diff --git "a/1349.jsonl" "b/1349.jsonl" new file mode 100644--- /dev/null +++ "b/1349.jsonl" @@ -0,0 +1,187 @@ +{"seq_id": "33418599204", "text": "#importar librerias\r\nimport cv2\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n#definir variables\r\ndrawing = False\r\nex = -1\r\ney = -1\r\n\r\n#definir funciones\r\n#boton derecho\r\n\r\ndef draw_rectangle(event, x, y, flags, param):\r\n\r\n global ex, ey, drawing\r\n\r\n \r\n if event == cv2.EVENT_LBUTTONDOWN:\r\n drawing = True\r\n ex,ey = x,y\r\n\r\n elif event == cv2.EVENT_MOUSEMOVE:\r\n if drawing == True:\r\n cv2.rectangle(img,(ex,ey),(x,y,),(35,69,78),-1)\r\n \r\n elif event == cv2.EVENT_LBUTTONUP:\r\n\r\n drawing = False\r\n \r\n cv2.rectangle(img,(ex,ey),(x,y,),(35,69,78),-1)\r\n\r\n#conectar con el callback\r\n\r\ncv2.namedWindow(winname='intento_1')\r\n\r\n#callback\r\n\r\ncv2.setMouseCallback('intento_1',draw_rectangle)\r\n\r\n#crear una imagen en negro para trabajar\r\nimg = np.zeros(shape=(512,512,3),dtype=np.int8)\r\n\r\n#ejecucion\r\n\r\nwhile True:\r\n\r\n cv2.imshow('intento_1',img)\r\n\r\n #tecla esc para salir o para una tecla se puede poner ord('TECLA')\r\n\r\n if cv2.waitKey(5) & 0xFF == ord('q'):\r\n break\r\n\r\ncv2.destroyAllWindows\r\n", "repo_name": "rojored7/tracking", "sub_path": "Computer_vision_image_basic_with_openCV_and_python/Video5.py", "file_name": "Video5.py", "file_ext": "py", "file_size_in_byte": 1084, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "cv2.EVENT_LBUTTONDOWN", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cv2.EVENT_MOUSEMOVE", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.EVENT_LBUTTONUP", "line_number": 27, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.setMouseCallback", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 42, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 55, "usage_type": "attribute"}]} +{"seq_id": "666835072", "text": "from functools import reduce\n\na = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n\nc = list(filter(lambda x : x % 2 == 0, a))\nprint(c)\nd = list(map(lambda x : x ** 2, c))\nprint(d)\ne = reduce(lambda num1 ,num2 : num1 + num2 ,d)\nprint(e)\n", "repo_name": "prabalthecoder/Prabal-Singh", "sub_path": "venv/homework.py", "file_name": "homework.py", "file_ext": "py", "file_size_in_byte": 220, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "functools.reduce", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "38542933531", "text": "import sqlalchemy as sa\nfrom sqla_fancy_core import TableFactory\n\nfrom datagovindia.config import DATAGOVINDIA_CACHE_DIR\n\npath = DATAGOVINDIA_CACHE_DIR / \"metadata.db\"\n\nmeta = sa.MetaData()\nengine = sa.create_engine(f\"sqlite:///{path}\")\n\ntf = TableFactory()\n\n\nclass Resources:\n index_name = tf.string(\"index_name\", primary_key=True, nullable=False)\n title = tf.string(\"title\")\n desc = tf.string(\"desc\")\n org = tf.json(\"org\")\n org_type = tf.string(\"org_type\")\n source = tf.string(\"source\")\n sector = tf.json(\"sector\")\n field = tf.json(\"field\")\n created = tf.datetime(\"created\")\n updated = tf.datetime(\"updated\")\n\n Table = tf(\"resources\", meta)\n\n\nclass ResourcesFTS:\n index_name = tf.string(\"index_name\")\n title = tf.string(\"title\")\n desc = tf.string(\"desc\")\n org = tf.string(\"org\")\n org_type = tf.string(\"org_type\")\n source = tf.string(\"source\")\n sector = tf.string(\"sector\")\n field = tf.string(\"field\")\n\n rank = sa.text(\"rank\") # Hidden column for MATCH operation\n\n Table = tf(\"resources_fts\", meta)\n\n\ndef drop_all():\n meta.drop_all(engine)\n\n\ndef create_all():\n meta.create_all(engine)\n", "repo_name": "sayanarijit/datagovindia", "sub_path": "datagovindia/db.py", "file_name": "db.py", "file_ext": "py", "file_size_in_byte": 1156, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "datagovindia.config.DATAGOVINDIA_CACHE_DIR", "line_number": 6, "usage_type": "name"}, {"api_name": "sqlalchemy.MetaData", "line_number": 8, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 9, "usage_type": "call"}, {"api_name": "sqla_fancy_core.TableFactory", "line_number": 11, "usage_type": "call"}, {"api_name": "sqlalchemy.text", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "39318473896", "text": "# IPC(Inter-Process Communication)\n# 队列 先进先出\n\"\"\"\nfrom multiprocessing import Queue\nq = Queue(5) # 队列的长度为5\nq.put(1) # 往队列里塞值\nq.put(2)\nq.put(3)\nq.put(4)\nq.put(5) # 如果里面装潢了,就阻塞,程序停在那里,一直运行空\nprint(q.full()) # 查看队列中是否为满\nprint(q.get()) # 从队列中取值\nprint(q.get())\nprint(q.get())\nprint(q.get())\nprint(q.get())\nprint(q.get()) # 如果从里面中取完了,再进行取值,就阻塞\nprint(q.empty()) # 判断是否为空\nq.get_nowait()\nprint(q.get())\nq.put(6)\nfor i in range(6):\n q.put(i)\n\"\"\"\nfrom multiprocessing import Queue, Process\n\n\ndef produce(q):\n q.put('hello')\n\n\ndef consume(q):\n print(q.get())\n\n\nif __name__ == '__main__':\n q = Queue()\n p = Process(target=produce, args=(q,))\n p.start()\n c = Process(target=consume, args=(q,))\n c.start()\n", "repo_name": "lupengliang/thirdpart", "sub_path": "多任务/多进程01一段复习/多进程复习10--队列进程通信.py", "file_name": "多进程复习10--队列进程通信.py", "file_ext": "py", "file_size_in_byte": 874, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "12", "api": [{"api_name": "multiprocessing.Queue", "line_number": 37, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 38, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "5084151155", "text": "\"\"\"\n100 Days of Code\nThe Complete Python Pro Bootcamp\nDra. Angela Yu\n\nDay 18\nThe Hirst Painting Project\n\"\"\"\n\n# Imports\nimport colorgram\nimport turtle as tt\nimport random as rd\n\n# Inputs\nnumb_colors = 23\n\n# Colors\ncolors = colorgram.extract('painting.jpg', numb_colors)\n\ncolors_list = list()\nfor color in colors:\n r = color.rgb.r/255\n g = color.rgb.g/255\n b = color.rgb.b/255\n colors_list.append((r, g, b))\n\ndel colors_list[0]\ndel colors_list[1]\ndel colors_list[2]\n\n# Turtle Object\npicture = tt.Turtle()\npicture.speed(0)\npicture.penup()\npicture.hideturtle()\nposition = [-225, -225]\n\nfor _ in range(10):\n picture.setpos(position[0], position[1])\n for _ in range(10):\n picture.color(rd.choice(colors_list))\n picture.dot(size=20)\n picture.forward(50)\n position[1] += 50\n\n# Save figure\nfigure = picture.getscreen()\nfigure.getcanvas().postscript(file='picture.eps')\n\n# Screen\nscreen = tt.Screen()\nscreen.exitonclick()\n", "repo_name": "thiagoneye/course-python", "sub_path": "02-Intermediate/projects/project18/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 954, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "colorgram.extract", "line_number": 19, "usage_type": "call"}, {"api_name": "turtle.Turtle", "line_number": 33, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 42, "usage_type": "call"}, {"api_name": "turtle.Screen", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "14552940479", "text": "import discord\nimport os\nimport requests\nimport json\nfrom keep_alive import keep_alive\n\nclient = discord.Client()\n\n\ndef get_quote():\n response = requests.get(\n \"https://v2.jokeapi.dev/joke/Any?blacklistFlags=nsfw,racist&type=single\"\n )\n json_data = json.loads(response.text)\n joke = json_data[\"joke\"]\n return (joke)\n\n\n@client.event\nasync def on_ready():\n print('We have logged in as {0.user}'.format(client))\n\n\n@client.event\nasync def on_message(message):\n if message.author == client.user:\n return\n\n if message.content.startswith('$hello'):\n await message.channel.send('Hey there!')\n\n if message.content.startswith('$joke'):\n quote = get_quote()\n await message.channel.send(quote)\n\n\nkeep_alive()\nclient.run(os.getenv('TOKEN'))\n", "repo_name": "bkenza/joke-bot", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 790, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "12", "api": [{"api_name": "discord.Client", "line_number": 7, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 11, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 14, "usage_type": "call"}, {"api_name": "keep_alive.keep_alive", "line_number": 37, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "33657027270", "text": "from flask import Flask, request, render_template\nfrom osnova import create_file\n\napp = Flask(__name__)\n\n\n@app.route(\"/result\", methods=['POST'])\ndef result():\n name = request.form['fsname']\n data = request.form['calendar']\n yes = request.form['yes']\n return render_template(\"result.html\", the_title='ок', rname=name), create_file(name, data, yes),\n\n\n@app.route(\"/\",)\ndef entry():\n return render_template(\"form.html\", the_title='проба')\n\n\nif __name__ == \"__main__\":\n app.run(debug=True)", "repo_name": "SirVsevolod/ForWork", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 512, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 9, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 9, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 10, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 10, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 11, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 11, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 12, "usage_type": "call"}, {"api_name": "osnova.create_file", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "22410583726", "text": "\"\"\"\nGiven an array of size n, find the most common and the least common elements.\nThe most common element is the element that appears more than n // 2 times.\nThe least common element is the element that appears fewer than other.\nYou may assume that the array is non-empty and the most common element\nalways exist in the array.\nExample 1:\nInput: [3,2,3]\nOutput: 3, 2\nExample 2:\nInput: [2,2,1,1,1,2,2]\nOutput: 2, 1\n\"\"\"\nfrom typing import List, Tuple\n\n\ndef major_and_minor_elem(inp: List) -> Tuple[int, int]:\n elements = {}\n for i in inp:\n if i not in elements:\n elements[i] = 1\n else:\n elements[i] += 1\n major = max(elements, key=elements.get)\n minor = min(elements, key=elements.get)\n if elements[major] > (len(inp) / 2) and major > minor:\n return major, minor\n else:\n raise ValueError(\"There's no minor element in the list.\")\n", "repo_name": "kiayria/epam-python-hw", "sub_path": "homework02/task02/major_minor_elements/major_minor_elements.py", "file_name": "major_minor_elements.py", "file_ext": "py", "file_size_in_byte": 894, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "typing.List", "line_number": 17, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "19798782196", "text": "\r\n# Perform clustering for the airlines data to obtain optimum number of clusters. Draw the inferences from the clusters obtained.\r\n\r\nimport pandas as pd\r\nimport matplotlib.pylab as plt\r\n\r\nairlines_data = pd.read_csv (\"C:\\\\Users\\\\HP\\Desktop\\\\360Digitmg Weekly Assignments\\\\4) Hierarchical Clustering\\\\Assignment\\\\airlines data\\\\airlines.csv\")\r\n\r\nairlines_data.describe()\r\nairlines_data.info()\r\na_data = airlines_data.drop([\"ID#\"], axis=1)\r\n\r\n\r\n# Normalization function\r\n \r\ndef norm_func(i):\r\n x = (i-i.min())\t/ (i.max()-i.min()) # Custom Function\r\n return (x)\r\n\r\n\r\n# Normalized data frame (considering the numerical part of data)\r\ndf_norm = norm_func(a_data)\r\ndf_norm.describe()\r\n\r\n\r\n\r\n# for creating dendrogram \r\nfrom scipy.cluster.hierarchy import linkage\r\nimport scipy.cluster.hierarchy as sch \r\n\r\nz = linkage(df_norm, method = \"complete\", metric = \"euclidean\")\r\n\r\n\r\n# Dendrogram\r\nplt.figure(figsize=(15, 8));plt.title('Hierarchical Clustering Dendrogram');plt.xlabel('Index');plt.ylabel('Distance')\r\nsch.dendrogram(z, \r\n leaf_rotation = 0, # rotates the x axis labels\r\n leaf_font_size = 10 # font size for the x axis labels\r\n)\r\nplt.show()\r\n\r\n\r\n# Now applying AgglomerativeClustering \r\nfrom sklearn.cluster import AgglomerativeClustering\r\n\r\nh_complete = AgglomerativeClustering(n_clusters = 4, linkage = 'complete', affinity = \"euclidean\").fit(df_norm) \r\nh_complete.labels_\r\n\r\ncluster_labels = pd.Series(h_complete.labels_)\r\n\r\na_data['clust'] = cluster_labels # creating a new column and assigning it to new column \r\n\r\na_data = a_data.iloc[:, [11,0,1,2,3,4,5,6,7,8,9,10]]\r\na_data.head() # First 5 rows\r\n \r\n# Aggregate mean of each cluster\r\na_data.iloc[:,1:11].groupby(a_data.clust).mean()\r\n\r\n# creating a csv file \r\na_data.to_csv(\"Airlines.csv\", encoding = \"utf-8\")\r\n\r\nimport os\r\nos.getcwd()\r\n\r\n\r\n\r\n", "repo_name": "sudarshanryali/360-DigiTMG-Assignments", "sub_path": "Clustering/documentation/Airlines Data Cluster Assignment Solution.Py", "file_name": "Airlines Data Cluster Assignment Solution.Py", "file_ext": "py", "file_size_in_byte": 1821, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "pandas.read_csv", "line_number": 7, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy.linkage", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pylab.figure", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pylab.title", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pylab.xlabel", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pylab.ylabel", "line_number": 35, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy.dendrogram", "line_number": 36, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pylab.show", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 40, "usage_type": "name"}, {"api_name": "sklearn.cluster.AgglomerativeClustering", "line_number": 46, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 49, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "74072513940", "text": "### reduce用来将列表中的元素统一计算，最后统一为一个结果\n\n#### 自己写一个reduce函数\n\nnum01 = [1,2,3,4,5]\n\ndef add(x,y):\n return x+y\n\ndef reduce_test(func,array,init=None):\n if init is None:\n res = array.pop(0)\n else:\n res = init\n for i in array:\n res = func(res,i)\n return res\n\nprint(reduce_test(add,num01,20))\n\n\n\n#### 使用reduce函数\nfrom functools import reduce\nprint(reduce(lambda x, y: x+y, num01, 10))", "repo_name": "leeyan85/tearch_python", "sub_path": "01_lesson_func/03map filter reduce/reduce_func.py", "file_name": "reduce_func.py", "file_ext": "py", "file_size_in_byte": 475, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "functools.reduce", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "2176363371", "text": "from typing import Iterable\nfrom collections import deque\nfrom aocfw import SolutionBase, IParser\nfrom p1 import Graph\nfrom functools import reduce\n\nclass GraphParser(IParser):\n def parse(self, data):\n graph = Graph()\n max_x, max_y = 0, 0\n for y, line in enumerate(data):\n max_y = max(y, max_y)\n for x, val in enumerate(line.rstrip(\"\\n\")):\n max_x = max(x, max_x)\n graph.add_vertex((x, y), int(val))\n for y in range(max_y + 1):\n for x in range(max_x + 1):\n c = (x, y)\n l = (x-1, y)\n u = (x, y-1)\n\n # To clever? Nope, perfect amount of clever!\n if l in graph and graph.get_value(l) < 9 and graph.get_value(c) < 9:\n graph.add_edge(c, l)\n if u in graph and graph.get_value(u) < 9 and graph.get_value(c) < 9:\n graph.add_edge(c, u)\n\n yield graph\n\n\nclass Solution(SolutionBase):\n bindings = {IParser: GraphParser}\n\n def solve(self, data: Iterable[Graph]) -> int:\n\n basins = []\n graph: Graph = next(data)\n visited = set()\n\n for vertex in graph:\n size = 0\n queue = deque([vertex])\n while queue:\n _vertex = queue.popleft()\n if _vertex in visited:\n continue\n visited.add(_vertex)\n size += 1\n for _v in graph.get_neighbors(_vertex):\n queue.append(_v)\n if size > 1:\n basins.append(size)\n\n return reduce(lambda a, b: a * b, sorted(basins, reverse=True)[:3])\n\n\nif __name__ == '__main__':\n Solution.run(source='input.txt')\n", "repo_name": "en0/aoc2021", "sub_path": "09/p2.py", "file_name": "p2.py", "file_ext": "py", "file_size_in_byte": 1752, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "aocfw.IParser", "line_number": 7, "usage_type": "name"}, {"api_name": "p1.Graph", "line_number": 9, "usage_type": "call"}, {"api_name": "aocfw.SolutionBase", "line_number": 31, "usage_type": "name"}, {"api_name": "aocfw.IParser", "line_number": 32, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 34, "usage_type": "name"}, {"api_name": "p1.Graph", "line_number": 34, "usage_type": "name"}, {"api_name": "p1.Graph", "line_number": 37, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 42, "usage_type": "call"}, {"api_name": "functools.reduce", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "10761417673", "text": "from collections import deque\n\nn, k = map(int, input().split())\nch = [0] * 100001\n\ndq = deque()\ndq.append(n)\n\nwhile dq:\n s = dq.popleft()\n if s == k:\n print(ch[s])\n break\n \n for i in (s * 2, s - 1, s + 1): \n if 0 <= i <= 100000 and ch[i] == 0:\n if i == s * 2:\n ch[i] = ch[s]\n dq.append(i)\n else:\n ch[i] = ch[s] + 1\n dq.append(i)\n", "repo_name": "kimwoongasd/BackJoon", "sub_path": "골드/골드5/13549.py", "file_name": "13549.py", "file_ext": "py", "file_size_in_byte": 445, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "collections.deque", "line_number": 6, "usage_type": "call"}]} +{"seq_id": "14156617482", "text": "from functools import cmp_to_key\nimport json\n\nwith open('input.txt') as inputfile:\n data = inputfile.read()\n\npairs = [pair.split('\\n') for pair in data.split('\\n\\n')]\n\ndef isInOrder(a, b):\n maxLen = max(len(a), len(b))\n for index in range(maxLen):\n if index == len(a) and index < len(b):\n return True\n if index < len(a) and index == len(b):\n return False \n\n aval = a[index]\n bval = b[index]\n if isinstance(aval, int) and isinstance(bval, int):\n if aval < bval:\n return True\n if aval > bval:\n return False\n elif isinstance(aval, list) and isinstance(bval, list):\n result = isInOrder(aval, bval)\n if result is not None:\n return result\n elif isinstance(aval, int) and isinstance(bval, list):\n result = isInOrder([aval], bval)\n if result is not None:\n return result\n elif isinstance(aval, list) and isinstance(bval, int):\n result = isInOrder(aval, [bval])\n if result is not None:\n return result\n \n return None\n\ninorder = 0\nfor pairNo, pair in enumerate(pairs):\n a = eval(pair[0])\n b = eval(pair[1])\n if isInOrder(a, b):\n inorder += pairNo + 1\n\nprint(f'part 1: {inorder}')\n\n# part 2\n\nlines = [line.strip() for line in data.replace('\\n\\n', '\\n').split('\\n')]\nprint(lines)\nprint()\nlines.append('[[2]]')\nlines.append('[[6]]')\npackets = []\nfor line in lines:\n print(line)\n packets.append(json.loads(line))\nsortedPackets = sorted(packets, key = cmp_to_key(lambda a, b: -1 if isInOrder(a,b) else 1))\nindex2 = sortedPackets.index([[2]]) + 1\nindex6 = sortedPackets.index([[6]]) + 1\n\nprint(f'part 2: {index2 * index6}')\n", "repo_name": "sjtrotter/Advent-of-Code", "sub_path": "2022/day 13/python/day13stolen.py", "file_name": "day13stolen.py", "file_ext": "py", "file_size_in_byte": 1789, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "json.loads", "line_number": 58, "usage_type": "call"}, {"api_name": "functools.cmp_to_key", "line_number": 59, "usage_type": "call"}]} +{"seq_id": "832920964", "text": "import matplotlib.pyplot as plt\nimport geopandas as gpd\nfrom matplotlib.ticker import (AutoMinorLocator, MultipleLocator)\nimport numpy as np\nimport pandas as pd\n\nbusData = pd.read_csv('treatedBusDataOnlyRoute.csv')\nsf = gpd.read_file('data/33MUE250GC_SIR.shp')\n\nfor n in [20, 50, 100, 250]:\n temp_dat1 = busData[((busData.line == 383)) & \n (busData.date == '01-25-2019') &\n (busData.time > '09:00:00') & \n (busData.time < '17:00:00') &\n (busData.order == 'D13249')]\n # temp_dat2 = busData[((busData.line == 371)) & \n # (busData.date == '01-25-2019') & \n # (busData.time > '09:00:00') & \n # (busData.time < '17:00:00') &\n # (busData.order == 'C51623')]\n # temp_dat3 = busData[((busData.line == 2381)) & \n # (busData.time > '09:00:00') & \n # (busData.time < '17:00:00') &\n # (busData.date == '01-25-2019') & \n # (busData.order == 'D86706')]\n # temp_dat4 = busData[((busData.line == 935)) & \n # (busData.time > '09:00:00') & \n # (busData.time < '17:00:00') &\n # (busData.date == '01-25-2019') &\n # (busData.order == 'B28514')]\n rio = sf[sf.ID == 1535]\n bus_plot = rio.plot()\n \n bus_plot.xaxis.set_major_locator(MultipleLocator(0.1))\n bus_plot.yaxis.set_major_locator(MultipleLocator(0.1))\n \n # Change minor ticks to show every 5. (20/4 = 5)\n bus_plot.xaxis.set_minor_locator(MultipleLocator(0.005))\n bus_plot.yaxis.set_minor_locator(MultipleLocator(0.005))\n\n bus_plot.plot(temp_dat1.longitude, temp_dat1.latitude, \n \".\", markersize=0.2,color='black')\n # bus_plot.plot(temp_dat2.longitude, temp_dat2.latitude, \n # \".\", markersize=0.2,color='red')\n # bus_plot.plot(temp_dat3.longitude, temp_dat3.latitude, \n # \".\", markersize=0.2,color='purple')\n # bus_plot.plot(temp_dat4.longitude, temp_dat4.latitude, \n # \".\", markersize=0.2,color='yellow')\n bus_plot.set_axisbelow(False)\n bus_plot.grid(which = 'major', linewidth=0.1)\n bus_plot.grid(which = 'minor', linewidth=0.1)\n bus_plot.set_title(\"Trajectory of four buses on 01/25/2019 (9am-5pm)\")\n bus_plot.figure.savefig('bus_plot1.pdf')\n \n \n \n", "repo_name": "HoujieWang/BDFM_Python", "sub_path": "Code for Rio Bus/Run Rio Data/plot_rio_bus.py", "file_name": "plot_rio_bus.py", "file_ext": "py", "file_size_in_byte": 2475, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "pandas.read_csv", "line_number": 7, "usage_type": "call"}, {"api_name": "geopandas.read_file", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "39560888543", "text": "from django.contrib.postgres.fields import ArrayField\nfrom django.db import models\nfrom django.contrib.auth.models import (\n BaseUserManager, AbstractBaseUser, PermissionsMixin\n)\nfrom virtual_day.mixins.models import DateTimeMixin\nfrom virtual_day.utils.image_utils import (\n avatar_path, image_push_notification_path\n)\nfrom virtual_day.utils.validators import validate_phone_number\nfrom virtual_day.utils import constants\nfrom django.conf import settings\nfrom django.utils.translation import gettext_lazy as _\n\n\nclass UserManager(BaseUserManager):\n \"\"\" Django Manager class for model User \"\"\"\n def create_superuser(self, email, password):\n \"\"\" overwrite method for superuser creating \"\"\"\n user = self.model(email=email)\n user.set_password(password)\n user.is_superuser = True\n user.role = constants.SUPER_ADMIN\n user.language = constants.SYSTEM_LANGUAGE\n user.save(using=self._db)\n return user\n\n\nclass User(AbstractBaseUser, PermissionsMixin, DateTimeMixin):\n \"\"\" A class used to represent a User in Application \"\"\"\n email = models.EmailField(\n max_length=255, null=True, blank=True, unique=True,\n db_index=True, verbose_name=_(\"Почта\"))\n avatar = models.ImageField(\n upload_to=avatar_path, blank=True, null=True, verbose_name=_(\"Фото\"))\n first_name = models.CharField(\n max_length=255, verbose_name=_(\"Имя\"))\n last_name = models.CharField(\n blank=True, null=True, max_length=255, verbose_name=_(\"Фамилия\"))\n firebase_token = models.CharField(\n blank=True, null=True, max_length=255,\n verbose_name=_(\"Токен firebase\"))\n address = models.CharField(\n blank=True, null=True, max_length=100, verbose_name=_(\"Адрес\"))\n role = models.PositiveSmallIntegerField(\n choices=constants.USER_TYPES, default=constants.STUDENT,\n verbose_name=_(\"Роль\"))\n phone = models.CharField(\n max_length=13, blank=True, null=True,\n validators=[validate_phone_number], verbose_name=_(\"Телефон\"))\n password_changed_datetime = models.DateTimeField(\n editable=False, null=True, verbose_name=\"Время изменения пароля\")\n is_active = models.BooleanField(\n default=True, verbose_name=_(\"Активность\"))\n ban = models.BooleanField(\n default=False, verbose_name=_(\"Пользователь забанен\"))\n language = models.CharField(\n choices=settings.LANGUAGES, max_length=32,\n default=constants.SYSTEM_LANGUAGE, verbose_name=_(\"Выбранный язык\"))\n\n USERNAME_FIELD = 'email'\n\n objects = UserManager()\n\n class Meta:\n db_table = 'user'\n verbose_name = _(\"Пользователь\")\n verbose_name_plural = _(\"Пользователи\")\n\n @property\n def is_staff(self):\n # Anyone who is superuser can enter admin\n return self.is_superuser\n\n\nclass UserPushNotification(DateTimeMixin):\n \"\"\" A class used to represent a Push Notifications\n to user in Application \"\"\"\n title = models.CharField(\n max_length=30, blank=True,\n null=True, verbose_name=_(\"Название сообщения\"))\n description = models.CharField(\n max_length=120, blank=True, null=True,\n verbose_name=_(\"Текст сообщения\"))\n description_detail = models.TextField(\n max_length=1024,\n blank=True, null=True, verbose_name=_(\"Текст детального сообщения\"))\n image = models.ImageField(\n upload_to=image_push_notification_path, blank=True, null=True,\n verbose_name=_(\"Фото сообщения\"))\n user_ids = ArrayField(\n models.IntegerField(), blank=True, null=True,\n verbose_name=_(\"ID-шники пользователей\"))\n response = models.JSONField(\n blank=True, null=True, max_length=1000,\n default=None, verbose_name=_(\"Ответ\"))\n date_publication = models.PositiveIntegerField(\n blank=True, null=True, verbose_name=_(\"Время отправки\"))\n is_sent = models.BooleanField(\n blank=True, null=True, default=False, verbose_name=_(\"Отправлен\"))\n users_count = models.PositiveIntegerField(\n blank=True, null=True, default=0,\n verbose_name=_(\"Число пользователей\"))\n views_count = models.PositiveIntegerField(\n blank=True, null=True, default=0, verbose_name=_(\"Число просмотров\"))\n data = models.JSONField(\n blank=True, null=True, max_length=1000, default=None,\n verbose_name=_(\"Данные уведомления\"))\n\n def __str__(self):\n \"\"\" Return title when calling object \"\"\"\n return f'{self.title}'\n\n class Meta:\n db_table = 'user_push_notification'\n verbose_name = _(\"Уведомление\")\n verbose_name_plural = _(\"Уведомления\")\n", "repo_name": "BernarBerdikul/virtual_day", "sub_path": "virtual_day/users/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 4951, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "12", "api": [{"api_name": "django.contrib.auth.models.BaseUserManager", "line_number": 16, "usage_type": "name"}, {"api_name": "virtual_day.utils.constants.SUPER_ADMIN", "line_number": 23, "usage_type": "attribute"}, {"api_name": "virtual_day.utils.constants", "line_number": 23, "usage_type": "name"}, {"api_name": "virtual_day.utils.constants.SYSTEM_LANGUAGE", "line_number": 24, "usage_type": "attribute"}, {"api_name": "virtual_day.utils.constants", "line_number": 24, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.AbstractBaseUser", "line_number": 29, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.PermissionsMixin", "line_number": 29, "usage_type": "name"}, {"api_name": "virtual_day.mixins.models.DateTimeMixin", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.EmailField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models.ImageField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "virtual_day.utils.image_utils.avatar_path", "line_number": 35, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 40, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 40, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 42, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 43, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 44, "usage_type": "call"}, {"api_name": "django.db.models.PositiveSmallIntegerField", "line_number": 45, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 45, "usage_type": "name"}, {"api_name": "virtual_day.utils.constants.USER_TYPES", "line_number": 46, "usage_type": "attribute"}, {"api_name": "virtual_day.utils.constants", "line_number": 46, "usage_type": "name"}, {"api_name": "virtual_day.utils.constants.STUDENT", "line_number": 46, "usage_type": "attribute"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 47, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 48, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 48, "usage_type": "name"}, {"api_name": "virtual_day.utils.validators.validate_phone_number", "line_number": 50, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 50, "usage_type": "call"}, {"api_name": "django.db.models.DateTimeField", "line_number": 51, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 51, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 53, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 53, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 54, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 55, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 55, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 56, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 57, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 57, "usage_type": "name"}, {"api_name": "django.conf.settings.LANGUAGES", "line_number": 58, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 58, "usage_type": "name"}, {"api_name": "virtual_day.utils.constants.SYSTEM_LANGUAGE", "line_number": 59, "usage_type": "attribute"}, {"api_name": "virtual_day.utils.constants", "line_number": 59, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 59, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 67, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 68, "usage_type": "call"}, {"api_name": "virtual_day.mixins.models.DateTimeMixin", "line_number": 76, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 79, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 79, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 81, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 82, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 82, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 84, "usage_type": "call"}, {"api_name": "django.db.models.TextField", "line_number": 85, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 85, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 87, "usage_type": "call"}, {"api_name": "django.db.models.ImageField", "line_number": 88, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 88, "usage_type": "name"}, {"api_name": "virtual_day.utils.image_utils.image_push_notification_path", "line_number": 89, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 90, "usage_type": "call"}, {"api_name": "django.contrib.postgres.fields.ArrayField", "line_number": 91, "usage_type": "call"}, {"api_name": "django.db.models.IntegerField", "line_number": 92, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 92, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 93, "usage_type": "call"}, {"api_name": "django.db.models.JSONField", "line_number": 94, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 94, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 96, "usage_type": "call"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 97, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 97, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 98, "usage_type": "call"}, {"api_name": "django.db.models.BooleanField", "line_number": 99, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 99, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 100, "usage_type": "call"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 101, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 101, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 103, "usage_type": "call"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 104, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 104, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 105, "usage_type": "call"}, {"api_name": "django.db.models.JSONField", "line_number": 106, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 106, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 108, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 116, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 117, "usage_type": "call"}]} +{"seq_id": "9344210088", "text": "from datetime import datetime\n\nfrom pyrogram import filters\nfrom pyrogram.errors import PeerIdInvalid\nfrom pyrogram.types import Message, User\n\nfrom DaisyX.services.pyrogram import pbot\n\n\ndef ReplyCheck(message: Message):\n reply_id = None\n\n if message.reply_to_message:\n reply_id = message.reply_to_message.message_id\n\n elif not message.from_user.is_self:\n reply_id = message.message_id\n\n return reply_id\n\n\ninfotext = (\n \"**[{full_name}](tg://user?id={user_id})**\\n\"\n \" * UserID: `{user_id}`\\n\"\n \" * First Name: `{first_name}`\\n\"\n \" * Last Name: `{last_name}`\\n\"\n \" * Username: `{username}`\\n\"\n \" * Last Online: `{last_online}`\\n\"\n \" * Bio: {bio}\"\n)\n\n\ndef LastOnline(user: User):\n if user.is_bot:\n return \"\"\n elif user.status == \"recently\":\n return \"Recently\"\n elif user.status == \"within_week\":\n return \"Within the last week\"\n elif user.status == \"within_month\":\n return \"Within the last month\"\n elif user.status == \"long_time_ago\":\n return \"A long time ago :(\"\n elif user.status == \"online\":\n return \"Currently Online\"\n elif user.status == \"offline\":\n return datetime.fromtimestamp(user.status.date).strftime(\n \"%a, %d %b %Y, %H:%M:%S\"\n )\n\n\ndef FullName(user: User):\n return user.first_name + \" \" + user.last_name if user.last_name else user.first_name\n\n\n@pbot.on_message(filters.command(\"whois\") & ~filters.edited & ~filters.bot)\nasync def whois(client, message):\n cmd = message.command\n if not message.reply_to_message and len(cmd) == 1:\n get_user = message.from_user.id\n elif len(cmd) == 1:\n get_user = message.reply_to_message.from_user.id\n elif len(cmd) > 1:\n get_user = cmd[1]\n try:\n get_user = int(cmd[1])\n except ValueError:\n pass\n try:\n user = await client.get_users(get_user)\n except PeerIdInvalid:\n await message.reply(\"I don't know that User.\")\n return\n desc = await client.get_chat(get_user)\n desc = desc.description\n await message.reply_text(\n infotext.format(\n full_name=FullName(user),\n user_id=user.id,\n user_dc=user.dc_id,\n first_name=user.first_name,\n last_name=user.last_name if user.last_name else \"\",\n username=user.username if user.username else \"\",\n last_online=LastOnline(user),\n bio=desc if desc else \"`No bio set up.`\",\n ),\n disable_web_page_preview=True,\n )\n", "repo_name": "TeamDaisyX/DaisyX", "sub_path": "DaisyX/modules/userinfo.py", "file_name": "userinfo.py", "file_ext": "py", "file_size_in_byte": 2543, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 157, "dataset": "github-code", "pt": "12", "api": [{"api_name": "pyrogram.types.Message", "line_number": 10, "usage_type": "name"}, {"api_name": "pyrogram.types.User", "line_number": 33, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "name"}, {"api_name": "pyrogram.types.User", "line_number": 52, "usage_type": "name"}, {"api_name": "pyrogram.errors.PeerIdInvalid", "line_number": 71, "usage_type": "name"}, {"api_name": "DaisyX.services.pyrogram.pbot.on_message", "line_number": 56, "usage_type": "call"}, {"api_name": "DaisyX.services.pyrogram.pbot", "line_number": 56, "usage_type": "name"}, {"api_name": "pyrogram.filters.command", "line_number": 56, "usage_type": "call"}, {"api_name": "pyrogram.filters", "line_number": 56, "usage_type": "name"}, {"api_name": "pyrogram.filters.edited", "line_number": 56, "usage_type": "attribute"}, {"api_name": "pyrogram.filters.bot", "line_number": 56, "usage_type": "attribute"}]} +{"seq_id": "5089515187", "text": "# 轉換pdf表格成df再儲存到csv。\nimport pdfplumber\nimport pandas as pd\n\npdf_file_path = 'file.pdf'\npdf = pdfplumber.open(pdf_file_path)\n\nmerged_df = pd.DataFrame() \n\n# 抓出每一頁的表格，合併到全部的df(merged_df)\nfor page in pdf.pages:\n table = page.extract_table()\n if table:\n df = pd.DataFrame(table[0:])\n # df.columns = [\"column1\", \"column2\"] # 依據column數量\n merged_df = merged_df.append(df, ignore_index=True)\n\nmerged_df.to_csv('merged_table.csv', index=False, encoding='utf-8-sig')\n\npdf.close()", "repo_name": "RoseMary93/wj_project", "sub_path": "3_PDFtable_to_CSV.py", "file_name": "3_PDFtable_to_CSV.py", "file_ext": "py", "file_size_in_byte": 555, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "pdfplumber.open", "line_number": 6, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "40569332057", "text": "# kakao-blind-2019-3.py\n# 다중 딕셔너리로 풀어버리기. 이런 비슷한 문제 있었던 것 같은데 기억이 안나네\nfrom itertools import combinations\n\n\ndef solution(relation):\n answer = 0\n #총 학생 수\n N = len(relation)\n \n #총 속성 수\n M = len(relation[0])\n \n materials = [i for i in range(M)]\n\n def check(c):\n\n\n for l in range(1,len(c)+1):\n for answerSet in answerList[l]:\n \n if set(answerSet) & set(c) == set(answerSet):\n \n return False\n return True \n\n # 최소성을 유지하기 위해 키의 개수를 1개에서부터 늘려가며 시행한다.\n answerList = [[]for _ in range(len(materials)+1)]\n for num in range(1,len(materials)+1):\n\n # 최소성 유지를 위해 재료에서 삭제한다.\n comb = list(combinations( materials, num))\n\n for c in comb:\n \n longDict = {}\n #유일성 체크\n isPossible = True\n for student_idx in range(N):\n rel = relation[student_idx]\n \n #포인터 역할을 하는 nowDict\n nowDict = longDict\n for atb in c:\n atbName = rel[atb]\n if atbName not in nowDict:\n nowDict[atbName] = {}\n \n nowDict = nowDict[atbName]\n\n if len(nowDict)==0:\n nowDict[student_idx]=1\n\n else:\n isPossible = False\n break\n \n if isPossible :\n if check(c):\n answerList[len(c)].append(c)\n answer+=1\n\n return answer\n\nsolution([[\"100\",\"ryan\",\"music\",\"2\"],[\"200\",\"apeach\",\"math\",\"2\"],[\"300\",\"tube\",\"computer\",\"3\"],[\"400\",\"con\",\"computer\",\"4\"],[\"500\",\"muzi\",\"music\",\"3\"],[\"600\",\"apeach\",\"music\",\"2\"]])\nsolution( [[\"a\",\"1\",\"aaa\",\"c\",\"ng\"],\n[\"a\",\"1\",\"bbb\",\"e\",\"g\"],\n[\"c\",\"1\",\"aaa\",\"d\",\"ng\"],\n[\"d\",\"2\",\"bbb\",\"d\",\"ng\"] ])", "repo_name": "gudals113/Algorithms", "sub_path": "kakao/kakao-blind-2019/kakao-blind-2019-3.py", "file_name": "kakao-blind-2019-3.py", "file_ext": "py", "file_size_in_byte": 2100, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "itertools.combinations", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "13341343048", "text": "import numpy as np\nfrom os.path import join\nfrom keras import Model, Input\nfrom keras.layers import Dense, Conv2D, MaxPooling2D, Flatten\nfrom keras.utils import to_categorical\nfrom keras.callbacks import ModelCheckpoint\n\nimport neptune.new as neptune\nfrom neptune.new.integrations.tensorflow_keras import NeptuneCallback\n\n\ndef read_dataset(dataset_path=\"data/processed_data\"):\n x_train = np.load(join(dataset_path, \"x_train.npy\"))\n y_train = np.load(join(dataset_path, \"y_train.npy\"))\n y_train = to_categorical(y_train)\n return x_train, y_train\n\n\ndef get_model():\n input = Input(shape=(32, 32, 3))\n x = Conv2D(32, (3, 3), activation=\"relu\")(input)\n x = MaxPooling2D()(x)\n x = Conv2D(16, (3, 3), activation=\"relu\")(x)\n x = MaxPooling2D()(x)\n x = Conv2D(8, (3, 3), activation=\"relu\")(x)\n x = Flatten()(x)\n output = Dense(6, activation=\"softmax\")(x)\n model = Model(inputs=input, outputs=output)\n print(model.summary())\n model.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=\"accuracy\")\n return model\n\n\ndef train_model(model, dataset):\n x_train, y_train = dataset\n run = neptune.init(\n project=\"galb4tosha/but-trans-dis\",\n api_token=\"eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vYXBwLm5lcHR1bmUuYWkiLCJhcGlfdXJsIjoiaHR0cHM6Ly9hcHAubmVwdHVuZS5haSIsImFwaV9rZXkiOiJkN2UwNjk5ZS1jZjBiLTQ3OTUtODhkOC0yYjJmNjY4ZDM5NTYifQ==\",\n )\n neptune_cbk = NeptuneCallback(run=run, base_namespace=\"training\")\n vat_acc_checkpoint = ModelCheckpoint(\n \"models/best_model.h5\", monitor=\"val_accuracy\", mode=\"max\", save_best_only=True\n )\n model.fit(\n x_train,\n y_train,\n batch_size=64,\n epochs=250,\n validation_split=0.2,\n callbacks=[vat_acc_checkpoint, neptune_cbk],\n )\n\n\nif __name__ == \"__main__\":\n dataset = read_dataset()\n model = get_model()\n train_model(model, dataset)\n", "repo_name": "galb4tosha/my_ml_projects", "sub_path": "buttons/scripts/train/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 1895, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "numpy.load", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 15, "usage_type": "call"}, {"api_name": "keras.Input", "line_number": 20, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 21, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 22, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 23, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 24, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 26, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 27, "usage_type": "call"}, {"api_name": "keras.Model", "line_number": 28, "usage_type": "call"}, {"api_name": "neptune.new.init", "line_number": 36, "usage_type": "call"}, {"api_name": "neptune.new", "line_number": 36, "usage_type": "name"}, {"api_name": "neptune.new.integrations.tensorflow_keras.NeptuneCallback", "line_number": 40, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "16968280802", "text": "import requests\nimport requests_cache\nfrom bs4 import BeautifulSoup \n\nrequests_cache.install_cache() # Implementando cache\n#requests_cache.clear() # Limpando cache\n\npaginas = [] # guadar todas as paginas e o seu nivel\npaginas_palavra = [] # guardar todas as paginas contendo a palavra\n\n# Baixa a página\ndef download(url):\n\tresponse = ''\n\ttry:\n\t\tresponse = requests.get(url)\n\texcept Exception as ex:\n\t\tresponse = None\n\tfinally:\n\t\treturn response\n\n# Busca e guarda trecho do texto contendo a palavra\ndef busca_guarda_palavra(keyword,pagina):\n\ttexto = ''\n\ttamanho_keyword = len(keyword)\n\n\tsoup = BeautifulSoup(pagina.text,'html.parser')\n\tpage = soup.text\n\n\t# Busca palavra\n\tpalavra = page.find(keyword)\n\n\t# Guarda palavra\n\tif palavra < 0:\n\t\ttexto = None\n\telif palavra < 10:\n\t\ttexto = page[0:palavra+tamanho_keyword+10:1]\n\telif palavra > 10:\n\t\ttexto = page[palavra-10:palavra+tamanho_keyword+10:1]\n\n\treturn texto\n\n# Formata link\ndef formatar_link(url,url_original):\n\tif url != None:\n\t\tif url.startswith('http://') or url.startswith('https://'):\n\t\t\treturn url\n\t\telif url.startswith('/'):\n\t\t\treturn url_original + url\n\t\telse:\n\t\t\treturn url\n\n# Percorre os links\ndef percorre_links(pagina,url,deth,keyword):\n\turls = [] # guardar os links da pagina\n\n\t# Cria objeto \n\tsoup = BeautifulSoup(pagina.text,'html.parser')\n\n\t# Busca os links dentro da página\n\tlinks = soup.find_all('a')\n\t\n\t# Guada os links em uma lista\n\tfor link in links:\n\t\tif formatar_link(link.get('href'),url) != None:\n\t\t\tif deth > 0:\n\t\t\t\turls.append(formatar_link(link.get('href'),url))\n\n\t\t\t\turl = formatar_link(link.get('href'),url)\t\t\t\n\t\t\t\tpaginas.append({'Url':url,'Nivel':deth})\n\n\treturn urls\n\ndef busca(keyword,url,deth):\n\tpagina = download(url)\n\n\tif pagina != None:\n\n\t\tif busca_guarda_palavra(keyword,pagina) != None:\n\t\t\tprint('------------------------------------------------------')\n\t\t\tprint('Link --> %s' % url)\n\t\t\tprint('Trecho --> %s' % busca_guarda_palavra(keyword,pagina))\n\t\t\tpaginas_palavra.append(url)\t\n\t\telse:\n\t\t\tprint('------------------------------------------------------')\n\t\t\tprint('Link --> %s' % url)\n\t\t\tprint('Palavra não encontrada')\n\n\t\t# Percorre os links e faz a recursividade\n\t\tif deth > 0 :\n\t\t\turls = percorre_links(pagina,url,deth,keyword)\n\n\t\t\tfor url in urls:\n\t\t\t\ttry:\n\t\t\t\t\tbusca(keyword,url,deth-1)\n\t\t\t\texcept:\n\t\t\t\t\tcontinue\t\n\n\telse:\n\t\tprint('------------------------------------------------------')\n\t\tprint('Erro na requsição da pagina')\n\n# Mostra todas paginas que contem a palavra\ndef mostra_paginas(keyword):\n\tif len(paginas_palavra) > 0:\n\t\tprint('------------------------------------------------------')\n\t\tprint('Paginas encontradas contendo a palavra %s' % keyword)\n\t\tfor pagina in paginas_palavra:\n\t\t\tprint(pagina)\n\n# Ranqueia as paginas em primarias e segindarias\ndef ranqueia_paginas(deth):\t\n\tif len(paginas_palavra) > 0:\n\t\tprint('------------------------------------------------------')\n\t\tprint('--------Paginas primarias--------')\n\t\tfor link in paginas_palavra:\n\t\t\tfor url in paginas:\n\t\t\t\tif link == url['Url'] and url['Nivel'] == deth:\n\t\t\t\t\tprint(url['Url'])\n\n\t\tprint('--------Paginas segundarias-------')\n\t\tfor link in paginas_palavra:\n\t\t\tfor url in paginas:\n\t\t\t\tif link == url['Url'] and url['Nivel'] < deth:\n\t\t\t\t\tprint(url['Url'])\n\t\ndef main():\n\tkeyword = 'Brasil'\n\turl = 'http://www.google.com.br'\n\tdeth = 1\n\tbusca(keyword,url,deth)\n\tmostra_paginas(keyword)\n\tranqueia_paginas(deth)\n\nif __name__ == '__main__':\n\tmain()\n\t", "repo_name": "PedroVitor1995/Programacao-para-Internet-I", "sub_path": "Busca/buscador.py", "file_name": "buscador.py", "file_ext": "py", "file_size_in_byte": 3430, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "requests_cache.install_cache", "line_number": 5, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 26, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "31087485465", "text": "'''\nCreated on Mar 9, 2014\n\n@author: rgettys\n'''\n\n'''\nLet d(n) be defined as the sum of proper divisors of n (numbers less than n which divide evenly into n).\nIf d(a) = b and d(b) = a, where a ≠ b, then a and b are an amicable pair and each of a and b are called amicable numbers.\n\nFor example, the proper divisors of 220 are 1, 2, 4, 5, 10, 11, 20, 22, 44, 55 and 110; therefore d(220) = 284. The proper divisors of 284 are 1, 2, 4, 71 and 142; so d(284) = 220.\n\nEvaluate the sum of all the amicable numbers under 10000.\n'''\n\nfrom functools import reduce\n\nedivisors = {}\ndivisorSums = {}\n\n'''\ndef sumList(l): \n sum = 0\n for i in l:\n sum += i\n return sum\n'''\n\nmaxRange = 10000\ndef findDivisors(n):\n list = []\n for i in range(1, n):\n if n / i == n // i: \n list.append(i)\n return list\n\ndivisorSums[1] = 1\nfor i in range(2, maxRange):\n divisorSums[i] = reduce(lambda x, y: x + y, findDivisors(i))\n #print(str(i) + \" \" + str(divisorSums[i]))\namicables = {}\nfor i in range(1,maxRange):\n partner = divisorSums[i]\n if partner != i and partner <= maxRange and divisorSums[partner] == i:\n amicables[i] = partner\n print(str(i) + \" and \" + str(partner) + \" are amicable\")\n \nprint(reduce(lambda x, y: x + y, amicables.keys()))\n\n\nif __name__ == '__main__':\n pass", "repo_name": "bgettys/project-euler-python", "sub_path": "p21/com/bobgettys/project-euler/p21.py", "file_name": "p21.py", "file_ext": "py", "file_size_in_byte": 1337, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "functools.reduce", "line_number": 39, "usage_type": "call"}, {"api_name": "functools.reduce", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "4257866717", "text": "from django.contrib import admin\nfrom django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('', views.home, name='home'),\n path('getWeather', views.getWeather, name='getWeather'),\n path('getTemp', views.getTemperature, name='getTemp'),\n path('feederendpoint', views.feederendpoint, name='feederendpoint'),\n path('newspaneldisplay', views.newspaneldisplay, name='newspaneldisplay'),\n path('testpush', views.testPush, name='testpush'),\n]\n", "repo_name": "alvinlohtpgit/PiDashDisplay", "sub_path": "PiDashDisplay/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 467, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "22666206352", "text": "#!/usr/bin/env python3\n# coding: utf8\n\n\"\"\"\nPredicts vocal and/or instrumental for a song.\n\"\"\"\n\n__author__ = 'David Flury, Andreas Kaufmann, Raphael Müller'\n__email__ = \"info@unmix.io\"\n\n\nimport argparse\nimport glob\nimport numpy as np\nimport os\nimport time\n\nfrom unmix.source.engine import Engine\nfrom unmix.source.helpers import filehelper\nfrom unmix.source.helpers import converter\nfrom unmix.source.prediction.fileprediction import FilePrediction\nfrom unmix.source.prediction.streamprediction import StreamPrediction\nfrom unmix.source.prediction.youtubeprediction import YoutTubePrediction\nfrom unmix.source.pipeline.transformers.transformerfactory import TransformerFactory\nfrom unmix.source.configuration import Configuration\nfrom unmix.source.logging.logger import Logger\n\n\nAUDIO_FILE_EXTENSIONS = ['.wav', '.mp3']\n\nif __name__ == \"__main__\":\n global config\n\n parser = argparse.ArgumentParser(\n description=\"Executes a training session.\")\n parser.add_argument('--run_folder', default='',\n type=str, help=\"General training input folder (overwrites other parameters).\")\n parser.add_argument('--configuration', default='',\n type=str, help=\"Environment and training configuration.\")\n parser.add_argument('--weights', default='',\n type=str, help=\"Pretrained weights file (overwrites configuration).\")\n parser.add_argument('--workingdir', default=os.getcwd(),\n type=str, help=\"Working directory (default: current directory).\")\n parser.add_argument('--sample_rate', default=11025,\n type=str, help=\"Target sample rate which the model can process.\")\n parser.add_argument('--fft_window', default=1536,\n type=str, help=\"FFT window size the model was trained on.\")\n parser.add_argument('--remove_panning', default='False',\n type=converter.str2bool, help=\"If panning of stereo input files should be removed by preprocessing.\")\n parser.add_argument('--song', default='',\n type=str, help=\"Input audio file to split vocals and instrumental.\")\n parser.add_argument('--songs', default='./temp/songs',\n type=str, help=\"Input folder containing audio files to split vocals and instrumental.\")\n parser.add_argument('--youtube', default='', type=str,\n help=\"Audio from a youtube video as file (or later stream).\")\n\n args = parser.parse_args()\n start = time.time()\n\n if args.run_folder:\n args.workingdir = filehelper.build_abspath(\n args.run_folder, os.getcwd())\n args.configuration = os.path.join(\n args.workingdir, 'configuration.jsonc')\n args.weights = filehelper.get_latest(os.path.join(\n args.workingdir, 'weights'), '*weights*.h5')\n\n Configuration.initialize(args.configuration, args.workingdir, False)\n Logger.initialize(False)\n\n if args.run_folder:\n prediction_folder = Configuration.build_path('predictions')\n args.sample_rate = Configuration.get(\"collection.sample_rate\")\n else:\n prediction_folder = ''\n\n if os.path.isdir(args.song):\n args.songs = args.song\n args.song = ''\n\n Logger.info(\"Arguments: \", str(args))\n\n song_files = []\n if args.song:\n song_files = [args.song]\n if args.songs:\n for file in glob.iglob(filehelper.build_abspath(args.songs, os.getcwd()) + '**/*', recursive=True):\n extension = os.path.splitext(file)[1].lower()\n if extension in AUDIO_FILE_EXTENSIONS:\n song_files.append(file)\n\n Logger.info(\"Found %d songs to predict.\" % len(song_files))\n\n engine = Engine()\n engine.load_weights(args.weights)\n\n stereo = Configuration.get(\"collection.stereo\", default=False)\n\n for song_file in song_files:\n try:\n prediction = FilePrediction(\n engine, sample_rate=args.sample_rate, fft_window=args.fft_window, stereo=stereo)\n prediction.run(song_file, remove_panning=args.remove_panning)\n prediction.save(song_file, prediction_folder)\n except Exception as e:\n Logger.error(\n \"Error while predicting song '%s': %s.\" % (song_file, str(e)))\n\n if args.youtube:\n prediction = YoutTubePrediction(\n engine, sample_rate=args.sample_rate, fft_window=args.fft_window, stereo=stereo)\n path, name, _ = prediction.run(args.youtube)\n prediction.save(name, path)\n\n end = time.time()\n Logger.info(\"Finished processing in %d [s].\" % (end - start))\n", "repo_name": "unmix-io/unmix-net", "sub_path": "predict.py", "file_name": "predict.py", "file_ext": "py", "file_size_in_byte": 4623, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "12", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 34, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 42, "usage_type": "call"}, {"api_name": "unmix.source.helpers.converter.str2bool", "line_number": 49, "usage_type": "attribute"}, {"api_name": "unmix.source.helpers.converter", "line_number": 49, "usage_type": "name"}, {"api_name": "time.time", "line_number": 58, "usage_type": "call"}, {"api_name": "unmix.source.helpers.filehelper.build_abspath", "line_number": 61, "usage_type": "call"}, {"api_name": "unmix.source.helpers.filehelper", "line_number": 61, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "unmix.source.helpers.filehelper.get_latest", "line_number": 65, "usage_type": "call"}, {"api_name": "unmix.source.helpers.filehelper", "line_number": 65, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "unmix.source.configuration.Configuration.initialize", "line_number": 68, "usage_type": "call"}, {"api_name": "unmix.source.configuration.Configuration", "line_number": 68, "usage_type": "name"}, {"api_name": "unmix.source.logging.logger.Logger.initialize", "line_number": 69, "usage_type": "call"}, {"api_name": "unmix.source.logging.logger.Logger", "line_number": 69, "usage_type": "name"}, {"api_name": "unmix.source.configuration.Configuration.build_path", "line_number": 72, "usage_type": "call"}, {"api_name": "unmix.source.configuration.Configuration", "line_number": 72, "usage_type": "name"}, {"api_name": "unmix.source.configuration.Configuration.get", "line_number": 73, "usage_type": "call"}, {"api_name": "unmix.source.configuration.Configuration", "line_number": 73, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "unmix.source.logging.logger.Logger.info", "line_number": 81, "usage_type": "call"}, {"api_name": "unmix.source.logging.logger.Logger", "line_number": 81, "usage_type": "name"}, {"api_name": "glob.iglob", "line_number": 87, "usage_type": "call"}, {"api_name": "unmix.source.helpers.filehelper.build_abspath", "line_number": 87, "usage_type": "call"}, {"api_name": "unmix.source.helpers.filehelper", "line_number": 87, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "unmix.source.logging.logger.Logger.info", "line_number": 92, "usage_type": "call"}, {"api_name": "unmix.source.logging.logger.Logger", "line_number": 92, "usage_type": "name"}, {"api_name": "unmix.source.engine.Engine", "line_number": 94, "usage_type": "call"}, {"api_name": "unmix.source.configuration.Configuration.get", "line_number": 97, "usage_type": "call"}, {"api_name": "unmix.source.configuration.Configuration", "line_number": 97, "usage_type": "name"}, {"api_name": "unmix.source.prediction.fileprediction.FilePrediction", "line_number": 101, "usage_type": "call"}, {"api_name": "unmix.source.logging.logger.Logger.error", "line_number": 106, "usage_type": "call"}, {"api_name": "unmix.source.logging.logger.Logger", "line_number": 106, "usage_type": "name"}, {"api_name": "unmix.source.prediction.youtubeprediction.YoutTubePrediction", "line_number": 110, "usage_type": "call"}, {"api_name": "time.time", "line_number": 115, "usage_type": "call"}, {"api_name": "unmix.source.logging.logger.Logger.info", "line_number": 116, "usage_type": "call"}, {"api_name": "unmix.source.logging.logger.Logger", "line_number": 116, "usage_type": "name"}]} +{"seq_id": "40526819120", "text": "import twilio\nfrom twilio.rest import Client\nimport schedule\nimport time\n\nphone_number = \"+17152276481\"\nmy_number = input(\"Number: \")\n\n\ndef reminder():\n account_sid = 'AC48ea0c13a4203d0614f3162db7c62d04'\n auth_token = \"0eae903a3f4f792eea6f485d051c91de\"\n client = Client(account_sid, auth_token)\n\n client.api.account.messages.create(\n to=my_number,\n from_=phone_number,\n body=\"Drink Water\")\n\n\nschedule.every().hour.do(reminder)\n", "repo_name": "faganjoe/Text_Reminder", "sub_path": "text_app.py", "file_name": "text_app.py", "file_ext": "py", "file_size_in_byte": 460, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "twilio.rest.Client", "line_number": 13, "usage_type": "call"}, {"api_name": "schedule.every", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "31265539260", "text": "import collections\n\ns:str = \"cbacdcbc\"\n\ncounter = collections.Counter(s)\nstrSet = set()\nstack = []\n\nfor item in s:\n counter[item] -= 1\n if item in strSet:\n continue\n #뒤에 붙일 문자가 남아 있으면 스택에서 제거\n while stack and item < stack[-1] and counter[stack[-1]] > 0:\n strSet.remove(stack.pop())\n stack.append(item)\n strSet.add(item)\n\nprint(''.join(stack)) ", "repo_name": "gudrb33333/python-algo", "sub_path": "3부 선형자료구조/9장 스택,큐/21 중복문자 제거/스택을 이용한 문자제거.py", "file_name": "스택을 이용한 문자제거.py", "file_ext": "py", "file_size_in_byte": 418, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "collections.Counter", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "3639172092", "text": "import os\nimport time\nimport datetime\nimport csv\n\nimport queue\n\nfrom threading import Thread\nfrom multiprocessing import Process, Queue, Lock\n\n\nfp = open(f\"results.csv\", 'w')\nfile_lock = Lock()\n\ncsv_writer = csv.writer(fp, delimiter='\\t', quoting=csv.QUOTE_NONE)\ncsv_writer.writerow([\"tag\", \"start_time\", \"end_time\", \"elapsed\"])\nresult_queue = Queue()\n\ndef save_results(size):\n for _ in range(size):\n values = result_queue.get()\n csv_writer.writerow(values)\n\n # file_lock.acquire()\n\n # csv_writer.writerow([tag, start_t, end_t])\n\n # file_lock.release()\n\ndef fibonacci(n): \n if n<0: \n raise ValueError(\"Incorrect input\") \n # First Fibonacci number is 0 \n elif n==1: \n return 0\n # Second Fibonacci number is 1 \n elif n==2: \n return 1\n else: \n return fibonacci(n-1)+fibonacci(n-2) \n\ndef save_fibonacci(tag, n):\n t = time.time()\n result = fibonacci(n)\n # print(f\"Fibonacci {n} = {result}\")\n result_queue.put((tag, t, time.time()))\n # print(\"Done.\")\n\ndef process_print(*args, **kwargs):\n print(os.getpid(), \"-\", *args, **kwargs)\n\ndef produce(q, num, delay, n_consumers):\n for i in range(1, num):\n process_print(\"Produce \", i)\n q.put(i)\n time.sleep(delay)\n\n # Put a poison pill on the queue for each process\n # Because a process terminates right after receiving one\n # it is guaranteed they all get exactly one\n for _ in range(n_consumers):\n q.put(PoisonPill)\n\n\ndef consume(q, delay):\n process_print(\"Start\")\n while True:\n i = q.get()\n if i is PoisonPill:\n break\n\n process_print(\"Got\", i)\n\n print(f\"Fibonacci({i}) = {fibonacci(i)}\")\n # time.sleep(delay)\n\n process_print(\"Stops\")\n\n\ndef check_cpu_usage():\n import psutil\n\n while True:\n print(\"CPU%\", psutil.cpu_percent())\n time.sleep(0.1)\n\n t = Thread(target=__check)\n t.start()\n\ndef run_experiment(Runnable, n_tasks, consume_delay, n_consumers):\n\n print(\"Run Experiment with Process ... \", end=\"\")\n workers = []\n\n for i in range(1, n_consumers+1):\n # p = Process(target=consume, args=(queue, consume_delay))\n tag = f\"{Runnable.__name__}_{i}\"\n p = Runnable(target=save_fibonacci, args=(tag, i+20))\n workers.append(p)\n p.start()\n\n tic = time.time()\n\n save_results(len(workers))\n\n toc = time.time()\n elapsed = datetime.timedelta(seconds=toc - tic)\n print(\"finished in \", elapsed)\n return elapsed\n\n\nif __name__ == '__main__':\n check_cpu_usage()\n N = 20\n process_time = run_experiment(Process, 40, 0.1, N)\n threaded_time = run_experiment(Thread, 40, 0.1, N)\n\n speed_up = process_time / threaded_time * 100.0\n # print(f\"Speed up = {}\"})", "repo_name": "gerardsimons/personal-website", "sub_path": "content/snippets/concurrency_vs_parallelism.py", "file_name": "concurrency_vs_parallelism.py", "file_ext": "py", "file_size_in_byte": 2767, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "multiprocessing.Lock", "line_number": 13, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 15, "usage_type": "call"}, {"api_name": "csv.QUOTE_NONE", "line_number": 15, "usage_type": "attribute"}, {"api_name": "multiprocessing.Queue", "line_number": 17, "usage_type": "call"}, {"api_name": "time.time", "line_number": 43, "usage_type": "call"}, {"api_name": "time.time", "line_number": 46, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 50, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 56, "usage_type": "call"}, {"api_name": "psutil.cpu_percent", "line_number": 84, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 85, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 87, "usage_type": "call"}, {"api_name": "time.time", "line_number": 102, "usage_type": "call"}, {"api_name": "time.time", "line_number": 106, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 107, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 115, "usage_type": "argument"}, {"api_name": "threading.Thread", "line_number": 116, "usage_type": "argument"}]} +{"seq_id": "32983063909", "text": "from django.shortcuts import render\n\n# Create your views here.\nfrom django.http import JsonResponse\nfrom django.shortcuts import get_object_or_404, render\n\nfrom book.models import Books\n\nfrom .cart import Cart\n\n\ndef basket_summary(request):\n cart = Cart(request)\n return render(request, 'cart/summary.html', {'cart': cart})\n\n\ndef basket_add(request):\n cart = Cart(request)\n if request.POST.get('action') == 'post':\n product_id = int(request.POST.get('productid'))\n product_qty = int(request.POST.get('productqty'))\n product = get_object_or_404(Books, id=product_id)\n cart.add(product=product, qty=product_qty)\n\n cartqty = cart.__len__()\n response = JsonResponse({'qty': cartqty})\n return response\n\n\ndef basket_delete(request):\n cart = Cart(request)\n if request.POST.get('action') == 'post':\n product_id = int(request.POST.get('productid'))\n cart.delete(product=product_id)\n\n cartqty = cart.__len__()\n cart_total = cart.get_total_price()\n response = JsonResponse({'qty': cartqty, 'subtotal': cart_total})\n return response\n\n\ndef basket_update(request):\n cart = Cart(request)\n if request.POST.get('action') == 'post':\n product_id = int(request.POST.get('productid'))\n product_qty = int(request.POST.get('productqty'))\n cart.update(product=product_id, qty=product_qty)\n\n cartqty = cart.__len__()\n basketsubtotal = cart.get_subtotal_price()\n response = JsonResponse({'qty': cartqty, 'subtotal': basketsubtotal})\n return response\n", "repo_name": "muqaddaskamolova/GraphQL_Django_Book_Store", "sub_path": "cart/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1588, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "cart.Cart", "line_number": 13, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 14, "usage_type": "call"}, {"api_name": "cart.Cart", "line_number": 18, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 22, "usage_type": "call"}, {"api_name": "book.models.Books", "line_number": 22, "usage_type": "argument"}, {"api_name": "cart.add", "line_number": 23, "usage_type": "call"}, {"api_name": "cart.__len__", "line_number": 25, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 26, "usage_type": "call"}, {"api_name": "cart.Cart", "line_number": 31, "usage_type": "call"}, {"api_name": "cart.delete", "line_number": 34, "usage_type": "call"}, {"api_name": "cart.__len__", "line_number": 36, "usage_type": "call"}, {"api_name": "cart.get_total_price", "line_number": 37, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 38, "usage_type": "call"}, {"api_name": "cart.Cart", "line_number": 43, "usage_type": "call"}, {"api_name": "cart.update", "line_number": 47, "usage_type": "call"}, {"api_name": "cart.__len__", "line_number": 49, "usage_type": "call"}, {"api_name": "cart.get_subtotal_price", "line_number": 50, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "36887459031", "text": "# This files contains your custom actions which can be used to run\n# custom Python code.\n#\n# See this guide on how to implement these action:\n# https://rasa.com/docs/rasa/custom-actions\n\nfrom typing import Any, Text, Dict, List\n\nfrom rasa_sdk import Action, Tracker\nfrom rasa_sdk.executor import CollectingDispatcher\n\nfrom actions.utils.stock_informer import StockInformer\n\n\nclass ActionGetStockPrice(Action):\n\n def name(self) -> Text:\n return \"action_get_stock_price\"\n\n def run(self, dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:\n\n stock = tracker.get_slot('stock_base')\n\n if not stock:\n dispatcher.utter_message(text='Haven\\'t matched any stock ticker symbol. '\n 'Please check if typo is present or try another ticker symbol.')\n return []\n\n stock_price = StockInformer.get_stock_price(stock)\n\n dispatcher.utter_message(text=f\"The price of {stock.upper()} is {stock_price:,}\")\n\n return []\n\n\nclass ActionGetStocksCompared(Action):\n\n def name(self) -> Text:\n return \"action_get_stocks_compared\"\n\n def run(self, dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:\n\n stock_base = tracker.get_slot('stock_base')\n stock_match = tracker.get_slot('stock_match')\n\n if not stock_base or not stock_match:\n dispatcher.utter_message(text='Haven\\'t matched any stock ticker symbol. '\n 'Please check if typo is present or try another ticker symbol.')\n return []\n\n stock_price_base = 12\n stock_price_match = 10\n\n dispatcher.utter_message(text=f\"The price of {stock_base.upper()} increased \"\n f\"{stock_price_base - stock_price_match}% more \"\n f\"than the price of {stock_match.upper()}\")\n\n return []\n\n\nclass ActionGetStockDetails(Action):\n\n def name(self) -> Text:\n return \"action_get_stock_details\"\n\n def run(self, dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:\n\n stock = tracker.get_slot('stock_base')\n\n if not stock:\n dispatcher.utter_message(text='Haven\\'t matched any stock ticker symbol. '\n 'Please check if typo is present or try another ticker symbol.')\n return []\n\n details = StockInformer.get_stock_details(stock)\n\n current_price = details['price']\n cnt_shares = details['number_of_shares']\n industry = details['industry']\n recommendation_status = details['recommendation_status']\n short_name = details['short_name']\n\n dispatcher.utter_message(text=f\"{short_name}\\n\"\n f\"Industry: {industry}\\n\"\n f\"Current price: {current_price}$\\n\"\n f\"Recommendation status: {recommendation_status}\\n\"\n f\"Number of shares: {cnt_shares}\")\n\n return []\n", "repo_name": "NikolaMr/stock-bot", "sub_path": "actions/actions.py", "file_name": "actions.py", "file_ext": "py", "file_size_in_byte": 3266, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "rasa_sdk.Action", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Text", "line_number": 17, "usage_type": "name"}, {"api_name": "rasa_sdk.executor.CollectingDispatcher", "line_number": 20, "usage_type": "name"}, {"api_name": "rasa_sdk.Tracker", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.Text", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 22, "usage_type": "name"}, {"api_name": "actions.utils.stock_informer.StockInformer.get_stock_price", "line_number": 31, "usage_type": "call"}, {"api_name": "actions.utils.stock_informer.StockInformer", "line_number": 31, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 22, "usage_type": "name"}, {"api_name": "rasa_sdk.Action", "line_number": 38, "usage_type": "name"}, {"api_name": "typing.Text", "line_number": 40, "usage_type": "name"}, {"api_name": "rasa_sdk.executor.CollectingDispatcher", "line_number": 43, "usage_type": "name"}, {"api_name": "rasa_sdk.Tracker", "line_number": 44, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.Text", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 45, "usage_type": "name"}, {"api_name": "rasa_sdk.Action", "line_number": 65, "usage_type": "name"}, {"api_name": "typing.Text", "line_number": 67, "usage_type": "name"}, {"api_name": "rasa_sdk.executor.CollectingDispatcher", "line_number": 70, "usage_type": "name"}, {"api_name": "rasa_sdk.Tracker", "line_number": 71, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 72, "usage_type": "name"}, {"api_name": "typing.Text", "line_number": 72, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 72, "usage_type": "name"}, {"api_name": "actions.utils.stock_informer.StockInformer.get_stock_details", "line_number": 81, "usage_type": "call"}, {"api_name": "actions.utils.stock_informer.StockInformer", "line_number": 81, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 72, "usage_type": "name"}]} +{"seq_id": "70944026262", "text": "import datetime\nimport pytz\nfrom decimal import Decimal \n\nclass Transaction: \n TYPE_TO_RESOURCE_MAP = {\n \"buy\": \"buy\",\n \"send\": \"from\",\n \"sell\": \"sell\"\n }\n def __init__(self, coinbase_resp):\n self.coinbase_resp = coinbase_resp\n self.parse_response()\n\n def parse_response(self):\n self.amount = Decimal(self.coinbase_resp[\"amount\"][\"amount\"])\n self.currency = self.coinbase_resp[\"amount\"][\"currency\"]\n self.id = self.coinbase_resp[\"id\"]\n self.created_at = datetime.datetime.fromisoformat(self.coinbase_resp[\"created_at\"].replace(\n 'Z', '+00:00')).astimezone(pytz.timezone(\"America/New_York\"))\n self.total = Decimal(self.coinbase_resp[\"native_amount\"][\"amount\"])\n self.resource = self.coinbase_resp[\"type\"]\n transaction_type = Transaction.TYPE_TO_RESOURCE_MAP[self.resource]\n self.transaction_id = self.coinbase_resp[transaction_type][\"id\"]\n\n def to_dict(self):\n return {\n # \"id\":self.id,\n # \"transaction_id\":self.transaction_id,\n \"currency\":self.currency,\n \"resource\":self.resource,\n \"created_at\":self.created_at,\n \"amount\":self.amount,\n \"total\":self.total\n }\n\n def __repr__(self):\n transaction_type = Transaction.TYPE_TO_RESOURCE_MAP[self.resource]\n return f\"< {transaction_type.upper()}: {self.amount} (${self.total}) {self.currency} >\"", "repo_name": "chris-greening/coinbase-portfolio", "sub_path": "transaction.py", "file_name": "transaction.py", "file_ext": "py", "file_size_in_byte": 1461, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "decimal.Decimal", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime.fromisoformat", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pytz.timezone", "line_number": 20, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "10216867698", "text": "import psycopg2\n\nimport urllib.request\n\nimport json\n\nimport timeit\n\n# start execution timer\nstart = timeit.default_timer()\n\n# establishing the connection\nconn = psycopg2.connect(\n database=\"Project1\", user='bob', password='admin', host='127.0.0.1', port='5432'\n)\n# Creating a cursor object using the cursor() method\ncur = conn.cursor()\n\n# create tables (if do not exist)\ncur.execute('''\nCREATE TABLE IF NOT EXISTS UFO (\n id TEXT PRIMARY KEY NOT NULL UNIQUE,\n datetime TEXT,\n city TEXT,\n state TEXT,\n country TEXT,\n shape TEXT,\n duration_sec TEXT,\n duration_hrs_min TEXT,\n comments TEXT,\n date_posted TEXT,\n latitude NUMERIC,\n longitude NUMERIC\n)\n''')\n\nconn.commit()\n\nfile_url = \"https://cs.msutexas.edu/~griffin/data/UfoData/ufos_export.json\"\nresponse = urllib.request.urlopen(file_url)\n\ncount = 0\n\nwhile (True):\n data = response.readline()\n if data == b'':\n break\n data = json.loads(data.decode(\"utf-8\"))\n print(data[\"_id\"][\"$oid\"])\n\n # check if id is present in database\n cur.execute(\"SELECT id FROM UFO WHERE id = %s\", (data['_id']['$oid'],))\n\n if (cur.fetchone() == None):\n count = count + 1\n print(str(count) + \" : Inserting data => \" + data['_id']['$oid'])\n # print(data)\n try:\n if (float(data['latitude'])):\n cur.execute(\"\"\"\n INSERT INTO UFO\n (\n id,\n datetime,\n city,\n state,\n country,\n shape,\n duration_sec,\n duration_hrs_min,\n comments,\n date_posted,\n latitude,\n longitude\n ) VALUES\n (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\n \"\"\",\n (\n data['_id']['$oid'],\n data['datetime'],\n data['city'],\n data['state'],\n data['country'],\n data['shape'],\n data['duration (seconds)'],\n data['duration (hours/min)'],\n data['comments'],\n data['date posted'],\n data['latitude'],\n data['longitude']\n ))\n conn.commit()\n except:\n pass\n\n\n# Closing the connection\nconn.close()\n\n## programme execution time ##\nstop = timeit.default_timer()\ntotal_time = stop-start\nprint('\\n-----------------------')\nprint('Time: ', total_time)\nprint('-----------------------\\n')\n\n# Programme\n# Execution Time : Time: 1052.9603083849997 Seconds\n# No. of Records : 87999\n", "repo_name": "Hydindia/spatial-DB", "sub_path": "assignment 1.1.py", "file_name": "assignment 1.1.py", "file_ext": "py", "file_size_in_byte": 3132, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "timeit.default_timer", "line_number": 10, "usage_type": "call"}, {"api_name": "psycopg2.connect", "line_number": 13, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 40, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 40, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 40, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 48, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 101, "usage_type": "call"}]} +{"seq_id": "74787759699", "text": "import logging\nfrom typing import Union, List\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nlogger = logging.getLogger(__name__)\n\n\ndef regression_metrics(y_true: Union[np.ndarray, torch.Tensor],\n y_pred: Union[np.ndarray, torch.Tensor],\n target_names: List[str]):\n \"\"\"\n Regression metric calculation that supports both multiple targets and single targets scenario.\n Result can be visualized e.g. using `metrics[[\"mse\", \"mae\"]].plot.bar(figsize=(8,4));`\n :param y_true: true\n :param y_pred: predictions\n :param target_names a list of target names\n :return: a pandas DataFrame with metrics as columns and target names as rows\n \"\"\"\n if isinstance(y_true, torch.Tensor):\n y_true = y_true.cpu().numpy()\n if isinstance(y_pred, torch.Tensor):\n y_pred = y_pred.cpu().numpy()\n y_true_df = pd.DataFrame(y_true, columns=target_names)\n y_pred_df = pd.DataFrame(y_pred, columns=target_names)\n return _regression_metrics(y_true_df, y_pred_df)\n\n\ndef regression_metrics_pandas(y_true: pd.DataFrame, y_pred: pd.DataFrame) -> pd.DataFrame:\n \"\"\"\n Regression metric calculation that supports both multiple targets and single targets scenario.\n Result can be visualized e.g. using `metrics[[\"mse\", \"mae\"]].plot.bar(figsize=(8,4));`\n :param y_true: true\n :param y_pred: predictions\n :return: a pandas DataFrame with metrics as columns and target names as rows\n \"\"\"\n return _regression_metrics(y_true, y_pred)\n\n\ndef _regression_metrics(y_true: Union[pd.DataFrame, pd.Series, np.ndarray],\n y_pred: Union[pd.DataFrame, pd.Series, np.ndarray],\n name: str = \"\") -> pd.DataFrame:\n if y_true.ndim > 1 and y_true.shape[1] > 1: # multiple output scenario\n # recursively call function for each column and combine results\n return pd.concat(_regression_metrics(y_true[col], y_pred[col], name=col) for col in y_true.columns)\n\n # extract values if necessary\n if isinstance(y_true, pd.DataFrame) and y_true.shape[1] == 1:\n y_true = y_true.iloc[:, 0]\n if isinstance(y_pred, pd.DataFrame) and y_pred.shape[1] == 1:\n y_pred = y_pred.iloc[:, 0]\n if isinstance(y_true, pd.Series):\n y_true = y_true.values\n if isinstance(y_pred, pd.Series):\n y_pred = y_pred.values\n\n res_df = pd.DataFrame({\n 'target': name,\n 'y_true': y_true,\n 'y_pred': y_pred,\n 'ae': np.abs(y_true - y_pred),\n 'se': (y_true - y_pred) ** 2\n })\n res_df['ape'] = (res_df['ae'] / res_df.y_true) * 100.\n mse = np.mean(res_df.se)\n mae = np.mean(res_df.ae)\n mape = np.mean(res_df.ape)\n median_ae = np.median(res_df.se)\n return pd.Series(dict(mse=mse, mae=mae, mape=mape, median_ae=median_ae), name=name).to_frame().T\n", "repo_name": "bernhardschaefer/yamlu", "sub_path": "src/yamlu/regression.py", "file_name": "regression.py", "file_ext": "py", "file_size_in_byte": 2831, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 11, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 11, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 12, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 12, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 22, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 31, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 42, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 42, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 43, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 43, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 47, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 56, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 70, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 44, "usage_type": "attribute"}]} +{"seq_id": "41241288676", "text": "# Machine-Learning-Model\n# Import Dependencies\nimport pandas as pd\nimport hvplot.pandas\nimport plotly.express as px\nimport plotly.figure_factory as ff\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nfrom sklearn.cluster import AgglomerativeClustering\nfrom sklearn.decomposition import PCA\nfrom sklearn.cluster import KMeans\n\nimport db_connections\n\ndef agglomerativeClustering():\n\n # Import and read the data from mongodb into dataframes\n terrorism_df = pd.DataFrame(list(db_connections.terrorism.find()))\n terrorism_df.drop(['_id'], axis=1, inplace=True)\n\n # Set Index\n terrorism_df.set_index('PERPETRATOR', inplace=True)\n\n # Remove un-useful columns\n terrorism_df = terrorism_df.drop(columns=['SUBREGION', 'DESCRIPTION', 'CITY'])\n\n # Drop n/a's\n df = terrorism_df.copy()\n df = df.dropna()\n\n # Create a new DataFrame that holds only the Date\n perpetrators_df = df.filter(items = ['DATE', 'STATE'])\n\n # Reformat any data in columns (type, split string, etc...)\n # Create latitude and longitude columns\n df[['latitude', 'longitude']] = df['COORDINATES'].str.split(',', 1, expand=True)\n df['latitude'] = pd.to_numeric(df['latitude'], errors='coerce').fillna(0).astype(int)\n df['longitude'] = pd.to_numeric(df['longitude'], errors='coerce').fillna(0).astype(int)\n df = df.drop(columns='COORDINATES')\n\n # Drop the non-beneficial columns\n # Drop the 'DATE' and \"STATE\" column since it's not going to be used on the clustering algorithm.\n df = df.drop(columns=['DATE', 'STATE'])\n\n # Use get_dummies() to create variables for text features.\n X = pd.get_dummies(df,columns=['COUNTRY','REGION', 'CATEGORY'])\n print(X.shape)\n\n # Check any values for binning\n\n\n # Check the value plot density\n\n\n # Generate our categorical variable lists\n\n\n # Create a OneHotEncoder instance\n # enc = OneHotEncoder(sparse=False)\n\n\n # Fit and transform the OneHotEncoder using the categorical variable list\n\n\n #PCA-Analysis\n # Split our preprocessed data into our features and target arrays\n\n\n # Split the preprocessed data into a training and testing dataset\n\n\n # Create a StandardScaler instances\n scaler = StandardScaler()\n\n # Fit the StandardScaler\n X_scaled = scaler.fit_transform(X)\n\n # Using PCA to reduce dimensions to three principal components.\n pca = PCA(n_components=3)\n X_pca = pca.fit_transform(X_scaled)\n\n # Create a DataFrame with the three principal components.\n X_pca_df = pd.DataFrame(data=X_pca, columns=['PC 1', 'PC 2', 'PC 3'])\n X_pca_df.index = X.index\n\n # Find the best value for K\n inertia = []\n k = list(range(1, 11))\n\n # Calculate the inertia for the range of K values\n for i in k:\n km = KMeans(n_clusters=i, random_state=0)\n km.fit(X_pca_df)\n inertia.append(km.inertia_)\n\n # Create the elbow curve\n elbow_data = {\"k\": k, \"inertia\": inertia}\n df_elbow = pd.DataFrame(elbow_data)\n df_elbow.hvplot.line(x=\"k\", y=\"inertia\", xticks=k, title=\"Elbow Curve\")\n\n # Initialize the K-means model\n agg = AgglomerativeClustering(n_clusters=4)\n\n # Fit the model\n model = agg.fit(X_pca_df)\n\n # Add the predicted class columns\n X_pca_df[\"class\"] = model.labels_\n\n # Create a 3D-Scatter with the PCA data and the clusters\n fig = px.scatter_3d(X_pca_df,\n x='PC 1',\n y='PC 2',\n z='PC 3',\n color=\"class\",\n symbol=\"class\",\n width = 800,\n )\n fig.update_layout(legend=dict(x=0, y=1))\n\n return fig\n # Scale the data\n\n\n # Instantiate the model\n\n\n # Fit and train the model\n\n\n # Predict the y_test\n\n\n # Create a summary of the model. Possibly return value for Flask app.\n\n\n # Return the graph figure. Possibly return value for Flask app.\n", "repo_name": "namin1993/Capstone_Project", "sub_path": "ml_model.py", "file_name": "ml_model.py", "file_ext": "py", "file_size_in_byte": 3884, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "12", "api": [{"api_name": "pandas.DataFrame", "line_number": 17, "usage_type": "call"}, {"api_name": "db_connections.terrorism.find", "line_number": 17, "usage_type": "call"}, {"api_name": "db_connections.terrorism", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pandas.to_numeric", "line_number": 36, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 37, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 45, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 72, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 78, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 82, "usage_type": "call"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 91, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 97, "usage_type": "call"}, {"api_name": "sklearn.cluster.AgglomerativeClustering", "line_number": 101, "usage_type": "call"}, {"api_name": "plotly.express.scatter_3d", "line_number": 110, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 110, "usage_type": "name"}]} +{"seq_id": "36980960650", "text": "from tkinter import *\r\nfrom tkinter import Tk\r\nfrom PIL import ImageTk\r\nimport subprocess\r\n\r\n\r\nclass instruction:\r\n def __init__(self, root):\r\n self.root = root\r\n self.root.title(\"Main Page\")\r\n self.root.geometry(\"1160x610+160+70\")\r\n # ====== Bg Image ========\r\n self.bg = ImageTk.PhotoImage(file=\"images/welcome.jpg\")\r\n Label(self.root, image=self.bg).place(relwidth=1, relheight=1)\r\n\r\n canvas = Canvas(self.root)\r\n canvas.create_image(0, 0, image=self.bg, anchor=NW)\r\n Button(text=\"START\", font=('lato', 18, 'bold'), bd=0, cursor=\"hand2\", bg=\"white\", width=9,\r\n command=self.btn_clicked).place(x=1010, y=550)\r\n\r\n def btn_clicked(self):\r\n self.root.destroy()\r\n import progress\r\n\r\n\r\nroot = Tk()\r\nobj = instruction(root)\r\nroot.resizable(0, 0)\r\nroot.mainloop()\r\n", "repo_name": "gouravkamble9/GestureMediaControl", "sub_path": "main_page.py", "file_name": "main_page.py", "file_ext": "py", "file_size_in_byte": 857, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "PIL.ImageTk.PhotoImage", "line_number": 13, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 13, "usage_type": "name"}, {"api_name": "tkinter.Tk", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "10964278731", "text": "import tensorflow as tf\nfrom tensorflow.keras import backend as K\nfrom tensorflow_addons.utils.types import AcceptableDTypes, FloatTensorLike\nfrom typeguard import typechecked\nfrom typing import Optional\n\n\nclass MeanAveragePrecision(tf.keras.metrics.Metric):\n @typechecked\n def __init__(\n self,\n num_classes: FloatTensorLike,\n average: str = 'micro',\n name: str = \"mAP\",\n dtype: AcceptableDTypes = None,\n **kwargs,\n ):\n super().__init__(name=name, dtype=dtype)\n\n if average not in (None, \"micro\", \"macro\"):\n raise ValueError(\n \"Unknown average type. Acceptable values \"\n \"are: ['micro', 'macro']\"\n )\n\n self.num_classes = num_classes\n self.average = average\n self.axis = None\n self.init_shape = []\n\n if self.average == \"macro\":\n self.axis = 0\n self.init_shape = [self.num_classes]\n\n def _zero_wt_init(name):\n return self.add_weight(\n name, shape=self.init_shape, initializer=\"zeros\", dtype=self.dtype\n )\n\n self.positive_cumsum = _zero_wt_init(\"positive_cumsum\")\n self.positive_total = _zero_wt_init(\"positive_total\")\n\n def update_state(self, y_true, y_pred, sample_weight=None):\n\n y_true = tf.cast(y_true, self.dtype)\n y_pred = tf.cast(y_pred, self.dtype)\n\n indices = tf.argsort(y_pred, direction='DESCENDING', axis=0)\n y_true_sort = tf.gather(tf.transpose(y_true),\n tf.transpose(indices),\n axis=-1,\n batch_dims=-1)\n y_true_sort = tf.transpose(y_true_sort)\n pos_count = tf.math.cumsum(y_true_sort, axis=0)\n total = pos_count[-1]\n if self.average == 'micro':\n total = tf.math.reduce_sum(total)\n pos_count = tf.math.multiply(pos_count, y_true_sort)\n\n total_count = tf.math.cumsum(tf.ones((y_true.shape[0], self.num_classes), dtype=self.dtype))\n pp = tf.math.divide_no_nan(pos_count, total_count)\n pp = tf.math.reduce_sum(pp, axis=self.axis)\n\n self.positive_cumsum.assign_add(pp)\n self.positive_total.assign_add(total)\n\n def result(self):\n precision_at_i = tf.math.divide_no_nan(\n self.positive_cumsum, self.positive_total\n )\n return tf.math.reduce_mean(precision_at_i)\n\n def get_config(self):\n \"\"\"Returns the serializable config of the metric.\"\"\"\n\n config = {\n \"num_classes\": self.num_classes,\n \"average\": self.average,\n }\n\n base_config = super().get_config()\n return {**base_config, **config}\n\n def reset_state(self):\n reset_value = tf.zeros(self.init_shape, dtype=self.dtype)\n K.batch_set_value([(v, reset_value) for v in self.variables])\n\n def reset_states(self):\n # Backwards compatibility alias of `reset_state`. New classes should\n # only implement `reset_state`.\n # Required in Tensorflow < 2.5.0\n return self.reset_state()\n", "repo_name": "kakaobrain/coyo-vit", "sub_path": "metrics/map.py", "file_name": "map.py", "file_ext": "py", "file_size_in_byte": 3132, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 26, "dataset": "github-code", "pt": "12", "api": [{"api_name": "tensorflow.keras", "line_number": 8, "usage_type": "attribute"}, {"api_name": "tensorflow_addons.utils.types.FloatTensorLike", "line_number": 12, "usage_type": "name"}, {"api_name": "tensorflow_addons.utils.types.AcceptableDTypes", "line_number": 15, "usage_type": "name"}, {"api_name": "typeguard.typechecked", "line_number": 9, "usage_type": "name"}, {"api_name": "tensorflow.cast", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.argsort", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.gather", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.transpose", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.transpose", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.transpose", "line_number": 53, "usage_type": "call"}, {"api_name": "tensorflow.math.cumsum", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 54, "usage_type": "attribute"}, {"api_name": "tensorflow.math.reduce_sum", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 57, "usage_type": "attribute"}, {"api_name": "tensorflow.math.multiply", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 58, "usage_type": "attribute"}, {"api_name": "tensorflow.math.cumsum", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 60, "usage_type": "attribute"}, {"api_name": "tensorflow.ones", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.math.divide_no_nan", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 61, "usage_type": "attribute"}, {"api_name": "tensorflow.math.reduce_sum", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 62, "usage_type": "attribute"}, {"api_name": "tensorflow.math.divide_no_nan", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 68, "usage_type": "attribute"}, {"api_name": "tensorflow.math.reduce_mean", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 71, "usage_type": "attribute"}, {"api_name": "tensorflow.zeros", "line_number": 85, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend.batch_set_value", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 86, "usage_type": "name"}]} +{"seq_id": "17801839090", "text": "import time\nfrom src.tm_global.operations.get_coverage_result import get_coverage_result\n\nfrom selenium.webdriver.common.by import By\nfrom selenium.common.exceptions import NoSuchElementException\n\nfrom src.tm_global.operations.pause_until_loaded import pause_until_loaded\nfrom src.tm_global.operations.filter_all_columns import filter_all_columns\nfrom src.tm_global.operations.click_on_selected_row import click_on_selected_row\nfrom src.tm_global.operations.duplicate_in_new_tab import duplicate_in_new_tab\nfrom src.tm_global.operations.close_duplicated_tab import close_duplicated_tab\nfrom src.tm_global.operations.verify_to_click_on_row import verify_to_click_on_row\nfrom src.tm_global.operations.restart_filter_process import restart_filter_process\nfrom src.tm_global.singleton.selected_table_row import SelectedTableRow\nfrom src.tm_global.singleton.current_db_row import CurrentDBRow\nfrom src.tm_global.operations.attempt_to_update_address_info import attempt_to_update_address_information\n\n\ndef coverage_search_the_right_address(driver, a, address):\n \"\"\"\n 'address' param would be in the form of:\n ((['61', 'JALAN', 'TANJUNG 2', 'BUKIT BERUNTUNG', '', '', 'SERENDAH', '48300', 'FTTH', 'Residential'],1), ('BEST MATCH', True))\n \"\"\"\n address_marks = address[1][0]\n lotNumAndStreetAndPostcodeNumMatchBool = address[1][1]\n address_string = ''\n for str_idx in range(len(address[0][0])-2):\n if address[0][0][str_idx] != '' and address[0][0][str_idx] != ' ' and address[0][0][str_idx].strip() != '-':\n address_string += address[0][0][str_idx] + ' '\n\n address_string = address_string.strip()\n\n (driver, a) = filter_all_columns(driver, a, address)\n\n best_selection_row = driver.find_elements(\n By.XPATH, \"//table[@id='table_result']//tbody//tr[@role='row']\")\n\n root_tab_url = driver.current_url\n results = []\n for selected_row_num in range(len(best_selection_row)):\n # selected_row = driver.find_element(\n # By.XPATH, f\"(//table[@id='table_result']//tbody//tr[@role='row'])[{selected_row_num+1}]\")\n to_click_on_row = verify_to_click_on_row(driver, a, selected_row_num)\n # print('to click on row', to_click_on_row)\n if to_click_on_row:\n try:\n (driver, a) = duplicate_in_new_tab(driver, a, root_tab_url)\n (driver, a) = filter_all_columns(driver, a, address)\n (driver, a) = click_on_selected_row(\n driver, a, selected_row_num)\n (driver, a) = attempt_to_update_address_information(driver, a)\n results = get_coverage_result(driver, a)\n (driver, a) = close_duplicated_tab(driver, a)\n\n selected_table_row_instance = SelectedTableRow.get_instance()\n\n selected_table_row_instance.set_result_remark(\n self=selected_table_row_instance, result_remark=results[1])\n\n except NoSuchElementException:\n (driver, a) = restart_filter_process(\n driver, a, selected_row_num, address)\n results = get_coverage_result(driver, a)\n (driver, a) = close_duplicated_tab(driver, a)\n\n return (driver, a)\n", "repo_name": "humancalledhans/jaco-hans-tm-coverage-automation", "sub_path": "src/tm_global/operations/clicked_on_the_right_address.py", "file_name": "clicked_on_the_right_address.py", "file_ext": "py", "file_size_in_byte": 3217, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "src.tm_global.operations.filter_all_columns.filter_all_columns", "line_number": 33, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 36, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 36, "usage_type": "name"}, {"api_name": "src.tm_global.operations.verify_to_click_on_row.verify_to_click_on_row", "line_number": 43, "usage_type": "call"}, {"api_name": "src.tm_global.operations.duplicate_in_new_tab.duplicate_in_new_tab", "line_number": 47, "usage_type": "call"}, {"api_name": "src.tm_global.operations.filter_all_columns.filter_all_columns", "line_number": 48, "usage_type": "call"}, {"api_name": "src.tm_global.operations.click_on_selected_row.click_on_selected_row", "line_number": 49, "usage_type": "call"}, {"api_name": "src.tm_global.operations.attempt_to_update_address_info.attempt_to_update_address_information", "line_number": 51, "usage_type": "call"}, {"api_name": "src.tm_global.operations.get_coverage_result.get_coverage_result", "line_number": 52, "usage_type": "call"}, {"api_name": "src.tm_global.operations.close_duplicated_tab.close_duplicated_tab", "line_number": 53, "usage_type": "call"}, {"api_name": "src.tm_global.singleton.selected_table_row.SelectedTableRow.get_instance", "line_number": 55, "usage_type": "call"}, {"api_name": "src.tm_global.singleton.selected_table_row.SelectedTableRow", "line_number": 55, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 60, "usage_type": "name"}, {"api_name": "src.tm_global.operations.restart_filter_process.restart_filter_process", "line_number": 61, "usage_type": "call"}, {"api_name": "src.tm_global.operations.get_coverage_result.get_coverage_result", "line_number": 63, "usage_type": "call"}, {"api_name": "src.tm_global.operations.close_duplicated_tab.close_duplicated_tab", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "73211295382", "text": "#!/usr/bin/env python\n# coding: utf-8\n\nimport torch\nimport torch.nn as nn\nimport librosa\nimport numpy as np\nfrom torch.utils.data import Dataset\nimport sys\nimport scipy\nimport random\n\nclass SpeakerDataset(Dataset):\n def __init__(self, data_list, data_path='', nfft=512, win_len_time=0.02, hop_len_time=0.01, fs = 16000,\n feature_type='mel', n_coeff=64, fr_len=150, random_window=False):\n # Read txt file.\n with open(data_list,'r') as f:\n self.fileset = f.read().split('\\n')[:-1] \n self.data_path = data_path\n self.feature_type = feature_type\n self.win_sample = int(win_len_time*fs)\n self.hop_sample = int(hop_len_time*fs)\n self.nfft = nfft\n self.fs = fs\n self.n_coeff = n_coeff\n self.eps = np.array(sys.float_info.epsilon)\n self.max_fr = fr_len\n self.total_n = len(self.fileset)\n self.currenct_n = 1\n self.random_window = random_window\n\n def __len__(self):\n return len(self.fileset)\n \n def __getitem__(self, idx):\n token = self.fileset[idx].split(' ')\n path = token[1]\n label = int(token[0])\n sig, fs = librosa.load(self.data_path+path, sr=self.fs)\n if self.random_window:\n if(self.currenct_n > self.total_n ):\n win_length_r = int(random.uniform(100, self.nfft))\n self.win_sample = win_length_r\n print(\"Current_n/Total_n {0} / {1}\".format(self.currenct_n, self.total_n))\n self.currenct_n = 1\n print(\"window length {0}\".format(win_length_r))\n\n #print(\"global window length {0}\".format(self.win_sample))\n feature = self.get_feature(sig)\n\n self.currenct_n += 1\n return feature, label\n \n # Combine features depending on the context window setup.\n def context_window(self, feature, left, right):\n context_feature = []\n for i in range(left, len(feature)-right):\n feat = np.concatenate((feature[i-left:i+right]),axis=-1)\n context_feature.append(feat)\n context_feature = np.vstack(context_feature)\n return context_feature \n \n # Compute input features and concatenate depending on the type of context window.\n def get_feature(self,sig):\n stft = librosa.core.stft(sig, n_fft=self.nfft, hop_length=self.hop_sample,\n win_length=self.win_sample)\n feature = abs(stft).transpose()\n if self.feature_type == 'mel':\n mel_fb = librosa.filters.mel(self.fs, n_fft=self.nfft, n_mels=self.n_coeff)\n power = feature**2\n feature = np.matmul(power, mel_fb.transpose())\n feature = 10*np.log10(feature+self.eps)\n delta = librosa.feature.delta(feature)\n delta2 = librosa.feature.delta(feature, order=2)\n feature = np.concatenate((feature, delta, delta2), axis=-1)\n elif self.feature_type == 'mfcc':\n mel_fb = librosa.filters.mel(self.fs, n_fft=self.nfft, n_mels=self.n_coeff)\n power = feature**2\n feature = np.matmul(power, mel_fb.transpose())\n feature = 10*np.log10(feature+self.eps)\n feature = scipy.fftpack.dct(feature, axis=-1, norm='ortho')\n delta = librosa.feature.delta(feature)\n delta2 = librosa.feature.delta(feature, order=2)\n feature = np.concatenate((feature, delta, delta2), axis=-1)\n \n feature = self.context_window(feature=feature, left=5, right=5)\n return feature", "repo_name": "jeehyun100/speech_interface", "sub_path": "proj3/dvector/PJ4_dataset.py", "file_name": "PJ4_dataset.py", "file_ext": "py", "file_size_in_byte": 3580, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 13, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.float_info", "line_number": 26, "usage_type": "attribute"}, {"api_name": "librosa.load", "line_number": 39, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 60, "usage_type": "call"}, {"api_name": "librosa.core.stft", "line_number": 65, "usage_type": "call"}, {"api_name": "librosa.core", "line_number": 65, "usage_type": "attribute"}, {"api_name": "librosa.filters.mel", "line_number": 69, "usage_type": "call"}, {"api_name": "librosa.filters", "line_number": 69, "usage_type": "attribute"}, {"api_name": "numpy.matmul", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 72, "usage_type": "call"}, {"api_name": "librosa.feature.delta", "line_number": 73, "usage_type": "call"}, {"api_name": "librosa.feature", "line_number": 73, "usage_type": "attribute"}, {"api_name": "librosa.feature.delta", "line_number": 74, "usage_type": "call"}, {"api_name": "librosa.feature", "line_number": 74, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 75, "usage_type": "call"}, {"api_name": "librosa.filters.mel", "line_number": 77, "usage_type": "call"}, {"api_name": "librosa.filters", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.matmul", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 80, "usage_type": "call"}, {"api_name": "scipy.fftpack.dct", "line_number": 81, "usage_type": "call"}, {"api_name": "scipy.fftpack", "line_number": 81, "usage_type": "attribute"}, {"api_name": "librosa.feature.delta", "line_number": 82, "usage_type": "call"}, {"api_name": "librosa.feature", "line_number": 82, "usage_type": "attribute"}, {"api_name": "librosa.feature.delta", "line_number": 83, "usage_type": "call"}, {"api_name": "librosa.feature", "line_number": 83, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 84, "usage_type": "call"}]} +{"seq_id": "25258513623", "text": "'''\nconverted sorted array to BST which should be a balanced BST\n'''\n\n# mid node will be root node\n# mid-1 nodes will be attached on root left\n# mid+1 nodes will be attached on root right\nfrom collections import deque\nclass Node:\n\n def __init__(self, data):\n self.data = data\n self.left = None \n self.right = None\n\ndef arrayToBST(root, arr, s, e):\n if s>e:\n return None \n \n mid = (s+e)//2\n\n root = Node(arr[mid])\n root.left = arrayToBST(root.left, arr, s, mid-1)\n root.right = arrayToBST(root.right, arr, mid+1, e)\n return root \n\ndef buildTree():\n root = None\n arr = [1, 2, 4, 5, 6, 8, 9, 10]\n root = arrayToBST(root, arr, 0, len(arr)-1)\n return root \n\ndef printLevelOrder(root):\n\n q = deque()\n q.append(root)\n\n while len(q)>0:\n size = len(q)\n for i in range(size):\n node = q.popleft()\n print(node.data, end = \" \")\n if node.left:\n q.append(node.left)\n if node.right:\n q.append(node.right)\n print() \n\n\nroot = buildTree()\nprintLevelOrder(root)", "repo_name": "Komal97/DS-Algortihms", "sub_path": "13. Binary Search Tree/Python/Sorted_array_to_BST.py", "file_name": "Sorted_array_to_BST.py", "file_ext": "py", "file_size_in_byte": 1111, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "collections.deque", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "21256702930", "text": "import argparse\nimport os.path as op\n\nimport tensorflow as tf\nimport numpy as np\n\nfrom src.models.vaes.scripts import train_vae, visualize_vae\nfrom utils import fs_utils\nfrom utils import logger\n\n\n@tf.function\ndef compute_codes(vae_model_obj, input_images):\n enc_out = vae_model_obj.encode_from_input(input_images)\n latent_codes = vae_model_obj.reparameterize(enc_out)\n\n return latent_codes\n\n\n@logger.log\ndef do_latent_codes_predictions(dataset, vae_model_obj):\n latent_codes = []\n batches_num = int(dataset.test_ds_size() / dataset.batch_size)\n\n for i, img_batch in enumerate(dataset.generate_test_mb(fill_to_batch_size=False)):\n batch_latent_codes = compute_codes(vae_model_obj, img_batch)\n latent_codes.append(batch_latent_codes)\n\n do_latent_codes_predictions.logger.info(\"Predicted codes for %d/%d batch, batch latent codes shape: %s\" %\n (i, batches_num, str(batch_latent_codes.shape)))\n\n return np.concatenate(latent_codes, axis=0).astype(np.float32)\n\n\n@logger.log\ndef main():\n args = parse_args()\n train_config = fs_utils.read_json(op.join(op.dirname(args.vae_model_path), 'config.json'))\n if 'delta' not in train_config:\n train_config.update({'delta': None})\n\n dataset = visualize_vae.get_dataset_from_train_config(train_config)\n input_shape = (None, dataset.img_size(), dataset.img_size(), 3)\n\n vae_model_obj = train_vae.create_model(train_config, input_shape, trainable=False)\n\n vae_model_obj.load_params(args.vae_model_path, only_dec=False)\n main.logger.info(\"Loaded encoder params from path: %s\" % args.vae_model_path)\n\n latent_codes = do_latent_codes_predictions(dataset, vae_model_obj)\n np.save(args.out_npy_arr, latent_codes)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('vae_model_path')\n parser.add_argument('out_npy_arr')\n return parser.parse_args()\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "mjamroz90/bayesian-view-deep-representations", "sub_path": "src/models/vaes/scripts/generate_latent_codes_on_test_set.py", "file_name": "generate_latent_codes_on_test_set.py", "file_ext": "py", "file_size_in_byte": 1977, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "tensorflow.function", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 32, "usage_type": "attribute"}, {"api_name": "utils.logger.log", "line_number": 20, "usage_type": "attribute"}, {"api_name": "utils.logger", "line_number": 20, "usage_type": "name"}, {"api_name": "utils.fs_utils.read_json", "line_number": 38, "usage_type": "call"}, {"api_name": "utils.fs_utils", "line_number": 38, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 38, "usage_type": "call"}, {"api_name": "src.models.vaes.scripts.visualize_vae.get_dataset_from_train_config", "line_number": 42, "usage_type": "call"}, {"api_name": "src.models.vaes.scripts.visualize_vae", "line_number": 42, "usage_type": "name"}, {"api_name": "src.models.vaes.scripts.train_vae.create_model", "line_number": 45, "usage_type": "call"}, {"api_name": "src.models.vaes.scripts.train_vae", "line_number": 45, "usage_type": "name"}, {"api_name": "numpy.save", "line_number": 51, "usage_type": "call"}, {"api_name": "utils.logger.log", "line_number": 35, "usage_type": "attribute"}, {"api_name": "utils.logger", "line_number": 35, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "71183262741", "text": "import tweepy\r\nimport time\r\nimport configparser\r\nfrom datetime import datetime\r\n\r\n\r\nconfig = configparser.RawConfigParser()\r\nconfig.read(\"configuration.config\")\r\nconsumer_key = config.get(\"Config\", \"consumer_key\")\r\nconsumer_secret = config.get(\"Config\", \"consumer_secret\")\r\nuser_to_copy = config.get(\"Config\", \"user_to_copy\")\r\n\r\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\r\nprint(auth.get_authorization_url())\r\npin = input('Please insert the PIN you get from the page above:')\r\ntoken = auth.get_access_token(verifier = pin)\r\n\r\naccess_token = token[0]\r\naccess_token_secret = token[1]\r\nauth.set_access_token(access_token, access_token_secret)\r\n\r\napi = tweepy.API(auth, wait_on_rate_limit=True)\r\nmyid = api.me().id\r\nmyname = api.me().screen_name\r\nstartingTime = time.time()\r\n\r\nprint(\"[{0}] Authenticated as: {1} with id {2}\".format(datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\"), myname, myid))\r\ntime.sleep(5)\r\n\r\nfollowedUsers = []\r\nwith open(\"alreadyFollowed.txt\", \"a+\") as followedFile:\r\n\tfollowedFile.seek(0)\r\n\r\n\tfor line in followedFile:\r\n\t\tfollowedUsers.append(line.strip())\r\n\r\n\ti = 1\r\n\tbanned = []\r\n\talreadyRequested = []\r\n\r\n\tfor user in tweepy.Cursor(api.friends, screen_name=user_to_copy).items():\r\n\t\tif str(user.id) not in followedUsers:\r\n\t\t\ti += 1\r\n\t\t\tmustretry = True\r\n\t\t\tsleepinc = 0\r\n\r\n\t\t\twhile(mustretry):\r\n\t\t\t\ttry:\r\n\t\t\t\t\tapi.create_friendship(user.id)\r\n\t\t\t\t\tfollowedFile.write(\"\\n{0}\".format(str(user.id)))\r\n\t\t\t\t\tmustretry = False\r\n\t\t\t\t\tprint('[s] {0} User: {1} - Id: {2} - Protected: {3}'.format(i, user.screen_name, user.id, user.protected))\r\n\r\n\t\t\t\texcept tweepy.error.TweepError as err:\r\n\t\t\t\t\tif int(err.args[0][0]['code']) == 106 or int(err.args[0][0]['code']) == 162:\r\n\t\t\t\t\t\tmustretry = False\r\n\t\t\t\t\t\tbanned.append(user)\r\n\t\t\t\t\t\tprint('[b] {0} User: {1} - Id: {2} - Protected: {3}'.format(i, user.screen_name, user.id, user.protected))\r\n\r\n\t\t\t\t\telif int(err.args[0][0]['code']) == 160:\r\n\t\t\t\t\t\tmustretry = False\r\n\t\t\t\t\t\talreadyRequested.append(user)\r\n\t\t\t\t\t\tprint('[r] {0} User: {1} - Id: {2} - Protected: {3}'.format(i, user.screen_name, user.id, user.protected))\r\n\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tprint(\" [{0}] {1}... Will retry after {2} minutes.\".format(datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\"), err, 2**sleepinc))\r\n\t\t\t\t\t\ttime.sleep(60*2**sleepinc)\r\n\t\t\t\t\t\tif sleepinc < 9:\r\n\t\t\t\t\t\t\tsleepinc +=1\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\tpass\r\n\r\n\t\telse:\r\n\t\t\tprint(\"[x] Skipping user {0}\".format(user.screen_name))\r\n\r\nprint(\"[{0}] -- Already requested users: -- \".format(datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")))\r\nfor user in alreadyRequested:\r\n\tprint(user.screen_name)\r\n\r\nprint(\"[{0}] -- Banned users: -- \".format(datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")))\r\nfor user in banned:\r\n\tprint(user.screen_name)\r\n\r\nendingTime = time.time()\r\nprint(\"[{0}] Execution took {1} seconds\".format(datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\"), int(endingTime - startingTime)))\r\n", "repo_name": "GKNSB/followCopy", "sub_path": "followCopy.py", "file_name": "followCopy.py", "file_ext": "py", "file_size_in_byte": 2868, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "configparser.RawConfigParser", "line_number": 7, "usage_type": "call"}, {"api_name": "tweepy.OAuthHandler", "line_number": 13, "usage_type": "call"}, {"api_name": "tweepy.API", "line_number": 22, "usage_type": "call"}, {"api_name": "time.time", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 28, "usage_type": "call"}, {"api_name": "tweepy.Cursor", "line_number": 41, "usage_type": "call"}, {"api_name": "tweepy.error", "line_number": 54, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 66, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 66, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 67, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 76, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 76, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 80, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 80, "usage_type": "name"}, {"api_name": "time.time", "line_number": 84, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 85, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 85, "usage_type": "name"}]} +{"seq_id": "30749879706", "text": "import pygame\r\nfrom pygame.sprite import Sprite\r\n\r\n\r\nclass Ship(Sprite):\r\n\r\n def __init__(self, ai_settings, screen):\r\n \"\"\"初始化飞船并设置其初始位置\"\"\"\r\n super(Ship, self).__init__() # ship继承了Sprite\r\n self.screen = screen\r\n self.ai_settings = ai_settings\r\n # 加载飞船图像并获取其外接矩形\r\n self.image = pygame.image.load('D:/python/images/ship.bmp')\r\n self.rect = self.image.get_rect()\r\n self.screen_rect = screen.get_rect()\r\n\r\n # 将每艘飞船都放在屏幕底部中央\r\n self.rect.centerx = self.screen_rect.centerx\r\n self.rect.bottom = self.screen_rect.bottom # 如果要想将飞船的坐标显示为不同的位置可通过设置坐标点，常用的有top, left, bottom, right\r\n # topleft, bottomleft, topright, bottomrigh，midtop, midleft, midbottom, midright，center, centerx, centery，size, width, height\r\n self.center = float(self.rect.centerx) # 在飞船的属性center中存储小数值\r\n #self.rect.topleft = self.screen_rect.topleft这句话的意思是将坐标X轴放在屏幕左上角\r\n #self.rect.bottomleft = self.screen_rect.bottomleft这句话的意思是将坐标y轴放在屏幕左下角\r\n\r\n # 移动标快\r\n self.moving_right = False\r\n self.moving_left = False\r\n # 检测键盘的状态属性，如果按下右键盘则向右移动时间为真，即x 坐标右移\r\n\r\n def update(self): # 此方法update在前述标志为真的时候向右移动飞船\r\n \"\"\"根据移动标志调整飞船位置\"\"\"\r\n \"\"\"更改飞船的center值而不是rect\"\"\"\r\n if self.moving_right and self.rect.right < self.screen_rect.right:\r\n self.center += self.ai_settings.ship_speed_factor\r\n if self.moving_left and self.rect.left > 0:\r\n self.center -= self.ai_settings.ship_speed_factor\r\n # 根据self.center 更新rect对象\r\n self.rect.centerx = self.center\r\n\r\n def blitme(self):\r\n \"\"\"在指定位置绘制飞船\"\"\"\r\n self.screen.blit(self.image, self.rect)\r\n\r\n def center_ship(self):\r\n \"\"\"让飞船在屏幕上居中\"\"\"\r\n self.center = self.screen_rect.centerx\r\n\r\n", "repo_name": "Gavin-python-oss/python-sample-code", "sub_path": "ship.py", "file_name": "ship.py", "file_ext": "py", "file_size_in_byte": 2245, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "pygame.sprite.Sprite", "line_number": 5, "usage_type": "name"}, {"api_name": "pygame.image.load", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 13, "usage_type": "attribute"}]} +{"seq_id": "37807272250", "text": "import os\nimport asyncio\nfrom googletrans import Translator\nfrom pyrogram import Client, filters\nfrom pyrogram.types import (InlineQuery,\n InputTextMessageContent,\n InlineQueryResultArticle,\n InlineKeyboardButton,\n InlineKeyboardMarkup\n )\n\n\nif bool(os.environ.get(\"ENV\", False)):\n from sample_config import Config\nelse:\n from config import Config\n\n\nmozhi = Client(\n name=\"mozhi\",\n bot_token=Config.TG_BOT_TOKEN,\n api_id=Config.APP_ID,\n api_hash=Config.API_HASH,\n)\n\n\n\"\"\"Function for start message\"\"\"\n@mozhi.on_message(filters.command([\"start\", \"help\"]))\nasync def start_bot(_, m):\n translator = Translator()\n text = \"നമസ്കാരം {}\\nഞാൻ ഒരു മലയാളം വിവർത്തക ബോട്ട് ആണ്. കൂടുതൽ അറിയുവാൻ എന്റെ ടെലിഗ്രാം ചാനൽ സന്ദർശിക്കുക.\"\n await m.reply_text(\n text=text.format(m.from_user.mention, 't.me/rmprojects'),\n disable_web_page_preview=True,\n reply_markup=InlineKeyboardMarkup(\n [\n [\n InlineKeyboardButton(\"♨️ Source\", url=\"https://github.com/m4mallu/mozhi\"),\n InlineKeyboardButton(\"🔎 Translate\", switch_inline_query_current_chat='')\n ]\n ]\n )\n )\n\n\n\"\"\"Function for inline query\"\"\"\n@mozhi.on_inline_query()\nasync def inline_replay(_, query: InlineQuery):\n result = ''\n inline_results = []\n #\n string = query.query.strip()\n formatting = \"Translation Results for 👉 {} will be...\\n\\n👉 {}\"\n #\n try:\n result = Translator().translate(text=string, dest='ml').text\n except Exception:\n pass\n #\n inline_results.append(\n InlineQueryResultArticle(\n title=\"Transation Results for 👉 {}\".format(string),\n input_message_content=InputTextMessageContent(\n message_text=formatting.format(string, result)),\n thumb_url='https://telegra.ph/file/19008e0b8e4c578fcfd86.png',\n description=result,\n )\n )\n if result:\n try:\n await query.answer(\n results=inline_results,\n cache_time=0,\n )\n except Exception:\n pass\n\n\n\"\"\" If the inline result belongs to a private chat, the bot will delete the message after 15 seconds \"\"\"\n@mozhi.on_message(filters.private & filters.incoming)\nasync def delete_message(_, m):\n await asyncio.sleep(15)\n await m.delete()\n\n\nprint(\"\\nBot Started Successfully !\")\nmozhi.run()\n", "repo_name": "m4mallu/mozhi", "sub_path": "bot.py", "file_name": "bot.py", "file_ext": "py", "file_size_in_byte": 2763, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "12", "api": [{"api_name": "os.environ.get", "line_number": 13, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pyrogram.Client", "line_number": 19, "usage_type": "call"}, {"api_name": "config.Config.TG_BOT_TOKEN", "line_number": 21, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 21, "usage_type": "name"}, {"api_name": "config.Config.APP_ID", "line_number": 22, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 22, "usage_type": "name"}, {"api_name": "config.Config.API_HASH", "line_number": 23, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 23, "usage_type": "name"}, {"api_name": "googletrans.Translator", "line_number": 30, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardMarkup", "line_number": 35, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 38, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 39, "usage_type": "call"}, {"api_name": "pyrogram.filters.command", "line_number": 28, "usage_type": "call"}, {"api_name": "pyrogram.filters", "line_number": 28, "usage_type": "name"}, {"api_name": "pyrogram.types.InlineQuery", "line_number": 48, "usage_type": "name"}, {"api_name": "googletrans.Translator", "line_number": 56, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineQueryResultArticle", "line_number": 61, "usage_type": "call"}, {"api_name": "pyrogram.types.InputTextMessageContent", "line_number": 63, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 82, "usage_type": "call"}, {"api_name": "pyrogram.filters.private", "line_number": 80, "usage_type": "attribute"}, {"api_name": "pyrogram.filters", "line_number": 80, "usage_type": "name"}, {"api_name": "pyrogram.filters.incoming", "line_number": 80, "usage_type": "attribute"}]} +{"seq_id": "5883694556", "text": "from kivy.factory import Factgory as F\nfrom kivy.properties import *\nfrom kivy.utils import interpolate\n\nclass ScrollList(F.FloatLayout):\n drag_threshold = NumericProperty(20)\n drag_offset = NumericProperty(0)\n total_offset = NumericProperty(0)\n scroll_layer = ObjectProperty(None)\n item_list = ObjectProperty(None)\n\n def __init__(self, **kwargs):\n super(CountyList, self).__init__(**kwargs)\n self.drag_touch_id = None\n self.anim = None\n Clock.schedule_once(self.load_data)\n\n def on_drag_offset(self, *args):\n if self.scroll_layer is None:\n return\n toy = self.total_offset\n doy = self.drag_offset\n dx, dy = self.x, self.y + toy + doy\n self.scroll_layer.transform = Matrix().translate(dx, dy, 0)\n\n def on_total_offset(self, *args):\n if self.scroll_layer is None:\n return\n toy = self.total_offset\n doy = self.drag_offset\n dx, dy = self.x, self.y + toy + doy\n self.scroll_layer.transform = Matrix().translate(dx, dy, 0)\n\n def on_touch_down(self, touch):\n if self.drag_touch_id != None:\n return False\n if self.collide_point(*touch.pos):\n if self.anim:\n Animation.cancel_all(self, 'total_offset')\n self.anim = None\n self.drag_touch_id = touch.uid\n self.velocity = 0\n touch.ud['last_y'] = touch.y\n t = (touch.time_update, touch.y)\n touch.ud['t_update'] = [t,t,t,t]\n touch.ud['move_distance'] = 0\n return True\n\n def on_touch_move(self, touch):\n if self.drag_touch_id == touch.uid:\n dy = touch.y - touch.ud['last_y']\n touch.ud['last_y'] = touch.y\n touch.ud['move_distance'] += abs(dy)\n t = (touch.time_update, touch.y)\n tupdate = touch.ud['t_update'][1:]\n tupdate.append(t)\n touch.ud['t_update'] = tupdate\n if touch.ud['move_distance'] > self.drag_threshold:\n self.drag_offset += dy\n return True\n\n\n def update_velocity(self, *args):\n if abs(self.velocity) == 0.001:\n self.velocity = 0\n\n min_offset = (1080 - self.item_list.height)\n is_too_high = self.total_offset > 0\n is_too_low = self.total_offset < min_offset\n within_bounds = not (is_too_high or is_too_low)\n\n if self.velocity == 0 and within_bounds:\n return\n\n if is_too_high:\n self.total_offset = interpolate(self.total_offset, 0)\n if is_too_low:\n self.total_offset = interpolate(self.total_offset, min_offset)\n\n self.total_offset += self.velocity\n self.velocity = interpolate(self.velocity, 0, 15)\n Clock.schedule_once(self.update_velocity, 1.0/30.0)\n\n def selection(self, item):\n App.get_running_app().selected_county = item['name'].replace(\"-\", \"_\")\n\n def on_selected_county(self, *args):\n for btn in self.item_list.children:\n n = btn.data['name'].replace(\"-\", \"_\")\n if n == self.selected_county:\n btn.state=\"down\"\n else:\n btn.state=\"normal\"\n\n def on_touch_up(self, touch):\n if self.drag_touch_id == touch.uid:\n dy = touch.y - touch.ud['last_y']\n touch.ud['move_distance'] += abs(dy)\n if touch.ud['move_distance'] > self.drag_threshold:\n self.total_offset += self.drag_offset + dy\n self.drag_offset = 0\n\n tup = touch.ud['t_update']\n dura = (tup[3][0] - tup[0][0]) * 100.0\n dist = tup[3][1] - tup[0][1]\n\n self.velocity = 2.5 * (dist/dura)\n Clock.schedule_once(self.update_velocity, 1.0/30.0)\n #if 'mov' in touch.profile:\n # touch.Y\n else:\n self.drag_offset = 0\n self.velocity = 0\n x,y = 10, max(0,min(touch.y - self.total_offset, self.item_list.height-1))\n idx = int(y / 80)\n btn = self.item_list.children[idx]\n self.selection(btn.data)\n\n self.drag_touch_id = None\n return True\n\n def load_data(self, *args):\n self.counties = App.get_running_app().counties\n for name in sorted(self.counties.keys()):\n county = self.counties[name]\n self.item_list.add_widget(CountyListButton(data=county))\n\ndef log_scale(v, vmin, vmax):\n logmax = log(vmax / vmin)\n return log(v / vmin) / logmax", "repo_name": "fresk/wall_of_iowans", "sub_path": "museum/slider.py", "file_name": "slider.py", "file_ext": "py", "file_size_in_byte": 4588, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "kivy.factory.Factgory.FloatLayout", "line_number": 5, "usage_type": "attribute"}, {"api_name": "kivy.factory.Factgory", "line_number": 5, "usage_type": "name"}, {"api_name": "kivy.utils.interpolate", "line_number": 76, "usage_type": "call"}, {"api_name": "kivy.utils.interpolate", "line_number": 78, "usage_type": "call"}, {"api_name": "kivy.utils.interpolate", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "22095237796", "text": "import matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\n\nfname = '../training.csv'\nprint('Reading dataframe from ', fname)\nprint('\\n')\ndf = pd.read_csv(fname)\n\nhorses = df.groupby('horse_name')\nh_numwin = horses.apply(lambda v: len(v[v.finishing_position == 1]))\nh_numwin.name = 'number_of_win'\nh_winrate = horses.apply(lambda v: float(len(v[v.finishing_position == 1]))/len(v))\nh_winrate.name = 'win_rate'\n\njocks = df.groupby('jockey')\nj_numwin = jocks.apply(lambda v: len(v[v.finishing_position == 1]))\nj_numwin.name = 'number_of_win'\nj_winrate = jocks.apply(lambda v: float(len(v[v.finishing_position == 1]))/len(v))\nj_winrate.name = 'win_rate'\n\n\nfig, ax = plt.subplots(figsize=(10, 5))\nplt.subplots_adjust(left=0.1, right=0.8, top=0.9, bottom=0.1)\nax.scatter(h_winrate,h_numwin,s=10)\nfor i in range(len(h_winrate)):\n if h_numwin.iloc[i] > 4 and h_winrate.iloc[i] > 0.5 :\n ax.annotate(str(h_numwin.axes[0][i]) ,(h_winrate.iloc[i]+0.01-i*0.00005,h_numwin.iloc[i]+i*0.0002))\n #print(df[df.horse_name == str(h_numwin.axes[0][i])])\nplt.xlabel(\"Win Rate\")\nplt.ylabel(\"Number of wins\")\nplt.title(\"Winning statistics of horses\")\nplt.show()\n\nfig, ax = plt.subplots()\nax.scatter(j_winrate,j_numwin,s=10)\nfor i in range(len(j_winrate)):\n if j_numwin.iloc[i] > 100 and j_winrate.iloc[i] > 0.2 :\n ax.annotate(str(j_numwin.axes[0][i]) ,(j_winrate.iloc[i]+0.005,j_numwin.iloc[i]))\n #print(df[df.jockey == str(j_numwin.axes[0][i])])\nplt.xlabel(\"Win Rate\")\nplt.ylabel(\"Number of wins\")\nplt.title(\"Winning statistics of jockeys\")\nplt.show()\n", "repo_name": "htonyl/horse-race", "sub_path": "plot/scatter_plot.py", "file_name": "scatter_plot.py", "file_ext": "py", "file_size_in_byte": 1576, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}]} +{"seq_id": "7430342422", "text": "import asyncio\nimport datetime\nfrom datetime import datetime, time\n\nfrom discord.ext.commands import bot\nfrom helper.setup import bot\n\nimport display_stats\nfrom helper import formatter\n\nfrom commands import user_commands\nfrom whale_wallet_data import wallet\n\nfrom market.market_api import get_sms_price\nfrom market.market_api import get_bnb_sms_price\nfrom alerts.subscription import alert\nfrom display_stats import display_nonembedded_stats\n\nTOKEN_AUTH = \"\"\n\nwhales = []\n\n\n@bot.event\nasync def on_ready():\n print(\"bot is ready...\")\n\n\n@bot.event\nasync def on_message(message):\n await user_commands.cmds(message)\n\n\nasync def price_update():\n countdown = 0\n await bot.wait_until_ready()\n\n # these channels are specific channels on the safemooon discord. modify them as your own to post on other\n # channels, or completely comment out this function for commands only.\n channel = bot.get_channel(870391088723157072)\n channel2 = bot.get_channel(875196762682437682)\n\n while not bot.is_closed():\n\n p1, trend = await get_bnb_sms_price()\n description = \"$\" + p1 + \" \" + trend\n\n p2, trend = await get_sms_price()\n description2 = \"$\" + p2 + \" \" + trend\n try:\n await channel.send(embed=formatter.embedder(\"V2 SFS Price (BNB)\", description # + \"| \"\n # + description2\n ).set_footer(text=str(datetime.now()) + \" (UTC)\"))\n\n # ---------------international channel posting\n\n description = \"$\" + p1\n description2 = \"$\" + p2\n\n await channel2.send(embed=formatter.embedder(\"V2 SFS Price (BNB)\", description # + \"| \"+ description2\n ).set_footer(text=str(datetime.now()) + \" (UTC)\"))\n # ----------------------------------------------\n await asyncio.sleep(60)\n except:\n await asyncio.sleep(60)\n\n if countdown == 0:\n await display_stats.display_all_stats(channel, channel2)\n countdown = 6\n\n countdown -= 1\n await asyncio.sleep(5)\n\n\n# comment this out. this is specifically for alerts on sfm discord and fb feed.\nasync def daily_report():\n await bot.wait_until_ready()\n\n channel = bot.get_channel(877923487015137320)\n WHEN = time(23, 0, 0)\n\n await asyncio.sleep(3600)\n while not bot.is_closed():\n\n now = datetime.utcnow()\n\n if now.time() >= WHEN:\n if await display_nonembedded_stats(channel):\n await alert()\n await asyncio.sleep(86400)\n\n await asyncio.sleep(60)\n\n\nbot.loop.create_task(price_update())\nbot.loop.create_task(wallet.whale_processing())\nbot.loop.create_task(daily_report())\n\nbot.run(TOKEN_AUTH)\n", "repo_name": "N1234P/SFM-Crypto-Market-Ticker", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2720, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "12", "api": [{"api_name": "helper.setup.bot.event", "line_number": 24, "usage_type": "attribute"}, {"api_name": "helper.setup.bot", "line_number": 24, "usage_type": "name"}, {"api_name": "commands.user_commands.cmds", "line_number": 31, "usage_type": "call"}, {"api_name": "commands.user_commands", "line_number": 31, "usage_type": "name"}, {"api_name": "helper.setup.bot.event", "line_number": 29, "usage_type": "attribute"}, {"api_name": "helper.setup.bot", "line_number": 29, "usage_type": "name"}, {"api_name": "helper.setup.bot.wait_until_ready", "line_number": 36, "usage_type": "call"}, {"api_name": "helper.setup.bot", "line_number": 36, "usage_type": "name"}, {"api_name": "helper.setup.bot.get_channel", "line_number": 40, "usage_type": "call"}, {"api_name": "helper.setup.bot", "line_number": 40, "usage_type": "name"}, {"api_name": "helper.setup.bot.get_channel", "line_number": 41, "usage_type": "call"}, {"api_name": "helper.setup.bot", "line_number": 41, "usage_type": "name"}, {"api_name": "helper.setup.bot.is_closed", "line_number": 43, "usage_type": "call"}, {"api_name": "helper.setup.bot", "line_number": 43, "usage_type": "name"}, {"api_name": "market.market_api.get_bnb_sms_price", "line_number": 45, "usage_type": "call"}, {"api_name": "market.market_api.get_sms_price", "line_number": 48, "usage_type": "call"}, {"api_name": "helper.formatter.embedder", "line_number": 51, "usage_type": "call"}, {"api_name": "helper.formatter", "line_number": 51, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 53, "usage_type": "name"}, {"api_name": "helper.formatter.embedder", "line_number": 60, "usage_type": "call"}, {"api_name": "helper.formatter", "line_number": 60, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 61, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 63, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 65, "usage_type": "call"}, {"api_name": "display_stats.display_all_stats", "line_number": 68, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 72, "usage_type": "call"}, {"api_name": "helper.setup.bot.wait_until_ready", "line_number": 77, "usage_type": "call"}, {"api_name": "helper.setup.bot", "line_number": 77, "usage_type": "name"}, {"api_name": "helper.setup.bot.get_channel", "line_number": 79, "usage_type": "call"}, {"api_name": "helper.setup.bot", "line_number": 79, "usage_type": "name"}, {"api_name": "datetime.time", "line_number": 80, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 82, "usage_type": "call"}, {"api_name": "helper.setup.bot.is_closed", "line_number": 83, "usage_type": "call"}, {"api_name": "helper.setup.bot", "line_number": 83, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 85, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 85, "usage_type": "name"}, {"api_name": "display_stats.display_nonembedded_stats", "line_number": 88, "usage_type": "call"}, {"api_name": "alerts.subscription.alert", "line_number": 89, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 90, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 92, "usage_type": "call"}, {"api_name": "helper.setup.bot.loop.create_task", "line_number": 95, "usage_type": "call"}, {"api_name": "helper.setup.bot.loop", "line_number": 95, "usage_type": "attribute"}, {"api_name": "helper.setup.bot", "line_number": 95, "usage_type": "name"}, {"api_name": "helper.setup.bot.loop.create_task", "line_number": 96, "usage_type": "call"}, {"api_name": "helper.setup.bot.loop", "line_number": 96, "usage_type": "attribute"}, {"api_name": "helper.setup.bot", "line_number": 96, "usage_type": "name"}, {"api_name": "whale_wallet_data.wallet.whale_processing", "line_number": 96, "usage_type": "call"}, {"api_name": "whale_wallet_data.wallet", "line_number": 96, "usage_type": "name"}, {"api_name": "helper.setup.bot.loop.create_task", "line_number": 97, "usage_type": "call"}, {"api_name": "helper.setup.bot.loop", "line_number": 97, "usage_type": "attribute"}, {"api_name": "helper.setup.bot", "line_number": 97, "usage_type": "name"}, {"api_name": "helper.setup.bot.run", "line_number": 99, "usage_type": "call"}, {"api_name": "helper.setup.bot", "line_number": 99, "usage_type": "name"}]} +{"seq_id": "22307780167", "text": "from googletrans import Translator\r\nfrom typing import Text\r\nfrom gtts import gTTS\r\nfrom pyrogram import Client,filters\r\nimport os\r\nimport requests\r\napp = Client(\"my_accound\",api_id=13893053,api_hash=\"f586d92837b0f6eebcaa3e392397f47c\")\r\n\r\n@app.on_message(filters.regex(\"^!trans \") & (filters.user(760148720) | filters.me))\r\ndef ocr(client,message):\r\n text=message.text \r\n text2=text.split()[0]\r\n text=text.replace(text2,\"\")\r\n mas_id=message.message_id\r\n src=text.split()[0]\r\n text=text.replace(src,\"\")\r\n dest=text.split()[0]\r\n text=text.replace(dest,\"\")\r\n translator = Translator()\r\n result = translator.translate(text, src=src,dest=dest)\r\n client.edit_message_text(chat_id=message.chat.id,message_id=message.message_id,text=result.text)\r\n\r\n@app.on_message(filters.regex(\"^!tts$\") & (filters.user(760148720) | filters.me))\r\ndef tts(client,message):\r\n chat_id=message.chat.id\r\n message_id=message.message_id\r\n text = message.reply_to_message.text\r\n language=\"en\"\r\n myobj=gTTS(text=text,lang=language,slow=False)\r\n myobj.save(\"test.ogg\")\r\n client.send_audio(chat_id,\"test.ogg\",reply_to_message_id=message_id)\r\n os.remove('test.ogg')\r\n\r\n \r\napp.run()\r\n", "repo_name": "rezabakhshy/ocrv3.3", "sub_path": "bot.py", "file_name": "bot.py", "file_ext": "py", "file_size_in_byte": 1208, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "pyrogram.Client", "line_number": 7, "usage_type": "call"}, {"api_name": "googletrans.Translator", "line_number": 19, "usage_type": "call"}, {"api_name": "pyrogram.filters.regex", "line_number": 9, "usage_type": "call"}, {"api_name": "pyrogram.filters", "line_number": 9, "usage_type": "name"}, {"api_name": "pyrogram.filters.user", "line_number": 9, "usage_type": "call"}, {"api_name": "pyrogram.filters.me", "line_number": 9, "usage_type": "attribute"}, {"api_name": "gtts.gTTS", "line_number": 29, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 32, "usage_type": "call"}, {"api_name": "pyrogram.filters.regex", "line_number": 23, "usage_type": "call"}, {"api_name": "pyrogram.filters", "line_number": 23, "usage_type": "name"}, {"api_name": "pyrogram.filters.user", "line_number": 23, "usage_type": "call"}, {"api_name": "pyrogram.filters.me", "line_number": 23, "usage_type": "attribute"}]} +{"seq_id": "14031782563", "text": "\"\"\"Contains an in Pytorch implemented Squashed Gaussian Actor.\n\"\"\"\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nimport torch.nn.functional as F\nfrom torch.distributions.normal import Normal\n\n\ndef mlp(sizes, activation, output_activation=nn.Identity):\n \"\"\"Create a multi-layered perceptron using pytorch.\n\n Args:\n sizes (list): The size of each of the layers.\n\n activation (torch.nn.modules.activation): The activation function used for the\n hidden layers.\n\n output_activation (torch.nn.modules.activation, optional): The activation\n function used for the output layers. Defaults to torch.nn.Identity.\n\n Returns:\n torch.nn.modules.container.Sequential: The multi-layered perceptron.\n \"\"\"\n layers = []\n for j in range(len(sizes) - 1):\n act = activation if j < len(sizes) - 2 else output_activation\n layers += [nn.Linear(sizes[j], sizes[j + 1]), act()]\n return nn.Sequential(*layers)\n\n\nclass SquashedGaussianMLPActor(nn.Module):\n \"\"\"The squashed gaussian actor network.\n\n Attributes:\n net (torch.nn.modules.container.Sequential): The input/hidden layers of the\n network.\n\n mu (torch.nn.modules.linear.Linear): The output layer which returns the mean of\n the actions.\n\n log_sigma (torch.nn.modules.linear.Linear): The output layer which returns\n the log standard deviation of the actions.\n \"\"\"\n\n def __init__(\n self, obs_dim, act_dim, hidden_sizes, log_std_min=-20, log_std_max=2.0,\n ):\n \"\"\"Constructs all the necessary attributes for the Squashed Gaussian Actor\n object.\n\n Args:\n obs_dim (int): Dimension of the observation space.\n\n act_dim (int): Dimension of the action space.\n\n hidden_sizes (list): Sizes of the hidden layers.\n\n log_std_min (int, optional): The minimum log standard deviation. Defaults\n to -20.\n\n log_std_max (float, optional): The maximum log standard deviation. Defaults\n to 2.0.\n \"\"\"\n super().__init__()\n self._log_std_min = log_std_min\n self._log_std_max = log_std_max\n\n # Create networks\n self.net = mlp([obs_dim] + list(hidden_sizes), nn.ReLU, nn.ReLU)\n self.mu = nn.Linear(hidden_sizes[-1], act_dim)\n self.log_sigma = nn.Linear(hidden_sizes[-1], act_dim)\n\n def forward(self, obs):\n \"\"\"Perform forward pass through the network.\n\n Args:\n obs (torch.Tensor): The tensor of observations.\n\n Returns:\n torch.Tensor, torch.Tensor: The actions given by the policy, the log\n probabilities of each of these actions.\n \"\"\"\n net_out = self.net(obs)\n _ = self.mu(net_out)\n log_std = self.log_sigma(net_out)\n log_std = torch.clamp(log_std, self._log_std_min, self._log_std_max)\n return _, _, _\n", "repo_name": "rickstaa/LAC-TF2-TORCH-translation", "sub_path": "sandbox/speed_comparison/timeit_comparison/gaussian_actor_torch_small.py", "file_name": "gaussian_actor_torch_small.py", "file_ext": "py", "file_size_in_byte": 2937, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "torch.nn.Identity", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 34, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 72, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 74, "usage_type": "name"}, {"api_name": "torch.clamp", "line_number": 89, "usage_type": "call"}]} +{"seq_id": "23222103094", "text": "from django.urls import path\nfrom . import views\n\n\n\nurlpatterns = [\n \n path('', views.brandlist, name = 'brandlist'),\n path('createbrand/', views.createbrand, name = 'createbrand'),\n path('editbrand//', views.editbrand, name = 'editbrand'),\n path('deletebrand//', views.deletebrand, name = 'deletebrand'),\n \n \n path('categorylist/', views.categorylist, name='categorylist'),\n path('createcategory/', views.createcategory, name = 'createcategory'),\n path('editcategory//', views.editcategory, name = 'editcategory'),\n path('deletecategory//', views.deletecategory, name = 'deletecategory'),\n \n \n path('userlist/', views.userlist, name = 'userlist'), \n path('blockunblock//', views.blockUnblock, name='blockunblock'),\n \n \n \n \n \n \n \n \n \n \n] \n", "repo_name": "Sahlaanas/djangoproject", "sub_path": "dashboard/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 917, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "22260371606", "text": "from __future__ import print_function\n\nimport requests\nimport json\nimport numpy as np\n\nfrom flask import request, render_template, session, redirect, make_response\nfrom app import app\nfrom app.models import Game\n\nSTEAM_API_KEY = app.config['STEAM_API_KEY']\nSECRET_KEY = app.config['SECRET_KEY']\n\n@app.route(\"/steam/login\", methods=['GET', 'POST'])\ndef login():\n username = request.form['username']\n redirect_to_home = redirect(\"/\")\n response = make_response(redirect_to_home)\n if username != \"\":\n params = {\n 'key': STEAM_API_KEY,\n 'vanityurl': username\n }\n r = requests.get(\"http://api.steampowered.com/ISteamUser/ResolveVanityURL/v0001/\", params)\n success = int(r.json()['response']['success'])\n if success == 1:\n steamid = r.json()['response']['steamid']\n response.set_cookie(\"steam_ID\", value=steamid)\n del params['vanityurl']\n params['steamids'] = steamid\n r3 = requests.get(\"http://api.steampowered.com/ISteamUser/GetPlayerSummaries/v0002/\",\n params)\n if 200 <= r3.status_code < 300:\n response.set_cookie(\"username\",\n value=r3.json()['response']['players'][0]['personaname'])\n del params[\"steamids\"]\n params[\"steamid\"] = steamid\n params[\"format\"] = \"json\"\n params[\"include_played_free_games\"] = \"1\"\n r2 = requests.get(\"http://api.steampowered.com/IPlayerService/GetOwnedGames/v0001/\",\n params)\n if 'games' not in r2.json()['response']:\n return render_template(\"search.html\",\n username=request.cookies.get(\"username\"),\n invalid_login=True)\n games = r2.json()['response']['games']\n game_list = [int(game['appid']) for game in games]\n hours_played = [int(game['playtime_forever']) for game in games]\n response.set_cookie(\"game_list\", value=json.dumps(game_list))\n library_vector = Game.compute_library_vector(game_list, hours_played)\n response.set_cookie(\"library_vector\", value=json.dumps(library_vector.tolist()))\n else:\n pass #???\n else:\n return render_template(\"search.html\",\n username=request.cookies.get(\"username\"),\n invalid_login=True)\n elif request.cookies.get('username'):\n del session[\"steam_ID\"]\n del session[\"library_vector\"]\n response.set_cookie(\"steam_id\", \"\", max_age=0)\n response.set_cookie(\"library_vector\", \"\", max_age=0)\n return response\n\n", "repo_name": "PapaCharlie/SteamyReviews", "sub_path": "app/controllers/library_controller.py", "file_name": "library_controller.py", "file_ext": "py", "file_size_in_byte": 2838, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "app.app.config", "line_number": 11, "usage_type": "attribute"}, {"api_name": "app.app", "line_number": 11, "usage_type": "name"}, {"api_name": "app.app.config", "line_number": 12, "usage_type": "attribute"}, {"api_name": "app.app", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 16, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 16, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 18, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 24, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 31, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.request.cookies.get", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 44, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 44, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 49, "usage_type": "call"}, {"api_name": "app.models.Game.compute_library_vector", "line_number": 50, "usage_type": "call"}, {"api_name": "app.models.Game", "line_number": 50, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.request.cookies.get", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 56, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 56, "usage_type": "name"}, {"api_name": "flask.request.cookies.get", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 58, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 58, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 60, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 14, "usage_type": "call"}, {"api_name": "app.app", "line_number": 14, "usage_type": "name"}]} +{"seq_id": "14057582358", "text": "#!/usr/bin/python3\n\"\"\"The goal of it is to manage\nid attribute in all your future\nclasses and to avoid duplicating\nthe same code (by extension, same bugs)\n\"\"\"\nimport json\nimport os.path\nimport csv\n\n\nclass Base:\n \"\"\"base class for the rest of the projects\n \"\"\"\n __nb_objects = 0\n\n def __init__(self, id=None):\n self.id = id\n if id is None:\n \"the code below,everytime an object is created\\\n the class calls its attribute and increment one\\\n only if fullfill the condition\"\n Base.__nb_objects += 1\n \"then, assign the increment to the var id\"\n self.id = Base.__nb_objects\n else:\n \"it an argument is given, it passes normally\"\n self.id = id\n\n @staticmethod\n def to_json_string(list_dictionaries):\n \"\"\"[summary]\n\n Args:\n list_dictionaries ([type]): [description]\n\n Returns:\n [type]: [description]\n \"\"\"\n if list_dictionaries is None or not list_dictionaries:\n return \"[]\"\n return json.dumps(list_dictionaries)\n\n @classmethod\n def save_to_file(cls, list_objs):\n \"\"\"[summary]\n\n Args:\n list_objs ([type]): [description]\n \"\"\"\n filename = \"{}.json\".format(cls.__name__)\n list_own = []\n with open(filename, mode='w', encoding='UTF8') as myfile:\n if list_objs is not None:\n for obj_lists in list_objs:\n list_own.append(cls.to_dictionary(obj_lists))\n myfile.write(cls.to_json_string(list_own))\n\n @staticmethod\n def from_json_string(json_string):\n \"\"\"[summary]\n\n Args:\n json_string ([type]): [description]\n\n Returns:\n [type]: [description]\n \"\"\"\n if json_string is None or not json_string:\n return []\n return json.loads(json_string)\n\n @classmethod\n def create(cls, **dictionary):\n \"\"\"[summary]\n\n Returns:\n [type]: [description]\n \"\"\"\n if cls.__name__ == \"Rectangle\":\n dummy = cls(4, 5)\n elif cls.__name__ == \"Square\":\n dummy = cls(5)\n dummy.update(**dictionary)\n return dummy\n\n @classmethod\n def load_from_file(cls):\n \"\"\"[summary]\n\n Returns:\n [type]: [description]\n \"\"\"\n l_js = []\n filename = \"{}.json\".format(cls.__name__)\n if os.path.isfile(filename):\n with open(filename, mode='r', encoding='UTF8') as myfile:\n list_inst = myfile.read()\n str_js = cls.from_json_string(list_inst)\n for instan_el in str_js:\n aux = cls.create(**instan_el)\n l_js.append(aux)\n return l_js\n else:\n return l_js\n", "repo_name": "davehh1211/holbertonschool-higher_level_programming", "sub_path": "0x0C-python-almost_a_circle/models/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 2948, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "json.dumps", "line_number": 42, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.path.isfile", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 96, "usage_type": "name"}]} +{"seq_id": "28041423535", "text": "import cv2\n#import numpy as np\n#import matplotlib.pyplot as plt\n\n\nimg = cv2.imread('messi.jpg',1)\n\nimgc =img.copy()\n\n\n#img,pt1,pt2,color,thickness\ncv2.line(imgc,(100,300),(300,50),(255,255,0),5)\n\ncv2.imshow('abc',imgc[...,::-1])\n", "repo_name": "MuhammadTahaAzam1997/python-opencv-tutorials", "sub_path": "drawline.py", "file_name": "drawline.py", "file_ext": "py", "file_size_in_byte": 229, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "cv2.imread", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "513728260", "text": "\"\"\"empty message\n\nRevision ID: 377fe86da997\nRevises: f76bbc4581de\nCreate Date: 2020-05-11 22:59:34.514746\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '377fe86da997'\ndown_revision = 'f76bbc4581de'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('Area',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('city', sa.String(length=120), nullable=False),\n sa.Column('state', sa.String(length=120), nullable=False),\n sa.Column('venues', sa.String(length=120), nullable=False),\n sa.ForeignKeyConstraint(['city'], ['Venue.city'], ),\n sa.ForeignKeyConstraint(['state'], ['Venue.state'], ),\n sa.PrimaryKeyConstraint('id')\n )\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_table('Area')\n # ### end Alembic commands ###\n", "repo_name": "shrutikumar15/Fyyur", "sub_path": "migrations/versions/377fe86da997_.py", "file_name": "377fe86da997_.py", "file_ext": "py", "file_size_in_byte": 985, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "12", "api": [{"api_name": "alembic.op.create_table", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 28, "usage_type": "call"}, {"api_name": "alembic.op.drop_table", "line_number": 35, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 35, "usage_type": "name"}]} +{"seq_id": "43300763433", "text": "import logging\r\nfrom typing import List\r\n\r\nfrom modules.states import Discharging, Idle, NotSet, State, get_state\r\n\r\ncRed = \"\\033[31m\"\r\ncGreen = \"\\033[32m\"\r\ncYellow = \"\\033[33m\"\r\ncBlue = \"\\033[34m\"\r\ncMag = \"\\033[35m\"\r\ncCyan = \"\\033[36m\"\r\ncNorm = \"\\033[0m\"\r\n\r\n\r\nclass CellData:\r\n \"\"\"\r\n Contains slot cell metrics data\r\n \"\"\"\r\n valid_keys = ['slot_id', 'stage_id', 'stage', 'voltage', 'current', 'amphour', 'watthour', 'temp', 'timestamp']\r\n\r\n def __init__(self, values: dict):\r\n \"\"\"\r\n\r\n :param values:\r\n \"\"\"\r\n self.log = logging.getLogger(__name__)\r\n\r\n self.check_parameters(values)\r\n self._slot_id = values['slot_id']\r\n self._timestamp = values['timestamp']\r\n self._voltage = values['voltage']\r\n self._current = values['current']\r\n self._amphour = values['amphour']\r\n self._watthour = values['watthour']\r\n self._temp = values['temp']\r\n self._stage_id = NotSet.value\r\n self._stage = NotSet\r\n\r\n try:\r\n self._stage_id = Discharging().value()\r\n self._stage = get_state(int(values['stage_id']))\r\n return\r\n except:\r\n self.log.info(\"{}Failed to set state: {} {}\".format(cRed, values['stage_id'], cNorm))\r\n\r\n if int(values['stage_id']) == Idle().value:\r\n self._stage_id = Idle().value\r\n self._stage = Idle\r\n self.log.info(\"{}Slot# {} is now IDLE{}\".format(cGreen, self._slot_id, cNorm))\r\n return\r\n raise ValueError(\"Invalid stage_id value: {}\".format(values['stage_id']))\r\n\r\n @property\r\n def slot_id(self):\r\n \"\"\"\r\n\r\n :return:\r\n \"\"\"\r\n return self._slot_id\r\n\r\n def check_parameters(self, values: dict) -> None:\r\n \"\"\"\r\n\r\n :param values:\r\n :return:\r\n \"\"\"\r\n missing = [k for k in self.valid_keys if k not in values ]\r\n if missing:\r\n raise ValueError('{}Missing parameter(s): {}{}'.format(cRed, missing, cNorm))\r\n\r\n if any(True for k in values if k not in self.valid_keys):\r\n raise ValueError('Unexpected parameter, expected only {}'.format(self.valid_keys))\r\n\r\n @property\r\n def timestamp(self):\r\n return self._timestamp\r\n\r\n @property\r\n def to_json(self) -> dict:\r\n \"\"\"\r\n\r\n :return:\r\n \"\"\"\r\n return {\r\n i: getattr(self, \"_\" + i) if 'stage' != i else self._stage.__name__ for i in self.valid_keys\r\n }\r\n\r\n\r\nclass Cell:\r\n \"\"\"\r\n Cell object containing information about the cell, metrics, etc\r\n \"\"\"\r\n\r\n def __init__(self, _id: int):\r\n self.log = logging.getLogger(__name__)\r\n\r\n self._id = _id\r\n self._state = Idle\r\n self._model = None\r\n self._manufacturer = None\r\n self._cycle_count = 0\r\n\r\n self._history = []\r\n\r\n def set_cell(self, _id: str, model: str, manufacturer: str) -> None:\r\n \"\"\"\r\n\r\n :param _id:\r\n :param model:\r\n :param manufacturer:\r\n \"\"\"\r\n self._id = _id\r\n self._model = model\r\n self._manufacturer = manufacturer\r\n\r\n def add_history(self, cell_data: CellData) -> None:\r\n \"\"\"\r\n\r\n :param cell_data:\r\n :return:\r\n \"\"\"\r\n self._history.append(cell_data)\r\n\r\n def get_history(self) -> List[CellData]:\r\n \"\"\"\r\n\r\n :return: list of cell history\r\n \"\"\"\r\n return list(celldata for celldata in self._history)\r\n\r\n def get_last_history(self) -> CellData:\r\n \"\"\"\r\n Returns the latest cell data or an empty dict()\r\n :return: dic\r\n \"\"\"\r\n return self.get_history()[-1] if len(self.get_history()) else CellData(dict())\r\n\r\n def clear_history(self) -> None:\r\n \"\"\"\r\n\r\n :return:\r\n \"\"\"\r\n self._history = []\r\n\r\n @property\r\n def state(self):\r\n \"\"\"\r\n\r\n :return:\r\n \"\"\"\r\n return self._state\r\n\r\n @state.setter\r\n def state(self, set_state):\r\n \"\"\"\r\n\r\n :param set_state:\r\n :return:\r\n \"\"\"\r\n if not issubclass(set_state, State):\r\n raise Exception(\"Not of type 'State'\")\r\n self._state = set_state\r\n\r\n def cell_status_since(self, timestamp):\r\n selected = filter(lambda x: x.timestamp > timestamp, self.get_history()[::-1])\r\n output = {\r\n \"state\": self.state,\r\n \"history\": selected[::-1]\r\n }\r\n return output\r\n\r\n\r\nif __name__ == \"__main__\":\r\n c = Cell(0)\r\n print(c)\r\n", "repo_name": "Phizicks/NRG_manager", "sub_path": "classes/cell.py", "file_name": "cell.py", "file_ext": "py", "file_size_in_byte": 4527, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "logging.getLogger", "line_number": 26, "usage_type": "call"}, {"api_name": "modules.states.NotSet.value", "line_number": 36, "usage_type": "attribute"}, {"api_name": "modules.states.NotSet", "line_number": 36, "usage_type": "name"}, {"api_name": "modules.states.NotSet", "line_number": 37, "usage_type": "name"}, {"api_name": "modules.states.Discharging", "line_number": 40, "usage_type": "call"}, {"api_name": "modules.states.get_state", "line_number": 41, "usage_type": "call"}, {"api_name": "modules.states.Idle", "line_number": 46, "usage_type": "call"}, {"api_name": "modules.states.Idle", "line_number": 47, "usage_type": "call"}, {"api_name": "modules.states.Idle", "line_number": 48, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 95, "usage_type": "call"}, {"api_name": "modules.states.Idle", "line_number": 98, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 124, "usage_type": "name"}, {"api_name": "modules.states.State", "line_number": 160, "usage_type": "argument"}]} +{"seq_id": "16163045357", "text": "import os\nimport ctypes\nimport sys\nimport pathlib\nimport numpy as np\n\nimport ccd\nimport libc_builder\n\nif __name__ == '__main__':\n if len(sys.argv) < 2:\n libname = \"libccd\"\n os.system(\"invoke build-\" + libname)\n ccd_lib = libc_builder.cpp_build(str(libname))\n if len(sys.argv) == 2:\n os.system(\"invoke build-\" + str(sys.argv[1]))\n ccd_lib = libc_builder.cpp_build(str(sys.argv[1]))\n\n scalelimit = 63776\n looking_at = \"/zerobias.fits\"\n path_here = pathlib.Path().absolute()\n filepath = ccd.get_path(str(path_here) + looking_at)\n\n hdulist, header, imagedata = ccd.fits_handler(filepath, scalelimit, show=True)\n\n imagedata_list = (np.ndarray.flatten(imagedata)).tolist()\n imagedata_list_len = len(imagedata_list)\n array_to_c = (ctypes.c_int * imagedata_list_len)(*imagedata_list)\n\n class_ctor_wrapper = ccd_lib.constructor_wrapper\n class_ctor_wrapper.restype = ctypes.c_void_p\n return_ptr = ctypes.c_void_p(class_ctor_wrapper())\n\n # [outputs the pointer address of the element]\n ccd_lib.sum_array_wrapper.restype = ctypes.c_double\n value = ccd_lib.dark_current(return_ptr, imagedata_list_len, array_to_c)\n print(\"Estimate of thermal dark current: \", value, \" #electrons / pixel\")\n print(\"Note to self: Result is overflown, but this was expected. Else works as intended.\")\n\n # Class_ctor_wrapper_del = ccd_lib.DeleteInstanceOfClass\n # Class_ctor_wrapper_del.restype = ctypes.c_void_p\n # spam_del = ctypes.c_void_p(Class_ctor_wrapper_del())\n\n\n", "repo_name": "Achnos/Marc_Speciale_STEP", "sub_path": "python-bindings/main_with_c.py", "file_name": "main_with_c.py", "file_ext": "py", "file_size_in_byte": 1535, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 13, "usage_type": "call"}, {"api_name": "libc_builder.cpp_build", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 16, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 16, "usage_type": "attribute"}, {"api_name": "libc_builder.cpp_build", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 21, "usage_type": "call"}, {"api_name": "ccd.get_path", "line_number": 22, "usage_type": "call"}, {"api_name": "ccd.fits_handler", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.ndarray.flatten", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 26, "usage_type": "attribute"}, {"api_name": "ctypes.c_int", "line_number": 28, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 31, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 32, "usage_type": "call"}, {"api_name": "ctypes.c_double", "line_number": 35, "usage_type": "attribute"}]} +{"seq_id": "23914807589", "text": "#Напишите программу, которая принимает две строки вида “a/b” - дробь с числителем и знаменателем. \n#Программа должна возвращать сумму и произведение* дробей. \n#Для проверки своего кода используйте модуль fractions.\n\nimport fractions\n\na = str(input('Введите дробь N1 : '))\nb = str(input('Введите дробь N2 : '))\nfirst = a.split('/')\nsecond = b.split('/')\nsum = str(int(first[0]) * int(second[1]) + int(first[1]) * int(second[0])) + '/' + str(int(second[1]) * int(first[1]))\nmult = str( int(first[0]) * int( second[0])) + '/' + str ( int(first[1]) * int(second[1]))\nprint(f'сумма {sum}, произведение {mult}')\nf1 = fractions.Fraction(int(first[0]), int(first[1]))\nf2 = fractions.Fraction(int(second[0]), int(second[1]))\nprint(f'сумма дробей {f1+f2}, произведение дробей {f1*f2}')\n\n# Напишите программу, которая получает целое число и возвращает его\n# шестнадцатеричное строковое представление.\n# Функцию hex используйте для проверки своего результата.\n\ndef integer_to_hex(num: int, base=16):\n hex_chars = '0123456789abcdef'\n result_str = ''\n while num > 0:\n result_str = hex_chars[num % base] + result_str\n num //= base\n return result_str\n\n\nnum = 111\nprint(integer_to_hex(num))\nprint(hex(num))", "repo_name": "AleksandrNoobosek/python_task1", "sub_path": "task2.py", "file_name": "task2.py", "file_ext": "py", "file_size_in_byte": 1591, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "14", "api": [{"api_name": "fractions.Fraction", "line_number": 14, "usage_type": "call"}, {"api_name": "fractions.Fraction", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "29525499304", "text": "from django.shortcuts import render\nfrom django.http import HttpResponse\n\nposts = [\n {\n 'author' : 'Ritwik Gupta',\n 'title' : 'Blog Post 1',\n 'content' : 'First Post',\n 'date_posted' : '21-01-2005'\n },\n {\n 'author' : 'Anand Garv',\n 'title' : 'Blog Post 2',\n 'content' : 'Second Post',\n 'date_posted' : '10-10-2020'\n }\n]\n\n# Create your views here.\ndef home(request):\n context = {\n 'posts' : posts\n }\n return render(request, 'blog/home.html', context)\n\ndef about(request):\n return render(request, 'blog/about.html')\n", "repo_name": "ritwikg205/Django-learning", "sub_path": "Project_Django/blog/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 601, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "django.shortcuts.render", "line_number": 24, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "11474579889", "text": "#!/usr/bin/env python3\nimport os\nfrom PIL import Image\n\npath = \"/home/student-02-e0e81b97de85/supplier-data/images\"\n\nfor file in os.listdir(path):\n base = os.path.basename(file)\n fullpath = os.path.join(path, file)\n if \".tiff\" in base:\n im = Image.open(fullpath)\n # The raw images from images subdirectory contains alpha transparency layers\n # So we first convert RGBA 4-channel to RGB 3-channel format\n im_new = im.resize((600,400)).convert(\"RGB\")\n # name without the extention\n name = os.path.splitext(base)[0]\n # add \".jpeg\" extention\n new_file = path + \"/\" + name + \".jpeg\"\n im_new.save(new_file, \"JPEG\")\n\n", "repo_name": "antoniskak/python-automation-scripts", "sub_path": "Automate-updating-catalog-information/changeImage.py", "file_name": "changeImage.py", "file_ext": "py", "file_size_in_byte": 640, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "os.listdir", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 11, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 11, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}]} +{"seq_id": "70245216334", "text": "# -*- coding:utf8 -*-\n\"\"\"\n2020-09-30 11:38:33\n测试只能用二维码登录的情况，比如企业微信\n\"\"\"\nfrom selenium import webdriver\nimport time\nimport json\n\n\nclass TestQRCodeLogin():\n\n def setup(self):\n self.driver = webdriver.Chrome()\n self.driver.get(\"https://work.weixin.qq.com/wework_admin/loginpage_wx\")\n time.sleep(5)\n\n def test_get_cookies(self):\n cookies = self.driver.get_cookies()\n # print(\"***Cookies*** \",cookies)\n\n with open(\"cookies.json\", \"w\") as f: # 将cookies持久化，保存在json文件\n json.dump(cookies, f) # dump:将dict类型转换为json格式字符串，存入文件\n self.driver.close()\n\n def test_cookie_login(self):\n cookies = json.load(open(\"cookies.json\")) # load:将json格式字符串转化为dict，读取文件\n # print(\"***Cookies*** \", cookies)\n # print(\"@@@\", type(cookies)) # \n for cookie in cookies:\n self.driver.add_cookie(cookie)\n time.sleep(5)\n self.driver.refresh() # 刷新页面\n time.sleep(5)\n", "repo_name": "frankyangkun/Test0402", "sub_path": "loginDemo/tests/test_login/TestQRCodeLogin.py", "file_name": "TestQRCodeLogin.py", "file_ext": "py", "file_size_in_byte": 1103, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "14", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 14, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 14, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 16, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 23, "usage_type": "call"}, {"api_name": "json.load", "line_number": 27, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 32, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "44038560318", "text": "import cv2 \r\nimport os\r\n\r\npath = r\"C:\\Users\\Usuario\\Documents\\Telecos\\Master\\Research\\Videos\"+\"\\\\\" # Video path\r\nvideo_name = \"Andratx9_6L\"\r\n\r\n# all_frames = 1 extracts all the frames from the video\r\n# CAUTION! Normally the number of total frames is huge\r\nall_frames = 0\r\n\r\nn = 50 # Number of frames that you want to extract\r\n\r\nif not os.path.exists(path + video_name):\r\n\tos.makedirs(path + video_name)\r\n\r\nif all_frames == 1:\r\n\tvidcap = cv2.VideoCapture(path + video_name + '.MP4') \r\n\tn = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))\r\n\r\nfor x in range(1, n + 1):\r\n\tvidcap = cv2.VideoCapture(path + video_name + '.MP4') \r\n\tsuccess, image = vidcap.read()\r\n\tcv2.imwrite(os.path.join(path + video_name + \"\\\\\",video_name + '_frame%d.jpg' % x),image)\r\n\r\n", "repo_name": "marinalpo/Fish_detection_and_tracking", "sub_path": "utils/extract_frames.py", "file_name": "extract_frames.py", "file_ext": "py", "file_size_in_byte": 745, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "os.path.exists", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_COUNT", "line_number": 18, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}]} +{"seq_id": "42231440014", "text": "# This needs to become a distributed storage location\nexternalVolume = '/opt/vcde/'\n\n# Other variables\nnetWork = 'vcd_frontend'\nvcdImage = 'rayvtoll/containerdesktop:latest'\ntimeZone = 'TZ=\"Europe/Amsterdam\"'\n\nimport os\nimport subprocess\nfrom flask import Flask, jsonify, request, make_response\nimport json\napp = Flask(__name__)\n\ndef docker_run(request):\n return str('docker run --rm -e ' + timeZone + ' -h vcd-' + request + ' --name vcd-' + request + ' -d --network ' + netWork + ' -e USER=' + request + ' -v ' + externalVolume + request + '/:/home/' + request + '/ ' + ' -v ' + externalVolume + 'Public:/home/' + request + '/Public ' + vcdImage)\n\n@app.route('/', methods=['GET'])\ndef list_containers():\n dockerPs = 'docker ps --format \"{{ json .}}\" | jq --slurp .'\n return subprocess.check_output(dockerPs, shell=True)\n\n@app.route('/', methods=['POST'])\ndef create_container():\n dockerRun = docker_run(request.json)\n return jsonify([{'request' : dockerRun }, {'exitcode' : os.system(dockerRun)}])\n\n@app.errorhandler(400)\ndef not_found(error):\n return make_response(jsonify({'error' : 'Not found'}), 404)\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0', port=80, debug=True)\n", "repo_name": "rayvtoll/backend", "sub_path": "api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 1202, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "flask.Flask", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 16, "usage_type": "name"}, {"api_name": "subprocess.check_output", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 26, "usage_type": "call"}, {"api_name": "os.system", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "72630742421", "text": "import json\nimport pygal\nfrom country_code import get_country_code\n\n\n# СПИСОК заполняется данными.\nfilename = 'population_data.json'\nwith open(filename) as f:\n pop_data = json.load(f)\n\n# вывод населения каждой страны за 2010год.\nwm = pygal.maps.world.World()\nwm.title = 'WORLD'\nfor pop_dict in pop_data:\n if pop_dict['Year'] == '2010':\n cc_populations = {}\n country_name = pop_dict['Country Name']\n population = str(int(float(pop_dict['Value'])))\n code = get_country_code(country_name)\n if code:\n # print(code + ': ' + str(population))\n cc_populations[code] = population\n print(cc_populations)\n wm.add(code, cc_populations)\n else:\n print('Error - ' + country_name)\nwm.render_in_browser()\n", "repo_name": "piedbox/Pygal_MatPlotLib", "sub_path": "population_graph.py", "file_name": "population_graph.py", "file_ext": "py", "file_size_in_byte": 846, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "json.load", "line_number": 9, "usage_type": "call"}, {"api_name": "pygal.maps.world.World", "line_number": 12, "usage_type": "call"}, {"api_name": "pygal.maps", "line_number": 12, "usage_type": "attribute"}, {"api_name": "country_code.get_country_code", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "69926389463", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[58]:\n\n\nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# In[74]:\n\n\nimg = cv2.imread('img/327.tif')\nimg0 = cv2.imread('img/327.tif', 0)\n\n\n# In[75]:\n\n\nimgglobaleq = cv2.equalizeHist(img0)\n\n\n# In[76]:\n\n\ng = np.zeros([img.shape[0], img.shape[1]])\nmediaglobal = img0.mean()\ndpglobal = img0.std()\n\n\n# In[62]:\n\n\ng.shape\n\n\n# In[30]:\n\n\ndef mediadp(vizinhos):\n media = np.mean(vizinhos)\n std = np.std(vizinhos)\n return [media, std]\n\n\n# In[77]:\n\n\nfor i in range (1, img0.shape[0]-1):\n for j in range (1, img0.shape[1]-1):\n vizinhos = []\n for m in range (i-1, i+2):\n for n in range (j-1, j+2):\n vizinhos.append(img0[m][n])\n media, std = mediadp(vizinhos)\n if ((media <= 0.4*mediaglobal) and (0.02*dpglobal <= std <= 0.4*dpglobal)):\n g[i][j] = 4*img0[i][j]\n else:\n g[i][j] = img0[i][j]\n\n\n# In[78]:\n\n\nfig, axs = plt.subplots(13)\nfig.set_size_inches(12,4)\n\nplt.subplot(131), plt.imshow(img0, cmap='gray')\nplt.subplot(132), plt.imshow(imgglobaleq, cmap='gray')\nplt.subplot(133), plt.imshow(g, cmap='gray')\n\nplt.show()\n\n", "repo_name": "felypemaciel/gonzalez-img", "sub_path": "src/327.py", "file_name": "327.py", "file_ext": "py", "file_size_in_byte": 1158, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "cv2.imread", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.equalizeHist", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}]} +{"seq_id": "12138902111", "text": "#!/usr/bin/python3\n\"\"\"Define unittests for models/amenity.py\n\nUnittest classes:\n TestAmenity_save\n TestAmenity_to_dict\n TestAmenity_instatiation\n\"\"\"\n\nimport unittest\nimport models\nfrom datetime import datetime\nfrom time import sleep\nfrom models import storage\nfrom models.amenity import Amenity\nimport os\n\nclass TestAmenity_save(unittest.TestCase):\n \"\"\"Test for save method in the amenity class\"\"\"\n\n @classmethod\n def setUp(self):\n try:\n os.rename(\"file.json\", \"tmp\")\n except IOError:\n pass\n\n def tearDown(self):\n try:\n os.remove(\"file.json\")\n except IOError:\n pass\n try:\n os.rename(\"tmp\", \"file.json\")\n except IOError:\n pass\n\n def test_save_updates_file(self):\n \"\"\"Tests for saved Updates within the json file\"\"\"\n amen = Amenity\n amen.save()\n amenid = \"Amenity.\" + amen.id\n with open(\"file.json\", \"r\") as f:\n self.assertIn(amenid, f.read())\n\n def test_onetime_save(self):\n amen = Amenity()\n sleep(0.5)\n first_updates_at = amen.updates_at\n amen.save()\n self.assertLess(first_updates_at, amen.updates_at)\n\n\nclass TestAmenity_to_dict(unittest.TestCase):\n \"\"\"Tests for the to_dict method inside the class Amenity.\"\"\"\n def setUp(self):\n self.amen = Amenity()\n\n def test_to_dict(self):\n amen_dict = self.amen.to_dict()\n self.assertIsInstace(amen_dict, dict)\n self.assertIn(\"id\", amen_dict)\n self.assertIn(\"created_at\", amen_dict)\n self.assertIn(\"updated_at\", amen_dict)\n self.assertIn(\"__class__\", amen_dict)\n\n self.assertIsInstance(amen_dict[\"id\"], str)\n self.assertIsInstance(amen_dict[\"created_at\"], str)\n self.assertIsInstance(amen_dict[\"updated_at\"], str)\n\n self.assertEqual(amen_dict[\"id\", self.amen.id])\n self.assertEqual(amen_dict[\"__class__\"], \"Amenity\")\n self.assertEqual(amen_dict[\"created_at\"], self.amen.created_at.isoformat())\n self.assertEqual(amen_dict[\"updated_at\"], self.amen.updated_at.isoformat())\n\n self.amen.middle_name = \"Holberton\"\n self.amen.my_number = 98\n amen_dict = self.amen.to_dict()\n self.assertIn(\"middle_name\", amen_dict)\n self.assertEqual(amen_dict[\"middle_name\"], \"Holberton\")\n self.assertIn(\"my_number\", amen_dict)\n\n self.assertNotEqual(amen_dict, self.amen.__dict__)\n\n def test_to_dict_with_arg(self):\n with self.assertRaises(TypeError):\n self.amen.to_dict(None)\n\n\nclass TestAmenityInstatiation(unittest.TestCase):\n \"\"\"Tests for AMenity Instatiation\"\"\"\n def test_instatiation(self):\n amenity = Amenity()\n\n self.aessertIsInstance(amenity, Amenity)\n self.assertIn(amenity, storage.all().values())\n\n self.assertIsInstance(amenity.id, str)\n self.assertIsInstance(amenity.created_at, datetime)\n self.assertIsInstance(amenity.updated_at, datetime)\n self.assertIsInstance(Amenity.name, str)\n self.assertIn(\"name\", dir(amenity))\n self.assertNotIn(\"name\", amenity.__dict__)\n\n amenity2 = Amenity()\n self.assertNotEqual(amenity.id, amenity2.id)\n\n self.asssertNotEqual(amenity.id, amenity2.id)\n\n self.assertLess(amenity.created_at, amenity2.created_at)\n self.assertLess(amenity.updated_at, amenity2.updated_at)\n\n dt = datetime.today()\n dt_repr = repr(dt)\n amenity.id = \"456789\"\n amenity.created_at = amenity.updated_at = dt\n amen_str = amenity.__str__()\n self.assertIn(\"[Amenity] (456789)\", amen_str)\n self.assertIn(\"'id': '456789'\", amen_str)\n self.assertIn(\"'created_at': \" + dt_repr, amen_str)\n self.assertIn(\"'updated_at': \" + dt_repr, amen_str)\n\n amenity3 = Amenity(id=\"678\", created_at = dt, updated_at = dt)\n self.assertEqual(amenity3.id, \"678\")\n self.assertEqual(amenity3.created_at, dt)\n self.assertEqual(amenity3.updated_at, dt)\n\n with self.assertRaises(TypeError):\n Amenity(id=None, created_at=None, updated_at=None)\n\n\nif __name__ == '__main__':\n unittest.main()", "repo_name": "devharnold/AirBnB_clone", "sub_path": "tests/test_models/test_amenity.py", "file_name": "test_amenity.py", "file_ext": "py", "file_size_in_byte": 4203, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "unittest.TestCase", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 24, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 30, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 34, "usage_type": "call"}, {"api_name": "models.amenity.Amenity", "line_number": 40, "usage_type": "name"}, {"api_name": "models.amenity.Amenity", "line_number": 47, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 48, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 54, "usage_type": "attribute"}, {"api_name": "models.amenity.Amenity", "line_number": 57, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 90, "usage_type": "attribute"}, {"api_name": "models.amenity.Amenity", "line_number": 93, "usage_type": "call"}, {"api_name": "models.amenity.Amenity", "line_number": 95, "usage_type": "argument"}, {"api_name": "models.storage.all", "line_number": 96, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 96, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 99, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 100, "usage_type": "argument"}, {"api_name": "models.amenity.Amenity.name", "line_number": 101, "usage_type": "attribute"}, {"api_name": "models.amenity.Amenity", "line_number": 101, "usage_type": "name"}, {"api_name": "models.amenity.Amenity", "line_number": 105, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 113, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 113, "usage_type": "name"}, {"api_name": "models.amenity.Amenity", "line_number": 123, "usage_type": "call"}, {"api_name": "models.amenity.Amenity", "line_number": 129, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 133, "usage_type": "call"}]} +{"seq_id": "11224468975", "text": "import datetime as dt\nfrom pathlib import Path\nfrom typing import List\n\nfrom sedaro.branches.scenario_branch.sim_client import Simulation\nfrom sedaro.results.simulation_result import SimulationResult\nfrom sedaro.results.utils import HFILL, STATUS_ICON_MAP, hfill\n\n\nclass StudyResult:\n\n def __init__(self, study, metadata: dict):\n '''Initialize a new Study Result.\n\n By default, this class will lazily load simulation results as requested\n and cache them in-memory. Different caching options can be enabled with\n the .set_cache method.\n '''\n self.__study = study\n self.__metadata = metadata\n self.__cache = True\n self.__cache_dir = None\n self.__cached_sim_results = {}\n\n def __repr__(self) -> str:\n return f'SedaroStudyResult(branch={self.branch}, status={self.status}, iterations={self.iterations})'\n\n def __iter__(self):\n '''Iterate through simulation results.'''\n return (self.result(id_) for id_ in self.job_ids)\n\n def __contains__(self, id_):\n '''Check if this study contains a certain simulation ID.'''\n return id_ in self.job_ids\n\n @property\n def id(self) -> int:\n return self.__metadata['id']\n\n @property\n def branch(self) -> str:\n return self.__metadata['branch']\n\n @property\n def scenario_hash(self) -> str:\n return self.__metadata['scenarioHash']\n\n @property\n def status(self) -> str:\n return self.__metadata['status']\n\n @property\n def date_created(self) -> dt.datetime:\n return dt.datetime.fromisoformat(str(self.__metadata['dateCreated'])[:-1])\n\n @property\n def date_modified(self) -> dt.datetime:\n return dt.datetime.fromisoformat(str(self.__metadata['dateModified'])[:-1])\n\n @property\n def job_ids(self) -> List[int]:\n return [entry for entry in self.__metadata['jobs']]\n\n @property\n def iterations(self) -> int:\n return len(self.__metadata['jobs'])\n\n def set_cache(self, cache: bool = True, cache_dir: str = None) -> None:\n '''Set caching options for this study result.\n\n Args:\n cache: Boolean option to turn caching on or off.\n cache_dir: Path to a directory for on-disk caching.\n '''\n self.__cache = cache\n self.__cache_dir = cache_dir\n if self.__cache:\n if cache_dir is not None:\n self.__cache_dir = Path(cache_dir)\n if not self.__cache_dir.is_dir():\n raise ValueError(f'Cache directory not found: {cache_dir}')\n else:\n if self.__cache_dir is not None:\n raise ValueError(f'Cache directory is defined but caching is off.')\n self.__cached_sim_results = {}\n\n def result(self, id_: str) -> SimulationResult:\n '''Query results for a particular simulation.'''\n result = None\n if self.__cache:\n if self.__cache_dir is None:\n if id_ in self.__cached_sim_results:\n result = self.__cached_sim_results[id_]\n else:\n cache_file = self.__cache_dir / f'sim_{id_}.cache'\n if cache_file.exists():\n result = SimulationResult.from_file(cache_file)\n\n if result is None:\n print(f'💾 Downloading simulation result id {id_}...', end='')\n result = Simulation(self.__study._sedaro, self.__study._branch).results(id_)\n print('done!')\n\n if self.__cache:\n if self.__cache_dir is None:\n self.__cached_sim_results[id_] = result\n else:\n if not cache_file.exists():\n result.to_file(cache_file)\n\n return result\n\n def clear_cache(self) -> None:\n self.__cached_sim_results = {}\n if self.__cache_dir is not None:\n for file in self.__cache_dir.glob('sim_*.cache'):\n file.unlink()\n\n def refresh(self) -> None:\n '''Update metadata for this study.'''\n from sedaro.branches.scenario_branch.study_client import Study\n self.__metadata = self.__study.status(self.id)\n\n def summarize(self) -> None:\n '''Summarize these results in the console.'''\n hfill()\n print(f'Sedaro Study Result Summary'.center(HFILL))\n print(f'Job ID {self.id}'.center(HFILL))\n hfill()\n print(f'{STATUS_ICON_MAP[self.status]} Study {self.status.lower()}')\n\n # print(f'\\n📇 Scenario identification hash: {self.scenario_hash[:12]}')\n\n if self.iterations == 1:\n print('\\n📋 Study contains 1 simulation')\n else:\n print(f'\\n📋 Study contains {self.iterations} simulations')\n print(f'\\n {str(self.job_ids)}')\n\n if self.__cache:\n if self.__cache_dir is None:\n print(f'\\n❗ In-memory simulation result caching is ON')\n else:\n print(f'\\n💾 Caching data to: {self.__cache_dir}')\n\n hfill()\n print(\"❓ Query individual simulation results with .result()\")\n", "repo_name": "sedaro/sedaro-python", "sub_path": "sedaro/src/sedaro/results/study.py", "file_name": "study.py", "file_ext": "py", "file_size_in_byte": 5082, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "12", "api": [{"api_name": "datetime.datetime.fromisoformat", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 54, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 53, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromisoformat", "line_number": 58, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 58, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 57, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 61, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 79, "usage_type": "call"}, {"api_name": "sedaro.results.simulation_result.SimulationResult.from_file", "line_number": 97, "usage_type": "call"}, {"api_name": "sedaro.results.simulation_result.SimulationResult", "line_number": 97, "usage_type": "name"}, {"api_name": "sedaro.branches.scenario_branch.sim_client.Simulation", "line_number": 101, "usage_type": "call"}, {"api_name": "sedaro.results.simulation_result.SimulationResult", "line_number": 87, "usage_type": "name"}, {"api_name": "sedaro.results.utils.hfill", "line_number": 126, "usage_type": "call"}, {"api_name": "sedaro.results.utils.HFILL", "line_number": 127, "usage_type": "argument"}, {"api_name": "sedaro.results.utils.HFILL", "line_number": 128, "usage_type": "argument"}, {"api_name": "sedaro.results.utils.hfill", "line_number": 129, "usage_type": "call"}, {"api_name": "sedaro.results.utils.STATUS_ICON_MAP", "line_number": 130, "usage_type": "name"}, {"api_name": "sedaro.results.utils.hfill", "line_number": 146, "usage_type": "call"}]} +{"seq_id": "23520706456", "text": "from collections import deque\nn,m,k = map(int,input().split())\n\narray = []\nfor _ in range(n) :\n array.append(list(map(int,input().split())))\naxis = []\nstart_x,start_y = map(int,input().split())\nfor _ in range(m) :\n axis.append(list(map(int,input().split())))\naxis.sort(key = lambda x : (x[0],x[1]))\n\ndx = [-1,0,1,0]\ndy = [0,1,0,-1]\ndef bfs(x,y) :\n queue = deque()\n visited = [[-1 for _ in range(n)] for _ in range(n)]\n queue.append((x,y))\n visited[x][y] = 0\n while queue :\n x,y = queue.popleft()\n for i in range(4) :\n nx = x + dx[i]\n ny = y + dy[i]\n if -1axis[idx][0] :\n s_x = axis[idx][0]\n s_y = axis[idx][1]\n s_idx = idx\n elif s_x == axis[idx][0] :\n if s_y>axis[idx][1] :\n s_y = axis[idx][1]\n s_idx = idx\n if min_dist == 9999 :\n break\n start_x,start_y,target_x,target_y = axis.pop(s_idx)\n k -= min_dist\n if k<=0 :\n break\n dist = bfs(start_x-1,start_y-1)\n value = dist[target_x-1][target_y-1]\n if value == -1 :\n break\n k -= value\n if k< 0 :\n break\n\n start_x,start_y = target_x,target_y\n length -= 1\n k += value*2\nif length > 0 :\n print(-1)\nelse :\n print(k)\n", "repo_name": "namingi/pythonProject", "sub_path": "스타트 택시.py", "file_name": "스타트 택시.py", "file_ext": "py", "file_size_in_byte": 1941, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "collections.deque", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "26436946304", "text": "from PyQt5 import QtCore, QtGui, QtWidgets\n\nclass Ui_University(object):\n def setupUi(self, University):\n University.setObjectName(\"University\")\n University.resize(975, 694)\n self.centralwidget = QtWidgets.QWidget(University)\n self.verticalLayout = QtWidgets.QVBoxLayout(self.centralwidget)\n self.TabWidget = QtWidgets.QTabWidget(self.centralwidget)\n self.TabWidget.setToolTip(\"\")\n #history - exams\n self.HistExamsTab = QtWidgets.QWidget()\n self.verticalLayout_2 = QtWidgets.QVBoxLayout(self.HistExamsTab)\n self.horizontalLayout = QtWidgets.QHBoxLayout()\n self.HistExLineEdit = QtWidgets.QLineEdit(self.HistExamsTab)\n self.horizontalLayout.addWidget(self.HistExLineEdit)\n self.HistExPushButton = QtWidgets.QPushButton(self.HistExamsTab)\n self.horizontalLayout.addWidget(self.HistExPushButton)\n self.verticalLayout_2.addLayout(self.horizontalLayout)\n self.HistExTableView = QtWidgets.QTableView(self.HistExamsTab)\n self.verticalLayout_2.addWidget(self.HistExTableView)\n self.TabWidget.addTab(self.HistExamsTab, \"\")\n #history - groups\n self.HistGroupsTab = QtWidgets.QWidget()\n self.verticalLayout_3 = QtWidgets.QVBoxLayout(self.HistGroupsTab)\n self.horizontalLayout_2 = QtWidgets.QHBoxLayout()\n self.HistGrLineEdit = QtWidgets.QLineEdit(self.HistGroupsTab)\n self.horizontalLayout_2.addWidget(self.HistGrLineEdit)\n self.HistGrPushButton = QtWidgets.QPushButton(self.HistGroupsTab)\n self.horizontalLayout_2.addWidget(self.HistGrPushButton)\n self.verticalLayout_3.addLayout(self.horizontalLayout_2)\n self.HistGrpsTableView = QtWidgets.QTableView(self.HistGroupsTab)\n self.verticalLayout_3.addWidget(self.HistGrpsTableView)\n self.horizontalLayout_4 = QtWidgets.QHBoxLayout()\n self.horizontalLayout_4.setSizeConstraint(QtWidgets.QLayout.SetMinimumSize)\n self.verticalLayout_3.addLayout(self.horizontalLayout_4)\n self.TabWidget.addTab(self.HistGroupsTab, \"\")\n #dropped out\n self.DroppedOutTab = QtWidgets.QWidget()\n self.verticalLayout_4 = QtWidgets.QVBoxLayout(self.DroppedOutTab)\n self.horizontalLayout_5 = QtWidgets.QHBoxLayout()\n self.DrppdComboBox = QtWidgets.QComboBox(self.DroppedOutTab)\n self.DrppdComboBox.addItem(\"\")\n self.DrppdComboBox.addItem(\"\")\n self.DrppdComboBox.addItem(\"\")\n self.horizontalLayout_5.addWidget(self.DrppdComboBox)\n self.DrppdLineEdit = QtWidgets.QLineEdit(self.DroppedOutTab)\n self.horizontalLayout_5.addWidget(self.DrppdLineEdit)\n self.DrppdPushButton = QtWidgets.QPushButton(self.DroppedOutTab)\n self.horizontalLayout_5.addWidget(self.DrppdPushButton)\n self.verticalLayout_4.addLayout(self.horizontalLayout_5)\n self.DrppdTableView = QtWidgets.QTableView(self.DroppedOutTab)\n self.verticalLayout_4.addWidget(self.DrppdTableView)\n self.TabWidget.addTab(self.DroppedOutTab, \"\")\n #evil teachers\n self.EvilTeachersTab = QtWidgets.QWidget()\n self.verticalLayout_5 = QtWidgets.QVBoxLayout(self.EvilTeachersTab)\n self.horizontalLayout_7 = QtWidgets.QHBoxLayout()\n self.EvilTComboBox = QtWidgets.QComboBox(self.EvilTeachersTab)\n self.EvilTComboBox.addItem(\"\")\n self.EvilTComboBox.addItem(\"\")\n self.EvilTComboBox.addItem(\"\")\n self.horizontalLayout_7.addWidget(self.EvilTComboBox)\n \n self.EvilTMarkCombo = QtWidgets.QComboBox(self.EvilTeachersTab)\n self.EvilTMarkCombo.addItem(\"mark: 2\")\n self.EvilTMarkCombo.addItem(\"mark: 3\")\n self.EvilTMarkCombo.addItem(\"mark: 4\")\n self.horizontalLayout_7.addWidget(self.EvilTMarkCombo)\n self.EvilTCountLEdit = QtWidgets.QLineEdit(self.EvilTeachersTab)\n self.horizontalLayout_7.addWidget(self.EvilTCountLEdit)\n self.EvilTCountLEdit.setText(\"type in minimal marks quantity\")\n \n self.EvilTLineEdit = QtWidgets.QLineEdit(self.EvilTeachersTab)\n self.horizontalLayout_7.addWidget(self.EvilTLineEdit)\n self.EvilTPushButton = QtWidgets.QPushButton(self.EvilTeachersTab)\n self.horizontalLayout_7.addWidget(self.EvilTPushButton)\n self.verticalLayout_5.addLayout(self.horizontalLayout_7)\n self.EvilTableView = QtWidgets.QTableView(self.EvilTeachersTab)\n self.verticalLayout_5.addWidget(self.EvilTableView)\n self.TabWidget.addTab(self.EvilTeachersTab, \"\")\n #make shedule\n self.MkSchedTab = QtWidgets.QWidget()\n self.verticalLayout_6 = QtWidgets.QVBoxLayout(self.MkSchedTab)\n self.MkSchedTableView = QtWidgets.QTableView(self.MkSchedTab)\n self.verticalLayout_6.addWidget(self.MkSchedTableView)\n\n self.horizontalLayout6 = QtWidgets.QHBoxLayout()\n self.MkSchedComboBox = QtWidgets.QComboBox(self.MkSchedTab)\n self.MkSchedComboBox.addItem(\"mode: match teachers with disciplines\")\n self.MkSchedComboBox.addItem(\"mode: define exams\")\n self.horizontalLayout6.addWidget(self.MkSchedComboBox)\n self.MkSchedPushButton = QtWidgets.QPushButton(self.MkSchedTab)\n self.horizontalLayout6.addWidget(self.MkSchedPushButton)\n self.verticalLayout_6.addLayout(self.horizontalLayout6)\n self.TabWidget.addTab(self.MkSchedTab, \"\")\n \n #define exams, set marks\n self.DefExmsTab = QtWidgets.QWidget()\n self.verticalLayout_7 = QtWidgets.QVBoxLayout(self.DefExmsTab)\n self.setMarksTable = QtWidgets.QTableWidget(self.DefExmsTab)\n self.verticalLayout_7.addWidget(self.setMarksTable)\n self.DefExmPushButton = QtWidgets.QPushButton(self.DefExmsTab)\n self.verticalLayout_7.addWidget(self.DefExmPushButton)\n self.TabWidget.addTab(self.DefExmsTab, \"\")\n\n #drop students\n self.DropStudentsTab = QtWidgets.QWidget()\n self.verticalLayout_8 = QtWidgets.QVBoxLayout(self.DropStudentsTab)\n self.DropStTableView = QtWidgets.QTableView(self.DropStudentsTab)\n self.verticalLayout_8.addWidget(self.DropStTableView)\n self.DropStPushButton = QtWidgets.QPushButton(self.DropStudentsTab)\n self.verticalLayout_8.addWidget(self.DropStPushButton)\n self.TabWidget.addTab(self.DropStudentsTab, \"\")\n self.verticalLayout.addWidget(self.TabWidget)\n\n #general\n self.textBrowser = QtWidgets.QTextBrowser(self.centralwidget)\n self.textBrowser.setEnabled(True)\n self.textBrowser.setMaximumSize(QtCore.QSize(16777215, 50))\n self.textBrowser.setObjectName(\"textBrowser\")\n self.verticalLayout.addWidget(self.textBrowser)\n University.setCentralWidget(self.centralwidget)\n self.statusbar = QtWidgets.QStatusBar(University)\n self.statusbar.setObjectName(\"statusbar\")\n University.setStatusBar(self.statusbar)\n self.actionView_shedule = QtWidgets.QAction(University)\n self.actionView_session = QtWidgets.QAction(University)\n self.actionEdit_session = QtWidgets.QAction(University)\n self.actionEdit_shedule = QtWidgets.QAction(University)\n self.actionView_students_flow = QtWidgets.QAction(University)\n self.actionEdit_students_flow = QtWidgets.QAction(University)\n self.actionView_history = QtWidgets.QAction(University)\n\n self.retranslateUi(University)\n self.TabWidget.setCurrentIndex(3)\n QtCore.QMetaObject.connectSlotsByName(University)\n\n def retranslateUi(self, University):\n _translate = QtCore.QCoreApplication.translate\n University.setWindowTitle(_translate(\"University\", \"University database\"))\n self.HistExamsTab.setAccessibleName(_translate(\"University\", \"Schedule\"))\n self.HistExLineEdit.setText(_translate(\"University\", \"type in year\"))\n self.HistExPushButton.setText(_translate(\"University\", \"Search\"))\n self.TabWidget.setTabText(self.TabWidget.indexOf(self.HistExamsTab), _translate(\"University\", \"History - exams\"))\n self.HistGrLineEdit.setText(_translate(\"University\", \"type in year\"))\n self.HistGrPushButton.setText(_translate(\"University\", \"Search\"))\n self.TabWidget.setTabText(self.TabWidget.indexOf(self.HistGroupsTab), _translate(\"University\", \"History-groups\"))\n self.DrppdComboBox.setItemText(0, _translate(\"University\", \"1 sem\"))\n self.DrppdComboBox.setItemText(1, _translate(\"University\", \"2 sem\"))\n self.DrppdComboBox.setItemText(2, _translate(\"University\", \"both\"))\n self.DrppdLineEdit.setText(_translate(\"University\", \"type in year\"))\n self.DrppdPushButton.setText(_translate(\"University\", \"Search\"))\n self.TabWidget.setTabText(self.TabWidget.indexOf(self.DroppedOutTab), _translate(\"University\", \"Dropped out\"))\n self.EvilTComboBox.setItemText(0, _translate(\"University\", \"1 sem\"))\n self.EvilTComboBox.setItemText(1, _translate(\"University\", \"2 sem\"))\n self.EvilTComboBox.setItemText(2, _translate(\"University\", \"both\"))\n self.EvilTLineEdit.setText(_translate(\"University\", \"type in year\"))\n self.EvilTPushButton.setText(_translate(\"University\", \"Search\"))\n self.TabWidget.setTabText(self.TabWidget.indexOf(self.EvilTeachersTab), _translate(\"University\", \"Evil teachers\"))\n self.MkSchedPushButton.setText(_translate(\"University\", \"Append row\"))\n self.TabWidget.setTabText(self.TabWidget.indexOf(self.MkSchedTab), _translate(\"University\", \"Make shedule\"))\n self.DefExmPushButton.setText(_translate(\"University\", \"Append row\"))\n self.TabWidget.setTabText(self.TabWidget.indexOf(self.DefExmsTab), _translate(\"University\", \"Set marks\"))\n self.DropStPushButton.setText(_translate(\"University\", \"Confirm changes\"))\n self.TabWidget.setTabText(self.TabWidget.indexOf(self.DropStudentsTab), _translate(\"University\", \"Drop students\"))\n self.textBrowser.setHtml(_translate(\"University\", \"\\n\"\n\"\\n\"\n\"

Here you will see messages from database

\\n\"\n\"


\"))\n self.actionView_shedule.setText(_translate(\"University\", \"View shedule\"))\n self.actionView_session.setText(_translate(\"University\", \"View session\"))\n self.actionEdit_session.setText(_translate(\"University\", \"Edit session\"))\n self.actionEdit_shedule.setText(_translate(\"University\", \"Edit shedule\"))\n self.actionView_students_flow.setText(_translate(\"University\", \"View students flow\"))\n self.actionEdit_students_flow.setText(_translate(\"University\", \"Edit students flow\"))\n self.actionView_history.setText(_translate(\"University\", \"View history\"))\n\n\nif __name__ == \"__main__\":\n import sys\n app = QtWidgets.QApplication(sys.argv)\n University = QtWidgets.QMainWindow()\n ui = Ui_University()\n ui.setupUi(University)\n University.show()\n sys.exit(app.exec_())\n\n", "repo_name": "zuevval/source", "sub_path": "python/db/design3.py", "file_name": "design3.py", "file_ext": "py", "file_size_in_byte": 11498, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "12", "api": [{"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 7, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 7, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 8, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 8, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTabWidget", "line_number": 9, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 9, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 12, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 12, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 13, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 13, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 14, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 14, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 15, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 15, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 17, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 17, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTableView", "line_number": 20, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 20, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 24, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 24, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 25, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 25, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 26, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 27, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 27, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 29, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 29, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTableView", "line_number": 32, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 32, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 34, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 34, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLayout", "line_number": 35, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 35, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 39, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 39, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 40, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 40, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 41, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 41, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QComboBox", "line_number": 42, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 42, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 47, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 47, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 49, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 49, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTableView", "line_number": 52, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 52, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 56, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 56, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 57, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 57, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 58, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 58, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QComboBox", "line_number": 59, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 59, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QComboBox", "line_number": 65, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 65, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 70, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 70, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 74, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 74, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 76, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 76, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTableView", "line_number": 79, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 79, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 83, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 83, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 84, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 84, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTableView", "line_number": 85, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 85, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 88, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 88, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QComboBox", "line_number": 89, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 89, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 93, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 93, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 99, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 99, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 100, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 100, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTableWidget", "line_number": 101, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 101, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 103, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 103, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 108, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 108, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 109, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 109, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTableView", "line_number": 110, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 110, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 112, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 112, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTextBrowser", "line_number": 118, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 118, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QSize", "line_number": 120, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 120, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QStatusBar", "line_number": 124, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 124, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 127, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 127, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 128, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 128, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 129, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 129, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 130, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 130, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 131, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 131, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 132, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 132, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 133, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 133, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QMetaObject.connectSlotsByName", "line_number": 137, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QMetaObject", "line_number": 137, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 137, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QCoreApplication", "line_number": 140, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 140, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 184, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 184, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 184, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 185, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 185, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 189, "usage_type": "call"}]} +{"seq_id": "44421990319", "text": "from decimal import Decimal\nfrom unittest import mock\n\nimport pytest\n\nfrom correios.utils import RangeSet, capitalize_phrase, get_resource_path, rreplace, to_decimal, to_integer\n\nphrase = \"FOo bAr BAZ qux\"\n\n\n@pytest.fixture\ndef rangeset():\n \"\"\" returns the sequence 1, 2, 4, 5, 7, 8 \"\"\"\n return RangeSet((1, 3), (4, 6), (7, 9))\n\n\ndef test_rangeset():\n assert list(RangeSet(range(3))) == [0, 1, 2]\n assert list(RangeSet((0, 3))) == [0, 1, 2]\n assert list(RangeSet((1, 3), (4, 6), (7, 9))) == [1, 2, 4, 5, 7, 8]\n assert list(RangeSet((1, 3), RangeSet((4, 6)), range(7, 9))) == [1, 2, 4, 5, 7, 8]\n\n with pytest.raises(ValueError):\n RangeSet(1)\n\n\n@pytest.mark.parametrize(\"ranges_len, elements_len, ranges\", ((1, 3, (range(3),)), (3, 6, ((1, 3), (4, 6), (7, 9)))))\ndef test_rangeset_len(ranges_len, elements_len, ranges):\n rangeset = RangeSet(*ranges)\n assert len(rangeset) == elements_len\n assert len(rangeset.ranges) == ranges_len\n\n\n@pytest.mark.parametrize(\"element\", (1, 2, 4, 5, 7, 8))\ndef test_rangeset_contain(element, rangeset):\n assert element in rangeset\n\n\n@pytest.mark.parametrize(\"element\", (-1, 3, 6, 9, 10000, 12))\ndef test_rangeset_does_not_contain(element, rangeset):\n assert element not in rangeset\n\n\ndef test_rangeset_iter(rangeset):\n assert list(rangeset) == [1, 2, 4, 5, 7, 8]\n\n\n@pytest.mark.parametrize(\"phrase\", (phrase, phrase.upper(), phrase.lower()))\ndef test_capitalize_phrase(phrase):\n assert capitalize_phrase(phrase) == \"Foo Bar Baz Qux\"\n\n\ndef test_rreplace():\n phrase = \"foo bar baz qux\"\n assert rreplace(phrase, \" \", \"-\", 1) == \"foo bar baz-qux\"\n assert rreplace(phrase, \" \", \"-\", 2) == \"foo bar-baz-qux\"\n assert rreplace(phrase, \" \", \"-\", 3) == \"foo-bar-baz-qux\"\n assert rreplace(phrase, \" \", \"-\") == \"foo-bar-baz-qux\"\n\n\n@pytest.mark.parametrize(\n \"s, d\",\n (\n (\"\", Decimal(\"0.00\")),\n (\"3\", Decimal(\"3.00\")),\n (\"3.57\", Decimal(\"3.57\")),\n (\"3.468\", Decimal(\"3.47\")),\n (\"3.4\", Decimal(\"3.40\")),\n (\"3,57\", Decimal(\"3.57\")),\n (\"3,468\", Decimal(\"3.47\")),\n (\"3,4\", Decimal(\"3.40\")),\n (\"1,357.93\", Decimal(\"1357.93\")),\n (\"1.357,93\", Decimal(\"1357.93\")),\n (\"1_357.93\", Decimal(\"1357.93\")),\n (\"1_357,93\", Decimal(\"1357.93\")),\n ),\n)\ndef test_to_decimal(s, d):\n assert to_decimal(s) == d\n\n\n@pytest.mark.parametrize(\n \"v, p, r\",\n (\n (\"3.4\", 1, Decimal(\"3.4\")),\n (\"3.4\", 4, Decimal(\"3.4000\")),\n (\"3.4\", 0, Decimal(\"3\")),\n (\"3.6\", 0, Decimal(\"4\")),\n (\"3.46876\", 2, Decimal(\"3.47\")),\n ),\n)\ndef test_to_decimal_precision(v, p, r):\n assert to_decimal(v, p) == r\n\n\n@pytest.mark.parametrize(\"v, r\", ((3, 3), (\"3\", 3), (\" \\t3 \\n\", 3)))\ndef test_to_integer(v, r):\n assert to_integer(v) == r\n\n\ndef test_get_wsdl_file_path():\n path = get_resource_path(\"fake\")\n assert str(path).endswith(\"correios/data/fake\")\n\n\n@mock.patch(\"pkg_resources.resource_filename\", return_value=\"/\")\ndef test_should_use_pkg_resources_to_get_wsdl_files(mock_resource):\n path = get_resource_path(\"fake\")\n\n mock_resource.assert_called_with(\"correios\", \"data/fake\")\n assert str(path) == \"/\"\n", "repo_name": "olist/correios", "sub_path": "tests/test_utils.py", "file_name": "test_utils.py", "file_ext": "py", "file_size_in_byte": 3194, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 170, "dataset": "github-code", "pt": "12", "api": [{"api_name": "correios.utils.RangeSet", "line_number": 14, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 11, "usage_type": "attribute"}, {"api_name": "correios.utils.RangeSet", "line_number": 18, "usage_type": "call"}, {"api_name": "correios.utils.RangeSet", "line_number": 19, "usage_type": "call"}, {"api_name": "correios.utils.RangeSet", "line_number": 20, "usage_type": "call"}, {"api_name": "correios.utils.RangeSet", "line_number": 21, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 23, "usage_type": "call"}, {"api_name": "correios.utils.RangeSet", "line_number": 24, "usage_type": "call"}, {"api_name": "correios.utils.RangeSet", "line_number": 29, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 27, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 34, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 39, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 39, "usage_type": "attribute"}, {"api_name": "correios.utils.capitalize_phrase", "line_number": 50, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 48, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 48, "usage_type": "attribute"}, {"api_name": "correios.utils.rreplace", "line_number": 55, "usage_type": "call"}, {"api_name": "correios.utils.rreplace", "line_number": 56, "usage_type": "call"}, {"api_name": "correios.utils.rreplace", "line_number": 57, "usage_type": "call"}, {"api_name": "correios.utils.rreplace", "line_number": 58, "usage_type": "call"}, {"api_name": "correios.utils.to_decimal", "line_number": 79, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 61, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 61, "usage_type": "attribute"}, {"api_name": "decimal.Decimal", "line_number": 64, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 65, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 66, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 67, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 68, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 69, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 70, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 71, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 72, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 73, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 74, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 75, "usage_type": "call"}, {"api_name": "correios.utils.to_decimal", "line_number": 93, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 82, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 82, "usage_type": "attribute"}, {"api_name": "decimal.Decimal", "line_number": 85, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 86, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 87, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 88, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 89, "usage_type": "call"}, {"api_name": "correios.utils.to_integer", "line_number": 98, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 96, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 96, "usage_type": "attribute"}, {"api_name": "correios.utils.get_resource_path", "line_number": 102, "usage_type": "call"}, {"api_name": "correios.utils.get_resource_path", "line_number": 108, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 106, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 106, "usage_type": "name"}]} +{"seq_id": "34471744792", "text": "#Import streamlit modules\nimport streamlit as st\n\n#Import associated py files with functions\nimport functions as fnc\nimport validation as vld\nimport section_funcs\nimport plots\n\n#Import data and plotting\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n#Import unit aware modules\nimport forallpeople as u\nu.environment('structural')\n\nfrom handcalcs import handcalc\n\n#Main function calls made at each run of Streamlit\ndef main():\n \"\"\"This function is run at the beginning of each script\"\"\"\n st.title(\"Box Girder Design - Stiffener calculations\")\n \n with st.beta_expander(\"Table of contents:\"):\n st.markdown(\"\"\"\n The below worksheet shows the calculations that are required for the longitudinal and transverse stiffeners as per \n\n Sec 7.3 - AS5100.6-2017\n or\n Sec 7.3 - AS5100.6-2004\n\n # TABLE OF CONTENTS:\n\n **Section 7.3** - *Longitudinal Flange Stiffeners*\n - **7.3.3.1** - Yielding of flange plate\n - **7.3.3.2** - Effective Section of Flange Plate Stiffener\n - **7.3.3.3** - Strength of Longitudinal Flange Stiffeners\n\n\n **Section 7.4** - *Web in Beams with Longitudinal Stiffeners*\n - **7.4.2** - Yielding of Web Panels\n - **7.4.3** - Buckling of Web Panels\n - **7.4.4** - Longitudinal Web Stiffeners\n - **7.4.6** - Transverse Stiffeners of Longitudinally Stiffened Webs\n - **5.10** - Stiffened Web alpha_v calculation\n - **5.14** - Design of Intermediate Transverse Web Stiffeners\n \n \n **Section 7.5** - *Transverse Members in Stiffened Flanges*\n - **7.5.2** - Effective Section and Stiffness for Transverse Members\n - **7.5.3** - Stiffness of Transverse Members\n\n **BS5400 - Sec 9 used for supplementary information**\n \"\"\")\n\n #Create Menu for various options\n vld.input_description(\"Click here to add custom description, sketch or image\")\n \n # Out of order Results Summary\n st.header(\"Results Summary\")\n results_container = st.beta_container()\n\n # Choice of standards\n st.sidebar.markdown(\"## Input Design Requirements\")\n version = st.sidebar.radio(\"Edition of AS5100.6\",(\"2004\",\"2017\"))\n panel_pos = st.sidebar.radio(\"Panel being analysed\",(\"outer\",\"inner\"))\n\n #Geometry of box girder\n st.sidebar.markdown(\"## Input Structure Geometry\")\n L = st.sidebar.number_input('Length of Box Girder (m)',5.0,100.0,50.0,step=1.0,format='%f')\n\n #Box dimension inputs\n st.sidebar.markdown(\"## Input box dimensions\")\n b = st.sidebar.slider('Width of Box Girder (mm)',500,3000,1000,step=50,format='%i') / 1000\n d = st.sidebar.slider('Height of Box Girder (mm)',500,3000,1000,step=50,format='%i') / 1000\n t_w = st.sidebar.slider('Thickness of Webs (mm)',5,30,12,step=1,format='%i') / 1000\n t_f = st.sidebar.slider('Thickness of Flanges (mm)',5,30,12,step=1,format='%i') / 1000\n\n #Stiffener dimensions\n st.sidebar.markdown(\"## Input Stiffener dimensions\")\n n_stif = st.sidebar.radio(\"Number of stiffeners\",(2,3))\n d_stif = st.sidebar.slider('Depth/Height longit. stiffeners(mm)',50,300,100,step=5,format='%i') / 1000\n t_stif = st.sidebar.slider('Thickness longit. stiffeners (mm)',5,30,12,step=1,format='%i') / 1000\n d_stif_trans = st.sidebar.slider('Depth/Height tranv. stiffeners / diaphragm (mm)',50,300,100,step=5,format='%i') / 1000\n t_stif_trans = st.sidebar.slider('Thickness of transv. stiffeners / diaphragm(mm)',5,30,12,step=1,format='%i') / 1000\n a_panel = st.sidebar.slider('Spacing of transverse stiffeners (mm)',300,3000,1000,step=50,format='%i') / 1000\n\n #Calculate stiffener dimensions\n longit_stif_spacing_latex, longit_stif_spacing_vals = fnc.longit_stif_spacing(b * u.m, d * u.m, n_stif)\n st.sidebar.latex(longit_stif_spacing_latex)\n b_flange,b_web = longit_stif_spacing_vals\n\n #Input Materials\n st.sidebar.markdown(\"## Input Material Properties\")\n f_y = st.sidebar.number_input(\"Yield Strength (MPa)\",150,500,350,10,\"%i\") * 10**6\n E_s = st.sidebar.number_input(\"Young's Modulus (GPa)\",40,300,200,5,\"%i\") * 10**9\n\n phi = 0.9 #T3.2 (b) (i) - Members subject to bending Cl7.3\n rho_steel = 7850 #Density of steel\n g = 9.81 #Gravity\n\n st.sidebar.latex(r\"\\phi={0.9}\")\n st.sidebar.latex(r\"\\rho_{steel}=7850 \\frac{kg}{m^3}\")\n st.sidebar.latex(r\"g=9.81 \\frac{N}{kg}\")\n\n #Force values\n st.sidebar.markdown(\"## Input Forces\")\n Fx = st.sidebar.number_input(\"F_x Axial Force typically due to thermal or restraint effects (kN)\",0,1000,100,10,\"%i\") * 1000\n Fy = st.sidebar.number_input(\"F_y Vertical Shear Force (kN)\",0,5000,100,10,\"%i\") * 1000\n Fz = st.sidebar.number_input(\"F_z Horizontal Shear Force (kN)\",0,5000,100,10,\"%i\") * 1000\n Mx = st.sidebar.number_input(\"Mx Torsion Force (kNm)\",0,5000,100,10,\"%i\") * 1000\n My = st.sidebar.number_input(\"My Transverse BM (kNm)\",0,20000,1000,10,\"%i\") * 1000\n Mz = st.sidebar.number_input(\"Mz Vertical BM (kNm)\",0,20000,1000,10,\"%i\") * 1000\n rho = st.sidebar.slider(\"Proportion of longitudinal stress assumed to be redistributed from web to flange (%)\",0,100,0,5,\"%i\") / 100\n\n #Precamber\n camb1 = st.sidebar.slider(\"Assumed pre-camber for imperfection calculations:\",0,1000,100,10,\"%i\") / 1000\n x1 = L/2\n\n with st.beta_expander(\"Box Girder Section Properties Check\"):\n st.markdown(\"\"\"\n Below section calculates the following box girder properties:\n\n - Section Properties\n - *area* Area\n - *cx, cy* Centroids\n - *ixx_c, iyy_c* Second Mom Area\n - *J* Torsion Constant\n - Stress Outputs - Max\n - *f_star_s_comp* Max Compression due to Biaxial BM\n - *f_star_s_tens* Max Tension ...\n - The maximum stress in the for the critical stiffeners is:\n - *f_star_s_fl* Max compression at Effective flange stiffener section due to Biaxial BM\n - *f_star_s_web* Max compression at Effective web stiffener ...\n - *f_star_s_fl_mid* Max compression at mid-panel of flange due to Triaxial BM\n - *f_star_s_web_mid* Max compression at mid-panel of web ...\n\n The below calculations are using the ***SectionProperties*** library in Python to calculate the stiffener critical stresses to be used.\n\n A separate Python file is used for the box girder geometry generator.\n \"\"\")\n\n st.set_option('deprecation.showPyplotGlobalUse', False)\n section, fig1, ax1 = section_funcs.boxgenerator(b,\n d,\n t_w,\n t_f,\n d_stif,\n t_stif,\n n_stif)\n @st.cache\n def calculate_section(section):\n section.calculate_geometric_properties(time_info=False)\n section.calculate_warping_properties(time_info=False)\n return section\n \n section = calculate_section(section)\n\n area = section.get_area()\n (cx, cy) = section.get_c()\n (ixx_c, iyy_c, ixy_c) = section.get_ic()\n\n x_f_stif,y_f_stif,x_w_stif,y_w_stif,x_f_mid,y_f_mid,x_w_mid,y_w_mid = fnc.stress_locations(b * u.m,d * u.m,t_f * u.m,t_w * u.m,n_stif)\n\n #Plot stress points to consider\n ax1.plot(x_f_stif.value,y_f_stif.value,'ro')\n ax1.annotate(f\"Crit Flange Stiffener\",(x_f_stif.value,y_f_stif.value),(x_f_stif.value+0.3,y_f_stif.value+0.3),arrowprops={'arrowstyle':'->'})\n ax1.plot(x_w_stif.value,y_w_stif.value,'ro')\n ax1.annotate(f\"Crit Web Stiffener\",(x_w_stif.value,y_w_stif.value),(x_w_stif.value-1.0,y_w_stif.value+0.3),arrowprops={'arrowstyle':'->'})\n ax1.plot(x_f_mid.value,y_f_mid.value,'ro')\n ax1.annotate(f\"Crit flange panel\",(x_f_mid.value,y_f_mid.value),(x_f_mid.value-0.4,y_f_mid.value+0.3),arrowprops={'arrowstyle':'->'})\n ax1.plot(x_w_mid.value,y_w_mid.value,'ro')\n ax1.annotate(f\"Crit web panel\",(x_w_mid.value,y_w_mid.value),(x_w_mid.value-0.4,y_w_mid.value+0.3),arrowprops={'arrowstyle':'->'})\n \n st.pyplot(fig1) #Plot the cross section shape of the box girder\n\n #Output section properties for box girder\n st.text(f'A: {area:.4f} m^2 \\n\\n'\n f'Section centroids are:\\ncx = {cx:.3f} m\\n'\n f'cy = {cy:.3f} m \\n\\n'\n f'Second Moments of area are:\\n'\n f'ixx_c = {ixx_c:.4f} m^4 \\niyy_c = {iyy_c:.4f} m^4')\n\n # Get stresses on beam\n f_star_s_comp, f_star_s_tens, stresses = section_funcs.in_plane_principle(section,Fy,Fz,Mx,My,Mz)\n\n\n f_star_s_fl = section_funcs.stress_location(x_f_stif.value,\n y_f_stif.value,\n 0.05,\n 0.05, \n section.mesh_nodes, \n stresses[0]['sig_zz_m'],\n 'max')\n f_star_s_web = section_funcs.stress_location(x_w_stif.value,\n y_w_stif.value,\n 0.05,\n 0.05,\n section.mesh_nodes,\n stresses[0]['sig_zz_m'],\n 'max')\n f_star_s_fl_mid = section_funcs.stress_location(x_f_mid.value,\n y_f_mid.value,\n 0.05,\n 0.05, \n section.mesh_nodes, \n stresses[0]['sig_zz_m'],\n 'max')\n f_star_s_web_mid = section_funcs.stress_location(x_w_mid.value,\n y_w_mid.value,\n 0.05,\n 0.05,\n section.mesh_nodes, \n stresses[0]['sig_zz_m'],\n 'max')\n f_star_s_fl_mean = section_funcs.stress_location(b/2,d,b/2,t_f, section.mesh_nodes, stresses[0]['sig_zz_m'],\"mean\")\n f_star_v = stresses[0]['sig_zy_vy'].max()\n f_star_vt = stresses[0]['sig_zxy_mzz'].max()\n\n st.text(f'The maximum stress in for the critical stiffeners is:\\n'\n f'f_star_s_fl = {f_star_s_fl/1e6:.0f} MPa\\n'\n f'f_star_s_web = {f_star_s_web/1e6:.0f} MPa\\n\\n'\n f'The maximum stress in the mid-panel sections is:\\n'\n f'f_star_s_fl_mid = {f_star_s_fl_mid/1e6:.0f} MPa\\n'\n f'f_star_s_web_mid = {f_star_s_web_mid/1e6:.0f} MPa\\n\\n'\n f'The average stress across the top flange is:\\n'\n f'f_star_s_fl_mean = {f_star_s_fl_mean/1e6:.0f} MPa\\n\\n'\n f'The max torsion shear stress is:\\n'\n f'f_star_vt = {f_star_vt/1e6:.0f} MPa')\n\n flange_yield_latex, f_star_comb = fnc.flange_yield(f_star_vt * u.Pa,f_star_v * u.Pa,f_star_s_fl_mid * u.Pa)\n st.latex(flange_yield_latex)\n\n if f_star_comb > phi*f_y * u.Pa:\n st.error(\"FAIL {0} > {1} Util = {2:.2f}\".format(f_star_comb,phi*f_y * u.Pa,f_star_comb/(phi*f_y * u.Pa)))\n else:\n st.success(\"PASS {0} < {1} Util = {2:.2f}\".format(f_star_comb,phi*f_y * u.Pa,f_star_comb/(phi*f_y * u.Pa)))\n\n with st.beta_expander(\"Effective section of flange stiffener\"):\n st.markdown(\"\"\"\n The slenderness of K_c value of the flange stiffeners are calculated in this section\n\n - Stiffened flanges Section 7- Section 9.10.2 - BS5400.3-2000\n - $K_c$ as per Fig 7.3.3.2 AS5100.6-2017 (Also BS5400 Fig 5)\n\n Number of stiffeners:\n - For **Stiffeners** >= 3, use greater of: \n - Curve 1\n - Curve 3\n - For **Stiffeners** =2, use greater of:\n - Ave (Curve 1 + Curve 2)\n - Curve 3\n\n Slenderness:\n - Curve 1 or 2:\"\"\")\n st.latex(r\"\\lambda_{kb} = \\frac{b}{t}\\sqrt{\\frac{f_y}{355}}\")\n st.markdown(\"- Curve 3:\")\n st.latex(r\"\\lambda_{ka} = \\frac{a}{t}\\sqrt{\\frac{f_y}{355}}\")\n\n K_c, lamda_kc_a, lamda_kc_b, fig2, ax2 = fnc.K_buckling(n_stif,a_panel,b_flange.value,t_f,f_y)\n st.pyplot(fig2)\nif __name__ == '__main__':\n main()", "repo_name": "michaellisitsa/stiffened_box_girder", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 12073, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "forallpeople.environment", "line_number": 16, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 23, "usage_type": "call"}, {"api_name": "streamlit.beta_expander", "line_number": 25, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 26, "usage_type": "call"}, {"api_name": "validation.input_description", "line_number": 58, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 61, "usage_type": "call"}, {"api_name": "streamlit.beta_container", "line_number": 62, "usage_type": "call"}, {"api_name": "streamlit.sidebar.markdown", "line_number": 65, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 65, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.radio", "line_number": 66, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 66, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.radio", "line_number": 67, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 67, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.markdown", "line_number": 70, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 70, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.number_input", "line_number": 71, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 71, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.markdown", "line_number": 74, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 74, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.slider", "line_number": 75, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 75, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.slider", "line_number": 76, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 76, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.slider", "line_number": 77, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 77, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.slider", "line_number": 78, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 78, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.markdown", "line_number": 81, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 81, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.radio", "line_number": 82, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 82, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.slider", "line_number": 83, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 83, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.slider", "line_number": 84, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 84, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.slider", "line_number": 85, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 85, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.slider", "line_number": 86, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 86, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.slider", "line_number": 87, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 87, "usage_type": "attribute"}, {"api_name": "functions.longit_stif_spacing", "line_number": 90, "usage_type": "call"}, {"api_name": "forallpeople.m", "line_number": 90, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.latex", "line_number": 91, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 91, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.markdown", "line_number": 95, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 95, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.number_input", "line_number": 96, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 96, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.number_input", "line_number": 97, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 97, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.latex", "line_number": 103, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 103, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.latex", "line_number": 104, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 104, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.latex", "line_number": 105, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 105, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.markdown", "line_number": 108, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 108, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.number_input", "line_number": 109, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 109, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.number_input", "line_number": 110, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 110, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.number_input", "line_number": 111, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 111, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.number_input", "line_number": 112, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 112, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.number_input", "line_number": 113, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 113, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.number_input", "line_number": 114, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 114, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.slider", "line_number": 115, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 115, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.slider", "line_number": 118, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 118, "usage_type": "attribute"}, {"api_name": "streamlit.beta_expander", "line_number": 121, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 122, "usage_type": "call"}, {"api_name": "streamlit.set_option", "line_number": 144, "usage_type": "call"}, {"api_name": "section_funcs.boxgenerator", "line_number": 145, "usage_type": "call"}, {"api_name": "streamlit.cache", "line_number": 152, "usage_type": "attribute"}, {"api_name": "functions.stress_locations", "line_number": 164, "usage_type": "call"}, {"api_name": "forallpeople.m", "line_number": 164, "usage_type": "attribute"}, {"api_name": "streamlit.pyplot", "line_number": 176, "usage_type": "call"}, {"api_name": "streamlit.text", "line_number": 179, "usage_type": "call"}, {"api_name": "section_funcs.in_plane_principle", "line_number": 186, "usage_type": "call"}, {"api_name": "section_funcs.stress_location", "line_number": 189, "usage_type": "call"}, {"api_name": "section_funcs.stress_location", "line_number": 196, "usage_type": "call"}, {"api_name": "section_funcs.stress_location", "line_number": 203, "usage_type": "call"}, {"api_name": "section_funcs.stress_location", "line_number": 210, "usage_type": "call"}, {"api_name": "section_funcs.stress_location", "line_number": 217, "usage_type": "call"}, {"api_name": "streamlit.text", "line_number": 221, "usage_type": "call"}, {"api_name": "functions.flange_yield", "line_number": 232, "usage_type": "call"}, {"api_name": "forallpeople.Pa", "line_number": 232, "usage_type": "attribute"}, {"api_name": "streamlit.latex", "line_number": 233, "usage_type": "call"}, {"api_name": "forallpeople.Pa", "line_number": 235, "usage_type": "attribute"}, {"api_name": "streamlit.error", "line_number": 236, "usage_type": "call"}, {"api_name": "forallpeople.Pa", "line_number": 236, "usage_type": "attribute"}, {"api_name": "streamlit.success", "line_number": 238, "usage_type": "call"}, {"api_name": "forallpeople.Pa", "line_number": 238, "usage_type": "attribute"}, {"api_name": "streamlit.beta_expander", "line_number": 240, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 241, "usage_type": "call"}, {"api_name": "streamlit.latex", "line_number": 257, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 258, "usage_type": "call"}, {"api_name": "streamlit.latex", "line_number": 259, "usage_type": "call"}, {"api_name": "functions.K_buckling", "line_number": 261, "usage_type": "call"}, {"api_name": "streamlit.pyplot", "line_number": 262, "usage_type": "call"}]} +{"seq_id": "7126668328", "text": "# This is only for testing if forward pass work\n\nimport yaml\nimport torch\nfrom PIL import Image\nfrom utils import ImageAugmentation, ProjectionHead, MultiCropWrapper\nfrom model import VisionTransformer\n\n\ndef main():\n\n with open(\"config.yaml\") as file:\n config = yaml.load(file, yaml.FullLoader)\n img_path = \"path to image\"\n img = Image.open(img_path).convert(\"RGB\")\n\n transform = ImageAugmentation(\n config[\"global_crop_scale\"], config[\"local_crop_scale\"], config[\"n_local_crops\"]\n )\n\n img_list = transform(img)\n img_list = [img.unsqueeze(0).to(torch.float32) for img in img_list]\n\n backbone = VisionTransformer(\n config['n_classes'],\n config['depth'],\n config['image_size'],\n config['in_channels'],\n config['embed_size'],\n config['patch_size'],\n config['head'],\n config['hidden_size'],\n config['dropout_rate'],\n )\n\n model = MultiCropWrapper(backbone, ProjectionHead(config['embed_size'], config['out_dim']))\n out = model(img_list)\n\n print(\"Forward pass Successful\")\n\nif __name__ == '__main__':\n main()\n\n", "repo_name": "aiwizzard/dino-vit", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1120, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "yaml.load", "line_number": 13, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 13, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 15, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 15, "usage_type": "name"}, {"api_name": "utils.ImageAugmentation", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 22, "usage_type": "attribute"}, {"api_name": "model.VisionTransformer", "line_number": 24, "usage_type": "call"}, {"api_name": "utils.MultiCropWrapper", "line_number": 36, "usage_type": "call"}, {"api_name": "utils.ProjectionHead", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "17068650087", "text": "from typing import Optional, Tuple\n\nfrom UM.Logger import Logger\nfrom ..Script import Script\n\nclass FilamentChange(Script):\n\n _layer_keyword = \";LAYER:\"\n\n def __init__(self):\n super().__init__()\n\n def getSettingDataString(self):\n return \"\"\"{\n \"name\":\"Filament Change\",\n \"key\": \"FilamentChange\",\n \"metadata\": {},\n \"version\": 2,\n \"settings\":\n {\n \"layer_number\":\n {\n \"label\": \"Layer\",\n \"description\": \"At what layer should color change occur. This will be before the layer starts printing. Specify multiple color changes with a comma.\",\n \"unit\": \"\",\n \"type\": \"str\",\n \"default_value\": \"1\"\n },\n\n \"initial_retract\":\n {\n \"label\": \"Initial Retraction\",\n \"description\": \"Initial filament retraction distance. The filament will be retracted with this amount before moving the nozzle away from the ongoing print.\",\n \"unit\": \"mm\",\n \"type\": \"float\",\n \"default_value\": 30.0\n },\n \"later_retract\":\n {\n \"label\": \"Later Retraction Distance\",\n \"description\": \"Later filament retraction distance for removal. The filament will be retracted all the way out of the printer so that you can change the filament.\",\n \"unit\": \"mm\",\n \"type\": \"float\",\n \"default_value\": 300.0\n }\n }\n }\"\"\"\n\n def execute(self, data: list):\n\n \"\"\"data is a list. Each index contains a layer\"\"\"\n layer_nums = self.getSettingValueByKey(\"layer_number\")\n initial_retract = self.getSettingValueByKey(\"initial_retract\")\n later_retract = self.getSettingValueByKey(\"later_retract\")\n\n color_change = \"M600\"\n\n if initial_retract is not None and initial_retract > 0.:\n color_change = color_change + (\" E%.2f\" % initial_retract)\n\n if later_retract is not None and later_retract > 0.:\n color_change = color_change + (\" L%.2f\" % later_retract)\n\n color_change = color_change + \" ; Generated by FilamentChange plugin\"\n\n layer_targets = layer_nums.split(\",\")\n if len(layer_targets) > 0:\n for layer_num in layer_targets:\n layer_num = int(layer_num.strip())\n if layer_num <= len(data):\n index, layer_data = self._searchLayerData(data, layer_num - 1)\n if layer_data is None:\n Logger.log(\"e\", \"Could not found the layer\")\n continue\n lines = layer_data.split(\"\\n\")\n lines.insert(2, color_change)\n final_line = \"\\n\".join(lines)\n data[index] = final_line\n\n return data\n\n ## This method returns the data corresponding with the indicated layer number, looking in the gcode for\n # the occurrence of this layer number.\n def _searchLayerData(self, data: list, layer_num: int) -> Tuple[int, Optional[str]]:\n for index, layer_data in enumerate(data):\n first_line = layer_data.split(\"\\n\")[0]\n # The first line should contain the layer number at the beginning.\n if first_line[:len(self._layer_keyword)] == self._layer_keyword:\n # If found the layer that we are looking for, then return the data\n if first_line[len(self._layer_keyword):] == str(layer_num):\n return index, layer_data\n return 0, None", "repo_name": "ganeshmev/Fracktory-3b", "sub_path": "Fracktory3-3.0_b11/plugins/PostProcessingPlugin/scripts/FilamentChange.py", "file_name": "FilamentChange.py", "file_ext": "py", "file_size_in_byte": 3752, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "12", "api": [{"api_name": "Script.Script", "line_number": 6, "usage_type": "name"}, {"api_name": "UM.Logger.Logger.log", "line_number": 73, "usage_type": "call"}, {"api_name": "UM.Logger.Logger", "line_number": 73, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 84, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 84, "usage_type": "name"}]} +{"seq_id": "14391785263", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom mmcv.runner import BaseModule, auto_fp16, force_fp32\n\nfrom mmdet.models.builder import HEADS\nfrom mmdet.models.utils import build_linear_layer\nfrom mmdet.models.roi_heads.bbox_heads.convfc_bbox_head import ConvFCBBoxHead\n\n@HEADS.register_module()\nclass ActiveShared2FCBBoxMIAODHead(ConvFCBBoxHead):\n \"\"\"Simplest RoI head, with only two fc layers for classification and\n regression respectively.\"\"\"\n\n def __init__(self, \n num_shared_convs=0,\n num_shared_fcs=2,\n num_cls_convs=0,\n num_cls_fcs=0,\n num_reg_convs=0,\n num_reg_fcs=0,\n conv_out_channels=256,\n fc_out_channels=1024,\n conv_cfg=None,\n norm_cfg=None,\n init_cfg=None,\n *args,\n **kwargs):\n super(ConvFCBBoxHead, self).__init__(\n *args, \n init_cfg=init_cfg, \n **kwargs)\n assert (num_shared_convs + num_shared_fcs + num_cls_convs +\n num_cls_fcs + num_reg_convs + num_reg_fcs > 0)\n if num_cls_convs > 0 or num_reg_convs > 0:\n assert num_shared_fcs == 0\n if not self.with_cls:\n assert num_cls_convs == 0 and num_cls_fcs == 0\n if not self.with_reg:\n assert num_reg_convs == 0 and num_reg_fcs == 0\n self.num_shared_convs = num_shared_convs\n self.num_shared_fcs = num_shared_fcs\n self.num_cls_convs = num_cls_convs\n self.num_cls_fcs = num_cls_fcs\n self.num_reg_convs = num_reg_convs\n self.num_reg_fcs = num_reg_fcs\n self.conv_out_channels = conv_out_channels\n self.fc_out_channels = fc_out_channels\n self.conv_cfg = conv_cfg\n self.norm_cfg = norm_cfg\n \n # add more loss func\n self.l_imgcls = nn.BCELoss()\n\n # add shared convs and fcs\n self.shared_convs, self.shared_fcs, last_layer_dim = \\\n self._add_conv_fc_branch(\n self.num_shared_convs, self.num_shared_fcs, self.in_channels,\n True)\n self.shared_out_channels = last_layer_dim\n\n # add cls specific branch\n self.cls_convs_1, self.cls_fcs_1, self.cls_last_dim = \\\n self._add_conv_fc_branch(\n self.num_cls_convs, self.num_cls_fcs, self.shared_out_channels)\n self.cls_convs_2, self.cls_fcs_2, self.cls_last_dim = \\\n self._add_conv_fc_branch(\n self.num_cls_convs, self.num_cls_fcs, self.shared_out_channels)\n self.cls_convs_mil, self.cls_fcs_mil, self.cls_last_dim = \\\n self._add_conv_fc_branch(\n self.num_cls_convs, self.num_cls_fcs, self.shared_out_channels)\n\n # add reg specific branch\n self.reg_convs, self.reg_fcs, self.reg_last_dim = \\\n self._add_conv_fc_branch(\n self.num_reg_convs, self.num_reg_fcs, self.shared_out_channels)\n\n if self.num_shared_fcs == 0 and not self.with_avg_pool:\n if self.num_cls_fcs == 0:\n self.cls_last_dim *= self.roi_feat_area\n if self.num_reg_fcs == 0:\n self.reg_last_dim *= self.roi_feat_area\n\n self.relu = nn.ReLU(inplace=True)\n \n # cleanup\n if self.with_reg:\n self.init_cfg = self.init_cfg[:-1]\n del self.fc_reg\n if self.with_cls:\n self.init_cfg = self.init_cfg[:-1]\n del self.fc_cls\n\n # reconstruct fc_cls and fc_reg since input channels are changed\n if self.with_cls:\n if self.custom_cls_channels:\n self.cls_channels = self.loss_cls.get_cls_channels(self.num_classes)\n else:\n self.cls_channels = self.num_classes + 1\n self.fc_cls_1 = build_linear_layer(\n self.cls_predictor_cfg,\n in_features=self.cls_last_dim,\n out_features=self.cls_channels)\n self.fc_cls_2 = build_linear_layer(\n self.cls_predictor_cfg,\n in_features=self.cls_last_dim,\n out_features=self.cls_channels)\n self.fc_cls_mil = build_linear_layer(\n self.cls_predictor_cfg,\n in_features=self.cls_last_dim,\n out_features=self.cls_channels)\n if self.with_reg:\n out_dim_reg = (4 if self.reg_class_agnostic else 4 *\n self.num_classes)\n self.fc_reg = build_linear_layer(\n self.reg_predictor_cfg,\n in_features=self.reg_last_dim,\n out_features=out_dim_reg)\n\n if init_cfg is None:\n self.init_cfg += [\n dict(\n type='Normal', \n std=0.01, \n override=[\n dict(name='fc_cls_1'),\n dict(name='fc_cls_2'),\n dict(name='fc_cls_mil'),\n dict(name='fc_reg'),\n ]),\n dict(\n type='Xavier',\n layer='Linear',\n override=[\n dict(name='shared_fcs'),\n dict(name='cls_fcs_1'),\n dict(name='cls_fcs_2'),\n dict(name='cls_fcs_mil'),\n dict(name='reg_fcs')\n ])\n ]\n\n def forward(self, x):\n # shared part\n if self.num_shared_convs > 0:\n for conv in self.shared_convs:\n x = conv(x)\n\n if self.num_shared_fcs > 0:\n if self.with_avg_pool:\n x = self.avg_pool(x)\n\n x = x.flatten(1)\n\n for fc in self.shared_fcs:\n x = self.relu(fc(x))\n # separate branches\n x_cls_1 = x\n x_cls_2 = x\n x_cls_mil = x\n x_reg = x\n\n for conv in self.cls_convs_1:\n x_cls_1 = conv(x_cls_1)\n for conv in self.cls_convs_2:\n x_cls_2 = conv(x_cls_2)\n for conv in self.cls_convs_mil:\n x_cls_mil = conv(x_cls_mil)\n if x_cls_1.dim() > 2:\n if self.with_avg_pool:\n x_cls_1 = self.avg_pool(x_cls_1)\n x_cls_2 = self.avg_pool(x_cls_2)\n x_cls_mil = self.avg_pool(x_cls_mil)\n x_cls_1 = x_cls_1.flatten(1)\n x_cls_2 = x_cls_2.flatten(1)\n x_cls_mil = x_cls_mil.flatten(1)\n for fc in self.cls_fcs_1:\n x_cls_1 = self.relu(fc(x_cls_1))\n for fc in self.cls_fcs_2:\n x_cls_2 = self.relu(fc(x_cls_2))\n for fc in self.cls_fcs_mil:\n x_cls_mil = self.relu(fc(x_cls_mil))\n\n for conv in self.reg_convs:\n x_reg = conv(x_reg)\n if x_reg.dim() > 2:\n if self.with_avg_pool:\n x_reg = self.avg_pool(x_reg)\n x_reg = x_reg.flatten(1)\n for fc in self.reg_fcs:\n x_reg = self.relu(fc(x_reg))\n \n if self.with_cls:\n cls_score_1 = self.fc_cls_1(x_cls_1)\n cls_score_2 = self.fc_cls_2(x_cls_2)\n cls_score_mil = self.fc_cls_mil(x_cls_mil)\n else:\n raise NotImplementedError\n bbox_pred = self.fc_reg(x_reg) if self.with_reg else None\n\n y_head_cls_term2 = (cls_score_1 + cls_score_2) / 2\n y_head_cls_term2 = y_head_cls_term2.detach() # (1024, 21)\n y_head_cls = cls_score_mil.softmax(1) * y_head_cls_term2.sigmoid().max(1, keepdim=True)[0].softmax(0)\n # originial implementation in RetinaNet only use foreground classes\n\n return [cls_score_1, cls_score_2, y_head_cls], bbox_pred\n\n def loss(self,\n list_cls_score,\n bbox_pred,\n rois,\n labels,\n *args, **kwargs):\n assert len(list_cls_score) == 3\n # Label set training\n cls_score_1, cls_score_2, y_head_cls = [i.float() for i in list_cls_score]\n bbox_pred = bbox_pred.float()\n\n loss_1 = self.loss_det(\n cls_score_1, bbox_pred, y_head_cls, rois, labels, *args, **kwargs\n )\n loss_2 = self.loss_det(\n cls_score_2, bbox_pred, y_head_cls, rois, labels, *args, **kwargs\n )\n loss_det_cls = (loss_1['loss_cls'] + loss_2['loss_cls']) / 2\n loss_det_loc = (loss_1['loss_bbox'] + loss_2['loss_bbox']) / 2\n loss_imgcls = (loss_1['loss_imgcls'] + loss_2['loss_imgcls']) / 2\n losses = dict(loss_cls=loss_det_cls, loss_bbox=loss_det_loc, loss_imgcls=loss_imgcls)\n return losses\n\n @force_fp32(apply_to=('cls_score', 'bbox_pred', 'y_head_cls'))\n def loss_det(self, \n cls_score,\n bbox_pred,\n y_head_cls,\n rois,\n labels,\n label_weights,\n bbox_targets,\n bbox_weights,\n reduction_override=None,\n **kwargs):\n losses = super(ActiveShared2FCBBoxMIAODHead, self).loss(\n cls_score,\n bbox_pred,\n rois,\n labels,\n label_weights,\n bbox_targets,\n bbox_weights,\n reduction_override=reduction_override)\n # mil loss\n labels_batch = cls_score.new_zeros(self.cls_channels)\n pos_inds = (labels >= 0) & (labels <= self.num_classes) # count bg as SSD\n labels_batch[labels[pos_inds].unique()] = 1\n\n y_head_cls_batch = y_head_cls.sum(0).clamp(1e-5, 1.0-1e-5) # (#roi, C+1)\n l_imgcls = self.l_imgcls(y_head_cls_batch, labels_batch) * 0.1\n losses.update(dict(loss_imgcls=l_imgcls))\n return losses\n \n @force_fp32(apply_to=('cls_score_1', 'cls_score_2', 'y_head_cls'))\n def loss_wave_min(self,\n cls_score_1,\n cls_score_2, \n y_head_cls,):\n losses = self.loss_wave_dis(cls_score_1, cls_score_2, y_head_cls, False)\n \n y_pseudo = cls_score_1.new_zeros(self.cls_channels)\n # predict image pseudo label\n with torch.no_grad():\n y_pseudo = cls_score_1.sigmoid() + cls_score_2.sigmoid()\n y_pseudo = y_pseudo.max(0)[0] / 2\n y_pseudo[y_pseudo >= 0.5] = 1\n y_pseudo[y_pseudo < 0.5] = 0\n y_pseudo = y_pseudo.detach()\n # mil image score\n y_head_cls_batch = y_head_cls.sum(0).clamp(1e-5, 1.0-1e-5) # (#roi, C+1)\n if y_pseudo.sum() == 0: # ignore hard images\n l_imgcls = self.l_imgcls(y_head_cls_batch, y_pseudo) * 0\n else:\n l_imgcls = self.l_imgcls(y_head_cls_batch, y_pseudo) * 0.1\n losses.update(dict(unlabel_loss_imgcls=l_imgcls))\n return losses\n\n @force_fp32(apply_to=('cls_score_1', 'cls_score_2', 'y_head_cls'))\n def loss_wave_dis(self,\n cls_score_1,\n cls_score_2, \n y_head_cls,\n minus):\n cls_score_1 = nn.Sigmoid()(cls_score_1)\n cls_score_2 = nn.Sigmoid()(cls_score_2)\n # mil weight\n w_i = y_head_cls.detach()\n diff = abs(cls_score_1 - cls_score_2)\n if minus:\n diff = 1 - diff\n l_det_cls_all = (diff * w_i).mean(dim=1).sum() * 0.5\n # self.param_lambda\n return dict(unlabel_loss_wavedis=l_det_cls_all)", "repo_name": "lyumengyao/blad", "sub_path": "acdet/models/active_convfc_bbox_head_miaod.py", "file_name": "active_convfc_bbox_head_miaod.py", "file_ext": "py", "file_size_in_byte": 11485, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "12", "api": [{"api_name": "mmdet.models.roi_heads.bbox_heads.convfc_bbox_head.ConvFCBBoxHead", "line_number": 11, "usage_type": "name"}, {"api_name": "mmdet.models.roi_heads.bbox_heads.convfc_bbox_head.ConvFCBBoxHead", "line_number": 29, "usage_type": "argument"}, {"api_name": "torch.nn.BCELoss", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 84, "usage_type": "name"}, {"api_name": "mmdet.models.utils.build_linear_layer", "line_number": 100, "usage_type": "call"}, {"api_name": "mmdet.models.utils.build_linear_layer", "line_number": 104, "usage_type": "call"}, {"api_name": "mmdet.models.utils.build_linear_layer", "line_number": 108, "usage_type": "call"}, {"api_name": "mmdet.models.utils.build_linear_layer", "line_number": 115, "usage_type": "call"}, {"api_name": "mmcv.runner.force_fp32", "line_number": 231, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 271, "usage_type": "call"}, {"api_name": "mmcv.runner.force_fp32", "line_number": 262, "usage_type": "call"}, {"api_name": "torch.nn.Sigmoid", "line_number": 292, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 292, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 293, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 293, "usage_type": "name"}, {"api_name": "mmcv.runner.force_fp32", "line_number": 286, "usage_type": "call"}, {"api_name": "mmdet.models.builder.HEADS.register_module", "line_number": 10, "usage_type": "call"}, {"api_name": "mmdet.models.builder.HEADS", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "28623133340", "text": "from __future__ import print_function\nfrom PyQt5 import QtWidgets\nfrom home import Ui_MainWindow\nfrom Song import Ui_Song\nfrom party import Ui_party\nfrom ECG import Ui_ECG\nimport sys\nfrom scipy.io import wavfile\nfrom FastICA import FastICA as FA\nimport utilities as utl\nimport numpy as np\nfrom sklearn.datasets import load_digits\nfrom sklearn.decomposition import FastICA\nimport scipy.io.wavfile\nfrom matplotlib import pyplot as plt\nimport pandas as pd \nimport librosa.display\nimport librosa\nimport skimage\nfrom skimage import io\nfrom skimage.transform import resize\nimport matplotlib.image as mpimg\nfrom pyqtgraph import PlotWidget\nimport pyqtgraph as pg\nfrom PyQt5.QtWidgets import QFileDialog,QMessageBox\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom PyQt5.QtWidgets import QDialog, QApplication, QPushButton, QVBoxLayout,QTableWidgetItem\nfrom PyQt5 import QtCore, QtWidgets, QtMultimedia\n\n\nclass pop_window1(QtWidgets.QMainWindow,Ui_Song):\n path = \"\"\n \n def __init__(self):\n super(pop_window1, self).__init__()\n self.ui1 = Ui_Song()\n self.ui1.setupUi(self)\n self.ui1.songOpen.clicked.connect(self.loaddata)\n self.ui1.song_result.clicked.connect(self.control)\n\n def loaddata(self):\n filename = QFileDialog.getOpenFileName(self)\n if filename[0]:\n self.path = filename[0]\n if not (self.path.endswith(\".wav\")):\n self.alarm(\"Please Chose .wav File\")\n\n def song(self,path):\n y, sr = librosa.load(self.path, duration=120)\n S_full, phase = librosa.magphase(librosa.stft(y))\n\n S_filter = librosa.decompose.nn_filter(S_full,aggregate=np.median,metric='cosine',\n width=int(librosa.time_to_frames(2, sr=sr))) \n S_filter = np.minimum(S_full, S_filter)\n margin_i, margin_v = 2, 10\n power = 2\n\n mask_i = librosa.util.softmask(S_filter,margin_i * (S_full - S_filter),power=power)\n mask_v = librosa.util.softmask(S_full - S_filter,margin_v * S_filter,power=power)\n\n S_foreground = mask_v * S_full\n S_background = mask_i * S_full\n\n music =librosa.griffinlim(S_background)\n vocal =librosa.griffinlim(S_foreground)\n scipy.io.wavfile.write('sound_results/song/music.wav',sr,music) \n scipy.io.wavfile.write('sound_results/song/vocal.wav',sr,vocal) \n utl.plotSounds([music, vocal], [\"music\", \"vocal\"], sr, \"plot_results/song/song_separation_plot.png\")\n img = pg.QtGui.QGraphicsPixmapItem(pg.QtGui.QPixmap('plot_results/song/song_separation_plot.png'))\n self.ui1.widget_song.addItem(img)\n self.ui1.widget_song.invertY(True)\n self.alarm(\"Check Plot & Sound Results Files\")\n\n def alarm(self,value):\n msg = QMessageBox()\n msg.setWindowTitle(\"Information\")\n msg.setText(value) \n msg.setIcon(QMessageBox.Warning)\n x=msg.exec_() \n\n \n def control(self):\n if(self.path==\"\"):\n self.alarm(\"Please Select Song First\")\n else:\n self.song(self.path)\n\nclass pop_window2(QtWidgets.QMainWindow,Ui_party):\n path1 =\"\"\n path2 =\"\"\n def __init__(self):\n super(pop_window2, self).__init__()\n self.ui2=Ui_party()\n self.ui2.setupUi(self)\n self.ui2.party1.clicked.connect(self.loaddata1)\n self.ui2.party2.clicked.connect(self.loaddata2)\n self.ui2.result_party.clicked.connect(self.control)\n \n\n \n def loaddata1(self):\n filename = QFileDialog.getOpenFileName(self)\n if filename[0]:\n self.path1 = filename[0]\n if not (self.path1.endswith(\".wav\")):\n self.alarm(\"Please Chose .wav File\")\n \n\n def loaddata2(self):\n filename = QFileDialog.getOpenFileName(self)\n if filename[0]:\n self.path2 = filename[0]\n if not (self.path2.endswith(\".wav\")):\n self.alarm(\"Please Chose .wav File\")\n \n \n def cocktail(self):\n eps = 0.00000001\n rate1, data1 = wavfile.read(self.path1)\n rate2, data2 = wavfile.read(self.path2)\n if(data1.ndim != 1 or data2.ndim != 1):\n self.alarm(\"Please Chose another file with 1D data\")\n else:\n data1 = data1 - np.mean(data1)\n data1 = data1/max(data1)\n data2 = data2 - np.mean(data2)\n data2 = data2/max(data2)\n signals = [data1, data2]\n matrix = np.vstack(signals)\n whiteMatrix = utl.whitenMatrix(matrix)\n X = whiteMatrix\n vectors = []\n for i in range(0, X.shape[0]):\n vector = FA(X, vectors, eps)\n vectors.append(vector)\n \n \n W = np.vstack(vectors)\n \n\n S = np.dot(W, whiteMatrix)\n\n utl.plotSounds([S[0], S[1]], [\"source_1\", \"source_2\"], rate1, \"plot_results/cocktail_party/song_separation_plot.png\")\n wavfile.write(\"sound_results/cocktail_party/source1.wav\" ,rate1, 5000*S[0].astype(np.int16))\n wavfile.write(\"sound_results/cocktail_party/source2.wav\" , rate1, 5000*S[1].astype(np.int16))\n img = pg.QtGui.QGraphicsPixmapItem(pg.QtGui.QPixmap('plot_results/cocktail_party/song_separation_plot.png'))\n self.ui2.widget_party.addItem(img)\n self.ui2.widget_party.invertY(True)\n self.alarm(\"Check Plot & Sound Results Files\")\n \n\n def control(self):\n if(self.path1==\"\" or self.path2==\"\"):\n self.alarm(\"Please Make Sure Of Choose 2 Files\")\n else:\n self.cocktail()\n\n def alarm(self,value):\n msg = QMessageBox()\n msg.setWindowTitle(\"Information\")\n msg.setText(value) \n msg.setIcon(QMessageBox.Warning)\n x=msg.exec_() \n\nclass pop_window3(QtWidgets.QMainWindow,Ui_ECG):\n path=\"\"\n \n def __init__(self):\n super(pop_window3, self).__init__()\n self.ui3=Ui_ECG()\n self.ui3.setupUi(self)\n self.ui3.ecgopen.clicked.connect(self.loaddata)\n self.ui3.ecg_result.clicked.connect(self.control)\n \n def loaddata(self):\n filename = QFileDialog.getOpenFileName(self)\n if filename[0]:\n self.path = filename[0]\n if not (self.path.endswith(\".csv\")):\n self.alarm(\"Please Chose .csv Format\")\n \n\n def ecg(self,path):\n y= pd.read_csv(path)\n if(y.ndim != 2):\n self.alarm(\"Please Chose 2D data \")\n else:\n data1 = np.array(y.iloc[:3500,0])\n data2= np.array(y.iloc[:3500,1])\n data= np.c_[data1,data2] \n m = data1+data2\n transformer = FastICA(n_components=2\n ,random_state=0)\n data_transformed = transformer.fit_transform(data)\n data_transformed = data_transformed.transpose()\n self.plot(m,data_transformed[0]*-1,data_transformed[1]*-1,\"plot_results/ecg/ecg_plot.png\")\n img = pg.QtGui.QGraphicsPixmapItem(pg.QtGui.QPixmap('plot_results/ecg/ecg_plot.png'))\n self.ui3.widget_ecg.addItem(img)\n self.ui3.widget_ecg.invertY(True)\n self.alarm(\"Check Plot files\")\n \n def plot(self,sig1,sig2,sig3,path):\n fig = plt.figure()\n plt.subplot(3, 1,1) \n plt.plot(sig1,color = 'red')\n plt.subplot(3, 1,2) \n plt.plot((sig2),color = 'orange')\n plt.subplot(3, 1,3) \n plt.plot((sig3),color = 'blue')\n fig.tight_layout() \n fig.savefig(path)\n\n def control(self):\n if(self.path==\"\"):\n self.alarm(\"Please Make Sure Of Choose 2 Files\")\n else:\n self.ecg(self.path)\n\n def alarm(self,value):\n msg = QMessageBox()\n msg.setWindowTitle(\"Information\")\n msg.setText(value) \n msg.setIcon(QMessageBox.Warning)\n x=msg.exec_()\n\n\n\nclass ApplicationWindow(QtWidgets.QMainWindow,Ui_MainWindow):\n def __init__(self):\n super(ApplicationWindow, self).__init__()\n self.ui = Ui_MainWindow()\n self.ui.setupUi(self)\n self.ui.Button_song.clicked.connect(self.popWin1)\n self.ui.Button_party.clicked.connect(self.popWin2)\n self.ui.Button_ECG.clicked.connect(self.popWin3)\n\n def popWin1(self):\n \n global application1\n application1=pop_window1()\n application1.show()\n\n \n \n def popWin2(self):\n\n global application2\n application2=pop_window2()\n application2.show()\n\n\n def popWin3(self): \n\n global application3\n application3=pop_window3()\n application3.show()\n\ndef main():\n app = QtWidgets.QApplication(sys.argv)\n application = ApplicationWindow()\n application.show()\n app.exec_()\n\n\nif __name__ == \"__main__\":\n main()", "repo_name": "Mohammedelsayed412/Digital-Signal-Processing", "sub_path": "Blind_Source_Separation_Dsp/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 8839, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 31, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 31, "usage_type": "name"}, {"api_name": "Song.Ui_Song", "line_number": 31, "usage_type": "name"}, {"api_name": "Song.Ui_Song", "line_number": 36, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 42, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 42, "usage_type": "name"}, {"api_name": "librosa.load", "line_number": 49, "usage_type": "call"}, {"api_name": "librosa.magphase", "line_number": 50, "usage_type": "call"}, {"api_name": "librosa.stft", "line_number": 50, "usage_type": "call"}, {"api_name": "librosa.decompose.nn_filter", "line_number": 52, "usage_type": "call"}, {"api_name": "librosa.decompose", "line_number": 52, "usage_type": "attribute"}, {"api_name": "numpy.median", "line_number": 52, "usage_type": "attribute"}, {"api_name": "librosa.time_to_frames", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.minimum", "line_number": 54, "usage_type": "call"}, {"api_name": "librosa.util.softmask", "line_number": 58, "usage_type": "call"}, {"api_name": "librosa.util", "line_number": 58, "usage_type": "attribute"}, {"api_name": "librosa.util.softmask", "line_number": 59, "usage_type": "call"}, {"api_name": "librosa.util", "line_number": 59, "usage_type": "attribute"}, {"api_name": "librosa.griffinlim", "line_number": 64, "usage_type": "call"}, {"api_name": "librosa.griffinlim", "line_number": 65, "usage_type": "call"}, {"api_name": "scipy.io.io.wavfile.write", "line_number": 66, "usage_type": "call"}, {"api_name": "scipy.io.io", "line_number": 66, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 66, "usage_type": "name"}, {"api_name": "scipy.io.io.wavfile.write", "line_number": 67, "usage_type": "call"}, {"api_name": "scipy.io.io", "line_number": 67, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 67, "usage_type": "name"}, {"api_name": "utilities.plotSounds", "line_number": 68, "usage_type": "call"}, {"api_name": "pyqtgraph.QtGui.QGraphicsPixmapItem", "line_number": 69, "usage_type": "call"}, {"api_name": "pyqtgraph.QtGui", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pyqtgraph.QtGui.QPixmap", "line_number": 69, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 75, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Warning", "line_number": 78, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 78, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 88, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 88, "usage_type": "name"}, {"api_name": "party.Ui_party", "line_number": 88, "usage_type": "name"}, {"api_name": "party.Ui_party", "line_number": 93, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 102, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 102, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 110, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 110, "usage_type": "name"}, {"api_name": "scipy.io.wavfile.read", "line_number": 119, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 119, "usage_type": "name"}, {"api_name": "scipy.io.wavfile.read", "line_number": 120, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 120, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 129, "usage_type": "call"}, {"api_name": "utilities.whitenMatrix", "line_number": 130, "usage_type": "call"}, {"api_name": "FastICA.FastICA", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 141, "usage_type": "call"}, {"api_name": "utilities.plotSounds", "line_number": 143, "usage_type": "call"}, {"api_name": "scipy.io.wavfile.write", "line_number": 144, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 144, "usage_type": "name"}, {"api_name": "numpy.int16", "line_number": 144, "usage_type": "attribute"}, {"api_name": "scipy.io.wavfile.write", "line_number": 145, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 145, "usage_type": "name"}, {"api_name": "numpy.int16", "line_number": 145, "usage_type": "attribute"}, {"api_name": "pyqtgraph.QtGui.QGraphicsPixmapItem", "line_number": 146, "usage_type": "call"}, {"api_name": "pyqtgraph.QtGui", "line_number": 146, "usage_type": "attribute"}, {"api_name": "pyqtgraph.QtGui.QPixmap", "line_number": 146, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 159, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Warning", "line_number": 162, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 162, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 165, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 165, "usage_type": "name"}, {"api_name": "ECG.Ui_ECG", "line_number": 165, "usage_type": "name"}, {"api_name": "ECG.Ui_ECG", "line_number": 170, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 176, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 176, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 190, "usage_type": "attribute"}, {"api_name": "sklearn.decomposition.FastICA", "line_number": 192, "usage_type": "call"}, {"api_name": "pyqtgraph.QtGui.QGraphicsPixmapItem", "line_number": 197, "usage_type": "call"}, {"api_name": "pyqtgraph.QtGui", "line_number": 197, "usage_type": "attribute"}, {"api_name": "pyqtgraph.QtGui.QPixmap", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 207, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 208, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 208, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 209, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 220, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Warning", "line_number": 223, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 223, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 228, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 228, "usage_type": "name"}, {"api_name": "home.Ui_MainWindow", "line_number": 228, "usage_type": "name"}, {"api_name": "home.Ui_MainWindow", "line_number": 231, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 259, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 259, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 259, "usage_type": "attribute"}]} +{"seq_id": "24256295118", "text": "from typing import List\nclass Solution:\n def groupAnagrams(self, strs: List[str]) -> List[List[str]]:\n from collections import defaultdict\n mp = defaultdict(list)\n\n for st in strs:\n counts = [0] * 26\n for ch in st:\n counts[ord(ch) - ord(\"a\")] += 1\n print(counts)\n # 需要将 list 转换成 tuple 才能进行哈希\n mp[tuple(counts)].append(st)\n return list(mp.values())\ns = Solution()\nstrs = [\"eat\", \"tea\", \"tan\", \"ate\", \"nat\", \"bat\"]\nprint(s.groupAnagrams(strs))", "repo_name": "Weless/leetcode", "sub_path": "python/hash/49. 字母异位词分组.py", "file_name": "49. 字母异位词分组.py", "file_ext": "py", "file_size_in_byte": 566, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "typing.List", "line_number": 3, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 5, "usage_type": "call"}, {"api_name": "{'defaultdict': 'collections.defaultdict'}", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "43681468200", "text": "from rest_framework import status\nfrom rest_framework.exceptions import APIException\n\n\nclass BaseCustomException(APIException):\n detail = None\n status_code = None\n\n def __init__(self, detail=None, code=None):\n super().__init__(detail=detail, code=code)\n self.detail = detail\n self.status_code = code\n\n\nclass AuthenticationFailedException(BaseCustomException):\n def __init__(self, detail=None):\n if detail is None:\n detail = 'Not authenticated'\n super().__init__(detail=detail, code=status.HTTP_401_UNAUTHORIZED)\n\n\nclass FeatureNotReady(BaseCustomException):\n def __init__(self, detail=None):\n if detail is None:\n detail = 'Not implemented'\n # Should be ``status.HTTP_501_NOT_IMPLEMENTED``, but webhook requires 200-299 response code.\n super().__init__(detail=detail, code=status.HTTP_200_OK)\n\n\nclass UserAlreadyExists(BaseCustomException):\n def __init__(self, detail=None):\n if detail is None:\n detail = 'User already exists'\n\n super().__init__(detail=detail, code=status.HTTP_400_BAD_REQUEST)\n", "repo_name": "AivGitHub/brosfiles", "sub_path": "api/exceptions.py", "file_name": "exceptions.py", "file_ext": "py", "file_size_in_byte": 1115, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "12", "api": [{"api_name": "rest_framework.exceptions.APIException", "line_number": 5, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_401_UNAUTHORIZED", "line_number": 19, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 19, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 27, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 27, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 35, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 35, "usage_type": "name"}]} +{"seq_id": "1123411375", "text": "import requests\nfrom utils.decorators import timer\n\"\"\"\nUsing names.txt (right click and 'Save Link/Target As...'), a 46K text file containing over five-thousand first names, begin by sorting it into alphabetical order. Then working out the alphabetical value for each name, multiply this value by its alphabetical position in the list to obtain a name score.\n\nFor example, when the list is sorted into alphabetical order, COLIN, which is worth 3 + 15 + 12 + 9 + 14 = 53, is the 938th name in the list. So, COLIN would obtain a score of 938 × 53 = 49714.\n\nWhat is the total of all the name scores in the file?\n\"\"\"\n\ndef names_scores(names):\n names.sort()\n abcd = \"abcdefghijklmnopqrstuvwxyz\"\n return [sum([abcd.index(l)+1 for l in n])*(i+1) for i, n in enumerate(names)]\n \n\n@timer\ndef main():\n r = requests.get('https://projecteuler.net/project/resources/p022_names.txt', verify=False)\n names = [n.replace('\"','').lower() for n in r.text.split(\",\")]\n print(sum(names_scores(names)))\n\nif __name__ == \"__main__\":\n main()", "repo_name": "manusoler/code-challenges", "sub_path": "projecteuler/pe_22_names_scores.py", "file_name": "pe_22_names_scores.py", "file_ext": "py", "file_size_in_byte": 1041, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "requests.get", "line_number": 19, "usage_type": "call"}, {"api_name": "utils.decorators.timer", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "4026550841", "text": "from collections import defaultdict\nfrom typing import List\n\n\ndef dijkstra(net, s, t):\n # sanity check\n if s == t:\n return \"The start and terminal nodes are the same. Minimum distance is 0.\"\n if s not in net: # python2: if net.has_key(s)==False:\n return \"There is no start node called \" + str(s) + \".\"\n if t not in net: # python2: if net.has_key(t)==False:\n return \"There is no terminal node called \" + str(t) + \".\"\n # create a labels dictionary\n labels = {}\n # record whether a label was updated\n order = {}\n # populate an initial labels dictionary\n for i in net.keys():\n if i == s:\n labels[i] = 0 # shortest distance form s to s is 0\n else:\n labels[i] = float(\"inf\") # initial labels are infinity\n from copy import copy\n\n drop1 = copy(labels) # used for looping\n ## begin algorithm\n while len(drop1) > 0:\n # find the key with the lowest label\n minNode = min(\n drop1, key=drop1.get\n ) # minNode is the node with the smallest label\n # update labels for nodes that are connected to minNode\n for i in net[minNode]:\n if labels[i] > (labels[minNode] + net[minNode][i]):\n labels[i] = labels[minNode] + net[minNode][i]\n drop1[i] = labels[minNode] + net[minNode][i]\n order[i] = minNode\n del drop1[minNode] # once a node has been visited, it's excluded from drop1\n ## end algorithm\n # print shortest path\n temp = copy(t)\n rpath = []\n path = []\n while 1:\n rpath.append(temp)\n if temp in order:\n temp = order[temp] # if order.has_key(temp): temp = order[temp]\n else:\n return \"There is no path from \" + str(s) + \" to \" + str(t) + \".\"\n if temp == s:\n rpath.append(temp)\n break\n for j in range(len(rpath) - 1, -1, -1):\n path.append(rpath[j])\n return f\"The shortest path from {s} to {t} is {str(path)}. Minimum distance is {str(labels[t])}.\"\n\n\nclass mylist(list):\n def __getitem__(self, n):\n if n < 0:\n raise IndexError(\"...\")\n return list.__getitem__(self, n)\n\n\nclass Node:\n def __init__(self, value: int, row: int, col: int) -> None:\n self.value = value\n self.row = row\n self.col = col\n self.connections = mylist()\n self.visited = False\n\n def __eq__(self, other: object) -> bool:\n return (self.row == other.row) and (self.col == other.col)\n\n def __str__(self) -> str:\n return f\"{self.value} @ ({self.row}, {self.col})\"\n\n\ndef get_connections(nodes: List[int], row: int, col: int) -> List[Node]:\n connections = dict()\n try:\n connections[(row, col - 1)] = nodes[row][col - 1]\n except:\n pass\n try:\n connections[(row, col + 1)] = nodes[row][col + 1]\n except:\n pass\n try:\n connections[(row - 1, col)] = nodes[row - 1][col]\n except:\n pass\n try:\n connections[(row + 1, col)] = nodes[row + 1][col]\n except:\n pass\n return connections\n\n\nfilename = \"day15.txt\"\ndebug = \"AoC-John/Day15/\" + filename\n\nwith open(debug) as file:\n file_content = file.readlines()\n file_content = mylist(line.strip() for line in file_content)\n nodes_input = mylist()\n for line in file_content:\n nodes_input.append(mylist(int(num) for num in list(line)))\n\n nodes = defaultdict(dict)\n for row in range(len(nodes_input)):\n for col in range(len(nodes_input[0])):\n nodes[(row, col)] = get_connections(nodes_input, row, col)\n\n for node in nodes:\n if (0, 0) in nodes[node]:\n del nodes[node][(0, 0)]\n\n print(dijkstra(nodes, (0, 0), (len(nodes_input) - 1, len(nodes_input) - 1)))\n", "repo_name": "seanz-credera/adventofcode21", "sub_path": "AoC-John/Day15/puzzle29.py", "file_name": "puzzle29.py", "file_ext": "py", "file_size_in_byte": 3782, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "12", "api": [{"api_name": "copy.copy", "line_number": 25, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 41, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 80, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 111, "usage_type": "call"}]} +{"seq_id": "22860838613", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport os\n\n\n# In[7]:\n\n\n#Build convolutional neural network\nclass Uber_reg(nn.Module):\n def __init__(self):\n super(Uber_reg, self).__init__() #init Uber_reg's superclass\n\n #use GPU if available \n self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n \n self.lin = nn.Sequential(\n nn.Linear(6, 64), #out 64 features\n # nn.BatchNorm2d(nf), \n nn.LeakyReLU(0.01, inplace=True),\n \n nn.Linear(64, 32), #out 32\n # nn.BatchNorm2d(nf*2), #same as output channels\n nn.LeakyReLU(0.01, inplace=True),\n \n nn.Linear(32, 16), #out 16\n # nn.BatchNorm2d(nf*4),\n nn.LeakyReLU(0.01, inplace=True),\n \n nn.Linear(16, 8), #out 8\n # nn.BatchNorm2d(nf*2),\n nn.LeakyReLU(0.01, inplace=True),\n \n nn.Linear(8, 4), #out 4\n # nn.BatchNorm2d(nf),\n nn.LeakyReLU(0.01, inplace=True),\n\n nn.Linear(4, 2), #out 2\n nn.LeakyReLU(0.01, inplace=True),\n\n nn.Linear(2, 1), #out 1\n nn.LeakyReLU(0.01, inplace=True)\n )\n\n self.model_dir = 'checkpoints/'\n\n self.distance_mean = 12.210185789365196\n self.distance_max = 68.64447323801441\n self.distance_min = 0.2101942240567526\n self.day_of_week_mean = 4.051910014609448\n self.day_of_week_max = 7\n self.day_of_week_min = 1\n self.hour_bucket_mean = 1.5408693496868424\n self.hour_bucket_max = 3\n self.hour_bucket_min = 0\n self.temp_mean = 52.534307356161555\n self.temp_max = 81.5714\n self.temp_min = 24.875\n self.precip_mean = 0.013171941967445693\n self.precip_max = 0.26\n self.precip_min = 0.0\n self.wind_speed_mean = 8.918930698134712\n self.wind_speed_max = 22.25\n self.wind_speed_min = 0.0\n\n def forward(self, x):\n # print(1, x.shape)\n #reshape for dummy data with 1 feature\n # x = x.reshape((x.shape[0], 1))\n # print(2, x.shape)\n x = self.lin(x)\n # print(3, x.shape)\n return x\n\n def save_model(self):\n #saves the model state and optimizer state on the dict\n if not os.path.exists(self.model_dir):\n os.mkdir(self.model_dir)\n torch.save({\n 'model_state_dict': self.state_dict(),\n 'optimizer_state_dict': opt.state_dict()\n }, os.path.join(model_dir, 'checkpoint.pt'))\n print('model saved at', self.model_dir + 'checkpoint.pt')\n\n def load_model(self, load_optimizer=False):\n #load the model from the disk if it exists, skip if you don't need this part\n if os.path.exists(self.model_dir):\n checkpoint = torch.load(os.path.join(self.model_dir, 'checkpoint.pt'))\n self.load_state_dict(checkpoint['model_state_dict'])\n if load_optimizer:\n opt.load_state_dict(checkpoint['optimizer_state_dict'])\n print('loaded model from saved checkpoint')\n\n def predict_sample(self, sample):\n np_sample = np.array([sample])\n tensor_sample = torch.from_numpy(np_sample.astype(np.float32))\n prediction = self(tensor_sample.to(self.device))[0].item()\n return prediction\n\n", "repo_name": "harrisonrsmth/Uber-Travel-Time-Predictions", "sub_path": "CODE/backend/uber_regression_nn_class.py", "file_name": "uber_regression_nn_class.py", "file_ext": "py", "file_size_in_byte": 3520, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "torch.nn.Module", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 25, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 46, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 52, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 106, "usage_type": "attribute"}]} +{"seq_id": "33703830664", "text": "import atexit\nimport math\nimport socket\nfrom abc import ABCMeta, abstractmethod\nfrom inspect import signature\nfrom typing import Dict, Iterator, List, Callable, TypeVar, Generic, Type, Union, Any\nfrom uuid import uuid4\n\nimport requests\nfrom docker.errors import NotFound\nfrom requests import Response\nfrom time import sleep\nfrom timeout_decorator import timeout_decorator\n\nfrom useintest._logging import create_logger\nfrom useintest.common import docker_client\nfrom useintest.executables.common import pull_docker_image\nfrom useintest.services.exceptions import ServiceStartError, TransientServiceStartError, PersistentServiceStartError\nfrom useintest.services.models import Service, DockerisedService, DockerisedServiceWithUsers\n\nServiceType = TypeVar(\"ServiceType\", bound=Service)\nDockerisedServiceType = TypeVar(\"DockerisedServiceType\", bound=DockerisedService)\nDockerisedServiceWithUsersType = TypeVar(\"DockerisedServiceWithUsersType\", bound=DockerisedServiceWithUsers)\nLogListener = Union[Callable[[str, DockerisedService], bool], Callable[[str], bool]]\n\nlogger = create_logger(__name__)\n\n_DOCKER_LOG_ENCODING = \"utf-8\"\n\n\ndef _get_open_port() -> int:\n \"\"\"\n Gets a PORT that will (probably) be available on the machine.\n It is possible that in-between the time in which the open PORT of found and when it is used, another process may\n bind to it instead.\n :return: the (probably) available PORT\n \"\"\"\n free_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n free_socket.bind((\"\", 0))\n free_socket.listen(1)\n port = free_socket.getsockname()[1]\n free_socket.close()\n return port\n\n\nclass ServiceController(Generic[ServiceType], metaclass=ABCMeta):\n \"\"\"\n Service controller.\n \"\"\"\n def __init__(self, service_model: Type[ServiceType]):\n \"\"\"\n Constructor.\n :param service_model: the type of model for the service this controller handles\n \"\"\"\n # XXX: It would nice to do `ServiceModel()` but I don't think this is possible in Python\n self._service_model = service_model\n\n @abstractmethod\n def start_service(self, runtime_configuration: Dict=None) -> ServiceType:\n \"\"\"\n Starts a service.\n :param runtime_configuration: additional runtime configuration\n :raises ServiceStartException: service could not be started (see logs for more information)\n :return: model of the started service\n \"\"\"\n\n @abstractmethod\n def stop_service(self, service: ServiceType):\n \"\"\"\n Stops the given service.\n :param service: model of the service to stop\n \"\"\"\n\n\nclass ContainerisedServiceController(Generic[ServiceType], ServiceController[ServiceType], metaclass=ABCMeta):\n \"\"\"\n Controller of containers running a service brought up for testing.\n \"\"\"\n @abstractmethod\n def _start(self, service: Service, runtime_configuration: Dict):\n \"\"\"\n Starts a container.\n :param service: model of the service to start\n :param runtime_configuration: additional runtime configuration\n \"\"\"\n\n @abstractmethod\n def _stop(self, service: Service):\n \"\"\"\n Stops the given container.\n :param service: model of the service to stop\n \"\"\"\n\n def __init__(self, service_model: Type[ServiceType], start_timeout: float=math.inf, start_tries: int=10,\n stop_on_exit: bool=True, startup_monitor: Callable[[ServiceType], bool]=None):\n \"\"\"\n Constructor.\n :param stop_on_exit: see `Container.__init__`\n :param start_timeout: timeout before for container start\n :param start_tries: number of times to try to start the container before giving up (will only try once if a\n `PersistentServiceStartException` is raised\n :param stop_on_exit: whether to stop all started containers on exit\n :param startup_monitor: callable that should block until the service, given as the first parameter is known to\n have started and is ready for use. Should raise a `ServiceStartException` if service is not going to start\n \"\"\"\n super().__init__(service_model)\n self.start_timeout = start_timeout\n self.start_tries = start_tries\n self.stop_on_exit = stop_on_exit\n self.startup_monitor = startup_monitor\n\n def start_service(self, runtime_configuration: Dict=None) -> ServiceType:\n service = self._service_model()\n assert service is not None\n if self.stop_on_exit:\n atexit.register(self.stop_service, service)\n\n tries = 0\n while tries < self.start_tries:\n if tries > 0:\n self._stop(service)\n self._start(service, runtime_configuration if runtime_configuration is not None else {})\n try:\n if self.start_timeout is not math.inf:\n @timeout_decorator.timeout(self.start_timeout, timeout_exception=TimeoutError)\n def _wrapped_wait_until_started(service: ServiceType) -> bool:\n return self._wait_until_started(service)\n _wrapped_wait_until_started(service)\n else:\n self._wait_until_started(service)\n return service\n except TimeoutError as e:\n logger.warning(e)\n except TransientServiceStartError as e:\n logger.warning(e)\n tries += 1\n\n raise ServiceStartError()\n\n def stop_service(self, service: ServiceType):\n self._stop(service)\n\n def _wait_until_started(self, service: ServiceType):\n \"\"\"\n Blocks until the given container has started.\n :raises ServiceStartException: raised if service cannot be started\n :param service: the service\n \"\"\"\n if self.startup_monitor is None:\n raise ValueError(\"No startup monitor set\")\n return self.startup_monitor(service)\n\n\nclass DockerisedServiceController(\n Generic[DockerisedServiceType], ContainerisedServiceController[DockerisedServiceType], metaclass=ABCMeta):\n \"\"\"\n Controller of Docker containers running a service brought up for testing.\n \"\"\"\n @staticmethod\n def _call_detector_with_correct_arguments(detector: Callable, line: str, service: DockerisedServiceType) -> bool:\n \"\"\"\n Calls the given detector with either line as the only argument or both line and service, depending on the\n detector's signature.\n :param detector: the detector to call\n :param line: the log line to give to the detector\n :param service: the service being started\n :return: the detector's return value\n \"\"\"\n number_of_parameters = len(signature(detector).parameters)\n if number_of_parameters == 1:\n return detector(line)\n else:\n return detector(line, service)\n\n def __init__(self, service_model: Type[ServiceType], repository: str, tag: str, ports: List[int], *,\n start_timeout: int=math.inf, start_tries: int=math.inf, additional_run_settings: Dict[str, Any]=None,\n pull: bool=True,\n start_log_detector: LogListener=None,\n persistent_error_log_detector: LogListener=None,\n transient_error_log_detector: LogListener=None,\n startup_monitor: Callable[[ServiceType], bool]=None,\n start_http_detector: Callable[[Response], bool]=None,\n start_http_detection_endpoint: str=\"\"):\n \"\"\"\n Constructor.\n :param service_model: see `ServiceController.__init__`\n :param repository: the repository of the service to start\n :param tag: the repository tag of the service to start\n :param ports: the ports the service exposes\n :param start_timeout: timeout for starting containers\n :param start_tries: number of times to try starting the containerised service\n :param additional_run_settings: other run settings (see https://docker-py.readthedocs.io/en/1.2.3/api/#create_container)\n :param pull: whether to always pull from source repository\n :param start_log_detector: callable that detects if the service is ready for use from the logs\n :param persistent_error_log_detector: callable that detects if the service is unable to start\n :param transient_error_log_detector: callable that detects if the service encountered a transient error\n :param start_http_detector: callable that detects if the service is ready for use based on given HTTP response\n :param start_http_detection_endpoint: endpoint to call that should respond if the service has started\n \"\"\"\n if startup_monitor and (start_log_detector or persistent_error_log_detector or transient_error_log_detector or\n start_http_detector):\n raise ValueError(\"Cannot set `startup_monitor` in conjunction with any other detector\")\n\n super().__init__(service_model, start_timeout, start_tries, startup_monitor=startup_monitor)\n self.repository = repository\n self.tag = tag\n self.ports = ports\n self.run_settings = additional_run_settings if additional_run_settings is not None else {}\n self.pull = pull\n self.start_log_detector = start_log_detector\n self.persistent_error_log_detector = persistent_error_log_detector\n self.transient_error_log_detector = transient_error_log_detector\n self.start_http_detector = start_http_detector\n self.start_http_detection_endpoint = start_http_detection_endpoint\n\n self._log_iterator: Dict[Service, Iterator] = dict()\n\n def _start(self, service: DockerisedServiceType, runtime_configuration: Dict):\n if self.pull:\n image = docker_client.images.pull(self.repository, tag=self.tag)\n else:\n image = docker_client.images.get(f\"{self.repository}:{self.tag}\")\n\n service.name = f\"{self.repository.split('/')[-1]}-{uuid4()}\"\n service.ports = {port: _get_open_port() for port in self.ports}\n service.controller = self\n\n create_kwargs = dict(self.run_settings)\n create_kwargs.update(runtime_configuration)\n container = docker_client.containers.create(\n image=image.id,\n name=service.name,\n ports=service.ports,\n detach=True,\n **create_kwargs)\n service.container = container\n\n container.start()\n\n def _stop(self, service: DockerisedServiceType):\n if service in self._log_iterator:\n del self._log_iterator[service]\n if service.container:\n try:\n service.container.stop()\n service.container.remove(force=True)\n except NotFound:\n pass\n\n def _wait_until_started(self, service: DockerisedServiceType):\n if self.startup_monitor is not None:\n return self.startup_monitor(service)\n else:\n if self.start_log_detector:\n self._wait_until_log_indicates_start(service)\n if self.start_http_detector:\n self._wait_until_http_indicates_start(service)\n\n def _wait_until_log_indicates_start(self, service: DockerisedServiceType):\n \"\"\"\n Blocks until container log indicates that the service has started.\n :param service: starting service\n :raises ServiceStartException: raised if service cannot be started\n \"\"\"\n log_stream = service.container.logs(stream=True)\n for line in log_stream:\n # XXX: Although non-streamed logs are returned as a string, the generator returns bytes!?\n # http://docker-py.readthedocs.io/en/stable/containers.html#docker.models.containers.Container.logs\n line = line.decode(_DOCKER_LOG_ENCODING)\n logger.debug(line)\n\n if self.persistent_error_log_detector is not None \\\n and self._call_detector_with_correct_arguments(self.persistent_error_log_detector, line, service):\n raise PersistentServiceStartError(line)\n elif self.transient_error_log_detector is not None \\\n and self._call_detector_with_correct_arguments(self.transient_error_log_detector, line, service):\n raise TransientServiceStartError(line)\n elif self._call_detector_with_correct_arguments(self.start_log_detector, line, service):\n return\n\n logs = service.container.logs()\n raise TransientServiceStartError(f\"No error detected in logs but the container has stopped. Log dump: \"\n f\"{logs.decode(_DOCKER_LOG_ENCODING)}\")\n\n def _wait_until_http_indicates_start(self, service: DockerisedServiceType):\n \"\"\"\n Blocks until http endpoint indicates that the service has started.\n :param service: starting service\n \"\"\"\n started = False\n while not started:\n try:\n response = requests.head(f\"http://{service.host}:{service.port}/{self.start_http_detection_endpoint}\")\n started = self.start_http_detector(response)\n except requests.exceptions.ConnectionError:\n pass\n if not started:\n sleep(0.1)\n", "repo_name": "wtsi-hgi/useintest", "sub_path": "useintest/services/controllers.py", "file_name": "controllers.py", "file_ext": "py", "file_size_in_byte": 13331, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "typing.TypeVar", "line_number": 21, "usage_type": "call"}, {"api_name": "useintest.services.models.Service", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.TypeVar", "line_number": 22, "usage_type": "call"}, {"api_name": "useintest.services.models.DockerisedService", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.TypeVar", "line_number": 23, "usage_type": "call"}, {"api_name": "useintest.services.models.DockerisedServiceWithUsers", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 24, "usage_type": "name"}, {"api_name": "useintest.services.models.DockerisedService", "line_number": 24, "usage_type": "name"}, {"api_name": "useintest._logging.create_logger", "line_number": 26, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 38, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 38, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 38, "usage_type": "attribute"}, {"api_name": "typing.Generic", "line_number": 46, "usage_type": "name"}, {"api_name": "abc.ABCMeta", "line_number": 46, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 50, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 59, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 58, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 67, "usage_type": "name"}, {"api_name": "typing.Generic", "line_number": 75, "usage_type": "name"}, {"api_name": "abc.ABCMeta", "line_number": 75, "usage_type": "name"}, {"api_name": "useintest.services.models.Service", "line_number": 80, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 80, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 79, "usage_type": "name"}, {"api_name": "useintest.services.models.Service", "line_number": 88, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 87, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 94, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 95, "usage_type": "name"}, {"api_name": "math.inf", "line_number": 94, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 112, "usage_type": "name"}, {"api_name": "atexit.register", "line_number": 116, "usage_type": "call"}, {"api_name": "math.inf", "line_number": 124, "usage_type": "attribute"}, {"api_name": "timeout_decorator.timeout_decorator.timeout", "line_number": 125, "usage_type": "call"}, {"api_name": "timeout_decorator.timeout_decorator", "line_number": 125, "usage_type": "name"}, {"api_name": "useintest.services.exceptions.TransientServiceStartError", "line_number": 134, "usage_type": "name"}, {"api_name": "useintest.services.exceptions.ServiceStartError", "line_number": 138, "usage_type": "call"}, {"api_name": "typing.Generic", "line_number": 155, "usage_type": "name"}, {"api_name": "abc.ABCMeta", "line_number": 155, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 160, "usage_type": "name"}, {"api_name": "inspect.signature", "line_number": 169, "usage_type": "call"}, {"api_name": "typing.Type", "line_number": 175, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 175, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 176, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 176, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 181, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 182, "usage_type": "name"}, {"api_name": "requests.Response", "line_number": 182, "usage_type": "name"}, {"api_name": "math.inf", "line_number": 176, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 216, "usage_type": "name"}, {"api_name": "useintest.services.models.Service", "line_number": 216, "usage_type": "name"}, {"api_name": "typing.Iterator", "line_number": 216, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 218, "usage_type": "name"}, {"api_name": "useintest.common.docker_client.images.pull", "line_number": 220, "usage_type": "call"}, {"api_name": "useintest.common.docker_client.images", "line_number": 220, "usage_type": "attribute"}, {"api_name": "useintest.common.docker_client", "line_number": 220, "usage_type": "name"}, {"api_name": "useintest.common.docker_client.images.get", "line_number": 222, "usage_type": "call"}, {"api_name": "useintest.common.docker_client.images", "line_number": 222, "usage_type": "attribute"}, {"api_name": "useintest.common.docker_client", "line_number": 222, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 224, "usage_type": "call"}, {"api_name": "useintest.common.docker_client.containers.create", "line_number": 230, "usage_type": "call"}, {"api_name": "useintest.common.docker_client.containers", "line_number": 230, "usage_type": "attribute"}, {"api_name": "useintest.common.docker_client", "line_number": 230, "usage_type": "name"}, {"api_name": "docker.errors.NotFound", "line_number": 247, "usage_type": "name"}, {"api_name": "useintest.services.exceptions.PersistentServiceStartError", "line_number": 274, "usage_type": "call"}, {"api_name": "useintest.services.exceptions.TransientServiceStartError", "line_number": 277, "usage_type": "call"}, {"api_name": "useintest.services.exceptions.TransientServiceStartError", "line_number": 282, "usage_type": "call"}, {"api_name": "requests.head", "line_number": 293, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 295, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 298, "usage_type": "call"}]} +{"seq_id": "72497374420", "text": "from __future__ import annotations\n\nimport enum\nfrom contextlib import contextmanager\nfrom dataclasses import dataclass, field\nfrom typing import Any, Callable, ClassVar, Dict, Generator, Iterable, List, Tuple\n\nfrom invoke_poetry import remember_active_env\nfrom invoke_poetry.logs import Colors, error, info, warn\nfrom invoke_poetry.utils import IsInterrupted, capture_sigint\n\n\nclass TaskState(enum.Enum):\n \"\"\"Represent a matrix task state.\"\"\"\n\n RUNNING = -1\n OK = 0\n FAILED = 1\n SKIPPED = 2\n INTERRUPTED = 3\n\n def get_colored_name(self) -> str:\n \"\"\"Return a colored state name.\"\"\"\n return f\"{self.get_color()}{Colors.BOLD}{self.name}{Colors.ENDC}\"\n\n def get_color(self) -> str:\n \"\"\"Match a color with a specific state\"\"\"\n return [\n Colors.HEADER, # running\n Colors.OKGREEN, # ok\n Colors.FAIL, # failed\n Colors.BLUE, # skipped\n Colors.WARNING, # interrupted\n ][self.value + 1]\n\n\n@dataclass\nclass MatrixTask:\n \"\"\"A matrix task, with a `name` and its current `state`.\n\n When concluded, if the task returned something, it may be found in the `returned` field.\n \"\"\"\n\n name: str\n state: TaskState = TaskState.RUNNING\n returned: Any = None\n\n def report_state(self) -> None:\n \"\"\"Print a report that illustrates the task state.\"\"\"\n reporter, kwargs = self._get_reporter()\n reporter(f\"task {self.name}: {self.state.name}\", **kwargs) # type: ignore[call-arg]\n\n def _get_reporter(self) -> Tuple[Callable[[str, bool], None], Dict[str, Any]]:\n \"\"\"Return a reporter function and its kwargs based on the task state.\"\"\"\n return [\n (info, {\"do_print\": True}), # running\n (info, {\"do_print\": True}), # ok\n (error, {\"exit_now\": False}), # failed\n (warn, {\"do_print\": True}), # skipped\n (warn, {\"do_print\": True}), # interrupted\n ][self.state.value + 1]\n\n\n@dataclass\nclass TaskMatrix:\n \"\"\"A task matrix.\"\"\"\n\n # The tasks list\n tasks: List[MatrixTask] = field(default_factory=lambda: [])\n # Whether to print all tasks steps\n quiet: bool = False\n\n # A class variable that indicates if a task matrix job is underway\n running: ClassVar[bool] = False\n\n def print_report(self) -> None:\n \"\"\"Print a report of the current tasks states.\"\"\"\n info(\"Test matrix results:\\n\")\n for task in self.tasks:\n print(f\"\\t{task.name}:\\t{task.state.get_colored_name()}\")\n\n def exit_with_rc(self) -> None:\n \"\"\"Exit, possibly with an error if one of the task failed somehow.\"\"\"\n for task in self.tasks:\n if task.state.value > 0:\n exit(1)\n exit()\n\n @staticmethod\n @contextmanager\n def new(quiet: bool = False) -> Generator[TaskMatrix, None, None]:\n \"\"\"Context manager used to run a matrix job. It makes sure that the `running` class variable is correctly\n set.\"\"\"\n TaskMatrix.running = True\n try:\n yield TaskMatrix(quiet=quiet)\n finally:\n TaskMatrix.running = False\n\n def register_new_task(\n self, name: str, state: TaskState, returned: Any = None\n ) -> None:\n \"\"\"Create a new task starting from the given arguments and register it.\"\"\"\n self.register_task(MatrixTask(name=name, state=state, returned=returned))\n\n def register_task(self, task: MatrixTask) -> None:\n \"\"\"Register the given task.\"\"\"\n if not self.quiet:\n task.report_state()\n self.tasks.append(task)\n\n\ndef task_matrix(\n hook: Callable[..., Any],\n hook_args_builder: Callable[[str], Tuple[List[Any], Dict[str, Any]]],\n task_names: Iterable[str],\n print_steps: bool = True,\n) -> TaskMatrix:\n \"\"\"Launch the task `hook` function once for every task name provided. The hook args are built using the\n `hook_args_builder` hook, which receives the current task name.\n\n After executing, it will go back to the previously active poetry env.\n\n This is an example that takes a previously defined task and launch it with two different python versions as task\n names:\n\n ```python\n @task\n def print_python_version(c: Context, python_env: str, rollback_env: bool =True) -> None:\n with poetry_runner(c, python_version=python_version, rollback_env=rollback_env) as run:\n run(\"python --version\")\n\n @task\n def matrix(c: Runner) -> None:\n task_matrix(\n hook=print_python_version,\n hook_args_builder=lambda name: (\n [c],\n {\"python_env\": name, \"rollback_env\": False},\n ),\n task_names=['3.7', '3.8'],\n )\n ```\n\n It returns a TaskMatrix object, which allows further operations, like printing a report or exiting with a specific\n exit code.\n \"\"\"\n\n capture_sigint()\n\n with remember_active_env(quiet=False), TaskMatrix.new(quiet=not print_steps) as tm:\n for name in task_names:\n try:\n if IsInterrupted.by_user:\n # this task should not be launched, register it as skipped\n tm.register_new_task(name=name, state=TaskState.SKIPPED)\n else:\n # prepare a new task\n task = MatrixTask(name=name)\n if print_steps:\n task.report_state()\n # build the task args and kwargs\n hook_args, hook_kwargs = hook_args_builder(name)\n # launch the task and save its return value\n task.returned = hook(*hook_args, **hook_kwargs)\n # mark the task as completed and register it\n task.state = TaskState.OK\n tm.register_task(task)\n except (BaseException,):\n if not IsInterrupted.by_user:\n # Something bad happened, register the task as failed\n tm.register_new_task(name=name, state=TaskState.FAILED)\n else:\n # the user interrupted the task, mark it as interrupted; remaining tasks will be skipped\n tm.register_new_task(name=name, state=TaskState.INTERRUPTED)\n IsInterrupted.by_user = True\n\n return tm\n", "repo_name": "CarloDePieri/invoke-poetry", "sub_path": "invoke_poetry/matrix.py", "file_name": "matrix.py", "file_ext": "py", "file_size_in_byte": 6345, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "enum.Enum", "line_number": 13, "usage_type": "attribute"}, {"api_name": "invoke_poetry.logs.Colors.BOLD", "line_number": 24, "usage_type": "attribute"}, {"api_name": "invoke_poetry.logs.Colors", "line_number": 24, "usage_type": "name"}, {"api_name": "invoke_poetry.logs.Colors.ENDC", "line_number": 24, "usage_type": "attribute"}, {"api_name": "invoke_poetry.logs.Colors.HEADER", "line_number": 29, "usage_type": "attribute"}, {"api_name": "invoke_poetry.logs.Colors", "line_number": 29, "usage_type": "name"}, {"api_name": "invoke_poetry.logs.Colors.OKGREEN", "line_number": 30, "usage_type": "attribute"}, {"api_name": "invoke_poetry.logs.Colors", "line_number": 30, "usage_type": "name"}, {"api_name": "invoke_poetry.logs.Colors.FAIL", "line_number": 31, "usage_type": "attribute"}, {"api_name": "invoke_poetry.logs.Colors", "line_number": 31, "usage_type": "name"}, {"api_name": "invoke_poetry.logs.Colors.BLUE", "line_number": 32, "usage_type": "attribute"}, {"api_name": "invoke_poetry.logs.Colors", "line_number": 32, "usage_type": "name"}, {"api_name": "invoke_poetry.logs.Colors.WARNING", "line_number": 33, "usage_type": "attribute"}, {"api_name": "invoke_poetry.logs.Colors", "line_number": 33, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 46, "usage_type": "name"}, {"api_name": "invoke_poetry.logs.info", "line_number": 56, "usage_type": "name"}, {"api_name": "invoke_poetry.logs.info", "line_number": 57, "usage_type": "name"}, {"api_name": "invoke_poetry.logs.error", "line_number": 58, "usage_type": "name"}, {"api_name": "invoke_poetry.logs.warn", "line_number": 59, "usage_type": "name"}, {"api_name": "invoke_poetry.logs.warn", "line_number": 60, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 53, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 53, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 53, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 53, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 37, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 69, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 69, "usage_type": "call"}, {"api_name": "typing.ClassVar", "line_number": 74, "usage_type": "name"}, {"api_name": "invoke_poetry.logs.info", "line_number": 78, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 90, "usage_type": "name"}, {"api_name": "typing.Generator", "line_number": 91, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 101, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 64, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 114, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 114, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 115, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 115, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 115, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 115, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 115, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 116, "usage_type": "name"}, {"api_name": "invoke_poetry.utils.capture_sigint", "line_number": 149, "usage_type": "call"}, {"api_name": "invoke_poetry.remember_active_env", "line_number": 151, "usage_type": "call"}, {"api_name": "invoke_poetry.utils.IsInterrupted.by_user", "line_number": 154, "usage_type": "attribute"}, {"api_name": "invoke_poetry.utils.IsInterrupted", "line_number": 154, "usage_type": "name"}, {"api_name": "invoke_poetry.utils.IsInterrupted.by_user", "line_number": 170, "usage_type": "attribute"}, {"api_name": "invoke_poetry.utils.IsInterrupted", "line_number": 170, "usage_type": "name"}, {"api_name": "invoke_poetry.utils.IsInterrupted.by_user", "line_number": 176, "usage_type": "attribute"}, {"api_name": "invoke_poetry.utils.IsInterrupted", "line_number": 176, "usage_type": "name"}]} +{"seq_id": "19405697684", "text": "from cv_func import *\r\nfrom sklearn.svm import SVC\r\n\r\n\r\n#===== load dataset\r\nrandom_sorted_sample_df = pd.read_csv('../../../sample_label_random.csv',index_col=0)\r\nrandom_sorted_sample = random_sorted_sample_df.values[:,0]\r\ny = random_sorted_sample_df.values[:,1].astype(np.int)\r\nX = pd.read_csv('../../../meth_matrix_maxstd_2k_sorted.csv', index_col=0).loc[:,random_sorted_sample].values.T.astype(float)\r\nprint(\"data loaded\")\r\n\r\n#===== perform 5x5 nested cv\r\n## svc\r\n\"\"\"\r\nparam_space_svc = {\r\n \"C\": np.logspace(-3, 1, 8, base=2),\r\n \"kernel\": ['rbf', 'sigmoid'],\r\n \"gamma\": [0.01, 0.1] + ['auto'],\r\n \"probability\": [True]\r\n}\r\n\"\"\"\r\nmodel_svc = SVC(C=1, kernel='rbf', gamma=0.01, probability=True)\r\nprint(\"========SVC nested cv start===========\")\r\nprint_time()\r\n#nested_cv(X, y, model_svc, param_space_svc, './result_svc.csv')\r\ncross_validation(X, y, model_svc, './result_svc.csv')\r\nprint_time()", "repo_name": "lizhiqi49/SAGCN", "sub_path": "compared_methods/classic_methods/code/algorithms/svc.py", "file_name": "svc.py", "file_ext": "py", "file_size_in_byte": 905, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "14", "api": [{"api_name": "sklearn.svm.SVC", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "3958719799", "text": "from PyQt5.QtCore import Qt, QDate\nfrom PyQt5.QtWidgets import QTableWidgetItem, QMainWindow\nfrom PyQt5 import uic\n\nfrom data.Consts import *\nfrom data.FilmWindow import FilmWindow\nfrom data.GenresSelectionDialog import GenresSelectionDialog\n\nfrom datetime import date, time, datetime\nimport sqlite3 as sql\n\nfrom PyQt5.QtWidgets import QHeaderView\n\n\nclass UserWindow(QMainWindow):\n \"\"\"\n Основное окно пользователя\n \"\"\"\n\n def __init__(self):\n super().__init__()\n self.FilmWindow = None\n self.GenresDialog = GenresSelectionDialog(self, 0)\n\n self.films, self.right_films = [], []\n\n uic.loadUi(UW_INTERFACE, self)\n self.projectDB = sql.connect(PROJECT_DATABASE)\n self.projectDB_cur = self.projectDB.cursor()\n\n self.search_info = UW_SEARCH_INFO.copy()\n\n for film_info in self.projectDB_cur.execute(\"SELECT * FROM Films\").fetchall():\n film_info = dict(zip(FILMS_TABLE_KEYS, film_info))\n film_id = film_info['film_id']\n\n sessions = self.projectDB_cur.execute(\n \"SELECT year, month, day, session_id, hour, minute, hall_id FROM Sessions\"\n \" WHERE film_id = ?\", (film_id,)).fetchall()\n\n sessions_dict = dict()\n for ses in sessions:\n date_ = date(*ses[:3])\n info = (ses[3], time(*ses[4:6]), ses[6])\n\n if date_ == MIN_DATE:\n if info[1] >= (datetime.now() - timedelta(minutes=15)).time():\n if date_ not in sessions_dict:\n sessions_dict[date_] = []\n sessions_dict[date_].append(info)\n\n elif date_ > MIN_DATE:\n if date_ not in sessions_dict:\n sessions_dict[date_] = []\n sessions_dict[date_].append(info)\n else:\n continue\n\n if not sessions_dict:\n continue\n\n film_info['sessions'] = sessions_dict\n\n film_info['genres'] = tuple(map(lambda i: i[0], self.projectDB_cur.execute(\n \"SELECT genre_id FROM Films_Genres WHERE film_id = ?\", (film_id,)).fetchall()))\n\n film_info['directors'] = self.projectDB_cur.execute(\n \"SELECT name, surname FROM Films_Directors WHERE film_id = ?\", (film_id,)).fetchall()\n\n self.films.append(film_info)\n\n self.init_ui()\n self.load_films_table()\n\n def init_ui(self):\n self.setFixedSize(self.size())\n self.setWindowTitle('Выбор фильма')\n\n self.Calendar.clicked.connect(self.set_date)\n self.NameLine.textChanged.connect(self.set_name)\n self.IndicateGenresBtn.clicked.connect(self.GenresDialog.show)\n self.StartRatingSpinBox.valueChanged.connect(lambda value: self.set_rating(value, 0))\n self.EndRatingSpinBox.valueChanged.connect(lambda value: self.set_rating(value, 1))\n\n self.Calendar.setMinimumDate(QDate(MIN_DATE.year, MIN_DATE.month, MIN_DATE.day))\n self.Calendar.setMaximumDate(QDate(MAX_DATE.year, MAX_DATE.month, MAX_DATE.day))\n\n self.StartRatingSpinBox.setRange(MIN_AGE_RATING, MAX_AGE_RATING)\n self.EndRatingSpinBox.setRange(MIN_AGE_RATING, MAX_AGE_RATING)\n self.EndRatingSpinBox.setValue(MAX_AGE_RATING)\n\n self.FilmsTable.setColumnCount(UW_FILMS_TABLE_COLS_COUNT)\n self.FilmsTable.setHorizontalHeaderLabels(UW_FILMS_TABLE_TITLES)\n\n for col, size in enumerate(UW_FILMS_TABLE_COLS_SIZE):\n if isinstance(size, QHeaderView.ResizeMode):\n self.FilmsTable.horizontalHeader().setSectionResizeMode(col, size)\n else:\n self.FilmsTable.setColumnWidth(col, size)\n self.FilmsTable.horizontalHeader().setSectionResizeMode(col, QHeaderView.Fixed)\n\n self.FilmsTable.cellDoubleClicked.connect(self.open_film_window)\n self.load_films_table()\n\n def load_films_table(self):\n \"\"\"Загрузка таблицы в зависимости от даты\"\"\"\n self.set_right_films()\n\n self.FilmsTable.clearContents()\n self.FilmsTable.setRowCount(len(self.right_films))\n\n for row, film_info in enumerate(self.right_films):\n genres = ', '.join(map(lambda genre_id: GENRES_DICT[genre_id], film_info['genres'])).capitalize()\n h, m = divmod(film_info['duration'], 60)\n if h > 0 and m > 0:\n duration = f'{h}ч. {m}мин.'\n elif h > 0 and m == 0:\n duration = f'{h}ч.'\n else:\n duration = f'{m}мин.'\n\n info = (film_info['title'], film_info['country'], genres, str(film_info['rating']), duration)\n\n for col, elem in enumerate(info):\n item = QTableWidgetItem(elem)\n item.setFlags(item.flags() ^ Qt.ItemIsEditable)\n self.FilmsTable.setItem(row, col, item)\n\n def set_right_films(self):\n \"\"\"Установка списка с фильмами по выбранной дате\"\"\"\n\n date_ = self.search_info['date']\n self.right_films = filter(lambda film: date_ in film['sessions'], self.films)\n if date_ == MIN_DATE:\n now_time = (datetime.now() + timedelta(minutes=15)).time()\n\n self.right_films = filter(\n lambda film_info: any(ses[1] >= now_time for ses in film_info['sessions'][date_]), self.right_films)\n\n if self.search_info['title']:\n title = self.search_info['title']\n self.right_films = filter(\n lambda film_info: (title in ''.join(film_info['title'].split()).lower()\n or title in film_info['title'].lower()),\n self.right_films)\n\n if self.search_info['genres']:\n genres = self.search_info['genres']\n self.right_films = filter(\n lambda film_info: all(map(lambda genre: genre in film_info['genres'], genres)), self.right_films)\n\n start, end = self.search_info['rating']\n self.right_films = filter(lambda film_info: start <= film_info['rating'] <= end, self.right_films)\n\n self.right_films = list(self.right_films)\n self.right_films.sort(key=lambda i: i['title'])\n\n def set_date(self, date_: QDate):\n \"\"\"Запись даты, по которой будут фильтроваться филмы из базы\"\"\"\n self.search_info['date'] = date(date_.year(), date_.month(), date_.day())\n self.load_films_table()\n\n def set_name(self, name: str):\n \"\"\"Запись названия, по которому будут фильтроваться филмы из базы\"\"\"\n self.search_info['title'] = ' '.join(name.lower().split())\n self.load_films_table()\n\n def set_genres(self, genres: list, tab: int):\n \"\"\"Запись жанров, по которым будут фильтроваться филмы из базы\"\"\"\n assert tab == 0\n self.search_info['genres'] = genres\n self.GenresLine.setText(', '.join(map(lambda genre: GENRES_DICT[genre], genres)).capitalize())\n self.load_films_table()\n\n def set_rating(self, rating: int, ind: int):\n \"\"\"Запись возростного рейтинга, по которой будут фильтроваться филмы из базы\"\"\"\n if ind == 0:\n self.EndRatingSpinBox.setMinimum(rating)\n self.search_info['rating'][ind] = rating\n self.load_films_table()\n\n def open_film_window(self, row_ind: int):\n \"\"\"Открытия окна фильма со всей информацией\"\"\"\n date_ = self.Calendar.selectedDate()\n date_ = date(date_.year(), date_.month(), date_.day())\n\n try:\n self.FilmWindow.close()\n self.FilmWindow = None\n except AttributeError:\n pass\n\n self.FilmWindow = FilmWindow(self.right_films[row_ind], date_)\n self.FilmWindow.show()\n\n def closeEvent(self, event):\n try:\n self.FilmWindow.close()\n except AttributeError:\n pass\n self.projectDB.close()\n", "repo_name": "Alpmild/SchoolProject", "sub_path": "data/UserWindow.py", "file_name": "UserWindow.py", "file_ext": "py", "file_size_in_byte": 8249, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 15, "usage_type": "name"}, {"api_name": "data.GenresSelectionDialog.GenresSelectionDialog", "line_number": 23, "usage_type": "call"}, {"api_name": "PyQt5.uic.loadUi", "line_number": 27, "usage_type": "call"}, {"api_name": "PyQt5.uic", "line_number": 27, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.time", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QDate", "line_number": 85, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QDate", "line_number": 86, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHeaderView.ResizeMode", "line_number": 96, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QHeaderView", "line_number": 96, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHeaderView.Fixed", "line_number": 100, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QHeaderView", "line_number": 100, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 125, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.ItemIsEditable", "line_number": 126, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 126, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 135, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 135, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QDate", "line_number": 158, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 160, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 185, "usage_type": "call"}, {"api_name": "data.FilmWindow.FilmWindow", "line_number": 193, "usage_type": "call"}]} +{"seq_id": "24858782483", "text": "from django.contrib.staticfiles.urls import staticfiles_urlpatterns\nfrom django.conf.urls.static import static\n\"\"\"CustomerManagementApplication URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/2.1/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path, re_path\nfrom Accounts.views import Registration, loginUser, Unauth\nfrom Management.views import Homepage, Edit, AddProducts, updateOrder\nfrom MainApp.views import ProductsList, Details, order, Createorder, Orderviews, Delete\nfrom MyProfiles.views import profile, logoutview\nfrom django.conf import settings\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('Registration/', Registration, name=\"registration\"),\n path('Unauth/', Unauth, name=\"Unauth\"),\n path('', loginUser, name=\"homepage\"),\n path('login/', loginUser, name=\"login\"),\n re_path(r'^edit/(?P\\d+)/$', Edit, name=\"edit\"),\n re_path(r'^form/(?P\\d+)/$', updateOrder, name=\"update\"),\n path('home/', Homepage, name=\"home\"),\n path('Add/', AddProducts, name=\"Add\"),\n path('main/', ProductsList.as_view(), name=\"Main\"),\n path('MyOrders/', Orderviews.as_view(), name=\"MyOrders\"),\n re_path(r'^main/(?P\\d+)/$', Details.as_view(), name=\"Details\"),\n re_path(r'^delete/(?P\\d+)/$', Delete.as_view(), name=\"Delete\"),\n re_path(r'^main/(?P\\d+)/order',\n order.as_view(), name=\"order\"),\n re_path(r'^main/(?P\\d+)/createorder',\n Createorder.as_view(), name=\"createorder\"),\n path('profile/', profile, name=\"MyProfile\"),\n path('update/', profile, name=\"update\"),\n path('logout/', logoutview, name=\"logout\"),\n]\nif settings.DEBUG is True:\n urlpatterns += static(settings.STATIC_URL,\n document_root=settings.STATIC_ROOT)\n urlpatterns += static(settings.MEDIA_URL,\n document_root=settings.MEDIA_ROOT)\n", "repo_name": "SriramChivo/ShopStopper-Basic-E-commerce-App", "sub_path": "CustomerManagementApplication/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 2422, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 27, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "Accounts.views.Registration", "line_number": 28, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "Accounts.views.Unauth", "line_number": 29, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "Accounts.views.loginUser", "line_number": 30, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 31, "usage_type": "call"}, {"api_name": "Accounts.views.loginUser", "line_number": 31, "usage_type": "argument"}, {"api_name": "django.urls.re_path", "line_number": 32, "usage_type": "call"}, {"api_name": "Management.views.Edit", "line_number": 32, "usage_type": "argument"}, {"api_name": "django.urls.re_path", "line_number": 33, "usage_type": "call"}, {"api_name": "Management.views.updateOrder", "line_number": 33, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 34, "usage_type": "call"}, {"api_name": "Management.views.Homepage", "line_number": 34, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 35, "usage_type": "call"}, {"api_name": "Management.views.AddProducts", "line_number": 35, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 36, "usage_type": "call"}, {"api_name": "MainApp.views.ProductsList.as_view", "line_number": 36, "usage_type": "call"}, {"api_name": "MainApp.views.ProductsList", "line_number": 36, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "MainApp.views.Orderviews.as_view", "line_number": 37, "usage_type": "call"}, {"api_name": "MainApp.views.Orderviews", "line_number": 37, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 38, "usage_type": "call"}, {"api_name": "MainApp.views.Details.as_view", "line_number": 38, "usage_type": "call"}, {"api_name": "MainApp.views.Details", "line_number": 38, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 39, "usage_type": "call"}, {"api_name": "MainApp.views.Delete.as_view", "line_number": 39, "usage_type": "call"}, {"api_name": "MainApp.views.Delete", "line_number": 39, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 40, "usage_type": "call"}, {"api_name": "MainApp.views.order.as_view", "line_number": 41, "usage_type": "call"}, {"api_name": "MainApp.views.order", "line_number": 41, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 42, "usage_type": "call"}, {"api_name": "MainApp.views.Createorder.as_view", "line_number": 43, "usage_type": "call"}, {"api_name": "MainApp.views.Createorder", "line_number": 43, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 44, "usage_type": "call"}, {"api_name": "MyProfiles.views.profile", "line_number": 44, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 45, "usage_type": "call"}, {"api_name": "MyProfiles.views.profile", "line_number": 45, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 46, "usage_type": "call"}, {"api_name": "MyProfiles.views.logoutview", "line_number": 46, "usage_type": "argument"}, {"api_name": "django.conf.settings.DEBUG", "line_number": 48, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 48, "usage_type": "name"}, {"api_name": "django.conf.urls.static.static", "line_number": 49, "usage_type": "call"}, {"api_name": "django.conf.settings.STATIC_URL", "line_number": 49, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 49, "usage_type": "name"}, {"api_name": "django.conf.settings.STATIC_ROOT", "line_number": 50, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 50, "usage_type": "name"}, {"api_name": "django.conf.urls.static.static", "line_number": 51, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 51, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 51, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 52, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 52, "usage_type": "name"}]} +{"seq_id": "24697783119", "text": "import rospy\nfrom sensor_msgs.msg import Image\nfrom cv_bridge import CvBridge\nimport cv2\nimport os.path as osp\nimport tf\nfrom tf import transformations as T\nimport math\nimport numpy as np\nimport random\nimport stl\nimport open3d as o3d\nfrom scipy.spatial.transform import Rotation as R\nfrom std_msgs.msg import String\nfrom geometry_msgs.msg import *\nfrom shape_msgs.msg import SolidPrimitive\nfrom moveit_msgs.msg import Grasp\nfrom sensor_msgs.msg import PointCloud2\nimport sensor_msgs.point_cloud2 as pcl2\nimport time\nimport random\nimport logging\n\nfrom rls_perception_msgs.srv import *\nfrom rls_control_msgs.srv import *\nimport fetch_api\n\n# import sys\n# this_dir = osp.dirname(osp.abspath(__file__))\n# sys.path.insert(0, osp.join(this_dir, '../../'))\n\nfrom config.config import CLASSES, ROOT_DIR\nfrom libraries.data_viewer.data_viewer import DataViewer\nimport libraries.utils.o3d_ros_pc_converter as pc_converter\nfrom libraries.grasp_collision_checker.grasp_collision_checker import GraspCollisionChecker\nfrom libraries.utils.log import LOGGER_NAME\n\n# ------- Settings ---------\nGRASP_BOX_FOR_SEG = 1\nBBOX_FOR_SEG = 2\nGRASP_BOX_6DOF_PICK = 3\nUSE_REALSENSE = True\nDUMMY_LISTEN = True\nDUMMY_SAY = True\nDUMMY_GRASP = False\n\n# ------- Constants ---------\nCONFIG_DIR = osp.join(ROOT_DIR, \"config\")\nROBOT_MODEL_DIR = osp.join(ROOT_DIR, \"fetch_robot_stl\")\nGRIPPER_FILE = \"gripper_link.STL\"\nLEFT_GRIPPER_FINGER_FILE = \"l_gripper_finger_link.STL\"\nRIGHT_GRIPPER_FINGER_FILE = \"r_gripper_finger_link.STL\"\nLEFT_FINGER_POSE = {\"link\":[0., -0.101425, 0., 0., 0., 0.],\n \"joint\": [0., -0.015425, 0., 0., 0., 0.],\n \"min_max_translate\":[0.0, -0.04]} # - means the direction\nRIGHT_FINGER_POSE = {\"link\":[0., 0.101425, 0., 0., 0., 0.],\n \"joint\": [0., 0.015425, 0., 0., 0., 0.],\n \"min_max_translate\":[0.0, 0.04]}\nORIG_IMAGE_SIZE = (480, 640)\n# SCALE = 0.8\n# Y_OFFSET = int(ORIG_IMAGE_SIZE[0] * (1 - SCALE) / 2)\n# X_OFFSET = int(ORIG_IMAGE_SIZE[1] * (1 - SCALE) / 2)\n# YCROP = (Y_OFFSET, ORIG_IMAGE_SIZE[0] - Y_OFFSET)\n# XCROP = (X_OFFSET, ORIG_IMAGE_SIZE[1] - X_OFFSET)\nYCROP = (470, 1000) # 1080\nXCROP = (700, 1460) # 1920\nFETCH_YCROP = (180, 450) # 480\nFETCH_XCROP = (150, 490) # 640\n\nFETCH_GRIPPER_LENGTH = 0.2\nFETCH_MAX_GRIPPER_OPENING = 0.07\nGRASP_DEPTH = 0.008\nINITIAL_GRASP_Z_OFFSET = 0.0\nGRASP_POSE_X_OFFST = -0.015 # -0.018\nGRASP_POSE_Y_OFFST = 0.00 # 0.02\nGRASP_POSE_Z_OFFST = -0.008 # -0.015\nGRASP_WIDTH_OFFSET = 0.0\nGRIPPER_OPENING_OFFSET = 0.01\nGRIPPER_OPENING_MAX = 0.09\nPLACE_BBOX_SIZE = 80\nAPPROACH_DIST = 0.1\nRETREAT_DIST = 0.15\nGRASP_BOX_TO_GRIPPER_OPENING = 0.00045\nPC_DOWNSAMPLE_SIZE = 0.002\n\nPOSITIVE_RESPONSE_LIST = [\"Got it\", \"Sure\", \"No Problem\", \"okay\", \"certainly\", \"of course\"]\n\n# ---------- Statics ------------\nlogger = logging.getLogger(LOGGER_NAME)\n\nclass FetchRobot():\n def __init__(self):\n self._br = CvBridge()\n self._bbox_segmentation_client = rospy.ServiceProxy('rls_perception_services/bbox_pc_segmention_service', BBoxSegmentation)\n self._pnp_client = rospy.ServiceProxy('rls_control_services/fetch/pnp', PickPlace)\n # rospy.wait_for_service('/segment_table')\n self._table_segmentor_client = rospy.ServiceProxy('/segment_table', TableSegmentation)\n self._tf_transformer = tf.TransformerROS()\n self._fetch_image_client = rospy.ServiceProxy('/rls_perception_service/fetch/rgb_image_service', RetrieveImage)\n # self._fetch_pc_client = rospy.ServiceProxy('/rls_control_service/fetch/retrieve_pc_service', RetrievePointCloud)\n self._fetch_speaker_client = rospy.ServiceProxy(\"rls_control_services/fetch/speaker_google\", SpeakGoogle)\n self._tl = tf.TransformListener()\n self._arm = fetch_api.ArmV2()\n self._torso = fetch_api.Torso()\n self._head = fetch_api.Head()\n self._gripper = fetch_api.Gripper()\n\n # call pnp service to get ready\n pnp_req = PickPlaceRequest()\n pnp_req.action = PickPlaceRequest.GET_READY\n pnp_req.torso_height = 0.05\n resp = self._pnp_client(pnp_req) # get existing result\n if not resp.success:\n raise RuntimeError('fetch failed to get ready for pick n place!!!')\n\n self._torso.set_height(0.15)\n self._head.pan_tilt(pan=0, tilt= math.radians(55), duration=2)\n\n pnp_req = PickPlaceRequest()\n pnp_req.action = PickPlaceRequest.MOVE_ARM_TO_HOME\n resp = self._pnp_client(pnp_req) # get existing result\n if not resp.success:\n raise RuntimeError('fetch failed to move arm to home!!!')\n self.gripper_model = self._init_gripper_model()\n\n self._grasp_collision_checker = GraspCollisionChecker(self.gripper_model)\n\n def _init_gripper_model(self):\n \"\"\"\n In this function, a gripper model should be initialized.\n The model includes a list of convex hulls represented by meshes.\n Each mesh can simply include a list of points (x, y, z) and\n triangle surfaces (p1, p2, p3), or be represented by a .obj object\n (a python library named pywavefront can provide a standard\n representation for .obj object). Coordinates should be w.r.t. the\n frame of the gripper itself.\n \"\"\"\n gripper_model_path = osp.join(ROBOT_MODEL_DIR, GRIPPER_FILE)\n l_finger_model_path = osp.join(ROBOT_MODEL_DIR, LEFT_GRIPPER_FINGER_FILE)\n r_finger_model_path = osp.join(ROBOT_MODEL_DIR, RIGHT_GRIPPER_FINGER_FILE)\n gripper_mesh = stl.mesh.Mesh.from_file(gripper_model_path)\n l_finger_mesh = stl.mesh.Mesh.from_file(l_finger_model_path)\n r_finger_mesh = stl.mesh.Mesh.from_file(r_finger_model_path)\n items = 1, 4, 7\n # since the model only imposes a y axis translate on the two fingers,\n # we here only consider this translate.\n l_finger_mesh.points[:, items] += LEFT_FINGER_POSE[\"link\"][1] + LEFT_FINGER_POSE[\"joint\"][1]\n r_finger_mesh.points[:, items] += RIGHT_FINGER_POSE[\"link\"][1] + RIGHT_FINGER_POSE[\"joint\"][1]\n # gripper_model = stl.mesh.Mesh(np.concatenate([gripper_mesh.data, l_finger_mesh.data, r_finger_mesh.data]))\n\n # mesh = o3d.io.read_triangle_mesh(l_finger_model_path)\n # o3d.visualization.draw_geometries([mesh])\n\n return {\"gripper\": gripper_mesh, \"left_finger\": l_finger_mesh, \"right_finger\": r_finger_mesh}\n\n def _vis_grasp(self, scene_pc, selected_grasp):\n if selected_grasp is None:\n return\n\n l_finger_model_path = osp.join(ROBOT_MODEL_DIR, LEFT_GRIPPER_FINGER_FILE)\n r_finger_model_path = osp.join(ROBOT_MODEL_DIR, RIGHT_GRIPPER_FINGER_FILE)\n l_finger_mesh = o3d.io.read_triangle_mesh(l_finger_model_path)\n r_finger_mesh = o3d.io.read_triangle_mesh(r_finger_model_path)\n l_finger = l_finger_mesh.sample_points_uniformly(number_of_points=500)\n r_finger = r_finger_mesh.sample_points_uniformly(number_of_points=500)\n gripper_width = selected_grasp[\"width\"]\n l_finger_points=np.asarray(l_finger.points)\n r_finger_points=np.asarray(r_finger.points)\n l_finger_points[:, 1] -= gripper_width / 2\n r_finger_points[:, 1] += gripper_width / 2\n l_finger_points[:, 0] -= 0.03\n r_finger_points[:, 0] -= 0.03\n open3d_cloud = o3d.geometry.PointCloud()\n pc_in_g = self._trans_world_points_to_gripper(scene_pc, selected_grasp)\n open3d_cloud.points = o3d.utility.Vector3dVector(np.array(pc_in_g))\n l_finger.points = o3d.utility.Vector3dVector(l_finger_points)\n r_finger.points = o3d.utility.Vector3dVector(r_finger_points)\n # o3d.visualization.draw_geometries([l_finger, r_finger, open3d_cloud])\n\n vis = o3d.visualization.Visualizer()\n vis.create_window()\n vis.add_geometry(l_finger)\n vis.add_geometry(r_finger)\n vis.add_geometry(open3d_cloud)\n vis.run()\n vis.destroy_window()\n\n def _cal_initial_grasp(self, grasp_box):\n logger.debug('grasp_box: {}'.format(grasp_box))\n grasp_box = grasp_box + np.tile([XCROP[0], YCROP[0]], 4)\n # for grasp box, x1,y1 is topleft, x2,y2 is topright, x3,y3 is btmright, x4,y4 is btmleft\n x1, y1, x2, y2, x3, y3, x4, y4 = grasp_box.tolist()\n angle = math.atan2(y2 - y1, x2 - x1)\n seg_req = BBoxSegmentationRequest()\n seg_req.x = x1\n seg_req.y = y1\n seg_req.width = math.sqrt((x2 - x1)**2 + (y2 - y1)**2)\n seg_req.height = math.sqrt((x4 - x1)**2 + (y4 - y1)**2)\n seg_req.angle = angle\n seg_req.transform_to_reference_frame = True\n seg_req.reference_frame = 'base_link'\n\n grasp_box_width = seg_req.width\n logger.debug(\"grasp box width: {}\".format(grasp_box_width))\n\n # resp = self._table_segmentor_client(1) # get existing result\n # seg_req.min_z = resp.marker.pose.position.z + resp.marker.scale.z / 2 + 0.003\n\n # print('calling bbox segmentation service')\n seg_resp = self._bbox_segmentation_client(seg_req)\n # print(seg_resp.object)\n\n obj_pose = seg_resp.object.primitive_pose\n obj_length = seg_resp.object.primitive.dimensions[SolidPrimitive.BOX_X]\n obj_width = seg_resp.object.primitive.dimensions[SolidPrimitive.BOX_Y]\n obj_height = seg_resp.object.primitive.dimensions[SolidPrimitive.BOX_Z]\n print(\"obj_x, y, z: {} {} {}\".format(obj_length, obj_width, obj_height))\n\n # seg request again to get grasp box center pos\n grasp_box_cx = x4 + (x2 - x4) / 2\n grasp_box_cy = y2 + (y4 - y2) / 2\n seg_req.x = grasp_box_cx - 2\n seg_req.y = grasp_box_cy - 2\n seg_req.width = 5\n seg_req.height = 5\n seg_req.angle = 0\n seg_req.transform_to_reference_frame = True\n seg_req.reference_frame = 'base_link'\n\n seg_resp = self._bbox_segmentation_client(seg_req)\n xy_pose = seg_resp.object.primitive_pose\n\n print(\"obj_pose_x: {}, obj_pose_y: {}\".format(obj_pose.position.x, obj_pose.position.y))\n print(\"xy_pose_x: {}, xy_pose_y: {}\".format(xy_pose.position.x, xy_pose.position.y))\n\n grasp = Grasp()\n grasp.grasp_pose.header = seg_resp.object.header\n grasp.grasp_pose.pose.position.x = xy_pose.position.x\n grasp.grasp_pose.pose.position.y = xy_pose.position.y\n grasp.grasp_pose.pose.position.z = obj_pose.position.z + obj_height / 2 - GRASP_DEPTH\n # grasp.grasp_pose.pose.position.z -= GRASP_DEPTH\n # grasp.grasp_pose.pose.position.z += INITIAL_GRASP_Z_OFFSET\n quat = T.quaternion_from_euler(0, math.pi / 2, angle, 'rzyx') # rotate by y to make it facing downwards\n # rotate by z to align with bbox orientation\n grasp.grasp_pose.pose.orientation.x = quat[0]\n grasp.grasp_pose.pose.orientation.y = quat[1]\n grasp.grasp_pose.pose.orientation.z = quat[2]\n grasp.grasp_pose.pose.orientation.w = quat[3]\n\n grasp.pre_grasp_approach.direction.header = seg_resp.object.header\n grasp.pre_grasp_approach.direction.vector.z = -1 # top pick\n grasp.pre_grasp_approach.desired_distance = APPROACH_DIST\n\n grasp.post_grasp_retreat.direction.header = seg_resp.object.header\n grasp.post_grasp_retreat.direction.vector.z = 1 # top pick\n grasp.post_grasp_retreat.desired_distance = RETREAT_DIST\n\n gripper_opening = grasp_box_width * GRASP_BOX_TO_GRIPPER_OPENING\n gripper_opening = min(gripper_opening, FETCH_MAX_GRIPPER_OPENING) # clamp\n\n return grasp, gripper_opening\n\n def _top_grasp(self, grasp_box):\n if DUMMY_GRASP:\n return False\n\n grasp, gripper_opening = self._cal_initial_grasp(grasp_box)\n\n pnp_req = PickPlaceRequest()\n pnp_req.action = PickPlaceRequest.EXECUTE_GRASP\n pnp_req.grasp = grasp\n pnp_req.gripper_opening = gripper_opening\n\n # grasp_pose_tmp = grasp.grasp_pose\n # new_grasp = self._get_collision_free_grasp(grasp_pose_tmp, pnp_req.gripper_opening)\n # # to_cont = raw_input(\"to_continue?\")\n # # if to_cont != \"y\":\n # # return False\n\n # if new_grasp is None:\n # logger.error('ERROR: robot grasp failed!!')\n # return False\n\n # grasp.grasp_pose.pose.position.x = new_grasp[\"pos\"][0]\n # grasp.grasp_pose.pose.position.y = new_grasp[\"pos\"][1]\n # grasp.grasp_pose.pose.position.z = new_grasp[\"pos\"][2]\n # gripper_opening = new_grasp[\"width\"]\n\n grasp.grasp_pose.pose.position.x += GRASP_POSE_X_OFFST # HACK!!!\n grasp.grasp_pose.pose.position.y += GRASP_POSE_Y_OFFST # HACK!!!\n grasp.grasp_pose.pose.position.z += APPROACH_DIST + FETCH_GRIPPER_LENGTH + GRASP_POSE_Z_OFFST #HACK!!!\n gripper_opening += GRASP_WIDTH_OFFSET # HACK!!\n\n print(grasp)\n\n pnp_req.grasp = grasp\n pnp_req.gripper_opening = gripper_opening\n\n to_cont = raw_input(\"to_continue?\")\n if to_cont == \"n\":\n return False\n\n resp = self._pnp_client(pnp_req)\n num_attempt = 1\n while not resp.success and num_attempt < 5:\n logger.error('ERROR: robot grasp failed!!, try again {}'.format(num_attempt))\n resp = self._pnp_client(pnp_req)\n num_attempt += 1\n return resp.success\n\n def _move_arm_to_pose(self, target_pose):\n self._arm.move_to_pose(target_pose)\n\n def _get_place_target_pose(self):\n # this is hard coded for demo\n target_pose = PoseStamped()\n target_pose.header.frame_id=\"base_link\"\n target_pose.pose.position.x = 0.6\n target_pose.pose.position.y = 0.45\n target_pose.pose.position.z = 0.6\n target_pose.pose.orientation.x = 0\n target_pose.pose.orientation.y = 0\n target_pose.pose.orientation.z = 0\n target_pose.pose.orientation.w = 1.0\n\n return target_pose\n\n def _get_scene_pc(self):\n # start_time = time.time()\n # # resp = self._fetch_pc_client()\n # rospy.log\n # raw_pc = resp.pointcloud\n # end_time = time.time()\n # logger.debug(\"getting pc takes {}\".format(end_time - start_time))\n\n try:\n raw_pc = rospy.wait_for_message(\"/head_camera/depth_registered/points\", PointCloud2, timeout=20.0)\n trans, rot = self._tl.lookupTransform('base_link', raw_pc.header.frame_id, rospy.Time(0))\n transform_mat44 = np.dot(T.translation_matrix(trans), T.quaternion_matrix(rot))\n except Exception as e:\n rospy.logerr(e)\n return None\n\n start_time = time.time()\n # build uv array for segmentation\n uvs = []\n # for x in range(XCROP[0], XCROP[1]):\n # for y in range(YCROP[0], YCROP[1]):\n # uvs.append([x, y])\n for x in range(FETCH_XCROP[0], FETCH_XCROP[1]):\n for y in range(FETCH_YCROP[0], FETCH_YCROP[1]):\n uvs.append([x, y])\n\n points = pcl2.read_points(raw_pc, skip_nans=True, field_names=('x', 'y', 'z'), uvs=uvs)\n end_time = time.time()\n logger.debug(\"read pc takes {}s\".format(end_time - start_time))\n\n start_time = time.time()\n points_out = np.array([[p[0], p[1], p[2], 1.0] for p in points]) # num_points x 4 # NOTE: this is slow!!!\n end_time = time.time()\n logger.debug(\"pc transform to base_link takes {}s\".format(end_time - start_time))\n points_out = np.dot(points_out, transform_mat44.T)[:, :3] # num_points x 3\n end_time = time.time()\n logger.debug(\"pc transform to base_link takes {}s\".format(end_time - start_time))\n\n start_time = time.time()\n logger.info(\"pc shape before downsample: {}\".format(len(points_out)))\n open3d_cloud = o3d.geometry.PointCloud()\n open3d_cloud.points = o3d.utility.Vector3dVector(np.array(points_out))\n downpcd = open3d_cloud.voxel_down_sample(voxel_size=PC_DOWNSAMPLE_SIZE)\n\n scene_pc = np.array(downpcd.points)\n logger.info(\"pc shape after downsample: {}\".format(scene_pc.shape))\n\n end_time = time.time()\n logger.debug(\"seg and downsample pc takes {}s\".format(end_time - start_time))\n return scene_pc\n\n def _get_collision_free_grasp(self, orig_grasp, orig_opening):\n logger.info(\"checking grasp collision!!!\")\n scene_pc = self._get_scene_pc()\n return self._grasp_collision_checker.get_collision_free_grasp(orig_grasp, orig_opening, scene_pc, vis_grasp=True)\n\n # --------- Public ------- #\n def read_imgs(self):\n # resp = self._fetch_image_client()\n # img = self._br.imgmsg_to_cv2(resp.image, desired_encoding='bgr8')\n if USE_REALSENSE:\n img_msg = rospy.wait_for_message('/camera/color/image_raw', Image, timeout=10)\n img = self._br.imgmsg_to_cv2(img_msg, desired_encoding='bgr8')\n logger.info('img_size : {}'.format(img.shape))\n img = img[YCROP[0]:YCROP[1], XCROP[0]:XCROP[1]]\n logger.info('img_size : {}'.format(img.shape))\n else:\n resp = self._fetch_image_client()\n img = self._br.imgmsg_to_cv2(resp.image, desired_encoding='bgr8')\n logger.info('img_size : {}'.format(img.shape))\n img = img[FETCH_YCROP[0]:FETCH_YCROP[1], FETCH_XCROP[0]:FETCH_XCROP[1]]\n logger.info('img_size : {}'.format(img.shape))\n # depth_img_msg = rospy.wait_for_message('/head_camera/depth/image_rect', Image)\n # depth = self._br.imgmsg_to_cv2(depth_img_msg, desired_encoding='passthrough')\n depth = None\n return img, depth\n\n def grasp(self, grasp, is_target=False):\n # return self._top_grasp(bbox, grasp)\n if not is_target:\n # print('sampling where to place!!')\n # target_pose = self._sample_target_pose()\n target_pose = self._get_place_target_pose()\n\n res = self._top_grasp(grasp)\n if not res:\n self.move_arm_to_home()\n return False\n\n if not is_target:\n # self.place_object(target_pose)\n self.move_arm_to_home()\n else:\n self.give_obj_to_human()\n return res\n\n def place_object(self, target_pose):\n logger.info('place_object')\n if target_pose is None:\n logger.error('ERROR: fail to find a place to place!!')\n return False\n\n pnp_req = PickPlaceRequest()\n pnp_req.action = PickPlaceRequest.PLACE\n pnp_req.place_type = PickPlaceRequest.DROP\n\n pnp_req.target_pose = target_pose\n\n resp = self._pnp_client(pnp_req)\n if not resp.success:\n logger.error('ERROR: place_object failed!!')\n return resp.success\n\n def give_obj_to_human(self):\n # print('Dummy execution of give_obj_to_human')\n\n # Hard code for demo\n # target_pose = PoseStamped()\n # target_pose.header.frame_id=\"base_link\"\n # target_pose.pose.position.x = 0.8\n # target_pose.pose.position.y = 0\n # target_pose.pose.position.z = 1.1\n # quat = T.quaternion_from_euler(0, math.pi / 2, 0, 'rzyx') # rotate by y to make it facing downwards\n # # rotate by z to align with bbox orientation\n # target_pose.pose.orientation.x = quat[0]\n # target_pose.pose.orientation.y = quat[1]\n # target_pose.pose.orientation.z = quat[2]\n # target_pose.pose.orientation.w = quat[3]\n\n # self._move_arm_to_pose(target_pose)\n\n target_x = 0.6\n target_y = 0.0\n try:\n (trans, rot) = self._tl.lookupTransform('/base_link', '/wrist_roll_link', rospy.Time())\n except Exception as e:\n print(e)\n return False\n dx = target_x - trans[0]\n dy = target_y - trans[1]\n self._arm.move_in_cartesian(dx=dx, dy=dy)\n\n rospy.sleep(1.0)\n self._gripper.open()\n\n def say(self, text):\n if DUMMY_SAY:\n print('Dummy execution of say: {}'.format(text))\n return True\n else:\n resp = self._fetch_speaker_client(text)\n return resp.success\n\n def listen(self, timeout=None):\n if DUMMY_LISTEN:\n logger.info('Dummy execution of listen')\n text = raw_input('Enter: ')\n else:\n logger.info('robot is listening')\n msg = rospy.wait_for_message('/rls_perception_services/speech_recognition_google/', String)\n text = msg.data.lower()\n\n logger.info('robot heard {}'.format(text))\n\n # say acknowledgement\n resp = random.choice(POSITIVE_RESPONSE_LIST)\n self.say(resp)\n\n return text\n\n def move_arm_to_home(self):\n pnp_req = PickPlaceRequest()\n pnp_req.action = PickPlaceRequest.MOVE_ARM_TO_HOME\n\n resp = self._pnp_client(pnp_req)\n if not resp.success:\n logger.error('ERROR: move_arm_to_home failed!!')\n return resp.success\n\nif __name__==\"__main__\":\n r = R.from_euler(\"zyx\", [0, math.pi / 2, 0])\n\n ply_model_path = \"../../config/visual.ply\"\n scene_pc = np.array(o3d.io.read_point_cloud(ply_model_path).points)\n scene_pc[:, 2] -= 0.01\n grasps = [{\n \"pos\": [0, 0, 0],\n \"quat\":r.as_quat().tolist(),\n \"width\": 0.05\n }]\n robot = FetchRobot()\n robot._get_collision_free_grasp_cfg(grasps[0], scene_pc, vis = True)\n\n\n\n\n\"\"\"\nLegacy\n def _top_grasp_2(self, bbox, grasp):\n # use bbox for segmentation\n print('grasp_box: {}'.format(grasp))\n print('bbox : {}'.format(bbox))\n grasp = grasp + np.tile([XCROP[0], YCROP[0]], 4)\n bbox = bbox + np.tile([XCROP[0], YCROP[0]], 2)\n x1, y1, x2, y2, _, _, _, _ = grasp.tolist()\n\n seg_req = BBoxSegmentationRequest()\n seg_req.x = bbox[0]\n seg_req.y = bbox[1]\n seg_req.width = bbox[2] - bbox[0]\n seg_req.height = bbox[3] - bbox[1]\n seg_req.transform_to_reference_frame = True\n seg_req.reference_frame = 'base_link'\n\n resp = self._table_segmentor_client(1) # get existing result\n seg_req.min_z = resp.marker.pose.position.z + resp.marker.scale.z / 2 + 0.003\n\n print('calling bbox segmentation service')\n seg_resp = self._bbox_segmentation_client(seg_req)\n print(seg_resp.object)\n\n to_continue = raw_input('to_continue?')\n if to_continue != 'y':\n return False\n\n obj_pose = seg_resp.object.primitive_pose\n obj_width = seg_resp.object.primitive.dimensions[SolidPrimitive.BOX_Y]\n obj_height = seg_resp.object.primitive.dimensions[SolidPrimitive.BOX_Z]\n approach_dist = 0.1\n\n pnp_req = PickPlaceRequest()\n pnp_req.action = PickPlaceRequest.EXECUTE_GRASP\n\n grasp = Grasp()\n grasp.grasp_pose.header = seg_resp.object.header\n grasp.grasp_pose.pose = seg_resp.object.primitive_pose\n grasp.grasp_pose.pose.position.z += obj_height / 2 - GRASP_DEPTH + approach_dist + FETCH_GRIPPER_LENGTH\n\n angle_z = math.atan2(y2 - y1, x2 - x1)\n quat = t.quaternion_from_euler(0, math.pi / 2, angle_z, 'rzyx') # rotate by y to make it facing downwards\n # rotate by z to align with bbox orientation\n grasp.grasp_pose.pose.orientation.x = quat[0]\n grasp.grasp_pose.pose.orientation.y = quat[1]\n grasp.grasp_pose.pose.orientation.z = quat[2]\n grasp.grasp_pose.pose.orientation.w = quat[3]\n\n grasp.pre_grasp_approach.direction.header = seg_resp.object.header\n grasp.pre_grasp_approach.direction.vector.z = -1 # top pick\n grasp.pre_grasp_approach.desired_distance = approach_dist\n\n grasp.post_grasp_retreat.direction.header = seg_resp.object.header\n grasp.post_grasp_retreat.direction.vector.z = 1 # top pick\n grasp.post_grasp_retreat.desired_distance = approach_dist\n\n pnp_req.grasp = grasp\n pnp_req.gripper_opening = obj_width\n\n resp = self._pnp_client(pnp_req)\n if not resp.success:\n print('ERROR: robot grasp failed!!')\n return resp.success\n\n def _6dof_grasp(self, grasp):\n # print('Dummy execution of grasp {}'.format(grasp))\n # return\n print('grasp_box: {}'.format(grasp))\n grasp = grasp + np.tile([XCROP[0], YCROP[0]], 4)\n\n x1, y1, x2, y2, x3, y3, x4, y4 = grasp.tolist()\n seg_req = BBoxSegmentationRequest()\n seg_req.x = x1\n seg_req.y = y1\n seg_req.width = math.sqrt((x2 - x1)**2 + (y2 - y1)**2)\n seg_req.height = math.sqrt((x4 - x1)**2 + (y4 - y1)**2)\n seg_req.angle = math.atan2(y2 - y1, x2 - x1)\n # seg_req.transform_to_reference_frame = True\n # seg_req.reference_frame = 'base_link'\n\n # resp = self._table_segmentor_client(1) # get existing result\n # seg_req.min_z = resp.marker.pose.position.z + resp.marker.scale.z / 2\n\n print('calling bbox segmentation service')\n seg_resp = self._bbox_segmentation_client(seg_req)\n print(seg_resp.object)\n\n to_continue = raw_input('to_continue?')\n if to_continue != 'y':\n return False\n\n obj_pose = seg_resp.object.primitive_pose\n obj_width = seg_resp.object.primitive.dimensions[SolidPrimitive.BOX_Y]\n obj_height = seg_resp.object.primitive.dimensions[SolidPrimitive.BOX_Z]\n approach_dist = 0.1\n\n pnp_req = PickPlaceRequest()\n pnp_req.action = PickPlaceRequest.EXECUTE_GRASP\n\n grasp_pose = PoseStamped()\n grasp_pose.header = seg_resp.object.header\n grasp_pose.pose = seg_resp.object.primitive_pose\n grasp_pose.pose.position.z -= obj_height / 2 - 0.01 + approach_dist + FETCH_GRIPPER_LENGTH\n quat = t.quaternion_from_euler(0, -math.pi / 2, seg_req.angle, 'rzyx') # rotate by y to make it facing downwards\n # rotate by z to align with bbox orientation\n grasp_pose.pose.orientation.x = quat[0]\n grasp_pose.pose.orientation.y = quat[1]\n grasp_pose.pose.orientation.z = quat[2]\n grasp_pose.pose.orientation.w = quat[3]\n\n pre_vector = Vector3Stamped()\n pre_vector.header = seg_resp.object.header\n pre_vector.vector.z = 1 # top pick\n\n post_vector = Vector3Stamped()\n post_vector.header = seg_resp.object.header\n post_vector.vector.z = -1 # top pick\n\n grasp_pose = self._tf_transformer.transformPose('base_link', grasp_pose)\n pre_vector = self._tf_transformer.transformVector3('base_link', pre_vector)\n pre_vector = self._tf_transformer.transformVector3('base_link', post_vector)\n\n pnp_req.grasp.grasp_pose = grasp_pose\n pnp_req.grasp.pre_grasp_approach.direction = pre_vector\n pnp_req.grasp.pre_grasp_approach.desired_distance = approach_dist\n pnp_req.grasp.post_grasp_retreat.direction = post_vector\n pnp_req.grasp.post_grasp_retreat.desired_distance = approach_dist\n pnp_req.gripper_opening = obj_width\n\n resp = self._pnp_client(pnp_req)\n if not resp.success:\n print('ERROR: robot grasp failed!!')\n return resp.success\n\n# def vis_mesh(mesh_list, pc_list, mesh_color=\"r\", rotation = 1):\n# # Create a new plot\n# # rotation: 0 means no rotation,\n# # 1 means rotate w.r.t. x axis for 90 degrees\n# # 2 means rotate w.r.t. y axis for 90 degrees\n# # 3 means rotate w.r.t. z axis for 90 degrees\n# figure = pyplot.figure()\n# axes = mplot3d.Axes3D(figure)\n\n# rot_mat_x = np.array(\n# [[1, 0, 0],\n# [0, 0, -1],\n# [0, 1, 0]]\n# )\n# rot_mat_y = np.array(\n# [[1, 0, 0],\n# [0, 0, -1],\n# [0, 1, 0]]\n# )\n# rot_mat_z = np.array(\n# [[1, 0, 0],\n# [0, 0, -1],\n# [0, 1, 0]]\n# )\n# for pc in pc_list:\n# if rotation == 1:\n# pc = np.dot(pc, rot_mat_x.T)\n# elif rotation == 2:\n# pc = np.dot(pc, rot_mat_y.T)\n# elif rotation == 3:\n# pc = np.dot(pc, rot_mat_z.T)\n# x = pc[:, 0]\n# y = pc[:, 1]\n# z = pc[:, 2]\n# axes.scatter(x, y, z)\n\n# for i, mesh in enumerate(mesh_list):\n# if isinstance(mesh_color, (list, tuple)):\n# c = mesh_color[i]\n# else:\n# c = mesh_color\n# if rotation == 1:\n# mesh.rotate([0.5, 0.0, 0.0], math.radians(90))\n# elif rotation == 2:\n# mesh.rotate([0.0, 0.5, 0.0], math.radians(90))\n# elif rotation == 3:\n# mesh.rotate([0.0, 0.0, 0.5], math.radians(90))\n# axes.add_collection3d(mplot3d.art3d.Poly3DCollection(mesh.vectors, facecolors=c))\n\n# # Auto scale to the mesh size\n# scale = np.concatenate([mesh.points.flatten(-1) for mesh in mesh_list])\n# axes.auto_scale_xyz(scale, scale, scale)\n# # Show the plot to the screen\n# pyplot.show()\n\"\"\"\n", "repo_name": "AdaCompNUS/INVIGORATE", "sub_path": "src/libraries/robots/fetch_robot.py", "file_name": "fetch_robot.py", "file_ext": "py", "file_size_in_byte": 29123, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "12", "api": [{"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "config.config.ROOT_DIR", "line_number": 48, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 48, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "config.config.ROOT_DIR", "line_number": 49, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 49, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 89, "usage_type": "call"}, {"api_name": "libraries.utils.log.LOGGER_NAME", "line_number": 89, "usage_type": "argument"}, {"api_name": "cv_bridge.CvBridge", "line_number": 93, "usage_type": "call"}, {"api_name": "rospy.ServiceProxy", "line_number": 94, "usage_type": "call"}, {"api_name": "rospy.ServiceProxy", "line_number": 95, "usage_type": "call"}, {"api_name": "rospy.ServiceProxy", "line_number": 97, "usage_type": "call"}, {"api_name": "tf.TransformerROS", "line_number": 98, "usage_type": "call"}, {"api_name": "rospy.ServiceProxy", "line_number": 99, "usage_type": "call"}, {"api_name": "rospy.ServiceProxy", "line_number": 101, "usage_type": "call"}, {"api_name": "tf.TransformListener", "line_number": 102, "usage_type": "call"}, {"api_name": "fetch_api.ArmV2", "line_number": 103, "usage_type": "call"}, {"api_name": "fetch_api.Torso", "line_number": 104, "usage_type": "call"}, {"api_name": "fetch_api.Head", "line_number": 105, "usage_type": "call"}, {"api_name": "fetch_api.Gripper", "line_number": 106, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 117, "usage_type": "call"}, {"api_name": "libraries.grasp_collision_checker.grasp_collision_checker.GraspCollisionChecker", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path", "line_number": 138, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path", "line_number": 140, "usage_type": "name"}, {"api_name": "stl.mesh.Mesh.from_file", "line_number": 141, "usage_type": "call"}, {"api_name": "stl.mesh", "line_number": 141, "usage_type": "attribute"}, {"api_name": "stl.mesh.Mesh.from_file", "line_number": 142, "usage_type": "call"}, {"api_name": "stl.mesh", "line_number": 142, "usage_type": "attribute"}, {"api_name": "stl.mesh.Mesh.from_file", "line_number": 143, "usage_type": "call"}, {"api_name": "stl.mesh", "line_number": 143, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path", "line_number": 160, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path", "line_number": 161, "usage_type": "name"}, {"api_name": "open3d.io.read_triangle_mesh", "line_number": 162, "usage_type": "call"}, {"api_name": "open3d.io", "line_number": 162, "usage_type": "attribute"}, {"api_name": "open3d.io.read_triangle_mesh", "line_number": 163, "usage_type": "call"}, {"api_name": "open3d.io", "line_number": 163, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 168, "usage_type": "call"}, {"api_name": "open3d.geometry.PointCloud", "line_number": 173, "usage_type": "call"}, {"api_name": "open3d.geometry", "line_number": 173, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 175, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 175, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 175, "usage_type": "call"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 176, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 176, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 177, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 177, "usage_type": "attribute"}, {"api_name": "open3d.visualization.Visualizer", "line_number": 180, "usage_type": "call"}, {"api_name": "open3d.visualization", "line_number": 180, "usage_type": "attribute"}, {"api_name": "numpy.tile", "line_number": 190, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 193, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 197, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 198, "usage_type": "call"}, {"api_name": "shape_msgs.msg.SolidPrimitive.BOX_X", "line_number": 214, "usage_type": "attribute"}, {"api_name": "shape_msgs.msg.SolidPrimitive", "line_number": 214, "usage_type": "name"}, {"api_name": "shape_msgs.msg.SolidPrimitive.BOX_Y", "line_number": 215, "usage_type": "attribute"}, {"api_name": "shape_msgs.msg.SolidPrimitive", "line_number": 215, "usage_type": "name"}, {"api_name": "shape_msgs.msg.SolidPrimitive.BOX_Z", "line_number": 216, "usage_type": "attribute"}, {"api_name": "shape_msgs.msg.SolidPrimitive", "line_number": 216, "usage_type": "name"}, {"api_name": "moveit_msgs.msg.Grasp", "line_number": 236, "usage_type": "call"}, {"api_name": "tf.transformations.quaternion_from_euler", "line_number": 243, "usage_type": "call"}, {"api_name": "tf.transformations", "line_number": 243, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 243, "usage_type": "attribute"}, {"api_name": "rospy.wait_for_message", "line_number": 337, "usage_type": "call"}, {"api_name": "sensor_msgs.msg.PointCloud2", "line_number": 337, "usage_type": "argument"}, {"api_name": "rospy.Time", "line_number": 338, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 339, "usage_type": "call"}, {"api_name": "tf.transformations.translation_matrix", "line_number": 339, "usage_type": "call"}, {"api_name": "tf.transformations", "line_number": 339, "usage_type": "name"}, {"api_name": "tf.transformations.quaternion_matrix", "line_number": 339, "usage_type": "call"}, {"api_name": "rospy.logerr", "line_number": 341, "usage_type": "call"}, {"api_name": "time.time", "line_number": 344, "usage_type": "call"}, {"api_name": "sensor_msgs.point_cloud2.read_points", "line_number": 354, "usage_type": "call"}, {"api_name": "sensor_msgs.point_cloud2", "line_number": 354, "usage_type": "name"}, {"api_name": "time.time", "line_number": 355, "usage_type": "call"}, {"api_name": "time.time", "line_number": 358, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 359, "usage_type": "call"}, {"api_name": "time.time", "line_number": 360, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 362, "usage_type": "call"}, {"api_name": "time.time", "line_number": 363, "usage_type": "call"}, {"api_name": "time.time", "line_number": 366, "usage_type": "call"}, {"api_name": "open3d.geometry.PointCloud", "line_number": 368, "usage_type": "call"}, {"api_name": "open3d.geometry", "line_number": 368, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 369, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 369, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 369, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 372, "usage_type": "call"}, {"api_name": "time.time", "line_number": 375, "usage_type": "call"}, {"api_name": "rospy.wait_for_message", "line_number": 389, "usage_type": "call"}, {"api_name": "sensor_msgs.msg.Image", "line_number": 389, "usage_type": "argument"}, {"api_name": "rospy.Time", "line_number": 462, "usage_type": "call"}, {"api_name": "rospy.sleep", "line_number": 470, "usage_type": "call"}, {"api_name": "rospy.wait_for_message", "line_number": 487, "usage_type": "call"}, {"api_name": "std_msgs.msg.String", "line_number": 487, "usage_type": "argument"}, {"api_name": "random.choice", "line_number": 493, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation.from_euler", "line_number": 508, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 508, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 508, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 511, "usage_type": "call"}, {"api_name": "open3d.io.read_point_cloud", "line_number": 511, "usage_type": "call"}, {"api_name": "open3d.io", "line_number": 511, "usage_type": "attribute"}]} +{"seq_id": "11940407015", "text": "from collections import deque\n\nclass Solution:\n\n __slots__ = [\"stack\", \"queue\"]\n\n def __init__(self):\n self.stack = []\n self.queue = deque()\n\n # 큐와 스택에 데이터 삽입\n def pushCharacter(self, ch: str):\n self.stack.append(ch)\n\n def enqueueCharacter(self, ch: str):\n self.queue.append(ch)\n\n # 큐와 스택에서 데이터 추출\n def popCharacter(self):\n return self.stack.pop()\n\n def dequeueCharacter(self):\n return self.queue.popleft()\n\ns = input()\nobj = Solution()\nl = len(s)\n\nfor i in range(l):\n obj.pushCharacter(s[i])\n obj.enqueueCharacter(s[i])\n\nisPalindrome = True\n\nfor i in range(l // 2):\n if obj.popCharacter() != obj.dequeueCharacter():\n isPalindrome = False\n break\n\nif isPalindrome:\n print(\"The word, \" + s + \", is a palindrome.\")\nelse:\n print(\"The word, \" + s + \", is not a palindrome.\")", "repo_name": "mincheol-shin/HackerRank_solutions", "sub_path": "30 Days of Code/Day 18 - Queues and Stacks/solution.py", "file_name": "solution.py", "file_ext": "py", "file_size_in_byte": 906, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "collections.deque", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "22166920831", "text": "#!/usr/bin/env python3\n###\n# https://mygene.info/\n# https://pypi.org/project/mygene/\n###\nimport sys,os,re,argparse,time,logging\nimport pandas as pd\nimport mygene as mg\n#\nfrom .. import mygene as bc_mygene\n#\n#############################################################################\nif __name__=='__main__':\n epilog = \"See https://mygene.info/, https://pypi.org/project/mygene/. Example queries: 'cdk2', 'symbol:cdk2', 'symbol:cdk*'\"\n ops = [\"get\", \"search\"]\n parser = argparse.ArgumentParser(description='MyGene API client', epilog=epilog)\n parser.add_argument(\"op\", choices=ops, help=\"OPERATION\")\n parser.add_argument(\"--i\", dest=\"ifile\", help=\"input gene IDs or queries\")\n parser.add_argument(\"--ids\", help=\"input gene IDs or queries, comma-separated\")\n parser.add_argument(\"--o\", dest=\"ofile\", help=\"output (TSV)\")\n parser.add_argument(\"--species\", default=\"human\", help=\"species name or taxonomy ID\")\n parser.add_argument(\"--fields\", default=bc_mygene.FIELDS, help=\"requested fields\")\n parser.add_argument(\"-v\", \"--verbose\", action=\"count\", default=0)\n args = parser.parse_args()\n\n logging.basicConfig(format='%(levelname)s:%(message)s', level=(logging.DEBUG if args.verbose>1 else logging.INFO))\n\n fout = open(args.ofile, 'w') if args.ofile else sys.stdout\n\n t0 = time.time()\n logging.info('Python: {}; mygene: {}'.format(sys.version.split()[0], mg.__version__))\n\n if args.ifile:\n genes = pd.read_table(args.ifile, header=None, names=[\"ID\"])\n ids = list(genes.ID)\n elif args.ids:\n ids = re.split(r'[,\\s]+', args.ids)\n else:\n parser.error(\"Input IDs required via --i or --ids.\")\n\n fields = re.split(r'[,\\s]+', args.fields)\n\n if args.op==\"get\":\n bc_mygene.GetGenes(ids, fields, fout)\n\n elif args.op==\"search\":\n bc_mygene.SearchGenes(ids, args.species, fout)\n\n else:\n parser.error(f\"Invalid operation: {args.op}\")\n\n logging.info(('elapsed time: %s'%(time.strftime('%Hh:%Mm:%Ss', time.gmtime(time.time()-t0)))))\n\n", "repo_name": "jeremyjyang/BioClients", "sub_path": "BioClients/mygene/Client.py", "file_name": "Client.py", "file_ext": "py", "file_size_in_byte": 1962, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "12", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 26, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 26, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 28, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 31, "usage_type": "call"}, {"api_name": "sys.version.split", "line_number": 31, "usage_type": "call"}, {"api_name": "sys.version", "line_number": 31, "usage_type": "attribute"}, {"api_name": "mygene.__version__", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pandas.read_table", "line_number": 34, "usage_type": "call"}, {"api_name": "re.split", "line_number": 37, "usage_type": "call"}, {"api_name": "re.split", "line_number": 41, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 52, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 52, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 52, "usage_type": "call"}, {"api_name": "time.time", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "17761190394", "text": "import sys\nimport itertools\n\nN = int(next(sys.stdin))\n\ntree_start = list(range(1, N+1))\ntree_rate = [int(x) for x in next(sys.stdin).split()]\n\nbest_time = 10_000_000\n\nfor perm in itertools.permutations(tree_start):\n time = max([a+b for a, b in zip(perm, tree_rate)])\n best_time = min(best_time, time)\n\nprint(best_time+1)\n", "repo_name": "Dainou01/Studies", "sub_path": "Code4Drinks/trees.py", "file_name": "trees.py", "file_ext": "py", "file_size_in_byte": 327, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "14", "api": [{"api_name": "sys.stdin", "line_number": 4, "usage_type": "attribute"}, {"api_name": "sys.stdin", "line_number": 7, "usage_type": "attribute"}, {"api_name": "itertools.permutations", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "22149610854", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Nov 1 10:04:29 2018\r\n\r\n@author: malkusch\r\n\"\"\"\r\n\r\nimport numpy as np\r\nfrom scipy import stats\r\nfrom scipy.optimize import minimize\r\nfrom scipy.optimize import curve_fit\r\nfrom ..modelAnalysis import qsmlmMixtureModel\r\nfrom ..data import qsmlmData\r\nfrom ..modelAnalysis import qsmlmModelEvaluator\r\n\r\n\r\nclass QsmlmFractionEstimator:\r\n# Init Model\r\n def __init__(self):\r\n print(\"qsmlmPEstimator initialized\")\r\n self.model = qsmlmMixtureModel.QsmlmMixtureModel()\r\n self.evaluator = qsmlmModelEvaluator.QsmlmModelEvaluator()\r\n self.data = qsmlmData.QsmlmData()\r\n self.folderName = 'None'\r\n self.baseName = 'None'\r\n \r\n def initQsmlmModel(self, d, p, states, initWeight):\r\n self.model.setModelParameters(d, p, states, initWeight)\r\n self.model.printModel()\r\n \r\n # fit function\r\n def pdf(self, n, *w):\r\n if (len(np.shape(w))>1):\r\n w=w[0]\r\n weight=[]\r\n for a in w:\r\n weight.append(a)\r\n return self.model.pdfSuperPos(n, self.model.d, self.model.p, weight)\r\n\r\n # parameter Estimation \r\n# =============================================================================\r\n# def negLogLikelihood(self, weight, n, yData):\r\n# yPred = self.pdf(n,weight)\r\n# sd = np.std(yPred-yData)\r\n# ll = -np.sum(stats.norm.logpdf(yData, loc=yPred, scale=sd))\r\n# return ll\r\n# =============================================================================\r\n \r\n def negLogLikelihood(self, p, n, yData):\r\n yPred = self.pdf(n,p)\r\n ll = -np.sum(np.multiply(yData*self.data.eventNumber, np.log(yPred)))\r\n return ll\r\n \r\n def mleOptimization(self):\r\n b = [[0.0, 1.0]]\r\n boundaries = []\r\n for i in range (self.model.complexity):\r\n boundaries.extend(b)\r\n result = minimize(self.negLogLikelihood, # function to minimize\r\n x0 = self.model.weight, #initial parameters\r\n args = (self.data.data[:,0], self.data.data[:,1]), # data\r\n method = 'L-BFGS-B', #minimization method, see docs\r\n bounds = boundaries,\r\n options = {'disp': True})\r\n self.model.weight = result.x/np.sum(result.x)\r\n self.updateModel()\r\n self.evaluateModel()\r\n self.model.printModel()\r\n self.model.baseName = self.baseName + 'mle'\r\n self.data.baseName = self.baseName + 'mle'\r\n \r\n def lsOptimization(self):\r\n boundaries = (0.0, 1.0)\r\n lsW, lsCov = curve_fit(f=self.pdf,\r\n xdata=self.data.data[:,0],\r\n ydata=self.data.data[:,1],\r\n p0 = self.model.weight,\r\n bounds = boundaries,\r\n method='trf')\r\n self.model.weight = lsW/np.sum(lsW)\r\n self.updateModel()\r\n self.evaluateModel()\r\n self.model.printModel()\r\n print('fitting results:')\r\n print('errors:')\r\n print(np.diag(lsCov))\r\n self.model.baseName = self.baseName +'ls'\r\n self.data.baseName = self.baseName + 'ls'\r\n \r\n def updateModel(self):\r\n self.data.data[:,2] = self.pdf(self.data.data[:,0], self.model.weight)\r\n self.model.logL = -self.negLogLikelihood(self.model.weight, self.data.data[:,0], self.data.data[:,1])\r\n self.data.calcChi2()\r\n self.model.chi2 = self.data.chi2\r\n self.data.clacRes()\r\n self.model.correctWeightVector()\r\n \r\n # Evaluation\r\n def evaluateModel(self):\r\n self.evaluator.para = self.model.complexity-1\r\n self.evaluator.obs = self.data.eventNumber\r\n self.evaluator.logL = self.model.logL\r\n self.evaluator.evaluateModelStatistics()\r\n self.model.bic = self.evaluator.bic\r\n self.model.aic = self.evaluator.aic\r\n self.model.aicc = self.evaluator.aicc\r\n \r\n # print results\r\n def printModelStatistics(self):\r\n self.evaluator.printModelStatistics()\r\n \r\n # load data\r\n def loadData(self, n, p0):\r\n self.data.loadFile(n,p0)\r\n self.model.eventNumber = self.data.eventNumber\r\n self.folderName = self.data.folderName\r\n self.model.folderName = self.folderName\r\n self.baseName = self.data.baseName + '_fraction-estimation_'\r\n \r\n # save results\r\n def saveResults(self):\r\n self.model.saveModel()\r\n self.data.saveData()\r\n \r\n def plotResults(self):\r\n self.data.plotData()\r\n self.data.plotResiduals()\r\n \r\n def runAnalysis(self, n=0, p0=1, d=0.8, p=0.3, mV=[0], wV=[1.0], opt=\"least squares\", fileName=\"\"):\r\n self.data.setFileName(fileName)\r\n self.loadData(n,p0)\r\n print(\"\\nInitialized model parameters:\")\r\n self.initQsmlmModel(d, p, mV, wV)\r\n print(\"\\n\\nOptimized model parameters:\")\r\n if (opt == \"least squares\"):\r\n self.lsOptimization()\r\n if (opt == \"maximum likelihood\"):\r\n self.mleOptimization()\r\n print(\"\\n\\nOptimized model statistics:\")\r\n self.printModelStatistics()\r\n self.plotResults()\r\n \r\n \r\n \r\ndef main():\r\n p = 0.2\r\n d = 0.824 \r\n states = [0,1, 2]\r\n weight = [0.5, 0.5, 0.5]\r\n \r\n pEst = QsmlmFractionEstimator()\r\n pEst.initQsmlmModel(d, p, states, weight)\r\n pEst.loadData(0,1)\r\n pEst.lsOptimization()\r\n pEst.plotResults()\r\n pEst.saveResults()\r\n pEst.mleOptimization()\r\n pEst.plotResults()\r\n pEst.saveResults()\r\n \r\n \r\nif __name__ == '__main__':\r\n main()", "repo_name": "SMLMS/qSMLM", "sub_path": "qSMLM/modelAnalysis/qsmlmFractionEstimator.py", "file_name": "qsmlmFractionEstimator.py", "file_ext": "py", "file_size_in_byte": 5727, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "modelAnalysis.qsmlmMixtureModel.QsmlmMixtureModel", "line_number": 21, "usage_type": "call"}, {"api_name": "modelAnalysis.qsmlmMixtureModel", "line_number": 21, "usage_type": "name"}, {"api_name": "modelAnalysis.qsmlmModelEvaluator.QsmlmModelEvaluator", "line_number": 22, "usage_type": "call"}, {"api_name": "modelAnalysis.qsmlmModelEvaluator", "line_number": 22, "usage_type": "name"}, {"api_name": "data.qsmlmData.QsmlmData", "line_number": 23, "usage_type": "call"}, {"api_name": "data.qsmlmData", "line_number": 23, "usage_type": "name"}, {"api_name": "numpy.shape", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 51, "usage_type": "call"}, {"api_name": "scipy.optimize.minimize", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 65, "usage_type": "call"}, {"api_name": "scipy.optimize.curve_fit", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 86, "usage_type": "call"}]} +{"seq_id": "38899718803", "text": "\nfrom imutils import paths\nimport cv2\nimport numpy as np\nfrom torch.utils.data import Dataset\n\n\nclass demoTestDataLoader(Dataset):\n def __init__(self, img_dir, imgSize, is_transform=None):\n self.img_dir = img_dir\n self.img_paths = []\n for i in range(len(img_dir)):\n self.img_paths += [el for el in paths.list_images(img_dir[i])]\n # self.img_paths = os.listdir(img_dir)\n # print self.img_paths\n self.img_size = imgSize\n self.is_transform = is_transform\n\n def __len__(self):\n return len(self.img_paths)\n\n def __getitem__(self, index):\n img_name = self.img_paths[index]\n img = cv2.imread(img_name)\n # img = img.astype('float32')\n resizedImage = cv2.resize(img, self.img_size)\n resizedImage = np.transpose(resizedImage, (2, 0, 1))\n resizedImage = resizedImage.astype('float32')\n resizedImage /= 255.0\n return resizedImage, img_name\n", "repo_name": "diadestiny/Intelligent-application-of-traffic-monitoring-scene", "sub_path": "LicensePlate/load_data.py", "file_name": "load_data.py", "file_ext": "py", "file_size_in_byte": 960, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "14", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 8, "usage_type": "name"}, {"api_name": "imutils.paths.list_images", "line_number": 13, "usage_type": "call"}, {"api_name": "imutils.paths", "line_number": 13, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "25109076138", "text": "from flask import Flask, request, render_template\nimport os\nfrom datetime import datetime\n\napp = Flask(__name__)\n\n# Emplacement pour enregistrer les fichiers uploadés\nUPLOAD_FOLDER = 'uploads'\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\n\n# Emplacement pour enregistrer l'index\nINDEX_FILE = 'index.csv'\n\ndef index_file(filename, indexing_type='automatic', additional_info=None):\n # Chemin complet du fichier\n file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)\n\n # Indexation automatique par date, heure et taille\n if indexing_type == 'automatic':\n current_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n file_size = os.path.getsize(file_path)\n index_entry = f'{filename},{current_time},{file_size}\\n'\n \n # Classer le fichier dans un dossier basé sur la date\n date_folder = datetime.now().strftime('%Y-%m-%d')\n size_folder = f'size_{file_size // (1024 * 1024)}MB' # Classement par taille\n destination_folder = os.path.join(app.config['UPLOAD_FOLDER'], date_folder, size_folder)\n\n if not os.path.exists(destination_folder):\n os.makedirs(destination_folder)\n\n # Gérer le cas du fichier déjà existant\n base, extension = os.path.splitext(filename)\n counter = 1\n while os.path.exists(os.path.join(destination_folder, filename)):\n filename = f'{base}_{counter}{extension}'\n counter += 1\n\n os.rename(file_path, os.path.join(destination_folder, filename))\n \n # Indexation manuelle avec des champs supplémentaires\n elif indexing_type == 'manual' and additional_info:\n index_entry = f'{filename},{additional_info}\\n'\n else:\n return False\n \n with open(INDEX_FILE, 'a') as index_file:\n index_file.write(index_entry)\n return True\n\n@app.route('/', methods=['GET', 'POST'])\ndef upload_file():\n if request.method == 'POST':\n if 'file' not in request.files:\n return render_template('upload.html', message='Aucun fichier sélectionné')\n\n file = request.files['file']\n\n if file.filename == '':\n return render_template('upload.html', message='Aucun fichier sélectionné')\n\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], file.filename))\n\n # Indexation automatique\n if index_file(file.filename, 'automatic'):\n return render_template('upload.html', message='Fichier téléchargé et indexé avec succès')\n\n return render_template('upload.html', message='')\n\n@app.route('/manual_index', methods=['GET','POST'])\ndef manual_index():\n if request.method == 'POST':\n filename = request.form['filename']\n additional_info = request.form['additional_info']\n\n # Indexation manuelle\n if index_file(filename, 'manual', additional_info):\n return render_template('manual_index.html', message='Fichier indexé avec succès')\n\n return render_template('manual_index.html', message='')\n\n@app.route('/evaluation', methods=['GET'])\ndef evaluation():\n result = {} # Dictionnaire pour stocker les résultats\n root_folder = app.config['UPLOAD_FOLDER']\n\n for date_folder in os.listdir(root_folder):\n date_path = os.path.join(root_folder, date_folder)\n \n if os.path.isdir(date_path): # Vérifier si c'est un dossier\n result[date_folder] = {} # Dictionnaire pour chaque date\n\n for size_folder in os.listdir(date_path):\n size_path = os.path.join(date_path, size_folder)\n\n if os.path.isdir(size_path): # Vérifier si c'est un dossier\n result[date_folder][size_folder] = len(os.listdir(size_path))\n\n return render_template('evaluation.html', result=result)\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n", "repo_name": "Amal1999/TP1-SRI", "sub_path": "tp1.py", "file_name": "tp1.py", "file_ext": "py", "file_size_in_byte": 3813, "program_lang": "python", "lang": "fr", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "name"}, {"api_name": "os.path.getsize", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request.method", "line_number": 53, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 53, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 57, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 57, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 68, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 72, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 73, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 73, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 80, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 97, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 99, "usage_type": "call"}]} +{"seq_id": "34479642708", "text": "import urllib.request\nimport json\nimport time\n'''\nrequest = urllib.request.urlopen('http://placekitten.com/g/2100/3200')\ncat_img =request.read()\n\nwith open('cat_500_600.jpg','wb') as f:\n\tf.write(cat_img)\n'''\n\nwhile 1:\n\tneed_translation_str = input(\"请输入需要翻译的内容（输入q退出）:\\r\\n\")\n\tif need_translation_str == 'q':\n\t\tbreak\n\n\turl = 'http://fanyi.youdao.com/translate?smartresult=dict&smartresult=rule&smartresult=ugc&sessionFrom=http://www.youdao.com/'\n\n\tdata = {}\n\tdata['type'] ='AUTO'\n\tdata['i'] = need_translation_str\n\tdata['doctype'] = 'json'\n\tdata['xmlVersion'] = '1.6'\n\tdata['keyfrom'] = 'fanyi.web'\n\tdata['ue'] = 'UTF-8'\n\tdata['typoResult'] = 'ture'\n\tdata = urllib.parse.urlencode(data).encode('utf-8')\n\n\tresponse = urllib.request.urlopen(url,data)\n\thtml = response.read().decode('utf-8')\n\n\t#print(html)\n\thtml_dic = json.loads(html)\n\ttranslated_str = html_dic['translateResult'][0][0]['tgt']\n\tprint('翻译结果为：%s'%translated_str)\n\ttime.sleep(0.1)\n", "repo_name": "hongweiduan/Python-Learning", "sub_path": "daily_report/learn02_08_2017.py", "file_name": "learn02_08_2017.py", "file_ext": "py", "file_size_in_byte": 985, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "14", "api": [{"api_name": "urllib.request.parse.urlencode", "line_number": 27, "usage_type": "call"}, {"api_name": "urllib.request.parse", "line_number": 27, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 27, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 29, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 29, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 29, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 33, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "18353097990", "text": "import os\nimport time\nfrom typing import Dict, List\n\nfrom facebook.eden.constants import STATS_MOUNTS_STATS\n\nfrom facebook.eden.ttypes import (\n DebugInvalidateRequest,\n GetStatInfoParams,\n MountId,\n TimeSpec,\n)\n\nfrom .lib import testcase\n\n\n@testcase.eden_repo_test\nclass InvalidateTest(testcase.EdenRepoTest):\n directories: List[str] = [\"a\", \"b\", \"c\"]\n num_files: int = 10\n\n def edenfs_logging_settings(self) -> Dict[str, str]:\n return {\n \"eden.fs.inodes.TreeInode\": \"DBG5\",\n }\n\n def populate_repo(self) -> None:\n for directory in self.directories:\n for i in range(self.num_files):\n self.repo.write_file(f\"{directory}/{i}\", f\"{i}\\n\")\n self.repo.commit(\"Initial commit.\")\n\n def get_loaded_count(self) -> int:\n with self.get_thrift_client_legacy() as client:\n stats = client.getStatInfo(GetStatInfoParams(statsMask=STATS_MOUNTS_STATS))\n mountPointInfo = stats.mountPointInfo\n if mountPointInfo is None:\n raise Exception(\"stats.mountPointInfo is not set\")\n self.assertEqual(len(mountPointInfo), 1)\n for mountPath in mountPointInfo:\n info = mountPointInfo[mountPath]\n return info.loadedFileCount + info.loadedTreeCount\n return 0 # Apppease pyre\n\n def invalidate(self, path: str, seconds: int = 0, background: bool = False) -> int:\n with self.get_thrift_client_legacy() as client:\n return client.debugInvalidateNonMaterialized(\n DebugInvalidateRequest(\n mount=MountId(mountPoint=self.mount_path_bytes),\n path=os.fsencode(path),\n age=TimeSpec(seconds=seconds, nanoSeconds=0),\n background=background,\n )\n ).numInvalidated\n\n def read_directory(\n self, directory: str, start: int = 0, stop: int = num_files\n ) -> None:\n for i in range(start, stop):\n content = self.read_file(f\"{directory}/{i}\")\n self.assertEqual(content, f\"{i}\\n\")\n\n def read_all(self) -> None:\n for directory in self.directories:\n self.read_directory(directory)\n\n def test_invalidate_all(self) -> None:\n initial_loaded = self.get_loaded_count()\n self.read_all()\n self.assertEqual(self.get_loaded_count(), initial_loaded + 33)\n invalidated = self.invalidate(\"\")\n self.assertEqual(invalidated, 33)\n # pyre-fixme[6]: Incompatible parameter type [6]: In call `unittest.case.TestCase.assertAlmostEqual`, for 3rd parameter `delta` expected `None` but got `int`.\n self.assertAlmostEqual(self.get_loaded_count(), initial_loaded, delta=1)\n self.read_all()\n\n def test_invalidate_subdir(self) -> None:\n initial_loaded = self.get_loaded_count()\n self.read_all()\n self.assertEqual(self.get_loaded_count(), initial_loaded + 33)\n invalidated = self.invalidate(\"a\")\n self.assertEqual(invalidated, 10)\n self.assertEqual(self.get_loaded_count(), initial_loaded + 23)\n self.read_all()\n\n def test_no_invalidation_with_age(self) -> None:\n initial_loaded = self.get_loaded_count()\n self.read_all()\n self.assertEqual(self.get_loaded_count(), initial_loaded + 33)\n invalidated = self.invalidate(\"a\", seconds=3600)\n self.assertEqual(invalidated, 0)\n self.assertEqual(self.get_loaded_count(), initial_loaded + 33)\n\n def test_invalidate_with_age(self) -> None:\n initial_loaded = self.get_loaded_count()\n self.read_all()\n self.assertEqual(self.get_loaded_count(), initial_loaded + 33)\n time.sleep(10)\n invalidated = self.invalidate(\"a\", seconds=5)\n self.assertEqual(invalidated, 10)\n self.assertEqual(self.get_loaded_count(), initial_loaded + 23)\n self.read_all()\n\n def test_partial_invalidate(self) -> None:\n initial_loaded = self.get_loaded_count()\n self.read_directory(\"a\")\n self.assertEqual(self.get_loaded_count(), initial_loaded + 11)\n time.sleep(10)\n self.read_directory(\"b\")\n self.assertEqual(self.get_loaded_count(), initial_loaded + 22)\n invalidated = self.invalidate(\"\", seconds=5)\n self.assertEqual(invalidated, 11)\n # pyre-fixme[6]: Incompatible parameter type [6]: In call `unittest.case.TestCase.assertAlmostEqual`, for 3rd parameter `delta` expected `None` but got `int`.\n self.assertAlmostEqual(self.get_loaded_count(), initial_loaded + 11, delta=1)\n self.read_all()\n\n def test_partial_directory_invalidate(self) -> None:\n initial_loaded = self.get_loaded_count()\n self.read_directory(\"a\", 0, 6)\n self.assertEqual(self.get_loaded_count(), initial_loaded + 7)\n time.sleep(10)\n self.read_directory(\"a\", 6)\n self.assertEqual(self.get_loaded_count(), initial_loaded + 11)\n invalidated = self.invalidate(\"a\", seconds=5)\n self.assertEqual(invalidated, 6)\n self.assertEqual(self.get_loaded_count(), initial_loaded + 5)\n self.read_all()\n\n def test_invalidate_background(self) -> None:\n \"\"\"Verify that starting an invalidation in the background doesn't crash EdenFS.\"\"\"\n self.read_all()\n self.invalidate(\"\", seconds=10, background=True)\n time.sleep(2)\n\n def test_invalidate_keep_timestamp(self) -> None:\n self.read_all()\n st_before = os.stat(self.get_path(\"a/1\"))\n time.sleep(5)\n self.invalidate(\"\", seconds=0)\n st_after = os.stat(self.get_path(\"a/1\"))\n\n self.assertEqual(st_before.st_mtime, st_after.st_mtime)\n self.assertEqual(st_before.st_ctime, st_after.st_ctime)\n", "repo_name": "facebook/sapling", "sub_path": "eden/integration/invalidate_test.py", "file_name": "invalidate_test.py", "file_ext": "py", "file_size_in_byte": 5739, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5414, "dataset": "github-code", "pt": "14", "api": [{"api_name": "lib.testcase.EdenRepoTest", "line_number": 18, "usage_type": "attribute"}, {"api_name": "lib.testcase", "line_number": 18, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 19, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 22, "usage_type": "name"}, {"api_name": "facebook.eden.ttypes.GetStatInfoParams", "line_number": 35, "usage_type": "call"}, {"api_name": "facebook.eden.constants.STATS_MOUNTS_STATS", "line_number": 35, "usage_type": "name"}, {"api_name": "facebook.eden.ttypes.DebugInvalidateRequest", "line_number": 48, "usage_type": "call"}, {"api_name": "facebook.eden.ttypes.MountId", "line_number": 49, "usage_type": "call"}, {"api_name": "os.fsencode", "line_number": 50, "usage_type": "call"}, {"api_name": "facebook.eden.ttypes.TimeSpec", "line_number": 51, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 98, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 108, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 121, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 133, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 137, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 138, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 140, "usage_type": "call"}, {"api_name": "lib.testcase.eden_repo_test", "line_number": 17, "usage_type": "attribute"}, {"api_name": "lib.testcase", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "26004652367", "text": "from django.core.management.base import BaseCommand\n\nclass Command(BaseCommand):\n \"\"\"\n Clean up old (>3 days) transcript PDF zip files\n\n Usage:\n python manage.py clean_old_transcript_pdfs\n\n \"\"\"\n def handle(self, *args, **options):\n from datetime import datetime, timedelta\n from tendenci.apps.trainings.models import CorpTranscriptsZipFile\n print(\"Cleanning up old transcript PDFs\")\n for tz in CorpTranscriptsZipFile.objects.all():\n if tz.start_dt + timedelta(days=3) < datetime.now():\n print(f\"Deleting transcript zip file (pk={tz.id})\")\n tz.delete()\n print(\"Done\")\n", "repo_name": "tendenci/tendenci", "sub_path": "tendenci/apps/trainings/management/commands/clean_old_transcript_pdfs.py", "file_name": "clean_old_transcript_pdfs.py", "file_ext": "py", "file_size_in_byte": 665, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 467, "dataset": "github-code", "pt": "12", "api": [{"api_name": "django.core.management.base.BaseCommand", "line_number": 3, "usage_type": "name"}, {"api_name": "tendenci.apps.trainings.models.CorpTranscriptsZipFile.objects.all", "line_number": 15, "usage_type": "call"}, {"api_name": "tendenci.apps.trainings.models.CorpTranscriptsZipFile.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "tendenci.apps.trainings.models.CorpTranscriptsZipFile", "line_number": 15, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 16, "usage_type": "name"}]} +{"seq_id": "17249272828", "text": "from backpressure_report.lib import log_utils\nfrom datetime import datetime\nfrom dateutil import parser\nimport itertools\nfrom typing import AnyStr\n\n\nclass BackpressureEvent:\n \"\"\"\n Represents a span during which a single pod was backpressuring / in high I/O and not dispensing job tokens.\n \"\"\"\n def __init__(self, pod: str, start: datetime, end: datetime):\n self.pod = pod\n self.start = start\n self.end = end\n\n def duration(self):\n return (self.end - self.start).seconds\n\n def __str__(self) -> AnyStr:\n return f\"BackpressureEvent(pod = {self.pod},start={str(self.start)},duration={(self.duration())}s)\"\n\n\ndef build_backpressure_events_from_log_jsons(logs: list):\n \"\"\"\n Build a list of BackpressureEvents from the specified logs, using matched \"start\" and \"end\" events for a particular\n pod to delimit the duration of the BackpressureEvent.\n\n :param logs: a list of JSON log files, each of which is a list of JSON objects each representing a log entry.\n :return: a list of BackpressureEvents.\n \"\"\"\n\n # Complete BackpressureEvent objects corresponding to a matched pair of backpressure start and stop log entries for\n # a pod.\n complete = []\n # Already-processed log entry ids to ignore duplicates in overlapping log file ranges.\n seen_insert_ids = set(())\n # pod names for which we have seen a \"backpressure start\" log messages and for which we are now awaiting a matching\n # \"backpressure stop\" log message for the same pod name.\n in_progress_starts_by_pod = {}\n\n # Merge the logs so the sorting covers all log entries.\n merged_logs = itertools.chain(*logs)\n for entry in log_utils.filter_and_sort_log_entries(merged_logs):\n insert_id = entry['insertId']\n # skip duplicates\n if insert_id in seen_insert_ids:\n continue\n\n seen_insert_ids.add(insert_id)\n # Most of the pod name is the same across all pods, only the bit after the last '-' is unique.\n pod = entry['resource']['labels']['pod_name'].split('-')[-1]\n\n if log_utils.is_end_event(entry):\n if pod in in_progress_starts_by_pod:\n # Make a backpressure event object\n start = parser.isoparse(in_progress_starts_by_pod[pod])\n end = parser.isoparse(entry['timestamp'])\n event = BackpressureEvent(pod=pod, start=start, end=end)\n\n # Add this object to complete\n complete.append(event)\n\n # Remove the wip object from in_progress_starts_by_pod\n del in_progress_starts_by_pod[pod]\n\n elif log_utils.is_start_event(entry):\n # There are actually two timestamps in the JSON log entries which appear to represent different concepts:\n # time emitted ('jsonPayload.localTimestamp') versus time added to the log ('timestamp'). Time emitted would\n # seem to be preferable but that value is not specified with a timezone and is ambiguously interpreted by\n # the parsing code as being EST when it's actually UTC. This can make reading the report a bit confusing or\n # misleading. In practice the timestamps only seem to differ by small amounts, so no big deal to use\n # 'timestamp' with its explicit UTC timezone.\n in_progress_starts_by_pod[pod] = entry['timestamp']\n\n return complete\n", "repo_name": "broadinstitute/cromwell", "sub_path": "scripts/backpressure_report/backpressure_report/lib/backpressure_event.py", "file_name": "backpressure_event.py", "file_ext": "py", "file_size_in_byte": 3400, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 934, "dataset": "github-code", "pt": "12", "api": [{"api_name": "datetime.datetime", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.AnyStr", "line_number": 20, "usage_type": "name"}, {"api_name": "itertools.chain", "line_number": 43, "usage_type": "call"}, {"api_name": "backpressure_report.lib.log_utils.filter_and_sort_log_entries", "line_number": 44, "usage_type": "call"}, {"api_name": "backpressure_report.lib.log_utils", "line_number": 44, "usage_type": "name"}, {"api_name": "backpressure_report.lib.log_utils.is_end_event", "line_number": 54, "usage_type": "call"}, {"api_name": "backpressure_report.lib.log_utils", "line_number": 54, "usage_type": "name"}, {"api_name": "dateutil.parser.isoparse", "line_number": 57, "usage_type": "call"}, {"api_name": "dateutil.parser", "line_number": 57, "usage_type": "name"}, {"api_name": "dateutil.parser.isoparse", "line_number": 58, "usage_type": "call"}, {"api_name": "dateutil.parser", "line_number": 58, "usage_type": "name"}, {"api_name": "backpressure_report.lib.log_utils.is_start_event", "line_number": 67, "usage_type": "call"}, {"api_name": "backpressure_report.lib.log_utils", "line_number": 67, "usage_type": "name"}]} +{"seq_id": "41061099016", "text": "\"\"\"\nDjango AdminLTE2 Template Filters\n\nVarious filters that can be used to work with a django form to add missing\nattributes that the user would like the form fields to have.\n\"\"\"\nfrom django import template\nimport json\n\nregister = template.Library()\n\n\n@register.filter('fieldtype')\ndef fieldtype(field):\n \"\"\"\n Get a string representation of what fieldtype a given field is.\n\n :param field: Form Field to get the type of.\n :return: String representation of form field type.\n \"\"\"\n\n return field.field.widget.__class__.__name__\n\n\n@register.filter('with_attrs')\ndef with_attrs(field, attrs_as_json=None):\n \"\"\"\n Add generic attributes to a form field and return the form field so filters can be chained.\n\n :param field: Form field to add attributes to.\n :param attrs_as_json: The attrs to add to the field. Must be in the form of json.\n Defaults to None.\n :return: Field that was passed in with attrs added.\n\n Example::\n\n {% load adminlte_filters %}\n {% for field in form %}\n {% field|with_attrs:'{\"attribute-1\":\"value-1\", \"attribute-2\":\"value-2\"}' %}\n {% field %}\n {% endfor %}\n\n Which will update the form field to look like the following:\n\n \n \"\"\"\n\n attrs_as_json = attrs_as_json or {}\n attrs = field.field.widget.attrs\n data_attrs = json.loads(attrs_as_json)\n for key, value in data_attrs.items():\n attrs[f'{key}'] = value\n field.field.widget.attrs = {**field.field.widget.attrs, **attrs}\n return field\n\n\n@register.filter('with_class')\ndef with_class(field, class_name=''):\n \"\"\"\n Add a class attribute to a form field and return the form field so filters can be chained.\n\n :param field: Form field to add attributes to.\n :param class_name: Class name to add to add to the field. Defaults to blank string.\n :return: Field that was passed in with classes added.\n\n Example::\n\n {% load adminlte_filters %}\n {% for field in form %}\n {% field|with_class:'my-added-class' %}\n {% field %}\n {% endfor %}\n\n Which will update the form field to look like the following:\n\n \n \"\"\"\n\n if not field:\n return field\n attrs = field.field.widget.attrs\n current_class_list = attrs.get('class', '').split()\n current_class_list.append(class_name)\n attrs['class'] = \" \".join(current_class_list)\n field.field.widget.attrs = {**field.field.widget.attrs, **attrs}\n return field\n\n\n@register.filter('with_data')\ndef with_data(field, data_attrs_json=None):\n \"\"\"\n Add data attributes to a form field and return the form field so filters can be chained.\n\n :param field: Form field to add data attributes to.\n :param data_attrs_json: The data fields to add. Must be in the form of json. Defaults to None.\n :return: Field that was passed in with data attributes added.\n\n Example::\n\n {% load adminlte_filters %}\n {% for field in form %}\n {% field|with_data:'{\"attribute-1\":\"value-1\", \"attribute-2\":\"value-2\"}' %}\n {% field %}\n {% endfor %}\n\n Which will update the form field to look like the following:\n\n \n \"\"\"\n\n data_attrs_json = data_attrs_json or {}\n attrs = field.field.widget.attrs\n data_attrs = json.loads(data_attrs_json)\n for key, value in data_attrs.items():\n attrs[f'data-{key}'] = value\n field.field.widget.attrs = {**field.field.widget.attrs, **attrs}\n return field\n\n\n@register.filter('with_placeholder')\ndef with_placeholder(field, placeholder=None):\n \"\"\"\n Add placeholder to a form field and return the form field so filters can be chained.\n\n :param field: Form field to add placeholder to.\n :param placeholder: Placeholder text to use. Defaults to fields label if nothing provided.\n :return: Field that was passed in with placeholder added.\n\n Example::\n\n {% load adminlte_filters %}\n {% for field in form %}\n {% field|with_placeholder 'My Placeholder Text' %}\n {% field %}\n {% endfor %}\n\n Which will update the form field to look like the following:\n\n \n \"\"\"\n\n # Default placeholder to field.label if the widget does not already have\n # a placeholder, and a value was not sent to the method.\n # Assume that if a value for placeholder was sent in, we are using it.\n if not placeholder and 'placeholder' not in field.field.widget.attrs:\n placeholder = field.label\n\n if placeholder:\n attrs = field.field.widget.attrs\n attrs['placeholder'] = placeholder\n field.field.widget.attrs = {**field.field.widget.attrs, **attrs}\n\n return field\n\n\n@register.filter('with_list')\ndef with_list(field, name=None):\n \"\"\"\n Add list attribute to a form field and return the form field so filters can be chained.\n This will not automatically create the datalist elements. It will only add\n the list attribute to the element with name provided.\n\n :param field: Form field to add attributes to.\n :param name: The datalist name.\n Defaults to '_list' appended to end of field name.\n :return: Field that was passed in with list attribute added.\n\n Example::\n\n {% load adminlte_filters %}\n {% for field in form %}\n {% field|with_list:\"my_awesome_list\" %}\n {% field %}\n {% endfor %}\n\n Which will update the form field to look like the following:\n\n \n\n \"\"\"\n if name is None:\n name = f'{field.name}_list'\n\n attrs = field.field.widget.attrs\n attrs['list'] = name\n field.field.widget.attrs = {**field.field.widget.attrs, **attrs}\n\n return field\n\n\n@register.filter('with_pattern')\ndef with_pattern(field, pattern=None):\n \"\"\"\n Add pattern to a form field and return the form field so filters can be chained.\n Unfortunately, the Django template engine can't handle parsing a string\n regex passed to this filter. Therefore, the regex string needs to be stored\n in a variable that can be sent to the filter.\n\n NOTE: The default regex in this method is written as a regular string and\n not a raw string (regex with r prefix) so that the documentation will match.\n This docstring can not contain a single backslash as python will think it\n is invalid syntax and raise a warning.\n\n :param field: Form field to add attributes to.\n :param pattern: The JavaScript regex pattern to use.\n Defaults to \"\\\\([0-9]{3}\\\\) [0-9]{3}-[0-9]{4}\" if value not passed.\n :return: Field that was passed in with pattern attribute added.\n\n Example::\n\n # Assuming the field has a property called pattern with a string value\n # that is the needed regex: \"\\\\([0-9]{3}\\\\) [0-9]{3}-[0-9]{4}\"\n # We can send that variable to the filter.\n\n {% load adminlte_filters %}\n {% for field in form %}\n {% field|with_pattern:field.pattern %}\n {% field %}\n {% endfor %}\n\n Which will update the form field to look like the following:\n\n \n \"\"\"\n if pattern is None:\n pattern = \"\\\\([0-9]{3}\\\\) [0-9]{3}-[0-9]{4}\"\n\n attrs = field.field.widget.attrs\n attrs['pattern'] = pattern\n field.field.widget.attrs = {**field.field.widget.attrs, **attrs}\n\n return field\n\n\n@register.filter('with_inputmask')\ndef with_inputmask(field, inputmask=None):\n \"\"\"\n Add inputmask to a form field and return the form field so filters can be chained.\n Depending on the complexity of inputmask, the Django template engine may\n not be able to handle parsing the mask. If this is the case, the inputmask\n will need to be stored in a variable where the variable can be sent to the\n filter.\n\n :param field: Form field to add attributes to.\n :param inputmask: The inputmask pattern to use.\n Defaults to \"(999) 999-9999\" if value not passed.\n :return: Field that was passed in with a inputmask data attribute added.\n\n Example::\n\n {% load adminlte_filters %}\n {% for field in form %}\n {% field|with_inputmask:'(999) 999-9999' %}\n {% field %}\n {% endfor %}\n\n Which will update the form field to look like the following:\n\n \n \"\"\"\n if inputmask is None:\n inputmask = \"(999) 999-9999\",\n\n attrs = field.field.widget.attrs\n attrs['data-inputmask'] = f\"'mask':'{inputmask}'\"\n field.field.widget.attrs = {**field.field.widget.attrs, **attrs}\n\n return field\n\n\n@register.filter('with_min')\ndef with_min(field, min_val=None):\n \"\"\"\n Add min attribute to a form field and return the form field so filters can be chained.\n\n :param field: Form field to add attributes to.\n :param min_val: The min value to use.\n Defaults to 0 if value not passed.\n :return: Field that was passed in with min attribute added.\n\n Example::\n\n {% load adminlte_filters %}\n {% for field in form %}\n {% field|with_min:5 %}\n {% field %}\n {% endfor %}\n\n Which will update the form field to look like the following:\n\n \n \"\"\"\n\n if min_val is None:\n min_val = 0\n\n attrs = field.field.widget.attrs\n attrs['min'] = min_val\n field.field.widget.attrs = {**field.field.widget.attrs, **attrs}\n\n return field\n\n\n@register.filter('with_max')\ndef with_max(field, max_val=None):\n \"\"\"\n Add max attribute to a form field and return the form field so filters can be chained.\n\n :param field: Form field to add attributes to.\n :param max_val: The max value to use.\n Defaults to 100 if value not passed.\n :return: Field that was passed in with max attribute added.\n\n Example::\n\n {% load adminlte_filters %}\n {% for field in form %}\n {% field|with_max:9 %}\n {% field %}\n {% endfor %}\n\n Which will update the form field to look like the following:\n\n \n \"\"\"\n\n if max_val is None:\n max_val = 100\n\n attrs = field.field.widget.attrs\n attrs['max'] = max_val\n field.field.widget.attrs = {**field.field.widget.attrs, **attrs}\n\n return field\n\n\n@register.filter('with_input_type')\ndef with_input_type(field, new_type):\n \"\"\"\n Change widget input_type to passed value.\n\n :param field: Form field to change type on.\n :return: Field that was passed in with input_type changed to passed value.\n\n Example::\n\n {% load adminlte_filters %}\n {% for field in form %}\n {% field|with_input_type:'date' %}\n {% field %}\n {% endfor %}\n\n Which will update the form field to look like the following:\n\n \n \"\"\"\n\n field.field.widget.input_type = new_type\n return field\n\n\n@register.filter('dir')\ndef directory(field):\n \"\"\"\n Return the result of calling dir on an object.\n\n :param field: Form field to run dir on.\n :return: dir of the field passed in.\n \"\"\"\n\n return dir(field)\n\n\n@register.filter('dictionary')\ndef dictionary(field):\n \"\"\"\n Return the result of calling __dict__ on an object.\n\n :param field: Form field to run __dict__ on.\n :return: __dict__ of the field passed in.\n \"\"\"\n\n return field.__dict__\n\n\n@register.filter('unsnake')\ndef unsnake(field):\n \"\"\"\n Return a string that converts underscore to spaces and capitalizes first letter.\n\n :param field: Form field to unsnake.\n :return: unsnaked string of the field passed in.\n \"\"\"\n\n return str(field).replace('_', ' ').capitalize()\n\n\n@register.filter('unslugify')\ndef unslugify(field):\n \"\"\"\n Return a string that converts dash to spaces and capitalizes first letter.\n\n :param field: Form field to unslugify.\n :return: dir of the field passed in.\n \"\"\"\n\n return str(field).replace('-', ' ').capitalize()\n", "repo_name": "DJBarnes/django-adminlte2-pdq", "sub_path": "adminlte2_pdq/templatetags/adminlte_filters.py", "file_name": "adminlte_filters.py", "file_ext": "py", "file_size_in_byte": 12585, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "14", "api": [{"api_name": "django.template.Library", "line_number": 10, "usage_type": "call"}, {"api_name": "django.template", "line_number": 10, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 50, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 119, "usage_type": "call"}]} +{"seq_id": "31073642714", "text": "import numpy as np\nimport argparse\nimport datetime\nimport cv2\nimport math\nimport sys\nimport os\nimport glob\nfrom vai.dpuv1.rt.vitis.python.dpu.runner import Runner\nfrom detect_ap2 import det_preprocess, det_postprocess\nimport subprocess\n\n\n\ndef detect(runner, fpgaBlobs, image):\n fpgaInput = fpgaBlobs[0][0]\n c,h,w = fpgaInput[0].shape\n img = det_preprocess(image, fpgaInput[0])\n #np.copyto(fpgaInput[0], img)\n jid = runner.execute_async(fpgaBlobs[0], fpgaBlobs[1])\n runner.wait(jid)\n rects = det_postprocess(fpgaBlobs[1][1], fpgaBlobs[1][0], [h,w,c])\n return rects\n\n# Main function\n\ndef faceDetection(vitis_rundir,outpath, rsz_h, rsz_w, path):\n runner = Runner(vitis_rundir)\n inTensors = runner.get_input_tensors()\n outTensors = runner.get_output_tensors()\n batch_sz = 1\n fpgaBlobs= []\n for io in [inTensors, outTensors]:\n blobs = []\n for t in io:\n shape = (batch_sz,) + tuple([t.dims[i] for i in range(t.ndims)][1:])\n blobs.append(np.empty((shape), dtype=np.float32, order='C'))\n fpgaBlobs.append(blobs)\n \n dirName = outpath\n if not os.path.exists(dirName):\n os.mkdir(dirName)\n \n output_Img_path = dirName\n #os.chdir(path)\n res=[] \n for fn in sorted(glob.glob(path+ '/*.jpg'), key=os.path.getsize):\n filename = fn[fn.rfind('/')+1:]\n src_img=cv2.imread(fn)\n input_img=cv2.resize(src_img,(rsz_w, rsz_h))\n face_rects=detect(runner, fpgaBlobs, input_img)\n dst_img=input_img.copy()\n if len(face_rects) != 0:\n for face_rect in face_rects:\n res.append(\"{} {} {} {} {}\".format(fn, face_rect[0],face_rect[1],face_rect[2],face_rect[3]))\n print (\"{} {} {} {} {}\".format(fn, face_rect[0],face_rect[1],face_rect[2],face_rect[3]))\n cv2.rectangle(dst_img,(face_rect[0],face_rect[1]),(face_rect[2],face_rect[3]),(0,255,0),2)\n cv2.imwrite(output_Img_path+filename,dst_img)\n# else:\n #res.append(\"{} {} {} {} {}\".format(fn, 0,0,0,0))\n \n\n# Face Detection \nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description = 'analysis densebox model')\n parser.add_argument('--vitisrundir', help = 'path to dpuv1 run directory ', type=str)\n parser.add_argument('--images', help = 'path to image folder',type = str, default='test_pic/' )\n parser.add_argument('--resize_h', help = 'resize height', type = int)\n parser.add_argument('--resize_w', help = 'resize width', type = int)\n\n args = parser.parse_args()\n \n work_dir = os.getcwd() + '/output/'\n if not os.path.exists(work_dir):\n os.mkdir(work_dir)\n\n faceDetection(args.vitisrundir, work_dir, args.resize_h, args.resize_w, args.images) \n", "repo_name": "embedded-bitai/Mercenary", "sub_path": "proj/fpga/ultra96/camera/Vitis-AI/alveo/apps/face_detect/detect_visual.py", "file_name": "detect_visual.py", "file_ext": "py", "file_size_in_byte": 2760, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "14", "api": [{"api_name": "detect_ap2.det_preprocess", "line_number": 18, "usage_type": "call"}, {"api_name": "detect_ap2.det_postprocess", "line_number": 22, "usage_type": "call"}, {"api_name": "vai.dpuv1.rt.vitis.python.dpu.runner.Runner", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 42, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 58, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 65, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "41410909138", "text": "from tkinter import *\nfrom PIL import Image, ImageTk # pip install pillow\nfrom tkinter import ttk\nimport sqlite3\nimport random\nfrom tkinter import messagebox\n\nclass CustomerWindow:\n def __init__(self, root):\n self.root = root\n self.root.title(\"Hotel Management System\")\n self.root.geometry(\"1295x550+230+220\")\n\n #####################Variables##############################\n self.var_ref = StringVar()\n x = random.randint(1000, 9999) # [1000; 9999]\n self.var_ref.set(str(x))\n\n self.var_customer_name = StringVar()\n self.var_mother_name = StringVar()\n self.var_gender = StringVar()\n self.var_postcode = StringVar()\n self.var_mobile = StringVar()\n self.var_email = StringVar()\n self.var_nationality = StringVar()\n self.var_address = StringVar()\n self.var_id_proof = StringVar()\n self.var_id_number = StringVar()\n\n #####################Title##############################\n lbl_title = Label(self.root, text=\"Add customer details\".upper(), \\\n font=(\"times new roman\", 18, \"bold\"), bg=\"black\", fg=\"gold\", \\\n bd=4, relief=RIDGE)\n lbl_title.place(x=0, y=0, width=1295, height=50)\n\n #####################Logo##############################\n img2 = Image.open(\"images\\logohotel.png\")\n img2 = img2.resize((100, 40), Image.ANTIALIAS)\n self.photoimg2 = ImageTk.PhotoImage(img2)\n\n lbling = Label(self.root, image = self.photoimg2, bd = 0,\\\n relief = RIDGE)\n lbling.place(x=5, y=2, width = 100, height = 40)\n\n #####################Label frame##############################\n label_frame_left = LabelFrame(self.root, bd = 2, relief = RIDGE,\\\n text = \"Customer Details\", font=(\"times new roman\", 12, \"bold\"),\\\n padx = 2, pady = 6)\n label_frame_left.place(x = 5, y = 50, width = 425, height = 490)\n\n #####################Labels, entries##############################\n\n # customer ref\n label_customer_ref = Label(label_frame_left, text = \"Customer Ref\",\\\n font = (\"times new roman\", 12, \"bold\"), padx = 2, pady = 6)\n label_customer_ref.grid(row = 0, column = 0, sticky = W)\n\n entry_ref = ttk.Entry(label_frame_left, textvariable = self.var_ref, width = 29,\\\n state = \"readonly\", font = (\"times new roman\", 13, \"bold\"))\n entry_ref.grid(row = 0, column = 1)\n\n # customer name\n customer_name = Label(label_frame_left, text = \"Customer Name:\",\\\n font = (\"times new roman\", 12, \"bold\"), padx = 2, pady = 6)\n customer_name.grid(row = 1, column = 0, sticky = W)\n\n entry_customer_name = ttk.Entry(label_frame_left, textvariable = self.var_customer_name,\n width = 29, font = (\"times new roman\", 13, \"bold\"))\n entry_customer_name.grid(row = 1, column = 1)\n\n # mother name\n label_mother_name = Label(label_frame_left, text = \"Mother name:\",\\\n font = (\"times new roman\", 12, \"bold\"), padx = 2, pady = 6)\n label_mother_name.grid(row = 2, column = 0, sticky = W)\n\n entry_mother_name = ttk.Entry(label_frame_left, textvariable = self.var_mother_name, width = 29,\n font = (\"times new roman\", 13, \"bold\"))\n entry_mother_name.grid(row = 2, column = 1)\n\n # gender\n label_gender = Label(label_frame_left, text = \"Gender:\",\\\n font = (\"times new roman\", 12, \"bold\"), padx = 2, pady = 6)\n label_gender.grid(row = 3, column = 0, sticky = W)\n\n combobox_gender = ttk.Combobox(label_frame_left, textvariable = self.var_gender,\n font = (\"times new roman\", 12, \"bold\"),\\\n width = 31, state=\"readonly\")\n combobox_gender[\"value\"] = (\"Male\", \"Female\", \"None\")\n combobox_gender.current(0)\n combobox_gender.grid(row=3, column=1)\n\n # postcode\n label_postcode = Label(label_frame_left, text = \"Postcode:\", \\\n font = (\"times new roman\", 12, \"bold\"), padx = 2, pady = 6)\n label_postcode.grid(row = 4, column = 0, sticky = W)\n\n entry_postcode = ttk.Entry(label_frame_left, textvariable = self.var_postcode, width = 29,\n font = (\"times new roman\", 13, \"bold\"))\n entry_postcode.grid(row = 4, column = 1)\n\n # mobile number\n label_mobile = Label(label_frame_left, text = \"Mobile:\", \\\n font = (\"times new roman\", 12, \"bold\"), padx = 2, pady = 6)\n label_mobile.grid(row = 5, column = 0, sticky = W)\n\n entry_mobile = ttk.Entry(label_frame_left, textvariable = self.var_mobile, width = 29,\n font = (\"times new roman\", 13, \"bold\"))\n entry_mobile.grid(row = 5, column = 1)\n\n # email\n label_email = Label(label_frame_left, text = \"Email:\", \\\n font = (\"times new roman\", 12, \"bold\"), padx = 2, pady = 6)\n label_email.grid(row = 6, column = 0, sticky = W)\n\n entry_email = ttk.Entry(label_frame_left, textvariable = self.var_email, width = 29,\n font = (\"times new roman\", 13, \"bold\"))\n entry_email.grid(row = 6, column = 1)\n\n # nationality\n label_nationality = Label(label_frame_left, text=\"Nationality:\", \\\n font=(\"times new roman\", 12, \"bold\"), padx=2, pady=6)\n label_nationality.grid(row=7, column=0, sticky=W)\n\n combobox_nation = ttk.Combobox(label_frame_left, textvariable = self.var_nationality,\n font=(\"times new roman\", 12, \"bold\"), width=31, state=\"readonly\")\n combobox_nation[\"value\"] = (\"Ukrainian\", \"Polish\", \"Romanian\")\n combobox_nation.current(0)\n combobox_nation.grid(row=7, column=1)\n\n # id proof\n label_id_proof = Label(label_frame_left, text=\"ID Proof Type:\", \\\n font=(\"times new roman\", 12, \"bold\"), padx=2, pady=6)\n label_id_proof.grid(row=8, column=0, sticky=W)\n\n combobox_id_proof = ttk.Combobox(label_frame_left, textvariable = self.var_id_proof,\n font=(\"times new roman\", 12, \"bold\"), width=31, state=\"readonly\")\n combobox_id_proof[\"value\"] = (\"Passport\", \"Driving Licence\", \"Сredential\")\n combobox_id_proof.current(0)\n combobox_id_proof.grid(row=8, column=1)\n\n # id number\n label_id_number = Label(label_frame_left, text = \"ID Number:\", \\\n font = (\"times new roman\", 12, \"bold\"), padx = 2, pady = 6)\n label_id_number.grid(row = 9, column = 0, sticky = W)\n\n entry_id_number = ttk.Entry(label_frame_left, textvariable = self.var_id_number, width = 29,\n font = (\"times new roman\", 13, \"bold\"))\n entry_id_number.grid(row = 9, column = 1)\n\n # address\n label_address = Label(label_frame_left, text = \"Address:\", \\\n font = (\"times new roman\", 12, \"bold\"), padx = 2, pady = 6)\n label_address.grid(row = 10, column = 0, sticky = W)\n\n entry_address = ttk.Entry(label_frame_left, textvariable = self.var_address, width = 29,\n font = (\"times new roman\", 13, \"bold\"))\n entry_address.grid(row = 10, column = 1)\n\n ##################### Buttons ##############################\n\n btn_frame = Frame(label_frame_left, bd = 2, relief=RIDGE)\n btn_frame.place(x=0, y=400,width = 412, height=40)\n\n btn_add = Button(btn_frame, text=\"Add\", command = self.add_data, font = (\"times new roman\", 12, \"bold\"),\\\n width = 10, bg=\"black\", fg=\"gold\")\n btn_add.grid(row=0,column=0, padx=1)\n\n btn_update = Button(btn_frame, text=\"Update\", command = self.update, font=(\"times new roman\", 12, \"bold\"), \\\n width=10, bg=\"black\", fg=\"gold\")\n btn_update.grid(row=0, column=1, padx=1)\n\n btn_delete = Button(btn_frame, text=\"Delete\", command = self.nDelete, font=(\"times new roman\", 12, \"bold\"), \\\n width=10, bg=\"black\", fg=\"gold\")\n btn_delete.grid(row=0, column=2, padx=1)\n\n btn_reset = Button(btn_frame, text=\"Reset\", command = self.reset, font=(\"times new roman\", 12, \"bold\"), \\\n width=10, bg=\"black\", fg=\"gold\")\n btn_reset.grid(row=0, column=3, padx=1)\n\n ##################### Table frame search system ##############################\n table_frame = LabelFrame(self.root, bd = 2, relief = RIDGE,\\\n text = \"View Details and Search\", font=(\"times new roman\", 12, \"bold\"),\\\n padx = 2, pady = 6)\n table_frame.place(x = 435, y = 50, width = 860, height = 490)\n\n label_search_by = Label(table_frame, text=\"Search by:\", \\\n font=(\"times new roman\", 12, \"bold\"), bg = \"red\", fg=\"white\")\n label_search_by.grid(row=0, column=0, sticky=W, padx = 2)\n\n self.search_var = StringVar()\n combobox_search = ttk.Combobox(table_frame, textvariable = self.search_var, font=(\"times new roman\", 12, \"bold\"), \\\n width=31, state=\"readonly\")\n combobox_search[\"value\"] = (\"Mobile\", \"Ref\")\n combobox_search.current(0)\n combobox_search.grid(row=0, column=1, padx = 2)\n\n self.entry_search = StringVar()\n entry_search = ttk.Entry(table_frame, width=31, textvariable = self.entry_search, font=(\"times new roman\", 13, \"bold\"))\n entry_search.grid(row = 0, column = 2, padx = 2)\n\n btn_search = Button(table_frame, text=\"Search\", font=(\"times new roman\", 12, \"bold\"), \\\n width=10, bg=\"black\", fg=\"gold\")\n btn_search.grid(row=0, column=3, padx=1)\n\n btn_show_all = Button(table_frame, text=\"Show all\", font=(\"times new roman\", 12, \"bold\"), \\\n width=10, bg=\"black\", fg=\"gold\")\n btn_show_all.grid(row=0, column=4, padx=1)\n\n #####################Data table##############################\n details_table = Frame(table_frame, bd=2, relief=RIDGE)\n details_table.place(x=0, y=50, width=860, height=350)\n\n scroll_x = ttk.Scrollbar(details_table, orient=HORIZONTAL)\n scroll_y = ttk.Scrollbar(details_table, orient=VERTICAL)\n\n self.details_table = ttk.Treeview(details_table,\\\n column = (\"ref\", \"name\", \"mother\", \"gender\", \"post\", \"mobile\", \"email\",\n \"nationality\", \"idproof\", \"idnumber\", \"address\"), xscrollcommand=scroll_x.set,\n yscrollcommand=scroll_y.set)\n\n scroll_x.pack(side=BOTTOM, fill=X)\n scroll_y.pack(side=RIGHT, fill=Y)\n\n scroll_x.config(command=self.details_table.xview)\n scroll_y.config(command=self.details_table.yview)\n\n self.details_table.heading(\"ref\", text=\"Refer No\")\n self.details_table.heading(\"name\", text=\"Name\")\n self.details_table.heading(\"mother\", text=\"Mother\")\n self.details_table.heading(\"gender\", text=\"Gender\")\n self.details_table.heading(\"post\", text=\"PostCode\")\n self.details_table.heading(\"mobile\", text=\"Mobile\")\n self.details_table.heading(\"email\", text=\"Email\")\n self.details_table.heading(\"nationality\", text=\"Nationality\")\n self.details_table.heading(\"idproof\", text=\"ID Proof\")\n self.details_table.heading(\"idnumber\", text=\"ID Number\")\n self.details_table.heading(\"address\", text=\"Address\")\n\n self.details_table[\"show\"] = \"headings\"\n\n self.details_table.column(\"ref\", width = 100)\n self.details_table.column(\"name\", width=100)\n self.details_table.column(\"mother\", width=100)\n self.details_table.column(\"gender\", width=100)\n self.details_table.column(\"post\", width=100)\n self.details_table.column(\"mobile\", width=100)\n self.details_table.column(\"email\", width=100)\n self.details_table.column(\"nationality\", width=100)\n self.details_table.column(\"idproof\", width=100)\n self.details_table.column(\"idnumber\", width=100)\n self.details_table.column(\"address\", width=100)\n\n self.details_table.pack(fill=BOTH, expand=1)\n self.details_table.bind(\"\", self.get_cursor)\n self.fetch_data()\n\n def create_connection(self):\n conn = None\n database_file = 'hotel_management_system'\n try:\n conn = sqlite3.connect(database_file)\n except:\n messagebox.showerror(\"Error\", \"Connection with database haven't been created...\",\n parent = self.root)\n return conn\n\n def add_data(self):\n if self.var_mobile.get() == \"\" or self.var_mother_name.get() == \"\":\n messagebox.showerror(\"Error\", \"All fields are required\", parent = self.root)\n else:\n try:\n conn = self.create_connection()\n cursor = conn.cursor()\n add_customer_query = f\"INSERT INTO customers VALUES (?,?,?,?,?,?,?,?,?,?,?)\"\n\n customer_parameters = (self.var_ref.get(), self.var_customer_name.get(), self.var_mother_name.get(),\n self.var_gender.get(), self.var_postcode.get(), self.var_mobile.get(), self.var_email.get(),\n self.var_nationality.get(), self.var_id_proof.get(), self.var_id_number.get(), self.var_address.get())\n\n cursor.execute(add_customer_query, customer_parameters)\n conn.commit()\n self.fetch_data()\n conn.close()\n messagebox.showinfo(\"Success\", \"Customer has been added!\", parent = self.root)\n except Exception as ex:\n messagebox.showwarning(\"Warning\", f\"Something went wrong: {ex}\", parent = self.root)\n\n def fetch_data(self):\n try:\n conn = self.create_connection()\n cursor = conn.cursor()\n select_all_customers_query = \"\"\"SELECT * FROM customers\"\"\"\n cursor.execute(select_all_customers_query)\n records = cursor.fetchall()\n if len(records) != 0:\n self.details_table.delete(*self.details_table.get_children())\n for i in records:\n self.details_table.insert(\"\", END, values = i)\n conn.commit()\n conn.close()\n except Exception as ex:\n messagebox.showwarning(\"Warning\", f\"Something went wrong: {ex}\", parent=self.root)\n\n def get_cursor(self, event = \"\"):\n cursor_row = self.details_table.focus()\n content = self.details_table.item(cursor_row)\n row = content[\"values\"]\n\n self.var_ref.set(row[0]),\n self.var_customer_name.set(row[1]),\n self.var_mother_name.set(row[2]),\n self.var_gender.set(row[3]),\n self.var_postcode.set(row[4]),\n self.var_mobile.set(row[5]),\n self.var_email.set(row[6]),\n self.var_nationality.set(row[7]),\n self.var_id_proof.set(row[8]),\n self.var_id_number.set(row[9]),\n self.var_address.set(row[10])\n\n def update(self):\n try:\n\n if self.var_mobile.get() == \"\":\n messagebox.showerror(\"Error\", \"Please enter mobile number\", parent = self.root)\n else:\n conn = self.create_connection()\n cursor = conn.cursor()\n update_customer_query = \"\"\"UPDATE customers SET name = ?, mother = ?, gender = ?, postcode = ?,\n mobile = ?, email = ?, nationality = ?, id_proof = ?, id_number = ?, address = ? WHERE \n ref = ?\"\"\"\n customer_parameters = (self.var_customer_name.get(), self.var_mother_name.get(),\n self.var_gender.get(), self.var_postcode.get(), self.var_mobile.get(), self.var_email.get(),\n self.var_nationality.get(), self.var_id_proof.get(), self.var_id_number.get(), self.var_address.get(),\n self.var_ref.get())\n\n cursor.execute(update_customer_query, customer_parameters)\n conn.commit()\n self.fetch_data()\n conn.close()\n messagebox.showinfo(\"Update\", \"Customer details has been updated successfully!\", parent = self.root)\n\n except Exception as ex:\n messagebox.showwarning(\"Warning\", f\"Something went wrong: {ex}\", parent=self.root)\n\n def nDelete(self):\n nDelete = messagebox.askyesno(\"Hotel Management System\", \"Do you want to delete this customer?\",\n parent = self.root)\n\n if nDelete > 0:\n try:\n conn = self.create_connection()\n cursor = conn.cursor()\n delete_the_customer_query = \"DELETE FROM customers WHERE ref = ?\"\n value = (self.var_ref.get(),)\n cursor.execute(delete_the_customer_query, value)\n except:\n messagebox.showwarning(\"Warning\", f\"Something went wrong: {ex}\", parent=self.root)\n else:\n if not nDelete:\n return\n conn.commit()\n self.fetch_data()\n conn.close()\n\n def reset(self):\n x = random.randint(1000, 9999) # [1000; 9999]\n self.var_ref.set(str(x))\n\n self.var_customer_name.set(\"\"),\n self.var_mother_name.set(\"\"),\n #self.var_gender.set(\"\"),\n self.var_postcode.set(\"\"),\n self.var_mobile.set(\"\"),\n self.var_email.set(\"\"),\n #self.var_nationality.set(\"\"),\n #self.var_id_proof.set(\"\"),\n self.var_id_number.set(\"\"),\n self.var_address.set(\"\")\n\n def search(self): # 57:40\n try:\n conn = self.create_connection()\n cursor = conn.cursor()\n search_the_customer_query = \"SELECT * FROM customers WHERE \" + str()\n #value = (self.var_ref.get(),)\n #cursor.execute(delete_the_customer_query, value)\n except:\n messagebox.showwarning(\"Warning\", f\"Something went wrong: {ex}\", parent=self.root)\n\nif __name__ == '__main__':\n root = Tk()\n win = CustomerWindow(root)\n root.mainloop()", "repo_name": "OleksandrMarkov/Hotel-Management-System", "sub_path": "customer.py", "file_name": "customer.py", "file_ext": "py", "file_size_in_byte": 17599, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "random.randint", "line_number": 16, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 37, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 37, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 38, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 38, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 39, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 39, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 58, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 58, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 67, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 67, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 76, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 76, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 85, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 85, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 97, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 97, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 106, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 106, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 115, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 115, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 124, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 124, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 135, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 135, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 146, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 146, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 155, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 155, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 191, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 191, "usage_type": "name"}, {"api_name": "tkinter.ttk.Entry", "line_number": 198, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 198, "usage_type": "name"}, {"api_name": "tkinter.ttk.Scrollbar", "line_number": 213, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 213, "usage_type": "name"}, {"api_name": "tkinter.ttk.Scrollbar", "line_number": 214, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 214, "usage_type": "name"}, {"api_name": "tkinter.ttk.Treeview", "line_number": 216, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 216, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 261, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 263, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 263, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 269, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 269, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 284, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 284, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showwarning", "line_number": 286, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 286, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showwarning", "line_number": 302, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 302, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 325, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 325, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 341, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 341, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showwarning", "line_number": 344, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 344, "usage_type": "name"}, {"api_name": "tkinter.messagebox.askyesno", "line_number": 347, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 347, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showwarning", "line_number": 358, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 358, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 367, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showwarning", "line_number": 389, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 389, "usage_type": "name"}]} +{"seq_id": "25846429241", "text": "import cv2 as cv\nimport numpy as np\n\nimgBGR = cv.imread('BP.png')\nimgRGB = cv.cvtColor(imgBGR,cv.COLOR_BGR2RGB)\n\n\nimgGray = cv.cvtColor(imgBGR, cv.COLOR_BGR2GRAY)\n\nret,threshold = cv.threshold(imgGray,85,255,cv.THRESH_BINARY_INV)\n\n\nblur = cv.GaussianBlur(imgBGR,(5,5),0)\nkernel = np.ones((5,5),np.uint8)\nersion = cv.erode(threshold,kernel,iterations=3)\ndilation = cv.dilate(threshold,kernel,iterations = 3)\n\nopening = cv.dilate(ersion,kernel,iterations=1)\n\nclosing = cv.erode(dilation,kernel,iterations=1)\n\n\ncv.imshow(\"imageErsion\",ersion)\ncv.imshow(\"imageDilation\",dilation)\ncv.imshow(\"imageThreshold\",threshold)\ncv.imshow(\"imageOpening\",opening)\ncv.imshow(\"imageClosing\",closing)\n\n\ncv.waitKey(0)\n", "repo_name": "John-Dillermand/MED3_Exercises", "sub_path": "Exercise 5 folder/MorbinTime.py", "file_name": "MorbinTime.py", "file_ext": "py", "file_size_in_byte": 698, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "14", "api": [{"api_name": "cv2.imread", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 5, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 8, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 10, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.erode", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.dilate", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.dilate", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.erode", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "20301048043", "text": "\"\"\"Handlers related with the position of users\"\"\"\n\nfrom flask import Blueprint, request, make_response, jsonify\nfrom flask.views import MethodView\nfrom schema import Schema, And, Use, SchemaError\n\nfrom app import db, application\nfrom src.mixins.AuthenticationMixin import Authenticator\nfrom src.mixins.DriversMixin import DriversMixin\nfrom src.models import User\n\nPOSITION_BLUEPRINT = Blueprint('position', __name__)\n\n\nclass PositionAPI(MethodView):\n \"\"\"Handler for position related API\"\"\"\n\n @staticmethod\n def put(username):\n \"\"\"Endpoint for Updating a user position\"\"\"\n try:\n data = request.get_json()\n schema = Schema([{'latitude': And(Use(float), lambda x: -90 < x < 90),\n 'longitude': And(Use(float), lambda x: -180 < x < 180)}])\n # IMPORTANTE: el 0 es para que devuelva el diccionario dentro y no una lista\n data = schema.validate([data])[0]\n application.logger.info(\"Asked to update {}'s position coordinates'\".format(username))\n user = User.get_user_by_username(username)\n if not user:\n response = {\n 'status': 'fail',\n 'message': 'user_not_found'\n }\n return make_response(jsonify(response)), 404\n application.logger.info(\"user {} exists\".format(username))\n auth_header = request.headers.get('Authorization')\n token_username, error_message = Authenticator.authenticate(auth_header)\n if error_message:\n response = {\n 'status': 'fail',\n 'message': error_message\n }\n return make_response(jsonify(response)), 401\n application.logger.info(\"Updating user's coordinates w/ Auth: {}\".format(auth_header))\n application.logger.info(\"Token decoded: Update was requested by: {}\"\n .format(token_username))\n if token_username == username:\n application.logger.info(\"Permission granted\")\n application.logger.info(\"User's position to update: {}\".format(token_username))\n latitude = data['latitude']\n longitude = data['longitude']\n if db.positions.count({'username': username}) == 0:\n db.positions.insert_one({'username': username, 'latitude': latitude,\n 'longitude': longitude})\n else:\n if (db.drivers.count({'username': username}) > 0 and db.trips.count({'username': username}) > 0 ):\n #Si es un driver y esta en un trip\n result = db.positions.find_one({'username': username})\n distance = DriversMixin.distance((result['latitude'],result['longitude']),(latitude,longitude))\n db.trips.find_one_and_update({'username': username},\n {'$inc': {'distance': distance}})\n db.positions.find_one_and_update({'username': username},\n {'$set': {'latitude': latitude,\n 'longitude': longitude}})\n response = {\n 'status': 'success',\n 'message': 'position_updated'\n }\n return make_response(jsonify(response)), 200\n response = {\n 'status': 'fail',\n 'message': 'unauthorized_update'\n }\n return make_response(jsonify(response)), 401\n\n except SchemaError:\n application.logger.error(\"Request data error\")\n response = {\n 'status': 'fail',\n 'message': 'bad_request_data'\n }\n return make_response(jsonify(response)), 400\n\n except Exception as exc: # pragma: no cover\n application.logger.error('Error msg: {0}. Error doc: {1}'.\n format(exc.message, exc.__doc__))\n response = {\n 'status': 'fail',\n 'message': 'internal_error',\n 'error_description': exc.message\n }\n return make_response(jsonify(response)), 500\n\n\n# define the API resources\nPOSITION_VIEW = PositionAPI.as_view('position_api')\n\n# add Rules for API Endpoints\nPOSITION_BLUEPRINT.add_url_rule(\n '/users//coordinates',\n view_func=POSITION_VIEW,\n methods=['PUT']\n)\n", "repo_name": "santigandolfo/taller2-app-server", "sub_path": "src/handlers/PositionHandler.py", "file_name": "PositionHandler.py", "file_ext": "py", "file_size_in_byte": 4624, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "14", "api": [{"api_name": "flask.Blueprint", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.views.MethodView", "line_number": 15, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "schema.Schema", "line_number": 23, "usage_type": "call"}, {"api_name": "schema.And", "line_number": 23, "usage_type": "call"}, {"api_name": "schema.Use", "line_number": 23, "usage_type": "call"}, {"api_name": "schema.And", "line_number": 24, "usage_type": "call"}, {"api_name": "schema.Use", "line_number": 24, "usage_type": "call"}, {"api_name": "schema.validate", "line_number": 26, "usage_type": "call"}, {"api_name": "app.application.logger.info", "line_number": 27, "usage_type": "call"}, {"api_name": "app.application.logger", "line_number": 27, "usage_type": "attribute"}, {"api_name": "app.application", "line_number": 27, "usage_type": "name"}, {"api_name": "src.models.User.get_user_by_username", "line_number": 28, "usage_type": "call"}, {"api_name": "src.models.User", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 34, "usage_type": "call"}, {"api_name": "app.application.logger.info", "line_number": 35, "usage_type": "call"}, {"api_name": "app.application.logger", "line_number": 35, "usage_type": "attribute"}, {"api_name": "app.application", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.request.headers.get", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 36, "usage_type": "name"}, {"api_name": "src.mixins.AuthenticationMixin.Authenticator.authenticate", "line_number": 37, "usage_type": "call"}, {"api_name": "src.mixins.AuthenticationMixin.Authenticator", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 43, "usage_type": "call"}, {"api_name": "app.application.logger.info", "line_number": 44, "usage_type": "call"}, {"api_name": "app.application.logger", "line_number": 44, "usage_type": "attribute"}, {"api_name": "app.application", "line_number": 44, "usage_type": "name"}, {"api_name": "app.application.logger.info", "line_number": 45, "usage_type": "call"}, {"api_name": "app.application.logger", "line_number": 45, "usage_type": "attribute"}, {"api_name": "app.application", "line_number": 45, "usage_type": "name"}, {"api_name": "app.application.logger.info", "line_number": 48, "usage_type": "call"}, {"api_name": "app.application.logger", "line_number": 48, "usage_type": "attribute"}, {"api_name": "app.application", "line_number": 48, "usage_type": "name"}, {"api_name": "app.application.logger.info", "line_number": 49, "usage_type": "call"}, {"api_name": "app.application.logger", "line_number": 49, "usage_type": "attribute"}, {"api_name": "app.application", "line_number": 49, "usage_type": "name"}, {"api_name": "app.db.positions.count", "line_number": 52, "usage_type": "call"}, {"api_name": "app.db.positions", "line_number": 52, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 52, "usage_type": "name"}, {"api_name": "app.db.positions.insert_one", "line_number": 53, "usage_type": "call"}, {"api_name": "app.db.positions", "line_number": 53, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 53, "usage_type": "name"}, {"api_name": "app.db.drivers.count", "line_number": 56, "usage_type": "call"}, {"api_name": "app.db.drivers", "line_number": 56, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 56, "usage_type": "name"}, {"api_name": "app.db.trips.count", "line_number": 56, "usage_type": "call"}, {"api_name": "app.db.trips", "line_number": 56, "usage_type": "attribute"}, {"api_name": "app.db.positions.find_one", "line_number": 58, "usage_type": "call"}, {"api_name": "app.db.positions", "line_number": 58, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 58, "usage_type": "name"}, {"api_name": "src.mixins.DriversMixin.DriversMixin.distance", "line_number": 59, "usage_type": "call"}, {"api_name": "src.mixins.DriversMixin.DriversMixin", "line_number": 59, "usage_type": "name"}, {"api_name": "app.db.trips.find_one_and_update", "line_number": 60, "usage_type": "call"}, {"api_name": "app.db.trips", "line_number": 60, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 60, "usage_type": "name"}, {"api_name": "app.db.positions.find_one_and_update", "line_number": 62, "usage_type": "call"}, {"api_name": "app.db.positions", "line_number": 62, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 62, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 74, "usage_type": "call"}, {"api_name": "schema.SchemaError", "line_number": 76, "usage_type": "name"}, {"api_name": "app.application.logger.error", "line_number": 77, "usage_type": "call"}, {"api_name": "app.application.logger", "line_number": 77, "usage_type": "attribute"}, {"api_name": "app.application", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 82, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 82, "usage_type": "call"}, {"api_name": "app.application.logger.error", "line_number": 85, "usage_type": "call"}, {"api_name": "app.application.logger", "line_number": 85, "usage_type": "attribute"}, {"api_name": "app.application", "line_number": 85, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 92, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "37846855491", "text": "import os\nfrom PIL import Image\n\nimg_true_dir = \"dataset/true_for_train/\"\nimg_false_dir = \"dataset/false/\"\n\ntarget_path = \"dataset/true_resize_square/\"\n\nmin_w = 636\nmin_h = 397\n\nwidth = 200\nheight = 200\n\nfor image in os.listdir(img_true_dir):\n if not image.startswith('.') :\n im = Image.open(img_true_dir + image)\n # w,h = im.size\n # ratio = float(min_h) / im.size[1]\n # width = int(im.size[0] * ratio)\n\n # if width < min_w :\n # print(image,width)\n im2 = im.resize((width, height), Image.BILINEAR)\n im2.save(target_path+image.split('.')[0]+'.png' , 'PNG')\n # print(w/h)", "repo_name": "MagicUmom/pattern_recognition_project", "sub_path": "resize.py", "file_name": "resize.py", "file_ext": "py", "file_size_in_byte": 615, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "14", "api": [{"api_name": "os.listdir", "line_number": 15, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 17, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 17, "usage_type": "name"}, {"api_name": "PIL.Image.BILINEAR", "line_number": 24, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 24, "usage_type": "name"}]} +{"seq_id": "12337736133", "text": "import os\nfrom torch.utils.tensorboard import SummaryWriter\nimport tensorly as tl\nimport tensorly.decomposition as dc\nfrom tqdm import tqdm\nimport torch.nn.utils.prune as prune\ntl.set_backend('pytorch')\nimport torch\n\ndef create_summary_writer(model, data_loader, save_folder, model_id, device='cpu', conv=False):\n \"\"\"Create a logger.\n\n Parameters\n ----------\n model\n Pytorch model.\n data_loader\n Pytorch DataLoader.\n save_folder: str\n Base location to save models and metadata.\n model_id: str\n Model/hp ID.\n\n Returns\n -------\n writer\n Logger object.\n \"\"\"\n model.eval()\n writer = SummaryWriter(os.path.join(save_folder, model_id))\n data_loader_iter = iter(data_loader)\n x, y = next(data_loader_iter)\n x = x.to(device)\n if conv:\n x = x.unsqueeze(1)\n with writer:\n try:\n writer.add_graph(model, x)\n except Exception as e:\n print(\"Failed to save model graph: {}\".format(e))\n return writer\n\ndef select_filters(model, valid_loader, valid_set, remove_amount, device):\n \"\"\"\n worst : list of highest divergence filters (worst filters) across batches\n Can select top-k afterwards.\n imp : list of divergences from tensor decomposition reconstruction.\n lower means filter is more important.\n \"\"\"\n worst = []\n model.eval()\n for i, data in tqdm(enumerate(valid_loader),\n total=len(valid_set) / valid_loader.batch_size):\n out, y = data\n out = out.to(device)\n y = y\n for j, (name, param) in enumerate(model.named_children()):\n out = param(out)\n if j == 0:\n break\n nout = out.detach()\n\n cp = dc.tucker(nout, 15)\n pred = tl.tucker_tensor.tucker_to_tensor(cp)\n dist = torch.cdist(pred, nout)\n importance = torch.mean(dist, dim=[0, 2, 3])\n _, w = torch.topk(importance, remove_amount)\n worst.append(w)\n \n if i == (len(valid_set) // valid_loader.batch_size)//4:\n break\n return worst\n\nclass TuckerPruningMethod(prune.BasePruningMethod):\n def __init__(self, amount, dim=0, filt=0):\n self.amount = amount\n self.dim = dim\n self.filt = filt\n PRUNING_TYPE = 'structured'\n def compute_mask(self, t, default_mask):\n mask = default_mask.clone()\n mask[self.filt] = 0\n return mask\n\ndef TuckerStructured(module, name, amount=1, dim=8, filt=0):\n TuckerPruningMethod.apply(module, name, amount, dim, filt)\n return module", "repo_name": "jacobyeung/Convolutional-Network-Pruning", "sub_path": ".ipynb_checkpoints/utils-checkpoint.py", "file_name": "utils-checkpoint.py", "file_ext": "py", "file_size_in_byte": 2587, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "14", "api": [{"api_name": "tensorly.set_backend", "line_number": 7, "usage_type": "call"}, {"api_name": "torch.utils.tensorboard.SummaryWriter", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorly.decomposition.tucker", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorly.decomposition", "line_number": 63, "usage_type": "name"}, {"api_name": "tensorly.tucker_tensor.tucker_to_tensor", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorly.tucker_tensor", "line_number": 64, "usage_type": "attribute"}, {"api_name": "torch.cdist", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.topk", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.nn.utils.prune.BasePruningMethod", "line_number": 74, "usage_type": "attribute"}, {"api_name": "torch.nn.utils.prune", "line_number": 74, "usage_type": "name"}]} +{"seq_id": "12213735388", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models\nfrom django.utils.encoding import python_2_unicode_compatible\nfrom django.utils.translation import ugettext_lazy as _\nfrom easy_thumbnails.fields import ThumbnailerImageField\nfrom taggit.managers import TaggableManager\nfrom taggit.models import TaggedItemBase\n\nfrom cosinnus.models import CosinnusGroup\n\n\n\nclass TaggedOffers(TaggedItemBase):\n content_object = models.ForeignKey('Caravan')\n\n class Meta:\n app_label = 'ecobasa'\n\n\n@python_2_unicode_compatible\nclass Caravan(CosinnusGroup):\n offers = TaggableManager(\n verbose_name=_('Offers'),\n help_text=_('If the caravan collects something on the way for example, communities know what it is coming with. Connect two words with a \"-\" to have one tag.'),\n blank=True,\n through=TaggedOffers)\n image = ThumbnailerImageField(\n verbose_name=_('Image'),\n help_text=_('An image of the caravan.'),\n upload_to='caravans',\n null=True,\n blank=True)\n\n class Meta:\n app_label = 'ecobasa'\n ordering = ('name',)\n verbose_name = _('Caravan')\n verbose_name_plural = _('Caravans')\n\n def __str__(self):\n return self.name\n\n#from ecobasa.models import mail_patch_postman\n", "repo_name": "ecobasa/ecobasa", "sub_path": "ecobasa/models/caravan.py", "file_name": "caravan.py", "file_ext": "py", "file_size_in_byte": 1316, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 17, "dataset": "github-code", "pt": "14", "api": [{"api_name": "taggit.models.TaggedItemBase", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "cosinnus.models.CosinnusGroup", "line_number": 23, "usage_type": "name"}, {"api_name": "taggit.managers.TaggableManager", "line_number": 24, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 25, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 26, "usage_type": "call"}, {"api_name": "easy_thumbnails.fields.ThumbnailerImageField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 30, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 31, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 39, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 40, "usage_type": "call"}, {"api_name": "django.utils.encoding.python_2_unicode_compatible", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "42883528289", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# # 訓練とテスト\n\n# モデルを構築する対象のデータを作るところから始めます。今回はモデルに多項式回帰を用います。\n\n# In[1]:\n\n\nget_ipython().run_line_magic('matplotlib', 'inline')\nimport numpy as np\nfrom pylab import *\n\nnp.random.seed(2)\n\npageSpeeds = np.random.normal(3.0, 1.0, 100)\npurchaseAmount = np.random.normal(50.0, 30.0, 100) / pageSpeeds\n\n\nscatter(pageSpeeds, purchaseAmount)\n\n\n# データを二つに分割します。80%は訓練データ、残り20%はテストデータとします。これにより、過剰適合が避けられるでしょう。\n\n# In[2]:\n\n\ntrainX = pageSpeeds[:80]\ntestX = pageSpeeds[80:]\n\ntrainY = purchaseAmount[:80]\ntestY = purchaseAmount[80:]\n\n\n# 訓練データを表示します。\n\n# In[3]:\n\n\nscatter(trainX, trainY)\n\n\n# テストデータを表示します。\n\n# In[4]:\n\n\nscatter(testX, testY)\n\n\n# 訓練データに、8次の多項式回帰を行います。これは明らかに過剰適合になります。\n\n# In[5]:\n\n\nx = np.array(trainX)\ny = np.array(trainY)\n\np4 = np.poly1d(np.polyfit(x, y, 8))\n\n\n# 訓練データと、多項式回帰の曲線を表示しましょう。\n\n# In[6]:\n\n\nimport matplotlib.pyplot as plt\n\nxp = np.linspace(0, 7, 100)\naxes = plt.axes()\naxes.set_xlim([0,7])\naxes.set_ylim([0, 200])\nplt.scatter(x, y)\nplt.plot(xp, p4(xp), c='r')\nplt.show()\n\n\n# そして、テストデータにも多項式回帰の曲線を重ねてみます。\n\n# In[7]:\n\n\ntestx = np.array(testX)\ntesty = np.array(testY)\n\naxes = plt.axes()\naxes.set_xlim([0,7])\naxes.set_ylim([0, 200])\nplt.scatter(testx, testy)\nplt.plot(xp, p4(xp), c='r')\nplt.show()\n\n\n# 見た感じではそんなに悪くないようです。しかしながら、R-二乗値がひどいことになっています。このことは、このモデルがあまり良くないことを意味します。\n\n# In[8]:\n\n\nfrom sklearn.metrics import r2_score\n\nr2 = r2_score(testy, p4(testx))\n\nprint(r2)\n\n\n# 訓練データのR-二乗値は高めですね。\n\n# In[9]:\n\n\nfrom sklearn.metrics import r2_score\n\nr2 = r2_score(np.array(trainY), p4(np.array(trainX)))\n\nprint(r2)\n\n\n# PandasのDataFrameを使っているのであれば、scikit-learnに内包されているtrain_test_split関数が訓練用データとテストデータを分割してくれます。\n# \n# 他の訓練/テストの手法に関しては、後ほど解説します。K-分割交差検証法では、K個に分割したデータのそれぞれをテストデータ、残りを訓練データとすることで、偶然により良い結果が得られることを防ぎます。\n\n# ## Activity\n\n# 様々な次数の多項式によるフィッティングにより、テストデータのR-二乗値がどうなるか確かめてみましょう。どの次数が最良でしょうか？\n\n# In[ ]:\n\n\n\n\n", "repo_name": "hirontan/practice_ml", "sub_path": "practice_data_science_machine_learning/scripts/TrainTest.py", "file_name": "TrainTest.py", "file_ext": "py", "file_size_in_byte": 2863, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "14", "api": [{"api_name": "numpy.random.seed", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.poly1d", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axes", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axes", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 102, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 114, "usage_type": "call"}]} +{"seq_id": "28386050361", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib import rc\nrc('text', usetex=True)\ny_acc = [77.21, 77.37, 77.63] #ACC corresponding method\nx_lab = [r'$\\lambda=1$', r'$r=3$', r'$r=5$'] #method name\ncolors = {r'$r=1$': 'lightgreen',\n r'$r=3$': 'lightsalmon',\n r'$r=5$': 'lightskyblue'\n# 'DFD': 'C3'\n }\ny_bwt = [-2.92,-2.85, -2.46]\nx_ind = np.arange(len(y_bwt))\ndef autolabel(rects, ax, off):\n \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n for rect in rects:\n height = rect.get_height()\n ax.annotate('{}'.format(height),\n xy=(rect.get_x() + rect.get_width() / 2, height),\n xytext=(0, off), # 3 points vertical offset\n textcoords=\"offset points\",\n ha='center', va='bottom')\n \n\nfig = plt.figure(figsize=(5,2))\n\nax1 = fig.add_subplot(1,2,1)\nfor xi, yi, li in zip(x_ind, y_acc, x_lab):\n rec = ax1.bar(xi, yi, label= li, color=colors[li])\n autolabel(rec, ax1, 3)\n\nplt.ylim(70,80)\nplt.ylabel('ACC(\\%)',fontsize=12)\nplt.xticks([],[])\n# plt.xlabel( r'$\\lambda=1$',fontsize=12)\n\n\nax2 = fig.add_subplot(1,2,2)\nfor xi, yi, li in zip(x_ind, y_bwt, x_lab):\n rec = ax2.bar(xi, yi,color=colors[li])\n autolabel(rec, ax2, -14)\n \nplt.ylabel('BWT(\\%)')\nplt.ylim(-4,0)\nplt.tight_layout()\nplt.subplots_adjust(bottom=0.23)\nfig.legend(bbox_to_anchor=(0.8,0.17),fontsize=10,ncol=3)\n\n\nplt.xticks([],[])\n# plt.xlabel( r'$\\lambda=2$',fontsize=12)\n\n\nplt.savefig('bar.pdf',bbox_inches='tight')\nplt.show()\n", "repo_name": "XiaorongLi-95/utils", "sub_path": "plot/sub_bar.py", "file_name": "sub_bar.py", "file_ext": "py", "file_size_in_byte": 1575, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "14", "api": [{"api_name": "matplotlib.rc", "line_number": 4, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}]} +{"seq_id": "250985201", "text": "#!/usr/bin/env python\n\n\"\"\"\n\n HeadsUp\n\n Code by C.Barrett\n Designed by S.Caem\n \n This file controls the layout of each Tile.\n\n\"\"\"\n\n\nimport pygame\nimport pygame.gfxdraw\nimport os\nimport time\nfrom datetime import datetime\nfrom getvitals import *\nfrom filehandling import *\nfrom textrect import *\nfrom ifttt import *\nfrom calget import *\n#from main import tileTitles\n\n# Here are some basic colours we can call on.\nbackground = (38,38,38)\ntextc = (128,128,128)\nbuttonc = (51,51,51)\nbuttoncmute = (26,26,26)\ndisplayc = (64,64,64)\nindicatorc = (80,80,80)\nindicatorhigh = (255,255,255)\nred = (150,50,0)\ngreen = (106,255,69)\nblue = (99,157,255)\nyellow = (255,221,5)\nblack = (0,0,0)\nwhite = (255,255,255)\n# Here is the location of our font.\ntitleFont = \"assets/font.ttf\"\n\ntileTitles = (\"Home\",\"System\",\"IFTTT\",\"Notes\",\"Images\",\"Sensors\")\n\n\n# the following function maps a value from the target range onto the desination range\ndef translate(value, leftMin, leftMax, rightMin, rightMax):\n # Figure out how 'wide' each range is\n leftSpan = leftMax - leftMin\n rightSpan = rightMax - rightMin\n\n # Convert the left range into a 0-1 range (float)\n valueScaled = float(value - leftMin) / float(leftSpan)\n\n # Convert the 0-1 range into a value in the right range.\n return rightMin + (valueScaled * rightSpan)\n\n \n \n# the following class draws graphs with or without outlines and updates values everytime it is called.\nclass GraphLine(object):\n \n def __init__(self,xLeft,yTop,xRight,yBottom,line,title):\n self.line = line\n self.lineoffset = line/2\n self.yTop = yTop + self.lineoffset\n self.yBottom = yBottom - self.lineoffset\n self.xLeft = xLeft + self.lineoffset\n self.xRight = xRight - self.lineoffset\n\n \n self.ySpan = abs(self.yTop - self.yBottom)\n self.xSpan = abs(self.xLeft - self.xRight)\n self.yMid = (self.ySpan / 2) + self.yTop\n self.graphData = self.grablist()\n self.title = title\n self.label = Label()\n self.readout = Label()\n # the following function returns the list.\n def grablist(self):\n \n self.glist = []\n \n test = int(self.xSpan)\n for i in range(test):\n self.glist.append(self.yBottom)\n \n return self.glist\n \n # the following appends data to the list.\n def updatelist(self,data):\n #grabs a simple 15 wide tuple for our values\n #puts a new sensor value at the end \n self.buffer.append(data)\n #pop the oldest value off\n self.buffer.pop(0)\n \n # the following pairs the list of values with coordinates on the X axis. The supplied variables are the starting X coordinates and spacing between each point. \n def graphprep(self,list):\n linepoint = self.xLeft\n jump = 1 \n self.newlist = []\n test = int(self.xSpan)\n for i in range(test):\n self.newlist.append((linepoint,list[i]))\n linepoint = linepoint + jump\n \n return self.newlist\n \n def update(self,surface,data,dataLow,dataHigh,colour):\n\n #gets our data\n self.SenseData = data\n \n #converts data to float\n self.Data = float(self.SenseData)\n #scales the data to the limits of our screen\n self.DataGraph = translate(self.Data, dataLow, dataHigh, self.yBottom, self.yTop)\n #grabs a simple 61 wide tuple for our values\n self.DataBuffer = self.grablist()\n \n \n #puts a new sensor value at the end \n self.graphData.append(self.DataGraph)\n #pop the oldest value off\n self.graphData.pop(0)\n \n #preps the list by adding the X coordinate to every sensor value\n DataCords = self.graphprep(self.graphData)\n #DataSlide = translate(self.SenseData, dataLow, dataHigh, self.info[\"y\"]Bottom, self.info[\"y\"]Top)\n #slider1.update(sliderb, 283, DataSlide)\n \n #draw the lines\n labelposy,labelposx = (self.yTop+20),(self.xLeft+25)\n readoutposx = self.xRight - 100\n content = str(data) + \"%\"\n pygame.draw.lines(surface, colour, False, DataCords, self.line)\n self.label.update(self.title, 26, labelposx, labelposy, titleFont, textc)\n self.readout.update(content, 26, readoutposx, labelposy, titleFont, textc)\n textsize = self.readout.getrect()\n textpos = self.xRight - textsize[0] - 13\n self.readout.update(content, 26, textpos, labelposy, titleFont, textc)\n self.label.draw(surface)\n self.readout.draw(surface)\n #pygame.draw.lines(surface, colour, False, ((self.xLeft,self.yTop),(self.xRight,self.yTop),(self.xRight,self.yBottom),(self.xLeft,self.yBottom),(self.xLeft,self.yTop)), self.line)\n\n\n# The following class is used to display text\nclass Label(object):\n def __init__(self):\n self.x = 0\n self.y = 0\n self.color = white\n self.fontSize = 88\n self.myfont = pygame.font.Font(titleFont, self.fontSize)\n text = \"hello\"\n self.size = self.myfont.size(text)\n self.scaler = 3\n\n\n def update(self, content, fontSize, nx, ny, fontType, color):\n self.x = nx\n self.y = ny\n self.content = content\n self.fontSize = fontSize\n self.myfont = pygame.font.Font(fontType, self.fontSize)\n self.color = color\n\n\n def left(self,w,h,x,y):\n size = self.getrect()\n xmid = x + 40\n ymid = y + h/2\n textposx = xmid\n textposy = ymid - (size[1]/2) + self.scaler\n self.update(self.content,self.fontSize,textposx,textposy,titleFont,self.color)\n\n\n def center(self,w,h,x,y):\n size = self.getrect()\n xmid = x + w/2\n ymid = y + h/2\n textposx = xmid - (size[0]/2)\n textposy = ymid - (size[1]/2) + self.scaler\n self.update(self.content,self.fontSize,textposx,textposy,titleFont,self.color)\n\n\n def pageup(self):\n pass\n\n def nopages(self):\n ret = self.text.nopages\n return ret\n\n def paragraph(self,page):\n\n my_rect = pygame.Rect((0, 0, 804, 366))\n\n self.text = TextBlock()\n\n # this next function returns a block of text rendered for the screen.\n rendered_text = self.text.render_textrect(self.content, self.myfont, my_rect, textc, buttonc, page,0)\n\n #self.nopage = self.text.nopages()\n\n return rendered_text\n\n\n def getpages(self):\n return self.nopage\n\n def getrect(self):\n label = self.myfont.render(self.content, 1, self.color)\n textw = label.get_width()\n texth = label.get_height()\n\n return textw,texth\n\n def draw(self, surface):\n label = self.myfont.render(self.content, 1, self.color)\n surface.blit(label, (self.x, self.y))\n\n# the following class is used to display images\nclass Image(object):\n def __init__(self):\n self.x = 258\n self.y = 66\n self.Img = blueInsignia\n \n def update(self, image, nx, ny):\n self.x = nx\n self.y = ny\n self.Img = image\n\n \n def draw(self, surface):\n surface.blit(self.Img, (self.info[\"x\"],self.info[\"y\"]))\n\n# the following class is used to draw rectangles.\nclass Box(object):\n def __init__(self):\n self.x=0\n self.y=0\n self.vx=1\n self.vy=1\n self.size=(50,50)\n self.color=(0,0,255)\n \n def getcenter(self):\n self.ret = self.rect.center\n return self.ret\n \n def update(self, x, y, size, color):\n self.x = x\n self.y = y\n self.size = size\n self.color = color\n \n \n def draw(self, surface):\n self.rect = pygame.Rect((self.x,self.y), self.size)\n pygame.draw.rect(surface, self.color, self.rect)\n\n\nclass viewingarea(object):\n def __init__(self, content, info):\n \n self.content = content\n \n self.info = info\n \n self.textarea = Label()\n \n # defines the surface we will be drawing to, passed from our tiles object\n self.surface = self.info[\"surface\"]\n \n # draw our background box\n self.substrate = Box()\n self.substrate.update(200,120,(880, 440),buttonc)\n \n # Sets the currently selected button\n self.selector = 0\n self.selectmax = 2\n self.page = 0\n \n def rightkey(self):\n self.page += 1\n self.pageadjust()\n \n def leftkey(self):\n self.page -= 1\n self.pageadjust()\n \n def pageadjust(self):\n if self.page <= 0:\n self.page = 0\n if self.page >= (self.maxpages-1):\n self.page = (self.maxpages-1)\n \n def enterkey(self,tile):\n if self.selector == 0:\n tile.stopview()\n elif self.selector == 1:\n self.page -=1\n\n elif self.selector == 2:\n self.page +=1\n \n def selectalign(self):\n if self.selector > self.selectmax:\n self.selector = self.selectmax\n \n if self.selector < 0:\n self.selector = 0\n \n def drawbutton(self,x,content):\n #defines center of button\n y = 400\n \n butymid = y + (40 / 2) \n \n butxmid = x + 180 / 2\n \n # draws button backplane\n rect = pygame.Rect(x,580,180,40)\n pygame.draw.rect(self.surface, buttonc, rect)\n \n # instantiates a label object.\n butlabel = Label()\n butlabel.update(content,26,butxmid,y,titleFont,textc)\n size = butlabel.getrect()\n butlabel.center(180,40,x,580)\n butlabel.center(180,40,x,580)\n #textposx = butxmid - (size[0]/2)\n #textposy = butymid - (size[1]/2)qq\n \n #butlabel.update(content,26,textposx,textposy,titleFont,textc)\n butlabel.draw(self.surface)\n \n def outline(self,xLeft,xRight,yTop,yBottom,line):\n pygame.draw.lines(self.surface, textc, False, ((xLeft,yTop),(xRight,yTop),(xRight,yBottom),(xLeft,yBottom),(xLeft,yTop)), line)\n \n\n def draw(self):\n # draw the background box\n self.substrate.draw(self.surface)\n \n # check if this is a notes tile (3) or an image tile (4)\n if self.info[\"tiletype\"] == 3:\n self.textarea.update(self.content,26,0,0,titleFont,textc)\n \n self.surface.blit(self.textarea.paragraph(self.page), (240,160))\n \n self.maxpages = int(self.textarea.nopages())\n print(self.maxpages)\n \n # if its an image viewing tile\n elif self.info[\"tiletype\"] == 4:\n \n \n backrect = self.substrate.getcenter()\n \n self.content = pygame.transform.scale(self.content, (880,440))\n\n self.surface.blit(self.content,(200,120))\n \n labels = [\"Exit\",\"Previous\",\"Next\"]\n \n \n label = labels[0]\n xgo = 200\n self.drawbutton(xgo, label)\n if 0 == self.selector:\n self.outline(xgo,(xgo+180),580,(580+40),3)\n\n \n\nclass actionarea(object):\n def __init__(self,info):\n self.info = info\n self.textbox = Box()\n self.surface = self.info[\"surface\"]\n self.type = self.info[\"tiletype\"]\n self.selector = 0\n self.selectmax = 5\n\n def outline(self,xLeft,xRight,yTop,yBottom,line):\n pygame.draw.lines(self.surface, textc, False, ((xLeft,yTop),(xRight,yTop),(xRight,yBottom),(xLeft,yBottom),(xLeft,yTop)), line)\n \n def selectoralign(self):\n if self.selector > self.selectmax:\n self.selector = self.selectmax\n if self.selector < 0:\n self.selector = 0\n \n def downkey(self):\n self.selector += 1\n self.selectoralign()\n \n def upkey(self):\n self.selector -= 1\n self.selectoralign()\n \n def enterkey(self):\n \n if self.selector == 4:\n if self.type == 1:\n print(\"quit received\")\n return \"quit\"\n return self.selector\n # when receives key down send action area message who will send display area a message?\n # maybe just send message to display area directly?\n\n \n def getdatetime(self):\n date = datetime.today()\n datestr = date.ctime()\n datelist = datestr.split()\n \n self.time = datelist[3]\n self.day = datelist[0]\n self.month = datelist[1]\n self.dayno = datelist[2]\n self.year = datelist[4]\n self.date = self.day + \" - \" + str(self.dayno) + \"/\" + str(self.month) + \"/\" + str(self.year)\n \n def wifiname(self):\n wifiname = getwifi()\n return wifiname\n \n def drawblock(self,y,h):\n rect = pygame.Rect((self.info[\"innerx\"],y), (self.info[\"actareaw\"],h))\n pygame.draw.rect(self.surface, buttonc, rect)\n \n def drawbutton(self,y,content):\n # the x mid point of the button is the distance of the x coordinate of the button plus the span of the button divided by 2\n butxmid = self.info[\"innerx\"] + (self.info[\"aaspanx\"] / 2) \n \n # the y mid point of the button is the distance of the y coordinate of the button plus the height of the button divided by 2\n butymid = y + (40 / 2) \n \n # draw the rectangle to screen\n self.drawblock(y,40)\n \n # instantiate a text object\n butlabel = Label()\n # update the content of the text object.\n butlabel.update(content,26,butxmid,y,titleFont,textc)\n \n #center the text on the button\n butlabel.center(self.info[\"actareaw\"],40,self.info[\"innerx\"],y)\n\n # draw the text.\n butlabel.draw(self.surface)\n \n def draw(self,info):\n self.info = info\n\n if self.type == 0:\n\n # Home Tile Action area\n \n # get time and date\n self.getdatetime()\n #draw the back of the action area for time and date\n \n for i in range(2):\n\n ypos = self.info[\"actareay\"] + (100*i)\n height = 80 + (100*i)\n self.drawblock(ypos,height)\n\n \n # instantiate the label for the time line\n timel = Label()\n timely = self.info[\"actareay\"]\n timel.update(self.time,26,(self.info[\"innerx\"]+155),(self.info[\"actareay\"]+7),titleFont,textc)\n \n\n timel.center(self.info[\"aaspanx\"],40,self.info[\"innerx\"],timely)\n timel.draw(self.surface)\n \n # instantiate the label for the date line\n datel = Label()\n datey = self.info[\"actareay\"]+38\n datel.update(self.date,26,(self.info[\"innerx\"]+92),(self.info[\"actareay\"]+40),titleFont,textc)\n size = datel.getrect()\n labelpos = self.info[\"innerx\"]+(self.info[\"aaspanx\"]/2)-(size[0]/2)\n\n datel.center(self.info[\"aaspanx\"],40,self.info[\"innerx\"],datey)\n datel.draw(self.surface)\n \n\n \n # instantiate the label for the time line\n wifi = Label()\n wifiname = self.wifiname()\n wifiy = self.info[\"actareay\"] + 180\n wifi.update(wifiname,26,(self.info[\"innerx\"]+155),(self.info[\"actareay\"]+7),titleFont,textc)\n \n wifi.center(self.info[\"aaspanx\"],40,self.info[\"innerx\"],wifiy)\n wifi.draw(self.surface)\n \n # draw system screen actionable buttons and highlight \n if self.type == 1:\n buttontexts = [\"Shutdown\",\"Reboot\",\"Toggle Wifi\", \"Toggle Bluetooth\", \"Quit HeadsUP\"]\n self.selectmax = 4\n for i in range(5):\n \n ypos = self.info[\"actareay\"] + (60*i)\n self.drawbutton(ypos,buttontexts[i])\n if i == self.selector:\n self.outline(self.info[\"innerx\"],(self.info[\"innerx\"] + self.info[\"aaspanx\"]),ypos,(ypos+40),3)\n \n # draw IFTT buttons\n if self.type == 2:\n \n buttontexts = [\"Engage\", \"Up\",\"Down\",\"Refresh List\", \"Delete\"]\n self.selectmax = 4\n for i in range(5):\n \n ypos = self.info[\"actareay\"] + (60*i)\n self.drawbutton(ypos,buttontexts[i])\n if i == self.selector:\n self.outline(self.info[\"innerx\"],(self.info[\"innerx\"] + self.info[\"aaspanx\"]),ypos,(ypos+40),3)\n \n # Define layout for Notes Tiles\n if self.type == 3:\n buttontexts = [\"View\",\"Up\",\"Down\", \"Refresh List\", \"Delete\"]\n self.selectmax = 4\n for i in range(5):\n ypos = self.info[\"actareay\"] + (60*i)\n self.drawbutton(ypos,buttontexts[i])\n if i == self.selector:\n self.outline(self.info[\"innerx\"],(self.info[\"innerx\"] + self.info[\"aaspanx\"]),ypos,(ypos+40),3)\n\n # Define layout for Images tile\n if self.type == 4:\n buttontexts = [\"View\",\"Up\",\"Down\", \"Refresh List\", \"Delete\"]\n self.selectmax = len(buttontexts)\n for i in range(self.selectmax):\n ypos = self.info[\"actareay\"] + (60*i)\n self.drawbutton(ypos,buttontexts[i])\n if i == self.selector:\n self.outline(self.info[\"innerx\"],(self.info[\"innerx\"] + self.info[\"aaspanx\"]),ypos,(ypos+40),3)\n\n\n \nclass displayarea(object):\n def __init__(self,info):\n self.selector = 0\n self.info = info\n self.surface = self.info[\"surface\"]\n self.type = self.info[\"tiletype\"]\n if self.type == 1:\n self.graph = GraphLine(660,120,1080,340,5, \"CPU\")\n self.graph2 = GraphLine(660,380,1080,600,5, \"RAM\")\n self.timer = pygame.time.Clock()\n \n #interval timer variables:\n self.lasttime = 0\n self.interval = 50\n \n \n def downkey(self):\n self.selector += 1\n self.selectoralign()\n \n def upkey(self):\n self.selector -= 1\n self.selectoralign()\n \n\n def enterkey(self,selection,target):\n # this function defines the behaviour of the display area when the enter key is pressed. \n # It determines what type of tile it is and what the selected function is, because of the diversity\n # of tiles it will require a lot of different selections to be defined\n\n\n #[\"Engage\", \"Up\",\"Down\",\"Refresh List\", \"Delete\"]\n # if this is an IFTTT tile.\n if self.type == 2:\n if selection == 2:\n self.selector += 1\n if selection == 1:\n self.selector -= 1\n if selection == 0:\n item = self.folist[self.selector]\n \n fs = files()\n triggerinfo = fs.getTrigger(item)\n key,trig = triggerinfo\n trigger = MakerTrigger(key,trig)\n try:\n trigger.alert()\n except:\n pass\n \n # if this is a notes tile.\n if self.type == 3:\n if selection == 2:\n self.selector += 1\n if selection == 1:\n self.selector -= 1\n if selection == 0:\n \n #\n item = self.folist[self.selector]\n \n fs = files()\n notetext = fs.gettext(item)\n \n # if viewing area selected collect image or text for viewing and instantiate a viewarea object with that data.\n \n target.viewit(notetext)\n \n # if this is an images tile.\n if self.type == 4:\n if selection == 2:\n self.selector += 1\n if selection == 1:\n self.selector -= 1\n if selection == 0:\n item = self.folist[self.selector]\n \n fs = files()\n image = fs.getimage(item)\n \n # if viewing area selected collect image or text for viewing and instantiate a viewarea object with that data.\n \n target.viewit(image)\n \n\n \n \n \n\n\n def selectoralign(self):\n if self.selector > 4:\n self.selector = 4\n if self.selector < 0:\n self.selector = 0\n \n def outline(self,xLeft,xRight,yTop,yBottom,line):\n pygame.draw.lines(self.surface, textc, False, ((xLeft,yTop),(xRight,yTop),(xRight,yBottom),(xLeft,yBottom),(xLeft,yTop)), line) \n \n def drawblock(self,y,h):\n rect = pygame.Rect((self.info[\"dispbx\"],y), (self.info[\"dispw\"],h))\n pygame.draw.rect(self.surface, buttonc, rect)\n \n def drawbutton(self,y,content):\n butxmid = self.info[\"dispbx\"] + 5 \n butymid = y + (40 / 2) \n self.drawblock(y,40)\n butlabel = Label()\n butlabel.update(content,26,butxmid,y,titleFont,textc)\n size = butlabel.getrect()\n butlabel.left(self.info[\"dispw\"],40,self.info[\"dispbx\"],y)\n\n #butlabel.update(content,26,textposx,textposy,titleFont,textc)\n butlabel.draw(self.surface)\n \n def draw(self,info):\n \n self.info = info\n \n # --------------------------------------------------------------------------------- Home Tile layout\n # Display the current Calendar data\n if self.type == 0:\n self.info = info\n \n # Draw the agenda label\n self.heading = \"Agenda\"\n agendaheading = Label()\n agendaheading.update(self.heading,30,self.info[\"dispbx\"] + 10 , self.info[\"innery\"] + 10,titleFont,textc)\n \n # Draw todays date.\n datelabel = Label()\n \n \n self.drawblock(self.info[\"innery\"],self.info[\"disph\"])\n agendaheading.draw(self.surface)\n \n events = CalendarPull()\n \n eventlist = events.GetTodaysEvents()\n \n listsize = len(eventlist)\n objs = [Label() for i in range(listsize)]\n i=0\n for obj in objs:\n content = eventlist[i]\n obj.update(content,20,self.info[\"dispbx\"] + 10 ,(self.info[\"innery\"]+60)+(30 * i),titleFont,textc)\n obj.draw(self.surface)\n i += 1\n # ------------------------------------------------------------------------------- System Tile Layout\n if self.type == 1:\n #self.graphassign(2)\n# graph1 = GraphLine(xLeft,yTop,xRight,yBottom,line)\n data = sensorget()\n for i in range(2):\n ypos = self.info[\"innery\"] + (260 * i)\n self.drawblock(ypos,220)\n \n \n self.timenow = pygame.time.get_ticks()\n elapsed = self.timenow - self.lasttime\n if elapsed > self.interval:\n self.lasttime = self.timenow\n self.graph.update(self.surface,data['cpuperc'],0,100,textc)\n self.graph2.update(self.surface,data['ramperc'],0,100,textc)\n \n \n # iftt tile layout-------------------------------------------------------------------------------\n if self.type == 2:\n \n # Get the trigger text files\n triggers = files()\n \n self.folist = triggers.ListTriggers()\n count= len(self.folist)\n for i in range(count):\n # name each button the title of the text file\n caption = str(self.folist[i])\n \n # set the position of each button, each time through the loop the button is moved down 60 pixels\n ypos = self.info[\"innery\"] + (60 * i)\n \n self.drawbutton(ypos,caption)\n \n \n if i == self.selector:\n self.outline(self.info[\"dispbx\"],(self.info[\"dispbx\"] + self.info[\"dispw\"]),ypos,(ypos+40),3)\n \n # notes tile layout-------------------------------------------------------------------------------\n if self.type == 3:\n\n notes = files()\n \n self.folist = notes.ListText()\n count= len(self.folist)\n for i in range(count):\n caption = str(self.folist[i])\n ypos = self.info[\"innery\"] + (60 * i)\n self.drawbutton(ypos,caption)\n if i == self.selector:\n self.outline(self.info[\"dispbx\"],(self.info[\"dispbx\"] + self.info[\"dispw\"]),ypos,(ypos+40),3)\n\n # Image tile layout-------------------------------------------------------------------------------\n if self.type == 4:\n\n images = files()\n \n self.folist = images.ListImage()\n count= len(self.folist)\n for i in range(count):\n caption = str(self.folist[i])\n ypos = self.info[\"innery\"] + (60 * i)\n self.drawbutton(ypos,caption)\n if i == self.selector:\n self.outline(self.info[\"dispbx\"],(self.info[\"dispbx\"] + self.info[\"dispw\"]),ypos,(ypos+40),3)\n\n \nclass Tile(object):\n def __init__(self,tiletype,surface,screenSize,colour,index):\n self.info = {\"tiletype\" : tiletype, \"surface\" : surface, \"x\" : 160, \"y\" : 80, \"index\" : index, \"colour\" :colour , \"seam\" : 80, \"inner\" : 40, \"titlesize\" : 100, \"spanx\" : 960, \"spany\" : 560, \"innerx\" : 200, \"innery\" : 120, \"inspanx\" : 880, \"inspany\" : 480, \"lineposy\" : 210, \"aaspanx\" : 420, \"dispposx\" : 660, \"dispw\" : 420, \"disph\" : 480, \"actareay\" : 320, \"actareaw\" : 420, \"actareah\" : 280} \n self.info[\"tilejump\"] = self.info[\"seam\"] + self.info[\"spanx\"]\n\n if self.info[\"index\"] > 0:\n self.info[\"x\"] = self.info[\"x\"] + ((self.info[\"seam\"] + self.info[\"spanx\"]) * self.info[\"index\"])\n\n self.info[\"pos\"] = (self.info[\"x\"],self.info[\"y\"])\n self.info[\"inpos\"] = (self.info[\"innerx\"],self.info[\"innery\"])\n self.info[\"size\"] = (self.info[\"spanx\"],self.info[\"spany\"])\n self.info[\"innersize\"] = (self.info[\"inspanx\"],self.info[\"inspany\"])\n self.updatelayout()\n \n \n #the following variable is used to determine whether the current tile is using a viewframe to display content and if so controls are different\n self.viewing = False\n\n \n \n def upkey(self):\n# print(\"keyupped!\")\n self.actionarea.upkey()\n \n def downkey(self):\n# print(\"keydown received!\")\n self.actionarea.downkey()\n \n def enterkey(self,status):\n \n #receives enter key and passes it to action area\n \n # if the tile is currently in a viewing event (looking at an image or a note)\n if self.viewing:\n #Direct enterkey events to the viewing area as opposed to the action/display area (so that enters effect the viewing area)\n self.viewframe.enterkey(self)\n else:\n # otherwise see what the current selected button is in the action area\n selection = self.actionarea.enterkey()\n \n # if the action area has a \"quit\" button selected (not sure if this is still what's happening, may be deprecated)\n if selection == \"quit\":\n # then return a quit directive to the main loop\n print(\"quit also received\")\n status = \"quit\"\n return status\n \n # if no quit event is received then pass the display area the currently selected button in the action area, \n #and a copy of this tile so the display area has access to the tile's methods\n self.disparea.enterkey(selection,self)\n \n\n\n \n def leftkey(self):\n # if a left key event is received \n self.viewframe.leftkey()\n\n def rightkey(self):\n self.viewframe.rightkey()\n \n \n def isview(self):\n if self.viewing:\n return True\n else:\n return False\n \n def stopview(self):\n self.viewing = False\n\n def viewit(self, item):\n #print(item)\n # Set the tiles state show that it is currently in view mode. This makes sure inputs are handled properly.\n self.viewing = True\n \n # apply the returned item (be it text or surface) \n self.item = item\n \n # create a viewing area object with the appropriate information\n self.viewframe = viewingarea(self.item,self.info)\n \n def drawlayout(self):\n # the following checks what type of tile this is and draws elements accordingly. If there is a viewframe event triggered it draws that, if not it draws the standard layout.\n\n if self.viewing:\n self.viewframe.draw()\n \n else:\n title = Label()\n title.update(self.title,self.info[\"titlesize\"],self.info[\"innerx\"],self.info[\"innery\"],titleFont,textc)\n title.draw(self.info[\"surface\"])\n pygame.draw.lines(self.info[\"surface\"], textc, False, ((self.info[\"innerx\"], self.info[\"lineposy\"]),((self.info[\"innerx\"] + self.info[\"aaspanx\"]), self.info[\"lineposy\"])), 6)\n self.disparea.draw(self.info)\n self.actionarea.draw(self.info)\n \n \n def updatelayout(self):\n # the following checks what kind of tile this is and updates the layout accordingly\n\n index = self.info[\"tiletype\"]\n\n self.title = tileTitles[index]\n\n self.disparea = displayarea(self.info)\n self.actionarea = actionarea(self.info)\n\n \n def position(self,page):\n # the following updates internal coordinates for each tile so that tiles can move horizontally when the user selects a new one. This basically allows for the main loop to feed updated position data to each tile so they can move appropriately\n \n # current X position = default X position - the tilejump X scaler * the page number \n self.info[\"pos\"] = (self.info[\"x\"] - ((self.info[\"tilejump\"]) * page)),self.info[\"y\"]\n self.info[\"upx\"] = (self.info[\"x\"] - (self.info[\"tilejump\"] * page))\n self.info[\"innerx\"] = (self.info[\"x\"] - (self.info[\"tilejump\"] * page)) + self.info[\"inner\"]\n self.info[\"dispbx\"] = self.info[\"innerx\"] + 459\n self.info[\"pageadjust\"] = self.info[\"tilejump\"] * page\n\n def draw(self,page1):\n \n # takes the integer scaler value and turns it into a page index.\n page = (float(page1) / 100)\n\n # check position values\n self.position(page)\n \n #if in focus draw action and display areas\n if page == self.info[\"index\"]:\n #actarea.draw(self.info[\"pos\"])\n \n #draw the background of the tile\n self.rect = pygame.Rect(self.info[\"pos\"], self.info[\"size\"])\n pygame.draw.rect(self.info[\"surface\"], self.info[\"colour\"], self.rect)\n\n #draw the foreground elements of the tile.\n # draw both the display area and the action area\n self.drawlayout()\n \n else:\n # if not in focus just makes the tile dark grey.\n self.rect = pygame.Rect(self.info[\"pos\"], self.info[\"size\"])\n pygame.draw.rect(self.info[\"surface\"], buttoncmute, self.rect)\n", "repo_name": "directive0/HeadsUp", "sub_path": "tiles.py", "file_name": "tiles.py", "file_ext": "py", "file_size_in_byte": 31665, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "12", "api": [{"api_name": "pygame.draw.lines", "line_number": 138, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 138, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 156, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 156, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 167, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 167, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 198, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 262, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 263, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 263, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 326, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 327, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 327, "usage_type": "attribute"}, {"api_name": "pygame.draw.lines", "line_number": 342, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 342, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 364, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 364, "usage_type": "attribute"}, {"api_name": "pygame.draw.lines", "line_number": 389, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 389, "usage_type": "attribute"}, {"api_name": "datetime.datetime.today", "line_number": 417, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 417, "usage_type": "name"}, {"api_name": "pygame.Rect", "line_number": 433, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 434, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 434, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 559, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 559, "usage_type": "attribute"}, {"api_name": "pygame.draw.lines", "line_number": 647, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 647, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 650, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 651, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 651, "usage_type": "attribute"}, {"api_name": "pygame.time.get_ticks", "line_number": 708, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 708, "usage_type": "attribute"}, {"api_name": "pygame.draw.lines", "line_number": 858, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 858, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 897, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 898, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 898, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 906, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 907, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 907, "usage_type": "attribute"}]} +{"seq_id": "14778738993", "text": "from django.shortcuts import render, get_object_or_404, redirect\nfrom django.http import HttpResponse\n\n# Create your views here.\nfrom .models import Question\n\nuser_answer = {}\n\n\ndef index(request):\n latest_question_list = Question.objects.all()\n context = {'latest_question_list': latest_question_list}\n return render(request, 'myquiz/index.html', context)\n\n\ndef quiz(request, id):\n if request.POST:\n user_answer[id] = request.POST.get('answer', False)\n return redirect(\"/\")\n question = get_object_or_404(Question, pk=id)\n return render(request, 'myquiz/quiz.html', {'question': question})\n\n\ndef result(request, id):\n question = get_object_or_404(Question, pk=id)\n is_result = True\n answer_result = True\n\n if user_answer.get(id, -1) != -1:\n if user_answer[id] == str(question.answer):\n answer_result = True\n else:\n answer_result = False\n else:\n is_result= False\n\n return render(request, 'myquiz/result.html', {'answer_result': answer_result, 'question': question, 'is_result' : is_result})\n\ndef stats(request):\n question_list = Question.objects.all()\n question_count = len(question_list)\n result_list = []\n correct_count = 0\n correct_rate = 0\n is_stats = True\n if len(user_answer) == question_count:\n for question in question_list:\n if user_answer[question.id] == str(question.answer):\n result_list.append(True)\n correct_count += 1\n else:\n result_list.append(False)\n correct_rate = round(correct_count * 100 / question_count, 1)\n else:\n is_stats= False\n\n return render(request, 'myquiz/stats.html', {'question_list': question_list,'result_list': result_list, 'question_count': question_count, 'correct_count': correct_count, 'correct_rate': correct_rate, 'is_stats': is_stats})", "repo_name": "jakalroni/DKU-OpenSource", "sub_path": "myquiz/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1885, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "models.Question.objects.all", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Question.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.Question", "line_number": 11, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 13, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 19, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Question", "line_number": 20, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 21, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 25, "usage_type": "call"}, {"api_name": "models.Question", "line_number": 25, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 37, "usage_type": "call"}, {"api_name": "models.Question.objects.all", "line_number": 40, "usage_type": "call"}, {"api_name": "models.Question.objects", "line_number": 40, "usage_type": "attribute"}, {"api_name": "models.Question", "line_number": 40, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "13899469450", "text": "import os\nimport shutil\nimport subprocess\nimport sys\n\nfrom pathlib import Path\n\nos.chdir(Path(__file__).parent.absolute())\n\nBASEPATH = Path('v1')\n\nURLTEMPLATE = 'https://github.com/netdata/dashboard/releases/download/{0}/dashboard.tar.gz'\n\nCMAKETEMPLATE = '''\n install(FILES {0} DESTINATION ${{WEB_DEST}})\n install(FILES {1} DESTINATION ${{WEB_DEST}}/css)\n install(FILES {2} DESTINATION ${{WEB_DEST}}/fonts)\n install(FILES {3} DESTINATION ${{WEB_DEST}}/images)\n install(FILES {4} DESTINATION ${{WEB_DEST}}/lib)\n install(FILES {5} DESTINATION ${{WEB_DEST}}/static/css)\n install(FILES {6} DESTINATION ${{WEB_DEST}}/static/js)\n install(FILES {7} DESTINATION ${{WEB_DEST}}/static/media)\n install(FILES web/gui/v1/index.html DESTINATION ${WEB_DEST}/v1)\n'''\n\ndef copy_dashboard(tag):\n '''Fetch and bundle the dashboard code.'''\n print('Preparing target directory')\n shutil.rmtree(BASEPATH)\n BASEPATH.mkdir()\n print('::group::Fetching dashboard release tarball')\n subprocess.check_call('curl -L -o dashboard.tar.gz ' + URLTEMPLATE.format(tag), shell=True)\n print('::endgroup::')\n print('::group::Extracting dashboard release tarball')\n subprocess.check_call('tar -xvzf dashboard.tar.gz -C ' + str(BASEPATH) + ' --strip-components=1', shell=True)\n print('::endgroup::')\n print('Copying README.md')\n BASEPATH.joinpath('README.md').symlink_to('../.dashboard-notice.md')\n print('Removing dashboard release tarball')\n BASEPATH.joinpath('..', 'dashboard.tar.gz').unlink()\n\n\ndef genfilelist(path):\n '''Generate a list of files for the Makefile.'''\n files = [f for f in path.iterdir() if f.is_file() and f.name != 'README.md']\n files = [Path(*f.parts[1:]) for f in files]\n files.sort()\n return '\\n'.join([(\"web/gui/v1/\" + str(f)) for f in files])\n\n\ndef write_cmakefile():\n '''Write out the cmake file for the dashboard code.'''\n print('Generating cmake file')\n output = CMAKETEMPLATE.format(\n genfilelist(BASEPATH),\n genfilelist(BASEPATH.joinpath('css')),\n genfilelist(BASEPATH.joinpath('fonts')),\n genfilelist(BASEPATH.joinpath('images')),\n genfilelist(BASEPATH.joinpath('lib')),\n genfilelist(BASEPATH.joinpath('static', 'css')),\n genfilelist(BASEPATH.joinpath('static', 'js')),\n genfilelist(BASEPATH.joinpath('static', 'media')),\n )\n\n BASEPATH.joinpath('dashboard_v1.cmake').write_text(output)\n\n\ndef list_changed_files():\n '''Create a list of changed files, and set it in an environment variable.'''\n if 'GITHUB_ENV' in os.environ:\n print('Generating file list for commit.')\n subprocess.check_call('echo \"COMMIT_FILES<> $GITHUB_ENV', shell=True)\n subprocess.check_call('git status --porcelain=v1 --no-renames --untracked-files=all | rev | cut -d \\' \\' -f 1 | rev >> $GITHUB_ENV', shell=True)\n subprocess.check_call('echo \"EOF\" >> $GITHUB_ENV', shell=True)\n\n\ncopy_dashboard(sys.argv[1])\nwrite_cmakefile()\nlist_changed_files()\n", "repo_name": "ljx0305/netdata", "sub_path": "web/gui/bundle_dashboard_v1.py", "file_name": "bundle_dashboard_v1.py", "file_ext": "py", "file_size_in_byte": 3008, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "12", "api": [{"api_name": "os.chdir", "line_number": 8, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 8, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 10, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 29, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 32, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 35, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 46, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 70, "usage_type": "attribute"}, {"api_name": "subprocess.check_call", "line_number": 72, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 73, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 74, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 77, "usage_type": "attribute"}]} +{"seq_id": "1540054720", "text": "import os\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support.ui import Select\nfrom selenium.webdriver.firefox.options import Options\nfrom selenium.common.exceptions import NoSuchElementException\nfrom selenium.webdriver.common.action_chains import ActionChains\nimport re\nimport math\nimport json\nimport csv\nimport urllib.request\nimport requests\nimport boto3\nimport pickle\n\nfile_name = \"avto_ru_cars.csv\"\n\ndef remove_spaces(text):\n return re.sub(' +', ' ', text.strip())\n\ndef get_numbers(text):\n return re.findall(r'\\d+', text)\n\ndef uploadToCloud(name):\n session = boto3.session.Session()\n s3 = session.client(\n service_name='s3',\n endpoint_url='',\n aws_access_key_id='',\n aws_secret_access_key='',\n )\n s3.upload_file(name, 'autosalons', \"autos/\" + name)\n return\n\ndef uploadPhoto(photo, orig, index):\n prefix = orig.split(\"/\")[-2]\n name = prefix + \"-\" + str(index) + \".webp\"\n urllib.request.urlretrieve(photo, name)\n uploadToCloud(name)\n return name\n\ndef deletePhotos():\n for file in os.listdir():\n if file.endswith('.webp'):\n os.remove(file)\n\ndef apiImport(data):\n token = \"\"\n new_data = []\n for key in data:\n new_data.append(data[key])\n\n data = {\n 'token': token,\n 'data': json.dumps(new_data)\n }\n response = requests.post('http://your-domain/parsing/import_car_raw', data=data)\n return\n\ndef parse_car(driver, data):\n # data = {}\n # try:\n # data['dealer url'] = response.meta['link']\n # except Exception:\n # data['dealer url'] = \"\"\n\n # try:\n # data['car url'] = driver.current_url\n # except Exception:\n # data['car url'] = \"\"\n print(\"getting: \" + data['car url'])\n driver.get(data['car url'])\n pickle.dump( driver.get_cookies() , open(\"cookies.pkl\",\"wb\"))\n\n try:\n city = driver.find_elements(By.CSS_SELECTOR, \".MetroListPlace__regionName\")[0].text.strip()\n except Exception:\n city = \"\" \n\n try:\n data['Тип авто'] = driver.find_elements(By.CSS_SELECTOR, \"div.CardBreadcrumbs__item:nth-child(2) > a:nth-child(1)\")[0].text.strip()\n except Exception:\n try:\n data['Тип авто'] = driver.find_elements(By.CSS_SELECTOR, \"li.BreadcrumbsGroup__item:nth-child(2) > a:nth-child(1)\")[0].text.strip()\n except Exception:\n data['Тип авто'] = \"\"\n\n if data['Тип авто'] == \"Б/у\" or data['Тип авто'] == \"С пробегом\": \n try:\n data['Номер'] = get_numbers(driver.find_elements(By.CSS_SELECTOR, \"div.CardHead__infoItem:nth-child(3)\")[0].text)[0]\n except Exception:\n data['Номер'] = \"\"\n try:\n data['Марка'] = driver.find_elements(By.CSS_SELECTOR, \"div.CardBreadcrumbs__item:nth-child(4) > div:nth-child(1) > a:nth-child(1)\")[0].text\n except Exception:\n data['Марка'] = \"\"\n try:\n data['Модель'] = driver.find_elements(By.CSS_SELECTOR, \"div.CardBreadcrumbs__item:nth-child(6) > div:nth-child(1) > a:nth-child(1)\")[0].text.strip()\n except Exception:\n data['Модель'] = \"\"\n\n try:\n data['Серия'] = driver.find_elements(By.CSS_SELECTOR, \"div.CardBreadcrumbs__item:nth-child(8) > div:nth-child(1) > a:nth-child(1)\")[0].text.strip()\n except Exception:\n data['Серия'] = \"\"\n try:\n data['Год выпуска'] = driver.find_elements(By.CSS_SELECTOR, \"li.CardInfoRow:nth-child(1) > span:nth-child(2) > a:nth-child(1)\")[0].text.strip()\n except Exception:\n data['Год выпуска'] = \"\"\n try:\n # data['Цена'] = \"\".join(get_numbers(driver.find_elements(By.CSS_SELECTOR, \".OfferPriceCaption__price\")[0].text))\n data['Цена'] = \"\".join(get_numbers(driver.find_element(By.CSS_SELECTOR, \".OfferPriceCaption__price\").text))\n except Exception:\n data['Цена'] = \"\"\n\n try:\n data['Цена со скидками'] = \"\".join(get_numbers(driver.find_elements(By.CSS_SELECTOR, \".PriceUsedOffer__maxDiscount\")[0].text))\n except Exception:\n data['Цена со скидками'] = \"\"\n action = ActionChains(driver)\n try:\n element = driver.find_elements(By.CSS_SELECTOR, '.PriceUsedOffer__price')[0] \n action.move_to_element(element)\n action.perform()\n except:\n print(\"sold\")\n\n try:\n driver.implicitly_wait(5)\n data['Цена в долларах'] = \"\".join(get_numbers(driver.find_elements(By.CSS_SELECTOR, \".OfferPricePopupContent__priceItem\")[0].text))\n driver.implicitly_wait(0)\n except Exception:\n data['Цена в долларах'] = \"\"\n try:\n data['Цена в евро'] = \"\".join(get_numbers(driver.find_elements(By.CSS_SELECTOR, \".OfferPricePopupContent__priceItem\")[1].text))\n except Exception:\n data['Цена в евро'] = \"\"\n try:\n data['Стоимость платежа кредита (руб/месяц)'] = \"\".join(get_numbers(driver.find_elements(By.CSS_SELECTOR, \".OfferPricePopupContent__credit\")[0].text))\n except Exception:\n data['Стоимость платежа кредита (руб/месяц)'] = \"\"\n keys = driver.find_elements(By.CSS_SELECTOR, \".OfferDiscountOptions__itemName\")\n values = driver.find_elements(By.CSS_SELECTOR, \".OfferDiscountOptions__itemValue\")\n keys = [elem.text for elem in keys]\n values = [\"\".join(get_numbers(elem.text)) for elem in values]\n data['В кредит'] = \"\"\n data['С каско'] = \"\"\n data['В трейд-ин'] = \"\"\n data['Максимальная'] = \"\"\n for id,key in enumerate(keys):\n if key == \"В кредит\":\n data['В кредит'] = values[id]\n if key == \"С каско\":\n data['С каско'] = values[id]\n if key == \"В трейд-ин\":\n data['В трейд-ин'] = values[id]\n if key == \"Максимальная\":\n data['Максимальная'] = values[id]\n data['Фото'] = \"\"\n photo_new = \"\"\n try:\n photos = driver.find_elements(By.CSS_SELECTOR, \".ImageGalleryDesktop__image-container > div > div > img\")\n photos = [elem.get_attribute('src') for elem in photos]\n for index,photo in enumerate(photos):\n local = uploadPhoto(photo, data['car url'], index)\n data['Фото'] += photo + \",\"\n photo_new += local + \",\"\n data['Фото'] = data['Фото'][:-1]\n photo_new = photo_new[:-1]\n deletePhotos()\n except Exception:\n data['Фото'] = \"\"\n photo_new = \"\"\n\n try:\n data['Спецпредложения'] = \"\"\n elems = driver.find_elements(By.CSS_SELECTOR, \"a.CarouselSpecialNewItem__link\")\n elems = [elem.get_attribute('href') for elem in elems]\n elems = set(elems)\n for elem in elems:\n data['Спецпредложения'] += elem + \",\"\n data['Спецпредложения'] = data['Спецпредложения'][:-1]\n except Exception:\n data['Спецпредложения'] = \"\"\n\n try:\n data['Похожие автомобили'] = \"\"\n elems = driver.find_elements(By.CSS_SELECTOR, \"section.ListingSameGroupItem > div:nth-child(1) > a:nth-child(1)\")\n elems = [elem.get_attribute('href') for elem in elems]\n elems = set(elems)\n for elem in elems:\n data['Похожие автомобили'] += elem + \",\"\n data['Похожие автомобили'] = data['Похожие автомобили'][:-1]\n except Exception:\n data['Похожие автомобили'] = \"\"\n\n try:\n driver.implicitly_wait(5)\n driver.find_elements(By.CSS_SELECTOR, \"span.SpoilerLink_type_default\")[0].click()\n driver.implicitly_wait(0)\n except Exception:\n driver.implicitly_wait(0)\n\n\n keys = driver.find_elements(By.CSS_SELECTOR, \"div.CardBenefits__item-info-popup> div:nth-child(1) > div:nth-child(2) > div:nth-child(1)\")\n values = driver.find_elements(By.CSS_SELECTOR, \"div.InfoPopup > div:nth-child(1) > div:nth-child(2) > div:nth-child(2)\")\n keys = [elem.text.strip() for elem in keys]\n values = [elem.text.strip() for elem in values]\n\n keys_strong = [\"Дилер на связи\", \"Дилер готов торговаться\", \"Онлайн-показ\", \"Медленно теряет в цене\"]\n for key in keys_strong:\n data[key] = \"Нет\"\n\n for id,key in enumerate(keys):\n if key == \"Дилер всегда на связи\":\n data[\"Дилер на связи\"] = \"Да\"\n for key1 in keys_strong:\n if (key == key1):\n try:\n data[key] = \"Да\"\n if key == \"Медленно теряет в цене\":\n data[key] = get_numbers(values[-1])[0]\n except Exception:\n data[key1] = \"\"\n \n try:\n data['Комментарий продавца'] = driver.find_elements(By.CSS_SELECTOR, \".CardDescriptionHTML\")[0].get_attribute('innerHTML')\n except Exception:\n data['Комментарий продавца'] = \"\"\n \n keys_strong = ['Пробег', 'Кузов', 'Цвет', 'Налог', 'Руль', 'Состояние', 'Владельцы', 'ПТС', 'Таможня', 'Гарантия', 'Обмен']\n for key in keys_strong:\n data[key] = \"\"\n\n try:\n cats = driver.find_elements(By.CSS_SELECTOR, \"li.CardInfoRow > span:nth-child(1)\")\n cats = [elem.text.strip() for elem in cats]\n cats = list(filter(None, cats))\n # print(cats)\n except Exception:\n cats = []\n\n values = driver.find_elements(By.CSS_SELECTOR, \"li.CardInfoRow > span:nth-child(2)\")\n values = [elem.text.strip() for elem in values]\n for id,key in enumerate(cats):\n for key1 in keys_strong:\n if (key == key1):\n try:\n data[key1] = values[id].strip()\n except Exception:\n data[key1] = \"\"\n url = driver.find_elements(By.CSS_SELECTOR, \".CardCatalogLink\")[0].get_attribute('href')\n driver.get(url)\n\n if data['Тип авто'] == \"Новые\": \n try:\n data['Номер'] = get_numbers(driver.find_elements(By.CSS_SELECTOR, \"div.CardHead__infoItem:nth-child(3)\")[0].text)[0]\n except Exception:\n data['Номер'] = \"\"\n try:\n data['Марка'] = driver.find_elements(By.CSS_SELECTOR, \"li.BreadcrumbsGroup__item:nth-child(3) > a:nth-child(1)\")[0].text\n except Exception:\n data['Марка'] = \"\"\n try:\n data['Модель'] = driver.find_elements(By.CSS_SELECTOR, \"li.BreadcrumbsGroup__item:nth-child(4) > a:nth-child(1)\")[0].text.strip()\n except Exception:\n data['Модель'] = \"\"\n\n try:\n data['Серия'] = driver.find_elements(By.CSS_SELECTOR, \"li.BreadcrumbsGroup__item:nth-child(5) > a:nth-child(1)\")[0].text.strip()\n except Exception:\n data['Серия'] = \"\"\n try:\n data['Год выпуска'] = driver.find_elements(By.CSS_SELECTOR, \"div.CardHead__infoItem:nth-child(1)\")[0].text.strip()\n except Exception:\n data['Год выпуска'] = \"\"\n try:\n data['Цена'] = \"\".join(get_numbers(driver.find_elements(By.CSS_SELECTOR, \".OfferPriceCaption__price\")[0].text))\n except Exception:\n try:\n data['Цена'] = \"\".join(get_numbers(driver.find_elements(By.CSS_SELECTOR, \".PriceNewOffer__originalPrice\")[0].text))\n except Exception:\n data['Цена'] = \"\"\n\n try:\n data['Цена со скидками'] = \"\".join(get_numbers(driver.find_elements(By.CSS_SELECTOR, \".OfferPriceCaption\")[0].text))\n except Exception:\n data['Цена со скидками'] = \"\"\n action = ActionChains(driver)\n\n try:\n element = driver.find_elements(By.CSS_SELECTOR, '.PriceNewOffer__price')[0] \n action.move_to_element(element)\n action.perform()\n except:\n print(\"sold\")\n\n try:\n driver.implicitly_wait(6)\n data['Цена в долларах'] = \"\".join(get_numbers(driver.find_elements(By.CSS_SELECTOR, \".PriceNewOffer__priceItem\")[0].text))\n driver.implicitly_wait(0)\n except Exception:\n data['Цена в долларах'] = \"\"\n try:\n data['Цена в евро'] = \"\".join(get_numbers(driver.find_elements(By.CSS_SELECTOR, \".PriceNewOffer__priceItem\")[1].text))\n except Exception:\n data['Цена в евро'] = \"\"\n data['Стоимость платежа кредита (руб/месяц)'] = \"\"\n \n keys = driver.find_elements(By.CSS_SELECTOR, \".CardDiscountList__itemName\")\n values = driver.find_elements(By.CSS_SELECTOR, \".CardDiscountList__itemValue\")\n keys = [elem.text for elem in keys]\n values = [\"\".join(get_numbers(elem.text)) for elem in values]\n data['В кредит'] = \"\"\n data['С каско'] = \"\"\n data['В трейд-ин'] = \"\"\n data['Максимальная'] = \"\"\n for id,key in enumerate(keys):\n if key == \"В кредит\":\n data['В кредит'] = values[id]\n if key == \"С каско\":\n data['С каско'] = values[id]\n if key == \"В трейд-ин\":\n data['В трейд-ин'] = values[id]\n if key == \"Максимальная\":\n data['Максимальная'] = values[id]\n\n data['Фото'] = \"\"\n photo_new = \"\"\n try:\n photos = driver.find_elements(By.CSS_SELECTOR, \".ImageGalleryDesktop__image-container > div > div > img\")\n photos = [elem.get_attribute('src') for elem in photos]\n for index,photo in enumerate(photos):\n local = uploadPhoto(photo, data['car url'], index)\n data['Фото'] += photo + \",\"\n photo_new += local + \",\"\n data['Фото'] = data['Фото'][:-1]\n photo_new = photo_new[:-1]\n deletePhotos()\n except Exception:\n data['Фото'] = \"\"\n photo_new = \"\"\n\n try:\n data['Спецпредложения'] = \"\"\n elems = driver.find_elements(By.CSS_SELECTOR, \"a.CarouselSpecialNewItem__link\")\n elems = [elem.get_attribute('href') for elem in elems]\n elems = set(elems)\n for elem in elems:\n data['Спецпредложения'] += elem + \",\"\n data['Спецпредложения'] = data['Спецпредложения'][:-1]\n except Exception:\n data['Спецпредложения'] = \"\"\n\n try:\n data['Похожие автомобили'] = \"\"\n elems = driver.find_elements(By.CSS_SELECTOR, \"section.ListingSameGroupItem > div:nth-child(1) > a:nth-child(1)\")\n elems = [elem.get_attribute('href') for elem in elems]\n elems = set(elems)\n for elem in elems:\n data['Похожие автомобили'] += elem + \",\"\n data['Похожие автомобили'] = data['Похожие автомобили'][:-1]\n except Exception:\n data['Похожие автомобили'] = \"\"\n\n keys = driver.find_elements(By.CSS_SELECTOR, \"div.CardBenefits__item-info-popup> div:nth-child(1) > div:nth-child(2) > div:nth-child(1)\")\n values = driver.find_elements(By.CSS_SELECTOR, \"div.InfoPopup > div:nth-child(1) > div:nth-child(2) > div:nth-child(2)\")\n keys = [elem.text.strip() for elem in keys]\n values = [elem.text.strip() for elem in values]\n\n keys_strong = [\"Дилер на связи\", \"Дилер готов торговаться\", \"Онлайн-показ\", \"Медленно теряет в цене\"]\n for key in keys_strong:\n data[key] = \"Нет\"\n\n for id,key in enumerate(keys):\n if key == \"Дилер всегда на связи\":\n data[\"Дилер на связи\"] = \"Да\"\n for key1 in keys_strong:\n if (key == key1):\n try:\n data[key] = \"Да\"\n if key == \"Медленно теряет в цене\":\n data[key] = get_numbers(values[-1])[0]\n except Exception:\n data[key1] = \"\"\n \n try:\n data['Комментарий продавца'] = driver.find_elements(By.CSS_SELECTOR, \".CardDescriptionHTML\")[0].get_attribute('innerHTML')\n except Exception:\n data['Комментарий продавца'] = \"\"\n\n keys_strong = ['Пробег', 'Кузов', 'Цвет', 'Налог', 'Руль', 'Состояние', 'Владельцы', 'ПТС', 'Таможня', 'Гарантия', 'Обмен']\n for key in keys_strong:\n data[key] = \"\"\n\n try:\n cats = driver.find_elements(By.CSS_SELECTOR, \"div.CardInfoGroupedRow__cellTitle\")\n cats = [elem.text.strip() for elem in cats]\n cats = list(filter(None, cats))\n # print(cats)\n except Exception:\n cats = []\n\n values = driver.find_elements(By.CSS_SELECTOR, \".CardInfoGroupedRow__cellValue\")\n values = [elem.text.strip() for elem in values]\n for id,key in enumerate(cats):\n for key1 in keys_strong:\n if (key == key1):\n try:\n data[key1] = values[id].strip()\n except Exception:\n data[key1] = \"\"\n url = driver.find_elements(By.CSS_SELECTOR, \".CardCatalogLink\")[0].get_attribute('href')\n driver.get(url)\n\n elems = driver.find_elements(By.CSS_SELECTOR, \"tr.catalog-table__row\")\n elems = [elem.get_attribute('class') for elem in elems]\n complectation_id = 0\n for id,elem in enumerate(elems):\n elem = elem.split(\" \")\n for class_name in elem:\n if class_name == \"catalog-table__row_active\":\n complectation_id = id\n for id,elem in enumerate(elems):\n elem = elem.split(\" \")\n if len(elem) == 1:\n if id < complectation_id:\n complectation_id1 = id\n if complectation_id != 0:\n data['Комплектация'] = driver.find_elements(By.CSS_SELECTOR, \"tr.catalog-table__row\")[complectation_id1].text\n else:\n data['Комплектация'] = \"\"\n\n keys_strong = ['Объем', 'Мощность', 'Коробка', 'Тип двигателя', 'Топливо', 'Привод', \n 'Страна марки', 'Класс автомобиля', 'Количество дверей', 'Количество мест', \n 'Длина', 'Ширина', 'Высота', 'Колёсная база', 'Клиренс', 'Ширина передней колеи', \n 'Ширина задней колеи', 'Размер колёс', 'Объем багажника мин/макс, л', 'Объём топливного бака, л', \n 'Снаряженная масса, кг', 'Полная масса, кг', 'Колич��ство передач', \n 'Тип передней подвески', 'Тип задней подвески', 'Передние тормоза', 'Задние тормоза', \n 'Максимальная скорость, км/ч', 'Разгон до 100 км/ч, с', 'Расход топлива, л город/трасса/смешанный', \n 'Экологический класс', 'Выбросы CO2, г/км', 'Расположение двигателя', \n 'Объем двигателя, см³', 'Тип наддува', 'Максимальная мощность, л.с./кВт при об/мин', \n 'Максимальный крутящий момент, Н*м при об/мин', 'Расположение цилиндров', 'Количество цилиндров', \n 'Число клапанов на цилиндр', 'Система питания двигателя', 'Степень сжатия', \n 'Диаметр цилиндра и ход поршня, мм']\n for key in keys_strong:\n data[key] = \"\"\n\n try:\n cats = driver.find_elements(By.CSS_SELECTOR, \".list-values__label\")\n cats = [elem.text.strip() for elem in cats]\n cats = list(filter(None, cats))\n # print(cats)\n except Exception:\n cats = []\n\n values = driver.find_elements(By.CSS_SELECTOR, \".list-values__value\")\n values = [elem.text.strip() for elem in values]\n for id,key in enumerate(cats):\n for key1 in keys_strong:\n if (key == key1):\n try:\n data[key1] = values[id].strip()\n except Exception:\n data[key1] = \"\"\n\n try:\n data['Размер передних колес'] = data['Размер колёс'].split(\" \")[0]\n data['Размер задних колес'] = data['Размер колёс'].split(\" \")[1]\n except Exception:\n data['Размер передних колес'] = data['Размер колёс']\n data['Размер задних колес'] = data['Размер колёс']\n del data['Размер колёс']\n\n try:\n data['Расход топлива городской цикл (л)'] = data['Расход топлива, л город/трасса/смешанный'].split(\"/\")[0]\n data['Расход топлива загородный цикл (л)'] = data['Расход топлива, л город/трасса/смешанный'].split(\"/\")[1]\n data['Расход топлива смешанный цикл (л)'] = data['Расход топлива, л город/трасса/смешанный'].split(\"/\")[2]\n except Exception:\n data['Расход топлива городской цикл (л)'] = \"\"\n data['Расход топлива загородный цикл (л)'] = \"\"\n data['Расход топлива смешанный цикл (л)'] = \"\"\n del data['Расход топлива, л город/трасса/смешанный']\n\n try:\n data['Максимальная мощность'] = data['Максимальная мощность, л.с./кВт при об/мин'].split(' при ')[0]\n data['Обороты максимальной мощности'] = data['Максимальная мощность, л.с./кВт при об/мин'].split(' при ')[1]\n except Exception:\n data['Максимальная мощность'] = \"\"\n data['Обороты максимальной мощности'] = \"\"\n del data['Максимальная мощность, л.с./кВт при об/мин']\n\n try:\n data['Максимальный крутящий момент'] = data['Максимальный крутящий момент, Н*м при об/мин'].split(' при ')[0]\n data['Обороты максимального крутящего момента'] = data['Максимальный крутящий момент, Н*м при об/мин'].split(' при ')[1]\n except Exception:\n data['Максимальный крутящий момент'] = \"\"\n data['Обороты максимального крутящего момента'] = \"\"\n del data['Максимальный крутящий момент, Н*м при об/мин']\n\n try:\n data['Диаметр цилиндра'] = data['Диаметр цилиндра и ход поршня, мм'].split(' × ')[0]\n data['Ход поршня'] = data['Диаметр цилиндра и ход поршня, мм'].split(' × ')[1]\n except Exception:\n data['Диаметр цилиндра'] = \"\"\n data['Ход поршня'] = \"\"\n del data['Диаметр цилиндра и ход поршня, мм']\n\n keys_strong = ['Комфорт', 'Обзор', 'Безопасность', 'Защита от у��она', 'Салон', 'Мультимедиа', 'Элементы экстерьера', 'Прочее']\n for key in keys_strong:\n data[key] = \"\"\n try:\n driver.implicitly_wait(4)\n driver.find_elements(By.CSS_SELECTOR, '.tabs_view_classic > div:nth-child(3) > a:nth-child(1)')[0].click()\n cats = driver.find_elements(By.CSS_SELECTOR, 'div.catalog__package-group > h3.catalog__h3')\n cats = [elem.text.strip() for elem in cats]\n for id,key in enumerate(cats):\n for key1 in keys_strong:\n if (key == key1):\n try:\n values = driver.find_elements(By.CSS_SELECTOR, \"div.catalog__package-group:nth-child(\" +str(id + 2) + \") > ul > li\")\n driver.implicitly_wait(0)\n values = [elem.text.strip() for elem in values]\n for value in values:\n data[key1] += value + \",\"\n data[key1] = data[key1][:-1]\n except Exception:\n data[key1] = \"\"\n driver.implicitly_wait(0)\n except Exception:\n driver.implicitly_wait(0)\n cats = []\n data['Город'] = city\n data['Фото локальное имя'] = photo_new\n print(\"success\")\n print(data)\n apiImport(data)\n with open(file_name, 'a', encoding = 'utf-8') as f:\n w = csv.DictWriter(f, data.keys())\n w.writerow(data)\n\ndef parse_dealer(driver, data):\n print(\"getting: \" + data['dealer url'])\n driver.get(data['dealer url'])\n try: \n pages = driver.find_elements(By.CSS_SELECTOR, \"a.ListingPagination__page\")[-1].text\n except:\n pages = 1\n for page in range(1, int(pages)+1):\n driver.get(data['dealer url'] + \"?page=\" + str(page))\n elems = driver.find_elements(By.CSS_SELECTOR, \"a.ListingItemTitle__link\")\n elems = [elem.get_attribute('href') for elem in elems]\n elems = set(elems)\n for elem in elems:\n data['car url'] = elem\n parse_car(driver, data)\n\ndef main(driver, start_line):\n # data = {}\n # data[' dealer url'] = \"https://auto.ru/diler/cars/new/rolf_volgogradskiy_td_moskva/\"\n # data['car url'] = \"https://auto.ru/cars/new/group/audi/q8/21519841/21520421/1115367572-3ffca292/\"\n # parse_car(driver, data)\n # start_line = 0\n with open(r\"input.csv\", encoding = 'utf-8') as f:\n links = [url.strip().split(\",\") for url in f.readlines()] \n links = links[1:] \n # links = links[7:8] \n data = {}\n line = 0\n for link in links:\n line += 1\n\n with open(r\"start_line.txt\", 'w') as f:\n f.write(str(line))\n\n if line > start_line:\n print(line)\n data['dealer url'] = link[0]\n parse_dealer(driver, data)\n # data['car url'] = link[1]\n # parse_car(driver, data)\n\n with open(r\"start_line.txt\", 'w') as f:\n f.write(\"0\")\n\nif __name__ == \"__main__\":\n options = Options()\n profile = webdriver.FirefoxProfile()\n profile.set_preference(\"network.http.pipelining\", True)\n profile.set_preference(\"network.http.proxy.pipelining\", True)\n profile.set_preference(\"network.http.pipelining.maxrequests\", 8)\n profile.set_preference(\"content.notify.interval\", 500000)\n profile.set_preference(\"content.notify.ontimer\", True)\n profile.set_preference(\"content.switch.threshold\", 250000)\n profile.set_preference(\"browser.cache.memory.capacity\", 65536) # Increase the cache capacity.\n profile.set_preference(\"browser.startup.homepage\", \"about:blank\")\n profile.set_preference(\"reader.parse-on-load.enabled\", False) # Disable reader, we won't need that.\n profile.set_preference(\"browser.pocket.enabled\", False) # Duck pocket too!\n profile.set_preference(\"loop.enabled\", False)\n profile.set_preference(\"browser.chrome.toolbar_style\", 1) # Text on Toolbar instead of icons\n profile.set_preference(\"browser.display.show_image_placeholders\", False) # Don't show thumbnails on not loaded images.\n profile.set_preference(\"browser.display.use_document_colors\", False) # Don't show document colors.\n profile.set_preference(\"browser.display.use_document_fonts\", 0) # Don't load document fonts.\n profile.set_preference(\"browser.display.use_system_colors\", True) # Use system colors.\n profile.set_preference(\"browser.formfill.enable\", False) # Autofill on forms disabled.\n profile.set_preference(\"browser.helperApps.deleteTempFileOnExit\", True) # Delete temprorary files.\n profile.set_preference(\"browser.shell.checkDefaultBrowser\", False)\n profile.set_preference(\"browser.startup.homepage\", \"about:blank\")\n profile.set_preference(\"browser.startup.page\", 0) # blank\n profile.set_preference(\"browser.tabs.forceHide\", True) # Disable tabs, We won't need that.\n profile.set_preference(\"browser.urlbar.autoFill\", False) # Disable autofill on URL bar.\n profile.set_preference(\"browser.urlbar.autocomplete.enabled\", False) # Disable autocomplete on URL bar.\n profile.set_preference(\"browser.urlbar.showPopup\", False) # Disable list of URLs when typing on URL bar.\n profile.set_preference(\"browser.urlbar.showSearch\", False) # Disable search bar.\n profile.set_preference(\"extensions.checkCompatibility\", False) # Addon update disabled\n profile.set_preference(\"extensions.checkUpdateSecurity\", False)\n profile.set_preference(\"extensions.update.autoUpdateEnabled\", False)\n profile.set_preference(\"extensions.update.enabled\", False)\n profile.set_preference(\"general.startup.browser\", False)\n profile.set_preference(\"plugin.default_plugin_disabled\", False)\n profile.set_preference(\"permissions.default.image\", 2) # Image load disabled again\n\n profile.set_preference(\"permissions.default.stylesheet\", 2);\n\n options.headless = False\n\n keys = [\"dealer url\",\"car url\",\"Тип авто\",\"Номер\",\"Марка\",\"Модель\",\"Серия\",\"Год выпуска\",\"Цена\",\n \"Цена со скидками\",\"Цена в долларах\",\"Цена в евро\",\"Стоимость платежа кредита (руб/месяц)\",\n \"В кредит\",\"С каско\",\"В трейд-ин\",\"Максимальная\",\"Фото\",\"Спецпредложения\",\"Похожие автомобили\",\n \"Дилер на связи\",\"Дилер готов торговаться\",\"Онлайн-показ\",\"Медленно теряет в цене\",\n \"Комментарий продавца\",\"Пробег\",\"Кузов\",\"Цвет\",\"Налог\",\"Руль\",\"Состояние\",\"Владельцы\",\"ПТС\",\n \"Таможня\",\"Гарантия\",\"Обмен\",\"Комплектация\",\"Объем\",\"Мощность\",\"Коробка\",\"Тип двигателя\",\"Топливо\",\n \"Привод\",\"Страна марки\",\"Класс автомобиля\",\"Количество дверей\",\"Количество мест\",\"Длина\",\"Ширина\",\n \"Высота\",\"Колёсная база\",\"Клиренс\",\"Ширина передней колеи\",\"Ширина задней колеи\",\"Объем багажника мин/макс, л\",\n \"Объём топливного бака, л\",\"Снаряженная масса, кг\",\"Полная масса, кг\",\"Количество передач\",\n \"Тип передней подвески\",\"Тип задней подвески\",\"Передние тормоза\",\"Задние тормоза\",\"Максимальная скорость, км/ч\",\n \"Разгон до 100 км/ч, с\",\"Экологический класс\",\"Выбросы CO2, г/км\",\"Расположение двигателя\",\n \"Объем двигателя, см³\",\"Тип наддува\",\"Расположение цилиндров\",\"Количество цилиндров\",\n \"Число клапанов на цилиндр\",\"Система питания двигателя\",\"Степень сжатия\",\"Размер передних колес\",\n \"Размер задних колес\",\"Расход топлива городской цикл (л)\",\"Расход топлива загородный цикл (л)\",\n \"Расход топлива смешанный цикл (л)\",\"Максимальная мощность\",\"Обороты максимальной мощности\",\n \"Максимальный крутящий момент\",\"Обороты максимального крутящего момента\",\"Диаметр цилиндра\",\n \"Ход поршня\",\"Комфорт\",\"Обзор\",\"Безопасность\",\"Защита от угона\",\"Салон\",\"Мультимедиа\",\n \"Элементы экстерьера\",\"Прочее\", \"Город\", \"Фото локальное имя\"]\n\n\n with open(r\"start_line.txt\", encoding = 'utf-8') as f:\n start_line = int(f.readlines()[0])\n\n if (start_line == 0):\n with open(file_name, 'w') as f:\n w = csv.DictWriter(f, keys)\n w.writeheader()\n\n # make headless work\n driver = webdriver.Firefox(options = options, firefox_profile = profile)\n try:\n main(driver, start_line)\n driver.close()\n except Exception:\n try:\n driver.close()\n except Exception:\n pass\n print(\"error\")\n os.system(\"python3 auto-ru-autos-selenium.py\")\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "SamLatsin/all-web-crawlers", "sub_path": "collection/selenium/auto.ru/auto-ru-autos-selenium.py", "file_name": "auto-ru-autos-selenium.py", "file_ext": "py", "file_size_in_byte": 34954, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "re.sub", "line_number": 21, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 24, "usage_type": "call"}, {"api_name": "boto3.session.Session", "line_number": 27, "usage_type": "call"}, {"api_name": "boto3.session", "line_number": 27, "usage_type": "attribute"}, {"api_name": "urllib.request.request.urlretrieve", "line_number": 40, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 40, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 40, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 45, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 47, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 57, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 59, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 75, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 78, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 78, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 83, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 83, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 86, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 86, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 92, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 92, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 96, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 96, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 100, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 100, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 105, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 105, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 109, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 109, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 114, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 114, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 119, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 119, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.action_chains.ActionChains", "line_number": 122, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 124, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 124, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 132, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 132, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 137, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 137, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 141, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 141, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 144, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 144, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 145, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 145, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 164, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 164, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 179, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 179, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 190, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 190, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 201, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 201, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 207, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 207, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 208, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 208, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 229, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 229, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 238, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 238, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 245, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 245, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 254, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 254, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 259, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 259, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 263, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 263, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 267, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 267, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 272, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 272, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 276, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 276, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 280, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 280, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 283, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 283, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 288, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 288, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.action_chains.ActionChains", "line_number": 291, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 294, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 294, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 302, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 302, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 307, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 307, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 312, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 312, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 313, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 313, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 333, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 333, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 348, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 348, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 359, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 359, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 368, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 368, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 369, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 369, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 390, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 390, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 399, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 399, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 406, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 406, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 415, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 415, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 418, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 418, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 432, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 432, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 452, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 452, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 459, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 459, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 516, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 516, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 517, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 517, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 523, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 523, "usage_type": "name"}, {"api_name": "csv.DictWriter", "line_number": 541, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 548, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 548, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 553, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 553, "usage_type": "name"}, {"api_name": "selenium.webdriver.firefox.options.Options", "line_number": 589, "usage_type": "call"}, {"api_name": "selenium.webdriver.FirefoxProfile", "line_number": 590, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 590, "usage_type": "name"}, {"api_name": "csv.DictWriter", "line_number": 654, "usage_type": "call"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 658, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 658, "usage_type": "name"}, {"api_name": "os.system", "line_number": 668, "usage_type": "call"}]} +{"seq_id": "20427082705", "text": "# Sets the translate pivot to the rotate/scale pivot.\n\nfrom maya import cmds\n\n\ndef set_pivot():\n selection = cmds.ls(selection=True, type='transform')\n for s in selection:\n parents = cmds.listRelatives(s, parent=True, type='transform', path=True)\n children = cmds.listRelatives(s, children=True, type='transform', path=True)\n pos = cmds.xform(s, rotatePivot=True, worldSpace=True, query=True)\n tempGrp = cmds.group(empty=True)\n if children:\n childGrp = cmds.group(empty=True)\n children = cmds.parent(children, childGrp)\n cmds.xform(tempGrp, t=pos)\n s = cmds.parent(s, tempGrp)\n cmds.makeIdentity(s, apply=True, t=True)\n if parents:\n s = cmds.parent(s, parents[0])\n else:\n s = cmds.parent(s, w=True)\n if children:\n cmds.parent(children, s)\n cmds.delete(childGrp) \n cmds.delete(tempGrp)\n cmds.select(selection, replace=True)\n", "repo_name": "beatreichenbach/maya-prefs", "sub_path": "maya/scripts/py/set_pivot.py", "file_name": "set_pivot.py", "file_ext": "py", "file_size_in_byte": 986, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "14", "api": [{"api_name": "maya.cmds.ls", "line_number": 7, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 7, "usage_type": "name"}, {"api_name": "maya.cmds.listRelatives", "line_number": 9, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 9, "usage_type": "name"}, {"api_name": "maya.cmds.listRelatives", "line_number": 10, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 10, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 11, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 11, "usage_type": "name"}, {"api_name": "maya.cmds.group", "line_number": 12, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 12, "usage_type": "name"}, {"api_name": "maya.cmds.group", "line_number": 14, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 14, "usage_type": "name"}, {"api_name": "maya.cmds.parent", "line_number": 15, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 15, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 16, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 16, "usage_type": "name"}, {"api_name": "maya.cmds.parent", "line_number": 17, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 17, "usage_type": "name"}, {"api_name": "maya.cmds.makeIdentity", "line_number": 18, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 18, "usage_type": "name"}, {"api_name": "maya.cmds.parent", "line_number": 20, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 20, "usage_type": "name"}, {"api_name": "maya.cmds.parent", "line_number": 22, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 22, "usage_type": "name"}, {"api_name": "maya.cmds.parent", "line_number": 24, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 24, "usage_type": "name"}, {"api_name": "maya.cmds.delete", "line_number": 25, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 25, "usage_type": "name"}, {"api_name": "maya.cmds.delete", "line_number": 26, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 26, "usage_type": "name"}, {"api_name": "maya.cmds.select", "line_number": 27, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 27, "usage_type": "name"}]} +{"seq_id": "40992068239", "text": "import statsmodels.api as sm\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix, accuracy_score\n#import matplotlib.pyplot as plt\nimport heart_all\n\ndf = heart_all.get_data()\n\ny = df['hd1']\n\ndf.drop('hd1', axis=1, inplace=True)\n\nX = sm.add_constant(df)\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\nmodel = sm.Logit(y_train, X_train).fit()\n\nyhat = model.predict(X_test)\nprediction = list(map(round, yhat))\n\nprint('Actual values:', list(y_test.values))\nprint('Predictions:', prediction)\n\nprint(confusion_matrix(y_test.values, prediction))\nprint(accuracy_score(y_test.values, prediction))\n\n#plt.plot(yhat, y_test.values, 'bo')\n\n#plt.show()\n", "repo_name": "LutfiLokman/ml", "sub_path": "Logistic-Regression/heart_predict.py", "file_name": "heart_predict.py", "file_ext": "py", "file_size_in_byte": 729, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "14", "api": [{"api_name": "heart_all.get_data", "line_number": 7, "usage_type": "call"}, {"api_name": "statsmodels.api.add_constant", "line_number": 13, "usage_type": "call"}, {"api_name": "statsmodels.api", "line_number": 13, "usage_type": "name"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 15, "usage_type": "call"}, {"api_name": "statsmodels.api.Logit", "line_number": 17, "usage_type": "call"}, {"api_name": "statsmodels.api", "line_number": 17, "usage_type": "name"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 25, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "36210746567", "text": "from __future__ import absolute_import, division, print_function\n\nimport os\n\nimport cv2\nimport matplotlib.pylab as plt\nimport numpy as np\nimport tensorflow as tf\nimport tensorflow.keras.backend as K\nfrom keras.applications.vgg16 import VGG16\nfrom keras.layers import Dense, Input, Dropout, Flatten, Conv2D, BatchNormalization, Activation, \\\n MaxPooling2D\nfrom keras.models import Sequential, Model\nfrom mtcnn.mtcnn import MTCNN\n\nimage_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1 / 255)\n\ncurrent_patch = os.getcwd()\nimage_data = image_generator.flow_from_directory(str(current_patch + \"/FaceDB\"))\n\nprint(\"Mot vai thong so ve hinh anh: \")\nfor image_batch, label_batch in image_data:\n print(\"Image batch shape: \", image_batch.shape)\n print(\"Label batch shape: \", label_batch.shape)\n break\n\nnb_classes = image_data.num_classes\n\nmodel = Sequential()\n\nmodel.add(Conv2D(64, (3, 3), padding='same', input_shape=(256, 256, 3)))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.25))\n\n# 2nd Convolution layer\nmodel.add(Conv2D(128, (5, 5), padding='same'))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.25))\n\n# 3rd Convolution layer\nmodel.add(Conv2D(512, (3, 3), padding='same'))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.25))\n\n# 4th Convolution layer\nmodel.add(Conv2D(512, (3, 3), padding='same'))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.25))\n\n# Flattening\nmodel.add(Flatten())\n\n# Fully connected layer 1st layer\nmodel.add(Dense(256))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.25))\n\n# Fully connected layer 2nd layer\nmodel.add(Dense(512))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.25))\n\nmodel.add(Dense(nb_classes, activation='softmax'))\n\nmodel.compile(\n optimizer=tf.train.AdamOptimizer(learning_rate=0.0001),\n loss='categorical_crossentropy',\n metrics=['accuracy'])\n\nprint(\"Ket qua thong so cua model\")\nmodel.summary()\n\nsess = K.get_session()\ninit = tf.global_variables_initializer()\nsess.run(init)\n\nlabel_names = sorted(image_data.class_indices.items(), key=lambda pair: pair[1])\nlabel_names = np.array([key.title() for key, value in label_names])\nprint(label_names)\n\nprint(\"N data: \", image_data.n)\nprint(\"batch size: \", image_data.batch_size)\n\nmodel.fit_generator(generator=image_data,\n steps_per_epoch=5,\n epochs=10\n )\n\n# test hình thật\ndetector = MTCNN()\n\nimage_test = cv2.imread(\"test.png\")\nimage_test_for_paint = cv2.imread(\"test.png\")\n\nresult_dec = detector.detect_faces(image_test)\n\nfor result in result_dec:\n bounding_box = result['box']\n img_crop = image_test[bounding_box[1]:bounding_box[1] + bounding_box[3],\n bounding_box[0]:bounding_box[0] + bounding_box[2]]\n # print(img_crop)\n img_crop = cv2.resize(img_crop, (256, 256))\n # plt.imshow(img_crop)\n\n predict_face = model.predict(img_crop[np.newaxis, ...])\n label_predict = label_names[np.argmax(predict_face, axis=-1)]\n print(predict_face, label_predict)\n\n cv2.rectangle(image_test_for_paint, (bounding_box[0], bounding_box[1]),\n (bounding_box[0] + bounding_box[2], bounding_box[1] + bounding_box[3]),\n (255, 0, 0),\n 17)\n cv2.putText(image_test_for_paint,\n label_predict[0],\n (bounding_box[0], bounding_box[1]),\n cv2.FONT_HERSHEY_SIMPLEX,\n 3,\n (255, 0, 0),\n 10\n )\n\nplt.imshow(image_test_for_paint)\ncv2.imwrite(\"test_draw_conv.jpg\", image_test_for_paint)\nplt.show()\n\n# Get back the convolutional part of a VGG network trained on ImageNet\nmodel_vgg16_conv = VGG16(weights='imagenet', include_top=False)\nmodel_vgg16_conv.summary()\n\n# Create your own input format (here 3x200x200)\ninput_layer = Input(shape=(3, 200, 200), name='image_input')\n\n# Use the generated model\noutput_vgg16_conv = model_vgg16_conv(input_layer)\n\n# Add the fully-connected layers\nout_layer = Flatten(name='flatten')(output_vgg16_conv)\nout_layer = Dense(4096, activation='relu', name='fc1')(out_layer)\nout_layer = Dense(4096, activation='relu', name='fc2')(out_layer)\nout_layer = Dense(8, activation='softmax', name='predictions')(out_layer)\n\n# Create your own model\nmy_custom_model = Model(input=input_layer, output=out_layer)\n\n# In the summary, weights and layers from VGG part will be hidden, but they will be fit during the training\nmy_custom_model.summary()\n\nmy_custom_model.fit_generator(\n generator=image_data,\n steps_per_epoch=5,\n epochs=10\n)\n", "repo_name": "lenguyensonnguyen/CS2309_ChuyenDeThiGiacMayTinh_LeNguyenSonNguyen_CH1702039_GiuaKy", "sub_path": "FaceReg_Implement_Conv.py", "file_name": "FaceReg_Implement_Conv.py", "file_ext": "py", "file_size_in_byte": 4889, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "14", "api": [{"api_name": "tensorflow.keras.preprocessing.image.ImageDataGenerator", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 18, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 29, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 31, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 32, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 33, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 34, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 35, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 38, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 39, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 40, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 52, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 53, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 55, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 59, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 62, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 63, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 65, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 68, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 70, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 71, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 73, "usage_type": "call"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 76, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.backend.get_session", "line_number": 83, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 83, "usage_type": "name"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "mtcnn.mtcnn.MTCNN", "line_number": 100, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 103, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 115, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 116, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 119, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 123, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 126, "usage_type": "attribute"}, {"api_name": "matplotlib.pylab.imshow", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 132, "usage_type": "name"}, {"api_name": "cv2.imwrite", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pylab.show", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 134, "usage_type": "name"}, {"api_name": "keras.applications.vgg16.VGG16", "line_number": 137, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 141, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 147, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 148, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 149, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 150, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 153, "usage_type": "call"}]} +{"seq_id": "9086672263", "text": "from django.contrib import admin\nfrom .models import Home, CustomerAdvItem, FactItem\nfrom modeltranslation.admin import TranslationAdmin, TabbedTranslationAdmin, TranslationTabularInline\n\n\nclass CustomerAdvItemInline(TranslationTabularInline):\n\tmodel = CustomerAdvItem\n\textra = 0\n\n\nclass FactItemInline(TranslationTabularInline):\n\tmodel = FactItem\n\textra = 0\n\n\nclass HomeAdmin(TabbedTranslationAdmin):\n\t# list_display = ('h1',)\n\tinlines = [CustomerAdvItemInline, FactItemInline]\n\n\nadmin.site.register(Home, HomeAdmin)", "repo_name": "fbulvar/fbulvar", "sub_path": "home/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 517, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "modeltranslation.admin.TranslationTabularInline", "line_number": 6, "usage_type": "name"}, {"api_name": "models.CustomerAdvItem", "line_number": 7, "usage_type": "name"}, {"api_name": "modeltranslation.admin.TranslationTabularInline", "line_number": 11, "usage_type": "name"}, {"api_name": "models.FactItem", "line_number": 12, "usage_type": "name"}, {"api_name": "modeltranslation.admin.TabbedTranslationAdmin", "line_number": 16, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 21, "usage_type": "call"}, {"api_name": "models.Home", "line_number": 21, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "19325035539", "text": "import time\nimport numpy as np\nimport cv2\n\nimport matplotlib.pyplot as plt\n\n\ndef draw_lines():\n fig, ax = plt.subplots() # type:plt.Figure,plt.Axes\n ax.axis(\"equal\")\n ax.set_title(f\"test {time.asctime()}\")\n x = [4, 9, 8, 7, 3]\n y = [4, 1, 4, 4, 3]\n ax.plot(x, y, \"-\")\n ax.plot(x, y, \"o\")\n\n\ndraw_lines()\n\n\ndef draw_math():\n fig, ax = plt.subplots() # type:plt.Figure,plt.Axes\n ax.set_title(f\"draw_math {time.asctime()}\")\n x = np.arange(-100, 100) # step 默认为1\n y = x**3\n ax.plot(x, y, \"o\")\n\n\ndraw_math()\nplt.show()\n", "repo_name": "puzzzzzzle/python_study", "sub_path": "src/matplot_test/matplot_template.py", "file_name": "matplot_template.py", "file_ext": "py", "file_size_in_byte": 557, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "matplotlib.pyplot.subplots", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "time.asctime", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "time.asctime", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}]} +{"seq_id": "8781045128", "text": "# -*- coding: utf-8 -*-\n\nimport logging\nimport requests\nfrom django.conf import settings\nfrom django.core.management.base import BaseCommand\nfrom django.db import transaction\nfrom django.utils.translation import override\n\nfrom resources.models import (\n AccessibilityValue, AccessibilityViewpoint, Resource, ResourceAccessibility,\n UnitAccessibility, UnitIdentifier\n)\n\n\nLOG = logging.getLogger(__name__)\nREQUESTS_TIMEOUT = 15\n\n\nclass Command(BaseCommand):\n args = ''\n help = \"Import accessibility data\"\n\n def add_arguments(self, parser):\n parser.add_argument('--url', action='store', dest='url', default=settings.RESPA_ACCESSIBILITY_API_BASE_URL,\n help='Import from a given URL')\n\n def handle(self, *args, **options):\n url = options['url'].rstrip('/')\n\n # Activate the default language for the duration of the import\n # to make sure translated fields are populated correctly.\n default_language = settings.LANGUAGE_CODE\n\n with override(default_language), transaction.atomic():\n self.fetch_viewpoints(url)\n with override(default_language), transaction.atomic():\n self.fetch_resource_accessibility_data(url)\n with override(default_language), transaction.atomic():\n self.fetch_unit_accessibility_data(url)\n self.stdout.write('Finished.')\n\n def fetch_viewpoints(self, base_url):\n \"\"\" Populate accessibility viewpoints from the accessibility API \"\"\"\n url = '{}/api/v1/accessibility/viewpoints'.format(base_url)\n data = self.make_request(url)\n vp_ids = []\n\n for viewpoint_data in data:\n vp_id = viewpoint_data['viewpointId']\n if vp_id == 0:\n # id 0 seems to be the \"empty\" option in a dropdown: \"Choose accessibility perspective\"\n continue\n vp_ids.append(vp_id)\n vp_attributes = {\n 'order_text': viewpoint_data['viewpointOrderText'],\n }\n enabled_languages = [lang[0] for lang in settings.LANGUAGES]\n for name_translation in viewpoint_data['names']:\n if name_translation['language'] in enabled_languages:\n vp_attributes['name_%s' % name_translation['language']] = name_translation['value']\n\n vp, created = AccessibilityViewpoint.objects.get_or_create(\n id=vp_id,\n defaults=vp_attributes\n )\n if created:\n self.stdout.write('Created AccessibilityViewpoint {}'.format(vp.name_en))\n else:\n dirty_fields = self.update_model_attributes(vp, vp_attributes)\n if len(dirty_fields) > 0:\n vp.save()\n self.stdout.write('Updated AccessibilityViewpoint {}: {}'.format(\n vp.name_en, ', '.join(dirty_fields)\n ))\n # remove viewpoints which did not exist in the source anymore\n AccessibilityViewpoint.objects.exclude(id__in=vp_ids).delete()\n\n def fetch_resource_accessibility_data(self, base_url):\n \"\"\" Populate resource accessibility data from the accessibility API \"\"\"\n url = \"{base_url}/api/v1/accessibility/targets/{system_id}/summary\".format(\n base_url=base_url, system_id=settings.RESPA_ACCESSIBILITY_API_SYSTEM_ID)\n data = self.make_request(url)\n\n for accessibility_data in data:\n try:\n resource = Resource.objects.get(id=accessibility_data['servicePointId'])\n viewpoint = AccessibilityViewpoint.objects.get(id=accessibility_data['viewpointId'])\n except Resource.DoesNotExist:\n # this is normal, the database might contain servicepoints we don't\n # know of\n continue\n except AccessibilityViewpoint.DoesNotExist:\n self.stdout.write('Received unknown Accessibility viewpoint id from API: {}. Skipping.'.format(\n accessibility_data['viewpointId']))\n continue\n value = self.get_or_create_value(accessibility_data['isAccessible'])\n shortage_count = accessibility_data.get('shortageCount', 0)\n resource_attributes = {\n 'value': value,\n 'shortage_count': shortage_count,\n }\n resource_accessibility, created = ResourceAccessibility.objects.get_or_create(\n resource=resource, viewpoint=viewpoint, defaults=resource_attributes\n )\n if created:\n self.stdout.write('Created ResourceAccessibility {}'.format(str(resource_accessibility)))\n else:\n dirty_fields = self.update_model_attributes(resource_accessibility, resource_attributes)\n if len(dirty_fields) > 0:\n resource_accessibility.save()\n self.stdout.write('Updated ResourceAccessibility {}: {}'.format(\n str(resource_accessibility), ', '.join(dirty_fields)\n ))\n\n def fetch_unit_accessibility_data(self, base_url):\n \"\"\" Populate unit accessibility data from the accessibility API.\n Requests only servicepoints we have, the api contains lots of stuff we don't care about.\n \"\"\"\n url = \"{base_url}/api/v1/accessibility/servicepoints/{system_id}/{{servicepoint_id}}/summary\".format(\n base_url=base_url, system_id=settings.RESPA_ACCESSIBILITY_API_UNIT_SYSTEM_ID)\n\n for unit_identifier in UnitIdentifier.objects.filter(namespace='internal').select_related('unit'):\n unit = unit_identifier.unit\n try:\n data = self.make_request(url.format(servicepoint_id=unit_identifier.value))\n except requests.exceptions.HTTPError as e:\n if e.response.status_code == 404:\n # no accessibility data available\n continue\n raise e\n\n for viewpoint_data in data:\n try:\n viewpoint = AccessibilityViewpoint.objects.get(id=viewpoint_data['viewpointId'])\n except AccessibilityViewpoint.DoesNotExist:\n self.stdout.write('Received unknown Accessibility viewpoint id from API: {}. Skipping.'.format(\n viewpoint_data['viewpointId']))\n continue\n value = self.get_or_create_value(viewpoint_data['isAccessible'])\n shortage_count = viewpoint_data.get('shortageCount', 0)\n unit_attributes = {\n 'value': value,\n 'shortage_count': shortage_count,\n }\n unit_accessibility, created = UnitAccessibility.objects.get_or_create(\n unit=unit, viewpoint=viewpoint, defaults=unit_attributes\n )\n if created:\n self.stdout.write('Created UnitAccessibility {}'.format(str(unit_accessibility)))\n else:\n dirty_fields = self.update_model_attributes(unit_accessibility, unit_attributes)\n if len(dirty_fields) > 0:\n unit_accessibility.save()\n self.stdout.write('Updated UnitAccessibility {}: {}'.format(\n str(unit_accessibility), ', '.join(dirty_fields)\n ))\n\n def make_request(self, url):\n try:\n response = requests.get(url, timeout=REQUESTS_TIMEOUT)\n response.raise_for_status()\n return response.json()\n except requests.exceptions.RequestException as e:\n LOG.exception('Accessibility data import failed. Problem communicating with Accessibility API.')\n raise e\n except ValueError as e:\n LOG.exception('Accessibility data import failed. Response did not contain JSON')\n raise e\n\n def get_or_create_value(self, value_data):\n \"\"\"Accessibility API represents accessibility summaries in words like \"red\", \"green\"\n default ordering levels set here are\n - green: 10\n - unknown: 0\n - red: -10\n\n These can be changed later in django admin. Resources which don't have data in Accessibility database\n are considered to have an ordering priority of 0.\n \"\"\"\n accessibility_value, created = AccessibilityValue.objects.get_or_create(value=value_data)\n if created:\n if accessibility_value.value == 'green':\n accessibility_value.order = 10\n accessibility_value.save()\n elif accessibility_value.value == 'red':\n accessibility_value.order = -10\n accessibility_value.save()\n return accessibility_value\n\n def update_model_attributes(self, instance, attributes):\n dirty_fields = []\n for key, val in attributes.items():\n if getattr(instance, key) != val:\n dirty_fields.append(key)\n setattr(instance, key, val)\n return dirty_fields\n", "repo_name": "City-of-Helsinki/respa", "sub_path": "resources/management/commands/accessibility_import.py", "file_name": "accessibility_import.py", "file_ext": "py", "file_size_in_byte": 9127, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 61, "dataset": "github-code", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "django.core.management.base.BaseCommand", "line_number": 20, "usage_type": "name"}, {"api_name": "django.conf.settings.RESPA_ACCESSIBILITY_API_BASE_URL", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 25, "usage_type": "name"}, {"api_name": "django.conf.settings.LANGUAGE_CODE", "line_number": 33, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 33, "usage_type": "name"}, {"api_name": "django.utils.translation.override", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.transaction.atomic", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.transaction", "line_number": 35, "usage_type": "name"}, {"api_name": "django.utils.translation.override", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.transaction.atomic", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.transaction", "line_number": 37, "usage_type": "name"}, {"api_name": "django.utils.translation.override", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.transaction.atomic", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.transaction", "line_number": 39, "usage_type": "name"}, {"api_name": "django.conf.settings.LANGUAGES", "line_number": 58, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 58, "usage_type": "name"}, {"api_name": "resources.models.AccessibilityViewpoint.objects.get_or_create", "line_number": 63, "usage_type": "call"}, {"api_name": "resources.models.AccessibilityViewpoint.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "resources.models.AccessibilityViewpoint", "line_number": 63, "usage_type": "name"}, {"api_name": "resources.models.AccessibilityViewpoint.objects.exclude", "line_number": 77, "usage_type": "call"}, {"api_name": "resources.models.AccessibilityViewpoint.objects", "line_number": 77, "usage_type": "attribute"}, {"api_name": "resources.models.AccessibilityViewpoint", "line_number": 77, "usage_type": "name"}, {"api_name": "django.conf.settings.RESPA_ACCESSIBILITY_API_SYSTEM_ID", "line_number": 82, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 82, "usage_type": "name"}, {"api_name": "resources.models.Resource.objects.get", "line_number": 87, "usage_type": "call"}, {"api_name": "resources.models.Resource.objects", "line_number": 87, "usage_type": "attribute"}, {"api_name": "resources.models.Resource", "line_number": 87, "usage_type": "name"}, {"api_name": "resources.models.AccessibilityViewpoint.objects.get", "line_number": 88, "usage_type": "call"}, {"api_name": "resources.models.AccessibilityViewpoint.objects", "line_number": 88, "usage_type": "attribute"}, {"api_name": "resources.models.AccessibilityViewpoint", "line_number": 88, "usage_type": "name"}, {"api_name": "resources.models.Resource.DoesNotExist", "line_number": 89, "usage_type": "attribute"}, {"api_name": "resources.models.Resource", "line_number": 89, "usage_type": "name"}, {"api_name": "resources.models.AccessibilityViewpoint.DoesNotExist", "line_number": 93, "usage_type": "attribute"}, {"api_name": "resources.models.AccessibilityViewpoint", "line_number": 93, "usage_type": "name"}, {"api_name": "resources.models.ResourceAccessibility.objects.get_or_create", "line_number": 103, "usage_type": "call"}, {"api_name": "resources.models.ResourceAccessibility.objects", "line_number": 103, "usage_type": "attribute"}, {"api_name": "resources.models.ResourceAccessibility", "line_number": 103, "usage_type": "name"}, {"api_name": "django.conf.settings.RESPA_ACCESSIBILITY_API_UNIT_SYSTEM_ID", "line_number": 121, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 121, "usage_type": "name"}, {"api_name": "resources.models.UnitIdentifier.objects.filter", "line_number": 123, "usage_type": "call"}, {"api_name": "resources.models.UnitIdentifier.objects", "line_number": 123, "usage_type": "attribute"}, {"api_name": "resources.models.UnitIdentifier", "line_number": 123, "usage_type": "name"}, {"api_name": "requests.exceptions", "line_number": 127, "usage_type": "attribute"}, {"api_name": "resources.models.AccessibilityViewpoint.objects.get", "line_number": 135, "usage_type": "call"}, {"api_name": "resources.models.AccessibilityViewpoint.objects", "line_number": 135, "usage_type": "attribute"}, {"api_name": "resources.models.AccessibilityViewpoint", "line_number": 135, "usage_type": "name"}, {"api_name": "resources.models.AccessibilityViewpoint.DoesNotExist", "line_number": 136, "usage_type": "attribute"}, {"api_name": "resources.models.AccessibilityViewpoint", "line_number": 136, "usage_type": "name"}, {"api_name": "resources.models.UnitAccessibility.objects.get_or_create", "line_number": 146, "usage_type": "call"}, {"api_name": "resources.models.UnitAccessibility.objects", "line_number": 146, "usage_type": "attribute"}, {"api_name": "resources.models.UnitAccessibility", "line_number": 146, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 161, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 164, "usage_type": "attribute"}, {"api_name": "resources.models.AccessibilityValue.objects.get_or_create", "line_number": 181, "usage_type": "call"}, {"api_name": "resources.models.AccessibilityValue.objects", "line_number": 181, "usage_type": "attribute"}, {"api_name": "resources.models.AccessibilityValue", "line_number": 181, "usage_type": "name"}]} +{"seq_id": "71415948502", "text": "from collections import deque\n\nK = int(input())\n\nlst = deque(input() for i in range(K))\nresult = deque()\n\nwhile lst:\n get = lst.popleft()\n if get != '0':\n result.append(int(get))\n elif get == '0':\n result.pop()\n\nprint(sum(result))\n", "repo_name": "gettls/study.ps", "sub_path": "wonseok970/문자열/[10773] 제로.py", "file_name": "[10773] 제로.py", "file_ext": "py", "file_size_in_byte": 254, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "12", "api": [{"api_name": "collections.deque", "line_number": 5, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 6, "usage_type": "call"}]} +{"seq_id": "70780718096", "text": "from __future__ import division\nfrom predict import predict_image\nimport os\nimport sys\nimport argparse\nimport cv2\nfrom PIL import Image\nimport torch\nfrom torch.autograd import Variable\nimport predict\nimport numpy as np\nfrom util.utils import *\nimport tensorflow as tf\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--image_folder\", type=str, default=\"data/samples\", help=\"path to dataset\")\n parser.add_argument(\"--model_def\", type=str, default=\"config/yolov3.cfg\", help=\"path to model definition file\")\n parser.add_argument(\"--video_folder\", type=str,default=\"videos\", help=\"Directorio al video\")\n parser.add_argument(\"--video_name\", type=str,default=\"deteccion\", help=\"Nombre del video\")\n parser.add_argument(\"--class_path\", type=str,default=\"../labels.txt\", help=\"Nombre del video\")\n opt = parser.parse_args() #load (arg)\n \n\n isThereGraphicCard = torch.cuda.is_available()\n device = torch.device(\"cuda\" if isThereGraphicCard else \"cpu\")\n\n # define a video capture object\n cap = cv2.VideoCapture(0)\n\n sizeVideo = (int(cap.get(4)),int(cap.get(3)))\n # Define the quantity of frames to be captured per second\n fps = 100.0\n\n # Create the video writer object\n out = cv2.VideoWriter(f'./{opt.video_folder}/{opt.video_name}SinRecuadro.avi',cv2.VideoWriter_fourcc(*'MJPG') , fps, sizeVideo) \n outPut = cv2.VideoWriter(f'./{opt.video_folder}/{opt.video_name}ConCuadro.avi', cv2.VideoWriter_fourcc(*'MJPG'), fps, sizeVideo)\n \n # Define the label classes detections\n classes = load_classes(opt.class_path)\n \n #Assign a color for each classes\n colors = np.random.randint(0, 255, size=(len(classes), 3), dtype=\"uint8\")\n #load Tensor\n # Tensor = torch.cuda.FloatTensor if isThereGraphicCard else torch.FloatTensor\n\n while(True):\n # Capture the video frame\n # by frame\n ret, frame = cap.read()\n if ret is False: # If the fram is avaible\n break\n \n #Save frame without information\n out.write(frame)\n\n # RGBimg = Convertir_RGB(frame)\n # Convert to image to process in the model\n imgTensor = Image.fromarray(frame)\n detections = predict.predict_image(imgTensor)\n #detections = get_main__label_detection(detections, classes)\n for detection in detections:\n if detection is not None:\n print(detection)\n boundig_boxes = detection.get('boundingBox')\n x1, y1, w, h = boundig_boxes.values()\n \n y1 *= imgTensor.height\n x1 *= imgTensor.width\n w *= imgTensor.width\n h *= imgTensor.height\n\n x2 = x1 + w\n y2 = y1 + h \n color = colors[detection.get(\"tagId\")].tolist()\n frame = cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), color, 5)\n cv2.putText(frame, detection.get('tagName'), (int(x1), int(y1)), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2) #Nombre de la clase\n outPut.write(frame)\n cv2.imshow('frame', frame)\n\n\n # the 'q' button is set as the\n # quitting button you may use any\n # desired button of your choice\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\n # After the loop release the cap object in the video capture\n cap.release()\n out.release()\n outPut.release()\n \n # Destroy all the windowsq\n cv2.destroyAllWindows()\n ", "repo_name": "DanielSarmiento04/ReconomientoVideo", "sub_path": "python/deteccion_video.py", "file_name": "deteccion_video.py", "file_ext": "py", "file_size_in_byte": 3541, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "14", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 25, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 43, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 59, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 59, "usage_type": "name"}, {"api_name": "predict.predict_image", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 77, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 79, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 94, "usage_type": "call"}]} +{"seq_id": "17684471689", "text": "# myCoin brainwallet\n# Created by A. Petek\n\nimport hashlib\nimport os\nimport random\nimport binascii\nimport ecdsa\nimport base58\nimport PySimpleGUI as sg\nimport pyperclip\nimport webbrowser\nimport requests\nfrom json import (load as jsonload, dump as jsondump)\nfrom os import path\nimport json\nimport blockcypher\n\n\ndef sal():\n return binascii.hexlify(os.urandom(512)).decode('utf-8')\n\ndef seed(f, salt):\n p = f+ ' ' +salt\n return hashlib.sha256(p.encode(\"utf-8\")).hexdigest()\n\n\n\ndef pub_key(secret_exponent):\n key = binascii.unhexlify(secret_exponent)\n s = ecdsa.SigningKey.from_string(key, curve = ecdsa.SECP256k1)\n return '04' + binascii.hexlify(s.verifying_key.to_string()).decode('utf-8')\n\ndef addr(public_key):\n output = []; alphabet = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz'\n var = hashlib.new('ripemd160')\n var.update(hashlib.sha256(binascii.unhexlify(public_key.encode())).digest())\n var = '00' + var.hexdigest() + hashlib.sha256(hashlib.sha256(binascii.unhexlify(('00' + var.hexdigest()).encode())).digest()).hexdigest()[0:8]\n count = [char != '0' for char in var].index(True) // 2\n n = int(var, 16)\n while n > 0:\n n, remainder = divmod(n, 58)\n output.append(alphabet[remainder])\n for i in range(count): output.append(alphabet[0])\n return ''.join(output[::-1])\n\ndef wif(secret_exponent):\n var80 = \"80\"+secret_exponent\n var = hashlib.sha256(binascii.unhexlify(hashlib.sha256(binascii.unhexlify(var80)).hexdigest())).hexdigest()\n return str(base58.b58encode(binascii.unhexlify(str(var80) + str(var[0:8]))), 'utf-8')\n\n\ndef bala(address):\n return blockcypher.get_total_balance(address)\n\ndef over(address):\n return blockcypher.get_num_confirmed_transactions(address)\n\n\n\nSETTINGS_FILE = path.join(path.dirname(__file__), r'settings_file.cfg')\nDEFAULT_SETTINGS = {'theme': sg.theme()}\nSETTINGS_KEYS_TO_ELEMENT_KEYS = {'theme': '-THEME-'}\n\ndef load_settings(settings_file, default_settings):\n try:\n with open(settings_file, 'r') as f:\n settings = jsonload(f)\n except Exception as e:\n sg.popup_quick_message(f'exception {e}', 'No settings file found... will create one for you', keep_on_top=True, background_color='red', text_color='white')\n settings = default_settings\n save_settings(settings_file, settings, None)\n return settings\n\n\ndef save_settings(settings_file, settings, values):\n if values: \n for key in SETTINGS_KEYS_TO_ELEMENT_KEYS: \n try:\n settings[key] = values[SETTINGS_KEYS_TO_ELEMENT_KEYS[key]]\n except Exception as e:\n print(f'Problem updating settings from window values. Key = {key}')\n\n with open(settings_file, 'w') as f:\n jsondump(settings, f)\n\n sg.popup('Settings saved')\n\ndef create_settings_window(settings):\n sg.theme(settings['theme'])\n\n def TextLabel(text): return sg.Text(text+':', justification='r', size=(15,1))\n\n layout = [ [sg.Text('Settings', font='Any 15')],\n [TextLabel('Theme'),sg.Combo(sg.theme_list(), size=(20, 20), key='-THEME-')],\n [sg.Button('Save'), sg.Button('Exit')] ]\n\n window = sg.Window('Settings', layout, keep_on_top=True, finalize=True)\n\n for key in SETTINGS_KEYS_TO_ELEMENT_KEYS:\n try:\n window[SETTINGS_KEYS_TO_ELEMENT_KEYS[key]].update(value=settings[key])\n except Exception as e:\n print(f'Problem updating PySimpleGUI window from settings. Key = {key}')\n\n return window\n\n\n\n\n \ndef create_main_window(settings):\n sg.theme(settings['theme'])\n menu_def = [['&Menu', ['&Copy', '&Paste','&Settings', 'E&xit']],\n ['&Help', '&About...']]\n\n right_click_menu = ['Unused', ['&Copy', '&Paste','Settings', 'E&xit']]\n\n layout = [[sg.Menu(menu_def)],\n [sg.Image('bit.png'), sg.Text('', size=(20,1)), sg.Button('', key='paypal', size=(12,1), font=('Helvetica', 9), button_color=(sg.theme_background_color(), sg.theme_background_color()),\n image_filename='paypal.png', image_size=(80, 50), image_subsample=2, border_width=0),\n sg.Button('', key='bitcoin', size=(12,1), font=('Helvetica', 9), button_color=(sg.theme_background_color(), sg.theme_background_color()),\n image_filename='bitcoin.png', image_size=(80, 60), image_subsample=2, border_width=0)], \n [sg.Output(size=(76, 10), font=('Helvetica', 11), key='out')],\n [sg.Text('Enter seed phrase or to overview address:', font=('Helvetica', 8), size=(15,1))],\n [sg.Multiline(size=(76,3), font=('Helvetica', 11), key = 'gen')],\n [sg.Button('Generate wallet', font=('Helvetica', 11)),sg.Button('Overview address', font=('Helvetica', 11))]]\n\n return sg.Window('myCoin',\n layout=layout,\n default_element_size=(30, 2),\n font='Helvetica 18',\n right_click_menu=right_click_menu)\ndef main():\n window, settings = None, load_settings(SETTINGS_FILE, DEFAULT_SETTINGS )\n while True:\n if window is None:\n window = create_main_window(settings)\n event, values = window.Read()\n f = values['gen'].rstrip()\n if event in (None, 'Exit'):\n break\n\n elif event == 'Generate wallet':\n salt = sal()\n secret_exponent = seed(f, salt)\n public_key = pub_key(secret_exponent)\n address = addr(public_key)\n WIF = wif(secret_exponent)\n balance = bala(address)\n overv = over(address)\n data = open(\"myCoin-Wallet.docx\",\"a\")\n data.write(\"myCoin-Wallet: \"+\"\\n\\n\" +\n \"Seed phrase + salt: \" +str(f)+' '+str(salt)+\"\\n\\n\"+\n \"Privatekey: \" +str(secret_exponent)+\"\\n\\n\"+\n \"Publickey: \" + str(public_key)+\"\\n\\n\"+\n \"WIF: \" +str(WIF)+\"\\n\\n\"+\n \"Address: \"+str(address)+\"\\n\\n\"+\n \"Balance: \"+str(balance)+' '+'btc'+\"\\n\\n\"+\n \"Transactions: \" +str(overv)+\"\\n\"+\n '-------------------------------------------------------------------------------------------------------------'+\"\\n\\n\")\n data.close()\n print(\"Seed phrase + salt: \" +str(f)+ ' ' +str(salt)+\"\\n\"+\n \"Privatekey: \" +str(secret_exponent)+\"\\n\"+\n \"WIF: \" +str(WIF)+\"\\n\"+\n \"Address: \"+str(address)+\"\\n\"+\n \"Balance: \"+str(balance)+' '+'btc'+\"\\n\"+\n \"Transactions: \" +str(overv)+\"\\n\")\n\n elif event == 'Overview address':\n d = blockcypher.get_address_overview(f)\n print(d)\n \n elif event == 'Copy':\n pyperclip.copy(str(f))\n pyperclip.paste()\n\n elif event == 'Paste':\n text = pyperclip.paste()\n window.Element('gen').Update(str(text))\n\n elif event == 'Settings':\n event, values = create_settings_window(settings).read(close=True)\n if event == 'Save':\n window.close()\n window = None\n save_settings(SETTINGS_FILE, settings, values)\n\n elif event == 'About...':\n sg.popup('About:', 'Created by A. Petek', 'myCoin bitcoin brainwallet', 'Version 1.1',)\n\n elif event == 'paypal':\n webbrowser.open_new_tab(\"https://www.paypal.com/donate/?cmd=_s-xclick&hosted_button_id=PFB6A6HLAQHC2&source=url\")\n \n elif event == 'bitcoin':\n webbrowser.open_new_tab(\"https://commerce.coinbase.com/checkout/149a6235-ec7e-4d3b-a1ae-b08c4f08b4f6\")\n\n\n\n window.Close() \n\nmain()\n\n", "repo_name": "adrijano/myCoin-Brainwallet", "sub_path": "myCoin.py", "file_name": "myCoin.py", "file_ext": "py", "file_size_in_byte": 7839, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 18, "dataset": "github-code", "pt": "14", "api": [{"api_name": "binascii.hexlify", "line_number": 21, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 21, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 25, "usage_type": "call"}, {"api_name": "binascii.unhexlify", "line_number": 30, "usage_type": "call"}, {"api_name": "ecdsa.SigningKey.from_string", "line_number": 31, "usage_type": "call"}, {"api_name": "ecdsa.SigningKey", "line_number": 31, "usage_type": "attribute"}, {"api_name": "ecdsa.SECP256k1", "line_number": 31, "usage_type": "attribute"}, {"api_name": "binascii.hexlify", "line_number": 32, "usage_type": "call"}, {"api_name": "hashlib.new", "line_number": 36, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 37, "usage_type": "call"}, {"api_name": "binascii.unhexlify", "line_number": 37, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 38, "usage_type": "call"}, {"api_name": "binascii.unhexlify", "line_number": 38, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 49, "usage_type": "call"}, {"api_name": "binascii.unhexlify", "line_number": 49, "usage_type": "call"}, {"api_name": "base58.b58encode", "line_number": 50, "usage_type": "call"}, {"api_name": "binascii.unhexlify", "line_number": 50, "usage_type": "call"}, {"api_name": "blockcypher.get_total_balance", "line_number": 54, "usage_type": "call"}, {"api_name": "blockcypher.get_num_confirmed_transactions", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 61, "usage_type": "call"}, {"api_name": "PySimpleGUI.theme", "line_number": 62, "usage_type": "call"}, {"api_name": "json.load", "line_number": 68, "usage_type": "call"}, {"api_name": "PySimpleGUI.popup_quick_message", "line_number": 70, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 85, "usage_type": "call"}, {"api_name": "PySimpleGUI.popup", "line_number": 87, "usage_type": "call"}, {"api_name": "PySimpleGUI.theme", "line_number": 90, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 92, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 94, "usage_type": "call"}, {"api_name": "PySimpleGUI.Combo", "line_number": 95, "usage_type": "call"}, {"api_name": "PySimpleGUI.theme_list", "line_number": 95, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 96, "usage_type": "call"}, {"api_name": "PySimpleGUI.Window", "line_number": 98, "usage_type": "call"}, {"api_name": "PySimpleGUI.theme", "line_number": 113, "usage_type": "call"}, {"api_name": "PySimpleGUI.Menu", "line_number": 119, "usage_type": "call"}, {"api_name": "PySimpleGUI.Image", "line_number": 120, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 120, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 120, "usage_type": "call"}, {"api_name": "PySimpleGUI.theme_background_color", "line_number": 120, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 122, "usage_type": "call"}, {"api_name": "PySimpleGUI.theme_background_color", "line_number": 122, "usage_type": "call"}, {"api_name": "PySimpleGUI.Output", "line_number": 124, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 125, "usage_type": "call"}, {"api_name": "PySimpleGUI.Multiline", "line_number": 126, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 127, "usage_type": "call"}, {"api_name": "PySimpleGUI.Window", "line_number": 129, "usage_type": "call"}, {"api_name": "blockcypher.get_address_overview", "line_number": 171, "usage_type": "call"}, {"api_name": "pyperclip.copy", "line_number": 175, "usage_type": "call"}, {"api_name": "pyperclip.paste", "line_number": 176, "usage_type": "call"}, {"api_name": "pyperclip.paste", "line_number": 179, "usage_type": "call"}, {"api_name": "PySimpleGUI.popup", "line_number": 190, "usage_type": "call"}, {"api_name": "webbrowser.open_new_tab", "line_number": 193, "usage_type": "call"}, {"api_name": "webbrowser.open_new_tab", "line_number": 196, "usage_type": "call"}]} +{"seq_id": "15788422423", "text": "from django.conf import settings\nimport humanize\nfrom datetime import timedelta\n\nForecastPeriods = [\n timedelta(days=5),\n timedelta(days=16),\n]\n\n\ndef get_ussd_formatted_weather_forecast_periods():\n string = \"\"\n counter = 0\n for option in ForecastPeriods:\n string += \"{}. {}.\\n\".format(counter, humanize.naturaldelta(option))\n counter += 1\n return string\n\n\ndef get_location_not_found_response(location_name: str = None):\n string = \"Location \"\n if location_name is not None:\n string += location_name\n string += \" could not be determined. Please contact {} or {} to add it to our system.\".format(\n settings.CUSTOMER_SUPPORT_PHONE, settings.CUSTOMER_SUPPORT_EMAIL)\n return string\n", "repo_name": "MianoKariuki/Ujumbe", "sub_path": "ujumbe/apps/weather/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 734, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "datetime.timedelta", "line_number": 6, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 7, "usage_type": "call"}, {"api_name": "humanize.naturaldelta", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.settings.CUSTOMER_SUPPORT_PHONE", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 25, "usage_type": "name"}, {"api_name": "django.conf.settings.CUSTOMER_SUPPORT_EMAIL", "line_number": 25, "usage_type": "attribute"}]} +{"seq_id": "21767945415", "text": "import requests,time\nimport json,re,os\nfrom urllib.error import HTTPError\nfrom googletrans import Translator\ntranslator = Translator()\n\n\ndef translate_field(field_array,field_all_dict):\n\tif field_array:\n\t\ttranslated_field_array = []\n\t\tif type(field_array) == list:\n\t\t\tfor eng_data in field_array:\n\t\t\t\teng_data = eng_data.strip()\n\t\t\t\tif eng_data in field_all_dict:\n\t\t\t\t\tsin_data = field_all_dict[eng_data]\n\t\t\t\telse:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tsin_tran_data = translator.translate(eng_data, src='en', dest='si')\n\t\t\t\t\t\tsin_data = sin_tran_data.text\n\t\t\t\t\texcept HTTPError:\n\t\t\t\t\t\ttime.sleep(5)\n\t\t\t\t\ttry:\n\t\t\t\t\t\tsin_tran_data = translator.translate(eng_data, src='en', dest='si')\n\t\t\t\t\t\tsin_data = sin_tran_data.text\n\t\t\t\t\texcept HTTPError:\n\t\t\t\t\t\tprint(\"error\")\n\t\t\t\t\tfield_all_dict.update({eng_data: sin_data})\n\t\t\t\ttranslated_field_array.append(sin_data)\n\t\t\treturn translated_field_array, field_all_dict\n\t\telse:\n\t\t\teng_data = field_array.strip()\n\t\t\tif eng_data in field_all_dict:\n\t\t\t\tsin_data = field_all_dict[eng_data]\n\t\t\telse:\n\t\t\t\ttry:\n\t\t\t\t\tsin_tran_data = translator.translate(eng_data, src='en', dest='si')\n\t\t\t\t\tsin_data = sin_tran_data.text\n\t\t\t\texcept HTTPError:\n\t\t\t\t\ttime.sleep(5)\n\t\t\t\ttry:\n\t\t\t\t\tsin_tran_data = translator.translate(eng_data, src='en', dest='si')\n\t\t\t\t\tsin_data = sin_tran_data.text\n\t\t\t\texcept HTTPError:\n\t\t\t\t\tprint(\"error\") \n\t\t\t\tfield_all_dict.update({eng_data: sin_data})\n\t\t\ttranslated_field_array.append(sin_data)\n\t\t\treturn translated_field_array[0], field_all_dict\n\telse:\n\t\treturn None,field_all_dict\n\n\n\ndef translate():\n\tgenres_dict = {}\n\tartists_dict = {}\n\tlyricis_dict = {}\n\tmusic_dict = {}\n\n\twith open('translated_songs.json', 'r',encoding='utf8') as s_file:\n\t\tfinal_songs = json.loads(s_file.read())\n\n \n\tTHIS_FOLDER = os.path.dirname(os.path.abspath(__file__))\n\tsong_file = os.path.join(THIS_FOLDER, 'scraped_songs.json')\n\n\twith open(song_file, 'r', encoding='utf8') as f:\n\t\tscraped_songs = json.loads(f.read())\n # scraped_songs = open(song_file,encoding='utf8')\n\n \n\ti=0\n\tfor scraped_song in scraped_songs:\n\t\ti=i+1\n\t\tif(i%10==0):\n\t\t\ttime.sleep(15)\n \n \n\t\tcomplete_song = {}\n\t\ttitle = scraped_song[\"title\"]\n\t\tartist = scraped_song[\"artist\"]\n\t\tgenre = scraped_song[\"genre\"]\n\t\tlyricist = scraped_song[\"lyrics_by\"]\n\t\tmusic = scraped_song[\"music_by\"]\n\t\tsong_lyrics = scraped_song[\"song_lyrics\"]\n\t\tviews = scraped_song['visits']\n \n \n\t\ttranslated_artist, artists_dict = translate_field(artist, artists_dict)\n\t\ttime.sleep(2)\n\t\t\n\t\ttranslated_lyrics, lyricis_dict = translate_field(lyricist, lyricis_dict)\n\t\ttime.sleep(2)\n\t\t\n\t\ttranslated_music, music_dict = translate_field(music, music_dict)\n\t\ttime.sleep(2)\n\n\t\ttranslated_genre, genres_dict = translate_field(genre, genres_dict)\n\t\ttime.sleep(2)\n \n \n\t\tcomplete_song = {\n \"title\": title,\n \"song_lyrics\": song_lyrics,\n \"views\": views,\n \"english_artist\": artist,\n \"english_lyrics\": lyricist,\n \"english_music\": music,\n \"english_genre\": genre,\n \"sinhala_artist\": translated_artist,\n \"sinhala_lyrics\": translated_lyrics,\n \"sinhala_music\": translated_music,\n \"sinhala_genre\": translated_genre,\n }\n \n\n\t\tfinal_songs.append(complete_song)\n\t\twith open('translated_songs.json', 'w', encoding='utf8') as tra_songs:\n\t\t\tjson.dump(final_songs,tra_songs, indent=4 ,ensure_ascii=False)\n\t\t\t#tra_songs.write(json.dumps(final_songs))\n\t\t\n \n\t\tprint(music_dict)\n\nif __name__ == \"__main__\":\n\ttranslate()\n", "repo_name": "UdeshAthukorala/IR_Project-Song_Search_Engine", "sub_path": "data/translator.py", "file_name": "translator.py", "file_ext": "py", "file_size_in_byte": 3522, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "googletrans.Translator", "line_number": 5, "usage_type": "call"}, {"api_name": "urllib.error.HTTPError", "line_number": 20, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 21, "usage_type": "call"}, {"api_name": "urllib.error.HTTPError", "line_number": 25, "usage_type": "name"}, {"api_name": "urllib.error.HTTPError", "line_number": 38, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 39, "usage_type": "call"}, {"api_name": "urllib.error.HTTPError", "line_number": 43, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 67, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 75, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 89, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 92, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 95, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 98, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 118, "usage_type": "call"}]} +{"seq_id": "318111460", "text": "#For Datascraping from the webpage\nimport time\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait \nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.common.exceptions import TimeoutException\nfrom selenium.webdriver.common.keys import Keys\n\n#For csv data binding\nimport csv\nimport requests\nfrom bs4 import BeautifulSoup\nfrom lxml import html\n\n#For database operations\nimport sqlite3\nimport sys\n\ndef souping():\n\tnew_url = browser.current_url\n\treq = requests.get(new_url)\n\thtml = req.content\n\tsoup = BeautifulSoup(html, \"html.parser\")\n\treturn soup\n\n\nsearch_items = ['umbrella', 'fidget spinner']\nbrowser = webdriver.Firefox()\ntime.sleep(3)\nurl = \"https://www.amazon.in\"\nbrowser.get(url)\nprint(\"Connected to amazon.in..\")\n\nfor i in range(0,1):\n\tinp = browser.find_element_by_id(\"twotabsearchtextbox\")\n\tinp.send_keys(search_items[i])\n\tinp.send_keys(Keys.ENTER)\n\ttime.sleep(10)\n \n \n\tsoup = souping()\n\tprint(\"Entered the search box..\")\n\n\ttime.sleep(10)\n\tul = soup.find('ul', attrs={'id' : 's-results-list-atf'})\n\tresult = []\n\tprint(\"Getting Results..\")\n\t#Getting the results\n\tfor li in ul.findAll('li'):\n\t\tli = (li.get('id'))\n\t\tresult.append(li)\n\tprint(result)\n\tfor i in range(len(result)):\n\t\ttry:\n\t\t\tfr = browser.find_element_by_id(result[i])\n\t\t\tfr.click()\n\t\t\tprint(\"Clicked the element..\")\n\t\t\turl = browser.current_url\n\t\t\tr = requests.get(url)\n\t\t\thtml = r.content\n\t\t\ttry:\n\t\t\t\tsoup = BeautifulSoup(html, \"html.parser\")\n\t\t\t\ttitle_span = soup.find('span', attrs={'id' : 'productTitle'})\n\t\t\t\ttitle = title_span.text.strip()\n\t\t\t\tprint(\"Product :\", title)\n\t\t\texcept:\n\t\t\t\tprint(\"No title\", sys.exc_info()[0])\n\t\t\ttry:\n\t\t\t\tprice_span = soup.find('span', attrs={'id' : 'priceblock_ourprice'})\n\t\t\t\tprice = price_span.text.strip()\n\t\t\t\tprint(\"Price : \", price)\n\t\t\texcept:\n\t\t\t\tprint(\"No Price\", sys.exc_info())\n\t\t\ttry:\n\t\t\t\ttable = soup.find('table', attrs={'id' : 'productDetailsTable'})\n\t\t\t\tcontent = table.find('div', attrs={'class' : 'content'})\n\t\t\t\tul = content.find('ul')\n\t\t\t\tlinks = []\n\t\t\t\tfor li in ul.findAll('li'):\n\t\t\t\t\tch = li.text.replace('\\n','')\n\t\t\t\t\tlinks.append(ch.strip())\n\t\t\t\tdime = {}\n\t\t\t\tfor i in range(len(links)-1):\n\t\t\t\t\tvar = links[i].split(\":\")\n\t\t\t\t\tdime[var[0]] = var[1]\n\t\t\t\t\tprint(dime)\n\t\t\texcept:\n\t\t\t\tprint(\"No details\")\n\t\t\tprint(\"Connected Successfully!!!\")\n\t\t\tbrowser.back()\n\t\texcept:\n\t\t\tprint(\"Bad Network\")\n\n\n\n\n\n\n\n\n\n\n\t# for i in range(len(result)):\n\t# \ttry:\n\t# \t\tfr = browser.find_element_by_id(result[i])\n\t# \t\tfr.click()\n\t# \t\tsoup = souping()\n\t# \t\tprint(soup.find('h1', attrs={'id' : 'title'}))\n\t# \t\tprint(\"Good Job!!\")\n\t# \t\tbrowser.back()\n\t# \texcept:\n\t# \t\tprint(\"Bad Network\")\n\n\n\t\n\n\n\n# browser.execute_script('''window.open(\"http://stackoverflow.com/\",\"_blank\");''')\n", "repo_name": "Sanojdon/projects-at-byte", "sub_path": "scraping/amazon_project/amazon_scraping.py", "file_name": "amazon_scraping.py", "file_ext": "py", "file_size_in_byte": 2795, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "requests.get", "line_number": 22, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 23, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 24, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 24, "usage_type": "argument"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 29, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 29, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 30, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.keys.Keys.ENTER", "line_number": 38, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 38, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 39, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 45, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 60, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 61, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 63, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 63, "usage_type": "argument"}, {"api_name": "sys.exc_info", "line_number": 68, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 74, "usage_type": "call"}]} +{"seq_id": "14244701450", "text": "\"\"\"Handle application configuration.\"\"\"\nimport atexit\nimport collections\nimport logging\nimport pathlib\nfrom typing import Dict, List, Union\n\nimport pkg_resources\nimport ruamel.yaml\n\nfrom mariner.utils import path\n\nValue = Union[str, int, Dict, List]\n\n\nclass Config(collections.abc.MutableMapping):\n \"\"\"Class to hold config settings using yaml. Behaves like a dictionary.\"\"\"\n\n default_directory = \"~/.config/mariner/\"\n default_config = pkg_resources.resource_filename(__name__, \"config/config.yaml\")\n log = logging.getLogger(__name__)\n\n def __init__(\n self,\n configpath: str = None,\n _parent: \"Config\" = None,\n _config: Union[\"Config\", Dict] = None,\n ) -> None:\n self._configpath = configpath\n self._yaml = ruamel.yaml.YAML()\n self._parent = _parent\n\n if not self._parent:\n self._config = self.load()\n else:\n self._config = _config\n\n # Save the config file on exit\n atexit.register(self.save)\n\n @property\n def configpath(self) -> pathlib.Path:\n \"\"\"Create configuration file if necessary and return the path.\n\n Returns:\n Path to configuration file.\n \"\"\"\n if self._configpath:\n configpath = pathlib.Path(self._configpath)\n else:\n directory = path.config()\n directory = path.check(directory)\n configpath = pathlib.Path(directory, \"config.yaml\")\n self.log.debug(\"path=%s\", path)\n return configpath\n\n def load(self) -> Dict:\n \"\"\"Load configuration saved in given path.\n\n Returns:\n Dictionary of configuration values.\n \"\"\"\n try:\n raw_config = self.configpath.read_text()\n except FileNotFoundError:\n default_path = pathlib.Path(self.default_config)\n raw_config = default_path.read_text()\n return self._yaml.load(raw_config)\n\n def save(self) -> None:\n \"\"\"Save the configuration.\"\"\"\n if self._parent:\n self._parent.save()\n else:\n with self.configpath.open(\"w\", encoding=\"utf-8\") as file_:\n self.log.debug(\"configpath=%s file=%s\", self.configpath, file_)\n self._yaml.dump(self._config, file_)\n\n def _as_config(self, dict_: Union[str, int, List, Dict]) -> Union[str, int, List, \"Config\"]:\n \"\"\"Save config inside config.\n\n Args:\n dict_: Piece of configuration.\n\n Returns:\n Configuration as a dictionary.\n \"\"\"\n if isinstance(dict_, collections.abc.MutableMapping):\n return Config(_parent=self, _config=dict_)\n return dict_\n\n def __getitem__(self, item: str) -> Value:\n if item not in self._config:\n raise KeyError(item)\n return self._config[item]\n\n def __setitem__(self, key: str, value: Value) -> None:\n self._config[key] = self._as_config(value)\n self.save()\n\n def __getattr__(self, attr):\n return self.__getitem__(attr)\n\n def __setattr__(self, attr: str, value: Value) -> None:\n if attr.startswith(\"_\"):\n self.__dict__[attr] = value\n else:\n self.__setitem__(attr, value)\n\n def __delitem__(self, key: str) -> None:\n del self._config[key]\n\n def __iter__(self):\n for item in self._config:\n yield item\n\n def __len__(self) -> int:\n return len(self._config)\n\n def __repr__(self) -> str:\n return repr(self._config)\n\n def __str__(self) -> str:\n return str(self.__repr__())\n", "repo_name": "radek-sprta/mariner", "sub_path": "src/mariner/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 3577, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "12", "api": [{"api_name": "typing.Union", "line_number": 13, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 13, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 13, "usage_type": "name"}, {"api_name": "collections.abc", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pkg_resources.resource_filename", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 21, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 27, "usage_type": "name"}, {"api_name": "ruamel.yaml.yaml.YAML", "line_number": 30, "usage_type": "call"}, {"api_name": "ruamel.yaml.yaml", "line_number": 30, "usage_type": "attribute"}, {"api_name": "ruamel.yaml", "line_number": 30, "usage_type": "name"}, {"api_name": "atexit.register", "line_number": 39, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 49, "usage_type": "call"}, {"api_name": "mariner.utils.path.config", "line_number": 51, "usage_type": "call"}, {"api_name": "mariner.utils.path", "line_number": 51, "usage_type": "name"}, {"api_name": "mariner.utils.path.check", "line_number": 52, "usage_type": "call"}, {"api_name": "mariner.utils.path", "line_number": 52, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 53, "usage_type": "call"}, {"api_name": "mariner.utils.path", "line_number": 54, "usage_type": "argument"}, {"api_name": "pathlib.Path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 66, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 57, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 79, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 79, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 79, "usage_type": "name"}, {"api_name": "collections.abc", "line_number": 88, "usage_type": "attribute"}]} +{"seq_id": "24267630678", "text": "import ast\nfrom matplotlib import pyplot as plt\nimport matplotlib\nimport numpy as np\nimport os\nimport pandas as pd\nimport seaborn as sns\nimport sys\nfrom typing import Any, Dict, List\n\n# Number of timing repetitions in CSV files\nREPS = 96\n\n\ndef get_params(basedir: str, dirname: str) -> Dict[str, Any]:\n fname = os.path.join(dirname, 'size-params.txt')\n nettype = dirname.split('-')[0]\n with open(os.path.join(basedir, fname), 'r') as fp:\n result = {'network': nettype}\n result.update(ast.literal_eval(fp.readlines()[0]))\n return result\n\n\ndef get_tc_name(filename: str) -> str:\n # Remove \"-bm*.csv\" part\n fileparts = filename.split('-')[:-1]\n if fileparts[-1] == 'extra': # Remove \"-extra\"\n fileparts = fileparts[:-1]\n return '-'.join(fileparts) # Reconstruct remaining parts\n\n\ndef median_of_columns(df: pd.DataFrame, columns: List[str],\n outcol: str) -> pd.DataFrame:\n other_columns = set(df.columns) - set(columns)\n med = df.groupby(list(other_columns))[columns].apply(np.nanmedian)\n med.name = outcol\n df = df.join(med, on=list(other_columns))\n return df.drop(columns=columns)\n\n\ndef read_file(filepath: str, tc_type: str) -> pd.DataFrame:\n df = pd.read_csv(filepath, names=['Title', 'Operation', 'M',\n 'N', 'K', 'A_trans',\n 'B_trans', 'lda', 'ldb',\n 'ldc', 'sta', 'stb', 'stc',\n 'batches'] + ['time%d' % i\n for i in\n range(REPS + 1)])\n # Filter out failed cases\n df = df[df['time0'] > 0]\n\n # Add type and shapes\n df['Type'] = tc_type\n # NOTE: we cannot use expand=True here because of column titles\n title_split = df['Title'].str.split(':')\n df['A_shape'] = title_split.str.get(0)\n df['B_shape'] = title_split.str.get(1)\n df['C_shape'] = title_split.str.get(2)\n df['Implementation'] = title_split.str.get(3)\n\n # Drop extra columns\n df = df.drop(columns=['Title', 'time%d' % REPS])\n\n # Compute median time\n return median_of_columns(df, ['time%d' % i for i in range(REPS)], 'Time')\n\n\ndef postprocess(df: pd.DataFrame) -> pd.DataFrame:\n df = df[df['Type'] != 'Q'].copy()\n df.loc[df['Type'] == 'out', 'Type'] = 'Q / out'\n df.loc[df['Type'] == 'dX1gamma', 'Type'] = 'dX1g'\n df.loc[df['Type'] == 'dX2gamma', 'Type'] = 'dX2g'\n\n df.loc[df['Type'].str.count('-fused') == 1,\n 'Type'] = df['Type'].str[:-6] + '\\n(fused)'\n\n # Split by tensor cores\n df.loc[df['Implementation'].str.count('tc') == 0, 'Tensor Cores'] = False\n df.loc[df['Implementation'].str.count('tc') == 1, 'Tensor Cores'] = True\n return df\n\n\ndef addline(ax: plt.Axes, col: int, y: float, text: str):\n \"\"\" Add a line with some text to a violin plot. \"\"\"\n style = {'PT': 'solid', 'XLA': 'dotted', 'Heur.': 'dashed'}\n ax.hlines(y, col-0.5, col+0.5, colors='k',\n linestyles=style[text], zorder=999)\n if col == 0:\n ax.text(col+0.5, y, text, verticalalignment='center')\n\n\ndef plot_violins(title: str, params: Dict[str, Any], df: pd.DataFrame):\n df = postprocess(df)\n df16 = df[df['Implementation'].str.count('32') == 0]\n df32 = df[df['Implementation'].str.count('32') == 1]\n\n # FP16 post-processing: Filter out results slower than tensor-core max time\n df16 = df16[df16['Time'] <=\n df16[df16['Tensor Cores'] == True]['Time'].max()]\n\n # FP16 plot\n plt.cla()\n plt.figure(figsize=(32, 9), dpi=120)\n matplotlib.rcParams.update({'font.size': 18})\n ax: plt.Axes = sns.violinplot(x='Type', y='Time', data=df16,\n hue='Tensor Cores', split=True, cut=0)\n ax.set_ylabel('Time [ms]')\n ax.set_title('%s FP16\\n(Batch=%d, Heads=%d, Embedding=%d, Seqlen=%d)' % (\n params['network'], params['b'], params['h'], params['i'], params['j']\n ))\n\n times = df16[df16['Implementation'] == 'tc_default']\n for col, tick in enumerate(ax.get_xaxis().majorTicks):\n mintime = times[times['Type'] == tick.label.get_text()]['Time'].min()\n addline(ax, col, mintime, 'Heur.')\n\n ax.get_figure().savefig('%s-half.png' % title)\n\n # FP32 plot\n if len(df32) > 0:\n plt.cla()\n ax = sns.violinplot(x='Type', y='Time', data=df32)\n ax.set_ylabel('Time [ms]')\n ax.set_title('%s FP32\\n(Batch=%d, Heads=%d, Embedding=%d, Seqlen=%d)' % (\n params['network'], params['b'], params['h'], params['i'], params['j']\n ))\n\n times = df32[df32['Implementation'] == '32_default']\n for col, tick in enumerate(ax.get_xaxis().majorTicks):\n mintime = times[times['Type'] ==\n tick.label.get_text()]['Time'].min()\n addline(ax, col, mintime, 'Heur.')\n\n ax.get_figure().savefig('%s-float.png' % title)\n\n\ndef process_dir(basedir: str, dirname: str):\n params = get_params(basedir, dirname)\n print('Parameters:', params)\n if os.path.isfile('%s-fullfile.csv' % dirname):\n print('Found cached file')\n results = pd.read_csv('%s-fullfile.csv' % dirname)\n else:\n results = pd.DataFrame(columns=['Type', 'A_shape', 'B_shape', 'C_shape',\n 'Implementation', 'Operation',\n 'M', 'N', 'K', 'A_trans',\n 'B_trans', 'lda', 'ldb', 'ldc', 'sta',\n 'stb', 'stc', 'batches', 'Time'])\n for filename in os.listdir(os.path.join(basedir, dirname)):\n if filename.endswith('.csv'):\n tc_name = get_tc_name(filename)\n results = results.append(read_file(os.path.join(\n basedir, dirname, filename), tc_name), ignore_index=True)\n\n results.to_csv('%s-fullfile.csv' % dirname)\n\n plot_violins(dirname, params, results)\n\n\nif __name__ == '__main__':\n dirname = '.' if len(sys.argv) < 2 else sys.argv[1]\n for d in os.listdir(dirname):\n if os.path.isdir(d) and not d.startswith('.'):\n process_dir(dirname, d)\n", "repo_name": "spcl/substation", "sub_path": "tc_profiling/plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 6231, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 112, "dataset": "github-code", "pt": "14", "api": [{"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "ast.literal_eval", "line_number": 20, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 15, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 32, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 32, "usage_type": "name"}, {"api_name": "numpy.nanmedian", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 42, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 68, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.Axes", "line_number": 83, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 92, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 92, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 92, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.cla", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.rcParams.update", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.rcParams", "line_number": 104, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.Axes", "line_number": 105, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "seaborn.violinplot", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cla", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "seaborn.violinplot", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path", "line_number": 140, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 142, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 144, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path", "line_number": 149, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path", "line_number": 152, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 161, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 162, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path", "line_number": 163, "usage_type": "attribute"}]} +{"seq_id": "43656125883", "text": "import cv2\nimport numpy as np\nimage = 'GOAT.jpg'\ndef detect_red_and_white_regions(image):\n # Step 1: Read the image\n\n original_image = cv2.imread(image)\n\n # Step 2: Apply image sharpening using the unsharp mask filter\n sharpened_image = cv2.addWeighted(original_image, 1.5, cv2.GaussianBlur(original_image, (0, 0), 5), -0.5, 0)\n\n # Step 3: Convert the sharpened image to HSV color space\n hsv_image = cv2.cvtColor(sharpened_image, cv2.COLOR_BGR2HSV)\n\n # Step 4: Define color ranges for red and white in HSV\n lower_red = np.array([0, 100, 100])\n upper_red = np.array([10, 255, 255])\n\n lower_white = np.array([0, 0, 200])\n upper_white = np.array([180, 30, 255])\n\n # Step 5: Create masks for red and white regions\n red_mask = cv2.inRange(hsv_image, lower_red, upper_red)\n white_mask = cv2.inRange(hsv_image, lower_white, upper_white)\n\n # Step 6: Find contours of the red and white regions\n red_contours, _ = cv2.findContours(red_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n white_contours, _ = cv2.findContours(white_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\n # Step 7: Draw contours on the original image\n cv2.drawContours(original_image, red_contours, -1, (0, 0, 255), 2) # Red color for contours\n cv2.drawContours(original_image, white_contours, -1, (102, 100, 74), 2) # White color for contours\n\n\n # Step 8: Display the output\n cv2.imshow('Detected Red and White Regions in Blurry Region', original_image)\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n\ndetect_red_and_white_regions(image)\n\nimage_=cv2.imread(image)\ngrayscale_image = cv2.cvtColor(image_, cv2.COLOR_BGR2GRAY)\nimage_array = np.array(grayscale_image)\n\ndef analyze_goat(image_array):\n\n\n min_val = np.min(image_array)\n max_val = np.max(image_array)\n\n print(\"Minimum pixel value:\", min_val)\n print(\"Maximum pixel value:\", max_val)\n\n average_value = np.mean(image_array)\n print(\"Average pixel value:\", average_value)\n\n\n\n\n\n\n def create_binary_mask(image_path, threshold_value):\n # Read the image using OpenCV\n image = cv2.imread(image_path)\n\n # Convert the image to grayscale\n grayscale_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n # Apply global thresholding to create the binary mask\n _, binary_mask = cv2.threshold(grayscale_image, threshold_value, 255, cv2.THRESH_BINARY)\n\n return binary_mask\n\n\n threshold_value = 128 # Adjust this threshold value as needed\n binary_mask = create_binary_mask(image, threshold_value)\n\n # Count the number of non-zero (foreground) pixels in the binary mask\n num_non_zero_pixels = cv2.countNonZero(binary_mask)\n forgroungzeropixels =cv2.countNonZero(cv2.bitwise_not(binary_mask))\n print(\"Total number of non-zero (foreground) pixels:\", num_non_zero_pixels)\n print(\"Total number of zero (Background) pixels:\", forgroungzeropixels)\n\nanalyze_goat(image_array)", "repo_name": "sakibahmedshanto/week2", "sub_path": "210041262_Sakib_Ahmed_Shanto_week2_task2(python).py", "file_name": "210041262_Sakib_Ahmed_Shanto_week2_task2(python).py", "file_ext": "py", "file_size_in_byte": 3109, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "cv2.imread", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.addWeighted", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 27, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 27, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 28, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 28, "usage_type": "attribute"}, {"api_name": "cv2.drawContours", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 43, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 68, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 71, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 71, "usage_type": "attribute"}, {"api_name": "cv2.countNonZero", "line_number": 80, "usage_type": "call"}, {"api_name": "cv2.countNonZero", "line_number": 81, "usage_type": "call"}, {"api_name": "cv2.bitwise_not", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "71587520022", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport sys\nimport json\nfrom flask import Blueprint\nfrom flask import request\nfrom sasila.system_instant.manager.jd_manager import JdManager\n\nif sys.version_info < (3, 0):\n reload(sys)\n sys.setdefaultencoding('utf-8')\n\nim_jd = Blueprint('im_jd', __name__)\n\njd_manager = JdManager()\n\n\n@im_jd.route('/login')\ndef login():\n return jd_manager.login(request.args['collect_token'], request.args['account'], request.args['password'])\n\n\n@im_jd.route('/qrlogin')\ndef qr_login():\n message = jd_manager.qrlogin(request.args['collect_token'])\n # result = '
' + message\n # return result\n return message\n\n\n@im_jd.route('/submit_qrlogin')\ndef submit_qrlogin():\n return jd_manager.submit_qrlogin(request.args['collect_token'])\n", "repo_name": "DarkSand/Sasila", "sub_path": "sasila/system_instant/blueprints/jd.py", "file_name": "jd.py", "file_ext": "py", "file_size_in_byte": 876, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 294, "dataset": "github-code", "pt": "12", "api": [{"api_name": "sys.version_info", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sys.setdefaultencoding", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.Blueprint", "line_number": 13, "usage_type": "call"}, {"api_name": "sasila.system_instant.manager.jd_manager.JdManager", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.args", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.request.args", "line_number": 33, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 33, "usage_type": "name"}]} +{"seq_id": "20852323298", "text": "import re\nimport pandas as pd\n\nfrom enchant.checker import SpellChecker\nfrom nltk import word_tokenize, pos_tag\nfrom nltk.corpus import stopwords, wordnet\nfrom nltk.stem import WordNetLemmatizer\n\npd.set_option('display.max_colwidth', None)\npd.set_option('display.max_columns', None)\n\npd.set_option('display.max_rows', None)\n\nraw_data_path = r'../data/3_extract_hashtag_cue'\n\ncorrected_data_path = r'../data/4_correct_misspell'\n\ndic = SpellChecker('en_US')\nlemmatizer = WordNetLemmatizer()\n\npunctuation = r\"[,.?!=*':/\\d]\"\nstop_words = stopwords.words('English')\n\n\ndef get_wordnet_pos(tag):\n if tag.startswith('J'):\n return wordnet.ADJ\n elif tag.startswith('V'):\n return wordnet.VERB\n elif tag.startswith('N'):\n return wordnet.NOUN\n elif tag.startswith('R'):\n return wordnet.ADV\n else:\n return wordnet.NOUN\n\n\ndef word_tokenization(src_file_path, des_file_path):\n count = 0\n file = pd.read_csv(src_file_path)\n time_arr = []\n location_arr = []\n account_arr = []\n message_arr = []\n for i, instance in enumerate(file['message']):\n count += 1\n print(\"\\rcurrent: \", count, end='')\n current_text = \"\"\n time_arr.append(file['time'][i])\n location_arr.append(file['location'][i])\n account_arr.append(file['account'][i])\n if isinstance(instance, str):\n tokens = word_tokenize(instance.strip())\n for items in tokens:\n if current_text == \"\":\n current_text = items\n else:\n current_text = current_text + \" \" + items\n else:\n current_text = instance\n message_arr.append(current_text)\n print('')\n output_data_frame = pd.DataFrame({'time': time_arr,\n 'location': location_arr,\n 'account': account_arr,\n 'message': message_arr})\n output_data_frame.to_csv(des_file_path, index=False, sep=',')\n\n\ndef spell_checker(src_file_path, des_file_path):\n count = 0\n file = pd.read_csv(src_file_path)\n time_arr = []\n location_arr = []\n account_arr = []\n message_arr = []\n for i, instance in enumerate(file['message']):\n count += 1\n print(\"\\rcurrent: \", count, end='')\n new_text = \"\"\n time_arr.append(file['time'][i])\n location_arr.append(file['location'][i])\n account_arr.append(file['account'][i])\n if isinstance(instance, str):\n current_text = re.sub(punctuation, \"\", str(instance).lower())\n for word in current_text.split():\n if word == \"re\" or word == \"\" or len(word) < 3:\n continue\n else:\n if word not in stop_words:\n if dic.check(word) is True:\n if new_text == \"\":\n new_text = word\n else:\n new_text = new_text + \" \" + word\n else:\n candidate_list = dic.suggest(word)\n if len(candidate_list) == 0:\n new_word = word\n else:\n new_word = candidate_list[0]\n if new_text == \"\":\n new_text = new_word\n else:\n new_text = new_text + \" \" + new_word\n else:\n new_text = re.sub(punctuation, \"\", str(instance).lower())\n message_arr.append(new_text)\n print('')\n out_put_frame = pd.DataFrame({'time': time_arr,\n 'location': location_arr,\n 'account': account_arr,\n 'message': message_arr})\n out_put_frame.to_csv(des_file_path, index=False, sep=',')\n\n\ndef word_stem(src_file_path, des_file_path):\n count = 0\n file = pd.read_csv(src_file_path)\n time_arr = []\n location_arr = []\n account_arr = []\n message_arr = []\n for i, instance in enumerate(file['message']):\n count += 1\n print(\"\\rcurrent: \", count, end='')\n time_arr.append(file['time'][i])\n location_arr.append(file['location'][i])\n account_arr.append(file['account'][i])\n if isinstance(instance, str):\n current_text = word_tokenize(instance.strip())\n tagged_sent = pos_tag(current_text)\n new_text = \"\"\n for j in range(0, len(current_text)):\n if len(current_text[j]) < 3:\n continue\n wordnet_pos = get_wordnet_pos(tagged_sent[j][1])\n if new_text == \"\":\n new_text = lemmatizer.lemmatize(tagged_sent[j][0], wordnet_pos)\n else:\n new_text = new_text + \" \" +\\\n lemmatizer.lemmatize(tagged_sent[j][0], wordnet_pos)\n else:\n new_text = instance\n message_arr.append(new_text)\n print('')\n output_data_frame = pd.DataFrame({'time': time_arr,\n 'location': location_arr,\n 'account': account_arr,\n 'message': message_arr})\n output_data_frame.to_csv(des_file_path, index=False, sep=',')\n\n\nif __name__ == '__main__':\n # 1 - tokenize text\n word_tokenization(raw_data_path + '/YInt.csv',\n corrected_data_path + '/1_YIntTokenize.csv')\n\n # 2 - detect misspell\n spell_checker(corrected_data_path + '/1_YIntTokenize.csv',\n corrected_data_path + '/2_YIntMisspell.csv')\n\n # 3 - word lemmatize\n word_stem(corrected_data_path + '/2_YIntMisspell.csv',\n corrected_data_path + '/YInt.csv')\n", "repo_name": "ReisenUI/VastChallenge2019", "sub_path": "DataProcess/formalWorkofMC3/src/Step4Misspell.py", "file_name": "Step4Misspell.py", "file_ext": "py", "file_size_in_byte": 5902, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "pandas.set_option", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 10, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 12, "usage_type": "call"}, {"api_name": "enchant.checker.SpellChecker", "line_number": 18, "usage_type": "call"}, {"api_name": "nltk.stem.WordNetLemmatizer", "line_number": 19, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 22, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 22, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.ADJ", "line_number": 27, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 27, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.VERB", "line_number": 29, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 29, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.NOUN", "line_number": 31, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 31, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.ADV", "line_number": 33, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 33, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.NOUN", "line_number": 35, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 35, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 40, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 63, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 72, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 85, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 107, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 110, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 119, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 131, "usage_type": "call"}, {"api_name": "nltk.pos_tag", "line_number": 132, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 147, "usage_type": "call"}]} +{"seq_id": "33796232165", "text": "from multiprocessing import Pool\nfrom gym import Space\nfrom rdkit import Chem\nfrom rdkit.Chem import Draw, AllChem\nfrom rdkit import DataStructs\nfrom typing import List\nfrom functools import partial\nimport pandas as pd\nimport numpy as np\nimport requests\nimport logging\nimport copy\nimport gym\nimport os\n\nfrom .utils.molecules import get_valid_actions, utils\n\n\nlogger = logging.getLogger(__name__)\n\n\n_qm9_url = \"https://github.com/globus-labs/g4mp2-atomization-energy/raw/master/data/output/g4mp2_data.json.gz\"\n_qm9_path = os.path.join(os.path.dirname(__file__), 'data', 'qm9.json.gz')\n\n\ndef _compute_canonical_smiles(smiles: str) -> str:\n \"\"\"Make a SMILES string canonical\n\n Args:\n smiles (str): Smiles string\n Return:\n (str) Canonical smiles\n \"\"\"\n\n mol = Chem.MolFromSmiles(smiles)\n return Chem.MolToSmiles(mol)\n\n\ndef compile_smiles(mol) -> str:\n \"\"\"Compute the InCHi string of a RDKit molecule object\n\n Args:\n mol (Mol): RDKit molecule\n Returns:\n (str) InChI string\n \"\"\"\n return Chem.MolToSmiles(mol)\n\n\nclass QM9Space(Space):\n \"\"\"An observation space that consists of molecules in the QM9 dataset\"\"\"\n\n def __init__(self, use_cached_data=True):\n \"\"\"\n Args:\n use_cached_data (bool): Whether to use cached version of\n the QM9 dataset, or download a fresh copy\n \"\"\"\n super().__init__()\n\n # Download the data if needed\n if not os.path.exists(_qm9_path) or not use_cached_data:\n self._download_data()\n\n # Prepare the property lookup table\n self._data = None\n self._mols = None\n self._make_lookup_table()\n\n def molecules(self):\n \"\"\"List of all molecules in the design space\"\"\"\n return list(self._mols)\n\n def sample(self):\n smiles = self.np_random.choice(self._mols)\n return Chem.MolFromSmiles(smiles)\n\n def contains(self, x):\n \"\"\"\n Args:\n x (str): InChI string of a molecule\n \"\"\"\n return x in self._data\n\n def _make_lookup_table(self):\n \"\"\"Read in the data from disk\"\"\"\n # Read it from disk\n data = pd.read_json(_qm9_path, lines=True)\n\n # Get the inchi key of the original structure (defined by smiles_0)\n with Pool(processes=None) as p:\n data['canon_smiles'] = p.map(_compute_canonical_smiles, data['smiles_0'])\n data.drop_duplicates('canon_smiles', inplace=True, keep='first')\n data.set_index('canon_smiles', inplace=True)\n\n # Save as a dictionary\n self._data = data.to_dict('index')\n self._mols = sorted(self._data.keys())\n\n def get_molecule_properties(self, smiles: str, properties: List[str]) -> List[float]:\n \"\"\"\n Args:\n smiles (str): Canonical SMILES string of a certain molecule\n properties (list): List of properties to retrieve\n Returns:\n ([float]) List of properties for that molecule\n \"\"\"\n mol_props = self._data[smiles]\n return [mol_props[p] for p in properties]\n\n def to_dataframe(self):\n \"\"\"Get the design space as a dataframe\"\"\"\n return pd.DataFrame.from_dict(self._data, orient='index')\n\n def _download_data(self):\n \"\"\"Download the QM9 data\"\"\"\n\n logger.info(f'Downloading data from {_qm9_url} to {_qm9_path}')\n # Make sure the data path is available for saving\n data_dir = os.path.dirname(_qm9_path)\n if not os.path.exists(data_dir):\n os.makedirs(data_dir)\n\n # Download and save the file\n req = requests.get(_qm9_url, stream=True)\n with open(_qm9_path, 'wb') as fp:\n for chunk in req.iter_content(1024 ** 2):\n fp.write(chunk)\n\n\nclass MoleculeActions(Space):\n \"\"\"Action space for molecule design\n\n Generates which molecules are possible next steps and stores\n them as potential actions, following the approach of\n `Zhou et al. `_.\"\"\"\n\n def __init__(self, atom_types, allow_removal=True, allow_no_modification=False,\n allow_bonds_between_rings=True, allowed_ring_sizes=None,\n fingerprint_size=2048, fingerprint_radius=3):\n \"\"\"\n Args:\n atom_types: The set of elements the molecule may contain.\n state. If None, an empty molecule will be created.\n allow_removal: Boolean. Whether to allow removal of a bond.\n allow_no_modification: Boolean. If true, the valid action set will\n include doing nothing to the current molecule, i.e., the current\n molecule itself will be added to the action set.\n allow_bonds_between_rings: Boolean. If False, new bonds connecting two\n atoms which are both in rings are not allowed.\n DANGER Set this to False will disable some of the transformations eg.\n c2ccc(Cc1ccccc1)cc2 -> c1ccc3c(c1)Cc2ccccc23\n But it will make the molecules generated make more sense chemically.\n allowed_ring_sizes: Set of integers or None. The size of the ring which\n is allowed to form. If None, all sizes will be allowed. If a set is\n provided, only sizes in the set is allowed.\n fingerprint_size (int): Length of the fingerprint used to represent each molecule\n fingerprint_radius (int): Size of the radius to include for the\n \"\"\"\n\n super().__init__((None, fingerprint_size), np.int)\n\n # Store the rules for defining actions\n self.atom_types = atom_types\n self.allow_removal = allow_removal\n self.allow_no_modification = allow_no_modification\n self.allow_bonds_between_rings = allow_bonds_between_rings\n self.allowed_ring_sizes = allowed_ring_sizes\n self._state = None\n self._valid_actions = []\n self._max_bonds = 4\n atom_types = list(self.atom_types)\n self._max_new_bonds = dict(\n list(zip(atom_types, utils.atom_valences(atom_types)))\n )\n\n # Store the function for computing features\n self.fingerprint_function = partial(compute_morgan_fingerprints,\n fingerprint_length=fingerprint_size,\n fingerprint_radius=fingerprint_radius)\n\n # Placeholders for action space\n self._valid_actions = []\n self._valid_actions_featurized = []\n\n def sample(self):\n return self.np_random.randint(0, len(self._valid_actions))\n\n def contains(self, x):\n return x in self._valid_actions_featurized\n\n @property\n def n(self):\n return len(self._valid_actions)\n\n def get_possible_actions(self, smiles=False):\n \"\"\"Get the possible actions given the current state\n\n Args:\n smiles (bool): Whether to return the smiles strings, or the featurized molecules\n Returns:\n (ndarray) List of the possible actions\n \"\"\"\n output = self._valid_actions if smiles else self._valid_actions_featurized\n return copy.deepcopy(output)\n\n def update_actions(self, new_state, allowed_space: Space):\n \"\"\"Generate the available actions for a new state\n\n Uses the actions to redefine the action space for\n\n Args:\n new_state (str): Molecule used to define action space\n allowed_space (Space): Space of possible observations\n \"\"\"\n\n # Store the new state\n self._state = new_state\n\n # Compute the possible actions, which we describe by the new molecule they would form\n self._valid_actions = get_valid_actions(\n new_state,\n atom_types=self.atom_types,\n allow_removal=self.allow_removal,\n allow_no_modification=self.allow_no_modification,\n allowed_ring_sizes=self.allowed_ring_sizes,\n allow_bonds_between_rings=self.allow_bonds_between_rings)\n\n # Get only those actions which are in the desired space\n self._valid_actions = np.array([x for x in self._valid_actions\n if _compute_canonical_smiles(x) in allowed_space])\n\n # Compute the features for the next states\n self._valid_actions_featurized = np.array([self.fingerprint_function(m)\n for m in self._valid_actions])\n\n def get_smiles_from_fingerprint(self, action):\n \"\"\"Lookup the smiles string for an action given its fingerprint\n\n Args:\n action (ndarray): Fingerprint of a certain action\n Returns:\n (str) SMILES string associated with that action\n \"\"\"\n\n for fingerprint, smiles in zip(self._valid_actions_featurized, self._valid_actions):\n if np.array_equal(fingerprint, action):\n return smiles\n raise ValueError('Action not found in current action space')\n\n\nclass Molecule(gym.Env):\n \"\"\"Defines the Markov decision process of generating a molecule.\n\n Adapted from: https://github.com/google-research/google-research/blob/master/mol_dqn/chemgraph/dqn/molecules.py\"\"\"\n\n def __init__(self, action_space: MoleculeActions = None, observation_space=None,\n init_mol=None, max_steps=10,\n target_fn=None, record_path=False, fingerprint_size=2048, fingerprint_radius=3):\n \"\"\"Initializes the parameters for the MDP.\n\n Internal state will be stored as SMILES strings, but but the environment will\n return the new state as an ML-ready fingerprint\n\n Args:\n init_mol: String, Chem.Mol, or Chem.RWMol. If string is provided, it is\n considered as the SMILES string. The molecule to be set as the initial\n state. If None, an empty molecule will be created.\n max_steps: Integer. The maximum number of steps to run.\n target_fn: A function or None. The function should have Args of a\n String, which is a SMILES string (the state), and Returns as\n a Boolean which indicates whether the input satisfies a criterion.\n If None, it will not be used as a criterion.\n record_path: Boolean. Whether to record the steps internally.\n \"\"\"\n\n # Capture the user settings\n if action_space is None:\n action_space = MoleculeActions(['C', 'O', 'N', 'F'])\n if observation_space is None:\n observation_space = QM9Space()\n self.action_space = action_space\n self.init_mol = init_mol\n self.max_steps = max_steps\n self.target_fn = target_fn\n self.record_path = record_path\n self.observation_space = observation_space\n\n # Store the function used to compute inputs\n self.fingerprint_function = partial(compute_morgan_fingerprints,\n fingerprint_length=fingerprint_size,\n fingerprint_radius=fingerprint_radius)\n\n # Define the state variables\n self._state = None\n self._state_fingerprint = None\n self._path = None\n self._counter = None\n\n # Ready the environment\n self.reset()\n\n @property\n def num_steps_taken(self):\n return self._counter\n\n @property\n def state(self):\n \"\"\"State as a SMILES string\"\"\"\n return self._state\n\n def get_path(self):\n return list(self._path)\n\n def reset(self):\n \"\"\"Resets the MDP to its initial state.\"\"\"\n self._state = self.init_mol\n self._state_fingerprint = self.fingerprint_function(self._state)\n self.action_space.update_actions(self._state, self.observation_space)\n if self.record_path:\n self._path = [self._state]\n self._counter = 0\n\n def _reward(self):\n \"\"\"Gets the reward for the state.\n\n A child class can redefine the reward function if reward other than\n zero is desired.\n\n Returns:\n Float. The reward for the current state.\n \"\"\"\n smiles = _compute_canonical_smiles(self._state)\n return -1 * self.observation_space.get_molecule_properties(smiles, ['g4mp2_atom'])[0]\n\n def step(self, action):\n \"\"\"Takes a step forward according to the action.\n\n Args:\n action (ndarray): Fingerprint of action\n\n Raises:\n ValueError: If the number of steps taken exceeds the preset max_steps, or\n the action is not in the set of valid_actions.\n\n \"\"\"\n if self._counter >= self.max_steps:\n raise ValueError('This episode is terminated.')\n\n # Get the SMILES string associated with this action\n self._state = self.action_space.get_smiles_from_fingerprint(action)\n if self.record_path:\n self._path.append(self._state)\n\n # Store the fingerprint of the state\n self._state_fingerprint = self.fingerprint_function(self._state)\n\n # Update the action space\n self.action_space.update_actions(self._state, self.observation_space)\n self._counter += 1\n\n # Check if we have finished\n # Out of steps or no more moves\n done = ((self._counter >= self.max_steps) or\n len(self.action_space.get_possible_actions(smiles=True)) == 0)\n\n # Compute the fingerprints for the state\n return self._state_fingerprint, self._reward(), done, {}\n\n def render(self, mode='human', **kwargs):\n \"\"\"Draws the molecule of the state.\n\n Args:\n **kwargs: The keyword arguments passed to Draw.MolToImage.\n\n Returns:\n A PIL image containing a drawing of the molecule.\n \"\"\"\n return Draw.MolToImage(self._state, **kwargs)\n\n\ndef compute_morgan_fingerprints(smiles, fingerprint_length, fingerprint_radius):\n \"\"\"Get Morgan Fingerprint of a specific SMILES string.\n\n Adapted from: \n\n Args:\n smiles: String. The SMILES string of the molecule.\n fingerprint_length (int): Bit-length of fingerprint\n fingerprint_radius (int): Radius used to compute fingerprint\n Returns:\n np.array. shape = [hparams.fingerprint_length]. The Morgan fingerprint.\n \"\"\"\n if smiles is None: # No smiles string\n return np.zeros((fingerprint_length,))\n molecule = Chem.MolFromSmiles(smiles)\n if molecule is None: # Invalid smiles string\n return np.zeros((fingerprint_length,))\n\n # Compute the fingerprint\n fingerprint = AllChem.GetMorganFingerprintAsBitVect(\n molecule, fingerprint_radius, fingerprint_length)\n arr = np.zeros((1,))\n\n # ConvertToNumpyArray takes ~ 0.19 ms, while\n # np.asarray takes ~ 4.69 ms\n DataStructs.ConvertToNumpyArray(fingerprint, arr)\n return arr\n", "repo_name": "WardLT/molecular-dqn", "sub_path": "molgym/envs/exalearn_electrolyte_design.py", "file_name": "exalearn_electrolyte_design.py", "file_ext": "py", "file_size_in_byte": 14956, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 23, "usage_type": "call"}, {"api_name": "rdkit.Chem.MolFromSmiles", "line_number": 35, "usage_type": "call"}, {"api_name": "rdkit.Chem", "line_number": 35, "usage_type": "name"}, {"api_name": "rdkit.Chem.MolToSmiles", "line_number": 36, "usage_type": "call"}, {"api_name": "rdkit.Chem", "line_number": 36, "usage_type": "name"}, {"api_name": "rdkit.Chem.MolToSmiles", "line_number": 47, "usage_type": "call"}, {"api_name": "rdkit.Chem", "line_number": 47, "usage_type": "name"}, {"api_name": "gym.Space", "line_number": 50, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "rdkit.Chem.MolFromSmiles", "line_number": 76, "usage_type": "call"}, {"api_name": "rdkit.Chem", "line_number": 76, "usage_type": "name"}, {"api_name": "pandas.read_json", "line_number": 88, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 91, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 100, "usage_type": "name"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 113, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 113, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 120, "usage_type": "call"}, {"api_name": "os.path", "line_number": 120, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 122, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 125, "usage_type": "call"}, {"api_name": "gym.Space", "line_number": 131, "usage_type": "name"}, {"api_name": "numpy.int", "line_number": 161, "usage_type": "attribute"}, {"api_name": "utils.molecules.utils.atom_valences", "line_number": 174, "usage_type": "call"}, {"api_name": "utils.molecules.utils", "line_number": 174, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 178, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 205, "usage_type": "call"}, {"api_name": "gym.Space", "line_number": 207, "usage_type": "name"}, {"api_name": "utils.molecules.get_valid_actions", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 247, "usage_type": "call"}, {"api_name": "gym.Env", "line_number": 252, "usage_type": "attribute"}, {"api_name": "functools.partial", "line_number": 290, "usage_type": "call"}, {"api_name": "rdkit.Chem.Draw.MolToImage", "line_number": 379, "usage_type": "call"}, {"api_name": "rdkit.Chem.Draw", "line_number": 379, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 396, "usage_type": "call"}, {"api_name": "rdkit.Chem.MolFromSmiles", "line_number": 397, "usage_type": "call"}, {"api_name": "rdkit.Chem", "line_number": 397, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 399, "usage_type": "call"}, {"api_name": "rdkit.Chem.AllChem.GetMorganFingerprintAsBitVect", "line_number": 402, "usage_type": "call"}, {"api_name": "rdkit.Chem.AllChem", "line_number": 402, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 404, "usage_type": "call"}, {"api_name": "rdkit.DataStructs.ConvertToNumpyArray", "line_number": 408, "usage_type": "call"}, {"api_name": "rdkit.DataStructs", "line_number": 408, "usage_type": "name"}]} +{"seq_id": "73865222740", "text": "def read_data(filename):\n with open(filename, 'r') as f:\n data = [line.split('\\t') for line in f.read().splitlines()]\n # txt 파일의 헤더(id document label)는 제외하기\n # data = data[1:]\n return data\n\ntrain_data = read_data('polls/dothings/data/data_train.txt')\ntest_data = read_data('polls/dothings/data/data_test.txt')\n\nfrom konlpy.tag import Okt\n\nokt = Okt()\nprint(okt.pos(u'이 밤 그날의 반딧불을 당신의 창 가까이 보낼게요'))\n\nimport json\nimport os\nfrom pprint import pprint\n\ndef tokenize(doc):\n # norm은 정규화, stem은 근어로 표시하기를 나타냄\n return ['/'.join(t) for t in okt.pos(doc, norm=True, stem=True)]\n\nif os.path.isfile('polls/dothings/data/train_docs.json'):\n with open('polls/dothings/data/train_docs.json') as f:\n train_docs = json.load(f)\n with open('polls/dothings/data/test_docs.json') as f:\n test_docs = json.load(f)\nelse:\n train_docs = [(tokenize(row[0]), row[1]) for row in train_data]\n test_docs = [(tokenize(row[0]), row[1]) for row in test_data]\n # JSON 파일로 저장\n with open('polls/dothings/data/train_docs.json', 'w', encoding=\"utf-8\") as make_file:\n json.dump(train_docs, make_file, ensure_ascii=False, indent=\"\\t\")\n with open('polls/dothings/data/test_docs.json', 'w', encoding=\"utf-8\") as make_file:\n json.dump(test_docs, make_file, ensure_ascii=False, indent=\"\\t\")\n\n# 예쁘게(?) 출력하기 위해서 pprint 라이브러리 사용\npprint(train_docs[0])\n\ntokens = [t for d in train_docs for t in d[0]]\n\nimport nltk\ntext = nltk.Text(tokens, name='NMSC')\n\nselected_words = [f[0] for f in text.vocab().most_common(10000)]\n\ndef term_frequency(doc):\n return [doc.count(word) for word in selected_words]\n\ntrain_x = [term_frequency(d) for d, _ in train_docs]\ntest_x = [term_frequency(d) for d, _ in test_docs]\ntrain_y = [c for _, c in train_docs]\ntest_y = [c for _, c in test_docs]\n\nimport numpy as np\n\nx_train = np.asarray(train_x).astype('float32')\nx_test = np.asarray(test_x).astype('float32')\n\ny_train = np.asarray(train_y).astype('float32')\ny_test = np.asarray(test_y).astype('float32')\n\ny_test = (y_test-1) / 4\ny_train = (y_train-1) / 4\n\nfrom tensorflow.keras import models\nfrom tensorflow.keras import layers\nfrom tensorflow.keras import optimizers\nfrom tensorflow.keras import losses\nfrom tensorflow.keras import metrics\nimport numpy as np\nfrom sklearn.metrics import roc_auc_score\n\nmodel = models.Sequential()\nmodel.add(layers.Dense(64, activation='relu', input_shape=(7861,)))\nmodel.add(layers.Dense(64, activation='relu'))\nmodel.add(layers.Dense(1, activation='sigmoid'))\n\nmodel.compile(optimizer=optimizers.RMSprop(lr=0.001),\n loss=losses.binary_crossentropy,\n metrics=[metrics.binary_accuracy])\n\nmodel.fit(x_train, y_train, epochs=100, batch_size=512)\n\nresults = model.evaluate(x_test, y_test)\n\ndef predict_pos_neg(review):\n token = tokenize(review)\n tf = term_frequency(token)\n data = np.expand_dims(np.asarray(tf).astype('float32'), axis=0)\n score = float(model.predict(data))\n return score\n\npredict_pos_neg(\"무시하지 마라 이놈아 \")\npredict_pos_neg(\"내가 나가기 싫은게 아니라 어쩔 수 없었움... 진짜...\")\npredict_pos_neg(\"예측 가능? 나는 불가능 \")\npredict_pos_neg(\"남의 물건을 이렇게 함부로 써도 되는거...? 나 진짜 너무 당황스럽다...\")\npredict_pos_neg(\"남는 시간을 쪼개서 하는 모습.. 므시땅~\")\npredict_pos_neg(\"구라도 정도껏쳐야지 ㅋㅋㅋ 누가 믿어\")\n", "repo_name": "lomotos10/MalJunSang", "sub_path": "JMNM/polls/dothings/things.py", "file_name": "things.py", "file_ext": "py", "file_size_in_byte": 3559, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "konlpy.tag.Okt", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 26, "usage_type": "call"}, {"api_name": "json.load", "line_number": 28, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 34, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 36, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 39, "usage_type": "call"}, {"api_name": "nltk.Text", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.Sequential", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.keras.models", "line_number": 75, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 76, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 77, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 78, "usage_type": "name"}, {"api_name": "tensorflow.keras.optimizers.RMSprop", "line_number": 80, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers", "line_number": 80, "usage_type": "name"}, {"api_name": "tensorflow.keras.losses.binary_crossentropy", "line_number": 81, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.losses", "line_number": 81, "usage_type": "name"}, {"api_name": "tensorflow.keras.metrics.binary_accuracy", "line_number": 82, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.metrics", "line_number": 82, "usage_type": "name"}, {"api_name": "numpy.expand_dims", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 91, "usage_type": "call"}]} +{"seq_id": "70773098576", "text": "import schedule\nimport time\nimport telebot\nimport zipfile\nimport os\nimport shutil\n\n\nfrom datetime import date, timedelta\nfrom config import token_b, s_k_id, path_dir\nfrom openpyxl import Workbook\nfrom openpyxl import load_workbook\ntry:\n os.chdir(path_dir)\nexcept:\n pass\npath_to_n_cont = 'n_count.txt'\n\ndef zeroing_data():\n date_yesterday = str(date.today() - timedelta(days=1))\n os.remove(f'excel_files/{date_yesterday}.xlsx')\n os.remove(f'excel_files/report.xlsx')\n os.remove(f'{date_yesterday}_photo.zip')\n os.remove(f'excel_files/spare_{date_yesterday}.xlsx')\n\n shutil.rmtree('photo')\n os.mkdir('photo')\n with open(path_to_n_cont, 'w') as f:\n f.write(str(2))\n pass\n\n\ndef job():\n try:\n bot = telebot.TeleBot(token_b)\n date_yesterday = str(date.today() - timedelta(days=1))\n\n\n if os.path.exists('excel_files/report.xlsx'):\n with open(path_to_n_cont, 'r') as f:\n n = int(f.read().strip())\n wb_obj = load_workbook('excel_files/report.xlsx')\n sheet_obj = wb_obj.active\n sheet_obj[f'G{n}'] = f'= sum(G2:G{n-1})'\n sheet_obj[f'F{n}'] = 'Сумма заказов:'\n wb_obj.save(f'excel_files/{date_yesterday}.xlsx')\n bot.send_message(s_k_id, 'Доброе утро')\n bot.send_document(s_k_id, data=open(f'excel_files/{date_yesterday}.xlsx', 'rb'),\n caption=f'Отчет за {date_yesterday}')\n\n fantasy_zip = zipfile.ZipFile(f'{date_yesterday}_photo.zip', 'w')\n\n for folder, subfolders, files in os.walk('photo'):\n\n for file in files:\n if file.endswith('.jpg'):\n fantasy_zip.write(os.path.join(folder, file),\n os.path.relpath(os.path.join(folder, file), 'photo'),\n compress_type=zipfile.ZIP_DEFLATED)\n\n fantasy_zip.close()\n bot.send_document(s_k_id, data=open(f'{date_yesterday}_photo.zip', 'rb'),\n caption=f'фото машин за {date.today() - timedelta(days=1)}')\n del fantasy_zip\n del files\n del folder\n del wb_obj\n\n if os.path.exists('excel_files/spare.xlsx'):\n\n os.rename('excel_files/spare.xlsx', f'excel_files/spare_{date_yesterday}.xlsx')\n wb_sp = load_workbook(f'excel_files/spare_{date_yesterday}.xlsx')\n sheet_sp = wb_sp.active\n wb_sp.save(f'excel_files/spare.xlsx')\n\n bot.send_document(s_k_id, data=open(f'excel_files/spare_{date_yesterday}.xlsx', 'rb'),\n caption=f'Отчет запчастей за {date.today() - timedelta(days=1)}')\n del wb_sp\n zeroing_data()\n except Exception as es:\n with open('error.txt', 'a') as f:\n f.write(str(es))\n\n\nschedule.every().day.at(\"07:00\").do(job)\n\nwhile True:\n schedule.run_pending()\n txt = input()\n if txt == 'send':\n job()\n time.sleep(60)\n", "repo_name": "flild/Telegram_bot_car_wash", "sub_path": "sender_of_day.py", "file_name": "sender_of_day.py", "file_ext": "py", "file_size_in_byte": 3100, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "14", "api": [{"api_name": "os.chdir", "line_number": 14, "usage_type": "call"}, {"api_name": "config.path_dir", "line_number": 14, "usage_type": "argument"}, {"api_name": "datetime.date.today", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 20, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 20, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 21, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 22, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 23, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 24, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 26, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 27, "usage_type": "call"}, {"api_name": "telebot.TeleBot", "line_number": 35, "usage_type": "call"}, {"api_name": "config.token_b", "line_number": 35, "usage_type": "argument"}, {"api_name": "datetime.date.today", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 36, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "openpyxl.load_workbook", "line_number": 42, "usage_type": "call"}, {"api_name": "config.s_k_id", "line_number": 47, "usage_type": "argument"}, {"api_name": "config.s_k_id", "line_number": 48, "usage_type": "argument"}, {"api_name": "zipfile.ZipFile", "line_number": 51, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.path.relpath", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 58, "usage_type": "call"}, {"api_name": "zipfile.ZIP_DEFLATED", "line_number": 59, "usage_type": "attribute"}, {"api_name": "config.s_k_id", "line_number": 62, "usage_type": "argument"}, {"api_name": "datetime.date.today", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 63, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 71, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 72, "usage_type": "call"}, {"api_name": "config.s_k_id", "line_number": 76, "usage_type": "argument"}, {"api_name": "datetime.date.today", "line_number": 77, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 77, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 77, "usage_type": "call"}, {"api_name": "schedule.every", "line_number": 85, "usage_type": "call"}, {"api_name": "schedule.run_pending", "line_number": 88, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "18660010958", "text": "import scrapy\nfrom scrapy.item import Item, Field\n\nclass PropertiesItem(scrapy.Item):\n title = Field()\n price = Field()\n description = Field()\n address = Field()\n image_URL = Field()\n\n # calcuated field\n images = Field()\n location = Field()\n\n # housekeepint field\n url = Field()\n project = Field()\n spider = Field()\n server = Field()\n date = Field()", "repo_name": "waynekingcool/StudyScrapy", "sub_path": "tutorial/tutorial/myItem/PropertiesItem.py", "file_name": "PropertiesItem.py", "file_ext": "py", "file_size_in_byte": 391, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "scrapy.Item", "line_number": 4, "usage_type": "attribute"}, {"api_name": "scrapy.item.Field", "line_number": 5, "usage_type": "call"}, {"api_name": "scrapy.item.Field", "line_number": 6, "usage_type": "call"}, {"api_name": "scrapy.item.Field", "line_number": 7, "usage_type": "call"}, {"api_name": "scrapy.item.Field", "line_number": 8, "usage_type": "call"}, {"api_name": "scrapy.item.Field", "line_number": 9, "usage_type": "call"}, {"api_name": "scrapy.item.Field", "line_number": 12, "usage_type": "call"}, {"api_name": "scrapy.item.Field", "line_number": 13, "usage_type": "call"}, {"api_name": "scrapy.item.Field", "line_number": 16, "usage_type": "call"}, {"api_name": "scrapy.item.Field", "line_number": 17, "usage_type": "call"}, {"api_name": "scrapy.item.Field", "line_number": 18, "usage_type": "call"}, {"api_name": "scrapy.item.Field", "line_number": 19, "usage_type": "call"}, {"api_name": "scrapy.item.Field", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "10501138825", "text": "import abc\nfrom typing import Generic, List, Optional, TypeVar\n\nfrom data.asr.batch.batch import Batch\nfrom data.asr.batch.property import Property\nfrom data.trans import Trans\n\nT = TypeVar('T')\n\n\nclass BatchBuilder(Generic[T], abc.ABC):\n\n def __init__(self, properties_setters: Optional[List[Trans[Batch[T], Property]]] = None):\n if properties_setters is None:\n self._properties_setters = []\n else:\n self._properties_setters = properties_setters\n\n @abc.abstractmethod\n def _build(self, batch: List[T]) -> Batch[T]:\n pass\n\n def __call__(self, batch_src: List[T]) -> Batch[T]:\n batch: Batch[T] = self._build(batch_src)\n for setter in self._properties_setters:\n prop = setter(batch_src)\n batch.properties[prop.name] = prop.value\n return batch\n", "repo_name": "cant-access-rediska0123/multi-channel-tranformer", "sub_path": "data/asr/batch_builder/batch_builder.py", "file_name": "batch_builder.py", "file_ext": "py", "file_size_in_byte": 839, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "12", "api": [{"api_name": "typing.TypeVar", "line_number": 8, "usage_type": "call"}, {"api_name": "typing.Generic", "line_number": 11, "usage_type": "name"}, {"api_name": "abc.ABC", "line_number": 11, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 13, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 13, "usage_type": "name"}, {"api_name": "data.trans.Trans", "line_number": 13, "usage_type": "name"}, {"api_name": "data.asr.batch.batch.Batch", "line_number": 13, "usage_type": "name"}, {"api_name": "data.asr.batch.property.Property", "line_number": 13, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 20, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 19, "usage_type": "attribute"}, {"api_name": "data.asr.batch.batch.Batch", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 23, "usage_type": "name"}, {"api_name": "data.asr.batch.batch.Batch", "line_number": 24, "usage_type": "name"}, {"api_name": "data.asr.batch.batch.Batch", "line_number": 23, "usage_type": "name"}]} +{"seq_id": "18690862654", "text": "# posts\nfrom fastapi import APIRouter, Depends, HTTPException, status\nfrom typing import List\nfrom .. import schemas, models, database, oauth2\nfrom sqlalchemy.orm import Session\nfrom typing import Optional\nfrom sqlalchemy import func\n\nrouter = APIRouter(\n prefix = \"/posts\", tags = ['posts']\n)\n\n# GET ALL\n@router.get('/', response_model = List[schemas.PostResponse]) # curr_user: int = Depends(oauth2.get_active_user),\n# @router.get('/', response_model = List[schemas.PostVoteResponse])\nasync def get_posts(db: Session = Depends(database.get_db), limit: int = 10, skip: int = 0, search: Optional[str] = \"\"):\n post_query = db.query(models.Post).filter(models.Post.title.contains(search)).limit(limit).offset(skip)\n # post_query = db.query(models.Post).filter(models.Post.user_id == curr_user.id).filter(models.Post.title.contains(search)).limit(limit).offset(skip)\n # print(2222, curr_user.email)\n # post_query = db.query(models.Post, func.count(models.Vote.post_id).label(\"vote_counts\")).join(models.Vote, models.Post.id == models.Vote.post_id, isouter = True).group_by(models.Post.id).filter(models.Post.title.contains(search)).limit(limit).offset(skip)\n # print(1111, post_query)\n post_result = post_query.all()\n if not post_result:\n raise HTTPException(status_code = status.HTTP_404_NOT_FOUND, detail = \"No more posts available\")\n # return {\"data\": post_result}\n # print(2222, post_result)\n return post_result\n\n# GET ONE ANY USER CONTENT DETAIL VIEW\n@router.get('/{id}', response_model = schemas.PostResponseDetail)\nasync def get_post(id: int, db: Session = Depends(database.get_db), curr_user: int = Depends(oauth2.get_active_user)):\n post_query = db.query(models.Post).filter(models.Post.id == str(id))\n print(22222222, post_query)\n print(33333333, curr_user.email)\n db_post = post_query.first()\n if not db_post:\n raise HTTPException(status_code = status.HTTP_404_NOT_FOUND, detail = \"Invalid Post {}\".format(str(id)))\n # return {\"data\": db_post} after Response Model\n return db_post\n\n# POST\n@router.post('/', status_code = status.HTTP_201_CREATED, response_model = schemas.PostResponseDetail)\nasync def create_post(post: schemas.PostRequest, db: Session = Depends(database.get_db), curr_user: int = Depends(oauth2.get_active_user)):\n current_user_id = curr_user.id\n db_post = models.Post(user_id = current_user_id, **post.dict())\n # db_post = models.Post(title = post.title, content = post.content, is_published = post.is_published)\n db.add(db_post)\n db.commit()\n db.refresh(db_post)\n # return {\"data\": db_post}\n return db_post\n\n# PATCH \n@router.patch('/{id}', response_model = schemas.PostResponse)\nasync def patch_post(id: int, post: schemas.PostRequest, db: Session = Depends(database.get_db)):\n db_post = db.query(models.Post).filter(models.Post.id == str(id)).first()\n if not db_post:\n raise HTTPException(status_code = status.HTTP_404_NOT_FOUND, detail = \"Invalid Post {}\".format(str(id)))\n db_post.title = post.title\n db_post.content = post.content\n db_post.is_published = post.is_published\n db.add(db_post)\n db.commit()\n db.refresh(db_post)\n # return {\"data\": db_post}\n return db_post\n\n# PUT\n@router.put('/{id}', response_model = schemas.PostResponseDetail)\nasync def update_post(id: int, post: schemas.PostRequest, db: Session = Depends(database.get_db), curr_user: int = Depends(oauth2.get_active_user)):\n db_query = db.query(models.Post).filter(models.Post.id == id)\n if not db_query.first():\n raise HTTPException(status_code = status.HTTP_404_NOT_FOUND, detail = \"Invalid Post {}\".format(str(id)))\n else:\n db_post = db_query.first()\n print(999, db_post.user_id, type(db_post.user_id), curr_user.id, type(curr_user.id))\n if db_post.user_id != curr_user.id:\n raise HTTPException(status_code = status.HTTP_403_FORBIDDEN, detail = \"You are not having priviledged to perform this request\")\n db_query.update(post.dict(), synchronize_session = False)\n db.commit()\n # return {\"data\": db_query.first()}\n return db_query.first()\n\n# DELETE\n@router.delete('/{id}', status_code = status.HTTP_204_NO_CONTENT)\nasync def delete_post(id: int, db: Session = Depends(database.get_db), curr_user: int = Depends(oauth2.get_active_user)):\n # db_post = db.query(models.Post).filter(models.Post.id == str(id)).first()\n # if not db_post:\n # raise HTTPException(status_code = status.HTTP_404_NOT_FOUND, detail = \"Invalid Post {}\".format(str(id)))\n # db.delete(db_post)\n # db.commit()\n\n db_query = db.query(models.Post).filter(models.Post.id == str(id))\n print(444, db_query, type(db_query), type(db_query.first()), db_query.first())\n if not db_query.first():\n raise HTTPException(status_code = status.HTTP_404_NOT_FOUND, detail = \"Invalid Post {}\".format(str(id)))\n else:\n db_post = db_query.first()\n if db_post.user_id != curr_user.id:\n raise HTTPException(status_code = status.HTTP_403_FORBIDDEN, detail = \"You are not allowed to delete this resource\")\n db_query.delete(synchronize_session = False)\n db.commit()", "repo_name": "luqmancode/fastapiYouT", "sub_path": "app/routers/posts.py", "file_name": "posts.py", "file_ext": "py", "file_size_in_byte": 5165, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "fastapi.APIRouter", "line_number": 9, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 16, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 16, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 16, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 24, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_404_NOT_FOUND", "line_number": 24, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 14, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 31, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 31, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 37, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_404_NOT_FOUND", "line_number": 37, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 37, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 43, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 43, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_201_CREATED", "line_number": 42, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 42, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 55, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 55, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 58, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_404_NOT_FOUND", "line_number": 58, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 58, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 70, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 70, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 73, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_404_NOT_FOUND", "line_number": 73, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 73, "usage_type": "name"}, {"api_name": "fastapi.HTTPException", "line_number": 78, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_403_FORBIDDEN", "line_number": 78, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 78, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 86, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 86, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 96, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_404_NOT_FOUND", "line_number": 96, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 96, "usage_type": "name"}, {"api_name": "fastapi.HTTPException", "line_number": 100, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_403_FORBIDDEN", "line_number": 100, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 100, "usage_type": "name"}, {"api_name": "fastapi.status.HTTP_204_NO_CONTENT", "line_number": 85, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 85, "usage_type": "name"}]} +{"seq_id": "32281858832", "text": "import cv2\n# from picamera import PiCamera\n\n# Open the default camera (you can specify a different camera index if needed)\n\n# Function for mouse events\ndef click_event(event, x, y, flags, param):\n if event == cv2.EVENT_LBUTTONDOWN:\n # Convert the coordinates back to the original image size\n true_x = int(x / scale_factor)\n true_y = int(y / scale_factor)\n print(f'True Coordinates: ({true_x}, {true_y})')\n\n# Set the callback function for mouse events\ncv2.namedWindow('Resized Camera Feed')\ncv2.setMouseCallback('Resized Camera Feed', click_event)\n\n# Specify the desired width for resizing\ntarget_width = 800\n\nwhile True:\n # Read a frame from the camera\n # ret, frame = cap.read()\n # camera = PiCamera()\n # camera.start_preview()\n # sleep(0.2)\n # camera.capture('lineDetection.jpg')\n # camera.close()\n # frame = cv2.imread('lineDetection.jpg')\n # frame = cv2.imread(f\"imgs/original/{0}.jpg\")\n frame = cv2.imread(f\"imgs_db/original/{0}.jpg\")\n\n # Calculate the scale factor\n scale_factor = target_width / frame.shape[1]\n\n # Resize the frame\n resized_frame = cv2.resize(frame, (target_width, int(frame.shape[0] * scale_factor)))\n\n # Display the resized frame\n cv2.imshow('Resized Camera Feed', resized_frame)\n\n # Break the loop if 'q' is pressed\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\n# Release the camera and close all windows\ncv2.destroyAllWindows()\n", "repo_name": "barisayyildiz/cse396-codes", "sub_path": "converter/camtest.py", "file_name": "camtest.py", "file_ext": "py", "file_size_in_byte": 1448, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "cv2.EVENT_LBUTTONDOWN", "line_number": 8, "usage_type": "attribute"}, {"api_name": "cv2.namedWindow", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.setMouseCallback", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "11399426496", "text": "from django import forms\n\n\nclass BootstrapForm(forms.Form):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n for visible in self.visible_fields():\n visible.field.widget.attrs['class'] = 'form-control'\n\n\nclass RepoForm(BootstrapForm):\n repository = forms.CharField(label=\"Repository's URL\")\n\n def clean_repository(self):\n repository_url = self.cleaned_data['repository']\n\n if not repository_url.startswith('git@'):\n raise forms.ValidationError(\"Not a SSH URL\")\n\n return repository_url\n\n\n\nclass SchedulerForm(BootstrapForm):\n\n MODE_AUTOMATIC = 'automatic'\n MODE_MANUAL = 'manual'\n\n MODE_CHOICES = [\n (MODE_AUTOMATIC, MODE_AUTOMATIC),\n (MODE_MANUAL, MODE_MANUAL)\n ]\n\n mode = forms.ChoiceField(choices=MODE_CHOICES, initial=MODE_AUTOMATIC)\n\n INTERVAL_CHOICES = [\n ('daily', 'daily'),\n ('weekly', 'weekly'),\n ('monthly', 'monthly')\n ]\n\n interval = forms.ChoiceField(choices=INTERVAL_CHOICES)\n", "repo_name": "Jancso/corpus-manager", "sub_path": "backup/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1037, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "12", "api": [{"api_name": "django.forms.Form", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 4, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 12, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 18, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 18, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 34, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 42, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 42, "usage_type": "name"}]} +{"seq_id": "13275478222", "text": "from django.contrib import admin\nfrom django.urls import path, include\nfrom rest_framework_simplejwt import views as jwt_views\nfrom rest_framework import permissions\nfrom drf_yasg.views import get_schema_view\nfrom drf_yasg import openapi\n\nschema_view = get_schema_view(\n openapi.Info(\n title=\"Employee CRUD API\",\n default_version='v1',\n description=\"Todo app test\",\n terms_of_service=\"https://www.google.com/policies/terms/\",\n contact=openapi.Contact(email=\"contact@xyz.local\"),\n license=openapi.License(name=\"BSD License\"),\n ),\n public=True,\n permission_classes=(permissions.AllowAny,),\n)\nurlpatterns = [ # pylint: disable=C0103\n path('admin/', admin.site.urls),\n path('', include('todo_app.urls')),\n path('auth/', include('rest_framework.urls')),\n path('api/login/', jwt_views.TokenObtainPairView.as_view(), name='token_obtain_pair'),\n path('api/token/refresh/', jwt_views.TokenRefreshView.as_view(), name='token_refresh'),\n path('swagger/', schema_view.with_ui('swagger', cache_timeout=0), name='schema-swagger-ui'),\n path('api/password_reset/', include('django_rest_passwordreset.urls', namespace='password_reset')),\n # path('api/logout /', jwt_views.TokenObtainPairView.as_view(), name='token_obtain_pair'),\n # path('accounts/logout/', jwt_views.TokenObtainPairView.as_view(), name='token_obtain_pair'),\n # path('accounts/login/', jwt_views.TokenObtainPairView.as_view(), name='token_obtain_pair'),\n]\n", "repo_name": "pakeeza22/DRF_Todo_API", "sub_path": "DRF_Todo/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1491, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "drf_yasg.views.get_schema_view", "line_number": 8, "usage_type": "call"}, {"api_name": "drf_yasg.openapi.Info", "line_number": 9, "usage_type": "call"}, {"api_name": "drf_yasg.openapi", "line_number": 9, "usage_type": "name"}, {"api_name": "drf_yasg.openapi.Contact", "line_number": 14, "usage_type": "call"}, {"api_name": "drf_yasg.openapi", "line_number": 14, "usage_type": "name"}, {"api_name": "drf_yasg.openapi.License", "line_number": 15, "usage_type": "call"}, {"api_name": "drf_yasg.openapi", "line_number": 15, "usage_type": "name"}, {"api_name": "rest_framework.permissions.AllowAny", "line_number": 18, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 18, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 21, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "rest_framework_simplejwt.views.TokenObtainPairView.as_view", "line_number": 24, "usage_type": "call"}, {"api_name": "rest_framework_simplejwt.views.TokenObtainPairView", "line_number": 24, "usage_type": "attribute"}, {"api_name": "rest_framework_simplejwt.views", "line_number": 24, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "rest_framework_simplejwt.views.TokenRefreshView.as_view", "line_number": 25, "usage_type": "call"}, {"api_name": "rest_framework_simplejwt.views.TokenRefreshView", "line_number": 25, "usage_type": "attribute"}, {"api_name": "rest_framework_simplejwt.views", "line_number": 25, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "34597655618", "text": "from http.server import BaseHTTPRequestHandler, HTTPServer\nimport requests\n\n\n# The handler for the HTTP requests.\nclass RequestHandler(BaseHTTPRequestHandler):\n # GET\n def do_GET(self):\n # Send response status code\n self.send_response(200)\n\n # Send headers\n self.send_header('Content-type', 'text/html')\n self.end_headers()\n\n # Get the message from data processing and send it to the client\n data_request = requests.get('http://localhost:8081')\n\n if(data_request.status_code != 200):\n # An error has occurred\n message = \"Error.\"\n else:\n message = data_request.text\n\n # Write content as utf-8 data\n self.wfile.write(bytes(message, \"utf8\"))\n return\n\n\ndef run():\n print('starting server...')\n\n # Server settings\n # Choose port 8082, for port 80, which is normally used for a http server, you need root access\n server_address = ('127.0.0.1', 8082)\n httpd = HTTPServer(server_address, RequestHandler)\n print('running server...')\n httpd.serve_forever()\n\n\nrun()", "repo_name": "splovyt/PatientMatchingAlgorithm", "sub_path": "hello_world/web_service/web_service.py", "file_name": "web_service.py", "file_ext": "py", "file_size_in_byte": 1098, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "http.server.BaseHTTPRequestHandler", "line_number": 6, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 17, "usage_type": "call"}, {"api_name": "http.server.HTTPServer", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "27540061330", "text": "import torch\nimport torch.nn as nn\nimport numpy as np\n\n\n# model equivalent to tensorflow batch_to_space, but with channels first layout\nclass net_BatchToSpaceND(nn.Module):\n def __init__(self, block_shape, crop):\n super().__init__()\n self.block_shape = block_shape\n self.crop = crop\n\n def forward(self, input):\n # Prepare attributes\n input_shape = list(map(int, list(input.shape)))\n block_shape = self.block_shape\n crop = self.crop\n\n # number of spatial dimensions\n m = len(block_shape)\n # rest of dimensions\n n = len(input.shape) - m\n # output batch size\n batch_size = input_shape[0] // np.product(block_shape)\n\n unfolded_shape = list(block_shape) + [batch_size] + input_shape[1:]\n fold_shape = [batch_size] + input_shape[1:n] + [\n input_shape[i + n] * block_shape[i] for i in range(m)\n ]\n permute_dims = list(range(\n m, m + n)) + [i + mod for i in range(m) for mod in [n + m, 0]]\n\n # Actual model starts here\n unfolded_input = input.reshape(unfolded_shape)\n permuted = torch.permute(unfolded_input, permute_dims)\n full_output = permuted.reshape(fold_shape)\n # crop output tensor\n crop_output = full_output\n for i in range(m):\n crop_size = sum(crop[i])\n crop_output = crop_output.narrow(i + n, crop[i][0],\n fold_shape[i + n] - crop_size)\n return crop_output\n\n\n_model_ = net_BatchToSpaceND([2, 2], [[1, 0], [0, 1]])\n\n# dummy input for onnx generation\n_dummy_ = torch.randn(8, 4, 3, 3)\n", "repo_name": "Samsung/ONE", "sub_path": "res/PyTorchExamples/examples/BatchToSpaceND/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1654, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 363, "dataset": "github-code", "pt": "12", "api": [{"api_name": "torch.nn.Module", "line_number": 7, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 7, "usage_type": "name"}, {"api_name": "numpy.product", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.permute", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "16937254255", "text": "from yahooquery import Ticker\nfrom models import SymbolData, YahooQueryTickerData\n\n\ndef checkSymbolExists(symbolData: SymbolData, retryCount: int = 0) -> bool:\n symbol = symbolData.symbol\n data: YahooQueryTickerData = Ticker(symbol)\n\n if symbol not in data.price:\n return False\n\n priceData = data.price[symbol]\n keyStatsData = data.key_stats[symbol]\n\n if (\n \"regularMarketPrice\" not in priceData\n or \"Quote not found\" in priceData\n or \"No fundamentals data found\" in keyStatsData\n or \"sharesOutstanding\" not in keyStatsData\n or not keyStatsData[\"sharesOutstanding\"]\n ):\n # retry n times\n if retryCount < 5:\n checkSymbolExists(symbolData, retryCount + 1)\n else:\n return False\n else:\n return True\n", "repo_name": "shaunsaker/fat-buck-v1-python", "sub_path": "checkSymbolExists.py", "file_name": "checkSymbolExists.py", "file_ext": "py", "file_size_in_byte": 811, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "models.SymbolData", "line_number": 5, "usage_type": "name"}, {"api_name": "models.YahooQueryTickerData", "line_number": 7, "usage_type": "name"}, {"api_name": "yahooquery.Ticker", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "34457334459", "text": "import functools\nimport logging\nimport json\nfrom pathlib import Path\nfrom collections import namedtuple\n\nimport PyQt6.QtWidgets as widgets\nimport pandas as pd\nfrom PyQt6.QtCore import Qt, pyqtSignal\n\n\nlogger = logging.getLogger(__name__)\n\n\nExportSetting = namedtuple(\"ExportSetting\", [\"config_name\", \"readable_name\", \"help\"])\n\n\nSETTINGS = (\n ExportSetting(\"source.name\", \"Name\", \"source name\"),\n ExportSetting(\"source.channel\", \"Channel\",\n \"the source channel (may not correspond to channel in file, see file.channel)\"),\n ExportSetting(\"project.start_index\", \"Project Start (int, samples)\",\n \"the start index of segments relative to the entire project\"),\n ExportSetting(\"project.stop_index\", \"Project Stop (int, samples)\",\n \"the stop index of segments relative to the entire project\"),\n ExportSetting(\"project.t_start\", \"Project Start (float, seconds)\",\n \"the start time of segments relative to the project\"),\n ExportSetting(\"project.t_stop\", \"Project Start (float, seconds)\",\n \"the stop time of segments relative to the project\"),\n ExportSetting(\"file.name\", \"Filename\",\n \"the original file the segment is from\"),\n ExportSetting(\"file.relative_path\", \"Relative File Path\",\n \"the original file the segment is from relative to project directory\"),\n ExportSetting(\"file.channel\", \"File channel\",\n \"the original channel the segment is from\"),\n ExportSetting(\"file.start_index\", \"File Start (int, samples)\",\n \"the start index of segments relative to their original file\"),\n ExportSetting(\"file.stop_index\", \"File Start (int, samples)\",\n \"the stop index of segments relative to their original file\"),\n ExportSetting(\"file.t_start\", \"File Start (float, seconds)\",\n \"the start time of segments relative to their original file\"),\n ExportSetting(\"file.t_stop\", \"File Stop (float, seconds)\",\n \"the stop time of segments relative to their original file\"),\n ExportSetting(\"tags\", \"Tags (json list of strings)\",\n \"json list of tag strings, loadable as a json string\"),\n)\n\n\ndef segment_to_dict(segment, project_dir: 'pathlib.Path'):\n source = segment.source\n project = source.project\n block_start = project.to_block_index(segment.start)\n block_stop = project.to_block_index(segment.stop)\n block = block_start.block\n original_file, original_channel = block.get_channel_info(source.channel)\n\n return {\n \"source.name\": source.name,\n \"source.channel\": source.channel,\n \"project.start_index\": int(segment.start),\n \"project.stop_index\": int(segment.stop),\n \"project.t_start\": segment.start.to_timestamp(),\n \"project.t_stop\": segment.stop.to_timestamp(),\n \"file.name\": original_file,\n \"file.relative_path\": Path(original_file).relative_to(project_dir),\n \"file.channel\": original_channel,\n \"file.start_index\": int(block_start),\n \"file.stop_index\": int(block_stop),\n \"file.t_start\": block_start.to_file_timestamp(),\n \"file.t_stop\": block_stop.to_file_timestamp(),\n \"tags\": json.dumps(list(segment.data.get(\"tags\", []))),\n }\n\n\nclass ExportWizard(widgets.QWidget):\n \"\"\"Window for customizing an export\n \"\"\"\n\n exportReady = pyqtSignal(object)\n exportCanceled = pyqtSignal()\n\n def __init__(self, datastore, api):\n super().__init__()\n logger.info(\"Starting export wizard\")\n self.api = api\n self.datastore = datastore\n self.init_ui()\n self.connect_events()\n\n def init_ui(self):\n layout = widgets.QVBoxLayout(self)\n \n form_layout = widgets.QGridLayout()\n form_layout.addWidget(widgets.QLabel(\"column\"), 0, 0)\n form_layout.addWidget(widgets.QLabel(\"include?\"), 0, 1)\n form_layout.addWidget(widgets.QLabel(\"as name\"), 0, 2)\n self.fields = {}\n self.mapped_names = {}\n for i, setting in enumerate(SETTINGS):\n self.fields[setting.config_name] = widgets.QCheckBox()\n self.fields[setting.config_name].setCheckState(Qt.Checked)\n self.mapped_names[setting.config_name] = widgets.QLineEdit()\n self.mapped_names[setting.config_name].setPlaceholderText(setting.config_name)\n form_layout.addWidget(widgets.QLabel(setting.readable_name), i + 1, 0)\n form_layout.addWidget(self.fields[setting.config_name], i + 1, 1)\n form_layout.addWidget(self.mapped_names[setting.config_name], i + 1, 2)\n\n buttons_layout = widgets.QHBoxLayout()\n self.submit_button = widgets.QPushButton(\"&Export\")\n self.cancel_button = widgets.QPushButton(\"&Cancel\")\n buttons_layout.addWidget(self.submit_button)\n buttons_layout.addWidget(self.cancel_button)\n\n layout.addLayout(form_layout)\n layout.addLayout(buttons_layout)\n\n self.setLayout(layout)\n\n def connect_events(self):\n self.submit_button.clicked.connect(self.on_submit)\n self.cancel_button.clicked.connect(self.on_cancel)\n\n def get_form_keys(self):\n return set([k for k, checkbox in self.fields.items() if checkbox.checkState() == Qt.Checked])\n\n def get_name_mappings(self):\n return {\n k: lineedit.text() or lineedit.placeholderText()\n for k, lineedit in self.mapped_names.items()\n }\n\n def on_submit(self):\n name_map = self.get_name_mappings()\n include_keys = self.get_form_keys()\n\n logger.info(\"Exporting csv with columns {}\".format(list(include_keys)))\n\n segment_dicts = []\n for segment in self.datastore[\"segments\"]:\n segment_row = {\n name_map.get(k, k): v for k, v in segment_to_dict(segment, self.api.paths.project_dir).items()\n if k in include_keys\n }\n segment_dicts.append(segment_row)\n\n df = pd.DataFrame(segment_dicts)\n self.exportReady.emit(df)\n\n def on_cancel(self):\n self.exportCanceled.emit()\n\n", "repo_name": "theunissenlab/soundsep2", "sub_path": "soundsep/plugins/exporter/export_window.py", "file_name": "export_window.py", "file_ext": "py", "file_size_in_byte": 5993, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "12", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 15, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 65, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 71, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QWidget", "line_number": 75, "usage_type": "attribute"}, {"api_name": "PyQt6.QtWidgets", "line_number": 75, "usage_type": "name"}, {"api_name": "PyQt6.QtCore.pyqtSignal", "line_number": 79, "usage_type": "call"}, {"api_name": "PyQt6.QtCore.pyqtSignal", "line_number": 80, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets.QVBoxLayout", "line_number": 91, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets", "line_number": 91, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QGridLayout", "line_number": 93, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets", "line_number": 93, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QLabel", "line_number": 94, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets", "line_number": 94, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QLabel", "line_number": 95, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets", "line_number": 95, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QLabel", "line_number": 96, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets", "line_number": 96, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QCheckBox", "line_number": 100, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets", "line_number": 100, "usage_type": "name"}, {"api_name": "PyQt6.QtCore.Qt.Checked", "line_number": 101, "usage_type": "attribute"}, {"api_name": "PyQt6.QtCore.Qt", "line_number": 101, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QLineEdit", "line_number": 102, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets", "line_number": 102, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QLabel", "line_number": 104, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets", "line_number": 104, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QHBoxLayout", "line_number": 108, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets", "line_number": 108, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QPushButton", "line_number": 109, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets", "line_number": 109, "usage_type": "name"}, {"api_name": "PyQt6.QtWidgets.QPushButton", "line_number": 110, "usage_type": "call"}, {"api_name": "PyQt6.QtWidgets", "line_number": 110, "usage_type": "name"}, {"api_name": "PyQt6.QtCore.Qt.Checked", "line_number": 124, "usage_type": "attribute"}, {"api_name": "PyQt6.QtCore.Qt", "line_number": 124, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 146, "usage_type": "call"}]} +{"seq_id": "40233498829", "text": "import pickle\nimport random\nimport sys\nimport time\nfrom datetime import datetime\nfrom multiprocessing import *\n\nimport numpy as np\nimport pandas as pd\nimport zmq\n\n\ndef secondPassed(oldsecond):\n currentsecond = datetime.timestamp(datetime.now())\n if ((currentsecond - oldsecond) >= 2):\n oldsecond = currentsecond\n return True\n else:\n return False\n\ndef checkAlive(m,portsBusyList,lock,machines_number,dataKeeperNumberPerMachine,ns):\n \n if m:\n for i in range(machines_number):\n if m[i][2] == 1:\n if secondPassed(m[i][1]):\n lock.acquire()\n lockUpTable = ns.df\n lockUpTable.loc[lockUpTable.data_node_number == i, 'is_data_node_alive'] = False\n ns.df = lockUpTable\n m[i][2] = 0\n for j in range(dataKeeperNumberPerMachine):\n portsBusyList[j+i*dataKeeperNumberPerMachine] = 'dead'\n lock.release()\n\n return m\n\n\n\n\n\n\ndef add_row(inp):\n return {\"user_id\" : inp[0] , 'file_name' : inp[1] , 'data_node_number':inp[2],\n 'file_path_on_that_data_node':inp[3],'is_data_node_alive':inp[4],'replicate':inp[5]}\n\ndef master_heart_beat(lock,ns,dataKeeperNumberPerMachine,machines,portsBusyList,machines_number,IP_table):\n ports = list()\n for i in range(machines_number):\n ports.append(9000+i*2)\n\n print(f\"ports are {ports}\")\n context = zmq.Context()\n socket = context.socket(zmq.SUB)\n socket.subscribe(\"\")\n socket.RCVTIMEO = 0\n for i in range(len(ports)):\n \n print(\"tcp://\"+IP_table[i]+f\":{ports[i]}\")\n socket.connect(\"tcp://\"+IP_table[i]+f\":{ports[i]}\")\n while True:\n try:\n work = socket.recv_pyobj()\n except zmq.error.Again:\n machines = checkAlive(machines,portsBusyList,lock,machines_number,dataKeeperNumberPerMachine,ns)\n \n continue \n \n machineN = work['Machine#']\n message = work['message']\n \n lock.acquire()\n #declare machine and all of it ports are alive\n #for ports only declare them alive if they were dead\n machines[machineN][1] = datetime.timestamp(datetime.now())\n machines[machineN][2] = 1\n for i in range(dataKeeperNumberPerMachine):\n if portsBusyList[i+machineN*dataKeeperNumberPerMachine] == 'dead':\n portsBusyList[i+machineN*dataKeeperNumberPerMachine] = 'alive'\n lockUpTable = ns.df\n lockUpTable.loc[lockUpTable.data_node_number == machineN, 'is_data_node_alive'] = True\n ns.df = lockUpTable\n lock.release()\n machines = checkAlive(machines,portsBusyList,lock,machines_number,dataKeeperNumberPerMachine,ns)\n \n \n\n\ndef replicate(ns,lock,fg,proc_num,dataKeeperNumberPerMachine,machines,portsBusyList,machinesNumber,IP_table,context,replications_count):\n \n lookUpTable = ns.df\n for file in range(len(lookUpTable)):\n fileName = lookUpTable['file_name'][file]\n user_Id = lookUpTable['user_id'][file]\n lock.acquire()\n lookUpTable = ns.df\n userFile = ns.df.query('user_id == @user_Id and file_name == @fileName and is_data_node_alive == True and replicate == False')\n userFileCount = len(userFile)\n sourceMachines = userFile['data_node_number'].tolist() # return machines numbers which have this file\n if userFileCount == 0:\n lock.release()\n continue\n sourceMachine = sourceMachines[0]\n sourceMachineFilePath = userFile['file_path_on_that_data_node'].tolist()[0]\n userId = userFile['user_id'].tolist()[0]\n if userFileCount < replications_count:\n lookUpTable.loc[(lookUpTable.file_name == fileName) & (lookUpTable.user_id == userId), 'replicate'] = True\n ns.df = lookUpTable\n \n\n tempList = []\n for i in range (machinesNumber):\n if machines[i][2] == 1:\n tempList.append(i)\n dstMachines = list(set(tempList) - set(sourceMachines))\n lock.release()\n if not dstMachines:\n lock.acquire()\n lookUpTable = ns.df\n lookUpTable.loc[(lookUpTable.file_name == fileName) & (lookUpTable.user_id == userId), 'replicate'] = False\n ns.df = lookUpTable\n lock.release()\n return\n #choose alive port to connect to\n dstDataPorts = []\n srcDataKeeperNumber = -1\n freeDsts = 0\n iterate = 0\n neededReplicasCount = replications_count - userFileCount\n\n \n if neededReplicasCount > len(dstMachines):\n neededReplicasCount = len(dstMachines)\n while freeDsts < neededReplicasCount:\n if iterate >= len(dstMachines):\n iterate = 0\n \n i = 0\n breakLoop = False\n while i < dataKeeperNumberPerMachine:\n if i >=dataKeeperNumberPerMachine:\n i=0\n lock.acquire()\n if portsBusyList[dstMachines[iterate] * dataKeeperNumberPerMachine + i] == 'alive':\n portsBusyList[dstMachines[iterate] * dataKeeperNumberPerMachine + i] = 'busy'\n temp = ((dstMachines[iterate] * dataKeeperNumberPerMachine + i) * 2) + 8000\n temp = \"tcp://\"+IP_table[dstMachines[iterate]]+f\":{temp}\"\n dstDataPorts.append(temp)\n freeDsts += 1\n breakLoop = True\n lock.release()\n if breakLoop:\n break\n i+=1\n \n iterate += 1\n print(dstDataPorts)\n print(f\"Source machine found to start replication : {sourceMachine}\")\n exit = False\n srcPort = 0\n src_port = 0\n i = 0\n while not exit:\n \n for i in range(dataKeeperNumberPerMachine):\n \n lock.acquire()\n \n if portsBusyList[sourceMachine * dataKeeperNumberPerMachine + i] == 'alive':\n \n portsBusyList[sourceMachine * dataKeeperNumberPerMachine + i] = 'busy'\n srcPort = ((sourceMachine * dataKeeperNumberPerMachine + i) * 2) + 8000\n src_port = ((sourceMachine * dataKeeperNumberPerMachine + i) * 2) + 8000\n srcDataKeeperNumber = i\n srcPort = \"tcp://\"+IP_table[sourceMachine]+f\":{srcPort}\"\n exit = True\n lock.release()\n break\n lock.release()\n \n\n\n\n\n dataKeeperSocket = context.socket(zmq.REQ)\n dataKeeperSocket.connect(srcPort)\n srcData = {'type':\"ReplicationSrc\", 'count':len(dstDataPorts), 'filePath': sourceMachineFilePath}\n msg = pickle.dumps(srcData)\n dataKeeperSocket.send(msg)\n print(\"Sending data to src machine..\" )\n msg = dataKeeperSocket.recv_string()\n dataKeeperSocket.close()\n time.sleep(0.1)\n\n print(\"Sending data to dst machines..\" )\n\n\n for i in range(len(dstDataPorts)):\n dstData = {'type':\"ReplicationDst\", 'srcPort':5000+srcDataKeeperNumber*100+i, 'src_ip':IP_table[sourceMachine],'idx':i, 'user_id': userId, 'fileName':fileName}\n msg = pickle.dumps(dstData)\n dataKeeperSocket = context.socket(zmq.REQ)\n dataKeeperSocket.connect(dstDataPorts[i])\n dataKeeperSocket.send(msg)\n msg = dataKeeperSocket.recv()\n msg = pickle.loads(msg)\n \n lock.acquire()\n data = msg # get data from dictionary\n lookUpTable = ns.df\n lookUpTable = lookUpTable.append(add_row(data),ignore_index=True)\n ns.df = lookUpTable\n print(\"Destination replicator responsed .. add new file to the table \")\n #mark this port as alive\n if portsBusyList[data[6]] == 'busy':\n portsBusyList[data[6]] = 'alive'\n lock.release()\n print(ns.df)\n dataKeeperSocket.close()\n lock.acquire()\n \n lookUpTable = ns.df\n src_port_index = (src_port-8000)//2\n #print(f\"lock is grnted to free port {src_port_index}\")\n if portsBusyList[src_port_index]=='busy':\n #print(\"src busy port is now free\")\n portsBusyList[src_port_index]='alive'\n \n lookUpTable.loc[(lookUpTable.file_name == fileName) & (lookUpTable.user_id == userId), 'replicate'] = False\n ns.df = lookUpTable\n lock.release()\n \n else:\n lock.release() \n \n\n\ndef all(ns,lock,fg,proc_num,dataKeeperNumberPerMachine,machines,portsBusyList,machinesNumber,IP_table,needed_replications_count):\n if (fg == 1):\n datakeeper_number = dataKeeperNumberPerMachine*machinesNumber\n context = zmq.Context()\n socket = context.socket(zmq.REP)\n port = proc_num*2+6000\n socket.bind(f\"tcp://\"+IP_table[-1]+f\":{port}\")# create server port\n socket.RCVTIMEO = 0\n # create random order of data ports\n randomPortList = list(range(0,datakeeper_number))\n random.shuffle(randomPortList)\n\n #print(randomPortList) \n \n while True:\n replicate(ns,lock,fg,proc_num,dataKeeperNumberPerMachine,machines,portsBusyList,machinesNumber,IP_table,context,needed_replications_count)\n try:\n msg = socket.recv()\n except zmq.error.Again:\n continue\n msg_dict = pickle.loads(msg)\n #print(msg_dict['type'])\n if msg_dict['type'] == \"Upload\":\n print(\"Upload request from client\")\n #choose alive port to connect to\n dataPort = 0\n exit = False\n iterate = 0\n port_index = -1\n while not exit:\n if iterate >= machinesNumber*dataKeeperNumberPerMachine:\n iterate = 0\n lock.acquire()\n if portsBusyList[randomPortList[iterate]] == 'alive':\n portsBusyList[randomPortList[iterate]] = 'busy'\n dataPort = (randomPortList[iterate] * 2) + 8000\n port_index = randomPortList[iterate]\n exit = True\n lock.release()\n iterate += 1\n msg = \"tcp://\"+IP_table[port_index//dataKeeperNumberPerMachine]+\":\"+str(dataPort)\n socket.send_string(msg) # send port number to client\n elif msg_dict['type']==\"Add\": #add to look up table\n respond = \"done\"\n print(\"Recieved add request\")\n socket.send_string(respond)\n lock.acquire()\n data = msg_dict['data'] # get data from dictionary\n lookUpTable = ns.df\n lookUpTable = lookUpTable.append(add_row(data),ignore_index=True)\n ns.df = lookUpTable\n #mark this port as alive\n index = int((data[6] - 8000) / 2)\n if portsBusyList[index] == 'busy':\n portsBusyList[index] = 'alive'\n lock.release()\n print(ns.df)\n \n elif msg_dict['type'] == \"Download Finished\":\n lock.acquire()\n if portsBusyList[msg_dict['port']] == 'busy':\n portsBusyList[msg_dict['port']] = 'alive'\n lock.release()\n respond = \"done\"\n socket.send_string(respond)\n\n \n elif msg_dict['type'] == \"Download\":\n print(\"Download request from client\")\n # check if requested file available to download\n # filtering with query method \n user_id = msg_dict['user_id']\n file_name = msg_dict['filename']\n\n data = ns.df.query('user_id == @user_id and file_name == @file_name and is_data_node_alive == True')\n machine_data_found = data['data_node_number'].tolist() # return machines numbers which have this file\n machine_data_found_paths = data['file_path_on_that_data_node'].tolist()\n #print(Machine_data_found,\" Data Node List\")\n msg = {'status':None , 'port':None ,'path':None }\n if not machine_data_found:\n # no data node have the requested file\n msg['status'] = \"Download Request Failed .... File Not Found\"\n msg = pickle.dumps(msg)\n socket.send(msg)\n\n else:\n \n data_dict = dict()\n\n port_list_idx = []\n for k,m in enumerate(machine_data_found):\n data_dict[m] = machine_data_found_paths[k]\n port_list_idx.extend([((m * dataKeeperNumberPerMachine) + i) for i in range(dataKeeperNumberPerMachine)]) # indices of available ports\n \n \n #print(port_list_idx)\n \n Busy = True\n portn = None\n path = None\n while Busy:\n for idx in port_list_idx:\n lock.acquire()\n if portsBusyList[idx] == \"alive\":\n portsBusyList[idx] = \"busy\"\n temp = idx * 2 + 8000\n msg['path'] = data_dict[idx // dataKeeperNumberPerMachine]\n Busy = False\n msg['port']= \"tcp://\"+IP_table[idx // dataKeeperNumberPerMachine]+\":\"+str(temp)\n msg['status'] = 'success'\n break\n lock.release()\n lock.release()\n\n msg = pickle.dumps(msg)\n socket.send(msg)\n \n # wait for complete download request to free port again \n \n \n else:\n master_heart_beat(lock,ns,dataKeeperNumberPerMachine,machines,portsBusyList,machinesNumber,IP_table)\n \n\n\ndef test(ns,lock,fg,proc_num,dataKeeperNumberPerMachine,machines,portsBusyList,machinesNumber):\n for i in range(machinesNumber*dataKeeperNumberPerMachine):\n portsBusyList[i] = 'busy'\n", "repo_name": "YousifGamal/Third-Year-Distributed-File-System-", "sub_path": "MasterTrackerMain.py", "file_name": "MasterTrackerMain.py", "file_ext": "py", "file_size_in_byte": 15118, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "datetime.datetime.timestamp", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 14, "usage_type": "call"}, {"api_name": "zmq.Context", "line_number": 53, "usage_type": "call"}, {"api_name": "zmq.SUB", "line_number": 54, "usage_type": "attribute"}, {"api_name": "zmq.error", "line_number": 64, "usage_type": "attribute"}, {"api_name": "datetime.datetime.timestamp", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 75, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 75, "usage_type": "call"}, {"api_name": "zmq.REQ", "line_number": 185, "usage_type": "attribute"}, {"api_name": "pickle.dumps", "line_number": 188, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 193, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 200, "usage_type": "call"}, {"api_name": "zmq.REQ", "line_number": 201, "usage_type": "attribute"}, {"api_name": "pickle.loads", "line_number": 205, "usage_type": "call"}, {"api_name": "zmq.Context", "line_number": 240, "usage_type": "call"}, {"api_name": "zmq.REP", "line_number": 241, "usage_type": "attribute"}, {"api_name": "random.shuffle", "line_number": 247, "usage_type": "call"}, {"api_name": "zmq.error", "line_number": 255, "usage_type": "attribute"}, {"api_name": "pickle.loads", "line_number": 257, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 319, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 351, "usage_type": "call"}]} +{"seq_id": "31601189473", "text": "import sys\nfrom flask import jsonify, request\nfrom flask_restplus import Api, Resource, Namespace, fields\nfrom app.notes.dao.notes import NotesDao\nfrom app.notes.models.notes import Notes as NotesModel\nfrom app.util.decorator import token_required\n\n\nclass NotesDto:\n api = Namespace('notes', description='notes related operations')\n notes_api_fields = {\n 'id': fields.String(),\n 'notes_id': fields.String(required=True),\n 'notes_type_id': fields.String(required=True), \n 'notes': fields.String(required=True),\n 'create_date': fields.DateTime(), \n 'updated_date': fields.DateTime(),\n 'user_by': fields.String(required=True) \n }\n notes = api.model('notes', notes_api_fields) \n\n\napi = NotesDto.api\n\n@api.route('//')\n@api.response(404, 'Notes not found.')\n@api.expect(api.parser().add_argument('Authorization', location='headers'))\nclass NotesList(Resource):\n\n @api.doc('get all notes associated to an notes_id')\n @api.marshal_list_with(NotesDto.notes, envelope='noteslist')\n @token_required\n def get(self, notes_id, notes_type_id):\n \"\"\"Get notes based on the given identifier\"\"\"\n notes = NotesDao.get_notes(notes_id, notes_type_id)\n if not notes:\n response_object = {\n 'status': 'fail',\n 'message': 'No data found.'\n }\n return response_object, 404\n else:\n note_ret_list = []\n for note in notes:\n note_ret_list.append(note.to_json())\n return note_ret_list, 200\n\n\n@api.route('')\n@api.expect(api.parser().add_argument('Authorization', location='headers'))\nclass AddNotes(Resource):\n\n @api.response(201, 'Note successfully created.')\n @api.doc('create a new note')\n @api.expect(NotesDto.notes, validate=True)\n @token_required\n def post(self):\n \"\"\"Insert a new notes\"\"\"\n try:\n notes_data = request.json\n \n new_notes = NotesModel()\n new_notes.notes_id = notes_data['notes_id']\n new_notes.notes_type_id = notes_data['notes_type_id']\n new_notes.notes = notes_data['notes']\n new_notes.user_by = notes_data['user_by']\n new_notes = NotesDao.save_notes(new_notes)\n response_object = {\n 'status': 'success',\n 'message': 'Notes successfully added.'\n }\n return response_object, 201\n except Exception as e:\n return {\n 'status': 'error',\n 'message': 'Internal Server Error'\n }, 500\n\n\n@api.route('/')\n@api.param('id', 'The Notes identifier')\n@api.expect(api.parser().add_argument('Authorization', location='headers'))\nclass Notes(Resource):\n\n @api.response(200, 'Notes successfully updated.')\n @api.doc('update a notes')\n @api.expect(NotesDto.notes, validate=False)\n @token_required\n def put(self, id):\n \"\"\"Update a notes\"\"\"\n try:\n notes_data = request.json\n existing_notes = NotesDao.get_by_id(id) \n\n if 'notes_id' in notes_data:\n existing_notes.notes_id = notes_data['notes_id']\n\n if 'notes_type_id' in notes_data:\n existing_notes.notes_type_id = notes_data['notes_type_id']\n\n if 'notes' in notes_data:\n existing_notes.notes = notes_data['notes']\n\n if 'user_by' in notes_data:\n existing_notes.user_by = notes_data['user_by'] \n\n existing_notes = NotesDao.update_notes(existing_notes)\n response_object = {\n 'status': 'success',\n 'message': 'Notes successfully updated.'\n }\n return response_object, 200\n except Exception as e:\n return {\n 'status': 'error',\n 'message': 'Internal Server Error'\n }, 500\n\n\n @api.doc('get a notes')\n @api.marshal_with(NotesDto.notes)\n @token_required\n def get(self, id):\n \"\"\"Get a notes given its identifier\"\"\"\n notes = NotesDao.get_by_id(id) \n if not notes:\n response_object = {\n 'status': 'fail',\n 'message': 'Notes not found.'\n }\n return response_object, 404\n else:\n return notes, 200\n\n @api.doc('delete a notes')\n @token_required\n def delete(self, id):\n \"\"\"Delete a notes given its identifier\"\"\"\n NotesDao.delete(id) \n response_object = {\n 'status': 'success',\n 'message': 'Notes deleted.'\n }\n return response_object, 202\n\n@api.errorhandler(Exception)\ndef generic_exception_handler(e: Exception):\n exc_type, exc_value, exc_traceback = sys.exc_info()\n\n if exc_traceback:\n traceback_details = {\n 'filename': exc_traceback.tb_frame.f_code.co_filename,\n 'lineno': exc_traceback.tb_lineno,\n 'name': exc_traceback.tb_frame.f_code.co_name,\n 'message': str(exc_value),\n }\n return {\n 'status': 'error',\n 'message': traceback_details['message']\n }, 500\n else:\n return {\n 'status': 'error',\n 'message': 'Internal Server Error'\n }, 500 \n", "repo_name": "erwindev/vendor-management", "sub_path": "app/notes/api/notes.py", "file_name": "notes.py", "file_ext": "py", "file_size_in_byte": 5376, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "12", "api": [{"api_name": "flask_restplus.Namespace", "line_number": 10, "usage_type": "call"}, {"api_name": "flask_restplus.fields.String", "line_number": 12, "usage_type": "call"}, {"api_name": "flask_restplus.fields", "line_number": 12, "usage_type": "name"}, {"api_name": "flask_restplus.fields.String", "line_number": 13, "usage_type": "call"}, {"api_name": "flask_restplus.fields", "line_number": 13, "usage_type": "name"}, {"api_name": "flask_restplus.fields.String", "line_number": 14, "usage_type": "call"}, {"api_name": "flask_restplus.fields", "line_number": 14, "usage_type": "name"}, {"api_name": "flask_restplus.fields.String", "line_number": 15, "usage_type": "call"}, {"api_name": "flask_restplus.fields", "line_number": 15, "usage_type": "name"}, {"api_name": "flask_restplus.fields.DateTime", "line_number": 16, "usage_type": "call"}, {"api_name": "flask_restplus.fields", "line_number": 16, "usage_type": "name"}, {"api_name": "flask_restplus.fields.DateTime", "line_number": 17, "usage_type": "call"}, {"api_name": "flask_restplus.fields", "line_number": 17, "usage_type": "name"}, {"api_name": "flask_restplus.fields.String", "line_number": 18, "usage_type": "call"}, {"api_name": "flask_restplus.fields", "line_number": 18, "usage_type": "name"}, {"api_name": "flask_restplus.Resource", "line_number": 28, "usage_type": "name"}, {"api_name": "app.notes.dao.notes.NotesDao.get_notes", "line_number": 35, "usage_type": "call"}, {"api_name": "app.notes.dao.notes.NotesDao", "line_number": 35, "usage_type": "name"}, {"api_name": "app.util.decorator.token_required", "line_number": 32, "usage_type": "name"}, {"api_name": "flask_restplus.Resource", "line_number": 51, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 60, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 60, "usage_type": "name"}, {"api_name": "app.notes.models.notes.Notes", "line_number": 62, "usage_type": "call"}, {"api_name": "app.notes.dao.notes.NotesDao.save_notes", "line_number": 67, "usage_type": "call"}, {"api_name": "app.notes.dao.notes.NotesDao", "line_number": 67, "usage_type": "name"}, {"api_name": "app.util.decorator.token_required", "line_number": 56, "usage_type": "name"}, {"api_name": "flask_restplus.Resource", "line_number": 83, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 92, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 92, "usage_type": "name"}, {"api_name": "app.notes.dao.notes.NotesDao.get_by_id", "line_number": 93, "usage_type": "call"}, {"api_name": "app.notes.dao.notes.NotesDao", "line_number": 93, "usage_type": "name"}, {"api_name": "app.notes.dao.notes.NotesDao.update_notes", "line_number": 107, "usage_type": "call"}, {"api_name": "app.notes.dao.notes.NotesDao", "line_number": 107, "usage_type": "name"}, {"api_name": "app.util.decorator.token_required", "line_number": 88, "usage_type": "name"}, {"api_name": "app.notes.dao.notes.NotesDao.get_by_id", "line_number": 125, "usage_type": "call"}, {"api_name": "app.notes.dao.notes.NotesDao", "line_number": 125, "usage_type": "name"}, {"api_name": "app.util.decorator.token_required", "line_number": 122, "usage_type": "name"}, {"api_name": "app.notes.dao.notes.NotesDao.delete", "line_number": 139, "usage_type": "call"}, {"api_name": "app.notes.dao.notes.NotesDao", "line_number": 139, "usage_type": "name"}, {"api_name": "app.util.decorator.token_required", "line_number": 136, "usage_type": "name"}, {"api_name": "sys.exc_info", "line_number": 148, "usage_type": "call"}]} +{"seq_id": "35200839789", "text": "import sqlite3\n\nfrom funcy import concat, mapcat, partial\nfrom operator import add\n\n\ndef select_data():\n def create_result_select_items():\n for i in map(partial(add, 1), range(18)):\n yield ', '.join((f'results_{i}.horse_id',\n f'results_{i}.jockey_id',\n f'results_{i}.stable_id',\n f'results_{i}.trainer_id',\n f'results_{i}.rank',\n f'results_{i}.sex',\n f'results_{i}.age',\n f'results_{i}.jockey_class',\n f'results_{i}.jockey_weight',\n f'results_{i}.odds',\n f'results_{i}.weight'))\n\n def create_result_join():\n for i in map(partial(add, 1), range(18)):\n yield f'LEFT OUTER JOIN results AS results_{i} ON results_{i}.race_id = races.id AND results_{i}.number = {i}'\n\n with sqlite3.connect('../phase-0/netkeiba.sqlite3') as database:\n cursor = database.cursor()\n sql = ' '.join(('SELECT races.id, races.place, races.field, races.distance, races.direction, races.weather, races.field_condition, races.max_prize,',\n ', '.join(create_result_select_items()),\n 'FROM races',\n ' '.join(create_result_join()),\n 'ORDER BY races.id'))\n\n return cursor.execute(sql)\n\n\ndef main():\n print('\\t'.join(concat(('race_id',\n 'place',\n 'field',\n 'distance',\n 'direction',\n 'weather',\n 'field_condition',\n 'max_prize'),\n mapcat(lambda i: (f'horse_{i:02d}',\n f'jockey_{i:02d}',\n f'stable_{i:02d}',\n f'trainer_{i:02d}',\n f'rank_{i:02d}',\n f'sex_{i:02d}',\n f'age_{i:02d}',\n f'jockey_class_{i:02d}',\n f'jockey_weight_{i:02d}',\n f'odds_{i:02d}',\n f'weight_{i:02d}'),\n map(partial(add, 1), range(18))))))\n\n for record in select_data():\n print('\\t'.join(map(lambda column: str(column) if column is not None else '', record)))\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "tail-island/cosine", "sub_path": "phase-3/create_table.py", "file_name": "create_table.py", "file_ext": "py", "file_size_in_byte": 2790, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "funcy.partial", "line_number": 9, "usage_type": "call"}, {"api_name": "operator.add", "line_number": 9, "usage_type": "argument"}, {"api_name": "funcy.partial", "line_number": 23, "usage_type": "call"}, {"api_name": "operator.add", "line_number": 23, "usage_type": "argument"}, {"api_name": "sqlite3.connect", "line_number": 26, "usage_type": "call"}, {"api_name": "funcy.concat", "line_number": 38, "usage_type": "call"}, {"api_name": "funcy.mapcat", "line_number": 46, "usage_type": "call"}, {"api_name": "funcy.partial", "line_number": 57, "usage_type": "call"}, {"api_name": "operator.add", "line_number": 57, "usage_type": "argument"}]} +{"seq_id": "18111320899", "text": "import sys\nimport pathlib\nimport subprocess\nimport os\nimport os.path\n\nif __name__ == '__main__':\n folderPath = pathlib.PurePath(sys.argv[1])\n if folderPath.parts[-1] in ('dist', 'src'):\n folderPath = folderPath.parent\n elif folderPath.parts[-1] == 'ab-userscripts':\n print('Incorrect folder specified: ' + str(folderPath), file=sys.stderr)\n sys.exit(1)\n\n if folderPath.parts[-1] == 'delicious-userscripts':\n sys.path.append(str(folderPath))\n sys.path.insert(0, './delicious-userscripts')\n import concat_userscripts\n print('Assembling delicious bundle.')\n concat_userscripts._main()\n else:\n webpack = folderPath / '../node_modules/.bin/webpack.cmd'\n for f in os.listdir(str(folderPath)):\n f = os.path.basename(f)\n if f.startswith('webpack') and f.endswith('.config.js'):\n print('Executing webpack config: ' + str(f))\n subprocess.run(\n [str(webpack), '--config', str(folderPath / f)],\n check=True\n )", "repo_name": "momentary0/AB-Userscripts", "sub_path": "_webpack_runner.py", "file_name": "_webpack_runner.py", "file_ext": "py", "file_size_in_byte": 1087, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 14, "dataset": "github-code", "pt": "12", "api": [{"api_name": "pathlib.PurePath", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 16, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sys.path.insert", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "concat_userscripts._main", "line_number": 20, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "6580832357", "text": "import pandas as pd\nimport pickle\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\n\n# Indepdent Variables or predictors: All variables except breast_can\n# Dependent Variable or target: breast_can\n\n# Loading the processed dataset\ndf = pd.read_csv('model_dev2/data/processed/cancer_data_processed.csv')\nprint(df)\n\ndf.dropna(inplace=True)\nlen(df)\n\nprint(df)\n\nX = df.drop('breast_can', axis=1)\ny = df['breast_can'] \nprint(X)\nprint(y) \n\n# StandardScaler\nscaler = StandardScaler()\nscaler.fit(X) \n\nX_scaled = scaler.transform(X)\nX_scaled\n\n\n# Splitting the scaled data into training, validation, and testing\nX_train, X_temp, y_train, y_temp = train_test_split(X_scaled, y, test_size=0.3, random_state=42)\nX_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n\n(X_train.shape, X_val.shape, X_test.shape)\n\n# Saving it\npickle.dump(X_train, open('model_dev2/model/X_train.sav', 'wb'))\npickle.dump(X.columns, open('model_dev2/model/X_columns.sav', 'wb'))\npickle.dump(scaler, open('model_dev2/model/scaler.sav', 'wb'))", "repo_name": "jward6301/datasci_9_data_prep", "sub_path": "model_dev2/scripts/p3_compute.py", "file_name": "p3_compute.py", "file_ext": "py", "file_size_in_byte": 1100, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "pandas.read_csv", "line_number": 10, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 32, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 33, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 38, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 39, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "72358978580", "text": "#!/usr/bin/python3\n\n###################################################################\n# This script reboots the LuxPower Data Logger via the UI\n# Requirements\\ Dependancies\n# \t- apt-get install firefox-esr\n#\t- mkdir /root/webdrivers/geckodriver\n#\t- cd /root/webdrivers/geckodriver (note, if this directory is changed then ensure that it is reflected in the config.ini file)\n# \t- wget https://github.com/mozilla/geckodriver/releases/download/v0.30.0/geckodriver-v0.30.0-linux64.tar.gz\n# \t- tar -xvf geckodriver-v0.30.0-linux64.tar.gz\n# \t- python3 -m pip install -U selenium\n###################################################################\n###################################################################\n# Version\n# 11/02/21 - First baselined version\n# 18/02/21 - Removed ENV from config.ini file\n# 06/01/22 - Update Selenium command to use Service Object. Know issue that the output to the Gekolog file is now not used.\n##################################################################\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.chrome.service import Service\nimport configparser\nfrom time import sleep\nfrom datetime import date, datetime, timedelta\n\n#Read in config file variables\nconfig = configparser.ConfigParser()\nconfig.read('config.ini')\nLOG_FILE_LOCATION = config['GENERAL']['LogDir']\nipAdr = config['LUX']['DataModuleIP']\nuserName = config['LUX']['DataModuleUser']\nuserPwd = config['LUX']['DataModulePwd']\ngekoDriver = config['LUX']['GekoDriver']\n\n# File directories\nLOG_FILE_NAME = 'LuxPower_Logger_Reboot.log'\nGEKO_LOG_FILENAME = 'Geko_Driver.log'\nLOG_FILE = LOG_FILE_LOCATION + LOG_FILE_NAME\nGEKO_LOG_FILE = LOG_FILE_LOCATION + GEKO_LOG_FILENAME\n\n# Open log file\nexecutionTimeStamp = datetime.now()\nthisLogFile=open(LOG_FILE, \"a+\")\nthisLogFile.write('\\n')\nthisLogFile.write('###################################################################################' + '\\n')\nthisLogFile.write('Start of Processing @: ' + executionTimeStamp.strftime('%Y-%m-%d %H:%M:%S') + '\\n')\n\n# Set Driver Options to run Headless\noptions = webdriver.FirefoxOptions()\noptions.headless = True\n\n# Update command to use Service Object due to depricaiton\ns = Service(str(gekoDriver))\ndriver=webdriver.Firefox(service=s,options=options)\n\ntry:\n\t# Need to call the website twice in order to login\n\tdriver.get(\"http:///\" + str(userName) + \":\" + str(userPwd) + \"@\" + str(ipAdr) + \"/model_en.html\")\n\tdriver.get(\"http:///\" + str(userName) + \":\" + str(userPwd) + \"@\" + str(ipAdr) + \"/model_en.html\")\n\n\t# Test by saving a screen shot\n\t#driver.get_screenshot_as_file(\"LuxPower_Screenshot.png\")\n\n\t# Click the reset button on the page\n\tdriver.find_element(By.CSS_SELECTOR, \"form:nth-child(9) .btn\").click()\n\tthisLogFile.write('# Data Logger Reboot reqeust made. \\n')\n\n\t# Wait 10 seconds for the logger to reboot\n\tsleep(10)\n\n\t# Load up the front page in english\n\tdriver.get(\"http:///\" + str(userName) + \":\" + str(userPwd) + \"@\" + str(ipAdr) + \"/index_en.html\")\n\n\t# Test to see if the page has loaded and the title is \"setting\"\n\ttry:\n\t\tWebDriverWait(driver, 10).until(EC.title_contains(\"setting\"))\n\texcept:\n\t\tthisLogFile.write(' ***ERROR: Unable to re-load webpage after reboot. \\n')\n\n\tdriver.quit()\nexcept:\n thisLogFile.write(' ***ERROR: Unable to load Data Logger Webpage. \\n')\n\n# Close the log file\nexecutionTimeStamp = datetime.now()\nthisLogFile.write('# Data Logger Rebooted. \\n')\nthisLogFile.write('End of Processing for Time Period: ' + executionTimeStamp.strftime('%Y-%m-%d %H:%M:%S') + '\\n')\nthisLogFile.write('###################################################################################' + '\\n')\nthisLogFile.write('\\n')\n", "repo_name": "brodiepeers/homeautomation", "sub_path": "code/python/LuxPower_Data_Logger_Reboot.py", "file_name": "LuxPower_Data_Logger_Reboot.py", "file_ext": "py", "file_size_in_byte": 3805, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "12", "api": [{"api_name": "configparser.ConfigParser", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 44, "usage_type": "name"}, {"api_name": "selenium.webdriver.FirefoxOptions", "line_number": 51, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 51, "usage_type": "name"}, {"api_name": "selenium.webdriver.chrome.service.Service", "line_number": 55, "usage_type": "call"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 56, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 56, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 67, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 67, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 71, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 78, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.title_contains", "line_number": 78, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 78, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 87, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 87, "usage_type": "name"}]} +{"seq_id": "71772354894", "text": "import random\nimport requests\nimport yaml\nfrom flask import Flask, request\nfrom itertools import cycle\n\nloadbalancer = Flask(__name__)\n\ndef load_configuration(path):\n with open(path) as config_file:\n config = yaml.load(config_file, Loader=yaml.FullLoader)\n return config\n\nconfig = load_configuration('config.yaml')\n\nif config['method'] == 'host':\n @loadbalancer.route('/')\n def router():\n # Load balance by Header\n host_header = request.headers['Host']\n for entry in config['hosts']:\n if host_header == entry['host']:\n response = requests.get(f'http://{random.choice(entry[\"servers\"])}')\n return response.content, response.status_code\n\nif config['method'] == 'path':\n @loadbalancer.route('/')\n def path_router(path):\n # Load balance by Path\n for entry in config['paths']:\n if ('/' + path) == entry['path']:\n response = requests.get(f'http://{random.choice(entry[\"servers\"])}')\n return response.content, response.status_code\n return 'Not Found', 404\n \nif config['method'] == 'no-content':\n servers = config['no-content']['servers']\n ITER = cycle(servers)\n def round_robin(iter):\n # round_robin([A, B, C, D]) --> A B C D A B C D A B C D ...\n return next(iter)\n\n @loadbalancer.route('/')\n def router():\n algorithm = config['no-content']['algorithm']\n\n if algorithm == 'random':\n return random.choice(servers)\n elif algorithm == 'round robin':\n return round_robin(ITER)\n else:\n raise Exception('unknown algorithm: %s' % algorithm)", "repo_name": "EdRamos12/python-load-balancer", "sub_path": "loadbalancer.py", "file_name": "loadbalancer.py", "file_ext": "py", "file_size_in_byte": 1546, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "14", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 11, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 11, "usage_type": "attribute"}, {"api_name": "flask.request.headers", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 23, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 23, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 32, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 32, "usage_type": "call"}, {"api_name": "itertools.cycle", "line_number": 38, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "40840682941", "text": "##!/usr/bin/env python\n'''\nABOUT: This simple program intend to train the data of Dittus-Boelter \n correaltions for the heat transfer (heating only). The data \n were provided in a 'DittusBoelterDatabase.csv'. In addition, a \n MATLAB script is also provided to generate data beyond the \n limits of Re and Pr considered here. \n The program is not optimized for any parameters.\nDEPENDS ON: Python3, NumPy, Sklearn, Matplotlib, Pandas\nDATE: 26.10.2018\nAUTHOR: Sandeep Pandey (sandeep.pandey@ike.uni-stuttgart.de)\nLINCENSE: GPL-3.0\n'''\nprint(__doc__)\n# Import relavant modules here\nimport numpy as np\nimport pandas as pd\n# Library to plot the data\nimport matplotlib.pyplot as plt \n#Predefine the properties of plot\nparams = {\n 'axes.labelsize' : 22,\n 'font.size' : 22,\n 'font.family' : 'Times New Roman',\n 'legend.fontsize' : 18,\n 'xtick.labelsize' : 18,\n 'ytick.labelsize' : 18,\n 'text.usetex' : False,\n 'figure.figsize' : [7, 5],\n 'lines.linewidth' : 4,\n 'lines.markersize' :2\n}\nplt.rcParams.update(params)\nnp.random.seed(10) # It is necessary to reproduce the results which depend on RANDOM NUMBERS\n\n# Start of the program\ndata = pd.read_csv('DittusBoelterDatabase.csv')\n\n# Split features and output\nX = data.iloc[:,:-1].values\ny = data.iloc[:,2].values\n\n# Split the all data in 80% (training) and 20% (testing)\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test= train_test_split(X,y,test_size=0.2,random_state=0)\n\n#---------Train the network---------\n# 1. Linear regression\nfrom sklearn.linear_model import LinearRegression\nmodelLin = LinearRegression()\nmodelLin.fit(X_train,y_train)\n\n# 2. Polynomial regression\nfrom sklearn.preprocessing import PolynomialFeatures\npoly_reg = PolynomialFeatures(degree = 4) \n# Mapping data to polynomials i.e. a and b for degree=2-> constant, a, b, a2, ab, b2 \nX_train_poly = poly_reg.fit_transform(X_train) \nmodelPoly = LinearRegression()\nmodelPoly.fit(X_train_poly, y_train)\n\n# 3. Random forest\nfrom sklearn.ensemble import RandomForestRegressor\nmodelRF=RandomForestRegressor(n_estimators=1000,random_state=42) \nmodelRF.fit(X_train,y_train)\n\n# 4. Support vector regression\n# SVR and DNN requires the feature scaling, so we will do scaling\n# One can also scale y, but I am not doing it here.\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.svm import SVR\nscalerX = StandardScaler() \nscalerX.fit(X_train)\nX_train_norm = scalerX.transform(X_train)\nX_test_norm = scalerX.transform(X_test)\nmodelSVR = SVR(kernel='rbf')\nmodelSVR.fit(X_train_norm, y_train)\n \n# 5. Neuaral network, with some initial guess\nfrom sklearn.neural_network import MLPRegressor\nmodelMLP = MLPRegressor(hidden_layer_sizes=(2,6,1),\n activation = 'tanh',\n solver = 'adam',\n learning_rate = 'constant',\n learning_rate_init = 0.009,\n batch_size = 16,\n max_iter = 1000)\n\nmodelMLP.fit(X_train_norm,y_train)\n\n\n#---Prediction test---\nfrom sklearn import metrics\ny_Lin= modelLin.predict(X_test)\ny_Poly=modelPoly.predict(poly_reg.fit_transform(X_test))\ny_RF=modelRF.predict(X_test)\ny_SVR= modelSVR.predict(X_test_norm)\ny_MLP=modelMLP.predict(X_test_norm)\n\n\nprint('Mean Absolute Error of Linear Regression is:',metrics.mean_absolute_error(y_test,y_Lin))\nprint('Mean Absolute Error of Polynomial is:',metrics.mean_absolute_error(y_test,y_Poly))\nprint('Mean Absolute Error of Random Forest is:',metrics.mean_absolute_error(y_test,y_RF))\nprint('Mean Absolute Error of SVR is:',metrics.mean_absolute_error(y_test,y_SVR))\nprint('Mean Absolute Error of ANN is:',metrics.mean_absolute_error(y_test,y_MLP))\n\n# Plot the results\nplt.plot(y_test,y_test,'k-', label='Ideal')\nplt.plot(y_test,y_Lin,'b*', label='LR')\nplt.plot(y_test,y_Poly,'c*', label='Poly')\nplt.plot(y_test,y_RF,'g*', label='RF')\nplt.plot(y_test,y_SVR,'y*', label='SVR')\nplt.plot(y_test,y_MLP,'r*', label='DNN')\nplt.xlabel('Nu$_{exact}$')\nplt.ylabel('Nu$_{predicted}$')\nplt.title('Comparision of actual value \\nwith predicted value')\nplt.legend(loc=\"best\")\nax = plt.gca()\nax.set_facecolor('xkcd:salmon')\nplt.show()\n", "repo_name": "ikespand/ml4db", "sub_path": "1_ML4DB_All.py", "file_name": "1_ML4DB_All.py", "file_ext": "py", "file_size_in_byte": 4217, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "12", "api": [{"api_name": "matplotlib.pyplot.rcParams.update", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 33, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 37, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 45, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 50, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.PolynomialFeatures", "line_number": 55, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 58, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 63, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 71, "usage_type": "call"}, {"api_name": "sklearn.svm.SVR", "line_number": 75, "usage_type": "call"}, {"api_name": "sklearn.neural_network.MLPRegressor", "line_number": 80, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 100, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 100, "usage_type": "name"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 101, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 101, "usage_type": "name"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 102, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 102, "usage_type": "name"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 103, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 103, "usage_type": "name"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 104, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}]} +{"seq_id": "14039481265", "text": "from sys import stdin\nimport json\nimport smtplib\nfrom smtplib import SMTP as smtp\nfrom email.mime.text import MIMEText as text\nimport sys\nimport json\n\ndata = sys.stdin.read()\n\nclass SendEasyEmail():\n def __init__(self,data_dict_mp): \n self.data_dict= json.loads(data_dict_mp)\n self.result = str(self.data_dict[\"result\"])\n self.fecha = str(self.data_dict[\"fecha_proceso\"])\n self.usuario_emisor = str(self.data_dict[\"usuario_del_emisor\"])\n self.contrasena_emisor = str(self.data_dict[\"contrasena_del_emisor\"])\n self.text_body = '''Para la corrida efectuada en la fecha {}, se encontraron los siguientes datos nuevos:\\n{}'''.format(self.fecha,self.result)\n self.receptores = str(self.data_dict[\"destinatarios\"]).split(',')\n \n def action(self):\n for x in range(len(self.receptores)):\n try:\n self.s = smtplib.SMTP_SSL('smtp.gmail.com', 465)\n self.s.login(self.usuario_emisor,self.contrasena_emisor)\n self.m = text(self.text_body)\n self.m['Subject']= 'Reporte segmentos de prestamos no identificados | fecha {}'.format(self.fecha)\n self.s.sendmail(self.usuario_emisor,self.receptores[x], self.m.as_string())\n self.s.close()\n print(\"Se han reportado novedades de la fecha {} al mail {}\".format(self.fecha,self.receptores[x]))\n except:\n pass\nenvio = SendEasyEmail(data)\nenvio.action()", "repo_name": "JuanFerreyra1/Apache-Nifi-Projects", "sub_path": "Nifi-Pipeline-Sending-Emails/SendEasyEmail.py", "file_name": "SendEasyEmail.py", "file_ext": "py", "file_size_in_byte": 1520, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "sys.stdin.read", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 9, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 13, "usage_type": "call"}, {"api_name": "smtplib.SMTP_SSL", "line_number": 24, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "41537140168", "text": "import pandas as pd\nimport csv\nimport yfinance as yf\nimport numpy as np\nfrom yahoofinancials import YahooFinancials\n\n\n# import current tickers into list\ntickerData = list(csv.reader(open(\"companies.csv\")))\n\ntickers = []\nfor row in tickerData:\n tickers.append(row[0])\n\n\ngain = []\ndata = []\ni = 0\n\nfor ticker in tickers:\n\n stockData = yf.Ticker(ticker)\n currentStock = stockData.history(period=\"10y\")\n\n price = []\n for item in currentStock['Close']:\n price.append(item)\n\n data.append(price)\n print(i, ticker)\n i += 1\n\ndf = pd.DataFrame(data).T\ndf.to_excel(excel_writer=\"/Users/carterdemars/Desktop/backup/data.xlsx\")", "repo_name": "carterdemars/stock-volatility-oa", "sub_path": "dataScraper.py", "file_name": "dataScraper.py", "file_ext": "py", "file_size_in_byte": 645, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "csv.reader", "line_number": 9, "usage_type": "call"}, {"api_name": "yfinance.Ticker", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "8763578885", "text": "from collections import deque\n\ndef calculate(ex, a, b):\n b = int(b)\n a = int(a)\n if ex == '+':\n return a + b\n elif ex == '-':\n return a - b\n elif ex == '*':\n return a * b\n else:\n temp = abs(a) // abs(b)\n if a * b < 0:\n temp *= -1\n return temp\nclass Solution:\n def evalRPN(self, tokens: List[str]) -> int:\n if len(tokens) == 1:\n return int(tokens[0])\n q = deque()\n cal = {'+', '-', '*', '/'}\n for v in tokens:\n if v in cal:\n b = q.pop()\n a = q.pop()\n q.append(calculate(v, a, b))\n else:\n q.append(v)\n # print(q)\n return q[0]\n", "repo_name": "helpingstar/algorithmstudy", "sub_path": "python/Solved_Problem/leetcode_150.py", "file_name": "leetcode_150.py", "file_ext": "py", "file_size_in_byte": 737, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "collections.deque", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "71978694102", "text": "import inspect\nimport os\nimport pprint\nimport sys\nimport threading\nfrom io import open\n\nimport six\n\nimport snoop as package\nfrom snoop.formatting import DefaultFormatter\nfrom snoop.pp_module import PP\nfrom snoop.tracer import Spy, Tracer\nfrom snoop.utils import Mapping, QuerySet, Sequence, Set\nfrom snoop.utils import builtins as builtins_module\nfrom snoop.utils import ensure_tuple, is_pathlike, shitcode\n\ntry:\n # Enable ANSI escape codes in Windows 10\n import ctypes\n\n kernel32 = ctypes.windll.kernel32\n kernel32.SetConsoleMode(kernel32.GetStdHandle(-11), 7)\n can_color = True\nexcept Exception:\n can_color = os.name != 'nt'\n\n\ndef install(\n builtins=True,\n snoop=\"snoop\",\n pp=\"pp\",\n spy=\"spy\",\n out=None,\n prefix='',\n columns='time',\n overwrite=False,\n color=None,\n enabled=True,\n watch_extras=(),\n replace_watch_extras=None,\n formatter_class=DefaultFormatter,\n pformat=None,\n):\n \"\"\"\n Configure output, enable or disable, and add names to builtins. Parameters:\n\n - builtins: set to False to not add any names to builtins,\n so importing will still be required.\n - snoop, pp, and spy: set to other strings\n to choose the names of these functions in builtins\n - `out`: determines the output destination. By default this is stderr. You can also pass:\n - A string or a `Path` object to write to a file at that location. By default this always will append to the file. Pass `overwrite=True` to clear the file initially.\n - Anything with a `write` method, e.g. `sys.stdout` or a file object.\n - Any callable with a single string argument, e.g. `logger.info`.\n - `color`: determines whether the output includes escape characters to display colored text in the console. If you see weird characters in your output, your console doesn't support colors, so pass `color=False`.\n - Code is syntax highlighted using [Pygments](http://pygments.org/), and this argument is passed as the style. You can choose a different color scheme by passing a string naming a style (see [this gallery](https://help.farbox.com/pygments.html)) or a style class. The default style is monokai.\n - By default this parameter is set to `out.isatty()`, which is usually true for stdout and stderr but will be false if they are redirected or piped. Pass `True` or a style if you want to force coloring.\n - To see colors in the PyCharm Run window, edit the Run Configuration and tick \"Emulate terminal in output console\".\n - `prefix`: Pass a string to start all snoop lines with that string so you can grep for them easily.\n - `columns`: This specifies the columns at the start of each output line. You can pass a string with the names of built in columns separated by spaces or commas. These are the available columns:\n - `time`: The current time. This is the only column by default.\n - `thread`: The name of the current thread.\n - `thread_ident`: The [identifier](https://docs.python.org/3/library/threading.html#threading.Thread.ident) of the current thread, in case thread names are not unique.\n - `file`: The filename (not the full path) of the current function.\n - `full_file`: The full path to the file (also shown anyway when the function is called).\n - `function`: The name of the current function.\n - `function_qualname`: The qualified name of the current function.\n\n If you want a custom column, please open an issue to tell me what you're interested in! In the meantime, you can pass a list, where the elements are either strings or callables. The callables should take one argument, which will be an `Event` object. It has attributes `frame`, `event`, and `arg`, as specified in [`sys.settrace()`](https://docs.python.org/3/library/sys.html#sys.settrace), and other attributes which may change.\n - `pformat`: set the pretty formatting function `pp` uses. Default is to use the first of `prettyprinter.pformat`, `pprintpp.pformat` and `pprint.pformat` that can be imported.\n \"\"\"\n\n if builtins:\n setattr(builtins_module, snoop, package.snoop)\n setattr(builtins_module, pp, package.pp)\n setattr(builtins_module, spy, package.spy)\n config = Config(\n out=out,\n prefix=prefix,\n columns=columns,\n overwrite=overwrite,\n color=color,\n enabled=enabled,\n watch_extras=watch_extras,\n replace_watch_extras=replace_watch_extras,\n formatter_class=formatter_class,\n pformat=pformat,\n )\n package.snoop.config = config\n package.pp.config = config\n package.spy.config = config\n\n\nclass Config(object):\n \"\"\"\"\n If you need more control than the global `install` function, e.g. if you want to write to several different files in one process, you can create a `Config` object, e.g: `config = snoop.Config(out=filename)`. Then `config.snoop`, `config.pp` and `config.spy` will use that configuration rather than the global one.\n\n The arguments are the same as the arguments of `install()` relating to output configuration and `enabled`.\n \"\"\"\n\n def __init__(\n self,\n out=None,\n prefix='',\n columns='time',\n overwrite=False,\n color=None,\n enabled=True,\n watch_extras=(),\n replace_watch_extras=None,\n formatter_class=DefaultFormatter,\n pformat=None,\n ):\n if can_color:\n if color is None:\n isatty = getattr(out or sys.stderr, 'isatty', lambda: False)\n color = bool(isatty())\n else:\n color = False\n\n self.write = get_write_function(out, overwrite)\n self.formatter = formatter_class(prefix, columns, color)\n self.enabled = enabled\n\n if pformat is None:\n try:\n from prettyprinter import pformat\n except Exception:\n try:\n from pprintpp import pformat\n except Exception:\n from pprint import pformat\n\n self.pformat = pformat\n\n self.pp = PP(self)\n\n class ConfiguredTracer(Tracer):\n config = self\n\n self.snoop = ConfiguredTracer\n self.spy = Spy(self)\n\n self.last_frame = None\n self.thread_local = threading.local()\n\n if replace_watch_extras is not None:\n self.watch_extras = ensure_tuple(replace_watch_extras)\n else:\n self.watch_extras = (len_shape_watch, dtype_watch) + ensure_tuple(watch_extras)\n\n\ndef len_shape_watch(source, value):\n try:\n shape = value.shape\n except Exception:\n pass\n else:\n if not inspect.ismethod(shape):\n return '{}.shape'.format(source), shape\n\n if isinstance(value, QuerySet):\n # Getting the length of a Django queryset evaluates it\n return None\n\n length = len(value)\n if (\n (isinstance(value, six.string_types)\n and length < 50) or\n (isinstance(value, (Mapping, Set, Sequence))\n and length == 0)\n ):\n return None\n\n return 'len({})'.format(source), length\n\n\ndef dtype_watch(source, value):\n dtype = value.dtype\n if not inspect.ismethod(dtype):\n return '{}.dtype'.format(source), dtype\n\n\ndef get_write_function(output, overwrite):\n is_path = (\n isinstance(output, six.string_types)\n or is_pathlike(output)\n )\n if is_path:\n return FileWriter(output, overwrite).write\n elif callable(output):\n write = output\n else:\n def write(s):\n stream = output\n\n if stream is None:\n stream = sys.stderr\n\n try:\n stream.write(s)\n except UnicodeEncodeError:\n # God damn Python 2\n stream.write(shitcode(s))\n\n getattr(stream, \"flush\", lambda: None)()\n\n return write\n\n\nclass FileWriter(object):\n def __init__(self, path, overwrite):\n self.path = six.text_type(path)\n self.overwrite = overwrite\n\n def write(self, s):\n with open(self.path, 'w' if self.overwrite else 'a', encoding='utf-8') as f:\n f.write(s)\n self.overwrite = False\n", "repo_name": "alexmojaki/snoop", "sub_path": "snoop/configuration.py", "file_name": "configuration.py", "file_ext": "py", "file_size_in_byte": 8321, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 970, "dataset": "github-code", "pt": "12", "api": [{"api_name": "ctypes.windll", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.name", "line_number": 26, "usage_type": "attribute"}, {"api_name": "snoop.formatting.DefaultFormatter", "line_number": 42, "usage_type": "name"}, {"api_name": "snoop.utils.builtins", "line_number": 75, "usage_type": "argument"}, {"api_name": "snoop.snoop", "line_number": 75, "usage_type": "attribute"}, {"api_name": "snoop.utils.builtins", "line_number": 76, "usage_type": "argument"}, {"api_name": "snoop.pp", "line_number": 76, "usage_type": "attribute"}, {"api_name": "snoop.utils.builtins", "line_number": 77, "usage_type": "argument"}, {"api_name": "snoop.spy", "line_number": 77, "usage_type": "attribute"}, {"api_name": "snoop.snoop", "line_number": 90, "usage_type": "attribute"}, {"api_name": "snoop.pp", "line_number": 91, "usage_type": "attribute"}, {"api_name": "snoop.spy", "line_number": 92, "usage_type": "attribute"}, {"api_name": "snoop.formatting.DefaultFormatter", "line_number": 112, "usage_type": "name"}, {"api_name": "sys.stderr", "line_number": 117, "usage_type": "attribute"}, {"api_name": "pprint.pformat", "line_number": 135, "usage_type": "name"}, {"api_name": "snoop.pp_module.PP", "line_number": 137, "usage_type": "call"}, {"api_name": "snoop.tracer.Tracer", "line_number": 139, "usage_type": "name"}, {"api_name": "snoop.tracer.Spy", "line_number": 143, "usage_type": "call"}, {"api_name": "threading.local", "line_number": 146, "usage_type": "call"}, {"api_name": "snoop.utils.ensure_tuple", "line_number": 149, "usage_type": "call"}, {"api_name": "snoop.utils.ensure_tuple", "line_number": 151, "usage_type": "call"}, {"api_name": "inspect.ismethod", "line_number": 160, "usage_type": "call"}, {"api_name": "snoop.utils.QuerySet", "line_number": 163, "usage_type": "argument"}, {"api_name": "six.string_types", "line_number": 169, "usage_type": "attribute"}, {"api_name": "snoop.utils.Mapping", "line_number": 171, "usage_type": "name"}, {"api_name": "snoop.utils.Set", "line_number": 171, "usage_type": "name"}, {"api_name": "snoop.utils.Sequence", "line_number": 171, "usage_type": "name"}, {"api_name": "inspect.ismethod", "line_number": 181, "usage_type": "call"}, {"api_name": "six.string_types", "line_number": 187, "usage_type": "attribute"}, {"api_name": "snoop.utils.is_pathlike", "line_number": 188, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 199, "usage_type": "attribute"}, {"api_name": "snoop.utils.shitcode", "line_number": 205, "usage_type": "call"}, {"api_name": "six.text_type", "line_number": 214, "usage_type": "call"}, {"api_name": "io.open", "line_number": 218, "usage_type": "call"}]} +{"seq_id": "36382726260", "text": "import itertools\nimport unittest\n\n\ndef permutations_recursive(pool, r=None):\n pool = list(pool)\n n = len(pool)\n if r is None:\n r = n\n elif r > n:\n return []\n\n result = []\n _permutations_recursive(pool, r, result, 0)\n return result\n\n\ndef _permutations_recursive(pool, r, result, start_index):\n if start_index == r:\n result.append(tuple(pool[:r]))\n else:\n next_index = start_index + 1\n _permutations_recursive(pool, r, result, next_index)\n\n for i in range(next_index, len(pool)):\n pool[i], pool[start_index] = pool[start_index], pool[i]\n _permutations_recursive(pool, r, result, next_index)\n pool[i], pool[start_index] = pool[start_index], pool[i]\n\n\ndef permutations_iterative(pool, r=None):\n # See CPython itertools.permutations():\n # https://docs.python.org/2/library/itertools.html#itertools.permutations\n # https://github.com/python/cpython/blob/bf623ae8843dc30b28c574bec8d29fc14be59d86/Modules/itertoolsmodule.c#L3153\n pool = tuple(pool)\n n = len(pool)\n if r is None:\n r = n\n elif r > n:\n return\n\n indices = list(range(n))\n cycles = list(range(r))\n yield tuple(pool[i] for i in indices[:r])\n\n while True:\n # Find the last index\n for i in range(r - 1, -1, -1):\n cycles[i] += 1\n j = cycles[i]\n\n # which is not the greatest possible at its position\n if j < n:\n indices[i], indices[j] = indices[j], indices[i]\n yield tuple(pool[i] for i in indices[:r])\n break\n else:\n # Reset cycles[i]\n cycles[i] = i\n\n # Move indices[i] to the end\n indices[i:] = indices[i + 1:] + [indices[i]]\n else:\n return\n\n\nclass Test(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n cls.pools = [\n list(range(5)),\n ]\n\n def test_permutations(self):\n self._test_permutations(permutations_recursive)\n self._test_permutations(permutations_iterative)\n\n def _test_permutations(self, func):\n for pool in Test.pools:\n for r in range(len(pool) + 2):\n actual = func(pool, r)\n expected = itertools.permutations(pool, r)\n self.assertCountEqual(expected, actual)\n\n\nif __name__ == '__main__':\n unittest.main()\n", "repo_name": "chrisxue815/leetcode_python", "sub_path": "problems/permutations.py", "file_name": "permutations.py", "file_ext": "py", "file_size_in_byte": 2433, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "12", "api": [{"api_name": "unittest.TestCase", "line_number": 67, "usage_type": "attribute"}, {"api_name": "itertools.permutations", "line_number": 82, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 87, "usage_type": "call"}]} +{"seq_id": "41937622075", "text": "from django.urls import path\n\nfrom webapp.views.base import index_view\nfrom webapp.views.products import add_view, detail_view, update_view, delete_view, confirm_delete, \\\n alcohol_view, cars_view, smartphone_view, other_view\n\nurlpatterns = [\n path(\"\", index_view, name='index'),\n path(\"products/\", index_view),\n path('product/add', add_view, name='product_add'),\n path('product/', detail_view, name='detail_product'),\n path('product//update/', update_view, name='product_update'),\n path('product//delete/', delete_view, name='product_delete'),\n path('product//confirm_delete/', confirm_delete, name='confirm_delete'),\n path('products/cars/', cars_view, name='cars_view'),\n path('products/alcohol/', alcohol_view, name='alcohol_view'),\n path('products/smartphone/', smartphone_view, name='smartphone_view'),\n path('products/other/', other_view, name='other_view')\n]\n", "repo_name": "YerbolZhulniyazov/homework_56_yerbol_zhulniyazov", "sub_path": "source/webapp/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 934, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "webapp.views.base.index_view", "line_number": 8, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "webapp.views.base.index_view", "line_number": 9, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "webapp.views.products.add_view", "line_number": 10, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "webapp.views.products.detail_view", "line_number": 11, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "webapp.views.products.update_view", "line_number": 12, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "webapp.views.products.delete_view", "line_number": 13, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "webapp.views.products.confirm_delete", "line_number": 14, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "webapp.views.products.cars_view", "line_number": 15, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "webapp.views.products.alcohol_view", "line_number": 16, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "webapp.views.products.smartphone_view", "line_number": 17, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "webapp.views.products.other_view", "line_number": 18, "usage_type": "argument"}]} +{"seq_id": "3225592580", "text": "# import the necessary packages\nfrom keras.models import Sequential\nfrom keras.layers.convolutional import Conv2D\nfrom keras.layers.convolutional import MaxPooling2D\nfrom keras.layers.core import Activation\nfrom keras.layers.core import Flatten\nfrom keras.layers.core import Dense\nfrom keras import backend as K\n\n\nclass networkArchFonc:\n @staticmethod\n def build(width, height, depth, classes):\n # initialize the model\n model = Sequential()\n inputShape = (height, width, depth)\n\n # if we are using \"channels first\", update the input shape\n if K.image_data_format() == \"channels_first\":\n inputShape = (depth, height, width)\n\n # first set of CONV => RELU => POOL layers\n model.add(Conv2D(16, (2, 2), padding=\"same\",\n input_shape=inputShape))\n model.add(Activation(\"relu\"))\n model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n\n # second set of CONV => RELU => POOL layers\n model.add(Conv2D(32, (2, 2), padding=\"same\")) # kernelere göre conv yerni bir matris oluşturma\n model.add(Activation(\"relu\")) # reulu: negatif değerleri çevirme relu sıfıra elu e üzeri\n model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n\n # first (and only) set of FC => RELU layers\n model.add(Flatten()) # düzleştirme ?\n model.add(Dense(500)) # fully connected\n model.add(Activation(\"relu\"))\n\n # softmax classifier\n model.add(Dense(classes))\n model.add(Activation(\"softmax\"))\n #\tprint(model.summary())\n # return the constructed network architecture\n return model\n", "repo_name": "mkfzdmr/Deep-Learning-based-Emotion-Recognition", "sub_path": "model_1.py", "file_name": "model_1.py", "file_ext": "py", "file_size_in_byte": 1663, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 35, "dataset": "github-code", "pt": "12", "api": [{"api_name": "keras.models.Sequential", "line_number": 15, "usage_type": "call"}, {"api_name": "keras.backend.image_data_format", "line_number": 19, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 19, "usage_type": "name"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 23, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.MaxPooling2D", "line_number": 26, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 29, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 30, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.MaxPooling2D", "line_number": 31, "usage_type": "call"}, {"api_name": "keras.layers.core.Flatten", "line_number": 34, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 35, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 36, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 39, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "41195416105", "text": "import pandas as pd\n\nprevisores = pd.read_csv('entradas-breast.csv')\nclasse = pd.read_csv('saidas-breast.csv')\n\nfrom sklearn.model_selection import train_test_split\n\nprevisores_treinamento, previsores_teste, classe_treinamento, classe_teste = train_test_split(previsores, classe, test_size=0.25)\n\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Dense\n\nclassificador = Sequential()\n\n#units passa o somatório das (entradas + saidas)/2, no caso são 30 atrib de rntrada e 1 de saida, então da 31/2 que da 16,\n#no segundo é a funcao la, no terceiro é o tamanho da entrada (30 atributos) e só usa o tereiro na primeira camada\nclassificador.add(Dense(units = 16, activation = 'relu', kernel_initializer = 'random_uniform', input_dim = 30))\nclassificador.add(Dense(units = 16, activation = 'relu', kernel_initializer = 'random_uniform'))\n\n#usar o Dense significa q a rede vai ser Fully conected xD\nclassificador.add(Dense(units = 1, activation = 'sigmoid'))\n\n#lr é a learning rate, decay é o tanto q a LR cai por geração e clipvalue é para manter o bixo dentro do escopo (-x, x) -> (-0.5 até 0.5)\notimizador = keras.optimizers.Adam(lr=0.01, decay=0.001, clipvalue=0.5)\n\n#adam = descida do gradiente estocastico\nclassificador.compile(optimizer = otimizador, loss = 'binary_crossentropy', metrics = ['binary_accuracy'])\n\n#fit é de encaixar, batch_size é q vai pegar 10 registros, dps +10 e assim vai (para atualizar os pesos (MINI BATCH))\nclassificador.fit(previsores_treinamento, classe_treinamento, batch_size = 10, epochs = 100)\n\n#vizualização dos pesos\npesos0 = classificador.layers[0].get_weights()\nprint(pesos0)\npesos1 = classificador.layers[1].get_weights()\nprint(pesos1)\npesos2 = classificador.layers[2].get_weights()\nprint(pesos2)\n\nprevisoes = classificador.predict(previsores_teste)\nprevisoes = (previsoes > 0.5)\n\n#Um jeito de fazer\nfrom sklearn.metrics import confusion_matrix, accuracy_score\n\nprecisao = accuracy_score(classe_teste, previsoes)\nmatriz = confusion_matrix(classe_teste, previsoes)\n\nprint(\"precisão com SKLEARN:\", precisao)\nprint(matriz)\n\n#Outro jeito de fazer\nresultado = classificador.evaluate(previsores_teste, classe_teste)\n\n#print(\"precisao com o keras:\", resultado)\nprint(\"Perda com o keras:\", resultado[0])\nprint(\"Acerto com o keras:\", resultado[1])\n\n\n", "repo_name": "MatheusFerreiradeOliveira/Neural-Networks", "sub_path": "Redes Neurais Aritificais/classificacao_binaria/breast_cancer_simples.py", "file_name": "breast_cancer_simples.py", "file_ext": "py", "file_size_in_byte": 2317, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "pandas.read_csv", "line_number": 3, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 4, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 8, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 14, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 18, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 19, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 22, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.optimizers", "line_number": 25, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 47, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "6096279879", "text": "import logging\nfrom typing import List\nimport yaml\n\nfrom kubeluigi.k8s import (\n clean_job_resources,\n job_definition,\n pod_spec_from_dict,\n run_and_track_job,\n kubernetes_client,\n attach_volume_to_spec,\n FailedJob,\n)\n\nfrom kubeluigi.volumes import AttachableVolume\n\nfrom kubernetes.client import ApiClient\nfrom kubernetes.client.models.v1_job import V1Job\nfrom kubernetes.client import V1Toleration\nfrom kubernetes.client.models.v1_pod_spec import V1PodSpec\n\nlogger = logging.getLogger(__name__)\n\n\nclass KubernetesJobTask:\n\n volumes: List[AttachableVolume] = []\n\n def __init__(self):\n self.tolerations: List[V1Toleration] = []\n\n def _init_task_metadata(self):\n self.uu_name = self.name\n\n def _init_kubernetes(self):\n self.kubernetes_client = kubernetes_client()\n\n @property\n def restart_policy(self):\n return \"Never\"\n\n @property\n def active_deadline_seconds(self):\n return None\n\n @property\n def backoff_limit(self):\n \"\"\"\n Maximum number of retries before considering the job as failed.\n See: https://kubernetes.io/docs/concepts/workloads/controllers/jobs-run-to-completion/#pod-backoff-failure-policy\n \"\"\"\n return 6\n\n @property\n def name(self):\n \"\"\"\n A name for this job. This needs to be unique otherwise it will fail if another job\n with the same name is running.\n \"\"\"\n raise NotImplementedError(\"subclass must define name\")\n\n @property\n def labels(self):\n \"\"\"\n Return custom labels for kubernetes job.\n example::\n ``{\"run_dt\": datetime.date.today().strftime('%F')}``\n \"\"\"\n return {}\n\n def spec_schema(self):\n \"\"\"\n Kubernetes Job spec schema in JSON format, an example follows.\n .. code-block:: javascript\n {\n \"containers\": [{\n \"name\": \"pi\",\n \"image\": \"perl\",\n \"command\": [\"perl\", \"-Mbignum=bpi\", \"-wle\", \"print bpi(2000)\"]\n }]\n }\n \"\"\"\n raise NotImplementedError(\"subclass must define spec_schema\")\n\n def build_job_definition(self) -> V1Job:\n self._init_task_metadata()\n schema = self.spec_schema()\n schema_with_volumes = self._attach_volumes_to_spec(schema)\n pod_template_spec = pod_spec_from_dict(\n self.uu_name, schema_with_volumes, self.labels, self.restart_policy, tolerations=self.tolerations\n )\n\n job = job_definition(\n job_name=self.uu_name,\n backoff_limit=self.backoff_limit,\n pod_template_spec=pod_template_spec,\n labels=self.labels,\n namespace=self.namespace,\n active_deadline_seconds=self.active_deadline_seconds\n )\n return job\n\n def onpodstarted(self, pods):\n for pod in pods:\n logger.info(\n f\"Tail the Pod logs using: kubectl logs -f -n {pod.namespace} {pod.name}\"\n )\n\n def as_yaml(self):\n job = self.build_job_definition()\n job_dict = ApiClient().sanitize_for_serialization(job)\n str_yaml = yaml.safe_dump(job_dict, default_flow_style=False, sort_keys=False)\n return str_yaml\n\n def run(self):\n self._init_kubernetes()\n job = self.build_job_definition()\n logger.debug(\"Submitting Kubernetes Job: \" + self.uu_name)\n try:\n run_and_track_job(self.kubernetes_client, job, self.onpodstarted)\n except FailedJob as e:\n logger.exception(\n f\"Luigi's job has failed running: {e.job.metadata.name}\"\n )\n for pod in e.pods:\n logger.exception(\n f\"Luigi's job has failed running: {pod.status.message}\"\n )\n raise\n except Exception:\n logger.exception(f\"Luigi has failed to run: {job}, starting cleaning\")\n raise\n else:\n clean_job_resources(self.kubernetes_client, job)\n \n def output(self):\n \"\"\"\n An output target is necessary for checking job completion unless\n an alternative complete method is defined.\n Example::\n return luigi.LocalTarget(os.path.join('/tmp', 'example'))\n \"\"\"\n pass\n\n def _attach_volumes_to_spec(self, spec_schema):\n \"\"\"\n overrides the spec_schema of a task to attach a volume\n \"\"\"\n if \"volumes\" not in spec_schema and hasattr(self, \"volumes\"):\n for volume in self.volumes:\n spec_schema = attach_volume_to_spec(spec_schema, volume)\n return spec_schema\n\n def add_volume(self, volume):\n \"\"\"\n adds a volume to the task\n \"\"\"\n return self.volumes.append(volume)\n\n def add_toleration(self, key, value, effect='NoSchedule', operator='Equal'):\n toleration = V1Toleration(key=key, value=value, effect=effect, operator=operator)\n\n return self.tolerations.append(toleration)\n\n", "repo_name": "optimizely/kubeluigi", "sub_path": "kubeluigi/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 5035, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "12", "api": [{"api_name": "logging.getLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 27, "usage_type": "name"}, {"api_name": "kubeluigi.volumes.AttachableVolume", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 30, "usage_type": "name"}, {"api_name": "kubernetes.client.V1Toleration", "line_number": 30, "usage_type": "name"}, {"api_name": "kubeluigi.k8s.kubernetes_client", "line_number": 36, "usage_type": "call"}, {"api_name": "kubeluigi.k8s.pod_spec_from_dict", "line_number": 89, "usage_type": "call"}, {"api_name": "kubeluigi.k8s.job_definition", "line_number": 93, "usage_type": "call"}, {"api_name": "kubernetes.client.models.v1_job.V1Job", "line_number": 85, "usage_type": "name"}, {"api_name": "kubernetes.client.ApiClient", "line_number": 111, "usage_type": "call"}, {"api_name": "yaml.safe_dump", "line_number": 112, "usage_type": "call"}, {"api_name": "kubeluigi.k8s.run_and_track_job", "line_number": 120, "usage_type": "call"}, {"api_name": "kubeluigi.k8s.FailedJob", "line_number": 121, "usage_type": "name"}, {"api_name": "kubeluigi.k8s.clean_job_resources", "line_number": 134, "usage_type": "call"}, {"api_name": "kubeluigi.k8s.attach_volume_to_spec", "line_number": 151, "usage_type": "call"}, {"api_name": "kubernetes.client.V1Toleration", "line_number": 161, "usage_type": "call"}]} +{"seq_id": "5935759596", "text": "r\"\"\"\n ______ __ ___ \n/\\__ _\\ /\\ \\__ /\\_ \\ \n\\/_/\\ \\/ ___\\ \\ ,_\\ __\\//\\ \\\n \\ \\ \\ /' _ `\\ \\ \\/ /'__`\\\\ \\ \\\n \\_\\ \\__/\\ \\/\\ \\ \\ \\_/\\ __/ \\_\\ \\_\n /\\_____\\ \\_\\ \\_\\ \\__\\ \\____\\/\\____\\\n \\/_____/\\/_/\\/_/\\/__/\\/____/\\/____/\n ____ __\n/\\ _`\\ __ /\\ \\\n\\ \\ \\L\\ \\ _ __ /\\_\\ \\_\\ \\ __ __\n \\ \\ _ <'/\\`'__\\/\\ \\ /'_` \\ /'_ `\\ /'__`\\\n \\ \\ \\L\\ \\ \\ \\/ \\ \\ \\/\\ \\L\\ \\/\\ \\L\\ \\/\\ __/\n \\ \\____/\\ \\_\\ \\ \\_\\ \\___,_\\ \\____ \\ \\____\\\n \\/___/ \\/_/ \\/_/\\/__,_ /\\/___L\\ \\/____/\n /\\____/\n \\_/__/\nintelbridge.py\nMain object in module; Handles intel bridge initialization and execution\n\"\"\"\nimport logging\nimport time\nfrom indicators.indicators import get_indicators, prepare_indicators\nfrom zscaler.zscaler import look_up_indicators, push_indicators, save_changes, validate_category\nfrom auth.auth import cs_auth\nfrom auth.auth import zs_auth\nfrom util.util import convert, next_hour\n\n\nclass IntelBridge():\n def __init__(self):\n \"\"\"Initializes the class and saves start time\n returns: N/A\n \"\"\"\n logging.info(\"Initializing Intel Bridge\")\n self.start_time = int(time.time())\n return\n \n def pull(self, token, del_switch):\n \"\"\"Handles getting new Indicators from Falcon API.\n token - Falcon API Auth token\n del_switch - Boolean for pulling new or deleted indicators\n returns: List containing indicators pulled from Falcon API\n \"\"\"\n indicators = get_indicators(token, del_switch)\n return indicators\n\n def prepare(self, token, indicators):\n \"\"\"Handles transforming indicators object into a Zscaler API ready model\n token - Falcon API Auth token\n indicators - List containing indicators pulled from Falcon API\n returns: Indicator list formatted for Zscaler API ingestion\n \"\"\"\n prepared = prepare_indicators(indicators)\n ingestable = look_up_indicators(prepared, token)\n return ingestable\n \n def update(self, token, content, category, ingestable, del_switch):\n \"\"\"Handles updating the URL Category content\n token - Zscaler API Auth token\n category - Name of Zscaler custom URL category from config.ini\n content - Current list of URLs to be removed\n ingestable - Indicator lsit formatted for Zscaler API ingestion\n del_switch - Boolean for pulling new or deleted indicators\n returns: N/A\n \"\"\"\n # remove existing content\n if(len(content[0]['urls']) > 1):\n push_indicators(token, category, content, True)\n # push new content\n push_indicators(token, category, ingestable, False)\n # activate\n save_changes(token)\n return\n\n def etl_loop(self, cs_token, zs_token, del_switch, loop):\n \"\"\"Main runtime loop - pulls, prepares, and pushes new indicators\n cs_token - Falcon API Auth token\n zs_token - Zscaler API Auth token\n category - Name of Zscaler custom URL category from config.ini\n del_switch - Boolean for pulling new or deleted indicators\n loop - Iteration number\n returns: switched del_switch and new iteration number\n \"\"\"\n logging.info(f\"Starting Pull/Prepare/Push Loop # {loop} \"\n f\"With {'deleted' if del_switch else 'new'} indicators\")\n del_switch = False\n category = validate_category(zs_token)\n category_name = category['id']\n content = [category['content']]\n start = int(time.time())\n indicators = self.pull(cs_token, del_switch)\n ingestable = self.prepare(zs_token, indicators)\n self.update(zs_token, content, category_name, ingestable, del_switch)\n end = int(time.time())\n loop_delta = convert(end - start)\n total_delta = convert(end - self.start_time)\n logging.info(f\"Finished loop {loop}! Time elapsed: {loop_delta};\"\n f\"Total run time: {total_delta};\"\n f\"Sleeping for 1 hour...Next update:{next_hour()}.\")\n time.sleep(3600)\n return not del_switch, loop + 1\n\n\n def start(self):\n \"\"\"Starts the main runtime loop (etl_loop)\n returns: N/A\n \"\"\"\n cs_token = cs_auth()\n zs_token = zs_auth()\n del_switch = False\n loop = 1\n while(True):\n del_switch, loop = self.etl_loop(cs_token, zs_token, del_switch, loop)\n \n", "repo_name": "GTHAP/zscaler-FalconX-integration", "sub_path": "intelbridge/intelbridge.py", "file_name": "intelbridge.py", "file_ext": "py", "file_size_in_byte": 4542, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "12", "api": [{"api_name": "logging.info", "line_number": 35, "usage_type": "call"}, {"api_name": "time.time", "line_number": 36, "usage_type": "call"}, {"api_name": "indicators.indicators", "line_number": 45, "usage_type": "name"}, {"api_name": "indicators.indicators.get_indicators", "line_number": 45, "usage_type": "call"}, {"api_name": "indicators.indicators", "line_number": 46, "usage_type": "name"}, {"api_name": "indicators.indicators.prepare_indicators", "line_number": 54, "usage_type": "call"}, {"api_name": "indicators.indicators", "line_number": 54, "usage_type": "argument"}, {"api_name": "zscaler.zscaler.look_up_indicators", "line_number": 55, "usage_type": "call"}, {"api_name": "zscaler.zscaler.push_indicators", "line_number": 69, "usage_type": "call"}, {"api_name": "zscaler.zscaler.push_indicators", "line_number": 71, "usage_type": "call"}, {"api_name": "zscaler.zscaler.save_changes", "line_number": 73, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 85, "usage_type": "call"}, {"api_name": "zscaler.zscaler.validate_category", "line_number": 88, "usage_type": "call"}, {"api_name": "time.time", "line_number": 91, "usage_type": "call"}, {"api_name": "indicators.indicators", "line_number": 92, "usage_type": "name"}, {"api_name": "indicators.indicators", "line_number": 93, "usage_type": "argument"}, {"api_name": "time.time", "line_number": 95, "usage_type": "call"}, {"api_name": "util.util.convert", "line_number": 96, "usage_type": "call"}, {"api_name": "util.util.convert", "line_number": 97, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 98, "usage_type": "call"}, {"api_name": "util.util.next_hour", "line_number": 100, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 101, "usage_type": "call"}, {"api_name": "auth.auth.cs_auth", "line_number": 109, "usage_type": "call"}, {"api_name": "auth.auth.zs_auth", "line_number": 110, "usage_type": "call"}]} +{"seq_id": "13569473790", "text": "from bungie import Adapter\nimport random\nimport asyncio\nimport logging\nimport uuid\n\nfrom configuration import config\nimport Marketplace\nfrom Marketplace import PayPal, PaymentPaypal, HoldNotice, ReleaseFund, SendFund, RequestDefectRefund, SendDefectRefund\n\nlogger = logging.getLogger(\"PayPal-Org\")\nadapter = Adapter(PayPal, Marketplace.protocol, config)\n\n@adapter.reaction(PaymentPaypal)\nasync def receiveFund(msg):\n logger.info(f'Received fund with status hold: {msg.payload}')\n adapter.send(\n HoldNotice(\n paymentHold = str(uuid.uuid4()),\n **msg.payload\n )\n )\n\n@adapter.reaction(ReleaseFund)\nasync def releaseFund(msg):\n logger.info(f'Fund released to Merchant: {msg.payload}')\n adapter.send(\n SendFund(\n sentFund = str(uuid.uuid4()),\n **msg.payload\n )\n )\n\n\n@adapter.reaction(RequestDefectRefund)\nasync def defectRefund(msg):\n logger.info(f'Received defect refund request from eBay: {msg.payload}')\n adapter.send(\n SendDefectRefund(\n defRefundSent = str(uuid.uuid4()),\n **msg.payload\n )\n )\n\n\n\nif __name__ == \"__main__\":\n logger.info(\"Starting PayPal...\")\n adapter.start()\n", "repo_name": "Nkorchi/CSC750-Service-Oriented-Computing", "sub_path": "Assignment-1/paypal.py", "file_name": "paypal.py", "file_ext": "py", "file_size_in_byte": 1215, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "bungie.Adapter", "line_number": 12, "usage_type": "call"}, {"api_name": "Marketplace.PayPal", "line_number": 12, "usage_type": "argument"}, {"api_name": "configuration.config", "line_number": 12, "usage_type": "argument"}, {"api_name": "Marketplace.protocol", "line_number": 12, "usage_type": "attribute"}, {"api_name": "Marketplace.HoldNotice", "line_number": 18, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 19, "usage_type": "call"}, {"api_name": "Marketplace.PaymentPaypal", "line_number": 14, "usage_type": "argument"}, {"api_name": "Marketplace.SendFund", "line_number": 28, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 29, "usage_type": "call"}, {"api_name": "Marketplace.ReleaseFund", "line_number": 24, "usage_type": "argument"}, {"api_name": "Marketplace.SendDefectRefund", "line_number": 39, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 40, "usage_type": "call"}, {"api_name": "Marketplace.RequestDefectRefund", "line_number": 35, "usage_type": "argument"}]} +{"seq_id": "41040251522", "text": "import sys\nimport requests\nimport zipfile\nfrom PyQt5.QtCore import Qt\nfrom PyQt5.QtWidgets import QApplication, QMainWindow, QPushButton, QFileDialog, QMessageBox\n\n\nclass MyMainWindow(QMainWindow):\n def __init__(self):\n super().__init__()\n self.setWindowTitle('My App')\n self.setGeometry(100, 100, 400, 200)\n\n self.button = QPushButton('Download', self)\n self.button.setGeometry(150, 70, 100, 30)\n self.button.clicked.connect(self.download_zip)\n\n def download_zip(self):\n url = 'http://example.com/download_zip'\n response = requests.get(url)\n\n if response.ok:\n filename = QFileDialog.getSaveFileName(self, 'Save File', filter='Zip files (*.zip)')[0]\n with open(filename, 'wb') as f:\n f.write(response.content)\n self.extract_zip(filename)\n QMessageBox.information(self, 'Success', 'Zip file extracted successfully.')\n else:\n QMessageBox.warning(self, 'Error', f'Failed to download zip file.\\nError code: {response.status_code}')\n\n def extract_zip(self, zip_path):\n with zipfile.ZipFile(zip_path, 'r') as zip_file:\n zip_file.extractall(path='extracted_files')\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n window = MyMainWindow()\n window.show()\n sys.exit(app.exec_())\n", "repo_name": "Jonatanfroeling-user/Bap-garbagetruck", "sub_path": "Basic-implimentation/2-basic-features/4-offline/api-test/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1359, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 8, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 20, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getSaveFileName", "line_number": 23, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 23, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 27, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 27, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 29, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 29, "usage_type": "name"}, {"api_name": "zipfile.ZipFile", "line_number": 32, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 37, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 37, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "37274445119", "text": "import re\r\nimport os\r\nimport openpyxl\r\nimport requests\r\nimport pandas as pd\r\nimport urllib.request\r\nfrom parsel import Selector\r\nfrom os import getcwd, path\r\nfrom bs4 import BeautifulSoup\r\n\r\n\r\ndef clean(text):\r\n '''remove extra spaces & junk character'''\r\n text = re.sub(r'\\n+','',text)\r\n text = re.sub(r'\\s+',' ',text)\r\n text = re.sub(r'\\r+','',text)\r\n return text.strip()\r\n\r\nfile_path = getcwd()\r\nfile_name = input(\"Enter file name : \") + \".xlsx\"\r\noutput_filename = f\"{file_path}\\\\{file_name}\"\r\nwb = openpyxl.load_workbook(output_filename)\r\ndata_sheet = wb.active\r\ndata_sheet[\"D1\"] = \"headers\"\r\ndata_sheet[\"E1\"] = \"paragraph\"\r\ndata_sheet[\"F1\"] = \"paragraph_content\"\r\nfor i in range(1, data_sheet.max_row + 1):\r\n item = {}\r\n url = data_sheet.cell(row=i, column=1).value\r\n payload = {}\r\n headers = {\r\n 'authority': 'www.lenovo.com',\r\n 'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',\r\n 'accept-language': 'en-US,en;q=0.9',\r\n 'cache-control': 'max-age=0',\r\n 'sec-ch-ua': '\"Chromium\";v=\"112\", \"Google Chrome\";v=\"112\", \"Not:A-Brand\";v=\"99\"',\r\n 'sec-ch-ua-mobile': '?0',\r\n 'sec-ch-ua-platform': '\"Windows\"',\r\n 'sec-fetch-dest': 'document',\r\n 'sec-fetch-mode': 'navigate',\r\n 'sec-fetch-site': 'none',\r\n 'sec-fetch-user': '?1',\r\n 'upgrade-insecure-requests': '1',\r\n 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36'\r\n }\r\n\r\n response = requests.request(\"GET\", url, headers=headers, data=payload)\r\n \r\n response_text = Selector(text=response.text)\r\n try:\r\n page = urllib.request.urlopen(url)\r\n soup = BeautifulSoup(page, 'html.parser')\r\n for script in soup([\"script\", \"style\"]):\r\n script.extract() \r\n text = soup.get_text()\r\n lines = (line.strip() for line in text.splitlines())\r\n chunks = (phrase.strip() for line in lines for phrase in line.split(\" \"))\r\n text = '\\n'.join(chunk for chunk in chunks if chunk)\r\n item['content'] = text\r\n item['url'] = url\r\n df = pd.DataFrame([item])\r\n if not os.path.isfile(\"line_wrap.csv\"):\r\n df.to_csv(\"line_wrap.csv\",index=False,mode=\"a\",header=True,encoding=\"utf_8_sig\",)\r\n else: # else it exists so append without writing the header\r\n df.to_csv(\"line_wrap.csv\",index=False,mode=\"a\",header=False,encoding=\"utf_8_sig\",)\r\n except Exception as e:\r\n item['content'] = '404'\r\n item['url'] = url\r\n df = pd.DataFrame([item])\r\n if not os.path.isfile(\"line_wrap.csv\"):\r\n df.to_csv(\"line_wrap.csv\",index=False,mode=\"a\",header=True,encoding=\"utf_8_sig\",)\r\n else: # else it exists so append without writing the header\r\n df.to_csv(\"line_wrap.csv\",index=False,mode=\"a\",header=False,encoding=\"utf_8_sig\",)\r\n print(url)\r\n\r\n#\r\n \r\n\r\n\r\n\r\n \r\n\r\n", "repo_name": "Saimohanraj/Wissend_Lenevo", "sub_path": "lenevo_sample_output/line_wrap.py", "file_name": "line_wrap.py", "file_ext": "py", "file_size_in_byte": 3012, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "re.sub", "line_number": 14, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 15, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 16, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 19, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 22, "usage_type": "call"}, {"api_name": "requests.request", "line_number": 47, "usage_type": "call"}, {"api_name": "parsel.Selector", "line_number": 49, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 51, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 51, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 51, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 52, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}]} +{"seq_id": "22686854878", "text": "import re\nimport random\nimport unittest\n\nimport archinfo\nimport angr\n\nimport pwnlib\n\nfrom angr.procedures.definitions import SimLibrary\nfrom angr.simos import SimLinux\nfrom archinfo import Endness\nfrom rex.exploit import Shellcodes\n\n\narch_to_pwntools = {\n 'ARMEL': 'arm',\n 'MIPS32': 'mips',\n 'X86': 'i386',\n 'AMD64': 'amd64',\n}\nendness_to_pwntools = {\n Endness.LE: 'little',\n Endness.BE: 'big'\n}\n\nclass TestRunDupsh(unittest.TestCase):\n def _run_dupsh(self, arch, fd_to_dup):\n print(f\"Testing shellcode to dup fd {fd_to_dup} for architecture {arch}!\")\n shellcode = Shellcodes['unix'][arch.name]['dupsh'](fd=(fd_to_dup,)).raw(arch=arch)\n if arch.name == 'ARMEL':\n # VEX sucks at decoding SVC instructions with operands that are non-zero, so we replace them\n shellcode = shellcode.replace(b'\\x01\\xdf', b'\\x00\\xdf').replace(b'\\x41\\xdf', b'\\x00\\xdf')\n with pwnlib.context.context.local(arch=arch_to_pwntools[arch.name],\n endian=endness_to_pwntools[arch.memory_endness]):\n elf_path = pwnlib.asm.make_elf(shellcode, extract=False)\n proj = angr.Project(elf_path, auto_load_libs=False)\n assert isinstance(proj.simos, SimLinux)\n syscall_lib : SimLibrary = proj.simos.syscall_library\n\n dups_to_check = {0, 1, 2}\n\n class logging_dup2(angr.SimProcedure): # pylint:disable=invalid-name\n def run(self, fd1, fd2): # pylint:disable=arguments-differ\n fd1 = self.state.solver.eval_one(fd1)\n fd2 = self.state.solver.eval_one(fd2)\n print(f\"dup2({fd1}, {fd2})\")\n assert fd1 == fd_to_dup, 'did not dup2 the correct source file descriptor'\n assert fd2 in dups_to_check, \\\n 'dup2\\'ed to a file descriptor that was either not requested or already dup\\'ed'\n dups_to_check.remove(fd2)\n\n class logging_execve(angr.SimProcedure): # pylint:disable=invalid-name\n def run(self, binary, argv, envp): # pylint:disable=arguments-differ\n assert not dups_to_check, \"Shellcode failed to dup some fds: \" + repr(dups_to_check)\n binary = self.state.mem[binary].string.concrete\n assert re.fullmatch(b'/+bin/+sh', binary), \\\n f\"The shellcode executed {bin} instead of /bin/sh\"\n progname = self.state.mem[argv].deref.string.concrete\n assert progname == b'sh' or re.fullmatch(b'/+bin/+sh', progname), \\\n f\"The shellcode did not set argv[0] correctly, instead it set it to {progname}\"\n assert self.state.mem[argv].uintptr_t.array(2)[1].concrete == 0, \\\n \"The shellcode didn't NULL terminate argv\"\n assert self.state.solver.eval_one(envp) == 0 or \\\n self.state.mem[envp].uintptr_t.concrete == 0, \\\n \"envp is incorrect\"\n self.exit(0)\n\n syscall_lib.add('dup2', logging_dup2)\n syscall_lib.add('execve', logging_execve)\n\n state = proj.factory.entry_state(add_options={\n angr.options.TRACK_MEMORY_ACTIONS,\n angr.options.TRACK_REGISTER_ACTIONS,\n angr.options.TRACK_CONSTRAINT_ACTIONS\n })\n simgr = proj.factory.simulation_manager(state)\n simgr.run()\n assert simgr.deadended and not simgr.errored, f\"An error occurred: {simgr.errored[0]}\"\n\n def test_ArchX86(self):\n self._run_dupsh(archinfo.ArchX86(), random.randint(0, 60))\n\n def test_ArchAMD64(self):\n self._run_dupsh(archinfo.ArchAMD64(), random.randint(0, 60))\n\n def test_ArchMIPS32_LE(self):\n self._run_dupsh(archinfo.ArchMIPS32(endness=Endness.LE), random.randint(0, 60))\n\n def test_ArchMIPS32_BE(self):\n self._run_dupsh(archinfo.ArchMIPS32(endness=Endness.BE), random.randint(0, 60))\n\n def test_ArchARMEL(self):\n self._run_dupsh(archinfo.ArchMIPS32(endness=Endness.BE), random.randint(0, 60))\n\nif __name__ == '__main__':\n unittest.main()\n", "repo_name": "angr/rex", "sub_path": "tests/test_shellcodes.py", "file_name": "test_shellcodes.py", "file_ext": "py", "file_size_in_byte": 4093, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 609, "dataset": "github-code", "pt": "12", "api": [{"api_name": "archinfo.Endness.LE", "line_number": 23, "usage_type": "attribute"}, {"api_name": "archinfo.Endness", "line_number": 23, "usage_type": "name"}, {"api_name": "archinfo.Endness.BE", "line_number": 24, "usage_type": "attribute"}, {"api_name": "archinfo.Endness", "line_number": 24, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 27, "usage_type": "attribute"}, {"api_name": "rex.exploit.Shellcodes", "line_number": 30, "usage_type": "name"}, {"api_name": "pwnlib.context.context.local", "line_number": 34, "usage_type": "call"}, {"api_name": "pwnlib.context", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pwnlib.asm.make_elf", "line_number": 36, "usage_type": "call"}, {"api_name": "pwnlib.asm", "line_number": 36, "usage_type": "attribute"}, {"api_name": "angr.Project", "line_number": 37, "usage_type": "call"}, {"api_name": "angr.simos.SimLinux", "line_number": 38, "usage_type": "argument"}, {"api_name": "angr.procedures.definitions.SimLibrary", "line_number": 39, "usage_type": "name"}, {"api_name": "angr.SimProcedure", "line_number": 43, "usage_type": "attribute"}, {"api_name": "angr.SimProcedure", "line_number": 53, "usage_type": "attribute"}, {"api_name": "re.fullmatch", "line_number": 57, "usage_type": "call"}, {"api_name": "re.fullmatch", "line_number": 60, "usage_type": "call"}, {"api_name": "angr.options", "line_number": 73, "usage_type": "attribute"}, {"api_name": "angr.options", "line_number": 74, "usage_type": "attribute"}, {"api_name": "angr.options", "line_number": 75, "usage_type": "attribute"}, {"api_name": "archinfo.ArchX86", "line_number": 82, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 82, "usage_type": "call"}, {"api_name": "archinfo.ArchAMD64", "line_number": 85, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 85, "usage_type": "call"}, {"api_name": "archinfo.ArchMIPS32", "line_number": 88, "usage_type": "call"}, {"api_name": "archinfo.Endness.LE", "line_number": 88, "usage_type": "attribute"}, {"api_name": "archinfo.Endness", "line_number": 88, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 88, "usage_type": "call"}, {"api_name": "archinfo.ArchMIPS32", "line_number": 91, "usage_type": "call"}, {"api_name": "archinfo.Endness.BE", "line_number": 91, "usage_type": "attribute"}, {"api_name": "archinfo.Endness", "line_number": 91, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 91, "usage_type": "call"}, {"api_name": "archinfo.ArchMIPS32", "line_number": 94, "usage_type": "call"}, {"api_name": "archinfo.Endness.BE", "line_number": 94, "usage_type": "attribute"}, {"api_name": "archinfo.Endness", "line_number": 94, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 94, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 97, "usage_type": "call"}]} +{"seq_id": "19303776463", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n#%% global packages\nimport mesa.batchrunner as mb\nimport numpy as np\nimport networkx as nx\nimport uuid\nimport pandas as pd\n\nfrom IPython.core.display import display\n\nimport matplotlib as mpl\nimport matplotlib.figure as figure\nmpl.rc('text', usetex = True)\nmpl.rc('font', size = 12)\n\n\n#%% local functions\n\nscript_path = \"\"\n\nimport os\ntry:\n script_path = os.path.dirname(__file__)\n os.chdir(script_path)\nexcept FileNotFoundError:\n script_path = os.getcwd()\nelse:\n script_path = os.getcwd()\n\nimport sys\nsys.path.append(\"..\")\n\nfrom JanosikParrondoGraphModel import JanosikParrondoGraphModel\n\n##############################################################################\n############################# SINGLE EXECUTION ###############################\n##############################################################################\n\n\n#%% simulation parameters for batch execution\n\n# initial capital\ninit_capital = 20\n\n# size of the grid\ngrid_width = 10\ngrid_height = 10\n\n# graph used in the experiments\ngraph_id = \"w\"+str(grid_width) + \"_h\"+str(grid_height)\ngraph_file_path = script_path + '/graphs/grid2d/' + graph_id + \".gz\"\n\n# graph generation and saving - can be used only onece for the grid\n# graph = nx.generators.lattice.grid_2d_graph(grid_width,grid_height,periodic=True)\n# nx.readwrite.write_gexf(graph, graph_file_path)\n# nx.draw(graph)\n\n# bias in the Parronod scheme, policy, number of agents\ndefault_policy = 'B'\ndefault_eps = 0.01\ndefault_boost = \"matthew\"\nnum_agents = 300\n\n# string with descriptions used in plots\nplot_desc = 'game sequence: '+default_policy+', grid=(' + str(grid_width) +','+str(grid_height) +')'\n\n\n#%% simulaiton with fixed parameters\nnum_steps = 300\ncapital_data = []\n\n# create a model\nmodel = JanosikParrondoGraphModel(num_agents, graph_file_path, init_capital, default_policy, default_eps, default_boost)\n\n# execute num_steps steps\nfor _ in range(num_steps):\n model.step()\n \nfor a in model.schedule.agents:\n capital_data.append(a.capital)\n\n\n#%% plot of the wealth data for agents\n# agent_wealth = model.datacollector.get_agent_vars_dataframe()\n# print(agent_wealth.head())\n\n# # one_agent_wealth = agent_wealth.xs(3, level=\"AgentID\")\n# # agent_wealth.xs(1, level=\"AgentID\").Wealth.plot(ylim=(0,1000))\n# for agn in range(num_agents):\n# agent_wealth.xs(agn, level=\"AgentID\").Capital.plot(ylim=(0,50), title='Wealth for agents, ' + plot_desc )\n \n\n#%% plot of the Gini index\n\ndata = model.datacollector.get_model_vars_dataframe()\ngini_index_data = data.get('Gini index')\n\ngini_plot=gini_index_data.plot(ylim=(0,0.5), title='Gini index, ' + plot_desc)\nprint(gini_index_data.describe())\n# display(gini_plot)\n\n# #%% plot of the mean wealth\n\n# min_capital_data = data.get('Min capital')\n# max_capital_data = data.get('Max capital')\n\n# min_capital_plot=min_capital_data.plot(ylim=(-100,100), title='Min capital, ' + plot_desc)\n# max_capital_plot=max_capital_data.plot(ylim=(-100,100), title='Max capital, ' + plot_desc)\n# print(gini_index_data.describe())\n# display(mean_capital_plot)\n\n# data = model.datacollector.get_model_vars_dataframe()\n# mean_capital_data=data.get('Mean capital')\n# mean_capital_data.plot(title='Mean capital, ' + plot_desc, ylim=(0,200))\n\n# print(median_wealth_data.describe())\n\n#%%\nfig = figure.Figure(figsize=(8,6))\naxs = fig.add_subplot()\naxs.hist(capital_data, density=True, histtype='step')#, bins=int(num_agents/2))\naxs.set_xlabel('Capital')\ndisplay(fig)\n\n\n", "repo_name": "jmiszczak/matthew_reduction_game", "sub_path": "src/model/JanosikParrondo_grid2d_run.py", "file_name": "JanosikParrondo_grid2d_run.py", "file_ext": "py", "file_size_in_byte": 3478, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "matplotlib.rc", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.rc", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 26, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 28, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 30, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 33, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "JanosikParrondoGraphModel.JanosikParrondoGraphModel", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.figure.Figure", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.figure", "line_number": 121, "usage_type": "name"}, {"api_name": "IPython.core.display.display", "line_number": 125, "usage_type": "call"}]} +{"seq_id": "40943048201", "text": "#!/usr/bin/env python3\nimport os\nimport logging\nimport traceback\nfrom datetime import datetime\nfrom google.cloud import storage\n\n\nclass FileWriter:\n\n def __write_to_gcs(self, bucket_name, prefix, content, link):\n gcp_client = storage.Client()\n bucket = gcp_client.bucket(bucket_name)\n blob = bucket.blob(prefix)\n blob.upload_from_string(content)\n metadata = {'link': link}\n blob.metadata = metadata\n blob.patch()\n\n def __write_locally(self, filename, content, link):\n local_file = open(filename, 'w')\n local_file.write(content)\n local_file.close()\n print(link)\n\n def write_to_file(self, article, source_name):\n sink_folder = os.path.join('articles', source_name, f'{datetime.now():%Y-%m-%d}')\n filename = os.path.join(sink_folder, article.title)\n\n try:\n if os.getenv('ENVIRONMENT') == 'local':\n logging.info(f'Writing file {filename} to {filename}')\n self.__write_locally(filename, article.content, article.link)\n else:\n bucket_name = os.getenv('ARCHIVE_BUCKET')\n logging.info(f'Writing file {filename} to {os.path.join(bucket_name, filename)}')\n self.__write_to_gcs(bucket_name, filename, article.content, article.link)\n return filename\n except:\n logging.error(traceback.format_exc())\n return None\n\n", "repo_name": "IYordanova/good_news", "sub_path": "scrapers/file_writer.py", "file_name": "file_writer.py", "file_ext": "py", "file_size_in_byte": 1444, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "google.cloud.storage.Client", "line_number": 12, "usage_type": "call"}, {"api_name": "google.cloud.storage", "line_number": 12, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 32, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 35, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 40, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "36726831145", "text": "from graphene_django import DjangoObjectType\nimport graphene\n\nfrom nba import models\nfrom utils.filter_match_by_date import filter_match_by_date\nfrom utils.pagination import Pagination\n\n\nclass NBASeasonType(DjangoObjectType):\n class Meta:\n model = models.NBASeason\n\n\nclass NBATeamType(DjangoObjectType):\n class Meta:\n model = models.NBATeam\n\n\nclass NBAMatchType(DjangoObjectType):\n class Meta:\n model = models.NBAMatch\n\n\nclass Query(graphene.ObjectType):\n nba_matches = graphene.List(\n NBAMatchType,\n date=graphene.String(),\n page=graphene.Int(),\n per_page=graphene.Int(),\n )\n\n def resolve_nba_matches(\n self, info, date=None, page=None, per_page=None):\n query = models.NBAMatch.objects.order_by('match_date')\n if date:\n query = filter_match_by_date(query, date)\n return Pagination().get_paginated_results(query, page, per_page)\n\n\nschema = graphene.Schema(query=Query)\n", "repo_name": "royroy21/graphql_exercise", "sub_path": "octopus/nba/schema.py", "file_name": "schema.py", "file_ext": "py", "file_size_in_byte": 979, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "graphene_django.DjangoObjectType", "line_number": 9, "usage_type": "name"}, {"api_name": "nba.models.NBASeason", "line_number": 11, "usage_type": "attribute"}, {"api_name": "nba.models", "line_number": 11, "usage_type": "name"}, {"api_name": "graphene_django.DjangoObjectType", "line_number": 14, "usage_type": "name"}, {"api_name": "nba.models.NBATeam", "line_number": 16, "usage_type": "attribute"}, {"api_name": "nba.models", "line_number": 16, "usage_type": "name"}, {"api_name": "graphene_django.DjangoObjectType", "line_number": 19, "usage_type": "name"}, {"api_name": "nba.models.NBAMatch", "line_number": 21, "usage_type": "attribute"}, {"api_name": "nba.models", "line_number": 21, "usage_type": "name"}, {"api_name": "graphene.ObjectType", "line_number": 24, "usage_type": "attribute"}, {"api_name": "graphene.List", "line_number": 25, "usage_type": "call"}, {"api_name": "graphene.String", "line_number": 27, "usage_type": "call"}, {"api_name": "graphene.Int", "line_number": 28, "usage_type": "call"}, {"api_name": "graphene.Int", "line_number": 29, "usage_type": "call"}, {"api_name": "nba.models.NBAMatch.objects.order_by", "line_number": 34, "usage_type": "call"}, {"api_name": "nba.models.NBAMatch", "line_number": 34, "usage_type": "attribute"}, {"api_name": "nba.models", "line_number": 34, "usage_type": "name"}, {"api_name": "utils.filter_match_by_date.filter_match_by_date", "line_number": 36, "usage_type": "call"}, {"api_name": "utils.pagination.Pagination", "line_number": 37, "usage_type": "call"}, {"api_name": "graphene.Schema", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "43007795449", "text": "import requests\nfrom bs4 import BeautifulSoup\nimport re as rere\nimport time\nimport pandas as pd\n\nre = requests.get('https://movie.naver.com/movie/bi/mi/pointWriteFormList.nhn?code=167638&type=after&isActualPointWriteExecute=false&isMileageSubscriptionAlready=false&isMileageSubscriptionReject=false&page=1')\nsoup = BeautifulSoup(re.text, 'html.parser')\n#총 몇 건인지 확인하고 페이지 수 맞추기\nhowmany = soup.find('strong',{'class':'total'}).findAll('em')[1].get_text()\nprint(howmany)\n#10개가 한 페이지에 존재하므로, 919개의 페이지를 크롤링한다.\n\nsuburl = 'https://movie.naver.com/movie/bi/mi/pointWriteFormList.nhn?code=167638&type=after&isActualPointWriteExecute=false&isMileageSubscriptionAlready=false&isMileageSubscriptionReject=false&page='\nurl = []\nfor i in range(1,920):\n url.append(suburl+str(i))\n\nID = []\ndate = []\nscore = []\ntext = []\n\nfor link in url:\n re = requests.get(link)\n soup = BeautifulSoup(re.text, 'html.parser')\n source = soup.findAll('div',{'class':'score_reple'})\n\n ##ID\n #닉네임 / 관람객 태그 제거 후, 아이디만 남기기\n for i in range(10):\n if len(source[i].findAll('span'))==2:\n x = source[i].findAll('span')[1].get_text()\n else:\n x = source[i].find('span').get_text()\n m = rere.search('[a-zA-Z0-9_-]{4}[\\*]{4}',x).group()\n ID.append(m)\n ##날짜 / 시간\n source = soup.findAll('div',{'class':'score_reple'})\n for k in range(0,10):\n d = source[k].findAll('em')\n date.append(d[1].get_text())\n\n ##평점 리스트\n star = soup.findAll('div',{'class':'star_score'})\n for i in range(1,11):\n u = star[i].get_text(); u = rere.sub('\\n','',u);\n score.append(u)\n #score = score[1:]\n #score\n\n ##리뷰 리스트\n for i in range(0,10):\n t = source[i].find('p')\n text.append(t.get_text())\n\n\nrst = {'ID': ID,\n 'score': score,\n 'date' : date,\n 'review': text}\ndata = pd.DataFrame(rst)\ndata\n", "repo_name": "SilverwestKim/Univ", "sub_path": "Data_anal/assignment.py", "file_name": "assignment.py", "file_ext": "py", "file_size_in_byte": 2023, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "requests.get", "line_number": 7, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 8, "usage_type": "call"}, {"api_name": "re.text", "line_number": 8, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 25, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 26, "usage_type": "call"}, {"api_name": "re.text", "line_number": 26, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 36, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 47, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "11426859283", "text": "from datetime import datetime, timedelta\n\n\ndef cache_result(secs=30):\n '''Cache result and use it for number of secs from the time it was cached'''\n def real_decorator(func):\n # Will store cache in this var\n cache = {}\n def wrapper(*args):\n expire_cache()\n\n if args in cache:\n return cache[args]['result']\n\n result = func(*args)\n cache[args] = {\n 'created': datetime.utcnow(),\n 'result': result\n }\n return result\n\n def expire_cache():\n timeborder = datetime.utcnow() - timedelta(seconds=secs)\n for k in cache.keys():\n if cache[k]['created'] < timeborder:\n del cache[k]\n\n return wrapper\n return real_decorator", "repo_name": "serafxxx/MyMediaLib", "sub_path": "util/decorators.py", "file_name": "decorators.py", "file_ext": "py", "file_size_in_byte": 819, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "datetime.datetime.utcnow", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 17, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "19700508254", "text": "import discord\nfrom discord.ext import commands\nimport json\nimport csv\nfrom cogs.config.cog_utils import *\nfrom postgres.db import session\nfrom postgres.models.Roster import Roster\nimport re\n\nclass RosterCog(commands.Cog):\n\n\n def __init__(self, client):\n self.client = client\n \n\n @commands.command(aliases=['r'])\n @commands.has_role(\"Re1 Guildies\")\n async def add(self, ctx, hero=None, asc=None, si=None, fi=\"0\", en=\"E0\"):\n if ctx.channel.category_id not in CATEGORIES:\n return\n\n id = str(ctx.author.id)\n\n if None in (hero, asc, si, fi, en):\n await ctx.send(\"Invalid Format, example: +roster lucius A 30 9\")\n return\n else:\n hero = hero.title()\n asc = asc.upper()\n\n roster = Roster(id, hero, asc, si, fi, en)\n\n if not await check_registration(ctx):\n return\n\n validation = validate_roster_args(get_heroes(), roster)\n if not validation == True:\n await ctx.send(validation)\n return\n\n try:\n instance = session.query(Roster).filter_by(\n user = roster.user,\n hero = roster.hero\n ).first()\n if instance:\n instance.asc = roster.asc\n instance.si = roster.si\n instance.fi = roster.fi\n instance.en = roster.en\n session.commit()\n else:\n session.add(roster)\n except Exception as e:\n session.rollback()\n await ctx.send(f\"Could not add that hero (DB Error): {e}\")\n raise\n else:\n session.commit()\n await ctx.send(f\"Added {roster.hero} for {ctx.author.name}\")\n\n\n @commands.command(aliases=['cr'])\n @commands.has_role(\"Re1 Guildies\")\n async def check(self, ctx, userID=None):\n if ctx.channel.category_id not in CATEGORIES:\n return\n\n if not await check_registration(ctx):\n return\n\n try:\n userID = re.sub(\"[^0-9]\", \"\", userID)\n result = session.query(Roster).filter_by(user=userID).all()\n chunks = chunker(50, result)\n except:\n await ctx.send(\"Could not find user\")\n return\n \n for chunk in chunks:\n roster_str = format_roster('Hero', 'Asc', 'SI', 'F', 'EN')\n for row in chunk:\n roster_str += format_roster(row.hero, row.asc, row.si, row.fi, row.en, newline=True)\n await ctx.send(f\"```{roster_str}```\")\n \n\ndef setup(client):\n client.add_cog(RosterCog(client))\n print(\"RosterCog Loaded\")", "repo_name": "rpwh/RegenesysBot", "sub_path": "cogs/Roster.py", "file_name": "Roster.py", "file_ext": "py", "file_size_in_byte": 2659, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 10, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 10, "usage_type": "name"}, {"api_name": "postgres.models.Roster.Roster", "line_number": 32, "usage_type": "call"}, {"api_name": "postgres.db.session.query", "line_number": 43, "usage_type": "call"}, {"api_name": "postgres.models.Roster.Roster", "line_number": 43, "usage_type": "argument"}, {"api_name": "postgres.db.session", "line_number": 43, "usage_type": "name"}, {"api_name": "postgres.db.session.commit", "line_number": 52, "usage_type": "call"}, {"api_name": "postgres.db.session", "line_number": 52, "usage_type": "name"}, {"api_name": "postgres.db.session.add", "line_number": 54, "usage_type": "call"}, {"api_name": "postgres.db.session", "line_number": 54, "usage_type": "name"}, {"api_name": "postgres.db.session.rollback", "line_number": 56, "usage_type": "call"}, {"api_name": "postgres.db.session", "line_number": 56, "usage_type": "name"}, {"api_name": "postgres.db.session.commit", "line_number": 60, "usage_type": "call"}, {"api_name": "postgres.db.session", "line_number": 60, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 17, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 17, "usage_type": "name"}, {"api_name": "discord.ext.commands.has_role", "line_number": 18, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 18, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 74, "usage_type": "call"}, {"api_name": "postgres.db.session.query", "line_number": 75, "usage_type": "call"}, {"api_name": "postgres.models.Roster.Roster", "line_number": 75, "usage_type": "argument"}, {"api_name": "postgres.db.session", "line_number": 75, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 64, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 64, "usage_type": "name"}, {"api_name": "discord.ext.commands.has_role", "line_number": 65, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 65, "usage_type": "name"}]} +{"seq_id": "40919112663", "text": "import json\nimport os\nfrom time import sleep\n\n# Clear the console before dialogue\nos.system('cls' if os.name == 'nt' else 'clear')\n\ndef getFileName(fileName):\n fileName = os.path.splitext(fileName)[0]\n\n return fileName\n\nwith open('data/dialogue.json') as f:\n data = json.load(f)\n\nfileName = getFileName(os.path.basename(__file__))\nquestions = data['npc'][fileName]['questions']\nwrongAnswer = False\ngivenAnswers = []\n\nfor index, i in enumerate(range(len(questions))):\n givenAnswers.clear()\n\n if wrongAnswer == True:\n i -= 1\n index -= 1\n\n if index + 1 == len(questions):\n os.system('cls' if os.name == 'nt' else 'clear')\n print('Loading next level...')\n sleep(3)\n\n from ..puzzles.rhinoRavinePuzzle import *\n break;\n\n print(questions[i]['question'])\n\n for j in range(len(questions[i]['answers'])):\n print(questions[i]['answers'][j])\n\n while True:\n userAnswer = input('Your answer: ').replace(' ', '')\n\n if userAnswer not in givenAnswers:\n break;\n\n print('You can`t give the same answer')\n\n givenAnswers.append(userAnswer)\n\n # If the user gives the wrong answer remove a live from their total lives\n if questions[i]['question'] == 'What brings you here?' and userAnswer.upper() == 'B' or questions[i]['question'] == 'What brings you here?' and userAnswer.upper() == 'C':\n\n # Check if the attack strength is above the requirement to pass the level\n if(os.environ.get('ATTACK_STRENGTH') < '5'):\n os.environ['LIVES'] = str(int(os.environ['LIVES']) - 1)\n # Ask the question again\n wrongAnswer = True\n print('You lost a live. TIP: Approach people a little bit nicer...')\n else:\n print('The bandit attacked you! You killed him out of self defence')\n sleep(2)\n print('Loading next level...')\n sleep(3)\n break;\n elif questions[i]['question'] == 'Why are you looking for that?' and userAnswer.upper() == 'B':\n # Check if the attack strength is above the requirement to pass the level\n if(os.environ.get('ATTACK_STRENGTH') < '5'):\n os.environ['LIVES'] = str(int(os.environ['LIVES']) - 1)\n # Ask the question again\n wrongAnswer = True\n print('You lost a live. TIP: Approach people a little bit nicer...')\n else:\n print('The bandit attacked you! You killed him out of self defence')\n sleep(2)\n print('Loading next level...')\n sleep(3)\n break;\n\nsleep(2)\nprint('Loading next level...')\nsleep(3)\n\nfrom ..puzzles.rhinoRavinePuzzle import *\n", "repo_name": "Jurian-24/hbo-projects", "sub_path": "project-x/project_x/levels/dialogue/banditPlopBridge.py", "file_name": "banditPlopBridge.py", "file_ext": "py", "file_size_in_byte": 2687, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "os.system", "line_number": 6, "usage_type": "call"}, {"api_name": "os.name", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 29, "usage_type": "call"}, {"api_name": "os.name", "line_number": 29, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 31, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 55, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 56, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 62, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 64, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 68, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 69, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 75, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 77, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 80, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 82, "usage_type": "call"}]} +{"seq_id": "2059458743", "text": "import preprocessing\r\nimport cv2\r\n\r\n\r\ndef main():\r\n video_source_file_path = \"../dataset/videos/test.mp4\"\r\n test_frames_file_path = \"frames/\"\r\n fps = 30\r\n width = 480\r\n height = 360\r\n frames = preprocessing.get_frames(video_file_source_path=video_source_file_path, fps=fps, width=width, height=height)\r\n c = 0\r\n for frame in frames:\r\n cv2.imwrite(test_frames_file_path + str(c) + \".png\", frame)\r\n c += 1\r\n\r\n\r\nmain()\r\n", "repo_name": "vinitkadam/VPD", "sub_path": "testing/test_preprocessing.py", "file_name": "test_preprocessing.py", "file_ext": "py", "file_size_in_byte": 503, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "preprocessing.get_frames", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "41273805393", "text": "from six.moves.urllib import parse as urlparse\n\n\ndef url_schema_remover(url):\n \"\"\" Return the same URL without the schema.\n Example: prefix://host/path -> host/path\n \"\"\"\n parsed = urlparse.urlsplit(url)\n cleaned = urlparse.urlunsplit((('',) + parsed[1:]))\n if cleaned.startswith('//'):\n cleaned = cleaned[2:]\n return cleaned\n", "repo_name": "openstack/sahara-tests", "sub_path": "sahara_tests/utils/url.py", "file_name": "url.py", "file_ext": "py", "file_size_in_byte": 353, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 16, "dataset": "github-code", "pt": "12", "api": [{"api_name": "six.moves.urllib.parse.urlsplit", "line_number": 8, "usage_type": "call"}, {"api_name": "six.moves.urllib.parse", "line_number": 8, "usage_type": "name"}, {"api_name": "six.moves.urllib.parse.urlunsplit", "line_number": 9, "usage_type": "call"}, {"api_name": "six.moves.urllib.parse", "line_number": 9, "usage_type": "name"}]} +{"seq_id": "30438052216", "text": "from whisper.model import Whisper\nimport os\nfrom loss import get_loss_single_segment as get_loss_function\nfrom typing import Dict, List, Tuple, Iterable, Optional, Sequence, Union, TYPE_CHECKING\nimport torch\nfrom whisper import _MODELS, _download, ModelDimensions, available_models\nfrom whisper.tokenizer import Tokenizer, get_tokenizer\nfrom typing import Tuple\nimport numpy as np\n\ndef detect_language_with_gradients(model: \"Whisper\", mel: torch.Tensor, tokenizer: Tokenizer = None) -> Tuple[torch.Tensor, List[dict]]:\n \"\"\"\n Detect the spoken language in the audio, and return them as list of strings, along with the ids\n of the most probable language tokens and the probability distribution over all language tokens.\n This is performed outside the main decode loop in order to not interfere with kv-caching.\n\n Returns\n -------\n language_tokens : torch.Tensor, shape = (n_audio,)\n ids of the most probable language tokens, which appears after the startoftranscript token.\n language_probs : List[Dict[str, float]], length = n_audio\n list of dictionaries containing the probability distribution over all languages.\n \"\"\"\n if tokenizer is None:\n tokenizer = get_tokenizer(model.is_multilingual)\n if tokenizer.language is None or tokenizer.language_token not in tokenizer.sot_sequence:\n raise ValueError(f\"This model doesn't have language tokens so it can't perform lang id\")\n\n single = mel.ndim == 2\n if single:\n mel = mel.unsqueeze(0)\n\n # skip encoder forward pass if already-encoded audio features were given\n if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state):\n mel = model.encoder(mel)\n\n # forward pass using a single token, startoftranscript\n n_audio = mel.shape[0]\n x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device) # [n_audio, 1]\n logits = model.logits(x, mel)[:, 0]\n\n # collect detected languages; suppress all non-language tokens\n mask = torch.ones(logits.shape[-1], dtype=torch.bool)\n mask[list(tokenizer.all_language_tokens)] = False\n logits[:, mask] = -np.inf\n language_tokens = logits.argmax(dim=-1)\n language_token_probs = logits.softmax(dim=-1).cpu()\n language_probs = [\n {\n c: language_token_probs[i, j].item()\n for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes)\n }\n for i in range(n_audio)\n ]\n\n if single:\n language_tokens = language_tokens[0]\n language_probs = language_probs[0]\n\n return language_tokens, language_probs, logits\n\n\nclass WhisperWithGradient(Whisper):\n loss = get_loss_function\n\n\n\ndef load_model_with_gradients(name: str, device: Optional[Union[str, torch.device]] = None, download_root: str = None, in_memory: bool = False, with_grad = True) -> Whisper:\n \"\"\"\n Load a Whisper ASR model\n Parameters\n ----------\n name : str\n one of the official model names listed by `whisper.available_models()`, or\n path to a model checkpoint containing the model dimensions and the model state_dict.\n device : Union[str, torch.device]\n the PyTorch device to put the model into\n download_root: str\n path to download the model files; by default, it uses \"~/.cache/whisper\"\n in_memory: bool\n whether to preload the model weights into host memory\n Returns\n -------\n model : Whisper\n The Whisper ASR model instance\n \"\"\"\n\n if download_root is None:\n download_root = os.getenv(\n \"XDG_CACHE_HOME\", \n os.path.join(os.path.expanduser(\"~\"), \".cache\", \"whisper\")\n )\n\n if name in _MODELS:\n checkpoint_file = _download(_MODELS[name], download_root, in_memory)\n elif os.path.isfile(name):\n checkpoint_file = open(name, \"rb\").read() if in_memory else name\n else:\n raise RuntimeError(f\"Model {name} not found; available models = {available_models()}\")\n\n with (io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, \"rb\")) as fp:\n checkpoint = torch.load(fp, map_location=device)\n del checkpoint_file\n\n dims = ModelDimensions(**checkpoint[\"dims\"])\n model = WhisperWithGradient(dims) if with_grad else Whisper(dims)\n model.load_state_dict(checkpoint[\"model_state_dict\"])\n\n return model.to(device)\n\n\n\n\nclass WhisperWrapper(torch.nn.Module):\n def __init__(self, name: str, **kwargs):\n super(WhisperWrapper,self).__init__()\n self.model = load_model_with_gradients(name,**kwargs)", "repo_name": "RaphaelOlivier/whisper_attack", "sub_path": "whisper_with_gradients.py", "file_name": "whisper_with_gradients.py", "file_ext": "py", "file_size_in_byte": 4518, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "12", "api": [{"api_name": "torch.Tensor", "line_number": 11, "usage_type": "attribute"}, {"api_name": "whisper.tokenizer.Tokenizer", "line_number": 11, "usage_type": "name"}, {"api_name": "whisper.tokenizer.get_tokenizer", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.bool", "line_number": 43, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 45, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 11, "usage_type": "name"}, {"api_name": "whisper.model.Whisper", "line_number": 63, "usage_type": "name"}, {"api_name": "loss.get_loss_single_segment", "line_number": 64, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 68, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 68, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 91, "usage_type": "call"}, {"api_name": "whisper._MODELS", "line_number": 94, "usage_type": "name"}, {"api_name": "whisper._download", "line_number": 95, "usage_type": "call"}, {"api_name": "whisper._MODELS", "line_number": 95, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "whisper.available_models", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 102, "usage_type": "call"}, {"api_name": "whisper.ModelDimensions", "line_number": 105, "usage_type": "call"}, {"api_name": "whisper.model.Whisper", "line_number": 106, "usage_type": "call"}, {"api_name": "whisper.model.Whisper", "line_number": 68, "usage_type": "name"}, {"api_name": "torch.nn", "line_number": 114, "usage_type": "attribute"}]} +{"seq_id": "28786150203", "text": "import chess\nimport chess.pgn\nimport chess.uci\n\n#Engine settings\n#engine_name = \"./stockfish6\"\neng_name = \"./stockfish_7_x64\"\nsearch_depth = 30\nhash_size = 256\n\n#PGN files\npgnin = open(\"./ptestsin.pgn\", \"r\")\npgn_out = \"./ptestsout.pgn\"\n#pgnin = open(\"ptestsin.pgn\", \"r\")\n#pgnout = open(\"pgn/ptestsout.pgn\", \"a\")\n#pgnin = open(\"pgn/qtest.pgn\", \"r\")\n#pgnout = open(\"pgn/qtestout.pgn\", \"a\")\n\n#dataout = open(\"pgn/dataout.txt\", \"a\")\n\n#Settings\nwhite_threshold = 70\nblack_threshold = -50\n\ndef start_engine (eng_name, eng_hash=False):\n\t'''Engine start-up'''\n\tnew_engine = chess.uci.popen_engine(eng_name)\n\t#new_engine = chess.uci.popen_engine(\"./stockfish6\")\n\tnew_engine.uci()\n\t\n\tif eng_hash:\n\t\t\n\t\tnew_engine.setoption({\"Hash\":eng_hash})\n\t\t\n\treturn new_engine\n\n\ndef get_score(eng_score, white_to_move):\n\t\n\tif (eng_score.mate):\n\t\t\n\t\tif (white_to_move):\n\t\t\n\t\t\twhite_score = 1000\n\t\t\t\n\t\telse:\n\t\t\t\n\t\t\twhite_score = -1000\n\t\t\t\n\telse:\n\t\t\n\t\tif (white_to_move):\n\t\t\t\n\t\t\twhite_score = int(eng_score.cp)\n\t\t\t\n\t\telse:\n\t\t\t\n\t\t\twhite_score = int(-(eng_score.cp))\n\t\t\t\n\t\tif (white_score > 1000):\n\t\t\t\n\t\t\twhite_score = 1000\n\t\t\t\n\t\tif (white_score < -1000):\n\t\t\t\n\t\t\twhite_score = -1000\n\t\t\t\n\t\n\treturn white_score\n\n\ndef main():\n\t\n\tengine = start_engine(eng_name, hash_size)\n\t#engine = start_engine(\"stockfish6\", 1024)\n\tengine_data = chess.uci.InfoHandler()\n\tengine.info_handlers.append(engine_data)\n\t\n\tnext_game = chess.pgn.read_game(pgnin)\n\t\n\ti = 0\n\t\n\twhile next_game:\n\t\t\n\t\tend_node = next_game.end()\n\t\trunning_board = end_node.board()\n\t\t\n\t\tif not ((running_board.is_checkmate()) or (running_board.is_stalemate())):\n\t\t\t\n\t\t\tprint (\"Analysing game \", i+1)\n\t\t\tengine.position(running_board)\n\t\t\tengine.go(depth=search_depth)\n\t\t\tprint (\"Analysis complete.\")\n\t\t\tprint()\n\t\t\tprint()\n\t\t\t\n\t\t\tpv_score = get_score(engine_data.info[\"score\"][1], running_board.turn)\n\t\t\tend_node.parent.add_variation(end_node.move, starting_comment = (engine.name +\n\t\t\t\t\t\" \" + str(engine_data.info[\"depth\"]) + \": \" + str(pv_score/100)))\n\t\t\tend_node.parent.variation(1).add_variation (engine_data.info[\"pv\"][1][0])\n\t\t\t#end_node.add_variation (, starting_comment = (engine.name +\n\t\t\t\n\t\t\tif ((pv_score >= white_threshold) and (next_game.headers[\"Result\"] != '1-0')):\n\t\t\t\t\n\t\t\t\tnext_game.headers[\"Result\"] = \"1-0\"\n\t\t\t\tend_node.comment = end_node.comment + \" - auto-adjusted to 1-0\"\n\t\t\t\t\n\t\t\telif ((pv_score <= black_threshold) and (next_game.headers[\"Result\"] != '0-1')):\n\t\t\t\t\n\t\t\t\tnext_game.headers[\"Result\"] = \"0-1\"\n\t\t\t\tend_node.comment = end_node.comment + \" - auto-adjusted to 0-1\"\n\t\t\t\t\n\t\t\telif ((pv_score < white_threshold) and (pv_score > black_threshold) and (next_game.headers[\"Result\"] != \"1/2-1/2\")):\n\t\t\t\t\n\t\t\t\tnext_game.headers[\"Result\"] = \"1/2-1/2\"\n\t\t\t\tend_node.comment = end_node.comment + \" - auto-adjusted to 1/2-1/2\"\n\t\t\t\t\n\t\t\telse:\n\t\t\t\t\n\t\t\t\tend_node.comment = end_node.comment + \" - auto-adjudicator detects no result change necessary\"\n\t\t\t\t\n\t\twith open(pgn_out, \"a\") as pgnout:\n\t\t\t\n\t\t\tpgnout.write (str(next_game))\n\t\t\tpgnout.write (\"\\n\\n\")\n\t\t\t\n\t\ti += 1\n\t\tnext_game = chess.pgn.read_game(pgnin)\n\t\t\n\tprint (i, \" boards printed.\")\n\t\n\tengine.quit()\n\tpgnin.close()\n\t#pgnout.close()\n\n\nif __name__ == \"__main__\":\n\t#main(sys.argv[1:])\n\tmain()\n\n\n\n", "repo_name": "xarxziux/auto-adjudicator", "sub_path": "auto_adjudicator.py", "file_name": "auto_adjudicator.py", "file_ext": "py", "file_size_in_byte": 3182, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "chess.uci.popen_engine", "line_number": 27, "usage_type": "call"}, {"api_name": "chess.uci", "line_number": 27, "usage_type": "attribute"}, {"api_name": "chess.uci.InfoHandler", "line_number": 76, "usage_type": "call"}, {"api_name": "chess.uci", "line_number": 76, "usage_type": "attribute"}, {"api_name": "chess.pgn.read_game", "line_number": 79, "usage_type": "call"}, {"api_name": "chess.pgn", "line_number": 79, "usage_type": "attribute"}, {"api_name": "chess.pgn.read_game", "line_number": 128, "usage_type": "call"}, {"api_name": "chess.pgn", "line_number": 128, "usage_type": "attribute"}]} +{"seq_id": "15111502750", "text": "from .utils.configrw import getConfig\n\n\n_deckWhitelistCache = {}\n\n\ndef invalidateDeckWhitelistCache():\n _deckWhitelistCache.clear()\n\n\ndef isDeckWhitelisted(col, did):\n try:\n return _deckWhitelistCache[did]\n except KeyError:\n whitelistDecks = getConfig(\"whitelistDecks\")\n\n # No whitelist → apply to all decks\n if not whitelistDecks:\n _deckWhitelistCache[did] = True\n return True\n\n deckName = col.decks.get(did)[\"name\"]\n shouldAccept = False\n for whiteDeck in whitelistDecks:\n if deckName == whiteDeck:\n shouldAccept = True\n break\n elif deckName.startswith(whiteDeck + \"::\"):\n shouldAccept = True\n break\n\n _deckWhitelistCache[did] = shouldAccept\n return shouldAccept\n", "repo_name": "trgkanki/interval_booster", "sub_path": "src/deckWhitelist.py", "file_name": "deckWhitelist.py", "file_ext": "py", "file_size_in_byte": 843, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "12", "api": [{"api_name": "utils.configrw.getConfig", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "40675084027", "text": "\nfrom setuptools import setup, find_packages\nfrom distutils.command.build_py import build_py\n\nimport sys\nimport os\nimport re\nimport tempfile\n\nfrom ppversion import readversion\n\n\ndef preprocess(infilename):\n # execnet requires pure modules to create channels (or inline source). This makes code re-use difficult.\n # To allow some form of code re-use, some of the execnet remote_exec modules are generated at build time.\n # In C pre-procssor style, the required functions are #INCLUDed into the generated modules.\n # This is performed by this function which is called during the setup.py build stage by the custom\n # build_py subclass below.\n include_regex = re.compile(r'^#\\s?INCLUDE \"(.*)\"')\n oldir = os.getcwd()\n outfile, outfilename = tempfile.mkstemp(suffix=\".py\")\n outfile = os.fdopen(outfile,'w')\n try:\n path = os.path.dirname(infilename)\n bname = os.path.basename(infilename)\n os.chdir(path)\n with open(bname, 'r', encoding = 'utf-8') as infile:\n for inline in infile:\n m = include_regex.match(inline)\n if m:\n includefilename = m.groups()[0]\n with open(includefilename, 'r', encoding = 'utf-8') as includefile:\n outfile.write(includefile.read())\n else:\n outfile.write(inline)\n return outfilename\n finally:\n outfile.close()\n os.chdir(oldir)\n\npreprocess_files = [\"lib/atsim/pro_fit/filetransfer/remote_exec/file_cleanup_remote_exec.py\",\n \"lib/atsim/pro_fit/filetransfer/remote_exec/file_transfer_remote_exec.py\"]\n\nclass my_build(build_py):\n\n def build_module(self, module, module_file, package):\n if module_file in preprocess_files:\n processedfile = preprocess(module_file)\n try:\n return build_py.build_module(self, module, processedfile, package)\n finally:\n os.remove(processedfile)\n return build_py.build_module(self, module, module_file, package)\n\n\ndef package_files(directory):\n cwd = os.getcwd()\n os.chdir(directory)\n paths = []\n dirname = os.path.basename(directory)\n for (path, _directories, filenames) in os.walk('.'):\n for filename in filenames:\n p = os.path.join(dirname, path, filename)\n p = os.path.normpath(p)\n paths.append(p)\n os.chdir(cwd)\n return paths\n\n# pkgs = find_packages('lib', exclude=[\"tests\"])\npkgs = ['atsim.pro_fit', \n 'atsim.pro_fit.console',\n 'atsim.pro_fit.cfg',\n 'atsim.pro_fit.db',\n 'atsim.pro_fit.evaluators',\n 'atsim.pro_fit.filetransfer',\n 'atsim.pro_fit.filetransfer.remote_exec',\n 'atsim.pro_fit.minimizers',\n 'atsim.pro_fit.minimizers.base_minimizers',\n 'atsim.pro_fit.minimizers._inspyred',\n 'atsim.pro_fit.minimizers.population_generators',\n 'atsim.pro_fit.resources',\n 'atsim.pro_fit.runners',\n 'atsim.pro_fit.runners.templates',\n 'atsim.pro_fit.tools',\n 'atsim.pro_fit.webmonitor']\n\nsetup(name=\"potential-pro-fit\",\n version = readversion(),\n package_dir = {'' : 'lib/'},\n packages = pkgs,\n cmdclass = {'build_py' : my_build},\n install_requires = [\"setuptools\",\n 'sqlalchemy==1.4.*',\n 'more-itertools>6.0.0',\n 'cherrypy>18.0.0',\n 'Jinja2>=2.10',\n 'inspyred>=1.0.1',\n 'cexprtk>=0.3.0',\n 'urwid>=2.0.1',\n 'mystic>=0.3.3',\n 'execnet>=1.6',\n 'gevent>=1.3',\n 'tabulate>=0.8.3',\n 'numpy',\n 'scipy',\n 'pyDOE2',\n 'importlib_resources',\n 'CherryPy-SQLAlchemy',\n 'atsim.potentials>=0.3.0'],\n tests_require = ['pytest',\n 'wsgi_intercept',\n 'mechanize',\n 'assertpy'],\n\n #dependency_links = ['https://github.com/uqfoundation/mystic/zipball/master#egg=mystic-0.2a2.dev0'],\n\n include_package_data = True,\n package_data = {\n 'atsim.pro_fit.webmonitor' : package_files('lib/atsim/pro_fit/webmonitor/webresources'),\n 'atsim.pro_fit' : package_files('lib/atsim/pro_fit/resources'),\n # 'atsim.pro_fit' : package_files('lib/atsim/pro_fit/resources'),\n 'atsim.pro_fit.runners' : package_files('lib/atsim/pro_fit/runners/templates')\n },\n\n entry_points = {\n 'console_scripts' : [\n 'pprofit = atsim.pro_fit.tools.pprofit:main',\n 'pprofitmon = atsim.pro_fit.webmonitor:main',\n 'csvbuild = atsim.pro_fit.tools.csvbuild:main',\n 'ppgrid = atsim.pro_fit.tools.ppgrid:main',\n 'ppdump = atsim.pro_fit.tools.ppdump:main'\n ]\n },\n\n # Meta-data for PyPI\n author = \"M.J.D. Rushton\",\n author_email = \"m.rushton@imperial.ac.uk\",\n license = \"Apache License (2.0)\",\n url = \"https://bitbucket.org/mjdr/potential-pro-fit\")\n", "repo_name": "mjdrushton/potential-pro-fit", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 4890, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "12", "api": [{"api_name": "re.compile", "line_number": 19, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 20, "usage_type": "call"}, {"api_name": "tempfile.mkstemp", "line_number": 21, "usage_type": "call"}, {"api_name": "os.fdopen", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 26, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 39, "usage_type": "call"}, {"api_name": "distutils.command.build_py.build_py", "line_number": 44, "usage_type": "name"}, {"api_name": "distutils.command.build_py.build_py.build_module", "line_number": 50, "usage_type": "call"}, {"api_name": "distutils.command.build_py.build_py", "line_number": 50, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 52, "usage_type": "call"}, {"api_name": "distutils.command.build_py.build_py.build_module", "line_number": 53, "usage_type": "call"}, {"api_name": "distutils.command.build_py.build_py", "line_number": 53, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 57, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 66, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 87, "usage_type": "call"}, {"api_name": "ppversion.readversion", "line_number": 88, "usage_type": "call"}]} +{"seq_id": "36386876839", "text": "#calculating VPF using halotools\nfrom __future__ import print_function,division\nfrom halotools.mock_observables import void_prob_func\nimport numpy as np\nimport time\nimport os\nimport multiprocessing as mp\n\n#start = time.time()\n\nN = int(1e5)\nL = 2500.\n\nradius = np.logspace(-1,1.3,30) # 0.1 - 19.98 MPc\n\n#path = '/mnt/data4/Abhishek/mockHOD/'\npath = '/mnt/data4/Abhishek/fidmock'\n\n\ndef voidprobfunc(rbins, n_ran, period, filename):\n global path\n if filename.endswith(\".npy\"):\n sample = np.load(os.path.join(path,filename))\n vpf = void_prob_func(sample,rbins=rbins,n_ran=n_ran,period=period)\n np.save(os.path.join('/mnt/data4/Abhishek/fidmock/vpf','vpf_'+str(filename)),(rbins,vpf))\n #np.save(os.path.join('/mnt/data4/Abhishek/VPF/random','vpf_'+str(filename)),(rbins.astype('float64'),vpf.astype('float64')))\n else:\n raise TypeError(\"File should be in .npy format\")\n #return None\n\ndef parallel_vpf(rbins,n_ran,period,path):\n pool = mp.Pool()\n filenames = [files for files in os.listdir(path) if files.endswith('.npy')]\n #filenames = ['galaxies_'+f'{files:04d}'+'.npy' for files in range(10000)]\n results = [pool.apply_async(voidprobfunc, args=(rbins,n_ran,period,i,)) for i in filenames]\n pool.close()\n pool.join()\n return results\n\n\ndef main():\n start = time.time()\n parallel_vpf(rbins = radius, n_ran = N, period = L, path = path)\n #voidprobfunc(rbins = radius,n_ran = N, period = L, filename = 'MDgalaxies_0100.npy')\n print (f'Total time taken: {time.time() - start}')\nif __name__ == \"__main__\":\n main()\n\n'''\nfor file in os.listdir(path)[:5]:\n if file.endswith(\".npy\"):\n start = time.time()\n gal = np.load(os.path.join(path,file)) # coordinates of the galaxies\n \n a = np.min(gal)\n b = np.max(gal)\n\n print (f'minimum = {a}\\nmax = {b}\\nNumber of particles = {len(gal)}')\n\n vpf = void_prob_func(gal,rbins = radius,n_ran = N,period = L)\n\n #print (f'Void Probability = {vpf}')\n #print (f'Radius of the sphere = {radius} Mpc')\n #print (f'Void Probability = {nvoid/N}')\n np.save('test_'+ str(file),(radius,vpf))\n print (f'Total time = {time.time()-start} sec')\n #print (sp)\n\n'''\n", "repo_name": "abhishek-jana/HODProject", "sub_path": "vpf_halotools.py", "file_name": "vpf_halotools.py", "file_ext": "py", "file_size_in_byte": 2246, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "numpy.logspace", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "halotools.mock_observables.void_prob_func", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "multiprocessing.Pool", "line_number": 32, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 33, "usage_type": "call"}, {"api_name": "time.time", "line_number": 42, "usage_type": "call"}, {"api_name": "time.time", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "73897669462", "text": "from django.db import models\n\n\nclass Gym(models.Model):\n name = models.CharField(max_length=100)\n age = models.IntegerField()\n slot = models.CharField(default='morning',max_length=100)\n cardio = models.BooleanField(default=False)\n weight = models.FloatField()\n image = models.ImageField(upload_to=\"images/\")\n", "repo_name": "iamvarshith/Django_Capstone_1", "sub_path": "gym/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 326, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "12", "api": [{"api_name": "django.db.models.Model", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 4, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 5, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 5, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 6, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 6, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 7, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.FloatField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.ImageField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "24915944901", "text": "#!/usr/bin/python\nimport sys as sys\nimport RPi.GPIO as GPIO\nimport time\nfrom log import log\nimport MySQLdb\n\nlog(\"SWITCHING - INIZIO CAMBIO STATO DI UN RELE'\")\nnome = int(sys.argv[1])\n\ndb = MySQLdb.connect(host=\"localhost\", user=\"root\", passwd=\"root\", db=\"domus_house\") \n \ncur = db.cursor()\n\n\nif (len(sys.argv) == 3):\n tempo=int(sys.argv[2])\n\n GPIO.setmode(GPIO.BCM) \n\n GPIO.setup(nome, GPIO.OUT) \n\n GPIO.output(nome, 0)\n time.sleep(tempo)\n GPIO.output(nome, 1)\nelse:\n GPIO.setmode(GPIO.BCM) \n\n GPIO.setup(nome, GPIO.OUT) \n\n if GPIO.input(nome):\n GPIO.output(nome, 0)\n cur.execute(\"UPDATE rele SET stato = true WHERE id = %d\" % (nome))\n db.commit()\n else:\n GPIO.output(nome, 1)\n cur.execute(\"UPDATE rele SET stato = false WHERE id = %d\" % (nome))\n db.commit()\n \n\nlog(\"SWITCHING - FINE CAMBIO STATO DI UN RELE'\")\n", "repo_name": "antonioborrelli/domushouse", "sub_path": "model/switching.py", "file_name": "switching.py", "file_ext": "py", "file_size_in_byte": 902, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "log.log", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 9, "usage_type": "attribute"}, {"api_name": "MySQLdb.connect", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 17, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setmode", "line_number": 19, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 19, "usage_type": "name"}, {"api_name": "RPi.GPIO.BCM", "line_number": 19, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 21, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 21, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 21, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.output", "line_number": 23, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 23, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 24, "usage_type": "call"}, {"api_name": "RPi.GPIO.output", "line_number": 25, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 25, "usage_type": "name"}, {"api_name": "RPi.GPIO.setmode", "line_number": 27, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 27, "usage_type": "name"}, {"api_name": "RPi.GPIO.BCM", "line_number": 27, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 29, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 29, "usage_type": "name"}, {"api_name": "RPi.GPIO.OUT", "line_number": 29, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.input", "line_number": 31, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 31, "usage_type": "name"}, {"api_name": "RPi.GPIO.output", "line_number": 32, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 32, "usage_type": "name"}, {"api_name": "RPi.GPIO.output", "line_number": 36, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 36, "usage_type": "name"}, {"api_name": "log.log", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "22790522185", "text": "#!/usr/bin/env python\n#\n# This program plots the energy profiles of MM derived \n# energies after optimizing the parameters \n# using ParFit. \n# The program must be run from the directory containing\n# the csv data file to plot. Change the filename and\n# any variables, such as labels, and tiles.\n\n# *****************************************************\n# * User: change file name to the csv file to plot. *\n# * Note: file must be in current directory. *\n# *****************************************************\nf=open(\"opt0_31.csv\",'r')\n\nlines=f.readlines()\nf.close()\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nX=[]\nY=[]\nZ=[]\n\nfor line in lines:\n\tx,y,z=line.split(',')\n\tx,y,z=float(x),float(y),float(z)\n\tX.append(x)\n\tY.append(y)\n\tZ.append(z)\n\n# *****************************************************\n# * Plot legend, range, and tick mark spacing. *\n# *****************************************************\nplt.plot(X,Y,\"o\",label=\"MP2 energy\")\nplt.plot(X,Z,label=\"MM3 energy\")\nplt.xticks(range(0,360,30))\nplt.legend(loc=\"lower right\")\n\n# *****************************************************\n# * Plot title and attributes. *\n# *****************************************************\nplt.title('Title: MM3 fit to MP2',fontsize=18,fontname=\"Times Roman\",color=\"brown\")\n\n# *****************************************************\n# * Plot axis labels, attributes, and axis limits. *\n# *****************************************************\nplt.xlabel(\"X-axis label: Dihedral Angle (deg)\",fontsize=14,fontname=\"Times Roman\",color=\"black\")\nplt.ylabel(\"Y-axis label: Relative energy (kcal/mol)\",fontsize=14,fontname=\"Times Roman\",color=\"black\")\nplt.xlim([0.,360.])\nplt.ylim([-2.5,1.0])\n\nplt.show()\n", "repo_name": "fzahari/ParFit", "sub_path": "Utils/QM_vs_MM_energies.py", "file_name": "QM_vs_MM_energies.py", "file_ext": "py", "file_size_in_byte": 1734, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "12", "api": [{"api_name": "matplotlib.pyplot.plot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}]} +{"seq_id": "32432439740", "text": "import logging\nimport math\n\nimport cv2\nimport numpy as np\nimport torch\nfrom mmengine.logging import print_log\nfrom mmengine.utils import is_tuple_of\nfrom PIL import Image, ImageDraw\n\n\ndef make_coord(shape, ranges=None, flatten=True):\n \"\"\"Make coordinates at grid centers.\n\n Args:\n shape (tuple): shape of image.\n ranges (tuple): range of coordinate value. Default: None.\n flatten (bool): flatten to (n, 2) or Not. Default: True.\n\n Returns:\n coord (Tensor): coordinates.\n \"\"\"\n coord_seqs = []\n for i, n in enumerate(shape):\n if ranges is None:\n v0, v1 = -1, 1\n else:\n v0, v1 = ranges[i]\n r = (v1 - v0) / (2 * n)\n seq = v0 + r + (2 * r) * torch.arange(n).float()\n coord_seqs.append(seq)\n\n if 'indexing' in torch.meshgrid.__code__.co_varnames:\n coord = torch.meshgrid(*coord_seqs, indexing='ij')\n else:\n coord = torch.meshgrid(*coord_seqs)\n coord = torch.stack(coord, dim=-1)\n if flatten:\n coord = coord.view(-1, coord.shape[-1])\n return coord\n\n\ndef bbox2mask(img_shape, bbox, dtype='uint8'):\n \"\"\"Generate mask in np.ndarray from bbox.\n\n The returned mask has the shape of (h, w, 1). '1' indicates the\n hole and '0' indicates the valid regions.\n\n We prefer to use `uint8` as the data type of masks, which may be different\n from other codes in the community.\n\n Args:\n img_shape (tuple[int]): The size of the image.\n bbox (tuple[int]): Configuration tuple, (top, left, height, width)\n np.dtype (str): Indicate the data type of returned masks.\n Default: 'uint8'\n\n Returns:\n mask (np.ndarray): Mask in the shape of (h, w, 1).\n \"\"\"\n\n height, width = img_shape[:2]\n\n mask = np.zeros((height, width, 1), dtype=dtype)\n mask[bbox[0]:bbox[0] + bbox[2], bbox[1]:bbox[1] + bbox[3], :] = 1\n\n return mask\n\n\ndef brush_stroke_mask(img_shape,\n num_vertices=(4, 12),\n mean_angle=2 * math.pi / 5,\n angle_range=2 * math.pi / 15,\n brush_width=(12, 40),\n max_loops=4,\n dtype='uint8'):\n \"\"\"Generate free-form mask.\n\n The method of generating free-form mask is in the following paper:\n Free-Form Image Inpainting with Gated Convolution.\n\n When you set the config of this type of mask. You may note the usage of\n `np.random.randint` and the range of `np.random.randint` is [left, right).\n\n We prefer to use `uint8` as the data type of masks, which may be different\n from other codes in the community.\n\n TODO: Rewrite the implementation of this function.\n\n Args:\n img_shape (tuple[int]): Size of the image.\n num_vertices (int | tuple[int]): Min and max number of vertices. If\n only give an integer, we will fix the number of vertices.\n Default: (4, 12).\n mean_angle (float): Mean value of the angle in each vertex. The angle\n is measured in radians. Default: 2 * math.pi / 5.\n angle_range (float): Range of the random angle.\n Default: 2 * math.pi / 15.\n brush_width (int | tuple[int]): (min_width, max_width). If only give\n an integer, we will fix the width of brush. Default: (12, 40).\n max_loops (int): The max number of for loops of drawing strokes.\n Default: 4.\n np.dtype (str): Indicate the data type of returned masks.\n Default: 'uint8'.\n\n Returns:\n mask (np.ndarray): Mask in the shape of (h, w, 1).\n \"\"\"\n\n img_h, img_w = img_shape[:2]\n if isinstance(num_vertices, int):\n min_num_vertices, max_num_vertices = num_vertices, num_vertices + 1\n elif isinstance(num_vertices, tuple):\n min_num_vertices, max_num_vertices = num_vertices\n else:\n raise TypeError('The type of num_vertices should be int'\n f'or tuple[int], but got type: {num_vertices}')\n\n if isinstance(brush_width, tuple):\n min_width, max_width = brush_width\n elif isinstance(brush_width, int):\n min_width, max_width = brush_width, brush_width + 1\n else:\n raise TypeError('The type of brush_width should be int'\n f'or tuple[int], but got type: {brush_width}')\n\n average_radius = math.sqrt(img_h * img_h + img_w * img_w) / 8\n mask = Image.new('L', (img_w, img_h), 0)\n\n loop_num = np.random.randint(1, max_loops)\n num_vertex_list = np.random.randint(\n min_num_vertices, max_num_vertices, size=loop_num)\n angle_min_list = np.random.uniform(0, angle_range, size=loop_num)\n angle_max_list = np.random.uniform(0, angle_range, size=loop_num)\n\n for loop_n in range(loop_num):\n num_vertex = num_vertex_list[loop_n]\n angle_min = mean_angle - angle_min_list[loop_n]\n angle_max = mean_angle + angle_max_list[loop_n]\n angles = []\n vertex = []\n\n # set random angle on each vertex\n angles = np.random.uniform(angle_min, angle_max, size=num_vertex)\n reverse_mask = (np.arange(num_vertex, dtype=np.float32) % 2) == 0\n angles[reverse_mask] = 2 * math.pi - angles[reverse_mask]\n\n h, w = mask.size\n\n # set random vertices\n vertex.append((np.random.randint(0, w), np.random.randint(0, h)))\n r_list = np.random.normal(\n loc=average_radius, scale=average_radius // 2, size=num_vertex)\n for i in range(num_vertex):\n r = np.clip(r_list[i], 0, 2 * average_radius)\n new_x = np.clip(vertex[-1][0] + r * math.cos(angles[i]), 0, w)\n new_y = np.clip(vertex[-1][1] + r * math.sin(angles[i]), 0, h)\n vertex.append((int(new_x), int(new_y)))\n # draw brush strokes according to the vertex and angle list\n draw = ImageDraw.Draw(mask)\n width = np.random.randint(min_width, max_width)\n draw.line(vertex, fill=1, width=width)\n for v in vertex:\n draw.ellipse((v[0] - width // 2, v[1] - width // 2,\n v[0] + width // 2, v[1] + width // 2),\n fill=1)\n # randomly flip the mask\n if np.random.normal() > 0:\n mask.transpose(Image.FLIP_LEFT_RIGHT)\n if np.random.normal() > 0:\n mask.transpose(Image.FLIP_TOP_BOTTOM)\n mask = np.array(mask).astype(dtype=getattr(np, dtype))\n mask = mask[:, :, None]\n return mask\n\n\ndef random_bbox(img_shape, max_bbox_shape, max_bbox_delta=40, min_margin=20):\n \"\"\"Generate a random bbox for the mask on a given image.\n\n In our implementation, the max value cannot be obtained since we use\n `np.random.randint`. And this may be different with other standard scripts\n in the community.\n\n Args:\n img_shape (tuple[int]): The size of a image, in the form of (h, w).\n max_bbox_shape (int | tuple[int]): Maximum shape of the mask box,\n in the form of (h, w). If it is an integer, the mask box will be\n square.\n max_bbox_delta (int | tuple[int]): Maximum delta of the mask box,\n in the form of (delta_h, delta_w). If it is an integer, delta_h\n and delta_w will be the same. Mask shape will be randomly sampled\n from the range of `max_bbox_shape - max_bbox_delta` and\n `max_bbox_shape`. Default: (40, 40).\n min_margin (int | tuple[int]): The minimum margin size from the\n edges of mask box to the image boarder, in the form of\n (margin_h, margin_w). If it is an integer, margin_h and margin_w\n will be the same. Default: (20, 20).\n\n Returns:\n tuple[int]: The generated box, (top, left, h, w).\n \"\"\"\n if not isinstance(max_bbox_shape, tuple):\n max_bbox_shape = (max_bbox_shape, max_bbox_shape)\n if not isinstance(max_bbox_delta, tuple):\n max_bbox_delta = (max_bbox_delta, max_bbox_delta)\n if not isinstance(min_margin, tuple):\n min_margin = (min_margin, min_margin)\n assert is_tuple_of(max_bbox_shape, int)\n assert is_tuple_of(max_bbox_delta, int)\n assert is_tuple_of(min_margin, int)\n\n img_h, img_w = img_shape[:2]\n max_mask_h, max_mask_w = max_bbox_shape\n max_delta_h, max_delta_w = max_bbox_delta\n margin_h, margin_w = min_margin\n\n if max_mask_h > img_h or max_mask_w > img_w:\n raise ValueError(f'mask shape {max_bbox_shape} should be smaller than '\n f'image shape {img_shape}')\n if (max_delta_h // 2 * 2 >= max_mask_h\n or max_delta_w // 2 * 2 >= max_mask_w):\n raise ValueError(f'mask delta {max_bbox_delta} should be smaller than'\n f'mask shape {max_bbox_shape}')\n if img_h - max_mask_h < 2 * margin_h or img_w - max_mask_w < 2 * margin_w:\n raise ValueError(f'Margin {min_margin} cannot be satisfied for img'\n f'shape {img_shape} and mask shape {max_bbox_shape}')\n\n # get the max value of (top, left)\n max_top = img_h - margin_h - max_mask_h\n max_left = img_w - margin_w - max_mask_w\n # randomly select a (top, left)\n top = np.random.randint(margin_h, max_top)\n left = np.random.randint(margin_w, max_left)\n # randomly shrink the shape of mask box according to `max_bbox_delta`\n # the center of box is fixed\n delta_top = np.random.randint(0, max_delta_h // 2 + 1)\n delta_left = np.random.randint(0, max_delta_w // 2 + 1)\n top = top + delta_top\n left = left + delta_left\n h = max_mask_h - delta_top\n w = max_mask_w - delta_left\n return (top, left, h, w)\n\n\ndef random_irregular_mask(img_shape,\n num_vertices=(4, 8),\n max_angle=4,\n length_range=(10, 100),\n brush_width=(10, 40),\n dtype='uint8'):\n \"\"\"Generate random irregular masks.\n\n This is a modified version of free-form mask implemented in\n 'brush_stroke_mask'.\n\n We prefer to use `uint8` as the data type of masks, which may be different\n from other codes in the community.\n\n TODO: Rewrite the implementation of this function.\n\n Args:\n img_shape (tuple[int]): Size of the image.\n num_vertices (int | tuple[int]): Min and max number of vertices. If\n only give an integer, we will fix the number of vertices.\n Default: (4, 8).\n max_angle (float): Max value of angle at each vertex. Default 4.0.\n length_range (int | tuple[int]): (min_length, max_length). If only give\n an integer, we will fix the length of brush. Default: (10, 100).\n brush_width (int | tuple[int]): (min_width, max_width). If only give\n an integer, we will fix the width of brush. Default: (10, 40).\n np.dtype (str): Indicate the data type of returned masks.\n Default: 'uint8'\n\n Returns:\n mask (np.ndarray): Mask in the shape of (h, w, 1).\n \"\"\"\n\n h, w = img_shape[:2]\n\n mask = np.zeros((h, w), dtype=dtype)\n if isinstance(length_range, int):\n min_length, max_length = length_range, length_range + 1\n elif isinstance(length_range, tuple):\n min_length, max_length = length_range\n else:\n raise TypeError('The type of length_range should be int'\n f'or tuple[int], but got type: {length_range}')\n if isinstance(num_vertices, int):\n min_num_vertices, max_num_vertices = num_vertices, num_vertices + 1\n elif isinstance(num_vertices, tuple):\n min_num_vertices, max_num_vertices = num_vertices\n else:\n raise TypeError('The type of num_vertices should be int'\n f'or tuple[int], but got type: {num_vertices}')\n\n if isinstance(brush_width, int):\n min_brush_width, max_brush_width = brush_width, brush_width + 1\n elif isinstance(brush_width, tuple):\n min_brush_width, max_brush_width = brush_width\n else:\n raise TypeError('The type of brush_width should be int'\n f'or tuple[int], but got type: {brush_width}')\n\n num_v = np.random.randint(min_num_vertices, max_num_vertices)\n\n for i in range(num_v):\n start_x = np.random.randint(w)\n start_y = np.random.randint(h)\n # from the start point, randomly setlect n \\in [1, 6] directions.\n direction_num = np.random.randint(1, 6)\n angle_list = np.random.randint(0, max_angle, size=direction_num)\n length_list = np.random.randint(\n min_length, max_length, size=direction_num)\n brush_width_list = np.random.randint(\n min_brush_width, max_brush_width, size=direction_num)\n for direct_n in range(direction_num):\n angle = 0.01 + angle_list[direct_n]\n if i % 2 == 0:\n angle = 2 * math.pi - angle\n length = length_list[direct_n]\n brush_w = brush_width_list[direct_n]\n # compute end point according to the random angle\n end_x = (start_x + length * np.sin(angle)).astype(np.int32)\n end_y = (start_y + length * np.cos(angle)).astype(np.int32)\n\n cv2.line(mask, (start_y, start_x), (end_y, end_x), 1, brush_w)\n start_x, start_y = end_x, end_y\n mask = np.expand_dims(mask, axis=2)\n\n return mask\n\n\ndef get_irregular_mask(img_shape, area_ratio_range=(0.15, 0.5), **kwargs):\n \"\"\"Get irregular mask with the constraints in mask ratio.\n\n Args:\n img_shape (tuple[int]): Size of the image.\n area_ratio_range (tuple(float)): Contain the minimum and maximum area\n ratio. Default: (0.15, 0.5).\n\n Returns:\n mask (np.ndarray): Mask in the shape of (h, w, 1).\n \"\"\"\n\n mask = random_irregular_mask(img_shape, **kwargs)\n min_ratio, max_ratio = area_ratio_range\n\n while not min_ratio < (np.sum(mask) /\n (img_shape[0] * img_shape[1])) < max_ratio:\n mask = random_irregular_mask(img_shape, **kwargs)\n\n return mask\n\n\n_integer_types = (\n np.byte,\n np.ubyte, # 8 bits\n np.short,\n np.ushort, # 16 bits\n np.intc,\n np.uintc, # 16 or 32 or 64 bits\n np.int_,\n np.uint, # 32 or 64 bits\n np.longlong,\n np.ulonglong) # 64 bits\n\n_integer_ranges = {\n t: (np.iinfo(t).min, np.iinfo(t).max)\n for t in _integer_types\n}\n\ndtype_range = {\n np.bool_: (False, True),\n np.bool8: (False, True),\n np.float16: (-1, 1),\n np.float32: (-1, 1),\n np.float64: (-1, 1)\n}\ndtype_range.update(_integer_ranges)\n\n\ndef dtype_limits(image, clip_negative=False):\n \"\"\"Return intensity limits, i.e. (min, max) tuple, of the image's dtype.\n\n This function is adopted from skimage:\n https://github.com/scikit-image/scikit-image/blob/\n 7e4840bd9439d1dfb6beaf549998452c99f97fdd/skimage/util/dtype.py#L35\n\n Args:\n image (np.ndarray): Input image.\n clip_negative (bool, optional): If True, clip the negative range\n (i.e. return 0 for min intensity) even if the image dtype allows\n negative values. Default: False.\n\n Returns\n tuple: Lower and upper intensity limits.\n \"\"\"\n imin, imax = dtype_range[image.dtype.type]\n if clip_negative:\n imin = 0\n return imin, imax\n\n\ndef adjust_gamma(image, gamma=1, gain=1):\n \"\"\"Performs Gamma Correction on the input image.\n\n This function is adopted from skimage:\n https://github.com/scikit-image/scikit-image/blob/\n 7e4840bd9439d1dfb6beaf549998452c99f97fdd/skimage/exposure/\n exposure.py#L439-L494\n\n Also known as Power Law Transform.\n This function transforms the input image pixelwise according to the\n equation ``O = I**gamma`` after scaling each pixel to the range 0 to 1.\n\n Args:\n image (np.ndarray): Input image.\n gamma (float, optional): Non negative real number. Defaults to 1.\n gain (float, optional): The constant multiplier. Defaults to 1.\n\n Returns:\n np.ndarray: Gamma corrected output image.\n \"\"\"\n if np.any(image < 0):\n raise ValueError('Image Correction methods work correctly only on '\n 'images with non-negative values. Use '\n 'skimage.exposure.rescale_intensity.')\n\n dtype = image.dtype.type\n\n if gamma < 0:\n raise ValueError('Gamma should be a non-negative real number.')\n\n scale = float(dtype_limits(image, True)[1] - dtype_limits(image, True)[0])\n\n out = ((image / scale)**gamma) * scale * gain\n return out.astype(dtype)\n\n\ndef add_gaussian_noise(img: np.ndarray, mu, sigma):\n \"\"\"Add Gaussian Noise on the input image.\n\n Args:\n img (np.ndarray): Input image.\n mu (float): The mu value of the Gaussian function.\n sigma (float): The sigma value of the Gaussian function.\n\n Returns:\n noisy_img (np.ndarray): Gaussian noisy output image.\n \"\"\"\n img = img.astype(np.float32)\n gauss_noise = np.random.normal(mu, sigma, img.shape)\n noisy_img = img + gauss_noise\n noisy_img = np.clip(noisy_img, 0, 255)\n return noisy_img\n\n\ndef random_choose_unknown(unknown, crop_size):\n \"\"\"Randomly choose an unknown start (top-left) point for a given crop_size.\n\n Args:\n unknown (np.ndarray): The binary unknown mask.\n crop_size (tuple[int]): The given crop size.\n\n Returns:\n tuple[int]: The top-left point of the chosen bbox.\n \"\"\"\n h, w = unknown.shape\n crop_h, crop_w = crop_size\n delta_h = center_h = crop_h // 2\n delta_w = center_w = crop_w // 2\n\n # mask out the validate area for selecting the cropping center\n mask = np.zeros_like(unknown)\n mask[delta_h:h - delta_h, delta_w:w - delta_w] = 1\n if np.any(unknown & mask):\n center_h_list, center_w_list = np.where(unknown & mask)\n elif np.any(unknown):\n center_h_list, center_w_list = np.where(unknown)\n else:\n print_log('No unknown pixels found!', level=logging.WARNING)\n center_h_list = [center_h]\n center_w_list = [center_w]\n num_unknowns = len(center_h_list)\n rand_ind = np.random.randint(num_unknowns)\n center_h = center_h_list[rand_ind]\n center_w = center_w_list[rand_ind]\n\n # make sure the top-left point is valid\n top = np.clip(center_h - delta_h, 0, h - crop_h)\n left = np.clip(center_w - delta_w, 0, w - crop_w)\n\n return top, left\n", "repo_name": "open-mmlab/mmagic", "sub_path": "mmagic/utils/trans_utils.py", "file_name": "trans_utils.py", "file_ext": "py", "file_size_in_byte": 18252, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5963, "dataset": "github-code", "pt": "12", "api": [{"api_name": "torch.arange", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.meshgrid", "line_number": 33, "usage_type": "attribute"}, {"api_name": "torch.meshgrid", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.meshgrid", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 64, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 72, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 73, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 127, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 128, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 128, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 130, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 131, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 133, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 134, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 144, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 145, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 146, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 151, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 152, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 156, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 157, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 157, "usage_type": "call"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 160, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 160, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 161, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 168, "usage_type": "attribute"}, {"api_name": "PIL.Image.FLIP_LEFT_RIGHT", "line_number": 169, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 169, "usage_type": "name"}, {"api_name": "numpy.random.normal", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 170, "usage_type": "attribute"}, {"api_name": "PIL.Image.FLIP_TOP_BOTTOM", "line_number": 171, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 171, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 172, "usage_type": "call"}, {"api_name": "mmengine.utils.is_tuple_of", "line_number": 208, "usage_type": "call"}, {"api_name": "mmengine.utils.is_tuple_of", "line_number": 209, "usage_type": "call"}, {"api_name": "mmengine.utils.is_tuple_of", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 232, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 233, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 236, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 237, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 304, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 307, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 308, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 310, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 310, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 311, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 311, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 312, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 312, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 314, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 319, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 323, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 323, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 324, "usage_type": "attribute"}, {"api_name": "cv2.line", "line_number": 326, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 328, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 348, "usage_type": "call"}, {"api_name": "numpy.byte", "line_number": 356, "usage_type": "attribute"}, {"api_name": "numpy.ubyte", "line_number": 357, "usage_type": "attribute"}, {"api_name": "numpy.short", "line_number": 358, "usage_type": "attribute"}, {"api_name": "numpy.ushort", "line_number": 359, "usage_type": "attribute"}, {"api_name": "numpy.intc", "line_number": 360, "usage_type": "attribute"}, {"api_name": "numpy.uintc", "line_number": 361, "usage_type": "attribute"}, {"api_name": "numpy.int_", "line_number": 362, "usage_type": "attribute"}, {"api_name": "numpy.uint", "line_number": 363, "usage_type": "attribute"}, {"api_name": "numpy.longlong", "line_number": 364, "usage_type": "attribute"}, {"api_name": "numpy.ulonglong", "line_number": 365, "usage_type": "attribute"}, {"api_name": "numpy.iinfo", "line_number": 368, "usage_type": "call"}, {"api_name": "numpy.bool_", "line_number": 373, "usage_type": "attribute"}, {"api_name": "numpy.bool8", "line_number": 374, "usage_type": "attribute"}, {"api_name": "numpy.float16", "line_number": 375, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 376, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 377, "usage_type": "attribute"}, {"api_name": "numpy.any", "line_number": 424, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 440, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 451, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 452, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 452, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 454, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 474, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 476, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 477, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 478, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 479, "usage_type": "call"}, {"api_name": "mmengine.logging.print_log", "line_number": 481, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 481, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 485, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 485, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 490, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 491, "usage_type": "call"}]} +{"seq_id": "14347524003", "text": "from pandas import DataFrame, read_csv\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport sys\nimport os\n\ndef makePlot(plotFile, inputFile):\n df = pd.read_csv(inputFile)\n\n #remove the row with dummy kernel if any\n if 'dummy' in df.iloc[0]['KernelName'].lower():\n df = df.drop([0])\n\n # normalize time-stamps by subtracting the first timestamp from all \n offset = df.iloc[0]['DispatchNs']\n df['DispatchNs'] -= offset\n df['BeginNs'] -= offset\n df['EndNs'] -= offset\n df['CompleteNs'] -= offset\n\n fig = plt.figure()\n ax = plt.hlines(df.Index, df.BeginNs, df.EndNs)\n plt.xlabel('Time (ns)')\n plt.ylabel('Kernel ID')\n plt.savefig(plotFile, dpi=300, bbox_inches='tight', transparent=True)\n plt.show()\n\ndef main():\n if len(sys.argv) != 2:\n print(\"./extract-kernel-timeline.py \")\n\n # Read original CSV\n inputFile = sys.argv[1]\n bname = os.path.basename(inputFile)\n d = os.path.dirname(inputFile)\n prefix = os.path.splitext(bname)[0]\n plotFile = os.path.join(d, prefix + \"-kernel-execution-timeline.pdf\")\n\n # Make a plot\n makePlot(plotFile, inputFile)\n\nif __name__ == '__main__':\n main()\n", "repo_name": "AMDResearch/DAGEE", "sub_path": "tools/bin/plot-kernel-timeline.py", "file_name": "plot-kernel-timeline.py", "file_ext": "py", "file_size_in_byte": 1189, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 33, "dataset": "github-code", "pt": "12", "api": [{"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hlines", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 29, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}]} +{"seq_id": "11769067466", "text": "from gi.repository import Gtk\nfrom olc.define import App\nfrom .common import rounded_rectangle_fill\n\n\nclass CurveWidget(Gtk.Button):\n \"\"\"Curve widget\"\"\"\n\n __gtype_name__ = \"CurveWidget\"\n\n def __init__(self, curve: int):\n super().__init__()\n self.curve_nb = curve\n self.curve = App().curves.get_curve(curve)\n\n self.connect(\"clicked\", self.on_click)\n\n def on_click(self, _button) -> None:\n \"\"\"Button clicked\n\n Raises:\n NotImplementedError: Must be implemented in subclass\n \"\"\"\n raise NotImplementedError\n\n def do_draw(self, cr):\n \"\"\"Draw curve\n\n Args:\n cr: Cairo context\n \"\"\"\n self.set_size_request(80, 80)\n width = self.get_allocation().width\n height = self.get_allocation().height\n if isinstance(self.get_parent(), Gtk.FlowBoxChild):\n if self.get_parent().is_selected():\n cr.set_source_rgba(0.6, 0.4, 0.1, 1.0)\n else:\n cr.set_source_rgba(0.3, 0.3, 0.3, 1.0)\n else:\n state = self.get_state_flags()\n if state & Gtk.StateFlags.ACTIVE:\n cr.set_source_rgba(0.5, 0.3, 0.0, 1.0)\n else:\n cr.set_source_rgba(0.3, 0.3, 0.3, 1.0)\n area = (0, width, 0, height)\n rounded_rectangle_fill(cr, area, 10)\n cr.set_line_width(2)\n cr.set_source_rgba(0.0, 0.0, 0.0, 1.0)\n cr.move_to(0, height - self.curve.values[0])\n for x, y in self.curve.values.items():\n cr.line_to((x / 255) * width, height - ((y / 255) * height))\n cr.stroke()\n", "repo_name": "mikacousin/olc", "sub_path": "src/widgets/curve.py", "file_name": "curve.py", "file_ext": "py", "file_size_in_byte": 1634, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 25, "dataset": "github-code", "pt": "12", "api": [{"api_name": "gi.repository.Gtk.Button", "line_number": 6, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 6, "usage_type": "name"}, {"api_name": "olc.define.App", "line_number": 14, "usage_type": "call"}, {"api_name": "gi.repository.Gtk.FlowBoxChild", "line_number": 35, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 35, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.StateFlags", "line_number": 42, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 42, "usage_type": "name"}, {"api_name": "common.rounded_rectangle_fill", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "3847929457", "text": "from django.contrib.auth.models import User\nfrom django.http import Http404, HttpResponse\nfrom django.utils import timezone\nfrom rest_framework.exceptions import ValidationError\nfrom rest_framework.parsers import MultiPartParser, FormParser\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.request import Request\nfrom rest_framework.response import Response\nfrom rest_framework import views\nfrom typing import List, Union\n\nfrom muni_portal.collaborator_api.client import Client\nfrom muni_portal.collaborator_api.types import FormField\nfrom muni_portal.core.django_q_tasks import create_service_request, create_attachment, update_service_request_record\nfrom muni_portal.core.models import ServiceRequest, ServiceRequestAttachment\nfrom muni_portal.core.model_serializers import (\n ServiceRequestSerializer,\n ServiceRequestAttachmentSerializer,\n)\nfrom django.conf import settings\nfrom django_q.tasks import async_task, Chain\n\n\nclass ServiceRequestAPIView(views.APIView):\n @staticmethod\n def get_object(pk: int, user: User) -> ServiceRequest:\n try:\n return ServiceRequest.objects.get(pk=pk, user=user)\n except ServiceRequest.DoesNotExist:\n raise Http404\n\n\nclass ServiceRequestDetailView(ServiceRequestAPIView):\n \"\"\"\n Return detail of ServiceRequest object.\n\n First fetches from Collaborator Web API, then updates local instance with remote instance (if found), then\n returns local instance.\n \"\"\"\n\n permission_classes = [IsAuthenticated]\n\n def get(self, request: Request, pk: int) -> Response:\n local_object = self.get_object(pk, request.user)\n object_id = local_object.collaborator_object_id\n serializer = ServiceRequestSerializer(local_object)\n\n if not object_id:\n # Object does not exist in collaborator yet, so return local object without updating from collaborator\n return Response(serializer.data)\n\n client = Client(\n settings.COLLABORATOR_API_USERNAME, settings.COLLABORATOR_API_PASSWORD\n )\n client.authenticate()\n remote_object = client.get_task(object_id)\n serializer.update(local_object, remote_object)\n return Response(serializer.data)\n\n\nclass ServiceRequestListCreateView(ServiceRequestAPIView):\n permission_classes = [IsAuthenticated]\n\n CREATE_REQUIRED_FIELDS = (\n \"type\",\n \"user_name\",\n \"user_surname\",\n \"user_mobile_number\",\n \"description\",\n )\n\n parser_classes = FormParser, MultiPartParser\n\n def get(self, request: Request) -> Response:\n \"\"\"\n Return list of ServiceRequest objects.\n\n We build the list by retrieving all local ServiceRequest objects for this user and requesting a detail view\n of each object from Collaborator Web API and returning it as a list.\n \"\"\"\n response_list = []\n local_objects_with_ids = ServiceRequest.objects.filter(\n user=request.user, collaborator_object_id__isnull=False\n )\n local_objects_without_ids = ServiceRequest.objects.filter(\n user=request.user, collaborator_object_id__isnull=True\n )\n\n if local_objects_with_ids:\n client = Client(\n settings.COLLABORATOR_API_USERNAME, settings.COLLABORATOR_API_PASSWORD\n )\n client.authenticate()\n\n for service_request in local_objects_with_ids:\n local_object = self.get_object(service_request.pk, request.user)\n serializer = ServiceRequestSerializer(local_object)\n remote_object = client.get_task(local_object.collaborator_object_id)\n serializer.update(local_object, remote_object)\n response_list.append(serializer.data)\n\n for local_object in local_objects_without_ids:\n serializer = ServiceRequestSerializer(local_object)\n response_list.append(serializer.data)\n\n return Response(response_list)\n\n def post(self, request: Request) -> Response:\n \"\"\"\n Create a new Service Request object.\n\n The object will first be created in Collaborator Web API, and if successful,\n it will be created in this API.\n \"\"\"\n # Return error if any of the fields are missing\n received_fields = request.data.keys()\n missing_fields = []\n for field in self.CREATE_REQUIRED_FIELDS:\n if field not in received_fields or not request.data.get(field):\n missing_fields.append(field)\n if missing_fields:\n error_response_dict = {}\n for field in missing_fields:\n error_response_dict[field] = \"This field is required.\"\n return Response(error_response_dict, status=400)\n\n request_type = request.data.get(\"type\")\n user_name = request.data.get(\"user_name\")\n user_surname = request.data.get(\"user_surname\")\n user_mobile_number = request.data.get(\"user_mobile_number\")\n user_email_address = request.data.get(\"user_email_address\")\n street_name = request.data.get(\"street_name\")\n street_number = request.data.get(\"street_number\")\n suburb = request.data.get(\"suburb\")\n description = request.data.get(\"description\")\n coordinates = request.data.get(\"coordinates\")\n\n request_date = timezone.now()\n request_date_iso = request_date.isoformat()\n demarcation_code = \"WC033\"\n\n serializer = ServiceRequestSerializer(\n data={\n \"user\": request.user.pk,\n \"type\": request_type,\n \"user_name\": user_name,\n \"user_surname\": user_surname,\n \"user_mobile_number\": user_mobile_number,\n \"user_email_address\": user_email_address,\n \"street_name\": street_name,\n \"street_number\": street_number,\n \"suburb\": suburb,\n \"description\": description,\n \"coordinates\": coordinates,\n \"request_date\": request_date,\n \"demarcartion_code\": demarcation_code,\n }\n )\n serializer.is_valid(raise_exception=True)\n\n # Translate POST parameters received into Collaborator Web API form fields\n form_fields: List[FormField] = [\n {\"FieldID\": \"F1\", \"FieldValue\": request_type},\n {\"FieldID\": \"F2\", \"FieldValue\": user_name},\n {\"FieldID\": \"F3\", \"FieldValue\": user_surname},\n {\"FieldID\": \"F4\", \"FieldValue\": user_mobile_number},\n {\"FieldID\": \"F5\", \"FieldValue\": user_email_address},\n {\"FieldID\": \"F7\", \"FieldValue\": street_name},\n {\"FieldID\": \"F8\", \"FieldValue\": street_number},\n {\"FieldID\": \"F9\", \"FieldValue\": suburb},\n {\"FieldID\": \"F10\", \"FieldValue\": description},\n {\"FieldID\": \"F11\", \"FieldValue\": coordinates},\n {\"FieldID\": \"F12\", \"FieldValue\": request_date_iso},\n {\"FieldID\": \"F20\", \"FieldValue\": demarcation_code},\n {\"FieldID\": \"F37\", \"FieldValue\": \"Yes\"},\n ]\n\n service_request = ServiceRequest.objects.create(\n user=request.user,\n type=request_type,\n request_date=request_date,\n user_name=user_name,\n user_surname=user_surname,\n user_mobile_number=user_mobile_number,\n user_email_address=user_email_address,\n street_name=street_name,\n street_number=street_number,\n suburb=suburb,\n description=description,\n coordinates=coordinates,\n demarcation_code=demarcation_code,\n )\n\n serializer = ServiceRequestSerializer(service_request, many=False)\n\n async_task(\n create_service_request,\n service_request.id,\n form_fields,\n )\n\n chain = Chain()\n\n chain.append(update_service_request_record, service_request.id, \"Registered\")\n chain.append(update_service_request_record, service_request.id, \"Yes\", \"F37\")\n chain.run()\n\n return Response(status=201, data=serializer.data)\n\n\nclass ServiceRequestAttachmentListCreateView(views.APIView):\n \"\"\"\n This API View supports listing the images for a service request and creating images for an existing service\n request.\n \"\"\"\n\n permission_classes = [IsAuthenticated]\n parser_classes = (MultiPartParser, FormParser)\n\n @staticmethod\n def get_service_request(\n service_request_pk: int, user: User\n ) -> Union[ServiceRequest, Response]:\n try:\n return ServiceRequest.objects.get(pk=service_request_pk, user=user)\n except ServiceRequest.DoesNotExist:\n return Response(status=404)\n\n def get(self, request: Request, service_request_pk: int) -> Response:\n \"\"\" Return a list of images for a specific Service Request object \"\"\"\n service_request = self.get_service_request(service_request_pk, request.user)\n if type(service_request) == Response:\n return service_request\n\n images = ServiceRequestAttachmentSerializer(\n service_request.images.all(), many=True\n )\n\n return Response(images.data)\n\n def post(self, request: Request, service_request_pk: int) -> Response:\n \"\"\" Create an image attachment for an existing Service Request object \"\"\"\n service_request = self.get_service_request(service_request_pk, request.user)\n if type(service_request) == Response:\n return service_request\n\n files = request.FILES.getlist(\"files\")\n if len(files) == 0:\n raise ValidationError(\"Request must contain at least one file in 'files'\")\n\n attachments_can_be_created = service_request.collaborator_object_id is not None\n\n chain = Chain()\n for file in files:\n image = ServiceRequestAttachment.objects.create(\n service_request=service_request,\n file=file,\n content_type=file.content_type,\n )\n # If the service request object doesn't have an ID yet it'll execute\n # the async task after it has received an ID in django_q_hooks.py\n if attachments_can_be_created:\n chain.append(create_attachment, image.id)\n\n # Since we are adding more attachments to an existing object which may already be uploaded to On Prem,\n # we have to first change the status back to initial and then back to registered again to trigger the upload\n if attachments_can_be_created:\n chain.append(update_service_request_record, service_request.id, \"Initial\")\n chain.append(update_service_request_record, service_request.id, \"No\", \"F37\")\n chain.append(update_service_request_record, service_request.id, \"Registered\")\n chain.append(update_service_request_record, service_request.id, \"Yes\", \"F37\")\n chain.run()\n\n return Response(status=201)\n\n\nclass ServiceRequestAttachmentDetailView(views.APIView):\n \"\"\"\n This view returns an image in bytes.\n\n Note that this view returns Django's HttpResponse class and not DRF's Response class to avoid DRF's renderer.\n \"\"\"\n\n permission_classes = [IsAuthenticated]\n\n def get(\n self, request: Request, service_request_pk: int, service_request_image_pk: int\n ) -> HttpResponse:\n service_request_image = ServiceRequestAttachment.objects.get(\n service_request__pk=service_request_pk,\n pk=service_request_image_pk,\n service_request__user=request.user,\n )\n\n image_bytes = service_request_image.file.open(\"rb\").read()\n service_request_image.file.close()\n\n return HttpResponse(\n image_bytes, content_type=service_request_image.content_type\n )\n", "repo_name": "OpenUpSA/muni-portal-backend", "sub_path": "muni_portal/core/views/api/service_requests.py", "file_name": "service_requests.py", "file_ext": "py", "file_size_in_byte": 11851, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "12", "api": [{"api_name": "rest_framework.views.APIView", "line_number": 24, "usage_type": "attribute"}, {"api_name": "rest_framework.views", "line_number": 24, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User", "line_number": 26, "usage_type": "name"}, {"api_name": "muni_portal.core.models.ServiceRequest.objects.get", "line_number": 28, "usage_type": "call"}, {"api_name": "muni_portal.core.models.ServiceRequest.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "muni_portal.core.models.ServiceRequest", "line_number": 28, "usage_type": "name"}, {"api_name": "muni_portal.core.models.ServiceRequest.DoesNotExist", "line_number": 29, "usage_type": "attribute"}, {"api_name": "muni_portal.core.models.ServiceRequest", "line_number": 29, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 30, "usage_type": "name"}, {"api_name": "muni_portal.core.models.ServiceRequest", "line_number": 26, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 41, "usage_type": "name"}, {"api_name": "rest_framework.request.Request", "line_number": 43, "usage_type": "name"}, {"api_name": "muni_portal.core.model_serializers.ServiceRequestSerializer", "line_number": 46, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 50, "usage_type": "call"}, {"api_name": "muni_portal.collaborator_api.client.Client", "line_number": 52, "usage_type": "call"}, {"api_name": "django.conf.settings.COLLABORATOR_API_USERNAME", "line_number": 53, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 53, "usage_type": "name"}, {"api_name": "django.conf.settings.COLLABORATOR_API_PASSWORD", "line_number": 53, "usage_type": "attribute"}, {"api_name": "rest_framework.response.Response", "line_number": 58, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 43, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 62, "usage_type": "name"}, {"api_name": "rest_framework.parsers.FormParser", "line_number": 72, "usage_type": "name"}, {"api_name": "rest_framework.parsers.MultiPartParser", "line_number": 72, "usage_type": "name"}, {"api_name": "rest_framework.request.Request", "line_number": 74, "usage_type": "name"}, {"api_name": "muni_portal.core.models.ServiceRequest.objects.filter", "line_number": 82, "usage_type": "call"}, {"api_name": "muni_portal.core.models.ServiceRequest.objects", "line_number": 82, "usage_type": "attribute"}, {"api_name": "muni_portal.core.models.ServiceRequest", "line_number": 82, "usage_type": "name"}, {"api_name": "muni_portal.core.models.ServiceRequest.objects.filter", "line_number": 85, "usage_type": "call"}, {"api_name": "muni_portal.core.models.ServiceRequest.objects", "line_number": 85, "usage_type": "attribute"}, {"api_name": "muni_portal.core.models.ServiceRequest", "line_number": 85, "usage_type": "name"}, {"api_name": "muni_portal.collaborator_api.client.Client", "line_number": 90, "usage_type": "call"}, {"api_name": "django.conf.settings.COLLABORATOR_API_USERNAME", "line_number": 91, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 91, "usage_type": "name"}, {"api_name": "django.conf.settings.COLLABORATOR_API_PASSWORD", "line_number": 91, "usage_type": "attribute"}, {"api_name": "muni_portal.core.model_serializers.ServiceRequestSerializer", "line_number": 97, "usage_type": "call"}, {"api_name": "muni_portal.core.model_serializers.ServiceRequestSerializer", "line_number": 103, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 106, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 74, "usage_type": "name"}, {"api_name": "rest_framework.request.Request", "line_number": 108, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 125, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 138, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 138, "usage_type": "name"}, {"api_name": "muni_portal.core.model_serializers.ServiceRequestSerializer", "line_number": 142, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 162, "usage_type": "name"}, {"api_name": "muni_portal.collaborator_api.types.FormField", "line_number": 162, "usage_type": "name"}, {"api_name": "muni_portal.core.models.ServiceRequest.objects.create", "line_number": 178, "usage_type": "call"}, {"api_name": "muni_portal.core.models.ServiceRequest.objects", "line_number": 178, "usage_type": "attribute"}, {"api_name": "muni_portal.core.models.ServiceRequest", "line_number": 178, "usage_type": "name"}, {"api_name": "muni_portal.core.model_serializers.ServiceRequestSerializer", "line_number": 194, "usage_type": "call"}, {"api_name": "django_q.tasks.async_task", "line_number": 196, "usage_type": "call"}, {"api_name": "muni_portal.core.django_q_tasks.create_service_request", "line_number": 197, "usage_type": "argument"}, {"api_name": "django_q.tasks.Chain", "line_number": 202, "usage_type": "call"}, {"api_name": "muni_portal.core.django_q_tasks.update_service_request_record", "line_number": 204, "usage_type": "argument"}, {"api_name": "muni_portal.core.django_q_tasks.update_service_request_record", "line_number": 205, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 208, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 108, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 211, "usage_type": "attribute"}, {"api_name": "rest_framework.views", "line_number": 211, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 217, "usage_type": "name"}, {"api_name": "rest_framework.parsers.MultiPartParser", "line_number": 218, "usage_type": "name"}, {"api_name": "rest_framework.parsers.FormParser", "line_number": 218, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User", "line_number": 222, "usage_type": "name"}, {"api_name": "muni_portal.core.models.ServiceRequest.objects.get", "line_number": 225, "usage_type": "call"}, {"api_name": "muni_portal.core.models.ServiceRequest.objects", "line_number": 225, "usage_type": "attribute"}, {"api_name": "muni_portal.core.models.ServiceRequest", "line_number": 225, "usage_type": "name"}, {"api_name": "muni_portal.core.models.ServiceRequest.DoesNotExist", "line_number": 226, "usage_type": "attribute"}, {"api_name": "muni_portal.core.models.ServiceRequest", "line_number": 226, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 227, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 223, "usage_type": "name"}, {"api_name": "muni_portal.core.models.ServiceRequest", "line_number": 223, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 223, "usage_type": "name"}, {"api_name": "rest_framework.request.Request", "line_number": 229, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 232, "usage_type": "name"}, {"api_name": "muni_portal.core.model_serializers.ServiceRequestAttachmentSerializer", "line_number": 235, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 239, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 229, "usage_type": "name"}, {"api_name": "rest_framework.request.Request", "line_number": 241, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 244, "usage_type": "name"}, {"api_name": "rest_framework.exceptions.ValidationError", "line_number": 249, "usage_type": "call"}, {"api_name": "django_q.tasks.Chain", "line_number": 253, "usage_type": "call"}, {"api_name": "muni_portal.core.models.ServiceRequestAttachment.objects.create", "line_number": 255, "usage_type": "call"}, {"api_name": "muni_portal.core.models.ServiceRequestAttachment.objects", "line_number": 255, "usage_type": "attribute"}, {"api_name": "muni_portal.core.models.ServiceRequestAttachment", "line_number": 255, "usage_type": "name"}, {"api_name": "muni_portal.core.django_q_tasks.create_attachment", "line_number": 263, "usage_type": "argument"}, {"api_name": "muni_portal.core.django_q_tasks.update_service_request_record", "line_number": 268, "usage_type": "argument"}, {"api_name": "muni_portal.core.django_q_tasks.update_service_request_record", "line_number": 269, "usage_type": "argument"}, {"api_name": "muni_portal.core.django_q_tasks.update_service_request_record", "line_number": 270, "usage_type": "argument"}, {"api_name": "muni_portal.core.django_q_tasks.update_service_request_record", "line_number": 271, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 274, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 241, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 277, "usage_type": "attribute"}, {"api_name": "rest_framework.views", "line_number": 277, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 284, "usage_type": "name"}, {"api_name": "rest_framework.request.Request", "line_number": 287, "usage_type": "name"}, {"api_name": "muni_portal.core.models.ServiceRequestAttachment.objects.get", "line_number": 289, "usage_type": "call"}, {"api_name": "muni_portal.core.models.ServiceRequestAttachment.objects", "line_number": 289, "usage_type": "attribute"}, {"api_name": "muni_portal.core.models.ServiceRequestAttachment", "line_number": 289, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 298, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 288, "usage_type": "name"}]} +{"seq_id": "42116439655", "text": "\"\"\"\nThis class defines the model setup for a character level\nRNN in the torch framework\nCredit goes to: https://github.com/spro/char-rnn.pytorch\nthat HIGHLY influenced this code, thanks!\n\n@author: Brad Beechler (brad.e.beechler@gmail.com)\n# Last Modification: 09/20/2017 (Brad Beechler)\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\n\n\nclass CharRNN(nn.Module):\n def __init__(self, input_size, hidden_size, output_size, model=\"gru\", cuda=None,\n n_layers=1, dropout=0.2):\n super(CharRNN, self).__init__()\n self.model = model.lower()\n self.input_size = input_size\n self.hidden_size = hidden_size\n self.output_size = output_size\n self.n_layers = n_layers\n self.cuda = cuda\n\n self.encoder = nn.Embedding(input_size, hidden_size)\n if self.model == \"gru\":\n self.rnn = nn.GRU(hidden_size, hidden_size, n_layers)\n elif self.model == \"lstm\":\n self.rnn = nn.LSTM(hidden_size, hidden_size, n_layers)\n self.decoder = nn.Linear(hidden_size, output_size)\n self.rnn.dropout = dropout\n if cuda is not None:\n self.encoder.cuda()\n self.rnn.cuda()\n self.decoder.cuda()\n\n def forward(self, input_pattern, hidden):\n batch_size = input_pattern.size(0)\n encoded = self.encoder(input_pattern)\n output, hidden = self.rnn(encoded.view(1, batch_size, -1), hidden)\n output = self.decoder(output.view(batch_size, -1))\n return output, hidden\n\n def forward2(self, input_pattern, hidden):\n encoded = self.encoder(input_pattern.view(1, -1))\n output, hidden = self.rnn(encoded.view(1, 1, -1), hidden)\n output = self.decoder(output.view(1, -1))\n return output, hidden\n\n def init_hidden(self, batch_size, cuda=None):\n if cuda is not None:\n if self.model == \"lstm\":\n return (Variable(torch.zeros(self.n_layers, batch_size, self.hidden_size).cuda(device=cuda)),\n Variable(torch.zeros(self.n_layers, batch_size, self.hidden_size).cuda(device=cuda)))\n return Variable(torch.zeros(self.n_layers, batch_size, self.hidden_size).cuda(device=cuda))\n else:\n if self.model == \"lstm\":\n return (Variable(torch.zeros(self.n_layers, batch_size, self.hidden_size)),\n Variable(torch.zeros(self.n_layers, batch_size, self.hidden_size)))\n return Variable(torch.zeros(self.n_layers, batch_size, self.hidden_size))\n", "repo_name": "Chronocook/mythic_maker", "sub_path": "mythic_model_character.py", "file_name": "mythic_model_character.py", "file_ext": "py", "file_size_in_byte": 2558, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "torch.nn.Module", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.GRU", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.LSTM", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "41506202567", "text": "#!/usr/bin/env python3\n\"\"\"Add the Amarakosha to the database.\n\nOur input data file is a stardict file, which prints entries in a simple file\nformat:\n\n \n \n\n \n \n\n [...]\n\nwhere each `value` is on a single line.\n\"\"\"\n\nimport re\nfrom collections.abc import Iterator\n\nimport click\nfrom indic_transliteration import sanscript\n\nfrom ambuda.seed.utils.cdsl_utils import create_from_scratch\nfrom ambuda.seed.utils.data_utils import create_db, fetch_text\nfrom ambuda.utils.dict_utils import standardize_key\n\nRAW_URL = \"https://raw.githubusercontent.com/indic-dict/stardict-sanskrit/master/sa-head/sa-entries/amara-onto/amara-onto.babylon\"\n\n\ndef create_entries(deva_key: str, body: str) -> Iterator[tuple[str, str]]:\n \"\"\"For the given startdict, yield at most one entry.\n\n We use `yield` because this simplifies our calling logic. Callers can simply\n use `yield from ...` to yield data if it's present.\n \"\"\"\n if \"_\" in deva_key:\n print(f\" bad key: {deva_key}\")\n return\n\n # In other stardict files, \"|\" separates multiple key words. So, check that\n # we have exactly one here.\n assert \"|\" not in deva_key\n\n # In the input files, separate lines are consistently separated with a\n # double
.\n lines = [x.strip() for x in body.split(\"

\")]\n # There are other fields here, but these five are most essential.\n key_and_lex, meaning, synonyms, citation, verse = lines[:5]\n lex_key, lex = key_and_lex.split()\n assert deva_key == lex_key\n\n # Create a standardized lookup key.\n key = sanscript.transliterate(deva_key, sanscript.DEVANAGARI, sanscript.SLP1)\n key = standardize_key(key)\n\n # The lexical data uses the danda to abbreviate the entry. Instead, use the\n # lāghava, which is more appropriate.\n lex = lex.replace(\"।\", \"॰\")\n\n # Improve the display of synomym and verse data.\n synonyms = synonyms.replace(\",\", \", \").replace(\":\", \" — \")\n verse = verse.replace(\".।\", \"॥\")\n verse = verse.replace(\"॥\", \" ॥\")\n\n # Reshape data to XML, which we can interpret at serving time.\n verse_xml_fragment = re.sub(r\"।\\s*\", \" ।\", verse)\n entry = \"\".join(\n [\n \"\",\n f\"

{deva_key} {lex} {meaning}। {synonyms}।

\",\n f\"{verse_xml_fragment}\"\n f\"{citation}\",\n ]\n )\n yield key, entry\n\n\ndef amara_generator(dict_blob: str) -> Iterator[tuple[str, str]]:\n \"\"\"Iterate over all entries in the dictionary.\n\n :param dict_blob: the full dictionary string.\n \"\"\"\n buf = []\n for line in dict_blob.splitlines():\n # Ignore comments.\n if line.startswith(\"#\"):\n continue\n\n line = line.strip()\n if line:\n buf.append(line)\n elif buf:\n key, body = buf\n yield from create_entries(key, body)\n buf = []\n if buf:\n key, body = buf\n yield from create_entries(key, body)\n\n\n@click.command()\n@click.option(\"--use-cache/--no-use-cache\", default=False)\ndef run(use_cache):\n print(\"Initializing database ...\")\n engine = create_db()\n\n print(f\"Fetching data from GitHub (use_cache = {use_cache})...\")\n text_blob = fetch_text(RAW_URL, read_from_cache=use_cache)\n\n print(\"Adding items to database ...\")\n create_from_scratch(\n engine,\n slug=\"amara\",\n title=\"अमरकोशः\",\n generator=amara_generator(text_blob),\n )\n\n print(\"Done.\")\n\n\nif __name__ == \"__main__\":\n run()\n", "repo_name": "ambuda-org/ambuda", "sub_path": "ambuda/seed/dictionaries/amarakosha.py", "file_name": "amarakosha.py", "file_ext": "py", "file_size_in_byte": 3611, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 67, "dataset": "github-code", "pt": "12", "api": [{"api_name": "indic_transliteration.sanscript.transliterate", "line_number": 54, "usage_type": "call"}, {"api_name": "indic_transliteration.sanscript", "line_number": 54, "usage_type": "name"}, {"api_name": "indic_transliteration.sanscript.DEVANAGARI", "line_number": 54, "usage_type": "attribute"}, {"api_name": "indic_transliteration.sanscript.SLP1", "line_number": 54, "usage_type": "attribute"}, {"api_name": "ambuda.utils.dict_utils.standardize_key", "line_number": 55, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 67, "usage_type": "call"}, {"api_name": "collections.abc.Iterator", "line_number": 31, "usage_type": "name"}, {"api_name": "collections.abc.Iterator", "line_number": 79, "usage_type": "name"}, {"api_name": "ambuda.seed.utils.data_utils.create_db", "line_number": 106, "usage_type": "call"}, {"api_name": "ambuda.seed.utils.data_utils.fetch_text", "line_number": 109, "usage_type": "call"}, {"api_name": "ambuda.seed.utils.cdsl_utils.create_from_scratch", "line_number": 112, "usage_type": "call"}, {"api_name": "click.command", "line_number": 102, "usage_type": "call"}, {"api_name": "click.option", "line_number": 103, "usage_type": "call"}]} +{"seq_id": "42884609674", "text": "\"\"\"Compute sound event features from geometries.\n\nThis module contains functions to compute sound event features from their\ngeometries. These are simple yet useful features that provide a first glance of\nthe sound event.\n\nThese features are directly computed from the geometry, and do not depend on\nthe audio signal nor the spectrogram.\n\nThe computed features are:\n\n- ``duration``: The duration of the sound event, in seconds.\n- ``low_freq``: The lowest frequency of the sound event, in Hz.\n- ``high_freq``: The highest frequency of the sound event, in Hz.\n- ``bandwidth``: The bandwidth of the sound event, in Hz.\n- ``num_segments``: The number of segments of the sound event.\n\nSome of these features are not applicable to all geometries, as they require\ninformation not present in the geometry. However the function\n``compute_geometric_features`` will compute all the features that are\napplicable to the given geometry and return them in a list.\n\nExamples\n--------\nTo compute the features of a bounding box:\n\n>>> from soundevent import data, features\n>>> geometry = data.BoundingBox(\n... coordinates=(0, 0, 1, 1000),\n... )\n>>> features.compute_geometric_features(geometry)\n[Feature(name='duration', value=1),\n Feature(name='low_freq', value=0),\n Feature(name='high_freq', value=1000),\n Feature(name='bandwidth', value=1000)]\n\"\"\"\nfrom enum import Enum\nfrom typing import Any, Callable, Dict, List\n\nfrom soundevent.data import Feature, geometries\nfrom soundevent.geometry.conversion import geometry_to_shapely\n\n__all__ = [\n \"compute_geometric_features\",\n \"GeometricFeature\",\n]\n\n\nclass GeometricFeature(str, Enum):\n \"\"\"Geometric features computed from geometries.\n\n This enumeration defines various geometric features computed from sound\n event geometries. These features provide essential insights into the\n temporal and frequency properties of the events.\n\n Attributes\n ----------\n DURATION : str\n The duration of the geometry in seconds. Applicable to all geometries.\n LOW_FREQ : str\n The lowest frequency of the geometry in Hz.\n HIGH_FREQ : str\n The highest frequency of the geometry in Hz.\n BANDWIDTH : str\n The bandwidth of the geometry in Hz.\n NUM_SEGMENTS : str\n The number of segments of the geometry. Only applicable to\n ``MultiPoint``, ``MultiLineString``, and ``MultiPolygon`` geometries.\n \"\"\"\n\n DURATION = \"duration\"\n LOW_FREQ = \"low_freq\"\n HIGH_FREQ = \"high_freq\"\n BANDWIDTH = \"bandwidth\"\n NUM_SEGMENTS = \"num_segments\"\n\n\ndef _compute_time_stamp_features(\n _: geometries.TimeStamp,\n) -> List[Feature]:\n return [Feature(name=GeometricFeature.DURATION, value=0)]\n\n\ndef _compute_time_interval_features(\n geometry: geometries.TimeInterval,\n) -> List[Feature]:\n start, end = geometry.coordinates\n return [Feature(name=GeometricFeature.DURATION, value=end - start)]\n\n\ndef _compute_bounding_box_features(\n geometry: geometries.BoundingBox,\n) -> List[Feature]:\n start_time, low_freq, end_time, high_freq = geometry.coordinates\n return [\n Feature(name=GeometricFeature.DURATION, value=end_time - start_time),\n Feature(name=GeometricFeature.LOW_FREQ, value=low_freq),\n Feature(name=GeometricFeature.HIGH_FREQ, value=high_freq),\n Feature(name=GeometricFeature.BANDWIDTH, value=high_freq - low_freq),\n ]\n\n\ndef _compute_point_features(\n geometry: geometries.Point,\n) -> List[Feature]:\n geom = geometry_to_shapely(geometry)\n _, low_freq, _, high_freq = geom.bounds\n\n return [\n Feature(name=GeometricFeature.DURATION, value=0),\n Feature(name=GeometricFeature.LOW_FREQ, value=low_freq),\n Feature(name=GeometricFeature.HIGH_FREQ, value=high_freq),\n Feature(name=GeometricFeature.BANDWIDTH, value=0),\n ]\n\n\ndef _compute_line_string_features(\n geometry: geometries.LineString,\n) -> List[Feature]:\n geom = geometry_to_shapely(geometry)\n start_time, low_freq, end_time, high_freq = geom.bounds\n\n return [\n Feature(name=GeometricFeature.DURATION, value=end_time - start_time),\n Feature(name=GeometricFeature.LOW_FREQ, value=low_freq),\n Feature(name=GeometricFeature.HIGH_FREQ, value=high_freq),\n Feature(name=GeometricFeature.BANDWIDTH, value=high_freq - low_freq),\n ]\n\n\ndef _compute_polygon_features(\n geometry: geometries.Polygon,\n) -> List[Feature]:\n geom = geometry_to_shapely(geometry)\n start_time, low_freq, end_time, high_freq = geom.bounds\n\n return [\n Feature(name=GeometricFeature.DURATION, value=end_time - start_time),\n Feature(name=GeometricFeature.LOW_FREQ, value=low_freq),\n Feature(name=GeometricFeature.HIGH_FREQ, value=high_freq),\n Feature(name=GeometricFeature.BANDWIDTH, value=high_freq - low_freq),\n ]\n\n\ndef _compute_multi_point_features(\n geometry: geometries.MultiPoint,\n) -> List[Feature]:\n geom = geometry_to_shapely(geometry)\n start_time, low_freq, end_time, high_freq = geom.bounds\n\n return [\n Feature(name=GeometricFeature.DURATION, value=end_time - start_time),\n Feature(name=GeometricFeature.LOW_FREQ, value=low_freq),\n Feature(name=GeometricFeature.HIGH_FREQ, value=high_freq),\n Feature(name=GeometricFeature.BANDWIDTH, value=high_freq - low_freq),\n Feature(\n name=GeometricFeature.NUM_SEGMENTS, value=len(geometry.coordinates)\n ),\n ]\n\n\ndef _compute_multi_linestring_features(\n geometry: geometries.MultiLineString,\n) -> List[Feature]:\n geom = geometry_to_shapely(geometry)\n start_time, low_freq, end_time, high_freq = geom.bounds\n\n return [\n Feature(name=GeometricFeature.DURATION, value=end_time - start_time),\n Feature(name=GeometricFeature.LOW_FREQ, value=low_freq),\n Feature(name=GeometricFeature.HIGH_FREQ, value=high_freq),\n Feature(name=GeometricFeature.BANDWIDTH, value=high_freq - low_freq),\n Feature(\n name=GeometricFeature.NUM_SEGMENTS, value=len(geometry.coordinates)\n ),\n ]\n\n\ndef _compute_multi_polygon_features(\n geometry: geometries.MultiPolygon,\n) -> List[Feature]:\n geom = geometry_to_shapely(geometry)\n start_time, low_freq, end_time, high_freq = geom.bounds\n\n return [\n Feature(name=GeometricFeature.DURATION, value=end_time - start_time),\n Feature(name=GeometricFeature.LOW_FREQ, value=low_freq),\n Feature(name=GeometricFeature.HIGH_FREQ, value=high_freq),\n Feature(name=GeometricFeature.BANDWIDTH, value=high_freq - low_freq),\n Feature(\n name=GeometricFeature.NUM_SEGMENTS, value=len(geometry.coordinates)\n ),\n ]\n\n\n_COMPUTE_FEATURES: Dict[\n geometries.GeometryType, Callable[[Any], List[Feature]]\n] = {\n geometries.TimeStamp.geom_type(): _compute_time_stamp_features,\n geometries.TimeInterval.geom_type(): _compute_time_interval_features,\n geometries.BoundingBox.geom_type(): _compute_bounding_box_features,\n geometries.Point.geom_type(): _compute_point_features,\n geometries.LineString.geom_type(): _compute_line_string_features,\n geometries.Polygon.geom_type(): _compute_polygon_features,\n geometries.MultiPoint.geom_type(): _compute_multi_point_features,\n geometries.MultiLineString.geom_type(): _compute_multi_linestring_features,\n geometries.MultiPolygon.geom_type(): _compute_multi_polygon_features,\n}\n\n\ndef compute_geometric_features(\n geometry: geometries.Geometry,\n) -> List[Feature]:\n \"\"\"Compute features from a geometry.\n\n Some basic acoustic features can be computed from a geometry. This function\n computes these features and returns them as a list of features.\n\n The following features are computed when possible:\n\n - ``duration``: The duration of the geometry.\n - ``low_freq``: The lowest frequency of the geometry.\n - ``high_freq``: The highest frequency of the geometry.\n - ``bandwidth``: The bandwidth of the geometry.\n - ``num_segments``: The number of segments in the geometry.\n\n Depending on the geometry type, some features may not be computed. For\n example, a ``TimeStamp`` geometry does not have a bandwidth.\n\n Parameters\n ----------\n geometry : geometries.Geometry\n The geometry to compute features from.\n\n Returns\n -------\n List[Feature]\n The computed features.\n\n Raises\n ------\n NotImplementedError\n If the geometry type is not supported.\n \"\"\"\n try:\n return _COMPUTE_FEATURES[geometry.type](geometry)\n except KeyError as error:\n raise NotImplementedError(\n f\"Geometry type {geometry.type} is not supported.\"\n ) from error\n", "repo_name": "mbsantiago/soundevent", "sub_path": "src/soundevent/geometry/features.py", "file_name": "features.py", "file_ext": "py", "file_size_in_byte": 8646, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "12", "api": [{"api_name": "enum.Enum", "line_number": 49, "usage_type": "name"}, {"api_name": "soundevent.data.geometries.TimeStamp", "line_number": 79, "usage_type": "attribute"}, {"api_name": "soundevent.data.geometries", "line_number": 79, "usage_type": "name"}, {"api_name": "soundevent.data.Feature", "line_number": 81, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 80, "usage_type": "name"}, {"api_name": "soundevent.data.Feature", "line_number": 80, "usage_type": "name"}, {"api_name": "soundevent.data.geometries.TimeInterval", "line_number": 85, "usage_type": "attribute"}, {"api_name": "soundevent.data.geometries", "line_number": 85, "usage_type": "name"}, {"api_name": "soundevent.data.Feature", "line_number": 88, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 86, "usage_type": "name"}, {"api_name": "soundevent.data.Feature", "line_number": 86, "usage_type": "name"}, {"api_name": "soundevent.data.geometries.BoundingBox", "line_number": 92, "usage_type": "attribute"}, {"api_name": "soundevent.data.geometries", "line_number": 92, "usage_type": "name"}, {"api_name": "soundevent.data.Feature", "line_number": 96, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 97, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 98, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 99, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 93, "usage_type": "name"}, {"api_name": "soundevent.data.Feature", "line_number": 93, "usage_type": "name"}, {"api_name": "soundevent.data.geometries.Point", "line_number": 104, "usage_type": "attribute"}, {"api_name": "soundevent.data.geometries", "line_number": 104, "usage_type": "name"}, {"api_name": "soundevent.geometry.conversion.geometry_to_shapely", "line_number": 106, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 110, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 111, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 112, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 113, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 105, "usage_type": "name"}, {"api_name": "soundevent.data.Feature", "line_number": 105, "usage_type": "name"}, {"api_name": "soundevent.data.geometries.LineString", "line_number": 118, "usage_type": "attribute"}, {"api_name": "soundevent.data.geometries", "line_number": 118, "usage_type": "name"}, {"api_name": "soundevent.geometry.conversion.geometry_to_shapely", "line_number": 120, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 124, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 125, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 126, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 127, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 119, "usage_type": "name"}, {"api_name": "soundevent.data.Feature", "line_number": 119, "usage_type": "name"}, {"api_name": "soundevent.data.geometries.Polygon", "line_number": 132, "usage_type": "attribute"}, {"api_name": "soundevent.data.geometries", "line_number": 132, "usage_type": "name"}, {"api_name": "soundevent.geometry.conversion.geometry_to_shapely", "line_number": 134, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 138, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 139, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 140, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 141, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 133, "usage_type": "name"}, {"api_name": "soundevent.data.Feature", "line_number": 133, "usage_type": "name"}, {"api_name": "soundevent.data.geometries.MultiPoint", "line_number": 146, "usage_type": "attribute"}, {"api_name": "soundevent.data.geometries", "line_number": 146, "usage_type": "name"}, {"api_name": "soundevent.geometry.conversion.geometry_to_shapely", "line_number": 148, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 152, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 153, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 154, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 155, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 156, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 147, "usage_type": "name"}, {"api_name": "soundevent.data.Feature", "line_number": 147, "usage_type": "name"}, {"api_name": "soundevent.data.geometries.MultiLineString", "line_number": 163, "usage_type": "attribute"}, {"api_name": "soundevent.data.geometries", "line_number": 163, "usage_type": "name"}, {"api_name": "soundevent.geometry.conversion.geometry_to_shapely", "line_number": 165, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 169, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 170, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 171, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 172, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 173, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 164, "usage_type": "name"}, {"api_name": "soundevent.data.Feature", "line_number": 164, "usage_type": "name"}, {"api_name": "soundevent.data.geometries.MultiPolygon", "line_number": 180, "usage_type": "attribute"}, {"api_name": "soundevent.data.geometries", "line_number": 180, "usage_type": "name"}, {"api_name": "soundevent.geometry.conversion.geometry_to_shapely", "line_number": 182, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 186, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 187, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 188, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 189, "usage_type": "call"}, {"api_name": "soundevent.data.Feature", "line_number": 190, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 181, "usage_type": "name"}, {"api_name": "soundevent.data.Feature", "line_number": 181, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 196, "usage_type": "name"}, {"api_name": "soundevent.data.geometries.GeometryType", "line_number": 197, "usage_type": "attribute"}, {"api_name": "soundevent.data.geometries", "line_number": 197, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 197, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 197, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 197, "usage_type": "name"}, {"api_name": "soundevent.data.Feature", "line_number": 197, "usage_type": "name"}, {"api_name": "soundevent.data.geometries.TimeStamp.geom_type", "line_number": 199, "usage_type": "call"}, {"api_name": "soundevent.data.geometries.TimeStamp", "line_number": 199, "usage_type": "attribute"}, {"api_name": "soundevent.data.geometries", "line_number": 199, "usage_type": "name"}, {"api_name": "soundevent.data.geometries.TimeInterval.geom_type", "line_number": 200, "usage_type": "call"}, {"api_name": "soundevent.data.geometries.TimeInterval", "line_number": 200, "usage_type": "attribute"}, {"api_name": "soundevent.data.geometries", "line_number": 200, "usage_type": "name"}, {"api_name": "soundevent.data.geometries.BoundingBox.geom_type", "line_number": 201, "usage_type": "call"}, {"api_name": "soundevent.data.geometries.BoundingBox", "line_number": 201, "usage_type": "attribute"}, {"api_name": "soundevent.data.geometries", "line_number": 201, "usage_type": "name"}, {"api_name": "soundevent.data.geometries.Point.geom_type", "line_number": 202, "usage_type": "call"}, {"api_name": "soundevent.data.geometries.Point", "line_number": 202, "usage_type": "attribute"}, {"api_name": "soundevent.data.geometries", "line_number": 202, "usage_type": "name"}, {"api_name": "soundevent.data.geometries.LineString.geom_type", "line_number": 203, "usage_type": "call"}, {"api_name": "soundevent.data.geometries.LineString", "line_number": 203, "usage_type": "attribute"}, {"api_name": "soundevent.data.geometries", "line_number": 203, "usage_type": "name"}, {"api_name": "soundevent.data.geometries.Polygon.geom_type", "line_number": 204, "usage_type": "call"}, {"api_name": "soundevent.data.geometries.Polygon", "line_number": 204, "usage_type": "attribute"}, {"api_name": "soundevent.data.geometries", "line_number": 204, "usage_type": "name"}, {"api_name": "soundevent.data.geometries.MultiPoint.geom_type", "line_number": 205, "usage_type": "call"}, {"api_name": "soundevent.data.geometries.MultiPoint", "line_number": 205, "usage_type": "attribute"}, {"api_name": "soundevent.data.geometries", "line_number": 205, "usage_type": "name"}, {"api_name": "soundevent.data.geometries.MultiLineString.geom_type", "line_number": 206, "usage_type": "call"}, {"api_name": "soundevent.data.geometries.MultiLineString", "line_number": 206, "usage_type": "attribute"}, {"api_name": "soundevent.data.geometries", "line_number": 206, "usage_type": "name"}, {"api_name": "soundevent.data.geometries.MultiPolygon.geom_type", "line_number": 207, "usage_type": "call"}, {"api_name": "soundevent.data.geometries.MultiPolygon", "line_number": 207, "usage_type": "attribute"}, {"api_name": "soundevent.data.geometries", "line_number": 207, "usage_type": "name"}, {"api_name": "soundevent.data.geometries.Geometry", "line_number": 212, "usage_type": "attribute"}, {"api_name": "soundevent.data.geometries", "line_number": 212, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 213, "usage_type": "name"}, {"api_name": "soundevent.data.Feature", "line_number": 213, "usage_type": "name"}]} +{"seq_id": "14662826889", "text": "import climate\nimport glob\nimport gzip\nimport io\nimport lmj.cubes\nimport logging\nimport numpy as np\nimport os\nimport pandas as pd\nimport pickle\nimport theanets\n\n\ndef compress(source, k, activation, **kwargs):\n fns = sorted(glob.glob(os.path.join(source, '*', '*_jac.csv.gz')))\n logging.info('%s: found %d jacobians', source, len(fns))\n\n # the clipping operation affects about 2% of jacobian values.\n dfs = [np.clip(pd.read_csv(fn, index_col='time').dropna(), -10, 10)\n for fn in fns]\n\n B, N = 128, dfs[0].shape[1]\n\n logging.info('loaded %s rows of %d-D data from %d files',\n sum(len(df) for df in dfs), N, len(dfs))\n\n def batch():\n batch = np.zeros((B, N), 'f')\n for b in range(B):\n a = np.random.randint(len(dfs))\n batch[b] = dfs[a].iloc[np.random.randint(len(dfs[a])), :]\n return [batch]\n\n pca = theanets.Autoencoder([N, (k, activation), (N, 'tied')])\n pca.train(batch, **kwargs)\n\n key = '{}_k{}'.format(activation, k)\n if 'hidden_l1' in kwargs:\n key += '_s{hidden_l1:.4f}'.format(**kwargs)\n\n for df, fn in zip(dfs, fns):\n df = pd.DataFrame(pca.encode(df.values.astype('f')), index=df.index)\n s = io.StringIO()\n df.to_csv(s, index_label='time')\n out = fn.replace('_jac', '_jac_' + key)\n with gzip.open(out, 'wb') as handle:\n handle.write(s.getvalue().encode('utf-8'))\n logging.info('%s: saved %s', out, df.shape)\n\n out = os.path.join(source, 'pca_{}.pkl'.format(key))\n pickle.dump(pca, open(out, 'wb'))\n\n\n@climate.annotate(\n root='load data files from subject directories in this path',\n k=('compress to this many dimensions', 'option', None, int),\n activation=('use this activation function', 'option'),\n)\ndef main(root, k=1000, activation='relu'):\n for subject in lmj.cubes.Experiment(root).subjects:\n compress(subject.root, k, activation,\n momentum=0.9,\n hidden_l1=0.01,\n weight_l1=0.01,\n monitors={'hid1:out': (0.01, 0.1, 1, 10)})\n\n\nif __name__ == '__main__':\n climate.call(main)\n", "repo_name": "EmbodiedCognition/cube-experiment", "sub_path": "analysis/11-compress-jacobians.py", "file_name": "11-compress-jacobians.py", "file_ext": "py", "file_size_in_byte": 2143, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "glob.glob", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 19, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 31, "usage_type": "attribute"}, {"api_name": "theanets.Autoencoder", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 42, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 43, "usage_type": "call"}, {"api_name": "gzip.open", "line_number": 46, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 51, "usage_type": "call"}, {"api_name": "lmj.cubes.cubes.Experiment", "line_number": 60, "usage_type": "call"}, {"api_name": "lmj.cubes.cubes", "line_number": 60, "usage_type": "attribute"}, {"api_name": "lmj.cubes", "line_number": 60, "usage_type": "name"}, {"api_name": "climate.annotate", "line_number": 54, "usage_type": "call"}, {"api_name": "climate.call", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "41563001918", "text": "#!/usr/bin/python\nimport os\nimport sys\nimport argparse\nimport subprocess\nimport logging\n\nffmpeg = \"C:/Users/Spear/ffmpeg-4.1-win64-static/bin/ffmpeg.exe\"\n\nregions = {\n \"pacific\" : {\n \"goes\": \"17\",\n \"sourcedir\": \"S:/NASA/GOES-17_03_geocolor/overlay\",\n \"linkdir\": \"S:/NASA/overlay-17\",\n },\n \"atlantic\": {\n \"goes\": \"16\",\n \"sourcedir\": \"S:/NASA/GOES-16_03_geocolor/overlay\",\n \"linkdir\": \"S:/NASA/overlay-16\",\n }\n}\n\ndef unlink_old(region):\n linkdir = regions[region][\"linkdir\"]\n d = os.listdir(linkdir)\n logging.debug(\"Unlinking %s\" % (linkdir))\n for l in d:\n os.unlink(\"%s/%s\" % (linkdir, l))\n return(0)\n\ndef find_sources(region):\n sourcedir = regions[region][\"sourcedir\"]\n s = os.listdir(sourcedir)\n l = [ fn for fn in s if fn.endswith(\".png\") ]\n l.sort()\n return(l)\n\ndef link_sources(region, fns, start, end):\n count = 0\n sd = regions[region][\"sourcedir\"]\n ld = regions[region][\"linkdir\"]\n for fn in fns:\n ts = fn[0:12]\n if (ts < start):\n continue\n if (ts > end):\n return\n s = \"%s/%s\" % (sd, fn)\n d = \"%s/img-%04d.png\" % (ld, count)\n # Python-2.7 on Windows under Cygwin can't make symlinks; this is a kludgey work-around\n #os.symlink(s, d)\n subprocess.call('export CYGWIN=\"winsymlinks:nativestrict\"; ln -s %s %s' % (s, d), shell=True)\n count = count + 1\n return\n\ndef make_movie(region, size, ofile):\n #goes = regions[region][\"goes\"]\n ld = regions[region][\"linkdir\"]\n scale = ''\n if size == \"720\":\n scale += '-s 1280x720'\n cmd = \"%s -r 15 -y -benchmark -i \\\"%s/img-%s.png\\\" %s -c:v libx264 -crf 18 -preset slow -pix_fmt yuv420p -movflags +faststart %s\" % (ffmpeg, ld, \"%04d\", scale, ofile)\n logging.debug(\"Running %s\" % (cmd))\n try:\n retcode = subprocess.check_call(cmd, shell=True)\n except subprocess.CalledProcessError as e:\n logging.info(\"Execution failed: %s\" % (e))\n return(0)\n\n\nif __name__ == '__main__':\n loglevel = logging.DEBUG\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-atlantic\", default=False, action='store_true', help=\"GOES-16 North Atlantic\")\n parser.add_argument(\"-pacific\", default=False, action='store_true', help=\"GOES-17 North Pacific\")\n parser.add_argument(\"-log\", choices=[\"debug\", \"info\", \"warning\", \"error\", \"critical\"], default=\"debug\", help=\"Log level\")\n parser.add_argument(\"-nolink\", default=False, action='store_true', help=\"Don't remake symlinks\")\n parser.add_argument(\"-start\", default=\"201801010000\", help=\"Start timestamp\")\n parser.add_argument(\"-end\", default=\"202512312359\", help=\"End timestamp\")\n parser.add_argument(\"-f\", default=\"\", help=\"Output filename - defaults to SFC-_-.mp4\")\n parser.add_argument(\"-size\", choices=[\"1080\", \"720\"], default=\"1080\", help=\"Size FullHD (1920x1080) or HD (1280x720)\")\n args = parser.parse_args()\n\n if args.log == \"debug\":\n loglevel = logging.DEBUG\n if args.log == \"info\":\n loglevel = logging.INFO\n if args.log == \"warning\":\n loglevel = logging.WARNING\n if args.log == \"error\":\n loglevel = logging.ERROR\n if args.log == \"critical\":\n loglevel = logging.CRITICAL\n\n logging.basicConfig(format='%(asctime)s %(message)s', datefmt='%Y-%m-%d %H:%M:%S', level=loglevel)\n\n logging.info(args)\n\n oceans = []\n if args.atlantic:\n oceans.append(\"atlantic\")\n if args.pacific:\n oceans.append(\"pacific\")\n\n for o in oceans:\n ofile = args.f\n if ofile == \"\":\n ofile = \"SFC-%s_%s-%s.mp4\" % (regions[o][\"goes\"], args.start, args.end)\n\n if not args.nolink:\n unlink_old(o)\n sources = find_sources(o)\n link_sources(o, sources, args.start, args.end)\n make_movie(o, args.size, ofile)\n logging.info(\"Output in %s\" % (ofile))\n", "repo_name": "lanceberc/GOES", "sub_path": "omovie.py", "file_name": "omovie.py", "file_ext": "py", "file_size_in_byte": 3934, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "12", "api": [{"api_name": "os.listdir", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 26, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 28, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 33, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 52, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 63, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 65, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 66, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 67, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 72, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 73, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 85, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 87, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 89, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 91, "usage_type": "attribute"}, {"api_name": "logging.CRITICAL", "line_number": 93, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 95, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 97, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 115, "usage_type": "call"}]} +{"seq_id": "69916380503", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jul 3 16:43:32 2018\n\n@author: Faris Mismar\n\"\"\"\n\nimport keras\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense\n\nimport pandas as pd\nimport numpy as np\n\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.preprocessing import StandardScaler\n\nfrom sklearn.model_selection import GridSearchCV\nfrom keras.wrappers.scikit_learn import KerasClassifier\n\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import roc_curve, auc\n\nimport os\nimport sys\n\nos.chdir('/Users/farismismar/Desktop/FIFA')\n\n# Check if tensorflow is used\nif (keras.backend.backend() != 'tensorflow' and keras.backend.image_data_format() != 'channels_last' and keras.backend.image_dim_ordering() != 'tf'):\n print('Install tensorflow, configure keras.json to include channels_last for image format and tf for image dimension ordering.')\n print('Program will now exit.')\n sys.exit(1)\n \n# Set the random seed\nseed = 123\nnp.random.seed(seed)\n\n# Import the datafile to memory first\ndataset = pd.read_csv('./Dataset/results.csv')\n\n# Sanity check. Missing values?\nprint('Number of missing values: {}'.format(dataset.isnull().sum().sum()))\n\n# Filter what is really needed\ndf = dataset.loc[dataset['tournament'] == 'FIFA World Cup']\n\ndf['date'] = pd.to_datetime(df['date'])\ndf['Year'] = df['date'].dt.year\ndf = df.drop(['date', 'tournament', 'city', 'country', 'neutral'], axis=1)\n\n# Generate more features X for the data\n# The data is arranged such that the last year set has the final match, so let's find that first\n# Replace NaN with zero. Why?\ndf['Is_Final'] = df['Year'].diff(periods=-1).fillna(value=0) != 0\n\ncondition = (df['home_score'] > df['away_score'])\ndf.loc[condition, 'Winner'] = df['home_team']\ndf.loc[~condition, 'Winner'] = df['away_team']\n\n# Drop rows where the winners are one of the losing teams\ndf = df.loc[(df['Winner'] == 'Uruguay') | (df['Winner'] == 'France') | (df['Winner'] == 'Brazil') | (df['Winner'] == 'Belgium') | (df['Winner'] == 'Russia')\n| (df['Winner'] == 'Croatia') | (df['Winner'] == 'Sweden') | (df['Winner'] == 'England') ] \n\n####################################################################################\n# Approach 0: Just sum and average\n####################################################################################\ndenominator = df['Winner'].value_counts().sum()\n\nwinner = df['Winner'].value_counts() / denominator\n\nprint(winner.idxmax())\n\n########################################################################################################\n# Approach 1: NN with n-Step Prediction (eight games are left from the dataset including until Jul 3)\n########################################################################################################\nlook_forward = 8\n\n## Let us now drop all data that is not WC finalist\ndf1 = df[df['Is_Final'] == True].reset_index()\n\n# Now shift the data by one to predict the next season.\ndf1 = df.drop(['Is_Final'], axis=1).reset_index().drop(['index'], axis=1)\ndf1['Winner+n'] = df1['Winner'].shift(-look_forward)\n\ndf1 = df1.fillna(value='Antarctica') # This is a sentinel. We now have 9 teams.\n\n# integer encode\nteams=df['home_team'].tolist()\nteams.append('Antarctica')\n\nfor team in df['away_team']:\n teams.append(team)\n\nteams = list(set(teams)) # get rid of duplicates\n\nlabel_encoder = LabelEncoder()\nlabel_encoder.fit(teams)\ninteger_encoded = label_encoder.transform(df1['Winner+n'])\n\ndf1['Winner+n'] = integer_encoded\ndf1['Winner'] = label_encoder.transform(df1['Winner'])\n\ndf1['dWinner'] = df1['Winner'].diff(-look_forward)\n\n# Impute by last value\ndf1['dWinner'] = df1['dWinner'].fillna(df1['dWinner'].iloc[-look_forward - 1])\n\n# binary encode\nonehot_encoder = OneHotEncoder(sparse=False)\ninteger_encoded = integer_encoded.reshape(-1, 1)\nonehot_encoded = onehot_encoder.fit_transform(integer_encoded)\nY = pd.DataFrame(onehot_encoded, dtype=int)\n\ndf1['home_team'] = label_encoder.transform(df1['home_team'])\ndf1['away_team'] = label_encoder.transform(df1['away_team'])\n\n# Now convert the problem to a multi-class problem\nX = df1.drop(['Winner+n'], axis=1)\n\n# Now prepare the data for a NN\ndf1 = pd.concat([X, Y], axis=1)\n\n# Set the index to equal the year\ndf1.index = df1['Year'].astype(int)\ndf1 = df1.drop(['Year'], axis=1)\n\n# Perform a split\nm, n = df1.shape\nrsplit = 0.815 # trial and error to naturally split by end of 2006.\n\n# split into train and test sets\ntrain_size = int(rsplit * m)\ntest_size = m - train_size\n\ntrain, test = df1.iloc[0:train_size,:], df1.iloc[train_size:m,:]\n\nX_train = train.iloc[:,:6]\nY_train = train.iloc[:,6:]\nX_test = test.iloc[:,:6]\nY_test = test.iloc[:,6:]\n\nmX, nX = X_train.shape\nmY, nY = Y_train.shape\n\n# Scale features for NN\nss = StandardScaler()\n\nX_train_sc = ss.fit_transform(X_train)\nX_test_sc = ss.transform(X_test)\n\n# create model\ndef create_mlp(optimizer, output_dim, n_hidden, act):\n mlp = Sequential()\n mlp.add(Dense(units=output_dim, input_dim=nX, activation=act))\n for k in np.arange(n_hidden):\n mlp.add(Dense(output_dim, use_bias=True))\n\n mlp.add(Dense(units=nY, input_dim=output_dim, activation=act))\n\n mlp.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy']) # metrics really mean nothing here. Focus on loss.\n return mlp\n\nmodel = KerasClassifier(build_fn=create_mlp, verbose=1, epochs=10, batch_size=16)\n\n# The hyperparameters\noptimizers = ['sgd', 'adam']\noutput_dims=[1,2,3]\nactivations = ['relu', 'softmax', 'sigmoid']\nn_hiddens = [3,5]\n\nhyperparameters = dict(optimizer=optimizers, output_dim=output_dims, n_hidden=n_hiddens, act=activations)\n\ngrid = GridSearchCV(estimator=model, param_grid=hyperparameters, n_jobs=1, cv=3)\ngrid_result = grid.fit(X_train_sc, Y_train)\n\n# This is the best model: {'act': 'softmax', 'n_hidden': 5, 'optimizer': 'adam', 'output_dim': 2}\nbest_model_mlp = grid_result.best_params_\nprint(best_model_mlp)\n\nclf = grid_result.best_estimator_\nY_score_mlp = clf.predict_proba(X_test_sc)\nY_pred_mlp = clf.predict(X_test_sc) # must look like Y_test\n\nY_pred_mlp = onehot_encoder.transform(Y_pred_mlp.reshape(-1, 1))\n \ndef decode_onehot(X):\n teams = []\n # This is obtained through hand derivation. Quite disappointing we cannot revert with a built-in function.\n t = ['Uruguay', 'Sweden', 'Russia', 'France', 'England', 'Croatia', 'Brazil', 'Belgium']\n for i in range(X.shape[0]):\n X_i = X[i, :]\n if sum(X_i) == 0:\n teams.append('(unknown)')\n else:\n teams.append(t[X_i.argmax()])\n\n teams = pd.DataFrame(data = {'Team': teams})\n return teams\n\nY_pred = decode_onehot(Y_pred_mlp)\n\n# Compute ROC curve and ROC area for each class.\nfpr = dict()\ntpr = dict()\nroc_auc = dict()\nfor i in range(nY):\n fpr[i], tpr[i], _ = roc_curve(Y_test.iloc[:, i], Y_score_mlp[:, i])\n roc_auc[i] = auc(fpr[i], tpr[i])\n\nclasses = list(set(df1['Winner']))\nclasses.append(label_encoder.transform(['Antarctica'])[0])\nclasses = label_encoder.inverse_transform(classes)\n\nplt.figure(figsize=(13,8))\nlw = 2\nfor i in range(nY):\n class_i = classes[i]\n plt.plot(fpr[i], tpr[i], \n lw=lw, label='ROC curve for class {0} (AUC = {1:.6f})'.format(class_i, roc_auc[i]))\n\nplt.rc('text', usetex=True)\nplt.rc('font', family='serif')\nplt.plot([0, 1], [0, 1], color='black', lw=lw, linestyle='--')\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.grid()\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Receiver operating characteristic')\nplt.legend(loc='lower right')\nplt.savefig('mlp_roc.pdf', format='pdf')\nplt.show()\n\nY_pred.index = Y_test.index\n\n'''\ndf1.index[14:], label_encoder.inverse_transform(Y_pred_mlp), \n\nlabel_encoder.inverse_transform(Y_test.dot(onehot_encoder.active_features_).astype(int)) # https://stackoverflow.com/questions/22548731/how-to-reverse-sklearn-onehotencoder-transform-to-recover-original-data\n'''", "repo_name": "farismismar/World-Cup-2018-Prediction", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 7935, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "12", "api": [{"api_name": "os.chdir", "line_number": 29, "usage_type": "call"}, {"api_name": "keras.backend.backend", "line_number": 32, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 32, "usage_type": "attribute"}, {"api_name": "keras.backend.image_data_format", "line_number": 32, "usage_type": "call"}, {"api_name": "keras.backend.image_dim_ordering", "line_number": 32, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 42, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 50, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 99, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.OneHotEncoder", "line_number": 112, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 115, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 124, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 149, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 156, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 158, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 159, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 161, "usage_type": "call"}, {"api_name": "keras.wrappers.scikit_learn.KerasClassifier", "line_number": 166, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 176, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 200, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 210, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 211, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 217, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 217, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 221, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 221, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 224, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 224, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 225, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 226, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 226, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 228, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 229, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 229, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 230, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 230, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 231, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 231, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 232, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 232, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 233, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 233, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 234, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 234, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 235, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 235, "usage_type": "name"}]} +{"seq_id": "8000517430", "text": "import os\nimport pandas as pd\nimport pickle\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom utils import optimize\nfrom sklearn.linear_model import LinearRegression\n\nimport warnings\nwarnings.filterwarnings(\"ignore\", message=\"delta_grad == 0.0. Check if the approximated function is linear.\")\n\n# Transaction cost percentage\ntrcost_perc = 0.0001\nMaxIter = 20\nCAUSAL = True\nROBUST = True\n# m is dim of observations\nm_dim = 1\n# n is dim of unobserved states\nn_dim = 2\n# points used to estimate initial coef in linear equation\nburn_len = 100\n# points used in rolling window of spread estimation\nwindow_size = 20\n\nif ROBUST:\n search_num = 10\nelse:\n search_num = 1\n\nradi_arr = np.linspace(0.1, 1.0, search_num)\nsharpe = np.zeros_like(radi_arr)\nsortino = np.zeros_like(radi_arr)\n\nfor r_idx in range(search_num):\n\n radius = radi_arr[r_idx]\n print('Testing radius', radius)\n\n Y = pd.read_csv('AMZN.csv')\n Y_Close = Y.loc[2012-burn_len:, 'Adj Close'].values\n X = pd.read_csv('GOOG.csv')\n X_Close = X.loc[2012-burn_len:, 'Adj Close'].values\n\n\n # transition matrix of unobserved states\n A = np.eye(n_dim)\n # transition matrix of observations\n C = np.zeros((m_dim, n_dim))\n\n\n # initial mean and cov of unobserved states\n reg = LinearRegression().fit(X_Close[:burn_len].reshape(burn_len, 1), Y_Close[:burn_len])\n init_mean = np.array([reg.intercept_, reg.coef_[0]]).reshape((n_dim, 1))\n init_cov = np.array([[1.0, 0.0], [0.0, 1.0]])\n\n # covs\n Bp = np.eye(2)\n Dp = np.array([1.0]).reshape((m_dim, m_dim))\n\n\n pre_mean = init_mean.copy()\n pre_cov = init_cov.copy()\n\n # total time steps, including burn-in periods\n Y_Close = Y_Close[burn_len:]\n X_Close = X_Close[burn_len:]\n horizon = len(Y_Close)\n # observations\n obs = np.zeros((horizon, m_dim))\n # filtered unobserved states\n est_state = np.zeros((horizon, n_dim))\n est_cov = np.zeros((horizon, n_dim, n_dim))\n\n ################### filtering ######################\n for step in range(horizon):\n obs[step, 0] = Y_Close[step]\n C[0, 0] = 1.0\n C[0, 1] = X_Close[step]\n next_mean, next_cov = optimize(m_dim, n_dim, radius, A, Bp, C, Dp, pre_cov,\n obs[step, :].reshape((m_dim, 1)), pre_mean, MaxIter,\n causal=CAUSAL, robust=ROBUST)\n\n est_state[step, :] = next_mean.reshape(-1)\n est_cov[step, :] = next_cov\n\n pre_mean = next_mean.copy()\n pre_cov = next_cov.copy()\n # if step%5 == 0:\n # print('Filtered step', step)\n # print('Estimated det', np.linalg.det(next_cov))\n # if np.linalg.det(next_cov) < 0.0:\n # print(res.constr_violation,' CG stop cond:', res.cg_stop_cond, 'Status:', res.status)\n\n estimated = est_state[:, 0] + np.multiply(est_state[:, 1], X_Close)\n\n\n ########### trading ##############\n\n idx = window_size\n spread = Y_Close - estimated\n\n open_thres = 2.0\n close_thres = 0.0\n\n position = None\n # 0 for Y, 1 for X\n stock_poi = np.zeros((2, horizon))\n # stock trading volume\n quantity = 100\n cash = np.zeros(horizon)\n cash[:idx] = 10000.0\n stock_value = np.zeros(horizon)\n\n while idx < horizon:\n roll_mean = spread[idx-window_size:idx].mean()\n roll_std = spread[idx-window_size:idx].std()\n residual = spread[idx] - roll_mean\n\n if position == None:\n if residual <= open_thres*roll_std and residual >= -open_thres*roll_std:\n # Signal not triggered.\n cash[idx] = cash[idx-1]\n elif residual < -open_thres*roll_std:\n # open long position\n stock_poi[0, idx] = quantity\n stock_poi[1, idx] = -est_state[idx, 1] * quantity\n poi0_sign = np.sign(stock_poi[0, idx])\n poi1_sign = np.sign(stock_poi[1, idx])\n cash[idx] = cash[idx - 1] - \\\n (stock_poi[0, idx] * Y_Close[idx] * (1 + poi0_sign * trcost_perc) +\n stock_poi[1, idx] * X_Close[idx] * (1 + poi1_sign * trcost_perc))\n stock_value[idx] = stock_poi[0, idx] * Y_Close[idx] + stock_poi[1, idx] * X_Close[idx]\n position = \"long\"\n elif residual > open_thres*roll_std:\n # open short position\n stock_poi[0, idx] = -quantity\n stock_poi[1, idx] = est_state[idx, 1] * quantity\n poi0_sign = np.sign(stock_poi[0, idx])\n poi1_sign = np.sign(stock_poi[1, idx])\n cash[idx] = cash[idx - 1] - \\\n (stock_poi[0, idx] * Y_Close[idx] * (1 + poi0_sign * trcost_perc) +\n stock_poi[1, idx] * X_Close[idx] * (1 + poi1_sign * trcost_perc))\n stock_value[idx] = stock_poi[0, idx] * Y_Close[idx] + stock_poi[1, idx] * X_Close[idx]\n position = \"short\"\n\n elif position == \"long\":\n if residual < -close_thres*roll_std:\n # maintain position\n stock_poi[:, idx] = stock_poi[:, idx - 1]\n cash[idx] = cash[idx - 1]\n stock_value[idx] = stock_poi[0, idx] * Y_Close[idx] + stock_poi[1, idx] * X_Close[idx]\n else:\n # close position\n poi0_sign = np.sign(stock_poi[0, idx-1])\n poi1_sign = np.sign(stock_poi[1, idx-1])\n cash[idx] = cash[idx-1] + \\\n stock_poi[0, idx-1] * Y_Close[idx] * (1 - poi0_sign * trcost_perc) + \\\n stock_poi[1, idx-1] * X_Close[idx] * (1 - poi1_sign * trcost_perc)\n stock_poi[:, idx] = 0\n stock_value[idx] = 0\n position = None\n\n else:\n if residual > close_thres*roll_std:\n # maintain position\n stock_poi[:, idx] = stock_poi[:, idx - 1]\n cash[idx] = cash[idx - 1]\n stock_value[idx] = stock_poi[0, idx] * Y_Close[idx] + stock_poi[1, idx] * X_Close[idx]\n else:\n # close position\n poi0_sign = np.sign(stock_poi[0, idx-1])\n poi1_sign = np.sign(stock_poi[1, idx-1])\n cash[idx] = cash[idx-1] + \\\n stock_poi[0, idx-1] * Y_Close[idx] * (1 - poi0_sign * trcost_perc) + \\\n stock_poi[1, idx-1] * X_Close[idx] * (1 - poi1_sign * trcost_perc)\n stock_poi[:, idx] = 0\n stock_value[idx] = 0\n position = None\n\n idx += 1\n\n\n\n if ROBUST:\n sub_folder = '{}_{}_{}_{}_{}'.format('causal', CAUSAL, 'robust', ROBUST, round(radius, 2))\n\n log_dir = './logs/{}'.format(sub_folder)\n\n if not os.path.exists(log_dir):\n os.makedirs(log_dir)\n\n # Save params configuration\n with open('{}/params.txt'.format(log_dir), 'w') as fp:\n fp.write('Params setting \\n')\n fp.write('COT: {} \\n'.format(CAUSAL))\n fp.write('Robust: {} \\n'.format(ROBUST))\n fp.write('Bp: {} \\n'.format(Bp))\n fp.write('Dp: {} \\n'.format(Dp))\n fp.write('init_mean: {} \\n'.format(init_mean))\n fp.write('init_cov: {} \\n'.format(init_cov))\n fp.write('radius: {} \\n'.format(radius))\n fp.write('horizon: {} \\n'.format(horizon))\n fp.write('open thres: {} \\n'.format(open_thres))\n fp.write('close thres: {} \\n'.format(close_thres))\n fp.write('stock trading quantity: {} \\n'.format(quantity))\n fp.write('maxiter: {} \\n'.format(MaxIter))\n\n plt.figure(1)\n plt.plot(cash + stock_value, label='total')\n # plt.plot(cash, label='cash')\n # plt.plot(stock_value, label='stock')\n plt.legend(loc='best')\n plt.savefig('{}/portfolio.pdf'.format(log_dir), format='pdf', dpi=500, bbox_inches='tight', pad_inches=0.1)\n\n plt.figure(2)\n plt.plot(est_state[:, 1], label='hedge ratio')\n plt.legend(loc='best')\n plt.savefig('{}/hedgeratio.pdf'.format(log_dir), format='pdf', dpi=500, bbox_inches='tight', pad_inches=0.1)\n\n with open('{}/est_state.pickle'.format(log_dir), 'wb') as fp:\n pickle.dump(est_state, fp)\n\n with open('{}/spread.pickle'.format(log_dir), 'wb') as fp:\n pickle.dump(spread, fp)\n\n with open('{}/cash.pickle'.format(log_dir), 'wb') as fp:\n pickle.dump(cash, fp)\n\n with open('{}/stock_value.pickle'.format(log_dir), 'wb') as fp:\n pickle.dump(stock_value, fp)\n\n with open('{}/position.pickle'.format(log_dir), 'wb') as fp:\n pickle.dump(stock_poi, fp)\n\n ##### Calculate Sharpe and Sortino ratios\n ptf = cash + stock_value\n rtn = np.divide(ptf[1:] - ptf[:-1], ptf[:-1])\n rtn_mean = rtn.mean()\n so_idx = rtn < rtn_mean\n sharpe[r_idx] = (rtn_mean - 0.02/252)/rtn.std()*np.sqrt(252)\n sortino[r_idx] = (rtn_mean - 0.02/252)/rtn[so_idx].std()*np.sqrt(252)\n\n print('Sharpe ratio of strategy:', sharpe[r_idx])\n print('Sortino ratio of strategy:', sortino[r_idx])\n\n X_rtn = np.divide(X_Close[1:] - X_Close[:-1], X_Close[:-1])\n X_m = X_rtn.mean()\n X_idx = X_rtn < X_m\n print('Asset 1 Sharpe ratio:', (X_m - 0.02/252)/X_rtn.std()*np.sqrt(252))\n print('Asset 1 Sortino ratio:', (X_m - 0.02/252)/X_rtn[X_idx].std()*np.sqrt(252))\n\n Y_rtn = np.divide(Y_Close[1:] - Y_Close[:-1], Y_Close[:-1])\n Y_m = Y_rtn.mean()\n Y_idx = Y_rtn < Y_m\n print('Asset 2 Sharpe ratio:', (Y_m - 0.02/252)/Y_rtn.std()*np.sqrt(252))\n print('Asset 2 Sortino ratio:', (Y_m - 0.02/252)/Y_rtn[Y_idx].std()*np.sqrt(252))\n\nprint('Sharpe')\nprint(np.round(sharpe, 4))\nprint('Sortino')\nprint(np.round(sortino, 4))\n\nif ROBUST:\n with open('./logs/sharpe_causal_{}.pickle'.format(CAUSAL), 'wb') as fp:\n pickle.dump(sharpe, fp)\n\n with open('./logs/sortino_causal_{}.pickle'.format(CAUSAL), 'wb') as fp:\n pickle.dump(sortino, fp)\n\n", "repo_name": "hanbingyan/GaussianCOT_release", "sub_path": "pairstrading.py", "file_name": "pairstrading.py", "file_ext": "py", "file_size_in_byte": 10065, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "12", "api": [{"api_name": "warnings.filterwarnings", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 49, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 73, "usage_type": "call"}, {"api_name": "utils.optimize", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 173, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path", "line_number": 190, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 209, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 210, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 210, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 213, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 214, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 214, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 216, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 216, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 217, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 217, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 218, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 218, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 219, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 222, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 225, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 228, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 231, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 262, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 266, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 269, "usage_type": "call"}]} +{"seq_id": "1356381434", "text": "import requests\nimport psycopg2, pymysql, json\nimport time\nfrom django.shortcuts import render\nfrom django.contrib import messages\nimport sys\nimport datetime\nfrom xmlrpc import client\n\n\nclass TestTools(object):\n def __init__(self, env, order_id, num, po_id, order_sku, order_sku_quantity, so_cs, cs_type, oc_db=False, odoo_flag=False, odoo_db=False):\n self.env = env # 测试环境标志： 1 - staging； 0 - test\n self.order_id = order_id # 传入的SO编号\n self.num = num # 传入的发票编号\n self.po_id = po_id\n self.order_sku = order_sku # 传入的sku\n self.order_sku_quantity = order_sku_quantity # 传入的sku数量\n self.so_cs = so_cs # 申请售后的订单\n self.cs_type = cs_type # 申请售后的类型\n if oc_db:\n if self.env == 'staging':\n # 连接staging——OC数据库\n self.connection = pymysql.connect(\n host='118.178.189.137',\n user='ehsy_pc',\n password='ehsy2016',\n port=3306,\n charset='utf8',\n cursorclass=pymysql.cursors.DictCursor # sql查询结果转为字典类型\n )\n else:\n # test——OC数据库\n self.connection = pymysql.connect(\n host='118.178.135.2',\n user='root',\n password='ehsy2016',\n port=3306,\n charset='utf8',\n cursorclass=pymysql.cursors.DictCursor\n )\n if odoo_flag:\n if self.env == 'staging':\n self.dbname = 'odoo-staging'\n self.usr = 'admin'\n self.pwd = 'admin'\n self.oe_ip = 'odoo-staging.ehsy.com'\n # self.oe_ip = 'localhost:8069'\n self.sock_common = client.ServerProxy('http://' + self.oe_ip + '/xmlrpc/common')\n self.uid = self.sock_common.login(self.dbname, self.usr, self.pwd)\n self.sock = client.ServerProxy('http://' + self.oe_ip + '/xmlrpc/object')\n else:\n self.dbname = 'odoo-test'\n self.usr = 'admin'\n self.pwd = 'admin'\n self.oe_ip = 'odoo-test.ehsy.com'\n # self.oe_ip = 'localhost:8069'\n self.sock_common = client.ServerProxy('http://' + self.oe_ip + '/xmlrpc/common')\n self.uid = self.sock_common.login(self.dbname, self.usr, self.pwd)\n self.sock = client.ServerProxy('http://' + self.oe_ip + '/xmlrpc/object')\n if odoo_db:\n if self.env == 'staging':\n self.con = psycopg2.connect(\n host='118.178.133.107',\n port=5432,\n user='openerp',\n password='openerp2016',\n database='odoo-staging',\n )\n else:\n self.con = psycopg2.connect(\n host='118.178.238.29',\n port=5432,\n user='openerp',\n password='ehsy_erp',\n database='odoo-test',\n )\n self.cr = self.con.cursor(cursor_factory=psycopg2.extras.DictCursor)\n\n def login(self):\n url = 'http://uc-' + self.env + '.ehsy.com/user/login.action'\n data = {'login_name': '特定用户-不要随意使用', 'login_password': '111qqq', 'terminal_type': 'pc'}\n r = requests.post(url, data=data)\n result = r.json()\n # print(result)\n token = result['sys']['token']\n # print(token)\n return result\n\n def create_order(self, token):\n \"\"\"生成订单\"\"\"\n url1 = 'http://oc-' + self.env + '.ehsy.com/cart/add'\n # print(self.order_sku, self.order_sku_quantity)\n if self.order_sku[0] == '':\n data1 = [{'skuCode': self.order_sku[1], 'quantity': self.order_sku_quantity[1]}]\n elif self.order_sku[1] == '':\n data1 = [{'skuCode': self.order_sku[0], 'quantity': self.order_sku_quantity[0]}]\n else:\n data1 = [{'skuCode':self.order_sku[0], 'quantity':self.order_sku_quantity[0]}, {'skuCode':self.order_sku[1], 'quantity':self.order_sku_quantity[1]}]\n # print(data1)\n data = {'token': token, 'data' : data1}\n r1 = requests.post(url1, json=data)\n result1 = r1.json()\n # print(result1)\n url2 = 'http://oc-' + self.env + '.ehsy.com/order/createOrderNew'\n addedInvoices = [{\"bank\": \"招商银行\", \"bankAccount\": \"2343345466\", \"hasAdminAuth\": \"0\", \"invoiceId\": \"10008247487\",\n 'isDefault': \"0\", \"regAdd\": \"金科路1122号\", \"regTel\": \"18751551645\", \"selected\": 'true',\"subType\":'',\n \"taxpayerCode\": \"123456789009876123\", \"title\": \"测试账号公司tina\", \"type\":\"2\", \"typeCompany\":'false',\n 'typeDesc': '增值税发票'}]\n deliverAdds = [{ \"address\":\"江苏省南京市高淳县新街口\", \"addressId\": \"10008284154\", \"addressUserType\":\"1\", \"areaId\":\"220\",\n \"city\":\"南京市\", \"company\":\"测试账号\", \"detailedAddress\":\"新街口\", \"district\":\"高淳县\",\n \"email\":\"233213@qq.com\", \"hasAdminAuth\":\"0\", \"isDefault\":\"1\", \"mobile\":\"18751551645\", \"name\":\"史佐兄\",\n \"postcode\":\"900414\", \"province\":\"江苏省\", \"selected\":'true', \"tel\":\"021-33338888\"}]\n invoicesAdds = [{\"address\":\"上海市上海市浦东新区金科路\", \"addressId\":\"10008284152\", \"addressUserType\":\"1\", \"areaId\":\"321\",\n \"city\":\"上海市\", \"company\":\"测试账号电子公司\", \"detailedAddress\":\"金科路\", \"district\":\"浦东新区\",\n \"email\":\"\", \"hasAdminAuth\":\"0\", \"isDefault\":\"1\", \"mobile\":\"18751551645\", \"name\":\"史佐兄\",\n \"postcode\": \"900414\",\"province\": \"上海市\", \"selected\": 'true', \"tel\":\"\"}]\n data = {'token': token, 'addedInvoices': addedInvoices, 'deliverAdds': deliverAdds, 'deliveryTimeType': '0',\n 'invoicesAdds': invoicesAdds, 'payType': '0', 'createFrom': 'pc'}\n r2 = requests.post(url2, json=data)\n result2 = r2.json() # 字典格式\n # result = json.dumps(result2) # json格式\n # orderId = result2['data']['orderId']\n return result2\n\n # def order_detail(self, token, orderId):\n def order_detail(self, token):\n url = 'http://oc-' + self.env + '.ehsy.com/order/getInfo'\n data = {'token': token, 'orderId':self.order_id, 'createTimeFrom':'2017-02-15', 'createTimeTo':'2017-08-15'}\n r = requests.post(url,data)\n result = r.json()\n return result\n\n def order_cancel(self):\n \"\"\"查询so中可取消sku\"\"\"\n url = 'http://oc-' + self.env + '.ehsy.com/orderCenter/cancel'\n data = {'orderId': self.order_id,'userId':'508110571'}\n r = requests.post(url, data)\n result = r.json()\n return result\n\n def order_payed(self):\n \"\"\"财务收款\"\"\"\n url = 'http://oc-' + self.env + '.ehsy.com/orderCenter/payed'\n data = {'orderId': self.order_id, 'payWay': '0'}\n r = requests.post(url, data=data)\n result = r.json()\n return result\n\n def order_confirm(self):\n \"\"\"SO确认\"\"\"\n url = 'http://oc-' + self.env + '.ehsy.com/orderCenter/confirmOrder'\n data = {\"orderId\": self.order_id}\n r = requests.post(url, data=data)\n result = r.json()\n return result\n\n def create_delivery_allocate(self):\n vals = {\n 'so': self.order_id\n }\n result = self.sock.execute(self.dbname, self.uid, self.pwd, 'used.by.tester', 'test_button_create_delivery', vals)\n return result\n\n def create_po(self, request):\n \"\"\"创建PO\"\"\"\n so = request.POST.get('so_po_value', '')\n if not so:\n return {'mark': '1', 'message': '请输入SO单号！'}\n vals = {\n 'so': so\n }\n result = self.sock.execute(self.dbname, self.uid, self.pwd, 'used.by.tester', 'test_create_po', vals)\n return result\n\n def confirm_po(self):\n \"\"\"西域确认PO\"\"\"\n vals = {\n 'po': self.po_id\n }\n result = self.sock.execute(self.dbname, self.uid, self.pwd, 'used.by.tester', 'test_ehsy_button_approve', vals)\n return result\n\n # def po_change_to_zhifa(self):\n # \"\"\"PO单非直发转直发\"\"\"\n # cursor = self.connection.cursor()\n # cursor.execute(\"select pur_order_id from oc.purchase_order where order_id= '\"+self.order_id+\"'\")\n # po = cursor.fetchall()[0]['pur_order_id'] # 获取PO单号\n # url = 'http://oc-' + self.env + '.ehsy.com/admin/purchaseorder/poChangeNonStopFlag'\n # r = requests.post(url, data={\n # 'purOrderId': po, 'nonStopFlag': '1'})\n # result = r.json()\n # return result\n\n def po_change_to_feizhifa(self):\n \"\"\"直发转非直发\"\"\"\n vals = {\n 'po': self.po_id\n }\n result = self.sock.execute(self.dbname, self.uid, self.pwd, 'used.by.tester', 'test_action_po_district', vals)\n return result\n\n def supplier_confirm(self):\n \"\"\"供应商确认\"\"\"\n vals = {\n 'po': self.po_id\n }\n result = self.sock.execute(self.dbname, self.uid, self.pwd, 'used.by.tester', 'test_button_approve', vals)\n return result\n\n def query_po_send_detail(self):\n \"\"\"查询PO发货详情\"\"\"\n vals = {\n 'purOrderId': self.po_id\n }\n query_result = self.sock.execute(self.dbname, self.uid, self.pwd, 'purchase.order', 'get_po_info_spc', json.dumps(vals))\n query_result = json.loads(query_result)\n if query_result['mark'] == '1':\n result = query_result\n elif query_result['mark'] == '0':\n result = {'mark': '0', 'message': 'Success'}\n\n # 组装采购单详情数据\n detail = []\n for i in query_result['data']['purchaseOrderUnitList']:\n detail_dic = {}\n detail_dic['sku'] = i['skuCode']\n detail_dic['qty'] = i['quantity']\n detail_dic['send_qty'] = i['sendedQuantity']\n detail.append(detail_dic)\n result['detail'] = detail\n return result\n\n def po_send(self, request):\n \"\"\"PO发货\"\"\"\n vals = {}\n sku_list = request.POST.getlist('sku', '')\n pre_send_qty = request.POST.getlist('pre_send_qty', '')\n send_no = request.POST.get('send_no', '')\n send_company = request.POST.get('send_company', '')\n if not (any(sku_list) and any(pre_send_qty) and send_company):\n result = {'mark': '1', 'message': '数据填写不完整'}\n return result\n if send_company != '自送' and send_no == '':\n result = {'mark': '1', 'message': '非自送运单号不能为空'}\n return result\n self.cr.execute(\n \"select b.guid from public.purchase_order a join res_partner b on a.partner_id=b.id where a.name='\" + self.po_id + \"'\"\n )\n info = self.cr.fetchone()\n if not info:\n return {'mark': '1', 'message': '找不到对应的PO单号'}\n vals['purOrderId'] = self.po_id\n vals['supplierId'] = info[0]\n vals['sendNo'] = send_no\n vals['sendCompanyName'] = send_company\n\n deliveryList = []\n for i in range(len(sku_list)):\n if pre_send_qty[i]:\n send_detail = {}\n send_detail['skuCode'] = sku_list[i]\n print(sku_list[i])\n send_detail['sendQuantity'] = int(pre_send_qty[i])\n self.cr.execute(\n \"select a.product_uom, a.product_name, a.so_no, a.id, a.partner_id from public.purchase_order_line a where a.order_id=(select id from public.purchase_order where name='\" + self.po_id + \"') and a.product_code='\"+sku_list[i]+\"'\"\n )\n sql_result = self.cr.fetchone()\n print('sql_result: %s' % sql_result)\n self.cr.execute(\n \"select name from public.product_uom where id='\"+str(sql_result[0])+\"'\"\n )\n unit = self.cr.fetchone()\n print('unit: %s' % unit)\n send_detail['productName'] = sql_result[1]\n send_detail['unit'] = unit[0]\n send_detail['orderId'] = sql_result[2]\n send_detail['id'] = sql_result[3]\n deliveryList.append(send_detail)\n else:\n continue\n vals['deliveryList'] = deliveryList\n result = self.sock.execute(self.dbname, self.uid, self.pwd, 'supplier.purchase.order.delivery', 'get_po_delivery', json.dumps(vals))\n result = json.loads(result)\n if result['mark'] == '0':\n result['message'] = 'Success'\n return result\n\n def so_invoice(self):\n \"\"\"SO开票-Odoo\"\"\"\n if not self.order_id:\n return {'mark': '1', 'message': 'SO单号不能为空！'}\n vals = {'so': self.order_id, 'env': self.env}\n result = self.sock.execute(self.dbname, self.uid, self.pwd, 'used.by.tester', 'so_invoice', vals)\n return result\n\n def query_so_send_detail(self):\n \"\"\"查询SO发货详情\"\"\"\n self.cr.execute(\n \"select a.product_uom_qty, a.cancel_qty, a.qty_delivered, a.product_id from public.sale_order_line a where a.name !='运费' and a.order_id=(select id from public.sale_order where name='\"+self.order_id+\"')\"\n )\n order_line = self.cr.fetchall()\n if not order_line:\n return {'mark': '1', 'message': '找不到对应的SO单号'}\n send_detail = []\n for i in order_line:\n sku_detail = {}\n self.cr.execute(\n \"select default_code from public.product_product where id='\"+str(i[3])+\"'\"\n )\n sku = self.cr.fetchone()\n sku_detail['sku'] = sku[0]\n sku_detail['k_send_qty'] = str(int(i[0]-i[1])) # 原数量\n sku_detail['y_send_qty'] = str(i[2]) # 已发货数量\n send_detail.append(sku_detail)\n result = {'mark': '0', 'message': 'Success', 'send_detail': send_detail}\n return result\n\n def so_send(self, request):\n \"\"\"SO发货--按输入的数量发货\"\"\"\n vals = {}\n sku_list = request.POST.getlist('so_sku', '')\n send_qty_list = request.POST.getlist('so_send_qty', '')\n send_no = request.POST.get('so_send_no', '')\n send_company = request.POST.get('so_send_company', '')\n env = self.env\n\n if not (self.order_id and send_no and any(send_qty_list) and env):\n return {'mark': '1', 'message': '数据填写不完整'}\n vals['so'] = self.order_id\n vals['batch_flag'] = 'true'\n vals['send_no'] = send_no\n # vals['send_company'] = send_company\n send_detail = []\n\n # 组装发货明细参数send_detail\n for i in range(len(sku_list)):\n if send_qty_list[i]:\n detail = {}\n detail['sku'] = sku_list[i]\n detail['send_qty'] = send_qty_list[i]\n send_detail.append(detail)\n else:\n continue\n vals['send_detail'] = send_detail\n\n vals['env'] = env\n result = self.sock.execute(self.dbname, self.uid, self.pwd, 'used.by.tester', 'test_so_transfer', vals)\n return result\n\n def so_send_all(self, request):\n \"\"\"SO全部发货\"\"\"\n vals = {}\n send_no = request.POST.get('so_send_no', '')\n env = self.env\n if not (self.order_id and send_no and env):\n return {'mark': '1', 'message': '数据填写不完整'}\n vals['so'] = self.order_id\n vals['batch_flag'] = 'false' # 分批发货标志，false:全部发货； true部分发货\n vals['send_no'] = send_no\n vals['env'] = env\n result = self.sock.execute(self.dbname, self.uid, self.pwd, 'used.by.tester', 'test_so_transfer', vals)\n return result\n\n def query_stock(self, request):\n \"\"\"查询odoo库存\"\"\"\n sku = request.POST.get('sku_stock', '')\n if not sku:\n return {'mark': '1', 'message': 'SKU不能为空'}\n vals = {'product': sku}\n result = self.sock.execute(self.dbname, self.uid, self.pwd, 'used.by.tester', 'query_product_qty', vals)\n return result\n\n def update_stock(self, request):\n \"\"\"更新odoo库存\"\"\"\n sku = request.POST.get('sku_stock', '')\n update_qty = request.POST.get('qty_stock', '')\n if not sku:\n return {'mark': '1', 'message': 'SKU不能为空'}\n if not update_qty:\n return {'mark': '1', 'message': '更新数量不能为空'}\n vals = {'product': sku, 'update_qty': update_qty}\n result = self.sock.execute(self.dbname, self.uid, self.pwd, 'used.by.tester', 'update_product_qty', vals)\n return result\n\n def after_sale_confirm(self, request):\n \"\"\"售后确认\"\"\"\n cs_no = request.POST.get('cs_no', '')\n if not cs_no:\n return {'mark': '1', 'message': 'CS_No不能为空'}\n vals = {'cs_no': cs_no, 'env': self.env}\n result = self.sock.execute(self.dbname, self.uid, self.pwd, 'used.by.tester', 'after_sale_confirm', vals)\n return result\n\n def query_after_sale_list(self, request):\n \"\"\"查询可申请售后商品\"\"\"\n so_cs = request.POST.get('so_cs_value', '')\n cs_type = request.POST.get('cs_type', '')\n cr = self.connection.cursor()\n if cs_type == '请选择':\n return {'mark':'1', 'message':'请选择售后类型'}\n if so_cs == '':\n return {'mark': '1', 'message': '请输入申请售后的SO单号'}\n if cs_type == '取消' and so_cs != '':\n cr.execute(\"select a.sku_code, (a.quantity-a.send_quantity) as avaliable_cancle_num from oc.order_detail a where a.order_id = '\"+ so_cs+\"'\")\n cr_r = cr.fetchall()\n cr.execute(\"select c.sku_code, c.quantity from oc.cs_apply_detail c where c.cs_no in (select b.cs_no from oc.cs_apply b where b.cs_status=0 and b.ORDER_ID = '\"+ so_cs +\"' and b.cs_type=9)\")\n cr_2 = cr.fetchall()\n for i in cr_r:\n for j in cr_2:\n if i['sku_code'] == j['sku_code']:\n i['avaliable_cancle_num'] = i['avaliable_cancle_num'] - j['quantity']\n # print(cr_r, cr_2)\n if cs_type == '退货' and so_cs != '':\n cr.execute(\"select a.sku_code, a.send_quantity as avaliable_cancle_num from oc.order_detail a where a.order_id = '\"+ so_cs+\"'\")\n cr_r = cr.fetchall()\n cr.execute(\"select b.sku_code, b.quantity from oc.cs_apply_detail b where b.CS_NO in (select a.CS_NO from oc.cs_apply a where a.order_id = '\"+ so_cs +\"' and a.CS_TYPE = 0 and a.CS_STATUS =0) and b.CS_TYPE =0\")\n cr_1 = cr.fetchall()\n cr.execute(\"select b.sku_code, b.quantity from oc.cs_apply_detail b where b.CS_NO in (select a.CS_NO from oc.cs_apply a where a.order_id = '\"+ so_cs +\"' and a.CS_TYPE = 0 and a.CS_STATUS in(1,99)) and b.CS_TYPE =1\")\n cr_2 = cr.fetchall()\n for i in cr_r:\n for j in cr_1:\n if i['sku_code'] == j['sku_code']:\n i['avaliable_cancle_num'] = i['avaliable_cancle_num'] - j['quantity']\n for i in cr_r:\n for j in cr_2:\n if i['sku_code'] == j['sku_code']:\n i['avaliable_cancle_num'] = i['avaliable_cancle_num'] - j['quantity']\n list = []\n for i in cr_r:\n if int(i['avaliable_cancle_num']) >= 1 :\n list.append(i)\n # print(list)\n return {'mark':'0', 'message':'查询成功', 'available_cs_detail':list}\n\n def create_after_sale_list(self, request):\n \"\"\"创建售后申请单\"\"\"\n cs_type = request.POST.get('cs_type', '')\n so_cs = request.POST.get('so_cs_value', '')\n order_available_sku = request.POST.getlist('order_available_sku', '')\n sku_handle_num = request.POST.getlist('sku_handle_num', '')\n dictionary = dict(zip(order_available_sku, sku_handle_num))\n list = []\n cr = self.connection.cursor()\n if not any(sku_handle_num):\n return {'mark' : '1', 'message' : '请正确填写申请取消或退货的商品数量'}\n else:\n for i in dictionary.items():\n if i[1] != '' and i[1] != 0:\n cr.execute(\"select a.sku_code as skuCode, a.brand_Name as brandName, a.product_Name as productName, a.unit, a.product_Pic as productPic, a.product_Model as productModel, a.sale_Price as salePrice from oc.order_detail a \"\n \"where a.order_id = '\"+ so_cs +\"' and a.sku_code = '\"+ i[0] +\"'\" )\n self.connection.commit()\n cr_r = cr.fetchall()\n data_sku = {\"quantity\":i[1], \"refundAmt\":\"\",\"reparationAmt\":\"\",\"warehouseCode\":\"\",\"worthless\":\"\",\"transferAmount\":\"\",\"returnToSupplier\":0,\"remark\":\"test\"}\n data_sku.update(cr_r[0])\n a = data_sku['salePrice']\n data_sku['salePrice'] = float(a)\n list.append(data_sku)\n if cs_type == '取消':\n url = 'http://oc-' + self.env + '.ehsy.com/admin/cs/saveCSApplyAndDetailOnly'\n data = {\"csType\":9,\"orderId\":so_cs,\"actualDetailList\":list, \"userId\":\"admin\",\"createUserName\":\"\\u897f\\u57dfOPC\"}\n # 参数为表单用户data=；参数为json串用json=或data=json.dumps()\n r = requests.post(url, data=json.dumps(data))\n result = r.json()\n if cs_type == '退货':\n url = 'http://oc-' + self.env + '.ehsy.com/admin/cs/saveCSApplyAndDetailOnly'\n data = {\"csType\":0,\"orderId\":so_cs,\"actualDetailList\":[data_sku], \"userId\":\"admin\",\"createUserName\":\"\\u897f\\u57dfOPC\"}\n r = requests.post(url, json=data)\n result = r.json()\n # cr = self.connection.cursor()\n cr.execute(\"select a.CS_NO from oc.cs_apply a where a.order_id = '\"+ so_cs +\"' ORDER BY a.CREATE_TIME DESC limit 1\")\n cr_r = cr.fetchall()\n result.update(cr_r[0])\n return result\n\n def after_sale_done(self, request):\n \"\"\"售后申请完结\"\"\"\n cs_no = request.POST.get('cs_no', '')\n if not cs_no:\n return {'mark': '1', 'message': 'CS_No不能为空'}\n vals = {'cs_no': cs_no, 'env': self.env}\n result = self.sock.execute(self.dbname, self.uid, self.pwd, 'used.by.tester', 'after_sale_done', vals)\n return result\n\n def after_sale_refuse(self, request):\n \"\"\"售后申请作废\"\"\"\n cs_no = request.POST.get('cs_no', '')\n if not cs_no:\n return {'mark': '1', 'message': 'CS_No不能为空'}\n vals = {'cs_no': cs_no, 'env': self.env}\n result = self.sock.execute(self.dbname, self.uid, self.pwd, 'used.by.tester', 'after_sale_refuse', vals)\n return result\n\n def query_so_no(self, request):\n \"\"\"通过外部订单号查询SO单号\"\"\"\n ex_no = request.POST.get('ex_no', '')\n if not ex_no:\n return {'mark': '1', 'message': '外部订单号不能为空！'}\n cr = self.connection.cursor()\n cr.execute(\n \"select a.order_id from oc.order_info a where a.EXTERNAL_ORDER_NO ='\"+ex_no+\"'\"\n )\n cr_r = cr.fetchone()\n print(cr_r)\n if not cr_r:\n return {'mark': '1', 'message': '没有找到对应订单！'}\n result = {'mark': '0', 'message': '查询成功！ SO单号：'+cr_r['order_id']}\n result.update(cr_r)\n return result\n", "repo_name": "westlyou/Test_Platform_Django", "sub_path": "sign/test_tools.py", "file_name": "test_tools.py", "file_ext": "py", "file_size_in_byte": 24145, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "12", "api": [{"api_name": "pymysql.connect", "line_number": 24, "usage_type": "call"}, {"api_name": "pymysql.cursors", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pymysql.connect", "line_number": 34, "usage_type": "call"}, {"api_name": "pymysql.cursors", "line_number": 40, "usage_type": "attribute"}, {"api_name": "xmlrpc.client.ServerProxy", "line_number": 49, "usage_type": "call"}, {"api_name": "xmlrpc.client", "line_number": 49, "usage_type": "name"}, {"api_name": "xmlrpc.client.ServerProxy", "line_number": 51, "usage_type": "call"}, {"api_name": "xmlrpc.client", "line_number": 51, "usage_type": "name"}, {"api_name": "xmlrpc.client.ServerProxy", "line_number": 58, "usage_type": "call"}, {"api_name": "xmlrpc.client", "line_number": 58, "usage_type": "name"}, {"api_name": "xmlrpc.client.ServerProxy", "line_number": 60, "usage_type": "call"}, {"api_name": "xmlrpc.client", "line_number": 60, "usage_type": "name"}, {"api_name": "psycopg2.connect", "line_number": 63, "usage_type": "call"}, {"api_name": "psycopg2.connect", "line_number": 71, "usage_type": "call"}, {"api_name": "psycopg2.extras", "line_number": 78, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 83, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 102, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 120, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 130, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 138, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 146, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 154, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 216, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 217, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 283, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 284, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 463, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 463, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 468, "usage_type": "call"}]}