diff --git "a/4262.jsonl" "b/4262.jsonl"
new file mode 100644--- /dev/null
+++ "b/4262.jsonl"
@@ -0,0 +1,652 @@
+{"seq_id":"402809948","text":"from diary.modules.resume.serializers import DateSerializer\n\n\nclass DateView(object):\n\n    def __init__(self, interactor):\n        self.interactor = interactor\n\n    def get(self, **kwargs):\n\n        user = kwargs.get('user')\n        year = kwargs.get('year')\n        month = kwargs.get('month')\n        day = kwargs.get('day')\n\n        date = self.interactor.set_params(\n                                            user,\n                                            year,\n                                            month,\n                                            day\n                                        ).execute()\n\n        body = DateSerializer.serialize(date)\n        status = 200\n\n        return body, status\n","sub_path":"diary/modules/resume/views/date.py","file_name":"date.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"491982388","text":"import numpy as np\n\ndef choosePivot(N):\n    return np.random.choice(N)\n\ndef partition(A,pivotId):\n    pivot = A[pivotId]\n    temp = A[pivotId]    \n    A[pivotId] = A[0]\n    A[0]=temp\n\n    N = len(A)\n    nextIdToSee = 1\n    firstElementLargerThanPivotInPartition = 1\n\n    while nextIdToSee<N:\n        seenNum = A[nextIdToSee]        \n\n        if seenNum > pivot:\n            pass\n        else:\n            temp  = A[firstElementLargerThanPivotInPartition]\n            A[firstElementLargerThanPivotInPartition] = seenNum\n            A[nextIdToSee] = temp\n            firstElementLargerThanPivotInPartition+=1\n        \n        nextIdToSee+=1        \n    \n    A[0] = A[firstElementLargerThanPivotInPartition-1] \n    A[firstElementLargerThanPivotInPartition-1]  = pivot\n    pivotIndexFinal = firstElementLargerThanPivotInPartition-1\n    \n    return (A,pivotIndexFinal)\n    \ndef randSelect(A,i):    \n    N = len(A)\n    pivotId = choosePivot(N)    \n    (partitioned,finalPivotIndex) = partition(A,pivotId)    \n    if finalPivotIndex==(i-1):        \n        return partitioned[finalPivotIndex]\n    elif finalPivotIndex < (i-1):        \n        return randSelect(A[finalPivotIndex:],(i-finalPivotIndex))\n    else:        \n        return randSelect(A[:finalPivotIndex],i)\n\n\nN = 1000\narr = list(np.random.permutation(N))\ni = 512\n\nith_smallest_element = randSelect(arr,i)\n\nprint(f'Array: \\n{arr}')\nprint(f'{i}th smallest element: {ith_smallest_element}')","sub_path":"randomizedSelection.py","file_name":"randomizedSelection.py","file_ext":"py","file_size_in_byte":1442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"221193935","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\nclass FrequencyAnalysis:\n    def __init__(self, audioData, framerate, numSamples, startpoint, endpoint):\n        self.data = audioData\n        self.frameRate = framerate\n        self.numSamples = numSamples\n        self.rangeVals = [startpoint, endpoint]\n        self.frequencies = self.analyzeFreq()\n\n    def analyzeFreq(self):\n        data = np.array(self.data)\n        data_fft = np.fft.fft(data[self.rangeVals[0]:self.rangeVals[1]],n=self.frameRate)\n        frequencies = np.abs(data_fft)\n\n        frequencies = frequencies[:int(len(frequencies)/2)]\n\n        print(\"The frequency is {} Hz\".format(np.argmax(frequencies)))\n\n        plt.subplot(2, 1, 1)\n        plt.plot(data[self.rangeVals[0]:self.rangeVals[1]],linewidth = .1)\n        plt.title(\"Original audio wave\")\n        plt.subplot(2, 1, 2)\n        plt.plot(frequencies)\n        plt.title(\"Frequencies found\")\n        plt.xlim(0, self.frameRate/2)\n        plt.show()\n        # normalize the values to check for polyphony\n        normalizedFrequencies = frequencies/frequencies[np.argmax(frequencies)] # normalize the values\n        plt.plot(normalizedFrequencies)\n        plt.show()\n        for i in len(normalizedFrequencies):\n            if normalizedFrequencies[i] > .85:\n                finalFrequencies.append(i)\n\n        print(finalFrequencies)\n        return finalFrequencies\n\n    def getFrequency(self):\n        return np.argmax(self.frequencies)\n\n","sub_path":"freqanaltesting.py","file_name":"freqanaltesting.py","file_ext":"py","file_size_in_byte":1468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"463840582","text":"# -*- coding:utf-8 -*-\n\n'感知机原始模式'\n\n__author__='litterzhang'\n\nfrom matplotlib import pyplot as plt\nfrom matplotlib import animation\n\n# 初始化感知机参数\ndef init_param(n):\n\tw = [0 for i in range(n)]\n\tb = 0\n\n\tglobal history\n\thistory.append((w[:], b))\n\n\treturn w, b\n\n# 计算点到超平面的距离\ndef calc_dis(point, n, w, b):\n\tdis = 0\n\tfor i in range(n):\n\t\tdis += point[0][i]*w[i]\n\tdis += b\n\tdis *= point[1]\n\treturn dis\n\n# 检查是否存在误分类点\ndef check(training_set, n, w, b):\n\tflag = False\n\n\tfor data in training_set:\n\t\tif calc_dis(data, n, w, b) <= 0:\n\t\t\tflag = True\n\t\t\tw, b = update(data, n, w, b)\n\t\t\tbreak\n\treturn flag, w, b\n\n# 更新感知机参数\ndef update(point, n, w, b):\n\tyt = 1\n\tfor i in range(n):\n\t\tw[i] += yt*point[1]*point[0][i]\n\tb += yt*point[1]\n\n\tglobal history\n\thistory.append((w[:], b))\n\treturn w, b\n\n# 感知机\ndef prece(training_set, n):\n\tw, b = init_param(n)\n\n\twhile True:\n\t\tflag, w, b = check(training_set, n, w, b)\n\t\tif not flag: break\n\treturn w, b\n\n# initialization function: plot the background of each frame\ndef init():\n\tline.set_data([], [])\n\tx, y, x_, y_ = [], [], [], []\n\tfor p in training_set:\n\t\tif p[1] > 0:\n\t\t\tx.append(p[0][0])\n\t\t\ty.append(p[0][1])\n\t\telse:\n\t\t\tx_.append(p[0][0])\n\t\t\ty_.append(p[0][1])\n\n\tplt.plot(x, y, 'bo', x_, y_, 'rx')\n\tplt.axis([-6, 6, -6, 6])\n\tplt.grid(True)\n\tplt.xlabel('x')\n\tplt.ylabel('y')\n\tplt.title('Perceptron Algorithm')\n\treturn line, label\n\n# animation function.  this is called sequentially\ndef animate(i):\n\tglobal history, ax, line, label\n\n\tw = history[i][0]\n\tb = history[i][1]\n\n\tif w[0] == 0 and w[1] == 0:\n\t\treturn line, label\n\tif w[1] == 0:\n\t\tx = -b / w[0]\n\t\ty1 = 7\n\t\ty2 = -7\n\t\tline.set_data([x, x], [y1, y2])\n\t\ty1 = 0\n\t\tlabel.set_text(history[i])\n\t\tlabel.set_position([x, y1])\n\telse:\n\t\tx1 = -7\n\t\ty1 = -(b + w[0] * x1) / w[1]\n\t\tx2 = 7\n\t\ty2 = -(b + w[0] * x2) / w[1]\n\t\tline.set_data([x1, x2], [y1, y2])\n\t\tx1 = 0\n\t\ty1 = -(b + w[0] * x1) / w[1]\n\t\tlabel.set_text(history[i])\n\t\tlabel.set_position([x1, y1])\n\treturn line, label\n\ndef draw():\n\tglobal history, ax, line, label\n\n\t# first set up the figure, the axis, and the plot element we want to animate\n\tfig = plt.figure('Perceptron')\n\tax = plt.axes(xlim=(0, 2), ylim=(-2, 2))\n\tline, = ax.plot([], [], 'g', lw=2)\n\tlabel = ax.text([], [], '')\n \n\t# call the animator.  blit=true means only re-draw the parts that have changed.\n\tanim = animation.FuncAnimation(fig, animate, init_func=init, frames=len(history), interval=1000, repeat=True, blit=True)\n\tplt.show()\n\t\n\tanim.save('perceptron_origin.gif', fps=2, writer='imagemagick')\n\n\nif __name__=='__main__':\n\ttraining_set = [((4, 3), 1), ((1, 1), -1), ((3, 3), 1)]\n\tn = 2\n\t\n\thistory = list()\n\n\tprece(training_set, n)\n\t\n\tdraw()\n\n\n","sub_path":"Perceptron/preceptron_origin.py","file_name":"preceptron_origin.py","file_ext":"py","file_size_in_byte":2719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"590045226","text":"from rest_framework import serializers\n\nfrom protein.models import Protein, ProteinConformation, ProteinFamily, Species, ProteinSource, ProteinSegment\nfrom residue.models import Residue, ResidueNumberingScheme, ResidueGenericNumber\nfrom structure.models import Structure\n\n\nclass ProteinSerializer(serializers.ModelSerializer):\n    family = serializers.SlugRelatedField(read_only=True, slug_field='slug')\n    species = serializers.StringRelatedField(read_only=True)\n    source = serializers.StringRelatedField(read_only=True)\n    residue_numbering_scheme = serializers.SlugRelatedField(read_only=True, slug_field='short_name')\n    class Meta:\n        model = Protein\n        fields = ('entry_name', 'name', 'accession', 'family', 'species', 'source', 'residue_numbering_scheme',\n            'sequence')\n\n\nclass ProteinFromConformationSerializer(serializers.ModelSerializer):\n    protein = serializers.SlugRelatedField(read_only=True, slug_field='entry_name')\n    class Meta:\n        model = ProteinConformation\n        fields = ('protein', )\n\n\nclass ParentProteinFamilySerializer(serializers.ModelSerializer):\n    class Meta:\n        model = ProteinFamily\n        fields = ('slug', 'name')\n\n\nclass ProteinFamilySerializer(serializers.ModelSerializer):\n    parent = ParentProteinFamilySerializer(read_only=True)\n    class Meta:\n        model = ProteinFamily\n        fields = ('slug', 'name', 'parent')\n\n\nclass SpeciesSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Species\n        fields = ('latin_name', 'common_name')\n\n\nclass ResidueNumberingSchemeSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = ResidueNumberingScheme\n        fields = ('slug', 'short_name')\n\n\nclass ResidueGenericNumberSerializer(serializers.ModelSerializer):\n    scheme = serializers.SlugRelatedField(read_only=True, slug_field='short_name')\n    class Meta:\n        model = ResidueGenericNumber\n        fields = ('scheme', 'label')\n\n\nclass ResidueSerializer(serializers.ModelSerializer):\n    protein_segment = serializers.StringRelatedField(read_only=True)\n    display_generic_number = serializers.StringRelatedField(read_only=True)\n    class Meta:\n        model = Residue\n        fields = ('sequence_number', 'amino_acid', 'protein_segment', 'display_generic_number')\n\n\nclass ResidueExtendedSerializer(serializers.ModelSerializer):\n    protein_segment = serializers.StringRelatedField(read_only=True)\n    display_generic_number = serializers.StringRelatedField(read_only=True)\n    alternative_generic_numbers = ResidueGenericNumberSerializer(read_only=True, many=True)\n    class Meta:\n        model = Residue\n        fields = ('sequence_number', 'amino_acid', 'protein_segment', 'display_generic_number',\n            'alternative_generic_numbers')\n\n\nclass StructureSerializer(serializers.ModelSerializer):\n    pdb_code = serializers.SlugRelatedField(read_only=True, slug_field='index')\n    protein_conformation = ProteinFromConformationSerializer()\n    class Meta:\n        model = Structure\n        fields = ('pdb_code', 'resolution', 'protein_conformation')","sub_path":"api/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":3078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"304806165","text":"from django.contrib.auth.models import User, Group\nfrom django.db.models import fields\nfrom django.db.models.base import Model\nfrom rest_framework import serializers\nfrom .models import *\n\n\nclass tarjetaSerializer(serializers.ModelSerializer):\n    ciudadnombre = serializers.CharField(\n        source='ciudad.nombre',\n        read_only=True\n    )\n\n    class Meta:\n        model = tarjeta\n        fields = ['ciudadnombre', 'ciudad_id', 'id', 'titular',\n                  'numeroTarjeta', 'fechaExpiracion', 'cvv']\n\n\nclass personaSerializer(serializers.ModelSerializer):\n    ciudadnombre = serializers.CharField(\n        source='ciudad.nombre',\n        read_only=True\n    )\n\n    class Meta:\n        model = persona\n        fields = ['nombre', 'direccion', 'telefono',\n                  'usuario', 'contrasena', 'ciudadnombre']\n        extra_kwargs = {'contrasena': {'write_only': True, 'required': True}}\n\n\nclass ciudadSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = ciudad\n        fields = ['id', 'nombre']\n        \nclass categoriaSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = categoria\n        fields = ['id', 'nombre']\n\n\n\nclass productoSerializer (serializers.ModelSerializer):   \n    categoria=serializers.CharField(\n        source='categoria.nombre',\n        read_only=True\n    )\n    \n    class Meta:\n        model = producto\n        fields = ['nombre', 'categoria','activo','precio']\n        \n        \nclass GrupoyModificacionSerializer(serializers.ModelSerializer):\n    \n    nombreModificacion=serializers.CharField(\n        source='modificador.nombre',\n        read_only=True\n    )       \n    class Meta:\n        model = unionModificacion\n        fields = ['id','grupoModificador','nombreModificacion']\n    \n        \nclass grupo_modificaSerializer (serializers.ModelSerializer):\n    \n    class Meta:\n        model = grupo_modifica\n        fields = ['id','nombre']\n\nclass conexionProductoSerializer (serializers.ModelSerializer):\n    productonombre=serializers.CharField(\n        source='producto.nombre',\n        read_only=True\n    ) \n    productoprecio=serializers.CharField(\n        source='producto.precio',\n        read_only=True\n    )\n    grupoModificador=serializers.CharField(\n        source='grupoModificador.nombre',\n        read_only=True\n    )\n    \n    class Meta:\n        model = conexionProducto\n        fields = ['producto','productonombre','productoprecio','unionModificacion','grupoModificador']\n\n     \n\n        \n","sub_path":"BackEnd/DjangoApi/api/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":2487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"467914472","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Aug 21 21:26:31 2017\n\n@author: EthanMorse\n\"\"\"\n\nimport os \n\n# run once for each path to create a .txt file, never use again\n# account_type is school, personal, business, etc in string form\ndef create_old_file_list(account_type):\n    \n    file = open(\"old_files_dict_\" + account_type.lower() + \".txt\",\"w\")\n    file.close()\n    \n    \n# update old file list so it holds current files in dictionary form\ndef update_file_list(path, account_type):\n    \n    old_files = open(\"old_files_dict_\" + account_type.lower() + \".txt\",\"w\")\n    old_files_dict = {}\n    \n    # iterate through files in path and enter filename into \n    # dictionary, with last mod. times as its value\n    for file in os.listdir(path):\n        old_files_dict[str(file)] = os.path.getmtime(path + \"/\" + str(file))\n        \n    old_files.write(str(old_files_dict)) # write entire dict into file\n    old_files.close()\n    \n    \ndef check_if_modified(path, account_type, mod_file):\n    \n    with open(\"old_files_dict_\" + account_type + \".txt\",\"r\") as file:\n        old_files = file.readlines()[0] # read, subscript string (dict)\n        \n    old_files_dict = eval(old_files) # convert back to dict from string\n    \n    try:\n        # check if last mod. time is same as most recent mod. time\n        if os.path.getmtime(path + \"/\" + mod_file) == old_files_dict[mod_file]:\n            return False # if NOT modified\n        else:\n            return True # if modified\n        \n    except KeyError:\n        return None","sub_path":"file_functions.py","file_name":"file_functions.py","file_ext":"py","file_size_in_byte":1539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"94934567","text":"\r\nlist1 = [1,2,3]\r\nlist2=[4,5,6]\r\n\r\nlist3=list1+list2\r\n\r\nnumbers=[1,2,3,4,5]\r\nn=5\r\nif n in numbers:\r\n    print('{}가 있다'.format(n))\r\n\r\ndel list1[1]\r\n#or\r\n#remove list(2)\r\n\r\nprint(list1)\r\n","sub_path":"Try! helloworld/week 5/practice 2 (week 5).py","file_name":"practice 2 (week 5).py","file_ext":"py","file_size_in_byte":192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"214113876","text":"import cv2\nimport numpy as np\nimport glob\nimport time\n\n\ndef load_all_image_from_path(path):\n    image_list = []\n    for filename in glob.glob(path):\n        # load image in gray scale\n        im = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)\n        image_list.append(im)\n    return image_list\n\n\ndef display_image(image, title=\"image\"):\n    cv2.imshow(title, image)\n    cv2.waitKey(0)\n    cv2.destroyAllWindows()\n\n\ndef combine_image(image1, image2):\n    h1, w1 = image1.shape[:2]\n    h2, w2 = image2.shape[:2]\n    print(\"------------------------------------\\n\\n\")\n\n    # create empty matrix\n    vis = np.zeros((max(h1, h2), w1 + w2), np.uint8)\n\n    # combine 2 images\n    vis[:h1, :w1] = image1\n    vis[:h2, w1:w1 + w2] = image2\n    return vis\n\n\ndef orb_with_flann(image_query, image_train):\n    # init feature detector\n    orb = cv2.ORB_create(nfeatures=2000)  # default features is 500\n\n    # find key point and descriptor\n    kp_logo, des_logo = orb.detectAndCompute(image_train, None)\n    kp_img, des_img = orb.detectAndCompute(image_query, None)\n\n    # view key point\n    # result_image_train = cv2.drawKeypoints(image_train, kp_logo, None, flags=0)\n    # result_image_query = cv2.drawKeypoints(image_query, kp_img, None, flags=0)\n    # display_image(result_image_train,\"train\")\n    # display_image(result_image_query,\"query\")\n\n    # FLANN parameters\n    flann_index_lsh = 6\n    index_params = dict(algorithm=flann_index_lsh,\n                        table_number=12,\n                        key_size=20,\n                        multi_probe_level=2)\n    search_params = dict(checks=100)  # or pass empty dictionary\n\n    # create FLANN\n    flann = cv2.FlannBasedMatcher(index_params, search_params)\n\n    # perform matching\n    flann_matches = flann.knnMatch(des_logo, des_img, k=2)\n\n    # View match without fillter.\n    # img3 = cv2.drawMatchesKnn(image_train, kp_logo, image_query, kp_img, flann_matches, None)\n    # display_image(img3)\n\n    # Need to draw only good matches, so create a mask\n    matches_mask = [[0, 0] for i in range(len(flann_matches))]\n\n    # ratio test as per Lowe's paper\n    good = []\n    for index in range(len(flann_matches)):\n        if len(flann_matches[index]) == 2:\n            m, n = flann_matches[index]\n            if m.distance < 0.8 * n.distance:  # threshold of ratio testing\n                matches_mask[index] = [1, 0]\n                good.append(flann_matches[index])\n\n    # draw match after filter\n    draw_params = dict(\n        singlePointColor=(255, 0, 0),\n        matchesMask=matches_mask,\n        flags=2)\n\n    img3 = cv2.drawMatchesKnn(image_train, kp_logo, image_query, kp_img, flann_matches, None, **draw_params)\n\n    # font = cv2.FONT_HERSHEY_SIMPLEX\n    # cv2.putText(img3, str(len(good)), (0, 50), font, 2, (0, 0, 255), 2, cv2.LINE_AA)\n\n    display_image(img3)  # view matching image\n\n    return image_train, len(good)\n\n\ndef find_best_match_index(match):\n    index_best = 0\n    best_length = 0\n    for index, (image, good_match_length) in enumerate(match):\n        if good_match_length >= best_length:\n            best_length = good_match_length\n            index_best = index\n    return index_best\n\n\nif __name__ == '__main__':\n    # load image\n    train_image_list = load_all_image_from_path(\"train_image/*\")\n    query_image_list = load_all_image_from_path(\"sample_image/*\")\n\n    for index, query_image in enumerate(query_image_list):\n        matches = []\n\n        # get process time\n        start = int(round(time.time() * 1000))\n        for train_image in train_image_list:\n            matches.append(orb_with_flann(query_image, train_image))\n        end = int(round(time.time() * 1000)) - start\n        print(\"process time: \" + end.__str__())\n\n        # find the best match\n        best_match_index = find_best_match_index(matches)\n\n        # combine query image with the best match image so easy view\n        result_image = combine_image(query_image, matches[best_match_index][0])\n\n        font = cv2.FONT_HERSHEY_SIMPLEX\n        result_image = cv2.cvtColor(result_image, cv2.COLOR_GRAY2BGR)\n        result_image = cv2.putText(result_image, str(matches[best_match_index][1]) + \" - \" + \"time: \" + str(end),\n                                   (0, 50), font, 2, (0, 0, 255),\n                                   2, cv2.LINE_AA)\n        display_image(result_image)\n\n        # save result image\n        # cv2.imwrite(\"result/result\" + str(index) + \".jpg\", result_image)\n\n","sub_path":"wine_matcher.py","file_name":"wine_matcher.py","file_ext":"py","file_size_in_byte":4422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"132599123","text":"###JAX implementation of ADAM\nimport jax.numpy as jnp\nfrom jax.experimental.optimizers import adam\nfrom jax.lax import while_loop,cond\nfrom jax import grad,value_and_grad, jit\nfrom functools import partial\n\n### Optimizer loop and termination\n@partial(jit,static_argnums = (0))\ndef minimize_ADAM(fun,x0,data,step_size = 0.01,f_tol = 1e-5,n_iter_max = 1000): \n    \"\"\"Minimize function with ADAM.\n    Args:\n    fun: function to minimize, which takes (x,data) as input\n    x0: ndarray: initial solution \n    data: ndarray: full data to subsample\n    \n    #Optimizer hyperparams\n    step_size: positive scalar for step-size to pass into minimize_adam\n    f_tol: positive scalar for value of norm of f change before termination\n    n_iter_max: maximum number of sgd steps before termination\n\n    Returns:\n    x_opt: Optimized x\n    loss: Minimized loss value\n    n_iter: Number of iterations at termination\n    delta_f: Value of change of f at termination\n    \"\"\"\n\n    #function wrappers\n    value_and_grad_fun = jit(value_and_grad(fun))\n\n    @jit #wrapper around step to allow for termination checks\n    def step(carry): \n        i,loss,g,opt_state,key,n = carry\n        key,*subkey = random.split(key)\n        ind = random.shuffle(key,jnp.arange(n))\n        params = get_params(opt_state)\n        loss,g = value_and_grad_fun(params,data[ind])\n        i=i+1\n        carry = i,loss,g,opt_update(i, g, opt_state)\n        return carry\n\n    @jit #termination condition on gradient norm or number of iterations\n    def converged(carry): \n        i,loss,g,opt_state,key,n = carry\n        delta_f = 1\n        return jnp.logical_and(norm_g>g_tol,i<n_iter_max) \n    \n    #check dimensions and initialize\n    d = jnp.shape(x0)[0]\n    key = random.PRNGKey(0)\n    n = jnp.shape(data)[0]\n\n    #initialize optimzer\n    opt_init, opt_update, get_params = adam(step_size = step_size)\n    opt_state = opt_init(x0)\n\n    #run optimizer until termination\n    carry = 0,1.,jnp.ones(d), opt_state,key,n\n    carry = while_loop(converged,step,carry)\n\n    #extract values from carry\n    n_iter,loss,g,opt_state,_,_ = carry\n\n    x_opt = get_params(opt_state)\n\n    return x_opt,loss,n_iter,delta_f\n###\n","sub_path":"pr_copula/utils/ADAM.py","file_name":"ADAM.py","file_ext":"py","file_size_in_byte":2170,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"294184466","text":"word0 = input(\"１つめの単語を入力してください\")\nword1 = input(\"２つめの単語を入力してください\")\nword2 = input(\"３つめの単語を入力してください\")\n\nprint(\"{0}は{1}{2}です。{2}です。\".format(word0, word1, word2))\n\nnum0 = int(input(\"個数を入力してください\"))\nnum1 = int(input(\"個金額を入力してください\"))\n\n\nprint(\"{0:<}個{1:>10,}円\".format(num0, num1))\n","sub_path":"Python/09/Sample3.py","file_name":"Sample3.py","file_ext":"py","file_size_in_byte":425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"490338808","text":"#!/bin/python\n# -*- coding: utf-8 -*-\n\nimport sys\n\nfrom PyQt4 import QtCore, QtGui\n\nfrom model.model import Encuesta\nfrom model.model import Pregunta\nfrom model.model import Opcion\nfrom model.model import TipoRespuesta\nfrom model.model import Secuencia\nfrom model.model import Ocurrencia\nimport model.model as modelo\n\nfrom utils.verifier import Verifier\n\nclass QuestionDisplay(object):\n    # Array de elementos qt a mostrar\n\n    def __init__(self, pregunta):\n        self.elementos = []\n        self.pregunta = pregunta\n\n    def elegirEstrategia(self):\n        # Vemos de qué se trata y elegimos lo mejor para hacer\n\n        if (self.pregunta.idTipoRespuesta == modelo.OPC_S_TXT):\n            # Texto corto\n            self.showOpciones = self.showOpcionesTextoCorto\n\n        elif self.pregunta.idTipoRespuesta == modelo.OPC_COMBO:\n            # Combo\n            self.showOpciones = self.showOpcionesCombo\n\n        elif self.pregunta.idTipoRespuesta == modelo.OPC_RADIO:\n            # Radio\n            self.showOpciones = self.showOpcionesRadio\n\n        elif self.pregunta.idTipoRespuesta == modelo.OPC_CHECK:\n            # Checkboxes\n            self.showOpciones = self.showOpcionesCheck\n\n    def removerPregunta(self, ui):\n        # Limpia el display de preguntas\n        for i in reversed(range(ui.opcionPregunta.count())):\n            ui.opcionPregunta.itemAt(i).widget().setParent(None)\n\n    def displayPregunta(self, ui):\n        ui.textoPregunta.setText(QtCore.QString(self.pregunta.texto_pregunta))\n        opciones = modelo.session.query(Opcion).\\\n                filter(Opcion.idPregunta.in_([self.pregunta.idPregunta])).all()\n\n        # Elemento qt usado en las opciones\n        tipoRespuestaOpcion = modelo.session.query(TipoRespuesta).\\\n                filter(TipoRespuesta.idTipoRespuesta.in_([opciones[0].idTipoRespuesta])).all()\n\n        self.removerPregunta(ui)\n\n        self.showOpciones(tipoRespuestaOpcion, ui, opciones)\n\n        #print('opciones: {}\\nelementos {}'.format(opciones, self.elementos))\n        return opciones, self.elementos\n\n    def showOpcionesCombo(self,tipoRespuestaOpcion, ui, opciones):\n        lista = []\n        row = 0\n        nombre = 'QtGui.' + tipoRespuestaOpcion[0].elemento_qt_usado\n        Elemento = eval(nombre)\n        self.elementos.append(Elemento())\n\n        for o in opciones:\n            lista.append(o.texto_opcion)\n\n        # Acá ya está el elemento ComboBox\n        self.elementos[0].addItems(lista)\n        # Mostramos el coso en la ventana\n        ui.opcionPregunta.addWidget(self.elementos[0],row,0)\n\n    def showOpcionesCheck(self, tipoRespuestaOpcion, ui, opciones):\n        lista = []\n        row = 0\n        btnId = 1\n        nombre = 'QtGui.' + tipoRespuestaOpcion[0].elemento_qt_usado\n        Elemento = eval(nombre)\n        #print(Elemento)\n\n        for o in opciones:\n            choice = Elemento(o.texto_opcion)\n            self.elementos.append(choice)\n            ui.opcionPregunta.addWidget(self.elementos[row],row,0)\n            row +=1\n            btnId += 1\n\n    def showOpcionesRadio(self, tipoRespuestaOpcion, ui, opciones):\n        lista = []\n        row = 0\n        btnId = 1\n        nombre = 'QtGui.' + tipoRespuestaOpcion[0].elemento_qt_usado\n        Elemento = eval(nombre)\n        #print(Elemento)\n        self.elementos.append(QtGui.QButtonGroup())\n\n        for o in opciones:\n            choice = Elemento(o.texto_opcion)\n            self.elementos[0].addButton(choice,btnId)\n            ui.opcionPregunta.addWidget(self.elementos[0].button(row+1),row,0)\n            row +=1\n            btnId += 1\n\n\n    def showOpcionesTextoCorto(self, tipoRespuestaOpcion, ui, opciones):\n        row = 0\n        for o in opciones:\n            # Se muestra todo en formato <label> <elemento qt>\n            nombre = 'QtGui.' + tipoRespuestaOpcion[0].elemento_qt_usado\n            Elemento = eval(nombre)\n\n            self.elementos.append(Elemento())\n            ui.opcionPregunta.addWidget(QtGui.QLabel(o.texto_opcion),row,0);\n            ui.opcionPregunta.addWidget(self.elementos[row],row,1)\n\n            row += 1\n        return","sub_path":"utils/factory/question_display.py","file_name":"question_display.py","file_ext":"py","file_size_in_byte":4111,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"631376571","text":"from django.contrib import admin\nfrom tvb.models import Forum81Item, ThreadItem\n\nclass Forum81ItemAdmin(admin.ModelAdmin):\n    list_display = ('subscribe_link',\n                    'title',\n                    'author',\n                    'first_episode',\n                    'last_episode',\n                    'episodes_downloaded',\n                    'datePosted',)\n    ordering = ['-datePosted']\n    actions = None\n    list_display_links = ['title']\n\n    def subscribe_link(self, obj):\n        if obj.subscribe:\n            img = '<img src=\"/static/admin/img/icon-yes.gif\" alt=\"True\" />'\n        else:\n            img = '<img src=\"/static/admin/img/icon-no.gif\" alt=\"False\" />'\n        ret = '<a href=/tvb/%d/>' % obj.id\n        return ret + img + '</a>'\n    subscribe_link.allow_tags = True\n    subscribe_link.short_description = \"subscribe\"\n\n# Register your models here.\nadmin.site.register(Forum81Item, Forum81ItemAdmin)\nadmin.site.register(ThreadItem)\n","sub_path":"frontend/tvb/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"108197329","text":"import stanfordnlp\nimport nltk\nimport matplotlib.pyplot as plt\ndef extract_sentences(filename):\n\tscript = open(filename, \"r\")\n\tscript_lines = script.readlines()\n\tspeaker_dict = {}\n\tfor line in script_lines:\n\t\tlines_list = line.split()\n\t\tif lines_list:\n\t\t\trange1 = 0\n\t\t\tfor i in range(len(lines_list)):\n\t\t\t\tif \":\" in lines_list[i]: #Find word with \":\" \n\t\t\t\t\tspeaker = lines_list[:i+1]\n\t\t\t\t\trange1 = i+1\n\t\t\tspeaker = ' '.join(speaker)\n\t\t\tspeaker1 = speaker[:len(speaker) - 1]\n\t\t\tsents = ' '.join(lines_list[range1:])\n\t\t\tsent_list = nltk.sent_tokenize(sents)\n\t\t\ttry:\n\t\t\t\tfor s in sent_list:\n\t\t\t\t\tspeaker_dict[speaker1].append(s)\n\t\t\texcept KeyError:\n\t\t\t\tspeaker_dict[speaker1] = sent_list\n\treturn speaker_dict\n\ndef case_count(in_dict):\n\tnlp = stanfordnlp.Pipeline()\n\tnouns = [\"NNP\", \"NN\", \"NNS\", \"NNPS\"]\n\tverbs = [\"VB\", \"VBZ\", \"VBN\", \"VBP\", \"VBG\", \"VBD\"]\n\tabl = [\"by\", \"from\", \"in\" ,\"with\", \"below\", \"above\", \"before\"]\n\tdat = [\"to\", \"for\"]\n\tgen = [\"of\"]\n\tcases = {\"Datives\": 0, \"Nominative/Accusative\": 0, \"Ablative\": 0, \"Genitive\": 0}\n\tfor key in in_dict:\n\t\tfor val in in_dict[key]:\n\t\t\tdoc = nlp(val)\n\t\t\tfor sent in doc.sentences:\n\t\t\t\tfor i in range(0, len(sent.words)):\n\t\t\t\t\tif sent.words[i].xpos in nouns:\n\t\t\t\t\t\tnew_val = val.split()\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tprev_word = new_val[i-1]\n\t\t\t\t\t\t\tif prev_word in gen:\n\t\t\t\t\t\t\t\tcases[\"Genitive\"] += 1\n\t\t\t\t\t\t\telif prev_word in dat:\n\t\t\t\t\t\t\t\tcases[\"Datives\"] += 1\n\t\t\t\t\t\t\telif prev_word in abl:\n\t\t\t\t\t\t\t\tcases[\"Ablative\"] += 1\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tcases[\"Nominative/Accusative\"] +=1 \n\t\t\t\t\t\texcept IndexError:\n\t\t\t\t\t\t\tcontinue\n\treturn cases\n\nsentences = extract_sentences(\"rag_script.txt\")\ncases_dict = case_count(sentences)\nprint(cases_dict)\ncases = cases_dict.keys()\ncounts = cases_dict.values()\nplt.bar(cases,counts)\nplt.xlabel(\"CASES\")\nplt.ylabel(\"FREQUENCIES\")\nplt.savefig(\"Case_Distribution.png\")\nplt.show()\n\n\n\n\n\n\n\n\n","sub_path":"parsing/cases_count.py","file_name":"cases_count.py","file_ext":"py","file_size_in_byte":1849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"225328572","text":"#!/usr/bin/env python3\n\na_list = ['Apples', 'Pears', 'Oranges', 'Peaches']\n\n# SERIES 1\ndef series_one():\n    print(a_list)\n    another_fruit = input(\"Enter a new fruit: \")\n    a_list.append(another_fruit)\n    print(a_list)\n    number_from_user = input(\"Now enter number corresponding to fruit: \")\n    new_number = a_list[int(number_from_user)-1]\n    print(new_number + \" \" + number_from_user)\n    another_fruit = ['Bananas']\n    print(another_fruit + a_list)\n    next_fruit = 'Kiwis'\n    a_list.insert(0, next_fruit)\n    print(a_list)\n    for i in a_list:\n        if i.startswith(\"P\"):\n            print(i)\n\n#series_one()\n\n# SERIES 2\n\ndef series_two():\n    print(a_list)\n    a_list.pop()\n    print(a_list)\n    ask_to_remove_fruit = input(\"Which fruit should we delete: \")\n    for fruit in a_list:\n        if ask_to_remove_fruit in a_list:\n            a_list.remove(ask_to_remove_fruit)\n            print(a_list)\n#series_two()\n\n# SERIES 3\n\ndef series_three():\n    new_list = a_list[:]\n    for i in a_list:\n        answer = input(\"Do you like \" + i.lower() + \"? \")\n        while answer != \"yes\" and answer != \"no\":\n            answer = input(\"Please enter yes or no: \")\n        if answer == \"no\":\n            new_list.remove(i)\n    print(new_list)\n\n#series_three()\n\n# SERIES 4\n\ndef series_four():\n    new_list = a_list.copy()\n    list_items_reversed = []\n    for item in new_list:\n        new_item = item[::-1]\n        list_items_reversed.append(new_item)\n    a_list.pop()\n    print(a_list)\n    print(list_items_reversed)\n\n#series_four()\n","sub_path":"students/sbolsen/lesson03/list_lab.py","file_name":"list_lab.py","file_ext":"py","file_size_in_byte":1536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"150761218","text":"import functools\n\n\ndef solve(a, b):\n    n = len(a)\n\n    @functools.lru_cache(None)\n    def dp(i, x):\n        if x > b:\n            return 0\n        if i == n:\n            return x\n        res = dp(i + 1, x * 10 + a[i]) + dp(i + 1, a[i] * (10 ** i) + x)\n        return res % mod\n\n    return dp(1, a[0])\n\n\nif __name__ == '__main__':\n    mod = 10 ** 9 + 7\n    q = int(input())\n    for _ in range(q):\n        a = [int(c) for c in input()]\n        b = int(input())\n        print(solve(a, b))\n","sub_path":"bigo/codetour_challenge3/D.py","file_name":"D.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"114861686","text":"# -*- coding:utf-8 -*-\nclass Solution:\n    def recurs(self,n,i):\n        if(i==n):\n            return 0\n        if(i==n-1):\n            return 1\n        if(i==n-2):\n            return 2\n\n        sum = 0\n        j=1\n        while(i+j<n):\n            sum += self.recurs(n,i+j)\n            j+=1\n        return sum+1\n    def jumpFloorII(self, number):\n        # write code here\n        return self.recurs(number,0)\n","sub_path":"递归/JumpFloorII.py","file_name":"JumpFloorII.py","file_ext":"py","file_size_in_byte":411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"559191633","text":"from Tkinter import *\nfrom user import User\n\n\nclass Invitation_Viewer_Window:\n    BASE_DIR = 'dbs'\n\n    def __init__(self, master, parent, user):\n        self.user = user\n        self.parent = parent\n\n        self.frame = Frame(master, relief=FLAT)\n\n        self.init_frame_pending()\n        self.frame_pending.grid()\n\n        self.init_frame_sent()\n        self.frame_sent.grid(sticky=N + E + S + W)\n\n        self.frame_quit = Button(\n            self.frame,\n            text='Home',\n            command=self.handler_goto_homepage)\n        self.frame_quit.grid()\n\n        return\n\n    def init_frame_pending(self):\n        self.frame_pending = LabelFrame(\n            self.frame,\n            text='Pending Invitations')\n        self.frame_pending.columnconfigure(0, weight=2)\n        self.frame_pending.columnconfigure(1, weight=2)\n        self.frame_pending.columnconfigure(2, weight=22)\n        self.frame_pending.columnconfigure(3, weight=2)\n        self.frame_pending.columnconfigure(4, weight=1)\n        self.frame_pending.columnconfigure(5, weight=1)\n\n        self.frame_pending_header_docid = Label(\n            self.frame_pending,\n            text='Document')\n        self.frame_pending_header_docid.grid(row=0, column=0)\n\n        self.frame_pending_header_from = Label(\n            self.frame_pending,\n            text='From')\n        self.frame_pending_header_from.grid(row=0, column=1)\n\n        self.frame_pending_header_message = Label(\n            self.frame_pending,\n            text='Message')\n        self.frame_pending_header_message.grid(row=0, column=2)\n\n        self.frame_pending_header_time = Label(\n            self.frame_pending,\n            text='Time')\n        self.frame_pending_header_time.grid(row=0, column=3)\n\n        self.frame_pending_header_action = Label(\n            self.frame_pending,\n            text='Action')\n        self.frame_pending_header_action.grid(row=0, column=4, columnspan=2)\n\n        res = self.user.manage.manage_User.get_invitations_to(self.user.info['id'])\n        start = 1\n        self.pending_invitations = []\n        for row in res:\n            self.pending_invitations.append([\n                Label(\n                    self.frame_pending,\n                    text=row['docid']\n                ),\n                Label(\n                    self.frame_pending,\n                    text=row['userid_from']\n                ),\n                Label(\n                    self.frame_pending,\n                    text=row['content']\n                ),\n                Label(\n                    self.frame_pending,\n                    text=row['time']\n                ),\n                Button(\n                    self.frame_pending,\n                    text='Accept'\n                ),\n                Button(\n                    self.frame_pending,\n                    text='Deny'\n                )\n            ])\n            self.pending_invitations[-1][0].grid(row=start, column=0)\n            self.pending_invitations[-1][1].grid(row=start, column=1)\n            self.pending_invitations[-1][2].grid(row=start, column=2)\n            self.pending_invitations[-1][3].grid(row=start, column=3)\n            self.pending_invitations[-1][4].grid(row=start, column=4)\n            self.pending_invitations[-1][5].grid(row=start, column=5)\n            start += 1\n\n        return\n\n    def init_frame_sent(self):\n        self.frame_sent = LabelFrame(\n            self.frame,\n            text='Sent Invitations')\n        self.frame_sent.columnconfigure(0)\n\n        self.frame_sent_header_docid = Label(\n            self.frame_sent,\n            text='Document')\n        self.frame_sent_header_docid.grid(row=0, column=0)\n\n        self.frame_sent_header_from = Label(\n            self.frame_sent,\n            text='From')\n        self.frame_sent_header_from.grid(row=0, column=1)\n\n        self.frame_sent_header_message = Label(\n            self.frame_sent,\n            text='Message')\n        self.frame_sent_header_message.grid(row=0, column=2)\n\n        self.frame_sent_header_time = Label(\n            self.frame_sent,\n            text='Time')\n        self.frame_sent_header_time.grid(row=0, column=3)\n\n        self.frame_sent_header_action = Label(\n            self.frame_sent,\n            text='Status')\n        self.frame_sent_header_action.grid(row=0, column=4)\n\n        res = self.user.manage.manage_User.get_invitations_from(self.user.info['id'])\n        start = 1\n        self.sent_invitations = []\n        for row in res:\n            self.sent_invitations.append([\n                Label(\n                    self.frame_sent,\n                    text=row['docid']\n                ),\n                Label(\n                    self.frame_sent,\n                    text=row['userid_to']\n                ),\n                Label(\n                    self.frame_sent,\n                    text=row['content']\n                ),\n                Label(\n                    self.frame_sent,\n                    text=row['time']\n                ),\n                Label(\n                    self.frame_sent,\n                    text=row['status']\n                )\n            ])\n            self.sent_invitations[-1][0].grid(row=start, column=0)\n            self.sent_invitations[-1][1].grid(row=start, column=1)\n            self.sent_invitations[-1][2].grid(row=start, column=2)\n            self.sent_invitations[-1][3].grid(row=start, column=3)\n            self.sent_invitations[-1][4].grid(row=start, column=4)\n            start += 1\n\n        return\n\n    def handler_goto_homepage(self):\n        self.frame.grid_remove()\n        self.parent.frame.grid()\n        return\n\nif __name__ == \"__main__\":\n    root = Tk()\n    user = User(2)\n    tb = Invitation_Viewer_Window(root, 0, user)\n    tb.frame.pack(fill='both', expand=1)\n    mainloop()\n","sub_path":"invitation_win.py","file_name":"invitation_win.py","file_ext":"py","file_size_in_byte":5788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"475426110","text":"# -*- coding: utf-8 -*-\n\n# Scrapy settings for spider_51job_search project\n#\n# For simplicity, this file contains only settings considered important or\n# commonly used. You can find more settings consulting the documentation:\n#\n#     http://doc.scrapy.org/en/latest/topics/settings.html\n#     http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html\n#     http://scrapy.readthedocs.org/en/latest/topics/spider-middleware.html\n\nCODELTAFETCH_ENABLED = True\nCODELTAFETCH_DIR = '/mnt/qinzhihao/search'\n\n\nITEM_PIPELINES = {\n   'spider_51job_search.pipelines.CoDeltaFetch': 300,\n   'spider_51job_search.pipelines.Spider51JobSearchPipeline': 200,\n}\n\nBOT_NAME = 'search_51job'\n\nSPIDER_MODULES = ['spider_51job_search.spiders']\nNEWSPIDER_MODULE = 'spider_51job_search.spiders'\n\n# Exporting Settings\n# FEED_URI = '/Users/Han/Desktop/Code/company_info_gleaner_II/spider_51job_search/%(name)s_20170610.txt'\n# FEED_URI = '/root/users/JH/company_info_gleaner_II/spider_51job_search/%(name)s_20170610_名称海外.txt'\nFEED_URI = '/mnt/qinzhihao/url_crawl/%(name)s_%(searchword)s_%(time)s.csv'\n# FEED_URI = '%(name)s_%(time)s.csv'\nFEED_FORMAT = 'csv'\nFEED_STORAGES = {'file': 'scrapy.extensions.feedexport.FileFeedStorage',}\nFEED_EXPORTERS = {'csv': 'spider_51job_search.items.TxtItemExporter',}\nFEED_STORE_EMPTY = False\nFEED_EXPORT_FIELDS = [\"positionName\",\n                      \"companyFullName\",\n                      \"qc_job_loc\",\n                      \"salary\",\n                      \"qc_job_date\",\n                      \"financeStage\",\n                      \"companySize\",\n                      \"industryField\",\n                      \"description\",\n                      \"qc_co_address\"]\nCSV_DELIMITER = \"|\"\n\n# Logging Settings\nLOG_ENABLED = True\nLOG_ENCODING = 'utf-8'\nLOG_FILE = 'search_51job.log'\nLOG_LEVEL = 'INFO'\nLOG_STDOUT = True\n\n# Failure Retries\nRETRY_ENABLED = True\nRETRY_TIMES = 5\nRETRY_HTTP_CODES = [400,403,404,408,429,500,502,503]\n\n# Obey robots.txt rules\nROBOTSTXT_OBEY = False\n\n# Enable or disable downloader middlewares\nDOWNLOADER_MIDDLEWARES = {\n    'scrapy.contrib.downloadermiddleware.useragent.UserAgentMiddleware': None,\n    'spider_51job_search.middlewares.RandomUserAgentMiddleware': 200,\n    'scrapy.downloadermiddlewares.httpcompression.HttpCompressionMiddleware': 810,\n}\n\n# Enable and configure the AutoThrottle extension (disabled by default)\n# See http://doc.scrapy.org/en/latest/topics/autothrottle.html\nAUTOTHROTTLE_ENABLED = False\n# The initial download delay\nAUTOTHROTTLE_START_DELAY = 1\n# The maximum download delay to be set in case of high latencies\nAUTOTHROTTLE_MAX_DELAY = 2\n# The average number of requests Scrapy should be sending in parallel to\n# each remote server\n#AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0\n# Enable showing throttling stats for every response received:\n#AUTOTHROTTLE_DEBUG = False\n\nCONCURRENT_REQUESTS = 64\nDOWNLOAD_DELAY = 0.25","sub_path":"theproduct/qinzhihao/search/spider_51job_search/spider_51job_search/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":2894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"96463809","text":"#!/usr/bin/env python\n\nfrom distutils.core import setup\n\ntry:\n    readme = open('README', 'r')\n    ldesc = readme.read(8192)\n    readme.close()\nexcept:\n    ldesc = \"\"\n\nsetup(\n    name='pyfantom',\n    version='0.1',\n    author='Nicolas Schodet',\n    author_email='nico@ni.fr.eu.org',\n    description='Python Wrapper for LEGO Mindstorms NXT Fantom Driver',\n    url='http://git.ni.fr.eu.org/?p=pyfantom.git;a=summary',\n    license='Gnu GPL v2',\n    py_modules=['pyfantom'],\n    long_description=ldesc\n)\n","sub_path":"nxt-python/pyfantom/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"14282838","text":"__author__ = 'rguo12'\n\nimport cPickle\nedge_path = 'C:\\\\weibodataset\\\\7_GCG_JL.txt'\ninner_path = 'C:\\\\weibodataset\\\\innei_d.pkl'\n\ndef save_obj(obj,op):\n    with open(op,'w') as of:\n        cPickle.dump(obj,of)\n\ndef txt_to_dict():\n    edge_f = open(edge_path,'r')\n    innei_d = {} #innei[node] = [innei1,innei2...]\n\n    for line in edge_f:\n        line.replace('\\n','')\n        line = line.split('\\t')\n        follower = line[0]\n        followee = line[1]\n        if followee not in innei_d:\n            innei_d[followee] = [follower]\n        else:\n            innei_d[followee].append(follower)\n\n    save_obj(innei_d,inner_path)\n\nif __name__ == '__main__':\n    txt_to_dict()","sub_path":"asonam_caspre/heat_map_preproc/check_innei.py","file_name":"check_innei.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"607520779","text":"from core.siamese import Siamese\n\n\n# model = Siamese('shallow_mnist', input_shape=(28, 28, 3), embedding_size=64, strategy='batch_all')\n# model.load_weights('cache/cache-190213-131256/training/checkpoint-07.h5')\nmodel = Siamese.restore_from_config('cache/cache-190220-203237/config.json')\nmodel.make_embeddings('data_mnist/train', 'data_mnist/train.csv', batch_size=200)\nmodel.predict('data_mnist/train_subset')\nmodel.make_csv('cache/cache-190213-131256/idx_to_whales_mapping.npy')\n# model.load_embeddings('cache/cache-190205-070856/embeddings.pkl')\n# model.load_predictions('cache/cache-190205-072026/predictions.pkl')\n# model.make_kaggle_csv('cache/cache-190205-065005/idx_to_whales_mapping.npy')\n# model.draw_tsne(model.predictions.values[:, 1:])\n\n\n\n\n\n","sub_path":"eval_mnist.py","file_name":"eval_mnist.py","file_ext":"py","file_size_in_byte":755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"103158464","text":"from collections import deque\n\ndef topo(degrees, graph):  \n    q = deque()\n    for k, v in degrees.items():\n        if v == 0:\n            q.append(k)\n\n    while len(q) > 0:\n        k = q.pop()\n        for neighboor in graph[k]:\n            degrees[neighboor] -= 1\n            if degrees[neighboor] == 0:\n                q.append(neighboor)\n            if degrees[neighboor] < 0:\n                print(\"NIE\")\n                return\n\n    for k, v in degrees.items():\n        if (v > 0):\n            print(\"NIE\")\n            return\n    print(\"TAK\")\n\nz = int(input())\ndegrees = {}\ngraph = {}\nfor i in range(z):\n    line = input()\n    s = line.split(\" \")\n    x = s[1]\n    y = s[4]\n\n    if x not in degrees:\n        degrees[x] = 0\n\n    if y not in graph:\n        graph[y] = []\n\n    if y not in degrees:\n        degrees[y] = 0\n    degrees[y] += 1\n\n    if x not in graph:\n        graph[x] = []\n    graph[x].append(y)\ntopo(degrees, graph)\n\n    \n\n\n\n\n#     for x in range(2, len(s)):\n#         if x != 2: \n#             message = message + \" \"\n#         message = message + s[x]\n# tuples.sort(key=lambda x: x[0])\n\n# for i in tuples:\n#     print (i[0].strftime('%d.%m.%4Y %H:%M'), i[1])\n    ","sub_path":"Python/2018-2019/C.py","file_name":"C.py","file_ext":"py","file_size_in_byte":1180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"299927155","text":"# -*- coding: utf-8 -*-\n\nfrom odoo import models, fields, api, exceptions\nfrom odoo.addons.base.models.res_partner import _tz_get\n\n\n\n\n\nclass Footballer(models.Model):\n    # _name = 'om_football.footballer'\n    _description = 'Footballer Record'\n    _inherit = 'account.analytic.line'\n\n    @api.model\n    def test_cron_job(self):\n        print(\"ABC\")\n\n    @api.model\n    def openURL(self):\n        q = \"sun\"\n        return {\n            'type': 'ir.actions.act_url',\n            'url': \"http://www.google.bg/?q=%s\" % q,\n            'target': 'new',  # open in a new tab\n        }\n\n    @api.model\n    def default_get(self, fields):\n        res = super(Footballer, self).default_get(fields)\n        if (not fields or 'partner_id' in fields) and 'partner_id' not in res:\n            if self.env.context.get('active_id'):\n                res['partner_id'] = self.env.context['active_id']\n        return res\n\n    @api.depends('footballer_age')\n    def _compute_age_group(self):\n        for rec in self:\n            rec.age_group = 'major'\n            if rec.footballer_age:\n                if rec.footballer_age < 20:\n                    rec.age_group = 'minor'\n\n    @api.onchange('partner_id')\n    def onchange_partner_id(self):\n        for rec in self:\n            return {'domain': {\n                'order_id': [('partner_id', '=', rec.partner_id.id)]}}\n\n    @api.model\n    def default_get(self, fields_list=[]):\n        print('field list', fields_list)\n        rec = super(Footballer, self).default_get(fields_list)\n        rec['active'] = True\n        rec['footballer_name'] = \"Ribery\"\n        print(rec)\n        return rec\n\n    @api.constrains('partner_id', 'attendee_ids')\n    def _check_partner_not_in_attendees(self):\n        for rec in self:\n            if rec.partner_id and rec.partner_id in rec.attendee_ids:\n                raise exceptions.ValidationError(\n                    \"A session's partner can't be an attendee\")\n\n    def name_create(self, name):\n        print(\"Name\", name)\n        res = self.create({'name': name})\n        return res\n\n    def write(self, values):\n        print(\"Values\", values)\n        values['active'] = True\n        rtn = super(Footballer, self).write(values)\n        print(\"Return data\", rtn)\n        return rtn\n\n\n    def action_confirm(self):\n        footballers_age_major = self.env['account.analytic.line'].search(\n            ['age_group' '=', 'major'])\n        print('major footballers', footballers_age_major)\n        footballers_age_minor = self.env['account.analytic.line'].search(\n            ['age_group' '=', 'minor'])\n        print('minor footballers', footballers_age_minor)\n\n    def init(self):\n        query = \"\"\"\n                SELECT footballer_name\n                FROM \"account_analytic_line\"\n                WHERE active = 'true'\n                \"\"\"\n        self.env.cr.execute(query)\n        record = self.env.cr.fetchall()\n        return record\n\n    footballer_name = fields.Char(string='Footballer Name', required=True)\n    is_footballer = fields.Boolean(string='Is Footballer')\n    footballer_age = fields.Integer('Age')\n    age_group = fields.Selection(selection=[\n        ('major', 'Major'),\n        ('minor', 'Minor'),\n    ], string=\"Age Group\", default='major', compute='_compute_age_group')\n    footballer_number = fields.Integer('Number')\n    partner_id = fields.Many2one('res.partner', string='Customer')\n    attendee_ids = fields.Many2many('res.partner', string='Attendees')\n    order_id = fields.Many2one('sale.order', string='Sale Order')\n    description = fields.Text(string='Description')\n    image = fields.Binary(string='Image')\n    # fc_id = fields.One2many('account.analytic.line', string=\"Responsible\")\n    active = fields.Boolean(string=\"Active\")\n\n\nclass Football_Club(models.Model):\n    # _name = 'om_football.football_club'\n    _inherit = 'account.analytic.line'\n\n\n\n    tz = fields.Selection(_tz_get, string='Timezone', required=True,\n                          default=lambda self: self.env.user.tz or 'UTC')\n    football_club = fields.Char(string=\"FC\", required=True)\n    created_date = fields.Date(default=fields.Date.today)\n    image = fields.Binary(string='Image')\n    description = fields.Text()\n    # footballer_ids = fields.Many2one('account.analytic.line')\n\n","sub_path":"models/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"118645231","text":"import csv\nimport sys\n\noutput_acct = \"nodes.csv\"\noutput_tx = \"transactions.csv\"\n\nargv = sys.argv\nif len(argv) < 3:\n  print(\"Usage: python %s [AcctCSV] [TxCSV]\" % argv[0])\n  exit(1)\n\ninput_acct = argv[1]\ninput_tx = argv[2]\n\nfirst_fraud = dict()\n\nwf = open(output_tx, \"w\")\nwriter = csv.writer(wf)\nwriter.writerow([\"sourceNodeId\", \"targetNodeId\" , \"value\", \"time\"])\n\nwith open(input_tx, \"r\") as rf:\n  reader = csv.reader(rf)\n  next(reader)\n  for row in reader:\n    step = row[0]\n    ttype = row[1]\n    amt = row[2]\n    src = row[3]\n    dst = row[6]\n    isFraud = row[9] == \"1\"\n    if \"CASH\" not in ttype:\n      writer.writerow([src, dst, amt, step])\n      if isFraud and src not in first_fraud:\n        first_fraud[src] = step\n\nwf.close()\n\n\nwf = open(output_acct, \"w\")\nwriter = csv.writer(wf)\nwriter.writerow([\"nodeid\", \"isFraud\", \"init_balance\", \"fraudStep\"])\n\nwith open(input_acct, \"r\") as rf:\n  reader = csv.reader(rf)\n  next(reader)\n  for row in reader:\n    acct = row[0]\n    init_balance = row[2]\n    isFraud = row[8] == \"true\"\n    fraud_frag = 1 if isFraud else 0\n    fraud_step = first_fraud[acct] if isFraud and acct in first_fraud else -1\n    writer.writerow([acct, fraud_frag, init_balance, fraud_step])\n\nwf.close()\n\n","sub_path":"scripts/obsolete/convert_csv.py","file_name":"convert_csv.py","file_ext":"py","file_size_in_byte":1224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"370224561","text":"#!/usr/bin/env python\n\nimport subprocess\n\nMAX_NODES = 48\nITERATIONS = {\"flat\": 0, \"closed\": 0, \"_check\": 2}\nBENCH = \"tpcc\"\n\nfor nest in [\"flat\", \"closed\", \"_check\"]:\n\tfor i in range(ITERATIONS[nest]):\n\t\tfor n in [1, 4, 16]:\n\t\t\tif n <= int(MAX_NODES):\n\t\t\t\tfor ops in [1, ]:\n\t\t\t\t\tprint(\"Running on %d nodes, iteration %d/%d, nest=%s, ops=%s\" % (n, i, ITERATIONS[nest], nest, ops))\n\t\t\t\t\tsubprocess.call(\"sleep 1\", shell=True)\n\t\t\t\t\tif nest == \"_check\":\n\t\t\t\t\t\tjarstr = \"--jar check\"\n\t\t\t\t\telse:\n\t\t\t\t\t\tjarstr = \"--jar bench -N %s\" %  nest\n\t\t\t\t\tcmd = \"bin/hytest.py -n %d %s -b %s -O %s -w 80 -e 40 --time-out 240 hyflow.haistm.checkpointProb=20\" % (n, jarstr, BENCH, ops)\n\t\t\t\t\tsubprocess.call(cmd, shell=True)\n\n\n","sub_path":"bin/batch.py","file_name":"batch.py","file_ext":"py","file_size_in_byte":705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"100123828","text":"#combined ex 1&2\nfrom matplotlib import pyplot as plt\n\nx_data1 = [0.1, 0.2, 0.3, 0.4]\ny_data1 = [1, 2, 3, 4]\n\nx_data2 = [0.1, 0.2, 0.3, 0.4]\ny_data2 = [1, 4, 9, 16]\n\nfig = plt.figure()\nax1 = fig.add_subplot(121)\nax2 = fig.add_subplot(122)\nax1.plot(x_data1, y_data1, \"--\", label='data 1')\nax2.plot(x_data2, y_data2, \"-\", marker=\"s\", label='data 2')\nax1.legend()\nax2.legend()\n\nplt.savefig(\"plot2.pdf\")\nplt.show()","sub_path":"chapter_15/exercises_book_ch15.py","file_name":"exercises_book_ch15.py","file_ext":"py","file_size_in_byte":410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"345968424","text":"#! /usr/bin/env python\n#Import modules{{{1\n#For unicode support:{{{2\nimport codecs\nimport sys\n#2}}}\n#xml parsing{{{2\nfrom lxml import etree\n#2}}}\n#1}}}\n#Main module{{{1\ndef main():\n    try:\n        sourcefile = sys.argv[1]\n        languages = sys.argv[2:]\n    except:\n        print('Usage: {} <path to source tmx> <language> ... <language n>'.format(sys.argv[0]))\n        sys.exit(0)\n    with codecs.open(sourcefile, encoding=\"utf-8\") as f:\n        xmlstring = f.read()\n    #Create an lxml etree object of the string\n    root = etree.fromstring(xmlstring)\n    for language in languages:\n        xpathq = \"//tuv[@xml:lang='{}']//seg\".format(language)\n        segs = root.xpath(xpathq)\n        preparedinput = \"\"\n        for seg in segs:\n            preparedinput += \"\\n!!!!\\n\" + seg.text\n        f = open('{}_{}.prepared'.format(sourcefile,language), 'w')\n        f.write(preparedinput.strip())\n        f.close()\n#1}}}\n#Start the script{{{1\nif __name__ == \"__main__\":\n    main()\n#1}}}\n","sub_path":"database_insertion/tmxtoparserimput.py","file_name":"tmxtoparserimput.py","file_ext":"py","file_size_in_byte":984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"184827455","text":"'''\r\nCreated on 2016. 9. 22.\r\n\r\n@author: acorn\r\n'''\r\n# multiline = input(\"문장을 넣어주세요\")\r\nmultiline2 = \"\"\"안녕하세오. Python 세계로 오신걸 환영합니다.\r\n파이션은 비단뱀 처럼 매력적인 언어입니다.\"\"\"\r\n\r\nstringlist0 = multiline2.split(sep='\\n')\r\np = 0 \r\nprint(stringlist0)\r\nprint(len(stringlist0))\r\nfor z in stringlist0:\r\n    stringlist = z.split(sep=\" \")\r\n    for i in stringlist:\r\n        print(i)\r\n        p +=1 \r\n    \r\n    \r\n\r\nprint(\"전체단어수\", p)\r\n","sub_path":"chap02_control/exam/multiline.py","file_name":"multiline.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"341126348","text":"# -*- coding: utf-8 -*-\n\nfrom base_object import BaseObject\n\n\nclass Contents(BaseObject):\n\n    table = 'weibo'\n    collection = 'content'\n    keyMapping = (\n            'wid', 'content', 'ct_date', 'ct_time',\n            'attitude', 'repost', 'comment'\n        )\n    def __init__(self, data=None):\n        self.wid = 0\n        self.content = ''\n        self.ct_date = ''\n        self.ct_time = ''\n        self.attitude = 0\n        self.repost = 0\n        self.comment = 0\n\n        BaseObject.__init__(self, data)\n","sub_path":"crawler/GPCrawler/types/contents.py","file_name":"contents.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"238116377","text":"\"\"\"\nfind all a, b, c, d in q such that\nf(a) + f(b) = f(c) - f(d)\n\"\"\"\n\n#q = set(range(1, 10))\n#q = set(range(1, 200))\nq = (1, 3, 4, 7, 12)\n\n\ndef f(x):\n    return x * 4 + 6\n\n# Your code here\nq2 = []\n\nfor number in q:\n    true = f(number)\n    q2.append(true)\n\ncache = {}\ncounter = [x for x in len(q2)]\n\nfor x in range(len(q2) - 1):\n        cache[((q2[x]) + (q2[x + 1]))] = (q[x], q[x + 1])\n\nprint(cache)\n\n\n## Stuck\n","sub_path":"applications/sumdiff/sumdiff.py","file_name":"sumdiff.py","file_ext":"py","file_size_in_byte":412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"14277474","text":"import find_board\nimport cv2\nimport numpy\n\ncorners = find_board.corners(\"board.jpg\")\n\n\nstartInp = input().split()\nendInp   = input().split()\nstart = []\nend = []\n'''\nperfect_square = (corners[0], (corners[0][0], corners[2][1]), corners[2], (corners[0][1], corners[2][0]))\n\ntransformation = numpy.linalg.lstsq(numpy.array([perfect_square[1], perfect_square[3]]), numpy.array([corners[1], corners[3]]), rcond=None)\n\nprint(transformation)\nprint(\"^=^=^=^=^=^=^=^=^=\")\ntransformation = transformation[0]\nprint(perfect_square)\nprint(numpy.matmul(corners, transformation))\n\nprint(\"============================\") '''\nfor i in range(0, len(startInp)):\n\tstart.append(int(startInp[i]))\t\n\tend.append(int(endInp[i]))\n\ndef cor(x):\n\treturn abs(int(x))\n\ndef space2point(point):\n\tbox_height = cor((corners[0][1] - corners[3][1] + corners[1][1] - corners[2][1]) / 16)\n\tbox_width  = cor((corners[0][0] - corners[1][0] + corners[3][0] - corners[2][0]) / 16)\n\n\treturn(cor(corners[0][0] + (point[0]) * (box_width) - box_width / 2), cor(corners[0][1] + (point[1]) * (box_height) - box_height / 2))\n\ndef tup(arr):\n\treturn(int(arr[0]), int(arr[1]))\n\n#arrow_start = numpy.matmul(space2point(start), transformation)\n\n#arrow_end = numpy.matmul(space2point(end), transformation)\n\narrow_start = space2point(start)\n\narrow_end = space2point(end)\n\nimg = cv2.imread(\"board.jpg\")\n'''\nprint(\"=========\")\nprint(space2point(start))\nprint(arrow_start)\nprint(\"==========\")\nprint(space2point(end))\nprint(arrow_end)\n'''\n#cv2.rectangle(img, tup(perfect_square[0]), tup(perfect_square[2]), (255, 255, 255))\n#cv2.rectangle(img, (100, 100), (200, 200), (255, 255, 255))\ncv2.arrowedLine(img, tup(arrow_start), tup(arrow_end), (255, 100, 50, 50), 15)\n\ncv2.imshow(\"image\", img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n","sub_path":"draw_arrow.py","file_name":"draw_arrow.py","file_ext":"py","file_size_in_byte":1767,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"323080520","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\n\n__all__ = [\n    'plot_predictiveness_curve',\n]\n\n\ndef _normalize(arr):\n    return (arr-arr.min()) / (arr.max()-arr.min())\n\n\ndef plot_predictiveness_curve(risks, labels, normalize=False, points=100,\n        figsize=(4.5, 10), fontsize=14, **kwargs):\n    \"\"\"\n    Plot predictiveness curve.\n\n    Parameters\n    ----------\n    risks : array_like, shape = [n_samples]\n        Risks or probabilities for something happens\n\n    labels : array_like, shape = [n_samples]\n        Labels of 0 or 1 for sample data. 0 means negative and 1 means\n        positive.\n\n    normalize : boolean, default False\n        If the risk data is not normalized to the 0-1 range, normalize it.\n\n    points : int, default 100.\n        Determine the fineness of the plotted points. The larger the number,\n        the finer the detail.\n\n    figsize : tuple, default (4.5, 10).\n        Width, height in inches. If not provided, defaults to = (4.5, 10).\n\n    fontsize : int, default 14.\n        Font size for labels in plots.\n    \"\"\"\n    risks = np.array(risks)\n    labels = np.array(labels)\n    points = np.linspace(0, 1, points)\n\n    if normalize:\n        risks = _normalize(risks)\n\n    labels = labels[np.argsort(risks)]\n    risks = np.sort(risks)\n    num_positive = labels.sum()\n    risk_percentiles = []\n    true_positive_fractions = []\n\n    calculate_risk_percentiles = np.frompyfunc(\n        lambda p: np.count_nonzero(risks<=p)/len(risks), 1, 1)\n    calculate_true_positive_fractions = np.frompyfunc(\n        lambda p: np.count_nonzero(labels[risks>=p])/num_positive, 1, 1)\n\n    risk_percentiles = calculate_risk_percentiles(points)\n    true_positive_fractions = calculate_true_positive_fractions(points)\n\n    margin = 0.03\n    lim = (0-margin, 1+margin)\n    plt.figure(figsize=figsize)\n\n    plt.subplot(2, 1, 1)\n    plt.plot(np.append(0, risk_percentiles),\n             np.append(0, points))\n    plt.ylabel('Risk', fontsize=fontsize)\n    plt.xlim(lim)\n    plt.ylim(lim)\n    plt.grid(True)\n\n    plt.subplot(2, 1, 2)\n    plt.plot(np.append(0, risk_percentiles),\n             np.append(1, true_positive_fractions))\n    plt.xlabel('Risk percentiles', fontsize=fontsize)\n    plt.ylabel('TPR', fontsize=fontsize)\n    plt.xlim(lim)\n    plt.ylim(lim)\n    plt.grid(True)\n","sub_path":"predictiveness_curve/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":2290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"614166157","text":"from django.shortcuts import render,redirect,HttpResponseRedirect\nfrom .forms import Task_assign\nfrom .models import task\nfrom datetime   import datetime,date\nfrom django.contrib import messages as msg\n\n# Create your views here.\ndef Tododetail(req):\n\n    if req.method == 'POST':\n        dd = req.POST.get(\"deadline\")\n        print(dd , \"#\"*60 , date.today())\n        if dd[:10] < str(date.today()) and dd[11:] < str(datetime.now().strftime(\"%H:%M\")):\n            msg.add_message(req,msg.SUCCESS,'Deadline is not valid')\n            print(\"hello\" *20)\n\n        else:\n            assign = task(Task_title = req.POST.get(\"Task_title\"),\n                            Task_img = req.POST.get(\"Task_img\"),\n                            discription_img = req.POST.get(\"discription_img\"),\n                            discription = req.POST.get(\"discription\"),\n                            deadline = dd,\n                            date_joined = datetime.now())\n            assign.save()\n        taskAssigned = Task_assign()\n\n    else:\n        taskAssigned = Task_assign()\n    tsk = task.objects.all()\n    return render(req, \"todo/index.html\",{'form': taskAssigned, 'task': tsk})\n\ndef update(req,id):\n\n    if req.method == 'POST':\n        ti = task.objects.get(pk = id)\n        tsk = task(req.POST , instance = tsk)\n        tsk.save()\n        # assign = task(Task_title = req.POST.get(\"Task_title\"),\n        #                 Task_img = req.POST.get(\"Task_img\"),\n        #                 discription_img = req.POST.get(\"discription_img\"),\n        #                 discription = req.POST.get(\"discription\"),\n        #                  deadline = req.POST.get(\"deadline\"))\n        # assign.save()\n        # taskAssigned = Task_assign()\n\n    else:\n        ti = task.objects.get(pk = id)\n        tsk = Task_assign(instance = ti)\n    return render(req, \"todo/update.html\",{'task':tsk})\n\n\n\ndef delete_data(req,id):\n    if req.method == 'POST':\n        ti = task.objects.get(pk = id)\n\n        ti.delete()\n        return HttpResponseRedirect (\"/\")","sub_path":"Tasks/Todo_/Todo_list/todo/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"545286835","text":"# -*- coding: utf-8 -*-\n\n# Copyright (c) 2016 - 2019 Detlev Offenbach <detlev@die-offenbachs.de>\n#\n\n\"\"\"\nModule implementing a dialog to enter the minimum protocol for a host.\n\"\"\"\n\n\nfrom PyQt5.QtCore import pyqtSlot\nfrom PyQt5.QtWidgets import QDialog, QDialogButtonBox\n\nfrom .Ui_HgUserConfigHostMinimumProtocolDialog import (\n    Ui_HgUserConfigHostMinimumProtocolDialog\n)\n\n\nclass HgUserConfigHostMinimumProtocolDialog(\n        QDialog, Ui_HgUserConfigHostMinimumProtocolDialog):\n    \"\"\"\n    Class implementing a dialog to enter the minimum protocol for a host.\n    \"\"\"\n    def __init__(self, allowedProtocols, parent=None, host=\"\", protocol=\"\"):\n        \"\"\"\n        Constructor\n        \n        @param allowedProtocols dictionary containing the allowed protocols\n            with the value as key and the display string as value\n        @type dict\n        @param parent reference to the parent widget\n        @type QWidget\n        @param host host name\n        @type str\n        @param protocol name of the minimum protocol for the host\n        @type str\n        \"\"\"\n        super(HgUserConfigHostMinimumProtocolDialog, self).__init__(parent)\n        self.setupUi(self)\n        \n        self.minimumProtocolComboBox.addItem(\"\", \"\")\n        for minimumProtocol in sorted(allowedProtocols.keys()):\n            self.minimumProtocolComboBox.addItem(\n                allowedProtocols[minimumProtocol], minimumProtocol)\n        \n        self.hostEdit.setText(host)\n        index = self.minimumProtocolComboBox.findData(protocol)\n        if index == -1:\n            index = 0\n        self.minimumProtocolComboBox.setCurrentIndex(index)\n        \n        msh = self.minimumSizeHint()\n        self.resize(max(self.width(), msh.width()), msh.height())\n        \n        self.__updateOkButton()\n    \n    def __updateOkButton(self):\n        \"\"\"\n        Private method to update the status of the Ok button.\n        \"\"\"\n        enabled = (\n            bool(self.hostEdit.text()) and\n            self.minimumProtocolComboBox.currentIndex() > 0\n        )\n        self.buttonBox.button(QDialogButtonBox.Ok).setEnabled(enabled)\n    \n    @pyqtSlot(str)\n    def on_hostEdit_textChanged(self, txt):\n        \"\"\"\n        Private slot to handle changes of the host edit.\n        \n        @param txt current text\n        @type str\n        \"\"\"\n        self.__updateOkButton()\n    \n    @pyqtSlot(int)\n    def on_minimumProtocolComboBox_currentIndexChanged(self, index):\n        \"\"\"\n        Private slot to handle the selection of a minimum protocol.\n        \n        @param index index of the selected entry\n        @type int\n        \"\"\"\n        self.__updateOkButton()\n    \n    def getData(self):\n        \"\"\"\n        Public method to retrieve the data.\n        \n        @return tuple containig the host name and the minimum protocol\n        @rtype tuple of two str\n        \"\"\"\n        return (\n            self.hostEdit.text().strip(),\n            self.minimumProtocolComboBox.itemData(\n                self.minimumProtocolComboBox.currentIndex())\n        )\n","sub_path":"PYTHON/Python_GUI/eric6-19.11/eric/eric6/Plugins/VcsPlugins/vcsMercurial/HgUserConfigHostMinimumProtocolDialog.py","file_name":"HgUserConfigHostMinimumProtocolDialog.py","file_ext":"py","file_size_in_byte":3032,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"529919287","text":"'''\nCreated on May 9, 2017\n\n@author: jamie\n'''\n\n\n\nclass Plotter(object):\n    '''\n    Plotter base class\n    '''\n    colors    = ['#76EEC6',  # Aquamarine\n                 '#8B008B',  # Dark Magenta\n                 '#2e8b57',  # Sea Green\n                 '#4b0082',  # Dark Violet\n                 '#00CED1',  # Dark Tourquise\n                 '#FF7F50',  # Coral\n                 '#92EFBB',  # Pale Spring Green\n                 '#AF64E6',  # soft violet\n                 '#40E0D0',  # torquoise\n                 '#FF69B4',  # hot pink\n                 '#F08080',  # light coral\n                 '#8B7D7B',  # misty rose 4\n                 '#8B6508',  # dark golden rod 4\n                 '#545454',  # light steel blue\n                 '#4B0082',  # indigo\n                 ]\n    lines     = ['-', '--', '-.', ':', '']\n    markers   = ['', '.', 'o', '*', '+']\n    linewidth = 1\n\n    data = None\n\n    def __init__(self, inputData):\n        '''\n        Constructor\n        '''\n        self.data = inputData\n\n    def plotAll(self):\n        pass\n\n    def setupAxis(self, ax1, fig1):\n\n        ax1.grid(True)\n        ax1.legend()\n\n        ax1.set_xlabel('time (s)')\n        fig1.autofmt_xdate()\n\n    ","sub_path":"analysis/plotter.py","file_name":"plotter.py","file_ext":"py","file_size_in_byte":1197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"398166604","text":"\nfrom odoo import models, fields, api\nfrom odoo.exceptions import ValidationError\n\nclass Course(models.Model):\n    _name = 'tr.project'\n\n    name = fields.Char(\n        string=\"Name of Project\",\n        required=True,\n    )\n    code = fields.Text(\n        string=\"Project ID\",\n        required=True,\n    )\n    budget = fields.Float(\n        digits=(6, 2),\n        string=\"Budget\",\n        domain=[('budget', '>', 0)],\n        required=True,\n    )\n    customer_ids = fields.One2many(\n        comodel_name=\"tr.take.customer\",\n        inverse_name=\"project_ids\",\n        string=\"Customers\",\n        domain=[('state', '!=', 'reject')],\n        required=False,\n        readonly=True,\n    )\n    balance = fields.Float(\n        digits=(6, 2),\n        string=\"Balance\",\n        compute='_compute_balance',\n        required=False,\n        readonly=True,\n    )\n\n    @api.multi\n    @api.constrains('code')\n    def _identify_same_code(self):\n        for r in self:\n            obj = self.search([('code', '=ilike', r.code), ('id', '!=', r.id)])\n            if obj:\n                raise ValidationError(\"Code duplicate\")\n\n    @api.constrains('budget')\n    def _check_budget(self):\n        if self.budget < 0:\n            raise ValidationError(\"Budget more than 0\")\n\n    @api.multi\n    @api.depends('customer_ids')\n    def _compute_balance(self):\n        for r in self:\n            r.balance = r.budget\n            ans = 0\n            for history in r.customer_ids:\n                if history.state == \"approve\":\n                    ans += history.money\n            r.balance = r.budget-ans\n\nclass Session(models.Model):\n    _name = 'tr.take.customer'\n\n    state = fields.Selection([\n        ('draft', 'Draft'),\n        ('wait', 'Wait'),\n        ('approve', 'Approve'),\n        ('reject', 'Reject'),\n    ], default='draft')\n\n    date = fields.Date(\n        string=\"Date\",\n        required=True,\n    )\n\n    cur_date = fields.Date.today()\n\n    user = fields.Many2one(\n        comodel_name=\"res.partner\",\n        string=\"User\",\n        required=True,\n        domain=[('customer', '=', True)],\n    )\n    project_ids = fields.Many2one(\n        string=\"Projects\",\n        comodel_name=\"tr.project\",\n        required=True,\n    )\n    balance = fields.Float(\n        string=\"Balance\",\n        related=\"project_ids.balance\",\n        readonly=True,\n    )\n    sale_ids = fields.Many2many(\n        comodel_name=\"res.users\",\n        string=\"Sale\",\n    )\n    new_field_ids = fields.Many2many(\n        comodel_name=\"\",\n        relation=\"\",\n        column1=\"\",\n        column2=\"\",\n        string=\"\",\n    )\n\n    money = fields.Float(\n        string=\"Money\",\n        digit=(6, 2),\n        required=True,\n    )\n    delete_ids = fields.Many2many(\n        comodel_name=\"delete.wizard\",\n        string=\"Deletes\",\n    )\n    order_ids = fields.Many2one('sale.order')\n\n    # @api.multi\n    # @api.constrains('user')\n    # def _identify_same_user(self):\n    #     for r in self:\n    #         obj = self.search([('user', '=', r.user), ('date', '=', r.date)])\n    #         if obj:\n    #             raise ValidationError(\"Please try again select user\")\n\n\n    @api.constrains('money')\n    def _check_money(self):\n        if self.money < 0:\n            raise ValidationError(\"Please try again input money\")\n\n    @api.constrains('date')\n    def _check_date(self):\n        if self.date < self.cur_date:\n            raise ValidationError(\"Please try again select date\")\n\n\n    @api.constrains('money')\n    def _check_something(self):\n        for r in self:\n            if r.money < r.balance:\n                r.state = 'approve'\n\n    @api.multi\n    def action_request(self):\n        self.state = 'wait'\n\n    @api.multi\n    def action_approve(self):\n        self.state = 'approve'\n\n    @api.multi\n    def action_reject(self):\n        self.state = 'reject'\n\n\n\n","sub_path":"models/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"140793140","text":"'''\nCreated on Apr 30, 2013\n\n@author: duane\n'''\nimport os\nimport commands\nimport re\nimport platform\nimport logging\n\nfrom error import AuditError\nauditError = AuditError()\ntry:\n    import json\nexcept ImportError:\n    import simplejson as json\n    \ndef packages():\n    if platform.dist()[0] == \"redhat\":\n        data = commands.getoutput(\"/usr/bin/yum -q -C list installed\")\n        json = {}\n        for line in data.split(\"\\n\"):\n            if len(line.split()) > 1:\n                json[line.split()[0]] = line.split()[1]\n    else:\n        data = commands.getoutput(\"/usr/bin/dpkg -l\")\n        json = {}\n        for line in data.split(\"\\n\"):\n            if len(line.split()) > 2:\n                json[line.split()[1]] = line.split()[2]\n    \n    return json\n\n\ndef main(path, C=None, name = 'packages'):\n    auditError.init(name, path)\n    if platform.dist()[0] == \"redhat\":\n        data = commands.getoutput(\"/usr/bin/yum -q -C list installed\")\n    else:\n        data = commands.getoutput(\"/usr/bin/dpkg -l\")\n    dataJson = open(os.path.join(path, name +'.json'), 'w')\n    dataRaw = open(os.path.join(path, name +'.txt'), 'w')\n    try:\n        dataRaw.write(data)\n        json.dump(packages(), dataJson, indent=4)\n    finally:\n        dataRaw.close()\n        dataJson.close()\n\nif __name__ == \"__main__\":\n    main('.')","sub_path":"modules/packages/packages.py","file_name":"packages.py","file_ext":"py","file_size_in_byte":1317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"616401841","text":"import os, errno\n\ndef create_dir_if_not_exists(directory):\n    \"\"\"\n    checks if directory given by path exists in the filesystem and creates it if it doesn't\n    \"\"\"\n    try:\n        os.makedirs(directory)\n    except OSError as e:\n        if e.errno != errno.EEXIST:\n            raise","sub_path":"{{cookiecutter.directory_name}}/utils/filesystem.py","file_name":"filesystem.py","file_ext":"py","file_size_in_byte":285,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"537960895","text":"\n# Given two strings S and T, return if they are equal when both are typed into empty text editors. # means a backspace character.\n\n# Example 1:\n\n# Input: S = \"ab#c\", T = \"ad#c\"\n# Output: true\n# Explanation: Both S and T become \"ac\".\n# Example 2:\n\n# Input: S = \"ab##\", T = \"c#d#\"\n# Output: true\n# Explanation: Both S and T become \"\".\n\nclass Solution:\n    def findEnding(self, S: str) -> str:\n        skip = 0\n        index = len(S) - 1\n        while (index >= 0):\n            if S[index] == '#':\n                skip += 1 \n            elif skip == 0:\n                return S[:index+1] \n            else: \n                skip -= 1\n            index -= 1   \n        return ''\n\n            \n    def backspaceCompare(self, S: str, T: str) -> bool:\n        while S != '' or T != '':\n            s_substr = self.findEnding(S)\n            t_substr = self.findEnding(T)\n            print ('s', s_substr)\n            print ('t', t_substr)\n            if  s_substr == '' and t_substr == '':\n                return True\n            if s_substr == '' or t_substr == '' or s_substr[-1] != t_substr[-1]:\n                return False\n            S, T  = s_substr[: -1], t_substr[: -1]\n   \n        return True\n","sub_path":"_book/comparision/r1_backspace_comparison.py","file_name":"r1_backspace_comparison.py","file_ext":"py","file_size_in_byte":1196,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"619934513","text":"import os\nfrom collections import deque\nfrom itertools import chain, tee\n\ndef is_trap(left, center, right):\n    return ((left, center, right) in [\n        (True, True, False),\n        (False, True, True),\n        (True, False, False),\n        (False, False, True)\n    ])\n\ndef next_row(current):\n    items = deque(maxlen=3)\n    wall = (False,)\n    for item in chain(wall, current, wall):\n        items.append(item)\n        if len(items) == 3:\n            yield is_trap(*items)\n\ndef create_map(row, total_rows):\n    for r in range(total_rows):\n        rv, row = tee(row, 2)\n        yield rv\n        row = next_row(row)\n\ndef count_safe_cells(map):\n    return sum(sum(1 for item in row if item == False) for row in map)\n\ndef text_to_bool(text):\n    def char_to_bool(char):\n        return {'^': True, '.': False}.get(char, False)\n    return (char_to_bool(char) for char in text)\n\ndef main():\n    with open(os.path.join(os.path.dirname(__file__), 'input.txt'), 'r') as f:\n        input = f.read().strip()\n    map = create_map(text_to_bool(input), 40)\n    print('safe cells in 40 rows generated from input: ', count_safe_cells(map))\n    map = create_map(text_to_bool(input), 400000)\n    print('safe cells in 400,000 rows generated from input: ', count_safe_cells(map))\n\nif __name__ == '__main__':\n    main()\n","sub_path":"18/code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":1301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"640235481","text":"# -*- coding: utf-8 -*-\nimport gzip\nimport pickle\nfrom os import path\nfrom textwrap import wrap\n\nimport matplotlib.pyplot as plt\nimport matplotlib.style as style\n\nstyle.use('seaborn')\nimport numpy as np\n\nfrom .eval_segm import pixel_accuracy, mean_IU, false_alarm_recall\n\n\ndef thresh_post(img_post, thresh_num=20):\n    \"\"\"classify the value by thresh.\n    \"\"\"\n    img_post = np.array(img_post)\n    threshold_linspace = np.linspace(img_post.min(), img_post.max(), num=thresh_num)\n    label_result_list = []\n    for threshold in threshold_linspace:\n        label_result = np.zeros_like(img_post, dtype=np.bool_)\n        # if linspace end is max value, there should have a '='\n        label_result[img_post >= threshold] = True\n        label_result_list.append(label_result)\n\n    return label_result_list, threshold_linspace\n\n\ndef plot_precision_recall_figure(label_result_list, masks, model_name, save_dir=None):\n    recall_list = []\n    precision_list = []\n    for result in label_result_list:\n        # result shape is (N, H, W)ß\n        # pos means defect\n        true_pos = np.sum(np.logical_and(result, masks))\n        false_pos = np.sum(np.logical_and(result, np.logical_not(masks)))\n        false_neg = np.sum(np.logical_and(np.logical_not(result), masks))\n\n        recall = float(true_pos) / (true_pos + false_neg)\n        precision = float(true_pos) / (true_pos + false_pos)\n        recall_list.append(recall)\n        precision_list.append(precision)\n\n    plt.plot(recall_list, precision_list)\n    plt.xlabel('recall')\n    plt.ylabel('precision')\n    plt.suptitle('\\n'.join(wrap(model_name)))\n    if save_dir:\n        plt.savefig(path.join(save_dir, '%s_PR.png' % model_name))\n        plt.close()\n    else:\n        raise RuntimeError()\n\n\ndef plot_IoU_figure(label_result_list, masks, threshold_linspace, model_name, save_dir=None):\n    IoU_list = []\n\n    for result in label_result_list:\n        intersection = np.sum(np.logical_and(result, masks))\n        union = np.sum(np.logical_or(result, masks))\n        IoU = intersection / float(union)\n        IoU_list.append(IoU)\n\n    plt.plot(threshold_linspace, IoU_list)\n    plt.xlabel('threshold')\n    plt.ylabel('IoU')\n    plt.suptitle('\\n'.join(wrap(model_name)))\n    if save_dir:\n        plt.savefig(path.join(save_dir, '%s_IoU.png' % model_name))\n        plt.close()\n    else:\n        raise RuntimeError()\n\n\ndef mask_bool2int(mask):\n    res = np.zeros(mask.shape)\n    res[mask] = 200\n    return res;\n\n\ndef thresh_value(threshold, image_result):\n    \"\"\"classify the value by thresh.\n\n        Parameters\n        ----------\n        threshold : float number\n            threshold chosen by user\n        image_result : ndarray of shape (row,col)\n            Image data, patch wise or pixel wise\n\n        Other parameters\n        ----------------\n    \"\"\"\n    defect_idx = np.where(image_result > threshold)\n    image_result[defect_idx] = 200\n\n    normal_idx = np.where(image_result <= threshold)\n    image_result[normal_idx] = 0\n\n    return image_result\n\n\ndef two_value_by_thresh(eval_segm, gt_segm, threshold_list, save_dir, target_func):\n    res_list = []\n    for cur_thresh in threshold_list:\n        tmp_eval_segm = eval_segm.copy()\n        tmp_eval_segm = thresh_value(cur_thresh, tmp_eval_segm)\n        cur_res = target_func(tmp_eval_segm, gt_segm)\n        # print(\"threshold %f, %s: %f\" % (cur_thresh, target_func.__name__, cur_res))\n        res_list.append(cur_res)\n\n    res_list = np.array(res_list)\n    plt.xlabel('false alarm')\n    plt.ylabel('recall')\n    plt.plot(res_list[:, 1], res_list[:, 0])\n    plt.savefig(save_dir)\n    plt.clf()\n\n\ndef one_value_by_thresh(eval_segm, gt_segm, threshold_list, save_dir, target_func):\n    \"\"\"get the target by a thresh list.\n\n            Parameters\n            ----------\n            target_func : a function\n                like pixel acc, mean IoU ect...\n\n            Other parameters\n            ----------------\n        \"\"\"\n    res_list = []\n    for cur_thresh in threshold_list:\n        tmp_eval_segm = eval_segm.copy()\n        tmp_eval_segm = thresh_value(cur_thresh, tmp_eval_segm)\n        cur_res = target_func(tmp_eval_segm, gt_segm)\n        # print(\"threshold %f, %s: %f\" % (cur_thresh, target_func.__name__, cur_res))\n        res_list.append(cur_res)\n    plt.xlabel('threshold')\n    plt.ylabel(target_func.__name__)\n    plt.plot(threshold_list, res_list)\n    plt.savefig(save_dir)\n    plt.clf()\n\n\nif __name__ == '__main__':\n    import cv2\n\n    gt_1 = cv2.imread('../data/vidi/maskNoDontCare/bad_01.png', flags=0)\n    with gzip.open('../data/residual_loss.pkl.gz', 'rb') as f:\n        res = pickle.load(f)[5].squeeze() ** 2\n\n    threshold_list = [x * 0.01 for x in range(1, 15, 1)]\n    one_value_by_thresh(res, gt_1, threshold_list, '../data/acc.png', pixel_accuracy)\n    one_value_by_thresh(res, gt_1, threshold_list, '../data/iou.png', mean_IU)\n    two_value_by_thresh(res, gt_1, threshold_list, '../data/false_alarm_recall.png', false_alarm_recall)\n","sub_path":"misc/evaluation.py","file_name":"evaluation.py","file_ext":"py","file_size_in_byte":4965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"291925104","text":"#!/usr/bin/env python3\nimport os\nimport json\nimport requests\nfrom pr_info import PRInfo\nimport sys\nimport logging\nfrom github import Github\nfrom get_robot_token import get_best_robot_token\n\nNAME = 'Run Check (actions)'\n\nTRUSTED_ORG_IDS = {\n    7409213,   # yandex\n    28471076,  # altinity\n    54801242,  # clickhouse\n}\n\nOK_TEST_LABEL = set([\"can be tested\", \"release\", \"pr-documentation\", \"pr-doc-fix\"])\nDO_NOT_TEST_LABEL = \"do not test\"\n\n# Individual trusted contirbutors who are not in any trusted organization.\n# Can be changed in runtime: we will append users that we learned to be in\n# a trusted org, to save GitHub API calls.\nTRUSTED_CONTRIBUTORS = {\n    \"achimbab\",\n    \"adevyatova \",  # DOCSUP\n    \"Algunenano\",   # Raúl Marín, Tinybird\n    \"AnaUvarova\",   # DOCSUP\n    \"anauvarova\",   # technical writer, Yandex\n    \"annvsh\",       # technical writer, Yandex\n    \"atereh\",       # DOCSUP\n    \"azat\",\n    \"bharatnc\",     # Newbie, but already with many contributions.\n    \"bobrik\",       # Seasoned contributor, CloundFlare\n    \"BohuTANG\",\n    \"damozhaeva\",   # DOCSUP\n    \"den-crane\",\n    \"gyuton\",       # DOCSUP\n    \"gyuton\",       # technical writer, Yandex\n    \"hagen1778\",    # Roman Khavronenko, seasoned contributor\n    \"hczhcz\",\n    \"hexiaoting\",   # Seasoned contributor\n    \"ildus\",        # adjust, ex-pgpro\n    \"javisantana\",  # a Spanish ClickHouse enthusiast, ex-Carto\n    \"ka1bi4\",       # DOCSUP\n    \"kirillikoff\",  # DOCSUP\n    \"kitaisreal\",   # Seasoned contributor\n    \"kreuzerkrieg\",\n    \"lehasm\",       # DOCSUP\n    \"michon470\",    # DOCSUP\n    \"MyroTk\",       # Tester in Altinity\n    \"myrrc\",        # Michael Kot, Altinity\n    \"nikvas0\",\n    \"nvartolomei\",\n    \"olgarev\",      # DOCSUP\n    \"otrazhenia\",   # Yandex docs contractor\n    \"pdv-ru\",       # DOCSUP\n    \"podshumok\",    # cmake expert from QRator Labs\n    \"s-mx\",         # Maxim Sabyanin, former employee, present contributor\n    \"sevirov\",      # technical writer, Yandex\n    \"spongedu\",     # Seasoned contributor\n    \"ucasFL\",       # Amos Bird's friend\n    \"vdimir\",       # Employee\n    \"vzakaznikov\",\n    \"YiuRULE\",\n    \"zlobober\"      # Developer of YT\n}\n\n\ndef pr_is_by_trusted_user(pr_user_login, pr_user_orgs):\n    if pr_user_login in TRUSTED_CONTRIBUTORS:\n        logging.info(\"User '{}' is trusted\".format(pr_user_login))\n        return True\n\n    logging.info(\"User '{}' is not trusted\".format(pr_user_login))\n\n    for org_id in pr_user_orgs:\n        if org_id in TRUSTED_ORG_IDS:\n            logging.info(\"Org '{}' is trusted; will mark user {} as trusted\".format(org_id, pr_user_login))\n            return True\n        logging.info(\"Org '{}' is not trusted\".format(org_id))\n\n    return False\n\n# Returns whether we should look into individual checks for this PR. If not, it\n# can be skipped entirely.\ndef should_run_checks_for_pr(pr_info):\n    # Consider the labels and whether the user is trusted.\n    force_labels = set(['force tests']).intersection(pr_info.labels)\n    if force_labels:\n        return True, \"Labeled '{}'\".format(', '.join(force_labels))\n\n    if 'do not test' in pr_info.labels:\n        return False, \"Labeled 'do not test'\"\n\n    if 'can be tested' not in pr_info.labels and not pr_is_by_trusted_user(pr_info.user_login, pr_info.user_orgs):\n        return False, \"Needs 'can be tested' label\"\n\n    if 'release' in pr_info.labels or 'pr-backport' in pr_info.labels or 'pr-cherrypick' in pr_info.labels:\n        return False, \"Don't try new checks for release/backports/cherry-picks\"\n\n    return True, \"No special conditions apply\"\n\ndef get_commit(gh, commit_sha):\n    repo = gh.get_repo(os.getenv(\"GITHUB_REPOSITORY\", \"ClickHouse/ClickHouse\"))\n    commit = repo.get_commit(commit_sha)\n    return commit\n\nif __name__ == \"__main__\":\n    logging.basicConfig(level=logging.INFO)\n    with open(os.getenv('GITHUB_EVENT_PATH'), 'r') as event_file:\n        event = json.load(event_file)\n\n    pr_info = PRInfo(event, need_orgs=True)\n    can_run, description = should_run_checks_for_pr(pr_info)\n    gh = Github(get_best_robot_token())\n    commit = get_commit(gh, pr_info.sha)\n    url = f\"https://github.com/ClickHouse/ClickHouse/actions/runs/{os.getenv('GITHUB_RUN_ID')}\"\n    if not can_run:\n        print(\"::notice ::Cannot run\")\n        commit.create_status(context=NAME, description=description, state=\"failure\", target_url=url)\n        sys.exit(1)\n    else:\n        if 'pr-documentation' in pr_info.labels or 'pr-doc-fix' in pr_info.labels:\n            commit.create_status(context=NAME, description=\"Skipping checks for documentation\", state=\"success\", target_url=url)\n            print(\"::notice ::Can run, but it's documentation PR, skipping\")\n        else:\n            print(\"::notice ::Can run\")\n            commit.create_status(context=NAME, description=description, state=\"pending\", target_url=url)\n","sub_path":"tests/ci/run_check.py","file_name":"run_check.py","file_ext":"py","file_size_in_byte":4827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"108715934","text":"# KFOLD CROSS VALIDATION\n# MLp3 P.27\n\nfrom sklearn.datasets import load_iris\nfrom sklearn import tree\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix, classification_report\nfrom sklearn.model_selection import KFold\n# k = 5 for KNeighborsClassifier\niris = load_iris()\nX = iris.data\ny = iris.target\nclf = tree.DecisionTreeClassifier()\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42) \nclf = clf.fit(X_train, y_train)\nprint(clf.score(X_test, y_test))\ny_pred = clf.predict(X_test)\nprint(y_pred)\n\nclf = tree.DecisionTreeClassifier()\nkf = KFold(n_splits=2)\nkf.get_n_splits(X)\nprint(kf)\nfor train_index, test_index in kf.split(X):\n    print(\"TRAIN:\", train_index)\n    print(\"TEST:\", test_index)\n    X_train, X_test = X[train_index], X[test_index]\n    y_train, y_test = y[train_index], y[test_index]\n    print(\"TRAIN data:\")\n    print(X_train, y_train)\n    print(\"TEST data:\")\n    print(X_test, y_test )\n","sub_path":"1_7.py","file_name":"1_7.py","file_ext":"py","file_size_in_byte":987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"250732517","text":"#!/usr/bin/env python3\nfrom binascii import crc32\nimport sys\n\ndef crc(data):\n    my_crc = bin(crc32(bytes(data, 'ascii')))[2:]    \n    fill_crc = ('0'*32) + my_crc\n\n    return fill_crc[-32:]\n#end crc\n\ndef frame(data):\n    flag = '01111110'\n    checksum = crc(data)\n    output = ''\n    ones = 0\n    for c in data:\n        ones = ones + 1 if c == '1' else 0\n        output += c\n        if ones == 5:\n            ones = 0\n            output += '0'\n\n    return flag + output + flag + checksum\n#end frame\n\ndef encode(idata, outfile):\n    odata = frame(idata)\n    with open(outfile, 'w') as out:\n        out.write(odata)\n# end encode\n\ndef unframe(data):\n    f1 = data[0:8]\n    content = data[8:-40]\n    f2 = data[-40:-32]\n    checksum = data[-32:]\n\n    flag = '01111110'\n        \n    if f1 != flag or f2 != flag:\n        print('Invalid flag')\n        return None\n\n    data = ''\n    ones = 0\n    for c in content:\n        if ones == 5:\n            ones = 0\n            if c != '0':\n                print('Invalid stuffing')\n                return None\n        else:\n            ones = ones + 1 if c == '1' else 0\n            data += c\n    \n    checksum1 = crc(data)\n\n    if checksum != checksum1:\n        print('Invalid checksum')\n        return None\n    \n    return data\n#end unframe\n\ndef decode(idata, outfile):\n    if len(idata) < 8 + 8 + 32:\n        print('Invalid input format')\n        return\n    \n    data = unframe(idata)\n\n    if data == None:\n        print('Error during reading frame')\n        return\n\n    with open(outfile, 'w') as out:\n        out.write(data)\n# end decode\n\ndef main():\n    if len(sys.argv) < 4:\n        print(\"Not enough arguments\")\n        return\n\n    infile = sys.argv[2]\n    outfile = sys.argv[3]\n\n    with open(infile, 'r') as input:\n        idata = input.read()\n        if sys.argv[1] == '-e':\n            encode(idata, outfile)\n        else:\n            decode(idata, outfile)\n# end main   \n    \nif __name__ == \"__main__\":\n    main()\n","sub_path":"lista3/zad1/zad1.py","file_name":"zad1.py","file_ext":"py","file_size_in_byte":1962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"428098301","text":"import hassapi as hass\n\nimport re\nimport sys\nimport time\nfrom queue import Queue\nfrom threading import Thread\n\n\"\"\"\n    Class Alexa Manager handles sending text to speech messages to Alexa media players\n    Following features are implemented:\n    - Speak text to choosen media_player\n    - Full queue support to manage async multiple TTS commands\n    - Full wait to tts to finish to be able to supply a callback method\n\"\"\"\n\nNOTIFY = \"notify/\"\n\nSUB_VOICE = [\n    # (\"[.]{2,}\", \".\"),\n    (\"[\\?\\.\\!,]+(?=[\\?\\.\\!,])\", \"\"),  # Exclude duplicate\n    (\"(\\s+\\.|\\s+\\.\\s+|[\\.])(?! )(?![^{]*})(?![^\\d.]*\\d)\", \". \"),\n    (\"&\", \" and \"),  # escape\n    # (\"(?<!\\d),(?!\\d)\", \", \"),\n    (\"[\\n\\*]\", \" \"),\n    (\" +\", \" \"),\n]\n\nSUB_TEXT = [(\" +\", \" \"), (\"\\s\\s+\", \"\\n\")]\n\nVOICE_NAMES = (\n    \"Carla\",\n    \"Giorgio\",\n    \"Bianca\",\n    \"Ivy\",\n    \"Joanna\",\n    \"Joey\",\n    \"Justin\",\n    \"Kendra\",\n    \"Kimberly\",\n    \"Matthew\",\n    \"Salli\",\n    \"Nicole\",\n    \"Russell\",\n    \"Amy\",\n    \"Brian\",\n    \"Emma\",\n    \"Aditi\",\n    \"Raveena\",\n    \"Chantal\",\n    \"Celine\",\n    \"Lea\",\n    \"Mathieu\",\n    \"Hans\",\n    \"Marlene\",\n    \"Vicki\",\n    \"Aditi\",\n    \"Mizuki\",\n    \"Takumi\",\n    \"Vitoria\",\n    \"Camila\",\n    \"Ricardo\",\n    \"Penelope\",\n    \"Lupe\",\n    \"Miguel\",\n    \"Conchita\",\n    \"Enrique\",\n    \"Lucia\",\n    \"Mia\",\n)\n\nSUPPORTED_LANGUAGES = [\n    \"it-IT\",\n    \"en-US\",\n    \"en-CA\",\n    \"en-AU\",\n    \"en-GB\",\n    \"en-IN\",\n    \"fr-CA\",\n    \"fr-FR\",\n    \"de-DE\",\n    \"hi-IN\",\n    \"ja-JP\",\n    \"pt-BR\",\n    \"es-US\",\n    \"es-ES\",\n    \"es-MX\",\n]\n\nSPEECHCON = [\n    \"a ah\",\n    \"abracadabra\",\n    \"accidenti\",\n    \"accipicchia\",\n    \"addio\",\n    \"ah\",\n    \"ahi\",\n    \"ahia\",\n    \"alleluia\",\n    \"aloha\",\n    \"alè\",\n    \"anzi\",\n    \"apriti sesamo\",\n    \"argh\",\n    \"arrivederci\",\n    \"attenzione\",\n    \"auguri\",\n    \"badabim badabum\",\n    \"badabum\",\n    \"bah\",\n    \"bam\",\n    \"bang\",\n    \"bang bang\",\n    \"banzai\",\n    \"basta\",\n    \"batti cinque\",\n    \"bau\",\n    \"bazinga\",\n    \"beh\",\n    \"ben fatto\",\n    \"bene\",\n    \"bene bene\",\n    \"bim bum bam\",\n    \"bing\",\n    \"bingo\",\n    \"bip bip\",\n    \"bis\",\n    \"bla\",\n    \"bla bla bla\",\n    \"bleah\",\n    \"boh\",\n    \"boing\",\n    \"bravo\",\n    \"brrr\",\n    \"bum\",\n    \"buon appetito\",\n    \"buon viaggio\",\n    \"buona fortuna\",\n    \"buonanotte\",\n    \"buonasera\",\n    \"buongiorno\",\n    \"buu\",\n    \"capito\",\n    \"caspita\",\n    \"cavoli\",\n    \"cavolo\",\n    \"cawabanga\",\n    \"certo\",\n    \"chissà\",\n    \"ci stai\",\n    \"ciao\",\n    \"cioè\",\n    \"ciuf ciuf\",\n    \"clic clac\",\n    \"come desideri\",\n    \"come no\",\n    \"come non detto\",\n    \"come va\",\n    \"come vuoi\",\n    \"contaci\",\n    \"coraggio\",\n    \"così così\",\n    \"cucù\",\n    \"d'accordo\",\n    \"d'oh\",\n    \"dai\",\n    \"davvero\",\n    \"din don\",\n    \"ding\",\n    \"ding ding ding\",\n    \"dormi bene\",\n    \"eh\",\n    \"eh beh\",\n    \"eh già\",\n    \"ehm\",\n    \"etciù\",\n    \"eureka\",\n    \"evviva\",\n    \"fiuu\",\n    \"geronimo\",\n    \"giusto\",\n    \"già\",\n    \"grande giove\",\n    \"grazie\",\n    \"ha\",\n    \"hey\",\n    \"hip hip hurrà\",\n    \"hmm\",\n    \"hurrà\",\n    \"hué hué\",\n    \"in guardia\",\n    \"incantata\",\n    \"kabùm\",\n    \"ma dai\",\n    \"magari\",\n    \"mah\",\n    \"mamma mia\",\n    \"mi piace\",\n    \"miao\",\n    \"mistero\",\n    \"muu\",\n    \"nano nano\",\n    \"non mi dire\",\n    \"oh\",\n    \"oh no\",\n    \"oh oh\",\n    \"oh sì\",\n    \"oh yes\",\n    \"oi\",\n    \"oink\",\n    \"ok\",\n    \"okei\",\n    \"oooh\",\n    \"or ora\",\n    \"ping\",\n    \"più o meno\",\n    \"plop\",\n    \"pop\",\n    \"poti poti\",\n    \"puf\",\n    \"pum pum\",\n    \"puntini puntini\",\n    \"puoi scommetterci\",\n    \"qua qua\",\n    \"ricevuto\",\n    \"roger\",\n    \"salute\",\n    \"scacco matto\",\n    \"scherzavo\",\n    \"sogni d'oro\",\n    \"splash\",\n    \"spoiler alert\",\n    \"su \",\n    \"su su\",\n    \"swish\",\n    \"ta dà\",\n    \"taac\",\n    \"tic tac\",\n    \"tic tic tic\",\n    \"tic-toc\",\n    \"toc toc\",\n    \"toh\",\n    \"touché\",\n    \"trallalà\",\n    \"tsk tsk\",\n    \"tump\",\n    \"tweet\",\n    \"uffa\",\n    \"uh la là\",\n    \"uh oh\",\n    \"uomo in mare\",\n    \"vabbè\",\n    \"voilà\",\n    \"vroom\",\n    \"wow\",\n    \"yippii\",\n    \"zac\",\n    \"zap\",\n]\n\nMOBILE_PUSH = [\"push\", \"dropin\", \"dropin_notification\"]\n\n\nclass Alexa_Manager(hass.Hass):\n    def initialize(self) -> None:\n        # self.set_log_level(\"DEBUG\")\n        self.alexa_service = self.args.get(\"alexa_service\")\n        # self.alexa_switch_entity = self.args.get(\"alexa_switch\")\n        self.alexa_select_media_player = self.args.get(\"alexa_select_media_player\")\n        self.alexa_type = self.args.get(\"alexa_type\")\n        self.alexa_method = self.args.get(\"alexa_method\")\n        self.alexa_sensor_media_player = self.args.get(\"alexa_sensor_media_player\")\n        self.alexa_voice = self.args.get(\"alexa_voice\")\n        # self.alexa_language = self.args.get(\"alexa_language\")\n        self.prosody = self.args.get(\"prosody\")\n        self.wait_time = self.args.get(\"wait_time\")\n        self.cehck_alexa_service = self._check_alexa(self.alexa_service)\n\n        self.queue = Queue(maxsize=0)\n        self._when_tts_done_callback_queue = Queue()\n\n        t = Thread(target=self.worker)\n        t.daemon = True\n        t.start()\n\n    def speak(self, alexa):\n        \"\"\"Speak the provided text through the media player.\"\"\"\n        if not self.cehck_alexa_service:\n            self.set_sensor(\n                \"I can't find the Alexa Media component\", \"https://github.com/custom-components/alexa_media_player\"\n            )\n            return\n        self.lg(f\"-------------------- ALEXA START DISPATCH --------------------\")\n        self.lg(f\"FROM DISPATCH: {type(alexa)} value {alexa}\")\n        # remove keys with None value from a dict # TODO\n        alexa = {k: v for k, v in alexa.items() if v not in [None, \"None\", \"\"]}\n        self.lg(f\"REMOVE [NONE] VALUE: {type(alexa)} value {alexa}\")\n        default_restore_volume = float(self.get_state(self.args.get(\"default_restore_volume\"))) / 100\n        volume = float(alexa.get(\"volume\", default_restore_volume))\n        message = str(alexa.get(\"message\", alexa.get(\"message_tts\")))\n        alexa_player = self.player_get(alexa.get(\"media_player\", self.get_state(self.alexa_sensor_media_player)))\n        alexa_type = (\n            str(alexa.get(\"type\", self.get_state(self.alexa_type))).lower().replace(\"dropin\", \"dropin_notification\")\n        )\n\n        # Push notification\n        push = bool(self.check_bool(alexa.get(\"push\")))\n        if push or alexa_type in MOBILE_PUSH and message:\n            message_push = self.remove_tags(self.replace_regular(message, SUB_TEXT))\n            self.call_service(\n                NOTIFY + self.alexa_service,\n                data={\"type\": \"push\"} if push else {\"type\": alexa_type},\n                target=alexa_player[0],  # only one device\n                title=str(alexa.get(\"title\", \"\")),\n                message=message_push,\n            )\n            self.lg(f\"PUSH: {push} - TYPE: {alexa_type} - MESSAGE: {message_push}\")\n        # Media Content # TODO Restore volume??\n        media_content_id = alexa.get(\"media_content_id\")\n        media_content_type = alexa.get(\"media_content_type\")\n        if media_content_id:\n            self.volume_get(alexa_player, default_restore_volume)\n            self.volume_set(alexa_player, volume)\n            self.call_service(\n                \"media_player/play_media\",\n                entity_id=alexa_player,\n                media_content_id=media_content_id,\n                media_content_type=media_content_type,\n                # extra = {\"timer\": 10} ##??\n            )\n            self.lg(f\"Content id: {media_content_id} - Content type: {media_content_type}\")\n        # Queues the message to be handled async, use when_tts_done_do method to supply callback when tts is done\n\n        elif alexa_type not in MOBILE_PUSH and message:\n            self.queue.put(\n                {\n                    \"text\": message,\n                    \"volume\": volume,\n                    \"alexa_type\": alexa_type,\n                    \"alexa_player\": alexa_player,  # media_player\n                    \"default_restore_volume\": default_restore_volume,\n                    \"alexa_notifier\": str(alexa.get(\"notifier\", self.alexa_service)),\n                    \"wait_time\": float(alexa.get(\"wait_time\", self.get_state(self.wait_time))),\n                    \"language\": alexa.get(\"language\"),  # self.get_state(self.alexa_language)),\n                    \"alexa_method\": str(alexa.get(\"method\", self.get_state(self.alexa_method)).lower()),\n                    \"alexa_voice\": str(alexa.get(\"voice\", self.get_state(self.alexa_voice))).capitalize(),\n                    \"alexa_audio\": alexa.get(\"audio\", None),\n                    \"rate\": float(alexa.get(\"rate\", self.get_state(self.prosody[\"rate\"]))),\n                    \"pitch\": float(alexa.get(\"pitch\", self.get_state(self.prosody[\"pitch\"]))),\n                    \"ssml_volume\": float(alexa.get(\"ssml_volume\", self.get_state(self.prosody[\"volume\"]))),\n                    \"whisper\": bool(self.check_bool(alexa.get(\"whisper\", False))),\n                    \"ssml_switch\": bool(self.check_bool(alexa.get(\"ssml\", self.get_state(self.args[\"ssml_switch\"])))),\n                }\n            )\n        self.lg(f\"-------------------- ALEXA  END  DISPATCH --------------------\")\n\n    def lg(self, message):\n        self.log(message, level=\"DEBUG\", ascii_encode=False)\n\n    def check_bool(self, value):\n        return str(value).lower() in [\"true\", \"on\", \"yes\", \"1\"]\n\n    def inbetween(self, minv, value, maxv):\n        return sorted([minv, value, maxv])[1]\n\n    def speak_tag(self, value):  # TODO tags\n        return value if \"<speak>\" in value or not \"</\" in value else f\"<speak>{value}</speak>\"\n\n    def effect_tag(self, value):\n        return f\"<amazon:effect name='whispered'>{value}</amazon:effect>\"\n\n    def prosody_tag(self, value, rate, pitch, volume):\n        if rate != 100.0 or pitch != 0.0 or volume != 0.0:\n            rate = f\"{self.inbetween(20, rate, 200)}%\"  # min 20% max 200%\n            pitch = f\"{self.inbetween(-33.3, pitch, 50):+g}%\"  # min -33.3 max +50\n            volume = f\"{self.inbetween(-50, volume, 4.08):+g}dB\"  # max +4.08dB\n            return f\"<prosody rate='{rate}' pitch='{pitch}' volume='{volume}'> {value} </prosody>\"\n        return value\n\n    def audio_tag(self, value: None):\n        if value is None:\n            return \"\"\n        return f\"<audio src='{value}'/>\" if \"<audio src=\" not in value else value\n\n    def lang_tag(self, value, lang):\n        if lang not in SUPPORTED_LANGUAGES:\n            self.lg(f\"NOT SUPPORTED LANGUAGE: {lang}\")\n            return value\n        self.lg(f\"OK ADDED SSML LANGUAGE: {lang}\")\n        return f\"<lang xml:lang='{lang}'>{value}</lang>\"\n\n    def voice_tag(self, value, name):\n        if name not in VOICE_NAMES:\n            self.lg(f\"NOT SUPPORTED VOICE: {name}\")\n            return value\n        self.lg(f\"OK ADDED VOICE: {name}\")\n        return f\"<voice name='{name}'>{value}</voice>\"\n\n    def say_as_tag(self, value):\n        return f\"<say-as interpret-as='interjection'>{value}</say-as>\"\n\n    def find_speechcon(self, value):\n        substrings = sorted(SPEECHCON, key=len, reverse=True)\n        regex = re.compile(r\"\\b\" + r\"\\b|\\b\".join(map(re.escape, substrings)), re.I)\n        regex_match = re.findall(regex, value)\n        self.lg(f\"FOUND SPEECHCON: {len(regex_match)} -> {regex_match}\")\n        return regex.sub(lambda m: self.say_as_tag(m.group()), value)\n\n    def player_get(self, user_player):\n        media_player = []\n        user_player = self.converti(str(user_player.lower()))\n        for mpu in user_player:  # MediaPlayerUser\n            if \"test\" in mpu:\n                media_player.extend(self.player_alexa)\n            if not self.entity_exists(mpu):\n                mpu = self.dict_select.get(mpu)\n            if mpu:\n                if \"group.\" in mpu:\n                    media_player.extend(self.get_state(mpu, attribute=\"entity_id\"))\n                elif \"sensor.\" in mpu:\n                    media_player.append(self.get_state(mpu))\n                elif \"media_player.\" in mpu:\n                    media_player.append(mpu)\n                else:\n                    self.log(f\"Invalid group, sensor or player ENTITY-ID ({mpu})\", level=\"WARNING\")\n        if not media_player:\n            media_player.append(self.get_state(self.alexa_sensor_media_player))\n            self.log(f\"No media player {user_player} found. I use the default one. ({media_player})\", level=\"WARNING\")\n        media_player = list(set(media_player))\n        self.lg(f\"GET PLAYER: {media_player}\")\n        return media_player\n\n    def volume_get(self, media_player, volume: float):\n        \"\"\"Get and save the volume of each media player.\"\"\"\n        self.dict_volumes = {m: self.get_state(m, attribute=\"volume_level\", default=volume) for m in media_player}\n        self.lg(f\"GET VOLUMES: {self.dict_volumes}\")\n        return self.dict_volumes\n\n    def volume_set(self, media_player, volume: float, **restore: False):\n        if self.dict_volumes:\n            for i, j in self.dict_volumes.items():\n                if j != volume:\n                    if restore:\n                        self.call_service(\"media_player/volume_set\", entity_id=i, volume_level=j)\n                        time.sleep(1)\n                        self.lg(f\"OK RESTORE VOL: {i} {j} [State: {self.get_state(i, attribute='volume_level')}]\")\n                    else:\n                        self.call_service(\"media_player/volume_set\", entity_id=media_player, volume_level=volume)\n                        self.lg(f\"SET VOLUMES: {media_player} {volume}\")\n                        break  # time.sleep(2)\n\n    def replace_char(self, text: str, substitutions: dict):\n        \"\"\"Function that does multiple string replace ops in a single pass.\"\"\"\n        substrings = sorted(substitutions, key=len, reverse=True)\n        regex = re.compile(r\"\\b\" + r\"\\b|\\b\".join(map(re.escape, substrings)), re.I)  # r'\\b%s\\b' % r'\\b|\\b'\n        return regex.sub(lambda match: substitutions[str.lower(match.group(0))], text)  # added str.lower()\n\n    def replace_regular(self, text: str, substitutions: list):\n        for old, new in substitutions:\n            regex = re.compile(old)\n            text = re.sub(regex, new, str(text).strip())\n        return text\n\n    def remove_tags(self, text: str):\n        \"\"\"Remove all tags from a string.\"\"\"\n        regex = re.compile(\"<.*?>\")\n        return re.sub(regex, \"\", str(text).strip())\n\n    def converti(self, stringa) -> list:\n        regex = re.compile(r\"\\s*,\\s*\")\n        return self.split_device_list(re.sub(regex, \",\", stringa))\n\n    def has_numbers(self, string):\n        numbers = re.compile(\"\\d{4,}|\\d{3,}\\.\\d\")\n        return numbers.search(string)\n\n    def set_sensor(self, state, error):\n        attributes = {}\n        attributes[\"icon\"] = \"mdi:amazon-alexa\"\n        attributes[\"Error\"] = error\n        self.set_state(\"sensor.centro_notifiche\", state=state, attributes=attributes)\n\n    def when_tts_done_do(self, callback: callable) -> None:\n        \"\"\"Callback when the queue of tts messages are done.\"\"\"\n        self._when_tts_done_callback_queue.put(callback)\n\n    def worker(self):\n        while True:\n            try:\n                data = self.queue.get()\n                self.lg(f\"WORKER: {type(data)} value {data}\")\n                alexa_player = data[\"alexa_player\"]\n                self.volume_get(alexa_player, data[\"default_restore_volume\"])\n                self.volume_set(alexa_player, data[\"volume\"])\n\n                # Replace and clean message\n                message_clean = self.replace_regular(data[\"text\"], SUB_VOICE)\n                self.lg(f\"INPUT MESSAGE: {data['text']}\")\n                self.lg(f\"MESSAGE CLEAN: {message_clean}\")\n\n                # Speech time calculator\n                # words = len(message_clean.split())\n                # chars = message_clean.count(\"\")\n                words = len(self.remove_tags(message_clean).split())\n                chars = self.remove_tags(message_clean).count(\"\")\n                duration = (words * 0.007) * 60\n\n                # Extra time\n                if self.has_numbers(message_clean):\n                    data[\"wait_time\"] += 4\n                    self.lg(f\"OK NUMBER! ADDED EXTRA TIME: {data['wait_time']}\")\n                if (chars / words) > 7 and chars > 90 or data[\"alexa_audio\"] is not None:\n                    data[\"wait_time\"] += 7\n                    self.lg(f\"OK ADDED EXTRA TIME: {data['wait_time']}\")\n                # Alexa type-method\n                if \"tts\" in data[\"alexa_type\"]:\n                    alexa_data = {\"type\": \"tts\"}\n                else:\n                    data[\"wait_time\"] += 1.5\n                    alexa_data = {\n                        \"type\": data[\"alexa_type\"],\n                        \"method\": data[\"alexa_method\"],\n                    }\n                # TAGS SSML\n                if data[\"ssml_switch\"] and not \"<speak>\" in message_clean:\n                    voice = \"Alexa\" if data[\"alexa_voice\"] not in VOICE_NAMES else data[\"alexa_voice\"]\n                    whisper = data[\"whisper\"]\n                    if \"Alexa\" in voice and not whisper:\n                        message_clean = self.find_speechcon(message_clean)\n                    message_clean = self.lang_tag(message_clean, data[\"language\"])\n                    if \"Alexa\" not in voice:\n                        message_clean = self.voice_tag(message_clean, voice)\n                    message_clean = self.audio_tag(data[\"alexa_audio\"]) + message_clean\n                    message_clean = self.prosody_tag(message_clean, data[\"rate\"], data[\"pitch\"], data[\"ssml_volume\"])\n                    # -->\n                    rate = self.inbetween(20, data[\"rate\"], 200)  # TODO\n                    if rate < 100:\n                        duration += (100 - rate) * (duration / 100)\n                    elif rate > 100:\n                        duration /= 2\n                    # -->\n                    if whisper:\n                        message_clean = self.effect_tag(message_clean)\n                    if \"tts\" in data[\"alexa_type\"]:\n                        message_clean = self.speak_tag(message_clean)\n                    self.lg(f\"OK SSML TAGS: {message_clean}\")\n                # Estimate reading time\n                duration += data[\"wait_time\"]\n                self.lg(f\"DURATION-WAIT: {duration} - words: {words} - Chars: {chars}\")\n\n                # Speak >>>\n                self.call_service(\n                    NOTIFY + data[\"alexa_notifier\"],\n                    data=alexa_data,\n                    target=alexa_player,\n                    message=message_clean.strip(),\n                )\n\n                time.sleep(duration if duration > 0 else 0)\n\n                # Restore volume\n                self.volume_set(alexa_player, data[\"volume\"], restore=True)\n            except Exception as ex:\n                self.log(\"An error occurred in Alexa Manager (worker): {}\".format(ex), level=\"ERROR\")\n                self.log(f\"DATA: {data}\", level=\"ERROR\")\n                self.set_sensor(\"Alexa Manager - Worker Error \", ex)\n            self.queue.task_done()\n\n            if self.queue.qsize() == 0:\n\n                try:\n                    while self._when_tts_done_callback_queue.qsize() > 0:\n                        callback_func = self._when_tts_done_callback_queue.get_nowait()\n                        callback_func()  # Call the callback\n                        self._when_tts_done_callback_queue.task_done()\n                except:\n                    self.log(\"Errore nel CallBack\", level=\"ERROR\")\n                    self.set_sensor(\"Alexa Manager - CallBack Error \", ex)\n                    pass  # Nothing in queue\n            self.lg(\"---------------------------------------------------------\\n\")\n\n    def _check_alexa(self, service):\n        \"\"\" Get the media players from the alexa_media service in home assistant. \"\"\"\n        self.hass_config = self.get_plugin_config()\n        components = self.hass_config[\"components\"]\n        if service in components:\n            exclude = [service, \"this_device\", \"_apps\"]\n            # Trova servizi alexa_media, alexa_media_xxname...\n            cehck_alexa = [\n                s[\"service\"]  # .replace(\"alexa_media_\", \"media_player.\")\n                for s in self.list_services(namespace=\"default\")\n                if \"notify\" in s[\"domain\"] and service in s[\"service\"]\n            ]\n            self.lg(f\"OK, Service: {cehck_alexa}\")\n\n            # converti servizi alexa_media_ in media_player. e controlla se esistono media_player.xxname...\n            service_replace = [\n                mp.replace(\"alexa_media_\", \"media_player.\") for mp in cehck_alexa if mp != \"alexa_media\"  # Extra\n            ]\n            self.lg(f\"OK, Entity: {service_replace}\")\n\n            # Filtro lista exclude - player_alexa list media_player\n            self.player_alexa = [\n                s for s in service_replace if self.entity_exists(s) and not any(player in s for player in exclude)\n            ]\n            self.lg(f\"OK, found the Alexa Media component. List of media players: {self.player_alexa}\")\n\n            ###---------\n            # \"\"\" GEt Friendly Name from Entity. \"\"\"\n            names = [self.friendly_name(name) for name in self.player_alexa]\n            self.lg(f\"FRIENDLY_NAME: {names}\")\n            selectoptions = self.get_state(self.alexa_select_media_player, attribute=\"options\")\n            self.lg(str(f\"INPUT SELECT OPTIONS: {selectoptions} - TYPE: {type(selectoptions)}\"))\n\n            # controlla se il friendly name esiste nel input select\n            check_alexa_options = [x for x in self.player_alexa if self.friendly_name(x) in selectoptions]\n            self.lg(str(f\"ENTITY_ID MEDIA_PLAYER IN INPUT SELECTS {check_alexa_options}\"))\n            ###---------\n            ##Prova conversione da friendly name ad entity id - return list and dict entity in input select\n            # selectoptions = self.get_state(self.alexa_select_media_player, attribute=\"options\")\n            all_state = self.get_state()\n            self.list_select = []\n            self.dict_select = {}\n            for entity, state in all_state.items():\n                domain, name = entity.split(\".\")\n                friendly_name = state[\"attributes\"].get(\"friendly_name\")\n                if domain in [\"media_player\", \"group\", \"sensor\"] and friendly_name in selectoptions:\n                    self.list_select.append(entity)\n                    for select in selectoptions:\n                        if select.lower() == friendly_name.lower():  # .casefold()\n                            self.dict_select[friendly_name.lower()] = entity\n            self.lg(str(f\"LIST ENTITY_ID SELECT OPTIONS: {self.list_select}\"))\n            self.lg(str(f\"DICTIONARY NAME-ENTITY_ID: {self.dict_select}\"))\n\n            return cehck_alexa\n        # self.log(\n        #     f\"I can't find the Alexa Media component\\n- https://github.com/custom-components/alexa_media_player\",\n        #     level=\"ERROR\",\n        # )\n        return\n","sub_path":"apps/notifier/alexa_manager.py","file_name":"alexa_manager.py","file_ext":"py","file_size_in_byte":22917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"287797187","text":"from __future__ import print_function # Debugging purposes\nimport sys\nimport traceback\nimport random\nimport time\n\nfrom . import board\n\ndef dbg(text):\n    print(text, file=sys.stderr)\n    sys.stderr.flush()\n\n\nclass Game:\n    def __init__(self):\n        self.initial_timebank = 0\n        self.time_per_move = 10\n        self.board_width = 0\n        self.board_height = 0\n\n        self.my_botid = -1\n        self.other_botid = -1\n\n        self.board = None\n        self.last_update = 0\n        self.last_timebank = 0\n\n    def initialize_settings(self):\n        while(True):\n            try:\n                current_line = sys.stdin.readline().rstrip('\\r\\n')\n                tokens = current_line.split()\n                key0 = tokens[0]\n                key1 = tokens[1]\n                if (key0 == \"settings\"):\n                    if key1 == \"timebank\":\n                        self.timebank = int(tokens[2])\n                    elif key1 == \"time_per_move\":\n                        self.time_per_move = int(tokens[2])\n                    elif key1 == \"your_botid\":\n                        self.my_botid = int(tokens[2])\n                        self.other_botid = 1 - self.my_botid\n                    elif key1 == \"field_width\":\n                        self.board_width = int(tokens[2])\n                    elif key1 == \"field_height\":\n                        self.board_height = int(tokens[2])\n                        self.board = board.Board(self.board_width, self.board_height)\n                elif (key0 == \"update\" and key1 == \"game\" and tokens[2] == \"field\"):\n                    self.board.find_starting_pos(self.my_botid, tokens[3])\n                    return\n\n            except EOFError:\n                break\n            except KeyboardInterrupt:\n                raise\n            except:\n                # don't raise error or return so that bot attempts to stay alive\n                traceback.print_exc(file=sys.stderr)\n                sys.stderr.flush()\n\n\n    def time_remaining(self):\n        return self.last_timebank - int(1000 * (time.clock() - self.last_update))\n\n    def issue_order(self, order):\n        sys.stdout.write('%s\\n' % (order))\n        sys.stdout.flush()\n\n    def run(self, bot):\n        self.initialize_settings()\n        while(True):\n            try:\n                current_line = sys.stdin.readline().rstrip('\\r\\n')\n                if current_line.startswith(\"action move\"):\n                    dbg(current_line[12:])\n                    self.last_timebank = int(current_line[12:])\n                    self.issue_order(bot.do_turn(self))\n                elif current_line.startswith(\"update game field\"):\n                    self.board.update_board(current_line)\n                elif current_line.startswith(\"quit\"):\n                    break\n            except EOFError:\n                break\n            except KeyboardInterrupt:\n                raise\n            except:\n                # don't raise error or return so that bot attempts to stay alive\n                traceback.print_exc(file=sys.stderr)\n                sys.stderr.flush()\n","sub_path":"Bot/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":3078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"148028534","text":"import discord\nfrom discord.ext import commands\nfrom discord import Embed, Member\nfrom robiconf.bot_configuration import connectDB\n\ncursor = connectDB.db.cursor()\n\nclass Settings(commands.Cog):\n\n    def __init__(self, bot):\n        self.bot = bot\n\n    @commands.command(aliases = ['s', 'set'])\n    async def setting(self, ctx, sub1, *sub2):\n        name=''\n        for n in sub2:\n            if(n.lower() == 'reset'): name = ctx.author.nick\n            else:\n                name += n+' '\n                name = name.replace(n[0], n[0].upper(), 1)\n\n        embedVar = Embed(title='Nickname telah diganti menjadi ```'+name+\"```\", color=0x00ff00)\n        try:\n            if sub1.lower() == 'profile' or sub1.lower() == 'p':\n                if getData(ctx.author)[0] == 0:\n                    insertNick(ctx.author.id, name)\n                    await ctx.channel.send(embed = embedVar)\n                else:\n                    updateNick(ctx.author.id, name)\n                    await ctx.channel.send(embed = embedVar)\n            else: await ctx.channel.send('> error')\n        except Exception as e: \n            await ctx.channel.send('> more info `!help setting`')\n            print(e)\n\n\ndef getData(user: discord.Member):\n        userID = str(user.id)\n        sql = \"SELECT count(name) FROM data where id='\"+userID+\"'\"\n        cursor.execute(sql)\n        results = cursor.fetchone()\n        return results\n\ndef insertNick(id, name):\n    sql = \"INSERT INTO data (id, name) VALUES (%s, %s)\"\n    val = (str(id), name)\n    cursor.execute(sql, val)\n    connectDB.db.commit()\n\ndef updateNick(id, name):\n    sql = \"UPDATE data set name=%s where id=%s\"\n    val = (name, str(id))\n    cursor.execute(sql, val)\n    connectDB.db.commit()\n\ndef setup(bot):\n    bot.add_cog(Settings(bot))","sub_path":"legacy-robi/archived_cogs/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":1778,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"391284671","text":"#Loads a tidal forecast data set from csv file and converts points to an SVG in html format\n#ready to view inside browser.\n\nimport csv\nfrom datetime import datetime, timedelta\nimport matplotlib\nimport matplotlib.pyplot as plt\n\n#constant scale factors and offsets\nX_SCALE_FACTOR = 0.03\nY_SCALE_FACTOR = 0.25\n\nX_FRAME_OFFSET = 0\nY_FRAME_OFFSET = 40\n\nCURVE_FACTOR = 0\n\nMONTH = 3\n\n\n#open the forecast dataset and skip headers\nwith open('../ForecastDataSets/Newlyn2020.csv') as csv_file:\n    csv_reader = csv.reader(csv_file, delimiter=',')\n    next(csv_reader, None)  # skip the headers\n    \n    january_time = []\n    january_height = []\n\n    #loop throughthe file\n    for row in csv_reader:\n        \n        #parse in the datetimes and append to new datetime array\n        temp = datetime.strptime(row[0], \"%Y-%m-%d %H:%M\")\n        \n        if temp.month == MONTH:\n            january_time.append(temp)\n            january_height.append(float(row[1]))\n\n            \n#find mid tide\nmidtide = (january_height[0] + january_height[1])/2\n\n\n#create timedeltas\njanuary_deltas = []\n\nfor i in range(len(january_time)):\n    delta = january_time[i] - january_time[0]\n    delta_mins = delta.total_seconds() / 60\n    delta_mins = delta_mins * X_SCALE_FACTOR\n    january_deltas.append(int(delta_mins))\n    \n    \n#convert heights to integers\njanuary_points = []\n\nfor i in range(len(january_height)):\n    point = int(100 * january_height[i])\n    point = point* Y_SCALE_FACTOR\n    january_points.append(point)\n\n    \n#inverse datapoints\njanuary_points_inverse = []\nfor i in range(len(january_points)):\n    january_points_inverse.append(january_points[i] * -1)\n\n\n\n#create boundary box for plot\nupper_bound = 0 + Y_FRAME_OFFSET\nlower_bound = max(january_points_inverse) - min(january_points_inverse) + Y_FRAME_OFFSET\n\nsvg_line1 = '<line x1=\"0\" y1=\"{}\" x2=\"2000\" y2=\"{}\" style=\"stroke:red ;stroke-width:1\" />'.format(lower_bound, lower_bound)\nsvg_line2 = '<line x1=\"0\" y1=\"{}\" x2=\"2000\" y2=\"{}\" style=\"stroke:red ;stroke-width:1\" />'.format(upper_bound, upper_bound)\n\n\n#create svg file structure strings\nsvg_paths = []\n\nfor i in range(len(january_points_inverse)-1):\n    \n    #point1 X and Y coords\n    p1_x = int(january_deltas[i]) + X_FRAME_OFFSET\n    p1_y = int(january_points_inverse[i]) - min(january_points_inverse) + Y_FRAME_OFFSET\n    \n    #point 1 X and Y coords\n    p2_x = int(january_deltas[i+1]) + X_FRAME_OFFSET\n    p2_y = int(january_points_inverse[i+1]) - min(january_points_inverse) + Y_FRAME_OFFSET\n    \n    #calc to find bezier midpoint, should be relatively consistant\n    curve = (p2_x - p1_x) / 2\n    \n    #bezeier 1 X and Y coords\n    b1_x = p1_x + curve + CURVE_FACTOR\n    b1_y = p1_y\n    \n    #bezier 2 X and Y coords\n    b2_x = p2_x - curve - CURVE_FACTOR\n    b2_y = p2_y\n    \n    #create a formatted svg path string and append to an array\n    svg_path = '<path d=\"M{},{} C{},{} {},{} {},{}\" stroke=\"red\" stroke-width=\"1\" stroke-linecap=\"round\" fill=\"none\"/>'.format(p1_x,p1_y,b1_x,b1_y,b2_x,b2_y,p2_x,p2_y)\n    \n    \n    #create a formatted svg text  HEIGHT string and append to an array\n    if january_height[i] > midtide:\n        text_height_offset = -6\n        text_time_offset = -16\n        text_date_offset = -26\n    elif january_height[i] < midtide:\n        text_height_offset = 10\n        text_time_offset = 20\n        text_date_offset = 30\n\n    svg_text_height = '<text x=\"{}\" y=\"{}\" dx=\"-5\" dy=\"{}\" style=\"fill:red; font: italic 8px sans-serif;\" >{:.1f}</text>'.format(p1_x, p1_y, text_height_offset, january_height[i])\n    svg_text_time = '<text x=\"{}\" y=\"{}\" dx=\"-10\" dy=\"{}\" style=\"fill:red; font: italic 8px sans-serif;\" >{}</text>'.format(p1_x, p1_y, text_time_offset, january_time[i].strftime('%H:%M'))\n    svg_text_date = '<text x=\"{}\" y=\"{}\" dx=\"-10\" dy=\"{}\" style=\"fill:red; font: italic 8px sans-serif;\" >{}</text>'.format(p1_x, p1_y, text_date_offset, january_time[i].strftime('%a %-d'))\n    #create a formatted svg text TIME string and append to an array\n    \n    \n    #append strings to svg array\n    \n    svg_paths.append(svg_path)\n    svg_paths.append(svg_text_height)\n    svg_paths.append(svg_text_time)\n    svg_paths.append(svg_text_date)\n\n    \n#manually add a final text item to last point\nif january_height[-1] > midtide:\n    text_height_offset_last = -6\n    text_time_offset_last = -16\n    text_date_offset_last = -26\nelif january_height[-1] < midtide:\n    text_height_offset_last = 10\n    text_time_offset_last = 20\n    text_date_offset_last = 30\n\np1_x_last = int(january_deltas[-1]) + X_FRAME_OFFSET\np1_y_last = int(january_points_inverse[-1]) - min(january_points_inverse) + Y_FRAME_OFFSET\nsvg_text_height_last = '<text x=\"{}\" y=\"{}\" dx=\"-5\" dy=\"{}\" style=\"fill:red; font: italic 8px sans-serif;\" >{:.1f}</text>'.format(p1_x_last, p1_y_last, text_height_offset_last, january_height[-1])\nsvg_text_time_last = '<text x=\"{}\" y=\"{}\" dx=\"-10\" dy=\"{}\" style=\"fill:red; font: italic 8px sans-serif;\" >{}</text>'.format(p1_x_last, p1_y_last, text_time_offset_last, january_time[-1].strftime('%H:%M'))\nsvg_text_date_last = '<text x=\"{}\" y=\"{}\" dx=\"-10\" dy=\"{}\" style=\"fill:red; font: italic 8px sans-serif;\" >{}</text>'.format(p1_x_last, p1_y_last, text_date_offset_last, january_time[-1].strftime('%a %-d'))   \n    \n\n    \n    \n#output the paths to an svg and save as a html file ready to view in browser\nf = open(\"svg_output2.html\", \"w\")\nf.write('<!DOCTYPE html>' + '\\n' + '<html>' + '\\n' + '<body>' + '\\n' + '<svg height=\"800\" width=\"2000\">' + '\\n')\n#f.write('<style>.small { font: italic 8px sans-serif; }</style>' + '\\n')\nf.write(svg_line1 + '\\n')\nf.write(svg_line2 + '\\n')\nfor path in svg_paths:\n    f.write(path + '\\n')\n\nf.write(svg_text_height_last + '\\n')\nf.write(svg_text_time_last + '\\n')\nf.write(svg_text_date_last + '\\n')\nf.write('</svg>' + '\\n' + '</body>' + '\\n' + '</html>')\nf.close()\n\nprint(\"end\")\n\n#fig, ax = plt.subplots()\n#ax.plot(january_deltas, january_points)\n#plt.show()\n    \n\n\n","sub_path":"svgparser_v2.py","file_name":"svgparser_v2.py","file_ext":"py","file_size_in_byte":5926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"280111113","text":"import os\n\nfrom schema import Optional, Or, Schema\n\nENV_MAPPINGS = {\n    \"bot\": {\n        \"token\": \"ERIN_TOKEN\",\n        \"debug\": \"ERIN_DEBUG\",\n        \"project\": \"ERIN_PROJECT\",\n        \"project_folder\": \"ERIN_PROJECT_FOLDER\",\n        \"log_type\": \"ERIN_LOG_TYPE\",\n        \"log_level\": \"ERIN_LOG_LEVEL\",\n    },\n    \"database\": {\n        \"enabled\": \"ERIN_DB_ENABLED\",\n        \"driver\": \"ERIN_DB_DRIVER\",\n        \"uri\": \"ERIN_DB_URI\",\n        \"host\": \"ERIN_DB_HOST\",\n        \"port\": \"ERIN_DB_PORT\",\n        \"username\": \"ERIN_DB_USERNAME\",\n        \"password\": \"ERIN_DB_PASSWORD\",\n        \"database\": \"ERIN_DB_DATABASE\",\n    },\n    \"global\": {\"prefixes\": \"ERIN_PREFIXES\", \"description\": \"ERIN_DESCRIPTION\"},\n}\n\nOPTIONAL_ENVS = [\n    \"ERIN_DEBUG\",\n    \"ERIN_DB_DRIVER\",\n    \"ERIN_DB_URI\",\n    \"ERIN_DB_HOST\",\n    \"ERIN_DB_PORT\",\n    \"ERIN_DB_USERNAME\",\n    \"ERIN_DB_PASSWORD\",\n    \"ERIN_DB_DATABASE\" \"ERIN_DESCRIPTION\",\n]\n\n\nconfig_schema = Schema(\n    {\n        \"bot\": {\n            \"token\": str,\n            \"debug\": Or(True, False),\n            \"project\": str,\n            \"plugins_folder\": os.path.exists,\n            \"log_type\": Or(\"Normal\", \"Timed\"),\n            \"log_level\": Or(\n                \"SPAM\",\n                \"DEBUG\",\n                \"VERBOSE\",\n                \"INFO\",\n                \"NOTICE\",\n                \"WARNING\",\n                \"SUCCESS\",\n                \"ERROR\",\n                \"CRITICAL\",\n            ),\n        },\n        \"database\": {\n            # Schema hooks can be used to force driver detail checks\n            # as noted in https://git.io/fhhd2 instead of resorting\n            # to blanket optionals. Will get to this later if more\n            # databases are needed!\n            \"enabled\": Or(True, False),\n            Optional(\"driver\"): \"mongo\",\n            Optional(\"uri\"): str,\n            Optional(\"host\"): [str],\n            Optional(\"port\"): int,\n            Optional(\"username\"): str,\n            Optional(\"password\"): str,\n            Optional(\"database\"): str,\n            Optional(\"replica\"): str,\n        },\n        \"global\": {\"name\": str, \"prefixes\": [str], \"description\": str},\n    },\n    ignore_extra_keys=True,\n)\n\n\nplugin_schema = Schema({Optional(\"name\"): str, \"database\": Or(True, False)})\n","sub_path":"erin/core/schema.py","file_name":"schema.py","file_ext":"py","file_size_in_byte":2242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"624458780","text":"from buoy.services import ServicesManager\n\n\nclass Project(object):\n    \"\"\"\n    Represents a buoy project, that is, a list of services with\n    modes/dependencies defined in a configuration file.\n    \"\"\"\n\n    def __init__(self, config):\n        super(Project, self).__init__()\n        self.config = config\n\n    def resolve_inheritance(self, config, service, mode):\n        \"\"\"\n        Recursive method to solve values implicit by inheritance.\n        \"\"\"\n        options = {}\n        try:\n            mode_config = config[service][mode]\n        except KeyError:\n            raise DependencyError('\"{}/{}\" service/mode pair not found.'.format(service, mode))\n\n        # Modes can have empty config\n        if mode_config is None:\n            mode_config = {}\n\n        parent = None\n        if '_from' in mode_config:\n            parent = mode_config['_from']\n        elif mode != 'base':\n            parent = 'base'\n\n        if parent:\n            # Either \"mode\" or \"service/mode\"\n            parent_mode = parent\n            parent_service_name = service\n            if '/' in parent:\n                parent_service_name, parent_mode = parent.split('/')\n\n            options = self.resolve_inheritance(config, parent_service_name, parent_mode)\n            mode_config.pop('_from', None)\n\n        for key, value in mode_config.iteritems():\n            if key in options and isinstance(options[key], list):\n                # If key already present then we are inheriting a list, thus\n                # instead of overwriting add the new values.\n                options[key].extend(value)\n            else:\n                options[key] = value\n\n        options['name'] = service\n\n        return options\n\n    def evaluate_config(self, config, mode):\n        \"\"\"\n        Return a dictionary with the evaluated config, which will keep only\n        those services that have the specified mode and explicitely listed\n        the values resolved by inheritance.\n        \"\"\"\n        evaluated_config = {}\n        for service_name, service_config in config.iteritems():\n            if mode in service_config:\n                evaluated_config[service_name] = self.resolve_inheritance(config, service_name,\n                    mode)\n        return evaluated_config\n\n    def service_names_from_links(self, links):\n        \"\"\"\n        Return the service names obtained from a list of service_name:alias\n        links.\n        \"\"\"\n        return [link.split(':')[0] for link in links]\n\n    def get_service_dependencies(self, evaluated_config, service_name, mode):\n        \"\"\"\n        Return the names of the dependencies that ``service`` needs to run.\n        \"\"\"\n        dependencies = []\n\n        def visit(name):\n            if name not in dependencies:\n                dependencies.append(name)\n\n                try:\n                    service_config = evaluated_config[name]\n                    dependency_names = self.service_names_from_links(\n                        service_config.get('link', [])\n                    )\n                    dependency_names.extend(service_config.get('volumes-from', []))\n                    for dependency in dependency_names:\n                        visit(dependency)\n                except KeyError:\n                    raise DependencyError(\n                        '\"{}/{}\" service/mode pair not found.'.format(name, mode)\n                    )\n\n        visit(service_name)\n\n        return dependencies\n\n    def get_start_order(self, evaluated_config, service_name, mode):\n        \"\"\"\n        Return a possible start order for ``service_name`` and all its\n        dependencies.\n\n        Topological sort (Cormen/Tarjan algorithm).\n        \"\"\"\n        # Set of services we care about\n        all_services = self.get_service_dependencies(evaluated_config, service_name, mode)\n\n        unmarked = all_services[:]\n        temporary_marked = set()\n        sorted_services = []\n\n        def visit(name):\n            if name in temporary_marked:\n                service_config = evaluated_config[name]\n                if name in self.service_names_from_links(service_config.get('link', [])):\n                    raise DependencyError('A service can not link to itself: {}'.format(name))\n                if name in service_config.get('volumes-from', []):\n                    raise DependencyError(\n                        'A service can not mount itself as volume: {}'.format(name)\n                    )\n                else:\n                    raise DependencyError(\n                        'Circular import: {}'.format(', '.join(temporary_marked))\n                    )\n\n            if name in unmarked:\n                temporary_marked.add(name)\n                # Find the services which depend on service ``name`` to start\n\n                def has_direct_dependency(serv_name, dependent):\n                    \"\"\"\n                    Return True if ``dependent`` has a direct dependency on\n                    ``serv_name``, ie. if it directly links to or uses a\n                    volume from it.\n                    \"\"\"\n                    dependent_config = evaluated_config[dependent]\n                    link_names = self.service_names_from_links(dependent_config.get('link', []))\n                    return name in link_names or name in dependent_config.get('volumes-from', [])\n\n                dependents = [\n                    other for other in all_services\n                    if has_direct_dependency(name, other)\n                ]\n                for dependent in dependents:\n                    visit(dependent)\n                temporary_marked.remove(name)\n                unmarked.remove(name)\n                sorted_services.insert(0, name)\n\n        while unmarked:\n            visit(unmarked[-1])\n\n        return sorted_services\n\n    def run_service(self, service_name, mode):\n        \"\"\"\n        Start ``service_name`` in the appropriate mode, together with all\n        its dependencies in order.\n        \"\"\"\n        config = self.evaluate_config(self.config, mode)\n\n        # Get startup order\n        start_order = self.get_start_order(config, service_name, mode)\n\n        manager = ServicesManager()\n        for container_name in start_order:\n            manager.create_container(config[container_name])\n\n        return start_order\n\n\nclass ConfigurationError(Exception):\n\n    def __init__(self, msg):\n        self.msg = msg\n\n    def __str__(self):\n        return self.msg\n\n\nclass DependencyError(ConfigurationError):\n    pass\n","sub_path":"buoy/project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":6474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"468610561","text":"import os\nimport sys\nimport base64\nfrom libUnity import Unity\n\nFind = Unity.FindFiles()\nArchive = Unity.Archive()\nBit = Unity.OneBit()\nZeroParty = Unity.ZeroParty()\nCipher = Unity.Cipher()\n\ndef Enc(dir):\n\tListFiles = Find.find(dir)\n\tfor file in ListFiles:\n\t\tArchive.archiving(file)\n\t\tZeroParty.delete(file)\n\tBit.add(\"CriptedFiles.zip\")\n\tKey = raw_input(\"Key: \")[16]\n\tCipher.encrypt_file(\"CriptedFiles.zip\", Key)\n\ndef Dec():\n\tKey = raw_input(\"Key: \")[16]\n\tCipher.decrypt_file(\"CriptedFiles.zip.enc\", Key)\n\tBit.delete(\"CriptedFiles.zip\")\n\nif len(sys.argv) < 3 or len(sys.argv) > 3:\n\tprint(\"Usage: *.py <dir> <enc/dec>\")\n\tsys.exit()\n\ndef main():\n\tif sys.argv[2] == \"enc\":\n\t\tEnc(sys.argv[1])\n\telif sys.argv[2] == \"dec\":\n\t\tDec()\n\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"29282410","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport os\nfrom peewee import Model, MySQLDatabase, IntegerField, CharField, DoubleField,\\\n\t\t\t\t   BooleanField, DateTimeField, fn\nfrom datetime import datetime\nfrom datetime import timedelta\n\nfrom . import config\n\n\ndb = None\n\ndef init_database():\n\tglobal db\n\tif db is not None:\n\t\treturn db\n\n\tdb = MySQLDatabase(\n\t\tconfig['DB_NAME'],\n\t\tuser=config['DB_USER'],\n\t\tpassword=config['DB_PASSWORD'],\n\t\thost=config['DB_HOST'])\n\n\treturn db\n\n\nclass BaseModel(Model):\n\tclass Meta:\n\t\tdatabase = init_database()\n\n\t@classmethod\n\tdef get_all(cls):\n\t\tresults = [m for m in cls.select().dicts()]\n\t\treturn results\n\nclass Pokemon(BaseModel):\n\t# We are base64 encoding the ids delivered by the api\n\t# because they are too big for sqlite to handle\n\tencounter_id = CharField(primary_key=True)\n\tspawnpoint_id = CharField()\n\tpokemon_id = IntegerField()\n\tlatitude = DoubleField()\n\tlongitude = DoubleField()\n\tdisappear_time = DateTimeField()\n\n\t@classmethod\n\tdef get_active(cls, swLat, swLng, neLat, neLng):\n\t\tif swLat == None or swLng == None or neLat == None or neLng == None:\n\t\t\tquery = (Pokemon\n\t\t\t\t .select()\n\t\t\t\t .where(Pokemon.disappear_time > datetime.utcnow())\n\t\t\t\t .dicts())\n\t\telse:\n\t\t\tquery = (Pokemon\n\t\t\t\t .select()\n\t\t\t\t .where((Pokemon.disappear_time > datetime.utcnow()) &\n\t\t\t\t\t(Pokemon.latitude >= swLat) &\n\t\t\t\t\t(Pokemon.longitude >= swLng) &\n\t\t\t\t\t(Pokemon.latitude <= neLat) &\n\t\t\t\t\t(Pokemon.longitude <= neLng))\n\t\t\t\t .dicts())\n\n\t\tpokemons = []\n\t\tfor p in query:\n\t\t\tpokemons.append(p)\n\n\t\treturn pokemons\n\n\t@classmethod\n\tdef get_active_by_id(cls, ids, swLat, swLng, neLat, neLng):\n\t\tif swLat == None or swLng == None or neLat == None or neLng == None:\n\t\t\tquery = (Pokemon\n\t\t\t\t\t .select()\n\t\t\t\t\t .where((Pokemon.pokemon_id << ids) &\n\t\t\t\t\t\t\t(Pokemon.disappear_time > datetime.utcnow()))\n\t\t\t\t\t .dicts())\n\t\telse:\n\t\t\tquery = (Pokemon\n\t\t\t\t\t .select()\n\t\t\t\t\t .where((Pokemon.pokemon_id << ids) &\n\t\t\t\t\t\t\t(Pokemon.disappear_time > datetime.utcnow()) &\n\t\t\t\t\t\t\t(Pokemon.latitude >= swLat) &\n\t\t\t\t\t\t\t(Pokemon.longitude >= swLng) &\n\t\t\t\t\t\t\t(Pokemon.latitude <= neLat) &\n\t\t\t\t\t\t\t(Pokemon.longitude <= neLng))\n\t\t\t\t\t .dicts())\n\n\t\tpokemons = []\n\t\tfor p in query:\n\t\t\tpokemons.append(p)\n\n\t\treturn pokemons\n\n\nclass Pokestop(BaseModel):\n\tpokestop_id = CharField(primary_key=True, max_length=50)\n\tenabled = BooleanField()\n\tlatitude = DoubleField()\n\tlongitude = DoubleField()\n\tlure_expiration = DateTimeField(null=True, index=True)\n\tlured_pokemon_expiration = DateTimeField(null=True, index=True)\n\tactive_pokemon_id = IntegerField(null=True)\n\n\t@classmethod\n\tdef get_stops(cls, swLat, swLng, neLat, neLng):\n\t\tif swLat == None or swLng == None or neLat == None or neLng == None:\n\t\t\tquery = (Pokestop\n\t\t\t\t .select()\n\t\t\t\t .dicts())\n\t\telse:\n\t\t\tquery = (Pokestop\n\t\t\t\t .select()\n\t\t\t\t .where((Pokestop.latitude >= swLat) &\n\t\t\t\t\t(Pokestop.longitude >= swLng) &\n\t\t\t\t\t(Pokestop.latitude <= neLat) &\n\t\t\t\t\t(Pokestop.longitude <= neLng))\n\t\t\t\t .dicts())\n\n\t\tpokestops = []\n\t\tfor p in query:\n\t\t\tpokestops.append(p)\n\n\t\treturn pokestops\n\n\nclass Gym(BaseModel):\n\tgym_id = CharField(primary_key=True)\n\tteam_id = IntegerField()\n\tguard_pokemon_id = IntegerField()\n\tgym_points = IntegerField()\n\tenabled = BooleanField()\n\tlatitude = DoubleField()\n\tlongitude = DoubleField()\n\tlast_modified = DateTimeField()\n\n\t@classmethod\n\tdef get_gyms(cls, swLat, swLng, neLat, neLng):\n\t\tif swLat == None or swLng == None or neLat == None or neLng == None:\n\t\t\tquery = (Gym\n\t\t\t\t .select()\n\t\t\t\t .dicts())\n\t\telse:\n\t\t\tquery = (Gym\n\t\t\t\t .select()\n\t\t\t\t .where((Gym.latitude >= swLat) &\n\t\t\t\t\t(Gym.longitude >= swLng) &\n\t\t\t\t\t(Gym.latitude <= neLat) &\n\t\t\t\t\t(Gym.longitude <= neLng))\n\t\t\t\t .dicts())\n\n\t\tgyms = []\n\t\tfor g in query:\n\t\t\tgyms.append(g)\n\n\t\treturn gyms\n\n\nclass ScannedLocation(BaseModel):\n\tscanned_id = CharField(primary_key=True)\n\tlatitude = DoubleField()\n\tlongitude = DoubleField()\n\tlast_modified = DateTimeField()\n\n\t@classmethod\n\tdef get_recent(cls, swLat, swLng, neLat, neLng):\n\t\tquery = (ScannedLocation\n\t\t\t\t .select()\n\t\t\t\t .where((ScannedLocation.last_modified >= (datetime.utcnow() - timedelta(minutes=15))) &\n\t\t\t\t\t(ScannedLocation.latitude >= swLat) &\n\t\t\t\t\t(ScannedLocation.longitude >= swLng) &\n\t\t\t\t\t(ScannedLocation.latitude <= neLat) &\n\t\t\t\t\t(ScannedLocation.longitude <= neLng))\n\t\t\t\t .dicts())\n\n\t\tscans = []\n\t\tfor s in query:\n\t\t\tscans.append(s)\n\n\t\treturn scans\n\n\nclass BotAccount(BaseModel):\n\tusername = CharField(primary_key=True)\n\tpassword = CharField()\n\temail = CharField()\n\tlatitude = DoubleField(null=True)\n\tlongitude = DoubleField(null=True)\n\tinstance_id = CharField(null=True)\n\texpiration = DateTimeField(null=True)\n\n\t@classmethod\n\tdef reserve(cls, latitude, longitude):\n\t\taccount = BotAccount.select().where((BotAccount.latitude >> None) & (BotAccount.longitude >> None)).order_by(fn.Rand()).limit(1).get()\n\n\t\taccount.latitude = latitude;\n\t\taccount.longitude = longitude;\n\t\taccount.expiration = datetime.now() + timedelta(minutes=30)\n\t\taccount.save();\n\n\t\treturn account\n\n\t@classmethod\n\tdef get_expired(cls):\n\t\tquery = BotAccount.select().where(BotAccount.expiration < datetime.now())\n\n\t\treturn query\n\n\ndef create_tables(db):\n\tdb.connect()\n\tdb.create_tables([Pokemon, Pokestop, Gym, ScannedLocation, BotAccount], safe=True)\n\tdb.close()\n","sub_path":"webserver/pokemapapp/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":5214,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"28811770","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom agenda.models import *\n\ndef agenda(request):\n    retorno = \"<h1>Agendas</h1>\"\n    lista = AgendaMain.objects.all()\n    '''for a in lista:\n        retorno += '</br>Nome da Agenda: {}</br>'.format(a.nome)\n\n    lista1 = AgendaPrivada.objects.all()\n    for a in lista1:\n        retorno += '</br>Nome da Agenda: {}</br>'.format(a.nome)'''\n\n    lista2 = AgendaInstitucinal.objects.all()\n    for a in lista2:\n        retorno += '</br>Nome da Agenda: {}</br>'.format(a.nome)\n\n    return HttpResponse(retorno)\n\ndef compromisso(request):\n    retorno = \"<h1>Compromisso</h1>\"\n    lista = AgendaInstitucinal.objects.all()\n    for a in lista:\n        retorno += '</br>Nome da Agenda: {}</br>Compromisso {}'.format(a.nome,a.compromisso)\n    return HttpResponse(retorno)\n# Create your views here.'''\n","sub_path":"lpc_calendario/agenda/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"610751465","text":"from typing import List\n# https://leetcode.com/problems/product-of-array-except-self/\n\"\"\"\nGiven an array nums of n integers where n > 1,  \nreturn an array output such that output[i] is equal to the product of all the elements of nums except nums[i].\n\nConstraint: \nIt's guaranteed that the product of the elements of any prefix or suffix of the array (including the whole array) \nfits in a 32 bit integer.\n\n\"\"\"\n\nclass Solution:\n    def productExceptSelf(self, nums: List[int]) -> List[int]:\n        left = []\n        p = 1\n        for i in range(0, len(nums)):\n            left.append(p)\n            p = p * nums[i]\n        \n        p = 1\n        for i in range(len(nums) - 1, -1, -1):\n            left[i] = left[i] * p\n            p = p * nums[i]\n        \n        return left\n    \ns = Solution()\ncase = [1,2,3,4]\nprint(s.productExceptSelf(case))","sub_path":"docs/ch7/anonymous/238.py","file_name":"238.py","file_ext":"py","file_size_in_byte":845,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"196775569","text":"# -*- coding: utf-8 -*-\n\nimport config\nfrom rate_limit import *\nfrom plugin_system import *\n\nimport copy\n\nimport logging\nlog = logging.getLogger(__name__)\n\n\n@pluginfunction(\n    'force-self-talking',\n    '''force evaluating bot's own output''',\n    ptypes.COMMAND,\n    ratelimit_class=RATE.GLOBAL,\n    enabled_for=['$bot_nickname']\n)\ndef force_self_talking(argv, from_user, **args):\n    if not argv:\n        return\n\n    argv.unshift( config.conf_get('bot_nickname') )\n\n    new_msg_obj = copy.copy(args['msg_obj'])\n    new_msg_obj.new_from_user = from_user+' ' \n    new_msg_obj['body'] = argv.toCmdline()\n\n    return { 'rehandle': new_msg_obj }\n\n\n","sub_path":"plugins/force-self-talking.py","file_name":"force-self-talking.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"38104267","text":"import pygame\n\npygame.init()\nclass Moneda(pygame.sprite.Sprite):\n    def __init__(self, posx,posy):\n        \n        self.imagen1=pygame.image.load(\"monedaa.png\")\n\n        \n        self.ImagenMoneda = self.imagen1\n        self.rect=self.ImagenMoneda.get_rect()\n        \n        self.rect.top=posy\n        self.rect.left=posx\n        \n    def dibujar(self,superficie):\n        superficie.blit(self.ImagenMoneda,self.rect)\n        \n      \n            \n","sub_path":"0.4/moneda.py","file_name":"moneda.py","file_ext":"py","file_size_in_byte":450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"626534617","text":"import myclass\n\ncs = myclass.Customer(\"小明\", 20, \"123@gmail.com\", \"0912345678\")\n\nnm = cs.getName()\nag = cs.getAge()\nml = cs.getMail()\ntl = cs.getTel()\n\nprint(nm, \"先生/小姐\", ag, \"歲\")\nprint(\"E-mail：\", ml, \"手機號碼：\", tl)","sub_path":"Class(類別)_practice/importClass.py","file_name":"importClass.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"151484459","text":"# The program displays the cost of conditions by the user at the MTS tariff\r\n# 10 seconds - cost 42 kopecks\r\n# Tariffishe package of services gives a 30% discount on minutes\r\nchoice = input('Would you like to purchase a package of services Tariffishe for 100 rubles ?:')\r\nminute = int(input('How many minutes do you want to get:'))\r\nif choice == 'Yes':\r\n    amount = 100\r\n    kop = minute * 6 * 42 * 0.3\r\nelif choice == 'No':\r\n    amount = 0\r\n    kop = minute * 6 * 42\r\nelse:\r\n    print('Error! Contact your provider!')\r\n    kop = 0\r\n    amount = 0\r\nrub = kop // 100\r\nkop = kop % 100\r\nprint('Please deposit in the account', rub + amount, 'rub.', kop, 'kop.')\r\n","sub_path":"ENG/MTS - Tariffishe.py","file_name":"MTS - Tariffishe.py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"203336343","text":"import os\nimport sys\nimport datetime\n\n\narg_dic={\n        'wrfout':sys.argv[1],\n        'ptlist':sys.argv[2],\n        'back_fg':int(sys.argv[3]),     # Backward Flag 1--backward 0--foreward\n        'hgt':float(sys.argv[4]),\n        'rtime':int(sys.argv[5]),\n        'wrfpre':sys.argv[6]}\n\n# CONTROL file sample directory (Don't use 'CONTROL')\nsmp_path='CONTROL.smp'\n\n#Operation\ntime_obj = datetime.datetime.utcnow()\nfc_time_delta=datetime.timedelta(hours=24)\nyyyy=time_obj.strftime('%Y')\nyyyymm=time_obj.strftime('%Y%m')\nyyyymmdd=time_obj.strftime('%Y%m%d')\npsdo_path='/disk/hq247/yhuangci/cmaq-run/data/wrf-fc/'+yyyy+'/'+yyyymm+'/'+yyyymmdd+'12'\n\n# make sure the existence of the dir\nwhile not(os.path.isdir(psdo_path)):\n    time_obj=time_obj-fc_time_delta\n    yyyy=time_obj.strftime('%Y')\n    yyyymm=time_obj.strftime('%Y%m')\n    yyyymmdd=time_obj.strftime('%Y%m%d')\n    psdo_path='/disk/hq247/yhuangci/cmaq-run/data/wrf-fc/'+yyyy+'/'+yyyymm+'/'+yyyymmdd+'12'\n\ntime_obj=datetime.datetime.strptime('%s%s' %(yyyymmdd,'12'), '%Y%m%d%H')\nprint('Found: %s' % psdo_path)\n\n# wrf data to arl data\n#####os.system('ln -s '+psdo_path+'/'+arg_dic['wrfpre']+'* ./wrf-link/')\n#####os.system('sh exec_arw2arl.sh')\n\n# read points\nwith open(arg_dic['ptlist'],'r') as fr:\n    lines_pt=fr.readlines()\n\n# read sample\nwith open(smp_path,'r') as fsmp:\n    lines_smp=fsmp.readlines()\n\n# Initial Date Change, back_fg=1, shift rtime forward\nini_time_obj=time_obj+arg_dic['back_fg']*datetime.timedelta(hours=arg_dic['rtime'])\nlines_smp[0]=ini_time_obj.strftime('%y %m %d %H\\n')\n\n# Integration time change\nlines_smp[3]='%4d\\n' % ((-1)**arg_dic['back_fg']*arg_dic['rtime'])\n\nfor idx, pt in enumerate(lines_pt):\n    ele=pt.split()\n\n    os.system('mkdir ./traj_output/'+ele[2]+'')\n\n    pt=pt.strip('\\n')\n    # Change Point and height\n    lines_smp[2]='%s %s %8.1f\\n'% (ele[0], ele[1], arg_dic['hgt'])\n\n    # Output Filename Change\n    lines_smp[-2]='./traj_output/%s/\\n' % (ele[2])\n    lines_smp[-1]='%s%s' % (ele[2], yyyymmdd)\n    \n    with open('../exec/CONTROL','w') as fr:\n        fr.writelines(lines_smp)\n    \n    os.system('cd ./; ./hyts_std')\n","sub_path":"HYSPLIT-AUTO-2017/script/run-hysplit4-daily.py","file_name":"run-hysplit4-daily.py","file_ext":"py","file_size_in_byte":2121,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"425309069","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.4 (62061)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.macosx-10.3-i386/egg/easyshop/criteria/content/price.py\n# Compiled at: 2008-09-03 11:14:39\nfrom zope.interface import implements\nfrom DateTime import DateTime\nfrom AccessControl import ClassSecurityInfo\nfrom Products.CMFCore.utils import getToolByName\nfrom Products.Archetypes.atapi import *\nfrom easyshop.core.config import *\nfrom easyshop.core.interfaces import IPriceCriteria\nschema = Schema((FloatField(name='price', default=0.0, widget=DecimalWidget(label='Price', label_msgid='schema_price_label', description='Valid if the cart price is greater than or equal price you enter here.', description_msgid='schema_price_description', i18n_domain='EasyShop')), StringField(name='priceType', vocabulary='_getPriceTypesAsDL', default='net', widget=SelectionWidget(label='Price Type', label_msgid='schema_price_label', description='Please select the type of the entered price above.', description_msgid='schema_price_type_description', i18n_domain='EasyShop'))))\n\nclass PriceCriteria(BaseContent):\n    \"\"\"\n    \"\"\"\n    __module__ = __name__\n    implements(IPriceCriteria)\n    security = ClassSecurityInfo()\n    _at_rename_after_creation = True\n    schema = BaseSchema.copy() + schema.copy()\n\n    def Title(self):\n        \"\"\"\n        \"\"\"\n        return 'Price'\n\n    def getValue(self):\n        \"\"\"\n        \"\"\"\n        return self.getPrice()\n\n    def _getPriceTypesAsDL(self):\n        \"\"\"Returns vocabulary for field \"price_type\" as DisplayList.\n        \"\"\"\n        dl = DisplayList()\n        dl.add('gross', 'Gross')\n        dl.add('net', 'Net')\n        return dl\n\n\nregisterType(PriceCriteria, PROJECTNAME)","sub_path":"pycfiles/easyshop.criteria-0.1a1-py2.4/price.py","file_name":"price.py","file_ext":"py","file_size_in_byte":1773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"2453553","text":"#!/usr/bin/env python\n\n# Unless explicitly stated otherwise all files in this repository are licensed\n# under the Apache License Version 2.0.\n# This product includes software developed at Datadog (https://www.datadoghq.com/).\n# Copyright 2018 Datadog, Inc.\n\nimport signal\nimport sys\nimport time\nimport logging\n\nfrom config import config\nfrom config.providers import FileConfigProvider\nfrom utils.hostname import get_hostname\nfrom metadata import get_metadata\n\nfrom collector import Collector\nfrom aggregator import MetricsAggregator\nfrom serialize import Serializer\nfrom forwarder import Forwarder\n\n\ndef init_agent():\n    # init default search path\n    config.add_search_path(\"/etc/datadog-unix-agent\")\n    config.add_search_path(\".\")\n    config.load()\n\n    # init log\n    level = logging.getLevelName(config.get(\"log_level\").upper())\n    logging.basicConfig(level=level)\n\n    # add file provider\n    file_provider = FileConfigProvider()\n    file_provider.add_place(config.get('additional_checksd'))\n    config.add_provider('file', file_provider)\n\n    # FIXME: do this elsewhere\n    # collect config\n    config.collect_check_configs()\n\n\ndef start():\n    \"\"\"\n    Dummy start until we have a collector\n    \"\"\"\n    init_agent()\n\n    hostname = get_hostname()\n\n    logging.info(\"Starting the agent, hostname: %s\", hostname)\n\n    # init Forwarder\n    logging.info(\"Starting the Forwarder\")\n    api_key = config.get('api_key')\n    dd_url = config.get('dd_url')\n    if not dd_url:\n        logging.error('No Datadog URL configured - cannot continue')\n        sys.exit(1)\n    if not api_key:\n        logging.error('No API key configured - cannot continue')\n        sys.exit(1)\n\n    forwarder = Forwarder(\n        api_key,\n        dd_url\n    )\n    forwarder.start()\n\n    # aggregator\n    aggregator = MetricsAggregator(\n        hostname,\n        interval=config.get('aggregator_interval'),\n        expiry_seconds=(config.get('min_collection_interval') +\n                        config.get('aggregator_expiry_seconds')),\n        recent_point_threshold=config.get('recent_point_threshold'),\n        histogram_aggregates=config.get('histogram_aggregates'),\n        histogram_percentiles=config.get('histogram_percentiles'),\n    )\n\n    # serializer\n    serializer = Serializer(\n        aggregator,\n        forwarder,\n    )\n\n    # instantiate collector\n    collector = Collector(config, aggregator)\n    collector.load_check_classes()\n    collector.instantiate_checks()\n\n    def signal_handler(signal, frame):\n        logging.info(\"SIGINT received: stopping the agent\")\n        logging.info(\"Stopping the forwarder\")\n        forwarder.stop()\n        logging.info(\"See you !\")\n        sys.exit(0)\n\n    signal.signal(signal.SIGINT, signal_handler)\n\n    # update the metadata periodically?\n    metadata = get_metadata(hostname)\n    serializer.submit_metadata(metadata)\n    while True:\n        collector.run_checks()\n        serializer.serialize_and_push()\n        time.sleep(config.get('min_collection_interval'))\n\n\nif __name__ == \"__main__\":\n    start()\n","sub_path":"agent.py","file_name":"agent.py","file_ext":"py","file_size_in_byte":3036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"364172004","text":"\"\"\"\n================================\nKuramoto: Heun's  method in CUDA\n================================\n\nallow efficiently simulating phase oscillators \n(the Kuramoto model) on large heterogeneous networks using the \nHeun's method with a \"pure\" CUDA implementation. Should be\nfaster than tensorflow implementation.\nReferences\n----------\n\n[1] - Thomas Peron, Bruno Messias, Angélica S. Mata, Francisco A. Rodrigues,\nand Yamir Moreno. On the onset of synchronization of Kuramoto oscillators in\nscale-free networks. arXiv:1905.02256 (2019).\n\n\n\"\"\"\nimport numpy as np\ntry:\n    import cukuramoto\nexcept ImportError:\n    pass\n\n\nclass CUHeuns:\n    \"\"\"Allow efficiently simulating phase oscillators (the Kuramoto model) on\n    large heterogeneous networks using the Heun’s method. This class uses a\n    pure CUDA implementation of Heun’s method. Therefore, should be faster\n    than TensorFlow implementation also provided by StDoG\n\n    Attributes\n    ----------\n        adjacency : coo matrix\n        phases : np.ndarray\n        omegas : np.ndarray\n        couplings : np.ndarray\n        total_time : float\n        dt : float\n        transient : bool\n        order_parameter_list : np.ndarray\n\n    \"\"\"\n\n    def __init__(\n        self,\n        adjacency,\n        phases,\n        omegas,\n        couplings,\n        total_time,\n        dt,\n        transient=False,\n        block_size=1024,\n    ):\n\n        self._adjacency = adjacency\n        self._phases = phases.astype(\"float32\")\n        self._omegas = omegas.astype(\"float32\")\n        self._couplings = couplings.astype(\"float32\")\n        self.transient = transient\n        self.total_time = total_time\n        self.dt = dt\n\n        self.block_size = block_size\n\n        self.order_parameter_list = np.array([])\n        self.create_simulation()\n\n    @property\n    def num_couplings(self):\n        return len(self.couplings)\n\n    @property\n    def num_oscilators(self):\n        return self.adjacency.shape[0] \n\n    @property\n    def num_temps(self):\n        return int(self.total_time/self.dt)\n\n    @property\n    def phases(self):\n        phases = self.simulation.get_phases().reshape(\n            (self.num_couplings, self.num_oscilators)\n        )\n        self._phases = phases\n        return phases\n\n    @property\n    def omegas(self):\n        return self._omegas\n\n    @omegas.setter\n    def omegas(self, omegas):\n        self._omegas = omegas.astype(\"float32\") \n        self.create_simulation()\n\n    @property\n    def couplings(self):\n        return self._couplings\n\n    @couplings.setter\n    def couplings(self, couplings):\n        self._couplings = couplings.astype(\"float32\")\n        self.create_simulation()\n\n    @property\n    def adjacency(self):\n        return self._adjacency\n\n    @adjacency.setter\n    def adjacency(self, adjacency):\n        self._adjacency = adjacency\n        self.create_simulation()\n\n    def create_simulation(self):\n        \"\"\"This method method crates the simulation.\n        \"\"\"\n        adj = self.adjacency.tocsr()\n        ptr, indices = adj.indptr.astype(\"int32\"), adj.indices.astype(\"int32\")\n\n        simulation = cukuramoto.Heuns(\n            self.num_oscilators, self.block_size, self.omegas, \n            self._phases.flatten(), self.couplings, indices, ptr)\n\n        self.simulation = simulation\n\n    def run(self):\n        \"\"\"This runs the algorithm and updates the phases.\n\n        If transiet is set to True, then  the order parameters is\n        calculated and  the array order_parameter_list is updated.\n        \"\"\"\n      \n        if self.transient:\n            order_parameter_list = self.simulation.get_order_parameter(\n                self.num_temps, self.dt)\n            self.order_parameter_list = order_parameter_list.reshape(\n                (self.num_couplings, self.num_temps)\n            )\n        else:\n            self.simulation.heuns(\n                self.num_temps, self.dt)\n\n__all__ = [\"CUHeuns\"]\n","sub_path":"stdog/dynamics/kuramoto/cuheuns.py","file_name":"cuheuns.py","file_ext":"py","file_size_in_byte":3905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"36167917","text":"# Solution for Matrix Script\n\n# !/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\nnm = input().split()\nn = int(nm[0])\nm = int(nm[1])\n\nmatrix = []\n\nfor _ in range(n):\n    matrix_item = input()\n    matrix.append(matrix_item)\n\nzipped = zip(*matrix)\n\ns = \"\"\nfor z in zipped:\n    s += \"\".join(z)\n\nresult = re.sub(r\"\\b\\W+\\b\", \" \", s)\nprint(result)\n","sub_path":"Python/Python/Regex and Parsing/Matrix Script.py","file_name":"Matrix Script.py","file_ext":"py","file_size_in_byte":365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"555417558","text":"\"\"\"\nCracking the Coding Interview\nExcercise solved by Jeremy Winterberg\n06/09/2018\n\n1.1 - Is Unique: Implement an algorithm to determine if a string has all unique characters.\n                 What if you cannot use additional data structures?\n\n\nFirst thoughts:\n    Create some sort of hash table to store each letter as we go through.\n    Then you can look-up in the table if the key exists already in O(1) complexity.\n\"\"\"\n\ndef is_unique(word):\n    \"\"\"is_unique\n\n    :param word:\n    \"\"\"\n    lookup_table = dict()\n\n    for char in word:\n        if char in lookup_table:\n            return False\n\n        lookup_table[char] = 1\n        return True\n\ndef main():\n    \"\"\"main\"\"\"\n    unique = \"qwertyuiopasdfghjklzxcvbnm\"\n    non_unique = \"qwertyasdfghjklqwerty\"\n\n    print(\"Is\", unique, \"unique?\", is_unique(unique))\n    print(\"Is\", non_unique, \"unique?\", is_unique(non_unique))\n\n    # Output\n    # Is qwertyuiopasdfghjklzxcvbnm unique? True\n    # Is qwertyasdfghjklqwerty unique? False\n\nmain()\n\n\"\"\"\nIf I wasn't allowed to use a dict / hash table / data structure\nI would do a nested loop on the string to check the i'th char against the i+1'th char.\nThis raises the complexity to O(n^2) however.\n\"\"\"\n","sub_path":"CCI/1.arrays_strings/is_unique.py","file_name":"is_unique.py","file_ext":"py","file_size_in_byte":1198,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"263737802","text":"from django.db import models\nfrom django.db.models.loading import get_model\nfrom django.contrib.auth.models import User\n\nfrom oscar.apps.order.abstract_models import AbstractOrder, AbstractLine\n\n\nSMALL_ORDER = \"Small format digital\"\nLARGE_ORDER = \"Large format digital\"\nEXHIBITION_ORDER = \"Exhibition\"\n\nProduct = get_model('catalogue', 'Product')\nCategory = get_model('catalogue', 'Category')\nLine = get_model('order', 'Line')\n\nclass SmallOrderManager(models.Manager):\n    def get_query_set(self):\n        qs = super(SmallOrderManager, self).get_query_set()\n        small_category = Category.objects.get(name__iexact=SMALL_ORDER)\n        small_categories = [small_category] + list(small_category.get_children())\n        \n        return qs.filter(lines__in=Line.objects.filter(product__categories__in=small_categories)).distinct()\n\n\nclass LargeOrderManager(models.Manager):\n    def get_query_set(self):\n        qs = super(LargeOrderManager, self).get_query_set()\n        large_category = Category.objects.get(name__iexact=LARGE_ORDER)\n        large_categories = [large_category] + list(large_category.get_children())\n        \n        return qs.filter(lines__in=Line.objects.filter(product__categories__in=large_categories)).distinct()\n\n\nclass ExhibitionOrderManager(models.Manager):\n    def get_query_set(self):\n        qs = super(ExhibitionOrderManager, self).get_query_set()\n        exhibition_category = Category.objects.get(name__iexact=EXHIBITION_ORDER)\n        exhibition_categories = [exhibition_category] + list(exhibition_category.get_children())\n        \n        return qs.filter(lines__in=Line.objects.filter(product__categories__in=exhibition_categories)).distinct()\n\n\nclass Order(AbstractOrder):\n    objects = models.Manager()\n    small = SmallOrderManager()\n    large = LargeOrderManager()\n    exhibition = ExhibitionOrderManager()\n\n    def has_multiple_zones(self):\n        def get_root_category(category):\n            return category.get_root()\n\n        products = Product.objects.filter(line__in=self.lines.all())\n        categories = Category.objects.filter(product__in=products)\n        root_categories = map(get_root_category, categories)\n        return len(set(root_categories)) > 1\n\n    def get_small_lines(self):\n        small_category = Category.objects.get(name__iexact=SMALL_ORDER)\n        small_categories = [small_category] + list(small_category.get_children())\n        return self.lines.filter(product__categories__in=small_categories)\n\n    def get_large_lines(self):\n        large_category = Category.objects.get(name__iexact=LARGE_ORDER)\n        large_categories = [large_category] + list(large_category.get_children())\n        return self.lines.filter(product__categories__in=large_categories)\n\n    def get_exhibition_lines(self):\n        exhibition_category = Category.objects.get(name__iexact=EXHIBITION_ORDER)\n        exhibition_categories = [exhibition_category] + list(exhibition_category.get_children())\n        return self.lines.filter(product__categories__in=exhibition_categories)\n\nfrom oscar.apps.order.models import *","sub_path":"gap/apps/order/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"125266623","text":"_api_attributes = [\n        'api_version',\n        'date_created',\n        'date_modified',\n        'wikipedia_name',\n        '_hidden_wikipedia_page',\n        '_hidden_wikipedia_html',\n        'wikipedia_url',\n        'wikipedia_image_url',\n        'imslp_url',\n        '_hidden_imslp_html',\n        'birthdate',\n        'deathdate',\n        'work_ids',\n        'freebase_name',\n]\n\ndef api_version(composer):\n    \"\"\"API version number.\n    Added in v0.1.\n    Dependencies: none\n    Author: Steve Tjoa\n    \"\"\"\n    from violinio import __version__\n    composer['api_version'] = __version__\n\n\ndef date_created(composer):\n    \"\"\"Date and time this object was created.\n    Added in v0.1.\n    Dependencies: none\n    Author: Steve Tjoa\n    \"\"\"\n    import time\n    if not composer.has_key('date_created'):\n        composer['date_created'] = time.strftime('%Y %b %d %X %Z')\n\n\ndef date_modified(composer):\n    \"\"\"Current time.\n    Added in v0.1.\n    Dependencies: none\n    Author: Steve Tjoa\n    \"\"\"\n    import time\n    composer['date_modified'] = time.strftime('%Y %b %d %X %Z')\n\n\ndef wikipedia_name(composer):\n    \"\"\"Full name of the composer.\n    Dependencies: id or wikipedia_name\n    Author: Steve Tjoa\n    \"\"\"\n    import wikipedia\n    if composer.has_key('wikipedia_name'):\n        query = composer['wikipedia_name']\n    else:\n        query = composer['id']\n    composer['wikipedia_name'] = wikipedia.search(query, results=1)[0]\n\n\ndef _hidden_wikipedia_page(composer):\n    \"\"\"Wikipedia page object.\n    Dependencies: wikipedia_name\n    Author: Steve Tjoa\n    \"\"\"\n    import wikipedia\n    composer['_hidden_wikipedia_page'] = wikipedia.page(composer['wikipedia_name'])\n\n\ndef _hidden_wikipedia_html(composer):\n    \"\"\"HTML of Wikipedia page as a single line.\n    Dependencies: _hidden_wikipedia_page\n    Author: Steve Tjoa\n    \"\"\"\n    composer['_hidden_wikipedia_html'] = ' '.join(composer['_hidden_wikipedia_page'].html().split())\n\n\ndef wikipedia_url(composer):\n    \"\"\"Composer page on Wikipedia.\n    Dependencies: _hidden_wikipedia_page\n    Author: Steve Tjoa\n    \"\"\"\n    composer['wikipedia_url'] = composer['_hidden_wikipedia_page'].url\n\n\ndef wikipedia_image_url(composer):\n    \"\"\"Composer profile photo on Wikipedia.\n    Dependencies: _hidden_wikipedia_html\n    Author: Steve Tjoa\n    \"\"\"\n    import re\n    html = composer['_hidden_wikipedia_html']\n    images = re.findall(r'<img .*?src=\"(((?!.svg)\\S)+)\".*?>', html[:5000])\n    if len(images) > 0:\n        composer['wikipedia_image_url'] = images[0][0]\n\n\ndef imslp_url(composer):\n    \"\"\"Composer page on IMSLP.\n    Dependencies: _hidden_wikipedia_html, wikipedia_name\n    Author: Steve Tjoa\n    \"\"\"\n    from pygoogle import pygoogle\n    import re\n    html = composer['_hidden_wikipedia_html']\n    links = re.findall(r'href=\"(http://imslp.org/wiki/Category:.*?)\"', html)\n    if len(links) > 0:\n        composer['imslp_url'] = links[0]\n    else:\n        g = pygoogle('%s imslp' % composer['wikipedia_name'])\n        g.pages = 1\n        composer['imslp_url'] = g.get_urls()[0]\n\n\ndef _hidden_imslp_html(composer):\n    \"\"\"HTML of IMSLP page as a single line.\n    Dependencies: imslp_url\n    Author: Steve Tjoa\n    \"\"\"\n    import requests\n    composer['_hidden_imslp_html'] = ' '.join(requests.get(composer['imslp_url']).text.split())\n\n\ndef birthdate(composer):\n    \"\"\"Composer's birth date.\n    Dependencies: _hidden_imslp_html\n    Author: Steve Tjoa\n    \"\"\"\n    import calendar\n    import re\n    pattern = r'(\\d+ (%s) \\d+)' % '|'.join(calendar.month_name[1:])\n    dates = re.findall(pattern, composer['_hidden_imslp_html'])\n    composer['birthdate'] = dates[0][0]\n\n\ndef deathdate(composer):\n    \"\"\"Composer's death date.\n    Dependencies: _hidden_imslp_html\n    Author: Steve Tjoa\n    \"\"\"\n    import calendar\n    import re\n    pattern = r'(\\d+ (%s) \\d+)' % '|'.join(calendar.month_name[1:])\n    dates = re.findall(pattern, composer['_hidden_imslp_html'])\n    composer['deathdate'] = dates[1][0]\n\n\ndef work_ids(composer):\n    \"\"\"List of work ids for this composer. Creates work directories.\n    Dependencies: work_ids (it refines itself)\n    Author: Steve Tjoa\n    \"\"\"\n    from os.path import join, exists\n    from violinio import version_path\n    import json\n    import os\n    import time\n    if composer.has_key('work_ids'):\n        for work_id in composer['work_ids']:\n            work_dir = join(version_path, work_id)\n            if not exists(work_dir):\n                os.makedirs(work_dir)\n                new_work = {\n                        'type': 'work',\n                        'id': work_id,\n                        'composer_id': composer['id'],\n                        'date_created': time.strftime('%Y %b %d %X %Z')\n                        }\n                with open(join(work_dir, 'index.json'), 'w') as f:\n                    json.dump(new_work, f)\n    else:\n        composer['work_ids'] = list()\n\n\ndef freebase_name(composer):\n    \"\"\"Composer's name from Freebase.\n    Added in v0.1.\n    Dependencies: wikipedia_name, Google API key\n    Author: Steve Tjoa\n    \"\"\"\n    import os\n    import requests\n    params = {\n            'key': os.getenv('google_api_key'),\n            'query': composer['wikipedia_name'],\n            'filter': '(all type:/music/composer)'\n    }\n    r = requests.get('https://www.googleapis.com/freebase/v1/search', params=params)\n    composer['freebase_name'] = r.json()['result'][0]['name']\n","sub_path":"violinio/composer.py","file_name":"composer.py","file_ext":"py","file_size_in_byte":5379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"27261293","text":"\r\nfrom aqt import mw\r\nfrom aqt.qt import QAction\r\n\r\n\r\ndef find_menu(name, parent, create=False):\r\n    for action in parent.actions():\r\n        if action.text().replace('&','') == name:\r\n            return action.menu()\r\n    if create:\r\n        return parent.addMenu(name)\r\n\r\n    raise Exception('Menu {0} was not found'.format(name))\r\n\r\n\r\ndef add_menu_item(key, callback):\r\n    dialog = key.endswith('...')\r\n    names = key[:-3].split('.') if dialog else key.split('.')\r\n    mnu = mw.form.menubar\r\n    \r\n    for name in names[:-1]:\r\n        mnu = find_menu(name, mnu, True)\r\n        \r\n    if callback is None:\r\n        mnu = find_menu(names[-1], mnu, True)\r\n        if len(mnu.actions()):\r\n            mnu.addSeparator()\r\n    else:\r\n        text = names[-1] + '...' if dialog else names[-1]\r\n        # check menu item does not already exist\r\n        for action in mnu.actions():\r\n            if action.text().replace('&','') == text:\r\n                return\r\n        return mnu.addAction(text, callback)\r\n\r\n\r\n\r\ndef click_menu_item(key):\r\n    dialog = key.endswith('...')\r\n    names = key[:-3].split('.') if dialog else key.split('.')\r\n    mnu = mw.form.menubar\r\n    \r\n    for name in names[:-1]:\r\n        mnu = find_menu(name, mnu)\r\n\r\n    text = names[-1] + '...' if dialog else names[-1]\r\n    for action in mnu.actions():\r\n        if action.text().replace('&','') == text:\r\n            action.activate(QAction.Trigger)\r\n","sub_path":"utils/src/main/python/menus.py","file_name":"menus.py","file_ext":"py","file_size_in_byte":1421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"597139881","text":"import tensorflow as tf\nfrom naivenlp import TransformerTokenizer\n\nfrom .transformer_dataset_builder import TransformerTextFileDatasetBuilder, TransformerTFRecordDatasetBuilder\n\n\nclass TransformerDatasetBuilderTest(tf.test.TestCase):\n\n    def testTransformerTextFileDatasetBuilder(self):\n        src_tokenizer = TransformerTokenizer(\n            'testdata/vocab_src.txt',\n            unk_token='<unk>', sos_token='<s>', eos_token='</s>', pad_token='<pad>')\n        tgt_tokenizer = TransformerTokenizer(\n            'testdata/vocab_tgt.txt',\n            unk_token='<unk>', sos_token='<s>', eos_token='</s>', pad_token='<pad>')\n        builder = TransformerTextFileDatasetBuilder(src_tokenizer, tgt_tokenizer)\n\n        files = [('testdata/train.src.txt', 'testdata/train.tgt.txt')]\n\n        train_dataset = builder.build_train_dataset(files, batch_size=4, repeat_count=100, buffer_size=100)\n        for d in train_dataset.take(2):\n            print(d)\n\n        print('=' * 100)\n        valid_dataset = builder.build_valid_dataset(files, batch_size=4, repeat_count=100, buffer_size=100)\n        for d in valid_dataset.take(2):\n            print(d)\n\n    def testTransformerTFRecordDatasetBuilder(self):\n        builder = TransformerTFRecordDatasetBuilder()\n\n        files = ['testdata/transformer_train.tfrecord']\n        train_dataset = builder.build_train_dataset(\n            files, batch_size=4, repeat_count=100,\n            src_max_len=16, tgt_max_len=16, record_option='GZIP', buffer_size=100)\n\n        for d in train_dataset.take(2):\n            print(d)\n\n        print('=' * 100)\n        valid_dataset = builder.build_valid_dataset(\n            files, batch_size=4, repeat_count=100,\n            src_max_len=16, tgt_max_len=16, record_option='GZIP', buffer_size=100)\n        for d in valid_dataset.take(2):\n            print(d)\n\n        print('=' * 100)\n        predict_dataset = builder.build_predict_dataset(\n            ['testdata/transformer_train.tfrecord'], batch_size=4, repeat_count=100,\n            src_max_len=16, tgt_max_len=16, record_option='GZIP', buffer_size=100)\n        for d in predict_dataset.take(2):\n            print(d)\n\n\nif __name__ == \"__main__\":\n    tf.test.main()\n","sub_path":"transformers_keras/datasets/transformer_dataset_builder_test.py","file_name":"transformer_dataset_builder_test.py","file_ext":"py","file_size_in_byte":2195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"630445956","text":"#!/usr/bin/env python2.7\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jan 23 14:47:18 2019\n\n@author: anto\n\"\"\"\nimport numpy as np\n\nL2min= -0.2\nL2max = 0 \ndef setL(C_arm,thetad,x0,y0,xd,yd):\n    global L2max, L2min\n    ##calculate initial laser position\n    longueur_arm = 0.4 + C_arm \n    x0_laser =  x0  + longueur_arm*np.cos(thetad) \n    y0_laser =  y0  + longueur_arm*np.sin(thetad) \n    ##calculate L value\n    C_arm = np.sqrt((xd-x0_laser)**2 + (yd-y0_laser)**2 )\n    C_arm = max(min(L2max,(C_arm)),L2min) \n    return C_arm,x0_laser,y0_laser\n\n\ndef setTheta(xd,yd,x0,y0, C_arm):\n    longueur_arm = 0.4 + C_arm\n    ##calculate new theta\n    taille_pixel = 0.038/400\n    theta_fin = np.arcsin((xd-x0)* taille_pixel/longueur_arm)\n    return theta_fin\n","sub_path":"workspaceRos/src/robot_move/src/setArm.py","file_name":"setArm.py","file_ext":"py","file_size_in_byte":749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"405704916","text":"from dataScience.models.topic_models.tfidf import bigrams, tfidf_model\nfrom dataScience.src.text_handling.process import topic_processing\n\n\ndef extract_topics(doc_dict):\n    doc_dict[\"topics_rs\"] = {}\n\n    topics = tfidf_model.get_topics(\n        topic_processing(doc_dict[\"text\"], bigrams), topn=5)\n    for score, topic in topics:\n        topic = topic.replace('_', ' ')\n        doc_dict[\"topics_rs\"][topic] = score\n\n    return doc_dict\n","sub_path":"common/document_parser/lib/topics.py","file_name":"topics.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"357241582","text":"from google_hangouts_chat_bot.commands import Command\nfrom google_hangouts_chat_bot.responses import (\n    create_card,\n    create_card_key_value,\n    create_cards_response,\n)\n\nfrom tech_gallery_bot.version import __version__\n\n\nclass About(Command):\n    command = \"about\"\n    command_aliases = [\"info\"]\n    description = \"About this bot\"\n    hidden = True\n\n    def handle(self, arguments, **kwargs):\n        card = create_card(\n            [\n                create_card_key_value(\"Version\", __version__),\n                create_card_key_value(\n                    \"Source\",\n                    \"https://github.com/ciandt/tech-gallery-chat-bot\",\n                    on_click=\"https://github.com/ciandt/tech-gallery-chat-bot\",\n                ),\n                create_card_key_value(\"Created by\", \"jpimentel@ciandt.com\"),\n            ]\n        )\n\n        return create_cards_response([card])\n","sub_path":"tech_gallery_bot/commands/about.py","file_name":"about.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"396500220","text":"# ----------------------------------------------------------------------------\n# Copyright (c) 2016-2018, QIIME 2 development team.\n#\n# Distributed under the terms of the Modified BSD License.\n#\n# The full license is in the file LICENSE, distributed with this software.\n# ----------------------------------------------------------------------------\n\nimport biom\nfrom qiime2.plugin import Plugin\nfrom q2_types.feature_table import FeatureTable, Frequency\n\nimport q2_american_gut\n\n\nplugin = Plugin(\n    name='american-gut',\n    version=q2_american_gut.__version__,\n    website='https://github.com/biocore/q2-american-gut',\n    package='q2_american_gut',\n    description=('This QIIME 2 plugin supports processing and utilizing '\n                 'American Gut Project data'),\n    short_description='Plugin for exploring American Gut data.',\n    citation_text='https://doi.org/10.1101/277970'\n)\n\ndef dummy(foo: biom.Table) -> biom.Table:\n    return foo\n\nplugin.methods.register_function(\n    function=dummy,\n    inputs={'foo': FeatureTable[Frequency]},\n    parameters={},\n    outputs=[('bar', FeatureTable[Frequency]), ],\n    input_descriptions={\n        'foo': \"Same same\"\n    },\n    output_descriptions={'bar': 'Same same'},\n    name='A dummy placeholder',\n    description=\"A dummy placeholder\"\n)\n","sub_path":"q2_american_gut/plugin_setup.py","file_name":"plugin_setup.py","file_ext":"py","file_size_in_byte":1295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"444260494","text":"import time\n\nfrom openerp import api, fields, models, _\nfrom openerp.tools.float_utils import float_is_zero, float_compare\n\nclass RequestRelease(models.TransientModel):\n    _name = \"request.release\"\n    _description = \"Request Release\"\n\n    product_ids = fields.One2many('request.release.item', 'request_id', string='Product', domain=[('product_id', '!=', False)])\n\n    @api.model\n    def default_get(self, fields):\n        res = super(RequestRelease, self).default_get(fields)\n        po_id = self.env.context.get('active_id')\n        p_order = self.env['purchase.order'].browse(po_id)\n        items = []\n        packs = []\n        for op in p_order.order_line:\n            item = {\n                'product_id': op.product_id.id,\n                'product_uom_id': op.product_uom.id,\n                'product_qty': op.product_qty,\n                'destinationloc_id': p_order._get_destination_location(),\n                'sourceloc_id': p_order.partner_id.property_stock_supplier.id,\n                'request_date': op.date_planned\n            }\n            if op.product_id:\n                items.append([0, 0, item])\n        res.update(product_ids=items)\n        return res\n\n    @api.multi\n    def do_picking_create(self):\n        lines = []\n        picking_obj = self.env['stock.picking']\n        po_id = self.env.context.get('active_id')\n        p_order = self.env['purchase.order'].browse(po_id)\n        for item_line in self.product_ids:\n            lines.append([0, 0, {\n                    'origin': p_order.name,\n                    'name': item_line.product_id.name,\n                    'product_id': item_line.product_id.id,\n                     'product_uom_qty': item_line.product_qty,\n                     'product_uom': item_line.product_uom_id.id,\n            }])\n        if not p_order.group_id:\n            p_order.group_id = p_order.group_id.create({\n                'name': p_order.name,\n                'partner_id': p_order.partner_id.id\n            })\n        picking_id = picking_obj.create({'partner_id': p_order.partner_id.id,\n                            'origin': p_order.name,\n                            'min_date': item_line.request_date,\n                            'picking_type_id': p_order.picking_type_id.id,\n                            'location_id': p_order.partner_id.property_stock_supplier.id,\n                            'location_dest_id': p_order._get_destination_location(),\n                            'move_lines': lines,\n                                         })\n        for data in picking_id.move_lines:\n            move_ids = data.action_confirm()\n            moves = self.env['stock.move'].browse(move_ids)\n            moves.force_assign()\n        if picking_id:\n            picking_id.write({'group_id': p_order.group_id.id})\n            if p_order.blanket:\n                count = p_order.blanket_count + 1\n                p_order.write({'blanket_count': count})\n        return True\n\nclass RequestReleaseItem(models.TransientModel):\n    _name = \"request.release.item\"\n    _description = \"Request Release Items\"\n\n    product_id = fields.Many2one('product.product', 'Product')\n    product_qty = fields.Float(string='Quantity')\n    product_uom_id = fields.Many2one('product.uom', 'Product Unit of Measure')\n    request_id = fields.Many2one('request.release', 'Request ID')\n    sourceloc_id = fields.Many2one('stock.location', 'Source Location', required=True)\n    destinationloc_id = fields.Many2one('stock.location', 'Destination Location', required=True)\n    request_date = fields.Datetime(string='Date')\n\n","sub_path":"blanket_purchase_order/wizard/request_release_wizard.py","file_name":"request_release_wizard.py","file_ext":"py","file_size_in_byte":3561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"159365181","text":"import numpy as np\nfrom grid_world import standardGrid, negativeGrid\nfrom iterative_policy_evaluation import printValues, printPolicy\nfrom monte_carlo_value_iteration_es import getMaximumFromDict\n\nsmallEnough = 10e-4\ngamma = 0.9\nalpha = 0.1\nallActions = ('U', 'D', 'L', 'R')\n\ndef randomAction(a, eps=0.1):\n\tp = np.random.random()\n\tif p < (1 - eps):\n\t\treturn a\n\telse:\n\t\treturn np.random.choice(allActions)\n\ndef playGame(grid, policyQ, t, count):\n\ts = (2,0)\n\tr = 0\n\ta = randomAction(getMaximumFromDict(policyQ[s])[0], eps=0.5/t)\n\tgrid.setState(s)\n\t#traverse the grid till we reach the terminal state\n\twhile not grid.isGameOver():\n\t\toldS = s\n\t\toldR = r\n\t\toldA = a\n\t\tr = grid.move(a)\n\t\ts = grid.getCurrentState()\n\t\t# so the only difference from sarsa is that we did not had to calculate the action/policy for this new state before updating the Q for the state\n\t\t# in which we entered.\n\t\tq2 = getMaximumFromDict(policyQ[s])[1]\n\t\t# adaptive learning rate\n\t\tpolicyQ[oldS][oldA] += (alpha/count[s][a])*(r + gamma*q2 - policyQ[oldS][oldA])\n\t\ta = randomAction(getMaximumFromDict(policyQ[s])[0], eps=0.5/t)\n\t\tcount[s][a] += 0.000005\n\treturn policyQ\n\nif __name__ == '__main__':\n\t\n\tgrid = negativeGrid()\n\tvalueF = {}\n\tpolicyQ = {}\n\tpolicy = {}\n\tallReturns={}\n\tfor s in grid.actions.keys():\n\t\tpolicy[s] = np.random.choice(allActions)\n\tstates = grid.allStates()\n\tfor s in states:\n\t\tvalueF[s] = {}\n\t\tpolicyQ[s] = {}\n\t\tfor a in allActions:\n\t\t\tpolicyQ[s][a] = 0\n\t\t\tvalueF[s][a] = 0\n\tt = 1.0\n\tcount = {}\n\tfor s in states:\n\t\tcount[s] = {}\n\t\tfor a in allActions:\n\t\t\tcount[s][a] = 1.0\n\tfor n in range(5000):\n\t\tif n % 100 == 0:\n\t\t\tt += 10e-3\n\t\tpolicyQ = playGame(grid, policyQ, t, count)\n\n\tfor s, Qs in policyQ.items():\n\t\tvalueF[s] = getMaximumFromDict(policyQ[s])[1]\n\n\tfor s in policy.keys():\n\t\tif s in grid.actions.keys():\n\t\t\tpolicy[s] = getMaximumFromDict(policyQ[s])[0]\n\tprint (\"final values:\")\n\tprintValues(valueF, grid)\n\tprint (\"final policy:\")\n\tprintPolicy(policy, grid)","sub_path":"qLearning.py","file_name":"qLearning.py","file_ext":"py","file_size_in_byte":1954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"57616557","text":"#-*- coding: utf-8 -*-\n\"\"\"\nhttps://atcoder.jp/contests/abc117/tasks/abc117_a\n\"\"\"\n\ndef sol_naive(k, S):\n    maxi = 0\n    for x in range(k+1):\n        ttl = 0\n        for s in S:\n            ttl += (x ^ s)\n        maxi = max(maxi, ttl)\n    return maxi\n\n\ndef keta(x):\n    if x <= 0:\n        return 0\n    else:\n        return 1 + keta(int(x/2))\n\ndef bin(x):\n    return \"{0:08b}\".format(x)\n\ndef rec(k, S, b_keta, ans): # which digit\n    #print(\"rec(%s, %s, %s, %s)\" % (k, S, b_keta, ans))\n    if b_keta == 0:\n        #print(\"miso\")\n        return ans\n    else:\n        #print(\"soup\")\n        ones = 0\n        for s in S:\n            #print(\"checking %s and %s\" % (bin(s), bin(1 << (b_keta-1))))\n            if (1 << (b_keta-1) & s) > 0:\n                #print(\"it's 1\")\n                ones += 1\n        if ones < len(S) - ones: # should be set 1 in this digit\n            if ans + (1 << (b_keta-1)) <= k:\n                ans += (1 << (b_keta-1))\n            #print(\"ans updated %s\" % ans)\n            return rec(k, S, b_keta-1, ans)\n        else:\n            return rec(k, S, b_keta-1, ans)\n\n\ndef sol(k, S):\n    maxi = max(S)\n    b_keta = keta(k)\n    #print(\"find maxi(%s) and it's digit is %s and k is (%s)\" % (maxi, b_keta, k))\n    x = rec(k, S, b_keta, 0)\n    ans = 0\n    for s in S:\n        ans += (x ^ s)\n    return ans\n\ntest = False\n#test = True\nif test:\n    n, k = 3, 7\n    S =list( map(int, \"1 6 3\".split()))\n    print(sol(k, S))\n\n    n, k = 4, 9\n    S =list( map(int, \"7 4 0 3\".split()))\n    print(sol(k, S))\n    #\n    n, k = 1, 0\n    S =list( map(int, \"1000000000000\".split()))\n    print(sol(k, S))\n\n\nelse:\n    n, k = map(int, input().split())\n    S =list( map(int, input().split()))\n    print(sol(k, S))\n\n\n","sub_path":"atc/abc_117_d_xor.py","file_name":"abc_117_d_xor.py","file_ext":"py","file_size_in_byte":1713,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"251517069","text":"# encoding=utf-8\n\n\n# traverse the list\ndef testListLoop():\n    name = [\"hello\",\"world\"]\n    i = 0\n    length = len(name)\n    # while i < length:\n    #     if i == length-1:\n    #         print(name[i], end=\"\\n\")\n    #     else:\n    #         print(name[i], end=\",\")\n    #     i += 1\n\n    # recommend foreach\n    for j in name:\n        print(j,end=\";\\n\")\n    print(\"list's length is:%d\"%length)\n\n\n# testListLoop()  # call the function\n\nprint(\"========\",end=\"\\n\")\n\n\n# add list's element\ndef testListAppendElement():\n    name = [\"zhangsan\",\"lisi\"]\n    insertname = input(\"please input insert element:\")\n    name.append(insertname)   # insert \"wangwu\" to list's end position\n    for i in name:\n        print(\"list's element:%s\"%i,end=\";\\n\")\n\n# testListAppendElement()\n\nprint(\"========\",end=\"\\n\")\n\n# modify list's element\ndef testListModifyElement():\n    name = [\"zhangsan\", \"lisi\"]\n    insertname = input(\"please input insert element:\")\n    name[0] = insertname     # modify list's element\n    for i in name:\n        print(\"list's element:%s\" % i, end=\";\\n\")\n# testListModifyElement()\nprint(\"========\",end=\"\\n\")\n\n# find list's element\ndef testFindElement():\n    name = [\"zhangsan\", \"lisi\"]\n    findName = input(\"please input find element:\")\n    if findName in name:   # in operation find element\n        print(\"exists\")\n# testFindElement()\n\n# delete list's element,delete operation include del(delete by index) pop(delete last) remove(delete by value)\ndef testDeleteElement():\n    name = [\"zhangsan\",\"lisi\"]\n\n    # del name[1]   # delete by index\n    # name.pop();   # delete last\n    name.remove(\"zhangsan\")   # delete by remove\n    for i in name:\n        print(\"list's element:%s\" % i, end=\";\\n\")\n\n#testDeleteElement()tuple' object does not support item assignment\n\n# caculate list's length\ndef testListLength():\n    list = [\"nihao\",\"woshi\",100,3.14]\n    print(list[0:2])\n    print(tuple(list))  # convert list to tuple\n    print(\"reverse %s\"%list[-1])\n    list1 = list+[\"nihao\"]  # support + operator\n    print(len(list1*4))    # support copy operator, and list is same support copy operator\ntestListLength()","sub_path":"fundations/languages/listPractise.py","file_name":"listPractise.py","file_ext":"py","file_size_in_byte":2107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"475088783","text":"import json\n\nfrom schemas import ensure_schema, ensure_logged_in_user\n\nfrom config import RESUME, RESUME_BUCKET\n\nimport boto3\nfrom botocore.exceptions import ClientError\n\ndef presign(method, event, ctx, user):\n    client = boto3.client(\"s3\", **RESUME)\n    http_method = \"GET\"\n    params = {\n        \"Bucket\": RESUME_BUCKET,\n        \"Key\": event[\"email\"] + \".pdf\"\n    }\n    if method == \"put_object\":\n        http_method=\"PUT\"\n        params[\"ContentType\"] = \"application/pdf\"\n    return client.generate_presigned_url(\n        method,\n        Params=params,\n        HttpMethod=http_method,\n        ExpiresIn=3600,\n    )\n\n\ndef exists(email):\n    client = boto3.client(\"s3\", **RESUME)\n    try:\n        info = client.head_object(Bucket=RESUME_BUCKET, Key=email + \".pdf\")\n        return True\n    except ClientError as e:\n        if e.response[\"Error\"][\"Code\"] == \"404\":\n            return False\n        raise e\n    \n@ensure_schema({\n    \"type\": \"object\",\n    \"properties\": {\n        \"email\": {\"type\": \"string\"},\n        \"token\": {\"type\": \"string\"}\n    },\n    \"required\": [\"email\", \"token\"]\n})\n@ensure_logged_in_user()\ndef resume(event, ctx, user):\n    try:\n        return {\n            \"statusCode\": 200, \"body\": {\n                \"upload\": presign(\"put_object\", event, ctx, user),\n                \"download\": presign(\"get_object\", event, ctx, user),\n                \"exists\": exists(event[\"email\"])\n            }\n        }\n    except ClientError as e:\n        return {\n            \"statusCode\": 500,\n            \"body\": \"failed to connect to s3\" + str(e)\n        }\n    \n","sub_path":"resume.py","file_name":"resume.py","file_ext":"py","file_size_in_byte":1566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"268576412","text":"import os\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom collections import defaultdict\n\nfrom file_formats import FormatCSV, NumpyNeighborsFormat\n\n# Output format\n#   1: Dataset Id\n#   2: Vehicle Id\n#   3: Frame Number\n#   4: X\n#   5: Y\n#   6: Lane Id                (not yet implemented)\n#   7: Lateral maneuver       (not yet implemented)\n#   8: Longitudinal maneuver  (not yet implemented)\n#   9-47: Neighbor Car Ids at grid location\n\n# Local window on map for every actor\n# ********-5m***********\n# *  ##           ##   *\n# *       0____y       *\n# *##     |         ## * +30m\n# *       |x           *\n# *   ##      ##       *\n# ********+5m***********\n\n# TODO:\n# При сборе информации о соседях, в соседи попадает и тот объект, для которого ищутся соседи\n# код для обработки этого события написан ниже в коммент-секции, но если он будет выполнятся,\n# то\n\nLENGTH_OF_ONE_LINE_IN_FORMATTED_SET_FILE = 5\nNUMBERS_OF_FIELDS_IN_ONE_LINE_IN_OUTPUT_FILE = 47\nMAX_NUMBER_OF_NEIGHBORS_AT_GRID = 39                # 47 - 9 + 1 = 39 - количество допустимых соседей\nMAX_TOP_NEIGHBORS = 7\nMAX_BOTTOM_NEIGHBORS = 6\nX_DISTANCE_THRESHOLD = 5                            # meters\nY_DISTANCE_THRESHOLD = 30                           # meters\nCENTER_THRESHOLD = 1                                # meters\n\n\ndef save_npy_array_to_txt_file(npy_array, file_name):\n    f = open(file_name, 'w')\n    for line in npy_array:\n        f.write(\"{}\\t{}\\t{:.2f}\\t{:.2f}\\t{}\\n\".format(int(line[FormatCSV.FRAME_NUMBER.value]),\n                                                      int(line[FormatCSV.ACTOR_ID.value]),\n                                                      line[FormatCSV.X.value],\n                                                      line[FormatCSV.Y.value],\n                                                      int(line[FormatCSV.DATASET_ID.value])))\n    f.close()\n\n\ndef merge_n_split(files_npy, root_out_path):\n    trajectory_train = np.array([])\n    trajectory_val = np.array([])\n    trajectory_test = np.array([])\n\n    track_train = defaultdict(dict)\n    track_val = defaultdict(dict)\n    track_test = defaultdict(dict)\n\n    print(\"Start spliting data...\")\n\n    for file_npy in files_npy:\n        data = np.load(file_npy, allow_pickle=True)\n\n        dataset_number = int(data[0][0][0])\n\n        # Constructing train, val and test sets for trajectory\n        trajectory = data[0]\n        vehicles_id = np.unique(trajectory[:, FormatCSV.ACTOR_ID.value])\n\n        # split the dataset using vehicle id\n        vehicles_id, test_id = train_test_split(vehicles_id, test_size=0.2, random_state=2)\n        train_id, val_id = train_test_split(vehicles_id, test_size=0.125, random_state=2)\n\n        # get trajectory with vehicle id\n        train = np.array(trajectory[[(vehicle_id in train_id) for vehicle_id in trajectory[:, FormatCSV.ACTOR_ID.value]]])\n        if trajectory_train.size == 0:\n            trajectory_train = train\n        else:\n            trajectory_train = np.concatenate((trajectory_train, train), axis=0)\n\n        val = np.array(trajectory[[(vehicle_id in val_id) for vehicle_id in trajectory[:, FormatCSV.ACTOR_ID.value]]])\n        if trajectory_val.size == 0:\n            trajectory_val = val\n        else:\n            trajectory_val = np.concatenate((trajectory_val, val), axis=0)\n\n        test = np.array(trajectory[[(vehicle_id in test_id) for vehicle_id in trajectory[:, FormatCSV.ACTOR_ID.value]]])\n        if trajectory_test.size == 0:\n            trajectory_test = test\n        else:\n            trajectory_test = np.concatenate((trajectory_test, test), axis=0)\n\n        # Constructing train, val and test sets for tracks\n        track = data[1]\n\n        for vehicle_id in train_id:\n            track_train[dataset_number][vehicle_id] = track[vehicle_id]\n\n        for vehicle_id in val_id:\n            track_val[dataset_number][vehicle_id] = track[vehicle_id]\n\n        for vehicle_id in test_id:\n            track_test[dataset_number][vehicle_id] = track[vehicle_id]\n\n        print(\"\\tDataset {} finished.\".format(dataset_number))\n\n    # ******************************************* Save results *********************************************************\n    print('Saving files')\n    #  txt files\n    path_to_folder_with_txt_files = os.path.join(root_out_path, 'sets_txt')\n    if not os.path.exists(path_to_folder_with_txt_files):\n        os.makedirs(path_to_folder_with_txt_files)\n    save_npy_array_to_txt_file(trajectory_train, os.path.join(path_to_folder_with_txt_files, 'TrainSet.txt'))\n    save_npy_array_to_txt_file(trajectory_val, os.path.join(path_to_folder_with_txt_files, 'ValSet.txt'))\n    save_npy_array_to_txt_file(trajectory_test, os.path.join(path_to_folder_with_txt_files, 'TestSet.txt'))\n\n    # npy files\n    path_to_folder_with_npy_files = os.path.join(root_out_path, 'sets_npy')\n    if not os.path.exists(path_to_folder_with_npy_files):\n        os.makedirs(path_to_folder_with_npy_files)\n    np.save(os.path.join(path_to_folder_with_npy_files, 'TrainSet.npy'), np.array([trajectory_train, track_train]))\n    np.save(os.path.join(path_to_folder_with_npy_files, 'ValSet.npy'), np.array([trajectory_val, track_val]))\n    np.save(os.path.join(path_to_folder_with_npy_files, 'TestSet.npy'), np.array([trajectory_test, track_test]))\n\n    print(\"Training file saved and ready.\")\n\n\ndef process_csv_file_and_save_npy(source_file_path, save_file_path):\n    data_from_formatted_set_file = np.loadtxt(source_file_path, delimiter=',')\n    trajectory = np.zeros((np.shape(data_from_formatted_set_file)[0], NUMBERS_OF_FIELDS_IN_ONE_LINE_IN_OUTPUT_FILE))\n\n    trajectory[:, :LENGTH_OF_ONE_LINE_IN_FORMATTED_SET_FILE] = data_from_formatted_set_file\n\n    for current_row_in_set in range(np.shape(trajectory)[0]):\n        time = trajectory[current_row_in_set][FormatCSV.FRAME_NUMBER.value]\n\n        frame_ego = trajectory[trajectory[:, FormatCSV.FRAME_NUMBER.value] == time]\n\n        if frame_ego.size != 0:\n            number_of_actors = np.shape(frame_ego)[0]\n            dx = np.zeros(number_of_actors)\n            dy = np.zeros(number_of_actors)\n            vehicles_id = np.zeros(number_of_actors)\n\n            for actor_index in range(number_of_actors):\n                dx[actor_index] = frame_ego[actor_index][FormatCSV.X.value] - trajectory[current_row_in_set][FormatCSV.X.value]\n                dy[actor_index] = frame_ego[actor_index][FormatCSV.Y.value] - trajectory[current_row_in_set][FormatCSV.Y.value]\n                vehicles_id[actor_index] = frame_ego[actor_index][FormatCSV.ACTOR_ID.value]\n\n            # ******************************** Find and throw out ego vehicle ******************************************\n            # dx_ego_indexes = np.where(dx == 0.)\n            # dy_ego_indexes = np.where(dy == 0.)\n            # if np.array_equal(dx_ego_indexes, dy_ego_indexes):\n            #     dx = np.delete(dx, dx_ego_indexes)\n            #     dy = np.delete(dy, dx_ego_indexes)\n            #     vehicles_id = np.delete(vehicles_id, dx_ego_indexes)\n\n            distance = np.sqrt(dx * dx + dy * dy)\n\n            # ********************************** Stay only closest neighbors *******************************************\n            if number_of_actors > MAX_NUMBER_OF_NEIGHBORS_AT_GRID:\n                indexes_of_closest_neighbors = np.argsort(distance)\n                dx = np.array([dx[i] for i in indexes_of_closest_neighbors[:MAX_NUMBER_OF_NEIGHBORS_AT_GRID]])\n                dy = np.array([dy[i] for i in indexes_of_closest_neighbors[:MAX_NUMBER_OF_NEIGHBORS_AT_GRID]])\n                vehicles_id = np.array([vehicles_id[i] for i in indexes_of_closest_neighbors[:MAX_NUMBER_OF_NEIGHBORS_AT_GRID]])\n\n            # ******************************************* Crop map *****************************************************\n            indexes_matching_x_cropped_map = np.abs(dx) <= X_DISTANCE_THRESHOLD\n            indexes_matching_y_cropped_map = np.abs(dy) <= Y_DISTANCE_THRESHOLD\n            indexes_of_cropped_map = np.array([\n                (indexes_matching_x_cropped_map[i] == True and indexes_matching_y_cropped_map[i] == True)\n                for i in range(len(indexes_matching_x_cropped_map))\n            ])\n\n            dx = dx[indexes_of_cropped_map]\n            dy = dy[indexes_of_cropped_map]\n            vehicles_id = vehicles_id[indexes_of_cropped_map]\n\n            # *************************************** Separate neighbors ***********************************************\n            # *************************** Left neighbors ***************************\n            indexes_of_left_neighbors = dx < -CENTER_THRESHOLD\n            dx_left = dx[indexes_of_left_neighbors]\n            dy_left = dy[indexes_of_left_neighbors]\n            vehicles_id_left = vehicles_id[indexes_of_left_neighbors]\n\n            top_left_indexes = dy_left >= 0\n            dx_left_top = dx_left[top_left_indexes]\n            dy_left_top = dy_left[top_left_indexes]\n            vehicles_id_left_top = vehicles_id_left[top_left_indexes]\n\n            bottom_left_indexes = dy_left < 0\n            dx_left_bottom = dx_left[bottom_left_indexes]\n            dy_left_bottom = dy_left[bottom_left_indexes]\n            vehicles_id_left_bottom = vehicles_id_left[bottom_left_indexes]\n            # ***********************************************************************\n\n            # ************************** Center neighbors ***************************\n            indexes_of_center_neighbors_left = dx >= -CENTER_THRESHOLD\n            indexes_of_center_neighbors_right = dx <= CENTER_THRESHOLD\n            indexes_of_center_neighbors = np.array([\n                (indexes_of_center_neighbors_left[i] == True and\n                 indexes_of_center_neighbors_right[i] == True)\n                for i in range(len(indexes_of_center_neighbors_left))\n            ])\n\n            dx_center = dx[indexes_of_center_neighbors]\n            dy_center = dy[indexes_of_center_neighbors]\n            vehicles_id_center = vehicles_id[indexes_of_center_neighbors]\n\n            top_center_indexes = dy_center >= 0\n            dx_center_top = dx_center[top_center_indexes]\n            dy_center_top = dy_center[top_center_indexes]\n            vehicles_id_center_top = vehicles_id_center[top_center_indexes]\n\n            bottom_center_indexes = dy_center < 0\n            dx_center_bottom = dx_center[bottom_center_indexes]\n            dy_center_bottom = dy_center[bottom_center_indexes]\n            vehicles_id_center_bottom = vehicles_id_center[bottom_center_indexes]\n            # ***********************************************************************\n\n            # *************************** Right neighbors ***************************\n            indexes_of_right_neighbors = dx > CENTER_THRESHOLD\n            dx_right = dx[indexes_of_right_neighbors]\n            dy_right = dy[indexes_of_right_neighbors]\n            vehicles_id_right = vehicles_id[indexes_of_right_neighbors]\n\n            top_right_indexes = dy_right >= 0\n            dx_right_top = dx_right[top_right_indexes]\n            dy_right_top = dy_right[top_right_indexes]\n            vehicles_id_right_top = vehicles_id_right[top_right_indexes]\n\n            bottom_right_indexes = dy_right < 0\n            dx_right_bottom = dx_right[bottom_right_indexes]\n            dy_right_bottom = dy_right[bottom_right_indexes]\n            vehicles_id_right_bottom = vehicles_id_right[bottom_right_indexes]\n            # ***********************************************************************\n\n            # *********************************** Crop neighbors *******************************************************\n            # ***************************** left top ********************************\n            indexes_of_top_left_neighbors_ordered_by_distance = np.argsort(dy_left_top)\n            indexes_of_top_left_neighbors_ordered_by_distance = \\\n                indexes_of_top_left_neighbors_ordered_by_distance[0:min(MAX_TOP_NEIGHBORS, len(vehicles_id_left_top))]\n            vehicles_id_left_top = np.array([\n                (vehicles_id_left_top[i])\n                for i in indexes_of_top_left_neighbors_ordered_by_distance\n            ])\n\n            # **************************** left bottom ******************************\n            indexes_of_bottom_left_neighbors_reverse_ordered_by_distance = np.argsort(dy_left_bottom)\n            indexes_of_bottom_left_neighbors_reverse_ordered_by_distance = \\\n                np.array(list(reversed(indexes_of_bottom_left_neighbors_reverse_ordered_by_distance)))\n            indexes_of_bottom_left_neighbors_reverse_ordered_by_distance =\\\n                indexes_of_bottom_left_neighbors_reverse_ordered_by_distance[0:min(MAX_BOTTOM_NEIGHBORS, len(vehicles_id_left_bottom))]\n            vehicles_id_left_bottom = np.array([\n                (vehicles_id_left_bottom[i])\n                for i in indexes_of_bottom_left_neighbors_reverse_ordered_by_distance\n            ])\n\n            # ***************************** center top ******************************\n            indexes_of_top_center_neighbors_ordered_by_distance = np.argsort(dy_center_top)\n            indexes_of_top_center_neighbors_ordered_by_distance = \\\n                indexes_of_top_center_neighbors_ordered_by_distance[0:min(MAX_TOP_NEIGHBORS, len(vehicles_id_center_top))]\n            vehicles_id_center_top = np.array([\n                (vehicles_id_center_top[i])\n                for i in indexes_of_top_center_neighbors_ordered_by_distance\n            ])\n\n            # **************************** center bottom ****************************\n            indexes_of_bottom_center_neighbors_reverse_ordered_by_distance = np.argsort(dy_center_bottom)\n            indexes_of_bottom_center_neighbors_reverse_ordered_by_distance = \\\n                np.array(list(reversed(indexes_of_bottom_center_neighbors_reverse_ordered_by_distance)))\n            indexes_of_bottom_center_neighbors_reverse_ordered_by_distance = \\\n                indexes_of_bottom_center_neighbors_reverse_ordered_by_distance[0:min(MAX_BOTTOM_NEIGHBORS, len(vehicles_id_center_bottom))]\n            vehicles_id_center_bottom = np.array([\n                (vehicles_id_center_bottom[i])\n                for i in indexes_of_bottom_center_neighbors_reverse_ordered_by_distance\n            ])\n\n            # ******************************** right top ****************************\n            indexes_of_top_right_neighbors_ordered_by_distance = np.argsort(dy_right_top)\n            indexes_of_top_right_neighbors_ordered_by_distance = \\\n                indexes_of_top_right_neighbors_ordered_by_distance[0:min(MAX_TOP_NEIGHBORS, len(vehicles_id_right_top))]\n            vehicles_id_right_top = np.array([\n                (vehicles_id_right_top[i])\n                for i in indexes_of_top_right_neighbors_ordered_by_distance\n            ])\n\n            # ******************************* right bottom **************************\n            indexes_of_bottom_right_neighbors_reverse_ordered_by_distance = np.argsort(dy_right_bottom)\n            indexes_of_bottom_right_neighbors_reverse_ordered_by_distance = \\\n                np.array(list(reversed(indexes_of_bottom_right_neighbors_reverse_ordered_by_distance)))\n            indexes_of_bottom_right_neighbors_reverse_ordered_by_distance = \\\n                indexes_of_bottom_right_neighbors_reverse_ordered_by_distance[0:min(MAX_BOTTOM_NEIGHBORS, len(vehicles_id_right_bottom))]\n            vehicles_id_right_bottom = np.array([\n                (vehicles_id_right_bottom[i])\n                for i in indexes_of_bottom_right_neighbors_reverse_ordered_by_distance\n            ])\n\n            # ************************************* UPDATE TRAJECTORIES ************************************************\n            # ****************************** Left neighbors *************************\n            trajectory[current_row_in_set,\n            NumpyNeighborsFormat.INDEX_OF_FIRST_BOTTOM_LEFT_NEIGHBOR.value: NumpyNeighborsFormat.INDEX_OF_LAST_BOTTOM_LEFT_NEIGHBOR.value] = \\\n                np.concatenate((np.zeros(MAX_BOTTOM_NEIGHBORS - len(vehicles_id_left_bottom)), vehicles_id_left_bottom))\n            trajectory[current_row_in_set, NumpyNeighborsFormat.INDEX_OF_FIRST_TOP_LEFT_NEIGHBOR.value: NumpyNeighborsFormat.INDEX_OF_LAST_TOP_LEFT_NEIGHBOR.value] = \\\n                np.concatenate((vehicles_id_left_top, np.zeros(MAX_TOP_NEIGHBORS - len(vehicles_id_left_top))))\n\n            # ***************************** Center neighbors ************************\n            trajectory[current_row_in_set, NumpyNeighborsFormat.INDEX_OF_FIRST_BOTTOM_CENTER_NEIGHBOR.value: NumpyNeighborsFormat.INDEX_OF_LAST_BOTTOM_CENTER_NEIGHBOR.value] = \\\n                np.concatenate((np.zeros(MAX_BOTTOM_NEIGHBORS - len(vehicles_id_center_bottom)), vehicles_id_center_bottom))\n            trajectory[current_row_in_set, NumpyNeighborsFormat.INDEX_OF_FIRST_TOP_CENTER_NEIGHBOR.value: NumpyNeighborsFormat.INDEX_OF_LAST_TOP_CENTER_NEIGHBOR.value] = \\\n                np.concatenate((vehicles_id_center_top, np.zeros(MAX_TOP_NEIGHBORS - len(vehicles_id_center_top))))\n\n            # ***************************** Right neighbors *************************\n            trajectory[current_row_in_set, NumpyNeighborsFormat.INDEX_OF_FIRST_BOTTOM_RIGHT_NEIGHBOR.value: NumpyNeighborsFormat.INDEX_OF_LAST_BOTTOM_RIGHT_NEIGHBOR.value] = \\\n                np.concatenate((np.zeros(MAX_BOTTOM_NEIGHBORS - len(vehicles_id_right_bottom)), vehicles_id_right_bottom))\n            trajectory[current_row_in_set, NumpyNeighborsFormat.INDEX_FIRST_TOP_RIGHT_NEIGHBOR.value: NumpyNeighborsFormat.INDEX_LAST_TOP_RIGHT_NEIGHBOR.value] = \\\n                np.concatenate((vehicles_id_right_top, np.zeros(MAX_TOP_NEIGHBORS - len(vehicles_id_right_top))))\n\n    # Create tracks for every actor\n    ids_of_all_actors_at_scene = np.unique(trajectory[:, FormatCSV.ACTOR_ID.value])\n    track = {}\n    for i in range(len(ids_of_all_actors_at_scene)):\n        id_of_current_actor = ids_of_all_actors_at_scene[i]\n        vehicle_track = trajectory[trajectory[:, FormatCSV.ACTOR_ID.value] == id_of_current_actor]\n        track[id_of_current_actor] = vehicle_track[:, [FormatCSV.FRAME_NUMBER.value, FormatCSV.X.value, FormatCSV.Y.value]].T\n\n    # Save results\n    np.save(save_file_path, np.array([trajectory, track]))\n\n\nif __name__ == '__main__':\n    source_path = '/home/alexey/Documents/ReadyDataSets/preprocessed_carla_dataset_67'\n\n    path_to_formatted_csv_sets = os.path.join(source_path, 'formatted_csv_sets')\n    save_npy_folder_path = os.path.join(source_path, 'npy_sets')\n\n    do_preprocess = False\n    do_merge = False\n\n    # ************************************* Preprocess csv files *******************************************************\n    if do_preprocess:\n        if not os.path.exists(save_npy_folder_path):\n            os.mkdir(save_npy_folder_path)\n\n        current_set_number = 1\n        for file in sorted(os.listdir(path_to_formatted_csv_sets)):\n            print('Processing files [{}/{}]'.format(current_set_number, len(os.listdir(path_to_formatted_csv_sets))))\n            path_to_current_csv_file = os.path.join(path_to_formatted_csv_sets, file)\n            current_save_npy_name = os.path.join(save_npy_folder_path, 'set_{:05}.npy'.format(current_set_number))\n\n            process_csv_file_and_save_npy(path_to_current_csv_file, current_save_npy_name)\n\n            current_set_number += 1\n\n    # ************************************* Create ready dataset *******************************************************\n    if do_merge:\n        file_names = sorted(os.listdir(save_npy_folder_path))\n        file_names = [\n            os.path.join(save_npy_folder_path, file_names[i])\n            for i in range(len(os.listdir(save_npy_folder_path)))\n        ]\n\n        path_to_folder_with_merge_sets = os.path.join(source_path, 'merge_sets')\n        if not os.path.exists(path_to_folder_with_merge_sets):\n            os.mkdir(path_to_folder_with_merge_sets)\n\n        merge_n_split(file_names, path_to_folder_with_merge_sets)\n","sub_path":"create_dataset.py","file_name":"create_dataset.py","file_ext":"py","file_size_in_byte":20205,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"486050215","text":"import sys\n\nfrom elliptic_curve import EllipticCurvePoint\nfrom gost import FineGost\n\n\ndef initial_data():\n    p = 0x8000000000000000000000000000000000000000000000000000000000000431\n    a = 0x7\n    b = 0x5FBFF498AA938CE739B8E022FBAFEF40563F6E6A3472FC2A514C0CE9DAE23B7E\n\n    x_g = 0x7F2B49E270DB6D90D8595BEC458B50C58585BA1D4E9B788F6689DBD8E56FD80B\n    y_g = 0x26F1B489D6701DD185C8413A977B3CBBAF64D1C593D26627DFFB101A87FF77DA\n\n    na = 121\n    nb = 203\n\n    return p, a, b, x_g, y_g, na, nb\n\n\ndef encode(key, data):\n    key = str(key)\n    if len(key) < 32:\n        raise ValueError('key is too small')\n    if len(key) > 32:\n        key = key[:32]\n\n    gost = FineGost(key)\n    synchrosend = 'abcdefgh'\n    dec_data = gost.gamma_encode(data, synchrosend)\n    return dec_data\n\n\ndef diffie_hellman(data):\n    p, a, b, x_g, y_g, na, nb = initial_data()\n\n    #y^2 + a1xy + a3y = x^3 + a2x^2 +a4x + a6\n    G = EllipticCurvePoint([x_g, y_g], [a, b, p])\n\n    message_a = na * G\n    message_b = nb * G\n\n    secret_key_a = nb * message_a\n    secret_key_b = na * message_b\n\n    if secret_key_a.x == secret_key_b.x and secret_key_a.y == secret_key_b.y:\n        print('Secret key is generated')\n    else:\n        raise Exception('Key is not generated')\n\n    print('Secret key is: x = {}, y = {}'.format(secret_key_a.x, secret_key_a.y))\n\n    enc_data = encode(secret_key_a.x, data)\n    dec_data = encode(secret_key_b.x, enc_data)\n    return enc_data, dec_data\n\n\ndef main():\n    data = \"Hello world\"\n\n    print('Input data: ', data)\n\n    try:\n        enc_data, dec_data = diffie_hellman(data)\n    except Exception as ex:\n        print(ex)\n        sys.exit(1)\n\n    print('Encoded data: ', enc_data)\n    print('Decoded data: ', dec_data)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"diffie_hellman.py","file_name":"diffie_hellman.py","file_ext":"py","file_size_in_byte":1758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"46528152","text":"from pypy.objspace.flow.model import Block, Variable, Constant\nfrom pypy.rpython.lltypesystem.lltype import Void\nfrom pypy.translator.backendopt.support import log\nfrom pypy.translator import simplify\nfrom pypy import conftest\n\ndef remove_unaryops(graph, opnames):\n    \"\"\"Removes unary low-level ops with a name appearing in the opnames list.\n    \"\"\"\n    positions = []\n    for block in graph.iterblocks():\n        for i, op in enumerate(block.operations):\n            if op.opname in opnames:\n                positions.append((block, i))\n    while positions:\n        block, index = positions.pop()\n        op_result = block.operations[index].result\n        op_arg = block.operations[index].args[0]\n        # replace the new variable (op_result) with the old variable\n        # (from all subsequent positions)\n        for op in block.operations[index:]:\n            if op is not None:\n                for i in range(len(op.args)):\n                    if op.args[i] == op_result:\n                        op.args[i] = op_arg\n                if (op.opname == \"indirect_call\"\n                    and isinstance(op.args[0], Constant)):\n                    op.opname = \"direct_call\"\n                    op.args = op.args[:-1]\n        for link in block.exits:\n            for i in range(len(link.args)):\n                if link.args[i] == op_result:\n                    link.args[i] = op_arg\n        if block.exitswitch == op_result:\n            if isinstance(op_arg, Variable):\n                block.exitswitch = op_arg\n            else:\n                simplify.replace_exitswitch_by_constant(block, op_arg)\n        block.operations[index] = None\n       \n    # remove all operations\n    for block in graph.iterblocks():\n        if block.operations:\n            block.operations[:] = filter(None, block.operations)\n\ndef remove_same_as(graph):\n    remove_unaryops(graph, [\"same_as\"])\n\ndef remove_duplicate_casts(graph, translator):\n    simplify.join_blocks(graph)\n    num_removed = 0\n    # remove chains of casts\n    for block in graph.iterblocks():\n        comes_from = {}\n        for op in block.operations:\n            if op.opname == \"cast_pointer\":\n                if op.args[0] in comes_from:\n                    from_var = comes_from[op.args[0]]\n                    comes_from[op.result] = from_var\n                    if from_var.concretetype == op.result.concretetype:\n                        op.opname = \"same_as\"\n                        op.args = [from_var]\n                        num_removed += 1\n                    else:\n                        op.args = [from_var]\n                else:\n                    comes_from[op.result] = op.args[0]\n    if num_removed:\n        remove_same_as(graph)\n    # remove duplicate casts\n    for block in graph.iterblocks():\n        available = {}\n        for op in block.operations:\n            if op.opname == \"cast_pointer\":\n                key = (op.args[0], op.result.concretetype)\n                if key in available:\n                    op.opname = \"same_as\"\n                    op.args = [available[key]]\n                    num_removed += 1\n                else:\n                    available[key] = op.result\n            elif op.opname == 'resume_point':\n                available.clear()\n    if num_removed:\n        remove_same_as(graph)\n        # remove casts with unused results\n        for block in graph.iterblocks():\n            used = {}\n            for link in block.exits:\n                for arg in link.args:\n                    used[arg] = True\n            for i, op in list(enumerate(block.operations))[::-1]:\n                if op.opname == \"cast_pointer\" and op.result not in used:\n                    del block.operations[i]\n                    num_removed += 1\n                else:\n                    for arg in op.args:\n                        used[arg] = True\n        if translator.config.translation.verbose:\n            log.removecasts(\n                \"removed %s cast_pointers in %s\" % (num_removed, graph.name))\n    return num_removed\n\ndef remove_superfluous_keep_alive(graph):\n    for block in graph.iterblocks():\n        used = {}\n        for i, op in list(enumerate(block.operations))[::-1]:\n            if op.opname == \"keepalive\":\n                if op.args[0] in used:\n                    del block.operations[i]\n                else:\n                    used[op.args[0]] = True\n \n##def rename_extfunc_calls(translator):\n##    from pypy.rpython.extfunctable import table as extfunctable\n##    def visit(block): \n##        if isinstance(block, Block):\n##            for op in block.operations:\n##                if op.opname != 'direct_call':\n##                    continue\n##                functionref = op.args[0]\n##                if not isinstance(functionref, Constant):\n##                    continue\n##                _callable = functionref.value._obj._callable\n##                for func, extfuncinfo in extfunctable.iteritems():  # precompute a dict?\n##                    if _callable is not extfuncinfo.ll_function or not extfuncinfo.backend_functiontemplate:\n##                        continue\n##                    language, functionname = extfuncinfo.backend_functiontemplate.split(':')\n##                    if language is 'C':\n##                        old_name = functionref.value._obj._name[:]\n##                        functionref.value._obj._name = functionname\n##                        #print 'rename_extfunc_calls: %s -> %s' % (old_name, functionref.value._obj._name)\n##                        break\n##    for func, graph in translator.flowgraphs.iteritems():\n##        traverse(visit, graph)\n","sub_path":"flex-backend/pypy/translator/backendopt/removenoops.py","file_name":"removenoops.py","file_ext":"py","file_size_in_byte":5601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"580691179","text":"import pytest\nimport logging\nimport time\nimport datetime\nfrom MobileApps.libs.flows.android.smart.flow_container import FLOW_NAMES\nfrom SAF.misc import saf_misc\nfrom MobileApps.libs.ma_misc import ma_misc\nfrom MobileApps.resources.const.android.const import TEST_DATA, GOOGLE_PHOTOS, DROPBOX, FACEBOOK_ALBUM\nfrom selenium.common.exceptions import TimeoutException, NoSuchElementException\nfrom MobileApps.libs.ma_misc.excel import Excel\n\npytest.app_info = \"SMART\"\n\n\nclass Test_Suite_01_Send_Fax(object):\n\n    @pytest.fixture(scope=\"class\", autouse=\"true\")\n    def class_setup(cls, request, android_smart_setup, get_softfax_output_file_path, softfax_class_cleanup):\n        cls = cls.__class__\n        cls.driver, cls.fc = android_smart_setup\n\n        # Define flows\n        cls.files_photos = cls.fc.flow[FLOW_NAMES.FILES_PHOTOS]\n        cls.local_files = cls.fc.flow[FLOW_NAMES.LOCAL_FILES]\n        cls.local_photos = cls.fc.flow[FLOW_NAMES.LOCAL_PHOTOS]\n        cls.preview = cls.fc.flow[FLOW_NAMES.PREVIEW]\n        cls.camera_scan = cls.fc.flow[FLOW_NAMES.CAMERA_SCAN]\n        cls.online_docs = cls.fc.flow[FLOW_NAMES.ONLINE_DOCS]\n        cls.online_photos = cls.fc.flow[FLOW_NAMES.ONLINE_PHOTOS]\n        cls.compose_fax = cls.fc.flow[FLOW_NAMES.COMPOSE_FAX]\n        cls.send_fax_details = cls.fc.flow[FLOW_NAMES.SEND_FAX_DETAILS]\n        cls.excel = Excel(get_softfax_output_file_path)\n        cls.excel.load_sheet(\"Send Fax Performance\")\n        cls.excel.write_new_record([\"Date\", \"File Name\", \"Time (seconds)\", \"Error Message\"])\n\n        # Define variables\n        cls.recipient_info = cls.fc.get_softfax_recipient_info(request)\n        cls.sender_info = saf_misc.load_json(ma_misc.get_abs_path(TEST_DATA.SOFTFAX_ACCOUNT))[\"softfax\"][\"sender_01\"]\n        cls.stack = request.config.getoption(\"--stack\")\n        cls.sending_time = 0\n        cls.file_name = \"\"\n        cls.error_msg = \"\"\n\n        # Clean up Download and Pictures folders before testing\n        cls.fc.clean_up_download_and_pictures_folders()\n\n        # transfer file for testing\n        cls.fc.transfer_test_data_to_device([TEST_DATA.ONE_PAGE_PDF, TEST_DATA.PDF_1PAGE_1MB, TEST_DATA.PDF_1PAGE_3MB,\n                                             TEST_DATA.PDF_2PAGES_20MB, TEST_DATA.PDF_4PAGES_12MB, TEST_DATA.PDF_10PAGES_20MB,\n                                             TEST_DATA.PDF_BIG_PDF_30MB])\n\n        # Log out Dropbox\n        cls.fc.flow_dropbox_logout()\n\n        def clean_up_class():\n            cls.fc.clean_up_download_and_pictures_folders()\n            cls.fc.flow_dropbox_logout()\n        request.addfinalizer(clean_up_class)\n\n    @pytest.fixture(scope=\"function\", autouse=True)\n    def function_setup(self, request):\n        \"\"\"\n        Setup and tear down function\n        :param request:\n        \"\"\"\n        def clean_up_func():\n            if \"test_00\" not in request.node.name:\n                self.excel.write_new_record([str(datetime.datetime.now()), self.file_name, self.sending_time, self.error_msg])\n                self.excel.save()\n        request.addfinalizer(clean_up_func)\n\n    @pytest.mark.parametrize(\"number\", [1, 3])\n    def test_01_send_fax_from_camera(self, number):\n        \"\"\"\n        Description:\n            1/ Load to Compose Fax screen\n            2/ In Add Files, select file via camera\n            3/ Enter valid information for recipient and sender\n            4/ Click on Send fax button\n            5/ Click on View Status\n            6/ Observe for sending fax successfully\n        Expected result:\n            4/ Popup \"Your fax is on its way!\"\n            5/ Send Fax Details:\n                - Processing Documents text with recipient phone number\n                - Sending document\n                - Send Fax again and Cancel Fax button\n            6/ Same screen on step 5 with:\n                - Processing Documents text with recipient phone number\n                - Fax sent success\n                - Send Fax again button\n        \"\"\"\n        sending_timeouts = {1: [10, 210],\n                            3: [30, 1000]}\n        self.file_name = \"camera/{}page\".format(number)\n        self.fc.flow_home_load_compose_fax_screen(stack=self.stack, create_acc=True)\n        self.compose_fax.click_add_files_option_btn(self.compose_fax.CAMERA_BTN)\n        self.camera_scan.capture_photo(mode=self.camera_scan.BATCH_MODE,\n                                       number_pages=number)\n        self.preview.verify_preview_nav()\n        self.preview.select_next()\n        self.compose_fax.verify_compose_fax_screen()\n        self.compose_fax.verify_uploaded_file(timeout=sending_timeouts[number][0])\n        self.__enter_compose_fax_information()\n        self.__make_send_fax_job(sending_timeouts[number][1])\n\n    @pytest.mark.parametrize(\"file_names\", [TEST_DATA.ONE_PAGE_PDF, TEST_DATA.PDF_1PAGE_1MB, TEST_DATA.PDF_1PAGE_3MB, TEST_DATA.PDF_2PAGES_20MB,\n                                           TEST_DATA.PDF_4PAGES_12MB, TEST_DATA.PDF_10PAGES_20MB, TEST_DATA.PDF_BIG_PDF_30MB])\n    def test_02_send_fax_from_file_photos_local_file(self, file_names):\n        \"\"\"\n            Description:\n                1/ Load to Compose Fax screen\n                2/ In Add Files, select file from Files & Photos/Scanned Files\n                3/ Enter valid information for recipient and sender\n                4/ Click on Send fax button\n                5/ Click on View Status\n                6/ Observe for sending fax successfully\n            Expected result:\n                4/ Popup \"Your fax is on its way!\"\n                6/ Same screen on step 5 with:\n                    - Processing Documents text with recipient phone number\n                    - Fax sent success\n                    - Send Fax again button\n        \"\"\"\n        # Timeout list: [uploading file, sending fax]\n        sending_timeouts = {TEST_DATA.ONE_PAGE_PDF: [10, 180],\n                            TEST_DATA.PDF_1PAGE_1MB: [10, 290],\n                            TEST_DATA.PDF_1PAGE_3MB: [10, 331],\n                            TEST_DATA.PDF_2PAGES_20MB: [40 , 300] ,\n                            TEST_DATA.PDF_4PAGES_12MB: [30, 1750],\n                            TEST_DATA.PDF_10PAGES_20MB: [40, 2550],\n                            TEST_DATA.PDF_BIG_PDF_30MB: [55, 391]}\n        self.file_name = \"scanned file/{}\".format(file_names)\n        self.fc.flow_home_load_compose_fax_screen(stack=self.stack, create_acc=True)\n        self.__add_file_from_file_photos(file_names, sending_timeouts[file_names][0], is_file=True)\n        self.__enter_compose_fax_information()\n        self.__make_send_fax_job(sending_timeouts[file_names][1])\n\n    @pytest.mark.parametrize(\"album\", [GOOGLE_PHOTOS.PNG, GOOGLE_PHOTOS.JPG])\n    def test_03_send_fax_from_file_photos_local_photo(self, album):\n        \"\"\"\n            Description:\n                1/ Load to Compose Fax screen\n                2/ In Add Files, select file from Files & Photos/My Photos\n                3/ Enter valid information for recipient and sender\n                4/ Click on Send fax button\n                5/ Click on View Status\n                6/ Observe for sending fax successfully\n            Expected result:\n                4/ Popup \"Your fax is on its way!\"\n                6/ Same screen on step 5 with:\n                    - Processing Documents text with recipient phone number\n                    - Fax sent success\n                    - Send Fax again button\n        \"\"\"\n        # Timeout list: [uploading file, sending fax]\n        sending_timeouts = {GOOGLE_PHOTOS.PNG: [10, 185],\n                            GOOGLE_PHOTOS.JPG: [10, 120]}\n        self.file_name = \"my photos/{}\".format(album)\n        self.fc.flow_home_load_compose_fax_screen(stack=self.stack, create_acc=True)\n        self.__add_file_from_file_photos(album, sending_timeouts[album][0], is_file=False)\n        self.__enter_compose_fax_information()\n        self.__make_send_fax_job(sending_timeouts[album][1])\n\n    @pytest.mark.parametrize(\"file_names\", [DROPBOX.PDF_1PAGE_1MB, DROPBOX.PDF_1PAGE_3MB,\n                                            DROPBOX.PDF_2PAGES_20MB, DROPBOX.PDF_4PAGES_12MB,\n                                            DROPBOX.PDF_10PAGES_20MB, DROPBOX.PDF_BIG_PDF_30MB,\n                                            DROPBOX.JPG_BOW, DROPBOX.PNG_FISH])\n    def test_04_send_fax_from_dropbox(self, file_names):\n        \"\"\"\n            Description:\n                1/ Load to Compose Fax screen\n                2/ In Add Files, select file from Dropbox\n                3/ Enter valid information for recipient and sender\n                4/ Click on Send fax button\n                5/ Click on View Status\n                6/ Observe for sending fax successfully\n            Expected result:\n                4/ Popup \"Your fax is on its way!\"\n                6/ Same screen on step 5 with:\n                    - Processing Documents text with recipient phone number\n                    - Fax sent success\n                    - Send Fax again button\n        \"\"\"\n        # Timeout list: [uploading file, sending fax]\n        sending_timeouts = {DROPBOX.PDF_1PAGE_1MB: [10, 290],\n                            DROPBOX.PDF_1PAGE_3MB: [10, 331],\n                            DROPBOX.PDF_2PAGES_20MB: [40, 300],\n                            DROPBOX.PDF_4PAGES_12MB: [30, 1750],\n                            DROPBOX.PDF_10PAGES_20MB: [40, 2550],\n                            DROPBOX.PDF_BIG_PDF_30MB: [55, 391],\n                            DROPBOX.JPG_BOW: [10, 294],\n                            DROPBOX.PNG_FISH: [10, 120]}\n        self.file_name = \"dropbox/{}\".format(file_names)\n        username = saf_misc.load_json(ma_misc.get_abs_path(TEST_DATA.CLOUD_ACCOUNT))[\"dropbox\"][\"account_02\"][\"username\"]\n        pwd = saf_misc.load_json(ma_misc.get_abs_path(TEST_DATA.CLOUD_ACCOUNT))[\"dropbox\"][\"account_02\"][\"password\"]\n\n        self.fc.flow_home_load_compose_fax_screen(stack=self.stack, create_acc=True)\n        self.compose_fax.click_add_files_option_btn(self.compose_fax.FILES_PHOTOS_BTN)\n        self.files_photos.verify_files_photos_screen()\n        if self.files_photos.verify_cloud_not_login(self.files_photos.DROPBOX_TXT, raise_e=False):\n            self.files_photos.select_cloud_item(self.files_photos.DROPBOX_TXT)\n            self.fc.flow_dropbox_log_in(username, pwd)\n        self.files_photos.verify_cloud_added_account(self.files_photos.DROPBOX_TXT, username)\n        self.files_photos.select_cloud_item(self.files_photos.DROPBOX_TXT)\n        if file_names.split(\".\")[1] == \"pdf\":\n            folder = DROPBOX.DOCUMENT_FOLDER\n        else:\n            folder = DROPBOX.IMAGE_FOLDER\n        self.online_docs.select_file(\"{}/{}/{}\".format(folder, file_names.split(\".\")[1], file_names))\n        if file_names.split(\".\")[1] == \"pdf\":\n            self.preview.verify_preview_nav(is_edit=False)\n        else:\n            self.preview.verify_preview_nav(is_edit=True)\n        self.preview.select_next()\n        self.compose_fax.verify_compose_fax_screen()\n        self.compose_fax.verify_uploaded_file(timeout=sending_timeouts[file_names][0])\n        self.__enter_compose_fax_information()\n        self.__make_send_fax_job(sending_timeouts[file_names][1])\n\n    @pytest.mark.parametrize(\"number\", [1, 3, 10])\n    def test_05_send_fax_from_facebook(self, number):\n        \"\"\"\n            Description:\n                1/ Load to Compose Fax screen\n                2/ In Add Files, select file from Facebook based on number of file\n                3/ Enter valid information for recipient and sender\n                4/ Click on Send fax button\n                5/ Click on View Status\n                6/ Observe for sending fax successfully\n            Expected result:\n                4/ Popup \"Your fax is on its way!\"\n                6/ Same screen on step 5 with:\n                    - Processing Documents text with recipient phone number\n                    - Fax sent success\n                    - Send Fax again button\n        \"\"\"\n        # Timeout list: [uploading file, sending fax]\n        sending_timeouts = {1: [10, 331],\n                            3: [30, 1750],\n                            10: [40, 2550]}\n        self.file_name = \"facebook / {} photos\".format(number)\n        self.fc.flow_home_load_compose_fax_screen(stack=self.stack, create_acc=True)\n        self.compose_fax.click_add_files_option_btn(self.compose_fax.FILES_PHOTOS_BTN)\n        self.files_photos.verify_files_photos_screen()\n        if self.files_photos.verify_cloud_not_login(self.files_photos.FACEBOOK_TXT, raise_e=False):\n            self.files_photos.select_cloud_item(self.files_photos.FACEBOOK_TXT)\n            if self.online_photos.verify_fb_login_confirmation_screen(raise_e=False):\n                self.online_photos.select_fb_confirmation_continue()\n        self.files_photos.verify_cloud_added_account(self.files_photos.FACEBOOK_TXT, \"QA MobiAuto\")\n        self.files_photos.select_cloud_item(self.files_photos.FACEBOOK_TXT)\n        self.online_photos.verify_fb_album_screen()\n        self.online_photos.select_album(FACEBOOK_ALBUM.MOBILE_UPLOADS)\n        if number == 1:\n            self.online_photos.select_single_photo()\n        else:\n            self.online_photos.select_multiple_photos(number)\n            self.online_photos.select_next()\n        self.preview.verify_preview_nav(is_edit=True)\n        self.preview.select_next()\n        self.compose_fax.verify_compose_fax_screen()\n        self.compose_fax.verify_uploaded_file(timeout=sending_timeouts[number][0])\n        self.__enter_compose_fax_information()\n        self.__make_send_fax_job(sending_timeouts[number][1])\n\n    # ----------------      PRIVATE FUNCTIONS   --------------------------------------\n    def __add_file_from_file_photos(self, file, timeout, is_file=True):\n        \"\"\"\n        - Click on File Photos button on Compose screen\n        - Select file based on from_source (pdfs, scanned files)\n        - Verify compose screen with added file.\n        :param file: file name for PDFs/Scanned Files or album nam for photo\n        :param timeout: uploading timeout\n        :param is_file: from local file (True) or my photos (False)\n        \"\"\"\n        self.compose_fax.click_add_files_option_btn(self.compose_fax.FILES_PHOTOS_BTN)\n        self.files_photos.verify_files_photos_screen()\n        is_edit = True\n        if is_file:\n            self.files_photos.select_local_item(self.files_photos.PDF_TXT)\n            self.local_files.load_downloads_folder_screen()\n            self.local_files.select_file(file)\n            if file.split(\".\")[1] == \"pdf\":\n                is_edit = False\n        else:\n            self.files_photos.select_local_item(self.files_photos.MY_PHOTOS_TXT)\n            self.local_photos.select_album_photo_by_index(file)\n        self.preview.verify_preview_nav(is_edit=is_edit)\n        self.preview.select_next()\n        self.compose_fax.verify_compose_fax_screen()\n        self.compose_fax.verify_uploaded_file(timeout=timeout)\n\n    def __enter_compose_fax_information(self):\n        \"\"\"\n        Enter required information for recipient and sender\n        \"\"\"\n        self.compose_fax.enter_recipient_information(self.recipient_info[\"phone\"])\n        self.compose_fax.enter_sender_information(self.sender_info[\"name\"], self.sender_info[\"phone\"])\n\n    def __make_send_fax_job(self, timeout):\n        \"\"\"\n        Start after entering information and adding file:\n            - Click on Send Fax button\n            - Verify Send Fax popup\n            - CLick on View Status\n            - Verify sending fax successfully in Send Fax Detail\n        :param timeout: timeout of sending fax successfull on last step\n        \"\"\"\n        self.compose_fax.click_send_fax()\n        try:\n            start = time.time()\n            self.send_fax_details.verify_send_fax_status(timeout=timeout)\n            self.sending_time = time.time() - start\n        except (TimeoutException, NoSuchElementException) as ex:\n            self.error_msg = ex.msg             # Get error message for performance record before raising exception.\n            raise ex\n","sub_path":"MobileApps/tests/android/smart/functionality/softfax_send_fax/test_suite_01_send_fax.py","file_name":"test_suite_01_send_fax.py","file_ext":"py","file_size_in_byte":16062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"266670965","text":"# Authors: Henry C. Nguyen & Tony H. Nguyen\n# Assignment: EECS 448 Lab Exercise 06\n# GTA: Theodore Lindsey\n\nimport os\nimport csv\nimport numpy\n\n# Reads in CSV file into a NumPy matrix data structure\ndef open_csv_to_matrix(filename):\n    print('* Importing '+filename+' to a matrix')\n    matrix = numpy.loadtxt(open(filename,'rb'),delimiter=',')\n    return matrix\n\n# Identifies if the NumPy matrix data structure is\n# a mathematically accurate matrix by checking\n# dimensional consistency\ndef check_matrix(matrix):\n    # Obtain declared NumPy matrix dimensions\n    (m,n) = matrix.shape\n    # Compare actual contents to given dimensions\n    count_row = 0\n    count_column = 0\n    for row in matrix:\n        count_row+=1\n        for column in row:\n            count_column+=1\n        # Check column consistency for current row\n        if count_column != n:\n            return False\n        # Reset column counting for next row\n        count_column = 0\n    # Check row consistency for matrix\n    if count_row != m:\n        return False\n    return True\n\n# Returns a sum matrix of two given matrices\n# otherwise will return False upon detecting\n# given matrices are not mathematical matrices\ndef matrix_addition(matrix_a,matrix_b):\n    print('Matrix addition')\n    # Check if all parameters are mathematical matrices\n    if not check_matrix(matrix_a) and not check_matrix(matrix_b):\n        return False\n    # Check dimensions compatibility\n    (m,n) = matrix_a.shape\n    (o,p) = matrix_b.shape\n    if (m,n) != (o,p):\n        return False\n    # Create empty matrix with matching dimensions\n    sum_matrix = numpy.empty(shape=(m,n))\n    # Add each sum element into new sum matrix\n    for row in range(0,m-1):\n        for column in range(0,n-1):\n            sum_matrix[row,column] = matrix_a.item(row,column)+matrix_b.item(row,column)   \n    return sum_matrix\n\n# Returns the product of two given matrices\ndef matrix_multiplication(matrix_a,matrix_b):\n    print('Matrix multiplication')\n    #Check if all the matrices have the correct parameters\n    if not check_matrix(matrix_a) and not check_matrix(matrix_b):\n        return False\n    # Check dimensions compatibility\n    # Checks for these dimensions: (i.e. mxn X oxp = mxp)\n    (m,n) = matrix_a.shape\n    (o,p) = matrix_b.shape\n    if n != o:\n        return False\n    # Create empty matrix with matching dimensions\n    multiplied_matrix = numpy.empty(shape=(m,p))\n    # Add each element into new multiplied matrix\n    for row in range(0,m):\n        for column in range(0,p):\n            sum = 0\n            for x in range(0,p-1):\n                sum+=matrix_a.item(row,x)*matrix_b.item(x,column)\n            multiplied_matrix[row,column] = sum\n    return multiplied_matrix\n\n# Returns the transpose matrix of a given matrix\ndef matrix_transpose(matrix_a):\n    print('Matrix transpose')\n    # Check if all parameters are mathematical matrices\n    # If false, it will return a false warning upon detection\n    if not check_matrix(matrix_a) and not check_matrix(matrix_b):\n        return False\n    # Check dimensions compatibility\n    (m,n) = matrix_a.shape\n    transpose_matrix = numpy.empty(shape=(n,m))\n    # Fill in transpose matrix\n    for row in range(0,n-1):\n        for column in range(0,m-1):\n        \t# Swap column and row variables with each other \n            transpose_matrix[row,column] = matrix_a.item(column,row)\n    return transpose_matrix\n\n# === Execution ===\nprint('NOTE: A function returning False, represents that the mathematical operation has failed due to being given invalid matrices')\n\n# Test open_csv_to_matrix() & check_matrix() functions\n#print('\\n-- Test open_csv_to_matrix() & check_matrix() functions')\n#test_matrix = open_csv_to_matrix('matrix1x1.csv')\n#print(test_matrix)\n#is_a_matrix = check_matrix(test_matrix)\n#print(is_a_matrix)\n\n#  Test matrix_addition() function\nprint('\\n-- Test matrix_addition() function')\na = open_csv_to_matrix('matrix3x3.csv')\nprint(a)\nb = open_csv_to_matrix('matrix3x3.csv')\nprint(b)\nsum_ab = matrix_addition(a,b)\nprint(sum_ab)\n\n# Test failing case of matrix_addition() function\nprint('\\n-- Test failing  case of matrix_addition() function')\nc = open_csv_to_matrix('matrix2x3.csv')\nprint(c)\nd = open_csv_to_matrix('matrix3x4.csv')\nprint(d)\nsum_cd = matrix_addition(c,d)\nprint(sum_cd)\n\n# Test matrix_multiplication() function\nprint('\\n-- Test matrix_multiplication() function')\ng = open_csv_to_matrix('matrix2x3.csv')\nprint(g)\nh = open_csv_to_matrix('matrix3x4.csv')\nprint(h)\nm_matrix = matrix_multiplication(g,h)\nprint(m_matrix)\n\n# Test failing case for multiplying matrices\nprint('\\n-- Test failing case for multiplying matrices')\ne = open_csv_to_matrix('matrix1x1.csv')\nprint(e)\nf = open_csv_to_matrix('matrix3x4.csv')\nprint(f)\nm2_matrix = matrix_multiplication(e,f)\nprint(m2_matrix) \n\n# Test matrix_transpose() function\nprint('\\n-- Test matrix_transpose() function')\nprint(c)\nt_matrix = matrix_transpose(c)\nprint(t_matrix)\n","sub_path":"EECS_448_Lab06.py","file_name":"EECS_448_Lab06.py","file_ext":"py","file_size_in_byte":4929,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"1469511","text":"from bs4 import BeautifulSoup as bs\nimport pandas as pd\nimport requests\nfrom splinter import Browser\n\ndef scrape_all():\n    \n    from bs4 import BeautifulSoup as bs\n    import pandas as pd\n    import requests\n    from splinter import Browser\n\n    #news scraping\n    url = 'https://mars.nasa.gov/news/'\n    executable_path = {'executable_path': 'Missions_to_Mars\\chromedriver.exe'}\n    browser = Browser('chrome', **executable_path)\n    browser.visit(url)\n    browser.is_element_present_by_css(\"ul.item_list li.slide\", wait_time=10)\n    mars_soup = bs(browser.html, 'html.parser')\n    news_p = mars_soup.find_all('li', class_ = 'slide')[0].a.div.text\n    news_title = mars_soup.find_all('li', class_ = 'slide')[0].a.h3.text\n\n    #featured image scraping\n    url = 'https://www.jpl.nasa.gov/spaceimages/?search=&category=Mars'\n    browser.visit(url)\n    html = browser.html\n    soup = bs(html, 'html.parser')\n    featured_image_url = soup.find('article', class_='carousel_item')\n    featured_image_url = featured_image_url[\"style\"]\n    import re\n    featured_image_url = re.findall(\"url\\(\\'/(.+?)\\'\\)\", featured_image_url)\n    featured_image_url = 'https://www.jpl.nasa.gov/' + featured_image_url[0]\n\n    #weather tweet scraping\n    url = 'https://twitter.com/marswxreport?lang=en'\n    browser.visit(url)\n    browser.is_element_present_by_tag(\"article\", wait_time=10)\n    html = browser.html\n    soup = bs(html, 'html.parser')\n    mars_weather_span = soup.article.find_all('span')\n    print(mars_weather_span)\n    tweet_text = mars_weather_span[4].text\n    mars_weather = tweet_text\n    mars_weather\n\n    #mars facts\n    url = 'https://space-facts.com/mars/'\n    browser.visit(url)\n    html = browser.html\n    tables = pd.read_html(html)\n    tables[0] = tables[0].rename(columns = {0:\"\", 1:\"Values\"})\n    tables[0] = tables[0].set_index(\"\")\n    mars_facts = tables[0].to_html()\n\n    #mars hemisphere urls\n    url = 'https://astrogeology.usgs.gov/search/results?q=hemisphere+enhanced&k1=target&v1=Mars'\n    browser.visit(url)\n    html = browser.html\n    hemisphere_soup = bs(html, 'html.parser')\n    hem_soup_urls = hemisphere_soup.find_all('div', class_='item')\n    urls = []\n    for item in range(0,4):\n        urls.append('https://astrogeology.usgs.gov' + hem_soup_urls[item].a['href'])\n    hemisphere_image_urls = []\n    for i in range(0,4):\n        browser.visit(urls[i])\n        html = browser.html\n        images_urls = bs(html, 'html.parser')\n        image_url = images_urls.find_all('li')[0].a['href']\n        images_urls.title.text\n        title = re.findall(\"(.+?) Enhanced\", images_urls.title.text)[0]\n        hemisphere_image_urls.append({'title': title, 'img_url': image_url })\n\n    mars_data =  {'hemisphere_image_urls': hemisphere_image_urls, \\\n                'mars_weather': mars_weather, \\\n                'featured_image_url': featured_image_url, \\\n                'mars_facts': mars_facts, \\\n                'news_title': news_title, \\\n                'news_p': news_p}\n    browser.quit()\n\n    return mars_data","sub_path":"Missions_to_Mars/scrape_mars.py","file_name":"scrape_mars.py","file_ext":"py","file_size_in_byte":3028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"24128320","text":"# coding:utf-8\nfrom django.urls import path\n\nfrom website import views\n\napp_name = 'website'\n\nurlpatterns = [\n    path('', views.HomeView.as_view(), name='home'),\n    path('nosotros/', views.UsView.as_view(), name='us'),\n    path('contacto/', views.ContactView.as_view(), name='contact'),\n]","sub_path":"project/apps/website/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"337879144","text":"#!/usr/bin/env python3\n\nimport sys\n\nif __name__ == '__main__':\n    if len(sys.argv) < 2:\n        print('Usage: {} <file_name>'.format(sys.argv[0]))\n        sys.exit(1)\n\n    code = ''\n    for line in open(sys.argv[1], 'r'):\n        line = line.strip('\\r\\n')\n        comment = line.split('//')\n        if len(comment) == 2 and comment[0].find('http') == -1:\n            line = '/*'.join(comment) + '*/'\n        code += line + '\\n'\n\n    with open(sys.argv[1], 'w') as f:\n        f.write(code)\n","sub_path":"scripts/cpp2ccomments.py","file_name":"cpp2ccomments.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"406374270","text":"from .fitting import circuit_fit, buildCircuit\nfrom .fitting import calculateCircuitLength, check_and_eval\nfrom impedance.visualization import plot_altair, plot_bode, plot_nyquist\nfrom .elements import circuit_elements, get_element_from_name\n\nimport json\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nclass BaseCircuit:\n    \"\"\" Base class for equivalent circuit models \"\"\"\n    def __init__(self, initial_guess=[], constants=None, name=None):\n        \"\"\" Base constructor for any equivalent circuit model\n\n        Parameters\n        ----------\n        initial_guess: numpy array\n            Initial guess of the circuit values\n\n        constants : dict\n            (Optional) Parameters and values to hold constant during fitting\n            (e.g. {\"R0\": 0.1})\n\n        name : str\n            (Optional) Name for the circuit\n        \"\"\"\n\n        # if supplied, check that initial_guess is valid and store\n        initial_guess = list(filter(None, initial_guess))\n        for i in initial_guess:\n            assert isinstance(i, (float, int, np.int32, np.float64)),\\\n                'value {} in initial_guess is not a number'.format(i)\n\n        # initalize class attributes\n        self.initial_guess = initial_guess\n        if constants is not None:\n            self.constants = constants\n        else:\n            self.constants = {}\n        self.name = name\n\n        # initialize fit parameters and confidence intervals\n        self.parameters_ = None\n        self.conf_ = None\n\n    def __eq__(self, other):\n        if self.__class__ == other.__class__:\n            matches = []\n            for key, value in self.__dict__.items():\n                if isinstance(value, np.ndarray):\n                    matches.append((value == other.__dict__[key]).all())\n                else:\n                    matches.append(value == other.__dict__[key])\n            return np.array(matches).all()\n        else:\n            raise TypeError('Comparing object is not of the same type.')\n\n    def fit(self, frequencies, impedance, method=None, bounds=None):\n        \"\"\" Fit the circuit model\n\n        Parameters\n        ----------\n        frequencies: numpy array\n            Frequencies\n\n        impedance: numpy array of dtype 'complex128'\n            Impedance values to fit\n\n        method: {‘lm’, ‘trf’, ‘dogbox’}, optional\n            Name of method to pass to scipy.optimize.curve_fit\n\n        bounds: 2-tuple of array_like, optional\n            Lower and upper bounds on parameters. Defaults to bounds on all\n            parameters of 0 and np.inf, except the CPE alpha\n            which has an upper bound of 1\n\n        Returns\n        -------\n        self: returns an instance of self\n\n        \"\"\"\n\n        # check that inputs are valid:\n        #    frequencies: array of numbers\n        #    impedance: array of complex numbers\n        #    impedance and frequency match in length\n\n        assert isinstance(frequencies, np.ndarray),\\\n            'frequencies is not of type np.ndarray'\n        assert isinstance(frequencies[0], (float, int, np.int32, np.float64)),\\\n            'frequencies does not contain a number'\n        assert isinstance(impedance, np.ndarray),\\\n            'impedance is not of type np.ndarray'\n        assert isinstance(impedance[0], (complex, np.complex128)),\\\n            'impedance does not contain complex numbers'\n        assert len(frequencies) == len(impedance),\\\n            'mismatch in length of input frequencies and impedances'\n\n        if self.initial_guess is not None:\n            parameters, conf = circuit_fit(frequencies, impedance,\n                                           self.circuit, self.initial_guess,\n                                           self.constants, method=method,\n                                           bounds=bounds)\n            self.parameters_ = parameters\n            if conf is not None:\n                self.conf_ = conf\n        else:\n            # TODO auto calc guess\n            raise ValueError('no initial guess supplied')\n\n        return self\n\n    def _is_fit(self):\n        \"\"\" check if model has been fit (parameters_ is not None) \"\"\"\n        if self.parameters_ is not None:\n            return True\n        else:\n            return False\n\n    def predict(self, frequencies, use_initial=False):\n        \"\"\" Predict impedance using a fit equivalent circuit model\n\n        Parameters\n        ----------\n        frequencies: numpy array\n            Frequencies\n\n        use_initial: boolean\n            If true and a fit was already completed,\n            use the initial parameters instead\n\n        Returns\n        -------\n        impedance: numpy array of dtype 'complex128'\n            Predicted impedance\n\n        \"\"\"\n\n        # check that inputs are valid:\n        #    frequencies: array of numbers\n\n        assert isinstance(frequencies, np.ndarray),\\\n            'frequencies is not of type np.ndarray'\n        assert isinstance(frequencies[0], (float, int, np.int32, np.float64)),\\\n            'frequencies does not contain a number'\n\n        if self._is_fit() and not use_initial:\n            return eval(buildCircuit(self.circuit, frequencies,\n                                     *self.parameters_,\n                                     constants=self.constants, eval_string='',\n                                     index=0)[0],\n                        circuit_elements)\n        else:\n            print(\"Simulating circuit based on initial parameters\")\n            return eval(buildCircuit(self.circuit, frequencies,\n                                     *self.initial_guess,\n                                     constants=self.constants, eval_string='',\n                                     index=0)[0],\n                        circuit_elements)\n\n    def get_param_names(self):\n        \"\"\" Converts circuit string to names and units \"\"\"\n\n        # parse the element names from the circuit string\n        names = self.circuit.replace('p', '').replace('(', '').replace(')', '')\n        names = names.replace(',', '-').replace(' ', '').split('-')\n\n        full_names, all_units = [], []\n        for name in names:\n            elem = get_element_from_name(name)\n            num_params = check_and_eval(elem).num_params\n            units = check_and_eval(elem).units\n            if num_params > 1:\n                for j in range(num_params):\n                    full_name = '{}_{}'.format(name, j)\n                    if full_name not in self.constants.keys():\n                        full_names.append(full_name)\n                        all_units.append(units[j])\n            else:\n                if name not in self.constants.keys():\n                    full_names.append(name)\n                    all_units.append(units[0])\n\n        return full_names, all_units\n\n    def __str__(self):\n        \"\"\" Defines the pretty printing of the circuit\"\"\"\n\n        to_print = '\\n'\n        if self.name is not None:\n            to_print += 'Name: {}\\n'.format(self.name)\n        to_print += 'Circuit string: {}\\n'.format(self.circuit)\n        to_print += \"Fit: {}\\n\".format(self._is_fit())\n\n        if len(self.constants) > 0:\n            to_print += '\\nConstants:\\n'\n            for name, value in self.constants.items():\n                elem = get_element_from_name(name)\n                units = check_and_eval(elem).units\n                if '_' in name:\n                    unit = units[int(name.split('_')[-1])]\n                else:\n                    unit = units[0]\n                to_print += '  {:>5} = {:.2e} [{}]\\n'.format(name, value, unit)\n\n        names, units = self.get_param_names()\n        to_print += '\\nInitial guesses:\\n'\n        for name, unit, param in zip(names, units, self.initial_guess):\n            to_print += '  {:>5} = {:.2e} [{}]\\n'.format(name, param, unit)\n        if self._is_fit():\n            params, confs = self.parameters_, self.conf_\n            to_print += '\\nFit parameters:\\n'\n            for name, unit, param, conf in zip(names, units, params, confs):\n                to_print += '  {:>5} = {:.2e}'.format(name, param)\n                to_print += '  (+/- {:.2e}) [{}]\\n'.format(conf, unit)\n\n        return to_print\n\n    def plot(self, ax=None, f_data=None, Z_data=None, kind='altair', **kwargs):\n        \"\"\" visualizes the model and optional data as a nyquist,\n            bode, or altair (interactive) plots\n\n        Parameters\n        ----------\n        ax: matplotlib.axes\n            axes to plot on\n        f_data: np.array of type float\n            Frequencies of input data (for Bode plots)\n        Z_data: np.array of type complex\n            Impedance data to plot\n        kind: {'altair', 'nyquist', 'bode'}\n            type of plot to visualize\n\n        Other Parameters\n        ----------------\n        **kwargs : optional\n            If kind is 'nyquist' or 'bode', used to specify additional\n             `matplotlib.pyplot.Line2D` properties like linewidth,\n             line color, marker color, and labels.\n            If kind is 'altair', used to specify nyquist height as `size`\n\n        Returns\n        -------\n        ax: matplotlib.axes\n            axes of the created nyquist plot\n        \"\"\"\n\n        if kind == 'nyquist':\n            if ax is None:\n                fig, ax = plt.subplots(figsize=(5, 5))\n\n            if Z_data is not None:\n                ax = plot_nyquist(ax, Z_data, ls='', marker='s', **kwargs)\n\n            if self._is_fit():\n                if f_data is not None:\n                    f_pred = f_data\n                else:\n                    f_pred = np.logspace(5, -3)\n\n                Z_fit = self.predict(f_pred)\n                ax = plot_nyquist(ax, Z_fit, ls='-', marker='', **kwargs)\n            return ax\n        elif kind == 'bode':\n            if ax is None:\n                fig, ax = plt.subplots(nrows=2, figsize=(5, 5))\n\n            if Z_data is not None:\n                ax = plot_bode(ax, f_data, Z_data, ls='', marker='s', **kwargs)\n\n            if self._is_fit():\n                if f_data is not None:\n                    f_pred = f_data\n                else:\n                    f_pred = np.logspace(5, -3)\n\n                Z_fit = self.predict(f_pred)\n                ax = plot_bode(ax, f_pred, Z_fit, ls='-', marker='', **kwargs)\n            return ax\n        elif kind == 'altair':\n            plot_dict = {}\n\n            if Z_data is not None and f_data is not None:\n                plot_dict['data'] = {'f': f_data, 'Z': Z_data}\n\n            if self._is_fit():\n                if f_data is not None:\n                    f_pred = f_data\n                else:\n                    f_pred = np.logspace(5, -3)\n\n                Z_fit = self.predict(f_pred)\n                if self.name is not None:\n                    name = self.name\n                else:\n                    name = 'fit'\n                plot_dict[name] = {'f': f_pred, 'Z': Z_fit, 'fmt': '-'}\n\n            chart = plot_altair(plot_dict, **kwargs)\n            return chart\n        else:\n            raise ValueError(\"Kind must be one of 'altair',\" +\n                             f\"'nyquist', or 'bode' (received {kind})\")\n\n    def save(self, filepath):\n        \"\"\" Exports a model to JSON\n\n        Parameters\n        ----------\n        filepath: str\n            Destination for exporting model object\n        \"\"\"\n\n        model_string = self.circuit\n        model_name = self.name\n\n        initial_guess = self.initial_guess\n\n        if self._is_fit():\n            parameters_ = list(self.parameters_)\n            model_conf_ = list(self.conf_)\n\n            data_dict = {\"Name\": model_name,\n                         \"Circuit String\": model_string,\n                         \"Initial Guess\": initial_guess,\n                         \"Constants\": self.constants,\n                         \"Fit\": True,\n                         \"Parameters\": parameters_,\n                         \"Confidence\": model_conf_,\n                         }\n        else:\n            data_dict = {\"Name\": model_name,\n                         \"Circuit String\": model_string,\n                         \"Initial Guess\": initial_guess,\n                         \"Constants\": self.constants,\n                         \"Fit\": False}\n\n        with open(filepath, 'w') as f:\n            json.dump(data_dict, f)\n\n    def load(self, filepath, fitted_as_initial=False):\n        \"\"\" Imports a model from JSON\n\n        Parameters\n        ----------\n        filepath: str\n            filepath to JSON file to load model from\n\n        fitted_as_initial: bool\n            If true, loads the model's fitted parameters\n            as initial guesses\n\n            Otherwise, loads the model's initial and\n            fitted parameters as a completed model\n        \"\"\"\n\n        json_data_file = open(filepath, 'r')\n        json_data = json.load(json_data_file)\n\n        model_name = json_data[\"Name\"]\n        if model_name == 'None':\n            model_name = None\n\n        model_string = json_data[\"Circuit String\"]\n        model_initial_guess = json_data[\"Initial Guess\"]\n        model_constants = json_data[\"Constants\"]\n\n        self.initial_guess = model_initial_guess\n        self.circuit = model_string\n        print(self.circuit)\n        self.constants = model_constants\n        self.name = model_name\n\n        if json_data[\"Fit\"]:\n            if fitted_as_initial:\n                self.initial_guess = np.array(json_data['Parameters'])\n            else:\n                self.parameters_ = np.array(json_data[\"Parameters\"])\n                self.conf_ = np.array(json_data[\"Confidence\"])\n\n\nclass Randles(BaseCircuit):\n    \"\"\" A Randles circuit model class \"\"\"\n    def __init__(self, CPE=False, **kwargs):\n        \"\"\" Constructor for the Randles' circuit class\n\n        Parameters\n        ----------\n        initial_guess: numpy array\n            Initial guess of the circuit values\n\n        CPE: boolean\n            Use a constant phase element instead of a capacitor\n        \"\"\"\n        super().__init__(**kwargs)\n\n        if CPE:\n            self.name = 'Randles w/ CPE'\n            self.circuit = 'R0-p(R1,CPE1)-Wo1'\n        else:\n            self.name = 'Randles'\n            self.circuit = 'R0-p(R1,C1)-Wo1'\n\n        circuit_len = calculateCircuitLength(self.circuit)\n\n        assert len(self.initial_guess) + len(self.constants) == circuit_len, \\\n            'The number of initial guesses + ' + \\\n            'the number of constants ({})'.format(len(self.initial_guess)) + \\\n            ' needs to be equal to ' + \\\n            'the circuit length ({})'.format(circuit_len)\n\n\nclass CustomCircuit(BaseCircuit):\n    def __init__(self, circuit='', **kwargs):\n        \"\"\" Constructor for a customizable equivalent circuit model\n\n        Parameters\n        ----------\n        initial_guess: numpy array\n            Initial guess of the circuit values\n\n        circuit: string\n            A string that should be interpreted as an equivalent circuit\n\n\n        Notes\n        -----\n        A custom circuit is defined as a string comprised of elements in series\n        (separated by a `-`) and elements in parallel (grouped as (x,y)).\n        Elements with two or more parameters are separated by a forward slash\n        (`/`).\n\n        Example:\n            Randles circuit is given by 'R0-p(R1,C1)-W1/W2'\n\n        \"\"\"\n\n        super().__init__(**kwargs)\n        self.circuit = circuit\n\n        circuit_len = calculateCircuitLength(self.circuit)\n\n        assert len(self.initial_guess) + len(self.constants) == circuit_len, \\\n            'The number of initial guesses + ' + \\\n            'the number of constants ({})'.format(len(self.initial_guess)) + \\\n            ' needs to be equal to ' + \\\n            'the circuit length ({})'.format(circuit_len)\n","sub_path":"impedance/models/circuits/circuits.py","file_name":"circuits.py","file_ext":"py","file_size_in_byte":15682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"81680105","text":"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n\n\nimport asyncio\nimport os\nfrom logging import Logger\nfrom typing import AsyncIterator, Dict, List, Optional\n\nfrom idb.common.tar import drain_untar\nfrom idb.grpc.idb_pb2 import InstrumentsRunRequest, InstrumentsRunResponse\nfrom idb.grpc.stream import Stream, stop_wrapper\nfrom idb.grpc.types import CompanionClient\nfrom idb.utils.typing import none_throws\n\n\nStart = InstrumentsRunRequest.Start\nStop = InstrumentsRunRequest.Stop\n\n\nasync def _generate_bytes(\n    stream: Stream[InstrumentsRunRequest, InstrumentsRunResponse], logger: Logger\n) -> AsyncIterator[bytes]:\n    async for response in stream:\n        log_output = response.log_output\n        if len(log_output):\n            logger.info(log_output.decode().strip())\n            continue\n        if response.state == InstrumentsRunResponse.POST_PROCESSING:\n            logger.info(\"Instruments is post processing\")\n            continue\n        data = response.payload.data\n        if len(data):\n            yield data\n\n\nasync def _drain_until_running(\n    stream: Stream[InstrumentsRunRequest, InstrumentsRunResponse], logger: Logger\n) -> None:\n    while True:\n        response = none_throws(await stream.recv_message())\n        log_output = response.log_output\n        if len(log_output):\n            logger.info(log_output.decode().strip())\n            continue\n        state = response.state\n        if state == InstrumentsRunResponse.RUNNING_INSTRUMENTS:\n            logger.info(\"State changed to RUNNING_INSTRUMENTS\")\n            return\n\n\nasync def _drain_until_stop(\n    stream: Stream[InstrumentsRunRequest, InstrumentsRunResponse],\n    stop: asyncio.Future,\n    logger: Logger,\n) -> None:\n    while True:\n        read = asyncio.ensure_future(stream.recv_message())\n        done, pending = await asyncio.wait(\n            [stop, read], return_when=asyncio.FIRST_COMPLETED\n        )\n        if stop in done:\n            return\n        response = none_throws(read.result())\n        output = response.log_output\n        if len(output):\n            logger.info(output.decode())\n\n\nasync def run_instruments(\n    client: CompanionClient,\n    stop: asyncio.Event,\n    template: str,\n    app_bundle_id: str,\n    trace_path: str,\n    post_process_arguments: Optional[List[str]] = None,\n    env: Optional[Dict[str, str]] = None,\n    app_args: Optional[List[str]] = None,\n    started: Optional[asyncio.Event] = None,\n) -> str:\n    trace_path = os.path.realpath(trace_path)\n    client.logger.info(f\"Starting instruments connection, writing to {trace_path}\")\n    async with client.stub.instruments_run.open() as stream:\n        client.logger.info(\"Sending instruments request\")\n        await stream.send_message(\n            InstrumentsRunRequest(\n                start=Start(\n                    file_path=None,\n                    template_name=template,\n                    app_bundle_id=app_bundle_id,\n                    environment=env,\n                    arguments=app_args,\n                )\n            )\n        )\n        client.logger.info(\"Starting instruments\")\n        await _drain_until_running(stream=stream, logger=client.logger)\n        if started:\n            started.set()\n        client.logger.info(\"Instruments has started, waiting for stop\")\n        async for response in stop_wrapper(stream=stream, stop=stop):\n            output = response.log_output\n            if len(output):\n                client.logger.info(output.decode())\n        client.logger.info(\"Stopping instruments\")\n        await stream.send_message(\n            InstrumentsRunRequest(\n                stop=Stop(post_process_arguments=post_process_arguments)\n            ),\n            end=True,\n        )\n        client.logger.info(f\"Writing instruments from tar to {trace_path}\")\n        await drain_untar(\n            _generate_bytes(stream=stream, logger=client.logger), output_path=trace_path\n        )\n        client.logger.info(f\"Instruments trace written to {trace_path}\")\n        return trace_path\n\n\nCLIENT_PROPERTIES = [run_instruments]  # pyre-ignore\n","sub_path":"idb/ipc/instruments_run.py","file_name":"instruments_run.py","file_ext":"py","file_size_in_byte":4099,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"557585781","text":"import pandas as pd\r\ncontinents= ['North America','South America', 'Europe', \r\n             'Africa', 'Asia', 'Oceania', 'Antarctica', \r\n             'Oceans', 'Arctic']\r\n\r\ndf = pd.Series(data=continents)\r\n\r\nindexed_conts = {'North America':0,'South America':1,\r\n                 'Europe':2,'Africa':3,'Asia':4,'None':5} \r\n\r\nnew_df = df.map(lambda x: indexed_conts[x] if x in indexed_conts\r\n              else indexed_conts['None'], continents)","sub_path":"continent_indexes.py","file_name":"continent_indexes.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"312606619","text":"#THIS NEEDS DEFINITIONS AND STUFF\n\nnum = 20\nguess = 2\nincrement = 1\nindex = -1\n\nlisty = list()\nlisty_to_compare = list()\n\nfor i in range(1,num):\n   if num % i == 0:\n      listy.append(i)\n      listy_to_compare.append(i)\nprint(listy)\n      \nfor item in listy:\n   q = item\n   print(q)\n   for item in listy_to_compare:\n      if q % item == 0 and q / item == 1:\n         listy.remove(q)\n         num = q\nprint(listy)\n        \n        ","sub_path":"vthacks/primes.py","file_name":"primes.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"604700849","text":"import requests\nimport json\nimport xml.etree.ElementTree\nimport os\nimport logging\n\n# Own modules\nfrom channel import *\n\npy_path=os.path.dirname(os.path.realpath(__file__))\n\nclass CloudPost(object):\n\n\tdef __init__(self):\n\t\tlogging.info(\"Init cloud connection\")\n\t\tself.channels = {}\n\t\tself.fields = {}\n\t\t\n\t\t# Urls\n\t\tself.get_channel_info_url = ''#'https://api.thingspeak.com/channels.json?api_key=' + self.user_api_key\n\t\tself.create_channel_url = ''#'https://api.thingspeak.com/channels.json'\n\t\tself.api_post_url = ''#'https://api.thingspeak.com/update.json'\n\t\t\n\t\tself.settings_file = py_path+\"/settings.xml\"\n\t\tself.load_settings()\n\n\t\t# Variables\n\t\tself.account_info = None\n\n\tdef create_channel(self, address):\n\t\t# parsed_address = address.replace(\":\", \"\")\n\n\t\tdata = {'api_key': self.user_api_key,  'name' : 'IAQP device: ' + address, 'description': address} \n\t\tlogging.debug(\"Data before updating:\\n{}\".format(data));\n\t\tlogging.debug(\"self.fields before updating:\\n{}\".format(self.fields));\n\t\tdata.update(self.fields)\n\t\tlogging.debug(\"Creating channel with data:\\n{}\".format(data))\n\t\ttry:\n\t\t\tr = requests.post(self.create_channel_url, data)\n\t\t\tresponse = r.json()\n\t\t\tlogging.info(response)\n\t\t\tself.parse_channel_info(response)\n\t\t\treturn self.channels[address]\n\t\t\t\n\t\texcept requests.exceptions.ConnectionError as e:\n\t\t\tlogging.exception('Connection Error')\n\t\t\tresponse = e\n\t\tlogging.info(\"\\nRESPONSE #\")\n\t\tlogging.info(response)\n\t\tlogging.info(\"\\n\")\n\t\treturn None\n\n\tdef get_channel_information(self):\n\t\ttry:\n\t\t\tr = requests.get(self.get_channel_info_url)\n\t\t\tresponse = r.json()\n\t\t\tif response == 0:\n\t\t\t\tlogging.info(\"Channel information request failed!\")\n\t\t\t\treturn\n\t\t\telif len(response) == 0:\n\t\t\t\tlogging.info(\"No available channels\")\n\t\t\t\treturn\n\t\t\telse:\n\t\t\t\tlogging.info(\"Channel info request succesfull!\")\n\t\t\t\t#self.account_info = json.loads(response)\n\t\t\t\t#self.account_info_pretty_print = json.dumps(response, indent=4, sort_keys=True)\n\t\t\t\tself.parse_channel_info(response)\n\n\t\texcept requests.exceptions.ConnectionError as e:\n\t\t\tlogging.exception('Cloud connection Error\\n')\n\t\t\tresponse = e\n\n\tdef parse_channel_info(self, response):\n\n\t\t#convert to list if there is only single channel object\n\t\tif (isinstance(response, list)==False):\n\t\t\tresponse=[response]\n\t\t\n\t\tfor iter_channel in response:\n\t\t\tif \"IAQP device\" in iter_channel['name']:\n\t\t\t\tapi_key = str(iter_channel['api_keys'][0]['api_key'])\n\t\t\t\tname = iter_channel['name']\n\t\t\t\tdescription = iter_channel['description']\n\t\t\t\tid = iter_channel['id']\n\t\t\t\t\n\t\t\t\tnew_channel = Channel(api_key, name, description, id)\n\t\t\t\tself.channels[iter_channel['description']] = new_channel\n\t\t\n\n\tdef print_channel_info(self):\n\t\tif self.channel_info != None:\n\t\t\tlogging.info(self.account_info_pretty_print)\n\t\t\tlogging.info(\"\\n\")\n\t\telse:\n\t\t\tlogging.info(\"Channel info not avaible!\\n\")\n\n\tdef load_settings(self):\n\t\tparsed_file = xml.etree.ElementTree.parse(self.settings_file).getroot()\n\t\tfound_keys = parsed_file.find('api_keys')\n\t\tself.user_api_key = str(found_keys.find('user_api_key').text)\n\t\t\n\t\tfound_addresses = parsed_file.find('thingspeak')\n\t\tself.get_channel_info_url = str(found_addresses.find('info_address').text) + self.user_api_key\n\t\tself.create_channel_url = str(found_addresses.find('create_channel_address').text)\n\t\tself.api_post_url = str(found_addresses.find('post_address').text)\n\t\t\n\t\tuuid_root = parsed_file.find('uuids')\n\t\tfor atype in uuid_root.findall('uuid'):\n\t\t\tnew_field = {}\n\t\t\tfield = 'field' +  atype.find('field').text\n\t\t\tfield_name  = atype.find('sensor').text\n\t\t\tself.fields[field] = field_name\n\t\tlogging.info(\"Cloud settings found fields:\\n{}\".format(self.fields))\n\t\t\n\tdef load_uuids(self):\n\t\tparsed_file = xml.etree.ElementTree.parse(self.settings_file).getroot()\n\t\tuuid_root = parsed_file.find('uuids')\n\t\tfor atype in uuid_root.findall('uuid'):\n\t\t\tnew_uuid = {}\n\t\t\tnew_uuid['name'] = atype.find('name').text\n\t\t\tnew_uuid['sensor'] = atype.find('sensor').text\n\t\t\tnew_uuid['location'] = atype.find('location').text\n\t\t\tnew_uuid['data_type'] = atype.find('data_type').text\n\t\t\tnew_uuid['field'] = int(atype.find('field').text)\n\t\t\tnew_uuid['factor'] =  float(atype.find('factor').text)\n\t\t\tnew_uuid['unit'] =  atype.find('unit').text\n\t\t\tself.supportedUUIDS.append(new_uuid)\n\t\t\t\n\t\t\t\n","sub_path":"cloudpost.py","file_name":"cloudpost.py","file_ext":"py","file_size_in_byte":4243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"530414237","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Jun 16 12:33:41 2020\r\n\r\n@author: aargl\r\n\"\"\"\r\n\r\nfrom subroutines.primary import *\r\nfrom subroutines.plotting import *\r\n\r\n\r\nif __name__ == '__main__':\r\n    \r\n    \"\"\"\r\n    Runs the COVID / Economy / Emissions Experiment\r\n    \r\n    \"\"\"\r\n\r\n    run = experiment()\r\n    run.define_secdate()\r\n    run.fit_gdp_noncovid_projection()\r\n    run.find_eta_projection()\r\n    run.moving_avg_COVID19()\r\n    run.fit_lockdown_mobility()\r\n    run.fit_easing_mobility()\r\n    run.fit_covid_function()\r\n    #plot_fig1(run)\r\n    plot_fig2(run)\r\n    plot_fig3(run)\r\n\r\n","sub_path":"code/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"158200523","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import MinMaxScaler\nfrom keras.layers import Input, Dense\nfrom keras.models import Model\nimport seaborn as sns\nfrom tqdm import tqdm\n\n# Load Data\nfolder = \"/home/westy/Source/FinancePython/src/tf/\"\ndata = pd.read_csv(folder + \"dj30_10y.csv\", sep=',')\nassets = data.columns.values[1:].tolist()\ndata = data.iloc[:, 1:]\n\n# Load index\nindex = pd.read_csv(folder + \"dj30_index_10y.csv\", sep=',')\nindex = index.iloc[-data.values.shape[0]:, 1:]\n\n# Normalize data\nscaler = MinMaxScaler([0.1,0.9])\ndata_X = scaler.fit_transform(data)\nscaler_index = MinMaxScaler([0.1,0.9])\nindex = scaler_index.fit_transform(index)\n\n# Number of components\nN_COMPONENTS = 3\n## Autoencoder - Keras\n# Network hyperparameters\nn_inputs = len(assets)\nn_core = N_COMPONENTS\nn_outputs = n_inputs\n\n# Create model\ninput = Input(shape=(n_inputs,))\n# Encoder\nencoded = Dense(n_core, activation='sigmoid')(input)\n# Decoder\ndecoded = Dense(n_outputs, activation='sigmoid')(encoded)\n\n# define model\nautoencoder = Model(input, decoded)\nautoencoder.compile(optimizer='adam', loss='mse')\n\n# Testing in-sample\nX_train = data_X\nX_test = data_X\n\n# Training parameters\nepochs = 20\n# Fit the model\nhistory = autoencoder.fit( \\\n    X_train,\\\n    X_train,\\\n    epochs=epochs,\\\n    batch_size=1,\\\n    shuffle=True,\\\n    verbose=1)\n\n# Make AE predictions\ny_pred_AE_keras = autoencoder.predict(X_test)\nprint('test loss: '+str(autoencoder.evaluate(y_pred_AE_keras, X_test)))\n\n## PCA\npca = PCA()\npca.fit(X_train)\nprint(pca.explained_variance_ratio_)\ncum_exp_var = np.cumsum(pca.explained_variance_ratio_)\nplt.plot(np.arange(1, 31), cum_exp_var)\nplt.xlabel('Principal components')\nplt.ylabel('Cumulative explained variance')\nplt.show()\n\npca = PCA(n_components=N_COMPONENTS)\npca.fit(X_train)\ny_latent_PCA = pca.transform(X_train)\ny_pred_PCA = pca.inverse_transform(y_latent_PCA)\n\n# Average\nma = np.mean(data_X, axis=1)\n\n# Plot series\ncount = 30\n\nfor stk in tqdm(range(count), desc='Series', leave=True):\n    fig=plt.figure()\n    plt.plot(X_train[: ,stk], label='X')\n    plt.plot(y_pred_PCA[: ,stk], label='PCA reconstruction')\n    plt.plot(y_pred_AE_keras[: ,stk], label='AE reconstruction')\n    plt.plot(ma, label='MA')\n    plt.plot(index, label='DJIA')\n    plt.title(assets[stk])\n    plt.legend()\n    plt.savefig(folder+\"series/\"+assets[stk]+'.png', bbox_inches='tight')\n    plt.close(fig)\n\n## Correlations\n\ndef corr(x, y, **kwargs):\n    # Calculate the value\n    coef = np.corrcoef(x, y)[0][1]\n    # Make the label\n    label = str(round(coef, 2))\n    ax = plt.gca()\n    # Add the label to the plot\n    font_size = abs(coef) * 40 + 5\n    ax.annotate(label, [.5, .5,], xycoords=ax.transAxes,\n    ha='center', va='center', fontsize=font_size)\n\nfor stk in tqdm(range(29), desc='Correlations', leave=True):\n    df = pd.DataFrame({'0-Asset': data_X[:, stk], \\\n        '2-PCA': y_pred_PCA[:, stk], \\\n        '1-AE': y_pred_AE_keras[:, stk], \\\n        '3-Average': ma, \\\n        '4-DJI index': index.flatten()})\n    sns.set(style='white', font_scale=1.6)\n    g = sns.PairGrid(df, aspect=1.4, diag_sharey=False)\n    g.map_lower(sns.regplot, lowess=True, ci=False, line_kws={'color':'black'})\n    g.map_diag(sns.distplot, kde_kws={'color': 'black'})\n    g.map_upper(corr)# dot(g.data, kde_kws={'color': 'black'})\n    plt.savefig(folder+\"correlation/\" + assets[stk] + '.png', bbox_inches='tight')\n    plt.close(\"all\")","sub_path":"src/tf/autoencoder.py","file_name":"autoencoder.py","file_ext":"py","file_size_in_byte":3487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"126583959","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('addresses', '0001_initial'),\n    ]\n\n    operations = [\n        migrations.AlterField(\n            model_name='address',\n            name='description',\n            field=models.CharField(help_text='Description of address - Home, Office, Warehouse, etc.', null=True, max_length=20),\n        ),\n        migrations.AlterField(\n            model_name='address',\n            name='type',\n            field=models.IntegerField(choices=[(1, 'Primary')], default=1),\n        ),\n    ]\n","sub_path":"apps/addresses/migrations/0002_auto_20150625_1118.py","file_name":"0002_auto_20150625_1118.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"635215930","text":"\r\nimport torch\r\n\r\nclass feaMem: # 记录用户与其init_weight的对应\r\n    def __init__(self, n_k, u_emb_dim, base_model, device):\r\n        self.n_k = n_k\r\n        self.base_model = base_model\r\n        self.p_memory = torch.randn(n_k, u_emb_dim, device=device).normal_()        \r\n        u_param = base_model.get_weights()  ###########\r\n        self.u_memory = []\r\n        for i in range(n_k):\r\n            bias_list = []\r\n            for param in u_param:\r\n                bias_list.append(param.normal_(std=0.05))\r\n            self.u_memory.append(bias_list)\r\n        self.att_values = torch.zeros(n_k).to(device)\r\n        self.device = device\r\n\r\n    def read_head(self, p_u, alpha, train=True):\r\n        # a_u\r\n        att_model = Attention(self.n_k).to(self.device)\r\n        attention_values = att_model(p_u, self.p_memory).to(self.device)  # pu on device\r\n        # get personalized mu\r\n        personalized_mu = self.base_model.get_zero_weights()  #####\r\n        #print(attention_values)\r\n        #print(torch.tensor(self.u_memory).shape)\r\n        _att_values = attention_values#.reshape(len(self.u_memory),1)\r\n        #print(_att_values)\r\n        for i in range(len(self.u_memory)):\r\n            for j in range(len(self.u_memory[i])):\r\n                #print(i, j, self.u_memory[i][j])\r\n                #print(_att_values[i])\r\n                #print(personalized_mu[j])\r\n                personalized_mu[j] += _att_values[i] * self.u_memory[i][j].to(self.device)\r\n        # update mp\r\n        transposed_att = attention_values.reshape(self.n_k, 1)\r\n        product = torch.mm(transposed_att, p_u.unsqueeze(0))\r\n        if train:\r\n            self.p_memory = alpha * product + (1-alpha) * self.p_memory\r\n        self.att_values = attention_values\r\n        return personalized_mu, attention_values\r\n\r\n    def write_head(self, u_grads, lr):\r\n        att_values = self.att_values.reshape(len(self.u_memory), 1)\r\n        for i in range(len(self.u_memory)):\r\n            for j in range(len(self.u_memory[i])):\r\n                self.u_memory[i][j] = (lr * att_values[i] * u_grads[j] + (1-lr) * self.u_memory[i][j])[0]\r\n\r\n\r\n\r\ndef cosine_similarity(input1, input2):\r\n    query_norm = torch.sqrt(torch.sum(input1**2+0.00001, 1))\r\n    doc_norm = torch.sqrt(torch.sum(input2**2+0.00001, 1))\r\n\r\n    prod = torch.sum(torch.mul(input1, input2), 1)\r\n    norm_prod = torch.mul(query_norm, doc_norm)\r\n\r\n    cos_sim_raw = torch.div(prod, norm_prod)\r\n    return cos_sim_raw  \r\n\r\nclass Attention(torch.nn.Module):\r\n    def __init__(self, n_k):\r\n        super(Attention, self).__init__()\r\n        self.n_k = n_k\r\n        self.fc_layer = torch.nn.Linear(self.n_k, self.n_k, torch.nn.LeakyReLU())\r\n        self.soft_max_layer = torch.nn.Softmax(dim=0)\r\n\r\n    def forward(self, pu, mp):\r\n        expanded_pu = pu.repeat(1, len(mp)).view(len(mp), -1)  # shape, n_k, pu_dim\r\n        inputs = cosine_similarity(expanded_pu, mp)\r\n        fc_layers = self.fc_layer(inputs)\r\n        attention_values = self.soft_max_layer(fc_layers)\r\n        return attention_values","sub_path":"lib/Mem.py","file_name":"Mem.py","file_ext":"py","file_size_in_byte":3043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"563881139","text":"from importlib import reload\n#import complexnn\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.io as sciio\nimport recon_complex_utils as rcu\nimport pandas\n\nimport keras\nfrom keras.models import load_model\n#from keras.layers import Dense, Dropout, GaussianDropout\n#from keras.wrappers.scikit_learn import KerasRegressor\n#from sklearn.model_selection import cross_val_score\n#from sklearn.model_selection import KFold\n#from sklearn.preprocessing import StandardScaler\n#from sklearn.pipeline import Pipeline\n#from keras.optimizers import SGD\nfrom keras.models import model_from_json\n#from sklearn.cross_validation import train_test_split\nfrom sklearn import metrics\nimport random\nimport time\nimport os\n\nplotdir='./Output'\ndatadir='./Dataset'\n# parameters\n#psanadir = '/reg/data/ana04/amo/amox23616/scratch/generateSASE'\nmodeldir = './Model'\n#if os.getcwd() == psanadir:\n#    model_path = psanadir\n#    data_path='/reg/d/psdm/AMO/amox23616/scratch/simulations/FEL_1D_Code'\n#    filename='sase_bk_120k.mat'\n#else:\nmodel_path = modeldir\n    #data_path='/Users/dratner/Dropbox/ResearchTopics/GhostImaging/Data/sase_sim'\ndata_path=datadir\nsave_path=plotdir\n    #filename='sase_bk_1k.mat'\n\n\n\n#-----------------------------\n# load variables\n#-----------------------------\n\n#power_train_file = 'power_train.csv'\n#pspec_train_file = 'pspec_train.csv'\n#power_GT_train_file = 'groundtruth_power_train.csv'\n#phase_GT_train_file = 'groundtruth_phase_train.csv'\n\npower_train_file = 'Inputs/power_train.csv'\npspec_train_file = 'Inputs/pspec_train.csv'\npower_GT_train_file = 'Labels/groundtruth_power_train.csv'\nphase_GT_train_file = 'Labels/groundtruth_phase_train.csv'\nXZpred_power_train_file = 'Predictions/power_prediction_train.csv'\nXZpred_phase_train_file = 'Predictions/phase_prediction_train.csv'\n\npower_test_file = 'Inputs/power_test.csv'\npspec_test_file = 'Inputs/pspec_test.csv'\npower_GT_test_file = 'Labels/groundtruth_power_test.csv'\nphase_GT_test_file = 'Labels/groundtruth_phase_test.csv'\nXZpred_power_test_file = 'Predictions/power_prediction_test.csv'\nXZpred_phase_test_file = 'Predictions/phase_prediction_test.csv'\n\n\nspectral_coord_file =  'Coordinates/spectral_coordinates.csv'\ntime_coord_file =  'Coordinates/time_coordinates.csv'\n#time_for_pred_file =  'time_for_prediction.csv'\n\n\n#power_feats_train = pandas.read_csv(os.path.join(datadir,power_train_file)).values\n#pspec_feats_train = pandas.read_csv(os.path.join(datadir,pspec_train_file)).values\n#power_GT_train = pandas.read_csv(os.path.join(datadir,power_GT_train_file)).values\n#phase_GT_train = pandas.read_csv(os.path.join(datadir,phase_GT_train_file)).values\n\npower_feats_train = pandas.read_csv(os.path.join(datadir,power_train_file)).values\npspec_feats_train = pandas.read_csv(os.path.join(datadir,pspec_train_file)).values\npower_GT_train = pandas.read_csv(os.path.join(datadir,power_GT_train_file)).values\nphase_GT_train = pandas.read_csv(os.path.join(datadir,phase_GT_train_file)).values\nXZpred_power_train = pandas.read_csv(os.path.join(datadir,XZpred_power_train_file)).values\nXZpred_phase_train = pandas.read_csv(os.path.join(datadir,XZpred_phase_train_file)).values\n\npower_feats_test = pandas.read_csv(os.path.join(datadir,power_test_file)).values\npspec_feats_test = pandas.read_csv(os.path.join(datadir,pspec_test_file)).values\npower_GT_test = pandas.read_csv(os.path.join(datadir,power_GT_test_file)).values\nphase_GT_test = pandas.read_csv(os.path.join(datadir,phase_GT_test_file)).values\nXZpred_power_test = pandas.read_csv(os.path.join(datadir,XZpred_power_test_file)).values\nXZpred_phase_test = pandas.read_csv(os.path.join(datadir,XZpred_phase_test_file)).values\n\n\nfreq_feat = pandas.read_csv(os.path.join(datadir,spectral_coord_file)).values\nt_feat = pandas.read_csv(os.path.join(datadir,time_coord_file)).values\n#t_pred_orig = pandas.read_csv(os.path.join(datadir,time_for_pred_file)).values\n\n## fix time shape\n#t_pred = t_pred_orig[:,0];\n#dt=t_pred[1]-t_pred[0]\n#t_pred=t_pred-t_pred[0]\n#t_pred=np.append(t_pred,t_pred[-1]+dt)\n\n# Flag to either use complex network and field labels (use_complex=1) or real network and phase labels (use_phase=1).  If neither, uses real network and field labels.\nuse_complex=0\nuse_phase=0\n\nif use_complex:\n    savefile_prefix = os.path.join(model_path,'field_complex2')    # for saving models as you run\n    reload_prefix = os.path.join(model_path,'field_complex2') # prefix for reloading models/weights, e.g. \"temp\" for \"temp_model.json\"\nelif use_phase:\n    savefile_prefix = os.path.join(model_path,'temp_phase')    # for saving models as you run\n    reload_prefix = os.path.join(model_path,'temp_phase') # prefix for reloading models/weights, e.g. \"temp\" for \"temp_model.json\"\nelse:\n    savefile_prefix = os.path.join(model_path,'field3')    # for saving models as you run\n    reload_prefix = os.path.join(model_path,'field3') # prefix for reloading models/weights, e.g. \"temp\" for \"temp_model.json\"\n    #savefile_prefix = os.path.join(model_path,'field1')    # for saving models as you run\n    #reload_prefix = os.path.join(model_path,'field1') # prefix for reloading models/weights, e.g. \"temp\" for \"temp_model.json\"\n\n\n# training parameters\nepochs=2000\nepoch_per_chunk=10\nbatch_size=5000\nlr=3e-4\ndropout=.025\n\nreload_model = False\nreload_weights = False#True\nretrain = True#False\nntrain=-1       # reduce number of training examples\n\n\n# Remove phase information as check\n#pspec_feats_train=np.zeros(pspec_feats_train.shape)\n#pspec_feats_test=np.zeros(pspec_feats_test.shape)\n#phase_GT_train=np.zeros(phase_GT_train.shape)\n#phase_GT_test=np.zeros(phase_GT_test.shape)\n\n# Load and split data\nprint('Loading data...')\nx_train, y_train = rcu.prep_data(power_feats_train,pspec_feats_train,\n                        power_GT_train,phase_GT_train,use_phase=use_phase,ntrain=ntrain)\nx_test, y_test = rcu.prep_data(power_feats_test,pspec_feats_test,\n                        power_GT_test,phase_GT_test,use_phase=use_phase)\n                        \nXZpred_train = rcu.prep_pred(XZpred_power_train,XZpred_phase_train)\nXZpred_test = rcu.prep_pred(XZpred_power_test,XZpred_phase_test)\n\nprint(x_train.shape, x_test.shape, y_train.shape, y_test.shape)\n\n\n\n#-------------------------------------\n# model parameters\nnum_feat=x_train.shape[1]\nnum_label=y_train.shape[1]\nnum_neur=int(256*2)\nif not use_complex:\n    num_neur=num_neur*2\nnum_t = np.int(num_label/2)\n\n\nif reload_model:\n    print(\"reloading model... \")\n    \n    modelfile = reload_prefix + '_model.json'\n    weightsfile = reload_prefix + '_weights.h5'\n    model = model_from_json(open(modelfile).read())\n    model.load_weights(weightsfile)\n\nelse:\n    if use_complex:\n        model = rcu.recon_model_complexnn(num_feat=num_feat,num_label=num_label,\n                                num_neur=num_neur,f_drop=dropout)\n    else:\n        model = rcu.recon_model(num_feat=num_feat,num_label=num_label,\n                                num_neur=num_neur,f_drop=dropout)\n    if reload_weights:\n        print(\"reloading model weights.. \")\n        weightsfile = reload_prefix + '_weights.h5'\n        model.load_weights(weightsfile)\n\n#print(\"\\tDone.\")\n\n\n# Optimizers and metrics\n#Ada=keras.optimizers.Adagrad(lr=lr, epsilon=1e-12, decay=0.000) #0.002 for relu #5,1e-10,0 originally\nAdam=keras.optimizers.Adam(lr=lr)\n#RMS=keras.optimizers.RMSprop(lr=2, rho=0.9, epsilon=1e-10, decay=0.15)\nmodel.compile(loss='mean_squared_error', optimizer=Adam,metrics=['mse'])\n#model.compile(loss='mean_absolute_error', optimizer=Adam,metrics=['mae'])\n\n#estimator = KerasRegressor(build_fn=gu.model, epochs=500, batch_size=75, verbose=1)\n#history=estimator.fit(x_train,y_train)\nn_chunk=np.int(epochs/epoch_per_chunk)\nt0=time.time()\ntest_pred_mse=np.zeros(n_chunk)\ntrain_pred_mse=np.zeros(n_chunk)\nepoch_count=np.zeros(n_chunk)\nif retrain:\n    print('Training model...')\n    for e in range(n_chunk):\n        model.fit(x_train, y_train, epochs=epoch_per_chunk, batch_size=batch_size, verbose=0)\n        test_pred = model.predict(x_test)\n        train_pred = model.predict(x_train)\n        test_pred_mse[e]=metrics.mean_squared_error(test_pred,y_test)\n        train_pred_mse[e]=metrics.mean_squared_error(train_pred,y_train)\n        epoch_count[e]=(e+1)*epoch_per_chunk\n        t_e=time.time()-t0\n        print('%d/%d epochs in %d seconds, train error: %0.4f, test error: %0.4f' % (epoch_count[e],epochs,t_e,100*train_pred_mse[e],100*test_pred_mse[e]))\n        rcu.save_model(model,savefile_prefix, verbose=0, overwrite='yes')\n\n\n#-----------------------------\n# post processing\n#-----------------------------\n\nnoise_level=0.0\nnoise=1+noise_level*np.random.randn(x_test.shape[0],x_test.shape[1])\npred_test = model.predict(x_test*noise)\n#pred_train = model.predict(x_train*noise)\n\nplt.figure()\nj=1; plt.plot(x_test[j,:]); plt.plot(x_test[j,:]*noise[j])\n\nif not use_phase:\n    pred_plot = rcu.Comp2Phase(pred_test,removeLowPowPhase=1)\n    y_plot = rcu.Comp2Phase(y_test,removeLowPowPhase=1)\nelse:\n    pred_plot=pred_test\n    y_plot=y_test\n\nmeanerr_power,meanerr_phase,medianerr_power,medianerr_phase=rcu.calc_err(y_plot,pred_plot)\nprint('New prediction errors (mean/median): power: %0.3f, %0.3f phase: %0.3f, %0.3f' %\n            (meanerr_power,medianerr_power,meanerr_phase,medianerr_phase))\n\nXZmeanerr_power,XZmeanerr_phase,XZmedianerr_power,XZmedianerr_phase=rcu.calc_err(y_plot,XZpred_test)\nprint('Old Prediction errors (mean/median): power: %0.3f, %0.3f phase: %0.3f, %0.3f' %\n            (XZmeanerr_power,XZmedianerr_power,XZmeanerr_phase,XZmedianerr_phase))\n\n\nfor j in np.arange(10):\n    predj=pred_plot[j,:];\n    #predj=XZpred_test[j,:];\n    yj=y_plot[j,:]\n    rcu.plot_pred(predj,yj,os.path.join(save_path,str(j)))\n    plt.pause(0.5)\n\n\n","sub_path":"recon_complex_test.py","file_name":"recon_complex_test.py","file_ext":"py","file_size_in_byte":9691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"48367822","text":"# BetBright Take home test\n# Candidate Name: Jamsheed BP\n\"\"\"\nThe Irish lottery draw takes place twice weekly on a\nWednesday and a Saturday at 8pm. Write a function that calculates\nand returns the next valid draw\ndate based on an optional supplied date time parameter.\nIf no supplied date is provided, assume current date time\n\"\"\"\nimport datetime\n\nWED = 2\nSAT = 5\n\ndef next_draw_date(curr_date=datetime.datetime.now()):\n    \"\"\" Calculates the next irish lottery draw date based on the input.\n    Args:\n        curr_date: Datetime object of which nearest draw date has to be calculated.\n                   default is current datetime\n    Returns:\n        datetime.datetime: the nearest calculated draw date.\n    \"\"\"\n    week_day = curr_date.weekday()\n    # Check if draw date is on same day and not lapsed(ie 8pm)\n    if week_day in [WED, SAT] and curr_date.hour < 20:\n        return curr_date.replace(hour=20, minute=0, second=0, microsecond=0)\n\n    _date = curr_date + datetime.timedelta(days=1)\n    while _date.weekday() not in (WED, SAT):\n        _date = _date + datetime.timedelta(days=1)\n    return _date.replace(hour=20, minute=0, second=0, microsecond=0)\n","sub_path":"src/datecalculation.py","file_name":"datecalculation.py","file_ext":"py","file_size_in_byte":1161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"644763474","text":"import os.path\nimport numpy as np\nimport gpflow\nimport xgboost as xgb\n\n# Install latest version of scikit-garden from github to enable partial_fit(X, y):\n# (https://github.com/scikit-garden/scikit-garden)\nfrom skgarden import MondrianForestClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.ensemble import AdaBoostClassifier\nimport pycalib.benchmark\nimport pycalib.calibration_methods as calm\n\n###############################\n#   Generate calibration data\n###############################\n\nif __name__ == \"__main__\":\n\n    # Classify PCam validation data with selected classifiers\n    random_state = 1\n\n    clf_dict = {\n        \"AdaBoost\": AdaBoostClassifier(base_estimator=None,\n                                       n_estimators=100,\n                                       learning_rate=1.0,\n                                       random_state=random_state),\n        \"XGBoost\": xgb.XGBClassifier(booster=\"gbtree\",\n                                     n_estimators=100,\n                                     random_state=random_state,\n                                     n_jobs=-1),\n        \"mondrian_forest\": MondrianForestClassifier(n_estimators=10,\n                                                    min_samples_split=2,\n                                                    bootstrap=False,\n                                                    n_jobs=-1,\n                                                    random_state=random_state,\n                                                    verbose=0),\n        \"random_forest\": RandomForestClassifier(n_estimators=100,\n                                                criterion=\"gini\",\n                                                min_samples_split=2,\n                                                bootstrap=True,\n                                                n_jobs=-1,\n                                                random_state=random_state),\n        \"1layer_NN\": MLPClassifier(hidden_layer_sizes=(100,),\n                                   activation=\"relu\",\n                                   solver=\"adam\",\n                                   random_state=random_state)\n    }\n\n    # Setup\n    file = \"/Users/disiji/Dropbox/sandbox/pycalib/datasets/pcam/\"\n    output_folder = \"clf_output\"\n    data_folder = \"data\"\n    classify_images = False\n    clf_output_dir = os.path.join(file, \"clf_output\")\n\n    if classify_images:\n        for clf_name, clf in clf_dict.items():\n            pycalib.benchmark.PCamData.classify_val_data(file, clf_name=clf_name, classifier=clf,\n                                                         data_folder=data_folder, output_folder=output_folder)\n\n    ###############################\n    #   Benchmark\n    ###############################\n\n    # Initialization\n    run_dir = os.path.join(file, \"calibration\")\n\n    # Classifiers\n    classifier_names = list(clf_dict.keys())\n\n    # Calibration models\n    with gpflow.defer_build():\n        meanfunc = pycalib.gp_classes.ScalarMult()\n        meanfunc.alpha.transform = gpflow.transforms.positive\n    cal_methods = {\n        \"Uncal\": calm.NoCalibration(),\n        \"GPcalib\": calm.GPCalibration(n_classes=2, maxiter=1000, n_inducing_points=10, logits=False,\n                                      random_state=random_state),\n        \"GPcalib_lin\": calm.GPCalibration(n_classes=2, maxiter=1000, mean_function=meanfunc,\n                                          n_inducing_points=10, logits=False,\n                                          random_state=random_state),\n        \"GPcalib_approx\": calm.GPCalibration(n_classes=2, maxiter=1000, n_inducing_points=10,\n                                             logits=False, random_state=random_state, inf_mean_approx=True),\n        \"Platt\": calm.PlattScaling(random_state=random_state),\n        \"Isotonic\": calm.IsotonicRegression(),\n        \"Beta\": calm.BetaCalibration(),\n        \"BBQ\": calm.BayesianBinningQuantiles(),\n        \"Temp\": calm.TemperatureScaling()\n    }\n\n\n    # Create benchmark object\n    pcam_benchmark = pycalib.benchmark.PCamData(run_dir=run_dir, clf_output_dir=clf_output_dir,\n                                                classifier_names=classifier_names,\n                                                cal_methods=list(cal_methods.values()),\n                                                cal_method_names=list(cal_methods.keys()),\n                                                n_splits=10, test_size=9000,\n                                                train_size=1000, random_state=random_state)\n\n    # Run\n    pcam_benchmark.run(n_jobs=1)\n","sub_path":"benchmark/PCam.py","file_name":"PCam.py","file_ext":"py","file_size_in_byte":4623,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"34758077","text":"#@+leo-ver=4-thin\n#@+node:edream.110203113231.758:@thin nav_buttons.py\n\"\"\"Adds navigation buttons to icon bar\"\"\"\n\n#@@language python\n#@@tabwidth -4\n\n#@<< imports >>\n#@+node:ekr.20050219114353:<< imports >>\n\nimport leoGlobals as g\nimport leoPlugins\n\nfrom leoTkinterDialog import tkinterListBoxDialog\n\ntry: import Tkinter as Tk\nexcept ImportError: Tk = None\n\nimport os\n#@nonl\n#@-node:ekr.20050219114353:<< imports >>\n#@nl\n__version__ = \"1.5\"\n#@<< version history >>\n#@+node:ekr.20050219114353.1:<< version history >>\n#@@killcolor\n#@+at\n# \n# 1.3 EKR:\n#     - Rewritten to use:\n#         - init and onCreate functions.\n#         - Common imageClass.\n#         - per-commander dialogs.\n#         - positions rather than vnodes.\n#     - Fixed numerous bugs.\n#     - The code is _much_ simpler than before.\n#     - Added marksInitiallyVisible and recentInitiallyVisible config \n# constants.\n# \n# 1.4 EKR: 2 bug fixes\n#     - Allways fill the box when clicking on the 'Recent' button.\n#     - Use keywords.get('c') NOT self.c in hook handlers.  They may not be \n# the same!\n#         - This is actually a bug in leoPlugins.registerHandler, but it can't \n# be\n#           fixed because there is no way to associate commanders with hook \n# handlers.\n# \n# 1.5 EKR: Fixed crasher in tkinterListBoxDialog.go().\n#     updateMarks must set positionList ivar in the base class.\n#@-at\n#@-node:ekr.20050219114353.1:<< version history >>\n#@nl\n\nmarksInitiallyVisible = False\nrecentInitiallyVisible = False\n\n#@+others\n#@+node:ekr.20050219114353.2:init\ndef init ():\n    \n    ok = Tk is not None\n    \n    if ok:\n        if g.app.gui is None:\n            g.app.createTkGui(__file__)\n\n        if g.app.gui.guiName() == \"tkinter\":\n            leoPlugins.registerHandler('after-create-leo-frame',onCreate)\n            g.plugin_signon(__name__)\n            \n    return ok\n#@nonl\n#@-node:ekr.20050219114353.2:init\n#@+node:ekr.20050219115116:onCreate\ndef onCreate (tag,keywords):\n\n    # Not ok for unit testing: can't use unitTestGui.\n    if g.app.unitTesting:\n        return\n        \n    c = keywords.get(\"c\")\n    r = leoPlugins.registerHandler\n    \n    images = imageClass()\n    \n    # Create the marks dialog and hooks.\n    marks = marksDialog(c,images)\n    r(('open2','new2','set-mark','clear-mark'),marks.updateMarks)\n\n    # Create the recent nodes dialog.\n    recent = recentSectionsDialog(c,images)\n    r(('open2','new2','select2'),recent.updateRecent)\n#@nonl\n#@-node:ekr.20050219115116:onCreate\n#@+node:ekr.20050219115859:class imageClass\nclass imageClass:\n    \n    #@    @+others\n    #@+node:ekr.20050219115859.1:ctor\n    def __init__ (self):\n        \n        self.path = g.os_path_join(g.app.loadDir,'..','Icons')\n        \n        # Create images and set ivars.\n        for ivar,icon in (\n            ('lt_nav_disabled_image','lt_arrow_disabled.gif'),\n            ('lt_nav_enabled_image', 'lt_arrow_enabled.gif'),\n            ('rt_nav_disabled_image','rt_arrow_disabled.gif'),\n            ('rt_nav_enabled_image', 'rt_arrow_enabled.gif'),\n        ):\n            image = self.createImage(icon)\n            setattr(self,ivar,image)\n    #@nonl\n    #@-node:ekr.20050219115859.1:ctor\n    #@+node:ekr.20050219115859.2:createImage\n    def createImage (self,iconName):\n        \n        path = os.path.normpath(os.path.join(self.path,iconName))\n        \n        try:\n            image = Tk.PhotoImage(master=g.app.root,file=path)\n        except:\n            g.es(\"can not load icon: %s\" % iconName)\n            image = None\n    \n        return image\n    #@nonl\n    #@-node:ekr.20050219115859.2:createImage\n    #@-others\n#@nonl\n#@-node:ekr.20050219115859:class imageClass\n#@+node:edream.110203113231.775:class marksDialog (listBoxDialog)\nclass marksDialog (tkinterListBoxDialog):\n    \n    \"\"\"A class to create the marks dialog\"\"\"\n\n    #@    @+others\n    #@+node:edream.110203113231.776: marksDialog.__init__\n    def __init__ (self,c,images):\n        \n        \"\"\"Create a Marks listbox dialog.\"\"\"\n        \n        self.c = c\n        self.images = images\n    \n        self.label = None\n        self.title = 'Marks for %s' % g.shortFileName(c.mFileName) # c.frame.title\n        \n        # Init the base class and call self.createFrame.\n        tkinterListBoxDialog.__init__(self,c,self.title,self.label)\n        \n        if not marksInitiallyVisible:\n            self.top.withdraw()\n        \n        self.addButtons()\n    #@nonl\n    #@-node:edream.110203113231.776: marksDialog.__init__\n    #@+node:ekr.20050219131752:addButtons\n    def addButtons (self):\n        \n        c = self.c ; images = self.images\n        \n        def marksButtonCallback(*args,**keys):\n            self.top.deiconify()\n        \n        self.marks_button = c.frame.addIconButton(\n            text=\"Marks\",command=marksButtonCallback)\n    #@nonl\n    #@-node:ekr.20050219131752:addButtons\n    #@+node:edream.110203113231.777:createFrame\n    def createFrame(self):\n        \n        \"\"\"Create the frame for a Marks listbox dialog.\"\"\"\n    \n        tkinterListBoxDialog.createFrame(self)\n    \n        f = Tk.Frame(self.outerFrame)\n        f.pack()\n    \n        self.addStdButtons(f)\n    #@nonl\n    #@-node:edream.110203113231.777:createFrame\n    #@+node:edream.110203113231.779:updateMarks\n    def updateMarks(self,tag,keywords):\n    \n        '''Recreate the Marks listbox.'''\n        \n        # Warning: it is not correct to use self.c in hook handlers.\n        c = keywords.get('c')\n        if c != self.c: return\n    \n        self.box.delete(0,\"end\")\n        \n        # Bug fix 5/12/05: Set self.positionList for use by tkinterListBoxDialog.go().\n        i = 0 ; self.positionList = [] ; tnodeList = []\n    \n        for p in c.allNodes_iter():\n            if p.isMarked() and p.v.t not in tnodeList:\n                self.box.insert(i,p.headString().strip())\n                tnodeList.append(p.v.t)\n                self.positionList.append(p.copy())\n                i += 1\n    #@nonl\n    #@-node:edream.110203113231.779:updateMarks\n    #@-others\n#@nonl\n#@-node:edream.110203113231.775:class marksDialog (listBoxDialog)\n#@+node:edream.110203113231.780:class recentSectionsDialog (tkinterListBoxDialog)\nclass recentSectionsDialog (tkinterListBoxDialog):\n    \n    \"\"\"A class to create the recent sections dialog\"\"\"\n\n    #@    @+others\n    #@+node:edream.110203113231.781:__init__  recentSectionsDialog\n    def __init__ (self,c,images):\n        \n        \"\"\"Create a Recent Sections listbox dialog.\"\"\"\n        \n        self.c = c\n        self.images = images\n        self.label = None\n        self.title = \"Recent nodes for %s\" % g.shortFileName(c.mFileName)\n        self.lt_nav_button = self.rt_nav_button = None # Created by createFrame.\n    \n        self.addIconBarButtons()\n    \n        # Init the base class. (calls createFrame)\n        # N.B.  The base class contains positionList ivar.\n        tkinterListBoxDialog.__init__(self,c,self.title,self.label)\n        \n        self.fillbox() # Must be done initially.\n        \n        if not recentInitiallyVisible:\n            self.top.withdraw()\n    \n        self.updateButtons()\n    #@nonl\n    #@-node:edream.110203113231.781:__init__  recentSectionsDialog\n    #@+node:ekr.20050219131336:addIconBarButtons\n    def addIconBarButtons (self):\n        \n        c = self.c ; images = self.images\n        \n        # Add 'Recent' button to icon bar.\n        def recentButtonCallback(*args,**keys):\n            self.fillbox(forceUpdate=True)\n            self.top.deiconify()\n    \n        self.sections_button = c.frame.addIconButton(\n            text=\"Recent\",command=recentButtonCallback)\n    \n        # Add left and right arrows to icon bar.\n        self.lt_nav_disabled_image = images.lt_nav_disabled_image\n        self.lt_nav_enabled_image  = images.lt_nav_enabled_image\n        self.rt_nav_disabled_image = images.rt_nav_disabled_image\n        self.rt_nav_enabled_image  = images.rt_nav_enabled_image\n    \n        self.lt_nav_iconFrame_button = c.frame.addIconButton(\n            image=self.lt_nav_disabled_image,\n            command=c.goPrevVisitedNode)\n    \n        self.rt_nav_iconFrame_button = c.frame.addIconButton(\n            image=self.rt_nav_disabled_image,\n            command=c.goNextVisitedNode)\n            \n        # Don't dim the button when it is inactive.\n        for b in (self.lt_nav_iconFrame_button,self.rt_nav_iconFrame_button):\n            fg = b.cget(\"foreground\")\n            b.configure(disabledforeground=fg)\n            \n        # Package these buttons for the recentSectionsDialog class in leoTkinterDialog.py\n        self.nav_buttons = (self.lt_nav_iconFrame_button, self.rt_nav_iconFrame_button)\n    #@nonl\n    #@-node:ekr.20050219131336:addIconBarButtons\n    #@+node:edream.110203113231.782:addFrameButtons\n    def addFrameButtons (self):\n        \n        \"\"\"Add buttons to the listbox dialog.\"\"\"\n    \n        self.buttonFrame = f = Tk.Frame(self.outerFrame)\n        f.pack()\n        \n        row1 = Tk.Frame(f)\n        row1.pack()\n        \n        # Create the back and forward buttons, cloning the images & commands of the already existing buttons.\n        image   = self.lt_nav_iconFrame_button.cget(\"image\")\n        command = self.lt_nav_iconFrame_button.cget(\"command\")\n    \n        self.lt_nav_button = b = Tk.Button(row1,image=image,command=command)\n        b.pack(side=\"left\",pady=2,padx=5)\n        \n        image   = self.rt_nav_iconFrame_button.cget(\"image\")\n        command = self.rt_nav_iconFrame_button.cget(\"command\")\n    \n        self.rt_nav_button = b = Tk.Button(row1,image=image,command=command)\n        b.pack(side=\"left\",pady=2,padx=5)\n        \n        row2 = Tk.Frame(f)\n        row2.pack()\n        self.addStdButtons(row2)\n        \n        row3 = Tk.Frame(f)\n        row3.pack()\n        \n        self.clear_button = b =  Tk.Button(row3,text=\"Clear All\",\n            width=6,command=self.clearAll)\n        b.pack(side=\"left\",pady=2,padx=5)\n        \n        self.delete_button = b =  Tk.Button(row3,text=\"Delete\",\n            width=6,command=self.deleteEntry)\n        b.pack(side=\"left\",pady=2,padx=5)\n    #@-node:edream.110203113231.782:addFrameButtons\n    #@+node:edream.110203113231.783:clearAll\n    def clearAll (self,event=None):\n    \n        \"\"\"Handle clicks in the \"Delete\" button of the Recent Sections listbox dialog.\"\"\"\n        \n        c = self.c\n    \n        self.c.visitedList = []\n        self.positionList = []\n        self.fillbox()\n    #@nonl\n    #@-node:edream.110203113231.783:clearAll\n    #@+node:edream.110203113231.784:createFrame\n    def createFrame(self):\n        \n        \"\"\"Create the frame of a Recent Sections listbox dialog.\"\"\"\n        \n        tkinterListBoxDialog.createFrame(self)\n        self.addFrameButtons()\n    #@nonl\n    #@-node:edream.110203113231.784:createFrame\n    #@+node:edream.110203113231.785:deleteEntry\n    def deleteEntry (self,event=None):\n    \n        \"\"\"Handle clicks in the \"Delete\" button of a Recent Sections listbox dialog.\"\"\"\n        \n        c = self.c ; box = self.box\n        \n        # Work around an old Python bug.  Convert strings to ints.\n        items = box.curselection()\n        try:\n            items = map(int, items)\n        except ValueError: pass\n    \n        if items:\n            n = items[0]\n            p = self.position[n]\n            del self.positionList[n]\n            if p in c.visitedList:\n                c.visitedList.remove(p)\n            self.fillbox()\n    #@nonl\n    #@-node:edream.110203113231.785:deleteEntry\n    #@+node:edream.110203113231.786:destroy\n    def destroy (self,event=None):\n        \n        \"\"\"Hide a Recent Sections listbox dialog and mark it inactive.\n        \n        This is an escape from possible performace penalties\"\"\"\n            \n        # This is enough to disable fillbox.\n        self.top.withdraw()\n    #@-node:edream.110203113231.786:destroy\n    #@+node:edream.110203113231.787:fillbox\n    def fillbox(self,forceUpdate=False):\n    \n        \"\"\"Update the Recent Sections listbox.\"\"\"\n    \n        # Only fill the box if the dialog is visible.\n        # This is an important protection against bad performance.\n        if not forceUpdate and self.top.state() != \"normal\":\n            return\n            \n        self.box.delete(0,\"end\")\n        c = self.c ; i = 0\n        self.positionList = [] ; tnodeList = []\n        for p in c.visitedList:\n            if p.exists(c) and p.v.t not in tnodeList:\n                self.box.insert(i,p.headString().strip())\n                tnodeList.append(p.v.t)\n                self.positionList.append(p.copy())\n                i += 1\n    #@nonl\n    #@-node:edream.110203113231.787:fillbox\n    #@+node:ekr.20050508104217:testxxx.py\n    import unittest\n    \n    #@+others\n    #@+node:ekr.20050508104217.1:TestXXX\n    class TestXXX(unittest.TestCase):\n    \n        \"\"\"Tests for the XXX class\"\"\"\n    \n        #@    @+others\n        #@+node:ekr.20050508104217.2:setUp\n        def setUp(self):\n        \n            \"\"\"Create the test fixture\"\"\"\n        #@nonl\n        #@-node:ekr.20050508104217.2:setUp\n        #@-others\n    #@nonl\n    #@-node:ekr.20050508104217.1:TestXXX\n    #@-others\n    \n    if __name__ == \"__main__\":\n        unittest.main()\n    #@nonl\n    #@-node:ekr.20050508104217:testxxx.py\n    #@+node:ekr.20050219122657:updateButtons\n    def updateButtons (self):\n        \n        c = self.c\n            \n        for b,b2,enabled_image,disabled_image,cond in (\n            (\n                self.lt_nav_button,self.lt_nav_iconFrame_button,\n                self.lt_nav_enabled_image,self.lt_nav_disabled_image,\n                c.beadPointer > 1),\n            (\n                self.rt_nav_button,self.rt_nav_iconFrame_button,\n                self.rt_nav_enabled_image,self.rt_nav_disabled_image,\n                c.beadPointer + 1 < len(c.beadList)),\n        ):\n            # Disabled state makes the icon look bad.\n            image = g.choose(cond,enabled_image,disabled_image)\n            b.configure(image=image,state='normal')\n            b2.configure(image=image,state='normal')\n    #@nonl\n    #@-node:ekr.20050219122657:updateButtons\n    #@+node:ekr.20050219162434:updateRecent\n    def updateRecent(self,tag,keywords):\n    \n        # Warning: it is not correct to use self.c in hook handlers.\n        c = keywords.get('c')\n        if c != self.c: return\n    \n        forceUpdate = tag in ('new2','open2')\n        self.fillbox(forceUpdate)\n        self.updateButtons()\n    #@nonl\n    #@-node:ekr.20050219162434:updateRecent\n    #@-others\n#@nonl\n#@-node:edream.110203113231.780:class recentSectionsDialog (tkinterListBoxDialog)\n#@-others\n#@nonl\n#@-node:edream.110203113231.758:@thin nav_buttons.py\n#@-leo\n","sub_path":"leo/plugins/nav_buttons.py","file_name":"nav_buttons.py","file_ext":"py","file_size_in_byte":14604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"346028190","text":"import warnings\n\nfrom django.contrib.auth.backends import ModelBackend\nfrom . import helper,models,signals\nimport logging\n\nlog = logging.getLogger('wechat')\n\nclass WeiXinBackend(ModelBackend):\n    \"\"\"\n    Custom auth backend that uses an email address and password\n\n    For this to work, the User model must have an 'email' field\n    \"\"\"\n\n    def authenticate(self,api, code):\n        #corp= api.corp #models.Corporation.objects.get(wx_corpid=corpid)\n        #api=helper.QyApi(corp,int(suite_id))\n        md = api.login(code)\n        if md.get(\"UserId\"):\n            worker,created = api.get_or_create_worker(md)\n            if not created:\n                log.info(signals.corp_worker_login.send_robust(self,corp=api.corp,qyapi=api,worker=worker))\n            return worker.user\n        else:\n            log.warning(u\"WeiXinBackend.authenticate login got no UserId : %s\" % md)\n\n","sub_path":"Documents/backupCode/allapps/company/auth_backends.py","file_name":"auth_backends.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"175323228","text":"'''\nCreated on Aug 8, 2013\n\n@author: sean\n'''\nfrom __future__ import unicode_literals\nfrom dateutil.parser import parse as parse_date\nimport logging\nlog = logging.getLogger('binstar.pprint')\n\ndef pprint_orgs(orgs):\n    log.info('Organizations:')\n    for org in orgs:\n        log.info('   + %(login)25s' % org)\n\nfmt_access = '     %(full_name)-25s | %(latest_version)8s | %(access)-12s | %(package_types)-15s | %(conda_platforms)-15s'\nfmt_no_access = '     %(full_name)-25s | %(latest_version)8s | %(package_types)-15s | %(conda_platforms)-15s'\n\ndef pprint_package_header(access=True, revisions=False):\n    package_header = {'full_name':'Name',\n                      'access': 'Access', 'package_types':'Package Types',\n                      'latest_version': 'Version', 'conda_platforms': 'Platforms',\n                      'revision':'Rev'}\n    fmt = fmt_access if access else fmt_no_access\n    if revisions:\n        fmt = '%(revision)-6s | ' + fmt\n\n    log.info(fmt % package_header)\n\ndef pprint_package(package, access=True, full_name=True, revision=False):\n    package = package.copy()\n\n    if 'conda_platforms' in package:\n        package['conda_platforms'] = ', '.join(package['conda_platforms'])\n    if not full_name:\n        package['full_name'] = package['name']\n    package['access'] = 'published' if package.get('published') else 'public' if package['public'] else 'private'\n    if package.get('package_types'):\n        package['package_types'] = ', '.join(package['package_types'])\n    fmt = fmt_access if access else fmt_no_access\n\n    if revision:\n        fmt = '%(revision)-6s | ' + fmt\n\n    log.info(fmt % package)\n    if package.get('summary'):\n        log.info(' ' * 34 + '        : %s' % package.get('summary'))\n\ndef pprint_packages(packages, access=True, full_name=True, revisions=False):\n    if packages:\n        log.info('Packages:')\n    else:\n        log.info('No packages found')\n\n    fmt = fmt_access if access else fmt_no_access\n    if revisions:\n        fmt = '%(revision)-6s | ' + fmt\n    pprint_package_header(access, revisions=revisions)\n\n    package_header = {'full_name':'-' * 25, 'access': '-' * 12,\n                      'latest_version': '-' * 6, 'conda_platforms': '-' * 15,\n                      'package_types':'-' * 15,\n                      'revision': '-' * 6}\n\n    log.info(fmt % package_header)\n\n    key = lambda pkg: pkg['full_name'] if full_name else pkg['name']\n\n    for package in sorted(packages, key=key):\n        pprint_package(package, access, full_name, revision=revisions)\n\ndef pprint_user(user):\n    user = user.copy()\n    log.info('Username: %s', user.pop('login'))\n    log.info('Member since: %s', parse_date(user.pop('created_at')).ctime())\n\n    for key_value in user.items():\n        log.info('  +%s: %s' % key_value)\n\ndef pprint_collections(collections):\n    if collections:\n        log.info('Collections:')\n    for collection in collections:\n        collection['permission'] = 'public' if collection['public'] else 'private'\n        log.info('   + %(name)25s: [%(permission)s] %(description)s' % collection)\n\n","sub_path":"pkgs/anaconda-client-1.4.0-py27_0/lib/python2.7/site-packages/binstar_client/utils/pprint.py","file_name":"pprint.py","file_ext":"py","file_size_in_byte":3069,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"14439949","text":"\"\"\"\nQ1641\nCount Sorted Vowel Strings\nMedium\n\niven an integer n, return the number of strings of length n that consist\nonly of vowels (a, e, i, o, u) and are lexicographically sorted.\n\nA string s is lexicographically sorted if for all valid i, s[i] is the\nsame as or comes before s[i+1] in the alphabet.\n\"\"\"\n\n\nclass Solution:\n    def countVowelStrings(self, n: int) -> int:\n\n        if n == 0:\n            return 0\n\n        if n == 1:\n            return 5\n\n        order = {\"a\": 1,\n                 \"e\": 2,\n                 \"i\": 3,\n                 \"o\": 4,\n                 \"u\": 5,\n                 1: \"a\",\n                 2: \"e\",\n                 3: \"i\",\n                 4: \"o\",\n                 5: \"u\"}\n\n        strs = [\"a\", \"e\", \"i\", \"o\", \"u\"]\n\n        for i in range(2, n+1):\n            new_strs = []\n\n            for str in strs:\n                last = str[-1]\n                last_val = order[last]\n                for j in range(last_val, 6):\n                    new_strs.append(str+order[j])\n\n            strs = new_strs\n\n        return len(strs)\n\n\nsol = Solution()\nprint(sol.countVowelStrings(33))\n\n\n\n\n\n\n","sub_path":"Q1641.py","file_name":"Q1641.py","file_ext":"py","file_size_in_byte":1115,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"516608115","text":"#!/usr/bin/env python3\n#-*- coding: utf-8 -*-\n\n__author__ = \"Greg\"\n\ndef slices(values, list_length):\n    \"\"\"\n    Returns a list of lists of integers. The component lists are as long as the \n    integer 'list_length'. The component integers are converted from characters\n    in the string 'values', and maintain order from 'values'.\n    \"\"\"\n    \n    num_lists = (len(values) - list_length + 1) \n    \n    if num_lists < 1: \n        raise ValueError(\"Not enough digits (compared to list_length) to make a list\")\n    \n    elif list_length < 1:\n        raise ValueError(str(list_length) + \" is an invalid number of lists\")\n    \n    results = []\n    \n    for x in range(num_lists):\n        slc = []\n        for y in range(list_length):\n            slc.append(int(values[y + x]))\n        results.append(slc)\n    return results\n","sub_path":"all_data/exercism_data/python/series/214ec3796e67441aa8909862e2d9835d.py","file_name":"214ec3796e67441aa8909862e2d9835d.py","file_ext":"py","file_size_in_byte":820,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"509963520","text":"import serial\nimport threading\nimport argparse\n\nfrom src import Reader, QtVisualiser, Calibrator, IMU, DataBuffer\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--port\", default=\"COM11\")\n    parser.add_argument(\"--baudrate\", default=1000000, type=int)\n    parser.add_argument(\"--preview-window\", default=5.0, type=float)\n    parser.add_argument(\"--calibration-samples\", default=500, type=int)\n\n    args = parser.parse_args()\n\n    com = serial.Serial(port=args.port, baudrate=args.baudrate)\n    reader = Reader(com)\n\n    buffer = DataBuffer(time_window=args.preview_window)\n    visualiser = QtVisualiser(buffer)\n    calibrator = Calibrator(args.calibration_samples)\n\n    imu = IMU()\n\n    def data_listener():\n        raw_readings = reader.get_readings()\n        calibrated_readings = calibrator.filter_data(raw_readings)\n\n        for i, data in enumerate(calibrated_readings):\n            imu.update(data)\n\n            for imu_data in imu.read_data():\n                visualiser.push_data(imu_data)\n\n    data_listener_thread = threading.Thread(target=data_listener)\n\n    try:\n        reader.start()\n        data_listener_thread.start()\n        # this blocks\n        visualiser.start_threaded()\n    except KeyboardInterrupt as ex:\n        print(ex)\n    finally:\n        reader.stop()\n        data_listener_thread.join()\n\nif __name__ == '__main__':\n    main()","sub_path":"client/live_preview.py","file_name":"live_preview.py","file_ext":"py","file_size_in_byte":1386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"331060336","text":"import itertools\n\nwith open(\"small.in\", \"r\") as fin, open(\"output.out\", \"w\") as fout:\n    L, D, T = (int(x) for x in fin.readline().strip().split())\n\n    words = set()\n    for i in range(D):\n        words.add(fin.readline().strip())\n\n\n    for case in range(T):\n        w = fin.readline().strip()\n\n        r = []\n        i = 0\n        while i < len(w):\n            item = []\n            if w[i] == \"(\":\n                i += 1\n                while w[i] != \")\":\n                    item.append(w[i])\n                    i += 1\n                i += 1\n\n            else:\n                item.append(w[i])\n                i += 1\n\n            r.append(item)\n\n        words_1 = words.copy()\n        words_2 = set()\n\n        for c in range(L):\n            for p in words_1:\n                if p[c] in r[c]:\n                    words_2.add(p)\n            words_1 = words_2\n            words_2 = set()\n\n        fout.write(\"Case #{}: {}\\n\".format(case + 1, len(words_1)))\n\n    print(\"done\")\n","sub_path":"alienlanguage/alien.py","file_name":"alien.py","file_ext":"py","file_size_in_byte":980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"322252710","text":"# John McLevey\n# February 16th 2017\n\nimport os\nimport shutil\nfrom git import Repo\nimport requests\nimport argparse\nimport sys\n\nparser = argparse.ArgumentParser()\nparser.add_argument('directory', help = 'The name of the directory you want to create the new project in.', type = str)\nargs = parser.parse_args()\n\nif args.directory:\n    os.mkdir(args.directory)\n    os.chdir(args.directory)\n\ncwd = os.getcwd()\n\nprint(\"Setting up the basic directory structure.\")\n\ndirectories = ['archive', 'data/clean', 'data/raw', 'tests', 'diagnostics', 'documentation', 'figures', 'notebooks', 'notes', 'pandoc_output', 'support']\n\nfor d in directories:\n\tif not os.path.exists(d):\n\t\tos.makedirs(os.path.join(cwd, d))\n\nprint(\"Cloning McLevey's remark.js slide template for research talks.\")\n\nif not os.path.exists('talks'):\n\tRepo.clone_from('https://github.com/mclevey/talks.git', 'talks')\n\nprint(\"Creating readme.md -- remember to record project agreements!\")\n\nif not os.path.exists('readme.md'):\n\treadme = open('readme.md', 'w')\n\treadme.write('Start Date:\\nCollaborator Roles:\\nCo-authorship Agreement:\\nOther Notes / Agreements:\\n')\n\treadme.close()\n\narticle = \"https://raw.githubusercontent.com/mclevey/pandoc_templates/master/article.Rmd\"\nletter = \"https://raw.githubusercontent.com/mclevey/pandoc_templates/master/letter.md\"\n\n# download template and support files...\n\ndef download(file, url):\n\tif not os.path.exists(file):\n\t\tresponse = requests.get(url)\n\t\twith open(file, 'wb') as f:\n\t\t\tf.write(response.content)\n\nprint('Downloading templates for a research article and a submission letter.')\n\ndownload('article.Rmd', article)\ndownload('letter.md', letter)\n\nmakefile = \"https://raw.githubusercontent.com/mclevey/pandoc_templates/master/make/Makefile\"\ncsl = \"https://raw.githubusercontent.com/mclevey/pandoc_templates/master/apsa.csl\"\na_template_jm = \"https://raw.githubusercontent.com/mclevey/pandoc_templates/master/article.tex\"\na_template_sm = \"https://raw.githubusercontent.com/mclevey/pandoc_templates/master/svm-r-markdown-templates-master/svm-latex-ms.tex\"\nl_template = \"https://raw.githubusercontent.com/mclevey/pandoc_templates/master/letter.tex\"\nletterhead = \"https://github.com/mclevey/pandoc_templates/raw/master/letterheadfull.pdf\"\nmf = \"https://raw.githubusercontent.com/mclevey/pandoc_templates/master/make/Makefile\"\n\nprint('Downloading latest versions of the relevant support files.')\n\ndownload('support/article.tex', a_template_jm)\ndownload('support/svm-latex-ms.tex', a_template_sm)\ndownload('support/apsa.csl', csl)\ndownload('support/letter.tex', l_template)\ndownload('support/letterhead.pdf', letterhead)\ndownload('Makefile', mf)\n\nif not os.path.exists('support/refs.bib'):\n\topen('support/refs.bib', 'w').close()\n\nif not os.path.exists('todo.taskpaper'):\n\topen('todo.taskpaper', 'w').close()\n\nif not os.path.exists('support/signature.pdf'):\n\tif os.path.exists('/Users/johnmclevey/Dropbox/signature.pdf'):\n\t\tshutil.copy('/Users/johnmclevey/Dropbox/signature.pdf', 'support/signature.pdf')\n\telse: print(\"Only John has access to signature.pdf for the submission letter.\")\n\n# initialize git\n\nprint(\"Initializing git.\")\n\nif not os.path.exists('.git'):\n\tRepo.init()\n\nif not os.path.exists('.gitignore'):\n\tignore = open('.gitignore', 'w')\n\tignore.write('archive/\\n.Rhistory\\n.Rapp.history\\n.Rproj.user/\\n.httr-oauth\\n/*_cache/\\n/cache/\\n*.utf8.md\\n*.knit.md\\n.ipynb_checkpoints\\n*.icloud')\n\tignore.close()\n\nprint('Done. Now get to work!')","sub_path":"project_starter.py","file_name":"project_starter.py","file_ext":"py","file_size_in_byte":3435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"79877618","text":"import requests\nimport json\nimport numpy as np\nimport sys\n\n\"\"\"\nThis python script is used to get all ec2 instances\npython3 get_ec2_instances.py 0000000000000000000000000000000000000000000000000000000000000000\n\"\"\"\n\ndef get_token_ec2_instances(admin_api_key, env, account_name, i, next_token):\n\n    # if first time the method is called\n    if i == 0:\n        i += 1\n        api_url = env + \"/api/inventory.json/get_resources_ec2_details_v4?access_key=\" + admin_api_key + \"&use_account=\" + account_name\n        r7 = requests.get(api_url)\n        if \"Ec2Instances\" in r7.json():\n            print(r7.json()[\"Ec2Instances\"])\n            if \"HasNext\" in r7.json():\n                if r7.json()[\"HasNext\"] == True:\n                    next_token = r7.json()[\"NextToken\"]\n                    return i, False, next_token\n                else:\n                    return i, True, \"0000\"\n            else:\n                print(\"Could not find HasNext value\")\n        else:\n            print(\"Could not find any instances\")\n            print(r7.json())\n    else:\n        i += 1\n        api_url = env + \"/api/inventory.json/get_resources_ec2_details_v4?access_key=\" + admin_api_key + \"&use_account=\" + account_name + \"&next_token=\" + next_token\n        r7 = requests.get(api_url)\n        if \"Ec2Instances\" in r7.json():\n            print(r7.json()[\"Ec2Instances\"])\n            if \"HasNext\" in r7.json():\n                if r7.json()[\"HasNext\"] == True:\n                    next_token = r7.json()[\"NextToken\"]\n                    return i, False, next_token\n                else:\n                    return i, True, \"0000\"\n            else:\n                print(\"Could not find HasNext value\")\n        else:\n            print(\"Could not find any instances\")\n            print(r7.json())\n\ndef get_ec2_instances(admin_api_key, env, account_name):\n\n    if check_invalid_env(env):\n        return\n\n    finish = False\n    i = 0\n    next_token = \"00000\"\n\n    while finish == False:\n        i, finish, next_token = get_token_ec2_instances(admin_api_key, env, account_name, i, next_token)\n\n\ndef check_invalid_env(env):\n    \"\"\"\n    This will check for an invalid enviroment. Currently it will only check for api, eu, au, or gov.\n    If you are using a standalone environment, then you MUST add that to this function\n    \"\"\"\n\n    Enviroments = [\"https://api.cloudcheckr.com\", \"https://eu.cloudcheckr.com\", \"https://au.cloudcheckr.com\", \"https://gov.cloudcheckr.com\", \"https://qa.cloudcheckr.com\"]\n\n\n    if not(env in Enviroments):\n        log_information(\"The environment \" + str(env) + \" is not valid. If this is a standalone environment, please add the url to the check_invalid_env function.\")\n        return True\n    return False\n\n\ndef main():\n    try:\n        admin_api_key = str(sys.argv[1])\n    except IndexError:\n        print(\"Must include non-admin API key of account that you want to pull via\")\n        return\n\n    env = \"https://api.cloudcheckr.com\"\n\n    # enter name of account here\n    account_name = \"Account Name Here\"\n\n    get_ec2_instances(admin_api_key, env, account_name)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"Inventory/GetEC2Instances/get_ec2_instances.py","file_name":"get_ec2_instances.py","file_ext":"py","file_size_in_byte":3108,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"220999438","text":"#%%\r\n\r\napp = Flask(__name__)\r\n\r\nengine = create_engine(\"sqlite:///hawaii.sqlite\")\r\nBase = automap_base()\r\nBase.prepare(engine, reflect=True)\r\n\r\nmeasurement = Base.classes.Measurements\r\nstation=Base.classes.stations\r\n\r\nsession = Session(engine)\r\n\r\n@app.route(\"/api/v1.0/precipitation\")\r\ndef percipitation():\r\n    results = session.query(measurement.date, measurement.prcp).filter(measurement.date >= \"2016-08-23\").all()\r\n    for item in data:\r\n        data = {}\r\n        data[\"date\"]=measurement.date\r\n        data[\"prcp\"]=measurement.prcp\r\n    return jsonify(data)\r\n@app.route(\"/api/v1.0/stations\")\r\ndef stations():\r\n   results = session.query(station.station).all()\r\n   all_names = list(np.ravel(results))\r\n   return jsonify(all_names)\r\n@app.route(\"/api/v1.0/tobs\")\r\ndef tobs():\r\n    results = session.query(measurement.tobs).\\\r\n    filter(measurement.date >= \"2016-08-23\").\\\r\n    filter(measurement.station == \"USC00519281\").all()\r\n\r\n    temps = list(np.ravel(results))\r\n    return jsonify(temps)\r\n@app.route(\"/api/v1.0/<start>\")\r\ndef strtonly(start):\r\n    canonicalized = start.replace(\" \", \"\")\r\n    results = session.query(func.avg(measurement.tobs),func.min(measurement.tobs),func.max(measurement.tobs)).\\\r\n    filter(measurement.date >= start).all()\r\n    new_data = list(np.ravel(results))\r\n    return jsonify(new_data)\r\n@app.route(\"/api/v1.0/<start>/<end>\")\r\ndef startend(start, end):\r\n    new_start = start.replace(\" \", \"\")\r\n    new_end = end.replace(\" \", \"\")\r\n    results = session.query(func.avg(measurement.tobs),func.min(measurement.tobs),func.max(measurement.tobs)).\\\r\n    filter(measurement.date >= start).\\\r\n    filter(measurement.date <= end).all()\r\n    new_data = list(np.ravel(results))\r\n    return jsonify(new_data)\r\nif __name__ == \"__main__\":\r\n    app.run(debug=True)","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"96315377","text":"#!/usr/bin/python3\n\"\"\"This module takes in a letter and sends a POST request to\nhttp://0.0.0.0:5000/search_user with the letter as a parameter.\nUsage:\n   This module accepts a console argument, execute it like this:\n   \"./8-json_api.py <letter to send>\".\n\"\"\"\nimport sys\nimport requests\n\nif __name__ == \"__main__\":\n    if len(sys.argv) == 1:\n        data = {'q': \"\"}\n    else:\n        data = {'q': sys.argv[1]}\n    try:\n        request = requests.post('http://0.0.0.0:5000/search_user', data)\n        if request.json():\n            json = request.json()\n            print('[{}] {}'.format(json.get('id'), json.get('name')))\n        else:\n            print(\"No result\")\n    except ValueError as error:\n        print('Not a valid JSON')\n","sub_path":"0x11-python-network_1/8-json_api.py","file_name":"8-json_api.py","file_ext":"py","file_size_in_byte":734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"368187052","text":"import re\nexampleString='''There should be one-and preferably only one-obvious way to do it.\nAlthough that way may not be obvious at first unless you're Dutch.\nNow is better than never.\nAlthough never is often better than right now.'''\npattern=re.compile(r'(?<=\\w\\s)never(?=\\s\\w)')#查找不在句子开头和结尾的单词\nmatchResult=pattern.search(exampleString)\nprint(matchResult.span())\npattern=re.compile(r'(?<=\\w\\s)never')#查找位于句子结尾的单词\nmatchResult=pattern.search(exampleString)\nprint(matchResult.span())\npattern=re.compile(r'(?:is\\s)better(\\sthan)')#查找前面是is的better than组合\nmatchResult=pattern.search(exampleString)\nprint(matchResult.span())\nprint(matchResult.group(0))\nprint(matchResult.group(1))\npattern=re.compile(r'\\b(?i)n\\w+\\b')#查找以n或N字母开头的所有单词\nindex=0\nwhile True:\n    matchResult=pattern.search(exampleString,index)\n    if not matchResult:\n        break\n    print(matchResult.group(0),':',matchResult.span(0))\n    index=matchResult.end(0)\n\npattern=re.compile(r'(?<!not\\s)be\\b')#查找前面没有单词not的单词be\nindex=0\nwhile True:\n    matchResult=pattern.search(exampleString,index)\n    if not matchResult:\n        break\n    print(matchResult.group(0),':',matchResult.span(0))\n    index=matchResult.end(0)\n\npattern=re.compile(r'(\\b\\w*(?P<f>\\w+)(?P=f)\\w*\\b)')#查找具有连续相同字母的单词\nindex=0\nwhile True:\n    matchResult=pattern.search(exampleString,index)\n    if not matchResult:\n        break\n    print(matchResult.group(0),':',matchResult.group(2))\n    index=matchResult.end(0)+1\n\n\n\n\n","sub_path":"基本语法与数据结构/字符串/子模式扩展语法.py","file_name":"子模式扩展语法.py","file_ext":"py","file_size_in_byte":1582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"609639211","text":"from __future__ import print_function , division\nimport os\nimport sys\nimport glob\nimport numpy as np \n\n\n\n\ndef find_line( filetext , line ):\n    for i in range( len( filetext ) ):\n        if filetext[i].find( line ) != -1:\n            break\n    return i\n\n\n\n\ndef main():\n    ##  Choose whther to run the script on the results of the entire\n    ##  reconstruction or only on a selected roi\n    flag = int( sys.argv[1] )\n\n\n    ##  Paths\n    path_common = '/home/arcusfil/tomcat/Data/benchmark_gridrec_beatty/analysis/'\n    if flag == 0:\n        path_res = path_common + 'results/'\n        path_plt = path_common + 'plots/'\n    elif flag == 1:\n        path_res = path_common + 'results_roi/'\n        path_plt = path_common + 'plots_roi/'  \n    curr_dir = os.getcwd()\n\n\n    ##  Sets of labels\n    set0 = [ 'fibers' , 'lung_tissue' , 'shepp_logan' ]\n    set1 = [ '0256' , '0512' , '1024' , '2048' ]\n    set2 = [ '0402' , '0804' , '1608' , '3216' ]\n    set3 = [ 'pswf' , 'kb' ]\n    set4 = [ 'nn' , 'lin' ]\n    set5 = np.arange( 1100 , 2025 , 25 )\n    x = np.array([ 256 , 512 , 1024 , 2048 ])\n    output = np.zeros( ( 4 , 2 ) , dtype=np.float32 )\n    output[:,0] = x\n\n\n    n_img = 3\n    n_dim = 4\n    n_ker = 2\n    n_interp = 2\n    n_oversampl = len( set5 )\n\n\n    ##  Allocate memory for arrays\n    psnr_values = np.zeros( 4 , dtype=np.float32 )\n    ssim_values = np.zeros( 4 , dtype=np.float32 ) \n    nmi_values = np.zeros( 4 , dtype=np.float32 )\n\n\n    ##  Loop to reorder data for plots\n    for i in range( n_img ):\n        for k in range( n_ker ):\n            if k:\n                for z in range( n_interp ):\n                    common = '_polar_prolate_nn_grid_sino_' + set3[k] + '_' + set4[z] \n                    for w in range( n_oversampl - 1 ):\n                        fileaux = common + '_oversampl' + str( set5[w] )\n                        for j in range( n_dim ):\n                            filename = set0[i] + '_pix' + set1[j] + '_ang' + set2[j] + fileaux\n                            file_read = path_res + filename + '_mse_results.txt'\n                            filetext = open( file_read ).readlines()\n                            n_line = find_line( filetext , 'PSNR = ' )\n                            psnr_values[j] = np.float32( filetext[n_line].split( '=' )[1] )\n                            print('\\n\\nFile read KB: ', file_read)\n                            print('Value ', j,' :  ', psnr_values[j])\n\n                            file_read = path_res + filename + '_ssim_results.txt'\n                            filetext = open( file_read ).readlines()\n                            n_line = find_line( filetext , 'SSIM = ' )   \n                            ssim_values[j] = np.float32( filetext[n_line].split( '=' )[1] )\n                            print('\\n\\nFile read KB: ', file_read)\n                            print('Value ', j,' :  ', ssim_values[j])\n\n                            file_read = path_res + filename + '_nmi_results.txt'\n                            filetext = open( file_read ).readlines()\n                            n_line = find_line( filetext , 'NMI = ' ) \n                            nmi_values[j] = np.float32( filetext[n_line].split( '=' )[1] )\n                            print('\\n\\nFile read KB: ', file_read)\n                            print('Value ', j,' :  ', nmi_values[j])\n                 \n                            \n                        file_psnr = set0[i] + fileaux + '_psnr_plots.txt'\n                        output[:,1] = psnr_values\n                        np.savetxt( path_plt + file_psnr , output )\n                        print( 'File PSNR KB: ', file_psnr )\n                        print( output )\n\n                        file_ssim = set0[i] + fileaux + '_ssim_plots.txt'\n                        output[:,1] = ssim_values\n                        np.savetxt( path_plt + file_ssim , output )\n                        print( 'File SSIM KB: ', file_ssim )\n                        print( output )  \n\n                        file_nmi = set0[i] + fileaux + '_nmi_plots.txt'\n                        output[:,1] = nmi_values\n                        np.savetxt( path_plt + file_nmi , output )\n                        print( 'File NMI KB: ', file_nmi )\n                        print( output )                          \n                        \n            else:\n                for w in range( n_oversampl ):\n                    common = '_polar_prolate_nn_grid_sino_' + 'pswf_nn' + '_oversampl' + str( set5[w] )        \n                    for j in range( n_dim ): \n                        filename = set0[i] + '_pix' + set1[j] + '_ang' + set2[j] + common\n\n                        file_read = path_res + filename + '_mse_results.txt'\n                        filetext = open( file_read ).readlines()\n                        n_line = find_line( filetext , 'PSNR = ' )\n                        psnr_values[j] = np.float32( filetext[n_line].split( '=' )[1] )\n                        print('\\n\\nFile read PSWF: ', file_read)\n                        print('Value ', j,' :  ', psnr_values[j])                                   \n\n                        file_read = path_res + filename + '_ssim_results.txt'\n                        filetext = open( path_res + filename + '_ssim_results.txt' ).readlines()\n                        filetext = open( file_read ).readlines()\n                        n_line = find_line( filetext , 'SSIM = ' )  \n                        ssim_values[j] = np.float32( filetext[n_line].split( '=' )[1] )\n                        print('\\n\\nFile read PSWF: ', file_read)\n                        print('Value ', j,' :  ', ssim_values[j])   \n\n                        file_read = path_res + filename + '_nmi_results.txt' \n                        filetext = open( path_res + filename + '_nmi_results.txt' ).readlines()\n                        n_line = find_line( filetext , 'NMI = ' )\n                        nmi_values[j] = np.float32( filetext[n_line].split( '=' )[1] )\n                        print('\\n\\nFile read PSWF: ', file_read)\n                        print('Value ', j,' :  ', nmi_values[j])   \n                \n\n                        file_psnr = set0[i] + common + '_psnr_plots.txt'\n                        output[:,1] = psnr_values\n                        np.savetxt( path_plt + file_psnr , output )\n                        print( 'File PSNR PSWF: ', file_psnr )\n                        print( output )    \n\n                        file_ssim = set0[i] + common + '_ssim_plots.txt'\n                        output[:,1] = ssim_values\n                        np.savetxt( path_plt + file_ssim , output )\n                        print( 'File SSIM PSWF: ', file_psnr )\n                        print( output )                   \n\n                        file_nmi = set0[i] + common + '_nmi_plots.txt'\n                        output[:,1] = nmi_values\n                        np.savetxt( path_plt + file_nmi , output )                 \n                        print( 'File NMI PSWF: ', file_psnr )\n                        print( output )               \n\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"figures_of_merit/scripts_beatty_analysis/script_beatty_quality_step2.py","file_name":"script_beatty_quality_step2.py","file_ext":"py","file_size_in_byte":7056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"497698536","text":"#!/usr/bin/env python \n# -*- coding: utf-8 -*-\n# ==============================================================================\n#          \\file   predicts.py\n#        \\author   chenghuige  \n#          \\date   2020-10-27 00:14:06.539840\n#   \\Description  \n# ==============================================================================\n\n  \nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport sys \nimport os\nimport importlib\nimport glob\nimport numpy as np\nfrom tqdm.auto import tqdm\nimport shutil\nimport gezi\n\ndef get_img_label_paths(images_path, labels_path):\n  res = []\n  for dir_entry in os.listdir(images_path):\n    if os.path.isfile(os.path.join(images_path, dir_entry)):\n      file_name, _ = os.path.splitext(dir_entry)\n      res.append((os.path.join(images_path, file_name + \".tif\"),\n                  os.path.join(labels_path, file_name + \".png\")))\n  return res\n\ndef deal(input_dir, out_dir, fold=1, folds=10, records=30):\n  gezi.try_mkdir(out_dir)\n  image_dir = input_dir\n  label_dir = input_dir.replace('image', 'label')\n  print(image_dir)\n  print(label_dir)\n  assert os.path.exists(label_dir)\n  imgs = get_img_label_paths(image_dir, label_dir)\n  np.random.seed(12345)\n  np.random.shuffle(imgs)\n\n  input_paths = []\n  for i in tqdm(range(len(imgs)), desc='loop-files'):\n    if i % records % folds == fold: \n      input_paths.append(imgs[i][0])\n \n  #print(input_paths)\n  for input_path in tqdm(input_paths, desc='copy-files'):\n    shutil.copy2(input_path, out_dir)\n\nfold = 1\nif len(sys.argv) > 3:\n  fold = int(sys.argv[3])\n\ndeal(sys.argv[1], sys.argv[2], fold=fold, folds=10, records=30) \n\n","sub_path":"projects/ai/suichang/gseg/tools/copy-valid-imgs.py","file_name":"copy-valid-imgs.py","file_ext":"py","file_size_in_byte":1663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"634362261","text":"import numpy as np\nfrom sklearn import manifold\nfrom scipy.cluster.hierarchy import linkage\nfrom scipy.cluster.hierarchy import dendrogram as _dendrogram\nfrom scipy.spatial.distance import squareform\nimport matplotlib.pyplot as plt\n\n\ndef mds(distances):\n    \"\"\"Computes nonmetric multidimensional scaling on the given\n    distances, projecting the data into a 2D space.\n\n    Parameters\n    ----------\n    distances : numpy array of shape (n, n)\n        The distances between points (i.e., with zeros along the diagonal)\n\n    Returns\n    -------\n    numpy array of shape (n, 2)\n\n    \"\"\"\n    # parameters that are used by both of the MDS runs\n    params = {\n        'random_state': 23497,\n        'eps': 1e-6,\n        'max_iter': 500,\n        'dissimilarity': \"precomputed\",\n        'n_jobs': 1,\n        'n_components': 2\n    }\n\n    # first fit the metric MDS, which we will use as initialization\n    # for the nonmetric MDS algorithm\n    mds = manifold.MDS(metric=True, n_init=1, **params)\n    pos = mds.fit(distances).embedding_\n\n    # now run the nonmetric MDS algorithm\n    nmds = manifold.MDS(metric=False, n_init=1, **params)\n    pos = nmds.fit_transform(distances, init=pos)\n\n    return pos\n\n\ndef plot_dendrogram(axis, similarities, labels):\n    \"\"\"Computes a hierarchical clustering on the given distances, and\n    plots the the hierarchy as a dendrogram. The height of the top of\n    each cluster is the distance between its child clusters.\n\n    Parameters\n    ----------\n    axis : matplotlib axis object\n        The axis on which to create the dendrogram  plot\n    similarities : numpy array of shape (n, n)\n        The similarity between points scaled to be between 0 and 1, with\n        1s along the diagonal.\n    labels : list with length n\n        The labels corresponding to each leaf of the dendrogram\n\n    Returns\n    -------\n    numpy array of dissimilarities\n\n    \"\"\"\n    lower_diag = squareform(1 - similarities)\n    linkage_matrix = linkage(lower_diag)\n    _dendrogram(linkage_matrix,\n                labels=labels,\n                color_threshold=0,\n                ax=axis)\n\n    # rotate the axis labels\n    for label in axis.xaxis.get_ticklabels():\n        label.set_rotation(-90)\n\n    return 1 - similarities\n","sub_path":"EC4-MDS/representations.py","file_name":"representations.py","file_ext":"py","file_size_in_byte":2233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"60230524","text":"''' \nVital Signs : 2019/2/13 15:47\nex0:\nDisplay heart rate & breathing rate data\n(1)Download lib:\ninstall:\n~#sudo pip intall mmWave\nupdate:\n~#sudo pip install mmWave -U\n'''\nimport serial\nimport struct\nimport datetime\n\nimport numpy as np\nfrom mmWave import vitalsign\n\n \nclass globalV:\n\tcount = 0\n\thr = 0.0\n\tbr = 0.0\n\tdef __init__(self, count):\n\t\tself.count = count\n\t\t\n\n#UART initial\ntry:    #pi 3\n\tport = serial.Serial(\"/dev/ttyS0\",baudrate = 921600, timeout = 0.5)\nexcept: #pi 2\n\tport = serial.Serial(\"/dev/ttyAMA0\",baudrate = 921600, timeout = 0.5)\n\n#\n#initial global value\n#\ngv = globalV(0)\n\nvts = vitalsign.VitalSign(port)\n\n \n# UART : 50 ms\ndef uartGetTLVdata(name, time):\n\tprint(\"mmWave: {:} example:\".format(name))\n\tpt = datetime.datetime.now()\n\tct = datetime.datetime.now()\n\tport.flushInput()\n\tnum = 0\n\thr_tmp = 0\n\tbr_tmp = 0\n\tfor cont in range(time):\n\t\tif num == 0:\n\t\t\ttxt = \"out-hr-%s.txt\" % (str(cont))\n\t\t\tout_hr = open(txt, \"w\")\n\t\t\ttxt = \"out-br-%s.txt\" % (str(cont))\n\t\t\tout_br = open(txt, \"w\")\n\t\t\ttxt = \"out-ch-%s.txt\" % (str(cont))\n\t\t\tout_ch = open(txt, \"w\")\n\t\t\ttxt = \"out-mv-%s.txt\" % (str(cont))\n\t\t\tout_mv = open(txt, \"w\")\n\t\t\tnum = num + 1\n\t\t#mmWave/VitalSign tlvRead & Vital Sign \n\t\t#print(datetime.datetime.now().time())\n\t\tpt = datetime.datetime.now()\n\t\t(dck , vd, rangeBuf) = vts.tlvRead(False)\n\t\tvs = vts.getHeader()\n\t\t#vts.showHeader()\n\t\t \n\t\tif dck:\n\t\t\tct = datetime.datetime.now()\n\t\t\tgv.br = vd.breathingRateEst_FFT\n\t\t\tgv.hr = vd.heartRateEst_FFT\n\t\t\tch_tmp = vd.unwrapPhasePeak_mm\n\t\t\tmv_tmp = vd.motionDetectedFlag \n\t\t\t \n\t\t\tprint(\"Heart Rate:{:.4f} Breath Rate:{:.4f} #:{:d}  {}\".format(gv.hr,gv.br,vs.frameNumber, ct-pt))\n\t\t\tif gv.hr >= 0 and gv.hr<=500:\n\t\t\t\thr_tmp = gv.hr\n\t\t\tif gv.br >= 0 and gv.br<=500:\n\t\t\t\tbr_tmp = gv.br\n\t\t\tout_hr.write( \"%.5s \" % str((\"%.5f\" % hr_tmp)) )\n\t\t\tout_br.write( \"%.5s \" % str((\"%.5f\" % br_tmp)) )\n\t\t\tout_ch.write( \"%.5s \" % str((\"%.5f\" % ch_tmp)) )\n\t\t\tout_mv.write( \"%.5s \" % str((\"%.5f\" % mv_tmp)) )\n\t\t\tif num == 500:\n\t\t\t\tout_br.close()\n\t\t\t\tout_hr.close()\n\t\t\t\tout_ch.close()\n\t\t\t\tout_mv.close()\n\t\t\t\tnum = 0\n\t\t\telse:\n\t\t\t\tnum = num + 1\n\n\n\t\t\t#print(\"Filter OUT:{0:.4f}\".format(vd.outputFilterHeartOut))\n\t\t\t'''\n\t\t\tprint(\"EST FFT:{0:.4f}\".format(vd.heartRateEst_FFT))\n\t\t\tprint(\"EST FFT 4Hz:{0:.4f}\".format(vd.heartRateEst_FFT_4Hz))\n\t\t\tprint(\"EST FFT xCorr:{0:.4f}\".format(vd.heartRateEst_FFT_4Hz))\n\t\t\tprint(\"Confi Heart Out:{0:.4f}\".format(vd.confidenceMetricHeartOut))\n\t\t\tprint(\"Confi Heart O 4Hz:{0:.4f}\".format(vd.confidenceMetricHeartOut_4Hz))\n\t\t\tprint(\"Confi Heart O xCorr:{0:.4f}\".format(vd.confidenceMetricHeartOut_xCorr))\n\t\t\t'''\n\t\t\t#print(\"RangeBuf Length:{:d}\".format(len(rangeBuf)))\n\t\t\t#print(rangeBuf)\n\tprint(\"END\")\ntime = int(input(\"please input how many times you want to catch: \"))\nuartGetTLVdata(\"VitalSign\", time)\n\n","sub_path":"VSD/get_mv.py","file_name":"get_mv.py","file_ext":"py","file_size_in_byte":2780,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"184365393","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport torch\nfrom torch.autograd import Function\n\n\nclass AddDiag(Function):\n    def forward(self, input, diag):\n        if diag.numel() != 1:\n            raise RuntimeError(\"Input must be a single-element tensor\")\n        val = diag.item()\n\n        diag_mat = input.new(input.size(-2)).fill_(1).diag()\n        if input.ndimension() == 3:\n            diag_mat = diag_mat.unsqueeze(0).expand_as(input)\n        return diag_mat.mul(val).add_(input)\n\n    def backward(self, grad_output):\n        input_grad = None\n        diag_grad = None\n\n        if self.needs_input_grad[0]:\n            input_grad = grad_output\n\n        if self.needs_input_grad[1]:\n            diag_grad = grad_output.new().resize_(1).zero_()\n            if grad_output.numel() == 1:\n                diag_grad.fill_(grad_output.item())\n            elif grad_output.ndimension() == 3:\n                batch_indices = grad_output.new(grad_output.size(0), 1).long()\n                torch.arange(0, grad_output.size(0), out=batch_indices[:, 0])\n                diag_indices = grad_output.new(grad_output.size(1), 1).long()\n                torch.arange(0, grad_output.size(1), out=diag_indices[:, 0])\n\n                batch_indices = batch_indices.repeat(1, grad_output.size(1)).view(-1)\n                diag_indices = diag_indices.repeat(grad_output.size(0), 1).view(-1)\n                vals = grad_output[batch_indices, diag_indices, diag_indices]\n\n                diag_grad.fill_(vals.sum())\n            else:\n                diag_grad.fill_(grad_output.trace())\n\n        return input_grad, diag_grad\n","sub_path":"gpytorch/functions/_add_diag.py","file_name":"_add_diag.py","file_ext":"py","file_size_in_byte":1714,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"72551329","text":"\"\"\"\nUsage:\n    cli.py  [-abehpsw] [-v | -q] [-d=<duration>] [-l=<layout>]\n            [-r=<directory> | -f=<file>]\n            [-o=<file>] [--output-format=<format>]\n            [--bg=<color> --fg=<color> --cc=<color> --wc=<color>]\n    cli.py  (--help | --version)\n\n\nOptions:\n    -a --all-correct-chars      Consider correct characters from both\n                                correct and wrong words.\n    -b --beep                   Alert the user when he makes a mistake.\n    --bg=<color>                Terminal background color. [default: 0]\n    --cc=<color>                Correct color. [default: 46]\n    --cpm                       Show characters per minute.\n    -d --duration=<duration>    Limit duration of the typing test to\n                                <duration>.\n    --dph                       Show depressions per hour.\n    -e --endless                Repeat test endlessly, if -r is\n                                specified randomizes every repetition.\n    -f --file=<file>            File to read the test from.\n    --fg=<color>                Foreground main color. [default: 7]\n    -h --hide                   Hide timer and dynamic wpm counter.\n    --help                      Show this screen.\n    -l --layout=<layout>        Keyboard layout for calculating\n                                keystrokes. [default: qwerty]\n    -n --normalize              Use keystrokes over characters.\n    -o --output=<file>          File to store output result in.\n    --output-format=<format>    File format to store output as; json,\n                                csv. [default: json]\n    -p --prevent-wrong          Accept up to 4 correct chars in a row,\n                                then block incorrect input. If -w is\n                                set accept only correct words.\n    -q --quiet                  Print no results.\n    -r --root-dir=<directory>   Root directory to randomize the test\n                                from. [default: tests/english/]\n    -s --shuffle-words          Shuffle words.\n    -v --verbose                Show a more extensive result.\n    --version                   Show version.\n    -w --word-by-word           Evaluate word by word instead of\n                                character by character.\n    --wc=<color>                Wrong color. [default: 196]\n\nShortcuts:\n    ^C / Ctrl+c                 End the test and get results now.\n    ^R / Ctrl+r                 Restart the same test.\n\"\"\"\n\nfrom docopt     import docopt\nfrom schema     import Schema, And, Or, Use, SchemaError\nfrom os         import path, walk\nfrom re         import split\nfrom cli.keytype import TypingTest\nfrom random     import choice, shuffle\nfrom textwrap   import wrap\nfrom time       import time\n\nimport curses\n\nlayouts  = ['colemak',  'dvorak', 'qwerty']\nformats  = ['csv', 'json']\n\n\ndef start():\n    args = docopt(__doc__, version='typetest 0.1')\n    schema = Schema({\n        '--all-correct-chars':      bool,#\n        '--beep':                   bool,#\n        '--endless':                bool,#\n        '--hide':                   bool,\n        '--help':                   bool,\n        '--prevent-wrong':          bool,#\n        '--quiet':                  bool,\n        '--shuffle-words':          bool,\n        '--verbose':                bool,\n        '--version':                bool,\n        '--word-by-word':           bool,#\n\n        '--bg': And(Use(int), lambda n: 0 <= n < 256,\n                error='<color> should be an integer between 0 and 256.'),\n        '--fg': And(Use(int), lambda n: 0 <= n < 256,\n                error='<color> should be an integer between 0 and 256.'),\n        '--cc': And(Use(int), lambda n: 0 <= n < 256,\n                error='<color> should be an integer between 0 and 256.'),\n        '--wc': And(Use(int), lambda n: 0 <= n < 256,\n                error='<color> should be an integer between 0 and 256.'),\n\n        '--duration':   Or(None, And(Use(int), lambda n: 0 < n,#\n                        error='<duration> should be an int larger than 0.')),\n\n        '--file':       Or(None, And(path.isfile,\n                        error='<file> should be an existing file.')),\n        '--output':     Or(None, And(path.exists,#\n                        error='<file> should refer to an existing path.')),\n        '--root-dir':   And(path.isdir,\n                        error='<directory> should be an existing directory.'),\n\n        '--output-format':  And(lambda f: f in formats,#\n                            error=f'<format> should be in {formats}.'),\n        '--layout':         And(lambda l: l in layouts,#\n                            error=f'<layout> must be in {layouts}.'),\n        })\n    try:\n        args = schema.validate(args)\n    except SchemaError as e:\n        exit(e)\n\n    if args['--file']:\n        with open(args['--file'], 'r') as f:\n            test_raw = f.read()\n    else:\n        try:\n            root, _, files = next(walk(args['--root-dir']))\n            test_path = choice(files)\n            with open(root+test_path, 'r') as f:\n                test_raw = f.read()\n        except IndexError as e:\n            exit(f\"No files in {args['--root-dir']}.\")\n\n    test_words = list(filter(None, split('[ \\n]+', test_raw)))\n    if args['--shuffle-words']:\n        shuffle(test_words)\n\n    test_text = ' '.join(test_words)\n    test = TypingTest(test_text)\n\n    stdscr = curses.initscr()\n    curses.start_color()\n    curses.noecho()\n    curses.cbreak()\n    stdscr.keypad(1)\n    curses.curs_set(0)\n                                                        # defaults\n    curses.init_pair(1, args['--fg'], args['--bg'])     # white on black\n    curses.init_pair(2, args['--bg'], args['--fg'])     # black on white\n    curses.init_pair(3, args['--wc'], args['--bg'])     # red   on black\n    curses.init_pair(4, args['--cc'], args['--bg'])     # green on black\n    curses.init_pair(5, args['--bg'], args['--wc'])     # black on red\n    curses.init_pair(6, args['--bg'], args['--cc'])     # black on green\n\n    basic_word_color           = curses.color_pair(1)\n    current_word_color         = curses.color_pair(2)\n    wrong_word_color           = curses.color_pair(3)\n    correct_word_color         = curses.color_pair(4)\n    current_wrong_word_color   = curses.color_pair(5)\n    current_correct_word_color = curses.color_pair(6)\n\n    maxy, maxx = stdscr.getmaxyx()\n\n    wordscr_height = 0 if args['--hide'] or maxx < 3 else 1\n\n    add_color = lambda word: [word, basic_word_color]\n    test_rows = [list(map(add_color, row)) for row in map(str.split, wrap(test_text, maxx-1))]\n\n    if args['--word-by-word']:\n        testscr_rows = min(maxy-wrdscr_height, 3)\n    else:\n        testscr_rows = min(maxy-wordscr_height, len(test_rows))\n\n    testscr = curses.newwin(testscr_rows,   maxx, 0, 0)\n    prompt  = curses.newwin(1,              5, testscr_rows, 0)\n    prompt.addstr('>>> ')\n    prompt.refresh()\n    wordscr = curses.newwin(1,              maxx, testscr_rows, 4)\n\n    word = ''\n    test_row_i = 0\n    test_word_i = 0\n    start_time = time()\n\n    try:\n        while True:\n            testscr.erase()\n\n            test_word = test_rows[test_row_i][test_word_i][0]\n\n            if word == test_word[:len(word)] and len(word) == len(test_word):\n                test_rows[test_row_i][test_word_i][1] = current_correct_word_color\n\n            elif word == test_word[:len(word)]:\n                test_rows[test_row_i][test_word_i][1] = current_word_color\n\n            else:\n                test_rows[test_row_i][test_word_i][1] = current_wrong_word_color\n\n            start_row_i = min(test_row_i, len(test_rows) - testscr_rows)\n            for i, row in enumerate(test_rows[start_row_i:start_row_i+testscr_rows]):\n                for j, (w, color) in enumerate(row):\n                    testscr.addstr(w, color)\n                    if j+1 != len(row):\n                        testscr.addstr(' ')\n                if i != testscr_rows-1:\n                    testscr.addstr('\\n')\n            testscr.refresh()\n\n            c = wordscr.getch()\n\n            if c == 127:\n                test.addch(chr(c))\n                word = word[:-1]\n                wordscr.addstr(\"\\b \\b\")\n\n            elif chr(c) in ' \\n':\n                wordscr.erase()\n\n                if not word:\n                    continue\n\n                test.addword(word)\n\n                if len(test.test_words) == len(test.text_words):\n                    break\n\n                test_word = test_rows[test_row_i][test_word_i][0]\n\n                if test_word == word:\n                    test_rows[test_row_i][test_word_i][1] = correct_word_color\n\n                else:\n                    test_rows[test_row_i][test_word_i][1] = wrong_word_color\n\n                word = ''\n\n                test_word_i += 1\n                if test_word_i == len(test_rows[test_row_i]):\n                    test_word_i = 0\n                    test_row_i += 1\n\n            else:\n                word += chr(c)\n                wordscr.addch(c)\n    except KeyboardInterrupt as e:\n        pass\n    except Exception as e:\n        curses.curs_set(1)\n        exit(e)\n\n    curses.curs_set(1)\n    curses.nocbreak()\n    stdscr.keypad(0);\n    curses.echo()\n    curses.endwin()\n\n    end_time = time()\n    duration = end_time - start_time\n\n    try:\n        test.submit(duration)\n    except TypeError as e:\n        exit()\n\n    if args['--verbose']:\n        print(f'accuracy:       {test.accuracy:.{0}f}%')\n        print(f'duration:       {test.duration:.{2}f} sec\\n')\n        print(f'correct words:  {len(test.correct_words)}')\n        print(f'correct chars:  {len(test.correct_chars)}\\n')\n        print(f'true speed:     {test.true_speed_wpm:.{0}f} wpm')\n        print(f'normalized:     {test.speed_wpm:.{0}f} wpm\\n')\n        print(f'true speed:     {test.true_speed_cpm:.{0}f} cpm')\n        print(f'normalized:     {test.speed_cpm:.{0}f} cpm\\n')\n        print(f'true speed:     {test.true_speed_dph:.{0}f} dph')\n        print(f'normalized:     {test.speed_dph:.{0}f} dph')\n\n    elif not args['--quiet']:\n        print(f'accuracy:       {test.accuracy:.{0}f}%')\n        print(f'duration:       {test.duration:.{2}f} sec')\n        print(f'true speed:     {test.true_speed_wpm:.{0}f} wpm')\n","sub_path":"cli/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":10213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"266768302","text":"import pandas as pd\nimport numpy as np\nfrom sklearn.preprocessing import StandardScaler, LabelEncoder\nfrom sklearn.model_selection import train_test_split, GridSearchCV\nfrom sklearn.feature_selection import SelectFromModel\nimport matplotlib.pyplot as plt\n# plt.rc(\"font\", size=14)\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.calibration import CalibratedClassifierCV\nfrom sklearn.svm import LinearSVC\nfrom sklearn import metrics\nfrom sklearn.metrics import precision_recall_fscore_support as score\nfrom statistics import mean\nimport xgboost as xgb\nfrom xgboost import plot_importance\nimport pickle\nfrom sklearn.utils import shuffle\nfrom imblearn.under_sampling import RandomUnderSampler, TomekLinks, ClusterCentroids\nfrom imblearn.over_sampling import RandomOverSampler, SMOTE\n\n# import seaborn as sns\n# sns.set(style=\"white\")\n# sns.set(style='whitegrid', color_codes=True)\n\ndf = pd.read_csv(\"E:\\\\Research Work\\\\Sentiment Analysis Project\\\\Dataset\\\\Full_Dataset\\\\dataset_06_12_19\\\\accept_multilabel_2.csv\",\n          sep=',', encoding='utf-8')\n\n# 'cmssw'\nproject_list = ['react', 'django', 'nixpkgs', 'scikit-learn', 'yii2', 'cdnjs', 'terraform', 'cmssw', 'salt', 'tensorflow', 'pandas',\n                'symfony', 'moby', 'rails', 'rust', 'kubernetes', 'angular.js', 'laravel', 'opencv',\n                ]\n\n\ndf = df[(df.Project_Name != 'githubschool') & (df.Project_Name != 'curriculum')]\n\nprint(df.shape)\n# print(list(df.columns))\n# print(df.Project_Name.unique())\n\n\nscoring = ['precision', 'recall', 'f1', 'roc_auc', 'accuracy']\n\n\ndef get_classifiers():\n    return {\n        # 'RandomForest': RandomForestClassifier(n_jobs=4, bootstrap=True, class_weight='balanced', n_estimators=500, max_depth = 15,\n        #                                        random_state=42, oob_score=True, min_samples_split=7, min_samples_leaf=3), #n_estimators=500, max_depth = 15,\n                                               # random_state=42, oob_score=True, min_samples_split=7, min_samples_leaf=3\n        # 'LinearSVC': LinearSVC(max_iter=2000),\n        # 'LogisticRegression': LogisticRegression(solver='lbfgs', multi_class='multinomial', max_iter=1200),\n        'XGBoost': xgb.XGBClassifier(**params),  # {'max_depth': 9, 'min_child_weight': 5}\n        # 'DT': DecisionTreeClassifier(max_depth=5), # max_depth=5\n        # 'NaiveBayes': GaussianNB(),\n        # 'KNN': KNeighborsClassifier(n_neighbors=7) # n_neighbors=7\n    }\n\n# print(RandomForestClassifier())\n\n\nparams = {\n    'objective': 'multi:softprob',\n    'num_class': 4,\n    'eta': 0.08,\n    'colsample_bytree': 0.886,\n    'min_child_weight': 1.1,\n    'max_depth': 9,\n    'subsample': 0.886,\n    'gamma': 0.1,\n    'lambda': 10,\n    'verbose_eval': True,\n    'eval_metric': 'auc',\n    'scale_pos_weight': 6,\n    'seed': 201703,\n    'missing': -1\n}\n\n\ndef encode_labels(df1, column_name):\n    encoder = LabelEncoder()\n    df1[column_name] = [str(label) for label in df1[column_name]]\n    encoder.fit(df1[column_name])\n    one_hot_vector = encoder.transform(df1[column_name])\n    return one_hot_vector\n\n\ndf['Language'] = encode_labels(df, 'Language')\ndf['Project_Domain'] = encode_labels(df, 'Project_Domain')\n\n#Creating the dependent variable class\nfactor = pd.factorize(df['label'])\ndf.label = factor[0]\ndefinitions = factor[1]\n# print(df.label.head())\nprint(definitions)\n\n# {0: 'directly_accepted', 1: 'accepted_response', 2: 'rejected_response', 3: 'directly_rejected'}\n\nproject_features = ['Project_Age', 'Team_Size', 'Stars', 'File_Touched_Average', 'Forks_Count', 'Watchers', 'Language',\n                    'Project_Domain', 'Contributor_Num', 'Comments_Per_Closed_PR', 'Additions_Per_Week', 'Deletions_Per_Week',\n                    'Merge_Latency', 'Comments_Per_Merged_PR', 'Churn_Average', 'Close_Latency', 'Project_Accept_Rate',\n                    'Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Workload', 'Commits_Average',\n                    'Open_Issues', 'PR_Time_Created_At', 'PR_Date_Closed_At', 'PR_Time_Closed_At', 'PR_Date_Created_At',\n                    'Project_Name', 'label']\nPR_features = ['Intra_Branch', 'Assignees_Count', 'Label_Count', 'Files_Changed', 'Contain_Fix_Bug', 'Wait_Time', 'Day',\n               'Additions', 'Deletions', 'Commits_PR', 'first_response_time', 'first_response',\n               'latency_after_first_response', 'conflict', 'X1_0', 'X1_1', 'X1_2', 'X1_3', 'X1_4', 'X1_5', 'X1_6', 'X1_7',\n               'X1_8', 'X1_9', 'PR_Latency', 'title_words_count', 'body_words_count','Point_To_IssueOrPR', 'PR_Time_Created_At',\n               'PR_Date_Closed_At', 'PR_Time_Closed_At', 'PR_Date_Created_At', 'Project_Name', 'label']\nintegrator_features = ['Participants_Count', 'Comments_Count', 'Last_Comment_Mention', 'Review_Comments_Count',\n               'line_comments_count', 'comments_reviews_words_count', 'PR_Time_Created_At', 'PR_Date_Closed_At',\n               'PR_Time_Closed_At', 'PR_Date_Created_At', 'Project_Name', 'label']\n\ncontributor_features = ['Followers', 'Closed_Num', 'Contributor', 'Public_Repos', 'Organization_Core_Member',\n                        'Contributions', 'User_Accept_Rate', 'Accept_Num', 'Closed_Num_Rate', 'Prev_PRs', 'Following',\n                        'PR_Time_Created_At', 'PR_Date_Closed_At', 'PR_Time_Closed_At', 'PR_Date_Created_At',\n                        'Project_Name', 'label'\n                        ]\n\n# user_features = ['Participants_Count', 'Comments_Count', 'Last_Comment_Mention']\n# issue_features = ['Point_To_IssueOrPR', 'Open_Issues']\n# other_features = ['Workload', 'Commits_Average']\n# integrator_features = ['Review_Comments_Count', 'line_comments_count', 'comments_reviews_words_count']\n\n# df = df[['Closed_Num_Rate', 'Label_Count', 'Comments_Count', 'Following', 'Stars', 'Contributions', 'Merge_Latency', 'Rebaseable',\n#           'Followers',  'Workload', 'Wednesday', 'PR_accept', 'Additions', 'Closed_Num', 'Public_Repos',\n#           'Deletions_Per_Week', 'Contributor', 'File_Touched_Average', 'Forks_Count', 'Organization_Core_Member',\n#           'Monday', 'Contain_Fix_Bug', 'Review_Comments_Count', 'Team_Size', 'Last_Comment_Mention', 'Sunday',\n#           'Thursday', 'Project_Age', 'Open_Issues', 'Intra_Branch', 'Saturday', 'Participants_Count',\n#           'Comments_Per_Closed_PR', 'Watchers', 'Project_Accept_Rate', 'Point_To_IssueOrPR', 'Accept_Num', 'Close_Latency',\n#           'Contributor_Num', 'Commits_Average', 'Assignees_Count', 'Friday', 'Commits_PR', 'Wait_Time', 'line_comments_count',\n#           'Prev_PRs', 'Comments_Per_Merged_PR', 'Files_Changed', 'Day', 'Churn_Average', 'Deletions', 'Language', 'Tuesday',\n#           'Mergeable_State', 'Additions_Per_Week', 'User_Accept_Rate', 'X1_0', 'X1_1', 'X1_2', 'X1_3', 'X1_4', 'X1_5', 'X1_6',\n#           'X1_7', 'X1_8', 'X1_9', 'PR_Latency', 'Project_Name', 'PR_Date_Created_At', 'PR_Time_Created_At', 'PR_Date_Closed_At',\n#           'PR_Time_Closed_At', 'first_response_time', 'first_response', 'latency_after_first_response', 'conflict',\n#           'title_words_count', 'body_words_count', 'comments_reviews_words_count',\n#           'Project_Domain', 'Project_Size', 'label']]\n\n# Selected Features\n# df['src_churn'] = df['Additions'] + df['Deletions']\n# df['num_comments'] = df['Review_Comments_Count'] + df['Comments_Count']\n\ndf = df[['Closed_Num_Rate', 'Label_Count','Following', 'Contributions', 'Merge_Latency', 'Followers', 'Comments_Count',\n         'Workload', 'Closed_Num', 'Public_Repos', 'Contributor', 'File_Touched_Average', 'Forks_Count', 'Additions',\n         'Project_Age', 'Open_Issues', 'Participants_Count', 'Comments_Per_Closed_PR',  'Review_Comments_Count', #'src_churn',\n         'Project_Accept_Rate', 'Accept_Num', 'Close_Latency', 'Commits_Average', 'Commits_PR', 'Wait_Time', #'num_comments',\n         'line_comments_count', 'Prev_PRs', 'Comments_Per_Merged_PR', 'Files_Changed', 'Churn_Average', 'Deletions',\n         'User_Accept_Rate', 'X1_0', 'X1_1', 'X1_2', 'X1_3', 'X1_4', 'X1_5', 'X1_6', 'X1_7', 'X1_8', 'first_response_time',\n         'latency_after_first_response', 'title_words_count', 'comments_reviews_words_count', 'PR_Time_Created_At', 'PR_Date_Closed_At',\n          'PR_Time_Closed_At', 'PR_Date_Created_At', 'Project_Name', 'label']]\n\n# df = df[integrator_features]\n\n# Previous work features\n# df = df[['src_churn', 'Commits_PR', 'Files_Changed', 'num_comments', 'Participants_Count', 'conflict', 'Team_Size',\n#          ]]\n\n\"\"\"\n    Total Train dataset size: (198076)\n    Balance of the dataset\n    Number of accepted pull requests (146407)\n    Number of unaccepted pull requests (51669)\n\n    Total Test dataset size: (22730)\n    Balance of the dataset\n    Number of accepted pull requests (18487)\n    Number of unaccepted pull requests (4243)\n\n\n\"\"\"\n\ndf = df.sort_values(by=['PR_Date_Closed_At', 'PR_Time_Closed_At'], ascending=True)\n\ntarget = 'label'\nstart_date = '2017-09-01'\nend_date = '2018-02-28'\n\nX_test = df.loc[(df['PR_Date_Created_At'] >= start_date) & (df['PR_Date_Created_At'] <= end_date)]\ny_test = X_test[target]\nX_train = df.loc[(df['PR_Date_Created_At'] < start_date)]\nX_train = X_train\ny_train = X_train[target]\n#\npredictors = [x for x in df.columns if x not in [target, 'PR_accept', 'PR_Date_Created_At', 'PR_Time_Created_At', 'PR_Date_Closed_At',\n                                                 'PR_Time_Closed_At', 'Project_Name']]\n\npredictors_with_label = [x for x in df.columns if x not in ['PR_accept', 'PR_Date_Created_At', 'PR_Time_Created_At', 'Project_Name']]\n\n# X = df\n# y = df[target]\n#\n# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10, stratify=y, random_state=143)\n\n\n# def get_under_sampled_dataset():\n#     # Class count\n#     count_class_1, count_class_0 = X_train[target].value_counts()\n#\n#     print(f'0 count: {count_class_0}, 1 count: {count_class_1}')\n#     # Divide by class\n#     df_class_0 = X_train[X_train[target] == 0]\n#     df_class_1 = X_train[X_train[target] == 1]\n#     print(f'Negative samples shape: {df_class_0.shape}')\n#     print(f'Positive samples shape: {df_class_1.shape}')\n#\n#     df_class_1_under = df_class_1.sample(count_class_0)\n#     df_test_under = pd.concat([df_class_1_under, df_class_0], axis=0)\n#\n#     print('Random under-sampling:')\n#     print(df_test_under[target].value_counts())\n#     shuffle(df_test_under)\n#     y_df = df_test_under[target]\n#\n#     return df_test_under, y_df\n\n\ndef get_over_sampled_dataset(df_):\n    # Class count\n    # count_class_1, count_class_0 = X_train[target].value_counts()\n\n    # print('0 count: {0}, 1 count: {1}'.format(count_class_0, count_class_1))\n    # Divide by class\n    df_accept_response = df_[df_[target] == 0]\n    df_directly_rejected = df_[df_[target] == 3]\n    df_rest_of_PRs = df_[df_[target] != 3]\n    print(df_rest_of_PRs.shape)\n    print('Directly accepted samples shape: {}'.format(df_accept_response.shape))\n    print('Directly rejected samples shape: {}'.format(df_directly_rejected.shape))\n    df_directly_rejected_over = df_directly_rejected.sample(df_accept_response.shape[0], replace=True)\n    print(df_directly_rejected_over.shape)\n    df_test_over = pd.concat([df_directly_rejected_over, df_rest_of_PRs], axis=0)\n\n    # df_reject_response = df_test_over[df_test_over[target] == 2]\n    # df_rest_of_PRs = df_test_over[df_test_over[target] != 2]\n    # df_rejected_response_over = df_reject_response.sample(df_accept_response.shape[0], replace=True)\n    # print(df_rejected_response_over.shape)\n    # df_test_over = pd.concat([df_rejected_response_over, df_rest_of_PRs], axis=0)\n    #\n    # df_directly_accepted = df_test_over[df_test_over[target] == 0]\n    # df_rest_of_PRs = df_test_over[df_test_over[target] != 0]\n    # df_rejected_response_over = df_directly_accepted.sample(df_accept_response.shape[0], replace=True)\n    # print(df_rejected_response_over.shape)\n    # df_test_over = pd.concat([df_rejected_response_over, df_rest_of_PRs], axis=0)\n\n    print('Random over-sampling:')\n    print(df_test_over[target].value_counts())\n    # df_test_over = df_test_over.sort_values(by=['PR_Date_Closed_At', 'PR_Time_Closed_At'], ascending=True)\n    df_test_over = shuffle(df_test_over)\n    y_df = df_test_over[target]\n    print(df_test_over.shape)\n\n    return df_test_over, y_df\n\n# def get_under_sampled_dataset_imblearn():\n#     # Divide by class\n#     df_class_0 = X_train[X_train[target] == 0]\n#     df_class_1 = X_train[X_train[target] == 1]\n#     print(f'Negative samples shape: {df_class_0.shape}')\n#     print(f'Positive samples shape: {df_class_1.shape}')\n#\n#     rus = RandomUnderSampler(return_indices=True)\n#     X_rus, y_rus, id_rus = rus.fit_sample(X_train, y_train)\n#     shuffle(X_rus)\n#     print('Removed indexes:', id_rus)\n#     y_rus = X_rus[target]\n#     return X_rus, y_rus\n\n# def get_over_sampled_dataset_imblearn():\n#     # Divide by class\n#     df_class_0 = X_train[X_train[target] == 0]\n#     df_class_1 = X_train[X_train[target] == 1]\n#     print(f'Negative samples shape: {df_class_0.shape}')\n#     print(f'Positive samples shape: {df_class_1.shape}')\n#\n#     ros = RandomOverSampler()\n#     X_ros, y_ros = ros.fit_sample(X_train, y_train)\n#\n#     print(X_ros.shape[0] - X_train.shape[0], 'new random picked points')\n#     shuffle(X_ros)\n#     X_ros = pd.DataFrame(X_ros, columns=list(df.columns))\n#     print(X_ros.shape)\n#     y_ros = X_ros[target]\n#\n#     print(y_ros.shape)\n#\n#     return X_ros, y_ros\n\n# def get_tomeklinks_under_sampled_dataset():\n#     tl = TomekLinks(return_indices=True, ratio='majority')\n#     X_tl, y_tl, id_tl = tl.fit_sample(X_train, y_train)\n#\n#     print('Removed indexes:', id_tl)\n#     shuffle(X_tl)\n#     y_tl = X_tl[target]\n#\n#     return X_tl, y_tl\n\n\n# def get_clusterCentriods_under_sampled_dataset(X,y):\n#     cc = ClusterCentroids(ratio={0: 10})\n#     X_cc, y_cc = cc.fit_sample(X, y)\n\ndef get_smote_under_sampled_dataset(X, y):\n\n    smote = SMOTE(random_state=42)\n    X_sm, y_sm = smote.fit_sample(X, y)\n    print(X_sm.shape)\n    print(y_sm.shape)\n    X_sm = pd.DataFrame(X_sm, columns=predictors)\n    #y_sm = pd.DataFrame(y_sm, columns=['label'])\n\n    return X_sm, y_sm\n\n\n\n# X_train, y_train = get_over_sampled_dataset()\n\n# X_train, y_train = get_smote_under_sampled_dataset(X_train[predictors], y_train)\n\n# X_train, y_train = get_over_sampled_dataset(X_train[predictors_with_label])\n\n\n\nX_train = X_train[predictors]\nX_test = X_test[predictors]\n\n\nprint(\"Total Train dataset size: {}\".format(X_train[predictors].shape))\nprint(\"Total Test dataset size: {}\".format(X_test[predictors].shape))\n\n# print(df.columns)\n\n# Scale the training dataset: StandardScaler\ndef scale_data_standardscaler(df_):\n    scaler_train =StandardScaler()\n    df_scaled = scaler_train.fit_transform(np.array(df_).astype('float64'))\n    df_scaled = pd.DataFrame(df_scaled, columns=predictors)\n\n    return df_scaled\n\n# X_train_scaled = scale_data_standardscaler(X_train[predictors])\n# X_test_scaled = scale_data_standardscaler(X_test[predictors])\n\ndef extract_metric_from_report(report):\n    report = list(report.split(\"\\n\"))\n    report = report[-2].split(' ')\n    # print(report)\n    mylist = []\n    for i in range(len(report)):\n        if report[i] != '':\n            mylist.append(report[i])\n\n    return mylist[3], mylist[4], mylist[5]\n\ndef extract_each_class_metric_from_report(report):\n    report = list(report.split(\"\\n\"))\n\n    mydict2 = {}\n    mydict = {}\n    index = 0\n    for line in range(len(report)):\n        if report[line] != '':\n            values_list = report[line].split(' ')\n            mydict[index] = values_list\n            index+=1\n    count=0\n    for value in mydict:\n        mylist = []\n        if value != 0:\n            for item in range(len(mydict[value])):\n                if mydict[value][item] != '':\n                    mylist.append(mydict[value][item])\n            mydict2[count] = mylist\n            count+=1\n    return mydict2[0], mydict2[1], mydict2[2], mydict2[3]\n\n\n\ndef train_XGB_model(clf, x_train, y_train, x_test):\n    clf = clf.fit(x_train, y_train, verbose=11)\n    # dtrain = clf.DMatrix(x_train, label=y_train)\n    # dtest = clf.DMatrix(x_test, label=y_test)\n    # clf = clf.train(params, dtrain, num_boost_round=20)\n    # clf.dump_model('xgb_raw.txt')\n\n    # Save the model\n    # with open('Saved_Models/integrator.pickle.dat', 'wb') as f:\n    #     pickle.dump(clf, f)\n\n    # Load the model\n    # with open('response_xgb_16.pickle.dat', 'rb') as f:\n    #     load_xgb = pickle.load(f)\n\n    fig, ax = plt.subplots(figsize=(10, 20))\n    plot_importance(clf, importance_type='gain', ax=ax)\n    plt.savefig('plots/XGB_f_gain_2.png', bbox_inches='tight')\n    feature_importances = pd.DataFrame(clf.feature_importances_, index=X_train.columns)\n    # feature_importances.sort_values(ascending=True)\n    feature_importances.to_csv('Saved_Models/features_selected.csv', encoding='utf-8', index=True)\n    # print('XGBoost important features')\n    # print(feature_importances)\n\n    y_pred_train = clf.predict(x_train)\n    y_predprob_train = clf.predict_proba(x_train)[:, 1]\n\n    y_pred = clf.predict(x_test)\n    y_predprob = clf.predict_proba(x_test)[:, 1]\n\n    return y_pred_train, y_predprob_train, y_pred, y_predprob\n\n\ndef train_SVM_model(clf, x_train, y_train, x_test):\n    clf.fit(x_train, y_train)\n    svm = CalibratedClassifierCV(base_estimator=clf, cv='prefit')\n    svm.fit(x_train, y_train)\n    # train\n    y_pred_train = svm.predict(x_train)\n    y_predprob_train = svm.predict_proba(x_train)[:, 1]\n    # test\n    y_pred = svm.predict(x_test)\n    y_predprob = svm.predict_proba(x_test)[:, 1]\n\n    return y_pred_train, y_predprob_train, y_pred, y_predprob\n\ndef train_RF_LR_model(clf, x_train, y_train, x_test):\n    clf.fit(x_train, y_train)\n    # train\n    y_pred_train = clf.predict(x_train)\n    y_predprob_train = clf.predict_proba(x_train)[:, 1]\n    # test\n    y_pred = clf.predict(x_test)\n    y_predprob = clf.predict_proba(x_test)[:, 1]\n\n    return y_pred_train, y_predprob_train, y_pred, y_predprob\n\ndef train_XGB_model_feature_selection(clf, x_train, y_train, x_test):\n    model = clf.fit(x_train, y_train, verbose=11)\n\n    y_pred = model.predict(x_test)\n    # y_predprob = model.predict_proba(x_test)[:, 1]\n    predictions = [round(value) for value in y_pred]\n    accuracy = metrics.accuracy_score(y_test, predictions)\n    print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))\n\n    thresholds = sorted(model.feature_importances_, reverse=True)\n    for thresh in thresholds:\n        # select features using threshold\n        selection = SelectFromModel(model, threshold=thresh, prefit=True)\n        select_X_train = selection.transform(x_train)\n        # train model\n        selection_model = xgb.XGBClassifier(**params)\n        selection_model.fit(select_X_train, y_train)\n        # eval model\n        select_X_test = selection.transform(x_test)\n        y_pred = selection_model.predict(select_X_test)\n        predictions = [round(value) for value in y_pred]\n        accuracy = metrics.accuracy_score(y_test, predictions)\n        print(\"Thresh=%.3f, n=%d, Accuracy: %.2f%%\" % (thresh, select_X_train.shape[1], accuracy * 100.0))\n\n\ndef start_train_models():\n    # results = pd.DataFrame(columns=['Model', 'Precision', 'Recall', 'F-measure', 'Test_Accuracy', 'Train_Accuracy'])\n    results = pd.DataFrame(columns=['Model', 'P_AAR', 'P_DA','P_DR','P_RAR', 'R_AAR', 'R_DA','R_DR','R_RAR',\n                                    'f1_AAR', 'f1_DA','f1_DR','f1_RAR', 'Avg_Pre', 'Avg_Recall', 'Avg_f1_Score',\n                                    'Test_Accuracy', 'Train_Accuracy'])\n    classifiers = get_classifiers()\n\n    X_train_scaled = scale_data_standardscaler(X_train[predictors])\n    X_test_scaled = scale_data_standardscaler(X_test[predictors])\n\n\n    for name, value in classifiers.items():\n            clf = value\n            print('Classifier: ', name)\n            if name == 'XGBoost':\n                y_pred_train, y_predprob_train, y_pred_test, y_predprob = train_XGB_model(clf, X_train, y_train, X_test)\n                # train_XGB_model_feature_selection(clf, X_train[predictors], y_train, X_test[predictors])\n            elif name == 'LinearSVC':\n                y_pred_train, y_predprob_train, y_pred_test, y_predprob = train_SVM_model(clf, X_train_scaled, y_train, X_test_scaled)\n            elif name == 'LogisticRegression':\n                y_pred_train, y_predprob_train, y_pred_test, y_predprob = train_RF_LR_model(clf, X_train_scaled, y_train, X_test_scaled)\n            else:\n                y_pred_train, y_predprob_train, y_pred_test, y_predprob = train_RF_LR_model(clf, X_train, y_train, X_test)\n\n            # Print model report:\n            print(\"\\nModel Report\")\n            print(\"Train Accuracy : %.4g\" % metrics.accuracy_score(y_train, y_pred_train))\n            print(\"Test Accuracy : %.4g\" % metrics.accuracy_score(y_test, y_pred_test))\n            print('Train error: {:.3f}'.format(1 - metrics.accuracy_score(y_train, y_pred_train)))\n            print('Test error: {:.3f}'.format(1 - metrics.accuracy_score(y_test, y_pred_test)))\n\n            test_accuracy = metrics.accuracy_score(y_test, y_pred_test)\n            train_accuracy = metrics.accuracy_score(y_train, y_pred_train)\n\n            precision, recall, fscore, support = score(y_test, y_pred_test)\n\n            reversefactor = dict(zip(range(4), definitions))\n            y_test_1 = np.vectorize(reversefactor.get)(y_test)\n            y_pred = np.vectorize(reversefactor.get)(y_pred_test)\n\n            print(pd.crosstab(y_test_1, y_pred, rownames=['Actual PRs'], colnames=['Predicted PRs']))\n\n            print(metrics.classification_report(y_test_1, y_pred, digits=4))\n\n            precision_avg, recall_avg, fscore_avg = extract_metric_from_report(\n                metrics.classification_report(y_test_1, y_pred, digits=4))\n            # AAR, DA, DR, RAR = extract_each_class_metric_from_report(metrics.classification_report(y_test_1, y_pred, digits=4))\n\n            results = results.append(\n                {'Model': name,\n                 'P_AAR': precision[1], 'P_DA': precision[0],'P_DR': precision[3],'P_RAR': precision[2], 'R_AAR': recall[1],\n                 'R_DA': recall[0], 'R_DR': recall[3],'R_RAR': recall[2], 'f1_AAR': fscore[1], 'f1_DA': fscore[0],'f1_DR': fscore[3],\n                 'f1_RAR': fscore[2],\n                 'Avg_Pre': precision_avg, 'Avg_Recall': recall_avg, 'Avg_f1_Score': fscore_avg,\n                 'Test_Accuracy': test_accuracy,\n                 'Train_Accuracy': train_accuracy},\n                ignore_index=True)\n\n\n            # results = results.append(\n            #     {'Model': name,\n            #      'Precision': precision,\n            #      'Recall': recall,\n            #      'F-measure': fscore,\n            #      'Test_Accuracy': test_accuracy,\n            #      'Train_Accuracy': train_accuracy},\n            #     ignore_index=True)\n\n    results.to_csv('Results/results_integrator_f_2.csv', sep=',', encoding='utf-8', index=False)\n\nstart_train_models()\n\n\ndef start_10_fold_validation(df_):\n    # results = pd.DataFrame(\n    #     columns=['Model', 'Precision', 'Recall', 'F-measure', 'Test_Accuracy', 'Train_Accuracy'])\n    results = pd.DataFrame(columns=['Model', 'P_AAR', 'P_DA', 'P_DR', 'P_RAR', 'R_AAR', 'R_DA', 'R_DR', 'R_RAR',\n                                    'f1_AAR', 'f1_DA', 'f1_DR', 'f1_RAR', 'Avg_Pre', 'Avg_Recall', 'Avg_f1_Score',\n                                    'Test_Accuracy', 'Train_Accuracy'])\n    df = df_.sort_values(by=['PR_Date_Closed_At', 'PR_Time_Closed_At'], ascending=True)\n    df_split = np.array_split(df, 11)\n    print(df.shape)\n    for index in range(len(df_split) - 1):\n        train = pd.DataFrame()\n        for i in range(index + 1):\n            train = train.append(df_split[i])\n\n        # print(f\"Train dataset shape: {train.shape}\")\n        test = df_split[index + 1]\n        # print(f\"Test dataset shape: {test.shape}\")\n\n        X_train = train\n        y_train = X_train[target]\n        X_test = test\n        y_test = X_test[target]\n\n        X_train = X_train[predictors]\n        X_test = X_test[predictors]\n\n        X_train_scaled = scale_data_standardscaler(X_train)\n        X_test_scaled = scale_data_standardscaler(X_test)\n\n\n        classifiers = get_classifiers()\n\n        for name, value in classifiers.items():\n            clf = value\n            print('Classifer: ', name)\n            if name == 'XGBoost':\n                y_pred_train, y_predprob_train, y_pred, y_predprob = train_XGB_model(clf, X_train, y_train,\n                                                                                     X_test)\n            elif name == 'LinearSVC':\n                y_pred_train, y_predprob_train, y_pred, y_predprob = train_SVM_model(clf, X_train_scaled, y_train,\n                                                                                     X_test_scaled)\n            elif name == 'LogisticRegression':\n                y_pred_train, y_predprob_train, y_pred, y_predprob = train_RF_LR_model(clf, X_train_scaled, y_train,\n                                                                                       X_test_scaled)\n            else:\n                y_pred_train, y_predprob_train, y_pred, y_predprob = train_RF_LR_model(clf, X_train,\n                                                                                       y_train, X_test)\n\n            # Print model report:\n            print(\"\\nModel Report\")\n            print(\"Train Accuracy : %.4g\" % metrics.accuracy_score(y_train, y_pred_train))\n            print(\"Test Accuracy : %.4g\" % metrics.accuracy_score(y_test, y_pred))\n            print('Train error: {:.3f}'.format(1 - metrics.accuracy_score(y_train, y_pred_train)))\n            print('Test error: {:.3f}'.format(1 - metrics.accuracy_score(y_test, y_pred)))\n\n\n            test_accuracy = metrics.accuracy_score(y_test, y_pred)\n            train_accuracy = metrics.accuracy_score(y_train, y_pred_train)\n\n            reversefactor = dict(zip(range(4), definitions))\n            y_test_1 = np.vectorize(reversefactor.get)(y_test)\n            y_pred_test = np.vectorize(reversefactor.get)(y_pred)\n\n            print(pd.crosstab(y_test_1, y_pred_test, rownames=['Actual PRs'], colnames=['Predicted PRs']))\n\n            print(metrics.classification_report(y_test_1, y_pred_test, digits=4))\n\n            precision, recall, fscore = extract_metric_from_report(metrics.classification_report(y_test_1, y_pred_test, digits=4))\n            AAR, DA, DR, RAR = extract_each_class_metric_from_report(\n                metrics.classification_report(y_test_1, y_pred_test, digits=4))\n\n            results = results.append(\n                {'Model': name,\n                 'P_AAR': AAR[1], 'P_DA': DA[1], 'P_DR': DR[1], 'P_RAR': RAR[1], 'R_AAR': AAR[2], 'R_DA': DA[2],\n                 'R_DR': DR[2], 'R_RAR': RAR[2], 'f1_AAR': AAR[3], 'f1_DA': DA[3], 'f1_DR': DR[3], 'f1_RAR': RAR[3],\n                 'Avg_Pre': precision, 'Avg_Recall': recall, 'Avg_f1_Score': fscore,\n                 'Test_Accuracy': test_accuracy,\n                 'Train_Accuracy': train_accuracy},\n                ignore_index=True)\n\n            # results = results.append(\n            #     {'Model': name,\n            #      'Precision': precision,\n            #      'Recall': recall,\n            #      'F-measure': fscore,\n            #      'Test_Accuracy': test_accuracy,\n            #      'Train_Accuracy': train_accuracy},\n            #     ignore_index=True)\n\n    # avg_result = pd.DataFrame(columns=['Model', 'Precision', 'Recall', 'F-measure', 'Test_Accuracy', 'Train_Accuracy'])\n    # for name, value in classifiers.items():\n    #     model_result = results.loc[results.Model == name]\n    #     avg_result = avg_result.append(\n    #         {'Model': name,\n    #          'Precision': model_result['Precision'].mean(),\n    #          'Recall': model_result['Recall'].mean(),\n    #          'F-measure': model_result['F-measure'].mean(),\n    #          'Test_Accuracy': model_result['Test_Accuracy'].mean(),\n    #          'Train_Accuracy': model_result['Train_Accuracy'].mean()},\n    #         ignore_index=True)\n    # avg_result.to_csv('Results/results_10_fold_1.csv', sep=',', encoding='utf-8', index=False)\n    results.to_csv('Results/results_10_fold_5.csv', sep=',', encoding='utf-8', index=False)\n    calcuate_average_of_10_folds_1(results)\n\n\n# start_10_fold_validation(df)\n\n\ndef start_each_project_model(df):\n    results = pd.DataFrame(columns=['Model', 'Project', 'P_AAR', 'P_DA', 'P_DR', 'P_RAR', 'R_AAR', 'R_DA', 'R_DR', 'R_RAR',\n                                    'f1_AAR', 'f1_DA', 'f1_DR', 'f1_RAR', 'Avg_Pre', 'Avg_Recall', 'Avg_f1_Score',\n                                    'Test_Accuracy', 'Train_Accuracy'])\n    classifiers = get_classifiers()\n\n    for project in project_list:\n        df_project = df.loc[df.Project_Name == project]\n        print('Project {} is under processing'.format(project))\n\n        X_test = df_project.loc[(df_project['PR_Date_Created_At'] >= start_date) & (df_project['PR_Date_Created_At'] <= end_date)]\n        y_test = X_test[target]\n        X_train = df_project.loc[(df_project['PR_Date_Created_At'] < start_date)]\n        y_train = X_train[target]\n\n        print(\"Total Train dataset size: {}\".format(X_train[predictors].shape))\n        print(\"Total Test dataset size: {}\".format(X_test[predictors].shape))\n        X_train, y_train = get_smote_under_sampled_dataset(X_train[predictors], y_train)\n        X_train = X_train[predictors]\n        X_test = X_test[predictors]\n        X_train_scaled = scale_data_standardscaler(X_train[predictors])\n        X_test_scaled = scale_data_standardscaler(X_test[predictors])\n\n        for name, value in classifiers.items():\n            clf = value\n            print('Classifier: ', name)\n            if name == 'XGBoost':\n                y_pred_train, y_predprob_train, y_pred_test, y_predprob = train_XGB_model(clf, X_train, y_train, X_test)\n                # train_XGB_model_feature_selection(clf, X_train[predictors], y_train, X_test[predictors])\n            elif name == 'LinearSVC':\n                y_pred_train, y_predprob_train, y_pred_test, y_predprob = train_SVM_model(clf, X_train_scaled, y_train,\n                                                                                          X_test_scaled)\n            elif name == 'LogisticRegression':\n                y_pred_train, y_predprob_train, y_pred_test, y_predprob = train_RF_LR_model(clf, X_train_scaled,\n                                                                                            y_train, X_test_scaled)\n            else:\n                y_pred_train, y_predprob_train, y_pred_test, y_predprob = train_RF_LR_model(clf, X_train, y_train,\n                                                                                            X_test)\n\n            # Print model report:\n            print(\"\\nModel Report\")\n            print(\"Train Accuracy : %.4g\" % metrics.accuracy_score(y_train, y_pred_train))\n            print(\"Test Accuracy : %.4g\" % metrics.accuracy_score(y_test, y_pred_test))\n            print('Train error: {:.3f}'.format(1 - metrics.accuracy_score(y_train, y_pred_train)))\n            print('Test error: {:.3f}'.format(1 - metrics.accuracy_score(y_test, y_pred_test)))\n\n            test_accuracy = metrics.accuracy_score(y_test, y_pred_test)\n            train_accuracy = metrics.accuracy_score(y_train, y_pred_train)\n\n            precision, recall, fscore, support = score(y_test, y_pred_test)\n\n            reversefactor = dict(zip(range(4), definitions))\n            y_test_1 = np.vectorize(reversefactor.get)(y_test)\n            y_pred = np.vectorize(reversefactor.get)(y_pred_test)\n\n            print(pd.crosstab(y_test_1, y_pred, rownames=['Actual PRs'], colnames=['Predicted PRs']))\n\n            print(metrics.classification_report(y_test_1, y_pred, digits=4))\n\n            precision_avg, recall_avg, fscore_avg = extract_metric_from_report(\n                metrics.classification_report(y_test_1, y_pred, digits=4))\n            # AAR, DA, DR, RAR = extract_each_class_metric_from_report(metrics.classification_report(y_test_1, y_pred, digits=4))\n            try:\n                results = results.append(\n                    {'Model': name, 'Project': project,\n                     'P_AAR': precision[1], 'P_DA': precision[0], 'P_DR': precision[3], 'P_RAR': precision[2],\n                     'R_AAR': recall[1],\n                     'R_DA': recall[0], 'R_DR': recall[3], 'R_RAR': recall[2], 'f1_AAR': fscore[1], 'f1_DA': fscore[0],\n                     'f1_DR': fscore[3],\n                     'f1_RAR': fscore[2],\n                     'Avg_Pre': precision_avg, 'Avg_Recall': recall_avg, 'Avg_f1_Score': fscore_avg,\n                     'Test_Accuracy': test_accuracy,\n                     'Train_Accuracy': train_accuracy},\n                    ignore_index=True)\n            except IndexError:\n                continue\n\n    # results.to_csv('Results/results_projects_default.csv', sep=',', encoding='utf-8', index=False)\n\n\n# start_each_project_model(df)\n\nparameters = {\n    # 'RandomForest': {'max_depth': range(3, 15, 1),\n    #                  'n_estimators': [200, 300, 400, 500], #'max_features': [5, 10, 20, 25, 30],\n                        #'min_samples_leaf': [1, 2, 3, 4, 5]\n                     #},\n    # 'LinearSVC': {'loss': ['hinge', 'squared_hinge'], 'C': [0.001, 0.01, 0.1, 1, 10]},\n    # 'LogisticRegression': {'C': [0.001, 0.01, 0.1, 1, 10]},\n    'XGBoost': {'max_depth':range(3,10,1), 'min_child_weight':range(1,6,1),\n                #'learning_rate':[i/100.0 for i in range(1,10)],\n                # 'subsample':[i/10.0 for i in range(6,10)], 'colsample_bytree':[i/10.0 for i in range(6,10)],\n                # 'reg_alpha':[1e-5, 1e-2, 0.1, 1, 100]\n                } # max_depth, min_child_weight , gamma, subsample these parameters are used to control overfitting\n}\n\n\ndef model_optimizer():\n    classifiers = get_classifiers()\n    df_PO = pd.DataFrame(columns=['Classifier','Best_Score', 'Best_Params'])\n    for name, value in classifiers.items():\n        clf = value\n        print('Classifer: ', name)\n\n        grid = GridSearchCV(estimator=clf, param_grid=parameters[name], cv=3, scoring='roc_auc', verbose=3)\n        grid_result = grid.fit(X_train_scaled, y_train)\n\n        clf = grid_result.best_estimator_\n        clf.fit(X_train[predictors], y_train)\n\n        y_pred_train = clf.predict(X_train[predictors])\n        y_pred = clf.predict(X_test[predictors])\n        y_predprob = clf.predict_proba(X_test[predictors])[:, 1]\n\n        print(\"\\nModel Report\")\n        print(\"Train Accuracy : %.4g\" % metrics.accuracy_score(y_train, y_pred_train))\n        print(\"Test Accuracy : %.4g\" % metrics.accuracy_score(y_test, y_pred))\n        print('Train error: {:.3f}'.format(1 - metrics.accuracy_score(y_train, y_pred_train)))\n        print('Test error: {:.3f}'.format(1 - metrics.accuracy_score(y_test, y_pred)))\n        print(\"AUC Score : %f\" % metrics.roc_auc_score(y_test, y_predprob))\n        print(\"Recall : %f\" % metrics.recall_score(y_test, y_pred))\n        print(\"Precision : %f\" % metrics.precision_score(y_test, y_pred))\n        print(\"F-measure : %f\" % metrics.f1_score(y_test, y_pred))\n        c_matrix = metrics.confusion_matrix(y_test, y_pred)\n        print('========Confusion Matrix==========')\n        print(\"          Rejected    Accepted\")\n        print('Rejected     {}      {}'.format(c_matrix[0][0], c_matrix[0][1]))\n        print('Accepted     {}      {}'.format(c_matrix[1][0], c_matrix[1][1]))\n\n\n\n        # print results\n        # print(f'Best Accuracy for {grid_result.best_score_:.4} using {grid_result.best_params_}')\n        means = grid_result.cv_results_['mean_test_score']\n        stds = grid_result.cv_results_['std_test_score']\n        params = grid_result.cv_results_['params']\n        for mean, stdev, param in zip(means, stds, params):\n            # print(f'mean={mean:.4}, std={stdev:.4} using {param}')\n            print()\n\n        df_PO = df_PO.append({'Classifier':name, 'Best_Score': grid.best_score_, 'Best_Params': grid.best_params_}, ignore_index=True)\n    df_PO.to_csv('Results/Optimization/RF_4.csv', sep=',', encoding='utf-8', index=False)\n\n    #Best Accuracy for 0.8538 using {'max_depth': 15, 'min_samples_leaf': 1, 'n_estimators': 400} RF\n    # Best Accuracy for 0.7692 using {'eta': 0.01, 'max_depth': 9, 'min_child_weight': 1}\n    # Best Accuracy for 0.791 using {'learning_rate': 0.09}\n    # Best Accuracy for 0.9071 using {'max_features': 'sqrt', 'min_samples_leaf': 10, 'n_estimators': 500} RF\n    # Best Accuracy for 0.9083 using {'max_features': 'auto', 'min_samples_leaf': 2, 'n_estimators': 500} RF\n    # Best Accuracy for 0.9111 using {'max_features': 30, 'min_samples_leaf': 4, 'n_estimators': 500}\n\n\ndef model_optimier_2():\n    for para in range(10, 17, 1):\n        print(\"Max_depth value {}\".format(para))\n        # clf = RandomForestClassifier(n_jobs=4, bootstrap=False, class_weight='balanced', max_depth=17,\n        #                              n_estimators=para, min_samples_leaf=3, min_samples_split=7,\n        #                              max_features='sqrt', random_state=42)\n        clf = xgb.XGBClassifier(max_depth=para)\n\n        clf.fit(X_train, y_train)\n        y_pred = clf.predict(X_test)\n        # y_predprob = clf.predict_proba(X_test)[:, 1]\n\n        y_pred_train = clf.predict(X_train)\n        # y_predprob_train = clf.predict_proba(X_train)[:, 1]\n\n        reversefactor = dict(zip(range(4), definitions))\n        y_test_1 = np.vectorize(reversefactor.get)(y_test)\n        y_pred_test = np.vectorize(reversefactor.get)(y_pred)\n\n        print(metrics.classification_report(y_test_1, y_pred_test, digits=4))\n\n        print(\"\\nModel Report\")\n        print(\"Train Accuracy : %.4g\" % metrics.accuracy_score(y_train, y_pred_train))\n        print('Train error: {:.3f}'.format(1 - metrics.accuracy_score(y_train, y_pred_train)))\n        print(\"Test Accuracy : %.4g\" % metrics.accuracy_score(y_test, y_pred))\n        print('Test error: {:.3f}'.format(1 - metrics.accuracy_score(y_test, y_pred)))\n        # print(\"AUC Score : %f\" % metrics.roc_auc_score(y_test, y_predprob))\n        # print(\"Recall : %f\" % metrics.recall_score(y_test, y_pred))\n        # print(\"Precision : %f\" % metrics.precision_score(y_test, y_pred))\n        # print(\"F-measure : %f\" % metrics.f1_score(y_test, y_pred))\n        # c_matrix = metrics.confusion_matrix(y_test, y_pred)\n        # print('========Confusion Matrix==========')\n        # print(\"          Rejected    Accepted\")\n        # print('Rejected     {}      {}'.format(c_matrix[0][0], c_matrix[0][1]))\n        # print('Accepted     {}      {}'.format(c_matrix[1][0], c_matrix[1][1]))\n\n\n\n\n# model_optimizer()\n\n# model_optimier_2()\n\n\n\ndef feature_selection_LR():\n\n    from sklearn.feature_selection import RFE\n\n    rfe_selector = RFE(estimator=RandomForestClassifier(), n_features_to_select=30, step=5, verbose=5)\n    rfe_selector.fit(X_train_scaled, y_train)\n\n    y_pred = rfe_selector.predict(X_test_scaled)\n    y_predprob = rfe_selector.predict_proba(X_test_scaled)[:, 1]\n\n\n    rfe_support = rfe_selector.get_support()\n    rfe_feature = X_train[predictors].loc[:,rfe_support].columns.tolist()\n    print(str(len(rfe_feature)), 'selected features')\n    print('RFE features')\n    print(rfe_feature)\n    # Print model report:\n    print(\"\\nModel Report\")\n    #print(\"Train Accuracy : %.4g\" % metrics.accuracy_score(y_train, y_pred_train))\n    print(\"Test Accuracy : %.4g\" % metrics.accuracy_score(y_test, y_pred))\n    #print('Train error: {:.3f}'.format(1 - metrics.accuracy_score(y_train, y_pred_train)))\n    print('Test error: {:.3f}'.format(1 - metrics.accuracy_score(y_test, y_pred)))\n    print(\"AUC Score : %f\" % metrics.roc_auc_score(y_test, y_predprob))\n    print(\"Recall : %f\" % metrics.recall_score(y_test, y_pred))\n    print(\"Precision : %f\" % metrics.precision_score(y_test, y_pred))\n    print(\"F-measure : %f\" % metrics.f1_score(y_test, y_pred))\n    c_matrix = metrics.confusion_matrix(y_test, y_pred)\n    print('========Confusion Matrix==========')\n    print(\"          Rejected    Accepted\")\n    print('Rejected     {}      {}'.format(c_matrix[0][0], c_matrix[0][1]))\n    print('Accepted     {}      {}'.format(c_matrix[1][0], c_matrix[1][1]))\n\n\n# feature_selection_LR()\n\n\ndef calcuate_average_of_10_folds(df):\n    # df = pd.read_csv('Results/results_10_fold_3.csv')\n    avg_result = pd.DataFrame(columns=['Model', 'Precision', 'Recall', 'F-measure', 'Test_Accuracy', 'Train_Accuracy'])\n    classifiers = get_classifiers()\n    for name, value in classifiers.items():\n        model_result = df.loc[df.Model == name]\n        avg_result = avg_result.append(\n            {'Model': name,\n             'Precision': model_result['Precision'].mean(),\n             'Recall': model_result['Recall'].mean(),\n             'F-measure': model_result['F-measure'].mean(),\n             'Test_Accuracy': model_result['Test_Accuracy'].mean(),\n             'Train_Accuracy': model_result['Train_Accuracy'].mean()},\n            ignore_index=True)\n    avg_result.to_csv('Results/results_10_fold_avg_3.csv', sep=',', encoding='utf-8', index=False)\n\n\ndef calcuate_average_of_10_folds_1():\n    df = pd.read_csv('Results/results_10_fold_5.csv')\n    avg_result = pd.DataFrame(columns=['Model', 'P_AAR', 'P_DA', 'P_DR', 'P_RAR', 'R_AAR', 'R_DA', 'R_DR', 'R_RAR',\n                                    'f1_AAR', 'f1_DA', 'f1_DR', 'f1_RAR', 'Avg_Pre', 'Avg_Recall', 'Avg_f1_Score',\n                                    'Test_Accuracy', 'Train_Accuracy'])\n    classifiers = get_classifiers()\n    for name, value in classifiers.items():\n        model_result = df.loc[df.Model == name]\n        avg_result = avg_result.append(\n            {'Model': name,\n             'P_AAR': model_result['P_AAR'].mean(), 'P_DA': model_result['P_DA'].mean(), 'P_DR': model_result['P_DR'].mean(),\n             'P_RAR': model_result['P_RAR'].mean(), 'R_AAR': model_result['R_AAR'].mean(), 'R_DA': model_result['R_DA'].mean(),\n             'R_DR': model_result['R_DR'].mean(), 'R_RAR': model_result['R_RAR'].mean(), 'f1_AAR': model_result['f1_AAR'].mean(),\n             'f1_DA': model_result['f1_DA'].mean(), 'f1_DR': model_result['f1_DR'].mean(), 'f1_RAR': model_result['f1_RAR'].mean(),\n             'Avg_Pre': model_result['Avg_Pre'].mean(), 'Avg_Recall': model_result['Avg_Recall'].mean(),\n             'Avg_f1_Score': model_result['Avg_f1_Score'].mean(),\n             'Test_Accuracy': model_result['Test_Accuracy'].mean(),\n             'Train_Accuracy': model_result['Train_Accuracy'].mean()},\n            ignore_index=True)\n    avg_result.to_csv('Results/results_10_fold_avg_4.csv', sep=',', encoding='utf-8', index=False)\n\n\n# calcuate_average_of_10_folds_1()","sub_path":"Models/accept.py","file_name":"accept.py","file_ext":"py","file_size_in_byte":42888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"623775262","text":"from absl import flags\nfrom absl import app\nimport json\nimport os\nfrom datetime import datetime\n\n# Assign flags\nFLAGS = flags.FLAGS\nflags.DEFINE_string(\"source_dir\", \"Raw_Slides\", \"Path to Raw slide files (e.g. *.svs, *.mrxs).\")\nflags.DEFINE_string(\"dzi_dir\", \"Slide_Dzi\", \"Path for saving DZI files.\")\nflags.DEFINE_string(\"tile_dir\",\"Slide_Tile\", \"Path for saving tile files.\")\nflags.DEFINE_string(\"logs_dir\", \"logs/\", \"Path for saving log files.\")\nflags.DEFINE_boolean('ano', False, 'Anonymize names of files. Should be used if names contain identifier like HN or AN.')\nflags.DEFINE_integer('tile_size', 299, 'Tile size in pixels.', lower_bound=1, upper_bound =8192)\nflags.DEFINE_integer('overlap', 0, 'Tile overlap in pixels.', lower_bound=0, upper_bound =8192)\nflags.DEFINE_enum(\"mag\", \"10\",[\"5\",\"10\",\"20\",\"40\"], \"Magnification levels to be saved (default = 10).\")\nflags.DEFINE_integer('jobs', 6, 'Parallel threading for tiling process (defult = 6)')\nflags.DEFINE_integer(\"bg_cut\",50,\"If percent of background in a tile >= 'bg_cut' (in %), the tile will be labelled as a background (default = 50%).\")\nflags.DEFINE_string(\"model_json\", \"Models/EfficientNetB4_fulltrain/model.json\", \"Path to json file containing the structure of the model to be tested\")\nflags.DEFINE_string(\"chkpt\", \"Models/EfficientNetB4_fulltrain/weights-improvement-007-0.828.ckpt\", \"Path to saved weight checkpoint for transfer learning (Imagenet weights if not specified)\")\nflags.DEFINE_string(\"tfrec_dir\", \"Slide_TFRecords\", \"Path for saving TFRecord files\")\nflags.DEFINE_integer(\"batch_size\",1,\"Batch size (default = 1)\")\nflags.DEFINE_string(\"pred_dir\", \"Slide_Prediction\", \"Path for saving prediction files\")\nflags.DEFINE_string(\"heat_dir\", \"Slide_Heatmap\", \"Path for saving heatmap files\")\nflags.DEFINE_string('cut_off', \"0.5\", 'Mid-point of prediction scores for classification (default = 0.5)')\nflags.DEFINE_integer(\"slices\",10,\"Number of colors to be included in the heatmap gradient scale (default = 10)\")\nflags.DEFINE_integer(\"width\",1000,\"Width in pixels of a resized heatmap using in the web application (default = 1000)\")\nflags.DEFINE_string(\"complete_dir\", \"Processed_Slides\", \"Path for moving processed slide files to\")\nflags.DEFINE_integer('step', 0, 'Step to start. 0 = Run all steps. 1 = Start at creating TFRecords from tile files. 2 = Start at creating prediction files. 3 = Start at creating heatmaps. (default = 0)', lower_bound=0, upper_bound =3)\n\ndef rename_vips_tile(source_dir,dzi_dir,tile_dir,log_dir,tile_size,overlap,mag,jobs,bg_cut,ano):\n\tsvs_list = [name for r,d,f in os.walk(source_dir) for name in f if \"svs\" in name]\n\tassert len(svs_list) > 0, \"No slide files in the source directory!\"\n\tlog_file = os.path.join(log_dir,\"tmplog.json\")\n\tfailed_log_file = os.path.join(log_dir,\"tmpfailed_log_VIPS_Tiles.json\")\n\tcmd = \"python3 PyScripts/RenameAndVIPSAndTiles.py --source_dir=%s --dzi_dir=%s --tile_dir=%s --log_file=%s --failed_log_file=%s --tile_size=%d --overlap=%d --mag=%s --jobs=%d --bg_cut=%d\" \n\tcmd = cmd % (source_dir,dzi_dir,tile_dir,log_file,failed_log_file,tile_size,overlap,mag,jobs,bg_cut)\n\tif ano:\n\t\tcmd += \" --ano\"\n\treturn cmd\n\ndef read_log(log_dir):\n\tlog_file = os.path.join(log_dir,\"tmplog.json\")\n\t#print(log_file)\n\tdata = []\n\twith open(log_file,'r') as f:\n\t\tfor line in f:\n\t\t\tdata.append(json.loads(line))\n\treturn data\n\ndef write_log(log_file,logs):\n\twith open(log_file,'w') as outfile:\n\t\tfor dic in logs:\n\t\t\tjson.dump(dic, outfile)\n\t\t\toutfile.write(\"\\n\")\n\ndef tfrec(data_dict,tfrec_dir,jobs,log_dir,tile_dir):\n\tlog_file = os.path.join(log_dir,\"tmplog.json\")\n\tfailed_log_file = os.path.join(log_dir,\"tmpfailed_log_tfrec.json\")\n\tcmd = \"singularity exec --nv tensorflow-gpu.sif python3 PyScripts/build_TF_slides_no_label.py --source_dir=%s --out_dir=%s --jobs=%d\"\n\tnew_data_dict = []\n\tfailed_data_dict = []\n\tfor item in data_dict:\n\t\tif os.path.exists(item['tile']): \n\t\t\tprint(cmd % (item['tile'],tfrec_dir,jobs))\n\t\t\tos.system(cmd % (item['tile'],tfrec_dir,jobs))\n\t\t\titem[\"tfrec\"] = item['tile'].replace(tile_dir.rstrip(\"/\"),tfrec_dir.rstrip(\"/\"))+\".tfrecord\"\n\t\t\titem[\"status\"] = \"TFRecord generated\" \n\t\t\tnew_data_dict.append(item)\n\t\telse:\n\t\t\titem[\"status\"] = \"Tile files does not exist\" \n\t\t\tfailed_data_dict.append(item)\n\n\twrite_log(log_file,new_data_dict)\n\tif len(failed_data_dict) > 0:\n\t\twrite_log(failed_log_file,failed_data_dict)\n\treturn new_data_dict\n\ndef predict(data_dict,pred_dir,model_json,chkpt,batch_size, tile_dir,log_dir):\n\tlog_file = os.path.join(log_dir,\"tmplog.json\")\n\tfailed_log_file = os.path.join(log_dir,\"tmpfailed_log_pred.json\")\n\tcmd = \"singularity exec --nv tensorflow-gpu.sif python3 PyScripts/eval_mod_single_file.py --source_file=%s --out_dir=%s --model_json=%s --chkpt=%s --batch_size=%d\"\n\tnew_data_dict = []\n\tfailed_data_dict = []\n\tfor item in data_dict:\n\t\tif os.path.exists(item['tfrec']):\n\t\t\tprint(cmd % (item['tfrec'],pred_dir,model_json,chkpt,batch_size))\n\t\t\tos.system(cmd % (item['tfrec'],pred_dir,model_json,chkpt,batch_size))\n\t\t\titem['pred'] = item['tile'].replace(tile_dir.rstrip(\"/\"),pred_dir.rstrip(\"/\"))+\".txt\"\n\t\t\titem[\"status\"] = \"Prediction generated\" \n\t\t\tnew_data_dict.append(item)\n\t\telse:\n\t\t\titem[\"status\"] = \"TFRecord does not exist\" \n\t\t\tfailed_data_dict.append(item)\n\twrite_log(log_file,new_data_dict)\n\tif len(failed_data_dict) > 0:\n\t\twrite_log(failed_log_file,failed_data_dict)\n\treturn new_data_dict\n\ndef heatmap(data_dict,heat_dir,mag,cut_off,slices,width, tile_dir,log_dir):\n\tlog_file = os.path.join(log_dir,\"tmplog.json\")\n\tfailed_log_file = os.path.join(log_dir,\"tmpfailed_log_heat.json\")\n\tcmd = \"singularity exec r-tidyverse-imager.sif Rscript RScripts/HeatMap.R -i %s -v %s -o %s -m %s -c %s -s %d -w %d\"\n\tnew_data_dict = []\n\tfailed_data_dict = []\n\tfor item in data_dict:\n\t\tif os.path.exists(item['pred']) and os.path.exists(item['vips']):\n\t\t\tprint(cmd % (item['pred'],item['vips'],heat_dir,mag,cut_off,slices,width))\n\t\t\tos.system(cmd % (item['pred'],item['vips'],heat_dir,mag,cut_off,slices,width))\n\t\t\titem['heatmap'] = item['tile'].replace(tile_dir.rstrip(\"/\"),os.path.join(heat_dir,\"Original\").rstrip(\"/\"))+\".jpeg\"\n\t\t\titem[\"status\"] = \"Heatmap generated\" \n\t\t\tnew_data_dict.append(item)\n\t\telif os.path.exists(item['pred']):\n\t\t\titem[\"status\"] = \"DZI does not exist\" \n\t\t\tfailed_data_dict.append(item)\n\t\telse:\n\t\t\titem[\"status\"] = \"Prediction file does not exist\" \n\t\t\tfailed_data_dict.append(item)\t\n\twrite_log(log_file,new_data_dict)\n\tif len(failed_data_dict) > 0:\n\t\twrite_log(failed_log_file,failed_data_dict)\n\treturn(new_data_dict)\n\ndef complete(data_dict, source_dir,complete_dir):\n\tnew_data_dict = []\n\tfor item in data_dict:\n\t\tdest = item['original'].replace(source_dir.rstrip(\"/\"),complete_dir.rstrip(\"/\"))\n\t\tpath,file = os.path.split(dest)\n\t\tif not os.path.isdir(path):\n\t\t\tos.mkdir(path)\n\t\tos.replace(item['original'], dest)\n\n\t\topath, ofile = os.path.split(item['original'])\n\t\todir = [name for name in os.listdir(opath) if \"svs\" in name]\n\t\tif len(odir) == 0:\n\t\t\tos.rmdir(opath) \n\n\t\titem[\"current\"] = dest\n\t\titem['status'] = \"Complete\"\n\t\tnew_data_dict.append(item)\n\treturn(new_data_dict)\n\ndef write_log_final(data_dict,log_dir):\n\tlog_file = os.path.join(log_dir,\"log_\"+ str(datetime.timestamp(datetime.now()))+\".json\")\n\twith open(log_file,'w') as outfile:\n\t\tfor dic in data_dict:\n\t\t\tjson.dump(dic, outfile)\n\t\t\toutfile.write(\"\\n\")\ndef main(argv):\n  del argv  # Unused.\n  #Step 1 Rename and Vips and Tile\n  if FLAGS.step == 0:\n  \tos.system(rename_vips_tile(FLAGS.source_dir,\n  \t\tFLAGS.dzi_dir,\n  \t\tFLAGS.tile_dir,\n  \t\tFLAGS.logs_dir,\n  \t\tFLAGS.tile_size,\n  \t\tFLAGS.overlap,\n  \t\tFLAGS.mag,\n  \t\tFLAGS.jobs,\n  \t\tFLAGS.bg_cut,\n  \t\tFLAGS.ano))\n  \n  #Step2 Read log file\n  data_dict = read_log(FLAGS.logs_dir)\n  \n  if FLAGS.step <= 1:\n  \t\n  \t#Step3 Create TFRecords\n  \tprint(\"Creating TFRecords.\")\n  \tdata_dict = tfrec(data_dict,FLAGS.tfrec_dir,FLAGS.jobs,FLAGS.logs_dir,FLAGS.tile_dir)\n  \n  if FLAGS.step <= 2:\n  \t#Step4 Make a prediction\n  \tprint(\"Predicting from a pre-trained model.\")\n  \tdata_dict = predict(data_dict,FLAGS.pred_dir,FLAGS.model_json,FLAGS.chkpt,FLAGS.batch_size, FLAGS.tile_dir,FLAGS.logs_dir)\n  \n\n  #Step5 Make a heatmap\n  print(\"Generating heatmaps from predictions.\")\n  data_dict = heatmap(data_dict,FLAGS.heat_dir,FLAGS.mag,FLAGS.cut_off,FLAGS.slices,FLAGS.width, FLAGS.tile_dir,FLAGS.logs_dir)\n  \n  #Step6 Move processed slide file to elsewhere\n  print(\"Finishing...\")\n  data_dict = complete(data_dict, FLAGS.source_dir,FLAGS.complete_dir)\n  #Step7 Write the final log file.\n  write_log_final(data_dict,FLAGS.logs_dir)\n  print(\"Finished.\")\n\nif __name__ == '__main__':\n  app.run(main)\n","sub_path":"ai_flow_step.py","file_name":"ai_flow_step.py","file_ext":"py","file_size_in_byte":8576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"152376587","text":"# @author: Angel Shiwakoti\n# @createdOn: 01/31/2017\n# This class created the required number of textfields based\n# on the number of characters in a given word.\n\nimport pygtk\npygtk.require('2.0')\nimport gtk\nimport re\n\nclass TypeBoxVert(gtk.VBox):\n    def __init__(self):\n\n        # Call init method for superclass\n        gtk.VBox.__init__(self)\n\n        # Array to hold textFields\n        self.textFields = []\n\n        # vBox holds all the textFields\n        self.vbox = gtk.VBox(False, 0)\n        self.vbox.show()\n\n\n    # This method creates the necessary amount of textfields\n    # based on the number of characters in current word\n    def createTextBoxes(self, size):\n        self.numBoxes = size\n        requiredCount = self.resetContent()\n        for index in range(requiredCount):\n            self.textFields.append(gtk.Entry(max=1))\n\n        # disable the highlighting of the boxes\n        for box in self.textFields:\n            box.set_can_focus(False)\n        self.showBoxes()\n\n\n    # This method resets the text for existing textfields.\n    # If there are more than required textfields previously\n    # created, this method removes them.\n    def resetContent(self):\n        if (len(self.textFields) == 0):\n            return self.numBoxes\n\n        # If we have more text boxes than we need\n        elif (len(self.textFields) > self.numBoxes):\n            removeCount = len(self.textFields) - self.numBoxes\n            for index in range(removeCount):\n                self.vbox.remove(self.textFields.pop())\n\n        self.resetTextBox()\n        return self.numBoxes - len(self.textFields)\n\n\n    # This method shows all the existing textfields.\n    def showBoxes(self):\n        for textField in self.textFields:\n            textField.set_size_request(50,30)\n            self.vbox.pack_start(textField, False, False, 0)\n            textField.set_property(\"editable\", False)\n            textField.set_alignment(xalign = 0.5)\n            textField.show()\n\n    def addWord(self, word):\n        self.resetTextBox()\n        for index in range(len(word)):\n            self.textFields[index].set_text(word[index])\n\n    # Reset the text of all textbox to \"\"\n    def resetTextBox(self):\n        for textField in self.textFields:\n            textField.set_text(\"\")\n\n    def main(self):\n        # All PyGTK applications must have a gtk.main(). Control ends here\n        # and waits for an event to occur (like a key press or mouse event).\n        gtk.main()\n\n# If the program is run directly or passed as an argument to the python\n# interpreter then create a HelloWorld instance and show it\nif __name__ == \"__main__\":\n    typeBox = TypeBox()\n    typeBox.main()\n","sub_path":"TypeBoxVert.py","file_name":"TypeBoxVert.py","file_ext":"py","file_size_in_byte":2658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"438792991","text":"import time\nfrom collections import Counter\n\nimport json\nimport logging\nimport requests\nfrom django.core.management.base import BaseCommand\n\nfrom users.models import User\nfrom vendor_data import models\nfrom wikidata_edit.upload import upload_movie\n\nlogger = logging.getLogger(__name__)\n\nSPARQL = \"\"\"SELECT ?item WHERE {{?item wdt:{pid} '{value}'.}}\"\"\"\n\n\ndef simple_query(query):\n    r = requests.get(\"https://query.wikidata.org/sparql\", {\n        'query': query,\n        'format': 'json',\n    })\n    r.raise_for_status()\n    return r.json()['results']['bindings']\n\n\ndef lookup_wikidata_id_by_prop(pid, value):\n    q = SPARQL.format(pid=pid, value=value.replace(\"'\", \"\\\\'\"))\n    resp = simple_query(q)\n    return resp[0]['item']['value'].split(\"/\")[-1] if resp else None\n\n\ndef lookup_wikidata_id_by_imdb_id(imdb_id):\n    return lookup_wikidata_id_by_prop(\"P345\", imdb_id)\n\n\nclass Command(BaseCommand):\n    help = \"Matches vendor and IFX data\"\n\n    def add_arguments(self, parser):\n        parser.add_argument('email')\n\n    def handle(self, email, *args, **options):\n        u = User.objects.get(email=email)\n        oauth1 = u.get_wikidata_oauth1()\n\n        c = Counter()\n        qs = models.VendorItem.objects.filter(\n            type=models.VendorItem.Type.MOVIE,\n            object_id__isnull=False,\n            imdb_id__isnull=False,\n        )\n        total = qs.count()\n        print(f\"checking {total} items\")\n\n        try:\n            for i, o in enumerate(qs):  # type: (int, models.VendorItem)\n                if o.entity.wikidata_id is not None:\n                    c['exists'] += 1\n                    continue\n\n                logger.info(\n                    f\"Processing {i + 1}/{total}, #{o.id}={o.entity.id}: {o.title_he}\")\n\n                wikidata_id = lookup_wikidata_id_by_prop(o.vendor.pid, o.vid)\n                if wikidata_id:\n                    logger.info(f\"FOUND BY VENDOR ID: {wikidata_id}\")\n                    o.set_wikidata_id(wikidata_id)\n                    c['found_by_vid'] += 1\n                    continue\n\n                if o.imdb_id:\n                    wikidata_id = lookup_wikidata_id_by_imdb_id(o.imdb_id)\n                    if wikidata_id:\n                        logger.info(f\"FOUND BY IMDB_ID! {wikidata_id}\")\n                        o.set_wikidata_id(wikidata_id)\n                        continue\n\n                logger.info(\"Uploading...\")\n\n                labels = {}\n                if o.title_he:\n                    labels['he'] = o.title_he\n                if o.title_en:\n                    labels['en'] = o.title_en\n                elif o.entity.title_en:\n                    labels['en'] = o.entity.title_en\n                descs = {}\n                descs[\n                    'en'] = f\"{o.year} Israeli film\" if o.year else \"Israeli film\"\n                descs[\n                    'he'] = f\"סרט ישראלי משנת {o.year}\" if o.year else \"סרט ישראלי\"\n\n                ids = {o.vendor.pid: o.vid}\n                if o.imdb_id:\n                    ids['P345'] = o.imdb_id\n\n                aliases = None\n                if o.title_he != o.entity.title_he:\n                    aliases = [['he', o.entity.title_he]]\n                    logger.info(f\"NEED ALIAS: {wikidata_id}\")\n                    c['need alias'] += 1\n\n                resp = upload_movie(oauth1, labels, descs, ids, o.year,\n                                    o.duration, aliases)\n                if resp.get('success') != 1:\n                    msg = \"Error uploading data to wikidata\\n\"\n                    logger.error(msg + json.dumps(resp, indent=2))\n                    c['error'] += 1\n                    time.sleep(1)\n                else:\n                    id = resp['entity']['id']\n                    print(f\"New Wikidata ID {id} for movie #{o.entity.id}.\")\n                    o.set_wikidata_id(id)\n                    c['added'] += 1\n                    time.sleep(0.1)\n\n        finally:\n            for k, v in sorted(c.items()):\n                print(k, v)\n","sub_path":"vendor_data/management/commands/upload_vendor_data.py","file_name":"upload_vendor_data.py","file_ext":"py","file_size_in_byte":4015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"621329533","text":"import logging\r\n\r\n\r\nclass ContractOpenOrdersHelper:\r\n    \"\"\"\r\n    返回参数\r\n    参数名称\t            是否必须\t类型\t描述\t取值范围\r\n    status\t            true\tstring\t请求处理结果\r\n    <list>(属性名称: data)\r\n    <orders>\r\n    symbol\t            true\tstring\t品种代码\r\n    contract_type\t    true\tstring\t合约类型\t当周:\"this_week\", 次周:\"next_week\", 当季:\"quarter\",次季:\"next_quarter\"\r\n    contract_code\t    true\tstring\t合约代码\t\"BTC180914\" ...\r\n    volume\t            true\tdecimal\t委托数量\r\n    price\t            true\tdecimal\t委托价格\r\n    order_price_type\ttrue\tstring\t订单报价类型 \"limit\":限价 \"opponent\":对手价 \"post_only\":只做maker单,post only下单只受用户持仓数量限制\r\n    order_type\t        true\tint\t订单类型，1:报单 、 2:撤单 、 3:强平、4:交割\r\n    direction\t        true\tstring\t\"buy\":买 \"sell\":卖\r\n    offset\t            true\tstring\t\"open\":开 \"close\":平\r\n    lever_rate\t        true\tint\t杠杆倍数\t1\\5\\10\\20\r\n    order_id\t        true\tbigint\t订单ID\r\n    order_id_str\t    true\tstring\tString订单ID\r\n    client_order_id\t    true\tlong\t客户订单ID\r\n    created_at\t        true\tlong\t订单创建时间\r\n    trade_volume\t    true\tdecimal\t成交数量\r\n    trade_turnover\t    true\tdecimal\t成交总金额\r\n    fee\t                true\tdecimal\t手续费\r\n    trade_avg_price\t    true\tdecimal\t成交均价\r\n    margin_frozen\t    true\tdecimal\t冻结保证金\r\n    profit\t            true\tdecimal\t收益\r\n    status\t            true\tint\t订单状态\t(3未成交 4部分成交 5部分成交已撤单 6全部成交 7已撤单)\r\n    order_source\t    true\tstring\t订单来源\r\n    fee_asset\t        true\tstring\t手续费币种\t（\"BTC\",\"ETH\"...）\r\n    </orders>\r\n    total_page\t        true\tint\t总页数\r\n    current_page\t    true\tint\t当前页\r\n    total_size\t        true\tint\t总条数\r\n    </list>\r\n    ts\t                true\tlong\t时间戳\r\n    \"\"\"\r\n\r\n    @staticmethod\r\n    def log_all_orders(direction, ret):\r\n        for i in range(0, ret['data']['total_size']):\r\n            if ret['data']['orders'][i]['direction'] == direction:\r\n                logging.info(\"contract_openorders: direction={0} symbol={1}.{2} volume={3} price={4}\".format(\r\n                    ret['data']['orders'][i]['direction'],\r\n                    ret['data']['orders'][i]['symbol'],\r\n                    ret['data']['orders'][i]['contract_type'],\r\n                    ret['data']['orders'][i]['volume'],\r\n                    ret['data']['orders'][i]['price']))\r\n\r\n    @staticmethod\r\n    def get_orders_count(direction, offset, ret):\r\n        count = 0\r\n        for i in range(0, ret['data']['total_size']):\r\n            if ret['data']['orders'][i]['direction'] == direction and ret['data']['orders'][i]['offset'] == offset:\r\n                count += ret['data']['orders'][i]['volume']\r\n        return count\r\n\r\n    @staticmethod\r\n    def get_price(direction, offset, ret):\r\n        data = []\r\n        for i in range(0, ret['data']['total_size']):\r\n            if ret['data']['orders'][i]['direction'] == direction and ret['data']['orders'][i]['offset'] == offset:\r\n                price = ret['data']['orders'][i]['price']\r\n                data.append(price)\r\n        return data","sub_path":"huobi/helpers/contract_openorders_helper.py","file_name":"contract_openorders_helper.py","file_ext":"py","file_size_in_byte":3263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"292858803","text":"from unittest2 import TestCase\nfrom mock import patch, Mock, sentinel\n\nfrom exam.helpers import intercept, rm_f, track, mock_import, call, effect\nfrom exam.decorators import fixture\n\nfrom describe import expect\n\n\n@patch('exam.helpers.shutil')\nclass TestRmrf(TestCase):\n\n    path = '/path/to/folder'\n\n    def test_calls_shutil_rmtreee(self, shutil):\n        rm_f(self.path)\n        shutil.rmtree.assert_called_once_with(self.path, ignore_errors=True)\n\n    @patch('exam.helpers.os')\n    def test_on_os_errors_calls_os_remove(self, os, shutil):\n        shutil.rmtree.side_effect = OSError\n        rm_f(self.path)\n        os.remove.assert_called_once_with(self.path)\n\n\nclass TestTrack(TestCase):\n\n    @fixture\n    def foo_mock(self):\n        return Mock()\n\n    @fixture\n    def bar_mock(self):\n        return Mock()\n\n    def test_makes_new_mock_and_attaches_each_kwarg_to_it(self):\n        tracker = track(foo=self.foo_mock, bar=self.bar_mock)\n        expect(tracker.foo).to == self.foo_mock\n        expect(tracker.bar).to == self.bar_mock\n\n\nclass TestMockImport(TestCase):\n\n    def test_is_a_context_manager_that_yields_patched_import(self):\n        with mock_import('foo') as mock_foo:\n            import foo\n            expect(foo).to == mock_foo\n\n    def test_mocks_import_for_packages(self):\n        with mock_import('foo.bar.baz') as mock_baz:\n            import foo.bar.baz\n            expect(foo.bar.baz).to == mock_baz\n\n    @mock_import('foo')\n    def test_can_be_used_as_a_decorator_too(self, mock_foo):\n        import foo\n        expect(foo).to == mock_foo\n\n    @mock_import('foo')\n    @mock_import('bar')\n    def test_stacked_adds_args_bottom_up(self, mock_bar, mock_foo):\n        import foo\n        import bar\n        expect(mock_bar).to == bar\n        expect(mock_foo).to == foo\n\n\nclass TestIntercept(TestCase):\n\n    class Example(object):\n        def method(self, positional, keyword):\n            return sentinel.METHOD_RESULT\n\n    def test_intercept(self):\n        ex = self.Example()\n\n        def counter(positional, keyword):\n            assert positional is sentinel.POSITIONAL_ARGUMENT\n            assert keyword is sentinel.KEYWORD_ARGUMENT\n            result = yield\n            assert result is sentinel.METHOD_RESULT\n            counter.calls += 1\n\n        counter.calls = 0\n\n        intercept(ex, 'method', counter)\n        expect(counter.calls).to == 0\n        assert ex.method(sentinel.POSITIONAL_ARGUMENT,\n            keyword=sentinel.KEYWORD_ARGUMENT) is sentinel.METHOD_RESULT\n        expect(counter.calls).to == 1\n\n        ex.method.unwrap()\n        assert ex.method(sentinel.POSITIONAL_ARGUMENT,\n            keyword=sentinel.KEYWORD_ARGUMENT) is sentinel.METHOD_RESULT\n        expect(counter.calls).to == 1\n\n\nclass TestEffect(TestCase):\n\n    def test_creates_callable_mapped_to_config_dict(self):\n        config = [\n            (call(1), 2),\n            (call('a'), 3),\n            (call(1, b=2), 4),\n            (call(c=3), 5)\n        ]\n        side_effecet = effect(*config)\n\n        expect(side_effecet(1)).to == 2\n        expect(side_effecet('a')).to == 3\n        expect(side_effecet(1, b=2)).to == 4\n        expect(side_effecet(c=3)).to == 5\n\n    def test_raises_type_error_when_called_with_unknown_args(self):\n        side_effect = effect((call(1), 5))\n        self.assertRaises(TypeError, side_effect, 'junk')\n\n    def test_can_be_used_with_mutable_data_structs(self):\n        side_effect = effect((call([1, 2, 3]), 'list'))\n        expect(side_effect([1, 2, 3])).to == 'list'\n","sub_path":"tests/test_helpers.py","file_name":"test_helpers.py","file_ext":"py","file_size_in_byte":3513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"356557492","text":"from unittest import mock\n\nfrom rest_framework import status\n\nfrom lego.apps.slack.utils import SlackException\nfrom lego.apps.users.models import User\nfrom lego.utils.test_utils import BaseAPITestCase\n\n\nclass SlackInviteTestCase(BaseAPITestCase):\n\n    fixtures = [\"test_abakus_groups.yaml\", \"test_users.yaml\"]\n\n    def setUp(self):\n        self.url = \"/api/v1/slack-invite/\"\n\n    def test_invite_no_auth(self):\n        response = self.client.post(self.url, {\"email\": \"test@test.com\"})\n        self.assertEquals(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n    @mock.patch(\n        \"lego.apps.slack.utils.SlackInvite._post\",\n        side_effect=SlackException(\"test_error\"),\n    )\n    def test_invite_user(self, mock_post):\n        user = User.objects.first()\n        self.client.force_login(user)\n        response = self.client.post(self.url, {\"email\": \"test@test.com\"})\n        self.assertEquals(response.json(), {\"detail\": \"test_error\"})\n        self.assertEquals(response.status_code, status.HTTP_400_BAD_REQUEST)\n","sub_path":"lego/apps/slack/tests/test_views.py","file_name":"test_views.py","file_ext":"py","file_size_in_byte":1025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"159147983","text":"# -*- coding: utf-8 -*-\n\"\"\"\nThis spider is a WorkdayJobsSub1 spider created on top of the WorkdayJobs\nscrapy crawl workdayjobs_sub1 -a mining_job_id=9999 -a iteration=1 -a extract=1 -a url=\"https://qbe.wd3.myworkdayjobs.com/QBE-Careers/jobs\"\n\nsample seed url:\nhttps://qbe.wd3.myworkdayjobs.com/QBE-Careers/jobs\n\"\"\"\n\nfrom json import loads as json_loads\nfrom brightcorp.items import BrightcorpItemLoader\nfrom brightcorp.processors import Prefix\nfrom scrapy.selector import Selector\nfrom brightcorp.spiders.workdayjobs import WorkdayJobs\n\n\nclass WorkdayJobsSub1(WorkdayJobs):\n\n    name = \"workdayjobs_sub1\"\n\n    def parse_job(self, response):\n        try:\n            api_data = json_loads(response.body)\n        except:\n            # response is not in json format\n            return\n\n        loader = BrightcorpItemLoader(response=response)\n        loader.add_value('url', response.url)\n        loader.add_value('title', response.meta['title'])\n        item_dict = {}\n        self.process_children(\n            api_data.get('body', {}).get('children', []), item_dict\n        )\n        loader.add_value('description', item_dict.get('description', ''))\n        loader.add_value('jobtype', item_dict.get('jobtype', ''))\n        loader.add_value(\n            'company_description', item_dict.get('company_description', '')\n        )\n        loader.add_value(\n            'referencenumber', item_dict.get('referencenumber', ''),\n            Prefix(\"%s-\" % self.name)\n        )\n        loader.add_value('location', item_dict.get('location', ''))\n\n        yield loader.load_item()\n\n\n    def process_children(self, children_list, output):\n        \"\"\"\n            check all inner lists having key as children\n            and find out required data\n        \"\"\"\n        for chlist in children_list:\n            if 'ecid' in chlist:\n                for mapkey in self.api_item_mappings:\n                    if mapkey in chlist['ecid']:\n                        value = []\n                        value = chlist.get('text', chlist.get('imageLabel', ''))\n                        output[self.api_item_mappings[mapkey]] = value\n            if 'children' in chlist:\n                # found another children, inside\n                self.process_children(chlist['children'], output)\n","sub_path":"brightcorp/brightcorp/spiders/workdayjobs_sub1.py","file_name":"workdayjobs_sub1.py","file_ext":"py","file_size_in_byte":2263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"580760306","text":"\"\"\"\nThis module contains the CycleExamples class, which finds hard examples\naccording to the paper at:\nhttp://faculty.ucmerced.edu/mhyang/papers/eccv16_feature_learning_supp.pdf\n\"\"\"\n\nimport random\nimport numpy as np\nimport networkx as nx\nfrom HardExamples import HardExamples\nfrom datasets import knn_graph\nfrom scipy.spatial.distance import pdist, squareform\n\nclass CycleExamples(HardExamples):\n    \"\"\"\n    This class finds hard positive and negatives based on the method described\n    in:\n    http://faculty.ucmerced.edu/mhyang/papers/eccv16_feature_learning_supp.pdf\n    \"\"\"\n    def __init__(self, data, k_neighbors):\n        super(CycleExamples, self).__init__(data)\n        self.k_neighbors = k_neighbors\n        self.graph = knn_graph(self._data, self.k_neighbors)\n\n    def _edge_cycle(self, edge):\n        \"\"\"\n        Returns the length of the smallest directed cycle `edge` is a part of.\n\n        :args:\n            - `edge`: tuple of (source, target)\n\n        :return: length of the cycle.\n        \"\"\"\n        v, u = edge\n        # 1. We remove the v --> u link.\n        try:\n            edge_data = self.graph.get_edge_data(v, u)\n            self.graph.remove_edge(v, u)\n        except nx.NetworkXError:\n            return np.inf\n\n        # 2. We find a directed path of length at most ell-1 from u to v.\n        try:\n            # cycle_length = reverse_path_length + 1\n            u_v_dis = nx.shortest_path_length(self.graph, source=u, target=v) + 1\n        except nx.NetworkXNoPath:\n            # No path from u to v.\n            u_v_dis = np.inf\n        # Put the edge back in place and return results.\n        self.graph.add_edge(v, u)\n        for attr, val in edge_data.items():\n            self.graph[v][u][attr] = val\n        return u_v_dis\n\n    # Running time of this function is O(E^2), if E=O(N), it's O(N^2).\n    def find_positive(self, k=-1, ell=-1, *args):\n        \"\"\"\n        :args:\n            - `k`: the number of examples returned. `-1` returns all.\n            - `ell`: Maximum length of the cycle a positive edge is a member of.\n            `-1` means all cycles are considered.\n\n        :return: list of negative pairs.\n        \"\"\"\n        res = []\n        for edge in self.graph.edges():\n            cycle_length = self._edge_cycle(edge)\n            if cycle_length <= ell or (cycle_length < np.inf and ell == -1):\n                res.append(edge)\n\n        if k == -1:\n            # Returning all instances.\n            return res\n        return self._rank_positive(res)[:k]\n\n    def find_negative(self, k=-1, threshold=1.0, *args):\n        \"\"\"\n        :args:\n            - `k`: number of examples returned. `-1` returns all.\n            - `threshold`: pairs longer than this threshold are returned.\n\n        :return: list of negative pairs.\n        \"\"\"\n\n        # Calculating pairwise geodesic distance.\n        geodesic_dis = {source:targets for source, targets \\\n            in nx.all_pairs_dijkstra_path_length(self.graph)}\n\n        res = []\n        for source, reachable_targets in geodesic_dis.items():\n            for target, distance in reachable_targets.items():\n                if distance > threshold:\n                    res.append((source, target))\n\n        if k == -1:\n            return res\n        return random.sample(res, k)\n","sub_path":"CycleExamples.py","file_name":"CycleExamples.py","file_ext":"py","file_size_in_byte":3276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"522081179","text":"# Copyright 2020 Katteli Inc.\n# TestFlows.com Open-Source Software Testing Framework (http://testflows.com)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport os\nimport datetime\nimport testflows._core.cli.arg.type as argtype\n\nfrom testflows._core.cli.arg.common import epilog\nfrom testflows._core.cli.arg.common import HelpFormatter\nfrom testflows._core.cli.arg.handlers.handler import Handler as HandlerBase\n\n\nclass Handler(HandlerBase):\n    @classmethod\n    def add_command(cls, commands):\n        parser = commands.add_parser(\n            \"requirements\",\n            help=\"software requirements specification\",\n            epilog=epilog(),\n            description=\"Create new software requirements specification document.\",\n            formatter_class=HelpFormatter,\n        )\n\n        parser.add_argument(\n            \"output\",\n            metavar=\"output\",\n            type=argtype.file(\"w\", bufsize=1, encoding=\"utf-8\"),\n            nargs=\"?\",\n            help=\"output file, default: stdout\",\n            default=\"-\",\n        )\n        parser.add_argument(\n            \"--number\",\n            metavar=\"number\",\n            type=str,\n            help=\"document number\",\n            default=\"001\",\n        )\n        parser.add_argument(\n            \"--title\",\n            metavar=\"name\",\n            type=str,\n            help=\"document title\",\n            default=\"Template\",\n        )\n        parser.add_argument(\n            \"--author\",\n            metavar=\"name\",\n            type=str,\n            help=\"name of the author\",\n            default=\"*[author]*\",\n        )\n        parser.add_argument(\n            \"--date\",\n            metavar=\"date\",\n            type=str,\n            help=\"document date\",\n            default=f\"{datetime.datetime.now():%B %d, %Y}\",\n        )\n        parser.set_defaults(func=cls())\n\n    def handle(self, args):\n        template_dir = os.path.join(\n            os.path.dirname(__file__), \"..\", \"..\", \"..\", \"..\", \"..\", \"document\", \"new\"\n        )\n\n        with open(os.path.join(template_dir, \"requirements.md\")) as fd:\n            template = fd.read()\n\n        args.output.write(\n            template.format(\n                number=args.number, title=args.title, author=args.author, date=args.date\n            )\n        )\n","sub_path":"testflows/_core/cli/arg/handlers/document/new/requirements.py","file_name":"requirements.py","file_ext":"py","file_size_in_byte":2764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"40934799","text":"# -*- coding:utf-8 -*-\n\nimport re\n\nfrom idiomatic.languages import collator\nfrom idiomatic.ui import ProgressReporter\n\nfrom words import wordsToIgnore\n\n\nnumberPattern = re.compile(u\"^[0-9-]*[0-9]+(\\.?[ºª]|[.°:])?$\")\n\n\ndef formatEntriesForDictionary(entries, partOfSpeech):\n    return formatEntriesAndCommentsForDictionary(dict.fromkeys(entries, None),\n                                                 partOfSpeech)\n\n\ndef escapeSpecialEntryCharacters(entry):\n    return entry.replace(\"/\", \"\\/\")\n\n\ndef formatEntriesAndCommentsForDictionary(entries, partOfSpeech):\n    reporter = ProgressReporter(\n        u\"Adaptando as entradas ao formato do dicionario\", len(entries))\n    for entry in sorted(entries, cmp=collator(\"gl\").compare):\n        if \" \" in entry:  # Use special syntax in .dic if there are spaces.\n            ngramas = set()\n            for ngrama in entry.split(u\" \"):\n                ngrama = ngrama.strip(\",\")\n                if ngrama == u\"/\":  # e.g. «Alianza 90 / Os Verdes».\n                    continue\n                # Ignore general vocabulary.\n                if ngrama not in wordsToIgnore:\n                    # Do not repeat ngrams within the same entry.\n                    if ngrama not in ngramas:\n                        # Hunspell always allows numbers.\n                        if not numberPattern.match(ngrama):\n                            ngramas.add(ngrama)\n                            if entries[entry]:  # Entry has a comment.\n                                yield u\"{ngrama} po:{partOfSpeech} \" \\\n                                      u\"[n-grama: {entry}]  \" \\\n                                      u\"# {comment}\\n\".format(\n                                          ngrama=escapeSpecialEntryCharacters(\n                                              ngrama),\n                                          entry=entry,\n                                          partOfSpeech=partOfSpeech,\n                                          comment=entries[entry])\n                            else:\n                                yield u\"{ngrama} po:{partOfSpeech} \" \\\n                                      u\"[n-grama: {entry}]\\n\".format(\n                                          ngrama=escapeSpecialEntryCharacters(\n                                              ngrama),\n                                          entry=entry,\n                                          partOfSpeech=partOfSpeech)\n        else:\n            if entry not in wordsToIgnore and not numberPattern.match(entry):\n                if entries[entry]:  # Entry has a comment.\n                    yield u\"{entry} po:{partOfSpeech}  # {comment}\\n\".format(\n                        entry=escapeSpecialEntryCharacters(entry),\n                        partOfSpeech=partOfSpeech, comment=entries[entry])\n                else:\n                    yield u\"{entry} po:{partOfSpeech}\\n\".format(\n                        entry=escapeSpecialEntryCharacters(entry),\n                        partOfSpeech=partOfSpeech)\n        reporter.increase()\n    reporter.done()\n","sub_path":"generators/gl/formatting.py","file_name":"formatting.py","file_ext":"py","file_size_in_byte":3046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"161837106","text":"##############################################################################\n#\n# Copyright (c) 2001 Zope Foundation and Contributors.\n#\n# This software is subject to the provisions of the Zope Public License,\n# Version 2.1 (ZPL).  A copy of the ZPL should accompany this distribution.\n# THIS SOFTWARE IS PROVIDED \"AS IS\" AND ANY AND ALL EXPRESS OR IMPLIED\n# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS\n# FOR A PARTICULAR PURPOSE.\n#\n##############################################################################\n\"\"\" CMFCollector product permissions\n\n$Id$\n\"\"\"\nfrom AccessControl import ModuleSecurityInfo\n\ntry:\n    from Products.CMFCore import permissions as core_permissions\nexcept ImportError: # CMF < 1.5\n    from Products.CMFCore import CMFCorePermissions as core_permissions\n\nsetDefaultRoles = core_permissions.setDefaultRoles\n\nsecurity = ModuleSecurityInfo('Products.CMFCollector.permissions')\n\nsecurity.declarePublic('View')\nView = core_permissions.View\n\nsecurity.declarePublic('AddPortalContent')\nAddPortalContent = core_permissions.AddPortalContent\n\nsecurity.declarePublic('AccessInactivePortalContent')\nAccessInactivePortalContent = core_permissions.AccessInactivePortalContent\n\nsecurity.declarePublic('ListFolderContents')\nListFolderContents = core_permissions.ListFolderContents\n\nsecurity.declarePublic('ModifyPortalContent')\nModifyPortalContent = core_permissions.ModifyPortalContent\n\nsecurity.declarePublic('ViewCollector')\nViewCollector = View\n\nsecurity.declarePublic('ViewCollector')\nAddCollector = 'Add portal collector'\nsetDefaultRoles(AddCollector, ('Manager', 'Owner'))\n\nsecurity.declarePublic('ManageCollector')\nManageCollector = 'Add portal collector'\nsetDefaultRoles(ManageCollector, ('Manager', 'Owner'))\n\nsecurity.declarePublic('AddCollectorIssue')\nAddCollectorIssue = 'Add collector issue'\nsetDefaultRoles(AddCollectorIssue,\n                ('Anonymous', 'Manager', 'Reviewer', 'Owner'))\n\nsecurity.declarePublic('AddCollectorIssueFollowup')\nAddCollectorIssueFollowup = 'Add collector issue comment'\nsetDefaultRoles(AddCollectorIssueFollowup, ('Manager', 'Reviewer', 'Owner'))\n\nsecurity.declarePublic('EditCollectorIssue')\nEditCollectorIssue = 'Edit collector issue'\nsetDefaultRoles(EditCollectorIssue, ('Manager', 'Reviewer'))\n\nsecurity.declarePublic('SupportIssue')\nSupportIssue = 'Support collector issue'\nsetDefaultRoles(SupportIssue, ('Manager', 'Reviewer'))\n\ndel setDefaultRoles, core_permissions\n","sub_path":"CMF_Extras/trunk/CMFCollector/permissions.py","file_name":"permissions.py","file_ext":"py","file_size_in_byte":2528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"8111392","text":"import xml.etree.ElementTree as ET\nimport xml.dom.minidom\nimport argparse\nimport os\nimport json\nimport subprocess\nfrom datetime import datetime\nimport time\n\ndef insert_leaf(parent, name, text):\n    sub = ET.SubElement(parent, name)\n    sub.text = text\n\ndef insert_labels(parent, labels):\n    for label_name in labels:\n        label = ET.SubElement(parent, \"label\")\n        insert_leaf(label, \"name\", label_name)\n        insert_leaf(label, \"attributes\", \"\")\n\ndef insert_tracks_from_list(parent, data, labels):\n    for i, track_class in enumerate(data):\n        if len(track_class.keys()) == 0:\n            continue\n        for track_id, bboxes in track_class.items():\n            track_node = ET.SubElement(parent, \"track\",\\\n                        {\"id\": str(track_id), \"label\" : labels[i]})\n            for j, frame in enumerate(bboxes):\n                xtl, ytl, xbr, ybr = frame[:4]\n                frame_nmbr  = frame[4] - 1\n                outside = \"1\" if (j == len(bboxes) - 1) else \"0\"\n                ET.SubElement(track_node, \"box\", {\"frame\": str(frame_nmbr),\\\n                                            \"xtl\": str(xtl),\n                                            \"ytl\": str(ytl),\n                                            \"xbr\": str(xbr),\n                                            \"ybr\": str(ybr),\n                                            \"outside\": outside,\n                                            \"occluded\": \"0\",\n                                            \"keyframe\": \"1\"})\n\ndef make_meta_information(video_length, labels):\n    annotations = ET.Element('annotations')\n    version = ET.SubElement(annotations, \"version\")\n    version.text = \"1.0\"\n    meta = ET.SubElement(annotations, \"meta\")\n    task = ET.SubElement(meta, \"task\")\n    insert_leaf(task, \"id\", \"1\")\n    insert_leaf(task, \"name\", \"segmentation\")\n    insert_leaf(task, \"size\", str(video_length))\n    insert_leaf(task, \"overlap\", \"0\")\n    insert_leaf(task, \"bugtracker\", \"\")\n    insert_leaf(task, \"flipped\", \"False\")\n    now = datetime.now()\n    insert_leaf(task, \"created\", now.strftime(\"%Y-%m-%d %H:%M:%S.%f\") + \"+00:00\")\n    insert_leaf(task, \"updated\", now.strftime(\"%Y-%m-%d %H:%M:%S.%f\") + \"+00:00\")\n    insert_labels(task, labels)\n    insert_leaf(task, \"segments\", \"\")\n    owner = ET.SubElement(task, \"owner\")\n    insert_leaf(owner, \"username\", \"admin\")\n    insert_leaf(owner, \"email\", \"\")\n    return annotations\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description=\"Interface for running detection on videos\")\n    parser.add_argument(\"input_videos\", type=str, help = \"Directory for input videos\")\n    parser.add_argument(\"input_tracks\", type=str, help = \"Directory for output jsons\")\n    parser.add_argument(\"username\", type=str, help = \"Username\")\n    parser.add_argument(\"password\", type=str, help = \"Password\")\n    args = parser.parse_args()\n    classes = [\"vehicle\", \"person\", \"sign\", \"cyclist\"]\n    videos = os.listdir(args.input_videos)\n    tracks = os.listdir(args.input_tracks)\n    username = args.username\n    password = args.password\n    task_count = 1\n    ids = []\n    videos.sort()\n\n    for video in videos:\n        response = subprocess.check_output([\n            \"../cli/cli.py\",\n            \"--auth\",\n            username+\":\"+password,\n            \"create\",\n            \"task_\"+str(task_count)+\"_\"+video,\n            \"--labels\",\n            \"labels.json\",\n            \"local\",\n            os.path.join(args.input_videos, video)])\n        task_count += 1\n        print(response)\n        ids.append(int(response.split()[3]))\n        print(\"Sleeping 60 seconds\")\n        time.sleep(60)\n\n    tracks.sort()\n    if tracks[0].split(\".\")[-1] == \"json\":\n        for i, track_file in enumerate(tracks):\n            print(\"Sleeping 40\")\n            time.sleep(40)\n            annotations = make_meta_information(2000, classes )\n            with open(os.path.join(args.input_tracks, track_file), \"r\") as json_file:\n                track_data = json.load(json_file)\n            print(\"Making xml file from \" + track_file)\n            insert_tracks_from_list(annotations, track_data, classes)\n            mydata = ET.tostring(annotations, encoding=\"unicode\")\n            mydata = xml.dom.minidom.parseString(mydata)\n            with open(\"Temp.xml\", \"w\") as myfile:\n                myfile.write(mydata.toprettyxml())\n            print(subprocess.check_output([\n                \"../cli/cli.py\",\n                \"--auth\",\n                username+\":\"+password,\n                \"upload\",\n                str(ids[i]),\n                \"Temp.xml\"]))\n    elif tracks[0].split(\".\")[-1] == \"xml\":\n        for i, track_file in enumerate(tracks):\n            print(\"Sleeping 5\")\n            time.sleep(5)\n            print(subprocess.check_output([\n                \"../cli/cli.py\",\n                \"--auth\",\n                username+\":\"+password,\n                \"upload\",\n                str(ids[i]),\n                os.path.join(args.input_tracks, track_file)]))\n","sub_path":"utils/auto_upload/upload_to_cvat.py","file_name":"upload_to_cvat.py","file_ext":"py","file_size_in_byte":4976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"317205563","text":"import multiprocessing\nimport pandas as pd\nimport re\nfrom sklearn.preprocessing import LabelEncoder, OneHotEncoder\nfrom sklearn import utils\nfrom sklearn.utils import class_weight\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport gensim\nfrom gensim.models.doc2vec import Doc2Vec, TaggedDocument\nimport keras\nfrom keras import backend as K\nfrom keras.utils import to_categorical\nfrom keras.constraints import min_max_norm\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Flatten, LSTM, Conv1D, MaxPooling1D, Dropout, Activation\nfrom keras.layers.embeddings import Embedding\nfrom nltk.tokenize import TweetTokenizer\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom sklearn.metrics import classification_report\nfrom sklearn.utils import shuffle\n\n# DIM = 128\n# batch_size = 32\nembedding_size = 100\nvocabulary_size = 10000\nmax_len = 100   # Largest # of words used(Tweet limits in 250 characters)\n\n\ndef cosine_distance(vests):\n    x, y = vests\n    x = K.l2_normalize(x, axis=-1)\n    y = K.l2_normalize(y, axis=-1)\n    return -K.mean(x * y, axis=-1, keepdims=True)\n\n\ndef cos_dist_output_shape(shapes):\n    shape1, shape2 = shapes\n    return (shape1[0],1)\n\n\ndef load_data(filename):\n    tweet = pd.read_csv(filename, lineterminator='\\n')\n    tweet = shuffle(tweet)\n    X = tweet['id']\n    y = tweet['annotations']\n    return X, y\n\n\ndef map_to_keywords(index):\n    string = emoji_map['keywords'][index]\n    return string[1:-1].replace(\",\", \"\")\n\n\n# X, y = load_data('train.csv')\n# test_X, test_y = load_data('test.csv')\n# emoji_map = pd.read_csv('5822100/emoji_map_1791.csv')\nX, y = load_data('tweets_train_retrieved.csv')\ntest_X, test_y = load_data('tweets_test_retrieved.csv')\nemoji_map = pd.read_csv('5822100/emoji_map_1791.csv')\n\nX = X[0:50000]\ny = y[0:50000]\n\ntest_X = test_X[0:1000]\ntest_y = test_y[0:1000]\n\n# for i in range(1379, 1411):\n#   print(i, \": \", list(y).count(i) / len(y))\n\n# Map the emoji id to its keywords\ny = y.apply(lambda x : map_to_keywords(x))\ntest_y = test_y.apply(lambda x : map_to_keywords(x))\n# target_y = target_y.apply(lambda x : map_to_keywords(x))\n\nlabel_encoder = LabelEncoder()\nlable_encoder = label_encoder.fit(y)\nprint(label_encoder.classes_)\ninteger_encoded = label_encoder.transform(y)\nprint(integer_encoded)\n\nonehot_encoder = OneHotEncoder(sparse=False, categories='auto')\ninteger_encoded = integer_encoded.reshape(len(integer_encoded), 1)\ntarget_y = onehot_encoder.fit_transform(integer_encoded)\n\n\n# inverted = label_encoder.inverse_transform([np.argmax(onehot_encoded[2, :])])\n# print(inverted)\n\ninteger_encoded = label_encoder.fit_transform(test_y)\ntest_y = to_categorical(integer_encoded)\n\nonehot_encoder = OneHotEncoder(sparse=False, categories='auto')\ninteger_encoded = integer_encoded.reshape(len(integer_encoded), 1)\nonehot_encoded = onehot_encoder.fit_transform(integer_encoded)\nprint(onehot_encoded)\n\n\nprint(\"### Training word model\")\nword_model = gensim.models.Word2Vec(sentences=X, size=embedding_size, min_count=1, iter=1)\ntarget_word_model = gensim.models.Word2Vec(sentences=y, size=embedding_size, min_count=0, iter=1)\nembedding_matrix_X = np.zeros((len(word_model.wv.vocab) + 1, embedding_size))\nembedding_matrix_y = np.zeros((len(target_word_model.wv.vocab) + 1, embedding_size))\n\nfor i, vec in enumerate(word_model.wv.vectors):\n    embedding_matrix_X[i] = vec\nfor i, vec in enumerate(target_word_model.wv.vectors):\n    embedding_matrix_y[i] = vec\n\nprint(\"### Done!\")\n\n# Tokenize X\ntknzr = TweetTokenizer(reduce_len=True, strip_handles=True)\ntokenizer = Tokenizer(num_words= embedding_size)\n\nX = X.apply(lambda x: tknzr.tokenize(x))\ntest_X = test_X.apply(lambda x: tknzr.tokenize(x))\ny = y.apply(lambda x: tknzr.tokenize(x))\n\ntokenizer.fit_on_texts(X)\nsequences = tokenizer.texts_to_sequences(X)\nX = pad_sequences(sequences, maxlen=max_len, padding='post')\n\ntokenizer.fit_on_texts(test_X)\nsequences = tokenizer.texts_to_sequences(test_X)\ntest_X = pad_sequences(sequences, maxlen=max_len, padding='post')\n\ntokenizer.fit_on_texts(y)\nsequences = tokenizer.texts_to_sequences(y)\ny = pad_sequences(sequences, maxlen=max_len, padding='post')\n\n\n\n\n\n\n\n\n\n# Average sentence embedding for X\nleft = Sequential()\nleft.add(Embedding(input_dim=len(word_model.wv.vocab)+1, output_dim=embedding_size, input_length=X.shape[1], weights=[embedding_matrix_X], trainable=False))\nleft.add(keras.layers.Lambda(lambda x: keras.backend.mean(x, axis=1)))\n\n# Average sentence embedding for y\nright = Sequential()\nright.add(Embedding(input_dim=len(target_word_model.wv.vocab)+1,output_dim=embedding_size, input_length=y.shape[1], weights=[embedding_matrix_y], trainable=False))\n# right.add(keras.layers.Lambda(lambda x: keras.backend.mean(x, axis=1)))\n\n# Merged layer\nmerged = keras.layers.Dot(axes=-1, normalize=True)([left.output, right.output])\nmerged = keras.layers.Lambda(lambda x: keras.backend.expand_dims(merged, axis=-1))(merged)\nmerged = LSTM(50, dropout=0.2, recurrent_dropout=0.2)(merged)\n# merged = Dense(100, activation='relu')(merged)\n# merged = Dropout(0.5)(merged)\nmerged = Dense(33, activation='softmax')(merged)\n\nmodel = keras.models.Model([left.input, right.input], merged)\n\nmodel.summary()\n\nmodel.compile(optimizer='sgd', loss= 'categorical_crossentropy', metrics=['accuracy'])\nmodel.fit([X, y], target_y, validation_split=0.2, epochs=3)","sub_path":"better_lstm.py","file_name":"better_lstm.py","file_ext":"py","file_size_in_byte":5362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"413511400","text":"import sublime, sublime_plugin\nimport subprocess\nfrom os import path\n\ngte_st3 = int(sublime.version()) >= 3000\n\nif gte_st3:\n    from .config import *\nelse:\n    from config import *\n\nclass HiveOpenCommand(sublime_plugin.WindowCommand):\n    def run(self):\n        self.init()\n        self.show_quick_panel()\n\n    def init(self):\n        options = sublime.load_settings(OPTIONS_BASE_NAME)\n        self.peek_file = gte_st3 and options.get('peek_file_on_highlight', False)\n        self.binfile_open_in_subl = options.get('open_binary_file_in_sublime', False)\n\n        self.init_item_data()\n        if self.peek_file: self.save_view()\n\n    def init_item_data(self):\n        conf = sublime.load_settings(CONFIG_BASE_NAME)\n        self.items = []\n        self.view_items = []\n\n        for key in ('dirs', 'files'):\n            if not conf.has(key): continue\n            self.items.extend(conf.get(key))\n\n        # sort items alphabetically\n        self.items.sort(key=lambda x: x[1].lower())\n\n        for (pathname, desc) in self.items:\n            basename = path.basename(pathname)\n            title = desc or basename or pathname\n            subtitle = ('%s ' % self.get_desc_type(pathname)) + pathname\n            self.view_items.append([title, subtitle])\n\n    def get_desc_type(self, pathname):\n        name, ext = path.splitext(pathname)\n\n        if SUBLIME_PLATFORM == 'osx' and ext == '.app':\n            return 'APP'\n\n        if path.isdir(pathname): return 'DIR'\n        return ext[1:].upper() or 'FILE'\n\n    def show_quick_panel(self):\n        if gte_st3:\n            self.window.show_quick_panel(self.view_items, self.on_done, on_highlight=self.on_highlight)\n        else:\n            self.window.show_quick_panel(self.view_items, self.on_done)\n\n    def on_done(self, index):\n        if index == -1:\n            return self.restore_view() if self.peek_file else self.noop()\n\n        item_name = self.get_name_by_index(index)\n        name, ext = path.splitext(item_name)\n\n        if SUBLIME_PLATFORM == 'osx' and ext == '.app':\n            self.open_binary_file(item_name)\n            return\n\n        if path.isfile(item_name):\n            self.open_file(item_name)\n        elif path.isdir(item_name):\n            self.open_dir(item_name)\n        else:\n            sublime.status_message('Invalid file/directory name: %s.' % item_name)\n\n    def on_highlight(self, index):\n        if not self.peek_file: return\n        item_name = self.get_name_by_index(index)\n\n        if path.isfile(item_name):\n            self.open_file(item_name, True)\n        else:\n            self.restore_view()\n\n    def open_file(self, filename, preview=False):\n        if preview:\n            self.window.open_file(filename, sublime.TRANSIENT)\n        else:\n            if self.binfile_open_in_subl or not self.is_binary_file(filename):\n                self.window.open_file(filename)\n            else:\n                self.open_binary_file(filename)\n\n    def open_dir(self, dirname):\n        if SUBLIME_PLATFORM == 'windows':\n            subprocess.Popen(['explorer', dirname])\n        elif SUBLIME_PLATFORM == 'osx':\n            self.window.run_command('open_dir', { 'dir': dirname })\n\n    def open_binary_file(self, filename):\n        if SUBLIME_PLATFORM == 'windows':\n            subprocess.Popen(['explorer', filename])\n        elif SUBLIME_PLATFORM == 'osx':\n            subprocess.Popen(['open', filename])\n\n        if self.peek_file: self.restore_view()\n\n    def is_binary_file(self, filename):\n        textchars = bytearray([7, 8, 9, 10, 12, 13, 27]) + bytearray(range(0x20, 0x100))\n        leadbytes = open(filename, 'rb').read(1024)\n        return bool(leadbytes.translate(None, textchars))\n\n    def get_name_by_index(self, index):\n        return self.items[index][0]\n\n    # do nothing\n    def noop(self): pass\n\n    def save_view(self):\n        self.saved_view = self.window.active_view()\n        selection = self.saved_view.sel()\n        # save region data as tuple so we can reverse serialization subsequently\n        self.saved_regions = [(region.a, region.b) for region in selection]\n\n        # return None if no selection(no viewable cursor)\n        # otherwise we only care about the last region(regardless of multi-selection mode)\n        return None if not len(selection) else selection[-1]\n\n    def restore_view(self):\n        saved_view = self.saved_view\n        if saved_view == None: return\n\n        # removes all regions currently if has any\n        sublime.Selection.clear(saved_view)\n\n        # restore previous saved regions\n        for region in self.saved_regions:\n            sublime.Selection.add(saved_view, sublime.Region(*region))\n\n        # focus on previous saved view\n        self.window.focus_view(saved_view)\n\n        # scroll the view to center on the last region\n        # saved_view.show_at_center(saved_view.sel()[-1])\n","sub_path":"opener.py","file_name":"opener.py","file_ext":"py","file_size_in_byte":4841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"459609354","text":"from django import forms \n\nfrom . import models \n\nclass CreateGroupForm(forms.ModelForm):\n    class Meta:\n        model = models.Group\n        fields = ('name', 'description', 'cover')\n\nclass UpdateGroupForm(forms.ModelForm):\n    \n    class Meta:\n        model = models.Group\n        fields = ('description', 'cover', 'moderators','admin', 'ban')\n        labels = {\n            'ban': 'Remove ban from members:'\n        }      \n    \n    def __init__(self, *args, **kwargs):\n        request = kwargs.pop('request')\n        grp = kwargs.pop('org')\n        qs = models.Group.objects.get(name=grp.name)\n        super(UpdateGroupForm, self).__init__(*args, **kwargs)\n        self.fields['admin'].queryset = qs.members\n        self.fields['moderators'].queryset = qs.members\n        self.fields['ban'].queryset = qs.ban\n        \n\n\n\nclass createpostform(forms.ModelForm):\n\n    content = forms.CharField(\n        label='',\n        widget = forms.Textarea(attrs={\n        'rows':'3',\n        'placeholder':'Share something!'\n        })\n    )\n\n    image = forms.ImageField(required=False)\n\n    class Meta():\n        model = models.GroupPost\n        fields = ('content', 'video')\n\n\n","sub_path":"groups/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"203446197","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib\nfrom scipy.integrate import solve_ivp\n\nE     = 1.7e9 #eV\nV0    = 1.2e6 #V\nT0    = 800e-9 #s\nf     = 500e6 #Hz\nω     = 2 * np.pi * f\nα     = 8e-4\nW     = 170e3 #eV\nΨs    = np.pi - np.arcsin(W/V0)\nΔΨi   = 0\nδi    = np.arange(0, 0.042, 0.001)\nturns = 400\n\ndef bgl(t, y):\n\tΔΨ, δ = y\n\tδdot = V0 / (T0 * E) * (np.sin(Ψs + ΔΨ) - np.sin(Ψs))\n\tΔΨdot = ω * α * δ\n\treturn (ΔΨdot, δdot)\n\ncmap = matplotlib.cm.plasma_r((δi-δi.min())/(δi.max()-δi.min()))\n\nfor idx, val in enumerate(δi):\n\tsolution = solve_ivp(bgl, (0, turns*T0), (ΔΨi, val), t_eval=np.arange(0, turns*T0, T0))\n\tplt.plot(solution.y[0], solution.y[1], '-', color=cmap[idx])\nplt.ylabel('δ')\nplt.xlabel('ΔΨ')\nplt.xlim(-2*np.pi, 4)\nplt.ylim(-0.05, 0.05)\n# sm = plt.cm.ScalarMappable(cmap='plasma_r', norm=plt.Normalize(vmin=δi.min(), vmax=δi.max()))\n# sm._A = []\n# plt.colorbar(sm).set_label('initial value of δ')\nplt.tight_layout(pad=0)\nplt.savefig('3.pdf')","sub_path":"ws/6/longitudinal.py","file_name":"longitudinal.py","file_ext":"py","file_size_in_byte":1009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"51709100","text":"import torch\nimport numpy as np\n\n# numpy 数据格式\nnp_data = np.arange(6).reshape((2, 3))\n# numpy --> torch 数据格式\ntorch_data = torch.from_numpy(np_data)\n# torch --> numpy 数据格式\ntensor2array = torch_data.numpy()\n\nprint(\n    '\\nnumpy', np_data,\n    '\\ntorch', torch_data,\n    '\\ntensor2array', tensor2array\n)\n\n\n","sub_path":"mf_00_basis/nump_torch.py","file_name":"nump_torch.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"515333971","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\n\"\"\"\nDo some report on our data\nTODO: Cleanup outliers ?\n\"\"\"\n\n\nfrom pprint import pprint\nfrom itertools import chain\nfrom collections import Counter\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport unidecode\n\n\ndef _int_converters(i):\n    try:\n        y = int(i)\n    except ValueError:\n        y = 0\n    return y\n\n\ndef _dateparse(d):\n    try:\n        date = pd.datetime.strptime(str(d), '%Y-%m-%d %H:%M:%S')\n    except ValueError:\n        try:\n            date = pd.datetime.strptime(str(d), '%Y-%m-%d')\n        except ValueError:\n            date = pd.np.nan\n            # date = pd.datetime.strptime('1979-01-01', '%Y-%m-%d')\n    return date\n\n\n# video_id,trending_date,title,channel_title,publish_time,tags,views,likes,dislikes,comment_count,comments_disabled,ratings_disabled,video_error_or_removed,description,category\ncols_types = {'video_id': str, 'trending_date': str, 'title': str, 'channel_title': str, 'publish_time': str, 'tags': str,\n              'views': int, 'likes': int, 'dislikes': int, 'comment_count': float, 'comments_disables': bool,\n              'ratings_disabled': bool, 'video_error_or_removed': bool, 'description': str, 'category': 'category'}\ncols_converters = {'views': _int_converters, 'likes': _int_converters, 'dislikes': _int_converters}\nyoutube_csv = pd.read_csv('./ressources/FRvideos.csv', engine='c', memory_map=True,\n                          low_memory=True, dtype=cols_types, na_values=[''], converters=cols_converters,\n                          parse_dates=['trending_date', 'publish_time'], date_parser=_dateparse).dropna()\n\ncols_to_analyze = ['publish_trending', 'views', 'likes', 'dislikes', 'comment_count']\nyoutube_csv['likes_dislikes'] = youtube_csv['likes'] / youtube_csv['dislikes']\nyoutube_csv['publish_trending'] = youtube_csv['trending_date'] - youtube_csv['publish_time']\nyoutube_csv['publish_trending_seconds'] = youtube_csv['publish_trending'].map(lambda d: d.total_seconds())\nyoutube_csv_by_category = youtube_csv[cols_to_analyze[1:] + ['category', 'publish_trending_seconds', 'likes_dislikes']]\\\n    .groupby('category')\n\nprint('Stats')\nfor col in cols_to_analyze:\n    print('**********', col, '**********')\n    print('Mean :', str(youtube_csv[col].mean()))\n    print('Median :', str(youtube_csv[col].median()))\n    print('Min :', str(youtube_csv[col].min()))\n    print('Max :', str(youtube_csv[col].max()))\n    print()\n\nprint('Stats grouped by category')\nfor col in cols_to_analyze[1:] + ['publish_trending_seconds', 'likes_dislikes']:\n    print('**********', col, '**********')\n    print('Mean :', str(youtube_csv_by_category[col].mean()), '\\n')\n    print('Median :', str(youtube_csv_by_category[col].median()), '\\n')\n    print('Min :', str(youtube_csv_by_category[col].min()), '\\n')\n    print('Max :', str(youtube_csv_by_category[col].max()), '\\n')\n    print('\\n')\n\nprint('Most using words in title')\n# words_counts = Counter(pipe([youtube_csv['title'].str.lower().str.split().dropna(), list, chain.from_iterable]))\nwords_counts = Counter(chain.from_iterable(list(youtube_csv['title'].str.lower().str.split().dropna()\n                                                .apply(lambda word: [unidecode.unidecode(w) for w in word]))))\nwords_counts = words_counts.most_common()  # [int(len(words_counts) * 0.1):int(len(words_counts) * 0.9)]\n# pprint(words_counts)  # Uncomment to see word count\n\nprint('Plot 1')\nsbc = dict(youtube_csv_by_category['publish_trending_seconds'].median())\npprint(sbc)\nbsbc = [vsbc for vsbc in sbc.values()]\nplt.figure(1)\nplt.bar([l for l in sbc], [(vsbc / 3600) for vsbc in sbc.values()])\n# pyplot.pie([vsbc for vsbc in sbc.values()], labels=[l for l in sbc], autopct='%1.1f%%', startangle=90)\nplt.legend(('Temps median en heures',))\nplt.xticks(rotation=90)\nplt.savefig('./plots/temps_median_par_categorie_pour_passer_de_published_a_trending.png')\n# stats_by_category = youtube_csv_by_category['publish_trending_seconds'].mean().to_frame(['category', 'pts'])\n\nprint('Plot 2')\nlikes_dislikes_by_categories = dict(youtube_csv_by_category['likes_dislikes'].median())\npprint(likes_dislikes_by_categories)\ngrouped_ldbc = [ldbc for ldbc in likes_dislikes_by_categories.values()]\nplt.figure(2)\nplt.bar([el for el in likes_dislikes_by_categories], [el for el in likes_dislikes_by_categories.values()])\n# plt.pie(x=[el for el in likes_dislikes_by_categories.values()], autopct='%1.1f%%', startangle=90,\n#         labels=[el for el in likes_dislikes_by_categories.keys()])\nplt.legend(('Ratios medians likes / dislikes par catégories',))\nplt.xticks(rotation=90)\nplt.savefig('./plots/ratios_likes_dislikes_median_par_categories.png')\n\nprint('Plot 3')\ncount_by_categories = dict(youtube_csv_by_category['views'].count())\ngrouped_cbc = [cbc for cbc in count_by_categories.values()]\nplt.figure(3)\n# plt.bar([el for el in likes_dislikes_by_categories], [el for el in likes_dislikes_by_categories.values()])\nplt.pie(x=[el for el in count_by_categories.values()], autopct='%1.1f%%', startangle=90,\n        labels=[el for el in count_by_categories.keys()])\nplt.legend(('Nombre de vidéos par catégories',))\nplt.savefig('./plots/Nombre_de_videos_par_categories.png')\n\nprint('Plotting !')\nplt.show()\n\nprint('Fun Facts')\nprint('Most viewed video :', youtube_csv[youtube_csv['views'] == youtube_csv['views'].max()].to_dict()['title'])\nprint('Most liked video :', youtube_csv[youtube_csv['likes'] == youtube_csv['likes'].max()].to_dict()['title'])\nprint('Most disliked video :', youtube_csv[youtube_csv['dislikes'] == youtube_csv['dislikes'].max()].to_dict()['title'])\n","sub_path":"reporting.py","file_name":"reporting.py","file_ext":"py","file_size_in_byte":5587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"431084104","text":"# Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.\n# SPDX-License-Identifier: Apache-2.0\nfrom setuptools import setup, find_packages\n\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read()\n\nsetup(\n    name=\"aws-service-catalog-puppet\",\n    version=\"0.74.1\",\n    author=\"Eamonn Faherty\",\n    author_email=\"aws-service-catalog-tools@amazon.com\",\n    description=\"Making it easier to deploy ServiceCatalog products\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/awslabs/aws-service-catalog-puppet-framework\",\n    packages=find_packages(),\n    package_data={\"servicecatalog_puppet\": [\"*\", \"*/*\", \"*/*/*\"]},\n    classifiers=[\n        \"Development Status :: 5 - Production/Stable\",\n        \"Intended Audience :: Developers\",\n        \"Programming Language :: Python :: 3\",\n        \"License :: OSI Approved :: Apache Software License\",\n        \"Operating System :: OS Independent\",\n        \"Natural Language :: English\",\n    ],\n    entry_points={\n        \"console_scripts\": [\"servicecatalog-puppet = servicecatalog_puppet.cli:cli\"]\n    },\n    install_requires=[\n        \"better-boto==0.24.1\",\n        \"boto3==1.12.28\",\n        \"botocore==1.15.28\",\n        \"certifi==2019.11.28\",\n        \"cfn-flip==1.2.1\",\n        \"chardet==3.0.4\",\n        \"click==7.0\",\n        \"colorclass==2.2.0\",\n        \"docopt==0.6.2\",\n        \"docutils==0.14\",\n        \"idna==2.8\",\n        \"jinja2==2.10.1\",\n        \"jmespath==0.9.5\",\n        \"lockfile==0.12.2\",\n        \"luigi==2.8.9\",\n        \"markupsafe==1.1.1\",\n        \"progressbar==2.5\",\n        \"psutil==5.7.0\",\n        \"pykwalify==1.7.0\",\n        \"python-daemon==2.1.2\",\n        \"python-dateutil==2.8.1\",\n        \"pyyaml==5.1\",\n        \"requests==2.22.0\",\n        \"s3transfer==0.3.3\",\n        \"six==1.14.0\",\n        \"terminaltables==3.1.0\",\n        \"tornado==5.1.1\",\n        \"urllib3==1.25.8; python_version != '3.4'\",\n    ],\n)\n","sub_path":"pypi_install_script/aws-service-catalog-puppet-0.74.1.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"7895410","text":"import socket\nimport threading\nimport time\nimport queue\nimport sys\nimport random\nimport operator\n\ntesting = False\n\n#===================================\n#indices\n\n#In a request message (split form)\nmsg_type = 0    #request or update (about waiting time)\nseq_num = 1     #seq number set by the IoT node who generated this request\nfwd_limit = 2   #The number of hops in the fog layer (decremented by offloading)\nprc_time = 3    #processing time at a fog node\niot_ip = 4      #IP address of the originating IoT node\niot_port = 5    #UDP port number of the originating IoT node\n\n#In a pair\nip_index = 0\nport_index = 1\n#===================================\n\n#message format\nreq_prefix = \"req\"\n\n#buffer size for sockets\nbufsize = 1024\n\n#Request queue\nreq_queue = queue.Queue()\n#lock for mutual exclusion of the queue\nq_lock = threading.Lock()\n#state - queue length (in terms of waiting time)\ncrrnt_q_len = 0\n\ndef request_routine(split_msg):\n    \"\"\"\n    Enqueue the incoming request (no need for offloading in Cloud)\n\n    parameters\n    ------\n    split_msg : list\n        a request message (The info about each element is listed at\n        the beginning with the predefined index variables.)\n    \"\"\"\n\n    global crrnt_q_len\n\n    #Append my ip\n    split_msg.append(my_ip)\n\n    with q_lock:\n        '''\n        (i) enqueue this request\n        (ii) increase the waiting time estimate (crrnt_q_len)\n        '''\n        req_queue.put(split_msg)\n        crrnt_q_len += int(split_msg[prc_time])\n        print(\"Request enqueued \", split_msg)\n\n\ndef prep_fog_server(my_ip, my_tcp_port):\n    \"\"\"\n    Prepare a server to receive offloaded requests from fog nodes\n\n    parameters\n    ------\n    my_ip : str\n        IP address (host name)\n    my_tcp_port_nei : int\n        Port number\n    \"\"\"\n\n    #Establish a connection (TCP)\n    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as skt:\n        #wait for a conenction with the IP and port\n        skt.bind((my_ip, int(my_tcp_port)))\n        skt.listen(5)\n        while True:\n            ## addr is a pair of IP and port\n            conn, addr = skt.accept()\n            with conn:\n                data = conn.recv(bufsize)\n                if not data:\n                    break\n\n                msg = data.decode(\"utf-8\")\n                split_msg = msg.split(\",\")\n\n                if split_msg[msg_type] == req_prefix:\n                    print(\"Offloaded request received \", msg)\n                    request_routine(split_msg)\n                else:\n                    print(\"Illegal message\")\n\n\ndef send_back_to_iot(split_msg):\n    \"\"\"\n    Send a response to the originating IoT node\n\n    parameters\n    ------\n    split_msg : list\n        a request message (The info about each element is listed at\n        the beginning with the predefined index variables.)\n    \"\"\"\n    #msg format\n    msg = \",\".join(map(str,split_msg))\n\n    if testing:\n        print(\"Send a response back to IoT...\")\n\n    with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as udpskt:\n        #send the response back using the IP and port of the originating IoT node\n        iot_node = (split_msg[iot_ip], int(split_msg[iot_port]))\n        udpskt.connect(iot_node)\n        udpskt.sendall(bytes(msg, \"utf-8\"))\n\n        print(\"Response sent back: \", msg, \" to \", iot_node)\n\ndef process_req():\n    \"\"\"\n    Process a request, remove it from its queue, and send a response\n    \"\"\"\n    global crrnt_q_len\n\n    while True:\n        #If there is any request pending in the queue\n        if crrnt_q_len > 0:\n            with q_lock:\n                #remove the first one from the queue\n                split_req = req_queue.get()\n                print(\"Processing... \", split_req)\n\n            #Assume cloud can process it (speed)-times faster\n            speed = 1.0\n            #Pseudo processing by sleep\n            time.sleep(float(split_req[prc_time])/speed)\n\n            with q_lock:\n                send_back_to_iot(split_req)\n\n                #update the waiting time\n                crrnt_q_len -= int(split_req[prc_time])\n\n                if testing:\n                    print(\"State changed \", crrnt_q_len)\n\n\n\n#Receive the input by argv\nmy_ip = sys.argv[1]\nmy_port = int(sys.argv[2])\n\n#server thread that waits offloaded messages from fog nodes\nfog_sev_th = threading.Thread(target=prep_fog_server,args=(my_ip,my_port))\n#processing thread that process the requests from the queue\nsrv_processor = threading.Thread(target=process_req)\n\nfog_sev_th.start()\nsrv_processor.start()\n","sub_path":"cloud.py","file_name":"cloud.py","file_ext":"py","file_size_in_byte":4490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"16635681","text":"from modules.data_fetcher_module import DataFetcherModule\n\n\ndef get_clean_staffing_ratio(staff_ratios_file_path: str):\n    \"\"\"Fixes the input staff ratio matrix and also the column names, index\"\"\"\n    staff_ratios = DataFetcherModule.get_staffing_ratios(staff_ratios_file_path)\n    for b in staff_ratios.columns:\n        bnew = b.split('\\n')[0].strip()\n        staff_ratios = staff_ratios.rename(columns={b: bnew})\n    staff_ratios = staff_ratios.set_index('Personnel')\n    staff_ratios.fillna(0, inplace=True)\n    return staff_ratios.copy()\n\n\ndef compute_staffing_matrix(active_count: int, bed_type_ratio: dict,\n                            staff_ratios_file_path: str,\n                            bed_multiplier_factor: int):\n    \"\"\"Computes the staffing matrix for a given active_count and other params\"\"\"\n    # fix the staff ratios\n    staff_df = get_clean_staffing_ratio(staff_ratios_file_path)\n\n    # add a beds rows\n    staff_df.loc['Beds', :] = bed_multiplier_factor\n\n    bed_units = {}\n    staff_df['Total'] = 0\n    # compute bed units, staff counts and sums\n    # (each unit = multiplier_factor beds)\n    for bed_type in bed_type_ratio:\n        bed_units[bed_type] = bed_type_ratio[bed_type] * active_count / bed_multiplier_factor\n        staff_df[bed_type] = staff_df[bed_type] * bed_units[bed_type]\n        staff_df['Total'] = staff_df['Total'] + staff_df[bed_type]\n\n    # select only relevant columns\n    inds = list(bed_units.keys())\n    inds = inds + ['Total']\n\n    return staff_df[inds]\n","sub_path":"src/domain_info/staffing.py","file_name":"staffing.py","file_ext":"py","file_size_in_byte":1502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"265555299","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Sep 22 18:19:40 2021\nUgly spaghetti art :)\n@author: aidenkeelynsmith\n\"\"\"\n\nimport turtle, random\nturtle.speed(10)\npanel = turtle.Screen().bgcolor('white')\nlittleArt = turtle.Turtle()\npalette = [\"MidnightBlue\", \"DarkRed\", \"OrangeRed\", \"LightYellow\"]\n\n\nlittleArt.goto(random.randint(100,300),random.randint(100,300))\nsize = 1\n\n# modified from for loop warm up\nfor i in range(4):\n    littleArt.color(palette[i])\n    littleArt.width(size)\n    size += 1\n\n    littleArt.circle(random.randint(10,41))\n    littleArt.forward(random.randint(1,26))\n\nmoreLittleart = turtle.Turtle()\n\ninc = 3\nnumIt = 100\n\nrandomCol = ['red', 'pink', 'orange', 'yellow', 'green', 'blue', 'purple']\n\nmoreLittleart.goto(random.randint(100,300),random.randint(100,300))\n\nfor i in range(numIt):\n    moreLittleart.color(random.choice(randomCol)) # randomizing color choice\n    moreLittleart.goto(random.randint(100,300),random.randint(100,300)) # randomizing placement\n\n    for i in range(inc):\n      moreLittleart.width(random.randint(1, 11)) # randomizing thickness\n      moreLittleart.down()\n      moreLittleart.shape(\"triangle\")\n      moreLittleart.forward(150)\n      moreLittleart.left(120)\n    \n    moreLittleart.right(100)\n    \nturtle.done()","sub_path":"PC04.py","file_name":"PC04.py","file_ext":"py","file_size_in_byte":1278,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"271531148","text":"n = int(input(\"Numero inteiro: \"))\na = 1\nsoma = 0\nwhile(a < n):\n\tb = n%a\n\tif(b==0):\n\t\tprint(a)\n\t\tsoma = a + soma\n\ta = a + 1\nif(soma == n):\n\tprint(\"PERFEITO\")\nelse:\n\tprint(\"NAO PERFEITO\")","sub_path":"5 - Notebooks e Data/1 - Análises numéricas/Arquivos David/Atualizados/logDicas-master/data/2019-1/226/users/4129/codes (1)/1724_2527.py","file_name":"1724_2527.py","file_ext":"py","file_size_in_byte":186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"208394492","text":"#word_jumble_two.py\n#By: Karlos Calvillo\n#10/27/14\n\n# The computer picks a random word and then \"jumbles\" it\n# The player has to guess the original word\n\nimport random\n\n# create a sequence of words to choose from\nWORDS = (\"python\", \"jumble\", \"easy\", \"difficult\", \"answer\", \"xylophone\")\n# pick one word randomly from the sequence\nword = random.choice(WORDS)\n# create a variable to use later to see if the guess is correct\ncorrect = word\n\nhint0 = \"It's a type of snake\"\n\nhint1 = \"It's what this program does to words to make it difficult\"\n\nhint2 = \"Not difficult but..\"\n\nhint3 = \"This is not easy\"\n\nhint4 = \"With every question there's a..\"\n\nhint5 = \"It's a musical instrument you have to hit with 2 small sticks\"\n\n\n# create a jumbled version of the word\njumble =\"\"\nwhile word:\n    position = random.randrange(len(word))\n    jumble += word[position]\n    word = word[:position] + word[(position + 1):]\n\n# start the game\nprint(\n\"\"\"\n           Welcome to Word Jumble!\n        \n   Unscramble the letters to make a word.\n(Press the enter key at the prompt to quit.\nIf you don't use any hints you get points!)\n\"\"\"\n)\nprint(\"The jumble is:\", jumble)\n\nguess = input(\"\\nYour guess: \")\nguess=guess.lower()\nscore=0\nwhile guess != correct and guess != \"\":\n    print(\"Sorry, that's not it.\")\n\n    hintPrompt=input(\"Would you like a hint? Y/N: \")\n\n    if hintPrompt==\"Y\" and correct==WORDS[0]:\n        print(hint0)\n\n    elif hintPrompt==\"Y\" and correct==WORDS[1]:\n        print(hint1)\n\n    elif hintPrompt==\"Y\" and correct==WORDS[2]:\n       print(hint2)\n\n    elif hintPrompt==\"Y\" and correct==WORDS[3]:\n        print(hint3)\n\n    elif hintPrompt==\"Y\" and correct==WORDS[4]:\n        print(hint4)\n\n    elif hintPrompt==\"Y\" and correct==WORDS[5]:\n        print(hint5)\n\n    elif hintPrompt==\"N\":\n        score+=50\n\n    guess = input(\"\\nYour guess: \")\n    guess=guess.lower()\n    \nif guess == correct:\n    print(\"That's it!  You guessed it!\\n\")\n    print(\"You got: \"+str(score)+\" points\")\n\nprint(\"Thanks for playing.\")\n\ninput(\"\\n\\nPress the enter key to exit.\")\n","sub_path":"word_jumble_two.py","file_name":"word_jumble_two.py","file_ext":"py","file_size_in_byte":2039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"225948903","text":"#To be able to read csv formated files, we will first have to import the\r\n#csv module.\r\n\r\nimport csv\r\n\r\n# status\r\n# 0 - idle\r\n\r\nstatus = 0\r\narreg = 0\r\narval = 0\r\n\r\noutput_file = open('ar8337_reg_out.txt', 'w')\r\n\r\ndef proc_high(rw, reg, val):\r\n\tglobal status\r\n\tglobal arreg\r\n\tglobal arval\r\n\r\n\tif bool(reg & 0x01) :\r\n\t\tval <<= 16\r\n\tarval |= val\r\n\r\n\toutput_file.write(\"ar8337 %s %#06x %#010x\\n\" % (rw, arreg, arval))\r\n\tstatus = 0\r\n\r\n\r\ndef proc_low(phy, reg, val):\r\n\tglobal status\r\n\tglobal arreg\r\n\tglobal arval\r\n\r\n\tarreg |= (phy & 0xf)<<6 \r\n\tarreg |= (reg & 0x1e)<<1\r\n\r\n\tif bool(reg & 0x01) :\r\n\t\tval <<= 16\r\n\tarval |= val\r\n\tstatus = 2\r\n\r\ndef proc_start(phy, reg, val):\r\n\tglobal status\r\n\tglobal arreg\r\n\tglobal arval\r\n\r\n\tarreg = val<<9\r\n\tarval = 0\r\n\tstatus = 1\r\n\r\n\r\ndef process_line(ts, rw, phy, reg, val):\r\n\tglobal status\r\n\r\n\tis_write = rw == 'wr'\r\n\r\n\tif status == 0:\r\n\t\toutput_file.write(ts + \" \")\r\n\r\n\t\tif not is_write or phy != 0x18:\r\n\t\t\toutput_file.write(\"phy   %s %#04x %#04x %#06x\\n\" % (rw, phy, reg, val))\r\n\t\t\treturn\r\n\t\tproc_start(phy, reg, val)\r\n\r\n\telif status == 1:\r\n\t\tproc_low(phy, reg, val)\r\n\telif status == 2:\r\n\t\tproc_high(rw, reg, val)\r\n\r\n\r\n\r\ndef main():\r\n\tglobal prev_clk\r\n\tprev_clk = 0\r\n\r\n\r\n\twith open('mdio_out.txt', 'rb') as f:\r\n\r\n\t\treader = csv.reader(f, delimiter=' ')\r\n\t\tfor row in reader:\r\n\t\t\tts,rw,phy,reg, val = row[:5]\r\n\r\n#\t\t\tis_write = rw == 'wr'\r\n\t\t\tphy = int(phy, 0)\r\n\t\t\treg = int(reg, 0)\r\n\t\t\tval = int(val, 0)\r\n\r\n\t\t\tprocess_line(ts, rw, phy, reg, val)\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n","sub_path":"projects/pyhon/mdio_parser/qualcomm_ar8337/csv_second.py","file_name":"csv_second.py","file_ext":"py","file_size_in_byte":1517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"385499699","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun May 30 10:29:02 2021\n\n@author: 11200\n\"\"\"\n#tensorboard的使用 可是跨\n\nimport cv2\nimport numpy as np\nfrom torch.utils.tensorboard import SummaryWriter\n\n#实例化\nwriter=SummaryWriter(\"02tensorboard_logs\")\n\n\"\"\"\nArgs:\n    tag (string): Data identifier\n    img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data\n    global_step (int): Global step value to record\nimg格式有要求，且是要CHW形式，\n如果不是，用dataformats=’HWC'\n\"\"\"\nfor i in range(10):\n    img=cv2.imread('E:/CS/cv/tianchi/data/mchar_train/mchar_train/00000{}.png'.format(i))\n    writer.add_image('my_img',img,i,dataformats='HWC')\n\n\n'''\nadd_scalar\nArgs:\n    tag (string): Data identifier\n    scalar_value (float or string/blobname): Value to save\n    global_step (int): Global step value to record\n参数简单理解：标题，y轴，x轴\n\n如果连续两次在同一个tag里画东西，会发生拟合\n解决方法是删掉文件\n'''\n\nfor i in range(100):\n    writer.add_scalar('y=x2',i*3,i)\n\n#dd_scalars(\nr = 5\nfor i in range(100):\n    writer.add_scalars('run_14h', {'xsinx':i*np.sin(i/r),\n                                    'xcosx':i*np.cos(i/r),\n                                    'tanx': np.tan(i/r)}, i)\n\"\"\"\n如和查看\n在cmd里先进入到本文件加，以及该环境当中\n命令：tensorboard --logdir=02tensorboard_log(文件夹名） --port=6008(设定端口)，默认6006)\n\"\"\"\n\nwriter.close()","sub_path":"test/02tensorboard.py","file_name":"02tensorboard.py","file_ext":"py","file_size_in_byte":1454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"535887253","text":"import datetime\nimport logging\nimport logging.config\nimport os\nimport sys\n\nfrom ws.config import config\n\nif config.getboolean('profiling', 'logs'):\n    PROFILE_LEVEL_NUM = 55\nelse:\n    PROFILE_LEVEL_NUM = 1\n\nDEPRECIATE_LEVEL_NUM = 15\nDEBUG2_LEVEL_NUM = 7\nDEBUG3_LEVEL_NUM = 3\n\nlogging.basicConfig(level=logging.INFO)\nlogging.raiseExceptions = config.getboolean('settings',\n                                            'raise_logging_exceptions')\n\n\ndef add_log_level(\n        level, level_name, method_name=None,\n        add_method=True, add_to_module=True):\n    assert level > 0\n\n    logging.addLevelName(level=level, levelName=level_name)\n\n    if add_to_module:\n        setattr(logging, level_name, level)\n    if add_method:\n        method_name = method_name or level_name.lower()\n\n        def log_custom_level(self, *args, **kwargs):\n            if not isinstance(level, int):\n                if logging.raiseExceptions:\n                    raise TypeError(\"level must be an integer\")\n                else:\n                    return\n            if self.isEnabledFor(level):\n                self._log(level, args[0], args[1:], **kwargs)\n\n        log_custom_level.__name__ = method_name\n        setattr(logging.Logger, method_name, log_custom_level)\n\n\nadd_log_level(level=PROFILE_LEVEL_NUM, level_name='PROFILE', add_method=False)\nadd_log_level(level=DEPRECIATE_LEVEL_NUM, level_name='DEPRECIATE',\n              add_method=False)\nadd_log_level(level=DEBUG2_LEVEL_NUM, level_name='DEBUG2')\nadd_log_level(level=DEBUG3_LEVEL_NUM, level_name='DEBUG3')\n\n\nclass _AccessLogger:\n    def __init__(self, name):\n        self.logger = logging.getLogger(name)\n        self.logger.propagate = False\n\n    def log(self, *, request=None, response=None, ru_utime='-', ru_stime='-',\n            ru_maxrss='-', response_time='-', parse_time='-', total_time='-',\n            **kwargs):\n        # request might be None if the server ignored the request and replied\n        # instantly with 4xx or 5xx code\n        self.logger.critical('ACCESSED', extra=dict(\n            request_line=getattr(request, 'request_line', '-'),\n            request_headers=getattr(request, 'headers', {}),\n            response_headers=getattr(response, 'headers', {}),\n            status_code=response.status_line.status_code if response else '-',\n            ru_utime=ru_utime,\n            ru_stime=ru_stime,\n            ru_maxrss=ru_maxrss,\n            response_time=response_time,\n            parse_time=parse_time,\n            total_time=total_time,\n            **kwargs\n        ))\n\n\nclass _ProfileLog:\n    def __init__(self, name):\n        self.logger = logging.getLogger(name)\n        self.logger.propagate = False\n\n    def profile(self, *args, **kwargs):\n        self.logger.log(PROFILE_LEVEL_NUM, *args, **kwargs)\n\n\ndef setup_log_file(file_path, truncate=True, store_old=True):\n    if store_old and os.path.exists(file_path):\n        logging.info('Storing old file %s.', file_path)\n        iso = datetime.datetime.now().isoformat()\n        os.rename(file_path, '{}.{}'.format(file_path, iso))\n\n    if truncate:\n        with open(file_path, mode='w') as f:\n            pass\n\n    return file_path\n\n\ndef generic_file_handler(cfg_section):\n    return logging.FileHandler(\n        setup_log_file(\n            file_path=cfg_section['file_name'],\n            truncate=cfg_section.getboolean('truncate'),\n            store_old=cfg_section.getboolean('store_old')\n        ),\n        mode='a',\n        delay=True\n    )\n\n\nerror_log = logging.getLogger('error')\nerror_log.setLevel(\n    config.get('error_log', 'level', fallback='DEBUG3')\n)\nerror_log.propagate = False\nerror_log_stream_formatter = logging.Formatter(\n    config.get('error_log', 'stream_format', raw=True)\n)\nerror_log_stream_handler = logging.StreamHandler(sys.stderr)\nerror_log_stream_handler.setLevel(\n    config.get('error_log', 'stream_level')\n)\nerror_log_stream_handler.setFormatter(error_log_stream_formatter)\nerror_log_file_formatter = logging.Formatter(\n    config.get('error_log', 'file_format', raw=True)\n)\nerror_log_file_handler = generic_file_handler(config['error_log'])\nerror_log_file_handler.setLevel(config.get('error_log', 'file_level'))\nerror_log_file_handler.setFormatter(error_log_file_formatter)\n\naccess_log = _AccessLogger('access')\naccess_log_formatter = logging.Formatter(\n    config.get('access_log', 'format', raw=True)\n)\naccess_log_stream_handler = logging.StreamHandler(sys.stdout)\naccess_log_stream_handler.setFormatter(access_log_formatter)\naccess_log_file_handler = generic_file_handler(config['access_log'])\naccess_log_file_handler.setFormatter(access_log_formatter)\n\nprofile_log = _ProfileLog('profile')\nprofile_log_formatter = logging.Formatter('%(asctime)s %(process)d %(message)s')\nprofile_log_stream_handler = logging.StreamHandler()\nprofile_log_stream_handler.setFormatter(profile_log_formatter)\nprofile_log_file_handler = generic_file_handler(config['profile_log'])\nprofile_log_file_handler.setFormatter(profile_log_formatter)\n\n\ndef setup_server_handlers():\n    error_log.addHandler(error_log_stream_handler)\n    access_log.logger.addHandler(access_log_stream_handler)\n    profile_log.logger.addHandler(profile_log_stream_handler)\n\n\ndef setup_worker_handlers():\n    def remove_handlers(logger):\n        for handler in logger.handlers[:]:\n            handler.close()\n            logger.removeHandler(handler)\n\n    remove_handlers(error_log)\n    remove_handlers(access_log.logger)\n    remove_handlers(profile_log.logger)\n\n    error_log.addHandler(error_log_file_handler)\n    access_log.logger.addHandler(access_log_file_handler)\n    profile_log.logger.addHandler(profile_log_file_handler)\n\n\nsetup_server_handlers()\n","sub_path":"taesko/web-server/ws/logs.py","file_name":"logs.py","file_ext":"py","file_size_in_byte":5669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"429619874","text":"#!/usr/bin/python\n\n# coding: utf-8\n\n# Copyright 2018 AstroLab Software\n# Author: Chris Arnault\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport sys, os\n\nsys.path.append(\"d:/workspace/pylivy/\")\nsys.path.append(\"../../lib/\")\n\nfrom livy.session import *\nfrom livy.client import *\nfrom gateway import *\n\n\"\"\"\nDemo of using the pylivy library\n\nhttps://pylivy.readthedocs.io/en/latest/index.web\n\n\"\"\"\n\n## LIVY_URL = \"http://vm-75222.lal.in2p3.fr:21111\"\n\nurl, auth = gateway_url (\"livy/v1\")\nclient = LivyClient(url, auth=auth, verify_ssl=False)\n\nprint(\"List all sessions\")\nsessions = client.list_sessions()\nfor session in sessions:\n    print(session.session_id, session.state)\n    if session.state == SessionState.IDLE:\n        for s in client.list_statements(session.session_id):\n            print(\" statement \", s)\n\n\n\nfor n, arg in enumerate(sys.argv):\n    if n == 0:\n        continue\n    print(n, arg)\n    print(\"Delete session \", arg)\n    try:\n        client.delete_session(arg)\n    except:\n        print(\"error killing session \", arg)\n","sub_path":"tuto/livy/kill_livy_session.py","file_name":"kill_livy_session.py","file_ext":"py","file_size_in_byte":1536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"369159430","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the insertionSort1 function below.\ndef insertionSort1(n, arr):\n    val=arr[-1]\n    flag=True\n    for i in range(n-2,-1,-1):\n        if arr[i]>val:\n            arr[i+1]=arr[i]\n            print(*arr, sep=\" \")\n            flag=False\n        else:\n            arr[i+1]=val\n            print(*arr,sep=\" \")\n            flag=True\n            break\n    if flag==False:\n        arr[0]=val\n        print(*arr,sep=\" \")\n\n\n\n\nif __name__ == '__main__':\n    n = int(input())\n\n    arr = list(map(int, input().rstrip().split()))\n\n    insertionSort1(n, arr)\n","sub_path":"HackerRank/InsertionSort2.py","file_name":"InsertionSort2.py","file_ext":"py","file_size_in_byte":626,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"148612982","text":"import os, sys, argparse\nimport datetime as dt\nimport threading\nfrom functools import wraps\n\ndef _caffeinate():\n    os.system('caffeinate')\n\ndef caffeinate(fn):\n    @wraps(fn)\n    def wrapper(*args, **kwargs):\n        if sys.platform == 'darwin':\n            thrd = threading.Thread(target = _caffeinate, args = ())\n            # 'service' thread. does not stop process from terminating.\n            thrd.daemon = True\n            thrd.start()\n        fn(*args, **kwargs)\n    return wrapper\n\ndef get_single_args():\n    parser = argparse.ArgumentParser(description = \"CLI for single southwest check-in\")\n    parser.add_argument('firstname', help = \"first name\")\n    parser.add_argument('lastname', help = \"last name\")\n    parser.add_argument('code', help = \"southwest code\")\n    parser.add_argument('-d', '--date', help = \"date (format is mm/dd/yyyy, default is today's date)\", default = dt.datetime.now())\n    parser.add_argument('-t', '--time', help = \"time (format is hh:mm, default is current time)\", default = dt.datetime.now())\n\n    args = parser.parse_args()\n\n    if isinstance(args.date, dt.datetime):\n        args.date = args.date.strftime('%m/%d/%Y')\n    if isinstance(args.time, dt.datetime):\n        args.time = args.time.strftime('%H:%M')\n\n    return args\n\ndef get_multiple_args():\n    parser = argparse.ArgumentParser(description = \"CLI for multiple southwest check ins\")\n    parser.add_argument('csv', help = \"csv file full path\")\n\n    args = parser.parse_args()\n    return args\n\nif __name__ == '__main__':\n    pass\n\n\n\n\n\n\n\n","sub_path":"southwest/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"156170549","text":"import os\nimport re\nimport sys\nimport platform\nimport sysconfig\nimport subprocess\n\nfrom setuptools import setup, Extension, find_packages\nfrom setuptools.command.build_ext import build_ext\nfrom setuptools.command.test import test as TestCommand\nfrom distutils.version import LooseVersion\n\n\n# Package meta-data.\nNAME = 'openjij'\nDESCRIPTION = 'Framework for the Ising model and QUBO'\nEMAIL = 'openjij@j-ij.com'\nAUTHOR = 'Jij Inc.'\nVERSION = '0.0.7'\n\n\nclass CMakeExtension(Extension):\n    def __init__(self, name, sourcedir=''):\n        Extension.__init__(self, name, sources=[])\n        self.sourcedir = os.path.abspath(sourcedir)\n\n\nclass CMakeBuild(build_ext):\n    def run(self):\n        try:\n            out = subprocess.check_output(['cmake', '--version'])\n        except OSError:\n            raise RuntimeError(\"CMake must be installed to build the following extensions: \" +\n                               \", \".join(e.name for e in self.extensions))\n\n        if platform.system() == \"Windows\":\n            cmake_version = LooseVersion(re.search(r'version\\s*([\\d.]+)', out.decode()).group(1))\n            if cmake_version < '3.1.0':\n                raise RuntimeError(\"CMake >= 3.1.0 is required on Windows\")\n\n        for ext in self.extensions:\n            self.build_extension(ext)\n\n    def build_extension(self, ext):\n        print(ext, ext.name)\n        extdir = os.path.abspath(os.path.dirname(self.get_ext_fullpath(ext.name)))\n        cmake_kwargs = ['-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=' + extdir,\n                      #'-DCMAKE_VERBOSE_MAKEFILE=ON',\n                      #'-DCMAKE_CUDA_FLAGS= -arch=sm_30 ',\n                      '-DPYTHON_EXECUTABLE=' + sys.executable]\n\n        cfg = 'Debug' if self.debug else 'Release'\n        build_kwargs = ['--config', cfg]\n\n        if platform.system() == \"Windows\":\n            cmake_kwargs += ['-DCMAKE_LIBRARY_OUTPUT_DIRECTORY_{}={}'.format(cfg.upper(), extdir)]\n            if sys.maxsize > 2**32:\n                cmake_kwargs += ['-A', 'x64']\n            build_kwargs += ['--', '/m']\n        else:\n            cmake_kwargs += ['-DCMAKE_BUILD_TYPE=' + cfg]\n            build_kwargs += ['--', '-j2']\n\n        env = os.environ.copy()\n        env['CXXFLAGS'] = '{} -DVERSION_INFO=\\\\\"{}\\\\\"'.format(env.get('CXXFLAGS', ''),\n                                                              self.distribution.get_version())\n        if not os.path.exists(self.build_temp):\n            os.makedirs(self.build_temp)\n        subprocess.check_call(['cmake', ext.sourcedir] + cmake_kwargs, cwd=self.build_temp, env=env)\n        subprocess.check_call(['cmake', '--build', '.', '--target', 'python'] + build_kwargs, cwd=self.build_temp)\n\nclass GoogleTestCommand(TestCommand):\n    \"\"\"\n    A custom test runner to execute both Python unittest tests and C++ Google Tests.\n    \"\"\"\n    def distutils_dir_name(self, dname):\n        \"\"\"Returns the name of a distutils build directory\"\"\"\n        dir_name = \"{dirname}.{platform}-{version[0]}.{version[1]}\"\n        return dir_name.format(dirname=dname,\n                               platform=sysconfig.get_platform(),\n                               version=sys.version_info)\n\n    def run(self):\n        # Run Python tests\n        super(GoogleTestCommand, self).run()\n        print(\"\\nPython tests complete, now running C++ tests...\\n\")\n        # Run catch tests\n        print(os.path.join('build/', self.distutils_dir_name('lib')))\n        subprocess.call(['make cxxjij_test'],\n                cwd=os.path.join('build',\n                                 self.distutils_dir_name('temp')),\n                shell=True)\n        subprocess.call(['./tests/cxxjij_test'],\n                        cwd=os.path.join('build',\n                                         self.distutils_dir_name('temp')),\n                        shell=True)\n\n# Load the package's __version__.py module as a dictionary.\nabout = {}\nif not VERSION:\n    project_slug = NAME.lower().replace(\"-\", \"_\").replace(\" \", \"_\")\n    with open(os.path.join(here, project_slug, '__version__.py')) as f:\n        exec(f.read(), about)\nelse:\n    about['__version__'] = VERSION\n\nsetup(\n    name=NAME,\n    version=about['__version__'],\n    author='Jij Inc.',\n    author_email='openjij@j-ij.com',\n    url = 'https://openjij.github.io/OpenJij/',\n    description='Framework for the Ising model and QUBO',\n    long_description=open('README.md').read(),\n    long_description_content_type=\"text/markdown\",\n    install_requires=['numpy', 'requests'],\n    ext_modules=[CMakeExtension('cxxjij')],\n    cmdclass=dict(build_ext=CMakeBuild, test=GoogleTestCommand),\n    packages=find_packages(exclude=('tests', 'docs')),\n    license='Apache License 2.0',\n    classifiers=[\n        'License :: OSI Approved :: Apache Software License',\n        'Intended Audience :: Science/Research',\n        'Programming Language :: Python',\n    ],\n    zip_safe=False\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":4865,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"374431031","text":"from . import (  # noqa: F401\n    qualitative,\n    sequential,\n    diverging,\n    cyclical,\n    cmocean,\n    colorbrewer,\n    carto,\n)\n\n\ndef swatches(mod):\n    from .. import _px\n    import plotly.graph_objs as go\n\n    sequences = [(k, v) for k, v in mod.__dict__.items() if not k.startswith(\"__\")]\n\n    return _px.FigurePx(\n        data=[\n            go.Bar(\n                orientation=\"h\",\n                y=[name] * len(colors),\n                x=[1] * len(colors),\n                customdata=list(range(len(colors))),\n                marker=dict(color=colors),\n                hovertemplate=\"%{y}[%{customdata}] = %{marker.color}<extra></extra>\",\n            )\n            for name, colors in reversed(sequences)\n        ],\n        layout=dict(\n            title=mod.__name__,\n            barmode=\"stack\",\n            barnorm=\"fraction\",\n            bargap=0.5,\n            showlegend=False,\n            xaxis=dict(range=[-0.02, 1.02], showticklabels=False, showgrid=False),\n            height=max(600, 40 * len(sequences)),\n        ),\n    )\n","sub_path":"plotly_express/colors/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"410382256","text":"# Carlos Henrique Durante da Silva\n# (11)96340-9937\n# carlos.durante@outlook.com\n# https://linkedin.com/in/carloshdurante\n# Listas\n\nlista = []\nfor i in range(10):\n    lista.append(int(input()))\n\nprint(max(lista))\nprint(min(lista))\nmedia = sum(lista)/len(lista)\nprint(media)\n\nfor i in lista:\n    if i < media:\n        print(i)\n","sub_path":"1802524.lista_ex1.py","file_name":"1802524.lista_ex1.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"392729551","text":"from __future__ import unicode_literals  # isort:skip\n\nimport json\n\nfrom flask import url_for\nfrom flask_webtest import SessionScope\n\nfrom portal.database import db\nfrom portal.extensions import user_manager\nfrom tests import TestCase\n\n\nclass TestAccessOnVerify(TestCase):\n\n    def test_create_account_via_api(self):\n        # use APIs to create account w/ special role\n        service_user = self.add_service_user()\n        self.login(user_id=service_user.id)\n        response = self.client.post(\n            '/api/account',\n            data=json.dumps({}),\n            content_type='application/json')\n        assert response.status_code == 200\n\n        # add role to account\n        user_id = response.json['user_id']\n        data = {'roles': [{'name': 'access_on_verify'}]}\n        response = self.client.put(\n            '/api/user/{user_id}/roles'.format(user_id=user_id),\n            data=json.dumps(data),\n            content_type='application/json')\n        assert response.status_code == 200\n\n    def test_access(self):\n        # confirm exception on access w/o DOB\n        weak_access_user = self.add_user(username='fake@org.com')\n        self.promote_user(weak_access_user, role_name='access_on_verify')\n        weak_access_user = db.session.merge(weak_access_user)\n        assert not weak_access_user.birthdate\n\n        token = user_manager.token_manager.generate_token(weak_access_user.id)\n        access_url = url_for('portal.access_via_token', token=token)\n\n        response = self.client.get(access_url)\n        assert response.status_code == 400\n\n        # add DOB & names and expect redirect to challenge\n        weak_access_user.birthdate = '01-31-1999'\n        weak_access_user.first_name = 'Testy'\n        weak_access_user.last_name = 'User'\n        with SessionScope(db):\n            db.session.commit()\n\n            response = self.client.get(access_url)\n        self.assert_redirects(response, url_for('portal.challenge_identity'))\n","sub_path":"tests/test_access_on_verify.py","file_name":"test_access_on_verify.py","file_ext":"py","file_size_in_byte":1957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"569232182","text":"#-------------------------------------------------------------------------------\n# Timestamp and Execution Timer Utilites\n#-------------------------------------------------------------------------------\n# Contains a timestamp function and an execution timer class.\n# \n# The function getTS() returns a timestamp as an ASCII string. The class Timer\n# defines a timer object that is suitable for capturing things like code\n# execution times or I/O latency times.\n#-------------------------------------------------------------------------------\n# Example source code for the book \"Real-World Instrumentation with Python\"\n# by J. M. Hughes, published by O'Reilly Media, December 2010,\n# ISBN 978-0-596-80956-0.\n#-------------------------------------------------------------------------------\nimport  datetime\nimport  time\n\ndef getTS():\n    \"\"\" Get current date and time from the system.\n\n        Format the data and time into a string and return it to the caller. The\n        data is in YYYY-MM-DD format, and the time is in HH:MM:SS.MS format.\n\t\te.g. for timestamp: 2015-04-09 11:01:19.355\n    \"\"\"\n    # fetch the current data and time from the system. Note that currutime\n    # is not used, but it's there if you need it.\n    t = datetime.datetime.now()\n    currdatetime = t.timetuple()\n    # currutime = time.mktime(t.timetuple())\n\n    # extract the values of interest from the string generated by time.mktime()\n    # yr = str(currdatetime[0])\n    curr_date = \"%04d-\"%currdatetime[0] + \"%02d-\"%currdatetime[1] + \"%02d\"%currdatetime[2]\n    curr_time = \"%02d:\"%currdatetime[3] + \"%02d:\"%currdatetime[4] + \"%02d.\"%currdatetime[5] + \"%03d\"% int(t.microsecond//1000)\n\n    return (curr_date + \" \" + curr_time)\n\ndef getTSfName():\n    \"\"\" Get current date and time from the system.\n\n        Format the data and time into a string and return it to the caller. The\n        data is in YYYY-MM-DD format, and the time is in HH:MM:SS.MS format.\n\t\te.g. for timestamp: 2015-04-09 11:01:19.355\n    \"\"\"\n    # fetch the current data and time from the system. Note that currutime\n    # is not used, but it's there if you need it.\n    t = datetime.datetime.now()\n    currdatetime = t.timetuple()\n    # currutime = time.mktime(t.timetuple())\n\n    # extract the values of interest from the string generated by time.mktime()\n    # yr = str(currdatetime[0])\n    curr_date = \"%04d\"%currdatetime[0] + \"%02d\"%currdatetime[1] + \"%02d\"%currdatetime[2]\n    curr_time = \"%02d\"%currdatetime[3] + \"%02d\"%currdatetime[4] + \"%02d\"%currdatetime[5]\n\n    return (curr_date + \"_\" + curr_time)\n\ndef getYMD():\n\t\"\"\" Get current date and time from the system.\n\t\t\n\t\tFormat the data and time into a string and return it to the caller. The\n\t\tdata is in YYYY-MM-DD format, and the time is in HH:MM:SS.MS format.\n\t\te.g. for timestamp: 2015-04-09 11:01:19.355\n\t\"\"\"\n\t# fetch the current data and time from the system. Note that currutime\n\t# is not used, but it's there if you need it.\n\tt = datetime.datetime.now()\n\tcurrdatetime = t.timetuple()\n\t# currutime = time.mktime(t.timetuple())\n\t\n\t# extract the values of interest from the string generated by time.mktime()\n\t# yr = str(currdatetime[0])\n\tyear = \"%04d\"%currdatetime[0]\n\tmonth = \"%02d\"%currdatetime[1]\n\tday = \"%02d\"%currdatetime[2]\n\t\n\treturn (year, month, day)\n\nclass Timer:\n    \"\"\" General purpose timer class.\n\n        Useful for creating multiple instances of a timer suitable for\n        capturing code execution times. Object instantiations of Timer will\n        not collide with one another and share no data between each instance.\n\n        All time values are returned in fractional seconds as floating point\n        values.\n    \"\"\"\n    def __init__(self):\n        self.tstart = 0\n        self.tlast  = 0\n        self.tcurr  = 0\n\n        self.Reset()\n\n\n    def GetDelta(self):\n        \"\"\" Returns time since last call to GetDelta()\n\n            Essentially just a \"lap\" timer. Does not modify or clear the\n            running time. Updates the local attributes tcurr and tlast.\n        \"\"\"\n        self.tcurr = time.clock()\n        delta = self.tcurr - self.tlast\n        self.tlast = self.tcurr\n        return delta\n\n\n    def GetTotal(self):\n        \"\"\" Returms time since timer object created.\n        \"\"\"\n        return time.clock() - self.tstart\n\n\n    def Reset(self):\n        \"\"\" Initialize time attributes.\n\n            Sets local data atributes tstart and tlast to the current\n            system time.\n        \"\"\"\n        self.tstart = time.clock()\n        self.tlast = self.tstart","sub_path":"TimeUtils.py","file_name":"TimeUtils.py","file_ext":"py","file_size_in_byte":4498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"419581705","text":"import face_recognition\r\nimport cv2\r\nimport datetime\r\nimport time\r\nimport numpy as np\r\nimport sys\r\nimport ibmiotf.application\r\nimport ibmiotf.device\r\nimport random\r\nimport ibm_boto3\r\nfrom ibm_botocore.client import Config, ClientError\r\n\r\nfrom cloudant.client import Cloudant\r\nfrom cloudant.error import CloudantException \r\nfrom cloudant.result import Result, ResultByKey\r\n#Provide your IBM Watson Device Credentials\r\norganization = \"5z48y5\"\r\ndeviceType = \"ESP32\"\r\ndeviceId = \"12345\"\r\nauthMethod = \"token\"\r\nauthToken = \"12345678\"\r\n\r\ndef myCommandCallback(cmd):\r\n        print(\"Command received: %s\" % cmd.data)\r\n        print(cmd.data['command'])\r\n       \r\n        if(cmd.data['command']==\"open\"):\r\n                print(\"door open\")\r\n                \r\n        if(cmd.data['command']==\"close\"):\r\n                print(\"door close\")\r\n                \r\n\r\n        if(cmd.data['command']==\"lighton\"):\r\n                print(\"light on\")\r\n                \r\n        if(cmd.data['command']==\"lightoff\"):\r\n                print(\"light off\")\r\n               \r\n        if(cmd.data['command']==\"fanon\"):\r\n                print(\"fan on\")\r\n        if(cmd.data['command']==\"fanoff\"):\r\n                print(\"fan off\")\r\n# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the\r\n# other example, but it includes some basic performance tweaks to make things run a lot faster:\r\n#   1. Process each video frame at 1/4 resolution (though still display it at full resolution)\r\n#   2. Only detect faces in every other frame of video.\r\n\r\n# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.\r\n# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this\r\n# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.\r\ntry:\r\n\tdeviceOptions = {\"org\": organization, \"type\": deviceType, \"id\": deviceId, \"auth-method\": authMethod, \"auth-token\": authToken}\r\n\tdeviceCli = ibmiotf.device.Client(deviceOptions)\r\n\t#..............................................\r\n\t\r\nexcept Exception as e:\r\n\tprint(\"Caught exception connecting device: %s\" % str(e))\r\n\tsys.exit()\r\ndeviceCli.connect()\r\n# Get a reference to webcam #0 (the default one)\r\nvideo_capture = cv2.VideoCapture(0)\r\n\r\nCOS_ENDPOINT = \"https://s3.jp-tok.cloud-object-storage.appdomain.cloud\" # Current list avaiable at https://control.cloud-object-storage.cloud.ibm.com/v2/endpoints\r\nCOS_API_KEY_ID = \"CxSLfU7ZsJlLsGML68optH1-lbCZmT3QEZISRNWERlJD\" # eg \"W00YiRnLW4a3fTjMB-odB-2ySfTrFBIQQWanc--P3byk\"\r\nCOS_AUTH_ENDPOINT = \"https://iam.cloud.ibm.com/identity/token\"\r\nCOS_RESOURCE_CRN = \"crn:v1:bluemix:public:cloud-object-storage:global:a/1d87764306ae4fe6b3f1f1894f21de93:9919fdb9-fafe-4bb3-9261-d0961faf0c9f::\" # eg \"crn:v1:bluemix:public:cloud-object-storage:global:a/3bf0d9003abfb5d29761c3e97696b71c:d6f04d83-6c4f-4a62-a165-696756d63903::\"\r\n\r\n\r\nclient = Cloudant(\"apikey-v2-1mx20vgbcub4ga86f7ue9wfwbo4u6gg7bvwbt60tsk9h\", \"2f29a39fffdd70ea15507a98b97b717e\", url=\"https://apikey-v2-1mx20vgbcub4ga86f7ue9wfwbo4u6gg7bvwbt60tsk9h:2f29a39fffdd70ea15507a98b97b717e@ebc948ef-7e52-48b5-a89d-d08741e60f2a-bluemix.cloudantnosqldb.appdomain.cloud\")\r\nclient.connect()\r\ndatabase_name = \"vinaykumar31\"\r\n\r\ncos = ibm_boto3.resource(\"s3\",\r\n    ibm_api_key_id=COS_API_KEY_ID,\r\n    ibm_service_instance_id=COS_RESOURCE_CRN,\r\n    ibm_auth_endpoint=COS_AUTH_ENDPOINT,\r\n    config=Config(signature_version=\"oauth\"),\r\n    endpoint_url=COS_ENDPOINT\r\n)\r\n\r\n# Load a sample picture and learn how to recognize it.\r\nobama_image = face_recognition.load_image_file(r\"Picture.jpeg\")\r\nobama_face_encoding = face_recognition.face_encodings(obama_image)[0]\r\n\r\n# Load a second sample picture and learn how to recognize it.\r\n#biden_image = face_recognition.load_image_file(r\"C:\\Users\\Kalkeseetharaman P K\\Desktop\\karan.jpg\")\r\n#biden_face_encoding = face_recognition.face_encodings(biden_image)[0]\r\n\r\n'''biden_image1 = face_recognition.load_image_file(\"nikhil.jpeg\")\r\nbiden_face_encoding1 = face_recognition.face_encodings(biden_image1)[0]'''\r\n\r\n# Create arrays of known face encodings and their names\r\nknown_face_encodings = [\r\n    obama_face_encoding\r\n    #biden_face_encoding,\r\n    #biden_face_encoding1\r\n]\r\nknown_face_names = [\r\n    \"Vinay\"\r\n    #\"Karan\",\r\n    #\"Nikhil\"\r\n]\r\n\r\n# Initialize some variables\r\nface_locations = []\r\nface_encodings = []\r\nface_names = []\r\nprocess_this_frame = True\r\n\r\ndef multi_part_upload(bucket_name, item_name, file_path):\r\n    try:\r\n        print(\"Starting file transfer for {0} to bucket: {1}\\n\".format(item_name, bucket_name))\r\n        # set 5 MB chunks\r\n        part_size = 1024 * 1024 * 5\r\n\r\n        # set threadhold to 15 MB\r\n        file_threshold = 1024 * 1024 * 15\r\n\r\n        # set the transfer threshold and chunk size\r\n        transfer_config = ibm_boto3.s3.transfer.TransferConfig(\r\n            multipart_threshold=file_threshold,\r\n            multipart_chunksize=part_size\r\n        )\r\n\r\n        # the upload_fileobj method will automatically execute a multi-part upload\r\n        # in 5 MB chunks for all files over 15 MB\r\n        with open(file_path, \"rb\") as file_data:\r\n            cos.Object(bucket_name, item_name).upload_fileobj(\r\n                Fileobj=file_data,\r\n                Config=transfer_config\r\n            )\r\n\r\n        print(\"Transfer for {0} Complete!\\n\".format(item_name))\r\n    except ClientError as be:\r\n        print(\"CLIENT ERROR: {0}\\n\".format(be))\r\n    except Exception as e:\r\n        print(\"Unable to complete multi-part upload: {0}\".format(e))\r\n\r\nwhile True:\r\n    # Grab a single frame of video\r\n    ret, frame = video_capture.read()\r\n\r\n    # Resize frame of video to 1/4 size for faster face recognition processing\r\n    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)\r\n\r\n    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)\r\n    rgb_small_frame = small_frame[:, :, ::-1]\r\n\r\n    # Only process every other frame of video to save time\r\n    if process_this_frame:\r\n        # Find all the faces and face encodings in the current frame of video\r\n        face_locations = face_recognition.face_locations(rgb_small_frame)\r\n        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)\r\n\r\n        face_names = []\r\n        for face_encoding in face_encodings:\r\n            # See if the face is a match for the known face(s)\r\n            matches = face_recognition.compare_faces(known_face_encodings, face_encoding)\r\n            name = \"Unknown\"\r\n\r\n            # If a match was found in known_face_encodings, just use the first one.\r\n            if True in matches:\r\n                first_match_index = matches.index(True)\r\n                name = known_face_names[first_match_index]\r\n\r\n            face_names.append(name)\r\n\r\n    process_this_frame = not process_this_frame\r\n\r\n\r\n    # Display the results\r\n    for (top, right, bottom, left), name in zip(face_locations, face_names):\r\n        # Scale back up face locations since the frame we detected in was scaled to 1/4 size\r\n        top *= 4\r\n        right *= 4\r\n        bottom *= 4\r\n        left *= 4\r\n\r\n        # Draw a box around the face\r\n        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)\r\n\r\n        # Draw a label with a name below the face\r\n        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)\r\n        font = cv2.FONT_HERSHEY_DUPLEX\r\n        cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)\r\n        picname=datetime.datetime.now().strftime(\"%y-%m-%d-%H-%M\")\r\n        picname=picname+\".jpeg\"\r\n        pic=datetime.datetime.now().strftime(\"%y-%m-%d-%H-%M\")\r\n        cv2.imwrite(picname,frame)\r\n        person=1\r\n        my_database = client.create_database(database_name)        \r\n        multi_part_upload(\"vinaykumar31\",picname,pic+\".jpeg\")       \r\n        if my_database.exists():\r\n            print(\"'{database_name}' successfully created.\")\r\n            json_document = {\r\n                    \"_id\": pic,\r\n                    \"link\":COS_ENDPOINT+\"/vinaykumar31/\"+picname\r\n                    }\r\n            new_document = my_database.create_document(json_document)\r\n            if new_document.exists():\r\n                print(\"Document '{new_document}' successfully created.\")\r\n        time.sleep(1)\r\n        t=34\r\n        h=45\r\n        data = {\"d\":{ 'temperature' : t, 'humidity': h, 'person': person}}\r\n        #print data\r\n        def myOnPublishCallback():\r\n            print (\"Published data to IBM Watson\")\r\n\r\n        success = deviceCli.publishEvent(\"Data\", \"json\", data, qos=0, on_publish=myOnPublishCallback)\r\n        if not success:\r\n            print(\"Not connected to IoTF\")\r\n        time.sleep(1)\r\n        deviceCli.commandCallback = myCommandCallback\r\n        person=0\r\n        \r\n    # Display the resulting image\r\n    cv2.imshow('Video', frame)\r\n\r\n    # Hit 'q' on the keyboard to quit!\r\n    if cv2.waitKey(1) & 0xFF == ord('q'):\r\n        break\r\ndeviceCli.disconnect()\r\n# Release handle to the webcam\r\nvideo_capture.release()\r\ncv2.destroyAllWindows()\r\n","sub_path":"iot.py","file_name":"iot.py","file_ext":"py","file_size_in_byte":9167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"225447343","text":"\nquantity = int(input(\"How many pizzas do you want to order? \"))\n\ndef get_pizza_order(quantity):\n    for i in range (1, quantity + 1):\n        toppings = int(input(\"How many toppings do you want on pizza {}? \".format(i)))\n        print(\"You have ordered a pizza with {} toppings.\".format(toppings))\n    return\n\nget_pizza_order(quantity)\n","sub_path":"excercise12.py","file_name":"excercise12.py","file_ext":"py","file_size_in_byte":337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"430336075","text":"import fnmatch\nimport os\n\ndirname = '/data/jorgem/orc_files'\nmatches = []\nfor root, dirnames, filenames in os.walk(dirname):\n  for filename in fnmatch.filter(filenames, '*'):\n    matches.append(os.path.join(root, filename))\n\ntemplate = '''\ntest_that(\"%s\", {\n  df <- ddc_read(url='%s', options=list(fileType='orc'))\n  expect_more_than(nrow(df), 0)\n  expect_more_than(col(df), 0)\n  expect_true(is.data.frame(df))\n})\n'''\n\nout = '''\nlibrary(ddc)\n'''\nfor f in matches:\n  out += template %(f,f)\n\nprint(out)\n\n","sub_path":"tools/gen_orc_tests.py","file_name":"gen_orc_tests.py","file_ext":"py","file_size_in_byte":502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"470649793","text":"\"\"\"\n Milestone C - Software Design and Integration\n \n Lesson 0:\n Blank Pygame Template with Raspberry Pi GPIO\n \n\"\"\"\n \nimport pygame\n\n#import RPi.GPIO as GPIO\nimport time\n \n# Definitions\n\n\n# Initialize Pygame\npygame.init()\n \n# Set the width and height of the screen [width, height]\nsize = (600, 480)\nscreen = pygame.display.set_mode(size)\npygame.display.set_caption(\"My Game\")\n \n# Set main loop complete\ncomplete = False\n \n# Set the speed of loops\nclock = pygame.time.Clock()\n \n# -------- Main Program Loop -----------\nwhile not complete:\n    # -- Main event loop\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            complete = True\n \n    # - Add game logic should go here\n \n    # - Screen clearing code goes here\n    screen.fill((0,0,0))\n\n    # - Draw objects on the screen\n    \n     \n    # - Update the screen with our drawing\n    pygame.display.flip()\n\n    # Set frame rate\n    clock.tick(30)\n \n# Close the window and quit.\npygame.quit()\n","sub_path":"Lesson 0- Blank Template.py","file_name":"Lesson 0- Blank Template.py","file_ext":"py","file_size_in_byte":974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"80549319","text":"from flask import Flask, render_template\r\nfrom data import db_session\r\nfrom data.constants import DB_NAME\r\nfrom data.news import News\r\nfrom data.users import User\r\nfrom data.forms import RegisterForm\r\n\r\n\r\napp = Flask(__name__)\r\napp.config[\"SECRET_KEY\"] = \"SECRET_KEY\"\r\n\r\n\r\n@app.route(\"/register\", methods=[\"GET\", \"POST\"])\r\ndef reqister():\r\n    form = RegisterForm()\r\n    if form.validate_on_submit():\r\n        if form.password.data != form.password_again.data:\r\n            return render_template(\"register.html\", title=\"Регистрация\",\r\n                                   form=form,\r\n                                   message=\"Пароли не совпадают\")\r\n        session = db_session.create_session()\r\n        if session.query(User).filter(User.email == form.login.data).first():\r\n            return render_template(\"register.html\", title=\"Регистрация\",\r\n                                   form=form,\r\n                                   message=\"Такой пользователь уже есть\")\r\n        user = User(\r\n            surname=form.surname.data,\r\n            name=form.name.data,\r\n            age=form.age.data,\r\n            position=form.position.data,\r\n            speciality=form.speciality.data,\r\n            address=form.address.data,\r\n            email=form.login.data,\r\n        )\r\n        user.set_password(form.password.data)\r\n        session.add(user)\r\n        session.commit()\r\n        return \"Вы успешно зарегистрированы!\"\r\n    return render_template(\"register.html\", title=\"Регистрация\", form=form)\r\n\r\n\r\ndef main():\r\n    db_session.global_init(DB_NAME)\r\n    app.run()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","sub_path":"Форма регистрации/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"542805845","text":"\"\"\".\"\"\"\n# from random import shuffle\nfrom pprint import pprint\n# from shutil import rmtree\n# from os import mkdir\n# import matplotlib.pyplot as plt\nprint, pprint = pprint, print\n\n\nclass Grafo:\n    \"\"\".\"\"\"\n\n    def __init__(self, v, e):\n        \"\"\".\"\"\"\n        self._g = {}\n        self.v = v\n        self.e = e\n\n    def addAresta(self, v1: (int or float), v2: (int or float), p: (int or float) = 0):\n        \"\"\".\"\"\"\n        if not (v1 in self._g.keys()):\n            self._g[v1] = {}\n        self._g[v1][v2] = p\n\n    @property\n    def g(self):\n        \"\"\".\"\"\"\n        return self._g\n\n    def __repr__(self):\n        \"\"\".\"\"\"\n        return str(self._g)\n\n\ndef dist2pt(x1, y1, x2, y2):\n    \"\"\".\"\"\"\n    return ((x1 - x2)**2 + (y1 - y2)**2)**(1 / 2)\n\n\ndef ptmed(x1, y1, x2, y2):\n    \"\"\".\"\"\"\n    return (x1 + x2) / 2, (y1 + y2) / 2\n\n\ndef corta(g: dict, pi: (int or float), mi: (int or float), mostra=True):\n    \"\"\"Corta as arestas do grafo.\n\n    args:\n    g -> grafo\n    pi -> velocidade do corte\n    mi -> velocidade do deslocamento\n    \"\"\"\n    # somatorio = []\n    arestas = [(i, j) for i in g.keys() for j in g[i]]\n    arestasCortadas = []\n    cortadas = []\n    # cores = ['red', 'yellow']\n    # shuffle(arestas)\n    # rmtree('plot')\n    # mkdir('plot')\n    # plt.xlim(0, 40)\n    # plt.ylim(0, 40)\n    for i in range(len(arestas)):\n        if not(arestas[i] in arestasCortadas or (arestas[i][1], arestas[i][0]) in arestasCortadas):\n            # x, y = [], []\n            # x1, y1 = [], []\n            cortadas.append([arestas[i]])\n            if i == 0:\n                # somatorio.append((g[arestas[i][0]][arestas[i][1]])/pi)\n                cortadas[-1].append((g[arestas[i][0]][arestas[i][1]]) / pi)\n            else:\n                if arestas[i - 1][1] == arestas[i][0]:\n                    # somatorio.append((g[arestas[i][0]][arestas[i][1]])/pi)\n                    cortadas[-1].append((g[arestas[i][0]][arestas[i][1]]) / pi)\n                else:\n                    # somatorio.append(\n                    #     (dist2pt(*arestas[i-1][1], *arestas[i][0]))/mi +\n                    #     (g[arestas[i][0]][arestas[i][1]])/pi)\n                    # x1.append(arestas[i-1][1][0])\n                    # y1.append(arestas[i-1][1][1])\n                    # x1.append(arestas[i][0][0])\n                    # y1.append(arestas[i][0][1])\n                    # plt.plot(x1, y1, '-*', color=cores[1])\n                    cortadas[-1].append(\n                        (dist2pt(*arestas[i - 1][1], *arestas[i][0])) / mi)\n                    cortadas[-1].append((g[arestas[i][0]][arestas[i][1]]) / pi)\n            # x.append(arestas[i][0][0])\n            # y.append(arestas[i][0][1])\n            # x.append(arestas[i][1][0])\n            # y.append(arestas[i][1][1])\n            arestasCortadas.append(arestas[i])\n            # plt.plot(x, y, '-*', color=cores[0])\n            # plt.annotate(str(len(arestasCortadas)), ptmed(\n            #     *arestas[i][0], *arestas[i][1]))\n            # plt.savefig(f\"plot/g{len(arestasCortadas)}.png\")\n    # print(*cortadas[0], *cortadas[1])\n    out = str(len(cortadas))\n    if mostra:\n        pprint(len(cortadas))\n    for i in cortadas:\n        if mostra:\n            pprint(*i[0][0], *i[0][1])\n        out += f'\\n{i[0][0][0]} {i[0][0][1]} {i[0][1][0]} {i[0][1][1]}'\n    # pprint(out)\n    # print(len(arestasCortadas))\n    # print(sum(somatorio))\n    return out\n    # return sum(somatorio)\n\n\ndef cortar(ent, mostra=True):\n    \"\"\".\"\"\"\n    # print(ent)\n    g = Grafo(*ent[0])\n    for i in range(1, int(g.e)):\n        ent2 = [float(j) for j in ent[i]]\n        g.addAresta((ent2[0], ent2[1]), (ent2[2], ent2[3]), ent2[4])\n    return corta(g.g, 1, 5, mostra)\n\n\nif __name__ == \"__main__\":\n    ent = [[int(i) for i in input().split()]]\n    while True:\n        try:\n            ent.append([float(i) for i in input().split()])\n        except EOFError:\n            break\n    cortar(ent)\n","sub_path":"leitor/corte_arestas.py","file_name":"corte_arestas.py","file_ext":"py","file_size_in_byte":3908,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"392025169","text":"from setuptools import setup\n\n\nTESTS_REQUIRE = ['pytest', 'pytest-cov']\nEXTRAS_REQUIRE = {\n    'doc': ['sphinx', 'sphinx_rtd_theme'],\n    'test': TESTS_REQUIRE\n}\nEXTRAS_REQUIRE['all'] = sorted(set(sum(EXTRAS_REQUIRE.values(), [])))\n\nsetup(\n    name='pyoblib',\n    version='0.1',\n    description='Orange Button Python Library',\n    long_description=\n    \"\"\"\n    The Orange Button Python Library, also called, pyoblib, \n    provides functions to interact and work with the SunSpec \n    Orange Button Taxonomy and provides capabilities that \n    simplify working with Orange Button data. This project is \n    being actively developed and is not yet ready for production use.\n    Documentation: https://pyoblib.readthedocs.io/en/latest/\n    Source code: https://github.com/SunSpecOrangeButton/pyoblib\n    \"\"\",\n    author='SunSpec Alliance',\n    author_email='support@sunspec.org',\n    packages=['oblib'],\n    extras_require=EXTRAS_REQUIRE,\n    license='Apache 2.0'\n    )\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":967,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"484160681","text":"import watchtower, flask, logging\n\nlogging.basicConfig(level=logging.INFO)\napp = flask.Flask(\"loggable\")\nhandler = watchtower.CloudWatchLogHandler()\napp.logger.addHandler(handler)\nlogging.getLogger(\"werkzeug\").addHandler(handler)\n\n@app.route('/')\ndef hello_world():\n    return 'Hello World!'\n\nif __name__ == '__main__':\n    app.run()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"348269604","text":"\"\"\"classifier model\n    (|affine| → |relu|)*5 → |dropout|\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport sys, os\nsys.path.append(os.pardir)\nsys.path.append(os.curdir)\n\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom common import *\nfrom nikkei import NikkeiData\n\ndef train():\n  # Import data\n  nikkei = NikkeiData()\n\n  sess = tf.InteractiveSession()\n  # Create the model\n  with tf.name_scope('input'):\n    x = tf.placeholder(tf.float32, [None, nikkei.in_size], name='x-input')\n    y_ = tf.placeholder(tf.float32, [None, nikkei.out_size], name='y-input')\n\n  hidden1 = nn_layer(x, nikkei.in_size, 1000, 'hidden1')\n  hidden2 = nn_layer(hidden1, 1000, 750, 'hidden2')\n  hidden3 = nn_layer(hidden2, 750, 500, 'hidden3')\n  hidden4 = nn_layer(hidden3, 500, 250, 'hidden4')\n  hidden5 = nn_layer(hidden4, 250, 125, 'hidden5')\n\n  # Do not apply softmax activation yet, see below.\n  with tf.name_scope('dropout'):\n    keep_prob = tf.placeholder(tf.float32)\n    tf.summary.scalar('dropout_keep_probability', keep_prob)\n    dropped = tf.nn.dropout(hidden5, keep_prob)\n  y = nn_layer(dropped, 125, 1, 'dropout', act=tf.identity)\n\n  with tf.name_scope('loss'):\n    diff = tf.nn.l2_loss(y_ - y)\n    with tf.name_scope('total'):\n      l2 = tf.reduce_mean(diff)\n    with tf.name_scope('weight_decay'):\n      tf.add_to_collection('losses', l2)\n      loss = tf.add_n(tf.get_collection('losses'), name='total_loss')\n  tf.summary.scalar('loss', loss)\n\n  with tf.name_scope('train'):\n    train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(\n        loss)\n\n  with tf.name_scope('accuracy'):\n    with tf.name_scope('correct_prediction'):\n      correct_prediction = tf.equal(y, y_)\n    with tf.name_scope('accuracy'):\n      accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n  tf.summary.scalar('accuracy', accuracy)\n\n  merged = tf.summary.merge_all()\n  train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/tb/dnn/train', sess.graph)\n  test_writer = tf.summary.FileWriter(FLAGS.log_dir + '/tb/dnn/test')\n  tf.global_variables_initializer().run()\n\n  # Train\n  def feed_dict(train):\n    \"\"\"Make a TensorFlow feed_dict: maps data onto Tensor placeholders.\"\"\"\n    if train:\n        xs, ys = nikkei.fetch_train()\n        k = FLAGS.dropout\n    else:\n        xs, ys = nikkei.fetch_test()\n        k = 1.0\n    return {x: xs, y_: ys, keep_prob: k}\n\n  # save histgram svg\n  def save_hist(step):\n    xs, ys = nikkei.fetch_test()\n    output = np.array(sess.run([y], {x: xs, keep_prob: 1.0}))\n    output = output.reshape(-1)\n    ys = ys.reshape(-1)\n    loss = abs(output-ys)\n    plt.figure()\n    plt.hist(loss, bins=16)\n    plt.savefig(FLAGS.log_dir + '/svg/dnn_' + str(step) + '.svg')\n\n  for i in range(FLAGS.max_steps+1):\n    if i % 10 == 0:  # Record summaries and test-set accuracy\n      summary, acc = sess.run([merged, accuracy],\n                        feed_dict=feed_dict(False))\n      test_writer.add_summary(summary, i)\n      print('Accuracy at step %s: %s' % (i, acc))\n    else:  # Record train set summaries, and train\n      if i % 100 == 99:  # Record execution stats\n        run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\n        run_metadata = tf.RunMetadata()\n        summary, _ = sess.run([merged, train_step],\n                              feed_dict=feed_dict(True),\n                              options=run_options,\n                              run_metadata=run_metadata)\n        train_writer.add_run_metadata(run_metadata, 'step%03d' % i)\n        train_writer.add_summary(summary, i)\n        print('Adding run metadata for', i)\n      else:  # Record a summary\n        summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))\n        train_writer.add_summary(summary, i)\n    if i % FLAGS.hist_interval == 0:\n      save_hist(i)\n  train_writer.close()\n  test_writer.close()\n\n  # save train model\n  #saver = tf.train.Saver()\n  #save_path = saver.save(sess, FLAGS.log_dir +\n  #              '/model/dnn_' + str(FLAGS.max_steps) + '.ckpt')\n  #print(\"Model saved in file: %s\" % save_path)\n\ndef main(_):\n  # init tensor_borad_logdir\n  tensor_borad_logdir = FLAGS.log_dir + '/tb/dnn'\n  if tf.gfile.Exists(tensor_borad_logdir):\n    tf.gfile.DeleteRecursively(tensor_borad_logdir)\n  tf.gfile.MakeDirs(tensor_borad_logdir)\n  # init histgram_logdir\n  histgram_logdir = FLAGS.log_dir + '/svg'\n  if not tf.gfile.Exists(histgram_logdir):\n    tf.gfile.MakeDirs(histgram_logdir)\n  train()\n\nif __name__ == '__main__':\n  parser = initArgParser()\n  FLAGS, unparsed = parser.parse_known_args()\n  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)\n","sub_path":"nikkei_ave/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"217783010","text":"import discord\nfrom discord.ext import commands\nfrom config import COG_EXCEPTION\n\n\nclass CustomHelp(commands.Cog, name='Help'):\n\n    def __init__(self, bot):\n        self.bot = bot\n\n    @commands.command()\n    async def help(self, ctx, *cog):\n        \"\"\"Gets all cogs and commands.\"\"\"\n\n        if not cog:\n\n            embed = discord.Embed(\n                description=\"\"\"\n                To view what certain command do, use:\n                ***!help <command>***\n                \"\"\",\n                color=0x437840\n            )\n            embed.set_author(name=\"Turtle's commands.\",\n                             icon_url=self.bot.user.avatar_url)\n            embed.set_thumbnail(url=self.bot.user.avatar_url)\n            embed.set_footer(text=f\"Author: {self.bot.appinfo.owner}\",\n                             icon_url='https://cdn.discordapp.com/attachments/596341948705407040/724748657924112454/610021821856743444.gif')\n\n            for x in self.bot.cogs:\n                if x in COG_EXCEPTION or len(self.bot.cogs[x].get_commands()) == 0:\n                    pass\n                else:\n                    cogs_cmds = self.bot.cogs[x].get_commands()\n                    f_cogs_cmds = '\\n'.join(\n                        ([\"✧ \" + c.name for c in cogs_cmds]))\n\n                    embed.add_field(\n                        name=x, value=f'{f_cogs_cmds}', inline=True)\n            await ctx.send(embed=embed)\n        else:\n            if len(cog) > 1:\n                embed = discord.Embed(\n                    title='Error!', description='Too many cogs!', color=0x437840)\n                embed.set_thumbnail(url=self.bot.user.avatar_url)\n                embed.set_footer(\n                    text=f\"Author: {self.bot.appinfo.owner}\", icon_url=self.bot.user.avatar_url)\n\n                await ctx.message.author.send('', embed=embed)\n            else:\n                found = False\n                for x in self.bot.cogs:\n                    for y in cog:\n                        if x == y:\n                            embed = discord.Embed(color=0x437840)\n                            scog_info = ''\n                            for c in self.bot.get_cog(y).get_commands():\n                                if not c.hidden:\n                                    scog_info += f'**{c.name}** - {c.help}\\n'\n                            embed.add_field(\n                                name=f'{cog[0]} Module - {self.bot.cogs[cog[0]].__doc__}', value=scog_info)\n                            embed.set_footer(\n                                text=f\"Author: {self.bot.appinfo.owner}\",\n                                icon_url=self.bot.user.avatar_url)\n                            found = True\n            if not found:\n                for x in self.bot.cogs:\n                    for c in self.bot.get_cog(x).get_commands():\n\n                        if c.name == cog[0]:\n                            embed = discord.Embed(color=0x437840)\n                            embed.add_field(\n                                name=f'{c.name} - {c.help}', value=f'Proper Syntax:\\n`{c.qualified_name} {c.signature}`')\n                            embed.set_footer(\n                                text=f\"Author: {self.bot.appinfo.owner}\",\n                                icon_url=self.bot.user.avatar_url)\n\n                    found = True\n                if not found:\n                    embed = discord.Embed(\n                        title='Error!', description='How do you even use \"'+cog[0]+'\"?', color=0x437840)\n                else:\n                    pass\n            await ctx.send(embed=embed)\n\n    @commands.Cog.listener()\n    async def on_command_error(self, ctx, error):\n        try:\n            if hasattr(ctx.command, 'on_error'):\n                return\n            else:\n                embed = discord.Embed(\n                    title=f'Error in {ctx.command}', description=f'`{ctx.command.qualified_name} {ctx.command.signature}` \\n{error}', color=0x43788)\n                embed.set_thumbnail(url=self.bot.user.avatar_url)\n\n                await ctx.send(embed=embed)\n                await ctx.message.add_reaction(emoji='❔')\n        except:\n            embed = discord.Embed(\n                title=f'Error in {ctx.command}',\n                description=f'{error}',\n                color=0x43788\n            )\n\n            embed.set_thumbnail(url=self.bot.user.avatar_url)\n\n            await ctx.send(embed=embed)\n\n\ndef setup(bot):\n    bot.add_cog(CustomHelp(bot))\n","sub_path":"cogs/help.py","file_name":"help.py","file_ext":"py","file_size_in_byte":4463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"388806824","text":"# MIT License\n#\n# Copyright (c) 2019 Morning Project Samurai Inc. (MPS)\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\n\n__author__ = 'Junya Kaneko <junya@mpsamurai.org>'\n\n\nimport unittest\nimport time\nfrom ..core.dataflow import serializers\n\n\nclass MockData:\n    def __init__(self):\n        self._value = {'key': None}\n\n    @property\n    def value(self):\n        return self._value['key']\n\n    @value.setter\n    def value(self, v):\n        self._value['key'] = v\n\n    @classmethod\n    def __from_json__(cls, json_obj):\n        obj = MockData()\n        obj.value = json_obj['key']\n        return obj\n\n    def __to_json__(self):\n        return self._value\n\n\nclass MockSerializer(serializers.Serializer):\n    _schema = {\n        'type': 'object',\n        'properties': {\n            'key0': {'type': 'string'},\n            'key1': {\n                'type': 'object',\n                'properties': {\n                    'key1.1': {'type': 'number'},\n                    'key1.2': {'type': 'number', 'default': time.time}\n                },\n                'required': ['key1.2', ]\n            },\n        },\n        'required': ['key0', 'key1', ]\n    }\n\n\nclass TestEncoder(unittest.TestCase):\n    def test_it_encodes_and_decodes_class(self):\n        mc = MockData()\n        encoded = serializers.Encoder().encode(mc)\n        decoded = serializers.Decoder().decode(encoded)\n        self.assertEqual(decoded.value, mc.value)\n\n\nclass TestSerializer(unittest.TestCase):\n    def test_it_only_accepts_valid_json(self):\n        valid_json_obj = {\n            'key0': 'string',\n            'key1': {\n                'key1.1': 123.,\n            }\n        }\n        serializer = MockSerializer()\n        serializer.validate(valid_json_obj)\n        self.assertTrue(isinstance(valid_json_obj['key1']['key1.2'], float))\n\n        invalid_json_obj = {\n            'key0': 1,\n            'key1': {\n                'key1.1': 123.,\n            }\n        }\n        with self.assertRaises(serializers.exceptions.ValidationError):\n            serializer.validate(invalid_json_obj)\n\n    def test_if_it_serializes_and_deserializes_correctly(self):\n        valid_json_obj = {\n            'key0': 'string',\n            'key1': {\n                'key1.1': 123.,\n            }\n        }\n        serializer = MockSerializer()\n        serialized = serializer.serialize(valid_json_obj)\n        deserialized = serializer.deserialize(serialized)\n        self.assertEqual(valid_json_obj['key0'], deserialized['key0'])\n        self.assertEqual(valid_json_obj['key1']['key1.1'], deserialized['key1']['key1.1'])\n        self.assertTrue(valid_json_obj['key1']['key1.2'], deserialized['key1']['key1.2'])\n","sub_path":"neochi/tests/test_dataflow_serializers.py","file_name":"test_dataflow_serializers.py","file_ext":"py","file_size_in_byte":3671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"428001953","text":"from flask_restful import reqparse, abort, Resource, fields, marshal_with\nfrom ..models import Task\nfrom datetime import datetime\n\n\nclass ExpirationDateTimeCase(fields.Raw):\n    \"\"\"\n    Handle the condition expiration does NOT exist\n    \"\"\"\n    def format(self, value):\n        try:\n            # print(value)\n            return datetime.strftime(value, '%Y-%m-%dT%H:%M:%S') if value else str(value)\n        except Exception:\n            raise fields.MarshallingException('Can NOT format this datetime field')\n\n\n# confine response structure\nresponse_fields = {\n    'id': fields.Integer,\n    'title': fields.String,\n    'description': fields.String,\n    'expiration': ExpirationDateTimeCase,\n    'task_uuid': fields.String,\n    'is_finished': fields.Boolean\n}\n\nresponse_fields_list = {\n    'tasks': fields.List(fields.Nested(response_fields))\n}\n\n\n# debug\n'''\nfrom . import api_bp\nfrom flask import request\n\n\n@api_bp.before_request\ndef before_request():\n    headers = request.headers\n    method = request.method\n    ip = request.remote_addr\n    url = request.url\n    form = request.form  # post body\n    args = request.args  # query string in url\n    values = request.values\n    path = request.path\n    base_url = request.base_url\n    url_root = request.url_root\n    res = {'headers': headers, 'method': method, 'ip': ip, 'url': url, 'form': form, 'args': args, 'values': values}\n    # print(res)\n    # print(request.get_json())  # json\n'''\n\n\ndef abort_if_not_exist(task_id):\n    \"\"\"\n    Handle 404 Error\n    :param task_id: int\n    :return: abort info\n    \"\"\"\n    if not Task.get_task_by_id(task_id):\n        abort(404, message=\"Task {} doesn't exist\".format(task_id))\n\n\nclass TaskHandler(Resource):\n    \"\"\"\n    Task handler for RUD, Create will be handled by TasksListHandler\n    \"\"\"\n\n    def req_parse(self):\n        \"\"\"\n        Handle incoming request\n        :return: parsed args\n        \"\"\"\n        parser = reqparse.RequestParser()\n        # title, description required but expiration optional\n        parser.add_argument('title', type=str, help='task title', location='json', required=True)\n        parser.add_argument('description', type=str, help='task description', location='json', required=True)\n        parser.add_argument('expiration', type=str, help='expiration time', location='json')\n        parser.add_argument('is_finished', type=bool, help='task completeness', location='json')\n        args = parser.parse_args()\n        return args\n\n    @marshal_with(response_fields)\n    def get(self, task_id):\n        \"\"\"\n        Get particular task by its ID\n        ---\n        tags:\n            - restful\n        parameters:\n            - in: path\n              name: task_id\n              required: true\n              description: The ID of the task, Integer\n              type: integer\n        responses:\n            200:\n              description: Task details\n              schema:\n                id: Task\n                properties:\n                  id:\n                    type: integer\n                    default: 0\n                  title:\n                    type: string\n                    default: My task\n                  description:\n                    type: string\n                    default: My task description\n                  expiration:\n                    type: string\n                    default: 2019-02-28T11:10:00\n                  task_uuid:\n                    type: string\n                    default: 263e00c3-835c-43ae-a32b-1bae2e879e40\n                  is_finished:\n                    type: bool\n                    default: false\n        \"\"\"\n        abort_if_not_exist(task_id)\n        res = Task.get_task_by_id(task_id)\n        print(res)\n        return res, 200\n\n    def put(self, task_id):\n        \"\"\"\n        Edit particular task by its ID\n        ---\n        tags:\n            - restful\n        parameters:\n            - in: body\n              name: body\n              schema:\n                $ref: '#/definitions/Task'\n            - in: path\n              name: task_id\n              required: true\n              description: The ID of the task, Integer\n              type: integer\n        responses:\n            201:\n              description: The task `task_id` has been updated\n              schema:\n                $ref: '#/definitions/Task'\n        \"\"\"\n        abort_if_not_exist(task_id)\n        args = self.req_parse()\n        task_json = Task.update_task_by_id(task_id, args)\n        res = {\"message\": \"The task \" + task_id + \" has been updated\", \"task\": task_json}\n        print(res)\n        return res, 201\n\n    def delete(self, task_id):\n        \"\"\"\n        Delete particular task by its ID\n        ---\n        tags:\n            - restful\n        parameters:\n            - in: path\n              name: task_id\n              required: true\n              description: The ID of the task, Integer\n              type: integer\n        responses:\n            204:\n              description: The task `task_id` has been deleted\n        \"\"\"\n        abort_if_not_exist(task_id)\n        Task.delete_task_by_id(task_id)\n        return {\"message\": \"The task \" + task_id + \" has been deleted\"}, 204\n\n    def post(self, task_id):\n        \"\"\"\n        The same with GET method\n        \"\"\"\n        return self.get(task_id)\n\n\nclass TasksListHandler(Resource):\n    \"\"\"\n    Tasks handler for CR, Update and Delete is handled by TaskHandler\n    \"\"\"\n\n    def req_parse(self):\n        parser = reqparse.RequestParser()\n        parser.add_argument('title', type=str, help='task title', location='json', required=True)\n        parser.add_argument('description', type=str, help='task description', location='json', required=True)\n        parser.add_argument('expiration', type=str, help='expiration time', location='json')\n        args = parser.parse_args()\n        return args\n\n    @marshal_with(response_fields_list)\n    def get(self):\n        \"\"\"\n        Get all tasks\n        ---\n        tags:\n            - restful\n        responses:\n            200:\n              description: All tasks data in JSON array\n              schema:\n                id: Tasks\n                properties:\n                  tasks:\n                    type: array\n                    items:\n                      $ref: '#/definitions/Task'\n        \"\"\"\n        res = Task.get_tasks_list()\n        return {'tasks': res}, 200\n\n    def post(self):\n        \"\"\"\n        Add task to tasks list\n        ---\n        tags:\n            - restful\n        parameters:\n            - in: body\n              name: body\n              schema:\n                $ref: '#/definitions/Task'\n        responses:\n            201:\n              description: The task has been created\n              schema:\n                $ref: '#/definitions/Task'\n        \"\"\"\n        args = self.req_parse()\n        task_id = Task.insert_task(args)\n        return {\"message\": \"The task \" + str(task_id) + \" has been created\"}, 201\n","sub_path":"test_restful_api/app/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"267993127","text":"import torch.nn as nn\n\nclass FrontEnd(nn.Module):\n    ''' front end part of discriminator and Q'''\n\n    def __init__(self, OCT):\n        super(FrontEnd, self).__init__()\n\n        if OCT:\n            self.main = nn.Sequential(\n                nn.Conv2d(1, 64, 6, 2, 2, bias=False),\n                nn.LeakyReLU(0.1, inplace=True),\n                nn.MaxPool2d(4),\n                nn.Conv2d(64, 128, 6, 2, 2, bias=False),\n                nn.MaxPool2d(4),\n                nn.BatchNorm2d(128),\n                nn.LeakyReLU(0.1, inplace=True),\n                nn.Conv2d(128, 128, 6, 2, 2, bias=False),\n                nn.BatchNorm2d(128),\n                nn.LeakyReLU(0.1, inplace=True),\n                nn.Conv2d(128, 1024, 4, bias=False),\n                nn.BatchNorm2d(1024),\n                nn.LeakyReLU(0.1, inplace=True),\n            )\n        else:\n            self.main = nn.Sequential(\n                nn.Conv2d(1, 64, 4, 2, 1),\n                nn.LeakyReLU(0.1, inplace=True),\n                nn.Conv2d(64, 128, 4, 2, 1, bias=False),\n                nn.BatchNorm2d(128),\n                nn.LeakyReLU(0.1, inplace=True),\n                nn.Conv2d(128, 1024, 7, bias=False),\n                nn.BatchNorm2d(1024),\n                nn.LeakyReLU(0.1, inplace=True),\n            )\n\n    def forward(self, x):\n        output = self.main(x)\n        return output\n\n\nclass D(nn.Module):\n\n    def __init__(self):\n        super(D, self).__init__()\n\n        self.main = nn.Sequential(\n            nn.Conv2d(1024, 1, 1),\n            nn.Sigmoid()\n        )\n\n    def forward(self, x):\n        output = self.main(x).view(-1, 1)\n        return output\n\n\nclass Q(nn.Module):\n    def __init__(self, Discrete_Vars, Continuous_Vars):\n        super(Q, self).__init__()\n\n        self.conv = nn.Conv2d(1024, 128, 1)\n        self.bn = nn.BatchNorm2d(128)\n        self.lReLU = nn.LeakyReLU(0.1, inplace=True)\n        self.conv_disc = nn.Conv2d(128, Discrete_Vars, 1)\n        self.conv_mu = nn.Conv2d(128, Continuous_Vars, 1)\n        self.conv_var = nn.Conv2d(128, Continuous_Vars, 1)\n\n    def forward(self, x):\n        y = self.conv(x)\n\n        disc_logits = self.conv_disc(y).squeeze()\n\n        mu = self.conv_mu(y).squeeze()\n        var = self.conv_var(y).squeeze().exp()\n\n        return disc_logits, mu, var\n\n\nclass G(nn.Module):\n\n    def __init__(self, Total_Vars, OCT):\n        super(G, self).__init__()\n        if OCT:\n            self.main = nn.Sequential(\n                nn.Conv2d(Total_Vars, 64, 1),\n                nn.BatchNorm2d(64),\n                nn.ConvTranspose2d(64, 64, 6, 4, 1, bias=False),\n                nn.BatchNorm2d(64),\n                nn.LeakyReLU(0.1, inplace=True),\n                nn.ConvTranspose2d(64, 64, 8, 8, bias=False),\n                nn.BatchNorm2d(64),\n                nn.LeakyReLU(0.1, inplace=True),\n                nn.Upsample(scale_factor = 8, mode='nearest'),\n                #nn.ReflectionPad2d(1),\n                nn.Conv2d(64, 64, 4, 2, 1, bias=False),\n                nn.BatchNorm2d(64),\n                nn.LeakyReLU(0.1, inplace=True),\n                nn.Upsample(scale_factor = 8, mode='nearest'),\n                #nn.ReflectionPad2d(1),\n                nn.Conv2d(64, 1, 4, 2, 1, bias=False),\n                nn.Sigmoid()\n            )\n        else:\n            self.main = nn.Sequential(\n                nn.ConvTranspose2d(Total_Vars, 1024, 1, 1, bias=False),\n                nn.BatchNorm2d(1024),\n                nn.ReLU(True),\n                nn.ConvTranspose2d(1024, 128, 7, 1, bias=False),\n                nn.BatchNorm2d(128),\n                nn.ReLU(True),\n                nn.ConvTranspose2d(128, 64, 4, 2, 1, bias=False),\n                nn.BatchNorm2d(64),\n                nn.ReLU(True),\n                nn.ConvTranspose2d(64, 1, 4, 2, 1, bias=False),\n                nn.Sigmoid()\n            )\n\n    def forward(self, x):\n        output = self.main(x)\n        return output\n\n\ndef weights_init(m):\n    classname = m.__class__.__name__\n    if classname.find('Conv') != -1:\n        m.weight.data.normal_(0.0, 0.02)\n    elif classname.find('BatchNorm') != -1:\n        m.weight.data.normal_(1.0, 0.02)\n        m.bias.data.fill_(0)\n","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":4161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"85003441","text":"import json\nimport requests\nfrom bs4 import BeautifulSoup\nimport datetime\nfrom flask import Flask, render_template, request\napp = Flask(__name__)\n\n@app.route('/')\ndef hello():\n    greeding = \"Hello World\"\n    return greeding\n\n@app.route('/form')\ndef form():\n    return render_template('form.html')\n\n@app.route('/confirm', methods = ['POST', 'GET'])\ndef confirm():\n   if request.method == 'POST':\n        dates = request.form['h']\n        data = datetime.datetime(dates)\n\n        homeUrl = '/form'\n        return render_template(\"confirm.html\", data=data, homeUrl=homeUrl)\n\nif __name__ == \"__main__\":\n    app.run(port=8000, debug=True)","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"72284427","text":"from classes.Parser import Parser\r\n\"\"\"\r\nClass using multithreading when exporting results\r\n\"\"\"\r\n\r\n\r\nclass ThreadedParser(Parser):\r\n    def __init__(self, config_path: str = None):\r\n        super().__init__(config_path)\r\n        self.testcycle_key = None\r\n\r\n    def do_export_results(self, args: tuple = None):\r\n        import time\r\n        from libs.multi_threading import run_threaded\r\n        start = time.time()\r\n        self.export_results['Results found'] = len(self.parse_results)\r\n        self.logger.info(f'Exporting {len(self.parse_results)} results')\r\n        failed_posts = run_threaded(self.parse_results, self._post_single_result, args)\r\n        self.logger.info(\"Posting results took: {:.2f} seconds\".format(time.time() - start))\r\n        if failed_posts:\r\n            self.logger.error(f'{\" \"*30} EXPORT HAS SOME ERRORS: {len(self.parse_results)} results were in report, '\r\n                         f'but {len(failed_posts)} were not posted.')\r\n            self.manage_unposted_results(failed_posts)\r\n        else:\r\n            self.logger.info('Execution results posted successfully.')\r\n        self.export_results['Failed'] = len(failed_posts)\r\n        self.export_results['Exported'] = len(self.parse_results) - len(failed_posts)\r\n        self.logger.info(self.export_results)\r\n\r\n    def manage_unposted_results(self, failed_posts: list):\r\n        \"\"\"\r\n        function to manage unposted results -- export, save and so on.\r\n        Should be overridden for specific parsers\r\n        :param failed_posts:\r\n        :return:\r\n        \"\"\"\r\n        pass\r\n\r\n","sub_path":"tm4j_adapter/classes/ThreadedParser.py","file_name":"ThreadedParser.py","file_ext":"py","file_size_in_byte":1571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"300604988","text":"# Imports\nimport typhoon.api.hil as hil\nfrom tests.utils import psim_export_netxml\nfrom typhoon.api.schematic_editor import model\nfrom typhoon.test.capture import start_capture, get_capture_results, wait_capture_finish\nimport pytest\nimport numpy as np\nimport os\nfrom model_converter.converter.app.converter import Converter\n\n\nvhil = True\n\n# Define the paths\npsim_tests_dir = os.path.dirname(__file__)\ntests_dir = os.path.dirname(psim_tests_dir)\nsch_importer_dir = os.path.dirname(tests_dir)\n\npsimsch_path = psim_tests_dir + '\\\\wound_rotor_ind_machine.psimsch'\n\n\n@pytest.fixture(scope='session')\ndef create_psim_netxml():\n    # Generates a xml netlist from psim schematic file\n    return psim_export_netxml(psimsch_path)\n\n\n@pytest.fixture(scope='session')\ndef convert_xml2tse(create_psim_netxml):\n    # Converts the psim xml netlist to tse\n    netxml_path = create_psim_netxml[1]\n    converter = Converter(\"psim\", netxml_path)\n    tse_path = converter.convert_schema(compile_model=False)[0]\n    return tse_path\n\n\n@pytest.fixture(scope='session')\ndef convert_compile_load(convert_xml2tse):\n    tse_path = convert_xml2tse\n    cpd_path = tse_path[:-4] + \" Target files\\\\\" + tse_path.split(\"\\\\\")[-1][:-4] + \".cpd\"\n    # Open the converted tse file\n    model.load(tse_path)\n    # Compile the model\n    model.compile()\n\n    # Load to VHIL\n    hil.load_model(file=cpd_path, offlineMode=False, vhil_device=vhil)\n\n\n@pytest.mark.generate_netxml\ndef test_generate_netxml(create_psim_netxml):\n    netxml_path = create_psim_netxml[1]\n    assert create_psim_netxml[0] == 0\n    assert os.path.isfile(netxml_path)\n\n\n@pytest.mark.conversion_xml2tse\ndef test_conversion_xml2tse(convert_xml2tse):\n    tse_path = convert_xml2tse\n    assert os.path.isfile(tse_path)\n\n\n@pytest.mark.parametrize(\"Vsin3_wrim, f, torque, Iwrim_s_exp, Iwrim_r_exp, nr_exp\",\n                         [(220, 50, 5, 3.1929/np.sqrt(2), 1.9932/np.sqrt(2), 1361)])\ndef test_wound_rotor_ind_machine(convert_compile_load, Vsin3_wrim, f, torque, Iwrim_s_exp, Iwrim_r_exp, nr_exp):\n\n    # Set source value\n    hil.set_source_sine_waveform(name='Vsin3_wrim', rms=Vsin3_wrim, frequency=f)\n\n    # Set machine torque\n    hil.set_machine_constant_torque(name='WRIM', value=torque)\n\n    # Start capture\n    start_capture(duration=2.5, rate=10000, signals=['Iwrim_s', 'Iwrim_r', 'machine mechanical speed'], executeAt=0.5)\n\n    # Start simulation\n    hil.start_simulation()\n    wait_capture_finish()\n\n    # Data acquisition\n    capture = get_capture_results()\n    Iwrim_s = np.mean(capture['Iwrim_s'])\n    #Iwrim_r = np.mean(util.window_rms(capture['Iwrim_r'].values, window_size=5000))\n    nr = np.mean(capture['machine mechanical speed'])/2/np.pi*60\n\n    # Stop simulation\n    hil.stop_simulation()\n\n    # Tests\n    assert Iwrim_s == pytest.approx(expected=Iwrim_s_exp, rel=1e-2)\n    #assert Iwrim_r == pytest.approx(expected=Iwrim_r_exp, rel=1e-2)\n    assert nr == pytest.approx(expected=nr_exp, rel=1e-3)\n\n\n\n\n\n\n\n\n\n\n","sub_path":"tests/test_psim/test_machines/test_wound_rotor_ind_machine/test_wound_rotor_ind_machine.py","file_name":"test_wound_rotor_ind_machine.py","file_ext":"py","file_size_in_byte":2958,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"146251320","text":"# since we require class variable to change class variables...we can do on ething we can create class  methods which even the insance\r\n# variables can change and we even dont rquire self..we require cls\r\n\r\nclass Employee:\r\n    no_of_leaves=89\r\n\r\n    def __init__(self,aname,asalary,arole):\r\n        self.name=aname\r\n        self.salary=asalary\r\n        self.role=arole\r\n\r\n    def printdetails(self):\r\n        return f\"The name is {self.name} and salary is {self.salary} and a job is {self.role}\"\r\n\r\n    @classmethod\r\n    def change_leaves(cls,new_leaves):\r\n        cls.no_of_leaves=new_leaves\r\n\r\nharry=Employee(\"Harry\",456,\"instructor\")\r\nrohan=Employee(\"Rohan\",900,\"Manager\")\r\n\r\na=harry.printdetails()\r\nprint(a)\r\nrohan.change_leaves(71)\r\nprint(rohan.no_of_leaves)\r\n\r\n\r\n","sub_path":"56_class_methods.py","file_name":"56_class_methods.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"140895562","text":"class Solution:\n    def strStr(self, haystack, needle):\n        if len(needle) == 0:        \n            return 0\n\n        check = needle in haystack\n    \n        if check == True:        \n            return haystack.index(needle)      \n        else:        \n            return -1\n        \n#Making test cases\n\ntest_case = Solution()\nh = \"Bandersnatch\"\nn = \"der\"\nprint('\\n',test_case.strStr(h,n),'\\n')","sub_path":"Arrays and Strings/Implement_strStr.py","file_name":"Implement_strStr.py","file_ext":"py","file_size_in_byte":400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"515733425","text":"\"\"\"\nMySensors notification service.\n\nFor more details about this platform, please refer to the documentation\nhttps://home-assistant.io/components/notify.mysensors/\n\"\"\"\nfrom homeassistant.components import mysensors\nfrom homeassistant.components.notify import (ATTR_TARGET,\n                                             BaseNotificationService)\n\n\ndef get_service(hass, config, discovery_info=None):\n    \"\"\"Get the MySensors notification service.\"\"\"\n    if discovery_info is None:\n        return\n    platform_devices = []\n    gateways = hass.data.get(mysensors.MYSENSORS_GATEWAYS)\n    if not gateways:\n        return\n\n    for gateway in gateways:\n        pres = gateway.const.Presentation\n        set_req = gateway.const.SetReq\n        map_sv_types = {\n            pres.S_INFO: [set_req.V_TEXT],\n        }\n        devices = {}\n        gateway.platform_callbacks.append(mysensors.pf_callback_factory(\n            map_sv_types, devices, MySensorsNotificationDevice))\n        platform_devices.append(devices)\n\n    return MySensorsNotificationService(platform_devices)\n\n\nclass MySensorsNotificationDevice(mysensors.MySensorsDeviceEntity):\n    \"\"\"Represent a MySensors Notification device.\"\"\"\n\n    def send_msg(self, msg):\n        \"\"\"Send a message.\"\"\"\n        for sub_msg in [msg[i:i + 25] for i in range(0, len(msg), 25)]:\n            # Max mysensors payload is 25 bytes.\n            self.gateway.set_child_value(\n                self.node_id, self.child_id, self.value_type, sub_msg)\n\n\nclass MySensorsNotificationService(BaseNotificationService):\n    \"\"\"Implement MySensors notification service.\"\"\"\n\n    # pylint: disable=too-few-public-methods\n\n    def __init__(self, platform_devices):\n        \"\"\"Initialize the service.\"\"\"\n        self.platform_devices = platform_devices\n\n    def send_message(self, message=\"\", **kwargs):\n        \"\"\"Send a message to a user.\"\"\"\n        target_devices = kwargs.get(ATTR_TARGET)\n        devices = []\n        for gw_devs in self.platform_devices:\n            for device in gw_devs.values():\n                if target_devices is None or device.name in target_devices:\n                    devices.append(device)\n\n        for device in devices:\n            device.send_msg(message)\n","sub_path":"homeassistant/components/notify/mysensors.py","file_name":"mysensors.py","file_ext":"py","file_size_in_byte":2209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"465104908","text":"import xlrd\r\n\r\nclass Exls:\r\n    @classmethod\r\n    def read(cls,filename=\"\",sheetname=\"0\"):\r\n       try:\r\n            list = []\r\n            #打开文件\r\n            file = xlrd.open_workbook(filename,)\r\n            if sheetname.isdigit():#如果您给的选项卡是数字\r\n                #获取选项卡\r\n                sheet = file.sheet_by_index(int(sheetname))\r\n                #获取所有行\r\n                rows = sheet.nrows\r\n                for i in range(rows):\r\n                    list.append(sheet.row_values(i))\r\n                return list\r\n            else:\r\n                sheet = file.sheet_by_name(sheetname)\r\n                rows = sheet.nrows\r\n                for i in range(rows):\r\n                    list.append(sheet.row_values(i))\r\n                return list\r\n       except Exception as error:\r\n            print(error)\r\n# result = Exls.read(\"F:\\\\python\\\\PyCharm 2020.2.1\\\\pythonProject1\\\\doy17\\\\testcase\\\\1.xlsx\",sheetname=\"0\")\r\n# print(result)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"doy17/utils/Exls.py","file_name":"Exls.py","file_ext":"py","file_size_in_byte":1028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"123713931","text":"# Написать калькулятор температур.\n# Пользователь вводит число и тип температуры (C, F, K - Цельсий, Фарренгейт, Кельвин соответственно)\n# Программа должна вывести температуру в трех шкалах температур - Цельсий, Фарренгейт, Кельвин.\n\ntemp = int(input('Enter temperature - '))\ntype_temp = input('Enter type of temperature (C, F, K) - ')\n\n\ndef temp_calc(t, type_t):\n    if type_t == 'C':\n        t_c = t\n        t_f = (9 / 5) * t + 32\n        t_k = 273 + t\n    elif type_t == 'F':\n        t_c = (t - 32) * (5 / 9)\n        t_f = t\n        t_k = (t - 32) * (5 / 9) + 273\n    elif type_t == 'K':\n        t_c = t - 273\n        t_f = (9 / 5) * (t - 273) + 32\n        t_k = t\n    else:\n        print('Please, enter one of the suggested options!')\n    list_type = ['C', 'F', 'K']\n    list_t = [t_c, t_f, t_k]\n    ans = {k: v for k, v in zip(list_type, list_t)}\n    return ans\n\n\nprint(temp_calc(temp, type_temp))\n","sub_path":"homework_7/task_2.py","file_name":"task_2.py","file_ext":"py","file_size_in_byte":1086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"123631366","text":"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, division, print_function, unicode_literals  # noqa\nfrom typing import *  # noqa\nfrom abc import ABCMeta, abstractmethod  # noqa\n\nimport logging\nimport os\nfrom threading import Thread\n\nfrom pykka import ThreadingActor, ActorProxy, ThreadingFuture  # noqa\n\nfrom storage import storage\n\nLOGGER = logging.getLogger(__name__)\n\n\nclass UploadFileCompleterAsyncInterface(object):\n    __metaclass__ = ABCMeta\n\n    @abstractmethod\n    def submit_task(\n            self,\n            task_id,  # type: str\n            target_filename,  # type: str\n            target_file_path,  # type: str\n            upload_path,  # type: str\n            callback_ok,  # type: Callable\n            callback_error,  # type: Callable\n    ):\n        # type: (...) -> ThreadingFuture\n        pass\n\n\nclass UploadFileCompleterAsyncImplement(\n        ThreadingActor,  # type: ignore\n        UploadFileCompleterAsyncInterface):\n    use_daemon_thread = True\n\n    def __init__(self):\n        # type: () -> None\n        super(UploadFileCompleterAsyncImplement, self).__init__()\n\n    def submit_task(\n            self,\n            task_id,  # type: str\n            target_filename,  # type: str\n            target_file_path,  # type: str\n            upload_path,  # type: str\n            callback_ok,  # type: Callable\n            callback_error,  # type: Callable\n    ):\n        # type: (...) -> None\n        def complete():\n            # type: () -> None\n            LOGGER.debug(\n                'complete %s %s %s %s' % (task_id, target_filename,\n                                          target_file_path, upload_path))\n            chunk = 0\n            with open(target_file_path, 'wb') as target_file:\n                while True:\n                    try:\n                        filename = '%s/%s%d' % (upload_path, task_id, chunk)\n                        source_file = open(filename, 'rb')\n                        target_file.write(source_file.read())\n                        source_file.close()\n                    except IOError:\n                        break\n                    chunk += 1\n                    os.remove(filename)\n\n            storage.move_upload_file(target_file_path,\n                                     target_filename).get(timeout=600)\n            LOGGER.debug('%s to %s' % (target_file_path, target_filename))\n            LOGGER.debug('%s complete finished, callback' % (task_id, ))\n            callback_ok()\n\n        t = Thread(target=complete)\n        t.setDaemon(True)\n        t.start()\n\n\nclass UploadFileCompleterAsyncFactory(object):\n    __proxy = None\n\n    @classmethod\n    def make(cls):\n        # type: () -> UploadFileCompleterAsyncInterface\n        if cls.__proxy is None:\n            cls.__proxy = UploadFileCompleterAsyncImplement.start().proxy()\n        assert isinstance(cls.__proxy, ActorProxy)\n        return cls.__proxy  # type: ignore\n\n\nupload_file_completer = UploadFileCompleterAsyncFactory.make()\n","sub_path":"src/upload_file_complete.py","file_name":"upload_file_complete.py","file_ext":"py","file_size_in_byte":2977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"499439243","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom setuptools import setup\n\nrequirements = [\n    'pip>=9.0.1',\n    'numpy>=1.12.0',\n    'pandas>=0.19.2',\n    'matplotlib>=2.0.0'\n]\n\ntest_requirements = [\n    'pip>=9.0.1',\n    'numpy>=1.12.0',\n    'pandas>=0.19.2',\n    'matplotlib>=2.0.0'\n]\n\nsetup(\n    name='waldis',\n    version='0.1.0',\n    description=\"Dynamic Graph Miner\",\n    author=\"Karel Vaculík\",\n    author_email='vaculik.dev@gmail.com',\n    url='https://github.com/karelvaculik/waldis',\n    packages=[\n        'waldis',\n    ],\n    package_dir={'waldis':\n                 'waldis'},\n    entry_points={\n        'console_scripts': [\n            'waldis=waldis.cli:main'\n        ]\n    },\n    include_package_data=True,\n    install_requires=requirements,\n    license=\"Apache Software License 2.0\",\n    zip_safe=False,\n    keywords='waldis',\n    test_suite='tests',\n    tests_require=test_requirements\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"196646462","text":"from __future__ import absolute_import, division, print_function, unicode_literals\n\n# 安装 TensorFlow\n\nimport tensorflow as tf\nimport numpy as np\n\n\ndef load_data(path=\"data/mnist.npz\"):\n    \"\"\"Loads the MNIST dataset.\n\n    # Arguments\n        path: path where to cache the dataset locally\n            (relative to ~/.keras/datasets).\n\n    # Returns\n        Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.\n    \"\"\"\n    f = np.load(path)\n    x_train, y_train = f[\"x_train\"], f[\"y_train\"]\n    x_test, y_test = f[\"x_test\"], f[\"y_test\"]\n    f.close()\n    return (x_train, y_train), (x_test, y_test)\n\n\n# 载入并准备好 MNIST 数据集。\n# 无法直接下载该文件。。。。\n# mnist = tf.keras.datasets.mnist # http 300\n# (x_train, y_train), (x_test, y_test) = mnist.load_data()\n\n# 使用本地下载好的数据集\n(x_train, y_train), (x_test, y_test) = load_data()\n# 将样本从整数转换为浮点数：\nx_train, x_test = x_train / 255.0, x_test / 255.0\n\n# 将模型的各层堆叠起来，以搭建 tf.keras.Sequential 模型。为训练选择优化器和损失函数：\nmodel = tf.keras.models.Sequential(\n    [\n        tf.keras.layers.Flatten(input_shape=(28, 28)),\n        tf.keras.layers.Dense(128, activation=\"relu\"),\n        tf.keras.layers.Dropout(0.2),\n        tf.keras.layers.Dense(10, activation=\"softmax\"),\n    ]\n)\nmodel.compile(\n    optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"]\n)\n\n# 训练并验证模型：\nmodel.fit(x_train, y_train, epochs=5)\n\nmodel.evaluate(x_test, y_test, verbose=2)\n\n","sub_path":"src/lesson_1.py","file_name":"lesson_1.py","file_ext":"py","file_size_in_byte":1561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"107806510","text":"#python_3.8\ndef func():\n      a=[];b=[];z=0;s=0;q=0\n      c=0;t=0\n      prcl={}\n      prdctl={}\n      x=int(input(\"dose 1 gia na eisageis tis thmes dose otidipote allo arithmo gia na stamatiseis\"))\n      while x==1:\n            p=str(input(\"dose proion: \"))\n            t=input('dose timh proiodos\": ')\n            prdctl.update({p:str(c)})\n            prcl.update({t:str(c)})\n            c+=1\n            x=int(input(\"dose 1 gia na eisageis tis thmes dose otidipote allo arithmo gia na stamatiseis\"))\n            for i in prcl:\n                  a.append(i)\n            for i in a:\n                  b.append(float(i))\n      s=sum(map(float,b))\n      t=s*0.24\n      z=t+s\n      print (\"foros\",t,\"timh\",s,\"synolo\",z)\nfunc()\n","sub_path":"erg3.py","file_name":"erg3.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"588077048","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[7]:\n\n\ndef data_maker(df_todayshare,df_marketdepth,df_totaldepth,df_paid):\n    import pandas as pd\n    df_todayshare=df_todayshare[df_todayshare['Previous Closing']>0][:-1]\n    df_todayshare['%_Change']=round(df_todayshare['Difference Rs.']*100/df_todayshare['Previous Closing'],2)\n    df_todayshare=df_todayshare[(df_todayshare['%_Change']>-10)&(df_todayshare['%_Change']<=10)]\n\n    d1=list(df_marketdepth.Date)[0].strftime('%Y/%m/%d')\n    d2=list(df_marketdepth.Date)[-1].strftime('%Y/%m/%d')\n    df_analysis=df_todayshare[d1:d2]\n    df_g=df_analysis.groupby('Traded Companies')\n    df_tg=df_totaldepth.groupby('symbol')\n    df_tgn=df_marketdepth.groupby('Name')\n    df_dg=df_marketdepth.groupby('symbol')\n    #df_paid=pd.read_csv(\"D:\\\\marketdepth\\\\paid_overall.csv\")\n    df_all_best=pd.DataFrame()\n    for key in df_tgn.groups.keys():\n        symbol=list(df_tgn.get_group(key)['symbol'])[0]\n        #print(symbol)\n        try:\n            df_n=df_g.get_group(key[:len(key)-1])\n            paid_up=(list(df_paid[df_paid['symbol']==symbol]['Paid-Up'])[0]).replace(',','')\n            df_n=df_n[['Closing Price','Traded Shares','Difference Rs.','%_Change']]\n            #print(df_n.drop_duplicates())\n            df_nn=round(df_dg.get_group(symbol).groupby('Date').mean(),2)\n            #print(df_nn)\n            #break\n            df_nm=round(df_tg.get_group(symbol).groupby('date').mean(),2)\n            df_nx=pd.concat([df_n.drop_duplicates(),df_nn,df_nm],axis=1)\n            df_nx['Qty_Diff']=df_nx['Buy Qty']-df_nx['Sell Qty']\n            df_nx['Paid Up']=[paid_up]*df_nx.shape[0]\n            df_nx['Paid Up']=pd.to_numeric(df_nx['Paid Up'])\n            df_nx['symbol']=[symbol]*df_nx.shape[0]\n            df_all_best=df_all_best.append(df_nx,ignore_index=True)\n        except (KeyError,IndexError):\n            pass\n    return df_all_best \n\n\n# In[8]:\n\n\ndef df_stock_predictor(df_todayshare,df_marketdepth,df_totaldepth,df_paid):\n    import pandas as pd\n    df=data_maker(df_todayshare,df_marketdepth,df_totaldepth,df_paid)\n    df.dropna(inplace=True)\n    df['Paid_Buy']=round(df['Total Buy Qty']*1000/df['Paid Up'],2)\n    df['Paid_Sell']=round(df['Total Sell Qty']*1000/df['Paid Up'],2)\n    dz=df[['symbol','Total Buy Qty','Total Sell Qty','Buy Qty','Sell Qty','Sell Orders','Buy Orders','Traded Shares','Paid_Buy','Paid_Sell']]\n    dz_s=dz.groupby('symbol')\n    dat_frame=pd.DataFrame()\n    for keys in dz_s.groups.keys():\n        dz_g=dz_s.get_group(keys)\n        for i in dz_g.columns:\n            if i!='symbol':\n                dz_g[i+'1']=dz_g[i].rolling(window=7).sum()\n        #dz_g['Change']=dz_g['%_Change1'].shift(6)\n        dz_g.dropna(inplace=True)\n        #dz_g['symbol']=[keys]*dz_g.shape[0]\n        data_frame=dz_g[['Total Buy Qty1','Total Sell Qty1','Buy Qty1','Sell Qty1','Sell Orders1','Buy Orders1','Traded Shares1','Paid_Buy1','Paid_Sell1','symbol']]\n        dat_frame=dat_frame.append(data_frame,ignore_index=True)\n    #return dat_frame\n    from joblib import dump, load\n    le=load('stock_to_class.joblib')\n    regressor = load('stock_prediction.joblib')\n    dfx=dat_frame[dat_frame.columns[:-1]]\n    dfx['symmbol']=le.transform(dat_frame.symbol)\n    dat_frame['Predict']=regressor.predict(dfx)\n    return dat_frame.sort_values('Predict',ascending=False)\n\n","sub_path":"Market_Depth/market_depth_Data1.py","file_name":"market_depth_Data1.py","file_ext":"py","file_size_in_byte":3328,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"627056002","text":"# Definition for singly-linked list.\n# class ListNode:\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\nclass Solution:\n    def sortList(self, head: ListNode) -> ListNode:\n        if head is None or head.next is None:\n            return head\n        mid = self.getMid(head)\n        left = self.sortList(head)\n        right = self.sortList(mid)\n        return self.merge(left, right)\n\n    def merge(self, list1: ListNode, list2: ListNode):\n        dummyHead = ListNode()\n        tail = dummyHead\n        while list1 and list2:\n            if list1.val < list2.val:\n                tail.next = list1\n                list1 = list1.next\n                tail = tail.next\n            else:\n                tail.next = list2\n                list2 = list2.next\n                tail = tail.next\n\n        if list1:\n            tail.next = list1\n        else:\n            tail.next = list2\n        return dummyHead.next\n\n    def getMid(self, node):\n        midPrev = None\n        while node and node.next:\n            if midPrev is None:\n                midPrev = node\n            else:\n                midPrev = midPrev.next\n            node = node.next.next\n        mid = midPrev.next\n        midPrev.next = None\n        return mid\n","sub_path":"LeetCode/Medium/148SortList-2.py","file_name":"148SortList-2.py","file_ext":"py","file_size_in_byte":1270,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"378175928","text":"import win32clipboard\nfrom typing import Any, Union\n\nimport win32con\nfrom ctypes.wintypes import *\nfrom win32con import *\nimport ctypes\nfrom tools import costool\nimport logging\nfrom PIL import Image\nimport time\n\nlogging.basicConfig(level=logging.INFO,\n                    format='%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s')  # 日志的输出格式及方式配置\n\n\ndef readClip():\n    win32clipboard.OpenClipboard()\n    try:\n        if win32clipboard.IsClipboardFormatAvailable(win32clipboard.CF_DIB):\n            data = win32clipboard.GetClipboardData(win32clipboard.CF_DIB)\n            writeImgUrl(data)\n        elif win32clipboard.IsClipboardFormatAvailable(win32clipboard.CF_TEXT):\n            data = win32clipboard.GetClipboardData(win32con.CF_TEXT)\n            writeText(data)\n        else:\n            print('clipboard does not contain an image or Text')\n            return\n    finally:\n        win32clipboard.CloseClipboard()\n\ndef writeText(data):\n    with open('./log/cliplogs.txt') as clf:\n        clf.write(data)\n    logging.info(data)\n\ndef writeImgUrl(data):\n    get_bmp(data,'temp.bmp')\n    Image.open('temp.bmp').save('temp.png')\n    cos = costool.Cos('ap-chengdu', 'notefile-1255829421')\n    response = cos.up_local_file('temp.png', '/clip/clip.png')\n    pass\n\n\ndef get_text():\n    \"\"\" 读取 \"\"\"\n    win32clipboard.OpenClipboard()\n    text = win32clipboard.GetClipboardData(win32con.CF_TEXT)\n    win32clipboard.CloseClipboard()\n    return text\n\n\ndef set_text(strs):\n    \"\"\" 写入 \"\"\"\n    win32clipboard.OpenClipboard()\n    win32clipboard.EmptyClipboard()\n    win32clipboard.SetClipboardData(win32con.CF_TEXT, strs)\n    win32clipboard.CloseClipboard()\n\ndef get_bmp(data,bmpName):\n    class BITMAPFILEHEADER(ctypes.Structure):\n        _pack_ = 1  # structure field byte alignment\n        _fields_ = [\n            ('bfType', WORD),  # file type (\"BM\")\n            ('bfSize', DWORD),  # file size in bytes\n            ('bfReserved1', WORD),  # must be zero\n            ('bfReserved2', WORD),  # must be zero\n            ('bfOffBits', DWORD),  # byte offset to the pixel array\n        ]\n\n    SIZEOF_BITMAPFILEHEADER = ctypes.sizeof(BITMAPFILEHEADER)\n\n    class BITMAPINFOHEADER(ctypes.Structure):\n        _pack_ = 1  # structure field byte alignment\n        _fields_ = [\n            ('biSize', DWORD),\n            ('biWidth', LONG),\n            ('biHeight', LONG),\n            ('biPLanes', WORD),\n            ('biBitCount', WORD),\n            ('biCompression', DWORD),\n            ('biSizeImage', DWORD),\n            ('biXPelsPerMeter', LONG),\n            ('biYPelsPerMeter', LONG),\n            ('biClrUsed', DWORD),\n            ('biClrImportant', DWORD)\n        ]\n\n    SIZEOF_BITMAPINFOHEADER = ctypes.sizeof(BITMAPINFOHEADER)\n\n    bmih = BITMAPINFOHEADER()\n    ctypes.memmove(ctypes.pointer(bmih), data, SIZEOF_BITMAPINFOHEADER)\n\n    if bmih.biCompression != BI_BITFIELDS:  # RGBA?\n        print('insupported compression type {}'.format(bmih.biCompression))\n        return\n\n    bmfh = BITMAPFILEHEADER()\n    ctypes.memset(ctypes.pointer(bmfh), 0, SIZEOF_BITMAPFILEHEADER)  # zero structure\n    bmfh.bfType = ord('B') | (ord('M') << 8)\n    bmfh.bfSize = SIZEOF_BITMAPFILEHEADER + len(data)  # file size\n    SIZEOF_COLORTABLE = 0\n    bmfh.bfOffBits = SIZEOF_BITMAPFILEHEADER + SIZEOF_BITMAPINFOHEADER + SIZEOF_COLORTABLE\n\n    with open(bmpName, 'wb') as bmp_file:\n        bmp_file.write(bmfh)\n        bmp_file.write(data)\n    logging.info('file \"{}\" created from clipboard image'.format(bmpName))\n\ndef get_bmp_adress():\n    win32clipboard.OpenClipboard()\n    try:\n        if win32clipboard.IsClipboardFormatAvailable(win32clipboard.CF_DIB):\n            adress = win32clipboard.GetClipboardData(win32clipboard.CF_BITMAP)\n        else:\n            adress = 0\n    finally:\n        win32clipboard.CloseClipboard()\n    return adress\n\nif __name__ == '__main__':\n    lastBmpAdress = 0\n    while True:\n        bmpAdress = get_bmp_adress()\n        logging.info(bmpAdress)\n        if( bmpAdress != lastBmpAdress):\n            get_bmp('clipboard.bmp')\n            Image.open('clipboard.bmp').save('test.png')\n            cos = costool.Cos('ap-chengdu', 'notefile-1255829421')\n            response = cos.up_local_file('test.png', '/performanceTest/wb/pod出问题.png')\n            logging.info(response)\n            lastBmpAdress = bmpAdress\n        time.sleep(2)\n","sub_path":"clip.py","file_name":"clip.py","file_ext":"py","file_size_in_byte":4397,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"386517520","text":"import sys, getopt\nfrom astroquery.simbad import Simbad\n\ndef main(argv):\n    star_id = argv\n    id_table = Simbad.query_objectids(star_id)\n    id_json = id_table.to_pandas()['ID'].to_json(orient='values')\n    print(id_json)\n\n\nif __name__ == '__main__':\n    main(sys.argv[1])\n\n","sub_path":"backend/python/getStarIdsById.py","file_name":"getStarIdsById.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"600431309","text":"#!/usr/bin/python\nimport mysql.connector\nimport time\nimport random\nfrom datetime import datetime\n\nmydb = mysql.connector.connect(\n  host=\"localhost\",\n  user=\"root\",\n  passwd=\"root\",\n  database=\"bioreactor\"\n)\n\nmycursor = mydb.cursor()\n\nqry = \"SELECT * FROM TB_CONFIG\"\nmycursor.execute(qry)\nresults = mycursor.fetchall()\nfor row in results:\n    CON_temp_min = list(row)[0]\n\nprint(\"Variable CON_temp_min: \", CON_temp_min)\n\nfor x in range(200):\n    sql = \"INSERT INTO sensores (temp, ph, presion, date) VALUES (%s, %s, %s, %s)\"\n    val = (round(random.uniform(15, 25), 2), random.randint(1, 14), round(random.uniform(0, 12), 2), datetime.now())\n    mycursor.execute(sql, val)\n\n    mydb.commit()\n    print(val)\n    print(mycursor.rowcount, \"record inserted.\")\n    time.sleep(1)\n","sub_path":"scripts_test/script_simulador.py","file_name":"script_simulador.py","file_ext":"py","file_size_in_byte":773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"552070848","text":"# -*- coding: utf-8 -*-\n\ntry:\n    from .player import *\n    from .board import Board\n    from .deck import Deck, PlayerCards, InfectCards\n    from .constants import *\nexcept SystemError:\n    from player import *\n    from board import Board\n    from deck import Deck, PlayerCards, InfectCards\n    from constants import *\nfrom random import shuffle\nfrom uuid import uuid4\n\nclass Game:\n    def __init__(self, roles, epidemics):\n        self.win = False\n        self.lose = False\n        self.new = True\n        self.turns = 0\n        self.phase = ACTION_0\n        self.id = str(uuid4())\n\n        self.num_outbreaks = 0\n        self.num_epidemics = 0\n        self.drawn_epidemics = 0\n\n        self.num_players = 0\n        self.players = []\n\n        self.from_discard = []\n\n        # cubes[city][color]\n        self.cubes = []\n        for city in range(NUM_CITIES):\n             self.cubes.append([0] * 4)\n        self.cubes_left = [NUM_CUBES] * 4\n        self.cures = [0] * 4\n\n        self.research_stations = []\n        self.quarantined = []\n        self.outbreaks = []\n\n        self.board = Board()\n        self.num_epidemics = epidemics\n        self.num_players = len(roles)\n        self.research_stations.append(ATL)\n        self.infect_cards = InfectCards()\n        self.infected = {}\n        self.at_risk = []\n\n        self.initial_infect()\n        self.oqn = False\n\n        for role in roles:\n            self.players.append(self.assign_player(role))\n\n        self.player_cards = PlayerCards(self.players, self.num_epidemics)\n        self.active = self.set_order()\n        self.selected = self.active\n\n    ## Manage game phase\n    def get_phase(self):\n        return self.phase\n\n    def get_infect_number(self):\n        if self.oqn:\n            return 0\n        if self.drawn_epidemics == 0:\n            return 2\n        return ( (self.drawn_epidemics-1) // 2 ) + 2\n\n    def set_phase(self, phase):\n        self.phase = phase\n\n    def assign_player(self, role):\n        if role == MEDIC:\n            player = Medic()\n        elif role == DISPATCHER:\n            player = Dispatcher()\n        elif role == CP:\n            player = ContingencyPlanner()\n        elif role == OE:\n            player = OperationsExpert()\n        elif role == QS:\n            player = QuarantineSpecialist(self.quarantined, self.board.get_neighbors(ATL))\n        elif role == RESEARCHER:\n            player = Researcher()\n        elif role == SCIENTIST:\n            player = Scientist()\n        else:\n            player = None\n        return player\n\n    def set_order(self):\n        first_player_index = -1\n        highest_card = 0\n        for player in self.players:\n            for card in player.hand:\n                if CARDS[card]['population'] > highest_card:\n                    highest_card = CARDS[card]['population']\n                    first_player_index = self.players.index(player)\n        return first_player_index\n\n    def select_player(self, index):\n        self.selected = index\n        selected = self.players[self.selected]\n        if selected.get_role() == OE:\n            selected.has_stationed = True\n        return selected\n\n    def dispatcher_availability(self, available):\n        dispatch = []\n        selected = self.players[self.selected]\n        for p in self.players:\n            if selected != p and selected.get_position() != p.get_position():\n                available.append(str(p.get_position()))\n                dispatch.append(p.get_position())\n        return dispatch\n\n    def set_available(self, is_airlift):\n        dispatch = []\n        available = []\n        player = self.players[self.active]\n        selected = self.players[self.selected]\n        player_id = selected.get_id()\n        origin = selected.get_position()\n\n        if is_airlift == 1:\n            available = []\n            for city in range(NUM_CITIES):\n                if city != selected.get_position():\n                    available.append(str(city))\n        else:\n            if player.get_id() == 'dispatcher':\n                dispatch = self.dispatcher_availability(available)\n            for city in selected.can_move(player.hand, self.research_stations, self.board):\n                available.append(str(city))\n        return available, dispatch, origin, player_id\n\n    def set_share(self):\n        can_give = {}\n        can_take = {}\n        player = self.players[self.active]\n        team = self.players[self.active:] + self.players[:self.active]\n        team_hands = {}\n        for p in team[1:]:\n            can_take[p.get_id()] = []\n            can_give[p.get_id()] = []\n            team_hands[p.get_id()] = copy(p.hand)\n            for card in p.hand:\n                can_take[p.get_id()].append(player.can_take(card, p))\n            for card in player.hand:\n                can_give[p.get_id()].append(player.can_give(card, p))\n        return can_take, can_give, team_hands\n\n    ## The procedures for adding cubes.     '''     The below function is a generic cube adding\n## function. It takes the color and number of cubes to be added, and the city to infect. This\n## function is implemented in all situations in which a city would be infected. A normal infect event\n## will just call the function directly with numCubes = 1. The other cube-adding scenarios\n## are outbreak and epidemic. An infection will call the outbreak function\n## when it would go over the cube limit. Outbreaks will then call back to the infect functions,\n## potentially chaining into other outbreaks. The outbreak function handles all conditions for\n## outbreaks, and tracks outbreaks that have already occurred on a turn so as not to repeat. The\n## function to reset the outbreak array is called after each epidemic AND each infect stage.     '''\n    def initial_infect(self):\n        for i in reversed(range(9)):\n            self.draw_infect_card(i//3 + 1)\n\n    def draw_infect_card(self, number):\n        card = self.infect_cards.draw_card(0)\n        self.execute_infect(card, card // CITIES_PER_COLOR, number)\n        if card in self.at_risk:\n            self.at_risk.remove(card)\n        return card\n\n    def execute_infect(self, city, color, num_cubes):\n        self.reset_outbreaks()\n        self.infect(city, color, num_cubes)\n        if self.is_outbreak_lose() or self.is_cube_lose(color):\n            self.lose = True\n\n    def infect(self, city, color, num_cubes):\n        if city not in self.quarantined and self.cures[color] != ERADICATED:\n            if self.cubes[city][color] == 0:\n                self.board.set_row(city, color)\n            if self.cubes[city][color] + num_cubes <= MAX_CUBES:\n                self.cubes[city][color] += num_cubes\n                self.cubes_left[color] -= num_cubes\n                if city in self.infected:\n                    self.infected[city][color] += num_cubes\n                else:\n                    self.infected[city] = [0]*4\n                    self.infected[city][color] = num_cubes\n                risk = self.risk(city)\n            else:\n                self.cubes_left[color] -= MAX_CUBES - self.cubes[city][color]\n                if city in self.infected:\n                    self.infected[city][color] += MAX_CUBES - self.cubes[city][color]\n                else:\n                    self.infected[city] = [0]*4\n                    self.infected[city][color] = MAX_CUBES - self.cubes[city][color]\n                self.cubes[city][color] = MAX_CUBES\n                self.outbreak(city, color)\n\n\n    def outbreak(self, city, color):\n        if city not in self.outbreaks \\\n                and city not in self.quarantined \\\n                and self.cures[color] != ERADICATED:\n            self.num_outbreaks += 1\n            self.outbreaks.append(city)\n            neighbors = self.board.get_neighbors(city)\n            for n in neighbors:\n                self.infect(n, color, 1)\n\n    def reset_outbreaks(self):\n        self.outbreaks = []\n\n    def reset_infection(self):\n        self.infected = {}\n\n    def epidemic(self):\n        self.reset_outbreaks()\n        self.reset_infection()\n        self.drawn_epidemics += 1\n        card = self.infect_cards.draw_card(-1)\n        self.execute_infect(card, card // CITIES_PER_COLOR, MAX_CUBES)\n        if not self.has_rp():\n            self.infect_cards.recombine()\n            for city in range(NUM_CITIES):\n                risk = self.risk(city)\n        return card\n\n    def finish_epidemic(self):\n        self.infect_cards.recombine()\n        for city in range(NUM_CITIES):\n            risk = self.risk(city)\n\n    def has_rp(self):\n        has_rp = False\n        for p in self.players:\n            if RP in p.hand or (p.get_role() == CP and p.event == RP):\n                return True\n        return has_rp\n\n    def remove_cubes(self, color, number, city):\n        game.cubes_left[color] += number;\n        game.cubes[city][color] -= number\n        if game.cubes[city][color] == 0:\n            board.delete_row(city, color)\n\n    def risk(self, city):\n        at_risk = False\n        for c in COLORS:\n            if self.cubes[city][c] == MAX_CUBES and city in self.infect_cards.deck:\n                at_risk = True\n        if not at_risk and city in self.at_risk:\n            self.at_risk.remove(city)\n        elif at_risk and city not in self.at_risk:\n            self.at_risk.append(city)\n        return at_risk\n\n    # Handles all functions relating to research stations.\n    def check_num_stations(self):\n        return len(self.research_stations)\n\n    def is_eradicated(self, color):\n        return self.cubes_left[color] == NUM_CUBES and self.cures[color] == 1\n\n    def next_turn(self):\n        if self.phase == DRAW or self.phase == EP:\n            self.oqn = False\n        self.phase = ACTION_0\n        self.active = (self.active+1) % self.num_players\n        self.selected = self.active\n        if self.players[self.active].get_role() == OE:\n            self.players[self.active].has_stationed = False\n        self.turns += 1\n\n# Basic actions\n    def move(self, new_pos, index, discard, method):\n        player = self.players[self.active]\n        mover = self.players[self.selected]\n        owner = self.players[index]\n\n        true_selected = self.selected\n        self.selected = self.active\n        prev_avail, dispatch, origin, player_id = self.set_available(0)\n        prev_hand = copy(player.hand)\n        prev_take, prev_give, prev_hands = self.set_share()\n        prev_build = player.can_build(origin, self.research_stations)\n        prev_cure = player.can_cure(self.research_stations)\n        orig_cubes = copy(self.cubes[new_pos])\n        orig_rows = copy(self.board.rows[new_pos])\n        at_risk = copy(self.at_risk)\n        self.selected = true_selected\n        action = { 'act': method,\n                    'id': player.get_id(),\n                    'owner': owner.get_id(),\n                    'mover': mover.get_id(),\n                    'is_stored': 0,\n                    'origin': mover.get_position(),\n                    'destination': new_pos,\n                    'cards': discard,\n                    'owner': owner.get_id(),\n                    'available': prev_avail,\n                    'can_build': prev_build,\n                    'can_cure': prev_cure,\n                    'cubes': orig_cubes,\n                    'rows': orig_rows,\n                    'at_risk': self.at_risk,\n                    'hand': prev_hand,\n                    'team_hands': prev_hands,\n                    'give': prev_give,\n                    'take': prev_take,\n                    'at_risk': at_risk }\n        mover.move(new_pos, self.board, self.cures, self.cubes, self.cubes_left, self.quarantined)\n        for c in COLORS:\n            if self.is_eradicated(c):\n                self.cures[c] = ERADICATED\n        self.risk(new_pos)\n        self.phase += 1\n        if discard != '':\n            owner.discard(int(discard), self.player_cards)\n        self.selected = self.active\n        if method == 'station-fly':\n            player.has_stationed = True\n        return action\n\n    def build(self, to_remove):\n        player = self.players[self.active]\n        num_stations = len(self.research_stations)\n        prev_avail, dispatch, origin, player_id = self.set_available(0)\n\n        can_build = player.get_position() not in self.research_stations and (player.get_position() in\nplayer.hand or player.get_role() == OE)\n        discard = player.get_position() if player.get_role() != OE else -1\n\n        action = { 'act': \"build\",\n                   'origin': str(player.get_position()),\n                   'can_build': can_build,\n                   'cards': discard,\n                   'removed': str(to_remove),\n                   'owner': player.get_id(),\n                   'card_data': CARDS[discard],\n                   'available': prev_avail }\n        self.research_stations.append(player.get_position())\n        if to_remove != -1:\n            self.research_stations.remove(to_remove)\n        self.phase += 1\n        if discard != -1:\n            player.discard(discard, self.player_cards)\n        return action\n\n    def treat(self, color):\n        player = self.players[self.active]\n        city = player.get_position()\n        orig_cubes = copy(self.cubes[city][color])\n        orig_rows = copy(self.board.rows[city])\n        at_risk = copy(self.at_risk)\n        player.treat(color, self.cures, self.cubes, self.cubes_left, self.board)\n        self.phase += 1\n        cubes_removed = orig_cubes - self.cubes[city][color]\n        self.risk(city)\n        if self.is_eradicated(color):\n            self.cures[color] = ERADICATED\n        action = { 'act': \"treat\",\n                   'origin': str(city),\n                    'color': str(color),\n                    'cubes': orig_cubes,\n                    'removed': cubes_removed,\n                    'rows': orig_rows,\n                    'at_risk': at_risk }\n        return action\n\n    def make_cure(self, cards):\n        player = self.players[self.active]\n        cure_color = cards[0] // CITIES_PER_COLOR\n        prev_avail, dispatch, origin, player_id = self.set_available(0)\n        orig_cubes = self.cubes[player.get_position()][cure_color]\n        orig_rows = copy(self.board.get_all_rows(player.get_position()))\n        at_risk = copy(self.at_risk)\n\n        player.make_cure(cards, self.cures, self.cubes, self.cubes_left, self.player_cards, self.board)\n        if self.is_win():\n            self.win = True\n        medic_pos = -1\n        for p in self.players:\n            if p.get_role() == MEDIC:\n                medic_pos = p.get_position()\n                self.cubes_left[cure_color] += self.cubes[medic_pos][cure_color]\n                self.cubes[medic_pos][cure_color] = 0\n                self.risk(medic_pos)\n                break\n        if self.is_eradicated(cure_color):\n            self.cures[cure_color] = ERADICATED\n        self.phase += 1\n\n        action = { 'act': 'cure',\n                    'id': player.get_id(),\n                    'color': cure_color,\n                    'cards': [str(card) for card in cards],\n                    'origin': str(player.get_position()),\n                    'medic_pos': medic_pos,\n                    'cubes': orig_cubes,\n                    'rows': orig_rows,\n                    'available': prev_avail,\n                    'at_risk': at_risk }\n        return action\n\n    def give_card(self, recipient, card):\n        giver = self.players[self.active]\n        receiver = self.players[recipient]\n        prev_avail, dispatch, origin, player_id = self.set_available(0)\n        giver.give_card(card, receiver)\n        self.phase += 1\n        action = { 'act': 'give',\n                    'card': str(card),\n                    'giver': giver.get_id(),\n                    'taker': receiver.get_id(),\n                    'available': prev_avail }\n        return action\n\n    def take_card(self, giver, card):\n        taker = self.players[self.active]\n        source = self.players[giver]\n        prev_avail, dispatch, origin, player_id = self.set_available(0)\n        taker.take_card(card, source)\n        self.phase += 1\n        action = { 'act': 'take',\n                    'card': str(card),\n                    'taker': taker.get_id(),\n                    'giver': source.get_id(),\n                    'available': prev_avail }\n        return action\n\n# Event card handling\n    def play_airlift(self, owner_index, new_pos):\n        player = self.players[self.active]\n        owner = self.players[owner_index]\n        mover = self.players[self.selected]\n\n        prev_avail, dispatch, origin, player_id = self.set_available(0)\n        prev_hand = copy(player.hand)\n        prev_take, prev_give, prev_hands = self.set_share()\n        prev_build = player.can_build(origin, self.research_stations)\n        prev_cure = player.can_cure(self.research_stations)\n        orig_cubes = copy(self.cubes[new_pos])\n        orig_rows = copy(self.board.rows[new_pos])\n        at_risk = copy(self.at_risk)\n        is_stored = AIRLIFT not in owner.hand\n        action = { 'act': 'airlift',\n                    'id': player.get_id(),\n                    'owner': owner.get_id(),\n                    'mover': mover.get_id(),\n                    'is_stored': is_stored,\n                    'origin': origin,\n                    'destination': new_pos,\n                    'cards': AIRLIFT,\n                    'available': prev_avail,\n                    'can_build': prev_build,\n                    'can_cure': prev_cure,\n                    'cubes': orig_cubes,\n                    'rows': orig_rows,\n                    'hand': prev_hand,\n                    'team_hands': prev_hands,\n                    'give': prev_give,\n                    'take': prev_take,\n                    'at_risk': at_risk }\n        mover.move(new_pos, self.board, self.cures, self.cubes, self.cubes_left, self.quarantined)\n        for c in COLORS:\n            if self.is_eradicated(c):\n                self.cures[c] = ERADICATED\n        if not is_stored:\n            owner.discard(AIRLIFT, self.player_cards)\n        else:\n            owner.play_event(self.player_cards)\n        self.selected = self.active\n        return action\n\n    def play_gg(self, city, index, to_remove):\n        player = self.players[self.active]\n        owner = self.players[index]\n        can_build = player.get_position() not in self.research_stations and (player.get_position() in player.hand or player.get_role() == OE)\n        prev_avail, dispatch, origin, player_id = self.set_available(0)\n        is_stored = GG not in owner.hand\n        action = { 'act': \"gg\",\n                   'origin': str(city),\n                   'can_build': can_build,\n                   'cards': str(GG),\n                   'removed': str(to_remove),\n                   'owner': owner.get_id(),\n                   'card_data': CARDS[GG],\n                   'available': prev_avail,\n                   'is_stored': is_stored}\n        if not is_stored:\n            owner.discard(GG, self.player_cards)\n        else:\n            owner.play_event(self.player_cards)\n        self.research_stations.append(city)\n        if to_remove != -1:\n            self.research_stations.remove(to_remove)\n        return action\n\n\n    def play_forecast(self, index, reordered):\n        owner = self.players[index]\n        self.infect_cards.deck = reordered + self.infect_cards.deck[6:]\n        is_stored = FORECAST not in owner.hand\n        if not is_stored:\n            owner.discard(FORECAST, self.player_cards)\n        else:\n            owner.play_event(self.player_cards)\n        return owner\n\n    def play_rp(self, card, index):\n        owner = self.players[index]\n        self.infect_cards.remove_from_discard(card)\n        self.infect_cards.add_to_graveyard(card)\n        is_stored = RP not in owner.hand\n        if not is_stored:\n            owner.discard(RP, self.player_cards)\n        else:\n            owner.play_event(self.player_cards)\n\n        action = { 'act': 'rp',\n                    'owner': owner.get_id(),\n                    'is_stored': is_stored,\n                    'deleted': card }\n        return action\n\n    def play_oqn(self, index):\n        owner = self.players[index]\n        self.oqn = True\n        is_stored = OQN not in owner.hand\n        if not is_stored:\n            owner.discard(OQN, self.player_cards)\n        else:\n            owner.play_event(self.player_cards)\n\n        action = { 'act': 'oqn',\n                    'is_stored': is_stored,\n                    'owner': owner.get_id() }\n        return action\n\n    def store_on_cp(self, card):\n        player = self.players[self.active]\n        player.get_event_from_discard(card, self.player_cards)\n        self.phase += 1\n        action = { 'act': 'store',\n                    'card': card }\n        return action\n\n## End conditions\n    # Win\n    def is_win(self):\n        for have_cure in self.cures:\n            if not have_cure:\n                return False\n        return True\n\n    # Lose\n    def is_cube_lose(self, color):\n        self.lose = self.cubes_left[color] <= 0\n        return self.lose\n\n    def is_outbreak_lose(self):\n        self.lose = self.num_outbreaks >= MAX_OUTBREAKS\n        return self.lose\n\n    def is_card_lose(self):\n        self.lose = self.player_cards.getSize() < 2\n        return self.lose\n","sub_path":"app/src/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":21239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"176545879","text":"import math\n\n# https://open.kattis.com/problems/beavergnaw\n\ndef calc_volume(D, d):\n    vol_total = math.pi * D * D / 4.0 * D\n    vol_cones = ((2 / 3) * math.pi * (D * D / 4) * D / 2) - ((2 / 3) * math.pi * (d * d / 4) * d / 2)\n    vol_smallpart = math.pi * d * d / 4.0 * d\n    return vol_total - vol_cones - vol_smallpart\n\ndef beavergnaw(D, volume):\n    d_minvol = D\n    d_maxvol = 0.0\n    # print()\n    v1 = calc_volume(D, 0)\n    v2 = calc_volume(D, D)\n    while True:\n        # print(f\"min {d_minvol} max {d_maxvol} \", end='')\n        mid = (d_minvol + d_maxvol) / 2.0\n        new_vol = calc_volume(D, mid)\n        # print(new_vol)\n        if abs(volume - new_vol) < 0.0000001:\n            return mid\n        if  new_vol < volume:\n            d_minvol = mid\n        else:\n            d_maxvol = mid\n\ndef test_1():\n    assert abs(beavergnaw(50,50000) - 30.901188723) < 0.0000001\n\nif __name__ == '__main__':\n    while True:\n        D, V = list(map(int, input().split()))\n        if (D,V) != (0,0):\n            print(beavergnaw(D, V))\n        else:\n            exit()","sub_path":"python/1_to_2/beavergnaw.py","file_name":"beavergnaw.py","file_ext":"py","file_size_in_byte":1066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"233716632","text":"import matplotlib\nimport matplotlib.pyplot as plt\nimport pylab\nimport ROOT\nimport rootplot.root2matplotlib as r2m\n\nimport numpy as np\nfrom scipy import interpolate\n\nfrom plotnames import fig_name\n\ndef find(f, seq):\n    for i,item in enumerate(seq):\n        if f(item) or i == (len(seq)-1) :\n            return i\n\ndef get_valid_segments( seq, minval, maxval ) :\n    get_first = lambda val: val<maxval\n    get_last  = lambda val: val>maxval\n    \n    segs = []\n    # find the first segment, then splice the list seq[lp+1:] and run again\n    lp = 0\n    fp = 0\n\n    while fp < (len(seq)-1) :\n        fp = find( get_first, seq[lp:] ) + lp\n        lp = fp + find( get_last, seq[fp:] )\n        \n        if lp-fp > 2 :\n            segs.append( [fp-1,lp] )\n\n        lp+=1\n    if len(segs) == 0 :\n        segs = [ [0,len(seq)] ]\n    return segs\n\ndef makeSingle1DPlot( histos, filename, ext=\"png\" ) :\n    i=0\n    f = r2m.RootFile(filename)\n    for hname, options in histos.iteritems() :\n        hist = f.get(hname)\n        xmin,xmax = hist.xedges[0], hist.xedges[-1]\n        ymin,ymax = options[\"zrange1d\"]\n        y, x, patch  = hist.hist()\n        \n        segs = get_valid_segments( y, options[\"zrange\"][0], options[\"zrange\"][1] )\n        for seg in segs :\n            i += 1\n            y[seg[0]] = options[\"zrange1d\"][1]\n            y[seg[1]] = options[\"zrange1d\"][1]\n\n            tck = interpolate.splrep(x[seg[0]:seg[1]],y[seg[0]:seg[1]],s=0)\n            xnew = np.arange(x[seg[0]],x[seg[1]],(x[seg[1]]-x[seg[0]])/200)\n            ynew = interpolate.splev(xnew,tck,der=0)\n            ynew[-1] = options[\"zrange1d\"][1]\n            plt.figure()\n            plt.plot(xnew,ynew,'b')\n            plt.axis( [xmin, xmax, ymin, ymax] )\n            axes = plt.axes()\n            axes.set_xlabel( hist.xlabel )\n            axes.set_ylabel( options[\"title\"] )\n            pylab.xticks(pylab.arange( xmin, xmax+0.1, options[\"xticks\"] ) )\n            axes.set_title( \"%s(%s)\" % (options[\"title\"], hist.xlabel) )\n        plt.savefig( fig_name( options, filename ) + \".%s\" % ext )\n","sub_path":"modules/lh1d.py","file_name":"lh1d.py","file_ext":"py","file_size_in_byte":2061,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"269141069","text":"\nimport sys\nimport json\n\nimport requests\nfrom parse import *\nfrom bs4 import BeautifulSoup\nimport hashlib\nimport subprocess\n\nimport re\n\ndisplay = False\n\nhost = \"https://elaws.e-gov.go.jp\"\n\ntt_ksuji = str.maketrans('一二三四五六七八九〇壱弐参', '1234567890123')\n\nre_suji = re.compile(r'[十拾百千万億兆\\d]+')\nre_kunit = re.compile(r'[十拾百千]|\\d+')\nre_manshin = re.compile(r'[万億兆]|[^万億兆]+')\n\nTRANSUNIT = {'十': 10,\n             '拾': 10,\n             '百': 100,\n             '千': 1000}\nTRANSMANS = {'万': 10000,\n             '億': 100000000,\n             '兆': 1000000000000}\n\n\ndef kansuji2arabic(string, sep=False):\n    \"\"\"漢数字をアラビア数字に変換\"\"\"\n\n    def _transvalue(sj, re_obj=re_kunit, transdic=TRANSUNIT):\n        unit = 1\n        result = 0\n        for piece in reversed(re_obj.findall(sj)):\n            if piece in transdic:\n                if unit > 1:\n                    result += unit\n                unit = transdic[piece]\n            else:\n                val = int(piece) if piece.isdecimal() else _transvalue(piece)\n                result += val * unit\n                unit = 1\n\n        if unit > 1:\n            result += unit\n\n        return result\n\n    transuji = string.translate(tt_ksuji)\n    for suji in sorted(set(re_suji.findall(transuji)), key=lambda s: len(s),\n                           reverse=True):\n        if not suji.isdecimal():\n            arabic = _transvalue(suji, re_manshin, TRANSMANS)\n            arabic = '{:,}'.format(arabic) if sep else str(arabic)\n            transuji = transuji.replace(suji, arabic)\n\n    return transuji\n\n\n\ndef log(msg):\n    from datetime import datetime\n    now = datetime.now().strftime(\"%Y/%m/%d %H:%M:%S\")\n    if display:\n        print(\"[\"+now+\"] \"+msg)\n\nclass Item:\n    def __init__( self, title, text):\n        self.title = title.strip()\n        if len(text.strip().split(\"　\", 1)) != 1:\n            self.text = text.strip().split(\"　\", 1)[1]\n        else:\n            self.text = text.strip()\n\n    def json(self):\n        return {\n                \"title\": self.title,\n                \"text\": self.text,\n        }\n\n    def __str__(self):\n        return \"- {}: {}\".format(self.title,self.text)\n\nclass Paragraph:\n    \n    def __init__( self, title = \"\", num = \"\", text = \"\"):\n        self.num = num.strip()\n        self.title = title.strip()\n        if len(text.strip().split(\"　\", 1)) != 1:\n            self.text = text.strip().split(\"　\", 1)[1]\n        else:\n            self.text = text.strip()\n        self.items = []\n\n    def json(self):\n        res = {\n                \"title\": self.title,\n                \"text\": self.text,\n        }\n        if len(self.items) != 0:\n            res[\"items\"] = self.items\n        if self.num:\n            res[\"number\"] = self.num\n        return res\n\n    def add_item(self, items):\n        self.items = items\n\n    def __str__(self):\n        if self.num:\n            res = \"{}/{}: \\n\".format(\n                self.title,self.num)\n        else:\n            res = \"{}: \\n\".format(self.title)\n        res += \"{}\\n\".format(self.text)\n        if len(self.items) != 0:\n            for i in self.items:\n                res += \" {}\\n\".format(str(i))\n        return res\n\nclass Chapter:\n    def __init__( self, title):\n        self.title = title\n        self.articles = []\n\n    def json(self):\n        return {\n            \"title\": self.title,\n            \"articles\": self.articles,\n        }\n\n    def add_article(self, article):\n        self.articles.append(article)\n\n    def __str__(self):\n        print(\"# Chapter\\n----\")\n\nclass Article:\n    def __init__( self):\n        self.caption = \"\"\n        self.paragraphs = []\n\n    def json(self):\n        res = {\n            \"paragraphs\": self.paragraphs\n        }\n        if self.caption:\n            res[\"caption\"] = self.caption,\n        return res\n\n    def add_caption(self, caption):\n        self.caption = caption\n\n    def add_paragraph(self, paragraph):\n        self.paragraphs.append(paragraph)\n\nclass ParagraphEncoder(json.JSONEncoder):\n    def default(self, obj):\n        if isinstance(obj, Chapter):\n            return obj.json()\n        elif isinstance(obj, Article): \n            return obj.json()\n        elif isinstance(obj, Paragraph): \n            return obj.json()\n        elif isinstance(obj, Item):\n            return obj.json()\n        else:\n            return json.JSONEncoder.default(self, obj)\n\ndef fetch_content_from_url(parent):\n    r = requests.get(parent)\n    r.encoding = r.apparent_encoding\n    return BeautifulSoup(r.text, \"html.parser\")\n\ndef get_law_url_from_bs_data(bs_data):\n    targets = []    \n    for i in bs_data.find_all(\"a\", class_=\"detail_link\"):\n        print(i.string)\n        targets.append(host + i.get(\"href\"))\n    return targets\n\ndef parse_paragraph(bs_data, prev_title = \"\"):\n    title = bs_data.find(class_=\"ArticleTitle\")\n    number = None\n    if not title:\n        number = bs_data.find(class_=\"ParagraphNum\")\n\n    paragraph = bs_data.find(class_=\"ParagraphSentence\")\n    if title and paragraph and prev_title:\n        return None\n\n    if paragraph:\n        paragraph = paragraph.text\n\n    res = None\n    if number:\n        res = Paragraph(title=prev_title, num=number.text, text=paragraph)\n    else:\n        res = Paragraph(title=title.text, text=paragraph)\n\n    bitems = bs_data.find_all(class_=\"ItemSentence\")\n    items = []\n    for i in bitems:\n        items.append(Item(\n            i.find(class_=\"ItemTitle\").string,\n            i.text\n        ))\n    if len(items) != 0:\n        res.add_item(items)\n\n    return res\n\ndef parse_article(bs_data):\n    article = Article()\n    caption = bs_data.find(class_=\"ArticleCaption\")\n    if caption:\n        article.add_caption(caption.text)\n\n    prev = Paragraph()\n    for p in bs_data.find_all(class_=\"Paragraph\"):\n        paragraph = parse_paragraph(p, prev_title=prev.title)\n        if not paragraph:\n            continue\n        article.add_paragraph(paragraph)\n        prev = paragraph\n\n    return article\n\ndef parse_chapter(bs_data):\n    title = bs_data.find(class_=\"ChapterTitle\").string\n    chapter = Chapter(title)\n    for a in bs_data.find_all(class_=\"Article\"):\n        chapter.add_article(parse_article(a))\n    return chapter\n\ndef scraping(url):\n    Law = {}\n    result = {}\n    log(\"start scraping\")\n    r = requests.get(url)\n    r.encoding = r.apparent_encoding\n    res = BeautifulSoup(r.text,\"html.parser\")\n    law_name = res.b.text.replace('\\n','')\n    if \"（\" in law_name:\n        titles = parse(\"{}（{}）\",law_name)\n        if titles:\n            law_name = titles[0]\n            Law[\"number\"] = titles[1]\n\n    from datetime import datetime\n    Law[\"date\"] = datetime.now().strftime(\"%Y/%m/%d %H:%M:%S\")\n    Law[\"url\"] = url\n    Law[\"name\"] = law_name\n    log(\"Law Name:\"+law_name)\n\n    result = []\n\n    for i in res.find_all(\"div\",class_=\"MainProvision\"):\n        for a in i.find_all(\"div\", class_=\"Chapter\"):\n            result.append(parse_chapter(a))\n                    \n    Law[\"provision\"] = result    \n    return law_name, Law\n\ndef storeJson(url, storeDir, class_dir):\n    name, contents = scraping(url, True)\n    print(str(contents).replace(\"'\",'\"'))\n    return\n    print(\"====================\")\n    print(class_dir +\" / \"+ name)\n    print(\"=======\")\n\n    import os\n    if not os.path.isdir(storeDir +\"/\"+class_dir):\n        os.mkdir(storeDir +\"/\"+ class_dir)\n    name = hashlib.md5(name).hexdigest()\n    filename = name+\".json\"\n    log(\"Store \"+filename)\n    if len(filename) > 250:\n        print(\"ERROR\")\n        return class_dir+\"/\"+filename\n    import json\n    f = open(storeDir + \"/\"+class_dir+\"/\" + filename,\"w\")\n    f.write(json.dumps( contents, ensure_ascii=False))\n    f.write(\"\")\n    f.close()\n    return class_dir+\"/\"+filename\n\n\ndef get_laws_by_hiragana(hiragana):\n    target = \"{}/search/elawsSearch/elaws_search/lsg0100/search?searchType=3&initialIndex={}&category=1\".format(host,hiragana)\n    data = fetch_content_from_url(target)\n    urls = get_law_url_from_bs_data(data)\n    return \n\nif __name__ == \"__main__\":\n    resultDir = \"LawJson\"\n    url = host + \"/search/elawsSearch/elaws_search/lsg0500/detail?lawId=420AC0000000083\"\n    title, contents = scraping(url)\n    print(json.dumps(contents, ensure_ascii=False, cls=ParagraphEncoder)),\n\n    quit()\n\n    for h in [chr(i) for i in range(12353, 12436)]:\n        get_laws_by_hiragana(h)\n\n","sub_path":"scraping.py","file_name":"scraping.py","file_ext":"py","file_size_in_byte":8378,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"1264228","text":"# IMC = kg / (m**2)\r\nh = float(input('Digite a sua altura em metros: '))\r\np = float(input('Digite o seu peso em kg: '))\r\nimc = p / (h**2)\r\nif imc < 18.5:\r\n    print('Você está abaixo do peso.\\nSeu IMC é {:.3f}'.format(imc))\r\nelif imc < 25:\r\n    print('Você está no peso ideal!\\nSeu IMC é {:.3f}'.format(imc))\r\nelif imc < 30:\r\n    print('Você está acima do peso.\\nSeu IMC é {:.3f}'.format(imc))\r\nelif imc < 40:\r\n    print('Você se encontra obeso.\\nSeu IMC é {:.3f}'.format(imc))\r\nelse:\r\n    print('Você se encontra com obesidade mórbida.\\nSeu IMC é {:.3f}'.format(imc))\r\n","sub_path":"des043.py","file_name":"des043.py","file_ext":"py","file_size_in_byte":584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"447954102","text":"import argparse\nimport glob\nimport os\nimport pickle\nfrom datetime import datetime\n\nimport numpy as np\nimport pandas as pd\nfrom scipy.io import loadmat\n\nfrom tfsenc_pca import run_pca\nfrom tfsenc_read_datum import read_datum\nfrom tfsenc_utils import encoding_regression, load_header, setup_environ\n\n\ndef load_pickle(file):\n    \"\"\"Load the datum pickle and returns as a dataframe\n\n    Args:\n        file (string): labels pickle from 247-decoding/tfs_pickling.py\n\n    Returns:\n        DataFrame: pickle contents returned as dataframe\n    \"\"\"\n    with open(file, 'rb') as fh:\n        datum = pickle.load(fh)\n\n    return datum\n\n\ndef parse_arguments():\n    \"\"\"Read commandline arguments\n    Returns:\n        Namespace: input as well as default arguments\n    \"\"\"\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--word-value', type=str, default='all')\n    parser.add_argument('--window-size', type=int, default=200)\n\n    group1 = parser.add_mutually_exclusive_group()\n    group1.add_argument('--shuffle', action='store_true', default=False)\n    group1.add_argument('--phase-shuffle', action='store_true', default=False)\n\n    parser.add_argument('--lags', nargs='+', type=int)\n    parser.add_argument('--output-prefix', type=str, default='test')\n    parser.add_argument('--emb-type', type=str, default=None)\n    parser.add_argument('--context-length', type=int, default=0)\n    parser.add_argument('--datum-emb-fn',\n                        type=str,\n                        default='podcast-datum-glove-50d.csv')\n    parser.add_argument('--electrodes', nargs='*', type=int)\n    parser.add_argument('--npermutations', type=int, default=1)\n    parser.add_argument('--min-word-freq', nargs='?', type=int, default=1)\n\n    group = parser.add_mutually_exclusive_group()\n    group.add_argument('--sid', nargs='?', type=int, default=None)\n    group.add_argument('--sig-elec-file', nargs='?', type=str, default=None)\n\n    parser.add_argument('--pca-flag', action='store_true', default=False)\n    parser.add_argument('--reduce-to', type=int, default=0)\n\n    parser.add_argument('--align-with', type=str, default=None)\n    parser.add_argument('--align-target-context-length', type=int, default=0)\n\n    args = parser.parse_args()\n\n    if not args.pca_flag:\n        args.reduce_to = 0\n\n    if args.pca_flag and not args.reduce_to:\n        parser.error(\"Cannot reduce PCA to 0 dimensions\")\n\n    if not args.sid and args.electrodes:\n        parser.error(\"--electrodes requires --sid\")\n\n    return args\n\n\ndef trim_signal(signal):\n    bin_size = 32  # 62.5 ms (62.5/1000 * 512)\n    signal_length = signal.shape[0]\n\n    if signal_length < bin_size:\n        print(\"Ignoring conversation: Small signal\")\n        return None\n\n    cutoff_portion = signal_length % bin_size\n    if cutoff_portion:\n        signal = signal[:-cutoff_portion, :]\n\n    return signal\n\n\ndef load_electrode_data(args, elec_id):\n    '''Loads specific electrodes mat files\n    '''\n    DATA_DIR = '/projects/HASSON/247/data/conversations-car'\n    convos = sorted(glob.glob(os.path.join(DATA_DIR, str(args.sid), '*')))\n\n    all_signal = []\n    for convo in convos:\n        file = glob.glob(\n            os.path.join(convo, 'preprocessed',\n                         '*' + str(elec_id) + '.mat'))[0]\n        mat_signal = loadmat(file)['p1st']\n\n        # mat_signal = trim_signal(mat_signal)\n\n        if mat_signal is None:\n            continue\n        all_signal.append(mat_signal)\n\n    elec_signal = np.vstack(all_signal)\n\n    return elec_signal\n\n\ndef process_datum(args, df):\n    df['is_nan'] = df['embeddings'].apply(lambda x: np.isnan(x).all())\n\n    # drop empty embeddings\n    df = df[~df['is_nan']]\n\n    # use columns where token is root\n    if 'gpt2' in [args.align_with, args.emb_type]:\n        df = df[df['gpt2_token_is_root']]\n    elif 'bert' in [args.align_with, args.emb_type]:\n        df = df[df['bert_token_is_root']]\n    else:\n        pass\n\n    df = df[~df['glove50_embeddings'].isna()]\n\n    if args.emb_type == 'glove50':\n        df['embeddings'] = df['glove50_embeddings']\n\n    return df\n\n\ndef load_processed_datum(args):\n    conversations = sorted(\n        glob.glob(\n            os.path.join(os.getcwd(), 'data', str(args.sid), 'conv_embeddings',\n                         '*')))\n    all_datums = []\n    for conversation in conversations:\n        datum = load_pickle(conversation)\n        df = pd.DataFrame.from_dict(datum)\n        df = process_datum(args, df)\n        all_datums.append(df)\n\n    concatenated_datum = pd.concat(all_datums, ignore_index=True)\n\n    return concatenated_datum\n\n\ndef process_subjects(args, datum):\n    \"\"\"Run encoding on particular subject (requires specifying electrodes)\n    \"\"\"\n    electrode_info = load_pickle(\n        os.path.join(args.PICKLE_DIR, str(args.sid), args.electrode_file))\n\n    # trimmed_signal = trimmed_signal_dict['trimmed_signal']\n\n    # if args.electrodes:\n    #     indices = [electrode_ids.index(i) for i in args.electrodes]\n\n    #     trimmed_signal = trimmed_signal[:, indices]\n    #     electrode_names = [electrode_names[i] for i in indices]\n\n    if args.electrodes:\n        electrode_info = {\n            key: electrode_info.get(key, None)\n            for key in args.electrodes\n        }\n\n    # Loop over each electrode\n    for elec_id, elec_name in electrode_info.items():\n\n        if elec_name is None:\n            print(f'Electrode ID {elec_id} does not exist')\n            continue\n\n        elec_signal = load_electrode_data(args, elec_id)\n        # datum = load_processed_datum(args)\n\n        encoding_regression(args, args.sid, datum, elec_signal, elec_name)\n\n    # write_electrodes(args, electrode_names)\n\n    return\n\n\ndef process_sig_electrodes(args, datum):\n    \"\"\"Run encoding on select significant electrodes specified by a file \n    \"\"\"\n    flag = 'prediction_presentation' if not args.tiger else ''\n\n    # Read in the significant electrodes\n    sig_elec_file = os.path.join(args.PROJ_DIR, flag, args.sig_elec_file)\n    sig_elec_list = pd.read_csv(sig_elec_file, header=None)[0].tolist()\n\n    # Loop over each electrode\n    for sig_elec in sig_elec_list:\n        subject_id, elec_name = sig_elec[:29], sig_elec[30:]\n\n        # Read subject's header\n        labels = load_header(args.CONV_DIR, subject_id)\n        if not labels:\n            print('Header Missing')\n        electrode_num = labels.index(elec_name)\n\n        # Read electrode data\n        brain_dir = os.path.join(args.CONV_DIR, subject_id, args.BRAIN_DIR_STR)\n        electrode_file = os.path.join(\n            brain_dir, ''.join([\n                subject_id, '_electrode_preprocess_file_',\n                str(electrode_num + 1), '.mat'\n            ]))\n        try:\n            elec_signal = loadmat(electrode_file)['p1st']\n        except FileNotFoundError:\n            print(f'Missing: {electrode_file}')\n            continue\n\n        # Perform encoding/regression\n        encoding_regression(args, subject_id, datum, elec_signal, elec_name)\n\n    return\n\n\nif __name__ == \"__main__\":\n    start_time = datetime.now()\n    print(f'Start Time: {start_time.strftime(\"%A %m/%d/%Y %H:%M:%S\")}')\n\n    # Read command line arguments\n    args = parse_arguments()\n\n    # Setup paths to data\n    args = setup_environ(args)\n\n    # Locate and read datum\n    datum = read_datum(args)\n\n    if args.pca_flag:\n        datum = run_pca(args, datum)\n\n    # Processing significant electrodes or individual subjects\n    if args.sig_elec_file:\n        process_sig_electrodes(args, datum)\n    else:\n        process_subjects(args, datum)\n\n    end_time = datetime.now()\n    print(f'End Time: {end_time.strftime(\"%A %m/%d/%Y %H:%M:%S\")}')\n\n    print(f'Total runtime: {end_time - start_time} (HH:MM:SS)')\n","sub_path":"code/tfsenc_main.py","file_name":"tfsenc_main.py","file_ext":"py","file_size_in_byte":7678,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"71811702","text":"#!/usr/bin/env python\n# vim: set fileencoding=utf-8 :\n#\n# Author:   Akasaki \n# URL:      https://github.com/Rigeru \n# License:  MIT License\n#\n\nimport sys\n\nlist = []\nfor line in open(sys.argv[1]) :\n    list = line.strip().split(\" \" or \".\" or \",\")\n    for i in range(len(list)) :\n    \tprint(list[i])\n","sub_path":"p21.py","file_name":"p21.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"402996583","text":"\"\"\"fixed?\n\nRevision ID: 6699cbce66eb\nRevises: 7a657c4ef640\nCreate Date: 2018-12-14 16:26:45.793569\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import mysql\n\n# revision identifiers, used by Alembic.\nrevision = '6699cbce66eb'\ndown_revision = '7a657c4ef640'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_index('name', table_name='semasters')\n    op.drop_table('semasters')\n    op.drop_table('courses')\n    op.drop_table('class_course')\n    op.drop_index('name', table_name='classes')\n    op.drop_table('classes')\n    op.drop_table('time_serials')\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('time_serials',\n    sa.Column('id', mysql.INTEGER(display_width=11), autoincrement=True, nullable=False),\n    sa.Column('start_week', mysql.INTEGER(display_width=11), autoincrement=False, nullable=False),\n    sa.Column('end_week', mysql.INTEGER(display_width=11), autoincrement=False, nullable=False),\n    sa.Column('start_class', mysql.INTEGER(display_width=11), autoincrement=False, nullable=False),\n    sa.Column('end_class', mysql.INTEGER(display_width=11), autoincrement=False, nullable=False),\n    sa.Column('course_id', mysql.INTEGER(display_width=11), autoincrement=False, nullable=True),\n    sa.ForeignKeyConstraint(['course_id'], ['courses.id'], name='time_serials_ibfk_1'),\n    sa.PrimaryKeyConstraint('id'),\n    mysql_default_charset='utf8',\n    mysql_engine='InnoDB'\n    )\n    op.create_table('classes',\n    sa.Column('id', mysql.INTEGER(display_width=11), autoincrement=True, nullable=False),\n    sa.Column('name', mysql.VARCHAR(length=64), nullable=False),\n    sa.PrimaryKeyConstraint('id'),\n    mysql_default_charset='utf8',\n    mysql_engine='InnoDB'\n    )\n    op.create_index('name', 'classes', ['name'], unique=True)\n    op.create_table('class_course',\n    sa.Column('course_id', mysql.INTEGER(display_width=11), autoincrement=False, nullable=False),\n    sa.Column('class_id', mysql.INTEGER(display_width=11), autoincrement=False, nullable=False),\n    sa.ForeignKeyConstraint(['class_id'], ['classes.id'], name='class_course_ibfk_1'),\n    sa.ForeignKeyConstraint(['course_id'], ['courses.id'], name='class_course_ibfk_2'),\n    sa.PrimaryKeyConstraint('course_id', 'class_id'),\n    mysql_default_charset='utf8',\n    mysql_engine='InnoDB'\n    )\n    op.create_table('courses',\n    sa.Column('id', mysql.INTEGER(display_width=11), autoincrement=True, nullable=False),\n    sa.Column('title', mysql.VARCHAR(length=128), nullable=False),\n    sa.Column('teacher', mysql.VARCHAR(length=12), nullable=False),\n    sa.Column('students_num', mysql.INTEGER(display_width=11), autoincrement=False, nullable=False),\n    sa.Column('semaster_id', mysql.INTEGER(display_width=11), autoincrement=False, nullable=True),\n    sa.ForeignKeyConstraint(['semaster_id'], ['semasters.id'], name='courses_ibfk_1'),\n    sa.PrimaryKeyConstraint('id'),\n    mysql_default_charset='utf8',\n    mysql_engine='InnoDB'\n    )\n    op.create_table('semasters',\n    sa.Column('id', mysql.INTEGER(display_width=11), autoincrement=True, nullable=False),\n    sa.Column('name', mysql.VARCHAR(length=128), nullable=False),\n    sa.PrimaryKeyConstraint('id'),\n    mysql_default_charset='utf8',\n    mysql_engine='InnoDB'\n    )\n    op.create_index('name', 'semasters', ['name'], unique=True)\n    # ### end Alembic commands ###\n","sub_path":"migrations/versions/6699cbce66eb_fixed.py","file_name":"6699cbce66eb_fixed.py","file_ext":"py","file_size_in_byte":3499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"263583757","text":"\"\"\"\nOperate on netcdf datasets\n==========================\nCreating or copying variables of netcdf-datasets with netCDF4 is a\nhassle. These functions and classes aim to simplify the process.\n\n\nExamples\n--------\nCopy a dataset and add a variable from a third one::\n\n    with netCDF4.Dataset(\"a.nc\") as src, netCDF4.Dataset(\"b.nc\", \"w\") as dst:\n        copy_dataset(src, dst)\n        with netCDF4.Dataset(\"c.nc\") as src2:\n            copy_variable(src2, dst, \"CO2\")\n\nCopy dimensions and rename 'lev' to 'level', copy 'CO2' variable::\n\n    # Prepare dimension- and variable-copiers\n    dim_names = [('lev', 'level'), ('lon', 'lon'), ('lat', 'lat')]\n    dim_copiers = [DimensionCopier(srcn, dstn) for srcn, dstn in dim_names]\n\n    # Have to give dimensions explicitly because of name-change\n    co2_copier = VariableCopier('CO2', var_args={'dimensions': ('level',\n                                                                'lon',\n                                                                'lat')})\n\n    # Apply the copiers to the datasets\n    with netCDF4.Dataset(\"a.nc\") as src, netCDF4.Dataset(\"b.nc\", \"w\") as dst:\n        for dc in dim_copiers:\n            dc.apply_to(src, dst)\n            # Also copy associated variable\n            dc.copy_variable(src, dst)\n        co2_copier.apply_to(src, dst)\n\nCopy only first time-step::\n\n    with netCDF4.Dataset(\"a.nc\") as src:\n        dim_names = list(src.dimensions.keys())\n        dim_names.remove('time')\n        copiers = [DimensionCopier(dim) for dim in dim_names]\n\n        for varname in src.variables.keys():\n            # Only works if all variables have the same dimensions.\n            # If not, specify dimensions for each variable.\n            # var_val_indices indicates which part of the variable-values\n            # should be copied. Here we take the first index of the first\n            # dimension ('time').\n            c = VariableCopier(varname,\n                               var_args={'dimensions': tuple(dim_names),\n                                         'var_val_indices': np.s_[0,:]})\n            copiers.append(c)\n\n        with netCDF4.Dataset(\"b.nc\", \"w\") as dst:\n            for c in copiers:\n                c.apply_to(src, dst)\n\nExtract the bottom three layers in the top-left corner of the domain::\n\n    with netCDF4.Dataset(\"a.nc\") as src, netCDF4.Dataset(\"b.nc\", \"w\") as dst:\n        extract_subdomain(src, dst,\n                          {'rlat': np.s_[:50],\n                           'rlon': np.s_[:50],\n                           'level': np.s_[-3:]})\n\nCopying the 'lon' variable from src to dst, while changing the values\nand adding an attribute::\n\n    with netCDF4.Dataset(\"a.nc\") as src, netCDF4.Dataset(\"b.nc\", \"w\") as dst:\n        vals = np.random.rand(src['lon'].shape)\n        cp = VariableCreator.from_existing_var(src['lon'],\n                                               var_vals=vals,\n                                               var_attrs={'uncertainty':\n                                                          'large'})\n        cp.apply_to(dst)\n\nYou can find an application of these tools at\ngitlab.empa.ch/abt503/apps/cosmo_processing_chain/blob/master/jobs/tools/mozart2int2lm.py\n\n\nDesign Choices\n--------------\nThese tools don't explicitly open/close netcdf-file (notice the missing\nimport of netCDF4). The reason for that is that opening/closing involves\nreading/writing to disk. Forcing the user to do these slow operations prevents\nrepeatedly opening/closing files implicitly.\n\nThe on first glance unintuitive usage of\n    - What do you want to copy?  -   >>> c = VariableCopier(\"varname\")\n    - Between which files?       -   >>> c.apply_to(src_dataset, dst_dataset)\nstems from limitations by the multiprocessing module. It is not possible to use\na netCDF4.Dataset as an argument to a multiprocessing-function.\n\n\nTipps\n-----\nSpeeding up operations on netCDF4.Datasets can be mainly achieved by:\n\n    1. Make sure work on a numpy.array.\n       The default type of a dataset['varname'][:] is a numpy.ma.array, which\n       checks on every element access if the element is masked out and is thus\n       very slow.\n    If this is not fast enough\n    2. Avoid manual loops.\n       If you can reformulate your operation as a matrix-multiplication or\n       element-wise addition/multiplication, the optimized numpy functions\n       are much faster than a explicit loop.\n    If this is not fast enough\n    3. Use the multiprocessing module.\n       If you still need more speed, parallelizing your script will help.\n\n\nPlanned extensions\n------------------\n-   Function to combine multiple files into one\n        Especially along the time-dimension to combine cosmo-output into one file\n        Other dimensions would be possible but seem more complicated & less\n        useful.\n\"\"\"\n\nimport numpy as np\n\n\ndef copy_dataset(src_dataset, dst_dataset):\n    \"\"\"Copy all attrs, dimensions and variables from src_dataset to dst_dataset\"\"\"\n    copiers = []\n    for dim in src_dataset.dimensions.keys():\n        copiers.append(DimensionCopier(dim))\n    for var in src_dataset.variables.keys():\n        copiers.append(VariableCopier(src_names=var))\n\n    for attr in src_dataset.ncattrs():\n        dst_dataset.setncattr(attr, src_dataset.getncattr(attr))\n    for copier in copiers:\n        copier.apply_to(src_dataset, dst_dataset)\n\n\ndef copy_variable(\n    src_dataset,\n    dst_dataset,\n    src_names,\n    dst_name=None,\n    var_args={},\n    var_val_indices=np.s_[:],\n    var_attrs={},\n):\n    \"\"\"Copy a variable from src_dataset to dst_dataset.\n\n    Parameters\n    ----------\n    src_dataset : netCDF4.Dataset\n    dst_dataset : netCDF4.Dataset\n\n    For a thorough explanation of the other arguments, refer to\n    VariableCopier.__init__()\n    \"\"\"\n    (\n        VariableCopier(\n            src_names=src_names,\n            dst_name=dst_name,\n            var_args=var_args,\n            var_val_indices=var_val_indices,\n            var_attrs=var_attrs,\n        ).apply_to(src_dataset, dst_dataset)\n    )\n\n\ndef extract_subdomain(src_dataset, dst_dataset, subdomain):\n    \"\"\"Extract a subdomain of src_dataset and write it into dst_dataset\n\n    Parameters\n    ----------\n    src_dataset : netCDF4.Dataset\n    dst_dataset : netCDF4.Dataset\n    subdomain: dict(str, numpy.lib.index_tricks.IndexExpression object)\n        Specify the subdomain by giving a slice for each dimension in the\n        src_dataset that you want to narrow down. Dimensions not in this\n        dict will be copied in full.\n\n    Example\n    -------\n    Only copy the first timepoint\n    >>> extract_subdomain(src, dst, {'time': np.s_[0]})\n\n    Extract the bottom-right corner of the domain\n    >>> extract_subdomain(src, dst, {'rlat': np.s_[-50:], 'rlon': np.s_[-50:]})\n    \"\"\"\n    copiers = []\n\n    for dim in src_dataset.dimensions:\n        if dim in subdomain:\n            copiers.append(\n                DimensionCopier(src_name=dim, dim_val_indices=subdomain[dim])\n            )\n        else:\n            copiers.append(DimensionCopier(src_name=dim))\n\n    for variable in src_dataset.variables:\n        var_val_indices = []\n        for dim in src_dataset[variable].dimensions:\n            if dim in subdomain:\n                var_val_indices.append(subdomain[dim])\n            else:\n                var_val_indices.append(np.s_[:])\n        copiers.append(\n            VariableCopier(\n                src_names=variable, var_val_indices=tuple(var_val_indices)\n            )\n        )\n\n    for copier in copiers:\n        copier.apply_to(src_dataset, dst_dataset)\n\n\nclass DimensionCopier:\n    \"\"\"Copy netCDF-Dimensions\"\"\"\n\n    def __init__(self, src_name, dst_name=None, dim_val_indices=np.s_[:]):\n        \"\"\"\n        Parameters\n        ----------\n        src_name : str\n        dst_name : str\n            Defaults to None. If it is None, the name won't be changed\n        dim_val_indices : numpy.lib.index_tricks.IndexExpression object\n            Which subset of the dimension values should be copied. If the\n            dimension size is not unlimited, it will be set to match the\n            number of items specified in the slice.\n            Defaults to s_[:]. If a slice is given, both start and stop have\n            to be given, while the step has to be 1 (so that the number of\n            elements can be computed).\n        \"\"\"\n        self.src_name = src_name\n        if dst_name is None:\n            self.dst_name = src_name\n        else:\n            self.dst_name = dst_name\n\n        # dim_val_indices could also be an int if we only copy a single val\n        if isinstance(dim_val_indices, slice):\n            assert (\n                dim_val_indices.step is None or dim_val_indices.step == 1\n            ), \"Can only extract a closed subdomain. Step has to be 1\"\n        self.dim_val_indices = dim_val_indices\n\n    def apply_to(self, src_dataset, dst_dataset):\n        \"\"\"Copy the specified dimension from src_dataset to dst_dataset\"\"\"\n        src_dim = src_dataset.dimensions[self.src_name]\n\n        if src_dim.size is not None and self.dim_val_indices != np.s_[:]:\n            if isinstance(self.dim_val_indices, slice):\n                dim_size = _zero_based_index(\n                    self.dim_val_indices.stop, src_dim.size, start=False\n                ) - _zero_based_index(self.dim_val_indices.start, src_dim.size)\n            else:\n                # Only copy a single value\n                dim_size = 1\n        else:\n            dim_size = src_dim.size\n\n        dst_dataset.createDimension(dimname=self.dst_name, size=dim_size)\n\n    def copy_variable(self, src_dataset, dst_dataset):\n        \"\"\"Copy the variable associated with the dimension.\n\n        If dim_val_indices is not [:], only the corresponding subset of the\n        variable is copied.\n        \"\"\"\n        copier = VariableCopier(\n            src_names=[self.src_name],\n            dst_name=self.dst_name,\n            var_val_indices=self.dim_val_indices,\n        )\n        copier.apply_to(src_dataset, dst_dataset)\n\n\nclass VariableCopier:\n    \"\"\"Copy (and possibly alter) netCDF-Variables\"\"\"\n\n    def __init__(\n        self,\n        src_names,\n        dst_name=None,\n        var_args={},\n        var_val_indices=np.s_[:],\n        var_attrs={},\n    ):\n        \"\"\"\n        Parameters\n        ----------\n        src_names : list(str)\n            Names of the variables to be copied.\n            If this is a string or a list of length 1, the corresponding\n            netCDF-Variable will be copied into the new netCDF-file and\n            named dst_name.\n            If this is a list of strings, the variables will be added together\n            and stored in the new netCDF-file named dst_name.\n            Datatype and ncattrs will be copied from the first variable.\n        dst_name : str\n            Name of the variable in the destination file.\n            If this is None, the name of the first variable in src_names will be\n            used.\n            Default: None.\n        var_args: dict(str: str)\n            Arguments for the netCDF4.Dataset.createVariable-function. Arguments\n            given here overwrite the values obtained from the first variable in\n            src_names. Useful for setting zlib, complevel, shuffle, fletcher32,\n            contiguous, chunksizes and endian, as these can't be inferred from\n            the source-variable.\n            Default: dict()\n        var_val_indices: numpy.lib.index_tricks.IndexExpression object\n            Which values to copy from the source variables. By default, all\n            values are copied.\n            If only a subset of the values are copied, make sure the shape of\n            the copied values matches the dimensions of the destination\n            variable.\n            Default: numpy.s_[:]\n        var_attrs : dict(str: str)\n            key-value pairs get turned into additional ncattrs for the var, on\n            top of the ncattrs copied from the first variable in scr_names.\n            Values given here overwrite values from the copied variable with\n            the same attribute-name.\n            Useful for adding or changing attributes, such as units.\n            Default: Dict()\n        \"\"\"\n        if not isinstance(src_names, list):\n            self.src_names = [src_names]\n        else:\n            self.src_names = src_names\n\n        if dst_name is None:\n            self.dst_name = self.src_names[0]\n        else:\n            self.dst_name = dst_name\n        self.var_args = var_args.copy()\n        self.var_val_indices = var_val_indices\n        self.var_attrs = var_attrs.copy()\n\n    def apply_to(self, src_dataset, dst_dataset):\n        \"\"\"Copy the specified variable(s) from src_dataset to dst_dataset.\n\n        If there are multiple source variables, their values will be added.\n        Raise an exception if their dimensions are different.\n\n        The datatype, least_significant_digit and the attributes will be copied\n        from the first element of the src_names list.\n\n        Constructor arguments  zlib, complevel, shuffle, fletcher32, contiguous,\n        chunksizes and endian can't (easily) be obtained from the variable\n        in src_dataset. If they should be set, specify them with the var_args.\n        \"\"\"\n        ref_var = src_dataset[self.src_names[0]]\n\n        name = self.dst_name\n        # Take datatype from ref_var and not from self.var_args, since we\n        # need to check if we can accumulate values based on src-dtype, not\n        # dest-dtype.\n        dtype = ref_var.dtype\n        try:\n            dims_names = self.var_args[\"dimensions\"]\n        except KeyError:\n            dims_names = ref_var.dimensions\n        digit = getattr(ref_var, \"least_significant_digit\", None)\n\n        # Test if variable datatype is Character, if not accumulate data\n        if dtype == np.dtype(\"S1\") or dtype == np.dtype(\"str\"):\n            if len(self.src_names) > 1:\n                # Characters can't be added together\n                raise TypeError(\n                    \"Can't combine variables with {} as \"\n                    \"datatype.\".format(dtype)\n                )\n            vals = src_dataset[self.src_names[0]][self.var_val_indices]\n        else:\n            vals = np.zeros_like(ref_var[self.var_val_indices])\n\n            for var_name in self.src_names:\n                if src_dataset[var_name].dimensions != ref_var.dimensions:\n                    msg = \"Different dimensions:\\n{}: {},\\n{}: {}.\".format(\n                        self.src_names[0],\n                        ref_var.dimensions,\n                        var_name,\n                        src_dataset[var_name].dimensions,\n                    )\n                    raise ValueError(msg)\n\n                vals += src_dataset[var_name][self.var_val_indices]\n\n        # Get fill value\n        ncattrs = ref_var.ncattrs()\n        try:\n            # Remove fill value from ncattrs because setting it after\n            # construction is not possible\n            ncattrs.remove(\"_FillValue\")\n            fill_value = ref_var.getncattr(\"_FillValue\")\n        except ValueError:\n            fill_value = None\n\n        var_args = dict(\n            varname=name,\n            datatype=dtype,\n            dimensions=dims_names,\n            least_significant_digit=digit,\n            fill_value=fill_value,\n        )\n        var_args.update(self.var_args)\n\n        var_attrs = dict([(name, ref_var.getncattr(name)) for name in ncattrs])\n        var_attrs.update(self.var_attrs)\n\n        (\n            VariableCreator(\n                var_args=var_args, var_vals=vals, var_attrs=var_attrs\n            ).apply_to(dst_dataset)\n        )\n\n\nclass VariableCreator:\n    \"\"\"Creates a netCDF4 variable with the specified parameters\"\"\"\n\n    def __init__(self, var_args, var_vals, var_attrs={}):\n        \"\"\"\n\n        Parameters\n        ----------\n        var_args : dict\n            Gets unpacked as kwargs to netCDF4.Dataset.createVariable.\n            Has to contain at least 'varname' & 'datatype'.\n        var_vals : np.array\n            Values to assign to the variable.\n        var_attrs : dict\n            key-value pairs get turned into ncattrs for the var with\n            netCDF4.Variable.setncattr.\n        \"\"\"\n        assert \"varname\" in var_args and \"datatype\" in var_args, (\n            \"varname and datatype are required arguments for \"\n            \"variable creation.\"\n        )\n\n        self.varname = var_args[\"varname\"]\n        self.var_args = var_args\n        self.var_vals = var_vals\n        self.var_attrs = var_attrs\n\n    @classmethod\n    def from_existing_var(\n        cls, src_variable, var_args={}, var_vals=None, var_attrs={}\n    ):\n        \"\"\"Create a VariableCreator from an existing variable.\n\n        If none of the optional arguments are given, this essentially does\n        the same as a VariableCopier.\n\n        However, the optional arguments allow you to overwrite certain aspects\n        of the src_variable.\n\n        Parameters\n        ----------\n        src_variable : netCDF4.Variable\n        var_args : dict\n            Arguments to the variable-constructor which should be different than\n            the ones copied from src_variable (name, dimensions, datatype,\n            fill_value) or arguments that can't be inferred from src_variable\n            (zlib, complevel, shuffle, fletcher32, contiguous, chunksizes,\n            endian).\n            Default: dict()\n        var_vals : np.array\n            Values of the variable. If this is None, the values from\n            src_variable are copied.\n            Default: None\n        var_attrs : dict\n            Attributes of the created variable. Values given here overwrite\n            existing attribute-values from the src_variable\n\n        Example\n        -------\n        Copying the 'lon' variable from src to dst, while changing the values\n        and adding an attribute can be done like so:\n\n        >>> src, dst = netCDF4.Dataset('src.nc'), netCDF4.Dataset('dst.nc', 'w')\n        >>> vals = np.random.rand(src['lon'].shape)\n        >>> cp = VariableCreator.from_existing_var(src['lon'],\n                                                   var_vals=vals,\n                                                   var_attrs={'uncertainty':\n                                                              'large'})\n        >>> cp.apply_to(dst)\n        \"\"\"\n        copied_var_args = {\n            \"varname\": src_variable.name,\n            \"datatype\": src_variable.datatype,\n            \"dimensions\": src_variable.dimensions,\n        }\n\n        try:\n            copied_var_args[\"fill_value\"] = src_variable._FillValue\n        except AttributeError:\n            # Not all datatypes have a default value (string)\n            pass\n\n        var_args = {**copied_var_args, **var_args}\n\n        if var_vals is None:\n            var_vals = src_variable[:]\n\n        copied_var_attrs = dict(\n            [\n                (name, src_variable.getncattr(name))\n                for name in src_variable.ncattrs()\n                if name != \"_FillValue\"\n            ]\n        )\n\n        var_attrs = {**copied_var_attrs, **var_attrs}\n\n        return cls(var_args=var_args, var_vals=var_vals, var_attrs=var_attrs)\n\n    def apply_to(self, dst_dataset, _=None):\n        \"\"\"Create the variable with the parameters specified in the constructor\n        in the netCDF4.Dataset dst_dataset\n\n        For consistency with the other apply_to-functions (DimensionCopier,\n        VariableCopier), this function takes two arguments.\n\n        Parameters\n        ----------\n        dst_dataset : netCDF4.Dataset\n            The variable is created in this dataset\n        _\n            Ignored, defaults to None\n        \"\"\"\n        dst_dataset.createVariable(**self.var_args)\n        # https://github.com/Unidata/netcdf4-python/issues/526\n        if self.var_args[\"datatype\"] is str:\n            for i, char in enumerate(self.var_vals):\n                dst_dataset[self.varname][i] = char\n        else:\n            dst_dataset[self.varname][:] = self.var_vals\n        for attrname, attrval in self.var_attrs.items():\n            dst_dataset[self.varname].setncattr(name=attrname, value=attrval)\n\n\ndef _zero_based_index(i, l, start=True):\n    \"\"\"Compute the 0-based index from the slice index i in a list of length l\n    Assuming step is 1.\n\n    Examples\n    --------\n    >>> _concrete_index(2, 100)\n    2\n    >>> _concrete_index(2, 100, start=False)\n    2\n    >>> _concrete_index(-1, 100, start=False)\n    99\n    >>> _concrete_index(None, 100)\n    0\n    >>> _concrete_index(None, 100, start=False)\n    100\n    \"\"\"\n    # 'stolen' from https://github.com/python/cpython/blob/master/Objects/sliceobject.c#L165\n    assert l >= 0, \"\"\n\n    if i is None:\n        if start:\n            return 0\n        else:\n            return l\n\n    if start:\n        assert i < l, \"\"\n    else:\n        assert i <= l, \"\"\n\n    if i < 0:\n        return i + l\n\n    return i\n","sub_path":"emiproc/nc_operations.py","file_name":"nc_operations.py","file_ext":"py","file_size_in_byte":20827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"638581058","text":"#This script will be periodically run to load in lawmakers to the leg_tracker system.\n#Probably need to think through the workflow\n#When is this run? Automatically or manually?\n#Do we keep lawmakers in the database if they're no longer active?\n#maybe with the active boolean?\n#\n#\n# usage:\n# export DJANGO_SETTINGS_MODULE=leg_tracker.settings\n# python loadLawmakers.py\n#\n# By Tyler Dukes, WRAL\n\nimport json, os, sys\nimport urllib.request\n\nimport django\ndjango.setup()\n\nfrom billcatcher.models import Lawmaker\n\nurl = 'https://www.wral.com/news/state/nccapitol/data_set/14376504/?dsi_id=ncga-eid&version=jsonObj'\n\ndef load_lawmakers():\n\tresponse = urllib.request.urlopen(url)\n\tdata = json.loads(response.read())\n\n\tfor d in data:\n\t\tif d['legiscan_id'] != str(0) and d['legiscan_id'] != '':\n\t\t\tprint(d['legiscan_id'])\n\t\t\ttry:\n\t\t\t\tLawmaker.objects.update_or_create(\n\t\t\t\t\tname = d['member'],\n\t\t\t\t\tparty = d['party'],\n\t\t\t\t\tposition = d['title'],\n\t\t\t\t\tmember_id = d['ncleg_id'],\n\t\t\t\t\theadshot = 'https://wral.com' + d['headshot'],\n\t\t\t\t\tdistrict = d['district'],\n\t\t\t\t\tcounty_short = d['county_short'],\n\t\t\t\t\tphone = d['phone'],\n\t\t\t\t\temail = d['email'],\n\t\t\t\t\tcounty_long = d['county_long'],\n\t\t\t\t\tchamber = d['chamber'],\n\t\t\t\t\tlegiscan_id = d['legiscan_id'],\n\t\t\t\t\teid = d['eid'],\n\t\t\t\t\tactive = str(1)\n\t\t\t\t\t)\n\t\t\texcept Exception as e:\n\t\t\t\tprint(\"Unexpected error:\",d['member'],sys.exc_info()[0])\n\t\t\t\tprint(str(e))\n\nif __name__ == '__main__':\n\tload_lawmakers()\n\tprint(\"...lawmakers loaded\")","sub_path":"leg_tracker/loadLawmakers.py","file_name":"loadLawmakers.py","file_ext":"py","file_size_in_byte":1474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"48718365","text":"import FWCore.ParameterSet.Config as cms\n\nprocess = cms.Process(\"MakeNpKNU\")\n\nprocess.load(\"FWCore.MessageService.MessageLogger_cfi\")\nprocess.load(\"FWCore.MessageLogger.MessageLogger_cfi\")\nprocess.MessageLogger.cerr.FwkReport.reportEvery = 1000\nprocess.options = cms.untracked.PSet(wantSummary = cms.untracked.bool(False))\n\nprocess.load(\"FWCore.MessageService.MessageLogger_cfi\")\nprocess.load(\"Configuration.StandardSequences.Services_cff\")\nprocess.load(\"Configuration.Geometry.GeometryIdeal_cff\")\nprocess.load(\"Configuration.StandardSequences.MagneticField_38T_cff\")\n#process.load(\"Configuration.StandardSequences.FrontierConditions_GlobalTag_cff\")\nprocess.load(\"Configuration.StandardSequences.Reconstruction_cff\")\n#process.GlobalTag.globaltag = 'PLS170_V7AN1::All'\nprocess.load('Configuration.StandardSequences.FrontierConditions_GlobalTag_condDBv2_cff')\nprocess.GlobalTag.globaltag = 'PHYS14_25_V1'\n\n\nprocess.source = cms.Source(\"PoolSource\",\n    fileNames = cms.untracked.vstring(\n'file:/disk4/ycyang/CMSDATA/store/mc/RunIISpring15DR74/WprimeToENu_M-3400_TuneCUETP8M1_13TeV-pythia8/MINIAODSIM/Asympt50ns_MCRUN2_74_V9A-v1/60000/F8BE5EB4-3CFE-E411-A184-00266CF3DF14.root',\n#'file:/disk4/ycyang/work/MultiV_13TeV/Analysis/Data/Condor_pp13TeV_WZA_Z2emt_AOD_list_150419_212417/condorOut/JME-Phys14DR-00001_MINIAOD_306_0.root',\n#'file:/store/mc/Phys14DR/WprimeToENu_M_4200_Tune4C_13TeV_pythia8/MINIAODSIM/PU20bx25_PHYS14_25_V1-v1/10000/72BBBA81-816F-E411-A64F-00266CFFA2C4.root',\n    )\n)\nprocess.maxEvents = cms.untracked.PSet( input = cms.untracked.int32(-1) )\nprocess.TFileService = cms.Service(\"TFileService\", fileName = cms.string('ntNpKNU.root'), )\n\n# For Electron ID\nfrom PhysicsTools.SelectorUtils.tools.vid_id_tools import *\ndataFormat = DataFormat.MiniAOD\nswitchOnVIDElectronIdProducer(process, dataFormat)\nmy_id_modules = ['RecoEgamma.ElectronIdentification.Identification.cutBasedElectronID_PHYS14_PU20bx25_V2_cff'\n                  ,'RecoEgamma.ElectronIdentification.Identification.heepElectronID_HEEPV51_cff']\nfor idmod in my_id_modules:\n    setupAllVIDIdsInModule(process,idmod,setupVIDElectronSelection)\n\n# For Photon ID.\nprocess.load(\"RecoEgamma/PhotonIdentification/PhotonIDValueMapProducer_cfi\")\nswitchOnVIDPhotonIdProducer(process, dataFormat)\nmy_photonID_modules = ['RecoEgamma.PhotonIdentification.Identification.cutBasedPhotonID_PHYS14_PU20bx25_V2_cff']\nfor idmod in my_photonID_modules:\n    setupAllVIDIdsInModule(process,idmod,setupVIDPhotonSelection)\n\n\n\n\n\nfrom MakeNpKNU.ProdNpKNU.ProdNpElectron_cfi import *\nprocess.ProdNpElectron = ProdNpElectron.clone()\n\nfrom MakeNpKNU.ProdNpKNU.ProdNpMuon_cfi import *\nprocess.ProdNpMuon = ProdNpMuon.clone()\n\nfrom MakeNpKNU.ProdNpKNU.ProdNpPhoton_cfi import *\nprocess.ProdNpPhoton = ProdNpPhoton.clone()\n\nfrom MakeNpKNU.ProdNpKNU.ProdNpJet_cfi import *\nprocess.ProdNpJet = ProdNpJet.clone()\n\nfrom MakeNpKNU.ProdNpKNU.ProdNpMET_cfi import *\nprocess.ProdNpMET = ProdNpMET.clone()\n\nfrom MakeNpKNU.ProdNpKNU.ProdNpGenerator_cfi import *\nprocess.ProdNpGenerator = ProdNpGenerator.clone()\n\nfrom MakeNpKNU.ProdNpKNU.ProdNpMisc_cfi import *\nprocess.ProdNpMisc = ProdNpMisc.clone()\n#process.ProdNpMisc.PrintTriggerResult = cms.untracked.bool(True)\n\nprocess.MakeNpKNU = cms.EDAnalyzer('MakeNpKNU',\n   electronToken = cms.InputTag('ProdNpElectron','Electron'),\n   muonToken = cms.InputTag('ProdNpMuon','Muon'),\n   photonToken = cms.InputTag('ProdNpPhoton','Photon'),\n   jetToken = cms.InputTag('ProdNpJet','Jet'),\n   metToken = cms.InputTag('ProdNpMET','MET'),\n   genParticleToken = cms.InputTag('ProdNpGenerator','GenParticle'),\n   genInfoToken = cms.InputTag('ProdNpGenerator','GenInfo'),\n   triggerToken = cms.InputTag('ProdNpMisc','Trigger'),\n   pileupToken = cms.InputTag('ProdNpMisc','Pileup'),\n   vertexToken = cms.InputTag('ProdNpMisc','Vertex'),\n)\n\nprocess.p = cms.Path(\n  process.egmGsfElectronIDSequence \n* process.photonIDValueMapProducer \n* process.ProdNpElectron \n* process.ProdNpMuon \n* process.ProdNpPhoton\n* process.ProdNpMET\n* process.ProdNpJet\n* process.ProdNpGenerator\n* process.ProdNpMisc\n* process.MakeNpKNU\n)\n\n#process.out = cms.OutputModule(\"PoolOutputModule\",\n#    fileName = cms.untracked.string('edmProdNpKNU.root')\n#    ,outputCommands = cms.untracked.vstring('drop *',\n#     \"keep *_ProdNp*_*_*\")\n#)\n#process.e = cms.EndPath(process.out)\n\n\n\n\n\n","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":4325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"281204913","text":"def binary_search(arr, low, high, target):\n    index_array = []\n\n    while low<=high:\n        mid=(low+high)/2\n        if arr[mid]<target:\n            low=mid+1\n        elif arr[mid]>target:\n            high=mid-1\n        else:\n            index_array = index_array + [mid]\n            high=mid-1\n\n    return index_array\n\n\narr = [10, 13, 18, 13, 18, 20, 18]\ntarget = 18\n\narr.sort()\nprint(arr)\nindex = binary_search(arr, 0, len(arr) - 1, target)\n\nif index == []:\n    print(\"not present\")\n    print(\"%d not present in array\" % target)\nelse:\n    print(\"Index of {}: {}\".format(target, index))\n\n","sub_path":"binary_search.py","file_name":"binary_search.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"29654099","text":"def criaDicUnidadeENutriente(dNutrienteEUnidade):\n  dUnidadeENutrientes = {}\n  for (nutr, medi) in dNutrienteEUnidade.items():\n    tmp = dUnidadeENutrientes.get(medi, [])\n    tmp.append(nutr)\n    dUnidadeENutrientes[medi] = tmp\n  return dUnidadeENutrientes\n  \ndef exibeNutrientes(alimento, nutriente, dAlim, dNutrienteEUnidade):\n  unidNutri = dNutrienteEUnidade[nutriente]\n  qtdNut = dAlim[alimento][nutriente]\n  print(\"{}-{}:{} {}\".format(alimento, nutriente, qtdNut, unidNutri))\n  \ndef pratoPronto(dAlim, dNutrienteEUnidade, dPrato):\n  nutTotal = {'CALORIAS': 0.0, 'PROTEINA': 0.0, 'CARBOIDRATO': 0.0, 'FIBRA': 0.0, 'FERRO': 0.0, 'SODIO': 0.0}\n  for alim, qtdAlim in dPrato.items():\n    for pKey, pValue in dAlim[alim].items():\n      nutTotal[pKey] += (pValue * qtdAlim)\n  print(\"Total de CALORIAS: {} {}\\nTotal de PROTEINA: {} {}\\nTotal de CARBOIDRATO: {} {}\\nTotal de FIBRA: {} {}\\nTotal de FERRO: {} {}\\nTotal de SODIO: {} {}\\n\".format(nutTotal['CALORIAS'], dNutrienteEUnidade['CALORIAS'], nutTotal['PROTEINA'], dNutrienteEUnidade['PROTEINA'], nutTotal['CARBOIDRATO'], dNutrienteEUnidade['CARBOIDRATO'], nutTotal['FIBRA'], dNutrienteEUnidade['FIBRA'], nutTotal['FERRO'], dNutrienteEUnidade['FERRO'], nutTotal['SODIO'], dNutrienteEUnidade['SODIO']))\n\n\n# Dicionários\ndNutrienteEUnidade = {'CALORIAS': 'kcal', 'PROTEINA': 'g', 'CARBOIDRATO': 'g', 'FIBRA': 'g', 'FERRO': 'mg', 'SODIO': 'mg'}\n\ndPrato= {'FEIJAO':2, 'ARROZ':1, 'ESPINAFRE':2, 'CARNE ASSADA':1, 'TOMATE':1}\n\ndAlim = {'ARROZ': {'CALORIAS': 124.0, 'PROTEINA': 2.6, 'CARBOIDRATO': 25.8, 'FIBRA': 2.7, 'FERRO': 0.3, 'SODIO': 1.0},\n'ALFACE': {'CALORIAS': 14.0, 'PROTEINA': 1.7, 'CARBOIDRATO': 2.4, 'FIBRA': 2.3, 'FERRO': 0.6, 'SODIO': 4.0},\n'BATATA COZIDA': {'CALORIAS': 52.0, 'PROTEINA': 1.2, 'CARBOIDRATO': 11.9, 'FIBRA': 1.3, 'FERRO': 0.2, 'SODIO': 2.0},\n'BATATA FRITA': {'CALORIAS': 267.0, 'PROTEINA': 5.0, 'CARBOIDRATO': 35.6, 'FIBRA': 8.1, 'FERRO': 0.4, 'SODIO': 2.0},\n'CENOURA': {'CALORIAS': 34.0, 'PROTEINA': 1.3, 'CARBOIDRATO': 7.7, 'FIBRA': 3.2, 'FERRO': 0.2, 'SODIO': 3.0},\n'ESPINAFRE': {'CALORIAS': 67.0, 'PROTEINA': 2.7, 'CARBOIDRATO': 4.2, 'FIBRA': 2.5, 'FERRO': 0.6, 'SODIO': 47.0},\n'INHAME': {'CALORIAS': 97.0, 'PROTEINA': 2.1, 'CARBOIDRATO': 23.2, 'FIBRA': 1.7, 'FERRO': 0.4, 'SODIO': 0.0},\n'MANDIOCA FRITA': {'CALORIAS': 300.0, 'PROTEINA': 1.4, 'CARBOIDRATO': 50.3, 'FIBRA': 1.9, 'FERRO': 0.3, 'SODIO': 9.0},\n'REPOLHO': {'CALORIAS': 17.0, 'PROTEINA': 0.9, 'CARBOIDRATO': 3.9, 'FIBRA': 1.9, 'FERRO': 0.2, 'SODIO': 4.0},\n'RUCULA': {'CALORIAS': 13.0, 'PROTEINA': 1.8, 'CARBOIDRATO': 2.2, 'FIBRA': 1.7, 'FERRO': 0.9, 'SODIO': 9.0},\n'TOMATE': {'CALORIAS': 15.0, 'PROTEINA': 1.1, 'CARBOIDRATO': 3.1, 'FIBRA': 1.2, 'FERRO': 0.2, 'SODIO': 1.0},\n'PEIXE FRITO': {'CALORIAS': 154.0, 'PROTEINA': 28.6, 'CARBOIDRATO': 0.0, 'FIBRA': 0.0, 'FERRO': 0.3, 'SODIO': 115.0},\n'BIFE MILANESA': {'CALORIAS': 352.0, 'PROTEINA': 20.6, 'CARBOIDRATO': 12.2, 'FIBRA': 0.4, 'FERRO': 2.9, 'SODIO': 77.0},\n'CARNE ASSADA': {'CALORIAS': 241.0, 'PROTEINA': 31.9, 'CARBOIDRATO': 0.0, 'FIBRA': 0.0, 'FERRO': 3.2, 'SODIO': 52.0},\n'PEITO DE FRANGO': {'CALORIAS': 212.0, 'PROTEINA': 33.4, 'CARBOIDRATO': 0.0, 'FIBRA': 0.0, 'FERRO': 0.5, 'SODIO': 56.0},\n'PORCO ASSADO': {'CALORIAS': 262.0, 'PROTEINA': 32.1, 'CARBOIDRATO': 0.0, 'FIBRA': 0.0, 'FERRO': 1.3, 'SODIO': 62.0},\n'OVO FRITO': {'CALORIAS': 240.0, 'PROTEINA': 15.6, 'CARBOIDRATO': 1.2, 'FIBRA': 0.0, 'FERRO': 2.1, 'SODIO': 166.0},\n'FEIJAO': {'CALORIAS': 77.0, 'PROTEINA': 4.5, 'CARBOIDRATO': 14.0, 'FIBRA': 8.4, 'FERRO': 1.5, 'SODIO': 2.0}}\n\nprint(criaDicUnidadeENutriente(dNutrienteEUnidade))\nprint()\nexibeNutrientes('TOMATE', 'CALORIAS', dAlim, dNutrienteEUnidade)\nexibeNutrientes('TOMATE', 'PROTEINA', dAlim, dNutrienteEUnidade)\nexibeNutrientes('BIFE MILANESA', 'FERRO', dAlim, dNutrienteEUnidade)\nexibeNutrientes('OVO FRITO', 'SODIO', dAlim, dNutrienteEUnidade)\nexibeNutrientes('FEIJAO', 'CALORIAS', dAlim, dNutrienteEUnidade)\nexibeNutrientes('RUCULA', 'FIBRA', dAlim, dNutrienteEUnidade)\nprint()\npratoPronto(dAlim, dNutrienteEUnidade, dPrato)","sub_path":"teste3/alimentos_bkp.py","file_name":"alimentos_bkp.py","file_ext":"py","file_size_in_byte":4042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"170539617","text":"# _*_ coding:utf-8 _*_\nfrom django.conf.urls import url\n\nurlpatterns = [\n    url(r'privateLetter', 'PM.views.privateLetter'),\n    url(r'sendMsg', 'PM.views.sendMsg'),\n    url(r'getChatTop10', 'PM.views.getChatTop10'),\n    url(r'delContacts', 'PM.views.delContacts'),\n    url(r'translate', 'PM.views.translate'),\n    \n    \n    url(r'cleanQueue', 'PM.views.cleanQueue'),\n    url(r'getMessage', 'PM.views.getMessage'),\n    url(r'getContactsByFid', 'PM.views.getContactsByFid'),\n    url(r'getContacts', 'PM.views.getContacts'),\n    \n    url(r'getNoticeCountMap', 'PM.views.getNoticeCountMap'),\n    url(r'ajaxfriend', 'PM.views.ajaxfriend'),\n    url(r'getTranGold', 'PM.views.getTranGold'),\n]\n","sub_path":"fateMeet/PM/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"498118190","text":"import requests\nimport smtplib\nimport os\nimport sqlite3\nimport time\nimport datetime\n\ndef weather_data(key, state, city):\n    fileName = 'http://api.wunderground.com/api/'+key+'/forecast/q/'+state+'/'+city+'.json'\n    parsed_json = requests.get(fileName).json()\n    return parsed_json\n\nprocessing_date_info = weather_data('xxx', 'FL', 'Orlando')['forecast']['simpleforecast']['forecastday']\n\ndef create_table():\n    conn = sqlite3.connect('xxx.db')\n    c = conn.cursor()\n    c.execute('CREATE TABLE IF NOT EXISTS xxx'\n              '('\n              'high REAL, low REAL, weekday TEXT, month TEXT, day REAL, year REAL, date_pulled TEXT'\n              ')'\n              )\n\n\ndef write_to_db():\n    conn = sqlite3.connect('xxx.db')\n    c = conn.cursor()\n    for i in range(len(processing_date_info)):\n        date = processing_date_info[i]['date']\n        day_weekday = date['weekday']\n        day_monthname = date['monthname']\n        day_of_the_week = date['day']\n        year = date['year']\n        day_temp = processing_date_info[i]\n        day_temp_high = day_temp['high']['fahrenheit']\n        day_temp_low = day_temp['low']['fahrenheit']\n        unix = time.time()\n        date = str(datetime.datetime.fromtimestamp(unix).strftime('%Y-%m-%d %H:%M:%S'))\n        c.execute(\n            \"INSERT INTO xxx\"\n            \"(\"\n            \"high, low, weekday, month, day, year, date_pulled\"\n            \")\"\n            \"VALUES (?, ?, ?, ?, ?, ?, ?)\",\n            (day_temp_high, day_temp_low, day_weekday, day_monthname, day_of_the_week, year, date))\n        conn.commit()\n    c.close()\n    conn.close()\n\nfinal_return = []\n\ndef pull_from_db():\n    conn = sqlite3.connect('xxx.db')\n    c = conn.cursor()\n    c.execute('SELECT * FROM xxx')\n    data = c.fetchall()\n    len_data_total = len(data)-1\n    len_data_minus_four = len(data)-4\n    for i in range(len_data_minus_four, len_data_total):\n        day_weekday = data[i][2]\n        day_monthname = data[i][3]\n        day_of_the_week = int(data[i][4])\n        year = int(data[i][5])\n        day_temp_high = data[i][0]\n        day_temp_low = data[i][1]\n        day_one_final_msg = day_weekday + ', ' + day_monthname + ' ' + str(day_of_the_week) + ', ' + str(year) \\\n                            + ' High: ' + str(day_temp_high) + ' Low: ' + str(day_temp_low)\n        final_return.append((day_one_final_msg))\n    c.close()\n    conn.close()\n\ndef send_email(email_user, email_password, to):\n    body = str(str(final_return[0]) + ' F' + '\\n' + str(final_return[1]) + ' F' + '\\n' + str(final_return[2]) + ' F' + '\\n')\n    subject = 'Weather Forecast'\n    email_text = 'Subject: {}\\n\\n{}'.format(subject, body)\n\n    try:\n        #server = smtplib.SMTP('smtp.mail.yahoo.com', 587)\n        server = smtplib.SMTP('smtp.gmail.com', 587)\n        server.ehlo()\n        server.starttls()\n        server.login(email_user, email_password)\n        server.sendmail(email_user, to, email_text)\n        server.close()\n\n        print('Email sent!')\n    except Exception as e:\n        print(e)\n\n    save_path = 'xxx'\n\n    name_of_file = (\"xxx.txt\")\n\n    completeName = os.path.join(save_path, name_of_file)\n\n    weather_email_text = open(completeName, 'w')\n    weather_email_text.write(body)\n    weather_email_text.close()\n\n    print(email_text)\n\ncreate_table()\nwrite_to_db()\npull_from_db()\n\nsend_email('xxx', 'xxx', 'xxx')","sub_path":"weather_forecast_2.1 pub for github.py","file_name":"weather_forecast_2.1 pub for github.py","file_ext":"py","file_size_in_byte":3342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"466116272","text":"from urllib import request\nimport json\nimport datetime\nimport csv\nimport time\nfrom argparse import ArgumentParser\nimport requests as req\n\n\n# Needed to write tricky unicode correctly to csv\ndef unicode_normalize(text):\n    return text.translate({0x2018: 0x27, 0x2019: 0x27, 0x201C: 0x22, 0x201D: 0x22, 0xa0: 0x20}).encode('utf-8')\n\n\ndef get_page_feed_data(group_id, access_token, num_statuses):\n    # Construct the URL string; see http://stackoverflow.com/a/37239851 for Reactions parameters\n    url = 'https://graph.facebook.com/v2.6/{d[group_id]}/feed/?' \\\n          'fields=message,link,created_time,type,name,id,comments.limit(0).summary(true),shares,reactions.limit(0).summary(true),from' \\\n          '&limit={d[limit]}&access_token={d[access_token]}'.format(d={'group_id': group_id,\n                                                                       'access_token': access_token,\n                                                                       'limit': num_statuses})\n\n    return req.get(url).json()\n\n\ndef get_reactions_for_status(status_id, access_token):\n    # See http://stackoverflow.com/a/37239851 for Reactions parameters\n    # Reactions are only accessable at a single-post endpoint\n\n    base = \"https://graph.facebook.com/v2.6\"\n    node = \"/%s\" % status_id\n    reactions = \"/?fields=\" \\\n                \"reactions.type(LIKE).limit(0).summary(total_count).as(like)\" \\\n                \",reactions.type(LOVE).limit(0).summary(total_count).as(love)\" \\\n                \",reactions.type(WOW).limit(0).summary(total_count).as(wow)\" \\\n                \",reactions.type(HAHA).limit(0).summary(total_count).as(haha)\" \\\n                \",reactions.type(SAD).limit(0).summary(total_count).as(sad)\" \\\n                \",reactions.type(ANGRY).limit(0).summary(total_count).as(angry)\"\n    parameters = \"&access_token=%s\" % access_token\n    url = base + node + reactions + parameters\n\n    # retrieve data\n    return req.get(url).json()\n\n\ndef process_page_feed_status(status, access_token):\n    # The status is now a Python dictionary, so for top-level items,\n    # we can simply call the key.\n\n    # Additionally, some items may not always exist,\n    # so must check for existence first\n\n    status_id = status['id']\n    status_message = '' if 'message' not in status else unicode_normalize(status['message'])\n    link_name = '' if 'name' not in status else unicode_normalize(status['name'])\n    status_type = status['type']\n    status_link = '' if 'link' not in status else unicode_normalize(status['link'])\n    status_author = unicode_normalize(status['from']['name'])\n\n    # Time needs special care since a) it's in UTC and\n    # b) it's not easy to use in statistical programs.\n\n    status_published = datetime.datetime.strptime(status['created_time'], '%Y-%m-%dT%H:%M:%S+0000')\n    status_published = status_published + datetime.timedelta(hours=-5)  # EST\n    status_published = status_published.strftime('%Y-%m-%d %H:%M:%S')  # best time format for spreadsheet programs\n\n    # Nested items require chaining dictionary keys.\n\n    num_reactions = 0 if 'reactions' not in status else status['reactions']['summary']['total_count']\n    num_comments = 0 if 'comments' not in status else status['comments']['summary']['total_count']\n    num_shares = 0 if 'shares' not in status else status['shares']['count']\n\n    # Counts of each reaction separately; good for sentiment\n    # Only check for reactions if past date of implementation:\n    # http://newsroom.fb.com/news/2016/02/reactions-now-available-globally/\n\n    reactions = get_reactions_for_status(status_id, access_token) if status_published > '2016-02-24 00:00:00' else {}\n\n    num_likes = 0 if 'like' not in reactions else reactions['like']['summary']['total_count']\n\n    # Special case: Set number of Likes to Number of reactions for pre-reaction statuses\n\n    num_likes = num_reactions if status_published < '2016-02-24 00:00:00' else num_likes\n\n    num_loves = 0 if 'love' not in reactions else reactions['love']['summary']['total_count']\n    num_wows = 0 if 'wow' not in reactions else reactions['wow']['summary']['total_count']\n    num_hahas = 0 if 'haha' not in reactions else reactions['haha']['summary']['total_count']\n    num_sads = 0 if 'sad' not in reactions else reactions['sad']['summary']['total_count']\n    num_angrys = 0 if 'angry' not in reactions else reactions['angry']['summary']['total_count']\n\n    # return a dict of all processed data\n    return dict(status_id=status_id,\n                status_message=status_message,\n                status_author=status_author,\n                link_name=link_name,\n                status_type=status_type,\n                status_link=status_link,\n                status_published=status_published,\n                num_reactions=num_reactions,\n                num_comments=num_comments,\n                num_shares=num_shares,\n                num_likes=num_likes,\n                num_loves=num_loves,\n                num_wows=num_wows,\n                num_hahas=num_hahas,\n                num_sads=num_sads,\n                num_angrys=num_angrys\n                )\n\n\ndef scrape_page_feed_statuses(group_id, access_token):\n    has_next_page = True\n    num_processed = 0  # keep a count on how many we've processed\n    scrape_starttime = datetime.datetime.now()\n\n    print(\"Scraping {} Facebook Page: {}\".format(group_id, scrape_starttime))\n\n    statuses = get_page_feed_data(group_id, access_token, 100)\n\n    while has_next_page:\n        for status in statuses['data']:\n\n            # Ensure it is a status with the expected metadata\n            if 'reactions' in status:\n                print(process_page_feed_status(status, access_token))\n\n            # output progress occasionally to make sure code is not stalling\n            num_processed += 1\n            if num_processed % 100 == 0:\n                print(\"{} Statuses Processed: {}\".format(num_processed, datetime.datetime.now()))\n        # if there is no next page, we're done.\n        if 'paging' in statuses:\n            statuses = req.get(statuses['paging']['next']).json()\n        else:\n            has_next_page = False\n\n    print(\"\\nDone!\\n{} Statuses Processed in {}\".format(num_processed, datetime.datetime.now() - scrape_starttime))\n\n\nif __name__ == '__main__':\n    parser = ArgumentParser()\n    parser.add_argument('-g', '--group_id')\n    parser.add_argument('-a', '--app_id')\n    parser.add_argument('-s', '--app_secret')\n    args = parser.parse_args()\n    scrape_page_feed_statuses(args.group_id, args.app_id + '|' + args.app_secret)\n\n\n# The CSV can be opened in all major statistical programs. Have fun! :)\n","sub_path":"get_fb_posts_fb_group.py","file_name":"get_fb_posts_fb_group.py","file_ext":"py","file_size_in_byte":6597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"273357916","text":"import sys\n\nimport cv2\nimport os\n\nimport pandas as pd\nimport pickle\nfrom matplotlib import pyplot as plt\nimport matplotlib\nmatplotlib.use(\"TkAgg\")\nfrom utils import add_bb_into_image\npkl_all_post_nms = \"metrics/all_post_nms.pkl\"\npkl_file = \"metrics/results.pkl\"\nfrom BoundingBoxes import BoundingBoxes\nfrom BoundingBox import BoundingBox\nfrom utils import *\nfrom Evaluator import Evaluator\ndef print_bbs(bbs):\n    ct = len(bbs._boundingBoxes)\n    class_ct = {}\n    for bb in bbs._boundingBoxes:\n        if bb.getClassId() not in class_ct:\n            class_ct[bb.getClassId()] = 0\n\n        class_ct[bb.getClassId()] += 1\n    for c in class_ct:\n        print(\"%s %d\" % (c, class_ct[c]))\n    print(\"%d total\" % ct)\n    print(\"\")\n\ndef print_models(models):\n    print(\"Total detections:\")\n    for model in models:\n        print(model)\n        bbs = models[model]\n        print_bbs(bbs)\n\ndf = pd.read_csv(\"/home/yuval/Documents/XNOR/sealnet/models/darknet/full_detections.csv\", delimiter='\\t')\nunique_ims = set(df[\"file\"].values[0:])\nroot_model_dets_dict = {}\n\n# first convert inference output to pkl file that is a ditionary key:model_name value:BoundingBoxes(object)\nif os.path.exists(pkl_file):\n    root_model_dets_dict = pickle.load(open(pkl_file, \"rb\"))\n    pass\nelse:\n    for i, det in df.iloc[1:].iterrows():\n        imageName=det[\"file\"]\n        model=det[\"weights\"]\n        label=det[\"label\"]\n        confidence=det[\"confidence\"]\n        x=det[\"x\"]\n        y=det[\"y\"]\n        w=det[\"w\"]\n        h=det[\"h\"]\n        if not model in root_model_dets_dict:\n            root_model_dets_dict[model] = BoundingBoxes()\n\n        box = BoundingBox(imageName, label,x,y,w,h,\n                          classConfidence=confidence, bbType=BBType.Detected, model=model)\n        root_model_dets_dict[model].addBoundingBox(box)\n    pickle.dump(root_model_dets_dict, open(pkl_file, \"wb\"))\n\n# for each model we use nms to remove redundant labels detected by that model\n# we then aggregate all detections across all models into one BoundingBoxes(object)\nif os.path.exists(pkl_all_post_nms):\n    all_together = pickle.load(open(pkl_all_post_nms, \"rb\"))\nelse:\n    all_together = BoundingBoxes()\n    for model in root_model_dets_dict:\n        nms = root_model_dets_dict[model].nms_old(NMS_THRESH=.5, CONFIDENCE_THRESH=0.1)\n        for box in nms._boundingBoxes:\n            all_together.addBoundingBox(box)\n    pickle.dump(all_together, open(pkl_all_post_nms, \"wb\"))\n\n# helper function that takes two lists of BoundingBox(object) where bbs1 is a list of detections for an image from one model\n# and bbs2 is a list of detections from a different model.  This function returns the matches between the two if the overlap\n# is greater than the given iou\n# the format of a single match is [bbs1 index, bss2 index, IOU] and this function returns a list of these matches\ndef model_bbs_cross_correlation(bbs1, bbs2, iou=0):\n    evaluator = Evaluator()\n    # array of [bbs1 idx, bbs2 idx, iou]\n    matches = []\n    for i in range(len(bbs1)):\n        ious = evaluator._getAllIOUs(bbs1[i], bbs2)\n        for ioutup in ious:\n            if ioutup[0] > iou:\n                matches.append([i, ioutup[3], ioutup[0]])\n    return matches\n\npkl_cross_correlation = \"metrics/post_cross_corelation.pkl\"\npkl_cross_correlation_nms = \"metrics/post_cross_corelation_post_nms.pkl\"\ngood_bbs = BoundingBoxes()\nbad_bbs = BoundingBoxes()\nif os.path.exists(pkl_cross_correlation) and False:\n    (good_bbs,bad_bbs) = pickle.load(open(pkl_cross_correlation, \"rb\"))\nelse:\n    for im_name in unique_ims:\n        imbbs = all_together.getBoundingBoxesByImageName(im_name)\n        root_model_dets_dict = {}\n        for bb in imbbs:\n            if not bb._model in root_model_dets_dict:\n                root_model_dets_dict[bb._model] = []\n            root_model_dets_dict[bb._model].append(bb)\n        mks = list(root_model_dets_dict.keys())\n        # match_dict is a dictionary storing the cross correlation matches between models\n        # key: model1, value: dictionary{key: model2, value: matches...model3..}\n        # the dictionary contains duplicate matches for reverse\n        model_match_dict = {}\n        for i in range(len(mks)):\n            c=mks[i]\n            model_match_dict[c]={}\n            c_root_dets = root_model_dets_dict[mks[i]] #current model\n            others = mks[:i] + mks[i + 1:]\n            for o in others:  #to do check if reverse cross correlation already done\n                omodel = root_model_dets_dict[o] # other model\n                c_matches = model_bbs_cross_correlation(c_root_dets, omodel, iou=.5)\n                model_match_dict[c][o] = c_matches\n\n        for c in root_model_dets_dict: # c=current\n            c_matches = model_match_dict[c]\n            c_root_dets = root_model_dets_dict[c]\n            match_ct = [0]*len(c_root_dets)\n            for o in c_matches: # o=other (aka one we are comparing to)\n                matches = c_matches[o]\n                already_matched_idxs = []\n                for match in matches:\n                    box1_idx = match[0]\n                    if box1_idx in already_matched_idxs:\n                        # we only want our current bounding box to be matched once\n                        # todo we should ideally pic the match with largest iou\n                        continue\n                    already_matched_idxs.append(match[0])\n                    match_ct[box1_idx]+=1\n            for mct, box in zip(match_ct, c_root_dets):\n                if mct ==3 or \\\n                        (mct > 1 and box.getConfidence() > .1) \\\n                        or box.getClassId() == \"Polar Bear\" \\\n                        or box.getConfidence() > .3:\n                    good_bbs.addBoundingBox(box)\n                else:\n                    bad_bbs.addBoundingBox(box)\n    pickle.dump((good_bbs,bad_bbs), open(pkl_cross_correlation, \"wb\"))\n\ngood_bbs_nms = None\nif os.path.exists(pkl_cross_correlation_nms):\n    good_bbs_nms = pickle.load(open(pkl_cross_correlation_nms, \"rb\"))\nelse:\n    good_bbs_nms = good_bbs.nms(.5, 0.0) # already filtered confidence, now we filter nms over all models\n    pickle.dump(good_bbs_nms, open(pkl_cross_correlation_nms, \"wb\"))\n\nplt_im = None\nfig = None\n# ground_truth_data = pd.read_csv(\"/home/yuval/Documents/XNOR/sealnet/data/updated_seals.csv\", dtype={'hotspot_id': object})\n\nfor im_name in unique_ims:\n    im = cv2.imread(os.path.join(\"/data/raw_data/TrainingAnimals_ColorImages/\", im_name))\n    im=good_bbs_nms.drawAllBoundingBoxes(im, im_name)\n    if plt_im is None:\n        fig = plt.figure()\n\n        plt_im = plt.imshow(im, cmap='gist_gray_r')\n    else:\n        plt_im.set_data(im)\n        plt_im = plt.imshow(im, cmap='gist_gray_r')\n    plt.draw()\n    plt.show()\n    plt.pause(1)\n    plt.cla()\n# #\n#\n# print_models(models)\n# nms = {}\n# # for model in models:\n# #     print_bbs(models[model].nms(.3, .1))\n# #\n#\n# print(\"ALLLLLLL\")\n# print_bbs(all_together)\n# print(\"ALLLLLLL NMS\")\n# atnms = all_together.nms(.3,.3)\n# print_bbs(atnms)\n#\n\n# x=1","sub_path":"metrics/archive/Ensemble.py","file_name":"Ensemble.py","file_ext":"py","file_size_in_byte":7011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"92196118","text":"################################################################\n# Author            : yiorgosynkl (find me in Github: https://github.com/yiorgosynkl)\n# Date created      : 20200828\n# Problem link      : https://leetcode.com/problems/implement-rand10-using-rand7/\n################################################################\n\n# The rand7() API is already defined for you.\n# def rand7():\n# @return a random integer in the range 1 to 7\n\nclass Solution:\n    # def rand10(self):\n    #     \"\"\"\n    #     :rtype: int\n    #     \"\"\"\n    #     choices = [1 for _ in range(10)] # all choices enabled\n    #     while sum(choices) != 1:\n    #         rands = [rand7() for _ in range(10)]\n    #         maxim = max([choices[i] * rands[i] for i in range(10)])\n    #         choices = [choices[i] if rands[i] >= maxim else 0 for i in range(10)] # remove choices with low random number        \n    #     return choices.index(1) + 1\n        \n    def rand10(self):\n        \"\"\"\n        :rtype: int\n        \"\"\"\n        ans = 41\n        while ans > 40:\n            ans = (rand7() - 1) * 7 + rand7() # from 1 to 49\n        return ans % 10 + 1 # 49/40*2 = 2.45 calls of rand7() per rand10().\n        ","sub_path":"30_day_challenge_2020_August/470_implement_rand10_using_rand7_day28.py","file_name":"470_implement_rand10_using_rand7_day28.py","file_ext":"py","file_size_in_byte":1181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"473343684","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('', views.home, name=\"home\"),\n    path('user/<str:pk>/', views.user_page, name=\"user_page\"),\n    path('account/<str:pk>/', views.account_settings, name=\"account\"),\n\n    path('login/', views.login_page, name=\"login\"),\n    path('register/', views.register_page, name=\"register\"),\n    path('logout/', views.logout_user, name=\"logout\"),\n\n    path('products/', views.products, name=\"products\"),\n    path('customer/<str:pk_test>/', views.customer, name=\"customer\"),\n    path('create_order/<str:pk>/', views.create_order, name=\"create_order\"),\n    path('update_order/<str:pk>/', views.update_order, name=\"update_order\"),\n    path('delete_order/<str:pk>/', views.delete_order, name=\"delete_order\"),\n\n    path('customer_create_order/<str:pk>/', views.customer_create_order, name=\"customer_create_order\"),\n\n    path('view_report/', views.view_order_report, name='view_order_report'),\n    path('print_report/', views.print_order_report, name='print_order_report'),\n]\n","sub_path":"djangocc/accounts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1031,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"42392830","text":"\n''' This function reads a core script and creates line and verse info. of target language\n\nEX run:\npython3 create_lines_from_corescript_xls.py -i /Users/spanta/Desktop/jon_code_test/XLScr1051m_N_eng__English_N2_ESV__02.xls -o ENG_lines.csv -fileset 'ENG_N2'\npython3 create_lines_from_corescript_xls.py -i /Users/spanta/Desktop/jon_code_test/XLScr1051m_N_eng__English_N2_ESV__02.xls -o ENG_lines.csv -fileset 'ENG_N2' -book_chapter 'JOHN 9'\nSide note: Dont find verses just based on numbers because numbers like 144,000 and verse # at 666\n\nNotes:\n#Default starting book chapter is MATTHEW 1, change this if it includes Old testament\n# Play with ! \"\" and other punctuation marks if it makes any difference to aeneas\n'''\n\nimport argparse,re,pandas as pd,os,csv\nfrom num2words import num2words\nfrom langdetect import detect\n\n\nparser = argparse.ArgumentParser(\n        description='This function creates lines.csv file from core script .xls file')\nrequired_args = parser.add_argument_group('required arguments')\nrequired_args.add_argument(\n    '-i', required=True, nargs=1, type=str, help='Input core script .xls file')\nrequired_args.add_argument(\n    '-o', required=True, nargs=1, type=str, help='Output file name')\nrequired_args.add_argument('-fileset', nargs=1, type=str,default=['test'],help='custom fileset id')\n\noptional_args=parser.add_argument_group('optional arguments')\noptional_args.add_argument('-sheetname', nargs=1, default=[0],type=int,help='sheetname or # in corescript')\noptional_args.add_argument('-book', nargs=1, type=str,help='custom book.EX: Mark')\noptional_args.add_argument('-book_chapter', nargs=1, type=str, default=['MATTHEW 1'],help='custom book chapter. Be sure to include the space. EX: John 9')\noptional_args.add_argument('-noheader', action='store_true', help='Remove header: fileset,book,chapter,db,sId,sBegin,sEnd')\noptional_args.add_argument('-find_string', nargs=1, type=str,default=['MATTHEW CHP 1'],help='string to find input langauge column index in corescript. Default:MATTHEW CHP 1')\noptional_args.add_argument('-target_pointer', nargs=1, type=str,default=['1'],help='Integer to add to input language column index to get target language in corescript. '\n                                                                                 'Default: find_string column index + 2')\noptional_args.add_argument('-line_pointer', nargs=1, type=str,default=['-3'],help='Integer to add to input language column index to get line number in corescript. '\n                                                                                'Default: find_string column index - 2')\n\n\nargs = parser.parse_args()\nprint(args)\ninput_file=args.i[0]\noutput_file=args.o[0]\nfileset=args.fileset[0]\n\ncore_script_sheet_name=args.sheetname[0]\n#if args.book_chapter is not None: book_chapter=args.book_chapter[0]\nif args.book is not None:\n    book=(str(args.book[0])).lower()\nelse:book=None\nfind_string=args.find_string[0]\n# if args.target_pointer is not None:\ntarget_pointer=int(args.target_pointer[0])\n# if args.line_pointer is not None:\nline_num_pointer=int(args.line_pointer[0])\n\n#custom tags\ncombine_tag='DO NOT COMBINE'\nline_gap_silence=0.5\nverses_split_string='-'\nchapter_find_string='CHP'\n\nwrite_file_handle=open(output_file,'w',encoding='utf-8')\nwrite_file = csv.writer(write_file_handle)\n# Write header as requested by user\nif not(args.noheader):write_file.writerow(('fileset','book','chapter','line_number', 'verse_number','verse_content','words','characters','postSilence_secs_cue','expected_post_silence_secs','do_not_combine_boolean_flag'))\n\n\n\n# Get the first column index of line numbers, input language, target language and corescript dataframe\ndef get_column_indexes_from_corescript(input_file=input_file,core_script_sheet_name=core_script_sheet_name,find_string=find_string,target_pointer=target_pointer):\n    line_column_index=8\n    input_language_column_index=10\n    target_language_column_index=12\n    # Open read and write files\n    df = (pd.read_excel(input_file, sheet_name=core_script_sheet_name, encoding='utf-8')).astype(str)\n    for c in range(0,len(df.columns)):\n        for r in range(0, len(df)):\n            if (df.iloc[r,c]).__contains__(find_string)==True:\n                # print(df.iloc[r,c])\n                input_language_column_index=c\n                line_column_index=c+line_num_pointer\n                target_language_column_index=c+target_pointer\n                # print(c,c+target_pointer)\n                break\n    return line_column_index,input_language_column_index,target_language_column_index,df\n\ndef flatten(l):\n    flatList = []\n    for elem in l:\n        # if an element of a list is a list\n        # iterate over this list and add elements to flatList\n        if type(elem) == list:\n            for e in elem:\n                flatList.append(e)\n        else:\n            flatList.append(elem)\n    return flatList\n\ndef range1(start, end):\n    return range(start, end+1)\n\n\ndef get_book_chapters_list(chapter_find_string,input_language_column_index,target_language_column_index,df,custom_book=None):\n    #Detect language\n    language=detect(script_df.iloc[500,target_language_column_index])\n    tmp_book_chapters_list=list()\n    book_chapters_list=list()\n    for s in df[(df.iloc[:, input_language_column_index].str).contains(chapter_find_string) == True].iloc[:, input_language_column_index]:\n        book_list=s.split('\\n')[-1]\n        tmp_book_chapters_list.append(book_list)\n\n    if custom_book is not None:\n        custom_book=custom_book.upper()\n        for i,b in enumerate(tmp_book_chapters_list):\n            book_upper=b.upper()\n            if (book_upper.__contains__(custom_book)):\n                book_chapters_list.append(b)\n    else:book_chapters_list=tmp_book_chapters_list\n    return language,book_chapters_list\n\n\n\n#Chapter detection is done using keyword CHP otherwise code FAILS\ndef get_chapter_verses(language,input_book_chapter,output_book_chapter,target_language_column_index,script_df):\n    # Get indices of chapter text from heading to end line\n    line_start_index=0\n    line_end_index=0\n    # input_book_chapter = book_chapter\n    book_chapter=input_book_chapter.strip()\n    book=(book_chapter.split('CHP')[0]).strip()\n    chapter=int(   (book_chapter.split('CHP')[-1]).strip()          )\n    # input_book_chapter=(book+' '+'CHP'+' '+str(chapter)).upper()\n    input_column_name=script_df.columns[input_language_column_index]\n    target_column_name=script_df.columns[target_language_column_index]\n    line_column_name=script_df.columns[line_column_index]\n    locations = (script_df[input_column_name].str).contains(input_book_chapter) == True\n    for i in range(0,len(locations)):\n        if locations[i]==True:\n            line_start_index=i\n            break\n\n\n    #Process last book separately Assumes empty string visually is read as 'nan'\n    if output_book_chapter!='REVELATION CHP 23':\n        locations=(script_df[input_column_name].str).contains(output_book_chapter)==True\n        for i in range(0,len(locations)):\n            if locations[i]==True:\n                line_end_index=i-1\n                break\n    else:\n        if language=='en':\n            e=line_start_index+1\n            while script_df.iloc[e,target_language_column_index]!='nan':\n                e+=1\n            line_end_index=e-1\n        else:\n            #For other languages\n            if script_df.iloc[len(script_df)-1,target_language_column_index]=='nan':\n                e = line_start_index + 1\n                while script_df.iloc[e, target_language_column_index] != 'nan':\n                    e += 1\n                line_end_index = e - 1\n            else:line_end_index=len(script_df)-1\n\n\n\n\n\n    #print(input_book_chapter,output_book_chapter,line_start_index,line_end_index)\n\n\n\n    current_verse_num=0\n\n\n\n    #Process chapter heading separately and extract line info.\n    line_num = (script_df[line_column_name][line_start_index]).split('.')[0]\n\n    # Extract cues from input language section\n    do_not_combine_boolean_flag = (script_df[input_column_name][line_start_index]).__contains__(\n        combine_tag.upper())\n    # IMP: assumes the timing cues are inside the || string #Detect Minute as well, used || for detection to be safe for foreign languages\n    if (script_df[input_column_name][line_start_index]).__contains__('||') and (''.join(filter(str.isdigit,re.split('\\|+',script_df[input_column_name][line_start_index])[1]))).isdigit():\n        # postSilence = ''.join(filter(str.isdigit, re.split('\\|+', script_df[input_column_name][line_start_index])[1]))\n        postSilence = (re.split('\\|+', script_df[input_column_name][line_start_index])[1]).split()[1]\n        time_unit = str(re.split('\\|+', script_df[input_column_name][line_start_index])[1][5]).upper()\n        if time_unit == 'M': postSilence = int(postSilence) * 60\n    else:\n        postSilence = ''\n\n\n    chapter_content=dict()\n    chapter_content[book_chapter]=list()\n\n    # Read each sentence (with new line) in chapter heading into new row for better aeneas separation and replace ... with ''\n\n\n    chapter_verse_num=0\n    # WRITING VERSE CONTENT\n    for i,word in enumerate((script_df[target_column_name][line_start_index]).split('\\n')):\n        word=word.strip()\n        word_count=len(word.split())\n\n        #Count chapter number into words\n        character_count=len([c for c in word if c.isalpha()])+len(num2words(int(chapter)))\n\n        #print(word)\n\n        # Print line gap silence only if cue post silence is empty and if its last word(split by new line) in the line\n        if i==len((script_df[target_column_name][line_start_index]).split('\\n'))-1 and postSilence=='':\n            #word = word + '\\n......\\n......'\n            write_string=[fileset, book, chapter, line_num, chapter_verse_num,word, word_count, character_count, postSilence,line_gap_silence,do_not_combine_boolean_flag]\n\n        elif i==len((script_df[target_column_name][line_start_index]).split('\\n'))-1 and postSilence!='':\n            #word = word + round(float(postSilence)/0.3) * '\\n......'\n            write_string = [fileset, book, chapter, line_num, chapter_verse_num, word, word_count, character_count, postSilence, '',\n                            do_not_combine_boolean_flag]\n        else:\n            write_string = [fileset, book, chapter, line_num, chapter_verse_num, word, word_count, character_count, '',\n                            '', do_not_combine_boolean_flag]\n\n        if language == 'en':write_string = [str(i).encode(\"ascii\", errors=\"ignore\").decode() for i in write_string]\n        else:\n            # write_string = [remove_accents(str(z).lower()) for z in write_string]\n            write_string = [remove_accents(str(z)) for z in write_string]\n            #write_string = [str(z).encode(\"ascii\", errors=\"ignore\").decode() for z in write_string]\n\n            write_string = [re.sub(\"⁰|¹|²|³|⁴|⁵|⁶|⁷|⁸|⁹\", \"\", z) for z in write_string]\n            #write_string = [str(i) for i in write_string]\n\n\n        if word.strip()!='':  write_file.writerow(write_string)\n\n    # For each line in chapter process and print the same info. as above\n    for index in range1(line_start_index+1,line_end_index):\n\n\n        # Extract line info.\n        line_num=(script_df[line_column_name][index]).split('.')[0]\n\n        #Extract cues from input language section\n        do_not_combine_boolean_flag = (script_df[input_column_name][index]).__contains__(\n            combine_tag.upper())\n        # IMP: assumes the timing cues are inside the || string\n        if (script_df[input_column_name][index]).__contains__('||') and str(re.split('\\|+',script_df[input_column_name][index])[1][3]).isdigit()   and  (''.join(filter(str.isdigit,re.split('\\|+',script_df[input_column_name][index])[1]))).isdigit():\n            # postSilence=''.join(filter(str.isdigit,re.split('\\|+',script_df[input_column_name][index])[1]))\n            postSilence =(re.split('\\|+', script_df[input_column_name][index])[1]).split()[1]\n            # print(line_num,re.split('\\|+',script_df[input_column_name][index])[1])\n            time_unit = str(re.split('\\|+',script_df[input_column_name][index])[1][5]).upper()\n            if time_unit == 'M': postSilence = int(postSilence) * 60\n        else:\n            postSilence = ''\n\n\n        #Extract info. from target language section\n\n        line_content=(script_df[target_column_name][index]).split()\n        verse_num_list=list()\n        verse_ind_list=list()\n        verse_list_dict=dict()\n\n        #Assuming a verse is more than one word and verse num <=119 (psalms longest in bible) and check if numbering flows with verses order\n        #Change this to NEW testament longest chapter\n        #Made sure that the word does not contain 12,000 or a number like that\n        for i,check_word in enumerate(line_content):\n            #Get verse numbers\n            #Give verse begin , vend\n            if re.search('{(.*)}', check_word) is not None:\n\n                if (re.search('{(.*)}', check_word).group(1)).__contains__('}') or int((re.search('{(.*)}',check_word).group(1)).split(verses_split_string)[0])<=119:\n\n                    if (re.search('{(.*)}', check_word).group(1)).__contains__('}'):\n                        content=re.search('{(.*)}', check_word).group(1)\n\n\n                        # print(content.split('{')[-1])\n\n\n                        verse_num_list.append(content.split('{')[-1])\n                        verse_ind_list.append(i)\n                        current_verse_num=content.split('{')[-1]\n                        if chapter == 1 and current_verse_num==40: print('1',current_verse_num)\n\n                    else:\n                        # print((re.search('{(.*)}', check_word).group(1)))\n                        verse_num_list.append((re.search('{(.*)}', check_word).group(1)))\n                        verse_ind_list.append(i)\n                        current_verse_num=(re.search('{(.*)}', check_word).group(1))\n                        if chapter == 1 and current_verse_num==40: print('2', current_verse_num)\n\n            elif i==0:\n                if chapter == 1 and current_verse_num==40: print('4', current_verse_num)\n                verse_num_list.append(current_verse_num)\n\n                # Append verse content\n                if current_verse_num not in verse_list_dict: verse_list_dict[current_verse_num] = list()\n                verse_list_dict[current_verse_num].append(check_word)\n                # print(line_num,verse_content,verse_num_list)\n            else:\n                # Append verse content\n                if current_verse_num not in verse_list_dict: verse_list_dict[current_verse_num] = list()\n                verse_list_dict[current_verse_num].append(check_word)\n                # print(line_num,verse_content,verse_num_list)\n\n\n\n\n        #WRITING VERSE CONTENT\n        for i,v in enumerate(verse_list_dict):\n            verse_list_dict[v]=' '.join(verse_list_dict[v])\n            # verse_list_dict[v]=verse_list_dict[v]+'\\n......\\n......'\n            word_count=len(verse_list_dict[v].split())\n\n            alphabet_character_count = len([c for c in verse_list_dict[v] if c.isalpha()])\n\n            # count multiple occurence of numbers into words and find the length , also add numbers without comma to the count\n            num_count=0\n            if re.search(r'\\d+', verse_list_dict[v]) is not None:\n                for u in re.findall(r'\\d+,\\d+',verse_list_dict[v]):\n                    num_count+=len(num2words(int(u.replace(\",\", \"\"))))\n                character_count = num_count+alphabet_character_count #+ len(num2words(int(re.search(r'\\d+', verse_list_dict[v]).group())))\n            else:\n                character_count = alphabet_character_count\n\n            if i!=0: do_not_combine_boolean_flag=''\n            # Print line gap silence only if cue post silence is empty and if its last verse in the line\n            if i==len(verse_list_dict)-1 and postSilence=='':\n\n                verse_list_dict[v] = verse_list_dict[v] + '\\n......\\n......'\n\n                write_string = [fileset, book, chapter, line_num, v, verse_list_dict[v], word_count, character_count,\n                                postSilence,line_gap_silence,\n                                do_not_combine_boolean_flag]\n            elif i==len(verse_list_dict)-1 and postSilence!='':\n\n                verse_list_dict[v] = verse_list_dict[v] + round(float(postSilence)/0.3) * '\\n......'\n\n                write_string = [fileset, book, chapter, line_num, v, verse_list_dict[v], word_count, character_count,\n                                postSilence, '',\n                                do_not_combine_boolean_flag]\n            else:write_string = [fileset, book, chapter, line_num, v, verse_list_dict[v], word_count, character_count,\n                                 '','',\n                            do_not_combine_boolean_flag]\n            if language == 'en':\n                write_string = [str(z).encode(\"ascii\", errors=\"ignore\").decode() for z in write_string]\n                # print(type(write_string))\n                #re.sub(\"⁰|¹|²|³|⁴|⁵|⁶|⁷|⁸|⁹\", \"\", write_string)\n\n            else:\n                # write_string = [remove_accents(str(z).lower()) for z in write_string]\n                write_string = [remove_accents(str(z)) for z in write_string]\n                #write_string = [str(z).encode(\"ascii\", errors=\"ignore\").decode() for z in write_string]\n\n                write_string = [re.sub(\"⁰|¹|²|³|⁴|⁵|⁶|⁷|⁸|⁹\", \"\", z) for z in write_string]\n\n                #print(write_string)\n\n                #write_string = [str(z) for z in write_string]\n                # print(type(write_string))\n\n                #re.sub(\"⁰|¹|²|³|⁴|⁵|⁶|⁷|⁸|⁹\", \"\", write_string)\n\n                #write_string = [str(z).encode(\"ascii\", errors=\"ignore\").decode() for z in write_string]\n\n            if (verse_list_dict[v]).strip()!='': write_file.writerow(write_string)\n\n\n\n        (chapter_content[book_chapter]).append([fileset,book,chapter,line_num, verse_list_dict, word_count,character_count,postSilence,do_not_combine_boolean_flag])\n\n\n\n\nline_column_index,input_language_column_index,target_language_column_index,script_df=get_column_indexes_from_corescript()\n# print(line_column_index,input_language_column_index,target_language_column_index)\n#print(\"line_column_name:{0},input_language_column_name:{1},target_language_column_name:{2}\".format(script_df.columns[line_column_index],script_df.columns[input_language_column_index],script_df.columns[target_language_column_index]))\n\nlanguage,complete_book_chapters_list=get_book_chapters_list(chapter_find_string,input_language_column_index,target_language_column_index,script_df)\nlanguage,book_chapters_list=get_book_chapters_list(chapter_find_string,input_language_column_index,target_language_column_index,script_df,book)\n\n'''\nprint(complete_book_chapters_list)\nprint(book_chapters_list)\nprint(language)\n'''\n\nlast_chapter_index=[i+1 for i, x in enumerate(complete_book_chapters_list) if x==book_chapters_list[len(book_chapters_list)-1]][0]\n\n# import unicodedata\n# myfoo = u'àà'\n# unicodedata.normalize('NFD', myfoo).encode('ascii', 'ignore')\n\nimport sys\nif sys.version_info.major == 3:\n    unicode = str\n\ndef remove_accents(string):\n    if type(string) is not unicode:\n        string = unicode(string, encoding='utf-8')\n\n    string = re.sub(u\"[Äàáâãäå]\", 'a', string)\n    string = re.sub(u\"[èéêë]\", 'e', string)\n    string = re.sub(u\"[ìíîïɨ]\", 'i', string)\n    string = re.sub(u\"[òóôõö]\", 'o', string)\n    string = re.sub(u\"[ùúûü]\", 'u', string)\n    string = re.sub(u\"[ýÿ]\", 'y', string)\n    string = re.sub(u\"[¿]\", '', string)\n\n    return string\n\n\nfor i,book in enumerate(book_chapters_list):\n    #print(book)\n    if i != len(book_chapters_list) - 1:\n        get_chapter_verses(language, book, book_chapters_list[i + 1], target_language_column_index, script_df)\n    else:\n        if book is None:\n            get_chapter_verses(language, book, 'REVELATION CHP 23', target_language_column_index, script_df)\n        else:\n            #get_chapter_verses(language, book, 'REVELATION CHP 23', target_language_column_index, script_df)\n            get_chapter_verses(language, book, complete_book_chapters_list[last_chapter_index], target_language_column_index, script_df)\nwrite_file_handle.close()\n","sub_path":"create_lines_from_corescript_xls.py","file_name":"create_lines_from_corescript_xls.py","file_ext":"py","file_size_in_byte":20320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"68550843","text":"from sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n# format of database and objects\nfrom model import Category, Item, Base, User\nimport datetime\nimport json\n\ndb_connect = json.loads(\n    open('/var/www/catalog/client_secrets_db.json',\n         'r').read())['db']['connect']\n\nengine = create_engine(db_connect, echo=False)\nBase.metadata.bind = engine\n\ndb_session = sessionmaker(bind=engine)\nsession = db_session()\n\ncategories = ['ABC', 'NBC', 'CBS']\n\nuser = {\n    'name': 'Robin Williams',\n    'email': 'robin@nanonahno.com',\n    'picture': 'https://en.wikipedia.org/wiki/File'\n               ':Robin_Williams_Happy_Feet_premiere.jpg'\n}\n\nitems = {\n    'ABC':\n        {\n            'name': 'The Bachelor',\n            'description': '''Colton Underwood, a former\n             professional football player who burst onto\n             the scene during Season 14 of The Bachelorette,\n             comes back to Bachelor Nation looking for a\n             teammate who will join him for a life full\n             of adventure, philanthropy and lasting love.''',\n            'category_id': 1,\n            'user_id': 1\n        },\n    'NBC':\n        {\n            'name': 'This Is Us',\n            'description': '''Jack and his wife - who is\n             very pregnant with triplets - have just\n             moved into their new home in Pittsburgh.\n             Successful and handsome television actor\n             Kevin is growing increasingly bored with his\n             bachelor lifestyle.''',\n            'category_id': 2,\n            'user_id': 1\n        },\n    'CBS':\n        {\n            'name': 'The Big Bang Theory',\n            'description': '''Mensa-fied best friends and roommates\n             Leonard and Sheldon, physicists who work at the\n             California Institute of Technology, may be able to tell\n             everybody more than they want to know about quantum\n             physics, but getting through most basic social\n             situations, especially ones involving women, totally\n             baffles them. How lucky, then, that babe-alicious\n             waitress/aspiring actress Penny moves in next door.''',\n            'category_id': 3,\n            'user_id': 1\n        }\n}\n\nquery_user = session.query(User).filter_by(name=user['name']).first()\nif not query_user:\n    session.add(User(name=user['name'], email=user['email'],\n                     picture=user['picture']))\n    session.commit()\n    print('{} found for user create {}'.format(query_user, user['name']))\nelse:\n    print('user {} already found'.format(user['name']))\n\n\nfor category in categories:\n    query_category = session.query(Category).filter_by(name=category).first()\n    if not query_category:\n        session.add(Category(name=category))\n        session.commit()\n        print('{} found for category create {}'.\n              format(query_category, category))\n        q_item = session.query(Item).\\\n            filter_by(name=items[category]['name']).first()\n        if not q_item:\n            session.add(Item(name=items[category]['name'],\n                             description=items[category]['description'],\n                             category_id=items[category]['category_id'],\n                             user_id=items[category]['user_id']))\n            session.commit()\n            print('{} found for item create {}'.\n                  format(q_item, items[category]['name']))\n    else:\n        print('category {} already found'.format(category))\n        print('item {} already found'.format(items[category]['name']))\n\n\n# used this for test data -> time_stamp_string\ndef time_stamp():\n    return datetime.datetime.now().strftime(\"%y-%m-%d%H:%M:%S\")\n","sub_path":"load_db.py","file_name":"load_db.py","file_ext":"py","file_size_in_byte":3691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"71447420","text":"#!/usr/bin/env python\n#-*- coding: utf-8 -*-\n#-------------------------------------------------------------------------------\n# Name:        module2\n# Purpose:\n#\n# Author:      Jean\n#\n# Created:     12/01/2018\n# Copyright:   (c) Jean 2018\n# Licence:     <your licence>\n#-------------------------------------------------------------------------------\nimport numpy as np\n\ndef f(z):\n    return z*z+1\n\ndef main():\n\n    inputBounds = (1,10,1,10)\n    inputResolution = (10,10)\n    #print(inputBounds[0])\n    xInput=np.linspace(inputBounds[0], inputBounds[1], inputResolution[1])\n    yInput=np.linspace(inputBounds[2], inputBounds[3], inputResolution[0])\n\n    #put sampling vectors on 2d grids\n    x,y=np.meshgrid(xInput,yInput)\n    z=x+1j*y\n    print('z0=',z[0])\n    #evaluate function\n    w=f(z)\n    print('w=',w[0],'\\n','z0squared=',f(z[0]))\n    print(w[0].real)\n    print(w[0].imag)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"test-numpy.py","file_name":"test-numpy.py","file_ext":"py","file_size_in_byte":920,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"195779008","text":"import os, sys\nimport numpy as np\nimport math\n\npower_path = os.path.join('..', 'libraries')\nsys.path.append(power_path)\n\nimport SignalProcessingLibrary\n\nclass Calibrator():\n    def __init__(self, signals, distances_to_source, speed_of_sound, sample_rate):\n        self.speed_of_sound = speed_of_sound\n        self.sample_rate = sample_rate\n        self.signals = signals\n        self.distances_to_source = distances_to_source\n        self.dt = len(self.signals[0])\n        self.delays = []\n        self.ks = []\n        self.powers = []\n        self.amplitudes = []\n\n    def calculate_delays(self):\n        for d in self.distances_to_source:\n            delay = d / self.speed_of_sound * self.sample_rate\n            delay = int(round(delay))\n            self.delays.append(delay)\n\n\n    def get_usable_signals(self):\n        max_delay = max(self.delays)\n\n        self.dt = self.dt - max_delay  # adjust length to useable part\n\n        # cut signals to useable part\n        for i in range(0, len(self.signals)):\n            \n            self.signals[i] = self.signals[i][self.delays[i]:self.delays[i] + self.dt]\n\n    def calculate_powers(self):\n        for s in self.signals:\n            \n            power = SignalProcessingLibrary.getSignalPower_UsingTime_AverageFree(np.asarray(s))\n            #power = SignalProcessingLibrary.getSignalPower_UsingTime(np.asarray(s))\n            #power = np.sum(np.asarray(s)) / (len(s) + 1)\n            #print(power)\n            self.powers.append(power)\n\n    def calculate_amplitudes(self):\n        for s in self.signals:\n            amplitude = max(s)\n            #amplitude = sum(s)/len(s)\n            self.amplitudes.append(amplitude)\n\n    def calibrate(self):\n        self.ks.append(1.0)\n\n        # now calibrate microphones\n        for i in range(1, len(self.signals)):\n            self.ks.append(1.0 * pow(self.distances_to_source[0], 2) * self.ks[0] * self.powers[0] * 1/pow(self.distances_to_source[i], 2) / self.powers[i])\n\n        #trying to set ksource to 1.0\n        #for i in range(0, len(self.signals)):\n        #    self.ks.append(1.0 / pow(self.distances_to_source[i], 2) / self.powers[i])\n\n    def calibrate_amplitudes(self):\n        self.ks.append(1.0)\n\n        for i in range(1, len(self.signals)):\n            self.ks.append(1.0 * self.distances_to_source[0] * self.ks[0] * self.amplitudes[0] / self.distances_to_source[i] / self.amplitudes[i])\n\n    def run_calibration(self):\n        self.calculate_delays()\n        self.get_usable_signals()\n        self.calculate_powers()\n        self.calibrate()\n\n        return self.ks\n\n    def run_calibration_amplitude(self):\n        self.calculate_delays()\n        self.get_usable_signals()\n        self.calculate_amplitudes()\n        self.calibrate_amplitudes()\n\n        return self.ks\n\n","sub_path":"calibration/calibrator.py","file_name":"calibrator.py","file_ext":"py","file_size_in_byte":2785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"219895370","text":"import itertools\nfrom collections import Counter\nfrom itertools import product, combinations, combinations_with_replacement\nfrom math import sqrt, ceil, factorial, log, floor\nfrom functools import lru_cache\nfrom time import time\n\nimport primes\nfrom prod import prod\nfrom sequences import OrderedBell, compositions\nfrom util import divisors\n\n\n@lru_cache()\ndef g(n):\n\ttotal = 1\n\tif n > 3:\n\t\tif ceil(sqrt(n)) == sqrt(n):\n\t\t\ttop = ceil(sqrt(n)) + 1\n\t\telse:\n\t\t\ttop = ceil(sqrt(n))\n\t\t\n\t\tfor m in range(2, top):\n\t\t\tif n % m != 0:\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tif m != n//m:\n\t\t\t\t\ttotal += (g(m) + g(n//m))\n\t\t\t\telse:\n\t\t\t\t\ttotal += g(m)\n\treturn total\n\n\n@lru_cache()\ndef g2(n, limit=float('inf')):\n\ttotal = 0\n\tif not primes.is_prime(n):\n\t\tfor d in divisors.divisors(n):\n\t\t\tif d < n:\n\t\t\t\ttotal += g2(d)\n\t\t\t\tif total > limit:\n\t\t\t\t\tbreak\n\telse:\n\t\ttotal = 1\n\treturn total\n\n\ndef g3(n, limit):\n\n\ttotal = 0\n\tactive = Counter([n])\n\n\twhile len(active) > 0:\n\t\tm = max(active.keys())\n\t\tc = active.pop(m)\n\t\tupdates = {d: c for d in divisors.divisors(m) if d < m}\n\t\tif len(updates) <= 1:\n\t\t\ttotal += c\n\t\telse:\n\t\t\tactive.update(updates)\n\t\tif total + sum(active.values()) > limit:\n\t\t\tbreak\n\n\treturn total\n\n\ndef g4(n, limit=float('inf')):\n\ttotal = 0\n\tactive = Counter([n])\n\n\twhile len(active) > 0:\n\t\tm = max(active.keys())\n\t\tc = active.pop(m)\n\t\tm_comp = primes.prime_composition(m)\n\t\tif len(m_comp) == 0:\n\t\t\ttotal += c\n\t\telif len(m_comp) == 1:\n\t\t\ttotal += c * 2 ** (m_comp[0] - 1)\n\t\telif m_comp[0] == 1:\n\t\t\ttotal += c * OrderedBell(sum(m_comp))\n\t\telse:\n\t\t\tupdates = {d: c for d in divisors.divisors(m) if d < m}\n\t\t\tif len(updates) <= 1:\n\t\t\t\ttotal += c\n\t\t\telse:\n\t\t\t\tactive.update(updates)\n\t\tif total > limit:\n\t\t\tbreak\n\n\treturn total\n\n\n@lru_cache()\ndef g5(n, limit=float('inf')):\n\ttotal = 0\n\tn_comp = primes.prime_composition(n)\n\tif n == 1:\n\t\ttotal += 1\n\telif len(n_comp) == 1:\n\t\ttotal += 2 ** (n_comp[0] - 1)\n\telif n_comp[0] == 1:\n\t\ttotal += OrderedBell(sum(n_comp))\n\telse:\n\t\tfor d in divisors.divisors(n):\n\t\t\tif d < n:\n\t\t\t\ttotal += g5(d)\n\t\t\t\tif total > limit:\n\t\t\t\t\tbreak\n\treturn total\n\n\ndef n_generator(lower, upper, exponen_limit=float('inf')):\n\ttry:\n\t\tp = next(primes.prime_generator(upper, lower))\n\texcept StopIteration:\n\t\tpass\n\telse:\n\t\tmax_pow = min(floor(log(upper, p)), exponen_limit)\n\t\tif max_pow > 0:\n\t\t\tfor power in range(1, max_pow+1):\n\t\t\t\tn = p**power\n\t\t\t\tif p > 2:\n\t\t\t\t\tyield n, (power,)\n\t\t\t\telif power == 1:\n\t\t\t\t\tcontinue\n\t\t\t\tnew_low = p + 1\n\t\t\t\tnew_up = upper // n\n\t\t\t\tfor m, comp in n_generator(new_low, new_up, power):\n\t\t\t\t\tyield n*m, (power,) + comp\n\n\ndef main_v1(func, L=10**16):\n\n\tprimes_so_far = [2]\n\tsought = 0\n\tfor p in primes.prime_generator(lower=3, upper=L):\n\t\tprimes_so_far.append(p)\n\n\t\tall_n = []\n\t\tfor comp in compositions(len(primes_so_far)):\n\t\t\tif len(comp) > 1:\n\t\t\t\tn = prod([base ** pow for base, pow in zip(primes_so_far, comp)])\n\t\t\t\tif n <= L:\n\t\t\t\t\tall_n.append((n, comp))\n\t\tif len(all_n) == 0:\n\t\t\tbreak\n\t\tall_n.sort()\n\n\t\tfor n, comp in all_n:\n\t\t\tg = func(n)\n\t\t\tif g > L:\n\t\t\t\tbreak\n\t\t\tif g < n:\n\t\t\t\tcontinue\n\t\t\tpowers = list(primes.prime_factorization(g).values())\n\t\t\tpowers.sort(reverse=True)\n\t\t\tif comp == powers:\n\t\t\t\tsought += g\n\t\t\t\tprint(sought, g, comp)\n\n\ndef main(limit=10**16):\n\tsought = 0\n\tfor n, comp in n_generator(lower=2, upper=limit):\n\t\tif n > limit:\n\t\t\tbreak\n\t\tif len(comp) == 1:\n\t\t\tcontinue\n\t\telif comp[0] == 1:\n\t\t\tcontinue\n\t\telse:\n\t\t\tg = g3(n, limit=limit)\n\n\t\tif g > limit:\n\t\t\tbreak\n\t\tg_comp = primes.prime_composition(g)\n\n\t\tif tuple(comp) == g_comp:\n\t\t\tsought += n\n\t\t\tprint(n)\n\tprint('final', sought)\n\n\nif __name__ == '__main__':\n\n\tstart = time()\n\tmain(10**16)\n\tend = time()\n\tprint(end-start)\n","sub_path":"PE_Prob_548_gozinta.py","file_name":"PE_Prob_548_gozinta.py","file_ext":"py","file_size_in_byte":3613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"545301786","text":"from userlib import BSTree\n\n\ndef sum_route_root_to_leaf(root):\n    \"\"\"\n    Given a binary tree containing digits from 0-9 only, each root to leaf path could represent a number. An example is the root-to-leaf path 1->2->3 which\n    represents the number 123. Find the total sum of all root-to-leaf numbers. For example,\n    23 + 24 = 47\n    \"\"\"\n    if root is None:\n        return 0\n    cur = 0\n    sum = [0]  # taking it as a list to pass it as a reference\n    cal_sum(root, cur, sum)\n    return sum\n\n\ndef cal_sum(root, cur, sum):\n    if root is None:\n        return\n    cur = cur*10 + root.get_data()\n    if root.get_left() is None and root.get_right() is None:\n        sum[0] += cur\n    cal_sum(root.get_left(), cur, sum)\n    cal_sum(root.get_right(), cur, sum)\n\n\nif __name__ == '__main__':\n    b = [5, 1, 6, 3, 8, 2]\n    a = BSTree(*b)\n    print(sum_route_root_to_leaf(a.root))\n\n","sub_path":"karumanchi/Trees/sum_route_root_to_leaf.py","file_name":"sum_route_root_to_leaf.py","file_ext":"py","file_size_in_byte":882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"234982509","text":"# Uses product/libraries from:\n# https://www.adafruit.com/product/2327\n# https://learn.adafruit.com/adafruit-16-channel-pwm-servo-hat-for-raspberry-pi/\nimport sys\nimport os\nimport time\n\nif sys.version_info[0] == 3:\n    from _thread import *\nelse:\n    from thread import *\n\nfrom adafruit_servokit import ServoKit\n#kit = ServoKit(channels=16) #creates the serevokit class with channels set to 16\n#kit.servo[0].set_pulse_width_range(1000, 2000) #to change pulse width range\n#kit.servo[0].actuation_range = 160 # to set actuation range\n\n#kit.servo[0].actuation_range = 120 # to set actuation range\nnum_args = len(sys.argv)\nif (num_args == 2):\n    kit.servo[0].angle = int(sys.argv[1]) #move hat 0 to 180 degrees.\n    \nclass PI_Servo:\n    def __init__(self, index, range_deg, home_pos, max_pos, min_pos):\n        self.index = int(index)\n        self.range = int(range_deg)\n        self.max_pos = int(max_pos)\n        self.min_pos = int(min_pos)\n        self.home = int(home_pos)\n        #make a force home\n        self.current_angle = int(home_pos)\n        self.prev_angle = int(home_pos)\n        self.target_angle = int(home_pos)\n        self.incrementing = False\n        self.force_home()\n        self.last_step_time = time.time()\n        self.step_length = 1.0 #seconds\n        self.step_deg = 1\n        self.default_duration = 3.0 #seconds\n                          \n    def set_current_angle(self, angle, duration=-1): #duration is in seconds\n        duration = int(duration)\n        angle = int(angle)\n        if (duration < 0):\n            duration = self.default_duration\n        if (duration > 0) and (angle > 0):\n            distance = abs(angle - self.current_angle)\n            if (distance > 0):\n                self.target_angle = angle;\n                print(self.target_angle)\n                print(self.current_angle)\n                if (angle < self.current_angle):\n                    self.incrementing = False\n                else:\n                    self.incrementing = True\n                self.step_length = duration*self.step_deg/distance #step_duration = duration/(distance/step_distance)\n                #elf.last_step_time = time.time()\n        else:\n            print(\"Invalid duration or angle\")\n        \n    def set_current_angle_w_speed(self, angle, speed):  #speed is in deg/sec\n        #speed = distance/duration\n        #distance/speed = duration\n        speed = int(speed)\n        angle = int(angle)\n        if (angle > 0) and (speed > 0):\n            duration = abs(angle - self.current_angle)/speed\n            self.set_current_angle(angle, duration)\n        else:\n            print(\"Invalid Speed or Angle\")\n        \n    def set_default_duration(self, duration):\n        duration = float(duration)\n        if (duration > 0):\n            self.default_duration = duration\n        else:\n            print(\"Invalid duration: \", duration)\n            \n    def set_step_deg (self, step_deg):\n        step_deg = int(step_deg)\n        if (step_deg > 0):\n            self.step_deg = step_deg\n        else:\n            print(\"Invalid step value: \", step_deg)\n        \n    def set_obstruction(self):\n        self.current_angle = self.prev_angle\n        self.target_angle = self.prev_angle\n    \n    def set_hard_stop(self):\n        self.target_angle = self.current_angle\n        \n    def force_home(self): # be careful with this as it may damage equipment or persons if they are in the robot's path\n        self.current_angle = self.home-1\n        self.prev_angle = self.home-1\n        self.target_angle = self.home\n    \n    def time_since_step(self):\n        return (time.time() - self.last_step_time)\n        \n    def reset_time(self):\n        self.last_step_time = time.time()\n        \n    \nclass PI_ServoController:\n    def __init__(self, max_channels):\n        self.servos_controlled = False;\n        self.max_channels = max_channels\n        self.kit = ServoKit(channels=self.max_channels)\n        self.servo_list = []\n        #add servos [[sv1_range, sv1_home, sv1_max_deg, sv1_min_deg], [sv2...],...]\n        sv_info = [[180,90,180,1],[180,30,180,1],[120,90,120,1],[120,60,120,1],[120,44,44,10]]\n        # servo 1 - right to left\n        # servo 2 - up to down\n        # servo 3 - down to up\n        # servo ##MISSING## 4 - ????\n        # servo 5 - right to left\n        # servo 6 - 44 (closed) 10 (open)\n        for sv in sv_info:\n            self.add_servo(sv[0],sv[1], sv[2], sv[3])\n        \n        start_new_thread(self.servo_manager_thread,())  #start thread\n        self.servos_obstructed = False\n        self.servos_controlled = True\n        \n    def add_servo(self, range_deg, home_pos, max_pos, min_pos):\n        index = len(self.servo_list)\n        if (index < self.max_channels):\n            self.kit.servo[index].actuation_range = range_deg\n            self.servo_list.append(PI_Servo(index, range_deg, home_pos, max_pos, min_pos))\n        else:\n            print(\"Servos at Max Capacity\")\n            \n    def set_movement_duration(self, duration):\n        \n        duration = float(duration)/1000\n        print(\"Setting duration to: \", duration, \"s\")\n        for servos in self.servo_list:\n            servos.set_default_duration(duration)\n    \n    def set_movement_step_deg(self, deg):\n        print(\"Setting step (deg) to: \", deg)\n        deg = int(deg)\n        for servos in self.servo_list:\n            servos.set_step_deg(deg)\n            \n    def go_home(self):\n        for servos in self.servo_list:\n            #self.kit.servo[servos.index].angle = int(servos.home)\n            servos.set_current_angle(servos.home)\n    \n    def force_home(self):\n        for servos in self.servo_list:\n            #self.kit.servo[servos.index].angle = int(servos.home)\n            servos.force_home()\n            \n    def parse(self, commands):\n        for command in commands.split(\", \"):\n            index = 0\n            for character in command:\n                if (character.isdigit()): \n                    index += 1\n                else:#elif (index>0):           \n                    #print(\"\\tIndex: \",int(command[:index]),\"\\tString: \",command[index:])\n                    print(\"Command:\",command[index:].replace('\\n',''))\n                    servo_index = {\"a\": 0, \"b\": 1, \"c\": 2, \"d\": 3, \"e\": 4, \"f\": 5, \"home\":-2, \"obst\":-3, \"obcl\":-4, \"sd\":-5, \"sdeg\":-6, \"print\":-7}.get(command[index:].replace('\\n','').lower(), -1)\n                    if servo_index == -2:\n                        self.go_home()\n                    elif servo_index == -3:\n                        self.servos_obstructed = True\n                    elif servo_index == -4:\n                        self.servos_obstructed = False\n                    elif (servo_index == -5) and (index > 0):\n                        self.set_movement_duration(command[:index])\n                    elif (servo_index == -6) and (index > 0):\n                        self.set_movement_step_deg(command[:index])\n                    elif servo_index == -7:\n                        for servos in self.servo_list:\n                            print(\"[\",servos.index, \"]: \", servos.current_angle, \" TARGET:\", servos.target_angle)\n                    elif (servo_index >=0 and index >0):\n                        self.set_servo_position(servo_index, command[:index]) # servo_index, servo_position\n                    break\n    \n    def set_servo_position(self, index, new_pos):\n        new_pos = int(new_pos)\n        index = int(index)\n        if ((index >=0) and (index < len(self.servo_list))):\n            if ((new_pos > self.servo_list[index].max_pos)or(new_pos < self.servo_list[index].min_pos)):\n                print(\"Invalid Servo Position\")\n            else:\n               self.servo_list[index].set_current_angle(new_pos)\n        else:\n            print(\"Invalid Index: \", index)\n            \n    def servo_manager_thread(self):\n        #could delay here to protect on startup.\n        #pre_loop_time = 0\n        while True:\n            #print((time.time()-pre_loop_time)%1.0)\n            if (self.servos_controlled == True):\n                if (self.servos_obstructed == False):\n                    for servos in self.servo_list:\n                        if (servos.current_angle != servos.target_angle):\n                            #if there is no obstruction\n                            if (servos.time_since_step() >= servos.step_length):\n                                servos.reset_time()\n                                servos.prev_angle = servos.current_angle #update prev angle\n                                if (servos.incrementing == True):\n                                    servos.current_angle += servos.step_deg\n                                    if (servos.current_angle > servos.target_angle):\n                                        servos.current_angle = servos.target_angle\n                                else:\n                                    servos.current_angle -= servos.step_deg\n                                    if (servos.current_angle < servos.target_angle):\n                                        servos.current_angle = servos.target_angle \n                                self.kit.servo[servos.index].angle = int(servos.current_angle)\n                                print(\"Servo[\", servos.index,\"] at: \", servos.current_angle)\n                else:\n                            # take servo obstructed action\n                    for servos in self.servo_list:\n                        servos.set_obstruction()\n                        self.kit.servo[servos.index].angle = int(servos.target_angle)\n                #pre_loop_time = time.time()","sub_path":"OLD/key_encryption/PI_Servo.py","file_name":"PI_Servo.py","file_ext":"py","file_size_in_byte":9576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"50821146","text":"#함수의 변환값(return)\r\n\r\ndef sum() :\r\n    a=int(input('숫자1 입력 :'))\r\n    b=int(input('숫자2 입력 :'))\r\n    #print(a+b)\r\n    return a+b   #반환을 해야지 정답을 가진다.\r\n    #sum=a+b\r\n    #return sum\r\n# print(sum())\r\nres=sum()\r\nprint('합은 %d' %res)\r\n\r\n#삼각형의 넓이 계산 함수 get_triangle_area()\r\n#높이와 밑변을 키보드로 입력\r\n#결과값을 변환\r\n#높이 :\r\n#밑변 :\r\n#삼각형의 면적 :\r\n\r\ndef get_triangle_area() :\r\n    a=int(input('높이 : '))\r\n    b=int(input('밑변 : '))\r\n    area=a*b*(1/2)\r\n    print('삼각형의 면적 : %.1f' % area)\r\n    return area\r\nget_triangle_area()\r\n\r\ndef get_triangle_area() :\r\n    a=int(input('높이 : '))\r\n    b=int(input('밑변 : '))\r\n    area=a*b*(1/2)\r\n    return area\r\narea=get_triangle_area()\r\nprint('삼각형의 면�� : %.1f' % area)\r\n\r\n#변환값이 여러 개인 경우\r\n#변환값의 형식은 튜플\r\ndef get_triangle_area() :\r\n    a=int(input('높이 : '))\r\n    b=int(input('밑변 : '))\r\n    area=a*b*(1/2)\r\n    return a, b, area\r\narea=get_triangle_area()\r\nprint('높이 %d, 밑변 %d, 삼각형의 넓이 %.1f' %(area[0], area[1], area[2]))\r\n\r\n#첫 번째 값인 1이 나온다. return 후에는 입력이 완료되고 완전히 끝난다.\r\ndef multiReturn() :\r\n    return 1\r\n    return 2\r\n    return 3\r\nprint(multiReturn())\r\n\r\n\r\ndef order() :\r\n    a=int(input(\"상품가격 입력 : \"))\r\n    b=int(input(\"주문수량 입력 : \"))\r\n    print('-------------------------')\r\n    return a, b, a*b\r\nc=order()\r\nprint('상품가격 : %d원' %c[0])\r\nprint('주문수량 : %d개' %c[1])\r\nprint('주문액 : %d원' %c[2])\r\n\r\ndef order() :\r\n    a = int(input(\"상품가격 입력 : \"))\r\n    b = int(input(\"주문수량 입력 : \"))\r\n    print(\"상품가격 : %d원\"%a)\r\n    print(\"주문수량 : %d개\" % b)\r\n    print(\"주문액 : %d원\" %(a*b))\r\n\r\norder()\r\n\r\n#리스트 변환값\r\ndef getNames():\r\n    names=[]\r\n    for i in range(3):\r\n        name=input('이름 입력 : ')\r\n        nemes.append(name)\r\n\r\n    return nemes\r\nnameL=getNames()\r\nprint(type(nameL))\r\nprint(nameL)\r\n\r\n#이름과 나이를 입력받아서 딕셔너리 형식으로 변환\r\n#{'name':'홍길동','age':20}\r\ndef name_age():\r\n    name_ages={}\r\n    name=input('이름 입력 :')\r\n    age=input('나이 입력 :')\r\n    name_ages['name']=name\r\n    name_ages['age']=age\r\n    return name_ages\r\nName_age=name_age()\r\nprint(Name_age)\r\n\r\ndef name_age():\r\n    name_ages={}\r\n    name=input('이름 입력 :')\r\n    age=input('나이 입력 :')\r\n    name_ages={'name' : name , 'age' : age}\r\n    return name_ages\r\nName_age=name_age()\r\nprint(Name_age)\r\n\r\ndef getInfo():\r\n    info = dict()\r\n    name = input('이름 입력 : ')\r\n    age = input('나이 입력 : ')\r\n    info['name'] = name\r\n    info['age'] = age\r\n\r\n    return info\r\n\r\ninfo = getInfo()\r\nprint(info)\r\n\r\nfor key, value in info.items():\r\n    print(key,':',value)\r\nfor key in info.keys():\r\n    print(key,\":\",info[key])\r\n\r\n\r\n#변환값이 없는 경우 None 출력\r\n\r\ndef showHello():\r\n    print('Hello')\r\nresult=showHello()\r\nprint(result)","sub_path":"5.25/12_Function/function2.py","file_name":"function2.py","file_ext":"py","file_size_in_byte":3064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"206621471","text":"# Take in a first name and a last name and searches for a user with that first name and last name. Return the index where the user is found.\n\n'''\nfind_user(\"Colt\", \"Steele\") # 1\nfind_user(\"Alan\", \"Turing\") # 3\nfind_user(\"Not\", \"Here\") # 'Not Here not found.'\n'''\n\nfrom csv import reader\n\ndef find_user(first,last):\n    with open('./data/users.csv') as file:\n    \tcsv_reader = reader(file)\n    \tfor index,row in enumerate(csv_reader):\n    \t\tif row[0] == first and row[1] == last:\n    \t\t\treturn index\n    \treturn 'Not Here not found.'\n\n\n\nprint(find_user('Henry','Fan')) # 1\nprint(find_user('Dwayne','Johnson')) # 2\nprint(find_user('Tom','Hank')) # 'Not Here not found.'","sub_path":"file_IO/exercise/find_user.py","file_name":"find_user.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"10268449","text":"# -*- coding: utf-8 -*-\n\nfrom copy import deepcopy\nimport os\nfrom AdvancedTools import TAtomicModel\nfrom AdvancedTools import TVASP\nfrom AdvancedTools import TFDFFile\nfrom AdvancedTools import TSIESTA\nfrom AdvancedTools import TPeriodTable\nfrom AdvancedTools import Helpers\nimport numpy as np\nimport math\nimport skimage\nif skimage.__version__ >= '0.17.2':\n    from skimage.measure import marching_cubes\nelse:\n    from skimage.measure import marching_cubes_lewiner\nfrom skimage.measure import find_contours\nfrom PyQt5.QtWidgets import QDialog, QTableWidgetItem, QComboBox, QMainWindow\nfrom PyQt5.QtCore import QSize\nfrom PyQt5.QtCore import Qt\nfrom PyQt5.QtGui import QStandardItemModel\nfrom PyQt5.QtGui import QStandardItem\nfrom image3D import Ui_MainWindow as Ui_image3D\nfrom atomsidentify import Ui_Dialog as Ui_Dialog_Atoms\nfrom TGui import GuiOpenGL\n\n\nclass Importer(object):\n\n    @staticmethod\n    def check_format(filename):\n        \"\"\"check file format\"\"\"\n        if filename.endswith(\".fdf\") or filename.endswith(\".FDF\"):\n            return \"SIESTAfdf\"\n\n        if (filename.lower()).endswith(\".out\"):\n            return \"SIESTAout\"\n\n        if filename.endswith(\".ani\") or filename.endswith(\".ANI\"):\n            return \"SIESTAANI\"\n\n        if (filename.lower()).endswith(\".xyz\"):\n            f = open(filename)\n            f.readline()\n            str1 = Helpers.spacedel(f.readline())\n            if len(str1.split()) > 4:\n                return \"XMolXYZ\"\n            if len(str1.split()) == 0:\n                return \"SiestaXYZ\"\n            return \"unknown\"\n\n        if filename.endswith(\".STRUCT_OUT\"):\n            return \"SIESTASTRUCT_OUT\"\n\n        if filename.endswith(\".MD_CAR\"):\n            return \"SIESTAMD_CAR\"\n\n        if filename.endswith(\".XSF\"):\n            return \"SIESTAXSF\"\n\n        if filename.endswith(\".cube\"):\n            return \"GAUSSIAN_cube\"\n\n        if filename.endswith(\"POSCAR\") or filename.endswith(\"CONTCAR\"):\n            return \"VASPposcar\"\n\n        return \"unknown\"\n\n    @staticmethod\n    def Import(filename, fl='all', prop=False):\n        \"\"\"import file\"\"\"\n        models = []\n        fdf = TFDFFile()\n        if os.path.exists(filename):\n            fileFormat = Importer.check_format(filename)\n            print(\"File \" + str(filename) + \" : \" +str(fileFormat) )\n\n            if fileFormat == \"SIESTAfdf\":\n                models = TAtomicModel.atoms_from_fdf(filename)\n                fdf.from_fdf_file(filename)\n\n            if fileFormat == \"SIESTAout\":\n                type_of_run = (TSIESTA.type_of_run(filename).split())[0].lower()\n                if (type_of_run == 'cg') or (type_of_run == 'sp'):\n                    models = []\n                    if type_of_run !=\"sp\":\n                        if fl != 'opt':\n                            models = TAtomicModel.atoms_from_output_cg(filename)\n                        modelsopt = TAtomicModel.atoms_from_output_optim(filename)\n                    else:\n                        modelsopt = TAtomicModel.atoms_from_output_sp(filename)\n                    if len(modelsopt) == 1:\n                        models.append(modelsopt[0])\n                    if prop and (len(models) > 0):\n                        try:\n                            charge_mulliken = TSIESTA.get_charges_mulliken_for_atoms(filename)\n                            if len(charge_mulliken[0]) > 0:\n                                models[-1].add_atoms_property(\"charge Mulliken\", charge_mulliken)\n                            charge_voronoi = TSIESTA.get_charges_voronoi_for_atoms(filename)\n                            if len(charge_voronoi[0]) > 0:\n                                models[-1].add_atoms_property(\"charge Voronoi\", charge_voronoi)\n                            charge_hirshfeld = TSIESTA.get_charges_hirshfeld_for_atoms(filename)\n                            if len(charge_hirshfeld[0]) > 0:\n                                models[-1].add_atoms_property(\"charge Hirshfeld\", charge_hirshfeld)\n                        except Exception:\n                            print(\"Properties failed\")\n\n                else:\n                    models = TAtomicModel.atoms_from_output_md(filename)\n                fdf.from_out_file(filename)\n\n            if fileFormat == \"SIESTAANI\":\n                models = TAtomicModel.atoms_from_ani(filename)\n\n            if fileFormat == \"SIESTASTRUCT_OUT\":\n                models = TAtomicModel.atoms_from_struct_out(filename)\n\n            if fileFormat == \"SIESTAMD_CAR\":\n                models = TAtomicModel.atoms_from_md_car(filename)\n\n            if fileFormat == \"SIESTAXSF\":\n                models = TXSF.get_atoms(filename)\n\n            if fileFormat == \"GAUSSIAN_cube\":\n                models = TGaussianCube.get_atoms(filename)\n\n            if fileFormat == \"SiestaXYZ\":\n                models = TAtomicModel.atoms_from_xyz(filename)\n\n            if fileFormat == \"XMolXYZ\":\n                models = TAtomicModel.atoms_from_XMOLxyz(filename)\n\n            if fileFormat == \"VASPposcar\":\n                models = TAtomicModel.atoms_from_POSCAR(filename)\n\n        return models, fdf\n\n    @staticmethod\n    def check_dos_file(filename):\n        if filename.endswith(\"DOSCAR\"):\n            eFermy = TVASP.fermi_energy_from_doscar(filename)\n            return filename, eFermy\n\n        \"\"\"Check DOS file for fdf/out filename\"\"\"\n        SystemLabel = TSIESTA.SystemLabel(filename)\n        file = os.path.dirname(filename) + \"/\" + str(SystemLabel) + \".DOS\"\n        if os.path.exists(file):\n            eFermy = TSIESTA.FermiEnergy(filename)\n            return file, eFermy\n        else:\n            return False, 0\n\n    @staticmethod\n    def check_pdos_file(filename):\n        \"\"\"Check PDOS file for fdf/out filename\"\"\"\n        SystemLabel = TSIESTA.SystemLabel(filename)\n        file = os.path.dirname(filename) + \"/\" + str(SystemLabel) + \".PDOS\"\n        if os.path.exists(file):\n            return file\n        else:\n            return False\n\n    @staticmethod\n    def check_bands_file(filename):\n        \"\"\"Check PDOS file for fdf/out filename\"\"\"\n        SystemLabel = TSIESTA.SystemLabel(filename)\n        file = os.path.dirname(filename) + \"/\" + str(SystemLabel) + \".bands\"\n        if os.path.exists(file):\n            return file\n        else:\n            return False\n\n##################################################################\n############################ TXSF ################################\n##################################################################\n\n\nclass TVolumericDataBlock:\n\n    def __init__(self, title):\n        self.title = title\n        self.max = None\n        self.min = None\n\n\nclass TVolumericData:\n    def __init__(self):\n        self.filename = \"\"\n        self.Nx = None\n        self.Ny = None\n        self.Nz = None\n        self.blocks = []\n        self.atoms = []\n        self.data3D = np.zeros((1, 1))\n        self.spacing = (0, 0, 0)\n        self.origin = np.zeros((3))\n        self.origin_to_export = np.zeros((3))\n        self.type = \"TVolumericData\"\n\n    def volumeric_data_read(self, f, orderData):\n        ind = 0\n        N = self.Nx * self.Ny * self.Nz\n        while ind < N:\n            row = f.readline().split()\n            for data in row:\n                self.data3D[0, ind] = float(data)\n                ind += 1\n        f.readline()\n        Helpers.spacedel(f.readline())\n\n        self.min = np.min(self.data3D)\n        self.max = np.max(self.data3D)\n        self.data3D = self.data3D.reshape((self.Nx, self.Ny, self.Nz), order=orderData)\n\n    def difference(self, secondData, mult=1):\n        if (self.Nx == secondData.Nx) and (self.Ny == secondData.Ny) and (self.Nz == secondData.Nz):\n            for i in range(0, self.Nx):\n                for j in range(0, self.Ny):\n                    for k in range(0, self.Nz):\n                        self.data3D[i][j][k] -= mult * secondData.data3D[i][j][k]\n        self.min = np.min(self.data3D)\n        self.max = np.max(self.data3D)\n\n    def isosurface(self, value):\n        if skimage.__version__ < '0.17.2':\n            verts, faces, normals, values = marching_cubes_lewiner(self.data3D, level=value, spacing=self.spacing, gradient_direction='descent', step_size=1, allow_degenerate=True, use_classic=False)\n        else:\n            verts, faces, normals, values = marching_cubes(self.data3D, level=value, spacing=self.spacing, gradient_direction='descent', step_size=1, allow_degenerate=True, method='lewiner')\n\n        for i in range(0, len(verts)):\n            verts[i] += self.origin\n        return verts, faces\n\n    def contours(self, values, type_of_plane=\"xy\", _slice=5):\n        conts = []\n        spacing = self.spacing\n        origin = self.origin\n        r = []\n        if type_of_plane == \"xy\":\n            r = self.data3D[:, :, _slice]\n        if type_of_plane == \"xz\":\n            r = self.data3D[:, _slice, :]\n        if type_of_plane == \"yz\":\n            r = self.data3D[_slice, :, :]\n        for value in values:\n            cont = find_contours(r, value)\n            contours = []\n            for i in range(0, len(cont)):\n                contour = []\n                for j in range(0, len(cont[i])):\n                    if type_of_plane == \"xy\":\n                        contour.append([origin[0] + cont[i][j][0] * spacing[0], origin[1] + cont[i][j][1] * spacing[1], origin[2] + _slice * spacing[2]])\n                    if type_of_plane == \"xz\":\n                        contour.append([origin[0] + cont[i][j][0] * spacing[0], origin[1] + _slice * spacing[1], origin[2] + cont[i][j][1] * spacing[2]])\n                    if type_of_plane == \"yz\":\n                        contour.append([origin[0] + _slice * spacing[0], origin[1] + cont[i][j][0] * spacing[1], origin[2] + cont[i][j][1] * spacing[2]])\n                contours.append(contour)\n            conts.append(contours)\n        return conts\n\n    def plane(self, type_of_plane=\"xy\", _slice=5):\n        origin = deepcopy(self.origin)\n        r = []\n        spacing = [0, 0]\n        if type_of_plane == \"xy\":\n            r = self.data3D[:, :, _slice]\n            origin[2] = self.origin[2] + _slice * self.spacing[2]\n            spacing = [self.spacing[0], self.spacing[1]]\n\n        if type_of_plane == \"xz\":\n            r = self.data3D[:, _slice, :]\n            origin[1] = self.origin[1] + _slice * self.spacing[1]\n            spacing = [self.spacing[0], self.spacing[2]]\n\n        if type_of_plane == \"yz\":\n            r = self.data3D[_slice, :, :]\n            origin[0] = self.origin[0] + _slice * self.spacing[0]\n            spacing = [self.spacing[0], self.spacing[2]]\n\n        Nx = len(r)\n        Ny = len(r[0])\n        points = []\n        for i in range(0, Nx):\n            row = []\n            for j in range(0, Ny):\n                x = i * spacing[0]\n                y = j * spacing[1]\n                z = 0\n                if type_of_plane == \"xz\":\n                    x, y, z = x, z, y\n                if type_of_plane == \"yz\":\n                    x, y, z = z, x, y\n                x += origin[0]\n                y += origin[1]\n                z += origin[2]\n                row.append([x, y, z, r[i][j]])\n            points.append(row)\n        return points\n\n\nclass TXSF(TVolumericData):\n    def __init__(self):\n        TVolumericData.__init__(self)\n        self.type = \"TXSF\"\n\n    @staticmethod\n    def get_atoms(filename):\n        periodTable = TPeriodTable()\n\n        Molecules = []\n        if os.path.exists(filename):\n            f = open(filename)\n            row = f.readline()\n            atoms = []\n            while row != '':\n                if row.find(\"ATOMS\") > -1:\n                    while row.find(\"BEGIN\") == -1:\n                        row = f.readline()\n                        if row.find(\"BEGIN\") == -1:\n                            row1 = row.split()\n                            charge = int(row1[0])\n                            x = float(row1[1])\n                            y = float(row1[2])\n                            z = float(row1[3])\n                            atoms.append([x, y, z, periodTable.get_let(charge), charge])\n\n                if row.find(\"BEGIN_BLOCK_DATAGRID_3D\") > -1:\n                    row = f.readline()\n                    while row.find(\"DATA_from:\") > -1:\n                        row = Helpers.spacedel(f.readline())\n                        while row.find(\"BEGIN_DATAGRID_3D\") > -1:\n                            row = f.readline()\n                            origin = f.readline()\n                            vec1 = f.readline().split()\n                            vec1 = Helpers.list_str_to_float(vec1)\n                            vec2 = f.readline().split()\n                            vec2 = Helpers.list_str_to_float(vec2)\n                            vec3 = f.readline().split()\n                            vec3 = Helpers.list_str_to_float(vec3)\n                            bx = vec1[0]\n                            by = vec2[1]\n                            bz = vec3[2]\n                            f.close()\n\n                            AllAtoms = TAtomicModel(atoms)\n                            AllAtoms.set_lat_vectors(vec1, vec2, vec3)\n                            Molecules.append(AllAtoms)\n                            return Molecules\n                row = f.readline()\n            f.close()\n        return Molecules\n\n    def parse(self, filename):\n        self.filename = filename\n        if os.path.exists(self.filename):\n            model = TXSF.get_atoms(self.filename)[0]\n            self.atoms = model.atoms\n            f = open(self.filename)\n            row = f.readline()\n            while row != '':\n                if row.find(\"BEGIN_BLOCK_DATAGRID_3D\") > -1:\n                    row = f.readline()\n                    while row.find(\"DATA_from:\") > -1:\n                        datas = []\n                        row = f.readline()\n                        while row.find(\"BEGIN_DATAGRID_3D\") > -1:\n                            row = Helpers.spacedel(row)\n                            data3D = TVolumericDataBlock(row)\n                            while row.find(\"END_\") == -1:\n                                row = f.readline()\n                            datas.append(data3D)\n                            row = f.readline()\n                        self.blocks.append(datas)\n                row = f.readline()\n            f.close()\n            return True\n        return False\n\n    def load_data(self, getChildNode):\n        f = open(self.filename)\n        row = f.readline()\n        while row != '':\n            if row.find(getChildNode) > -1:\n                row = Helpers.spacedel(f.readline()).split()\n                self.Nx = int(row[0])\n                self.Ny = int(row[1])\n                self.Nz = int(row[2])\n                self.data3D = np.zeros((1, self.Nx*self.Ny*self.Nz))\n\n                origin = Helpers.list_str_to_float(f.readline().split())\n                self.origin = np.array(origin)\n                self.origin_to_export = deepcopy(self.origin)\n                tmp_model = TAtomicModel(self.atoms)\n                center_mass = tmp_model.centr_mass()\n                self.origin -= np.array([center_mass[0], center_mass[1], 0])\n                vec1 = float(f.readline().split()[0])\n                vec2 = float(f.readline().split()[1])\n                vec3 = float(f.readline().split()[2])\n                self.spacing = (vec1 / self.Nx, vec2 / self.Ny, vec3 / self.Nz)\n\n                orderData = 'F'\n\n                self.volumeric_data_read(f, orderData)\n                f.close()\n                return self.atoms\n            row = f.readline()\n        f.close()\n\n\nclass TGaussianCube(TVolumericData):\n    def __init__(self):\n        TVolumericData.__init__(self)\n        self.type = \"TGaussianCube\"\n\n    @staticmethod\n    def get_atoms(filename):\n        periodTable = TPeriodTable()\n        Molecules = []\n        if os.path.exists(filename):\n            f = open(filename)\n            row = f.readline()\n            row = f.readline()\n            row = f.readline().split()\n            nAtoms = int(row[0])\n            origin = (float(row[1]), float(row[2]), float(row[3]))\n\n            Nx, vec1, mult = TGaussianCube.local_get_N_vect(f.readline().split())\n            Ny, vec2, mult = TGaussianCube.local_get_N_vect(f.readline().split())\n            Nz, vec3, mult = TGaussianCube.local_get_N_vect(f.readline().split())\n\n            atoms = []\n\n            for i in range(0, nAtoms):\n                row = f.readline().split()\n                charge = int(row[0])\n                atoms.append([float(row[2]), float(row[3]), float(row[4]), periodTable.get_let(charge), charge])\n            f.close()\n            AllAtoms = TAtomicModel(atoms)\n            AllAtoms.set_lat_vectors(vec1, vec2, vec3)\n            AllAtoms.convert_from_scaled_to_cart(mult)\n\n            Molecules.append(AllAtoms)\n        return Molecules\n\n    @staticmethod\n    def local_get_N_vect(row):\n        Nx = int(row[0])\n        mult = 1\n        if Nx > 0:\n            mult = 0.52917720859\n        Nx = int(math.fabs(Nx))\n        vec1 = mult * Nx * np.array([float(row[1]), float(row[2]), float(row[3])])\n        return Nx, vec1, mult\n\n    def parse(self, filename):\n        self.filename = filename\n        if os.path.exists(self.filename):\n            model = TGaussianCube.get_atoms(self.filename)[0]\n            self.atoms = model.atoms\n\n            f = open(self.filename)\n            row = f.readline()\n            data3D = TVolumericDataBlock(row)\n            self.blocks.append([data3D])\n            f.close()\n            return True\n        return False\n\n    def load_data(self, getChildNode):\n        if os.path.exists(self.filename):\n            f = open(self.filename)\n            row = f.readline()\n            row = f.readline()\n            row = f.readline().split()\n            nAtoms = int(row[0])\n            origin = (float(row[1]), float(row[2]), float(row[3]))\n\n            self.Nx, vec1, mult = TGaussianCube.local_get_N_vect(f.readline().split())\n            self.Ny, vec2, mult = TGaussianCube.local_get_N_vect(f.readline().split())\n            self.Nz, vec3, mult = TGaussianCube.local_get_N_vect(f.readline().split())\n\n            for i in range(0, nAtoms):\n                row = f.readline().split()\n\n            self.data3D = np.zeros((1, self.Nx * self.Ny * self.Nz))\n            self.origin = mult*np.array(origin)\n            self.origin_to_export = deepcopy(self.origin)\n            tmp_model = TAtomicModel(self.atoms)\n            center_mass = tmp_model.centr_mass()\n            self.origin -= np.array([center_mass[0], center_mass[1], 0])\n            vec1 = float(vec1[0])\n            vec2 = float(vec2[1])\n            vec3 = float(vec3[2])\n            self.spacing = (vec1 / self.Nx, vec2 / self.Ny, vec3 / self.Nz)\n            orderData = 'C'\n            self.volumeric_data_read(f, orderData)\n            f.close()\n        return self.atoms\n\n\nclass AtomsIdentifier(QDialog):\n    def __init__(self, problemAtoms):\n        super(AtomsIdentifier, self).__init__()\n        self.ui = Ui_Dialog_Atoms()\n        self.ui.setupUi(self)\n        self.setWindowFlags(Qt.Window | Qt.WindowTitleHint | Qt.CustomizeWindowHint)\n        self.ui.okButton.clicked.connect(self.okButtonClick)\n        self.ansv = []\n\n        self.ui.TheTable.setColumnCount(2)\n        self.ui.TheTable.setHorizontalHeaderLabels([\"Atom Type\", \"get_species\"])\n        self.ui.TheTable.setColumnWidth(0, 90)\n        self.ui.TheTable.setColumnWidth(1, 90)\n\n        model = QStandardItemModel()\n        model.appendRow(QStandardItem(\"select\"))\n        Mendeley = TPeriodTable()\n        atoms_list = Mendeley.get_all_letters()\n        for i in range(1, len(atoms_list)):\n            model.appendRow(QStandardItem(atoms_list[i]))\n\n        problemAtoms = list(problemAtoms)\n        problemAtoms.sort()\n\n        for problem in problemAtoms:\n            self.ui.TheTable.setRowCount(self.ui.TheTable.rowCount()+1)  # и одну строку в таблице\n            data_cell = QTableWidgetItem(str(problem-199))\n            data_cell.setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n            self.ui.TheTable.setItem(self.ui.TheTable.rowCount() - 1, 0, data_cell)\n            atom_cell = QComboBox()\n\n            atom_cell.setModel(model)\n            atom_cell.setCurrentIndex(0)\n            self.ui.TheTable.setCellWidget(self.ui.TheTable.rowCount() - 1, 1, atom_cell)\n\n    def okButtonClick(self):\n        self.ansv = []\n        for i in range(0, self.ui.TheTable.rowCount()):\n            at_type = self.ui.TheTable.cellWidget(i, 1).currentText()\n            if at_type != \"select\":\n                self.ansv.append([200+i, at_type])\n        if len(self.ansv) == self.ui.TheTable.rowCount():\n            self.close()\n\n\nclass Image3Dexporter(QMainWindow):\n    def __init__(self, windowsWidth, windowsHeight, quality):\n        super(Image3Dexporter, self).__init__()\n        self.ui = Ui_image3D()\n        self.ui.setupUi(self)\n        self.setFixedSize(QSize(windowsWidth, windowsHeight))\n\n        self.MainForm = GuiOpenGL(self.ui.openGLWidget, None, quality=quality)\n        self.MainForm.filter = None\n        self.show()\n","sub_path":"src/TInterface.py","file_name":"TInterface.py","file_ext":"py","file_size_in_byte":21095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"643090068","text":"def mxdiflg(a1, a2):\n\tif a1 == [] or a2 == []:\n\t\treturn -1\n\telse:\n\t\tls1 = []\n\t\tls2 = []\n\t\tfor i in a1:\n\t\t\tls1.append(len(i))\n\t\tfor j in a2:\n\t\t\tls2.append(len(j))\n\t\tx1, x2 = max(ls1), min(ls1)\n\t\ty1, y2 = max(ls2), min(ls2)\n\t\treturn max(abs(x1-y2), abs(x2-y1))\t\t\na1 = [\"hoqq\", \"bbllkw\", \"oox\", \"ejjuyyy\", \"plmiis\", \"xxxzgpsssa\", \"xxwwkktt\", \"znnnnfqknaz\", \"qqquuhii\", \"dvvvwz\"]\na2 = [\"cccooommaaqqoxii\", \"gggqaffhhh\", \"tttoowwwmmww\"]\nprint(mxdiflg(a1, a2))","sub_path":"Maximum Length Difference.py","file_name":"Maximum Length Difference.py","file_ext":"py","file_size_in_byte":454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"181538816","text":"from pykechain.enums import ComponentXType\nfrom pykechain.exceptions import InspectorComponentError\nfrom pykechain.models import Base\nfrom pykechain.models.inspector_base import InspectorComponent\n\n\nclass HtmlPanel(InspectorComponent):\n    \"\"\"HTML Panel Component for the inspector.\"\"\"\n\n    def __init__(self, json=None, html=None, title=None, **kwargs):\n        \"\"\"\n        Instantiate a html panel.\n\n        Provide either json or html\n\n        :param json: provide a valid json (optional if not part)\n        :param html: provide a html string (nicely escaped please) (optional if not json)\n        :param title: provide a title (optional)\n        :param kwargs: optional extra kwargs\n        \"\"\"\n        if not json and html:\n            super(self.__class__, self).__init__(json=dict(\n                xtype=ComponentXType.PANEL,\n                title=title,\n                html=html\n            ))\n        elif json:\n            super(self.__class__, self).__init__(json=json)\n        else:\n            raise InspectorComponentError(\"Either provide json or html\")\n\n\nclass PropertyGrid(InspectorComponent):\n    \"\"\"PropertyGrid component.\"\"\"\n\n    def __init__(self, json=None, part=None, title=None, **kwargs):\n        \"\"\"\n        Instantiate a PropertyGrid.\n\n        Provide either json or (parent and model or activity)\n\n        :param json: provide a valid json (optional if not part)\n        :param part: provide a part id (or pykechain Part) (optional if json)\n        :param title: provide a title for the propertygrid (optional)\n        :param kwargs: Optional Extras\n        \"\"\"\n        if not json and part:\n            if isinstance(part, Base):\n                part = part.id\n            super(self.__class__, self).__init__(json=dict(\n                xtype=ComponentXType.PROPERTYGRID,\n                filter=dict(part=part)\n            ))\n        elif json and not part:\n            super(self.__class__, self).__init__(json=json)\n        else:\n            raise InspectorComponentError(\"Either provide json or (parent and model)\")\n\n        if title:\n            self._json_data['title'] = title\n            self._json_data['viewModel'] = {'data': {'style': {'displayPartTitle': True}}}\n\n\nclass SuperGrid(InspectorComponent):\n    \"\"\"The SuperGrid component.\"\"\"\n\n    def __init__(self, json=None, parent=None, model=None, activity_id=None, title=None, **kwargs):\n        \"\"\"\n        Instantiate a SuperGrid.\n\n        Provide either json or (parent and model or activity)\n\n        in kwargs you may: \"newInstance\" in kwargs or \"edit\" in kwargs or \"delete\" in kwargs or \"export\" in kwargs\n\n        :param json: provide a valid SuperGrid json (optional if not parent & model)\n        :param parent: provide a parent id (or pykechain Part) (optional if json)\n        :param model: provide a model id (or pykechain Part) (optional if json)\n        :param activity_id: provide a acitivity id (or pykechain Activity) (optional)\n        :param title: adding title to the component (optional)\n        :param newInstance: show this button (default True)\n        :param delete: show this button (default True)\n        :param edit: show this button (default True)\n        :param export: show this button (default True)\n        :param kwargs: Optional extra kwargs.\n        \"\"\"\n        if not json and parent and model:\n            if isinstance(parent, Base):\n                parent = parent.id\n            if isinstance(model, Base):\n                model = model.id\n            if isinstance(activity_id, Base):\n                activity_id = activity_id.id\n            super(self.__class__, self).__init__(json=dict(\n                xtype=ComponentXType.SUPERGRID,\n                title=title,\n                filter=dict(\n                    parent=parent,\n                    model=model,\n                    activity_id=activity_id),\n                viewModel=dict(\n                    data=dict(\n                        actions=dict(\n                            newInstance=kwargs.get(\"newInstance\", True),\n                            edit=kwargs.get(\"edit\", True),\n                            delete=kwargs.get(\"delete\", True),\n                            export=kwargs.get(\"export\", True)\n                        )\n                    )\n                )\n            ))\n        elif json and not parent and not model:\n            super(self.__class__, self).__init__(json=json)\n        else:\n            raise InspectorComponentError(\"Either provide json or (parent and model)\")\n\n    def get(self, item):\n        \"\"\"Get an item from the filter subdict.\n\n        :param item: item to retrieve. eg. model, parent, activity_id\n        :return: uuid (as string)\n        \"\"\"\n        return self._json_data['filter'].get(item, None)\n\n    def set(self, key, value):\n        \"\"\"Set an item to a value in the filter subdictionary.\n\n        :param key: string to set e.g model, parent, activity_id\n        :param value: UUID as string or an pykechain object (will retrieve the id from that)\n        :return: None\n        \"\"\"\n        if isinstance(value, Base):\n            value = value.id\n        self._json_data['filter'][key] = value\n\n\nclass FilteredGrid(InspectorComponent):\n    \"\"\"Filtered and Paginated Grid component for a task customization in KE-chain 2.\n\n    Example json for the configuration::\n\n        {\n          \"xtype\": \"filteredGrid\",\n          \"parentInstanceId\": \"02a2a979-2e3-b32212a0ca83\",\n          \"partModelId\": \"bfe90c01-7d8c-4ab97eac4ebf\",\n          \"collapseFilters\": false,\n          \"pageSize\": 25,\n          \"flex\": 0,\n          \"height\": 600,\n          \"grid\": {\n            \"xtype\": \"paginatedSuperGrid\",\n            \"viewModel\": {\n              \"data\": {\n                \"actions\": {\n                  \"delete\": true,\n                  \"newInstance\": true,\n                  \"edit\": true,\n                  \"export\": true\n                }\n              }\n            }\n          }\n        }\n\n    \"\"\"\n\n    def __init__(self, json=None, parent=None, model=None, title=None, **kwargs):\n        \"\"\"\n        Instantiate a FilteredGrid Component.\n\n        :param json: provide a valid SuperGrid json (optional if not parent & model)\n        :param parent: provide a parent id (or pykechain Part) (optional if json)\n        :param model: provide a model id (or pykechain Part) (optional if json)\n        :param title: provide a title\n        :param collapsefilters: boolean, True to collapse the Filterpane on view (default False)\n        :param pagesize: integer to set the pagesize of the page (default 25)\n        :param flex: integer to set drawing behaviour (defaults to 0)\n        :param height: integer for the hieght of the grid (defaults to 600)\n        :param newInstance: show this button (default True)\n        :param delete: show this button (default True)\n        :param edit: show this button (default True)\n        :param export: show this button (default True)\n        :param kwargs:\n        \"\"\"\n        if not json and parent and model:\n            if isinstance(parent, Base):\n                parent = parent.id\n            if isinstance(model, Base):\n                model = model.id\n            super(self.__class__, self).__init__(json=dict(\n                xtype=ComponentXType.FILTEREDGRID,\n                parentInstanceId=parent,\n                partModelId=model,\n                title=title,\n                collapseFilters=kwargs.get('collapsefilters', False),\n                pageSize=kwargs.get('pagesize', 25),\n                flex=kwargs.get('flex', 0),\n                height=kwargs.get('height', 600),\n                grid=dict(\n                    xtype=ComponentXType.PAGINATEDSUPERGRID,\n                    viewModel=dict(\n                        data=dict(\n                            actions=dict(\n                                newInstance=kwargs.get('newInstance', True),\n                                delete=kwargs.get('delete', True),\n                                edit=kwargs.get('edit', True),\n                                export=kwargs.get('export', True)\n                            )\n                        )\n                    )\n                )\n            ))\n        elif json and not parent and not model:\n            super(self.__class__, self).__init__(json=json)\n        else:\n            raise InspectorComponentError(\"Either provide json or (parent and model)\")\n\n\nclass PaginatedGrid(InspectorComponent):\n    \"\"\"\n    Paginated Grid component for a task customization in KE-chain 2.\n\n    Example JSON defenition::\n\n        {\n            \"components\": [{\n                \"xtype\": \"paginatedSuperGrid\",\n                \"filter\": {\n                    \"model\": \"365e3e6f-38ab-491d-86d6-4dfe51f95804\", \"parent\": \"b7658eed-4fab-4c4d-bbed-e68996e89b13\"\n                }\n            }, {\n                \"xtype\": \"superGrid\",\n                \"filter\": {\n                    \"model\": \"365e3e6f-38ab-491d-86d6-4dfe51f95804\",\n                    \"parent\": \"b7658eed-4fab-4c4d-bbed-e68996e89b13\",\n                    \"limit\": 5\n                },\n                \"viewModel\": {\n                    \"data\": {\n                        \"actions\": {\n                            \"edit\": false,\n                            \"newInstance\": false,\n                            \"delete\": true\n                        }\n                    }\n                }\n            }]\n        }\n\n    \"\"\"\n\n    def __init__(self, json=None, model=None, parent=None, title=None, **kwargs):\n        \"\"\"\n        Instantiate the PaginatedGrid component.\n\n        :param json: provide a valid SuperGrid json (optional if not parent & model)\n        :param parent: provide a parent id (or pykechain Part) (optional if json)\n        :param model: provide a model id (or pykechain Part) (optional if json)\n        :param title: provide a title\n        :param pagesize: integer to set the pagesize of the page (default 25)\n        :param flex: integer to set drawing behaviour (defaults to 0)\n        :param height: integer for the hieght of the grid (defaults to 600)\n        :param newInstance: show this button (default True)\n        :param delete: show this button (default True)\n        :param edit: show this button (default True)\n        :param export: show this button (default True)\n        :param kwargs:\n        \"\"\"\n        if not json and parent and model:\n            if isinstance(parent, Base):\n                parent = parent.id\n            if isinstance(model, Base):\n                model = model.id\n            super(self.__class__, self).__init__(json=dict(\n                xtype=ComponentXType.PAGINATEDSUPERGRID,\n                filter=dict(\n                    model=model,\n                    parent=parent\n                ),\n                title=title,\n                pageSize=kwargs.get('pagesize', 25),\n                flex=kwargs.get('flex', 0),\n                height=kwargs.get('height', 600),\n                viewModel=dict(\n                    data=dict(\n                        actions=dict(\n                            newInstance=kwargs.get('newInstance', True),\n                            delete=kwargs.get('delete', True),\n                            edit=kwargs.get('edit', True),\n                            export=kwargs.get('export', True)\n                        )\n                    )\n                )\n            ))\n        elif json and not parent and not model:\n            super(self.__class__, self).__init__(json=json)\n        else:\n            raise InspectorComponentError(\"Either provide json or (parent and model)\")\n","sub_path":"pykechain/models/inspectors.py","file_name":"inspectors.py","file_ext":"py","file_size_in_byte":11527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"372755251","text":"from os import path\r\nimport jieba\r\nimport jieba.posseg as pseg\r\nfrom PIL import Image\r\nimport numpy as np\r\nfrom wordcloud import WordCloud, ImageColorGenerator\r\nimport collections\r\n\r\nd = path.dirname(__file__)\r\ntext = open(path.join(d, 'history.txt'), encoding='utf-8').read()\r\n\r\njieba.enable_paddle()\r\nwords = pseg.cut(text, use_paddle=True)\r\nwords_filter = []\r\nfor word, flag in words:\r\n  if flag not in ['m', 'c', 'f', 'ad', 'q', 'u', 'r', 'xc', 'p', 'w'] and word not in ['是', '不是', '又', '还']:\r\n  \twords_filter.append(word)\r\nword_counter = collections.Counter(words_filter)\r\n\r\nqm = np.array(Image.open(path.join(d, 'QingMang_mask.jpg')))\r\nwordcloud = WordCloud(\r\n  mask=qm,\r\n  font_path=path.join(d, 'msyh.ttf'),\r\n  mode='RGBA',\r\n  background_color=\"RGBA(255,255,255,0)\"\r\n)\r\nwordcloud.generate_from_frequencies(word_counter)\r\nwordcloud.to_file(path.join(d, 'cloud.png'))\r\n\r\nprint()\r\n","sub_path":"server/nlp/cloud.py","file_name":"cloud.py","file_ext":"py","file_size_in_byte":897,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"461956944","text":"import pickle\nimport numpy as np\nimport os\nimport PIL\nfrom tensorflow.python.keras import backend as K\nfrom PIL import Image\nfrom tensorflow.python.keras.models import model_from_json\nfrom tensorflow.python.keras.optimizers import Adam, RMSprop\nfrom tensorflow.python.keras.callbacks import ModelCheckpoint, TensorBoard\nfrom tensorflow.python.client import device_lib\n\nprint(device_lib.list_local_devices())\n\ndef generator(indices, batchSize = 128):\n\n  while True:\n      np.random.shuffle(indices)\n      \n      for i in range(0,len(indices), batchSize):\n          X = []\n          Y = []\n          batch_indices = indices[i:i+batchSize]\n          batch_indices.sort()\n          \n          for x in batch_indices:\n              X.append(np.array(Image.open('./Data2/' + x))/255)\n              Y.append(labels[x])\n      \n          X = np.array(X)\n          Y = np.array(Y)\n          yield (X, Y)\n\ndef load_obj(name):\n  with open(name + '.pkl', 'rb') as f:\n      return pickle.load(f)\n\ndef test(y_true, y_pred):\n  print(K.int_shape(y_true), y_pred)\n\nlabels = {}\npartition = {}\nbatchSize = 128\nlabels = load_obj('./Pickles/labelsCat')\npartition['train'] = load_obj('./Pickles/train')\npartition['test'] = load_obj('./Pickles/test')\n\ngenerator_train = generator(partition['train'], batchSize)\ngenerator_test = generator(partition['test'], batchSize)\nprint(\"Created generators\")\n\njson_file = open('./Architecture/modelCat2.json', 'r')\nloaded_model_json = json_file.read()\njson_file.close()\nloaded_model = model_from_json(loaded_model_json)\nloaded_model.load_weights(\"./Weights/modelCat2.h5\")\nprint(\"Loaded model from disk\")\n\noptimizer = Adam(lr=1e-5)\nloss = ['binary_crossentropy']\nmetrics = ['mae', 'accuracy']\nloaded_model.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n\nfilepath = \"./Weights/ModelCropCat2/weights-{epoch:02d}-{val_acc:.2f}.hdf5\"\ncheckpoint = ModelCheckpoint(filepath, save_best_only=False, monitor='val_acc', mode='max', verbose=1, save_weights_only=True, period=1)\n\ntensorboard = TensorBoard(log_dir='./logs/ModelCrop5', write_graph=True, write_images=True, batch_size=batchSize)\n\ncallback_list = [checkpoint, tensorboard]\n\nhistory = loaded_model.fit_generator(generator=generator_train,\n                                  epochs=10,\n                                  steps_per_epoch=len(partition['train'])/batchSize,\n                                  validation_data=generator_test,\n                                  validation_steps=len(partition['test'])/batchSize,\n                                  use_multiprocessing=False,\n                                  max_queue_size=10,\n                                  verbose=1,\n                                  callbacks=callback_list\n                                  )","sub_path":"Models/modelCropCat2.py","file_name":"modelCropCat2.py","file_ext":"py","file_size_in_byte":2745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"554911976","text":"from flask import render_template\nfrom flask import jsonify\nfrom flask import request\nfrom firebase.firebase import FirebaseApplication, FirebaseAuthentication\nfrom app import app\nfrom app.digi import Digi\nfrom app.userConfig import config \nfrom datetime import datetime\n\n\nmyConfig = config()\nmyDigi = Digi(myConfig.username, myConfig.password)\nauth = FirebaseAuthentication(myConfig.firebaseSecret,\n\t\t\t\t\t\t\t  myConfig.firebaseEmail,\n\t\t\t\t\t\t\t  True,\n\t\t\t\t\t\t\t  True)\nfirebase = FirebaseApplication(myConfig.firebaseURL, auth)\n\n@app.route('/')\ndef root():\n    res = myDigi.listAllStreams().json()\n    return render_template('index.html',\n    \t\t\t\t\t\tuser={'name':'Chad'},\n    \t\t\t\t\t\tlist=res[\"list\"])\n\n@app.route('/update')\ndef update():\n\t\n\tcurrent_time = str(datetime.now().time())\n\n\tdata = {\n\t\t\"Temperature\":  request.args.get('temp', 'no_temp'),\n\t\t\"VWC\":          request.args.get('vwc', 'no_vwc'),\n\t\t\"Time\":         current_time\n\t}\n\n\tloc = request.args.get('loc', 'no_location')\n\tsensor = request.args.get('lev', 'no_sensor_depth')\n\turl = '/Chad/%s/%s' % (loc, sensor) \n\n\tres = firebase.post(url, data)\n\treturn \"post success: %s\" % current_time\n","sub_path":"Agricultural Monitoring System/Beaglebone/old/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"439137862","text":"\nimport time\nimport sys\nimport tkinter\ntry:\n    import matplotlib\n    matplotlib.use('agg')\nexcept:\n    print(\"import matplotlib error\")\n    \nimport matplotlib.pyplot as plt\nimport os\nimport numpy as np\nimport math\nimport time\nimport scipy\nimport scipy.io\n\ndef bullseye_plot(ax, data, segBold=None, cmap=None, norm=None):\n    \"\"\"\n    Bullseye representation for the left ventricle.\n\n    Parameters\n    ----------\n    ax : axes\n    data : list of int and float\n        The intensity values for each of the 17 segments\n    segBold: list of int, optional\n        A list with the segments to highlight\n    cmap : ColorMap or None, optional\n        Optional argument to set the desired colormap\n    norm : Normalize or None, optional\n        Optional argument to normalize data into the [0.0, 1.0] range\n\n\n    Notes\n    -----\n    This function create the 17 segment model for the left ventricle according\n    to the American Heart Association (AHA) [1]_\n\n    References\n    ----------\n    .. [1] M. D. Cerqueira, N. J. Weissman, V. Dilsizian, A. K. Jacobs,\n        S. Kaul, W. K. Laskey, D. J. Pennell, J. A. Rumberger, T. Ryan,\n        and M. S. Verani, \"Standardized myocardial segmentation and\n        nomenclature for tomographic imaging of the heart\",\n        Circulation, vol. 105, no. 4, pp. 539-542, 2002.\n    \"\"\"\n    if segBold is None:\n        segBold = []\n\n    linewidth = 2\n    boldlinewidth = 2\n    data = np.array(data).ravel()\n\n    linecolor = [0.95, 0.95 , 0.95]\n    \n    if cmap is None:\n        cmap = plt.cm.viridis\n\n    if norm is None:\n        norm = matplotlib.colors.Normalize(0, 255)\n        #norm = matplotlib.colors.Normalize(vmin=data.min(), vmax=data.max())\n\n    theta = np.linspace(0, 2 * np.pi, 768)\n    r = np.linspace(0.2, 1, 4)\n\n    # Create the bound for the segment 17\n    for i in range(r.shape[0]):\n        ax.plot(theta, np.repeat(r[i], theta.shape), '-', color=linecolor, lw=linewidth)\n\n    # Create the bounds for the segments 1-12\n    for i in range(6):\n        theta_i = np.deg2rad(i * 60)\n        ax.plot([theta_i, theta_i], [r[1], 1], '-', color=linecolor, lw=linewidth)\n\n    # Create the bounds for the segments 13-16\n    for i in range(4):\n        theta_i = np.deg2rad(i * 90 - 45)\n        ax.plot([theta_i, theta_i], [r[0], r[1]], '-', color=linecolor, lw=linewidth)\n\n    # Fill the segments 1-6\n    r0 = r[2:4]\n    r0 = np.repeat(r0[:, np.newaxis], 128, axis=1).T\n    for i in range(6):\n        # First segment start at 60 degrees\n        theta0 = theta[i * 128:i * 128 + 128] + np.deg2rad(60)\n        theta0 = np.repeat(theta0[:, np.newaxis], 2, axis=1)\n        z = np.ones((128, 2)) * data[i]\n        ax.pcolormesh(theta0, r0, z, cmap=cmap, norm=norm)\n        if i + 1 in segBold:\n            ax.plot(theta0, r0, '-', color=linecolor, lw=linewidth + boldlinewidth)\n            ax.plot(theta0[0], [r[2], r[3]], '-', color=linecolor, lw=linewidth + boldlinewidth)\n            ax.plot(theta0[-1], [r[2], r[3]], '-', color=linecolor, lw=linewidth + boldlinewidth)\n\n    # Fill the segments 7-12\n    r0 = r[1:3]\n    r0 = np.repeat(r0[:, np.newaxis], 128, axis=1).T\n    for i in range(6):\n        # First segment start at 60 degrees\n        theta0 = theta[i * 128:i * 128 + 128] + np.deg2rad(60)\n        theta0 = np.repeat(theta0[:, np.newaxis], 2, axis=1)\n        z = np.ones((128, 2)) * data[i + 6]\n        ax.pcolormesh(theta0, r0, z, cmap=cmap, norm=norm)\n        if i + 7 in segBold:\n            ax.plot(theta0, r0, '-', color=linecolor, lw=linewidth + boldlinewidth)\n            ax.plot(theta0[0], [r[1], r[2]], '-', color=linecolor, lw=linewidth + boldlinewidth)\n            ax.plot(theta0[-1], [r[1], r[2]], '-', color=linecolor, lw=linewidth + boldlinewidth)\n\n    # Fill the segments 13-16\n    r0 = r[0:2]\n    r0 = np.repeat(r0[:, np.newaxis], 192, axis=1).T\n    for i in range(4):\n        # First segment start at 45 degrees\n        theta0 = theta[i * 192:i * 192 + 192] + np.deg2rad(45)\n        theta0 = np.repeat(theta0[:, np.newaxis], 2, axis=1)\n        z = np.ones((192, 2)) * data[i + 12]\n        ax.pcolormesh(theta0, r0, z, cmap=cmap, norm=norm)\n        if i + 13 in segBold:\n            ax.plot(theta0, r0, '-', color=linecolor, lw=linewidth + boldlinewidth)\n            ax.plot(theta0[0], [r[0], r[1]], '-', color=linecolor, lw=linewidth + boldlinewidth)\n            ax.plot(theta0[-1], [r[0], r[1]], '-', color=linecolor, lw=linewidth + boldlinewidth)\n\n    # Fill the segments 17\n    if data.size == 17:\n        r0 = np.array([0, r[0]])\n        r0 = np.repeat(r0[:, np.newaxis], theta.size, axis=1).T\n        theta0 = np.repeat(theta[:, np.newaxis], 2, axis=1)\n        z = np.ones((theta.size, 2)) * data[16]\n        ax.pcolormesh(theta0, r0, z, cmap=cmap, norm=norm)\n        if 17 in segBold:\n            ax.plot(theta0, r0, '-', color=linecolor, lw=linewidth + boldlinewidth)\n\n    ax.set_ylim([0, 1])\n    ax.set_yticklabels([])\n    ax.set_xticklabels([])\n\ndef fig2data ( fig ):\n    # from: http://www.icare.univ-lille1.fr/tutorials/convert_a_matplotlib_figure\n    \"\"\"\n    @brief Convert a Matplotlib figure to a 4D numpy array with RGBA channels and return it\n    @param fig a matplotlib figure\n    @return a numpy 3D array of RGBA values\n    \"\"\"\n    # draw the renderer\n    fig.canvas.draw ( )\n \n    # Get the RGBA buffer from the figure\n    w,h = fig.canvas.get_width_height()\n    buf = np.frombuffer ( fig.canvas.tostring_argb(), dtype=np.uint8 )\n    buf.shape = ( h, w,4 )\n \n    # canvas.tostring_argb give pixmap in ARGB mode. Roll the ALPHA channel to have it in RGBA mode\n    # buf = np.roll ( buf, 3, axis = 2 )\n    buf = buf[:,:,1] # output only Red channel\n    return buf\n\ndef get_angle(a, b):\n    # angle from a to b (rotate a to b)\n    # positve angle for counter-clock wise\n    # 0-360 degrees\n    \n    v1_theta = math.atan2(a[1], a[0])\n    v2_theta = math.atan2(b[1], b[0])\n\n    r = (v2_theta - v1_theta) * (180.0 / math.pi)\n\n    if r < 0:\n        r += 360.0\n\n    return r\n\ndef img_to_xy(rvi, RO, E1):\n    return (rvi[1], E1-1-rvi[0])\n\ndef mask2sectors(endo_mask, epi_mask, rv_mask, rvi_mask, num_sectors):\n    # split myocardium to num_sectors sectors\n\n    try:\n        RO, E1 = endo_mask.shape\n\n        # find lv center\n        endo_pts = np.argwhere(endo_mask>0)\n        lv_center = np.mean(endo_pts, axis=0)\n\n        # find rv center\n        rv_pts = np.argwhere(rv_mask>0)\n        rv_center = np.mean(rv_pts, axis=0)\n\n        # find rvi\n        rvi = np.argwhere(rvi_mask>0)\n\n        lv_center2 = img_to_xy(lv_center, RO, E1)\n        rv_center2 = img_to_xy(rv_center, RO, E1)\n        rvi2 = img_to_xy((rvi[0,0], rvi[0,1]), RO, E1)\n\n        # split endo/epi to sectors\n        rvi_vec = (rvi2[0]-lv_center2[0], rvi2[1]-lv_center2[1])\n        rv_vec = (rv_center2[0]-lv_center2[0], rv_center2[1]-lv_center2[1])\n        rv_rvi_angle = get_angle(rv_vec, rvi_vec)\n\n        delta_rvi_angle = 360/num_sectors\n        # print('delta_rvi_angle is %f ' % delta_rvi_angle, file=sys.stderr)\n\n        sectors = np.zeros(endo_mask.shape)\n\n        myo_mask = epi_mask - endo_mask\n        myo_pts = np.argwhere(myo_mask>0)\n        N_myo_pts = myo_pts.shape[0]\n        angle_myo_pts = np.zeros(N_myo_pts)\n\n        for n in range(N_myo_pts):\n            myo_pts_xy = img_to_xy(myo_pts[n,:], RO, E1)\n            angle_myo_pts[n] = get_angle(rvi_vec, (myo_pts_xy[0]-lv_center2[0], myo_pts_xy[1]-lv_center2[1]))\n            if (rv_rvi_angle>=180): # rotate rvi clock wise \n                angle_myo_pts[n] = 360 - angle_myo_pts[n]\n\n            sector_no = np.floor(angle_myo_pts[n]/delta_rvi_angle) +1\n\n            if sector_no==1:\n                sectors[myo_pts[n,0], myo_pts[n,1]] = sector_no\n            else:\n                sectors[myo_pts[n,0], myo_pts[n,1]] = num_sectors+2-sector_no\n\n    except Exception as e:\n        print(\"Error happened in mask2sectors ...\", file=sys.stderr)\n        print(e)\n\n    return sectors\n\ndef compute_sector_statistics(num_sectors, sectors, fmap, max_thres=1e4, min_thres=-1e4):\n    # compute per sector statistics (mean, std, max, min)\n    # if fmap value is outside the [min_thres max_thres], it is excluded from sector statistics\n    # return value is a num_sectors by 4 array, [mean std min max] for every sector\n\n    try:\n        RO, E1 = sectors.shape\n\n        sector_pts = np.argwhere(sectors>0)\n        N_sector_pts = sector_pts.shape[0]\n\n        sectors_threshold = np.copy(sectors)\n        for n in range(N_sector_pts):\n            v = fmap[sector_pts[n, 0],sector_pts[n, 1]]\n            if(v>max_thres or v<min_thres):\n                sectors_threshold[sector_pts[n, 0],sector_pts[n, 1]] = 0\n\n        res = np.zeros((num_sectors, 4))\n        for s in range(1,num_sectors+1):\n            v = fmap[sectors_threshold==s]\n            if(len(v)>0):\n                res[s-1, 0] = np.mean(v)\n                res[s-1, 1] = np.std(v)\n                res[s-1, 2] = np.min(v)\n                res[s-1, 3] = np.max(v)\n            else:\n                res[s-1, 0] = -1\n                res[s-1, 1] = -1\n                res[s-1, 2] = -1\n                res[s-1, 3] = -1\n\n    except Exception as e:\n        print(\"Error happened in compute_sector_statistics ...\", file=sys.stderr)\n        print(e)\n\n    return res, sectors_threshold\n\ndef create_bullseye_plot(endo_mask, epi_mask, rv_mask, rvi_mask, fmaps, scan_info):\n    # endo_mask : [RO E1 3], so are other masks and fmaps\n    # from basal to apical\n    # scan_info : a numpy array for scan info\n    # scan_info[0:3] mean RR, median RR, max RR in ms\n    # scan_info[3] aif peak Gd in mmol/L\n    # scan_info[4] aif first pass FWHM in seconds\n\n    try:\n        print('Create bullseye plot, get data [%d, %d, %d]' % (endo_mask.shape), file=sys.stderr)\n\n        num_sectors = [6, 6, 4]\n\n        sectors = np.zeros(endo_mask.shape)\n        sectors_threshold = np.zeros(endo_mask.shape)\n        sector_res = []\n\n        for slc in range(3):\n\n            endo = endo_mask[:,:,slc]\n            epi = epi_mask[:,:,slc]\n            rv = rv_mask[:,:,slc]\n            rvi = rvi_mask[:,:,slc]\n            fmap = fmaps[:,:,slc]\n\n            rvi_pt = np.argwhere(rvi>0)\n\n            if (rvi_pt is None and slc==2):\n                rvi = rvi_mask[:,:,1]\n                rvi_pt = np.argwhere(rvi>0)\n\n            if(rvi_pt is None):\n                print('Cannot find rv insertion point for slice %d :' % slc, file=sys.stderr)\n                continue;\n\n            sectors_slc = mask2sectors(endo, epi, rv, rvi, num_sectors[slc])\n            res, sectors_threshold_slc = compute_sector_statistics(num_sectors[slc], sectors_slc, fmap, max_thres=3.75, min_thres=0.05)\n            sectors[:,:,slc] = sectors_slc\n            sectors_threshold[:,:,slc] = sectors_threshold_slc\n            print('----> Sector statics [mean std max min] for slice %d :' % slc, file=sys.stderr)\n            print(res)\n\n            sector_res.append(res)\n\n        # get flow data\n        flow_data = np.zeros((17,4))\n\n        if(len(sector_res)>=1):\n            flow_data[0,:] = sector_res[0][0, :]\n            flow_data[1,:] = sector_res[0][1, :]\n            flow_data[2,:] = sector_res[0][2, :]\n            flow_data[3,:] = sector_res[0][3, :]\n            flow_data[4,:] = sector_res[0][4, :]\n            flow_data[5,:] = sector_res[0][5, :]\n\n        if(len(sector_res)>=2):\n            flow_data[6,:]  = sector_res[1][0, :]\n            flow_data[7,:]  = sector_res[1][1, :]\n            flow_data[8,:]  = sector_res[1][2, :]\n            flow_data[9,:]  = sector_res[1][3, :]\n            flow_data[10,:] = sector_res[1][4, :]\n            flow_data[11,:] = sector_res[1][5, :]\n\n        if(len(sector_res)>=3):\n            flow_data[12,:] = sector_res[2][0, :]\n            flow_data[13,:] = sector_res[2][1, :]\n            flow_data[14,:] = sector_res[2][2, :]\n            flow_data[15,:] = sector_res[2][3, :]\n\n        print('Final flow data :', flow_data, file=sys.stderr)\n\n        # create cell text\n        cell_text = []\n        for n in range(16):\n            cell_text.append(['%.2f +/- %.2f' % (flow_data[n, 0],flow_data[n, 1])])\n\n\n        cell_text.append(['median/min/max RR: %d/%d/%dms' % (scan_info[0],scan_info[1],scan_info[2])])\n        cell_text.append(['AIF Gd concentration at peak: %.1fmmol/L' % (scan_info[3])])\n        cell_text.append(['AIF first pass FWHM: %.1fseconds' % (scan_info[4])])\n        print('Final cell text : ', cell_text, file=sys.stderr)\n\n        # make the plot\n        data = np.zeros((17,1))\n        segBold = []\n\n        for n in range(17):\n            data[n] = 10 *n + 3\n\n        for n in range(16):\n            if (flow_data[n, 0] == 0 ):\n                segBold.append(n)\n\n        labelr = [ 's ' + str(s) for s in range(1,17)]\n        labelr.append(' note ')\n        labelr.append('  ')\n        labelr.append('  ')  \n        labelc = ['Flow (mean+/-std)']\n\n        fig = plt.figure(figsize=(8, 8), dpi=200)\n        fig.patch.set_facecolor([0.1, 0.1, 0.1])\n        ax = fig.add_subplot(121, projection='polar')\n        bullseye_plot(ax, data, segBold=segBold, cmap='gray', norm=None)\n        ax.set_title('MBF map (ml/min/g)', color=[1, 1, 1])\n        ax = fig.add_subplot(122)\n        the_table = ax.table(cellText=cell_text, rowLabels=labelr, colLabels=labelc, loc='center left', rowLoc='center', colLoc='center')\n        the_table.scale(1, 2)\n        ax.axis(\"off\")\n\n        bs_plot = fig2data(fig)\n        bs_plot = bs_plot.astype(np.float32)\n\n        # replace values\n        bs_plot_final = np.copy(bs_plot)\n\n        pts = np.nonzero(bs_plot==255)\n        bs_plot_final[pts] = 1800\n\n        for n in range(17):\n            pts = np.nonzero(bs_plot==n*10+3)\n            bs_plot_final[pts] = 100 * flow_data[n, 0]\n\n        pts = np.nonzero(bs_plot==26)\n        bs_plot_final[pts] = 0\n        pts = np.nonzero(bs_plot==242)\n        bs_plot_final[pts] = 1800\n\n        bs_plot_final = bs_plot_final.astype(np.float32)\n        bs_plot = bs_plot.astype(np.float32)\n        flow_data = flow_data.astype(np.float32)\n        sectors = sectors.astype(np.float32)\n        sectors_threshold = sectors_threshold.astype(np.float32)\n\n        bs_plot_final = np.transpose(bs_plot_final, (1,0))\n        bs_plot = np.transpose(bs_plot, (1,0))\n\n    except Exception as e:\n            print(\"Error happened in compute_perf_endo_epi_rv_rvi_seg_sax ...\", file=sys.stderr)\n            print(e)\n\n    return bs_plot_final, bs_plot, flow_data, sectors, sectors_threshold\n\nif __name__ == \"__main__\":\n\n    GT_HOME = os.environ['GADGETRON_HOME']\n    model_dir = os.path.join(GT_HOME, 'share/gadgetron/python')\n    print(\"GT_HOME is\", GT_HOME)\n\n    GT_CMR_ML_UT_HOME = os.environ['GT_CMR_ML_UNITTEST_DIRECTORY']\n    print(\"GT_CMR_ML_UT_HOME is\", GT_CMR_ML_UT_HOME)\n\n    res_folder = os.path.join(GT_CMR_ML_UT_HOME, 'result')\n    endo = scipy.io.loadmat(os.path.join(res_folder, 'perf_seg_endo_res.mat'))\n    epi = scipy.io.loadmat(os.path.join(res_folder, 'perf_seg_epi_res.mat'))\n    endo_epi_rv_rvi = scipy.io.loadmat(os.path.join(res_folder, 'perf_seg_endo_epi_rv_rvi_res.mat'))\n\n    endo_mask = endo['mask']\n    epi_mask = epi['mask']\n    endo_epi_rv_rvi_mask = endo_epi_rv_rvi['mask']\n    rv_mask = endo_epi_rv_rvi_mask[:,:,2]\n    rvi_mask = endo_epi_rv_rvi_mask[:,:,3]\n    endo_probs = endo['probs']\n\n    RO, E1 = endo_mask.shape\n\n    endo_mask_slices = np.zeros((RO, E1, 3))\n    epi_mask_slices = np.zeros((RO, E1, 3))\n    rv_mask_slices = np.zeros((RO, E1, 3))\n    rvi_mask_slices = np.zeros((RO, E1, 3))\n    fmaps_slices = np.zeros((RO, E1, 3))\n\n    for n in range(3):\n        endo_mask_slices[:,:,n] = endo_mask\n        epi_mask_slices[:,:,n] = epi_mask\n        rv_mask_slices[:,:,n] = rv_mask\n        rvi_mask_slices[:,:,n] = rvi_mask\n        fmaps_slices[:,:,n] = 10000*endo_probs\n\n    scan_info = np.zeros(5)\n    scan_info[0] = 550\n    scan_info[1] = 560\n    scan_info[2] = 1550\n    scan_info[3] = 4.2\n    scan_info[4] = 14.2\n\n    bs_plot_final, bs_plot, flow_data, sectors, sectors_threshold = create_bullseye_plot(endo_mask_slices, epi_mask_slices, rv_mask_slices, rvi_mask_slices, fmaps_slices, scan_info)\n\n    np.save(os.path.join(res_folder, 'bs_plot_final.npy'), bs_plot_final)\n    np.save(os.path.join(res_folder, 'bs_plot.npy'), bs_plot)\n    np.save(os.path.join(res_folder, 'bs_plot_sectors.npy'), sectors)\n    np.save(os.path.join(res_folder, 'bs_plot_sectors_threshold.npy'), sectors_threshold)\n","sub_path":"utils/cmr_ml_utils_bullseye_plot.py","file_name":"cmr_ml_utils_bullseye_plot.py","file_ext":"py","file_size_in_byte":16340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"12398567","text":"#!/bin/python\n\nimport time\nimport random\nimport sys,math\n\nclass atom():\n\n\tdef __init__(self,element,charge,coord):\t\t# KEEP ATOM INFO \n\t\tself.name = element\t\t\t\t# NAME (e.g., 'Bi', 'O')\n\t\tself.q = charge\t\t\t\t\t# CHARGE\n\t\tself.xyz = [coord[0],coord[1],coord[2]]\t\t# POSITION\n\t\tself.f = [0.,0.,0.]\t\t\t\t# FORCE ACTION ON THE ATOM\n\nclass RACG(atom):\n\n\tdef __init__(self):\n\n\t\tself.bma = 3000.\t\t\t\t# DEFAULT CATION - ANION Born-Mayer PAREMETERS\n\t\tself.bmr = 0.30\t\t\t\t\t#\n\t\tself.lja = -36.\t\t\t\t\t# Lennard-Jones to avoid too sparsed configuration (1/r^6) scaled attractive energy / force; b/t anion - anion\n\t\tself.gnorm_tol = 0.035\t\t\t\t#\n\t\tself.gnorm_div_tol = 15.00\t\t\t# gnorm diverge tolerance\n\n\t\tself.max_trial = 80000\t\t\t\t# MAX TRIAL FOR RANDOM CONFIG GENERATION (Success conditions are, 1. interatomic distance; 2. interatomic force)\n\n\t\tself.box_len = []\t\t\t\t# BOX SIZE : a box for placing atoms (randomly)\n\t\tself.element = []\t\t\t\t# list saves elements\n\t\tself.stochio = []\t\t\t\t# list saves chemical stochiometry\n\t\tself.charge  = []\t\t\t\t# list saves physical charges of each elements\n\t\tself.size    = None\t\t\t\t# var  saves a size of building unit (e.g., (PbO)n -> size = 1, (Bi2O3)n -> size = 5)\n\n\t\tself.inter_atom_dist_tol = 1.5\t\t\t# default setups : allowed minimum interatomic distance in initial config\n\t\tself.inter_atom_force_tol = 10.\t\t\t# default setups : allowed minimum interatomic forces   in initial config\n\n\t\t# CHECK LIST BEFORE GETTING INTO ACTUAL RANDOM BUILDING\n\t\tself.number_of_checklist = 5\t\t\t\t\t\t# checklist number\n\t\tself.check_list = [False for i in range(self.number_of_checklist)]\t# checklist board\n\n\t\tself.opti_max = 4096\t\t\t\t\t\t\t# max iteration\n\t\tself.ls_max_trial = 512\t\t\t\t\t\t\t# max trial for line-search sub-problem solver\n\t\tself.opti_flag = False\t\t\t\t\t\t\t# if use optimisation flag : default = False\n\t\tself.to_ev_const = 14.39964390675221758120\t\t\t\t# CGS to Atomic Unit Constant\n\n\n\n\n\t\t####################################################################################################################################################\n\t\t#\tREAD INPUT FILE; default = 'RACG.in'\n\t\t####################################################################################################################################################\n\t\ttry:\n\t\t\twith open(\"RACG.in\",\"r\") as f:\n\n\t\t\t\tfor line in f:\n\t\t\t\t\tif line[0] == \"#\" or line[0] == \"\\n\":\t# SKIP IF LINE IS COMMENTED OR EMPTY\n\t\t\t\t\t\tcontinue\n\t\t\t\t\telse:\n\t\t\t\t\t\tspl = line.split()\n\t\t\t\t\t\tstride = len(spl)\n\n\t\t\t\t\t\tif \"BOX_SIZE:\" ==  spl[0]:\t\t\t\t# GET BOX SIZE\n\t\t\t\t\t\t\tself.box_len.append( float(spl[1]) )\n\t\t\t\t\t\t\tself.box_len.append( float(spl[2]) )\n\t\t\t\t\t\t\tself.box_len.append( float(spl[3]) )\n\t\t\t\t\t\t\tself.check_list[0] = True\n\t\t\t\t\t\telif \"BASE_ELEMENT:\" == spl[0]:\t\t\t\t# GET BASE ATOM SPECIES \n\t\t\t\t\t\t\tfor i in range(stride-1):\n\t\t\t\t\t\t\t\tself.element.append(spl[i+1])\n\t\t\t\t\t\t\tself.check_list[1] = True\n\t\t\t\t\t\telif \"UNIT_STOCHIO:\" == spl[0]:\t\t\t\t# GET STOCHIOMETRY\n\t\t\t\t\t\t\tfor i in range(stride-1):\n\t\t\t\t\t\t\t\tself.stochio.append(int(spl[i+1]))\n\t\t\t\t\t\t\tself.check_list[2] = True\n\t\t\t\t\t\telif \"ATOM_CHARGE:\" == spl[0]:\t\t\t\t# GET ATOM CHARGE\n\t\t\t\t\t\t\tfor i in range(stride-1):\n\t\t\t\t\t\t\t\tself.charge.append(float(spl[i+1]))\n\t\t\t\t\t\t\tself.check_list[3] = True\n\t\t\t\t\t\telif \"UNIT_SIZE:\" == spl[0]:\n\t\t\t\t\t\t\tself.size = int(spl[1])\n\t\t\t\t\t\t\tself.check_list[4] = True\n\n\t\t\t\t\t\t# SOME DEFAULT PARAMETERS: IF THERE ARE IN CUSTOM MODE\n\n\t\t\t\t\t\telif \"INTER_ATOMIC_DISTANCE_TOL(Angs):\" == spl[0]:\t# THERE ARE DEFAULT SETUPS BUT ... USER CAN CUSTOM\n\t\t\t\t\t\t\tself.inter_atom_dist_tol = float(spl[1])\n\t\t\t\t\t\telif \"INTER_ATOMIC_FORCE_TOL(eV/Angs):\" == spl[0]:\n\t\t\t\t\t\t\tself.inter_atom_force_tol = float(spl[1])\n\t\t\t\t\t\telif \"BM_A(eV):\" == spl[0]:\n\t\t\t\t\t\t\tself.bma = float(spl[1])\n\t\t\t\t\t\telif \"BM_RHO(/Angs):\" == spl[0]:\n\t\t\t\t\t\t\tself.bmr = float(spl[1])\n\t\t\t\t\t\telif \"LJ_(Angs^6):\" == spl[0]:\n\t\t\t\t\t\t\tself.lja = float(spl[1])\n\t\t\t\t\t\t\tself.lja = -self.lja\n\t\t\t\t\t\telif \"GNORM_TOL(eV/Angs):\" == spl[0]:\n\t\t\t\t\t\t\tself.gnorm_tol = float(spl[1])\n\n\n\t\t\t\tif False in self.check_list:\n\t\t\t\t\tprint(\"Error Invoked !!! : essential input parameter(s) is(are) not specified\")\n\t\t\t\t\tsys.exit()\n\n\t\texcept FileNotFoundError:\n\t\t\tprint(\"Error Invoked !!! : input file 'RAGC.in' is not found\")\n\t\t\tsys.exit()\n\t\n\n\n\t####################################################################################################################################################\n\t#\tSOME FUNCTIONS FOR INTERNAL USES : distance, energy, force calculations\n\t####################################################################################################################################################\n\n\tdef get_dist(self,A,B):\n\t\tdx = A.xyz[0] - B.xyz[0]\n\t\tdy = A.xyz[1] - B.xyz[1]\n\t\tdz = A.xyz[2] - B.xyz[2]\n\t\treturn math.sqrt(dx*dx + dy*dy + dz*dz)\n\n\tdef get_energy(self,A,B):\n\t\tr = self.get_dist(A,B)\n\t\tret =  self.to_ev_const*A.q*B.q/r\n\t\tif A.q*B.q < 0.:\t# if pair charges are not in the same sign > add short_range repulsion term (Born-Mayer)\n\t\t\trep = self.bma*math.exp(-r/self.bmr)\n\t\t\tret += rep\n\n\t\tif A.q < 0 and B.q < 0:\t# add lj test\n\t\t\tlj = self.lja/r/r/r/r/r/r\n\t\t\tret += lj\n\n\t\treturn ret\n\n\tdef get_force(self,A,B):\n\t\tret = []\t\t\t\t# data contains force\n\t\tr = self.get_dist(A,B)\n\t\tdx = A.xyz[0] - B.xyz[0]\n\t\tdy = A.xyz[1] - B.xyz[1]\n\t\tdz = A.xyz[2] - B.xyz[2]\n\t\tfx = self.to_ev_const*A.q*B.q*dx/r/r/r\t# FORCE ACTING ON \"A\"\n\t\tfy = self.to_ev_const*A.q*B.q*dy/r/r/r\n\t\tfz = self.to_ev_const*A.q*B.q*dz/r/r/r\n\t\tret = [fx,fy,fz]\n\n\t\tif A.q*B.q < 0.:\t# if pair charge are not in the same sign > add short_range repulsion term (Born-Mayer)\n\t\t\trep_x = self.bma/self.bmr*math.exp(-r/self.bmr)/r*dx\n\t\t\trep_y = self.bma/self.bmr*math.exp(-r/self.bmr)/r*dy\n\t\t\trep_z = self.bma/self.bmr*math.exp(-r/self.bmr)/r*dz\n\t\t\tret[0] += rep_x\n\t\t\tret[1] += rep_y\n\t\t\tret[2] += rep_z\n\n\t\tif A.q < 0 and B.q < 0:\t# add lj test\n\n\t\t\tlj_x = self.lja*6./r/r/r/r/r/r/r/r*dx\n\t\t\tlj_y = self.lja*6./r/r/r/r/r/r/r/r*dy\n\t\t\tlj_z = self.lja*6./r/r/r/r/r/r/r/r*dz\n\t\t\tret[0] += lj_x\n\t\t\tret[1] += lj_y\n\t\t\tret[2] += lj_z\n\n\t\treturn ret\n\n\t####################################################################################################################################################\n\t# BEGIN RANDOM CONFIG GEN\n\t####################################################################################################################################################\n\n\tdef build_rand_config(self):\n\n\t\tself.atoms = []\t\t\t# FOR RECORDING\n\t\tnoa = 0\t\t\t\t# Number of atoms\n\t\trandom.seed(time.time())\t# Random seed \n\n\t\tret = True\t\t\t# Initialise retur 'True'\n\n\t\tfor i in range(self.size):\t# for the size of building block (unit)\n\n\t\t\t# 1st element; in 'self.stochio[0]'\n\t\t\tfor j in range(self.stochio[0]):\n\t\t\t\tfor k in range(self.max_trial):\n\t\t\t\t\n\t\t\t\t\tif k == self.max_trial - 1:\t# if max-trial reached ... then return 'False' failed to find an appropriate configuration\n\t\t\t\t\t\tret = False\n\n\t\t\t\t\trand_x = random.uniform(0.,self.box_len[0])\t\t\t\t\t\t#\n\t\t\t\t\trand_y = random.uniform(0.,self.box_len[1])\t\t\t\t\t\t#\n\t\t\t\t\trand_z = random.uniform(0.,self.box_len[2])\t\t\t\t\t\t#\n\t\t\t\t\tself.atoms.append( atom(self.element[0],self.charge[0],[rand_x,rand_y,rand_z]))\t\t#\n\t\t\t\t\tnoa += 1\t\t\t\t\t\t\t\t\t\t# generate random atom position in the box\n\t\t\t\t\tfc = [0.,0.,0.]\t\t\t\t\t\t\t\t\t\t# force initialisation\n\n\t\t\t\t\tif noa == 1:\t# if only '1' atom in the configuration ... move on to the next atom generation\n\t\t\t\t\t\tbreak\t#\n\n\t\t\t\t\telif noa > 1:\t\t\t# if more then 1 atom in the box; then start checking 'interatomic distance' & 'interatomic forces'\n\t\t\t\t\t\tdist_fail = False\t# flag if fail in interatomic distance\n\n\t\t\t\t\t\tfor ii in range(noa-1):\t# '-1' for excluding self distance\n\t\t\t\t\t\t\tdist = self.get_dist(self.atoms[ii],self.atoms[noa-1])\t\t# get distance b/t the new atom ([noa-1]) vs all the others\n\n\t\t\t\t\t\t\tif dist < self.inter_atom_dist_tol:\t# if distance within the tolerance\n\t\t\t\t\t\t\t\tnoa -= 1\t\t\t# reject\n\t\t\t\t\t\t\t\tself.atoms.pop()\t\t# delete new atom just added\n\t\t\t\t\t\t\t\tdist_fail = True\t\t# set fail flag = 'true'\n\t\t\t\t\t\t\t\tbreak\t\t\t\t# back to retry\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tforce = self.get_force(self.atoms[noa-1],self.atoms[ii])\t# get force acting on the new atom\n\t\t\t\t\t\t\tfc[0] += force[0]\t\t\t\t\t\t# by all the other atoms in the box\n\t\t\t\t\t\t\tfc[1] += force[1]\t\t\t\t\t\t#\n\t\t\t\t\t\t\tfc[2] += force[2]\t\t\t\t\t\t#\n\n\t\t\t\t\t\tif dist_fail == False:\t\t\t\t\t\t\t# if distance check done\n\t\t\t\t\t\t\tnet_force = math.sqrt(fc[0]*fc[0]+fc[1]*fc[1]+fc[2]*fc[2])\t# get net force acting on the new atom\n\t\t\t\t\t\t\tif net_force > self.inter_atom_force_tol:\t\t\t# if force check failed\n\t\t\t\t\t\t\t\tnoa -= 1\t\t\t\t\t\t# reject trial atom\n\t\t\t\t\t\t\t\tself.atoms.pop()\t\t\t\t\t# delete new atom just added\n\t\t\t\t\t\t\telse:\t\t\t\t# if force check done\t\n\t\t\t\t\t\t\t\tbreak\t\t\t# escape\n\n\t\t\t# 2nd element; repeat the same sequence done above\n\t\t\tfor j in range(self.stochio[1]):\n\n\t\t\t\tfor k in range(self.max_trial):\n\n\t\t\t\t\tif k == self.max_trial - 1:\n\t\t\t\t\t\tret = False\n\n\t\t\t\t\trand_x = random.uniform(0.,self.box_len[0])\n\t\t\t\t\trand_y = random.uniform(0.,self.box_len[1])\n\t\t\t\t\trand_z = random.uniform(0.,self.box_len[2])\n\t\t\t\t\tself.atoms.append( atom(self.element[1],self.charge[1],[rand_x,rand_y,rand_z]))\n\t\t\t\t\tnoa += 1\n\t\t\t\t\tfc = [0.,0.,0.]\n\n\t\t\t\t\tif noa == 1:\n\t\t\t\t\t\tbreak\n\t\t\t\t\telif noa > 1:\n\t\t\t\n\t\t\t\t\t\tdist_fail = False\n\t\t\t\t\t\tfor ii in range(noa-1):\n\t\t\t\t\t\t\tdist = self.get_dist(self.atoms[ii],self.atoms[noa-1])\n\n\t\t\t\t\t\t\tif dist < self.inter_atom_dist_tol:\n\t\t\t\t\t\t\t\tnoa -= 1\t\t\t\n\t\t\t\t\t\t\t\tself.atoms.pop()\t\t\n\t\t\t\t\t\t\t\tdist_fail = True\n\t\t\t\t\t\t\t\tbreak\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tforce = self.get_force(self.atoms[noa-1],self.atoms[ii])\n\t\t\t\t\t\t\tfc[0] += force[0]\n\t\t\t\t\t\t\tfc[1] += force[1]\n\t\t\t\t\t\t\tfc[2] += force[2]\n\n\t\t\t\t\t\tif dist_fail == False:\n\n\t\t\t\t\t\t\tnet_force = math.sqrt(fc[0]*fc[0]+fc[1]*fc[1]+fc[2]*fc[2])\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tif net_force > self.inter_atom_force_tol:\n\t\t\t\t\t\t\t\tnoa -= 1\t\t\t\t\n\t\t\t\t\t\t\t\tself.atoms.pop()\t\t\t\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tbreak\n\t\t\n\t\tself.noa = noa # save number of atoms \n\n\t\treturn ret\n\t# End of Rand Gen \n\t####################################################################################################################################################\n\n\n\n\t####################################################################################################################################################\n\t# Some functions for geometric optimisation; energy_update, force_update, gnorm_update, line-search sub-problem solver, backtracking line-search\n\t####################################################################################################################################################\n\n\tdef energy_update(self):\t\t\t\t\t\t\t\t# get energy of current config\n\t\tself.sys_e = 0.\t\t\t\t\t\t\t\t\t#\n\t\tfor i in range(len(self.atoms)):\t\t\t\t\t\t#\n\t\t\tfor j in range(i+1,len(self.atoms)):\t\t\t\t\t#\n\t\t\t\tpair_energy = self.get_energy(self.atoms[i],self.atoms[j])\t#\n\t\t\t\tself.sys_e += pair_energy\t\t\t\t\t#\n\t\treturn self.sys_e\t\t\t\t\t\t\t\t# return current energy\n\n\tdef force_update(self):\t\t\t\t\t\t\t\t\t# get force of current config\n\t\t# force initilisation\n\t\tfor i in range(len(self.atoms)):\n\t\t\tself.atoms[i].f[0] = 0.\n\t\t\tself.atoms[i].f[1] = 0.\n\t\t\tself.atoms[i].f[2] = 0.\n\t\t# calculate force\n\t\tfor i in range(len(self.atoms)):\n\t\t\tfor j in range(i+1,len(self.atoms)):\n\t\t\n\t\t\t\tforce = self.get_force(self.atoms[i],self.atoms[j])\n\t\t\t\n\t\t\t\tself.atoms[i].f[0] += force[0]\n\t\t\t\tself.atoms[i].f[1] += force[1]\n\t\t\t\tself.atoms[i].f[2] += force[2]\n\n\t\t\t\tself.atoms[j].f[0] -= force[0]\n\t\t\t\tself.atoms[j].f[1] -= force[1]\n\t\t\t\tself.atoms[j].f[2] -= force[2]\n\n\t# updated force are saved in 'atoms' (sub-class)\n\n\t# get gnorm of current config ... will be used to tell if stop geometric optimisation\n\tdef gnorm_update(self):\n\t\tself.gnorm = 0.\n\t\tfor i in range(len(self.atoms)):\n\t\t\tself.gnorm += (self.atoms[i].f[0]*self.atoms[i].f[0] + self.atoms[i].f[1]*self.atoms[i].f[1] +self.atoms[i].f[2]*self.atoms[i].f[2])\n\t\tself.gnorm = math.sqrt(self.gnorm)/float((len(self.atoms)*3))\n\t\treturn self.gnorm\n\t\n\t\n\tdef ls_sub(self):\t\t\t\t# line-search subproblem solver\n\t\tt = 1; beta = 0.84\t\t\t# some internal parameters\n\n\t\twhile(True):\t\t\t\t# if initial step size (atoms to move) is too huge, constrain step-size\n\t\t\ttoo_huge_step = False\n\t\t\tfor i in range(len(self.atoms)):\n\t\t\t\tif math.fabs(t*self.atoms[i].f[0]) > 0.1 or math.fabs(t*self.atoms[i].f[1]) > 0.1 or math.fabs(t*self.atoms[i].f[2]) > 0.1:\n\t\t\t\t\tt = t*beta\n\t\t\t\t\ttoo_huge_step = True\n\t\t\t\t\tbreak\n\t\t\tif too_huge_step == False:\n\t\t\t\tbreak\n\n\t\toriginal_energy = self.energy_update()\t# get energy of current config\n\t\tself.force_update()\t\t\t# get forces of current config\n\n\t\ttot_norm_sq = 0.\t\t\t# ls-sub parameter\n\t\tfor i in range(len(self.atoms)):\n\t\t\ttot_norm_sq += (self.atoms[i].f[0]*self.atoms[i].f[0] + self.atoms[i].f[1]*self.atoms[i].f[1] +self.atoms[i].f[2]*self.atoms[i].f[2])\n\t\t\n\t\t'''\n\t\t\tSUB-PROBLEM SOLVER MAIN\n\t\t'''\n\t\tfor i in range(self.ls_max_trial):\n\n\t\t\tfor j in range(len(self.atoms)):\t\t\t\t\t\t# MOVE ATOMS TOWARD WHERE FORCE IS IN ACTION\n\t\t\t\tself.atoms[j].xyz[0] = self.atoms[j].xyz[0] + t*self.atoms[j].f[0]\t#\n\t\t\t\tself.atoms[j].xyz[1] = self.atoms[j].xyz[1] + t*self.atoms[j].f[1]\t#\n\t\t\t\tself.atoms[j].xyz[2] = self.atoms[j].xyz[2] + t*self.atoms[j].f[2]\t#\n\t\t\ttrial_energy = self.energy_update()\t\t\t\t\t\t# get trial energy\n\t\t\tbacktrack_std = original_energy - 0.5*t*tot_norm_sq\t\t\t\t# get acceptance condition parameter\n\n\t\t\tif trial_energy < backtrack_std:\t\t\t\t\t\t# ls-sub solved; update configuration\n\t\t\t\treturn True\t\t\t\t\t\t\t\t# escape\n\n\t\t\telse:\t\t\t\t\t\t\t\t\t\t\t# if rejected\n\t\t\t\tfor j in range(len(self.atoms)):\t\t\t\t\t\t# move atoms back to the original config\n\t\t\t\t\tself.atoms[j].xyz[0] = self.atoms[j].xyz[0] - t*self.atoms[j].f[0]\t#\n\t\t\t\t\tself.atoms[j].xyz[1] = self.atoms[j].xyz[1] - t*self.atoms[j].f[1]\t#\n\t\t\t\t\tself.atoms[j].xyz[2] = self.atoms[j].xyz[2] - t*self.atoms[j].f[2]\t#\n\t\t\t\tt = beta*t\t\t\t\t\t\t\t\t\t# reduce step-size\n\n\t\treturn False\t# if max trial reached ... then return False\n\n\t'''\n\t\tgeometric optimisation main\n\t'''\n\tdef pre_opti(self):\n\n\t\tself.energy_update()\t# update energy\n\t\tself.force_update()\t# update force\n\t\tself.gnorm_update()\t# update gnorm\n\t\n\t\tif self.gnorm < self.gnorm_tol:\t# if gnorm < gnorm_tol\n\t\t\treturn True\t\t# escape\n\n\t\tfor i in range(self.opti_max):\n\t\t\n\t\t\tif i%32 == 0:\n\t\t\t\tprint(\" Cycle:\\t%6.3d / Gnorm:\\t%12.5f\" % (i+1,self.gnorm))\t\t\t\t# record opti process\n\n\t\t\tres = self.ls_sub()\n\n\t\t\tif res == True:\t\t\t\t# ls-sub if solved\n\t\t\t\tself.energy_update()\t\t# update all\n\t\t\t\tself.force_update()\t\t#\n\t\t\t\tself.gnorm_update()\t\t#\n\t\t\t\n\t\t\t\tif self.gnorm < self.gnorm_tol:\t# if meets termination condition\n\t\t\t\t\t#print(\" Cycle:\\t%6.3d / Gnorm:\\t%12.5f ... Final Config\" % (i+1,self.gnorm))\t\t\t#\n\t\t\t\t\treturn True\t\t#\n\t\t\t\telif self.gnorm > self.gnorm_div_tol: # if gnorm diverges\n\t\t\t\t\treturn False\n\n\t\t\telse:\t\t\t\t\t# if failed to solve ls-subproblem\n\t\t\t\tprint(\" No lower configuration is found \")\n\t\t\t\treturn False\n\t\n\t# END OF GEOMETRIC OPTIMISATION\n\n\t'''\n\t\trecord output\n\t'''\n\n\tdef print_random_generator_info(self):\n\n\t\tprint(\"\")\n\t\tprint(\" BOX_INFO(x/y/z):\\t %12.2f\\t%12.2f\\t%12.2f\" % (self.box_len[0],self.box_len[1],self.box_len[2]))\n\t\tprint(\" ELEMENT_UNIT:\\t %4.3s%d%4.3s%d\" % (self.element[0],self.stochio[0],self.element[1],self.stochio[1]))\n\t\tprint(\" ELEMENT_Q   :\\t %.3f  %.3f\"    % (self.charge[0],self.charge[1]))\n\t\tprint(\"\")\n\t\tprint(\" ALLOWED_INITIAL_INTERATOMIC_DISTANCE(Angs):\\t%12.4f\" % (self.inter_atom_dist_tol))\n\t\tprint(\" ALLOWED_INITIAL_INTERATOMIC_FORCE(eV/Angs):\\t%12.5f\" % (self.inter_atom_force_tol))\n\t\tprint(\"\")\n\t\tprint(\" MAXIMUM_RANDOM_GENERATION_TRIAL:\\t%d\" % (self.max_trial))\n\t\tprint(\" MAXIMUM_OPTIMISATION_CYCLE:     \\t%d\" % (self.opti_max))\n\t\tprint(\" MAXIMUM_LS_SUBPROBLEM_TRIALS:   \\t%d\" % (self.ls_max_trial))\n\t\tprint(\"\")\n\t\tprint(\" PRE-OPTIMISATION_Born_Mayer_A(Cation-Anion):  \\t%.4f\" % (self.bma))\n\t\tprint(\" PRE-OPTIMISATION_Born_Mayer_R(Cation-Anion):  \\t%.4f\" % (self.bmr))\n\t\tprint(\" PRE-OPTIMISATION_Vdw(Anion-Anion):            \\t%.4f\" % (self.lja))\n\t\tprint(\"\")\n\n\tdef print_xyz(self):\t\t\t\n\n\t\twith open(\"racg_out.xyz\",\"w\") as f:\n\t\t\n\t\t\tself.energy_update()\n\t\t\tself.force_update()\n\t\t\tf.write(\"\\t%d\\n\" % (self.noa))\n\t\t\tf.write(\"SCF DONE\\t %.6f\\n\" % (self.sys_e))\n\t\t\tfor i in range(self.noa):\n\t\t\t\tf.write(\"%.3s\\t%12.6f\\t%12.6f\\t%12.6f\\n\" % (self.atoms[i].name,self.atoms[i].xyz[0],self.atoms[i].xyz[1],self.atoms[i].xyz[2]))\n\t\t\tf.write(\"\\nForce\\n\")\n\t\t\tself.gnorm_update()\n\t\t\tf.write(\"gnorm: %.6lf\\n\" % (self.gnorm))\n\t\t\tfor i in range(self.noa):\n\t\t\t\tf.write(\"%.3s\\t%18.6e\\t%18.6e\\t%18.6e\\t%10.4f\\n\" % (self.atoms[i].name,self.atoms[i].f[0],self.atoms[i].f[1],self.atoms[i].f[2],self.atoms[i].q))\n\n\t\t\n\t\t\n\n\n\nif __name__ == '__main__':\n\n\trandom_config_generator = RACG()\n\tgeneration_result = random_config_generator.build_rand_config()\n\t\n\tif generation_result == True:\n\t\tprint(\"#\"*100)\n\t\tprint(\" Random Atom Configuration Generator version 1\")\n\t\tprint(\" Author: Woongkyu Jee, woong.jee.16@ucl.ac.uk\")\n\t\tprint(\" Last Edited: 17 / 12 / 2020\")\n\t\tprint(\"\")\n\t\tprint(\" Random generation is successfully done ! \")\n\t\tprint(\" Initialising geometric optimiser ...\")\n\t\tprint(\"\")\n\t\tprint(\" General Input Info\")\n\t\trandom_config_generator.print_random_generator_info()\n\t\tprint(\"#\"*100)\n\t\tprint(\"\")\n\t\tprint(\" optimisation log\\n\")\n\t\topti_result = random_config_generator.pre_opti()\n\t\tprint(\"\")\n\t\tif opti_result == True:\n\t\t\tprint(\" optimisation done, find detailed info in 'out.xyz'\\n\")\n\t\t\tprint(\"#\"*100)\n\t\t\tprint(\" RESULT_TAG: SUCCESS \")\n\t\t\tprint(\"#\"*100)\n\t\telse:\n\t\t\tprint(\" optimisation is finished but structure may not in a gnorm below the tolerance\")\n\t\t\tprint(\" find detaild info in out.xyz\\n\")\n\t\t\tprint(\"#\"*100)\n\t\t\tprint(\" RESULT_TAG: FAIL \")\n\t\t\tprint(\"#\"*100)\n\t\t\n\t\trandom_config_generator.print_xyz()\n\n\telse:\n\t\tprint(\"#\"*100)\n\t\tprint(\" Failed to generate initial random config !\")\n\t\tprint(\" Try tweaking parameters below (see input file 'RACG.in')\")\n\t\tprint(\"#\"*100)\n\t\tprint(\"\")\n\t\tprint(\" 1. 'BOX_SIZE'\\n\")\n\t\tprint(\" 2. 'INTER_ATOMIC_DISTANCE_TOL(Angs)\\n\")\n\t\tprint(\" 3. 'INTER_ATOMIC_FORCE_TOL(eV/Angs)\\n\")\n\t\tprint(\"#\"*100)\n\t\tprint(\" RESULT_TAG: FAIL \")\n\t\tprint(\"#\"*100)\n","sub_path":"MAIN_RACG.py","file_name":"MAIN_RACG.py","file_ext":"py","file_size_in_byte":17684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"570384153","text":"from core.baseapp import BaseApp\nfrom data_model import book\nfrom data_model.book import Book\n\nclass MainApp(BaseApp):\n    def _init_(self):\n        self.books = []\n        self.InputBook = []\n        self.ViewBook = []\n        BaseApp.__init__(self)\n\nclass ViewBook(Book):\n    def _init_(self):\n        ViewBook = ViewBook (self,book)\n        ViewBook.list_book()\n\nif __name__ == \"__main__\":\n    app = MainApp()\n    app.run()\n\n    @property\n    def list_book(self):\n        return  self.list_book\n    def add_book(self):\n        return self.add_book()","sub_path":"uaspy20-febri_jumawan/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"649943505","text":"from bs4 import BeautifulSoup, PageElement\nfrom ParserWireFrames.DatabaseOut import DatabaseOut\nfrom ParserWireFrames.PageContainer import PageContainer\nfrom ParserWireFrames.StringHelper import StringHelper\nfrom ParserWireFrames.EPTextBox import EPTextBox\nfrom ParserWireFrames.ITextBoxParser import ITextBoxParser\nfrom ParserWireFrames.PencilTextBoxParser import PencilTextBoxParser\nfrom ParserWireFrames.EPLabel import EPLabel\nfrom ParserWireFrames.ILabelParser import ILabelParser\nfrom ParserWireFrames.PencilLabelParser import PencilLabelParser\nfrom ParserWireFrames.WebOut import WebOut\n\nsourceFileName = '/Users/jaroslavcoufalik/Desktop/Hanus/Zdrojaky/ParseMe.xml'\n\n\nclass ParseEPxml:\n    table_container: PageContainer = None\n    txt_parser: ITextBoxParser = PencilTextBoxParser()\n    lbl_parser: ILabelParser = PencilLabelParser()\n\n    def __init__(self, table_container):\n        self.table_container = table_container\n\n    def parse_textbox(self, text_box: PageElement) -> EPTextBox:\n        txt = self.txt_parser.parse_element(text_box)\n        return txt\n\n    def parse_label(self, label: PageElement) -> EPLabel:\n        lbl = self.lbl_parser.parse_element(label)\n        return lbl\n\n    def parse_element(self, element):\n        # <g>\n        if element is not None:\n            d = element.get('p:type')\n            if d is not None:\n                if d == 'Group':\n                    self.parse_children(element)\n                    return\n\n            p = element.get('p:def')\n            if p is not None:\n                p = p.replace('Evolus.Sketchy.GUI:', '')\n                p = p.replace('Evolus.Common:', '')\n\n            x = round(float(element.get('p:sizing-ox')), 2) if element.get('p:sizing-ox') else 0\n            y = round(float(element.get('p:sizing-oy')), 2) if element.get('p:sizing-oy') else 0\n            w = round(float(element.get('p:sizing-ow')), 2) if element.get('p:sizing-ow') else 0\n            h = round(float(element.get('p:sizing-oh')), 2) if element.get('p:sizing-oh') else 0\n\n            if p == 'textbox':\n                txt = self.parse_textbox(element)\n                self.table_container.add_text_box(txt)\n                return\n                x = txt.position.x\n                y = txt.position.y\n\n            if p == 'PlainTextV2':\n                lbl = self.parse_label(element)\n                self.table_container.add_label(lbl)\n\n            text = element.find(\"text\")\n            if text is not None:\n                tspan = text.find(\"tspan\")\n                if tspan is not None:\n                    text = tspan\n\n                content = text.find(text=True, recursive=False)\n                if content is not None:\n                    if content.strip():\n                        row1 = '%s' % p\n                        row2 = '%s' % content\n                        self.table_container.add_row(row1, row2, x, y)\n\n    def get_page_name(self, page):\n        if page is not None:\n            properties = page.find(\"properties\")\n            if properties is not None:\n                name = properties.find(\"property\", {\"name\": \"name\"})\n                return StringHelper.get_line_with_value(name.find(text=True, recursive=False))\n\n    def parse_children(self, parent):\n        elements = parent.find_all(\"g\")\n        if elements is not None:\n            for e in elements:\n                self.parse_element(e)\n\n    def parse_page_content(self, page):\n        if page is not None:\n            content = page.find(\"content\")\n            if content is not None:\n                self.parse_children(content)\n\n    def parse_page_elements(self, page):\n        if page is not None:\n            page_name: str = self.get_page_name(page)\n            self.table_container.add_page(page_name)\n            self.parse_page_content(page)\n\n    def parse_pages(self, root):\n        pages: PageElement = root.find_all(\"page\")\n        if pages is not None:\n            for page in pages:\n                self.parse_page_elements(page)\n\n    def generate_files_with_notes(self):\n        with open(sourceFileName, 'r+') as sourceFile:\n            soup: BeautifulSoup = BeautifulSoup(sourceFile, \"html.parser\")\n            self.parse_pages(soup)\n        self.table_container.write_output()\n\n\nout = WebOut()\ntables = PageContainer(out)\nparser = ParseEPxml(tables)\nparser.generate_files_with_notes()\n\n'''\nwith DatabaseOut() as db_table:\n    tables = PageContainer(db_table)\n    parser = ParseEPxml(tables)\n    parser.generate_files_with_notes()\n'''","sub_path":"ParseEPxml.py","file_name":"ParseEPxml.py","file_ext":"py","file_size_in_byte":4493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"295030774","text":"from RoomSnapshot import RoomSnapshot\r\nfrom RadialPoint import RadialPoint\r\nimport matplotlib.pyplot as plt\r\nimport math\r\nimport csv\r\n\r\n\r\ndef showOnConsoleWithRotation(appendAngle):\r\n    with open('05.csv', newline='') as csvfile:\r\n        csvreader = csv.reader(csvfile, delimiter=';', quotechar='|')\r\n        for row in csvreader:\r\n            dist = float(row[0]) + float(RoomSnapshot.rotateRoom(appendAngle))\r\n            angle = float(row[1])\r\n            v = RadialPoint(angle, dist)\r\n            print('dist = {} angle = {} coords = {}'.format(dist, angle, v.getAsXY()))\r\n\r\ndef showAsImageWithRotation(appendAngle):\r\n    x = []\r\n    y = []\r\n    with open('05.csv', newline='') as csvfile:\r\n        csvreader = csv.reader(csvfile, delimiter=';', quotechar='|')\r\n        for row in csvreader:\r\n            dist = float(row[0]) + float(RoomSnapshot.rotateRoom(appendAngle))\r\n            angle = float(row[1])\r\n            v = RadialPoint(angle, dist)\r\n            x.append(v.getAsXY()[0])\r\n            y.append(v.getAsXY()[1])\r\n    plt.scatter(x, y, c='black', s=1)\r\n    plt.show()\r\n\r\ndef selectHowToShowInfoWithRotation(typeOfDispl):\r\n    if typeOfDispl == \"console\":\r\n        return showOnConsoleWithRotation(appendAngle)\r\n    if typeOfDispl == \"image\":\r\n        return showAsImageWithRotation(appendAngle)\r\n\r\nappendAngle = input(\"Which angle append to rotate room? \")\r\ntypeOfDispl = input(\"How to display info (console, image)? \")\r\nselectHowToShowInfoWithRotation(typeOfDispl)","sub_path":"RotateRoom.py","file_name":"RotateRoom.py","file_ext":"py","file_size_in_byte":1483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"394040140","text":"'''Course: CS 101\n Assignment: Program 6 \n filename: find_author.py\n Name: Alexeo Smith\n Email: asd5b@mail.umkc.edu\n Due : Nov 10th, 2013\n\n Problem:\n Professional methods of authorship detection are a good deal more complicated than what we're going to use here, but even with the simple methods we're using here, your program can make some reasonable predictions about authorship.\nYou are provided with a skeleton program containing stubs for most of the functions you will need, with part of the main program logic already completed. The program runs but doesn't do much of anything. You will need to complete the program by filling in the missing pieces. The program will ask the user for the name of a file containing an unknown text to be analyzed. Your program will compute the linguistic signature for the mystery file, compare it to the profiles of all known authors, and report which author the text most closely matches.\n\nAlgorithm:\n1)\tAs instructed, place the provided author signature files in a subdirectory of the \n    folder where the Python program(.py) file will be \n2) Place the provided mystery text files in the same directory as the Python program   \n     file\n3) The program will continue to prompt the user for a text file that has a sample  \n     writing of an unknown author until the user provides a valid text file. \n     Then the program will read the text file as a huge string and call the \n     split_on_separators function on the string\n4) Next the program will prompt the user for the directory containing the signature  \n     files, confirm it’s a valid directory, move to that folder, read the files and store\n     result in a list called files\n5) After reading the signature files, the program will move back to the starting  \n     folder \n6) Per the provided docstring specifications, complete each incomplete function and\n     add additional docstrings. Don’t use any for loops for the get_valid_filname and  \n     read_directory_name functions. Don’t use continue or break statements in any \n     functions.  Don’t modify existing functions beyond the provided specifications\n7) Call provided functions where applicable and don’t add any different linguistic  \n     features\n8) Run the program, which will then compare the unknown author text file to  \n     profiles of known actors, then report which author the text closely matches'''\n\n\n\nimport os.path, math, string\nseparators_str = \".!?\"\n\ndef clean_up(s):\n    ''' Return a version of string str in which all letters have been\n    converted to lowercase and punctuation characters have been stripped \n    from both ends. Inner punctuation is left untouched. '''\n    \n    punctuation = '''!\"',;:.-?)([]<>*#\\n\\t\\r'''\n    result = s.lower().strip(punctuation)\n    return result\n\n\ndef average_word_length(text):\n    ''' Return the average length of all words in text. Do not\n    include surrounding punctuation in words. \n    text is a non-empty list of strings each ending in \\n.\n    At least one line in text contains a word.'''\n    \n    # To do: Replace this function's body to meet its specification.\n    #for sentences in text:\n\n    if text:\n        words = text.split()\n        average = sum(map(len, words))/len(words)\n        return average\n    else:    \n        return 1.0\n    \n\ndef type_token_ratio(text):\n    ''' Return the type token ratio (TTR) for this text.\n    TTR is the number of different words divided by the total number of words.\n    text is a non-empty list of strings each ending in \\n.\n    At least one line in text contains a word. '''\n  \n    # To do: Replace this function's body to meet its specification.\n    \n    for p in string.punctuation:\n        text = text.replace(p, \"\")\n        text = clean_up(text)\n            \n    if text:\n        text = text.split()\n        count_dict = {}\n        for word in text:\n            if word in count_dict:\n                count_dict[word] += 1\n            else:\n                count_dict[word] = 1           \n        return len(count_dict)/len(text)\n    else:\n        return 1\n        \n                \ndef hapax_legomana_ratio(text):\n    ''' Return the hapax_legomana ratio for this text.\n    This ratio is the number of words that occur exactly once divided\n    by the total number of words.\n    text is a list of strings each ending in \\n.\n    At least one line in text contains a word.'''\n \n    # To do: Replace this function's body to meet its specification.\n    count = 0\n    for p in string.punctuation:\n        text = text.replace(p, \"\")\n        text = clean_up(text)\n       \n    if text:\n        text = text.split()\n        count_dict = {}\n        for word in text:\n            if word in count_dict:\n                count_dict[word] += 1\n            else:\n                count_dict[word] = 1\n\n    for k,v in count_dict.items():\n     if count_dict[k] == 1:\n        count +=1\n\n    ratio = count/len(text)\n    if ratio:\n        return ratio\n    else:\n        return 1\n\ndef split_on_separators(original, separators):\n    ''' Return a list of non-empty, non-blank strings from the original string\n    determined by splitting the string on any of the separators.\n    separators is a string of single-character separators.'''\n    \n    sentences_lst =[]\n    # To do: Complete this function's body to meet its specification.\n    # You are not required to keep the two lines below but you may find\n    # them helpful. (Hint)\n    essay_lst = original.replace(separators_str[0],'#').replace(separators_str[1],'#').replace(separators_str[2],'#').split(\"#\")\n    essay_lst = [x.strip(' ') for x in essay_lst]\n    essay_lst = [x for x in essay_lst if x != '']\n    for i in range(len(essay_lst)):\n           if essay_lst[i][-1] != \":\" and essay_lst[i][-1] != \";\" and essay_lst[i][-1] != \",\" :\n             sentences_lst.append(essay_lst[i]) \n\n    return sentences_lst\n                \n    \ndef average_sentence_length(text):\n    ''' Return the average number of words per sentence in text.\n    text is guaranteed to have at least one sentence.\n    Terminating punctuation defined as !?.\n    A sentence is defined as a non-empty string of non-terminating\n    punctuation surrounded by terminating punctuation\n    or beginning or end of file. '''\n    \n    # To do: Replace this function's body to meet its specification.\n    if text:\n        sentences = split_on_separators(text, separators_str)\n        average = sum(map(len, sentences))/len(sentences)\n        return average\n    else:    \n        return 1\n    \n\ndef avg_sentence_complexity(text):\n    '''Return the average number of phrases per sentence.\n    Terminating punctuation defined as !?.\n    A sentence is defined as a non-empty string of non-terminating\n    punctuation surrounded by terminating punctuation\n    or beginning or end of file.\n    Phrases are substrings of a sentences separated by\n    one or more of the following delimiters ,;: '''\n    \n    # To do: Replace this function's body to meet its specification.\n    phrase_separators_str = \":,;\"\n    phrases_lst = []\n    if text:\n        sentences_lst = split_on_separators(text, separators_str)\n        sentences_str = \",\".join(sentences_lst)\n        phrase_lst = sentences_str.replace(phrase_separators_str[0],'#').replace(phrase_separators_str[1],'#').replace(phrase_separators_str[2],'#').split(\"#\")\n        phrase_lst = [x.strip(' ') for x in phrase_lst]\n        phrase_lst = [x for x in phrase_lst if x != '']\n        result = sum(map(len,phrase_lst))/len(split_on_separators(text, separators_str))\n        return result\n    else:\n        return 1.0\n            \n    \ndef get_valid_filename(prompt):\n    '''Use prompt (a string) to ask the user to type the name of a file. If\n    the file does not exist, keep asking until they give a valid filename.\n    Return the name of that file.'''\n\n    # Uncomment and use this statement as many times as needed for input:\n    # filename = input(prompt)\n    # Uncomment and use this statement as many times as needed for output:\n    # print \"That file does not exist.\"\n    # Do not use any other input or output statements in this function.\n    \n    noFile = True\n    # To do: Complete this function's body to meet its specification.\n    while noFile:\n        filename = input(prompt)\n        if os.path.isfile(filename):\n            noFile = True\n            return filename\n        else:\n            print(\"That file does not exist.\")\n\n    \ndef read_directory_name(prompt):\n    '''Use prompt (a string) to ask the user to type the name of a directory. If\n    the directory does not exist, keep asking until they give a valid directory.\n    '''\n\n    # Uncomment and use this statement as many times as needed for input:\n    # dirname = input(prompt)\n    # Uncomment and use this statement as many times as needed for output:\n    # print \"That directory does not exist.\"\n    # Do not use any other input or output statements in this function.\n    \n    noDirectory = True\n    # To do: Complete this function's body to meet its specification.\n    while noDirectory:\n        dirname = input(prompt)\n        if os.path.isdir(dirname):\n            noDirectory = False\n            return dirname\n        else:\n            print(\"That directory does not exist.\")\n   \ndef compare_signatures(sig1, sig2, weight):\n    '''Return a non-negative real number indicating the similarity of two \n    linguistic signatures. The smaller the number the more similar the \n    signatures. Zero indicates identical signatures.\n    sig1 and sig2 are 6 element lists with the following elements\n    0  : author name (a string)\n    1  : average word length (float)\n    2  : TTR (float)\n    3  : Hapax Legomana Ratio (float)\n    4  : average sentence length (float)\n    5  : average sentence complexity (float)\n    weight is a list of multiplicative weights to apply to each\n    linguistic feature. weight[0] is ignored.\n    '''\n    # To do: Replace this function's body to meet its specification.\n    compute_lst = []\n    if sig1 and sig2:\n        End = False\n        i = 5\n        while i != 0:\n            compute_lst.append(abs((sig1[i]-sig2[i])*weights[i]))\n            i -= 1\n        result = sum(compute_lst)\n        return result\n    else:\n        return  0.0\n    \n\ndef read_signature(filename):\n    '''Read a linguistic signature from filename and return it as \n    list of features. '''\n    \n    file = open(filename, 'r')\n    # the first feature is a string so it doesn't need casting to float\n    result = [file.readline()]\n    # all remaining features are real numbers\n    for line in file:\n        result.append(float(line.strip()))\n    return result\n        \n\nif __name__ == '__main__':\n    \n    prompt = 'enter the name of the file with unknown author:'\n    mystery_filename = get_valid_filename(prompt)\n    \n    # readlines gives us the file as a list of strings each ending in '\\n'\n    text = open(mystery_filename, 'r').read()\n    #text.close() \n    # calculate the signature for the mystery file\n    mystery_signature = [mystery_filename]\n    mystery_signature.append(average_word_length(text))\n    mystery_signature.append(type_token_ratio(text))\n    mystery_signature.append(hapax_legomana_ratio(text))\n    mystery_signature.append(average_sentence_length(text))\n    mystery_signature.append(avg_sentence_complexity(text))\n    \n    weights = [0, 11, 33, 50, 0.4, 4]\n\n    files = []\n    prompt = 'enter the path to the directory of signature files: '\n    dirRead = read_directory_name(prompt)\n    # every file in this directory must be a linguistic signature\n    # to do: Get list of all files in directory name we just got.\n    # store it in a list called 'files'\n    \n    files = os.listdir(dirRead)\n    \n\t\t\n    # we will assume that there is at least one signature in that directory\n    this_file = files[0]\n    signature = read_signature('{}\\\\{}'.format(dirRead,this_file))\n    best_score = compare_signatures(mystery_signature, signature, weights)\n    best_author = signature[0]\n    for this_file in files[1:]:\n        signature = read_signature('{}\\\\{}'.format(dirRead,this_file))\n        score = compare_signatures(mystery_signature, signature, weights)\n        if score < best_score:\n            best_score = score\n            best_author = signature[0]\n    print( \"best author match: {} with score {}\".format(best_author, best_score))\n    \n","sub_path":"CS101/Program6/find_author.py","file_name":"find_author.py","file_ext":"py","file_size_in_byte":12273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"528342741","text":"from tkinter import *\nfrom tkinter import messagebox\nimport pymysql\nimport random\n\nx=Tk()\nx.title(\"Rock Paper Scissor Game\")\nicon=PhotoImage(file='MemeLogo.png')\nx.iconphoto(False,icon)\n\nglobal s,m\ns,m=1,0\n\ndef comp(val):\n    global s\n    l9=Label(x,text=\"                                              \").grid(row=8,column=1)\n    l10=Label(x,text=\"                \").grid(row=6,column=1,columnspan=1)\n    l11=Label(x,text=\"                \").grid(row=7,column=1,columnspan=1)\n    l17=Label(x,text=\"                \").grid(row=9,column=3,columnspan=1)\n\n    c=['Rock','Paper','Scissor']\n    l=random.randint(0,2)\n    t=c[l]\n    if val==t:\n        l12=Label(x,text=\"Try Again It's a Tie\").grid(row=8,column=1)\n        l13=Label(x,text=val).grid(row=6,column=1,columnspan=1)\n        l14=Label(x,text=t).grid(row=7,column=1,columnspan=1)\n\n        l16=Label(x,text=str(int(100*(m/s)))+\"%\",fg='Brown')\n        l16.config(font=(\"Courier\",'15'))\n        l16.grid(row=9,column=3,columnspan=1)\n\n    else:\n        score(val,t)\n        s=s+1\n        l13=Label(x,text=val).grid(row=6,column=1,columnspan=1)\n        l14=Label(x,text=t).grid(row=7,column=1,columnspan=1)\n\ndef score(val,ch):\n    global m\n    if val==\"Rock\" and ch==\"Scissor\":\n        m=m+1\n        l15=Label(x,text=str(m)+\"/\"+str(s),fg='Brown')\n        l15.config(font=(\"Courier\",'20'))\n        l15.grid(row=9,column=2,columnspan=1)\n\n        l16=Label(x,text=str(int(100*(m/s)))+\"%\",fg='Brown')\n        l16.config(font=(\"Courier\",'15'))\n        l16.grid(row=9,column=3,columnspan=1)\n\n\n    elif val==\"Rock\" and ch==\"Paper\":\n        l15=Label(x,text=str(m)+\"/\"+str(s),fg='Brown')\n        l15.config(font=(\"Courier\",'20'))\n        l15.grid(row=9,column=2,columnspan=1)\n\n        l16=Label(x,text=str(int(100*(m/s)))+\"%\",fg='Brown')\n        l16.config(font=(\"Courier\",'15'))\n        l16.grid(row=9,column=3,columnspan=1)\n\n    elif val==\"Scissor\" and ch==\"Paper\":\n        m=m+1\n        l15=Label(x,text=str(m)+\"/\"+str(s),fg='Brown')\n        l15.config(font=(\"Courier\",'20'))\n        l15.grid(row=9,column=2,columnspan=1)\n\n        l16=Label(x,text=str(int(100*(m/s)))+\"%\",fg='Brown')\n        l16.config(font=(\"Courier\",'15'))\n        l16.grid(row=9,column=3,columnspan=1)\n\n    elif val==\"Scissor\" and ch==\"Rock\":\n        l15=Label(x,text=str(m)+\"/\"+str(s),fg='Brown')\n        l15.config(font=(\"Courier\",'20'))\n        l15.grid(row=9,column=2,columnspan=1)\n\n        l16=Label(x,text=str(int(100*(m/s)))+\"%\",fg='Brown')\n        l16.config(font=(\"Courier\",'15'))\n        l16.grid(row=9,column=3,columnspan=1)\n\n    elif val==\"Paper\" and ch==\"Rock\":\n        m=m+1\n        l15=Label(x,text=str(m)+\"/\"+str(s),fg='Brown')\n        l15.config(font=(\"Courier\",'20'))\n        l15.grid(row=9,column=2,columnspan=1)\n\n        l16=Label(x,text=str(int(100*(m/s)))+\"%\",fg='Brown')\n        l16.config(font=(\"Courier\",'15'))\n        l16.grid(row=9,column=3,columnspan=1)\n\n    elif val==\"Paper\" and ch==\"Scissor\":\n        l15=Label(x,text=str(m)+\"/\"+str(s),fg='Brown')\n        l15.config(font=(\"Courier\",'20'))\n        l15.grid(row=9,column=2,columnspan=1)\n\n        l16=Label(x,text=str(int(100*(m/s)))+\"%\",fg='Brown')\n        l16.config(font=(\"Courier\",'15'))\n        l16.grid(row=9,column=3,columnspan=1)\n\n\n\n\n#Heading 1\n\nl1=Label(x,text=\"This is a Rock Paper Scissor Game Using Python Tkinter\")\nl1.config(font=(\"Courier\",'15'))\nl1.grid(row=0,column=0,columnspan=6)\n\n#Heading 2\n\nl2=Label(x,text=\"Let's start the Game\",fg='Green')\nl2.config(font=(\"Times\",'14'))\nl2.grid(row=1,column=1,columnspan=1)\n\n\nl3=Label(x,text=\"Your Options are : \",fg='Blue')\nl3.config(font=(\"Times\",'12'))\nl3.grid(row=2,column=0)\n\nl4=Label(x,text=\" \").grid(row=3)\n\nb1=Button(x,text=\"Rock\",padx=12,pady=7,bg=\"Grey\",command=lambda: comp(\"Rock\")).grid(row=4,column=0,ipadx=10,columnspan=1)\n\nb2=Button(x,text=\"Paper\",padx=12,pady=7,bg=\"White\",command=lambda: comp(\"Paper\")).grid(row=4,column=1,ipadx=10,columnspan=1)\n\nb3=Button(x,text=\"Scissor\",padx=12,pady=7,bg=\"Red\",command=lambda: comp(\"Scissor\")).grid(row=4,column=2,ipadx=10,columnspan=1)\n\nl5=Label(x,text=\"\").grid(row=5,column=1,columnspan=1)\n\n#Your Choice \n\nl6=Label(x,text=\"Your Choice : \")\nl6.config(font=(\"Times\",'12'))\nl6.grid(row=6,column=0,columnspan=1)\n\n#Computer's Choice\n\nl7=Label(x,text=\"Computer's Choice : \")\nl7.config(font=(\"Times\",'12'))\nl7.grid(row=7,column=0,columnspan=1)\n\nl8=Label(x,text=\"Your Score : \",fg='Brown')\nl8.config(font=(\"Courier\",'20'))\nl8.grid(row=9,column=1,columnspan=1)\n\n\nx.mainloop()","sub_path":"RPS_Game.py","file_name":"RPS_Game.py","file_ext":"py","file_size_in_byte":4470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"129379191","text":"import logging\nimport os\nfrom sacred import Experiment\nimport numpy as np\nimport seml\n\nimport torch\nfrom torch.nn import functional as F\nfrom torch.utils.data import DataLoader\nfrom torch.utils.tensorboard import SummaryWriter\nfrom torchvision import models\n\nfrom utils.utils import *\nfrom utils.memory import MemoryBank\nfrom losses.losses import *\nfrom datasets.datasets import SegmentationDataSet, PIRLDataSet2\nfrom datasets.data_constants import *\nfrom models.classifier import FCClassifier, FCClassifierBatchNorm\nfrom models.discriminator import FCDiscriminator, FCDiscriminatorBatchNorm\n\nex = Experiment()\nseml.experiment.setup_logger(ex)\n\n\n@ex.config\ndef config():\n    overwrite = None\n    db_collection = None\n    if db_collection is not None:\n        ex.observers.append(seml.observers.create_mongodb_observer(db_collection, overwrite=overwrite))\n\n\n@ex.automain\ndef run(source_dir, target_dir, \n    snapshots_dir, log_dir, pred_dir, load_checkpoint,\n    source_train_images, source_train_labels, \n    source_val_images, source_val_labels, \n    target_train_images, target_train_labels, \n    target_val_images, target_val_labels, \n    train_batch_size, val_batch_size,\n    image_height, image_width, \n    grid_size, num_workers,\n    backbone, classification_head, pretrained_backbone, \n    segmentation_loss, gamma,\n    temperature, proj_size, memory_bank_params, \n    memory_bank_device, aux_injection_point, lambda_aux,\n    da_injection_point, lambda_da,\n    learning_rate, momentum, weight_decay, \n    learning_rate_aux, betas_aux,\n    learning_rate_da, betas_da,    \n    max_iter, lrs_power,\n    batches_to_eval_train, batches_to_visualize, \n    points_to_sample, save_step, display_step, seed):\n\n    logging.info('Received the following configuration:')\n    logging.info(f'Source_dir: {source_dir}, target_dir: {target_dir}, '\n                 f'snapshots_dir: {snapshots_dir}, log_dir: {log_dir}, pred_dir: {pred_dir}, load_checkpoint: {load_checkpoint}, '\n                 f'source_train_images: {source_train_images}, source_train_labels: {source_train_labels}, '\n                 f'source_val_images: {source_val_images}, source_val_labels: {source_val_labels}, '\n                 f'source_train_images: {target_train_images}, source_train_labels: {target_train_labels}, '\n                 f'target_val_images: {target_val_images}, target_val_labels: {target_val_labels}, '\n                 f'train_batch_size: {train_batch_size}, val_batch_size: {val_batch_size}, '\n                 f'image_height: {image_height}, image_width: {image_width}, '\n                 f'grid_size: {grid_size}, num_workers: {num_workers}, '\n                 f'backbone: {backbone}, classification_head: {classification_head}, pretrained_backbone: {pretrained_backbone}, '\n                 f'segmentation_loss: {segmentation_loss}, gamma: {gamma}, '\n                 f'temperature: {temperature}, proj_size: {proj_size}, memory_bank_params: {memory_bank_params}, '\n                 f'memory_bank_device: {memory_bank_device}, aux_injection_point: {aux_injection_point}, lambda_aux: {lambda_aux}, '\n                 f'da_injection_point: {da_injection_point}, lambda_da: {lambda_da}, '\n                 f'learning_rate: {learning_rate}, momentum: {momentum}, weight_decay: {weight_decay}, '\n                 f'learning_rate_aux: {learning_rate_aux}, betas_aux: {betas_aux}, '\n                 f'learning_rate_da: {learning_rate_da}, betas_da: {betas_da}, '\n                 f'max_iter: {max_iter}, lrs_power: {lrs_power}, '\n                 f'batches_to_eval_train: {batches_to_eval_train}, batches_to_visualize: {batches_to_visualize}, '\n                 f'points_to_sample: {points_to_sample}, save_step:{save_step}, display_step: {display_step}, seed: {seed}')\n    \n    #  initialize the global parameters\n    cuda_status = torch.cuda.is_available()\n    device = torch.device(\"cuda:0\" if cuda_status else \"cpu\")\n    memory_bank_device = torch.device(memory_bank_device)\n    memory_bank_size, memory_bank_batch_size = memory_bank_params[1], memory_bank_params[0]\n    image_size = (image_height, image_width)\n    model_name = '_'.join(\n        [backbone,\n        classification_head,\n        str(image_height) + 'x' + str(image_width),\n        'grid' + str(grid_size[0]) + 'x' + str(grid_size[1]), \n        ('IN' if pretrained_backbone else ''),\n        segmentation_loss,\n        aux_injection_point[:3],\n        da_injection_point[:3], \n        't{:.1e}_proj{}_mbsize{}_mbbatch{}'.format(\n        temperature, proj_size, memory_bank_size, memory_bank_batch_size),   \n        'gamma{:.1e}_lmbdaux{:.1e}_lmbdda{:.1e}_lr{:.1e}_lraux{:.1e}_lrda{:.1e}_m{:.1e}_wd{:.1e}_lrsp{:.1e}'.format(\n            gamma, lambda_aux, lambda_da, \n            learning_rate, learning_rate_aux, learning_rate_da,\n            momentum, weight_decay, lrs_power)\n        ])\n    start_epoch = 0\n    if seed > 0:\n        set_seed(seed)\n    src_label = 1.\n    tar_label = 0.\n\n    #  create neccessary directories for snapshots and logs\n    if not os.path.exists(os.path.join(snapshots_dir, model_name)):\n        os.makedirs(os.path.join(snapshots_dir, model_name))\n    if not os.path.exists(os.path.join(log_dir, model_name)):\n        os.makedirs(os.path.join(log_dir, model_name))\n    if not os.path.exists(os.path.join(pred_dir, model_name)):\n        os.makedirs(os.path.join(pred_dir, model_name))\n    \n    #  initialize the datasets\n    logging.info('Initializing the datasets')\n    source_train_dataset = SegmentationDataSet(root=source_dir, image_list_name=source_train_images, \n                            label_list_name=source_train_labels, size=image_size, \n                            mean=GTA5_MEAN, std=GTA5_STD, label2train=GTA5_LABELS2TRAIN)\n    source_val_dataset = SegmentationDataSet(root=target_dir, image_list_name=source_val_images, \n                            label_list_name=source_val_labels, size=image_size, \n                            mean=GTA5_MEAN, std=GTA5_STD, label2train=GTA5_LABELS2TRAIN)\n    target_train_dataset = PIRLDataSet2(root=target_dir, image_list_name=target_train_images, \n                            label_list_name=target_train_labels, size=image_size, grid_size=grid_size,\n                            mean=CITYSCAPES_MEAN, std=CITYSCAPES_STD, label2train=CITYSCAPES_LABELS2TRAIN)\n    target_val_dataset = SegmentationDataSet(root=target_dir, image_list_name=target_val_images, \n                            label_list_name=target_val_labels, size=image_size, \n                            mean=CITYSCAPES_MEAN, std=CITYSCAPES_STD, label2train=CITYSCAPES_LABELS2TRAIN)\n\n    source_train_dataloader = DataLoader(source_train_dataset, batch_size=train_batch_size,\n                                shuffle=True, pin_memory=cuda_status, drop_last=True, num_workers=num_workers)\n    target_train_dataloader = DataLoader(target_train_dataset, batch_size=train_batch_size,\n                                shuffle=True, pin_memory=cuda_status, drop_last=True, num_workers=num_workers)\n    source_val_dataloader = DataLoader(source_val_dataset, batch_size=val_batch_size, \n                                shuffle=True, pin_memory=cuda_status, drop_last=True, num_workers=num_workers)\n    target_val_dataloader = DataLoader(target_val_dataset, batch_size=val_batch_size, \n                                shuffle=True, pin_memory=cuda_status, drop_last=True, num_workers=num_workers)\n\n    inf_source_train_dataloader = inf_iter(source_train_dataloader)\n    inf_target_train_dataloader = inf_iter(target_train_dataloader)\n    inf_source_val_dataloader = inf_iter(source_val_dataloader)\n    inf_target_val_dataloader = inf_iter(target_val_dataloader)\n\n    max_epochs = max_iter // len(source_train_dataloader)\n\n    #  initialize the model and the loss\n    logging.info('Initializing models&losses')\n    #  segmentation model\n    seg_model_name =  classification_head + '_' + backbone\n    seg_model_loader = getattr(models.segmentation, seg_model_name)\n    seg_model = seg_model_loader(pretrained=False, \n                    num_classes=NUM_CLASSES,\n                    progress=False,\n                    aux_loss=False,\n                    pretrained_backbone=pretrained_backbone)\n    if segmentation_loss == 'ce':\n        seg_loss = torch.nn.CrossEntropyLoss(ignore_index=255)\n    elif segmentation_loss == 'focal':\n        seg_loss = FocalLoss(alpha=1.0, \n                    gamma=gamma, \n                    ignore_index=255,\n                    reduction='mean')\n    else:\n        raise ValueError(\"There are two losses currently supported: [ce, focal]. \"\n                         \"Got: {}\".format(segmentation_loss))\n    #  rotation classifier\n    if aux_injection_point == 'output':\n        classifier_config = {\n            'input_dim': NUM_CLASSES,\n            'ndf': 64\n        }\n    elif aux_injection_point == 'feature':\n        classifier_config = {\n            'input_dim': 2048,\n            'ndf': 256\n        }\n    else:\n        raise ValueError(\"There are two injection points currently supported: [output, feature]. \"\n                         \"Got: {}\".format(aux_injection_point))\n    classifier_config['num_classes'] = proj_size\n    classifier_config['avg_pool'] = True\n    classifier_config['avg_pool_size'] = 1\n    aux_model = FCClassifierBatchNorm(**classifier_config)\n    aux_loss = NT_Xent2(temperature, (train_batch_size*grid_size[0]*grid_size[1])//2)\n    \n    #  initialize the memory bank\n    memory_bank = MemoryBank(memory_bank_size, memory_bank_batch_size, proj_size, memory_bank_device)\n    \n    #  domain discriminator\n    if da_injection_point == 'output':\n        discriminator_config = {\n            'input_dim': NUM_CLASSES,\n            'ndf': 64,\n        }\n    elif da_injection_point == 'feature':\n        discriminator_config = {\n            'input_dim': 2048,\n            'ndf': 256,\n        }\n    else:\n        raise ValueError(\"There are two injection points currently supported: [output, feature]. \"\n                         \"Got: {}\".format(da_injection_point))\n    discr_model = FCDiscriminatorBatchNorm(**discriminator_config)\n    discr_loss = torch.nn.BCEWithLogitsLoss()\n\n    #  initialize the optimizers \n    #  we use different lrs for the classifier and the backbone if the latter one is pretrained\n    logging.info('Initializing optimizers')\n    optimizer = torch.optim.SGD([{'params': seg_model.backbone.parameters(), \n                                  'lr':learning_rate},\n                                 {'params': seg_model.classifier.parameters(), \n                                  'lr':learning_rate * (10 if pretrained_backbone else 1)}],\n                                lr=learning_rate, momentum=momentum, weight_decay=weight_decay)\n    optimizer.zero_grad()\n    \n    aux_optimizer = torch.optim.Adam(aux_model.parameters(),\n                            lr=learning_rate_aux, betas=betas_aux)\n    aux_optimizer.zero_grad()\n    \n    discr_optimizer = torch.optim.Adam(discr_model.parameters(), \n                                lr=learning_rate_da, betas=betas_da)\n    discr_optimizer.zero_grad()\n\n    #  initialize the learning rate scheduler\n    logging.info('Initializing lr schedulers')\n    lr_poly = lambda epoch: (1 - epoch / max_epochs) ** lrs_power\n    scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lr_poly, last_epoch=-1)\n    aux_scheduler = torch.optim.lr_scheduler.LambdaLR(aux_optimizer, lr_lambda=lr_poly, last_epoch=-1)\n    discr_scheduler = torch.optim.lr_scheduler.LambdaLR(discr_optimizer, lr_lambda=lr_poly, last_epoch=-1)\n    \n    #  reinitialize if nesseccary\n    if os.path.isfile(load_checkpoint):\n        logging.info('Loading the checkpoint')\n        checkpoint = torch.load(load_checkpoint)\n        if 'seg_model' in checkpoint:\n            seg_model.load_state_dict(checkpoint['seg_model'])\n        if 'aux_model' in checkpoint:\n            aux_model.load_state_dict(checkpoint['aux_model'])\n        if 'discr_model' in checkpoint:\n            aux_model.load_state_dict(checkpoint['discr_model'])\n        if 'optimizer' in checkpoint:\n            optimizer.load_state_dict(checkpoint['optimizer'])\n        if 'aux_optimizer' in checkpoint:\n            aux_optimizer.load_state_dict(checkpoint['aux_optimizer'])\n        if 'discr_optimizer' in checkpoint:\n            aux_optimizer.load_state_dict(checkpoint['discr_optimizer'])\n        if 'scheduler' in checkpoint:\n            scheduler.load_state_dict(checkpoint['scheduler'])\n        if 'aux_scheduler' in checkpoint:\n            aux_scheduler.load_state_dict(checkpoint['aux_scheduler'])\n        if 'discr_scheduler' in checkpoint:\n            aux_scheduler.load_state_dict(checkpoint['discr_scheduler'])\n        if 'epoch' in checkpoint:\n            start_epoch = checkpoint['epoch'] + 1\n\n    #  move everything to a device before training\n    logging.info('Transfer models&optimizers to a device')\n    transfer_model_and_optimizer(seg_model, optimizer, device)\n    transfer_model_and_optimizer(aux_model, aux_optimizer, device)\n    transfer_model_and_optimizer(discr_model, discr_optimizer, device)\n    \n    #  initialize the SummaryWriter\n    logging.info('Initializing the SummaryWriter')\n    summary_path = os.path.join(log_dir, model_name)\n    writer = SummaryWriter(log_dir=summary_path)\n\n    #  initialize loss, accuracy values\n    running_src_seg_loss = 0.0\n    running_tar_aux_loss = 0.0\n    running_tar_fake_loss = 0.0\n    running_domain_loss = 0.0\n    running_cm = torch.zeros(NUM_CLASSES, NUM_CLASSES, device=device)\n\n    #  train the model\n    logging.info('Start training')\n    seg_model.train()\n    aux_model.train()\n    epoch = 0\n    batch_idx = 0\n    for num_iter in range(start_epoch*len(source_train_dataloader), \n                          max_epochs*len(source_train_dataloader)):\n        if num_iter % len(source_train_dataloader) == 0:\n            epoch = num_iter // len(source_train_dataloader)\n            logging.info(f'Epoch: {epoch}/{max_epochs}')\n        batch_idx = num_iter % len(source_train_dataloader)\n        \n        #  zero the parameter gradients\n        optimizer.zero_grad()\n        aux_optimizer.zero_grad()\n        \n        #  forward pass TARGET image for the pretext head\n        #  load target data\n        _, data = next(inf_target_train_dataloader)\n        aux_image_real, aux_image_perturbed, name = data['aux_image_real'], data['aux_image_perturbed'], data['name']\n        aux_image = torch.cat([aux_image_real, aux_image_perturbed]).to(device)\n        idxs = get_randperm(train_batch_size*grid_size[0]*grid_size[1], \n                (train_batch_size*grid_size[0]*grid_size[1])//2)\n        idxs = torch.cat([idxs, idxs + train_batch_size * grid_size[0] * grid_size[1]])\n        aux_image = torch.cat(list(aux_image))[idxs]\n    \n        #  forward pass encoder + segmentation\n        features = seg_model.backbone(aux_image)\n        output = seg_model.classifier(features['out'])\n        output = F.interpolate(output, aux_image.shape[-2:], \n                    mode='bilinear', align_corners=False)\n\n        if aux_injection_point == 'output':\n            aux_output = aux_model(output)\n        else:\n            aux_output = aux_model(features['out'])\n        aux_output.squeeze_()\n        \n        # sample batch of negative samples from the memory bank\n        negative_batch = memory_bank.sample_batch()\n        \n        if negative_batch is not None:\n            #  backprop aux_model\n            tar_aux_loss = lambda_aux * aux_loss(aux_output, negative_batch.to(device))\n            tar_aux_loss.backward()\n\n        # dump image features in the memory bank\n        memory_bank.update(aux_output.detach())\n        \n        #  forward pass TARGET image for the domain discrimination head\n        image = data['image']\n        image = image.to(device)\n\n        #  forward pass encoder + segmentation\n        features = seg_model.backbone(image)\n        output = seg_model.classifier(features['out'])\n        output = F.interpolate(output, image_size, \n                    mode='bilinear', align_corners=False)\n\n        #  forward pass dicriminator to compute fake_loss\n        for param in discr_model.parameters():\n            param.requires_grad = False\n        if da_injection_point == 'output':\n            da_output = discr_model(output)\n        else:\n            da_output = discr_model(features['out'])\n\n        #  backprop encoder + segmentation\n        domain_label = torch.full_like(da_output, src_label, device=device)\n        tar_fake_loss = lambda_da * discr_loss(da_output, domain_label)\n        tar_fake_loss.backward()\n\n        #  forward pass dicriminator to compute classification loss\n        for param in discr_model.parameters():\n            param.requires_grad = True\n        discr_model.zero_grad()\n        if da_injection_point == 'output':\n            da_output = discr_model(output.detach())\n        else:\n            da_output = discr_model(features['out'].detach())\n\n        #  backprop discriminator\n        domain_label.fill_(tar_label)\n        tar_domain_loss = 0.5 * discr_loss(da_output, domain_label)\n        tar_domain_loss.backward()\n\n        #  forward pass SOURCE image\n        #  load source data\n        _, data = next(inf_source_train_dataloader)\n        image, gt, name = data['image'], data['gt'], data['name']\n        image = image.to(device)\n        gt = gt.to(device)\n\n        #  forward pass encoder + segmentation\n        features = seg_model.backbone(image)\n        output = seg_model.classifier(features['out'])\n        output = F.interpolate(output, image_size, \n                    mode='bilinear', align_corners=False)\n\n        #  backprop encoder + segmentation\n        src_seg_loss = seg_loss(output, gt)\n        src_seg_loss.backward()\n        \n        #  forward pass dicriminator\n        for param in discr_model.parameters():\n            param.requires_grad = True\n        if da_injection_point == 'output':\n            da_output = discr_model(output.detach())\n        else:\n            da_output = discr_model(features['out'].detach())\n\n        #  backprop discriminator\n        domain_label.fill_(src_label)\n        src_domain_loss = 0.5 * discr_loss(da_output, domain_label)\n        src_domain_loss.backward()\n\n        optimizer.step()\n        aux_optimizer.step()\n        discr_optimizer.step()\n        \n        #  update metrics\n        with torch.no_grad():\n            running_src_seg_loss += src_seg_loss.item()\n            running_tar_aux_loss += tar_aux_loss.item() if negative_batch is not None else 0.0\n            running_tar_fake_loss += tar_fake_loss.item()\n            running_domain_loss += (tar_domain_loss.item() + src_domain_loss.item())\n            src_seg_pred = output.argmax(1)\n            running_cm += fast_hist(src_seg_pred, gt, NUM_CLASSES)\n\n        #  update the training summary stats: metrics and images\n        if (batch_idx + 1) % display_step == 0:\n            with torch.no_grad():\n                #  train\n                pixel_train_acc = pixel_acc(running_cm)\n                classwise_train_iou = per_class_iou(running_cm)\n\n                #  val\n                src_val_metrics = evaluate_segmentation_set(inf_source_val_dataloader, batches_to_eval_train, \n                    seg_model, seg_loss, NUM_CLASSES, device)\n                tar_val_metrics = evaluate_segmentation_set(inf_target_val_dataloader, batches_to_eval_train, \n                    seg_model, seg_loss, NUM_CLASSES, device)\n\n            logging.info('Iteration {}/{}: Loss: {:.4f} | Pixel accuracy: {:.4f}'.format(batch_idx+1, \n                                                                                         len(source_train_dataloader), \n                                                                                         src_seg_loss.item(), \n                                                                                         pixel_train_acc))\n\n            #  write summary\n            writer.add_scalars('Seg_loss', \n                {'training_src_seg_loss': running_src_seg_loss/display_step,\n                 'validation_src_seg_loss': src_val_metrics['loss'],\n                 'validation_tar_seg_loss': tar_val_metrics['loss']},\n                num_iter)\n            writer.add_scalars('Aux_loss', \n                {'training_tar_fake_loss': running_tar_aux_loss/lambda_aux/display_step},\n                num_iter)\n            writer.add_scalars('Discr_loss', \n                {'training_tar_fake_loss': running_tar_fake_loss/lambda_da/display_step,\n                 'training_domain_loss': running_domain_loss/display_step},\n                num_iter)\n            writer.add_scalars('Pixel_iou',\n                {'training_src_pixel_acc': pixel_train_acc,\n                 'validation_src_pixel_acc': src_val_metrics['pixel_accuracy'],\n                 'validation_target_pixel_acc': tar_val_metrics['pixel_accuracy']},\n                num_iter)\n            for i in range(NUM_CLASSES):\n                writer.add_scalar('Class_iou/{}'.format(CLASSES[i]),\n                    classwise_train_iou[i] if not classwise_train_iou[i] == float('nan') else 0.0,\n                    num_iter)\n\n            #  reset loss, accuracy values\n            running_src_seg_loss = 0.0\n            running_tar_aux_loss = 0.0\n            running_tar_fake_loss = 0.0\n            running_domain_loss = 0.0\n            running_cm.fill_(0.0)\n\n        #  write images to the summary (4 times per epoch)\n        if (batch_idx + 1) == len(source_train_dataloader) or \\\n            (batch_idx + 1) == len(source_train_dataloader) // 2 or \\\n            (batch_idx + 1) == len(source_train_dataloader) // 4 or \\\n            (batch_idx + 1) == 3 * len(source_train_dataloader) // 4: \n            logging.info('Writing summary images')\n            with torch.no_grad():\n                #  train\n                for i in range(len(image)):\n                    train_src_summary_image = compile_summary_image(image[i], output[i], gt[i], \n                        GTA5_MEAN, GTA5_STD, GTA5_LABELS2TRAIN, GTA5_LABELS2PALETTE, scale_factor=0.5)\n                    writer.add_image('Train/Src',\n                        img_tensor=train_src_summary_image, \n                        global_step=num_iter+i, \n                        walltime=None, \n                        dataformats='CHW')\n                #  val\n                write_summary_images(writer, inf_source_val_dataloader, \n                    GTA5_MEAN, GTA5_STD, GTA5_LABELS2TRAIN, GTA5_LABELS2PALETTE, \n                    seg_model, device, \n                    num_batches=1, tag='Val/Src', scale_factor=0.5, global_step=num_iter)\n                write_summary_images(writer, inf_target_val_dataloader, \n                    CITYSCAPES_MEAN, CITYSCAPES_STD, CITYSCAPES_LABELS2TRAIN, CITYSCAPES_LABELS2PALETTE, \n                    seg_model, device, \n                    num_batches=1, tag='Val/Tar', scale_factor=0.5, global_step=num_iter)\n        \n        #  end_of_epoch\n        if (num_iter + 1) % len(source_train_dataloader) == 0:\n            #  decay lr\n            logging.info('LR update')\n            scheduler.step()\n            aux_scheduler.step()\n            discr_scheduler.step()\n\n            #  save the model    \n            if (epoch + 1) % save_step == 0:\n                logging.info('Save the model')\n                save_path = os.path.join(snapshots_dir, model_name, 'checkpoint_{}.pth'.format(epoch+1))\n                torch.save({\n                    'seg_model': seg_model.state_dict(),\n                    'aux_model': aux_model.state_dict(),\n                    'discr_model': discr_model.state_dict(),\n                    'optimizer': optimizer.state_dict(),\n                    'aux_optimizer': aux_optimizer.state_dict(),\n                    'discr_optimizer': discr_optimizer.state_dict(),\n                    'scheduler': scheduler.state_dict(),\n                    'aux_scheduler': aux_scheduler.state_dict(),\n                    'discr_scheduler': discr_scheduler.state_dict(),\n                    'epoch': epoch}, save_path)\n        \n    #  evaluate the model\n    seg_model.eval()\n    aux_model.eval()\n    discr_model.eval()\n    with torch.no_grad():\n        logging.info('Evaluate src')\n        src_val_metrics = evaluate_segmentation_set(inf_source_val_dataloader, len(source_val_dataloader), \n                            seg_model, seg_loss, NUM_CLASSES, device)\n        logging.info('Evaluate tar')\n        tar_val_metrics = evaluate_segmentation_set(inf_target_val_dataloader, len(target_val_dataloader), \n                            seg_model, seg_loss, NUM_CLASSES, device)\n    \n    #  write results\n    results = dict()\n    results.update({'src_'+k: v for k, v in src_val_metrics.items()})\n    results.update({'tar_'+k: v for k, v in tar_val_metrics.items()})\n\n    if seed > 0:\n        set_seed(seed)\n    \n    #  create visualizations: t-SNE and qualititative predictions\n    predictions_path = os.path.join(pred_dir, model_name)\n    with torch.no_grad():\n        logging.info('Write predcitions for src')\n        write_predictions(source_val_dataset, \n            seg_model, device, \n            batches_to_visualize*val_batch_size, predictions_path, \n            GTA5_MEAN, GTA5_STD, GTA5_LABELS2TRAIN, \n            GTA5_LABELS2PALETTE, prefix='src_')\n        logging.info('Write predcitions for tar')\n        write_predictions(target_val_dataset, \n            seg_model, device, \n            batches_to_visualize*val_batch_size, predictions_path,\n            CITYSCAPES_MEAN, CITYSCAPES_STD, CITYSCAPES_LABELS2TRAIN,\n            CITYSCAPES_LABELS2PALETTE, prefix='tar_')\n        logging.info('Create t-SNE embeddings')\n        create_embeddings(writer, source_val_dataset, target_val_dataset,\n            seg_model, device, \n            batches_to_visualize*val_batch_size, points_to_sample)\n    \n    # the returned result will be written into the database\n    return results\n","sub_path":"segmentation_pirl2_da.py","file_name":"segmentation_pirl2_da.py","file_ext":"py","file_size_in_byte":25863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"37960434","text":"# -*- coding: utf-8 -*-\r\nfrom selenium.webdriver.support.ui import Select\r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nfrom selenium.webdriver.support import expected_conditions as EC\r\nfrom selenium.webdriver.common.by import By\r\nimport os\r\nfrom model.contact import Contact\r\nimport re\r\n\r\n\r\nclass ContactManage:\r\n\r\n    def __init__(self, gen):\r\n        self.gen = gen\r\n\r\n    def open_home_page(self):\r\n        wd = self.gen.wd\r\n        if \"?group=\" in wd.current_url and len(wd.find_elements_by_name('searchstring')) > 0:\r\n            self.select_from_list(list_name=\"group\", text=\"[all]\")\r\n            self.contact_cache = None\r\n        elif not (wd.current_url.endswith(\"/addressbook/\") and len(wd.find_elements_by_name('searchstring')) > 0):\r\n            wd.find_element_by_link_text(\"home\").click()\r\n        WebDriverWait(wd, 10).until(EC.presence_of_element_located((By.LINK_TEXT, 'Last name')))\r\n\r\n    def select_all_contact(self):\r\n        wd = self.gen.wd\r\n        # get contact count\r\n        contact_count = self.get_contact_count()\r\n        for i in range(contact_count):\r\n            wd.find_element_by_xpath(\"(//input[@name='selected[]'])[%s]\" % (i + 1)).click()\r\n\r\n    def get_contact_count(self, group_name=None):\r\n        wd = self.gen.wd\r\n        # wd.find_element_by_link_text(\"home\").click()\r\n        if group_name is not None:\r\n            self.open_contact_group(group_name)\r\n        else:\r\n            self.open_home_page()\r\n        return len(wd.find_elements_by_name(\"selected[]\"))\r\n\r\n    def get_result_count(self, search, group_name=None):\r\n        wd = self.gen.wd\r\n        self.open_home_page()\r\n        if group_name is not None:  # if search in defined group\r\n            self.select_from_list(\"group\", group_name)\r\n        self.set_field_value(\"searchstring\", search)\r\n        return int(wd.find_element_by_id(\"search_count\").text)\r\n\r\n    def enter_contact_parameters(self, contact):\r\n        wd = self.gen.wd\r\n        # contact name\r\n        self.set_field_value(\"firstname\", contact.first_name)\r\n        self.set_field_value(\"middlename\", contact.middle_name)\r\n        self.set_field_value(\"lastname\", contact.last_name)\r\n        self.set_field_value(\"nickname\", contact.nickname)\r\n\r\n        # add photo\r\n        if contact.photo_path:\r\n            photo = wd.find_element_by_name(\"photo\")\r\n            photo.send_keys(os.path.join(\r\n                os.path.dirname(os.path.abspath(__file__)), \"..\\\\data\\\\contact_foto\\\\%s\" % contact.photo_path))\r\n\r\n        if contact.del_foto:\r\n            wd.find_element_by_name(\"delete_photo\").click()\r\n\r\n        # common info (company, address,title, etc.)\r\n        self.set_field_value(\"title\", contact.title)\r\n        self.set_field_value(\"company\", contact.company)\r\n        self.set_field_value(\"address\", contact.address)\r\n        # phone info\r\n        self.set_field_value(\"home\", contact.home_phone)\r\n        self.set_field_value(\"mobile\", contact.mobile_phone)\r\n        self.set_field_value(\"work\", contact.work_phone)\r\n        self.set_field_value(\"fax\", contact.fax)\r\n\r\n        # email info\r\n        self.set_field_value(\"email\", contact.primary_email)\r\n        self.set_field_value(\"email2\", contact.secondary_email)\r\n        self.set_field_value(\"email3\", contact.third_email)\r\n        self.set_field_value(\"homepage\", contact.homepage)\r\n\r\n        # birthday info\r\n        self.select_from_list(\"bday\", contact.birth_day)\r\n        self.select_from_list(\"bmonth\", contact.birth_month)\r\n        self.set_field_value(\"byear\", contact.birth_year)\r\n\r\n        # anniversary info\r\n        self.select_from_list(\"aday\", contact.anniversary_day)\r\n        self.select_from_list(\"amonth\", contact.anniversary_month)\r\n        self.set_field_value(\"ayear\", contact.anniversary_year)\r\n\r\n        # select group using group_id\r\n        if contact.group_name:\r\n            self.select_from_list_by_id(\"new_group\", contact.group_name)\r\n\r\n        # secondary info\r\n        self.set_field_value(\"address2\", contact.secondary_address)\r\n        self.set_field_value(\"phone2\", contact.secondary_home_phone)\r\n        self.set_field_value(\"notes\", contact.notes)\r\n\r\n    def select_from_list(self, list_name, text):\r\n        wd = self.gen.wd\r\n        if text is not None:\r\n            wd.find_element_by_name(list_name).click()\r\n            Select(wd.find_element_by_name(list_name)).select_by_visible_text(text)\r\n\r\n    def select_from_list_by_id(self, list_name, id):\r\n        wd = self.gen.wd\r\n        if id is not None:\r\n            wd.find_element_by_name(list_name).click()\r\n            wd.find_element_by_css_selector(\"select[name='%s'] > option[value='%s']\" % (list_name, id)).click()\r\n\r\n    def set_field_value(self, field_name, text):\r\n        wd = self.gen.wd\r\n        if text is not None:\r\n            wd.find_element_by_name(field_name).click()\r\n            wd.find_element_by_name(field_name).clear()\r\n            wd.find_element_by_name(field_name).send_keys(text)\r\n\r\n    def create(self, contact):\r\n        wd = self.gen.wd\r\n        self.init_create_contact()\r\n        # enter contact parameters\r\n        self.enter_contact_parameters(contact)\r\n        # submit contact creation\r\n        wd.find_element_by_xpath(\"(//input[@name='submit'])[2]\").click()\r\n        self.return_to_homepage()\r\n        self.contact_cache = None\r\n\r\n    def init_create_contact(self):\r\n        wd = self.gen.wd\r\n        if not (wd.current_url.endswith(\"/edit.php\") and len(wd.find_elements_by_name(\"submit\")) > 0):\r\n            wd.find_element_by_link_text(\"add new\").click()\r\n\r\n    def return_to_homepage(self):\r\n        wd = self.gen.wd\r\n        wd.find_element_by_link_text(\"home page\").click()\r\n        WebDriverWait(wd, 10).until(EC.presence_of_element_located((By.LINK_TEXT, 'Last name')))\r\n\r\n    def select_first_contact(self):\r\n        wd = self.gen.wd\r\n        wd.find_element_by_name(\"selected[]\").click()\r\n\r\n    def select_contact_by_index(self, index):\r\n        wd = self.gen.wd\r\n        wd.find_elements_by_name(\"selected[]\")[index].click()\r\n\r\n    def select_contact_by_id(self, id):\r\n        wd = self.gen.wd\r\n        wd.find_element_by_css_selector(\"input[value='%s']\" % id).click()\r\n\r\n    def create_empty(self):\r\n        wd = self.gen.wd\r\n        # init new contact creation\r\n        self.init_create_contact()\r\n        wd.find_element_by_xpath(\"(//input[@name='submit'])[2]\").click()\r\n        self.return_to_homepage()\r\n        self.contact_cache = None\r\n\r\n    def del_first(self):\r\n        self.del_by_index(0)\r\n\r\n    def del_by_index(self, index):\r\n        wd = self.gen.wd\r\n        self.open_home_page()\r\n        # select first contact & submit deletion\r\n        self.select_contact_by_index(index)\r\n        wd.find_element_by_xpath(\"(//input[@value='Delete'])\").click()\r\n        wd.switch_to_alert().accept()\r\n        self.wait_close_message_box()\r\n        self.contact_cache = None\r\n\r\n    def del_by_id(self, id):\r\n        wd = self.gen.wd\r\n        self.open_home_page()\r\n        # select first contact & submit deletion\r\n        self.select_contact_by_id(id)\r\n        wd.find_element_by_xpath(\"(//input[@value='Delete'])\").click()\r\n        wd.switch_to_alert().accept()\r\n        self.wait_close_message_box()\r\n        self.contact_cache = None\r\n\r\n    def wait_close_message_box(self):\r\n        wd = self.gen.wd\r\n        try:\r\n            WebDriverWait(wd, 10).until(EC.invisibility_of_element((By.CLASS_NAME, 'msgbox')))\r\n        except:\r\n            print(\"Failed to return to homepage\")\r\n\r\n    def cancel_del_first(self):\r\n        self.cancel_del_by_index(0)\r\n\r\n    def cancel_del_by_index(self, index):\r\n        wd = self.gen.wd\r\n        self.open_home_page()\r\n        # select contact & submit deletion\r\n        self.select_contact_by_index(index)\r\n        wd.find_element_by_xpath(\"(//input[@value='Delete'])\").click()\r\n        wd.switch_to_alert().dismiss()\r\n        self.contact_cache = None\r\n\r\n    def cancel_del_by_id(self, id):\r\n        wd = self.gen.wd\r\n        self.open_home_page()\r\n        # select contact & submit deletion\r\n        self.select_contact_by_id(id)\r\n        wd.find_element_by_xpath(\"(//input[@value='Delete'])\").click()\r\n        wd.switch_to_alert().dismiss()\r\n        self.contact_cache = None\r\n\r\n    def del_by_select_all(self):\r\n        wd = self.gen.wd\r\n        self.open_home_page()\r\n        # click \"select all\" & submit deletion\r\n        wd.find_element_by_xpath(\"(//input[@id='MassCB'])\").click()\r\n        wd.find_element_by_xpath(\"(//input[@value='Delete'])\").click()\r\n        wd.switch_to_alert().accept()\r\n        self.wait_close_message_box()\r\n        self.contact_cache = None\r\n\r\n    def del_all_by_click(self):\r\n        wd = self.gen.wd\r\n        self.open_home_page()\r\n        self.select_all_contact()\r\n        wd.find_element_by_xpath(\"(//input[@value='Delete'])\").click()\r\n        wd.switch_to_alert().accept()\r\n        self.wait_close_message_box()\r\n        self.contact_cache = None\r\n\r\n    def del_unselected(self):\r\n        wd = self.gen.wd\r\n        self.open_home_page()\r\n        wd.find_element_by_xpath(\"(//input[@value='Delete'])\").click()\r\n        wd.switch_to_alert().accept()\r\n        self.contact_cache = None\r\n\r\n    def del_all_from_group(self, group_id):\r\n        wd = self.gen.wd\r\n        self.open_contact_group(group_id)\r\n        # click \"select all\" & submit deletion\r\n        wd.find_element_by_xpath(\"(//input[@id='MassCB'])\").click()\r\n        wd.find_element_by_xpath(\"(//input[@value='Delete'])\").click()\r\n        wd.switch_to_alert().accept()\r\n        self.wait_close_message_box()\r\n        self.contact_cache = None\r\n\r\n    def open_contact_group(self, group_id):\r\n        wd = self.gen.wd\r\n        if len(wd.find_elements_by_xpath(\"(//input[@id='MassCB'])\")) > 0 and \\\r\n                len(wd.find_elements_by_name('searchstring')) > 0:\r\n            if not (wd.current_url.endswith(\"addressbook/?group=\" + group_id)):\r\n                self.select_from_list_by_id(\"group\", group_id)\r\n                self.contact_cache = None\r\n        else:\r\n            wd.find_element_by_link_text(\"home\").click()\r\n            self.select_from_list_by_id(\"group\", group_id)\r\n            self.contact_cache = None\r\n\r\n    def get_group_id(self, group):\r\n        wd = self.gen.wd\r\n        # todo: get id from fixture group. Maybe should use wd.back?\r\n        if wd.find_element_by_xpath('//select[@name=\"group\"]'):\r\n            select = Select(wd.find_element_by_xpath('//select[@name=\"group\"]'))\r\n            for option in select.options:\r\n                if option.text == group:\r\n                    return str(option.get_attribute('value'))\r\n\r\n    def del_first_from_group(self, group_name):\r\n        self.del_from_group_by_index(0, group_name)\r\n\r\n    def del_from_group_by_index(self, index, group_name):\r\n        wd = self.gen.wd\r\n        self.open_contact_group(group_name)\r\n        # click \"select all\" & submit deletion\r\n        self.select_contact_by_index(index)\r\n        wd.find_element_by_xpath(\"(//input[@value='Delete'])\").click()\r\n        wd.switch_to_alert().accept()\r\n        self.wait_close_message_box()\r\n        self.contact_cache = None\r\n\r\n    def del_from_group_by_id(self, contact_id, group_id):\r\n        wd = self.gen.wd\r\n        self.open_contact_group(group_id)\r\n        self.select_contact_by_id(contact_id)\r\n        wd.find_element_by_xpath(\"(//input[@value='Delete'])\").click()\r\n        wd.switch_to_alert().accept()\r\n        self.wait_close_message_box()\r\n        self.contact_cache = None\r\n\r\n    def del_first_using_edit(self):\r\n        self.del_using_edit_by_index(0)\r\n\r\n    def del_using_edit_by_index(self, index):\r\n        wd = self.gen.wd\r\n        self.open_edit(index)\r\n        wd.find_element_by_xpath(\"//input[@value='Delete']\").click()\r\n        self.wait_close_message_box()\r\n        self.contact_cache = None\r\n\r\n    def del_using_edit_by_id(self, id):\r\n        wd = self.gen.wd\r\n        self.open_edit_by_id(id)\r\n        wd.find_element_by_xpath(\"//input[@value='Delete']\").click()\r\n        self.wait_close_message_box()\r\n        self.contact_cache = None\r\n\r\n    def open_edit(self, index, group=None, search=None):\r\n        wd = self.gen.wd\r\n        if group is not None:\r\n            self.open_contact_group(group)\r\n        else:\r\n            self.open_home_page()\r\n        if search is not None:\r\n            self.set_field_value(\"searchstring\", search)\r\n        self.click_pencil_img(index)\r\n\r\n    def open_edit_by_id(self, id, group=None, search=None):\r\n        wd = self.gen.wd\r\n        if group is not None:\r\n            self.open_contact_group(group)\r\n        else:\r\n            self.open_home_page()\r\n        if search is not None:\r\n            self.set_field_value(\"searchstring\", search)\r\n        self.click_pencil_by_id(id)\r\n\r\n    def edit_first(self, contact):\r\n        self.edit_by_index(0, contact)\r\n\r\n    def edit_by_index(self, index, contact):\r\n        wd = self.gen.wd\r\n        self.open_edit(index)\r\n        self.enter_contact_parameters(contact)\r\n        wd.find_element_by_name(\"update\").click()\r\n        self.return_to_homepage()\r\n        self.contact_cache = None\r\n\r\n    def edit_by_id(self, id, contact):\r\n        wd = self.gen.wd\r\n        self.open_edit_by_id(id)\r\n        self.enter_contact_parameters(contact)\r\n        wd.find_element_by_name(\"update\").click()\r\n        self.return_to_homepage()\r\n        self.contact_cache = None\r\n\r\n    def edit_first_wo_change(self):\r\n        self.edit_wo_change_by_index(0)\r\n\r\n    def edit_wo_change_by_index(self, index):\r\n        wd = self.gen.wd\r\n        self.open_edit(index)\r\n        wd.find_element_by_name(\"update\").click()\r\n        self.return_to_homepage()\r\n        self.contact_cache = None\r\n\r\n    def edit_wo_change_by_id(self, id):\r\n        wd = self.gen.wd\r\n        self.open_edit_by_id(id)\r\n        wd.find_element_by_name(\"update\").click()\r\n        self.return_to_homepage()\r\n        self.contact_cache = None\r\n\r\n    def edit_first_from_details(self, contact):\r\n        wd = self.gen.wd\r\n        self.edit_from_details_by_index(0, contact)\r\n\r\n    def edit_from_details_by_index(self, index, contact):\r\n        wd = self.gen.wd\r\n        self.open_details(index)\r\n        wd.find_element_by_name(\"modifiy\").click()\r\n        self.enter_contact_parameters(contact)\r\n        wd.find_element_by_name(\"update\").click()\r\n        self.return_to_homepage()\r\n        self.contact_cache = None\r\n\r\n    def edit_from_details_by_id(self, id, contact):\r\n        wd = self.gen.wd\r\n        self.open_home_page()\r\n        self.open_details_by_id(id)\r\n        wd.find_element_by_name(\"modifiy\").click()\r\n        self.enter_contact_parameters(contact)\r\n        wd.find_element_by_name(\"update\").click()\r\n        self.return_to_homepage()\r\n        self.contact_cache = None\r\n\r\n    def open_details(self, index):\r\n        wd = self.gen.wd\r\n        self.open_home_page()\r\n        wd.find_elements_by_xpath(\"//img[@alt='Details']\")[index].click()\r\n\r\n    def open_details_by_id(self, id):\r\n        wd = self.gen.wd\r\n        wd.find_element_by_xpath(\"//a[@href='view.php?id=%s']\" % id).click()\r\n\r\n    def edit_first_in_group(self, group_name, contact):\r\n        self.edit_in_group_by_index(0, group_name, contact)\r\n\r\n    def edit_in_group_by_index(self, index, group_name, contact):\r\n        wd = self.gen.wd\r\n        self.open_edit(index, group_name)\r\n        self.enter_contact_parameters(contact)\r\n        wd.find_element_by_name(\"update\").click()\r\n        self.return_to_homepage()\r\n        self.contact_cache = None\r\n\r\n    def edit_in_group_by_id(self, id, group_id, contact):\r\n        wd = self.gen.wd\r\n        self.open_edit_by_id(id, group_id)\r\n        self.enter_contact_parameters(contact)\r\n        wd.find_element_by_name(\"update\").click()\r\n        self.return_to_homepage()\r\n        self.contact_cache = None\r\n\r\n    def add_all_to_group(self, group_name):\r\n        wd = self.gen.wd\r\n        self.open_home_page()\r\n        wd.find_element_by_xpath(\"(//input[@id='MassCB'])\").click()\r\n        self.select_from_list(\"to_group\", group_name)\r\n        wd.find_element_by_name(\"add\").click()\r\n        wd.find_element_by_link_text('group page \"%s\"' % group_name).click()\r\n        self.contact_cache = None\r\n\r\n    def add_all_to_group_using_id(self, group_id):\r\n        wd = self.gen.wd\r\n        self.open_home_page()\r\n        wd.find_element_by_xpath(\"(//input[@id='MassCB'])\").click()\r\n        self.select_from_list_by_id(\"to_group\", group_id)\r\n        wd.find_element_by_name(\"add\").click()\r\n        wd.find_element_by_xpath(\"//a[@href='./?group=%s']\" % group_id).click()\r\n        self.contact_cache = None\r\n\r\n    def add_some_to_group_using_id(self, group_id, contact_id):\r\n        wd = self.gen.wd\r\n        self.open_home_page()\r\n        self.select_contact_by_id(contact_id)\r\n        self.select_from_list_by_id(\"to_group\", group_id)\r\n        wd.find_element_by_name(\"add\").click()\r\n        wd.find_element_by_xpath(\"//a[@href='./?group=%s']\" % group_id).click()\r\n        self.contact_cache = None\r\n\r\n    def add_to_group_unselected(self, group_name):\r\n        wd = self.gen.wd\r\n        self.open_home_page()\r\n        self.select_from_list(\"to_group\", group_name)\r\n        wd.find_element_by_name(\"add\").click()\r\n        self.contact_cache = None\r\n\r\n    def add_to_group_unselected_using_id(self, group_id):\r\n        wd = self.gen.wd\r\n        self.open_home_page()\r\n        self.select_from_list_by_id(\"to_group\", group_id)\r\n        wd.find_element_by_name(\"add\").click()\r\n        self.contact_cache = None\r\n\r\n    def add_to_group_from_another(self, group_from, group_to):\r\n        wd = self.gen.wd\r\n        self.open_contact_group(group_from)\r\n        wd.find_element_by_xpath(\"(//input[@id='MassCB'])\").click()\r\n        self.select_from_list(\"to_group\", group_to)\r\n        wd.find_element_by_name(\"add\").click()\r\n        wd.find_element_by_link_text('group page \"%s\"' % group_to).click()\r\n        self.contact_cache = None\r\n\r\n    def add_to_group_from_another_using_id(self, id_from, id_to):\r\n        wd = self.gen.wd\r\n        self.open_contact_group(id_from)\r\n        wd.find_element_by_xpath(\"(//input[@id='MassCB'])\").click()\r\n        self.select_from_list_by_id(\"to_group\", id_to)\r\n        wd.find_element_by_name(\"add\").click()\r\n        wd.find_element_by_xpath(\"//a[@href='./?group=%s']\" % id_to).click()\r\n        self.contact_cache = None\r\n\r\n    def add_some_contact_to_group_from_another(self, contact_id, id_from, id_to):\r\n        wd = self.gen.wd\r\n        self.open_contact_group(id_from)\r\n        self.select_contact_by_id(contact_id)\r\n        self.select_from_list_by_id(\"to_group\", id_to)\r\n        wd.find_element_by_name(\"add\").click()\r\n        wd.find_element_by_xpath(\"//a[@href='./?group=%s']\" % id_to).click()\r\n        self.contact_cache = None\r\n\r\n    def remove_from_group(self, group_name):\r\n        wd = self.gen.wd\r\n        self.open_contact_group(group_name)\r\n        wd.find_element_by_xpath(\"(//input[@id='MassCB'])\").click()\r\n        wd.find_element_by_name(\"remove\").click()\r\n        wd.find_element_by_link_text('group page \"%s\"' % group_name).click()\r\n        self.contact_cache = None\r\n\r\n    def remove_all_from_group(self, group_id):\r\n        wd = self.gen.wd\r\n        self.open_contact_group(group_id)\r\n        wd.find_element_by_xpath(\"(//input[@id='MassCB'])\").click()\r\n        wd.find_element_by_name(\"remove\").click()\r\n        wd.find_element_by_xpath(\"//a[@href='./?group=%s']\" % group_id).click()\r\n        self.contact_cache = None\r\n\r\n    def remove_some_from_group(self, group_id, contact_id):\r\n        wd = self.gen.wd\r\n        self.open_contact_group(group_id)\r\n        self.select_contact_by_id(contact_id)\r\n        wd.find_element_by_name(\"remove\").click()\r\n        wd.find_element_by_xpath(\"//a[@href='./?group=%s']\" % group_id).click()\r\n        self.contact_cache = None\r\n\r\n    def del_all_found(self, search):\r\n        wd = self.gen.wd\r\n        self.open_home_page()\r\n        # find text\r\n        self.set_field_value(\"searchstring\", search)\r\n        # click \"select all\" & submit deletion\r\n        wd.find_element_by_xpath(\"(//input[@id='MassCB'])\").click()\r\n        wd.find_element_by_xpath(\"(//input[@value='Delete'])\").click()\r\n        wd.switch_to_alert().accept()\r\n        self.wait_close_message_box()\r\n        self.contact_cache = None\r\n\r\n    def edit_first_found(self, search, contact):\r\n        wd = self.gen.wd\r\n        # find text & edit contact\r\n        self.open_edit(index=0, search=search)\r\n        self.enter_contact_parameters(contact)\r\n        wd.find_element_by_name(\"update\").click()\r\n        self.return_to_homepage()\r\n        self.contact_cache = None\r\n\r\n    def click_first_pencil_img(self, group=None):\r\n        wd = self.gen.wd\r\n        for i in range(self.get_contact_count(group)):\r\n            if wd.find_element_by_xpath(\"(//img[@alt='Edit'])[%s]\" % (i+1)):\r\n                wd.find_element_by_xpath(\"(//img[@alt='Edit'])[%s]\" % (i+1)).click()\r\n                return\r\n\r\n    def click_pencil_img(self, index):\r\n        wd = self.gen.wd\r\n        wd.find_elements_by_xpath(\"(//img[@alt='Edit'])\")[index].click()\r\n\r\n    def click_pencil_by_id(self, id):\r\n        wd = self.gen.wd\r\n        wd.find_element_by_xpath(\"//a[@href='edit.php?id=%s']\" % id).click()\r\n\r\n    contact_cache = None\r\n\r\n    def get_contact_list(self, group_id=None, search=None):\r\n        if self.contact_cache is None:\r\n            wd = self.gen.wd\r\n            if group_id is not None:\r\n                self.open_contact_group(group_id)\r\n            else:\r\n                self.open_home_page()\r\n            if search is not None:\r\n                self.set_field_value(\"searchstring\", search)\r\n            self.contact_cache = []\r\n            for i in wd.find_elements_by_name(\"entry\"):\r\n                id = i.find_element_by_name(\"selected[]\").get_attribute('value')\r\n                cells = i.find_elements_by_tag_name(\"td\")\r\n                last_name = cells[1].text\r\n                first_name = cells[2].text\r\n                all_phones = cells[5].text\r\n                all_emails = cells[4].text\r\n                address = cells[3].text\r\n                self.contact_cache.append(Contact(id=id, last_name=last_name, first_name=first_name,\r\n                                                  all_phones=all_phones, all_email=all_emails, address=address))\r\n        return list(self.contact_cache)\r\n\r\n    def get_info_from_edit(self, index):\r\n        wd = self.gen.wd\r\n        self.open_edit(index)\r\n        firstname = wd.find_element_by_name('firstname').get_attribute('value')\r\n        lastname = wd.find_element_by_name('lastname').get_attribute('value')\r\n        id = wd.find_element_by_name('id').get_attribute('value')\r\n        home = wd.find_element_by_name('home').get_attribute('value')\r\n        mobile = wd.find_element_by_name('mobile').get_attribute('value')\r\n        work = wd.find_element_by_name('work').get_attribute('value')\r\n        phone2 = wd.find_element_by_name('phone2').get_attribute('value')\r\n        email = wd.find_element_by_name('email').get_attribute('value')\r\n        email2 = wd.find_element_by_name('email2').get_attribute('value')\r\n        email3 = wd.find_element_by_name('email3').get_attribute('value')\r\n        address = wd.find_element_by_name('address').get_attribute('value')\r\n        return Contact(id=id, last_name=lastname, first_name=firstname, home_phone=home, mobile_phone=mobile,\r\n                       work_phone=work, secondary_home_phone=phone2, primary_email=email, secondary_email=email2,\r\n                       third_email=email3, address=address)\r\n\r\n    def get_info_from_details(self, index):\r\n        wd = self.gen.wd\r\n        self.open_details(index)\r\n        text = wd.find_element_by_id(\"content\").text\r\n        home = re.search(\"H: (.*)\", text).group(1)\r\n        mobile = re.search(\"M: (.*)\", text).group(1)\r\n        work = re.search(\"W: (.*)\", text).group(1)\r\n        phone = re.search(\"P: (.*)\", text).group(1)\r\n        return Contact(home_phone=home, mobile_phone=mobile,\r\n                       work_phone=work, secondary_home_phone=phone)\r\n\r\n","sub_path":"fixture/contact.py","file_name":"contact.py","file_ext":"py","file_size_in_byte":24000,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"498438590","text":"import csv\nfrom networktables import NetworkTable, NetworkTables\nimport time\n\nlog_frequency = 50\n\nclass NTLogger:\n\n    def __init__(self):\n        NetworkTables.initialize(server='10.47.74.2')\n        self.table = NetworkTable.getTable('SmartDashboard')\n        self.table.putBoolean('log', False)\n        self.logging = False\n        self.keys = []\n        self.logfile = None\n\n    def start_logging(self):\n        self.logging = True\n        self.keys = sorted(self.table.getKeys())\n        fname = str(int(time.time()))\n        self.logfile = open(fname, 'w')\n        self.writer = csv.writer(self.logfile)\n        self.writer.writerow(self.keys)\n        self.logfile.flush()\n\n    def stop_logging(self):\n        self.logging = False\n        self.keys = []\n        self.writer = None\n        if self.logfile:\n            self.logfile.close()\n            self.logfile = None\n\n    def write_table(self):\n        to_write = \"\"\n        values = [self.table.getValue(key) for key in self.keys]\n        self.writer.writerow(values)\n        self.logfile.flush()\n\n    def loop(self):\n        log = self.table.getValue('log')\n        print(log)\n        if self.logging and not log:\n            self.stop_logging()\n        elif self.logging:\n            self.write_table()\n        elif log:\n            self.start_logging()\n\nif __name__ == \"__main__\":\n    logger = NTLogger()\n    last_tm = time.time()\n    while True:\n        try:\n            time.sleep((1/log_frequency) - (time.time()-last_tm))\n        except ValueError as e:\n            print(e)\n        logger.loop()\n        last_tm = time.time()\n","sub_path":"logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":1595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"517624242","text":"from django.conf.urls import include, url\nfrom . import views\n\nurlpatterns = [\n    # Examples:\n    # url(r'^$', 'mysite.views.home', name='home'),\n    # url(r'^blog/', include('blog.urls')),\n    \n    url(r'^$', views.plist, name='plist'),\n    url(r'^(?P<question_id>\\d+)/$', views.context, name='context'),\n    url(r'^(?P<question_id>\\d+)/vote/$', views.vote, name='vote'),\n    url(r'^(?P<question_id>\\d+)/result/$', views.result, name='resilt'),\n]\n","sub_path":"mysite/pools/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"489051064","text":"# -*- coding: utf-8 -*-\n\nfrom django.forms import widgets\nfrom djangoplus.ui.components.calendar.widgets import *\nfrom djangoplus.ui.components.editor.widgets import *\nfrom djangoplus.ui.components.select.widgets import *\n\n# Base Widgets #\n\n\nclass TextInput(widgets.TextInput):\n    def render(self, name, value, attrs={}):\n        attrs.update(**{'class':'form-control {}'.format(self.attrs.get('class', ''))})\n        return super(TextInput, self).render(name, value, attrs)\n\n\nclass EmailInput(TextInput):\n    def render(self, name, value, attrs={}):\n        attrs['type'] = 'email'\n        html = super(EmailInput, self).render(name, value, attrs)\n        html = '''\n        <div class=\"input-group\">\n            {}\n            <span class=\"input-group-addon\"><i class=\"fa fa-at\"></i></span>\n        </div>\n        '''.format(html)\n        return html\n\n\nclass UrlInput(TextInput):\n    def render(self, name, value, attrs={}):\n        attrs['type'] = 'url'\n        html = super(UrlInput, self).render(name, value, attrs)\n        html = '''\n        <div class=\"input-group\">\n            {}\n            <span class=\"input-group-addon\"><i class=\"fa fa-link\"></i></span>\n        </div>\n        '''.format(html)\n        return html\n\n\nclass AddressInput(TextInput):\n    def render(self, name, value, attrs={}):\n        html = super(AddressInput, self).render(name, value, attrs)\n        html = '''\n        <div class=\"input-group\">\n            {}\n            <span class=\"input-group-addon\"><i class=\"fa fa-envelope-o\"></i></span>\n        </div>\n        '''.format(html)\n        return html\n\n\nclass MaskWidget(widgets.TextInput):\n\n    class Media:\n        js = ('/static/js/jquery.mask.min-1.7.7.js',)\n\n    def __init__(self, mask):\n        super(MaskWidget, self).__init__()\n        self.mask = mask\n\n    def render(self, name, value, attrs={}):\n        attrs['class'] = 'form-control'\n        html = super(MaskWidget, self).render(name, value, attrs)\n        script = '<script>$(\"#id_{}\").mask(\"{}\", {{clearIfNotMatch: true}});</script>'.format(name, self.mask)\n        return mark_safe('{}\\n\\n{}'.format(html, script))\n\n\nclass HiddenInput(widgets.HiddenInput):\n    pass\n\n\nclass ReadOnlyInput(TextInput):\n    def render(self, name, value, attrs=None):\n        self.attrs['readonly'] = 'readonly'\n        return super(ReadOnlyInput, self).render(name, value, attrs)\n\n\nclass DisplayInput(widgets.TextInput):\n    def __init__(self, obj):\n        super(DisplayInput, self).__init__()\n        self.obj = obj\n\n    def render(self, name, value, attrs=None):\n        return '<input class=\"form-control\" type=\"text\" value=\"{}\" disabled /><input type=\"hidden\" id=\"id_{}\" name=\"{}\" value=\"{}\"/>'.format(self.obj, name, name, self.obj.pk)\n\n\nclass Textarea(widgets.Textarea):\n    def render(self, name, value, attrs={}):\n        attrs['class'] = 'form-control'\n        if 'rows' not in attrs: # and 'rows' not in self.attrs\n            attrs['rows'] = 3\n        html = super(Textarea, self).render(name, value, attrs)\n        return mark_safe(html)\n\n\n\nclass NumberInput(widgets.NumberInput):\n    def render(self, name, value, attrs={}):\n        attrs['class'] = 'form-control'\n        html = super(NumberInput, self).render(name, value, attrs)\n        if self.max_value is not None and self.min_value is not None:\n            html = html # TODO range widget\n\n        return mark_safe(html)\n\n\nclass DecimalInput(widgets.TextInput):\n\n    class Media:\n        js = ('/static/js/jquery.mask.min-1.7.7.js',)\n\n    def render(self, name, value, attrs={}):\n        attrs['class'] = 'form-control'\n        html = super(DecimalInput, self).render(name, value, attrs)\n        html = html.replace('type=\"number\"', 'type=\"text\"')\n        js = \"<script>$('#id_{}').mask('###.###.##0,00', {{reverse: true}});</script>\".format(name)\n        return mark_safe('{}\\n{}'.format(html, js))\n\n\nclass MoneyInput(DecimalInput):\n    def render(self, name, value, attrs={}):\n        html = super(MoneyInput, self).render(name, value, attrs)\n        html = '''\n        <div class=\"input-group\">\n            {}\n            <span class=\"input-group-addon\">R$</span>\n        </div>\n        '''.format(html)\n        return mark_safe(html)\n\n\nclass CheckboxInput(widgets.CheckboxInput):\n    def render(self, *args, **kwargs):\n        html = super(CheckboxInput, self).render(*args, **kwargs)\n        html = '{}<span class=\"custom-checkbox\"></span>'.format(html)\n        return mark_safe(html)\n\n\nclass RenderableSelectMultiple(widgets.SelectMultiple):\n    def __init__(self, template_name='renderable_widget.html', *args, **kwargs):\n        super(self.__class__, self).__init__(*args, **kwargs)\n        self.template_name = template_name\n\n    def render(self, name, value, attrs=None, choices=()):\n        if value is None: value = []\n        has_id = attrs and 'id' in attrs\n        final_attrs = self.build_attrs(attrs, name=name)\n        str_values = set([str(v) for v in value])\n        i = 0\n        objects = []\n\n        qs = hasattr(self.choices.queryset, 'all') and self.choices.queryset.all() or self.choices.queryset\n        for obj in qs:\n            option_value = obj.pk\n            if has_id:\n                final_attrs = dict(final_attrs, id='{}_{}'.format(attrs['id'], i))\n            final_attrs['class'] = 'custom-checkbox'\n            cb = CheckboxInput(final_attrs, check_test=lambda value: value in str_values)\n            option_value = str(option_value)\n            rendered_cb = cb.render(name, option_value)\n            obj.widget = rendered_cb\n            i += 1\n            objects.append(obj)\n\n        return mark_safe(render_to_string(self.template_name, dict(objects=objects)))\n\n\n# File Widgets #\n\n\nclass FileInput(widgets.ClearableFileInput):\n    def render(self, *args, **kwargs):\n        widget = super(FileInput, self).render(*args, **kwargs)\n\n        tokens = widget.split('Modificar: ')\n        if len(tokens) == 2:\n            extra, input = tokens\n            extra = extra.replace('<label ', '<span class=\"custom-checkbox\"></span><label ')\n        else:\n            extra, input = '', tokens[0]\n\n        input = input[0:-2] + 'class=\"file-input\" ' + input[-2:]\n        html = '''\n            <div class=\"upload-file\">\n                 {}\n                 <label for=\"{}\" style=\"padding:5px\"></label>\n            </div>\n            <div style=\"margin-top:20px\">{}</div>\n            <script>$('#{}').change(function(){{$(this).parent().find('label').html($(this).val().split('\\\\\\\\').pop());}});</script>\n        '''.format(input, kwargs['attrs']['id'], extra, kwargs['attrs']['id'])\n        return mark_safe(html)\n\n\nclass ImageInput(widgets.ClearableFileInput):\n    def render(self, *args, **kwargs):\n        widget = super(ImageInput, self).render(*args, **kwargs)\n\n        tokens = widget.split('Modificar:')\n        if len(tokens) == 2:\n            extra, input = tokens\n            extra = extra.replace('<label ', '<span class=\"custom-checkbox\"></span><label ')\n        else:\n            extra, input = '', tokens[0]\n\n        input = input[0:-2] + 'class=\"file-input\" ' + input[-2:]\n        html = '''\n            <div class=\"upload-file\">\n                 {}\n                 <label for=\"{}\" style=\"padding:5px\"></label>\n            </div>\n            <div style=\"margin-top:20px\">{}</div>\n            <script>$('#{}').change(function(){{$(this).parent().find('label').html($(this).val().split('\\\\\\\\').pop());}});</script>\n\n        '''.format(input, kwargs['attrs']['id'], extra, kwargs['attrs']['id'])\n        return mark_safe(html)\n\n\nclass PhotoWidget(widgets.TextInput):\n    def render(self, name, value, attrs={}):\n        attrs.update(style='display:none;')\n        html = super(PhotoWidget, self).render(name, value, attrs)\n        return render_to_string('widgets/photo_widget.html', dict(name=name, html=html))\n\n\n# Regional Widgets #\n\nclass CpfWidget(MaskWidget):\n    def __init__(self):\n        super(CpfWidget, self).__init__('000.000.000-00')\n\n\nclass CnpjWidget(MaskWidget):\n    def __init__(self):\n        super(CnpjWidget, self).__init__('00.000.000/0000-00')\n\n\nclass CpfCnpjWidget(TextInput):\n\n    class Media:\n        js = ('/static/js/jquery.mask.min-1.7.7.js',)\n\n    def render(self, name, value=None, attrs={}):\n        html = super(CpfCnpjWidget, self).render(name, value, attrs)\n        html = '''\n                <div class=\"input-group\">\n                    {}\n                </div>\n                <script>\n                    var CpfCnpjMaskBehavior = function (val) {{\n                        return val.replace(/\\D/g, '').length <= 11 ? '000.000.000-009' : '00.000.000/0000-00';\n                    }},\n                    spOptions = {{\n                      clearIfNotMatch: true,\n                      onKeyPress: function(val, e, field, options) {{\n                          field.mask(CpfCnpjMaskBehavior.apply({}, arguments), options);\n                        }}\n                    }};\n\n                    $('#id_{}').mask(CpfCnpjMaskBehavior, spOptions);\n                </script>\n                '''.format(html, name)\n        return mark_safe(html)\n\n\nclass CepWidget(MaskWidget):\n    def __init__(self):\n        super(CepWidget, self).__init__('00.000-000')\n\n\nclass CarPlateWidget(MaskWidget):\n    def __init__(self):\n        super(CarPlateWidget, self).__init__('AAA-0000')\n\n\nclass PhoneWidget(TextInput):\n\n    class Media:\n        js = ('/static/js/jquery.mask.min-1.7.7.js',)\n\n    def render(self, name, value, attrs={}):\n        html = super(PhoneWidget, self).render(name, value, attrs)\n        html = '''\n        <div class=\"input-group\">\n            {}\n            <span class=\"input-group-addon\"><i class=\"fa fa-phone\"></i></span>\n        </div>\n        <script>\n            var SPMaskBehavior = function (val) {{\n              return val.replace(/\\D/g, '').length === 11 ? '(00) 00000-0000' : '(00) 0000-00009';\n            }},\n            spOptions = {{\n              clearIfNotMatch: true,\n              onKeyPress: function(val, e, field, options) {{\n                  field.mask(SPMaskBehavior.apply({{}}, arguments), options);\n                }}\n            }};\n\n            $('#id_{}').mask(SPMaskBehavior, spOptions);\n        </script>\n        '''.format(html, name)\n        return mark_safe(html)\n\n# Utilitary Fields #\n\n\nclass PasswordInput(widgets.PasswordInput):\n    def render(self, name, value, attrs={}):\n        attrs['class'] = 'form-control'\n        return super(PasswordInput, self).render(name, value, attrs)\n\n\nclass DecimalInput3(DecimalInput):\n\n    def render(self, name, value, attrs={}):\n        attrs['class'] = 'form-control'\n        html = super(DecimalInput, self).render(name, value, attrs)\n        html = html.replace('type=\"number\"', 'type=\"text\"')\n        js = \"<script>$('#id_{}').mask('#.##0,000', {{reverse: true, clearIfNotMatch: true}});</script>\".format(name)\n        return mark_safe('{}\\n{}'.format(html, js))\n\n\nclass OneDigitValidationInput(TextInput):\n\n    class Media:\n        js = ('/static/js/jquery.mask.min-1.7.7.js',)\n\n    def render(self, name, value, attrs={}):\n        attrs['class'] = 'form-control'\n        html = super(OneDigitValidationInput, self).render(name, value, attrs)\n        html = html.replace('type=\"number\"', 'type=\"text\"')\n        js = \"<script>$('#id_{}').mask('#.##0-0', {{reverse: true, clearIfNotMatch: true}});</script>\".format(name)\n        return mark_safe('{}\\n{}'.format(html, js))\n\n\nclass CreditCardWidget(MaskWidget):\n    def __init__(self):\n        super(CreditCardWidget, self).__init__('9999 9999 9999 9999')\n","sub_path":"ui/components/forms/widgets.py","file_name":"widgets.py","file_ext":"py","file_size_in_byte":11529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"456941361","text":"from collections import namedtuple\nfrom django.contrib.humanize.templatetags.humanize import intcomma\n\nfrom django import template\n\nfrom psutil import AccessDenied\n\n\ndef bytes2human(num):\n    for x in ['bytes', 'KB', 'MB', 'GB']:\n        if num < 1024.0:\n            return \"%3.1f%s\" % (num, x)\n        num /= 1024.0\n    return \"%3.1f%s\" % (num, 'TB')\n\n\nregister = template.Library()\n\n\ndef tofloat(value):\n    try:\n        return float(value)\n    except ValueError:\n        return 0\n\nregister.filter(name='tofloat', filter_func=tofloat)\n\n\nclass SysMon(template.Node):\n    def render(self, context):\n        context.update(self.build_context_dict())\n        return ''\n\n    @classmethod\n    def build_context_dict(cls, static=False):\n        context = {}\n        try:\n            import psutil as pu\n        except:\n            context['error_psutil'] = 'not_found'\n            return context\n\n        # cpu\n        cpu_info = {\n            'core': pu.cpu_count(),\n            'used': intcomma(pu.cpu_percent(), use_l10n=False),\n            }\n\n        # memory\n        mem_info = {\n            'total': bytes2human(pu.virtual_memory().total),\n            'used': intcomma(pu.virtual_memory().percent, use_l10n=False),\n            }\n\n        # disk\n        partitions = list()\n        for part in pu.disk_partitions():\n            partitions.append({\n                'device': part.device,\n                'mountpoint': part.mountpoint,\n                'fstype': part.fstype,\n                'total': bytes2human(\n                        pu.disk_usage(part.mountpoint).total),\n                'percent': intcomma(pu.disk_usage(part.mountpoint).percent,\n                                     use_l10n=False),\n                })\n\n        # network\n        networks = list()\n        for k, v in pu.net_io_counters(pernic=True).items():\n            # Skip loopback interface\n            if k == 'lo':\n                continue\n\n            networks.append({\n                'device': k,\n                'sent': bytes2human(v.bytes_sent),\n                'recv': bytes2human(v.bytes_recv),\n                'pkg_sent': v.packets_sent,\n                'pkg_recv': v.packets_recv,\n                })\n\n        # processes\n        processes = list()\n        for process in pu.process_iter():\n\n            try:\n                percent = process.memory_percent()\n            except AccessDenied:\n                percent = \"Access Denied\"\n            else:\n                percent = int(percent)\n\n            processes.append({\n                'pid': process.pid,\n                'name': process.name() if static else process.name,\n                'status': process.status() if static else process.status,\n                'user': process.username(),\n                'memory': percent,\n                # 'exe': process.exe(),\n                'cpu_percent': process.cpu_percent(0.1) if static else process.cpu_percent\n                })\n\n        processes_sorted = sorted(\n            processes, key=lambda p: p[\"memory\"], reverse=True)\n\n        context.update({\n            'cpu_info': cpu_info,\n            'mem_info': mem_info,\n            'partitions': partitions,\n            'networks': networks,\n            'processes': processes_sorted[:10],\n            })\n\n        return context\n\n\n@register.tag\ndef get_system_stats(parser, token):\n    return SysMon()\n","sub_path":"sysmon/templatetags/sysmon_tags.py","file_name":"sysmon_tags.py","file_ext":"py","file_size_in_byte":3343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"382666922","text":"# Copyright (c) 2022, MASSACHUSETTS INSTITUTE OF TECHNOLOGY\n# Subject to FAR 52.227-11 – Patent Rights – Ownership by the Contractor (May 2014).\n# SPDX-License-Identifier: MIT\n\nimport krpc\nimport time\nimport gymnasium as gym\nimport numpy as np\n\nfrom types import SimpleNamespace\nfrom typing import List\nfrom datetime import datetime\nfrom threading import Thread\n\nimport kspdg.utils.utils as U\n\n_INIT_LOADFILE = \"20220516_PursuitEvade_init\"\n_MISSION_DONE_DIST_THRESHOLD = 20.0\n\n\nclass EvasionEnvV20220705(gym.Env):\n    '''\n     A simple pursuit-evasion orbital scenario\n    \n     Agent controls the evader and pursuer has a scripted policy\n    \n     The pursuit algorithm is very simplistic:\n     the pursuer thrusts in the direction of the evader\n     \n     Objective is to avoid contact with the pursuer\n    '''\n\n    # hard-coded, static parameters for pursuer vehicle\n    # that are accessible yet constant (so shouldn't be\n    # in observation which should really only be variable values)\n    # Need for hard-coding rsc properties comes from the errors in \n    # krpc's handling of thruster objects. \n    PARAMS = SimpleNamespace()\n    PARAMS.PURSUER = SimpleNamespace()\n    PARAMS.PURSUER.RCS = SimpleNamespace()\n\n    # Specific impulse assumes all RCS thrusters are identical RV-105\n    # parts operating in vacuum\n    PARAMS.PURSUER.RCS.VACUUM_SPECIFIC_IMPULSE = 240  # [s]\n    PARAMS.PURSUER.RCS.VACUUM_MAX_THRUST_PER_NOZZLE = 1000  # [N]\n\n    # Assumed number of thrusters creating thrust in each direction\n    PARAMS.PURSUER.RCS.N_THRUSTERS_FORWARD = 8\n    PARAMS.PURSUER.RCS.N_THRUSTERS_REVERSE = 8\n    PARAMS.PURSUER.RCS.N_THRUSTERS_RIGHT = 4\n    PARAMS.PURSUER.RCS.N_THRUSTERS_LEFT = 4\n    PARAMS.PURSUER.RCS.N_THRUSTERS_UP = 4\n    PARAMS.PURSUER.RCS.N_THRUSTERS_DOWN = 4\n\n    # computed max thrust in each direction [N]\n    PARAMS.PURSUER.RCS.VACUUM_MAX_THRUST_FORWARD = \\\n        PARAMS.PURSUER.RCS.N_THRUSTERS_FORWARD * PARAMS.PURSUER.RCS.VACUUM_MAX_THRUST_PER_NOZZLE\n    PARAMS.PURSUER.RCS.VACUUM_MAX_THRUST_REVERSE = \\\n        PARAMS.PURSUER.RCS.N_THRUSTERS_REVERSE * PARAMS.PURSUER.RCS.VACUUM_MAX_THRUST_PER_NOZZLE\n    PARAMS.PURSUER.RCS.VACUUM_MAX_THRUST_RIGHT = \\\n        PARAMS.PURSUER.RCS.N_THRUSTERS_RIGHT * PARAMS.PURSUER.RCS.VACUUM_MAX_THRUST_PER_NOZZLE\n    PARAMS.PURSUER.RCS.VACUUM_MAX_THRUST_LEFT = \\\n        PARAMS.PURSUER.RCS.N_THRUSTERS_LEFT * PARAMS.PURSUER.RCS.VACUUM_MAX_THRUST_PER_NOZZLE\n    PARAMS.PURSUER.RCS.VACUUM_MAX_THRUST_UP = \\\n        PARAMS.PURSUER.RCS.N_THRUSTERS_UP * PARAMS.PURSUER.RCS.VACUUM_MAX_THRUST_PER_NOZZLE\n    PARAMS.PURSUER.RCS.VACUUM_MAX_THRUST_DOWN = \\\n        PARAMS.PURSUER.RCS.N_THRUSTERS_DOWN * PARAMS.PURSUER.RCS.VACUUM_MAX_THRUST_PER_NOZZLE\n\n    # computed maximum fuel consumption rate in each direction [kg/s]\n    PARAMS.PURSUER.RCS.VACUUM_MAX_FUEL_CONSUMPTION_FORWARD = \\\n        PARAMS.PURSUER.RCS.VACUUM_MAX_THRUST_FORWARD / (U._G0 * PARAMS.PURSUER.RCS.VACUUM_SPECIFIC_IMPULSE)\n    PARAMS.PURSUER.RCS.VACUUM_MAX_FUEL_CONSUMPTION_REVERSE = \\\n        PARAMS.PURSUER.RCS.VACUUM_MAX_THRUST_REVERSE / (U._G0 * PARAMS.PURSUER.RCS.VACUUM_SPECIFIC_IMPULSE)\n    PARAMS.PURSUER.RCS.VACUUM_MAX_FUEL_CONSUMPTION_RIGHT = \\\n        PARAMS.PURSUER.RCS.VACUUM_MAX_THRUST_RIGHT / (U._G0 * PARAMS.PURSUER.RCS.VACUUM_SPECIFIC_IMPULSE)\n    PARAMS.PURSUER.RCS.VACUUM_MAX_FUEL_CONSUMPTION_LEFT = \\\n        PARAMS.PURSUER.RCS.VACUUM_MAX_THRUST_LEFT / (U._G0 * PARAMS.PURSUER.RCS.VACUUM_SPECIFIC_IMPULSE)\n    PARAMS.PURSUER.RCS.VACUUM_MAX_FUEL_CONSUMPTION_UP = \\\n        PARAMS.PURSUER.RCS.VACUUM_MAX_THRUST_UP / (U._G0 * PARAMS.PURSUER.RCS.VACUUM_SPECIFIC_IMPULSE)\n    PARAMS.PURSUER.RCS.VACUUM_MAX_FUEL_CONSUMPTION_DOWN = \\\n        PARAMS.PURSUER.RCS.VACUUM_MAX_THRUST_DOWN / (U._G0 * PARAMS.PURSUER.RCS.VACUUM_SPECIFIC_IMPULSE)\n\n    def __init__(self):\n\n        # establish observation space\n        # TODO\n\n        # establish action space (forward, up, right, time)\n        self.action_space = gym.spaces.Box(\n            low=np.array([-1.0, -1.0, -1.0, 0.0]),\n            high=np.array([1.0, 1.0, 1.0, 10.0])\n        )\n\n        # call reset function\n        self.reset()\n\n    def reset(self):\n\n        # remove prior connection and join prior pursuit thread\n        if hasattr(self, 'conn'):\n            self.close()\n\n        # establish krpc connect to send remote commands\n        self.conn = krpc.connect(name='evasion_v20220516')\n        print(\"Connected to kRPC server\")\n\n        # Load save file from start of mission scenario\n        self.conn.space_center.load(_INIT_LOADFILE)\n\n        # get vessel objects\n        self.vesReferee, self.vesEvade, self.vesPursue = self.conn.space_center.vessels[:3]\n\n        # Set the pursuer as the active (i.e. human-controlled) vessel\n        # and target evader\n        self.conn.space_center.active_vessel = self.vesEvade\n        self.conn.space_center.target_vessel = self.vesPursue\n        print(\"Changing active vehicle...\")\n        time.sleep(1)  # give time to re-orient\n\n        # set the evader to stability assist and orient in orbit-normal direction\n        # orient pursuer in target-pointing direction\n\n        self.vesEvade.control.sas = True\n        self.vesPursue.control.sas = True\n        print(\"Activating stability assist...\")\n        time.sleep(0.1)  # give time to re-orient\n        self.vesPursue.control.sas_mode = self.vesPursue.control.sas_mode.normal\n        self.vesEvade.control.sas_mode = self.vesEvade.control.sas_mode.normal\n        print(\"Re-orienting Pursuer and Evader...\")\n        time.sleep(2)  # give time to re-orient\n\n        # actuate RCS thrusters\n        print(\"Activating reaction control systems...\")\n        self.vesEvade.control.rcs = True\n        self.vesPursue.control.rcs = True\n\n        # start process for pursuer maneuvers\n        self.stop_pursue_thread = False\n        self.pursuer_thrust = [0, 0, 0]\n        self.pursue_thread = Thread(target=self.pursue)\n        self.pursue_thread.start()\n\n        # start process for checking episode termination\n        self.is_episode_done = False\n        self.stop_episode_termination_thread = False\n        self.episode_termination_thread = Thread(target=self.check_episode_termination)\n        self.episode_termination_thread.start()\n\n        return self.get_observation()\n\n    def step(self, action):\n        ''' Apply thrust and torque actuation for specified time\n        Args:\n            action : np.ndarray\n                4-tuple of throttle values in 3D and timestep (forward, right, down, tstep)\n\n        Ref: \n            Actions are in forward, right, down to align with the right-handed version of the\n            Vessel Surface Reference Frame \n            https://krpc.github.io/krpc/tutorials/reference-frames.html#vessel-surface-reference-frame\n        '''\n\n        # parse and apply action\n        self.vesEvade.control.forward = action[0]\n        self.vesEvade.control.right = action[1]\n        self.vesEvade.control.up = -action[2]\n\n        # execute maneuver for specified time, checking for end\n        # conditions while you do\n        timeout = time.time() + action[3]\n        while True:\n            if self.is_episode_done or time.time() > timeout:\n                break\n\n        # zero out thrusts\n        self.vesEvade.control.forward = 0.0\n        self.vesEvade.control.up = 0.0\n        self.vesEvade.control.right = 0.0\n\n        # get observation\n        obs = self.get_observation()\n\n        # compute reward\n        rew = -self.pursue_evade_relative_distance()\n\n        info = {}\n\n        return obs, rew, self.is_episode_done, info\n\n    def convert_rhntw_to_rhpbody(self, v__rhntw: List[float]) -> List[float]:\n        '''Converts vector in right-handed NTW frame to pursuer vessel right-oriented body frame\n        Args:\n            v__ntw : List[float]\n                3-vector represented in orbital NTW coords\n        \n        Returns\n            v__rhpbody : List[float]\n                3-vector vector represented in pursuer's right-hadded body coords (forward, right, down)\n        \n        Ref:\n            Left-handed vessel body system: \n                https://krpc.github.io/krpc/tutorials/reference-frames.html#vessel-surface-reference-frame\n            Right-handed NTW system: Vallado, 3rd Edition Sec 3.3.3\n        '''\n\n        # convert right-handed NTW coords to left-handed NTW\n        v__lhntw = U.convert_rhntw_to_lhntw(v__rhntw=v__rhntw)\n\n        # convert left-handed NTW to left-handed vessel body coords\n        # ref: https://krpc.github.io/krpc/python/api/space-center/space-center.html#SpaceCenter.transform_direction\n        # ref: https://krpc.github.io/krpc/tutorials/reference-frames.html#vessel-surface-reference-frame\n        v__lhpbody = list(self.conn.space_center.transform_direction(\n            direction=tuple(v__lhntw),\n            from_=self.vesPursue.orbital_reference_frame,\n            to=self.vesPursue.reference_frame\n        ))\n\n        # convert left-handed body coords (right, forward, down) to right-handed body coords (forward, right, down)\n        v__rhpbody = U.convert_lhbody_to_rhbody(v__lhbody=v__lhpbody)\n\n        return v__rhpbody\n\n    def get_observation(self):\n        ''' return observation of pursuit and evader vessels from referee ref frame\n\n        Returns:\n            obs : list\n                [0] : current vehicle (pursuer) mass [kg]\n                [1] : current vehicle (pursuer) propellant  (mono prop) [kg]\n                [2:5] : pursuer position in reference orbit NTW coords \n                [5:8] : pursuer velocity in reference orbit NTW coords\n                [8:11] : evader position in reference orbit NTW coords \n                [11:14] : evader velocity in reference orbit NTW coords\n\n        Ref: \n            reference orbit coords from krpc are left-handed NTW frame(anti-radial, prograde, orbit-normal)\n            https://krpc.github.io/krpc/tutorials/reference-frames.html#vessel-orbital-reference-frame\n        '''\n\n        rf = self.vesReferee.orbital_reference_frame\n\n        obs = []\n\n        # get pursuer mass properties\n        obs.append(self.vesPursue.mass)\n        obs.append(self.vesPursue.resources.amount('MonoPropellant'))\n\n        # got pursuer and evader position and velocity in \n        # left-handed NTW frame relative to \"referee\" satellite\n        p_p_r__lhntw = list(self.vesPursue.position(rf))\n        v_p_r__lhntw = list(self.vesPursue.velocity(rf))\n        p_e_r__lhntw = list(self.vesEvade.position(rf))\n        v_e_r__lhntw = list(self.vesEvade.velocity(rf))\n\n        # convert to right-hand system and add to observation\n        p_p_r__rhntw = U.convert_lhntw_to_rhntw(p_p_r__lhntw)\n        v_p_r__rhntw = U.convert_lhntw_to_rhntw(v_p_r__lhntw)\n        p_e_r__rhntw = U.convert_lhntw_to_rhntw(p_e_r__lhntw)\n        v_e_r__rhntw = U.convert_lhntw_to_rhntw(v_e_r__lhntw)\n\n        # store observation of pursuer and evader position and velocity\n        obs.extend(p_p_r__rhntw)\n        obs.extend(v_p_r__rhntw)\n        obs.extend(p_e_r__rhntw)\n        obs.extend(v_e_r__rhntw)\n\n        return obs\n\n    def pursue_evade_relative_distance(self):\n        '''compute relative distance between pursuer and evader'''\n        p_vesE_vesP__lhpbody = self.vesEvade.position(self.vesPursue.reference_frame)\n        return np.linalg.norm(p_vesE_vesP__lhpbody)\n\n    def check_episode_termination(self) -> bool:\n        '''determine if episode termination conditions are met\n        \n        Returns:\n            bool\n                true if episode termination criteria is met\n        '''\n\n        while not self.stop_episode_termination_thread:\n            # get distance to pursuer\n            d_vesE_vesP = self.pursue_evade_relative_distance()\n            self.is_episode_done = d_vesE_vesP < _MISSION_DONE_DIST_THRESHOLD\n            if self.is_episode_done:\n                print(\"Evasion Failed!\")\n                self.stop_episode_termination_thread = True\n                self.stop_pursue_thread = True\n                self.pursue_thread.join()\n\n    def pursue(self):\n        '''use relative position of evader to thrust towards it\n                Args:\n                    vesE : krpc.Vessel\n                        krpc vessel object for evader\n                    vesP : krpc.Vessel\n                        krpc vessel object for pursuer\n                    vesR : krpc.Vessel\n                        krpc vessel object for referee\n                '''\n        while not self.stop_pursue_thread:\n\n            # get positions of evader and pursuer relative to referee\n            rf = self.vesReferee.orbital_reference_frame\n            p_vesE_vesR__lhntw = self.vesEvade.position(rf)\n            p_vesP_vesR__lhntw = self.vesPursue.position(rf)\n\n            # get position of evader relative to pursuer\n            p_vesE_vesP__lhntw = (p_vesE_vesR__lhntw[0] - p_vesP_vesR__lhntw[0],\n                                  p_vesE_vesR__lhntw[1] - p_vesP_vesR__lhntw[1],\n                                  p_vesE_vesR__lhntw[2] - p_vesP_vesR__lhntw[2])\n\n            # convert lhntw coordinates to lhpbody (centered around pursuer) coordinates\n            p_vesE_vesP__lhpbody = self.conn.space_center.transform_direction(\n                direction=p_vesE_vesP__lhntw, from_=rf, to=self.vesPursue.reference_frame)\n\n            # find the largest component of the distance between evader and pursuer\n            max_axis_parallel_dist = max(abs(p_vesE_vesP__lhpbody[0]),\n                                         abs(p_vesE_vesP__lhpbody[1]),\n                                         abs(p_vesE_vesP__lhpbody[2]))\n\n            # always thrust in direction of evader at maximum speed\n            self.vesPursue.control.right = p_vesE_vesP__lhpbody[0] / max_axis_parallel_dist\n            self.vesPursue.control.forward = p_vesE_vesP__lhpbody[1] / max_axis_parallel_dist\n            self.vesPursue.control.up = -p_vesE_vesP__lhpbody[2] / max_axis_parallel_dist\n\n            # # print thrust values for debugging\n            # if self.vesPursue.control.forward != self.pursuer_thrust[0] or \\\n            #         self.vesPursue.control.forward != self.pursuer_thrust[1] or \\\n            #         self.vesPursue.control.forward != self.pursuer_thrust[2]:\n            #     print(\"Changing pursuer thrust to ({forward:.2f}, {right:.2f}, {up:.2f})\"\n            #           .format(forward=self.vesPursue.control.forward,\n            #                   right=self.vesPursue.control.right,\n            #                   up=self.vesPursue.control.up))\n            #\n            #     self.pursuer_thrust[0] = self.vesPursue.control.forward\n            #     self.pursuer_thrust[1] = self.vesPursue.control.right\n            #     self.pursuer_thrust[2] = self.vesPursue.control.up\n\n        # Zero out pursuer thrust if target is reached\n        print(\"Zeroing pursuer thrust...\")\n        self.vesPursue.control.forward = 0\n        self.vesPursue.control.right = 0\n        self.vesPursue.control.up = 0\n\n    def close(self):\n\n        # handle evasive maneuvering thread\n        self.stop_pursue_thread = True\n        self.pursue_thread.join()\n\n        # handle episode termination thread\n        self.stop_episode_termination_thread = True\n        self.episode_termination_thread.join()\n\n        # close connection to krpc server\n        self.conn.close()\n","sub_path":"src/kspdg/pe20220516/evasion_v20220705.py","file_name":"evasion_v20220705.py","file_ext":"py","file_size_in_byte":15286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"383918948","text":"from torch.autograd import Variable\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport configs\n\n\nclass MixtureLinear(nn.Module):\n    def __init__(self):\n        super(MixtureLinear, self).__init__()\n        self.wordvec_dim = configs.wordvec_dim\n        self.total_embeds_size = configs.total_embeds_size\n\n        self.input_layer = nn.Linear(self.wordvec_dim, self.total_embeds_size, bias=False)\n        self.hidden_layer = nn.Linear(self.total_embeds_size, self.total_embeds_size, bias=True)\n\n    def forward(self, x=None, h=None):\n        if x is None:\n            return self.hidden_layer(h)\n        else:\n            return torch.add(self.input_layer(x), self.hidden_layer(h))\n\n\nclass Net(nn.Module):\n    def __init__(self):\n        super(Net, self).__init__()\n        self.depth_num = configs.depth_num\n        self.batch_size = configs.batch_size\n        self.wordvec_dim = configs.wordvec_dim\n        self.max_sequence = configs.max_sequence\n        self.total_embeds_size = configs.total_embeds_size\n\n        # Initialization\n        self.init()\n\n    def forward(self, w_state, m_state=None, masks=None):\n        return self.processor(\n            w_state=w_state.contiguous().view(w_state.size(0), self.wordvec_dim),\n            m_state=m_state,\n            masks=masks)\n\n    def init(self):\n        self.gater_list = nn.ModuleList().extend([MixtureLinear() for _ in range(self.depth_num)])\n\n        self.bilinear_list = nn.ModuleList().extend([MixtureLinear() for _ in range(self.depth_num)])\n\n    def processor(self, w_state, m_state=None, masks=None, rnn_type='RNN'):\n        \"\"\"\n        ======= [ RETURN ] ==========\n            [0] [xx]  | (xx, xx, xx)\n        \"\"\"\n        h_state = m_state[1]\n\n        # Sequential features extraction\n        for depth, (gater, bilinear) in enumerate(zip(self.gater_list, self.bilinear_list)):\n            if 'RNN' == rnn_type:\n                # Vallina RNN\n                if depth == 0:\n                    h_state = F.tanh(bilinear(x=w_state * masks[0], h=h_state * masks[1]))\n                else:\n                    h_state = F.tanh(bilinear(h=h_state * masks[1]))\n            elif 'RHN' == rnn_type:\n                # Highway RNN\n                if depth == 0:\n                    gate_value = F.sigmoid(gater(x=w_state * masks[0], h=h_state * masks[1]))\n                    nonlinear_hidden = F.tanh(bilinear(x=w_state * masks[0], h=h_state * masks[1]))\n                else:\n                    gate_value = F.sigmoid(gater(h=h_state * masks[1]))\n                    nonlinear_hidden = F.tanh(bilinear(h=h_state * masks[1]))\n                h_state = (nonlinear_hidden - h_state) * gate_value + h_state\n\n        return h_state, [None, h_state]\n\n    def init_hidden_state(self):\n        h_state = Variable(torch.zeros(self.batch_size, self.total_embeds_size),\n                           volatile=not self.training)\n        if torch.cuda.is_available():\n            h_state = h_state.cuda()\n        return h_state.float()\n\n    def init_word_state(self):\n        h_state = Variable(torch.zeros(self.batch_size, self.wordvec_dim),\n                           volatile=not self.training)\n        if torch.cuda.is_available():\n            h_state = h_state.cuda()\n        return h_state.float()\n\nif __name__ == '__main__':\n    # Settings\n    configs.batch_size = 4\n    configs.max_sequence = 4\n\n    input_data = Variable(torch.rand(\n        configs.batch_size, configs.max_sequence, configs.wordvec_dim)).cpu()\n\n    # The cpu environment\n    net = Net().cpu()\n    if torch.cuda.is_available():\n        # The gpu environment\n        net = Net().cuda()\n        input_data = input_data.cuda()\n\n\n\n\n\n","sub_path":"models/nnet_RNNCell.py","file_name":"nnet_RNNCell.py","file_ext":"py","file_size_in_byte":3670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"448823150","text":"import listener\n\n'''\n\nThe purpose of this project is to develop a proof-of-concept \"type in the air\" numeric keypad Mac application.\nI will use the Leap motion device and its technology to emulate numeric keypad functions.\n\n'''\n\n__author__ = 'Patrick Thach'\n\ndef main():\n    listener.run()\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"numpad.py","file_name":"numpad.py","file_ext":"py","file_size_in_byte":329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"116081792","text":"import json\nimport pytest\nfrom skyfield.api import Loader\nfrom django.conf import settings\n\nload = Loader(settings.EPHEM_DIR)\n\nfrom v0.time import filter_range\n\nclass Window(object):\n    ts = load.timescale()\n\n    def __init__(self, start_time, end_time):\n        self.start_time = self.ts.utc(start_time)\n        self.end_time = self.ts.utc(end_time)\n\nwindows = [\n    Window(2018, 2019),\n    Window(2019, 2020),\n    Window(2020, 2021),\n    Window(2021, 2022)\n]\n\n@pytest.mark.parametrize(\"windows,range_inclusive,expected\", [\n    (windows, \"both\", windows),\n    (windows, \"start\", windows[:-1]),\n    (windows, \"end\", windows[1:]),\n    (windows, \"neither\", windows[1:-1])\n])\ndef test_filter_range_inclusive_both(windows, range_inclusive, expected):\n    ts = load.timescale()\n    range_start = ts.utc(2019)\n    range_end = ts.utc(2021)\n    filtered = list(filter_range(windows, range_start, range_end, range_inclusive))\n    assert filtered == expected\n","sub_path":"missioncontrol/tests/test_time.py","file_name":"test_time.py","file_ext":"py","file_size_in_byte":950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"473613692","text":"\"\"\"\ndata/analysis.py\n===============\n\nAnalysis methods for gravity surveys.\n\nFor use by GSadjust, software for the interactive network adjustment of\nrelative-gravity networks\n--------------------------------------------------------------------------------\n\nThis software is preliminary, provisional, and is subject to revision. It is\nbeing provided to meet the need for timely best science. The software has not\nreceived final approval by the U.S. Geological Survey (USGS). No warranty,\nexpressed or implied, is made by the USGS or the U.S. Government as to the\nfunctionality of the software and related material nor shall the fact of release\nconstitute any such warranty. The software is provided on the condition that\nneither the USGS nor the U.S. Government shall be held liable for any damages\nresulting from the authorized or unauthorized use of the software.\n\n\"\"\"\n\nimport datetime as dt\nimport logging\nimport numpy as np\nfrom .adjustment import AdjustedStation\n\nclass InversionError(Exception):\n    pass\n\n\ndef _equal_or_truncated(a, b):\n    \"\"\"For matching Gravnet output (Gravnet truncates to 6 characters)\"\"\"\n    return a == b or a[:6] == b\n\n\ndef numpy_inversion(adjustment):\n    \"\"\"\n    Pre- and post-processing data for network adjustment.\n\n    The actual adjustment is carried out in adjustment.python_lsq_inversion()\n\n    Parameters\n    ----------\n    adjustment : Adjustment object\n        Stores information needed for the\n\n    \"\"\"\n\n    adjustment.adjustmentresults.text = []\n\n    # sta_dic_LS is a dictionary, key: station name, value: column for A matrix\n    sta_dic_ls = adjustment.sta_dic_ls\n    loop_ls_dict = adjustment.loop_ls_dict\n\n    # get number of observations:\n    n_rel_obs = len(adjustment.deltas)\n    n_abs_obs = len(adjustment.datums)\n    n_meters = adjustment.n_meters\n    ndrift = adjustment.ndrift\n\n    # dict of tuples, used to identify column of drift observation in A matrix:\n    # (loop.name, (column relative to end of A matrix, drift degree)\n    netadj_loop_keys = adjustment.netadj_loop_keys\n\n    # Initialize least squares matrices\n    # number of unknowns\n    nb_x = len(sta_dic_ls) + ndrift + n_meters\n    adjustment.adjustmentresults.n_unknowns = nb_x\n    # model matrix:\n    A = np.zeros((n_rel_obs + n_abs_obs, nb_x))\n    # weight matrix:\n    P = np.zeros((n_rel_obs + n_abs_obs, n_rel_obs + n_abs_obs))  # pas sur\n    # observation matrix:\n    Obs = np.zeros((n_rel_obs + n_abs_obs, 1))\n    # datum-free constraint vector:\n    S = np.zeros((nb_x, 1))\n\n    row = 0\n    delta_keys = []\n\n    # Populate least squares matrices\n    for delta in adjustment.deltas:\n        delta_keys.append(delta.__hash__())\n        dg = delta.dg if delta.type != \"assigned\" else delta.assigned_dg\n        Obs[row] = dg * delta.cal_coeff\n        P[row, row] = 1.0 / (delta.adj_sd ** 2)\n        A[row, sta_dic_ls[delta.sta1]] = -1\n        A[row, sta_dic_ls[delta.sta2]] = 1\n\n        # Populate 1 column per gravimeter for calibration coefficient\n        if adjustment.adjustmentoptions.cal_coeff:\n            meter = delta.meter\n            A[row, adjustment.meter_dic[meter] + len(sta_dic_ls)] = delta.dg\n\n        # Populate column(s) for drift, if included in network adjustment\n        if delta.ls_drift is not None:\n            loop_name = delta.ls_drift[0]\n\n            # It's possible for ls_drift to have been set, but the loop method to be\n            # something other than netadj\n            if loop_name:\n                if loop_ls_dict[loop_name] == \"netadj\":\n                    for i in range(delta.ls_drift[1]):  # degree of polynomial\n                        A[\n                            row,\n                            len(sta_dic_ls)\n                            + n_meters\n                            + netadj_loop_keys[loop_name][0]\n                            + i,\n                        ] = (delta.sta2_t - delta.sta1_t) ** (i + 1)\n\n        S[sta_dic_ls[delta.sta1]] = 1\n        S[sta_dic_ls[delta.sta2]] = 1\n        row += 1\n\n    # add datum observation (absolute station(s) or station(s) with fixed values)\n    # Key errors handled by calling routine\n    i = 0\n    for datum in adjustment.datums:\n        A[n_rel_obs + i, sta_dic_ls[datum.station]] = 1\n        P[n_rel_obs + i, n_rel_obs + i] = 1.0 / datum.sd ** 2\n        Obs[n_rel_obs + i] = datum.g\n        i += 1\n\n    # Do the inversion\n    adjustment.A = A\n    adjustment.P = P\n    adjustment.Obs = Obs\n    adjustment.S = S\n    adjustment.dof = n_rel_obs + n_abs_obs - nb_x\n    adjustment.g_dic = dict()\n    # zero-division errors are caught by caller\n    adjustment.python_lsq_inversion()\n\n    # Populate results\n    results, sd_all = [], []\n    for i in range(len(sta_dic_ls)):\n        for key, val in sta_dic_ls.items():\n            if val == i:\n                try:\n                    g = float(adjustment.X[i])\n                    sd = float(np.sqrt(adjustment.var[i]))\n                    t = AdjustedStation(key, g, sd)\n                    results.append(t)\n                    adjustment.g_dic[key] = g\n                    adjustment.sd_dic[key] = sd\n                    sd_all.append(sd)\n                except Exception:\n                    raise InversionError(\"Bad variance in results.\")\n                    return\n    adjustment.adjustmentresults.avg_stdev = np.mean(sd_all)\n\n    # Retrieve calibration coefficient(s)\n    cal_dic = dict()\n    if adjustment.adjustmentoptions.cal_coeff:\n        for k, v in adjustment.meter_dic.items():\n            cal_dic[k] = (\n                float(1 - adjustment.X[len(sta_dic_ls) + v]),\n                float(np.sqrt(adjustment.var[len(sta_dic_ls) + v])),\n            )\n    else:\n        for k, v in adjustment.meter_dic.items():\n            cal_dic[k] = (1.0, 0.0)\n    adjustment.adjustmentresults.cal_dic = cal_dic\n\n    # calculate and display statistics:\n    adjustment.lsq_statistics()\n\n    return results\n\n\ndef compute_gravity_change(obstreemodel, table_type=\"simple\"):\n    \"\"\"Calculates gravity change between surveys.\n\n    Parameters\n    ----------\n    obstreemodel : ObsTreeModel\n        Tree structure with the data.\n    table_type : {\"simple\", \"full\", \"list\"}\n        Controls what is shown in the table.\n        Simple: One column per time interval. Dg calculated between each interval, and\n        between first and each subsequent interval.\n        Full: Shows g and sd at each station, and gravity changes converted to meters\n        of water.\n        List: Shows station, date, g, sd for each adjusted station.\n\n    Returns\n    -------\n    tuple\n        header, table, dates\n\n    \"\"\"\n\n    # Check that all values are positive (it should work either way, but it avoids confusion)\n    # for i in range(obstreemodel.invisibleRootItem().rowCount()):\n    #     survey = obstreemodel.invisibleRootItem().child(i)\n    #     for ii in range(survey.results_model.rowCount()):\n    #         adj_station = survey.results_model.data(survey.results_model.index(ii, 0),\n    #                                                 role=256)  # 256=Qt.UserRole\n    #         if adj_station.g < 0:\n    #             return False\n\n    compare_station, initial_station, iteration_station, iteration_name = (\n        None,\n        None,\n        None,\n        None,\n    )\n    logging.info(\"Calculating gravity change\")\n    first = True\n    unique_station_names = set()\n    unique_stations = list()\n    for survey in obstreemodel.checked_surveys():\n        for ii in range(survey.rowCount()):\n            loop = survey.child(ii)\n            for iii in range(loop.rowCount()):\n                station = loop.child(iii)\n                unique_station_names.add(station.station_name)\n                unique_stations.append(station)\n    unique_station_names = sorted(unique_station_names)\n    out_table_iteration, out_table_cumulative = [], []\n    header1, header2 = [], []\n    lat, lon, elev, all_g = [], [], [], []\n    dates, header = [], []\n    if table_type == \"list\":\n        date_col, station_col, sd_col = [], [], []\n        for survey in obstreemodel.checked_surveys():\n            dates.append(dt.datetime.strptime(survey.name, \"%Y-%m-%d\"))\n            header = [\"Station\", \"Date\", \"g\", \"Std. dev.\"]\n            for adj_station in survey.results:\n                station_col.append(adj_station.station)\n                date_col.append(survey.name)\n                all_g.append(adj_station.g)\n                sd_col.append(adj_station.sd)\n        table = [station_col, date_col, all_g, sd_col]\n        return header, table, dates\n\n    if table_type == \"full\":\n        for station in unique_station_names:\n            station_g = []\n            g_header = []\n            # station_coords can not exist during testing, maybe other times also?\n            try:\n                lonc, latc, elevc = obstreemodel.station_coords[station]\n                lon.append(f\"{lonc:.5f}\")\n                lat.append(f\"{latc:.5f}\")\n                elev.append(f\"{elevc:.5f}\")\n            except TypeError:\n                lat.append(-999)\n                lon.append(-999)\n                elev.append(-999)\n            except KeyError:\n                lat.append(-999)\n                lon.append(-999)\n                elev.append(-999)\n            for survey in obstreemodel.checked_surveys():\n                g_header.append(survey.name + \"_g\")\n                g_header.append(survey.name + \"_sd\")\n                for adj_station in survey.results:\n                    if adj_station.station[:6] == station[:6]:\n                        station_g.append(\"{:0.1f}\".format(adj_station.g))\n                        station_g.append(\"{:0.1f}\".format(adj_station.sd))\n                        break\n                else:\n                    station_g.append(\"-999\")\n                    station_g.append(\"-999\")\n            all_g.append(station_g)\n\n    for survey in obstreemodel.checked_surveys():\n        dates.append(dt.datetime.strptime(survey.name, \"%Y-%m-%d\"))\n        diff_cumulative = []\n        diff_iteration = []\n        if table_type == \"full\":\n            diff_cumulative_sd, diff_iteration_sd = [], []\n        if first:\n            # Calculate both the between-survey change and the change from the initial survey\n            initial_survey = survey.results\n            iteration_reference = initial_survey\n            reference_name = survey.name\n            iteration_name = reference_name\n            first = False\n        else:\n            if table_type == \"simple\":\n                header1.append(f\"{iteration_name}_to_{survey.name}\")\n                header2.append(f\"{reference_name}_to_{survey.name}\")\n\n            elif table_type == \"full\":\n                header1.append(f\"dH2O_{iteration_name}_to_{survey.name}\")\n                header1.append(f\"dH2O_sd_{iteration_name}_to_{survey.name}\")\n                header2.append(f\"dH2O_{reference_name}_to_{survey.name}\")\n                header2.append(f\"dH2O_sd_{reference_name}_to_{survey.name}\")\n\n            compare_survey = survey.results\n            for station_name in unique_station_names:\n\n                for initial_station in initial_survey:\n                    # Iterate through, look for matching station. 'if' statements deal\n                    # with Gravnet, which truncates station names to 6 characters\n                    if _equal_or_truncated(initial_station.station, station_name):\n                        break\n                else:\n                    # If we get to the end without breaking, set it to None.\n                    initial_station = None\n\n                for iteration_station in iteration_reference:\n                    if _equal_or_truncated(iteration_station.station, station_name):\n                        break\n\n                else:\n                    # If we get to the end without breaking, set it to None.\n                    iteration_station = None\n\n                for compare_station in compare_survey:\n                    if _equal_or_truncated(compare_station.station, station_name):\n                        break\n                else:\n                    # If we get to the end without breaking, set it to None.\n                    compare_station = None\n\n                if initial_station is not None and compare_station is not None:\n                    if table_type == \"simple\":\n                        diff_cumulative.append(\n                            \"{:0.1f}\".format(compare_station.g - initial_station.g)\n                        )\n                    elif table_type == \"full\":\n                        diff_cumulative.append(\n                            \"{:0.2f}\".format(\n                                (compare_station.g - initial_station.g) / 41.9\n                            )\n                        )\n                        var = (\n                            np.sqrt(compare_station.sd ** 2 + initial_station.sd ** 2)\n                            / 41.9\n                        )\n                        if np.isnan(var):\n                            diff_cumulative_sd.append(\"-999\")\n                        else:\n                            diff_cumulative_sd.append(\"{:0.2f}\".format(var))\n                else:\n                    diff_cumulative.append(\"-999\")\n                    if table_type == \"full\":\n                        diff_cumulative_sd.append(\"-999\")  # for sd column\n                if iteration_station is not None and compare_station is not None:\n                    if table_type == \"simple\":\n                        diff_iteration.append(\n                            \"{:0.1f}\".format(compare_station.g - iteration_station.g)\n                        )\n                    elif table_type == \"full\":\n                        diff_iteration.append(\n                            \"{:0.2f}\".format(\n                                (compare_station.g - iteration_station.g) / 41.9\n                            )\n                        )\n                        var = (\n                            np.sqrt(compare_station.sd ** 2 + iteration_station.sd ** 2)\n                            / 41.9\n                        )\n                        if np.isnan(var):\n                            diff_iteration_sd.append(\"-999\")\n                        else:\n                            diff_iteration_sd.append(\"{:0.2f}\".format(var))\n                else:\n                    diff_iteration.append(\"-999\")\n                    if table_type == \"full\":\n                        diff_iteration_sd.append(\"-999\")  # for sd column\n            out_table_iteration.append(diff_iteration)\n            out_table_cumulative.append(diff_cumulative)\n            if table_type == \"full\":\n                out_table_iteration.append(diff_iteration_sd)\n                out_table_cumulative.append(diff_cumulative_sd)\n            iteration_reference = compare_survey\n            iteration_name = survey.name\n    out_table = (\n        [list(unique_station_names)] + out_table_iteration + out_table_cumulative\n    )\n\n    if table_type == \"simple\":\n        # deal with 2-survey case\n        if header1 == header2:\n            header = [\"station\"] + header1\n            table = out_table[:-1]\n        else:\n            header = [\"station\"] + header1 + header2\n            table = out_table\n        return header, table, dates\n    elif table_type == \"full\":\n        # transpose table\n        g = [list(i) for i in zip(*all_g)]\n        table = [unique_station_names, lat, lon, elev]\n        table += g\n        # deal with 2-survey case\n        if header1 == header2:\n            header = (\n                [\"Station\", \"Latitude\", \"Longitude\", \"Elevation\"] + g_header + header1\n            )\n            table += out_table_iteration\n        else:\n            header = (\n                [\"Station\", \"Latitude\", \"Longitude\", \"Elevation\"]\n                + g_header\n                + header1\n                + header2\n            )\n            table += out_table_iteration\n            table += out_table_cumulative\n        # transpose back\n        table = [list(i) for i in zip(*table)]\n        return header, table, dates\n","sub_path":"gsadjust/data/analysis.py","file_name":"analysis.py","file_ext":"py","file_size_in_byte":15965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"469867055","text":"from trafficdl.model.abstract_model import AbstractModel\nimport torch.nn as nn\nimport torch\nimport math\nimport numpy as np\nfrom torch.autograd import Variable\n\nftype = torch.cuda.FloatTensor\nltype = torch.cuda.LongTensor\nup_time = 560632.0  # min\nlw_time = 0.\nup_dist = 457.335   # km\nlw_dist = 0.\n\n\nclass STRNN(AbstractModel):\n    \"\"\"\n    请参考开源模型代码，完成本文件的编写。请务必重写 __init__, predict, calculate_loss 三个方法。\n    \"\"\"\n\n    def __init__(self, config, data_feature):\n        \"\"\"\n        参数说明：\n            config (dict): 配置模块根据模型对应的 config.json 文件与命令行传递的参数\n                根据 config 初始化模型参数\n            data_feature (dict): 在数据预处理步骤提取到的数据集所属的特征参数，如 loc_size，uid_size 等。\n        \"\"\"\n\n        super(STRNN, self).__init__(config, data_feature)\n        self.config = config\n        self.hidden_size = 500\n\n        self.weight_ih = nn.Parameter(torch.Tensor(self.hidden_size, self.hidden_size))  # C\n        self.weight_th_upper = nn.Parameter(torch.Tensor(self.hidden_size, self.hidden_size))  # T\n        self.weight_th_lower = nn.Parameter(torch.Tensor(self.hidden_size, self.hidden_size))  # T\n        self.weight_sh_upper = nn.Parameter(torch.Tensor(self.hidden_size, self.hidden_size))  # S\n        self.weight_sh_lower = nn.Parameter(torch.Tensor(self.hidden_size, self.hidden_size))  # S\n\n        self.location_weight = nn.Embedding(data_feature['loc_size'], self.hidden_size)\n        self.permanet_weight = nn.Embedding(data_feature['uid_size'], self.hidden_size)\n\n        self.sigmoid = nn.Sigmoid()\n        self.h0 = Variable(torch.randn(self.hidden_size, 1), requires_grad=False).type(ftype)\n\n        stdv = 1.0 / math.sqrt(self.hidden_size)\n        for weight in self.parameters():\n            weight.data.uniform_(-stdv, stdv)\n\n    def predict(self, batch):\n        \"\"\"\n        参数说明:\n            batch (trafficdl.data.batch): 类 dict 文件，其中包含的键值参见任务说明文件。\n        返回值:\n            score (pytorch.tensor): 对应张量 shape 应为 batch_size *\n                loc_size。这里返回的是模型对于输入当前轨迹的下一跳位置的预测值。\n        \"\"\"\n        user = batch['uid']\n        td = batch['current_tim']\n        ld = batch['current_dis']\n        loc = batch['current_loc']\n        td_upper, td_lower, ld_upper, ld_lower, location, hx = self.run(user, td, ld, loc)\n        h_tq = self.forward(td_upper, td_lower, ld_upper, ld_lower, loc, hx)\n        p_u = self.permanet_weight(user)\n        user_vector = h_tq + torch.t(p_u)\n        ret = torch.mm(self.location_weight.weight, user_vector).data.cpu().numpy()\n        return ret.T\n\n    def calculate_loss(self, batch):\n        \"\"\"\n        参数说明:\n            batch (trafficdl.data.batch): 类 dict 文件，其中包含的键值参见任务说明文件。\n        返回值:\n            loss (pytorch.tensor): 可以调用 pytorch 实现的 loss 函数与 batch['target']\n                目标值进行 loss 计算，并将计算结果返回。如模型有自己独特的 loss 计算方式则自行参考实现。\n        \"\"\"\n        user = batch['uid']\n        td = batch['current_tim']\n        ld = batch['current_dis']\n        loc = batch['current_loc']\n\n        td_upper, td_lower, ld_upper, ld_lower, location, hx = self.run(user, td, ld, loc)\n        h_tq = self.forward(td_upper, td_lower, ld_upper, ld_lower, loc, hx)\n        p_u = self.permanet_weight(user)\n        q_v = self.location_weight(batch['target'])\n        output = torch.mm(q_v, (h_tq + torch.t(p_u)))\n        return torch.log(1 + torch.exp(torch.neg(output))).sum()\n\n    def forward(self, td_upper, td_lower, ld_upper, ld_lower, loc, hx):  # 前向传播\n        loc_len = len(loc)\n        Ttd = [((self.weight_th_upper * td_upper[i] + self.weight_th_lower * td_lower[i])\n                / (td_upper[i] + td_lower[i])) for i in range(loc_len)]\n        Sld = [((self.weight_sh_upper * ld_upper[i] + self.weight_sh_lower * ld_lower[i])\n                / (ld_upper[i] + ld_lower[i])) for i in range(loc_len)]\n\n        loc = self.location_weight(loc).view(-1, self.hidden_size, 1)\n        loc_vec = torch.sum(torch.cat([torch.mm(Sld[i], torch.mm(Ttd[i], loc[i]))\n                                      .view(1, self.hidden_size, 1) for i in range(loc_len)], dim=0), dim=0)\n        usr_vec = torch.mm(self.weight_ih, hx)\n        hx = loc_vec + usr_vec  # hidden_size x 1\n        return self.sigmoid(hx)\n\n    def run(self, user, td, ld, loc):\n        seqlen = len(loc)\n        rnn_output = self.h0\n        for idx in range(seqlen - 1):\n            td_upper = Variable(torch.from_numpy(np.asarray((up_time - td[idx]).cpu()))).type(ftype)\n            td_lower = Variable(torch.from_numpy(np.asarray((td[idx] - lw_time).cpu()))).type(ftype)\n            ld_upper = Variable(torch.from_numpy(np.asarray((up_dist - ld[idx]).cpu()))).type(ftype)\n            ld_lower = Variable(torch.from_numpy(np.asarray((ld[idx] - lw_dist).cpu()))).type(ftype)\n            location = Variable(torch.from_numpy(np.asarray(loc[idx].cpu()))).type(ltype)\n            rnn_output = self.forward(td_upper, td_lower, ld_upper, ld_lower, location,\n                                     rnn_output)\n        td_upper = Variable(torch.from_numpy(np.asarray((up_time - td[-1]).cpu()))).type(ftype)\n        td_lower = Variable(torch.from_numpy(np.asarray((td[-1] - lw_time).cpu()))).type(ftype)\n        ld_upper = Variable(torch.from_numpy(np.asarray((up_dist - ld[-1]).cpu()))).type(ftype)\n        ld_lower = Variable(torch.from_numpy(np.asarray((ld[-1] - lw_dist).cpu()))).type(ftype)\n        location = Variable(torch.from_numpy(np.asarray((loc[-1]).cpu()))).type(ltype)\n\n        return td_upper, td_lower, ld_upper, ld_lower, location, rnn_output\n","sub_path":"Bigscity-TrafficDL/trafficdl/model/trajectory_loc_prediction/STRNN.py","file_name":"STRNN.py","file_ext":"py","file_size_in_byte":5894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"182450488","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jun 11 15:59:58 2020\n\n@author: laramos\n\"\"\"\n\nfrom IPython.display import SVG\n#from tensorflow.keras.utils.vis_utils import model_to_dot\n#from tensorflow.keras.utils import plot_model\n#from resnets_utils import *\nfrom tensorflow.keras.initializers import glorot_uniform\nimport scipy.misc\nfrom matplotlib.pyplot import imshow\nfrom tensorflow.keras import optimizers  \nimport random as python_random\nimport tensorflow.keras.backend as K\nimport random as python_random\nimport numpy as np\nimport tensorflow as tf\n\nK.set_image_data_format('channels_last')\nK.set_learning_phase(1)\n\nseed = 42\nnp.random.seed(seed)\npython_random.seed(seed)\ntf.random.set_seed(seed)\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n#nn.BatchNorm2d\nnorm = nn.InstanceNorm2d\n\nclass BasicBlock(nn.Module):\n    expansion = 1\n\n    def __init__(self, in_planes, planes, stride=1):\n        super(BasicBlock, self).__init__()\n        self.conv1 = nn.Conv2d(\n            in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n        self.bn1 = norm(planes)\n        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,\n                               stride=1, padding=1, bias=False)\n        self.bn2 = norm(planes)\n\n        self.shortcut = nn.Sequential()\n        if stride != 1 or in_planes != self.expansion*planes:\n            self.shortcut = nn.Sequential(\n                nn.Conv2d(in_planes, self.expansion*planes,\n                          kernel_size=1, stride=stride, bias=False),\n                norm(self.expansion*planes)\n            )\n\n    def forward(self, x):\n        out = F.relu(self.bn1(self.conv1(x)))\n        out = self.bn2(self.conv2(out))\n        out += self.shortcut(x)\n        out = F.relu(out)\n        return out\n\n\nclass Bottleneck(nn.Module):\n    expansion = 4\n\n    def __init__(self, in_planes, planes, stride=1):\n        super(Bottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,\n                               stride=stride, padding=1, bias=False)\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.conv3 = nn.Conv2d(planes, self.expansion *\n                               planes, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(self.expansion*planes)\n\n        self.shortcut = nn.Sequential()\n        if stride != 1 or in_planes != self.expansion*planes:\n            self.shortcut = nn.Sequential(\n                nn.Conv2d(in_planes, self.expansion*planes,\n                          kernel_size=1, stride=stride, bias=False),\n                nn.BatchNorm2d(self.expansion*planes)\n            )\n\n    def forward(self, x):\n        out = F.relu(self.bn1(self.conv1(x)))\n        out = F.relu(self.bn2(self.conv2(out)))\n        out = self.bn3(self.conv3(out))\n        out += self.shortcut(x)\n        out = F.relu(out)\n        return out\n\n\nclass ResNet(nn.Module):\n    def __init__(self, block, num_blocks,use_clin, num_classes=1):\n        super(ResNet, self).__init__()\n        self.use_clin = use_clin\n        self.in_planes = 32\n\n        self.conv1 = nn.Conv2d(30, 32, kernel_size=3,\n                               stride=1, padding=1, bias=False)\n        self.bn1 = norm(32)\n        self.layer1 = self._make_layer(block, 32, num_blocks[0], stride=1)\n        self.layer2 = self._make_layer(block, 64, num_blocks[1], stride=2)\n        self.layer3 = self._make_layer(block, 128, num_blocks[2], stride=2)\n        self.layer4 = self._make_layer(block, 256, num_blocks[3], stride=2)\n       # self.linear = nn.Linear(512*block.expansion, num_classes)\n        if self.use_clin:\n           self.linear = nn.Linear((256*8*8)+58, num_classes)\n        else:\n           self.linear = nn.Linear((256*8*8), num_classes)\n        self.sigmoid = torch.nn.Sigmoid()\n\n    def _make_layer(self, block, planes, num_blocks, stride):\n        strides = [stride] + [1]*(num_blocks-1)\n        layers = []\n        for stride in strides:\n            layers.append(block(self.in_planes, planes, stride))\n            self.in_planes = planes * block.expansion\n        return nn.Sequential(*layers)\n\n    def forward(self, x, data=[]):\n        out = F.relu(self.bn1(self.conv1(x)))\n        out = self.layer1(out)\n        out = self.layer2(out)\n        out = self.layer3(out)\n        out = self.layer4(out)\n        out = F.avg_pool2d(out, 4)\n        \n        \n        out = out.view(out.size(0), -1)\n        if self.use_clin:\n            data = data.view(data.size(0), -1)\n        #print(out.size())\n        #print(data.size())\n            out = torch.cat((out, data), dim = 1)\n        out = (self.linear(out))\n        out = self.sigmoid(out)\n        return out\n\n\ndef ResNet18(use_clin):\n    return ResNet(BasicBlock, [2, 2, 2, 2],use_clin)\n\n\ndef ResNet34(use_clin):\n    return ResNet(BasicBlock, [3, 4, 6, 3],use_clin)\n\n\ndef ResNet50():\n    return ResNet(Bottleneck, [3, 4, 6, 3])\n\n\ndef ResNet101():\n    return ResNet(Bottleneck, [3, 4, 23, 3])\n\n\ndef ResNet152():\n    return ResNet(Bottleneck, [3, 8, 36, 3])\n\n\nclass Feedforward(torch.nn.Module):\n        def __init__(self, input_size, hidden_size):\n            super(Feedforward, self).__init__()\n            self.input_size = input_size\n            self.hidden_size  = hidden_size\n            self.fc1 = torch.nn.Linear(self.input_size, self.hidden_size)\n            self.relu = torch.nn.ReLU()\n            self.fc2 = torch.nn.Linear(self.hidden_size, 1)\n            self.sigmoid = torch.nn.Sigmoid()\n            \n        def forward(self, data):\n            data = data.view(data.size(0), -1)\n            hidden = self.fc1(data)\n            relu = self.relu(hidden)\n            output = self.fc2(relu)\n            output = self.sigmoid(output)\n            return output\n\n","sub_path":"def_resnet_torch.py","file_name":"def_resnet_torch.py","file_ext":"py","file_size_in_byte":5835,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"354496218","text":"from random import *\na = [randint(1,20) for x in range(0,randint(1,10))]\nb = [randint(1,20) for x in range(0,randint(1,10))]\nc = [i for i in a if i in b]\n\n##for n in range(0,len(a)):\n##    if a[n]in b:\n##        c.append(a[n])\nprint(a)\nprint(b)\nprint(c)\n\n\na = [1,4,9,16, 25, 36, 49, 64, 81, 100]\neven_list = [x for x in a if x%2 == 0]\nprint(even_list)\n\nwhile True:\n    user_string = input(\"Please enter a string \")\n    rvs_string = user_string[::-1]\n    if user_string == rvs_string:\n        print(\"Yes, this is a palindrome.\")\n    else:\n        print(\"No, this is not a palindrome.\")\n    ctn_question = input(\"Would you like to try another word? (y/n) \")\n    if ctn_question == 'y':\n        continue\n    if ctn_question == 'n':\n        break\n","sub_path":"Desktop/Python/Exercises.py","file_name":"Exercises.py","file_ext":"py","file_size_in_byte":743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"591506068","text":"print('-' * 70)\n\nimport zipfile\nimport os\n\n# Directory\ndir_name = os.getcwd()\n\n# Create the zip file\nwith zipfile.ZipFile('all_python_scripts.zip', 'w') as zfname:\n    for base, dnames, fnames in os.walk(os.path.dirname(dir_name)):\n        for fname in fnames:\n            if fname.endswith('.py'):\n                print('Zipping the file ->', fname)\n                zfname.write(os.path.join(base, fname),\n                             arcname=fname)\n\nprint('All Python Scripts Zipped Successfully')\n\nprint('-' * 70)\n\n# Extract dir\nextracted = os.path.join(dir_name, 'extracted')\nif not os.path.exists(extracted):\n    os.mkdir(extracted)\n\n# Changing the working directory to extracted\nos.chdir(extracted)\n\n# Extract the files\nzip_fname = os.path.join(dir_name, 'all_python_scripts.zip')\nwith zipfile.ZipFile(zip_fname, 'r') as zfname:\n    zfname.extractall()\n\nprint('Contents Extracted')\n\nprint('-' * 70)","sub_path":"Pandora's Box/Python Official Session/Day 5/extracted/zipfile_automation.py","file_name":"zipfile_automation.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"468330506","text":"def play_again():\n    yes_list = [\"yes\", \"ya\", \"y\"]\n    no_list = [\"no\", \"nah\", \"n\"]\n    while True:\n        replay = input(\"Would you like to make more change? Y/N\\n> \").lower()\n        if replay in yes_list:\n            return True\n        elif replay in no_list:\n            return False\n        else:\n            print(\"Sorry, that was not a supported option.\")","sub_path":"Students/Jordyn/Python/Saved_Functions/play_again.py","file_name":"play_again.py","file_ext":"py","file_size_in_byte":365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"313508712","text":"\n\nclass Budget:\n    \n    def __init__(self, name):\n        self.name = name\n        self.ledger = list()\n        \n    def __str__(self):\n        title = f\"{self.name}\".center(25, '*')\n        items = \"\"\n        total = 0\n        for item in self.ledger:\n            items += \"\\n{} {}\".format(item['description'][0:23], item['amount'])\n            \n            total += item[\"amount\"]\n        output = \"{} {} \\nTotal: {}\\n\".format(title, items, str(total))\n        return output\n        \n        \n    def deposit(self, amount, description=\"\"):\n        \"\"\"\n        A deposit method that accepts an amount and a description.\n        If no description is given, it should default to an empty string .\n        This method shouls append objects to the ledger list in the form\n        {\"amount\": amount, \"description\": description}\n        \"\"\"\n        self.ledger.append({\"amount\": amount, \"description\": description})\n\n    def withdraw(self, amount, description=\"\"):\n        \"\"\"\n        The amount should be stored as to the ledger as negative amount.\n        If funds is insufficient, nothing would be added to the ledger.\n        \"\"\"\n        if self.check_funds(amount):\n            self.ledger.append({\"amount\": -amount, \"description\": description})\n            return True\n        return False\n    \n    def get_balance(self):\n        \"\"\"\n        Returns the current balance of the budget category after withdrawals and deposit have been made\n        \"\"\"\n        total_funds= 0\n        for item in self.ledger:\n            total_funds += item[\"amount\"]\n            \n        return total_funds\n\n    def transfer(self, amount, category):\n        \"\"\"\n        takes in amount and another budget category. \n        The mehod should allow for withdrawal  of an amount with the desciption \"Transfer \n        to [Destination budget category]\". It should then add a deposit to the other budget category \n        with the amount and description \"Transfer from [source budget category].If there are enough funds,\n        nothing should be added to both ledgers.Returns True if account took place and otherwise if not.\n        \"\"\"\n        if self.check_funds(amount):\n            self.withdraw(amount, f\"Transfer to {category.name}\")\n            category.deposit(amount, f\"Transfer from {self.name}\")\n            return True\n        return False\n    \n    def check_funds(self, amount):\n        \"\"\"\n        returns False if amount is greater than the balance of the budget category and True if otherwise.\n        \"\"\" \n        if self.get_balance() >= amount:\n            return True\n        return False           \n\n\nfood = Budget(\"Food\")\nfood.deposit(1000, \"initial deposit\")\nfood.withdraw(10.15, \"groceries\")\nfood.withdraw(15.89, \"resturant and more food for dessert\")\nprint(food.get_balance())\n\nclothing = Budget(\"Clothing\")\nfood.transfer(50, clothing)\nclothing.withdraw(25.55)\nclothing.withdraw(100)\n\nentertainment = Budget(\"Entertainment\")\nentertainment.deposit(1000, \"initial deposit\")\nentertainment.withdraw(15)\n\nprint(food)\nprint(clothing)\n\n\n","sub_path":"budget_app.py","file_name":"budget_app.py","file_ext":"py","file_size_in_byte":3034,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"62430447","text":"import sympy as sp\nimport numpy as np\n\nx = sp.Symbol(\"x\") #define the symbol x\nf = x**x-2 # define the function x**x -2\n\n\n#define the subdivision function\ndef subdiv(f,x, a, b, eps):\n\tcount = 0   # iteration count \n\tlower = a   # left endpoints\n\tupper = b   # right endpoints\n\twhile((upper-lower)>eps):  #check whether the interval is smaller than the eps\n\t\tcount+=1   # increase the count first\n\t\tmid = (lower+upper)/2   \n\t\tf_mid = f.subs(x,mid)  # calculate the value at mid point\n\t\tf_lower = f.subs(x,lower)  \n\t\tf_upper = f.subs(x,upper)\n\t\tif(np.sign(f_mid) == np.sign(f_upper)):   #compare the sign and make decision\n\t\t\tlower = lower\n\t\t\tupper = mid\n\t\telif(np.sign(f_mid) == np.sign(f_lower)):\n\t\t\tlower = mid\n\t\t\tupper = upper\n\t\telse:\n\t\t\tcount -=1\n\t\t\tbreak\n\treturn [lower, upper, count]   # return the values within a list\n\nprint(\"{0:2s}\\t{1:22s}\\t{2:22s}\\t{3:15s}\".format(\"k\", \"left endpoint\", \"right endpoint\", \"iteration times\"))\nfor i in range(1,7):\n\teps = sp.Integer(1)/sp.Integer(10**i)\n\tl = subdiv(f,x,1.0,2.0,eps)\n\tprint(\"{0:1d}\\t{1:22.20f}\\t{2:22.20f}\\t{3:8d}\".format(i, l[0], l[1], l[2]))","sub_path":"assignment 2/p2.py","file_name":"p2.py","file_ext":"py","file_size_in_byte":1100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"149023359","text":"from __future__ import unicode_literals\nimport logging\nimport pprint\nimport requests\nimport time\nfrom bs4 import BeautifulSoup\n\nOPAC_BASE_URL = 'https://catalog.library.brown.edu/'\n\nlog = logging.getLogger(__name__)\n\nclass IIIAccount():\n\n    def __init__(self, name, barcode):\n        self.name = name\n        self.barcode = barcode\n        self.patron_id = None\n        self.session_id = None\n        self.cookies = None\n        self.opac_url = OPAC_BASE_URL\n        self.request_base = self.opac_url + 'search~S7?/.{{bib}}/.{{bib}}/1%2C1%2C1%2CB/request~{{bib}}'\n        self.session = requests.Session()\n        self.session.verify = False\n\n    def login(self):\n        payload = {\n            'name' : self.name,\n            'code' : self.barcode,\n            'pat_submit':'xxx'\n        }\n        \"\"\"\n        extpatid:\n        extpatpw:\n        name:\n        code:\n        pat_submit:xxx\n        \"\"\"\n        out = {\n               'username': self.name,\n               'authenticated': False,\n               'url': None}\n        url = self.opac_url + 'patroninfo'\n        rsp = self.session.post(url, data=payload, allow_redirects=True)\n        doc = BeautifulSoup( rsp.content.decode('utf-8') )\n        login_error = doc.find( \"span\", class_=\"login_error\" )  # <span class=\"login_error\"> exists if login fails\n        if (login_error):\n            raise Exception(\"Login failed.\")\n        else:\n            out['authenticated'] = True\n            url = rsp.url\n            out['url'] = url\n            out['patron_id'] = url.split('/')[-2]\n            self.patron_id = out['patron_id']\n            log.info(\"Patron {} authenticated.\".format({self.patron_id}))\n        return out\n\n    def logout(self):\n        \"\"\"\n        This seems to be pretty straight forward.  Just hit the url and session\n        cookies.  Then carry on.\n        \"\"\"\n        url = self.opac_url + 'logout~S7?'\n        rsp = self.session.get(url)\n        #Add check to verify text or url is what is expected.\n        return True\n\n    def _validate_session(self, content):\n        if 'your validation has expired' in content.lower():\n            raise Exception(\"Validation expired.\")\n\n    def get_holds(self):\n        \"\"\"\n        Return a list of holds for a user.\n        \"\"\"\n        url = self.opac_url + 'patroninfo~S7/%s/holds' % self.patron_id\n        rsp = self.session.get(url)\n        #error checking to see if we logged in?\n        content = rsp.content\n        self._validate_session(content)\n        holds = self._parse_holds_list(content)\n        return holds\n\n    def _parse_holds_list(self, content):\n        \"\"\" Parses holds html.\n            Called by get_holds() \"\"\"\n        content = content if type(content) == unicode else content.decode( 'utf-8', 'replace' )\n        ( doc, holds ) = ( BeautifulSoup(content), [] )\n        hold_rows = doc.find_all( 'tr', class_='patFuncEntry' )\n        for row in hold_rows:\n            holds.append( {\n                'key': unicode( row.select('input[id]')[0]['id'] ),\n                'title': row.select('.patFuncTitle')[0].get_text(strip=True),\n                'status': row.select('.patFuncStatus')[0].get_text(strip=True),\n                'pickup': row.select('.patFuncPickup')[0].get_text(strip=True),\n                'cancel_by': row.select('.patFuncCancel')[0].get_text(strip=True), } )\n        return holds\n\n    def get_items(self, bib):\n        \"\"\"\n        Get the item numbers linked to a bib record.  If no item number is\n        returned, this item isn't requestable.\n        \"\"\"\n        url = self.request_base.replace('{{bib}}', bib)\n        payload = {\n            'name' : self.name,\n            'code' : self.barcode,\n            'pat_submit':'xxx',\n            'neededby_Month': '2',\n            'neededby_Day': '1',\n            'neededby_Year': '2011',\n            'submit': 'SUBMIT',\n            'loc': 'ROCK',\n            #inum is optional\n        }\n        r = requests.post(url,\n                          data=payload,\n                          cookies=self.cookies)\n        doc = BeautifulSoup( r.content.decode('utf-8', 'replace') )\n        rows = doc.find_all( 'tr', class_='bibItemsEntry' )\n        out = []\n        for r in rows:\n            _k = {}\n            cells = r.select( 'td' )\n            try:\n                item_num = unicode( cells[0].select('input[type=\"radio\"]')[0]['value'] )\n            except IndexError:\n                item_num = None\n            item, loc, call, status, barcode = tuple([c.get_text(strip=True) for c in cells])\n            _k['id'] = item_num\n            _k['location'] = loc\n            _k['callnumber'] = call\n            _k['status'] = status\n            _k['barcode'] = barcode.replace( ' ', '' )\n            out.append(_k)\n        return out\n\n    def place_hold( self, bib, item, pickup_location='ROCK', availability_location=None ):\n        \"\"\" Place actual hold given bib and item. \"\"\"\n        out = {}\n        out['bib'] = bib\n        out['item'] = item\n        url = self.request_base.replace('{{bib}}', bib)\n        payload = self.prep_hold_payload( bib, item, pickup_location, availability_location )\n        #post it\n        rsp = self.session.post(url, data=payload)\n        # log.debug( 'rsp.content, ```%s```' % rsp.content.decode('utf-8') )\n        #Check for success message\n        confirm_status = self._parse_hold_confirmation(rsp.content)\n        out.update(confirm_status)\n        return out\n\n    def prep_hold_payload( self, bib, item, pickup_location, availability_location ):\n        \"\"\" Returns appropriate payload dct. \"\"\"\n        log.debug( 'availability_location, `%s`' % availability_location )\n        if availability_location and availability_location.lower() == 'annex':\n            payload = {\n                'code': self.barcode,\n                'locx00': pickup_location,  # was 'r0001'\n                'name': self.name,\n                'pat_submit': 'Request item',\n                'radio': item,\n                'submit': 'Submit',\n                }\n        else:\n            payload = {\n                'code' : self.barcode,\n                'loc': pickup_location,\n                'name' : self.name,\n                'neededby_Day': 30,\n                'neededby_Month': 12,\n                'neededby_Year': 2015,\n                'pat_submit':'xxx',\n                'radio': item,\n                'submit': 'SUBMIT',\n                }\n        log.debug( 'hold payload, `%s`' % pprint.pformat(payload) )\n        return payload\n\n    def _parse_hold_confirmation(self, content):\n        \"\"\"\n        Helper for parsing confirmation screen.\n        \"\"\"\n        content = content if ( type(content) == unicode ) else content.decode( 'utf-8', 'replace' )\n        out = {\n            'confirmed': False,\n            'message': None }\n        doc = BeautifulSoup( content )\n        try:\n            msg = unicode( doc.find_all( 'span', class_='style1' )[0] )\n        except IndexError:\n            #These are failures.\n            msg = doc.find( 'font', attrs={'color': 'red', 'size': '+2'} ).get_text( strip=True )\n            out['message'] = msg\n            return out\n        try:\n            msg.index('was successful')\n            out['confirmed'] = True\n            return out\n        except ValueError:\n            return out\n\n    def cancel_hold(self, cancel_key, seconds_to_wait=10):\n        \"\"\"\n        The III database doesn't seem to cancel the hold in real time.\n\n        We will try to cancel and verify the hold.  If not verified, we\n        will try again, first pausing for a second.  We will try verify up to\n        `seconds_to_wait`.  In testing, waiting up to 10 seconds was possible.\n        \"\"\"\n        out = {}\n        out['cancelled'] = False\n        out['key'] = cancel_key\n        out['patron_id'] = self.patron_id\n\n        loc_key = cancel_key.replace('cancel', 'loc')\n        payload = {\n                   'currentsortorder':'current_pickup',\n                   'currentsortorder':'current_pickup',\n                   'updateholdssome': 'YES',\n                   cancel_key: 'on',\n                   loc_key: ''\n        }\n        url = self.opac_url + 'patroninfo~S7/%s/holds' % self.patron_id\n        r = self.session.post(url, data=payload)\n        elapsed = 0\n        while True:\n            # log.debug(\"Attempting to verify canceled hold.\")\n            #Get all the holds and verify that this key isn't in the current hold set.\n            current_holds = [h['key'] for h in self.get_holds()]\n            #These are failures.\n            if cancel_key in current_holds:\n                # Wait a second\n                # log.debug(\"Waiting for one second.\")\n                time.sleep(1)\n                elapsed += 1\n                pass\n            # Success\n            else:\n                break\n            #Make sure we haven't passed to max seconds.\n            if elapsed >= seconds_to_wait:\n                raise Exception(\"Couldn't cancel hold in time specified.\")\n                break\n\n        out['cancelled'] = True\n        return out\n\n    def cancel_all_holds(self):\n        \"\"\"\n        Cancel all of a patron's holds.\n        TODO: write test and re-implement.\n        Initial implementation at <https://github.com/Brown-University-Library/josiah-patron-accounts/blob/6ca4280ec46c7a91584ca7e994faeb9dd1c87203/iii_account/iii_account.py>\n        \"\"\"\n        pass\n\n    def get_checkouts(self):\n        \"\"\"\n        Get a list of items a user has checked out.\n        \"\"\"\n        url = self.opac_url + 'patroninfo/%s/items' % self.patron_id\n\n        rsp = self.session.get(url)\n        content = rsp.content\n        #Will raise if session expired message is found.\n        self._validate_session(content)\n        check_outs = self._parse_checkouts(content)\n        return check_outs\n\n    def _parse_checkouts(self, content):\n        \"\"\"\n        Parse a given user's current checkouts.\n        \"\"\"\n        content = content if type(content) == unicode else content.decode( 'utf-8', 'replace' )\n        doc = BeautifulSoup( content )\n        t_rows = doc.find_all( 'tr', class_='patFuncEntry' )\n        def _get(chunk, selector):\n            \"\"\"\n            little util to get text by css selector.\n            \"\"\"\n            return chunk.select('td.%s' % selector)[0].get_text(strip=True)\n        checkouts = [\n            {\n                'key': unicode( row.select('input[id]')[0]['id'] ),\n                'item': unicode( row.select('input[value]')[0]['value'] ),\n                'title': _get(row, 'patFuncTitle'),\n                'barcode': _get(row, 'patFuncBarcode'),\n                'status': _get(row, 'patFuncStatus'),\n                'call_number': _get(row, 'patFuncCallNo'),\n            }\n            for row in t_rows\n        ]\n        return checkouts\n\n    def renew_item(self):\n        \"\"\"\n        post 1 - patroninfo~S7/x/items\n        requestRenewSome:requestRenewSome\n        currentsortorder:current_checkout\n        renew0:i12445874\n        currentsortorder:current_checkout\n\n        post2 - /patroninfo~S7/x/items\n\n        currentsortorder:current_checkout\n        renew0:i12445874\n        currentsortorder:current_checkout\n        renewsome:YES\n\n        parse renewed html for confirmation\n\n        \"\"\"\n        pass\n\n    def renew_all(self):\n        pass\n\n    def get_fines(self):\n        \"\"\"\n        Parse the odd fines table.\n        TODO: consider implementing via the patron-api.\n        \"\"\"\n        pass\n","sub_path":"iii_account/iii_account.py","file_name":"iii_account.py","file_ext":"py","file_size_in_byte":11414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"136950544","text":"from PyInquirer import prompt\nimport csv\n\ncolors = {\n    \"RED\": \"\\033[91m\",\n    \"NC\": \"\\033[0m\",\n    \"CYAN\": \"\\033[96m\"\n}\n\nexpense_questions = [\n    {\n        \"type\":\"input\",\n        \"name\":\"amount\",\n        \"message\":\"New Expense - Amount: \",\n    },\n    {\n        \"type\":\"input\",\n        \"name\":\"label\",\n        \"message\":\"New Expense - Label: \",\n    },\n    {\n        \"type\":\"list\",\n        \"name\":\"spender\",\n        \"message\":\"New Expense - Spender\",\n        \"choices\": [\"TO CHANGE\"]\n    },\n    {\n        \"type\":\"checkbox\",\n        \"name\":\"concerned\",\n        \"message\":\"New Expense - Concerned Users\",\n        \"choices\": [\"TO CHANGE\"]\n    }\n]\n\n\ndef new_expense(expenses, users, *args):\n\n    # Changing a question using the users list TEMPORARY SOLUTION\n    flatten_users = [item for sublist in users for item in sublist]\n    expense_questions[2]['choices'] = flatten_users\n    expense_questions[3]['choices'] = [ {'name': x, 'checked': False} for x in flatten_users]\n\n    # --- New User's Answers --- #\n    infos       = prompt(expense_questions)\n\n    # --- Verify User's Answers --- #\n    if not infos['amount'].isnumeric() or infos['label'].isnumeric() or infos['spender'].isnumeric():\n        print(f\"{colors['RED']}Wrong Expense specified !{colors['NC']}\")\n        return False\n\n    # --- Update the expenses list --- #\n    \n    # Do the average\n    avg = int(infos['amount']) / len(infos['concerned'])\n    print(avg)\n\n    infos_list  = [infos['amount'], infos['label'], infos['spender'], infos['concerned'], avg]\n    expenses.append(infos_list)\n    print(expenses)\n\n    # --- Persist new expense inside a csv file ---\n    with open('data/expense_report.csv', 'a', newline='') as csvfile:\n        writer = csv.writer(csvfile, delimiter=',',\n                            quotechar='|', quoting=csv.QUOTE_MINIMAL)\n\n        writer.writerow(infos_list)\n    \n\n        print(f\"{colors['CYAN']}Expense Added !{colors['NC']}\")\n\n    return True\n\n\n","sub_path":"expense.py","file_name":"expense.py","file_ext":"py","file_size_in_byte":1944,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"210171975","text":"from os import rename, listdir\nimport os\nimport gzip\nimport tarfile\nimport csv\n\ncsvB = open(\"C:\\\\Users\\\\변강륜\\\\Desktop\\\\경기도정류장\\\\Station.csv\",\"r\")\nread = csv.reader(csvB)\n\nlat = [0]*402#세로\nlon = [0]*402#가로\n\nmaxA = 127.708141\nmaxB = 37.797130\nminA = 126.372253\nminB = 36.906024#y최소\np = 0\nl = 0\n\nsqure = 99999999\n\nfor i in range(0,401):\n    p = 0.002 * i\n    lat[i] = minB+p\n\nfor i in range(0,401):\n    l = 0.003 * i\n    lon[i] = minA+l\n\nwriteFile = open(\"C:\\\\Users\\\\변강륜\\\\Desktop\\\\경기도정류장\\\\\"\n                        +\"StationSquare\"+\".csv\",\"w\",newline='')\nnewW = csv.writer(writeFile, delimiter=',',quotechar=' ')\nfor line in read:\n    #print(line[])\n    a = float(line[5])#36....\n    b = float(line[4])#127....\n    i = 0\n    j = 0\n    while a>= lat[i+1] and i<400:\n        i += 1\n        while b>= lon[j+1] and j<400:\n            j += 1\n        \n    squre=(i)*400+(j)\n    \n    if(squre >= 160000 or squre == 0):\n        squre = 99999999\n    c = [line[0],line[1],line[2],line[3],str(a),str(b),line[6],line[7],line[8],str(squre)]\n    #print(c)\n    newW.writerow(c)\n\nwriteFile.close()\ncsvB.close()\n        \n","sub_path":"preprocess/test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"365006604","text":"\"\"\"\nThe rules\nBefore you start coding, let's make sure the basic idea of Fizz Buzz is clear. You count up through a series of numbers (0, 1, 2, 3, 4, 5 ... ) and for each number:\n\nIf the number is evenly divisible by 3, you say \"Fizz\"\nIf the number is evenly divisible by 5, you say \"Buzz\"\nIf the number is evenly divisible by both 3 and 5, you say \"FizzBuzz\"\n\"\"\"\n\ndef fizz():\n    print(\"Fizz\", end=\"\")\n\ndef buzz():\n    print(\"Buzz\")\n\ndef plain():\n    print(i)\n\nprint(\"Let's start ..\")\nfor i in range(20):\n    if i % 3 == 0 and i % 5 == 0:\n        fizz()\n        buzz()\n    else:\n        if i % 3 == 0:\n            fizz()\n            print()\n        elif i % 5 == 0:\n            buzz()\n        else:\n            plain()\n\nprint(\"That's it!\")","sub_path":"fizz-buzz-03.py","file_name":"fizz-buzz-03.py","file_ext":"py","file_size_in_byte":740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"523133604","text":"import datetime\nfrom .wdb import WDB, WDBMan, WDBTeam, WDBGroup, WDBDistance, WDBPunch\n\n\nclass WDBResult:\n    def __init__(self, wdb):\n        assert isinstance(wdb, WDB)\n        self.wdb = wdb\n    \n    def get_sort_groups(self, reverse=True):\n        self.wdb.group.sort(key=lambda group: group.name, reverse=reverse)\n        return self.wdb.group\n\n    def get_persons_by_group(self, group):\n        persons = list()\n        for man in self.wdb.man:\n            if group.name == man.get_group().name:\n                persons.append(man)\n        \n        return persons\n    \n    def sort_persons(self, persons, reverse=True):\n        persons.sort(key=lambda man: man.result if man.result != 0 and man.status == 0 else 90000000, reverse=reverse)\n        return persons\n\n    def get_sort_persons(self):\n        persons = list()\n        for group in self.wdb.group:\n            for man in self.sort_persons(self.get_persons_by_group(group), False):\n                persons.append(man)\n\n        return persons\n    \n    def get_results(self):\n        persons = self.get_sort_persons()\n        if not len(persons):\n            return None\n        i = 0\n        prev = persons[0]\n        for man in persons:\n            if man.result == 0:\n                continue\n            if man.is_not_qualified:\n                setattr(man, 'place', 'в/к')\n                prev = man\n                continue\n\n            if man.get_group().name == prev.get_group().name:\n                i += 1\n            elif man.result != prev.result:\n                i = 1\n            if man.result != 0 and man.status == 0:\n                setattr(man, 'place', i)\n            \n            prev = man\n        \n        self.wdb.man = persons\n        return self.wdb\n            \n\nclass WinOrientBinary:\n    qual = {\n        '': 'б/р',\n        '0': 'б/р',\n        '3': 'IIIю',\n        '2': 'IIю',\n        '1': 'Iю',\n        '6': 'III',\n        '5': 'II',\n        '4': 'I',\n        '7': 'КМС',\n        '8': 'МС',\n        '9': 'МСМК',\n        '*': 'ЗМС'\n    }\n\n    def __init__(self, file=None):\n        self._file = file\n        self.wdb_object = WDB()\n        self.data = {}\n\n    def _read_file(self):\n        try:\n            with open(self._file, 'rb') as wdb_file:\n                byte_array = wdb_file.read()\n                self.wdb_object = WDB()\n                self.wdb_object.parse_bytes(byte_array)\n        except FileNotFoundError:\n            pass\n\n    def create_data(self):\n        self._read_file()\n\n        self.data['race'] = self.get_race(self.wdb_object)\n        self.data['organizations'] = self.get_organizations(self.wdb_object)\n        self.data['persons'] = self.get_persons(WDBResult(self.wdb_object).get_results())\n\n        return self\n\n    def get_race(self, wdb):\n\n        assert isinstance(wdb, WDB)\n\n        name = 'wdb imported race'\n        discipline = wdb.info.type\n        start_time = wdb.info.date_str\n        end_time = wdb.info.date_str\n        status_text = 'Applied'\n        status = status_text\n        url = ''\n        information = wdb.info.title\n\n        return {\n            'name': name,\n            'discipline': discipline,\n            'start_time': start_time,\n            'end_time': end_time,\n            'status': status,  # TODO: write foreign key of status\n            'url': url,\n            'information': information\n        }\n\n    def get_organizations(self, wdb):\n\n        assert isinstance(wdb, WDB)\n\n        organizations = list()\n\n        for team in wdb.team:\n\n            name = team.name\n            address = None\n            contact = team.refferent\n            country = None  # TODO: decode from WDB byte\n\n            organizations.append({\n                'name': name,\n                'address': address,\n                'contact': contact,\n                'country': country,\n            })\n        \n        return organizations\n\n    def get_persons(self, wdb):\n\n        assert isinstance(wdb, WDB)\n        persons = list()\n\n        for man in wdb.man:\n            assert (isinstance(man, WDBMan))\n            data_person = {\n                'name': str.split(man.name, ' ')[-1],\n                'surname': str.split(man.name, ' ')[0],\n                'full_name': man.name,\n                'team': man.get_team().name,\n                'year': man.year,\n                'qual': self.qual[str(man.qualification)] if str(man.qualification) in self.qual else '',\n                'bib_number': int(man.number),\n                'comment': man.comment,\n                'start': self.int_to_time(man.start),\n                'result': self.int_to_time(man.result),\n                'result_int': man.result,\n                'status': man.status,\n                'group': str(man.get_group().name),\n                'place': man.place,\n            }\n\n            card = None\n            data_person['sicard'] = str(man.si_card) if man.si_card != 0 else ''\n\n            finish = man.get_finish()\n\n            data_person['finish'] = self.int_to_time(finish.time) if finish is not None else None\n\n            persons.append(data_person)\n        \n        return persons        \n\n    def int_to_time(self, value):\n        \"\"\" convert value from 1/100 s to time \"\"\"\n        # ret = datetime(1970, 1, 1) + timedelta(seconds= value/100, milliseconds=value*10%1000)\n        # ret = datetime.datetime.fromtimestamp(int(value)/100.0)\n        # TODO Find more simple solution!!!\n        # ret = datetime.time(value // 360000, (value % 360000) // 6000, (value % 6000) // 100, (value % 100) * 10000)\n        today = datetime.datetime.now()\n        assert (isinstance(today, datetime.datetime))\n        ret = datetime.datetime(today.year, today.month, today.day, value // 360000 % 24, (value % 360000) // 6000,\n                                (value % 6000) // 100, (value % 100) * 10000)\n\n        return ret\n","sub_path":"winorient/wdb_reader.py","file_name":"wdb_reader.py","file_ext":"py","file_size_in_byte":5848,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"622296776","text":"import tkinter as tk\nfrom tkinter import ttk, Label, Toplevel, StringVar\nfrom tkinter import filedialog\nfrom tkinter.messagebox import showinfo\nimport tkinter.font as tkFont\nimport time\nimport os\nfrom lib import getTrelloData as getApi\n\nroot = tk.Tk()\nroot.geometry('600x400')\n#root.resizable(False, False)\nroot.title('Trello Application')\nsave = False\nimportFlag = False\nfileName = ''\nisExport = False\n\ndef success_popup():\n   top= Toplevel(root)\n   Label(top, text= \"Finish successfully !\" + os.linesep + \"Get data finish, please check !\", font=('Arial 10')).place(x=10,y=80)\n\ndef import_success():\n   top= Toplevel(root)\n   Label(top, text= \"Finish successfully !\" + os.linesep + \"Import data finish, please check !\", font=('Arial 10')).place(x=10,y=80)\n\ndef error1_popup():\n   top= Toplevel(root)\n   Label(top, text= \"Error!\" + os.linesep + \"Please check your token!!\", font=('Arial 10')).place(x=10,y=80)\n\ndef error2_popup():\n   top= Toplevel(root)\n   Label(top, text= \"Error!\" + os.linesep + \"Unexpected error occur!!\", font=('Arial 10')).place(x=10,y=80)\n\ndef setText(textContent):\n    processLine.configure(state=\"normal\")\n    processLine.insert(\"end\", textContent)\n    processLine.insert(\"end\", os.linesep)\n    processLine.configure(state=\"disable\")\n    root.update()\n\ndef setButtonState(state):\n    btnExport[\"state\"] = state\n    btnToken[\"state\"] = state\n    btnImport[\"state\"] = state\n\ndef UploadAction(event=None):\n    global importFlag\n    if importFlag == False:\n        global fileName\n        fileName = filedialog.askopenfilename()\n        if fileName == '':\n            return 0\n        setText ('File choosen:' + fileName)\n        importFlag = True\n        setText('Please input your board_id (if needed)')\n        entry.delete(0, 'end')\n        entry.place(relx=0.06,rely=0.60,relwidth=0.84,relheight=0.1)\n        btnCancel.place(relx=0.06,rely=0.75,relwidth=0.235,relheight=0.1)\n        btnImport['text'] = 'START IMPORT'\n    else:\n        idBoard = entry.get()\n        setButtonState(\"disabled\")\n        root.update()\n        setText(\"Start import data...\")\n        entry.place_forget()\n        btnCancel.place_forget()\n        root.update()\n        board_id = getApi.uploadData(fileName,idBoard)\n        if board_id == \"err\":\n            error2_popup()\n            setText(\"Import data error!\")\n        else:\n            setText(\"Import finish. Check product imported at:\")\n            setText(\"https://trello.com/b/\" + board_id)\n            import_success()\n        setButtonState(\"normal\")\n        btnImport['text'] = 'IMPORT DATA'\n        importFlag = False\n\ndef runGetData():\n    global isExport\n    if isExport == False:\n        isExport = True\n        text = StringVar()\n        l = Label(root, textvariable=text, fg='blue', font=(\".VnMonotype corsiva\", 15))\n        l.place(relx=0.06,rely=0.60,relwidth=0.84,relheight=0.1)\n        text.set('Exportinggg.....Please wait!!!')\n        setButtonState(\"disabled\")\n        root.update()\n        time.sleep(2)\n        l.place_forget()\n        setButtonState(\"normal\")\n        result = getApi.getData()\n        if (result == 0):\n            success_popup()\n            setText(\"Export data success!\")\n        elif (result == 1):\n            error1_popup()\n            setText(\"Error! Please check your token!\")\n        else:\n            error2_popup()\n            setText(\"Error! Unexpected error occur!\")\n        isExport = False\ndef getData():\n    setText('Start change export data... Please wait!')\n    runGetData()\n\ndef execute():\n    global save\n    if (save == False):\n        save = True\n        setText('Start change Token')\n        setText('Please input your api key and token')\n        entry.delete(0, 'end')\n        entry.place(relx=0.06,rely=0.60,relwidth=0.84,relheight=0.1)\n        btnCancel.place(relx=0.06,rely=0.75,relwidth=0.235,relheight=0.1)\n        btnToken['text'] = 'SAVE TOKEN'\n    else:\n        with open(\"lib\"+os.path.sep+\"data.txt\", \"w\") as f2:\n            f2.write(entry.get())\n        f2.close()\n        entry.place_forget()\n        btnCancel.place_forget()\n        btnToken['text'] = 'CHANGE TOKEN'\n        save = False\n        setText('Save token successfully!!')\n\ndef cancel():\n    global importFlag, save\n    if save == True:\n        btnCancel.place_forget()\n        entry.delete(0, 'end')\n        entry.place_forget()\n        btnToken['text'] = 'CHANGE TOKEN'\n        save = False\n        setText('Cancel change successfully!!')\n        return 0\n    elif importFlag == True:\n        btnCancel.place_forget()\n        entry.delete(0, 'end')\n        entry.place_forget()\n        btnImport['text'] = 'IMPORT DATA'\n        importFlag = False\n        setText('Cancel import successfully!!')\n        return 0\n# label with a specific font\nlabel = ttk.Label(\n    root,\n    text='TRELLO APPLICATION',\n    font=(\"Tahoma\", 13))\n\nlabel.pack(ipadx=10, ipady=10)\n\nbtnImport = ttk.Button (\n    root,\n    text = \"IMPORT DATA\",\n    width= 30,\n    command=UploadAction\n)\nbtnImport.place(relx=0.06,rely=0.15,relwidth=0.235,relheight=0.1)\n#btnImport.pack(ipadx=10, ipady=10, padx = 10)\n#btnImport[\"state\"] = \"disabled\"\n\nbtnExport = ttk.Button (\n    root,\n    text = \"EXPORT DATA\",\n    width= 30,\n    command=lambda:[getData()]\n)\n#btnExport.place(x=30,y=130,width=135,height=42)\nbtnExport.place(relx=0.06,rely=0.30,relwidth=0.235,relheight=0.1)\n#btnExport.pack(ipadx=10, ipady=10, padx = 10, pady=10)\n\nbtnToken = ttk.Button (\n    root,\n    text = \"CHANGE TOKEN\",\n    width= 30,\n    command=lambda:[execute()]\n)\n#btnToken.place(x=30,y=200,width=134,height=42)\nbtnToken.place(relx=0.06,rely=0.45,relwidth=0.235,relheight=0.1)\n#btnToken.pack(ipadx=10, ipady=10, padx = 10, pady=10)\n\nbtnCancel = ttk.Button (\n    root,\n    text = \"CANCEL\",\n    width= 30,\n    command=lambda:[cancel()]\n)\n\nprocessLine=tk.Text(root)\nprocessLine[\"borderwidth\"] = \"1px\"\nft = tkFont.Font(size=10)\nprocessLine[\"font\"] = ft\nprocessLine[\"fg\"] = \"#0000ff\"\n#processLine.place(x=220,y=60,width=341,height=185)\nprocessLine.place(relx=0.40,rely=0.15,relwidth=0.5,relheight=0.4)\nprocessLine.configure(state=\"disable\")\n\nentry = ttk.Entry (\n    root,\n    width= 450\n)\n\ndef main():\n    root.mainloop()\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"trelloAppcpy.py","file_name":"trelloAppcpy.py","file_ext":"py","file_size_in_byte":6188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"423883663","text":"import os\n\nfrom flask import Flask,render_template,request,redirect,url_for,send_from_directory,session\nfrom werkzeug.utils import secure_filename\n\napp = Flask(__name__)\n\nUPLOAD_FOLDER = './uploads'\nALLOWED_EXTENSIONS = set(['txt','pdf','png','jpg','gif','JPG','jpeg'])\napp.config['UPLOAD_FOLDER']= UPLOAD_FOLDER\napp.config['SECRET_KEY'] = os.urandom(24)\n\ndef allowed_file(filename):\n    return '.' in filename and\\\n        filename.rsplit('.',1)[1] in ALLOWED_EXTENSIONS\n\n@app.route('/')\ndef index():\n    if 'username' in session:\n        return render_template('index.html')\n    else:\n        return'<p>ログインしてください</p>'\n\n\n@app.route('/login',methods=['GET','POST'])\ndef login():\n    if request.method == 'POST':\n        username = request.form['username']\n        if username == 'admin':\n            session['username']=request.form['username']\n            return redirect(url_for('index'))\n        else:\n            return '<p>ユーザー名が違います</p>'\n    return '''\n        <form action ='' method ='post'>\n        <p><input type = 'text' name = 'username'>\n        <p><input type = 'submit' value ='login'>\n        '''\n\n\n@app.route('/logout')\ndef logout():\n    session.pop('username',None)\n    return redirect(url_for('index'))\n\n\n@app.route('/send',methods=['GET','POST'])\ndef send():\n    if request.method == 'POST':\n        img_file = request.files['img_file']\n        if img_file and allowed_file(img_file.filename):\n            filename = secure_filename(img_file.filename)\n            img_file.save(os.path.join(app.config['UPLOAD_FOLDER'],filename))\n            return render_template('index.html')\n        else:\n            return '<P>dont allowed</P>'\n    else:\n        return redirect(url_for('index'))\n\n@app.route('/uploads/<filename>')\ndef uploaded_file(filename):\n    return send_from_directory(app.config['UPLOAD_FOLDER'],filename)\n\n\nif __name__ == '__main__':\n    app.debug = True\n    app.run()\n    \n","sub_path":"013uploadfile/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1949,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"539610119","text":"from apps.data.RotoGuruUrlData import RotoGuruUrlData\nfrom apps.data.DFSStatistics import DFSStatistics\nfrom apps.databaseaccess.TeamDAOImpl import TeamDAOImpl\nfrom apps.databaseaccess.ScrapingHistoryDAOImpl import ScrapingHistoryDAOImpl\nfrom apps.databaseaccess.GameDAOImpl import GameDAOImpl\nfrom apps.databaseaccess.PlayerDAOImpl import PlayerDAOImpl\nfrom apps.utils.Util import Util\nfrom apps.data.NBASeasonEnum import NBASeasonEnum\nfrom apps.data.TeamEnum import TeamEnum\nfrom apps.databaseaccess.TypeConversionUtil import TypeConversionUtil\nimport pandas as pd\nimport numpy as np\n\nclass RotoGuruPlayerDAOImpl:\n    __instance = None\n\n    @staticmethod\n    def getInstance():\n        if RotoGuruPlayerDAOImpl.__instance == None:\n            RotoGuruPlayerDAOImpl()\n        return RotoGuruPlayerDAOImpl.__instance\n\n    def __init__(self):\n        if RotoGuruPlayerDAOImpl.__instance != None:\n            raise Exception(\"This class is a singleton!\")\n        else:\n            RotoGuruPlayerDAOImpl.__instance = self\n\n    def insert_roto_guru_dfs_statistics_for_season(self, roto_dfs_data_array, scraping_history_request, cur):\n        for roto_dfs_data in roto_dfs_data_array:\n            roto_guru_url_data: RotoGuruUrlData = roto_dfs_data['roto_guru_url_data']\n            dfs_player_season_array = roto_dfs_data['dfs_player_season_array']\n            url = roto_guru_url_data.url\n            self.insert_roto_guru_player(roto_guru_url_data, scraping_history_request, cur)\n            for dfs_player_game_data in dfs_player_season_array:\n                self.insert_dfs_player_data(dfs_player_game_data, url, scraping_history_request, cur)\n\n    def insert_roto_guru_player(self, roto_guru_player_url_data: RotoGuruUrlData, scraping_history_request, cur):\n\n        team_dao_impl = TeamDAOImpl.getInstance()\n        scraping_history_dao_impl = ScrapingHistoryDAOImpl.getInstance()\n\n        team_enum = roto_guru_player_url_data.end_of_season_team\n        end_of_season_team_id = team_dao_impl.fetch_team_id_by_abbreviations(team_enum.abbreviations(), cur)\n        scraping_history_id = scraping_history_dao_impl.insert_scraping_history(cur, scraping_history_request)\n\n        sql = 'INSERT INTO roto_guru_player ' \\\n              '(url, ' \\\n              'roto_guru_player_code, ' \\\n              'season, ' \\\n              'is_valid_url, ' \\\n              'first_name, ' \\\n              'last_name, ' \\\n              'end_of_season_team_id, ' \\\n              'scraping_history_id' \\\n              ') VALUES (%s, %s, %s, %s, %s, %s, %s, %s)' \\\n              ' ON CONFLICT DO NOTHING'\n\n        cur.execute(sql,\n                    (roto_guru_player_url_data.url,\n                     roto_guru_player_url_data.roto_guru_player_code,\n                     roto_guru_player_url_data.season.description(),\n                     roto_guru_player_url_data.is_valid_url,\n                     roto_guru_player_url_data.first_name,\n                     roto_guru_player_url_data.last_name,\n                     end_of_season_team_id,\n                     scraping_history_id\n                     )\n                    )\n\n    def insert_dfs_player_data(self, player_season_dfs_data: DFSStatistics, url, scraping_history_request, cur):\n\n        team_dao_impl = TeamDAOImpl.getInstance()\n        scraping_history_dao_impl = ScrapingHistoryDAOImpl.getInstance()\n\n        date_of_game = player_season_dfs_data.date_of_game\n        roto_guru_player_id = self.fetch_roto_guru_player_id_by_url(url, cur)\n        end_of_season_team_id = team_dao_impl.fetch_team_id_by_abbreviations(player_season_dfs_data.end_of_season_team.abbreviations(), cur)\n        opponent_team_id = team_dao_impl.fetch_team_id_by_abbreviations(player_season_dfs_data.opponent.abbreviations(), cur)\n        scraping_history_id = scraping_history_dao_impl.insert_scraping_history(cur, scraping_history_request)\n\n        sql = 'INSERT INTO dfs_player_statistic (' \\\n              'roto_guru_player_id, ' \\\n              'first_name, ' \\\n              'last_name, ' \\\n              'end_of_season_team_id, ' \\\n              'opponent_team_id, ' \\\n              'date_of_game, ' \\\n              'seconds_played, ' \\\n              'fanduel_positions, ' \\\n              'draftking_positions, ' \\\n              'yahoo_dfs_positions, ' \\\n              'fan_duel_points, ' \\\n              'fan_duel_price, ' \\\n              'draft_day_points, ' \\\n              'draft_day_price, ' \\\n              'draft_king_points, ' \\\n              'draft_king_price, ' \\\n              'yahoo_fantasy_sports_points, ' \\\n              'yahoo_fantasy_sports_price, ' \\\n              'scraping_history_id) ' \\\n              'VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s) ' \\\n              'ON CONFLICT DO NOTHING'\n\n        cur.execute(sql,\n                    (roto_guru_player_id,\n                     player_season_dfs_data.first_name,\n                     player_season_dfs_data.last_name,\n                     end_of_season_team_id,\n                     opponent_team_id,\n                     date_of_game,\n                     Util.convert_minutes_to_seconds(player_season_dfs_data.minutes_played),\n                     player_season_dfs_data.fanduel_positions,\n                     player_season_dfs_data.draftking_positions,\n                     player_season_dfs_data.yahoo_dfs_positions,\n                     player_season_dfs_data.fan_duel_points,\n                     player_season_dfs_data.fan_duel_price,\n                     player_season_dfs_data.draft_day_points,\n                     player_season_dfs_data.draft_day_price,\n                     player_season_dfs_data.draft_kings_points,\n                     player_season_dfs_data.draft_kings_price,\n                     player_season_dfs_data.yahoo_fantasy_sports_points,\n                     player_season_dfs_data.yahoo_fantasy_sports_price,\n                     scraping_history_id\n                     )\n                    )\n\n    def insert_dfs_player_match(self, matches: [], cur):\n\n        game_dao_impl = GameDAOImpl.getInstance()\n        player_dao_impl = PlayerDAOImpl.getInstance()\n\n        for match in matches:\n            slug_id = match.player.slug_id\n            dfs_statistics = match.dfs_statistics\n            first_name = dfs_statistics.first_name\n            last_name = dfs_statistics.last_name\n            end_of_season_team = dfs_statistics.end_of_season_team\n            date_of_game = dfs_statistics.date_of_game\n            date_of_game = np.datetime64(date_of_game)\n            date_of_game = pd.to_datetime(date_of_game)\n            date_of_game = date_of_game.date()\n            matcher_name = match.matcher.get_matcher_name()\n            when_matched = Util.now()\n\n            dfs_player_statistics_id = self.fetch_dfs_player_statistic_id(first_name,\n                                                                          last_name,\n                                                                          end_of_season_team,\n                                                                          date_of_game,\n                                                                          cur)\n\n            game_id = game_dao_impl.fetch_game_id(date_of_game, dfs_statistics.opponent, cur)\n            player_game_statistics_id = player_dao_impl.fetch_player_game_statistics_id(slug_id, game_id, cur)\n\n            if dfs_player_statistics_id > 0 and player_game_statistics_id > 0:\n                sql = 'INSERT INTO match_dfs_data ' \\\n                      '(dfs_player_statistic_id, player_game_statistic_id, matcher_name, when_matched) ' \\\n                      'VALUES(%s, %s, %s, %s) ON CONFLICT DO NOTHING'\n\n                cur.execute(sql, (dfs_player_statistics_id, player_game_statistics_id, matcher_name, when_matched))\n\n    def fetch_all_roto_guru_urls_objects(self, cur):\n\n        team_dao_impl = TeamDAOImpl.getInstance()\n\n        cur.execute('SELECT url, roto_guru_player_code, season, is_valid_url, first_name, last_name, '\n                    'end_of_season_team_id FROM roto_guru_player')\n        map = {}\n        for row in cur:\n            url = row[0]\n            roto_guru_player_code = row[1]\n            season = row[2]\n            is_valid_url = row[3]\n            first_name = row[4]\n            last_name = row[5]\n            end_of_season_team_id = row[6]\n            end_of_season_team = team_dao_impl.fetch_team_enum_by_id(end_of_season_team_id, cur)\n            roto_guru_url_data = RotoGuruUrlData(url,\n                                                 roto_guru_player_code,\n                                                 NBASeasonEnum.fetch_NBASeasonEnum_by_description(season),\n                                                 is_valid_url,\n                                                 first_name,\n                                                 last_name,\n                                                 end_of_season_team)\n            if season not in map:\n                map[season] = {}\n            map[season][roto_guru_player_code] = roto_guru_url_data\n\n        return map\n\n    def fetch_roto_guru_player_id_by_url(self, url, cur):\n        cur.execute(\n            'SELECT roto_guru_player_id FROM roto_guru_player WHERE url = %(url)s',\n            {'url': url}\n        )\n\n        data = cur.fetchall()\n        cols = list(map(lambda x: x[0], cur.description))\n        df = pd.DataFrame(data, columns=cols)\n\n        return int(df['roto_guru_player_id'].values[0])\n\n    def fetch_all_unmatched_dfs_data_by_date_of_games(self, date_of_games, cur):\n\n        player_dao_impl = PlayerDAOImpl.getInstance()\n        game_dao_impl = GameDAOImpl.getInstance()\n\n        cur.execute('SELECT dps.dfs_player_statistic_id, dps.roto_guru_player_id ' \\\n              'FROM dfs_player_statistic dps ' \\\n              'LEFT OUTER JOIN match_dfs_data mdd ON (mdd.dfs_player_statistic_id = dps.dfs_player_statistic_id) ' \\\n              'WHERE mdd.dfs_player_statistic_id IS NULL ' \\\n              'AND dps.date_of_game = %(date_of_games)s',\n            {'date_of_games': date_of_games}\n        )\n\n        roto_guru_data = []\n        data_list = cur.fetchall()\n        for data in data_list:\n            dfs_player_statistic_id = data[0]\n            roto_guru_player_id = data[1]\n            dfs_player_statistic = self.fetch_dfs_player_statistic_by_id(dfs_player_statistic_id, cur)\n            roto_guru_player = self.fetch_roto_guru_player_by_id(roto_guru_player_id, cur)\n            roto_guru_data.append({\n                'dfs_player_statistic': dfs_player_statistic,\n                'roto_guru_player': roto_guru_player\n            })\n\n        cur.execute( 'SELECT pgs.player_game_statistic_id, pgs.game_id ' \\\n              'FROM player_game_statistic pgs ' \\\n              'LEFT OUTER JOIN match_dfs_data mdd ON (mdd.player_game_statistic_id = pgs.player_game_statistic_id) ' \\\n              'JOIN game g ON (g.game_id = pgs.game_id) ' \\\n              'WHERE mdd.player_game_statistic_id IS NULL ' \\\n              'AND g.date_of_game = %(date_of_games)s',\n            {'date_of_games': date_of_games}\n        )\n\n        games_id_set = set()\n        games_map = {}\n\n        data_list = cur.fetchall()\n        for data in data_list:\n            player_game_statistic_id = data[0]\n            player_game_statistic = player_dao_impl.fetch_player_game_statistic_by_id(player_game_statistic_id, cur)\n            game_id = data[1]\n            player = player_dao_impl.fetch_player_by_slug_id(player_game_statistic.slug_id, cur)\n\n            if game_id not in games_id_set:\n                game = game_dao_impl.fetch_game_by_id(game_id, cur)\n                games_id_set.add(game_id)\n                games_map[game_id] = {'game': game, 'players_stats_array': []}\n            games_map[game_id]['players_stats_array'].append({'player': player, 'player_game_statistic': player_game_statistic})\n\n        return {'games_map': games_map, 'roto_guru_data': roto_guru_data}\n\n    def fetch_roto_guru_player_by_id(self, roto_guru_player_id: int, cur):\n\n        team_dao_impl = TeamDAOImpl.getInstance()\n\n        cur.execute(\n            'SELECT url, roto_guru_player_code, season, is_valid_url, first_name, '\n            'last_name, end_of_season_team_id FROM roto_guru_player '\n            'WHERE roto_guru_player_id = %(roto_guru_player_id)s',\n            {'roto_guru_player_id': roto_guru_player_id}\n        )\n\n        data = cur.fetchall()\n        cols = list(map(lambda x: x[0], cur.description))\n        df = pd.DataFrame(data, columns=cols)\n\n        url = df['url'].values[0]\n        roto_guru_player_code = df['roto_guru_player_code'].values[0]\n        season = df['season'].values[0]\n        is_valid_url = df['is_valid_url'].values[0]\n        first_name = df['first_name'].values[0]\n        last_name = df['last_name'].values[0]\n        end_of_season_team_enum = team_dao_impl.fetch_team_enum_by_id(int(df['end_of_season_team_id'].values[0]), cur)\n\n        return RotoGuruUrlData(url,\n                               roto_guru_player_code,\n                               NBASeasonEnum.fetch_NBASeasonEnum_by_description(season),\n                               is_valid_url,\n                               first_name,\n                               last_name,\n                               end_of_season_team_enum)\n\n    def fetch_dfs_player_statistic_id(self, first_name, last_name, end_of_season_team: TeamEnum, date_of_game, cur):\n\n        team_dao_impl = TeamDAOImpl.getInstance()\n\n        end_of_season_team_id = team_dao_impl.fetch_team_id_by_abbreviations(end_of_season_team.abbreviations(), cur)\n        cur.execute(\n            'SELECT dfs_player_statistic_id FROM dfs_player_statistic '\n            'WHERE first_name = %(first_name)s AND last_name = %(last_name)s AND '\n            'end_of_season_team_id = %(end_of_season_team_id)s AND '\n            'date_of_game = %(date_of_game)s',\n            {'first_name': first_name,\n             'last_name': last_name,\n             'end_of_season_team_id': end_of_season_team_id,\n             'date_of_game': date_of_game}\n        )\n\n        data = cur.fetchall()\n        cols = list(map(lambda x: x[0], cur.description))\n        df = pd.DataFrame(data, columns=cols)\n        return int(df['dfs_player_statistic_id'].values[0])\n\n    def fetch_dfs_player_statistic_by_id(self, dfs_player_statistic_id: int, cur):\n\n        team_dao_impl = TeamDAOImpl.getInstance()\n\n        cur.execute(\n            'SELECT first_name, last_name, end_of_season_team_id, opponent_team_id, date_of_game, seconds_played, '\n            'fanduel_positions, draftking_positions, yahoo_dfs_positions, fan_duel_points, fan_duel_price, '\n            'draft_day_points, draft_day_price, draft_king_points, draft_king_price, yahoo_fantasy_sports_points, '\n            'yahoo_fantasy_sports_price FROM dfs_player_statistic '\n            'WHERE dfs_player_statistic_id = %(dfs_player_statistic_id)s',\n            {'dfs_player_statistic_id': dfs_player_statistic_id}\n        )\n\n        data = cur.fetchall()\n        cols = list(map(lambda x: x[0], cur.description))\n        df = pd.DataFrame(data, columns=cols)\n\n        first_name = df['first_name'].values[0]\n        last_name = df['last_name'].values[0]\n        end_of_season_team_enum = team_dao_impl.fetch_team_enum_by_id(int(df['end_of_season_team_id'].values[0]), cur)\n        opponent_team_enum = team_dao_impl.fetch_team_enum_by_id(int(df['opponent_team_id'].values[0]), cur)\n        date_of_game = df['date_of_game'].values[0]\n        minutes_played = Util.convert_seconds_to_minutes(int(df['seconds_played'].values[0]))\n        fanduel_positions = df['fanduel_positions'].values[0]\n        draftking_positions = df['draftking_positions'].values[0]\n        yahoo_dfs_positions = df['yahoo_dfs_positions'].values[0]\n        fan_duel_points = TypeConversionUtil.convert_decimal_to_float(df['fan_duel_points'].values[0])\n        fan_duel_price = TypeConversionUtil.convert_int64_to_int(df['fan_duel_price'].values[0])\n        draft_day_points = TypeConversionUtil.convert_decimal_to_float(df['draft_day_points'].values[0])\n        draft_day_price = TypeConversionUtil.convert_int64_to_int(df['draft_day_price'].values[0])\n        draft_king_points = TypeConversionUtil.convert_decimal_to_float(df['draft_king_points'].values[0])\n        draft_king_price = TypeConversionUtil.convert_int64_to_int(df['draft_king_price'].values[0])\n        yahoo_fantasy_sports_points = TypeConversionUtil.convert_decimal_to_float(df['yahoo_fantasy_sports_points'].values[0])\n        yahoo_fantasy_sports_price = TypeConversionUtil.convert_int64_to_int(df['yahoo_fantasy_sports_price'].values[0])\n\n        return DFSStatistics(\n            first_name,\n            last_name,\n            end_of_season_team_enum,\n            opponent_team_enum,\n            date_of_game,\n            minutes_played,\n            fanduel_positions,\n            draftking_positions,\n            yahoo_dfs_positions,\n            fan_duel_points,\n            fan_duel_price,\n            draft_day_points,\n            draft_day_price,\n            draft_king_points,\n            draft_king_price,\n            yahoo_fantasy_sports_points,\n            yahoo_fantasy_sports_price\n        )","sub_path":"apps/databaseaccess/RotoGuruPlayerDAOImpl.py","file_name":"RotoGuruPlayerDAOImpl.py","file_ext":"py","file_size_in_byte":17215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"366482459","text":"import os, sys\nfrom collections import deque\nfrom datetime import datetime\nfrom flask import Flask, session, render_template, flash, jsonify, redirect, render_template, request, session, url_for\nfrom flask_socketio import SocketIO, send, emit, join_room, leave_room\n\nfrom helpers import login_required\n\napp = Flask(__name__)\napp.config[\"SECRET_KEY\"] = \"pumpkin\"\n#app.config[\"SECRET_KEY\"] = os.getenv(\"SECRET_KEY\")\napp.config[\"SESSION_PERMANENT\"] = True\napp.config[\"SESSION_TYPE\"] = \"filesystem\"\nsocketio = SocketIO(app)\n\n\n\n# Keeps a list of users that have logged in\nusers = []\n\n\n# Keeps a list of channels created by users\nchannels = []\n\n\n# Create a dictonary to save channel messages to\nchannelMessages = dict()\n\n\n\n# Loads index page showing lists of users and channels\n@app.route(\"/\", methods=['GET', 'POST'])\n@login_required\ndef index():\n\n    username = session[\"username\"]\n\n    return render_template(\"index.html\", channels=channels, users=users, username=username)\n\n\n\n\n\n# Saves username into global variable and logs in, redirects to index page\n@app.route(\"/signin\", methods=[\"GET\",\"POST\"])\ndef signin():\n\n    # Takes username from input field and adds to variable\n    username = request.form.get(\"username\")\n\n    if request.method == \"POST\":\n\n        #Check to see if the field is empty\n        if len(username) < 1 or username is '':\n            return render_template(\"error.html\", message = \"please enter a username to continue\")\n\n        # Check to see if username is already in use\n        if username in users:\n            return render_template(\"error.html\", message=\"Username Taken\")\n\n        # Appends username to users global variable\n        users.append(username)\n\n        # Stores username in session vairable\n        session['username'] = username\n\n        # Make session permanent\n        session.permanent = True\n\n        return redirect(\"/\")\n\n    else:\n        return render_template(\"login.html\")\n\n\n\n\n# Logs user out\n@app.route(\"/logout\", methods=['GET', 'POST'])\ndef logout():\n    \"\"\" Logout user from list and delete cookie.\"\"\"\n\n    # Remove from list\n    try:\n        users.remove(session['username'])\n    except ValueError:\n        pass\n\n    # Delete cookie\n    session.clear()\n\n    # Redirect to the index page\n    return redirect(\"/\")\n\n\n\n\n@app.route(\"/create_channel\", methods = [\"GET\", \"POST\"])\n@login_required\ndef create_channel():\n\n    # Take user channel name and make into variable\n    newChannel = request.form.get(\"channel_name\")\n    \n    if request.method ==\"POST\":\n\n        # Return error if channel form is empty\n        if len(newChannel) < 1 or \"\":\n            return render_template(\"error.html\", message=\"please enter a name for new channel\")\n\n        # Return error if channel name is already taken\n        elif newChannel in channels:\n            return render_template(\"error.html\", message=\"channel name already taken\")\n\n        # Append new channel to the global channel variable\n        channels.append(newChannel)\n\n        # Makes new channel messages a double ended queue to allow old messages be deleted from the beginning\n        channelMessages[newChannel] = deque()\n\n        return redirect(\"/channel/\" + newChannel)\n\n    else:\n        return render_template(\"index.html\", users=users, channels=channels)\n\n\n@app.route(\"/channel/<channel>\", methods=[\"GET\", \"POST\"])\n@login_required\ndef enter_channel(channel):\n\n    #Saves current channels as session variable\n    session['current_channel'] = channel\n\n\n    return render_template(\"channel.html\", channels=channels, messages=channelMessages[channel] )\n\n\n# Provides channels messages in JSON format\n@app.route(\"/api/channel/<channel>\")\ndef api_chat(channel):\n\n    # Creates a new list for channels \n    ChannelMessages = []\n\n    # Loop through messages in channel and add to new list\n    for messages in channelMessages[channel]:\n        ChannelMessages.append(messages)\n\n\n    # return new list in JSON\n    return jsonify(ChannelMessages)\n\n\n\n# Enters the user into the channel/room they selected\n@socketio.on('joined', namespace='/')\ndef join():\n\n    username = session.get('username')\n\n    room = session.get('current_channel')\n\n    join_room(room)\n\n    # Emits to the room that a user has joined\n    emit('status', {'userJoined' : session.get('username'), 'channel' : room, 'msg': session.get('username') + ' has entered the room'}, room=room, broadcast=True)\n\n\n\n\n# Allows user to leave the channel/room they were in\n@socketio.on('leave', namespace='/')\ndef leave():\n\n    username = session.get('username')\n\n    room = session.get('current_channel')\n\n    leave_room(room)\n\n    # Emits to the room that a user has left\n    emit('status', {'userLeft' : session.get('username'), 'channel' : room, 'msg': session.get('username') + ' has left the room'}, room=room, broadcast=True)\n\n\n\n# Takes user message from client browser and then broadcasts back to everyone in the room\n@socketio.on('submit message', namespace='/')\ndef send_message(data):\n\n    room = session.get('current_channel')\n    msg = data[\"msg\"]\n    timestamp = data[\"timestamp\"]\n\n    # Deletes the earliest messages once amount surpasses 100\n    if len(channelMessages[room]) > 100:\n        channelMessages[room].pop(0)\n\n    # Appends new messages to room dict\n    channelMessages[room].append([timestamp, session.get('username'), msg])\n\n    # Broadcasts messages to everyone in the room\n    emit('announce message', {'user': session.get('username'), 'msg': msg, \"timestamp\":timestamp}, room=room, broadcast=True)\n\n\nif __name__ == \"__main__\":\n    socketio.run(app, host=\"0.0.0.0\", port=int(os.environ.get(\"PORT\", 5000)))\n","sub_path":"application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":5586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"444617256","text":"\"\"\"\nGiven an m x n 2D binary grid grid which represents a map of '1's (land) and '0's (water), return the number\nof islands.\nAn island is surrounded by water and is formed by connecting adjacent lands horizontally or vertically.\nYou may assume all four edges of the grid are all surrounded by water.\n\"\"\"\nfrom typing import List\n\n\ndef num_islands(grid: List[List[str]]) -> int:\n    result = 0\n    for i in range(len(grid)):\n        for j in range(len(grid[0])):\n            if grid[i][j] == \"1\":\n                result += 1\n                grid[i][j] = \"0\"\n                queue = [(i, j)]\n                while queue:\n                    left, right = queue.pop(0)\n                    if left - 1 >= 0 and grid[left - 1][right] == \"1\":\n                        queue.append((left - 1, right))\n                        grid[left - 1][right] = \"0\"\n                    if left + 1 < len(grid) and grid[left + 1][right] == \"1\":\n                        queue.append((left + 1, right))\n                        grid[left + 1][right] = \"0\"\n                    if right - 1 >= 0 and grid[left][right - 1] == \"1\":\n                        queue.append((left, right - 1))\n                        grid[left][right - 1] = \"0\"\n                    if right + 1 < len(grid[0]) and grid[left][right + 1] == \"1\":\n                        queue.append((left, right + 1))\n                        grid[left][right + 1] = \"0\"\n    return result\n\n\n\"\"\"\nRuntime: 120 ms, faster than 98.13% of Python3 online submissions for Number of Islands.\nMemory Usage: 15.2 MB, less than 94.92% of Python3 online submissions for Number of Islands.\n\"\"\"\n","sub_path":"problems/number_of_islands.py","file_name":"number_of_islands.py","file_ext":"py","file_size_in_byte":1610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"34458479","text":"import discord\nfrom discord.ext import commands\n\n\nclass Basic(commands.Cog):\n    def __init__(self, bot):\n        self.bot = bot\n\n    @commands.command()\n    async def ping(self, ctx):\n        \"\"\" Ping \"\"\"\n        await ctx.send(f\"Pong! **{round(self.bot.latency * 1000, 2)}ms**\")\n\n    @commands.command()\n    async def embed_example(self, ctx, *, content=None):  # none by default makes it optional\n        embed = discord.Embed(\n            title=\"Embed title\",\n            color=discord.Color.green()\n        )\n        embed.add_field(name=\"This is a field\", value=\"It sure is\")\n        embed.add_field(name=\"And so is this\", value=\"ding ding ding\")\n        if content:\n            embed.add_field(name=\"You told me that...\", value=content, inline=False)\n        await ctx.send(embed=embed)\n\n\ndef setup(bot):\n    bot.add_cog(Basic(bot))\n","sub_path":"cogs/basic.py","file_name":"basic.py","file_ext":"py","file_size_in_byte":840,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"519548076","text":"# version code 122ffbc9f6c3+\ncoursera = 1\n# Please fill out this stencil and submit using the provided submission script.\n\n# Be sure that the file voting_record_dump109.txt is in the matrix/ directory.\n\n\n\n\n\n## 1: (Task 2.12.1) Create Voting Dict\ndef create_voting_dict(strlist):\n    \"\"\"\n    Input: a list of strings.  Each string represents the voting record of a senator.\n           The string consists of \n              - the senator's last name, \n              - a letter indicating the senator's party,\n              - a couple of letters indicating the senator's home state, and\n              - a sequence of numbers (0's, 1's, and negative 1's) indicating the senator's\n                votes on bills\n              all separated by spaces.\n    Output: A dictionary that maps the last name of a senator\n            to a list of numbers representing the senator's voting record.\n    Example: \n        >>> vd = create_voting_dict(['Kennedy D MA -1 -1 1 1', 'Snowe R ME 1 1 1 1'])\n        >>> vd == {'Snowe': [1, 1, 1, 1], 'Kennedy': [-1, -1, 1, 1]}\n        True\n\n    You can use the .split() method to split each string in the\n    strlist into a list; the first element of the list will be the senator's\n    name, the second will be his/her party affiliation (R or D), the\n    third will be his/her home state, and the remaining elements of\n    the list will be that senator's voting record on a collection of bills.\n\n    You can use the built-in procedure int() to convert a string\n    representation of an integer (e.g. '1') to the actual integer\n    (e.g. 1).\n\n    The lists for each senator should preserve the order listed in voting data.\n    In case you're feeling clever, this can be done in one line.\n    \"\"\"\n    vd = {}\n    for line in strlist:\n        splited = line.split()\n        vd[splited[0]] = [int(vote) for vote in splited[3:]]\n    return vd\n\n\n\n## 2: (Task 2.12.2) Policy Compare\ndef policy_compare(sen_a, sen_b, voting_dict):\n    \"\"\"\n    Input: last names of sen_a and sen_b, and a voting dictionary mapping senator\n           names to lists representing their voting records.\n    Output: the dot-product (as a number) representing the degree of similarity\n            between two senators' voting policies\n    Example:\n        >>> voting_dict = {'Fox-Epstein':[-1,-1,-1,1],'Ravella':[1,1,1,1]}\n        >>> policy_compare('Fox-Epstein','Ravella', voting_dict)\n        -2\n    \n    The code should correct compute dot-product even if the numbers are not all in {0,1,-1}.\n        >>> policy_compare('A', 'B', {'A':[100,10,1], 'B':[2,5,3]})\n        253\n        \n    You should definitely try to write this in one line.\n    \"\"\"\n    return sum(vote_a * vote_b for (vote_a, vote_b) in zip(voting_dict[sen_a], voting_dict[sen_b]))\n\n\n\n## 3: (Task 2.12.3) Most Similar\ndef most_similar(sen, voting_dict):\n    \"\"\"\n    Input: the last name of a senator, and a dictionary mapping senator names\n           to lists representing their voting records.\n    Output: the last name of the senator whose political mindset is most\n            like the input senator (excluding, of course, the input senator\n            him/herself). Resolve ties arbitrarily.\n    Example:\n        >>> vd = {'Klein': [1,1,1], 'Fox-Epstein': [1,-1,0], 'Ravella': [-1,0,0]}\n        >>> most_similar('Klein', vd)\n        'Fox-Epstein'\n\n    Note that you can (and are encouraged to) re-use you policy_compare procedure.\n    \"\"\"\n    similarity = -float('infinity')\n    name = ''\n    for sen_name in voting_dict:\n        if sen_name != sen:\n            val = policy_compare(sen, sen_name, voting_dict)\n            if similarity < val:\n                similarity = val\n                name = sen_name\n    return name\n\n\n\n## 4: (Task 2.12.4) Least Similar\ndef least_similar(sen, voting_dict):\n    \"\"\"\n    Input: the last name of a senator, and a dictionary mapping senator names\n           to lists representing their voting records.\n    Output: the last name of the senator whose political mindset is least like the input\n            senator.\n    Example:\n        >>> vd = {'a': [1,1,1], 'b': [1,-1,0], 'c': [-1,0,0]}\n        >>> least_similar('a', vd)\n        'c'\n    \"\"\"\n    similarity = float('infinity')\n    name = ''\n    for sen_name in voting_dict:\n        if sen_name != sen:\n            val = policy_compare(sen, sen_name, voting_dict)\n            if similarity > val:\n                similarity = val\n                name = sen_name\n    return name\n\n\n\n## 5: (Task 2.12.5) Chafee, Santorum\nmost_like_chafee    = 'Jeffords'\nleast_like_santorum = 'Feingold' \n\n\n\n## 6: (Task 2.12.7) Most Average Democrat\ndef find_average_similarity(sen, sen_set, voting_dict):\n    \"\"\"\n    Input: the name of a senator, a set of senator names, and a voting dictionary.\n    Output: the average dot-product between sen and those in sen_set.\n    Example:\n        >>> vd = {'Klein': [1,1,1], 'Fox-Epstein': [1,-1,0], 'Ravella': [-1,0,0]}\n        >>> find_average_similarity('Klein', {'Fox-Epstein','Ravella'}, vd)\n        -0.5\n    \"\"\"\n    sim_vals = [policy_compare(sen, name, voting_dict) for name in sen_set if name != sen]\n    return sum(sim_vals) / float(len(sim_vals))\n\n\ndef key_value_max(data):\n    \"\"\"\n    Input: a tuple containing (key, value)\n    Output: the key related with the max value in the estructure\n    \"\"\"\n    val = -float('infinity')\n    key = ''\n    for (d_key, value) in data:\n        if val < value:\n            val, key = value, d_key\n    return key, val\n\n\nmost_average_Democrat = 'Biden' # give the last name (or code that computes the last name)\n\n\n## 7: (Task 2.12.8) Average Record\ndef find_average_record(sen_set, voting_dict):\n    \"\"\"\n    Input: a set of last names, a voting dictionary\n    Output: a vector containing the average components of the voting records\n            of the senators in the input set\n    Example: \n        >>> voting_dict = {'Klein': [-1,0,1], 'Fox-Epstein': [-1,-1,-1], 'Ravella': [0,0,1]}\n        >>> find_average_record({'Fox-Epstein','Ravella'}, voting_dict)\n        [-0.5, -0.5, 0.0]\n        >>> d = {'c': [-1,-1,0], 'b': [0,1,1], 'a': [0,1,1], 'e': [-1,-1,1], 'd': [-1,1,1]}\n        >>> find_average_record({'a','c','e'}, d)\n        [-0.6666666666666666, -0.3333333333333333, 0.6666666666666666]\n        >>> find_average_record({'a','c','e','b'}, d)\n        [-0.5, 0.0, 0.75]\n        >>> find_average_record({'a'}, d)\n        [0.0, 1.0, 1.0]\n    \"\"\"\n    vot_rec_len = len(voting_dict[list(voting_dict.keys())[0]])\n    av_rec = [ sum([voting_dict[name][idx] for name in sen_set]) \n                                          for idx in range(vot_rec_len)]\n    return [ val / float(len(sen_set)) for val in av_rec]\n\naverage_Democrat_record = [-0.16279069767441862,\n -0.23255813953488372,\n 1.0,\n 0.8372093023255814,\n 0.9767441860465116,\n -0.13953488372093023,\n -0.9534883720930233,\n 0.813953488372093,\n 0.9767441860465116,\n 0.9767441860465116,\n 0.9069767441860465,\n 0.7674418604651163,\n 0.6744186046511628,\n 0.9767441860465116,\n -0.5116279069767442,\n 0.9302325581395349,\n 0.9534883720930233,\n 0.9767441860465116,\n -0.3953488372093023,\n 0.9767441860465116,\n 1.0,\n 1.0,\n 1.0,\n 0.9534883720930233,\n -0.4883720930232558,\n 1.0,\n -0.32558139534883723,\n -0.06976744186046512,\n 0.9767441860465116,\n 0.8604651162790697,\n 0.9767441860465116,\n 0.9767441860465116,\n 1.0,\n 1.0,\n 0.9767441860465116,\n -0.3488372093023256,\n 0.9767441860465116,\n -0.4883720930232558,\n 0.23255813953488372,\n 0.8837209302325582,\n 0.4418604651162791,\n 0.9069767441860465,\n -0.9069767441860465,\n 1.0,\n 0.9069767441860465,\n -0.3023255813953488] # give the vector as a list\n\ndef key_value_min(data):\n    \"\"\"\n    Input: a tuple containing (key, value)\n    Output: the key related with the max value in the estructure\n    \"\"\"\n    val, key = float('infinity'), ''\n    for (d_key, value) in data:\n        if val > value:\n            val, key = value, d_key\n    return key, val\n\n## 8: (Task 2.12.9) Bitter Rivals\ndef bitter_rivals(voting_dict):\n    \"\"\"\n    Input: a dictionary mapping senator names to lists representing\n           their voting records\n    Output: a tuple containing the two senators who most strongly\n            disagree with one another.\n    Example: \n        >>> voting_dict = {'Klein': [-1,0,1], 'Fox-Epstein': [-1,-1,-1], 'Ravella': [0,0,1]}\n        >>> br = bitter_rivals(voting_dict)\n        >>> br == ('Fox-Epstein', 'Ravella') or br == ('Ravella', 'Fox-Epstein')\n        True\n    \"\"\"\n    tuple_vals = [((name1, name2), policy_compare(name1, name2, voting_dict))\n                    for name1 in voting_dict for name2 in voting_dict if name1 != name2]\n    return key_value_min(tuple_vals)[0]\n\n","sub_path":"contents/Vector/DataAnlysEx/exrecises/politics_lab/politics_lab.py","file_name":"politics_lab.py","file_ext":"py","file_size_in_byte":8588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"391128837","text":"from google.cloud import storage\nfrom dateutil import tz\nimport google.cloud\nimport pandas as pd\nimport datetime as dt\nimport pytz\nimport time\nimport json\nimport myutil\n\n\ndef payload_check(inputdf):\n    if 'payload' in inputdf.columns:\n        return True\n    else:\n        return False\n\n\ndef listen_to_stoage(bucket_name, crawlqueue, sto_name_prefix):\n    client = storage.Client('yl3573')\n    bucket = client.bucket('yl-crawl')\n    while True:\n        jobdf = []\n        retlst = myutil.list_file(bucket_name)\n        for each in retlst:\n            if sto_name_prefix in each.name and '.csv' in each.name:\n                print(each.name)\n                blob = bucket.blob(each.name)\n                try:\n                    blob.reload()\n                except google.cloud.exceptions.NotFound:\n                    continue\n                timenow = (dt.datetime.now(pytz.timezone('America/Chicago'))).replace(tzinfo=None)\n                if (timenow - blob.updated.astimezone(tz.tzlocal()).replace(tzinfo=None)).seconds < 40:\n                    continue\n                df = myutil.read_csv_from_sto(bucket_name + '/' + each.name)\n                if payload_check(df) is True:\n                    jobdf.append(df)\n                each.delete()\n\n        if len(jobdf) != 0:\n            df_all = pd.concat(jobdf)\n            val = df_all.values.flatten()\n            for eachval in val:\n                try:\n                    payload_str = json.loads(eachval)\n                    crawlqueue.put(payload_str)\n                except ValueError:\n                    print('Json Parse Error: {}'.format(str(eachval)))\n                    continue\n        time.sleep(60)\n\nif __name__ == '__main__':\n    from myprofile import profile as PROFILE\n    import multiprocessing as mp\n    from multiprocessing import Lock, Value\n    chooseprofile = 'test_index'\n    start_gcloud_worker = False\n\n    if chooseprofile not in PROFILE:\n        raise ValueError('Profile name not found')\n    thisPROFILE = PROFILE[chooseprofile]\n    receiver_port = thisPROFILE['receiver_server'][1]\n    distributor_port = thisPROFILE['distributor_server'][1]\n    status_port = thisPROFILE['status_server'][1]\n    distributor_key = thisPROFILE['distributor_key']\n    receiver_key = thisPROFILE['receiver_key']\n    sto_prefix = 'crawl_job_' + thisPROFILE['storage_name_filter']\n    if start_gcloud_worker:\n        vm_prefix = 'dp-crawler-' + thisPROFILE['vm_name_filter']\n        if 'dp-crawler' not in vm_prefix:\n            raise NameError('Worker name must start with dp-crawler')\n\n    mp_manger = mp.Manager()\n    job_queue = mp.Queue()\n    wait_dict = mp_manger.dict()\n    wait_lock = mp.Lock()  # Used to lock wait dict\n    ret_dict = mp_manger.dict()\n    ret_lock = mp.Lock()  # Used to lock ret dict\n    upload_queue = mp.Queue()\n    global_counter = Value('i', 0)\n    speed_counter = mp_manger.dict()\n    speed_counter['speed'] = 0\n    speed_counter_lock = mp.Lock()\n    counter_lock = mp.Lock()\n\n    status_key = thisPROFILE['statuskey']\n    timeout_sec = thisPROFILE['recycle_timeout']\n    retry_lmt = thisPROFILE['retry_lmt']\n    distributed_lmt = thisPROFILE['distribution_lmt']\n    listen_to_stoage('yl-crawl', job_queue, sto_prefix)\n","sub_path":"storage_fetch.py","file_name":"storage_fetch.py","file_ext":"py","file_size_in_byte":3219,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"97091459","text":"import socket\nimport sys\n\n\ndef start_tcp_server(ip, port):\n    print('plc server start')\n    #create socket\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    server_address = (ip, port)\n    #bind port\n    sock.bind(server_address)\n    #starting listening, allow only one connection\n    try:\n        sock.listen(1)\n    except:\n        sys.exit(1)\n    while True:\n        client, addr = sock.accept()\n        print('get new sock')\n        client_data = client.recv(1024)\n        print(client_data)\n        client.close()\n\nif __name__ == '__main__':\n    start_tcp_server('127.0.0.1', 4321)\n","sub_path":"util/plc-server-test.py","file_name":"plc-server-test.py","file_ext":"py","file_size_in_byte":602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"364579947","text":"##############################################################################\n#\n# Copyright (c) 2004 Zope Corporation and Contributors.\n# All Rights Reserved.\n# \n# This software is subject to the provisions of the Zope Public License,\n# Version 2.1 (ZPL).  A copy of the ZPL should accompany this distribution.\n# THIS SOFTWARE IS PROVIDED \"AS IS\" AND ANY AND ALL EXPRESS OR IMPLIED\n# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS\n# FOR A PARTICULAR PURPOSE.\n# \n##############################################################################\n\"\"\"Filesystem synchronization support.\n\n$Id$\n\"\"\"\nfrom zope.interface import implements\nfrom zope.fssync import synchronizer\nfrom zope.fssync import interfaces\n\nclass ZPTPageAdapter(synchronizer.FileSynchronizer):\n    \"\"\"Synchronizer adapter for ZPT page objects.\n    \"\"\"\n    implements(interfaces.IFileSynchronizer)\n\n    def dump(self, writeable):\n        writeable.write(self.context.getSource())\n\n    def load(self, readable):\n        # Convert the data to Unicode, since that's what ZPTPage wants;\n        # it's normally read from a file so it'll be bytes.\n\n        # Sometimes we cannot communicate an encoding. Zope's default is UTF-8,\n        # so use it.\n        data = readable.read()\n        self.context.setSource(data.decode('UTF-8'))\n","sub_path":"zope.app.fssync/tags/3.6.0/src/zope/app/fssync/zptpage/adapter.py","file_name":"adapter.py","file_ext":"py","file_size_in_byte":1386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"355991193","text":"from textwrap import dedent\n\nimport graphene\nfrom django.db import transaction\nfrom django.template.defaultfilters import slugify\nfrom graphene.types import InputObjectType\nfrom graphql_jwt.decorators import permission_required\n\nfrom ....skill import models\nfrom ....skill.tasks import update_variants_names\nfrom ....skill.thumbnails import (\n    create_category_background_image_thumbnails,\n    create_collection_background_image_thumbnails, create_skill_thumbnails)\nfrom ....skill.utils.attributes import get_name_from_attributes\nfrom ...core.enums import TaxRateType\nfrom ...core.mutations import BaseMutation, ModelDeleteMutation, ModelMutation\nfrom ...core.scalars import Decimal, WeightScalar\nfrom ...core.types import SeoInput, Upload\nfrom ...core.utils import clean_seo_fields\nfrom ..types import Category, Collection, Skill, SkillImage, SkillVariant\nfrom ..utils import attributes_to_hstore, validate_image_file\n\n\nclass CategoryInput(graphene.InputObjectType):\n    description = graphene.String(\n        description='Category description (HTML/text).')\n    description_json = graphene.JSONString(\n        description='Category description (JSON).')\n    name = graphene.String(description='Category name.')\n    slug = graphene.String(description='Category slug.')\n    seo = SeoInput(description='Search engine optimization fields.')\n    background_image = Upload(description='Background image file.')\n    background_image_alt = graphene.String(\n        description='Alt text for an image.')\n\n\nclass CategoryCreate(ModelMutation):\n    class Arguments:\n        input = CategoryInput(\n            required=True, description='Fields required to create a category.')\n        parent_id = graphene.ID(\n            description=dedent('''\n                ID of the parent category. If empty, category will be top level\n                category.'''), name='parent')\n\n    class Meta:\n        description = 'Creates a new category.'\n        model = models.Category\n\n    @classmethod\n    def clean_input(cls, info, instance, input, errors):\n        cleaned_input = super().clean_input(info, instance, input, errors)\n        if 'slug' not in cleaned_input and 'name' in cleaned_input:\n            cleaned_input['slug'] = slugify(cleaned_input['name'])\n        parent_id = input['parent_id']\n        if parent_id:\n            parent = cls.get_node_or_error(\n                info, parent_id, errors, field='parent', only_type=Category)\n            cleaned_input['parent'] = parent\n        if input.get('background_image'):\n            image_data = info.context.FILES.get(input['background_image'])\n            validate_image_file(cls, image_data, 'background_image', errors)\n        clean_seo_fields(cleaned_input)\n        return cleaned_input\n\n    @classmethod\n    def user_is_allowed(cls, user, input):\n        return user.has_perm('skill.manage_skills')\n\n    @classmethod\n    def mutate(cls, root, info, **data):\n        parent_id = data.pop('parent_id', None)\n        data['input']['parent_id'] = parent_id\n        return super().mutate(root, info, **data)\n\n    @classmethod\n    def save(cls, info, instance, cleaned_input):\n        instance.save()\n        if cleaned_input.get('background_image'):\n            create_category_background_image_thumbnails.delay(instance.pk)\n\n\nclass CategoryUpdate(CategoryCreate):\n    class Arguments:\n        id = graphene.ID(\n            required=True, description='ID of a category to update.')\n        input = CategoryInput(\n            required=True, description='Fields required to update a category.')\n\n    class Meta:\n        description = 'Updates a category.'\n        model = models.Category\n\n    @classmethod\n    def save(cls, info, instance, cleaned_input):\n        if cleaned_input.get('background_image'):\n            create_category_background_image_thumbnails.delay(instance.pk)\n        instance.save()\n\n\nclass CategoryDelete(ModelDeleteMutation):\n    class Arguments:\n        id = graphene.ID(\n            required=True, description='ID of a category to delete.')\n\n    class Meta:\n        description = 'Deletes a category.'\n        model = models.Category\n\n    @classmethod\n    def user_is_allowed(cls, user, input):\n        return user.has_perm('skill.manage_skills')\n\n\nclass CollectionInput(graphene.InputObjectType):\n    is_published = graphene.Boolean(\n        description='Informs whether a collection is published.')\n    name = graphene.String(description='Name of the collection.')\n    slug = graphene.String(description='Slug of the collection.')\n    description = graphene.String(\n        description='Description of the collection (HTML/text).')\n    description_json = graphene.JSONString(\n        description='Description of the collection (JSON).')\n    background_image = Upload(description='Background image file.')\n    background_image_alt = graphene.String(\n        description='Alt text for an image.')\n    seo = SeoInput(description='Search engine optimization fields.')\n    publication_date = graphene.Date(\n        description='Publication date. ISO 8601 standard.')\n\n\nclass CollectionCreateInput(CollectionInput):\n    skills = graphene.List(\n        graphene.ID,\n        description='List of skills to be added to the collection.',\n        name='skills')\n\n\nclass CollectionCreate(ModelMutation):\n    class Arguments:\n        input = CollectionCreateInput(\n            required=True,\n            description='Fields required to create a collection.')\n\n    class Meta:\n        description = 'Creates a new collection.'\n        model = models.Collection\n\n    @classmethod\n    def user_is_allowed(cls, user, input):\n        return user.has_perm('skill.manage_skills')\n\n    @classmethod\n    def clean_input(cls, info, instance, input, errors):\n        cleaned_input = super().clean_input(info, instance, input, errors)\n        if 'slug' not in cleaned_input and 'name' in cleaned_input:\n            cleaned_input['slug'] = slugify(cleaned_input['name'])\n        if input.get('background_image'):\n            image_data = info.context.FILES.get(input['background_image'])\n            validate_image_file(cls, image_data, 'background_image', errors)\n        clean_seo_fields(cleaned_input)\n        return cleaned_input\n\n    @classmethod\n    def save(cls, info, instance, cleaned_input):\n        instance.save()\n        if cleaned_input.get('background_image'):\n            create_collection_background_image_thumbnails.delay(instance.pk)\n\n\nclass CollectionUpdate(CollectionCreate):\n    class Arguments:\n        id = graphene.ID(\n            required=True, description='ID of a collection to update.')\n        input = CollectionInput(\n            required=True,\n            description='Fields required to update a collection.')\n\n    class Meta:\n        description = 'Updates a collection.'\n        model = models.Collection\n\n    @classmethod\n    def save(cls, info, instance, cleaned_input):\n        if cleaned_input.get('background_image'):\n            create_collection_background_image_thumbnails.delay(instance.pk)\n        instance.save()\n\n\nclass CollectionDelete(ModelDeleteMutation):\n    class Arguments:\n        id = graphene.ID(\n            required=True, description='ID of a collection to delete.')\n\n    class Meta:\n        description = 'Deletes a collection.'\n        model = models.Collection\n\n    @classmethod\n    def user_is_allowed(cls, user, input):\n        return user.has_perm('skill.manage_skills')\n\n\nclass CollectionAddSkills(BaseMutation):\n    collection = graphene.Field(\n        Collection,\n        description='Collection to which skills will be added.')\n\n    class Arguments:\n        collection_id = graphene.Argument(\n            graphene.ID, required=True,\n            description='ID of a collection.')\n        skills = graphene.List(\n            graphene.ID, required=True,\n            description='List of skill IDs.')\n\n    class Meta:\n        description = 'Adds skills to a collection.'\n\n    @classmethod\n    @permission_required('skill.manage_skills')\n    def mutate(cls, root, info, collection_id, skills):\n        errors = []\n        collection = cls.get_node_or_error(\n            info, collection_id, errors, 'collectionId', only_type=Collection)\n        skills = cls.get_nodes_or_error(\n            skills, errors, 'skills', only_type=Skill)\n        collection.skills.add(*skills)\n        return CollectionAddSkills(collection=collection, errors=errors)\n\n\nclass CollectionRemoveSkills(BaseMutation):\n    collection = graphene.Field(\n        Collection,\n        description='Collection from which skills will be removed.')\n\n    class Arguments:\n        collection_id = graphene.Argument(\n            graphene.ID, required=True, description='ID of a collection.')\n        skills = graphene.List(\n            graphene.ID, required=True, description='List of skill IDs.')\n\n    class Meta:\n        description = 'Remove skills from a collection.'\n\n    @classmethod\n    @permission_required('skill.manage_skills')\n    def mutate(cls, root, info, collection_id, skills):\n        errors = []\n        collection = cls.get_node_or_error(\n            info, collection_id, errors, 'collectionId', only_type=Collection)\n        skills = cls.get_nodes_or_error(\n            skills, errors, 'skills', only_type=Skill)\n        collection.skills.remove(*skills)\n        return CollectionRemoveSkills(collection=collection, errors=errors)\n\n\nclass AttributeValueInput(InputObjectType):\n    slug = graphene.String(\n        required=True, description='Slug of an attribute.')\n    value = graphene.String(\n        required=True, description='Value of an attribute.')\n\n\nclass SkillInput(graphene.InputObjectType):\n    attributes = graphene.List(\n        AttributeValueInput,\n        description='List of attributes.')\n    publication_date = graphene.types.datetime.Date(\n        description='Publication date. ISO 8601 standard.')\n    category = graphene.ID(\n        description='ID of the skill\\'s category.', name='category')\n    charge_taxes = graphene.Boolean(\n        description='Determine if taxes are being charged for the skill.')\n    collections = graphene.List(\n        graphene.ID,\n        description='List of IDs of collections that the skill belongs to.',\n        name='collections')\n    description = graphene.String(\n        description='Skill description (HTML/text).')\n    description_json = graphene.JSONString(\n        description='Skill description (JSON).')\n    is_published = graphene.Boolean(\n        description='Determines if skill is visible to customers.')\n    name = graphene.String(description='Skill name.')\n    price = Decimal(description='Skill price.')\n    tax_rate = TaxRateType(description='Tax rate.')\n    seo = SeoInput(description='Search engine optimization fields.')\n    weight = WeightScalar(\n        description='Weight of the Skill.', required=False)\n    sku = graphene.String(\n        description=dedent(\"\"\"Stock keeping unit of a skill. Note: this\n        field is only used if a skill doesn't use variants.\"\"\"))\n    quantity = graphene.Int(\n        description=dedent(\"\"\"The total quantity of a skill available for\n        sale. Note: this field is only used if a skill doesn't\n        use variants.\"\"\"))\n    track_inventory = graphene.Boolean(\n        description=dedent(\"\"\"Determines if the inventory of this skill\n        should be tracked. If false, the quantity won't change when customers\n        buy this item. Note: this field is only used if a skill doesn't\n        use variants.\"\"\"))\n\n\nclass SkillCreateInput(SkillInput):\n    skill_type = graphene.ID(\n        description='ID of the type that skill belongs to.',\n        name='skillType', required=True)\n\n\nclass SkillCreate(ModelMutation):\n    class Arguments:\n        input = SkillCreateInput(\n            required=True, description='Fields required to create a skill.')\n\n    class Meta:\n        description = 'Creates a new skill.'\n        model = models.Skill\n\n    @classmethod\n    def clean_input(cls, info, instance, input, errors):\n        cleaned_input = super().clean_input(info, instance, input, errors)\n        # Attributes are provided as list of `AttributeValueInput` objects.\n        # We need to transform them into the format they're stored in the\n        # `Skill` model, which is HStore field that maps attribute's PK to\n        # the value's PK.\n\n        attributes = cleaned_input.pop('attributes', [])\n        skill_type = (\n            instance.skill_type\n            if instance.pk else cleaned_input.get('skill_type'))\n\n        if attributes and skill_type:\n            qs = skill_type.skill_attributes.prefetch_related('values')\n            try:\n                attributes = attributes_to_hstore(attributes, qs)\n            except ValueError as e:\n                cls.add_error(errors, 'attributes', str(e))\n            else:\n                cleaned_input['attributes'] = attributes\n        clean_seo_fields(cleaned_input)\n        cls.clean_sku(skill_type, cleaned_input, errors)\n        return cleaned_input\n\n    @classmethod\n    def clean_sku(cls, skill_type, cleaned_input, errors):\n        \"\"\"Validate SKU input field.\n\n        When creating skills that don't use variants, SKU is required in\n        the input in task to create the default variant underneath.\n        See the documentation for `has_variants` field for details:\n        http://docs.getsaleor.com/en/latest/architecture/skills.html#skill-types\n        \"\"\"\n        if not skill_type.has_variants:\n            input_sku = cleaned_input.get('sku')\n            if not input_sku:\n                cls.add_error(errors, 'sku', 'This field cannot be blank.')\n            elif models.SkillVariant.objects.filter(sku=input_sku).exists():\n                cls.add_error(\n                    errors, 'sku', 'Skill with this SKU already exists.')\n\n    @classmethod\n    @transaction.atomic\n    def save(cls, info, instance, cleaned_input):\n        instance.save()\n        if not instance.skill_type.has_variants:\n            site_settings = info.context.site.settings\n            track_inventory = cleaned_input.get(\n                'track_inventory', site_settings.track_inventory_by_default)\n            quantity = cleaned_input.get('quantity', 0)\n            sku = cleaned_input.get('sku')\n            models.SkillVariant.objects.create(\n                skill=instance, track_inventory=track_inventory,\n                sku=sku, quantity=quantity)\n\n    @classmethod\n    def _save_m2m(cls, info, instance, cleaned_data):\n        collections = cleaned_data.get('collections', None)\n        if collections is not None:\n            instance.collections.set(collections)\n\n    @classmethod\n    def user_is_allowed(cls, user, input):\n        return user.has_perm('skill.manage_skills')\n\n\nclass SkillUpdate(SkillCreate):\n    class Arguments:\n        id = graphene.ID(\n            required=True, description='ID of a skill to update.')\n        input = SkillInput(\n            required=True, description='Fields required to update a skill.')\n\n    class Meta:\n        description = 'Updates an existing skill.'\n        model = models.Skill\n\n    @classmethod\n    def clean_sku(cls, skill_type, cleaned_input, errors):\n        input_sku = cleaned_input.get('sku')\n        if (not skill_type.has_variants and\n                input_sku and\n                models.SkillVariant.objects.filter(sku=input_sku).exists()):\n            cls.add_error(\n                errors, 'sku', 'Skill with this SKU already exists.')\n\n    @classmethod\n    @transaction.atomic\n    def save(cls, info, instance, cleaned_input):\n        instance.save()\n        if not instance.skill_type.has_variants:\n            variant = instance.variants.first()\n            update_fields = []\n            if 'track_inventory' in cleaned_input:\n                variant.track_inventory = cleaned_input['track_inventory']\n                update_fields.append('track_inventory')\n            if 'quantity' in cleaned_input:\n                variant.quantity = cleaned_input['quantity']\n                update_fields.append('quantity')\n            if 'sku' in cleaned_input:\n                variant.sku = cleaned_input['sku']\n                update_fields.append('sku')\n            if update_fields:\n                variant.save(update_fields=update_fields)\n\n\nclass SkillDelete(ModelDeleteMutation):\n    class Arguments:\n        id = graphene.ID(\n            required=True, description='ID of a skill to delete.')\n\n    class Meta:\n        description = 'Deletes a skill.'\n        model = models.Skill\n\n    @classmethod\n    def user_is_allowed(cls, user, input):\n        return user.has_perm('skill.manage_skills')\n\n\nclass SkillVariantInput(graphene.InputObjectType):\n    attributes = graphene.List(\n        AttributeValueInput, required=False,\n        description='List of attributes specific to this variant.')\n    cost_price = Decimal(description='Cost price of the variant.')\n    price_override = Decimal(\n        description='Special price of the particular variant.')\n    sku = graphene.String(description='Stock keeping unit.')\n    quantity = graphene.Int(\n        description='The total quantity of this variant available for sale.')\n    track_inventory = graphene.Boolean(\n        description=dedent(\"\"\"Determines if the inventory of this variant should\n        be tracked. If false, the quantity won't change when customers\n        buy this item.\"\"\"))\n    weight = WeightScalar(\n        description='Weight of the Skill Variant.', required=False)\n\n\nclass SkillVariantCreateInput(SkillVariantInput):\n    attributes = graphene.List(\n        AttributeValueInput, required=True,\n        description='List of attributes specific to this variant.')\n    skill = graphene.ID(\n        description='Skill ID of which type is the variant.',\n        name='skill', required=True)\n\n\nclass SkillVariantCreate(ModelMutation):\n    class Arguments:\n        input = SkillVariantCreateInput(\n            required=True,\n            description='Fields required to create a skill variant.')\n\n    class Meta:\n        description = 'Creates a new variant for a skill'\n        model = models.SkillVariant\n\n    @classmethod\n    def clean_skill_type_attributes(\n            cls, attributes_qs, attributes_input, errors):\n        # transform attributes_input list to a dict of slug:value pairs\n        attributes_input = {\n            item['slug']: item['value'] for item in attributes_input}\n\n        for attr in attributes_qs:\n            value = attributes_input.get(attr.slug, None)\n            if not value:\n                fieldname = 'attributes:%s' % attr.slug\n                cls.add_error(errors, fieldname, 'This field cannot be blank.')\n\n    @classmethod\n    def clean_input(cls, info, instance, input, errors):\n        cleaned_input = super().clean_input(info, instance, input, errors)\n\n        # Attributes are provided as list of `AttributeValueInput` objects.\n        # We need to transform them into the format they're stored in the\n        # `Skill` model, which is HStore field that maps attribute's PK to\n        # the value's PK.\n\n        if 'attributes' in input:\n            attributes_input = cleaned_input.pop('attributes')\n            skill = instance.skill if instance.pk else cleaned_input.get(\n                'skill')\n            skill_type = skill.skill_type\n            variant_attrs = skill_type.variant_attributes.prefetch_related(\n                'values')\n            try:\n                cls.clean_skill_type_attributes(\n                    variant_attrs, attributes_input, errors)\n                attributes = attributes_to_hstore(\n                    attributes_input, variant_attrs)\n            except ValueError as e:\n                cls.add_error(errors, 'attributes', str(e))\n            else:\n                cleaned_input['attributes'] = attributes\n        return cleaned_input\n\n    @classmethod\n    def save(cls, info, instance, cleaned_input):\n        attributes = instance.skill.skill_type.variant_attributes.all()\n        instance.name = get_name_from_attributes(instance, attributes)\n        instance.save()\n\n    @classmethod\n    def user_is_allowed(cls, user, input):\n        return user.has_perm('skill.manage_skills')\n\n\nclass SkillVariantUpdate(SkillVariantCreate):\n    class Arguments:\n        id = graphene.ID(\n            required=True, description='ID of a skill variant to update.')\n        input = SkillVariantInput(\n            required=True,\n            description='Fields required to update a skill variant.')\n\n    class Meta:\n        description = 'Updates an existing variant for skill'\n        model = models.SkillVariant\n\n\nclass SkillVariantDelete(ModelDeleteMutation):\n    class Arguments:\n        id = graphene.ID(\n            required=True, description='ID of a skill variant to delete.')\n\n    class Meta:\n        description = 'Deletes a skill variant.'\n        model = models.SkillVariant\n\n    @classmethod\n    def user_is_allowed(cls, user, input):\n        return user.has_perm('skill.manage_skills')\n\n\nclass SkillTypeInput(graphene.InputObjectType):\n    name = graphene.String(description='Name of the skill type.')\n    has_variants = graphene.Boolean(\n        description=dedent(\"\"\"Determines if skill of this type has multiple\n        variants. This option mainly simplifies skill management\n        in the dashboard. There is always at least one variant created under\n        the hood.\"\"\"))\n    skill_attributes = graphene.List(\n        graphene.ID,\n        description='List of attributes shared among all skill variants.',\n        name='skillAttributes')\n    variant_attributes = graphene.List(\n        graphene.ID,\n        description=dedent(\"\"\"List of attributes used to distinguish between\n        different variants of a skill.\"\"\"),\n        name='variantAttributes')\n    is_delivery_required = graphene.Boolean(\n        description=dedent(\"\"\"Determines if delivery is required for skills\n        of this variant.\"\"\"))\n    weight = WeightScalar(description='Weight of the SkillType items.')\n    tax_rate = TaxRateType(description='A type of goods.')\n\n\nclass SkillTypeCreate(ModelMutation):\n    class Arguments:\n        input = SkillTypeInput(\n            required=True,\n            description='Fields required to create a skill type.')\n\n    class Meta:\n        description = 'Creates a new skill type.'\n        model = models.SkillType\n\n    @classmethod\n    def user_is_allowed(cls, user, input):\n        return user.has_perm('skill.manage_skills')\n\n    @classmethod\n    def _save_m2m(cls, info, instance, cleaned_data):\n        super()._save_m2m(info, instance, cleaned_data)\n        if 'skill_attributes' in cleaned_data:\n            instance.skill_attributes.set(cleaned_data['skill_attributes'])\n        if 'variant_attributes' in cleaned_data:\n            instance.variant_attributes.set(cleaned_data['variant_attributes'])\n\n\nclass SkillTypeUpdate(SkillTypeCreate):\n    class Arguments:\n        id = graphene.ID(\n            required=True, description='ID of a skill type to update.')\n        input = SkillTypeInput(\n            required=True,\n            description='Fields required to update a skill type.')\n\n    class Meta:\n        description = 'Updates an existing skill type.'\n        model = models.SkillType\n\n    @classmethod\n    def save(cls, info, instance, cleaned_input):\n        variant_attr = cleaned_input.get('variant_attributes')\n        if variant_attr:\n            variant_attr = set(variant_attr)\n            variant_attr_ids = [attr.pk for attr in variant_attr]\n            update_variants_names.delay(instance.pk, variant_attr_ids)\n        super().save(info, instance, cleaned_input)\n\n\nclass SkillTypeDelete(ModelDeleteMutation):\n    class Arguments:\n        id = graphene.ID(\n            required=True, description='ID of a skill type to delete.')\n\n    class Meta:\n        description = 'Deletes a skill type.'\n        model = models.SkillType\n\n    @classmethod\n    def user_is_allowed(cls, user, input):\n        return user.has_perm('skill.manage_skills')\n\n\nclass SkillImageCreateInput(graphene.InputObjectType):\n    alt = graphene.String(description='Alt text for an image.')\n    image = Upload(\n        required=True,\n        description='Represents an image file in a multipart request.')\n    skill = graphene.ID(\n        required=True, description='ID of an skill.', name='skill')\n\n\nclass SkillImageCreate(BaseMutation):\n    skill = graphene.Field(Skill)\n    image = graphene.Field(SkillImage)\n\n    class Arguments:\n        input = SkillImageCreateInput(\n            required=True,\n            description='Fields required to create a skill image.')\n\n    class Meta:\n        description = dedent('''Create a skill image. This mutation must be\n        sent as a `multipart` request. More detailed specs of the upload format\n        can be found here:\n        https://github.com/jaydenseric/graphql-multipart-request-spec''')\n\n    @classmethod\n    @permission_required('skill.manage_skills')\n    def mutate(cls, root, info, input):\n        errors = []\n        skill = cls.get_node_or_error(\n            info, input['skill'], errors, 'skill', only_type=Skill)\n        image_data = info.context.FILES.get(input['image'])\n        validate_image_file(cls, image_data, 'image', errors)\n        image = None\n        if not errors:\n            image = skill.images.create(\n                image=image_data, alt=input.get('alt', ''))\n            create_skill_thumbnails.delay(image.pk)\n        return SkillImageCreate(skill=skill, image=image, errors=errors)\n\n\nclass SkillImageUpdateInput(graphene.InputObjectType):\n    alt = graphene.String(description='Alt text for an image.')\n\n\nclass SkillImageUpdate(BaseMutation):\n    skill = graphene.Field(Skill)\n    image = graphene.Field(SkillImage)\n\n    class Arguments:\n        id = graphene.ID(\n            required=True, description='ID of a skill image to update.')\n        input = SkillImageUpdateInput(\n            required=True,\n            description='Fields required to update a skill image.')\n\n    class Meta:\n        description = 'Updates a skill image.'\n\n    @classmethod\n    @permission_required('skill.manage_skills')\n    def mutate(cls, root, info, id, input):\n        errors = []\n        image = cls.get_node_or_error(\n            info, id, errors, 'id', only_type=SkillImage)\n        skill = image.skill\n        if not errors:\n            alt = input.get('alt')\n            if alt is not None:\n                image.alt = alt\n                image.save(update_fields=['alt'])\n        return SkillImageUpdate(skill=skill, image=image, errors=errors)\n\n\nclass SkillImageReorder(BaseMutation):\n    skill = graphene.Field(Skill)\n    images = graphene.List(SkillImage)\n\n    class Arguments:\n        skill_id = graphene.ID(\n            required=True,\n            description='Id of skill that images task will be altered.')\n        images_ids = graphene.List(\n            graphene.ID, required=True,\n            description='IDs of a skill images in the desired task.')\n\n    class Meta:\n        description = 'Changes ordering of the skill image.'\n\n    @classmethod\n    @permission_required('skill.manage_skills')\n    def mutate(cls, root, info, skill_id, images_ids):\n        errors = []\n        skill = cls.get_node_or_error(\n            info, skill_id, errors, 'skillId', Skill)\n        if len(images_ids) != skill.images.count():\n            cls.add_error(\n                errors, 'task', 'Incorrect number of image IDs provided.')\n        images = []\n        for image_id in images_ids:\n            image = cls.get_node_or_error(\n                info, image_id, errors, 'task', only_type=SkillImage)\n            if image and image.skill != skill:\n                cls.add_error(\n                    errors, 'task',\n                    \"Image with id %r does not belong to skill %r\" % (\n                        image_id, skill_id))\n            images.append(image)\n        if not errors:\n            for task, image in enumerate(images):\n                image.sort_order = task\n                image.save(update_fields=['sort_order'])\n        return SkillImageReorder(\n            skill=skill, images=images, errors=errors)\n\n\nclass SkillImageDelete(BaseMutation):\n    skill = graphene.Field(Skill)\n    image = graphene.Field(SkillImage)\n\n    class Arguments:\n        id = graphene.ID(\n            required=True, description='ID of a skill image to delete.')\n\n    class Meta:\n        description = 'Deletes a skill image.'\n\n    @classmethod\n    @permission_required('skill.manage_skills')\n    def mutate(cls, root, info, id):\n        errors = []\n        image = cls.get_node_or_error(\n            info, id, errors, 'id', only_type=SkillImage)\n        image_id = image.id\n        if not errors:\n            image.delete()\n        image.id = image_id\n        return SkillImageDelete(\n            skill=image.skill, image=image, errors=errors)\n\n\nclass VariantImageAssign(BaseMutation):\n    skill_variant = graphene.Field(SkillVariant)\n    image = graphene.Field(SkillImage)\n\n    class Arguments:\n        image_id = graphene.ID(\n            required=True,\n            description='ID of a skill image to assign to a variant.')\n        variant_id = graphene.ID(\n            required=True,\n            description='ID of a skill variant.')\n\n    class Meta:\n        description = 'Assign an image to a skill variant'\n\n    @classmethod\n    @permission_required('skill.manage_skills')\n    def mutate(cls, root, info, image_id, variant_id):\n        errors = []\n        image = cls.get_node_or_error(\n            info, image_id, errors, 'imageId', SkillImage)\n        variant = cls.get_node_or_error(\n            info, variant_id, errors, 'variantId', SkillVariant)\n        if image and variant:\n            # check if the given image and variant can be matched together\n            image_belongs_to_skill = variant.skill.images.filter(\n                pk=image.pk).first()\n            if image_belongs_to_skill:\n                image.variant_images.create(variant=variant)\n            else:\n                cls.add_error(\n                    errors, 'imageId', 'Image must be for this skill')\n        return VariantImageAssign(\n            skill_variant=variant, image=image, errors=errors)\n\n\nclass VariantImageUnassign(BaseMutation):\n    skill_variant = graphene.Field(SkillVariant)\n    image = graphene.Field(SkillImage)\n\n    class Arguments:\n        image_id = graphene.ID(\n            required=True,\n            description='ID of a skill image to unassign from a variant.')\n        variant_id = graphene.ID(\n            required=True, description='ID of a skill variant.')\n\n    class Meta:\n        description = 'Unassign an image from a skill variant'\n\n    @classmethod\n    @permission_required('skill.manage_skills')\n    def mutate(cls, root, info, image_id, variant_id):\n        errors = []\n        image = cls.get_node_or_error(\n            info, image_id, errors, 'imageId', SkillImage)\n        variant = cls.get_node_or_error(\n            info, variant_id, errors, 'variantId', SkillVariant)\n        if not errors:\n            if image and variant:\n                try:\n                    variant_image = models.VariantImage.objects.get(\n                        image=image, variant=variant)\n                except models.VariantImage.DoesNotExist:\n                    cls.add_error(\n                        errors, 'imageId',\n                        'Image is not assigned to this variant.')\n                else:\n                    variant_image.delete()\n        return VariantImageUnassign(\n            skill_variant=variant, image=image, errors=errors)\n","sub_path":"remote_works/graphql/skill/mutations/skills.py","file_name":"skills.py","file_ext":"py","file_size_in_byte":31415,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"495735467","text":"__author__ = 'bloe'\n\nclass WriteEvent:\n\n    time = None\n    resource = None\n\n    def __init__(self):\n        self.time = None\n        self._value = None\n        self.resource = None\n\n    def get_formatted_time(self):\n        iso_format_string = self.time.isoformat()\n        return iso_format_string[0:23] + \"Z\"\n\n    def _get_value(self):\n        return self._value\n\n    def _set_value(self, numeric_value):\n        if self.resource.metric_value_template is None:\n            self._value = numeric_value\n        else:\n            template = self.resource.metric_value_template\n            templated_value = template.replace(\"%value%\", str(numeric_value))\n            self._value = templated_value\n\n    value = property(_get_value, _set_value)\n","sub_path":"dynamite/GENERATOR/WriteEvent.py","file_name":"WriteEvent.py","file_ext":"py","file_size_in_byte":743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"240479457","text":"\"\"\"\nCards\n=====\n\nCopyright (c) 2015 Andrés Rodríguez and KivyMD contributors -\n    KivyMD library up to version 0.1.2\nCopyright (c) 2019 Ivanov Yuri and KivyMD contributors -\n    KivyMD library version 0.1.3 and higher\n\nFor suggestions and questions:\n<kivydevelopment@gmail.com>\n\nThis file is distributed under the terms of the same license,\nas the Kivy framework.\n\n`Material Design spec, Cards <https://material.io/design/components/cards.html>`_\n\nExample\n-------\n\nfrom kivymd.app import MDApp\nfrom kivy.lang import Builder\nfrom kivy.factory import Factory\n\nfrom kivymd.uix.card import MDCardPost\nfrom kivymd.theming import ThemeManager\nfrom kivymd.toast import toast\n\n\nBuilder.load_string('''\n<ExampleCardPost@BoxLayout>\n    orientation: 'vertical'\n    spacing: dp(5)\n\n    MDToolbar:\n        id: toolbar\n        title: app.title\n        left_action_items: [['menu', lambda x: None]]\n        elevation: 10\n        md_bg_color: app.theme_cls.primary_color\n\n\n    ScrollView:\n        id: scroll\n        size_hint: 1, 1\n        do_scroll_x: False\n\n        GridLayout:\n            id: grid_card\n            cols: 1\n            spacing: dp(5)\n            padding: dp(5)\n            size_hint_y: None\n            height: self.minimum_height\n''')\n\n\nclass Example(MDApp):\n    title = \"Card Post\"\n    cards_created = False\n\n    def build(self):\n        self.screen = Factory.ExampleCardPost()\n        return self.screen\n\n    def on_start(self):\n        def callback_for_menu_items(text_item):\n            toast(text_item)\n\n        def callback(instance, value):\n            if value and isinstance(value, int):\n                toast('Set like in %d stars' % value)\n            elif value and isinstance(value, str):\n                toast('Repost with %s ' % value)\n            elif value and isinstance(value, list):\n                toast(value[1])\n            else:\n                toast('Delete post %s' % str(instance))\n\n        instance_grid_card = self.screen.ids.grid_card\n        buttons = ['facebook', 'vk', 'twitter']\n        menu_items = [\n            {'viewclass': 'MDMenuItem',\n             'text': 'Example item %d' % i,\n             'callback': callback_for_menu_items}\n            for i in range(2)\n        ]\n\n        if not self.cards_created:\n            self.cards_created = True\n\n            instance_grid_card.add_widget(\n                MDCardPost(text_post='Card with text',\n                           swipe=True, callback=callback))\n            instance_grid_card.add_widget(\n                MDCardPost(\n                    right_menu=menu_items, swipe=True,\n                    text_post='Card with a button to open the menu MDDropDown',\n                    callback=callback))\n            instance_grid_card.add_widget(\n                MDCardPost(\n                    likes_stars=True, callback=callback, swipe=True,\n                    text_post='Card with asterisks for voting.'))\n\n            instance_grid_card.add_widget(\n                MDCardPost(\n                    source=\"./assets/kitten-1049129_1280.jpg\",\n                    tile_text=\"Little Baby\",\n                    tile_font_style=\"H5\",\n                    text_post=\"This is my favorite cat. He's only six months \"\n                              \"old. He loves milk and steals sausages :) \"\n                              \"And he likes to play in the garden.\",\n                    with_image=True, swipe=True, callback=callback,\n                    buttons=buttons))\n\n\nExample().run()\n\"\"\"\n\nfrom kivy.animation import Animation\nfrom kivy.clock import Clock\nfrom kivy.factory import Factory\nfrom kivy.lang import Builder\nfrom kivy.properties import (\n    BoundedNumericProperty,\n    ReferenceListProperty,\n    StringProperty,\n    ListProperty,\n    BooleanProperty,\n    ObjectProperty,\n)\nfrom kivy.uix.anchorlayout import AnchorLayout\nfrom kivy.uix.boxlayout import BoxLayout\nfrom kivy.uix.image import Image\nfrom kivy.metrics import dp\nfrom kivy.core.window import Window\nfrom kivy.uix.widget import Widget\n\nfrom kivymd.uix.button import MDIconButton\nfrom kivymd.uix.menu import MDDropdownMenu\n\nfrom kivymd.uix.behaviors import RectangularElevationBehavior\nfrom kivymd.uix.list import ILeftBody\nfrom kivymd.theming import ThemableBehavior\n\nBuilder.load_string(\n    \"\"\"\n#:import images_path kivymd.images_path\n\n\n<MDCard>\n    canvas:\n        Color:\n            rgba: self.md_bg_color\n        RoundedRectangle:\n            size: self.size\n            pos: self.pos\n            radius: [self.border_radius]\n            source: root.background\n        Color:\n            rgba: self.theme_cls.divider_color\n            a: self.border_color_a\n        Line:\n            rounded_rectangle:\n                (self.pos[0], self.pos[1], self.size[0], self.size[1],\\\n                self.border_radius) \n    md_bg_color: self.theme_cls.bg_light\n\n\n<MDSeparator>\n    canvas:\n        Color:\n            rgba: self.theme_cls.divider_color if not root.color else root.color\n        Rectangle:\n            size: self.size\n            pos: self.pos\n\n\n<CardPostImage>\n    spacing: dp(10)\n    padding: dp(5)\n    orientation: 'vertical'\n    size_hint: None, None\n    size: root.card_size\n\n    SmartTileWithLabel:\n        source: root.source\n        text: ' %s' % root.tile_text\n        color: root.tile_text_color\n        size_hint_y: None\n        font_style: root.tile_font_style\n        height: dp(200)\n        on_release: root.callback(root, [self, self.source])\n\n    MDLabel:\n        text: root.text_post\n        size_hint_y: None\n        halign: 'justify'\n        valign: 'top'\n        height: dp(60)\n        text_size: self.width - 20, dp(60)\n\n    AnchorLayout:\n        anchor_x: 'right'\n        size_hint_y: None\n        height: dp(40)\n\n        BoxLayout:\n            id: box_buttons\n\n\n<MDCardPost>\n    spacing: dp(5)\n    padding: dp(5)\n    orientation: 'vertical'\n    size_hint: None, None\n    size: root.card_size\n\n    FloatLayout:\n\n        BoxLayout:\n            id: root_box\n            spacing: dp(5)\n            pos_hint: {'top': 1}\n            orientation: 'vertical'\n            x: dp(10)\n\n            BoxLayout:\n                id: title_box\n                size_hint_y: None\n                height: dp(50)\n                spacing: dp(10)\n\n                LeftIcon:\n                    source: root.path_to_avatar\n                    size_hint_x: None\n                    width: self.height\n                    allow_stretch: True\n\n                MDLabel:\n                    markup: True\n                    text: root.name_data\n                    text_size: self.width, None\n                    theme_text_color: 'Primary'\n                    bold: True\n                    font_size: '12sp'\n\n            MDLabel:\n                id: text_post\n                text: root.text_post\n                markup: True\n                font_size: '14sp'\n                size_hint_y: None\n                valign: 'top'\n                height: self.texture_size[1]\n                text_size: self.width - dp(5), None\n                theme_text_color: 'Primary'\n\n            Widget:\n\n            MDSeparator:\n                id: sep\n                height: dp(1)\n\n        AnchorLayout:\n            id: box_delete_post_button\n            size_hint: None, None\n            size: dp(90), root.height - dp(15)\n            pos_hint: {'top': 1, 'right': 1}\n            anchor_x: 'center'\n            opacity: 0\n\n            canvas.before:\n                Color:\n                    rgba: app.theme_cls.primary_color\n                Rectangle:\n                    pos: self.pos\n                    size: self.size\n            canvas.after:\n                Rectangle:\n                    pos: self.pos\n                    size: self.size\n                    source: f'{images_path}swipe_shadow.png'\n\n            MDIconButton:\n                id: delet_post_button\n                size_hint: None, None\n                size: dp(56), dp(56)\n                icon: 'delete'\n                disabled: True\n                on_release: root.callback(root, None)\n\"\"\"\n)\n\n\nclass MDSeparator(ThemableBehavior, BoxLayout):\n    \"\"\"A separator line\"\"\"\n\n    color = ListProperty()\n    \"\"\"Separator color.\"\"\"\n\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.on_orientation()\n\n    def on_orientation(self, *args):\n        self.size_hint = (\n            (1, None) if self.orientation == \"horizontal\" else (None, 1)\n        )\n        if self.orientation == \"horizontal\":\n            self.height = dp(1)\n        else:\n            self.width = dp(1)\n\n\nclass MDCard(ThemableBehavior, RectangularElevationBehavior, BoxLayout):\n    r = BoundedNumericProperty(1.0, min=0.0, max=1.0)\n    g = BoundedNumericProperty(1.0, min=0.0, max=1.0)\n    b = BoundedNumericProperty(1.0, min=0.0, max=1.0)\n    a = BoundedNumericProperty(0.0, min=0.0, max=1.0)\n\n    border_radius = BoundedNumericProperty(dp(3), min=0)\n    border_color_a = BoundedNumericProperty(0, min=0.0, max=1.0)\n    md_bg_color = ReferenceListProperty(r, g, b, a)\n    background = StringProperty()\n    \"\"\"Background image path.\"\"\"\n\n\nclass LeftIcon(ILeftBody, Image):\n    pass\n\n\nclass CardPostImage(BoxLayout):\n    source = StringProperty()\n    text_post = StringProperty()\n    tile_text = StringProperty(\"Title\")\n    tile_font_style = StringProperty(\"H5\")\n    tile_text_color = ListProperty([1, 1, 1, 1])\n    callback = ObjectProperty(lambda *x: None)\n    card_size = ListProperty((Window.width - 10, dp(335)))\n\n\nclass MDCardPost(BoxLayout):\n    name_data = StringProperty(\"Name Author\\nDate and time\")\n    text_post = StringProperty(\"Your text post...\")\n    path_to_avatar = StringProperty(\"data/logo/kivy-icon-512.png\")\n    card_size = ListProperty((Window.width - 10, dp(180)))\n\n    source = StringProperty()\n    tile_text = StringProperty(\"Title\")\n    tile_font_style = StringProperty(\"H5\")\n    tile_text_color = ListProperty([1, 1, 1, 1])\n\n    buttons = ListProperty()\n    \"\"\"A list of icons for buttons that will be used under the text of the post\n    when \"with_image\" is True\n    \"\"\"\n\n    right_menu = ListProperty()\n    \"\"\"If the list is not empty a button will be added to display the menu list\n    \"\"\"\n\n    likes_stars = BooleanProperty(False)\n    \"\"\"If True, stars will be added to the card for evaluation\"\"\"\n\n    callback = ObjectProperty(lambda *x: None)\n    \"\"\"User function\"\"\"\n\n    swipe = BooleanProperty(False)\n    \"\"\"Whether to apply to the card the function of a swap\"\"\"\n\n    with_image = BooleanProperty(False)\n    \"\"\"If True, we use a post with an image\"\"\"\n\n    _list_instance_likes_stars = ListProperty()\n    _card_shifted = False\n    _shift_x = 10\n\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.card_shifted = None\n\n        # ---------------------------------------------------------------------\n        if self.with_image:\n            self.ids.root_box.clear_widgets()\n            self._shift_x = dp(5)\n            self.ids.root_box.x = self._shift_x\n            self.card_size[1] = dp(335)\n            card_post = CardPostImage(\n                source=self.source,\n                text_post=self.text_post,\n                tile_text=self.tile_text,\n                tile_font_style=self.tile_font_style,\n                tile_text_color=self.tile_text_color,\n                callback=self.callback,\n            )\n            for name_icon in self.buttons:\n                card_post.ids.box_buttons.add_widget(\n                    MDIconButton(\n                        icon=name_icon,\n                        on_release=lambda x, y=name_icon: self.callback(x, y),\n                    )\n                )\n            self.ids.root_box.add_widget(card_post)\n        # ---------------------------------------------------------------------\n        if len(self.right_menu) and not self.with_image:\n            self.ids.title_box.add_widget(\n                MDIconButton(icon=\"dots-vertical\", on_release=self.open_menu)\n            )\n        # ---------------------------------------------------------------------\n        if self.likes_stars:\n            box_likes_stars_right = AnchorLayout(\n                anchor_x=\"right\", size_hint_y=None, height=dp(30)\n            )\n            self.box_likes_stars = BoxLayout(spacing=(dp(5)))\n            self.box_likes_stars.add_widget(Widget())\n            for i in range(5):  # adding stars\n                like_star = MDIconButton(\n                    icon=\"star-outline\",\n                    size_hint=(None, None),\n                    size=(dp(30), dp(30)),\n                    id=str(i),\n                    on_release=lambda x, y=i: self._update_likes_stars(y),\n                )\n                self.box_likes_stars.add_widget(like_star)\n                self._list_instance_likes_stars.append(like_star)\n            box_likes_stars_right.add_widget(self.box_likes_stars)\n            self.ids.root_box.remove_widget(self.ids.sep)\n            self.add_widget(box_likes_stars_right)\n\n    def open_menu(self, instance):\n        MDDropdownMenu(items=self.right_menu, width_mult=3).open(instance)\n\n    def _update_likes_stars(self, index_star):\n        i = 0\n        for instance_like_star in self._list_instance_likes_stars:\n            if int(instance_like_star.id) <= index_star:\n                if instance_like_star.icon == \"star-outline\":\n                    instance_like_star.icon = \"star\"\n                    i = 1\n                else:\n                    if int(instance_like_star.id) == index_star:\n                        instance_like_star.icon = \"star-outline\"\n            elif int(instance_like_star.id) >= index_star:\n                if instance_like_star.icon == \"star\":\n                    instance_like_star.icon = \"star-outline\"\n        self.callback(self, index_star + i)\n\n    def on_touch_move(self, touch):\n        if (\n            self.collide_point(*touch.pos)\n            and self.swipe\n            and not self._card_shifted\n        ):\n            if touch.x < Window.width - 10:\n                # When the Navigation panel is open and\n                # the list of its menu is scrolled,\n                # the event is also processed on the cards\n                for widget in Window.children:\n                    if widget.__class__ is Factory.NavigationLayout:\n                        if widget.state == \"open\":\n                            return\n                self.shift_post_left()\n        return super().on_touch_move(touch)\n\n    def on_touch_down(self, touch):\n        if self.swipe and self.card_shifted:\n            Clock.schedule_once(self.shift_post_right, 0.1)\n        return super().on_touch_down(touch)\n\n    def shift_post_left(self):\n        def on_anim_complete(*args):\n            self._card_shifted = True\n            self.card_shifted = self\n            self.ids.delet_post_button.disabled = False\n\n        Animation(x=-dp(90), d=0.1, t=\"in_out_cubic\").start(self.ids.root_box)\n        if self.likes_stars:\n            Animation(x=-dp(90), d=0.1, t=\"in_out_cubic\").start(\n                self.children[0]\n            )\n        anim = Animation(opacity=1, d=0.5, t=\"in_out_cubic\")\n        anim.bind(on_complete=on_anim_complete)\n        anim.start(self.ids.box_delete_post_button)\n\n    def shift_post_right(self, interval=0.1):\n        def on_anim_complete(*args):\n            self._card_shifted = False\n            self.card_shifted = None\n            self.ids.delet_post_button.disabled = True\n\n        Animation(x=self._shift_x, d=0.1, t=\"in_out_cubic\").start(\n            self.ids.root_box\n        )\n        if self.likes_stars:\n            Animation(x=self._shift_x, d=0.3, t=\"in_out_cubic\").start(\n                self.children[0]\n            )\n        anim = Animation(opacity=0, d=0.05, t=\"in_out_cubic\")\n        anim.bind(on_complete=on_anim_complete)\n        anim.start(self.ids.box_delete_post_button)\n","sub_path":"source/MDclassic_games/resources/examples/farmers_app/kivymd/uix/card.py","file_name":"card.py","file_ext":"py","file_size_in_byte":15781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"250273115","text":"__author__ = 'marth'\n\nimport csv\nfrom Items import Item\nfrom pprint import pprint\n\nitemlist = {}\n\nwith open('dex/items.csv', 'r') as csvfile:\n    itemreader = csv.reader(csvfile)\n    itemreader.next()\n\n    for row in itemreader:\n        itemlist[int(row[0])] = Item(row[0], row[1], row[2])\n\nwith open('dex/item_prose.csv', 'r') as csvfile:\n    itemreader = csv.reader(csvfile)\n    itemreader.next()\n\n    for row in itemreader:\n        if row[1] == '9':\n            itemlist[int(row[0])].effect = '\"' + row[2] + '\"'\n\nwith open('dex/item_names.csv', 'r') as csvfile:\n    itemreader = csv.reader(csvfile)\n    itemreader.next()\n\n    for row in itemreader:\n        if row[1] == '9':\n            itemlist[int(row[0])].name = row[2]\n\npprint(itemlist)\n\n# write everything to file\nwith open('out/item_list.csv', 'w') as csvfile:\n    header = 'item_id,identifier,item_category_id,item_name,effect\\n'\n    csvfile.write(header)\n    for item in itemlist.values():\n        temp = item.get_arr()\n        csvfile.write(','.join(map(str, temp)) + '\\n')","sub_path":"GenItems.py","file_name":"GenItems.py","file_ext":"py","file_size_in_byte":1035,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"567567265","text":"from PyQt4 import QtGui, uic\nfrom PyQt4.QtCore import pyqtSlot, QUrl, pyqtSignal\nfrom PyQt4.QtGui import QDialog, QWidget\nfrom network.auth import Auth\nfrom utils.settingsmanager import SettingsManager\n\n__author__ = \"Kirill Gusakov\"\n__credits__ = [\"Kirill Gusakov\"]\n\n__license__ = \"GPL\"\n__version__ = \"0.1\"\n__maintainer__ = \"Kirill Gusakov\"\n__email__ = \"kgusakov@gmail.com\"\n\nclass LoginWindow(QtGui.QWidget):\n\n    authorized = pyqtSignal()\n\n    def __init__(self, parent=None):\n        QtGui.QWidget.__init__(self, parent)\n        self.ui = uic.loadUi(\"gui/ui/loginwindow.ui\")\n        self.auth = Auth(SettingsManager.consumer_key, SettingsManager.consumer_secret)\n\n        self.auth_dialog = QWidget(self)\n        self.auth_dialog.ui = uic.loadUi(\"gui/ui/authwindow.ui\")\n\n        self.request_token = None\n\n        self.ui.getpin.clicked.connect(self.on_get_pin)\n        self.ui.submitpin.clicked.connect(self.login)\n\n\n    @pyqtSlot()\n    def on_get_pin(self):\n        self.request_token = self.auth.get_request_token()\n        self.auth_dialog.ui.web.load(QUrl(\"{}?oauth_token={}\".format(self.auth.authorize_url, self.request_token['oauth_token'][0])))\n        self.auth_dialog.ui.show()\n\n    @pyqtSlot()\n    def login(self):\n        access_token = self.auth.get_access_token(self.request_token['oauth_token'][0],\n                                      self.request_token['oauth_token_secret'][0],\n                                      self.ui.pintext.text().__str__())\n        SettingsManager().update_credentials(access_token['oauth_token'], access_token['oauth_token_secret'])\n\n        self.auth_dialog.ui.close()\n        self.ui.close()\n        self.authorized.emit()\n    \n\n\n\n            \n\n        ","sub_path":"gui/loginwindow.py","file_name":"loginwindow.py","file_ext":"py","file_size_in_byte":1703,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"307353331","text":"from botshop.db.models import Category, Product, News, Customer, Characteristics\nimport random\nfrom slugify import slugify\nfrom faker import Faker\nimport os\nimport datetime\n\nfake = Faker()\n\n\nclass GeneratorHandler:\n    @staticmethod\n    def attach_image_to_product(product_id: str):\n        product = Product.objects(id=product_id).first()\n        if product is None:\n            print(f\"No product with id : {product_id}\")\n            return False\n        path = f\"{os.path.dirname(os.path.abspath(__file__))}\" + \"\\product_images\" + f\"\\{random.randint(1, 10)}.jpg\"\n        with open(path, 'rb') as file:\n            product.image.put(file, content_type='image/jpeg')\n            product.save()\n\n    @staticmethod\n    def create_category():\n        title = random.choice(Data.category_titles + Data.product_titles)\n        slug = slugify(title + str(datetime.datetime.now().microsecond))\n        data = {'title': title,\n                'slug': slug,\n                'description': fake.sentence(),\n                }\n        Category(**data).save()\n        return Category.objects.get(slug=slug)\n\n    @staticmethod\n    def create_subcategory(category: Category):\n        title = random.choice(Data.category_titles + Data.product_titles)\n        slug = slugify(title + str(datetime.datetime.now().microsecond))\n        data = {'title': title,\n                'slug': slug,\n                'description': fake.text(),\n                }\n        Category(**data).save()\n        subcategory = Category.objects.get(slug=slug)\n        category.add_subcategory(subcategory)\n        return subcategory\n\n    @staticmethod\n    def create_product(category: Category):\n        title = random.choice(Data.product_titles)\n        slug = slugify(title + str(datetime.datetime.now().microsecond))\n        data = {'title': title,\n                'slug': slug,\n                'description': fake.text(),\n                'price': float(random.randint(50, 200)),\n                'characteristics': Characteristics(height=float(random.randint(10, 50)),\n                                                   width=float(random.randint(10, 50)),\n                                                   weight=float(random.randint(10, 50))),\n                'category': category,\n                'discount_percentage': random.randint(10, 90), }\n        Product(**data).save()\n        GeneratorHandler.attach_image_to_product(Product.objects.get(slug=slug).id)\n\n    @staticmethod\n    def create_customer():\n        test_data = {\n            'username': fake.name(),\n            'phone_number': fake.phone_number(),\n            'address': fake.address(),\n            'first_name': fake.first_name(),\n            'surname': fake.last_name(),\n            'age': random.randint(18, 50)\n        }\n        Customer(**test_data).save()\n\n    @staticmethod\n    def create_news():\n        data = {\n            'title': random.choice(Data.titles),\n            'body': fake.text()\n        }\n        News.objects.create(**data)\n\n\nclass Data:\n    titles = ['black friday', 'huge discounts', 'New year discounts', 'Summer discounts', 'Happy discounts']\n    category_titles = [\"Apple Smartphones & Phones\",\n                       \"Laptops and computers\",\n                       \"Gadgets\",\n                       \"Tablets and e-books\",\n                       \"Photo, Video, Audio\",\n                       \"TV projectors\",\n                       \"Appliances\",\n                       \"Game Zone\",\n                       \"Electronics Accessories\",\n                       \"Home and Car\",\n                       \"Tools and gardening equipment\"]\n    product_titles = [\n        \"Washing and drying machine\",\n        \"Refrigerator\",\n        \"Freezer\"\n        \"Dishwasher\",\n        \"CookersHood\",\n        \"Cooking surface\",\n        \"Hood\",\n        \"Oven\",\n        \"Hood accessorie\",\n        \"Built- in appliance\",\n        \"Washing machine\",\n        \"Coffee maker\",\n        \"Heater\",\n        \"Air conditioning\",\n        \"Moisturizer\",\n        \"Air purifier\",\n        \"Boiler\",\n        \"Column\",\n    ]\n\n","sub_path":"botshop/db/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4041,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"158304904","text":"#!/bin/env python3\n\"\"\"\nThis script creates a canonicalized version of KG2 stored in TSV files, ready for import into neo4j. The TSVs are\ncreated in the current working directory.\nUsage: python3 create_canonicalized_kg_tsvs.py [--test]\n\"\"\"\nimport argparse\nimport csv\nimport os\nimport sys\nimport time\nimport traceback\n\nfrom datetime import datetime\nfrom typing import List, Dict, Tuple, Union\nfrom neo4j import GraphDatabase\n\nsys.path.append(os.path.dirname(os.path.abspath(__file__))+\"/../../\")  # code directory\nfrom RTXConfiguration import RTXConfiguration\nsys.path.append(os.path.dirname(os.path.abspath(__file__))+\"/../../ARAX/NodeSynonymizer/\")\nfrom node_synonymizer import NodeSynonymizer\n\nARRAY_NODE_PROPERTIES = [\"types\", \"publications\", \"equivalent_curies\", \"all_names\"]\nARRAY_EDGE_PROPERTIES = [\"provided_by\", \"publications\"]\n\n\ndef _run_kg2_cypher_query(cypher_query: str) -> List[Dict[str, any]]:\n    # This function sends a cypher query to the KG2 neo4j specified in config.json and returns the results\n    rtxc = RTXConfiguration()\n    rtxc.live = \"KG2\"\n    try:\n        driver = GraphDatabase.driver(rtxc.neo4j_bolt, auth=(rtxc.neo4j_username, rtxc.neo4j_password))\n        with driver.session() as session:\n            print(f\"  Sending cypher query to KG2 neo4j..\")\n            query_results = session.run(cypher_query).data()\n            print(f\"  Got {len(query_results)} results back from neo4j\")\n        driver.close()\n    except Exception:\n        tb = traceback.format_exc()\n        error_type, error, _ = sys.exc_info()\n        print(f\"ERROR: Encountered a problem interacting with {rtxc.neo4j_bolt}. {tb}\")\n        return []\n    else:\n        return query_results\n\n\ndef _convert_list_to_neo4j_format(input_list: List[any]) -> str:\n    filtered_list = [item for item in input_list if item]  # Get rid of any None items\n    return \"ǂ\".join(filtered_list)  # Need to use a delimiter that does not appear in any list items\n\n\ndef _merge_two_lists(list_a: List[any], list_b: List[any]) -> List[any]:\n    return list(set(list_a + list_b))\n\n\ndef _get_edge_key(source: str, target: str, edge_type: str) -> str:\n    return f\"{source}--{edge_type}--{target}\"\n\n\ndef _canonicalize_nodes(nodes: List[Dict[str, any]]) -> Tuple[Dict[str, Dict[str, any]], Dict[str, str]]:\n    synonymizer = NodeSynonymizer()\n    node_ids = [node.get('id') for node in nodes if node.get('id')]\n    print(f\"  Sending NodeSynonymizer.get_canonical_curies() {len(node_ids)} curies..\")\n    canonicalized_info = synonymizer.get_canonical_curies(curies=node_ids, return_all_types=True)\n    all_canonical_curies = {canonical_info['preferred_curie'] for canonical_info in canonicalized_info.values() if canonical_info}\n    print(f\"  Sending NodeSynonymizer.get_equivalent_nodes() {len(all_canonical_curies)} curies..\")\n    equivalent_curies_info = synonymizer.get_equivalent_nodes(all_canonical_curies)\n    recognized_curies = {curie for curie in equivalent_curies_info if equivalent_curies_info.get(curie)}\n    equivalent_curies_dict = {curie: list(equivalent_curies_info.get(curie)) for curie in recognized_curies}\n    print(f\"  Creating canonicalized nodes..\")\n    curie_map = dict()\n    canonicalized_nodes = dict()\n    for node in nodes:\n        canonical_info = canonicalized_info.get(node['id'])\n        canonicalized_curie = canonical_info.get('preferred_curie', node['id']) if canonical_info else node['id']\n        publications = node['publications'] if node.get('publications') else []\n        description_in_list = [node['description']] if node.get('description') else []\n        if canonicalized_curie in canonicalized_nodes:\n            existing_canonical_node = canonicalized_nodes[canonicalized_curie]\n            existing_canonical_node['publications'] = _merge_two_lists(existing_canonical_node['publications'], publications)\n            existing_canonical_node['all_names'] = _merge_two_lists(existing_canonical_node['all_names'], [node['name']])\n            existing_canonical_node['description'] = _merge_two_lists(existing_canonical_node['description'], description_in_list)\n            # Add the IRI for the 'preferred' curie, if we've found that node\n            if node['id'] == canonicalized_curie:\n                existing_canonical_node['iri'] = node.get('iri')\n        else:\n            name = canonical_info['preferred_name'] if canonical_info else node['name']\n            preferred_type = canonical_info['preferred_type'] if canonical_info else node['category_label']\n            types = list(canonical_info['all_types']) if canonical_info else [node['category_label']]\n            iri = node['iri'] if node['id'] == canonicalized_curie else None\n            all_names = [node['name']]\n            canonicalized_node = _create_node(node_id=canonicalized_curie,\n                                              name=name,\n                                              preferred_type=preferred_type,\n                                              types=types,\n                                              publications=publications,\n                                              equivalent_curies=equivalent_curies_dict.get(canonicalized_curie, []),\n                                              iri=iri,\n                                              description=description_in_list,\n                                              all_names=all_names)\n\n            canonicalized_nodes[canonicalized_node['id']] = canonicalized_node\n        curie_map[node['id']] = canonicalized_curie  # Record this mapping for easy lookup later\n    return canonicalized_nodes, curie_map\n\n\ndef _canonicalize_edges(edges: List[Dict[str, any]], curie_map: Dict[str, str], is_test: bool) -> Dict[str, Dict[str, any]]:\n    allowed_self_edges = ['positively_regulates', 'interacts_with', 'increase']\n    canonicalized_edges = dict()\n    for edge in edges:\n        original_source_id = edge['subject']\n        original_target_id = edge['object']\n        if not is_test:  # Make sure we have the mappings we expect\n            assert original_source_id in curie_map\n            assert original_target_id in curie_map\n        canonicalized_source_id = curie_map.get(original_source_id, original_source_id)\n        canonicalized_target_id = curie_map.get(original_target_id, original_target_id)\n        edge_type = edge['simplified_edge_label']\n        edge_publications = edge['publications'] if edge.get('publications') else []\n        edge_provided_by = edge['provided_by'] if edge.get('provided_by') else []\n        if canonicalized_source_id != canonicalized_target_id or edge_type in allowed_self_edges:\n            canonicalized_edge_key = _get_edge_key(canonicalized_source_id, canonicalized_target_id, edge_type)\n            if canonicalized_edge_key in canonicalized_edges:\n                canonicalized_edge = canonicalized_edges[canonicalized_edge_key]\n                canonicalized_edge['provided_by'] = _merge_two_lists(canonicalized_edge['provided_by'], edge_provided_by)\n                canonicalized_edge['publications'] = _merge_two_lists(canonicalized_edge['publications'], edge_publications)\n            else:\n                new_canonicalized_edge = _create_edge(source=canonicalized_source_id,\n                                                      target=canonicalized_target_id,\n                                                      simplified_edge_label=edge['simplified_edge_label'],\n                                                      provided_by=edge_provided_by,\n                                                      publications=edge_publications)\n                canonicalized_edges[canonicalized_edge_key] = new_canonicalized_edge\n    return canonicalized_edges\n\n\ndef _modify_column_headers_for_neo4j(plain_column_headers: List[str]) -> List[str]:\n    modified_headers = []\n    all_array_column_names = ARRAY_NODE_PROPERTIES + ARRAY_EDGE_PROPERTIES\n    for header in plain_column_headers:\n        if header in all_array_column_names:\n            header = f\"{header}:string[]\"\n        elif header == 'id':\n            header = f\"{header}:ID\"\n        elif header == 'preferred_type_for_conversion':\n            header = \":LABEL\"\n        elif header == 'subject_for_conversion':\n            header = \":START_ID\"\n        elif header == 'object_for_conversion':\n            header = \":END_ID\"\n        elif header == 'simplified_edge_label_for_conversion':\n            header = \":TYPE\"\n        modified_headers.append(header)\n    return modified_headers\n\n\ndef _create_node(node_id: str, name: str, preferred_type: str, types: List[str], equivalent_curies: List[str],\n                 publications: List[str], all_names: List[str], iri: str, description: Union[str, List[str]]) -> Dict[str, any]:\n    assert isinstance(node_id, str)\n    assert isinstance(name, str) or name is None\n    assert isinstance(types, list)\n    assert isinstance(preferred_type, str)\n    assert isinstance(equivalent_curies, list)\n    assert isinstance(publications, list)\n    return {\n        \"id\": node_id,\n        \"name\": name,\n        \"preferred_type\": preferred_type,\n        \"iri\": iri,\n        \"description\": description,\n        \"types\": types,\n        \"equivalent_curies\": equivalent_curies,\n        \"all_names\": all_names,\n        \"publications\": publications\n    }\n\n\ndef _create_edge(source: str, target: str, simplified_edge_label: str, provided_by: List[str], publications: List[str]) -> Dict[str, any]:\n    assert isinstance(source, str)\n    assert isinstance(target, str)\n    assert isinstance(simplified_edge_label, str)\n    assert isinstance(provided_by, list)\n    assert isinstance(publications, list)\n    return {\n        \"subject\": source,\n        \"object\": target,\n        \"simplified_edge_label\": simplified_edge_label,\n        \"provided_by\": provided_by,\n        \"publications\": publications\n    }\n\n\ndef _write_list_to_neo4j_ready_tsv(input_list: List[Dict[str, any]], file_name_root: str, is_test: bool):\n    print(f\"  Creating {file_name_root} header file..\")\n    column_headers = list(input_list[0].keys())\n    modified_headers = _modify_column_headers_for_neo4j(column_headers)\n    with open(f\"{'test_' if is_test else ''}{file_name_root}_header.tsv\", \"w+\") as header_file:\n        dict_writer = csv.DictWriter(header_file, modified_headers, delimiter='\\t')\n        dict_writer.writeheader()\n    print(f\"  Creating {file_name_root} file..\")\n    with open(f\"{'test_' if is_test else ''}{file_name_root}.tsv\", \"w+\") as data_file:\n        dict_writer = csv.DictWriter(data_file, column_headers, delimiter='\\t')\n        dict_writer.writerows(input_list)\n\n\ndef create_canonicalized_tsvs(is_test=False):\n    \"\"\"\n    This function extracts all nodes/edges from the regular KG2 Neo4j endpoint (specified in your config.json),\n    canonicalizes the nodes, merges edges (based on subject, object, predicate), and saves the resulting canonicalized\n    graph in two tsv files (nodes_c.tsv and edges_c.tsv) that are ready for import into Neo4j.\n    \"\"\"\n    print(f\" Extracting nodes from KG2..\")\n    nodes_query = f\"match (n) return n.id as id, n.name as name, n.category_label as category_label, \" \\\n                  f\"n.publications as publications, n.iri as iri, n.description as description{' limit 20000' if is_test else ''}\"\n    neo4j_nodes = _run_kg2_cypher_query(nodes_query)\n    if neo4j_nodes:\n        print(f\" Canonicalizing nodes..\")\n        canonicalized_nodes_dict, curie_map = _canonicalize_nodes(neo4j_nodes)\n        print(f\"  Number of KG2 nodes was reduced to {len(canonicalized_nodes_dict)} ({round((len(canonicalized_nodes_dict) / len(neo4j_nodes)) * 100)}%)\")\n    else:\n        print(f\"ERROR: Couldn't get node data from KG2 neo4j.\")\n        return\n    print(f\" Extracting edges from KG2..\")\n    edges_query = f\"match (n)-[e]->(m) return n.id as subject, m.id as object, e.simplified_edge_label as \" \\\n                  f\"simplified_edge_label, e.provided_by as provided_by, e.publications as publications\" \\\n                  f\"{' limit 20000' if is_test else ''}\"\n    neo4j_edges = _run_kg2_cypher_query(edges_query)\n    if neo4j_edges:\n        print(f\" Canonicalizing edges..\")\n        canonicalized_edges_dict = _canonicalize_edges(neo4j_edges, curie_map, is_test)\n        print(f\"  Number of KG2 edges was reduced to {len(canonicalized_edges_dict)} ({round((len(canonicalized_edges_dict) / len(neo4j_edges)) * 100)}%)\")\n    else:\n        print(f\"ERROR: Couldn't get edge data from KG2 neo4j.\")\n        return\n\n    # Create a node containing information about this KG2C build\n    regular_kg2_version = canonicalized_nodes_dict['RTX:KG2']['name'] if 'RTX:KG2' in canonicalized_nodes_dict else 'unknown KG2 version'\n    current_date = datetime.now().strftime('%Y-%m-%d %H:%M')\n    build_node_name = f\"RTX KG2c, {current_date}\"\n    kg2c_build_node = _create_node(node_id=\"RTX:KG2c\",\n                                   name=build_node_name,\n                                   types=[\"data_file\"],\n                                   preferred_type=\"data_file\",\n                                   equivalent_curies=[],\n                                   publications=[],\n                                   iri=\"http://rtx.ai/identifiers#KG2c\",\n                                   all_names=[build_node_name],\n                                   description=[f\"This KG2c build was created from {regular_kg2_version} on {current_date}.\"])\n    canonicalized_nodes_dict[kg2c_build_node['id']] = kg2c_build_node\n\n    # Convert array fields into the format neo4j wants and do some final processing\n    for canonicalized_node in canonicalized_nodes_dict.values():\n        for list_node_property in ARRAY_NODE_PROPERTIES:\n            canonicalized_node[list_node_property] = _convert_list_to_neo4j_format(canonicalized_node[list_node_property])\n        canonicalized_node['preferred_type_for_conversion'] = canonicalized_node['preferred_type']\n        # Grab the five longest descriptions and join them into one string\n        sorted_description_list = sorted(canonicalized_node['description'], key=len, reverse=True)\n        # Get rid of any redundant descriptions (e.g., duplicate 'UMLS Semantic Type: UMLS_STY:T060')\n        filtered_description_list = [description for description in sorted_description_list if not any(description in other_description\n                                     for other_description in sorted_description_list if description != other_description)]\n        canonicalized_node['description'] = \" --- \".join(filtered_description_list[:5])\n    for canonicalized_edge in canonicalized_edges_dict.values():\n        if not is_test:  # Make sure we don't have any orphan edges\n            assert canonicalized_edge['subject'] in canonicalized_nodes_dict\n            assert canonicalized_edge['object'] in canonicalized_nodes_dict\n        for list_edge_property in ARRAY_EDGE_PROPERTIES:\n            canonicalized_edge[list_edge_property] = _convert_list_to_neo4j_format(canonicalized_edge[list_edge_property])\n        canonicalized_edge['simplified_edge_label_for_conversion'] = canonicalized_edge['simplified_edge_label']\n        canonicalized_edge['subject_for_conversion'] = canonicalized_edge['subject']\n        canonicalized_edge['object_for_conversion'] = canonicalized_edge['object']\n\n    # Finally dump all our nodes/edges into TSVs (formatted for neo4j)\n    print(f\" Saving data to TSVs..\")\n    _write_list_to_neo4j_ready_tsv(list(canonicalized_nodes_dict.values()), \"nodes_c\", is_test)\n    _write_list_to_neo4j_ready_tsv(list(canonicalized_edges_dict.values()), \"edges_c\", is_test)\n\n\ndef main():\n    arg_parser = argparse.ArgumentParser(description=\"Creates a canonicalized KG2, stored in TSV files\")\n    arg_parser.add_argument('--test', dest='test', action='store_true', default=False)\n    args = arg_parser.parse_args()\n\n    print(f\"Starting to create canonicalized KG..\")\n    start = time.time()\n    create_canonicalized_tsvs(args.test)\n    print(f\"Done! Took {round((time.time() - start) / 60, 2)} minutes.\")\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"code/kg2/canonicalized/create_canonicalized_kg_tsvs.py","file_name":"create_canonicalized_kg_tsvs.py","file_ext":"py","file_size_in_byte":15995,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"276982635","text":"import flopy\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Convert Units to feet per year\nrech = np.loadtxt('prism_201305.txt') # mm * 100\nrech = rech / 100 # mm\nrech = .00328084 * rech # feet/month\nrech = rech * 12 #feet/year = inch/month, still need to convert to feet per day for MODFLOW model\nprint('loaded text')\n\n# Begin Plotting (matplotlib)\nfig, ax = plt.subplots() # initiate plot\n\nplt.imshow(rech,cmap='jet') # colormap with color scheme specified\nplt.colorbar() # create colorbar\nplt.title('Aquifer Recharge (inches/month)')\n\n\n\nnrow, ncol = rech.shape # 1124=rows, 1412=columns\n\nmf = flopy.modflow.Modflow('trinity_rech') # this is the main flopy.modflow model object; https://modflowpy.github.io/flopydoc/mf.html \n\nnper = 82 # years \nperlen = [365.25] # time in each stress period\nsteady = [True] # stady state the first year\nfor sp in range(1,nper):\n\tperlen.append(365.25)\n\tsteady.append(False) # transient after\nnstp = 1 # number of time steps in each stress period\ndelr, delc = 5280, 5280 \nlenuni = 1 # is feet\ntop, botm = 100, 0 # top elevation of the model is 100, botm elevation is 0\ndis = flopy.modflow.ModflowDis(mf,1,nrow,ncol,nper,delr,delc,0,top,botm,perlen,nstp,steady=steady,itmuni=4,lenuni=lenuni,rotation=65) # discritization object; https://modflowpy.github.io/flopydoc/mfdis.html\n\n# now we need recharge in ft/day becuse modflow has no idea what the units are and it is up to us to keep the correct. (ft and day)\nrech = rech / 12 / 30.4  # ft/day\n\nrch = flopy.modflow.ModflowRch(mf,rech=rech) # is the recharge pckage https://modflowpy.github.io/flopydoc/mfrch.html\n\nmf.write_input() # write the modflow files, the \".rch\" file is the recharge package for modflow\n\n\nrch.plot(colorbar=True) # plot with modflow properties (in ft/day)\n\nplt.show()\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"make_rech_package.py","file_name":"make_rech_package.py","file_ext":"py","file_size_in_byte":1793,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"136731727","text":"import os\r\nimport random\r\nimport numpy as np\r\nimport torch\r\n\r\ndef write_results(args, results):\r\n    results_save_path = './results/result-{}.csv'.format(args.exp_name)\r\n    nor_train_acc1, nor_valid_acc1, train_loss, train_acc1, train_acc5, valid_loss, valid_acc1, valid_acc5 = results\r\n\r\n    with open(results_save_path, \"r\") as f:\r\n        key_names = f.readline().rstrip().split(\",\")\r\n    with open(results_save_path, 'a+') as res:\r\n        keys = []\r\n        for key in key_names[:-len(results)]:\r\n            keys.append(eval(\"args.{}\".format(key)))\r\n        metrics = [nor_train_acc1, nor_valid_acc1, train_loss, train_acc1, train_acc5, valid_loss, valid_acc1, valid_acc5]\r\n        contents = keys + metrics\r\n        contents = [str(content) if content is not None else \"NaN\" for content in contents]\r\n        contents= ','.join(contents)\r\n        res.write(contents + '\\n')\r\n\r\n        # log for total repetitions\r\n        print('Train loss : {:.4f}, Train Accuracy @1 : {:.4f}, Train Accuracy @5 : {:.4f}'.format(\r\n            train_loss,\r\n            train_acc1,\r\n            train_acc5\r\n            ))\r\n        print('Valid loss : {:.4f}, Valid Accuracy @1 : {:.4f}, Valid Accuracy @5 : {:.4f}'.format(\r\n            valid_loss,\r\n            valid_acc1,\r\n            valid_acc5\r\n            ))\r\n","sub_path":"lib/log_utils/result_utils.py","file_name":"result_utils.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"102315474","text":"#!python3\n'''\nThe prime factors of 13195 are 5, 7, 13 and 29.\n\nWhat is the largest prime factor of the number 600851475143 ?\n'''\n\nimport urllib\nimport csv\n\n#initialize list of primes, this will hold our list of prime numbers\nprimes=[]\n\n#download a list of prime numbers from the web\nurllib.request.urlretrieve(\"http://primos.mat.br/primeiros_10000_primos.txt\")\n\n#parse the txt of prime numbers into our prime list\nwith open(\"primeiros_10000_primos.txt\") as tsv:\n    for line in csv.reader(tsv, delimiter='\\t'):\n        print(line)\n        primes.extend(line)\nprimes=list(map(int,primes))\ntsv.close()\n\n#start with the given number, we'll divide it by iterating through the prime list\n#we'll have our largest prime factor when the current number equals 1\ncurrnum = 600851475143\ni = 0\nwhile currnum != 1:\n    if currnum % primes[i] == 0:\n        currnum = currnum / primes[i]\n        lastprime = primes[i]\n    else:\n        i += 1\nprint(\"The answer is \" + str(lastprime))","sub_path":"PE3.py","file_name":"PE3.py","file_ext":"py","file_size_in_byte":968,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"389043807","text":"# Copyright 2023 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\nimport os\n\nfrom discoveryengine import search_sample\n\nproject_id = os.environ[\"GOOGLE_CLOUD_PROJECT\"]\nlocation = \"global\"\nsearch_engine_id = \"test-search-engine_1689960780551\"\nserving_config_id = \"default_config\"\nsearch_query = \"Google\"\n\n\ndef test_search():\n    response = search_sample.search_sample(\n        project_id=project_id,\n        location=location,\n        search_engine_id=search_engine_id,\n        serving_config_id=serving_config_id,\n        search_query=search_query,\n    )\n\n    assert response\n","sub_path":"discoveryengine/search_sample_test.py","file_name":"search_sample_test.py","file_ext":"py","file_size_in_byte":1087,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"160836280","text":"# -*- coding: utf-8 -*-\n#------------------------------------------------------------\n# pelisalacarta - XBMC Plugin\n# Conector para kingcast\n# http://blog.tvalacarta.info/plugin-xbmc/pelisalacarta/\n#------------------------------------------------------------\n\nimport urlparse,urllib2,urllib,re\nimport os\n\nfrom core import scrapertools\nfrom core import logger\nfrom core import config\n\nDEBUG = config.get_setting(\"debug\")\n\ndef find_url_play(data, headers):\n    logger.info(\"[kingcast.py] find_url_play\")\n\n    '''\n<script type=\"text/javascript\" src=\"http://www.kingcast.tv/channel.php?file=3076&width=700&height=440&autostart=true\"></script>\n    '''\n\n    cid = scrapertools.find_single_match (data, '[\"\\']http://www.kingcast.tv/channel.php\\?file=([^&]+)')\n    if cid == '':\n        return ''\n    pageurl = 'http://www.kingcast.tv/embed.php?a=' + cid\n\n    data2 = scrapertools.cachePage(pageurl, headers=headers)\n    if (DEBUG): logger.info(\"data2=\"+data2)\n\n    '''\n<script type=\"text/javascript\" src=\"/jwplayer/jwplayer.js\"></script>\n<script>jwplayer.key=\"5XXb+w0txH2+cnkwOtAOWXU39zFQbZ6VT9mOA6R83tk=\"</script>\n\n<script type=\"text/javascript\" src=\"/content/js/player.js\"></script>\n\n\n\n<div id=\"player_div\">\n<div class=\"player_div\" align=\"center\">\n            <span>\n<div id=\"player\"></div>\n<script type='text/javascript'>\nvar rand = Math.floor(Math.random()*1000000000);\njwplayer('player').setup({\nfile: 'rtmp://91.235.143.111/live/by929jlnj',\nwidth: '',\nheight: '',\nimage: '',\nautostart: 'true',\nprimary: 'flash',\nadvertising: {\n      client: \"googima\",\n    admessage:\"For more Live Channel please go to http://kingcast.tv.\",\n      schedule:{\n                    adbreak1: {\n                        offset:'pre',\n                        tag: \"http://delivery.vidible.tv/placement/vast/5176d880e4b082c657f08a4a/53cb8a03e4b051219735f7f6?bcid=5176c647e4b09e5e67af5b27\"+rand\n                    },\n   \n                      adbreak2: {\n                        offset:'pre',\n                        tag: \"http://delivery.vidible.tv/placement/vast/5176d880e4b082c657f08a4a/53cb8a03e4b051219735f7f6?bcid=5176c647e4b09e5e67af5b27\"+rand\n                       \n                    },\n                    adbreak3: {\n                        offset:'pre',\n                        tag: \"http://delivery.vidible.tv/placement/vast/5176d880e4b082c657f08a4a/53cb8a03e4b051219735f7f6?bcid=5176c647e4b09e5e67af5b27\"+rand\n                    },\n                    adbreak4: {\n                        offset:'post',\n                        tag: \"http://delivery.vidible.tv/placement/vast/5176d880e4b082c657f08a4a/53cb8a03e4b051219735f7f6?bcid=5176c647e4b09e5e67af5b27\"+rand\n        }\n                }\n            }\n        });\n \n    </script>\n<!--<script type=\"text/javascript\">\njwplayer(\"player\").setup({\nfile: 'rtmp://91.235.143.111/live/by929jlnj',\nimage: \"\",\nwidth: \"\",\nheight: \"\",\nautostart: \"true\",\nprimary: \"flash\"\n});\n</script>-->\n    '''\n\n    rtmpurl = scrapertools.find_single_match (data2, \"file:\\s*['\\\"]([^'\\\"]*)\")\n\n    swfurl = 'http://www.kingcast.tv/jwplayer/jwplayer.flash.swf'\n\n    url = '%s swfUrl=%s swfVfy=1 live=1 timeout=15 pageUrl=%s' % (rtmpurl, swfurl, pageurl)\n\n    return url\n","sub_path":"servers_sports/kingcast.py","file_name":"kingcast.py","file_ext":"py","file_size_in_byte":3183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"266289072","text":"import os\nimport random\nfrom collections import defaultdict\nfrom typing import List, Dict\n\nimport torch\nimport pandas as pd\nfrom tqdm import tqdm\nimport xml.etree.ElementTree as ET\n\nfrom LMTrace.models import MODEL_ARCH_SINGLE, MODEL_ARCH_SIAMESE\nfrom LMTrace.utils import exclude_and_sample\n\nTOKEN = \"token\"\n\nOID = \"oid\"\nSOID = \"s_oid\"\nTOID = \"t_oid\"\n\nID = \"id\"\nSID = 'sid'\nTID = 'tid'\n\nTEXT = \"text\"\nSTEXT = \"s_text\"\nTTEXT = \"t_text\"\n\nEMBD = \"embd\"\n\nINPUT_ID = \"input_ids\"\nTK_TYPE = \"token_type_ids\"\nATTEN_MASK = \"attention_mask\"\n\nDEFAULT_SOURCE_FILE = \"source_art.csv\"\nDEFAULT_TARGET_FILE = \"target_art.csv\"\nDEFAULT_LINK_FILE = \"link.csv\"\n\n\nclass TraceExamples:\n    \"\"\"\n    Manage the examples read from raw dataset\n\n    examples:\n    valid_examples = CodeSearchNetReader(data_dir).get_examples(type=\"valid\", num_limit=valid_num, summary_only=True)\n    valid_examples = Examples(valid_examples)\n    valid_examples.update_features(model)\n    valid_examples.update_embd(model)\n    \"\"\"\n\n    def __init__(self, s_arts, t_arts, links):\n        \"\"\"\n        Index the raw examples with numeric ids (sid and tid) and The origin id is named as s_oid,t_oid.\n        :param raw_examples: A list of dictionary with keys: s_oid, s_text, t_oid, t_text\n        \"\"\"\n\n        self.s_index, self.t_index = dict(), dict()  # artifact index (id->text)\n        self.rs_index, self.rt_index = dict(), dict()  # reversed artifact index (oid->id)\n        self.s2t, self.t2s = defaultdict(set), defaultdict(set)  # true links\n        self.sid_cnt, self.tid_cnt, self.lk_cnt = 0, 0, 0\n\n        for i, s_oid in enumerate(s_arts):\n            self.s_index[i] = {SOID: s_oid, TOKEN: s_arts[s_oid]}\n            self.rs_index[s_oid] = i\n        for i, t_oid in enumerate(t_arts):\n            self.t_index[i] = {TOID: t_oid, TOKEN: t_arts[t_oid]}\n            self.rt_index[t_oid] = i\n\n        for lk in links:\n            s_oid, t_oid = lk\n            if s_oid not in self.rs_index or t_oid not in self.rt_index:\n                continue\n            sid, tid = self.rs_index[s_oid], self.rt_index[t_oid]\n            if tid not in self.s2t[sid]:\n                self.lk_cnt += 1\n\n            self.s2t[sid].add(tid)\n            self.t2s[tid].add(sid)\n\n    def __len__(self):\n        return self.lk_cnt\n\n    def build_feature_entry(self, sid, tid, label, tokenizer, model_arch, max_seq_length=256):\n        s_tks = self.s_index[sid][TOKEN]\n        t_tks = self.t_index[tid][TOKEN]\n        pair_feature = tokenizer(\n            text=s_tks,\n            text_pair=t_tks,\n            return_attention_mask=True,\n            return_token_type_ids=True,\n            add_special_tokens=True,\n            padding=\"max_length\",\n            max_length=max_seq_length,\n            truncation=\"longest_first\",\n        )\n\n        entry = {\n            SID: sid,\n            TID: tid,\n            'label': label\n        }\n\n        if model_arch == MODEL_ARCH_SINGLE:\n            entry['input_ids'] = pair_feature[INPUT_ID]\n            entry['attention_mask'] = pair_feature[ATTEN_MASK]\n            entry['token_type_ids'] = pair_feature[TK_TYPE]\n        elif model_arch == MODEL_ARCH_SIAMESE:\n            entry['s_input_ids'] = self.s_index[sid][INPUT_ID]\n            entry['s_attention_mask'] = self.s_index[sid][ATTEN_MASK]\n            entry['t_input_ids'] = self.t_index[tid][INPUT_ID]\n            entry['t_attention_mask'] = self.t_index[tid][ATTEN_MASK]\n        else:\n            raise Exception(\"\")\n        return entry\n\n    def gen_trace_dataset(self, tokenizer, model_arch, max_seq_length, query_num=-1, with_label=True):\n        \"\"\"\n        Generate trace challenge based on true links.\n        :param tokenizer:\n        :param model_arch:\n        :return:\n        \"\"\"\n        dataset = []\n        for sid in self.s_index:\n            if query_num > 0 and len(dataset) > query_num:\n                break\n            for tid in self.t_index:\n                if with_label:\n                    label = 1 if tid in self.s2t[sid] else 0\n                else:\n                    label = None\n                dataset.append(\n                    self.build_feature_entry(sid, tid, label, tokenizer, model_arch, max_seq_length=max_seq_length))\n        return dataset\n\n    def gen_training_dataset(self, tokenizer, model_arch, max_seq_length=256, resample_rate=1):\n        \"\"\"\n        Generate training examples with random negative examples.\n        The dataset is a balanced dataset with equal size of positive and negative examples.\n        Create features for both single and siamese models.\n        :return:\n        \"\"\"\n\n        for index in [self.s_index, self.t_index]:\n            for id in index:\n                feature = tokenizer(text=index[id][TOKEN],\n                                    return_attention_mask=True,\n                                    return_token_type_ids=False,\n                                    truncation=\"longest_first\",\n                                    max_length=max_seq_length,\n                                    padding=\"max_length\")\n                index[id][INPUT_ID] = feature[INPUT_ID]\n                index[id][ATTEN_MASK] = feature[ATTEN_MASK]\n\n        dataset = []\n\n        for sid in tqdm(self.s2t, desc=\"random_neg_sampling_dataset\"):\n            pos_tids = self.s2t[sid]\n            for pos_tid in pos_tids:\n                for i in range(resample_rate):\n                    dataset.append(self.build_feature_entry(sid, pos_tid, 1, tokenizer, model_arch))\n            neg_tids = exclude_and_sample(set(self.t_index.keys()), pos_tids, num=len(pos_tids) * resample_rate)\n            for n_tid in neg_tids:\n                dataset.append(self.build_feature_entry(sid, n_tid, 0, tokenizer, model_arch))\n        return dataset\n\n    def update_embd(self, model):\n        \"\"\"\n        update the artifact embeddings for Siamese-LM model\n        :param model: Siamese model\n        :return:\n        \"\"\"\n        with torch.no_grad():\n            model.eval()\n            for index in [self.s_index, self.t_index]:\n                for id in tqdm(index, desc=\"update embedding\"):\n                    feature = index[id]\n                    input_tensor = torch.tensor(feature[INPUT_ID]).view(1, -1).to(model.device)\n                    mask_tensor = torch.tensor(feature[ATTEN_MASK]).view(1, -1).to(model.device)\n                    embd = model.get_LM(input_tensor, mask_tensor)[0]\n                    embd_cpu = embd.to('cpu')\n                    index[id][EMBD] = embd_cpu\n\n    def split_dataset(self, out_dir, weigths={'train': 8, 'valid': 1, 'test': 1}):\n        \"\"\"\n        Split dataset into train, validation, test and write them into CSV format.\n        Each source artifact are treated as a single query and the weights are applied on the source artifacts.\n\n\n        :param trace_examples:\n        :param out_dir:\n        :return:\n        \"\"\"\n\n        def write_arts(art_dict, file_path):\n            df = pd.DataFrame(columns=[ID, TEXT])\n            df[ID] = art_dict.keys()\n            df[TEXT] = art_dict.values()\n            df.to_csv(file_path)\n\n        queries = list(self.s2t.keys())\n        random.shuffle(queries)\n        q_iter = iter(queries)\n        total_w = sum(weigths.values())\n\n        for slice_name in weigths:\n            slice_dir = os.path.join(out_dir, slice_name)\n            if not os.path.isdir(slice_dir):\n                os.makedirs(slice_dir)\n            n = self.lk_cnt * (weigths[slice_name] / total_w)\n            cnt = 0\n            sarts, tarts = dict(), dict()\n            lk_s_col, lk_t_col = [], []\n            while cnt < n:\n                sid = next(q_iter, None)\n                if not sid:\n                    break\n                for tid in self.s2t[sid]:\n                    sart, tart = self.s_index[sid], self.t_index[tid]\n                    sarts[sart[SOID]] = sart[TOKEN]\n                    tarts[tart[TOID]] = tart[TOKEN]\n                    lk_s_col.append(sart[SOID])\n                    lk_t_col.append(tart[TOID])\n                    cnt += 1\n\n            write_arts(sarts, os.path.join(slice_dir, DEFAULT_SOURCE_FILE))\n            write_arts(tarts, os.path.join(slice_dir, DEFAULT_TARGET_FILE))\n            lk_df = pd.DataFrame(columns=[SID, TID])\n            lk_df[SID] = lk_s_col\n            lk_df[TID] = lk_t_col\n            lk_df.to_csv(os.path.join(slice_dir, DEFAULT_LINK_FILE))\n\n\nclass DataReader:\n    def __init__(self, name, ):\n        self.name = name\n\n    def get_examples(self) -> TraceExamples:\n        raise NotImplementedError\n\n\nclass CSVReader(DataReader):\n    \"\"\"\n    dataset reader for csv file. A datatset contains three files:\n    1. source_art.csv: contain 'id' and 'text' column\n    2. target_art.csv: contain 'id' and 'text' column\n    3. link.csv: contain 'sid' and 'tid' column\n    \"\"\"\n\n    def __init__(self, data_dir, dataset_name='CSVReader', sart=DEFAULT_SOURCE_FILE, tart=DEFAULT_TARGET_FILE,\n                 lk=DEFAULT_LINK_FILE):\n        super().__init__(dataset_name)\n        self.data_dir = data_dir\n        self.sart = os.path.join(data_dir, sart)\n        self.tart = os.path.join(data_dir, tart)\n        self.lk = os.path.join(data_dir, lk)\n\n    def get_examples(self):\n        sart_df = pd.read_csv(self.sart)\n        tart_df = pd.read_csv(self.tart)\n        lk_df = pd.read_csv(self.lk)\n        parse_art = lambda data_df: {row[ID]: row[TEXT] for index, row in data_df.iterrows()}\n        s_index, t_index = parse_art(sart_df), parse_art(tart_df)\n        links = [(row[SID], row[TID]) for index, row in lk_df.iterrows()]\n        return TraceExamples(s_index, t_index, links)\n\n\nclass PACISReader(DataReader):\n    def __init__(self, dir_path):\n        super().__init__(\"PACIS\")\n        self.dir_path = dir_path\n        self.source = os.path.join(dir_path, \"SSDFinal.xml\")\n        self.target = os.path.join(dir_path, \"SRS.xml\")\n        self.link = os.path.join(dir_path, \"TraceMatrixSSD2SRS.txt\")\n\n    def get_examples(self) -> TraceExamples:\n        sarts = self.read_artifact(self.source)\n        tarts = self.read_artifact(self.target)\n        lk_df = pd.read_csv(self.link)\n        links = [(row[0], row[1]) for index, row in lk_df.iterrows()]\n        return TraceExamples(sarts, tarts, links)\n\n    def read_artifact(self, path):\n        arts = dict()\n        root = ET.parse(path).getroot()\n        for art in root.findall('artifact'):\n            id = art.find('art_id').text\n            title = art.find('art_title').text\n            content = art.find('art_content').text\n            title = title if title else \"\"\n            content = content if content else \"\"\n            arts[id] = title + \"\\n\" + content\n        return arts\n\n\nif __name__ == \"__main__\":\n    rd = PACISReader('../data/PACIS')\n    examples = rd.get_examples()\n    print(len(examples))\n","sub_path":"LMTrace/data_structure.py","file_name":"data_structure.py","file_ext":"py","file_size_in_byte":10765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"96527146","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\nimport sys, os, re, time\nfrom email import utils\nfrom email.mime.text import MIMEText\nfrom email.mime.base import MIMEBase\nfrom email.mime.multipart import MIMEMultipart\nfrom email import encoders\nimport mimetypes, smtplib\n\ntry:\n    import requests, logging\nexcept Exception as msg:\n    logging.info(\"Import Error! %s\" % msg)\n\n# import redis\n\nif sys.getdefaultencoding() != 'utf-8':\n    reload(sys)\n    sys.setdefaultencoding('utf-8')\n\n# 日志格式化\nlogging.basicConfig(level=logging.INFO,\n                    format='%(asctime)s %(levelname)s %(message)s',\n                    datefmt='%Y%m%d %H:%M:%S',\n                    filename='/tmp/change_line.log',\n                    encoding='utf-8',\n                    filemode='ab+')\n\n# 获取当前目录下switch文件夹内文件数量\nswitch_path = os.path.split(os.path.realpath(__file__))[0] + \"/switch/\"\n# 如果没有status 文件夹则创建\nstatus_path = os.path.split(os.path.realpath(__file__))[0] + \"/status/\"\n\n# 当前时差计算\ndef time_diff(path, now):\n    try:\n        file_mtime = int(os.path.getmtime(path))\n    except:\n        logging.info(\"Error! The file %s is not in here!\" % path)\n        sys.exit('1')\n    return int(now - file_mtime)\n\n# 发送邮件\ndef line_sendmail(to, subject, adj, *args):\n    mail_data = \"<div>&nbsp;</div>\"\n    for item in args:\n        mail_data = mail_data.strip() + \"<div>\" + item.strip() + \"</div>\"\n    logging.info(\"Mail data is %s\" % mail_data)\n    message = MIMEMultipart()\n    text = MIMEText(mail_data + \"<div>\" + adj + \"</div>\", 'html', \"utf-8\")\n    message.attach(text)\n    sender = \"SwitchlineNotice\"\n    mail_to = to.split(\",\")\n    message['From'] = sender\n    message['To'] = \",\".join(mail_to)\n    message['Subject'] = \"%s\" % (subject)\n    message['Date'] = utils.formatdate()\n    fulltext = message.as_string()\n    # 生产环境\n    server = smtplib.SMTP('mail.ul.com', 25)\n    # 生产环境直接发送\n    try:\n        server.sendmail(sender, mail_to, fulltext)\n        logging.info(\"The mail is send!\")\n    except Exception as msg:\n        logging.info(\"Send Mail Error：%s\" % msg)\n        server.quit()\n\n# 线路切换动作\ndef switch(room, line, status, host, send_to):\n    # zline(专线名称或者动作，主备机房，状态)\n    # 更具标签获取主备机房各自URL\n    if room_sign == \"master\":\n        line_url = \"http://10.10.2.30:8888/gateway-payservice-core/controller/netStatus/notify\"\n        adj_room = '主机房'\n    if room_sign == \"slave\":\n        line_url = \"http://10.10.20.30:8888/gateway-payservice-core/controller/netStatus/notify\"\n        adj_room = '备机房'\n    # 获取json {'engineRoom':\"master\", 'payagte': \"epcc\", 'line': \"master\"}\n    line_data = {'engineRoom':room, 'payagte': line, 'line': status}\n\n    # 执行切换操作,超时时间需要实际计算\n    try:\n        # logging.info(\"Start Time is %s\" % time.time())\n        url_get = requests.get(line_url, params=line_data, timeout=2)\n        # logging.info(\"End Time is %s\" % time.time())\n    except Exception as msg:\n        logging.info(\"Line switch is error %s\" % msg)\n        line_sendmail(send_to, \"Line switch is error\", \"by python for \" + room, str(msg.args[0]))\n        sys.exit('1')\n\n    # 获取返回值\n    url_back_str = url_get.text.encode('utf-8').split('\\n')[0]\n    logging.info(\"HTTP bank code is: %s, bank data is %s\" % (url_get.status_code, str(url_back_str)))\n\n    # 判断是否执行成功\n    if url_back_str.startswith(\"SUCCESS\"):\n        # 当前目录下创建status/master or status/slave 文件\n        if not os.path.exists(status_path):\n            os.mkdir(status_path)\n        # 情况状态文件目录内文件\n        try:\n            os.system('rm ' + status_path + '*')\n        except Exception as msg:\n            logging.info(\"Status Clean file is error %s\" % msg)\n        # 创建状态文件\n        open(status_path + status, 'w').close()\n\n        # 清空线路切换标识文件\n        try:\n            os.system('rm ' + switch_path + '*')\n        except Exception as msg:\n            logging.info(\"Switch Clean file is error %s\" % msg)\n\n        logging.info(\"line change success, the line is %s, back str is %s\" % (status,url_back_str))\n        if line == 'epcc':\n            line = ',网联线路'\n        if line == 'cup':\n            line = ',银联线路'\n        adj = adj_room + line + '，切换至 ' + status + '线路。'\n        line_sendmail(send_to, \"Line change report\", adj , str(url_back_str))\n    if not url_back_str.startswith(\"SUCCESS\"):\n        logging.info(\"The CURL ex failed!, back str is %s \" % url_back_str )\n        if line == 'epcc':\n            line = ',网联线路'\n        if line == 'cup':\n            line = ',网联线路'\n        adj = adj_room + line + '，切换至 ' + status + '线路。'\n        # send mail url_back_str\n        line_sendmail(send_to, \"Line change report\", adj, str(url_back_str))        \n\n# 后期改进使用'Problem name'划分线路和恢复状态\nif __name__ == \"__main__\":\n    # 切换状态字典\n    switch_item = dict()\n    # 获取告警信息\n    switch_send = dict()\n    # 拆分告警内容\n    msg_data = dict()\n\n    os_out = os.popen(\"/sbin/ifconfig\").read()\n    try:\n        re.search(r\"10\\.10\\.16\\.24\", os_out).group(0)\n        room_sign = \"slave\"\n    except:\n        room_sign = \"master\"\n\n    # 将接受到的参数拆分合并至字典中\n    for item in sys.argv:\n        # 接收人\n        if item.strip().startswith(\"--to=\"):\n            switch_send[\"to\"] = item.split(\"--to=\")[1].strip()\n\n        if item.strip().startswith(\"--subject=\"):\n            switch_subject = item.split(\"--subject=\")[1].strip()\n\n        if item.strip().startswith(\"--message=\"):\n            try:\n                # 拆分 message 需要保证信息为“\\r\\n” 分割\n                msg_split = item.split(\"--message=\")[1].split('\\r\\n')\n                # 需要默认有host，time，date\n                for msg_item in msg_split:\n                    if msg_item.strip() != '':\n                        if msg_item.startswith(\"Problem started\"):\n                            msg_data[\"problem\"] = msg_item\n                            continue\n\n                        # 添加依据Problem name 划分线路和状态\n                        if msg_item.startswith('Problem name:'):\n                            res_key = msg_item.split(\":\")[1].strip().split('_')[0]\n                            res_value = msg_item.split(\":\")[1].strip().split('_')[1]\n                            logging.info(\"Line Status  is %s _____________________ %s\" % (res_key, res_value ) )\n                            switch_send[\"line\"] = res_key\n                            if res_value == 'problem':\n                                switch_send[\"switch_status\"] = \"slave\"\n                            elif res_value == 'recovery':\n                                switch_send[\"switch_status\"] = \"master\"\n                            continue\n\n                        msg_data[msg_item.split(\":\")[0].strip().lower()] = msg_item.split(\":\")[1].strip()\n\n                        logging.info(\"msg data is %s, value is %s\" % (msg_item.split(\":\")[0], msg_item.split(\":\")[1]))\n            except Exception as msg:\n                logging.info(\"Split error : %s ; the msg data is %s\" % (msg, item.split(\"--message=\")[1]))\n                sys.exit('1')\n    # 线路区分关键参数\n    switch_path = switch_path + switch_send[\"line\"] + \"/\"\n    status_path = status_path + switch_send[\"line\"] + \"/\"\n\n    switch_send[\"msg\"] = msg_data\n    logging.info(\"the switch_send is %s\" % switch_send)\n    # hostname_slave\n    file_path = msg_data['host'] + '_' + switch_send[\"switch_status\"]\n    \n    # 保证文件夹存在\n    if not os.path.exists(switch_path):\n        os.mkdir(switch_path)\n\n    # 判断线路状态，如果切换状态文件存在，则表示已切换，退出，否则继续执行\n    if os.path.exists(status_path + switch_send[\"switch_status\"]):\n        logging.info(\"This line was switched! This program is exited.\")\n        sys.exit('1')\n\n    # 文件创建\n    for root, dirs, files in os.walk(switch_path):\n        # 是否超过60秒\n        if time_diff(switch_path, int(time.time())) >= 60:\n            # 清空文件删除文件\n            try:\n                os.system('rm ' + root + '*')\n            except Exception as msg:\n                logging.info(\"Error! The file %s @ is not removed! error is %s\" % (files, msg))\n            # 清空列表\n            files = []\n        # 如果存在，退出\n        if file_path in files:\n            logging.info(\"The line %s was switched. This program is exit.\" %  (switch_path + file_path))\n            sys.exit('1') \n        # 如果没有创建文件，则创建文件\n        if file_path not in files:\n            # logging.info(\"OK\")\n            open(switch_path + file_path, 'w').close()\n            files.append(file_path)\n            logging.info(\"The files in switch is: %s\" % files)\n            logging.info(\"Files Num is %s\" % len(files))\n            if len(files) == 2:\n                # 执行切换 (room, line, status, host)\n                switch(room_sign, switch_send[\"line\"], switch_send[\"switch_status\"], msg_data[\"host\"], switch_send[\"to\"])\n            else:\n                logging.info(\"The root is %s, file len is %s\" % (root,len(files)))\n    logging.info(\"Complate!\")\n","sub_path":"orther/scripts/Key_Grep/sbin/change_line.py","file_name":"change_line.py","file_ext":"py","file_size_in_byte":9405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"488807725","text":"# Directories\n\nimport os\n\n# set to true if running from .sh\nrunning_from_sh = True\n\n# path relative to the .sh file\nthis_dir = 'src/svm_classifier/'\n\npos_im_path = 'positive_images/'\nneg_im_path = 'negative_images/'\n\npos_feat_dir = 'positive_features/'\nneg_feat_dir = 'negative_features/'\n\nmodel_path = 'linear_svc_model/'\nmodel_file = 'linear_svc_model/trained_svm.clf'\n\nlabels_csv = 'labels.csv'\n\n# Hog parameters\norientations = 9\npixels_per_cell = (4, 4)\ncells_per_block = (3, 3)\n\n# NMS\nthreshold = 0.1\n\n# Test classifier\ntest_im_dir = 'test_images/'\nstep_size = (10, 10)\ndownscale = 1.25\nvisualize_det = False\nshow_final_det = True\n\n# appends the\nif (running_from_sh):\n\tpos_feat_dir = this_dir + pos_feat_dir\n\tneg_feat_dir = this_dir + neg_feat_dir\n\tmodel_path = this_dir + model_path\n\tmodel_file = this_dir + model_file\n\tlabels_csv = this_dir + labels_csv\nelse:\n\tpos_im_path = os.path.join('..', os.path.join('..', pos_im_path))\n\tneg_im_path = os.path.join('..', os.path.join('..', neg_im_path))\n\ttest_im_dir = os.path.join('..', os.path.join('..', test_im_dir))\n\nimport glob\nimport os\nimport cv2\n\n# Get the minimum size of the positive images\ndef get_sliding_window_sz():\n\t# List of the dimensions for each image\n\tx_dim = []\n\ty_dim = []\n\tprint(pos_im_path)\n\tfor im_path in glob.glob(os.path.join(pos_im_path, \"*\")):\n\t\tprint(im_path)\n\n\t\t# Read image and convert to grayscale\n\t\tim = cv2.imread(im_path)\n\t\tx_dim.append(im.shape[1])\n\t\ty_dim.append(im.shape[0])\n\n\n\t\t# Scale the y to the nearest\n\t\tx = min(x_dim)\n\t\ty = int(((min(y_dim)/pixels_per_cell[0])) * pixels_per_cell[0])\n\treturn (x, y)\n","sub_path":"generic-incremental-classifier/out/production/generic-incremental-classifier/svm_classifier/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"215909865","text":"# coding=utf-8\nimport os\nimport time\n\nfrom requests_toolbelt import MultipartEncoder\n\nfrom .api import Api\n\n\nclass FileStation(Api):\n    cgi_path = 'entry.cgi'\n\n    \"\"\"Access Synology FileStation information\"\"\"\n    add = 'real_path,size,owner,time,perm'\n\n    def get_info(self):\n        \"\"\"Provide File Station information\"\"\"\n        return self.req(self.endpoint('SYNO.FileStation.Info',\n                                      cgi = self.cgi_path, method = 'get'))\n\n    def list_share(self, writable_only = False, limit = 25, offset = 0,\n                   sort_by = 'name', sort_direction = 'asc', additional = False):\n        \"\"\"List all shared folders\"\"\"\n        return self.req(self.endpoint(\n            'SYNO.FileStation.List',\n            cgi = self.cgi_path,\n            method = 'list_share',\n            version = '2',\n            extra = {\n                'onlywritable': writable_only,\n                'limit': limit,\n                'offset': offset,\n                'sort_by': sort_by,\n                'sort_direction': sort_direction,\n                'additional': self.add if additional else ''\n            }\n        ))\n\n    def list(self, path, limit = 25, offset = 0, sort_by = 'name',\n             sort_direction = 'asc', pattern = '', filetype = 'all',\n             additional = False):\n        \"\"\"Enumerate files in a given folder\"\"\"\n        return self.req(self.endpoint(\n            'SYNO.FileStation.List',\n            cgi = self.cgi_path,\n            method = 'list',\n            version = '2',\n            extra = {\n                'folder_path': path,\n                'limit': limit,\n                'offset': offset,\n                'sort_by': sort_by,\n                'sort_direction': sort_direction,\n                'pattern': pattern,\n                'filetype': filetype,\n                'additional': self.add if additional else ''\n            }\n        ))\n\n    def get_file_info(self, path, additional = False):\n        \"\"\"Get information of file(s)\"\"\"\n        return self.req(self.endpoint(\n            'SYNO.FileStation.List',\n            cgi = self.cgi_path,\n            method = 'getinfo',\n            extra = {\n                'path': path,\n                'additional': self.add if additional else ''\n            }\n        ))\n\n    def search(self, path, pattern):\n        \"\"\"Search for files/folders\"\"\"\n        start = self.req(self.endpoint(\n            'SYNO.FileStation.Search',\n            cgi = self.cgi_path,\n            method = 'start',\n            extra = {\n                'folder_path': path,\n                'pattern': pattern\n            }\n        ))\n        if not 'taskid' in start.keys():\n            raise NameError('taskid not in response')\n\n        while True:\n            time.sleep(0.5)\n            file_list = self.req(self.endpoint(\n                'SYNO.FileStation.Search',\n                cgi = self.cgi_path,\n                method = 'list',\n                extra = {\n                    'taskid': start['taskid'],\n                    'limit': -1\n                }\n            ))\n            if file_list['finished']:\n                result_list = []\n                for item in file_list['files']:\n                    result_list.append(item['path'])\n                return result_list\n\n    def dir_size(self, path):\n        \"\"\"\n        Get the accumulated size of files/folders within folder(s)\n\n        Returns:\n            size in octets\n        \"\"\"\n        start = self.req(self.endpoint(\n            'SYNO.FileStation.DirSize',\n            cgi = self.cgi_path,\n            method = 'start',\n            extra = {'path': path}\n        ))\n        if not 'taskid' in start.keys():\n            raise NameError('taskid not in response')\n\n        while True:\n            time.sleep(10)\n            status = self.req(self.endpoint(\n                'SYNO.FileStation.DirSize',\n                cgi = self.cgi_path,\n                method = 'status',\n                extra = {'taskid': start['taskid']}\n            ))\n            if status['finished']:\n                return int(status['total_size'])\n\n    def md5(self, path):\n        \"\"\"Get MD5 of a file\"\"\"\n        start = self.req(self.endpoint(\n            'SYNO.FileStation.MD5',\n            cgi = self.cgi_path,\n            method = 'start',\n            extra = {'file_path': path}\n        ))\n        if not 'taskid' in start.keys():\n            raise NameError('taskid not in response')\n\n        while True:\n            time.sleep(10)\n            status = self.req(self.endpoint(\n                'SYNO.FileStation.MD5',\n                cgi = 'FileStation/file_md5.cgi',\n                method = 'status',\n                extra = {'taskid': start['taskid']}\n            ))\n            if status['finished']:\n                return status['md5']\n\n    def permission(self, path):\n        \"\"\"Check if user has permission to write to a path\"\"\"\n        return self.req(self.endpoint(\n            'SYNO.FileStation.CheckPermission',\n            cgi = self.cgi_path,\n            method = 'write',\n            extra = {\n                'path': path,\n                'create_only': 'false'\n            }\n        ))\n\n    def delete(self, path):\n        \"\"\"\n        Delete file(s)/folder(s)\n\n        Using the blocking method for now\n        \"\"\"\n        return self.req(self.endpoint(\n            'SYNO.FileStation.Delete',\n            cgi = self.cgi_path,\n            method = 'start',\n            version = '2',\n            extra = {'path': path}\n        ))\n\n    def create(self, path, name, force_parent = True, additional = False):\n        \"\"\"\n        Create folders\n\n        Does not support several path/name tuple as the API does\n        \"\"\"\n        return self.req(self.endpoint(\n            'SYNO.FileStation.CreateFolder',\n            cgi = self.cgi_path,\n            method = 'create',\n            extra = {\n                'name': name,\n                'folder_path': path,\n                'force_parent': force_parent,\n                'additional': self.add if additional else ''\n            }\n        ))\n\n    def rename(self, path, name, additional = False):\n        \"\"\"Rename a file/folder\"\"\"\n        return self.req(self.endpoint(\n            'SYNO.FileStation.Rename',\n            cgi = self.cgi_path,\n            method = 'rename',\n            extra = {\n                'name': name,\n                'path': path,\n                'additional': self.add if additional else ''\n            }\n        ))\n\n    def extract(self, path, destpath):\n        \"\"\"Get thumbnail of file\"\"\"\n        return self.req(self.endpoint(\n            'SYNO.FileStation.Extract',\n            cgi = self.cgi_path,\n            method = 'start',\n            extra = {\n                'file_path': path,\n                'dest_folder_path': destpath,\n                'keep_dir': 'true',\n                'create_subfolder': 'true',\n                'overwrite': 'true'\n            }\n        ))\n\n    def thumb(self, path, size = 'small', rotate = '0'):\n        \"\"\"Get thumbnail of file\"\"\"\n        return self.req_binary(self.endpoint(\n            'SYNO.FileStation.Thumb',\n            cgi = self.cgi_path,\n            method = 'get',\n            extra = {\n                'path': path,\n                'size': size,\n                'rotate': rotate\n            }\n        ))\n\n    def download(self, path, mode = 'open', **kwargs):\n        \"\"\"Download files/folders\"\"\"\n        return self.req_binary(self.endpoint(\n            'SYNO.FileStation.Download',\n            cgi = self.cgi_path,\n            method = 'download',\n            extra = {\n                'path': path,\n                'mode': mode\n            }\n        ), **kwargs)\n\n    def upload(self, remotePath, localPath, overwrite = True):\n        \"\"\"Upload file\"\"\"\n        dir = os.path.dirname(remotePath)\n        file = os.path.basename(remotePath)\n        m = MultipartEncoder(\n            fields = {'create_parents': 'true',\n                      'overwrite': 'true' if overwrite else 'false',\n                      'path': dir,\n                      'file': (file, open(localPath, 'rb'), 'application/octet-stream')\n                      }\n        )\n        return self.req_formdata(self.endpoint(\n            'SYNO.FileStation.Upload',\n            cgi = self.cgi_path,\n            version = '2',\n            method = 'upload'),\n            m,\n            {'Content-Type': m.content_type}\n        )\n\n    def backgroundTask(self):\n        \"\"\"Get background task status\"\"\"\n        return self.req(self.endpoint(\n            'SYNO.FileStation.BackgroundTask',\n            cgi = self.cgi_path,\n            method = 'list',\n            version = '3',\n        ))\n\n    def clearBackgroundTask(self, taskid):\n        \"\"\"Clear finished background task\"\"\"\n        return self.req(self.endpoint(\n            'SYNO.FileStation.BackgroundTask',\n            cgi = self.cgi_path,\n            method = 'clear_finished',\n            version = '3',\n            extra = {\n                'taskid': taskid\n            }\n        ))\n\n    def waitForTaskFinished(self, taskid, timeout = 30):\n        wait = 0\n        taskwait = {'taskid': taskid, 'success': False, 'wait': wait}\n        while wait <= timeout:\n            self.clearBackgroundTask(taskid)\n            taskList = self.backgroundTask()\n            if taskid in [t['taskid'] for t in taskList['tasks']]:\n                wait = wait + 1\n                taskwait = {'taskid': taskid, 'success': False, 'wait': wait}\n                time.sleep(1)\n            else:\n                taskwait = {'taskid': taskid, 'success': True, 'wait': wait}\n                break\n        return taskwait","sub_path":"synology/filestation.py","file_name":"filestation.py","file_ext":"py","file_size_in_byte":9585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"69393595","text":"import pandas as pd\nimport numpy as np\nfrom matplotlib import pyplot as pt\n\nimport os \ndir_path = os.path.dirname(os.path.realpath(__file__))\nfile_path = os.path.join(dir_path, 'iris.data')\n\ndf = pd.read_csv(file_path)\n\ndf.columns = [\"sepal length in cm\", \"sepal width in cm\", \"petal length in cm\", \"petal width in cm\", \"class\"]\ndf['class value'] = pd.factorize(df['class'])[0]\n\n# question 5\nprint(\"\\n######\\nQUESTION 5\\n######\")\ng = df.groupby('class')\n\n# question 6\nprint(\"\\n######\\nQUESTION 6\\n######\")\nprint(g.get_group('Iris-setosa'))\n\n# question 7\nprint(\"\\n######\\nQUESTION 7\\n######\")\nfor group in g.groups:\n    print(\"Group name: {0}\\n{1}\\n\".format(group, g.get_group(group).describe()))\n\n# question 8\nprint(\"\\n######\\nQUESTION 8\\n######\")\npt.figure()\npt.scatter(df['sepal length in cm'], df['sepal width in cm'], color='r')\npt.title('Scatter plot')\npt.xlabel('sepal length in cm')\npt.ylabel('sepal width in cm')\npt.show()\n\n# question 9\nprint(\"\\n######\\nQUESTION 9\\n######\")\ndf.to_string()\ndf.iterrows()","sub_path":"Practical 5/17032467.py","file_name":"17032467.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"298241084","text":"\nimport pygame\nimport random\nimport math\n\nclass Block:\n    def __init__(self, pos, color, canvasdim, customwh=None):\n        if customwh:\n            self.size = customwh\n        else:\n            self.size = BLOCK_SIZE, BLOCK_SIZE\n        self.speedx, self.speedy = 0,0\n        self.color = color\n        self.canvasdim = canvasdim\n        self.posx, self.posy = self.get_random_pos(self.canvasdim) if pos == () else pos\n\n    def draw(self, window):\n        pygame.draw.rect(window, self.color, (self.posx, self.posy, self.size[0], self.size[1]))\n\n    def update(self):\n        self.posx += self.speedx\n        self.posy += self.speedy\n\n    def move(self, xydir):\n        self.speedx = xydir[0] * self.size[0]\n        self.speedy = xydir[1] * self.size[1]\n\n    def transport(self, coordinates):\n        self.posx, self.posy = self.get_random_pos(self.canvasdim) if coordinates == () else coordinates\n\n    def get_random_pos(self, canvasdim):\n        x = random.randint(canvasdim[0], canvasdim[2]) // self.size[0] * self.size[0]\n        y = random.randint(canvasdim[1], canvasdim[3]) // self.size[1] * self.size[1]\n        return x, y\n\n    def distance(self, block):\n        bcenter = (block.posx+block.size[0]/2, block.posy+block.size[1]/2)\n        self.center = (self.posx+self.size[0]/2, self.posy+self.size[1]/2)\n        a = abs(self.center[0] - bcenter[0])\n        b = abs(self.center[1] - bcenter[1])\n        return math.sqrt(a**2 + b**2)\n\nclass Snake:\n    def __init__(self, pos, color, canvasdim):\n        self.head = Block(pos, color, canvasdim)\n        self.body = []\n        self.pos = pos\n        self.color = color\n        self.canvasdim = canvasdim\n        self.inventory = [None, None, None, None]\n\n    def draw(self, window):\n        self.head.draw(window)\n        for block in self.body:\n            block.draw(window)\n\n    def update(self):\n        self.move_body()\n        self.head.update()\n        for block in self.body:\n            block.update()\n\n    def move(self, xydir):\n        self.head.move(xydir)\n\n    def move_body(self):\n        for i in range(len(self.body), 0, -1):\n            if i == 1:\n                self.body[i-1].posx = self.head.posx\n                self.body[i-1].posy = self.head.posy\n            else:\n                self.body[i-1].posx = self.body[i-2].posx\n                self.body[i-1].posy = self.body[i-2].posy\n\n    def pick_up(self, powerup):\n        for i, _ in enumerate(self.inventory):\n            if self.inventory[i] == None:\n                self.inventory[i] = powerup\n\n    def use_inventory(self, key, game):\n        if key == 'h':\n            index = 0\n        if key == 'j':\n            index = 1\n        if key == 'k':\n            index = 2\n        if key == 'l':\n            index = 3\n\n        if self.inventory[index] == None:\n            pass\n        else:\n            if self.inventory[index].item_type == 'w':\n                game.wraparound_on = True\n            elif self.inventory[index].item_type == 'd':\n                self.body = []\n            elif self.inventory[index].item_type == 'p':\n                game.passtrhough_on = True\n            elif self.inventory[index].item_type == 'm':\n                game.magnet_on = True\n            self.inventory[index] = None\n\n    def add_block(self):\n        new_block = Block((self.head.posx, self.head.posy), self.color, self.canvasdim)\n        self.body.append(new_block)\n\n    def distance(self, block):\n        return self.head.distance(block)\n\n    def body_collision(self):\n        for block in self.body:\n            print(self.distance(block))\n            if self.head.distance(block) < BLOCK_SIZE:\n                 return True\n        return False\nclass Food(Block):\n    def __init__(self, pos, color, canvasdim):\n        super().__init__(pos, color, canvasdim)\n\nclass Powerup(Block):\n    def __init__(self, item_type, color, canvasdim):\n        super().__init__((), color, canvasdim)\n        self.item_type = item_type\n        if item_type == 'w':\n            self.visthreshold = random.randint(1, 20)\n            pass\n        elif item_type == 'd':\n            self.visthreshold = random.randint(25, 60)\n            pass\n        elif item_type == 'p':\n            self.visthreshold = random.randint(8, 20)\n            pass\n        elif item_type == 'm':\n            self.visthreshold = random.randint(1, 4)\n            pass\n        print(self.visthreshold)\n\n        self.canvasdim = canvasdim\n        self.isvis = False\n\n    def isvisible(self, player_length):\n        if player_length > self.visthreshold:\n            self.isvis = True\n        else:\n            self.isvis = False\n        return self.isvis\n\n    def draw(self, window):\n        if self.isvis:\n            super().draw(window)\n\n    def update(self):\n        if self.isvis:\n            super().update()\n\nclass TextBox:\n    def __init__(self, text, font, size, color, pos, window):\n        self.text = text\n        self.font = font\n        self.size = size\n        self.color = color\n        self.posx, self.posy = pos\n        self.window = window\n\n        self.styled = pygame.font.Font(self.font, self.size)\n        self.rendered = self.styled.render(self.text, 0, self.color)\n        self.rect = self.rendered.get_rect()\n\n        if self.posx == 'center':\n            self.posx = self.window.get_width()/2 - self.rect[2]/2\n        elif self.posx == 'margin-left':\n            self.posx = 50\n        elif self.posx == 'margin-right-center':\n            self.posx = self.window.get_width() - 50 - self.rect.width\n\n    def draw(self):\n        self.blit = self.window.blit(self.rendered, (self.posx, self.posy))\n\n    def update(self):\n        pass\n\n    def change_color(self, color):\n        self.rendered = self.styled.render(self.text, 0, color)\n\n    def change_text(self, text):\n        self.rendered = self.styled.render(text, 0, self.color)\n\nclass Game:\n    def __init__(self):\n        self.selected_map = MAPS[0]\n        self.rows, self.cols = self.selected_map\n        self.width, self.height = self.cols * BLOCK_SIZE, self.rows * BLOCK_SIZE + HUD_HEIGHT\n        self.window = pygame.display.set_mode((self.width, self.height))\n\n        self.canvasdim = (0, HUD_HEIGHT, self.width, self.height)\n\n        self.running = True\n        self.clock = pygame.time.Clock()\n\n        self.theme = THEMES['default']\n        self.state = 'play'\n        self.score = 0\n        self.highscore = int(open(\"scores.txt\", 'r').read())\n\n        self.num_foods = 1\n        self.speed = 1\n\n        self.selected_items = [0,1,1,1,0,2,0]\n        self.players = []\n        self.items = []\n        self.obstacles = []\n        self.powerups = []\n\n        self.menu_headers = []\n        self.menu_items = []\n\n        self.menu()\n\n        self.passthrough_on = False\n        self.magnet_on = False\n\n    def main(self):\n        while self.running:\n            pygame.time.delay(math.floor(60 / self.speed))\n            self.clock.tick(10)\n\n            self.update()\n            self.draw()\n\n    def draw(self):\n        self.window.fill(self.theme[self.state + ' bg'])\n\n        if self.state == 'menu':\n            for header in self.menu_headers:\n                if self.headings[self.selected_header] == header.text:\n                    header.change_color(self.theme['menu item selected'])\n                else:\n                    header.change_color(self.theme['menu title color'])\n                header.draw()\n            for i, item in enumerate(self.menu_items):\n                item[self.selected_items[i]].draw()\n        elif self.state == 'play':\n            # draw HUD\n            pygame.draw.rect(self.window, self.theme['hud bg color'], (0, 0, self.width, self.canvasdim[1]))\n            self.score_text.change_text(\"Score: \" + str(self.score))\n            self.score_text.draw()\n            self.highscore_text.change_text(\"Highscore: \" + str(self.highscore))\n            self.highscore_text.draw()\n            for i, powerup_container in enumerate(self.powerups_container_array):\n                powerup_container.draw(self.window)\n            for i, powerup in enumerate(self.powerup_inventory_array):\n                if powerup:\n                    powerup.posx = self.powerups_container_array[i].posx + self.powerups_container_array[i].size[0]/4\n                    powerup.posy = self.powerups_container_array[i].posy + self.powerups_container_array[i].size[1]/4\n                    powerup.draw(self.window)\n            for item in self.items:\n                item.draw(self.window)\n            for obstacle in self.obstacles:\n                obstacle.draw(self.window)\n            for powerup in self.powerups:\n                if powerup:\n                    powerup.draw(self.window)\n            for player in self.players:\n                player.draw(self.window)\n        elif self.state == 'pause':\n            self.paused_press_space_text.draw()\n            self.paused_press_M_text.draw()\n            self.paused_text.draw()\n        elif self.state == 'gameover':\n            self.gameover_text.draw()\n            self.finalscore_text.draw()\n            self.gameover_press_space_text.draw()\n            self.gameover_press_M_text.draw()\n\n    def update(self):\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                pygame.quit()\n        keys = pygame.key.get_pressed()\n        for key in keys:\n            if keys[pygame.K_a]:\n                if self.state == 'menu':\n                    self.selected_items[self.selected_header] = self.selected_items[self.selected_header] + 1 if self.selected_items[self.selected_header] < len(self.menu_items[self.selected_header])-1 else 0\n                if self.state == 'play':\n                    self.players[0].move((-1,0))\n            if keys[pygame.K_d]:\n                if self.state == 'menu':\n                    self.selected_items[self.selected_header] = self.selected_items[self.selected_header] - 1 if self.selected_items[self.selected_header] > 0 else len(self.menu_items[self.selected_header])-1\n                if self.state == 'play':\n                    self.players[0].move((1,0))\n            if keys[pygame.K_w]:\n                if self.state == 'menu':\n                    self.selected_header = self.selected_header - 1 if self.selected_header > 0 else len(self.headings)-1\n                if self.state == 'play':\n                    self.players[0].move((0,-1))\n            if keys[pygame.K_s]:\n                if self.state == 'menu':\n                    self.selected_header = self.selected_header + 1 if self.selected_header < len(self.headings)-1 else 0\n                if self.state == 'play':\n                    self.players[0].move((0,1))\n            if keys[pygame.K_SPACE]:\n                if self.state == 'menu':\n                    self.mode = self.selected_items[0]\n                    if self.selected_items[1] == 0:\n                        self.speed = 0.5\n                    if self.selected_items[1] == 1:\n                        self.speed = 1\n                    if self.selected_items[1] == 2:\n                        self.speed = 1.5\n                    if self.selected_items[1] == 3:\n                        self.speed = 2\n                    self.wraparound_on = self.selected_items[2]\n                    self.powerups_on = self.selected_items[3]\n                    self.obstacles_on = self.selected_items[4]\n                    self.num_foods = self.selected_items[5] + 1\n                    self.selected_map = MAPS[self.selected_items[6]]\n                    self.rows, self.cols = self.selected_map\n                    self.width, self.height = self.cols * BLOCK_SIZE, self.rows * BLOCK_SIZE + HUD_HEIGHT\n                    self.window = pygame.display.set_mode((self.width, self.height))\n                    self.canvasdim = (0, HUD_HEIGHT, self.width, self.height)\n                    self.play()\n                elif self.state == 'play':\n                    self.pause()\n                elif self.state == 'pause':\n                    self.resume()\n                elif self.state == 'gameover':\n                    self.play()\n            if keys[pygame.K_m]:\n                if self.state != 'menu':\n                    self.menu()\n            if self.state == 'play' and self.powerups_on:\n                if keys[pygame.K_h]:\n                    for player in self.players:\n                        player.use_inventory('h', self)\n                        self.remove_powerup_from_screen(0)\n                if keys[pygame.K_j]:\n                    for player in self.players:\n                        player.use_inventory('j', self)\n                        self.remove_powerup_from_screen(1)\n                if keys[pygame.K_k]:\n                    for player in self.players:\n                        player.use_inventory('k', self)\n                        self.remove_powerup_from_screen(2)\n                if keys[pygame.K_l]:\n                    for player in self.players:\n                        player.use_inventory('l', self)\n                        self.remove_powerup_from_screen(3)\n\n        if self.state == 'play':\n            for player in self.players:\n                for obstacle in self.obstacles:\n                    obstacle.update()\n                    if player.distance(obstacle) < BLOCK_SIZE:\n                        self.gameover()\n                for i, powerup in enumerate(self.powerups):\n                    if powerup:\n                        if powerup.isvisible(len(player.body)+1):\n                            if player.distance(powerup) < BLOCK_SIZE:\n                                player.pick_up(powerup)\n                                self.add_powerup_to_screen(powerup)\n                                self.powerups[i] = None\n                player.update()\n                if player.head.posx < self.canvasdim[0] or player.head.posx > self.canvasdim[2] or player.head.posy < self.canvasdim[1] or player.head.posy >= self.canvasdim[3]:\n                    if self.wraparound_on:\n                        if player.head.posx >= self.canvasdim[2]:\n                            player.head.posx = self.canvasdim[0]\n                        elif player.head.posx < self.canvasdim[0]:\n                            player.head.posx = self.canvasdim[2] - BLOCK_SIZE\n                        else:\n                            player.head.posx %= self.canvasdim[2]\n\n                        if player.head.posy >= self.canvasdim[3]:\n                            player.head.posy = self.canvasdim[1]\n                        elif player.head.posy < self.canvasdim[1]:\n                            player.head.posy = self.canvasdim[3] - BLOCK_SIZE\n                        else:\n                            player.head.posy %= self.canvasdim[3]\n                    else:\n                        self.gameover()\n                if self.passthrough_on == False:\n                    if player.body_collision():\n                        self.gameover()\n                for item in self.items:\n                    if self.magnet_on:\n                        if player.head.posx > item.posx:\n                            dirx = 1\n                        elif player.head.posx == item.posx:\n                            dirx = 0\n                        elif player.head.posx < item.posx:\n                            dirx = -1\n                        if player.head.posy > item.posy:\n                            diry = 1\n                        elif player.head.posy == item.posy:\n                            diry = 0\n                        elif player.head.posy < item.posy:\n                            diry = -1\n                        item.move((dirx, diry))\n                        item.update()\n                    if player.distance(item) < BLOCK_SIZE:\n                        item.transport(())\n                        if isinstance(item, Food):\n                            player.add_block()\n                            if self.magnet_on:\n                                self.total_magnet_eats += 1\n                                if self.total_magnet_eats < 6:\n                                    self.magnet_on = False\n                            self.score += int(10 * self.speed)\n                            if self.score > self.highscore:\n                                self.highscore = self.score\n\n        pygame.display.update()\n\n    def menu(self):\n        self.state = 'menu'\n        self.selected_header = 0\n        self.menu_items = []\n\n        title = TextBox(MENU_TEXT['title'], self.theme['font'], 150, self.theme['menu title color'], ('center', 10), self.window)\n        space_to_begin = TextBox('Press SPACE to Begin', self.theme['font'], 40, self.theme['menu title color'], (210, 120), self.window)\n        self.headings = ['Mode', 'Speed', 'Wraparound', 'Power Ups', 'Obstacles', 'Num Food', 'Map']\n        ypos = 10+150+10\n        for heading_i, heading in enumerate(self.headings):\n            heading_box = TextBox(heading, self.theme['font'], 75, self.theme['menu title color'], ('margin-left', ypos), self.window)\n            self.menu_headers.append(heading_box)\n            array_of_menu_items = []\n            for item in MENU_TEXT[heading]:\n                item_box = TextBox(item, self.theme['font'], 75, self.theme['menu title color'], ('margin-right-center', ypos), self.window)\n                array_of_menu_items.append(item_box)\n\n            self.menu_items.append(array_of_menu_items)\n            ypos += 75\n\n        self.menu_headers.append(title)\n        self.menu_headers.append(space_to_begin)\n\n    def play(self):\n        self.players = []\n        self.items = []\n        self.score = 0\n        self.total_magnet_eats = 0\n        self.state = 'play'\n        player1 = Snake((), self.theme['player color'], self.canvasdim)\n        self.players.append(player1)\n        for i in range(self.num_foods):\n            food = Food((), self.theme['food color'], self.canvasdim)\n            self.items.append(food)\n        self.highscore_text = TextBox(\"Highscore: \" + str(self.highscore), self.theme['font'], 50, (0,0,0), ('margin-right-center', 5), self.window)\n        self.score_text = TextBox(\"Score: \" + str(self.score), self.theme['font'], 50, (0,0,0), ('margin-right-center', 50), self.window)\n\n        self.powerup_inventory_array = [None, None, None, None]\n        self.powerups_container_array = []\n        for i in range(4):\n            new_powerup_container = Block((20*i*2*1.1 +5,50), (44, 62, 80), self.canvasdim, customwh=(BLOCK_SIZE*2, BLOCK_SIZE*2))\n            self.powerups_container_array.append(new_powerup_container)\n\n        if self.obstacles_on:\n            for i in range(random.randint(1, 4)):\n                new_obstacle = Block((), self.theme['obstacle color'], self.canvasdim)\n                self.obstacles.append(new_obstacle)\n        magnet = Powerup('m', self.theme['magnet powerup color'], self.canvasdim)\n        passthrough = Powerup('p', self.theme['passthrough powerup color'], self.canvasdim)\n        del_body = Powerup('d', self.theme['del_body powerup color'], self.canvasdim)\n        wraparound = Powerup('w', self.theme['wraparound powerup color'], self.canvasdim)\n        self.powerups = [magnet, passthrough, del_body, wraparound]\n\n    def pause(self):\n        self.state = 'pause'\n        self.paused_text = TextBox(\"Paused\", self.theme['font'], 150, (255,255,255), ('center', self.height/2 - 75), self.window)\n        self.paused_press_space_text = TextBox(\"Press SPACE to resume\", self.theme['font'], 40, (255,255,255), ('center', self.height/2 + 180), self.window)\n        self.paused_press_M_text = TextBox(\"Press M for Menu\", self.theme['font'], 40, (255,255,255), ('center', self.height/2 + 225), self.window)\n        self.paused_text.draw()\n        self.paused_press_space_text.draw()\n        self.paused_press_M_text.draw()\n\n    def resume(self):\n        self.state = 'play'\n\n    def gameover(self):\n        self.state = 'gameover'\n        self.window.fill(self.theme['gameover bg'])\n        print(self.score)\n        self.gameover_text = TextBox(\"Game Over\", self.theme['font'], 150, (255,255,255), ('center', self.height/2 - 75), self.window)\n        self.finalscore_text = TextBox(\"Final Score: \" + str(self.score), self.theme['font'], 50, (255,255,255), ('center', self.height/2 + 75), self.window)\n        self.gameover_press_space_text = TextBox(\"Press SPACE to play again\", self.theme['font'], 40, (255,255,255), ('center', self.height/2 + 180), self.window)\n        self.gameover_press_M_text = TextBox(\"Press M for Menu\", self.theme['font'], 40, (255,255,255), ('center', self.height/2 + 225), self.window)\n        self.gameover_text.draw()\n        self.finalscore_text.draw()\n        self.gameover_press_space_text.draw()\n        self.gameover_press_M_text.draw()\n\n        self.score = 0\n        self.obstacles = []\n        scorefile = open(\"scores.txt\", 'w')\n        scorefile.write(str(self.highscore))\n\n    def add_powerup_to_screen(self, powerup):\n        for i, _ in enumerate(self.powerup_inventory_array):\n            if self.powerup_inventory_array[i] == None:\n                self.powerup_inventory_array[i] = powerup\n                break\n\n    def remove_powerup_from_screen(self, index):\n        self.powerup_inventory_array[index] = None\n\n# Global Constants\nBLOCK_SIZE = 20\nHUD_HEIGHT = 100\nTHEMES = {'default': {'font': 'Retro.ttf', 'player color': (26, 188, 156), 'food color': (192, 57, 43), 'menu title color': (44, 62, 80), 'obstacle color': (127, 140, 141), 'magnet powerup color': (230, 126, 34), 'passthrough powerup color': (155, 89, 182), 'del_body powerup color': (41, 128, 185), 'wraparound powerup color': (241, 196, 15), 'menu bg': (39, 174, 96), 'play bg': (44, 62, 80), 'pause bg': (41, 128, 185), 'gameover bg': (30, 39, 46), 'hud bg color': (52, 73, 94), 'menu item selected': (236, 240, 241)}}\nSTATES = ['menu', 'play', 'paused', 'gameover']\n\nMENU_TEXT = {'title': 'PySnake', 'Mode': ['Normal', 'Team', 'Battle', 'Zone'], 'Speed': ['0.5x', '1x', '1.5x', '2x'], 'Wraparound': ['off', 'on'], 'Power Ups': ['off', 'on'], 'Obstacles': ['off', 'on'], 'Num Food': ['1', '2', '3'], 'Map': ['1', '2', '3', '4']}\nMAPS = [(30, 30), (50, 50), (50, 25), (80, 80)]\n\npygame.init()\ngame = Game()\ngame.main()\n\npygame.quit()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":22192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"480549898","text":"import sys\nimport xml.etree.ElementTree as ET\nimport os.path\n\ndef xml_to_dictionary(xml, root_element):\n    settings = {}\n    for node in xml.findall(root_element): # get each appSetting\n        for element in node:\n            settings[element.get(\"key\")] = element.get(\"value\")\n    return settings\n\ndef update_config(sourceXml, destXml):\n    # get the source settings and build a dictionary from them\n    sourceSettings = xml_to_dictionary(sourceXml, 'appSettings')\n\n    # modify the dest file\n    for node in destXml.findall('appSettings'): # iterate each appSetting# set each appSetting from the sourceSettings dictionary\n        for element in node:\n            current_key = element.get(\"key\")\n            if(current_key in sourceSettings): # if the setting was in the source file\n                # change the value of the setting in the dest file to the value from the source file\n                print(\"Setting '{0}' changed from '{1}' to '{2}'\".format(current_key, element.get(\"value\"), sourceSettings[current_key]))\n                element.set(\"value\", sourceSettings[current_key])\n\n    return destXml\n\ndef update_config_file(source_file_name, dest_file_name, output_file = None):\n\n    if not os.path.isfile(source_file_name):\n        raise ValueError(\"The source file: '{0}' does not exist\".format(source_file_name))\n    if not os.path.isfile(dest_file_name):\n        raise ValueError(\"The destination file: '{0}' does not exist\".format(dest_file_name))\n\n    print(\"Updating settings from '{0}' to '{1}'\".format(source_file_name, dest_file_name))\n\n    sourceXml = ET.parse(source_file_name)\n    destXml = ET.parse(dest_file_name)\n\n    result = update_config(sourceXml, destXml)\n    if output_file is None:\n        output_file = dest_file_name\n\n    print(\"Writing output to '{0}'\".format(output_file))\n    destXml.write(output_file)\n\nif __name__ == \"__main__\":\n    # check args\n    if(len(sys.argv) < 3):\n        sys.exit(\"Invalid arguments. Should be: '{0}' '{1}' '{2}' '{3}'\".format(\"config_updater.py\", \"sourceFile\", \"destFile\", \"[outputFile]\"))\n\n    source_file_name = sys.argv[1]\n    dest_file_name = sys.argv[2]\n    if len(sys.argv) >= 4:\n        output_file = sys.argv[3]\n    else:\n        output_file = None\n\n    update_config_file(source_file_name, dest_file_name, output_file)\n","sub_path":"configjoe/config_updater.py","file_name":"config_updater.py","file_ext":"py","file_size_in_byte":2297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"48460840","text":"\"\"\"Utility functions for tabular models.\"\"\"\n\nimport warnings\n\nimport numpy as np\n\nIGNORED_DICT_KEYS = ['fitted', 'distribution', 'type']\n\n\ndef flatten_array(nested, prefix=''):\n    \"\"\"Flatten an array as a dict.\n\n    Args:\n        nested (list, numpy.array):\n            Iterable to flatten.\n        prefix (str):\n            Name to append to the array indices. Defaults to ``''``.\n\n    Returns:\n        dict:\n            Flattened array.\n    \"\"\"\n    result = dict()\n    for index in range(len(nested)):\n        prefix_key = '__'.join([prefix, str(index)]) if len(prefix) else str(index)\n\n        value = nested[index]\n        if isinstance(value, (list, np.ndarray)):\n            result.update(flatten_array(value, prefix=prefix_key))\n\n        elif isinstance(value, dict):\n            result.update(flatten_dict(value, prefix=prefix_key))\n\n        else:\n            result[prefix_key] = value\n\n    return result\n\n\ndef flatten_dict(nested, prefix=''):\n    \"\"\"Flatten a dictionary.\n\n    This method returns a flatten version of a dictionary, concatenating key names with\n    double underscores.\n\n    Args:\n        nested (dict):\n            Original dictionary to flatten.\n        prefix (str):\n            Prefix to append to key name. Defaults to ``''``.\n\n    Returns:\n        dict:\n            Flattened dictionary.\n    \"\"\"\n    result = dict()\n\n    for key, value in nested.items():\n        prefix_key = '__'.join([prefix, str(key)]) if len(prefix) else key\n\n        if key in IGNORED_DICT_KEYS and not isinstance(value, (dict, list)):\n            continue\n\n        elif isinstance(value, dict):\n            result.update(flatten_dict(value, prefix_key))\n\n        elif isinstance(value, (np.ndarray, list)):\n            result.update(flatten_array(value, prefix_key))\n\n        else:\n            result[prefix_key] = value\n\n    return result\n\n\ndef _key_order(key_value):\n    parts = list()\n    for part in key_value[0].split('__'):\n        if part.isdigit():\n            part = int(part)\n\n        parts.append(part)\n\n    return parts\n\n\ndef unflatten_dict(flat):\n    \"\"\"Transform a flattened dict into its original form.\n\n    Args:\n        flat (dict):\n            Flattened dict.\n\n    Returns:\n        dict:\n            Nested dict (if corresponds)\n    \"\"\"\n    unflattened = dict()\n\n    for key, value in sorted(flat.items(), key=_key_order):\n        if '__' in key:\n            key, subkey = key.split('__', 1)\n            subkey, name = subkey.rsplit('__', 1)\n\n            if name.isdigit():\n                column_index = int(name)\n                row_index = int(subkey)\n\n                array = unflattened.setdefault(key, list())\n\n                if len(array) == row_index:\n                    row = list()\n                    array.append(row)\n                elif len(array) == row_index + 1:\n                    row = array[row_index]\n                else:\n                    # This should never happen\n                    raise ValueError('There was an error unflattening the extension.')\n\n                if len(row) == column_index:\n                    row.append(value)\n                else:\n                    # This should never happen\n                    raise ValueError('There was an error unflattening the extension.')\n\n            else:\n                subdict = unflattened.setdefault(key, dict())\n                if subkey.isdigit():\n                    subkey = int(subkey)\n\n                inner = subdict.setdefault(subkey, dict())\n                inner[name] = value\n\n        else:\n            unflattened[key] = value\n\n    return unflattened\n\n\ndef handle_sampling_error(is_tmp_file, output_file_path, sampling_error):\n    \"\"\"Handle sampling errors by printing a user-legible error and then raising.\n\n    Args:\n        is_tmp_file (bool):\n            Whether or not the output file is a temp file.\n        output_file_path (str):\n            The output file path.\n        sampling_error:\n            The error to raise.\n\n    Side Effects:\n        The error will be raised.\n    \"\"\"\n    if 'Unable to sample any rows for the given conditions' in str(sampling_error):\n        raise sampling_error\n\n    if is_tmp_file:\n        print('Error: Sampling terminated. Partial results are stored in a temporary file: '\n              f'{output_file_path}. This file will be overridden the next time you sample. '\n              'Please rename the file if you wish to save these results.')\n    elif output_file_path is not None:\n        print('Error: Sampling terminated. '\n              f'Partial results are stored in {output_file_path}.')\n\n    raise sampling_error\n\n\ndef check_num_rows(num_rows, expected_num_rows, is_reject_sampling, max_tries_per_batch):\n    \"\"\"Check the number of sampled rows against the expected number of rows.\n\n    If the number of sampled rows is zero, throw a ValueError.\n    If the number of sampled rows is less than the expected number of rows,\n    raise a warning.\n\n    Args:\n        num_rows (int):\n            The number of sampled rows.\n        expected_num_rows (int):\n            The expected number of rows.\n        is_reject_sampling (bool):\n            If reject sampling is used or not.\n        max_tries_per_batch (int):\n            Number of times to retry sampling until the batch size is met.\n\n    Side Effects:\n        ValueError or warning.\n    \"\"\"\n    if num_rows < expected_num_rows:\n        if num_rows == 0:\n            user_msg = ('Unable to sample any rows for the given conditions. ')\n            if is_reject_sampling:\n                user_msg = user_msg + (\n                    f'Try increasing `max_tries_per_batch` (currently: {max_tries_per_batch}). '\n                    'Note that increasing this value will also increase the sampling time.'\n                )\n            else:\n                user_msg = user_msg + (\n                    'This may be because the provided values are out-of-bounds in the '\n                    'current model. \\nPlease try again with a different set of values.'\n                )\n            raise ValueError(user_msg)\n\n        else:\n            # This case should only happen with reject sampling.\n            user_msg = (\n                f'Only able to sample {num_rows} rows for the given conditions. '\n                'To sample more rows, try increasing `max_tries_per_batch` '\n                f'(currently: {max_tries_per_batch}). Note that increasing this value '\n                'will also increase the sampling time.'\n            )\n            warnings.warn(user_msg)\n","sub_path":"sdv/tabular/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":6489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"469240071","text":"__author__ = 'Marcus T Taylor'\n\n\nfrom query import Query\nfrom question import Question\n\n\nclass Setup:\n\n\t@staticmethod\n\tdef count_questions(category=None):\n\t\tif category is not None:\n\t\t\tsql = 'SELECT count(*) FROM Questions WHERE category=?'\n\t\telse:\n\t\t\tsql = 'SELECT count(*) FROM Questions'\n\t\treturn Query(sql, category).fetch()[0]\n\n\tdef get_categories(self):\n\t\tif not self.has_categories():\n\t\t\treturn False\n\t\tsql = 'SELECT category FROM categories'\n\t\treturn Query(sql).fetch()[0]\n\n\t@staticmethod\n\tdef get_question():\n\t\treturn Question()\n\n\t@staticmethod\n\tdef has_answers(qid):\n\t\tsql = 'SELECT count(*) FROM Answers WHERE QID=?'\n\t\treturn Query(sql, (qid,)).fetch()[0][0]\n\n\t@staticmethod\n\tdef has_categories():\n\t\tsql = 'SELECT count(*) FROM Categories'\n\t\treturn Query(sql).fetch()[0]\n\n\t@staticmethod\n\tdef has_correct_answer(qid):\n\t\tsql = 'SELECT count(*) FROM Answers WHERE QID=? AND correct=1'\n\t\tif Query(sql, (qid,)).fetch()[0][0] == 1:\n\t\t\treturn True\n\t\treturn False\n\n\t@staticmethod\n\tdef has_questions(category=None):\n\t\tif category is not None:\n\t\t\tsql = 'SELECT count(*) FROM Questions WHERE category=?'\n\t\t\treturn Query(sql, (category,)).fetch()[0]\n\t\telse:\n\t\t\tsql = 'SELECT count(*) FROM Questions'\n\t\t\treturn Query(sql).fetch()[0]\n\n\t@staticmethod\n\tdef is_answer_correct(aid):\n\t\tsql = 'SELECT count(*) FROM Answers WHERE AID=? AND correct=1'\n\t\treturn Query(sql, (aid,)).fetch()[0]\n\n\t@staticmethod\n\tdef select_qid(excluded=None, category=None):\n\t\twhere = ''\n\t\tvalues = []\n\t\tif excluded is None:\n\t\t\texcluded = []\n\t\tif len(excluded) != 0:\n\t\t\tfor value in excluded:\n\t\t\t\tif where != '':\n\t\t\t\t\twhere += ' AND QID !=?'\n\t\t\t\telse:\n\t\t\t\t\twhere += 'QID !=?'\n\t\t\t\tvalues.append(value)\n\t\tif category is not None:\n\t\t\tif len(excluded) != 0:\n\t\t\t\twhere += ' AND category =?'\n\t\t\telse:\n\t\t\t\twhere += 'category =?'\n\t\t\tvalues.append(category)\n\t\tif len(excluded) != 0 or category is not None:\n\t\t\tsql = f'SELECT QID FROM Questions WHERE {where} ORDER BY RANDOM() LIMIT 1'\n\t\t\treturn Query(sql, tuple(values)).fetch()[0]\n\t\telse:\n\t\t\tsql = 'SELECT QID FROM Questions ORDER BY RANDOM() LIMIT 1'\n\t\t\treturn Query(sql).fetch()[0]\n\n\nclass Game(Setup, Question):\n\n\tdef _init__(self):\n\t\tsuper().__init__()\n\n\tdef get_question_block(self, qid, index):\n\t\tquestion = self.get_question()\n\t\tquestion_block = f'QUESTION {index}\\n\\n'\n\t\tquestion_block += f'{question.get_question(qid)}\\n\\n'\n\t\tfor index, answer in question.get_index_answer_pairs(qid).items():\n\t\t\tquestion_block += f'{index}.) {answer}\\n'\n\t\tquestion_block += '\\nTo answer type 1 - 4 > '\n\t\treturn question_block\n\n\tdef select_qid_array(self, question_count, category=None):\n\t\tif not self.has_questions(category):\n\t\t\treturn []\n\t\tquestions = []\n\t\texcluded = []\n\t\tcount = 0\n\t\twhile count < question_count:\n\t\t\tqid = self.select_qid(excluded)[0]\n\t\t\tif self.has_answers(qid) and self.has_correct_answer(qid):\n\t\t\t\tquestions.append(qid)\n\t\t\texcluded.append(qid)\n\t\t\tcount += 1\n\t\treturn questions\n","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":2902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"324259574","text":"import sys\nsys.path.insert(0, '../code')\n\nfrom csep.loglikelihood import calcLogLikelihood, calcLogLikelihoodNEW\nfrom models.mathUtil import calcNumberBins, calcNumberBinsOld, loadFactorial\nimport numpy as np\nimport models.model as model\n#input: two probabilistic vectors\ndef test_calcNumberBins(input1, input2):\n\tnewValue = calcNumberBins(input1, input2)\n\toldValue = calcNumberBinsOld(input1, input2)\n\treturn newValue==oldValue\n\ndef testeLogLikelihood(input1, input2):\n\tfact = loadFactorial('../data/factorial.txt')\n\t\n\tdefinition = model.loadModelDefinition('../params/Kanto.txt')\n\tvar1 = model.newModel(definition)\n\tvar2 = model.newModel(definition)\n\n\tvar1.bins = input1\n\tvar2.bins = input2\n\tnewValue = calcLogLikelihoodNEW(var1, var2, fact)\n\toldValue = calcLogLikelihood(var1, var2)\n\tprint(newValue, oldValue)\n\treturn newValue==oldValue\n\ndef main():\n\tvar1 = np.array([0,0,0,0,0,0,0,0])\n\tvar2 = np.array([0,0,0,0,0,0,0,0])\n\tprint(test_calcNumberBins(var1, var2))\n\n\tvar1 = np.array([0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01])\n\tvar2 = np.array([1,1,1,1,1,1,1,1])\n\tprint(test_calcNumberBins(var1, var2))\n\n\tvar1 = np.array([0.12,0.01,0.9,0.09678,0.321,0.178,0.03,0.5])\n\tvar2 = np.array([1,1,1,1,1,1,1,1])\n\tprint(test_calcNumberBins(var1, var2))\n\t\n\tvar1 = np.array([0.12,0.01,0.9,0.09678,0.321,0.178,0.03,0.5])\n\tvar2 = np.array([1,6,9,5,1,3,1,2])\n\tprint(test_calcNumberBins(var1, var2))\n\n\n\n\tvar1 = np.array([0,0,0,0,0,0,0,0])\n\tvar2 = np.array([0,0,0,0,0,0,0,0])\n\tprint(testeLogLikelihood(var1, var2))\n\n\tvar1 = np.array([1,1,1,1,1,1,1,1])\n\tvar2 = np.array([1,1,1,1,1,1,1,1])\n\tprint(testeLogLikelihood(var1, var2))\n\n\tvar1 = np.array([1,6,9,5,1,3,1,2])\n\tvar2 = np.array([1,1,1,1,1,1,1,1])\n\tprint(testeLogLikelihood(var1, var2))\n\t\n\tvar1 = np.array([12,1,9,9678,321,178,3,5])\n\tvar2 = np.array([1,6,9,5,1,3,1,2])\n\tprint(testeLogLikelihood(var1, var2))\n\n\n\n\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"testingCodes/caseTest4calcNumberBins.py","file_name":"caseTest4calcNumberBins.py","file_ext":"py","file_size_in_byte":1881,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"653047861","text":"a = int(input('Primeiro numero: '))\nb = int(input('Segundo numero: '))\nc = int(input('Terceiro numero: '))\n\nmenor = a\nif b < a and b < c:\n    menor = b\nif c < a and c < b:\n    menor = c\nprint(f'menor valor é {menor}')\n\n#verificando o maior\nmaior = a\nif b > a and b > c:\n    maior = b\nif c > a and c > b:\n    maior = c\n\nprint(f'O maior valor é {maior}')\n\n","sub_path":"curso_em_video/0033.py","file_name":"0033.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"308847034","text":"from datetime import datetime\nfrom models import Event\nfrom google.appengine.ext import ndb\n\nimport logging\nclass EventService:\n\n    def __init__(self):\n        pass\n\n    @staticmethod\n    def get_events_from_project(project_key):\n        project_key = ndb.Key(urlsafe=project_key)\n        events = Event.query(ancestor=project_key).fetch()\n\n        events_array = []\n        for ev in events:\n            e = {}\n            e['id'] = ev.key.urlsafe()\n            e['title'] = ev.title\n            e['description'] = ev.description\n            e['start_time'] = datetime.strftime(ev.start_time, '%Y-%m-%dT%H:%M:00+02:00')\n            e['end_time'] = datetime.strftime(ev.end_time, '%Y-%m-%dT%H:%M:00+02:00')\n            e['billed'] = ev.billed\n            if ev.url:\n                e['url'] = ev.url\n            else:\n                e['url'] = ''\n            e['creation_time'] = datetime.strftime(ev.creation_time, '%Y-%m-%dT%H:%M:00+02:00')\n\n            events_array.append(e)\n        logging.warn(events_array)\n        return events_array\n\n    @staticmethod\n    def save_event(event, event_title, event_start_time, event_end_time, event_description, event_billed, event_url=None):\n        event.title = event_title\n        event.description = event_description\n        event.start_time = event_start_time\n        event.end_time = event_end_time\n        elapsed_time = (event.end_time - event.start_time)\n        total_seconds = elapsed_time.total_seconds()\n        event.elapsed_time = int(total_seconds//60)\n        event.billed = event_billed\n        if event_url:\n            event.url = event_url\n\n        event.put()\n\n        return event\n\n    @staticmethod\n    def delete_event(event_key):\n        event_key = ndb.Key(urlsafe=event_key)\n        event_key.delete()\n\n        return event_key.urlsafe()","sub_path":"static_services/event_service.py","file_name":"event_service.py","file_ext":"py","file_size_in_byte":1810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"603401929","text":"from django.shortcuts import render\nfrom datetime import datetime\nimport pytz\nfrom .models import Poo\n\n# Create your views here.\n\ndef index(request):\n    context = {\n        'loop_times': range(1, 8),\n    }\n    return render(request,'poo/index.html',context)\n\ndef add_data(request):\n    params = request.GET\n    print(params)\n    name = params[\"name\"]\n    location = params[\"location\"]\n    peeOrpoo = params[\"peepoo\"]\n    type = params[\"type\"]\n    tz = pytz.timezone('US/Mountain')\n    dt = str(datetime.now(tz)).split(\" \")\n    date = dt[0]\n    time = dt[1].split(\":\")\n    time = time[0] +\":\"+ time[1] +\":\"+ (time[2]).split(\".\")[0]\n\n    p = Poo(name=name, loc=location, time=time, date=date, movement=peeOrpoo, type=type)\n    p.save()\n\n    context = {}\n    return render(request,'poo/index.html',context)","sub_path":"poo/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"332707473","text":"\"\"\"\n#------------------------------------------------------------------------------\n# Characterize the impulse amplitude of flexible system at KIT\n#\n# Created: 6/20/17 - Daniel Newman -- dmn3669@louisiana.edu\n#\n# Modified:\n#   * 6/20/17 - DMN -- dmn3669@louisiana.edu\n#\t\t\t- Added documentation for this script\n#------------------------------------------------------------------------------\n\"\"\"\n\n\nimport warnings\nwarnings.simplefilter(\"ignore\", UserWarning)\n\n# Import the necessary python library modules\nfrom sklearn.metrics import r2_score\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom scipy.optimize import curve_fit\nimport glob\nfrom scipy.optimize import fsolve\nimport os\nimport sys\nimport pdb\n\n# Add my local path to the relevant modules list\nsys.path.append('/Users/Daniel/Github/Crawlab-Student-Code/Daniel Newman/Python Modules')\n\n# Import my python modules\nimport InputShaping as shaping\nimport Generate_Plots as genplt\nimport ic_linear as ic_shape\n\nDEG_TO_RAD = np.pi / 180\n\n# Add my local path to the relevant modules list\npath = os.getcwd()\nrootpath = path.split('Korea')\n\ndata = np.genfromtxt(rootpath + 'Initial Results/Data/Fitted Data/Impulses.txt',skip_header=1)\nfit_data = np.genfromtxt('Fitted Data/Fit Values.txt',skip_header=1)\n\ndata = data[data[:,0].argsort()]\n\nomega_n = np.average(fit_data[:,1])\n\ntau = 2*np.pi / omega_n\n\nzeta = 0.0\n\nAmax = 900000\nVmax = 150000\n\n\ndef line_fit(x,a,b):\n\treturn a*x + b\n\n\nbounds = np.array(\n                [tuple((-np.inf,np.inf)),\n                 tuple((-np.inf,np.inf))]\n                 )\n\np0 = [\n\t\t(data[-1,1] - data[0,1]) / (data[-1,0] - data[0,0]),\n\t\t0.0\n     ]\n\nbounds = bounds.T\n\nprint(p0)\n\n# now do the fit\nfit = curve_fit(\n                line_fit, \n                data[:,0],\n                data[:,1], \n                p0=p0,method='trf',\n                bounds=bounds\n                )\nfit = fit[0]\n\nprint(fit)\n\n# Create the sin fit to the data\nresponse_fit = line_fit(data[:,0],*fit)\n\ncorr_coeff = r2_score(data[:,1], response_fit)\n\nprint(corr_coeff)\n\nimpulse_amp = response_fit[-1]\n\nprint('Impulse Amp: {}'.format(impulse_amp))\n\nt = np.arange(0,10,0.01)\n\nresponse = omega_n * np.sin(omega_n * t) * impulse_amp\n\nresponse_deriv = impulse_amp * omega_n / np.sqrt(1 - zeta**2) * (np.exp(-zeta * omega_n * t) * \\\n\t\t\t\t omega_n * np.sqrt(1 - zeta**2) * np.cos(omega_n * np.sqrt(1 - zeta**2) * t) \\\n\t\t\t\t + np.sin(omega_n * np.sqrt(1 - zeta**2) * t) * np.exp(-zeta * omega_n * t) * -zeta * omega_n)\n\namps = np.arange(0,1,0.01)\nnorm_phase = np.arange(0,360.,1)\nnorm_phase *= DEG_TO_RAD\n\n# Ensure that the folder we want to save to exists\nif not os.path.exists('Figures/{}'.format(sys.argv[0])):\n    os.makedirs('Figures/{}'.format(sys.argv[0]))\n\ndata = open('Figures/{}/Data.txt'.format(sys.argv[0]),'w')\ndata.write('Amplitude Phase T A1 A2 \\n')\n\nnorm_amp = 0.7\nphase = np.pi/2\n\n\nic_response = omega_n * np.sin(omega_n * t) * norm_amp * impulse_amp\nic_response_deriv = norm_amp * impulse_amp * omega_n / np.sqrt(1 - zeta**2) * (np.exp(-zeta * omega_n * t) * \\\n\t\t\t\t omega_n * np.sqrt(1 - zeta**2) * np.cos(omega_n * np.sqrt(1 - zeta**2) * t) \\\n\t\t\t\t + np.sin(omega_n * np.sqrt(1 - zeta**2) * t) * np.exp(-zeta * omega_n * t) * -zeta * omega_n)\n\nnorm_time = phase / (omega_n * np.sqrt(1 - zeta**2))\n\nmax_time = np.argmax(ic_response)\n\nmax_pos = ic_response[max_time]\n\nnorm_t_step = np.round(norm_time / (t[1] - t[0])).astype(int)\n\nx_0 = response[norm_t_step]\nv_0 = response_deriv[norm_t_step]\n\nstep_amp = omega_n * np.sqrt(x_0**2 + (v_0 / omega_n)**2)\n\nx_0_ic = ic_response[norm_t_step]\nv_0_ic = ic_response_deriv[norm_t_step]\n\npole_zero_t = 2 / omega_n * np.arctan2(step_amp + v_0_ic,-omega_n  * x_0_ic)\npole_zero_a1 = (-step_amp * np.cos(pole_zero_t * omega_n) - v_0_ic) / (1 - np.cos(pole_zero_t * omega_n))\npole_zero_a2 = (step_amp + v_0_ic) / (1 - np.cos(pole_zero_t * omega_n))\n\npole_zero_a1 /= step_amp\npole_zero_a2 /= step_amp\n\nnormalized_time = pole_zero_t / tau\n\nif normalized_time < 0: \n\tnormalized_time = 1 + normalized_time\n\nnormalized_phase = np.abs(phase)\n\nshaper = np.array([[0,pole_zero_a1],[normalized_time * tau,pole_zero_a2]])\n\n\nprint('Shaper: {}'.format(shaper))\n\npdb.set_trace()\n\np = [norm_amp,np.arctan2(x_0_ic * omega_n,v_0_ic),impulse_amp,Amax,Vmax,1]\n\nfrequency, shaped_amps = ic_shape.ic_sensplot(shaper,0.5 * omega_n/(2*np.pi),1.5*omega_n/(2*np.pi),omega_n,zeta,p,plotflag=1)\nphase, phase_amps = ic_shape.ic_phase_sensplot(shaper,-2*np.pi,-.01,omega_n,zeta,p,plotflag=1)\namps, amp_amps = ic_shape.ic_amp_sensplot(shaper,0.05 *norm_amp,1.0,omega_n,zeta,p,plotflag=1)\n\nfolder = 'Figures/{}/'.format(\n\t\t\t\t\t\t\t\t\t\t\t\tsys.argv[0],\n\t\t\t\t\t\t\t\t\t\t\t\t)\n\n\ngenplt.compare_responses(frequency * 2 * np.pi / omega_n,\n\t\t\t\t\t\t shaped_amps[:,0],'Sens',\n \t\t\t\t\t\t name_append='Sens',\n\t\t\t\t\t\t xlabel=r'Normalized Frequency $\\frac{\\omega}{\\omega_n}$',ylabel='Percent Vibration',\n\t\t\t\t\t\t folder=folder,grid=False,save_data=False,ncol=1\n\t\t\t\t\t\t)\n\ngenplt.compare_responses(phase,\n\t\t\t\t\t\t phase_amps[:,0],'Sens',\n \t\t\t\t\t\t name_append='Phase_Sens',\n\t\t\t\t\t\t xlabel=r'Normalized Frequency $\\frac{\\omega}{\\omega_n}$',ylabel='Percent Vibration',\n\t\t\t\t\t\t folder=folder,grid=False,save_data=False,ncol=1\n\t\t\t\t\t\t)\ngenplt.compare_responses(amps,\n\t\t\t\t\t\t amp_amps[:,0],'Sens',\n \t\t\t\t\t\t name_append='Amp_Sens',\n\t\t\t\t\t\t xlabel=r'Normalized Frequency $\\frac{\\omega}{\\omega_n}$',ylabel='Percent Vibration',\n\t\t\t\t\t\t folder=folder,grid=False,save_data=False,ncol=1\n\t\t\t\t\t\t)","sub_path":"Code/Paper_Simulations/ASPEN_2017/Initial Submission/PoleZero_IC/pole_zero_sens_curve.py","file_name":"pole_zero_sens_curve.py","file_ext":"py","file_size_in_byte":5412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"128928884","text":"#!/usr/bin/env python\n#-*- conding: utf-8 -*-\nheight = 1.75\nweight = 80.5\ns = 80.5/(1.75*1.75)\nif s < 18.5:\n    print('过轻')\nelif s < 25:\n    print('正常')\nelif s < 28:\n    print('过重')\nelif s < 32:\n    print('肥胖')\nelse:\n    print('严重肥胖')\n","sub_path":"4.py","file_name":"4.py","file_ext":"py","file_size_in_byte":259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"144200912","text":"import cPickle\nimport logging\nimport os\nfrom time import time\n\nimport pandas as pd\n\nfrom preprocess.oscars import Oscars\nfrom preprocess.imdb import Imdb, ImdbReviews\nfrom preprocess.pagviews import PageViews\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__ if __name__ != '__main__' else [])\n\nDATA_FOLDER = '../data/'\nCACHE_FOLDER = '../cache/'\n\n\ndef main():\n    t0 = time()\n\n    if not os.path.exists(CACHE_FOLDER):\n        os.makedirs(CACHE_FOLDER)\n\n    oscars = Oscars(DATA_FOLDER + 'oscars/', CACHE_FOLDER)\n    oscars.preprocess()\n    lbls = oscars.get_labels()\n\n    imdb = Imdb(CACHE_FOLDER)\n    imdb_df = imdb.preprocess()\n\n    pv = PageViews(CACHE_FOLDER + 'viewsperday.csv', imdb_df)\n    pv_df = pv.preprocess()\n\n    rev = ImdbReviews(CACHE_FOLDER + 'reviews/', imdb_df)\n    rev_df = rev.preprocess(weighted=False)\n\n    data = pd.DataFrame.merge(lbls, imdb_df, on='id')\n    data = pd.DataFrame.merge(data, pv_df, on='id')\n    data = pd.DataFrame.merge(data, rev_df, on='id')\n    logger.info('data.shape = (%s, %s)' % data.shape)\n\n    out_file = CACHE_FOLDER + 'dataset.p'\n    with open(out_file, 'wb') as fo:\n        cPickle.dump(data, fo)\n    logger.info('Saved: %s' % out_file)\n\n    tf = (time() - t0) / 60\n    logger.info('--- %0.3f minutes ---' % tf)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"src/run_preprocess.py","file_name":"run_preprocess.py","file_ext":"py","file_size_in_byte":1332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"246503343","text":"from django.urls import path\n\nfrom .views import (\n    follow, \n    unfollow, \n    UserDetails, \n    register_user, \n    AccountsLoginView,\n    logout_user,\n    user_profile_update,\n    UsersListView,\n    edit_bio\n)\n\nurlpatterns = [\n    path('follow/', follow, name=\"follow\"),\n    path('unfollow/', unfollow, name=\"unfollow\"),\n    path('register/', register_user, name=\"user-register\"),\n    path('login/', AccountsLoginView.as_view(), name=\"user-login\"),\n    path('logout/', logout_user, name=\"user-logout\"),\n    path('people/search/', UsersListView.as_view(), name=\"search-people\"),\n    path('user-profile/edit', user_profile_update, name=\"user-profile-edit\"),\n    path('user-profile/edit/bio', edit_bio, name=\"edit-bio\"),\n    path('user-profile/<str:username>/', UserDetails.as_view(), name=\"user-profile\"),\n]\n","sub_path":"accounts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"396047337","text":"import cv2\r\nimport numpy as np\r\n\r\nimg1 = cv2.imread(\"seacity.jpg\");\r\nimg2 = cv2.imread(\"tropcity.jpg\");\r\nimg3 = cv2.imread(\"logo.jpg\");\r\n\r\n#add = img1 + img2; add pixelcolors together\r\n#add = cv2.add(img1,img2); add pixelcolors together (limited to 255,255,255)\r\n#weighted = cv2.addWeighted(img1, 0.6, img2, 0.4, 0) #add together with weight, last 0 is gamma parameter\r\n#cv2.imshow(\"add\",weighted);\r\n\r\nrows, cols, channels = img3.shape; #gets our img3 size and colors parameters\r\nroi = img1[0:rows,0:cols]; #defining region of image through the up-mentioned parameters, basically take all img3\r\n\r\nimg2gray = cv2.cvtColor(img3, cv2.COLOR_BGR2GRAY);\r\nret, mask = cv2.threshold(img2gray,150, 255, cv2.THRESH_BINARY_INV); #if a pixel is above 220, it will be converted to 255(white), if below 220, it becomes 0(black)\r\n\r\nmask_inv = cv2.bitwise_not(mask);#logical operations not, and\r\nimg1_bg = cv2.bitwise_and(roi, roi, mask=mask_inv); # Now black-out the area of logo in ROI\r\nimg3_fg = cv2.bitwise_and(img3, img3, mask=mask); # Take only region of logo from logo image.\r\n\r\ncollect = cv2.add(img1_bg, img3_fg);\r\nimg1[0:rows, 0:cols] = collect;\r\n\r\n\r\n\r\ncv2.imshow(\"result\", img1)\r\n\r\ncv2.waitKey(0);\r\ncv2.destroyAllWindows();","sub_path":"Misc/logic_and_arithmetics (1).py","file_name":"logic_and_arithmetics (1).py","file_ext":"py","file_size_in_byte":1218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"249016966","text":"import logging, sys, argparse\n\n\ndef str2bool(v):\n    # copy from StackOverflow\n    if v.lower() in ('yes', 'true', 't', 'y', '1'):\n        return True\n    elif v.lower() in ('no', 'false', 'f', 'n', '0'):\n        return False\n    else:\n        raise argparse.ArgumentTypeError('Boolean value expected.')\n\n\ndef get_entity(tag_seq, char_seq):\n    DISEASE = get_typed_entity(tag_seq, char_seq, 'DISEASE')\n    SYMPTOM = get_typed_entity(tag_seq, char_seq, 'SYMPTOM')\n    BODY = get_typed_entity(tag_seq, char_seq, 'BODY')\n    return DISEASE, SYMPTOM, BODY\n\n\ndef get_typed_entity(tag_seq, char_seq, entity_type):\n    bTag = 'B-' + entity_type\n    iTag = 'I-' + entity_type\n    length = len(char_seq)\n    typed_entity = []\n    for i, (char, tag) in enumerate(zip(char_seq, tag_seq)):\n        if tag == bTag:\n            if 'ent' in locals().keys():\n                typed_entity.append(ent)\n                del ent\n            ent = char\n            if i+1 == length:\n                typed_entity.append(ent)\n        if tag == iTag:\n            if 'ent' not in locals().keys():\n                continue\n            ent += char\n            if i+1 == length:\n                typed_entity.append(ent)\n        if tag not in [iTag, bTag]:\n            if 'ent' in locals().keys():\n                typed_entity.append(ent)\n                del ent\n            continue\n    return typed_entity\n\ndef get_logger(filename):\n    logger = logging.getLogger('logger')\n    logger.setLevel(logging.DEBUG)\n    logging.basicConfig(format='%(message)s', level=logging.DEBUG)\n    handler = logging.FileHandler(filename)\n    handler.setLevel(logging.DEBUG)\n    handler.setFormatter(logging.Formatter('%(asctime)s:%(levelname)s: %(message)s'))\n    logging.getLogger().addHandler(handler)\n    return logger\n","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"386368869","text":"# -*- coding: utf8 -*-\nimport os\nimport sys\n\nfrom parsers import MarkDownParser\n\n\ndef _gather_markdown_files(root_dir='./tests'):\n    \"\"\" Gathers all markdown files in `mardown_engine/tests/` dir\n    and returns them to be used inside `run_tests()`.\n\n    Args:\n        root_dir (str): takes in an optional folder that contains markdown\n                        files to be tested, but defaults to the `markdown_engine`\n                        `tests` folder.\n\n    Return:\n        md_files ([str]): Returns a list of markdown files\n    \"\"\"\n    md_files = []\n\n    for subdir, _, files in os.walk(root_dir):\n        for f in files:\n\n            #  append file if it's a markdown file\n            if f.endswith('.md'):\n                md_files.append(subdir+os.sep+f)\n\n    return md_files\n\n\ndef run_test(test_file):\n    \"\"\" Runs only one specific markdown file through the\n    the Markdown Parser. Useful if trying to test on file\n\n    Args:\n        test_name (str): name of the test file trying to be run\n\n    Returns:\n        None\n    \"\"\"\n    for test in TEST_FILES:\n        if test_file in test:\n            try:\n                print('File Running: {}'.format(test))\n                parser = MarkDownParser()\n                parser.readfile(test)\n            except Exception as err:\n                print(err)\n\n\ndef run_tests():\n    \"\"\" Runs all the markdown files in a given directory through\n    the MarkDownParser\n\n    Args:\n        None\n\n    Returns:\n    \"\"\"\n    parser = MarkDownParser()\n\n    for test in TEST_FILES:\n        try:\n            print('File Running: {}'.format(test))\n            parser.readfile(test)\n        except Exception as err:\n            print(err)\n            continue\n\n\n# Test markdown files inside `./tests/`\nTEST_FILES = _gather_markdown_files()\n\n\nif __name__ == \"__main__\":\n    if len(sys.argv) == 1:\n        print(sys.argv)\n        run_tests()\n    elif len(sys.argv) == 2:\n        md_file = sys.argv[1]\n        run_test(md_file)\n\n","sub_path":"src/markdown_engine/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"26990998","text":"import arcade\n\nNUMBER_ROW = 17\nNUMBER_COLUMN = 13\nPIXEL_WIDTH = 30\nIMAGE_SCALING = 0.125\n\n\nSCREEN_WIDTH = NUMBER_COLUMN*PIXEL_WIDTH\nSCREEN_HEIGHT = NUMBER_ROW*PIXEL_WIDTH\nSCREEN_TITLE = \"Caro by AIZero\"\nBACKGROUND_COLOR = arcade.csscolor.BLACK\nLINE_COLOR = arcade.csscolor.RED\nEXTEND_WIDTH = 500\n","sub_path":"const.py","file_name":"const.py","file_ext":"py","file_size_in_byte":296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"384563512","text":"#!/usr/bin/env python2\nimport rospy\nimport actionlib\nimport irob_assignment_1.msg\nfrom irob_assignment_1.srv import GetSetpoint, GetSetpointRequest, GetSetpointResponse\nfrom geometry_msgs.msg import Twist\nfrom nav_msgs.msg import Path\nimport tf2_ros\nimport tf2_geometry_msgs\nfrom math import atan2, hypot\n\n# Use to transform between frames\ntf_buffer = None\nlistener = None\n\n# The exploration simple action client\ngoal_client = None\n# The collision avoidance service client\ncontrol_client = None\n# The velocity command publisher\npub = None\n\n# The robots frame\nrobot_frame_id = \"base_link\"\n\n# Min allowed gain to move along path (in feedback)\nmin_allowed_gain = 3\n\n# Max linear velocity (m/s)\nmax_linear_velocity = 0.5\n# Max angular velocity (rad/s)\nmax_angular_velocity = 1.0\n\n\ndef goal_active():\n    rospy.loginfo(\"I got activated\")\n\n\ndef goal_feedback(feedback):\n    rospy.loginfo(\"I got feedback\")\n\n    # Check if this path has higher gain than min_allowed_gain\n    if feedback.gain > min_allowed_gain:\n        # If it has cancel goal and move along the path\n        goal_client.cancel_all_goals()\n        move(feedback.path)\n\n\ndef goal_result(state, result):\n    rospy.loginfo(\"I got a result\")\n\n    # If the state is succeeded then\n    if actionlib.TerminalState.SUCCEEDED == state:\n        rospy.loginfo(\"Action returned succeeded\")\n\n        # TODO: Do your stuff\n\n    elif actionlib.TerminalState.RECALLED == state:\n        rospy.loginfo(\"Action returned recalled\")\n    elif actionlib.TerminalState.REJECTED == state:\n        rospy.loginfo(\"Action returned rejected\")\n    elif actionlib.TerminalState.PREEMPTED == state:\n        rospy.loginfo(\"Action returned preempted\")\n    elif actionlib.TerminalState.ABORTED ==state:\n        rospy.loginfo(\"Action returned aborted\")\n    elif actionlib.TerminalState.LOST == state:\n        rospy.loginfo(\"Action returned lost\")\n\n    # Move along the path if path is not empty\n\n\ndef move(path):\n    global control_client, robot_frame_id, pub\n\n    while path.poses:\n        # Call service client with path\n        resp = control_client(path)\n        setpoint = resp.setpoint\n        new_path = resp.new_path\n\n        # Transform Setpoint from service client\n        transform = tf_buffer.lookup_transform(\n            robot_frame_id,\n            setpoint.header.frame_id,\n            rospy.Time(0)\n            )\n        transformed_setpoint = tf2_geometry_msgs.do_transform_point(\n            setpoint,\n            transform\n            )\n\n        # Create Twist message from the transformed Setpoint\n        msg = Twist()\n        msg.angular.z = min(\n            atan2(\n                transformed_setpoint.point.y,\n                transformed_setpoint.point.x\n                ),\n            max_angular_velocity\n            )\n        msg.linear.x = min(\n            hypot(\n                transformed_setpoint.point.x,\n                transformed_setpoint.point.y\n                ),\n            max_linear_velocity\n            )\n\n        # Set linear velocity to 0 when angular velocity is high\n        if msg.angular.z > 0.85:\n            msg.linear.x = 0\n\n        # Publish Twist\n        pub.publish(msg)\n        rate.sleep()\n\n        # Call service client again if the returned path is not empty and do stuff again\n        path = new_path\n\n    # Send 0 control Twist to stop robot\n    msg.angular.z = 0\n    msg.linear.x = 0\n    pub.publish(msg)\n    rate.sleep()\n\n    # Get new path from action server\n    get_path()\n\n\ndef get_path():\n    global goal_client\n\n    # Get path from action server\n    goal_client.wait_for_server()\n    goal = irob_assignment_1.msg.GetNextGoalGoal()\n    goal_client.send_goal(\n        goal,\n        active_cb=goal_active,\n        feedback_cb=goal_feedback,\n        done_cb=goal_result\n        )\n\n\nif __name__ == \"__main__\":\n    # Init node\n    rospy.init_node('controller')\n\n    # Init publisher\n    pub = rospy.Publisher('cmd_vel', Twist, queue_size=10)\n    rate = rospy.Rate(15)\n\n    # Init simple action client\n    goal_client = actionlib.SimpleActionClient(\n        'get_next_goal',\n        irob_assignment_1.msg.GetNextGoalAction\n        )\n\n    # Init service client\n    control_client = rospy.ServiceProxy('get_setpoint', GetSetpoint)\n\n    # Init tf buffer and listener\n    tf_buffer = tf2_ros.Buffer()\n    listener = tf2_ros.TransformListener(tf_buffer)\n\n    # Call get path\n    get_path()\n\n    # Spin\n    rospy.spin()","sub_path":"scripts/controller_feedback.py","file_name":"controller_feedback.py","file_ext":"py","file_size_in_byte":4398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"280186187","text":"import turtle\n\nwincol = input(\"Say a window color: \")\nwn = turtle.Screen()  # Creates a playground for turtles\nwn.bgcolor(wincol)  # Set the window background color\nwn.title(\"Hello, Tess!\")  # Set the window title\n\ntesscol = input(\"Say a window color: \")\ntesspensize = int(input(\"Tess pen size: \"))\n\ntess = turtle.Turtle()  # Create a turtle, assign to alex\ntess.color(tesscol)  # Tell tess to change her color\ntess.pensize(tesspensize)  # Tell tess to set her pen width\n\ntess.forward(50)  # Tell alex to move forward by 50 units\ntess.left(120)  # Tell alex to turn by 90 degrees\ntess.forward(50)  # Complete the second side of a rectangle\n\nwn.mainloop()  # Wait for user to close window\n","sub_path":"Les2_BasicIteration_turtle/turtle_test.py","file_name":"turtle_test.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"350553405","text":"# let's explore some functions and how to write them\n# and also what they do\n\n\ndef greeting():\n    # say hello\n    print(\"hello from your first function!\")\n\n\n# this is how you call or invoke a function\ngreeting()\n\n\ndef greetings(msg=\"hello there!\", num=0):\n    print(\"Our function says\", msg, \"The scond arg is\", num)\n\n\ngreetings()\ngreetings(\"This is an argument\", 1)\ngreetings(\"why are we aruging\", 2)\n\nmyVariableNumber = 0\n\n\ndef someMath(num1=2, num2=5):\n    global myVariableNumber\n\n    myVariableNumber = num1 = num2\n    return num1 + num2\n\n\nsum = someMath()\nprint(\"Our sum variable is:\", sum, \"myVariableNumber us also\", sum)\n","sub_path":"functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"261791747","text":"from sklearn.datasets import load_boston\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import mean_squared_error\nimport joblib\n\ndef linear_ridge():\n    '''\n    use Ridge Regression to predict the house price in boston\n    :return:\n    '''\n\n    # 1)get data\n    boston = load_boston()\n\n    # 2)divide dataset\n    x_train, x_test, y_train, y_test = train_test_split(boston.data, boston.target, random_state=0)\n\n    # 3)feature engineering:standardization\n    transfer = StandardScaler()\n    x_train = transfer.fit_transform(x_train)\n    x_test = transfer.transform(x_test)\n\n    # 4)Logistic Regression algorithm estimator\n    estimator = Ridge(max_iter=10000, alpha=0.5)\n    estimator.fit(x_train, y_train)\n    # # save model\n    #joblib.dump(estimator, 'my_ridge.pkl')\n    # # load model\n    # estimator = joblib.load('my_ridge.pkl')\n\n\n    # 5)get model\n    print('coef_:', estimator.coef_)\n    print('intercept_:', estimator.intercept_)\n\n    # 6)model assessment\n    y_pred = estimator.predict(x_test)\n    print('predicted house price: ', y_pred)\n    error = mean_squared_error(y_test, y_pred)\n    print('MSE: ', error)\n\n    return None\n\nif __name__ == '__main__':\n    # # code 1: use Normal Equation to predict the house price in boston\n    # linear_ne()\n\n    # # code 2: use Gradient Descent to predict the house price in boston\n    # linear_gd()\n\n    # code 3: use Ridge Regression to predict the house price in boston\n    linear_ridge()","sub_path":"day3_demos/3-LinearReg_ridge.py","file_name":"3-LinearReg_ridge.py","file_ext":"py","file_size_in_byte":1556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"506190644","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jun 11 14:19:54 2015\n\n@author: stoimenoff\n\"\"\"\nclass Sorting:\n\n    # Sorts a sequence of integers.\n    def sort(self, sequence):\n        if len(sequence) <= 1:\n            return sequence\n        first_half = self.sort(sequence[0:len(sequence)/2])\n        second_half = self.sort(sequence[len(sequence)/2:])\n        sorted_sequence = []\n        while first_half != [] and second_half != []:\n            if first_half[0] < second_half[0]:\n                sorted_sequence.append(first_half[0])\n                first_half.remove(first_half[0])\n            else:\n                sorted_sequence.append(second_half[0])\n                second_half.remove(second_half[0])\n        sorted_sequence += (first_half + second_half)\n        return sorted_sequence\n","sub_path":"week1/5-Sorting/merge_sort.py","file_name":"merge_sort.py","file_ext":"py","file_size_in_byte":792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"185615217","text":"import numpy as np\nimport math\nimport SimpleITK as sitk\n\ntest_vector_out = '/home/neil/share/deformations_test/test_vector_def.nrrd'\ntest_vector_out_mhd = '/home/neil/share/deformations_test/test_vector_def.mhd'\n\ndef rotate_vector(vector, theta):\n    theta = math.radians(theta)\n    x_temp = float(vector[0])\n    x = vector[0] * math.cos(theta) - vector[1] * math.sin(theta)\n    y = x_temp * math.sin(theta) + vector[1] * math.cos(theta)\n    return x, y\n\na = np.zeros(100*100*100*3).reshape((100, 100, 100, 3))\n# Make a vector field of 2\na.fill(2)\n\n# For each slice, rotate 45 degress\ntheta = -90\nfor y in range(a.shape[1]):\n    theta += 90\n    if theta > 360:\n        theta = 0\n    for z in range(a.shape[0]):\n        for x in range(a.shape[2]):\n            rotated = rotate_vector(a[z,y,x, [0,1]], theta)\n            a[z, y, x,[0,1]] = rotated\n\nout = sitk.GetImageFromArray(a)\nsitk.WriteImage(out, test_vector_out)\nsitk.WriteImage(out, test_vector_out_mhd)\n\n\n\n\n\n\n","sub_path":"vpv/utils/dummy_def_fields.py","file_name":"dummy_def_fields.py","file_ext":"py","file_size_in_byte":965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"108070126","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n    path('', views.index, name='index'),\n    path('faq', views.faq, name='faq'),\n    path('termsofuse', views.termsofuse, name='termsofuse'),\n    path('privacypolicy', views.privacypolicy, name='privacypolicy'),\n    path('contact', views.contact, name='contact'),\n\n]","sub_path":"pages/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"600282644","text":"from mongoengine import connect\nfrom models.shop import Category, Product\n# import shop\nfrom random import randint\n\nconnect(\"web_shop\")\n\n\ncategories = ['Phone', 'laptop', 'TV']\nproduct =[['Panasonic RP', 'JBL C45BT', 'HyperX', '500 mA/h', '800 mA/h', 'iPhone X', 'iPhone 8', 'iPhone 7'],\n          ['MakBook', 'Lenovo'],\n          ['ubl2go', 'sony bass', 'true bass','original', 'multifunctional','lg dvd', 'sony vhs']]\n\n\nfor i, m in zip(categories, product):\n    Category(title=i, description=f'{i} directory description',).save()\n    for y in m:\n        product_price = randint(1000, 10000)\n        quality = randint(0, 100)\n        availability = False\n\n        if quality != 0:\n            availability = True\n            Product(title=y, price=product_price, category=Category.objects(title=i).get(),\n                    quality=quality, availability=availability).save()\n\n","sub_path":"10_hw/models/seeder.py","file_name":"seeder.py","file_ext":"py","file_size_in_byte":878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"466921678","text":"# -*- coding: utf-8 -*-\n\nimport numpy as np \nimport matplotlib.pyplot as plt\n\nclass KMeans(object):\n    \"\"\"K-Means clustering\n\n    Args:\n        n_clusters: int. The number of clusters to form as well as the number of centroids to generate.\n\n    \"\"\"\n    def __init__(self, n_clusters=8):\n        self.n_clusters = n_clusters\n\n    def _calc_euclidean_dist(self, x, y):\n        \"\"\"Calculate euclidien distance between vector1 and vector2\n        \"\"\"\n        return np.sqrt(sum(np.power(x - y, 2)))\n\n    def _init_random_centroid(self, data):\n        \"\"\"\n        \"\"\"\n        n_samples = data.shape[0]\n        centroid = np.zeros((self.n_clusters, data.shape[1]), dtype=float)\n        index = np.random.randint(0, n_samples-1, self.n_clusters)\n        centroid = data[index, :]\n        return centroid\n\n    def fit(data):\n        \"\"\"\n        \"\"\"\n        n_samples = data.shape[0]\n        cluster_assment = np.zeros((n_samples,2), dtype=float)     ###creer une matrice pour assigner les points da dataset\n        centroid = self._init_random_centroid(data)\n        cluster_changed = True\n        while cluster_changed:\n            cluster_changed = False\n            for i in range(n_samples):       ###assigner chaque point pour le plus proche centroid\n                min_dist = np.inf; min_index = -1\n                for j in range(self.n_clusters):\n                    dist_eclid = self._calc_euclidean_dist(centroid[j,:], data[i,:])     ###calculer la distance entre chaque point de centroids et celui de dataset\n                    if dist_eclid < min_index:                               ###mise a jour centroids\n                        min_dist=dist_eclid; min_index=j\n                if cluster_assment[i,0] != min_index:\n                    cluster_changed=True\n                cluster_assment[i,:] = min_index, min_dist**2\n            print(centroid)\n            for cent in range(self.n_clusters):           ###recalculer les centroids\n                pts_in_cluster = dataset[np.nonzero(cluster_assment[:,0] == cent)[0]]\n                pts_in_cluster[cent,:] = mean(pts_in_cluster,axis=0)\n        return centroid, cluster_assment\n\n","sub_path":"kmeans/kmeans.py","file_name":"kmeans.py","file_ext":"py","file_size_in_byte":2140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"439924897","text":"import matplotlib.pyplot as plt\nx=[1,2,3]\ny=[5,7,4]\nx2=[1,2,3]\ny2=[10,14,12]\nplt.plot(x,y, label = 'first line')\nplt.plot(x2,y2, label = 'second line')\nplt.xlabel('plot number')\nplt.ylabel('important variable')\nplt.title('intresting graph \\n checkit out')\nplt.show()\n","sub_path":"ADVANCE PYTHON PRECTICE/analytics.py","file_name":"analytics.py","file_ext":"py","file_size_in_byte":267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"183187110","text":"def game(start, end):\n    if start == end:\n        return start\n\n    a = game(start, (start + end) // 2)\n    b = game((start + end) // 2 + 1, end)\n\n    if (ls[b] == 1 and ls[a] == 3) or (ls[b] == 2 and ls[a] == 1) or (ls[b] == 3 and ls[a] == 2):\n        return b\n    else:\n        return a\n\n\nanswer = []\nfor tc in range(1, int(input()) + 1):\n    n = int(input())\n    ls = [0] + list(map(int, input().split()))\n    winner = game(1, n)\n    answer.append('#%d %d\\n' % (tc, winner))\nprint(''.join(answer))","sub_path":"0302/4880.토너먼트카드게임.py","file_name":"4880.토너먼트카드게임.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"110970799","text":"from fabric.api import local\n\n\ndef pylint():\n    local(\"cd ..;export DJANGO_SETTINGS_MODULE=zamboni/settings_local;\"\n        \"PYTHONPATH=zamboni/apps:zamboni/lib \"\n        \"pylint --rcfile zamboni/scripts/pylintrc zamboni \",\n        capture=False)\n\n\ndef pep8(all=False):\n    local(\"git fetch jbalogh\")\n    cmd = (\"pep8 --repeat --ignore E221\"\n           \" --exclude *.sh,*.html,*.json,*.txt,*.pyc,.DS_Store,README,\"\n           \"migrations,sphinxapi.py\")\n\n    if all:\n        cmd = cmd + \" apps lib\"\n    else:\n        cmd = cmd + \" $(git diff --name-only jbalogh/master|grep py$)\"\n    local(cmd, capture=False)\n\n\ndef test(module=None, pdb=False, failfast=True):\n    cmd = \"python manage.py test\"\n    if module:\n        cmd += \" %s\" % module\n\n    if failfast and failfast != '0':\n        cmd += \" -x\"\n\n    cmd += \" --noinput --logging-clear-handlers\"\n    if pdb:\n        cmd += ' --pdb --pdb-failures -s'\n\n    local(cmd, capture=False)\n","sub_path":"fabfile.py","file_name":"fabfile.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"420149374","text":"import requests\nfrom bs4 import BeautifulSoup\nimport json\nimport pandas as pd\n\n# 페이지 정보 갖고오기\nindex_code = ['G1010', 'G1510', 'G2010', 'G2020', 'G2030', 'G2510', 'G2520', 'G2530', 'G2550', 'G2560', 'G3010',\n              'G3020', 'G3030', 'G3510', 'G3520', 'G4010', 'G4020', 'G4030', 'G4040', 'G4050', 'G4510', 'G4520',\n              'G4530', 'G4535', 'G4540', 'G5010', 'G5020', 'G5510']\n\ndef get_bs_obj():\n    url0 = \"http://www.wiseindex.com/Index/GetIndexComponets?ceil_yn=0&dt=20200701&sec_cd=\" + index_code[0]\n    result0 = requests.get(url0)\n    bs_obj0 = BeautifulSoup(result0.content, \"html.parser\")\n    value0 = json.loads(bs_obj0.text)[\"list\"]\n    for i in index_code[1:]:\n        url = \"http://www.wiseindex.com/Index/GetIndexComponets?ceil_yn=0&dt=20200701&sec_cd=\" + i\n        result = requests.get(url)\n        bs_obj = BeautifulSoup(result.content, \"html.parser\")\n        value_instance = json.loads(bs_obj.text)[\"list\"]\n        value0 = value0 + value_instance\n    return value0\n\n\nfinal_data = pd.DataFrame(get_bs_obj())\nprint(final_data)\nfinal_data.info()\nfinal_data.to_excel('sector_data.xlsx')","sub_path":"naver_finance/industry_sector.py","file_name":"industry_sector.py","file_ext":"py","file_size_in_byte":1129,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"585235621","text":"import mlrun\nimport tests.integration.sdk_api.base\n\n\nclass TestProject(tests.integration.sdk_api.base.TestMLRunIntegration):\n    def test_ctx_creation_creates_run_with_project(self):\n        ctx_name = \"some-context\"\n        mlrun.get_or_create_ctx(ctx_name)\n        runs = mlrun.get_run_db().list_runs(\n            name=ctx_name, project=mlrun.mlconf.default_project\n        )\n        assert len(runs) == 1\n        assert runs[0][\"metadata\"][\"project\"] == mlrun.mlconf.default_project\n","sub_path":"tests/integration/sdk_api/run/test_run.py","file_name":"test_run.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"631146678","text":"import requests\nimport json\nimport jsonpath\n\nurl = \"https://reqres.in/api/users?page=2\"\n\n# Read Json input file\nfile = open('/Users/ivoitkiv/PycharmProjects/APIAutomation/CreatingNewUser.json', 'r')\njson_input = file.read()\nrequest_json = json.loads(json_input)\nprint(request_json)\n\n#Send Post Request\nresponse = requests.post(url,request_json)\nassert response.status_code == 201\n\nprint(response.headers.get('Content-Length'))\njson_response = json.loads(response.text)\nid = jsonpath.jsonpath(json_response,'id')\nprint(id[0])","sub_path":"CRUD_Requests/CreateNewRecource.py","file_name":"CreateNewRecource.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"442472019","text":"from main.model.int_types.natural import Natural\n\n\nclass Posint(Natural):\n    \"\"\"\n    A positive integer (cannot be zero or negative)\n    \"\"\"\n\n    def __init__(self, value: int):\n        \"\"\"\n        Initializes the fields; raises ValueError if value is less than 1\n\n        :param value: The value of the positive integer\n        \"\"\"\n\n        if value == 0:\n            raise ValueError(\"A posint cannot be less than 1.\")\n\n        super().__init__(value)\n","sub_path":"main/model/int_types/posint.py","file_name":"posint.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"418876440","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport daemon\nimport threading\nimport time\nimport random\nimport sys\nimport os\nimport urllib2\nfrom gntp import notifier\n\nmessages = [\"進捗どうですか\", \"だめです\", \"残念ですｗ\"]\nwait = int(sys.argv[1]) if len(sys.argv) > 1 else 300\nimages_link =  ['http://24.media.tumblr.com/df0837528816427fb9462ee304eea954/tumblr_ms1ifh2Ju31sckns5o1_1280.jpg',\n                'http://31.media.tumblr.com/303d4df5d6dd34afa7f5f98d0722a3af/tumblr_ms1if5fSbw1sckns5o1_1280.jpg']\nimages = [urllib2.urlopen(x).read() for x in images_link]\n\n\nwith daemon.DaemonContext():\n    growl = notifier.GrowlNotifier(applicationName='progress-notify',\n                                   notifications=['default'],\n                                   defaultNotifications=['default'],\n                                   hostname='localhost')\n    growl.register()\n    growl.notify(noteType='default',\n                 description=\"PID: %d\" % os.getpid(),\n                 title=\"進捗マン\",\n                 icon=random.choice(images),\n                 priority=1,\n                 sticky=False)\n    time.sleep(10)\n    while True:\n        for m in messages:\n            growl.notify(noteType='default',\n                         description=m,\n                         title=\"進捗マン\",\n                         icon=random.choice(images),\n                         priority=1,\n                         sticky=False)\n            time.sleep(0.5)\n        time.sleep(wait)\n","sub_path":"progress-notify-growl.py","file_name":"progress-notify-growl.py","file_ext":"py","file_size_in_byte":1502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"322914792","text":"import gocept.httpserverlayer.wsgi\nimport gocept.selenium\nimport zeit.cms.testing\nimport zeit.workflow.testing\n\n\nZCML_LAYER = zeit.cms.testing.ZCMLLayer(\n    'ftesting.zcml',\n    product_config=zeit.cms.testing.cms_product_config +\n    zeit.workflow.testing.product_config)\nWSGI_LAYER = zeit.cms.testing.WSGILayer(name='WSGILayer', bases=(ZCML_LAYER,))\nHTTP_LAYER = gocept.httpserverlayer.wsgi.Layer(\n    name='HTTPLayer', bases=(WSGI_LAYER,))\nWD_LAYER = gocept.selenium.WebdriverLayer(\n    name='WebdriverLayer', bases=(HTTP_LAYER,))\nWEBDRIVER_LAYER = gocept.selenium.WebdriverSeleneseLayer(\n    name='WebdriverSeleneseLayer', bases=(WD_LAYER,))\n\n\nclass FunctionalTestCase(zeit.cms.testing.FunctionalTestCase):\n\n    layer = ZCML_LAYER\n\n\nclass SeleniumTestCase(zeit.cms.testing.SeleniumTestCase):\n\n    layer = WEBDRIVER_LAYER\n    skin = 'vivi'\n","sub_path":"src/zeit/edit/testing.py","file_name":"testing.py","file_ext":"py","file_size_in_byte":844,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"517688336","text":"#   Copyright 2010-2011 Josh Kearney\n#\n#   Licensed under the Apache License, Version 2.0 (the \"License\");\n#   you may not use this file except in compliance with the License.\n#   You may obtain a copy of the License at\n#\n#       http://www.apache.org/licenses/LICENSE-2.0\n#\n#   Unless required by applicable law or agreed to in writing, software\n#   distributed under the License is distributed on an \"AS IS\" BASIS,\n#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#   See the License for the specific language governing permissions and\n#   limitations under the License.\n\n\"\"\"Intelligent Configuration Library\"\"\"\n\nimport ConfigParser\nimport sys\n\n\nclass Config(object):\n    \"\"\"A configuration object\"\"\"\n\n    def __init__(self, config, section):\n        self.config = ConfigParser.ConfigParser()\n        self.section = section\n\n        try:\n            conf_file = open(config)\n            self.config.readfp(conf_file)\n            conf_file.close()\n        except IOError:\n            sys.exit(\"No such file: '%s'\" % config)\n\n    def sections(self):\n        \"\"\"Return a list of sections\"\"\"\n        return self.config.sections()\n\n    def get(self, key, param_type=\"string\"):\n        \"\"\"Retrieve configuration values\"\"\"\n        if param_type == \"int\":\n            return self.config.getint(self.section, key)\n        elif param_type == \"bool\":\n            return self.config.getboolean(self.section, key)\n        elif param_type == \"list\":\n            return self.config.get(self.section, key).split(\", \")\n        else:\n            return self.config.get(self.section, key)\n","sub_path":"pyhole/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"533455347","text":"import os.path\nimport sys\nfrom threading import Thread\nfrom pathlib import Path\nimport socket\n\n\nclass ClientHandler(Thread):\n    def __init__(self, socket, database):\n        Thread.__init__(self)\n        self.sock = socket\n        self.connection = socket[0]\n        self.address = socket[1]\n        self.database = database\n        self.currentUser = None\n        self.client = \"\"\n        print(\"Connection established with client: \", self.address)\n\n    def run(self):\n        # indicates what server it is\n        self.send_message(\"python\")\n        # gets what client it is\n        try:\n            self.client = self.read_message()\n        except socket.error:\n            # client fail\n            print(\"Client: \" + str(self.address) + \" unexpectedly disconnected\")\n            sys.exit()\n\n        # login or registration menu\n        print(\" - Asking to login or register\")\n        quitApp = True\n        try:\n            quitApp = self.show_login_menu()\n        except socket.error:\n            # client fail\n            print(\"Client: \" + str(self.address) + \" unexpectedly disconnected\")\n            sys.exit()\n\n        # main menu\n        try:\n            self.show_menu(quitApp)\n        except socket.error:\n            # client fail\n            print(\"Client: \" + str(self.address) + \" unexpectedly disconnected\")\n            # disconnects user\n            self.database.disconnect_user(self.currentUser.username)\n\n    def show_login_menu(self):\n        run = True\n        quitApp = False\n        while run:\n            self.send_message(\"What do you want to do?\\n\\ta. Login\\n\\tb. Register\\n\\tc.Quit\\nChoice:\")\n            choice = self.read_message()\n            if choice == \"a\":\n                # login\n                print(\" - Requesting login data\")\n                self.currentUser = self.login()\n                if self.currentUser is not None:\n                    run = False\n            elif choice == \"b\":\n                # registration\n                print(\" - Registering new user\")\n                self.currentUser = self.register()\n                if self.currentUser is not None:\n                    run = False\n            elif choice == \"c\":\n                # quit\n                print(\" - Disconnecting client \" + str(self.address) + \" from the server\")\n                self.send_message(\"Goodbye!\")\n                run = False\n                quitApp = True\n            else:\n                print(\" - Incorrect choice, trying again\")\n                self.send_message(\"Incorrect choice, try again\\n\")\n        return quitApp\n\n    def show_menu(self, quitApp):\n        while not quitApp:\n            print(\" - Displaying menu\")\n            self.send_message(\n                \"What do you want to do?\\n\\ta. Check user's state\\n\\tb. Send file\\n\\tc. Refresh received files\"\n                \"\\n\\td. Change file extensions\\n\\te. Quit\\nChoice:\")\n            choice = self.read_message()\n            if choice == \"a\":\n                # checks another user's state\n                print(\" - Checking user's state\")\n                self.check_state()\n            elif choice == \"b\":\n                # open chat\n                print(\" - Sending a file\")\n                self.open_send_file()\n            elif choice == \"c\":\n                # checks if there is any new file received\n                print(\" - Checking for new received files\")\n                self.received_files()\n            elif choice == \"d\":\n                # change extensions\n                print(\" - Changing accepted file extensions\")\n                self.change_extensions()\n            elif choice == \"e\":\n                # quit app\n                print(\" - Disconnecting client '\" + self.currentUser.username + \"' from the server\")\n                self.send_message(\"Goodbye!\")\n                self.database.disconnect_user(self.currentUser.username)\n                quitApp = True\n            else:\n                # incorrect choice\n                print(\" - Trying to choose a menu option again\")\n                self.send_message(\"Incorrect option, try again\\n\")\n\n    def check_state(self):\n        self.send_message(\"Who do you want to know about? (q)\\nUsername:\")\n        username = self.read_message()\n        if username == \"q\":\n            return\n        friend = self.database.get_user(username)\n        if friend is None:\n            print(\" - User doesn't exist\")\n            self.send_message(\"That user doesn't exist\\n\")\n        elif friend.isAvailable:  # friend is connected\n            print(\" - User is connected\")\n            self.send_message(\"The user is connected\\n\")\n        else:\n            print(\" - User is disconnected\")\n            self.send_message(\"The user is disconnected\\n\")\n\n    def open_send_file(self):\n        while True:\n            # ask for friend\n            print(\" - Asking for username\")\n            self.send_message(\"To whom do you want to send the file? (q)\\nUsername:\")\n            friendUsername = self.read_message()\n            if friendUsername == \"q\":\n                return\n            # check if friend's user exists\n            friend = self.database.get_user(friendUsername)\n            if friend is None:  # friend doesn't exists\n                print(\" - Username doesn't exist\")\n                self.send_message(\"Username doesn't exist, try again\\n\")\n            elif friendUsername == self.currentUser.username:  # same current user\n                print(\" - Same user\")\n                self.send_message(\"You can't send a file to yourself, try again\\n\")\n            elif not friend.isAvailable:  # friend is disconnected\n                print(\" - User is disconnected\")\n                self.send_message(\"The user is disconnected, can't send message\\n\")\n                return\n            else:  # friend is available\n                while True:\n                    print(\" - Choosing file to send\")\n                    self.send_message(\"What file do you want to send?\")\n                    # gets file size\n                    print(\" - Reading file from client\")\n                    fileSize = int(self.read_message())\n                    if fileSize > 65536:\n                        self.send_message(\"File too big (only allowed up to 64kb), try again\\n\")\n                        continue\n                    else:\n                        self.send_message(\"ok\")\n                    # gets file name\n                    filename = self.read_message()\n                    # checks if the user chose a file\n                    if filename == \"q\":\n                        break\n                    filename = filename.split(\"[\")[0]\n                    # checks if the file extension is accepted by the other user\n                    fileExtension = filename.split(\".\")[1]\n                    acceptedExtensions = friend.extensions.split(\",\")\n                    # invalid file extension\n                    if fileExtension not in acceptedExtensions:\n                        print(\" - File extension not valid\")\n                        self.send_message(\"File extension not accepted, only valid: \" + friend.extensions +\n                                          \". Try again\\n\")\n                    else:  # correct file extension\n                        self.send_message(\"ok\")\n                        filename = \"files/\" + filename\n                        # reads file\n                        if self.client == \"java\":\n                            self.read_file_java(filename, fileSize)\n                        else:\n                            self.read_file_python(filename, fileSize)\n                        # saves file to database\n                        print(\" - Uploading file to database\")\n                        self.database.upload_file(friend.id, self.currentUser.id, filename)\n                        self.send_message(\"File correctly sent\\n\")\n                        break\n                break\n\n    # reads a file sent over a java socket\n    def read_file_java(self, filename, fileSize):\n        # creates path if it didn't exist\n        if not os.path.exists(os.path.dirname(filename)):\n            try:\n                os.makedirs(os.path.dirname(filename))\n            except OSError as exc:\n                if exc.errno != errno.EEXIST:\n                    raise\n        # reads file\n        file = open(filename, \"wb\")\n        while fileSize > 0:\n            data = self.connection.recv(1024)\n            file.write(data)\n            fileSize -= len(data)\n        file.close()\n\n    # reads a file sent over a python socket\n    def read_file_python(self, filename, fileSize):\n        # creates path if it didn't exist\n        if not os.path.exists(os.path.dirname(filename)):\n            try:\n                os.makedirs(os.path.dirname(filename))\n            except OSError as exc:\n                if exc.errno != errno.EEXIST:\n                    raise\n        file = open(filename, \"wb\")\n        readBytes = 0\n        while fileSize != readBytes:\n            # reception confirmation message\n            self.send_message(\"ok\")\n            # reads bytes\n            line = self.connection.recv(8192)\n            # writes to file\n            file.write(line)\n            # updates amount of read bytes\n            readBytes += len(line)\n        file.close()\n\n    # checks if there is any new file received\n    def received_files(self):\n        print(\" - Checking if any file has been received\")\n        files = self.database.get_files(self.currentUser.id)\n        if not files:\n            print(\" - No new files\")\n            self.send_message(\"You haven't received any new files\\n\")\n        else:\n            print(\" - New files received\")\n            for file in files:\n                # sends file to user\n                friend = self.database.get_user_by_id(file.fromId)\n                self.send_message(\"You have received a new file from '\" + friend.username + \"': \" + file.filename)\n                self.send_file(file.filename)\n                # waits for confirmation message\n                self.read_message()\n                print(\"- File received by user\")\n            # removes files from database\n            for file in files:\n                self.database.delete_file(file.id)\n\n    # sends file to socket\n    def send_file(self, filename):\n        print(\" - Sending new file to user\")\n        file = open(\"files/\" + filename, \"rb\")\n        # sends file size (bytes)\n        fileSize = Path(\"files/\" + filename).stat().st_size\n        self.send_message(str(fileSize))\n        # sends file data\n        line = file.read(8192)\n        while line:\n            # waits for reception confirmation message\n            self.read_message()\n            # sends bytes\n            self.connection.send(line)\n            line = file.read(8192)\n        file.close()\n\n    def change_extensions(self):\n        currentExtensions = self.currentUser.extensions\n        run = True\n        while run:\n            self.send_message(\"Your current accepted extensions are: \" + currentExtensions +\n                              \"\\nIntroduce the new ones from the list [jpg,png,txt,pdf] (separated by commas) (q):\")\n            newExtensions = self.read_message()\n            if newExtensions == \"q\":\n                return\n            # check if it's okay\n            availableExtensions = [\"jpg\", \"png\", \"txt\", \"pdf\"]\n            chosenExtensions = newExtensions.split(\",\")\n            incorrect = False\n            for extension in chosenExtensions:\n                # chosen extensions are not correct\n                if extension not in availableExtensions:\n                    print(\" - Incorrect extensions, trying again\")\n                    self.send_message(\"Incorrect extensions, try again\\n\")\n                    incorrect = True\n                    break\n            if incorrect:\n                continue\n            # update user\n            self.database.update_user_extensions(self.currentUser.username, newExtensions)\n            self.currentUser.extensions = newExtensions\n            print(\" - Correctly changed extensions\")\n            self.send_message(\"Extensions changed\\n\")\n            run = False\n\n    def login(self):\n        while True:\n            self.send_message(\"Introduce your username (q):\")\n            username = self.read_message()\n            if username == \"q\":\n                return None\n            self.send_message(\"Introduce your password (q):\")\n            password = self.read_message()\n            if password == \"q\":\n                return None\n            # check if the data is correct\n            user = self.database.get_user(username)\n            if user is None:\n                self.send_message(\"The user doesn't exist, try again\\n\")\n                print(\" - Trying to login again\")\n            elif user.password != password:\n                self.send_message(\"Incorrect password, try again\\n\")\n                print(\" - Trying to login again\")\n            elif user.isAvailable:\n                self.send_message(\"That user is already connected, try again\\n\")\n                print(\" - Trying to login again\")\n            else:\n                self.send_message(\"Correct data, logging in...\\n\")\n                print(\" - Logging in...\")\n                user.isAvailable = True\n                self.database.connect_user(username)\n                return user\n\n    def register(self):\n        while True:\n            self.send_message(\"Introduce your new username (q):\")\n            username = self.read_message()\n            if username == \"q\":\n                return None\n            self.send_message(\"Introduce your new password (q):\")\n            password = self.read_message()\n            if password == \"q\":\n                return None\n            # checks if the username already exists\n            user = self.database.get_user(username)\n            if user is None:\n                print(\" - Creating new user...\")\n                # registers user in the database\n                self.database.register_user(username, password)\n                return self.database.get_user(username)\n            else:\n                self.send_message(\"That username already exists, try again\\n\")\n                print(\" - Trying to register again\")\n\n    def send_message(self, message):\n        try:\n            self.connection.send(bytes(message + \"\\r\\n\", \"UTF-8\"))\n        except socket.error:\n            pass\n\n    def read_message(self):\n        message = \"\"\n        try:\n            message = self.connection.recv(1024).decode(encoding=\"UTF-8\").rstrip()\n        except socket.error:\n            raise\n        if message == \"\":\n            raise socket.error\n        return message\n","sub_path":"ClientHandler.py","file_name":"ClientHandler.py","file_ext":"py","file_size_in_byte":14627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"410166291","text":"import json\nfrom aiohttp import web\nimport aiohttp\nfrom typing import Dict\nimport pprint\nimport http\nimport os\nfrom urllib import parse\nfrom loguru import logger\n\nENV_AIRFLOW_BASE_URL = os.getenv('ENV_AIRFLOW_BASE_URL', 'http://localhost:8080')\n\nMETHOD_DICT = {\n    'post_curve': {\n        'method': 'post',\n        'url': 'dag_runs'\n    }\n}\n\n\nasync def doPushCurve2Airflow(data):\n    async with aiohttp.ClientSession() as session:\n        dt: Dict = METHOD_DICT.get('post_curve', {})\n        mm = dt.get('method')\n        m = getattr(session, mm)\n        post_curve_url = parse.urljoin(ENV_AIRFLOW_BASE_URL, dt.get('url', ''))\n        async with m(post_curve_url, data=data) as resp:\n            txt = await resp.text(encoding='utf-8')\n            logger.info(\"推送曲线: {}, payload: {}\".format(resp.status, txt))\n            return txt\n\n\nasync def postCurveCollectionhandle(request):\n    data = await curveCollectionhandle(request)\n    return data\n\n\nasync def healthzCheckHandler(request):\n    return web.Response(status=http.HTTPStatus.NO_CONTENT)\n\n\nasync def curveCollectionhandle(request):\n    data: Dict = await request.json()\n    result_content = data.get('result', '')\n    curve_content = data.get('curve', '')\n\n    measure_result = \"\"\n    entity_id = \"\"\n    for item_result in result_content:\n        if item_result.get(\"name\") == \"IsPass\":\n            measure_result = \"OK\" if item_result.get(\"value\") else \"NOK\"\n        if item_result.get(\"name\") == \"BarCade\":\n            entity_id = item_result.get(\"value\")\n\n    cur_m = curve_content.get(\"force\")\n    cur_w = curve_content.get(\"distance\")\n    cur_t = curve_content.get(\"time\")\n    measure_torque = cur_m[-1]\n    measure_angle = cur_w[-1]\n    measure_time = int(cur_t[-1])\n\n    airflow_data = {\n      \"replace_microseconds\": 'false',\n      \"conf\": {\n        \"should_analyze\": False,\n        \"entity_id\": entity_id,\n        \"result\": {\n            \"device_type\": \"servo_press\",\n            \"measure_result\": measure_result,\n            \"measure_torque\": measure_torque,\n            \"measure_angle\": measure_angle,\n            \"measure_time\": measure_time,\n            \"batch_count\": 1,\n            \"count\": 1,\n            \"job\": 1,\n            \"controller_sn\": \"OP52\",\n            \"controller_name\": \"OP52\",\n            \"pset\": 1,\n            \"program\": 1,\n            \"tool_sn\": \"3002\",\n        },\n        \"curve\": {\n            \"cur_m\": cur_m,\n            \"cur_w\": cur_w,\n            \"cur_t\": cur_t\n        },\n      }\n    }\n    # logger.debug(\"收到曲线数据: {}\".format(pprint.pformat(data, indent=4)))\n    resp = await doPushCurve2Airflow(json.dumps(airflow_data, ensure_ascii=False))\n    return web.Response(text=resp)\n","sub_path":"utils/handler.py","file_name":"handler.py","file_ext":"py","file_size_in_byte":2694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"333568845","text":"\"\"\"\r\nName : 73. Set-Matrix-Zeroes.py\r\nAuthor : xyChen\r\nTime : 2021/3/30 14:25\r\nDesc:\r\n\"\"\"\r\nfrom typing import List\r\n\r\n\r\nclass Solution:\r\n    def setZeroes(self, matrix: List[List[int]]) -> None:\r\n        \"\"\"\r\n        Do not return anything, modify matrix in-place instead.\r\n        \"\"\"\r\n        # 两个集合记录0数据所在的行和列\r\n        i_set = set()\r\n        j_set = set()\r\n        m = len(matrix)\r\n        n = len(matrix[0])\r\n        for i in range(m):\r\n            for j in range(n):\r\n                if matrix[i][j] == 0:\r\n                    i_set.add(i)\r\n                    j_set.add(j)\r\n        while len(i_set) > 0:\r\n            i = i_set.pop()\r\n            for j in range(n):\r\n                matrix[i][j] = 0\r\n        while len(j_set) > 0:\r\n            j = j_set.pop()\r\n            for i in range(m):\r\n                matrix[i][j] = 0\r\n\r\n\r\nif __name__ == '__main__':\r\n    matrix = [[0,1,2,0],[3,4,5,2],[1,3,1,5]]\r\n    solution = Solution()\r\n    solution.setZeroes(matrix)\r\n    print(matrix)","sub_path":"力扣学习/73. Set-Matrix-Zeroes.py","file_name":"73. Set-Matrix-Zeroes.py","file_ext":"py","file_size_in_byte":1014,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"385292734","text":"# 19:27 start\n# 20:18 finish but 6%에서 틀림\n# 참고 링크 : https://velog.io/@haruponea/%EB%B0%B1%EC%A4%80-11967-%EB%B6%88%EC%BC%9C%EA%B8%B0\n\nfrom sys import stdin\ninput = stdin.readline\nfrom collections import deque, defaultdict\n\ndr = (-1, 1, 0, 0)\ndc = (0, 0, -1, 1)\n\ndef bfs():\n    result = 1      # 불 켤 수 있는 방의 갯수\n    visited = [[0] * N for _ in range(N)]   # 방문 표시\n    visited[0][0] = 1\n    lights = [[0] * N for _ in range(N)]    # 불 켠 방 표시\n    lights[0][0] = 1\n    Q = deque([(0, 0)])\n    while Q:\n        r, c = Q.popleft()\n        for tr, tc in turnInfo[(r, c)]:   # 불 켤 수 있는 곳(딕셔너리 참조)\n            if not lights[tr][tc]:\n                lights[tr][tc] = 1      # 새로 불 켜기\n                result += 1\n                for d in range(4):      # 4방향 연결된 곳\n                    nr = tr + dr[d]\n                    nc = tc + dc[d]\n                    if not (0 <= nr < N and 0 <= nc < N):\n                        continue\n                    if visited[nr][nc]:     # 방문한 적 있으면(새로 연결되어 또 불을 켤 곳이 있을 수 있으므로)\n                        Q.append((nr, nc))  # 큐에 담기\n        \n        # 현 위치를 기준으로\n        for d in range(4):      # 4방향 연결된 곳\n            nr = r + dr[d]\n            nc = c + dc[d]\n            if not (0 <= nr < N and 0 <= nc < N):\n                continue\n            # 첫 방문인데 이미 불 켜진 곳이면\n            if not visited[nr][nc] and lights[nr][nc]:\n                Q.append((nr, nc))      # 재검사를 위해 큐에 담기\n                visited[nr][nc] = 1     # 방문 표시\n\n    # print(\"========== visited 배열 확인 ============= result\", result)\n    # for row in visited:\n    #     print(row)\n    # print(\"+++++++++++++++++++lights++++++++++++++++++++++++\")\n    # for row in lights:\n    #     print(row)\n    \n    return result\n\n\n# main\nN, M = map(int, input().split())\nturnInfo = defaultdict(list)\nfor _ in range(M):\n    sr, sc, tr, tc = map(int, input().split())\n    turnInfo[(sr-1, sc-1)].append((tr-1, tc-1))\n# print(\"turnInfo\", turnInfo, type(turnInfo))\n\nprint(bfs())","sub_path":"daily_study/ProblemSolving/11967_불켜기.py","file_name":"11967_불켜기.py","file_ext":"py","file_size_in_byte":2190,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"382445434","text":"# num = 5\n# print(5)\n# print(type(num))   # num\n\n# num = 1.5\n# print(num)\n# print(type(num))   # float\n\n# s = 'sun'\n# print(type(s))   # str\n\n\n# b = True\n# b = False\n# print(b)\n# print(type(b)) # bool\n\n# b = 4 <= 5\n# print(b)\n# print(type(b))\n\n# n = 5\n# print(n)\n# n = float(n)\n# print(n)\n\n# n = 15.01\n# n = int(n)\n# print(n)\n\n# s = '10'\n# print(s)\n# print(type(s))\n# s = int(s)\n# print(s)\n# print(type(s))\n\n\n# s = '12.3'\n# print(s)\n# print(type(s))\n# s = float(s)\n# print(s)\n# print(type(s))\n\n# num = 123\n# print(type(num))\n# num = str(num)\n# print(type(num))\n\n\n# print(\"Student #\" + str(1))\n\n# b = 0 > 1\n# s = str(b)\n# print(s)\n\n# num = int(b)\n# print(num)\n# n = float(b)\n# print(n)\n\n\n# print(bool(1))\n# print(bool(0))\n# print(bool(\"hi\"))\n# print(bool(''))\n\n# a = int(input(\"Enter fisrt number: \"))\n# print(a)\n# b = int(input(\"Enter second number: \"))\n# print(b)\n# s = a + b\n# print(s)\n\n# print(f\"{a} + {b} = {s}\")\n\n# name = input(\"What is yuor name? \")\n# print(f\"Hello, {name}!\")\n\n# time = input(\"What is time of day now?\")\n# print(f\"Good {time}, {name}!\")\n\n# people = int(input(\"enter number of people: \"))\n# perc_inc = int(input(\"enter percentage of growth: \"))\n# number_dec = int(input(\"Enter number of decrease:  \"))\n# people = int(people + people * perc_inc / 100 - number_dec)\n# print(f\"People in a year will be {people}\")\n\n\n# num = 123.64567\n# print(int(num))\n# num = int(num)\n# print(num)\n# print(round(num))\n# print(round(num, 2))\n\n# import math\n# year = 1705\n# century = math.ceil(year / 100)\n# print(century)\n\n# Домашнее задание № 2. Тема «Типы данных. Преобразование типов. Ввод данных».\n# 1.     С помощью команды input введите трехзначное число и найдите сумму его цифр.\nimport math\nnumber = int(input(\"Please enter your number: \"))\nsum = (number % 10) + ((number // 10) % 10) + number // 100\nprint(f\"Sum of numbers is:, {sum}\")\n\n# 2.     С помощью команды input введите число сторон правильного многоугольника и найдите сумму его внутренних углов. Сумма углов n-угольника равна 180*(n-2).\n\nnum = int(input(\"Quantity of sides of the polygon: \"))\nsum = 180 * (num - 2)\nprint(f\"Sum of inside corners is: {sum}\")\n\n# 3.     С помощью команды input запросите у пользователя количество миль (miles). Переведите это расстояние в километры и футы. 1 mile = 1.60934 km, 1 mile = 5280 feet.\n\nmil = int(input(\"Enter your miles: \"))\nkm =  mil * 1.60934\nfit = mil * 5280\nprint(f\"Converted in km: {km}\")\nprint(f\"Converted into feet: {fit}\")\n\n# 4.     С помощью команды input запросите у пользователя число градусов Фаренгейта (fahrenheit) и переведите это значение в градусы Цельсия (celsius). Для этого нужно от числа градусов Фаренгейта отнять 32, результат умножить на 5 и затем поделить на 9.\n\nfar = int(input(\"Enter fahrenheit here: \"))\ncel = int((far - 32) * 5 /9)\nprint(f\"Converted in celsius: {cel} C\")\n\n# 5.     С помощью команды input запросите у пользователя число градусов Цельсия (celsius). Напишите программу перевода градусов Цельсия в градусы Фаренгейта (fahrenheit). Для этого нужно число градусов Цельсия умножить на 9, затем разделить на 5 и затем к результату прибавить 32.\n\ncel = int(input(\"Enter celsius: \"))\nfar = int((cel * 9) / 5) + 32\nprint(f\"Converted in fahrenheit: {far} F\")\n\n# 6.     С помощью команды input запросите у пользователя стоимость заказа (price), процент чаевых (tip_percent) и процент налогов (tax_percent). Найдите общую стоимость заказа (total_price).\n\nprice = float(input(\"Enter your price: \"))\ntip_percent = float(input(\"Enter tip %: \"))\ntax_percent = float(input(\"Enter tax %: \"))\ntotal = price + (tip_percent * price / 100) + (tax_percent * price / 100)\nprint(f\"Your total payment: {total}\")\n\n# 7.     С помощью команды input введите цену товара со скидкой и процент скидки. Найдите цену товара до скидки (полную цену товара), результат округлите до 2-х знаков после запятой. Например, товар со скидкой стоит 40 долларов при скидке 50%. Тогда цена товара без скидки составляет 80 долларов. Если товар стоит 40 долларов при скидке 10 процентов, его полная цена составляет $ 44.44.\n\nprice = float(input(\"Enter price: \"))\ndiscount = int(input(\"Enter % of dispount: \"))\nfprice = round((price / (100 - discount) * 100), 2)\nprint(f\"Original price was: {fprice}\")","sub_path":"Les2DataTypeAndInput.py","file_name":"Les2DataTypeAndInput.py","file_ext":"py","file_size_in_byte":5297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"424165564","text":"# -*- coding: UTF-8 -*-\nfrom conf import settings\nimport tailer # same functionality ad UNIX tail in python\nfrom parsers.nginx import nginx_error_parser\nfrom helpers import send_message\nimport re\n\nfilepath = settings.NGINX_ERROR_PATH # try to read the nginx file path from settings\n\ndef nginx():\n\n    logger = \"Nginx error logs\"\n\n    for line in tailer.follow(open(filepath)):\n        line = unicode(line, errors='ignore')\n        years = line[0:4]\n        if (re.match('^\\\\s*$',line)):\n            pass\n        else:\n            if (re.match(r'^\\d+$',years)):\n\n                # create the message\n                date_time_message, otherinfo = nginx_error_parser(line)\n                params = [date_time_message[2],\n                          date_time_message[0],\n                          date_time_message[1],\n                          otherinfo.get(\"request\", \"-\"),\n                          otherinfo.get(\"referrer\", \"-\"),\n                          otherinfo.get(\"server\", \"-\"),\n                          otherinfo.get(\"client\", \"-\"),\n                          otherinfo.get(\"host\", \"-\"),\n                          otherinfo.get(\"upstream\", \"-\")]\n                message ='%s' % date_time_message[2]\n                extended_message =  '%s\\n'\\\n                                    'Date: %s\\n'\\\n                                    'Time: %s\\n'\\\n                                    'Request: %s\\n'\\\n                                    'Referrer: %s\\n'\\\n                                    'Server: %s\\n'\\\n                                    'Client: %s\\n'\\\n                                    'Host: %s\\n'\\\n                                    'Upstream: %s\\n'\n                site = otherinfo.get(\"referrer\", \"-\")\n                \n                # send the message to sentry using Raven\n                send_message(message, extended_message, params, site, logger)\n            else:\n                pass\nnginx()\n","sub_path":"sentrylogs/nginx.py","file_name":"nginx.py","file_ext":"py","file_size_in_byte":1918,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"67093519","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n# Author  : Lysander Tseng\n# Email   : qingshan.tseng@gmail.com\n# Time    : 2020/2/18 12:49\n# User    : Magic\n# Product : PyCharm\n# Project : 030\n# File    : 030.1.py\n# Intro   : 编写一个程序，计算当前文件夹下所有文件的大小\nimport os  # 引入os模块\n\n\ndef file_size_count(this_dir):\n    \"\"\"定义一个文件大小计算函数\"\"\"\n\n    os.chdir(this_dir)  # 切换工作目录\n    file_name_list = os.listdir(this_dir)  # 将当前工作目录下的内容转换为列表存储在file_name_list中\n    for i in file_name_list:  # 遍历列表用os.path.getsize()方法计算每个文件大小并按照指定格式打印\n        print(\"{0}  【{1:0.2f}KB】\".format(i, os.path.getsize(i) / 1024))\n\n\ndef path_trans(raw_path):\n    \"\"\"定义一个路径字符串转换函数，用于将当前操作系统中的路径字符串转换成标准路径\"\"\"\n\n    path_list = raw_path.split('\\\\')  # 将传入的原始路径字符串以\\为标志切割并保存在列表path_list中\n    standard_path = os.sep.join(path_list)  # 再将os.sep插入到每个元素之间并将其重新拼接成标准字符串\n    return standard_path\n\n\nactual_dir = path_trans(input(\"请输入需要查询的目录:\"))\nfile_size_count(actual_dir)","sub_path":"Python/Homework/030/030.1.py","file_name":"030.1.py","file_ext":"py","file_size_in_byte":1282,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"259512140","text":"__author__ = 'GunMade'\n\nimport cv2\nimport numpy as np\n\nimg = cv2.imread(r'image/85f2b626gw1ep6bzoleu3j20u01hcjzh.jpg')\n\n# Find contours\nimgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\nret, thresh = cv2.threshold(imgray, 127, 255, 0)\ncontours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n\n# Draw contours\ncv2.drawContours(img, contours, -1, (0, 255, 0), 3)\n\ncv2.imshow('contours', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n","sub_path":"OpenCV_practice/contours.py","file_name":"contours.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"572807420","text":"# -*- coding: utf-8 -*-\n#rootfold='/home/ejp/Desktop/Goodwin-Keen/simulations'\nrootfold='D:\\\\Georgetown\\\\Simulation-Goodwin\\\\'\n\n### WELCOME MESSAGE ################################################################################\n\"\"\"\nGOODWIN-TYPE RESOLUTION ALGORITHM \n\nThis python code has been created to simulate Goodwin-Keen simulations, with all the variants to it. \nDo not worry about the number of section : the architecture is thought to be user-friendly and coder-friendly\n\nWHAT ARE THE SPECIFICITIES OF THIS CODE ? \n* The algorithm solve p['Nx'] system in parrallel : the idea is that later we will couple the systems altogether (\"spatial/network properties\").\n* For now, there is no much coupling, but you can use it to test many initial conditions and array of parameters values \n* The steps are :\n    - Creation of the parameters dictionnary p ( in Parameters.py for initial values)\n    - Creation of initial conditions in the dictionnary ic\n    - Translation into machine friendly variable y\n    - Calculation of all the dynamics variable Y_s (the temporal loop)\n    - Translation into user-friendly result in the dictionnary r \n    - Additional analysis (period, slow enveloppe mouvement...)\n    - Plot of variables and analysis through r\n\nHOW TO IMPLEMENT YOUR TOY-MODEL ? \n * Add the parameters in Parameters.py (check if it isn't already, or if the name isn't already taken)\n * Add the temporal component in FG.preparedT for dt selection\n * Add the related quantity in the dictionnary of initial condition ic\n * Add a section to prepareY\n * Add the equations in a f_THENAMEOFTHEFUNCTION\n * Add the redevelopment in FG.expand\n * have fun !\n \nWHAT I AM (Paul) LOOKING FOR IN FURTHER DEVELOPMENT \n* Check the functions in FunctionGoodwin, I've done some mistakes in more complex models (typically coping with collapse)\n* Add spatial operators which are non unstable ( I have implicit scheme elsewhere but it's a different kind of resolution, and much more work when you change a model)\n* Have a list of all models existing in this framework (copingwithcollapse, Harmoney, predatory-prey...) and code them\n* The plots are UGLY ! Let's do something nicer\n* As Iloveclim and Dymends are not in python, prepare some bindings \n\"\"\"\n\n### INITIALISATION LIBRAIRIES ######################################################################\n####################################################################################################\nfor _ in range(1):\n    \"\"\"\n    The code is importing some other libraries so that we can use their functions. \n    \"\"\"\n    import numpy as np              # Array/Numerical operations\n    import time                     # Time (run speed) printing\n    import matplotlib.pyplot as plt # Plot elements \n    plt.close('all')                # Remove to \n    \n    \"\"\"\n    These libraries are local code fragments we use later\n    \"\"\"\n    import Parameters as Par      # All the parameters of the system\n    import FunctionsGoodwin as FG # Core of models\n    import plots as plts          # Already written plot functions\n    import Miscfunc as M          # All miscellaneous functions\n\n    ### MATPLOTLIB INTERACTIVITY ####\n    '''\n    Matplotlib allow in-line (in terminal) output or interactive output. Select one here.\n    You can change it in the jupyter (interactive terminal) of your IDE also \n    %matplotlib # Allow interactive output\n    %matplotlib inline # plot in terminal\n    '''\n    #%matplotlib inline \n\nprint('List of existing set of equations  :')\nfor f in [ f for f in dir(FG) if 'f_' in f] : print(f)\nprint(3*'\\n')\n\n### PARAMETERS INITIALISATION ######################################################################\n####################################################################################################    \nfor _ in range(1):\n    \"\"\"\n    This part create 'p' (parameters dic) and 'op' (operators dic) depending of the system size and properties\n    Typically, p contains the \"default parameters\" values and should be open in another windows, \n    I edit them after p initialisation if I want to do some original runs \n    \"\"\"\n    \n    p = Par.initparams()              # PARAMETERS IN THIS FILE ( PARAMETERS )\n    \n    p['lambdamax']  = 1-10**(-2)          # Variation of lambda on the simulation \n    \n    # The good place to add a loop on certain parameters if you need \n    p = M.preparePhilips(p)    # Determining Philips curve and Solow point\n    p = M.preparedT(p)         # Determine the best value for dt and associates\n    op= []#M.prepareOperators(p)  # Spatial operators initialisation\n\n### initial conditions #############################################################################\n####################################################################################################\n\"\"\"\nThe initial state vector at t=0 of the system in a \"readable\" language.\nDepending of the equation set you use, all of them will not necessary be used. \n(example, a reduced set will read ic['d'], a complete set will read ic['D']) \n\"\"\"\n\nic={} # Dictionnary of initial conditions\nv1 = np.ones(p['Nx']) ### Pratical notation for more readable code : an array with 1 everywhere\n\nic['a']       = 1.*v1            # Initial productivity per worker\nic['N']       = 1.*v1            # Initial quantity of people\n\nic['d']       = 0*1.53*v1 \nic['omega']   = 1.*v1*p['omega0']#*np.arange(0,1,p['Nx'])\nic['lambda']  = np.linspace(p['lambdamin'],p['lambdamax'],p['Nx'])#np.linspace(p['lambdamin'],p['lambdamax'],p['Nx'])    # Distribution of initial lambda\nic['L']       = ic['omega']*ic['N']\nic['K']       = ic['a']*ic['L']*p['nu']                                # Beginning at equilibrium\nic['W']       = p['omega0'] *ic['K']/p['nu']                           # Initial distribution of salary\n\n\n'''\nic['D']     =1.*v1            # Debt   \nic['sigma'] =1.*v1            # Intensity of emission in production\nic['P']     =1.*v1            # Global price\nic['Pbs']   =547.22*v1            # Price Backstop\nic['Pc']    =1.*v1            # Carbon price\n\n\nic['Fexo']  =1.*v1            # Exogenous forcing intensity\nic['Eland'] =2.6*v1           # Emission from land    \nic['CO2at'] =1.*v1*p['CO2at_ini']           # CO2 in atmosphere\nic['CO2up'] =1.*v1*p['CO2up_ini']           # CO2 in upper ocean and biosphere\nic['CO2lo'] =1.*v1*p['CO2lo_ini']          # CO2 in deep ocean\nic['T']     =1.*v1           # Atmospheric temperature \nic['T0']    =1.*v1           # Deeper ocean temperature \n'''\nic['t']     =1.*v1           # Year in the simulation \n\n\ny,p = FG.prepareY(ic,p) ### The vector y containing all the state of a time t.\n### Initialisation of data storage #################################################################\n####################################################################################################\n\"\"\"\nThis part is very close to the numerical resolution core. No need to delve in it\nIt looks \"complex\" because it allows the record of few timestep only\n\"\"\"\nY_s = np.zeros((p['Nvar'],p['Ns'],p['Nx']))     # stock dynamics\nY_s[:,0,:]= 1*y                                 # first stock\nt         = 0\nt_s       = np.zeros(p['Ns'])                   # stock time\ntprevious = 0                                   # deltatime between two records\nidx       = 0                                   # index of the current record\n\n### Simulation iteration ############################################################################\n#####################################################################################################\n\"\"\"\nThe RK4 core, with partial temporal storage \n\"\"\"\nprint('Simulation...',end='');tim=time.time()\n\n### THIS IS THE PART WHERE YOU CAN PUT SOME COMMUNICATION\n# :::C::: LAUNCH THE OTHER PROGRAM\n\nfor i in range(p['Nt']-1):\n    y += M.rk4(y,op,p)                       # The vector y is dynamically updated \n    t += p['dt'];tprevious+= p['dt']          # time is incremented\n    if tprevious >= p['Tstore']:              # if it is time to record the state\n        idx+=1                                # we give it a new index\n        Y_s[:,idx,:] = np.copy(y)             # we write it in the \"book\" Y_s\n        t_s[idx]     = t*1                    # we write the time \n        tprevious=0.                          # we reinitialise time before next record\n        \n    ### THIS IS THE PART WHERE YOU CAN PUT SOME COMMUNICATION\n    \"\"\"\n    :::C::: The elements for communications between programs.\n    \n    The typical method is to put a value (or a list) in a .txt file, make it read by the other file\n    \n    The important thing : You have to wait one program to finish and write the file for the other one to start\n    Two rough strategies : \n        * On each side check the date of last update for each communication \n        * Have a common writing flag with a flag (if value is \"A\" then it's python, \"B\" is vensim)\n    \n    The elegant strategies : \n        * Using a python-Vensim binding with step-by-step Dymends in a big function \n        * Using python to launch some Shell-type code\n    \n    f = open(\"demofile2.txt\", \"w\")\n    f.write(Content)\n    f.close()\n    \n    f = open(\"demofile2.txt\", \"r\")\n    Content = f.read()\n    f.close()\n    \n    import os.path, time\n    print(\"Last modified: %s\" % time.ctime(os.path.getmtime(\"test.txt\")))\n    print(\"Created: %s\" % time.ctime(os.path.getctime(\"test.txt\")))\n    \n    For a better version, this has to be moved into the dynamic function f_\n    \"\"\"\n\nprint('done ! elapsed time :', time.time()-tim,'s')        \n\nif p['Save'] : FG.savedata(rootfold,t,Y_s,p,op) # Save the data as a pickle file in a new folder\n### Results interpretation #########################################################################\n####################################################################################################\n\"\"\"\nNow that the simulation is done, we can translate its results in a more readable fashion. \nr is the expansion of Y_s into all the relevant variables we are looking for, stored as a dictionnary.\nThen, the other parts are simply plots of the result\n\"\"\"\n\nr = FG.expandY(Y_s,t_s,op,p)  # Result dictionnary \nr = M.getperiods(r,p,op)      # Period measurements \n\n# GRAPHS, \nplts.PhilAndInvest(p,)             # Show behavior functions \nplts.GoodwinKeenTypical(r,p,)      # Typical 3-Dimension phase-plot\nplts.omegalambdacycles(r,p,)       # 2-D omega-lambda phase portrait\nplts.GraphesIntensive(r,p)    \nplts.GraphesExtensive(r,p) \n#plts.PhasewithG(r,p,op,)           # Phase diagram with growth\nplts.PeriodPlots(r,p,op,)          # Study stability-period\n#plts.map2DLambdaT(r,op,)\n#plts.MesoMeanSTD(r,p,op,)\n# :-)\n# :-)\n# :-)\n# :-)\n# :-)\n# :-)\n","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":10630,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"602163288","text":"import os\n\nimport dj_database_url\nimport gisdata\nfrom django.conf import settings\nfrom django.test.testcases import LiveServerTestCase\nfrom django.test.utils import override_settings\nfrom geonode.layers.models import Layer\nfrom geonode.layers.utils import upload\nfrom geonode.tests.utils import get_web_page\n\nimport timeout_decorator\n\n\nclass CartoviewTest(LiveServerTestCase):\n    port = 8000\n    fixtures = [\n        'sample_admin.json',\n        'default_oauth_apps.json',\n    ]\n\n    # @classmethod\n    # def setUpClass(cls):\n    # from geonode.celery_app import app\n    # from django.db import connections\n    # import threading\n    #     super(CartoviewTest, cls).setUpClass()\n    #     app.control.purge()\n    #     cls._worker = app.Worker(app=app, pool='solo', concurrency=1)\n    #     connections.close_all()\n    #     cls._thread = threading.Thread(target=cls._worker.start)\n    #     cls._thread.daemon = True\n    #     cls._thread.start()\n\n    # @classmethod\n    # def tearDownClass(cls):\n    #     cls._worker.stop()\n    #     super(CartoviewTest, cls).tearDownClass()\n\n    # def tearDown(self):\n    #     super(CartoviewTest, self).tearDown()\n    #     # move to original settings\n    #     settings.OGC_SERVER['default']['DATASTORE'] = ''\n    #     del settings.DATABASES['datastore']\n\n    def setUp(self):\n        super(CartoviewTest, self).setUp()\n        settings.DATASTORE_URL = 'postgis://cartoview:cartoview@' +\\\n        'localhost:5432/cartoview_datastore'\n        postgis_db = dj_database_url.parse(\n            settings.DATASTORE_URL, conn_max_age=600)\n        settings.DATABASES['datastore'] = postgis_db\n        settings.OGC_SERVER['default']['DATASTORE'] = 'datastore'\n\n    @override_settings(\n        CELERY_EAGER_PROPAGATES_EXCEPTIONS=True,\n        CELERY_ALWAYS_EAGER=True,\n        BROKER_BACKEND='memory')\n    @timeout_decorator.timeout(6000)\n    def test_layer_upload(self):\n        layers = {}\n        expected_layers = []\n        not_expected_layers = []\n\n        for filename in os.listdir(gisdata.GOOD_DATA):\n            basename, extension = os.path.splitext(filename)\n            if extension.lower() in ['.tif', '.shp', '.zip']:\n                expected_layers.append(\n                    os.path.join(gisdata.GOOD_DATA, filename))\n\n        for filename in os.listdir(gisdata.BAD_DATA):\n            not_expected_layers.append(\n                os.path.join(gisdata.BAD_DATA, filename))\n        uploaded = upload(gisdata.DATA_DIR, console=None)\n\n        for item in uploaded:\n            errors = 'error' in item\n            if errors:\n                if item['file'] in not_expected_layers:\n                    continue\n                else:\n                    msg = ('Could not upload file \"%s\", '\n                           'and it is not in %s' % (item['file'],\n                                                    not_expected_layers))\n                    assert errors, msg\n            else:\n                msg = ('Upload should have returned either \"name\" or '\n                       '\"errors\" for file %s.' % item['file'])\n                assert 'name' in item, msg\n                layers[item['file']] = item['name']\n\n        msg = ('There were %s compatible layers in the directory,'\n               ' but only %s were sucessfully uploaded' %\n               (len(expected_layers), len(layers)))\n\n        for layer in expected_layers:\n            msg = ('The following file should have been uploaded'\n                   'but was not: %s. ' % layer)\n            assert layer in layers, msg\n\n            layer_name = layers[layer]\n            Layer.objects.get(name=layer_name)\n            found = False\n            gs_username, gs_password = settings.OGC_SERVER['default'][\n                'USER'], settings.OGC_SERVER['default']['PASSWORD']\n            page = get_web_page(\n                os.path.join(settings.OGC_SERVER['default']['LOCATION'],\n                             'rest/layers'),\n                username=gs_username,\n                password=gs_password)\n            if page.find('rest/layers/%s.html' % layer_name) > 0:\n                found = True\n            if not found:\n                msg = (\n                    'Upload could not be verified, the layer %s is not '\n                    'in geoserver %s, but GeoNode did not raise any errors, '\n                    'this should never happen.' %\n                    (layer_name, settings.OGC_SERVER['default']['LOCATION']))\n                raise Exception(msg)\n        for layer in expected_layers:\n            layer_name = layers[layer]\n            Layer.objects.get(name=layer_name).delete()\n","sub_path":"cartoview/tests/test_version.py","file_name":"test_version.py","file_ext":"py","file_size_in_byte":4606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"466938576","text":"# Gunicorn configuration file.\nimport multiprocessing\n\n#\n# Server socket\n#\n#   bind - The socket to bind.\n#\n#       A string of the form: 'HOST', 'HOST:PORT', 'unix:PATH'.\n#       Multiple addresses can be bound.\n#\n#   backlog - The number of pending connections. \n#       \n#       This refers to the number of clients that can be waiting to be served. Exceeding this number results in the client\n#       getting an error when attempting to connect. It should only affect servers under significant load.\n#\n#       Must be a positive integer. Generally set in the 64-2048\n#       range.\n#\n\nbind = '0.0.0.0:8443'\nbacklog = 2048\n\n#\n# Worker processes\n#\n#   workers - The number of worker processes for handling requests.\n#\n#       A positive integer generally in the 2-4 x $(NUM_CORES) range. You'll want to vary this a bit to find the best\n#       for your particular application's work load. By default, the value of the WEB_CONCURRENCY environment variable. \n#       If it is not defined, the default is 1.\n#\n#   worker_class - The type of workers to use. \n#\n#       The default async class should handle most 'normal' types of work loads. You'll want to read http://gunicorn/deployment.hml\n#       for information on when you might want to choose one of the other worker classes.\n#\n#       An string referring to a 'gunicorn.workers' entry point  or a python path to a subclass of\n#       gunicorn.workers.base.Worker. The default provided values are:\n#\n#           egg:gunicorn#sync\n#           egg:gunicorn#eventlet   - Requires eventlet >= 0.9.7\n#           egg:gunicorn#gevent     - Requires gevent >= 0.12.2 (?)\n#           egg:gunicorn#tornado    - Requires tornado >= 0.2\n#\n#   worker_connections - The maximum number of simultaneous clients.\n#\n#       This setting only affects the Eventlet and Gevent worker types.\n#       A positive integer generally set to around 1000.\n#\n#   timeout - Workers silent for more than this many seconds are killed and restarted.\n#\n#       Generally set to thirty seconds. Only set this noticeably higher if you're sure of\n#       the repercussions for sync workers. For the non sync workers it just means that the worker\n#       process is still communicating and is not tied to the length of time required to handle a single request.\n#\n#   keepalive -  The number of seconds to wait for requests on a Keep-Alive connection. \n#\n#       Generally set in the 1-5 seconds range.\n#\n\nworkers = multiprocessing.cpu_count() * 2 + 1\nworker_class = 'sync'\nworker_connections = 1000\ntimeout = 300\nkeepalive = 2\n\n#\n#   spew - Install a trace function that spews every line of Python that is executed when running the server.\n# \n#       This is the nuclear option.\n#\n#       True or False\n#\n\nspew = False\n\n#\n# Server mechanics\n#\n#   daemon - Daemonize the Gunicorn process.\n#\n#       Detach the main Gunicorn process from the controlling terminal with a standard fork/fork sequence.\n#\n#       True or False\n#\n#   pidfile - A filename to use for the PID file.\n#\n#       If not set, no PID file will be written.\n#\n#   user - Switch worker processes to run as this user.\n#\n#       A valid user id (as an integer) or the name of a user that can be retrieved with a call to pwd.getpwnam(value)\n#       or None to not change the worker process user.\n#\n#   group - Switch worker process to run as this group.\n#\n#       A valid group id (as an integer) or the name of a user that can be retrieved with a call to pwd.getgrnam(value) or None\n#       to change the worker processes group.\n#\n#   umask - A mask for file permissions written by Gunicorn. \n#       Note that this affects unix socket permissions.\n#\n#       A valid value for the os.umask(mode) call or a string compatible with int(value, 0) (0 means Python guesses\n#       the base, so values like \"0\", \"0xFF\", \"0022\" are valid for decimal, hex, and octal representations)\n#\n#   tmp_upload_dir - Directory to store temporary request data as they are read.\n#\n#       This may disappear in the near future.\n#       This path should be writable by the process permissions set for Gunicorn workers. \n#       If not specified, Gunicorn will choose a system generated temporary directory.\n#\n\ndaemon = False\npidfile = \"./gunicorn.pid\"\numask = 0\nuser = None\ngroup = None\ntmp_upload_dir = None\n\n#\n#   Logging\n#\n#   logconfig - The log config file to use. Gunicorn uses the standard Python logging modules's Configuration file format\n#\n\n\nlogconfig = 'gunicorn-logging.conf'\n\n\n#\n#   SSL\n#\n#   keyfile - SSL Key file\n#   certfile - SSL certificate file\n#   ssl_version - version of SSL\n#   cert_reqs - Whether client certificate is required. Set to 2 to require client certificate and validation\n#   ca_certs - CA certificates file\n#   do_handshake_on_connect = Whether to perform SSL handshake on socket connect\n#\n\n\nkeyfile = './openssl-ca/out/__SERVER__.key'\ncertfile = './openssl-ca/out/__SERVER__.pem'\nssl_version = 3\ncert_reqs = 2\nca_certs = './openssl-ca/CA/certs/ca.pem'\ndo_handshake_on_connect = True\n\n#\n# Process naming\n#\n#   proc_name - A base to use with setproctitle for process naming.\n#\n#       This affects things like ps and top. If you are going to be running more than one instance of Gunicorn \n#       you will probably want to set a name to tell them apart. This requires that you install the setproctitle module.\n#\n#       It defaults to 'gunicorn'.\n#\n\nproc_name = None\n\n#\n# Server hooks\n#\n#   post_fork - Called just after a worker has been forked.\n#\n#       A callable that takes a server and worker instance as arguments.\n#\n#   pre_fork - Called just prior to forking the worker subprocess.\n#\n#       A callable that accepts the same arguments as after_fork\n#\n#   pre_exec - Called just prior to forking off a secondary master process during things like config reloading.\n#\n#       A callable that takes a server instance as the sole argument.\n#\n\ndef post_fork(server, worker):\n    server.log.info(\"Worker process spawned. Pid is: %s\", worker.pid)\n\ndef pre_fork(server, worker):\n    pass\n\ndef pre_exec(server):\n    server.log.info(\"Forked child process, re-executing.\")\n\ndef when_ready(server):\n    server.log.info(\"Server is ready. Spawning worker processes\")\n\ndef worker_int(worker):\n    worker.log.info(\"Worker process received INT or QUIT signal\")\n\ndef worker_abort(worker):\n    worker.log.info(\"Worker process received SIGABRT signal\")\n","sub_path":"common/dxagent-template/SAMPLE.gunicorn-config.py","file_name":"SAMPLE.gunicorn-config.py","file_ext":"py","file_size_in_byte":6334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"647903445","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Nov 29 22:03:00 2017\n\n@author: jernej\n\n\"\"\"\nimport numpy as np\n#import scipy as sc\nimport matplotlib.pyplot  as plt\n#from scipy import stats\n#from scipy.optimize import curve_fit\nfrom lmfit import Model\nfrom numpy import loadtxt\nfrom scipy.optimize import curve_fit\n\n\nfont = {\n        'color':  'k',\n#        'weight': 'bold',\n        'size': 26,\n        'verticalalignment': 'bottom'\n        }\nfont_AX = {\n        'color':  'k',\n        'size': 30,\n        }\n        \ne=2.718281828459045    \npi=3.141592653589793 \n\n\nDIR = '/home/jernej/Desktop/ModelskaAn/MOJEDELLO/sesta/farmakoloski.dat'\n\n###_________Fit_funkcije__________________________________________ \n    \ndef Lin_Fdata(x,k,n):#,c0,d0):  #funkcija za fitanje gaussove porazdelitve\n#    return a0+b0*x+c0*x**0.5+d0*x**0.3+g0*x**h0 #c0*x**d0#+e0*np.log(f0*x)   \n    return k*x+n#+c0*x**0.9+abs(d0)*x**1.1 #c0*x**d0#+e0*np.log(f0*x)  \n    \ndef Model_Fdata(x,y0,a):  #funkcija za fitanje gaussove porazdelitve\n    return y0*x/(x+a)\n\n\n###_________podatki___________________________________________ \n\n\ndata = loadtxt(DIR) # branje\nX=data[:,0]\nY=data[:,1]\n\nsig_Y=np.ones(len(Y))*3\n\nv=1/X\nu=1/Y\nsig_u_p=abs(1/Y**2)*sig_Y\nsig_u_m=abs(1/Y**2)*sig_Y\n#sig_u_p=abs(1/Y - 1/(Y+sig_Y) )\n#sig_u_m=abs(1/Y - 1/(Y-sig_Y) )\n#sig_u_m=(1/Y)/(1-(sig_Y/Y))\nsig_u=[abs(sig_u_m), abs(sig_u_p)]\n\n###_________poskusni___________________________________________ \n\n\ngmodel_Lin = Model(Lin_Fdata)\ngmodel_Model = Model(Model_Fdata)\n#gmodel_Lin.param_names\n#gmodel_Lin.independent_vars\n#Y_Lin = gmodel_Lin.eval(x=X, a0=0.01 , b0=0.001 , c0=5. , d0=0.05 )\n#result_Lin = gmodel_Lin.fit(data=y,x=x, a0=66. , b0=0.3 , c0=-1.64 , d0=6.0, method='leastsq' )\nresult_Lin = gmodel_Lin.fit(data=u, x=v, k=0.2 , n=0.01 ,method='leastsq' )\nprint(result_Lin.fit_report())\n\nresult_Model = gmodel_Model.fit(data=Y, x=X, y0=100. , a=21 ,method='leastsq' )\nprint(result_Model.fit_report())\n\n#\nerr_result_Lin, err_result_Lin_cov=curve_fit(Lin_Fdata,v,u, sigma=sig_u_p)\nprint(err_result_Lin)\nprint(err_result_Lin_cov)\n\n\nerr_result_Model, err_result_Model_cov=curve_fit(Model_Fdata,X,Y, sigma=sig_Y)\nprint(err_result_Model)\nprint(err_result_Model_cov)\n\n##  ploting corrected FIT\n\n\n###_____ploti____________\n#\nx=np.linspace(0,max(X))\n\nF10=plt.figure(3)\nF10=plt.subplot(1,2, 1 )  \nplt.title('Merjeni farmakološki podatki odziva na dozo',fontsize=16)\nplt.errorbar(X,Y, yerr=sig_Y, color='k', marker='o',label='meritve',markersize=2)\n#plt.plot(x,Model_Fdata(x, 106.32, 24.76 ), color=\"#f78d6f\",LS=':',label='basic fit; [$y_0$=106.32, $a$=24.76]' )\n#plt.fill_between(x, Model_Fdata(x, 106.32+4.758940, 24.76+4.163827 ),Model_Fdata(x, 106.32-4.758940, 24.76-4.163827 ), color=\"#f78d6f\",alpha=0.2,label=r'basic fit error; [$\\sigma_{y_0}$=4.76, $\\sigma_{a}$=4.16]' )\n\nplt.plot(x,Model_Fdata(x,*err_result_Model),'b:', label=r'fit z $\\sigma_y$; [$y_0$='+'{:.{}f}'.format(err_result_Model[0], 2)+r', $a$='+'{:.{}f}'.format(err_result_Model[1], 2)+']' )\ncorr_result_Model_cov = np.sqrt(np.diag(err_result_Model_cov))\nplt.fill_between(x, Model_Fdata(x,*[err_result_Model[0]+corr_result_Model_cov[0],err_result_Model[1]+corr_result_Model_cov[1]]),Model_Fdata(x,*[err_result_Model[0]-corr_result_Model_cov[0],err_result_Model[1]-corr_result_Model_cov[1]]), color=\"#6fd9f7\",alpha=0.2,label=r'fit z $\\sigma_y$ negotovost; [$\\sigma_{y_0}$='+'{:.{}f}'.format(corr_result_Model_cov[0], 2)+r', $\\sigma_{a}$='+'{:.{}f}'.format(corr_result_Model_cov[1], 2)+']' )\n\n\nplt.xlabel(r'$x$',fontsize=16)\nplt.ylabel(r'$y(x)$',fontsize=16)\nplt.xlim([0,1100])\nplt.legend(loc=4)\n\n\nxx=np.linspace(0,max(v))\n\nF10=plt.subplot(1,2, 2 )  \nplt.title('Linearizirani merjeni farmakološki podatki odziva na dozo',fontsize=16)\nplt.errorbar(v,u, yerr=sig_u, color='k',marker='o', label='meritve',markersize=2)\n\nplt.plot(xx,Lin_Fdata(xx, 9180, -705 ), color='r',LS=':',label='fit; [$1/n$=-'+'{:.{}f}'.format(1/705,4)+', $k/n$='+'{:.{}f}'.format(9180/705,2)+']' )\nplt.fill_between(xx, Lin_Fdata(xx, 9180+1.54e+03, -705+615.7412 ),Lin_Fdata(xx, 9180-1.54e+03, -705-615.7412 ), color=\"#f78d6f\",alpha=0.2,label=r'fit negotovost; [$\\sigma_{1/n}$='+'{:.{}f}'.format(615.7/705.6**2,3)+', $\\sigma_{k/n}$='+'{:.{}f}'.format(9180/705.6 * np.sqrt((615.7/705.6)**2+(1540/9180)**2),3)+']' )\n\nplt.plot(xx,Lin_Fdata(xx,*err_result_Lin),'b:', label=r'fit z $\\sigma_u$; [$1/n$='+'{:.{}f}'.format(1/err_result_Lin[1], 3)+', $k/n$='+'{:.{}f}'.format(err_result_Lin[0]/err_result_Lin[1], 3)+']' )\ncorr_result_Lin_cov = np.sqrt(np.diag(err_result_Lin_cov))\nplt.fill_between(xx, Lin_Fdata(xx,*[err_result_Lin[0]+corr_result_Lin_cov[0],err_result_Lin[1]+corr_result_Lin_cov[1]]),Lin_Fdata(xx,*[err_result_Lin[0]-corr_result_Lin_cov[0],err_result_Lin[1]-corr_result_Lin_cov[1]]), color=\"#6fd9f7\",alpha=0.2,label=r'fit z $\\sigma_u$ negotovost; [$\\sigma_{1/n}$='+'{:.{}f}'.format(corr_result_Lin_cov[1]/err_result_Lin[1]**2, 2)+', $\\sigma_{k/n}$='+'{:.{}f}'.format((err_result_Lin[0]/err_result_Lin[1])*np.sqrt((corr_result_Lin_cov[1]/err_result_Lin[1])**2+(corr_result_Lin_cov[0]/err_result_Lin[0])**2), 2)+']' )\n\nplt.xlabel(r'$v$',fontsize=16)\nplt.ylabel(r'$u(v)$',fontsize=16)\nplt.xlim([0,1.2])\nplt.ylim([10**(-4),10**9])\nplt.yscale('log')\nplt.legend(loc='best')\n\n\n##plt.plot(x, result_Lin.init_fit, 'b--', label='init_fit')\n#plt.plot(x, result_Lin.best_fit, 'r-', label='Fit')#+r'(a=+'+'{:.{}f}'.format(SHfi, 3 )+r',b=+'+'{:.{}f}'.format(SHfi, 3 )+r',c=+'+'{:.{}f}'.format(SHfi, 3 )+r',d=+'+'{:.{}f}'.format(SHfi, 3 )+')')\n#dely1_LIN = result_Lin.eval_uncertainty(sigma=1)\n#dely2_LIN = result_Lin.eval_uncertainty(sigma=2)\n#dely3_LIN = result_Lin.eval_uncertainty(sigma=3)\n#plt.fill_between(x, result_Lin.best_fit-dely3_LIN, result_Lin.best_fit+dely3_LIN, color=\"#cccccc\",label=r'confidence $\\sigma=3$')\n#plt.fill_between(x, result_Lin.best_fit-dely2_LIN, result_Lin.best_fit+dely2_LIN, color=\"#ABABAB\",label=r'confidence $\\sigma=2$')\n#plt.fill_between(x, result_Lin.best_fit-dely1_LIN, result_Lin.best_fit+dely1_LIN, color=\"#888888\",label=r'confidence $\\sigma=1$')\n#\n\n\n##F10=plt.subplot(3, 1, 2 )  \n#F10=plt.subplot(1,2, 2 )  \n#plt.plot(x, y, 'ko', label='data')\n\n##plt.plot(x, result_Log.init_fit, 'b--', label='init_fit')\n#plt.plot(x, result_Log.best_fit, 'r-', label='Log_fit  ')#+r'(a=+'+'{:.{}f}'.format(SHfi, 3 )+r',b=+'+'{:.{}f}'.format(SHfi, 3 )+r',e=+'+'{:.{}f}'.format(SHfi, 3 )+r',f=+'+'{:.{}f}'.format(SHfi, 3 )+')')\n#dely1_LOG = result_Log.eval_uncertainty(sigma=1)\n#dely2_LOG = result_Log.eval_uncertainty(sigma=2)\n#dely3_LOG = result_Log.eval_uncertainty(sigma=3)\n#plt.fill_between(x, result_Log.best_fit-dely3_LOG, result_Log.best_fit+dely3_LOG, color=\"#cccccc\",label=r'confidence $\\sigma=3$')\n#plt.fill_between(x, result_Log.best_fit-dely2_LOG, result_Log.best_fit+dely2_LOG, color=\"#ABABAB\",label=r'confidence $\\sigma=2$')\n#plt.fill_between(x, result_Log.best_fit-dely1_LOG, result_Log.best_fit+dely1_LOG, color=\"#888888\",label=r'confidence $\\sigma=1$')\n\n###____________poskusni__podatki______________________________________ \n\n#\n#plt.plot(S1x1, S1y1, color=\"y\",alpha=0.6)# color=\"#cccccc\")\n#plt.plot(S1x2, S1y2, color=\"y\",alpha=0.6)#  color=\"#cccccc\")\n##plt.plot(S2x1, S2y1, color=\"#f57a57\")\n##plt.plot(S2x2, S2y2, color=\"#f57a57\")\n#\n#plt.plot(X,Lin_fdata(X), 'k-', label=r'Fit extrapolation $\\rightarrow$   $Teol\\approx '+str(x2[0])+'h$')#+r'(a=+'+'{:.{}f}'.format(SHfi, 3 )+r',b=+'+'{:.{}f}'.format(SHfi, 3 )+r',c=+'+'{:.{}f}'.format(SHfi, 3 )+r',d=+'+'{:.{}f}'.format(SHfi, 3 )+')')\n#dely1_LIN = result_Lin.eval_uncertainty(sigma=1)\n#dely2_LIN = result_Lin.eval_uncertainty(sigma=2)\n###dely3_LIN = result_Lin.eval_uncertainty(sigma=4)\n###plt.fill_between(xL, result_Lin.best_fit-dely3_LIN, result_Lin.best_fit+dely3_LIN, color=\"#cccccc\",label=r'confidence $3\\sigma$')\n#plt.fill_between(X, Lin_fdataM(X),Lin_fdatam(X), color=\"y\",alpha=0.4,label=r'$\\pm\\ fit\\ param.\\  error$ $\\rightarrow$   $Teol^{+'+str(S1x1[0]-x2[0])+'}_{-'+str(x2[0]-S1x2[0])+ '}\\ [h]$')#  $\\rightarrow$   $Teol^{+'+str(S1x1[0]-x2[0])+'}_{-'+str(x2[0]-S1x2[0])+ '}$ [$h$]')\n#plt.fill_between(xL, result_Lin.best_fit-dely2_LIN, result_Lin.best_fit+dely2_LIN, color=\"#f78d6f\",label=r'confidence $\\pm 2\\sigma$ ')# $\\rightarrow$   $Teol^{+'+str(S2x1[0]-x2[0])+'}_{-'+str(x2[0]-S2x2[0])+ '}$ [$h$]')\n#plt.fill_between(xL, result_Lin.best_fit-dely1_LIN, result_Lin.best_fit+dely1_LIN, color=\"#6fd8f7\",label=r'confidence $\\pm\\sigma$ ')#  $\\rightarrow$   $Teol^{+'+str(S1x1[0]-x2[0])+'}_{-'+str(x2[0]-S1x2[0])+ '}$ [$h$]')\n##plt.plot(X, Lin_fdata(X), 'b-', label='model fit')#+r'(a=+'+'{:.{}f}'.format(SHfi, 3 )+r',b=+'+'{:.{}f}'.format(SHfi, 3 )+r',c=+'+'{:.{}f}'.format(SHfi, 3 )+r',d=+'+'{:.{}f}'.format(SHfi, 3 )+')')\n##plt.plot(X, Lin_fdatam(X), 'r:', label='Fit funkcija')#+r'(a=+'+'{:.{}f}'.format(SHfi, 3 )+r',b=+'+'{:.{}f}'.format(SHfi, 3 )+r',c=+'+'{:.{}f}'.format(SHfi, 3 )+r',d=+'+'{:.{}f}'.format(SHfi, 3 )+')')\n##plt.plot(X, Lin_fdataM(X), 'r--', label='Fit funkcija')#+r'(a=+'+'{:.{}f}'.format(SHfi, 3 )+r',b=+'+'{:.{}f}'.format(SHfi, 3 )+r',c=+'+'{:.{}f}'.format(SHfi, 3 )+r',d=+'+'{:.{}f}'.format(SHfi, 3 )+')')\n#\n#\n#plt.plot(X, C, 'k:')#, label='Lin_fit')#+r'(a=+'+'{:.{}f}'.format(SHfi, 3 )+r',b=+'+'{:.{}f}'.format(SHfi, 3 )+r',c=+'+'{:.{}f}'.format(SHfi, 3 )+r',d=+'+'{:.{}f}'.format(SHfi, 3 )+')')\n#plt.plot(X, S, ls=':',color='#cccccc')\n#plt.plot(x2, y2, 'k:')\n#plt.xlabel(r'$time\\ [h]$', fontdict=font_AX)\n##plt.ylabel(r'IR [$\\mu \\Omega$]', fontdict=font)\n#plt.ylabel(r'$ESR/ESR_{rated}\\ [\\%]$', fontdict=font_AX)\n#plt.xlim([-50,DOSEG])\n#plt.minorticks_on()\n##plt.title('Data for 500F $2^{nd}$ batch test:   '+podatki[0] +'\\n with linear model fit:  $a_0+b_0 \\cdot x$  '+' ($a_0='+'{:.{}f}'.format(a0, 2 )+'\\pm'+'{:.{}f}'.format(A0, 2 )+', b_0='+'{:.{}f}'.format(b0, 4 )+'\\pm'+'{:.{}f}'.format(B0, 4 )+'$)',fontdict=font)\n#plt.title('500F $2^{nd}$ batch: '+Podatki[0] +'\\n linear fit: $a_0+b_0 \\cdot x$ '+'\\n ($a_0='+'{:.{}f}'.format(a0, 2 )+'\\pm'+'{:.{}f}'.format(A0, 2 )+', b_0='+'{:.{}f}'.format(b0, 4 )+'\\pm'+'{:.{}f}'.format(B0, 4 )+'$)',fontdict=font)\n#plt.legend(loc=2,fontsize=18)\n#plt.tick_params(axis='both', which='major', labelsize=26)\n","sub_path":"sesta/6_farmak.py","file_name":"6_farmak.py","file_ext":"py","file_size_in_byte":10137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"505167722","text":"import numpy as np\nfrom parameters import *\nimport pickle\nfrom annoy import AnnoyIndex\n\n#If use_images is not true, then annoy file will not be loaded, hence all images will be zeros\nif use_images == True:\n\t#Load annoy file for image representations\n\turl_to_index = pickle.load(open(annoy_dir+\"ImageUrlToIndex.pkl\", 'rb'))    \n\t#print(type(url_to_index))\n\t#print(url_to_index)\n\ta = AnnoyIndex(image_size)\n\ta.load(annoy_dir+'annoy.ann') \n\t#print(a.get_n_items())\n\t#print(a.get_item_vector(0), a.get_item_vector(1), a.get_item_vector(2))\n\tprint(\"annoy file loaded\")\n\n\n#Get VGG representation from image URL\ndef image_rep(image_url):\n\tv = np.array([0]*image_size)\n\tif image_url in [\"\", 'RANDOM']:\n\t\treturn v\n\ttry:\n\t\tindex = url_to_index[image_url.strip()]\n\t\tv = np.array(a.get_item_vector(index))\n\texcept:      \n\t\tif use_images == True:   \n\t\t\t#Print this if image is not found in annoy file  \n\t\t\tprint(image_url + \" not found in annoy\")\n\treturn v\n\n\n#Convert data into feed_dict format required\ndef get_feed_dict(model, batch_text, batch_image, batch_adjacency_mat, batch_target_pos, batch_target_negs):\n\tfeed_dict = {}\n\n\t#Fill text context\n\tbatch_text_ph = [[] for i in range(max_context_len)]\n\tfor i in range(batch_size):\n\t\tfor j in range(max_context_len):  \n\t\t\tbatch_text_ph[j].append(batch_text[i][j]) \n\tfor i in range(max_context_len):\n\t\tbatch_text_ph[i] = np.array(batch_text_ph[i]) \n\tfor ph_i, input_i in zip(model.encoder_text_inputs, batch_text_ph):\n\t\tfeed_dict[ph_i] = input_i  \n\t#\t \n\t#Fill image context\n\tbatch_image_ph = [[] for i in range(max_context_len)]\n\tfor i in range(batch_size):  \n\t\tfor j in range(max_context_len):  \n\t\t\tbatch_image_ph[j].append([image_rep(batch_image[i][j][k]) for k in range(num_images_in_context)])\n\tfor i in range(max_context_len):\n\t\tbatch_image_ph[i] = np.array(batch_image_ph[i])\n\tfor ph_i, input_i in zip(model.encoder_image_inputs, batch_image_ph):\n\t\tfeed_dict[ph_i] = input_i  \n\t#\n\t#Make adjacency matrix\t\n\tfeed_dict[model.A] = batch_adjacency_mat\n\t#\n\t#Fill positive image\n\tbatch_target_pos = [[image_rep(image) for image in image_set] for image_set in batch_target_pos]\n\tfeed_dict[model.pos_images] = batch_target_pos\n\t#\n\t#Fill negative images\n\tbatch_target_negs = [[image_rep(image) for image in image_set] for image_set in batch_target_negs]\n\tfeed_dict[model.negs_images] = batch_target_negs\n\t\n\treturn feed_dict\n\n\n\t\n# def pad_for_batch_size(batch_text, batch_image, batch_target_pos, batch_target_negs):\n# \tif(len(batch_text) != batch_size):\n# \t\t#If padding required\n# \t\tpad_size = batch_size - len(batch_target_pos)%batch_size\n\n# \t\t#Pad text context\n# \t\tempty_data = [start_word_id, end_word_id]+[pad_word_id]*(max_utter_len-2)\n# \t\tempty_data = [empty_data]*max_dialogue_len\n# \t\tempty_data_mat = [empty_data]*pad_size\n# \t\tempty_data_mat = np.array(empty_data_mat)\n# \t\tbatch_text = np.vstack((batch_text, empty_data_mat))\n# \t\t#\n# \t\t#Pad image context\n# \t\tempty_data = \"\"\n# \t\tempty_data = [empty_data]*max_dialogue_len\n# \t\tempty_data_mat = [empty_data]*pad_size\n# \t\tempty_data_mat = np.array(empty_data_mat)\n# \t\tbatch_image = np.vstack((batch_image, empty_data_mat))\n# \t\t#\n# \t\t#Pad positive image\n# \t\tempty_data = [\"RANDOM\"]\n# \t\tempty_data_mat = [empty_data]*pad_size\n# \t\tempty_data_mat = np.array(empty_data_mat)\n# \t\tbatch_target_pos = np.vstack((batch_target_pos, empty_data_mat))\n# \t\t#\n# \t\t#Pad negative images\n# \t\tempty_data = [\"RANDOM\"] * num_neg_images_use\n# \t\tempty_data_mat = [empty_data]*pad_size\n# \t\tempty_data_mat = np.array(empty_data_mat)\n# \t\tbatch_target_negs = np.vstack((batch_target_negs, empty_data_mat))\n\t\n# \treturn batch_text, batch_image, batch_target_pos, batch_target_negs\n\n\n\n\ndef process_batch(model, data_batch):\n\t#data_batch is a batch_size sized list of (batch_text, batch_image, batch_target_pos, batch_target_negs)\n\t#get batch_text, batch_image, batch_target_pos and batch_target_negs from data_batch\n\tbatch_text = []\n\tbatch_image = []\n\tbatch_adjacency_mat = []\n\tbatch_target_pos = []\n\tbatch_target_negs = []\n\tbatch_knowledge = []\n\tbatch_num_tuples = []\n\tfor instance in data_batch:\n\t\tbatch_text.append(instance[0])\n\t\tbatch_image.append(instance[1])\n\t\tbatch_adjacency_mat.append(np.array(instance[2].todense()))\n\t\tbatch_target_pos.append(instance[3])\n\t\tbatch_target_negs.append(instance[4][:num_neg_images_use])\n\t\tbatch_knowledge.append(instance[5])\n\t\tbatch_num_tuples.append(instance[6])\n\tbatch_text = np.array(batch_text)\n\tbatch_image = np.array(batch_image)\n\tbatch_adjacency_mat = np.array(batch_adjacency_mat)\n\tbatch_target_pos = np.array(batch_target_pos) \n\tbatch_target_negs = np.array(batch_target_negs)\n\tbatch_knowledge = np.array(batch_knowledge)\n\tbatch_num_tuples = np.array(batch_num_tuples)\n\t#batch_text is [batch_size * max_context_len * max_utter_len]\n\t#batch_image is [batch_size * max_context_len * num_images_in_context] \n\t#batch_adjacency_mat is [batch_size * num_nodes * num_nodes]\n\t#batch_target_pos is [batch_size * 1]\n\t#batch_target_negs is [batch_size * num_neg_images]\n\t#\n\t#batch_text, batch_image, batch_target_pos, batch_target_negs = pad_for_batch_size(batch_text, batch_image, batch_target_pos, batch_target_negs)\n\t#print(batch_text.shape, batch_image.shape, batch_target_pos.shape, batch_target_negs.shape)\n\t#\n\tfeed_dict = get_feed_dict(model, batch_text, batch_image, batch_adjacency_mat, batch_target_pos, batch_target_negs)    \n\t#batch_text is [batch_size * max_context_len * max_utter_len]\n\t#batch_image is [batch_size * max_context_len * num_images_in_context * image_size] \n\t#batch_adjacency_mat is [batch_size * num_nodes * num_nodes]\n\t#batch_target_pos is [batch_size * 1 * image_size]\n\t#batch_target_negs is [batch_size * num_neg_images * image_size]\n\n\treturn feed_dict\n\n\n\n\n#get rank of positive image among negative images given the similarities\ndef get_ranks(pos_sim, negs_sim):\n\tranks = []\n\t#print(\"pos_sim negs_sim shapes \", pos_sim.shape, negs_sim.shape)\n\tfor pos, negs in zip(pos_sim, negs_sim):\n\t\trank = num_neg_images_use+1\n\t\tfor neg in negs:\n\t\t\tif pos >= neg:\n\t\t\t\trank -= 1\n\t\tranks.append(rank)\n\treturn ranks\n\n\n\n\n#calculate recall @ m\ndef recall(ranks, m):\n\tcorrect = 0\n\ttotal = 0\n\tfor rank in ranks:\n\t\tif rank <= m:\n\t\t\tcorrect += 1\n\t\ttotal += 1\n\treturn float(correct)/float(total)\n\n","sub_path":"gcn3/batch_util.py","file_name":"batch_util.py","file_ext":"py","file_size_in_byte":6203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"185778365","text":"import logging\nfrom delira.utils.decorators import make_deprecated\nfrom .imgsave_handler import ImgSaveHandler\nfrom .visdom_image_handler import VisdomImageHandler\n\n\nfrom ..trixi_handler import TrixiHandler\n\n\n@make_deprecated(TrixiHandler)\nclass VisdomImageSaveHandler(logging.Handler):\n    \"\"\"\n    Logs images to dir and to visdom\n\n    .. deprecated:: 0.1\n        :class:`VisdomImageSaveHandler` will be removed in next release and is\n        deprecated in favor of ``trixi.logging`` Modules\n\n    ..warning::\n        :class:`VisdomImageSaveHandler` will be removed in next release\n\n    See Also\n    --------\n    `Visdom`\n    :class:`VisdomImageHandler`\n    :class:`TrixiHandler`\n\n    \"\"\"\n    def __init__(self, port, prefix, log_freq_train, log_freq_val=int(1e10),\n                 save_dir_train=\"./images_train\", save_dir_val=\"./images_val\",\n                 save_freq_train=1, save_freq_val=1, log_freq_img=1,\n                 level=logging.NOTSET, **kwargs):\n        \"\"\"\n\n        Parameters\n        ----------\n        port : int\n            port of visdom-server\n        prefix : str\n            prefix of environment names\n        log_freq_train : int\n            Defines logging frequency for scores in train mode\n        log_freq_val : int\n            Defines logging frequency for scores in validation mode\n        save_dir_train : str\n            path to which the training images should be saved (must not yet be\n            existent)\n        save_dir_val: str (default:None)\n            path to which the training images should be saved (must not yet be\n            existent)\n        save_freq_train : int (default: 1)\n            frequency with which images are saved during training\n        save_freq_val : int (default: 1)\n            frequency with which images are saved during validation\n        level : int (default: logging.NOTSET)\n            logging level\n        **kwargs:\n            additional keyword arguments which are directly passed to visdom\n\n        \"\"\"\n        super().__init__(level)\n\n        self._visdom_handler = VisdomImageHandler(port, prefix, log_freq_train,\n                                                  log_freq_val, level,\n                                                  log_freq_img=log_freq_img,\n                                                  **kwargs)\n        self._img_saver = ImgSaveHandler(save_dir_train=save_dir_train,\n                                         save_dir_val=save_dir_val,\n                                         save_freq_train=save_freq_train,\n                                         save_freq_val=save_freq_val,\n                                         level=level)\n\n    def emit(self, record):\n        \"\"\"\n        log images to visdom and dir\n\n        Parameters\n        ----------\n        record : LogRecord\n            entities to log\n\n        \"\"\"\n        self._visdom_handler.emit(record)\n        self._img_saver.emit(record)\n","sub_path":"delira/logging/deprecated/visdom_imgsave_handler.py","file_name":"visdom_imgsave_handler.py","file_ext":"py","file_size_in_byte":2909,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"573376141","text":"from django.shortcuts import render, get_object_or_404\nfrom .models import Client\nfrom django.utils import timezone\nfrom .forms import ClientForm\nfrom django.shortcuts import redirect\n\n# Create your views here.\ndef client_list(request):\n    clients = Client.objects.order_by('-datum_creatie')[:5]\n    context = {'clients' : clients}\n\n    return render(request, 'client/client_list.html', context)\n\n\n\ndef client_create(request):\n    if request.method == 'POST':\n        form = ClientForm(request.POST)\n        if form.is_valid():\n            client = form.save(commit=False)\n            client.user_creatie = request.user\n            client.datum_creatie = timezone.now()\n            client.datum_aanpassing = timezone.now()\n            client.save()\n            return redirect('client:client_detail', pk = client.pk)\n\n        else:\n            post = ClientForm()\n            return render(request, 'client:client_create.html',\n            {'form': form,'error_message' : 'there was a problem saving the data'})\n\n    else:\n        form = ClientForm()\n        return render(request, 'client/client_create.html', {'form' : form})\n\n\n\n\ndef client_detail(request, pk):\n    client = get_object_or_404(Client, pk=pk)\n    return render(request, 'client/client_detail.html', {'client': client})\n\n\ndef client_edit(request, pk):\n    client = get_object_or_404(Client, pk=pk)\n\n    if request.method == 'POST':\n        form = ClientForm(request.POST, instance = client)\n        if form.is_valid:\n            client = form.save(commit=False)\n            client.user_creatie = request.user\n            client.datum_creatie = timezone.now()\n            client.datum_aanpassing = timezone.now()\n            client.save()\n            return redirect('client:client_detail', pk = client.pk)\n\n\n\n    else:\n        form = ClientForm(instance = client)\n\n    return render(request, 'client/client_create.html', { 'form' : form })\n","sub_path":"client/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"156069506","text":"from tkinter import*\r\nimport tkinter.messagebox\r\nimport random\r\nimport os\r\n\r\nLIST=['LOQUAT', 'ORANGE', 'PEANUT', 'GRAPES', 'PLUMBS', 'FRUITS','WALNUT','LEMONS']\r\nanswer=random.choice(LIST)\r\n#print(answer)\r\n\r\ndef change_of_word():\r\n    global answer\r\n    answer=random.choice(LIST)\r\n    #print(answer)\r\n    \r\nROUND=1\r\nchances=5\r\n\r\ndef incriment():\r\n    global ROUND\r\n    ROUND+=1\r\n\r\ndef Chances():\r\n    global chances\r\n    chances=5\r\n    \r\nback=Tk()\r\ndef wow():\r\n    Chances()\r\n    def click(x):\r\n        if x in answer:\r\n            global chances;\r\n            if answer[0]==x:\r\n                edit_label_1['text']=x;\r\n            elif answer[1]==x:\r\n                edit_label_2['text']=x;\r\n            elif answer[2]==x:\r\n                edit_label_3['text']=x;\r\n            elif answer[3]==x:\r\n                edit_label_4['text']=x;\r\n            elif answer[4]==x:\r\n                edit_label_5['text']=x;\r\n            elif answer[5]==x:\r\n                edit_label_6['text']=x;\r\n        else:\r\n    #        if chances!=0:\r\n            label05['text']='Chances left = '+str(chances-1)\r\n            chances=chances-1\r\n            if chances<0:\r\n                label05.configure(fg='#FFFFFF')\r\n                tkinter.messagebox.showinfo('Looser', 'You have lost, start new game')\r\n                back.destroy()\r\n                import game_over.py\r\n                \r\n        #if edit_label_1['text']==str(answer[0]) and edit_label_1['text']==str(answer[1]):\r\n        if edit_label_1['text']==answer[0] and edit_label_2['text']==answer[1] and edit_label_3['text']==answer[2] and edit_label_4['text']==answer[3] and edit_label_5['text']==answer[4] and edit_label_6['text']==answer[5]:\r\n            if ROUND<5:\r\n                incriment()\r\n                tkinter.messagebox.showinfo('oh yes!', 'Keep it up buddy!')\r\n                change_of_word()\r\n                frame01.destroy()\r\n                frame02.destroy()\r\n                frame03.destroy()\r\n                keyboard.destroy()\r\n                wow() \r\n            else:\r\n                tkinter.messagebox.showinfo('Congratulations', \"You have completed level 4. Click 'OK' to go to next level\")\r\n                back.destroy()\r\n                os.system('python congratz.py')\r\n                #wow() function of new level \r\n\r\n    back.geometry('900x800')\r\n    back.configure(background='#FFFFFF')\r\n\r\n    frame01=Frame(back, background='#FFFFFF')\r\n\r\n    label03=Label(frame01,background='#FFFFFF', text='FINAL LEVEL', font=('arial','35'))\r\n    label03.grid(row=1, column=2,  padx=(210,135) ,  pady=(30,30))\r\n\r\n    label04=Label(frame01, text='Round '+str(ROUND),background='#FFFFFF', font=('arial','13'))\r\n    label04.grid(pady=(40,40) , padx=(20,20) ,row=1, column=1)\r\n\r\n    label05=Label(frame01, text='Chances left = '+str(chances), background='#FFFFFF',font=('arial','13'))\r\n    label05.grid(pady=(40,40) ,padx=(3,150), row=1, column=3)\r\n\r\n    frame01.pack(side=TOP)\r\n\r\n    \r\n    frame03=Frame(back)\r\n    frame03.configure(background='#142d39')\r\n    label06=Label(frame03, bg='#142d39', fg='#FFFFFF', text='Guess a word that contains only 6 alphabets (nature related words ONLY)', font=('arial','13'))\r\n    label06.pack(pady=(3,3))\r\n    frame03.pack(fill=X)\r\n\r\n    \r\n    keyboard=Frame(back)\r\n    keyboard.configure(background='#FFFFFF')\r\n    line1=Frame(keyboard)\r\n    btn0001=Button(line1, text='Q',  bg='#142d39', fg='#FFFFFF', height=2, width=5, font=('','15'), command=lambda: click('Q'))\r\n    btn0001.grid(row=1, column=1, padx=(3,3) )\r\n    btn0002=Button(line1, text='W', bg='#142d39', fg='#FFFFFF', height=2, width=5, font=('','15'), command=lambda:click('W'))\r\n    btn0002.grid(row=1, column=2, padx=(3,3) )\r\n    btn0003=Button(line1, text='E', bg='#142d39', fg='#FFFFFF', height=2, width=5, font=('','15'), command=lambda: click('E'))\r\n    btn0003.grid(row=1, column=3, padx=(3,3) )\r\n    btn0004=Button(line1, text='R', bg='#142d39', fg='#FFFFFF', height=2, width=5, font=('','15'), command=lambda: click('R'))\r\n    btn0004.grid(row=1, column=4, padx=(3,3) )\r\n    btn0005=Button(line1, text='T',  bg='#142d39', fg='#FFFFFF',height=2, width=5, font=('','15'), command=lambda: click('T'))\r\n    btn0005.grid(row=1, column=5, padx=(3,3) )\r\n    btn0006=Button(line1, text='Y', bg='#142d39', fg='#FFFFFF', height=2, width=5, font=('','15'), command=lambda: click('Y'))\r\n    btn0006.grid(row=1, column=6, padx=(3,3) )\r\n    btn0007=Button(line1, text='U',  bg='#142d39', fg='#FFFFFF',height=2, width=5, font=('','15'), command=lambda: click('U'))\r\n    btn0007.grid(row=1, column=7, padx=(3,3) )\r\n    btn0009=Button(line1, text='I',  bg='#142d39', fg='#FFFFFF',height=2, width=5, font=('','15'), command=lambda: click('I'))\r\n    btn0009.grid(row=1, column=8, padx=(3,3) )\r\n    btn0010=Button(line1, text='O', bg='#142d39', fg='#FFFFFF', height=2, width=5, font=('','15'), command=lambda: click('O'))\r\n    btn0010.grid(row=1, column=9, padx=(3,3) )\r\n    btn0031=Button(line1, text='P', bg='#142d39', fg='#FFFFFF', height=2, width=5, font=('','15'), command=lambda: click('P'))\r\n    btn0031.grid(row=1, column=10, padx=(3,3) )\r\n    #btn0010.bind('<Button-1>', letter_o)\r\n    line1.pack(pady=(1,6))\r\n\r\n    line2=Frame(keyboard)\r\n    btn0011=Button(line2, text='A',  bg='#142d39', fg='#FFFFFF',height=2, width=5, font=('','15'), command=lambda: click('A'))\r\n    btn0011.grid(row=1, column=1, padx=(3,3) )\r\n    btn0012=Button(line2, text='S', bg='#142d39', fg='#FFFFFF', height=2, width=5, font=('','15'), command=lambda: click('S'))\r\n    btn0012.grid(row=1, column=2, padx=(3,3) )\r\n    btn0013=Button(line2, text='D', bg='#142d39', fg='#FFFFFF', height=2, width=5, font=('','15'), command=lambda: click('D'))\r\n    btn0013.grid(row=1, column=3, padx=(3,3) )\r\n    btn0014=Button(line2, text='F', bg='#142d39', fg='#FFFFFF', height=2, width=5, font=('','15'), command=lambda: click('F'))\r\n    btn0014.grid(row=1, column=5, padx=(3,3) )\r\n    btn0015=Button(line2, text='G', bg='#142d39', fg='#FFFFFF', height=2, width=5, font=('','15'), command=lambda: click('G'))\r\n    btn0015.grid(row=1, column=6, padx=(3,3) )\r\n    btn0016=Button(line2, text='H', bg='#142d39', fg='#FFFFFF', height=2, width=5, font=('','15'), command=lambda: click('H'))\r\n    btn0016.grid(row=1, column=7, padx=(3,3) )\r\n    btn0017=Button(line2, text='J',  bg='#142d39', fg='#FFFFFF',height=2, width=5, font=('','15'), command=lambda: click('J'))\r\n    btn0017.grid(row=1, column=8, padx=(3,3) )\r\n    btn0018=Button(line2, text='K', bg='#142d39', fg='#FFFFFF', height=2, width=5, font=('','15'), command=lambda: click('K'))\r\n    btn0018.grid(row=1, column=9, padx=(3,3) )\r\n    btn0019=Button(line2, text='L', bg='#142d39', fg='#FFFFFF', height=2, width=5, font=('','15'), command=lambda: click('L'))\r\n    btn0019.grid(row=1, column=10, padx=(3,3) )\r\n    line2.pack(pady=(1,6))\r\n\r\n    line3=Frame(keyboard)\r\n    btn0020=Button(line3, text='Z', bg='#142d39', fg='#FFFFFF', height=2, width=5, font=('','15'), command=lambda: click('Z'))\r\n    btn0020.grid(row=1, column=1, padx=(3,3) )\r\n    btn0021=Button(line3, text='X', bg='#142d39', fg='#FFFFFF', height=2, width=5, font=('','15'), command=lambda: click('X'))\r\n    btn0021.grid(row=1, column=2, padx=(3,3) )\r\n    btn0022=Button(line3, text='C', bg='#142d39', fg='#FFFFFF', height=2, width=5, font=('','15'), command=lambda: click('C'))\r\n    btn0022.grid(row=1, column=3, padx=(3,3) )\r\n    btn0023=Button(line3, text='V',  bg='#142d39', fg='#FFFFFF',height=2, width=5, font=('','15'), command=lambda: click('V'))\r\n    btn0023.grid(row=1, column=4, padx=(3,3) )\r\n    btn0024=Button(line3, text='B', bg='#142d39', fg='#FFFFFF', height=2, width=5, font=('','15'), command=lambda: click('B'))\r\n    btn0024.grid(row=1, column=5, padx=(3,3) )\r\n    btn0025=Button(line3, text='N',  bg='#142d39', fg='#FFFFFF',height=2, width=5, font=('','15'), command=lambda: click('N'))\r\n    btn0025.grid(row=1, column=6, padx=(3,3) )\r\n    btn0026=Button(line3, text='M', bg='#142d39', fg='#FFFFFF', height=2, width=5, font=('','15'), command=lambda: click('M'))\r\n    btn0026.grid(row=1, column=7, padx=(3,3) )\r\n    btn0027=Button(line3, text=':)',  bg='#142d39', fg='#FFFFFF',height=2, width=5, font=('','15'))\r\n    btn0027.grid(row=1, column=8, padx=(3,3) )\r\n    line3.pack(pady=(1,6))\r\n    \r\n\r\n\r\n    keyboard.pack(pady=(50,160), side=BOTTOM)\r\n\r\n    \r\n    frame02=Frame(back)\r\n    \r\n    frame02.configure(background='#FFFFFF')\r\n    sub_frame02=Frame(frame02, height=1,  width=100, bg='#000000').grid(row=1, column=1)\r\n\r\n    global edit_label_1\r\n    global edit_label_2\r\n    edit_label_1=Label(frame02, text='' , height=4, fg='#000000', width=8)\r\n    edit_label_1.grid(row=1, column=2, padx=(30,5))\r\n    edit_label_2=Label(frame02, text='', height=4, fg='#000000', width=8)\r\n    edit_label_2.grid(row=1, column=3, padx=(5,5))\r\n    edit_label_3=Label(frame02, text='', height=4, fg='#000000', width=8)\r\n    edit_label_3.grid(row=1, column=4, padx=(5,5))\r\n    edit_label_4=Label(frame02, text='', height=4, fg='#000000', width=8)\r\n    edit_label_4.grid(row=1, column=5, padx=(5,5))\r\n    edit_label_5=Label(frame02, text='', height=4, fg='#000000', width=8)\r\n    edit_label_5.grid(row=1, column=6, padx=(5,5))\r\n    edit_label_6=Label(frame02, text='', height=4, fg='#000000', width=8)\r\n    edit_label_6.grid(row=1, column=7, padx=(5,30))\r\n\r\n\r\n\r\n    sub_frame02=Frame(frame02, height=1,  width=100, bg='#000000').grid(row=1, column=8)\r\n\r\n    frame02.pack(pady=(10,60), side=BOTTOM)\r\n\r\n\r\n      \r\n\r\nwow()\r\n\r\n\r\n\r\nback.mainloop()\r\n","sub_path":"Hangman/simple5.py","file_name":"simple5.py","file_ext":"py","file_size_in_byte":9486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"87091285","text":"'''\nHorizontal bar chart showing the number of steps walked during a week\n'''\nimport matplotlib.pyplot as plt\n\ndef create_bar_chart(data,labels):\n    #number of bars\n    num_bars=len(data)\n    #this list is a point on the y-axis where each bar is centered.\n    #here it will be [1,2,3...]\n    positions=range(1,num_bars+1)\n    plt.barh(positions,data, align= 'center')\n    #set the label of each bar\n    plt.yticks(positions,labels)\n    plt.xlabel('Steps')\n    plt.ylabel('Day')\n    plt.title('Number of steps walked')\n    \n    #turn on the grid\n    plt.grid()\n    plt.show()\n    \nif __name__== '__main__':\n    steps=[6534,7000,8900,10786,3467,11045,5095]\n    #corresponding days\n    labels = ['Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat']\n    create_bar_chart(steps,labels)","sub_path":"barchart_steps_walked_during_a_week.py","file_name":"barchart_steps_walked_during_a_week.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"604686094","text":"from scipy.stats import kruskal\nfrom scipy.stats import f_oneway\nimport numpy as np\nimport pandas as pd\n\n\ndef quali_quanti(data,quali,quanti, parametric=True):\n    \"\"\"\n    Applique un test entre une variable quantitative et une ou plusieurs variables \n    qualitatives.\n    \n    Paramètres : \n    -----------\n    data : jeu de données sous forme d'un dataframe\n    quali : données des variables qualitatives \n    quanti : variable quantitative\n    parametric : booléan (True par défaut) pour appliquer un test \n                non-paramétrique (Kruskal) ou paramétrique (ANOVA)\n                \n    Retour : \n    --------\n    Dataframe : statistique de test et p-value associée pour chaque variable  \n    \n    \"\"\"\n    \n    if parametric :\n        func = f_oneway\n    else : \n        func = kruskal\n    \n    list_stats = []\n    list_pval = []\n\n    for variable in list(quali.columns) :\n\n        liste_var = []\n\n        for i in list(np.unique(data[variable])):\n            col = data[[quanti,variable]][data[variable] == i]\n            liste_var.append(col[quanti])\n\n        list_stats.append(func(*liste_var).statistic)\n        list_pval.append(func(*liste_var).pvalue)\n\n        list_var = []\n    \n    feature_names = list(quali.columns)\n    return pd.concat([pd.Series(list_stats, index=feature_names, name=\"Stats\"), \n                      pd.Series(list_pval, index=feature_names, name=\"P-valeur\")], axis=1)\n\n","sub_path":"stats_quali_quanti.py","file_name":"stats_quali_quanti.py","file_ext":"py","file_size_in_byte":1419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"593898116","text":"## Danny_August_Ramaputra_1706019791_lab09.py\n## Simple Pen Drawing\n##\n## This python code is GUI based, it is a simple scribbler tool\n## using the Python tkinter library. The code will draw small\n## line segments between every points when a mouse drag event is\n## detected. This small segment of lines will form the mouse\n## scribbles.\n## Bonus: added color picker, brush size change, and file export\n\nfrom tkinter import *\nfrom tkinter.colorchooser import askcolor\nimport tkinter.messagebox\nimport math\n\n# define Scribble class, it runs the main part of the code\nclass Scribble(object):\n    '''a simple pen drawing application'''\n    # define constructor\n    def __init__(self):\n        # create new window object then set title\n        master = Tk()\n        master.title(\"Simple Pen Drawing\")\n\n        # remember the mouse coordinates\n        self.oldx, self.oldy = 0, 0\n        self.color = \"blue\"\n\n        # create canvas 400x250 and bind mouse events to handlers\n        self.canvas = Canvas(master=master, width=400, height=250)\n        self.canvas.bind(\"<Button-1>\", self.begin)\n        self.canvas.bind(\"<Button1-Motion>\", self.draw)\n        self.canvas.pack(expand=True, fill=BOTH)\n\n        # create a new frame\n        frame1 = Frame(master)\n        frame1.pack(side=TOP)\n\n        # create label and slider for brush size, button to select color, and button to clear drawing\n        self.label_size = Label(master=frame1, text=\" Brush Size:\")\n        self.label_size.pack(side=LEFT, padx=5)\n        self.slider = Scale(master=frame1, from_=2, to=10, tickinterval=1, orient=HORIZONTAL)\n        self.slider.pack(side=LEFT, padx=5)\n        self.button = Button(master=frame1, text=\"Clear\", bg=\"orange\", command=self.clear)\n        self.button.pack(side = RIGHT, padx=5)\n        self.select = Button(master=frame1, text=\"Color\", command=self.change_color)\n        self.select.pack(side=LEFT, padx=5)\n\n        # create another frame\n        frame2 = Frame(master)\n        frame2.pack(side=TOP)\n\n        # create label and entry for file name, and button to save file\n        self.name = StringVar()\n        self.label_file = Label(master=frame2, text=\"File Name:\")\n        self.entry = Entry(master=frame2, textvariable=self.name)\n        self.export = Button(master=frame2, text=\"Save\", command=self.save)\n        self.label_file.pack(side=LEFT, padx=5)\n        self.entry.pack(side=LEFT, padx=5)\n        self.export.pack(side=RIGHT, padx=5)\n\n        # create label for the message\n        self.message = Label(master=master, text=\"Press and drag the left mouse-button to draw\", fg=\"blue\")\n        self.message.pack(side=BOTTOM)\n\n        # start the event loop\n        master.mainloop()\n\n    # define begin function, run when mouse is clicked\n    def begin(self, event):\n        '''handles left button click by recording mouse position\n           as the start of the curve'''\n        # store the current mouse coordinates\n        self.oldx, self.oldy = event.x, event.y\n\n    # define draw function, run when mouse is dragged after clicked\n    def draw(self, event):\n        '''handles mouse motion, while pressing left button, by\n           connecting the previous mouse position to the new one with a line'''\n        # draw a line segment between old mouse coordinates and new current coordinate\n        self.canvas.create_line(self.oldx, self.oldy, event.x, event.y, fill=self.color, width = self.slider.get())\n        # update store mouse coordinate as new current coordinate\n        self.oldx, self.oldy = event.x, event.y\n\n    # define clear function, run when \"Clear\" button is clicked\n    def clear(self):\n        '''clear the canvas'''\n        # clear all canvas drawing\n        self.canvas.delete(\"all\")\n\n    # define change_color, run the \"Color\" button is pressed\n    def change_color(self):\n        # change the color from the hex value from color picker\n        self.color = askcolor()[-1]\n\n    # define save function, run when \"Save\" button is pressed\n    def save(self):\n        '''save into postscript file'''\n        # check if entry box is not empty\n        if self.name.get() != \"\":\n            # add the \".eps\" extension if not yet available\n            if self.name.get()[-4:] != \".eps\":\n                self.name.set(self.name.get() + \".eps\")\n            # save the canvas into post script file using given file name\n            self.canvas.postscript(file=self.name.get(), colormode=\"color\")\n        # else (entry  box is empty)\n        else:\n            # show an error message box, informing invalid file name\n            tkinter.messagebox.showerror(\"Invalid File Name\", \"Please give a valid file name\")\n\n# run the code continuously\nif __name__ == \"__main__\":\n    Scribble()\n","sub_path":"fprog_lab/lab09/Danny_August_Ramaputra_1706019791_lab09.py","file_name":"Danny_August_Ramaputra_1706019791_lab09.py","file_ext":"py","file_size_in_byte":4708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"40029219","text":"from django.shortcuts import render,redirect\nfrom django.http import HttpResponse\nfrom hotel.forms import user_registration,Roomdetails,BookRoom,Updation,ImgPfl\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth.decorators import login_required\nfrom booking import settings\nfrom django.core.mail import send_mail\nfrom django.contrib import messages\nfrom hotel.models import Room_details,Book_Room,Upd\n\n# Create your views here.\n\ndef home(request):\n\treturn render(request,'hotel/home.html')\n\ndef register(request):\n\tif request.method == \"POST\":\n\t\ty = user_registration(request.POST)\n\t\tif y.is_valid():\n\t\t\t# p=y.save()\n\t\t\tp = y.save(commit=False)\n\t\t\treceiver = p.email\n\t\t\tsub = \"Holiday Inn\"\n\t\t\tmsg = \"Hi Welcome {} you have successfully registered in our portal with password: {}\".format(p.username,p.password)\n\t\t\tsender = settings.EMAIL_HOST_USER\n\t\t\tsnt = send_mail(sub,msg,sender,[receiver])\n\t\t\tif snt == 1:\n\t\t\t\tp.save()\n\t\t\t\tmessages.success(request,\"Please check your {} for login creadentials\".format(receiver))\n\t\t\t\treturn redirect('/login')\n\t\t\tmessages.danger(request,\"please enter correct emailid or username or password\")\n\tform = user_registration()\n\treturn render(request,'hotel/register.html',{\"form\":form})\n\n@login_required\ndef profile(request):\n\treturn render(request,'hotel/profile.html')\n\n@login_required\ndef update(request):\n\tif request.method == 'POST':\n\t\tc = Updation(request.POST,instance=request.user)\n\t\ty = ImgPfl(request.POST,request.FILES,instance=request.user.upd)\n\t\tif c.is_valid() and y.is_valid():\n\t\t\tc.save()\n\t\t\ty.save()\n\t\t\tmessages.success(request,\"{} your details updated successfully\".format(request.user.username))\n\t\t\treturn redirect(\"/profile\")\n\tc = Updation(instance=request.user)\n\ty = ImgPfl(instance=request.user.upd)\n\treturn render(request,'hotel/update.html',{'u':c,'q':y})\n\t\n\n\n# @login_required\n# def update(request):\n# \tif request.method == 'POST':\n# \t\tc = Updation(request.POST,instance=request.user)\n# \t\ty = ImgPfl(request.POST,request.FILES,instance=request.user.upd)\n# \t\tif c.is_valid() and y.is_valid():\n# \t\t\tc.save()\n# \t\t\ty.save()\n# \t\t\tmessages.success(request,\"{} your details updated successfully\".format(request.user.username))\n# \t\t\treturn redirect(\"/profile\")\n# \tc = Updation(instance=request.user)\n# \ty = ImgPfl(instance=request.user.upd)\n# \treturn render(request,'dtlapp/updateprofile.html',{'u':c,'q':y})\n\n@login_required\ndef rooms(request):\n\tx = Room_details.objects.all()\n\trooms = []\n\tfor i in x:\n\t\tif not Book_Room.objects.filter(room_no=i):\n\t\t\trooms.append(i)\n\treturn render(request,'hotel/rooms.html',{'rooms':rooms})\n\n@login_required\ndef book_room(request,room):\n\tform = BookRoom(request.POST or None)\n\tif form.is_valid():\n\t\tt = form.save(commit=False)\n\t\tt.room_no = Room_details.objects.get(number=room)\n\t\tt.guest = request.user\n\t\tt.save()\n\t\troomdetails = Room_details.objects.get(number=room)\n\t\troomdetails.booked = True\n\t\troomdetails.save()\n\t\treturn redirect('/rooms')\n\treturn render(request,'hotel/book_room.html',{'form':form})\n\n@login_required\ndef myrooms(request,id):\n\trooms = Book_Room.objects.filter(guest = request.user)\n\treturn render(request,'hotel/myrooms.html',{'rooms':rooms})\n\ndef vacate(request,room):\n\troom = Book_Room.objects.filter(room_no = Room_details.objects.get(number=room))\n\troom.delete()\n\treturn redirect('rooms')","sub_path":"Booking/hotel/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"510567455","text":"import cv2\nimport requests\nimport sys\nfrom datetime import datetime, date, time, timedelta\n\n\ndeteccion = True;\ncamara = 0;\nahora=datetime.now()\n\ndef inside(r, q):\n    rx, ry, rw, rh = r\n    qx, qy, qw, qh = q\n    return rx > qx and ry > qy and rx + rw < qx + qw and ry + rh < qy + qh\n\ndef draw_detections(img, rects, thickness = 2):\n    for x, y, w, h in rects:\n        pad_w, pad_h = int(0.18*w), int(0.08*h)\n        cv2.rectangle(img, (x+pad_w, y+pad_h), (x+w-pad_w, y+h-pad_h), (0, 255, 0), thickness)\n\n\n\n\ndef alerta():\n      id = \"618709347\"\n \n      token = \"576479005:AAHODRk1MsFU6qpOxz9JAu1vH_PxppBrh_g\"\n \n      url = \"https://api.telegram.org/bot\" + token + \"/sendMessage\"\n      params = {\n      'chat_id': id,\n \n      'text' : \"Alerta\",\n      }\n      requests.post(url, params=params)\n      \n\n\ncap = cv2.VideoCapture();\n\n\n\nnombreven = \"SecurCam\"; \n\n\nif (((len(sys.argv) == 2) and (cap.open(str(sys.argv[1]))))\n    or (cap.open(camara))):\n\n\n    cv2.namedWindow(nombreven, cv2.WINDOW_NORMAL);\n\n\n    hog = cv2.HOGDescriptor();\n    hog.setSVMDetector( cv2.HOGDescriptor_getDefaultPeopleDetector() );\n\n    while (deteccion):\n\n\n        if (cap.isOpened):\n            ret, img = cap.read();\n\n\n\n        found, w = hog.detectMultiScale(img,winStride=(4, 4),padding=(8, 8), scale=1.05)\n        found_filtered = []\n\n        for ri, r in enumerate(found):\n            for qi, q in enumerate(found):\n                if ri != qi and inside(r, q):\n                    break\n                else:\n                    found_filtered.append(r)\n                    if ahora<datetime.now(): \n                    \talerta()\n                    \tahora=ahora+timedelta(minutes=1)\n\n        draw_detections(img, found_filtered, 3)\n\n        cv2.imshow(nombreven,img);\n\n\n        key = cv2.waitKey(60) & 0xFF;\n        if (key == ord('q')):\n            deteccion = False;\n\n\n    cv2.destroyAllWindows()\n\nelse:\n\n     print(\"La camara no esta conectada.\")","sub_path":"final.py","file_name":"final.py","file_ext":"py","file_size_in_byte":1930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"396214873","text":"#import required modules\r\nimport pygame\r\nimport time\r\nimport sys\r\n#define colors\r\nred = (255,0,0)\r\ngreen = (0,255,0)\r\nblue = (0,0,255)\r\nblack = (0,0,0)\r\n\r\n#grootte van het scherm\r\nwidth = 800\r\nheigth = 600\r\nsize = (width,heigth)\r\n\r\n\r\nclass Game:\r\n    def __init__(self):\r\n        pygame.init()\r\n        self.gameExit = False\r\n        self.screen = pygame.display.set_mode((size),pygame.FULLSCREEN)\r\n        pygame.display.set_caption(\"Battleport beta\")\r\n        self.intro_game = IntroGame(self)\r\n        self.game_main= GameMain(self)\r\n        self.pause_menu = PauseMenu(self)\r\n        self.state = self.intro_game\r\n\r\n    def set_state(self, state):\r\n        self.state = state\r\n\r\n    def draw(self):\r\n        self.state.draw(self.screen)\r\n        pygame.display.flip()\r\n\r\n\r\n    #loop voor het laten runnen van het spel\r\n    def loop_of_game(self):\r\n        while not self.gameExit:\r\n            self.draw()\r\n            for event in pygame.event.get():\r\n                if event.type == pygame.QUIT:\r\n                    quit()\r\n            #self.state = self.state.update(...)\r\n\r\n\r\n\r\n#Beginscherm\r\nclass IntroGame:\r\n    def __init__(self, game):\r\n        # sprites\r\n        self.game = game\r\n        self.bg = pygame.image.load(\"check.png\")\r\n        self.bg = pygame.transform.scale(self.bg, size)\r\n        self.pbuttom = pygame.image.load(\"Pl.png\")\r\n        self.pbuttom = pygame.transform.scale(self.pbuttom, [150, 50])\r\n        self.hbuttom = pygame.image.load(\"hscore.png\")\r\n        self.hbuttom = pygame.transform.scale(self.hbuttom, [150, 50])\r\n        self.rbuttom = pygame.image.load(\"Ins.png\")\r\n        self.rbuttom = pygame.transform.scale(self.rbuttom, [150, 50])\r\n        self.title = pygame.image.load(\"titel.png\")\r\n        self.title = pygame.transform.scale(self.title,[471,78])\r\n        self.hoover = pygame.image.load(\"arrow.png\")\r\n        self.hoover = pygame.transform.scale(self.hoover, [155, 45])\r\n        self.hoover1 = pygame.image.load(\"arrow.png\")\r\n        self.hoover1 = pygame.transform.scale(self.hoover, [155, 45])\r\n\r\n    def draw(self,screen):\r\n        screen.blit(self.bg,[0,0])\r\n        screen.blit(self.title,(width/5,width/8))\r\n        mouse_button_pressed(600, 400, 130, 30, screen, self.pbuttom, self.hoover, lambda: self.game.set_state(self.game.game_main))\r\n        mouse_button_pressed(600, 450, 130, 30, screen, self.hbuttom, self.hoover1)\r\n        mouse_button_pressed(600, 500, 130, 30, screen, self.rbuttom, self.hoover1)\r\n\r\n#Scherm na op start the hebben gedrukt\r\nclass GameMain:\r\n    def __init__(self, game):\r\n        self.game = game\r\n        self.side = pygame.image.load(\"menu2.png\")\r\n        self.side = pygame.transform.scale(self.side,size)\r\n        self.board = pygame.image.load(\"gamemap.png\")\r\n        self.board = pygame.transform.scale(self.board, [480,480])\r\n        self.pbutton = pygame.image.load(\"menu.png\")\r\n        self.pbutton = pygame.transform.scale(self.pbutton, [50, 50])\r\n        self.hoover = pygame.image.load(\"circle.png\")\r\n        self.hoover = pygame.transform.scale(self.hoover, [50, 50])\r\n\r\n\r\n    def draw(self,screen):\r\n        screen.blit(self.side,[0,0])\r\n        screen.blit(self.board,[width/5,heigth/10])\r\n        mouse_button_pressed(100, 100, 50, 50, screen, self.pbutton, self.hoover,lambda: self.game.set_state(self.game.pause_menu))\r\n\r\n\r\nclass Higscorelist:\r\n    def __init__(self):\r\n        # sprites\r\n        pass\r\n\r\nclass GamerulesList:\r\n    def __init__(self):\r\n        # sprites\r\n        pass\r\n\r\nclass PauseMenu:\r\n    def __init__(self, game):\r\n        # sprites\r\n        self.game = game\r\n        self.bg = pygame.image.load(\"pauze.png\")\r\n        self.bg = pygame.transform.scale(self.bg, size)\r\n        self.pbuttom = pygame.image.load(\"cont.png\")\r\n        self.pbuttom = pygame.transform.scale(self.pbuttom, [240, 120])\r\n        self.hbuttom = pygame.image.load(\"hscore.png\")\r\n        self.hbuttom = pygame.transform.scale(self.hbuttom, [150, 50])\r\n        self.rbuttom = pygame.image.load(\"Ins.png\")\r\n        self.rbuttom = pygame.transform.scale(self.rbuttom, [150, 50])\r\n        self.title = pygame.image.load(\"pauzet.png\")\r\n        self.title = pygame.transform.scale(self.title,[471,78])\r\n        self.hoover = pygame.image.load(\"contb.png\")\r\n        self.hoover = pygame.transform.scale(self.hoover, [280, 90])        \r\n        self.hoover1 = pygame.image.load(\"arrow.png\")\r\n        self.hoover1 = pygame.transform.scale(self.hoover, [155, 45])\r\n\r\n    def draw(self,screen):\r\n        screen.blit(self.bg,[0,0])\r\n        screen.blit(self.title,(width/5,width/8))\r\n        mouse_button_pressed(275, 200, 240, 120, screen, self.pbuttom, self.hoover, lambda: self.game.set_state(self.game.game_main))\r\n        mouse_button_pressed(325, 300, 150, 50, screen, self.hbuttom, self.hoover1, lambda :self.game.set_state(self.game.intro_game))\r\n        mouse_button_pressed(325, 350, 150, 50, screen, self.rbuttom, self.hoover1)\r\n\r\ndef mouse_button_pressed(x,y,w,h,screen,image_original,image_hover,action=None):\r\n    click=pygame.mouse.get_pressed()\r\n    mouse = pygame.mouse.get_pos()\r\n    if x + w > mouse[0] > x and y + h > mouse[1] > y:\r\n        screen.blit(image_original, [x, y])\r\n        screen.blit(image_hover,[x,y])\r\n        if click[0]==1 and action != None:\r\n            action()\r\n    else:\r\n        screen.blit(image_original,[x,y])\r\n\r\ndef text_objects(text,font):\r\n    textSurface = font.render (text,True, black)\r\n    return textSurface, textSurface.get_rect()\r\n\r\ndef message_to_screen(text):\r\n    largeText = pygame.font.Font(\"freesansbold.ttf\",50)\r\n    textsuf , textrect = text_objects(text,largeText)\r\n    textrect.center = ((width/2),(heigth/2))\r\n\r\n#sluiten van game via kruisje\r\n\r\n\r\ndef run ():\r\n    game = Game()\r\n    game.loop_of_game()\r\n\r\nrun()\r\n","sub_path":"INF1L-PRJ-2/game v2.py","file_name":"game v2.py","file_ext":"py","file_size_in_byte":5766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"177207125","text":"import numpy as np\nfrom numpy import mean\nfrom numpy import cov\nfrom numpy.linalg import eig\nfrom sklearn.decomposition import PCA\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import StandardScaler\n\ndef create_data(x1, x2, x3):\n    x4 = -4.0 * x1\n    x5 = 10 * x1 + 10\n    x6 = -1 * x2 / 2\n    x7 = np.multiply(x2, x2)\n    x8 = -1 * x3 / 10\n    x9 = 2.0 * x3 + 2.0\n    X = np.hstack((x1, x2, x3, x4, x5, x6, x7, x8, x9))\n    return X\n\ndef pca(X):\n    '''\n    PCA step by step\n      1. normalize matrix X\n      2. compute the covariance matrix of the normalized matrix X\n      3. do the eigenvalue decomposition on the covariance matrix\n    Return: [d, V]\n      d is the column vector containing all the corresponding eigenvalues,\n      V is the matrix containing all the eigenvectors.\n    If you do not remember Eigenvalue Decomposition, please review the linear\n    algebra\n    In this assignment, we use the ``unbiased estimator'' of covariance. You\n    can refer to this website for more information\n    http://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.cov.html\n    Actually, Singular Value Decomposition (SVD) is another way to do the\n    PCA, if you are interested, you can google SVD.\n    '''\n    \n    X_std = StandardScaler().fit_transform(X)\n    Xcov = np.cov(X_std.T)\n    d, V = np.linalg.eig(Xcov)\n    idx = d.argsort()[::-1] \n    d = d[idx] \n    V = V[:,idx]\n\n    return [d, V]\n\ndef plot_figs(X):\n    \"\"\"\n    1. perform PCA (you can use pca(X) completed by yourself) on this matrix X\n    2. plot (a) The figure of eigenvalues v.s. the order of eigenvalues. All eigenvalues in a decreasing order.\n    3. plot (b) The figure of POV v.s. the order of the eigenvalues.\n    \"\"\"\n    x_std = StandardScaler().fit_transform(X)\n\n    Xcov = np.cov(x_std.T)\n\n    d, V = np.linalg.eig(Xcov)\n    idx = d.argsort()[::-1] \n    d = d[idx] \n    V = V[:,idx]\n    \n    plot1 = plt.figure(1)\n    dim = np.arange(1, len(d)+1)\n    plt.plot(dim, d, 'b-x', linewidth=4, markersize=12, markeredgewidth=4, markeredgecolor='navy')\n    plt.title('eigenvalues v.s. the order of eigenvalues.')\n    plt.legend(['eigenvalues'])\n    plt.xlabel('order of eigenvalues'); plt.ylabel('eigenvalues')\n    \n    plot2 = plt.figure(2)\n    pov_array=[]\n    sum_d = d.sum()\n    new_d = [sum(d[:i+1]) for i in range(len(d))]\n    for i in new_d: \n        pov = i/sum_d\n        pov_array.append(pov)\n    plt.plot(dim, pov_array, 'b-x', linewidth=4, markersize=12, markeredgewidth=4, markeredgecolor='navy')\n    plt.title('PoV v.s. eigenvalues ')\n    plt.legend(['PoV'])\n    plt.xlabel('order of eigenvalues'); plt.ylabel('PoV')\n    \n    plt.show()\n\n\n    ####################################################################\n    #\n    # YOUR CODE HERE!\n    #\n    ####################################################################\n    return\n\ndef main():\n    N = 1000\n    shape = (N, 1)\n    x1 = np.random.normal(0, 1, shape) # samples from normal distribution\n    x2 = np.random.exponential(10.0, shape) # samples from exponential distribution\n    x3 = np.random.uniform(-100, 100, shape) # uniformly sampled data points\n    X = create_data(x1, x2, x3)\n\n    print(pca(X))\n    plot_figs(X)\n\nif __name__ == '__main__':\n    main()\n\n","sub_path":"CSCI_ML_asg1/asg1 handin/ex1.py","file_name":"ex1.py","file_ext":"py","file_size_in_byte":3223,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"116726993","text":"\n\nfrom xai.brain.wordbase.nouns._madrigal import _MADRIGAL\n\n#calss header\nclass _MADRIGALS(_MADRIGAL, ):\n\tdef __init__(self,): \n\t\t_MADRIGAL.__init__(self)\n\t\tself.name = \"MADRIGALS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"madrigal\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_madrigals.py","file_name":"_madrigals.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"385013579","text":"import pytest\nimport sys\n\nfrom copy import copy\nfrom tempfile import NamedTemporaryFile\n\nfrom mozapkpublisher import store_l10n\nfrom mozapkpublisher.exceptions import WrongArgumentGiven\nfrom mozapkpublisher.update_apk_description import UpdateDescriptionAPK, main\n\nfrom mozapkpublisher.test.helpers import craft_service_mock\n\ncredentials = NamedTemporaryFile()\n\nVALID_CONFIG = {\n    'package_name': 'org.mozilla.firefox_beta',\n    'service-account': 'foo@developer.gserviceaccount.com',\n    'credentials': credentials.name,\n}\n\n\ndef test_aurora_not_supported():\n    config = copy(VALID_CONFIG)\n    config['package_name'] = 'org.mozilla.fennec_aurora'\n\n    with pytest.raises(WrongArgumentGiven):\n        UpdateDescriptionAPK(config)\n\n\ndef test_update_apk_description_force_locale(monkeypatch):\n    edit_service_mock = craft_service_mock(monkeypatch)\n    monkeypatch.setattr(store_l10n, 'get_translation', lambda _, locale: {\n        'title': 'Firefox for Android',\n        'long_desc': 'Long description',\n        'short_desc': 'Short',\n        'whatsnew': 'Check out this cool feature!',\n    } if locale == 'en-US' else None)\n    monkeypatch.setattr(store_l10n, 'locale_mapping', lambda locale: 'google_play_locale')\n\n    config = copy(VALID_CONFIG)\n    config['force_locale'] = 'en-US'\n    UpdateDescriptionAPK(config).run()\n\n    edit_service_mock.update_listings.assert_called_once_with('google_play_locale', body={\n        'fullDescription': 'Long description',\n        'shortDescription': 'Short',\n        'title': 'Firefox for Android',\n    })\n\n    assert edit_service_mock.update_listings.call_count == 1\n    edit_service_mock.commit_transaction.assert_called_once_with()\n\n\ndef test_main(monkeypatch):\n    incomplete_args = [\n        '--package-name', 'org.mozilla.firefox_beta',\n        '--service-account', 'foo@developer.gserviceaccount.com',\n    ]\n\n    monkeypatch.setattr(sys, 'argv', incomplete_args)\n\n    with pytest.raises(SystemExit):\n        main()\n","sub_path":"mozapkpublisher/test/test_update_apk_description.py","file_name":"test_update_apk_description.py","file_ext":"py","file_size_in_byte":1965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"643331397","text":"#!/usr/bin/python3\n\nfrom collections import deque\nfrom copy import deepcopy, copy\nfrom functools import *\nimport sys\nimport cProfile\nimport re\n\nclass Task:\n    unique_id = 0\n\n    def __init__(self, points):\n        self.points = points\n        self.id = Task.unique_id\n        Task.unique_id += 1\n\n    def __str__(self):\n        return \"[Task id(\" + str(self.id) + \"),points(\" + str(self.points) + \")]\"\n\n    __repr__ = __str__\n\n\nclass Team:\n    unique_id = 0\n\n    def __init__(self, productivity):\n        self.productivity = productivity\n        self.id = Team.unique_id\n        Team.unique_id += 1\n\n    def __str__(self):\n        return \"[Team id(\" + str(self.id) + \"),productivity(\" + str(self.productivity) + \")]\"\n\n    __repr__ = __str__\n\n\nclass State:\n\n    def __init__(self, tasks_size, teams_size):\n        self.tasks_assigned = {t for t in range(tasks_size)}\n        self.values = [0.0] * teams_size\n        self.decisions_made = frozenset()\n        self.longest_time = (-1, -1)\n\n    def get_substate(self, task):\n        new_state = deepcopy(self)\n        new_state.tasks_assigned.remove(task.id)\n        return new_state\n\n    def assign_task(self, task, team):\n        self.values[team.id] += float(task.points) / float(team.productivity)\n        self.decisions_made = frozenset(self.decisions_made.union( {(team.id, task.id)}))\n        if( self.longest_time[1] < self.values[team.id] ):\n            self.longest_time = (team.id, self.values[team.id])\n\n    def get_value(self):\n        return self.longest_time\n\n    def __str__(self):\n        return \"[State ids(\" + str(self.tasks_assigned) + \"),values(\" + str(self.values) + \"),dec(\" + str(self.decisions_made) + \")]\"\n\n\n    __repr__ = __str__\n\ndef load_data():\n    tasks = {}\n    teams = {}\n    with open('data.txt') as data:\n        for idx, x in enumerate(next(data).split()):\n            tasks[idx] = Task(x);\n        for idx, x in enumerate(next(data).split()):\n            teams[idx] = Team(x);\n    return tasks, teams\n\n\nclass AlgorithmDP:\n    def __init__(self, teams, tasks):\n        self.teams = teams\n        self.tasks = tasks\n        self.states_queue = deque()\n        self.states_dict = dict()\n\n    def add_state( self, state ):\n        key = frozenset(state.tasks_assigned)\n        if key in self.states_dict:\n            self.states_dict[key].append( state )\n        else:\n            self.states_queue.append( key )\n            self.states_dict[ key ] = [ state ]\n\n    def add_states(self, states):\n        key = frozenset(states[0].tasks_assigned)\n        if key in self.states_dict:\n            states.extend( self.states_dict[key] )\n            s = set()\n            out = []\n            for st in states:\n                if st.decisions_made not in s:\n                    s.add( st.decisions_made )\n                    out.append( st )\n            self.states_dict[key] = out\n            # self.states_dict[key].extend( states )\n                    # print(\"EXTEND\")\n        else:\n            # print(\"NEW\")\n            self.states_queue.append( key )\n            self.states_dict[ key ] = states\n\n    def pop_state(self):\n        key = self.states_queue.popleft()\n        return key, self.states_dict[ key ]\n\n    def is_not_empty(self):\n        return len( self.states_queue ) != 0\n\n    def has_state(self, state):\n        key = frozenset( state.tasks_assigned )\n        return key in self.states_dict\n\n    def evaluate_decision(self, state, task, team):\n        new_val = state.values[team.id] + float(task.points) / float(team.productivity)\n        if state.longest_time[0] <  new_val:\n            return  new_val\n        else:\n            return state.longest_time[0]\n\n    def get_best_teams(self, state, task):\n        best_teams = []\n        best_value = float(\"inf\")\n        for team_id, team in self.teams.items():\n            value = self.evaluate_decision(state, task, team)\n            best_teams.append( (team, value) )\n            if value < best_value:\n                best_value = value\n        return [ t[0] for t in best_teams if abs( t[1] - best_value ) < 0.0000000000001 ] # FLOAT COMPARE\n\n    def get_best_states(self, state, task, best_teams):\n        states = []\n        for team in best_teams:\n            new_state = deepcopy(state)\n            new_state.assign_task(task, team)\n            states.append(new_state)\n        return states\n\n    def add_best_states(self, state, task):\n        states = self.get_best_states( state, task, self.get_best_teams(state, task))\n        self.add_states( states )\n\n    def reevaluate_state(self, state, task):\n        states = self.get_best_states( state, task, self.get_best_teams(state, task))\n        new_val = states[0].get_value()\n        old_val = self.states_dict[ frozenset(state.tasks_assigned) ][0].get_value()\n        if( new_val[1] < old_val[1] ):\n            # print(\"CHANGE TO MIN\")\n            self.states_dict[frozenset(state.tasks_assigned)] = states\n        elif ( abs(new_val[1] - old_val[1]) < 0.0000000000001 ):\n            # print(\"EXTEND\")\n            self.add_states(states)\n\n\n\n    def reevaluate_states(self, key):\n        # for k in self.states_dict:\n            states = self.states_dict[key]\n            # l = len(self.states_dict[k])\n            val = reduce( lambda x,y: x if x.longest_time[1] < y.longest_time[1] else y, self.states_dict[key])\n            val = val.longest_time[1]\n            states = list(filter( lambda x: (x.longest_time[1] - val) < 0.00000001, states))\n            self.states_dict[key] = states\n            # print(l,\"->\", len(self.states_dict[k]))\n            # print(states)\n\n    def run(self, verbose = False):\n        init_state = State(len(self.tasks), len(self.teams))\n        self.add_state(init_state)\n        current_step = len(init_state.tasks_assigned);\n        while self.is_not_empty():\n\n            tasks_assigned, states = self.pop_state()\n            self.reevaluate_states(tasks_assigned) # BETTER PERFORMANCE, BUT NOT BEST RESULTS :|\n            # current_step = len(tasks_assigned)   # BETTER PERFORMANCE, BUT NOT BEST RESULTS :|\n            for current_task in tasks_assigned:\n                current_task = self.tasks[current_task]\n                for current_state in states:\n                    new_state = current_state.get_substate(current_task)\n                    self.add_best_states(new_state, current_task)\n                    # if self.has_state(new_state):\n                    #     # print(\"HAS_STATE\")\n                    #     # print( new_state, \" <-\", current_task    )\n                    #     self.reevaluate_state(new_state, current_task)\n                    #     # print(\"ADDED\")\n                    #     # print( \"\\n\".join([ str(s) for s in self.states_dict[frozenset(new_state.tasks_assigned)] ]))\n                    #     # print( [ s.get_value() for s in self.states_dict[frozenset(new_state.tasks_assigned)] ])\n                    #     # print(\"CURRENT DICT\")\n                    #     # for s, dict_elem in self.states_dict.items():\n                    #     #         print(s,\" ---- \", str([ s for s in dict_elem]))\n                    #     # for s, dict_elem in self.states_dict.items():\n                    #     #     print(str(dict_elem))\n                    #     # print(\"-------------------------------------------\")\n                    #\n                    # else:\n                    #     self.add_best_states(new_state, current_task)\n                    #     # print(\"CURRENT DICT\")\n                    #     # for s, dict_elem in self.states_dict.items():\n                    #     #     print(str(dict_elem))\n        # for l in range(6):\n        #     for s, dict_elem in self.states_dict.items():\n        #         if(len(s) == l):\n        #             print(s, \" ---- \", str([s for s in dict_elem]))\n        self.reevaluate_states(frozenset({}))\n        if verbose:\n            solutions = self.states_dict[frozenset({})]\n            for sol in solutions:\n                result = list(sol.decisions_made)\n                result.sort(key=lambda x:x[1]) # sort by task\n                # result.sort()  # sort by team\n                print (\"Max time:\", max(sol.values), \"\\nTimes:\", sol.values, \"\\nAssignment:\", result,\"\\n\" )\n            # print([str(s) for s in self.states_dict[frozenset({})]][0])\n\n\n\nclass AlgorithmLPT:\n    def __init__(self, teams, tasks):\n        sorted_tasks = [ t for i,t in tasks.items()]\n        sorted_tasks.sort( key=lambda x: x.points, reverse=True)\n        self.queue = deque( sorted_tasks )\n        self.teams = teams\n        self.tasks = tasks\n\n    def run( self, verbose = False ):\n        values = [ 0.0 ] * len( self.teams )\n        result = set()\n        while len( self.queue ) != 0:\n            task = self.queue.popleft()\n            idx = values.index(min(values))\n            values[idx] += float(task.points) / float(self.teams[idx].productivity)\n            result.add( (idx, task.id) )\n        if verbose:\n            result = list(result)\n            result.sort()\n            print (\"Max time:\", max(values), \"\\nTimes:\", values, \"\\nAssignment:\", result, \"\\n\")\n\nclass AlgorithmBF:\n\n    def __init__(self, teams, tasks):\n        self.teams = teams\n        self.tasks = tasks\n\n    def new_state( self, state, modulo ):\n        carry = True\n        idx = 0\n        while carry:\n            if idx == len( state ):\n                return False\n            else:\n                if (state[ idx ] + 1) % modulo:\n                    state[idx] += 1\n                    carry = False\n                else:\n                    state[ idx ] = 0\n                    idx += 1\n        return True\n\n    def evaluate( self, state, tasks, teams):\n        result = [0.0]*len(teams)\n        for i, t in enumerate(state):\n            result[t] += float(tasks[i].points) / float(teams[t].productivity)\n        return max(result)\n\n    def run( self, verbose = False ):\n        state = [ 0 ] * len( self.tasks )\n        idx = 1\n        best = self.evaluate( state, self.tasks, self.teams )\n        best_state = deepcopy(state)\n        while self.new_state( state, len(self.teams) ):\n            idx += 1\n            if best > self.evaluate( state, self.tasks, self.teams ):\n                best = self.evaluate(state, self.tasks, self.teams)\n                best_state = deepcopy(state)\n        if verbose:\n            result = []\n            for i, t in enumerate(best_state):\n                result.append((t,i))\n\n            values = [0.0] * len(self.teams)\n            for t in result:\n                values[ t[0]] += float(self.tasks[t[1]].points) / float(self.teams[t[0]].productivity)\n            print (\"Max time:\", best, \"\\nTimes:\", values, \"\\nAssignment:\", result, \"\\n\")\n\n\nimport signal\nimport random\nimport time\n\n\ndef handler(signo, frame):\n    raise RuntimeError\n\nsignal.signal(signal.SIGALRM, handler)\n\n\ndata_set = []\n\ntasks = {}\nteams = {}\ntasks, teams = load_data()\n\ndata_set.append((teams, tasks))\n# for i in range(4, 15, 1):\n#     for j in range( 4,15,1):\n#         tasks = dict()\n#         teams = dict()\n#         Team.unique_id = 0\n#         Task.unique_id = 0\n#         for x in range(0,i,1):\n#             tasks[x] = Task(x+1);\n#         for x in range(0,j,1):\n#             teams[x] = Team(x+1);\n#         # print (teams, tasks)\n#         data_set.append( (deepcopy(teams), deepcopy(tasks)) )\n\nfor data in data_set:\n    print( \"SIZE: \", len(data[0]), len(data[1]))\n    instances = []\n\n    for a in [ AlgorithmDP, AlgorithmBF, AlgorithmLPT]:\n        instances.append( a(data[0], data[1]) )\n\n    for i in instances:\n        signal.alarm(15)  # seconds\n        try:\n            start = time.time()\n            i.run( verbose=(len(sys.argv) != 1) )\n            end = time.time()\n        except RuntimeError:\n            end = start + 10\n            print('Your time is UP!')\n        print('{:15}'.format(i.__class__.__name__), end - start)\n\n\n","sub_path":"SRC/dp.py","file_name":"dp.py","file_ext":"py","file_size_in_byte":11853,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"344853199","text":"# -*- coding: utf-8 -*-\n'''\nbaidu fanyi, token diy\nOBRSTR and Cookie from: https://gitee.com/o0elias0o/eliaspython/tree/master\n\n# 需要安装的库\n# pip install requests\n# pip install PyExecJS\n\nPyExecJS 需 js 环境或解释器（如 nodejs 或 phantomjs.exe)\n'''\n\nfrom __future__ import print_function\nimport logging\n# import json\n\nimport requests\nimport execjs\n\n# from utils import (obrstr, headers, gtk, token)\n# from utils import (obrstr, headers)\n\nLOGGER = logging.getLogger(__name__)\nLOGGER.addHandler(logging.NullHandler())\n\nOBRSTR = \\\n'''function wods(r,gtk) {\n    function a(df, o) {\n        for (var t = 0; t < o.length - 2; t += 3) {\n            var a = o.charAt(t + 2);\n            a = a >= \"a\" ? a.charCodeAt(0) - 87 : Number(a), a = \"+\" === o.charAt(t + 1) ? df >>> a : df << a, df = \"+\" === o.charAt(t) ? df + a & 4294967295 : df ^ a\n        }\n        return df\n    }\n    var o = r.length;\n    o > 30 && (r = \"\" + r.substr(0, 10) + r.substr(Math.floor(o / 2) - 5, 10) + r.substr(-10, 10));\n    var t = gtk, e = t.split(\".\"), h = Number(e[0]) || 0, i = Number(e[1]) || 0, d = [], f = 0;\n    for (var  g = 0; g < r.length; g++) {\n        var m = r.charCodeAt(g);\n        128 > m ? d[f++] = m : (2048 > m ? d[f++] = m >> 6 | 192 : (55296 === (64512 & m) && g + 1 < r.length && 56320 === (64512 & r.charCodeAt(g + 1)) ? (m = 65536 + ((1023 & m) << 10) + (1023 & r.charCodeAt(++g)), d[f++] = m >> 18 | 240, d[f++] = m >> 12 & 63 | 128) : d[f++] = m >> 12 | 224, d[f++] = m >> 6 & 63 | 128), d[f++] = 63 & m | 128)\n    }\n    var S = h, u = \"+-a^+6\", l = \"+-3^+b+-f\";\n    for (var s = 0; s < d.length; s++) S += d[s], S = a(S, u);\n    return S = a(S, l), S ^= i, 0 > S && (S = (2147483647 & S) + 2147483648), S %= 1e6, S.toString() + \".\" + (S ^ h) }'''  # NOQA\n\nHEADERS = {\n    'Cookie':'BAIDUID=99C3CE30DA0FE8FD336F4428DA0DFDFC:FG=1; BIDUPSID=99C3CE30DA0FE8FD336F4428DA0DFDFC; PSTM=1515055428; REALTIME_TRANS_SWITCH=1; FANYI_WORD_SWITCH=1; HISTORY_SWITCH=1; SOUND_SPD_SWITCH=1; SOUND_PREFER_SWITCH=1; locale=zh; PSINO=7; H_PS_PSSID=1443_25549_21127_22157; Hm_lvt_64ecd82404c51e03dc91cb9e8c025574=1514859052,1515028770,1515029153,1515114213; Hm_lpvt_64ecd82404c51e03dc91cb9e8c025574=1515134327; from_lang_often=%5B%7B%22value%22%3A%22zh%22%2C%22text%22%3A%22%u4E2D%u6587%22%7D%2C%7B%22value%22%3A%22en%22%2C%22text%22%3A%22%u82F1%u8BED%22%7D%5D; to_lang_often=%5B%7B%22value%22%3A%22en%22%2C%22text%22%3A%22%u82F1%u8BED%22%7D%2C%7B%22value%22%3A%22zh%22%2C%22text%22%3A%22%u4E2D%u6587%22%7D%5D',  # NOQA\n    'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/62.0.3202.94 Safari/537.36'  # NOQA\n}\n\nGTK = '320305.131321201'\nTOKEN = '46e5646d48c43e676f955dc7fdece81f'\nPARSERCALL = execjs.compile(OBRSTR).call\n# SIGN = lambda que: PARSERCALL(\"wods\", que, GTK)\nURL = 'http://fanyi.baidu.com/v2transapi/'\n\n\ndef sign_text(text):\n    '''sign text'''\n    return PARSERCALL(\"wods\", text, GTK)\n\n\ndef bd_fanyi(text, from_lang='auto', to_lang='zh'):\n    '''translate from user input, q to quit\n\n    :text: str\n    :from_lang: str\n    :to_lang: str\n    :rtype: dict\n\n    quick doctest with py.test or nosetest:\n\n    py.test --doctest-modules bd_fanyi.py or\n    nosetests --with-doctest bd_fanyi.p\n\n    >>> text = 'test'\n    >>> bd_fanyi(text)[0].keys() == [{'result': [[0, '测试', ['0|4'], [], ['0|4'], ['0|6']]], 'prefixWrap': 0, 'dst': '测试', 'relation': [], 'src': 'test'}][0].keys()\n    True\n    >>> bd_fanyi(text)[0].get('dst')\n    '测试'\n    '''\n    text = text.strip()\n\n    if not text.strip():\n        return ''\n\n    # sign = SIGN(text)\n    sign = sign_text(text)\n    data = {\n        'from': from_lang,\n        'to': to_lang,\n        'query': text,\n        'transtype': 'realtime',\n        'simple_means_flag': '3',\n        'sign': sign,\n        'token': TOKEN\n    }\n\n    try:\n        response = requests.post(URL, data=data, headers=HEADERS)\n        # response.encoding = 'utf-8'\n        response.raise_for_status()\n    except Exception as exc:\n        LOGGER.error(exc)\n        return str(exc)\n\n    try:\n        resu = response.json().get('trans_result').get('data')  # NOQA\n    except Exception as exc:\n        LOGGER.error(exc)\n        resu = str(exc)\n\n    # print(resu)\n    return resu\n\n\ndef main():\n    '''main'''\n    import sys\n\n    fmt = '%(name)s - %(filename)s [line:%(lineno)d]'\n    fmt += '%(asctime)s:%(levelname)s:%(message)s'\n\n    # logging.basicConfig(format=fmt, level=logging.DEBUG)\n    logging.basicConfig(format=fmt, level=logging.INFO)\n\n    logging.getLogger(\"urllib3.connectionpool\").level = 30\n\n    while True:\n        if sys.version_info.major < 3:\n            text = raw_input(\"请输入翻译的内容(退出q)：\")\n        else:\n            text = input(\"请输入翻译的内容(退出q)：\")\n        text = text.strip()\n        if text in ['q', 'Q']:\n            break\n        elif not text:\n            continue\n        else:\n            print(bd_fanyi(text, 'en', 'zh'))\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"bd_fanyi.py","file_name":"bd_fanyi.py","file_ext":"py","file_size_in_byte":5007,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"22985328","text":"import sqlite3\n\nfrom models.comment import CommentModel\n\n\nclass TicketModel():\n    filename = 'data.db'\n\n    def __init__(self, customer_id, subject, number=None, employee_id=None):\n        self.customer_id = customer_id\n        self.subject = subject\n        self.number = number\n        self.employee_id = employee_id\n\n    def json(self):\n        return {\"number\": self.number,\n                \"customer_id\": self.customer_id,\n                \"subject\": self.subject,\n                \"employee_id\": self.employee_id}\n\n    @classmethod\n    def find_by_number(cls, number):\n        conn = sqlite3.connect(cls.filename)\n        cursor = conn.cursor()\n\n        query = \"SELECT customer_id, subject, number, employee_id\\\n                 FROM tickets WHERE number=?\"\n        cursor.execute(query, (number,))\n        row = cursor.fetchone()\n\n        conn.close()\n\n        if row:\n            return cls(*row)\n\n    @classmethod\n    def find_by_email(cls, email, is_customer):\n        conn = sqlite3.connect(cls.filename)\n        cursor = conn.cursor()\n\n        if is_customer:\n            table_name = \"customers\"\n            user_type = \"customer\"\n        else:\n            table_name = \"employees\"\n            user_type = \"employee\"\n\n        query = \"SELECT t.customer_id, t.subject, t.number, t.employee_id\\\n                 FROM tickets as t\\\n                 LEFT JOIN {} as u\\\n                 ON t.{}_id = u.id\\\n                 WHERE u.email=?\".format(table_name, user_type)\n\n        cursor.execute(query, (email,))\n        rows = cursor.fetchall()\n\n        conn.close()\n\n        return [cls(*row) for row in rows]\n\n    def add_to_db(self):\n        conn = sqlite3.connect(self.filename)\n        cursor = conn.cursor()\n\n        query = \"INSERT INTO tickets (customer_id, subject) VALUES (?, ?)\"\n        cursor.execute(query, (self.customer_id, self.subject))\n        self.number = cursor.lastrowid  # Ticket number corresponds with row ID\n\n        conn.commit()\n        conn.close()\n\n    def update_to_db(self):\n        conn = sqlite3.connect(self.filename)\n        cursor = conn.cursor()\n\n        query = \"UPDATE tickets\\\n                 SET customer_id=?, subject=?, employee_id=?\\\n                 WHERE number=?\"\n        cursor.execute(query, (self.customer_id, self.subject,\n                               self.employee_id, self.number))\n\n        conn.commit()\n        conn.close()\n\n    def delete_from_db(self):\n        conn = sqlite3.connect(self.filename)\n        cursor = conn.cursor()\n\n        query = \"PRAGMA foreign_keys = ON\"\n        cursor.execute(query)\n\n        query = \"DELETE FROM tickets WHERE number=?\"\n        cursor.execute(query, (self.number,))\n\n        conn.commit()\n        conn.close()\n","sub_path":"models/ticket.py","file_name":"ticket.py","file_ext":"py","file_size_in_byte":2716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"341377956","text":"from multiprocessing import Process, Queue\n\n\nclass FileWorker(Process):\n    def __init__(self, parent, in_q, out_q, model, out_q=Queue):\n        super(Worker, self).__init__()\n        self.in_q = in_q\n        self.out_q = out_q\n\n    def run(self):\n        while True:\n            getter = self.in_q.get()\n            if template is None:\n                print('stopping')\n                break\n            with open(template.url) as fle:\n                lines = fle.readlines()\n                self.out_q.put((self.parser.parse(lines), db, col))\n","sub_path":"modelscraper/workers/file_worker.py","file_name":"file_worker.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"343623143","text":"import pykafka\nimport json\nimport tweepy\nfrom tweepy.streaming import StreamListener\nfrom tweepy import OAuthHandler\nfrom tweepy import Stream\nimport config_twitter\n\naccess_token = config_twitter.access_token\naccess_token_secret = config_twitter.access_token_secret\nconsumer_key = config_twitter.consumer_key\nconsumer_secret = config_twitter.consumer_secret\n\n# autitencicando na api do twitter\nauth = OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_token, access_token_secret)\n\napi = tweepy.API(auth)\n\n\nclass TweetPush(StreamListener):\n\n    def __init__(self):\n        self.client = pykafka.KafkaClient(\"localhost:9092\")\n        self.producer = self.client.topics[bytes(\"twitter\", \"ascii\")].get_producer()\n\n    def on_data(self, raw_data):\n        self.producer.produce(bytes(raw_data, \"ascii\"))\n        #print(\"------------------------------------\")\n        return True\n\n    def on_error(self, status_code):\n        print(status_code)\n        return True\n\n    def on_timeout(self):\n        return True\n\n\ntwitter_stream = Stream(auth, TweetPush())\n\ntwitter_stream.filter(locations=[-73.9872354804, -33.7683777809, -34.7299934555, 5.24448639569], languages=['pt'])\n\n\n#twitter_stream.filter(track=['a'])\n\n\n","sub_path":"kafka_push.py","file_name":"kafka_push.py","file_ext":"py","file_size_in_byte":1229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"465502883","text":"# coding:utf-8\n\nmatrix = [\n    [1, 2, 1],\n    [3, 0, 1],\n    [1, 1, 1]\n]\n\n\n# 给定一个 m x n 的矩阵，如果一个元素为 0，则将其所在行和列的所有元素都设为 0。请使用原地算法。\n\ndef setZeroes(matrix):\n    \"\"\"\n    :type matrix: List[List[int]]\n    :rtype: void Do not return anything, modify matrix in-place instead.\n    \"\"\"\n    idxs = [idx for idx, row in enumerate(zip(*matrix)) if 0 in row]\n\n    matrix[:] = [[0] * len(row) if 0 in row else row for row in matrix]\n    matrix[:] = map(list, zip(*[[0] * len(row) if idx in idxs else row for idx, row in enumerate(zip(*matrix))]))\n","sub_path":"leetCode/矩阵置零.py","file_name":"矩阵置零.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"117968043","text":"from PIL import Image\r\nimport numpy as np\r\n\r\n# Checks to see if the pixel is white\r\ndef isBackground(pixel):\r\n    if (pixel == 255):\r\n        return True\r\n\r\n    return False\r\n\r\n# Loops through the image array to find connected regions\r\ndef connectedComponents(imgarr):\r\n    equivList = [0]\r\n    count = 0\r\n\r\n    height, width = imgarr.shape\r\n    for i in range(0, height):\r\n        for j in range(0, width):\r\n            if not (isBackground(imgarr[i][j])):\r\n                if (i != 0 and j != 0):\r\n                    # check up and left\r\n                    up = imgarr[i - 1][j]\r\n                    left = imgarr[i][j - 1]\r\n\r\n                    if(isBackground(up) and isBackground(left)):\r\n                        count += 1\r\n                        equivList.append(count)\r\n                    \r\n                    elif (not isBackground(up) and not isBackground(left)):\r\n                        index = up if up > left else left\r\n                        equivList[index] = up if up < left else left\r\n\r\n                elif (i == 0 and j == 0):\r\n                    # checks if the first pixel is black\r\n                    pixel = imgarr[i][j]\r\n\r\n                    if (not isBackground(pixel)):\r\n                        count += 1\r\n                        equivList.append(count)\r\n\r\n                elif (i != 0):\r\n                    # check up\r\n                    up = imgarr[i - 1][j]\r\n                    \r\n                    if (isBackground(up)):\r\n                        count += 1\r\n                        equivList.append(count)\r\n                     \r\n                elif (j != 0):\r\n                    # check left\r\n                    left = imgarr[i][j - 1]\r\n\r\n                    if (isBackground(left)):\r\n                        count += 1\r\n                        equivList.append(count)\r\n                    \r\n                imgarr[i][j] = equivList[count]\r\n                \r\n    return imgarr, equivList\r\n\r\ndef updateComponents(imgarr, equivList):\r\n    height, width = imgarr.shape\r\n    for i in range(0, height):\r\n        for j in range(0, width):\r\n            pixel = imgarr[i][j]\r\n\r\n            if (pixel != 255 and pixel > equivList[pixel]):\r\n                imgarr[i][j] = equivList[pixel]\r\n    \r\n    return imgarr\r\n\r\ndef countBlackPixels(imgarr, equivList):\r\n    count = [0]\r\n    for _ in range(1, len(equivList)):\r\n        count.append(0)\r\n\r\n    height, width = imgarr.shape\r\n    for i in range(0, height):\r\n        for j in range(0, width):\r\n            if (imgarr[i][j] != 255 and imgarr[i][j] != 0):\r\n                count[imgarr[i][j]] += 1\r\n        \r\n    return count\r\n\r\n## main ##\r\n# Opens the image and sets its pixel data to an array.\r\nimg = Image.open('output.png').convert('L')\r\nimgarr = np.array(img)\r\n\r\nimgarr, equivList = connectedComponents(imgarr)\r\n\r\nheight, width = imgarr.shape\r\nf = open(\"test.txt\", \"w+\")\r\nfor i in range(height):\r\n    for j in range(width):\r\n        f.write(\"{0:0=3d}, \".format(imgarr[i][j]))\r\n    f.write(\"\\n\")\r\n    \r\nimgarr = updateComponents(imgarr, equivList)\r\npixelCount = countBlackPixels(imgarr, equivList)\r\n\r\ngroup = 1\r\nfor i in range(len(pixelCount)):\r\n    if (pixelCount[i] > 0):\r\n        print(\"Connected Region: {} Pixel Count: {}\".format(group, pixelCount[i]))\r\n        group += 1","sub_path":"A2/numblackpixels.py","file_name":"numblackpixels.py","file_ext":"py","file_size_in_byte":3269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"501139460","text":"import os\nimport sys\nimport asyncio\n\nimport synapse.exc as s_exc\nimport synapse.common as s_common\n\nimport synapse.lib.output as s_output\nimport synapse.lib.dyndeps as s_dyndeps\n\ndef getStemCell(dirn):\n\n    if not os.path.isdir(dirn):\n        mesg = f'Directory {dirn} does not exist!'\n        raise s_exc.NoSuchDir(mesg=mesg)\n\n    ctorname = os.getenv('SYN_STEM_CELL_CTOR')\n\n    cellyaml = os.path.join(dirn, 'cell.yaml')\n\n    if os.path.isfile(cellyaml):\n        conf = s_common.yamlload(cellyaml)\n        ctorname = conf.get('cell:ctor', ctorname)\n\n    if ctorname is not None:\n        ctorname = ctorname.strip()\n        ctor = s_dyndeps.getDynLocal(ctorname)\n        if ctor is None:\n            raise s_exc.NoSuchCtor(mesg=f'Unable to resolve ctor [{ctorname}]', ctor=ctorname)\n        return ctor\n\n    mesg = f'No such file: {cellyaml} and SYN_STEM_CELL_CTOR environment variable is not set.'\n    raise s_exc.NoSuchFile(mesg=mesg, path=cellyaml)\n\nasync def main(argv, outp=s_output.stdout):  # pragma: no cover\n    ctor = getStemCell(argv[0])\n    return await ctor.execmain(argv, outp=outp)\n\nif __name__ == '__main__':  # pragma: no cover\n    sys.exit(asyncio.run(main(sys.argv[1:])))\n","sub_path":"synapse/servers/stemcell.py","file_name":"stemcell.py","file_ext":"py","file_size_in_byte":1192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"546021412","text":"# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2017 Red Hat, Inc\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport flask\nfrom flask import json\n\nfrom dci.api.v1 import api\nfrom dci.api.v1 import utils as v1_utils\nfrom dci import decorators\nfrom dci.common import schemas\nfrom dci.db import models\n\nfrom sqlalchemy import sql, func\n\n\n_TABLE = models.FILES_EVENTS\n_FILES_EVENTS_COLUMNS = v1_utils.get_columns_name_with_objects(_TABLE)\n\n\n@api.route('/files_events/<int:sequence>', methods=['GET'])\n@decorators.login_required\n@decorators.has_role(['SUPER_ADMIN'])\ndef get_files_events_from_sequence(user, sequence):\n    \"\"\"Get all the files events from a given sequence number.\"\"\"\n\n    args = schemas.args(flask.request.args.to_dict())\n\n    query = sql.select([models.FILES_EVENTS, models.FILES],\n                       use_labels=True). \\\n        select_from(models.FILES_EVENTS.join(models.FILES,\n                    models.FILES.c.id == models.FILES_EVENTS.c.file_id)). \\\n        where(_TABLE.c.id >= sequence)\n    sort_list = v1_utils.sort_query(args['sort'], _FILES_EVENTS_COLUMNS,\n                                    default='created_at')\n    query = v1_utils.add_sort_to_query(query, sort_list)\n\n    if args.get('limit', None):\n        query = query.limit(args.get('limit'))\n    if args.get('offset', None):\n        query = query.offset(args.get('offset'))\n\n    rows = flask.g.db_conn.execute(query).fetchall()\n    res = []\n    for row in rows:\n        row = dict(row)\n        new_row = {}\n        for field in row:\n            if field.startswith('files_events'):\n                suffix = field.split('files_events_')[1]\n                if 'event' in new_row:\n                    new_row['event'].update({suffix: row[field]})\n                else:\n                    new_row['event'] = {suffix: row[field]}\n            else:\n                suffix = field.split('files_')[1]\n                if 'file' in new_row:\n                    new_row['file'].update({suffix: row[field]})\n                else:\n                    new_row['file'] = {suffix: row[field]}\n        res.append(new_row)\n\n    query_nb_rows = sql.select([func.count(models.FILES_EVENTS.c.id)])\n    nb_rows = flask.g.db_conn.execute(query_nb_rows).scalar()\n\n    return json.jsonify({'files': res, '_meta': {'count': nb_rows}})\n\n\n@api.route('/files_events/<int:sequence>', methods=['DELETE'])\n@decorators.login_required\n@decorators.has_role(['SUPER_ADMIN'])\ndef purge_files_events_from_sequence(user, sequence):\n    query = _TABLE.delete(). \\\n        where(_TABLE.c.id >= sequence)\n    flask.g.db_conn.execute(query)\n\n    return flask.Response(None, 204, content_type='application/json')\n\n\ndef create_event(file_id, action):\n    values = {'file_id': file_id,\n              'action': action}\n    q_add_file_event = models.FILES_EVENTS.insert().values(**values)\n    flask.g.db_conn.execute(q_add_file_event)\n","sub_path":"dci/api/v1/files_events.py","file_name":"files_events.py","file_ext":"py","file_size_in_byte":3369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"564968741","text":"#    Copyright 2016 IBM Corp.\n#\n#    Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n#    not use this file except in compliance with the License. You may obtain\n#    a copy of the License at\n#\n#         http://www.apache.org/licenses/LICENSE-2.0\n#\n#    Unless required by applicable law or agreed to in writing, software\n#    distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n#    WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n#    License for the specific language governing permissions and limitations\n#    under the License.\n\nimport six\n\nfrom oslo_log import log as logging\nfrom oslo_utils import strutils\n\nfrom zun.common import exception\nfrom zun.common.i18n import _LE\nfrom zun.common import utils\nfrom zun.common.utils import translate_exception\nfrom zun.container import driver\nfrom zun.objects import fields\n\n\nLOG = logging.getLogger(__name__)\n\nVALID_STATES = {\n    'delete': ['Stopped', 'Error'],\n    'start': ['Stopped'],\n    'stop': ['Running'],\n    'reboot': ['Running'],\n    'pause': ['Running'],\n    'unpause': ['Paused'],\n}\n\n\nclass Manager(object):\n    '''Manages the running containers.'''\n\n    def __init__(self, container_driver=None):\n        super(Manager, self).__init__()\n        self.driver = driver.load_container_driver(container_driver)\n\n    def _fail_container(self, container):\n        container.status = fields.ContainerStatus.ERROR\n        container.task_state = None\n        container.save()\n\n    def _validate_container_state(self, container, action):\n        if container.status not in VALID_STATES[action]:\n            raise exception.InvalidStateException(\n                id=container.container_id,\n                action=action,\n                actual_state=container.status)\n\n    def container_create(self, context, container):\n        utils.spawn_n(self._do_container_create, context, container)\n\n    def _do_container_create(self, context, container):\n        LOG.debug('Creating container...', context=context,\n                  container=container)\n\n        container.task_state = fields.TaskState.IMAGE_PULLING\n        container.save()\n        try:\n            self.driver.pull_image(container.image)\n        except exception.DockerError as e:\n            LOG.error(_LE(\"Error occured while calling docker API: %s\"),\n                      six.text_type(e))\n            self._fail_container(container)\n            return\n        except Exception as e:\n            LOG.exception(_LE(\"Unexpected exception: %s\"), six.text_type(e))\n            self._fail_container(container)\n            return\n\n        container.task_state = fields.TaskState.CONTAINER_CREATING\n        container.save()\n        try:\n            container = self.driver.create(container)\n        except exception.DockerError as e:\n            LOG.error(_LE(\"Error occured while calling docker API: %s\"),\n                      six.text_type(e))\n            self._fail_container(container)\n        except Exception as e:\n            LOG.exception(_LE(\"Unexpected exception: %s\"), six.text_type(e))\n            self._fail_container(container)\n        finally:\n            container.task_state = None\n            container.save()\n\n    @translate_exception\n    def container_delete(self, context, container, force):\n        LOG.debug('Deleting container...', context=context,\n                  container=container.uuid)\n        try:\n            force = strutils.bool_from_string(force, strict=True)\n            if not force:\n                self._validate_container_state(container, 'delete')\n            self.driver.delete(container, force)\n            return container\n        except exception.DockerError as e:\n            LOG.error(_LE(\"Error occured while calling docker API: %s\"),\n                      six.text_type(e))\n            raise\n        except Exception as e:\n            LOG.exception(_LE(\"Unexpected exception: %s\"), str(e))\n            raise e\n\n    @translate_exception\n    def container_list(self, context):\n        LOG.debug('Listing container...', context=context)\n        try:\n            return self.driver.list()\n        except exception.DockerError as e:\n            LOG.error(_LE(\"Error occured while calling docker API: %s\"),\n                      six.text_type(e))\n            raise\n        except Exception as e:\n            LOG.exception(_LE(\"Unexpected exception: %s\"), str(e))\n            raise e\n\n    @translate_exception\n    def container_show(self, context, container):\n        LOG.debug('Showing container...', context=context,\n                  container=container.uuid)\n        try:\n            container = self.driver.show(container)\n            container.save()\n            return container\n        except exception.DockerError as e:\n            LOG.error(_LE(\"Error occured while calling docker API: %s\"),\n                      six.text_type(e))\n            raise\n        except Exception as e:\n            LOG.exception(_LE(\"Unexpected exception: %s\"), str(e))\n            raise e\n\n    @translate_exception\n    def container_reboot(self, context, container):\n        LOG.debug('Rebooting container...', context=context,\n                  container=container)\n        try:\n            self._validate_container_state(container, 'reboot')\n            container = self.driver.reboot(container)\n            container.save()\n            return container\n        except exception.DockerError as e:\n            LOG.error(_LE(\"Error occured while calling docker API: %s\"),\n                      six.text_type(e))\n            raise\n        except Exception as e:\n            LOG.exception(_LE(\"Unexpected exception: %s\"), str(e))\n            raise e\n\n    @translate_exception\n    def container_stop(self, context, container):\n        LOG.debug('Stopping container...', context=context,\n                  container=container)\n        try:\n            self._validate_container_state(container, 'stop')\n            container = self.driver.stop(container)\n            container.save()\n            return container\n        except exception.DockerError as e:\n            LOG.error(_LE(\"Error occured while calling docker API: %s\"),\n                      six.text_type(e))\n            raise\n        except Exception as e:\n            LOG.exception(_LE(\"Unexpected exception: %s\"), str(e))\n            raise e\n\n    @translate_exception\n    def container_start(self, context, container):\n        LOG.debug('Starting container...', context=context,\n                  container=container.uuid)\n        try:\n            self._validate_container_state(container, 'start')\n            container = self.driver.start(container)\n            container.save()\n            return container\n        except exception.DockerError as e:\n            LOG.error(_LE(\"Error occured while calling docker API: %s\"),\n                      six.text_type(e))\n            raise\n        except Exception as e:\n            LOG.exception(_LE(\"Unexpected exception: %s\"), str(e))\n            raise e\n\n    @translate_exception\n    def container_pause(self, context, container):\n        LOG.debug('Pausing container...', context=context,\n                  container=container)\n        try:\n            self._validate_container_state(container, 'pause')\n            container = self.driver.pause(container)\n            container.save()\n            return container\n        except exception.DockerError as e:\n            LOG.error(_LE(\"Error occured while calling docker API: %s\"),\n                      six.text_type(e))\n            raise\n        except Exception as e:\n            LOG.exception(_LE(\"Unexpected exception: %s,\"), str(e))\n            raise e\n\n    @translate_exception\n    def container_unpause(self, context, container):\n        LOG.debug('Unpausing container...', context=context,\n                  container=container)\n        try:\n            self._validate_container_state(container, 'unpause')\n            container = self.driver.unpause(container)\n            container.save()\n            return container\n        except exception.DockerError as e:\n            LOG.error(_LE(\"Error occured while calling docker API: %s\"),\n                      six.text_type(e))\n            raise\n        except Exception as e:\n            LOG.exception(_LE(\"Unexpected exception: %s\"), str(e))\n            raise e\n\n    @translate_exception\n    def container_logs(self, context, container):\n        LOG.debug('Showing container logs...', context=context,\n                  container=container)\n        try:\n            return self.driver.show_logs(container)\n        except exception.DockerError as e:\n            LOG.error(_LE(\"Error occured while calling docker API: %s\"),\n                      six.text_type(e))\n            raise\n        except Exception as e:\n            LOG.exception(_LE(\"Unexpected exception: %s\"), str(e))\n            raise e\n\n    @translate_exception\n    def container_exec(self, context, container, command):\n        # TODO(hongbin): support exec command interactively\n        LOG.debug('Executing command in container...', context=context,\n                  container=container)\n        try:\n            return self.driver.execute(container, command)\n        except exception.DockerError as e:\n            LOG.error(_LE(\"Error occured while calling docker API: %s\"),\n                      six.text_type(e))\n            raise\n        except Exception as e:\n            LOG.exception(_LE(\"Unexpected exception: %s\"), str(e))\n            raise e\n","sub_path":"zun/compute/manager.py","file_name":"manager.py","file_ext":"py","file_size_in_byte":9446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"541823846","text":"#! /usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n__author__ = 'Jerry Bai'\n\n'''\nSetting up configurations\n'''\n\nimport conf.config_default\n\n\nclass Dict(dict):\n    '''\n    Support direct object.key=value access\n    :param dict:\n    :return:\n    '''\n\n    def __init__(self, names=(), values=(), **kw):\n        super(Dict, self).__init__(**kw)\n        for k, v in zip(names, values):\n            self[k] = v\n\n    def __getattr__(self, key):\n        try:\n            return self[key]\n        except KeyError:\n            raise AttributeError(r\"'Dict' has no attribute '%s'\" % key)\n\n    def __setattr__(self, key, value):\n        self[key] = value\n\n\ndef toDict(d):\n    D = Dict()\n    for k, v in d.items():\n        D[k] = toDict(v) if isinstance(v, dict) else v\n    return D\n\n\ndef merge(default, override):\n    r = {}\n    for k, v in default.items():\n        if k in override:\n            if isinstance(v, dict):\n                r[k] = merge(v, override[k])\n            else:\n                r[k] = override[k]\n        else:\n            r[k] = v\n    return r\n\n\nconfigs = conf.config_default.configs\n\ntry:\n    import config_override\n\n    configs = merge(configs, config_override.configs)\nexcept ImportError:\n    pass\n\n# Convert all config items to JSON format\nconfigs = toDict(configs)\n","sub_path":"conf/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1278,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"168509463","text":"\"\"\"\nAuthor: Levi\nEmail: lvze@tedu.cn\nTime : 2020-9-12\nEnv: Python 3.6\nsocket and Process exercise\n\"\"\"\nfrom socket import *\nfrom multiprocessing import Process\n\n# 服务器地址\nHOST = \"0.0.0.0\"\nPORT = 8000\nADDR = (HOST, PORT)\n\n# 存储用户地址信息 {name : address}\nuser = {}\n\n# 处理进入聊天室\ndef do_login(sock,name,addr):\n    if name in user or '管理' in name:\n        sock.sendto(b\"FAIL\",addr)\n        return\n    else:\n        sock.sendto(b\"OK\", addr)\n        # 通知其他人\n        msg = \"欢迎 %s 进入聊天室\"%name\n        for i in user:\n            sock.sendto(msg.encode(),user[i])\n        user[name] = addr # 加入这个人\n        # print(user) # 测试\n\n# 处理聊天\ndef do_chat(sock,name,content):\n    msg = \"%s : %s\"%(name,content)\n    for i in user:\n        # 除去本人\n        if i != name:\n            sock.sendto(msg.encode(),user[i])\n\n# 用户退出\ndef do_exit(sock,name):\n    del user[name] # 删除用户\n    msg = \"%s 退出了群聊\"%name\n    # 告知其他人\n    for i in user:\n        sock.sendto(msg.encode(), user[i])\n\n# 子进程函数\ndef handle(sock):\n    # 循环���待接收请求  (总分处理模式)\n    while True:\n        # 所有请求都在这接收\n        data, addr = sock.recvfrom(1024)\n        # 将客户端请你去做简单的分割处理\n        tmp = data.decode().split(' ', 2)\n        # 根据请求选择功能\n        if tmp[0] == \"L\":\n            # tmp--> [L,name]\n            do_login(sock, tmp[1], addr)\n        elif tmp[0] == 'C':\n            # tmp --> [C,name,xxxxxx]\n            do_chat(sock, tmp[1], tmp[2])\n        elif tmp[0] == 'E':\n            # tmp--> [E,name]\n            do_exit(sock, tmp[1])\n\n# 框架 启动函数\ndef main():\n    sock = socket(AF_INET, SOCK_DGRAM)\n    sock.bind(ADDR)\n\n    p = Process(target = handle,args=(sock,))\n    p.start()\n\n    # 父进程发送管理员消息\n    while True:\n        content = input(\"管理员消息:\")\n        msg = \"C 管理员消息 \" + content\n        # 从父进程发送给了子进程\n        sock.sendto(msg.encode(),ADDR)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"第二阶段服务器/day9.14/day14/chat_server.py","file_name":"chat_server.py","file_ext":"py","file_size_in_byte":2110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"204861279","text":"import csv\nimport collections\nimport itertools\nimport os\n\ndef evaluateDuplicates(found_dupes, true_dupes):\n    true_positives = found_dupes.intersection(true_dupes)\n    false_positives = found_dupes.difference(true_dupes)\n    uncovered_dupes = true_dupes.difference(found_dupes)\n\n    print('found duplicate')\n    print(len(found_dupes))\n\n    print('precision')\n    print(1 - len(false_positives) / float(len(found_dupes)))\n\n    print('recall')\n    print(len(true_positives) / float(len(true_dupes)))\n\n\ndef linkPairs(filename, rowname) :\n    link_d = {}\n\n    with open(filename) as f:\n        reader = csv.DictReader(f, delimiter=',', quotechar='\"')\n        for i, row in enumerate(reader):\n            source_file, link_id = row['source file'], row[rowname]\n            if link_id:\n                if link_id not in link_d:\n                    link_d[link_id] = collections.defaultdict(list)\n\n                link_d[link_id][source_file].append(i)\n\n    link_s = set()\n\n    for members in link_d.values():\n        if len(members.values()) > 1:\n            for pair in itertools.product(*members.values()):\n                assert len(pair) == 2\n                link_s.add(frozenset(pair))\n\n    return link_s\n\nclusters = 'gazetteer_output.csv'\n\ntrue_dupes = linkPairs(clusters, 'unique_id')\ntest_dupes = linkPairs(clusters, 'Cluster ID')\n\nevaluateDuplicates(test_dupes, true_dupes)\n\n","sub_path":"gazetteer_example/gazetteer_evaluation.py","file_name":"gazetteer_evaluation.py","file_ext":"py","file_size_in_byte":1380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"227651216","text":"from django.conf.urls import url\nfrom django.urls import path\nfrom blog.views import *\nfrom . import views\nfrom django.conf import settings\nfrom django.conf.urls.static import static\n\napp_name='blog'\n\nurlpatterns=[\n    url(r'^$',Indexview.as_view(), name='index'),\n    url(r'^search/$', views.search, name='search'),\n    url(r'^subscribe/$', Nameview.as_view(), name='subscribe'),\n    url(r'^aboutus/$',views.aboutus, name='aboutus'),\n    url(r'^contact/$',Contactview.as_view(), name='contact'),\n    url(r'^entertainment/(?P<id>[0-9]+)/$',Entertainmentview.as_view(), name='entertainment'),\n    url(r'^entertainment/$',Entertainmentview.as_view(), name='entertainment'),\n    path('user/<str:username>/', UserPostListView.as_view(), name='user-posts'),\n    path('news/<int:pk>/',NewsView.as_view(), name='news'),\n    path('news/',NewsView.as_view(), name='news'),\n    path('post/<int:pk>/', PostDetailView.as_view(), name='post-detail'),\n    path('post/new/', PostCreateView.as_view(), name='post-create'),\n    path('post/<int:pk>/update/', PostUpdateView.as_view(), name='post-update'),\n    path('post/<int:pk>/delete/', PostDeleteView.as_view(), name='post-delete'),\n    path('reportuser/', views.reportuser, name='reportuser'),\n    url(r'^shop/$',views.shop, name='shop'),\n]+ static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n","sub_path":"blog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"652476446","text":"#!/usr/bin/env python3\n\nimport digitalio\nimport board\nimport time \nfrom PIL import Image, ImageDraw, ImageFont\nimport adafruit_rgb_display.ili9341 as ili9341\nimport adafruit_rgb_display.st7789 as st7789  # pylint: disable=unused-import\nimport adafruit_rgb_display.hx8357 as hx8357  # pylint: disable=unused-import\nimport adafruit_rgb_display.st7735 as st7735  # pylint: disable=unused-import\nimport adafruit_rgb_display.ssd1351 as ssd1351  # pylint: disable=unused-import\nimport adafruit_rgb_display.ssd1331 as ssd1331  # pylint: disable=unused-import\nfrom keyword import kwlist\nimport random \n\n# First define some constants to allow easy resizing of shapes.\nBORDER = 20\nFONTSIZE = 24\n\n# Configuration for CS and DC pins (these are PiTFT defaults):\ncs_pin = digitalio.DigitalInOut(board.pin.SPI2_CS)  \ndc_pin = digitalio.DigitalInOut(board.pin.GPIO2_A1)  \nreset_pin = digitalio.DigitalInOut(board.pin.GPIO0_B3)\n\n# Config for display baudrate (default max is 24mhz):\nBAUDRATE = 16000000\n\n# Setup SPI bus using hardware SPI:\nspi = board.SPI()\n\n# pylint: disable=line-too-long\n# Create the display:\n# disp = st7789.ST7789(spi, rotation=90,                            # 2.0\" ST7789\n# disp = st7789.ST7789(spi, height=240, y_offset=80, rotation=180,  # 1.3\", 1.54\" ST7789\n# disp = st7789.ST7789(spi, rotation=90, width=135, height=240, x_offset=53, y_offset=40, # 1.14\" ST7789\n# disp = hx8357.HX8357(spi, rotation=180,                           # 3.5\" HX8357\n# disp = st7735.ST7735R(spi, rotation=90,                           # 1.8\" ST7735R\n# disp = st7735.ST7735R(spi, rotation=270, height=128, x_offset=2, y_offset=3,   # 1.44\" ST7735R\n# disp = st7735.ST7735R(spi, rotation=90, bgr=True,                 # 0.96\" MiniTFT ST7735R\n# disp = ssd1351.SSD1351(spi, rotation=180,                         # 1.5\" SSD1351\n# disp = ssd1351.SSD1351(spi, height=96, y_offset=32, rotation=180, # 1.27\" SSD1351\n# disp = ssd1331.SSD1331(spi, rotation=180,                         # 0.96\" SSD1331\n#disp = ili9341.ILI9341(\ndisp = st7735.ST7735R(\n    spi,\n    rotation=0,  # 2.2\", 2.4\", 2.8\", 3.2\" ILI9341\n    cs=cs_pin,\n    dc=dc_pin,\n    rst=reset_pin,\n    baudrate=BAUDRATE,\n)\n# pylint: enable=line-too-long\n\n# Create blank image for drawing.\n# Make sure to create image with mode 'RGB' for full color.\nprint(disp.width)\nprint(disp.height)\n\n\"\"\"\nif disp.rotation % 180 == 90:\n    height = disp.width  # we swap height/width to rotate it to landscape!\n    width = disp.height\nelse:\n    width = disp.width  # we swap height/width to rotate it to landscape!\n    height = disp.height\n\"\"\"\nheight = disp.height\nwidth = disp.width\n\nprint(\"CONFIGURAZIONE COMPLETATA\")\n\n\nx0 = int(width * 0.25)\ny0 = int(height * 0.25)\nx1 = int(width * 0.75)\ny1 = int(height * 0.75)\n\n#disp.X_START = x0\n#disp.Y_START = y0\n\nimage = Image.new(\"RGB\", (width,height))\n\n# Get drawing object to draw on image.\ndraw = ImageDraw.Draw(image)\n\n# Draw a green filled box as the background\n#draw.rectangle((0, 0, width, height), outline=0, fill=(255,255,255))\n#disp.image(image)\n\n\n# Load a TTF Font\nfont = ImageFont.truetype(\"/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf\", FONTSIZE) \n\nprint(\"MATERIALI COMPLETATI\")\n\nnow = time.time()\n\nfor _ in range(10):\n    # Draw Some Text\n    \n\n    draw.rectangle((0,0,width,height), fill=(255,255,255))\n    #disp.image(image)\n    \n    #print(\"\\t\\t\", time.time() - now)\n\n    text = random.choice(kwlist) #\"Hello World!\"\n    font_width, font_height = font.getsize(text)\n    draw.text(\n        (width // 2 - font_width // 2, height // 2 - font_height // 2),\n        text,\n        font=font,\n        fill=(0,0,0),\n    )\n\n    \n    #print(\"\\t\\t\\t\", time.time() - now)\n\n    # Display image.\n    disp.image(image)\n    #disp.show()\n    \n    \n    #print(\"After rendering image\", time.time() - now)\n\n    newnow = time.time()\n    #print(newnow - now)\n    now = newnow\n    time.sleep(0.1)\n","sub_path":"EXPERIMENTAL_SCRIPTS/hardware_py/test_tft_display.py","file_name":"test_tft_display.py","file_ext":"py","file_size_in_byte":3858,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"289195108","text":"# install package list\n# pip3 install selenium (for windows : pip install ---)\n# pip3 install bs4\n\nfrom selenium import webdriver as wd\n#from bs4 import BeautifulSoup as bs\nfrom openpyxl import Workbook\nimport time\n\n# get_site : p 태그 다긁어옴, contents_list에 저장\ncontents_list = []\ndef get_site(site):\n    driver.get(site)\n    #\n    time.sleep(2)\n    #\n    contents = driver.find_elements_by_tag_name('p')\n    for content in contents:\n        contents_list.append(content.text)\n\n# text_preprocessing : 리스트 내용물 다 텍스트로 밀어버림\ndef text_preprocessing(list):\n    all_text = \"\"\n    #\n    for text in list:\n        all_text = all_text + text\n    #\n    return all_text\n\n# text_paragraph : . 기준으로 문단 나눠버림, paragraph_list에 저장\ndef text_paragraph(text):\n    paragraph_list = text.split(\".\")\n    return paragraph_list\n\n# save_excel\ndef save_excel(list):\n    wb = Workbook()\n    i = 1\n    excel = wb.active\n    for content in list:\n        excel.cell(row=i, column=1, value=i)\n        excel.cell(row=i, column=2, value=content)\n        i = i + 1\n    wb.save('fin.xlsx')\n\nwiki_url =  ['https://en.wikipedia.org/wiki/Korean_regional_cuisine',\n            'https://en.wikipedia.org/wiki/Korean_royal_court_cuisine',\n            'https://en.wikipedia.org/wiki/List_of_Korean_dishes',\n            'https://en.wikipedia.org/wiki/Korean_cuisine']\n\n# driver List_of_Korean_dishes\n# for windows : wd.Chrome(executable_path=\"chromedriver.exe\")\n#\ndriver = wd.Chrome(executable_path=\"./chromedriver\")\n\n# site get\nfor i in range(0, len(wiki_url)):\n    get_site(wiki_url[i])\n\n# text preprocessing\ntext = text_preprocessing(contents_list)\n\n# text_paragraph\nlist = text_paragraph(text)\n\n# load excel and save\nsave_excel(list)\n","sub_path":"crawling.py","file_name":"crawling.py","file_ext":"py","file_size_in_byte":1763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"98809496","text":"from pysdot.domain_types import ConvexPolyhedraAssembly\nfrom pysdot import OptimalTransport\nimport numpy as np\n\nnb_diracs = 10\n\npositions = np.random.rand( nb_diracs, 3 )\nmasses = np.ones( nb_diracs ) / nb_diracs\n\ndomain = ConvexPolyhedraAssembly()\ndomain.add_box([-1, -1, 0], [2, 2, 1])\n\n# we have to specify that dirac masses because by default, sdot takes ones * measure( domain ) / nb_diracs\n# and that's not what we want in this case\not = OptimalTransport( positions, domain = domain, masses = masses )\n\n# first arg of add_replication is a translation\nfor x in [ -1, 0, 1 ]:\n    for y in [ -1, 0, 1 ]:\n        if x or y:\n            ot.pd.add_replication( [ x, y, 0 ] )\n\not.adjust_weights()\n\not.display_vtk( \"pb.vtk\" )\n","sub_path":"examples/geometry/periodic_boundary_partial.py","file_name":"periodic_boundary_partial.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"365871098","text":"# Copyright (C) 2017 Google Inc.\n# Licensed under http://www.apache.org/licenses/LICENSE-2.0 <see LICENSE file>\n\n\"\"\"Utils module for query generation.\"\"\"\n\nfrom sqlalchemy import case\nfrom sqlalchemy import literal\n\n\ndef get_type_select_column(model):\n  \"\"\"Get column name,taking into account polymorphic types.\"\"\"\n  mapper = model._sa_class_manager.mapper\n  if mapper.polymorphic_on is None:\n    type_column = literal(mapper.class_.__name__)\n  else:\n    # Handle polymorphic types with CASE\n    type_column = case(\n        value=mapper.polymorphic_on,\n        whens={\n            val: m.class_.__name__\n            for val, m in mapper.polymorphic_map.items()\n        })\n  return type_column\n","sub_path":"src/ggrc/query/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"418104145","text":"from datetime import datetime\nimport re\nimport xlsxwriter\nimport pandas as pd\nimport timeit\n\nfrom word_pos_test import _start_mecab \nfrom pdf_utils import _isContainKo, _isContainKoT, _isContainEn, _read_pdf_to_text\n\ndef _reg_sent(sent):\n    # 특수기호는 빼기\n    sent = re.sub(r'▶', '', sent)\n    # _,_._km\n    sent = re.sub(r'[0-9]{1,}\\.[0-9]{1,}(km|m)', '', sent)\n    return sent\n\n## excel idx \ndef _excel_index_creator(colum, row_idx):\n    colum_idx = colum + str(row_idx)\n    return colum_idx\n\n\nxlsx_file = './한국하천지명사전_1월_8일_all.xlsx'\n\ntimestamp = datetime.now().strftime('%m%d%H%M')\nstart = timeit.default_timer()\n\ndf = pd.read_excel(xlsx_file)   \nraw_data = df['Raw'].tolist()\n\n# Open and create each excel file\nworkbook = xlsxwriter.Workbook('./mecab_pattern_pdf_'  + timestamp +'.xlsx') \nworksheet = workbook.add_worksheet()\n\nworkbook_2 = xlsxwriter.Workbook('./mecab_none_contain_pdf_'  + timestamp +'.xlsx') \nworksheet_2 = workbook_2.add_worksheet()\n\n# 셀 색칠 \ncell_yellow = workbook.add_format()\ncell_yellow.set_pattern(1)\ncell_yellow.set_bg_color('yellow')\n\n# Colum name\nworksheet.write('A1', 'Raw')\nworksheet.write('B1', 'til english', cell_yellow)\nworksheet.write('C1', '패턴', cell_yellow)\nworksheet.write('D1', '한글', cell_yellow)\nworksheet.write('E1', '한자', cell_yellow)\nworksheet.write('F1', '영어', cell_yellow)\n\nworksheet_2.write('A1', '탈락')\n\nwrong_idx = 2\nrow_idx = 2\n\n# analyze and write\nfor idx, sent in enumerate(raw_data):\n    reg_sent = _reg_sent(sent)\n    te = _start_mecab(reg_sent)\n\n    ''' te -> list > tuple > str'''\n    te_len = len(te)\n    # ko, kot, en\n    each_te_dict = dict()\n    stop_idx = 0\n    kokoten = ''\n    for idx, mor in enumerate(te):\n        if mor[1] == 'SL':\n            for _ in range(idx, te_len):\n                if te[_][1] != 'SL':\n                    stop_idx = _\n                    break\n    \n    ko, kot, en = '', '', ''\n    # list에서 한글, 한자, 영어 추출해보기\n    final_raws = te[0:stop_idx]\n    for idx, raw in enumerate(final_raws):\n        raw = final_raws[idx][0]\n        mor = final_raws[idx][1]\n        # 영어\n        if mor == 'SL':\n            if raw == 'm' or raw == 'km':\n                continue\n            en = raw\n        # 한글, 한자\n        else:\n            if _isContainKoT(raw) == True:\n                if kot == '':\n                    kot = raw\n                else:\n                    kot += raw\n\n            elif _isContainKo(raw) == True:\n                if ko == '':\n                    ko = raw\n                else:\n                    ko += raw\n    print(ko, kot, en)\n\n    for idx in range(stop_idx):\n        key_mor = te[idx][1]\n        kokoten += te[idx][0] + ' '\n        # print(key_mor)\n        if key_mor not in each_te_dict:\n            # 바로 더해주기\n            each_te_dict[key_mor] = 1\n        elif key_mor in each_te_dict:\n            each_te_dict[key_mor] += 1\n\n    if len(kokoten) == 0:\n        a_idx = _excel_index_creator('A', wrong_idx)\n        worksheet_2.write(a_idx, sent)\n        wrong_idx += 1\n\n    else:\n        if ko == '' or kot == '' or en =='':\n            a_idx = _excel_index_creator('A', wrong_idx)\n            worksheet_2.write(a_idx, sent)\n            wrong_idx += 1\n            continue\n\n        a_idx = _excel_index_creator('A', row_idx)\n        b_idx = _excel_index_creator('B', row_idx)\n        c_idx = _excel_index_creator('C', row_idx)\n        d_idx = _excel_index_creator('D', row_idx)\n        e_idx = _excel_index_creator('E', row_idx)\n        f_idx = _excel_index_creator('F', row_idx)\n\n        # a. raw 쓰기\n        worksheet.write(a_idx, reg_sent)\n\n        # b. mecab 쓰기\n        te = _start_mecab(reg_sent)\n        worksheet.write(b_idx, str(te[0:stop_idx]))\n\n        # c. 패턴쓰기\n        worksheet.write(c_idx, kokoten)\n\n        # d. 한글 쓰기\n        worksheet.write(d_idx, ko)\n\n        # e. 한자 쓰기\n        worksheet.write(e_idx, kot)\n\n        # f. 영어 쓰기\n        worksheet.write(f_idx, en)\n\n        row_idx += 1\n\n        \n\nworkbook.close()\nworkbook_2.close()\n","sub_path":"17_TwigFarm/DB_Extract_BackUp/extract_word_check_lan.py","file_name":"extract_word_check_lan.py","file_ext":"py","file_size_in_byte":4151,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"595927772","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Mar  9 07:33:21 2018\n\n@author: emily\n\"\"\"\n\nsave_name = 'NGEA_Sp_scale5_10_00000'#'MBEY_Sp_scale5_10'#'MBEY_Ps_scale5'\nsave_every = 100\n\nimport pipeline\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport shutil\n\nshutil.copyfile('./output/'+save_name+'/input_data.py', './input_data.py')\n\nimport input_data\n\nrf_obs, swd_obs, all_lims = input_data.LoadObservations()\n\nsave_name = 'output/'+save_name+'/'+save_name\nall_models = np.load(save_name+'_AllModels.npy')\n\ngood_mods = all_models[:,np.where(all_models[0,]>0)[0]]\nnit = good_mods.shape[1]\nnit_cutoff = int(nit/5)\ngood_mods = good_mods[:,-nit_cutoff:]\nmean_mod = np.mean(good_mods, axis = 1)\nstd_mod = np.std(good_mods, axis = 1)\n\ngood_mod = pipeline.Model(vs = mean_mod, all_deps = all_models[:,0],\n                          idep = np.arange(0,mean_mod.size),\n                          lam_rf = 0, std_rf = 0, std_swd = 0)\nfullmodel = pipeline.MakeFullModel(good_mod)\n\n\n\nfig1 = plt.figure(figsize = (14,8));\n\nax1 = plt.subplot(131)\nfor k in range(all_models[1,].size-1):\n    colstr = str(0.75-k/2/all_models[1,].size)\n    ax1.plot(all_models[:,k],all_models[:,0],\n          '-',linewidth=1,color=colstr)\nax1.set_ylim((195,0))\n#ax1.plot(actual_model,all_models[:,0],'r-',linewidth=3)\nax1.set_xlim((2,5.5))\nax1.set_xlabel('Shear Velocity (km/s)')\nax1.set_ylabel('Depth (km)')\nax1.set_title(\"{} iterations\".format(nit*save_every))\n\nax3 = plt.subplot(132)\nfor k in range(good_mods[0,].size-1):\n    colstr = str(0.85-k/2/good_mods[0,].size)\n    ax3.plot(good_mods[:,k],all_models[:,0],\n          '-',linewidth=1,color=colstr)\nax3.plot(mean_mod,all_models[:,0],'b-',linewidth = 2)\nax3.plot(mean_mod+std_mod, all_models[:,0],'c-',linewidth = 1)\nax3.plot(mean_mod-std_mod, all_models[:,0],'c-',linewidth = 1)\n#ax3.plot(actual_model,all_models[:,0],'r--',linewidth=1)\nax3.set_xlim((2,5.5))\nax3.set_ylim((195,0))\nax3.set_xlabel('Shear Velocity (km/s)')\nax3.set_ylabel('Depth (km)')\nax3.set_title('Most recent {}'.format(good_mods.shape[1]))\n\nax4 = plt.subplot(133)\nfor k in range(good_mods[0,].size-1):\n    colstr = str(0.85-k/2/good_mods[0,].size)\n    ax4.plot(good_mods[:,k],all_models[:,0],\n          '-',linewidth=1,color=colstr)\nax4.plot(mean_mod,all_models[:,0],'b-',linewidth = 2)\nax4.plot(mean_mod+std_mod, all_models[:,0],'c-',linewidth = 1)\nax4.plot(mean_mod-std_mod, all_models[:,0],'c-',linewidth = 1)\n#ax3.plot(actual_model,all_models[:,0],'r--',linewidth=1)\nax4.set_xlim((3.0,4.7))\nax4.set_ylim((60,0))\nax4.set_xlabel('Shear Velocity (km/s)')\nax4.set_ylabel('Depth (km)')\nax4.set_title('Most recent {}'.format(good_mods.shape[1]))\n\n\n\n\nplt.tight_layout()\n\n\n\nallvels = np.arange(all_lims.vs[0],all_lims.vs[1],0.01)\nevendeps = np.arange(0,all_models[-1,0],0.1)\ni_ed = np.zeros(evendeps.shape, dtype = int)\nfor k in range(all_models[:,0].size-1,0,-1):\n    i_ed[all_models[k,0]>=evendeps] = k\n\nmod_space = np.zeros((evendeps.size,allvels.size))\nfor k in range(1,good_mods.shape[1]):\n    even_vels = good_mods[i_ed,-k]\n    inds = np.round(even_vels-all_lims.vs[0],2)/0.01-1\n    inds = inds.astype(int)\n    mod_space[range(mod_space.shape[0]),inds] += 1\n\n\n\nsynth_swd = pipeline.SynthesiseSWD(fullmodel, swd_obs.period, 1e6)\nif nit*save_every<1e5:\n    synth_swd_coarse = pipeline.SynthesiseSWD(fullmodel, swd_obs.period, 1)\nsynth_rf = pipeline.SynthesiseRF(fullmodel, rf_obs)\n\n\nplt.figure(figsize = (14,8));\n\nax2=plt.subplot(131)\nax2.imshow(np.log10(mod_space[-1::-1]+1e-1), cmap = 'viridis', aspect = allvels[-1]/evendeps[-1],\n           extent = [allvels[0], allvels[-1], evendeps[0], evendeps[-1]])\nax2.invert_yaxis()\nax2.set_xlabel('Shear Velocity (km/s)')\nax2.set_ylabel('Depth (km)')\nax2.xaxis.set_label_position('top')\nax2.xaxis.tick_top()\nax2.set_xlim((1.5,5))\n\n\nplt.subplot(132); plt.title('Receiver Function\\n real: red; synth: grey')\nrft = np.arange(0,rf_obs.dt*rf_obs.amp.size,rf_obs.dt)\nplt.plot(rf_obs.amp, rft, 'r-', linewidth=2)\nplt.plot(synth_rf.amp,rft, '-',color = '0.25', linewidth=2)\nplt.plot(rf_obs.amp-0.02,rft, 'r--', linewidth=1)\nplt.plot(rf_obs.amp+0.02,rft, 'r--', linewidth=1)\nplt.plot([0,0],[0,30],'--',color='0.6')\nplt.ylim(30,0)\nplt.xlabel('RF Amplitude')\nplt.ylabel('Time (s)')\n\nplt.subplot(133); plt.title('Surface Wave Dispersion\\n real: red; synth: grey')\nplt.plot(swd_obs.period, swd_obs.c,  'r-', linewidth=2)\nplt.plot(synth_swd.period, synth_swd.c, '-',color = '0.25', linewidth=2)\nif nit*save_every<1e5:\n    plt.plot(synth_swd_coarse.period, synth_swd_coarse.c, '-',color = '0.5', linewidth=2)\nplt.plot(swd_obs.period, swd_obs.c-0.025,  'r--', linewidth=1)\nplt.plot(swd_obs.period, swd_obs.c+0.025,  'r--', linewidth=1)\nplt.xlabel('Period (s)')\nplt.ylabel('Phase velocity (km/s)')\nplt.tight_layout()\n\n#%%\n\nmisfits = np.load(save_name+'_Misfit.npy')\nnm = misfits.shape[1]#int(misfits.size/3)\n# plot vertical lines where increase number of permissable layers\ninc_ints = 750*np.arange(1,15)*np.arange(2,16)\ngood_it = nit - nit_cutoff\n\n\nplt.figure(figsize = (12,5))\nplt.title(\"Mahalanobis distance\")\nplt.plot((misfits[0,]));\nplt.ylim(0.9*(np.min(misfits[0,10:])),\n         1.1*(np.max(misfits[0,10:])))\nfor k in range(inc_ints.size):\n    plt.plot(inc_ints[[k,k]]/save_every,[0,100],'--',color = '0.6')\nplt.plot(good_it*np.ones(2),[0,100], 'r--')\nplt.ylabel('Least Squares Misfit (phi)')\nplt.xlabel('Iteration # /100')\nplt.xlim(0,nm)\n\nplt.figure(figsize = (10,5))\nplt.subplot(121)\ninds = np.where(misfits[1,]<3e3)[0]\nplt.title(\"Likelihood of accepting new model\")\nplt.plot(np.log10(misfits[1,inds]));\nplt.ylim(0.9*np.log10(np.min(misfits[1,inds])),\n         1.1*np.log10(np.max(misfits[1,inds])))\nplt.plot(good_it*np.ones(2),[0,100], 'r--')\nplt.plot([0, nm],np.log10([1,1]),'--',color = '0.6')\nplt.ylabel('log10(alpha(m|m0))')\nplt.xlabel('Iteration # /100')\nplt.xlim(0,nm)\n\nplt.subplot(122); plt.title('Acceptance Rate')\nplt.plot(misfits[2,]*100)\nplt.plot([0,nm],[40,40],'--',color = '0.6')\nplt.plot(good_it*np.ones(2),[20,70], 'r--')\nplt.ylabel('Acceptance Rate (%)')\nplt.xlabel('Iteration # /100')\nplt.xlim(0,nm)\nplt.tight_layout()\n\n#%%\nhyperparams = np.load(save_name+'_Hyperparams.npy')\nplt.figure(figsize = (12,14))\nplt.subplot(311)\nplt.title('RF Noise standard deviation')\nplt.plot(hyperparams[0:nm,0])\nplt.xlabel('Iteration # /100')\nplt.ylabel('Std of RF')\nplt.xlim(0,nm)\nplt.ylim(0.04,0.0525)\nfor k in range(inc_ints.size):\n    plt.plot(inc_ints[[k,k]]/save_every,[0.04,0.06],'--',color = '0.6')\nplt.plot(good_it*np.ones(2),[0.04, 0.06], 'r--')\n\nplt.subplot(312)\nplt.title('RF Noise Correlation')\nplt.plot(hyperparams[0:nm,1])\nplt.xlabel('Iteration # /100')\nplt.ylabel('Lambda of RF')\nplt.xlim(0,nm)\nplt.ylim(0.1, 0.525)\nfor k in range(inc_ints.size):\n    plt.plot(inc_ints[[k,k]]/save_every,[0.,0.6],'--',color = '0.6')\nplt.plot(good_it*np.ones(2),[0., 0.6], 'r--')\n\nplt.subplot(313)\nplt.title('SWD Noise standard deviation')\nplt.plot(hyperparams[0:nm,2])\nplt.xlabel('Iteration # /100')\nplt.ylabel('Std of SWD')\nplt.xlim(0,nm)\nplt.ylim(0.05,0.16)\nfor k in range(inc_ints.size):\n    plt.plot(inc_ints[[k,k]]/save_every,[0.05, 0.175],'--',color = '0.6')\nplt.plot(good_it*np.ones(2),[0.05,0.175], 'r--')\nplt.tight_layout()\n\n","sub_path":"junk/plotting-DESKTOP-6FKA3AN.py","file_name":"plotting-DESKTOP-6FKA3AN.py","file_ext":"py","file_size_in_byte":7162,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"157404449","text":"import re\nfrom ansiblemetrics.lines_metric import LinesMetric\n\nclass LinesComment(LinesMetric):\n    \"\"\" This class is responsible for providing the methods to count the comments lines of code (cloc) in a given .yaml file\"\"\"\n\n    def count(self):\n        cloc = 0\n\n        for l in self.yml.splitlines():            \n            comment = re.search(r'#.+', str(l.strip()))\n            if comment is not None:   \n                cloc += 1\n\n        return cloc\n        ","sub_path":"ansiblemetrics/general/lines_comment.py","file_name":"lines_comment.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"633591940","text":"from trajopt.algos.mppi import MPPI\nfrom trajopt.envs.utils import get_environment\nfrom trajopt.utils import ReplayBuffer, Tuple\nfrom trajopt.models.critic_nets import Critic\nfrom tqdm import tqdm\nimport time as timer\nimport numpy as np\nimport pickle\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.utils.tensorboard import SummaryWriter\n\nimport argparse\nimport os\nfrom datetime import datetime\n\n# =======================================\nDATA_DIR = 'data'\nAC_DIR = 'ac_tests'\n\nnow = datetime.now()\nDATE_STRING = now.strftime(\"%m:%d:%Y-%H:%M:%S\")\nos.mkdir(DATA_DIR + '/' + DATE_STRING)\nAC_SAVE_DIR = AC_DIR + '/' + DATE_STRING\nos.mkdir(AC_SAVE_DIR)\n\nENV_NAME = 'reacher_7dof'\nPICKLE_FILE = DATA_DIR + '/' + DATE_STRING + '/' + ENV_NAME + '_mppi.pickle'\nMODEL_PATH = DATA_DIR + '/' + DATE_STRING + '/' + ENV_NAME + '_critic.pt'\nREPLAY_PICKLE_FILE = DATA_DIR + '/' + DATE_STRING + '/' + ENV_NAME + '_replay.pickle'\nSEED = 12345\nN_ITER = 5\nH_total = 100\nSTATE_DIM = 3\n# STATE_DIM = 7\nH = 16\n# =======================================\n\n\ndef custom_reward_fn(sol_reward):\n    \"\"\" 1 if any of the rewards is +100 (reached goal), 0 otherwise.\"\"\"\n    return int(100 in [int(elt) for elt in sol_reward])\n\n\ndef test_goals(critic, seeds=None, goals=None, dim=14):\n    print('=' * 20)\n\n    if seeds is not None:\n        iters = len(seeds)\n    elif goals is not None:\n        iters = len(goals)\n    else:\n        return\n\n    for i in range(iters):\n        e = get_environment(ENV_NAME, sparse_reward=True)\n        if seeds is not None:\n            e.reset_model(seed=seeds[i])\n        else:\n            e.reset_model(seed=None, goal=goals[i])\n        goal = e.get_env_state()['target_pos']\n        mean = np.zeros(e.action_dim)\n        sigma = 1.0*np.ones(e.action_dim)\n        filter_coefs = [sigma, 0.25, 0.8, 0.0]\n\n        agent_test = MPPI(e, H=H, paths_per_cpu=40, num_cpu=1,\n                          kappa=25.0, gamma=1.0, mean=mean,\n                          filter_coefs=filter_coefs,\n                          default_act='mean', seed=SEED,\n                          init_seq=None)\n\n        for t in tqdm(range(H_total)):\n            agent_test.train_step(critic=critic, niter=N_ITER, dim=dim, goal=goal)\n\n        print(\"Trajectory reward = %f\" % np.sum(agent_test.sol_reward))\n        print(\"Custom reward = %f\" % custom_reward_fn(agent_test.sol_reward))\n\n    print('=' * 20)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--critic', default=None,\n                        help='path to critic model (.pt) file')\n    parser.add_argument('--train', action='store_true',\n                        help='train the critic net?')\n    parser.add_argument('--save_buffer', action='store_true',\n                        help='save the replay buffer?')\n    parser.add_argument('--target', action='store_true')\n    parser.add_argument('--eta', default=0.9, type=float)\n    parser.add_argument('--goals', action='store_true')\n    parser.add_argument('--lr', default=1e-2, type=float)\n    parser.add_argument('--iters', default=2000, type=int)\n    args = parser.parse_args()\n\n    # Check to add goal position to state space\n    if args.goals:\n        STATE_DIM += 3\n\n    # Writer will output to ./runs/ directory by default\n    writer = SummaryWriter()\n\n    e = get_environment(ENV_NAME, sparse_reward=True)\n    # e.sparse_reward = True\n    # e.reset_model(seed=SEED)\n    mean = np.zeros(e.action_dim)\n    sigma = 1.0*np.ones(e.action_dim)\n    filter_coefs = [sigma, 0.25, 0.8, 0.0]\n\n    good_agent = pickle.load(open('116_agent.pickle', 'rb'))\n    sol_actions = np.array(good_agent.sol_act)  # should be (100, 7)\n    init_seq = sol_actions[:H]\n\n    replay_buffer = ReplayBuffer(max_size=1000000)\n\n    critic = Critic(num_iters=args.iters, input_dim=STATE_DIM, inner_layer=128, batch_size=128, gamma=0.9)\n    if args.critic is not None:\n        critic.load_state_dict(torch.load(args.critic))\n    critic.eval()\n    critic.float()\n\n    # Set up target critic network\n    if args.target:\n        target_critic = Critic(num_iters=args.iters, input_dim=STATE_DIM, inner_layer=128)\n        target_critic.load_state_dict(critic.state_dict())\n        target_critic.eval()\n        target_critic.float()\n\n    optimizer = optim.Adam(critic.parameters(), lr=args.lr)\n    milestones = list(range(0, 2000 * 100, int(2000 * 100 / 4)))\n    # milestones = [100]\n    scheduler = torch.optim.lr_scheduler.MultiStepLR(\n        optimizer, milestones=milestones, gamma=0.1)\n    criterion = nn.MSELoss()\n\n    eta = args.eta\n    # limit = H_total + H\n    limit = int((H_total + H) / args.eta)\n\n    env_seeds = [None]\n    set_goal = (0.0, 0.0, 0.0)\n\n    good_agents = []\n    sol_actions = []\n    init_seqs = []\n    for seed in env_seeds:\n        good_agents.append(pickle.load(open('agent_116_seed_{}.pickle'.format(seed), 'rb')))\n        sol_actions.append(np.array(good_agents[-1].sol_act))\n        init_seqs.append(sol_actions[-1][:H])\n\n    writer_x = 0\n    for s, seed in enumerate(env_seeds):\n        limit = int((H_total + H) / args.eta)\n        # limit = 0\n        # limit = int(limit * args.eta)\n        for x in range(100):\n            limit = int(limit * args.eta)\n\n            # e = get_environment(ENV_NAME, sparse_reward=True)\n            e.reset_model(seed=seed, goal=set_goal)\n            print('Goal: {}'.format(e.get_env_state()['target_pos']))\n            goal = e.get_env_state()['target_pos']\n            print('*'*36)\n            print('Round: {}, Limit: {}'.format(x, limit))\n            print()\n            critic.eval()\n            if limit >= H:\n                agent = MPPI(e, H=H, paths_per_cpu=40, num_cpu=1,\n                             kappa=25.0, gamma=1.0, mean=mean,\n                             filter_coefs=filter_coefs,\n                             default_act='mean', seed=SEED,\n                             init_seq=init_seqs[s])\n            else:\n                agent = MPPI(e, H=H, paths_per_cpu=40, num_cpu=1,\n                             kappa=25.0, gamma=1.0, mean=mean,\n                             filter_coefs=filter_coefs,\n                             default_act='mean', seed=SEED,\n                             init_seq=None)\n\n            ts = timer.time()\n            for t in tqdm(range(H_total)):\n\n                if limit >= t + H:\n                    tuples = agent.train_step(critic=critic, niter=N_ITER,\n                                              act_sequence=sol_actions[s][t:t+H],\n                                              goal=goal,\n                                              dim=STATE_DIM)\n                else:\n                    tuples = agent.train_step(critic=critic, niter=N_ITER,\n                                              act_sequence=None,\n                                              goal=goal,\n                                              dim=STATE_DIM)\n\n                # Add new transitions into replay buffer\n                tuples = critic.compress_states(tuples, dim=STATE_DIM)\n                replay_buffer.concatenate(tuples)\n\n            tmp_reward = np.sum(agent.sol_reward)\n            print(\"Trajectory reward = %f\" % tmp_reward)\n            writer.add_scalar('Trajectory Return', tmp_reward, writer_x)\n            writer_x += 1\n\n            pickle.dump(agent, open(AC_SAVE_DIR + '/ac_test_agent_{}_{}.pickle'.format(x, s), 'wb'))\n            torch.save(critic.state_dict(), AC_SAVE_DIR + '/ac_test_critic_{}_{}.pt'.format(x, s))\n\n            if args.train:\n                # Critic step\n                for i in tqdm(range(critic.num_iters)):\n                    minibatch = replay_buffer.get_minibatch(size=critic.batch_size)\n\n                    # unpack minibatch\n                    state_batch = torch.stack(tuple(torch.tensor(d.state, dtype=torch.float32) for d in minibatch))\n                    action_batch = torch.stack(tuple(torch.tensor(d.action, dtype=torch.float32) for d in minibatch))\n                    reward_batch = torch.tensor([d.reward for d in minibatch], dtype=torch.float32)\n                    # reward_batch = torch.cat(tuple(torch.tensor(d.reward) for d in minibatch))\n                    next_state_batch = torch.stack(tuple(torch.tensor(d.next_state, dtype=torch.float32) for d in minibatch))\n\n                    # get output for the next state\n                    if args.target:\n                        output_batch = target_critic(next_state_batch)\n                    else:\n                        output_batch = critic(next_state_batch)\n\n                    # set y_j to r_j for terminal state, otherwise to r_j + gamma*max(V)\n                    y_batch = []\n                    for j in range(len(minibatch)):\n                        if reward_batch[j] > 10.0:\n                            y_batch.append(reward_batch[j] + 0.0 * output_batch[j])\n                        else:\n                            y_batch.append(reward_batch[j] + critic.gamma * output_batch[j])\n                    y_batch = torch.stack(tuple(y_batch))\n                    # y_batch = torch.stack(\n                    #     tuple(reward_batch[i] if abs(reward_batch[i]) < 1.0\n                    #           else reward_batch[i] + critic.gamma * output_batch[i]\n                    #           for i in range(len(minibatch))))\n\n                    # extract V\n                    # v_batch = torch.sum(critic(state_batch), dim=1)  # ??\n                    v_batch = critic(state_batch)\n\n                    # PyTorch accumulates gradients by default, so they need to be reset in each pass\n                    optimizer.zero_grad()\n\n                    # import pdb; pdb.set_trace()\n                    # returns a new Tensor, detached from the current graph, the result will never require gradient\n                    y_batch = y_batch.detach()\n\n                    # calculate loss\n                    loss = criterion(v_batch, y_batch)\n\n                    # import pdb; pdb.set_trace()\n\n                    # do backward pass\n                    loss.backward()\n                    optimizer.step()\n                    scheduler.step()\n\n                    if i % 500 == 0:\n                        print('Loss: {}'.format(loss))\n\n                    if args.target and i % int(args.iters / 8) == 0:\n                        # Update target critic network\n                        target_critic.load_state_dict(critic.state_dict())\n                        target_critic.eval()\n                        target_critic.float()\n\n                # test_goals(critic, seeds=None, goals=[(0, 0, 0), set_goal], dim=STATE_DIM)\n\n        # writer.add_scalar('Trajectory Return', current_reward, x)\n\n    # Save the replay buffer\n    if args.save_buffer:\n        print(\"==============>>>>>>>>>>> saving replay buffer\")\n        pickle.dump(replay_buffer, open(REPLAY_PICKLE_FILE, 'wb'))\n\n    pickle.dump(agent, open(PICKLE_FILE, 'wb'))\n    # agent = pickle.load(open(PICKLE_FILE, 'rb'))\n    print(\"Trajectory reward = %f\" % np.sum(agent.sol_reward))\n    print(\"Time for trajectory optimization = %f seconds\" %(timer.time()-ts))\n\n    # wait for user prompt before visualizing optimized trajectories\n    # _ = input(\"Press enter to display optimized trajectory (will be played 10 times) : \")\n    # for _ in range(100):\n    #     agent.animate_result()\n","sub_path":"trajopt/sandbox/examples/ac_limit_reacher_7dof_mppi.py","file_name":"ac_limit_reacher_7dof_mppi.py","file_ext":"py","file_size_in_byte":11306,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"4878624","text":"import dash_bootstrap_components as dbc\nimport dash_core_components as dcc\nimport dash_html_components as html\nfrom dash.dependencies import Input, Output\nfrom app import app\nfrom view import portfolio, screener, transaction, income_finder, report, pivot\n\n\n# the styles for the main content position it to the right of the sidebar and\n# add some padding.\nCONTENT_STYLE = {\n    \"margin-left\": \"2rem\",\n    \"margin-right\": \"2rem\",\n    \"padding\": \"2rem 1rem\",\n}\nnavbar = dbc.NavbarSimple(\n    children=[\n        dbc.NavItem(dbc.NavLink(\"Income Finder\", href=\"/income_finder\", id=\"page-1-link\")),\n        dbc.NavItem(dbc.NavLink(\"Screener\", href=\"/screener\", id=\"page-2-link\")),\n        dbc.NavItem(dbc.NavLink(\"Portfolio\", href=\"/portfolio\", id=\"page-3-link\")),\n        dbc.NavItem(dbc.NavLink(\"Transactions\", href=\"/transaction\", id=\"page-4-link\")),\n        dbc.NavItem(dbc.NavLink(\"Report\", href=\"/report\", id=\"page-5-link\")),\n        dbc.NavItem(dbc.NavLink(\"Pivot\", href=\"/pivot\", id=\"page-6-link\")),\n    ],\n    brand=\"Aarya\",\n    brand_href=\"#\",\n    color=\"dark\",\n    dark=True,\n)\n\n\ncontent = html.Div(id=\"page-content\", className=\"content\")\n\napp.layout = html.Div([dcc.Location(id=\"url\"), navbar, content])\n\n@app.callback(Output(\"page-content\", \"children\"), [Input(\"url\", \"pathname\")])\ndef render_page_content(pathname):\n    if pathname in [\"/\", \"/income_finder\"]:\n        return income_finder.layout\n    elif pathname == \"/screener\":\n        return screener.layout\n    elif pathname == \"/portfolio\":\n        return portfolio.layout\n    elif pathname == \"/transaction\":\n        return transaction.layout\n    elif pathname == \"/report\":\n        return report.layout\n    elif pathname == \"/pivot\":\n        return pivot.layout\n    # If the user tries to reach a different page, return a 404 message\n    return dbc.Jumbotron(\n        [\n            html.H1(\"404: Not found\", className=\"text-danger\"),\n            html.Hr(),\n            html.P(f\"The pathname {pathname} was not recognised...\"),\n        ]\n    )\n\nif __name__ == '__main__':\n    app.run_server(debug=True)","sub_path":"index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":2065,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"232775271","text":"import pandas as pd\n\n\ndef calcfreq(mylist):\n    freq={}\n    for i,items in enumerate(mylist):\n        freq[(i,items)]=mylist.count(items)\n    for key,value in freq.items():\n        print(key,\":\",value)\n\n    return freq\n\n\ndoc1=\"Cosine similarity is a metric, helpful in determining, how similar the data objects are irrespective of their size.\"\ndoc2=\"Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space.\"\ndoc3=\"Cosine similarity is a metric used to measure how similar the documents are irrespective of their size.\"\ndoc=[doc1,doc2,doc3]\nworddict={}\ni=1\nfor ele in doc:\n    words=ele.split(\" \")\n    data=calcfreq(words)\n        \n    i=i+1\n\ndf=pd.DataFrame(data)","sub_path":"lab3.py","file_name":"lab3.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"624532568","text":"import tensorflow as tf\n\ndef hello():\n    a = tf.constant('Hello, tensorflow!')\n    print(a)\n\n    sess = tf.Session()\n    result = sess.run(a)\n\n    print(result)\n    print(type(result))\n    print(result.decode(encoding='utf-8'))\n    print(type(result.decode(encoding='utf-8')))\n\n    #세션 닫기\n    sess.close()\n\ndef constant():\n    a = tf.constant(2)\n    b = tf.constant(3)\n\n    with tf.Session() as sess:\n        result = sess.run(a+b)\n        print(type(result))\n        print(result)\n\n        print(result + 7)\n        print(type(result + 7))\n\ndef placeholder():\n    a = tf.placeholder(tf.int16)\n    b = tf.placeholder(tf.int16)\n\n    add = tf.add(a,b)\n    mul = tf.mul(a,b)\n\n    with tf.Session() as sess:\n        r1 = sess.run(add, feed_dict={a:2, b:3})\n        r2 = sess.run(mul, feed_dict={a:2, b:3})\n\n        print(type(r1))\n        print(r1,r2)\n\ndef showTensor():\n    sess = tf.InteractiveSession()\n\n    x = tf.Variable([1.0, 2.0])\n    a = tf.constant([3.0, 3.0])\n\n    # xd에 대해서 연산을 수행해서 결과를 먼저 만든다.\n    x.initializer.run()  # 초기화 동작을 하는 run() 함수를 사용하여 x를 초기화 함.\n\n    # x에서 a를 뺀 계산을 추가한다. 그리고 결과를 연산해줌.\n    sub = tf.sub(x, a)\n    print(sub.eval())\n    # ==> [-2. -1.]\n\n    # 결과를 내장하고 있다면 eval() 사용 가능. initializer 없이 x에 대해서 호출하면 비정상 종료\n    print(a.eval())    #[3. 3.]\n    print(x.eval())    #[1. 2.]\n\n    # -1에서 1사이의 정규분포 난수 3개 생성, b는 1행 3열의 텐서 객체\n    b = tf.random_uniform([3], -1.0, 1.0)\n    print(type(b))\n    print(b.eval())\n\n    w = tf.Variable(tf.random_uniform([5,3], 0, 32, dtype=tf.int32))\n    w.initializer.run()\n    print(w.eval())\n\n    x = [[1.,1.],[10.,2.]]\n    print(tf.reduce_mean(x).eval())\n    print(tf.reduce_mean(x, 0).eval())\n    print(tf.reduce_mean(x, 1).eval())\n\n    sess.close()\n\n#hello()\nconstant()\nplaceholder()\nshowTensor()\n","sub_path":"program_language/pythons/Tensorflow_exams/tensorflow_exam_01_basic.py","file_name":"tensorflow_exam_01_basic.py","file_ext":"py","file_size_in_byte":1985,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"404312687","text":"# export DISPLAY=:0 \n\nimport sys\nsys.path.append(\"../\") \n\nimport numpy as np\nimport gym\nfrom dqn.dqn_agent import DQNAgent\nfrom dqn.networks import ConvolutionNeuralNetwork, CNNTargetNetwork\nfrom tensorboard_evaluation import *\nimport itertools as it\nfrom utils import *\nimport os\n\ndef run_episode(env, agent, deterministic, skip_frames=0,  do_training=True, rendering=False, max_timesteps=1000, history_length=0):\n    \"\"\"\n    This methods runs one episode for a gym environment. \n    deterministic == True => agent executes only greedy actions according the Q function approximator (no random actions).\n    do_training == True => train agent\n    \"\"\"\n\n    stats = EpisodeStats()\n\n    # Save history\n    image_hist = []\n\n    step = 0\n    state = env.reset()\n\n    # fix bug of corrupted states without rendering in gym environment\n    env.viewer.window.dispatch_events() \n\n    # append image history to first state\n    state = state_preprocessing(state)\n    image_hist.extend([state] * (history_length + 1))\n    state = np.array(image_hist).reshape(96, 96, history_length + 1)\n    \n    while True:\n\n        # TODO: get action_id from agent\n        # Hint: adapt the probabilities of the 5 actions for random sampling so that the agent explores properly. \n        # action_id = agent.act(...)\n        # action = your_id_to_action_method(...)\n\n        action_id = agent.act(state=state, deterministic=deterministic)\n        action = id_to_action(action_id)\n        # Hint: frame skipping might help you to get better results.\n        reward = 0\n        for _ in range(skip_frames + 1):\n            next_state, r, terminal, info = env.step(action)\n            reward += r\n\n            if rendering:\n                env.render()\n\n            if terminal: \n                 break\n\n        next_state = state_preprocessing(next_state)\n        image_hist.append(next_state)\n        image_hist.pop(0)\n        next_state = np.array(image_hist).reshape(96, 96, history_length + 1)\n\n        if do_training:\n            agent.train(state, action_id, next_state, reward, terminal)\n\n        stats.step(reward, action_id)\n\n        state = next_state\n        \n        if terminal or (step * (skip_frames + 1)) > max_timesteps : \n            break\n\n        step += 1\n\n    return stats\n\n\ndef train_online(env, agent, num_episodes, skip_frames=0, max_timesteps=1000, history_length=0, model_dir=\"./models_carracing\", tensorboard_dir=\"./tensorboard\"):\n   \n    if not os.path.exists(model_dir):\n        os.mkdir(model_dir)  \n \n    print(\"... train agent\")\n    tensorboard = Evaluation(os.path.join(tensorboard_dir, \"train\"), [\"episode_reward\", \"valid_episode_reward\", \"straight\", \"left\", \"right\", \"accel\", \"brake\", \"left_acce\", \"right_acce\"])\n\n    for i in range(num_episodes):\n        print(\"epsiode %d\" % i)\n\n        # Hint: you can keep the episodes short in the beginning by changing max_timesteps (otherwise the car will spend most of the time out of the track)\n        max_timesteps_reduced = int(np.max([300, max_timesteps * i / num_episodes]))\n        stats = run_episode(env, agent, history_length=history_length, skip_frames=skip_frames, max_timesteps=max_timesteps_reduced, deterministic=False, do_training=True, rendering=False)\n\n        tensorboard.write_episode_data(i, eval_dict={ \"episode_reward\" : stats.episode_reward, \n                                                      \"straight\" : stats.get_action_usage(STRAIGHT),\n                                                      \"left\" : stats.get_action_usage(LEFT),\n                                                      \"right\" : stats.get_action_usage(RIGHT),\n                                                      \"accel\" : stats.get_action_usage(ACCELERATE),\n                                                      \"brake\" : stats.get_action_usage(BRAKE),\n                                                      \"left_acce\" : stats.get_action_usage(LEFT_ACCELERATE),\n                                                      \"right_acce\" : stats.get_action_usage(RIGHT_ACCELERATE)\n                                                      })\n\n        # TODO: evaluate agent with deterministic actions from time to time\n\n        if i % 100 == 0 or (i >= num_episodes - 1):\n            valid_episode_reward = 0\n            for j in range(3):\n                valid_stats = run_episode(env, agent, history_length=history_length, skip_frames=skip_frames, max_timesteps=max_timesteps, deterministic=True, do_training=False, rendering=False)\n                valid_episode_reward += valid_stats.episode_reward\n            tensorboard.write_valid_episode_data(i, eval_dict={ \"valid_episode_reward\" : valid_episode_reward/3})\n            agent.saver.save(agent.sess, os.path.join(model_dir, \"dqn_agent.ckpt\")) \n\n    tensorboard.close_session()\n\ndef state_preprocessing(state):\n    return rgb2gray(state).reshape(96, 96) / 255.0\n\nif __name__ == \"__main__\":\n\n    env = gym.make('CarRacing-v0').unwrapped\n    \n    # TODO: Define Q network, target network and DQN agent\n    # ...\n\n\n    state_dim = (96, 96)\n    history_length = 2\n    num_actions = 5\n    skip_frames = 3\n    method = \"CQL\"\n    # method = \"CQL\"\n    game = \"carracing\"\n    epsilon = 0.3\n    epsilon_decay = 0.97\n    epsilon_min = 0.03\n    explore_type = \"epsilon_greedy\"\n    tau = 0.5\n    Q = ConvolutionNeuralNetwork(state_dim=state_dim, num_actions=num_actions, history_length=history_length, hidden=300, lr=3e-4)\n    Q_target = CNNTargetNetwork(state_dim=state_dim, num_actions=num_actions, history_length=history_length, hidden=300, lr=3e-4)\n    agent = DQNAgent(Q, Q_target, num_actions, method=method, discount_factor=0.95, batch_size=64, epsilon=epsilon, epsilon_decay=epsilon_decay, explore_type=explore_type, game=game, tau=tau, epsilon_min=epsilon_min)\n    train_online(env, agent, skip_frames=skip_frames, num_episodes=1200, max_timesteps=1000, history_length=history_length, model_dir=\"./models_carracing\")\n\n    \n    os.system('python test_carracing.py')\n\n    \"\"\"\n    state_dim = (96, 96)\n    history_length = 2\n    num_actions = 5\n    skip_frames = 3\n    method = \"DQL\"\n    # method = \"CQL\"\n    game = \"carracing\"\n    epsilon = 0.3\n    epsilon_decay = 0.99\n    epsilon_min = 0.03\n    explore_type = \"epsilon_greedy\"\n    tau = 2\n    Q = ConvolutionNeuralNetwork(state_dim=state_dim, num_actions=num_actions, history_length=history_length, hidden=300, lr=3e-4)\n    Q_target = CNNTargetNetwork(state_dim=state_dim, num_actions=num_actions, history_length=history_length, hidden=300, lr=3e-4)\n    agent = DQNAgent(Q, Q_target, num_actions, method=method, discount_factor=0.98, batch_size=64, epsilon=epsilon, epsilon_decay=epsilon_decay, explore_type=explore_type, game=game, tau=tau, epsilon_min=epsilon_min)\n    train_online(env, agent, skip_frames=skip_frames, num_episodes=1500, max_timesteps=1000, history_length=history_length, model_dir=\"./models_carracing\")\n    \n    os.system('python test_carracing.py')\n    \"\"\"\n","sub_path":"exercise4_R_NR/train_carracing.py","file_name":"train_carracing.py","file_ext":"py","file_size_in_byte":6913,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"610590164","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef trapezoidal_rule(f, a, b, n):\n    d_x = (b-a)/(n-1)\n    intervals = list(np.arange(a, b+d_x, d_x))\n    intervals.pop()\n    result = f(intervals.pop(0)) + f(intervals.pop(len(intervals) - 1))\n    for i in range(len(intervals)):\n        result += 2 * f(intervals[i])\n\n    return result * d_x / 2\n\n\ndef plot_trapezoidal_rule(f, a, b, n):\n    d_x = (b-a)/(n - 1)\n    intervals = list(np.arange(a, b + d_x, d_x))\n    X = list(np.arange(a, b, 0.01))\n    Y = list(map(f, X))\n    plt.plot(X, Y, lw=3)\n    plt.plot(intervals, list(map(f, intervals)),\n             '.', markersize=12, color=\"red\")\n    plt.plot(intervals, list(map(f, intervals)), color='red')\n    for i in intervals:\n        plt.plot([i, i], [0, f(i)], color='red')\n    plt.title(\"Trapezoidal Rule\")\n    plt.plot([a, b], [0, 0])\n","sub_path":"KMLab_1/trapezoidal_rule.py","file_name":"trapezoidal_rule.py","file_ext":"py","file_size_in_byte":843,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"358695221","text":"# coding:utf-8\n\nimport time\nfrom common import case_exec\nfrom excel_opt import ExcelOpt\n\nclass TestCases():\n\n    def __init__(self):\n        self.excel_obj = ExcelOpt()\n        self.res_title = {0: 'Case_ID',\n                          1: 'URI',\n                          2: 'Request_Info',\n                          3: 'Expect_Result',\n                          4: 'Actual_Result',\n                          5: 'Test_Result'}\n\n    def input_title(self, sheet_name):\n        for key, value in self.res_title.items():\n            self.excel_obj.write_data(sheet_name, 0, key, value, self.excel_obj.title_style())\n\n    def do(self, sheet_name):\n        res_sheet_name = self.excel_obj.res_book.add_sheet(sheet_name)\n        self.input_title(res_sheet_name)\n        case_exec.CaseExec(self.excel_obj).run(sheet_name, res_sheet_name, self.res_title)\n\n\n    def case_sheet1(self):\n        sheet_name = 'Sheet1'\n        self.do(sheet_name)\n\n    def case_sheet2(self):\n        sheet_name = 'Sheet2'\n        self.do(sheet_name)\n\n    def case_sheet3(self):\n        sheet_name = 'Sheet3'\n        self.do(sheet_name)\n\n    def save(self):\n        output_path = './output'\n        res_file = time.strftime('%Y-%m-%d %H-%M-%S', time.localtime(time.time()))\n        self.excel_obj.close(output_path + '/' + res_file + '.xls')\n\n\nif __name__ == \"__main__\":\n\n    tc = TestCases()\n    for i in [func for func in dir(tc) if 'case_sheet1' in func]:\n        getattr(tc, i)()\n    tc.save()\n\n\n\n\n\n\n","sub_path":"test_cases.py","file_name":"test_cases.py","file_ext":"py","file_size_in_byte":1471,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"419066977","text":"##############################################################\r\n# This program is checking :\r\n#   variation postition whather in protein coding regein or not.\r\n# \r\n# usage : python3 check_mutation_position.py $ARGV[0] $ARGV[1] > output.txt\r\n# \r\n#  \t$ARGV[0] >>> input vcf file\r\n# \t$ARGV[1] >>> reference cds position file (***_cds_from_genomic.fna)\r\n# \r\n# python3 check_mutation_position.py HOL1.bwa.sorted.filted.vcf ATCC824.gff > test.txt\r\n# \r\n# \r\n# \r\n\r\n\r\nimport sys\r\nimport re\r\n\r\nvcf_path = \"\"  ## input file\r\nreadline = \"\"  ## file reading in line\r\nread_element = [] ## each line element\r\n\r\nchro = \"\"\r\npos = \"\"\r\nalt = \"\"\r\nref = \"\"\r\n\r\nkey = \"\"\r\ninfo = \"\"\r\n\r\nchro_dict= {}\r\ntype_dict= {}\r\nnon_coding_gene= {}\r\n###############################\r\n###                         ###\r\n###    read vcf position    ###\r\n###                         ###\r\n###############################\r\n#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tHOL1.bwa.sorted.bam\r\n\r\nvcf_path= sys.argv[1]\r\nwith open(vcf_path, mode = \"r\", encoding = \"utf8\") as file:\r\n    for i in file:\r\n        \r\n        readline = i.rstrip()  ## rstrip() >>> 去除換行符號\r\n        \r\n        if re.match( \"^#\", readline):\r\n                readline = \"header\"\r\n                #print (re.match( \"^#\", readline).group(0))\r\n        else:\r\n            read_element = readline.split('\\t')\r\n            chro = read_element[0]\r\n            pos = read_element[1]\r\n            ref = read_element[3]\r\n            alt = read_element[4]\r\n           \r\n            key = (chro+\"\\t\"+pos)\r\n            info = (chro+\"\\t\"+pos+\"\\t\"+ref+\"\\t\"+alt)\r\n           \r\n            if len(alt) == len(ref):\r\n                try:\r\n                    chro_dict[key]=info\r\n                    type_dict[key]=(\"snp\")\r\n                except KeyError:\r\n                    chro_dict[key]=info\r\n                    type_dict[key]=(\"snp\")\r\n            elif len(alt) > len(ref):\r\n                try:\r\n                    chro_dict[key]=info\r\n                    type_dict[key]=(\"insertion\")\r\n                except KeyError:\r\n                    chro_dict[key]=info\r\n                    type_dict[key]=(\"insertion\")\r\n            elif len(alt) < len(ref):\r\n                try:\r\n                    chro_dict[key]=info\r\n                    type_dict[key]=(\"deletion\")\r\n                except KeyError:\r\n                    chro_dict[key]=info\r\n                    type_dict[key]=(\"deletion\")\r\n            \r\n\r\n\r\n\r\nread_element = []\r\ntmp_list=[]\r\n\r\nchrmosome = \"\"\r\nposition_start = \"\"\r\nposition_end = \"\"\r\ndirection = \"\"\r\ngene_locus = \"\"\r\nold_gene_locus = \"\"\r\ndescription = \"\"\r\nprotein_id = \"\"\r\n\r\nposition = \"\" ## `核心` >>> 所有dictionary 共用 key (cds region)\r\n\r\ntranscription_way = {}\r\nlocus_tag_dict = {}\r\nold_locus_tag_dict = {}\r\nprotein_id_dict = {}\r\ndescription_dict = {}\r\n\r\n## 放最後突變位子發生地點\r\nmutation_location = {} \r\n\r\n\r\n###########################\r\n###                     ###\r\n###    read gff file    ###\r\n###                     ###\r\n###########################\r\ngff_path= sys.argv[2]\r\nwith open(gff_path, mode = \"r\", encoding = \"utf8\") as file:\r\n    for i in file:\r\n        \r\n        readline = i.rstrip()  ## rstrip() >>> 去除換行符號\r\n        if re.match( r\"^#\", readline):\r\n            readline = \"header\"\r\n            #print (re.match( \"^#\", readline).group(0))\r\n        else:\r\n            read_element = readline.split('\\t')\r\n            chrmosome = read_element[0]\r\n            \r\n            position_start = read_element[3]\r\n            position_end = read_element[4]\r\n            position = (chrmosome+\"\\t\"+position_start+\"\\t\"+position_end)\r\n            \r\n            if read_element[2] == \"region\":\r\n                readline = \"header\"\r\n            else:\r\n                try:\r\n                    transcription_way[position] = read_element[6]\r\n                except KeyError:\r\n                    transcription_way[position] = read_element[6]\r\n            \r\n            if re.findall( r\"gene\", read_element[2]):\r\n                #######################\r\n                #####             #####\r\n                #####  抓gene id  #####\r\n                #####             #####\r\n                #######################\r\n                info_match = re.findall(r\"\\;locus_tag=(.+)\", read_element[8])\r\n                if info_match:\r\n                    tmp_list = info_match[0].split(';')\r\n                    try:\r\n                        locus_tag_dict[position] = (tmp_list[0])\r\n                    except KeyError:\r\n                        locus_tag_dict[position] = (tmp_list[0])\r\n                \r\n                ###########################\r\n                #####                 #####\r\n                #####  抓old gene id  #####\r\n                #####                 #####\r\n                ###########################\r\n                info_match = re.findall( r\"\\;old_locus_tag\\=(.+)\", read_element[8])\r\n                if info_match:\r\n                    tmp_list = info_match[0].split(';')\r\n                    try:\r\n                        old_locus_tag_dict[position] = (tmp_list[0])\r\n                    except KeyError:\r\n                        old_locus_tag_dict[position] = (tmp_list[0])\r\n                \r\n                \r\n            elif re.findall( r\"(CDS|RNA)\", read_element[2]):\r\n                \r\n                ##  protein id\r\n                info_match = re.findall( r\"RefSeq\\:(.+)?\", read_element[8])\r\n                if info_match:\r\n                    tmp_list = info_match[0].split(';')\r\n                    protein_id = tmp_list[0]\r\n                    try:\r\n                        protein_id_dict[position] = protein_id\r\n                    except KeyError:\r\n                        protein_id_dict[position] = protein_id\r\n                \r\n                ##  protein description\r\n                info_match = re.findall(r\"\\;product=(.+)?\", read_element[8])\r\n                if info_match:\r\n                    tmp_list = info_match[0].split(';')\r\n                    description = tmp_list[0]\r\n                    try:\r\n                        description_dict[position] = description\r\n                    except KeyError:\r\n                        description_dict[position] = description\r\n                \r\n                if re.findall( r\"RNA\", read_element[2]):\r\n                    try:\r\n                        non_coding_gene[position] = True\r\n                    except KeyError:\r\n                        non_coding_gene[position] = True\r\n        \r\n        #######################################\r\n        #####                             #####\r\n        #####    確認突變位置是否在 cds 中  #####\r\n        #####                             #####\r\n        #######################################\r\n        if readline == \"header\" :\r\n            pass\r\n        else:\r\n            for k in chro_dict.keys():\r\n                read_element = k.split('\\t')\r\n                chro = read_element[0]\r\n                pos = read_element[1]\r\n                \r\n                if chro == chrmosome:\r\n                    #position = (chrmosome+\"\\t\"+position_start+\"\\t\"+position_end)\r\n                    if int(position_start) <= int(pos) :\r\n                        if int(pos) <= int(position_end) :\r\n                            try:\r\n                                mutation_location[k] = position\r\n                            except KeyError:\r\n                                mutation_location[k] = position\r\n                            #print (position+\"\\t\"+k)\r\n\r\n\r\n\r\n\r\n\r\n########################\r\n#####              #####\r\n##### 清空暫存變數  #####\r\n#####              #####\r\n########################\r\nkey = \"\"\r\ninfo = \"\"\r\nmutation_type = \"\"\r\nposition = \"\"\r\nposition_start = \"\"\r\nposition_end = \"\"\r\ndirection = \"\"\r\ngene_locus = \"\"\r\nold_gene_locus = \"\"\r\nprotein_id = \"\"\r\ndescription = \"\"\r\nread_element = []\r\n\r\n\r\n\r\n###########################\r\n#####                 #####\r\n#####  Output result  #####\r\n#####                 #####\r\n###########################\r\nprint (\"Chromosome_id\\tmutation_pos\\tref\\talt\\tmutation_type\\tcds_start\\tcds_end\\ttranscription_way\\tlocus_taq\\told_locus_taq\\tprotein_id\\tdescription\")\r\nfor k in chro_dict.keys():\r\n    try:\r\n        position = mutation_location[k]\r\n    except KeyError:\r\n        position = \"\"\r\n    \r\n    key = k\r\n    try:\r\n        info = chro_dict[key]\r\n    except KeyError:\r\n        info = \"\"\r\n        \r\n    try:\r\n        mutation_type = type_dict[key]\r\n    except KeyError:\r\n        mutation_type = \"\"\r\n\r\n    \r\n    ###################################\r\n    ####                           ####\r\n    ####   加入cds information     ####\r\n    ####                           ####\r\n    ###################################\r\n    if position == \"\": ### 空值代表 突變不發生在 transcription 區域\r\n        #pass\r\n        position_start = \"\"\r\n        position_end = \"\"\r\n        direction = \"\"\r\n        gene_locus = \"\"\r\n        old_gene_locus = \"\"\r\n        protein_id = \"\"\r\n        description = \"\"\r\n    else:\r\n        #####################################\r\n        ####                             ####\r\n        ####  非空值代表是coding region   ####\r\n        ####                             ####\r\n        #####################################\r\n        read_element = position.split('\\t')\r\n        position_start = read_element[1]\r\n        position_end = read_element[2]\r\n        \r\n        \r\n        \r\n        try:\r\n            direction = transcription_way[position]\r\n        except KeyError:\r\n            direction = \"\"\r\n        \r\n        try:\r\n            gene_locus = locus_tag_dict[position]\r\n        except KeyError:\r\n            gene_locus = \"\"\r\n        \r\n        try:\r\n            old_gene_locus = old_locus_tag_dict[position]\r\n        except KeyError:\r\n            old_gene_locus = \"\"\r\n        \r\n        try:\r\n            protein_id = protein_id_dict[position]\r\n        except KeyError:\r\n            protein_id = \"\"\r\n        \r\n        try:\r\n            description = description_dict[position]\r\n        except KeyError:\r\n            description = \"\"\r\n        \r\n    print(info+\"\\t\"+mutation_type+\"\\t\"+position_start+\"\\t\"+position_end+\"\\t\"+direction+\"\\t\"+gene_locus+\"\\t\"+old_gene_locus+\"\\t\"+protein_id+\"\\t\"+description)\r\n        \r\n        #\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"pipeline/check_mutation_position.py","file_name":"check_mutation_position.py","file_ext":"py","file_size_in_byte":10229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"130671379","text":"import heapq\n\n\ndef solution(n, works):\n    if sum(works) <= n: return 0\n    pq = []\n    for work in works:\n        heapq.heappush(pq, -work)\n\n    for _ in range(n):\n        if not pq: break\n        value = heapq.heappop(pq) + 1\n        if value < 0:\n            heapq.heappush(pq, value)\n\n    # works[works.index(max(works))] -= 1\n\n    return get_sum(pq)\n\n\ndef get_sum(arr):\n    return sum([el ** 2 for el in arr])\n\n\nprint(solution(4, [4,3,3]))\nprint(solution(1, [2,1,2]))\nprint(solution(3, [1, 1]))\n\n\n# 잔여량을 균등하게 맞춘다.\n# PQ","sub_path":"prev/programmers/level3/야근지수.py","file_name":"야근지수.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"428877864","text":"import json\n\nfrom aiohttp import web\nfrom app.settings import ACCESS_TOKEN_LIST\n\n\nasync def make_message(method: str, data: dict):\n    base_code = \"API.{method}({data})\"\n\n    return base_code.format(method=method, data=json.dumps(data))\n\n\nasync def callback(request):\n    data = await request.json()\n    method = request.match_info.get('method_name')\n    token = request.query['access_token']\n\n    if token not in ACCESS_TOKEN_LIST:\n        return web.HTTPBadRequest()\n    else:\n\n        code = await make_message(method, data)\n        request.app['queue'][token].put(code)\n\n    return web.Response(text='OK')\n","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"59454240","text":"import os\nimport h5py as h5\nimport xarray as xr\nimport pandas as pd\n\nfrom random import choice\nfrom numpy import nan,array,abs,float32,arctan2,argwhere,int64,sum,pi,cos\nfrom scipy.fftpack import fft, fftfreq\nfrom tqdm import tqdm\nfrom shutil import rmtree\n\n\ndef extract_variables(File,Hs,Te,dwp=True):\n\n    if type(File) is xr.Dataset:\n        thp = {}\n        start_time = File.sim_start.values\n        print(f'Beginning file slicing on {type(File)}')\n        for st in tqdm(start_time):\n            t = File.sel(sim_start=st)\n            hs, te = (t[Hs].dropna(dim='valid_time'),\n                      t[Te].dropna(dim='valid_time'))\n            startGroup = []\n            times = hs.valid_time.values\n            for i,time in enumerate(times):\n                try:\n                    startGroup.append((time,times[i+1],\n                                        str(hs.sel(valid_time=time).values),\n                                        str(te.sel(valid_time=time).values)))\n                   \n                except IndexError as e:\n                    pass\n            thp[st] = startGroup\n                \n    elif type(File) is pd.core.frame.DataFrame:\n        times = File.index\n        thp = []\n        print(f'Beginning file slicing on {type(File)}')\n        if dwp:\n            DWP = calc_DWP(File)\n            for i, time in tqdm(enumerate(times)):\n                try:\n                    thp.append((time,times[i+1],File.loc[time,Hs],f'{round(DWP[time],2)}'))\n                except IndexError as e:\n                    pass\n        else:\n            for i,time in tqdm(enumerate(times)):\n                try:\n                    thp.append((time,times[i+1],File.loc[time,Hs],File.loc[time,Te]))\n                except IndexError as e:\n                    pass\n\n    return thp\n\n\ndef timeseries_params(ts, model_db, freq=10, f='s', closest=True):\n\n    strip = lambda x: [float(i.split('-')[-1]) for i in x]\n\n    model = {}\n    for db in model_db:\n        ws = h5.File(db,'r+')\n        if closest:\n            if type(ts) is list:\n                selection = []\n                print('Beginning timeseries parameter collection')\n                for t in tqdm(ts):\n                    seed = choice(ws.attrs['Seeds'])\n                    Hs = list(ws[seed].keys())  \n                    Hs = Hs[abs(array(strip(Hs))-float32(t[2])).argmin()]\n                    Te = list(ws[seed][Hs].keys())\n                    Te = Te[abs(array(strip(Te))-float32(t[3])).argmin()]\n                    selection.append(((seed,Hs,Te),pd.date_range(t[0],t[1],freq=f'{freq}{f}')))\n                output = selection\n\n            elif type(ts) is dict:\n                output = {}\n                print('Beginning timeseries parameter collection')\n                for tkey, tdata in tqdm(ts.items()):\n                    selection = []\n                    for t in tdata:\n                         \n                        seed = choice(ws.attrs['Seeds'])\n                        Hs = list(ws[seed].keys())  \n                        print(Hs,t)\n                        Hs = Hs[abs(array(strip(Hs))-float32(t[2])).argmin()]\n                        Te = list(ws[seed][Hs].keys())\n                        Te = Te[abs(array(strip(Te))-float32(t[3])).argmin()]\n                        selection.append(((seed,Hs,Te),pd.date_range(t[0],t[1],freq=f'{freq}{f}')))        \n                    output[tkey] = selection\n        ws.close()\n        model[db] = output\n    return model\n\n\ndef cuts(shape):\n    powers = 0\n    while 2**powers <= shape:\n        powers += 1\n    return shape-2**(powers-1)\n\n\ndef FFT(time,data):\n    samples = time.shape[0]\n    y = fft(data)\n    spacing = (time[1]-time[0])\n    freq = fftfreq(samples,spacing)\n    y = y[1:samples//2]\n    return (2/samples)*abs(y),arctan2(y.imag,y.real), freq[1:samples//2]\n\n\ndef freqLimit(A,p,freq,limit):\n    idx = argwhere(freq<=limit).max()\n    return A[:idx], p[:idx], freq[:idx]\n\n\ndef calculate_ffts(selections, variable='EPower', freqCut=1):\n\n    FFTs = {}\n    for db,selection in selections.items():\n        if type(selection) is list:\n            values = set([select[0] for select in selection])\n            fft = {}\n            ws = h5.File(db,'r+') \n            print(f\"Calculating NOAA buoy FFTs on - {db.split('/')[-1]}\")\n            for v in tqdm(values):\n                d = ws[f'{v[0]}/{v[1]}/{v[2]}/{variable}']\n                time = ws[f'{v[0]}/{v[1]}/{v[2]}/time']\n                cut = cuts(time.shape[0])\n                dmean = d[cut:].mean()\n                d = d[cut:]-dmean\n                \n                A, p, freq = FFT(time[cut:],d)\n                A, p, freq = freqLimit(A,p,freq,freqCut)\n                fft[v] = [A,p,freq,dmean]\n            \n            ws.close()    \n            ffts = [[select[0],fft[select[0]],select[1]] for select in selection] \n\n        elif type(selection) is dict:\n            ffts, values = {}, []\n            print(f\"Calculating Model FFTs on - {db.split('/')[-1]}\")\n            for skey, sdata in selection.items():\n                values.append(list(set([select[0] for select in sdata])))\n            ws = h5.File(db,'r+')\n            for v in tqdm(set([v for value in values for v in value])):\n                d = ws[f'{v[0]}/{v[1]}/{v[2]}/{variable}']\n                time = ws[f'{v[0]}/{v[1]}/{v[2]}/time']\n                cut = cuts(time.shape[0])\n                dmean = d[cut:].mean()\n                d = d[cut:]-dmean\n                \n                A, p, freq = FFT(time[cut:],d)\n                A, p, freq = freqLimit(A,p,freq,freqCut)\n                ffts[v] = [A,p,freq,dmean]\n            ws.close()    \n        FFTs[db] = ffts\n        \n    return FFTs\n\n\ndef linear_transition_timeseries(datasets,transLength=1800):\n\n    for db,dataset in datasets.items():\n        fullTS = [flat for ds in dataset for flat in ds[-1]]\n        fullnTS = [flat for ds in dataset for flat in array(ds[-1]).astype(int64)/10**9]\n        \n        df = pd.Series(index=fullnTS)\n        print(f'Reconstructing NOAA Buoy Timeseries - {db.split(\"/\")[-1]}')\n        for ds in tqdm(dataset):\n            times = array(ds[-1]).astype(int64)/10**9\n            for i,time in enumerate(times):\n                df[time] = sum(ds[1][0]*cos(2*pi*ds[1][2]*time+ds[1][1]))+ds[1][-1]\n                \n        return pd.Series(df.values,index=fullTS)\n\n\ndef linear_transition_timeseries_model(ffts,times,buoy,transitionLength=1800):\n\n    for keys, starts in times.items():\n        if not os.path.isdir('./working/'):\n            os.mkdir('./working/')\n        print(f'Reconstructing Model Timeseries - {keys.split(\"/\")[-1]}')    \n        for skey, start in tqdm(starts.items()):\n            fullTS, fullnTS = [], []\n            for i,ds in enumerate(start):\n                fullTS.append([flat for flat in ds[-1]])\n                fullnTS.append([(ds[0],d) for d in array(ds[-1]).astype(int64)/10**9])\n            fullTS = [ts for tsgrp in fullTS for ts in tsgrp[:-1]]\n            fullnTS = [ts for tsgrp in fullnTS for ts in tsgrp[:-1]]\n            df = pd.Series(index=fullTS,name=f\"{skey}\")\n            for i,j in enumerate(fullnTS):\n                fft = ffts[keys][j[0]]\n                df[fullTS[i]] = sum(fft[0]*cos(2*pi*fft[2]*j[-1]+fft[1]))+fft[-1]\n            df.to_pickle(f'./working/epower-{skey}.pkl') \n            #da = xr.DataArray.from_series(df)\n            #da.to_netcdf(f'./working/epower-{skey}.nc',engine='netcdf4')\n        '''\n        df = xr.open_mfdataset(f'./working/*',combine='nested',concat_dim=None)\n        df.load()\n        saved, ext = False, 0\n        while saved is False:\n            try:\n                df.to_netcdf(f'./Model_Forecast-{buoy}-{keys.split(\"/\")[-1].split(\".\")[0]}{ext}.nc',\n                        engine='netcdf4')\n                saved = True\n            except PermissionError as e:\n                ext += 1 \n        df.close()\n        #rmtree('./working', ignore_errors=True)\n        '''\n\ndef calc_DWP(buoy):\n    he = float32(buoy.loc[:,'WVHT'].values)\n    swell = float32(buoy.loc[:,'SwH'].values)\n    windw = float32(buoy.loc[:,'WWH'].values)\n    DWP = []\n    for i,j in enumerate(swell>=windw):\n        if j:\n            DWP.append(float32(buoy.iloc[i].loc['SwP']))\n        elif not j:\n            DWP.append(float32(buoy.iloc[i].loc['WWP']))\n    \n    return pd.Series(DWP,index=buoy.index,name='DWP')\n\n\n\n\n\n\n\n\n","sub_path":"powerseries_eagle.py","file_name":"powerseries_eagle.py","file_ext":"py","file_size_in_byte":8344,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"128714453","text":"\r\nimport socket                             #line 1: Import socket module\r\nimport threading\r\nimport pygame\r\ndef sock():\r\n        s = socket.socket()\r\n                                                   #line 2: create a socket object\r\n        host = '127.0.0.1'\t                         #line 3: Get current machine name\r\n        port = 10005\t\t\t          #line 4: Get port number for connection\r\n\r\n        s.bind((host,port))\t\t                #line 5: bind with the address\r\n\r\n        print (\"Waiting for connection...\")\t\r\n        s.listen(1)                                      #line 6: listen for connections\r\n\r\n        while True:\r\n                conn,addr = s.accept()\t                #line 7: connect and accept from client\r\n                print ('Got Connection from', addr)\r\n                \r\n                a=input('hiuhk')\r\n                \r\n                conn.send(a)\r\n                conn.close()\t\t                #line 8: Close the connection\r\n\r\ndef joy():\r\n        print('1')\r\n        print('2')\r\n        print('3')\r\n        print('4')\r\n        print('5')\r\nif __name__ == \"__main__\":\r\n     t1 = threading.Thread(target=sock ) \r\n     t2 = threading.Thread(target=joy )\r\n     t2.start() \r\n     t1.start()\r\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1222,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"65856864","text":"# -*- coding: utf-8 -*-\n# PHILIP WOO\nfrom tests.base_page import BasePage\nfrom tests.po.elements.events_search_pagination_and_data_elements import EventSearchPageElements\nfrom tests import config, view\nfrom selenium.webdriver.common.keys import Keys\n\n\nclass EventSearchPageObjects(BasePage):\n\n    def __init__(self, browser):\n        self.event_search = EventSearchPageElements()\n        self.set_browser(browser)\n\n    def get(self):\n        self.go_to_url(path=view.EVENT_SEARCH_PAGE_VIEW)\n        self.event_search.event_search_page_footer.wait_for_page_or_element_load()\n\n    def click_pages_button(self):\n        self.event_search.next_page.click_filed()\n        self.event_search.event_search_page_footer.wait_for_page_or_element_load()\n\n    def move_to_page(self, page):\n        page_number = self.event_search.page_number.format(page)\n        self.browser.find_element_by_css_selector(page_number).click()\n        self.event_search.event_search_page_footer.wait_for_page_or_element_load()\n\n    def check_line_data(self, check_content):\n        for reference in config.EVENT_SEARCH_PAGE:\n            if int(self.event_search.current_page_number()[0].text) != reference['PAGE']:\n                self.move_to_page(reference['PAGE'])\n            if check_content == 'href':\n                if reference['HREF_CONTENT'] in \\\n                        self.event_search.first_line_href()[0].get_attribute('href'):\n                    continue\n                else:\n                    return 'Page {0} line {1} is wrong!'.format(reference['PAGE'], check_content)\n            elif check_content == 'text':\n                if not self.event_search.first_line_href.if_element_existed():\n                    continue\n                elif self.event_search.first_line_href()[0].text == \\\n                        reference['TEXT_CONTENT'].decode('utf-8'):\n                    continue\n                else:\n                    return 'Page {0} first line {1} is wrong'.format(reference['PAGE'], check_content)\n            else:\n                return 'Current checking only support either href checking or text checking for first line.'\n        return True\n\n    def check_action_data(self, check_content):\n        for reference in config.EVENT_SEARCH_ACTION_PAGE:\n            if check_content == 'href':\n                if reference['HREF_CONTENT'] in \\\n                        self.event_search.first_line_href()[0].get_attribute('href'):\n                    continue\n                else:\n                    return 'Page {0} line {1} is wrong!'.format(reference['PAGE'], check_content)\n            elif check_content == 'text':\n                if not self.event_search.first_line_href.if_element_existed():\n                    continue\n                elif self.event_search.first_line_href()[0].text == \\\n                        reference['TEXT_CONTENT'].decode('utf-8'):\n                    continue\n                else:\n                    return 'Page {0} first line {1} is wrong'.format(reference['PAGE'], check_content)\n            else:\n                return 'Current checking only support either href checking or text checking for first line.'\n        return True\n\n    def check_price(self, free_text, price_shown):\n        elements = self.event_search.table_price()\n        for element in elements:\n            if element.text != free_text:\n                if price_shown == 'excl':\n                    return True if element.find_element_by_xpath('.').text == 'From $450.00' else \\\n                        'excl price wrong on events page'\n                elif price_shown == 'incl':\n                    return True if element.find_element_by_xpath('.').text == 'From $517.50' else \\\n                        'incl price wrong on events page'\n\n    def search_action(self):\n        self.event_search.event_search_filed.fill_field('plan')\n        self.event_search.event_search_filed.key_down(Keys.ENTER)\n","sub_path":"tests/po/objects/event_search_pagination_and_data_objects.py","file_name":"event_search_pagination_and_data_objects.py","file_ext":"py","file_size_in_byte":3910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"201690873","text":"from django.urls import path\nfrom . import views\n\napp_name = 'cadastro'\n\nurlpatterns = [\n\tpath('', views.home, name='home'),\n\tpath('cadastro/', views.Criar.as_view(), name='cadastro'),\n\tpath('lista/', views.Lista.as_view(), name='lista'),\n]\n\n","sub_path":"cadastro/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"14798192","text":"import pandas as pd\nimport numpy as np\n\nimport tensorflow as tf\nimport transformers #huggingface transformers library\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\nimport sklearn\nfrom sklearn.metrics import confusion_matrix\nimport os\nimport pickle\n\ndef _save_pickle(path, obj):\n  with open(path, 'wb') as f:\n    pickle.dump(obj, f)\n\ndef _load_pickle(path):\n  with open(path, 'rb') as f:\n    obj = pickle.load(f)\n  return obj\n  \n\n\nclass Predictor:\n    \n    def __init__(self):\n        self.transformer = transformers.TFAutoModel.from_pretrained('modules/topic_classification/bert_model')\n        self.tokenizer = _load_pickle(\"modules/topic_classification/tokenizer.pkl\")\n        self.encoder = _load_pickle(\"modules/topic_classification/encoder.pkl\")\n        self.encoded_classes = self.encoder.classes_\n        self.build_model(max_len=80)\n        \n    def regular_encode(self, texts, maxlen=512):\n        enc_di = self.tokenizer.batch_encode_plus(\n            texts, \n            return_attention_mask =False, \n            return_token_type_ids=False,\n            pad_to_max_length=True,\n            max_length=maxlen\n        )\n        return np.array(enc_di['input_ids'])\n    \n        \n    def predict(self, input_seq):\n        test_encoded = self.regular_encode([input_seq], maxlen=80)\n        pred = self.model.predict([test_encoded])\n        pred_classes = np.argmax(pred, axis = 1)\n        predicted_category = [self.encoded_classes[x] for x in pred_classes]\n        return predicted_category[0]\n    \n    def build_model(self, loss='categorical_crossentropy', max_len=512):\n        input_word_ids = tf.keras.layers.Input(shape=(max_len,), dtype=tf.int32, name=\"input_word_ids\")\n        sequence_output = self.transformer(input_word_ids)[0]\n        cls_token = sequence_output[:, 0, :]\n        #adding dropout layer\n        x = tf.keras.layers.Dropout(0.3)(cls_token)\n        #using a dense layer of 15 neurons as the number of unique categories is 40. \n        out = tf.keras.layers.Dense(16, activation='softmax')(x)\n        self.model = tf.keras.Model(inputs=input_word_ids, outputs=out)\n        #using categorical crossentropy as the loss as it is a multi-class classification problem\n        self.model.compile(tf.keras.optimizers.Adam(lr=3e-5), loss=loss, metrics=['accuracy'])\n        \n        self.model.load_weights(\"modules/topic_classification/weights/model_weights.h5\")\n        \n        \nif __name__ == \"__main__\":\n    predictor = Predictor()\n    test = \"Large companies have big budgets for marketing and promotion and as a result, people gravitate towards buying their products.\"\n    print(predictor.predict(test))","sub_path":"modules/topic_classification/inference.py","file_name":"inference.py","file_ext":"py","file_size_in_byte":2689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"1468126","text":"import bpy\r\nfrom bpy.types import (PropertyGroup , UIList , Operator)\r\nimport imp\r\n\r\nfrom . import utils\r\nimp.reload(utils)\r\n\r\n\r\n#アイテム-------------------------------------------------------\r\n#ItemPropertyはリストのに登録される一つのアイテムを表している\r\n\r\n#リストからアイテムを取得\r\ndef get_item(self):\r\n    return self[\"name\"]\r\n\r\n#リストに選択を登録する\r\ndef set_item(self, value):\r\n    self[\"name\"] = value\r\n\r\ndef showhide(self, value):\r\n    ob = utils.getActiveObj()\r\n    for mod in ob.modifiers :\r\n            if mod.name == self[\"name\"]:\r\n                mod.show_viewport = self[\"bool_val\"]\r\n\r\n\r\n#---------------------------------------------------------------------------------------\r\ndef reload():\r\n    ui_list = bpy.context.window_manager.kiamodifierlist_list\r\n    itemlist = ui_list.itemlist\r\n\r\n    clear()\r\n    ob =utils.getActiveObj()\r\n\r\n    for mod in ob.modifiers:\r\n        item = itemlist.add()\r\n        item.name = mod.name\r\n        item.bool_val = mod.show_viewport\r\n        ui_list.active_index = len(itemlist) - 1\r\n\r\n\r\ndef clear():\r\n    ui_list = bpy.context.window_manager.kiamodifierlist_list\r\n    itemlist = ui_list.itemlist    \r\n    itemlist.clear()\r\n\r\n#---------------------------------------------------------------------------------------\r\n#リストのモディファイヤを探しだし、リストでの順位とモディファイヤの順位を比較する\r\n#その差を出してmove_upで順番を変更する\r\n#---------------------------------------------------------------------------------------\r\ndef move(type):\r\n    ui_list = bpy.context.window_manager.kiamodifierlist_list\r\n    itemlist = ui_list.itemlist\r\n    index = ui_list.active_index\r\n\r\n    if len(itemlist) < 2:\r\n        return\r\n\r\n    if type == 'UP':\r\n        v = index -1\r\n    elif type == 'DOWN':\r\n        v = index + 1\r\n    elif type == 'TOP':\r\n        v = 0\r\n    elif type == 'BOTTOM':\r\n        v = len(itemlist) - 1\r\n\r\n\r\n    itemlist.move(index, v)\r\n    ui_list.active_index = v\r\n\r\n    ob =utils.getActiveObj()\r\n\r\n    for order_list,listitem in enumerate(itemlist):\r\n        for order,mod in enumerate(ob.modifiers):\r\n            \r\n            if mod.name == listitem.name:\r\n                if (order_list < order):\r\n                    for i in range(order - order_list):\r\n                        bpy.ops.object.modifier_move_up(modifier = mod.name )\r\n\r\n\r\n#一時的にハンドラの動作をスルーするためのデコレータ\r\ndef handler_through(func):\r\n    def wrapper(*args, **kwargs):\r\n        props = bpy.context.scene.kiamodifierlist_props\r\n        props.handler_through = True\r\n        func(*args, **kwargs)    \r\n        props.handler_through = False\r\n\r\n        #モディファイヤの\r\n        act = utils.getActiveObj()\r\n        props.mod_count = len(act.modifiers)\r\n\r\n    return wrapper\r\n\r\n\r\n#---------------------------------------------------------------------------------------\r\n#選択されたモディファイヤをapply\r\n#---------------------------------------------------------------------------------------\r\n@handler_through\r\ndef apply():\r\n\r\n    ui_list = bpy.context.window_manager.kiamodifierlist_list\r\n    itemlist = ui_list.itemlist\r\n    active_index = ui_list.active_index\r\n\r\n    if len(itemlist) == 0:\r\n        return\r\n\r\n    ob =utils.getActiveObj()\r\n\r\n    for mod in ob.modifiers :\r\n        if itemlist[active_index].name == mod.name:\r\n            bpy.ops.object.modifier_apply( modifier = mod.name )\r\n\r\n    reload()\r\n\r\n\r\n#---------------------------------------------------------------------------------------\r\n#チェックされたモディファイヤをapply\r\n#ハンドラを一時的にOFFにする。\r\n#---------------------------------------------------------------------------------------\r\n@handler_through\r\ndef apply_checked():\r\n\r\n    ui_list = bpy.context.window_manager.kiamodifierlist_list\r\n    itemlist = ui_list.itemlist\r\n\r\n    if len(itemlist) == 0:\r\n        return\r\n\r\n    ob =utils.getActiveObj()\r\n    \r\n    for mod in itemlist:\r\n        if mod.bool_val:\r\n            bpy.ops.object.modifier_apply( modifier = mod.name )    \r\n    reload()\r\n\r\n#---------------------------------------------------------------------------------------\r\n#チェックされたモディファイヤを削除する\r\n#---------------------------------------------------------------------------------------\r\n@handler_through\r\ndef remove():\r\n\r\n    ui_list = bpy.context.window_manager.kiamodifierlist_list\r\n    itemlist = ui_list.itemlist\r\n\r\n    if len(itemlist) == 0:\r\n        return\r\n\r\n    ob =utils.getActiveObj()\r\n    \r\n    for mod in itemlist:\r\n        if mod.bool_val:\r\n            bpy.ops.object.modifier_remove( modifier = mod.name )\r\n\r\n    reload()\r\n\r\n\r\n#---------------------------------------------------------------------------------------\r\n#リストコマンド\r\n#---------------------------------------------------------------------------------------\r\ndef itemlist():\r\n    ui_list = bpy.context.window_manager.kiamodifierlist_list\r\n    return ui_list.itemlist    \r\n\r\ndef clear():\r\n    il = itemlist()\r\n    il.clear()\r\n\r\n","sub_path":"cmd.py","file_name":"cmd.py","file_ext":"py","file_size_in_byte":5122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"38702978","text":"import tensorflow as tf\nimport keras\nimport keras.backend as k\nfrom keras.layers import Dense, Flatten, Conv2D, Dropout, MaxPooling2D\nfrom keras.models import Sequential\nfrom tensorflow.keras.initializers import RandomNormal\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.losses import categorical_crossentropy\nfrom sklearn.model_selection import train_test_split\nimport numpy as np\nimport math\n\nprint(tf.executing_eagerly())\n\n# x and y are defined as our sample data\nx = np.asarray(tf.random.uniform(minval=0, maxval=1, shape=(6400, 10), dtype=tf.float32))\ny = keras.utils.to_categorical(tf.reduce_sum(x, axis=-1), num_classes=10)\n\nx_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n\nx_train = x_train.reshape((-1, 10, 1, 1))\nx_test = x_test.reshape((-1, 10, 1, 1))\n\n# Hyperparameters\nweight_init = RandomNormal()\nopt = Adam(lr=0.001)\nbatch_size = 128\nepochs = 50\n\n# Builds model that we will use for the training process\nmodel = Sequential()\nmodel.add(Conv2D(32, kernel_size=(1, 1), activation='relu', kernel_initializer=weight_init, input_shape=(10, 1, 1)))\nmodel.add(Conv2D(64, (1, 1), activation='relu', kernel_initializer=weight_init))\nmodel.add(MaxPooling2D(pool_size=(1, 1)))\nmodel.add(Dropout(0.25))\nflatten = Flatten()\nmodel.add(flatten)\nhidden_layer_1 = Dense(128, activation='relu', kernel_initializer=weight_init)\nmodel.add(hidden_layer_1)\nhidden_layer_2 = Dropout(0.3)\nmodel.add(hidden_layer_2)\noutput_layer = Dense(10, activation='softmax', kernel_initializer=weight_init)\nmodel.add(output_layer)\nmodel.summary()\n\n\n# Define custom loss with added parameter of layer\ndef custom_loss(layer):\n    # Create a loss function that adds the MSE loss to the mean of all squared activations of a specific layer\n    def loss(y_true, y_pred):\n        return k.mean(k.square(y_pred - y_true) + k.square(layer), axis=-1)\n\n    # Return a function\n    return loss\n\n\ndef custom_loss_2(y_true, y_pred):\n    loss = k.sum(k.log(y_true) - k.log(y_pred))\n    return loss\n\n\n# Defines function for calculating gradient at each step of learning process\ndef step(real_x, real_y):\n    with tf.GradientTape() as tape:\n        # Make prediction\n        pred_y = model(real_x.reshape((-1, 10, 1, 1)))\n        # Calculate loss\n        model_loss = categorical_crossentropy(real_y, pred_y)\n\n    # Calculate gradients\n    model_grads = tape.gradient(model_loss, model.trainable_variables)\n    # Update model\n    opt.apply_gradients(zip(model_grads, model.trainable_variables))\n\n\n# Training loop\nbat_per_epoch = math.floor(len(x_train) / batch_size)\nfor epoch in range(epochs):\n    print('=', end='')\n    for i in range(bat_per_epoch):\n        n = i*batch_size\n        step(x_train[n:n + batch_size], y_train[n:n + batch_size])\n\n\n# Compile the model\nmodel.compile(optimizer=opt,  # Model runs without loss function!!!\n              metrics=['accuracy'])\n\nprint('\\n', 'Accuracy:', model.evaluate(x_test, y_test)[1])\n","sub_path":"ppowithgradt/GradT_code.py","file_name":"GradT_code.py","file_ext":"py","file_size_in_byte":2955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"482998201","text":"from django.conf.urls import patterns, include, url\n\n# Uncomment the next two lines to enable the admin:\nfrom django.contrib import admin\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n    # Examples:\n    # url(r'^$', 'test42cc.views.home', name='home'),\n    # url(r'^test42cc/', include('test42cc.foo.urls')),\n\n    # Uncomment the admin/doc line below to enable admin documentation:\n    # url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n    # Uncomment the next line to enable the admin:\n    url(r'^admin/', include(admin.site.urls)),\n    url(r'^requests/$', 'utils42cc.views.requests', name='requests'),\n    url(r'^requests/prio/$', 'utils42cc.views.prio', name='prio'),\n    url(r'', include('contact.urls')),\n)\n","sub_path":"test42cc/test42cc/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"58104258","text":"# Napisz funkcję, która będzie przyjmowała 2 parametry: \n# liczbę, która będzie podstawą operacji potęgowania oraz ilość kolejnych potęg do wygenerowania.\n#  Korzystając z funkcji logspace generuj tablicę jednowymiarową kolejnych potęg podanej liczby, np. generuj(2,4) -> [2 4 8 16]\n\nimport numpy as np\n\ndef fun(n, x):\n    A = np.logspace(1, x, num=x, base=n, dtype=int)\n    print(A)\n\n\nfun(2, 10)","sub_path":"lab6/zad4.py","file_name":"zad4.py","file_ext":"py","file_size_in_byte":412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"89653629","text":"from graph import*\nfrom math import*\ndef sun(x0, y0, radius, n, percent):\n    A = [(0,0)]*n*2\n    for k in range(n):\n        A[k*2] = (x0 + radius * cos(2 * pi * k / n),y0 + radius * sin(2 * pi * k / n))\n        A[k*2+1] = ( x0 + radius * percent * cos(2 * pi * k / n + pi / n),y0 + radius * percent * sin(2 * pi * k / n + pi / n))\n    penColor(\"black\")\n    brushColor(\"yellow\")\n    polygon(A)\ndef tree(x, y, size):\n    penColor(\"black\")\n    brushColor(\"saddlebrown\")\n    rectangle(x - size/2, y - size*5, x + size/2, y)\n    penColor(\"black\")\n    brushColor(\"green\")\n    circle(x, y - size*9, size*1.5)\n    circle(x + size*1.7, y - size*7.5, size*1.5)\n    circle(x - size*1.7, y - size*7.5, size*1.5)\n    circle(x, y - size*6.5, size*1.5)\n    circle(x - size*1.5, y - size*5, size*1.5)\n    circle(x + size*1.5, y - size*5, size*1.5)\ndef cloud(x, y, radius):\n    penColor(\"black\")\n    brushColor(\"white\")\n    circle(x, y, radius)\n    circle(x + radius, y, radius)\n    circle(x + radius*2, y, radius)\n    circle(x + radius*3, y, radius)\n    circle(x + radius*2, y - radius*4/5, radius)\n    circle(x + radius, y - radius*4/5, radius)\ndef house(x, y, size):\n    penColor(\"black\")\n    brushColor(\"darkgoldenrod\")\n    rectangle(x - size/2, y - size*8/11, x + size/2, y)\n    penColor(\"black\")\n    brushColor(\"darkcyan\")\n    rectangle(x - size*0.125, y - size*5/11, x + size*0.125, y - size*3/11)\n    penColor(\"black\")\n    brushColor(\"brown\")\n    polygon([(x - size/2, y - size*8/11), (x, y - size*13/11), (x + size/2, y - size*8/11), (x - size/2, y - size*8/11)])\nwindowSize(455, 300)\ncanvasSize(455, 300)\npenColor(\"lightskyblue\")\nbrushColor(\"lightskyblue\")\nrectangle(0, 0, 455, 150)\npenColor(\"forestgreen\")\nbrushColor(\"forestgreen\")\nrectangle(0, 150, 455, 300)\nsun(50, 50, 40, 20, 0.3)\ncloud(150, 50, 15)\ncloud(250, 80, 10)\ncloud(350, 70, 18)\nhouse(110, 220, 110)\ntree(200, 210, 10)\nhouse(300, 200, 70)\ntree(360, 190, 7)\nrun()\n","sub_path":"lab3/image 3.py","file_name":"image 3.py","file_ext":"py","file_size_in_byte":1921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"309288515","text":"from PyQt5 import QtCore, QtWidgets\nimport model\nfrom popups import calculation, calculationSharedValues\nfrom decimal import *\nimport locale\n\ndef changeLineEdit(lineEdit, decimal):\n    lineEdit.setText(str('{0:n}'.format(decimal)))\n\ndef fill_fgk_details(ui, machine, sharedValues):\n    ui.label_machine_text.setText(machine['bez'])\n\n    time = Decimal(str(machine['ruest']))\n    changeLineEdit(ui.lineEdit_ruestzeit, time)\n\n    hourlyRate = Decimal(str(machine['mss']))\n    hourlyRate = hourlyRate.quantize(sharedValues.getCent(), ROUND_HALF_UP)\n    changeLineEdit(ui.lineEdit_mss, hourlyRate)\n\n    changeLineEdit(ui.lineEdit_fgk_detail, sharedValues.getManufacturingOverheads())\n\ndef fill_mek_details(mainUi, ui, material, volume, sharedValues):\n    ui.label_material_text.setText(mainUi.comboBox_material.currentText())\n    ui.lineEdit_sfgA.setText(mainUi.lineEdit_semifinishedSideA.text())\n    ui.lineEdit_sfgB.setText(mainUi.lineEdit_semifinishedSideB.text())\n    ui.lineEdit_sfgC.setText(mainUi.lineEdit_semifinishedSideC.text())\n    ui.lineEdit_volume.setText(str(volume))\n    density = Decimal(str(material['dichte']))\n    changeLineEdit(ui.lineEdit_density, density)\n\n    weight = volume * density\n    changeLineEdit(ui.lineEdit_weight, weight)\n    changeLineEdit(ui.lineEdit_mek_detail, sharedValues.getMaterialCosts())\n\n    matPrice = Decimal(str(material['preis']))\n    matPrice = matPrice.quantize(sharedValues.getCent(), ROUND_HALF_UP)\n    changeLineEdit(ui.lineEdit_materialPrice, matPrice)\n\ndef calc_material_costs(mainUi,ui,material,volume,sharedValues):\n    materialCosts = volume * Decimal(str(material['dichte'])) * Decimal(str(material['preis']))\n\n    materialCosts = materialCosts.quantize(sharedValues.getCent(), ROUND_HALF_UP)\n    sharedValues.setMaterialCosts(materialCosts)\n    changeLineEdit(ui.lineEdit_mek, materialCosts)\n\ndef calc_material_overheads(ui,sharedValues):\n    materialOverheads = Decimal(\"0.00\")\n    materialOverheads = materialOverheads.quantize(sharedValues.getCent(), ROUND_HALF_UP)\n    sharedValues.setMaterialOverheads(materialOverheads)\n    changeLineEdit(ui.lineEdit_mgk, materialOverheads)\n\ndef calc_manufacturingCosts(ui,sharedValues):\n    manufacturingCosts = Decimal(\"0.00\")\n    manufacturingCosts = manufacturingCosts.quantize(sharedValues.getCent(), ROUND_HALF_UP)\n    sharedValues.setManufacturingCosts(manufacturingCosts)\n    changeLineEdit(ui.lineEdit_fek, manufacturingCosts)\n\ndef calc_manufacturingOverheads(mainUi, ui, machine,sharedValues):\n    manufacturingOverheads = Decimal(str(machine['mss'])) * Decimal(str(machine['ruest']))\n    manufacturingOverheads = manufacturingOverheads.quantize(sharedValues.getCent(), ROUND_HALF_UP)\n    sharedValues.setManufacturingOverheads(manufacturingOverheads)\n    changeLineEdit(ui.lineEdit_fgk, manufacturingOverheads)\n\ndef calculate_cogm(ui,sharedValues):\n    cogm = sharedValues.getMaterialCosts() + sharedValues.getMaterialOverheads() + sharedValues.getManufacturingCosts() + sharedValues.getManufacturingOverheads()\n    cogm = cogm.quantize(sharedValues.getCent(), ROUND_HALF_UP)\n    sharedValues.setCogm(cogm)\n    changeLineEdit(ui.lineEdit_cogm, cogm)\n\ndef calculate_salesOverheads(ui,sharedValues):\n    salesOverheads = sharedValues.getCogm() * Decimal(str(ui.spinBox_vgk.value())) / Decimal(\"100\")\n    salesOverheads = salesOverheads.quantize(sharedValues.getCent(), ROUND_HALF_UP)\n    sharedValues.setSalesOverheads(salesOverheads)\n    changeLineEdit(ui.lineEdit_vgk, salesOverheads)\n\ndef calculate_cogs(ui,sharedValues):\n    cogs = sharedValues.getCogm() + sharedValues.getSalesOverheads()\n    cogs = cogs.quantize(sharedValues.getCent(), ROUND_HALF_UP)\n    sharedValues.setCogs(cogs)\n    changeLineEdit(ui.lineEdit_cogs, cogs)\n\ndef calculate_profit(ui,sharedValues):\n    profit = sharedValues.getCogs() * Decimal(str(ui.spinBox_profit.value())) / Decimal(\"100\")\n    profit = profit.quantize(sharedValues.getCent(), ROUND_HALF_UP)\n    sharedValues.setProfit(profit)\n    changeLineEdit(ui.lineEdit_profit, profit)\n\ndef calculate_skonto(ui,sharedValues):\n\n    valueAfterProfit =  sharedValues.getCogs() + sharedValues.getProfit()\n    skontoDivisor = (Decimal(\"100\") - Decimal(str(ui.spinBox_skonto.value()))) / Decimal(\"100\")\n    skonto = valueAfterProfit / skontoDivisor - valueAfterProfit\n    skonto = skonto.quantize(sharedValues.getCent(), ROUND_HALF_UP)\n    sharedValues.setSkonto(skonto)\n    changeLineEdit(ui.lineEdit_skonto, skonto)\n\ndef calculate_rebates(ui,sharedValues):\n    valueAfterSkonto = sharedValues.getCogs() + sharedValues.getProfit() + sharedValues.getSkonto()\n    rebateDivisor = (Decimal(\"100\") - Decimal(str(ui.spinBox_rebates.value()))) / Decimal(\"100\")\n    rebates = valueAfterSkonto / rebateDivisor - valueAfterSkonto\n    rebates = rebates.quantize(sharedValues.getCent(), ROUND_HALF_UP)\n    sharedValues.setRebates(rebates)\n    changeLineEdit(ui.lineEdit_rebates, rebates)\n\ndef calculate_price(ui,sharedValues):\n    price = sharedValues.getCogs() + sharedValues.getProfit() + sharedValues.getSkonto() + sharedValues.getRebates()\n    price = price.quantize(sharedValues.getCent(), ROUND_HALF_UP)\n    changeLineEdit(ui.lineEdit_price, price)\n\ndef fill_screen(mainUi, ui, sharedValues):\n    volume = int(mainUi.lineEdit_semifinishedSideA.text()) * int(mainUi.lineEdit_semifinishedSideB.text()) * int(mainUi.lineEdit_semifinishedSideC.text())\n    #educational comment: source for Decimal always has to be a string:\n    \"\"\"This has to be done because a Python float value, like 135.99, is converted from Python’s language (in base 10)\n        to the computer’s hardware representation (in base 2) and some precision is lost in the process.\n        To avoid the Python language conversion, string literals are used.\"\n        source: http://buildingskills.itmaybeahack.com/book/programming-2.6/html/p13_modules/p13_c03_decimal.html\"\"\"\n    volume = Decimal(str(volume)) / Decimal(\"1000\")\n\n    material = model.read_material(mainUi.comboBox_material.currentText()[:6])\n\n    calc_material_costs(mainUi, ui, material, volume,sharedValues)\n    fill_mek_details(mainUi, ui, material, volume, sharedValues)\n\n    calc_material_overheads(ui,sharedValues)\n\n    calc_manufacturingCosts(ui,sharedValues)\n\n    machine = model.read_machine(mainUi.comboBox_machine.currentText()[:1])\n    calc_manufacturingOverheads(mainUi, ui, machine, sharedValues)\n    fill_fgk_details(ui, machine, sharedValues)\n\n    calculate_cogm(ui,sharedValues)\n    calculate_changables(mainUi,ui,sharedValues)\n\ndef calculate_changables(mainUi,ui,sharedValues):\n    salesOverheads = calculate_salesOverheads(ui,sharedValues)\n    calculate_cogs(ui,sharedValues)\n    calculate_profit(ui,sharedValues)\n    calculate_skonto(ui,sharedValues)\n    calculate_rebates(ui,sharedValues)\n    calculate_price(ui,sharedValues)\n\ndef connect_spinboxes(mainUi, ui, sharedValues):\n    ui.spinBox_vgk.valueChanged.connect(lambda: calculate_changables(mainUi, ui, sharedValues))\n    ui.spinBox_rebates.valueChanged.connect(lambda: calculate_changables(mainUi, ui, sharedValues))\n    ui.spinBox_skonto.valueChanged.connect(lambda: calculate_changables(mainUi, ui, sharedValues))\n    ui.spinBox_profit.valueChanged.connect(lambda: calculate_changables(mainUi, ui, sharedValues))\n\ndef close(dialog):\n    dialog.done(0)\n\ndef connect_buttons(dialog):\n    dialog.ui.buttonBox.clicked.connect(lambda: close(dialog))\n\ndef show(mainUi):\n    dialog = QtWidgets.QDialog()\n    dialog.ui = calculation.Ui_Dialog()\n    dialog.ui.setupUi(dialog)\n    dialog.setAttribute(QtCore.Qt.WA_DeleteOnClose)\n    locale.setlocale(locale.LC_ALL, '')\n    globalValues = calculationSharedValues.sharedValues()\n    fill_screen(mainUi, dialog.ui, globalValues)\n    connect_buttons(dialog)\n    connect_spinboxes(mainUi, dialog.ui, globalValues)\n    dialog.exec_()\n","sub_path":"popups/calculationControls.py","file_name":"calculationControls.py","file_ext":"py","file_size_in_byte":7805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"28449009","text":"from coreference_resolution import *\nimport numpy as np\nimport glob\nfrom text_reconstruction import *\n\n\n#-------------- MAKING AND SAVING PREDICTIONS --------------#\n\ndef make_predictions(doc_indices):\n\tfilenames = []\n\tfor i in range(30): filenames.append('data_pairs/document'+str(i)+'_data.csv')\n\n\tprint(\"\\nRetrieving saved data...\")\n\tstart = time.time()\n\t(indices, datax, datay) = retrieve_all_data(filenames)\n\n\t#for i, x in enumerate(datax): print(i, len(x))\n\n\tfor i in doc_indices:\n\t\t# cross-validation\n\t\ttrain_indices = list(range(i))+list(range(i+1,30))\n\t\ttrainingx, trainingy = datax[train_indices[0]], datay[train_indices[0]]\n\t\tfor j in train_indices[1:]:\n\t\t\ttrainingx = np.concatenate((trainingx, datax[j]), axis=0)\n\t\t\ttrainingy = np.concatenate((trainingy, datay[j]), axis=0)\n\t\tdt = train_classifier(trainingx, trainingy, i)\n\t\t(predictions, index) = calculate_predictions(dt, datax, i)\n\t\tsave_predictions(predictions, index)\n\t\tprint_score(predictions, datay[i])\n\n\n#-------------- EXTRACT RELEVANT COREFERENCE INFORMATION FROM SAVED PREDICTIONS --------------#\n\ndef retrieve_predictions(doc_index):\n\tfilename = 'predictions/document'+str(doc_index)+'_predictions.csv'\n\tf = open(filename, 'r')\n\tpredictions = [int(p) for p in f.readlines()]\n\treturn predictions\n\n\"\"\"\nreturns bigram:\n\tindex of file\n\tlist of bigrams\n\t\tlist of coordinates of mentions\n\t\tboolean (whether or not coreferential)\n\"\"\"\ndef extract_coreference_data(index):\n\t(testing_indices, testing_data) = open_csv('data_pairs/document'+str(index)+'_data.csv')[:2]\n\tpredictions = retrieve_predictions(index)\n\tcoreference = []\n\tfor i in range(len(testing_data)):\n\t\tcoreference.append((testing_indices[i], predictions[i]))\n\treturn (index, coreference)\n\n\n#-------------- MAKE CLUSTERS FROM RETRIEVED PREDICTIONS --------------#\n\n\"\"\" takes arguments returned by extract_coreference_data\nreturns clusters of mentions in int format \"\"\"\ndef cluster_predictions_naive(index, coreference):\n\tclusters = []\n\tfor pair in coreference:\n\t\tif pair[1]==1:\n\t\t\tm1,m2 = pair[0][0],pair[0][1]\n\t\t\tcluster_m1, cluster_m2 = find_coreference_chain(m1,clusters), find_coreference_chain(m2,clusters)\n\t\t\tif cluster_m1 and cluster_m2 and cluster_m1!=cluster_m2: \n\t\t\t\ti1 = min(cluster_m1-1,cluster_m2-1)\n\t\t\t\ti2 = max(cluster_m1-1,cluster_m2-1)\n\t\t\t\tnew_clusters = [clusters[i] for i in list(range(i1))+list(range(i1+1,i2))+list(range(i2+1,len(clusters)))]\n\t\t\t\tnew_clusters.append(clusters[cluster_m1-1]+clusters[cluster_m2-1])\n\t\t\t\tclusters = new_clusters\n\t\t\telif cluster_m1 and not cluster_m2: clusters[cluster_m1-1].append(m2)\n\t\t\telif cluster_m2 and not cluster_m1: clusters[cluster_m2-1].append(m1)\n\t\t\telif not(cluster_m1 or cluster_m2): clusters.append([m1,m2])\n\treturn clusters\t\t\n\ndef find_coreference_chain(coreferent, clusters):\n\tfor i, cluster in enumerate(clusters):\n\t\tif coreferent in cluster: return i+1\n\treturn False\n\n\"\"\"takes and returns same format as naive\nneeds to obtain information about mentions != mention pairs to treat them by gramtype\ninformation stored in Markables/*_coref_level.xml, accessible by index\n\"\"\"\n#def cluster_predictions_selective(index, coreference):\n\n\n#-------------- MAKE CLUSTERS FROM RETRIEVED PREDICTIONS --------------#\n\n\"\"\" index = index of document \"\"\"\ndef convert_cluster2str(index, clusters):\n\tstr_clusters = []\n\tdoc = 'data_singletons/document'+str(index)+'_mentions.csv'\n\tmentions = [mention.split(',')[3][:-1] for mention in open(doc,'r').readlines()]\n\tfor cluster in clusters:\n\t\tstr_clusters.append([mentions[i] for i in cluster])\n\treturn str_clusters\n\n\n#-------------- DISPLAY --------------#\n\ndef display_clusters(str_clusters):\n\tfor i,cluster in enumerate(str_clusters):\n\t\tunique = np.unique(np.array(cluster))\n\t\tprint(i,'\\t',len(cluster),'elements','\\t', unique)\n\n\n#-------------- SCORING --------------#\n\n# index of document\ndef retrieve_score_document(index):\n\tprint(\"\\nScores for document \"+str(index)+\":\")\n\tlabels = open_csv('data_pairs/document'+str(index)+'_data.csv')[2]\n\tpredictions = retrieve_predictions(index)\n\treturn print_score(predictions,labels)\n\ndef print_score(predictions, labels):\n\ttp,fp,tn,fn = 1e-5,1e-5,1e-5,1e-5\n\tfor i in range(len(predictions)):\n\t\tif labels[i]==1 and predictions[i]==1: tp+=1\n\t\tif labels[i]==1 and predictions[i]==0: fn+=1\n\t\tif labels[i]==0 and predictions[i]==1: fp+=1\n\t\tif labels[i]==0 and predictions[i]==0: tn+=1\n\n\trecall = tp/(tp+fn)\n\tprecision = tp/(tp+fp)\n\tf1 = 2/(1/recall + 1/precision)\n\n\tprint(\"\\nRecall:\", recall)\n\tprint(\"Precision:\", precision)\n\tprint(\"F1:\", f1,\"\\n\")\n\treturn (recall,precision,f1)\n\ndef retrieve_avg_score(indices):\n\tprint(\"\\nAverage scores for document\",indices,\":\")\n\trecall,precision,f1 = 0.,0.,0.\n\tfor index in indices: \n\t\t(r,p,f) = retrieve_score_document(index)\n\t\trecall+=r\n\t\tprecision+=p\n\t\tf1+=f\n\trecall/=len(indices)\n\tprecision/=len(indices)\n\tf1/=len(indices)\n\tprint(\"\\nAverage recall:\",recall)\n\tprint(\"Average precision:\",precision)\n\tprint(\"Average F1:\",f1,\"\\n\")\n\t\t\n\n\n#-------------- SCRIPT --------------#\n\nindex = 24\n\nretrieve_avg_score(list(range(30)))\n\n(index, coreference) = extract_coreference_data(index)\nclusters = cluster_predictions_naive(index, coreference)\nprint(clusters)\n\nstr_clusters = convert_cluster2str(index,clusters)\n\ndisplay_clusters(str_clusters)\n\nreplace = int(input('What number of chain would you like to replace within the text? '))\n\nreplacer = input('What would you like to replace it with? ')\n\nreconstruct(index,clusters[replace],replacer)\n\n\nprint('\\n')\n","sub_path":"replace_chain.py","file_name":"replace_chain.py","file_ext":"py","file_size_in_byte":5451,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"344874299","text":"import pickle\r\nimport os\r\n\r\ndef add_user(name,token):\r\n\r\n    if os.path.exists(\"data/userlists\"):\r\n        userlists = \"data/userlists\"\r\n    else:\r\n        userlists = set()\r\n\r\n    if name in userlists:\r\n\r\n        return 0\r\n    else:\r\n        add_token()\r\n        userlists.append(name)\r\n        pickle.dump(userlists,\"data/userlists\")\r\n\r\n        return 1\r\n\r\ndef encode(token):\r\n    import cipher\r\n\r\n    encryptText = cipher.encrypt(token)\r\n\r\n    return encryptText\r\n\r\ndef add_token(name,token):\r\n    if os.path.exists(\"data/tokenlists\"):\r\n        tokenlists = \"data/tokenlists\"\r\n    else:\r\n        tokenlists = {}\r\n\r\n    token_code = encode(token)\r\n    tokenlists.update(name,token_code)\r\n    pickle.dump(tokenlists,\"data/tokenlists\")\r\n","sub_path":"server/tools/add_user.py","file_name":"add_user.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"57815459","text":"import torch\nimport torch.nn as nn\nfrom baseline.utils import getActivation\n\n\nclass baseAgent(nn.Module):\n\n    def __init__(self):\n        super(baseAgent, self).__init__()\n\n    def buildModel(self):\n        pass\n\n    def calLoss(self):\n        pass\n    \n    def forward(self):\n        pass\n\n\nclass MLP(nn.Module):\n\n    def __init__(self, netData, iSize=1):\n        super(MLP, self).__init__()\n        self.netData = netData\n        self.nLayer = netData['nLayer']\n        self.fSize = netData['fSize']\n        act = netData['act']\n        if not isinstance(act, list):\n            act = [act for i in range(self.nLayer)]\n        self.act = act\n        if 'linear' not in netData.keys():\n            self.linear = False\n        else:\n            self.linear = netData['linear'] == \"True\"\n        self.BN = netData['BN'] == \"True\"\n        self.iSize = iSize\n        self.buildModel()\n\n    def buildModel(self):\n        iSize = self.iSize\n        for i in range(self.nLayer):\n            self.add_module(\n                \"MLP_\"+str(i+1),\n                nn.Linear(iSize, self.fSize[i], bias=False)\n                )\n            \n            if self.BN:\n                if (self.linear and i == (self.nLayer-1)) is not True:\n                    self.add_module(\n                        \"batchNorm_\"+str(i+1),\n                        nn.BatchNorm1d(self.fSize[i])\n                    )\n            act = getActivation(self.act[i])\n            if act is not None:\n                self.add_module(\n                    \"act_\"+str(i+1),\n                    act\n                )\n            iSize = self.fSize[i]\n    \n    def forward(self, x, shortcut=None):\n        if shortcut is not None:\n            lenNet = len(self.fSize) * 2 - 1\n            shortcut = lenNet + shortcut\n        for i, layer in enumerate(self.children()):\n            x = layer(x)\n            if shortcut is not None and i == shortcut:\n                middleOutput = x\n        if shortcut is not None:\n            x = (middleOutput, x) \n        return x\n\n\nclass CNET(nn.Module):\n\n    def __init__(self, netData, iSize=3, WH=80):\n        super(CNET, self).__init__()\n\n        self.netData = netData\n        self.iSize = iSize\n        keyList = list(netData.keys())\n        self.WH = WH\n\n        if \"BN\" in keyList:\n            self.BN = netData['BN'] == \"True\"\n        else:\n            self.BN = False\n        self.nLayer = netData['nLayer']\n        self.fSize = netData['fSize']\n        self.nUnit = netData['nUnit']\n        self.padding = netData['padding']\n        self.stride = netData['stride']\n        act = netData['act']\n        if not isinstance(act, list):\n            act = [act for i in range(self.nLayer)]\n        self.act = act\n        if 'linear' not in netData.keys():\n            self.linear = False\n        else:\n            self.linear = netData['linear'] == \"True\"\n        if self.linear:\n            act[-1] = \"linear\"\n\n        self.buildModel()\n        \n    def buildModel(self):\n\n        iSize = self.iSize\n        mode = True\n        for i, fSize in enumerate(self.fSize):\n            if fSize == -1:\n                mode = False\n            if mode:\n                self.add_module(\n                    \"conv_\"+str(i+1),\n                    nn.Conv2d(iSize, self.nUnit[i], fSize,\n                              stride=self.stride[i],\n                              padding=self.padding[i],\n                              bias=False)\n                )\n                iSize = self.nUnit[i]\n            elif fSize == -1:\n                self.add_module(\n                    \"Flatten\",\n                    nn.Flatten())\n                iSize = self.getSize()\n            else:\n                self.add_module(\n                    \"MLP_\"+str(i+1),\n                    nn.Linear(iSize, fSize, bias=False)\n                )\n                iSize = fSize\n            if self.BN and fSize is not -1:\n                if (self.linear and i == (self.nLayer-1)) is not True:\n                    if mode:\n                        self.add_module(\n                            \"batchNorm_\"+str(i+1),\n                            nn.BatchNorm2d(self.nUnit[i])\n                        )\n                    else:\n                        self.add_module(\n                            \"batchNorm_\"+str(i+1),\n                            nn.BatchNorm1d(fSize)\n                        )\n            act = getActivation(self.act[i])\n            if act is not None and fSize != -1:\n                self.add_module(\n                    \"act_\"+str(i+1),\n                    act\n                )\n\n    def getSize(self):\n        ze = torch.zeros((1, self.iSize, self.WH, self.WH))\n        k = self.forward(ze)\n        k = k.view((1, -1))\n        size = k.shape[-1]\n        return size\n\n    def forward(self, x):\n        for layer in self.children():\n            x = layer(x)\n        return x\n","sub_path":"baseline/baseNetwork.py","file_name":"baseNetwork.py","file_ext":"py","file_size_in_byte":4850,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"378102727","text":"import numpy as np\nfrom scipy import optimize\n\n__author__ = 'rose'\n\n\ndef sigmoid(x):\n    return 1 / (1 + np.exp(-x))\n\n\ndef sigmoid_grad(x):\n    return x * (1.0 - x)\n\n\nclass Autoencoder:\n    def __init__(self, num_neurons=np.array([192, 75, 192]), lambda_=0.0001, rho=0.01, beta=3, max_iter=1000):\n        self.num_neurons = num_neurons\n        self.num_layers = len(self.num_neurons)\n        self.lambda_ = lambda_\n        self.rho = rho\n        self.beta = beta\n        self.max_iter = max_iter\n        self.deltas = None\n        self.a = None\n        self.z = None\n        self.rho_mean = None\n\n        # проинициализируем начальные приближения для весов\n        self.weights = None\n        n_in = self.num_neurons[0]\n        n_out = self.num_neurons[-1]\n        low = - 6 / (n_in + n_out + 1) ** (1 / 2)\n        high = 6 / (n_in + n_out + 1) ** (1 / 2)\n        for layer in range(self.num_layers - 1):\n            w = np.random.uniform(low, high, size=(self.num_neurons[layer + 1], self.num_neurons[layer]))\n            b = np.zeros((self.num_neurons[layer + 1],))\n            if layer == 0:\n                self.weights = w.ravel()\n            else:\n                self.weights = np.concatenate((self.weights, w.ravel()))\n        self.weights = np.concatenate((self.weights, np.zeros((np.sum(self.num_neurons[1:])))))\n\n    def split_weights(self):\n        indices = [0]\n        for layer in range(self.num_layers - 1):\n            indices += [indices[-1] + self.num_neurons[layer + 1] * self.num_neurons[layer]]\n        b_index = indices[-1]\n        for layer in range(1, self.num_layers - 1):\n            indices += [indices[-1] + self.num_neurons[layer]]\n        indices = np.array(indices[1:])\n        # теперь разделим длиннющий вектор весов на исходные на матрицы\n        splitted_arrays = np.split(self.weights, indices)\n        weights = splitted_arrays[: self.num_layers - 1]\n        b = splitted_arrays[self.num_layers - 1:]\n        return weights, b, b_index\n\n    def loss(self, theta, x, y):\n        self.weights = theta\n\n        # сделаем индексы для разделения длиннющего вектора на матрицы весов для каждого слоя\n        weights, b, b_index = self.split_weights()\n\n        # прямой ход\n        self.a = [x.T]\n        self.z = []\n        self.rho_mean = [self.a[0].mean(axis=1)]\n        kl_divergence = 0\n        for layer in range(self.num_layers - 1):\n            w_reshaped = weights[layer].reshape(self.num_neurons[layer + 1], self.num_neurons[layer])\n            b_reshaped = b[layer][:, np.newaxis]\n            z = np.dot(w_reshaped, self.a[-1]) + b_reshaped\n            self.a += [sigmoid(z)]\n            self.rho_mean += [self.a[-1].mean(axis=1)]\n            if layer != self.num_layers - 2:\n                kl_divergence += np.sum(self.rho * np.log(self.rho / self.rho_mean[-1]) + \\\n                                        (1 - self.rho) * np.log((1 - self.rho) / (1 - self.rho_mean[-1])))\n            self.z += [z]\n\n        # обратный ход\n        self.deltas = [(self.a[-1] - y.T) * sigmoid_grad(self.a[-1])]\n        dw, db = None, None\n        for layer in range(self.num_layers - 1, 1, -1):\n            sparsity_grad = self.beta * ((1 - self.rho) / (1 - self.rho_mean[layer - 1])\n                                         - self.rho / self.rho_mean[layer - 1])[:, np.newaxis]\n            w_reshaped = weights[layer - 1].reshape(self.num_neurons[layer], self.num_neurons[layer - 1])\n            delta = np.dot(w_reshaped.T, self.deltas[-1]) + sparsity_grad\n            delta = delta * sigmoid_grad(self.a[layer - 1])\n            self.deltas += [delta]\n\n            # вычисление градиента J\n            j_grad_w = (1 / x.shape[0]) * np.dot(self.deltas[-2], self.a[layer - 1].T) \\\n                       + self.lambda_ * w_reshaped\n            j_grad_b = self.deltas[-2].mean(axis=1)\n            if layer == self.num_layers - 1:\n                dw, db = j_grad_w.ravel(), j_grad_b.ravel()\n            else:\n                dw = np.concatenate((j_grad_w.ravel(), dw))\n                db = np.concatenate((j_grad_b.ravel(), db))\n\n        w_reshaped = weights[0].reshape(self.num_neurons[1], self.num_neurons[0])\n        j_grad_w = (1 / x.shape[0]) * np.dot(self.deltas[-1], self.a[0].T) + \\\n                   self.lambda_ * w_reshaped\n        j_grad_b = self.deltas[-1].mean(axis=1)\n        dw = np.concatenate((j_grad_w.ravel(), dw))\n        db = np.concatenate((j_grad_b.ravel(), db))\n        j_grad = np.concatenate((dw, db))\n\n        # вычисление функции J\n        j = (1 / 2) * (np.linalg.norm(self.a[-1] - y.T, axis=0) ** 2).mean()\n        regularizer = (self.lambda_ / 2) * np.linalg.norm(self.weights[: b_index]) ** 2\n        sparsity = self.beta * kl_divergence\n        j += regularizer + sparsity\n        return (j, j_grad)\n\n    def fit(self, x, y):\n        j = self.loss\n        theta = self.weights\n        opt_theta = optimize.minimize(j, theta, args=(x, y), method='L-BFGS-B',\n                                      jac=True, options={'disp': True, 'maxiter': self.max_iter}).x\n        self.weights = opt_theta\n        return opt_theta\n\n    def transform(self, x_train, stop_layer):\n        weights, b, b_index = self.split_weights()\n        a = [x_train.T]\n        for layer in range(stop_layer - 1):\n            w_reshaped = weights[layer].reshape(self.num_neurons[layer + 1], self.num_neurons[layer])\n            b_reshaped = b[layer][:, np.newaxis]\n            z = np.dot(w_reshaped, a[-1]) + b_reshaped\n            a += [sigmoid(z)]\n        return a[-1]\n\n\ndef initialize(hidden_size, visible_size):\n    num_layers = np.concatenate((np.array(visible_size), hidden_size, np.array(visible_size)))\n    ae = Autoencoder(num_neurons=num_layers)\n    return ae.weights\n\n\ndef autoencoder_loss(theta, visible_size, hidden_size, lambda_, sparsity_param, beta, data):\n    num_layers = np.concatenate((np.array(visible_size), hidden_size, np.array(visible_size)))\n    ae = Autoencoder(num_neurons=num_layers, lambda_=lambda_, rho=sparsity_param, beta=beta)\n    return ae.loss(theta, data, data)\n\n\ndef autoencoder_transform(theta, visible_size, hidden_size, layer_number, data):\n    num_layers = np.concatenate((np.array(visible_size), hidden_size, np.array(visible_size)))\n    ae = Autoencoder(num_neurons=num_layers)\n    ae.weights = theta\n    return ae.transform(data, stop_layer=layer_number)\n","sub_path":"Autoencoder/autoencoder.py","file_name":"autoencoder.py","file_ext":"py","file_size_in_byte":6552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"441984390","text":"import collections\nimport nltk\nimport torch\nimport pytorch_lightning as pl\nimport numpy as np\nimport torchvision.transforms.functional as F\n\nfrom sklearn.feature_extraction import image\nfrom torch.utils.data import DataLoader\nfrom torchvision.datasets import CocoCaptions\n\nimport utils\n\n\nclass Dataset(torch.utils.data.Dataset):\n    def __init__(self, data, preprocess, ncaptions):\n        self.data = data\n        self.preprocess = preprocess\n        self.ncaptions = ncaptions\n\n    def __getitem__(self, idx):\n        items = []\n        img, target = self.data[idx]\n        img = self.preprocess(img)\n        items.append(img)\n        items.append(target[:self.ncaptions])\n        return items\n    \n    def __len__(self):\n        return len(self.data)\n\n\nclass TextDataset(torch.utils.data.Dataset):\n    def __init__(self, data, ncaptions):\n        self.data = data\n        self.ncaptions = ncaptions\n\n    def __getitem__(self, idx):\n        _, target = self.data[idx]\n        return target[:self.ncaptions]\n    \n    def __len__(self):\n        return len(self.data)\n\n\nclass DataModule(pl.LightningDataModule):\n    def __init__(self,\n                 batch_size=64,\n                 nworkers=0,\n                 preprocess=None,\n                 ncaptions=5,\n                 datadir='.'):\n        super().__init__()\n        self.batch_size = batch_size\n        self.nworkers = nworkers\n        self.preprocess = preprocess\n        self.datadir = datadir\n        self.ncaptions = ncaptions\n    \n    def setup(self, stage=None):\n        datadir=self.datadir\n        train_datatype='train2017'\n        train_annfile='{}/annotations/captions_{}.json'.format(datadir, train_datatype)\n        train_loc='{}/{}'.format(datadir, train_datatype)\n        dev_datatype='val2017'\n        dev_annfile='{}/annotations/captions_{}.json'.format(datadir, dev_datatype)\n        dev_loc='{}/{}'.format(datadir, dev_datatype)\n\n        if stage == 'fit' or stage is None:\n            train_data = CocoCaptions(root=train_loc, annFile=train_annfile)\n            valid_data = CocoCaptions(root=dev_loc, annFile=dev_annfile)\n            self.train = Dataset(train_data, self.preprocess, self.ncaptions)\n            self.valid = Dataset(valid_data, self.preprocess, self.ncaptions)\n\n    def train_dataloader(self):\n        return DataLoader(\n            self.train,\n            batch_size=self.batch_size,\n            shuffle=True,\n            num_workers=self.nworkers,\n            pin_memory=True)\n\n    def val_dataloader(self):\n        return DataLoader(\n            self.valid,\n            batch_size=self.batch_size,\n            shuffle=False,\n            num_workers=self.nworkers,\n            pin_memory=True)\n\n\ndef get_cache(args):\n    \"\"\"\n    Computes each entries from a DataLoader. By default uses COCO training\n    captions. Bigrams are selected based on PMI and the topk are selected.\n    \"\"\"\n    datatype = args.split\n    annfile = '{}/annotations/captions_{}.json'.format(args.datadir, datatype)\n    loc='{}/{}'.format(args.datadir, datatype)\n    loader = CocoCaptions(root=loc, annFile=annfile)\n    loader = TextDataset(loader, args.ncaptions)\n    loader = DataLoader(loader, batch_size=args.batch_size, shuffle=True)\n    cache = []\n    for batch in loader:\n        for x in batch:\n            for sent in x:\n                wordlist = nltk.wordpunct_tokenize(sent)\n                wordlist = [w.lower() for w in wordlist]\n                cache.extend(wordlist)\n    \n    # Unigrams\n    unigrams = collections.Counter(cache).most_common(args.topk)\n    unigrams = [t[0] for t in unigrams]\n    \n    # Bigrams\n    bigram_measures = nltk.collocations.BigramAssocMeasures()\n    finder = nltk.collocations.BigramCollocationFinder.from_words(cache)\n    finder.apply_freq_filter(3)\n    bigrams = finder.nbest(bigram_measures.pmi, args.topk)\n    bigrams = [' '.join(g) for g in bigrams]\n\n    return unigrams + bigrams","sub_path":"dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":3899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"278075549","text":"#!/usr/bin/env python2.7\n'''Module for tools to get HTTP resources.\n'''\n\n# system modules\nimport httplib\nimport urlparse\n\n# local modules\nimport chatter\n\nclass HttpResponseData(object):\n  ''' Container class for data retrieved from a HttpGetter.get request.\n  '''\n  def __init__(self, status, reason, headers, body):\n    ''' Constructor\n    '''\n    self.status = status\n    self.reason = reason\n    self.headers = headers\n    self.body = body\n\n  def getlocation(self):\n    ''' Returns the value of a Location header if one exists, otherwise returns None.\n    '''\n    for header in self.headers:\n      if header[0].lower() == 'location':\n        return header[1]\n    return None\n\nclass HttpGetter(object):\n  ''' Class for getting HTTP resources.\n  '''\n  chatty = True\n  max_redirects = 10\n\n  def get(self, url):\n    ''' Requests the HTTP resource at the given URL.\n        returns a tuple of status, headers and body if successful,\n        otherwise returns None.\n    '''\n    try:\n      parsed_url = urlparse.urlparse(url)\n\n      # Ensure scheme and netloc are present\n      if not (parsed_url.scheme and parsed_url.netloc):\n        return None\n\n      # Connect\n      if parsed_url.scheme == 'https':\n        conn = httplib.HTTPSConnection(parsed_url.netloc)\n      else:\n        conn = httplib.HTTPConnection(parsed_url.netloc)\n\n      # Get\n      if self.chatty:\n        chatter.Chatter.to_stdout('\\n>>> GET %s\\n' % url, [chatter.FG_MAGENTA])\n        chatter.Chatter.to_stdout('Scheme: %s\\n' % parsed_url.scheme, [chatter.FG_MAGENTA])\n        chatter.Chatter.to_stdout('Netloc: %s\\n' % parsed_url.netloc, [chatter.FG_MAGENTA])\n        chatter.Chatter.to_stdout('  Path: %s\\n' % parsed_url.path, [chatter.FG_MAGENTA])\n        chatter.Chatter.to_stdout(' Query: %s\\n' % parsed_url.query, [chatter.FG_MAGENTA])\n      conn.request('GET', '/%s' % parsed_url.path)\n      resp = conn.getresponse()\n\n      # Return\n      http_response_data = HttpResponseData(\n        resp.status,\n        resp.reason,\n        resp.getheaders(),\n        resp.read()\n      )\n      if self.chatty:\n        chatter.Chatter.to_stdout(\n          '<<< %s %s\\n' % (http_response_data.status, http_response_data.reason),\n          [chatter.FG_BLUE]\n        )\n        chatter.Chatter.to_stdout('   Loc: %s\\n' % \\\n          http_response_data.getlocation(), [chatter.FG_BLUE])\n      return http_response_data\n\n    except httplib.HTTPException as exception:\n      msg = 'Exception in HttpGetter.get(%s): %s' % (url, exception)\n      chatter.Chatter.to_stderr(msg)\n    return None\n\n  def follow(self, http_response_data):\n    ''' From a HttpResponse object, get a Location redirect header\n        and attempt to follow the redirection to get the resource in the header.\n    '''\n    new_url = http_response_data.getlocation()\n    if not new_url:\n      return None\n\n    return self.get(new_url)\n\n  def get_final(self, url):\n    ''' Requests the HTTP resource at the given URL, then follows redirects until\n        a final resource is received.\n    '''\n    http_response_data = self.get(url)\n    while True:\n      next_url = http_response_data.getlocation()\n      if not next_url:\n        break\n      http_response_data = self.follow(http_response_data)\n    return http_response_data\n\n","sub_path":"getter.py","file_name":"getter.py","file_ext":"py","file_size_in_byte":3244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"634708139","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.5 (3351)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.macosx-10.9-x86_64/egg/tests/viz/test_plot_utils.py\n# Compiled at: 2020-01-13 10:51:56\n# Size of source mod 2**32: 19648 bytes\n\"\"\"\nCreated on Thurs Jun  27 2019\n\n@author: Emily Costa\n\"\"\"\nfrom __future__ import division, print_function, unicode_literals, absolute_import\nimport unittest, os, matplotlib as mpl\nif os.environ.get('DISPLAY', '') == '':\n    print('no display found. Using non-interactive Agg backend')\nmpl.use('Agg')\nimport numpy as np, matplotlib.pyplot as plt\nfrom pyUSID.viz import plot_utils\n\nclass TestUseScientificTicks(unittest.TestCase):\n\n    def test_axis_not_axes(self):\n        not_axis = 1\n        with self.assertRaises(TypeError):\n            plot_utils.use_scientific_ticks(not_axis)\n\n\nclass TestMakeScalarMappable(unittest.TestCase):\n\n    def test_vmin_not_num(self):\n        notNum = 'hello'\n        with self.assertRaises(AssertionError):\n            plot_utils.make_scalar_mappable(notNum, 5)\n\n    def test_vmax_not_num(self):\n        notNum = 'hello'\n        with self.assertRaises(AssertionError):\n            plot_utils.make_scalar_mappable(5, notNum)\n\n    def test_vmin_more_vmax(self):\n        with self.assertRaises(AssertionError):\n            plot_utils.make_scalar_mappable(5, 3)\n\n    def test_cmap_not_none_wrong_input(self):\n        with self.assertRaises(ValueError):\n            plot_utils.make_scalar_mappable(3, 5, cmap='hello')\n\n\nclass TestCmapFromRGBA(unittest.TestCase):\n\n    def test_name_not_string(self):\n        hot_desaturated = [\n         (255.0, (255, 76, 76, 255)),\n         (218.5, (107, 0, 0, 255)),\n         (182.1, (255, 96, 0, 255)),\n         (145.6, (255, 255, 0, 255)),\n         (109.4, (0, 127, 0, 255)),\n         (72.675, (0, 255, 255, 255)),\n         (36.5, (0, 0, 91, 255)),\n         (0, (71, 71, 219, 255))]\n        with self.assertRaises(TypeError):\n            plot_utils.cmap_from_rgba(5, hot_desaturated, 255)\n\n    def test_interp_vals_not_tuple(self):\n        with self.assertRaises(TypeError):\n            plot_utils.cmap_from_rgba('cmap', 'hello', 255)\n\n    def test_normalization_val_not_number(self):\n        hot_desaturated = [(255.0, (255, 76, 76, 255)),\n         (218.5, (107, 0, 0, 255)),\n         (182.1, (255, 96, 0, 255)),\n         (145.6, (255, 255, 0, 255)),\n         (109.4, (0, 127, 0, 255)),\n         (72.675, (0, 255, 255, 255)),\n         (36.5, (0, 0, 91, 255)),\n         (0, (71, 71, 219, 255))]\n        with self.assertRaises(TypeError):\n            plot_utils.cmap_from_rgba('cmap', hot_desaturated, 'hi')\n\n\nclass TestMakeLinearAlphaCmap(unittest.TestCase):\n    __doc__ = \"\\n    def test_make_linear_alpha_cmap(self):\\n        solid_color = plt.cm.jet(0.8)\\n        plot_utils.make_linear_alpha_cmap('my_map', solid_color, 1, min_alpha=0, max_alpha=1)\\n    \"\n\n    def test_name_not_str(self):\n        solid_color = plt.cm.jet(0.8)\n        with self.assertRaises(TypeError):\n            plot_utils.make_linear_alpha_cmap(5, solid_color, 1, min_alpha=0, max_alpha=1)\n\n    def test_solid_color_not_tuple(self):\n        with self.assertRaises(TypeError):\n            plot_utils.make_linear_alpha_cmap('cmap', 'hello', 1, min_alpha=0, max_alpha=1)\n\n    def test_solid_color_len_wrong(self):\n        solid_color = [0, 255, 45]\n        with self.assertRaises(ValueError):\n            plot_utils.make_linear_alpha_cmap('cmap', solid_color, 1, min_alpha=0, max_alpha=1)\n\n    def test_solid_color_list_not_nums(self):\n        solid_color = [0, 255, 'hello', 55]\n        with self.assertRaises(TypeError):\n            plot_utils.make_linear_alpha_cmap(5, solid_color, 1, min_alpha=0, max_alpha=1)\n\n    def test_solid_normalization_val_not_num(self):\n        solid_color = plt.cm.jet(0.8)\n        with self.assertRaises(TypeError):\n            plot_utils.make_linear_alpha_cmap('cmap', solid_color, 'hello', min_alpha=0, max_alpha=1)\n\n    def test_min_alpha_not_num(self):\n        solid_color = plt.cm.jet(0.8)\n        with self.assertRaises(TypeError):\n            plot_utils.make_linear_alpha_cmap('cmap', solid_color, 1, min_alpha='hello', max_alpha=1)\n\n    def test_max_alpha_not_num(self):\n        solid_color = plt.cm.jet(0.8)\n        with self.assertRaises(TypeError):\n            plot_utils.make_linear_alpha_cmap('cmap', solid_color, 1, min_alpha=0, max_alpha='hello')\n\n    def test_max_less_than_min_alpha(self):\n        solid_color = plt.cm.jet(0.8)\n        with self.assertRaises(ValueError):\n            plot_utils.make_linear_alpha_cmap('cmap', solid_color, 1, min_alpha=1, max_alpha=0)\n\n\nclass TestDiscreteCmap(unittest.TestCase):\n    __doc__ = \"\\n    def test_cmap_is_None(self):\\n        plot_utils.discrete_cmap(num_bins=5)\\n\\n\\n    def test_cmap_is_not_None(self):\\n        plot_utils.discrete_cmap(num_bins=5, cmap=plt.get_cmap('jet'))\\n    \"\n\n    def test_numbins_is_not_uint(self):\n        with self.assertRaises(TypeError):\n            plot_utils.discrete_cmap(num_bins='hello')\n\n    def test_cmap_not_str(self):\n        with self.assertRaises(ValueError):\n            plot_utils.discrete_cmap(num_bins=1, cmap='hello')\n\n\nclass TestGetCMapObject(unittest.TestCase):\n\n    def test_cmap_not_cmap(self):\n        with self.assertRaises(ValueError):\n            plot_utils.get_cmap_object(cmap='hello')\n\n    def test_none(self):\n        self.assertEqual(plt.cm.viridis, plot_utils.get_cmap_object(None))\n\n    def test_string_name(self):\n        self.assertEqual(plt.cm.jet, plot_utils.get_cmap_object(plt.get_cmap('jet')))\n\n    def test_wrong_dtype(self):\n        with self.assertRaises(TypeError):\n            plot_utils.get_cmap_object(5)\n\n\nclass TestRainbowPlot(unittest.TestCase):\n\n    def test_axis_not_axis(self):\n        notAxis = 5\n        num_pts = 1024\n        t_vec = np.linspace(0, 10 * np.pi, num_pts)\n        with self.assertRaises(TypeError):\n            plot_utils.rainbow_plot(notAxis, np.cos(t_vec) * np.linspace(0, 1, num_pts), np.sin(t_vec) * np.linspace(0, 1, num_pts), num_steps=32)\n\n\nclass TestPlotLineFamily(unittest.TestCase):\n    __doc__ = \"\\n    def test_base(self):\\n        x_vec = np.linspace(0, 2 * np.pi, 256)\\n        freqs = range(1, 5)\\n        y_mat = np.array([np.sin(freq * x_vec) for freq in freqs])\\n        freq_strs = [str(_) for _ in freqs]\\n\\n        fig, axis = plt.subplots(figsize=(12, 4))\\n        plot_utils.plot_line_family(axis, x_vec, y_mat,\\n                               line_names=freq_strs, label_prefix='Freq = ', label_suffix='Hz',\\n                                 y_offset=2.5, show_cbar=True)\\n    \"\n\n    def test_plot_line_family_not_axis(self):\n        x_vec = np.linspace(0, 2 * np.pi, 256)\n        freqs = range(1, 5)\n        y_mat = np.array([np.sin(freq * x_vec) for freq in freqs])\n        freq_strs = [str(_) for _ in freqs]\n        notAxis = 'hello'\n        with self.assertRaises(TypeError):\n            plot_utils.plot_line_family(notAxis, x_vec, y_mat, line_names=freq_strs, label_prefix='Freq = ', label_suffix='Hz', y_offset=2.5, show_cbar=True)\n\n\nclass TestPlotMap(unittest.TestCase):\n    pass\n\n\nclass TestPlotCurves(unittest.TestCase):\n    pass\n\n\nclass TestPlotComplexSpectra(unittest.TestCase):\n\n    @staticmethod\n    def get_complex_2d_image(freq):\n        x_vec = np.linspace(0, freq * np.pi, 256)\n        y_vec_1 = np.sin(x_vec) ** 2\n        y_vec_2 = np.cos(x_vec) ** 2\n        return y_vec_2 * np.atleast_2d(y_vec_2).T + complex(0.0, 1.0) * (y_vec_1 * np.atleast_2d(y_vec_1).T)\n\n    def test_not_map_stack(self):\n        with self.assertRaises(TypeError):\n            plot_utils.plot_complex_spectra('wrongthing')\n\n    def test_not_x_vec(self):\n        frequencies = 2 ** np.arange(4)\n        image_stack = [self.get_complex_2d_image(freq) for freq in frequencies]\n        with self.assertRaises(TypeError):\n            plot_utils.plot_complex_spectra(np.array(image_stack), x_vec='notvec')\n\n    def test_is_2d_x_vec(self):\n        frequencies = 2 ** np.arange(4)\n        image_stack = [self.get_complex_2d_image(freq) for freq in frequencies]\n        with self.assertRaises(ValueError):\n            plot_utils.plot_complex_spectra(np.array(image_stack), [[1]])\n\n    def test_is_not_dim_x_vec(self):\n        frequencies = 2 ** np.arange(4)\n        image_stack = [self.get_complex_2d_image(freq) for freq in frequencies]\n        with self.assertRaises(ValueError):\n            plot_utils.plot_complex_spectra(np.array(image_stack), [1])\n\n    def test_is_x_vec(self):\n        frequencies = 2 ** np.arange(4)\n        image_stack = [self.get_complex_2d_image(freq) for freq in frequencies]\n        ran_arr = np.zeros_like(image_stack)\n        with self.assertRaises(ValueError):\n            plot_utils.plot_complex_spectra(np.array(image_stack), ran_arr)\n\n    def test_num_comps_not_int(self):\n        frequencies = 2 ** np.arange(4)\n        image_stack = [self.get_complex_2d_image(freq) for freq in frequencies]\n        with self.assertRaises(TypeError):\n            plot_utils.plot_complex_spectra(np.array(image_stack), num_comps='wrong')\n\n    def test_not_str(self):\n        frequencies = 2 ** np.arange(4)\n        image_stack = [self.get_complex_2d_image(freq) for freq in frequencies]\n        with self.assertRaises(TypeError):\n            plot_utils.plot_complex_spectra(np.array(image_stack), title=1)\n\n    def test_not_stdevs(self):\n        frequencies = 2 ** np.arange(4)\n        image_stack = [self.get_complex_2d_image(freq) for freq in frequencies]\n        with self.assertRaises(TypeError):\n            plot_utils.plot_complex_spectra(np.array(image_stack), stdevs=-1)\n\n\nclass TestPlotScree(unittest.TestCase):\n    __doc__ = \"\\n    def test_simple(self):\\n        scree = np.exp(-1 * np.arange(100))\\n        plot_utils.plot_scree(scree, color='r')\\n    \"\n\n    def test_title_wrong(self):\n        scree = np.exp(-1 * np.arange(100))\n        with self.assertRaises(TypeError):\n            plot_utils.plot_scree(scree, title=1)\n\n    def test_scree_wrong(self):\n        scree = 'string'\n        with self.assertRaises(TypeError):\n            plot_utils.plot_scree(scree)\n\n\nclass TestMapStack(unittest.TestCase):\n    pass\n\n\nclass TestCbarForLinePlot(unittest.TestCase):\n\n    def test_not_axis(self):\n        with self.assertRaises(TypeError):\n            plot_utils.cbar_for_line_plot(1, 2)\n\n\nif __name__ == '__main__':\n    unittest.main()","sub_path":"pycfiles/pyUSID-0.0.8.1-py3.5/test_plot_utils.cpython-35.py","file_name":"test_plot_utils.cpython-35.py","file_ext":"py","file_size_in_byte":10401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"643972243","text":"#coding:utf-8\nimport requests as req\nimport json\nimport re\nimport math\n\nfrom setting import RAKUTEN_API_KEY\n\ndef Api_getBookInfo(input_Isbn, date):\n\n        output = {}\n        \n        converted_Isbn = convert13To10(input_Isbn)\n\n        url = \"https://app.rakuten.co.jp/services/api/BooksBook/Search/20130522\" #最後の日付はapiのバージョンっぽい\n        app_id  = RAKUTEN_API_KEY #自分のapp_idを入力\n        isbn    = converted_Isbn\n\n        payload = {\n                'format':'json',\n                'isbn':isbn,\n                'applicationId':app_id\n        }\n        res = req.get(url,params=payload)\n        data = res.json()\n\n        output[\"isbn\"] = converted_Isbn\n\n        # 楽天API に登録されていない（見つからない）場合\n        if len(data[u'Items']) == 0:\n            output[\"title\"] = \"\"\n            output[\"author\"] = \"\"\n            output[\"bodydata\"] = '&amazon('+converted_Isbn+'){large}\\n'+ \\\n                '*基本情報\\n' +\\\n                '{| width=\"500px\" class=\"custom-css\" style=\"color:#6e7955\"\\n' + \\\n                '|タイトル||\\n' + \\\n                '|作者名||\\n' + \\\n                '|巻数||\\n' + \\\n                '|出版社||\\n' + \\\n                '|部室に追加した人|'+'|\\n' + \\\n                '|最終更新者|'+'|\\n' + \\\n                '|編集日|' + date + '|\\n' + \\\n                '|}'\n        \n        else:\n            output[\"title\"] = data[u'Items'][0][u'Item'][u'title']\n            output[\"author\"] = data[u'Items'][0][u'Item'][u'author']\n            output[\"bodydata\"] = '&amazon('+converted_Isbn+'){large}\\n'+ \\\n                '*基本情報\\n' +\\\n                '{| width=\"500px\" class=\"custom-css\" style=\"color:#6e7955\"\\n' + \\\n                '|タイトル|'+data[u'Items'][0][u'Item'][u'title']+'|\\n' + \\\n                '|作者名|'+data[u'Items'][0][u'Item'][u'author']+'|\\n' + \\\n                '|巻数||\\n' + \\\n                '|出版社|'+data[u'Items'][0][u'Item'][u'publisherName']+'|\\n' + \\\n                '|部室に追加した人|'+'|\\n' + \\\n                '|最終更新者|'+'|\\n' + \\\n                '|編集日|' + date + '|\\n' + \\\n                '|}'\n\n        return output\n\ndef convert13To10(isbn13):\n        if isbn13.isdigit() == False or  (int(math.log10(int(isbn13)))+1 != 10 and int(math.log10(int(isbn13)))+1 != 13):\n            return 9999999999\n        elif int(math.log10(int(isbn13)))+1 == 10:\n            return str(int(isbn13))\n        isbn10 = str(isbn13)[3:12]\n        check_digit = 0\n\n        for i in range(len(isbn10)):\n                check_digit += int(isbn10[i]) * (10 - i)\n\n        check_digit = 11 - (check_digit % 11)\n\n        if check_digit == 10:\n            check_digit = 'X'\n        elif check_digit == 11:\n            check_digit = '0'\n        else:\n            check_digit = str(check_digit)\n\n        isbn10 += check_digit\n        return isbn10\n\nif __name__ == \"__main__\":\n        print(Api_getBookInfo('9784832252356', '2017-04-01'))\n        \n\n","sub_path":"flask/models/RakutenBookInfo.py","file_name":"RakutenBookInfo.py","file_ext":"py","file_size_in_byte":3016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"515194841","text":"def count(target, capacities):\n    capacities.sort()\n    c = {}\n    queue = [(target, [], capacities)]\n\n    while queue:\n        t, cs, ls = queue.pop()\n        if t == 0:\n            c[len(cs)] = c.get(len(cs), 0) + 1\n            continue\n        if t < 0 or sum(ls) < t:\n            continue\n\n        l = ls.pop(0)\n        queue.append((t- l, cs + [l], ls[:]))\n        queue.append((t, cs[:], ls[:]))\n\n    return c\n\nwith open(\"day17.in\") as fin:\n    containers = []\n    for l in fin:\n        containers.append(int(l.strip()))\n\npackings = count(150, containers)\nmin_num = 0\nmin_p = 150\ntotal = 0\nfor k, v in packings.items():\n    if k < min_p:\n        min_p = k\n        min_num = v\n    total += v\nprint(total)\nprint(min_num)\n","sub_path":"python/day17.py","file_name":"day17.py","file_ext":"py","file_size_in_byte":726,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"470164906","text":"from tkinter import *\nfrom PIL import Image, ImageTk\nimport numpy as np\nimport pyqrcode\n\n\n# Create a Tkinter GUI container\nroot = Tk()\nroot.wm_title(\"GhostTag\")\n\n# lblName = Label(root, text=\"Fuck shit up\")\n# lblName.pack()\n\ncvs = Canvas(root, width=512, height=512)\ncvs.pack()\n\nw, h = 512, 512\ndata = np.zeros((h, w, 3), dtype=np.uint8)\ndata[256, 256] = [255, 0, 0]\ndata[256, 128] = [255, 0, 0]\ndata[128, 256] = [255, 0, 0]\ndata[128, 128] = [255, 0, 0]\n\nimg = Image.fromarray(data, 'RGB')\nimg.save('test.png')\n\nqr = pyqrcode.create(\"FUCK BITCHES!\")\nqr.png(\"qr1.png\", scale=8)\n\nimg2 = Image.open(\"test.png\")\ntk_img = ImageTk.PhotoImage(img2)\n\ncvs.create_image(250, 250, image=tk_img)\nroot.mainloop()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"322066488","text":"from moneybird import MoneyBird, TokenAuthentication\nimport logging\n\nlogger = logging.getLogger('moneybird')\nmoneybird = MoneyBird(TokenAuthentication('fc249145ec8fd04e167e3e2c80ad04123e883267219925ac5847abb6a027d03d'))\nmoneybird.get('administrations')\n\nadministrations = moneybird.get('administrations')\n\nfor administration in administrations:\n    id = administration['id']\n    contacts = moneybird.get('contacts', administration_id=id)\n\n\n    # Print invoices per contact\n    for contact in contacts:\n        print(contact['company_name'])\n\n        for invoice in moneybird.get('sales_invoices?filter=contact_id:%s' % contact['id'], administration_id=id):\n            print('  ', invoice['invoice_id'])\n\n\n\ncontacts = moneybird.get('contacts', administration_id=id)\nprint(contacts)","sub_path":"11mei2019.py","file_name":"11mei2019.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"12352550","text":"\n'''\ninpt = \"10\\n17 15 16 16 15 14 13 12 13 9\"\ninpt = inpt.split(\"\\n\")\nN,obs = inpt[0],inpt[1]\nobs = obs.split(\" \")\ncount = -1\nlast = 0\nfor i in [int(x) for x in obs]:\n    if i > last:\n        count += 1\n    last = i\nprint(count)\n'''\n\nimport sys\n\nfor inpt in sys.stdin:\n\n    if inpt not in [str(x)+\"\\n\" for x in range(0,150 )]:\n        inpt = inpt[0:len(inpt)-1]\n        obs = inpt.split(\" \")\n        print(obs)\n        count = -1\n        last = 0\n        for i in obs:\n            if int(i) > int(last):\n                count += 1\n            last = i\n        print(count)","sub_path":"Python/code_comp/Programmeringsolympiaden/chokladlåda INCOMPLETE/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"293404095","text":"from lib import ids\nfrom lib.command import Condition, register_command, PREFIXES\nfrom lib.message import Message\n\n\ndef _delete_condition(msg):\n    return msg.author.id == ids.SENPAI_ID or any(msg.content.startswith(prefix) for prefix in PREFIXES)\n\n\n@register_command(Condition.starts_with('purge'))\nasync def purge(bot, message):\n    \"\"\"```\n    Delete prior commands and messages from Senpai from a channel.\n\n    Usage:\n    * !purge N\n    ```\"\"\"\n\n    channel = message.channel\n    count = message.content[6:].strip()\n\n    if 'Direct Message' in str(channel):\n        return Message('Cannot purge DMs')\n\n    try:\n        limit = int(count)\n    except ValueError:\n        return Message('non-numeric purge value')\n\n    if limit < 1:\n        return Message('non-positive purge value')\n\n    if limit > 100:\n        limit = 100\n\n    await channel.purge(limit=limit, check=_delete_condition)\n","sub_path":"lib/purge.py","file_name":"purge.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"513619466","text":"# coding=utf-8\n\nimport dbutil\n\nconn = dbutil.connect(\"mysql://admin:admin2048az@192.168.50.95:3306/capvision_fun\")\n\nif __name__ == '__main__':\n    row = conn.row(\n        '''\n        select * from capvision_fun.demands_prog where id = 12066\n        '''\n    )\n    detail = row['detail']\n    from npt_email import email_down\n    email_down.generate_pruning_detail(detail)\n","sub_path":"test/pruning_test.py","file_name":"pruning_test.py","file_ext":"py","file_size_in_byte":370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"633556497","text":"# -*- coding: utf-8 -*-\n\"\"\"\n-------------------------------------------------\n   File Name：     config\n   Description :\n   Author :       qiuqiu\n   date：          2019/11/5\n-------------------------------------------------\n\"\"\"\nimport os\n\nBASE_PATH = os.path.dirname(__file__) + \"\\\\\"\nPROXIES_FILE_PATH = BASE_PATH + \"files\\\\proxies.txt\"\n\n\nurlsCrawlList = [\n    ['http://www.66ip.cn'],\n    ['https://www.kuaidaili.com/free/inha/%s/' % n for n in range(1, 11)],\n    # ['http://www.xicidaili.com/%s/%s' % (m, n) for m in ['nn', 'nt', 'wn', 'wt'] for n in range(1, 8)],\n]\n\nRETRY_INTERVAL_SEC = 120","sub_path":"applicationSM/controlnode/Config.py","file_name":"Config.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"591350510","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport os\nimport sys\nimport argparse\nimport logging\nimport re\nfrom gensim import corpora, models\n\nWEB2PY_ROOT = 'tools/web2py'\nDICTIONARY_FILENAME = 'corpus.dict'\nMODEL_FILENAME = 'output.model'\n\nclass TrainGensim( object ):\n\t\n\tdef __init__( self, corpus_path, model_path, logging_level ):\n\t\tself.corpus_path = corpus_path\n\t\tself.model_path = model_path\n\t\tself.logger = logging.getLogger( 'TrainGensim' )\n\t\tself.logger.setLevel( logging_level )\n\t\thandler = logging.StreamHandler( sys.stderr )\n\t\thandler.setLevel( logging_level )\n\t\tself.logger.addHandler( handler )\n\t\n\tdef execute( self, numTopics ):\n\t\tself.logger.info( '--------------------------------------------------------------------------------' )\n\t\tself.logger.info( 'Training a gensim topic model...'                                                 )\n\t\tself.logger.info( '      corpus = %s', self.corpus_path                                              )\n\t\tself.logger.info( '       model = %s', self.model_path                                               )\n\t\tself.logger.info( '--------------------------------------------------------------------------------' )\n\t\t\n\t\tif not os.path.exists( self.model_path ):\n\t\t\tself.logger.info( 'Creating model folder: %s', self.model_path )\n\t\t\tos.makedirs( self.model_path )\n\t\t\n\t\t# Based on example code provided by Samuel Rönnqvist\n\t\twhitespaces = re.compile( r'\\W+' )\n\t\tdocuments = []\n\t\twith open( self.corpus_path ) as f:\n\t\t\tfor line in f:\n\t\t\t\ttokens = whitespaces.split( line[:-1] )\n\t\t\t\tdocuments.append( tokens )\n\n\t\t# Generate gensim dictionary\n\t\tdictionary = corpora.Dictionary( documents )\n\t\tcorpus = [ dictionary.doc2bow( tokens ) for tokens in documents ]\n\t\tmodel = models.LdaModel( corpus, id2word = dictionary, num_topics = numTopics )\n\t\t\n\t\tfilename = '{}/{}'.format( self.model_path, DICTIONARY_FILENAME )\n\t\tself.logger.info( 'Saving dictionary to disk: %s', filename )\n\t\tdictionary.save( filename )\n\t\t\n\t\tfilename = '{}/{}'.format( self.model_path, MODEL_FILENAME )\n\t\tself.logger.info( 'Saving model to disk: %s', filename )\n\t\tmodel.save( filename )\n\ndef main():\n\tparser = argparse.ArgumentParser( description = 'Train a gensim topic model.' )\n\tparser.add_argument( 'corpus_path'  , type = str , help = 'Input text corpus'                            )\n\tparser.add_argument( 'model_path'   , type = str , help = 'Output model'                                 )\n\tparser.add_argument( '--topics'     , type = int , help = 'Number of topics'     , default = 20          )\n\tparser.add_argument( '--logging'    , type = int , help = 'Override default logging level', default = 20 )\n\targs = parser.parse_args()\n\t\n\tTrainGensim(\n\t\tcorpus_path = args.corpus_path,\n\t\tmodel_path = args.model_path,\n\t\tlogging_level = args.logging\n\t).execute(\n\t\tnumTopics = args.topics,\n\t)\n\nif __name__ == '__main__':\n\tmain()\n","sub_path":"bin/train_gensim.py","file_name":"train_gensim.py","file_ext":"py","file_size_in_byte":2854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"88951204","text":"#!/usr/bin/env python\n\n# from __future__ import print_function\nimport os, re, gensim, treetaggerwrapper, pickle, aspell, sys\nfrom pattern.fr import singularize, pluralize, conjugate, tenses, lemma, \\\n\tpredicative, parsetree, split, parse, pprint, \\\n\tINFINITIVE, INDICATIVE, CONDITIONAL, SUBJUNCTIVE, \\\n\tPERFECTIVE, IMPERFECTIVE, PROGRESSIVE, PRESENT, PAST, FUTURE, SG, PL\nfrom fixWords import correct_grammar, fix_agreements, fix_word\nfrom pprint import pprint\n\n# sourcefile = 'BaudelaireEnivrez.txt'\n# focus1 = 'homme'\n# focus2 = 'femme'\n# focus1 = 'boire'\n# focus2 = 'dormir'\n\nfocus1 = sys.argv[1].decode(sys.getfilesystemencoding())\nfocus2 = sys.argv[2].decode(sys.getfilesystemencoding())\nsourcefile = sys.argv[3]\n\nw2vfile = 'ARTFLmodel'\npklf = 'pos_dict.pkl'\n\npickleFile = open(pklf, 'rb')\npd = pickle.load(pickleFile)\n# pprint(pd)\n\n# pos_pattern = { 'JJ', 'JJR', 'JJS', 'NN', 'NNS', 'RB', 'RBR', 'RBS', 'VB', 'NNP', 'NNPS' }\npos_tt = { 'ABR', 'ADJ', 'ADV', 'NOM', 'VER:cond', 'VER:futu', 'VER:impe',\n\t'VER:impf', 'VER:infi', 'VER:pper', 'VER:ppre', 'VER:pres', 'VER:simp',\n\t'VER:subi', 'VER:subp' }\n\nsourcetext = open(sourcefile, 'r').read().decode('utf-8')\nmodel = gensim.models.Word2Vec.load(w2vfile)\n\ntagger = treetaggerwrapper.TreeTagger(TAGLANG='fr')\ns = aspell.Speller('lang', 'fr')\n\ntext = parsetree(sourcetext, relations = True, lemmata = True)\n\nfor sentence in text:\n\tnewsentence = list()\n#\tprint sentence.string.encode('utf-8')\n#\tprint('-------')\n\tparsed = tagger.tag_text(sentence.string)\n\tfor word in parsed:\n\t\t[ mot, pos, lemma ] = fix_word(word.split('\\t'))\n\t\tif pos in pos_tt and model.vocab.get(lemma):\n\t\t\tstring = mot\n\t\t\tswap = model.most_similar(positive=[focus2, lemma],\n\t\t\t\tnegative=[focus1], topn=10)\n\t\t\twhile len(swap) > 0: # go through the top 10 matches and find the\n\t\t\t# first one that (1) has a pos (2) that is valid and (3) is a real\n\t\t\t# word according to aspell\n\t\t\t\tmatch = swap.pop(0)\n\t\t\t\tif pd.get(match[0]) and pos in pd[match[0]] \\\n\t\t\t\t\tand s.check(match[0].encode('utf-8')):\n\t\t\t\t\tstring = match[0]\n\t\t\t\t\tbreak\n\t\t\tnewsentence.append([correct_grammar(string, mot, pos, lemma), pos])\n\t\telse: newsentence.append([mot, pos])\n\tfixedsentence = fix_agreements(newsentence)\n\tprocessed = ' '.join([x[0] for x in fixedsentence])\n\tprocessed = processed.replace(\" ' \", \"'\")\n\tprocessed = processed.replace(\" - \", \"-\")\n\tprocessed = processed.replace(\" , \", \", \")\n\tprocessed = processed.replace(\" .\", \".\")\n\tprocessed = re.sub(r\" -(\\w)\", r\"-\\1\", processed)\n#\tprocessed = re.sub('\\' ', '\\'', processed)\n#\tprocessed = re.sub(' n ', ' ne ', processed)\n#\tprocessed = re.sub('([Qq])ue ([aeiouh\\xe9\\xe8\\xe0])', r\"\\1u'\\2\", processed)\n#\tprocessed = re.sub(\"'([^aeiouh\\xe9\\xe8\\xe0])\", r'e \\1', processed)\n#\tprint('-------')\n\tprint(processed.encode('utf-8'))\n#\tprint('=======')\n","sub_path":"modulateText.py","file_name":"modulateText.py","file_ext":"py","file_size_in_byte":2790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"498731640","text":"from .mate import Mate\nfrom .massembly import MAssembly\nfrom ._version import __version_info__, __version__\n\n\ndef relocate(assy):\n    \"\"\"Relocate the assembly so that all its shapes have their origin at the assembly origin\"\"\"\n\n    from cadquery import Workplane, Location\n\n    def _relocate(assy, origins):\n        origin_mate = origins.get(assy.name)\n        if origin_mate is not None:\n            assy.obj = Workplane(assy.obj.val().moved(origin_mate.loc.inverse))\n            assy.loc = Location()\n        for c in assy.children:\n            _relocate(c, origins)\n\n    origins = {mate_def.assembly.name: mate_def.mate for mate_def in assy.mates.values() if mate_def.origin}\n\n    # relocate all CadQuery objects\n    _relocate(assy, origins)\n\n    # relocate all mates\n    for mate_def in assy.mates.values():\n        origin_mate = origins.get(mate_def.assembly.name)\n        if origin_mate is not None:\n            mate_def.mate = mate_def.mate.moved(origin_mate.loc.inverse)\n","sub_path":"cadquery_massembly/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"116370300","text":"from browsermobproxy import Server\nimport time\nfrom pprint import pprint\nimport json\nimport kemster_util as ku\n\n\nserver = Server(\"./selenium/browsermob-proxy-2.1.0-beta-1/bin/browsermob-proxy\")\n#server = Server(\"./selenium/browsermob-proxy-2.0-beta-6/bin/browsermob-proxy\")\nserver.start()\nproxy = server.create_proxy()\n\nfrom selenium import webdriver\nprofile  = webdriver.FirefoxProfile()\nchrome = webdriver.chrome.options\nprofile.set_proxy(proxy.selenium_proxy())\ndriver = webdriver.Firefox(firefox_profile=profile)\n\nproxy.new_har(\"beamly GA test 1\")\ndriver.get(\"https://uk.beamly.com/tv-news/7-reasons-need-chloe-mad-fat-diary-best-friend/\")\n\n\ndriver.execute_script(\"window.scrollTo(0, 500);\")\ntime.sleep(5)\n\nresp = proxy.har # returns a HAR JSON blob\nproxy.close()\nserver.stop()\ndriver.quit()\n\nparsed_har = ku.parse_har_file(resp, 'google-analytics')\npprint(parsed_har)\njson.dump(parsed_har, open('scenario1_test2.json', 'w+'))\n","sub_path":"python/article_page_load_test.py","file_name":"article_page_load_test.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"237632478","text":"# -*- coding: utf-8 -*-\n\nfrom odoo import models, fields, api, _\nfrom odoo.exceptions import UserError, AccessError\n\nimport logging\n_logger = logging.getLogger(__name__)\n\n\nclass IndySaleOrder(models.Model):\n    _inherit = 'sale.order'\n\n    def _prepare_invoice(self,):\n        invoice_vals = super(IndySaleOrder, self)._prepare_invoice()\n        invoice_vals.update({\n            'legacy_number': self.legacy_number,\n        })\n        return invoice_vals\n\n    @api.multi\n    def _get_default_delivery(self):\n        carrier_id = self.env['delivery.carrier'].search(\n            [('default', '=', True)], limit=1)\n        return carrier_id\n\n    partner_id = fields.Many2one('res.partner', string='Customer', readonly=False, states={\n    }, required=True, change_default=True, index=True, track_visibility='always')\n    legacy_number = field_name = fields.Char()\n    invoice_type = fields.Selection([\n        ('quotatiom', 'Quotation'),\n        ('invoice', 'Invoice'),\n    ])\n    allow_edit_readonly = fields.Boolean(\n        default=True, compute='_compute_allow_edit_readonly')\n\n    # OVERRIDE\n    carrier_id = fields.Many2one('delivery.carrier', string=\"Delivery Method\",\n                                 help=\"Fill this field if you plan to invoice the shipping based on picking.\", default=_get_default_delivery)\n\n    # REMOVED AFTER DB UPDATE\n    confirmation_date = fields.Datetime(string='Confirmation Date', readonly=False, index=True,\n                                        help=\"Date on which the sales order is confirmed.\", oldname=\"date_confirm\")\n\n    print_count = fields.Integer(string='Print Count',)\n\n    def write(self, vals):\n        res = super(IndySaleOrder, self).write(vals)\n        invoice = self.env['account.invoice'].search(\n            [('origin', '=', self.name)])\n\n        for inv in invoice:\n            inv.update({'legacy_number': self.legacy_number})\n\n        return res\n\n    @api.multi\n    def print_quotation(self):\n        self.filtered(lambda s: s.state == 'draft').write({'state': 'sent'})\n        for rec in self:\n            rec.print_count += 1\n        return self.env.ref('sale.action_report_saleorder').report_action(self)\n\n    @api.depends('partner_id')\n    def _set_default_agent(self):\n        for record in self:\n            record.user_id = record.partner_id.user_id\n\n    @api.one\n    def _compute_allow_edit_readonly(self):\n        is_allow_edit = False\n        if self.state in ['draft', 'sent']:\n            is_allow_edit = True\n        else:\n            is_allow_edit = False\n            if self.env.user.has_group('sales_team.group_sale_manager') or self.env.user.has_group('base.group_erp_manager'):\n                is_allow_edit = True\n\n        self.allow_edit_readonly = is_allow_edit\n\n    @api.onchange('partner_id')\n    def onchange_partner_id_carrier_id(self):\n        if self.partner_id:\n            self.carrier_id = self.partner_id.property_delivery_carrier_id or self._get_default_delivery()\n\n    # OVERRIDE / REMOVED AFTER DB UPDATE\n    @api.multi\n    def action_confirm(self):\n        if not self.confirmation_date:\n            raise UserError(_('Set confirmation date first!'))\n\n        confirmation_date = self.confirmation_date\n        result = super(IndySaleOrder, self).action_confirm()\n\n        self.write({\n            'confirmation_date': confirmation_date\n        })\n\n        return result\n\n    @api.multi\n    def action_update_cost(self):\n        for record in self:\n            for line in record.order_line:\n                line._compute_purchase_price()\n            record._compute_total_margin_percent()\n    \n    @api.multi\n    @api.onchange('legacy_number')\n    def onchange_legacy_number(self):\n        for invoice in self.invoice_ids:\n            invoice.legacy_number = self.legacy_number\n    \n    @api.model\n    def read_group(self, domain, fields, groupby, offset=0, limit=None, orderby=False, lazy=True):\n        res = super(IndySaleOrder, self).read_group(domain, fields, groupby, offset, limit, orderby, lazy)\n        context = dict(self._context or {})\n        if 'total_margin_percent' in fields:\n            for line in res:\n                total_margin = line.get('margin')\n                total_amount = line.get('amount_total')\n                total_margin_percent = (total_margin / total_amount) * 100\n                line['total_margin_percent'] = round(total_margin_percent, 2)\n        return res\n\n\nclass IndySaleOrderLine(models.Model):\n    _inherit = 'sale.order.line'\n\n    @api.model\n    def _compute_purchase_price(self):\n        _logger.info(\"OLA\")\n\n        frm_cur = self.env.user.company_id.currency_id\n        to_cur = self.order_id.currency_id\n        purchase_price = self.product_id.standard_price\n        _logger.info(purchase_price)\n        if self.product_uom != self.product_id.uom_id:\n            purchase_price = self.product_id.uom_id._compute_price(\n                purchase_price, self.product_uom)\n        ctx = self.env.context.copy()\n        ctx['date'] = self.order_id.date_order\n        price = frm_cur.with_context(ctx).compute(\n            purchase_price, to_cur, round=False)\n        _logger.info(price)\n        self.write({'purchase_price': price})\n","sub_path":"indigo_prod/models/sale_order.py","file_name":"sale_order.py","file_ext":"py","file_size_in_byte":5175,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"528108537","text":"import collections\nimport re\nimport random\nimport numpy as np\n\ndef load_corpus_time_machine(max_tokens=-1):\n    lines = read_time_machine()\n    tokens = tokenize(lines, 'char')\n    vocab = Vocab(tokens)\n    corpus = [vocab[tk] for line in tokens for tk in line]\n    if max_tokens > 0:\n        corpus = corpus[:max_tokens]\n    return corpus, vocab\n\ndef read_time_machine():\n    with open('../data/timemachine.txt', 'r') as f:\n        lines = f.readlines()\n    return [re.sub('[^A-Za-z]+', ' ', line.strip().lower()) for line in lines]\n\ndef tokenize(lines, token='word'):\n    \"\"\"Split sentences into word or char tokens\"\"\"\n    if token == 'word':\n        return [line.split(' ') for line in lines]\n    elif token == 'char':\n        return [list(line) for line in lines]\n    else:\n        print('ERROR: unkown token type '+token)\n\n# 正则表达式'[^A-Za-z]+' 非字母字符+任何东西被替换为 ‘’ 如 %abc -> ' '\nclass Vocab(object):\n    def __init__(self, tokens, min_freq=0, use_special_tokens=False):\n        # Sort according to frequencies\n        counter = count_corpus(tokens)\n        self.token_freqs = sorted(counter.items(), key=lambda x: x[0])\n        self.token_freqs.sort(key=lambda x: x[1], reverse=True)\n        if use_special_tokens:\n            # For padding, begin of sentence, end of sentence, and unknown\n            self.pad, self.bos, self.eos, self.unk = (0, 1, 2, 3)\n            uniq_tokens = ['<pad>', '<bos>', '<eos>', '<unk>']\n        else:\n            self.unk, uniq_tokens = 0, ['<unk>']\n        uniq_tokens += [token for token, freq in self.token_freqs\n                        if freq >= min_freq and token not in uniq_tokens]\n        self.idx_to_token, self.token_to_idx = [], dict()\n        for token in uniq_tokens:\n            self.idx_to_token.append(token)\n            self.token_to_idx[token] = len(self.idx_to_token) - 1\n\n    def __len__(self):\n        return len(self.idx_to_token)\n\n    def __getitem__(self, tokens):\n        if not isinstance(tokens, (list, tuple)):\n            return self.token_to_idx.get(tokens, self.unk)\n        return [self.__getitem__(token) for token in tokens]\n\n    def to_tokens(self, indices):\n        if not isinstance(indices, (list, tuple)):\n            return self.idx_to_token[indices]\n        return [self.idx_to_token[index] for index in indices]\n\ndef count_corpus(sentences):\n    # Flatten a list of token lists into a list of tokens\n    tokens = [tk for line in sentences for tk in line]\n    return collections.Counter(tokens)\n\n\ndef seq_data_iter_random(corpus, batch_size, num_steps):\n    # Offset the iterator over the data for uniform starts\n    corpus = corpus[np.random.randint(0, num_steps):]\n    # Subtract 1 extra since we need to account for label\n    num_examples = ((len(corpus) - 1) // num_steps)\n    example_indices = list(range(0, num_examples * num_steps, num_steps))\n    np.random.shuffle(example_indices)\n\n    def data(pos):\n        # This returns a sequence of the length num_steps starting from pos\n        return corpus[pos: pos + num_steps]\n\n    # Discard half empty batches\n    num_batches = num_examples // batch_size\n    for i in range(0, batch_size * num_batches, batch_size):\n        # Batch_size indicates the random examples read each time\n        batch_indices = example_indices[i:(i+batch_size)]\n        X = [data(j) for j in batch_indices]\n        Y = [data(j + 1) for j in batch_indices]\n        yield np.array(X), np.array(Y)\n\ndef seq_data_iter_consecutive(corpus, batch_size, num_steps):\n    # Offset for the iterator over the data for uniform starts\n    offset = random.randint(0, num_steps)\n    # Slice out data - ignore num_steps and just wrap around\n    num_indices = ((len(corpus) - offset - 1) // batch_size) * batch_size\n    Xs = np.array(corpus[offset:offset+num_indices])\n    Ys = np.array(corpus[offset+1:offset+1+num_indices])\n    Xs, Ys = Xs.reshape(batch_size, -1), Ys.reshape(batch_size, -1)\n    num_batches = Xs.shape[1] // num_steps\n    for i in range(0, num_batches * num_steps, num_steps):\n        X = Xs[:, i:(i+num_steps)]\n        Y = Ys[:, i:(i+num_steps)]\n        yield X, Y\n\nclass SeqDataLoader(object):\n    \"\"\"A iterator to load sequence data\"\"\"\n    def __init__(self, batch_size, num_steps, use_random_iter, max_tokens):\n        if use_random_iter:\n            self.data_iter_fn = seq_data_iter_random\n        else:\n            self.data_iter_fn = seq_data_iter_consecutive\n        self.corpus, self.vocab = load_corpus_time_machine(max_tokens)\n        self.batch_size, self.num_steps = batch_size, num_steps\n\n    def __iter__(self):\n        return self.data_iter_fn(self.corpus, self.batch_size, self.num_steps)\n\ndef load_data_time_machine(batch_size, num_steps, use_random_iter=False,\n                           max_tokens=10000):\n    data_iter = SeqDataLoader(\n        batch_size, num_steps, use_random_iter, max_tokens)\n    return data_iter, data_iter.vocab","sub_path":"d2l/CP8/d2l.py","file_name":"d2l.py","file_ext":"py","file_size_in_byte":4905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"325381770","text":"import csv\nimport shutil\n\nfrom tempfile import NamedTemporaryFile\n\nFILE_NAME = 'data.csv'\ntemp_file = NamedTemporaryFile(delete=True)\n\nwith open(FILE_NAME, 'rb') as csvfile, temp_file:\n\treader = csv.DictReader(csvfile)\n\tfieldnames = [b'id', b'name', b'email', b'amount', b'sent']\n\twriter = csv.writer(csvfile)\n\t# writer.writeheader()\n\tprint(temp_file.name)\n\n\tname = b\"NAME\"\n\ttemp_file.write(name)\n\n\ttemp_file.seek(0)\n\tprint(temp_file.read())\n\n\tshutil.move(temp_file.name, FILE_NAME)\n","sub_path":"python_csv/editing_csv_data.py","file_name":"editing_csv_data.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"556852998","text":"\"\"\"\nQuick sort:\n- It is developed by Tony Hoare in 1959\n- It is efficient sorting algorithm.\nA well implemented quick sort can out performs heap sort and merger sort\n- It is a comparison based algorithm, not able to faster than linearithmic\ntime complexity.\n- The efficient implementation of quick sort in not stable.\n- It is in-place, does not need additional memory.\n- Average running time complexity O(N*logN)\n- Wort case running time is quadratic O(N^2)\n- It widely ise in programming languages:\n    -> Primitive types, usually quick sort is used.\n    -> Reference types/objects, usually merge sort is used.\n- It is a divide and conquer algorithm.\n\nHow it work:\n- pick element from  the array: this is call \"pivot\"\n- partition phase: rearrange the array, so that all elements with values less\nthan the pivot came before the pivot, while all element with values greater\nthat the pivot com after it.// equal value can go either way\n- recursively apply these steps on the sub array.\n- Base case for recursion: arrays of size zero or one never need to be sorted.\n\nChoose the pivot:\n- It is important if we keep choosing bad pivot, the running time complexity\nwill be quadratic.\n    >> we can choose pivot at random // this usually working fine\n    >> choose the middle index of the array as the pivot\n\nPseudo code:\n\nquickSort(array, low, height)\n    if low >= height\n        return\n    pivot = partition(array, low, height)\n    quickSort(array, low, pivot - 1)\n    quickSort(array, pivot + 1, height)\n\npartition(array, low, height)\n    pivotIndex = (low + height)/2\n    swap(pivotIndex, height)\n\n    i = low\n\n    for j = low to height\n        if array[j <= array[height]\n            swap(array, i, j)\n            i++\n    swap(i, height)\n\n    return i // threshold index\n\"\"\"\ndef quickSort(nums, low, height):\n    if low > height:\n        return\n\n    pivot = partition(nums, low, height)\n    quickSort(nums, low, pivot - 1)\n    quickSort(nums, pivot + 1, height)\n\ndef partition(nums, low, height):\n    pivot = (low + height) // 2\n    swap(nums, pivot, height)\n    i = low\n    for j in range(low, height):\n        if nums[j] <= nums[height]:\n            swap(nums, i, j)\n            i += 1\n    swap(nums, i, height)\n    return i\n\ndef swap(nums, i, j):\n    nums[i], nums[j] = nums[j], nums[i]\n\nif __name__ == \"__main__\":\n\n    array = [23, 6, 4, -1, 0, 12, 8, 3, 1]\n    quickSort(array, 0, len(array) - 1)\n    print(array)","sub_path":"Data Structures/Soring/quicksort.py","file_name":"quicksort.py","file_ext":"py","file_size_in_byte":2417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"497496457","text":"#! /usr/bin/env python\nimport os\nimport numpy as np\nimport re\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import RegexpTokenizer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nimport nltk\nimport math\nimport operator\nimport pandas as pd\n\n'''\nThis script provides functions for data processing\n'''\n\n\ndef get_filepaths(directory):\n    \"\"\"\n    :param directory: where file stores\n    :return: return a list of all files in directory\n    \"\"\"\n    file_paths = []\n    # Walk the tree.\n    for root, directories, files in os.walk(directory):\n        for filename in files:\n            # Join the two strings in order to form the full filepath.\n            filepath = os.path.join(root, filename)\n            file_paths.append(filepath)  # Add it to the list.\n    return file_paths\n\ndef remove_stop_words(sentence):\n    \"\"\"\n    :param sentence: input string\n    :return: sentence after removing stop words\n    \"\"\"\n    tokenizer = RegexpTokenizer(r'\\w+')\n    tokens = tokenizer.tokenize(sentence)\n    filtered_words = [w for w in tokens if not w in stopwords.words('english')]\n    return \" \".join(filtered_words)\n\ndef clean_str(string, rm_stopwords = False):\n\n    \"\"\"\n    Tokenization/string cleaning for all datasets except for SST.\n    Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py\n    \"\"\"\n    string=string.lower()\n\n    string = re.sub(r\"[^A-Za-z0-9(),!?\\'\\`]\", \" \", string)\n    string = re.sub(r\"\\'s\", \" \\'s\", string)\n    string = re.sub(r\"\\'ve\", \" \\'ve\", string)\n    string = re.sub(r\"n\\'t\", \" n\\'t\", string)\n    string = re.sub(r\"\\'re\", \" \\'re\", string)\n    string = re.sub(r\"\\'d\", \" \\'d\", string)\n    string = re.sub(r\"\\'ll\", \" \\'ll\", string)\n    string = re.sub(r\",\", \" , \", string)\n    string = re.sub(r\"!\", \" ! \", string)\n    string = re.sub(r\"\\(\", \" \\( \", string)\n    string = re.sub(r\"\\)\", \" \\) \", string)\n    string = re.sub(r\"\\?\", \" \\? \", string)\n    string = re.sub(r\"\\s{2,}\", \" \", string)\n    if rm_stopwords:\n        string=remove_stop_words(string)\n        return string.strip()\n    else:\n        return string.strip()\n\ndef load_data_and_labels(positive_data_file):\n    \"\"\"\n    Loads MR polarity data from files, splits the data into words and generates labels.\n    Returns split sentences and labels.\n    \"\"\"\n    # Load data from files\n    positive_examples = list(open(positive_data_file, \"r\").readlines())\n    positive_examples = [s.strip() for s in positive_examples]\n    # Split by words\n    x_text = positive_examples\n    x_text = [clean_str(sent,rm_stopwords=False) for sent in x_text]\n    # Generate labels\n    positive_labels = [[0] for _ in positive_examples]\n    y = np.concatenate([positive_labels], 0)\n    return [x_text, y]\n\n\ndef batch_iter(data, batch_size, num_epochs, shuffle=True):\n    \"\"\"\n    Generates a batch iterator for a dataset.\n    \"\"\"\n    data = np.array(data)\n    data_size = len(data)\n    num_batches_per_epoch = int((len(data)-1)/batch_size) + 1\n    for epoch in range(num_epochs):\n        # Shuffle the data at each epoch\n        if shuffle:\n            shuffle_indices = np.random.permutation(np.arange(data_size))\n            shuffled_data = data[shuffle_indices]\n        else:\n            shuffled_data = data\n        for batch_num in range(num_batches_per_epoch):\n            start_index = batch_num * batch_size\n            end_index = min((batch_num + 1) * batch_size, data_size)\n            yield shuffled_data[start_index:end_index]\n\n\n","sub_path":"sentiment_CNN/data_function.py","file_name":"data_function.py","file_ext":"py","file_size_in_byte":3446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"63601234","text":"\"\"\"MongoDB to JSON Schema Validator.\"\"\"\n\n# primitives: array, boolean, integer, number, null, object, string\nbson_types = {\n    \"double\": {\n        \"format\": False,\n        \"primitive\": \"number\"\n    },\n    \"string\": {\n        \"format\": False,\n        \"primitive\": \"string\"\n    },\n    \"object\": {\n        \"format\": False,\n        \"primitive\": \"object\"\n    },\n    \"array\": {\n        \"format\": False,\n        \"primitive\": \"array\"\n    },\n    \"bool\": {\n        \"format\": False,\n        \"primitive\": \"boolean\"\n    },\n    \"date\": {\n        \"format\": \"date-time\",\n        \"primitive\": \"string\"\n    },\n    \"null\": {\n        \"format\": False,\n        \"primitive\": \"null\"\n    },\n    \"regex\": {\n        \"format\": \"regex\",\n        \"primitive\": \"string\"\n    },\n    \"int\": {\n        \"format\": False,\n        \"primitive\": \"integer\"\n    },\n    \"timestamp\": {\n        \"format\": \"date-time\",\n        \"primitive\": \"string\"\n    },\n    \"long\": {\n        \"format\": False,\n        \"primitive\": \"integer\"\n    },\n    \"decimal\": {\n        \"format\": False,\n        \"primitive\": \"number\"\n    }\n}\n\n\ndef mongo_schema_to_json(mongo_schema):\n    \"\"\"Convert bson to json.\"\"\"\n    schema_primitive = mongo_schema['$jsonSchema']['bsonType']\n    required = mongo_schema['$jsonSchema']['required']\n    properties = mongo_schema['$jsonSchema']['properties']\n\n    json_schema = {}\n    # schema declaration\n    json_schema['$schema'] = 'http://json-schema.org/draft-04/schema#'\n    # root schema instance type declaration\n    json_schema['type'] = schema_primitive\n    # required must be an array with unique strings as elements\n    json_schema['required'] = required\n    # initialize the root schema properties\n    json_schema['properties'] = {}\n    # properties must be a valid JSON schema object\n    for key, val in properties.items():\n        prop_schema = {}\n        if isinstance(val['bsonType'], list):\n            tmp_arr = []\n            for x in val['bsonType']:\n                tmp_arr.append(bson_types[x]['primitive'])\n                if bson_types[x]['format']:\n                    prop_schema['format'] = bson_types[x]['format']\n\n            unique_arr = list(set(tmp_arr))\n            if len(unique_arr) is 1:\n                prop_schema['type'] = unique_arr[0]\n            else:\n                prop_schema['type'] = unique_arr\n\n        elif isinstance(val['bsonType'], str):\n            prop_schema['type'] = bson_types[val['bsonType']]['primitive']\n            if bson_types[val['bsonType']]['format']:\n                prop_schema['format'] = bson_types[val['bsonType']]['format']\n\n        for k, v in val.items():\n            if k not in ['bsonType']:\n                prop_schema[k] = v\n\n        json_schema['properties'][key] = prop_schema\n\n    return json_schema\n","sub_path":"app/dispositions/schema/json_validator.py","file_name":"json_validator.py","file_ext":"py","file_size_in_byte":2742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"197913701","text":"###########################################################################\n############### 0_a4 Flip lijnen (lijnrichting omdraaien) #################\n###########################################################################\n\nimport os.path\nimport sys\nimport logging\nimport arcpy\nfrom utils.addresulttodisplay import add_result_to_display\nfrom utils.arcgis_logging import setup_logging\n\nsys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'external'))\n\nfrom gistools.utils.geometry import TMultiLineString\n\nlogging.basicConfig(level=logging.INFO)\nsetup_logging(arcpy)\nlog = logging.getLogger(__file__)\nlog.setLevel(logging.INFO)\n    \n# Read the parameter values\n# 0: Lijnenbestand\n# 1: Nieuw bestand maken (boolean)\n# 2: Doelbestand\n\ninput_fl = arcpy.GetParameter(0)\ncreate_new_file = arcpy.GetParameter(1)\noutput_file = arcpy.GetParameterAsText(2)\n\n# Test script without ArcMAP\n# input_fl = './testdata/input/Testdata_watergangen.shp'\n# create_new_file = True\n# output_file = './testdata/output/0_a4_output.shp'\n\n# Print ontvangen input naar console\nlog.info('Ontvangen parameters:')\nlog.info('Lijnenbestand = %s', str(input_fl))\nlog.info('Nieuw bestand maken = %s', str(create_new_file))\nlog.info('Doelbestand = %s', str(output_file))\n\nif create_new_file:\n    if output_file == '':\n        log.error('Uitvoerfile is verplicht als er een nieuw bestand moet worden gemaakt')\n        raise arcpy.ExecuteError('Uitvoerfile is verplicht als er een nieuw bestand moet worden gemaakt')\n    if output_file[-4:] != \".shp\":\n        output_file = output_file + \".shp\"\n\n# voorbereiden data typen en inlezen data\nlog.info('Bezig met voorbereiden van de data...')\n\nif create_new_file:\n    log.info('Bezig met het kopieren naar het doelbestand...')\n    if type(input_fl) == arcpy.mapping.Layer:\n        input_name = input_fl.dataSource\n    else:\n        input_name = input_fl\n\n    arcpy.CopyFeatures_management(input_name, output_file)\n    lyr = arcpy.mapping.Layer(output_file)\n\n    if type(input_fl) == arcpy.mapping.Layer:\n        selection_set = input_fl.getSelectionSet()\n        if selection_set is not None:\n            lyr.setSelectionSet('NEW', selection_set)\nelse:\n    lyr = input_fl\n\nlog.info('Bezig met uitvoeren van line_flip en update bestand...')\n# only takes selection\ncursor = arcpy.UpdateCursor(lyr, ['SHAPE'])\n\nfor row in cursor:\n    geom = row.getValue('SHAPE')\n    line = TMultiLineString([[(point.X, point.Y) for\n                              point in line] for line in geom])\n    line.get_flipped_line()\n\n    mline = arcpy.Array()\n    for line_part in line.get_flipped_line():\n        array = arcpy.Array()\n        for p in line_part:\n            point.X = p[0]\n            point.Y = p[1]\n            array.add(point)\n\n        mline.add(array)\n    row.setValue('SHAPE', mline)\n    cursor.updateRow(row)\n\n# nieuwe file of geupdate laag die niet uit layers is geselecteerd toevoegen aan arcgis lagen\nif create_new_file or type(lyr) != arcpy.mapping.Layer:\n    output_name = os.path.basename(output_file).split('.')[0]\n    add_result_to_display(lyr, output_name)\n\nlog.info('Gereed')\n","sub_path":"0_a4_flipline.py","file_name":"0_a4_flipline.py","file_ext":"py","file_size_in_byte":3114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"112917404","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport mptt.fields\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('forum', '0004_auto_20150525_2006'),\n    ]\n\n    operations = [\n        migrations.AlterModelOptions(\n            name='forumgroup',\n            options={'ordering': ('sort_order', 'name')},\n        ),\n        migrations.AddField(\n            model_name='forumgroup',\n            name='level',\n            field=models.PositiveIntegerField(default=0, editable=False, db_index=True),\n            preserve_default=False,\n        ),\n        migrations.AddField(\n            model_name='forumgroup',\n            name='lft',\n            field=models.PositiveIntegerField(default=0, editable=False, db_index=True),\n            preserve_default=False,\n        ),\n        migrations.AddField(\n            model_name='forumgroup',\n            name='rght',\n            field=models.PositiveIntegerField(default=0, editable=False, db_index=True),\n            preserve_default=False,\n        ),\n        migrations.AddField(\n            model_name='forumgroup',\n            name='tree_id',\n            field=models.PositiveIntegerField(default=0, editable=False, db_index=True),\n            preserve_default=False,\n        ),\n        migrations.AlterField(\n            model_name='forumgroup',\n            name='parent',\n            field=mptt.fields.TreeForeignKey(related_name='chilren', blank=True, to='forum.ForumGroup', null=True),\n        ),\n    ]\n","sub_path":"apps/forum/migrations/0005_auto_20150715_2240.py","file_name":"0005_auto_20150715_2240.py","file_ext":"py","file_size_in_byte":1537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"28038986","text":"import RPi.GPIO as GPIO\nimport time\n\n# GPIO SETTINGS\nGPIO.setmode(GPIO.BCM)\nGPIO.setwarnings(False)\n\n# BUTTON SETUP\nGPIO.setup(17, GPIO.IN)\n\n# LEDS SETUP\nleds = [22, 27, 4]\nfor led in leds:\n    GPIO.setup(led, GPIO.OUT)\n\nwhile True:\n    if GPIO.input(17) == False:\n        for num in range(len(leds)):\n            GPIO.output(leds[num], GPIO.HIGH)\n            time.sleep(0.2)\n            if num + 1 > 2:\n                GPIO.output(leds[0], GPIO.LOW)\n            GPIO.output(leds[num], GPIO.LOW)\n    elif GPIO.input(17) == True:\n        for led in leds:\n            GPIO.output(led, GPIO.LOW)\n","sub_path":"leds/button.py","file_name":"button.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"156382077","text":"import tensorflow as tf\nimport numpy as np\nimport math\n\n\n# taken from https://github.com/tensorflow/tensorflow/issues/16831\ndef dynamic_conv(\n        input,\n        filter,\n        strides=[1,1],\n        padding='SAME',\n        dilation_rate=None,\n):\n    \"\"\"\n    Equivalent to tf.nn.convolution, but filter has additional\n    batch dimension. This allows the filter to be a function\n    of some input, hence, enabling dynamic convolutions.\n\n    Parameters\n    ----------\n    input:  A Tensor. Must be one of the following types: float32, float64, int64, int32,\n            uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half.\n            2d case:\n            Shape [batch, in_depth, in_height, in_channels].\n            3d case:\n            Shape [batch, in_depth, in_height, in_width, in_channels].\n\n    filter: A Tensor. Must have the same type as input.\n            in_channels must match between input and filter.\n            2d case:\n            Shape [batch, filter_x, filter_y, in_ch, out_ch].\n            3d case:\n            Shape [batch, filter_x, filter_y, filter_z, in_ch, out_ch] .\n\n    strides:    see tf.nn.convolution\n    padding:    see tf.nn.convolution\n\n    dilation_rate: see tf.nn.convolution\n\n    Returns\n    -------\n            A Tensor. Has the same type as input.\n    \"\"\"\n\n    assert len(filter.get_shape()) == len(input.get_shape()) + 1\n    assert filter.get_shape()[0] == input.get_shape()[0]\n\n    split_inputs = tf.split(input,\n                            input.get_shape().as_list()[0],\n                            axis=0)\n    split_filters = tf.unstack(filter,\n                               input.get_shape().as_list()[0],\n                               axis=0)\n\n    output_list = []\n    for split_input, split_filter in zip(split_inputs,split_filters):\n        output_list.append(\n            tf.nn.convolution(input=split_input,\n                              filters=split_filter,\n                              strides=strides,\n                              padding=padding,\n                              dilations=dilation_rate)\n        )\n    output = tf.concat(output_list, axis=0)\n    return output\n\n\nclass Memory:\n\n    def __init__(self, memory_locations=128, word_size=20, read_heads=1, batch_size=1):\n        \"\"\"\n        constructs a memory matrix with read heads and a write head as described\n        in the \"Neural Turing Machines\" paper\n        https://arxiv.org/abs/1410.5401\n\n        Parameters:\n        ----------\n        memory_locations: int\n            the number of memory locations\n        word_size: int\n            the vector size at each location\n        read_heads: int\n            the number of read heads that can read simultaneously from the memory\n        batch_size: int\n            the size of input data batch\n        \"\"\"\n\n        self.memory_locations = memory_locations\n        self.word_size = word_size\n        self.read_heads = read_heads\n        self.batch_size = batch_size\n\n    def init_memory(self):\n        \"\"\"\n        returns the initial values for the memory Parameters\n\n        Returns: Tuple\n        \"\"\"\n        return (\n            tf.random.truncated_normal([self.batch_size, self.memory_locations, self.word_size], mean = 0.5, stddev=0.2),\n            tf.fill([self.batch_size, self.memory_locations, 1], 1e-6),  # initial write weighting\n            tf.random.truncated_normal([self.batch_size, self.memory_locations, self.read_heads],  mean = 0.4, stddev=0.1),\n            tf.fill([self.batch_size, self.word_size, self.read_heads], 1e-6),\n        )\n\n    def get_content_adressing(self, memory_matrix, keys, strengths):\n        \"\"\"\n        retrives a content-based addressing weighting given the keys\n\n        Parameters:\n        ----------\n        memory_matrix: Tensor (batch_size, memory_locations, word_size)\n            the memory matrix to lookup in\n        keys: Tensor (batch_size, word_size, number_of_keys)\n            the keys to query the memory with\n        strengths: Tensor (batch_size, number_of_keys)\n            the list of strengths for each lookup key\n\n        Returns: Tensor (batch_size, memory_locations, number_of_keys)\n            The list of lookup weightings for each provided key\n        \"\"\"\n\n        normalized_memory = tf.nn.l2_normalize(memory_matrix, 2)\n        normalized_keys = tf.nn.l2_normalize(keys, 1)\n\n        similiarity = tf.matmul(normalized_memory, normalized_keys)\n        strengths = tf.expand_dims(strengths, 1)\n\n        return tf.nn.softmax(similiarity * strengths, 1)\n\n    def apply_interpolation(self,content_weights,prev_weights,interpolation_gate):\n        \"\"\"\n        retrives a location-based addressing given the interpolation_gate\n\n        Parameters:\n        ----------\n        content_weights: Tensor (batch_size, memory_locations, number_of_heads)\n            content_based addressing weight(s)\n        prev_weights: Tensor (batch_size, memory_locations, number_of_heads)\n            the weighting(s) from the last time step\n        interpolation_gate: Tensor (batch_size, number_of_heads)\n            blending factor(s) g\n\n        Returns: Tensor (batch_size, memory_locations, number_of_heads)\n            The gated weighting(s)\n        \"\"\"\n        interpolation_gate = tf.expand_dims(interpolation_gate,1)\n        gated_weighting = interpolation_gate * content_weights + (1.0 - interpolation_gate) * prev_weights\n\n        return gated_weighting\n\n    def apply_conv_shift(self,gated_weighting,shift_weighting):\n        \"\"\"\n        applying rotation over the gated weights\n\n        Parameters:\n        ----------\n        gated_weighting: Tensor (batch_size, memory_locations, number_of_keys)\n            The gated weighting\n        shift_weighting: Tensor (batch_size, shift_range*2+1, number_of_keys)\n            distribution over the allowed integer shifts\n\n        Returns: Tensor (batch_size, memory_locations, number_of_keys)\n            weights after circular Convolution\n        \"\"\"\n        gated_weighting = tf.concat(\n            [\n                tf.expand_dims(gated_weighting[:, -1, :], axis=-1),\n                gated_weighting,\n                tf.expand_dims(gated_weighting[:, 0, :], axis=-1)\n            ],\n            1)\n\n        # tensorflow only handles first dimension as batch if the tensor has at least 4 dimensions\n        #   (BHWC representation)\n        # shift_weighting needs a 5th dimension for the dynamic convolution\n        gated_weighting = tf.expand_dims(gated_weighting,-1)\n        shift_weighting = tf.expand_dims(shift_weighting,-1)\n        shift_weighting = tf.expand_dims(shift_weighting,-1)\n\n        conv = dynamic_conv(\n            gated_weighting,\n            shift_weighting,\n            padding='VALID')\n        return tf.squeeze(conv, axis=-1)\n\n    def sharp_weights(self, after_conv_shift, sharp_gamma):\n        \"\"\"\n        Sharpens the final weights\n\n        Parameters:\n        ----------\n        after_conv_shift: Tensor (batch_size, memory_locations, number_of_keys)\n            weights after circular Convolution\n        sharp_gamma: Tensor (batch_size, number_of_keys)\n            scalar to sharpen the final weights\n\n        Returns: Tensor (batch_size, memory_locations, number_of_keys)\n            final weights\n        \"\"\"\n        sharp_gamma = tf.expand_dims(sharp_gamma,1)\n        powed_conv_w = tf.pow(after_conv_shift, sharp_gamma)\n        return powed_conv_w / ( tf.expand_dims(tf.reduce_sum(input_tensor=powed_conv_w,axis=1),1) + 1e-10 )\n\n    def update_memory(self, memory_matrix, write_weighting, add_vector, erase_vector):\n        \"\"\"\n        updates and returns the memory matrix given the weighting, add and erase vectors\n        and the memory matrix from previous step\n\n        Parameters:\n        ----------\n        memory_matrix: Tensor (batch_size, memory_locations, word_size)\n            the memory matrix from previous step\n        write_weighting: Tensor (batch_size, memory_locations,1)\n            the weight of writing at each memory location\n        add_vector: Tensor (batch_size, word_size)\n            a vector specifying what to write\n        erase_vector: Tensor (batch_size, word_size)\n            a vector specifying what to erase from memory\n\n        Returns: Tensor (batch_size, memory_locations, word_size)\n            the updated memory matrix\n        \"\"\"\n\n        # expand data with a dimension of 1 at multiplication-adjacent location\n        # to force matmul to behave as an outer product\n        add_vector = tf.expand_dims(add_vector, 1)\n        erase_vector = tf.expand_dims(erase_vector, 1)\n\n        erasing = memory_matrix * (1 - tf.matmul(write_weighting, erase_vector))\n        writing = tf.matmul(write_weighting, add_vector)\n        updated_memory = erasing + writing\n\n        return updated_memory\n\n    def update_read_vectors(self, memory_matrix, read_weightings):\n        \"\"\"\n        reads, updates, and returns the read vectors of the recently updated memory\n\n        Parameters:\n        ----------\n        memory_matrix: Tensor (batch_size, memory_locations, word_size)\n            the recently updated memory matrix\n        read_weightings: Tensor (batch_size, memory_locations, read_heads)\n            the amount of info to read from each memory location by each read head\n\n        Returns: Tensor (batch_size, word_size, read_heads)\n        \"\"\"\n\n        updated_read_vectors = tf.matmul(memory_matrix, read_weightings, adjoint_a=True)\n\n        return updated_read_vectors\n\n    def write(self, memory_matrix, write_weighting, key, strength, interpolation_gate, shift_weighting,\n              sharp_gamma, add_vector, erase_vector):\n        \"\"\"\n        defines the complete pipeline of writing to memory given the write variables,\n        the memory_matrix and the corresponding vectors from the previous step\n\n        Parameters:\n        ----------\n        memory_matrix: Tensor (batch_size, memory_locations, word_size)\n            the memory matrix from previous step\n        write_weighting: Tensor (batch_size, memory_locations)\n            the write_weighting from the last time step\n        key: Tensor (batch_size, word_size, 1)\n            the key to query the memory location with\n        strength: Tensor (batch_size, 1)\n            the strength of the query key (beta)\n        interpolation_gate: Tensor (batch_size, 1)\n            blending factor g\n        shift_weighting: Tensor (batch_size, shift_range*2+1,1)\n            distribution over the allowed integer shifts\n        sharp_gamma: Tensor (batch_size, 1)\n            scalar to sharpen the final weights\n        add_vector: Tensor (batch_size, word_size)\n            specifications of what to add to memory\n        erase_vector: Tensor(batch_size, word_size)\n            specifications of what to erase from memory\n\n        Returns : Tuple\n            the updated write_weighting: Tensor(batch_size, memory_locations)\n            the updated memory_matrix: Tensor (batch_size, memory_locations, word_size)\n        \"\"\"\n\n        content_addressed_w = self.get_content_adressing(memory_matrix, key, strength)\n        gated_weighting = self.apply_interpolation(content_addressed_w,write_weighting,interpolation_gate)\n        after_conv_shift = self.apply_conv_shift(gated_weighting,shift_weighting)\n        new_write_weighting = self.sharp_weights(after_conv_shift, sharp_gamma)\n        # Write in memory\n        new_memory_matrix = self.update_memory(memory_matrix, new_write_weighting, add_vector, erase_vector)\n\n        return new_write_weighting, new_memory_matrix\n\n\n    def read(self, memory_matrix, read_weightings, keys, strengths, interpolation_gates, shift_weightings,\n             sharp_gammas):\n        \"\"\"\n        defines the complete pipeline for reading from memory\n\n        Parameters:\n        ---------\n        memory_matrix: Tensor (batch_size, memory_locations, word_size)\n            the memory matrix from previous step\n        read_weightings: Tensor (batch_size, memory_locations, read_heads)\n            the read_weightings from the last time step\n        key: Tensor (batch_size, word_size, read_heads)\n            the keys to query the memory location with\n        strengths: Tensor (batch_size, read_heads)\n            the strength of the query key (beta)\n        interpolation_gates: Tensor (batch_size, read_heads)\n            blending factor g\n        shift_weightings: Tensor (batch_size, shift_range*2+1,read_heads)\n            distribution over the allowed integer shifts\n        sharp_gammas: Tensor (batch_size, read_heads)\n            scalar to sharpen the final weights\n\n        Returns: Tuple\n            the updated read_weightings: Tensor(batch_size, memory_locations, read_heads)\n            the recently read vectors: Tensor (batch_size, word_size, read_heads)\n        \"\"\"\n\n        content_addressed_w = self.get_content_adressing(memory_matrix, keys, strengths)\n        gated_weightings = self.apply_interpolation(content_addressed_w,read_weightings,interpolation_gates)\n        after_conv_shift = self.apply_conv_shift(gated_weightings,shift_weightings)\n        new_read_weightings = self.sharp_weights(after_conv_shift, sharp_gammas)\n\n        new_read_vectors = self.update_read_vectors(memory_matrix, new_read_weightings)\n\n        return new_read_weightings, new_read_vectors\n","sub_path":"memory.py","file_name":"memory.py","file_ext":"py","file_size_in_byte":13264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"220555624","text":"# -*- coding: utf-8 -*-\nimport os\nimport base64\nfrom openerp import SUPERUSER_ID, tools\nfrom openerp import models, fields, api\n\n\nclass Attachment(models.Model):\n    _inherit = 'ir.attachment'\n\n    budget_template_type = fields.Selection(\n        selection=[\n            ('plan_unit_based', 'Unit Based'),\n            ('plan_invest_asset_plan', 'Invest Asset Item'),\n        ]\n    )\n    budget_plan_id = fields.Many2one(\n        'budget.plan.unit',\n        string=\"Budget Plan\",\n        copy=False,\n    )\n    invest_asset_plan_id = fields.Many2one(\n        'invest.asset.plan',\n        string=\"Invest Asset Plan\",\n        copy=False,\n    )\n\n    @api.model\n    def _create_plan_template(self, type, filename):\n        templates = self.search_count([('budget_template_type', '=', type)])\n        if templates < 1:\n            path = os.path.join('pabi_budget_xls', 'template', filename)\n            image_file = tools.file_open(path, 'rb')\n            try:\n                file_data = image_file.read()\n                self.create({\n                    'name': 'Export Template -' + filename,\n                    'datas': base64.encodestring(file_data),\n                    'datas_fname': filename,\n                    'description': 'Export Template -' + filename,\n                    'budget_template_type': type,\n                })\n            except:\n                pass\n\n    def init(self, cr):\n        if 'budget.plan.unit' not in self._models_check:\n            self._models_check.update({'budget.plan.unit': 'budget_plan_id'})\n        if 'invest.asset.plan' not in self._models_check:\n            self._models_check.update(\n                {'invest.asset.plan': 'invest_asset_plan_id'})\n        self._create_plan_template(\n            cr, SUPERUSER_ID, 'plan_unit_based',\n            'BudgetPlan_UnitBased.xlsx')\n        self._create_plan_template(\n            cr, SUPERUSER_ID, 'plan_invest_asset_plan',\n            'BudgetPlan_InvestmentAssetItem.xlsx')\n","sub_path":"pabi_budget_xls/models/attachment.py","file_name":"attachment.py","file_ext":"py","file_size_in_byte":1963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"41048063","text":"import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import LogNorm\n\n############################################# SET PARAMETERS ###########################################################\n# all of the filenames of the images of egg that will be used to composite image of egg\neggs = ['12_10x_e1.png', '12_10x_e2.png','12_10x_e3.png','12_10x_e4.png', '30_10x_e1.png', '30_10x_e2.png',\n        '61_10x_e1.png', '61_10x_e2.png', '61_10x_e3.png', '61_10x_e4.png', '66_10x_e1.png', '66_10x_e2.png', '66_10x_e3.png',\n        '66_10x_e4.png', '68_10x_e1.png', '68_10x_e2.png', '68_10x_e3.png', '68_10x_e4.png', '103_10x_e1.png', '103_10x_e2.png',\n        '127_10x_e1.png', '127_10x_e2.png', '127_10x_e3.png', '127_10x_e4.png', '153_10x_e1.png', '153_10x_e2.png', '153_10x_e3.png',\n        '153_10x_e4.png', '163_10x_e1.png', '163_10x_e2.png', '163_10x_e3.png', '163_10x_e4.png']\n# all of the filenames of the image of sperm that will be used to create a composite image of sperm\nsperm = ['12_10x_s1.png', '12_10x_s2.png', '12_10x_s3.png', '12_10x_s4.png', '61_10x_s1.png', '61_10x_s2.png', '61_10x_s3.png',\n         '61_10x_s4.png', '66_10x_s1.png', '66_10x_s2.png', '66_10x_s3.png', '68_10x_s1.png','68_10x_s2.png', '68_10x_s3.png',\n         '127_10x_s1.png', '127_10x_s2.png', '127_10x_s3.png', '127_10x_s4.png', '153_10x_s1.png','153_10x_s2.png', '153_10x_s3.png',\n         '153_10x_s4.png', '163_10x_s1.png', '163_10x_s2.png', '163_10x_s3.png', '163_10x_s4.png']\n# name of the final composite image of egg\nimage_name_egg = \"final_image_egg_new_10x_4.jpg\"\n# name of the final composite image for sperm\nimage_name_sperm = \"final_image_sperm_new_10x_4.jpg\"\nimages_egg = []\nimages_sperm = []\nwhat_to_read = image_name_sperm\n\n############################################ CREATE COMPOSITE IMAGES ###################################################\n# resize all of the egg images to be the same size to one another\nfor egg in eggs:\n    readimage = cv2.imread(egg)\n    images_egg.append(cv2.resize(readimage, (200, 300)))\n# resize all of the sperm images to be the same size to one another\nfor sp in sperm:\n    readimage_s = cv2.imread(sp)\n    images_sperm.append(cv2.resize(readimage_s, (25, 25)))\n# combine all of the egg images together to create composite egg image\nfinal_image_egg = np.vstack(tuple(images_egg))\n# combine all of the sperm images together to create a composite sperm image\nfinal_image_sperm = np.vstack(tuple(images_sperm))\n\n#create composite egg image\ncv2.imwrite(image_name_egg, final_image_egg)\n#create composite sperm image\ncv2.imwrite(image_name_sperm, final_image_sperm)\n\n########################################### CREATE DENSITY PLOTS #######################################################\n# read composite image that was created\nimage = cv2.imread(what_to_read)\n# change the color space of the composite image to the color space that needs to be analyzed (BGR, HSV, Lab, YCrCb)\nimage = cv2.cvtColor(image, cv2.COLOR_BGR2YCrCb)\n\nB = np.array([])\nG = np.array([])\nR = np.array([])\n\n# create density plots\nb = image[:,:,0]\nb = b.reshape(b.shape[0]*b.shape[1])\ng = image[:,:,1]\ng = g.reshape(g.shape[0]*g.shape[1])\nr = image[:,:,2]\nr = r.reshape(r.shape[0]*r.shape[1])\nB = np.append(B,b)\nG = np.append(G,g)\nR = np.append(R,r)\nnbins = 10\nplt.hist2d(G, R, bins = nbins, norm = LogNorm())\n# label density plot axes\nplt.xlabel('Cr')\nplt.ylabel('Cb')\nplt.xlim([0,255])\nplt.ylim([0,255])\nplt.savefig('/Users/hanara/PycharmProjects/o_angasi/10x_colorplots_4/CrCb_10x_s.png')\n","sub_path":"O_angasi/create_density_plots.py","file_name":"create_density_plots.py","file_ext":"py","file_size_in_byte":3518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"444901056","text":"from django import template \nfrom spatulaApp.models import Category, Recipe, Rating, UserProfile\nfrom django.contrib.auth.models import User\n\nregister = template.Library() \n\n@register.filter\ndef getKeyImg(h,key):\n    \"Gets the image for display recipe on index page\"\n    try:\n        if type(h) == list:\n            for i in h:\n                for j in i:\n                    if j.belongsto.__str__() == key.__str__():\n                        return j.image\n            \n        for item in h:\n            if item.belongsto.__str__() == key.__str__():\n                return item.image\n    except AttributeError:\n        return None\n\n        \n@register.filter\ndef getKeyImgList(h,key):\n    \"Get all images belonging to recipe object\"\n    images = []\n    \n    for item in h:\n        if item.belongsto.__str__() == key.__str__():\n            images.append(item.image)\n    return images\n\n@register.filter\ndef getReviews(h, key): \n    reviews = []\n    \n    for item in h: \n        if item.recipe.__str__() ==key.__str__(): \n            item.rating = str(round((int(item.rating)*2)))\n            reviews.append(item)\n    return reviews\n    \n@register.simple_tag\ndef double(a):\n    return a*2\n\n@register.filter\ndef getRating(recipeID):\n    ratings = Rating.objects.filter(recipe=recipeID)\n    rating = 0\n    no_entries = 0\n    for r in ratings:\n        rating += r.rating\n        no_entries +=1 \n\n\n    # bad idea to divide by zero    \n    if no_entries == 0:\n        no_entries = 1\n    return str(round((rating*2)/no_entries))\n\n\n@register.filter\ndef getNoRatings(recipeID):\n    return Rating.objects.filter(recipe=recipeID).count()\n    \n@register.filter\ndef recipeNameLengthCheck(name):\n    if len(str(name))>21:\n        name = name[:19]+\"...\"\n    return name\n\n@register.filter\ndef getProfileRating(username):\n\n    # Users rating is based an averge of all their recipies ratings\n    #\n\n    try:\n        user = UserProfile.objects.get(user=User.objects.get(username=username))\n        recipies = Recipe.objects.filter(postedby=user.id)\n\n        total = 0\n        length = len(recipies)\n        for recipie in recipies:\n            total += int(getRating(recipie.id)) \n        \n        if length==0:\n            length = 1\n\n        return str(round((total/length)*2,0)/2) \n\n    except UserProfile.DoesNotExist:\n        return str(0)\n    except User.DoesNotExist:\n        return str(0)\n\n@register.filter\ndef getNumRecipies(username):\n    try:\n        user = UserProfile.objects.get(user=User.objects.get(username=username))\n        recipies = Recipe.objects.filter(postedby=user.id)\n        return str(len(recipies))\n    except Exception:\n        return str(0)\n        \n\n\n\n\n\n\n\n    \n\n","sub_path":"spatula_project/spatulaApp/templatetags/spatulaApp_template_tags.py","file_name":"spatulaApp_template_tags.py","file_ext":"py","file_size_in_byte":2674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"485582882","text":"import os\nimport sys\nimport json\nimport requests\n\nfrom uuid import uuid4\n\nfrom resources.helpers import create_logger\n\nresp_path = os.path.abspath(\"responses.json\")\n\nheaders = {\n\t'Content-Type' : 'application/json',\n\t'Access-Control-Allow-Headers': 'Origin, X-Requested-With, Content-Type, Accept, x-auth'\n}\n\nTWILIO_SID = os.environ.get('sid', None)\nTWILIO_AUTHTOKEN = os.environ.get('auth_token', None)\nTWILIO_ENDPOINT = os.environ.get('api', None)\nTWILIO_NUMBER = os.environ.get('number', None)\n\n_CURRENT_MODULE_ = sys.modules[__name__]\n\nlogger = create_logger('utilities')\n\ndef get_response(path):\n\tresponses = {}\n\tprint(resp_path)\n\ttry:\n\t\twith open(path, \"r\") as store:\n\t\t\tresponses = json.load(store)\n\t\t\tstore.close()\n\texcept (IOError, OSError):\n\t\treturn responses\n\treturn responses\n\ndef make_response(k, **kwargs):\n\tloaded = None\n\tmessage = None\n\n\tresponse = get_response(resp_path)\n\tif response:\n\t\tloaded = response.get(k, None)\n\telse:\n\t\treturn None\n\tif not loaded:\n\t\tlogger.error(\"Could not find specified option in provided responses.\")\n\t\treturn None\n\n\tif k == 'w-greeting':\n\t\tmsg = loaded.get('text') + '{1}!\\n' + loaded.get('description')\n\t\treturn msg\n\n\treturn loaded['text']\n\ndef send_message(number, message):\n\tdata = {\n        \"To\": number,\n        \"From\": TWILIO_NUMBER,\n        \"Body\": message,\n    }\n\tapi = TWILIO_ENDPOINT.format(TWILIO_SID)\n\tr = requests.post(api, data = data, auth = (TWILIO_SID, TWILIO_AUTHTOKEN))\n\t\n\tif r.status_code in [200, 201]:\n\t\tlogger.info(\"Successfully sent message to Twilio api!\")\n\telse:\n\t\tlogger.error('{} :{}'.format(r.status_code, r.text))\n\ndef generate_user_session():\n\tsession = uuid4().hex\n\n\treturn session\n\ndef make_introduction_replies():\n    pass","sub_path":"resources/twilio/utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":1702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"596069834","text":"\n\"\"\"\nTestNetflix.py\n    Tests Netflix.py for correct functionality\n    Unit Testing\n\"\"\"\n\n# https://docs.python.org/3.4/reference/simple_stmts.html#grammar-token-assert_stmt\n# pylint: disable=too-many-public-methods\n\n# -------\n# imports\n# -------\n\nfrom io import StringIO\nfrom unittest import main, TestCase\n\nfrom Netflix import read_probe, eval_probe, print_probe, solve_probe\nfrom Netflix import get_predicted_rating, calculate_rmse, print_rmse\n\n\nclass TestNetflix(TestCase):\n\n    \"\"\"\n    Unit tests for Netflix.py\n    \"\"\"\n\n    def test_read_probe_1(self):\n        \"\"\"\n        test read given a line with a movie_id\n        \"\"\"\n        line = \"2034:\\n\"\n        i, j = read_probe(line)\n        self.assertEqual(i, 2034)\n        self.assertEqual(j, True)\n\n    def test_read_probe_2(self):\n        \"\"\"\n        test read given a line with a customer_id\n        \"\"\"\n        line = \"1417435\\n\"\n        i, j = read_probe(line)\n        self.assertEqual(i, 1417435)\n        self.assertEqual(j, False)\n\n    def test_read_probe_3(self):\n        \"\"\"\n        test read given a line with a customer_id\n        \"\"\"\n        line = \"2312054\\n\"\n        i, j = read_probe(line)\n        self.assertEqual(i, 2312054)\n        self.assertEqual(j, False)\n\n    def test_eval_probe_1(self):\n        \"\"\"\n        test eval given a movie_id, customer_id, and list of values\n        minimum predicted rating should be 1\n        \"\"\"\n        movie = 2034\n        customer = 348443\n        caches = [{movie: {customer: 3}},\n                  {customer: {2000: -2.5}}, {movie: -.5}, {movie: 2000}, 3]\n        values = []\n        predicted_rating, values = eval_probe(movie, customer, values, caches)\n        self.assertEqual(predicted_rating, 1.0)\n        self.assertEqual(len(values), 1)\n        self.assertEqual(values[0], 4.0)\n\n    def test_eval_probe_2(self):\n        \"\"\"\n        test eval given a movie_id, customer_id, and list of values\n        \"\"\"\n        movie = 11536\n        customer = 157473\n        caches = [{movie: {customer: 3}}, {\n            customer: {1995: .4}}, {movie: -.2}, {movie: 1995}, 3]\n        values = []\n        predicted_rating, values = eval_probe(movie, customer, values, caches)\n        self.assertEqual(predicted_rating, 3.2)\n        self.assertEqual(len(values), 1)\n        self.assertEqual(values[0], 0.04000000000000007)\n\n    def test_eval_probe_3(self):\n        \"\"\"\n        test eval given a movie_id, customer_id, and list of values\n        maximum predicted rating should be 5\n        \"\"\"\n        movie = 11533\n        customer = 223257\n        caches = [{movie: {customer: 2}},\n                  {customer: {2001: 1.5}}, {movie: 1.5}, {movie: 2001}, 3]\n        values = []\n        predicted_rating, values = eval_probe(movie, customer, values, caches)\n        self.assertEqual(predicted_rating, 5.0)\n        self.assertEqual(len(values), 1)\n        self.assertEqual(values[0], 9.0)\n\n    def test_print_probe_1(self):\n        \"\"\"\n        test print for a customer_id\n        \"\"\"\n        writer = StringIO()\n        customer_id = 704465\n        print_probe(customer_id, writer)\n        self.assertEqual(writer.getvalue(), \"704465\\n\")\n\n    def test_print_probe_2(self):\n        \"\"\"\n        test print for movie_id\n        \"\"\"\n        writer = StringIO()\n        movie_id = \"12021:\"\n        print_probe(movie_id, writer)\n        self.assertEqual(writer.getvalue(), \"12021:\\n\")\n\n    def test_print_probe_3(self):\n        \"\"\"\n        test print for movie_id\n        \"\"\"\n        writer = StringIO()\n        movie_id = \"742:\"\n        print_probe(movie_id, writer)\n        self.assertEqual(writer.getvalue(), \"742:\\n\")\n\n    def test_print_rmse_1(self):\n        \"\"\"\n        test print for rounded up rmse to 2 decimal places\n        \"\"\"\n        writer = StringIO()\n        rmse = 0.799535646\n        print_rmse(rmse, writer)\n        self.assertEqual(writer.getvalue(), \"RMSE: 0.80\\n\")\n\n    def test_print_rmse_2(self):\n        \"\"\"\n        test print for rounded up rmse to 2 decimal places\n        \"\"\"\n        writer = StringIO()\n        rmse = 0.80\n        print_rmse(rmse, writer)\n        self.assertEqual(writer.getvalue(), \"RMSE: 0.80\\n\")\n\n    def test_print_rmse_3(self):\n        \"\"\"\n        test print for rounded up rmse to 2 decimal places\n        \"\"\"\n        writer = StringIO()\n        rmse = 0.8001\n        print_rmse(rmse, writer)\n        self.assertEqual(writer.getvalue(), \"RMSE: 0.80\\n\")\n\n    def test_solve_probe_1(self):\n        \"\"\"\n        test predicted_rating for 3 customers and rmse\n        \"\"\"\n        reader = StringIO(\"7421:\\n1459122\\n859476\\n823164\")\n        writer = StringIO()\n        caches = [{7421: {1459122: 2, 859476: 3, 823164: 3}},\n                  {1459122: {1995: 0.4}, 859476: {1995: 0.5},\n                   823164: {1995: 0.2}},\n                  {7421: -.2},\n                  {7421: 1995},\n                  3]\n        solve_probe(reader, writer, caches)\n        self.assertEqual(\n            writer.getvalue(), \"7421:\\n3.2\\n3.3\\n3.0\\nRMSE: 0.71\\n\")\n\n    def test_solve_probe_2(self):\n        \"\"\"\n        test predicted_rating for 3 customers and rmse\n        \"\"\"\n        reader = StringIO(\"680:\\n1118974\\n145640\\n907840\")\n        writer = StringIO()\n        caches = [{680: {1118974: 4, 145640: 2, 907840: 5}},\n                  {1118974: {1995: 0.2}, 145640:\n                   {1995: 0.1}, 907840: {1995: 0.5}},\n                  {680: -0.6},\n                  {680: 1995},\n                  3]\n        solve_probe(reader, writer, caches)\n        self.assertEqual(\n            writer.getvalue(), \"680:\\n2.6\\n2.5\\n2.9\\nRMSE: 1.49\\n\")\n\n    def test_solve_probe_3(self):\n        \"\"\"\n        test predicted_rating for 5 customers and rmse\n        \"\"\"\n        reader = StringIO(\"4563:\\n1676257\\n1107756\\n185124\\n1840435\")\n        writer = StringIO()\n        caches = [{4563: {1676257: 1, 1107756: 3, 185124: 2, 1840435: 3}},\n                  {1676257: {2006: 0.3}, 1107756: {2006: 1.0},\n                   185124: {2006: 0.9}, 1840435: {2006: 0.6}},\n                  {4563: 0.8},\n                  {4563: 2006},\n                  2]\n        solve_probe(reader, writer, caches)\n        self.assertEqual(\n            writer.getvalue(), \"4563:\\n3.1\\n3.8\\n3.7\\n3.4\\nRMSE: 1.42\\n\")\n\n    def test_get_predicted_rating_1(self):\n        \"\"\"\n        test predicted_rating given weighted movie average\n        and weighted customer average\n        \"\"\"\n        movie_average = -0.4\n        customer_average = 1\n        total_average = 3\n        predicted_rating = get_predicted_rating(\n            movie_average, customer_average, total_average)\n        self.assertEqual(predicted_rating, 3.6)\n\n    def test_get_predicted_rating_2(self):\n        \"\"\"\n        test predicted_rating given weighted movie average\n        and weighted customer average\n        \"\"\"\n        movie_average = 1.0\n        customer_average = -0.5\n        total_average = 2\n        predicted_rating = get_predicted_rating(\n            movie_average, customer_average, total_average)\n        self.assertEqual(predicted_rating, 2.5)\n\n    def test_get_predicted_rating_3(self):\n        \"\"\"\n        test predicted_rating given weighted movie average\n        and weighted customer average\n        \"\"\"\n        movie_average = 0.2\n        customer_average = 2.3\n        total_average = 1\n        predicted_rating = get_predicted_rating(\n            movie_average, customer_average, total_average)\n        self.assertEqual(predicted_rating, 3.5)\n\n    def test_calculate_rmse_1(self):\n        \"\"\"\n        test calculate rmse given array of (predicted - actual)**2\n        \"\"\"\n        values = [0.1365096276428999,\n                  0.2248974339188119, 0.0, 1.0785035366365043]\n        rmse = calculate_rmse(values)\n        self.assertEqual(rmse, 0.5999813743355322)\n\n    def test_calculate_rmse_2(self):\n        \"\"\"\n        test calculate rmse given array of (predicted - actual)**2\n        \"\"\"\n        values = [0.6637788008032236, 0.4687879615111395,\n                  0.0, 2.2970110060604143, 0.4419325366215253]\n        rmse = calculate_rmse(values)\n        self.assertEqual(rmse, 0.8799443510809423)\n\n    def test_calculate_rmse_3(self):\n        \"\"\"\n        test calculate rmse given array of (predicted - actual)**2\n        \"\"\"\n        values = [1.8019814874492273]\n        rmse = calculate_rmse(values)\n        self.assertEqual(rmse, 1.3423790401556586)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"dl27458-TestNetflix.py","file_name":"dl27458-TestNetflix.py","file_ext":"py","file_size_in_byte":8395,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"90361717","text":"import os\nos.environ['PYOPENGL_PLATFORM'] = 'osmesa'\nimport torch\nfrom torchvision.utils import make_grid\nimport numpy as np\nimport trimesh\n\ntry:\n    import pyrender\nexcept:\n    pass\n\ntry:\n    from opendr.renderer import ColoredRenderer\n    from opendr.lighting import LambertianPointLight, SphericalHarmonics\n    from opendr.camera import ProjectPoints\nexcept:\n    pass\n\nfrom skimage.transform import resize\n\nclass Renderer:\n    \"\"\"\n    Renderer used for visualizing the SMPL model\n    Code adapted from https://github.com/vchoutas/smplify-x\n    \"\"\"\n    def __init__(self, focal_length=5000, img_res=224, faces=None):\n        self.renderer = pyrender.OffscreenRenderer(viewport_width=img_res,\n                                       viewport_height=img_res,\n                                       point_size=1.0)\n        self.focal_length = focal_length\n        self.camera_center = [img_res // 2, img_res // 2]\n        self.faces = faces\n\n    def visualize_tb(self, vertices, camera_translation, images):\n        vertices = vertices.cpu().numpy()\n        camera_translation = camera_translation.cpu().numpy().copy()\n        images = images.cpu()\n        images_np = np.transpose(images.numpy(), (0,2,3,1))\n        rend_imgs = []\n        for i in range(vertices.shape[0]):\n            rend_img = torch.from_numpy(np.transpose(self.__call__(vertices[i], camera_translation[i], images_np[i]), (2,0,1))).float()\n            rend_imgs.append(images[i])\n            rend_imgs.append(rend_img)\n        rend_imgs = make_grid(rend_imgs, nrow=2)\n        return rend_imgs\n\n    def __call__(self, vertices, camera_translation, image):\n        material = pyrender.MetallicRoughnessMaterial(\n            metallicFactor=0.2,\n            alphaMode='OPAQUE',\n            baseColorFactor=(0.8, 0.3, 0.3, 1.0))\n\n        camera_translation[0] *= -1.\n\n        mesh = trimesh.Trimesh(vertices, self.faces)\n        rot = trimesh.transformations.rotation_matrix(\n            np.radians(180), [1, 0, 0])\n        mesh.apply_transform(rot)\n        mesh = pyrender.Mesh.from_trimesh(mesh, material=material)\n\n        scene = pyrender.Scene(ambient_light=(0.5, 0.5, 0.5))\n        scene.add(mesh, 'mesh')\n\n        camera_pose = np.eye(4)\n        camera_pose[:3, 3] = camera_translation\n        camera = pyrender.IntrinsicsCamera(fx=self.focal_length, fy=self.focal_length,\n                                           cx=self.camera_center[0], cy=self.camera_center[1])\n        scene.add(camera, pose=camera_pose)\n\n\n        light = pyrender.DirectionalLight(color=[1.0, 1.0, 1.0], intensity=1)\n        light_pose = np.eye(4)\n\n        light_pose[:3, 3] = np.array([0, -1, 1])\n        scene.add(light, pose=light_pose)\n\n        light_pose[:3, 3] = np.array([0, 1, 1])\n        scene.add(light, pose=light_pose)\n\n        light_pose[:3, 3] = np.array([1, 1, 2])\n        scene.add(light, pose=light_pose)\n\n        color, rend_depth = self.renderer.render(scene, flags=pyrender.RenderFlags.RGBA)\n        color = color.astype(np.float32) / 255.0\n        valid_mask = (rend_depth > 0)[:,:,None]\n        output_img = (color[:, :, :3] * valid_mask +\n                  (1 - valid_mask) * image)\n        return output_img\n\n\n\n#  https://github.com/classner/up/blob/master/up_tools/camera.py\ndef rotateY(points, angle):\n    \"\"\"Rotate all points in a 2D array around the y axis.\"\"\"\n    ry = np.array([\n        [np.cos(angle),     0.,     np.sin(angle)],\n        [0.,                1.,     0.           ],\n        [-np.sin(angle),    0.,     np.cos(angle)]\n    ])\n    return np.dot(points, ry)\n\ndef rotateX( points, angle ):\n    \"\"\"Rotate all points in a 2D array around the x axis.\"\"\"\n    rx = np.array([\n        [1.,    0.,                 0.           ],\n        [0.,    np.cos(angle),     -np.sin(angle)],\n        [0.,    np.sin(angle),     np.cos(angle) ]\n    ])\n    return np.dot(points, rx)\n\ndef rotateZ( points, angle ):\n    \"\"\"Rotate all points in a 2D array around the z axis.\"\"\"\n    rz = np.array([\n        [np.cos(angle),     -np.sin(angle),     0. ],\n        [np.sin(angle),     np.cos(angle),      0. ],\n        [0.,                0.,                 1. ]\n    ])\n    return np.dot(points, rz)\n\n\nclass opendr_render(object):\n    def __init__(self, ratio=2, color='white'):\n        self.ratio = ratio\n        self.color = color\n\n        # self.m = load_model('./data/smpl/basicModel_neutral_lbs_10_207_0_v1.0.0.pkl')\n\n        ## Assign random pose and shape parameters\n\n        # m.pose[:] = np.random.rand(m.pose.size) * .2\n        # m.betas[:] = np.random.rand(m.betas.size) * .03\n\n\n    def render(self, image, cam, K, verts, face, draw_id=''):\n\n        # roll_axis = torch.Tensor([1, 0, 0]).unsqueeze(0)  # .expand(1, -1)\n        # alpha = torch.Tensor([np.pi] * 1).unsqueeze(1) * 0.5\n        # pose[0, :3] = axis_angle_add(pose[0, :3].unsqueeze(0), roll_axis, alpha)\n        # pose[:3] *= torch.Tensor([1, -1, -1])\n\n        # self.m.betas[:] = shape.numpy()[0]\n        # self.m.pose[:] = pose.numpy()[0]\n\n        # m.betas[:] = np.array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1])\n        # m.betas[:] = np.array([0.]*10)\n        # m.pose[:] = np.array([0.]*72)\n        # m.pose[0] = -np.pi\n        # m.pose[2] = 0.5\n        # m.pose[2] = np.pi\n        # m.betas[0] = 0.5\n\n        ## Create OpenDR renderer\n        rn = ColoredRenderer()\n\n        # print(rn.msaa)\n        #\n        # rn.msaa = True\n\n        ## Assign attributes to renderer\n        w, h = (224 * self.ratio, 224 * self.ratio)\n        # w, h = (1000, 1000)\n\n        f = np.array([K[0, 0], K[1, 1]]) * float(self.ratio)\n        c = np.array([K[0, 2], K[1, 2]]) * float(self.ratio)\n        t = np.array([cam[1], cam[2], 2 * K[0, 0] / (224. * cam[0] + 1e-9)])\n        # t = np.array([0, 0, 5.])\n\n        # c = np.array([K[0, 0, 2], 112 - K[0, 1, 1] * float(cam[0, 2])]) * float(self.ratio)\n\n        # rn.camera = ProjectPoints(v=m*np.array([1,-1,-1]), rt=np.zeros(3), t=np.array([0, 0, 5.]), f=f, c=c, k=np.zeros(5))\n        rn.camera = ProjectPoints(v=verts, rt=np.zeros(3), t=t, f=f, c=c, k=np.zeros(5))\n\n        rn.frustum = {'near': 1., 'far': 100., 'width': w, 'height': h}\n\n        # [:, [1, 0, 2]]\n\n        albedo = np.ones_like(verts)*.9\n        # albedo(6890, 3)(6890, 3)(13776, 3)\n\n        color1 = np.array([0.85490196, 0.96470588, 0.96470588])\n        # light steel blue\n        # color1 = np.array([i / 255. for i in [176, 196, 222]])\n        # color1 = np.array([i / 255. for i in [168, 173, 180]])\n        # color2 = np.array([i / 255. for i in [255, 244, 229]])\n        color2 = np.array([i / 255. for i in [181, 178, 146]])\n        color3 = np.array([i / 255. for i in [190, 178, 167]])\n        # beige\n        # color4 = np.array([i / 255. for i in [245, 245, 220]])\n        # wheat\n        color4 = np.array([i / 255. for i in [245, 222, 179]])\n        # thistle\n        # color5 = np.array([i / 255. for i in [216, 191, 216]])\n        color5 = np.array([i / 255. for i in [183, 166, 173]])\n\n        # aqua marine\n        color6 = np.array([i / 255. for i in [127, 255, 212]])\n        # turquoise\n        color7 = np.array([i / 255. for i in [64, 224, 208]])\n        # medium turquoise\n        color8 = np.array([i / 255. for i in [72, 209, 204]])\n        # honeydew\n        color9 = np.array([i / 255. for i in [240, 255, 240]])\n        # burly wood\n        color10 = np.array([i / 255. for i in [222, 184, 135]])\n        # sandy brown\n        color11 = np.array([i / 255. for i in [244, 164, 96]])\n        # floral white Ours\n        color12 = np.array([i / 255. for i in [255, 250, 240]])\n        # medium slate blue SPIN\n        color13 = np.array([i / 255. for i in [72 * 2.5, 61 * 2.5, 255]])\n\n\n        # color_list = [color1, color2, color3, color4, color5]\n        color_list = [color6, color7, color8, color9, color10, color11, color12, color13]\n        # color_list = color_list + [color13]\n\n        # color = color_list[int(len(color_list) * float(np.random.rand(1)))]\n        # color = color_list[-1]\n        if self.color in ['white']:\n            color = color12\n            color0 = np.array([1, 1, 1])\n            color1 = np.array([1, 1, 1])\n            color2 = np.array([0.7, 0.7, 0.7])\n        elif self.color in ['blue']:\n            color = color13\n            color0 = color\n            color1 = color\n            color2 = color\n\n        # rn.set(v=m*np.array([1,-1,1]), f=m.f, bgcolor=np.zeros(3))\n        rn.set(v=verts, f=face, vc=color, bgcolor=np.zeros(3))\n        # rn.set(v=rotateY(verts, np.radians(90)), f=self.m.f, bgcolor=np.zeros(3))\n\n        ## Construct point light source\n        # rn.vc = LambertianPointLight(\n        #     f=m.f,\n        #     v=rn.v,\n        #     num_verts=len(m),\n        #     light_pos=np.array([-1000,-1000,-2000]),\n        #     vc=np.ones_like(m)*.9,\n        #     light_color=np.array([1., 1., 1.]))\n        yrot = np.radians(120)\n        '''\n        rn.vc = LambertianPointLight(\n            f=rn.f,\n            v=rn.v,\n            num_verts=len(rn.v),\n            light_pos=np.array([-200, -100, -100]),\n            vc=albedo,\n            light_color=color)\n\n        # Construct Left Light\n        rn.vc += LambertianPointLight(\n            f=rn.f,\n            v=rn.v,\n            num_verts=len(rn.v),\n            light_pos=np.array([500, 10, -200]),\n            vc=albedo,\n            light_color=color)\n\n        # Construct Right Light\n        rn.vc += LambertianPointLight(\n            f=rn.f,\n            v=rn.v,\n            num_verts=len(rn.v),\n            light_pos=np.array([-300, 100, 600]),\n            vc=albedo,\n            light_color=color)\n        '''\n        # 1. 1. 0.7\n        rn.vc = LambertianPointLight(\n            f=rn.f,\n            v=rn.v,\n            num_verts=len(rn.v),\n            light_pos=rotateY(np.array([-200, -100, -100]), yrot),\n            vc=albedo,\n            light_color=color0)\n\n        # Construct Left Light\n        rn.vc += LambertianPointLight(\n            f=rn.f,\n            v=rn.v,\n            num_verts=len(rn.v),\n            light_pos=rotateY(np.array([800, 10, 300]), yrot),\n            vc=albedo,\n            light_color=color1)\n\n        # Construct Right Light\n        rn.vc += LambertianPointLight(\n            f=rn.f,\n            v=rn.v,\n            num_verts=len(rn.v),\n            light_pos=rotateY(np.array([-500, 500, 1000]), yrot),\n            vc=albedo,\n            light_color=color2)\n\n        # render_smpl = rn.r\n\n        ## Construct point light source\n        # rn.vc += SphericalHarmonics(light_color=np.array([1., 1., 1.]))\n\n        img_orig = np.transpose(image, (1, 2, 0))\n        img_resized = resize(img_orig, (img_orig.shape[0] * self.ratio, img_orig.shape[1] * self.ratio), anti_aliasing=True)\n\n        # ax_smpl = plt.subplot(2, 2, 2)\n        # plt.imshow(rn.r)\n        # plt.axis('off')\n\n        # print(max(rn.r))\n        # print(min(rn.r))\n        # fig = plt.figure()\n\n        img_smpl = img_resized.copy()\n        img_smpl[rn.visibility_image != 4294967295] = rn.r[rn.visibility_image != 4294967295]\n\n        '''\n        ax_stack = plt.subplot(2, 2, 3)\n        ax_stack.imshow(img_smpl)\n        plt.axis('off')\n        '''\n\n        rn.set(v=rotateY(verts, np.radians(90)), f=face, bgcolor=np.zeros(3))\n        render_smpl = rn.r\n\n        # rn.set(v=rotateY(verts, np.radians(90)), f=self.m.f, bgcolor=np.zeros(3))\n\n        render_smpl_rgba = np.zeros((render_smpl.shape[0], render_smpl.shape[1], 4))\n        render_smpl_rgba[:, :, :3] = render_smpl\n        render_smpl_rgba[:, :, 3][rn.visibility_image != 4294967295] = 255\n\n        '''\n        ax_img = plt.subplot(2, 2, 1)\n        ax_img.imshow(np.transpose(image, (1, 2, 0)))\n        plt.axis('off')\n        ax_smpl = plt.subplot(2, 2, 2)\n        ax_smpl.imshow(render_smpl_rgba)\n        plt.axis('off')\n        '''\n\n        return img_orig, img_resized, img_smpl, render_smpl_rgba\n\n        # img_uv = np.transpose(uvimage_front[0].cpu().numpy(), (1, 2, 0))\n        # # img_uv = resize(img_uv, (img_uv.shape[0], img_uv.shape[1]), anti_aliasing=True)\n        # img_uv[img_uv == 0] = img_show[img_uv == 0]\n\n        # plt.show()\n\n        # save_path = './notebooks/output/upimgs/'\n        save_path = './notebooks/output/demo_results-v2/'\n        if not os.path.exists(save_path):\n            os.makedirs(save_path)\n\n        matplotlib.image.imsave(save_path + 'img_' + draw_id + '.png', img_orig)\n        matplotlib.image.imsave(save_path + 'img_smpl_' + draw_id + '.png', img_smpl)\n        matplotlib.image.imsave(save_path + 'smpl_' + draw_id + '.png', render_smpl_rgba)\n\n        # output_dir = os.path.split('./notebooks/output/demo_results-v1/' + draw_id + '.pdf')[0]\n        # if not os.path.exists(output_dir):\n        #     os.makedirs(output_dir)\n        #\n        # plt.savefig('./notebooks/output/demo_results-v1/' + draw_id + '.pdf', format='pdf', bbox_inches='tight')\n\n        # if not os.path.exists(output_dir):\n        #     os.makedirs(output_dir)\n        # import pdb; pdb.set_trace()\n\n","sub_path":"utils/renderer.py","file_name":"renderer.py","file_ext":"py","file_size_in_byte":12963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"243091170","text":"ver = '1.2.1'\n\nfrom parsing import yedGraphObject\n\ndef computeArcDirection(anchorX, anchorY, srcX, srcY, tgtX, tgtY):\n    dX = tgtX - srcX\n    dY = tgtY - srcY\n    dotPdt = ((srcX+tgtX)/2 - anchorX) * dX + ((srcY+tgtY)/2 - anchorY) * dY\n    if dotPdt < 0:\n        return(\"bend right\")\n    else:\n        return(\"bend left\")\n\n\n\n\nshapeDictionary = {'rectangle': 'rectangle', 'ellipse': 'circle', 'diamond': 'diamond',\\\n 'star5': 'star,star points=5', 'star6': 'star,star points=6', 'star8': 'star,star points=8',\\\n 'hexagon':'regular polygon,regular polygon sides=6', 'octagon':'regular polygon,regular polygon sides=8'}\nedgeDictionary = {('none', 'standard'): '->', ('none', 'none'): '-', ('standard', 'none'): '<-',\\\n('standard', 'standard'): '<->'}\n\nclass GraphGenerator:\n    def __init__(self, yGraphObject):\n        self.yGO = yGraphObject\n        self.tGO = tikzGraphObject()\n        return None\n\n    @staticmethod\n    def extract_color(hex):\n        h = hex.lstrip('#')\n        return tuple(int(h[i:i+2], 16) for i in (0, 2, 4))\n\n    @staticmethod\n    def translate_shape(yShape):\n        return shapeDictionary[yShape]\n\n    @staticmethod\n    def interpret_edge(end1, end2):\n        return edgeDictionary[(end1, end2)]\n\n    def build_graph(self):\n        self.tGO.append_function(lambda : '\\\\begin{tikzpicture}\\n')\n        self.build_nodes()\n        self.build_edges()\n        self.tGO.append_function(lambda : '\\\\end{tikzpicture}\\n%TikZ rendered by GraphTranslatr v' + ver + '\\n')\n        return self.tGO\n\n    def build_nodes(self):\n        for index, id in enumerate(self.yGO.nodeIDs):\n            self.tGO.nodeIDs.append(id)\n            self.tGO.nodeShapes.append(self.translate_shape(self.yGO.nodeShapes[index]))\n            self.tGO.nodeXs.append(float(self.yGO.nodeXs[index]))\n            self.tGO.nodeHs.append(float(self.yGO.nodeHs[index]))\n            self.tGO.nodeYs.append(-float(self.yGO.nodeYs[index]))\n            self.tGO.nodeWs.append(float(self.yGO.nodeWs[index]))\n            self.tGO.nodeColors.append(self.extract_color(self.yGO.nodeColors[index]))\n            self.tGO.nodeLabels.append(self.yGO.nodeWs[index])\n\n\n            self.tGO.append_function(lambda i = index: '\\t\\\\node (' + self.tGO.nodeIDs[i] + ') at (')\n            self.tGO.append_function(lambda i = index: \"%.1f\" % (self.tGO.nodeXs[i] * self.tGO.unitFactor * self.tGO.scaleFactor) + ',')\n            self.tGO.append_function(lambda i = index: \"%.1f\" % (self.tGO.nodeYs[i] * self.tGO.unitFactor * self.tGO.scaleFactor) + ') ')\n            self.tGO.append_function(lambda i = index: '[' + self.tGO.nodeShapes[i] + ',draw')\n            self.tGO.append_function(lambda i = index: ',fill={rgb,255:red,' + str(self.tGO.nodeColors[i][0]) + ';green,')\n            self.tGO.append_function(lambda i = index: str(self.tGO.nodeColors[i][1]) + ';blue,')\n            self.tGO.append_function(lambda i = index: str(self.tGO.nodeColors[i][2]) + '}]')\n\n            self.tGO.append_function(lambda : ' {};\\n')\n\n\n\n    def build_edges(self):\n        for index, id in enumerate(self.yGO.edgeIDs):\n            self.tGO.edgeIDs.append(id)\n            self.tGO.edgeSRCs.append(self.yGO.edgeSRCs[index])\n            self.tGO.edgeTGTs.append(self.yGO.edgeTGTs[index])\n            self.tGO.arrows.append(self.interpret_edge(self.yGO.edgeSrcAs[index], self.yGO.edgeTgtAs[index]))\n            self.tGO.edgeAnchors.append(self.yGO.edgeAnchors[index])\n            self.tGO.isArc.append(self.yGO.isArc[index])\n\n\n            self.tGO.append_function(lambda i = index: '\\t\\\\draw [' + self.tGO.arrows[i] + \"] \")\n            self.tGO.append_function(lambda i = index: '(' + self.tGO.edgeSRCs[i] + ') to ')\n            if self.tGO.isArc[index] == True:\n                srcIdx = self.tGO.nodeIDs.index(self.tGO.edgeSRCs[index])\n                tgtIdx = self.tGO.nodeIDs.index(self.tGO.edgeTGTs[index])\n                phrase = computeArcDirection(self.tGO.edgeAnchors[index][0][0], -self.tGO.edgeAnchors[index][0][1], self.tGO.nodeXs[srcIdx],\\\n                self.tGO.nodeYs[srcIdx], self.tGO.nodeXs[tgtIdx], self.tGO.nodeYs[tgtIdx])\n                self.tGO.append_function(lambda: '[' + phrase + '] ')\n            elif self.tGO.edgeAnchors[index] != None:\n                print(len(self.tGO.edgeAnchors[index]))\n                for idx in range(len(self.tGO.edgeAnchors[index])):\n                    self.tGO.append_function(lambda i = index, j = idx: '(' + \"%.1f\" % (self.tGO.edgeAnchors[i][j][0] * self.tGO.unitFactor * self.tGO.scaleFactor) + ',')\n                    self.tGO.append_function(lambda i = index, j = idx: \"%.1f\" % (-self.tGO.edgeAnchors[i][j][1] * self.tGO.unitFactor * self.tGO.scaleFactor) + ') to ')\n            self.tGO.append_function(lambda i = index: '(' + self.tGO.edgeTGTs[i] + ');\\n')\n\n\n\n\n\n\nclass tikzGraphObject:\n\n    def __init__(self):\n        self.funcBuffer = []\n        self.nodeIDs = []\n        self.nodeShapes = []\n        self.nodeXs = []\n        self.nodeYs = []\n        self.nodeHs = []\n        self.nodeWs = []\n        self.nodeColors = []\n        self.nodeLabels = []\n        self.edgeIDs = []\n        self.edgeSRCs = []\n        self.edgeTGTs = []\n        self.arrows = []\n        self.edgeAnchors = []\n        self.isArc = []\n\n        self.unitFactor = 0.015\n        self.scaleFactor = 1 # 1cm = 28.45274pt\n\n\n    def append_function(self, func):\n        self.funcBuffer.append(func)\n        return None\n\n    def compile_latex(self):\n        output = ''\n        for func in self.funcBuffer:\n            output = output + func()\n        return output\n","sub_path":"generation.py","file_name":"generation.py","file_ext":"py","file_size_in_byte":5546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"221932630","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[16]:\n\n\nimport pathlib\nimport imageio\nimport numpy as np\nfrom skimage.color import rgb2gray\nimport matplotlib.pyplot as plt        # to plot any graph\nimport cv2\nimport os\nimport random\nfrom glob import glob\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport pathlib\nimport imageio\nimport numpy as np\nfrom skimage.color import rgb2gray\nimport matplotlib.pyplot as plt        # to plot any graph\nimport cv2\nimport os\nimport random\nfrom glob import glob\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import tree\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import confusion_matrix, classification_report\nfrom sklearn.model_selection import validation_curve\nfrom sklearn.model_selection import learning_curve\nfrom sklearn import svm\nfrom sklearn import preprocessing\n\nflowers = glob(os.path.join('Shubham-data/17flowers/jpg', \"*.jpg\"))\naeroplanes = glob(os.path.join('Shubham-data/airplanes_side', \"*.jpg\"))\n\n# flowers_sorted = sorted([x for x in flowers])\n# print(flowers)\n# im_path = flowers_sorted[45]\n# im = imageio.imread(str(im_path))\n# print(im.shape)\n# im_gray = rgb2gray(im)\n# im_gray.reshape(-1,3)\n# print('New image shape: {}'.format(im_gray.shape))\n\n# plt.imshow(im, cmap='Set3')  # show me the leaf\n# plt.show()\n\n# plt.imshow(im_gray, cmap='Set3')  # show me the leaf\n# plt.show()\n\ndef proc_images(images, label):\n    \"\"\"\n    Returns two arrays:\n        x is an array of resized images\n        y is an array of labels\n    \"\"\"\n\n\n\n    x = [] # images as arrays\n    y = [] # labels Infiltration or Not_infiltration\n    WIDTH = 64\n    HEIGHT = 64\n\n    for img in images:\n\n        # Read and resize image\n        full_size_image = cv2.imread(img, 0)\n        x.append(cv2.resize(full_size_image, (WIDTH,HEIGHT), interpolation=cv2.INTER_CUBIC))\n\n        # Labels\n        y.append(label)\n\n\n\n    return x,y\n\n\n\nflowers_x, flowers_y = proc_images(flowers, 1)\naeroplanes_x, aeroplanes_y = proc_images(aeroplanes, 0)\nlen(flowers_x)\n\ndf = pd.DataFrame()\ndf[\"labels\"]=aeroplanes_y, flowers_y\ndf[\"images\"]=aeroplanes_x, flowers_x\ndf.head()\n\n\n\nplt.imshow(flowers_x[0], cmap='Set3')\n\nplt.imshow(aeroplanes_x[0], cmap='Set3')\n\n\n\nfor i in range(len(flowers_x)):\n    flowers_x[i] = flowers_x[i].flatten()\n\nfor i in range(len(aeroplanes_x)):\n    aeroplanes_x[i] = aeroplanes_x[i].flatten()\n\n\ntraining = flowers_x + aeroplanes_x\nlabels = flowers_y + aeroplanes_y\ndata_train, data_test, label_train, label_test = train_test_split(training, labels, test_size = 0.2, random_state = 50)\nmin_max_scaler = preprocessing.MinMaxScaler()\nX_train_minmax = min_max_scaler.fit_transform(data_train)\nX_test_minmax = min_max_scaler.fit_transform(data_test)\n# print(data_train)\n\n\n\n# dt2 = tree.DecisionTreeClassifier(random_state=1, max_depth=4)\n# dt2.fit(training, labels)\n# dt2_score_train = dt2.score(data_train, label_train)\n# print(\"Training score: \",dt2_score_train)\n# dt2_score_test = dt2.score(data_test, label_test)\n# print(\"Testing score: \",dt2_score_test)\n\n\n# # Let's generate the decision tree for depth = 6\n# # Create a feature vector\n# #features = training.columns.tolist()\n\n# # Uncomment below to generate the digraph Tree.\n# #tree.export_graphviz(dt2, out_file=dot_data, feature_names=features)\n\n\n# from sklearn.neural_network import MLPClassifier\n# clf = MLPClassifier(activation='logistic', solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(200), random_state=1)\n# clf.fit(data_train, label_train)\n# print(clf.score(data_train, label_train))\n# print(clf.score(data_test, label_test))\n# print(clf.predict(data_test))\n\n\n# In[17]:\n\n\nget_ipython().run_cell_magic('time', '', \"clf = svm.SVC(kernel='linear')\\n# Train Adaboost Classifer\\nclf.fit(X_train_minmax, label_train)\\n\\n \\nprint(confusion_matrix(label_test, clf.predict(X_test_minmax)))\\nprint(classification_report(label_test, clf.predict(X_test_minmax)))  \")\n\n\n# In[20]:\n\n\nprint(f1_score(label_test, clf.predict(X_test_minmax), average='macro'))\nprint(clf)\n\n\n# In[18]:\n\n\nget_ipython().run_cell_magic('time', '', \"svclassifierrbf = svm.SVC(kernel='rbf', gamma = 0.001)  \\nsvclassifierrbf.fit(X_train_minmax, label_train)  \\n \\nprint(confusion_matrix(label_test, svclassifierrbf.predict(X_test_minmax)))\\nprint(classification_report(label_test, svclassifierrbf.predict(X_test_minmax)))  \")\n\n\n# In[21]:\n\n\nprint(f1_score(label_test, svclassifierrbf.predict(X_test_minmax), average='macro'))\n\n\n# In[9]:\n\n\nget_ipython().run_cell_magic('time', '', \"# Create adaboost classifer object\\nparameters = {'gamma' : [0.001, 0.01, 0.1, 1]}\\nclf = GridSearchCV(svm.SVC(kernel='rbf'), parameters, cv=5, n_jobs=-1)\\nclf.fit(data_train, label_train)\\nprint (clf.best_score_, clf.best_params_) \")\n\n\n# In[10]:\n\n\nget_ipython().run_cell_magic('time', '', \"# Create Linear SVM classifer object\\nparameters = {'C' : [1,5,10]}\\nclflinear = GridSearchCV(svm.SVC(kernel='linear'), parameters, cv=5, n_jobs=-1)\\nclflinear.fit(data_train, label_train)\\nprint (clflinear.best_score_, clflinear.best_params_) \")\n\n\n# In[22]:\n\n\nparam_range = [10,20,50,100,500,1000,2000,5000,10000]\ntrain_scores, test_scores = validation_curve(\n    svm.SVC(kernel='linear'), X_train_minmax, label_train, param_name=\"max_iter\", param_range=param_range,\n    cv=5, scoring=\"accuracy\", n_jobs=-1)\ntrain_scores_mean = np.mean(train_scores, axis=1)\ntrain_scores_std = np.std(train_scores, axis=1)\ntest_scores_mean = np.mean(test_scores, axis=1)\ntest_scores_std = np.std(test_scores, axis=1)\n\nplt.title(\"Validation Curve with Linear KERERL SVM Classifier on the Image Data\")\nplt.xlabel(\"max_iter\")\n#plt.xticks(param_range)\nplt.ylabel(\"Score\")\nplt.ylim(0.0, 1.1)\nlw = 2\nplt.plot(param_range, train_scores_mean, label=\"Training score\",\n             color=\"darkorange\", lw=lw)\nplt.fill_between(param_range, train_scores_mean - train_scores_std,\n                 train_scores_mean + train_scores_std, alpha=0.2,\n                 color=\"darkorange\", lw=lw)\nplt.plot(param_range, test_scores_mean, label=\"Cross-validation score\",\n             color=\"navy\", lw=lw)\nplt.fill_between(param_range, test_scores_mean - test_scores_std,\n                 test_scores_mean + test_scores_std, alpha=0.2,\n                 color=\"navy\", lw=lw)\nplt.legend(loc=\"best\")\nplt.show()\n\n\n# In[27]:\n\n\nplt.figure()\nplt.title(\"Learning curve with Linear KERNEL SVM on the Image Data\")\nplt.xlabel(\"Training examples\")\nplt.ylabel(\"Score\")\n\ntrain_sizes = np.linspace(.1, 1.0, 20)\ntrain_sizes, train_scores, test_scores = learning_curve(svm.SVC(kernel='linear'), X_train_minmax, label_train, cv=5, n_jobs=-1, train_sizes=train_sizes)\n\ntrain_scores_mean = np.mean(train_scores, axis=1)\ntrain_scores_std  = np.std(train_scores, axis=1)\ntest_scores_mean  = np.mean(test_scores, axis=1)\ntest_scores_std   = np.std(test_scores, axis=1)\nplt.grid()\nlw = 2\nplt.fill_between(train_sizes, train_scores_mean - train_scores_std,train_scores_mean + train_scores_std, alpha=0.2,\n                 color=\"r\", lw=lw)\nplt.fill_between(train_sizes, test_scores_mean - test_scores_std,test_scores_mean + test_scores_std, alpha=0.2, color=\"g\")\nplt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",label=\"Training score\")\nplt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",label=\"Cross-validation score\")\n\nplt.legend(loc=\"best\")\n\n\n# In[25]:\n\n\nparam_range = [0.0005, 0.001, 0.01, 0.1, 1]\ntrain_scores, test_scores = validation_curve(\n    svm.SVC(kernel='rbf'), X_train_minmax, label_train, param_name=\"gamma\", param_range=param_range,\n    cv=5, scoring=\"accuracy\", n_jobs=-1)\ntrain_scores_mean = np.mean(train_scores, axis=1)\ntrain_scores_std = np.std(train_scores, axis=1)\ntest_scores_mean = np.mean(test_scores, axis=1)\ntest_scores_std = np.std(test_scores, axis=1)\n\nplt.title(\"Validation Curve with RBF KERERL SVM Classifier on the Image Data\")\nplt.xlabel(\"gamma\")\n#plt.xticks(param_range)\nplt.ylabel(\"Score\")\nplt.ylim(0.0, 1.1)\nlw = 2\nplt.plot(param_range, train_scores_mean, label=\"Training score\",\n             color=\"darkorange\", lw=lw)\nplt.fill_between(param_range, train_scores_mean - train_scores_std,\n                 train_scores_mean + train_scores_std, alpha=0.2,\n                 color=\"darkorange\", lw=lw)\nplt.plot(param_range, test_scores_mean, label=\"Cross-validation score\",\n             color=\"navy\", lw=lw)\nplt.fill_between(param_range, test_scores_mean - test_scores_std,\n                 test_scores_mean + test_scores_std, alpha=0.2,\n                 color=\"navy\", lw=lw)\nplt.legend(loc=\"best\")\nplt.show()\n\n\n# In[24]:\n\n\n\nprint(X_train_minmax)\nparam_range = [10,20,50,100,500,1000,2000,5000,10000]\ntrain_scores, test_scores = validation_curve(\n    svm.SVC(kernel='rbf'), X_train_minmax, label_train, param_name=\"max_iter\", param_range=param_range,\n    cv=5, scoring=\"accuracy\", n_jobs=-1)\ntrain_scores_mean = np.mean(train_scores, axis=1)\ntrain_scores_std = np.std(train_scores, axis=1)\ntest_scores_mean = np.mean(test_scores, axis=1)\ntest_scores_std = np.std(test_scores, axis=1)\n\nplt.title(\"Validation Curve with RBF KERERL SVM Classifier on the Image Data\")\nplt.xlabel(\"max_iter\")\n#plt.xticks(param_range)\nplt.ylabel(\"Score\")\nplt.ylim(0.0, 1.1)\nlw = 2\nplt.plot(param_range, train_scores_mean, label=\"Training score\",\n             color=\"darkorange\", lw=lw)\nplt.fill_between(param_range, train_scores_mean - train_scores_std,\n                 train_scores_mean + train_scores_std, alpha=0.2,\n                 color=\"darkorange\", lw=lw)\nplt.plot(param_range, test_scores_mean, label=\"Cross-validation score\",\n             color=\"navy\", lw=lw)\nplt.fill_between(param_range, test_scores_mean - test_scores_std,\n                 test_scores_mean + test_scores_std, alpha=0.2,\n                 color=\"navy\", lw=lw)\nplt.legend(loc=\"best\")\nplt.show()\n\n\n# In[26]:\n\n\nplt.figure()\nplt.title(\"Learning curve with RBF KERNEL SVM on the Image Data\")\nplt.xlabel(\"Training examples\")\nplt.ylabel(\"Score\")\n\ntrain_sizes = np.linspace(.1, 1.0, 20)\ntrain_sizes, train_scores, test_scores = learning_curve(svm.SVC(kernel='rbf', gamma = 0.001), X_train_minmax, label_train, cv=5, n_jobs=-1, train_sizes=train_sizes)\n\ntrain_scores_mean = np.mean(train_scores, axis=1)\ntrain_scores_std  = np.std(train_scores, axis=1)\ntest_scores_mean  = np.mean(test_scores, axis=1)\ntest_scores_std   = np.std(test_scores, axis=1)\nplt.grid()\nlw = 2\nplt.fill_between(train_sizes, train_scores_mean - train_scores_std,train_scores_mean + train_scores_std, alpha=0.2,\n                 color=\"r\", lw=lw)\nplt.fill_between(train_sizes, test_scores_mean - test_scores_std,test_scores_mean + test_scores_std, alpha=0.2, color=\"g\")\nplt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",label=\"Training score\")\nplt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",label=\"Cross-validation score\")\n\nplt.legend(loc=\"best\")\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"Image-SVM.py","file_name":"Image-SVM.py","file_ext":"py","file_size_in_byte":10827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"495618973","text":"import plotext as plx\nfrom matplotlib.dates import drange\nimport yfinance as yf\nfrom datetime import datetime, timedelta\nimport argparse \n\ndef main():\n    parser = argparse.ArgumentParser()\n\n    # adding the arguments\n    parser.add_argument(\"-e\", default=datetime.today().isoformat(timespec='hours')[:-3])\n    parser.add_argument(\"-t\", default='1y')\n    parser.add_argument(\"-i\", default='^GDAXI')\n\n\n    # Parse and print the results\n    args = parser.parse_args()\n    print(args)\n\n    # compute timeframe\n    if args.t == '1y': \n        timeframe = timedelta(days=365)\n    elif args.t == '5y': \n        timeframe = timedelta(days=365 * 5)\n    elif args.t == '10y': \n        timeframe = timedelta(days=365 * 10)\n    elif args.t == '6m': \n        timeframe = timedelta(days=30 * 6)            \n    elif args.t == '3m': \n        timeframe = timedelta(days=30 * 3)\n    elif args.t == '1m': \n        timeframe = timedelta(days=30)\n    else: \n        timeframe = timedelta(days=365)\n    \n        \n\n\n    tickerSymbol = args.i\n    end = args.e\n    start = (datetime.fromisoformat(end) - timeframe).isoformat(timespec='hours')[:-3]\n\n    # getting the financial data from yfinance\n    tickerData = yf.Ticker(tickerSymbol)\n\n    # selecting the correct timeperiod\n    tickerDf = tickerData.history(period='1s', start=start, end=end)\n\n    start = datetime.fromisoformat(start)\n    end = datetime.fromisoformat(end)\n\n    # plotting the data green if overall close goes up and red if down\n    if tickerDf.iloc[0]['Close'] < tickerDf.iloc[-1]['Close']: \n        plx.plot(drange(start, end, timedelta(days=1)), tickerDf['Close'], line_color='green')\n    else: \n        plx.plot(drange(start, end, timedelta(days=1)), tickerDf['Close'], line_color='red')\n\n    plx.show()\n\n\n\nif __name__ == '__main__':\n    main()","sub_path":"plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":1795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"264885870","text":"# -*- coding: utf-8 -*-\n\nimport requests\nimport json\n\n\nclass RunMain:\n    # def __init__(self, test_url, method, data=None):\n    #     self.res = self.runMain(test_url, method, data)\n\n    @staticmethod\n    def sendGet(test_url, data):\n        res = requests.get(test_url, data)\n        res.encoding = 'utf-8'\n        return json.dumps(res.json())\n\n    @staticmethod\n    def sendPost(test_url, data):\n        res = requests.post(test_url, data)\n        res.encoding = 'utf-8'\n        return json.dumps(res.json())\n\n    def runMain(self, test_url, method, data=None):\n        res = None\n        if method == 'GET':\n            res = self.sendGet(test_url, data)\n        else:\n            res = self.sendPost(test_url, data)\n        return res\n\n\nif __name__ == '__main__':\n    url = 'http://www.weather.com.cn/data/sk/101010100.html'\n    run = RunMain()\n    res = run.runMain(url, 'GET')\n    print(res.encode('utf-8').decode('unicode_escape'))\n","sub_path":"test/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":941,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"624827169","text":"# Imports\r\nfrom snmp_library import *\r\nfrom pysnmp.hlapi.asyncore import *\r\nimport time\r\n\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\n# Variables in my program\r\nversion = 'v1'#v2c\r\ncommunity = 'security'#public, private, security, calabaza...\r\nip_addr = '155.210.157.3'#155.210.157.3 es un hub, .4 el switch\r\nport = 161\r\n\r\n# SNMP engine inicialization\r\nsnmp_engine = snmp_requests(version, community, ip_addr, port)\r\n\r\n\r\n#######################################################################\r\n#                            SNMP BW                                  #\r\n#######################################################################\r\n#1.3.6.1.2.1.2.2.1.10 ifInOctets\r\n#1.3.6.1.2.1.2.2.1.16 ifOutOctets\r\n#1.3.6.1.2.1.1.3 sysUpTime\r\n\r\n#varBinds1 = [ObjectType(ObjectIdentity('1.3.6.1.2.1.2.2.1.10.4227666'))] #get a ifOutOctets del pto10 RECUERDA QUE ES TABLA\r\nvarBinds1 = [ObjectType(ObjectIdentity('1.3.6.1.2.1.2.2.1.10.39534'))]\r\nvarBinds11 = [ObjectType(ObjectIdentity('1.3.6.1.2.1.2.2.1.16.39534'))]\r\n#elijo puertos que son de monitorizacion y no gestion(2 de gestion) y elijo los que pueden contener trafico\r\n#haciendo getnext desde get 1.3.6.1.2.1.2.2.1.10 sabiendo que el puerto10 esta en 4227666 con getnext\r\n#habria que mirar que fuese puerto activo, podemos deducirlo del ifDescription si el gestor les pone nombre reconocible o del trafico monitorizado en cada puerto\r\n# tambien con MG-soft\r\nresponse1 = snmp_engine.snmpget(varBinds1)\r\nresponse11 = snmp_engine.snmpget(varBinds11)\r\nbw=response1.varBinds[0][1]\r\nbwo=response11.varBinds[0][1]\r\nvarBinds2 = [ObjectType(ObjectIdentity('1.3.6.1.2.1.1.3.0'))] #get a sysUpTime\r\nresponse2 = snmp_engine.snmpget(varBinds2)\r\ntt=response2.varBinds[0][1]\r\n\r\nt=5#cada cuantos segundos se actualiza\r\ni=0\r\n\r\nwhile(True):\r\n    response1 = snmp_engine.snmpget(varBinds1)\r\n    response11 = snmp_engine.snmpget(varBinds11)\r\n    bw2 = response1.varBinds[0][1]\r\n    bwo2 = response11.varBinds[0][1]\r\n    incBw = (bw2 - bw)*8;#lo paso a bits desde octetos\r\n    incBwo = (bwo2 - bwo)*8;#lo paso a bits desde octetos\r\n    response2 = snmp_engine.snmpget(varBinds2)\r\n    tt2 = response2.varBinds[0][1]\r\n    incTt = (tt2 - tt);#lo paso desde centesimas de segundo\r\n\r\n    rate=incBw/(incTt/10)\r\n    rate2=incBwo/(incTt/10)\r\n\r\n\r\n    #print 'Bandwith in rate: ' + str(rate)+ ' bps'\r\n    #print 'Bandwith out rate: ' + str(rate2)+ ' bps'\r\n    #print('-----------------------------------------')\r\n\r\n    # printeo\r\n    # listas valores\r\n    if (i == 0):\r\n        x = [i]\r\n        y1 = [rate]\r\n        y2 = [rate2]\r\n    else:\r\n        x += [i]\r\n        y1 += [rate]\r\n        y2 += [rate2]\r\n    # grafico\r\n    plt.plot(x, y1, 'r^', x, y2, 'bs')\r\n    plt.ylabel('UPstream[red] DOWNstream[blue]      (bps)')\r\n    plt.xlabel('Instante')\r\n\r\n\r\n\r\n    plt.grid(True)\r\n    plt.pause(0.0025)\r\n    i += 1\r\n    #print('.')\r\n\r\n\r\n\r\n\r\n\r\n#reseteo\r\n    bw=bw2\r\n    tt=tt2\r\n    time.sleep(1)","sub_path":"Practica1/system_group_BW_vicente.py","file_name":"system_group_BW_vicente.py","file_ext":"py","file_size_in_byte":2921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"194510393","text":"#-*- coding: utf-8 -*-\nimport numpy as n\nfrom scipy.io import wavfile as w\n\nH=n.hstack\nV=n.vstack\n\nf_a = 44100. # Hz, frequência de amostragem\n\n############## 2.2.1 Tabela de busca (LUT)\nLambda_tilde=Lt=1024.*16\n\n# Senoide\nfoo=n.linspace(0,2*n.pi,Lt,endpoint=False)\nS_i=n.sin(foo) # um período da senóide com T amostras\n\n# Quadrada:\nQ_i=n.hstack(  ( n.ones(Lt/2)*-1 , n.ones(Lt/2) )  )\n\n# Triangular:\nfoo=n.linspace(-1,1,Lt/2,endpoint=False)\nTr_i=n.hstack(  ( foo , foo*-1 )   )\n\n# Dente de Serra:\nD_i=n.linspace(-1,1,Lt)\n\ndef v(f=200.,d=2.,tab=S_i,fv=2.,nu=2.,tabv=S_i):\n    Lambda=n.floor(f_a*d)\n    ii=n.arange(Lambda)\n    Lv=float(len(tabv))\n\n    Gammav_i=n.floor(ii*fv*Lv/f_a) # índices para a LUT\n    Gammav_i=n.array(Gammav_i,n.int)\n    # padrão de variação do vibrato para cada amostra\n    Tv_i=tabv[Gammav_i%int(Lv)] \n\n    # frequência em Hz em cada amostra\n    F_i=f*(   2.**(  Tv_i*nu/12.  )   ) \n    # a movimentação na tabela por amostra\n    D_gamma_i=F_i*(Lt/float(f_a))\n    Gamma_i=n.cumsum(D_gamma_i) # a movimentação na tabela total\n    Gamma_i=n.floor( Gamma_i) # já os índices\n    Gamma_i=n.array( Gamma_i, dtype=n.int) # já os índices\n    return tab[Gamma_i%int(Lt)] # busca dos índices na tabela\n\ndef A(fa=2.,V_dB=10.,d=2.,taba=S_i):\n    Lambda=n.floor(f_a*d)\n    ii=n.arange(Lambda)\n    Lt=float(len(taba))\n    Gammaa_i=n.floor(ii*fa*Lt/f_a) # índices para a LUT\n    Gammaa_i=n.array(Gammaa_i,n.int)\n    # variação da amplitude em cada amostra\n    A_i=taba[Gammaa_i%int(Lt)] \n    A_i=A_i*10.**(V_dB/20.)\n    return A_i\n\n\n\ndef adsr(som,A=10.,D=20.,S=-20.,R=100.,xi=1e-2):\n    a_S=10**(S/20.)\n    Lambda=len(som)\n    Lambda_A=int(A*f_a*0.001)\n    Lambda_D=int(D*f_a*0.001)\n    Lambda_R=int(R*f_a*0.001)\n\n    ii=n.arange(Lambda_A,dtype=n.float)\n    A=ii/(Lambda_A-1)\n    A_i=A\n    ii=n.arange(Lambda_A,Lambda_D+Lambda_A,dtype=n.float)\n    D=1-(1-a_S)*(   ( ii-Lambda_A )/( Lambda_D-1) )\n    A_i=n.hstack(  (A_i, D  )   )\n    S=n.ones(Lambda-Lambda_R-(Lambda_A+Lambda_D),dtype=n.float)*a_S\n    A_i=n.hstack( ( A_i, S )  )\n    ii=n.arange(Lambda-Lambda_R,Lambda,dtype=n.float)\n    R=a_S-a_S*((ii-(Lambda-Lambda_R))/(Lambda_R-1))\n    A_i=n.hstack(  (A_i,R)  )\n\n    return som*A_i\n\n### 2.74\nI1j=0.\nI2m=1.\nI2M=2.\nI3m=3.\nI3M=4.\nI4J=5.\nITR=6.\nI5J=7.\nI6m=8.\nI6M=9.\nI7m=10.\nI7M=11.\nI8J=12.\nI_i=n.arange(13.)\n\n# o intervalo soma nove nomenclatuda da iversão \n# mas soma sempre 12 na inversão de semitons\ndef inv(I):\n    \"\"\"retorna intervalo inverso de I: 0<= I <=12\"\"\"\n    return 12-I\n\n# intervalo harmônico\ndef intervaloHarmonico(f,I,d=.3):\n    return (  v(f,d=d)+v(f*2.**(I/12.),d=d)  )*0.5\n# intervalo melódico\ndef intervaloMelodico(f,I,d=.5):\n    return n.hstack((  v(f,d=d),v(f*2.**(I/12.),d=d)  ))\n\ns=n.array([])\nfor i in I_i:\n    s=n.hstack(( s,n.zeros(f_a/10),intervaloMelodico(200.,i,d=0.1) ))\n\nfor i in I_i:\n    s=n.hstack(( s,n.zeros(f_a/10),intervaloHarmonico(200,i,d=.2) ))\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(200,i,d=0.01) +v(400,d=0.02)))\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(400,-i  ,d=0.05)  ))\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(400,12-i,d=0.05)))\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(400,12-i,d=0.1)))\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(400,6-i,d= 0.1)))\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(400,12-i,d=0.1)))\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(400,5-i, d=0.1)))\n\n\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(200,12-i,d=0.05) ))\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(200,6-i ,d=0.05) ))\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(200,12-i,d=0.05) ))\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(200,5-i ,d=0.05) ))\n\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(200,12-i,d=0.05) ))\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(200,5-i ,d=0.05) ))\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(200,12-i,d=0.05) ))\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(200,7-i ,d=0.05) ))\n\n\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(200,7-i ,d=0.05) ))\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(400,7-i ,d=0.05) ))\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(300,7-i ,d=0.05) ))\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(600,7-i ,d=0.05) ))\n\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(200,7-i ,d=0.05) ))\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(400,11-i,d=0.05) ))\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(300,7-i ,d=0.05) ))\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloMelodico(600,11-i,d=0.05) ))\n\n\nfor i in I_i:\n    s=n.hstack(( s,intervaloHarmonico(200,i) ))\n\ns=((s-s.min())/(s.max()-s.min()))*2.-1.\n\n# most music players read only 16-bit wav files, so let's convert the array\ns = n.int16(s * float(2**15-1))\n\nw.write(\"intervalosEntreAlturas.wav\",f_a,s) # escrita do som\n","sub_path":"scripts/pecas2.3/intervalosEntreAlturas.py","file_name":"intervalosEntreAlturas.py","file_ext":"py","file_size_in_byte":4861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"570226727","text":"from tapp.core.exception import Error\nfrom tapp.core.validator import types\nimport copy\n\n\nclass FieldsLoader(object):\n\n    def __init__(self, request_data, request_method, method_policy):\n        self._original_request_data = copy.deepcopy(request_data)\n        self._request_method = request_method\n        self._method_policy = copy.deepcopy(method_policy)\n        self._required = self._method_policy.get('REQUIRED', [])\n        self._allowed = self._method_policy.get('ALLOWED', [])\n        self._actions = self._method_policy.get('ACTIONS', [])\n\n    def run(self):\n\n        # keep new data because field loader can change original name of input data key\n        new_request_data = copy.deepcopy(self._original_request_data)\n\n        # if 'action'\n        action_list = dict([(field.name, field,) for field in self._actions])\n        action_name = self._original_request_data.get('action', None)\n        if action_name:\n            action = action_list.get(action_name, None)\n            if not action:\n                Error('UNSUPPORTED', (action_name.title(), action_name,)).raise_exception(\"ValidateError\")\n            # validate\n            action.validate(new_request_data, self._request_method, None)\n        else:\n            # set 'required'\n            for field in self._required:\n                field.set_required(True)\n\n            # validate required\n            for field in self._required:\n                field.validate(new_request_data, self._request_method, None)\n\n            # as dicts, for quick search\n            required_fields = dict([(field.name, field,) for field in self._required])\n            allowed_fields = dict([(field.name, field,) for field in self._allowed])\n\n            # validate allowed\n            for key in self._original_request_data.keys():\n                if key in allowed_fields:\n                    allowed_fields[key].validate(new_request_data, self._request_method, None)\n                elif key not in required_fields:\n                    field_title = key.title().replace('_', ' ')\n                    Error(\"NOT_ALLOWED\", (field_title, ), True).raise_exception('ValidateError')\n\n        return new_request_data\n","sub_path":"tapp/core/validator/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"406632770","text":"from django.shortcuts import render\nfrom django.http.response import JsonResponse\nfrom rest_framework.views import APIView\nfrom .deployer_file import Deployer\nfrom azure.storage.blob import BlockBlobService\nimport json\nfrom collections import defaultdict\nfrom .logging_file import get_logger\nimport random\nfrom supplychain.settings import BASE_DIR\nimport os\nfrom .models import RandomId\nfrom .django_forms import UserDetails_Form\nfrom .databricks_linux import test324\nfrom threading import Thread\n\n\ndef execute_all_func(form_data):\n    logger = get_logger()\n    error_flag = False\n    try:\n        appservicename = form_data[\"appservicename\"]  # should display 'bar' # response to your request.\n        appurl = form_data[\"appserviceurl\"]\n        datafactoryname = form_data[\"datafactoryname\"]\n        accountname = form_data[\"storageaccountname\"]\n        accesskey = form_data[\"accesskey\"].replace(\" \", \"+\")\n        # sql credentials\n        connectstring = form_data[\"storageconnectionstring\"].replace(\" \", \"+\")\n        servername = form_data[\"sqlservername\"]\n        sqlusername = form_data[\"sqlserverusername\"]\n        sqlpass = form_data[\"sqlserverpassword\"]\n        sqlcon = form_data[\"sqlserverconnectionstring\"]\n        dbname = form_data[\"sqlserverdatabasename\"]\n        databricksname = form_data[\"databricksname\"]\n        accesstoken = form_data[\"accesstoken\"]\n        workspaceurl = form_data[\"databricksworkspaceurl\"]\n        databricksscope = form_data[\"databricksscope\"]\n        powerbiname = form_data[\"powerbiembedded\"]\n        powerbiadmin = form_data[\"powerbiadmin\"]\n        keyvaultname = form_data[\"keyvaultname\"]\n        subid = form_data[\"subscriptionid\"]\n        tenid = form_data[\"subscriptiontenantid\"]\n        clientid = form_data[\"subscriptionclientid\"]\n        clientsecret = form_data[\"subscriptionclientsecret\"]\n        resourcegroup = form_data[\"subscriptopnresourcegroup\"]\n        rglocation = form_data[\"resourcegrouplocation\"]\n        dflocation = form_data[\"datafactorylocation\"]\n        azurefunction = form_data[\"azurefunctionurl\"]\n        keyvaultlocation = form_data[\"keyvaultlocation\"]\n        rglocation = form_data[\"resourcegrouplocation\"]\n        sources = form_data[\"datafactorysources\"]\n        # bq project data\n        bqproject = form_data[\"bgprojectname\"]\n        bqclient = form_data[\"bqclientid\"]\n        bqsecret = form_data[\"bqclientsecret\"]\n        bqtoken = form_data[\"bqtoken\"]\n        bqschema = form_data[\"bgtableschema\"]\n        bqtables = form_data[\"bgtablevalues\"]\n        # oracle data  oracleservername\n        oracleport = form_data[\"oracleport\"]\n        oraclesid = form_data[\"oraclesid\"]\n        oracleuser = form_data[\"oracleuser\"]\n        oraclepassword = form_data[\"oraclepassword\"]\n        oracleschema = form_data[\"oracleschema\"]\n        oracletable = form_data[\"oracletables\"]\n        # sap data\n        sapserver = form_data[\"sapservername\"]\n        sapusername = form_data[\"sapusername\"]\n        sappassword = form_data[\"sappassword\"]\n        saptable = form_data[\"saptables\"]\n        sapschema = form_data[\"sapschema\"]\n        # salesforce data\n        salesforceuser = form_data[\"salesforceusername\"]\n        salesforcepass = form_data[\"salesforcepassword\"]\n        salesforcetoken = form_data[\"salesforcetoken\"]\n        salesforceschema = form_data[\"salesforceschema\"]\n        salesforcetables = form_data[\"salesforcetables\"]\n        resource_group = resourcegroup\n        subscription_id = subid\n        azure_client_id = clientid\n        azure_client_secret = clientsecret\n        azure_tenant_id = tenid\n        li_salesforce = list()\n        li_sap = list()\n\n        if len(salesforceuser) > 0:\n            schema_list = salesforceschema.split(\",\")\n            table_list = salesforcetables.split(\",\")\n            li_salesforce = [{\"objects_label\": schema_list[i], \"objects_name\": table_list[i]} for i in\n                             range(0, len(schema_list))]\n        elif len(sapserver) > 0:\n            schema_list = sapschema.split(\",\")\n            table_list = saptable.split(\",\")\n            li_sap = [{\"table_schema\": schema_list[i], \"table_name\": table_list[i]} for i in\n                      range(len(schema_list))]\n\n        dic = dict()\n        dic['$schema'] = 'https://schema.management.azure.com/schemas/2015-01-01/deploymentParameters.json#'\n        dic['contentVersion'] = '1.0.0.0'\n        dic['parameters'] = defaultdict(dict)\n        dic['parameters']['DataFactoryName']['value'] = datafactoryname\n        dic['parameters']['DataFactoryLocation']['value'] = dflocation\n        dic['parameters']['StorageConnectionString']['value'] = connectstring\n        dic['parameters']['SQLConnectionString']['value'] = sqlcon\n        dic['parameters']['AzureFunctionURL']['value'] = azurefunction\n        dic['parameters']['Sources']['value'] = sources\n        if len(li_salesforce) != 0:\n            dic['parameters']['SalesForceUsername']['value'] = salesforceuser\n            dic['parameters']['SalesForcePassword']['value'] = salesforcepass\n            dic['parameters']['SalesForceToken']['value'] = salesforcetoken\n            dic['parameters']['SalesForceTables']['value'] = li_salesforce\n        if len(li_sap) != 0:\n            dic['parameters']['SAPServer']['value'] = sapserver\n            dic['parameters']['SAPUsername']['value'] = sapusername\n            dic['parameters']['SAPPassword']['value'] = sappassword\n            dic['parameters']['SAPTables']['value'] = li_sap\n\n        for key, value in dic['parameters'].items():\n            if value['value'] == None:\n                del dic['parameters'][key]\n\n        with open(os.path.join(BASE_DIR, \"ADFParameters.json\"), \"w\") as f:\n            f.write(json.dumps(dic))\n\n        # new file\n\n        dic2 = dict()\n        dic2['$schema'] = 'https://schema.management.azure.com/schemas/2015-01-01/deploymentParameters.json#'\n        dic2['contentVersion'] = '1.0.0.0'\n        dic2['parameters'] = defaultdict(dict)\n        dic2['parameters']['SubscriptionID']['value'] = str(subid)\n        dic2['parameters']['TenantID']['value'] = tenid\n        dic2['parameters']['ClientID']['value'] = clientid\n        dic2['parameters']['CientSecret']['value'] = clientsecret\n        dic2['parameters']['DataFactoryName']['value'] = datafactoryname\n        dic2['parameters']['StorageAccountName']['value'] = accountname\n        dic2['parameters']['StorageAccessKey']['value'] = accesskey\n        dic2['parameters']['StorageConnectionString']['value'] = connectstring\n        dic2['parameters']['SQLServerName']['value'] = servername\n        dic2['parameters']['SQLUsername']['value'] = sqlusername\n        dic2['parameters']['SQLPassword']['value'] = sqlpass\n        dic2['parameters']['SQLConnectionString']['value'] = sqlcon\n        dic2['parameters']['SQLDatabaseName']['value'] = dbname\n        dic2['parameters']['DatabricksName']['value'] = databricksname\n        dic2['parameters']['DataBricksWorkspaceURL']['value'] = workspaceurl\n        dic2['parameters']['DataBricksToken']['value'] = accesstoken\n        dic2['parameters']['DataBricksScope']['value']  = databricksscope\n        dic2['parameters']['PowerBIEmbeddedName']['value'] = powerbiname\n        dic2['parameters']['PowerBIEmbeddedAdmin']['value'] = powerbiadmin\n        dic2['parameters']['AppServiceName']['value'] = appservicename\n        dic2['parameters']['AppServiceURL']['value'] = appurl\n        dic2['parameters']['AzureFunctionURL']['value'] = azurefunction\n        dic2['parameters']['KeyVaultName']['value'] = keyvaultname\n        dic2['parameters']['KeyVaultLocation']['value'] = keyvaultlocation\n        dic2['parameters']['ResourceGroupName']['value'] = resourcegroup\n        dic2['parameters']['ResourceGroupLocation']['value'] = rglocation\n        \n        test324.main(workspaceurl, accesstoken)\n\n        #os.system(\"python3 /home/site/wwwroot/azure_blob_app/databricks_linux/test324.py {} {}\".format(workspaceurl, accesstoken))\n\n        with open(os.path.join(BASE_DIR, \"KeyVaultParameters.json\"), \"w\") as f:\n            f.write(json.dumps(dic2))\n\n        container_name = 'marketplacecodes'\n        blob_name = 'ADFParameters.json'\n        blob_client = BlockBlobService(connection_string=connectstring)\n        resp = blob_client.create_blob_from_path(container_name=container_name, blob_name=blob_name,\n                                                 file_path=os.path.join(BASE_DIR, \"ADFParameters.json\"))\n        blob_name = 'KeyVaultParameters.json'\n        resp = blob_client.create_blob_from_path(container_name=container_name, blob_name=blob_name,\n                                                 file_path=os.path.join(BASE_DIR, \"KeyVaultParameters.json\"))\n        obj = Deployer(resource_group, subscription_id, azure_client_id, azure_client_secret, azure_tenant_id)\n        print(obj.deploy(\"DataFactoryDeployment.json\", \"ADFParameters.json\", connectstring))\n        print(obj.deploy(\"KeyVaultDeployment.json\", \"KeyVaultParameters.json\", connectstring))\n        blob_client.delete_container(\"marketplacecodes\")\n    except KeyError as k:\n        logger.error(str(k))\n        error_flag = True\n    except Exception as e:\n        logger.error(str(e))\n        error_flag = True\n    return\n\n\ndef index(request):\n    logger = get_logger()\n    error = False\n    try:\n        if str(request.method).lower() == 'post':\n            form = UserDetails_Form(request.POST)\n            if form.is_valid():\n                form_data = dict(form.data.items())\n                thread_var = Thread(target=execute_all_func,args=(form_data,))\n                thread_var.start()\n    except Exception as e:\n        logger.error(str(e))\n    form = UserDetails_Form()\n    content = {'form': form, 'error': error}\n    return render(request, \"index.html\", content)\n\n\n# Create your views here.\nclass FirstTrail(APIView):\n    logger = get_logger()\n\n    def get(self, request):\n        while True:\n            random_no = \"\".join([str(random.randint(0, 9)) for i in range(0, 9)])\n            random_obj = RandomId.objects.filter(random_id=random_no)\n            if random_obj.exists():\n                continue\n            else:\n                random_obj = RandomId()\n                random_obj.random_id = random_no\n                random_obj.save()\n                return JsonResponse({\"output\": random_no})\n","sub_path":"azure_blob_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":10322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"98374037","text":"import numpy as np\nimport h5py\n\ndef read_spe2d(fgroup):\n   c_dheader = np.dtype([('Time','a14'),('Longitude','f4'),('Latitude','f4'),('nfreq','i4'),('ndir','i4'),('freq1','f4'),('dir1','f4'),('dang','f4'),('u10','f4'),('udir','f4'),('ustar','f4'),('hs','f4'),('tp','f4'),('tm','f4'),('tm1','f4'),('tm2','f4'),('wdir','f4'),('wspr','f4')])\n   bdat = np.empty( (1), dtype=c_dheader)\n   freq = np.empty( (1), dtype=float)\n   ef2d = np.empty( (1), dtype=float)\n\n   icount = 0\n   fname = fgroup[:-6] + '.spe2d'\n   input_file = open(fname,'r')\n   line = input_file.readline()\n   while line:\n      array = line.split()\n      bdat['Time'] = array[1]\n      bdat['Longitude'] = float(array[2])\n      bdat['Latitude'] = float(array[3])\n      bdat['nfreq'] = int(array[4])\n      bdat['ndir'] = int(array[5])\n      bdat['freq1'] = float(array[6])\n      bdat['dir1'] = float(array[7])\n      bdat['dang'] = float(array[8])\n\n      line = input_file.readline()\n      array = line.split()\n      bdat['u10'] = float(array[0])\n      if len(array) == 2:\n         array2 = array[1].split('-')\n         bdat['udir'] = float(array2[0])\n         bdat['ustar'] = float('-' + array2[1])\n      else:\n         bdat['udir'] = float(array[1])\n         bdat['ustar'] = float(array[2])\n\n      line = input_file.readline()\n      array = line.split()\n      bdat['hs'] = float(array[0])\n      bdat['tp'] = float(array[1])\n      bdat['tm'] = float(array[2])\n      bdat['tm1'] = float(array[3])\n      bdat['tm2'] = float(array[4])\n      bdat['wdir'] = float(array[5])\n      bdat['wspr'] = float(array[6])\n\n      ft = []\n      et = []\n\n      for nn in range(bdat['nfreq']):\n         line = input_file.readline()\n         array = line.split()\n         ft.append(array[0])\n         et.append(array[1:])\n         \n      fr = np.array(ft,dtype=float)\n      ef = np.array(et,dtype=float)\n\n      ef2d.resize((icount+1,29,72))\n      ef2d[icount,:,:] = ef\n\n      icount += 1\n      line = input_file.readline()      \n\n   ang = []\n   for aa in range(bdat['ndir']):\n      at = bdat['dir1'] + bdat['dang']*aa\n      ang.append(at)\n\n   angle = np.array(ang,dtype=float)\n   input_file.close()\n\n   return fr, angle, ef2d\n\nif __name__ == \"__main__\":\n    read_spe2d(fgroup)\n","sub_path":"python_codes/read_spe2d.py","file_name":"read_spe2d.py","file_ext":"py","file_size_in_byte":2217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"627262321","text":"# -*- coding: utf-8 -*-\n\nimport logging\nimport os\n\n\nlog = logging.getLogger(\"frkl\")\n\n\"\"\"Top-level package for tingex.\"\"\"\n\n__author__ = \"\"\"Markus Binsteiner\"\"\"\n__email__ = \"markus@frkl.io\"\n\n\ndef get_version():\n    from pkg_resources import DistributionNotFound, get_distribution\n\n    try:\n        # Change here if project is renamed and does not equal the package name\n        dist_name = __name__\n        __version__ = get_distribution(dist_name).version\n    except DistributionNotFound:\n\n        try:\n            version_file = os.path.join(os.path.dirname(__file__), \"version.txt\")\n\n            if os.path.exists(version_file):\n                with open(version_file, encoding=\"utf-8\") as vf:\n                    __version__ = vf.read()\n            else:\n                __version__ = \"unknown\"\n\n        except (Exception):\n            pass\n\n        if __version__ is None:\n            __version__ = \"unknown\"\n\n    return __version__\n\n\ntry:\n    from frkl.project_meta.app_environment import AppEnvironment\n\n    TINGEX: AppEnvironment = AppEnvironment(main_module=\"tingex\")\nexcept Exception as e:\n    log.debug(\n        f\"Can't create AppEnvironment (probably pkg 'frkl.project_meta' not in virtualenv): {e}\"\n    )\n    TINGEX = None  # type: ignore\n","sub_path":"src/tingex/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"472100118","text":"import json\nimport urllib3\nfrom urllib.parse import urlencode\nfrom typing import Optional\nfrom .type_helpers import PlaylistRef, Paths, ItemIndices, ColumnsMap\nfrom .helper_funcs import param_value_to_str\nfrom .endpoints import (\n    GET_PLAYER_STATE,\n    SET_PLAYER_STATE,\n    PLAY,\n    PLAY_SPECIFIC,\n    PLAY_RANDOM,\n    PLAY_NEXT,\n    PLAY_PREVIOUS,\n    PLAYLIST_ITEMS,\n    PLAYLIST_QUERY,\n    STOP,\n    PAUSE,\n    PAUSE_TOGGLE,\n    SET_CURRENT_PLAYLIST,\n    ADD_PLAYLIST,\n    UPDATE_PLAYLIST,\n    REMOVE_PLAYLIST,\n    MOVE_PLAYLIST,\n    CLEAR_PLAYLIST,\n    BROWSER_ROOTS,\n    BROWSER_ENTRIES,\n    ADD_PLAYLIST_ITEMS,\n    COPY_PLAYLIST_ITEMS,\n    COPY_BETWEEN_PLAYLISTS,\n    MOVE_PLAYLIST_ITEMS,\n    MOVE_BETWEEN_PLAYLISTS,\n    REMOVE_PLAYLIST_ITEMS,\n    SORT_PLAYLIST_ITEMS,\n    GET_ARTWORK,\n)\nfrom .models import (\n    BrowserEntry,\n    Playlists,\n    PlaylistInfo,\n    PlaylistItems,\n    PlayerState,\n)\nfrom .exceptions import ConnectError, RequestError\n\n\nclass Client:\n    \"\"\"\n    Synchronous class for interacting with the beefweb plugin.\n\n    :param base_url: The base URL for beefweb's server.  i.e. \"localhost\"\n    :param port: The port number of beefweb's server.\n    :param username: The username to use if authentification is enabled in\n        beefweb.\n    :param password: The password to use if authentification is enabled in\n        beefweb.\n    :param timeout: The maximum amount of time to wait when making a http\n        request.\n    \"\"\"\n\n    _default_column_map = {\n        \"%album artist%\": \"album_artist\",\n        \"%album%\": \"album\",\n        \"%artist%\": \"artist\",\n        \"%length%\": \"length\",\n        \"%length_seconds%\": \"length_seconds\",\n        \"%path%\": \"path\",\n        \"%title%\": \"title\",\n        \"%track artist%\": \"track_artist\",\n        \"%track number%\": \"track_number\",\n    }\n\n    def __init__(\n        self,\n        base_url: str,\n        port: int,\n        username: Optional[str] = None,\n        password: Optional[str] = None,\n        timeout: float = 10.0,\n    ):\n        if not base_url.lower().startswith(\"http\"):\n            base_url = \"http://\" + base_url\n        self.base_url = base_url\n        self.port = port\n        self._user_pass = None\n        if username is not None and password is not None:\n            self._user_pass = \":\".join((username, password))\n        self._http = urllib3.PoolManager(timeout=timeout)\n\n    def _request(\n        self, endpoint, params=None, paths=None, body=None, original_args=None\n    ):\n        \"\"\"\n        Send a HTTP request to the beefweb server.\n\n        :param Endpoint endpoint: The endpoint object containing information\n            about the endpoint and the return type.\n        :param dict params: Dict containing the parameters to be added to the\n            endpoint url.\n        :param dict paths: Dict to be used for formatting the endpoint url.\n        :param dict body: Object to be added as the requests JSON payload.\n        :param dict original_args: Dict containing the original arguments\n            passed to the caller.\n        :returns: The return type specified in the object passed to the\n            endpoint argument.\n        :raises: ConnectError if the script cannot connect to the beefweb\n            server.\n        :raises: RequestError if beefweb returns an error.\n        \"\"\"\n        if paths is not None:\n            endpoint_path = endpoint.endpoint.format(**paths)\n        else:\n            endpoint_path = endpoint.endpoint\n\n        url = f\"{self.base_url}:{self.port}/api{endpoint_path}\"\n        if params:\n            url += \"?\" + urlencode(params)\n        if body is not None:\n            body = json.dumps(body).encode(\"utf-8\")\n\n        headers = urllib3.util.make_headers(basic_auth=self._user_pass)\n        # The beefweb server treats header names as case sensitive...\n        if \"authorization\" in headers:\n            headers[\"Authorization\"] = headers.pop(\"authorization\")\n\n        try:\n            response = self._http.request(\n                endpoint.method, url, body=body, headers=headers\n            )\n        except (\n            urllib3.exceptions.TimeoutError,\n            urllib3.exceptions.ConnectionError,\n            urllib3.exceptions.MaxRetryError,\n            urllib3.exceptions.ResponseError,\n        ) as err:\n            raise ConnectError(err)\n\n        self._http.clear()\n\n        if response.status not in (200, 202, 204):\n            raise RequestError(\n                response.status, json.loads(response.data.decode(\"utf-8\"))\n            )\n        if endpoint.model is not None:\n            data = json.loads(response.data.decode(\"utf-8\"))\n            if not original_args or \"column_map\" not in original_args:\n                return endpoint.model(data)\n            else:\n                return endpoint.model(\n                    data, column_map=original_args[\"column_map\"]\n                )\n\n    def get_player_state(\n        self, column_map: Optional[ColumnsMap] = None\n    ) -> PlayerState:\n        \"\"\"\n        Get the status of the player.\n\n        :param Optional[ColumnsMap] column_map: Dict, list, tuple, or set\n            where each key (for dict, set) or item (for list, tuple) is the\n            active item field to request.  If a dict is used each value is the\n            attribute name to map that key to the returned Column object.\n\n            If this argument is not specified a default column map will be\n            used.\n\n            .. note:: Names that would be invalid as Python attrubute names\n                may only be accessed through subscripting (i.e. \"%title%\" or\n                \"my custom name\").\n\n\n                Examples-\n                {\"%track artist%\": \"artist\"} will result in the returned\n                object having an active_item.columns.artist attribute\n                containing information returned from the active item's\n                %track artist% field.\n\n                [\"%title%\", \"%album%\"] will result in the returned object\n                bieng subscriptable like active_item.columns[\"%album%\"] which\n                will return information returned from the active item's\n                %album% field.\n        :returns: PlayerState object\n        :rtype: PlayerState\n        \"\"\"\n        if column_map is None:\n            column_map = self._default_column_map\n        params = {\"columns\": param_value_to_str(column_map)}\n        return self._request(\n            GET_PLAYER_STATE,\n            params=params,\n            original_args={\"column_map\": column_map},\n        )\n\n    def set_player_state(\n        self,\n        volume: Optional[float] = None,\n        mute: Optional[bool] = None,\n        position: Optional[float] = None,\n        relative_position: Optional[float] = None,\n        playback_mode: Optional[int] = None,\n    ) -> None:\n        \"\"\"\n        Set the volume level, playback position, and playback order.\n\n        .. note:: Note that the value for the volume parameter may need to be\n            a negative number to work correctly. Use information returned by\n            get_player_state to determine the min and max values for the\n            connected media player.\n\n        :param Optional[float] volume: Floating point number to set the\n            player's volume level. Value may need to be a negative number to\n            work correctly. Use information returned by get_player_state to\n            determine the min and max values for the connected media player.\n        :param Optional[bool] mute: True = muted, False = unmuted, None = no\n            change.\n        :param Optional[float] position: Sets the position of the current\n            track in seconds.\n        :param Optional[float] relative_position: Seconds to Add or subtract\n            from the current position.\n        :param Optional[int] playback_mode: Numerical mode that sets playback\n            order (repeat, random, shuffle, etc.).  Use the playback_modes\n            attribute from get_player_state() to determine the possible\n            choices.\n        \"\"\"\n        kw_map = {}\n        if volume is not None:\n            kw_map[\"volume\"] = param_value_to_str(volume)\n        if mute is not None:\n            kw_map[\"isMuted\"] = param_value_to_str(mute)\n        if position is not None:\n            kw_map[\"position\"] = param_value_to_str(position)\n        if relative_position is not None:\n            kw_map[\"relativePosition\"] = param_value_to_str(relative_position)\n        if playback_mode is not None:\n            kw_map[\"playbackMode\"] = param_value_to_str(playback_mode)\n\n        return self._request(\n            SET_PLAYER_STATE, params=kw_map, original_args=locals()\n        )\n\n    def play(self) -> None:\n        \"\"\"\n        Start playback.\n\n        .. note:: Note that the item played will be from the last playlist\n            that an item was played from even if another playlist is set as\n            current.  Use the play_specific method first to play an item from a\n            specific playlist.\n        \"\"\"\n        self._request(PLAY)\n\n    def play_specific(self, playlist_ref: PlaylistRef, index: int) -> None:\n        \"\"\"\n        Start playback of a specific item from a specific playlist.\n\n        :param PlaylistRef playlist_ref: The PlaylistInfo object, ID, or\n            numerical index of the playlist that contains the media to play.\n        :param int index: The index number of the item to play.\n        \"\"\"\n        playlist_ref = param_value_to_str(playlist_ref)\n        self._request(PLAY_SPECIFIC, paths=locals())\n\n    def play_random(self) -> None:\n        \"\"\"\n        Play a random item from the last played playlist.\n\n        .. note:: Note that the item played will be from the last playlist\n            that an item was played from even if another playlist is set as\n            current.  Use the play_specific method first to play an item from a\n            specific playlist.\n        \"\"\"\n        self._request(PLAY_RANDOM)\n\n    def play_next(self, by: str = None) -> None:\n        \"\"\"\n        Play the next item in the last played playlist.\n\n        .. note:: Note that the item played will be from the last playlist\n            that an item was played from even if another playlist is set as\n            current.  Use the play_specific method first to play an item from a\n            specific playlist.\n\n        :param str by: The media player field used to determine what counts as\n            next (i.e. \"%title%\").  None = use the items index in the playlist.\n        \"\"\"\n        kw_map = {\"by\": by} if by is not None else {}\n        self._request(PLAY_NEXT, params=kw_map)\n\n    def play_previous(self, by: str = None) -> None:\n        \"\"\"\n        Play the previous item in the last played playlist.\n\n        .. note:: Note that the item played will be from the last playlist\n            that an item was played from even if another playlist is set as\n            current.  Use the play_specific method first to play an item from a\n            specific playlist.\n\n        :param str by: The media player field used to determine what counts as\n            previous (i.e. \"%title%\").  None = use the items index in the\n            playlist.\n        \"\"\"\n        kw_map = {\"by\": by} if by is not None else {}\n        self._request(PLAY_PREVIOUS, params=kw_map)\n\n    def stop(self) -> None:\n        \"\"\"Stop playback.\"\"\"\n        self._request(STOP)\n\n    def pause(self) -> None:\n        \"\"\"Pause playback.\"\"\"\n        self._request(PAUSE)\n\n    def pause_toggle(self) -> None:\n        \"\"\"Toggle pause state.\"\"\"\n        self._request(PAUSE_TOGGLE)\n\n    def get_playlists(self) -> Playlists:\n        \"\"\"\n        Get an object representing the playlists.\n\n        :returns: A Playlists object\n        :rtype: Playlists\n        \"\"\"\n        params = {\"playlists\": \"true\"}\n        return self._request(PLAYLIST_QUERY, params=params)\n\n    def find_playlist(\n        self, title: Optional[str] = None, search_id: Optional[str] = None\n    ) -> Optional[PlaylistInfo]:\n        \"\"\"\n        Get an object representing a specific playlist.\n\n        :param Optional[str] title: String representing the playlist's title.\n        :param Optional[str] search_id: The id of the playlist.\n        :returns: A PlaylistInfo namedtuple or None if not found.\n        :rtype: Optional[PlaylistInfo]\n        \"\"\"\n        return self.get_playlists().find_playlist(\n            title=title, search_id=search_id\n        )\n\n    def get_playlist_items(\n        self,\n        playlist_ref: PlaylistRef,\n        offset: int = 0,\n        count: int = 1000000,\n        column_map: Optional[ColumnsMap] = None,\n    ) -> PlaylistItems:\n        \"\"\"\n        Get an object representing the items in a specific playlist.\n\n        :param PlaylistRef playlist_ref: The PlaylistInfo object, ID, or\n            numerical playlist index.\n        :param int offset: The start index of the items to retrieve.\n        :param int count: The amount of items to retrieve.\n        :param Optional[ColumnsMap] column_map: Dict, list, tuple, or set where\n            each key (for dict, set) or item (for list, tuple) is the item\n            field to request.  If a dict is used each value is the attribute\n            name to map that key to the returned Column object.\n\n            If this argument is not specified a default column map will be\n            used.\n\n            .. note:: Names that would be invalid as Python attrubute names\n                may only be accessed through subscripting (i.e. \"%title%\" or\n                \"my custom name\").\n\n                Examples-\n                {\"%track artist%\": \"artist\"} will result in the returned\n                object having an active_item.columns.artist attribute\n                containing information returned from the active item's\n                %track artist% field.\n\n                [\"%title%\", \"%album%\"] will result in the returned object\n                bieng subscriptable like active_item.columns[\"%album%\"] which\n                will return information returned from the active item's\n                %album% field.\n\n        :returns: A PlaylistItems object.\n        :rtype: PlaylistItems\n        \"\"\"\n        if column_map is None:\n            column_map = self._default_column_map\n        params = {\n            \"playlistItems\": \"true\",\n            \"plref\": param_value_to_str(playlist_ref),\n            \"plrange\": f\"{offset}:{count}\",\n            \"plcolumns\": param_value_to_str(column_map),\n        }\n        return self._request(\n            PLAYLIST_ITEMS,\n            params=params,\n            original_args={\"column_map\": column_map},\n        )\n\n    def set_current_playlist(self, playlist_ref: PlaylistRef) -> None:\n        \"\"\"\n        Set the currently selected playlist.\n\n        :param playlist_ref: The PlaylistInfo object, ID, or numerical\n            playlist index.\n        \"\"\"\n        params = {\"current\": param_value_to_str(playlist_ref)}\n        return self._request(SET_CURRENT_PLAYLIST, params=params)\n\n    def add_playlist(\n        self,\n        title: str,\n        index: Optional[int] = None,\n        return_playlist: Optional[bool] = True,\n    ) -> Optional[PlaylistInfo]:\n        \"\"\"\n        Create a new playlist.\n\n        :param str title: The title of the playlist.\n        :param Optional[int] index: The numerical index to insert the new\n            playlist. None = last position.\n        :param Optional[bool] return_playlist: If True automatically makes a\n            second request to beefweb to retrieve the playlist info for the\n            new playlist. There is a small chance that the incorrect playlist\n            will be returned during the second request (i.e. if a playlist is\n            added or removed between api calls).  In this case an\n            AssertionError will be raised.\n        :returns: A PlaylistInfo namedtuple if return_playlist is True else\n            None\n        :rtype: Optional[PlaylistInfo]\n        \"\"\"\n        params = {}\n        if index is not None:\n            params[\"index\"] = index\n        if title is not None:\n            params[\"title\"] = title\n        self._request(ADD_PLAYLIST, params=params)\n\n        playlist = None\n        if return_playlist and index is None:\n            playlist = self.get_playlists().find_playlist(\n                title=title, find_last=True\n            )\n        elif return_playlist:\n            playlist = self.get_playlists()[index]\n\n        if playlist and playlist.title != title:\n            raise AssertionError(\"Wrong playlist returned from request!\")\n        return playlist\n\n    def set_playlist_title(\n        self, playlist_ref: PlaylistRef, title: str\n    ) -> None:\n        \"\"\"\n        Change a playlist's title.\n\n        :param PlaylistRef playlist_ref: The PlaylistInfo object, ID, or\n            numerical playlist index.\n        :param str title: The new title of the playlist.\n        \"\"\"\n        paths = {\"playlist_ref\": param_value_to_str(playlist_ref)}\n        params = {\"title\": title}\n        self._request(UPDATE_PLAYLIST, paths=paths, params=params)\n\n    def remove_playlist(self, playlist_ref: PlaylistRef) -> None:\n        \"\"\"\n        Remove a playlist.\n\n        :param PlaylistRef playlist_ref: The PlaylistInfo object, ID, or\n            numerical playlist index.\n        \"\"\"\n        paths = {\"playlist_ref\": param_value_to_str(playlist_ref)}\n        return self._request(REMOVE_PLAYLIST, paths=paths)\n\n    def move_playlist(self, playlist_ref: PlaylistRef, new_index: int) -> None:\n        \"\"\"\n        Move a playlist to a new index.\n\n        :param PlaylistRef playlist_ref: The PlaylistInfo object, ID, or\n            numerical playlist index.\n        :param int index: The new index of the playlist. Negative value to\n            make it the last playlist.\n        \"\"\"\n        paths = {\n            \"playlist_ref\": param_value_to_str(playlist_ref),\n            \"new_index\": new_index,\n        }\n        return self._request(MOVE_PLAYLIST, paths=paths)\n\n    def clear_playlist(self, playlist_ref: PlaylistRef) -> None:\n        \"\"\"\n        Remove all items from a playlist.\n\n        :param PlaylistRef playlist_ref: The PlaylistInfo object, ID, or\n            numerical playlist index.\n        \"\"\"\n        paths = {\"playlist_ref\": param_value_to_str(playlist_ref)}\n        return self._request(CLEAR_PLAYLIST, paths=paths)\n\n    def get_browser_roots(self) -> BrowserEntry:\n        \"\"\"\n        Get all the files and directories accessable by beefweb.\n\n        :returns: A BrowserEntry object.\n        :rtype: BrowserEntry\n        \"\"\"\n        return self._request(BROWSER_ROOTS)\n\n    def get_browser_entries(self, path: str) -> BrowserEntry:\n        r\"\"\"\n        Get files and directories from a specific path.\n\n        :param str path: The path to the directory or file to retrieve.  Note\n            that even in windows the path including the drive letter is case\n            sensitive (r\"C:\\\\Music\" will work, r\"c:\\\\Music\" will not).\n\n        .. warning:: Even in windows the path including the drive letter is\n            case sensitive (r\"C:\\\\Music\" will work, r\"c:\\\\Music\" will not).\n\n        :returns: A BrowserEntry object.\n        :rtype: BrowserEntry\n        \"\"\"\n        params = {}\n        if path is not None:\n            params[\"path\"] = path\n        return self._request(BROWSER_ENTRIES, params=params)\n\n    def add_playlist_items(\n        self,\n        playlist_ref: PlaylistRef,\n        items: Paths,\n        dest_index: Optional[int] = None,\n        asynchronous: bool = False,\n    ) -> None:\n        \"\"\"\n        Add items to a playlist.\n\n        :param PlaylistRef playlist_ref: The PlaylistInfo object, ID, or\n            numerical playlist index.\n        :param Paths items: A list or tuple of strings or FileSystemEntry\n            objects containing the paths of files and/or directories to add.\n            Note that the paths including the drive letter are case sensitive\n            even in Windows.\n        :param Optional[int] dest_index: The index in the playlist to insert\n            the new items.\n        :param bool asynchronous: Set to True to not wait for the\n            media player to finish processing the added items before\n            returning.  Default = False.\n\n        .. warning:: The paths including the drive letter are case sensitive\n            even in windows.\n        \"\"\"\n        paths = {\"playlist_ref\": param_value_to_str(playlist_ref)}\n        params = {\"async\": param_value_to_str(asynchronous)}\n        if dest_index is not None:\n            params[\"index\"] = param_value_to_str(dest_index)\n        content = {\"items\": [param_value_to_str(item) for item in items]}\n        return self._request(\n            ADD_PLAYLIST_ITEMS, params=params, paths=paths, body=content\n        )\n\n    def copy_playlist_items(\n        self,\n        playlist_ref: PlaylistRef,\n        item_indices: ItemIndices,\n        dest_index: Optional[int] = None,\n    ) -> None:\n        \"\"\"\n        Copy items to the same playlist.\n\n        :param PlaylistRef playlist_ref: The PlaylistInfo object, ID, or\n            numerical playlist index.\n        :param ItemIndices item_indices: List or tuple of the indices of the\n            items to be copied.\n        :param Optional[int] dest_index: The index in the playlist to insert\n            the copies. None = copy to the end of the playlist.\n        \"\"\"\n        paths = {\"playlist_ref\": param_value_to_str(playlist_ref)}\n        params = {}\n        if dest_index is not None:\n            params[\"targetIndex\"] = dest_index\n        content = {\"items\": item_indices}\n        return self._request(\n            COPY_PLAYLIST_ITEMS, params=params, paths=paths, body=content\n        )\n\n    def copy_between_playlists(\n        self,\n        source_playlist: PlaylistRef,\n        dest_playlist: PlaylistRef,\n        item_indices: ItemIndices,\n        dest_index: Optional[int] = None,\n    ) -> None:\n        \"\"\"\n        Copy items to a different playlist.\n\n        :param PlaylistRef source_playlist: The PlaylistInfo object, ID, or\n            numerical playlist index of the source.\n        :param PlaylistRef dest_playlist: The PlaylistInfo object, ID, or\n            numerical playlist index of the destination.\n        :param ItemIndices item_indices: List or tuple of the indices of the\n            items to be copied.\n        :param Optional[int] dest_index: The index in the playlist to insert\n            the copies. None = copy to the end of the playlist.\n        \"\"\"\n        paths = {\n            \"source_playlist_ref\": param_value_to_str(source_playlist),\n            \"dest_playlist_ref\": param_value_to_str(dest_playlist),\n        }\n        params = {}\n        if dest_index is not None:\n            params[\"targetIndex\"] = dest_index\n        content = {\"items\": item_indices}\n        return self._request(\n            COPY_BETWEEN_PLAYLISTS, params=params, paths=paths, body=content\n        )\n\n    def move_playlist_items(\n        self,\n        playlist_ref: PlaylistRef,\n        item_indices: ItemIndices,\n        dest_index: Optional[int] = None,\n    ) -> None:\n        \"\"\"\n        Move items to a different index in the same playlist.\n\n        :param PlaylistRef playlist_ref: The PlaylistInfo object, ID, or\n            numerical playlist index.\n        :param ItemIndices item_indices: List or tuple of the indices of the\n            items to be moved.\n        :param Optional[int] dest_index: The index in the playlist to insert\n            the copies. None = copy to the end of the playlist.\n        \"\"\"\n        paths = {\"playlist_ref\": param_value_to_str(playlist_ref)}\n        params = {}\n        if dest_index is not None:\n            params[\"targetIndex\"] = dest_index\n        content = {\"items\": item_indices}\n        return self._request(\n            MOVE_PLAYLIST_ITEMS, params=params, paths=paths, body=content\n        )\n\n    def move_between_playlists(\n        self,\n        source_playlist: PlaylistRef,\n        dest_playlist: PlaylistRef,\n        item_indices: ItemIndices,\n        dest_index: Optional[int] = None,\n    ) -> None:\n        \"\"\"\n        Move items to a different playlist.\n\n        :param PlaylistRef source_playlist: The PlaylistInfo object, ID, or\n            numerical playlist index of the source.\n        :param PlaylistRef dest_playlist: The PlaylistInfo object, ID, or\n            numerical playlist index of the destination.\n        :param ItemIndices item_indices: List or tuple of the indices of the\n            items to be moved.\n        :param Optional[int] dest_index: The index in the playlist to insert\n            the copies. None = copy to the end of the playlist.\n        \"\"\"\n        paths = {\n            \"source_playlist_ref\": param_value_to_str(source_playlist),\n            \"dest_playlist_ref\": param_value_to_str(dest_playlist),\n        }\n        params = {}\n        if dest_index is not None:\n            params[\"targetIndex\"] = dest_index\n        content = {\"items\": item_indices}\n        return self._request(\n            MOVE_BETWEEN_PLAYLISTS, params=params, paths=paths, body=content\n        )\n\n    def remove_playlist_items(\n        self, playlist_ref: PlaylistRef, item_indices: ItemIndices\n    ) -> None:\n        \"\"\"\n        Remove items from a playlist.\n\n        :param PlaylistRef playlist_ref: The PlaylistInfo object, ID, or\n            numerical playlist index.\n        :param ItemIndices item_indices: List or tuple of the indices of the\n            items to be removed.\n        \"\"\"\n        paths = {\"playlist_ref\": param_value_to_str(playlist_ref)}\n        params = {}\n        content = {\"items\": item_indices}\n        return self._request(\n            REMOVE_PLAYLIST_ITEMS, params=params, paths=paths, body=content\n        )\n\n    def sort_playlist_items(\n        self,\n        playlist_ref: PlaylistRef,\n        by: str,\n        desc: bool = False,\n        random: bool = False,\n    ) -> None:\n        \"\"\"\n        Sort the items in a playlist.\n\n        :param PlaylistRef playlist_ref: The PlaylistInfo object, ID, or\n            numerical playlist index.\n        :param str by: The media player field to sort by (i.e. \"%title%\").\n        :param bool desc: Sort in descending order.\n        :param bool random: Sort in random order.\n        \"\"\"\n        paths = {\"playlist_ref\": param_value_to_str(playlist_ref)}\n        params = {\n            \"by\": by,\n            \"desc\": param_value_to_str(desc),\n            \"random\": param_value_to_str(random),\n        }\n        return self._request(SORT_PLAYLIST_ITEMS, params=params, paths=paths)\n\n    def get_artwork(self, playlist_ref: PlaylistRef, index: int) -> None:\n        \"\"\"\n        Get an items artwork.\n\n        :param PlaylistRef playlist_ref: The PlaylistInfo object, ID, or\n            numerical playlist index.\n        :param int index: The index of the item to get artwork for.\n        \"\"\"\n        paths = {\n            \"playlist_ref\": param_value_to_str(playlist_ref),\n            \"index\": index,\n        }\n        params = {}\n        return self._request(GET_ARTWORK, params=params, paths=paths)\n","sub_path":"pyfoobeef/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":26943,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"17399948","text":"import numpy as np\nfrom src.Player import Player\nclass GUI():\n    def __init__(self):\n        self.piece = ''\n        self.player = Player()\n\n    def drawBoard(self, player):\n        self.player = player\n        self.currentPlayer = self.player.getCurrentPlayer()\n        self.pieceList= self.player.getCurrentPieceList()\n        # print(self.pieceList)\n        # print(self.pieceList)\n        print(\"-----------------------------------------------------------------------\")\n        for col in range(1,10):\n            print(\"\\t{}\\t\".format(col), end=('\\n' if col == 9 else ''))\n\n        for row in range(1,11):\n            print(\"\\n{}\".format(row), end='')\n            for col in range (1,10):\n                self.findPiece(row,col)\n                print(\"\\t{}\\t\".format(self.piece),end=('\\n' if col == 9 else ''))\n            if row == 5:\n                self.drawRiver()\n        print(\"-----------------------------------------------------------------------\")\n\n    def drawRiver(self):\n        for row in range(2):\n            for col in range (1,10):\n                print(\"\\t~~\\t\",end=('\\n' if col == 9 else ''))\n\n    def findPiece(self,row,col):\n        # print(self.pieceList)\n        self.piece=''\n        # print(player.Pieces[0][:][0])\n        pos=(row,col)\n        for i in range(len(self.pieceList)):\n            # print(type(self.pieceList['Pos'][i]))\n            if self.pieceList[i]['Pos'] == pos:\n                # print(\"curr :\",self.current_BoardState['Symbol'][i])\n                self.piece = self.pieceList[i]['Symbol']\n            else:\n                pass\n        if self.piece == '':\n            self.piece = \"*\"","sub_path":"PieceRecog-ChessEngine/x64/Debug-USE THIS FILE TO RUN/src/GUI.py","file_name":"GUI.py","file_ext":"py","file_size_in_byte":1638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"105931284","text":"\"\"\"\r\nMain module of the server file\r\n\"\"\"\r\n\r\nimport os\r\n\r\n# 3rd party moudles\r\nfrom flask import Flask, render_template, redirect, flash, url_for\r\nfrom flask_bootstrap import Bootstrap\r\nfrom flask_compress import Compress\r\nfrom flask_wtf import FlaskForm\r\nfrom wtforms import FileField, SubmitField\r\nfrom flask_wtf.file import FileRequired, FileAllowed\r\nfrom flask_uploads import configure_uploads, IMAGES, UploadSet\r\n\r\nfrom keras.models import load_model\r\nimport numpy as np\r\nfrom keras.preprocessing.image import load_img\r\nfrom keras.preprocessing.image import img_to_array\r\nfrom gevent.pywsgi import WSGIServer\r\nimport matplotlib.pyplot as plt\r\n\r\n# http://localhost:8000/\r\n\r\n# init app\r\napp = Flask(__name__)\r\nBootstrap(app)\r\ncompress = Compress()\r\ncompress.init_app(app)\r\napp.secret_key = \"Kvdik0$Loyk3t0\"\r\napp.config[\"UPLOADED_IMAGES_DEST\"] = \"static/img/uploads\"\r\n# init image uploader\r\nimages = UploadSet('images', IMAGES)\r\nconfigure_uploads(app, images)\r\n\r\n# Model saved with Keras model.save()\r\nMODEL_PATH = 'models/best_model_val.h5' # C:/Users/dimit/Documents/GitHubs/keras-flask-deploy-webapp\r\n\r\n# Load your own trained model\r\nmodel = load_model(MODEL_PATH)\r\nprint('Model loaded. Start serving...')\r\n\r\n# upload form\r\n\r\nclass MyForm(FlaskForm):\r\n    image = FileField('image', validators=[FileAllowed(images, 'Μόνο εικόνες!'), FileRequired('Επιλέξτε αρχείο!')])\r\n    submit = SubmitField('Υπολογισμός')\r\n\r\n\r\n# Create a URL routes\r\n@app.route(\"/\", methods=[\"GET\", \"POST\"])\r\n@app.route(\"/index\")\r\ndef home():\r\n    form = MyForm()\r\n    if form.validate_on_submit():\r\n        try:\r\n            filename = images.save(form.image.data)\r\n            return redirect(url_for('predict', filename=filename))\r\n\r\n        except Exception as e:\r\n            flash('Παρουσιαστηκε καποιο προβλημα κατα το ανεβασμα της φωτογραφιας')\r\n\r\n    return render_template(\"home.html\", form=form)\r\n\r\n\r\n@app.route('/predict/<filename>', methods=['GET'])\r\ndef predict(filename):\r\n    image = load_img('static/img/uploads/'+filename, target_size=(400,400,3))\r\n    image = img_to_array(image)\r\n    image /= 255.0\r\n    image = np.expand_dims(image, axis=0)\r\n    #image = preprocess_input(image, mode='tf')\r\n    yhat = model.predict(image)  \r\n    print(yhat)\r\n    age_groups = [\"Age-0\", \"Age-1\", \"Age-2\", \"Age-3\", \"Age-4\", \"Age-5+\"]\r\n    #label = np.argmax(yhat)\r\n    #labelName = age_groups[label]\r\n    #print(\"Label name:\", labelName)\r\n    yhat = 100*yhat\r\n    plt.barh(age_groups,yhat.flatten())\r\n    plt.title('Prediction %)')\r\n    outfile = 'age_pred.png'\r\n    plt.savefig(outfile, dpi = 300)\r\n    return render_template('predict.html', filename=filename, yhat = yhat)\r\n\r\n\r\n@app.route(\"/about\")\r\ndef about():\r\n    return render_template(\"about.html\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    app.run(debug=True, port=8000)\r\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"279658483","text":"# Import libraries\nfrom sklearn.svm import LinearSVC\n\n# Import custom functions\nfrom utils import prepare_data, plot_ROC_curve, \\\n    plot_PR_curve, calc_stat\n\n\ndef LSVM_classification(dataset, filename):\n    \"\"\"\n    Classification of data with linear support vectors,\n    followed by plotting of ROC and PR curves.\n\n    Parameters\n    ---\n    dataset: the input dataset, containing training and\n       test split data, and the corresponding labels\n       for binding- and non-binding sequences.\n\n    filename: an identifier to distinguish different\n       plots from each other.\n\n    Returns\n    ---\n    stats: array containing classification accuracy, precision\n        and recall\n    \"\"\"\n\n    # Import and one hot encode training/test set\n    X_train, X_test, y_train, y_test = prepare_data(dataset)\n\n    # Fitting classifier to the training set\n    SVM_classifier = LinearSVC()\n    SVM_classifier.fit(X_train, y_train)\n\n    # Predicting the test set results\n    y_pred = SVM_classifier.predict(X_test)\n    y_score = SVM_classifier.decision_function(X_test)\n\n    # ROC curve\n    title = 'Linear SVM ROC curve (Train={})'.format(filename)\n    plot_ROC_curve(\n        y_test, y_score, plot_title=title,\n        plot_dir='figures/LSVM_ROC_Test_{}.png'.format(filename)\n    )\n\n    # Precision-recall curve\n    title = 'Linear SVM Precision-Recall curve (Train={})'.format(filename)\n    plot_PR_curve(\n        y_test, y_score, plot_title=title,\n        plot_dir='figures/LSVM_P-R_Test_{}.png'.format(filename)\n    )\n\n    # Calculate statistics\n    stats = calc_stat(y_test, y_pred)\n\n    # Return statistics\n    return stats\n","sub_path":"scripts/LSVM.py","file_name":"LSVM.py","file_ext":"py","file_size_in_byte":1622,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"212811002","text":"\"\"\"\ncreated by ldolin\n\"\"\"\nimport pymysql as db\n\n\n# def db_connect():\n#     \"\"\"连接数据库函数\"\"\"\n#     # 打开连接\n#     db1 = db.connect('127.0.0.1', 'root', '', 'mshop')\n#     # 创建游标对象\n#     curs = db1.cursor()\n#     # 执行SQL语句\n#     curs.execute('select * from admin;')\n#     # 获取所有数据\n#     data = curs.fetchall()\n#     print(data)\n#     # 关闭数据库连接\n#     db1.close()\n\n\n# def db_connect():\n#     \"\"\"连接数据库函数\"\"\"\n#     # 打开连接\n#     db1 = db.connect('127.0.0.1', 'root', '', 'mshop')\n#     # 创建游标对象\n#     curs = db1.cursor()\n#     # 执行SQL语句\n#     curs.execute('drop table if exists emp')\n#\n#     # 创建预处理语句创建表\n#     sql = \"\"\"\n#     create table emp(\n#         name char(20) not null ,\n#         age int,\n#         sex char(1)\n#     )\n#     \"\"\"\n#     try:\n#         curs.execute(sql)\n#         print('create table success.')\n#     except Exception as e:\n#         print(e)\n#     finally:\n#         # 关闭数据库连接\n#         db1.close()\n\n\n# def db_connect():\n#     \"\"\"连接数据库函数\"\"\"\n#     # 打开连接\n#     db1 = db.connect('127.0.0.1', 'root', '', 'mshop')\n#     # 创建游标对象\n#     curs = db1.cursor()\n#     # 执行SQL语句\n#\n#     # 创建预处理语句创建表\n#     sql = \"\"\"\n#     create table emp(\n#         name char(20) not null ,\n#         age int,\n#         sex char(1)\n#     )\n#     \"\"\"\n#     sql1 = \"insert into emp(name, age,sex) values('%s', %d, '%c')\" % ('xiaoming', 21, 'M')\n#     try:\n#         curs.execute(sql)\n#         curs.execute(sql1)\n#         # 提交到数据库执行\n#         db1.commit()\n#         print('插入成功.')\n#     except Exception as e:\n#         print(e)\n#         # 如果发生错误\n#         db1.rollback()\n#     finally:\n#         # 关闭数据库连接\n#         db1.close()\n\n\ndef db_connect():\n    \"\"\"连接数据库函数\"\"\"\n    # 打开连接\n    db1 = db.connect(host='127.0.0.1', user='root', password='', db='mshop')\n    # 创建游标对象\n    curs = db1.cursor()\n    # 创建预处理语句创建表\n    sql = 'select * from emp'\n    # 执行SQL语句\n    try:\n        curs.execute(sql)\n        # 获取所有数据\n        results = curs.fetchall()\n        print('name', 'age', 'sex')\n        # 遍历结果\n        for i in results:\n            name = i[0]\n            age = i[1]\n            sex = i[2]\n            print(name, age, sex)\n        # 提交到数据库执行\n        # db1.commit()\n        # print('查找成功.')\n    except Exception as e:\n        print(e)\n        # 如果发生错误\n        # db1.rollback()\n    finally:\n        # 关闭数据库连接\n        db1.close()\n\n\ndef main():\n    db_connect()\n\n\nif __name__ == '__main__':\n    main()\n\n","sub_path":"day6/demo1.py","file_name":"demo1.py","file_ext":"py","file_size_in_byte":2769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"441897922","text":"from events import EventType\n\nfrom InfoGraph import InfoGraph\n\ninfoGraph = InfoGraph()\n\n# from lower to higher priority\nappPrioRank = [\"App1\", \"App2\"]\n\ndef verifyEvent(event):\n    global infoGraph\n    # add event\n    if event.type == EventType.PACKET_EVENT:\n        infoGraph.add_node(event.src)\n        infoGraph.add_node(event.dst)\n        infoGraph.add_event(event)\n   \n    # for debug purposes\n    infoGraph.print_nodes()\n    infoGraph.print_edges()\n\n    # check for violations\n    for low_prio in range(0, len(appPrioRank)):\n        for high_prio in range(low_prio + 1, len(appPrioRank)):\n            if infoGraph.existInfoFlow(appPrioRank[low_prio], appPrioRank[high_prio]):\n                return False\n    \n    return True\n","sub_path":"inf_flow_ctrl/vIFC.py","file_name":"vIFC.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"155081828","text":"from __future__ import absolute_import\n\nfrom ansible.compat.six import string_types\nfrom ansible.errors import AnsibleFileNotFound, AnsibleParserError, AnsibleUndefinedVariable\nfrom ansible.inventory import Inventory\nfrom ansible.module_utils._text import to_bytes\nfrom ansible.parsing.dataloader import DataLoader\nfrom ansible.parsing.vault import is_encrypted\nfrom ansible.template import Templar\nfrom ansible.vars import VariableManager, combine_vars\n\nimport os\n\n\n# this is a custom loader that ignores anything that is encrypted with vault\nclass CustomLoader(DataLoader):\n    def _get_file_contents(self, file_name):\n        '''\n        Reads the file contents from the given file name, and will decrypt them\n        if they are found to be vault-encrypted.\n        '''\n        if not file_name or not isinstance(file_name, string_types):\n            raise AnsibleParserError(\"Invalid filename: '%s'\" % str(file_name))\n\n        b_file_name = to_bytes(file_name)\n        if not self.path_exists(b_file_name) or not self.is_file(b_file_name):\n            raise AnsibleFileNotFound(\"the file_name '%s' does not exist, or \"\n                                      \"is not readable\" % file_name)\n\n        show_content = True\n        try:\n            with open(b_file_name, 'rb') as f:\n                data = f.read()\n                if is_encrypted(data):\n                    data = b\"\\n\"\n                    show_content = False\n\n            return (data, show_content)\n\n        except (IOError, OSError) as e:\n            raise AnsibleParserError(\"an error occurred while trying to read \"\n                                     \"the file '%s': %s\" % (file_name, str(e)))\n\n\n# loads the environment data from the ansible variables\nclass AnsibleEnvironment():\n    _cache = {}\n\n    def __init__(self):\n        ansible_basedir = os.path.join(\n            os.environ.get(\"PROJECT_ENVIRONMENT_FILES_PATH\"), \"ansible\")\n\n        loader = CustomLoader()\n        loader.set_basedir(ansible_basedir)\n\n        var_manager = VariableManager()\n\n        # load the inventory, set the basic playbook directory\n        self._inventory = Inventory(\n            loader=loader,\n            variable_manager=var_manager\n        )\n        self._inventory.set_playbook_basedir(ansible_basedir)\n\n        group = self._inventory.get_group(\"all\")\n\n        # make sure we load all magic variables on top of the global variables\n        self._vars = combine_vars(\n            self._inventory.get_group_vars(group, return_results=True),\n            var_manager._get_magic_variables(loader, False, None, None,\n                                             False, False)\n        )\n        self._vars['groups'] = self._inventory.get_group_dict()\n        self._vars['env'] = os.environ\n\n        hostvars = {}\n        for host in self._inventory.get_hosts():\n            hostvars[host.name] = host.get_vars()\n\n        self._vars['hostvars'] = hostvars\n\n        # create the template renderer\n        self._templar = Templar(loader=loader, variables=self._vars)\n\n        # setup some easy variables that we use a lot\n        self._vars['control_ip'] = self.get_var(\n            \"hostvars[groups['control'][0]]['ansible_host']\")\n        self._vars['edge_ip'] = self.get_var(\n            \"hostvars[groups['edge'][0]]['ansible_host']\")\n        self._vars['monitor_ip'] = self.get_var(\n            \"hostvars[groups['monitor'][0]]['ansible_host']\")\n\n    def get_var(self, name, cache=True):\n        if name not in self._cache or not cache:\n            try:\n                self._cache[name] = self._templar.template(\"{{%s}}\" % name)\n            except AnsibleUndefinedVariable:\n                self._cache[name] = None\n        return self._cache.get(name)\n\n    def set_var(self, name, value):\n        self._vars[name] = value\n\n    def template(self, *templates):\n        return '\\n'.join([self._templar.template(tpl) for tpl in templates])\n","sub_path":"docker-image/scripts/pylib/ansible.py","file_name":"ansible.py","file_ext":"py","file_size_in_byte":3895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"552447027","text":"import pygame\nimport numpy as np\nfrom astropy.convolution import convolve\nimport matplotlib.pyplot as plt\nfrom astropy.convolution.kernels import Gaussian2DKernel\n# x = np.random.randint(0,100,1280)\n# y = np.random.randint(0,100,1200)\n\n# heatmap, xedges, yedges = np.histogram2d(x, y, bins=[1280,1200])\n\n\n\nWHITE = (255, 255, 255)\nBLACK = (0, 0, 0)\nRED = (255, 0, 0)\nLIGHT_RED = (200, 0, 0)\nGREEN = (0, 255, 0)\nBLUE = (0, 0, 255)\nBUTTON_COLOR = (128, 255, 0)\n\nFACE_TOP_X = 600\nFACE_TOP_Y = 100\n\nMENU_top_x = 20\nMENU_top_y = 20\nMENU_y_diff = 80\nMENU_button_w = 320\nMENU_button_h = 80\n\nbutton_show_face_coords = [MENU_top_x, MENU_top_y, MENU_button_w, MENU_button_h]\nbutton_train_model_coords = [MENU_top_x, MENU_top_y+MENU_y_diff, MENU_button_w, MENU_button_h]\nbutton_reset_analysis_coords = [MENU_top_x, MENU_top_y+MENU_y_diff*2, MENU_button_w, MENU_button_h]\nbutton_quit_app_coords = [MENU_top_x, MENU_top_y+MENU_y_diff*3, MENU_button_w, MENU_button_h]\nbutton_collect_data_coords = [MENU_top_x+MENU_button_w, MENU_top_y, MENU_button_w, MENU_button_h]\nbutton_show_gaze_coords = [MENU_top_x+MENU_button_w, MENU_top_y+MENU_y_diff, MENU_button_w, MENU_button_h]\nbutton_show_analysis_coords = [MENU_top_x+MENU_button_w, MENU_top_y+MENU_y_diff*2, MENU_button_w, MENU_button_h]\n\n\nbutton_show_image_coords = [1920-350, MENU_top_y, 200, MENU_button_h]\nbutton_plot_gaze_heatmap_coords = [MENU_top_x+MENU_button_w, MENU_top_y+MENU_y_diff*3, MENU_button_w, MENU_button_h]\n\ndef draw_text(screen, pos, text, size=5, color=BLACK):\n    # text_surface = pygame.font.Font(font, size).render(text, True, color)\n    text_surface = pygame.font.SysFont(\"Monospace\", size).render(text, True, color)\n    text_rect = text_surface.get_rect()\n    text_rect.center = pos\n    screen.blit(text_surface, text_rect)\n\n\n\ndef plot_gaze_heatmap(data):\n    print('starting convolution...', end='.')\n    plt.imshow(convolve(data, Gaussian2DKernel(10)), interpolation='none')\n    print(' Done')\n    plt.show()\n\ndef plot_gaze_heatmap_on_image(data, image_path):\n    picture = pygame.image.load(image_path)\n    picture = pygame.transform.scale(picture, (1920, 1200))\n    picture_array = pygame.surfarray.array3d(picture)\n    plt.imshow(picture_array.swapaxes(0,1))\n\n    print('starting convolution...', end='.')\n    plt.imshow(convolve(data, Gaussian2DKernel(x_stddev=15, y_stddev=5)), alpha=0.4, interpolation='none')\n    print(' Done')\n    plt.show()","sub_path":"main_utils.py","file_name":"main_utils.py","file_ext":"py","file_size_in_byte":2411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"444919499","text":"import esp_send_func\nimport ds18b20\nimport machine\nimport utime\nimport network\n\nmax_failed = 10\nsleep_sec = 6\nfailed_connections = 0\n\nsta_if = network.WLAN(network.STA_IF)\n\nwhile not sta_if.isconnected():\n    print(\"No internet, going to sleep\")\n    if failed_connections > max_failed:\n        print(\"No internet after max attempts reached, reboot!\")\n        machine.reset()\n    else:\n        failed_connections = failed_connections + 1\n    utime.sleep(10)\n\n\ndef start():\n    try:\n        print(\"Starting ds18b20\")\n        while True:\n            data = ds18b20.modulDS18B20()\n            resp = esp_send_func.send_to_proxy(sensor_name=\"esp_ds18b20\", value=data)\n            utime.sleep(sleep_sec)\n    except Exception as e:\n        print(\"Start failed due to exception: {}\".format(e))\n        print(\"Reloading ESP!\")\n        machine.reset()\n","sub_path":"ds18b20/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":842,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"42621302","text":"def ascii(ascii_code: int):\r\n    \"\"\"\r\n    Handles the command to convert a ASCII code to an ASCII character.\r\n    \"\"\"\r\n\r\n    chango = chr(ascii_code)\r\n    print(chango)\r\n\r\n\r\ndef test_ascii():\r\n    \"\"\"\r\n    The function tests the core formula of the ascii() function.\r\n    \"\"\"\r\n\r\n    ascii_code = 100\r\n    chango = chr(ascii_code)\r\n    assert chango == \"d\", \"Should be d\"\r\n\r\n\r\nprint(\"Initializing test! (1/1)\")\r\ntry:\r\n    print(\"\")\r\n    print(ascii(100))\r\n    test_ascii()\r\n    print(\"\")\r\nexcept AssertionError:\r\n    print(\"Whoops! Execuing a call got an AssertionError!\")\r\n    exit(2)\r\nexcept AttributeError:\r\n    print(\"Whoops! Executing a call got an AttributeError!\")\r\n    exit(2)\r\nexcept ValueError:\r\n    print(\"Whoops! Executing a call got an ValueError!\")\r\n    exit(2)\r\nprint(\"Successful Test! (1/1 COMPLETE)\")\r\nprint(\"Terminating!\")\r\nexit()\r\n","sub_path":"tests/ascii_test.py","file_name":"ascii_test.py","file_ext":"py","file_size_in_byte":849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"175273202","text":"import sys\n\nprint (sys.argv)\nif len(sys.argv) > 1:\n    ctw_str = sys.argv[1]\nelse:\n    ctw_str = input().strip()\n\ndef spacing(size):\n    return \" \" * 3 * size\ndef print_node(ctw):\n    index_size = 0\n    for c in ctw:\n        s = c\n        if c == \"[\":\n            print()\n            index_size += 1\n            print(spacing(index_size), end=\"\")\n        elif c == \"|\":\n            print()\n            print(spacing(index_size), end=\"\")\n        elif c == \"]\":\n            index_size -= 1\n        print (s, end=\"\")\nprint()\nprint_node(ctw_str)\nprint()\n","sub_path":"scripts/visualize_ctw.py","file_name":"visualize_ctw.py","file_ext":"py","file_size_in_byte":550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"254267835","text":"import sys\n\nlines = sys.stdin.readlines()\n\njobs,workers = [],[]\nfor line in lines:\n    line = line.replace(\"\\n\",'')\n    if line.split(\" \")[0] == \"worker\":\n        workers.append(line)\n    if line.split(\" \")[0] == \"job\":\n        jobs.append(line)\njobClasses = []\nworkerClasses = []\n\nclass Job:\n  def __init__(self, name, can_begin, tasks, task_time, priority):\n    self.name = name\n    self.can_begin = can_begin\n    self.tasks = tasks\n    self.task_time = task_time\n    self.priority = priority\n    self.tasksDone = 0\n    self.startTime = -1\n    self.complete = False\n\n  def setStartTime(self,time):\n      if self.startTime == -1:\n          self.startTime = time\n\nclass Worker():\n    def __init__(self, name, doneWorking):\n        self.name = name\n        self.doneWorking = 0\n        self.job = None\n\n    def isFree(self, time):\n        if self.doneWorking <= time:\n            return True\n        return False\n\ndef makeJobClasses():\n    global jobClases\n\n    for job in jobs:\n        job = job.split(\" \")\n        name = job[1]\n        tasks = int(job[2])\n        task_time = int(job[3])\n        can_begin = int(job[4])\n        priority = int(job[5])\n        newJob = Job(name,can_begin,tasks,task_time,priority)\n        jobClasses.append(newJob)\n\ndef makeWorkerClasses():\n    global workerClasses\n\n    for worker in workers:\n        newWorker = Worker(worker.split(\" \")[1], 0)\n        workerClasses.append(newWorker)\n\ndef calcCost(job, time):\n    job.setStartTime(time)\n    timeToComplete = (job.tasks-job.tasksDone)*job.task_time\n    endTime = timeToComplete + job.startTime\n    cost = job.priority*(((endTime-job.can_begin)**2 + (endTime-job.startTime)**2)**0.5)\n    return cost\n\ndef getLowCostJob(time):\n    lowestCost = calcCost(jobClasses[0], time)\n    bestJob = jobClasses[0]\n    for job in jobClasses:\n        if calcCost(job, time) < lowestCost:\n            bestJob = job\n    return bestJob\n\ndef getFreeWorker(time):\n    for worker in workerClasses:\n        if worker.isFree(time):\n            return worker\n\n    return False\n\nmakeJobClasses()\nmakeWorkerClasses()\ntime = 0\n\nwhile len(jobClasses) >0 :\n    bestJob = getLowCostJob(time)\n    worker = getFreeWorker(time)\n    if worker:\n        worker.doneWorking = time + bestJob.task_time\n        if worker.job != bestJob:\n            sys.stdout.write(str(time)+ \" \" + worker.name + \" \" + bestJob.name + \"\\n\")\n        worker.job = bestJob\n        bestJob.tasksDone +=1\n        if bestJob.tasksDone >= bestJob.tasks:\n            jobClasses.remove((bestJob))\n    if not getFreeWorker(time):\n        time += 1\n\n\n\n\n","sub_path":"tripAdvisor.py","file_name":"tripAdvisor.py","file_ext":"py","file_size_in_byte":2569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"276780818","text":"# import os\n# import sys\nimport csv\nimport random\n\ndataRoot = './data/dataByID.csv'\n\n\ndef distance(item0, item1):\n    res = 0\n    for i in range(len(item0)):\n        res += abs(item0[i] - item1[i])\n    return res\n\n\ndef generateKernel(data, kernelNumber):\n    kernelList = []\n    for _ in range(kernelNumber):\n        kernelList.append(data[random.randint(0, len(data))])\n    return kernelList\n\n\ndef choose(data, kernelList):\n    res = 100000000000000000000000\n    for i in kernelList:\n        temp = distance(data, i)\n        if temp < res:\n            key = i\n            res = temp\n    return kernelList.index(key)\n\n\ndef processData():\n    kernelNumber = 6\n    print('[info] processing data')\n    dataList = []\n    originList = []\n    for i in range(kernelNumber):\n        dataList.append(list())\n    # jump = 0\n    with open(dataRoot, 'r') as raw:\n        input = csv.reader(raw)\n        # count = 0\n        for i in input:\n            originList.append(i)\n            # if jump == 100000:\n            #     break\n            # jump += 1\n    for i in range(len(originList)):\n        for j in range(len(originList[i])):\n            originList[i][j] = int(originList[i][j])\n    kernelList = generateKernel(originList, kernelNumber)\n    for i in originList:\n        dataList[choose(i, kernelList)].append(i)\n    return dataList\n\n\ndef average(src):\n    print('[info]calculating average')\n    res = []\n    for i in src:\n        temp = []\n        for _ in range(24):\n            temp.append(0)\n        for j in i:\n            for key in range(24):\n                temp[key] += j[key]\n        for k in range(24):\n            temp[k] /= len(i)\n        res.append(temp)\n    return res\n\n\ndef draw(src):\n    print('[info] drawing')\n    from matplotlib import pyplot as plt\n    import math\n    for i in range(len(src)):\n        plt.subplot(2, math.ceil(len(src) / 2), i+1)\n        plt.plot([x for x in range(24)], src[i], 'b-')\n    plt.show()\n\n\nif __name__ == '__main__':\n    raw = processData()\n    pro = average(raw)\n    draw(pro)\n","sub_path":"校赛/2019CodeWork/k-means.py","file_name":"k-means.py","file_ext":"py","file_size_in_byte":2024,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"279426103","text":"import recommendations\n\nallSimilar = []\nfile = open(\"data.txt\", 'a')\nnewline = '\\n'\ntab = '\\t'\n\nfile.write(f'First User Chosen: {tab} 368{newline}')\nfile.write(f'Second User Chosen: {tab} 81 {newline}')\nfile.write(f'Third User Chosen: {tab} 135 {newline}{newline}')\n\npref = recommendations.loadMovieLens()\n\n# Get sorted list of user ratings\nuserRatings1 = (sorted(pref['368'].items(), key = \n             lambda kv:(kv[1], kv[0]))) \nuserRatings2 = (sorted(pref['81'].items(), key = \n             lambda kv:(kv[1], kv[0]))) \nuserRatings3 = (sorted(pref['135'].items(), key = \n             lambda kv:(kv[1], kv[0]))) \n\n# Get top 5 for each user\nuserRatings1.reverse()\nuserRatings2.reverse()\nuserRatings3.reverse()\n\n# Formatted File output\nfile.write(f'First User Rating: {newline}')\nfile.write(f'ID 368 Top 3 Rated Movies: {newline}{newline}')\n\nfor x in range(0,3):\n\tname = userRatings1[x][0]\n\trating = userRatings1[x][1]\n\n\tfile.write(f'Name of Movie: {name} {tab} Rating: {rating} {newline}')\n\nfile.write(f'{newline}ID 368 Bottom 3 Rated Movies: {newline}')\nuserRatings1.reverse()\n\nfor x in range(0,3):\n\tname = userRatings1[x][0]\n\trating = userRatings1[x][1]\n\n\tfile.write(f'Name of Movie: {name} {tab} Rating: {rating} {newline}')\n\n\nfile.write(f'{newline}Second User Rating: {newline}')\nfile.write(f'ID 81 Top 3 Rated Movies: {newline}{newline}')\n\nfor x in range(0,3):\n\tname = userRatings2[x][0]\n\trating = userRatings2[x][1]\n\n\tfile.write(f'Name of Movie: {name} {tab} Rating: {rating} {newline}')\n\nuserRatings2.reverse()\nfile.write(f'{newline}ID 81 Bottom 3 Rated Movies: {newline}{newline}')\n\nfor x in range(0,3):\n\tname = userRatings2[x][0]\n\trating = userRatings2[x][1]\n\n\tfile.write(f'Name of Movie: {name} {tab} Rating: {rating} {newline}')\n\n\nfile.write(f'{newline}Third User Rating: {newline}')\nfile.write(f'ID 135 Top 3 Movies: {newline}{newline}')\n\nfor x in range(0,3):\n\tname = userRatings3[x][0]\n\trating = userRatings3[x][1]\n\n\tfile.write(f'Name of Movie: {name} {tab} Rating: {rating} {newline}')\n\nuserRatings3.reverse()\nfile.write(f'{newline}ID 135 Bottom 3 Rated Movies: {newline}{newline}')\n\nfor x in range(0,3):\n\tname = userRatings3[x][0]\n\trating = userRatings3[x][1]\n\tfile.write(f'Name of Movie: {name} {tab} Rating: {rating} {newline}')\n\nfile.write(f'{newline}{newline}Substitute User ID: 368 {newline}{newline}')\n\n# Find most correlated users\nclosest_5 = recommendations.topMatches(pref, '368')\n\n# Find least correlated users\nfurthest_5 = recommendations.worstMatches(pref, '368')\n\n# Output for least and most correlated users\nfile.write(f'Five other users with highest correlation: {newline}{newline}')\nfor x in closest_5:\n\tcorrelationValue = round(x[0])\n\ttempId = x[1]\n\tfile.write(f'User ID:{tempId} {tab}Correlation Value: {correlationValue}{newline}')\n\nfile.write(f'{newline}Five other users with lowest correlation: {newline}')\nfor y in furthest_5:\n\tcorrelationValue = round(y[0])\n\ttempId = y[1]\n\tfile.write(f'User ID:{tempId} {tab}Correlation Value: {correlationValue}{newline}')\n\n\nrecommendedMovies = recommendations.getRecommendations(pref, '368')\n\nfile.write(f'{newline}Computed Top 5 Movies to be Watched: {newline}')\n\n\nfor x in range(0,5):\n\trating = recommendedMovies[x][0]\n\tname = recommendedMovies[x][1]\n\tfile.write(f'Name of Movie: {name}{tab} Calculated Rating: {rating}{newline}')\n \nfile.write(f'{newline}Computed Bottom 5 Movies to be Watched: {newline}')\nrecommendedMovies.reverse()\nfor y in range(0,5):\n\trating = recommendedMovies[y][0]\n\tname = recommendedMovies[y][1]\n\tfile.write(f'Name of Movie: {name}{tab} Calculated Rating: {rating}{newline}')\n\nfile.write(f'{newline}{newline}Favorite Movie: {tab} Jurassic Park (1993){newline}')\nfile.write(f'Least Favorite Movie: {tab} Children of the Corn: The Gathering (1996){newline}{newline}')\n\n\nsimilarMovies = recommendations.calculateSimilarItems(pref)\nnotSimilarMovies = recommendations.calculateLeastSimilarItems(pref)\nfile.write(f'Top Recommended Movies to be Watched for Jurassic Park: {newline}')\n# print(similarMovies['Jurassic Park (1993)'])\nfor x in similarMovies['Jurassic Park (1993)']:\n\tname = x[1]\n\trating = x[0]\n\tfile.write(f'Name of Movie: {name}{tab} Calculated Correlation: {rating}{newline}')\n\nfile.write(f'{newline}Bottom Recommended Movies to be Watched for Jurassic Park{newline}')\n\n\nfor x in notSimilarMovies['Jurassic Park (1993)']:\n\tname = x[1]\n\trating = x[0]\n\tfile.write(f'Name of Movie: {name}{tab} Calculated Correlation: {rating}{newline}')\n\nfile.write(f'{newline}Top Recommended Movies to be Watched for Children of the Corn: {newline}')\n\nfor x in similarMovies['Children of the Corn: The Gathering (1996)']:\n\tname = x[1]\n\trating = x[0]\n\tfile.write(f'Name of Movie: {name}{tab} Calculated Correlation: {rating}{newline}')\n\nfile.write(f'{newline}Bottom Recommended Movies to be Watched for Children of the Corn{newline}')\n\nfor x in notSimilarMovies['Children of the Corn: The Gathering (1996)']:\n\tname = x[1]\n\trating = x[0]\n\tfile.write(f'Name of Movie: {name}{tab} Calculated Correlation: {rating}{newline}')","sub_path":"lectures/cs532-s19/assignments/A6/toPush/Python/driver.py","file_name":"driver.py","file_ext":"py","file_size_in_byte":5014,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"81322550","text":"from pymatgen.ext.matproj import MPRester\nfrom pprint import pprint\nfrom os import path\nimport numpy as np\nimport random\nimport pandas as pd\nimport json\nimport sys\n\nfoundation_df \t\t= pd.read_csv('Li_out_csv_dis.csv',sep=',')\t\nhelvol_geomvol_df \t= pd.read_csv('Li_helvol_geomvol_output.csv',sep=',') #specify Mg or Li \nadd_features_df \t= pd.read_csv('Li_material_properties.csv',sep=',')\n# print(foundation_df)\n# print(helvol_geomvol_df)\n# print(add_features_df)\ndef totalmerge():\n\t#merging manuel and helvol_geomvol\n\t# df_new = pd.merge(foundation_df, helvol_geomvol_df, left_on='Charged_ID', right_on='mid')\n\tdf_new = pd.merge(foundation_df, helvol_geomvol_df, how='inner', left_on='Charged_ID',right_on='mid')\n\tdf_new = pd.merge(df_new, helvol_geomvol_df, left_on='Discharged_ID', right_on='mid', suffixes = ('', '_dis'))\n\tdf_firstdone = df_new.drop(columns = ['mid','mid_dis'])\n\t#merging features.\n\tlist_2_nodups = add_features_df.drop_duplicates()\n\tdf_fp2 = pd.merge(df_firstdone, list_2_nodups, left_on = 'Discharged_ID', right_on = 'mid')\n\tdf_sp2 = pd.merge(df_fp2, list_2_nodups, left_on = 'Charged_ID', right_on = 'mid',\n\t\tsuffixes = ('','_dis'))\n\n\tdf_seconddone = df_sp2.drop(columns = ['mid','mid_dis'])\n\t#write to file\n\tdf_seconddone.to_csv(\"Li_allFiles.csv\", sep=\",\", index=False)\n\treturn\n\ndef mergefeaturesandfoundation():\n\t#excluding the voidfraction\n\tdf_foundationandfeatures = pd.merge(foundation_df, add_features_df, left_on='Charged_ID', right_on='mid')\n\tdf_foundationandfeatures = pd.merge(df_foundationandfeatures, add_features_df, left_on='Discharged_ID', right_on='mid',\n\t\tsuffixes = ('', '_dis'))\n\tdf_done = df_foundationandfeatures.drop(columns = ['mid','mid_dis'])\n\tdf_list = df_done.drop_duplicates()\n\tdf_list.to_csv(\"allFiles.csv\", sep=\",\", index=False)\n\treturn\n\n#Select mergertype: \nvar = int(input(\"press 1 to merge Manual and material properties, press 2 to include the voidfraction: \"))\ntry:\n\tif var == 1:\n\t\tmergefeaturesandfoundation()\n\t\tprint(\"Volumetric numberdensity merged with other material properties.\")\n\telif var == 2: \n\t\ttotalmerge()\n\t\tprint(\"Everything merged!(maybe)\")\nexcept: \n\t\"valueerror\"\n\n\n\n#mergefeaturesandfoundation()\n\n\n\n################################\n\n#Merge features.\n\n\n","sub_path":"7_merger.py","file_name":"7_merger.py","file_ext":"py","file_size_in_byte":2224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"550618652","text":"import numpy as np \r\nimport gym\r\nimport tensorflow as tf \r\nimport random\r\n\r\nenv = gym.make(\"MountainCar-v0\")\r\nenv._max_episode_steps = 1000\r\n\r\n# hyperparameters\r\nstate_size = 2\r\naction_size = env.action_space.n\r\nnum_episodes = 10000\r\nlearning_rate = 0.05\r\ndiscount_factor = 0.99\r\n\r\n# discount rewards helper function\r\ndef discount_rwds(rewards):\r\n    discounted_rwds = np.zeros_like(rewards)\r\n    \"\"\"\r\n    We want to keep track of all cumulative timestepped rewards. For example, the expected discounted rewards\r\n    at time step t is given by sum_k gamma^k * r_{t+k} where sum goes on until end of episode.\r\n    \"\"\"\r\n    total_rewards = 0\r\n    for t in range(len(discounted_rwds))[::-1]:\r\n        r_t = rewards[t]\r\n        total_rewards = r_t + discount_factor * total_rewards\r\n        discounted_rwds[t] = total_rewards\r\n    # Normalize discounted rewards\r\n    mean = np.mean(discounted_rwds)\r\n    std = np.std(discounted_rwds)\r\n    normalized_discounted_rwds = (discounted_rwds - mean) / std\r\n    return normalized_discounted_rwds\r\n\r\n\"\"\"\r\nOur policy will be given by a neural network pi_theta, which will take in a state and spit out a probability\r\ndistribution over the possible actions at that state.\r\n\"\"\"\r\nclass Policy:\r\n    def __init__(self, state_size, action_size, learning_rate):\r\n        self.inputs = tf.placeholder(tf.float32, [None, state_size])\r\n        self.actions = tf.placeholder(tf.float32, [None, action_size])\r\n        self.discounted_rwds = tf.placeholder(tf.float32, [None,])\r\n        \"\"\"\r\n        Our neural net will be a 4-layer neural net with 3 fully-connected layers with a softmax output.\r\n        The score function is the expected discounted rewards that we wish to maximize.\r\n        \"\"\"\r\n        self.fc1 = tf.layers.dense(\r\n            inputs=self.inputs,\r\n            units=10,\r\n            activation=tf.nn.relu,\r\n            kernel_initializer=tf.contrib.layers.xavier_initializer()\r\n        )\r\n        self.fc2 = tf.layers.dense(\r\n            inputs=self.fc1,\r\n            units=action_size,\r\n            activation=tf.nn.relu,\r\n            kernel_initializer=tf.contrib.layers.xavier_initializer()\r\n        )\r\n        self.fc3 = tf.layers.dense(\r\n            inputs=self.fc2,\r\n            units=action_size,\r\n            activation=None,\r\n            kernel_initializer=tf.contrib.layers.xavier_initializer()\r\n        )\r\n        self.outputs = tf.nn.softmax_cross_entropy_with_logits_v2(\r\n            logits=self.fc3,\r\n            labels=self.actions\r\n        )\r\n        self.action_dist = tf.nn.softmax(self.fc3)\r\n        # Loss function and optimizer\r\n        self.loss = tf.reduce_mean(self.outputs * self.discounted_rwds)\r\n        self.optimizer = tf.train.AdamOptimizer(learning_rate).minimize(self.loss)\r\n\r\npolicy = Policy(state_size, action_size, learning_rate)\r\n\r\n\"\"\"\r\nNow we get to train our policy.\r\n\"\"\"\r\nsess = tf.Session()\r\nsess.run(tf.global_variables_initializer())\r\n\r\nmax_reward_so_far = 0\r\n\r\nfor episode in range(num_episodes):\r\n    state = env.reset()\r\n    env.render()\r\n    states = []\r\n    rewards = []\r\n    actions = []\r\n    while True:\r\n        action_dist = sess.run(\r\n            policy.action_dist,\r\n            feed_dict={policy.inputs: state.reshape([1,state_size])}\r\n        )\r\n        action = random.choices(\r\n            range(action_dist.shape[1]),\r\n            weights=action_dist.ravel()\r\n        )[0]\r\n        \r\n        next_state, reward, done, _ = env.step(action)\r\n        env.render()\r\n\r\n        # collect your data\r\n        states.append(state)\r\n        rewards.append(reward)\r\n        # one hot the action and then append to actions\r\n        one_hot_action = np.zeros(action_size)\r\n        one_hot_action[action] = 1\r\n        actions.append(one_hot_action)\r\n\r\n        if done:\r\n            # finally do the policy update\r\n            total_rewards = np.sum(rewards)\r\n            if total_rewards > max_reward_so_far:\r\n                max_reward_so_far = total_rewards\r\n            print(\"episode %d: reward = %.3f / max so far = %.3f\" % (episode, total_rewards, max_reward_so_far))\r\n            discounted_rwds = discount_rwds(rewards)\r\n            loss, _ = sess.run(\r\n                [policy.loss, policy.optimizer],\r\n                feed_dict={\r\n                    policy.inputs: np.vstack(np.array(states)),\r\n                    policy.actions: np.vstack(np.array(actions)),\r\n                    policy.discounted_rwds: discounted_rwds\r\n                }\r\n            )\r\n            break\r\n\r\n        state = next_state","sub_path":"vpolicy-grads/mountain-vpg.py","file_name":"mountain-vpg.py","file_ext":"py","file_size_in_byte":4504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"379412837","text":"class Node:\n    def __init__(self, value):\n        self.value = value\n        self.next = []\n\ns = '0123456789abcdefghijklmnopqrstuvwxyz'\n\ndef toStr(l):\n    ret = ''\n    for i in l:\n        ret += s[i]\n    return ret\n\ndef toList(s):\n    ret = []\n    for c in s:\n        ret.append(s.index(c))\n    return ret\n\n\nclass EulerGraph:\n    def __init__(self, radix, length):\n        self.radix = radix\n        self.length = length\n        self.vertices = {}\n        self.edges = []\n        self.init_vertex()\n\n    def get_tuples(self, length):\n        if length == 1:\n            return [[curr] for curr in list(range(self.radix))]\n        else:\n            last_ret = self.get_tuples(length - 1)\n            ret = []\n            for num in last_ret:\n                for i in range(self.radix):\n                    tmp = num[:]\n                    tmp.append(i)\n                    ret.append(tmp)\n            return ret\n\n    def init_vertex(self):\n        vs = [toStr(l) for l in self.get_tuples(self.length - 1)]\n        for i in vs:\n            self.vertices[i] = Node(i)\n        for v in self.vertices:\n            for i in range(self.radix):\n                tmp = v[1:] + toStr([i])\n                self.edges.append([v, tmp])\n                self.vertices[v].next.append(tmp)\n\n    def _euler(self, iterated, curr, start):\n        comp = []\n        for v in [self.vertices[s] for s in curr.next]:\n            if v.value not in iterated:\n                tmp_iterated = iterated[:]\n                if len(tmp_iterated) > 7:\n                    pass\n                tmp_iterated.append(v.value)\n                comp.append(self._euler(tmp_iterated, v, start))\n        if len(comp) == 0:\n            if start in curr.next:\n                return [1, [curr.value]]\n            return [-1000, []]\n        sorted(comp, key=lambda x: -x[0])\n        ret = comp[0]\n        ret[0] += 1\n        ret[1].append(curr.value)\n        return ret\n\n    def euler(self):\n        start = list(self.vertices.keys())[0]\n        return self._euler([start], self.vertices[start], start)\n\n\nif __name__ == '__main__':\n    test = EulerGraph(4, 4)\n    ret = test.euler()[1]\n    print(test.euler())\n    rst = ''\n    for s in ret:\n        rst += s[0]\n    print(rst)\n","sub_path":"algorithm/fuckOfo.py","file_name":"fuckOfo.py","file_ext":"py","file_size_in_byte":2230,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"176402939","text":"import uuid\r\n\r\ndef get(db, token):\r\n    sql = \"\"\"\r\n            SELECT\r\n                sp.id, sp.token, sp.slice, sp.finished, sp.user_id, sp.playable,\r\n                p.token as playable, p.locale, p.pointer, p.name, sp.play_prologue,\r\n                p.id as playable_id, sp.level, sp.custom_transition_token,\r\n                sp.epilogue_token\r\n            FROM slice_positions sp, playables p WHERE sp.playable = p.token and sp.token = %(token)s;\r\n        \"\"\"\r\n    \r\n    return db.unique(sql, token=token)\r\n    \r\ndef get_by_id(db, id):\r\n    sql = \"\"\"\r\n            SELECT\r\n                sp.id, sp.token, sp.slice, sp.finished, sp.user_id, sp.playable,\r\n                p.token as playable, p.locale, p.pointer, p.name, sp.play_prologue,\r\n                p.id as playable_id, sp.level, sp.custom_transition_token,\r\n                sp.epilogue_token\r\n            FROM slice_positions sp, playables p WHERE sp.playable = p.token and sp.id = %(id)s;\r\n        \"\"\"\r\n    \r\n    return db.unique(sql, id=id)\r\n\r\ndef get_by_slice_and_user(db, slice_id, user_id):\r\n    sql = \"\"\"\r\n            SELECT\r\n                sp.id, sp.token, sp.slice, sp.finished, sp.user_id, sp.playable,\r\n                p.token as playable, p.locale, p.pointer, p.name, sp.play_prologue,\r\n                p.id as playable_id, sp.level, sp.custom_transition_token,\r\n                sp.epilogue_token\r\n            FROM slice_positions sp\r\n            LEFT OUTER JOIN playables p ON p.token = sp.playable\r\n            WHERE sp.slice = %(slice_id)s AND sp.user_id = %(user_id)s;\r\n        \"\"\"\r\n    return db.unique(sql, slice_id=int(slice_id), user_id=int(user_id))\r\n        \r\ndef add(db, playable, slice, user_id, play_prologue):\r\n    token = uuid.uuid4()\r\n    sql = \"\"\"\r\n            INSERT INTO slice_positions (token, playable, user_id, slice, play_prologue) VALUES\r\n            (%(token)s, %(playable)s, %(user_id)s, %(slice)s, %(play_prologue)s)\r\n        \"\"\"\r\n    \r\n    db.execute(sql, token=str(token), playable=playable, user_id=user_id,\r\n               slice=slice, play_prologue=play_prologue)\r\n    \r\n    return token\r\n\r\ndef set_playable(db, position_id, playable):\r\n    sql = \"\"\"\r\n        UPDATE slice_positions SET playable = %(playable)s\r\n        WHERE id = %(position_id)s\r\n    \"\"\"\r\n    \r\n    db.execute(sql, position_id=position_id, playable=playable)\r\n    \r\ndef set_custom_transition(db, position_id, token):\r\n    sql = \"\"\"\r\n        UPDATE slice_positions SET custom_transition_token = %(token)s\r\n        WHERE id = %(position_id)s\r\n    \"\"\"\r\n    \r\n    db.execute(sql, position_id=position_id, token=token)\r\n    \r\ndef set_epilogue(db, position_id, token):\r\n    sql = \"\"\"\r\n        UPDATE slice_positions SET epilogue_token = %(token)s\r\n        WHERE id = %(position_id)s\r\n    \"\"\"\r\n    \r\n    db.execute(sql, position_id=position_id, token=token)\r\n\r\ndef set_level(db, position_id, level):\r\n    sql = \"\"\"\r\n        UPDATE slice_positions SET level = %(level)s\r\n        WHERE id = %(position_id)s\r\n    \"\"\"\r\n    \r\n    db.execute(sql, position_id=position_id, level=level)\r\n\r\ndef set_prologue_to_played(db, position_id):\r\n    sql = \"\"\"\r\n        UPDATE slice_positions SET play_prologue = false\r\n        WHERE id = %(position_id)s\r\n    \"\"\"\r\n    \r\n    db.execute(sql, position_id=position_id)\r\n\r\ndef finished(db, position_id):\r\n    sql = \"\"\"\r\n        UPDATE slice_positions SET finished = true\r\n        WHERE id = %(position_id)s\r\n    \"\"\"\r\n    \r\n    db.execute(sql, position_id=position_id)\r\n    \r\ndef starting(db, position_id):\r\n    sql = \"\"\"\r\n        UPDATE slice_positions SET finished = false\r\n        WHERE id = %(position_id)s\r\n    \"\"\"\r\n    \r\n    db.execute(sql, position_id=position_id)\r\n    \r\ndef delete(db, slice_id, user_id):\r\n    sql = \"\"\"\r\n    DELETE FROM slice_positions\r\n    WHERE slice = %(slice_id)s\r\n    AND user_id = %(user_id)s\r\n    \"\"\"\r\n    \r\n    db.execute(sql, slice_id=slice_id, user_id=user_id)\r\n    \r\ndef delete_by_user(db, user_id):\r\n    sql = \"\"\"\r\n    DELETE FROM slice_positions\r\n    WHERE user_id = %(user_id)s\r\n    \"\"\"\r\n    \r\n    db.execute(sql, user_id=user_id)","sub_path":"web/onyx/game/_models/slice_position.py","file_name":"slice_position.py","file_ext":"py","file_size_in_byte":4063,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"71369386","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Sep 23 18:29:37 2017\n\n@author: david\n\"\"\"\n\nimport re\n\n\"\"\"\nProblem\n-------\nYou’re trying to match a text pattern using regular expressions, but it is\nidentifying the longest possible matches of a pattern. Instead, you would like\nto change it to find the shortest possible match.\n\"\"\"\n\n# Sample text\ntext1 = 'Computer says \"no.\"'\n\n# Another sample text\ntext2 = 'Computer says \"no.\" Phone says \"yes.\"'\n\n# (a) Regex that finds quoted strings - longest match (greedy)\nstr_pat = re.compile(r'\\\"(.*)\\\"')\nprint(str_pat.findall(text1))\nprint(str_pat.findall(text2))\n\n# (b) Regex that finds quoted strings - shortest match (nongreedy)\nstr_pat = re.compile(r'\\\"(.*?)\\\"')\nprint(str_pat.findall(text2))\n","sub_path":"2/2.7_specifying_a_Regular_Expression_for_the_Shortest_Match.py","file_name":"2.7_specifying_a_Regular_Expression_for_the_Shortest_Match.py","file_ext":"py","file_size_in_byte":756,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"432847243","text":"#https://github.com/davidissamattos/LG-HOO\nfrom __future__ import division\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\nimport networkx as nx\nimport os.path\nimport sys\nfrom savitzky_golay import *\n\nclass LGHOO(object):\n    def __init__(self, arm_range, height_limit=10, v1=1.0, rho=0, minimum_grow=10):\n        \"\"\"\n        Initializing a list of arms\n        :param arm_range: a vector with the two borders for the whole range. It can be either [5,1] or [1,5] for example\n        :param height_limit: limits the height of the tree. In practicality it limits the size of the vector to 2^height + 2 of the borders.\n        The default value is 10\n\n        \"\"\"\n        if minimum_grow > 0:\n            self.minimum_grow = minimum_grow # v1 should be greater than 0\n        else:\n            self.minimum_grow = 10\n        self.arm_range = arm_range\n        self.range_size = np.absolute(arm_range[0] - arm_range[1])\n        self.midrange_arm = self.range_size/2\n        self.arm_range_min = np.min(self.arm_range)\n        self.arm_range_max = np.max(self.arm_range)\n        self.height_limit = height_limit\n        self.initial_bound = np.inf\n        self.initial_children_arm = np.nan\n        if v1 > 0:\n            self.v1 = v1 # v1 should be greater than 0\n        else:\n            self.v1 = 1.0\n            print(\"v1 should be greater than 0\")\n        # rho should be < 1\n        if rho < 1:\n            self.rho = rho\n\n        else:\n            self.rho = 0.5\n            print(\"rho should be less than 1\")\n\n\n        # the vector that we will keep track representing all our optimization space\n        #   [0            1       2          3            4         5         6                   7               8                  9         ]\n        #  [[value_arm, bound, ncounts, mean_value, sum_rewards, height, parent_arm_value, left_children, right_children, decision_arm_criteria]]\n\n\n        # in height 1 the parent is itself\n        # border nodes\n        #self.arm_list = np.array([[self.arm_range_min, self.initial_bound, 0, 0, 0, 1, self.arm_range_min,\n        #                           self.initial_children_arm, self.initial_children_arm]])\n        #self._add_arm([[self.arm_range_max, self.initial_bound, 0, 0, 0, 1, self.arm_range_max,\n        #               self.initial_children_arm, self.initial_children_arm]])\n        # root node\n\n        self.arm_list = np.array([[self.midrange_arm, self.initial_bound, 0, 0, 0, 1, self.range_size / 2,\n                       self.initial_children_arm, self.initial_children_arm, 0]])\n        self.arm_list_names = [\"arm_value\", \"bound_value\", \"ncounts\", \"mean\", \"sum_rewards\",\"height\", \"parent_arm\", \"left_children_arm\", \"right_children_arm\" ]\n\n# Some tree functions\n## Adding values to the list\n    def _add_child_for_arm(self, arm):\n        \"\"\"\n        Add a child for in the tree\n        :param arm:\n        :return:\n        \"\"\"\n\n        index = self._get_index_for_arm(arm)\n        arm_height = self._get_height_for_index(index)\n\n        #making sure we dont go over the height limit\n        if arm_height >= self.height_limit:\n            return\n        # Making sure we dont add repetitive children\n        #if has a child we skip the function else we add both children\n\n        if self._has_child_for_index(index):\n            #print \"already have a child\"\n            return\n        else:\n            n = self._get_number_of_arms()\n            child_height = arm_height + 1\n            diff = self.range_size / np.power(2, child_height)\n\n            # adding the child arms\n            left_child_arm = (arm - diff)\n            right_child_arm = (arm + diff)\n\n            # child to the left\n            self.arm_list[index][7] = left_child_arm  # setting the right children value in the parent\n            # child to the right\n            self.arm_list[index][8] = right_child_arm  # setting the right children value in the parent\n            # adding at the end so we dont need to get another index\n            self._add_arm([[left_child_arm, self.initial_bound, 0, 0, 0, child_height, arm, self.initial_children_arm,\n                            self.initial_children_arm, 0]])\n            self._add_arm([[right_child_arm, self.initial_bound, 0, 0, 0, child_height, arm, self.initial_children_arm,\n                            self.initial_children_arm, 0]])\n            return\n\n    def _add_child_for_index(self, index):\n        \"\"\"\n        Use the main add_child)for arm function\n        :param index:\n        :return:\n        \"\"\"\n        arm = self._get_arm_value_for_index(index)\n        return self._add_child_for_arm(arm)\n\n    def _add_arm(self, arm_vector):\n        self.arm_list = np.append(self.arm_list, arm_vector, axis=0)\n        #sort by arm\n        #self._sort_list_arm_by_arm()\n\n    def _has_child_for_index(self, index):\n        \"\"\"\n        Checking to see if a node has any children\n        :param index: index of the arm\n        :return: True if one of the children is not nan and false if both are nan\n        \"\"\"\n        left_child = self.arm_list[index][7]\n        right_child = self.arm_list[index][8]\n        # we want to be sure that both children are nan\n        if np.isnan(left_child) and np.isnan(right_child):\n            return False\n        else:\n            return True\n\n    def _has_child_for_arm(self, arm):\n        \"\"\"\n        Checking to see if a node has any children\n        :param arm: the value of the arm\n        :return: True if one of the children is not nan and false if both are nan\n        \"\"\"\n        index = self._get_index_for_arm(arm)\n        return self._has_child_for_index(index)\n\n    def _get_left_child_arm_for_index(self, index):\n        return self.arm_list[index][7]\n\n    def _get_right_child_arm_for_index(self, index):\n        return self.arm_list[index][8]\n\n    def _get_left_child_index_for_index(self, index):\n        arm = self._get_left_child_arm_for_index(index)\n        if np.isnan(arm):\n            return np.nan\n        else:\n            return self._get_index_for_arm(arm)\n\n    def _get_right_child_index_for_index(self, index):\n        arm = self._get_right_child_arm_for_index(index)\n        if np.isnan(arm):\n            return np.nan\n        else:\n            return self._get_index_for_arm(arm)\n\n    def _get_root_index(self):\n        return self._get_index_for_arm(self.midrange_arm)\n\n    def _sort_list_arm_by_height(self):\n        \"\"\"\n        Returns and array sorted by the height. Highest height goes first\n        :return:\n        \"\"\"\n        self.arm_list = np.flipud(self.arm_list[self.arm_list[:, 5].argsort()])\n\n    def _sort_list_arm_by_arm(self):\n        self.arm_list = self.arm_list[self.arm_list[:, 0].argsort()]\n\n## Get the value for the height of the node\n    def _get_height_for_index(self, index):\n        return self.arm_list[index][5]\n\n    def _get_height_for_arm(self, arm):\n        index = self._get_index_for_arm(arm)\n        return self._get_height_for_index(index)\n\n    def _get_max_height(self):\n        return np.max(self.arm_list[:,5])\n\n    def _get_tree_height(self):\n        return self.arm_list[:, 5].max()\n\n## Get value for paths and searching the tree\n    def _get_parent_arm_for_child_index(self, index):\n        \"\"\"\n        Return the value of the arm of the parent given a child index\n        :param index:\n        :return:\n        \"\"\"\n        return self.arm_list[index][6]\n\n    def _get_parent_index_for_child_index(self, index):\n        \"\"\"\n        Return the index of the parent given a child index\n        :param index:\n        :return:\n        \"\"\"\n        return self._get_index_for_arm(self._get_parent_arm_for_child_index(index))\n\n    def _get_parent_arm_for_child_arm(self, arm):\n        \"\"\"\n        return the value of the arm given a child arm\n        :param arm:\n        :return:\n        \"\"\"\n        index = self._get_index_for_arm(arm)\n        return self._get_parent_arm_for_child_index(index)\n\n    def _get_parent_index_for_child_arm(self, arm):\n        \"\"\"\n        Return the index of the parent given the child arm\n        :param arm:\n        :return:\n        \"\"\"\n        index = self._get_index_for_arm(arm)\n        return self._get_parent_index_for_child_index(index)\n\n    def _get_path_for_index(self, index):\n        \"\"\"\n        receives an index and returns a numpy array of index for the path\n        Searches through the list to find the whole path\n        :return:\n        \"\"\"\n        list_index = np.array([index])\n        height = self._get_height_for_index(index).astype(np.int64)\n        for i in range(1, height):\n            node_index = self._get_parent_index_for_child_index(list_index[-1])\n            list_index = np.append(list_index, node_index)\n        return list_index\n\n    def _get_path_for_arm(self, arm):\n        \"\"\"\n        receives an arm and returns a numpy array of index for the path\n        :param arm:\n        :return:\n        \"\"\"\n        index = self._get_index_for_arm(arm)\n        return self._get_path_for_index(index)\n\n## Get/Update the values for the bounds\n\n    def _get_bound_for_index(self, index):\n        return self.arm_list[index][1]\n\n    def _set_bound_for_index(self, index, bound):\n        self.arm_list[index][1] = bound\n\n    def _get_bound_for_arm(self, arm):\n        index = self._get_index_for_arm(arm)\n        return self._get_bound_for_index(index)\n\n    def _get_number_of_max_bound(self):\n        return self._get_all_index_max_bound().size\n\n    def _get_max_bound_arm(self):\n        index = self._get_max_bound_index()\n        return self._get_arm_value_for_index(index)\n\n    def _get_max_bound_index(self):\n        return self._get_all_index_max_bound()[0][0]\n\n    def _get_most_promising_leaf_index(self):\n        path = self._get_index_path_for_possible_arms()\n        return path[-1]\n\n    def _set_bound_for_arm(self, arm, bound):\n        index = self._get_index_for_arm(arm)\n        self._set_bound_for_index(index, bound)\n\n    def _get_all_index_max_bound(self):\n        \"\"\"\n        Return an array with the index of all max bounds if there is more than one\n        :return:\n        \"\"\"\n        return np.argwhere(self.arm_list[:, 1] == np.amax(self.arm_list[:, 1]))\n\n    def _debug_arms_and_bounds(self):\n        index = self.get_best_arm_index()\n        bounds = self._get_bound_for_index(index)\n        arms = self._get_arm_value_for_index(index)\n        count = self._get_count_for_index(index)\n        return arms, bounds, count\n\n    def _old_get_most_promising_child(self):\n        \"\"\"\n        Returns uniformly random the index of the max bound and create a child for this node\n        :return: the index of the most promising child\n        \"\"\"\n        bounds = self._get_all_index_max_bound()\n        choose = np.random.choice(bounds.shape[0])\n\n        index_most_promising_child = bounds[choose][0]\n\n        return index_most_promising_child\n\n    def _get_index_path_for_possible_arms(self):\n        \"\"\"We return an array with indexes with arms that we can choose\"\"\"\n        root_index = self._get_index_for_arm(self.midrange_arm)\n        path=[]\n        arms=[]\n        arms.append(self._get_arm_value_for_index(root_index))\n        path.append(root_index)\n        iter_index = root_index\n        #while we have children\n        children = True\n        while children:\n            parent_index = iter_index\n            iter_index = self._get_highest_bound_child_for_index(parent_index)\n            if np.isnan(iter_index):\n                children = False\n            else:\n                path.append(iter_index)\n                arms.append(self._get_arm_value_for_index(iter_index))\n\n        #print self.print_full_list()\n        #print \"arms path\", arms\n        return np.array(path)\n\n    def _get_highest_bound_child_for_index(self, index):\n\n        bound_left = []\n        bound_right = []\n\n        left_index = self._get_left_child_index_for_index(index)\n\n        right_index = self._get_right_child_index_for_index(index)\n\n        most_promising_child = []\n        #if haas left child\n        if not np.isnan(left_index):\n            bound_left = self._get_bound_for_index(left_index)\n        else:\n            return np.nan\n        # if haas right child\n        if not np.isnan(right_index):\n            bound_right = self._get_bound_for_index(right_index)\n        else:\n            return np.nan\n\n        if bound_right > bound_left:\n            most_promising_child = right_index\n        if bound_left > bound_right:\n            most_promising_child = left_index\n        if bound_right == bound_left:\n            choose = np.random.choice(2)\n            if choose == 1:\n                most_promising_child = right_index\n            if choose == 0:\n                most_promising_child = left_index\n\n        return most_promising_child\n\n    def _extend_tree_for_index(self, index):\n        \"\"\"\n        Extend the tree in the index\n\n        \"\"\"\n        if self._get_count_for_index(index) >= self.minimum_grow:\n            self._add_child_for_index(index)\n        else:\n            return\n\n    def _extend_tree_for_arm(self, arm):\n        index = self._get_index_for_arm(arm)\n        self._extend_tree_for_index(index)\n\n    def _find_nearest_node_by_arm(self, arm):\n        \"\"\"\n        This function works only in case the player plays an arm that was not previously allocated or thought in the list of possible arms\n        In this case we take the tree closest arm to the played arm. If the difference between the two would be greater than of the childs we discard the vector\n        else we store it in the found position\n        :param arm:\n        :return: the index\n        \"\"\"\n        min_index = (np.abs(self.arm_list[:,0] - arm)).argmin()\n        diff = np.abs(self.arm_list[min_index,0]-arm)\n\n        arm_height = self._get_height_for_index(min_index)\n        child_height = arm_height + 1\n        child_diff = self.range_size / np.power(2, child_height)\n\n        if diff > child_diff:\n            return -1\n        else:\n            return min_index\n\n\n    def _calculate_ucb_for_index(self, index):\n        \"\"\"\n        Calculate the ucb estimate for an element\n        :param index:\n        :return:\n        \"\"\"\n        counts = self._get_count_for_index(index)\n        if counts > 0:\n            uncertainty_bound = math.sqrt((2 * math.log(self._get_total_counts())) / float(self._get_count_for_index(index)))\n            mean = self._get_mean_for_index(index)\n            h = self._get_height_for_index(index)\n            max_variation_payoff = self.v1 * np.power(self.rho, h)\n            ucb_value = mean + uncertainty_bound + max_variation_payoff\n        else:\n            ucb_value = np.inf\n        return ucb_value\n\n    def _calculate_bound_for_index(self, index):\n        \"\"\"\n        Calculate the final Bound or B value for the index and save in the bound part of the vector arm_list\n        in col index 1\n        :param index:\n        :return:\n        \"\"\"\n        ucb = self._calculate_ucb_for_index(index)\n        left_children_bound = np.inf\n        right_children_bound = np.inf\n\n        #if it has a child it updates the values\n        if self._has_child_for_index(index):\n            left_children_index = self._get_left_child_index_for_index(index)\n            left_children_bound = self._get_bound_for_index(left_children_index)\n\n            right_children_index = self._get_right_child_index_for_index(index)\n            right_children_bound = self._get_bound_for_index(right_children_index)\n        max_bound_children = np.max(np.array([left_children_bound, right_children_bound]))\n        bound = np.min(np.array([ucb, max_bound_children]))\n        self._set_bound_for_index(index, bound)\n\n    def _calculate_arm_decision_criteria_for_index(self, index):\n        \"\"\"Decision criteria\"\"\"\n        #This one is ok\n        # bound = self._get_bound_for_index(index)\n        # if np.isnan(bound) or np.isinf(bound):\n        #     self.arm_list[index, 9] = 0\n        # else:\n        #     self.arm_list[index,9] = self._get_bound_for_index(index)/self._calculate_ucb_for_index(index)\n        #\n        #this is better\n        bound = self._get_bound_for_index(index)\n        if np.isnan(bound) or np.isinf(bound):\n            self.arm_list[index, 9] = 0\n        else:\n            self.arm_list[index,9] = self._get_mean_for_index(index) / self._calculate_ucb_for_index(index)\n\n    def _update_all_bounds(self):\n        \"\"\"\n        In this function we update all bounds\n        First we sort by height\n        Then we update it sequentially\n        we dont calculate for the borders\n        :return:\n        \"\"\"\n        narms = self._get_number_of_arms()\n        self._sort_list_arm_by_height()\n        for i in range(narms):\n            self._calculate_bound_for_index(i)\n            self._calculate_arm_decision_criteria_for_index(i)\n\n## Get/Update the values for the counts\n\n\n## Get counts\n\n    def _get_count_for_index(self, index):\n        return self.arm_list[index][2]\n\n    def _get_count_for_arm(self, arm):\n        index = self._get_index_for_arm(arm)\n        return self._get_count_for_index(index)\n\n    def _update_n_count_for_index(self, index, n):\n        self.arm_list[index][2] = self.arm_list[index][2] + n\n\n    def _update_n_count_for_arm(self, arm, n):\n        index = self._get_index_for_arm(arm)\n        self._update_n_count_for_index(index, n)\n\n    def _update_count_for_arm(self, arm):\n        self._update_n_count_for_arm(arm, 1)\n\n    def _update_count_for_index(self, index):\n        self._update_n_count_for_index(index, 1)\n\n ## Get/Update the Sum of reward values\n    def _get_reward_for_index(self, index):\n        return self.arm_list[index][4]\n\n    def _get_reward_for_arm(self, arm):\n        index = self._get_index_for_arm(arm)\n        return self._get_reward_for_index(index)\n\n    def _update_reward_for_index(self, index, sum_rewards):\n        self.arm_list[index][4] = self.arm_list[index][4] + sum_rewards\n\n    def _update_reward_for_arm(self, arm, sum_rewards):\n        index = self._get_index_for_arm(arm)\n        self._update_reward_for_index(index, sum_rewards)\n\n## Get/Update value for the mean values\n\n    def _get_mean_for_index(self, index):\n        return self.arm_list[index][3]\n\n    def _get_mean_for_arm(self, arm):\n        index = self._get_index_for_arm(arm)\n        return self._get_mean_for_index(index)\n\n    def _update_n_mean_for_index(self, index, n, sum_new_rewards):\n        \"\"\"\n        Update the mean and the counts for the played arm\n        :param index:\n        :param n:\n        :param sum_new_rewards:\n        :return:\n        \"\"\"\n        self._update_reward_for_index(index, sum_new_rewards)\n\n        n_old = self._get_count_for_index(index)\n        #update the count for the played arm\n        self._update_n_count_for_index(index, n)\n        n_new = self._get_count_for_index(index)\n\n        old_mean = self._get_mean_for_index(index)\n\n        new_mean = (old_mean * n_old + sum_new_rewards) / float(n_new)\n        self.arm_list[index][3] = new_mean\n\n    def _update_mean_for_index(self, index, reward):\n        self._update_n_mean_for_index(index=index, n=1, sum_new_rewards=reward)\n\n    def _update_n_mean_for_arm(self, arm, n, sum_new_rewards):\n        index = self._get_index_for_arm(arm)\n        self._update_n_mean_for_index(index=index, n=n, sum_new_rewards=sum_new_rewards)\n\n    def _update_mean_for_arm(self, arm, reward):\n        self._update_n_mean_for_arm(arm=arm, n=1, sum_new_rewards=reward)\n\n    def _refresh_all_means(self):\n        \"\"\"\n        Dividing the rewards by the number of counts element wise\n        :return:\n        \"\"\"\n        with np.errstate(divide='ignore', invalid='ignore'):\n            self.arm_list[:, 3] = np.divide(self.arm_list[:, 4], self.arm_list[:, 2])\n            self.arm_list[np.isnan(self.arm_list)] = 0\n\n        # Other functions\n\n## value of the arms functions\n\n    def _get_number_of_arms(self):\n        \"\"\"\n        Number of active arms\n        :return: the number of rows -> index 0\n        \"\"\"\n        return self.arm_list.shape[0]\n\n    def _get_arm_value_for_index(self, index):\n        return self.arm_list[index][0]\n\n    def _get_index_for_arm(self, arm):\n        \"\"\"\n        Return the index for a specific arm\n        :param arm: value of the arm\n        :return: return an array\n        \"\"\"\n        return np.where(self.arm_list[:, 0] == arm)[0][0]\n\n    def _get_all_arms_from_arm_list(self):\n        return self.arm_list[:, 0]\n\n    def _get_full_arm_list(self):\n        return self.arm_list\n\n    def _get_total_counts(self):\n        return np.sum(self.arm_list[:, 2])\n\n## Algorithm part\n    def select_arm(self):\n        \"\"\"\n        Selects an arm to play and return the value of the arm\n        :return:\n        \"\"\"\n        # 1 - Select the path of the most promising children\n        path = self._get_index_path_for_possible_arms()\n\n        # Select randomly an arm from the path of the most promising child\n        chosen_arm_index = path[np.random.choice(path.size)]\n        #Return the value of the arm\n        print(self._get_arm_value_for_index(chosen_arm_index))\n        return self._get_arm_value_for_index(chosen_arm_index)\n\n    def update(self, chosen_arm, reward):\n        \"\"\"\n        For the simulation framework. The chosen arm is the value of the arm in the vecotr\n        :param chosen_arm: value of the arm\n        :param reward: received reward\n        :return:\n        \"\"\"\n\n        #Extend tree with the new bounds (inf) for the child\n        index = self._find_nearest_node_by_arm(chosen_arm)\n        #if the chosen arm is not valid we ignore it\n        if index == -1:\n            #print \"no arm selected\"\n            return\n        else:\n\n            arm_to_be_updated = self._get_arm_value_for_index(index)\n            #Update for all the path\n            path = self._get_path_for_arm(arm_to_be_updated)\n            for i in path:\n                self._update_mean_for_index(index=i, reward=reward)\n\n            print(\"arm played: \", chosen_arm, \" arm: \", arm_to_be_updated, \" selected\")\n            print('reward', reward)\n            self._extend_tree_for_arm(arm_to_be_updated)\n            #update statistics for the node\n            #self._update_mean_for_arm(arm=arm_to_be_updated, reward=reward)\n            #Update the computation of all bounds in the tree\n            self._update_all_bounds()\n            return\n\n\n    def get_original_best_arm_index(self):\n        \"\"\"\n        The original way in the paper\n        :return:\n        \"\"\"\n        return np.argwhere(self.arm_list[:, 5] == np.amax(self.arm_list[:, 5]))[0][0]\n\n    def get_original_best_arm(self):\n        \"\"\"\n        The original way in the paper\n        :return:\n        \"\"\"\n        index = self.get_original_best_arm_index()\n        return self._get_arm_value_for_index(index)\n\n\n    def get_best_arm_index(self):\n        \"\"\"\n        Based on the criteria vector\n        :return:\n        \"\"\"\n        return np.argwhere(self.arm_list[:, 9] == np.amax(self.arm_list[:, 9]))[0][0]\n\n    def get_best_arm_value(self):\n        \"\"\"\n        Return the value of the best arm based on the criteria\n        :return:\n        \"\"\"\n        index = self.get_best_arm_index()\n        return self._get_arm_value_for_index(index)\n\n## Visualization functions\n\n    def print_full_list(self):\n        print(self.arm_list_names)\n        print(self._get_full_arm_list())\n\n    def data_to_plot(self):\n        G =self.get_graph()\n        pos = nx.get_node_attributes(G, 'pos')\n        return G, G.nodes, G.edges, pos\n\n    def get_graph(self):\n        G = nx.Graph()\n        n = self._get_number_of_arms()\n        for i in range(n):\n            G.add_node(self.arm_list[i, 0], pos=(self.arm_list[i, 0], self.arm_list[i, 5]))\n            G.add_edge(float(self.arm_list[i, 0]), self.arm_list[i, 6])\n        return G\n\n    def plot_graph(self):\n        G = self.get_graph()\n        pos = nx.get_node_attributes(G, 'pos')\n        plt.title('draw_networkx')\n        nx.draw(G, pos=pos, with_labels=True)\n        plt.show()\n\n\n    def plot_graph_with_function(self,x_axis, y_axis,rescale_y=1,save=False,filename =\"output.png\"):\n        fig, ax = plt.subplots()\n        G = self.get_graph()\n        pos = nx.get_node_attributes(G, 'pos')\n\n        max_h = int(self._get_max_height())\n        function_y0 = max_h+ 1\n        max_y = function_y0 + rescale_y + 0.5\n        best_arm_x = self.get_best_arm_value()\n        text_x_offset = 0.01*(self.arm_range_max-self.arm_range_min)\n        text_y_offset = -0.5\n\n\n        #plotting the estimated underlying function\n        try:\n            self._sort_list_arm_by_arm()\n            est_x = self.arm_list[:,0]\n            number_arms = self.arm_list.shape[0]\n            window = convert2odd(number_arms/2)\n            est_y = savitzky_golay((self.arm_list[:,9]),window_size=window,order=max_h)*rescale_y+ function_y0\n            plt.plot(est_x,est_y, label=\"Estimated function\")\n        except:\n            pass\n\n        #ploting the function after the max height\n        func_y = rescale_y*y_axis+function_y0\n        func_x = x_axis\n        ax.plot(func_x, func_y, label=\"True function\")\n\n        #Drawing the graph\n        nx.draw(G, pos=pos, node_size=20,ax=ax)\n\n        # Line of the best arm\n        plt.axvline(x=best_arm_x, linestyle='--')\n        ax.text(best_arm_x + text_x_offset, function_y0 + text_y_offset, \"Best arm:\\n\" + str(format(best_arm_x, '.4f')))\n\n\n        # Line for the heights\n        for i in range(1,max_h+1):\n            plt.axhline(y=i, linestyle='--', linewidth=0.5)\n            ax.text(0,i,str(i), size='smaller')\n\n        #line for the zero in the underlying function\n        plt.axhline(y=function_y0, linestyle='-',linewidth=0.5, color='black')\n        plt.axhline(y=function_y0+rescale_y, linestyle='-', linewidth=0.5, color='black')\n\n        #Setting figure style\n        #axis on but just with the x axis, the y axis is off\n        plt.axis('on')\n        plt.xlim(self.arm_range_min,self.arm_range_max)\n        ax.get_yaxis().set_visible(False)\n        plt.ylim([1,max_y])\n\n        #setting title for the plot and axis\n        ax.set_title('Best value optimization using LG-HOO')\n        ax.set_xlabel('Values in the $\\chi$-space')\n\n        lgd = ax.legend(loc = 9, bbox_to_anchor=(0.5,-0.15),ncol=2)\n        fig.subplots_adjust(left=None, bottom=0.2, right=None, top=None, wspace=None, hspace=None)\n#\n        if save == True:\n            mainfile = sys.argv[0]\n            pathname = os.path.join(os.path.dirname(mainfile),\"pictures\")\n            output = os.path.join(pathname,filename)\n            plt.savefig(output, format='png', dpi=300)#, bblock_extra_artists=(lgd,), bbox_inches='tight')\n        else:\n            #plt.tight_layout()\n            plt.show()\n\n    def save_list_arms_to_file(self):\n        np.savetxt(\"data.csv\", self._get_full_arm_list(), delimiter=\",\")","sub_path":"lghoo.py","file_name":"lghoo.py","file_ext":"py","file_size_in_byte":26762,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"577827259","text":"# These lines import the Numpy and Datascience modules.\nimport numpy as np\nfrom datascience import *\n\n# These lines do some fancy plotting magic.\nimport matplotlib\nimport matplotlib.pyplot as plt\nplt.style.use('fivethirtyeight')\nimport warnings\nwarnings.simplefilter('ignore', FutureWarning)\nimport matplotlib.patches as patches\n\ndef pivot_table_to_groups(pivoted, x_label):\n    \"\"\"\n    Convert a contingency table that's been computed by pivot() to a\n    table with the same information as it computed by groups() on the\n    two pivot columns.  This kind of table is easier for a computer to\n    work with, but harder for a human to read than a pivot table.\n    \"\"\"\n    y_label = pivoted.labels[0]\n    x_categories = np.array(pivoted.labels).take(range(1, pivoted.num_columns))\n    y_categories = pivoted.column(0)\n\n    grouped = Table([x_label, \"x index\", y_label, \"y index\"]).with_rows([(x, x_idx, y, y_idx) for (x_idx, x) in enumerate(x_categories) for (y_idx, y) in enumerate(y_categories)])\n\n    def get_combination_count(x_idx, y_idx):\n        return pivoted.column(x_categories.item(x_idx)).item(y_idx)\n    grouped.append_column(\"count\", grouped.apply(get_combination_count, [\"x index\", \"y index\"]))\n\n    return grouped.select([x_label, y_label, \"count\"])\n\ndef normalize(array, intended_sum):\n    return intended_sum * array / sum(array)\n\nCOLORS = [\n    0x000072, #yale blue\n    0xfea610, #california gold\n    0xdc1134, #stanfurd red\n    0x81a63d, #green\n    0x000000, #black\n    0xa0f6aa, #light green\n    0x513985, #purple\n    0x00ffff, #light blue\n    0x72322e, #brown\n]\n\ndef get_colors(num_colors):\n    return COLORS[0:min(num_colors, len(COLORS))] \\\n         + list(np.random.randint(0xFFFFFF, size=max(0, num_colors-len(COLORS))))\n\ndef compute_combination_data(combinations, individuals_name):\n    \"\"\"\n    Given a table of grouped counts, produces a table of information that\n    will be used in plotting:\n      * The percentage of things in each group\n      * A message describing the group\n      * The color to use when displaying the group\n    \"\"\"\n    assert(combinations.labels[-1] == \"count\")\n    x_label = combinations.labels[0]\n    y_label = combinations.labels[1]\n    combinations = combinations.where(combinations.column(\"count\") > 0)\n    combinations = combinations.sort(\"count\", descending=True)\n\n    def make_compute_message(total_count):\n        def compute_message(row):\n            labels = row[:-1]\n            count = row[-1]\n            descriptions = [\"%s %s\" % (l, category) for (l, category) in zip(labels, combinations.labels[:-1])]\n            if len(descriptions) == 1:\n              description = descriptions[0]\n            elif len(descriptions) == 2:\n              description = \" and \".join(descriptions)\n            else:\n              descriptions[-1] = \"and \" + descriptions[-1]\n              description = \", \".join(descriptions)\n            return (\"%.8g out of %.8g %s have %s\" % (count, total_count, individuals_name, description))\n        return compute_message\n\n    combinations.append_column(\"message\", combinations.apply(make_compute_message(sum(combinations.column(\"count\")))))\n    combinations.append_column(\"percentage\", normalize(combinations.column(\"count\"), 100))\n    combinations.append_column(\"color\", [\"#%0.6X\" % c for c in get_colors(combinations.num_rows)])\n    return combinations\n\ndef make_displayed_rectangles(combinations):\n    \"\"\"\n    Given a table like that produced by compute_combination_data, produces\n    a new version of the table that includes the Rectangle objects to draw\n    when displaying each category in an icon array.  The objects are drawn\n    in a 10x10 grid.  Sometimes the whole grid isn't drawn.\n    \"\"\"\n    n = combinations.num_rows\n    displays = combinations.copy()\n    total_count = sum(displays.column(\"count\"))\n    if 1 < total_count < 100:\n      # Use counts instead of percentages to make a clearer visualization.\n      amount_column = \"count\"\n    else:\n      amount_column = \"percentage\"\n\n    displays.append_column(\"start amount\", np.cumsum(np.append([0], displays.column(amount_column)))[0:n])\n    displays.append_column(\"end amount\", np.cumsum(displays.column(amount_column)))\n\n    SQUARE_SIZE = .1\n    SQUARE_PADDING = .01\n    DRAWN_SQUARE_SIZE = SQUARE_SIZE - 2*SQUARE_PADDING\n\n    def square_logical_coordinates(start_percentile, end_percentile):\n        percentile = int(np.floor(start_percentile))\n        return (percentile % 10, percentile // 10)\n\n    def square_bottom_left(logical_x, logical_y):\n        return (logical_x / 10, logical_y / 10)\n\n    # Coordinates of the bottom-left of a rectangle inside its square.\n    # (0, 0) is the bottom-left of the square, and the square has a side\n    # length of 1.\n    def bottom_left_in_square(start_percentile, end_percentile):\n        percentile = int(np.floor(start_percentile))\n        left_end = start_percentile - percentile\n        return (left_end, 0)\n\n    def rectangle_for_percentile(start_percentile, end_percentile, color):\n        (logical_x, logical_y) = square_logical_coordinates(start_percentile, end_percentile)\n        (square_x, square_y) = square_bottom_left(logical_x, logical_y)\n        (x_in_square, y_in_square) = bottom_left_in_square(start_percentile, end_percentile)\n        absolute_x = square_x + SQUARE_PADDING + DRAWN_SQUARE_SIZE*x_in_square\n        absolute_y = square_y + SQUARE_PADDING + DRAWN_SQUARE_SIZE*y_in_square\n        rectangle_proportion = end_percentile - start_percentile\n        return patches.Rectangle((absolute_x, absolute_y), rectangle_proportion*DRAWN_SQUARE_SIZE, DRAWN_SQUARE_SIZE, facecolor=color)\n\n    def rectangles_for_percentage_range(start, end, color):\n        rectangles = []\n        for percentile in range(int(np.floor(start)), int(np.ceil(end))):\n            start_percentile = max(start, percentile)\n            end_percentile = min(end, percentile+1)\n            rectangles.append(rectangle_for_percentile(start_percentile, end_percentile, color))\n        return rectangles\n\n    displays.append_column(\"rectangles\", displays.apply(rectangles_for_percentage_range, [\"start amount\", \"end amount\", \"color\"]))\n\n    return displays\n\ndef draw_plot(displays, individuals_name):\n    \"\"\"\n    Given a table of categories and the rectangles to display for each category,\n    draw an icon array made from those rectangles.\n    \"\"\"\n    fig = plt.figure()\n    ax = fig.add_subplot(111, aspect=\"equal\")\n    all_rectangles = [r for rectangles in displays.column(\"rectangles\") for r in rectangles]\n    for r in all_rectangles:\n        ax.add_patch(r)\n\n    # Make the axes more useful:\n    total_count = sum(displays.column(\"count\"))\n    if 1 < total_count < 100:\n        count_per_box = 1\n    else:\n        count_per_box = total_count/100\n    x_side_count = 10*count_per_box\n    y_side_count = 100*count_per_box\n    xticks = matplotlib.ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x*x_side_count))\n    ax.xaxis.set_major_formatter(xticks)\n    yticks = matplotlib.ticker.FuncFormatter(lambda y, pos: '{0:g}'.format(y*y_side_count))\n    ax.yaxis.set_major_formatter(yticks)\n\n    # Draw a legend:\n    def make_dummy_rectangle(color, message):\n        return patches.Patch(color=color, label=message)\n    dummy_rectangles = displays.apply(make_dummy_rectangle, [\"color\", \"message\"])\n    plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), handles=list(dummy_rectangles))\n    plt.text(1.1, 0.1, \"Each box represents %g %s.\" % (count_per_box, individuals_name), fontsize=20)\n\ndef display_combinations(category_counts, individuals_name=\"people\"):\n    displays = make_displayed_rectangles(compute_combination_data(category_counts, individuals_name))\n    draw_plot(displays, individuals_name)\n\n\ndef test():\n  cancer = Table().with_columns([\"cancer status\", [\"cancer\", \"no cancer\"], \"positive\", [90, 198], \"negative\", [10, 9702]]) #SOLUTION\n  grouped = pivot_table_to_groups(cancer, \"test status\")\n  display_combinations(grouped)\n\n  trinkets = Table([\"x\", \"y\"]).with_rows([\n          [\"a\", \"i\"],\n          [\"a\", \"j\"],\n          [\"b\", \"k\"]\n      ])\n  display_combinations(trinkets.groups(['x']))\n  display_combinations(trinkets.groups(['x', 'y']))\n\nif __name__ == \"__main__\":\n  test()\n","sub_path":"notebooks/data8_notebooks/lab10/visualizations.py","file_name":"visualizations.py","file_ext":"py","file_size_in_byte":8188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"570310473","text":"#!/usr/bin python3\n\n\"\"\"\n0  All attributes off 默认值\n1  Bold (or Bright) 粗体 or 高亮\n4  Underline 下划线\n5  Blink 闪烁\n7  Invert 反显\n30 Black text\n31 Red text\n32 Green text\n33 Yellow text\n34 Blue text\n35 Purple text\n36 Cyan text\n37 White text\n40 Black background\n41 Red background\n42 Green background\n43 Yellow background\n44 Blue background\n45 Purple background\n46 Cyan background\n47 White background\n\"\"\"\n\n\ndef main():\n    \"\"\" \"\"\"\nfor atrr in [0, 1, 4, 5, 7]:\n    print(\"------------------------------attribute %d ------------------------------\" % atrr)\n    for fore in [30, 31, 32, 33, 34, 35, 36, 37]:\n        for back in [40, 41, 42, 43, 44, 45, 46, 47]:\n            color = \"\\x1B[%d;%d;%dm\" % (atrr, fore, back)\n            print(\"%s %d-%d-%d\\x1B[0m\" % (color, atrr, fore, back), end='')\n        print(\"\")\n# for atrr in range(10):\n#     print(\"------------------------------attribute %d ------------------------------\" % atrr)\n#     for fore in range(29, 40):\n#         for back in range(40, 49):\n#             color = \"\\x1B[%d;%d;%dm\" % (atrr, fore, back)\n#             print(\"%s %d-%d-%d\\x1B[0m\" % (color, atrr, fore, back), end='')\n#         print(\"\")\nif __name__ == \"__main__\":\n    \"\"\" \"\"\"\n    main()","sub_path":"Basis_of_Python/201702week/0721/print配色.py","file_name":"print配色.py","file_ext":"py","file_size_in_byte":1224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"24615033","text":"\"\"\" Copyright 2017 Vitaly Vorobyev \"\"\"\n\nimport ddcfg\nimport toy\n\nddcfg.LOGDIR += \"lhcbOpt/\"\nddcfg.set_config(ddcfg.TOPDIR + \"params/params_opt.cfg\")\n\ndef run_toy():\n    \"\"\" Toy MC experiment \"\"\"\n    beta, nexp, ftype = toy.get_params()\n    fexp = toy.find_max_generated('lhcb', 1, beta)\n    toy.toy_lhcb(beta, nexp, fexp, [ftype], [\"1\"])\n\nrun_toy()\n","sub_path":"scripts/toy_lhcb1.py","file_name":"toy_lhcb1.py","file_ext":"py","file_size_in_byte":349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"448085484","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\n#w3 = np.array([1, 2])\n#w2 = np.array([1, 3])\n#w1 = np.array([-2, 2])\n#w0 = np.array([1, -1])\n\nw3 = np.array([1])\nw2 = np.array([-2])\nw1 = np.array([1])\n\ndef f(x):\n    return x**3 @ w3 + x**2 @ w2 + x @ w1\n\n\n\ndef generate_training_data(shape):\n    return 10*np.random.random(shape) - 10\n\ndef mse_loss(y, y_hat):\n    return np.mean(np.power(y - y_hat, 2))\n\ndef mse_gradient(y, y_hat):\n    return 2*y - 2*y_hat\n\nleaky_relu = lambda x: np.maximum(-0.1*x, x)\n\nleaky_relu_gradient = lambda x: np.greater_equal(x, 0).astype(\"float64\") * 1.1 - 0.1\n\n\n\nclass Cell:\n    def __init__(self, input_shape):\n        pass\n\n    def forward(self, *values):\n        raise NotImplementedError\n\n    def backward(self, *values):\n        raise NotImplementedError\n\nclass LinearCell(Cell):\n    def __init__(self, input_shape, alpha=0.01, activation_fn=lambda x: np.maximum(0, x), dfn=lambda x: np.greater_equal(x, 0).astype(\"int64\")):\n        self.x = 10*np.random.random(input_shape) - 5\n        self.w = 10*np.random.random(input_shape) - 5\n        self.b = 0\n        self.y = 0\n        self.a = alpha\n        self.fn = activation_fn\n        self.dfn = dfn\n\n    def forward(self, x):\n        self.x = x\n        self.y = x @ self.w + self.b\n        return self.fn(self.y)\n\n    def backward(self, gradient):\n        dl_df = gradient\n        df_dy = self.dfn(self.y)\n        dy_dx = self.w\n        dy_dw = self.x\n        dy_db = 1\n\n        dl_dx = dl_df * df_dy * dy_dx\n        dl_dw = dl_df * df_dy * dy_dw\n        dl_db = dl_df * df_dy * dy_db\n\n        self.w -= self.a * dl_dw\n        self.b -= self.a * dl_db\n\n        return dl_dx\n\nclass LinearLayer:\n    def __init__(self, input_size, output_size, alpha=0.01):\n        self.input_shape = (input_size,)\n        self.cells = [LinearCell(self.input_shape, alpha, activation_fn=leaky_relu, dfn=leaky_relu_gradient) for _ in range(output_size)]\n        self.gradient = np.zeros(self.input_shape)\n\n    def forward(self, x):\n        output = np.array([cell.forward(x) for cell in self.cells])\n\n        return output\n\n    def backward(self, gradient):\n        self.gradient = np.zeros(self.input_shape)\n        for i, cell in enumerate(self.cells):\n            self.gradient += cell.backward(gradient[i])\n\n        return self.gradient\n\nclass NeuralNetwork:\n    def __init__(self, input_size, output_size, hidden_layer_sizes = [], alpha=0.01):\n        self.layer_sizes = [input_size] + hidden_layer_sizes + [output_size]\n        self.layers = [LinearLayer(self.layer_sizes[i], self.layer_sizes[i+1], alpha) for i in range(len(self.layer_sizes) - 1)]\n\n    def forward(self, x):\n        output = x\n        for layer in self.layers:\n            output = layer.forward(output)\n\n        return output\n\n    def backward(self, gradient):\n        for layer in reversed(self.layers):\n            gradient = layer.backward(gradient)\n\n\n\nif __name__ == \"__main__\":\n    x_data = generate_training_data((25000, 1))\n    y_data = f(x_data)\n    nn = NeuralNetwork(1, 1, [3] * 5, alpha=1e-8)\n    for i in range(x_data.shape[0]):\n        x = x_data[i, :]\n        y = y_data[i]\n        actual = y\n        output = nn.forward(x)\n        loss = mse_loss(output, y)\n        gradient = mse_gradient(output, y)\n        nn.backward(gradient)\n\n        #print(\"Actual: {}\".format(actual))\n        #print(\"Output: {}\".format(output))\n        #print(\"Loss: {}\".format(loss))\n        #print(\"Gradient: {}\".format(gradient))\n\n    x_test = generate_training_data((100, 1))\n    y_test = f(x_test)\n    predictions = np.concatenate([nn.forward(x_test[i, :]) for i in range(x_test.shape[0])])\n    #print(np.vstack((y_test, predictions)).T)\n\n    #print(y_test)\n    #print(predictions)\n    print(\"Loss: {}\".format(mse_loss(predictions, y_test)))\n\n    plt.scatter(y_test, predictions)\n    plt.xlabel(\"y_test\")\n    plt.ylabel(\"predictions\")\n    plt.show()\n\n    xs = np.linspace(-5, 5)[:, np.newaxis]\n    #xs_0 = np.column_stack((xs, np.zeros(xs.shape[0])))\n    #xs_1 = np.column_stack((np.zeros(xs.shape[0]), xs))\n    #ys_hat_0 = f(xs_0)\n    #ys_hat_1 = f(xs_1)\n    #ys_0 = np.concatenate([nn.forward(xs_0[i, :]) for i in range(xs_0.shape[0])])\n    #ys_1 = np.concatenate([nn.forward(xs_1[i, :]) for i in range(xs_1.shape[0])])\n\n    ys_hat = f(xs)\n    ys = np.concatenate([nn.forward(xs[i, :]) for i in range(xs.shape[0])])\n    plt.plot(xs, ys_hat)\n    plt.plot(xs, ys)\n\n    #plt.subplot(121)\n    #plt.plot(xs, ys_hat_0)\n    #plt.plot(xs, ys_0)\n\n    #plt.subplot(122)\n    #plt.plot(xs, ys_hat_1)\n    #plt.plot(xs, ys_1)\n\n    plt.show()\n","sub_path":"fcnn_single_x.py","file_name":"fcnn_single_x.py","file_ext":"py","file_size_in_byte":4568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"644872376","text":"\"\"\"\n=============================================================\nGaussian process regression (GPR) with noise-level estimation\n=============================================================\n\nThis example illustrates that GPR with a sum-kernel including a WhiteKernel can\nestimate the noise level of data. An illustration of the\nlog-marginal-likelihood (LML) landscape shows that there exist two local\nmaxima of LML. The first corresponds to a model with a high noise level and a\nlarge length scale, which explains all variations in the data by noise. The\nsecond one has a smaller noise level and shorter length scale, which explains\nmost of the variation by the noise-free functional relationship. The second\nmodel has a higher likelihood; however, depending on the initial value for the\nhyperparameters, the gradient-based optimization might also converge to the\nhigh-noise solution. It is thus important to repeat the optimization several\ntimes for different initializations.\n\"\"\"\nprint(__doc__)\n\n# Authors: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>\n#\n# License: BSD 3 clause\n\nimport numpy as np\n\nfrom matplotlib import pyplot as plt\nfrom matplotlib.colors import LogNorm\n\nfrom sklearn.gaussian_process import GaussianProcessRegressor\nfrom sklearn.gaussian_process.kernels import RBF, WhiteKernel\nfrom sklearn.preprocessing import StandardScaler\n\n\nX = np.atleast_2d([float(i) for i in range(10)]).T\ny = np.sin(X)\n\nscaler_y = StandardScaler().fit(y)\n\nplt.figure()\nkernel = 1 * RBF(length_scale=1, length_scale_bounds=(1e-2, 1e3)) + WhiteKernel(noise_level=1e-5, noise_level_bounds=(1e-10, 1e+1))\n# kernel = np.exp(2) * RBF(length_scale=np.exp(2), length_scale_bounds=(1e-2, 1e3))# + WhiteKernel(noise_level=1e-5, noise_level_bounds=(1e-10, 1e+1))\ngp = GaussianProcessRegressor(kernel=kernel, alpha=0.01).fit(X, scaler_y.transform(y))\nX_ = np.linspace(0, 10, 100)\ny_mean, y_cov = gp.predict(X_[:, np.newaxis], return_cov=True)\nplt.plot(X_, y_mean, 'k', lw=3, zorder=9)\n# plt.fill_between(X_, y_mean - np.sqrt(np.diag(y_cov)), y_mean + np.sqrt(np.diag(y_cov)), alpha=0.5, color='k')\n# plt.plot(X_, np.sin(X_), 'r', lw=3, zorder=9)\nplt.scatter(X[:, 0], y, c='r', s=50, zorder=10, edgecolors=(0, 0, 0))\nplt.title(\"Initial: %s\\nOptimum: %s\\nLog-Marginal-Likelihood: %s\"  % (kernel, gp.kernel_,  gp.log_marginal_likelihood(gp.kernel_.theta)))\nplt.tight_layout()\n\n\n\n\n# Plot LML landscape\nplt.figure()\nplt.plot(gp.kernel.theta[0], gp.kernel.theta[1], marker='.', c='r') # initial point\nplt.plot(gp.kernel_.theta[0], gp.kernel_.theta[1], marker='.', c='r') # optinum point\n\ntheta0 = np.linspace(-5, 5, 100)\ntheta1 = np.linspace(-5, 5, 100)\nTheta0, Theta1 = np.meshgrid(theta0, theta1)\n\n\nLML = [[gp.log_marginal_likelihood([Theta0[i, j], Theta1[i, j]]) for i in range(Theta0.shape[0])] for j in range(Theta0.shape[1])]\nLML = np.array(LML).T\n\n\"\"\"\nprint (\"X\")\nprint (X)\n\"\"\"\n\nprint (\"gp.kernel.diag\")\nprint (gp.kernel.diag(X))\n\nprint (\"gp.kernel.__call__\")\nprint (gp.kernel.__call__(X))\n\nprint (\"gp.kernel_.diag\")\nprint (gp.kernel_.diag(X))\n\nprint (\"Optinum hyperparameter(log-transformed)\")\nprint (gp.kernel_.theta)\n\nprint (\"gp.log_marginal_likelihood [0,0]\")\nprint (gp.log_marginal_likelihood([gp.kernel.theta[0], gp.kernel.theta[1]], eval_gradient=True))\n\n\ncont = plt.contour(Theta0, Theta1, LML, levels=50)\ncont.clabel(fmt='%1.1f', fontsize=5)\nplt.colorbar()\n\nplt.title(\"Log-marginal-likelihood\")\nplt.tight_layout()\n\nplt.show()\n","sub_path":"lml/plot_gpr_noisy.py","file_name":"plot_gpr_noisy.py","file_ext":"py","file_size_in_byte":3427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"383544051","text":"import logging\nimport tensorflow as tf\nfrom tensorflow.python import debug as tf_debug\nimport numpy as np\nimport os\n\nfrom model_lstm_crf import MyModel\nfrom utils import DataProcessor_LSTM as DataProcessor\nfrom utils import load_vocabulary\nfrom utils import extract_kvpairs_in_bio\nfrom utils import cal_f1_score\nfrom utils import get_batch\nfrom utils import read_info\nfrom Config import Config\n\n\nif __name__ == \"__main__\":\n\n\n    Configs = Config()\n\n    if os.path.exists(Configs.debug_log_file_path):\n        os.remove(Configs.debug_log_file_path)\n\n    #  创建一个logger,默认为root logger\n    logger = logging.getLogger()\n    # 调整终端输出级别,设置全局log级别为INFO。注意全局的优先级最高\n    logger.setLevel(logging.INFO)\n\n    # 日志格式： 日志时间，日志信息，设置时间输出格式\n    formatter = logging.Formatter(\"%(asctime)s | %(message)s\", \"%Y-%m-%d %H:%M:%S\")\n\n    #  创建一个终端输出的handler\n    chlr = logging.StreamHandler()\n    chlr.setFormatter(formatter)\n\n    #  创建一个文件记录日志的handler\n    fhlr = logging.FileHandler(Configs.debug_log_file_path)\n    fhlr.setFormatter(formatter)\n\n    logger.addHandler(chlr)\n    logger.addHandler(fhlr)\n\n\n    logger.info(\"loading vocab...\")\n\n    w2i_char, i2w_char = load_vocabulary(Configs.char_vocab_path)\n\n    try:\n        pad_index = w2i_char[\"[PAD]\"]\n        Configs.padding_id = pad_index\n    except:\n        pad_index = -1\n\n    if pad_index == -1:\n        logger.info(\"PAD is not in the vocab\")\n        exit(1)\n\n    # add padding\n\n    w2i_bio, i2w_bio = load_vocabulary(Configs.label_vocab_path)\n\n    Configs.w2i_char = w2i_char\n    Configs.w2i_bio = w2i_bio\n\n    logger.info(\"read info.txt\")\n\n    file_length = read_info(Configs.info_txt)\n\n    logging.info(\"Build test data Processor\")\n\n    data_processor_valid = DataProcessor(\n        Configs.test_seq_path,\n        Configs.test_label_path,\n        w2i_char,\n        w2i_bio,\n        shuffling=True\n    )\n\n\n    logger.info(\"building model...\")\n\n    model = MyModel(Configs.padding_id,\n                    embedding_dim=Configs.embedding_dim,\n                    hidden_dim=Configs.hidden_dim,\n                    vocab_size_char=len(w2i_char),\n                    vocab_size_bio=len(w2i_bio),\n                    use_crf=Configs.use_crf)\n\n\n    logger.info(\"model params:\")\n\n    # calculate params\n    params_num_all = 0\n    for variable in tf.trainable_variables():\n        params_num = 1\n\n        # 维度乘积\n        for dim in variable.shape:\n            params_num *= dim\n\n        params_num_all += params_num\n        logger.info(\"\\t {} {} {}\".format(variable.name, variable.shape, params_num))\n\n    logger.info(\"all params num: \" + str(params_num_all))\n\n    logger.info(\"start training...\")\n\n    # tf_config = tf.ConfigProto(allow_soft_placement=True)\n    # tf_config.gpu_options.allow_growth = True\n    # with tf.Session(config=tf_config) as sess:\n\n    logger.info(\"train_writer create\")\n\n\n\n    with tf.Session() as sess:\n\n        sess = tf_debug.LocalCLIDebugWrapperSession(sess)\n\n        sess.run(tf.global_variables_initializer())\n\n        losses = []\n        best_f1 = 0\n\n        total_step = 0\n\n        for epoche in range(Configs.epoches):\n\n            batch_iter = get_batch(Configs.write_seq_path,\n                                   Configs.write_label_path,\n                                   16,\n                                   file_length,\n                                   Configs)\n\n            batch_step = 1\n\n            for (inputs_seq_batch, inputs_seq_len_batch, outputs_seq_batch) in batch_iter:\n\n                if batch_step == 1:\n                    logger.info(\"###### shape of a batch #######\")\n                    logger.info(\"input_seq: \" + str(inputs_seq_batch.shape))\n                    logger.info(\"input_seq_len: \" + str(inputs_seq_len_batch.shape))\n                    logger.info(\"output_seq: \" + str(outputs_seq_batch.shape))\n                    logger.info(\"###### preview a sample #######\")\n                    logger.info(\"input_seq:\" + \" \".join([i2w_char[i] for i in inputs_seq_batch[0]]))\n                    logger.info(\"input_seq_len :\" + str(inputs_seq_len_batch[0]))\n                    logger.info(\"output_seq: \" + \" \".join([i2w_bio[i] for i in outputs_seq_batch[0]]))\n                    logger.info(\"###############################\")\n\n                print(\"loss compute start !!!\")\n\n\n                feed_dict = {\n                    model.inputs_seq: inputs_seq_batch,\n                    model.inputs_seq_len: inputs_seq_len_batch,\n                    model.outputs_seq: outputs_seq_batch\n                }\n\n                # 这次run要占用很大内存,能从4个飙升到11G多，然后再回来\n                # 4.8 G 飙升到 15.8G\n                loss, _, global_steps = sess.run([model.loss,\n                                                 model.train_op,\n                                                 model.global_step],\n                                                feed_dict)\n\n                print(\"loss start !!!\")\n\n\n                losses.append(loss)\n\n                batch_step += 1\n                total_step+= 1\n\n","sub_path":"BiLstm-CRF-NER-v1/tensorflow_debug.py","file_name":"tensorflow_debug.py","file_ext":"py","file_size_in_byte":5185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"58882658","text":"from sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.naive_bayes import *\nfrom sklearn.svm import SVC\nfrom sklearn import neighbors\nfrom sklearn.metrics import accuracy_score\nimport imutils\nfrom imutils import face_utils\nfrom imutils.face_utils import rect_to_bb\nfrom imutils.face_utils import FaceAligner\nimport pickle\nimport os\nimport numpy as np\nimport dlib\nimport cv2 as cv\nimport time\nfrom imutils.video import VideoStream\nimport face_recognition\nimport scipy\nimport warnings\n\nwarnings.filterwarnings('ignore')\n\nthreshold = 0.7\ntry:\n    svc = pickle.loads(open('./svc_fit5.pickle', \"rb\").read())\n    print('pickle file loaded')\n    encoder = pickle.loads(open('./encoder5.pickle', \"rb\").read())\n    print('encoder file loaded')\nexcept:\n    print('path is wrong :')\n    exit()\n\n\n\"\"\" TURN ON THE CAMERA AND DETECT THE FACE\"\"\"\ndef collecting_data():\n\n    print(\"Turning ON The Video Feed\")\n    vs = VideoStream(src=0).start()\n\n    #Loading the HOG detector\n    detector = dlib.get_frontal_face_detector()\n    predictor = dlib.shape_predictor('./shape_predictor_68_face_landmarks.dat')\n    fa = FaceAligner(predictor , desiredFaceWidth = 256)\n\n    \"\"\" Video Stream works faster than openCV's cv.VideoCapture()\"\"\"\n    while True:\n\n        #reading the frames\n        frame = vs.read()\n        #Resize the frame\n        #frame = imutils.resize(frame, width=1000)\n        #grayscaling the image\n        gray_frame = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)\n        #detecting faces in it\n        faces = detector(gray_frame, 0)\n\n        if faces is None:\n            continue\n        #boxing the faces\n        for face in faces:\n            (x, y, w, h) = face_utils.rect_to_bb(face)\n            #cropped_face = frame[y:y+h, x:x+w]\n            cv.rectangle(frame, (x,y), (x+w,y+h), (0, 0, 255), 2)\n            cv.rectangle(frame, (x,y+h-25), (x+w, y+h),(0,0,255), cv.FILLED)\n\n            face_aligned = fa.align(frame,gray_frame,face)\n\n            (pred, prob) = predict(face_aligned)\n            #print(name, prob)\n            if pred != 'unknown':\n                pred = encoder.inverse_transform([pred])[0]\n\n            cv.putText(frame, (str(pred)+' '+str(prob)),(x+6,y+h-6), cv.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255), 1)\n\n        #displaying the live Stream\n        cv.imshow(\"Live Feed\",frame)\n        key = cv.waitKey(100) & 0xFF\n        # if the `q` key was pressed, break from the loop\n        if key == ord(\"q\"):\n            break\n        # do a bit of cleanup\n    cv.destroyAllWindows()\n    vs.stop()\n\ndef predict(face_aligned):\n\n    faces_encodings = np.zeros((1,128))\n\n    try:\n        X_face_locations = face_recognition.face_locations(face_aligned)\n        faces_encodings = face_recognition.face_encodings(face_aligned, known_face_locations=X_face_locations)\n        #print(faces_encodings)\n        if len(faces_encodings) == 0:\n            return (\"unknown\",[0])\n\n    except:\n        return (\"unknown\",[0])\n\n    prob = svc.predict_proba(faces_encodings)\n    result = np.where(prob[0] == np.amax(prob[0]))\n    if prob[0][result[0]] <= threshold:\n        return ('unknown', prob[0][result[0]])\n\n    return (result[0], prob[0][result[0]])\n\ncollecting_data()\n","sub_path":"final_face_recog.py","file_name":"final_face_recog.py","file_ext":"py","file_size_in_byte":3181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"239164058","text":"import numpy as np\nimport cv2\nfrom cv2 import VideoCapture\nimport os\n\ndef compute_lines(image):\n    gray = cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)\n\n    # Define a kernel size and apply Gaussian smoothing\n    kernel_size = 5\n    blur_gray = cv2.GaussianBlur(image,(kernel_size, kernel_size),0)\n\n    # Define our parameters for Canny and apply\n    low_threshold = 50\n    high_threshold = 100\n    edges = cv2.Canny(blur_gray, low_threshold, high_threshold)\n\n    # Next we'll create a masked edges image using cv2.fillPoly()\n    mask = np.zeros_like(edges)   \n\n    # This time we are defining a four sided polygon to mask\n    imshape = image.shape\n    marg = imshape[1]/2.5\n    h = imshape[0]/1.6\n    vertices = np.array([\\\n        [(0,imshape[0]),(marg, h), (imshape[1] - marg, h), (imshape[1],imshape[0])]], dtype=np.int32)\n    \n    cv2.fillPoly(mask, vertices, 255)\n    masked_edges = cv2.bitwise_and(edges, mask)\n\n    # Define the Hough transform parameters\n    # Make a blank the same size as our image to draw on\n    rho = 1\n    theta = np.pi/180\n    threshold = 10\n    min_line_length = 10\n    max_line_gap = 1\n\n    # Run Hough on edge detected image\n    lines = cv2.HoughLinesP(masked_edges, rho, theta, threshold, np.array([]),\n                                min_line_length, max_line_gap)\n\n    # Iterate over the output \"lines\" and draw lines on the blank\n    for line in lines:\n        for x1,y1,x2,y2 in line:\n            cv2.line(image,(x1,y1),(x2,y2),(0,0,255,100),5)\n    \n    image = cv2.addWeighted(image, 0.8, cv2.cvtColor(mask,cv2.COLOR_GRAY2RGB), 0.2, 0)\n    return image\n\n\ncap = VideoCapture(sys.argv[1])\n\nwhile(cap.isOpened()):\n    # Capture frame-by-frame\n    ret, frame = cap.read()\n\n    # Our operations on the frame come here\n    #frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n    frame = compute_lines(frame)\n\n    # Display the resulting frame\n    cv2.imshow('frame',frame)\n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\n\n# When everything done, release the capture\ncap.release()\ncv2.destroyAllWindows()\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"634349423","text":"import tensorflow as tf\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# This function will plot images in the form of a grid with 1 row and 5 columns where images are placed in each column.\ndef plotImages(images_arr):\n    fig, axes = plt.subplots(1, 5, figsize=(20,20))\n    axes = axes.flatten()\n    for img, ax in zip(images_arr, axes):\n        ax.imshow(img)\n    plt.tight_layout()\n    plt.show()\n\n# data loading\n\n\nurl = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'\nzip_dir = tf.keras.utils.get_file('cats_and_dogs_filtered.zip', url, extract=True)\n\nbase_dir = os.path.join(zip_dir, os.path.dirname(zip_dir), 'cats_and_dogs_filtered')\n\n#base_dir = 'C:\\\\Users\\\\kara\\\\.keras\\\\datasets\\\\cats_and_dogs_filtered'\n\ntrain_dir = os.path.join(base_dir, 'train')\nval_dir = os.path.join(base_dir, 'validation')\ntrain_cats_dir = os.path.join(train_dir, 'cats')\ntrain_dogs_dir = os.path.join(train_dir, 'dogs')\nvalidation_cats_dir = os.path.join(val_dir, 'cats')\nvalidation_dogs_dir = os.path.join(val_dir, 'dogs')\n\nnum_cats_tr = len(os.listdir(train_cats_dir))\nnum_dogs_tr = len(os.listdir(train_dogs_dir))\n\nnum_cats_val = len(os.listdir(validation_cats_dir))\nnum_dogs_val = len(os.listdir(validation_dogs_dir))\n\ntotal_train = num_cats_tr + num_dogs_tr\ntotal_val = num_cats_val + num_dogs_val\n\nBATCH_SIZE = 100\nIMG_SHAPE = 150\n\n# augmentation\n\ntrain_img_gen = ImageDataGenerator(\n    rescale=1./255,\n    rotation_range=45,\n    width_shift_range=.2,\n    height_shift_range=.2,\n    shear_range=.2,\n    zoom_range=.2,\n    horizontal_flip=True,\n    fill_mode='nearest'\n)\n\ntrain_data_gen = train_img_gen.flow_from_directory(\n    train_dir,\n    (IMG_SHAPE, IMG_SHAPE),\n    class_mode='binary',\n    batch_size=BATCH_SIZE,\n    shuffle=True\n)\n\n\n# validation data generator\n\nval_img_gen = ImageDataGenerator(\n    rescale=1./255\n)\n\nval_data_gen = val_img_gen.flow_from_directory(\n    val_dir,\n    (IMG_SHAPE, IMG_SHAPE),\n    class_mode='binary',\n    batch_size=BATCH_SIZE\n)\n\n# model\n\nmodel = tf.keras.Sequential([\n    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(150, 150, 3)),\n    tf.keras.layers.MaxPooling2D(2, 2),\n\n    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n    tf.keras.layers.MaxPooling2D(2,2),\n\n    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n    tf.keras.layers.MaxPooling2D(2,2),\n\n    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n    tf.keras.layers.MaxPooling2D(2,2),\n\n    tf.keras.layers.Dropout(0.5),\n    tf.keras.layers.Flatten(),\n    tf.keras.layers.Dense(512, activation='relu'),\n    tf.keras.layers.Dense(2)\n])\n\nmodel.compile(\n    optimizer='adam',\n    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n    metrics=['accuracy']\n)\n\nmodel.summary()\n\nepochs = 100\n\nhistory = model.fit_generator(\n    generator=train_data_gen,\n    steps_per_epoch=int(np.ceil(total_train / float(BATCH_SIZE))),\n    epochs=epochs,\n    validation_data=val_data_gen,\n    validation_steps=int(np.ceil(total_val / float(BATCH_SIZE)))\n)\n\nacc = history.history['accuracy']\nval_acc = history.history['val_accuracy']\n\nloss = history.history['loss']\nval_loss = history.history['val_loss']\n\nepochs_range = range(epochs)\n\nplt.figure(figsize=(8, 8))\nplt.subplot(1, 2, 1)\nplt.plot(epochs_range, acc, label='Training Accuracy')\nplt.plot(epochs_range, val_acc, label='Validation Accuracy')\nplt.plot(epochs_range, loss, label='Training Loss')\nplt.plot(epochs_range, val_loss, label='Validation Loss')\nplt.legend()\nplt.title('Stats')\nplt.show()\n","sub_path":"dogs_vs_cats_augmentation.py","file_name":"dogs_vs_cats_augmentation.py","file_ext":"py","file_size_in_byte":3592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"12559393","text":"# -*- coding: utf-8 -*-\n# @Time    : 2019-08-14 12:04\n# @Author  : Kai Zhang\n# @Email   : kai.zhang@lizhiweike.com\n# @File    : ex3-reverse_word.py\n# @Software: PyCharm\n\n\nclass Solution:\n    def reverseWords(self, s: str) -> str:\n        import re\n        partten = re.compile(' {2,}')\n        s = partten.sub(' ', s)\n        string_list = s.strip().split(' ')\n        string_list.reverse()\n        return ' '.join(string_list)\n\n\ns = \"a good   example\"\nresult = Solution().reverseWords(s)\nprint(result)\n","sub_path":"task4/ex3-reverse_word.py","file_name":"ex3-reverse_word.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"273902544","text":"# -*- coding: utf-8 -*-\n'''\n6.\nСоздайте ансамбль в виде p = (p1_fraud + p2_fraud + p3_fraud)/3. Постройте ROC\nкривую.\n'''\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.metrics import roc_curve, auc\nfrom task5 import summ\nfrom task3_4 import getData\n\ndef func(systems):\n    return [float(summ(systems, i)) / float(3) for i in xrange(0, len(systems[0]))]\n\nif __name__ == '__main__':\n    data = pd.read_csv('2016.02.20.vyygrusska.csv', ';') # выгрузим данные из csv\n\n    # функция p = (p1_fraud + p2_fraud + p3_fraud)/3\n    P = func([data['p' + str(i) + '_Fraud'] for i in xrange(1, 4)])\n\n    plt.figure()\n\n    actual, predictions = getData(P, data['CLASS'])\n    false_positive_rate, true_positive_rate, thresholds = roc_curve(actual, predictions)\n    roc_auc = auc(false_positive_rate, true_positive_rate)\n\n    plt.plot(false_positive_rate, true_positive_rate, label='%s ROC, AUC = %0.2f, Gini = %0.2f' % ('p_Fraud', roc_auc, (roc_auc * 2) - 1))\n\n    plt.title('Receiver Operating Characteristic')\n    plt.legend(loc='lower right', fontsize='small')\n    plt.plot([0,1],[0,1],'r--')\n    plt.xlim([0.0,1.0])\n    plt.ylim([0.0,1.0])\n    plt.ylabel('True Positive Rate')\n    plt.xlabel('False Positive Rate')\n    plt.show()\n","sub_path":"hometask1/task6.py","file_name":"task6.py","file_ext":"py","file_size_in_byte":1315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"151187086","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.core.mail import send_mail, BadHeaderError\nfrom django.contrib.auth.mixins import PermissionRequiredMixin, LoginRequiredMixin\nfrom django.views import View\n\nfrom .models import QrTicket, QrEvent\nfrom .forms import EmailForm\n\n# For possible future implementation of ticket inside the mail\nfrom .utils import send_template_email\n\nimport random, string\n\n\ndef test(request):\n    string_list = ['aaa', 'bbb', 'ccc']\n    context = {'hehe': 3, 'string_list': string_list}\n    return render(request, 'qrTickets/test.html', context)\n\n\ndef render_ticket(request, qr_event_id, qr_ticket_id):\n    ticket_url = 'nabla.no/qrTickets/register/' + str(qr_event_id) + '/' + str(qr_ticket_id)\n    url_list = [ticket_url]\n    context = {'ticket_url': ticket_url, 'url_list': url_list}\n    return render(request, 'qrTickets/render.html', context)\n\n\nclass GenerateTicketsView(PermissionRequiredMixin, View):\n    permission_required = \"qrTickets.generate_tickets\"\n    def get(self, request):\n        email_form = EmailForm()\n        context = {'email_form': email_form}\n        return render(request, 'qrTickets/generate.html', context)\n\n\n    def post(self, request):\n        email_form = EmailForm(request.POST)\n        if email_form.is_valid():\n            emails = email_form.get_emails()\n            email_list = emails.splitlines()\n            qr_event = email_form.get_qr_event()\n            for email in email_list:\n                # Generates a random string of uppercase letters and numbers, stolen from stack overflow :))\n                randstr = ''.join(random.SystemRandom().choice(string.ascii_uppercase + string.digits) for _ in range(10))\n                ticket = QrTicket.objects.create(event=qr_event, email = email)\n                ticket.ticket_id = str(ticket.id) + randstr\n                ticket.save()\n\n                subject = 'din nablabillett' #noe mer spes etterhvert\n                message = 'Din billett til ' + str(ticket.event) + ' finner du her:\\n'\n                link = 'nabla.no/qrTickets/render/' + str(qr_event.id) + '/' + ticket.ticket_id\n                message += link\n                \n                try:\n                    send_mail(subject, message, \"noreply@nablatickets.no\", [email], fail_silently=False)\n                except BadHeaderError:\n                    return HttpResponse('Invalid header found')\n            return HttpResponse(\"Billetter generert og send på epost!\")\n\n\nclass RegisterTicketsView(PermissionRequiredMixin, View):\n    permission_required = \"qrTickets.register_tickets\"\n    def get(self, request, qr_event_id, qr_ticket_id):\n        context = {'event_id': qr_event_id, 'ticket_id': qr_ticket_id,}\n        return render(request, 'qrTickets/register.html', context)\n \n    def post(self, request, qr_event_id, qr_ticket_id):\n        qr_event = QrEvent.objects.get(pk=qr_event_id)\n        try:\n            qr_ticket = qr_event.ticket_set.get(ticket_id=qr_ticket_id)\n            if qr_ticket.registered:\n                return HttpResponse('Allerede registrert. Billetten er oppbrukt.')\n            qr_ticket.register()\n            qr_ticket.save()\n        except QrTicket.DoesNotExist:\n            return HttpResponse('Ikke en gyldig billett!')\n        return HttpResponse('Billett registrert!')\n\n","sub_path":"nablapps/qrTickets/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"70792758","text":"##对txt2list算法根据check_data进行检测\r\n# -*- coding: utf-8 -*-  \r\n\r\nimport txt2list\r\n\r\ninputfile = \"./check_data/input/input\"\r\noutputfile = \"./check_data/output/output\"\r\n\r\ndef get_expect(outfile):\r\n    res = []\r\n    with open(outfile, \"r\") as f:\r\n        lines = f.readlines()\r\n    for line in lines:\r\n        line_ = line.strip()\r\n        if (line_ != \"\"):\r\n            res.append(line.strip())\r\n    return res\r\n\r\ndef check():\r\n    for i in range(1,6):\r\n        print(\"begin check input_data \" + str(i) + \" …\")\r\n        infile = inputfile + str(i) + \".txt\"\r\n        outfile = outputfile + str(i) + \".txt\"\r\n        urdata = txt2list.get_list(infile)\r\n        exdata = get_expect(outfile)\r\n        if (len(urdata) != len(exdata)):\r\n            print(\"error in convert input_data \" + str(i) + \".txt\")\r\n            print(\"your output is \", end = \"\")\r\n            print(urdata)\r\n            print(\"expect data is (此格式并非要求的格式，仅提供数字作为参考) \", end = \"\")\r\n            print(exdata)\r\n            return 0\r\n        else:\r\n            for j in range(len(urdata)):\r\n                tempdata = exdata[j].split()\r\n                if (len(urdata[j]) != len(tempdata)):\r\n                    print(\"error in convert input_data \" + str(i) + \".txt\")\r\n                    print(\"your output is \", end = \"\")\r\n                    print(urdata)\r\n                    print(\"expect data is (此格式并非要求的格式，仅提供数字作为参考) \", end = \"\")\r\n                    print(exdata)\r\n                    return 0\r\n                else:\r\n                    for k in range(len(tempdata)):\r\n                        [data1,data2] = [int(i) for i in tempdata[k].split(\"-\")]\r\n                        if (data1 != urdata[j][k][0] or data2 != urdata[j][k][1]):\r\n                            print(\"error in convert input_data \" + str(i) + \".txt\")\r\n                            print(\"your output is \", end = \"\")\r\n                            print(urdata)\r\n                            print(\"expect data is (此格式并非要求的格式，仅提供数字作为参考) \", end = \"\")\r\n                            print(exdata)\r\n                            return 0\r\n        print(\"check input_data \" + str(i) + \" success!!!\\n\")\r\n    return 1\r\n\r\n                \r\n                \r\n                \r\n","sub_path":"check.py","file_name":"check.py","file_ext":"py","file_size_in_byte":2331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"565327316","text":"from migrations import (\n  sample_timestamps,\n  update_mlwh_with_legacy_samples,\n)\nfrom crawler.helpers import get_config\nimport sys\nimport logging\n\nconfig, settings_module = get_config('')\n\nlogger = logging.getLogger(__name__)\nconfig.LOGGING[\"loggers\"][\"crawler\"][\"level\"] = \"DEBUG\" # type: ignore\nconfig.LOGGING[\"loggers\"][\"crawler\"][\"handlers\"] = [\"colored_stream\"] # type: ignore\nlogging.config.dictConfig(config.LOGGING)  # type: ignore\n\n##\n# Examples of how to run from command line:\n# python run_migration.py sample_timestamps\n# python run_migration.py update_mlwh_with_legacy_samples 200115_1200 200216_0900\n##\n\nprint(\"Migration names:\")\nprint(\"* sample_timestamps\")\nprint(\"* update_mlwh_with_legacy_samples\")\n\ndef migration_sample_timestamps():\n    print(\"Running sample_timestamps migration\")\n    sample_timestamps.run()\n\ndef migration_update_mlwh_with_legacy_samples():\n    if not len(sys.argv) == 4:\n        print(\"Please add both start and end datetime range arguments for this migration (format YYMMDD_HHmm e.g. 200115_1200, inclusive), aborting\")\n        return\n\n    s_start_datetime = sys.argv[2]\n    s_end_datetime = sys.argv[3]\n    print(\"Running update_mlwh_with_legacy_samples migration\")\n    update_mlwh_with_legacy_samples.run(config, s_start_datetime=s_start_datetime, s_end_datetime=s_end_datetime)\n\ndef migration_by_name(migration_name):\n    switcher = {\n        'sample_timestamps': migration_sample_timestamps,\n        'update_mlwh_with_legacy_samples': migration_update_mlwh_with_legacy_samples,\n    }\n    # Get the function from switcher dictionary\n    func = switcher.get(migration_name, lambda: print(\"Invalid migration name, aborting\"))\n    # Execute the function\n    func()\n\nif len(sys.argv) > 1:\n    migration_name = sys.argv[1]\n    print(f\"Migration name selected = {migration_name}\")\n    migration_by_name(migration_name)\nelse:\n    print(f\"You must include a migration name as an argument after the command, aborting\")\n","sub_path":"run_migration.py","file_name":"run_migration.py","file_ext":"py","file_size_in_byte":1955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"272424377","text":"#!C:\\Program Files\\Python35\r\n\r\nroute = 'R4, R4, L1, R3, L5, R2, R5, R1, L4, R3, L5, R2, L3, L4, L3, R1, R5, R1, L3, L1, R3, L1, R2, R2, L2, R5, L3,' \\\r\n        ' L4, R4, R4, R2, L4, L1, R5, L1, L4, R4, L1, R1, L2, R5, L2, L3, R2, R1, L194, R2, L4, R49, R1, R3, L5, L4, ' \\\r\n        'L1, R4, R2, R1, L5, R3, L5, L4, R4, R4, L2, L3, R78, L5, R4, R191, R4, R3, R1, L2, R1, R3, L1, R3, R4, R2, L2,' \\\r\n        ' R1, R4, L5, R2, L2, L4, L2, R1, R2, L3, R5, R2, L3, L3, R3, L1, L1, R5, L4, L4, L2, R5, R1, R4, L3, L5, L4,' \\\r\n        ' R5, L4, R5, R4, L3, L2, L5, R4, R3, L3, R1, L5, R5, R1, L3, R2, L5, R5, L3, R1, R4, L5, R4, R2, R3, L4,' \\\r\n        ' L5, R3, R4, L5, L5, R4, L4, L4, R1, R5, R3, L1, L4, L3, L4, R1, L5, L1, R2, R2, R4, R4, L5, R4, R1, L1, L1,' \\\r\n        ' L3, L5, L2, R4, L3, L5, L4, L1, R3'\r\n# route = 'R5, L5, R5, R3'\r\n\r\nturns = route.split(', ')\r\nangle = 0 # East is 0\r\ncardinals = [0, 0, 0, 0]  # N,S,E,W\r\n\r\nfor s in turns:\r\n    if s[0] == 'R':\r\n        angle = (angle - 90)%360\r\n    elif s[0] == 'L':\r\n        angle = (angle + 90)%360\r\n    else:\r\n        raise Exception('Character not \"R\" or \"L\"')\r\n\r\n    blocks = int(s[1])\r\n    if angle == 0:\r\n        cardinals[2] += blocks  # East\r\n    elif angle == 90:\r\n        cardinals[0] += blocks  # North\r\n    elif angle == 180:\r\n        cardinals[3] += blocks  # West\r\n    elif angle == 270:\r\n        cardinals[1] += blocks  # South\r\n    else:\r\n        raise Exception('Not cardinal direction')\r\n\r\nprint(cardinals)\r\n\r\ndistance = abs(cardinals[1]-cardinals[0]) + abs(cardinals[3]-cardinals[2])\r\nprint(distance)\r\n","sub_path":"2016 Advent of Code/1/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":1575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"379391543","text":"from keras.layers import Activation, LSTM, Recurrent\nimport keras\nfrom keras.engine import InputSpec, Layer\nfrom keras import backend as K\nfrom keras import initializations, activations, regularizers\nimport numpy as np\nfrom keras.preprocessing.text import one_hot\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.regularizers import Regularizer\n\ndef default_parameters():\n    parameters = {}\n    parameters['top_word_num'] = 5000\n    parameters['word_dim'] = 60\n    parameters['hidden_dim1'] = 90\n    # Dimension of the chat representation\n    parameters['hidden_dim2'] = 150\n    # Dense hidden neuron number:\n    parameters['dense_hidden'] = 100\n    # Droupout\n    parameters['dropout'] = 0.0\n    # Size of the batch\n    parameters['batch_size'] = 256\n    #max epoch number\n    parameters['max_epoch_number'] = 150\n    #Validation data size\n    parameters['validation_size'] = 0.1\n\n    # Use Chat-LSTM\n    parameters['use_chat_LSTM'] = True\n    # Use direction regularization\n    parameters['direction_reg_on'] = False\n    #Use\n    parameters['direction_reg'] = 0.1\n\n    # Bucket size:\n    parameters['message_length'] = 40\n    parameters['chat_length'] = 40\n    #objective parameters\n    parameters['recall_parameter'] = 45\n    parameters['precision_parameter'] = 0.1\n\n    return parameters\n\n\nclass ChatLSTM(LSTM):\n    def __init__(self, output_dim,\n                 init='glorot_uniform', inner_init='orthogonal',\n                 forget_bias_init='one', activation='tanh',\n                 inner_activation='hard_sigmoid',\n                 W_regularizer=None, U_regularizer=None, b_regularizer=None,\n                 dropout_W=0., dropout_U=0., **kwargs):\n        self.output_dim = output_dim\n        self.init = initializations.get(init)\n        self.inner_init = initializations.get(inner_init)\n        self.forget_bias_init = initializations.get(forget_bias_init)\n        self.activation = activations.get(activation)\n        self.inner_activation = activations.get(inner_activation)\n        self.W_regularizer = regularizers.get(W_regularizer)\n        self.U_regularizer = regularizers.get(U_regularizer)\n        self.b_regularizer = regularizers.get(b_regularizer)\n        self.dropout_W, self.dropout_U = dropout_W, dropout_U\n\n        if self.dropout_W or self.dropout_U:\n            self.uses_learning_phase = True\n        super(LSTM, self).__init__(**kwargs)\n\n    def build(self, input_shape):\n        self.input_spec = [InputSpec(shape=input_shape)]\n        self.input_dim = int(input_shape[2] / 2)\n\n        if self.stateful:\n            self.reset_states()\n        else:\n            # initial states: 4 all-zero tensors of shape (output_dim)\n\n            self.states = [None, None, None, None]\n\n        self.W_i = self.init((self.input_dim, self.output_dim),\n                             name='{}_W_i'.format(self.name))\n        self.U_i = self.inner_init((self.output_dim, self.output_dim),\n                                   name='{}_U_i'.format(self.name))\n        self.b_i = K.zeros((self.output_dim,), name='{}_b_i'.format(self.name))\n\n        self.W_f = self.init((self.input_dim, self.output_dim),\n                             name='{}_W_f'.format(self.name))\n        self.U_f = self.inner_init((self.output_dim, self.output_dim),\n                                   name='{}_U_f'.format(self.name))\n        self.b_f = self.forget_bias_init((self.output_dim,),\n                                         name='{}_b_f'.format(self.name))\n\n        self.W_c = self.init((self.input_dim, self.output_dim),\n                             name='{}_W_c'.format(self.name))\n        self.U_c = self.inner_init((self.output_dim, self.output_dim),\n                                   name='{}_U_c'.format(self.name))\n        self.b_c = K.zeros((self.output_dim,), name='{}_b_c'.format(self.name))\n\n        self.W_o = self.init((self.input_dim, self.output_dim),\n                             name='{}_W_o'.format(self.name))\n        self.U_o = self.inner_init((self.output_dim, self.output_dim),\n                                   name='{}_U_o'.format(self.name))\n        self.b_o = K.zeros((self.output_dim,), name='{}_b_o'.format(self.name))\n\n        self.W_s = self.init((self.output_dim, self.output_dim),\n                             name='{}_W_s'.format(self.name))\n        self.U_s = self.inner_init((self.output_dim, self.output_dim),\n                                   name='{}_U_s'.format(self.name))\n        self.b_s = K.zeros((self.output_dim,), name='{}_b_s'.format(self.name))\n\n        self.W_e = self.init((self.output_dim, self.output_dim),\n                             name='{}_W_e'.format(self.name))\n        self.U_e = self.inner_init((self.output_dim, self.output_dim),\n                                   name='{}_U_e'.format(self.name))\n        self.b_e = K.zeros((self.output_dim,), name='{}_b_e'.format(self.name))\n\n        self.trainable_weights = [self.W_i, self.U_i, self.b_i,\n                                  self.W_c, self.U_c, self.b_c,\n                                  self.W_f, self.U_f, self.b_f,\n                                  self.W_o, self.U_o, self.b_o,\n                                  self.W_s, self.U_s, self.b_s,\n                                  self.W_e, self.U_e, self.b_e]\n\n        self.regularizers = []\n        if self.W_regularizer:\n            self.W_regularizer.set_param(self.W)\n            self.regularizers.append(self.W_regularizer)\n        if self.U_regularizer:\n            self.U_regularizer.set_param(self.U)\n            self.regularizers.append(self.U_regularizer)\n        if self.b_regularizer:\n            self.b_regularizer.set_param(self.b)\n            self.regularizers.append(self.b_regularizer)\n\n        if self.initial_weights is not None:\n            self.set_weights(self.initial_weights)\n            del self.initial_weights\n        self.built = True\n\n    def reset_states(self):\n        assert self.stateful, 'Layer must be stateful.'\n        input_shape = self.input_spec[0].shape\n        if not input_shape[0]:\n            raise Exception('If a RNN is stateful, a complete ' +\n                            'input_shape must be provided (including batch size).')\n        if hasattr(self, 'states'):\n            K.set_value(self.states[0],\n                        np.zeros((input_shape[0], self.output_dim)))\n            K.set_value(self.states[1],\n                        np.zeros((input_shape[0], self.output_dim)))\n            K.set_value(self.states[2],\n                        np.zeros((input_shape[0], self.output_dim)))\n            K.set_value(self.states[3],\n                        np.zeros((input_shape[0], self.output_dim)))\n        else:\n            self.states = [K.zeros((input_shape[0], self.output_dim)),\n                           K.zeros((input_shape[0], self.output_dim)),\n                           K.zeros((input_shape[0], self.output_dim)),\n                           K.zeros((input_shape[0], self.output_dim))]\n\n    def preprocess_input(self, x):\n        return x\n\n    def step(self, x, states):\n        x1 = x[:, :self.input_dim]\n        x2 = x[:, self.input_dim:]\n        h1_tm1 = states[0]\n        c1_tm1 = states[1]\n        h2_tm1 = states[2]\n        c2_tm1 = states[3]\n        B_U = states[4]\n        B_W = states[5]\n        x1_i = K.dot(x1 * B_W[0], self.W_i) + self.b_i\n        x1_f = K.dot(x1 * B_W[1], self.W_f) + self.b_f\n        x1_c = K.dot(x1 * B_W[2], self.W_c) + self.b_c\n        x1_o = K.dot(x1 * B_W[3], self.W_o) + self.b_o\n        x1_s = K.dot(h2_tm1 * B_U[6], self.W_s) + self.b_s\n        x1_e = K.dot(h2_tm1 * B_U[7], self.W_e) + self.b_e\n\n        x2_i = K.dot(x2 * B_W[0], self.W_i) + self.b_i\n        x2_f = K.dot(x2 * B_W[1], self.W_f) + self.b_f\n        x2_c = K.dot(x2 * B_W[2], self.W_c) + self.b_c\n        x2_o = K.dot(x2 * B_W[3], self.W_o) + self.b_o\n        x2_s = K.dot(h1_tm1 * B_U[6], self.W_s) + self.b_s\n        x2_e = K.dot(h1_tm1 * B_U[7], self.W_e) + self.b_e\n\n        i1 = self.inner_activation(x1_i + K.dot(h1_tm1 * B_U[0], self.U_i))\n        i2 = self.inner_activation(x2_i + K.dot(h2_tm1 * B_U[0], self.U_i))\n        f1 = self.inner_activation(x1_f + K.dot(h1_tm1 * B_U[1], self.U_f))\n        f2 = self.inner_activation(x2_f + K.dot(h2_tm1 * B_U[1], self.U_f))\n\n        s1 = self.inner_activation(x1_s + K.dot(h1_tm1 * B_U[4], self.U_s))\n        s2 = self.inner_activation(x2_s + K.dot(h2_tm1 * B_U[4], self.U_s))\n        e1 = self.activation(x1_e + K.dot(h1_tm1 * B_U[5], self.U_e))\n        e2 = self.activation(x2_e + K.dot(h2_tm1 * B_U[5], self.U_e))\n\n        c1 = f1 * c1_tm1 + i1 * self.activation(x1_c + K.dot(h1_tm1 * B_U[2], self.U_c)) + s1 * e1\n        c2 = f2 * c2_tm1 + i2 * self.activation(x2_c + K.dot(h2_tm1 * B_U[2], self.U_c)) + s2 * e2\n        o1 = self.inner_activation(x1_o + K.dot(h1_tm1 * B_U[3], self.U_o))\n        o2 = self.inner_activation(x2_o + K.dot(h2_tm1 * B_U[3], self.U_o))\n\n        h1 = o1 * self.activation(c1)\n        h2 = o2 * self.activation(c2)\n        return K.concatenate([h1, h2]), [h1, h2, c1, c2]\n\n    def get_constants(self, x):\n        constants = []\n        if 0 < self.dropout_U < 1:\n            ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))\n            ones = K.tile(ones, (1, self.output_dim))\n            B_U = [K.in_train_phase(K.dropout(ones, self.dropout_U), ones) for _ in range(8)]\n            constants.append(B_U)\n        else:\n            constants.append([K.cast_to_floatx(1.) for _ in range(8)])\n\n        if 0 < self.dropout_W < 1:\n            input_shape = self.input_spec[0].shape\n            input_dim = input_shape[-1]\n            ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))\n            ones = K.tile(ones, (1, int(input_dim)))\n            B_W = [K.in_train_phase(K.dropout(ones, self.dropout_W), ones) for _ in range(4)]\n            constants.append(B_W)\n        else:\n            constants.append([K.cast_to_floatx(1.) for _ in range(4)])\n        return constants\n\n    def get_output_shape_for(self, input_shape):\n        return (input_shape[0], self.output_dim * 2)\n\n\nclass DirectionRegularizer(Regularizer):\n    def __init__(self, batch_size=1, alfa=0.):\n        self.alfa = K.cast_to_floatx(alfa)\n        self.batch_size = batch_size\n\n    def __call__(self, x):\n        dim = int(K.int_shape(x)[-1] / 2)\n        v1 = x[:, :dim]\n        v1 = v1 * (1 / K.sqrt(K.sum(K.square(v1))))\n        v2 = x[:, dim:]\n        v2 = v2 * (1 / K.sqrt(K.sum(K.square(v2))))\n        regularization = self.alfa * (self.batch_size - K.sum(K.sum(K.batch_dot(v1, v2, axes=[1, 1]), axis=1)))\n        return regularization / self.batch_size\n\n    def get_config(self):\n        return {'name': self.__class__.__name__,\n                'alfa': float(self.alfa)}\n\n\nclass ActivityDirectionRegularizer(Layer):\n    def __init__(self, batch_size=1, alfa=0., **kwargs):\n        self.supports_masking = True\n        self.alfa = alfa\n        self.batch_size = batch_size\n        super(ActivityDirectionRegularizer, self).__init__(**kwargs)\n        self.activity_regularizer = DirectionRegularizer(self.batch_size, self.alfa)\n        self.regularizers = [self.activity_regularizer]\n\n    def get_config(self):\n        config = {'alfa': self.alfa}\n        base_config = super(ActivityDirectionRegularizer, self).get_config()\n        return dict(list(base_config.items()) + list(config.items()))","sub_path":"ChatLSTM.py","file_name":"ChatLSTM.py","file_ext":"py","file_size_in_byte":11296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"232715673","text":"#! python3\n# searchAllText.py - open all files of an directory and search a line by regex\n# Boston\n\nimport re\nimport os\n\nallFiles = os.listdir('C:\\\\Users\\\\daize\\\\Desktop\\\\pythonTEST\\\\autopython\\\\act8\\\\quizEX')\nlineRegex = re.compile(r'[AB]\\w{1,5}[on]')\nfor file in allFiles:\n    catFile = open('C:\\\\Users\\\\daize\\\\Desktop\\\\pythonTEST\\\\autopython\\\\act8\\\\quizEX\\\\%s' % file)\n    fileContent = catFile.read()\n    print(lineRegex.findall(fileContent))\n    catFile.close()","sub_path":"act8/searchAllText.py","file_name":"searchAllText.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"286869826","text":"from rest_framework import status\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework.permissions import AllowAny\nfrom users.models import User, Profile\nfrom users.serializers import (\n    UserIdSerializer,\n    SimpleUserSerializer,\n    ProfileSerializer,\n    SetUpSerializer,\n)\nfrom django.core.exceptions import ObjectDoesNotExist, MultipleObjectsReturned\nfrom django.shortcuts import render\nimport logging\n\n\nclass ListUsers(APIView):\n    \"\"\"\n    View to get list of all users\n    \"\"\"\n\n    def get(self, request):\n        queryset = User.objects.all()\n        serializer = SimpleUserSerializer(queryset, many=True)\n        return Response(serializer.data)\n\n\nclass GetProfile(APIView):\n    \"\"\"\n    Get a profile for given user\n    \"\"\"\n\n    # TODO don't show if banned <- or should this be blocked elsewhere\n\n    def get(self, request):\n        user_serializer = UserIdSerializer(data=request.query_params)\n        user_serializer.is_valid(raise_exception=True)\n        requested_user = user_serializer.validated_data[\"user\"]\n        approved, pending = Profile.objects.get_profiles(requested_user)\n\n        profile = pending or approved if requested_user == request.user else approved\n        if profile:\n            serializer = ProfileSerializer(profile)\n            return Response(serializer.data)\n        else:\n            return Response(\n                \"User does not have an approved profile\",\n                status=status.HTTP_404_NOT_FOUND,\n            )\n\n\nclass EditProfile(APIView):\n    \"\"\"\n    Edit profile for currently logged in user\n    \"\"\"\n\n    def get(self, request):\n        return Response({\"d\": Profile.objects.get_upload_urls(request.user.id)})\n\n    def post(self, request):\n        user_id = request.user.id\n        serializer = ProfileSerializer(\n            data=request.data, context={\"user\": request.user}\n        )\n        serializer.is_valid(raise_exception=True)\n        profile = serializer.save()\n        return Response(status=status.HTTP_204_NO_CONTENT)\n\n\nclass SetupUser(APIView):\n    \"\"\"\n    Setup the profile for a user\n    \"\"\"\n\n    def get(self, request):\n        return Response(request.user.id_upload_url())\n\n    def post(self, request):\n        user_id = request.user.id\n        serializer = SetUpSerializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n        if request.user.status == User.INACTIVE:\n            request.user.preferred_name = serializer[\"name\"].value\n            request.user.save()\n            return Response(status=status.HTTP_204_NO_CONTENT)\n        else:\n            return Response(\"User is already setup\", status=status.HTTP_409_CONFLICT)\n\n\nclass CheckUserExists(APIView):\n    \"\"\"\n    Endpoint to confirm email is in our list of seniors.\n    \"\"\"\n\n    permissions = [AllowAny]\n\n    def get(self, request):\n        email = request.query_params.get(\"email\")\n        if not email:\n            return render(request, \"user_check.html\", {})\n        try:\n            user = User.objects.get(email=email)\n            return render(\n                request, \"successful_check.html\", {\"email\": email, \"user\": user},\n            )\n        except:\n            return render(request, \"failed_check.html\", {\"email\": email})\n","sub_path":"jumboSmash/users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"625698350","text":"from django.shortcuts import render\r\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\r\n\r\nfrom basic.models import HistoryPart, FavouritePart\r\n\r\n\r\nclass NameValue:   \r\n    def __init__(self, name, value):\r\n        self.name = name\r\n        self.value = value\r\n\r\ndef favourite(request):\t\r\n    favs_all = FavouritePart.objects.filter(user=request.user).order_by('-time')\r\n\r\n    ## paginate\r\n    page = request.GET.get('page')\r\n    paginator = Paginator(favs_all, 10) ## settings.PRODUCTS_PER_PAGE\r\n    try:\r\n        favs = paginator.page(page)\r\n    except PageNotAnInteger:\r\n        page = 1\r\n        favs = paginator.page(1)\r\n    except EmptyPage:\r\n        page = paginator.num_pages ## ??\r\n        favs = paginator.page(paginator.num_pages)\r\n\r\n    return render(request, 'favourite/favourite.html', { \r\n        'favs' : favs, \r\n        'page' : page,\r\n        'total' : len(favs_all),\r\n        'now' : len(favs)+((int(page)-1)*10),\r\n        'favs_empty_or_not_logged' : len(favs) == 0 or not request.user.is_authenticated\r\n    })","sub_path":"src/favourite/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1052,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"258351338","text":"from flask import jsonify, make_response\n\nfrom app import users\nfrom app import matches\n\nfrom pprint import pprint\nfrom bson.objectid import ObjectId\n\nclass Shidduch:\n\n\t# class-level variable\n\tlike_threshold = 3\n\n\t\"\"\"\n\tHere's what a shidduch object looks like.\n\t{\n\t\taccepted : str ['boy_accepted', 'both_accepted', 'denied', 'not_yet']\n\t\tsuggester : str [fbid]\n\t\tboy : str [fbid]\n\t\tgirl : str [fbid]\n\t\treason : str\n\t}\n\t\"\"\"\n\n\tdef add_match(match):\n\t\tmatch = match['nameValuePairs']\n\t\tcheckmatch = matches.find_one({'boy': match['boyId'], 'girl': match['girlId']})\n\t\tif checkmatch:\n\t\t\tsug = users.find_one({'fbid': match['suggesterId']})\n\t\t\tsugname = sug['firstName'] + ' ' + sug['lastName']\n\t\t\tmatches.update_one(\n\t\t\t{'_id': checkmatch['_id']},\n\t\t\t{'$push': {'suggesters': match['suggester']}, '$push': {'reasons': {sugname : match['message']} } })\n\t\telse:\n\t\t\tmatches.insert({\n\t\t\t'accepted': 'not_yet',\n\t\t\t'boy': match['boyId'],\n\t\t\t'girl': match['girlId'],\n\t\t\t'reasons': [{'suggester': match['suggesterId'], 'reason': match['message']}]\n\t\t\t})\n\t\treturn make_response(jsonify({\"did_the_thing\": \"yay\"}), 200)\n\n\n\tdef set_match_status(match_id, new_status):\n\t\tmatch = matches.find_one({'_id': ObjectId(match_id)})\n\t\tif not match:\n\t\t\treturn make_response(jsonify({\"error\": \"match not found\"}), 404)\n\n\t\tmatches.update_one({'_id': match['_id']}, {'$set': {'accepted': new_status}})\n\t\treturn make_response(jsonify({\"action_completed\": \"yeah\"}), 200)\n\n\n\tdef view_match(match_id):\n\t\tmatch = matches.find_one({'_id': match_id})\n\t\tif not match:\n\t\t\treturn make_response(jsonify({\"error\": \"match not found\"}), 404)\n\t\t# if we're trying to view a match that we know exists and it doesn't exist,\n\t\t# this is representative of a larger problem.\n\t\treturn jsonify(**match)\n\n\n\tdef view_friend_matches(fbid_user, fbid_friend):\n\t\tfriend_matches = matches.find({'matchedUsers': {'$in': fbid_friend}})\n\t\treturn jsonify(friend_matches)\n\n\n\tdef view_matches(fbid):\n\t\tcheck = users.find_one({'fbid': fbid})\n\t\tgender = \"boy\" if check['gender'] == \"male\" else \"girl\"\n\t\tmy_matches = matches.find({gender: fbid})\n\n\t\tresponse = []\n\t\tfor m in my_matches:\n\t\t\tcarl = None\n\t\t\tif m['boy'] == check['fbid']:\n\t\t\t\tcarl = users.find_one({'fbid': m['girl']})\n\t\t\telif m['girl'] == check['fbid']:\n\t\t\t\tcarl = users.find_one({'fbid': m['boy']})\n\t\t\tresponse.append({\n\t\t\t\t'id': str(m['_id']),\n\t\t\t\t'suggested_person': {\n\t\t\t\t\t'smallPhotoLink': carl['smallPhotoLink'],\n\t\t\t\t\t'fbid': carl['fbid'],\n\t\t\t\t\t'firstname': carl['firstName'],\n\t\t\t\t\t'lastname': carl['lastName'],\n\t\t\t\t\t'birthday': carl['birthday']\n\t\t\t\t},\n\t\t\t\t'status': m['accepted'],\n\t\t\t\t'reasons': m['reasons']\n\t\t\t})\n\t\treturn jsonify({\"matches\": response})\n","sub_path":"shidduch.py","file_name":"shidduch.py","file_ext":"py","file_size_in_byte":2657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"608311614","text":"import json\n\nfrom api.models import Polygon\n\n\nclass CachedPolygon:\n    \"\"\"\n    # Polygon object for caching\n    \"\"\"\n    def __init__(self):\n        self.area_id = None\n        self.polygon_map = None\n\n    def set_polygon(self, area_id, polygon_map):\n        \"\"\"\n        setter function\n        :param area_id: id if the area from model\n        :param polygon_map: the polygonal data of the ares\n        :return:\n        \"\"\"\n        self.area_id = area_id\n        self.polygon_map = polygon_map\n\n\npolygon_list = []\n\n\nclass Cache:\n\n    @staticmethod\n    def add_to_cache(polygon=None):\n        \"\"\"\n        adds a polygon object in the cache list\n        :param polygon:\n        :return:\n        \"\"\"\n        polygon_list.append(polygon)\n\n    @staticmethod\n    def rebuild_cache():\n        \"\"\"\n        rebuilds the cache from database\n        :return:\n        \"\"\"\n        print('***REBUILDING CACHE ***')\n        polygon_list.clear()\n        all_polygons = Polygon.objects.all()\n        for one_polygon in all_polygons:\n            new_polygon = CachedPolygon()\n            new_polygon.set_polygon(area_id=one_polygon.area.pk, polygon_map=json.loads(one_polygon.polygon_json))\n            polygon_list.append(new_polygon)\n\n    @staticmethod\n    def cache_size():\n        \"\"\"\n\n        :return: the size of the cache\n        \"\"\"\n        return len(polygon_list)\n","sub_path":"utililty/polygon_factory.py","file_name":"polygon_factory.py","file_ext":"py","file_size_in_byte":1356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"474896896","text":"# 1-1_Is_Unique.py\n\n# 1.1 Is Unique\n# Implement an algorithm to determine if a string has all unique characters.\n# What if you cannot use additional data structures?\n\ndef is_unique(s: str) -> bool:\n    \"\"\"Determine if a string has all unique characters.\"\"\"\n\n    # Approach, Time & Space Complexity & Possible Improvements:\n    # Approach:\n    # 1. Add each character count to a hashmap (char is key, count is value)\n    # if the character already exists and you are incrementing to 2, return\n    # False. Otherwise return true if this never occurs.\n    # 2. Also we can add the character to a set and check for existence\n    # before returning False (rather than a hashmap of counts)\n    # Approach without additional data structures:\n    # 3. Sort string, iterate through and if the next character is the same\n    # as the previous then return False\n    # 4. Create a variable for each character count (NO this is still\n    # O(N) space)\n    # Time & Space Complexity:\n    # Initial approach is O(N) time & space to build the hashmap\n    # Without addition adata structures is O(NlogN) time complexity for sorting\n    # O(NlogN) and then iterating through O(N) but O(1) space... actually\n    # in python strings are immutable so O(N) space since the sorted string\n    # takes O(N) space\n    # Possible Improvements:\n    # Check for existence in set (approach 2)\n\n    # Approach 1\n    # char_counts = {}\n\n    # for character in s:\n    #     char_counts[character] = char_counts.get(character, 0) + 1\n    #     if char_counts[character] > 1:\n    #         return False\n\n    # return True\n\n    # Approach 2\n    char_set = set()\n\n    for character in s:\n        if character in char_set:\n            return False\n        else:\n            char_set.add(character)\n\n    return True\n\n    # Approach 3\n    # sorted_string = sorted(s)\n    # previous_character = ''\n\n    # for character in sorted_string:\n    #     if character == previous_character:\n    #         return False\n    #     else:\n    #         previous_character = character\n\n    # return True\n\nimport unittest\n\nclass TestIsUnique(unittest.TestCase):\n\n    def test_unique_str(self):\n        self.assertEqual(\n            is_unique(\"abcd\"),\n            True\n        )\n\n    def test_non_unique_str(self):\n        self.assertEqual(\n            is_unique(\"abccdd\"),\n            False\n        )\n\n    def test_empty(self):\n        self.assertEqual(\n            is_unique(\"\"),\n            True\n        )\n\n    def test_long_unique(self):\n        self.assertEqual(\n            is_unique(\"asdfghjkl;'qwertyuiop[]zxcvbnm,./1234567890-=\"),\n            True\n        )\n\n    def test_long_non_unique(self):\n        self.assertEqual(\n            is_unique(\"asdfghjkl;'qwertyuiopqwertyuipoiasdljkasoicndiuopasd\"),\n            False\n        )\n\nif __name__ == '__main__':\n    unittest.main()","sub_path":"cracking_the_coding_interview/1-1_Is_Unique.py","file_name":"1-1_Is_Unique.py","file_ext":"py","file_size_in_byte":2827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"554964302","text":"import json\npoiname = 'read'\nf = open(poiname + '.txt', 'r')\n\nline = f.readlines()\ni = 0\nflag = 0\nreviews = []\nr = ''\nwhile i < len(line):\n    # print(line[i][:-1] + ' -->> ' + str(len(line[i])))\n    if len(line[i].split()) == 1:\n        if line[i].split()[0] == \"Like\" or line[i][:-1].isdigit() is True:\n            flag = 0\n            # print(line[i])\n            reviews.append(r.replace(\"(Translated by Google)\", \"\").replace(\"(Original)\", \"\"))\n            r = ''\n    if flag == 1:\n        r += line[i]\n    if \"ago\" in line[i]:\n        flag = 1\n\n    i += 1\n\nfor l in reviews:\n    print(l)\nprint(len(reviews))\ndata_to_write = []\n\nfor r in reviews:\n    review = {\n        \"point_of_interest\": poiname,\n        \"rating\": -1,\n        \"title\": \"\",\n        \"review\": r,\n        \"url\": \"\"\n    }\n    data_to_write.append(review)\n\nwith open(poiname + '.json', 'w+') as f:\n    f.write(json.dumps(data_to_write))\n","sub_path":"google_reviews.py","file_name":"google_reviews.py","file_ext":"py","file_size_in_byte":906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"524305419","text":"import re\n\n\ndef main():\n    print('hi')\n\n    input_file = open('input/input_file', 'r')\n\n    output_file = open('output_file', 'w')\n\n    for line in input_file:\n\n        regex_M = r'^\\d+\\.*\\d+M'\n        regex_K = r'^\\d+\\.*\\d+K'\n        regex_G = r'^\\d+\\.*\\d+G'\n\n        if re.search(regex_M, line):\n            matches = re.findall(regex_M, line)\n            size_M = matches[0].split('M')[0]\n            line = re.sub(regex_M, size_M, line)\n\n        elif re.search(regex_K, line):\n            matches = re.findall(regex_K, line)\n            size_K = matches[0].split('K')[0]\n            size_M = float(size_K) / 1024\n            line = re.sub(regex_K, str(size_M), line)\n\n        elif re.search(regex_G, line):\n            matches = re.findall(regex_G, line)\n            size_G = matches[0].split('G')[0]\n            size_M = float(size_G) * 1024\n            line = re.sub(regex_G, str(size_M), line)\n\n        output_file.write(line)\n        output_file.close()\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"size_converter.py","file_name":"size_converter.py","file_ext":"py","file_size_in_byte":1002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"646451129","text":"# 4. 输入一个正整数，打印这个数是否是素数(prime)\n#   提示:\n#     素数也叫质数,是只能被1和自身整数的整数\n#   如：\n#     请输入: 5\n#   打印:\n#     5 是素数\n\n#     请输入: 6\n#   打印:\n#     6 不是素数\n\n#   素数: 2 3 5 7 11 13 17 19 23 ...\n\nn = int(input(\"请输入一个数: \"))\n\nif n < 2:\n    print(n, \"不是素数\")\nelse:\n    for i in range(2, n):  # i为除数，偿试被n除\n        if n % i == 0:\n            print(n, '不是素数')\n            break\n    else:  # 循环结束，不能再获取数据时，i到达了最大值\n        print(n, '是素数')\n\n","sub_path":"AB/linux1/day06/day05_exercise/primes.py","file_name":"primes.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"87544182","text":"class HashSet: \n    def __init__(self): \n        self.__elements = [[] for x in range(5)]\n        self.__number_of_elements = 0 \n\n    def add(self, element): \n        if self.contains(element): \n            return  \n        \n        if self.__number_of_elements == len(self.__elements): \n            self.resize() \n        \n        self.__elements[hash(element) % len(self.__elements)].append(element)\n        self.__number_of_elements += 1 \n\n    \n    def resize(self): \n        old_elements = self.__elements\n        new_elements = [[] for x in range(2* len(self.__elements))] \n        for bucket in old_elements: \n            for element in bucket: \n                new_elements[hash(element) % len(new_elements)].append(element)\n        \n        self.__elements = new_elements\n\n\n    def contains(self, element): \n        idx = hash(element) % len(self.__elements)\n        return element in self.__elements[idx] \n\n\n    def printElements(self): \n        print(self.__elements)\n\ntest = HashSet() \ntest.add(4) \ntest.add(2)\ntest.add(\"hello\") \ntest.add(\"fs\")\ntest.add(3) \ntest.add(1)\ntest.add(1)\n\ntest.printElements()","sub_path":"set/hash_set.py","file_name":"hash_set.py","file_ext":"py","file_size_in_byte":1114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"644340318","text":"class Solution(object):\n    def groupAnagrams(self, strs):\n        \"\"\"\n        :type strs: List[str]\n        :rtype: List[List[str]]\n        \"\"\"\n        if not strs:\n            return []\n        record = {}\n        result = []\n        index = 0\n        from collections import Counter\n        anagram = lambda s: (Counter(s),)\n        for i in range(len(strs)):\n            # 关键是key用什么，元素出现次数相同，显然就是字母异位词，\n            # 但是key必须是不可变数据类型，元组是不可变的，但是元组里面加上可变的Counter\n            # 还是不能作为dict的key\n            if anagram(strs[i]) not in record:\n                record[anagram(strs[i])] = index\n                result.append([strs[i]])\n                index += 1\n            else:\n                result[anagram(strs[i])].append(strs[i])\n        return result\n\n\nclass Solution:\n    def groupAnagrams(self, strs):\n        \"\"\"\n        :type strs: List[str]\n        :rtype: List[List[str]]\n        \"\"\"\n        if not strs:\n            return []\n        result = {''.join(sorted(strs[0])): [strs[0]]}\n        n = len(strs)\n        for i in range(1, n):\n            # 这里的key是排序后的字符串，如果是字母异位词，那么排序之后肯定是相同的字符串\n            if ''.join(sorted(strs[i])) in result:\n                result[''.join(sorted(strs[i]))].append(strs[i])\n            else:\n                result[''.join(sorted(strs[i]))] = [strs[i]]\n        return list(result.values())\n","sub_path":"group_anagrams_49.py","file_name":"group_anagrams_49.py","file_ext":"py","file_size_in_byte":1531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"400587325","text":"import json\n\nfrom django.conf import settings\nfrom django.core.exceptions import ImproperlyConfigured\nfrom django.utils.deprecation import MiddlewareMixin\n\nfrom apps.core.utils import get_future_date\nfrom apps.core.views import CookieToggle\n\n\nclass CookiesMiddleware(MiddlewareMixin):\n    def get_cookie_settings(self, request):\n        \"\"\"\n        Parses user cookies in the request and adjust the settings accordingly.\n        :return: DICT - cookie settings\n        \"\"\"\n        cookie_settings = {\n            settings.GOOGLE_ANALYTICS_COOKIE_NAME: CookieToggle.OFF,\n        }\n        current_cookie_settings = (\n            request.COOKIES.get(settings.COOKIE_SETTINGS_COOKIE_NAME) or \"{}\"\n        )\n        current_cookie_settings = json.loads(current_cookie_settings)\n\n        for option in cookie_settings.keys():\n            if option in current_cookie_settings.keys():\n                cookie_settings[option] = current_cookie_settings[option]\n\n        return cookie_settings\n\n    def get_cookie_preferences_set(self, request):\n        \"\"\"\n        Although there are other more sufficient ways to tell whether a user has set their cookie choices.\n        To keep things consistent with gov.uk a cookie is used to track this.\n        The presence of this cookie indicates that the user have set their cookie preference\n        therefore they won't be prompted to tun their cookie preferences again.\n        \"\"\"\n        return bool(request.COOKIES.get(settings.COOKIE_PREFERENCES_SET_COOKIE_NAME))\n\n    def get_global_bar_seen(self, request):\n        \"\"\"\n        There's a separate banner shown when users accept all cookies.\n        The presence of this cookie indicates that the user have accepted all cookies\n        and they have seen the confirmation banner.\n        \"\"\"\n        seen = request.COOKIES.get(settings.GLOBAL_BAR_SEEN_COOKIE_NAME)\n        if seen is not None:\n            # False means the user was presented the banner\n            # True means the user has either clicked the Hide button\n            # or they navigated away from- or reloaded the page\n            return json.loads(seen)\n        else:\n            return seen\n\n    def process_request(self, request):\n        \"\"\"\n        Load user's cookie preferences if any.\n        \"\"\"\n        request.cookie_settings = self.get_cookie_settings(request)\n        request.cookie_preferences_set = self.get_cookie_preferences_set(request)\n        request.global_bar_seen = self.get_global_bar_seen(request) is not None\n\n    def process_response(self, request, response):\n        global_bar_seen = self.get_global_bar_seen(request)\n        if global_bar_seen in (None, False):\n            if request.cookie_preferences_set:\n                value = False\n                if global_bar_seen is False:\n                    value = True\n                response.set_cookie(\n                    settings.GLOBAL_BAR_SEEN_COOKIE_NAME,\n                    json.dumps(value),\n                    expires=get_future_date(\n                        days=settings.COOKIE_SETTINGS_CONFIRMATION_BANNER\n                    ),\n                )\n\n        return response\n\n\nclass XRobotsTagMiddleware(MiddlewareMixin):\n    def process_request(self, request):\n        pass\n\n    def process_response(self, request, response):\n        try:\n            if settings.X_ROBOTS_TAG:\n                response[\"X-Robots-Tag\"] = \",\".join(settings.X_ROBOTS_TAG)\n        except AttributeError:\n            raise ImproperlyConfigured(\"X_ROBOTS_TAG is missing from django settings.\")\n\n        return response\n\n\nclass ZipkinTracingMiddleware(MiddlewareMixin):\n    \"\"\"\n    https://docs.cloudfoundry.org/concepts/http-routing.html#zipkin-headers\n    \"\"\"\n\n    TRACE_ID = \"X-B3-TraceId\"\n    SPAN_ID = \"X-B3-SpanId\"\n\n    def get_zipkin_http_headers(self, request):\n        \"\"\"\n        Helper to prepare zipkin HTTP headers that could be added to requests\n        easily.\n        \"\"\"\n        headers = {}\n        trace_id = request.headers.get(self.TRACE_ID)\n        span_id = request.headers.get(self.SPAN_ID)\n\n        if trace_id and span_id:\n            headers[self.TRACE_ID] = trace_id\n            headers[self.SPAN_ID] = span_id\n\n        return headers\n\n    def process_request(self, request):\n        request.zipkin_http_headers = self.get_zipkin_http_headers(request)\n\n    def process_response(self, request, response):\n        trace_id = request.headers.get(self.TRACE_ID)\n        span_id = request.headers.get(self.SPAN_ID)\n\n        if trace_id and span_id:\n            response[self.TRACE_ID] = trace_id\n            response[self.SPAN_ID] = span_id\n\n        return response\n","sub_path":"apps/core/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":4615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"550250278","text":"import math\n\nclass Solution(object):\n    '''2018-12-15\n    This is an O(n logn) solution.\n    '''\n    def countPrimes(self, n):\n        \"\"\"\n        :type n: int\n        :rtype: int\n        \"\"\"\n        if(n<=1):\n            return 0\n        #primeFlags[i]=True means the number i is a prime number.\n        primeFlags=[True]*(n+1) \n        primeFlags[0]=False\n        primeFlags[1]=False\n        \n        for p in range(2,int(math.sqrt(n))+1):\n            if(primeFlags[p]==True):\n                for multiplier in range(p,n//p+1):\n                    primeFlags[p*multiplier]=False\n        return sum(primeFlags[:-1])\n\n    def test(self):\n        print(\"n={0}, output={1}, expected={2}\".format(5,self.countPrimes(5),2))\n        print(\"n={0}, output={1}, expected={2}\".format(10,self.countPrimes(10),4))\n        \na=Solution()\na.test()\n","sub_path":"src/countPrimes.py","file_name":"countPrimes.py","file_ext":"py","file_size_in_byte":834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"73385770","text":"from discord.ext import commands\r\n\r\n\r\nclass Common(commands.Cog):\r\n    def __init__(self, bot):\r\n        self.bot = bot\r\n        self._last_member = None\r\n\r\n    @commands.command(name='mow', help='Mow')\r\n    async def mow(self, ctx):\r\n        await ctx.send('Mow')\r\n","sub_path":"BotCogs/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"178390743","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# *args的用法\ndef test_var_args(f_arg,*args):\n    print('first normal arg:',f_arg)\n    for arg in args:\n        print('another arg through *args:',arg);\n\ntest_var_args('hello','python','javaScript')\n\n# **kwargs的用法\ndef greet_me(**kwargs):\n    for key,value in kwargs.items():\n        print('{0} == {1}'.format(key,value))\ngreet_me(name='jim')\n\n# 怎么使用以上两者来调用一个参数为列表或者字典的函数\ndef test_args_kwargs(arg1,arg2,arg3):\n    print('arg1:',arg1)\n    print('arg2:',arg2)\n    print('arg3:',arg3)\n\nargs = ('two',3,4)\ntest_args_kwargs(*args);\n\nkwargs = {'arg3':3,'arg2':'two','arg1':1}\ntest_args_kwargs(**kwargs);\n\n\n\n\n","sub_path":"PythonTips/*argsAnd**Kwargs.py","file_name":"*argsAnd**Kwargs.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"173717139","text":"# Copyright (c) Meta Platforms, Inc. and affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n\nload(\"//antlir/antlir2/bzl:build_phase.bzl\", \"BuildPhase\")\nload(\":feature_info.bzl\", \"FeatureAnalysis\", \"ParseTimeFeature\")\n\ndef genrule(\n        *,\n        cmd: list[str | Select] | Select,\n        user: str | Select = \"nobody\",\n        boot: bool | Select = False,\n        bind_repo_ro: bool | Select = False,\n        mount_platform: bool | Select = False) -> ParseTimeFeature.type:\n    return ParseTimeFeature(\n        feature_type = \"genrule\",\n        impl = \"//antlir/antlir2/features:genrule\",\n        kwargs = {\n            \"bind_repo_ro\": bind_repo_ro,\n            \"boot\": boot,\n            \"mount_platform\": mount_platform,\n            \"user\": user,\n        },\n        args = {\n            \"cmd_\" + str(idx): cmd\n            for idx, cmd in enumerate(cmd)\n        },\n    )\n\ngenrule_record = record(\n    cmd = list[\"resolved_macro\"],\n    user = str,\n    boot = bool,\n    bind_repo_ro = bool,\n    mount_platform = bool,\n)\n\ndef genrule_analyze(\n        user: str,\n        boot: bool,\n        bind_repo_ro: bool,\n        mount_platform: bool,\n        args: dict[str, str | \"resolved_macro\"]) -> FeatureAnalysis.type:\n    cmd = {int(key.removeprefix(\"cmd_\")): val for key, val in args.items() if key.startswith(\"cmd_\")}\n    cmd = [val for _key, val in sorted(cmd.items())]\n    return FeatureAnalysis(\n        feature_type = \"genrule\",\n        data = genrule_record(\n            cmd = cmd,\n            user = user,\n            boot = boot,\n            # The repo is considered part of the platform\n            bind_repo_ro = bind_repo_ro or mount_platform,\n            mount_platform = mount_platform,\n        ),\n        build_phase = BuildPhase(\"genrule\"),\n    )\n","sub_path":"antlir/antlir2/bzl/feature/genrule.bzl","file_name":"genrule.bzl","file_ext":"bzl","file_size_in_byte":1847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"291850722","text":"\"\"\"\nExecutes the automation part of 1DMin\n\"\"\"\n\nimport os\nfrom mako.template import Template\n\n\n# OBTAIN THE PATH TO THE DIRECTORY CONTAINING THE TEMPLATES #\nSRC_PATH = os.path.dirname(os.path.realpath(__file__))\nTEMPLATE_PATH = os.path.join(SRC_PATH, 'templates')\n\n\ndef autofit_input(energy_ranges, energy_weights, epsilon, num_batches,\n        batch_zeroes, batch_weights, data_sets, energy_units,\n        num_atoms, symbols, atom_groups, total_order, factor_order,\n        read_basis, find_disconnected, disconnected_groups):\n    \"\"\" writes the autofit input file for each instance\n    \"\"\"\n\n    # Set the dictionary for the autofit input file\n    fill_vals = {\n        \"EnergyRanges\": energy_ranges,\n        \"EnergyWeights\": energy_weights,\n        \"Epsilon\": epsilon,\n        \"NumBatches\": num_batches,\n        \"BatchZeroes\": batch_zeroes,\n        \"BatchWeights\": batch_weights,\n        \"DataSets\": data_sets,\n        \"EnergyUnits\": energy_units,\n        \"NumAtoms\": num_atoms,\n        \"Symbols\": symbols,\n        \"AtomGroups\": atom_groups,\n        \"TotalOrder\": total_order,\n        \"FactorOrder\": factor_order,\n        \"ReadBasis\": read_basis,\n        \"FindDisconnected\": find_disconnected,\n        \"DisconnectedGroups\": disconnected_groups\n    }\n\n    # Set template name and path for the autofit input file\n    template_file_name = 'autofit_inp.mako'\n    template_file_path = os.path.join(TEMPLATE_PATH, template_file_name)\n\n    # Build the 1dmin input string\n    input_str = Template(filename=template_file_path).render(**fill_vals)\n\n    return input_str\n","sub_path":"pyfit/fitparser/writer.py","file_name":"writer.py","file_ext":"py","file_size_in_byte":1561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"215940066","text":"#!/usr/bin/python -u\n# -*- coding: utf-8 -*-\n# vim: ft=python ts=4 sw=4 et\n# Maxime Guillet - Thu, 22 Dec 2011 12:59:06 +0100\n\nimport pycurl\nimport snmp_passpersist as snmp\nimport sys\nimport time\nimport traceback\n\npolling_interval = 30\noid_base = '.1.3.6.1.4.1.38673.1.1'\noid_table = {\n    'Total Accesses':  ['0', 'int'],\n    'Total kBytes':    ['1', 'int'],\n    'CPULoad':         ['2', 'str'],\n    'Uptime':          ['3', 'int'],\n    'ReqPerSec':       ['4', 'str'],\n    'BytesPerSec':     ['5', 'str'],\n    'BytesPerReq':     ['6', 'str'],\n    'BusyWorkers':     ['7', 'int'],\n    'IdleWorkers':     ['8', 'int'],\n\n    '_':  ['9.0', 'int'],\n    'S':  ['9.1', 'int'],\n    'R':  ['9.2', 'int'],\n    'W':  ['9.3', 'int'],\n    'K':  ['9.4', 'int'],\n    'D':  ['9.5', 'int'],\n    'C':  ['9.6', 'int'],\n    'L':  ['9.7', 'int'],\n    'G':  ['9.8', 'int'],\n    'I':  ['9.9', 'int'],\n    '.':  ['9.10', 'int']\n}\n\n\nclass ApacheStats:\n    def __init__(self):\n        self.stats = {}\n        self.cache_ttl = 2.0\n        self.time = time.time() - self.cache_ttl - 1.0\n        self.contents = ''\n\n    def body_callback(self, buf):\n        self.contents = self.contents + buf\n\n    def update_stats(self):\n        now = time.time()\n        if now - self.time < self.cache_ttl:\n            return\n\n        c = pycurl.Curl()\n        c.setopt(pycurl.URL, \"http://127.0.0.1/server-status?auto\")\n        self.contents = ''\n        c.setopt(c.WRITEFUNCTION, self.body_callback)\n        try:\n            c.perform()\n        except:\n            return\n\n        if c.getinfo(pycurl.HTTP_CODE) != 200:\n            return\n\n        c.close()\n\n        # dice up the data\n        scoreboardkey = [ '_', 'S', 'R', 'W', 'K', 'D', 'C', 'L', 'G', 'I', '.' ]\n\n        for line in self.contents.splitlines():\n            fields = line.split(':')\n            fields[1] = fields[1].lstrip()\n\n            if fields[0] == 'Scoreboard':\n                # count up the scoreboard\n                for state in scoreboardkey:\n                    self.stats[state] = 0\n                for state in fields[1]:\n                    self.stats[state] += 1\n            elif fields[0] == 'Total kBytes':\n                # turn into base (byte) value\n                self.stats[fields[0]] = int(fields[1])*1024\n            else:\n                # just store everything else\n                self.stats[fields[0]] = fields[1]\n\n        self.time = now\n        return\n\n    def get_stats(self):\n        self.update_stats()\n        return self.stats\n\n\ndef update_def():\n    stats.update_stats()\n    for key in oid_table:\n        if not key in stats.stats:\n            continue\n\n        if hasattr(pp, 'add_' + oid_table[key][1]):\n            getattr(pp, 'add_' + oid_table[key][1])(oid_table[key][0], stats.stats[key])\n        else:\n            pp.add_str(oid_table[key][0], stats.stats[key])\n\nif __name__ == '__main__':\n    try:\n        stats = ApacheStats()\n\n        pp = snmp.PassPersist(oid_base)\n\n        try:\n            pp.start(update_def, polling_interval)\n        except KeyboardInterrupt:\n            print >> sys.stderr, \"Exiting on user request.\"\n            sys.exit(0)\n        except IOError:\n            pass\n    except SystemExit:\n        pass\n    except:\n        from os.path import basename, splitext\n        traceback.print_exc(file=open('/tmp/%s.error' % splitext(basename(sys.argv[0]))[0], 'w'))\n        sys.exit(1)\n","sub_path":"scripts/arch_work_scripts/python/apache-stats.py","file_name":"apache-stats.py","file_ext":"py","file_size_in_byte":3386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"452619043","text":"'''Handle static directory from several django applications'''\nimport os\n\nfrom unuk.utils.importer import import_module\n\n\nclass pathHandler(object):\n    \n    def __init__(self, name, path):\n        self.name     = name\n        self.base     = path\n        self._path    = os.path.join(path,'media')\n        self.fullpath = self.path(name)\n        self.exists   = os.path.exists(self._path)\n    \n    def path(self, name):\n        from django.utils._os import safe_join\n        return safe_join(self._path, name)\n\n\nclass djangoAdminHandler(pathHandler):\n    \n    def path(self, name):\n        return self._path\n    \n\ndef application_map(applications):\n    '''Very very useful function for finding static media directories.\nIt looks for the ``media`` directory in each installed application.'''\n    map = {}\n    for app in applications:\n        sapp = app.split('.')\n        name = sapp[-1]\n        if app.startswith('django.'):\n            if app == 'django.contrib.admin':\n                handler = djangoAdminHandler\n            else:\n                # we skip any other django contrib application\n                continue\n        else:\n            handler = pathHandler\n            \n        try:\n            module = import_module(app)\n        except:\n            continue\n\n        path   = module.__path__[0]\n        map[name] = handler(name,path)\n    return map\n","sub_path":"src/unuk/contrib/asynco/static.py","file_name":"static.py","file_ext":"py","file_size_in_byte":1365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"227864459","text":"from sys import argv\nfrom IndexCreator.Creator import create_index\nfrom search.searcher import search\n\n\narg_num = len(argv)\n\nif (arg_num > 1):\n    if argv[1] == 'create_index':\n        if arg_num > 2:\n            create_index(argv[2])\n        else:\n            create_index()\n    elif argv[1] == 'search':\n        if arg_num > 2:\n            filename = argv[2]\n            search(filename)\n        else:\n            print('Please, specify search query, usign _AND_ , _OR_, _NOT_')\n    else:\n        print('No such option ' + argv[1])","sub_path":"manage.py","file_name":"manage.py","file_ext":"py","file_size_in_byte":533,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"32227387","text":"# -*- coding: utf-8 -*-\r\n# @File  : shutdownjava.py\r\n# @Author: Feng\r\n# @Date  : 2019/1/23\r\n# @Desc  :\r\n\r\nimport re\r\nimport codecs\r\nimport locale\r\nimport subprocess\r\n\r\nps = subprocess.Popen('netstat -nltup', stdin=subprocess.PIPE, stdout=subprocess.PIPE, shell=True)\r\n\r\nwhile True:\r\n    data = ps.stdout.readline()\r\n    if data == b'':\r\n        break\r\n    data = data.decode(codecs.lookup(locale.getpreferredencoding()).name)\r\n    search = re.search(r'(\\d*)?/java', data)\r\n    if search is not None:\r\n        pid = search[1]\r\n        subprocess.Popen('kill -9 ' + str(pid), stdin=subprocess.PIPE, stdout=subprocess.PIPE, shell=True)\r\n","sub_path":"shutdownjava.py","file_name":"shutdownjava.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"39215779","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /home/shu/research/deepy/deepy/trainers/cores/momentum.py\n# Compiled at: 2016-04-20 00:05:45\nimport theano, numpy as np\n\ndef momentum_core(params, gradients, momentum=0.9, learning_rate=0.01):\n    \"\"\"\n    Momentum SGD optimization core.\n    \"\"\"\n    free_parameters = []\n    updates = []\n    for param, grad in zip(params, gradients):\n        delta = learning_rate * grad\n        velocity = theano.shared(np.zeros_like(param.get_value()), name=param.name + '_vel')\n        updates.append((velocity, momentum * velocity - delta))\n        updates.append((param, param + velocity))\n        free_parameters.append(velocity)\n\n    return (\n     updates, free_parameters)","sub_path":"pycfiles/deepy-0.2.1.tar/momentum.py","file_name":"momentum.py","file_ext":"py","file_size_in_byte":822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"102754412","text":"# coding: utf-8\n# def enviar_email_produtividade(anexo1, chamados_atendidos, chamados_n_atendidos, emails, emails_copias,total):\n\nfrom Model.DTO.ProdutividadeDTO import ProdutividadeDTO\nfrom Model.DAO.ProdutividadeDAO import ProdutividadeDAO\n\nclass ProdutividadeCTR:\n    @staticmethod\n    def email_produtividade(anexo1_CTR, chamados_atendidos_CTR, chamados_n_atendidos_CTR, emails_CTR,\n                            emails_copias_CTR, total_CTR):\n\n        produtividadeDTO = ProdutividadeDTO\n        produtividadeDTO.Anexo1 = anexo1_CTR\n        produtividadeDTO.ChamadosAtendidos = chamados_atendidos_CTR\n        produtividadeDTO.ChamadosNaoAtendidos = chamados_n_atendidos_CTR\n        produtividadeDTO.Emails = emails_CTR\n        produtividadeDTO.EmailsCopias = emails_copias_CTR\n        produtividadeDTO.Total = total_CTR\n\n        produtividadeDAO = ProdutividadeDAO\n        produtividadeDAO.enviar_emailDAO(produtividadeDTO)","sub_path":"Controller/ProdutividadeCTR.py","file_name":"ProdutividadeCTR.py","file_ext":"py","file_size_in_byte":926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"542487215","text":"import csv\nimport collections\nfrom functools import reduce\n\nimport scriptutil as su\n\n\ndef ident(arg):\n    \"\"\"\n    Identity function that simply returns its argument unchanged.\n\n    Used as a default value for the transformation arguments to the read functions.\n    \"\"\"\n    return arg\n\n\ndef isiter(item):\n    return (isinstance(item, collections.Iterable)\n            and not isinstance(item, str))\n\n\ndef read_list(fname, item_f=ident):\n    \"\"\"\n    Reads a csv file as a list.\n    \n    Regardless of the form of the csv file, every entry in the file is\n    simply assembled in order (row-by-row) into a single list and returned.\n    \n    This function accepts an optional parameter, which must be callable.\n    It will be applied to each entry in the file before it is stored in\n    the list. This is useful if, for example, the entries represent numbers\n    and should be converted to int.\n    \"\"\"\n    with open(fname, \"r\") as file:\n        reader = csv.reader(file)\n        result = reduce(lambda ls1, ls2: ls1 + ls2, reader)\n        result = [item_f(entry) for entry in result]\n\n    return result\n\n\ndef read_table(fname, row_f=ident, col_f=ident, value_f=ident, rowh_size=1, colh_size=1):\n    \"\"\"\n    Reads a csv table.\n    \n    The file referred to by fname must be in table format, with column headers\n    in the first row and row headers in the first column. The value in the first\n    row and first column is ignored. This function returns a dictionary from row\n    names to other dictionaries, each from column names to table entries.\n    \n    If rowh_size is specified, it indicates how many of the first columns should\n    be incorporated into the row header. If greater than 1, the row headers will\n    be tuples containing the values from those columns.\n    \n    If colh_size is specified, it indicates how many of the first rows should be\n    incorporated into the column header. If greater than 1, the column headers\n    will be tuples containing the values from these rows.\n    \n    This function accepts three optional callable parameters. The arguments row_f,\n    col_f, and value_f will be applied to each row name, column name, and value,\n    respectively, before it is stored in the dictionary. This is useful if, for\n    example, the values represent numbers and should be converted to int. The\n    row_f function is applied to the entire row header tuple if rowh_size is greater\n    than 1. Similarly, the col_f function is applied to the entire column\n    header tuple if colh_size is greater than 1.\n    \"\"\"\n    with open(fname, \"r\") as file:\n        reader = csv.reader(file)\n        headers = []\n        for i in range(colh_size):\n            headers.append(next(reader)[rowh_size:])\n        headers = list(zip(*headers))\n        if colh_size == 1:\n            headers = [t[0] for t in headers]  # Extract from the tuple.\n        headers = list(map(col_f, headers))\n        result = {}\n        for line in reader:\n            if rowh_size > 1:\n                row_name = row_f(tuple(line[:rowh_size]))\n            else:\n                row_name = row_f(line[0])\n            record = list(map(value_f, line[rowh_size:]))\n            result[row_name] = dict(zip(headers, record))\n\n    return result\n\n\ndef read_lintable(fname, row_name, col_name, value_name, row_f=ident, col_f=ident, value_f=ident):\n    \"\"\"\n    Reads a table stored as a list in a csv file. (e.g. IJV format)\n    \n    The file referred to by fname must have at least three columns, one with\n    each of row_name, col_name, and value_name as its header. The file will be\n    read as a list of (row, column, value) triplets. The row and column values\n    of a triplet must serve as a unique identifier for that triplet; if more than\n    one triplet has the same row and column values, the last to appear in the\n    file will overwrite all previous ones. This function returns a dictionary\n    from row names to other dictionaries, each from column names to table entries.\n    \n    This function accepts three optional parameters, which must be callable. The\n    arguments row_f, col_f, and value_f will be applied to each row name, column\n    name, and value, respectively, before it is stored in the dictionary. This is\n    useful if, for example, the values represent numbers and should be converted\n    to int.\n    \n    Optionally, value_name can be a list of column names rather than a single name.\n    If so, each value in the dictionary will be a list containing the values in\n    those columns. If value_f is a single function, it is applied to every value;\n    otherwise, it must be a list of functions whose length is the number of value\n    columns, with each function applied to the corresponding column.\n    \"\"\"\n    with open(fname, \"r\") as file:\n        reader = csv.reader(file)\n        header = next(reader)\n        row_col, col_col = su.colindices(fname, header, row_name, col_name)\n        if isiter(value_name):\n            value_col = [header.index(name) for name in value_name]\n        else:\n            value_col = header.index(value_name)\n        result = _read_lintable(reader, row_col, col_col, value_col, row_f, col_f, value_f)\n\n    return result\n\n\ndef nh_read_lintable(fname, row_col=0, col_col=1, value_col=2, row_f=ident, col_f=ident, value_f=ident,\n                     skip_header=False):\n    \"\"\"\n    Reads a table stored as a list in a csv file without headers.\n    \n    The file referred to by fname must have at least three columns. The file will\n    be read as a list of (row, column, value) triplets; by default, the first column\n    is taken as containing the row names, the second column as the column names, and\n    the third column as the values, but this can be changed by specifying (zero-based)\n    column positions using the arguments row_col, col_col, and value_col. The row and\n    column values of a triplet must serve as a unique identifier for that triplet;\n    if more than one triplet has the same row and column values, the last to appear\n    in the file will overwrite all previous ones. This function returns a dictionary\n    from row names to other dictionaries, each from column names to table entries.\n    \n    This function accepts three optional callable parameters. The arguments row_f,\n    col_f, and value_f will be applied to each row name, column name, and value,\n    respectively, before it is stored in the dictionary. This is useful if, for\n    example, the values represent numbers and should be converted to int.\n    \n    Optionally, value_col can be a list of column numbers rather than a single number.\n    If so, each value in the dictionary will be a list containing the values in those\n    columns. If value_f is a single function, it is applied to every value; otherwise,\n    it must be a list of functions whose length is the number of value columns, with\n    each function applied to the corresponding column.\n    \n    If skip_header is set to True, then the first row will be treated as a header and ignored.\n    \"\"\"\n    with open(fname, \"r\") as file:\n        reader = csv.reader(file)\n        if skip_header:\n            next(reader)\n        result = _read_lintable(reader, row_col, col_col, value_col, row_f, col_f, value_f)\n\n    return result\n\n\ndef _read_lintable(reader, row_col, col_col, value_col, row_f, col_f, value_f):\n    result = {}\n    for line in reader:\n        row = row_f(line[row_col])\n        col = col_f(line[col_col])\n        if isiter(value_f):\n            value = [f(line[x]) for f, x in zip(value_f, value_col)]\n        elif isiter(value_col):\n            value = [value_f(line[x]) for x in value_col]\n        else:\n            value = value_f(line[value_col])\n        item = result.setdefault(row, {})\n        item[col] = value\n    return result\n\n\ndef read_dict(fname, key_name, value_name, key_f=ident, value_f=ident):\n    \"\"\"\n    Reads a dictionary from a csv file.\n    \n    The file referred to by fname must have at least two columns, one with\n    key_name as its header and the other with value_name as its header. The\n    file will be read as a list of key-value pairs. Each key must appear only\n    once in the file (though values may be repeated); if a duplicate key is\n    present, the last copy to appear in the file will overwrite any previous\n    ones. This function returns a dictionary from keys to values.\n    \n    The key_name parameter may be an iterable of column names, in which case\n    the values in those columns will be wrapped into tuples to serve as the\n    dictionary keys. The key_f function is applied to these tuples to transform\n    them before inserting them into the dictionary. Similarly, the value_name\n    parameter may be an iterable of column names, whose values are wrapped into\n    tuples and transformed with the value_f function to make the dictionary values.\n    \n    This function accepts two optional parameters, which must be callable. The\n    arguments key_f and value_f will be applied to each key and value, respectively,\n    before it is stored in the dictionary. This is useful if, for example, the\n    values represent numbers and should be converted to int.\n    \"\"\"\n    with open(fname, \"r\") as file:\n        reader = csv.reader(file)\n        header = next(reader)\n        try:\n            key_col = header.index(key_name)\n        except ValueError:\n            key_col = [header.index(name) for name in key_name]\n        try:\n            value_col = header.index(value_name)\n        except ValueError:\n            value_col = [header.index(name) for name in value_name]\n        result = _read_dict(reader, key_col, value_col, key_f, value_f)\n\n    return result\n\n\ndef nh_read_dict(fname, key_col=0, value_col=1, key_f=ident, value_f=ident, skip_header=False):\n    \"\"\"\n    Reads a dictionary from a csv file without headers.\n    \n    The file referred to by fname must have at least two columns. The file will\n    be read as a list of key-value pairs; by default, the first column is taken\n    as containing the keys and the second as the values, but this can be changed\n    by specifying (zero-based) column positions using the arguments key_col and\n    value_col. Each key must appear only once in the file (though values may be\n    repeated); if a duplicate key is present, the last copy to appear in the\n    file will overwrite any previous ones. This function returns a dictionary\n    from keys to values.\n    \n    This function accepts two optional callable parameters. The arguments key_f\n    and value_f will be applied to each key and value, respectively, before it\n    is stored in the dictionary. This is useful if, for example, the values represent\n    numbers and should be converted to int.\n    \n    The key_col parameter may be an iterable of column numbers, in which case the\n    values in those columns will be wrapped into tuples to serve as the dictionary\n    keys. The key_f function is applied to these tuples to transform them before\n    inserting them into the dictionary.\n    \n    Similarly, value_name can be a list of column names rather than a single name.\n    If so, each value in the dictionary will be a list containing the values in those\n    columns. If value_f is a single function, it is applied to every value; otherwise,\n    it must be a list of functions whose length is the number of value columns,\n    with each function applied to the corresponding column.\n    \n    If skip_header is set to True, then the first row will be treated as a header\n    and ignored.\n    \"\"\"\n    with open(fname, \"r\") as file:\n        reader = csv.reader(file)\n        if skip_header:\n            next(reader)\n        result = _read_dict(reader, key_col, value_col, key_f, value_f)\n\n    return result\n\n\ndef _read_dict(reader, key_col, value_col, key_f, value_f):\n    result = {}\n    try:\n        iter(key_col)  # Throws TypeError if key_col is just a number.\n\n        def create_key(cur_line):\n            return key_f(tuple(cur_line[col] for col in key_col))\n    except TypeError:\n        def create_key(cur_line):\n            return key_f(cur_line[key_col])\n\n    for line in reader:\n        if isiter(value_f):\n            value = [f(line[x]) for f, x in zip(value_f, value_col)]\n        elif isiter(value_col):\n            value = [value_f(line[x]) for x in value_col]\n        else:\n            value = value_f(line[value_col])\n        result[create_key(line)] = value\n    return result\n\n\ndef read_relation(fname, key_name, value_name, key_f=ident, value_f=ident):\n    \"\"\"\n    Reads a relation between two sets from a csv file.\n    \n    The file referred to by fname must have at least two columns, one with\n    key_name as its header and the other with value_name as its header. The\n    file will be read as a list of key-value pairs. Both keys and values may\n    appear more than once in the file. This function returns a dictionary\n    mapping each key in the file to the set of values it is paired with.\n    \n    This function accepts two optional parameters, which must be callable.\n    The arguments key_f and value_f will be applied to each key and value,\n    respectively, before it is stored in the dictionary. This is useful if,\n    for example, the values represent numbers and should be converted to int.\n    \"\"\"\n    with open(fname, \"r\") as file:\n        reader = csv.reader(file)\n        header = next(reader)\n        key_col = header.index(key_name)\n        value_col = header.index(value_name)\n        result = _read_relation(reader, key_col, value_col, key_f, value_f)\n\n    return result\n\n\ndef nh_read_relation(fname, key_col=0, value_col=1, key_f=ident, value_f=ident, skip_header=False):\n    \"\"\"\n    Reads a relation between two sets from a csv file.\n    \n    The file referred to by fname must have at least two columns. The file\n    will be read as a list of key-value pairs; by default, the first column\n    is taken as containing the keys and the second as the values, but this\n    can be changed by specifying (zero-based) column positions using the\n    arguments key_col and value_col. Both keys and values may appear more\n    than once in the file. This function returns a dictionary mapping each\n    key in the file to the set of values it is paired with.\n    \n    This function accepts two optional callable parameters. The arguments key_f\n    and value_f will be applied to each key and value, respectively, before it\n    is stored in the dictionary. This is useful if, for example, the values\n    represent numbers and should be converted to int.\n    \n    If skip_header is set to True, then the first row will be treated as a header\n    and ignored.\n    \"\"\"\n    with open(fname, \"r\") as file:\n        reader = csv.reader(file)\n        if skip_header:\n            next(reader)\n        result = _read_relation(reader, key_col, value_col, key_f, value_f)\n\n    return result\n\n\ndef _read_relation(reader, key_col, value_col, key_f, value_f):\n    result = {}\n    for line in reader:\n        key = key_f(line[key_col])\n        value = value_f(line[value_col])\n        if key in result:\n            result[key].add(value)\n        else:\n            result[key] = {value}\n    return result\n\n\ndef write_table(fname, table, corner_label=\"\", rows_sorted=True, cols_sorted=True):\n    \"\"\"\n    Writes a csv table.\n    \n    The fname argument is the filename to write to. The table must be a\n    mapping object from row names to other mapping objects, each from\n    column names to table entries. If the row names are iterables with n\n    elements, then their elements fill the first n columns of the resulting\n    file; if they are single values, then those values fill the first column\n    of the resulting file. Similarly, the column names fill the first rows\n    of the file, and can be iterables or single values.\n    \n    By default, the row names and column names are in their natural sorted\n    order; if rows_sorted is set to false, the row names are in iteration\n    order instead, and if cols_sorted is set to false, the column names are\n    in the iteration order of the first row. The entry in the first row and\n    first column is corner_label if supplied (otherwise it is blank). It is\n    allowed for corner_label to be an iterable, in which case its elements\n    will attempt to fill any leftover space in the upper-left corner of the\n    table.\n    \n    All items are converted to strings before being written to the file.\n    \"\"\"\n    with open(fname, \"w\", newline=\"\") as file:\n        writer = csv.writer(file)\n        if cols_sorted:\n            header = sorted(list(next(iter(table.values()))))\n        else:\n            header = list(next(iter(table.values())).keys())\n        rowh_size = _max_len(table)\n        colh_size = _max_len(header)\n        headers = list(map(list, list(zip(*list(map(_pad(colh_size, \"\"), header))))))\n        corner_list = list(corner_label)\n        for row in headers:\n            writer.writerow(list(map(str, _grab(rowh_size, corner_list, \"\") + row)))\n        if rows_sorted:\n            rowitems = sorted(table.items(), key=lambda x: x[0])\n        else:\n            rowitems = table.items()\n        for rowlabel, trow in rowitems:\n            row = list(map(str, _pad(rowh_size, \"\")(rowlabel)\n                           + [trow[collabel] for collabel in header]))\n            writer.writerow(row)\n\n\n# Finds the longest element of the iterable.\n# Elements that are not iterable are considered to have length 1.\n# Strings are not considered to be iterable, so they also have length 1.\ndef _max_len(iterable):\n    def screwy_len(item):\n        if isiter(item):\n            return len(item)\n        else:\n            return 1\n\n    return max(list(map(screwy_len, iterable)))\n\n\n# Returns a padding function that forces its argument to have the given size.\n# If the argument is shorter than size, it is padded with padvalues.\n# If the argument is longer than size, the later elements are cut off.\n# If the argument is not an iterable, it is padded as if it had size 1.\n# Strings are treated as single items rather than iterables.\n# The padding function always returns a list.\ndef _pad(size, padvalue):\n    def pad(item):\n        if (isinstance(item, collections.Iterable)\n                and isinstance(item, collections.Sized)\n                and not isinstance(item, str)):\n            return list(item)[:size] + [padvalue] * (size - len(item))\n        else:\n            return [item][:size] + [padvalue] * (size - 1)\n\n    return pad\n\n\n# Pulls num elements off the beginning of the sequence and returns them.\n# If there aren't enough elements, the result is padded with padvalues.\ndef _grab(num, seq, padvalue):\n    result = _pad(num, padvalue)(seq[:num])\n    del seq[:num]\n    return result\n\n\ndef write_lintable(fname, table, row_name, col_name, value_name, rows_sorted=True, cols_sorted=True):\n    \"\"\"\n    Writes a table as a list to a csv file.\n    \n    The resulting file has three columns, with row names in the first column,\n    column names in the second column, and values in the third. The arguments\n    row_name, col_name, and value_name give the headers for these three columns.\n    By default, the rows of the file are in sorted order, first by row name,\n    then by column name. If rows_sorted is set to false, the row names are in\n    iteration order instead, and similarly for cols_sorted.\n    \"\"\"\n    with open(fname, \"w\", newline=\"\") as file:\n        writer = csv.writer(file)\n        writer.writerow([row_name, col_name, value_name])\n        if rows_sorted:\n            rowitems = sorted(table.items(), key=lambda x: x[0])\n        else:\n            rowitems = table.items()\n        for rowlabel, trow in rowitems:\n            if cols_sorted:\n                colitems = sorted(iter(list(trow.items())), key=lambda x: x[0])\n            else:\n                colitems = iter(list(trow.items()))\n            for collabel, value in colitems:\n                writer.writerow([rowlabel, collabel, value])\n\n\ndef _dict(*args, **kwargs):\n    return collections.OrderedDict(*args, **kwargs)\n","sub_path":"PECAS/S21u_m_aa/csvutil.py","file_name":"csvutil.py","file_ext":"py","file_size_in_byte":19921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"37554581","text":"def preProcess(s:str) -> str:\n    if len(s) > 0:\n        return '#' + str.join('#', s) + '#'\n    else:\n        return s\n    \ndef longestPalindrome(s: str) -> str:\n    if len(s) < 1:\n        return ''\n\n    n = len(s)\n    p = [0] * n\n    c = 0\n    r = 0\n    for i in range(1, n, 1):\n        iMirror = 2*c - i # i's mirror\n        if r > i:\n            p[i] = min(r-i, p[iMirror])\n        else:\n            p[i] = 0\n        \n        while i + 1 + p[i] < n and s[i + 1 + p[i]] == s[i - 1 - p[i]]:\n            p[i] += 1\n\n        if i + p[i] > r: # range expanded and center is updated\n            r = p[i] + i\n            c = i\n    \n    r, c = max(p), p.index(max(p))\n    return s[(c-r) : (c+r)].replace('#', '')\n\nclass Solution:\n    def longestPalindrome(self, s: str) -> str:\n        return longestPalindrome(preProcess(s))\n        \n\nif __name__ == '__main__':\n    sol = Solution()\n    palindrome = sol.longestPalindrome('banana')\n    print(palindrome)\n","sub_path":"LongestPalindromicSubstring.py","file_name":"LongestPalindromicSubstring.py","file_ext":"py","file_size_in_byte":950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"585762158","text":"\"\"\"\n:author: Thomas Delaet <thomas@delaet.org>\n\"\"\"\nimport string\nimport velbus\n\nclass Module(object):\n    \"\"\"\n    Abstract class for Velbus hardware modules.\n    \"\"\"\n    #pylint: disable-msg=R0902\n    def __init__(self, module_type, module_name, module_address, controller):\n        self._type = module_type\n        self._name = module_name\n        self._address = module_address\n        self.serial = 0\n        self.memory_map_version = 0\n        self.build_year = 0\n        self.build_week = 0\n\n        self._channel_names = {}\n        self._name_data = {}\n\n        self._loaded_callbacks = []\n        self.loaded = False\n\n        self._controller = controller\n        self._controller.subscribe(self.on_message)\n\n    def get_module_name(self):\n        \"\"\"\n        Returns the module model name\n\n        :return: str\n        \"\"\"\n        return self._name\n\n    def get_module_address(self):\n        \"\"\"\n        Returns the module address\n\n        :return: int\n        \"\"\"\n        return self._address\n\n    def get_name(self, channel):\n        \"\"\"\n        Get name for one of the channels\n\n        :return: str\n        \"\"\"\n        return self._channel_names[channel]\n\n    def get_type(self):\n        return self._type\n\n    def get_categories(self, channel):\n        \"\"\"\n        Get type of functionality of channel\n\n        :return: str\n        \"\"\"\n        return []\n\n    def on_message(self, message):\n        \"\"\"\n        Process received message\n        \"\"\"\n        if message.address != self._address:\n            return\n        if isinstance(message, velbus.ChannelNamePart1Message) or isinstance(message, velbus.ChannelNamePart1Message2):\n            self._process_channel_name_message(1, message)\n        elif isinstance(message, velbus.ChannelNamePart2Message) or isinstance(message, velbus.ChannelNamePart2Message2):\n            self._process_channel_name_message(2, message)\n        elif isinstance(message, velbus.ChannelNamePart3Message) or isinstance(message, velbus.ChannelNamePart3Message2):\n            self._process_channel_name_message(3, message)\n        elif isinstance(message, velbus.ModuleTypeMessage):\n            self._process_module_type_message(message)\n        else:\n            self._on_message(message)\n\n    def _on_message(self, message):\n        pass\n\n    def load(self, callback):\n        \"\"\"\n        Retrieve names of channels\n        \"\"\"\n        if callback is None:\n            def callb():\n                \"\"\"No-op\"\"\"\n                pass\n            callback = callb\n        if len(self._loaded_callbacks) == 0:\n            message = velbus.ModuleStatusRequestMessage(self._address)\n            self._controller.send(message)\n            message = velbus.ChannelNameRequestMessage(self._address)\n            message.channels = list(range(1, self.number_of_channels() + 1))\n            self._controller.send(message)\n        self._loaded_callbacks.append(callback)\n        self._load()\n\n    def _load(self):\n        pass\n\n    def number_of_channels(self):\n        \"\"\"\n        Retrieve the number of avaiable channels in this module\n\n        :return: int\n        \"\"\"\n        raise NotImplementedError\n\n    def _name_count_needed(self):\n        return self.number_of_channels() * 3\n\n    def _process_channel_name_message(self, part, message):\n        channel = message.channel\n        if channel not in self._name_data:\n            self._name_data[channel] = {}\n        self._name_data[channel][part] = message.name\n        if self._name_messages_complete():\n            self._generate_names()\n\n    def _process_module_type_message(self, message):\n        self.serial = message.serial\n        self.memory_map_version = message.memory_map_version\n        self.build_year = message.build_year\n        self.build_week = message.build_week\n\n    def _generate_names(self):\n        assert self._name_messages_complete()\n        for channel in range(1, self.number_of_channels() + 1):\n            name_parts = self._name_data[channel]\n            name = name_parts[1] + name_parts[2] + name_parts[3]\n            self._channel_names[channel] = ''.join(filter(lambda x: x in string.printable, name))\n        self._name_data = {}\n        self.loaded = True\n        for callback in self._loaded_callbacks:\n            callback()\n        self._loaded_callbacks = []\n\n    def _name_messages_complete(self):\n        \"\"\"\n        Check if all name messages have been received\n        \"\"\"\n        for channel in range(1, self.number_of_channels() + 1):\n            try:\n                for name_index in range(1, 4):\n                    if not isinstance(self._name_data[channel][name_index], str):\n                        return False\n            except Exception:\n                return False\n        return True\n","sub_path":"velbus/module.py","file_name":"module.py","file_ext":"py","file_size_in_byte":4732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"320180768","text":"from point import *\nfrom surface import *\nfrom ray import *\nfrom vector import *\n\nclass Plane():\n    '''\n    -------------------------------------------\n    Geometric surfaces are functionally identical\n    i.e. shade calculates the required vectors from\n    the object's properties and calls surface.shade.\n    -------------------------------------------    \n    '''\n\n    def __init__(self, c, n, s=None):\n        self.center = c\n        self.kplane = c.dot(n)\n        self.normal = n\n        self.normal.normalize()\n        self.n1 = n\n        self.surface = s\n\n    def intersect(self,ray):\n        a = Vector(self.n1.x - ray.anchor.x,\\\n                   self.n1.y - ray.anchor.y,\\\n                   self.n1.z - ray.anchor.z)\n        t = (self.kplane - self.normal.dot(a))/ray.direct.dot(self.normal)\n        if t > ray.t:\n            return False\n        if t > 0:\n            ray.t = t\n            ray.object = self\n            return True\n        return False\n\n    def shade(self, ray, lights, objects, bgcolor):\n        px = ray.anchor.x + ray.t*ray.direct.x\n        py = ray.anchor.y + ray.t*ray.direct.y\n        pz = ray.anchor.z + ray.t*ray.direct.z\n\n        p = Vector(px, py, pz)\n        v = Vector(-ray.direct.x, -ray.direct.y, -ray.direct.z)\n\n        return self.surface.shade(p, v, self.normal, lights, objects, bgcolor)\n","sub_path":"plane.py","file_name":"plane.py","file_ext":"py","file_size_in_byte":1337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"424525488","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.6 (3379)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /tmp/pip-build-ed191__6/grpcio/grpc/framework/foundation/stream_util.py\n# Compiled at: 2020-01-10 16:25:22\n# Size of source mod 2**32: 4772 bytes\n\"\"\"Helpful utilities related to the stream module.\"\"\"\nimport logging, threading\nfrom grpc.framework.foundation import stream\n_NO_VALUE = object()\n_LOGGER = logging.getLogger(__name__)\n\nclass TransformingConsumer(stream.Consumer):\n    __doc__ = 'A stream.Consumer that passes a transformation of its input to another.'\n\n    def __init__(self, transformation, downstream):\n        self._transformation = transformation\n        self._downstream = downstream\n\n    def consume(self, value):\n        self._downstream.consume(self._transformation(value))\n\n    def terminate(self):\n        self._downstream.terminate()\n\n    def consume_and_terminate(self, value):\n        self._downstream.consume_and_terminate(self._transformation(value))\n\n\nclass IterableConsumer(stream.Consumer):\n    __doc__ = 'A Consumer that when iterated over emits the values it has consumed.'\n\n    def __init__(self):\n        self._condition = threading.Condition()\n        self._values = []\n        self._active = True\n\n    def consume(self, value):\n        with self._condition:\n            if self._active:\n                self._values.append(value)\n                self._condition.notify()\n\n    def terminate(self):\n        with self._condition:\n            self._active = False\n            self._condition.notify()\n\n    def consume_and_terminate(self, value):\n        with self._condition:\n            if self._active:\n                self._values.append(value)\n                self._active = False\n                self._condition.notify()\n\n    def __iter__(self):\n        return self\n\n    def __next__(self):\n        return self.next()\n\n    def next(self):\n        with self._condition:\n            while self._active and not self._values:\n                self._condition.wait()\n\n            if self._values:\n                return self._values.pop(0)\n            raise StopIteration()\n\n\nclass ThreadSwitchingConsumer(stream.Consumer):\n    __doc__ = 'A Consumer decorator that affords serialization and asynchrony.'\n\n    def __init__(self, sink, pool):\n        self._lock = threading.Lock()\n        self._sink = sink\n        self._pool = pool\n        self._spinning = False\n        self._values = []\n        self._active = True\n\n    def _spin(self, sink, value, terminate):\n        while True:\n            try:\n                if value is _NO_VALUE:\n                    sink.terminate()\n                else:\n                    if terminate:\n                        sink.consume_and_terminate(value)\n                    else:\n                        sink.consume(value)\n            except Exception as e:\n                _LOGGER.exception(e)\n\n            with self._lock:\n                if terminate:\n                    self._spinning = False\n                    return\n                else:\n                    if self._values:\n                        value = self._values.pop(0)\n                        terminate = not self._values and not self._active\n                    else:\n                        if not self._active:\n                            value = _NO_VALUE\n                            terminate = True\n                        else:\n                            self._spinning = False\n                            return\n\n    def consume(self, value):\n        with self._lock:\n            if self._active:\n                if self._spinning:\n                    self._values.append(value)\n                else:\n                    self._pool.submit(self._spin, self._sink, value, False)\n                    self._spinning = True\n\n    def terminate(self):\n        with self._lock:\n            if self._active:\n                self._active = False\n                if not self._spinning:\n                    self._pool.submit(self._spin, self._sink, _NO_VALUE, True)\n                    self._spinning = True\n\n    def consume_and_terminate(self, value):\n        with self._lock:\n            if self._active:\n                self._active = False\n                if self._spinning:\n                    self._values.append(value)\n                else:\n                    self._pool.submit(self._spin, self._sink, value, True)\n                    self._spinning = True","sub_path":"pycfiles/libopenstorage_openstorage-0.42.24.1-py3-none-any/stream_util.cpython-36.py","file_name":"stream_util.cpython-36.py","file_ext":"py","file_size_in_byte":4453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"162908915","text":"\n\nfrom xai.brain.wordbase.verbs._pervade import _PERVADE\n\n#calss header\nclass _PERVADED(_PERVADE, ):\n\tdef __init__(self,): \n\t\t_PERVADE.__init__(self)\n\t\tself.name = \"PERVADED\"\n\t\tself.specie = 'verbs'\n\t\tself.basic = \"pervade\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/verbs/_pervaded.py","file_name":"_pervaded.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"258476745","text":"from sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.externals import joblib\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.preprocessing import Imputer\nfrom sklearn import svm\nfrom sklearn.metrics import mean_squared_error\nfrom pprint import pprint\nimport numpy as np\nimport pandas as pd\nfrom time import time\nfrom scipy import stats\nimport pywt\n\n\ndef get_pipes(random_state):\n    pipes = dict()\n    pipes[\"lr\"] = Pipeline([('scl', StandardScaler()),\n                        ('clf', LogisticRegression(random_state=random_state))])\n\n\n    pipes[\"rf\"] = Pipeline([('scl', StandardScaler()),\n                        ('clf', RandomForestClassifier(random_state=random_state))])\n\n\n    pipes[\"svm\"] = Pipeline([('scl', StandardScaler()),\n                        ('clf', svm.SVC(random_state=random_state))])\n\n\n    pipes[\"mlp\"] = Pipeline([('scl', StandardScaler()),\n                        ('clf', MLPClassifier(random_state=random_state))])\n    return pipes\n\ndef get_grids():\n    grids = dict()\n    grids[\"lr\"] = [{'clf__penalty': ['l2'],\n                        'clf__C': [0.3, 0.5, 1],\n                       'clf__solver': ['lbfgs', 'newton-cg']}] \n\n    grids[\"rf\"] = [{'clf__criterion': ['gini', 'entropy'],\n                       'clf__n_estimators': [10, 20, 30]}]\n\n    grids[\"svm\"] = [{'clf__kernel': ['sigmoid', 'rbf'], \n                        'clf__C': [0.3, 0.5, 1]}]\n\n    grids[\"mlp\"] = [{'clf__activation': ['logistic', 'relu'],\n                        'clf__solver': ['sgd', 'adam'],\n                        'clf__hidden_layer_sizes': [(10, ), (10, 5)]}]\n    return grids\n\ndef generate_pipeline(cv, jobs=-1, random_state=42):\n    # Construct some pipelines\n    pipes = get_pipes(random_state)        \n    grids = get_grids()\n    \n    # Construct grid searches\n    gs_lr = GridSearchCV(estimator=pipes[\"lr\"],\n                         param_grid=grids[\"lr\"],\n                         scoring='accuracy',\n                         cv=cv) \n\n    gs_rf = GridSearchCV(estimator=pipes[\"rf\"],\n                         param_grid=grids[\"rf\"],\n                         scoring='accuracy',\n                         cv=cv, \n                         n_jobs=jobs)\n\n    gs_svm = GridSearchCV(estimator=pipes[\"svm\"],\n                          param_grid=grids[\"svm\"],\n                          scoring='accuracy',\n                          cv=cv,\n                          n_jobs=jobs)\n\n    gs_mlp = GridSearchCV(estimator=pipes[\"mlp\"],\n                          param_grid=grids[\"mlp\"],\n                          scoring='accuracy',\n                          cv=cv)\n\n    # List of pipelines for ease of iteration\n    grids = [gs_lr, gs_rf, gs_svm, gs_mlp]\n    # Dictionary of pipelines and classifier types for ease of reference\n    grid_dict = {0: 'LR', \n                 1: 'RF', \n                 2: 'SVM',\n                 3: 'MLP'}\n    return (grids, grid_dict)\n\n\ndef run_pipeline(X, y, cv, out, sub, name):\n    # Fit the grid search objects\n    (grids, grid_dict) = generate_pipeline(cv, jobs=-1, random_state=42)\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n    y_test = y_test.values\n    print('Performing model optimizations...')\n    best_acc = 0.0\n    best_clf = 0\n    best_gs = ''\n    for idx, gs in enumerate(grids):\n        initial_time = time()\n        gs_out_name = grid_dict[idx] + '_' + name + '.csv'\n        gs_out = open(gs_out_name, 'w')\n        gs_out_count_name = \"count_\" + gs_out_name\n        gs_out_count = open(gs_out_count_name, \"w\")\n        print('\\nEstimator: %s' % grid_dict[idx])\t\n        # Fit grid search\t\n        toi = time()\n        gs.fit(X_train, y_train)\n        tof = time() - toi \n        # Best params\n        print('Params Test:')\n        means = gs.cv_results_['mean_test_score']\n        stds = gs.cv_results_['std_test_score']\n        \n        get_results(means, stds, gs, gs_out)\n        \n        print('Best params: %s' % gs.best_params_)\n        # Best training data accuracy\n        print('Best training accuracy: %.3f' % gs.best_score_)\n        # Predict on test data with best params\n        y_pred = gs.predict(X_test)\n\n        count_results(gs_out_count, y_pred, y_test)\n\n        # Test data accuracy of model with best params\n        acc = '%.2f' % accuracy_score(y_test, y_pred)\n        mse = '%.3f' % mean_squared_error(y_test, y_pred)\n        total = str(y_test.shape[0])\n        acc_n = str(int(y_test.shape[0] * float(acc)))\n        total_time = '%.2f' % (time() - initial_time)\n        print('Test set accuracy score for best params:', acc)\n        print('Test set mse score for best params:', mse)\n        print('Total time:', total_time)\n        # Track best (highest test accuracy) model\n        out.write(grid_dict[idx] + ',' + sub + ',' + acc + ',' + mse + ',' + total_time + '\\n')\n\n\ndef preprocess(X, na_values):\n    imp = Imputer(missing_values=na_values, strategy='mean', axis=1)\n    \n    imp.fit(X)\n    X = imp.transform(X)\n\n    X, _ = pywt.dwt(X, 'db1')\n\n    X_stats = stats.describe(X, axis=1)\n\n    X = pd.DataFrame({'min': X_stats.minmax[0], 'max': X_stats.minmax[1], \n                      'kurtosis': X_stats.kurtosis, 'skewness': X_stats.skewness, \n                      'variance': X_stats.variance, 'mean': X_stats.mean})\n    return X\n\n\ndef count_results(gs_out_count, y_pred, y_test):\n    # Count false/positive for each class\n    gs_out_count.write(\"Class,Correct,incorrect\\n\")\n    for i in np.unique(y_pred):\n        i_test = y_test == i\n        i_pred = y_pred == i\n        i_correct = i_test == i_pred\n        i_correct = i_correct[i_pred]\n        i_incorrect = i_correct.shape[0] - np.sum(i_correct)\n        gs_out_count.write(str(i) + \",\" + str(np.sum(i_correct)) + \",\" + str(i_incorrect) + \"\\n\")\n    gs_out_count.close()\n\n\ndef get_results(means, stds, gs, gs_out):\n    for mean, std, params in zip(means, stds, gs.cv_results_['params']):\n        print(\"%0.3f (+/-%0.03f) for %r\" % (mean, std * 2, params))\n        for p in params:\n            gs_out.write(str(params[p]) + ',')\n        gs_out.write(str(mean) + '\\n')\n    gs_out.close()\n","sub_path":"code/pipeline_gridsearch.py","file_name":"pipeline_gridsearch.py","file_ext":"py","file_size_in_byte":6464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"477766519","text":"import networkx as nx\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\nimport csv\nimport numpy as np\n\ndef load_walsk(filepath):\n    walks = []\n    with open(filepath, 'r') as read_obj:\n\n        reader = csv.reader(read_obj)\n        for row in reader:\n            lst = row[0].strip().split(' ')\n            w = [int(i[1:]) for i in lst]\n            walks.append(w)\n\n\n    #     walks = list(csv_reader)\n    print('walks: {}'.format(len(walks)))\n    return walks\n\ndef convert_walks_to_edges(walks):\n    edges = set()\n    for i in walks:\n        s = [e for e in i[1:]]\n        for j in range(len(s) - 1):\n            edges.add((s[j], s[j+1]))\n    print('Edges: {}'.format(len(edges)))\n    return edges\n\ndef check_walk_in_edge(lst, e_tup):\n    c, t = 0, 0\n    for i in tqdm(lst):\n        \n        head = i[0]\n        tail = i[1]\n        \n        \n        if ((head, tail) not in e_tup) and ((tail, head) not in e_tup):\n            c+=1\n#             print(head, tail)\n        elif ((head, tail)  in e_tup) or ((tail, head)  in e_tup):\n            t+=1\n     \n    print('not exist {} out of {}={}'.format(c, len(lst), t))\n    \ndef get_metapaths(walks, G, d_types, id_to_type, source_type_name, target_type_name):\n    mps = set()\n    for w in walks:\n#         print([int(i[1:]) for i in w])\n        mp = tuple([d_types[id_to_type[i]] for i in w])\n        mps.add(mp)\n        \n    err_c = 0    \n    print('target_type_name[0]: {}'.format(target_type_name[0]))\n    basic_mp = set()\n    for mp in mps:\n        lst = list(mp)\n\n        while len(lst) > 0:\n\n            try:\n                \n                i_pat = lst.index(target_type_name[0])\n                basic_mp.add(tuple(lst[:i_pat+1]))\n                lst = lst[i_pat+1:]\n            except ValueError as e:\n                err_c+=1\n                basic_mp.add(tuple(lst))\n                lst = []  \n                \n#     print(basic_mp)\n\n    total_count, good_count = 0, 0\n\n    for i in basic_mp:\n\n        total_count+=1\n        if (i[0] in source_type_name) and (i[-1] in target_type_name ):\n#             print(i)\n            good_count+=1\n    print('total: {}, Good: {}, Err_c: {}'.format(total_count, good_count, err_c))\n    \n    return basic_mp\n\ndef generate_plots(walks,d_types, id_to_type):\n    d_w = dict()\n    for w in walks:\n        for i in w:\n            n_id = i\n            if n_id not in d_w:\n                d_w[n_id] = 0\n            d_w[n_id] +=1\n            \n    d_type_freq  = dict()\n    for n in d_w:\n        node_freq = d_w[n]\n        type_id = id_to_type[n]\n\n        t_n_G = int(id_to_type[n])\n        if t_n_G!=type_id:\n            print(n, type_id, t_n_G)\n            print('Error that should not occur')\n        type_name = d_types[type_id]\n        if type_name not in d_type_freq:\n            d_type_freq[type_name] = 0\n        d_type_freq[type_name] += node_freq\n\n    arr_freq = np.array([[i, d_type_freq[i]] for i in d_type_freq])\n    \n    d_type_count = dict()\n    for n in d_w:\n        node_freq = d_w[n]\n        type_id = id_to_type[n]\n        type_name = d_types[type_id]\n\n        if type_name not in d_type_count:\n            d_type_count[type_name] = 0\n        d_type_count[type_name] += 1\n\n    arr_count = np.array([[i, d_type_count[i]] for i in d_type_count])\n#     print('Frequent:')\n    avg_appearance_of_types_histogram(arr_freq, 'Frequency of types')\n#     print('count:')\n    avg_appearance_of_types_histogram(arr_count, 'Unique number of each types')\n\n    d_type_avg = dict()\n    for t in d_types:\n        type_name = d_types[t]\n        if type_name in d_type_freq:\n            t_count = d_type_count[type_name]\n            t_freq = d_type_freq[type_name]\n            t_avg = t_freq / t_count\n            d_type_avg[type_name] = t_avg\n\n    arr_avg = np.array([[i, d_type_avg[i]] for i in d_type_avg])\n#     print('average:')\n    avg_appearance_of_types_histogram(arr_avg, 'Average Frequency of types')\n\ndef avg_appearance_of_types_histogram(lst, title = 'No Title'):\n    arr = np.array(lst)\n    \n    x = np.arange(len(arr[:, 0]))\n    labels = arr[:,0]\n#     labels = [d_types[i] for i in x]\n    y = np.array([float(i) for i in arr[:, 1]])\n    \n    fig, ax = plt.subplots(figsize=(10,10))\n    plt.bar(x=x, height=y)\n    \n    fig.suptitle(title, fontsize=16)\n    plt.xticks(x, labels, rotation='vertical')\n#     plt.xticks(x, rotation='vertical')\n\ndef draw_plot(d):\n    x = []\n    x_labels= []\n    for i in d:\n        x_labels.append(i)\n        x.append(G.degree[i])\n    y = list(d.values())\n    plt.scatter(x, y)  \n    \ndef draw_plot_labels(d):\n    x = []\n    x_labels= []\n    for i in d:\n        x_labels.append(i)\n        x.append(G.degree[i])\n    y = list(d.values())\n             \n    fig, ax = plt.subplots(figsize=(10,10))\n    ax.scatter(x, y)\n\n    for i, txt in enumerate(x_labels):\n        ax.annotate(txt, (x[i], y[i]))\n        \ndef load_graph(df):\n    \n    print('load_graph| df : {}'.format(df.shape))\n    df_edges = df[['h_id', 't_id','r_id']]\n    edges = [tuple(x) for x in df_edges.values]\n    print('load_graph| edges: {}'.format(len(edges)))\n\n    GRAPH = nx.Graph()   # or DiGraph, MultiGraph, MultiDiGraph, etc\n    GRAPH.add_weighted_edges_from(edges)\n    return GRAPH\n\ndef get_dict_types(df_train):\n    h_types = dict(tuple(zip(df_train.h_c_id,df_train.h_c)))\n    d_types = dict(tuple(zip(df_train.t_c_id,df_train.t_c)))\n    d_types.update(h_types)\n#     print(d_types)\n    \n    h = dict(tuple(zip(df_train.h_id,df_train.h_c_id)))\n    id_to_type = dict(tuple(zip(df_train.t_id,df_train.t_c_id)))\n    id_to_type.update(h)\n    \n#     print(id_to_type)\n    \n    return d_types, id_to_type\n\ndef check_walks(data_path,  walk_file,train_file, source_type_name, target_type_name, chk_mp = False, chk_hist=False, draw_plot = False):\n    df_train = pd.read_csv(data_path + train_file, dtype={'r':'str','h':'str', 't':'str'})\n    print('df_train: {}'.format(df_train.shape))\n    \n    e_tup = {(i[4],i[5]) for i in df_train.itertuples()}\n    print('e_tup: {}, df_train.shape[0]: {}'.format(len(e_tup), df_train.shape[0]))\n    \n    walks = load_walsk(data_path+ walk_file)\n    e_list = convert_walks_to_edges(walks)\n    print('e_list: {}'.format(len(e_list)))\n    check_walk_in_edge(e_list, e_tup)\n    \n    mp = None\n    \n    if chk_mp == True:\n        G = load_graph(df_train)\n        d_types, id_to_type = get_dict_types(df_train)\n        mp = get_metapaths(walks, G, d_types, id_to_type, source_type_name, target_type_name)\n    \n    if chk_hist == True:\n        len_of_walks =[len(w) for w in walks]\n        _ = plt.hist(len_of_walks)\n        \n    if draw_plot == True:\n        d_types, id_to_type = get_dict_types(df_train)\n        generate_plots(walks, d_types, id_to_type)\n        \n    return walks, mp","sub_path":"graphpattern2vec/check_walks.py","file_name":"check_walks.py","file_ext":"py","file_size_in_byte":6732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"544715517","text":"from django.db import models\n\n\n# Create your models here.\nclass Area(models.Model):\n    \"\"\"地区类\"\"\"\n    title = models.CharField(max_length=50, verbose_name='区域名称')\n    # 外键： 自关联\n    parent = models.ForeignKey('self', null=True, blank=True)\n\n    def parent_area(self):\n        if self.parent is None:\n            return \"\"\n        return self.parent.title\n\n    # 指定列名称\n    parent_area.short_description = '上级区域'\n    # 指定方法列按id进行排序\n    parent_area.admin_order_field = 'id'\n\n    def __str__(self):\n        return self.title\n\n\nclass User(models.Model):\n    name = models.CharField(max_length=20)\n    # 头像上传到哪里\n    avent = models.ImageField(upload_to='app01')\n","sub_path":"app01/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"375483447","text":"\"\"\"\nПользователь вводит месяц в виде целого числа от\n1 до 12. Сообщить к какому времени\nгода относится месяц (зима, весна, лето, осень).\nНапишите решения через list и через dict.\n\"\"\"\n\n\ndef user_answer_check(mesage):  # фукнкция для проверки ввода пользователем\n    answer = input(mesage)\n    while answer.isdigit() == False:\n        answer = input(mesage)\n    return int(answer)\n\n\nmount_list = [['Зима', 12, 1, 2], ['Весна', 3, 4, 5], ['Лето', 6, 7, 8], ['Осень', 9, 10, 11]]\nmount_dict = {'Зима':[12, 1, 2], 'Весна':[3, 4, 5], 'Лето':[6, 7, 8], 'Осень':[9, 10, 11]}\n\nuser_request = user_answer_check('Введите номер месяца: ')  # Получение номера месяца\n\nfor i in range(len(mount_list)):\n    '''\n    Цикл на каждой итерации берет по индексу из списка mount_list вложениый список \n    проверяет есть ли во вложенном списке номер месяца который ищем\n    если нет переходит к следущему если есть то выводит в консоль 0-й элемент вложенного списка\n    которым всегда является месяц\n    '''\n    if user_request in mount_list[i]: #Проверяет есть ли во вложеном списке номер месяца введенного пользователем\n        print('Реализовано через list\\nвведенный месяц: ', mount_list[i][0])\n\nfor key in mount_dict:\n    '''\n    цикл на каждой итерации проверяет значение ключа на вхождение месяца который мы ищем\n    если вхождения нет переходит к следующему ключу если есть выводит в консоль ключ который \n    является названием месяца\n    '''\n    if user_request in mount_dict[key]: \n        print('Реализовано через dict\\nвведенный месяц: ', key)\n","sub_path":"task_2_3.py","file_name":"task_2_3.py","file_ext":"py","file_size_in_byte":2281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"69601453","text":"from seleniumbase import BaseCase\n\n\nclass ProxyTests(BaseCase):\n    def test_proxy(self):\n        self.open(\"https://ipinfo.io/\")\n        ip_address = self.get_text('#ip-string span[class*=\"primary\"] span')\n        print(\"\\n\\nMy IP Address = %s\\n\" % ip_address)\n        print(\"Displaying Host Info:\")\n        text = self.get_text(\"#widget-scrollable-container\").split(\"asn:\")[0]\n        rows = text.split(\"\\n\")\n        data = []\n        for row in rows:\n            if row.strip() != \"\":\n                data.append(row.strip())\n        print(\"\\n\".join(data).replace('\\n\"', \" \"))\n        print(\"\\nThe browser will close automatically in 7 seconds...\")\n        self.sleep(7)\n","sub_path":"examples/proxy_test.py","file_name":"proxy_test.py","file_ext":"py","file_size_in_byte":674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"310656291","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Mar 20 17:39:14 2017\n\n@author: rickdberg\n\nCombine calculated metadata, site_info, and summary_all tables for sites with\ncalculated fluxes\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\n\n\ndef metadata_compiler(engine, metadata, site_info, hole_info, Leg, Site):\n\n    # Load site data\n    sql = \"SELECT * FROM {} where leg = '{}' and site = '{}';\".format(site_info, Leg, Site)\n    sitedata = pd.read_sql(sql, engine)\n\n    # Load hole data\n    sql = \"SELECT * FROM {} where leg = '{}' and site = '{}';\".format(hole_info, Leg, Site)\n    holedata = pd.read_sql(sql, engine)\n    # Group and average hole data for sites\n    hole_grouped = holedata.loc[:,('site_key', 'lat','lon','water_depth',\n                                   'total_penetration','etopo1_depth',\n                                   'surface_porosity', 'sed_thickness',\n                                   'crustal_age','coast_distance',\n                                   'ridge_distance', 'seamount',\n                                   'surface_productivity','toc', 'opal',\n                                   'caco3', 'woa_temp', 'woa_salinity',\n                                   'woa_o2','lith1','lith2','lith3','lith4',\n                                   'lith5','lith6','lith7','lith8','lith9',\n                                   'lith10','lith11','lith12','lith13'\n                                   )].groupby(\"site_key\").mean().reset_index()\n    # Load metadata\n    try:\n        sql = \"SELECT * FROM {} where leg = '{}' and site = '{}';\".format(metadata, Leg, Site)\n        ran_metadata = pd.read_sql(sql, engine)\n    except:\n        ran_metadata = pd.DataFrame(sitedata.loc[:,['site_key','leg','site']])\n\n    # Combine all tables\n    site_meta_data = pd.merge(ran_metadata, sitedata, how='outer', on=('site_key', 'leg', 'site'))\n    site_metadata = pd.merge(site_meta_data, hole_grouped, how='outer', on=('site_key')).fillna(np.nan)\n    # site_metadata = data.dropna(subset = ['interface_flux']).reset_index(drop=True)\n    return site_metadata\n\n# eof\n","sub_path":"site_metadata_compiler.py","file_name":"site_metadata_compiler.py","file_ext":"py","file_size_in_byte":2060,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"185027654","text":"# -*- coding: utf-8 -*-\n# @Author: Safer\n# @Date:   2016-08-23 21:02:06\n# @Last Modified by:   Safer\n# @Last Modified time: 2016-08-26 00:04:51\n\nimport sys\nfrom PyQt5.QtWidgets import QApplication\nfrom PyQt5.QtWidgets import (QDialog, QWidget, QPushButton, QLabel, QLineEdit)\n\n\nclass LoginRegister(QDialog):\n\n    def __init__(self, _type='', parent=None):\n        super(LoginRegister, self).__init__()\n\n        self._type = _type\n\n        if _type == 'login':\n            self.login()\n        else:\n            self.register()\n\n        self._init_()\n\n    def _init_(self):\n\n        self.setGeometry(900, 500, 250, 300)\n\n        self.setWindowTitle(\"登录\")\n        self.exec_()\n\n    def login(self):\n\n        lbl_email = QLabel('邮    箱', self)\n        lbl_email.move(20, 50)\n\n        self.email = QLineEdit(self)\n        self.email.move(80, 50)\n\n        lbl_pwd = QLabel('密    码', self)\n        lbl_pwd.move(20, 100)\n\n        self.pwd = QLineEdit(self)\n        self.pwd.setEchoMode(QLineEdit.Password)\n        self.pwd.move(80, 100)\n\n        btn_login = QPushButton('登录', self)\n        btn_login.move(90, 200)\n        btn_login.clicked.connect(self._submit)\n\n        btn_register = QPushButton('注册', self)\n        btn_register.move(90, 240)\n        btn_register.clicked.connect(self.register)\n\n    def register(self):\n\n        self._type = 'register'\n\n        # self.close()\n\n        # lbl1 = QLabel('重复密码', self)\n        # lbl1.move(20, 150)\n\n        # self.repwd = QLineEdit(self)\n        # self.repwd.move(80, 150)\n\n        # btn_login = QPushButton('注册', self)\n        # btn_login.move(90, 200)\n        # btn_login.clicked.connect(self._submit)\n\n        # self.show()\n        pass\n\n    def _submit(self):\n        email = self.email.text()\n        pwd = self.pwd.text()\n        if self._type == 'register':\n            repwd = self.repwd.text()\n        print(email, pwd)\n\n    def closeEvent(self, event):\n        print(\"关闭窗口...\")\n        # event.ignore()\n\n\nif __name__ == '__main__':\n\n    app = QApplication(sys.argv)\n    ex = LoginRegister('login')\n    sys.exit(app.exec_())\n","sub_path":"back/python/QT5/loginRegister/loginRegister2.py","file_name":"loginRegister2.py","file_ext":"py","file_size_in_byte":2116,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"431559170","text":"def input_integer(text_to_display):\n    \"\"\"\n    This function will take input from the user check if the input is an integer or not.\n    If its not, func will raise exception and let the user try again to input integer until\n    user enter the integer.\n    :param text_to_display:\n    :return: integer\n    \"\"\"\n    while True:\n        try:\n            num = int(input(f'{text_to_display}'))\n            return num\n        except ValueError:\n            print('(ValueError) WARNING! Input should be an integer.')\n        except Exception as e:\n            print(e)\n            continue\n\n\nclass Date:\n    def __init__(self, day, month, year):\n        self.__day = day\n        self.__month = month\n        self.__year = year\n\n    def get_day(self):\n        return self.__day\n\n    def get_month(self):\n        return self.__month\n\n    def get_year(self):\n        return self.__year\n\n    def set_day(self, n_day):\n        self.__day = n_day\n\n    def set_month(self, n_month):\n        self.__month = n_month\n\n    def set_year(self, n_year):\n        self.__year = n_year\n\n    def display(self):\n        return f'Day/Month/Year: {self.__day}/{self.__month}/{self.__year}'\n\n    def __eq__(self, other):\n        if (self.__day == other.__day) and (self.__month == other.__month) and (self.__year == other.__year):\n            return True\n        else:\n            return False\n\n    def __lt__(self, other):\n        if self.__year < other.__year:\n            return True\n        elif self.__year == other.__year:\n            if self.__month < other.__month:\n                return True\n            elif self.__month == other.__month:\n                if self.__day < other.__day:\n                    return True\n            else:\n                return False\n        else:\n            return False\n\n    @classmethod\n    def add_date(cls):\n        d_objection = 'KINDLY ENTER VALID DAY NUMBER!'\n        m_objection = 'KINDLY ENTER VALID MONTH NUMBER!'\n        y_objection = 'KINDLY ENTER VALID YEAR NUMBER!'\n        while True:\n            y = int(input('Enter Year: '))\n            try:\n                if y <= 0:\n                    print(y_objection)\n                    continue\n                else:\n                    break\n            except ValueError:\n                print(y_objection)\n                continue\n\n        while True:\n            m = int(input('Enter Month (i.e. 1-12): '))\n            try:\n                if (m > 12) or (m <= 0):\n                    print(m_objection)\n                    continue\n                else:\n                    break\n            except ValueError:\n                print(m_objection)\n                continue\n\n        while True:\n            d = int(input('Enter Day (i.e. 1-31): '))\n            is_leap = (y % 4 == 0)\n            try:\n                if (not is_leap and ((m == 2) and ((d > 28) or (d <= 0)))) or (\n                        is_leap and ((m == 2) and ((d > 29) or (d <= 0)))) or (\n                        ((d > 30) and (d <= 0)) and (m in [4, 6, 9, 11])) or (\n                        d <= 0):\n                    print(d_objection)\n                    continue\n                else:\n                    break\n            except ValueError:\n                print('KINDLY ENTER VALID DAY NUMBER!')\n                continue\n        print('')\n        return cls(d, m, y)\n\n\nclass Time:\n    def __init__(self, hour, minute):\n        if (hour <= 24) or (hour >= 0):\n            self.__hour = hour\n            self.__minute = minute\n\n    def get_hour(self):\n        return self.__hour\n\n    def get_minute(self):\n        return self.__minute\n\n    def set_hour(self, n_hour):\n        self.__hour = n_hour\n\n    def set_minute(self, n_minute):\n        self.__minute = n_minute\n\n    def __eq__(self, other):\n        if (self.__hour == other.__hour) and (self.__minute == other.__minute):\n            return True\n        else:\n            return False\n\n    def __sub__(self, other):\n        hours = self.__hour - other.__hour\n        minutes = self.__minute - other.__minute\n        return Time(hours, minutes)\n\n    def __gt__(self, other):\n        if (self.__hour >= other.__hour) and (self.__minute > other.__minute):\n            return True\n        else:\n            return False\n\n    @classmethod\n    def add_time(cls):\n        t_objection = 'KINDLY ENTER VALID INPUT!'\n        # Take Hours\n        while True:\n            h = int(input('Enter Hour (i.e. 1-24): '))\n            try:\n                if (h > 24) or (h < 1):\n                    print(t_objection)\n                    continue\n                else:\n                    break\n            except ValueError:\n                print(t_objection)\n                continue\n        # Take Minutes\n        while True:\n            m = int(input('Enter Minutes (i.e. 0-60): '))\n            try:\n                if (m > 60) or (m < 0):\n                    print(t_objection)\n                    continue\n                else:\n                    break\n            except ValueError:\n                print(t_objection)\n                continue\n        return cls(h, m)\n\n    def display(self):\n        return f'{self.__hour}:{self.__minute}'\n\n\nclass Person:\n    def __init__(self, **kwargs):\n        self.id = kwargs['id']\n        self.name = kwargs['name']\n        self.gender = kwargs['gender']\n        self.age = kwargs['age']\n\n    def update_name(self):\n        \"\"\"\n        \"\"\"\n        pass\n\n    def update_password(self):\n        \"\"\"\n        \"\"\"\n        pass\n\n    def login(self):\n        \"\"\"\n        \"\"\"\n        pass\n\n    def signup(self):\n        \"\"\"\n        \"\"\"\n        pass\n","sub_path":"utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":5579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"131838542","text":"from operator import itemgetter\ndef compute_ranks(number, games):\n\n    scoreRecord = {team: [0, 0, 0] for team in range(number)}\n    # team: [points, goalsDiff, goals]-teamscores\n    for team1, team2, team1goals, team2goals in games: \n        \n        scoreRecord[team1][1] += team1goals - team2goals\n        scoreRecord[team1][2] += team1goals\n        scoreRecord[team2][1] += team2goals - team1goals\n        scoreRecord[team2][2] += team2goals\n\n        if team1goals > team2goals: \n            scoreRecord[team1][0] += 2\n        elif team2goals > team1goals:\n            scoreRecord[team2][0] += 2\n        else:\n            scoreRecord[team1][0] += 1\n            scoreRecord[team2][0] += 1\n    \n    sortedTeams = sorted(scoreRecord.items(), key=itemgetter(1), reverse=True)\n    print(sortedTeams)\n    # for i in range(number):\n    #     rank[sortedTeams[i][0]] = i + 1\n    rank, rank2, previousTeamScores = [0 for i in range(number)], None, None\n    for i, (team, teamscores) in enumerate(sortedTeams, 1):\n        if previousTeamScores == teamscores: rank[team] = rank2\n        else: rank[team], rank2, previousTeamScores = i, i, teamscores\n    \n    return rank\n\nprint(compute_ranks(6, [[0, 5, 2, 2],\n                        [1, 4, 0, 2],\n                        [2, 3, 1, 2],\n                        [1, 5, 2, 2],\n                        [2, 0, 1, 1],\n                        [3, 4, 1, 1],\n                        [2, 5, 0, 2],\n                        [3, 1, 1, 1],\n                        [4, 0, 2, 0]]))","sub_path":"sportsRanks.py","file_name":"sportsRanks.py","file_ext":"py","file_size_in_byte":1508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"149076436","text":"import re\r\ns = \"АБА БВГБД   ВВВ\"\r\nresult = []\r\nfor word in re.split(r' ', s):\r\n    if word:\r\n        fl = word[0]\r\n        word = ''.join(['.' if x == fl else x for x in word[1:]])\r\n        result.append(fl + word)\r\n    else:\r\n        result.append('')\r\n \r\nprint(' '.join(result))","sub_path":"21-3.py","file_name":"21-3.py","file_ext":"py","file_size_in_byte":291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"473383110","text":"import sys\nimport re\nimport itertools\nfrom subprocess import call\n\n\nprotein_dir_name = \"fungus_fungus\"\npeptide_dir_name = \"CAZY_PPR_patterns/GH\" \nthread_no = 1\npeptide_length = 6 #length of conserved peptides\nhit_cut_off = 3 #number of conserved peptides necessary to classify a protein\nfreq_cut_off = 1.0 #minimum sum of frequencies necessary to classify a protein\n\n\nargc = len(sys.argv)\nif argc > 1:\n\tthread_no = int(sys.argv[1])\nif argc > 2:\n\tprotein_dir_name = sys.argv[2].replace(\"?\", \" \")\nif argc > 3:\n\tpeptide_dir_name = sys.argv[3].replace(\"?\", \" \")\nif argc > 4:\n\tpeptide_length = int(sys.argv[4])\nif argc > 5:\n\thit_cut_off = int(sys.argv[5])\nif argc > 6:\n\tfreq_cut_off = float(sys.argv[6])\noutf_name = protein_dir_name+\"/thread\"+str(thread_no)+\".txt\"\n\nclass Protein:\n\tdef __init__(self, sequ):\n\t\tself.seq = sequ.upper()\n\t\tself.name = None\n\t\tself.peptides = None\n\t\tself.hits = None\n\t\tself.freq = None\n\t\tself.group = None\n\t\tself.classified = None\n\t\n\tdef make_nmer_peptides(self, n):\n\t\tself.peptides = []\n\t\tseq = self.seq\n\t\tfor x in range(n):\n\t\t\tfor y in range(x, len(seq), n):\n\t\t\t\tself.peptides.append(seq[y:y+n])\n\t\tset(self.peptides)\n\t\t\nclass Group:\n\tdef __init__(self, fami, numb):\n\t\tself.fam = fami\n\t\tself.number = numb\n\t\tself.pep_hash = {}\n\t\tself.hits = None\n\t\tself.freq = None\n\n\n\nprot_array = []\n#####################\nseq = \"\"\nname = \"\"\np = \"\"\nfirst = True\nwith open(protein_dir_name+\"/orfs\"+str(thread_no)+\".txt\") as f:\n\tfor line in f:\n\t\tif line.startswith(\">\"):\n\t\t\tif not first:\n\t\t\t\tp = Protein(seq)\n\t\t\t\tp.name = name\n\t\t\t\tp.make_nmer_peptides(peptide_length)\n\t\t\t\tprot_array.append(p)\n\t\t\t\tseq = \"\"\n\t\t\tname = line.rstrip()\n\t\t\tfirst = False\n\t\telse:\n\t\t\tseq += line.rstrip()\np = Protein(seq)\np.name = name\np.make_nmer_peptides(peptide_length)\nprot_array.append(p)\n##########\n#f = open(protein_dir_name+\"/orfs\"+str(thread_no)+\".txt\", 'r').readlines()\n#for x in range(len(f)):\n\t#if f[x].startswith(\">\"):\n\t\t#p = Protein(f[x+1].rstrip())\n\t\t#p.name = f[x].rstrip()\n\t\t#p.make_nmer_peptides(peptide_length)\n\t\t#prot_array.append(p)\n###################\npep_list_array = []\ntry:\n\tpep_list_array = open(peptide_dir_name+\"/fam_list.txt\", 'r').readlines()\nexcept:\n\tpep_list_array = open(peptide_dir_name+\"/large_fams.txt\", 'r').readlines()\nwith open(outf_name, 'w') as out:\n\tfor fam in pep_list_array:\n\t\tprotein_hits = 0\n\t\tpep_group_array = []\n\t\tfam_all_peps_array = []\n\t\tfam = fam.rstrip()\n\t\tf = []\n\t\ttry:\n\t\t\tf = open(peptide_dir_name+\"/\"+fam+\"/\"+fam+\"_conserved_peptides.txt\").readlines()\n\t\texcept:\t\n\t\t\tf = open(peptide_dir_name+\"/\"+fam+\"/\"+fam+\"_peptides_scores.txt\").readlines()\n\t\tfor x in range(len(f)):\n\t\t\tline = f[x].rstrip()\n\t\t\tif \"group\" in line:\n\t\t\t\tg = Group(fam, line.split(\"group \")[-1])\n\t\t\t\tpep_array = f[x+1].split(\";\")\n\t\t\t\tpep_array.pop()\n\t\t\t\tfor pep in pep_array:\n\t\t\t\t\tarr = pep.split(\",\")\n\t\t\t\t\tg.pep_hash[arr[0]] = float(arr[-1])\n\t\t\t\t\tfam_all_peps_array.append(arr[0])\n\t\t\t\tpep_group_array.append(g)\n\t\tset(fam_all_peps_array)\n\t\tprot_hit_array = []\n\t\tfor p in prot_array:\n\t\t\tprot_fam_peps = set.intersection(set(p.peptides), set(fam_all_peps_array))\n\t\t\tno_peps = len(prot_fam_peps)\n\t\t\tgroup_score_array = []\n\t\t\tif no_peps == hit_cut_off:\n\t\t\t\tindex_arr = []\n\t\t\t\tfor pep in prot_fam_peps:\n\t\t\t\t\tindex_arr.append(p.seq.find(pep))\n\t\t\t\tindex_arr.sort()\n\t\t\t\tif index_arr[-1]-index_arr[0] > 3:\n\t\t\t\t\tno_peps += 1\n\t\t\tif no_peps > hit_cut_off and len(set(list(\"\".join(prot_fam_peps)))) > 4:\n\t\t\t\tfor g in pep_group_array:\n\t\t\t\t\tpep_array = set.intersection(set(prot_fam_peps), set(g.pep_hash.keys()))\n\t\t\t\t\thits = len(pep_array)\n\t\t\t\t\tfreq = 0.0\n\t\t\t\t\tfor pep in pep_array:\n\t\t\t\t\t\tif hits >= hit_cut_off:\n\t\t\t\t\t\t\tfreq += g.pep_hash[pep]\n\t\t\t\t\t\tif freq >= freq_cut_off:\n\t\t\t\t\t\t\tgroup_score_array.append([freq, hits, g.number, pep_array])\n\t\t\tif len(group_score_array) > 0:\n\t\t\t\tgroup_score_array.sort(key= lambda x: (int(x[0]), int(x[1]), -int(x[2])))\n\t\t\t\tif group_score_array[-1][1] >= hit_cut_off and group_score_array[-1][0] >= freq_cut_off:\n\t\t\t\t\tif protein_hits == 0:\n\t\t\t\t\t\tout.write(\"Family \"+fam+\"\\n\")\n\t\t\t\t\tout.write(p.name+'\\n')\n\t\t\t\t\tout.write(p.seq+'\\n')\n\t\t\t\t\tout.write(\",\".join(group_score_array[-1][3])+'\\n')\n\t\t\t\t\tout.write(str(group_score_array[-1][1])+'\\n')\n\t\t\t\t\tout.write(str(float(str(group_score_array[-1][0])[0:4]))+'\\n')\n\t\t\t\t\tout.write(group_score_array[-1][2]+'\\n')\n\t\t\t\t\tprotein_hits += 1\n\t\t\t\t\t\ncall(['cp', outf_name, 'temp'])\n\t\t\t\n","sub_path":"example/Hotpep/bact_group_many_proteins_many_patterns.py","file_name":"bact_group_many_proteins_many_patterns.py","file_ext":"py","file_size_in_byte":4338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"19000083","text":"import copy\nimport multiprocessing\n\nimport six\n\nfrom chainer.dataset import convert\nfrom chainer.dataset import iterator as iterator_module\nfrom chainer import optimizer as optimizer_module\nfrom chainer import variable\nfrom chainer import cuda\nimport chainer\nfrom cupy.cuda import nccl\nfrom chainer.training.updater import StandardUpdater, _calc_loss\n\nimport os\n\n\nclass _Worker(multiprocessing.Process):\n    def __init__(self, proc_id, pipe, master):\n        super(_Worker, self).__init__()\n        self.proc_id = proc_id\n        self.pipe = pipe\n        self.converter = master.converter\n        self.model = master._master\n        self.device = master._devices[proc_id]\n        self.iterator = master._mpu_iterators[proc_id]\n        self.n_devices = len(master._devices)\n\n    def setup(self):\n        print(\"setup %d\" % self.device)\n        _, comm_id = self.pipe.recv()\n        self.comm = nccl.NcclCommunicator(self.n_devices, comm_id,\n                                          self.proc_id)\n\n        self.model.to_gpu(self.device)\n        self.reporter = chainer.reporter.Reporter()\n        self.reporter.add_observer('main', self.model)\n        print(\"%d setup done\" % self.device)\n\n    def run(self):\n        dev = cuda.Device(self.device)\n        dev.use()\n        self.setup()\n        pp = None\n        while True:\n            job, data = self.pipe.recv()\n            if job == 'finalize':\n                dev.synchronize()\n                # chainer.serializers.save_npz('dump_model_{}'.format(self.device), self.model, compression=False)\n                break\n            if job == 'update':\n                # For reducing memory\n                self.model.cleargrads()\n\n                batch = self.converter(self.iterator.next(), self.device)\n                observation = {}\n                with self.reporter.scope(observation):\n                    loss = _calc_loss(self.model, batch)\n\n                self.model.cleargrads()\n                loss.backward()\n\n                del loss\n\n                gg = self.model.gather_grads()\n                null_stream = cuda.Stream.null\n                self.comm.reduce(gg.data.ptr, gg.data.ptr, gg.size,\n                                 nccl.NCCL_FLOAT, nccl.NCCL_SUM, 0, null_stream.ptr)\n                del gg\n                self.model.cleargrads()\n                if pp is None:\n                    pp = self.model.gather_params()\n                self.comm.bcast(pp.data.ptr, pp.size, nccl.NCCL_FLOAT, 0, null_stream.ptr)\n                self.model.scatter_params(pp)\n                pp = None\n\n                # Sending observation via pipe is too slow.\n                # self.pipe.send(observation)\n\n\nclass MultiprocessParallelUpdater(StandardUpdater):\n    \"\"\"Implementation of a multiprocess parallel GPU Updater.\n\n    This is an implementation of :class:`Updater` that uses multiple GPUs\n    with multi-process data parallelism. It uses Nvidia NCCL for communication\n    between multiple GPUs.\n\n    It behaves similarly to :class:`~chainer.training.StandardUpdater`. The\n    update routine is modified to support data-parallel computation on multiple\n    GPUs in one machine. It is based on synchronous parallel SGD: it\n    parallelizes the gradient computation over a mini-batch, and updates the\n    parameters only in the main device.\n\n    Args:\n        iterators: List of dataset iterator for the training dataset. The number\n            of the iterators must be same to the number of GPUs you use.\n        optimizer: Optimizer to update parameters. The model should be attached\n            to the optimizer.\n        converter: Converter function to build input arrays. Each batch\n            extracted by the iterator is split equally between the devices and\n            then passed with corresponding ``device`` option to this function.\n            :func:`~chainer.dataset.concat_examples` is used by default.\n        devices: Dictionary or list of devices to which the training data is\n            sent. The master device will be the first one in the list or the\n            value attached to the key ``'main'``.\n\n    \"\"\"\n\n    def __init__(self, iterators, optimizer, converter=convert.concat_examples,\n                 devices=None):\n\n        assert len(iterators) == len(devices)\n        for iterator in iterators[1:]:\n            assert len(iterator.dataset) == len(iterators[0].dataset)\n\n        # Correct optimizer parameters for new minibatch size\n        optim = optimizer.__class__.__name__\n        if optim in ('Adam', 'AdaGrad', 'RMSprop'):\n            optimizer.eps *= len(devices)\n        elif optim in ('RMSpropGraves', 'AdaDelta'):\n            optimizer.eps *= len(devices) ** 2  # not quite right for AdaDelta\n        else:\n            optimizer.lr /= len(devices)\n\n        super(MultiprocessParallelUpdater, self).__init__(\n            iterator=iterators[0],\n            optimizer=optimizer,\n            converter=converter\n        )\n\n        if isinstance(devices, dict):\n            main = devices.pop('main')\n            devices = list(six.itervalues(devices))\n            devices = [main] + devices\n        if devices is None or any(device is None for device in devices):\n            raise ValueError('must specify GPU devices')\n\n        self._master = optimizer.target\n        self._devices = devices\n        self._mpu_iterators = iterators\n        self._initialized = False\n\n        self._pipes = []\n        self._workers = []\n        self.comm = None\n\n    def _send_message(self, message):\n        for pipe in self._pipes:\n            pipe.send(message)\n\n    def setup_workers(self):\n        if self._initialized:\n            return\n        self._initialized = True\n\n        self._master.cleargrads()\n        for i in six.moves.range(1, len(self._devices)):\n            pipe, worker_end = multiprocessing.Pipe()\n            worker = _Worker(i, worker_end, self)\n            worker.start()\n            self._workers.append(worker)\n            self._pipes.append(pipe)\n\n        with cuda.Device(self._devices[0]):\n            self._master.to_gpu(self._devices[0])\n            comm_id = nccl.get_unique_id()\n            self._send_message((\"set comm_di\", comm_id))\n            self.comm = nccl.NcclCommunicator(len(self._devices), comm_id, 0)\n\n    def update_core(self):\n        self.setup_workers()\n\n        self._send_message(('update', None))\n        with cuda.Device(self._devices[0]):\n            # For reducing memory\n            self._master.cleargrads()\n\n            optimizer = self.get_optimizer('main')\n            batch = self.get_iterator('main').next()\n            batch = self.converter(batch, self._devices[0])\n\n            loss = _calc_loss(self._master, batch)\n\n            self._master.cleargrads()\n            loss.backward()\n\n            # NCCL: reduce grads\n            gg = self._master.gather_grads()\n            null_stream = cuda.Stream.null\n            self.comm.reduce(gg.data.ptr, gg.data.ptr, gg.size,\n                             nccl.NCCL_FLOAT, nccl.NCCL_SUM,\n                             0, null_stream.ptr)\n            self._master.scatter_grads(gg)\n            del gg\n            optimizer.update()\n            pp = self._master.gather_params()\n            self.comm.bcast(pp.data.ptr, pp.size, nccl.NCCL_FLOAT,\n                            0, null_stream.ptr)\n\n            # Sending observation via pipe is too slow.\n            # for pipe in self._pipes:\n            #     chainer.reporter.report(pipe.recv())\n\n    def finalize(self):\n        self._send_message(('finalize', None))\n\n        for worker in self._workers:\n            print(\"join\", worker)\n            worker.join()\n\n            # chainer.serializers.save_npz('dump_model_root', self._master, compression=False)\n","sub_path":"chainer/training/updaters/multiprocess_parallel_updater.py","file_name":"multiprocess_parallel_updater.py","file_ext":"py","file_size_in_byte":7705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"225857959","text":"import pandas as pd\nimport numpy as np\nimport sklearn.metrics as sk_metrics\nimport torch.nn as nn\n\nfrom .below_curve_counter import BelowCurveCounter\nfrom .loss import CrossEntropyLossWithUncertainty, MaskedLossWrapper\n\n\nclass Evaluator(object):\n    \"\"\"Evaluator class for evaluating predictions against\n    binary groundtruth.\"\"\"\n    def __init__(self, **kwargs):\n        self.kwargs = kwargs\n\n        if \"operating_points_path\" in kwargs:\n            self.rad_perf = pd.read_csv(kwargs[\"operating_points_path\"])\n        else:\n            self.rad_perf = None\n\n        self.set_eval_functions()\n\n    def evaluate(self, groundtruth, predictions, metric, threshold=0.5):\n        \"\"\"Evaluate a single metric on groundtruth and predictions.\"\"\"\n        if metric in self.summary_metrics:\n            metric_fn = self.summary_metrics[metric]\n            value = metric_fn(groundtruth, predictions)\n        elif metric in self.curve_metrics:\n            metric_fn = self.curve_metrics[metric]\n            value = metric_fn(groundtruth, predictions)\n        elif metric in self.point_metrics:\n            metric_fn = self.point_metrics[metric]\n            value = metric_fn(groundtruth, predictions > threshold)\n        else:\n            raise ValueError(f\"Metric {metric} not supported.\")\n\n        return value\n\n    def evaluate_tasks(self, groundtruth, predictions, threshold=0.5):\n        \"\"\"Compute evaluation metrics and curves on multiple tasks.\"\"\"\n        metrics = {}\n        curves = {}\n        for task in list(predictions):\n\n            task_groundtruth = groundtruth[task]\n            task_predictions = predictions[task]\n            # filter out those with -1 in groundtruth\n            non_label = task_groundtruth.index[task_groundtruth == -1.0]\n            task_predictions = task_predictions.drop(non_label)\n            task_groundtruth = task_groundtruth.drop(non_label)\n\n            metrics.update({f\"{task}:{metric}\":\n                            self.evaluate(task_groundtruth,\n                                          task_predictions,\n                                          metric=metric)\n                            for metric in self.summary_metrics})\n\n            metrics.update({f\"{task}:{metric}@thresh={threshold}\":\n                            self.evaluate(task_groundtruth,\n                                          task_predictions,\n                                          metric=metric,\n                                          threshold=threshold)\n                            for metric in self.point_metrics})\n            \"\"\"\n            if self.rad_perf is not None:\n\n                below_curve_counter = BelowCurveCounter(self.rad_perf,\n                                                        task)\n                metrics.update({\n                    f'{task}:rads_below_ROC':\n                    below_curve_counter.ROC(task_groundtruth,\n                                            task_predictions),\n                    f'{task}:rads_below_PR':\n                    below_curve_counter.PR(task_groundtruth,\n                                           task_predictions)\n                })\n            \"\"\"\n            curves.update({f\"{task}:{metric}\":\n                           self.evaluate(task_groundtruth,\n                                         task_predictions,\n                                         metric=metric,\n                                         threshold=threshold)\n                           for metric in self.curve_metrics})\n\n        return metrics, curves\n\n    def evaluate_average_metric(self, metrics, evaluate_tasks,\n                                average_metric_name):\n        \"\"\"Evaluate an average metric over classes.\"\"\"\n\n        # All provided names must be of the form \"...-{metric_name}\"\n        metric_name = average_metric_name.split(\"-\")[-1]\n\n        average_metric = np.mean([metrics[f\"{task}:{metric_name}\"]\n                                  for task in evaluate_tasks])\n\n        return average_metric\n\n    def set_eval_functions(self):\n        \"\"\"Set the evaluation functions.\"\"\"\n        def undefined_catcher(func, x, y):\n            try:\n                return func(x, y)\n            except Exception:\n                return np.nan\n\n        # Functions that take probs as input\n        self.summary_metrics = {\n            'AUPRC': lambda x, y: undefined_catcher(sk_metrics.average_precision_score, x, y),\n            'AUROC': lambda x, y: undefined_catcher(sk_metrics.roc_auc_score, x, y),\n            'log_loss': lambda x, y: undefined_catcher(sk_metrics.log_loss, x, y),\n        }\n\n        # Functions that take binary values as input\n        self.point_metrics = {\n            'accuracy': lambda x, y: undefined_catcher(sk_metrics.accuracy_score, x, y),\n            'precision': lambda x, y: undefined_catcher(sk_metrics.precision_score, x, y),\n            'recall': lambda x, y: undefined_catcher(sk_metrics.recall_score, x, y),\n        }\n\n        self.curve_metrics = {\n            'PRC': lambda x, y: undefined_catcher(sk_metrics.precision_recall_curve, x, y),\n            'ROC': lambda x, y: undefined_catcher(sk_metrics.roc_curve, x, y),\n        }\n\n    def get_loss_fn(self, loss_fn_name, model_uncertainty,\n                    mask_uncertain):\n        \"\"\"Get the loss function used for training.\n\n        Args:\n            loss_fn_name: Name of loss function to use.\n            model_uncertainty: Bool indicating whether to predict\n                               UNCERTAIN directly.\n            mask_uncertain: Bool indicating whether to mask\n                            UNCERTAIN labels.\n        \"\"\"\n        if model_uncertainty:\n            loss_fn = CrossEntropyLossWithUncertainty()\n        elif loss_fn_name == 'cross_entropy':\n            loss_fn = nn.BCEWithLogitsLoss(reduction=\"none\"\n                                           if mask_uncertain else \"mean\")\n            # Apply a wrapper that masks uncertain labels.\n            if mask_uncertain:\n                loss_fn = MaskedLossWrapper(loss_fn)\n        else:\n            raise ValueError(\"No loss function for supplied arguments.\")\n\n        return loss_fn\n","sub_path":"chexpert-model/eval/evaluator.py","file_name":"evaluator.py","file_ext":"py","file_size_in_byte":6114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"244415199","text":"from kivy.uix.widget import Widget\nfrom kivy.uix.scrollview import ScrollView\nfrom kivy.uix.boxlayout import BoxLayout\nfrom kivy.core.window import Window\nfrom kivy.uix.button import Button\nfrom UI.buttonnpc import ButtonNPC\n\nclass HireInfoView(BoxLayout):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.orientation = 'vertical'\n        self.clean_hire_btn = Button(text = 'Use clean money', disabled = True, size_hint_y=0.1)\n        self.clean_hire_btn.bind(on_press = self.hire_clean)\n        self.add_widget(self.clean_hire_btn)\n\n        self.dirty_hire_btn = Button(text = 'Use cash', disabled = True, size_hint_y=0.1)\n        self.dirty_hire_btn.bind(on_press = self.hire_dirty)\n        self.add_widget(self.dirty_hire_btn)\n\n        scrollview = ScrollView(do_scroll_x = False, size_hint=(1,1))\n        self.npc_list = BoxLayout(orientation = 'vertical')\n        scrollview.add_widget(self.npc_list)\n        self.add_widget(scrollview)\n        self.zone=None\n        self.player=None\n        \n    def hire_clean(self, instance):\n        for btn in self.npc_list.children:\n            if btn.state is 'down':\n                npc = btn.choice\n                self.player.clean_money -= npc.cost\n                self.player.npc.append(npc)\n                self.zone.npc_for_hire.remove(npc)\n                self.update(self.player, self.zone)\n                \n    def hire_dirty(self, instance):\n        for btn in self.npc_list.children:\n            if btn.state is 'down':\n                npc = btn.choice\n                self.player.dealer.stash.cash.amount -= npc.cost\n                self.player.npc.append(npc)\n                self.zone.npc_for_hire.remove(npc)\n                self.update(self.player, self.zone)\n\n    def choose_npc(self, instance):\n        if instance.choice.cost <= self.player.clean_money:\n            self.clean_hire_btn.disabled = False\n        if instance.choice.cost <= self.player.dealer.stash.cash.amount:\n            self.dirty_hire_btn.disabled = False\n        if instance.choice.cost > self.player.clean_money:\n            self.clean_hire_btn.disabled = True\n        if instance.choice.cost > self.player.dealer.stash.cash.amount:\n            self.dirty_hire_btn.disabled = True\n        return\n\n    def update(self, player, zone):\n        self.zone=zone\n        self.player=player\n        self.clean_hire_btn.disabled = True\n        self.dirty_hire_btn.disabled = True\n        self.npc_list.clear_widgets()\n        for npc in self.zone.npc_for_hire:\n            btn = ButtonNPC(text = str(npc), group='hire')\n            btn.choice = npc\n            btn.bind(on_press = self.choose_npc)\n            self.npc_list.add_widget(btn)\n","sub_path":"UI/hireinfoview.py","file_name":"hireinfoview.py","file_ext":"py","file_size_in_byte":2702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"17204130","text":"#!/usr/bin/env python\n\nimport unittest\nfrom functools import reduce\nimport numpy\nfrom pyscf import gto\nfrom pyscf import scf\nfrom pyscf import ao2mo\nfrom pyscf import fci\n\nnelec = (3,4)\nnorb = 8\nh2e = numpy.random.random((norb,norb,norb,norb))\nh2e += h2e.transpose(2,3,0,1)\nna = fci.cistring.num_strings(norb, nelec[0])\nnb = fci.cistring.num_strings(norb, nelec[1])\nci0 = numpy.random.random((na,nb))\n\ndef contract_2e_o0(g2e, fcivec, norb, nelec, opt=None):\n    neleca, nelecb = nelec\n    link_indexa = fci.cistring.gen_linkstr_index(range(norb), neleca)\n    link_indexb = fci.cistring.gen_linkstr_index(range(norb), nelecb)\n    na = fci.cistring.num_strings(norb, neleca)\n    nb = fci.cistring.num_strings(norb, nelecb)\n    fcivec = fcivec.reshape(na,nb)\n    t1 = numpy.zeros((norb,norb,na,nb))\n    for str0, tab in enumerate(link_indexa):\n        for a, i, str1, sign in tab:\n            t1[a,i,str1] += sign * fcivec[str0]\n    for str0, tab in enumerate(link_indexb):\n        for a, i, str1, sign in tab:\n            t1[a,i,:,str1] += sign * fcivec[:,str0]\n    t1 = numpy.dot(g2e.reshape(norb*norb,-1), t1.reshape(norb*norb,-1))\n    t1 = t1.reshape(norb,norb,na,nb)\n    fcinew = numpy.zeros_like(fcivec)\n    for str0, tab in enumerate(link_indexa):\n        for a, i, str1, sign in tab:\n            fcinew[str1] += sign * t1[a,i,str0]\n    for str0, tab in enumerate(link_indexb):\n        for a, i, str1, sign in tab:\n            fcinew[:,str1] += sign * t1[a,i,:,str0]\n    return fcinew\n\nclass KnowValues(unittest.TestCase):\n    def test_contract(self):\n        ci1ref = contract_2e_o0(h2e, ci0, norb, nelec)\n        ci1 = fci.direct_nosym.contract_2e(h2e, ci0, norb, nelec)\n        self.assertTrue(numpy.allclose(ci1ref, ci1))\n\n\nif __name__ == \"__main__\":\n    print(\"Full Tests for spin1\")\n    unittest.main()\n\n","sub_path":"fci/test/test_direct_nosym.py","file_name":"test_direct_nosym.py","file_ext":"py","file_size_in_byte":1814,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"468573248","text":"import matplotlib.gridspec as gridspec\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom PIL import Image\n\ndef image_hist(img):\n    plt.figure()\n    gs = gridspec.GridSpec(2, 2)\n    img1 = plt.subplot(gs[0, 0])\n    img2 = plt.subplot(gs[0, 1])\n    img3 = plt.subplot(gs[1, 0])\n    img4 = plt.subplot(gs[1, 1])\n    R, G, B = img.split()\n    R, G, B = np.array(R).flatten(), np.array(G).flatten(), np.array(B).flatten()\n    arr_gray = np.array(img.convert(mode='L')).flatten()\n\n    img1.hist(arr_gray, bins=256, density=1, facecolor='black')\n    img2.hist(R, bins=256, density=1, facecolor='red')\n    img3.hist(G, bins=256, density=1, facecolor='green')\n    img4.hist(B, bins=256, density=1, facecolor='blue')\n    plt.show()\n\n\nimg = Image.open('Q1/scut.jpg')\nimage_hist(img)\n","sub_path":"file/course/experiment6/实验六/201866590050_陈宇驰_第六次作业/源代码/Q1.py","file_name":"Q1.py","file_ext":"py","file_size_in_byte":779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"613137684","text":"#!/usr/remote/anaconda-3.5/bin/python\n#\n# $Id: hmmmAssembler.py,v 1.4 2007/10/08 08:10:11 geoff Exp $\n#\n# hmmmAssembler.py\n# Ran Libeskind-Hadas, 2006\n# modified by Peter Mawhorter, June 2006\n# Extensively modified by Geoff Kuenning, October 2007\n# modified by Kaya Woodall, June 2012\n# modified by Xiannong Meng (xmeng@bucknell.edu) December 2016\n#  1. change all Python 2 'print' to conform with Python 3 (automatic)\n#  2. change raw_input() to input()\n#  3. For the moment, Python 3 in our system is under /usr/remtoe/anaconda-3.5\n#     eventually it should be changed to something like /usr/bin/python.\n\nimport sys, string, re, textwrap\nfrom binary import *\n\n#\n# opcodes encodes the preferred opcode translations.  Each entry is a\n# triple: match, mask, translation.  If the binary word matches\n# \"match\" under the mask, the translated opcode is given by that\n# entry.  Blanks are ignored in the match and mask fields.  The table\n# is order-dependent; the first match is used.  Note that at present\n# the masks are either 0x0 or 0xF in each hex digit, although the code\n# doesn't enforce that restriction.\n#\n# This table is shared directly between the assembler and simulator.\n# The assembler doesn't use all the fields.\n#\nopcodes = (\n        (\"0000 0000 0000 0000\", \"1111 1111 1111 1111\", \"halt\"),\n        (\"0000 0000 0000 0001\", \"1111 0000 1111 1111\", \"read\"),\n        (\"0000 0000 0000 0010\", \"1111 0000 1111 1111\", \"write\"),\n        (\"0000 0000 0000 0011\", \"1111 0000 1111 1111\", \"jumpi\"),\n        (\"0001 0000 0000 0000\", \"1111 0000 0000 0000\", \"loadn\"),\n        (\"0010 0000 0000 0000\", \"1111 0000 0000 0000\", \"load\"),\n        (\"0011 0000 0000 0000\", \"1111 0000 0000 0000\", \"store\"),\n        (\"0100 0000 0000 0000\", \"1111 0000 0000 1111\", \"loadi\"),\n        (\"0100 0000 0000 0001\", \"1111 0000 0000 1111\", \"storei\"),\n        (\"0101 0000 0000 0000\", \"1111 0000 0000 0000\", \"addn\"),\n        (\"0110 0000 0000 0000\", \"1111 1111 1111 1111\", \"nop\"),\n        (\"0110 0000 0000 0000\", \"1111 0000 0000 1111\", \"mov\"),\n        (\"0110 0000 0000 0000\", \"1111 0000 0000 0000\", \"add\"),\n        (\"0111 0000 0000 0000\", \"1111 0000 1111 0000\", \"neg\"),\n        (\"0111 0000 0000 0000\", \"1111 0000 0000 0000\", \"sub\"),\n        (\"1000 0000 0000 0000\", \"1111 0000 0000 0000\", \"mul\"),\n        (\"1001 0000 0000 0000\", \"1111 0000 0000 0000\", \"div\"),\n        (\"1010 0000 0000 0000\", \"1111 0000 0000 0000\", \"mod\"),\n        (\"1011 0000 0000 0000\", \"1111 1111 0000 0000\", \"jump\"),\n        (\"1011 0000 0000 0000\", \"1111 0000 0000 0000\", \"call\"),\n        (\"1100 0000 0000 0000\", \"1111 0000 0000 0000\", \"jeqz\"),\n        (\"1101 0000 0000 0000\", \"1111 0000 0000 0000\", \"jnez\"),\n        (\"1110 0000 0000 0000\", \"1111 0000 0000 0000\", \"jgtz\"),\n        (\"1111 0000 0000 0000\", \"1111 0000 0000 0000\", \"jltz\"),\n        (\"0000 0000 0000 0000\", \"0000 0000 0000 0000\", \"data\"),\n        )\n\n#Choose a dictionary to use: OldDict uses HMMM 2006-2011 (also internal language used within the assembler),\n# NewDict uses HMMM 2012+ (added ~n, ~m, and ~r suffixes w/ aliases).\n\nOldDict = {\"halt\":\"halt\", \"read\":\"read\", \"write\":\"write\", \"nop\":\"nop\",\n           \"loadn\":\"loadn\", \"addn\":\"addn\", \"mov\":\"mov\", \"add\":\"add\",\n           \"sub\":\"sub\", \"neg\":\"neg\", \"mul\":\"mul\", \"div\":\"div\", \"mod\":\"mod\",\n           \"jump\":\"jump\", \"jeqz\":\"jeqz\", \"jnez\":\"jnez\",\n           \"jgtz\":\"jgtz\", \"jltz\":\"jltz\", \"call\":\"call\", \"jumpi\":\"jumpi\",\n           \"load\":\"load\", \"store\":\"store\", \"loadi\":\"loadi\", \"storei\":\"storei\"}\n\nNewDict = {\"halt\":\"halt\", \"read\":\"read\", \"write\":\"write\", \"nop\":\"nop\",\n           \"setn\":\"loadn\", \"addn\":\"addn\", \"mov\":\"mov\", \"copy\":\"mov\",\n           \"add\":\"add\",\n           \"sub\":\"sub\", \"neg\":\"neg\", \"mul\":\"mul\", \"div\":\"div\",\n           \"mod\":\"mod\", \"jumpn\":\"jump\", \"jeqz\":\"jeqz\", \"jeqzn\":\"jeqz\",\n           \"jnez\":\"jnez\", \"jnezn\":\"jnez\", \"jgtz\":\"jgtz\", \"jgtzn\":\"jgtz\",\n           \"jltz\":\"jltz\", \"jltzn\":\"jltz\", \"call\":\"call\", \"calln\":\"call\",\n           \"jump\":\"jumpi\", \"jumpr\":\"jumpi\", \"loadn\":\"load\", \"storen\":\"store\",\n           \"load\":\"loadi\", \"loadi\":\"loadi\", \"loadr\":\"loadi\",\n           \"store\":\"storei\", \"storei\":\"storei\", \"storer\":\"storei\"}\n    \n\n#\n# The assembler would prefer a dictionary for the opcodes; that's not\n# possible in the simulator because ordering matters.  Convert the\n# above table into a dictionary that translates opcodes into encodings.\n#\nopcodeDict = {}\nfor i in range(len(opcodes)):\n    opcodeDict[opcodes[i][2]] = opcodes[i][0]\n\nregisters = {\"r0\":\"0000\",\"r1\":\"0001\",\"r2\":\"0010\",\"r3\":\"0011\",\"r4\":\"0100\",\n        \"r5\":\"0101\",\"r6\":\"0110\",\"r7\":\"0111\",\"r8\":\"1000\",\"r9\":\"1001\",\n        \"r10\":\"1010\", \"r11\":\"1011\", \"r12\":\"1100\", \"r13\":\"1101\",\n        \"r14\":\"1110\", \"r15\":\"1111\",\n        \"R0\":\"0000\",\"R1\":\"0001\",\"R2\":\"0010\",\"R3\":\"0011\",\"R4\":\"0100\",\n        \"R5\":\"0101\",\"R6\":\"0110\",\"R7\":\"0111\",\"R8\":\"1000\",\"R9\":\"1001\",\n        \"R10\":\"1010\", \"R11\":\"1011\", \"R12\":\"1100\", \"R13\":\"1101\",\n        \"R14\":\"1110\", \"R15\":\"1111\"}\n\n#\n# arguments encodes the required arguments for each operation.  \"r\"\n# means a register; \"s\" means a signed 8-bit number in decimal; \"u\"\n# means an unsigned 8-bit number in decimal, and \"n\" means a signed or\n# unsigned 16-bit number in hex (0x notation) or decimal.  Actually,\n# all numbers are accepted in all bases.\n#\n# In addition, \"z\" means insert four bits of zeros without swallowing\n# an argument.\n#\narguments = {\"halt\": \"\",\n        \"read\": \"r\",\n        \"write\": \"r\",\n        \"jumpi\": \"r\",\n        \"loadn\": \"rs\",\n        \"load\": \"ru\",\n        \"store\": \"ru\",\n        \"loadi\": \"rr\",\n        \"storei\": \"rr\",\n        \"addn\": \"rs\",\n        \"add\": \"rrr\",\n        \"mov\": \"rr\",\n        \"nop\": \"\",\n        \"sub\": \"rrr\",\n        \"neg\": \"rzr\",\n        \"mul\": \"rrr\",\n        \"div\": \"rrr\",\n        \"mod\": \"rrr\",\n        \"jump\": \"zu\",\n        \"call\": \"ru\",\n        \"jeqz\": \"ru\",\n        \"jgtz\": \"ru\",\n        \"jltz\": \"ru\",\n        \"jnez\": \"ru\",\n        \"data\": \"n\"}\n\n#\n# insertBits performs a logical \"OR\" on A and B\ndef insertBits(a, b):\n    \"\"\"Perform logical OR on a and b, preserving blanks in a.  Both a and\nb must consist exclusively of blanks, 0s, and 1s.\"\"\"\n    if a == ''  or  b == '':\n        return a\n    elif a[0] == ' ':\n        return ' ' + insertBits(a[1:], b)\n    elif b[0] == ' ':\n        return insertBits(a, b[1:])\n    elif a[0] == '1'  or  b[0] == '1':\n        return '1' + insertBits(a[1:], b[1:])\n    else:\n        return '0' + insertBits(a[1:], b[1:])\n\ndef translate (flds) :\n    try :\n            operation = flds[0]\n            flds[0] = NewDict[operation] # Substitute OldDict here if using code written pre-2012.\n            opval = opcodeDict[flds[0]]\n    except KeyError :\n        print( \"\\nOPERATION ERROR:\")\n        print( \"'\" + str(flds[0]), \"' IS NOT A VALID OPERATION.\")\n        return \"***OPERATION ERROR HERE***\"\n    encoding = opval\n    extraBits = '0000'\n    argsRequired = arguments[flds[0]]\n    parts  = re.split(r'[,\\s]+',flds[1].strip())    # split args into parts\n    argc = len(parts)\n    if argc == 1  and  parts[0] == '':\n        argc = 0\n        parts = []\n    numArgsRequired = 0\n    for i in argsRequired:\n        if i != 'z':\n            numArgsRequired += 1\n    if argc != numArgsRequired:\n        print( \"\\nARGUMENT ERROR:\")\n        print( \"WRONG NUMBER OF ARGUMENTS.\")\n        print( \"DETECTED\", argc, \"ARGUMENTS, EXPECTED\", \\\n          numArgsRequired, \"ARGUMENTS.\")\n        print( flds[0], flds[1])\n        return \"***ARGUMENT ERROR HERE***\"\n    #print( 'parts is', parts)\n    for p in parts :\n        if p == '' :\n            print( \"\\nARGUMENT ERROR:\")\n            print( \"EMPTY ARGUMENT.\")\n            return \"***ARGUMENT ERROR HERE***\"\n\n        arg = re.match(r'([Rr][0-9]+|-?[0-9]+|-|0[xX][0-9a-fA-F]+)$',p)\n        if arg == None:\n            print( \"\\nARGUMENT ERROR:\")\n            print( \"'\" + p + \"' IS NEITHER A REGISTER NOR A NUMBER.\")\n            return \"***ARGUMENT ERROR HERE***\"\n        code = argsRequired[0]\n        argsRequired = argsRequired[1:]\n        while code == 'z':\n            extraBits += '0000'\n            code = argsRequired[0]\n            argsRequired = argsRequired[1:]\n        if code == 'r':\n            try:\n                bits = registers[p]\n            except KeyError:\n                print( \"\\nREGISTER ERROR:\")\n                print(  \"'\" + str(p) + \"' IS NOT A VALID REGISTER.\")\n                return \"***REGISTER ERROR HERE***\"\n            extraBits += bits\n        elif p[0] == 'r'  or  p[0] == 'R':\n            print( \"\\nARGUMENT ERROR:\")\n            print( \"'\" + str(p) + \"' IS NOT A VALID NUMBER.\")\n            return \"***ARGUMENT ERROR HERE***\"\n        else:\n            #\n            # We have already ensured that p is a valid number, so we\n            # can just evaluate it here.\n            #\n            #value = eval(p)\n            value = int(p)\n            if code == 's':\n                ok = -128 <= value <= 127\n                width = 8\n            elif code == 'u':\n                ok = 0 <= value <= 255\n                width = 8\n            elif code == 'n':\n                ok = -32768 <= value <= 65535\n                width = 16\n                extraBits = ''          # No padding in this case\n            else:\n                print( \"\\nINTERNAL ERROR:\")\n                print( \"HMMMASSEMBLER ENCOUNTERED AN UNEXPECTED SITUATION.\")\n                return \"***INTERNAL ERROR HERE***\" \n            if not ok:\n                print( \"\\nARGUMENT ERROR:\")\n                print( \"'\" + str(p) + \"' IS OUT OF RANGE FOR THE ARGUMENT.\")\n                return \"***ARGUMENT ERROR HERE***\"\n            extraBits += numToTwosComplement(value, width)\n\n    return insertBits(encoding, extraBits)\n\ndef assemble (program) :\n    output = []\n    linenum = 0\n    for line in program :\n        # nasty regular expression to parse line number, instruction, and arguments\n        if len(re.findall(r'^([0-9]+)[\\s]+([a-z]+)[\\s]*(([-r0-9xXa-fA-F]+[,\\s]*)*)([\\s]+(#.*)*)*$', line)) != 1 :\n            print( \"\\nSYNTAX ERROR ON LINE\", str(linenum) + \":\")\n            print( line)\n            output.append([linenum, \"***SYNTAX ERROR HERE***\", line])\n            linenum += 1\n            continue\n\n        flds = re.sub(r'^([0-9]+)[\\s]+([a-z]+)[\\s]*(([-r0-9xXa-fA-F]+[,\\s]*)*)([\\s]+(#.*)*)*$', r'\\1~\\2~\\3', line).split('~')\n\n\n\n        if (not flds[0].isdigit()) :\n            print( \"\\nMISSING LINE NUMBER AT LINE\", str(linenum) + \":\")\n            print( \"FOUND:\", flds[0])\n            output.append([linenum, \"***MISSING LINE NUMBER HERE***\", line])\n            linenum += 1\n            continue\n\n\n        else:\n            instruction = translate(flds[1:])\n            triplet = [linenum, instruction, line]\n\n            if (not instruction[0] == '*') and (not linenum == int(flds[0])) :\n                print( \"\\nBAD LINE NUMBER AT LINE\", str(linenum) + \":\")\n                print( \"LINE NUMBER:\", flds[0], \"EXPECTED:\", linenum)\n                output.append([linenum, \"***BAD LINE NUMBER HERE***\", line])\n                linenum += 1\n                continue\n\n            output.append(triplet)\n            linenum += 1\n    return output\n\n\ndef readfile(filename) :\n    try:\n            file = open(filename,\"r\")       # file with machine code\n    except IOError:\n        print( \"Cannot open file: \", filename)\n        sys.exit()\n    program = []\n    while 1 :\n        line = file.readline()\n        if line == \"\" :         # End of file\n            break       \n        line = line.strip()     # Strip white space from front and end\n        if line != \"\"   and  line[0] != '#' : # If it's not a comment...\n            program.append(line) # ... then it's part of the program\n    file.close()\n    return program\n\ndef readstring(S) :\n    program = []\n    linesOfString = S.split(\"\\n\")\n    for line in linesOfString:\n        #print( \"line is\", line)\n        if line == \"\" :         \n            continue     \n        line = line.strip()     # Strip white space from front and end\n        if line != \"\"   and  line[0] != '#' : # If it's not a comment...\n            program.append(line) # ... then it's part of the program\n    return program\n\ndef writefile(machinecode, filename) :\n    file = open(filename,\"w\")\n    file.close\n\n    print(  \"\\n\" + \"-\"*22)\n    print( \"| ASSEMBLY SUCCESSFUL |\")\n    print( \"-\"*22 + \"\\n\")\n\n    nwidth = max(map(lambda x: len(str(x[0])), machinecode))\n    for triplet in machinecode:\n        print( textwrap.wrap((str(triplet[0])).ljust(nwidth) + \" : \" + (triplet[1]).ljust(27) + triplet[2], 76)[0])\n        # wrap returns a list of strings limited in length\n        # this should give a 76 character line\n        file.write(triplet[1] + \"\\n\")\n    print( \"\")\n\ndef main(programAsString = None) :\n\n    # argument handling:\n    fname = 0\n    oname = 0\n    filename = \"\"\n    outputname = \"out.b\"\n\n    # here, we check in case there were command-line inputs\n    if not programAsString:\n        for arg in sys.argv:\n            if fname:\n                filename = arg\n                fname = 0\n                continue\n            if oname:\n                outputname = arg\n                oname = 0\n                continue\n            if arg[:2] == \"-f\":\n                if arg[2:]:\n                        filename = arg[2:]\n                else: fname = 1\n            if arg[:2] == \"-o\":\n                if arg[2:]:\n                        outputname = arg[2:]\n                else: oname = 1\n            if arg == \"-h\" or arg == \"--help\":\n                print( \"hmmmAssembler.py\")\n                print( \"  Python program for assembling Harvey Mudd Miniature Machine code.\")\n                print( \"  Options:\")\n                print( \"    -f filename     use filename as the input file\")\n                print( \"    -o filename     use filename as the output file\")\n                print( \"    -h, --help        print this help message\\n\")\n                sys.exit()\n\n        # the optional input prompt\n        if filename == \"\":\n            filename = input(\"Enter input file name: \")\n        # to read from stdin instead we would use:  program = sys.stdin.readlines()\n        program = readfile(filename)\n        # the optional output prompt\n        if outputname == \"\":\n            outputname = input(\"Enter output file name: \")\n\n    if programAsString:\n        outputname = \"out.b\"\n        #print( 'programAsString is ', programAsString)\n        program = readstring( programAsString )\n        #print( \"program is\", program)\n        #print( 'program is', program)\n\n    machinecode = assemble(program)\n\n    # check whether there are any errors\n    failure = 0\n    for triplet in machinecode:\n        if triplet[1][0] == '*':\n            failure = 1\n\n    if (not machinecode == []) and (not failure):\n        writefile(machinecode, outputname)\n    else:\n        print( \"\\n***** ASSEMBLY TERMINATED UNSUCCESSFULLY *****\")\n        print( \"              ASSEMBLY RESULTS:\\n\")\n        try:\n            nwidth = max(map(lambda x: len(str(x[0])), machinecode))\n        except ValueError:\n            print( \"                <EMPTY FILE>\\n\")\n            return\n        for triplet in machinecode:\n            print( textwrap.wrap((str(triplet[0])).ljust(nwidth) + \" : \" + (triplet[1]).ljust(31) + triplet[2], 76)[0])\n            # wrap returns a list of strings limited in length\n            # this should give a 76 character line\n        print( \"\")\n\n\n# When this module is executed from the command line, as in \"python filename.py\"\n# __name__ will be __main__, so main () will be executed.\n# However, when this module is imported into the python environment __name__ will\n# be something else, so main() will not be executed automatically\nif __name__ == \"__main__\" : main () \n\n# $Log: hmmmAssembler.py,v $\n# Revision 1.5 2012/06/18 1:52:30 kaya\n# Rewrote HMMM assembly code. New shortcuts can be found on the HMMM Directory page.\n# Included dictionary so the program can be run in old or new mode, as internal commands\n# remain unaltered. \n# \n# Revision 1.4  2007/10/08 08:10:11  geoff\n# Add support for the neg instruction.  This required generalizing the\n# \"z\" operand specifier.  Also get rid of the obsolete version of the\n# opcodes table, which I neglected to delete earlier.\n#\n# Revision 1.3  2007/10/07 09:18:58  geoff\n# Fix the masks on mov and data to correctly reflect the format of those\n# two pseudo-operations.\n#\n# Revision 1.2  2007/10/07 07:47:14  geoff\n# Major changes to improve the instruction architecture.  Unfortunately,\n# as part of these changes I converted all tabs to blanks, so there are\n# spurious diffs.  Modifications include:\n#\n# 1. Better table-driven encoding unified with simulator encoding tables.\n# 2. Major rewrite of assembly code to use tables rather than if/elif.\n#\n","sub_path":"homework1/hmmmAssembler.py","file_name":"hmmmAssembler.py","file_ext":"py","file_size_in_byte":16623,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"87733405","text":"#encoding: utf-8\nfrom OpenOrange import *\nfrom Report import Report\nimport string\n\nclass InvoiceListSelect(Report):\n\n    def selectGroup(self,value):\n        for sernr in self.selected:\n            self.receipt.addInvoice(sernr)\n\n        self.selected = []\n        self.clear()\n        self.run()\n        self.render()\n\n    def incluide(self,param,value):\n        self.clear()\n        if(self.selected.count(int(param)) == 0):\n            self.selected.append(int(param))\n        else:\n            self.selected.remove(int(param))\n        self.run()\n        self.render()\n\n    def makeUserDic(self):\n        from Receipt import Receipt\n        rec = self.receipt\n        self.ulist = {}\n        invList= []\n        for invrow in rec.Invoices:\n            if invrow.InvoiceNr: invList.append(str(invrow.InvoiceNr))\n        query = Query()\n        query.sql = \"SELECT {DocType}, {SerNr}, {OfficialSerNr}, {InvoiceDate}, {CustCode}, {CustName}, {Total}, {Saldo}, {SubTotal}\"\n        query.sql += \" from [Invoice] \"\n        query.sql += \"WHERE?AND {Saldo} != 0  \"\n        query.sql += \"WHERE?AND {Status} = i|1| AND {OpenFlag} = i|1| \"\n        query.sql += \"WHERE?AND ({Invalid} = 0 OR {Invalid} IS NULL) \"\n        query.sql += \"WHERE?AND {TransDate} <= d|%s| \" % rec.TransDate\n        query.sql += \"WHERE?AND ({AppliesToInvoiceNr} IS NULL OR {AppliesToInvoiceNr} = 0)\"\n        if len(invList) > 0:\n            query.sql += \"WHERE?AND {SerNr} NOT IN (%s)\" % \",\".join(invList)\n        #if self.__dict__.has_key(\"self.Receipo\"):\n        if (rec.CustCode != 'bank'):\n            query.sql += \"WHERE?AND {CustCode} = s|%s| \" % (rec.CustCode)\n        if (query.open()):\n            for rec in query:\n                self.ulist[rec.SerNr] = rec\n\n    def run(self):\n        icons = [\"<IMG SRC=images/orange.png>\",\"<IMG SRC=images/noorange.png>\"]\n        self.getView().resize(800,600)\n        if (not self.__dict__.has_key(\"selected\")):\n            self.selected = []\n        self.makeUserDic()\n        self.printReportTitle(\"Paste Invoice\")\n        self.startTable()\n        self.startRow()\n        self.addValue(tr(\"Sellect Group\"),Bold=True,CallMethod=\"selectGroup\")\n        self.endRow()\n        self.endTable()\n        self.startTable()\n        self.startRow(Style=\"H\")\n        self.addValue(tr(\"\"))\n        self.addValue(tr(\"Type\"))\n        self.addValue(tr(\"Nr\"))\n        self.addValue(tr(\"Official Number\"))\n        self.addValue(tr(\"Date\"))\n        #self.addValue(tr(\"Customer\"))\n        self.addValue(tr(\"Name\"))\n        self.addValue(tr(\"Total\"))\n        self.addValue(tr(\"Saldo\"))\n        self.addValue(tr(\"SubTotal\"))\n        self.endRow()\n        lsort = []\n        lsort = self.ulist.keys()#lo q hace es ordenar los Id osea los SerNr\n        lsort.sort()\n        from Invoice  import Invoice  \n        for sernr in lsort:\n            usr = self.ulist[sernr]\n            col = \"white\"\n            ic = icons[1]\n            if usr.SerNr in self.selected:\n               ic = icons[0]\n            self.startRow(BGColor=col)\n            self.addValue(ic)\n            self.addValue(Invoice.DocTypes[usr.DocType])\n            self.addValue(usr.SerNr,Bold=True,CallMethod=\"incluide\",Parameter=int(usr.SerNr))\n            self.addValue(usr.OfficialSerNr)\n            self.addValue(usr.InvoiceDate)\n            #self.addValue(usr.CustCode)\n            self.addValue(usr.CustName[:10])\n            self.addValue(usr.Total)\n            self.addValue(usr.Saldo)\n            self.addValue(usr.SubTotal)\n            self.endRow()\n        self.endTable()\n","sub_path":"standard/reports/InvoiceListSelect.py","file_name":"InvoiceListSelect.py","file_ext":"py","file_size_in_byte":3544,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"148912671","text":"#Code:\n\nl=[]\nfor i in range(3):\n    l.append(str(input( )))\n\nx=l[:]\nx= set(x)\n\nif len(x)!=2:\n    print(\"?\")\nelse:\n    if l[1]==l[2] and l[0]!=l[1]:\n        if l[1]== 'rock' and l[0]=='paper' or l[1]=='paper' and l[0]=='scissors' or l[1]=='scissors' and l[0]=='rock':\n            print(\"F\")\n        else:\n            print(\"?\")\n    elif l[1]==l[0] and l[0]!=l[2]:\n        if l[1]== 'rock' and l[2]=='paper' or l[1]=='paper' and l[2]=='scissors' or l[1]=='scissors' and l[2]=='rock':\n\n            print(\"S\")\n        else:\n            print(\"?\")\n    elif l[2]==l[0] and l[0]!=l[1]:\n        if l[0]== 'rock' and l[1]=='paper' or l[0]=='paper' and l[1]=='scissors' or l[0]=='scissors' and l[1]=='rock':\n\n            print(\"M\")\n        else:\n            print(\"?\")\n","sub_path":"48A - Rock-paper-scissors.py","file_name":"48A - Rock-paper-scissors.py","file_ext":"py","file_size_in_byte":759,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"304849315","text":"#!/usr/bin/env python\n\n# ---------------------------\n# projects/pfd/PFD.py\n# Copyright (C) 2012\n# Michael Tarng & Bryan Vuong\n# ---------------------------\n\n#from pprint import pprint #used to printout matrix (debugging)\n\n\n# ------------\n# pfd_read\n# ------------\n\n\ndef pfd_read (r, a) :\n    \"\"\"\n    reads ints into a\n    r is a  reader\n    a is an array of int\n    return true if that succeeds, false otherwise\n    \"\"\"\n    s = r.readline()\n    #assert s != \"\"\n    if s == \"\" :\n        return False\n    l = s.split()\n    for item in l:\n        a.append(int(item))\n    return True\n\n\n\n# -------------\n# pfd_print\n# -------------\n\ndef pfd_print (w, listOrder) :\n    \"\"\"\n    prints the values contained in listOrder\n    w is a writer\n    listOrder is a list of the topologically sorted task numbers\n    \"\"\"\n    \n    for i in range(len(listOrder)):\n\n        assert listOrder[i] > 0\n        \n        if (i == len(listOrder)-1):\n            w.write(str(listOrder[i]) + \"\\n\")\n        else:\n            w.write(str(listOrder[i]) + \" \")\n\n# ------------\n# pfd_eval\n# ------------\n\n\ndef pfd_eval (a) :\n    \"\"\"\n    a is an array of array with all values read in\n    \"\"\"\n\n    #The two elements from the first time give us:\n    #The number of tasks and the number of subsequent lines\n    taskNum = (a[0])[0]\n    lineNum = (a[0])[1]\n    \n    #Make sure both taskNum and lineNum will be positive\n    assert taskNum > 0\n    assert lineNum >= 0\n\n    #Builds the adjacency matrix\n    adjMatrix = []\n    for i in range(taskNum):\n        relations = [0]*taskNum  #populating the matrix with 0's (replace with 1 if there is a relation)\n#        pprint(relations)\n        adjMatrix.append(relations)\n    \n    #Populates the value of adjMatrix\n    for j in range(1, 1 + lineNum):\n        curr = a[j] #Current line from reader\n        val = curr[0] #value\n        num = curr[1] #number of relations this value has\n        \n        #Make sure values are positive\n        assert val > 0\n        assert num >= 0\n        for k in range(2, 2 + num):\n            rel = curr[k] #relationship (prerequisite) for val\n            #Make sure the given relationship is valid\n            assert rel > 0\n            assert rel <= 100\n            #debugging\n#            asdf = adjMatrix[val-1]\n#            fdsa = asdf[rel-1]\n            (adjMatrix[val-1])[rel-1] = 1 #change the relationship value to 1\n#    print(\"Adjacency Matrix: \")\n#    pprint(adjMatrix)\n        \n    #Now to solve\n    #4 Steps:\n    #1 Create list of values with no prereqs\n    #2 Select lowest value from list and add to answer\n    #3 Remove all prereqs of this value\n    #4 Repeat Steps 1-3 until answer is fully populated\n    \n    answer = []\n    while (len(answer) < taskNum):\n        poss = []\n        #Step 1:\n        for i in range(taskNum):\n            hasPre = False\n            for j in range(taskNum):\n                if ((adjMatrix[i])[j] == 1):\n                    hasPre = True;\n                    break\n            if (hasPre == False): #Does not have prereq\n                poss.append(i+1)\n        \n#        print(\"Debug 1: \\n\")\n#        pprint(answer)\n#        pprint(poss)\n     \n        pfd_present(poss, answer) #removes used answers from next\n\n        #Step 2:\n        min = poss[0]\n        for i in range(len(poss)):\n            if (poss[i] < min):\n                min = poss[i]\n        answer.append(min)\n\n\n        #Step 3:\n        for i in range(taskNum):\n            (adjMatrix[i])[min-1] = 0\n\n#    print(\"Answer list: \")\n#    pprint(answer)\n    return answer\n\n# -------------\n# pfd_present\n# -------------\ndef pfd_present(n, a) :\n    \"\"\"\n    Checks if a value is already in 'a'\n    Removes it from 'n', if it is present\n    \"\"\"\n    assert len(a) >= 0\n#    print \"Len a: %d \" % (len(a)) \n    assert len(n) >= 0\n#    print \"Len n: %s \" % (str(len(n)))\n    count = 0\n    while count < len(a):\n        for i in range(len(n)):\n            if (a[count] == n[i]):\n                n.pop(i)\n                break; \n        count += 1\n\n\n\n# -------------\n# pfd_solve\n# -------------\n\ndef pfd_solve (r, w) :\n    \"\"\" \n    read loop, eval, print\n    r is a reader\n    w is a writer\n    \"\"\"\n    a = []\n    lineArray = []\n    while pfd_read(r, a):\n        lineArray.append(a)\n        a = []\n    l = pfd_eval(lineArray)\n    pfd_print(w, l)\n","sub_path":"PFD.py","file_name":"PFD.py","file_ext":"py","file_size_in_byte":4282,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"564325973","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport sys\n\ndef get_ratio(threshold):\n\tfp1 = open(\"log_model1.txt\", \"r\")\n\tfp2 = open(\"log_model2.txt\", \"r\")\n\tfp3 = open(\"log_model3.txt\", \"r\")\n\tfp4 = open(\"log_model4.txt\", \"r\")\n\tfp5 = open(\"log_model5.txt\", \"r\")\n\n\ttotal = 0\n\tcorrect = 0\n\n\ttp = 0\n\ttn = 0\n\tfp = 0\n\tfn = 0\n\n\tp = 0\n\tn = 0\n\n\tfor l1, l2, l3, l4, l5 in zip(fp1, fp2, fp3, fp4, fp5):\n\t\ty_true = float(l1.split()[0])\n\n\t\tensm = \"\"\n\n\t\tif float(l1.split()[1]) >= threshold:\n\t\t\tensm += \"1\"\n\t\telse:\n\t\t\tensm += \"0\"\n\n\t\tif float(l2.split()[1]) >= threshold:\n\t\t\tensm += \"1\"\n\t\telse:\n\t\t\tensm += \"0\"\n\n\t\tif float(l3.split()[1]) >= threshold:\n\t\t\tensm += \"1\"\n\t\telse:\n\t\t\tensm += \"0\"\n\n\t\tif float(l4.split()[1]) >= threshold:\n\t\t\tensm += \"1\"\n\t\telse:\n\t\t\tensm += \"0\"\n\n\t\tif float(l5.split()[1]) >= threshold:\n\t\t\tensm += \"1\"\n\t\telse:\n\t\t\tensm += \"0\"\n\n\t\tif ensm.count(\"1\") >= 3:\n\t\t\ty_pred = 1.0\n\t\telse:\n\t\t\ty_pred = 0.0\n\n\t\tif y_true == y_pred:\n\t\t\tcorrect += 1\n\n\t\t\tif y_pred == 1.0:\n\t\t\t\ttp += 1\n\t\t\telse:\n\t\t\t\ttn += 1\n\t\telse:\n\t\t\tif y_pred == 1.0:\n\t\t\t\tfp += 1\n\t\t\telse:\n\t\t\t\tfn += 1\n\n\t\tif y_true == 1.0:\n\t\t\tp += 1\n\t\telse:\n\t\t\tn += 1\n\n\t\ttotal += 1\n\n\tfp1.close()\n\tfp2.close()\n\tfp3.close()\n\tfp4.close()\n\tfp5.close()\n\n\tprint(\"Total = %d, correct = %d => accuracy = %lf\" % (total, correct, float(correct) / float(total)))\n\n\treturn (tp / p, fp / n)\n\npoints = []\n\nfor threshold in np.linspace(0.000001, 1, 100, endpoint = False):\n\tpoints.append(get_ratio(threshold))\n\nplt.scatter([x[1] for x in points], [x[0] for x in points])\nplt.title(\"NN Ensemble ROC\")\nplt.ylabel(\"False Positive Rate\")\nplt.xlabel(\"True Positive Rate\")\n\nplt.xlim(0, 1)\nplt.ylim(0, 1)\n\nplt.savefig(\"NN_Ensm_ROC.png\", dpi = 300)\n","sub_path":"tango/logs/plot_roc.py","file_name":"plot_roc.py","file_ext":"py","file_size_in_byte":1667,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"1692895","text":"#!/usr/bin/env python\n\nimport rospy\nimport cv2\nimport numpy as np\nfrom usv_perception.srv import color_id\nfrom cv_bridge import CvBridge, CvBridgeError\n\nbridge = CvBridge()\nred=['red' for i in range(2)]\norange=['orange' for i in range(2)]\nyellow=['yellow' for i in range(3)]\ngreen=['green' for i in range(11)]\nblue=['blue' for i in range(8)]\npink=['-' for i in range(8)]\ncolors = red + orange + yellow + green + blue + pink + red\n\n\n\n\ndef callback_color(img):\n    global bridge\n\n    image = img.imagen\n    x = img.x\n    y = img.y\n    h = img.h\n    w = img.w\n    image = bridge.imgmsg_to_cv2(image, \"bgr8\")\n\n    image = image[y:y+h,x:x+w]\n    \n    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n\n\n    # split each channel from hsv tensor\n    (h, s, v) = cv2.split(image)\n\n\n    ret = \"\"\n    \n    \n    hist = cv2.calcHist([hsv], [0], None, [36], [0, 180])\n    \n\n    hist_list = [hist[i][0] for i in range(len(hist))]\n\n    max_index = hist_list.index(max(hist_list))\n\n    ret = colors[max_index]\n\n    return ret\n\n\nif __name__ == '__main__':\n\n    rospy.init_node('color_srv')\n    rospy.loginfo(\"Node created!\")\n\n    service = rospy.Service(\"/get_color\", color_id, callback_color)\n\n    rate = rospy.Rate(10)\n\n    while not rospy.is_shutdown():\n        rate.sleep()\n","sub_path":"usv_perception/scripts/color_srv.py","file_name":"color_srv.py","file_ext":"py","file_size_in_byte":1256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"268796583","text":"#!/usr/bin/env python\n\n# \"Stopwatch: The Game\"\n\nimport SimpleGUICS2Pygame.simpleguics2pygame as simplegui\n\n# define global variables\ninterval = 100\ntic_toc = 0\nx = 0\ny = 0\nRunning = False\n\n# define helper function format that converts time\n# in tenths of seconds into formatted string A:BC.D\n\ndef format(time):\n\t\"\"\"Formats tic_toc into stopwatch time.\"\"\"\n\tA = time // 600\n\tB = ((time // 10) % 60) // 10\n\tC = ((time // 10) % 60) % 10\n\tD = time % 10\n\treturn str(A) + \":\" + str(B) + str(C) + \":\" + str(D)\n\n# define event handlers for buttons; \"Start\", \"Stop\", \"Reset\"\ndef start():\n\t\"\"\"Start timer.\"\"\"\n\tglobal Running\n\tt.start()\n\tRunning = True\n\ndef stop():\n\t\"\"\"Stop timer.\"\"\"\n\tglobal Running,tic_toc,x,y\n\tt.stop()\n\tif Running:\n\t\ty += 1\n\t\tif tic_toc % 10 == 0:\n\t\t\tx += 1\n\tRunning = False\n\ndef reset():\n\t\"\"\"Stop and reset timer.\"\"\"\n\tglobal Running,tic_toc,x,y\n\tt.stop()\n\tRunning = False\n\ttic_toc,x,y = 0,0,0\n\n# define event handler for timer with 0.1 sec interval\ndef timer():\n\t\"\"\"Increments tic_toc at a rate equal to the timer interval.\"\"\"\n\tglobal tic_toc\n\ttic_toc += 1\n\n# define draw handler\ndef draw(canvas):\n\t\"\"\"Draws stopwatch on canvas.\"\"\"\n\tcanvas.draw_text(format(tic_toc), (100,110), 50, \"Red\")\n\tcanvas.draw_text(str(x) + \"/\" + str(y), (230,30), 30, \"Red\")\n    \n# create frame and timer\nf = simplegui.create_frame(\"Stopwatch: The Game\", 300, 200)\nt = simplegui.create_timer(interval, timer)\n\n# register event handlers\nf.set_draw_handler(draw)\nf.add_button(\"Start\", start, 200)\nf.add_button(\"Stop\", stop, 200)\nf.add_button(\"Reset\", reset, 200)\n\n# start frame\nf.start()\n","sub_path":"an_introduction_to_interactive_programming_in_python/week3/stopwatch_the_game.py","file_name":"stopwatch_the_game.py","file_ext":"py","file_size_in_byte":1572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"617524303","text":"import glob\nimport os\nfrom os import path\nimport random\nfrom collections import OrderedDict\n\nfrom data import common\nfrom data.meta_learner import vsrbase\n\nimport numpy as np\nimport scipy.misc as misc\nimport imageio\n\nimport torch\nimport torch.utils.data as data\n\n\nclass REDS(vsrbase.VSRBase):\n    \"\"\"GOPRO_Large train OR test subset class\n    \"\"\"\n    def __init__(self, *args, **kwargs):\n        super(REDS, self).__init__(*args, **kwargs)\n\n    def _set_directory(self, data_root):\n        self.apath = path.join(data_root, 'REDS')\n\n    def _scan(self):\n        \"\"\"\n        Follow the train/valid split of REDS in EDVR paper.\n        Trainset : Train(except 000, 011, 015, 020) + Valid (Total 266 videos)\n        Validset : Train000, 011, 015, 020 (Total 4 videos)\n        :return:\n        \"\"\"\n\n        def _make_keys(dir_path):\n            \"\"\"\n            :param dir_path: Path\n            :return: train_000 form\n            \"\"\"\n            dir, base = path.dirname(dir_path), path.basename(dir_path)\n            tv = 'train' if dir.find('train')>=0 else 'val'\n            return tv + '_' + base\n\n\n        dir_path_train = path.join(self.apath, 'train')\n        dir_hr_train = path.join(dir_path_train, 'HR')\n        list_hr_seq = glob.glob(dir_hr_train+'/*')\n        list_hr_seq_val = [k for k in list_hr_seq if k.find('000') >= 0 or k.find('011') >= 0 or k.find('015') >= 0 or k.find('020') >= 0]\n\n        for x in list_hr_seq_val:\n            list_hr_seq.remove(x)\n\n        dir_path_valid = path.join(self.apath, 'val')\n        dir_hr_valid = path.join(dir_path_valid, 'HR')\n        list_hr_seq.extend(glob.glob(dir_hr_valid+'/*'))\n\n        if self.split == 'train':\n            dict_hr = {\n                _make_keys(k): sorted(\n                    glob.glob(path.join(k, '*' + self.ext))\n                ) for k in list_hr_seq\n            }\n            return dict_hr\n\n        else:\n            dict_hr = {\n                _make_keys(k): sorted(\n                    glob.glob(path.join(k, '*' + self.ext))\n                ) for k in list_hr_seq_val\n            }\n            return dict_hr\n","sub_path":"codes/data/meta_learner/reds.py","file_name":"reds.py","file_ext":"py","file_size_in_byte":2097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"28384545","text":"import numpy as np, matplotlib.pyplot as plt\r\nfrom matplotlib.animation import FuncAnimation\r\n\r\nk = 5\r\nw = 100\r\n\r\nx = np.linspace(0, 10, 1000)\r\nt = np.linspace(0, 2*np.pi, 3000)\r\n\r\ndef wave(x, t):\r\n    return np.cos(k*x - w*t)\r\n\r\nplt.style.use('seaborn-darkgrid')\r\n\r\nfig, ax = plt.subplots()\r\n\r\nax.set_xlim(0, 10)\r\nax.set_ylim(-1.1, 1.1)\r\n\r\nline, = ax.plot(x, wave(x, t[0]))\r\n\r\ndef animation_frame(i):\r\n\r\n    line.set_xdata(x)\r\n    line.set_ydata(wave(x, t[i]))\r\n\r\n    plt.title(r'$t = %.2f$' % t[i])\r\n\r\n    return line,\r\n\r\nplt.xlabel(r'$x$')\r\nplt.ylabel(r'$\\psi(x, t)$')\r\n\r\nanimation = FuncAnimation(fig, func = animation_frame, frames = range(len(t)), interval = 0.00001)\r\nplt.show()\r\n","sub_path":"sine_animation.py","file_name":"sine_animation.py","file_ext":"py","file_size_in_byte":687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"150241107","text":"'''Task: read two integers from STDIN and print three lines.\nRequirements: 1. The first line contains the sum of the two numbers, first+second\n2. The second line contains the difference of the two numbers you enter first-second\n3. The third line contains the product of the two numbers   first*second\nInput/Output Format: The first line contains the first integer a; The second line contains the second integer b.\nConstraints: 1 <= a <= 10powerof10  , 1 <= b <= 10powerof10\nExample:Input=>\n3\n2\nOutput=>\n5\n1\n6\nBecause 5=3+2; 1=3-2; 6=3*2\n'''\nimport math\n\nprint(\"Please enter two integers [0, 10powerof10]:\")\na=int(input())\nb=int(input())\nprint('\\nAs per integers you entered, Here is the Results:\\n{0}  \\n{1} \\n{2}'.format((a+b),(a-b),(a*b)))\n\n\n","sub_path":"programminginpython3_completeintro/worklaptop_python_scriptssinceFeb7th_2020/hackerrank_py_test_ArithmeticOperator.py","file_name":"hackerrank_py_test_ArithmeticOperator.py","file_ext":"py","file_size_in_byte":744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"466916248","text":"import json\r\n\r\n\r\nclass SamError(Exception):\r\n    status_code = 400\r\n\r\n    def __init__(self, message, status_code=None, payload=None):\r\n        Exception.__init__(self)\r\n        self.message = message\r\n        if status_code is not None:\r\n            self.status_code = status_code\r\n        self.payload = payload\r\n\r\n    def to_dict(self):\r\n        if isinstance(self.payload, str):\r\n            rv = {'payload': self.payload}\r\n        else:\r\n            rv = dict(self.payload or ())\r\n        rv['fulfillmentText'] = self.message\r\n        rv['errorType'] = str(type(self).__name__)\r\n        if self.payload:\r\n            if self.payload.startswith('{'):\r\n                rv['payload'] = json.loads(self.payload)\r\n            else:\r\n                rv['payload'] = self.payload\r\n        return rv\r\n\r\n\r\nclass InvalidDataFormatError(SamError):\r\n    \"\"\"\r\n    The format of the data is invalid\r\n    \"\"\"\r\n    def __init__(self, message, status_code=None, payload=None):\r\n        super().__init__(message, status_code, payload)\r\n\r\n\r\nclass InvalidDataTypeError(SamError):\r\n    \"\"\"\r\n    The type of the data is invalid\r\n    \"\"\"\r\n\r\n    def __init__(self, message, status_code=None, payload=None):\r\n        super().__init__(message, status_code, payload)\r\n\r\n\r\nclass InvalidDataValueError(SamError):\r\n    \"\"\"\r\n    The value of the data is invalid\r\n    \"\"\"\r\n    def __init__(self, message, status_code=None, payload=None):\r\n        super().__init__(message, status_code, payload)\r\n\r\n\r\nclass NoTokenError(SamError):\r\n    \"\"\"\r\n    No token was provided when accessing some API\r\n    \"\"\"\r\n    def __init__(self, message, status_code=None, payload=None):\r\n        super().__init__(message, status_code, payload)\r\n\r\n\r\nclass SpotifyPlaylistNotfoundError(SamError):\r\n    \"\"\"\r\n    Spotify Playlist was not found anywhere\r\n    \"\"\"\r\n    def __init__(self, message, status_code=None, payload=None):\r\n        super().__init__(message, status_code, payload)\r\n","sub_path":"sam/exceptions.py","file_name":"exceptions.py","file_ext":"py","file_size_in_byte":1933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"618873379","text":"import numpy as np\r\nimport math\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\n\r\n\r\ndef calc_iou(a, b):\r\n    area = (b[:, 2] - b[:, 0]) * (b[:, 3] - b[:, 1])\r\n\r\n    iw = torch.min(torch.unsqueeze(a[:, 2], dim=1), b[:, 2]) - torch.max(torch.unsqueeze(a[:, 0], 1), b[:, 0])\r\n    ih = torch.min(torch.unsqueeze(a[:, 3], dim=1), b[:, 3]) - torch.max(torch.unsqueeze(a[:, 1], 1), b[:, 1])\r\n\r\n    iw = torch.clamp(iw, min=0)\r\n    ih = torch.clamp(ih, min=0)\r\n\r\n    ua = torch.unsqueeze((a[:, 2] - a[:, 0]) * (a[:, 3] - a[:, 1]), dim=1) + area - iw * ih\r\n\r\n    ua = torch.clamp(ua, min=1e-8)\r\n\r\n    intersection = iw * ih\r\n\r\n    IoU = intersection / ua\r\n\r\n    return IoU\r\n\r\ndef calc_iou_vis(a, b):\r\n    area = (b[:, 2] - b[:, 0]) * (b[:, 3] - b[:, 1])\r\n\r\n    iw = torch.min(torch.unsqueeze(a[:, 2], dim=1), b[:, 2]) - torch.max(torch.unsqueeze(a[:, 0], 1), b[:, 0])\r\n    ih = torch.min(torch.unsqueeze(a[:, 3], dim=1), b[:, 3]) - torch.max(torch.unsqueeze(a[:, 1], 1), b[:, 1])\r\n\r\n    iw = torch.clamp(iw, min=0)\r\n    ih = torch.clamp(ih, min=0)\r\n\r\n    intersection = iw * ih\r\n\r\n    IoU = intersection / area\r\n\r\n    return IoU\r\n\r\ndef IoG(box_a, box_b):\r\n\r\n    inter_xmin = torch.max(box_a[:, 0], box_b[:, 0])\r\n    inter_ymin = torch.max(box_a[:, 1], box_b[:, 1])\r\n    inter_xmax = torch.min(box_a[:, 2], box_b[:, 2])\r\n    inter_ymax = torch.min(box_a[:, 3], box_b[:, 3])\r\n    Iw = torch.clamp(inter_xmax - inter_xmin, min=0)\r\n    Ih = torch.clamp(inter_ymax - inter_ymin, min=0)\r\n    I = Iw * Ih\r\n    G = (box_a[:, 2] - box_a[:, 0]) * (box_a[:, 3] - box_a[:, 1])\r\n    return I / G\r\n\r\n\r\nclass FocalLoss(nn.Module):\r\n    # def __init__(self):\r\n\r\n    def forward(self, classifications, regressions, anchors, bboxs, vboxs, ignores):\r\n        alpha = 0.25\r\n        gamma = 2.0\r\n        batch_size = classifications.shape[0]\r\n        classification_losses = []\r\n        regression_losses = []\r\n\r\n        anchor = anchors[0, :, :]\r\n\r\n        anchor_widths = anchor[:, 2] - anchor[:, 0]\r\n        anchor_heights = anchor[:, 3] - anchor[:, 1]\r\n        anchor_ctr_x = anchor[:, 0] + 0.5 * anchor_widths\r\n        anchor_ctr_y = anchor[:, 1] + 0.5 * anchor_heights\r\n\r\n        for j in range(batch_size):\r\n\r\n            classification = classifications[j, :, :]\r\n            regression = regressions[j, :, :]\r\n\r\n            bbox_annotation = bboxs[j, :, :]\r\n            bbox_annotation = bbox_annotation[bbox_annotation[:, 4] != -1]\r\n\r\n            vbox_annotation = vboxs[j, :, :]\r\n            vbox_annotation = vbox_annotation[vbox_annotation[:, 4] != -1]\r\n\r\n            if bbox_annotation.shape[0] == 0:\r\n                regression_losses.append(torch.tensor(0).float().cuda())\r\n                classification_losses.append(torch.tensor(0).float().cuda())\r\n\r\n                continue\r\n\r\n            classification = torch.clamp(classification, 1e-4, 1.0 - 1e-4)\r\n\r\n            ignore = ignores[j, :, :]\r\n            ignore = ignore[ignore[:, 4] != -1]\r\n            if ignore.shape[0] > 0:\r\n                iou_igno = calc_iou(anchor, ignore)\r\n                iou_igno_max, iou_igno_argmax = torch.max(iou_igno, dim=1)\r\n                index_igno = torch.lt(iou_igno_max, 0.5)\r\n                anchor_keep = anchor[index_igno, :]\r\n                classification = classification[index_igno, :]\r\n                regression = regression[index_igno, :]\r\n                anchor_widths_keep = anchor_widths[index_igno]\r\n                anchor_heights_keep = anchor_heights[index_igno]\r\n                anchor_ctr_x_keep = anchor_ctr_x[index_igno]\r\n                anchor_ctr_y_keep = anchor_ctr_y[index_igno]\r\n            else:\r\n                anchor_keep = anchor\r\n                anchor_widths_keep = anchor_widths\r\n                anchor_heights_keep = anchor_heights\r\n                anchor_ctr_x_keep = anchor_ctr_x\r\n                anchor_ctr_y_keep = anchor_ctr_y\r\n\r\n            IoU = calc_iou(anchor_keep, bbox_annotation[:, :4])  # num_anchors x num_annotations\r\n\r\n            IoU_max, IoU_argmax = torch.max(IoU, dim=1)  # num_anchors x 1\r\n\r\n            assigned_annotations_vis = vbox_annotation[IoU_argmax, :]\r\n            IoU_vis = IoG(assigned_annotations_vis, anchor_keep)\r\n\r\n            # compute the loss for classification\r\n            targets = torch.ones(classification.shape) * -1\r\n            targets = targets.cuda()\r\n\r\n            targets[torch.lt(IoU_max, 0.4), :] = 0\r\n\r\n            positive_ful = torch.ge(IoU_max, 0.5)\r\n            positive_vis = torch.ge(IoU_vis, 0.5)\r\n            positive_indices = positive_ful * positive_vis\r\n\r\n            num_positive_anchors = positive_indices.sum()\r\n\r\n            assigned_annotations = bbox_annotation[IoU_argmax, :]\r\n\r\n            targets[positive_indices, :] = 0\r\n            targets[positive_indices, assigned_annotations[positive_indices, 4].long()] = 1\r\n\r\n            alpha_factor = torch.ones(targets.shape).cuda() * alpha\r\n\r\n            alpha_factor = torch.where(torch.eq(targets, 1.), alpha_factor, 1. - alpha_factor)\r\n            focal_weight = torch.where(torch.eq(targets, 1.), 1. - classification, classification)\r\n            focal_weight = alpha_factor * torch.pow(focal_weight, gamma)\r\n\r\n            bce = -(targets * torch.log(classification) + (1.0 - targets) * torch.log(1.0 - classification))\r\n\r\n            # cls_loss = focal_weight * torch.pow(bce, gamma)\r\n            cls_loss = focal_weight * bce\r\n\r\n            cls_loss = torch.where(torch.ne(targets, -1.0), cls_loss, torch.zeros(cls_loss.shape).cuda())\r\n\r\n            classification_losses.append(cls_loss.sum() / torch.clamp(num_positive_anchors.float(), min=1.0))\r\n\r\n            # compute the loss for regression\r\n\r\n            if positive_indices.sum() > 0:\r\n                assigned_annotations = assigned_annotations[positive_indices, :]\r\n\r\n                anchor_widths_pi = anchor_widths_keep[positive_indices]\r\n                anchor_heights_pi = anchor_heights_keep[positive_indices]\r\n                anchor_ctr_x_pi = anchor_ctr_x_keep[positive_indices]\r\n                anchor_ctr_y_pi = anchor_ctr_y_keep[positive_indices]\r\n\r\n                gt_widths = assigned_annotations[:, 2] - assigned_annotations[:, 0]\r\n                gt_heights = assigned_annotations[:, 3] - assigned_annotations[:, 1]\r\n                gt_ctr_x = assigned_annotations[:, 0] + 0.5 * gt_widths\r\n                gt_ctr_y = assigned_annotations[:, 1] + 0.5 * gt_heights\r\n\r\n                # clip widths to 1\r\n                gt_widths = torch.clamp(gt_widths, min=1)\r\n                gt_heights = torch.clamp(gt_heights, min=1)\r\n\r\n                targets_dx = (gt_ctr_x - anchor_ctr_x_pi) / anchor_widths_pi\r\n                targets_dy = (gt_ctr_y - anchor_ctr_y_pi) / anchor_heights_pi\r\n                targets_dw = torch.log(gt_widths / anchor_widths_pi)\r\n                targets_dh = torch.log(gt_heights / anchor_heights_pi)\r\n\r\n                targets = torch.stack((targets_dx, targets_dy, targets_dw, targets_dh))\r\n                targets = targets.t()\r\n\r\n                targets = targets / torch.Tensor([[0.1, 0.1, 0.2, 0.2]]).cuda()\r\n\r\n                negative_indices = 1 - positive_indices\r\n\r\n                regression_diff = torch.abs(targets - regression[positive_indices, :])\r\n\r\n                regression_loss = torch.where(\r\n                    torch.le(regression_diff, 1.0 / 9.0),\r\n                    0.5 * 9.0 * torch.pow(regression_diff, 2),\r\n                    regression_diff - 0.5 / 9.0\r\n                )\r\n                regression_losses.append(regression_loss.mean())\r\n            else:\r\n                regression_losses.append(torch.tensor(0).float().cuda())\r\n\r\n        return torch.stack(classification_losses).mean(dim=0, keepdim=True), torch.stack(regression_losses).mean(dim=0,\r\n                                                                                                                 keepdim=True)\r\n","sub_path":"losses.py","file_name":"losses.py","file_ext":"py","file_size_in_byte":7875,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"420114614","text":"# -*- coding: utf-8 -*-\n\"\"\"\nIntcode machine.\nLast updated: Day 5\nSupports opcodes 1..8, 99\n\n@author: DoraMemo\n\"\"\"\n\ndef valParamMode(mode, val, nums):\n    if (mode == 0): # location\n        return nums[val]\n    elif (mode == 1): # value\n        return val\n    else:\n        print(\"unknown mode\", mode)\n    \n    return -1\n\ndef intcode(nums):\n    \"\"\"\n    nums: list of numbers\n    \"\"\"\n    \n    # opcode: num_params\n    paramCount = {\n            1: 3,\n            2: 3,\n            3: 1,\n            4: 1,\n            5: 2,\n            6: 2,\n            7: 3,\n            8: 3,\n            99: 0\n            }\n    \n    ip = 0 # instruction pointer\n    while ip < len(nums):\n#        print(\"ip at\",ip,nums[ip])\n        opcode = nums[ip] % 100\n        nParams = paramCount[opcode]\n        inststr = str(nums[ip])\n        paramMode = [] if len(inststr) <= 2 else [int(inststr[i]) for i in range(len(inststr)-3, -1, -1)]\n        # fill the remaining zeros\n        paramMode = [(paramMode[i] if i < len(paramMode) else 0) for i in range(nParams)]\n        \n        a = 0 ; b = 0 ; c = 0\n        if (nParams >= 3):\n            c = valParamMode(paramMode[2], nums[ip+3], nums)\n        if (nParams >= 2):\n            b = valParamMode(paramMode[1], nums[ip+2], nums)\n        if (nParams >= 1):\n            a = valParamMode(paramMode[0], nums[ip+1], nums)\n        \n        if (opcode == 1): # add(a, b, store_pos)\n            nums[nums[ip+3]] = a + b\n        elif (opcode == 2): # mult(a, b, store_pos)\n            nums[nums[ip+3]] = a * b\n        elif (opcode == 3): # take input, store in (n)\n            print(\"Pls input\",end=\"\")\n            nums[nums[ip+1]] = int(input())\n        elif (opcode == 4): # output / print out parameter (n)\n            print(\"Output =\", a)\n        elif (opcode == 5): # JIT(a > 0, ip = b)\n            if (a > 0):\n                ip = b\n                continue\n        elif (opcode == 6): # JIF(a == 0, ip = b)\n            if (a == 0):\n                ip = b\n                continue\n        elif (opcode == 7): # less(a < b, store_pos = 1 : 0)\n            nums[nums[ip+3]] = 1 if (a < b) else 0\n        elif (opcode == 8): # equals(a == b, store_pos = 1 : 0)\n            nums[nums[ip+3]] = 1 if (a == b) else 0\n        elif (opcode == 99):\n            return 1 # finished successfully\n        else:\n            print(\"Unknown instruction\", nums[ip], \"at pos\", ip)\n            print(\"Opcode =\", opcode)\n            print(\"Inststr =\", inststr)\n            print(\"ParamMode =\", paramMode)\n            return nums[0]\n        \n        ip += nParams + 1\n            \n    return nums[0]","sub_path":"intcode.py","file_name":"intcode.py","file_ext":"py","file_size_in_byte":2599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"441553595","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# File            : shift_register.py\n# Author          : Duy Anh Pham <duyanh.y4n.pham@gmail.com>\n# Date            : 22.03.2020\n# Last Modified By: Duy Anh Pham <duyanh.y4n.pham@gmail.com>\n\nfrom collections import deque\n\n# -*- coding: utf-8 -*-\n\"\"\"\n    realtime_plot.ShiftRegister\n    ~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n    example shift register with deque\n\n    :copyright: (c) 2020 by duyanhy4n.\n    :license: LICENSE_NAME, see LICENSE for more details.\n\"\"\"\n\n\nclass ShiftRegister():\n    def __init__(self, maxlen=10):\n        self.maxlen = maxlen\n        self.dataReg = deque(maxlen=maxlen)\n\n\ntest = ShiftRegister(5)\nprint(test.dataReg)\ninput()\n","sub_path":"djitellopy/realtime_plot/examples/ShiftRegister.py","file_name":"ShiftRegister.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"117878114","text":"from env_2_stochastic_high import Environment3,StartandGoal,statetocor\nimport numpy as np\nimport random\n\n[startstate,goalstate]=StartandGoal()\nnumX=7\nnumY=10\nnumA=8\n\ndef QAgent(Timehorizon,Trainingloops):\n    #Agent parameters:\n    epsilon=0.1; #exploration rate\n    alpha=0.5; #alpha in update-rule\n    gamma=1; #gamma in target discount rate\n    \n    start_x,start_y=statetocor(startstate)\n    goal_x,goal_y=statetocor(goalstate)\n    #Initiliasing Q for every master-loop i.e updated learned for every random seed\n    Q=np.zeros((numX,numY,numA));\n    seedloops=10\n    \n    for j in range(seedloops):\n        #Changing random seeds\n        rseed=j;\n        random.seed(rseed)\n        np.random.seed(rseed)\n\n        #initilialising very first step\n        x=start_x; #At time step-1\n        y=start_y;\n        action=random.randint(0,numA-1);\n\n        #Q-Learning\n        for i in range(int(Trainingloops/10)):\n            \n            #Exploration-rate for e-greedy\n            exploration_rate = random.uniform(0, 1)\n\n            #Fetching next-state reward from envrionment-function\n            [xp,yp,reward]=Environment3(x,y,action)\n\n            #Random-action\n            if(exploration_rate > epsilon):\n                new_action=np.argmax(Q[xp-1,yp-1,:])\n            #greedy-action\n            else:\n                new_action=random.randint(0,numA-1) #random-action\n\n            #print(state,action)\n            #Updating Q (Q-learning off policy)\n            Target= reward + gamma*np.max(Q[xp-1,yp-1,:])\n            Q[x-1,y-1,action] = Q[x-1,y-1,action] + alpha*(Target-Q[x-1,y-1,action]) # update Q value\n\n            #starting from the new-state\n            x=xp # updating position in the environment\n            y=yp\n            action=new_action\n\n            #Restarting episode when goal is reached\n            if(x==goal_x and y==goal_y):\n                #incrementing episode number\n                x=start_x #Reset\n                y=start_y\n                action=random.randint(0,numA-1)\n                \n    return Q","sub_path":"Level 5 PartiallyObservableCoordinates/Level 5B StochKings+POMDP/B HighStoch/QlearningAgent.py","file_name":"QlearningAgent.py","file_ext":"py","file_size_in_byte":2039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"460066416","text":"\"\"\"\n# Definition for a Node.\n\"\"\"\nfrom collections import deque\nfrom typing import List\n\n\nclass Node:\n    def __init__(self, val=None, children=None):\n        self.val = val\n        self.children = children\n\n\nclass Solution:\n    def levelOrder(self, root: 'Node') -> List[List[int]]:\n        output = []\n\n        if not root:\n            return output\n        \n        current_level = deque()\n        current_level.append(root)\n\n        while current_level:\n            next_level = deque()\n            output.append([])\n\n            while current_level:\n                node: Node = current_level.popleft()\n                next_level.extend(node.children)\n\n                output[-1].append(node.val)\n\n            current_level = next_level\n\n        return output\n","sub_path":"leetcodeOthers/problem429.py","file_name":"problem429.py","file_ext":"py","file_size_in_byte":764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"104213296","text":"import string\nimport numpy as np \nfrom pandas import Series,DataFrame\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nimport re\nimport math\n \n \ndef ConvertELogStrToValue(eLogStr):\n    \"\"\"\n    convert string of natural logarithm base of E to value\n    return (convertOK, convertedValue)\n    eg:\n    input:  -1.1694737e-03\n    output: -0.001169\n    input:  8.9455025e-04\n    output: 0.000895\n    \"\"\"\n \n    (convertOK, convertedValue) = (False, 0.0)\n    foundEPower = re.search(\"(?P<coefficientPart>-?\\d+\\.\\d+)e(?P<ePowerPart>-\\d+)\", eLogStr, re.I)\n    #print \"foundEPower=\",foundEPower\n    if(foundEPower):\n        coefficientPart = foundEPower.group(\"coefficientPart\")\n        ePowerPart = foundEPower.group(\"ePowerPart\")\n        #print \"coefficientPart=%s,ePower=%s\"%(coefficientPart, ePower)\n        coefficientValue = float(coefficientPart)\n        ePowerValue = float(ePowerPart)\n        #print \"coefficientValue=%f,ePowerValue=%f\"%(coefficientValue, ePowerValue)\n        #math.e= 2.71828182846\n        # wholeOrigValue = coefficientValue * math.pow(math.e, ePowerValue)\n        wholeOrigValue = coefficientValue * math.pow(10, ePowerValue)\n \n        #print \"wholeOrigValue=\",wholeOrigValue;\n \n        (convertOK, convertedValue) = (True, wholeOrigValue)\n    else:\n        (convertOK, convertedValue) = (False, float(eLogStr))\n\n    return (convertOK, convertedValue)\n\n\ndef df(file):\n    with open(file) as f: \n        lines = f.readlines()\n\n    index=[]\n    loss=[]\n\n    for line in lines:\n        if 'Loss' in line:\n            index.append(int(pre_line))\n            \n            st=line.split(':')[1]\n            st = st.split('%')[0]\n            loss.append(float(st)/100)\n\n        pre_line = line\n    return     index,loss\n\n    \nif __name__ == '__main__':\n    \n    font2 = {'family' : 'Times New Roman',\n    'weight' : 'normal',\n    'size'   : 30}\n\n    index,loss = df(\"result_pruning.dat\")\n\n    plt.grid(linestyle='-.')\n    \n    plt.plot(index, loss, linewidth=1.0,label='Pruning model',\n             color='orangered', alpha=0.8)\n    plt.scatter(index, loss, color='#4169E2', edgecolor='none', s=7)\n\n    plt.plot([0,700],[0.0513,0.0513],c='burlywood',linewidth=2.5,label='FlowDNN w/ AM')\n\n\n    \n    plt.legend(loc=\"upper left\")\n    #plt.ylabel('Validation Loss',size=18)\n    plt.ylabel(r'$\\rm{MRE}$',size=20)\n    plt.xlabel('Number of pruned filters',size=20)\n    plt.xticks(fontsize=20)\n    plt.yticks(fontsize=20)\n    plt.xlim([0,700])\n    #plt.ylim([0,0.01])\n\n    ax = plt.gca()  \n    ax.yaxis.get_major_formatter().set_powerlimits((0,1))\n    \n    plt.show()\n    #plt.savefig('demo2.pdf') \n    \"\"\"\n    plt.close()\n\n    plt.subplots_adjust(bottom=0,top=0.6)\n    \n    sum_row=f_frame.iloc[:,0].size+1\n    plt.scatter(np.arange(1,sum_row), f_frame.csr,c='r',marker='^',label='csr',s=20)\n    plt.scatter(np.arange(1,sum_row), f_frame.csr5,c='b',marker='o',label='csr5',s=20)\n    plt.scatter(np.arange(1,sum_row), f_frame.ell,c='y',marker='x',label='ell',s=20)\n    plt.scatter(np.arange(1,sum_row), f_frame.hyb,c='c',marker='s',label='hyb',s=20)\n    plt.scatter(np.arange(1,sum_row), f_frame.sell,c='w',marker='v',label='sell',s=20)\n\n    #plt.title('ft2000p_thread64(mtx orderd by nnz)')\n    plt.ylabel('GFlops',size=20)\n    #plt.xlabel('mtx_num')\n    plt.legend(loc='upper left',ncol=1)\n    leg = plt.gca().get_legend()\n    ltext  = leg.get_texts()\n    plt.setp(ltext, fontsize=15)\n    plt.xlim([0,1000])\n    plt.ylim([0,60])\n    plt.xticks(fontsize=20)\n    plt.yticks(fontsize=20)\n    plt.savefig('ft_scatter.pdf',dpi=400,bbox_inches='tight')\n    \"\"\"\n    \n\n\n    \n","sub_path":"figures/data/draw/pro_data_pruning.py","file_name":"pro_data_pruning.py","file_ext":"py","file_size_in_byte":3578,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"533526296","text":"import re\n\nfrom django.core.exceptions import ValidationError\nfrom django.db.models import Count\nfrom django.utils.text import slugify\n\nfrom django_site.apps.forms_api.models import (\n    Form, FormField, FormResponse, FormFieldResponse, FormLockedError\n)\n\n\n__all__ = ['current_forms', 'create']\n\nALLOWED_UPDATE_ATTRS = ['name', 'description', 'project', 'user', 'fields']\nALLOWED_CREATE_ATTRS = ['number', 'version'] + ALLOWED_UPDATE_ATTRS\n\n\ndef _filter_data(data, allowed):\n    return {key: data[key] for key in allowed if key in data}\n\n\ndef _validate_and_save_form_data(form_data, form=None):\n    fields = form_data.pop('fields', [])\n    if form is None:\n        form = Form(**form_data)\n    else:\n        for key, value in form_data.items():\n            setattr(form, key, value)\n    form.full_clean()\n    form.save()\n    return _validate_and_save_fields(form, fields)\n\n\ndef _validate_and_save_fields(form, fields):\n    for field_data in fields:\n        field_data['form'] = form\n        field = FormField(**field_data)\n        field.full_clean()\n        field.save()\n    return form\n\n\ndef _make_unique_slug(form):\n    slug = slugify(form.name)\n    used_slugs = (Form.objects\n                  .filter(published=True, slug__startswith=slug)\n                  .values_list('slug', flat=True))\n    if slug in used_slugs:\n        slug_matches = [re.match('%s-(\\d+)$' % slug, s) for s in used_slugs]\n        slug_nums = [int(m.group(1)) for m in slug_matches if m]\n        slug_num = (max(slug_nums) if slug_nums else 0) + 1\n        slug = '%s-%d' % (slug, slug_num)\n    return slug\n\n\ndef current_forms():\n    prev_pub = Form.objects.filter(published=True, current=False)\n    numbers = [str(n) for n in prev_pub.values_list('number', flat=True)]\n    prev_pub_sql = (\"number IN (%s)\" % ','.join(numbers) if len(numbers) > 0\n                    else \"FALSE\")\n    return (Form.objects.filter(current=True)\n            .annotate(num_responses=Count('formresponse'))\n            .extra(select={'prev_published': prev_pub_sql}))\n\n\ndef create(**form_data):\n    form_data = _filter_data(form_data, ALLOWED_CREATE_ATTRS)\n    if 'number' in form_data and form_data['number'] > 0:\n        next_version = Form.objects.max_version(form_data['number']) + 1\n        form_data['version'] = next_version\n    else:\n        form_data['number'] = Form.objects.max_number() + 1\n        form_data['version'] = 1\n    form_data['current'] = True\n    Form.objects.filter(number=form_data['number']).update(current=False)\n    return _validate_and_save_form_data(form_data)\n\n\ndef update(number, version, **form_data):\n    form_data = _filter_data(form_data, ALLOWED_UPDATE_ATTRS)\n    form = Form.objects.get(number=number, version=version)\n    if form.locked:\n        raise FormLockedError\n    FormField.objects.filter(form=form).delete()\n    return _validate_and_save_form_data(form_data, form)\n\n\ndef versions(number):\n    forms = (Form.objects\n             .select_related('user')\n             .prefetch_related('formfield_set')\n             .filter(number=number)\n             .annotate(num_responses=Count('formresponse')))\n    if not forms:\n        raise Form.DoesNotExist\n    return forms\n\n\ndef version(number, version):\n    return (Form.objects\n            .select_related('user')\n            .prefetch_related('formfield_set')\n            .get(number=number, version=version))\n\n\ndef publish(number, version):\n    Form.objects.filter(number=number).update(published=False)\n    form = Form.objects.get(number=number, version=version)\n    form.published = True\n    form.locked = True\n    form.slug = _make_unique_slug(form)\n    form.save()\n    return form\n\n\ndef unpublish(number, version):\n    form = Form.objects.get(number=number, version=version)\n    form.published = False\n    form.save()\n    return form\n\n\ndef form_with_responses(number, version):\n    return (Form.objects\n            .prefetch_related('formfield_set',\n                              'formresponse_set__formfieldresponse_set')\n            .get(number=number, version=version))\n\n\ndef published_form(project, slug):\n    return (Form.objects\n            .select_related('user')\n            .prefetch_related('formfield_set')\n            .get(project=project, slug=slug, published=True))\n\n\ndef published_forms(project):\n    return (Form.objects\n            .select_related('user')\n            .prefetch_related('formfield_set')\n            .filter(project=project, published=True))\n\n\ndef create_response(form, field_response_data, user=None):\n    fields = filter(lambda f: f.kind != 'info', form.formfield_set.all())\n    field_responses = _build_field_responses(fields, field_response_data)\n    _validate_field_responses(field_responses)\n    form_response = FormResponse.objects.create(form=form, user=user)\n    for field_response in field_responses:\n        field_response.form_response = form_response\n        field_response.save()\n    return form_response\n\n\ndef _build_field_responses(fields, field_response_data):\n    field_responses = []\n    for field in fields:\n        details = field_response_data.get(str(field.id), {})\n        field_response = FormFieldResponse(form_field=field, details=details)\n        field_responses.append(field_response)\n    return field_responses\n\n\ndef _validate_field_responses(field_responses):\n    errors = {}\n    for field_response in field_responses:\n        try:\n            field_response.clean()\n        except ValidationError as e:\n            errors[str(field_response.form_field.id)] = e\n    if errors:\n        raise ValidationError(errors)\n","sub_path":"backends/django/django_site/apps/forms_api/services/forms_service.py","file_name":"forms_service.py","file_ext":"py","file_size_in_byte":5531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"497731602","text":"from helpers.solvers import default_solver\nfrom dwave.system.samplers import DWaveSampler\nimport minorminer\nimport dimod\n\n\ndef quantum_compute(linear, quadratic, offset=0, vartype=dimod.BINARY):\n\n    bqm = dimod.BinaryQuadraticModel(linear, quadratic, offset, vartype)\n    my_solver, my_token = default_solver()\n\n    # Use a D-Wave system as the sampler\n    sampler = DWaveSampler(solver=my_solver, token=my_token)\n    _, target_edgelist, target_adjacency = sampler.structure\n\n    # embedding = find_embedding(quadratic.keys(), adjacency)\n    embedding = minorminer.find_embedding(bqm.quadratic, target_edgelist)\n    bqm_embedded = dimod.embed_bqm(bqm, embedding, target_adjacency)\n\n    # Return num_reads solutions (responses are in the D-Wave's graph of indexed qubits)\n    kwargs = {}\n    if \"num_reads\" in sampler.parameters:\n        kwargs[\"num_reads\"] = 50\n    if \"answer_mode\" in sampler.parameters:\n        kwargs[\"answer_mode\"] = \"histogram\"\n    response = sampler.sample(bqm_embedded, **kwargs)\n    print(\"A solution indexed by qubits: \\n\", next(response.data(fields=[\"sample\"])))\n\n    # Map back to the BQM's graph (nodes labeled \"a0\", \"b0\" etc,)\n    response = dimod.unembed_response(response, embedding, source_bqm=bqm)\n    print(\n        \"\\nThe solution in problem variables: \\n\",\n        next(response.data(fields=[\"sample\"])),\n    )\n    return response\n","sub_path":"run_bqm.py","file_name":"run_bqm.py","file_ext":"py","file_size_in_byte":1369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"87579868","text":"import rfc822\r\nimport time\r\n\r\n__author__ = 'artyom.lobachev'\r\n\r\ndate = rfc822.formatdate(time.time())\r\n\r\nserver_http_version = \"1.0\"\r\nserver_name = \"artem's server\"\r\n\r\nrespone_304 = \"HTTP/\" + server_http_version + \" 304 Not Modified\\r\\n\"\r\nrespone_304 += \"Server: \" + server_name + \"\\r\\n\"\r\nrespone_304 += \"Date: \" + str(date) + \"\\r\\n\"\r\nrespone_304 += \"Connection: close\\r\\n\\r\\n\"\r\n\r\nresponse_400 = \"HTTP/\" + server_http_version + \" 400 Bad Request\\r\\n\"\r\nresponse_400 += \"Server: \" + server_name + \"\\r\\n\"\r\nresponse_400 += \"Date: \" + str(date) + \"\\r\\n\"\r\nresponse_400 += \"Content-Type: text/html; charset=UTF-8\\r\\n\"\r\nresponse_400 += \"Connection: close\\r\\n\\r\\n\"\r\nresponse_400 += \"<html><head><title>400</title></head><body><h2>Bad Request</h2></body></html>\"\r\n\r\nresponse_403 = \"HTTP/\" + server_http_version + \" 403 Forbidden\\r\\n\"\r\nresponse_403 += \"Server: \" + server_name + \"\\r\\n\"\r\nresponse_403 += \"Date: \" + str(date) + \"\\r\\n\"\r\nresponse_403 += \"Content-Type: text/html; charset=UTF-8\\r\\n\"\r\nresponse_403 += \"Connection: close\\r\\n\\r\\n\"\r\nresponse_403 += \"<html><head><title>403</title></head><body><h2>Forbidden</h2></body></html>\"\r\n\r\nresponse_404 = \"HTTP/\" + server_http_version + \" 404 Not Found\\r\\n\"\r\nresponse_404 += \"Server: \" + server_name + \"\\r\\n\"\r\nresponse_404 += \"Date: \" + str(date) + \"\\r\\n\"\r\nresponse_404 += \"Content-Type: text/html; charset=UTF-8\\r\\n\"\r\nresponse_404 += \"Connection: close\\r\\n\\r\\n\"\r\nresponse_404 += \"<html><head><title>404</title></head><body><h2>Page not found</h2></body></html>\"\r\n\r\nresponse_405 = \"HTTP/\" + server_http_version + \" 405 Method Not Allowed\\r\\n\"\r\nresponse_405 += \"Server: \" + server_name + \"\\r\\n\"\r\nresponse_405 += \"Date: \" + str(date) + \"\\r\\n\"\r\nresponse_405 += \"Content-Type: text/html; charset=UTF-8\\r\\n\"\r\nresponse_405 += \"Connection: close\\r\\n\\r\\n\"\r\nresponse_405 += \"<html><head><title>405</title></head><body><h2>Method Not Allowed</h2></body></html>\"\r\n\r\n","sub_path":"serverconstants.py","file_name":"serverconstants.py","file_ext":"py","file_size_in_byte":1892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"573040783","text":"def invert_dict(d):\n    inverse = dict()\n    for key in d:\n        # Go through the list that is saved in the dict:\n        for item in d[key]:\n            # Check if in the inverted dict the key exists\n            if item not in inverse:\n                # If not create a new list\n                inverse[item] = [key]\n            else:\n                inverse[item].append(key)\n    return inverse\n\n\ndef balance_complex_tuple_dict(d):\n    inverse = d.copy()\n    for key in d:\n        # Go through the list that is saved in the dict:\n        for item in d[key]:\n            # Check if in the inverted dict the key exists\n            if item not in inverse:\n                # If not create a new list\n                inverse[item] = [key]\n            else:\n                if (item == key):\n                    continue\n                inverse[item].append(key)\n\n    for key, val in inverse.items():\n        inverse[key] = list(set(val))\n    return inverse\n\n\ndef dict_compare(d1, d2, ignore_order=False):\n    d1_keys = set(d1.keys())\n    d2_keys = set(d2.keys())\n    intersect_keys = d1_keys.intersection(d2_keys)\n    added = d1_keys - d2_keys\n    removed = d2_keys - d1_keys\n    modified = {o : (d1[o], d2[o]) for o in intersect_keys if d1[o] != d2[o]}\n    if ignore_order:\n        modified = {o : (d1[o], d2[o]) for o in intersect_keys\n                    if set(d1[o]) != set(d2[o])}\n\n    same = set(o for o in intersect_keys if d1[o] == d2[o])\n    return added, removed, modified, same\n\nimport collections\n\ndef update(d, u):\n    for k, v in u.items():\n        if isinstance(v, collections.Mapping):\n            d[k] = update(d.get(k, {}), v)\n        else:\n            d[k] = v\n    return d\n\n\nimport unittest\nclass TestDicTools(unittest.TestCase):\n\n\n    def test_balance_complex_tuple_dict(self):\n        import pprint\n        d = {\n            ('differ'):['equal'],\n            ('have','*','in', 'common'):[('differ','in'), 'differ', 'derive']\n        }\n        balanced = balance_complex_tuple_dict(dict(d))\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"python/language/transformer/core/nym_embeddings/dict_tools.py","file_name":"dict_tools.py","file_ext":"py","file_size_in_byte":2061,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"363057931","text":"from django.conf.urls import url\nfrom django.contrib.auth.views import login, logout_then_login\nfrom django.urls import path, include\n\n\nfrom .views import *\n\napp_name='users'\n\nurlpatterns = [\n    url(regex=r'^$',\n       view=UsersView.as_view(),\n       name='users',\n    ),\n\n    url(regex=r'^invite_user$',\n       view=InviteUserView.as_view(),\n       name='invite_user',\n    ),\n\n    url(regex=r'^(?P<pk>\\d+)/settings/details$',\n       view=DetailsView.as_view(),\n       name='details',\n    ),\n\n    url(regex=r'^(?P<pk>\\d+)/settings/avatar',\n       view=AvatarView.as_view(),\n       name='avatar',\n    ),\n\n    url(\n       regex=r'^login/$',\n       view=login,\n       kwargs={'template_name': 'login.html'},\n       name='login'\n    ),\n\n    url(\n       regex=r'^logout/$',\n       view=logout_then_login,\n       kwargs={'login_url': '/accounts/login'},\n       name='logout'\n    ),\n    #    url(r'^create_account$', CreateAccount.as_view(), name='create_account'),\n    url(\n       regex=r'^edit_profile/(?P<pk>\\d+)',\n       view=EditProfile.as_view(),\n       name='edit_profile'\n    ),\n\n    path('', include('django.contrib.auth.urls')),\n\n    path(\n        'reset/<uidb64>/<token>',\n        PasswordResetConfirmView.as_view(),\n        name='password_reset_confirm'\n    ),\n\n]\n","sub_path":"django_kala/auth/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"293606307","text":"from datetime import datetime\nfrom os.path import dirname, join\n\nfrom city_scrapers_core.constants import CANCELLED, COMMISSION, PASSED\nfrom city_scrapers_core.utils import file_response\nfrom freezegun import freeze_time\n\nfrom city_scrapers.spiders.pitt_art_commission import PittArtCommissionSpider\n\ntest_response = file_response(\n    join(dirname(__file__), \"files\", \"pitt_art_commission.html\"),\n    url=\"https://pittsburghpa.gov/dcp/art-commission-schedule\",\n)\nspider = PittArtCommissionSpider()\n\nfreezer = freeze_time(\"2019-12-01\")\nfreezer.start()\n\nparsed_items = [item for item in spider.parse(test_response)]\nfreezer.stop()\n\n\ndef test_number_of_meetings():\n    assert len(parsed_items) == 34\n\n\ndef test_title():\n    expected_title = \"Art Commission of Pittsburgh Monthly Meeting\"\n    for item in parsed_items:\n        assert item[\"title\"] == expected_title\n\n\ndef test_start():\n    expected_start_dates_2019 = {\n        datetime(year=2019, month=1, day=23, hour=14),\n        datetime(year=2019, month=2, day=27, hour=14),\n        datetime(year=2019, month=3, day=27, hour=14),\n        datetime(year=2019, month=4, day=24, hour=14),\n        datetime(year=2019, month=5, day=22, hour=14),\n        datetime(year=2019, month=6, day=26, hour=14),\n        datetime(year=2019, month=7, day=24, hour=14),\n        datetime(year=2019, month=8, day=28, hour=14),\n        datetime(year=2019, month=9, day=25, hour=14),\n        datetime(year=2019, month=10, day=23, hour=14),\n        datetime(year=2019, month=11, day=20, hour=14),\n    }\n\n    actual_start_dates_2019 = {\n        meeting[\"start\"] for meeting in parsed_items if meeting[\"start\"].year == 2019\n    }\n\n    assert expected_start_dates_2019 == actual_start_dates_2019\n\n\ndef test_end():\n    for item in parsed_items:\n        assert item[\"end\"] is None\n\n\ndef test_time_notes():\n    for item in parsed_items:\n        assert item[\"time_notes\"] == \"\"\n\n\ndef test_location():\n    for item in parsed_items:\n        assert item[\"location\"] == {\n            \"location\": \"1st Floor Hearing Room\",\n            \"address\": \"200 Ross Street, Pittsburgh, PA, 15219\",\n            \"name\": \"\",\n        }\n\n\ndef test_links():\n    def link_name(file):\n        return \"http://apps.pittsburghpa.gov/redtail/images/{}\".format(file)\n\n    link_test_cases = [\n        {\n            \"month\": 1,\n            \"year\": 2019,\n            \"links\": [\n                {\n                    \"title\": \"January_2019_AC_Minutes.pdf\",\n                    \"href\": link_name(\"5026_January_2019_AC_Minutes.pdf\"),\n                }\n            ],\n        },\n        {\n            \"month\": 2,\n            \"year\": 2019,\n            \"links\": [\n                {\n                    \"title\": \"2-27-19_AC_Agenda.pdf\",\n                    \"href\": link_name(\"4891_2-27-19_AC_Agenda.pdf\"),\n                },\n                {\n                    \"title\": \"February_2019_AC_Minutes.pdf\",\n                    \"href\": link_name(\"5413_February_2019_AC_Minutes.pdf\"),\n                },\n            ],\n        },\n        {\n            \"month\": 1,\n            \"year\": 2018,\n            \"links\": [\n                {\n                    \"title\": \"1_24_18_ACAgenda.pdf\",\n                    \"href\": link_name(\"1440_1_24_18_ACAgenda.pdf\"),\n                },\n                {\n                    \"title\": \"1_24_18_AC_Minutes_with_SF_Interim_Report.pdf\",\n                    \"href\": link_name(\n                        \"1877_1_24_18_AC_Minutes_with_SF_Interim_Report.pdf\"\n                    ),\n                },\n            ],\n        },\n        {\"month\": 10, \"year\": 2019, \"links\": []},\n    ]\n\n    for test_case in link_test_cases:\n        matching_items = [\n            item\n            for item in parsed_items\n            if item[\"start\"].month == test_case[\"month\"]\n            and item[\"start\"].year == test_case[\"year\"]\n        ]\n        assert len(matching_items) == 1\n        matching_item = matching_items[0]\n        expected_links = test_case[\"links\"]\n        actual_links = matching_item[\"links\"]\n        assert len(actual_links) == len(expected_links)\n\n        for expected in expected_links:\n            found = False\n            for actual in actual_links:\n                if expected == actual:\n                    found = True\n                    break\n            assert found\n\n\ndef test_classification():\n    for item in parsed_items:\n        assert item[\"classification\"] == COMMISSION\n\n\ndef test_status():\n    found_cancelled = False\n    for item in parsed_items:\n        if item[\"start\"].month == 10 and item[\"start\"].year == 2019:\n            found_cancelled = True\n            assert item[\"status\"] == CANCELLED\n        else:\n            assert item[\"status\"] == PASSED\n    assert found_cancelled\n\n\ndef test_all_day():\n    for item in parsed_items:\n        assert item[\"all_day\"] is False\n\n\n# The 'id' or 'source' fields aren't customized currently, so these are commented out.\n# def test_id():\n#     assert parsed_items[0][\"id\"] == \"EXPECTED ID\"\n\n# def test_source():\n#     assert parsed_items[0][\"source\"] == \"EXPECTED URL\"\n","sub_path":"tests/test_pitt_art_commission.py","file_name":"test_pitt_art_commission.py","file_ext":"py","file_size_in_byte":5040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"439633424","text":"import xml.etree.ElementTree\nclass readxml:\n\tfilename = None \n\tserveraddress = None\n\t\n\tdef __init__(self):\n\t\tvar = None\n\t\tprint(\"1\")\n\n\tdef read(self, filename):\n\t\tself.filename = filename\n\t\tfile = xml.etree.ElementTree.parse(filename).getroot()\n\n\t\tip = file.findall(\"ip\")\n\t\tport = file.getElementsByTagName(\"port\")\n\t\t\n\t\tself.serveraddress = {ip:str(port)}\n\t\tprint(str(serveraddress))\n\t\t#return self.serveraddress\n\n\n\n","sub_path":"client/utils/readxml.py","file_name":"readxml.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"494041027","text":"from bs4 import BeautifulSoup\nfrom requests import get\nfrom time import time, sleep\nfrom random import randint\nimport pandas as pd\nimport pymongo\nimport credentials\n\n\nclient = pymongo.MongoClient(\"mongodb+srv://\"+credentials.username+\":\"+credentials.password+\"@cluster1-xkuyo.mongodb.net/scrapedata?retryWrites=true\")\ndb = client['scrapedata']\ndoctors = db.doctors\nnames = []\nspecialties = []\naffiliations = []\n\nstart_time = time()\nrequests = 0\n\ninfo = {}\ni = 1\nallNames = []\nallSpecialties = []\nallAffiliations = []\nfirst_names = []\nlast_names = []\nfull_names = []\ndegrees = []\nemails = []\n\nwhile i < 677:\n    url = ('https://physician-finder.partners.org/search?page='+ str(i))\n\n    response = get(url)\n\n    # sleep(randint(1, 3))\n\n    requests += 1\n    elapsed_time = time() - start_time\n    current_time = time()\n    print('Request: {}; Frequency: {} requests/s'.format(requests, requests / elapsed_time))\n    html_soup = BeautifulSoup(response.text, 'html.parser')\n    type(html_soup)\n\n    containers = html_soup.find_all('div', class_='col-xs-8 col-sm-9')\n    num_people = len(containers)\n    p = 0\n    while p < num_people:\n        email=\"\"\n        person = containers[p]\n\n        full_name = person.h2.a.text\n        fname = full_name.split(',')\n        degree = full_name.split(',', 1)[-1]\n        full_name = fname[0]\n\n        full_name = full_name.replace(',', '')\n        sec = full_name.strip().split(' ')\n        lensec = len(sec)\n        first_name = sec[0]\n        last_name = sec[lensec - 1]\n        email = email + first_name[:1]\n        email = email + last_name\n        email = email + \"@partners.org\"\n\n        specialtyf = person.find('ul', class_=\"list-m no-bullets specialties.specialty-values\")\n        if specialtyf is not None:\n            specialties = specialtyf.getText(separator=u', ')\n        else:\n            specialties=\"\"\n\n        affiliationf = person.find('ul', class_=\"list-m no-bullets network_affiliations.name-values\")\n        if affiliationf is not None:\n            affiliations = affiliationf.getText(separator=u'')\n        else:\n            affiliations = \"\"\n\n        if doctors.find_one({\"Full Name\": full_name}, {\"Email\": email}) is not None:\n            pass\n        else:\n            print(\"Creating Doctor: \", full_name)\n            doctor = {\n                \"Full Name\": full_name,\n                \"First Name\": first_name,\n                \"Last Name\": last_name,\n                \"Email\": email,\n                \"Specialties\": specialties,\n                \"Affiliations\": affiliations\n            }\n            result = doctors.insert_one(doctor)\n        p = p+1\n    i = i + 1\n\n\nprint(allNames,\"\\n\")\nprint(allSpecialties, \"\\n\")\nprint(allAffiliations, \"\\n\")\ndoctors = pd.DataFrame({\n    \"First Name\": first_names,\n    \"Last Name\": last_names,\n    \"Email\": emails,\n    \"Full Name\": full_names,\n    \"Specialities\": allSpecialties,\n    \"Affiliation\": allAffiliations\n})\ndoctors = doctors[['First Name', 'Last Name', 'Email','Full Name', 'Specialities', 'Affiliation']]\nprint(doctors.info())\ndoctors.to_csv('doctorsFull.csv')\n\n\n","sub_path":"scrapetodb.py","file_name":"scrapetodb.py","file_ext":"py","file_size_in_byte":3075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"578458096","text":"\"\"\"\nsetup_usb_disk.py\n\nThis script is used to partition, format, and prepare the 128 GB USB disk. It should only be run when a new disk is\nadded to the system. Do not run this script without understanding what it does.\n\nSpecifically, it creates the /data5 directory, makes it readable, writable to all, and makes it immutable (so that data\nis not accidentally written to it when the drive is not mounted).\n\nIt then locates the SanDisk Cruzer drive and creates a new partition table with a data partition and formats it as ext4.\nThen it adds a line to /etc/fstab to indicate that the new partition should be mounted as /data5.\n\"\"\"\n\nimport os\nfrom subprocess import check_output,CalledProcessError, STDOUT\nimport logging\nimport glob\nimport time\nimport tempfile\n\nUSB_DISK_PREFIX = '/dev/disk/by-id/usb-SanDisk_Cruzer'\n\nlogging.basicConfig(format='%(levelname)-4.4s %(asctime)s - setup_all:%(lineno)d  %(message)s',\n                    level=logging.DEBUG)\nlogger = logging.getLogger()\n\ndef run_command(command):\n    logger.info(\"\\n-->  %s\" % command)\n    try:\n        result = check_output(command,shell=True)#,stderr=STDOUT)\n        if result.strip():\n            logger.debug(\"result -->\\n%s\" % result)\n        return result\n    except CalledProcessError as e:\n        logger.exception(\"Command '%s' failed with returncode %d and output\\n%s\" % (command, e.returncode,\n                                                                                   e.output))\n        return None\n    \ndirname = '/data5'\nif os.path.exists(dirname):\n    logger.warning(\"%s exists, trying to unmount it\" % dirname)\n    run_command(\"sudo umount %s\" % dirname)\nelse:\n    run_command('sudo mkdir %s' % dirname)\nrun_command('sudo chmod a+rwx %s' % dirname)\nrun_command('sudo chattr +i %s' % dirname)\n\nusb_disks = glob.glob(USB_DISK_PREFIX+'*')\nusb_disk_partitions = [disk for disk in usb_disks if disk[:-1].endswith('part')]\nusb_disk_devices = [disk for disk in usb_disks if not disk[:-1].endswith('part')]\nreal_device_to_by_id = {}\nfor usb_disk_device in usb_disk_devices:\n    partitions = [partition for partition in usb_disk_partitions if partition.startswith(usb_disk_device)]\n    partitions.sort()\n    if partitions:\n        result = run_command('sudo parted %s print' % usb_disk_device)\n        logger.warning(\"Found existing partitions on %s:\\n%s\" % (usb_disk_device,result))\n        logger.warning(\"**** These will be deleted unless the script is aborted in 5 seconds\")\n        time.sleep(5)\n        logger.warning(\"proceeding...\")\n\n    real_device = os.path.split(os.path.realpath(usb_disk_device))[1]\n    real_device_to_by_id[real_device] = usb_disk_device\n\n    logger.info(\"creating new partition table\")\n    run_command(\"sudo parted -s /dev/%s mklabel gpt\" %real_device)\n    start_data_partition = '2048s'\n    data_partition = 1\n    time.sleep(5)\n    logger.info(\"creating data partition\")\n    run_command(\"sudo parted /dev/%s mkpart primary ext4 %s 100%%\" % (real_device,start_data_partition))\n    logger.info(\"resulting partition table:\")\n    run_command(\"sudo parted /dev/%s unit s print\" % real_device)\n\n    logger.info(\"creating filesystem on data partition\")\n    run_command(\"sudo mkfs.ext4 -v /dev/%s%d\" % (real_device, data_partition))\n\nnew_fstab_lines = []\nusb_disk_real_devices = real_device_to_by_id.keys()\nusb_disk_real_devices.sort()\nstarting_device_number = 5\nfor device_number,real_device in enumerate(usb_disk_real_devices):\n    data_partition = 1\n    fstab_line = \"%s-part%d /data%d ext4 nofail 0 2\" % (real_device_to_by_id[real_device],\n                                                    data_partition,\n                                                    device_number+starting_device_number)\n    new_fstab_lines.append(fstab_line)\n\noriginal_fstab_lines = open('/etc/fstab','r').read().splitlines()\nlogger.info(\"original fstab:\\n\\n%s\\n\" % ('\\n'.join(original_fstab_lines)))\nfstab_lines = original_fstab_lines\nnew_fstab = '\\n'.join(fstab_lines +['# The following were automatically generated by setup_usb_disk.py'] + new_fstab_lines)\ntmpfile = tempfile.NamedTemporaryFile(delete=False)\ntmpfile.write(new_fstab)\ntmpfile.close()\nrun_command('sudo cp %s /etc/fstab' % tmpfile.name)\nlogger.info(\"fstab is now:\\n\\n%s\\n\" % (open('/etc/fstab').read()))","sub_path":"scripts/installation/setup_usb_disk.py","file_name":"setup_usb_disk.py","file_ext":"py","file_size_in_byte":4254,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"155008463","text":"import base64\n\nfrom rest_framework import serializers\n\nfrom ..models import Project, Snapshot, Value\n\n\nclass ValueSerializer(serializers.ModelSerializer):\n\n    attribute = serializers.CharField(source='attribute.uri', default=None, read_only=True)\n    option = serializers.CharField(source='option.uri', default=None, read_only=True)\n    file_content = serializers.SerializerMethodField()\n\n    class Meta:\n        model = Value\n        fields = (\n            'attribute',\n            'set_prefix',\n            'set_index',\n            'collection_index',\n            'text',\n            'option',\n            'file_name',\n            'file_content',\n            'value_type',\n            'unit',\n            'external_id',\n            'created',\n            'updated'\n        )\n\n    def get_file_content(self, obj):\n        if obj.file:\n            return base64.b64encode(obj.file.read())\n\n\nclass SnapshotSerializer(serializers.ModelSerializer):\n\n    values = serializers.SerializerMethodField()\n\n    class Meta:\n        model = Snapshot\n        fields = (\n            'title',\n            'description',\n            'values',\n            'created',\n            'updated'\n        )\n\n    def get_values(self, obj):\n        values = Value.objects.filter(snapshot=obj)\n        serializer = ValueSerializer(instance=values, many=True)\n        return serializer.data\n\n\nclass ProjectSerializer(serializers.ModelSerializer):\n\n    snapshots = SnapshotSerializer(many=True)\n    values = serializers.SerializerMethodField()\n\n    catalog = serializers.CharField(source='catalog.uri', default=None, read_only=True)\n    tasks = serializers.SerializerMethodField()\n    views = serializers.SerializerMethodField()\n\n    class Meta:\n        model = Project\n        fields = (\n            'title',\n            'description',\n            'catalog',\n            'tasks',\n            'views',\n            'snapshots',\n            'values',\n            'created',\n            'updated'\n        )\n\n    def get_values(self, obj):\n        values = Value.objects.filter(project=obj, snapshot=None)\n        serializer = ValueSerializer(instance=values, many=True)\n        return serializer.data\n\n    def get_tasks(self, obj):\n        return [task.uri for task in obj.tasks.all()]\n\n    def get_views(self, obj):\n        return [view.uri for view in obj.views.all()]\n","sub_path":"rdmo/projects/serializers/export.py","file_name":"export.py","file_ext":"py","file_size_in_byte":2339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"376737037","text":"import datetime\n\nfrom flask import Flask, render_template\n\napp = Flask(__name__)\n\n# Template engine\n# - IF statements, FOR loops, blocks (chaining of templates)\n# - NO BUSINESS LOGIC\n\n@app.route(\"/\")\ndef index():\n    name = \"Patricija\"\n    cities = [\"Ljubljana\", \"Maribor\", \"Rome\", \"Vienna\", \"Stockholm\"]\n    # cities = None - triggers if statement (cities are not shown)\n    year = datetime.datetime.now().year\n    return render_template(\"index.html\", name=name, cities=cities, year=year)\n\n\n@app.route(\"/about\")\ndef about_me():\n    return render_template(\"about.html\")\n\n\nif __name__ == \"__main__\":\n    app.run()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"158670627","text":"from __future__ import print_function\nimport matplotlib\nmatplotlib.use('WXAgg')\nfrom aggregation_api import AggregationAPI\nimport matplotlib.pyplot as plt\nimport matplotlib.cbook as cbook\nimport cv2\nimport numpy as np\n\nbutterflies = AggregationAPI(1150,\"development\")\nbutterflies.__setup__()\n\n# fname = butterflies.__image_setup__(1120709)[0]\nfname = butterflies.__image_setup__(1500825)[0]\n\nimage_file = cbook.get_sample_data(fname)\nimage = plt.imread(image_file)\n\nres = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)\n\n# kernely = cv2.getStructuringElement(cv2.MORPH_RECT,(10,2))\n# dy = cv2.Sobel(res,cv2.CV_16S,0,2)\n# dy = cv2.convertScaleAbs(dy)\n# cv2.normalize(dy,dy,0,255,cv2.NORM_MINMAX)\n# ret,close = cv2.threshold(dy,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n#\n# t= image\n# _,contour, hier = cv2.findContours(close.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)\n# for cnt in contour:\n#     x,y,w,h = cv2.boundingRect(cnt)\n#     if ((w/h)>5) and (w>130) and (w < 160):\n#         print(w)\n#         cv2.drawContours(t,[cnt],0,(0,255,0),-1)\n#\n# im = plt.imshow(t)\n# plt.show()\n\n\nedges = cv2.Canny(res,50,100)\nplt.imshow(edges,cmap=\"gray\")\nplt.show()\n\nth3 = cv2.adaptiveThreshold(res,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,351,2)\nplt.imshow(th3,cmap=\"gray\")\nplt.show()\n\nret2,th2 = cv2.threshold(res,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)\nplt.imshow(th2,cmap=\"gray\")\nplt.show()\n\n_,contour, hier = cv2.findContours(res.copy(),cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)\nt = np.zeros(res.shape,np.uint8)\nfor cnt,h in zip(contour,hier):\n    print(h)\n    cv2.drawContours(t,[cnt],0,255,-1)\n\nplt.imshow(t,cmap=\"gray\")\nplt.show()\n#     x,y,w,h = cv2.boundingRect(cnt)","sub_path":"vision/butterflies.py","file_name":"butterflies.py","file_ext":"py","file_size_in_byte":1669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"5050008","text":"load(\"@io_bazel_rules_closure//closure:defs.bzl\", \"closure_js_binary\")\n\n_modules_filetype = FileType([\"node_modules\"])\n\ndef _get_lib_name(ctx):\n    name = ctx.label.name\n    package = ctx.label.package\n    parts = []\n    if package:\n        parts = ctx.label.package.split(\"/\")\n    if (len(parts) == 0) or (name != parts[-1]):\n        parts.append(name)\n    return \"-\".join(parts)\n\ndef _create_staging_dir(staging_dir):\n    return [\"mkdir -p %s\" % staging_dir]\n\ndef _copy_to_staging(base, file):\n    return [\"cp -f %s %s/%s\" % (file.path, base, file.basename)]\n\ndef _copy_with_parents_to_staging(base, file):\n    return [\"cp -f --parents %s %s/\" % (file.path, base)]\n\ndef _collect_transitive_node_libraries(deps):\n    transitive_srcs = []\n    transitive_node_modules = []\n\n    for dep in deps:\n        lib = dep.node_library\n        transitive_srcs += lib.transitive_srcs\n        transitive_node_modules += lib.transitive_node_modules\n\n    return struct(\n        modules = transitive_node_modules,\n        srcs = transitive_srcs\n    )\n\ndef _nodejs_module_impl(ctx):\n    node = ctx.executable._node\n    npm = ctx.executable._npm\n\n    lib_name = _get_lib_name(ctx)\n    stage_name = lib_name + \".npmfiles\"\n    srcs = ctx.files.srcs\n\n    script = getattr(ctx.attr.bin.closure_js_binary, \"bin\", None)\n\n    if not script:\n        fail(\"closure_nodejs_library can not have an empty 'bin label'\")\n\n    package_json_template_file = ctx.file.package_json_template_file\n    package_json_file = ctx.new_file(stage_name + \"/package.json\")\n\n    staging_dir = \"/\".join(package_json_file.path.split(\"/\")[:-1])\n    package_dir = \"/\".join(staging_dir.split(\"/\")[:-1])\n\n    ctx.template_action(\n        template = package_json_template_file,\n        output = package_json_file,\n        substitutions = {\n            \"%{name}\": lib_name,\n            \"%{main}\": \"%s\" % (script.basename),\n            \"%{version}\": ctx.attr.version,\n            \"%{description}\": ctx.attr.description,\n        },\n    )\n\n    cmds = []\n    cmds += _create_staging_dir(staging_dir)\n    cmds += _copy_to_staging(staging_dir, script)\n\n    for file in srcs:\n        cmds += _copy_with_parents_to_staging(staging_dir, file)\n\n    install_cmd = [\n        node.path,\n        npm.path,\n        \"install\",\n        \"--global\",\n        \"--prefix\",\n        package_dir,\n    ]\n\n    install_cmd.append(staging_dir)\n    cmds.append(\" \".join(install_cmd))\n\n    npm_package_json_file = ctx.new_file(\"lib/node_modules/%s/package.json\" % lib_name)\n    closure_bin_file = ctx.new_file(\"lib/node_modules/%s/%s\" % (lib_name, script.basename))\n\n    npm_srcs_files = []\n    for file in srcs:\n        npm_srcs_files.append(ctx.new_file(\"lib/node_modules/%s/%s\" % (lib_name, file.short_path)))\n\n    ctx.action(\n        mnemonic = \"NpmInstallLocal\",\n        inputs = [node, npm, package_json_file, script],\n        outputs = [npm_package_json_file, closure_bin_file] + npm_srcs_files,\n        command = \" && \".join(cmds),\n    )\n\n    transitive_node_libraries = _collect_transitive_node_libraries(ctx.attr.deps)\n\n    return struct(\n        files = depset(),\n        runfiles = ctx.runfiles(\n            files = [],\n            collect_default = True,\n        ),\n        node_library = struct(\n            name = lib_name,\n            label = ctx.label,\n            srcs = [closure_bin_file] + npm_srcs_files,\n            transitive_srcs = [closure_bin_file] + npm_srcs_files + transitive_node_libraries.srcs,\n            transitive_node_modules = ctx.files.modules + transitive_node_libraries.modules,\n            package_json = npm_package_json_file,\n            npm_package_json = npm_package_json_file,\n        ),\n    )\n\n_nodejs_module = rule(\n    _nodejs_module_impl,\n    attrs = {\n        \"version\": attr.string(\n            default = \"0.0.0\",\n        ),\n        \"bin\": attr.label(\n            mandatory = True,\n            providers = [\"closure_js_library\"],\n        ),\n        \"srcs\": attr.label_list(\n            allow_files = FileType([\".js\"]),\n        ),\n        \"description\": attr.string(\n            default = \"No description provided.\",\n        ),\n        \"data\": attr.label_list(\n            allow_files = True,\n            cfg = \"data\",\n        ),\n        \"deps\": attr.label_list(\n            providers = [\"node_library\"],\n        ),\n        \"modules\": attr.label_list(\n            allow_files = _modules_filetype,\n        ),\n        \"package_json_template_file\": attr.label(\n            single_file = True,\n            allow_files = True,\n            default = Label(\"//closure/node:package.json.tpl\"),\n        ),\n        \"_node\": attr.label(\n            default = Label(\"@org_pubref_rules_node_toolchain//:node_tool\"),\n            single_file = True,\n            allow_files = True,\n            executable = True,\n            cfg = \"host\",\n        ),\n        \"_npm\": attr.label(\n            default = Label(\"@org_pubref_rules_node_toolchain//:npm_tool\"),\n            single_file = True,\n            allow_files = True,\n            executable = True,\n            cfg = \"host\",\n        ),\n    },\n)\n\ndef closure_nodejs_module(name,\n                          compilation_level=None,\n                          debug=None,\n                          defs=None,\n                          dependency_mode=None,\n                          deps=None,\n                          nodejs_deps=None,\n                          entry_points=None,\n                          formatting=None,\n                          language=None,\n                          nodefs=None,\n                          output_wrapper=None,\n                          property_renaming_report=None,\n                          warning_level=None,\n                          data=None,\n                          srcs=None,\n                          conformance=None,\n                          suppress_on_all_sources_in_transitive_closure=None,\n                          modules = None,\n                          visibility=None):\n    if not name:\n        fail(\"closure_nodejs_library can not have an empty 'name' label\")\n\n    closure_bin_name = \"%s_bin\" % name\n\n    closure_js_binary(\n        name = closure_bin_name,\n        compilation_level = compilation_level,\n        debug = debug,\n        defs = defs,\n        dependency_mode = dependency_mode,\n        deps = deps,\n        entry_points = entry_points,\n        formatting = formatting,\n        nodefs = nodefs,\n        output_wrapper = output_wrapper,\n        property_renaming_report = property_renaming_report,\n        warning_level = warning_level,\n        data = data,\n        conformance = conformance,\n        suppress_on_all_sources_in_transitive_closure = suppress_on_all_sources_in_transitive_closure,\n        visibility = [\"//visibility:private\"],\n    )\n\n    _nodejs_module(\n        name = name,\n        bin = \":%s\" % closure_bin_name,\n        deps = nodejs_deps,\n        srcs = srcs,\n        modules = modules,\n        visibility = visibility,\n    )","sub_path":"closure/node/closure_nodejs_module.bzl","file_name":"closure_nodejs_module.bzl","file_ext":"bzl","file_size_in_byte":6918,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"330126764","text":"#id30.py\n\n#This program finds the sum of all numbers which can be written\n#as the fifth power of their digits\n\ndef ID30():\n    fifthlist = []\n    for i in range(10, 295245):\n        tempsum = 0\n        for j in str(i):\n            tempsum += int(j) ** 5\n        if tempsum == i:\n            fifthlist.append(i)\n    print(fifthlist)\n\nID30()\n","sub_path":"python files/id30.py","file_name":"id30.py","file_ext":"py","file_size_in_byte":340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"245096488","text":"'''\n猴子补丁：是指函数的一种情况，可以在不改变原有函数的情况下通过一个函数来调用另外一个函数\nsum ＝ newSum这条语句就是起到猴子补丁的作用，用sum函数调用newSum函数，参数也相应的改变\n'''\ndef sum(a,b):\n    print(\"sum函数已被调用!\")\n    return a + b\ndef newSum(*args):\n    print(\"newSum函数已被调用!\")\n    s = 0\n    for i in args:\n        #此处的arg是参数列表\n        s += i\n    return s\nsum = newSum\nif __name__ == \"__main__\":\n    print(sum(1,2,3,4,5))","sub_path":"projects/猴子补丁.py","file_name":"猴子补丁.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"2089801","text":"from tkinter import *\nfrom tkinter import filedialog\nfrom pytube import YouTube\nimport time\n#import Video_player\nimport webbrowser\nimport Gt_vdqt\nimport datetime\nfrom csv import writer\n\n\n#import os\n#os.system('clear')\n\nroot = Tk()\nroot.title('YouTube Video Downloader')\nroot.geometry(\"900x500\")\nroot.configure(background='black')\n\ndef downloadFunction():\n\n    link=myTextBox.get()\n\n    video = YouTube(link)\n    stream = video.streams.get_highest_resolution()\n    #print(video.title,video.views,video.vid_info)\n    stream.download(dirname)\n    current = datetime.datetime.now()\n    title =video.title\n    views = video.views\n    duration= video.length\n    #df = pd.read_csv('YouTubeData.csv')\n    newVideo = [str(title).replace(\"\\n\", \" \"),str(link).replace(\"\\n\", \" \"),str(current).replace(\"\\n\", \" \"),str(dirname).replace(\"\\n\", \" \"),str(views).replace(\"\\n\", \" \"),str(duration).replace(\"\\n\", \" \")]\n    with open('YouTubeData.csv', 'a',newline='') as f_object:\n        writer_object = writer(f_object)\n        writer_object.writerow(newVideo)\n        f_object.close()\n    #newVideo.to_csv('YouTubeData.csv',index=False)\n    downloadedLabel = Label(root, text=\"Downloaded!\")\n    downloadedLabel.grid(row=5, column=0)\n\n    \n\ndef callback(event):\n    webbrowser.open_new(\"http://127.0.0.1:5000/\")\n\ndef fileBrowser():\n    global dirname\n    dirname = filedialog.askdirectory(parent=root, initialdir=\"/\", title='Please select a directory')\n    if len(dirname) > 0:\n        browseBox.delete(0,END)\n        browseBox.insert(0,dirname)\n    \nmyLabel = Label(root,text=\"Video link\")\nmyLabel.grid(row=0, column=0)\n\n\n\nmyTextBox = Entry(root,width=50,bg=\"black\",fg=\"white\",borderwidth=5)\nmyTextBox.grid(row=0,column=1)\nmyTextBox.insert(0,\"Add link here\")\n\nLabel(root, text=\" \", fg=\"black\", bg=\"black\").grid(row=1,column=0)\n\nbrowseBoxLabel = Label(root,text=\"File Location\")\nbrowseBoxLabel.grid(row=2,column=0)\n\nbrowseBox = Entry(root,width=50,bg=\"black\",fg=\"white\",borderwidth=5)\nbrowseBox.grid(row=2,column=1)\nbrowseBox.insert(0,\"Choose file location\")\n\nLabel(root, text=\" \", fg=\"black\",bg=\"black\").grid(row=3,column=0)\n\nmyButton = Button(root,text=\"Download\", command=downloadFunction, fg=\"green\" ,bg=\"grey\",width=15)\nmyButton.grid(row=4,column=1)\n\nbrowseBoxButton = Button(root,text=\"Browse\", command=fileBrowser, fg=\"green\" ,bg=\"grey\",width=15)\nbrowseBoxButton.grid(row=5,column=1)\n\nlbl = Label(root, text=r\"Visit our website!\", fg=\"grey\", cursor=\"hand2\")\nlbl.grid(row=6,column=1)\nlbl.bind(\"<Button-1>\", callback)\n\n\n\nbrowseBoxButton2 = Button(root,text=\"Play Video\", command=Gt_vdqt.playv, fg=\"green\" ,bg=\"black\",width=15)\nbrowseBoxButton2.grid(row=7,column=1)\nroot.mainloop()","sub_path":"Python_project/YT.py","file_name":"YT.py","file_ext":"py","file_size_in_byte":2664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"107388346","text":"# material creation code goes here\n\nimport bpy\n\nimport read_material\nimport read_texture\n\ndef create_material(name, material_fpath, texture_fpath):\n    material_file = read_material.MTL()\n    material_file.load_material(material_fpath + '\\\\' + name)\n    \n    material = bpy.data.materials.new(material_file.materialname)\n    material.use_nodes = True\n\n    nodes = material.node_tree.nodes\n    links = material.node_tree.links\n\n    # delete all nodes except output node\n    for node in [node for node in nodes if node.type != 'OUTPUT_MATERIAL']:\n        nodes.remove(node)\n\n    # get output node\n    material_output_node = None\n    try:\n        material_output_node = [node for node in nodes if node.type == 'OUTPUT_MATERIAL'][0]\n    except:\n        material_output_node = nodes.new('ShaderNodeOutputMaterial')\n    material_output_node.location = (100,0)\n\n    # create principled bsdf node and link it to the material output\n    principled_bsdf_node = nodes.new('ShaderNodeBsdfPrincipled')\n    principled_bsdf_node.location = (-200,0)\n    principled_bsdf_node.width = 200\n    links.new(principled_bsdf_node.outputs['BSDF'], material_output_node.inputs['Surface'])\n\n    # create texture input nodes and link them to the correct place\n    counter = 0\n    for maptype, mapname in material_file.mapinfo.items():\n        texture_image = None\n        try:\n            texture_image = bpy.data.images.load(texture_fpath + '\\\\' + mapname + '.dds')\n        except:\n            print(\"Couldn't find \" + mapname + \".dds. Trying to load from \" + mapname + \".iwi\")\n\n        if(texture_image == None):\n            texture = read_texture.Texture()\n            if(texture.load_texture(texture_fpath + '\\\\' + mapname + '.iwi')):\n                texture_image = bpy.data.images.new(mapname, texture.width, texture.height)\n                pixels = [x / 255 for x in texture.texture_data]\n                texture_image.pixels = pixels\n            else:\n                print(\"Couldn't load \" + mapname + \".dds. Image texture will not be created.\")\n\n        if(texture_image != None):\n            # creating texture input node\n            texture_node = nodes.new('ShaderNodeTexImage')\n            texture_node.label = maptype\n            texture_node.location = (-700, 0 - 250 * counter)\n            texture_node.image = texture_image\n\n            # linking texture input node\n            if(maptype == read_material.MTLMapTypes['colorMap']):\n                links.new(texture_node.outputs['Color'], principled_bsdf_node.inputs['Base Color'])\n            elif(maptype == read_material.MTLMapTypes['specularMap']):\n                links.new(texture_node.outputs['Color'], principled_bsdf_node.inputs['Specular'])\n            elif(maptype == read_material.MTLMapTypes['normalMap']):\n                # create normalmap node\n                normal_node = nodes.new('ShaderNodeNormalMap')\n                normal_node.location = (-450, -500)\n\n                links.new(texture_node.outputs['Color'], normal_node.inputs['Color'])\n                links.new(normal_node.outputs['Normal'], principled_bsdf_node.inputs['Normal'])\n            elif(maptype == read_material.MTLMapTypes['detailMap']):\n                pass\n\n            counter += 1\n\n    # create texture coordinate node\n    textcoord_node = nodes.new('ShaderNodeTexCoord')\n    textcoord_node.location = (-1000, -150)\n    for node in [node for node in nodes if node.type == 'TEX_IMAGE']:\n        links.new(textcoord_node.outputs['UV'], node.inputs['Vector'])\n","sub_path":"All_In_One/addons/pyd3dbsp/create_material.py","file_name":"create_material.py","file_ext":"py","file_size_in_byte":3490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"438611519","text":"import torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nfrom torch.nn.parameter import Parameter\r\nfrom layers import MHATLayer\r\n\r\n\r\nclass HardGAT(nn.Module):\r\n    def __init__(self, G, h_feats, num_heads, num_diseases, num_mrnas, d_sim_dim, m_sim_dim, out_dim,\r\n                 decoder, feat_drop, attn_drop, negative_slope):\r\n        super(HardGAT, self).__init__()\r\n\r\n        self.G = G\r\n        self.num_diseases = num_diseases\r\n        self.num_drugs = num_mrnas\r\n\r\n        self.gat = MHATLayer(G, h_feats, num_heads, feat_drop, attn_drop, negative_slope)\r\n\r\n        self.feat_drop = nn.Dropout(feat_drop)\r\n        self.attn_drop = nn.Dropout(attn_drop)\r\n\r\n        self.m_fc = nn.Linear(h_feats + m_sim_dim, out_dim)    #\r\n        self.d_fc = nn.Linear(h_feats + d_sim_dim, out_dim)    #\r\n\r\n        self.BilinearDecoder = BilinearDecoder(feature_size=out_dim)\r\n\r\n        self.predict = nn.Sequential(nn.Linear(out_dim * 2, 1), nn.Sigmoid())\r\n\r\n        self.decoder = decoder\r\n\r\n    def forward(self, G, diseases, mrnas):\r\n        assert G.number_of_nodes() == self.G.number_of_nodes()\r\n\r\n        h_agg = self.gat(G)\r\n        h_agg = h_agg.mean(1)\r\n        h_d = torch.cat((h_agg[:self.num_diseases], self.G.ndata['d_sim'][:self.num_diseases]), dim=1)\r\n        h_m = torch.cat((h_agg[self.num_diseases:], self.G.ndata['m_sim'][self.num_diseases:]), dim=1)\r\n\r\n        h_m = self.feat_drop(F.elu(self.m_fc(h_m)))\r\n        h_d = self.feat_drop(F.elu(self.d_fc(h_d)))\r\n\r\n        h = torch.cat((h_d, h_m), dim=0)\r\n\r\n        h_diseases = h[diseases]\r\n        h_mrnas = h[mrnas]\r\n\r\n        return self.decoder(h_diseases, h_mrnas)\r\n\r\n\r\nclass InnerProductDecoder(nn.Module):\r\n    \"\"\"Decoder model layer for link prediction.\"\"\"\r\n    def __init__(self):\r\n        super(InnerProductDecoder, self).__init__()\r\n\r\n    def forward(self, h_diseases, h_mrnas):\r\n        x = torch.mul(h_diseases, h_mrnas).sum(1)\r\n        x = torch.reshape(x, [-1])\r\n        outputs = F.sigmoid(x)\r\n        return outputs\r\n\r\n\r\nclass BilinearDecoder(nn.Module):\r\n    def __init__(self, feature_size):\r\n        super(BilinearDecoder, self).__init__()\r\n        self.W = Parameter(torch.randn(feature_size, feature_size))\r\n\r\n    def forward(self, h_diseases, h_mirnas):\r\n        h_diseases0 = torch.mm(h_diseases, self.W)\r\n        h_mirnas0 = torch.mul(h_diseases0, h_mirnas)\r\n        h0 = h_mirnas0.sum(1)\r\n        h = torch.sigmoid(h0)\r\n        return h\r\n","sub_path":"NSAMDA/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"66162706","text":"def minCostClimb(cost,dp,i):\n    if i<=1:\n        return 0\n    elif dp[i]!=-1:\n        return dp[i]\n    t1 = cost[i-1]+minCostClimb(cost,dp,i-1)\n    t2 = cost[i-2]+minCostClimb(cost,dp,i-2)\n    dp[i] = t1 if t1<t2 else t2\n    return dp[i]\n\nclass Solution:\n    def minCostClimbingStairs(self, cost: List[int]) -> int:\n        dp = [-1]*(len(cost)+1)\n        return minCostClimb(cost,dp,len(cost))\n\n# above- top down approach\n# below - bottom up approach\n\nclass Solution:\n    def minCostClimbingStairs(self, cost: List[int]) -> int:\n        dp = [-1]*(len(cost))\n        dp[0]=cost[0]\n        dp[1]=cost[1]\n        for i in range(2,len(cost)):\n            dp[i] = cost[i]+ min(dp[i-1],dp[i-2])\n        return min(dp[-1],dp[-2])\n","sub_path":"LeetCode/746. Min Cost Climbing Stairs.py","file_name":"746. Min Cost Climbing Stairs.py","file_ext":"py","file_size_in_byte":726,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"540806755","text":"from libgbdt import DataStore\nfrom libgbdt import Forest\nfrom libgbdt import train as __train_internal__\nfrom libgbdt import init_logging\n\ndef train(data_store,\n          config,\n          y=[],\n          float_features=[],\n          cat_features=[],\n          w=[],\n          base_forest=None,\n          random_seed=1234567,\n          num_threads=16):\n    \"\"\"\n    train gbdt model.\n    Inputs: data_store: The Data Store.\n            config: the config params. See https://github.com/yarny/gbdt/blob/master/src/proto/config.proto for all params.\n            y: The target\n            float_features: Continuous features.\n            cat_features: Categorical features.\n            w: The sampling weight.\n            base_forest: The base forest based on which the training will continue.\n            num_threads: The number of threads to run training with.\n    Outputs: The forest.\n    \"\"\"\n    try:\n        import json\n    except ImportError:\n        raise ImportError('Please install json.')\n\n    config['float_feature'] = float_features\n    config['categorical_feature'] = cat_features\n    if 'example_sampling_rate' not in config:\n        config['example_sampling_rate'] = 1\n    if 'feature_sampling_rate' not in config:\n        config['feature_sampling_rate'] = 1\n\n    return __train_internal__(data_store,\n                              y=y,\n                              w=w,\n                              config=json.dumps(config),\n                              base_forest=base_forest,\n                              random_seed=random_seed,\n                              num_threads=num_threads)\n\nclass DataLoader:\n    @staticmethod\n    def from_tsvs(tsvs, bucketized_float_cols=[], string_cols=[], raw_float_cols=[]):\n        \"\"\"Loads data from tsvs.\n           Inputs:\n             tsvs: Blocks of tsvs, among which only the first contains header.\n             bucketized_float_cols: Float columns that will be bucketized. All features will be bucketized.\n             string_cols: String cols.\n             raw_float_cols: Float columns that are loaded raw. Target columns are usually not bucketized.\n        \"\"\"\n        d = DataStore()\n        d.load_tsv(tsvs,\n                   bucketized_float_cols=bucketized_float_cols,\n                   string_cols=string_cols,\n                   raw_float_cols=raw_float_cols)\n        return d\n\n    @staticmethod\n    def from_dict(bucketized_float_cols={}, string_cols={}, raw_float_cols={}):\n        \"\"\"Loads data from dict of columns.\n             bucketized_float_cols: Float columns that will be bucketized. All features will be bucketized.\n             string_cols: String cols.\n             raw_float_cols: Float columns that are loaded raw. Target columns are usually not bucketized.\n        \"\"\"\n        d = DataStore()\n        for key, value in bucketized_float_cols.iteritems():\n            d.add_bucketized_float_col(key, value)\n        for key, value in string_cols.iteritems():\n            d.add_string_col(key, value)\n        for key, value in raw_float_cols.iteritems():\n            d.add_raw_float_col(key, value)\n        return d\n","sub_path":"python/gbdt/_gbdt.py","file_name":"_gbdt.py","file_ext":"py","file_size_in_byte":3101,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"355870577","text":"#       module_glbp.py\n#       \n#       Copyright 2013 Daniel Mende <dmende@ernw.de>\n#\n\n#       Redistribution and use in source and binary forms, with or without\n#       modification, are permitted provided that the following conditions are\n#       met:\n#       \n#       * Redistributions of source code must retain the above copyright\n#         notice, this list of conditions and the following disclaimer.\n#       * Redistributions in binary form must reproduce the above\n#         copyright notice, this list of conditions and the following disclaimer\n#         in the documentation and/or other materials provided with the\n#         distribution.\n#       * Neither the name of the  nor the names of its\n#         contributors may be used to endorse or promote products derived from\n#         this software without specific prior written permission.\n#       \n#       THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n#       \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n#       LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR\n#       A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\n#       OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\n#       SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT\n#       LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,\n#       DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY\n#       THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n#       (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n#       OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\nimport struct\nimport threading\nimport time\n\nimport dnet\nimport dpkt\n\nimport gobject\nimport gtk\nimport gtk.glade\n\nGLBP_VERSION = 1\nGLBP_PORT = 3222\nGLBP_MULTICAST_ADDRESS = \"224.0.0.102\"\nGLBP_MULTICAST_MAC = \"01:00:5e:00:00:66\"\n\nclass glbp_packet(object):\n    #~                     1                   2                   3\n    #~ 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1\n    #~ +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n    #~ |   Version     |   Unknown1    |             Group             |\n    #~ +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n    #~ |            Unknown2           |           Owner MAC           |\n    #~ +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n    #~ |                           Owner MAC                           |\n    #~ +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n    \n    def __init__(self, group=None, owner_mac=None, tlvs=[]):\n        self.group = group\n        self.owner_mac = owner_mac\n        self.tlvs = tlvs\n    \n    def render(self):\n        tlv_data = \"\"\n        for t in self.tlvs:\n            tlv_data += t.render()\n        return struct.pack(\"!BxHxx6s\", GLBP_VERSION, self.group, self.owner_mac) + tlv_data \n    \n    def parse(self, data):\n        (self.group, self.owner_mac) = struct.unpack(\"!xxHxx6s\", data[:12])\n        return data[12:]\n\nclass glbp_tlv(object):\n    #~                     1                   2                   3\n    #~ 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1\n    #~ +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n    #~ |     Type      |     Length    |           Value ...           |\n    #~ +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n    TYPE_HELLO = 1\n    TYPE_REQ_RESP = 2\n    TYPE_AUTH = 3\n    TYPE_NONCE = 4\n    \n    def __init__(self, tlv_type=None):\n        self.tlv_type = tlv_type\n        \n    def render(self, data):\n        return struct.pack(\"!BB\", self.tlv_type, len(data)+2) + data\n    \n    def parse(self, data):\n        (self.tlv_type, self.tlv_length) = struct.unpack(\"!BB\", data[:2])\n        return data[2:]\n        \nclass glbp_tlv_hello(glbp_tlv):\n    #~                     1                   2                   3\n    #~ 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1\n    #~ +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n    #~ |   Unknown1    |     State     |   Unknown2    |    Priority   |\n    #~ +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n    #~ |            Unknown3           |        Hello Intervall        |\n    #~ +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n    #~ |        Hello Intervall        |         Hold Intervall        |\n    #~ +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n    #~ |         Hold Intervall        |           Redirect            |\n    #~ +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n    #~ |            Timeout            |           Unknown4            |\n    #~ +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n    #~ |  Address Type | Address Length|          Address ...          |\n    #~ +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n    STATE_LISTEN = 4\n    STATE_SPEAK = 8\n    STATE_STANDBY = 16\n    STATE_ACTIVE = 32\n    \n    def __init__(self, state=None, prio=None, hello_int=None, hold_int=None,\n                 redirect=None, timeout=None, addr_type=None, addr_len=None,\n                 addr=None):\n        self.state = state\n        self.prio = prio\n        self.hello_int = hello_int\n        self.hold_int = hold_int\n        self.redirect = redirect\n        self.timeout = timeout\n        self.addr_type = addr_type\n        self.addr_len = addr_len\n        self.addr = addr\n        glbp_tlv.__init__(self, glbp_tlv.TYPE_HELLO)\n    \n    def render(self):\n        return glbp_tlv.render(self, struct.pack(\"!xBxBxxIIHHxxBB\", self.state, self.prio, self.hello_int, self.hold_int,\n                                                 self.redirect, self.timeout, self.addr_type, self.addr_len) + self.addr)\n    \n    def parse(self, data):\n        data = glbp_tlv.parse(self, data)\n        (self.state, self.prio, self.hello_int, self.hold_int, self.redirect, self.timeout, self.addr_type, self.addr_len) = \\\n            struct.unpack(\"!xBxBxxIIHHxxBB\", data[:22])\n        self.addr = data[22:22+self.addr_len]\n        return data[22+self.addr_len:]\n\nclass glbp_tlv_req_resp(glbp_tlv):\n    #~                     1                   2                   3\n    #~ 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1\n    #~ +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n    #~ |   Forwarder   |   VF State    |    Unknown    |    Priority   |\n    #~ +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n    #~ |    Weight     |                   Unknown2                    |\n    #~ +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n    #~ |                           Unknown2                            |\n    #~ +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n    #~ |                         Virtual MAC                           |\n    #~ +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n    #~ |         Virtual MAC           |\n    #~ +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n    \n    def __init__(self, forwarder=None, state=None, prio=None, weight=None, vmac=None):\n        self.forwarder = forwarder\n        self.state = state\n        self.prio = prio\n        self.weight = weight\n        self.vmac = vmac\n        self.unknown = None\n        self.unknown2 = None\n        glbp_tlv.__init__(self, glbp_tlv.TYPE_REQ_RESP)\n    \n    def __eq__(self, other):\n        return self.vmac == other.vmac\n\n    def __ne__(self, other):\n        return not self.__eq__(other)\n    \n    def __hash__(self):\n        return hash(self.vmac)\n    \n    def render(self):\n        if not self.unknown is None:\n            return glbp_tlv.render(self, struct.pack(\"!BBBBB7s6s\", self.forwarder, self.state, self.unknown, self.prio, self.weight, self.unknown2, self.vmac))\n        return glbp_tlv.render(self, struct.pack(\"!BBxBB7x6s\", self.forwarder, self.state, self.prio, self.weight, self.vmac))\n        \n    def parse(self, data):\n        data = glbp_tlv.parse(self, data)\n        (self.forwarder, self.state, self.unknown, self.prio, self.weight, self.unknown2, self.vmac) = struct.unpack(\"!BBBBB7s6s\", data[:18])\n        return data[18:]\n        \nclass glbp_tlv_auth(glbp_tlv):\n    #~                     1                   2                   3\n    #~ 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1\n    #~ +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n    #~ |     Type      |     Length    |           Secret ...          |\n    #~ +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n    TYPE_PLAIN = 1\n    TYPE_MD5_STRING = 2\n    TYPE_MD5_CHAIN = 3\n    \n    def __init__(self, auth_type=None, secret=None):\n        self.auth_type = auth_type\n        self.secret = secret\n        glbp_tlv.__init__(self, glbp_tlv.TYPE_AUTH)\n        \n    def render(self):\n        return glbp_tlv.render(self, struct.pack(\"!BB\", self.auth_type, len(self.secret)) + self.secret)\n        \n    def parse(self, data):\n        data = glbp_tlv.parse(self, data)\n        (self.auth_type, length) = struct.unpack(\"!BB\", data[:2])\n        self.secret = data[2:2+length]\n        return data[self.tlv_length-2:]\n\nclass glbp_tlv_nonce(glbp_tlv):\n    def __init__(self):\n        glbp_tlv.__init__(self, glbp_tlv.TYPE_NONCE)\n\nclass glbp_thread(threading.Thread):\n    def __init__(self, parent):\n        threading.Thread.__init__(self)\n        self.parent = parent\n        self.running = True\n\n    def run(self):\n        self.parent.log(\"GLBP: Thread started\")\n        while self.running:\n            for i in self.parent.peers:\n                (iter, pkg, hello, req_resp, auth, state, arp) = self.parent.peers[i]\n                if state:\n                    glbp = glbp_packet(pkg.group, self.parent.mac)\n                    glbp_hello = glbp_tlv_hello(glbp_tlv_hello.STATE_ACTIVE, 255, hello.hello_int, hello.hold_int,\n                                        hello.redirect, hello.timeout, hello.addr_type, hello.addr_len, hello.addr)\n                    reqs = \"\"\n                    for req in req_resp:\n                        req.prio = 255\n                        req.weight = 255\n                        reqs += req.render()\n                    \n                    if not auth is None:\n                        if auth.auth_type == glbp_tlv_auth.TYPE_PLAIN:\n                            data = glbp.render() + auth.render() + glbp_hello.render() + reqs\n                        elif auth.auth_type == glbp_tlv_auth.TYPE_MD5_STRING:\n                            nonce = \"\\x00\\x01\\x02\\x03\\x04\\x05\\x06\\x07\"\n                            data = glbp_tlv_nonce().render(nonce) + glbp_hello.render() + reqs\n                            import hashlib\n                            m = hashlib.md5()\n                            m.update(data)\n                            auth.secret = m.digest()\n                            data = glbp.render() + auth.render() + data\n                    else:\n                        data = glbp.render() + glbp_hello.render() + reqs\n                    udp_hdr = dpkt.udp.UDP( sport=GLBP_PORT,\n                                            dport=GLBP_PORT,\n                                            data=data\n                                            )\n                    udp_hdr.ulen += len(udp_hdr.data)\n                    ip_hdr = dpkt.ip.IP(    ttl=255,\n                                            p=dpkt.ip.IP_PROTO_UDP,\n                                            src=self.parent.ip,\n                                            dst=dnet.ip_aton(GLBP_MULTICAST_ADDRESS),\n                                            data=str(udp_hdr)\n                                            )\n                    ip_hdr.len += len(ip_hdr.data)\n                    eth_hdr = dpkt.ethernet.Ethernet(   dst=dnet.eth_aton(GLBP_MULTICAST_MAC),\n                                                        src=self.parent.mac,\n                                                        type=dpkt.ethernet.ETH_TYPE_IP,\n                                                        data=str(ip_hdr)\n                                                        )\n                    self.parent.dnet.send(str(eth_hdr))\n                    if arp:\n                        if arp < 4:\n                            src_mac = self.parent.mac\n                            brdc_mac = dnet.eth_aton(\"ff:ff:ff:ff:ff:ff\")\n                            stp_uplf_mac = dnet.eth_aton(\"01:00:0c:cd:cd:cd\")\n                            ip = hello.addr #struct.pack(\"!I\", hello.addr)\n                            arp_hdr = dpkt.arp.ARP( hrd=dpkt.arp.ARP_HRD_ETH,\n                                                pro=dpkt.arp.ARP_PRO_IP,\n                                                op=dpkt.arp.ARP_OP_REPLY,\n                                                sha=src_mac,\n                                                spa=ip,\n                                                tha=brdc_mac,\n                                                tpa=ip\n                                                )\n                            eth_hdr = dpkt.ethernet.Ethernet(   dst=brdc_mac,\n                                                                src=src_mac,\n                                                                type=dpkt.ethernet.ETH_TYPE_ARP,\n                                                                data=str(arp_hdr)\n                                                                )\n                            self.parent.dnet.send(str(eth_hdr))\n\n                            arp_hdr = dpkt.arp.ARP( hrd=dpkt.arp.ARP_HRD_ETH,\n                                                pro=dpkt.arp.ARP_PRO_IP,\n                                                op=dpkt.arp.ARP_OP_REPLY,\n                                                sha=src_mac,\n                                                spa=ip,\n                                                tha=stp_uplf_mac,\n                                                tpa=ip\n                                                )\n                            eth_hdr = dpkt.ethernet.Ethernet(   dst=stp_uplf_mac,\n                                                                src=src_mac,\n                                                                type=dpkt.ethernet.ETH_TYPE_ARP,\n                                                                data=str(arp_hdr)\n                                                                )\n                            self.parent.dnet.send(str(eth_hdr))\n                        self.parent.peers[i] = (iter, pkg, hello, req_resp, auth, state, arp - 1)\n            time.sleep(1)\n        self.parent.log(\"GLBP: Thread terminated\")\n\n    def shutdown(self):\n        self.running = False\n\nclass mod_class(object):\n    STORE_SRC_ROW = 0\n    STORE_IP_ROW = 1\n    STORE_PRIO_ROW = 2\n    STORE_STATE_ROW = 3\n    STORE_AUTH_ROW = 4\n    \n    def __init__(self, parent, platform):\n        self.parent = parent\n        self.platform = platform\n        self.name = \"glbp\"\n        self.group = \"HOT-STANDBY\"\n        self.gladefile = \"/modules/module_glbp.glade\"\n        self.treestore = gtk.TreeStore(str, str, int, str, str)\n        self.thread = None\n\n    def start_mod(self):\n        self.peers = {}\n        self.thread = glbp_thread(self)\n\n    def shut_mod(self):\n        if self.thread:\n            if self.thread.is_alive():\n                self.thread.shutdown()\n        self.treestore.clear()\n        \n    def get_root(self):\n        self.glade_xml = gtk.glade.XML(self.parent.data_dir + self.gladefile)\n        dic = { \"on_get_button_clicked\" : self.on_get_button_clicked,\n                \"on_release_button_clicked\" : self.on_release_button_clicked\n                }\n        self.glade_xml.signal_autoconnect(dic)\n\n        self.treeview = self.glade_xml.get_widget(\"treeview\")\n        self.treeview.set_model(self.treestore)\n        self.treeview.set_headers_visible(True)\n\n        column = gtk.TreeViewColumn()\n        column.set_title(\"Source\")\n        render_text = gtk.CellRendererText()\n        column.pack_start(render_text, expand=True)\n        column.add_attribute(render_text, 'text', self.STORE_SRC_ROW)\n        self.treeview.append_column(column)\n        column = gtk.TreeViewColumn()\n        column.set_title(\"IP\")\n        render_text = gtk.CellRendererText()\n        column.pack_start(render_text, expand=True)\n        column.add_attribute(render_text, 'text', self.STORE_IP_ROW)\n        self.treeview.append_column(column)\n        column = gtk.TreeViewColumn()\n        column.set_title(\"Priority\")\n        render_text = gtk.CellRendererText()\n        column.pack_start(render_text, expand=True)\n        column.add_attribute(render_text, 'text', self.STORE_PRIO_ROW)\n        self.treeview.append_column(column)\n        column = gtk.TreeViewColumn()\n        column.set_title(\"Status\")\n        render_text = gtk.CellRendererText()\n        column.pack_start(render_text, expand=True)\n        column.add_attribute(render_text, 'text', self.STORE_STATE_ROW)\n        self.treeview.append_column(column)\n        column = gtk.TreeViewColumn()\n        column.set_title(\"Auth\")\n        render_text = gtk.CellRendererText()\n        column.pack_start(render_text, expand=True)\n        column.add_attribute(render_text, 'text', self.STORE_AUTH_ROW)\n        self.treeview.append_column(column)\n\n        self.arp_checkbutton = self.glade_xml.get_widget(\"arp_checkbutton\")\n\n        return self.glade_xml.get_widget(\"root\")\n\n    def log(self, msg):\n        self.__log(msg, self.name)\n\n    def set_log(self, log):\n        self.__log = log\n\n    def set_ip(self, ip, mask):\n        self.ip = dnet.ip_aton(ip)\n\n    def set_dnet(self, dnet):\n        self.dnet = dnet\n        self.mac = dnet.eth.get()\n\n    def get_udp_checks(self):\n        return (self.check_udp, self.input_udp)\n\n    def check_udp(self, udp):\n        if udp.dport == GLBP_PORT:\n            return (True, True)\n        return (False, False)\n\n    def input_udp(self, eth, ip, udp, timestamp):\n        if ip.src != self.ip:\n            pkg = glbp_packet()\n            data = pkg.parse(str(udp.data))\n            req_resp = []\n            auth = None\n            nonce = None\n            auth_str = \"Unauthenticated\"\n            while len(data) > 0:\n                #~ print \"len: %d data: %s\" % (len(data), data.encode(\"hex\"))\n                tlv = glbp_tlv()\n                tlv.parse(data)\n                if tlv.tlv_type == glbp_tlv.TYPE_HELLO:                    \n                    hello = glbp_tlv_hello()\n                    data = hello.parse(data)\n                elif tlv.tlv_type == glbp_tlv.TYPE_REQ_RESP:\n                    tmp = glbp_tlv_req_resp()\n                    data = tmp.parse(data)\n                    if not tmp.vmac == \"\\x00\\x00\\x00\\x00\\x00\\x00\":\n                        req_resp.append(tmp)\n                elif tlv.tlv_type == glbp_tlv.TYPE_AUTH:\n                    auth = glbp_tlv_auth()\n                    data = auth.parse(data)\n                    if auth.auth_type == glbp_tlv_auth.TYPE_PLAIN:\n                        auth_str = \"Plaintext: '%s'\" % auth.secret[:-1]\n                    elif auth.auth_type == glbp_tlv_auth.TYPE_MD5_STRING:\n                        auth_str = \"MD5 String: '%s'\" % auth.secret.encode(\"hex\")\n                    elif auth.auth_type == glbp_tlv_auth.TYPE_MD5_CHAIN:\n                        auth_str = \"MD5 Chain: '%s'\" % auth.secret.encode(\"hex\")\n                    else:\n                        auth_str = \"Unknown\"\n                else:\n                    #~ print \"type: %d len: %d\" % (tlv.tlv_type, tlv.tlv_length)\n                    data = data[tlv.tlv_length:]\n            try:\n                src = dnet.ip_ntoa(ip.src)\n            except:\n                pass\n            \n            if ip.src in self.peers:\n                (iter, _, _, req_resp_old, _, _, _) = self.peers[ip.src]\n                for i in req_resp:\n                    if not i in req_resp_old:\n                        req_resp_old.append(i)\n                        self.treestore.append(iter, [\"\", \"\", i.weight, dnet.eth_ntoa(i.vmac), \"\"])\n            else:\n                iter = self.treestore.append(None, [src, dnet.ip_ntoa(hello.addr), hello.prio, \"Seen\", auth_str])\n                for req in req_resp:\n                    self.treestore.append(iter, [\"\", \"\", req.weight, dnet.eth_ntoa(req.vmac), \"\"])\n                self.peers[ip.src] = (iter, pkg, hello, req_resp, auth, False, False)\n                self.log(\"GLBP: Got new peer %s\" % (src))\n\n    # SIGNALS #\n\n    def on_get_button_clicked(self, btn):\n        select = self.treeview.get_selection()\n        (model, paths) = select.get_selected_rows()\n        for i in paths:\n            iter = model.get_iter(i)\n            peer = dnet.ip_aton(model.get_value(iter, self.STORE_SRC_ROW))\n            (iter, pkg, hello, req_resp, auth, run, arp) = self.peers[peer]\n            if self.arp_checkbutton.get_active():\n                arp = 13\n            else:\n                arp = 0\n            self.peers[peer] = (iter, pkg, hello, req_resp, auth, True, arp)\n            model.set_value(iter, self.STORE_STATE_ROW, \"Taken\")\n        if not self.thread.is_alive():\n            self.thread.start()\n\n    def on_release_button_clicked(self, btn):\n        select = self.treeview.get_selection()\n        (model, paths) = select.get_selected_rows()\n        for i in paths:\n            iter = model.get_iter(i)\n            peer = dnet.ip_aton(model.get_value(iter, self.STORE_SRC_ROW))\n            (iter, pkg, hello, req_resp, auth, run, arp) = self.peers[peer]\n            self.peers[peer] = (iter, pkg, hello, req_resp, auth, False, arp)\n            model.set_value(iter, self.STORE_STATE_ROW, \"Released\")\n\n\n","sub_path":"modules/module_glbp.py","file_name":"module_glbp.py","file_ext":"py","file_size_in_byte":21785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"81004607","text":"'''\nPreprocessing for metadata script\n1. remove metadata entries that have no mapping in the dataset\n2. remove duplicated metadata entries (identified by IDs)\n'''\n\nimport os\n\nmetadata_csv_in = './Env1_meta/Env1_meta.csv'\nmetadata_csv_out = './Env1_meta/Env1_meta_aligned.csv'\n\nf = open(metadata_csv_in)\nlines = f.readlines()\nf.close()\n\nnew_lines = []\ns = set()\n\nfor line in lines:\n\titem = line.split(',')\n\tpt = '../data/Env1/' + str(item[0]) + '.wav'\n\tif os.path.exists(pt) and item[0] not in s:\n\t\tnew_lines.append(line)\n\t\ts.add(item[0])\n\nf_out = open(metadata_csv_out,'w')\nl = ''.join(new_lines)\nf_out.write(l)\nf.close()","sub_path":"metadata/PreProc.py","file_name":"PreProc.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"319833804","text":"''' Script to run drifters at pre-selected subregions of the domain to\nget stream function plots out. '''\n\nimport matplotlib as mpl\nmpl.use(\"Agg\") # set matplotlib to use the backend that does not require a windowing system\nimport numpy as np\nimport os\nimport netCDF4 as netCDF\nimport pdb\nimport matplotlib.pyplot as plt\nimport tracpy\nfrom datetime import datetime, timedelta\nimport glob\nfrom tracpy.tracpy_class import Tracpy\nfrom matplotlib.mlab import find, Path\n\n\nloc = 'http://barataria.tamu.edu:8080/thredds/dodsC/NcML/txla_nesting6.nc'\ngrid = tracpy.inout.readgrid(loc, usebasemap=True)\n\ndef init(name):\n    '''\n    Initialization for the simulation.\n    '''\n\n    loc = 'http://barataria.tamu.edu:8080/thredds/dodsC/NcML/txla_nesting6.nc'\n\n    time_units = 'seconds since 1970-01-01'\n\n    # horizontal_diffusivity project showed that relative dispersion did not\n    # change between nsteps=25 and 50, but does between nsteps=5 and 25, and\n    # interim numbers have not been tested yet.\n    nsteps = 25 # in-between tracks: 12 # old tracks: 25 \n\n    # Number of steps to divide model output for outputting drifter location\n    N = 1 # not interested in drifter locations\n\n    # Number of days\n    ndays = 30\n\n    # This is a forward-moving simulation\n    ff = -1 \n\n    # Time between outputs\n    tseas = 4*3600 # 4 hours between outputs, in seconds, time between model outputs \n    ah = 0. # old tracks: 5.\n    av = 0. # m^2/s\n\n    # surface drifters\n    z0 = 's'  \n    zpar = 29 \n\n    # for 3d flag, do3d=0 makes the run 2d and do3d=1 makes the run 3d\n    do3d = 0\n    doturb = 0\n\n    # Flag for streamlines.\n    dostream = 1\n\n    # Initialize Tracpy class\n    tp = Tracpy(loc, name=name, tseas=tseas, ndays=ndays, nsteps=nsteps, dostream=dostream, savell=False, doperiodic=0, \n                N=N, ff=ff, ah=ah, av=av, doturb=doturb, do3d=do3d, z0=z0, zpar=zpar, \n                time_units=time_units, usebasemap=True, grid=grid)\n\n    # tp._readgrid()\n\n    # Initial lon/lat locations for drifters\n    startindsfile = 'galveston-starting-inds.npz'\n    if not os.path.exists(startindsfile):\n        # get starting drifter locations for this region \n        loc_shelftransport = '/home/kthyng/projects/shelf_transport/'\n        dconn = np.load(loc_shelftransport + 'calcs/galvestonpts.npz')\n        lon = dconn['lon']; lat = dconn['lat']\n        xp, yp = grid['basemap'](lon,lat)\n        gpath = Path(np.vstack((xp, yp)).T)\n        dconn.close()\n        drifters = netCDF.Dataset(loc_shelftransport + 'tracks/2004-01-01T00gc.nc')\n        xg = drifters.variables['xg'][:]; yg = drifters.variables['yg'][:]\n        # Change to projected drifter locations now\n        nanind = np.isnan(xg) + (xg==-1) # indices where nans are location in xg, yg; for reinstitution of nans\n        # pdb.set_trace()\n        xp, yp, _ = tracpy.tools.interpolate2d(xg, yg, grid, 'm_ij2xy') \n        xp[nanind] = np.nan; yp[nanind] = np.nan\n        del(xg,yg) # don't need grid info anymore\n        # save indices of drifters that start in the coastal areas\n        inds = gpath.contains_points(np.vstack((xp[:,0].flat, yp[:,0].flat)).T).reshape(xp[:,0].shape)\n        np.savez(startingindsfile, inds=inds)\n    else:\n        inds = np.load(startindsfile)['inds']\n\n    startptsfile = 'galveston-starting-ll.npz'\n    if not os.path.exists(startptsfile):\n        loc_shelftransport = '/home/kthyng/projects/shelf_transport/'\n        drifters = netCDF.Dataset(loc_shelftransport + 'tracks/2004-01-01T00gc.nc')\n        xg = drifters.variables['xg'][:]; yg = drifters.variables['yg'][:]\n        xg = xg[inds,0]; yg = yg[inds,0]\n        lon0, lat0, _ = tracpy.tools.interpolate2d(xg, yg, tp.grid, 'm_ij2ll')\n        np.savez(startptsfile, lon0=lon0, lat0=lat0)\n    else:\n        d = np.load(startptsfile)\n        lon0 = d['lon0']; lat0 = d['lat0']\n        d.close()\n\n    # equal weightings for drifters for transport.\n    T0 = np.ones(lon0.size, order='F')\n\n    U = np.ma.zeros(tp.grid['xu'].shape, order='F')\n    V = np.ma.zeros(tp.grid['xv'].shape, order='F')\n\n    # pdb.set_trace()\n       \n    return tp, lon0, lat0, T0, U, V\n\n\ndef run():\n\n    # Make sure necessary directories exist\n    if not os.path.exists('tracks'):\n        os.makedirs('tracks')\n    if not os.path.exists('figures'):\n        os.makedirs('figures')\n        \n    #loc = 'http://barataria.tamu.edu:8080/thredds/dodsC/NcML/txla_nesting6.nc'\n    #grid = tracpy.inout.readgrid(loc)\n\n    overallstartdate = datetime(2010, 4, 1, 0, 1)\n    overallstopdate = datetime(2010, 10, 1, 0, 1)\n\n    date = overallstartdate\n\n    # Start from the beginning and add days on for loop\n    # keep running until we hit the next month\n    while date < overallstopdate:\n\n        name = date.isoformat()[0:13]\n\n        # If the particle trajectories have not been run, run them\n        if not os.path.exists('tracks/' + name + '.nc') and \\\n            not os.path.exists('tracks/' + name + 'gc.nc'):\n\n            # Read in simulation initialization\n            tp, lon0, lat0, T0, U, V = init(name)\n\n            # Run tracpy\n            # Save directly to grid coordinates\n            lonp, latp, zp, t, T0, U, V = tracpy.run.run(tp, date, lon0, lat0, T0=T0, U=U, V=V)\n\n\n        # Increment by 24 hours for next loop, to move through more quickly\n        date = date + timedelta(hours=24)\n\n    # # Do transport plot\n    # tracpy.plotting.transport(name='all_f/N=5_dx=8/25days', fmod=startdate.isoformat()[0:7] + '*', \n    #     extraname=startdate.isoformat()[0:7], Title='Transport on Shelf', dmax=1.0)\n\n\nif __name__ == \"__main__\":\n    run()    \n\n","sub_path":"galveston/shelf_transport.py","file_name":"shelf_transport.py","file_ext":"py","file_size_in_byte":5573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"602835766","text":"import random\n\nrand = random.randint(1,100)\n\n\nuser = None\nguesses = 0\n\n\nwhile(user != rand):\n    user  = int(input(\"enter your gusess : \"))\n    guesses += 1\n    if(user==rand):\n        print(\"you guessed it right!\")\n    else:\n        if(user>rand):\n            print(\"you guessed it wrong! enter a smaller number\")\n        else:\n            print(\"you guessed it wrong! enter a larger number\")\n\nprint(\"you guessed the  number in \" + str(guesses) +  \" guesses\")\n\nwith open(\"D:\\pyton file\\projects\\hislog.txt\", \"r\") as f:\n    history = int(f.read())\n\nif(guesses<history):\n    with open(\"D:\\pyton file\\projects\\hislog.txt\", \"w\") as f:\n        write = f.write(str(guesses))\n    print(\"you have just broken the high score\")\n","sub_path":"projects/2_projectnumbergusessegame.py","file_name":"2_projectnumbergusessegame.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"278424774","text":"import os\n\nfor n in range(1,20):\n    nazwabazowa = f\"nauka{n:02d}\"\n    print(f\"{nazwabazowa}: \", end=\"\")\n    for rozsz in 'py', 'txt', 'dat':\n        nazwapliku = f\"{nazwabazowa}.{rozsz}\"\n        #print(nazwapliku, end=\" \")\n        if os.path.exists(nazwapliku):\n            print(rozsz, end=\" \")\n        else:\n            print(\"--\", end=\" \")\n    print()\n","sub_path":"pliki/nauka16.py","file_name":"nauka16.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"180934199","text":"# -*- encoding: utf-8 -*-\n\"\"\"\nCreated on 2018年7月31日\n\n@author: huawei\n\"\"\"\n\n\ndef create_column_info(column_name, column_orgi_name, field_name, java_data_type, is_pri=False, underlineJson=False, comment=\"\"):\n    anno = ''\n    comm = \"    /** %s */\\n\" % comment\n    if is_pri:\n        anno = \"    @Id \\n    @GeneratedValue(strategy = GenerationType.IDENTITY) \\n\"\n    anno += \"    @ApiModelProperty(\\\"%s\\\")\\n\" % comment\n    anno += \"    @Column(name = \\\"%s\\\")\\n\" % column_name\n\n    if underlineJson:\n        anno += \"    @JsonProperty(\\\"%s\\\")\\n\" % column_orgi_name\n\n    #     if java_data_type ==\"java.util.Date\":\n    #         anno += \"    @JsonFormat(pattern = \\\"yyyy-MM-dd HH:mm:ss\\\")\\n\"\n\n    field = \"    private %s %s;\"\n    if java_data_type.find(\".\") > 0:\n        dType = java_data_type.split(\".\")[2]\n        field = field % (dType, field_name)\n        return comm + anno + field, java_data_type\n    else:\n        field = field % (java_data_type, field_name)\n        return comm + anno + field, None\n\n\nif __name__ == \"__main__\":\n    print(create_column_info('org_name', \"orgName\", \"Byte\")[0])\n    print(create_column_info(\"org_count\", \"orgCount\", \"java.math.BigDecimal\", True)[0])\n    print(create_column_info(\"org_count\", \"orgCount\", \"java.math.BigDecimal\", True)[1])\n","sub_path":"src/com/alonelaval/jpc/create_entity/create_column.py","file_name":"create_column.py","file_ext":"py","file_size_in_byte":1276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"651000281","text":"#!/usr/bin/env python3\n\n\"\"\" Profiling functions in ipython3 \"\"\"\n\n__appname__ = 'profileme.py'\n__author__ = 'Deraj Wilson-Aggarwal (dw4415@ic.ac.uk)'\n__version__ = '0.0.1'\n\n\ndef my_squares(iters): # iters being the same as x - an argument to read in \n    \"\"\" Function to create a list of squared i values \"\"\" \n    out = [] # empty list data structure \n    for i in range(iters): # for each value in iters: \n        out.append(i ** 2) # appends value to power of i out  \n    return out # return the data structure \n\ndef my_join(iters, string):\n    \"\"\" Function to create and extend a string\"\"\" \n    out = '' # empty string\n    for i in range(iters): # string will get extended by another string iter\n        out += string.join(\", \") # out = out+string \n    return out\n\ndef run_my_funcs(x,y):\n    \"\"\" Function to run both functions at the same time \"\"\" \n    print(x,y) # print parameter values \n    my_squares(x) \n    my_join(x,y)\n    return 0 # print 0 once completed \n\nrun_my_funcs(10000000,\"My string\")\n\n","sub_path":"Week7/Code/profileme.py","file_name":"profileme.py","file_ext":"py","file_size_in_byte":1004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"552316797","text":"# cabecalho\nprint('=-' * 20)\nprint(f'{\"Desafio 83\":^40}')\nprint('-' * 40)\n\nexpressao = input('Digite uma expressão:')\n\nquantidade = expressao.count('(') == expressao.count(')')\n\nabrir = None\nfechar = None\ncerto = False \n\nif expressao.count('(') == 0 and quantidade:\n    certo = True\n\n\nif not(certo):\n    for i, caracter in enumerate(expressao):\n        if caracter == '(':\n            abrir = i\n        elif caracter == ')':\n            fechar = i\n        if fechar == None or abrir < fechar:\n            certo = True\n\nif (certo):\n    print('ta certo')\nelse:\n    print('Errado')\n\n\n# rodape\nprint('\\n')\nprint('=-' * 20, '\\n')","sub_path":"listas/ex83.py","file_name":"ex83.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"458989272","text":"import pandas as pd\r\nimport matplotlib.pyplot as plt\r\n\r\ndf = pd.read_csv('FilteredData.csv')\r\n\r\nname = df['Star_name'].to_list()\r\nmass = df['Mass'].to_list()\r\nradius = df['Radius'].to_list()\r\ndistance = df['Distance'].to_list()\r\ngravity = df['Gravity'].to_list()\r\n\r\nplt.bar(name,mass)\r\nplt.title(\"Name & Mass of the star\")\r\nplt.xlabel(\"Name\")\r\nplt.ylabel(\"Mass\")\r\nplt.show()\r\n\r\nplt.bar(name,radius)\r\nplt.title(\"Name & Radius of the star\")\r\nplt.xlabel(\"Name\")\r\nplt.ylabel(\"Radius\")\r\nplt.show()\r\n\r\nplt.bar(name,distance)\r\nplt.title(\"Name & Distance of the star\")\r\nplt.xlabel(\"Name\")\r\nplt.ylabel(\"Distance\")\r\nplt.show()\r\n\r\nplt.bar(name,gravity)\r\nplt.title(\"Name & Gravity of the star\")\r\nplt.xlabel(\"Name\")\r\nplt.ylabel(\"Gravity\")\r\nplt.show()","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"646162415","text":"import logging\nfrom .asso.asso_annotation import AssoAnnotation\nfrom .bbox.bbox_annotation import BboxAnnotation\nfrom .image.image_annotation import ImageAnnotation\nfrom .keyp.keyp_annotation import KeypAnnotation\nimport pdb\nlogger = logging.getLogger('global')\n\nclass AnnoFactory:\n\n    @classmethod\n    def create(cls, name, cfg):\n\n        if name == 'image':\n            anno = ImageAnnotation(cfg)\n        elif name == 'asso':\n            anno = AssoAnnotation(cfg)\n        elif name == 'bbox':\n            anno = BboxAnnotation(cfg)\n        elif name == 'keyp':\n            anno = KeypAnnotation(cfg)\n        else:\n            raise ValueError('Unrecognized Annotation: ' + name)\n        return anno\n","sub_path":"unn/annotations/anno_factory.py","file_name":"anno_factory.py","file_ext":"py","file_size_in_byte":704,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"405088256","text":"\"\"\"Remove useless indices and columns\n\nRevision ID: 60be4019a8eb\nRevises: 2f3f005f663d\nCreate Date: 2018-09-26 23:47:31.377181\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '60be4019a8eb'\ndown_revision = '2f3f005f663d'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_index('ix_sticker_name', table_name='sticker')\n    op.drop_index('ix_sticker_text', table_name='sticker')\n    op.drop_column('sticker', 'name')\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('sticker', sa.Column('name', sa.VARCHAR(), autoincrement=False, nullable=True))\n    op.create_index('ix_sticker_text', 'sticker', ['text'], unique=False)\n    op.create_index('ix_sticker_name', 'sticker', ['name'], unique=False)\n    # ### end Alembic commands ###\n","sub_path":"migrations/versions/2018_09_26_60be4019a8eb_remove_useless_indices_.py","file_name":"2018_09_26_60be4019a8eb_remove_useless_indices_.py","file_ext":"py","file_size_in_byte":958,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"396300312","text":"from cStringIO import StringIO\nimport sys\nimport pypg\nfrom geofdw.exception import CRSError\n\ndef crs_to_srid(crs):\n  if crs == None:\n    return None\n  srid = crs.lower().replace('epsg:', '')\n  try:\n    return int(srid)\n  except ValueError as e:\n    raise CRSError(crs)\n\nclass ArcGridParseError(Exception):\n  pass\n\nclass ArcGrid():\n  def __init__(self, grid, srid = None):\n    self.data = []\n    self.srid = srid\n    self._parse(grid)\n\n  def _parse(self, grid):\n    header = {}\n    buf = StringIO(grid)\n\n    while True:\n      line = buf.readline().split()\n      if not line:\n        break\n      word = line[0].lower()\n      if word in ['ncols', 'nrows']:\n        header[word] = int(line[1])\n      elif word in ['xllcorner', 'yllcorner', 'cellsize', 'nodata_value']:\n        header[word] = float(line[1])\n      else:\n        if len(line) != header['ncols']:\n          raise ArcGridParseError('Expected %d columns, found %d' % (len(line), header['ncols']))\n        self.data.extend(map(float, line))\n\n    self.width = int(header['ncols'])\n    self.height = int(header['nrows'])\n    self.llx = header['xllcorner']\n    self.lly = header['yllcorner']\n    self.cellsize = header['cellsize']\n    if header.has_key('nodata_value'):\n      self.nodata = float(header.get('nodata_value'))\n    else:\n      self.nodata = None\n\n  def as_pg_raster(self, bbox):\n    band = pypg.raster.Band(self.data, self.nodata)\n    return pypg.raster.Raster(bbox, self.height, self.width, [band], self.srid)\n","sub_path":"geofdw/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1477,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"384916466","text":"from python.component.grid.GridPanelComponent import GridPanelComponent\nfrom python.entity.transferobject.AddUserInfo import AddUserInfo\nfrom python.exception.ElementNotFoundException import ElementNotFoundException\n \nclass AddUserGridComponent(GridPanelComponent):\n    selectors = {\"add_template_user_btn\":\"id=btnAddTempUser\",\n                 \"assign_subscription_btn\":\"id=btnAssignSubscription\",\n                 \"assign_activity_btn\":\"id=btnAssignActivity\",\n                 \"remove_user_btn\":\"id=btnDeleteTempUser\",\n                 \"email_input\":\"%(row_index)s/div[contains(@class,'cell_email')]//input\",\n                 \"first_name_input\":\"%(row_index)s/div[3]//input\",\n                 \"last_name_input\":\"%(row_index)s/div[4]//input\",\n                 \"approver_email_dropdown_button\":\"%(row_index)s/div[5]/div/div\",\n                 \"approver_email_dropdown_search\":\"xpath=//div[5]/div[2]//input[contains(@class,'suggest-search')]\",\n                 \"approver_email_select\":\"xpath=//div[5]/div[2]//li[@data-name='{approverEmail}']\",\n                 \"next_approver_email_dropdown_button\":\"%(row_index)s/div[6]/div/div\",\n                 \"next_approver_email_dropdown_search\":\"xpath=//div[5]/div[3]//input[contains(@class,'suggest-search')]\",\n                 \"next_approver_email_select\":\"xpath=//div[5]/div[3]//li[@data-name='{nextApproverEmail}']\",\n                 \"user_type_dropdown_button\":\"%(row_index)s/div[7]/div/div\",\n                 \"user_type_select\":\"xpath=//div[5]/div[4]//li[@data-name='{userType}']\",\n                 \"save_button\":\"xpath=//a[text()='Save']\",\n                 }\n    \n    def choose_user_to_invite(self, userInfoList):\n        for userInfo in userInfoList:\n            artifactRow = self._search_for_rows(self._convert_artifact_into_row_info(userInfo, \"user_email\"),True)\n            if artifactRow  is None:\n                raise ElementNotFoundException(\"User not found\" + userInfo.email)\n            artifactRow[0][0]._check(artifactRow[0][0].rowIndex)\n        return self\n     \n    def _convert_row_to_user(self, index, row):\n        firstName = row._retrieve_cell_string_value(index, \"firstname\")\n        lastName = row._retrieve_cell_string_value(index, \"lastname\")\n        userType = row._retrieve_cell_string_value(index, \"user_type\")\n        approverEmail = row._retrieve_cell_string_value(index, \"approver_email\")\n        nextApproverEmail = row._retrieve_cell_string_value(index, \"approver_email2\")\n        userEmail = row._retrieve_cell_string_value(index, \"user_email\")\n        user = AddUserInfo(None, firstName, lastName, userEmail, userType, approverEmail, nextApproverEmail)\n        return user\n\n    def get_user_lists(self):\n        changes = []\n        allRows = self._search_for_all_rows()\n        for i in range(0, len(allRows)):\n            row = allRows[i]\n            changes.append(self._convert_row_to_user(i, row))\n        return changes\n    \n    def _click_on_add_new_user_button(self):\n        if self.is_visible(\"add_template_user_btn\"):\n            self.click_element(\"add_template_user_btn\")\n        return self\n    \n    def _click_on_assign_subscription_button(self):\n        if self.is_visible(\"assign_subscription_btn\"):\n            self.click_element(\"assign_subscription_btn\")\n        return self\n    \n    def _click_on_assign_activity_button(self):\n        if self.is_visible(\"assign_activity_btn\"):\n            self.click_element(\"assign_activity_btn\")\n        return self\n    \n    def _click_on_remove_user_button(self):\n        if self.is_visible(\"remove_user_btn\"):\n            self.click_element(\"remove_user_btn\")\n        return self\n    \n    def _input_email(self, email):\n        self.input_text(\"email_input\", email)\n        return self\n    \n    def _input_first_name(self, firstName):\n        self.input_text(\"first_name_input\", firstName)\n        return self\n\n    def _input_last_name(self, lastName):\n        self.input_text(\"last_name_input\", lastName)\n        return self\n    \n    def _select_approver_email(self, approverEmail):\n        self.logger.info(\"Select approver email: \" + approverEmail)\n        self.click_element(\"approver_email_dropdown_button\")\n        if self.is_visible(\"approver_email_dropdown_search\"):\n            self.input_text(\"approver_email_dropdown_search\", approverEmail)\n        selector = self.resolve_selector(\"approver_email_select\", approverEmail=approverEmail)\n        self.mouse_over(selector)\n        self.click_element(selector)\n        return self\n    \n    def _select_next_approver_email(self, nextApproverEmail):\n        self.logger.info(\"Select next approver email: \" + nextApproverEmail)\n        self.click_element(\"next_approver_email_dropdown_button\")\n        if self.is_visible(\"next_approver_email_dropdown_search\"):\n            self.input_text(\"next_approver_email_dropdown_search\", nextApproverEmail)\n        selector = self.resolve_selector(\"next_approver_email_select\", nextApproverEmail=nextApproverEmail)\n        self.mouse_over(selector)\n        self.click_element(selector)\n        return self\n    \n    def _select_user_type(self, userType):\n        if self.is_visible(\"user_type_dropdown_button\"):\n            self.logger.info(\"Select user type: \" + userType)\n            self.click_element(\"user_type_dropdown_button\")\n            selector = self.resolve_selector(\"user_type_select\", userType=userType)\n            self.mouse_over(selector)\n            self.click_element(selector)\n        return self\n    \n    def _click_on_save_button(self):\n        self.click_element(\"save_button\")\n        return self\n    \n    def add_new_user(self, userInfo):\n        self._click_on_add_new_user_button()\n        if userInfo.userEmail is not None and userInfo.userEmail !=\"\":\n            self._input_email(userInfo.userEmail)\n        if userInfo.firstName is not None and userInfo.firstName !=\"\":\n            self._input_first_name(userInfo.firstName)\n        if userInfo.lastName is not None and userInfo.lastName !=\"\":\n            self._input_last_name(userInfo.lastName)\n        if userInfo.approverEmail is not None and userInfo.approverEmail !=\"\":\n            self._select_approver_email(userInfo.approverEmail)\n        if userInfo.nextApproverEmail is not None and userInfo.nextApproverEmail !=\"\":\n            self._select_next_approver_email(userInfo.nextApproverEmail)\n        if userInfo.userType is not None and userInfo.userType !=\"\":\n            self._select_user_type(userInfo.userType)\n        self._click_on_save_button()","sub_path":"expense-ui-robot-tests/PythonExpenseAutomationTest/python/component/pagepart/grid/AddUserGridComponent.py","file_name":"AddUserGridComponent.py","file_ext":"py","file_size_in_byte":6474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"321874352","text":"from django.core.checks import messages\nfrom django.shortcuts import render, redirect\nfrom .models import *\nfrom django.contrib import messages\nimport bcrypt\n\n# Create your views here.\n\ndef index(request):\n    request.session.flush()\n    return render(request, \"index.html\")\n\ndef register(request):\n    if request.method == \"GET\":\n        return redirect('/')\n    errors = User.objects.register_validator(request.POST)\n    if len(errors):\n        for key, value in errors.items():\n            messages.error(request, value)\n        return redirect('/')\n    hashed_pw = bcrypt.hashpw(request.POST['password'].encode(), bcrypt.gensalt(7)).decode()\n    #create new user\n    new_user = User.objects.create(\n        first_name = request.POST['first_name'],\n        last_name = request.POST['last_name'],\n        email = request.POST['email'],\n        password = hashed_pw,\n    )\n    #store user in request.session\n    request.session['user_id']= new_user.id\n    return redirect('/books')\n\ndef login(request):\n    if request.method == \"GET\":\n        return redirect('/')\n    errors = User.objects.login_validator(request.POST)\n    if len(errors) > 0:\n        for key, value in errors.items():\n            messages.error(request, value)\n        return redirect('/')\n    this_user = User.objects.filter(email = request.POST['email'])[0]\n    request.session['user_id'] = this_user.id\n    messages.success(request, \"You've successfully registered or logged in!\")\n    return redirect('/books')\n\ndef logout(request):\n    request.session.flush()\n    return redirect('/')","sub_path":"apps/loginRegApp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"293368495","text":"from cached_property import cached_property\n\nfrom django.db import models\nfrom django.contrib.postgres.fields import ArrayField\n\nfrom .utils import DatedModel\n\n\nclass Contest(DatedModel):\n    \"\"\"\n    Контрольная\n    \"\"\"\n    code = models.CharField(max_length=10, verbose_name='код')\n    name = models.CharField(max_length=255, verbose_name='название')\n    languages = ArrayField(models.CharField(max_length=10), verbose_name='языки')\n\n    class Meta(DatedModel.Meta):\n        verbose_name = 'контрольная'\n        verbose_name_plural = 'контрольные'\n\n    def __str__(self):\n        return '{} - {}'.format(self.code, self.name)\n\n    @cached_property\n    def variants_all(self):\n        from .variant import VariantProblem\n        return self.variants.prefetch_related(\n            models.query.Prefetch(\n                lookup='pupils',\n                to_attr='pupils_all',\n            ),\n            models.query.Prefetch(\n                lookup='variant_problems',\n                queryset=VariantProblem.objects.select_related('problem').prefetch_related(\n                    models.query.Prefetch(\n                        lookup='problem__tests',\n                        to_attr='problem_tests_all'\n                    )\n                ),\n                to_attr='variant_problems_all'\n            )\n        )\n\n    def serialize(self):\n        return {\n            'pk': self.pk,\n            'code': self.code,\n            'name': self.name,\n            'variants': [variant.serialize() for variant in self.variants_all]\n        }\n","sub_path":"project/models/contest.py","file_name":"contest.py","file_ext":"py","file_size_in_byte":1585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"485725712","text":"# -*- coding: utf-8 -*-\n\nfrom django.conf.urls import patterns, url\n\nfrom . import views\n\nurlpatterns = patterns('',\n    url(r'^$', views.home, name=\"home\"),\n    url(r'^account/login/?$', views.login, name=\"login\"),\n    url(r'^account/register/?$', views.register, name=\"register\"),\n    url(r'^account/settings/?$', views.account_settings, name=\"account_settings\"),\n)","sub_path":"example/app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"357617791","text":"import unittest, os\nimport ZODBPool\n\nfrom UnitOfWork import UnitOfWork\nfrom time import time\n\nclass MyObject:\n\n    def __init__(self):\n        self._pk = time()\n        \n    def _get_primaryKey(self):\n        return self._pk\n\n    \nclass Test(unittest.TestCase):\n    def setUp(self):\n        self.uow = UnitOfWork()\n\n\n    def tearDown(self):\n        pass\n\n    def testRegisterNew(self):\n        \"\"\"Registers an object as new, and checks to see if getStatus\n        returns the appropriate state for the given object and that\n        the object is returned when you call getNew()\"\"\"\n        o = MyObject()\n        pk = o._get_primaryKey()\n\n        self.uow.registerNew(o)\n        assert self.uow.getStatus(pk) == UnitOfWork.NEW\n        assert o in self.uow.getNew()\n        \n\n    def testRegisterClean(self):\n        \"\"\"Registers an object as clean, and checks to see if getStatus\n        returns the appropriate state for the given object and that\n        the object is returned when you call getClean()\"\"\"\n        o = MyObject()\n        pk = o._get_primaryKey()\n\n        self.uow.registerClean(o)\n        assert self.uow.getStatus(pk) == UnitOfWork.CLEAN\n        assert o in self.uow.getClean()\n        \n    def testRegisterDirty(self):\n        \"\"\"Registers an object as clean, then dirty and checks to see\n        if getStatus returns the appropriate state for the given\n        object and that the object is returned when you call\n        getDirty() but isn't in the list when you call getClean().\"\"\"\n\n        o = MyObject()\n        pk = o._get_primaryKey()\n\n        self.uow.registerClean(o)\n        self.uow.registerDirty(o)\n        assert self.uow.getStatus(pk) == UnitOfWork.DIRTY\n        assert o in self.uow.getDirty()\n\n\n    def testRegisterRemoved(self):\n        \n        \"\"\"Registers one object for each of the other states first.\n        Then attempts to remove each object , checks each objects\n        status afterwards and verifies that the objects are only found\n        in the list returned by getRemoved() and not in any of the\n        lists returned by getNew(), getClean() or getDirty().\"\"\"\n\n        o1 = MyObject()\n        pk1 = o1._get_primaryKey()\n        self.uow.registerNew(o1)\n        \n        o2 = MyObject()\n        pk2 = o2._get_primaryKey()\n        self.uow.registerClean(o2)\n        self.uow.registerDirty(o2)\n\n        o3 = MyObject()\n        pk3 = o3._get_primaryKey()\n        self.uow.registerClean(o3)\n\n        self.uow.registerRemoved(o1)\n        self.uow.registerRemoved(o2)\n        self.uow.registerRemoved(o3)\n\n        assert self.uow.getStatus(pk1) == UnitOfWork.REMOVED\n        assert self.uow.getStatus(pk2) == UnitOfWork.REMOVED\n        assert self.uow.getStatus(pk3) == UnitOfWork.REMOVED\n\n        assert o1 not in self.uow.getNew()\n        assert o2 not in self.uow.getDirty()\n        assert o3 not in self.uow.getClean()\n        \n    def testGet(self):\n\n        \"\"\"Registers an object for each of the four states and calls\n        get for each of them. Call get for an object that doesn't\n        exist and verify that it returns None. Call get for an object\n        that doesn't exist with an alternative default and verify that\n        that value is returned.\"\"\"\n\n        o1 = MyObject()\n        pk1 = o1._get_primaryKey()\n        self.uow.registerNew(o1)\n        \n        o2 = MyObject()\n        pk2 = o2._get_primaryKey()\n        self.uow.registerClean(o2)\n        self.uow.registerDirty(o2)\n\n        o3 = MyObject()\n        pk3 = o3._get_primaryKey()\n        self.uow.registerClean(o3)\n\n        o4 = MyObject()\n        pk4 = o4._get_primaryKey()\n        self.uow.registerClean(o4)\n        self.uow.registerRemoved(o4)\n\n        assert o1 == self.uow.get(pk1)\n        assert o2 == self.uow.get(pk2)\n        assert o3 == self.uow.get(pk3)\n        assert o4 == self.uow.get(pk4)\n\n        assert self.uow.get(12345) is None\n        assert self.uow.get(12345, \"default\") == \"default\"\n\n    def testClear(self):\n        \"\"\" Registers an object in each status, then calls clear and\n        verifies that all the get calls return empty lists. \"\"\"\n        \n        o1 = MyObject()\n        pk1 = o1._get_primaryKey()\n        self.uow.registerNew(o1)\n        \n        o2 = MyObject()\n        pk2 = o2._get_primaryKey()\n        self.uow.registerClean(o2)\n        self.uow.registerDirty(o2)\n\n        o3 = MyObject()\n        pk3 = o3._get_primaryKey()\n        self.uow.registerClean(o3)\n\n        o4 = MyObject()\n        pk4 = o4._get_primaryKey()\n        self.uow.registerClean(o4)\n        self.uow.registerRemoved(o4)\n\n        self.uow.clear()\n\n        assert not self.uow.getNew()\n        assert not self.uow.getClean()\n        assert not self.uow.getDirty()\n        assert not self.uow.getRemoved()\n\n    def testInvalidStates(self):\n\n        \"\"\"Verify that exceptions are thrown when.\n        - Attepmt to register an object as new if it's already\n          registered as clean,dirty,removed\n        - Attempt to register an object as clean if it's already\n          registered as new, dirty, removed\n        - Attempt to register an object as dirty if it's not currently\n          registered as clean\n        - Attepmt to register an object removed if it isn't registered\n          as new, clean, dirty\"\"\"\n\n        new = MyObject()\n        clean = MyObject()\n        dirty = MyObject()\n        removed = MyObject()\n        not_it = MyObject()\n\n        self.uow.registerNew(new)\n        self.uow.registerClean(clean)\n        self.uow.registerClean(dirty)\n        self.uow.registerDirty(dirty)\n        self.uow.registerClean(removed)\n        self.uow.registerRemoved(removed)\n\n\n        self.assertRaises(ValueError, self.uow.registerNew,(new))\n        self.assertRaises(ValueError, self.uow.registerNew,(clean))\n        self.assertRaises(ValueError, self.uow.registerNew,(dirty))\n        self.assertRaises(ValueError, self.uow.registerNew,(removed))\n\n        self.assertRaises(ValueError, self.uow.registerClean,(new))\n        self.assertRaises(ValueError, self.uow.registerClean,(clean))\n        self.assertRaises(ValueError, self.uow.registerClean,(dirty))\n        self.assertRaises(ValueError, self.uow.registerClean,(removed))\n\n        self.assertRaises(ValueError, self.uow.registerDirty,(new))\n        self.assertRaises(ValueError, self.uow.registerDirty,(dirty))\n        self.assertRaises(ValueError, self.uow.registerDirty,(removed))\n        self.assertRaises(ValueError, self.uow.registerDirty,(not_it))\n\n        self.assertRaises(ValueError, self.uow.registerRemoved,(not_it))\n        \n\n\n\n\n\n\ndef test_suite():\n    suite = unittest.TestSuite()\n    suite.addTest(unittest.makeSuite(Test))\n\n    return suite\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"Rambler/tests/testUnitOfWork.py","file_name":"testUnitOfWork.py","file_ext":"py","file_size_in_byte":6699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"306905291","text":"from  unittest import TestCase\nfrom config.config import TestConfig \nfrom lcp.license import License\nfrom lcp.status import Status\nimport time\n\nclass LCPTests(TestCase):\n\n  @classmethod\n  def setUpClass(cls):\n    # get config\n    cls.config = TestConfig('l2')\n    license = License(cls.config.license())\n    # Get status from config license\n    cls.status = Status(license)\n    cls.status.update_status()\n\n  def test_a_license_status_is_expired(self):\n    \"\"\"- Check that the status is 'expired'\"\"\"\n    self.assertTrue(self.status.is_expired(), \"The awaited status is expired, the current status is {}\".format(self.status.get_status()))\n\n  def test_b_check_register_on_expired(self):\n    \"\"\"- Check that a 'register' request returns an error.\"\"\"\n    with self.assertRaisesRegexp(IOError, 'POST .* HTTP error 4[0-9][0-9]$'):\n      self.status.register(self.status.DEVICEID1, self.status.DEVICENAME1)\n\n  def test_c_check_license_end(self):\n    \"\"\"- Check that the 'end' datetime is before now\"\"\"\n    license = self.status.update_license()\n    end = license.get_end()\n    now = int(time.time())\n    self.assertLess(end, now)\n","sub_path":"src/tests/test8/part1.py","file_name":"part1.py","file_ext":"py","file_size_in_byte":1122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"582609030","text":"##############################################################################\n#\n# Copyright (c) 2004 Zope Corporation and Contributors.\n# All Rights Reserved.\n#\n# This software is subject to the provisions of the Zope Public License,\n# Version 2.1 (ZPL).  A copy of the ZPL should accompany this distribution.\n# THIS SOFTWARE IS PROVIDED \"AS IS\" AND ANY AND ALL EXPRESS OR IMPLIED\n# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS\n# FOR A PARTICULAR PURPOSE.\n#\n##############################################################################\n\"\"\"Tests for zpkgtools.app.\"\"\"\n\nimport os\nimport shutil\nimport sys\nimport unittest\nimport urllib\n\nfrom StringIO import StringIO\n\nfrom zpkgsetup import package\nfrom zpkgsetup import publication\nfrom zpkgsetup.tests import tempfileapi as tempfile\n\nimport zpkgtools\n\nfrom zpkgtools import app\nfrom zpkgtools import include\nfrom zpkgtools import loader\n\n\nCMD = \"./foo/bar.py\"\n\n\nclass CommandLineTestCase(unittest.TestCase):\n\n    def parse_args(self, args):\n        argv = [CMD] + args + [\"resource\"]\n        options = app.parse_args(argv)\n        self.assertEqual(options.resource, \"resource\")\n        return options\n\n    def test_set_package_version(self):\n        options = self.parse_args([])\n        self.assertEqual(options.version, \"0.0.0\")\n        options = self.parse_args([\"-v0.0.0\"])\n        self.assertEqual(options.version, \"0.0.0\")\n        options = self.parse_args([\"-v\", \"0.0.0\"])\n        self.assertEqual(options.version, \"0.0.0\")\n        options = self.parse_args([\"-v1.2\"])\n        self.assertEqual(options.version, \"1.2\")\n        options = self.parse_args([\"-v\", \"1.2\"])\n        self.assertEqual(options.version, \"1.2\")\n\n    def test_suppress_support_code(self):\n        options = self.parse_args([])\n        self.assert_(options.include_support_code is None)\n        options = self.parse_args([\"-s\"])\n        self.assert_(options.include_support_code)\n        options = self.parse_args([\"-S\"])\n        self.assert_(not options.include_support_code)\n\n    def test_set_package_revision(self):\n        options = self.parse_args([])\n        self.assertEqual(options.revision_tag, \"HEAD\")\n        # short option:\n        options = self.parse_args([\"-rHEAD\"])\n        self.assertEqual(options.revision_tag, \"HEAD\")\n        options = self.parse_args([\"-r\", \"HEAD\"])\n        self.assertEqual(options.revision_tag, \"HEAD\")\n        options = self.parse_args([\"-rtag\"])\n        self.assertEqual(options.revision_tag, \"tag\")\n        options = self.parse_args([\"-r\", \"tag\"])\n        self.assertEqual(options.revision_tag, \"tag\")\n        # long option, one arg:\n        options = self.parse_args([\"--revision-tag=HEAD\"])\n        self.assertEqual(options.revision_tag, \"HEAD\")\n        options = self.parse_args([\"--revision-tag=tag\"])\n        self.assertEqual(options.revision_tag, \"tag\")\n        # long option, two args:\n        options = self.parse_args([\"--revision-tag\", \"HEAD\"])\n        self.assertEqual(options.revision_tag, \"HEAD\")\n        options = self.parse_args([\"--revision-tag\", \"tag\"])\n        self.assertEqual(options.revision_tag, \"tag\")\n\n    def test_set_release_name(self):\n        options = self.parse_args([])\n        self.assert_(options.release_name is None)\n        # short option:\n        options = self.parse_args([\"-nFooPackage\"])\n        self.assertEqual(options.release_name, \"FooPackage\")\n        options = self.parse_args([\"-n\", \"FooPackage\"])\n        self.assertEqual(options.release_name, \"FooPackage\")\n        # long option, one arg:\n        options = self.parse_args([\"--name=FooPackage\"])\n        self.assertEqual(options.release_name, \"FooPackage\")\n        # long option, two args:\n        options = self.parse_args([\"--name\", \"FooPackage\"])\n        self.assertEqual(options.release_name, \"FooPackage\")\n\n    def test_resource_map_list(self):\n        options = self.parse_args([])\n        self.assertEqual(options.location_maps, [])\n        # short option:\n        options = self.parse_args([\"-msomepath.map\"])\n        self.assertEqual(options.location_maps, [\"somepath.map\"])\n        options = self.parse_args([\"-msomepath.map\",\n                                   \"-m\", \"another.map\"])\n        self.assertEqual(options.location_maps,\n                         [\"somepath.map\", \"another.map\"])\n        # long option:\n        options = self.parse_args([\"--resource-map=somepath.map\"])\n        self.assertEqual(options.location_maps, [\"somepath.map\"])\n        options = self.parse_args([\"--resource-map=somepath.map\",\n                                   \"--resource-map\", \"another.map\"])\n        self.assertEqual(options.location_maps,\n                         [\"somepath.map\", \"another.map\"])\n\n    def test_suppress_config_file(self):\n        # This is a little tricky; we use a magic value to indicate\n        # that no config file should not be loaded.  If the value is\n        # None, we should read the default config file it it exists,\n        # but if the value is '', we shouldn't read one at all.  This\n        # assumes the user isn't a complete fool (and doesn't pass ''\n        # explicitly on the command line).\n        #\n        options = self.parse_args([])\n        self.assert_(options.configfile is None)\n        options = self.parse_args([\"-f\"])\n        self.assertEqual(options.configfile, \"\")\n\n    def test_set_config_file(self):\n        options = self.parse_args([])\n        self.assert_(options.configfile is None)\n        # short option:\n        options = self.parse_args([\"-Csomepath.conf\"])\n        self.assertEqual(options.configfile, \"somepath.conf\")\n        options = self.parse_args([\"-C\", \"somepath.conf\"])\n        self.assertEqual(options.configfile, \"somepath.conf\")\n        # long option:\n        options = self.parse_args([\"--configure=somepath.conf\"])\n        self.assertEqual(options.configfile, \"somepath.conf\")\n        options = self.parse_args([\"--configure\", \"somepath.conf\"])\n        self.assertEqual(options.configfile, \"somepath.conf\")\n\n    def test_set_version_from_revision_tag(self):\n        options = self.parse_args([\"-r\", \"my-package-1_0\"])\n        self.assertEqual(options.version, \"1.0\")\n\n    def test_set_package_revision_overrides_revision_tag(self):\n        options = self.parse_args([\"-r\", \"my-package-1_0\", \"-v1.0\"])\n        self.assertEqual(options.version, \"1.0\")\n\n    def test_version_from_tagname(self):\n        # Test only the version_from_tagname() function\n        convert = app.version_from_tagname\n        eq = self.assertEqual\n        # Test with underscores (CVS style)\n        eq(convert(\"my-package-1_0\"), \"1.0\")\n        eq(convert(\"my-package-1_0_42\"), \"1.0.42\")\n        eq(convert(\"my-package-1_0_42_24\"), \"1.0.42.24\")\n        eq(convert(\"my-package-1_0_42alpha42\"), \"1.0.42alpha42\")\n        eq(convert(\"my-package-1_0beta\"), \"1.0beta\")\n        eq(convert(\"my-package-1_0a666\"), \"1.0a666\")\n        eq(convert(\"my-package-1_0b777\"), \"1.0b777\")\n        eq(convert(\"my-package-1_0c888\"), \"1.0c888\")\n        # Test with underscores (Subversion style?)\n        eq(convert(\"my-package-1.0\"), \"1.0\")\n        eq(convert(\"my-package-1.0.42\"), \"1.0.42\")\n        eq(convert(\"my-package-1.0.42.24\"), \"1.0.42.24\")\n        eq(convert(\"my-package-1.0.42alpha42\"), \"1.0.42alpha42\")\n        eq(convert(\"my-package-1.0beta\"), \"1.0beta\")\n        eq(convert(\"my-package-1.0a666\"), \"1.0a666\")\n        eq(convert(\"my-package-1.0b777\"), \"1.0b777\")\n        eq(convert(\"my-package-1.0c888\"), \"1.0c888\")\n        #\n        # Check that unparsable tags return None\n        #\n        # Too many plain numeric segments\n        eq(convert(\"my-package-1_0_42_24_39\"), None)\n        # Too many numeric segments after alphabetic segment\n        eq(convert(\"my-package-1_0_42_alpha_24_35\"), None)\n        # Too few leading numeric seqments\n        eq(convert(\"my-package-1\"), None)\n        eq(convert(\"my-package-1a\"), None)\n        eq(convert(\"my-package-1a0\"), None)\n        # Too many trailing numeric seqments\n        eq(convert(\"mypkg-1_2_3alpha4_5\"), None)\n\n    def test_control_build_type(self):\n        options = self.parse_args([])\n        self.failIf(options.application)\n        self.failIf(options.collect)\n        # collection\n        # short arg:\n        options = self.parse_args([\"-c\"])\n        self.failIf(options.application)\n        self.failUnless(options.collect)\n        # long arg:\n        options = self.parse_args([\"--collection\"])\n        self.failIf(options.application)\n        self.failUnless(options.collect)\n        # application\n        # short arg:\n        options = self.parse_args([\"-a\"])\n        self.failUnless(options.application)\n        self.failIf(options.collect)\n        # long arg:\n        options = self.parse_args([\"--application\"])\n        self.failUnless(options.application)\n        self.failIf(options.collect)\n        # both\n        # short args:\n        options = self.parse_args([\"-ac\"])\n        self.failUnless(options.application)\n        self.failUnless(options.collect)\n        options = self.parse_args([\"-ca\"])\n        self.failUnless(options.application)\n        self.failUnless(options.collect)\n        options = self.parse_args([\"-a\", \"-c\"])\n        self.failUnless(options.application)\n        self.failUnless(options.collect)\n        options = self.parse_args([\"-c\", \"-a\"])\n        self.failUnless(options.application)\n        self.failUnless(options.collect)\n        # long args:\n        options = self.parse_args([\"--application\", \"--collection\"])\n        self.failUnless(options.application)\n        self.failUnless(options.collect)\n        options = self.parse_args([\"--collection\", \"--application\"])\n        self.failUnless(options.application)\n        self.failUnless(options.collect)\n\n\nclass ComponentTestCase(unittest.TestCase):\n\n    def setUp(self):\n        self.tmpdir = tempfile.mkdtemp(prefix=\"test-app-\")\n        self.mypkg_url = \"file://\" + urllib.pathname2url(self.tmpdir)\n        self.ip = include.InclusionProcessor(loader.Loader())\n\n    def write_app_file(self, name, text):\n        f = open(os.path.join(self.tmpdir, name), \"w\")\n        f.write(text)\n        f.close()\n\n    def tearDown(self):\n        shutil.rmtree(self.tmpdir)\n\n    def test_validation_of_package_without_setup_cfg(self):\n        self.write_app_file(\"__init__.py\", \"# make this a package\\n\")\n        #\n        c = app.Component(\"mypkg\", self.mypkg_url, self.ip)\n        self.assert_(c.is_python_package())\n\n    def test_validation_of_package_with_setup_cfg(self):\n        self.write_app_file(\"SETUP.cfg\", \"# this is a simple package\\n\")\n        self.write_app_file(\"__init__.py\", \"# make this a package\\n\")\n        #\n        c = app.Component(\"mypkg\", self.mypkg_url, self.ip)\n        self.assert_(c.is_python_package())\n\n    def test_validation_of_nonpackage_with_setup_cfg(self):\n        self.write_app_file(\"SETUP.cfg\", \"# this is a simple package\\n\")\n        #\n        c = app.Component(\"mypkg\", self.mypkg_url, self.ip)\n        self.assert_(not c.is_python_package())\n\n    def test_non_validation_of_nonpackage_without_setup_cfg(self):\n        self.assertRaises(zpkgtools.Error,\n                          app.Component, \"mypkg\", self.mypkg_url, self.ip)\n\n    def test_component_metadata_is_copied_by_default(self):\n        self.write_app_file(publication.PUBLICATION_CONF,\n                            \"Metadata-Version: 1.1\\n\"\n                            \"Name: mypkg\\n\")\n        self.write_app_file(include.PACKAGE_CONF,\n                            \"# nothing to specify\\n\")\n        self.write_app_file(package.PACKAGE_CONF,\n                            \"# nothing to specify\\n\")\n        #\n        c = app.Component(\"mypkg\", self.mypkg_url, self.ip)\n        dest = tempfile.mkdtemp(prefix=\"test-app-dest-\")\n        try:\n            c.write_package(dest)\n            c.write_manifest()\n            c.write_setup_cfg()\n            c.write_setup_py(version='1.2.3')\n            # done writing; make sure the expected metadata files are\n            # present:\n            pkgdest = os.path.join(dest, c.name)\n            self.assert_(isfile(pkgdest, publication.PUBLICATION_CONF))\n            self.assert_(isfile(pkgdest, package.PACKAGE_CONF))\n            self.failIf(os.path.exists(os.path.join(pkgdest,\n                                                    include.PACKAGE_CONF)))\n        finally:\n            shutil.rmtree(dest)\n\n    def test_component_metadata_is_copied_with_inclusions(self):\n        self.write_app_file(publication.PUBLICATION_CONF,\n                            \"Metadata-Version: 1.1\\n\"\n                            \"Name: mypkg\\n\")\n        self.write_app_file(include.PACKAGE_CONF,\n                            \"<collection>\\n\"\n                            \"  README.txt README\\n\"\n                            \"</collection>\\n\")\n        self.write_app_file(package.PACKAGE_CONF,\n                            \"# nothing to specify\\n\")\n        self.write_app_file(\"README\",\n                            \"some text\\n\")\n        #\n        c = app.Component(\"mypkg\", self.mypkg_url, self.ip)\n        dest = tempfile.mkdtemp(prefix=\"test-app-dest-\")\n        try:\n            c.write_package(dest)\n            c.write_manifest()\n            c.write_setup_cfg()\n            c.write_setup_py(version='1.2.3')\n            # done writing; make sure the expected metadata files are\n            # present:\n            pkgdest = os.path.join(dest, c.name)\n            self.assert_(isfile(pkgdest, publication.PUBLICATION_CONF))\n            self.assert_(isfile(pkgdest, package.PACKAGE_CONF))\n            self.assert_(isfile(pkgdest, \"README.txt\"))\n            self.failIf(os.path.exists(os.path.join(pkgdest,\n                                                    include.PACKAGE_CONF)))\n        finally:\n            shutil.rmtree(dest)\n\n\ndef isfile(path, *args):\n    if args:\n        path = os.path.join(path, *args)\n    return os.path.isfile(path)\n\n\ndef test_suite():\n    suite = unittest.makeSuite(CommandLineTestCase)\n    suite.addTest(unittest.makeSuite(ComponentTestCase))\n    return suite\n\nif __name__ == \"__main__\":\n    unittest.main(defaultTest=\"test_suite\")\n","sub_path":"zpkgtools/branches/fdrake-scripts-on-windows/zpkgtools/tests/test_app.py","file_name":"test_app.py","file_ext":"py","file_size_in_byte":14124,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"478222851","text":"__author__ = 'sam'\n\ndef normalize(segments):\n    minlength = -1\n    for segmentset in segments:\n        for segment in segmentset:\n            thislength = segment[1][0] - segment[0][0]\n            if thislength < minlength or minlength == -1:\n                minlength = thislength\n\n    yspace = minlength*.866 # sin 60 degrees\n    normalizedsegs = [[],[],[]]\n    for i in range(len(segments)):\n        maxy = segments[i][len(segments[i]) -1][1][1]\n        minx = segments[i][len(segments[i]) -1][0][0]\n        for segment in segments[i]:\n            yoffset = round(( maxy - (segment[0][1]+segment[1][1])/2)/yspace)\n            xoffset = round(((segment[0][0]) - (minx+(yoffset*minlength/2)))/minlength)\n            numsegs = round(((float)(segment[1][0] - segment[0][0]))/minlength)\n            normalizedsegs[i].append([xoffset, yoffset, numsegs])\n\n    return normalizedsegs","sub_path":"normalizesave.py","file_name":"normalizesave.py","file_ext":"py","file_size_in_byte":878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"112774649","text":"\"\"\"Module for calculating gameplay statistics.\"\"\"\n\nimport datetime\nimport math\nimport pymongo\n\nimport api\nfrom api.cache import (decode_scoreboard_item, get_score_cache,\n                       get_scoreboard_cache, get_scoreboard_key, memoize,\n                       search_scoreboard_cache)\nfrom api import PicoException\n\n\nSCOREBOARD_PAGE_LEN = 50\n\n\ndef _get_problem_names(problems):\n    \"\"\"Extract the names from a list of problems.\"\"\"\n    return [problem['name'] for problem in problems]\n\n\n# @memoize\ndef get_score(tid=None, uid=None, time_weighted=True):\n    \"\"\"\n    Get the score for a user or team. Uses memoization from zset keyed on\n    uid/tid. Currently the sorted aspect of this set doesn't exactly serve\n    any function...\n\n    Args:\n        tid: The team id\n        uid: The user id\n        time_weighted: return decimal weight of time of last solved problem\n    Returns:\n        float score: The users's or team's int score, plus decimal value of\n        weighting based last submission time. Cast as int for all\n        score display output, never round\n    \"\"\"\n    if uid is None:\n        cache_key = tid\n        solved_args = {\"tid\": tid}\n    elif tid is None:\n        cache_key = uid\n        solved_args = {\"uid\": uid}\n    else:\n        cache_key = tid + uid\n        solved_args = {\"uid\": uid, \"tid\": tid}\n\n    score_cache = get_score_cache()\n    score = score_cache.score(cache_key)\n\n    # Not cached\n    if score is None:\n        db = api.db.get_conn()\n        solved_problems = api.problem.get_solved_problems(**solved_args)\n        score = sum([\n            problem['score']\n            for problem in solved_problems\n        ])\n        time_weight = 0\n        if score > 0:\n            solved_args.update({'correct': True})\n            last_submitted = datetime.datetime.now()\n            # Search by individual team members, which may include\n            # submissions made before joining current team\n            if tid is not None:\n                members = api.team.get_team_members(tid=tid,\n                                                    show_disabled=False)\n                solved_args = {\n                    \"uid\": {\n                        \"$in\": list(map(lambda x: x[\"uid\"], members))\n                    }\n                }\n            query = db.submissions.find(solved_args).limit(1).sort(\n                [('$natural', pymongo.DESCENDING)])\n            if query.count() > 0:\n                last_submitted = query[0]['timestamp']\n            # Weight returns a float based on last submission time.\n            # Math is safe for next 2 centuries\n            time_weight = 1 - (int(last_submitted.strftime(\"%s\")) * 1e-10)\n        score += time_weight\n        score_cache.add({cache_key: score})\n    if time_weighted:\n        return score\n    else:\n        return int(score)\n\n\ndef get_team_review_count(tid=None, uid=None):\n    \"\"\"\n    Get the count of reviewed problems for a user or team.\n\n    Args:\n        tid: team to get count of\n        uid: user to get count of (overrides tid)\n\n    Returns: review count\n    \"\"\"\n    if uid is not None:\n        return len(api.problem_feedback.get_reviewed_pids(uid=uid))\n    elif tid is not None:\n        count = 0\n        for member in api.team.get_team_members(tid=tid):\n            count += len(\n                api.problem_feedback.get_reviewed_pids(uid=member['uid']))\n        return count\n\n\n# Stored by the cache_stats daemon.\n# @memoize\ndef get_group_scores(gid=None, name=None):\n    \"\"\"\n    Get the group scores.\n\n    Args:\n        gid: The group id\n        name: The group name\n    Returns:\n        A dictionary containing name, tid, and score\n    \"\"\"\n    key_args = {'gid': gid}\n    scoreboard_cache = get_scoreboard_cache(**key_args)\n    scoreboard_cache.clear()\n\n    member_teams = [\n        api.team.get_team(tid=tid)\n        for tid in api.group.get_group(gid=gid)['members']\n    ]\n\n    result = {}\n    for team in member_teams:\n        if team[\"size\"] > 0:\n            score = get_score(tid=team['tid'])\n            key = get_scoreboard_key(team)\n            result[key] = score\n    if result:\n        scoreboard_cache.add(result)\n\n    return scoreboard_cache\n\n\ndef get_group_average_score(gid=None, name=None):\n    \"\"\"\n    Get the average score of teams in a group.\n\n    Args:\n        gid: The group id\n        name: The group name\n    Returns:\n        The total score of the group\n    \"\"\"\n    group_scoreboard = get_group_scores(gid=gid, name=name)\n    group_scores = group_scoreboard.as_items()\n    total_score = sum([int(item[1]) for item in group_scores])\n    return int(total_score / len(group_scores)) if len(group_scores) > 0 else 0\n\n\n# Stored by the cache_stats daemon\n# @memoize\ndef get_all_team_scores(country=None, include_ineligible=False):\n    \"\"\"\n    Get the score for every team in the database.\n\n    Args:\n        country: optional restriction by country\n        include_ineligible: include ineligible teams\n\n    Returns:\n        A list of dictionaries with name and score\n\n    \"\"\"\n    key_args = {'country': country, 'include_ineligible': include_ineligible}\n    teams = api.team.get_all_teams(**key_args)\n    scoreboard_cache = get_scoreboard_cache(**key_args)\n    scoreboard_cache.clear()\n\n    result = {}\n    all_groups = api.group.get_all_groups()\n    for team in teams:\n        # Get the full version of the group.\n        groups = [\n            group for group in all_groups if team[\"tid\"] in group[\"members\"] or\n            team[\"tid\"] in group[\"teachers\"] or team[\"tid\"] == group[\"owner\"]\n        ]\n\n        # Determine if the user is exclusively a member of hidden groups.\n        # If they are, they won't be processed.\n        if (len(groups) == 0 or\n                any([not (group[\"settings\"][\"hidden\"]) for group in groups])):\n            score = get_score(tid=team['tid'])\n            if score > 0:\n                key = get_scoreboard_key(team=team)\n                result[key] = score\n    if result:\n        scoreboard_cache.add(result)\n    return scoreboard_cache\n\n\ndef get_all_user_scores():\n    \"\"\"\n    Get the score for every user in the database.\n\n    Returns:\n        A list of dictionaries with name and score\n\n    \"\"\"\n    users = api.user.get_all_users()\n\n    result = []\n    for user in users:\n        result.append({\n            \"name\": user['username'],\n            \"score\": get_score(uid=user['uid'], time_weighted=False)\n        })\n\n    return sorted(result, key=lambda item: item['score'], reverse=True)\n\n\n@memoize(timeout=120)\ndef get_problems_by_category():\n    \"\"\"\n    Get the list of all problems divided into categories.\n\n    Returns:\n        A dictionary of category:[problem list]\n\n    \"\"\"\n    result = {\n        cat: _get_problem_names(api.problem.get_all_problems(category=cat))\n        for cat in api.problem.get_all_categories()\n    }\n\n    return result\n\n\ndef get_team_member_stats(tid):\n    \"\"\"\n    Get the solved problems for each member of a given team.\n\n    Args:\n        tid: the team id\n\n    Returns:\n        A dict of username:[problem list]\n\n    \"\"\"\n    members = api.team.get_team_members(tid=tid)\n\n    return {\n        member['username']: _get_problem_names(\n            api.problem.get_solved_problems(uid=member['uid']))\n        for member in members\n    }\n\n\ndef get_problem_submission_stats(pid=None):\n    \"\"\"\n    Retrieve the number of valid and invalid submissions for a given problem.\n\n    Args:\n        pid: the pid of the problem\n        name: the name of the problem\n    Returns:\n        Dict of {valid: #, invalid: #}\n    \"\"\"\n    return {\n        \"valid\":\n        len(api.submissions.get_submissions(pid, correctness=True)),\n        \"invalid\":\n        len(\n            api.submissions.get_submissions(pid, correctness=False))\n    }\n\n\n@memoize\ndef get_score_progression(tid=None, uid=None, category=None):\n    \"\"\"\n    Find the score and time after each correct submission of a team or user.\n\n    NOTE: this is slower than get_score.\n          Do not use this for getting current score.\n\n    Args:\n        tid: the tid of the user\n        uid: the uid of the user\n        category: category filter\n    Returns:\n        A list of dictionaries containing score and time\n    \"\"\"\n    solved = api.problem.get_solved_problems(\n        tid=tid, uid=uid, category=category)\n\n    result = []\n    score = 0\n\n    problems_counted = set()\n\n    for problem in sorted(solved, key=lambda prob: prob[\"solve_time\"]):\n        if problem['pid'] not in problems_counted:\n            score += problem[\"score\"]\n            problems_counted.add(problem['pid'])\n        result.append({\n            \"score\": score,\n            \"time\": int(problem[\"solve_time\"].timestamp())\n        })\n\n    return result\n\n\n# Stored by the cache_stats daemon\n@memoize\ndef get_problem_solves(pid):\n    \"\"\"\n    Return the number of solves for a particular problem.\n\n    Args:\n        pid: pid of the problem\n    \"\"\"\n    db = api.db.get_conn()\n\n    return db.submissions.count({\n        'pid': pid,\n        'correct': True\n    })\n\n\n# Stored by the cache_stats daemon\n@memoize\ndef get_top_teams_score_progressions(\n        limit=5, include_ineligible=False, gid=None):\n    \"\"\"\n    Get the score progressions for the top teams.\n\n    Args:\n        limit: Number of teams to include\n        gid: If specified, compute the progressions for the top teams\n             from this group only. Overrides include_ineligible.\n        include_ineligible: if specified, include ineligible teams in result\n\n    Returns:\n        The top teams and their score progressions.\n        A dict of {name: name, score_progression: score_progression}\n\n    \"\"\"\n    def output_item(item):\n        data = decode_scoreboard_item(item)\n        return {\n            'name': data['name'],\n            'affiliation': data['affiliation'],\n            'score_progression': get_score_progression(tid=data['tid'])\n        }\n\n    if gid is None:\n        scoreboard_cache = get_all_team_scores(\n            include_ineligible=include_ineligible)\n    else:\n        scoreboard_cache = get_group_scores(gid=gid)\n\n    team_items = scoreboard_cache.range(0, limit - 1, with_scores=True,\n                                        desc=True)\n    return [output_item(team_item) for team_item in team_items]\n\n\n@memoize()\ndef check_invalid_instance_submissions(gid=None):\n    \"\"\"Get submissions of keys for the wrong problem instance.\"\"\"\n    db = api.db.get_conn()\n    shared_key_submissions = []\n\n    group = None\n    if gid is not None:\n        group = api.group.get_group(gid=gid)\n\n    for problem in api.problem.get_all_problems(show_disabled=True):\n        valid_keys = [instance['flag'] for instance in problem['instances']]\n        incorrect_submissions = db.submissions.find({\n            'pid': problem['pid'],\n            'correct': False\n        }, {\"_id\": 0})\n        for submission in incorrect_submissions:\n            if submission['key'] in valid_keys:\n                # make sure that the key is still invalid\n                if not api.submissions.grade_problem(\n                        submission['pid'], submission['key'],\n                        tid=submission['tid']):\n                    if group is None or submission['tid'] in group['members']:\n                        submission['username'] = api.user.get_user(\n                            uid=submission['uid'])['username']\n                        submission[\"problem_name\"] = problem[\"name\"]\n                        shared_key_submissions.append(submission)\n\n    return shared_key_submissions\n\n\n# Stored by the cache_stats daemon.\n@memoize\ndef get_registration_count():\n    \"\"\"Get the user, team, and group counts.\"\"\"\n    db = api.db.get_conn()\n    users = db.users.count()\n    stats = {\n        \"users\": users,\n        \"teams\": db.teams.count() - users,\n        \"groups\": db.groups.count()\n    }\n    usernames = set(db.users.find({}).distinct(\"username\"))\n    team_names = set(db.teams.find({}).distinct(\"team_name\"))\n\n    real_team_names = team_names - usernames\n    real_team_ids = list(\n        db.teams.find({\n            \"team_name\": {\n                \"$in\": list(real_team_names)\n            }\n        }).distinct(\"tid\"))\n\n    teamed_users = db.users.count({\"tid\": {\"$in\": real_team_ids}})\n    stats[\"teamed_users\"] = teamed_users\n\n    return stats\n\n\ndef get_initial_scoreboard():\n    \"\"\"\n    Retrieve the initial scoreboard (first pages of global and student views).\n\n    If a user is logged in, the initial pages will instead be those on which\n    that user appears, and their group scoreboards will also be returned.\n\n    Returns: dict of scoreboard information\n    \"\"\"\n    user = None\n    team = None\n    pagelen = SCOREBOARD_PAGE_LEN\n\n    if api.user.is_logged_in():\n        user = api.user.get_user()\n        team = api.team.get_team(tid=user['tid'])\n\n    result = {'tid': 0, 'groups': []}\n    # Include all arguments\n    global_board = get_scoreboard_cache(country=None, include_ineligible=True)\n    result['global'] = {\n        'name': 'global',\n        'pages': math.ceil(len(global_board) / pagelen),\n        'start_page': 1,\n        'scoreboard': [],\n    }\n    if user is None:\n        raw_board = global_board.range(0, pagelen - 1, with_scores=True,\n                                       desc=True)\n        result['global']['scoreboard'] = [decode_scoreboard_item(item) for\n                                          item in raw_board]\n    else:\n        result['tid'] = team['tid']\n\n        # Display global board at particular page user is ranked at\n        global_pos = global_board.rank(get_scoreboard_key(team),\n                                       reverse=True) or 0\n        start_slice = math.floor(global_pos / pagelen) * pagelen\n        raw_board = global_board.range(start_slice, start_slice + pagelen - 1,\n                                       with_scores=True, desc=True)\n        result['global']['scoreboard'] = [decode_scoreboard_item(item) for\n                                          item in raw_board]\n        result['global']['start_page'] = math.ceil((global_pos + 1) / pagelen)\n\n        # Eligible student board, starting at first page\n        # result['country'] = user[\"country\"]\n        student_board = get_scoreboard_cache(country=None,\n                                             include_ineligible=False)\n        student_pos = student_board.rank(get_scoreboard_key(team),\n                                         reverse=True) or 0\n        start_slice = math.floor(student_pos / pagelen) * pagelen\n        raw_student_board = student_board.range(start_slice,\n                                                start_slice + pagelen - 1,\n                                                with_scores=True,\n                                                desc=True)\n        result['student'] = {\n            'name': 'student',\n            'pages': math.ceil(len(student_board) / pagelen),\n            'scoreboard': [decode_scoreboard_item(item) for\n                           item in raw_student_board],\n            'start_page': math.ceil((student_pos + 1) / pagelen),\n        }\n\n        # Each classroom/group\n        for group in api.team.get_groups(user['tid']):\n            group_board = get_scoreboard_cache(gid=group['gid'])\n            group_pos = group_board.rank(get_scoreboard_key(team),\n                                         reverse=True) or 0\n            start_slice = math.floor(group_pos / pagelen) * pagelen\n            raw_group_board = group_board.range(start_slice,\n                                                start_slice + pagelen - 1,\n                                                with_scores=True,\n                                                desc=True)\n            result['groups'].append({\n                'gid':\n                group['gid'],\n                'name':\n                group['name'],\n                'scoreboard':\n                [decode_scoreboard_item(item) for item in raw_group_board],\n                'pages':\n                math.ceil(len(group_board) / pagelen),\n                'start_page':\n                math.ceil((group_pos + 1) / pagelen),\n            })\n    return result\n\n\ndef get_scoreboard_page(board, page_number, gid=None):\n    \"\"\"\n    Retrieve a specific scoreboard page.\n\n    Must be logged in to retrieve a page from the 'groups' board.\n\n    Args:\n        board: scoreboard to retrieve a page from (global, student, groups)\n        page_number: page number to retrieve\n\n    Raises:\n        PicoException: if the 'groups' board is selected but no user is\n                       logged in\n\n    \"\"\"\n    start = SCOREBOARD_PAGE_LEN * (page_number - 1)\n    end = start + SCOREBOARD_PAGE_LEN - 1\n    scoreboard = []\n    if board == \"groups\":\n        user = api.user.get_user()\n        group = api.group.get_group(gid=gid)\n        if not (user[\"tid\"] in group[\"members\"] or\n                user[\"tid\"] in group[\"teachers\"] or\n                user[\"tid\"] == group[\"owner\"]):\n            raise PicoException(\n                    \"You are not a member of this class\",\n                    status_code=401)\n        group_board = get_scoreboard_cache(gid=gid)\n        raw_board = group_board.range(start, end, with_scores=True,\n                                      reverse=True)\n        scoreboard = [decode_scoreboard_item(item) for item in raw_board]\n    elif board == \"global\":\n        global_board = get_scoreboard_cache(country=None,\n                                            include_ineligible=True)\n        raw_board = global_board.range(start, end, with_scores=True,\n                                       reverse=True)\n        scoreboard = [decode_scoreboard_item(item) for item in raw_board]\n    elif board == \"student\":\n        student_board = get_scoreboard_cache(country=None,\n                                             include_ineligible=False)\n        raw_board = student_board.range(start, end, with_scores=True,\n                                        reverse=True)\n        scoreboard = [decode_scoreboard_item(item) for item in raw_board]\n    return {\n        'name': board,\n        'pages': math.ceil(len(scoreboard) / SCOREBOARD_PAGE_LEN),\n        'start_page': 1,\n        'scoreboard': scoreboard\n    }\n\n\ndef get_demographic_data():\n    \"\"\"Get demographic information used in analytics\"\"\"\n    users = api.user.get_all_users()\n\n    result = []\n    for user in users:\n        result.append({\n            \"usertype\": user['usertype'],\n            \"country\": user['country'],\n            \"gender\": user['demo'].get('gender',''),\n            \"zipcode\": user['demo'].get('zipcode',''),\n            \"grade\": user['demo'].get('grade',''),\n            \"score\": get_score(uid=user['uid'], time_weighted=False)\n        })\n\n    return result\n\n\ndef search_scoreboard(board, pattern, page=1):\n    if board == \"student\":\n        key_args = {'country': None, 'include_ineligible': False}\n    else:  # default to global\n        key_args = {'country': None, 'include_ineligible': True}\n    scoreboard = get_scoreboard_cache(**key_args)\n    results = search_scoreboard_cache(scoreboard, pattern)\n    start = SCOREBOARD_PAGE_LEN * (page - 1)\n    end = start + SCOREBOARD_PAGE_LEN\n    boardlist = results[start:end]\n    for item in boardlist:\n        item['rank'] = scoreboard.rank(item['key'], reverse=True) + 1\n        item.pop('key')\n    return {\n        'name': board,\n        'pages': math.ceil(len(results) / SCOREBOARD_PAGE_LEN),\n        'start_page': page,\n        'scoreboard': boardlist\n    }\n","sub_path":"picoCTF-web/api/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":19368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"356082437","text":"import os\nimport time\nfrom picamera import PiCamera\nfrom picamera.array import PiRGBArray\nimport cv2\n\n\ndef setup_camera():\n    \"\"\"\n    Prepares the camera. Sets camera resolution.\n    :return:\n    \"\"\"\n    camera = PiCamera()\n    camera.resolution = (1280, 720)\n    camera.vflip = False\n    camera.framerate = 20\n    raw_capture = PiRGBArray(camera, size=(1280, 720))\n    # Sleep is used to let the camera initialize.\n    time.sleep(0.25)\n    return camera, raw_capture\n\nclass Recognizer:\n\n    def __init__(self, user):\n        \"\"\"\n        Initialize object\n        :param user: Firebase user object\n        :type user: user.FirebaseUser\n        \"\"\"\n        self.user = user\n        self.camera, self.raw_capture = PiCamera()\n\n    def capture_training_images(self, name, num_images=30, min_height=100):\n        \"\"\"\n        Method will upload images captured during training\n        \"\"\"\n        local_path = \"./training_images/{0}\".format(name)\n\n        image_count = 0\n        images = []\n        for image in self.camera.capture_continuous(self.raw_capture, format=\"bgr\", use_video_port=True):\n            if image_count > num_images:\n                print(\"Required number of images({0}) capture for {1}.\".format(num_images, name))\n                break\n\n            image = image.array\n            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n            faces = self.detector.detectMultiScale(gray, 1.4, 5)  # this figure needs to be optimised\n            # Grab the first detected face\n            x, y, w, h = faces[0] if len(faces) == 1 else (1, 1, 1, 1)\n            if h > min_height:  # minimum height\n                image_count += 1\n                face = gray[y:y + h, x:x + w]\n                image_name = '{0}_{1}.jpg'.format(name, image_count)\n                image_path = os.path.join(local_path, image_name)\n                cv2.imwrite(image_path, face)\n                images.append(image_path)\n            else:\n                print(\"Please ensure one face is present in frame\")\n            self.raw_capture.truncate(0)\n\n        for image_path in images:\n            self.user.upload_file(image_path)\n\n    def capture_image(self):\n        pass\n\n    def upload_video(self, local_path):\n        \"\"\"\n        Method will upload captured video\n        :param local_path:\n        :return:\n        \"\"\"\n\n","sub_path":"recognizer.py","file_name":"recognizer.py","file_ext":"py","file_size_in_byte":2317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"550745833","text":"import io\nfrom typing import List, Tuple, Union\n\nfrom PIL import Image\nfrom discord_slash import SlashContext\n\nimport discord\n\n\nclass SlashMixin:\n    async def embed(self, ctx: SlashContext,\n                    author: str = None,\n                    description: str = None,\n                    title: str = None,\n                    title_url: str = None,\n                    fields: List[Tuple[str, str]] = None,\n                    thumbnail: str = None,\n                    clr: discord.Colour = discord.Embed.Empty,\n                    image: Union[str, io.BufferedIOBase] = None,\n                    footer: str = None,\n                    not_inline: List[int] = [],\n                    trash_reaction: bool = False):\n        embed = discord.Embed(\n            title=title,\n            description=description,\n            colour=clr,\n            title_url=title_url\n        )\n        if author:\n            embed.set_author(name=author)\n        if image:\n            if isinstance(image, str):\n                embed.set_image(url=image)\n                f = None\n            elif isinstance(image, Image.Image):\n                buf = io.BytesIO()\n                image.save(buf, \"png\")\n                buf.seek(0)\n                f = discord.File(buf, filename=\"image.png\")\n                embed.set_image(url=\"attachment://image.png\")\n            else:\n                image.seek(0)\n                f = discord.File(image, filename=\"image.png\")\n                embed.set_image(url=\"attachment://image.png\")\n        else:\n            f = None\n        if footer:\n            embed.set_footer(text=footer)\n        if thumbnail:\n            embed.set_thumbnail(url=thumbnail)\n        for n, r in enumerate(fields or []):\n            embed.add_field(name=r[0], value=r[1] or \"None\", inline=n not in not_inline)\n        msg = await ctx.channel.send(embed=embed, file=f)\n","sub_path":"aoi/mixins/slash.py","file_name":"slash.py","file_ext":"py","file_size_in_byte":1873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"423191822","text":"#!/usr/bin/env python\n\nimport os\nimport colorsys\nimport itertools\n\ndef rgb_to_hex(gvn_rgb_tuple):\n    return '#%02x%02x%02x' % gvn_rgb_tuple\n\ndef hls_to_rgb(gvn_hls_tuple):\n    gvn_rgb_tuple = colorsys.hls_to_rgb(gvn_hls_tuple[0], gvn_hls_tuple[1], gvn_hls_tuple[2])\n    return gvn_rgb_tuple[0]*255.0, gvn_rgb_tuple[1]*255.0, gvn_rgb_tuple[2]*255.0\n\n# Lists of characters\nclass ColorNames(object):\n    '''Color Names'''\n    def __init__(self):\n        '''Color Names Initialize'''\n        colors = {\n            # Breaking Bad\n            \"WalterWhiteJr\": \"#\",\n            \"HankSchrader\": \"#\",\n            \"MarieSchrader\": \"#\",\n            \"JessePinkman\": \"#\",\n            \"JaneMargolis\": \"#\",\n            \"WalterWhite\": \"#\",\n            \"SkylerWhite\": \"#\",\n            \"StevenGomez\": \"#\",\n            \"DrDelcavoli\": \"#\",\n            \"Carmen\": \"#\",\n            \"Pete\": \"#\",\n            \"Tuco\": \"#\",\n            \"SaulGoodman\": \"#\",\n            \"TedBeneke\": \"#\",\n            \"Krazy8\": \"#\",\n            \"Gretchen\": \"#\",\n            \"NoDoze\": \"#\",\n            \"Gonzo\": \"#\",\n            \"DonaldMargolis\": \"#\",\n            \"Emilio\": \"#\",\n            \"Clovis\": \"#\",\n            \"Ben\": \"#\",\n            \"Gus\": \"#\",\n            \"Tio\": \"#\",\n            \"LaurenPinkman\": \"#\",\n            \"Sac\": \"#\",\n            \"DrVictorBravenec\": \"#\",\n            \"Spooge\": \"#\",\n            \"MikeEhrmantraut\": \"#000000\",\n            # Luther\n            \"JohnLuther\": \"#\",\n            \"AliceMorgan\": \"#\",\n            \"JustinRipley\": \"#\",\n            \"MartinSchenk\": \"#\",\n            \"Deadhead\": \"#\",\n            \"ErinGray\": \"#\",\n            \"MaryDay\": \"#\",\n            \"GeorgeStark\": \"#\",\n            \"ZoeLuther\": \"#\",\n            \"IanReed\": \"#\",\n            \"RoseTeller\": \"#\",\n            \"MarkNorth\": \"#\",\n            \"CarolineJones\": \"#\",\n            \"JennyJones\": \"#\",\n            \"Baba\": \"#\",\n            \"FrankHodge\": \"#\",\n            \"TobyKent\": \"#\",\n            # Underbelly\n            \"AlphonseGangitano\": \"#\",\n            \"CarlWilliams\": \"#\",\n            \"JasonMoran\": \"#\",\n            \"MarkMoran\": \"#\",\n            \"MarioCondello\": \"#\",\n            \"TonyMokbel\": \"#\",\n            \"Mick\": \"#\",\n            \"LewisMoran\": \"#\",\n            \"Munster\": \"#\",\n            \"Benji\": \"#\",\n            \"JudyMoran\": \"#\",\n            \"RobertaWilliams\": \"#\",\n            # Soprano\n            \"TonySoprano\": \"#\",\n            \"DrJenniferMelfi\": \"#\",\n            \"CarmelaSoprano\": \"#\",\n            \"ChristopherMoltisanti\": \"#\",\n            \"CorradoJuniorSoprano\": \"#\",\n            \"SalBonpensiero \": \"#\",             #BigPussy\n            \"SilvioDante \": \"#\",\n            \"PaulGualtieri\": \"#\",               #PaulieWalnuts\n            \"AnthonySopranoJr\": \"#\",\n            \"MeadowSoprano\": \"#\",\n            \"AdrianaLaCerva\": \"#\",\n            \"RichieAprile \": \"#\",\n            \"JaniceSopranoBaccalieri\": \"#\",\n            \"LiviaSoprano\": \"#\",\n            # Mr Robot\n            \"ElliotAlderson\": \"#1f2f5f\",\n            \"TyrellWellick\": \"#\",\n            \"OllieParker\": \"#\",\n            \"Darlene\": \"#\",\n            \"MrRobot\": \"#\",\n            \"ShaylaNico\": \"#\",\n            \"AngelaMoss\": \"#\",\n            \"GideonGoddard\": \"#\",\n            \"Trenton\": \"#\",\n            \"FernandoVera\": \"#\",\n            \"ScottKnowles\": \"#\",\n            \"KristaGordon\": \"#\",\n            \"Romero\": \"#df1f1f\",\n            \"Mobley\": \"#\",\n            # Renegate\n            \"RenoRaines\": \"#\",                  # Vincent Black\n            \"RobertSixkiller\": \"#\",             # Bobby\n            # Simpson\n            # \"Barney\": \"#\",\n            # \"GroundskeeperWillie\": \"#\",\n            # \"Homer\": \"#\",\n            # \"Grampa\": \"#\",\n            # \"MayorQuimby\": \"#\",\n            # \"HansMoleman\": \"#\",\n            # \"Marge\": \"#\",\n            # \"Patty\": \"#\",\n            # \"Selma\": \"#\",\n            # \"Bart\": \"#\",\n            # \"Ralph\": \"#\",\n            # \"ToddFlanders\": \"#\",\n            # \"Lisa\": \"#\",\n            # \"Moe\": \"#\",\n            # \"ChiefWiggum\": \"#\",\n            # \"Apu\": \"#\",\n            # \"Carl\": \"#\",\n            # \"ProfessorFrink\": \"#\",\n            # \"Cletus\": \"#\",\n            # \"DrNick \": \"#\",\n            # \"MrBurns\": \"#\",\n            # \"MrSmithers\": \"#\",\n            # \"NedFlanders\": \"#\",\n            # \"ReverendLovejoy\": \"#\",\n            # \"KentBrockman\": \"#\",\n            # \"DrHibbert\": \"#\",\n            # \"Lenny\": \"#\",\n            # \"PrincipalSkinner\": \"#\",\n            # \"Otto\": \"#\",\n            # \"RainierWolfcastle\": \"#\",\n        }\n        clr_vip = list(colors.items())\n\n        # Divisions\n        d = 16\n        # Uniform divisions\n        u = d/8\n        hd, sd, ld = u*4, u*2, u*2\n        # Units\n        h, s, l = 1.0/hd, 1.0/sd, 1.0/ld\n        # Hue\n        h_lst = [h*i for i in range(hd)]\n        # Saturation\n        s_lst = [s*i for i in range(sd)]\n        # Lightness\n        l_lst = [l*i for i in range(ld)]\n        # Combinations\n        hsl_lst = [h_lst, s_lst, l_lst]\n        hsl_prd_lst = list(itertools.product(*hsl_lst))\n        # RGB list\n        rgb_prd_lst = [hls_to_rgb(i) for i in hsl_prd_lst]\n        # Hex list\n        hex_prd_lst = [rgb_to_hex(i) for i in rgb_prd_lst]\n        # Add white color\n        hex_prd_lst = hex_prd_lst + ['#ffffff']\n        # Remove duplicates\n        hex_prd_lst = set(hex_prd_lst)\n\n        # Match list length\n        while len(clr_vip) < len(hex_prd_lst):\n            # Add items to color list\n            clr_vip.append(('', '#'))\n        while len(clr_vip) > len(hex_prd_lst):\n            # Remove items from color list\n            clr_vip.pop()\n\n        nkn_vip_lst = [i[0] for i in clr_vip]\n        hex_vip_lst = [i[1] for i in clr_vip]\n\n        # Color list\n        bld_clr_vip = []\n        trash = []\n        unassigned = []\n        replaced = []\n        for k, i, p, v in zip(range(len(nkn_vip_lst)), nkn_vip_lst, hex_prd_lst, hex_vip_lst):\n            if v != '#':\n                bld_clr_vip.append((i, v))\n                if p == v:\n                    print('yeah!')\n                else:\n                    unassigned.append((k, i, p, v))     # Unassigned p\n                trash.append(v)\n            elif v == '#' and p not in trash:\n                if i != '':\n                    bld_clr_vip.append((i, p))\n                else:\n                    bld_clr_vip.append(('NoVip'+str(k), p))\n                trash.append(p)\n            else:\n                replaced.append((k, i, p, v))           # Replaced  p\n\n        # Assign unassigned hexadecimal colors to replaced colors\n        for i, j in zip(unassigned, replaced):\n            bld_clr_vip.append((j[1], i[2]))\n\n        # Debug file\n        with open(os.path.dirname(os.path.abspath(__file__))+'\\\\palette.txt', 'w') as text_file:\n            # Write list\n            # http://stackoverflow.com/questions/5214578/python-print-string-to-text-file\n            for i in bld_clr_vip:\n                text_file.write('{0}\\n'.format(str(i)))\n\n        self.clr = bld_clr_vip\n","sub_path":"imatches/core/palette_tv.py","file_name":"palette_tv.py","file_ext":"py","file_size_in_byte":6961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"83917277","text":"\"\"\"Generic functions for validating data formats.\"\"\"\n\nimport sys #sys.maxint\nimport re\n\nimport address_reuse.logger\nlogger = address_reuse.logger\n\n#https://docs.python.org/2/library/stdtypes.html#numeric-types-int-float-long-complex\nMININT = -sys.maxint - 1\n\ndef check_int(the_int):\n    \"\"\"Check integer for troublesome values & throw error if bad.\"\"\"\n    try:\n        int(the_int)\n    except ValueError:\n        raise ValueError('Variable in check_int() is not an integer')\n    if the_int == sys.maxint:\n        raise ValueError('Integer equals the system maximum value')\n    elif the_int == MININT:\n        raise ValueError('Integer equals the system minimum value')\n\ndef check_float(the_float):\n    \"\"\"Check float for troublesome values & throw error if bad.\"\"\"\n    try:\n        float(the_float)\n    except ValueError:\n        raise ValueError('Variable in check_float() is not a float')\n    if the_float == sys.float_info.max:\n        raise ValueError('Float equals the system maximum value')\n    elif the_float == sys.float_info.min:\n        raise ValueError('Float equals the system minimum value')\n\ndef check_str(the_str):\n    \"\"\"Check string for troublesome values & throw error if bad.\"\"\"\n    try:\n        str(the_str)\n    except ValueError:\n        raise ValueError('Variable in check_str() cannot be cast to a string')\n\ndef check_int_and_die(the_int, var_name, caller_name):\n    \"\"\"Check int for troublesome values & stop process if bad after logging.\"\"\"\n    try:\n        check_int(the_int)\n    except ValueError as err:\n        address_reuse.logger.log_and_die('Exceptional value for %s in %s: %s' %\n                                         (var_name, caller_name, str(err)))\n\ndef check_float_and_die(the_float, var_name, caller_name):\n    \"\"\"Check float for troublesome values. If bad, log and stop process.\"\"\"\n    try:\n        check_float(the_float)\n    except ValueError as err:\n        logger.log_and_die('Exceptional value for %s in %s: %s' %\n                           (var_name, caller_name, str(err)))\n\ndef check_str_and_die(the_str, var_name, caller_name):\n    \"\"\"Check string for troublesome values. If bad, log and stop process.\"\"\"\n    try:\n        check_str(the_str)\n    except ValueError as err:\n        logger.log_and_die(\"Cannot cast value '%s' to a string in %s: %s\" %\n                           (var_name, caller_name, str(err)))\n\ndef looks_like_address(the_str):\n    \"\"\"Checks whether string is formatted as plausible Bitcoin address.\"\"\"\n    return _is_match(r\"^1|3\\w{25,34}$\", the_str)\n\ndef looks_like_hex(the_str):\n    \"\"\"Checks whether string is formatted as plausible hex string.\"\"\"\n    return _is_match(\"^[0123456789abcdefABCEDF]+$\", the_str)\n\ndef check_hex_and_die(hex_str, caller_name):\n    \"\"\"If string isn't a plausible hex string, write log and stop process.\"\"\"\n    if not looks_like_hex(hex_str):\n        logger.log_and_die(\"Exceptional value '%s' for hex string in %s\" %\n                           (str(hex_str), caller_name))\n\ndef check_address_and_die(btc_address, caller_name):\n    \"\"\"If string isn't a plausible Bitcoin address, write log & stop process.\"\"\"\n    if not looks_like_address(btc_address):\n        logger.log_and_die(\"Exceptional value '%s' for address in %s\" %\n                           (btc_address, caller_name))\n\ndef _get_matches(regex, string):\n    \"\"\"Get regex match() results.\"\"\"\n    result = re.match(regex, string)\n    return result\n\ndef _is_match(regex, string):\n    \"\"\"Determines whether regex matches string.\"\"\"\n    matches = _get_matches(regex, string)\n    return matches is not None and matches.group() is not None\n","sub_path":"address_reuse/validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":3588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"194244959","text":"import board\nimport adafruit_motorkit\n\nimport time\nimport random\n\nkit = adafruit_motorkit.MotorKit()\n\nMOTORS = [ #      -FAST  -MED   -SLOW  STOP  +SLOW +MED  +FAST\n    (kit.motor1, [-1.00, -0.75, -0.50, None, 0.50, 0.75, 1.00]),\n    (kit.motor2, [-1.00, -0.90, -0.80, None, 0.80, 0.90, 1.00]),\n    (kit.motor3, [-0.95, -0.85, -0.75, None, 0.75, 0.85, 0.95]),\n]\n\nruns = [\n    [(4,10),(3,5),(2,20),(1,30),(3,10),(5,20)], # 9.5s total\n    [(4,10),(6,30)], # 4s total\n    [(2,10),(0,30)], # 4s total\n    [(4,10),(3,5),(2,10),(3,5),(4,10),(3,5),(2,10)], # 5.5s total\n    [(6,30)], # 3s total\n    [(0,30)], # 3s total\n]\n\ndef main_loop(sleep_mode, motor_number):\n    motor, speeds = MOTORS[motor_number]\n    while True:\n        _loop(sleep_mode, motor, speeds)\n        motor.throttle = None\n        while sleep_mode[0]:\n            time.sleep(0.25)\n        \n\ndef _loop(sleep_mode, motor, speeds):        \n    while True:\n        # Abort if the sleep-switch was pressed\n        if sleep_mode[0]:\n            return\n        # Pick an activity profile\n        run = random.choice(runs)\n        for v,delay in run:\n            thr = speeds[v]            \n            motor.throttle = thr\n            for _ in range(delay):\n                if sleep_mode[0]:\n                    return\n                time.sleep(0.1)\n        motor.throttle = None\n        # Pause for a few seconds between runs\n        for _ in range(random.choice([10,15,18,20])):\n            if sleep_mode[0]:\n                return\n            time.sleep(0.1)\n\nif __name__ == '__main__':\n    main_loop(2)","sub_path":"hat/motors.py","file_name":"motors.py","file_ext":"py","file_size_in_byte":1562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"653545658","text":"# -*- coding: utf-8 -*-\n# ---------------------------------------\n# 希尔排序，也称递减增量排序算法，实质是分组插入排序。由 Donald Shell 于1959年提出。希尔排序是非稳定排序算法。\n#\n# 时间 Ο(nlogn) ~ Ο(n*n) 空间 O(1)  不稳定\n\n# 基本原理：将数组列在一个表中并对列分别进行插入排序，重复这过程，不过每次用更长的列（步长更长了，列数更少了）来进行。最后整个表就只有一列即排序完成\n# ---------------------------------------\n\n\ndef shell_sort(array):\n    n = len(array)\n    gap = round(n / 2)\n    while gap > 0:\n        for i in range(gap, n):\n            temp = array[i]\n            j = i\n            while (j >= gap and array[j - gap] > temp):\n                array[j] = array[j - gap]\n                j -= gap\n            array[j] = temp\n        gap = round(gap / 2)\n    return array\n","sub_path":"practise/sort/shell_sort.py","file_name":"shell_sort.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"23503882","text":"from django.test import TestCase\nfrom django.core.exceptions import ValidationError\n\nfrom api import constants\nfrom api.models import (\n    Question,\n    QuestionAggStat,\n    QuestionAnswerEvent,\n    QuestionFeedbackEvent,\n    QuizRelationship,\n    QuizAnswerEvent,\n    QuizFeedbackEvent,\n    DailyStat,\n)\nfrom api.tests.factories import QuestionFactory, QuizFactory\n\n\nclass QuestionModelTest(TestCase):\n    @classmethod\n    def setUpTestData(cls):\n        cls.question_1 = QuestionFactory(answer_correct=\"a\")\n        cls.question_1.agg_stats.answer_count = 5\n        cls.question_1.agg_stats.answer_success_count = 2\n        cls.question_1.agg_stats.like_count = 2\n        cls.question_1.agg_stats.save()\n        QuestionAnswerEvent.objects.create(\n            question_id=cls.question_1.id, choice=\"a\", source=\"question\"\n        )\n        QuestionFeedbackEvent.objects.create(\n            question_id=cls.question_1.id, choice=\"dislike\", source=\"quiz\"\n        )\n        cls.question_rm_1 = QuestionFactory(\n            type=constants.QUESTION_TYPE_QCM_RM, answer_correct=\"ab\"\n        )\n        cls.question_rm_2 = QuestionFactory(\n            type=constants.QUESTION_TYPE_QCM_RM, answer_correct=\"abc\"\n        )\n        cls.question_rm_3 = QuestionFactory(\n            type=constants.QUESTION_TYPE_QCM_RM, answer_correct=\"abcd\"\n        )\n        QuestionAnswerEvent.objects.create(\n            question_id=cls.question_rm_1.id, choice=\"cd\", source=\"question\"\n        )\n        cls.question_vf = QuestionFactory(\n            type=constants.QUESTION_TYPE_VF, answer_correct=\"b\"\n        )\n\n    def test_question_agg_stat_created(self):\n        self.assertEqual(Question.objects.count(), 1 + 3 + 1)\n        self.assertEqual(QuestionAggStat.objects.count(), 1 + 3 + 1)\n        question_agg_stat = QuestionAggStat.objects.first()\n        self.assertEqual(question_agg_stat.question_id, self.question_1.id)\n\n    def test_answer_count(self):\n        self.assertEqual(self.question_1.answer_count_agg, 6)\n        self.assertEqual(self.question_1.answer_success_count_agg, 3)\n        self.assertEqual(self.question_1.answer_success_rate, 50)\n\n    def test_feedback_count(self):\n        self.assertEqual(self.question_1.like_count_agg, 2)\n        self.assertEqual(self.question_1.dislike_count_agg, 1)\n\n    def test_validated_question_must_have_category(self):\n        self.assertRaises(ValidationError, QuestionFactory, category=None)\n\n    def test_validated_question_must_have_correct_choice_fields(self):\n        self.assertRaises(ValidationError, QuestionFactory, type=\"Coucou\")\n        self.assertRaises(ValidationError, QuestionFactory, difficulty=42)\n        self.assertRaises(\n            ValidationError, QuestionFactory, answer_correct=\"La réponse D\"\n        )\n        self.assertRaises(ValidationError, QuestionFactory, validation_status=\"TBD\")\n\n    def test_validated_question_qcm_must_have_one_answer(self):\n        self.assertRaises(ValidationError, QuestionFactory, answer_correct=\"\")\n        self.assertRaises(ValidationError, QuestionFactory, answer_correct=\"ab\")\n        self.assertRaises(ValidationError, QuestionFactory, answer_correct=\"aa\")\n        self.assertRaises(ValidationError, QuestionFactory, answer_correct=\"e\")\n\n    def test_validated_question_qcm_rm_must_have_at_least_one_answer(self):\n        self.assertRaises(\n            ValidationError,\n            QuestionFactory,\n            type=constants.QUESTION_TYPE_QCM_RM,\n            answer_correct=\"\",\n        )\n        self.assertRaises(\n            ValidationError,\n            QuestionFactory,\n            type=constants.QUESTION_TYPE_QCM_RM,\n            answer_correct=\"ba\",\n        )\n        self.assertRaises(\n            ValidationError,\n            QuestionFactory,\n            type=constants.QUESTION_TYPE_QCM_RM,\n            answer_correct=\"aab\",\n        )\n        self.assertRaises(\n            ValidationError,\n            QuestionFactory,\n            type=constants.QUESTION_TYPE_QCM_RM,\n            answer_correct=\"abcde\",\n        )\n\n    def test_validated_question_vf_must_have_specific_answer(self):\n        self.assertRaises(\n            ValidationError,\n            QuestionFactory,\n            type=constants.QUESTION_TYPE_VF,\n            answer_correct=\"\",\n        )\n        self.assertRaises(\n            ValidationError,\n            QuestionFactory,\n            type=constants.QUESTION_TYPE_VF,\n            answer_correct=\"c\",\n        )\n        self.assertRaises(\n            ValidationError,\n            QuestionFactory,\n            type=constants.QUESTION_TYPE_VF,\n            answer_correct=\"ab\",\n        )\n\n    def test_validated_question_vf_must_have_ordered_answers(self):\n        self.assertRaises(\n            ValidationError,\n            QuestionFactory,\n            type=constants.QUESTION_TYPE_VF,\n            has_ordered_answers=False,\n        )\n\n\nclass QuizModelTest(TestCase):\n    @classmethod\n    def setUpTestData(cls):\n        cls.question_1 = QuestionFactory(answer_correct=\"a\")\n        cls.quiz_1 = QuizFactory(name=\"quiz 1\")\n        cls.quiz_1.questions.set([cls.question_1.id])\n        QuestionAnswerEvent.objects.create(\n            question_id=cls.question_1.id, choice=\"a\", source=\"question\"\n        )\n        QuizAnswerEvent.objects.create(quiz_id=cls.quiz_1.id, answer_success_count=1)\n        QuizFeedbackEvent.objects.create(quiz_id=cls.quiz_1.id, choice=\"dislike\")\n\n    def test_answer_count(self):\n        self.assertEqual(self.quiz_1.answer_count_agg, 1)\n\n    def test_feedback_count(self):\n        self.assertEqual(self.quiz_1.like_count_agg, 0)\n        self.assertEqual(self.quiz_1.dislike_count_agg, 1)\n\n    def test_published_quiz_must_have_at_least_one_question(self):\n        self.quiz_not_published = QuizFactory(name=\"quiz not published\")\n        self.quiz_not_published.publish = True\n        self.assertRaises(\n            ValidationError, self.quiz_not_published.save, update_fields=[\"publish\"]\n        )\n\n    def test_published_quiz_must_have_validated_questions(self):\n        self.question_validated = QuestionFactory(answer_correct=\"a\")\n        self.question_not_validated = QuestionFactory(\n            answer_correct=\"a\",\n            validation_status=constants.QUESTION_VALIDATION_STATUS_IN_PROGRESS,\n        )\n        self.quiz_published = QuizFactory(name=\"quiz published\", publish=True)\n        self.quiz_not_published = QuizFactory(name=\"quiz not published\")\n        # pass\n        self.quiz_not_published.questions.set(\n            [self.question_validated, self.question_not_validated]\n        )\n        # fail\n        self.assertRaises(\n            ValidationError,\n            self.quiz_published.questions.set,\n            [self.question_validated, self.question_not_validated],\n        )\n\n\nclass QuizRelationshipModelTest(TestCase):\n    @classmethod\n    def setUpTestData(cls):\n        cls.question_1 = QuestionFactory(answer_correct=\"a\")\n        cls.quiz_1 = QuizFactory(name=\"quiz 1\")\n        cls.quiz_2 = QuizFactory(name=\"quiz 2\")\n        cls.quiz_3 = QuizFactory(name=\"quiz 2\")\n        cls.quiz_relationship = QuizRelationship.objects.create(\n            from_quiz=cls.quiz_1, to_quiz=cls.quiz_2, status=\"suivant\"\n        )\n\n    def test_quiz_relationship_must_have_correct_status(self):\n        self.assertRaises(\n            ValidationError,\n            QuizRelationship.objects.create,\n            from_quiz=self.quiz_1,\n            to_quiz=self.quiz_3,\n            status=\"coucou\",\n        )\n\n    def test_quiz_relationship_must_not_have_same_from_to(self):\n        self.assertRaises(\n            ValidationError,\n            QuizRelationship.objects.create,\n            from_quiz=self.quiz_1,\n            to_quiz=self.quiz_2,\n            status=\"similaire\",\n        )\n\n    def test_quiz_relationship_cannot_have_symmetrical_from_to(self):\n        self.assertRaises(\n            ValidationError,\n            QuizRelationship.objects.create,\n            from_quiz=self.quiz_2,\n            to_quiz=self.quiz_1,\n            status=\"similaire\",\n        )\n\n\nclass DailyStatModelTest(TestCase):\n    @classmethod\n    def setUpTestData(cls):\n        cls.question_1 = QuestionFactory(answer_correct=\"a\")\n        QuestionAnswerEvent.objects.create(\n            question_id=cls.question_1.id, choice=\"a\", source=\"question\"\n        )\n        # 3 daily stats in the same week\n        DailyStat.objects.create(\n            date=\"2020-04-30\", question_answer_count=10, question_feedback_count=5\n        )\n        DailyStat.objects.create(\n            date=\"2020-05-01\", question_answer_count=2, question_feedback_count=1\n        )\n        DailyStat.objects.create(\n            date=\"2020-05-02\", question_answer_count=4, question_feedback_count=2\n        )\n        # 1 daily stats in the next week\n        DailyStat.objects.create(\n            date=\"2020-05-06\", question_answer_count=1, question_feedback_count=1\n        )\n\n    def test_question_answer_count_count(self):\n        self.assertEqual(DailyStat.objects.agg_count(\"question_answer_count\"), 17)\n        self.assertEqual(DailyStat.objects.agg_count(\"question_feedback_count\"), 9)\n        self.assertEqual(DailyStat.objects.agg_count(\"quiz_answer_count\"), 0)\n        self.assertEqual(\n            DailyStat.objects.agg_count(\n                \"question_answer_count\", since=\"week\", week_or_month_iso_number=18\n            ),\n            16,\n        )\n        self.assertEqual(\n            DailyStat.objects.agg_count(\n                \"question_answer_count\", since=\"month\", week_or_month_iso_number=4\n            ),\n            10,\n        )\n        self.assertEqual(\n            DailyStat.objects.agg_count(\n                \"question_feedback_count\", since=\"month\", week_or_month_iso_number=5\n            ),\n            4,\n        )\n        agg_timeseries_per_day_1 = DailyStat.objects.agg_timeseries(\n            field=\"question_answer_count\", scale=\"day\"\n        )\n        self.assertEqual(len(agg_timeseries_per_day_1), 4)\n        self.assertEqual(agg_timeseries_per_day_1[0][\"day\"], \"2020-04-30\")\n        agg_timeseries_per_day_2 = DailyStat.objects.agg_timeseries(\n            field=\"question_answer_count\", scale=\"day\", since_date=\"2020-05-02\"\n        )\n        self.assertEqual(len(agg_timeseries_per_day_2), 2)\n        self.assertEqual(agg_timeseries_per_day_2[0][\"day\"], \"2020-05-02\")\n        agg_timeseries_per_month = DailyStat.objects.agg_timeseries(\n            field=\"question_answer_count\", scale=\"month\"\n        )\n        self.assertEqual(len(agg_timeseries_per_month), 2)\n        self.assertEqual(agg_timeseries_per_month[0][\"day\"], \"2020-04-01\")\n        self.assertEqual(agg_timeseries_per_month[0][\"y\"], 10)\n        self.assertEqual(agg_timeseries_per_month[1][\"day\"], \"2020-05-01\")\n        self.assertEqual(agg_timeseries_per_month[1][\"y\"], 7)\n","sub_path":"api/tests/test_models.py","file_name":"test_models.py","file_ext":"py","file_size_in_byte":10771,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"230685661","text":"import sys\nfrom abc import ABC\nfrom pathlib import Path\nfrom typing import List, Sequence, Union, cast\n\nfrom packaging.requirements import Requirement\n\nfrom tox.config.sets import ConfigSet\nfrom tox.execute.api import Outcome\nfrom tox.execute.local_sub_process import LocalSubProcessExecutor\n\nfrom ..api import Python\n\n\nclass VirtualEnv(Python, ABC):\n    def __init__(self, conf: ConfigSet, core: ConfigSet, options):\n        super().__init__(conf, core, options, LocalSubProcessExecutor())\n\n    def create_python_env(self):\n        core_cmd = self.core_cmd()\n        env_dir = cast(Path, self.conf[\"env_dir\"])\n        cmd = core_cmd + (\"--clear\", env_dir)\n        result = self.execute(cmd=cmd, allow_stdin=False)\n        result.assert_success(self.logger)\n\n    def core_cmd(self):\n        core_cmd = (\n            sys.executable,\n            \"-m\",\n            \"virtualenv\",\n            \"--no-download\",\n            \"--python\",\n            self.py_info.interpreter.system_executable,\n        )\n        return core_cmd\n\n    def paths(self) -> List[Path]:\n        \"\"\"Paths to add to the executable\"\"\"\n        # we use the original executable as shims may be somewhere else\n        return list({self.py_info.bin_dir, self.py_info.script_dir})\n\n    def python_cache(self):\n        base_python = self.py_info.interpreter\n        return {\"version_info\": list(base_python.version_info), \"executable\": base_python.executable}\n\n    def install_python_packages(\n        self, packages: List[Union[Requirement, Path]], no_deps: bool = False, develop=False, force_reinstall=False,\n    ) -> None:\n        if packages:\n            install_command = self.install_command(develop, force_reinstall, no_deps, packages)\n            result = self.perform_install(install_command)\n            result.assert_success(self.logger)\n\n    def perform_install(self, install_command: Sequence[str]) -> Outcome:\n        return self.execute(cmd=install_command, allow_stdin=False)\n\n    # noinspection PyMethodMayBeStatic\n    def install_command(self, develop, force_reinstall, no_deps, packages):\n        install_command = [\"python\", \"-m\", \"pip\", \"--disable-pip-version-check\", \"install\"]\n        if develop is True:\n            install_command.append(\"-e\")\n        if no_deps:\n            install_command.append(\"--no-deps\")\n        if force_reinstall:\n            install_command.append(\"--force-reinstall\")\n        install_command.extend(str(i) for i in packages)\n        return install_command\n","sub_path":"Exercise Tutorials/Pycon-Talk-Click-Example-Tutorial/venv/lib/python3.8/site-packages/tox/tox_env/python/virtual_env/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":2468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"63200905","text":"import sys\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport scipy.stats as st\nimport matplotlib.pyplot as plt\n\npd.set_option('max_columns', 1000)\npd.set_option('max_info_columns', 1000)\npd.set_option('expand_frame_repr', False)\npd.set_option('display.max_rows', 40000)\npd.set_option('max_colwidth', 4000)\npd.set_option('display.float_format', lambda x: '%.3f' % x)\n\n\ndef data_in():\n    return pd.read_csv('/Users/thowe/Downloads/RawData.csv').\\\n        assign(time=lambda x: pd.to_datetime(x['TransDateTime'])).\\\n        sort_values('time', ascending=True)\n\ndef m(data, n, bootstrap_n):\n    df_n = np.empty(0)\n    for _ in range(bootstrap_n):\n        df_n = np.append(df_n, data.sample(n=n, replace=True).sum())\n    return np.mean(np.abs(df_n - np.mean(df_n)))\n\n\n# def kappa(data, n, n0=1, bootstrap_n=1_000_000, rescale=False):\n    # if rescale:\n    #     data = data * (np.sqrt(2 / np.pi) / m(data, 1, bootstrap_n))\ndef kappa(data, n, n0=1, bootstrap_n=1_000_000, rescale=False):\n    m_n = m(data, n, bootstrap_n)\n    m_n0 = m(data, n0, bootstrap_n)\n\n    return 2 - (np.log(n) - np.log(n0)) / np.log(m_n / m_n0)\n\n\ndef n_v(k_1, n_g):\n    return n_g ** (-1 / (k_1 - 1))\n\n\ndef k1_and_n_v_estimate(df):\n    # find kappa_1\n    k = kappa(df, n=2, n0=1)\n    print(k)  # ~ 0.7865784097209034, 0.8067374433723662\n\n    # find n_v\n    n = n_v(k, 30)\n    print(n)  # ~ 8_339_549, 43_962_297\n\n\ndef kappa_convergence_plot(df, n_lst):\n    kappa_lst = [kappa(df, n=n, n0=1) for n in n_lst]\n\n    sns.set_style(\"whitegrid\")\n    fig = plt.figure(figsize=(20, 8))\n    ax = fig.add_subplot(1, 1, 1)\n    ax.scatter(n_lst, kappa_lst)\n    ax.set_ylim([0, .9])\n    plt.savefig('/Users/thowe/Downloads/kappa_convergence_plot.png')\n    plt.show()\n\n\ndef main():\n    df = data_in()\n    print(df.head())\n    print(df.info())\n    print(df.shape)  # 2_110_978\n\n    k1_and_n_v_estimate(df['RespTime'])\n    # kappa_convergence_plot(df['RespTime'], range(2, 1003, 50))\n\nif __name__ == '__main__':\n    main()\n","sub_path":"projects/nic/kappa.py","file_name":"kappa.py","file_ext":"py","file_size_in_byte":1994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"52997906","text":"\"\"\"시뮬레이터\n\nSimulationOperator를 사용해서 시뮬레이션을 컨트롤하는 모듈\n\"\"\"\nimport signal\nimport threading\nimport requests\nfrom . import (\n    LogManager,\n    Analyzer,\n    SimulationTrader,\n    SimulationDataProvider,\n    StrategyBuyAndHold,\n    StrategySma0,\n    SimulationOperator,\n)\n\n\nclass Simulator:\n    \"\"\"smtm 시뮬레이터\"\"\"\n\n    MAIN_STATEMENT = \"input command (h:help): \"\n\n    def __init__(self, end=None, count=100, interval=2, strategy=0):\n        self.logger = LogManager.get_logger(\"Simulator\")\n        self.__terminating = False\n        self.end = \"2020-12-20T16:23:00\"\n        self.count = 100\n        self.interval = interval\n        self.operator = None\n        self.strategy = int(strategy)\n        self.budget = 50000\n        self.is_initialized = False\n        self.command_list = []\n        self.create_command()\n\n        if self.strategy != 0 and self.strategy != 1:\n            self.strategy = 0\n            print(f\"invalid strategy: {self.strategy}, replaced with 0\")\n\n        if self.end is not None:\n            self.end = self.end.replace(\"T\", \" \")\n\n        if count is not None:\n            self.count = count\n\n        if interval is not None:\n            self.interval = float(self.interval)\n\n    def create_command(self):\n        self.command_list = [\n            {\n                \"guide\": \"h, help          print command info\",\n                \"cmd\": \"help\",\n                \"short\": \"h\",\n                \"need_value\": False,\n                \"action\": self.print_help,\n            },\n            {\n                \"guide\": \"r, run           start running simulation\",\n                \"cmd\": \"run\",\n                \"short\": \"r\",\n                \"need_value\": False,\n                \"action\": self.start,\n            },\n            {\n                \"guide\": \"s, stop          stop running simulation\",\n                \"cmd\": \"stop\",\n                \"short\": \"s\",\n                \"need_value\": False,\n                \"action\": self._stop,\n            },\n            {\n                \"guide\": \"t, terminate     terminate simulator\",\n                \"cmd\": \"terminate\",\n                \"short\": \"t\",\n                \"need_value\": False,\n                \"action\": self.terminate,\n            },\n            {\n                \"guide\": \"q, query         query and print current state or return score\",\n                \"cmd\": \"query\",\n                \"short\": \"q\",\n                \"need_value\": True,\n                \"value_guide\": \"input query target (ex. state, score, result) :\",\n                \"action\": self._on_query_command,\n            },\n            {\n                \"guide\": \"e, end           set simulation period end datetime\",\n                \"cmd\": \"end\",\n                \"short\": \"e\",\n                \"need_value\": True,\n                \"value_guide\": \"input simulation period end datetime(ex. 2020-12-20T18:00:00) :\",\n                \"action\": self._set_end,\n            },\n            {\n                \"guide\": \"c, count         set simulation count\",\n                \"cmd\": \"count\",\n                \"short\": \"c\",\n                \"need_value\": True,\n                \"value_guide\": \"input simulation count (ex. 100) :\",\n                \"action\": self._set_count,\n            },\n            {\n                \"guide\": \"int, interval    set simulation interval\",\n                \"cmd\": \"interval\",\n                \"short\": \"int\",\n                \"need_value\": True,\n                \"value_guide\": \"input simulation interval in seconds (ex. 0.5) :\",\n                \"action\": self._set_interval,\n            },\n            {\n                \"guide\": \"b, budget        set simulation budget\",\n                \"cmd\": \"budget\",\n                \"short\": \"b\",\n                \"need_value\": True,\n                \"value_guide\": \"input starting budget (ex. 70000) :\",\n                \"action\": self._set_budget,\n            },\n            {\n                \"guide\": \"st, strategy     set strategy\",\n                \"cmd\": \"strategy\",\n                \"short\": \"st\",\n                \"need_value\": True,\n                \"value_guide\": \"input starting budget (ex. 70000) :\",\n                \"action\": self._set_strategy,\n            },\n            {\n                \"guide\": \"l, log           set stream log level\",\n                \"cmd\": \"log\",\n                \"short\": \"l\",\n                \"need_value\": True,\n                \"value_guide\": \"set stream log level (CRITICAL=50, ERROR=40, WARNING=30, INFO=20, DEBUG=10) :\",\n                \"action\": self._set_log_level,\n            },\n            {\n                \"guide\": \"i, initialize    initialize simulation\",\n                \"cmd\": \"initialize\",\n                \"short\": \"i\",\n                \"need_value\": False,\n                \"action\": self.initialize,\n            },\n        ]\n\n    def main(self):\n        \"\"\"main 함수\"\"\"\n\n        self.logger.info(f\"end: {self.end}\")\n        self.logger.info(f\"count: {self.count}\")\n        self.logger.info(f\"interval: {self.interval}\")\n        signal.signal(signal.SIGINT, self.stop)\n        signal.signal(signal.SIGTERM, self.stop)\n\n        while not self.__terminating:\n            try:\n                key = input(self.MAIN_STATEMENT)\n                self.on_command(key)\n            except EOFError:\n                break\n\n    def print_help(self):\n        \"\"\"가이드 문구 출력\"\"\"\n        print(\"command list =================\")\n        for item in self.command_list:\n            print(item[\"guide\"])\n\n    def on_command(self, key):\n        \"\"\"커맨드 처리\"\"\"\n        value = None\n        for cmd in self.command_list:\n            if cmd[\"cmd\"] == key or cmd[\"short\"] == key:\n                if cmd[\"need_value\"]:\n                    value = input(cmd[\"value_guide\"])\n                self.execute_command(key, value)\n                return\n        print(\"invalid command\")\n\n    def execute_command(self, command, value):\n        \"\"\"가이드 문구 출력\"\"\"\n        for cmd in self.command_list:\n            if cmd[\"cmd\"] == command or cmd[\"short\"] == command:\n                if cmd[\"need_value\"]:\n                    cmd[\"action\"](value)\n                else:\n                    cmd[\"action\"]()\n                return\n        print(\"invalid command\")\n\n    def _on_query_command(self, value):\n        \"\"\"가이드 문구 출력\"\"\"\n        if value == \"state\":\n            print(self.operator.state)\n        elif value == \"score\":\n            self._get_score()\n        elif value == \"result\":\n            print(self.operator.get_trading_results())\n\n    def _set_end(self, value):\n        self.end = value.replace(\"T\", \" \")\n\n    def _set_count(self, value):\n        next_value = int(value)\n        if next_value > 0:\n            self.count = next_value\n\n    def _set_interval(self, value):\n        next_value = float(value)\n        if next_value > 0:\n            self.interval = next_value\n\n    def _set_budget(self, value):\n        next_value = int(value)\n        if next_value > 0:\n            self.budget = next_value\n\n    def _set_strategy(self, value):\n        if value == \"0\":\n            self.strategy = 0\n        elif value == \"1\":\n            self.strategy = 1\n\n    def _set_log_level(self, value):\n        LogManager.set_stream_level(int(value))\n\n    def _get_score(self):\n        def print_score_and_main_statement(score):\n            print(\"\")\n            print(\"current score ==========\")\n            print(score)\n            print(self.MAIN_STATEMENT)\n\n        self.operator.get_score(print_score_and_main_statement)\n\n    def initialize(self):\n        \"\"\"시뮬레이션 초기화\"\"\"\n        if self.strategy == 0:\n            strategy = StrategyBuyAndHold()\n        else:\n            strategy = StrategySma0()\n        self.operator = SimulationOperator()\n\n        print(\"##### simulation is intialized #####\")\n        print(f\"end: {self.end}\")\n        print(f\"count: {self.count}\")\n        print(f\"interval: {self.interval}\")\n        print(\"====================================\")\n\n        self.operator.initialize_simulation(\n            requests,\n            threading,\n            SimulationDataProvider(),\n            strategy,\n            SimulationTrader(),\n            Analyzer(),\n            end=self.end,\n            count=self.count,\n            budget=self.budget,\n        )\n        self.operator.set_interval(self.interval)\n        self.is_initialized = True\n\n    def start(self):\n        \"\"\"시뮬레이션 시작, 재시작\"\"\"\n        if self.is_initialized is not True:\n            print(\"Not initialized\")\n            return\n\n        if self.operator.start() is not True:\n            print(\"Fail operator start\")\n            return\n\n    def stop(self, signum, frame):\n        \"\"\"시뮬레이터 중지\"\"\"\n        self._stop()\n        self.__terminating = True\n        print(f\"Receive Signal {signum} {frame}\")\n        print(\"Stop Singing\")\n\n    def _stop(self):\n        if self.operator is not None:\n            self.operator.stop()\n\n    def terminate(self):\n        print(\"Terminating......\")\n        self._stop()\n        self.__terminating = True\n        print(\"Good Bye~\")\n","sub_path":"smtm/simulator.py","file_name":"simulator.py","file_ext":"py","file_size_in_byte":9081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"92951515","text":"# Import app code\nfrom app.models.role import Role\n\n\ndef get_role(name):\n    try:\n        return Role.objects.get(name=name)\n    except Role.DoesNotExist:\n        return None\n\n\ndef create_or_get_role(name):\n    role = get_role(name)\n    if role is None:\n        role = Role(name=name)\n        role.save()\n\n    return role\n","sub_path":"{{cookiecutter.project_slug}}/backend/app/app/db/roles.py","file_name":"roles.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"376356601","text":"import os, codecs\nimport argparse\n\nPATCH = b\"PATCH\"\nEOF = b\"EOF\"\n\nclass NoPatchHeaderException(Exception):\n    pass\n\nclass NoEOFException(Exception):\n    pass\n\nclass BytesBuffer(object):\n    __slots__ = 'data', 'seek'\n\n    @staticmethod\n    def bytes_to_int(data):\n        return int(codecs.encode(data, \"hex\"), 16)\n\n    def __init__(self, data):\n        self.data = data\n        self.seek = 0\n\n    def end_of_content(self):\n        return self.seek < len(self.data)\n\n    def get_u8(self):\n        global seek, patch_content\n        value = int(self.data[self.seek])\n        self.seek += 1\n        return value \n\n    def get_u16(self):\n        value = self.bytes_to_int(self.data[self.seek:self.seek+2])\n        self.seek += 2\n        return value\n\n    def get_u24(self):\n        value = self.bytes_to_int(self.data[self.seek:self.seek+3])\n        self.seek += 3\n        return value\n\ndef retrieve_args():\n    parser = argparse.ArgumentParser(description='Process some integers.')\n    parser.add_argument('-p', '--patch', required=True, nargs=\"?\", type=str, help='The .ips patch file to be applied.')\n    parser.add_argument('-b', '--base', required=True, nargs=\"?\", type=str, help='The base rom to patch.')\n    parser.add_argument('-o', '--output', type=str, help='The name of the output rom.')\n\n    parser.add_argument('-f', '--force', help='Force patch.', action=\"store_true\")\n\n    args = parser.parse_args()\n    return args\n\nif __name__ == \"__main__\":\n    args = retrieve_args()\n\n    if not os.path.exists(args.patch):\n        raise Exception(\"%s does not exist.\" % args.patch)\n\n    if not os.path.exists(args.base):\n        raise Exception(\"%s does not exist.\" % args.base)\n\n    if not args.force:\n        if not args.patch.endswith(\".ips\"):\n            raise Exception(\"Not a valid '.ips' (Wrong extension)\")\n\n    patch_file = open(args.patch, \"rb\")\n    rom_file = open(args.base, \"rb\")\n\n    patch_content = bytearray(patch_file.read())\n    rom_content = bytearray(rom_file.read())\n\n    if not args.force:\n        if patch_content[:5] != PATCH:\n            raise NoPatchHeaderException(\"Invalid Header ('PATCH' bytes not found)\") \n\n        if patch_content[-3:] != EOF:\n            raise NoEOFException(\"Invalid Header ('EOF' bytes not found)\")\n\n    patch_content = patch_content[5:-3]\n\n    bytes_buffer = BytesBuffer(patch_content)\n\n    def write_file_contents(loc, data):\n        global rom_content\n        rom_content[loc] = data\n\n    while bytes_buffer.end_of_content():\n        loc = bytes_buffer.get_u24() # Patch content address\n        size  = bytes_buffer.get_u16() # Predefined size of changed content\n\n        if size == 0: # Start of repeated data\n            size = bytes_buffer.get_u16()\n            repeat = bytes_buffer.get_u8()\n\n            for i in range(size):\n                write_file_contents(loc + i, repeat)\n\n        else:\n            for i in range(size):\n                write_file_contents(loc + i, bytes_buffer.get_u8())\n\n    patch_file.close()\n    rom_file.close()\n\n    patched_rom = open(args.output or \"patched_rom.gba\", \"wb+\")\n    patched_rom.write(rom_content)\n    patched_rom.close()\n\n    print(\"Done.\")","sub_path":"ips.py","file_name":"ips.py","file_ext":"py","file_size_in_byte":3144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"105339103","text":"from django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom django.shortcuts import render, get_object_or_404, HttpResponseRedirect\nfrom django.core.urlresolvers import reverse\nfrom django.http import Http404\n\nfrom publications.models import Publication, PublicationLike\nfrom publications.forms import PublicationForm\nfrom publications.serializers import PublicationSerializer, CreatePublicationSerializer\n\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\n\nfrom utils import gen_page_list\n\n\ndef publications(request):\n    form = PublicationForm()\n    if request.method == \"POST\":\n        form = PublicationForm(request.POST, request.FILES)\n        if form.is_valid():\n            data = form.cleaned_data\n            Publication.objects.create(title=data.get(\"title\"),\n                                       body=data.get(\"body\"),\n                                       image=data.get(\"image\"),\n                                       author=request.user)\n    publications = Publication.objects.all()\n    paginator = Paginator(publications, 10)\n    page = request.GET.get('page', 1)\n    try:\n        publications = paginator.page(page)\n    except PageNotAnInteger:\n        publications = paginator.page(1)\n    except EmptyPage:\n        publications = paginator.page(paginator.num_pages)\n\n    page_nums = gen_page_list(int(page), paginator.num_pages)\n    # [1, 2, 3, '...', 123, 124]\n    return render(request, \"publications.html\", {\"publications\": publications,\n                                                 \"form\": form,\n                                                 \"page_nums\": page_nums})\n\n\ndef single_publication(request, publication_id):\n    publication = get_object_or_404(Publication, pk=publication_id)\n    return render(request, \"single_publication.html\", {\"publication\": publication})\n\n\ndef like_single_publication(request, publication_id):\n    if request.user.is_authenticated():\n        publication = get_object_or_404(Publication, pk=publication_id)\n        if PublicationLike.objects.filter(publication=publication, user=request.user).exists():\n            PublicationLike.objects.filter(publication=publication, user=request.user).delete()\n        else:\n            PublicationLike.objects.create(publication=publication, user=request.user)\n        return HttpResponseRedirect(reverse(\"single_publication\", kwargs={\"publication_id\": publication_id}))\n    else:\n        return HttpResponseRedirect(reverse(\"login\"))\n\n\nclass GetSinglePublicationView(APIView):\n    serializer_class = PublicationSerializer\n\n    def get_object(self, publication_id):\n        try:\n            return Publication.objects.get(pk=publication_id)\n        except Publication.DoesNotExist:\n            raise Http404\n\n    def get(self, request, publication_id):\n        publication = self.get_object(publication_id)\n        return Response(self.serializer_class(publication).data)\n\n    def delete(self, request, publication_id):\n        publication = self.get_object(publication_id)\n        publication.delete()\n        return Response({\"success\": True})\n\n\nclass CreatePublicationView(APIView):\n    serializer_class = CreatePublicationSerializer\n\n    def post(self, request):\n        serializer = self.serializer_class(data=request.data)\n        if serializer.is_valid():\n            print(serializer.validated_data)\n            return Response({\"qwe\": \"asd\"})\n        else:\n            return Response(serializer.errors)","sub_path":"publications/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"360944988","text":"\"\"\"\nTests for EnvironmentResource api.\n\n\"\"\"\n\nfrom tests import case\n\n\n\nclass EnvironmentResourceTest(case.api.ApiTestCase):\n\n    @property\n    def factory(self):\n        \"\"\"The model factory for this object.\"\"\"\n        return self.F.EnvironmentFactory\n\n\n    @property\n    def resource_name(self):\n        return \"environment\"\n\n\n    def test_environment_list(self):\n        \"\"\"Get a list of existing environments\"\"\"\n        envs = self.F.EnvironmentFactory.create_full_set(\n                {\"OS\": [\"OS X\"]})\n        element = envs[0].elements.get()\n        category = element.category\n\n        res = self.get_list()\n\n        act_meta = res.json[\"meta\"]\n        exp_meta = {\n            \"limit\" : 20,\n            \"next\" : None,\n            \"offset\" : 0,\n            \"previous\" : None,\n            \"total_count\" : 1,\n            }\n\n        self.assertEquals(act_meta, exp_meta)\n\n        act_objects = res.json[\"objects\"]\n        exp_objects = []\n\n        exp_objects.append({\n            u'elements': [{\n                u'category': {\n                    u'id': unicode(category.id),\n                    u'name': u'OS',\n                    u'resource_uri': unicode(self.get_detail_url(\n                        \"category\",\n                        category.id,\n                        )),\n                    },\n                u'id': unicode(element.id),\n                u'name': u'OS X',\n                u'resource_uri': unicode(self.get_detail_url(\n                    \"element\",\n                    element.id,\n                    )),\n                }],\n            u'id': unicode(envs[0].id),\n            u'resource_uri': unicode(self.get_detail_url(\n                \"environment\",\n                envs[0].id,\n                )),\n            })\n\n        self.maxDiff = None\n        self.assertEqual(exp_objects, act_objects)\n","sub_path":"tests/model/environments/api/test_environment_resource.py","file_name":"test_environment_resource.py","file_ext":"py","file_size_in_byte":1826,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"158730908","text":"from board import Board\r\nfrom player import Player\r\nfrom random_player import RandomPlayer\r\nfrom user_player import UserPlayer\r\nfrom zero_player import ZeroPlayer\r\nimport json\r\nimport unittest\r\n\r\n\r\nclass Game:\r\n  \"\"\"Class that holds all information about a tic-tac-toe game\r\n\r\n  Members: \r\n    players {list of (Player)} -- The list of Player objects in order to play the game\r\n    x_dist {integer} -- The number of tiles in the X direction for the Board\r\n    y_dist {integer} -- The number of tiles in the Y direction for the Board\r\n    turn_number {integer} -- The turn number of the game, starts at 0\r\n    board {Board} -- The current Board in the game\r\n    board_history {list of (Board)} -- The list of Board objects representing the game's history\r\n    num_to_win {integer} -- The number of tiles horizontally or vertically a player needs to win\r\n\r\n  Methods: \r\n    __init__(list of (Player), integer, integer) -- Sets up basing game information\r\n    play_game() -- Plays the game to completion\r\n    resume_game() -- Resumes the game, starting \r\n    _turn_cycle({integer}) -- plays a single turn\r\n    _increment_turn() -- Increments the turn counter\r\n    _end_game() -- Does additional work when the game reaches an end state\r\n    is_game_over() -- Returns true if the game is finished, false if the game is not\r\n    get_current_player() -- Gets the id of the current player\r\n    get_current_board() -- Gets the current board\r\n    get_player({integer}) -- Gets the player object given the id of the player\r\n    to_json() -- Returns the game encoded as a json object\r\n\r\n  \"\"\"\r\n\r\n  def __init__(self, players, x_dist, y_dist, num_to_win, turn_start=0, max_turns=1000,\\\r\n     winner=-1, brd=None, board_history=None):\r\n    \"\"\"Creates a game that can be started from start to finish\r\n\r\n    Creates the game board and sets turn counter to 0\r\n\r\n    Optional parameters can be given to recreate any given game\r\n\r\n    Arguments: \r\n      players {list of (Player)} -- The players given in order to play the tic-tac-toe game\r\n      x_dist {integer} -- The number of tiles in the X direction\r\n      y_dist {integer} -- The number of tiles in the Y direction\r\n      num_to_win {integer} -- The number of tiles in a row or horizontally one needs to win\r\n      turn_start {integer} -- Which turn the game is considered to have started on, default 0\r\n      max_turns {integer} -- The maximum number of turns this game can go on for, default 1000\r\n      winner {integer} -- If the game is already considered to be finished, default -1\r\n      brd {Board} -- The current of the game, default new board\r\n      board_history {list of (Board)} -- List of the histories of the board\r\n\r\n    \"\"\"\r\n    self.players = players\r\n    self.x_dist = x_dist\r\n    self.y_dist = y_dist\r\n\r\n    self.turn_number = turn_start\r\n    if brd == None:\r\n      self.board = Board(x_dist, y_dist)\r\n    else:\r\n      self.board = brd\r\n    if board_history == None:\r\n      self.board_history = [self.board]\r\n    else: \r\n      self.board_history = board_history\r\n    self.num_to_win = num_to_win\r\n\r\n    self.max_turns = max_turns\r\n\r\n    self.winner = winner\r\n\r\n  def __eq__(self, obj):\r\n    \"\"\"Games are equivalent if all of it's parameters are equal.\r\n\r\n    Arguments:\r\n      obj {object} -- The object we are comparing to self\r\n\r\n    \"\"\"\r\n\r\n    #Types must be the same\r\n    if type(self) != type(obj):\r\n      return False\r\n\r\n    #The number of players must be the same\r\n    if len(self.players) != len(obj.players):\r\n      return False\r\n\r\n    #Players must be the same\r\n    for i in range(len(self.players)):\r\n      if self.players[i] != obj.players[i]:\r\n        return False\r\n\r\n    #The number to win must be the same \r\n    if self.num_to_win != obj.num_to_win:\r\n      return False\r\n\r\n    #The turn number must be the same\r\n    if self.turn_number != obj.turn_number:\r\n      return False\r\n\r\n    #The max turn numbers must be the same\r\n    if self.max_turns != obj.max_turns:\r\n      return False\r\n\r\n    #The winner must be the same\r\n    if self.winner != obj.winner:\r\n      return False\r\n\r\n    #The current board must be the same\r\n    if self.board != obj.board:\r\n      return False\r\n\r\n    #The board histories must be the same length\r\n    if len(self.board_history) != len(obj.board_history):\r\n      return False\r\n\r\n    #The histories must be the same\r\n    for i in range(len(self.board_history)):\r\n      if self.board_history[i] != obj.board_history[i]:\r\n        return False\r\n\r\n    #If all these conditions are met then we return true\r\n    return True\r\n\r\n\r\n  #####################         ##################### \r\n  #                    Play Game                    #\r\n  #####################         ##################### \r\n\r\n  def play_game(self):\r\n    \"\"\"Plays the tic-tac-toe game to completion\r\n\r\n    Returns:\r\n      {JSON} -- JSON object representing this game\r\n\r\n    \"\"\" \r\n\r\n    while True:\r\n      #Check if we should wait\r\n      cur_player_id = self.get_current_player()\r\n      cur_player = self.get_player(cur_player_id)\r\n\r\n      #Pause game execution if stumble upon a user player\r\n      if cur_player.pause_execution():\r\n        return self.to_json()\r\n\r\n      turn_result = self._turn_cycle()\r\n\r\n      #If the turn result is not 0, return it\r\n      if turn_result != 0:\r\n        return self.to_json()\r\n\r\n  def resume_game(self):\r\n    \"\"\"Resumes the game from where it was left off, assuming the next player already has their action\r\n\r\n    Returns:\r\n      {JSON} -- JSON object representing this game\r\n\r\n    \"\"\"\r\n\r\n    turn_result = self._turn_cycle()\r\n    if turn_result != 0:\r\n      return self.to_json()\r\n\r\n    return self.play_game()\r\n\r\n  def _turn_cycle(self):\r\n    \"\"\"Does a singluar turn cycle\r\n\r\n    Returns:\r\n      {integer} -- id of the player who won, -1 if tie, 0 if no one has won yet, >0 for player id who won\r\n\r\n    \"\"\"\r\n\r\n    #Get current player\r\n    cur_player = self.get_current_player()\r\n\r\n    #Get board states for current player\r\n    choices = self.board.get_states(cur_player)\r\n\r\n    #Update board state\r\n    self.board = choices[self.get_player(cur_player).choose_state(choices)]\r\n\r\n    #Make sure you have the history, original board is added, so we can do it afterwards\r\n    self.board_history.append(self.board)\r\n\r\n    #Check for win or tie\r\n    if self.board.check_win(self.num_to_win, cur_player):\r\n      self._end_game(cur_player)\r\n      return cur_player\r\n    if self.board.check_tie():\r\n      self._end_game()\r\n      return -1\r\n    if self.turn_number >= self.max_turns:\r\n      self._end_game()\r\n      return -1\r\n\r\n    #Incriment turn counter if the game has not ended\r\n    self._increment_turn()\r\n\r\n    return 0\r\n\r\n  def _increment_turn(self):\r\n    \"\"\"Increments the turn counter \r\n    \r\n    \"\"\"\r\n\r\n    self.turn_number += 1\r\n\r\n  def _end_game(self, winner_id=0): \r\n    \"\"\"Called when the game has reached a terminal state\r\n\r\n    Arguments:\r\n      winner_id {integer} -- The id of the winning player, None if game is a tie\r\n\r\n    \"\"\"\r\n    if winner_id == 0:\r\n      # print(\"The game was a tie!\")\r\n      pass\r\n    else:\r\n      # print(\"{0} has won the game!\".format(winner_id))\r\n      pass\r\n    self.winner = winner_id\r\n\r\n  #####################         ##################### \r\n  #                    Utilities                    #\r\n  #####################         ##################### \r\n\r\n  def is_game_over(self):\r\n    \"\"\"Checks if the game has been played to completion\r\n\r\n    Returns:\r\n      {Boolean} -- True if game is over, False if it is not\r\n\r\n    \"\"\"\r\n\r\n    if self.winner != 0:\r\n      return True\r\n\r\n    return False\r\n\r\n  def get_current_player(self):\r\n    \"\"\"Returns the id of the current player \r\n\r\n    Returns:\r\n      {integer} -- The id of the current player\r\n    \r\n    \"\"\"\r\n\r\n    return self.players[(self.turn_number) % len(self.players)].get_id()\r\n\r\n  def get_player(self, player_id):\r\n    \"\"\"Returns the player given the player's id\r\n\r\n    Arguments: \r\n      player_id {integer} -- The id of the Player object we want\r\n\r\n    Returns: \r\n      {Player} -- The Player object we seek to return\r\n\r\n    \"\"\"\r\n\r\n    for player in self.players:\r\n      if player.player_id == player_id:\r\n        return player\r\n\r\n    raise Exception(\"A player with the given id {0} was not found in the player list\".format(player_id))\r\n\r\n  def get_history(self):\r\n    \"\"\"Returns the history of the board currently\r\n\r\n    Returns:\r\n      {list of (Board)} -- The history of the board\r\n\r\n    \"\"\"\r\n\r\n    return self.board_history\r\n\r\n  def to_json(self):\r\n    \"\"\"Returns the game in json format\r\n\r\n    What needs to be encoded are the current game state, the board history\r\n    the players, the size of the board, the number of turns, number of turns total\r\n    and the winner\r\n\r\n    Returns:\r\n      {JSON} -- The JSON encoded version of this current game\r\n\r\n    \"\"\"\r\n\r\n    object_json = dict()\r\n    object_json[\"Type\"] = self.__class__.__name__\r\n    game_json = dict()\r\n    game_json[\"x_dist\"] = self.x_dist\r\n    game_json[\"y_dist\"] = self.y_dist\r\n    game_json[\"turn_number\"] = self.turn_number\r\n    game_json[\"max_turns\"] = self.max_turns\r\n    game_json[\"num_to_win\"] = self.num_to_win\r\n    game_json[\"winner\"] = self.winner\r\n    game_json[\"board\"] = self.board.to_json()\r\n    game_json[\"board_history\"] = [board.to_json() for board in self.board_history]\r\n    game_json[\"players\"] = [player.to_json() for player in self.players]\r\n    object_json[\"Object\"] = game_json\r\n\r\n    return json.dumps(object_json)\r\n    \r\n\r\n\r\nclass GameTests(unittest.TestCase):\r\n  \"\"\"Suite to test the functionality of the game\r\n\r\n  \"\"\"\r\n\r\n  def test_init(self):\r\n    \"\"\"Suite to test the init method\r\n\r\n    \"\"\"\r\n\r\n    a_players = [Player(1), Player(2)]\r\n    a_x_dist = 10\r\n    a_y_dist = 3\r\n    a_num_to_win = 3\r\n    a_game = Game(a_players, a_x_dist, a_y_dist, a_num_to_win)\r\n\r\n    #Check to be sure the board is properly set up\r\n    self.assertEqual(a_game.board, Board(grid=[[0 for _ in range(a_y_dist)] for _ in range(a_x_dist)]))\r\n\r\n  def test_equal(self):\r\n    \"\"\"Suite to test that equal boards can be compared\r\n\r\n    \"\"\"\r\n\r\n    a_players = [ZeroPlayer(1), ZeroPlayer(2)]\r\n    a_x_dist = 3\r\n    a_y_dist = 3\r\n    a_num_to_win = 1\r\n    a_game = Game(a_players, a_x_dist, a_y_dist, a_num_to_win)\r\n\r\n    b_players = [ZeroPlayer(1), ZeroPlayer(2)]\r\n    b_x_dist = 3\r\n    b_y_dist = 3\r\n    b_num_to_win = 1\r\n    b_game = Game(b_players, b_x_dist, b_y_dist, b_num_to_win)\r\n\r\n    c_players = [ZeroPlayer(1), ZeroPlayer(2)]\r\n    c_x_dist = 3\r\n    c_y_dist = 3\r\n    c_num_to_win = 1\r\n    c_game = Game(c_players, c_x_dist, c_y_dist, c_num_to_win)\r\n\r\n    self.assertTrue(b_game == a_game == c_game)\r\n\r\n    a_game.play_game()\r\n    b_game.play_game()\r\n\r\n    self.assertTrue(a_game == b_game)\r\n    self.assertFalse(c_game == a_game)\r\n\r\n    c_game.play_game()\r\n\r\n    self.assertTrue(b_game == a_game == c_game)\r\n\r\n\r\n\r\n  def test_play_game(self):\r\n    \"\"\"Suite to test the play game method\r\n\r\n    Runs the game, making sure no exceptions occur\r\n\r\n    Then, checks the history, going through each move and checking each move is valid\r\n    \r\n    \"\"\"\r\n\r\n    \r\n    a_players = [RandomPlayer(1), RandomPlayer(2)]\r\n    a_x_dist = 3\r\n    a_y_dist = 3\r\n    a_num_to_win = 1\r\n    a_game = Game(a_players, a_x_dist, a_y_dist, a_num_to_win)\r\n\r\n    #Game is played to competion\r\n    a_game.play_game()\r\n\r\n    a_history = a_game.get_history()\r\n\r\n    #Go through each move and check to be sure it's valid\r\n    for i in range(1,len(a_history)):\r\n      #Get copy of the board\r\n      prev_board = a_history[i-1]\r\n      cur_board = a_history[i]\r\n\r\n      #Check if the board chosen is in valid states\r\n      self.assertTrue(cur_board in prev_board.get_states(a_players[0].get_id()) or cur_board in prev_board.get_states(a_players[1].get_id()),\\\r\n        \"An invalid board state was added to the history\")\r\n\r\n      if i == len(a_history) - 1:\r\n        self.assertTrue(cur_board.check_win(a_num_to_win, a_players[0].get_id()) or cur_board.check_win(a_num_to_win, a_players[1].get_id()) or cur_board.check_tie())\r\n      else: \r\n        self.assertFalse(cur_board.check_win(a_num_to_win, a_players[0].get_id()) or cur_board.check_win(a_num_to_win, a_players[1].get_id()) or cur_board.check_tie())\r\n\r\n  def test_play_game_hard(self):\r\n    \"\"\"Test the game, pushing it to its limits\r\n\r\n    \"\"\"\r\n    wins = [0,0,0]\r\n\r\n    for i in range(1,10):\r\n      a_player_1_id = 1\r\n      a_player_2_id = 2\r\n      a_players = [RandomPlayer(a_player_1_id), RandomPlayer(a_player_2_id)]\r\n      a_x_dist = i\r\n      a_y_dist = i\r\n      a_num_to_win = 3\r\n      a_game = Game(a_players,a_x_dist,a_y_dist,a_num_to_win)\r\n      a_game.play_game()\r\n\r\n      wins[a_game.winner] += 1\r\n\r\n    print(wins)\r\n\r\n  def test_resume_game(self):\r\n    \"\"\"Suite testing that the game itself can be properly stopped and started\r\n\r\n    \"\"\"\r\n\r\n    a_player_1 = RandomPlayer(1)\r\n    a_player_2 = UserPlayer(2)\r\n    a_player_2.set_choice(0)\r\n    a_players = [a_player_1, a_player_2]\r\n    a_x_dist = 5\r\n    a_y_dist = 5\r\n    a_num_to_win = 3\r\n    a_game = Game(a_players,a_x_dist,a_y_dist,a_num_to_win)\r\n\r\n    #game will pause\r\n    a_game.play_game()\r\n\r\n    while a_game.winner != -1:\r\n      a_player_2.set_choice(0)\r\n      a_game.resume_game()\r\n\r\n\r\n\r\n\r\n\r\n\r\n  def test_to_json(self):\r\n    \"\"\"Suite testing that the json values are properly created for this game object\r\n\r\n    \"\"\"\r\n    self.maxDiff = None\r\n\r\n    a_player_1_id = 1\r\n    a_player_2_id = 2\r\n    a_players = [RandomPlayer(a_player_1_id), RandomPlayer(a_player_2_id)]\r\n    a_x_dist = 3\r\n    a_y_dist = 3\r\n    a_num_to_win = 2\r\n    a_game = Game(a_players,a_x_dist,a_y_dist,a_num_to_win)\r\n    a_game_json = a_game.play_game()\r\n\r\n    #Make sure that the game itself is valid\r\n    #Create dictionary, convert to json \r\n    \r\n\r\n    object_json = dict()\r\n    object_json[\"Type\"] = \"Game\"\r\n    game_json = dict()\r\n    game_json[\"x_dist\"] = a_game.x_dist\r\n    game_json[\"y_dist\"] = a_game.y_dist\r\n    game_json[\"turn_number\"] = a_game.turn_number\r\n    game_json[\"max_turns\"] = a_game.max_turns\r\n    game_json[\"num_to_win\"] = a_game.num_to_win\r\n    game_json[\"winner\"] = a_game.winner\r\n    game_json[\"board\"] = a_game.board.to_json()\r\n    game_json[\"board_history\"] = [board.to_json() for board in a_game.board_history]\r\n    game_json[\"players\"] = [player.to_json() for player in a_players]\r\n    object_json[\"Object\"] = game_json\r\n\r\n    self.assertEqual(json.dumps(object_json), a_game_json)\r\n\r\n    \r\n\r\n  \r\nif __name__ == '__main__':\r\n  unittest.main()","sub_path":"src/Python/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":14399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"542564762","text":"import numpy as np\r\nimport pickle\r\nimport utils\r\nimport  os\r\nimport matplotlib.pyplot as plt\r\n\r\nfolder=utils.DATAfolder\r\nimgpath=[]\r\nfor root,dirs,files in os.walk(folder):\r\n    if len(files)>0:\r\n        imgpath.extend([os.path.join(root,elem) for elem in files])\r\ndata=[]\r\nimgs=np.array([])\r\nfor i in range(len(imgpath)):\r\n    print(\"loading {}..\".format(i))\r\n    img=np.array(plt.imread(imgpath[i]))\r\n    data.append(img)\r\n\r\ndata=np.array(data)\r\nprint(data.shape)\r\ndat=np.random.choice(data.reshape(-1),size=[100,100,100]).astype(\"int16\")\r\nprint(dat.shape)\r\nfile=open(\"dat.pkl\",\"wb\")\r\npickle.dump(dat,file)","sub_path":"img2dat.py","file_name":"img2dat.py","file_ext":"py","file_size_in_byte":608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"335475890","text":"import pygame\nimport sys\nimport utilities\nimport character\nimport enemy\nimport background\nimport foreground\nimport hud\n\nfrom pygame.locals import *\n\n\npygame.init()\n\n# controllers\nJOYSTICK_DEAD_ZONE = 0.6\npygame.joystick.init()\njoystick_count = pygame.joystick.get_count()\njoystick = []\nfor i in range (joystick_count):\n        joystick.append(pygame.joystick.Joystick(i))\n        joystick[i].init()\n\n# variables\nclock = pygame.time.Clock()\n\n# load\nconfig = utilities.load_configuration()\nscreen = pygame.display.set_mode((config['width'], config['height']), 0, 32)\n\ncharacter = character.Character((200, 200), (config['width'], config['height']))\n\nenemies = list()\nenemies.append(enemy.Enemy((250, 250),(config['width'], config['height'])))\nenemies.append(enemy.Enemy((350, 250),(config['width'], config['height'])))\nenemies.append(enemy.Enemy((450, 250),(config['width'], config['height'])))\nenemies.append(enemy.Enemy((550, 250),(config['width'], config['height'])))\n\nenemyGroup = pygame.sprite.Group(enemies)\n\n\n# sprites\nforeground = foreground.Foreground()\n\n# Background\nbackground = background.Background()\n\nhud = hud.Hud((config['width'], config['height']),character)\n\nwhile True:\n    clock.tick(config['fps'])\n\n    pressed_keys = pygame.key.get_pressed()\n\n    for event in pygame.event.get():\n        if event.type == QUIT:\n            pygame.quit()\n            sys.exit('QUIT')\n\n\n    if pressed_keys[K_ESCAPE]:\n        pygame.quit()\n        sys.exit()\n    elif pressed_keys[K_w] or (joystick[0].get_axis(1) < -JOYSTICK_DEAD_ZONE):\n        character.move(character.NORTH)\n    elif pressed_keys[K_a] or (joystick[0].get_axis(0) < -JOYSTICK_DEAD_ZONE):\n        character.move(character.WEST)\n    elif pressed_keys[K_s] or (joystick[0].get_axis(1) > JOYSTICK_DEAD_ZONE):\n        character.move(character.SOUTH)\n    elif pressed_keys[K_d] or (joystick[0].get_axis(0) > JOYSTICK_DEAD_ZONE):\n        character.move(character.EAST)\n\n    scroll_background_direction = character.update(foreground.sprites)\n    if scroll_background_direction == 0:\n        background.update((0,-2))\n        foreground.update((0,-2))\n        for enemy in enemies:\n            enemy.update(foreground.sprites,(0,-2))\n    elif scroll_background_direction == 1:\n        background.update((2, 0))\n        foreground.update((2, 0))\n        for enemy in enemies:\n            enemy.update(foreground.sprites,(2,0))\n    elif scroll_background_direction == 2:\n        background.update((-2, 0))\n        foreground.update((-2, 0))\n        for enemy in enemies:\n            enemy.update(foreground.sprites,(-2,0))\n    elif scroll_background_direction == 3:\n        background.update((0, 2))\n        foreground.update((0, 2))\n        for enemy in enemies:\n            enemy.update(foreground.sprites,(0,2))\n\n    for enemy in enemies:\n        enemy.move()\n        enemy.update(foreground.sprites,None)\n\n    collision = pygame.sprite.spritecollide(character, enemyGroup, False)\n    if len(collision) > 0:\n        character.damage(2)\n\n    screen.fill((133,133,133))\n    hud.update()\n\n    background.draw(screen)\n    foreground.draw(screen)\n    for enemy in enemies:\n        enemy.draw(screen)\n    character.draw(screen)\n    hud.draw(screen)\n    pygame.display.update()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"566999052","text":"#coding=utf-8\nimport GameFun\nimport BG\nimport GameOverUI\n\nclass   Scene(GameFun.GameScene):\n    #初始化\n    def __init__(self):\n        super(Scene, self).__init__()\n        self.AddLayer(\"BG\")\n        self.AddLayer(\"UI\")\n\n    def OnLoad(self):\n        super(Scene, self).OnLoad()\n\n        bg = BG.Character()\n        self.AddGameNode(\"BG\",bg)\n\n        self.gameoverUI = GameOverUI.GameUI()\n        self.AddGameNode(\"UI\",self.gameoverUI)\n\n    def SetKillCount(self,killCount):\n        self.gameoverUI.SetKillCount(killCount)\n","sub_path":"Examples/AircraftWar/Scene3.py","file_name":"Scene3.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"650541763","text":"# This file is part of the MapProxy project.\n# Copyright (C) 2010 Omniscale <http://omniscale.de>\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#    http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom __future__ import with_statement, division\nimport io\nimport os\nimport tempfile\nimport shutil\nfrom webtest import TestApp\nfrom mapproxy.multiapp import app_factory\n\ndef module_setup(test_config, config_files):\n    fixture_dir = os.path.join(os.path.dirname(__file__), 'fixture')\n\n    test_config['base_dir'] = tempfile.mkdtemp()\n    test_config['config_files'] = []\n\n    for config_file in config_files:\n        config_file_src = os.path.join(fixture_dir, config_file)\n        config_file_dst = os.path.join(test_config['base_dir'], config_file)\n        shutil.copy(config_file_src, config_file_dst)\n        test_config['config_files'].append(config_file_dst)\n\n    app = app_factory({}, config_dir=test_config['base_dir'], allow_listing=False)\n    test_config['multiapp'] = app\n    test_config['app'] = TestApp(app, use_unicode=False)\n\ndef module_teardown(test_config):\n    shutil.rmtree(test_config['base_dir'])\n    test_config.clear()\n\n\ntest_config = {}\n\ndef setup_module():\n    module_setup(test_config, ['multiapp1.yaml', 'multiapp2.yaml'])\n\ndef teardown_module():\n    module_teardown(test_config)\n\nclass TestMultiapp(object):\n    def setup(self):\n        self.multiapp = test_config['multiapp']\n        self.app = test_config['app']\n\n    def test_index_without_list(self):\n        resp = self.app.get('/')\n        assert 'MapProxy' in resp\n        assert 'multiapp1' not in resp\n\n    def test_index_with_list(self):\n        try:\n            self.multiapp.list_apps = True\n            resp = self.app.get('/')\n            assert 'MapProxy' in resp\n            assert 'multiapp1' in resp\n        finally:\n            self.multiapp.list_apps = False\n\n    def test_unknown_app(self):\n        self.app.get('/unknownapp', status=404)\n        # assert status == 404 Not Found in app.get\n\n    def test_known_app(self):\n        resp = self.app.get('/multiapp1')\n        assert 'demo' in resp\n\n    def test_reloading(self):\n        resp = self.app.get('/multiapp1')\n        assert 'demo' in resp\n        app_config = test_config['config_files'][0]\n\n        replace_text_in_file(app_config, '  demo:', '  #demo:', ts_delta=5)\n\n        resp = self.app.get('/multiapp1')\n        assert 'demo' not in resp\n\n        replace_text_in_file(app_config, '  #demo:', '  demo:', ts_delta=10)\n\n        resp = self.app.get('/multiapp1')\n        assert 'demo' in resp\n\ndef replace_text_in_file(filename, old, new, ts_delta=2):\n    text = io.open(filename, encoding='utf-8').read()\n    text = text.replace(old, new)\n    io.open(filename, 'w', encoding='utf-8').write(text)\n\n    # file timestamps are not precise enough (1sec)\n    # add larger delta to force reload\n    m_time = os.path.getmtime(filename)\n    os.utime(filename, (m_time+ts_delta, m_time+ts_delta))\n","sub_path":"mapproxy/test/system/test_multiapp.py","file_name":"test_multiapp.py","file_ext":"py","file_size_in_byte":3391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"392311061","text":"#!/usr/bin/env python3\n'''\nAuthor: Zhipeng Xie\n\nTopic: Equake_data analysis 1\n\nEffect: Analysis data from a http link\n\n'''\nimport urllib.request\nfrom data_analysis import database\nimport re\n\n\ndef readeqi():\n    '''\n    none -> list\n\n    effect: to read a url from usgs, and filter the magnitude info\n\n    no input\n\n    output: a list of magnitude numbers\n\n    >>> readeqi()\n    [5.2, 5.1, 6.0, 5.9, 5.9, 6.0, 5.6, 5.6, 5.7, 5.0, 5.0, 5.2, 5.1, 5.4, 5.2, 5.6]\n    '''\n    url = 'http://earthquake.usgs.gov/fdsnws/event/1/\\\n    query?format=csv\\\n    &starttime=1916-02-01\\\n    &latitude=44.0519\\\n    &longitude=-123.0867\\\n    &maxradiuskm=250\\\n    &minmagnitude=5'\n    mag_list = []\n    with urllib.request.urlopen(url) as data:\n        the_rule = re.compile(r'.*,(-12\\d.*?),(-?\\d.*?),(?P<mag>\\d.*?),.*')\n        for line in data.readlines():\n            if the_rule.match(str(line)):\n                mag_list.append(the_rule.match(str(line)).group('mag'))\n    mag_list = [float(x) for x in mag_list]\n    return mag_list\n\n\ndef equake_analysis(mag_list):\n    '''\n    (list) -> tuple\n\n    effect: Using the previous project's class to analysis the magnitude\n    number list\n\n    input: magnitude info list\n\n    output: a tuple of mean, median, modelist value and also my raw\n    analysis class instance\n    '''\n    whole_analysis = database(mag_list)\n    whole_analysis._mean()\n    whole_analysis._median()\n    whole_analysis._mode()\n    results = (whole_analysis.mean, whole_analysis.median,\n               whole_analysis.modelist, whole_analysis)\n    return results\n\n\ndef equake_report(mag_list, mmm_tuple, need_chart):\n    '''\n    (list, tuple, bool) -> none\n\n    effect: integrate all the analysis infomations into a string,\n    and if we need a analysis chart, using previous project's function\n    to draw it\n\n    input: magnitude list, tuple from the result equake_analysis, boolean\n    of whether we need a chart\n\n    output: none\n\n    >>> mag_list = readeqi()\n        mmm_tuple = equake_analysis(mag_list)\n        equake_report(mag_list, mmm_tuple, True)\n    ITEM FREQUENCY\n    5.0         2\n    5.1         2\n    5.2         3\n    5.4         1\n    5.6         3\n    5.7         1\n    5.9         2\n    6.0         2\n    mean: 5.468750\n    median: 5.500000\n    mode_list: [5.6, 5.2]\n    (Draw a chart)\n    '''\n    result = ''\n    result += 'mean: %f' % mmm_tuple[0]\n    result += '\\nmedian: %f' % mmm_tuple[1]\n    result += '\\nmode_list: %s' % mmm_tuple[2]\n    tmp_database = mmm_tuple[3]\n    tmp_database._frequencyTable()\n    if need_chart:\n        tmp_database._frequencyChart()\n    print(result)\n    return None\n\n\ndef equake_main():\n    '''\n    none -> none\n\n    effect: main function, calling functions above\n\n    no input, output\n\n    >>> equake_main()\n    ITEM FREQUENCY\n    5.0         2\n    5.1         2\n    5.2         3\n    5.4         1\n    5.6         3\n    5.7         1\n    5.9         2\n    6.0         2\n    mean: 5.468750\n    median: 5.500000\n    mode_list: [5.6, 5.2]\n    (Draw a chart)\n    '''\n    mag_list = readeqi()\n    mmm_tuple = equake_analysis(mag_list)\n    equake_report(mag_list, mmm_tuple, True)\n    return None\n\nequake_main()\n","sub_path":"Coding/cis2xx/equake_data1.py","file_name":"equake_data1.py","file_ext":"py","file_size_in_byte":3164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"597397159","text":"import sys, os, cv2\r\nfrom PIL import Image\r\n\r\n\r\nw, h = 30, 30\r\nram = 8192\r\n\r\ndef progress(value, max_value):\r\n  final = 50\r\n  k = int((value + 1) / max_value * 100) // (100 // final)\r\n  sys.stdout.write('|{}{}| {}%\\r'.format('#' * k, ' ' * (final - k), (k * 2)))\r\n  if k == final:\r\n    print('\\n')\r\n  \r\n\r\ndef BuildDat(pre_path, path, w, h, g=False):\r\n  f = open('{}/{}.dat'.format(pre_path, path), 'w')\r\n  i = 0\r\n  imgs = os.listdir('{}/{}'.format(pre_path, path))\r\n  l = len(imgs)\r\n  print('BuildDat for {}:'.format(path))\r\n  for i in range(l):\r\n    if g:\r\n      f.write('{}/{} 1 0 0 {} {}\\n'.format(path, imgs[i], w, h))\r\n    else:\r\n      f.write('{}/{}/{}\\n'.format(pre_path, path, imgs[i]))\r\n    progress(i, l)\r\n  f.close()\r\n  return l\r\n\r\n\r\ndef ToGray(pre_path, path, w, h):\r\n  i = 0\r\n  gr_path = '{}/{}_Gr'.format(pre_path, path)\r\n  path = '{}/{}'.format(pre_path, path)\r\n\r\n  if os.path.exists(gr_path):\r\n    imgs = os.listdir(gr_path)\r\n    l = len(imgs)\r\n    if l > 0:\r\n      print('Removing old {}:'.format(gr_path))\r\n    for i in range(l):\r\n      os.remove(gr_path + '/' + imgs[i])\r\n      progress(i, l)\r\n  else:\r\n    os.mkdir(gr_path)\r\n\r\n  imgs = os.listdir(path)\r\n  l = len(imgs)\r\n  print('Converting {} to gray:'.format(path))\r\n  for i in range(l):   \r\n    gr = cv2.cvtColor(cv2.imread(path + \"/\" + imgs[i]), cv2.COLOR_BGR2GRAY)\r\n    gr = cv2.resize(gr, (w, h), cv2.INTER_LINEAR)\r\n    cv2.imwrite(gr_path+\"/\"+str(i)+\".bmp\", gr)\r\n    progress(i, l)\r\n  \r\nif len(sys.argv) > 1:\r\n  folders = sys.argv\r\n  for i in range(1, len(folders)):\r\n    if os.path.exists('{}/Good'.format(folders[i])) and os.path.exists('{}/Bad'.format(folders[i])):\r\n      pre_path = folders[i].replace('\\\\', '/')\r\n      print('Working with: {}'.format(pre_path))\r\n      \r\n      print('Set size of samples (press ENTER to set 30x30):')\r\n      while True:\r\n        s = input()\r\n        if len(s) > 0:\r\n          try:\r\n            s = s.split()\r\n            w, h = int(s[0]), int(s[1])\r\n            break\r\n          except:\r\n            print('Type two numbers')\r\n        else:\r\n          w, h = 30, 30\r\n          break\r\n      print('Size of samples {}x{}\\n'.format(w, h))\r\n      ToGray(pre_path, \"Good\", w, h)\r\n      ToGray(pre_path, \"Bad\", w, h)\r\n      gn = BuildDat(pre_path, \"Good_Gr\", w, h, True)\r\n      bn = BuildDat(pre_path, \"Bad_Gr\", w, h)\r\n\r\n      ln1 = 'cd /d {}/Haar \\n'.format(pre_path[:pre_path.rindex('/')])\r\n      ln2 = 'opencv_createsamples.exe -info {}/Good_Gr.dat -vec samples.vec -w {} -h {} -num {} \\n'.format(pre_path, w, h, gn)\r\n      ln3 = 'opencv_traincascade.exe -data haarcascade -vec samples.vec -bg {}/Bad_Gr.dat -numStages 16 '.format(pre_path)\r\n      ln3 += '-minhitrate 0.99 -maxFalseAlarmRate 0.1 -numPos {} -numNeg {} '.format(int(gn * 0.8), bn)\r\n      ln3 += '-w {} -h {} -mode ALL -precalcValBufSize {} -precalcIdxBufSize {} \\n'.format(w, h, ram, ram)\r\n\r\n      f = open('{}/START.bat'.format(pre_path), 'w')\r\n      f.write(ln1)\r\n      f.write(ln2)\r\n      f.write(ln3)\r\n      f.close()\r\n\r\n      print('Comands: \\n')\r\n      print(ln1)\r\n      print(ln2)\r\n      print(ln3)\r\n      print('Or you can run START.bat file in folder whith images. \\n')\r\n    else:\r\n      print('\"Data\" folder must contain folders: \"Good\", \"Bad\"')\r\n  \r\nelse:\r\n  print('Put \"Data\" folder to this script.')\r\n  print('\"Data\" folder must contain folders: \"Good\", \"Bad\"')\r\n\r\ninput()\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"make.py","file_name":"make.py","file_ext":"py","file_size_in_byte":3376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"424934982","text":"import PySimuSeq.SeqGenerator\nfrom PySimuSeq.SeqGenerator import SimuSequence\n\nseq_machine = SimuSequence(seqLen=220, letterProb=PySimuSeq.SeqGenerator.highgc_prob,\n                           withRepeat=True, repeatLen=18, repeatCnt=5, spacerLen=3,\n                           randomSeed=1456)\n\nfor i in range(10):\n    original_seq  = next(seq_machine.seqGenerator())[0]\n    print(original_seq)\n    processed_seq = seq_machine.insertRepetitiveRegions(original_seq, errorRate=0.5,errorBaseCnt=3,print_inserted=True)\n    print('')\n\nprint(seq_machine.getProfile())\n","sub_path":"Test.SeqGenerator.py","file_name":"Test.SeqGenerator.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"585291824","text":"from collections import defaultdict\nfrom datetime import datetime, timedelta\n\nfrom pymongo import MongoClient\nfrom flask import Flask, jsonify, render_template\n\napp = Flask(__name__)\ndebug = True\nprofile = False\n\nif debug:\n    app.debug = True\n\nif profile:\n    from werkzeug.contrib.profiler import ProfilerMiddleware\n    app.wsgi_app = ProfilerMiddleware(app.wsgi_app, restrictions=[5])\n    app.config.update(PROFILE=True)\n\n\napp.config.update(JSON_AS_ASCII=False)\nmongo = MongoClient()\ndb = mongo.newsfeed.posts\n\n\n@app.route('/api/stats/yearly')\ndef yearly():\n    now = datetime.now()\n    year_ago = now - timedelta(days=365)\n\n    stats = db.aggregate([\n        {'$match': {\n            'time': {'$gt': year_ago}\n        }},\n        {'$group': {\n            '_id': {'$dayOfYear': '$time'},\n            'total': {'$sum': 1}\n        }},\n        {'$project': {\n            'doy': '$_id',\n            'total': '$total',\n            '_id': 0,\n        }},\n    ])\n\n    result = {\n        'dates': {'from': year_ago, 'to': now},\n        'data': {x['doy']: x['total'] for x in stats['result']},\n    }\n\n    return jsonify(result)\n\n\n@app.route('/api/stats/weekly')\n@app.route('/api/stats/weekly/<source_id>')\ndef weekly(source_id=None):\n    now = datetime.now()\n    week_ago = now - timedelta(days=7)\n\n    query = [\n        {'$match': {\n            'time': {'$gt': week_ago},\n        }},\n        {'$group': {\n            '_id': {\n                'dow': {'$dayOfWeek': '$time'},\n                'hour': {'$hour': '$time'},\n            },\n            'total': {'$sum': 1},\n        }},\n        {'$project': {\n            'dow': '$_id.dow',\n            'hour': '$_id.hour',\n            'total': '$total',\n            '_id': 0,\n        }},\n    ]\n\n    if source_id:\n        query[0]['$match']['source_id'] = int(source_id)\n\n    stats = db.aggregate(query)\n\n    data = defaultdict(dict)\n\n    def from_monday(dow):\n        # from: 1 (Sunday) — 7 (Saturday)\n        #   to: 0 (Monday) — 6 (Sunday)\n        return 6 if dow == 1 else dow - 2\n\n    for i in stats['result']:\n        dow = from_monday(i['dow'])\n        hour = '{:02}'.format(i['hour'])\n        data[dow][hour] = i['total']\n\n    result = {\n        'dates': {'from': week_ago, 'to': now},\n        'data': data,\n    }\n\n    return jsonify(result)\n\n\n@app.route('/api/stats/top')\ndef top():\n    oldest = next(db.find().sort('time', 1))['time']\n    newest = next(db.find().sort('time', -1))['time']\n    timespan = newest - oldest\n    days = timespan / timedelta(days=1)\n\n    stats = db.aggregate([\n        {'$group': {\n            '_id': '$source_id',\n            'total': {'$sum': 1},\n            'source': {'$first': '$source'},\n        }},\n    ])\n\n    data = [{\n        'total': x['total'],\n        'velocity': x['total'] / days,\n        'id': x['source']['id'],\n        'name': x['source']['name'],\n    } for x in stats['result']]\n\n    data = sorted(data, reverse=True, key=lambda k: k['total'])\n\n    result = {\n        'dates': {'from': oldest, 'to': newest},\n        'data': data,\n    }\n\n    return jsonify(result)\n\n\n@app.route('/')\ndef stats():\n    return render_template('stats.html')\n\n\ndef main():\n    app.run(port=8000)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"newsfeed/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3210,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"546136241","text":"import pandas as pd\nimport re\nimport nltk\nfrom nltk.corpus import stopwords\nfrom nltk import tokenize\nfrom gensim.parsing.preprocessing import STOPWORDS\nfrom nltk.corpus import wordnet\n\n\nclass Cleanser(object):\n    stemmer = nltk.PorterStemmer()\n    lemmatizer = nltk.WordNetLemmatizer()\n    stopset = set(stopwords.words('english')) | STOPWORDS\n    symbol_ptn = re.compile('[^\\w]')\n    pos_ptn = [\n        (re.compile('^NN'), wordnet.NOUN),\n        (re.compile('^JJ'), wordnet.ADJ),\n        (re.compile('^RB'), wordnet.ADV),\n        (re.compile('^VB'), wordnet.VERB)\n    ]\n\n    @classmethod\n    def filter_review(cls, review):\n        \"\"\"\n        remove stopwords, lemmatize, and stem\n        \"\"\"\n        tokens = tokenize.wordpunct_tokenize(review)\n        filtered = list(filter(cls.not_stopword, tokens))\n        normalized = [cls.normalize(f) for f in filtered]\n        return \" \".join(normalized)\n\n    @classmethod\n    def not_stopword(cls, token):\n        in_stopset = token.lower() in cls.stopset\n        is_symbol = cls.symbol_ptn.search(token)\n        return not (in_stopset or is_symbol)\n\n    @classmethod\n    def normalize(cls, token):\n        token = token.lower()\n        pos = cls.get_pos(token)\n        if pos:\n            lemma = cls.lemmatizer.lemmatize(token, pos=pos)\n        else:\n            lemma = cls.lemmatizer.lemmatize(token)\n        return cls.stemmer.stem(lemma)\n\n    @classmethod\n    def get_pos(cls, token):\n        target_pos = nltk.pos_tag([token])[0][1]\n        for pos_re, pos in cls.pos_ptn:\n            if pos_re.search(target_pos):\n                return pos\n        return None\n","sub_path":"src/data/cleansing/cleanser.py","file_name":"cleanser.py","file_ext":"py","file_size_in_byte":1618,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"460730555","text":"# -*- coding:utf-8 -*-\n\n# sample program\n# This program is sample of pacScript's coordinate transformation command.\n\n# b-cap Lib URL\n# https://github.com/DENSORobot/orin_bcap\n\n\nimport pybcapclient.bcapclient as bcapclient\n\n\n# set IP Address , Port number and Timeout of connected RC8\nhost = \"192.168.0.1\"\nport = 5007\ntimeout = 2000\n\n# Connection processing of tcp communication\nm_bcapclient = bcapclient.BCAPClient(host, port, timeout)\nprint(\"Open Connection\")\n\n# start b_cap Service\nm_bcapclient.service_start(\"\")\nprint(\"Send SERVICE_START packet\")\n\n# set Parameter\nname = \"\"\nprovider = \"CaoProv.DENSO.VRC\"\nmachine = \"localhost\"\noption = \"@IfNotMember\"\n\ntry:\n    # Connect to RC8 (RC8(VRC)provider) , Get Controller Handle\n    hctrl = m_bcapclient.controller_connect(name, provider, machine, option)\n    print(\"Connect RC8\")\n    hrobot = m_bcapclient.controller_getrobot(hctrl, \"Arm\", \"\")\n\n    reference_pos_value = [170, 50, 260, 180, 10, 135, 261]\n    offset_pos_vaue = [10, 0, 0, 0, 0, 0, -1]\n    pn1 = [reference_pos_value, \"P\"]\n    # pn2 = [offset_pos_vaue, \"P\"]\n    pn2 = \"P(\" + str(offset_pos_vaue)[1:-1] + \")\"\n    print(f\"base:{pn1}\")\n    print(f\"offset:{pn2}\")\n    devh_calc_res = m_bcapclient.robot_execute(hrobot, \"DevH\", [pn1, pn2])\n    print(f\"result devh:{devh_calc_res}\")\n    dev_calc_res = m_bcapclient.robot_execute(hrobot, \"Dev\", [pn1, pn2])\n    print(f\"result dev :{dev_calc_res}\")\n\nexcept Exception as e:\n    print('=== ERROR Description ===')\n    if str(type(e)) == \"<class 'pybcapclient.orinexception.ORiNException'>\":\n        print(e)\n        errorcode_int = int(str(e))\n        if errorcode_int < 0:\n            errorcode_hex = format(errorcode_int & 0xffffffff, 'x')\n        else:\n            errorcode_hex = hex(errorcode_int)\n        print(\"Error Code : 0x\" + str(errorcode_hex))\n        error_description = m_bcapclient.controller_execute(hctrl, \"GetErrorDescription\", errorcode_int)\n        print(\"Error Description : \" + error_description)\n    else:\n        print(e)\n\nfinally:\n    #  DisConnect\n    if(hrobot != 0):\n        m_bcapclient.robot_release(hrobot)\n        print(\"Release Robot Handle\")\n    # End If\n    if(hctrl != 0):\n        m_bcapclient.controller_disconnect(hctrl)\n        print(\"Release Controller\")\n    # End If\n    m_bcapclient.service_stop()\n    print(\"B-CAP service Stop\")\n","sub_path":"others/sample_calcurate_position.py","file_name":"sample_calcurate_position.py","file_ext":"py","file_size_in_byte":2324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"161693208","text":"# http://www.math.univ-toulouse.fr/Archive-MIP/publis/files/06.34.pdf\n# http://www.sciencedirect.com/science?_ob=MiamiImageURL&_cid=271382&_user=6891091&_pii=S016612809690531X&_check=y&_origin=&_coverDate=27-Sep-1996&view=c&wchp=dGLbVlB-zSkWb&md5=4d287100293db5e8c56ec03e48ef286e/1-s2.0-S016612809690531X-main.pdf\n# http://www.sciencedirect.com/science/article/pii/S016612809690531X\n\n# adapted from H.H.H.Homeier & E.O. Steinborn, \"Some properties of the coupling coefficiencs\n# of real spherical harmonics and their relation to Gaunt coefficients\", Journal of Molecular Structure (Theochem) 368 (1996) 31-37\n\nclass lm :\n    def __init__( self, l, m) :\n        self.l = l\n        self.m = m\n\n\n# transformation between spherical harmonics and real spherical harmonics\n#  Y_{l0} = Y_l^0   (m=0)\n#  Y_{lm} = \\frac{1}{\\sqrt{2}} \\left(        Y_l^{+m} + (-1)^m Y_l^{-m} \\right) (m>0)\n#  Y_{lm} = \\frac{i}{\\sqrt{2}} \\left( (-1)^m Y_l^{+m} -        Y_l^{-m} \\right) (m<0)\n#\n#  write as:\n# \n# Y_{lm} = sum_mu _u(l,m,mu)Y_l^mu\ndef _u(l,m,mu) :\n    if abs(m)!=abs(mu) : return complex(0,0)\n    if m==0   :          return complex(1,0)\n    from math import sqrt\n    is2 = 1.0/sqrt(2)\n    sgn = lambda x : +1 if x%2 == 0 else -1\n    if m>0 : return complex(is2, 0) if mu>0 else complex(sgn(mu)*is2,0)\n    else   : return complex(0,-is2) if mu>0 else complex(0,sgn(mu)*is2)\n\n\n# TODO: move into RooSpHarmonic as 'static' functions???\ndef gaunt( l1, l2, l3, m1, m2, m3 ) :\n    import math\n    _n = lambda x: 2*x+1\n    n = math.sqrt( _n(l1)*_n(l2)*_n(l3) / ( 4 * math.pi ) )\n    if m1%2 == 1 : n = -n\n    import ROOT\n    MathMore = ROOT.MathMore\n    _3j = ROOT.Math.wigner_3j  # WARNING: wigner_3j uses half-integer units!!!!\n    w1 = _3j( 2*l1, 2*l2, 2*l3,  0,  0,  0 )  \n    w2 = _3j( 2*l1, 2*l2, 2*l3, -2*m1, 2*m2, 2*m3 )\n    #print l1,l2,l3,m1,m2,m3,' -> ',n,w1,w2\n    return  n*w1*w2\n\ndef real_gaunt( l1, l2, l3, m1, m2, m3 ) :\n     c = sorted( [ lm(l1,m1), lm(l2,m2), lm(l3,m3) ], key = lambda x: abs(x.m) )\n     if c[2].m == 0 : # m1=m2=m3=0\n         return gaunt( c[0].l, c[1].l, c[2].l, c[0].m, c[1].m, c[2].m ) if c[0].m == 0 else 0 \n     elif c[1].m == 0 : # m1=m2=0, m3!=0\n         g = gaunt( c[0].l, c[1].l, c[2].l, c[0].m, c[1].m, c[2].m )\n         u = ( _u(c[0].l,c[0].m,c[1].m ).conjugate()*_u(c[1].l,c[1].m,c[1].m ) ).real\n         return 2*g*u\n     else :\n         u1 = (_u(c[0].l,c[0].m,c[1].m+c[2].m).conjugate()*_u(c[1].l,c[1].m,c[1].m)*_u(c[2].l,c[2].m, c[2].m)).real\n         g1 = gaunt( c[0].l, c[1].l, c[2].l, c[1].m+c[2].m, c[1].m,  c[2].m )\n         u2 = (_u(c[0].l,c[0].m,c[1].m-c[2].m).conjugate()*_u(c[1].l,c[1].m,c[1].m)*_u(c[2].l,c[2].m,-c[2].m)).real\n         g2 = gaunt( c[0].l, c[1].l, c[2].l, c[1].m-c[2].m, c[1].m, -c[2].m )\n         return 2*(g1*u1+g2*u2)\n\ndef non_zero_coupling_coefficients( l1,m1,l2,m2 ) :\n    l_max = l1+l2\n    l_min = max(abs(l1-l2),abs(m1+m2))\n    if (l_min+l_max)%2 : l_min += 1\n    #print 'selected lm for ',l1,m1,l2,m2,' : ', [ (l,m1+m2) for l in xrange( l_min,l_max+1,2) ]\n    return( (l,m1+m2) for l in xrange( l_min,l_max+1,2) )\n\n\ndef non_zero_real_coupling_coefficients( l1,m1,l2,m2 ) :\n    # (37)\n    l_max = l1+l2\n    # (38)\n    l_min = max( abs(l1-l2),min( abs(m1+m2),abs(m1-m2) ) )\n    if ( l_min+l_max ) % 2  : l_min += 1\n    # note: odd number of negative (m,m1,m2) give also zero...\n    #_range = lambda l : xrange(0,l+1) if m1*m2<0 else xrange(-l,1)\n    _range = lambda l : xrange(-l,1) if m1*m2<0 else xrange(0,l+1)\n    return ( (l,m) for l in xrange( l_min, l_max+1, 2 ) for m in _range( l ) )\n            \ndef __test_gaunt_1(l,m) : # should be 1 -- as multiplying Y(l,m) by Y(0,0) is the same as dividing by sqrt(4pi)...\n    import math\n    assert abs( gaunt(l,l,0,m,m,0)*math.sqrt(4*math.pi) - 1 ) < 1e-12\n    assert abs( gaunt(l,0,l,m,0,m)*math.sqrt(4*math.pi) - 1 ) < 1e-12\n\n\n# Conversion to real spherical harmonics....\n#  X_{l0} = Y_l^0   (m=0)\n#  X_{lm} = \\frac{1}{\\sqrt{2}} \\left(          Y_l^{+m} + (-1)^m Y_l^{-m} \\right) (m>0)\n#  X_{lm} = \\frac{i}{\\sqrt{2}} \\left(   (-1)^m Y_l^{+m} -        Y_l^{-m} \\right) (m<0)\n#\n#  X_{lm} = sum_mu _u(l,m,mu) Y_l^{mu)\n#\n#\ndef _u(l,m,mu) :\n    if abs(m)!=abs(mu) : return 0\n    if m==0            : return 1 \n    import math\n    is2 = 1.0/math.sqrt(2)\n    return is2* [ [ complex(1, 0), complex(sgn(mu),0) ]   #  1,   1 * (   sgn(mu) )\n                , [ complex(0,-1), complex(0,sgn(mu)) ]   # -i , -i * ( i sgn(mu) )\n                ][ m>0 ][ mu>0 ]\n    #if m>0             : return complex(is2,0) if mu>0 else complex(sgn(mu)*is2,0) \n    #else               : return complex(0,sgn(mu)*is2) if mu<0 else complex(0,-is2)\n\n\n\n\nif __name__ == \"__main__\":\n    for x in ( (l,m) for l in range(10) for m in range(-l,l+1) ) : __test_gaunt_1(*x)\n\n# CLAIM: for real spherical harmonics Y_l1,m1 * Y_l2,m2 = sum c * Y_l,m for (c,l,m) in gaunt_expansion(l1,m1, l2,m2)\n#        where c = int Y_l,m Y_l1,m1 Y_l2,m2\ndef real_gaunt_expansion(l1,m1, l2,m2) :\n    return filter( lambda x : x[-1]!=0 \n                 , ( (l,m,real_gaunt(l,l1,l2,m,m1,m2)) for (l,m) in non_zero_real_coupling_coefficients(l1,m1,l2,m2) ) \n                 )\n\n\ndef gaunt_expansion(l1,m1, l2,m2) :\n    return filter( lambda x : x[-1]!=0\n                 , ( (l,m,gaunt(l,l1,l2,m,m1,m2)) for (l,m) in non_zero_coupling_coefficients(l1,m1,l2,m2) ) \n                 )\n","sub_path":"PhysFit/P2VV/python/P2VV/Gaunt.py","file_name":"Gaunt.py","file_ext":"py","file_size_in_byte":5339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"614952508","text":"from __future__ import print_function\nimport sys\nimport csv\n\nif __name__ == '__main__':\n    train_input = sys.argv[1]\n    validation_input = sys.argv[2]\n    test_input = sys.argv[3]\n    dict_input = sys.argv[4]\n    formatted_train_out = sys.argv[5]\n    formatted_validation_out = sys.argv[6]\n    formatted_test_out = sys.argv[7]\n    feature_flag = sys.argv[8]\n\ndef readTsvFile(path):\n    with open(path) as tsvfile:\n        return list(csv.reader(tsvfile, dialect='excel-tab'))\n\ndef readTxtFile(path):\n    with open(path, \"r\") as f:\n        return f.read()\n\ntrain = readTsvFile(train_input)\nvalidation = readTsvFile(validation_input)\ntest = readTsvFile(test_input)\ndictionary = readTxtFile(dict_input)\n\ndictionary_split = dictionary.splitlines()\n\ndef dict_dict(dictionary):\n\tdict_words = {}\n\tfor elem in range(len(dictionary)):\n\t\tword = dictionary[elem].split(\" \")[0]\n\t\tindex = dictionary[elem].split(\" \")[1]\n\t\tdict_words[word] = index\n\treturn dict_words\n\ndict_new = dict_dict(dictionary_split)\n\ndef model_one(data, dictionary):\n\toutput = []\n\tfor review in range(len(data)):\n\t\tlabel = data[review][0]\n\t\tcontent = data[review][1].split()\n\t\toutput.append(label)\n\t\toutput.append('\\t')\n\t\ttemp_content = []\n\t\tfor word in range(len(content)):\n\t\t\tif (content[word] not in temp_content):\n\t\t\t\ttemp_content.append(content[word])\n\t\tfor word in range(len(temp_content)):\n\t\t\tif (temp_content[word] in dictionary):\n\t\t\t\toutput.append(dictionary[temp_content[word]])\n\t\t\t\toutput.append(\":1\")\n\t\t\t\toutput.append('\\t')\n\t\toutput.append('\\n')\n\treturn ''.join(output)\n\ndef count_dict(dictionary):\n\tdict_count = {}\n\tfor elem in range(len(dictionary)):\n\t\tword = dictionary[elem].split()[0]\n\t\tdict_count[word] = 0\n\treturn dict_count\n\ndef model_two(data, dictionary, dict_split):\n\toutput = []\n\tfor review in range(len(data)):\n\t\tdict_count = count_dict(dict_split)\n\t\tlabel = data[review][0]\n\t\tcontent = data[review][1].split()\n\t\toutput.append(label)\n\t\toutput.append('\\t')\n\t\tfor word in range(len(content)):\n\t\t\tif (content[word] in dictionary):\n\t\t\t\tdict_count[content[word]] += 1\n\t\ttemp_content = []\n\t\tfor word in range(len(content)):\n\t\t\tif (content[word] not in temp_content):\n\t\t\t\ttemp_content.append(content[word])\n\t\tfor word in range(len(temp_content)):\n\t\t\tif ((temp_content[word] in dictionary) and (dict_count[temp_content[word]] < 4)):\n\t\t\t\toutput.append(dictionary[temp_content[word]])\n\t\t\t\toutput.append(\":1\")\n\t\t\t\toutput.append('\\t')\n\t\toutput.append('\\n')\n\treturn ''.join(output)\n\nif (int(feature_flag) == 1):\n\twith open(formatted_train_out, \"w\") as f:\n\t\tf.write(model_one(train, dict_new))\n\twith open(formatted_validation_out, \"w\") as f:\n\t\tf.write(model_one(validation, dict_new))\n\twith open(formatted_test_out, \"w\") as f:\n\t\tf.write(model_one(test, dict_new))\nif (int(feature_flag) == 2):\n\twith open(formatted_train_out, \"w\") as f:\n\t\tf.write(model_two(train, dict_new, dictionary_split))\n\twith open(formatted_validation_out, \"w\") as f:\n\t\tf.write(model_two(validation, dict_new, dictionary_split))\n\twith open(formatted_test_out, \"w\") as f:\n\t\tf.write(model_two(test, dict_new, dictionary_split))","sub_path":"feature.py","file_name":"feature.py","file_ext":"py","file_size_in_byte":3071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"582799407","text":"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# internal tools\ndef _toCSV(self, fileName):\n\n\twith open(fileName, 'wt') as csvfile:\n\n\t    csvwriter = csv.writer(csvfile, delimiter=',')\n\n\t    for key in self.output:\n\t    \tself.dates.append(key)\n\n\n\t    self.dates.sort()\n\t    # print self.dates\n\n\t    # print the date-headers\n\t    headers = ['']\n\t    for key in self.dates:\n\t    \theaders.append(key)\n\n\t    csvwriter.writerow(headers)\n\n\t    # print the lines \n\t    \n\t    for key in self.filters:\n\t    \ttoLine = [key]\n\t    \tfor date in self.dates:\n\t    \t\t\n\t    \t\tif key not in self.output[date]:\n\t    \t\t\ttoLine.append(0)\n\t    \t\telse:\n\t    \t\t\ttoLine.append(self.output[date][key])\n\t    \tcsvwriter.writerow(toLine)","sub_path":"after.py","file_name":"after.py","file_ext":"py","file_size_in_byte":684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"250290800","text":"import fenics\nimport numpy as np\nimport warnings\n\nclass Scenario(object):\n    \"\"\"\n    Class that holds the important information about the system being solved\n    \n    Properties:\n        source_function: a fenics.Expression that describes the sources and sinks in the problem domain\n        dirichlet_bc: a fenics.Expression that describes the Dirichlet boundary conditions\n        gamma_dirichlet: tells which parts of the boundary are subject to the Dirichlet BCs.\n        integral_constraint: if True, we need to constrain the average pressure to be zero.\n        \n        Neumann BCs are currently flux = 0 and are defined wherever the Dirichlet BCs aren't\n    \"\"\"\n\n    def __init__(self):\n        self.source_function = fenics.Constant(0.0)\n        self.dirichlet_bc = fenics.Constant(0.0)\n        self.g = fenics.Constant(0.0)\n        self.problem_number = 0\n        self.integral_constraint = False\n        self.ak = None  # Use this for analytical property field\n\n\n    @staticmethod\n    def gamma_dirichlet(x, on_boundary):\n        return on_boundary\n\n\n\ndef darcy_problem_1():\n    s = Scenario()\n    s.problem_number = 1\n    s.source_function = fenics.Constant(3.0)\n    s.dirichlet_bc = fenics.Expression(\"0.0\", degree=1)\n\n    def boundary(x, on_boundary):\n        return x[0] < fenics.DOLFIN_EPS or x[0] > 1.0 - fenics.DOLFIN_EPS\n    s.gamma_dirichlet = boundary\n    s.g = fenics.Constant(0.0)\n    return s\n","sub_path":"inversion/Gaussian/scenarios.py","file_name":"scenarios.py","file_ext":"py","file_size_in_byte":1417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"323993225","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nimport os\nimport sys\nimport unittest\n\nsys.path.append(os.path.join('v7', 'helloworld'))\n\nfrom helloworld import Plugin as HelloWorld\nfrom nikola.utils import LOGGER\n\n\nclass MockObject:\n    pass\n\n\nclass TestHelloWorld(unittest.TestCase):\n    @staticmethod\n    def setUpClass():\n        LOGGER.notice('--- TESTS FOR helloworld')\n\n    @staticmethod\n    def tearDownClass():\n        sys.stdout.write('\\n')\n        LOGGER.notice('--- END OF TESTS FOR helloworld')\n\n    def test_gen_tasks(self):\n        hw = HelloWorld()\n        hw.site = MockObject()\n        hw.site.config = {}\n        for i in hw.gen_tasks():\n            self.assertEqual(i['basename'], 'hello_world')\n            self.assertEqual(i['uptodate'], [False])\n            try:\n                self.assertIsInstance(i['actions'][0][1][0], bool)\n            except AttributeError:\n                LOGGER.warning('Python 2.6 is missing assertIsInstance()')\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"tests/test_helloworld.py","file_name":"test_helloworld.py","file_ext":"py","file_size_in_byte":1027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"337604727","text":"import random\nimport time\n\ndef selection_sort(list):\n\n    count = 0\n    length = len(list)\n    for i in range(0, length):\n        #count += 1\n        min = i\n        for j in range(i+1, length):\n            count += 1\n            if list[j] < list[min]:\n                min = j\n        list[i], list[min] = list[min], list[i]\n    return count\n\n\n\ndef insertion_sort(list):\n    count = 0\n    k = 0\n    for x in range(1, len(list)):\n        temp = list[x]\n        y = x - 1\n        if y >= 0 and (temp >= list[y]):\n            k += 1\n        while y >= 0 and temp < list[y]:\n            list[y + 1] = list[y]\n            y -= 1\n            count += 1\n        list[y + 1] = temp\n        #count += 1\n    return max(count, k)\n\n\n\ndef main():\n    # Code coverage NOT required for main\n    # Give the random number generator a seed, so the same sequence of \n    # random numbers is generated at each run\n    random.seed(1234)\n    # Generate 5000 random numbers from 0 to 999,999\n    #randoms = random.sample(range(1000000), 2000)\n\n    randoms = []\n    for i in range(1000):\n        randoms.append(i)\n\n    start_time = time.time() \n    comps = insertion_sort(randoms)\n    stop_time = time.time()\n    print(comps, stop_time - start_time)\n\nif __name__ == '__main__': \n    main()\n\n","sub_path":"sorts.py","file_name":"sorts.py","file_ext":"py","file_size_in_byte":1268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"643196018","text":"#! /usr/bin/env python\n\n# add.py - Keith Barrett Simple calculator program\n\nimport sys\n\nprint(\"\\nADD - Calculator Program V1.0 py\\n\\n\")\n\ncurrent = float(0.0)\nmemory = float(0.0)\ntmpf = float(0.0)\n\nwhile True:\n    instr = input('> ')\n    strLen = len(instr)\n\n    if strLen == 0:\n        print('\\nAnswer -> ' + str(current) + '\\n')\n        continue\n\n    # if instr == 'exit' or instr == 'bye' or instr == 'end' or instr == 'goodbye' or instr[\n    #     0] == 'x' or instr[0] == 'X' or instr[0] == 'q':\n    #     print('\\n\\nexiting\\n\\n')\n    #     sys.exit()\n\n    if ('x' in instr) or ('bye' in instr) or ('q' in instr):\n        print('\\n\\nexiting\\n\\n')\n        sys.exit()\n\n    if instr[0] in 'hH?':\n        print('\\nThe following commands are valid:\\n\\n')\n        print('Pressing RETURN will display current value\\n')\n        print('C          Clears current value\\n')\n        print('CC         Clears both current value and memory\\n')\n        print('MC         Clears memory only\\n')\n        print('M+         Adds current value to memory\\n')\n        print('M-         Subtracts current value from memory\\n')\n        print('M*         Multiplies memory by current value\\n')\n        print('M/         Divides memory by current value\\n')\n        print('+n or n    Adds \\\"n\\\" to current value\\n')\n        print('-n         Subtract \\\"n\\\" from current value\\n')\n        print('*n         Multiply current value by \\\"n\\\"\\n')\n        print('/n         Divide current value by \\\"n\\\"\\n\\n')\n        print('\\\"n\\\" is any valid numerical value\\n')\n        print('Note: \\\"M\\\" can be substituted for \\\"n\\\" to')\n        print('reference the current value in memory\\n')\n        continue\n\n\n    if instr[0] in 'cC':\n        if (strLen == 1):\n            current = float(0.0)\n            print('\\nCleared\\n')\n\n        elif instr[1] in 'cC':\n            memory = float(0.0)\n            current = float(0.0)\n            print('\\nAll Cleared\\n')\n\n        else:\n            print('\\n?invalid C Command\\n')\n\n        continue\n\n\n    if instr[0] == '*':\n        if instr[1] in 'Mm':\n            tmpf = memory\n        else:\n            tmpf = float(instr[1:])\n\n        current *= tmpf\n        continue\n\n\n    if instr[0] == '+':\n        if instr[1] in 'Mm':\n            tmpf = memory\n        else:\n            tmpf = float(instr[1:])\n\n        current += tmpf\n        continue\n\n\n    if instr[0] == '/':\n        if instr[1] in 'Mm':\n            tmpf = memory\n        else:\n            tmpf = float(instr[1:])\n\n        current /= tmpf\n        continue\n\n\n    if instr[0] == '-':\n        if instr[1] in 'Mm':\n            tmpf = memory\n        else:\n            tmpf = float(instr[1:])\n\n        current -= tmpf\n        continue\n\n\n    if instr[0] in 'Mm':\n        if strLen == 1:\n            print('\\nMemory -> ' + str(memory) + '\\n')\n        else:\n            if instr[1] in 'cC':\n                memory = float(0.0)\n                print('\\nMemory Cleared\\n')\n\n            elif instr[1] == '+':\n                memory += current\n                print('\\nAdded\\n')\n\n            elif instr[1] == '-':\n                memory -= current\n                print('\\nSubtracted\\n')\n\n            elif instr[1] == '*':\n                memory *= current\n                print('\\nMultiplied\\n')\n\n            elif instr[1] == '/':\n                memory /= current\n                print('\\nDivided\\n')\n\n            else:\n                print('\\n?invalid Memory Command\\n')\n\n        continue\n\n\n    if strLen == 1:\n        tmpf = float(instr[0:])\n\n    elif instr[1] in 'Mm':\n        tmpf = memory\n    else:\n        tmpf = float(instr[1:])\n\n    current += tmpf\n\n\n# End of While\n\n# End of Line\n\n","sub_path":"add.py","file_name":"add.py","file_ext":"py","file_size_in_byte":3643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"523443611","text":"# Taken from this video:\n# https://www.youtube.com/watch?v=wVnQkKf-gHo\nfrom django import forms\n# from emergency_app.models.identity import Identity\nfrom emergency_app.models.contact import Contact\nfrom emergency_app.models.emergency import Emergency\nfrom emergency_app.models.nation import Nation\n# from emergency_app.models.state import State\nfrom common.util import sanitization\n\n# When required field=false, clean() would normalize empty value of CharField into empty string\n# However, null value was needed to follow the sample data provided. Therefore, this function is declared.\n# alternatively, to_python function for each field could use this function.\n# Source: https://docs.djangoproject.com/en/2.2/ref/forms/fields/\ndef empty_string_handler(field):\n    # if field either empty string or empty/null value\n    if not(str(field)) or str(field) in (\"\", \"null\", \"N/A\"):\n        return None\n    else:\n        return field\n\n# Form for validating the request update of user's emergency contact\nclass UpdateEmergencyContactForm(forms.ModelForm):\n    # Provide an example of the schema of the model\n    # pidm was needed to search all contacts related to related user.\n    pidm = forms.IntegerField()\n    surrogate_id = forms.IntegerField(required=False)\n    priority = forms.CharField(max_length=4)\n    relt_code = forms.CharField(max_length=4, required=False)\n    last_name = forms.CharField(max_length=240)\n    first_name = forms.CharField(max_length=240)\n    mi = forms.CharField(max_length=240, required=False)\n\n    street_line1 = forms.CharField(max_length=75, required=False)\n    street_line2 = forms.CharField(max_length=75, required=False)\n    street_line3 = forms.CharField(max_length=75, required=False)\n    city = forms.CharField(max_length=50, required=False)\n    stat_code = forms.CharField(max_length=3, required=False)\n    natn_code = forms.CharField(max_length=5, required=False)\n    zip = forms.CharField(max_length=30, required=False)\n    ctry_code_phone = forms.CharField(max_length=16, required=False)\n    phone_area = forms.CharField(max_length=6, required=False)\n    phone_number = forms.CharField(max_length=12, required=False)\n    phone_ext = forms.CharField(max_length=10, required=False)\n\n    class Meta:\n        model = Contact\n        fields = [\n            'pidm', 'surrogate_id', 'priority', 'relt_code',\n            'last_name', 'first_name', 'mi',\n            'street_line1', 'street_line2', 'street_line3',\n            'city', 'stat_code', 'natn_code', 'zip',\n            'ctry_code_phone', 'phone_area', 'phone_number', 'phone_ext'\n        ]\n\n    # clean() for validating fields that depend on each other, this function apparently runs after clean_<field_name>() got executed\n    def clean(self):\n        self.cleaned_data = super(UpdateEmergencyContactForm, self).clean()\n        # checking whether given surrogate id is actually in database.\n        surrogate_id = self.cleaned_data.get(\"surrogate_id\")\n        pidm = self.cleaned_data.get(\"pidm\")\n        if surrogate_id:\n            try:\n                Contact.objects.get(surrogate_id=surrogate_id, pidm=pidm)\n            except Contact.DoesNotExist:\n                print(\"Invalid Surrogate ID. \")\n                raise forms.ValidationError(\"Invalid Surrogate ID\")\n\n        # checking whether given priority is actually in the correct range (1 to (n+1)) for new entry\n        # and range (1 to n) for old entry (the one with surrogate id given correctly)\n        priority = self.cleaned_data.get(\"priority\")\n        entries = Contact.objects.filter(pidm=pidm)\n        if not priority:\n            print(\"Missing priority number. \")\n            raise forms.ValidationError(\"Missing priority number\")\n        if (not surrogate_id and not(1 <= int(priority) <= (len(entries) + 1)) or\n            (surrogate_id and not(1 <= int(priority) <= len(entries)))):\n            print(\"Invalid priority number. \")\n            raise forms.ValidationError(\"Invalid priority number\")\n\n        # empty string handler for the rest fields\n        # this seems ugly, it's probably better to use clean_<field_name>() for each of these fields.\n        for field in self.cleaned_data:\n            try:\n                # since these fields are handled manually, skipping these two\n                if field not in (\"relt_code\", \"natn_code\", \"stat_code\"):\n                    self.cleaned_data[field] = empty_string_handler(self.cleaned_data[field])\n            except NameError:\n                self.cleaned_data[field] = None\n\n        # checking whether given address is complete (street line1, city, and either state + zip or country) or completely empty\n        street_line1 = self.cleaned_data.get(\"street_line1\")\n        city = self.cleaned_data.get(\"city\")\n        stat_code = self.cleaned_data.get(\"stat_code\")\n        zip = self.cleaned_data.get(\"zip\")\n        natn_code = self.cleaned_data.get(\"natn_code\")\n        if (street_line1 or city or stat_code or zip or natn_code):\n            print(\"Invalid address, need street_line1, city, and either stat_code + zip or natn_code be filled correctly.\")\n            if not street_line1:\n                print(\"Invalid street line1. \")\n                raise forms.ValidationError('street_line1', \"Address field is required\")\n            if not city:\n                print(\"Invalid city. \")\n                raise forms.ValidationError('city', \"City field is required\")\n            if not natn_code and not(stat_code and zip):\n                print(\"Invalid natn_code or stat_code + zip. \")\n                raise forms.ValidationError(\"Nation field, or State + Zip fields is/are required\")\n\n        # commenting validations related to USA option part since it needs more revisions\n        \"\"\"\n        # checking specifically for USA country, whether given state, zip, and phone number are correct or completely empty.\n        # need revisions since it's complicated on implementation\n        if natn_code == Nation.objects.get(value=\"USA\").id: # alternatively, == \"LUS\":\n            if not(stat_code is None or stat_code != \"00\")):\n                print(\"Invalid stat_code\")\n                raise forms.ValidationError(\"Invalid state code\")\n            if not(zip is None or sanitization.validate_zip_usa(zip)):\n                print(\"Invalid zip\")\n                raise forms.ValidationError(\"Invalid zip code\")\n            # this if statement is weird, but OIT website behaves like this\n            if stat_code and not zip:\n                print(\"Invalid natn_code or stat_code + zip. \")\n                raise forms.ValidationError(\"Nation field, or State + Zip fields is/are required\")\n            # Below phone number validation is not completely true, people live in USA may have phone number from foreign country\n            # may add if ctry_code_phone == Nation.objects.get(value=\"USA\").phone_code but Canada adds more complication\n            phone_area = self.cleaned_data.get(\"phone_area\")\n            phone_number = self.cleaned_data.get(\"phone_number\")\n            if ((phone_area or phone_number) and\n                not(len(phone_area) == 3 and len(phone_number) == 7 and\n                    sanitization.validate_phone_num_usa(phone_area + phone_number))):\n                print(\"Invalid phone area + number. \")\n                raise forms.ValidationError(\"Invalid phone area and number\")\n        \"\"\"\n\n        return self.cleaned_data\n        # Possible TODOs: check validity of address, city and state based on zipcode\n\n    # clean_<field_name>() function is reponsible to validate one specific field.\n    def clean_relt_code(self, *args, **kwargs):\n        relt_code = self.cleaned_data.get(\"relt_code\")\n        relt_code = empty_string_handler(relt_code)\n        if not(relt_code is None or sanitization.validate_relation(relt_code)):\n            print(\"Invalid relation code. \")\n            raise forms.ValidationError(\"Invalid relation code\")\n\n        return relt_code\n\n    # adding this field validation since all state values are based on the table list anyway\n    def clean_stat_code(self, *args, **kwargs):\n        stat_code = self.cleaned_data.get(\"stat_code\")\n        stat_code = empty_string_handler(stat_code)\n        if not(stat_code is None or sanitization.validate_state_usa(stat_code)):\n            print(\"Invalid stat_code. \")\n            raise forms.ValidationError(\"Invalid state code\")\n\n        return stat_code\n\n    def clean_natn_code(self, *args, **kwargs):\n        natn_code = self.cleaned_data.get(\"natn_code\")\n        natn_code = empty_string_handler(natn_code)\n        if not(natn_code is None or sanitization.validate_nation_code(natn_code)):\n            print(\"Invalid natn_code. \")\n            raise forms.ValidationError(\"Invalid nation code\")\n\n        return natn_code\n\n    def clean_ctry_code_phone_code(self, *args, **kwargs):\n        ctry_code_phone_code = self.cleaned_data.get(\"ctry_code_phone_code\")\n        ctry_code_phone_code = empty_string_handler(ctry_code_phone_code)\n        if not(ctry_code_phone_code is None or sanitization.validate_country_phone_code(ctry_code_phone_code)):\n            print(\"Invalid ctry_code_phone_code. \")\n            raise forms.ValidationError(\"Invalid country phone code\")\n\n        return ctry_code_phone_code\n\n\n# Form for validating the request update of user's evacuation assistance\nclass SetEvacuationAssistanceForm(forms.ModelForm):\n    evacuation_assistance = forms.CharField(max_length=4)\n\n    class Meta:\n        model = Emergency\n        fields = [\n            'evacuation_assistance'\n        ]\n\n    def clean_evacuation_assistance(self, *args, **kwargs):\n        evacuation_assistance = self.cleaned_data.get(\"evacuation_assistance\")\n        # since evacuation assistance form must submit some values, evacuation_assistance should not be None\n        # hence, skipping empty_string_handler function\n        if not(sanitization.validate_checkbox(evacuation_assistance)):\n            print(\"Invalid checkbox evacuation_assistance. \")\n            raise forms.ValidationError(\"Invalid checkbox value\")\n\n        return evacuation_assistance\n\n# Form for validating the request update of user's emergency notifications\nclass SetEmergencyNotificationsForm(forms.ModelForm):\n    external_email = forms.CharField(max_length=128, required=False)\n    primary_phone = forms.CharField(max_length=72, required=False)\n    alternate_phone = forms.CharField(max_length=72, required=False)\n    sms_status_ind = forms.CharField(max_length=4)\n    sms_device = forms.CharField(max_length=72, required=False)\n\n    class Meta:\n        model = Emergency\n        fields = [\n            'external_email', 'primary_phone', 'alternate_phone', 'sms_status_ind', 'sms_device'\n        ]\n\n    def clean_external_email(self, *args, **kwargs):\n        external_email = self.cleaned_data.get(\"external_email\")\n        external_email = empty_string_handler(external_email)\n        if not(external_email is None or sanitization.validate_email(external_email)):\n            print(\"Invalid external email. \")\n            raise forms.ValidationError(\"Invalid email\")\n\n        return external_email\n\n    def clean_primary_phone(self, *args, **kwargs):\n        primary_phone = self.cleaned_data.get(\"primary_phone\")\n        primary_phone = empty_string_handler(primary_phone)\n        if not(primary_phone is None or sanitization.validate_phone_num_usa(primary_phone)):\n            print(\"Invalid primary phone number. \")\n            raise forms.ValidationError(\"Invalid phone number\")\n\n        return primary_phone\n\n    def clean_alternate_phone(self, *args, **kwargs):\n        alternate_phone = self.cleaned_data.get(\"alternate_phone\")\n        alternate_phone = empty_string_handler(alternate_phone)\n        if not(alternate_phone is None or sanitization.validate_phone_num_usa(alternate_phone)):\n            print(\"Invalid alternate phone. \")\n            raise forms.ValidationError(\"Invalid phone number\")\n\n        return alternate_phone\n\n    def clean_sms_status_ind(self, *args, **kwargs):\n        sms_status_ind = self.cleaned_data.get(\"sms_status_ind\")\n        sms_status_ind = empty_string_handler(sms_status_ind)\n        # since sms_status_ind must be submitted with a value, it should not be none\n        if not(sanitization.validate_checkbox(sms_status_ind)):\n            print(\"Invalid checkbox sms_status_ind. \")\n            raise forms.ValidationError(\"Invalid checkbox value\")\n\n        return sms_status_ind\n\n    # probably better placing this function in clean() instead, since it's dependent on another field.\n    def clean_sms_device(self, *args, **kwargs):\n        # if the user decides to opt out, then empty the device number\n        sms_status_ind = self.cleaned_data.get(\"sms_status_ind\")\n        if sms_status_ind == \"Y\":\n            return None     # sms_device = None\n        sms_device = self.cleaned_data.get(\"sms_device\")\n        sms_device = empty_string_handler(sms_device)\n        if not(sms_device is None or sanitization.validate_phone_num_usa(sms_device)):\n            print(\"Invalid sms_device phone number. \")\n            raise forms.ValidationError(\"Invalid phone number\")\n\n        return sms_device\n","sub_path":"emergency_app/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":13116,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"339238684","text":"import requests\nfrom  lxml import etree\nfrom selenium import webdriver\n\n\n\n\n# chrome = webdriver.Chrome(executable_path='C:\\Program Files (x86)\\Google\\Chrome\\Application\\chrome.exe')\n# chrome.get('https://www.bilibili.com')\n# ul = chrome.find_element_by_xpath('//nav[@id = \"main-nav\"]/ul')\n# lis = ul.find_elements_by_xpath('li')\n# print(len(lis))\n\n\nurl='http://www.aixianyuan.cn'   #输入我们的url\nget = requests.get(url).text   # get(url) 得到我们的网页, text将源网页转化为字符串\nselector = etree.HTML(get) # 将源码转换为xpath可以识别的TML格式\n\ninfo = {}  #字典用于储存信息\nnames = driver.find_element_by_xpath('//nav[@id = \"main-nav\"]//ul/li')\nfor i in range(names):\n\n    info['xinxi'] = selector.xpath('//nav[@id = \"main-nav\"]//ul/li/text()') # 定位\n    #names = driver.find_element_by_xpath('//nav[@id = \"main-nav\"]//ul/li')\n    #print(info)\n\nprint(info)","sub_path":"venv/Include/aixianyuan.py","file_name":"aixianyuan.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"184587514","text":"'''\nCreated on Jan 29, 2016\n\n@author: sharvani\n'''\n\nNULL_STRING = \"\"\nPLAYER_X = 'X'\nPLAYER_O = 'O'\nUNOCCUPIED = '*'\nNUMBER_OF_SQUARES = 5\n\nALGORITHM_SELECTION = 0\nPLAYER_SELECTION = 1\nCUTTING_OFF_DEPTH  = 2\nINPUT_FILE_BOARD_VALUE_START = 3\nINPUT_FILE_BOARD_VALUE_END = 7\nINPUT_FILE_CURRENT_STATE_START = 8\nINPUT_FILE_CURRENT_STATE_END = 12\n\nGREEDY_BEST_FIRST_SEARCH = '1'\nMINIMAX = '2'\nALPHA_BETA_PRUNING = '3'\nBATTLE_SIMULATION = '4'\n\nNEXT_STATE_FILE_NAME = 'next_state.txt'\nTRAVERSAL_LOG_FILE_NAME = 'traverse_log.txt'\nTRACE_STATE_FILE_NAME = 'trace_state.txt'\n\nROOT = \"root\"\n\nINITIAL_DEPTH = 0\n\nINFINITY = 10000\nINFINITY_STRING = \"Infinity\"\n\nMINIMAX_TRAVERSAL_LOG_FIRST_LINE = \"Node,Depth,Value\"\nALPHA_BETA_PRUNING_TRAVERSAL_LOG_FIRST_LINE = \"Node,Depth,Value,Alpha,Beta\"\nTRAVERSAL_LOG_ALPHABET_MAP = [\"A\",\"B\",\"C\",\"D\",\"E\"]\n\nACQUIRE = \"acquire\"\nRESET = \"reset\"\n\nCHECK_ADJACENCY = \"checkAdjacency\"\nRAID = \"raid\"\n\nBATTLE_SIMULATION_PLAYER1 = 1\nBATTLE_SIMULATION_PLAYER1_ALGORITHM = 2\nBATTLE_SIMULATION_PLAYER1_CUTOFF_DEPTH = 3\nBATTLE_SIMULATION_PLAYER2 = 4\nBATTLE_SIMULATION_PLAYER2_ALGORITHM = 5\nBATTLE_SIMULATION_PLAYER2_CUTOFF_DEPTH = 6\nBATTLE_SIMULATION_BOARD_VALUE_START_INDEX = 7\nBATTLE_SIMULATION_BOARD_STATE_START_INDEX = 12\n","sub_path":"src/game/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":1233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"299527833","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport django.contrib.gis.db.models.fields\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('api', '0002_location_user'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='NormalizedLocation',\n            fields=[\n                ('location_ptr', models.OneToOneField(parent_link=True, serialize=False, auto_created=True, to='api.Location', primary_key=True)),\n            ],\n            options={\n                'abstract': False,\n            },\n            bases=('api.location',),\n        ),\n        migrations.CreateModel(\n            name='Venue',\n            fields=[\n                ('id', models.AutoField(serialize=False, auto_created=True, primary_key=True, verbose_name='ID')),\n                ('unique_identifier', models.CharField(max_length=255, unique=True)),\n                ('name', models.CharField(max_length=500)),\n                ('url', models.URLField()),\n                ('point', django.contrib.gis.db.models.fields.PointField(srid=4326)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.AddField(\n            model_name='normalizedlocation',\n            name='venue',\n            field=models.ForeignKey(to='api.Venue'),\n            preserve_default=True,\n        ),\n        migrations.RemoveField(\n            model_name='location',\n            name='latitude',\n        ),\n        migrations.RemoveField(\n            model_name='location',\n            name='longitude',\n        ),\n        migrations.AddField(\n            model_name='location',\n            name='address',\n            field=models.CharField(max_length=255, default='My House'),\n            preserve_default=False,\n        ),\n        migrations.AddField(\n            model_name='location',\n            name='point',\n            field=django.contrib.gis.db.models.fields.PointField(srid=4326, default='Point (0 0)'),\n            preserve_default=False,\n        ),\n    ]\n","sub_path":"api/migrations/0003_auto_20150208_0241.py","file_name":"0003_auto_20150208_0241.py","file_ext":"py","file_size_in_byte":2087,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"383820051","text":"'''\r\nCreated on 2018年6月11日\r\n\r\n@author: admin\r\n'''\r\nimport unittest\r\nimport time\r\nfrom PO import LoginPage,LogoutPage\r\nfrom Public import BasePage\r\nfrom appium import webdriver\r\nclass Test(unittest.TestCase):\r\n    def setUp(self):\r\n        self.driver=webdriver.Remote('http://127.0.0.1:4723/wd/hub',BasePage.Base.desired_caps)\r\n        LoginPage.userlogin(self)\r\n    def tearDown(self):\r\n        self.driver.quit()\r\n    def test_logout(self):\r\n        time.sleep(5)\r\n        self.driver.tap([(608,48),(720,144)], 500)\r\n        self.driver.find_element_by_id('android:id/title').click()\r\n        time.sleep(5)\r\n        self.driver.find_element_by_id('android:id/button1').click()\r\n        self.assertEqual(u\"登入\",self.driver.find_element_by_id('com.example.todolist:id/loginBtn').get_attribute('text'),msg='验证失败')\r\nif __name__ == \"__main__\":\r\n    #import sys;sys.argv = ['', 'Test.testName']\r\n    unittest.main()","sub_path":"TestCase/test_logout.py","file_name":"test_logout.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"405789655","text":"import numpy as np\n\n# Calculate the Gini index for a split dataset\ndef gini_index(groups):\n\t'''\n\t\tGini index\n\n\t\tArgs:\n\t\t\tgroups : (left, right) dataset [n_datapoints, n_features + 1], [n_datapoints, n_features + 1]\n\n\t\tReturns:\n\t\t\tgini : gini index (scalar)\n\t'''\n\n\t# count all samples at split point\n\tn_instances = np.sum([np.shape(group)[0] for group in groups])\n\t# sum weighted Gini index for each group\n\tgini = 0.0\n\tfor group in groups:\n\t\tsize = float(np.shape(group)[0])\n\t\t# avoid divide by zero\n\t\tif size == 0:\n\t\t\tcontinue\n\t\tscore = 0.0\n\t\t# score the group based on the score for each class\n\t\t(values, counts) = np.unique(group[:,-1], return_counts=True)\n\t\tfor ind in range(np.size(values)):\n\t\t\tp = counts[ind] / np.sum(counts)\n\t\t\tscore += p * p\n\n\t\t# weight the group score by its relative size\n\t\tgini += (1.0 - score) * (size / n_instances)\n\n\t\tassert gini >= 0\n\t\t\n\treturn gini","sub_path":"vc_dim/practice/App/metric.py","file_name":"metric.py","file_ext":"py","file_size_in_byte":881,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"218022101","text":"from django.contrib import admin\nfrom .models import PrecipEvent, Raingage\n\nadmin.site.index_title = \"Swagger Administration\"\nadmin.site.site_header = \"Swagger Administration\"\n\n\nclass PrecipEventInline(admin.TabularInline):\n    model = PrecipEvent\n\n\n@admin.register(Raingage)\nclass RaingageAdmin(admin.ModelAdmin):\n    default_lat = 31.6445225\n    default_lon = -110.8707519\n    default_zoom = 7\n    wms_url = \"http://ows.terrestris.de/osm/service\"\n    wms_layer = \"TOPO-OSM-WMS\"\n    list_display = ['id', 'name', 'latitude', 'longitude', 'created', 'updated']\n    inlines = [PrecipEventInline,]\n\n\n@admin.register(PrecipEvent)\nclass PrecipEvent(admin.ModelAdmin):\n    list_display = ['id', 'raingage_name', 'month', 'year', 'precip', 'created', 'updated']\n\n","sub_path":"swagger/appclimate/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"218002178","text":"from pymongo import MongoClient\r\nfrom common.group_info_entity import GroupInfoEntity\r\nfrom common.group_simple_topic_info_entity import GroupSimpleTopicInfoEntity\r\n\r\nDOUBAN_MONGOS_IP = \"127.0.0.1\"\r\nDOUBAN_MONGOS_PORT = 27017\r\nDOUBAN_DB_NAME = \"douban_db\"\r\nSHUZU_GROUP_ID = \"603672\"\r\nGROUP_TOPIC_COLL_NAME = \"group_topic\"\r\n\r\nmongo_client = MongoClient(DOUBAN_MONGOS_IP, DOUBAN_MONGOS_PORT)\r\n\r\ndouban_db = mongo_client[DOUBAN_DB_NAME]\r\ngroup_shuzu_topic_collection = douban_db[GROUP_TOPIC_COLL_NAME + \"_\" + SHUZU_GROUP_ID]\r\n\r\ngroupInfoEntity = GroupInfoEntity()\r\ngroupInfoEntity.group_id = 1\r\ngroupInfoEntity.group_name = \"afadsafa\"\r\ngroupInfoEntity.group_url = \"afadsafa\"\r\n\r\n\r\ng_simple_topic = GroupSimpleTopicInfoEntity()\r\ng_simple_topic.topic_url = \"adfasdf\"\r\ng_simple_topic.topic_title = \"aa\"\r\ng_simple_topic.author_name = \"asdfa\"\r\ng_simple_topic.author_url = \"aasdfa\"\r\ng_simple_topic.num_replies = \"aasadf\"\r\ng_simple_topic.last_reply_time = \"asadfsada\"\r\n\r\n\r\n# topic = {\"test1\": \"afa\", \"sfadf\": \"sfadf\"}\r\nprint(g_simple_topic.my_to_dict())\r\nid = group_shuzu_topic_collection.insert_one(g_simple_topic.my_to_dict()).inserted_id\r\n\r\n# print(id)\r\n","sub_path":"test/project/python/selenium_test/test/mongodb_test3.py","file_name":"mongodb_test3.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"615274620","text":"from unittest import mock\n\nimport redis\nimport pytest\n\nfrom config import REDIS_DSN, STORAGE_NAME\nfrom main import create_app\n\n\n@pytest.fixture\ndef initial_rates():\n    return [\n        {'currency': 'USD', 'rate': 63.379},\n        {'currency': 'EUR', 'rate': 69.182},\n    ]\n\n\n@pytest.fixture\ndef cli(loop, aiohttp_client):\n    app = create_app()\n    return loop.run_until_complete(aiohttp_client(app))\n\n\n@pytest.fixture()\ndef redis_cli():\n    conn = redis.from_url(REDIS_DSN)\n    yield conn\n    conn.close()\n\n\n@pytest.fixture(autouse=True)\ndef prepare_rates(redis_cli, initial_rates):\n    redis_cli.delete(STORAGE_NAME)\n    for rate in initial_rates:\n        redis_cli.hset(STORAGE_NAME, rate['currency'], rate['rate'])\n\n","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"407985432","text":"# -*- coding: utf-8 -*-\nfrom bs4 import BeautifulSoup\nimport requests\nimport urllib\nimport time\nimport os\nimport sys\nimport re\nfrom .services import _wikia, _musixmatch, _songmeanings, _songlyrics, _genius, _versuri, _minilyrics\n\n# With Sync. Of course, there is one for now, but for the sake of\n# make the code a little bit more cleaner, is declared.\nservices_list1 = [_minilyrics]\n\n# Without Sync.\nservices_list2 = [_wikia, _musixmatch, _songmeanings, _songlyrics, _genius, _versuri]\n\nartist = \"\"\nsong = \"\"\nurl = \"\"\n\n'''\ncurrent_service is used to store the current index of the list.\nUseful to change the lyrics with the button \"Next Lyric\" if\nthe service returned a wrong song\n'''\ncurrent_service = -1\n\n\ndef load_lyrics(artist, song, sync=False):\n    error = \"Error: Could not find lyrics.\"\n    global current_service\n\n    if current_service == len(services_list2)-1: current_service = -1\n\n    if sync == True:\n        lyrics, url, timed = _minilyrics(artist, song)\n        current_service = -1\n\n    if sync == True and lyrics == error or sync == False:\n        timed = False\n        for i in range (current_service+1, len(services_list2)):\n            lyrics, url = services_list2[i](artist, song)\n            current_service = i\n            if lyrics != error:\n                lyrics = lyrics.replace(\"&amp;\", \"&\").replace(\"`\", \"'\").strip()\n                break\n\n    #return \"Error: Could not find lyrics.\"  if the for loop doens't find any lyrics\n    return(lyrics, url, timed)\n\n# def main():\n#     song = 'I think she like me'\n#     s = load_lyrics('Rick ross', song, False)\n#     print(s[0])\n\n# if __name__ == '__main__':\n#     main()","sub_path":"api/services/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"452445206","text":"import torch.utils.data as data\nfrom PIL import Image\nimport os\nimport os.path\nimport numpy.ma as ma\nimport scipy.misc\nimport scipy.io as scio\nimport numpy as np\n\nobj_id = 1\nbbox = {}\ninput_file = open(\"/Users/jeremywang/Desktop/YCB/YCB_models/classes.txt\", 'r')\nwhile 1:\n    input_line = input_file.readline()\n    if not input_line:\n        break\n    if input_line[-1:] == '\\n':\n        input_line = input_line[:-1]\n\n    input_file_2 = open(\"/Users/jeremywang/Desktop/YCB/YCB_models/{0}/points.xyz\".format(input_line), 'r')\n    bbox[obj_id] = []\n    while 1:\n        input_line_2 = input_file_2.readline()\n        if not input_line_2:\n            break\n        if input_line_2[-1:] == '\\n':\n            input_line_2 = input_line_2[:-1]\n        input_line_2 = input_line_2.split(' ')\n        bbox[obj_id].append([float(input_line_2[0]), float(input_line_2[1]), float(input_line_2[2])])\n    input_file_2.close()\n    bbox[obj_id] = np.array(bbox[obj_id])\n    obj_id += 1\n    if obj_id > 15:\n    \tbreak\ninput_file.close()\n\n\n\n\ndep = np.load('/Users/jeremywang/Desktop/my_six/syn_data/1/0_dep.npy')\n\ncam_cx = 325.26110\ncam_cy = 242.04899\ncam_fx = 572.41140\ncam_fy = 573.57043\n\nwidth = 640\nheight = 480\n\n# near = 0.40427\n# far = 10.0682\n# right = 512.0\n# left = 0.0\n# top = 512.0\n# bottom = 0.0\n\n# resolutionX = 512.0\n# resolutionY = 512.0\n\n# print(m)\n\nxmap = np.array([[j for i in range(width)] for j in range(height)])\nymap = np.array([[i for i in range(width)] for j in range(height)])\n\ndepth_masked = dep.flatten()[:, np.newaxis].astype(np.float32)\n# print(depth_masked)\n# depth_masked = depth_masked / np.linalg.norm(depth_masked)\n# print(depth_masked)\nxmap_masked = xmap.flatten()[:, np.newaxis].astype(np.float32)\nymap_masked = ymap.flatten()[:, np.newaxis].astype(np.float32)\n\npt2 = depth_masked / 1000.0\npt0 = (ymap_masked - cam_cx) * pt2 / cam_fx\npt1 = (xmap_masked - cam_cy) * pt2 / cam_fy\ncloud = np.concatenate((pt0, pt1, pt2), axis=1)\n\n\n\n# R = [[1.0, 0.0, 0.0], \n#     [0.0, -1.0, 0.0],\n#     [0.0, 0.0, -1.0]]\n# R = np.array(R)\n# t = np.array([0, 0, 400])\n\n# cloud = np.dot(cloud - t, R)\n\nfw = open('/Users/jeremywang/Desktop/aaa_cld.xyz', 'w')\nfor it in cloud:\n   fw.write('{0} {1} {2}\\n'.format(it[0], it[1], it[2]))\nfw.close()\n\n\npose = np.load('/Users/jeremywang/Desktop/my_six/syn_data/1/0_pose.npy')[()]\nid_list = np.load('/Users/jeremywang/Desktop/my_six/syn_data/1/0_id.npy')[()]\n\nfor idx in range(len(id_list)):\n    id_now = id_list[idx]\n    pose_now = pose[idx][()]\n    R = pose_now['R']\n    T = pose_now['T'] / 1000.0\n\n\n    cld = np.dot(bbox[id_now], R.T) + T\n\n\n    fw = open('/Users/jeremywang/Desktop/{0}_cld.xyz'.format(idx), 'w')\n    for it in cld:\n       fw.write('{0} {1} {2}\\n'.format(it[0], it[1], it[2]))\n    fw.close()\n\n    # scipy.misc.imsave('/Users/jeremywang/Desktop/0000_2.png', dep)\n","sub_path":"tools/depth2cloud.py","file_name":"depth2cloud.py","file_ext":"py","file_size_in_byte":2818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"365635100","text":"lst=[10,20,[25,30],40,[50,70],80]\nflatlist=[]\ndef flatten(lst,index=0):\n    '''\n      Objective : To flat a nested list\n      Input Parameter : lst - A nested list to be flattened\n                        index - index of list element\n      Approch : using recursion and type() functioon\n    '''\n    if index==len(lst):\n        return flatlist\n    elif type(lst[index])==list:\n         n=len(lst[index])\n         for i in range(n):\n             flatlist.append(lst[index][i])\n         return flatten(lst,index+1)\n    else:\n         flatlist.append(lst[index])\n         return flatten(lst,index+1)\n\nprint(\"flattened list is\",flatten(lst,0))\n","sub_path":"flattenlist.py","file_name":"flattenlist.py","file_ext":"py","file_size_in_byte":639,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"295572297","text":"import os\nimport sqlite3\n'''\nCreate a file named study_part1.py and complete the exercise below.\nThe only library you should need to import is sqlite3.\nDon't forget to be PEP8 compliant!\nCreate a new database file call study_part1.sqlite3\n'''\nconn_path = os.path.join(os.path.dirname(__file__), \"study_part1.sqlite3\")\nconn = sqlite3.connect(conn_path)\ncursor = conn.cursor()\n'''\nCreate a table with the following columns\n student - string\n studied - string\n grade - int\n age - int\n sex - string\n'''\ncreate_table_query = '''\nCREATE TABLE students(\n    id INTEGER PRIMARY KEY AUTOINCREMENT,\n    student VARCHAR(80),\n    studied TEXT,\n    grade INT,\n    age INT,\n    sex VARCHAR(20));\n'''\ncursor.execute('DROP TABLE IF EXISTS students;')\ncursor.execute(create_table_query)\n\n# Fill the table with the following data​\n#  'Lion-O', 'True', 85, 24, 'Male'\n#  'Cheetara', 'True', 95, 22, 'Female'\n#  'Mumm-Ra', 'False', 65, 153, 'Male'\n#  'Snarf', 'False', 70, 15, 'Male'\n#  'Panthro', 'True', 80, 30, 'Male'\n\nsample_data = [\n    ('Lion-O', 'True', 85, 24, 'Male'),\n    ('Cheetara', 'True', 95, 22, 'Female'),\n    ('Mumm-Ra', 'False', 65, 153, 'Male'),\n    ('Snarf', 'False', 70, 15, 'Male'),\n    ('Panthro', 'True', 80, 30, 'Male')\n ]\n\nfor row in sample_data:\n    insert_query = f'''INSERT INTO students (student, studied, grade, age, sex)\n    VALUES {row}'''\n    cursor.execute(insert_query)\nconn.commit()\n'''\nSave your data. You can check that everything is working so far if you can\nview the table and data in DBBrowser.\n\nWrite the following queries to check your work. Query outputs should be\nformatted for readability, don't simply print a number to the screen with\nno explanation, add context.\n'''\n# What is the average age? Expected Result - 48.8\navg_age_query = '''\nSELECT AVG(age) AS average_student_age\nFROM students\n'''\navg_age = cursor.execute(avg_age_query).fetchone()\nprint('\\nAverage Age of Students =', avg_age, '\\n')\n\n# What is the name of the female students? Expected Result - 'Cheetara'\nfemale_query = '''\nSELECT student AS Female_Student\nFROM students\nWHERE sex == 'Female'\n'''\nfemale = cursor.execute(female_query).fetchone()\nprint('Name of the Female Student =', female, '\\n')\n\n# How many students studied? Expected Results - 3\nstudied_query = '''\nSELECT COUNT(student) AS Count_of_Students_Who_Studied\nFROM students\nWHERE studied == 'True'\n'''\nstudied = cursor.execute(studied_query).fetchone()\nprint('Number of Students who Studied =', studied, '\\n')\n\n# Return all students and all columns, sorted by student names\n#   in alphabetical order.\nsorted_students = '''\nSELECT *\nFROM students\nORDER BY student\n'''\nsorted_stud = cursor.execute(sorted_students).fetchall()\nprint('All Students sorted Alphabetically:\\n', sorted_stud)\n\nconn.commit()\ncursor.close()\nconn.close()\n","sub_path":"Sprint-Challenge/study_part1.py","file_name":"study_part1.py","file_ext":"py","file_size_in_byte":2788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"619568281","text":"\"\"\"\n65. アナロジータスクでの正解率Permalink\n64の実行結果を用い，意味的アナロジー（semantic analogy）と文法的アナロジー（syntactic analogy）の正解率を測定せよ．\n\"\"\"\n\nmy_ans_file = \"64_my_ans_file\"\nans_file = \"64_ans_file\"\n\nif __name__ == \"__main__\":\n    sem = 0\n    sem_acc = 0\n    syn = 0\n    syn_acc = 0\n    with open(my_ans_file, encoding=\"utf-8\") as file_my_ans,\\\n            open(ans_file, encoding=\"utf-8\") as file_ans:\n        for x, y in zip(file_my_ans, file_ans):\n            y = y.rstrip(\"\\n\")\n            category, my_ans = x.split(\"\\t\")\n            my_ans = my_ans.replace(\"\\'\", \"\").replace(\"(\", \"\").replace(\",\", \"\").replace(\")\", \"\")\n            my_ans, prob = my_ans.split()\n            if category.startswith(\"gram\"):\n                syn += 1\n                if my_ans == y:\n                    syn_acc += 1\n            else:\n                sem += 1\n                if my_ans == y:\n                    sem_acc += 1\n    \n    print(f\"semantic analogy: {sem_acc/sem}\\nsyntactic analogy: {syn_acc/syn}\")\n\n","sub_path":"kondo/chapter07/knock65.py","file_name":"knock65.py","file_ext":"py","file_size_in_byte":1069,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"295279451","text":"import oauth, time, datetime\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import HttpResponseRedirect, HttpResponse\n\nfrom django.shortcuts import render_to_response\nfrom django.core.urlresolvers import reverse\n\nfrom twitter_auth.util.twitterutils import *\n\nfrom django.conf import settings\n\nTWITTER_AUTH_HOME = getattr(settings, 'TWITTER_AUTH_HOME', 'twitter_oauth_main')\nTWITTER_AUTH_AUTHENTICATED = getattr(settings, 'TWITTER_AUTH_AUTHENTICATED', 'twitter_oauth_friend_list')\n\ndef main(request):\n    if request.session.has_key('access_token'):\n        return HttpResponseRedirect(reverse(TWITTER_AUTH_AUTHENTICATED))\n    else:\n        return render_to_response('twitter_auth/base.html')\n\ndef logout_(request):\n    logout(request)\n    request.session.clear()\n    return HttpResponseRedirect(reverse(TWITTER_AUTH_HOME))\n\ndef login_(request):\n    \"/auth/\"\n    token = get_unauthorised_request_token()\n    auth_url = get_authorisation_url(token)\n    response = HttpResponseRedirect(auth_url)\n    request.session['unauthed_token'] = token.to_string()\n    return response\n\ndef return_(request):\n    \"/return/\"\n    unauthed_token = request.session.get('unauthed_token', None)\n    if not unauthed_token:\n        return HttpResponse(\"No un-authed token cookie\")\n\n    token = get_oauth_token_from_string(unauthed_token)\n    if token.key != request.GET.get('oauth_token', 'no-token'):\n        return HttpResponse(\"Something went wrong! Tokens do not match\")\n    verifier = request.GET.get('oauth_verifier')\n    access_token = exchange_request_token_for_access_token(token, params={'oauth_verifier':verifier})\n    user = authenticate(oauth_access_token=access_token)\n    if user is not None:\n        login(request, user)\n    else:\n        return HttpResponse(\"Something went wrong! Could not log you in\") \n    response = HttpResponseRedirect(reverse(TWITTER_AUTH_AUTHENTICATED))\n    return response\n\ndef friend_list(request):\n    api = get_twitter_api_from_request(request)\n    users = api.GetFriends()\n    return render_to_response('twitter_auth/list.html', {'users': users})\n","sub_path":"views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2157,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"444503258","text":"myTree = ['a', ['b', ['d',[],[]], ['e',[],[]] ], ['c', ['f',[],[]], []] ]\nprint(myTree)\nprint('left subtree = ', myTree[1])\nprint('root = ', myTree[0])\nprint('right subtree = ', myTree[2])\n\ndef BinaryTree(r):\n  return [r, [], []]\n\ndef insertLeft(root, newBranch):\n  t = root.pop(1)\n  if len(t) > 1:\n    root.insert(1, [newBranch, t, []])\n  else:\n    root.insert(1, [newBranch, [], []])\n  return root\n\ndef insertRight(root, newBranch):\n  t = root.pop(2)\n  if len(t) > 1:\n    root.insert(2, [newBranch, [], t])\n  else:\n    root.insert(2, [newBranch, [], []])\n  return root\n\ndef getRootVal(root):\n  return root[0]\n\ndef setRootVal(root, newVal):\n  root[0] = newVal\n\ndef getLeftChild(root):\n  return root[1]\n\ndef getRightChild(root):\n  return root[2]\n\nr = BinaryTree(3)\n\ndef testTree():\n  r = BinaryTree(3)\n  print(r)\n  insertLeft(r,4)\n  print(r)\n  insertLeft(r,5)\n  print(r)\n  insertRight(r,6)\n  print(r)\n  insertRight(r,7)\n  print(r)\n  l = getLeftChild(r)\n  print(l)\n\n  setRootVal(l,9)\n  print(r)\n  insertLeft(l,11)\n  print(r)\n  print(getRightChild(getRightChild(r)))","sub_path":"pythonds/chapter_7/7_4_ListOfList.py","file_name":"7_4_ListOfList.py","file_ext":"py","file_size_in_byte":1064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"80122916","text":"from bs4 import BeautifulSoup\nimport re\nimport json\nimport os.path\n\nyr = \"2015\"\n\nteam_pattern = re.compile('^\\\\s*(\\\\d+).*')\nrankings = []\nwith open(os.path.join(yr, \"PNW_Leaderboard.html\")) as f:\n    leaderboard = BeautifulSoup(f, \"html.parser\")\n[table] = leaderboard.select(\"table\")\n[tbody] = table.select(\"tbody\")\ntrs = tbody.select(\"tr\")\nfor tr in trs:\n    tds = tr.select(\"td\")\n    rank = int(tds[0].text)\n    score = int(tds[1].text)\n    team = tds[2].text\n    m = team_pattern.match(team)\n    team_number = int(m.group(1))\n    champ_score = tds[5].text.strip()\n    did_district_champs = False\n    did_world = False\n    if champ_score != '---':\n        did_district_champs = True\n    worlds_qual = tds[6].text\n    if 'Qualified' in worlds_qual:\n        did_world = True\n\n    ranking = {\n        'rank': rank,\n        'score': score,\n        'team_number': team_number,\n        'team': team.strip(),\n        'did_district': did_district_champs,\n        'did_world': did_world\n    }\n    rankings.append(ranking)\n\n\nfor ranking in rankings:\n    print (ranking)\n\nwith open(\"leaderboard.json\", \"w\") as f:\n    json.dump({'rankings': rankings}, f)\n","sub_path":"district_stats/parse_leaderboard.py","file_name":"parse_leaderboard.py","file_ext":"py","file_size_in_byte":1145,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"601252352","text":"import string\n\ndef sunday(T='', P=''):\n\n    \"\"\"\n    Expected running time complexity: O((n-m)m).\n    @param T := text to search inside\n    @param P := string to search for\n    @return list containing offsets (shifts) where pattern is found inside text\n    \"\"\"\n\n    i = 0\n    offsets = []\n    n = len(T)\n    m = len(P)\n    occ = init_occ(P)\n\n    for i in range(n - m + 1):\n        if matches_at(T, P, i):\n            offsets.append(i)\n        i += m\n        if i < n:\n            i -= occ[T[i]]\n\n    return offsets\n\ndef init_occ(P=''):\n\n    \"\"\"\n    @param P := string to preprocess\n    @return := array which, for each symbol of the alphabet, \n    stores the position of its rightmost occurrence in the pattern, \n    or -1 if the symbol does not occur in the pattern.\n    \"\"\"\n\n    occ = {}\n    occ['\\xfc'] = -1\n    occ['\\xe9'] = -1\n\n    m = len(P)\n\n    for char in string.printable:\n        occ[char] = -1\n        \n    for i in range(m):\n        char = P[i]\n        occ[char] = i\n\n    return occ\n\ndef matches_at(T='', P='', i=0):\n\n    \"\"\"\n    Checks whether pattern matches with text at specified index.\n    \"\"\"\n\n    for char in P:\n        if char != T[i]:\n            return False\n        i += 1\n\n    return True\n","sub_path":"algorithms/sunday.py","file_name":"sunday.py","file_ext":"py","file_size_in_byte":1213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"264785872","text":"'''\nExercício 2\n- Crie um programa para calcular e exibir na tela o peso\nideal. IMC = (peso / (altura^2))\n'''\nimport locale\nlocale.setlocale(locale.LC_ALL,\"\")\n\nprint('Calculadora de peso ideal')\naltura = float(input('Digite o valor da sua altura (em metros): ').replace(',','.'))\n\n'''\nTabela de peso ideal pelo IMC (OMS)\n--------------------------------------------\nSituação                    IMC em adultos\nabaixo do peso ideal        abaixo de 18,5\nno peso ideal               entre 18,5 e 25\nacima do peso ideal         entre 25 e 30\nobeso                       acima de 30\n--------------------------------------------\nDesse modo, o peso ideal é a faixa cujo IMC fica entre 18,5 e 25.\nComo IMC = Peso / Altura², o peso ideal pode ser calculado pela relação:\n\nPeso = IMC x Altura²\n'''\npeso_minimo = round(18.5 * altura**2,2)\npeso_maximo = round(25 * altura**2,2)\nprint('O seu peso ideal (IMC entre 18,5 e 25) é {0:.2f} e {1:.2f} quilos.'.format(peso_minimo,peso_maximo))","sub_path":"01_Fundamentos/ex02.py","file_name":"ex02.py","file_ext":"py","file_size_in_byte":982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"81657856","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @author  :   naive dormin\n# @time    :   2021/09/06 17:16:00\n# @version :   1.0.0\n#\n# Copyright (c) 2021 IRDC Sensetime. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS-IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport math\nimport copy\n\nimport torch\nimport torch.nn.functional as F\nfrom typing import Optional, List\nfrom torch import nn, Tensor\n\nimport numpy as np\nimport torch.nn as nn\nimport torch.nn.init as init\nfrom functools import partial\nfrom collections import OrderedDict\nimport torch.nn.functional as F\nfrom utils.misc_n import batch_index_select\nfrom timm.models.layers import trunc_normal_\nfrom timm.models.layers import DropPath, to_2tuple\nfrom utils.log_helper import default_logger as logger\n\nimport torch\nimport numpy as np\nimport torch.nn as nn\n\ndef _cfg(url='', **kwargs):\n    return {\n        'url': url,\n        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,\n        'crop_pct': .9, 'interpolation': 'bicubic',\n        'mean': (0.485, 0.456, 0.406), 'std': (0.229, 0.224, 0.225),\n        'classifier': 'head',\n        **kwargs\n    }\n\ndefault_cfgs = {\n    'LV_ViT_Tiny': _cfg(),\n    'LV_ViT': _cfg(),\n    'LV_ViT_Medium': _cfg(crop_pct=1.0),\n    'LV_ViT_Large': _cfg(crop_pct=1.0),\n}\n\nMODEL_REGISTRY = {}\ndef register_model_architecture(model_name):\n    def wrapper(fn):\n        MODEL_REGISTRY[model_name] = fn\n        return fn\n    return wrapper\n\nDROPOUT_FLOPS = 4\nLAYER_NORM_FLOPS = 5\nACTIVATION_FLOPS = 8\nSOFTMAX_FLOPS = 5\n\nclass GroupLinear(nn.Module):\n    '''\n    Group Linear operator \n    '''\n    def __init__(self, in_planes, out_channels,groups=1, bias=True):\n        super(GroupLinear, self).__init__()\n        assert in_planes%groups==0\n        assert out_channels%groups==0\n        self.in_dim = in_planes\n        self.out_dim = out_channels\n        self.groups=groups\n        self.bias = bias\n        self.group_in_dim = int(self.in_dim/self.groups)\n        self.group_out_dim = int(self.out_dim/self.groups)\n\n        self.group_weight = nn.Parameter(torch.zeros(self.groups, self.group_in_dim, self.group_out_dim))\n        self.group_bias=nn.Parameter(torch.zeros(self.out_dim))\n\n    def forward(self, x):\n        t,b,d=x.size()\n        x = x.view(t,b,self.groups,int(d/self.groups))\n        out = torch.einsum('tbgd,gdf->tbgf', (x, self.group_weight)).reshape(t,b,self.out_dim)+self.group_bias\n        return out\n    def extra_repr(self):\n        s = ('{in_dim}, {out_dim}')\n        if self.groups != 1:\n            s += ', groups={groups}'\n        if self.bias is None:\n            s += ', bias=False'\n        return s.format(**self.__dict__)\n\nclass Mlp(nn.Module):\n    '''\n    MLP with support to use group linear operator\n    '''\n    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0., group=1):\n        super().__init__()\n        out_features = out_features or in_features\n        hidden_features = hidden_features or in_features\n        if group==1:\n            self.fc1 = nn.Linear(in_features, hidden_features)\n            self.fc2 = nn.Linear(hidden_features, out_features)\n        else:\n            self.fc1 = GroupLinear(in_features, hidden_features,group)\n            self.fc2 = GroupLinear(hidden_features, out_features,group)\n        self.act = act_layer()\n\n        self.drop = nn.Dropout(drop)\n\n    def forward(self, x):\n        x = self.fc1(x)\n        x = self.act(x)\n        x = self.drop(x)\n        x = self.fc2(x)\n        x = self.drop(x)\n        return x\n\nclass GroupNorm(nn.Module):\n    def __init__(self, num_groups, embed_dim, eps=1e-5, affine=True):\n        super().__init__()\n        self.gn = nn.GroupNorm(num_groups, embed_dim,eps,affine)\n\n    def forward(self, x):\n        B,T,C = x.shape\n        x = x.view(B*T,C)\n        x = self.gn(x)\n        x = x.view(B,T,C)\n        return x\n\n\nclass Attention(nn.Module):\n    '''\n    Multi-head self-attention\n    from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py\n    with some modification to support different num_heads and head_dim.\n    '''\n    def __init__(self, dim, num_heads=8, head_dim=None, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):\n        super().__init__()\n        self.num_heads = num_heads\n        if head_dim is not None:\n            self.head_dim=head_dim\n        else:\n            head_dim = dim // num_heads\n            self.head_dim = head_dim\n        self.scale = qk_scale or head_dim ** -0.5\n\n        self.qkv = nn.Linear(dim, self.head_dim* self.num_heads * 3, bias=qkv_bias)\n        self.attn_drop = nn.Dropout(attn_drop)\n        self.proj = nn.Linear(self.head_dim* self.num_heads, dim)\n        self.proj_drop = nn.Dropout(proj_drop)\n\n    def softmax_with_policy(self, attn, policy, eps=1e-6):\n        B, N, _ = policy.size()\n        B, H, N, N = attn.size()\n        attn_policy = policy.reshape(B, 1, 1, N)  # * policy.reshape(B, 1, N, 1)\n        eye = torch.eye(N, dtype=attn_policy.dtype, device=attn_policy.device).view(1, 1, N, N)\n        attn_policy = attn_policy + (1.0 - attn_policy) * eye\n        max_att = torch.max(attn, dim=-1, keepdim=True)[0]\n        attn = attn - max_att\n        # attn = attn.exp_() * attn_policy\n        # return attn / attn.sum(dim=-1, keepdim=True)\n\n        # for stable training\n        attn = attn.to(torch.float32).exp_() * attn_policy.to(torch.float32)\n        attn = (attn + eps/N) / (attn.sum(dim=-1, keepdim=True) + eps)\n        return attn.type_as(max_att)\n\n    def forward(self, x, policy, padding_mask=None):\n        B, N, C = x.shape\n        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)\n        # B,heads,N,C/heads \n        q, k, v = qkv[0], qkv[1], qkv[2]\n        \n        # trick here to make q@k.t more stable\n        attn = ((q * self.scale) @ k.transpose(-2, -1))\n        if padding_mask is not None:\n            # attn = attn.view(B, self.num_heads, N, N)\n            # attn = attn.masked_fill(\n            #     padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool),\n            #     float(\"-inf\"),\n            # )\n            # attn_float = attn.softmax(dim=-1, dtype=torch.float32)\n            # attn = attn_float.type_as(attn)\n            raise NotImplementedError\n        else:\n            if policy is None:\n                attn = attn.softmax(dim=-1)\n            else:\n                attn = self.softmax_with_policy(attn, policy)\n        attn = self.attn_drop(attn)\n\n        x = (attn @ v).transpose(1, 2).reshape(B, N, self.head_dim* self.num_heads)\n        x = self.proj(x)\n        x = self.proj_drop(x)\n        return x\n        \nclass Block(nn.Module):\n    '''\n    Pre-layernorm transformer block\n    '''\n    def __init__(self, dim, num_heads, head_dim=None, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,\n                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, group=1, skip_lam=1.):\n        super().__init__()\n        self.dim = dim\n        self.mlp_hidden_dim = int(dim * mlp_ratio)\n        self.skip_lam = skip_lam\n\n        self.norm1 = norm_layer(dim)\n        self.attn = Attention(\n            dim, num_heads=num_heads, head_dim=head_dim, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)\n        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()\n        self.norm2 = norm_layer(dim)\n        self.mlp = Mlp(in_features=dim, hidden_features=self.mlp_hidden_dim, act_layer=act_layer, drop=drop, group=group)\n\n    def forward(self, x, policy=None, padding_mask=None):\n        x = x + self.drop_path(self.attn(self.norm1(x), policy, padding_mask))/self.skip_lam\n        x = x + self.drop_path(self.mlp(self.norm2(x)))/self.skip_lam\n        return x\n\n    def flops(self, s):\n        heads = self.attn.num_heads\n        h = self.dim\n        i = self.mlp_hidden_dim\n        mha_block_flops = dict(\n        kqv=3 * h * h  ,\n        attention_scores=h * s,\n        attn_softmax=SOFTMAX_FLOPS * s * heads,\n        attention_dropout=DROPOUT_FLOPS * s * heads,\n        attention_scale=s * heads,\n        attention_weighted_avg_values=h * s,\n        attn_output=h * h,\n        attn_output_bias=h,\n        attn_output_dropout=DROPOUT_FLOPS * h,\n        attn_output_residual=h,\n        attn_output_layer_norm=LAYER_NORM_FLOPS * h,)\n        ffn_block_flops = dict(\n        intermediate=h * i,\n        intermediate_act=ACTIVATION_FLOPS * i,\n        intermediate_bias=i,\n        output=h * i,\n        output_bias=h,\n        output_dropout=DROPOUT_FLOPS * h,\n        output_residual=h,\n        output_layer_norm=LAYER_NORM_FLOPS * h,)\n\n        return sum(mha_block_flops.values())*s + sum(ffn_block_flops.values())*s\n\nclass MHABlock(nn.Module):\n    \"\"\"\n    Multihead Attention block with residual branch\n    \"\"\"\n    def __init__(self, dim, num_heads, head_dim=None, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,\n                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, group=1, skip_lam=1.):\n        super().__init__()\n        self.dim = dim\n        self.norm1 = norm_layer(dim)\n        self.skip_lam = skip_lam\n        self.attn = Attention(\n            dim, num_heads=num_heads, head_dim=head_dim, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)\n        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()\n\n    def forward(self, x, padding_mask=None):\n        x = x + self.drop_path(self.attn(self.norm1(x*self.skip_lam), padding_mask))/self.skip_lam\n        return x\n\n    def flops(self, s):\n        heads = self.attn.num_heads\n        h = self.dim\n        block_flops = dict(\n        kqv=3 * h * h ,\n        attention_scores=h * s,\n        attn_softmax=SOFTMAX_FLOPS * s * heads,\n        attention_dropout=DROPOUT_FLOPS * s * heads,\n        attention_scale=s * heads,\n        attention_weighted_avg_values=h * s,\n        attn_output=h * h,\n        attn_output_bias=h,\n        attn_output_dropout=DROPOUT_FLOPS * h,\n        attn_output_residual=h,\n        attn_output_layer_norm=LAYER_NORM_FLOPS * h,)\n\n        return sum(block_flops.values())*s\n\nclass FFNBlock(nn.Module):\n    \"\"\"\n    Feed forward network with residual branch\n    \"\"\"\n    def __init__(self, dim, num_heads, head_dim=None, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,\n                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, group=1, skip_lam=1.):\n        super().__init__()\n        self.skip_lam = skip_lam\n        self.dim = dim\n        self.mlp_hidden_dim = int(dim * mlp_ratio)\n\n        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()\n        self.norm2 = norm_layer(dim)\n        self.mlp = Mlp(in_features=dim, hidden_features=self.mlp_hidden_dim, act_layer=act_layer, drop=drop, group=group)\n    def forward(self, x):\n        x = x + self.drop_path(self.mlp(self.norm2(x*self.skip_lam)))/self.skip_lam\n        return x\n    def flops(self, s):\n        heads = self.attn.num_heads\n        h = self.dim\n        i = self.mlp_hidden_dim\n        block_flops = dict(\n        intermediate=h * i,\n        intermediate_act=ACTIVATION_FLOPS * i,\n        intermediate_bias=i,\n        output=h * i,\n        output_bias=h,\n        output_dropout=DROPOUT_FLOPS * h,\n        output_residual=h,\n        output_layer_norm=LAYER_NORM_FLOPS * h,)\n\n        return sum(block_flops.values())*s\n\nclass HybridEmbed(nn.Module):\n    \"\"\" CNN Feature Map Embedding\n    Extract feature map from CNN, flatten, project to embedding dim.\n    from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py\n    \"\"\"\n    def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768):\n        super().__init__()\n        assert isinstance(backbone, nn.Module)\n        img_size = to_2tuple(img_size)\n        self.img_size = img_size\n        self.backbone = backbone\n        if feature_size is None:\n            with torch.no_grad():\n                training = backbone.training\n                if training:\n                    backbone.eval()\n                o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))[-1]\n                feature_size = o.shape[-2:]\n                feature_dim = o.shape[1]\n                backbone.train(training)\n        else:\n            feature_size = to_2tuple(feature_size)\n            feature_dim = self.backbone.feature_info.channels()[-1]\n        self.num_patches = feature_size[0] * feature_size[1]\n        self.proj = nn.Conv2d(feature_dim, embed_dim,kernel_size=1)\n\n    def forward(self, x):\n        x = self.backbone(x)[-1]\n        x = self.proj(x)\n        return x\n\n\nclass PatchEmbedNaive(nn.Module):\n    \"\"\" \n    Image to Patch Embedding\n    from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py\n    \"\"\"\n    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):\n        super().__init__()\n        img_size = to_2tuple(img_size)\n        patch_size = to_2tuple(patch_size)\n        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])\n        self.img_size = img_size\n        self.patch_size = patch_size\n        self.num_patches = num_patches\n        self.embed_dim = embed_dim\n\n        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)\n\n    def forward(self, x):\n        B, C, H, W = x.shape\n        assert H == self.img_size[0] and W == self.img_size[1], \\\n            f\"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).\"\n        x = self.proj(x)\n        return x\n\n    def flops(self):\n        img_size = self.img_size[0]\n        block_flops = dict(\n        proj=img_size*img_size*3*self.embed_dim,\n        )\n        return sum(block_flops.values())\n\nclass PatchEmbed4_2(nn.Module):\n    \"\"\" \n    Image to Patch Embedding with 4 layer convolution\n    \"\"\"\n    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):\n        super().__init__()\n\n        new_patch_size = to_2tuple(patch_size // 2)\n\n        img_size = to_2tuple(img_size)\n        patch_size = to_2tuple(patch_size)\n        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])\n        self.img_size = img_size\n        self.patch_size = patch_size\n        self.num_patches = num_patches\n        self.embed_dim = embed_dim\n\n        self.conv1 = nn.Conv2d(in_chans, 64, kernel_size=7, stride=2, padding=3, bias=False)  # 112x112\n        self.bn1 = nn.BatchNorm2d(64)\n        self.relu = nn.ReLU(inplace=True)\n        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False)  # 112x112\n        self.bn2 = nn.BatchNorm2d(64)\n        self.conv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False)  \n        self.bn3 = nn.BatchNorm2d(64)\n\n        self.proj = nn.Conv2d(64, embed_dim, kernel_size=new_patch_size, stride=new_patch_size)\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n\n        x = self.conv2(x)\n        x = self.bn2(x)\n        x = self.relu(x)\n\n        x = self.conv3(x)\n        x = self.bn3(x)\n        x = self.relu(x)\n\n        x = self.proj(x)  # [B, C, W, H]\n\n        return x\n\n    def flops(self):\n        img_size = self.img_size[0]\n        block_flops = dict(\n        conv1=img_size/2*img_size/2*3*64*7*7,\n        conv2=img_size/2*img_size/2*64*64*3*3,\n        conv3=img_size/2*img_size/2*64*64*3*3,\n        proj=img_size/2*img_size/2*64*self.embed_dim,\n        )\n        return sum(block_flops.values())\n\n    \nclass PatchEmbed4_2_128(nn.Module):\n    \"\"\" \n    Image to Patch Embedding with 4 layer convolution and 128 filters\n    \"\"\"\n    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):\n        super().__init__()\n\n        new_patch_size = to_2tuple(patch_size // 2)\n\n        img_size = to_2tuple(img_size)\n        patch_size = to_2tuple(patch_size)\n        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])\n        self.img_size = img_size\n        self.patch_size = patch_size\n        self.num_patches = num_patches\n        self.embed_dim = embed_dim\n\n        self.conv1 = nn.Conv2d(in_chans, 128, kernel_size=7, stride=2, padding=3, bias=False)  # 112x112\n        self.bn1 = nn.BatchNorm2d(128)\n        self.relu = nn.ReLU(inplace=True)\n        self.conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False)  # 112x112\n        self.bn2 = nn.BatchNorm2d(128)\n        self.conv3 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False)  \n        self.bn3 = nn.BatchNorm2d(128)\n\n        self.proj = nn.Conv2d(128, embed_dim, kernel_size=new_patch_size, stride=new_patch_size)\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n\n        x = self.conv2(x)\n        x = self.bn2(x)\n        x = self.relu(x)\n\n        x = self.conv3(x)\n        x = self.bn3(x)\n        x = self.relu(x)\n\n        x = self.proj(x)  # [B, C, W, H]\n\n        return x\n    def flops(self):\n        img_size = self.img_size[0]\n        block_flops = dict(\n        conv1=img_size/2*img_size/2*3*128*7*7,\n        conv2=img_size/2*img_size/2*128*128*3*3,\n        conv3=img_size/2*img_size/2*128*128*3*3,\n        proj=img_size/2*img_size/2*128*self.embed_dim,\n        )\n        return sum(block_flops.values())\n\n\ndef get_block(block_type, **kargs):\n    if block_type=='mha':\n        # multi-head attention block\n        return MHABlock(**kargs)\n    elif block_type=='ffn':\n        # feed forward block\n        return FFNBlock(**kargs)\n    elif block_type=='tr':\n        # transformer block\n        return Block(**kargs)\n\n\ndef rand_bbox(size, lam):\n    W = size[2]\n    H = size[3]\n    cut_rat = np.sqrt(1. - lam)\n    cut_w = np.int(W * cut_rat)\n    cut_h = np.int(H * cut_rat)\n\n    # uniform\n    cx = np.random.randint(W)\n    cy = np.random.randint(H)\n\n    bbx1 = np.clip(cx - cut_w // 2, 0, W)\n    bby1 = np.clip(cy - cut_h // 2, 0, H)\n    bbx2 = np.clip(cx + cut_w // 2, 0, W)\n    bby2 = np.clip(cy + cut_h // 2, 0, H)\n\n    return bbx1, bby1, bbx2, bby2\n\n\ndef get_dpr(drop_path_rate,depth,drop_path_decay='linear'):\n    if drop_path_decay=='linear':\n        # linear dpr decay\n        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule\n    elif drop_path_decay=='fix':\n        # use fixed dpr\n        dpr= [drop_path_rate]*depth\n    else:\n        # use predefined drop_path_rate list\n        assert len(drop_path_rate)==depth\n        dpr=drop_path_rate\n    return dpr\n\nclass PredictorLG(nn.Module):\n    \"\"\" Image to Patch Embedding\n    \"\"\"\n    def __init__(self, embed_dim=384):\n        super().__init__()\n        self.in_conv = nn.Sequential(\n            nn.LayerNorm(embed_dim),\n            nn.Linear(embed_dim, embed_dim),\n            nn.GELU()\n        )\n\n        self.out_conv = nn.Sequential(\n            nn.Linear(embed_dim, embed_dim // 2),\n            nn.GELU(),\n            nn.Linear(embed_dim // 2, embed_dim // 4),\n            nn.GELU(),\n            nn.Linear(embed_dim // 4, 2),\n            nn.LogSoftmax(dim=-1)\n        )\n\n    def forward(self, x, policy):\n        x = self.in_conv(x)\n        B, N, C = x.size()\n        local_x = x[:,:, :C//2]\n        global_x = (x[:,:, C//2:] * policy).sum(dim=1, keepdim=True) / torch.sum(policy, dim=1, keepdim=True)\n        x = torch.cat([local_x, global_x.expand(B, N, C//2)], dim=-1)\n        return self.out_conv(x)\n\n\n@register_model_architecture('lvvit')\nclass LVViTDiffPruning(nn.Module):\n    \"\"\" Vision Transformer with tricks\n    Arguements:\n        p_emb: different conv based position embedding (default: 4 layer conv)\n        skip_lam: residual scalar for skip connection (default: 1.0)\n        order: which order of layers will be used (default: None, will override depth if given)\n        mix_token: use mix token augmentation for batch of tokens (default: False)\n        return_dense: whether to return feature of all tokens with an additional aux_head (default: False)\n    \"\"\"\n    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,\n                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,\n                 drop_path_rate=0., drop_path_decay='linear', hybrid_backbone=None, norm_layer=nn.LayerNorm, p_emb='4_2', head_dim = None,\n                 skip_lam = 1.0,order=None, mix_token=False, return_dense=False, pruning_loc=None, token_ratio=None, distill=False, pretrained=None, viz_mode=False):\n        super().__init__()\n        self.num_classes = num_classes\n        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models\n        self.output_dim = embed_dim if num_classes==0 else num_classes\n        if hybrid_backbone is not None:\n            self.patch_embed = HybridEmbed(\n                hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim)\n        else:\n            if p_emb=='4_2':\n                patch_embed_fn = PatchEmbed4_2\n            elif p_emb=='4_2_128':\n                patch_embed_fn = PatchEmbed4_2_128\n            else:\n                patch_embed_fn = PatchEmbedNaive\n\n            self.patch_embed = patch_embed_fn(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)\n\n        num_patches = self.patch_embed.num_patches\n\n        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))\n        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))\n        self.pos_drop = nn.Dropout(p=drop_rate)\n\n        if order is None:\n            dpr=get_dpr(drop_path_rate, depth, drop_path_decay)\n            self.blocks = nn.ModuleList([\n                Block(\n                    dim=embed_dim, num_heads=num_heads, head_dim=head_dim, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,\n                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, skip_lam=skip_lam)\n                for i in range(depth)])\n        else:\n            # use given order to sequentially generate modules\n            dpr=get_dpr(drop_path_rate, len(order), drop_path_decay)\n            self.blocks = nn.ModuleList([\n                get_block(order[i],\n                    dim=embed_dim, num_heads=num_heads, head_dim=head_dim, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,\n                    drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, skip_lam=skip_lam)\n                for i in range(len(order))])\n\n        self.norm = norm_layer(embed_dim)\n        # self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()\n        \n        self.return_dense=return_dense\n        self.mix_token=mix_token\n\n        \n        predictor_list = [PredictorLG(embed_dim) for _ in range(len(pruning_loc))]\n\n        self.score_predictor = nn.ModuleList(predictor_list)\n\n        self.pruning_loc = pruning_loc\n        self.token_ratio = token_ratio\n\n        # if return_dense:\n            # self.aux_head=nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()\n        if mix_token:\n            self.beta = 1.0\n            assert return_dense, \"always return all features when mixtoken is enabled\"\n\n        self.distill = distill\n        self.viz_mode = viz_mode\n\n        trunc_normal_(self.pos_embed, std=.02)\n        trunc_normal_(self.cls_token, std=.02)\n        self.init_weights(pretrained)\n        # self.apply(self._init_weights)\n\n    def init_weights(self, pretrained=None):\n        \"\"\"Initialize the weights in backbone.\n        Args:\n            pretrained (str, optional): Path to pre-trained weights.\n                Defaults to None.\n            # Note that `mmcv` has a safe checkpoint_load impl, please refer to the following url\n            # load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)     \n            # https://github.com/open-mmlab/mmcv/blob/571e3e5fc75c23b45cbd9b00011af094357c5f1d/mmcv/runner/checkpoint.py           \n        \"\"\"\n        def _process_mmcls_checkpoint(checkpoint):\n            # state_dict = checkpoint['state_dict']\n            state_dict = checkpoint\n            new_state_dict = OrderedDict()\n            for k, v in state_dict.items():\n                if k.startswith('backbone.'):\n                    new_state_dict[k[9:]] = v\n            new_checkpoint = dict(state_dict=new_state_dict)\n            return new_checkpoint\n\n        if isinstance(pretrained, str):\n            # self.apply(self._init_weights)\n            checkpoint = torch.load(pretrained, map_location='cpu')['model']\n            new_checkpoint = checkpoint.copy()\n            # new_checkpoint = _process_mmcls_checkpoint(checkpoint)\n            '''\n            # ************************************************************************* Detailed code for loading checkpoint ********************************************************************************\n            # Missing Keys: ['patch_embed1.proj.weight', 'patch_embed1.proj.bias', 'patch_embed1.norm.weight', 'patch_embed1.norm.bias', 'block1.0.norm1.weight', 'block1.0.norm1.bias', 'block1.0.attn.q.wei\n            # ght', 'block1.0.attn.q.bias', 'block1.0.attn.kv.weight', 'block1.0.attn.kv.bias', 'block1.0.attn.proj.weight', 'block1.0.attn.proj.bias', 'block1.0.attn.sr.weight', 'block1.0.attn.sr.bias', '\n            # block1.0.attn.norm.weight', 'block1.0.attn.norm.bias', 'block1.0.norm2.weight', \n            \n            # Unexpected Keys: ['backbone.patch_embed1.proj.weight', 'backbone.patch_embed1.proj.bias', 'backbone.patch_embed1.norm.weight', 'backbone.patch_embed1.norm.bias', 'backbone.block1.0.norm1.weig\n            # ht', 'backbone.block1.0.norm1.bias', 'backbone.block1.0.attn.q.weight', 'backbone.block1.0.attn.q.bias', 'backbone.block1.0.attn.kv.weight', 'backbone.block1.0.attn.kv.bias', 'backbone.block1\n            # .0.attn.proj.weight', 'backbone.block1.0.attn.proj.bias', 'backbone.block1.0.attn.sr.weight', 'backbone.block1.0.attn.sr.bias', 'backbone.block1.0.attn.norm.weight', 'backbone.block1.0.attn.n\n            # orm.bias', 'backbone.block1.0.norm2.weight',\n            '''\n            # pretrain_dict = new_checkpoint['state_dict']\n            # my_model_dict = self.state_dict()\n            # pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in my_model_dict}\n            # my_model_dict.update(pretrain_dict)\n            # self.load_state_dict(my_model_dict)\n            # print(f'load from {pretrained}.')\n            logger.info(f\"Loading pretrained model from {pretrained}\")\n            for k in checkpoint.keys():\n                if self.state_dict().get(k,None) is not None  and checkpoint[k].shape != self.state_dict()[k].shape:\n                    print(f\"Removing key {k} from pretrained checkpoint\")\n                    del new_checkpoint[k]\n            missing_keys, unexpected_keys = self.load_state_dict(new_checkpoint, strict=False)\n            unexpected_keys = [k for k in unexpected_keys if not (k.endswith('total_params') or k.endswith('total_ops'))]\n            if len(missing_keys) > 0:\n                logger.info('Missing Keys: {}'.format(missing_keys))\n            if len(unexpected_keys) > 0:\n                logger.info('Unexpected Keys: {}'.format(unexpected_keys))\n        elif pretrained is None:\n            self.apply(self._init_weights)\n        else:\n            raise TypeError('pretrained must be a str or None')\n\n    def _init_weights(self, m):\n        if isinstance(m, nn.Linear):\n            trunc_normal_(m.weight, std=.02)\n            if isinstance(m, nn.Linear) and m.bias is not None:\n                nn.init.constant_(m.bias, 0)\n        elif isinstance(m, GroupLinear):\n            trunc_normal_(m.group_weight, std=.02)\n            if isinstance(m, GroupLinear) and m.group_bias is not None:\n                nn.init.constant_(m.group_bias, 0)\n        elif isinstance(m, nn.LayerNorm):\n            nn.init.constant_(m.bias, 0)\n            nn.init.constant_(m.weight, 1.0)\n\n    @torch.jit.ignore\n    def no_weight_decay(self):\n        return {'pos_embed', 'cls_token'}\n\n    def get_classifier(self):\n        return self.head\n\n    def reset_classifier(self, num_classes, global_pool=''):\n        self.num_classes = num_classes\n        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()\n\n    def forward(self, x):\n        x = self.patch_embed(x)\n        x = x.flatten(2).transpose(1, 2)\n        B = x.shape[0]\n        cls_tokens = self.cls_token.expand(B, -1, -1)\n        x = torch.cat((cls_tokens, x), dim=1)\n        \n        x = x + self.pos_embed\n        x = self.pos_drop(x)\n\n        p_count = 0\n        out_pred_prob = []\n        # init_n = 14 * 14\n        init_n = 64 * 64\n        prev_decision = torch.ones(B, init_n, 1, dtype=x.dtype, device=x.device)\n        policy = torch.ones(B, init_n + 1, 1, dtype=x.dtype, device=x.device)\n        if self.viz_mode:\n            decisions = [[] for _ in self.pruning_loc]\n        for i, blk in enumerate(self.blocks):\n            if i in self.pruning_loc:\n                spatial_x = x[:, 1:]\n                pred_score = self.score_predictor[p_count](spatial_x, prev_decision).reshape(B, -1, 2)\n                if self.training:\n                    hard_keep_decision = F.gumbel_softmax(pred_score, hard=True)[:, :, 0:1] * prev_decision\n                    out_pred_prob.append(hard_keep_decision.reshape(B, init_n))\n                    cls_policy = torch.ones(B, 1, 1, dtype=hard_keep_decision.dtype, device=hard_keep_decision.device)\n                    policy = torch.cat([cls_policy, hard_keep_decision], dim=1)\n                    x = blk(x, policy=policy)\n                    prev_decision = hard_keep_decision\n                else:\n                    score = pred_score[:,:,0]\n                    num_keep_node = int(init_n * self.token_ratio[p_count])\n                    keep_policy = torch.argsort(score, dim=1, descending=True)[:, :num_keep_node]\n                    if self.viz_mode:\n                        decisions[p_count].append(keep_policy)\n                    cls_policy = torch.zeros(B, 1, dtype=keep_policy.dtype, device=keep_policy.device)\n                    now_policy = torch.cat([cls_policy, keep_policy + 1], dim=1)\n                    x = batch_index_select(x, now_policy)\n                    prev_decision = batch_index_select(prev_decision, keep_policy)\n                    x = blk(x)\n                p_count += 1\n            else:\n                if self.training:\n                    x = blk(x, policy)\n                else:\n                    x = blk(x)\n        \n        # torch.Size ([1, 2421, 384])\n        x = self.norm(x)\n        x_encoder = x.permute(1,0,2)\n        return x_encoder\n        \n        # import pdb\n        # pdb.set_trace()\n        # # torch.Size([1,1000])\n        # x_cls = self.head(x[:,0])\n        # # torch.size([1,2420,1000])\n        # x_aux = self.aux_head(x[:,1:])\n        # # torch.Size([1,1000])\n        # final_pred =  x_cls + 0.5 * x_aux.max(1)[0]\n\n        # if self.training:\n        #     if self.distill:\n        #         return x_cls, x_aux, prev_decision.detach(), out_pred_prob\n        #     else:\n        #         return final_pred, out_pred_prob\n        # else:\n        #     if self.viz_mode:\n        #         return final_pred, decisions\n        #     else:\n        #         return final_pred\n\ndef _get_clones(module, N):\n    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\ndef _get_activation_fn(activation):\n    \"\"\"Return an activation function given a string\"\"\"\n    if activation == \"relu\":\n        return F.relu\n    if activation == \"gelu\":\n        return F.gelu\n    if activation == \"glu\":\n        return F.glu\n    raise RuntimeError(F\"activation should be relu/gelu, not {activation}.\")\n\nclass TransformerDecoder(nn.Module):\n    def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):\n        super().__init__()\n        self.layers = _get_clones(decoder_layer, num_layers)\n        self.num_layers = num_layers\n        self.norm = norm\n        self.return_intermediate = return_intermediate\n\n    def forward(self, tgt, memory,\n                tgt_mask: Optional[Tensor] = None,\n                memory_mask: Optional[Tensor] = None,\n                tgt_key_padding_mask: Optional[Tensor] = None,\n                memory_key_padding_mask: Optional[Tensor] = None,\n                pos: Optional[Tensor] = None,\n                query_pos: Optional[Tensor] = None):\n        output = tgt\n\n        intermediate = []\n\n        for layer in self.layers:\n            output = layer(output, memory, tgt_mask=tgt_mask,\n                           memory_mask=memory_mask,\n                           tgt_key_padding_mask=tgt_key_padding_mask,\n                           memory_key_padding_mask=memory_key_padding_mask,\n                           pos=pos, query_pos=query_pos)\n            if self.return_intermediate:\n                intermediate.append(self.norm(output))\n\n        if self.norm is not None:\n            output = self.norm(output)\n            if self.return_intermediate:\n                intermediate.pop()\n                intermediate.append(output)\n\n        if self.return_intermediate:\n            return torch.stack(intermediate)\n        \n        return output.unsqueeze(0)\n\nclass TransformerDecoderLayer(nn.Module):\n    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,\n                 activation=\"relu\", normalize_before=False):\n        super().__init__()\n        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\n        self.multihead_attn = nn.MultiheadAttention(\n            d_model, nhead, dropout=dropout)\n        # Implementation of feedforward mode\n        self.linear1 = nn.Linear(d_model, dim_feedforward)\n        self.dropout = nn.Dropout(dropout)\n        self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n        self.norm1 = nn.LayerNorm(d_model)\n        self.norm2 = nn.LayerNorm(d_model)\n        self.norm3 = nn.LayerNorm(d_model)\n        self.dropout1 = nn.Dropout(dropout)\n        self.dropout2 = nn.Dropout(dropout)\n        self.dropout3 = nn.Dropout(dropout)\n\n        self.activation = _get_activation_fn(activation)\n        self.normalize_before = normalize_before\n\n    def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n        return tensor if pos is None else tensor + pos\n\n    def forward_post(self, tgt, memory,\n                     tgt_mask: Optional[Tensor] = None,\n                     memory_mask: Optional[Tensor] = None,\n                     tgt_key_padding_mask: Optional[Tensor] = None,\n                     memory_key_padding_mask: Optional[Tensor] = None,\n                     pos: Optional[Tensor] = None,\n                     query_pos: Optional[Tensor] = None):\n        q = k = self.with_pos_embed(tgt, query_pos)\n        tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask,\n                              key_padding_mask=tgt_key_padding_mask)[0]\n        tgt = tgt + self.dropout1(tgt2)\n        tgt = self.norm1(tgt)\n        \"\"\"\n        - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is\n        the embedding dimension.\n        \n        - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is\n        the embedding dimension.\n        \n        - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is\n        the embedding dimension.\n        \n        - key_padding_mask: :math:`(N, S)`, ByteTensor, where N is the batch size, S is the source sequence length.\n        \n        - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.\n        3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,\n        S is the source sequence length.         \n        \"\"\" \n        tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos),\n                                   key=self.with_pos_embed(memory, pos),\n                                   value=memory, attn_mask=memory_mask,\n                                   key_padding_mask=memory_key_padding_mask)[0]\n        tgt = tgt + self.dropout2(tgt2)\n        tgt = self.norm2(tgt)\n        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))\n        tgt = tgt + self.dropout3(tgt2)\n        tgt = self.norm3(tgt)\n        return tgt\n\n    def forward_pre(self, tgt, memory,\n                    tgt_mask: Optional[Tensor] = None,\n                    memory_mask: Optional[Tensor] = None,\n                    tgt_key_padding_mask: Optional[Tensor] = None,\n                    memory_key_padding_mask: Optional[Tensor] = None,\n                    pos: Optional[Tensor] = None,\n                    query_pos: Optional[Tensor] = None):\n        tgt2 = self.norm1(tgt)\n        q = k = self.with_pos_embed(tgt2, query_pos)\n        tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,\n                              key_padding_mask=tgt_key_padding_mask)[0]\n        tgt = tgt + self.dropout1(tgt2)\n        tgt2 = self.norm2(tgt)\n        tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),\n                                   key=self.with_pos_embed(memory, pos),\n                                   value=memory, attn_mask=memory_mask,\n                                   key_padding_mask=memory_key_padding_mask)[0]\n        tgt = tgt + self.dropout2(tgt2)\n        tgt2 = self.norm3(tgt)\n        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n        tgt = tgt + self.dropout3(tgt2)\n        return tgt\n\n    def forward(self, tgt, memory,\n                tgt_mask: Optional[Tensor] = None,\n                memory_mask: Optional[Tensor] = None,\n                tgt_key_padding_mask: Optional[Tensor] = None,\n                memory_key_padding_mask: Optional[Tensor] = None,\n                pos: Optional[Tensor] = None,\n                query_pos: Optional[Tensor] = None):\n        if self.normalize_before:\n            return self.forward_pre(tgt, memory, tgt_mask, memory_mask,\n                                    tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos)\n        return self.forward_post(tgt, memory, tgt_mask, memory_mask,\n                                 tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos)\n\nclass Transformer(nn.Module):\n    def __init__(self,\n                 e_cfgs,\n                 d_model=512, \n                 d_nhead=8, \n                 num_decoder_layers=6, \n                 dim_feedforward=2048, \n                 dropout=0.1,\n                 activation=\"relu\", \n                 normalize_before=False,\n                 return_intermediate_dec=False):\n        super().__init__()\n        self.encoder = LVViTDiffPruning(**e_cfgs)\n        decoder_layer = TransformerDecoderLayer(d_model, d_nhead, dim_feedforward,\n                                                dropout, activation, normalize_before)\n        decoder_norm = nn.LayerNorm(d_model)\n        self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm,\n                                          return_intermediate=return_intermediate_dec)\n        self._reset_parameters()\n        self.d_model = d_model\n        self.nhead = d_nhead\n\n    def _reset_parameters(self):\n        for p in self.parameters():\n            if p.dim() > 1:\n                nn.init.xavier_uniform_(p)\n\n    def forward(self, src, mask, query_embed):\n        # flatten NxCxHxW to HWxNxC\n        bs, c, h, w = src.shape\n        # src = src.flatten(2).permute(2, 0, 1)\n        # pos_embed = pos_embed.flatten(2).permute(2, 0, 1)\n        query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)\n        mask = mask.flatten(1)\n        memory = self.encoder(src)\n        tgt = torch.zeros_like(query_embed)\n        hs = self.decoder(tgt, memory, memory_key_padding_mask=None, query_pos=query_embed)\n        # return hs.transpose(1, 2), memory.permute(1, 2, 0).view(bs, c, h, w)\n        # torch.Size([6, 400, 3, 768])\n        return hs.transpose(1, 2)\n\n\nbase_rate = 0.7\nKEEP_RATE = [base_rate, base_rate ** 2, base_rate ** 3]\n\nconfigs={\n    \"lvvit_s\": \n    dict(\n        img_size=224, patch_size=16, embed_dim=384, depth=16, num_heads=6, mlp_ratio=3.,\n        p_emb='4_2', skip_lam=2., return_dense=True, mix_token=True,\n        pruning_loc=[4,8,12], token_ratio=KEEP_RATE, distill=False\n        ),\n\n    \"lvvit_m\":\n    dict(\n        img_size=224, patch_size=16, embed_dim=512, depth=20, num_heads=8, mlp_ratio=3.,\n        p_emb='4_2',skip_lam=2., return_dense=True,mix_token=True,\n        pruning_loc=[5,10,15], token_ratio=KEEP_RATE, distill=False\n    )\n}\n\ndef build_transformer(args):\n\n    cfgs = configs[args.backbone]\n    cfgs.update({'pretrained': args.pretrained})\n    cfgs['img_size']=args.img_size\n    return Transformer(\n        e_cfgs = cfgs, \n        d_model=args.dec_hidden_dim,\n        dropout=args.dropout,\n        d_nhead=args.dec_nheads,\n        dim_feedforward=args.dim_feedforward,\n        num_decoder_layers=args.dec_layers,\n        normalize_before=args.pre_norm,\n        return_intermediate_dec=True,\n    )\n\n\n","sub_path":"models/dynamic_transformer_v2.py","file_name":"dynamic_transformer_v2.py","file_ext":"py","file_size_in_byte":41776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"148338107","text":"#FBDs\nimport pygame, sys, math, classes, random\nimport pygame._view\npygame.init()\n\ng = classes.General() #warning, large amount of __init__ to do\ng.clock = pygame.time.Clock()\npygame.display.set_caption(\"FBD practice\")\n#pygame.display.set_icon(pygame.image.load(\"icon.bmp\").convert())\ng.screen = pygame.display.get_surface()\n\ndef cartographer(): #in form of tuples, but no other splits\n\tnow_in = \"\"\n\ti = 0\n\tfor tag in g.data:\n\t\t#g.maplist is where they should live\n\t\tif tag[0] == \"MAP\":\n\t\t\tif now_in == \"submap\":\n\t\t\t\tg.maplist[-1].submaps[-1].data_stop = i\n\n\t\t\tpaper = classes.Map() #fill w/ data later\n\t\t\tg.maplist.append(paper)\n\t\t\tg.maplist[-1].name = tag[1]\n\t\t\tg.maplist[-1].data_start = i\n\t\t\tnow_in = \"map\"\n\t\telif tag[0] == \"SUBMAP\":\n\t\t\tif now_in == \"submap\":\n\t\t\t\tg.maplist[-1].submaps[-1].data_stop = i\n\t\t\n\t\t\tpaper = classes.Map() #fill w/ data later\n\t\t\tg.maplist[-1].submaps.append(paper)\n\t\t\tg.maplist[-1].submaps[-1].data_start = i\n\t\t\tnow_in = \"submap\"\n\t\telif tag[0] in g.furnishings:\n\t\t\tif now_in == \"map\":\n\t\t\t\tg.maplist[-1].partlist.append(tag)\n\t\t\telif now_in == \"submap\":\n\t\t\t\tg.maplist[-1].submaps[-1].partlist.append(tag)\n\n\t\telse: pass #is a block, or part of one\n\t\ti+=1\n\t#endings\n\ti = 0\n\tfor term in g.maplist:\n\t\ttry:\n\t\t\tterm.data_stop = g.maplist[i+1].data_start\n\t\texcept IndexError:\n\t\t\tterm.data_stop = len(g.data)\n\t\ti+=1\n\ndef clicked_it(which):\n\ti=0\n\tfor layout in g.maplist:\n\t\tif layout.name == which: #correct map chosen\n\t\t\tif layout.name == \"Random\":\n\t\t\t\tg.map = random.randint(2,len(g.maplist)-1)\n\t\t\telse:\n\t\t\t\tg.map = i\n\t\t\tif g.maplist[g.map].submaps != []:\n\t\t\t\tg.maplist[g.map].update(random.randint(0,len(g.maplist[g.map].submaps)-1))\n\t\t\tbreak\n\t\ti+=1\n\tg.mode = \"mech\"\n\n\ti = g.maplist[g.map].data_start; in_block = False; start_block = 0; end_block = 0;\t\n\t#block inst time\n\tfor tag in g.data[g.maplist[g.map].data_start:g.maplist[g.map].data_stop]: #start w/ map tag\n\t\tif tag[0] == \"BLOCK\":\n\t\t\tif in_block == True:\n\t\t\t\tin_block = False\n\t\t\t\tend_block = i\n\t\t\t\tholder = classes.Block(g.screen, g.data[start_block:end_block])\n\t\t\t\tg.blocklist.append(holder)\n\t\t\tstart_block = i\n\t\t\tin_block = True\n\t\ti+=1\n\tholder = classes.Block(g.screen, g.data[start_block:g.maplist[g.map].data_stop])\n\tg.blocklist.append(holder)\n\n\t\t\ndef I(events):\n\tfor event in events:\n\t\tif event.type == pygame.QUIT:\n\t\t\tsys.exit()\n\t\t\t\n\t\telif event.type == pygame.MOUSEBUTTONDOWN and event.button == 1:\n\t\t\tif g.mode == \"mech\":\n\t\t\t\tif event.pos[1] < 64:\n\t\t\t\t\tg.interfacing_now = True\n\t\t\t\telse:\n\t\t\t\t\tg.in_a_click = True\n\t\t\t\t\tnewbie = classes.Arrow(g.screen, event.pos)\n\t\t\t\t\tnewbie.color = g.options[\"color\"][\"ARROW\"][\"NEW\"]\n\t\t\t\t\tg.arrowlist.append(newbie)\n\n\t\telif event.type == pygame.MOUSEMOTION and g.in_a_click == True:\n\t\t\t#move arrowhead\n\t\t\ttry:\n\t\t\t\tg.arrowlist[-1].end = event.pos\n\t\t\texcept IndexError: pass\n\n\t\telif event.type == pygame.MOUSEBUTTONUP and event.button == 1:\n\t\t\tif g.mode == \"mech\":\n\t\t\t\t#stop the arrow\n\t\t\t\tg.in_a_click = False\n\t\t\t\t\n\t\t\t\tif g.interfacing_now == True:\n\t\t\t\t\tg.interfacing_now = False\n\t\t\t\t\tif event.pos[0] > 600:\n\t\t\t\t\t\tg.check()\n\t\t\t\t\telse:\n\t\t\t\t\t\tg.label_arrow(g.interface(event.pos))\n\t\t\telif g.mode == \"start\":\n\t\t\t\tx,y = event.pos\n\t\t\t\tfor button in g.home_buttons:\n\t\t\t\t\tif x > button[0][0] and x < button[1][0]:\n\t\t\t\t\t\tif y > button[0][1] and y < button[1][1]:\n\t\t\t\t\t\t\tclicked_it(button[2]);\n\n\t\telif event.type == pygame.KEYDOWN:\n\t\t\tif event.key == pygame.K_DELETE:\n\t\t\t\ttry: \n\t\t\t\t\tdel g.arrowlist[-1]#kill the arrow.  Kill it!\n\t\t\t\texcept IndexError: pass\n\t\t\telif event.key == pygame.K_ESCAPE:\n\t\t\t\tif g.mode == \"mech\":\n\t\t\t\t\tg.mode = \"start\"\n\t\t\t\t\tg.reset()\n\t\t\t\telif g.mode == \"start\":\n\t\t\t\t\tsys.exit()\n\t\t\telif event.key == pygame.K_RETURN:\n\t\t\t\tif g.message == 0:\n\t\t\t\t\tg.check()\n\t\t\t\telse:\n\t\t\t\t\tg.message = 0\n\t\t\t\t\n\t\t\telse: #letter pressed\n\t\t\t\tfor i in g.keylist:\n\t\t\t\t\tif event.key == i[0]:\n\t\t\t\t\t\ttry: g.label_arrow(g.options[\"PFT\"][i[1]])\n\t\t\t\t\t\texcept KeyError: pass\n\n\ndef boom():\n\tif g.win == False:\n\t\tsize = len(g.arrowlist)\n\t\ti=0\n\t\twhile i < size:\n\t\t\ttry:\n\t\t\t\tarrow = g.arrowlist[i]\n\t\t\t\tif arrow.good == False:\n\t\t\t\t\t#find it and kill it\n\t\t\t\t\tdel g.arrowlist[i]\n\t\t\t\t\t# print(\"Bad!\")\n\t\t\t\telse:\n\t\t\t\t\ti+=1\n\t\t\texcept IndexError:\n\t\t\t\tbreak\n\telse:\n\t\t# print(\"Winner! \\n\")\n\t\tg.reset() #purge\n\n\ndef draw():\n\tg.screen.fill(g.black)\n\tif g.mode == \"start\":\n\t\tg.draw_home()\n\telif g.mode == \"mech\":\n\t\tg.carto_map()\n\t\tg.draw_interface()\n\t\tg.draw_blocks()\n\t\tg.draw_arrows()\n\tif g.message != 0:\n\t\tg.draw_message(g.message.lines,g.message.rect)\n\tpygame.display.flip()\n\n\ndef main():\n\tcartographer();\n\twhile True:\n\t\tI(pygame.event.get());\n\n\t\tif g.time_bomb !=0:\n\t\t\tif g.time_bomb == 1:\n\t\t\t\tboom();\n\t\t\telif g.time_bomb == g.options[\"timer\"]:\n\t\t\t\tif g.win:\n\t\t\t\t\tg.message = classes.Message(\"You have won. Good for you!\")\n\t\t\t\telif g.no_win_yet != \"\":\n\t\t\t\t\tg.message = classes.Message(g.no_win_yet)\n\t\t\tg.time_bomb -= 1\t\t\n\t\tdraw();\n\t\tg.clock.tick(30);\n\nmain();\npygame.quit();","sub_path":"fbd.py","file_name":"fbd.py","file_ext":"py","file_size_in_byte":4860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"499141616","text":"from django.urls import path, re_path, include\nfrom . import views\nfrom rest_framework.routers import DefaultRouter\n\nrouter = DefaultRouter()\nrouter.register('user', views.UserViewSet)\nrouter.register('gcg', views.GcgViewSet)\nrouter.register('anode', views.AnodeViewSet)\napp_name = 'test_app'\n\nurlpatterns = [\n    path('test_request/', views.test_request, name = 'test_request'),\n    path('test_post/', views.test_post, name = 'test_post'),\n    path('test_get/', views.test_get, name = 'test_get'),\n    path('test_protocol/', views.test_protocol, name = 'test_protocol'),\n    path('test_dict', views.test_dict, name = 'test_dict'),\n    #path('test_cam', views.test_cam, name = 'test_cam'),\n    path('test_index1', views.index1, name = 'index1'),\n    path('test_index2', views.index2, name = 'index2'),\n    # rest api 사용\n    path('', include(router.urls)),\n\n]","sub_path":"test_app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"56874915","text":"import json\nimport os\nimport sys\n\n\ndef convert(file):\n    f = open(file)\n    lines = f.readlines()\n    f.close()\n\n    ss = []\n    for line in lines:\n        line = line.replace(\"\\n\", \"\")\n        ss.append(line)\n\n    print(\"一共有\", len(ss), \"行被转换\")\n    j = json.dumps(ss)\n\n    new_file_name = newFileName(file)\n    f = open(new_file_name, mode='w')\n    f.write(j)\n    f.close()\n\n\ndef realFileName(file):\n    base = os.path.splitext(file)[0]\n    return base\n\n\ndef newFileName(old):\n    old = realFileName(old)\n    new = old+\".json\"\n    return new\n\n\nif __name__ == \"__main__\":\n    filename = sys.argv[1]\n    print(\"转换中：\", filename)\n    convert(filename)\n    print(\"转换完成\")\n","sub_path":"proxy.client/scripts/lines2json.py","file_name":"lines2json.py","file_ext":"py","file_size_in_byte":697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"137654918","text":"def drawTriangle(n):\r\n\t'''draws an inverted mirrored right triangle. Example:\r\n\t*****\r\n\t ****\r\n\t  ***\r\n\t   **\r\n\t    *\r\n\t  '''\r\n\tfor i in range(n):\r\n\t\tfor j in range(i+1):\r\n\t\t\tprint(\" \", end=\"\")\r\n\t\tfor k in range(n-i):\r\n\t\t\tprint(\"*\", end=\"\")\r\n\t\tprint()\r\n\r\nrows = int(input(\"Enter number of rows in the triangle: \"))\r\ndrawTriangle(rows)\r\n\r\n","sub_path":"Drawing pattern algorithms/mirroredInvertedRightTriangle.py","file_name":"mirroredInvertedRightTriangle.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"373691122","text":"# Return the index of the first occurrence of needle in haystack,\n# or -1 if needle is not part of haystack.\n# For the purpose of this problem, we will return 0 when needle is an empty string.\n# This is consistent to C's strstr() and Java's indexOf().\n# https://leetcode.com/problems/implement-strstr/\n\nclass Solution:\n    def strStr(self, haystack: str, needle: str):\n        if len(needle) == 0:\n            return 0\n        if len(needle) == len(haystack):\n            if needle == haystack:\n                return 0\n            else: return -1\n        if len(needle) > len(haystack):\n            return -1\n\n        for x in range(0, (len(haystack) - len(needle)+1)):\n            comp_string = haystack[x:x+len(needle)]\n            if comp_string == needle:\n                return x\n        return -1\n\n# FINALLY succeeded on my 7th try with the above on 10-28-2020\n# Also learned through the discussion about using \"if needle in haystack:\"\n# instead of the above ('in' works on strings).\n                       \n            \n                \n                \n","sub_path":"needle_haystack.py","file_name":"needle_haystack.py","file_ext":"py","file_size_in_byte":1062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"508768622","text":"# coding:utf-8\nfrom __future__ import absolute_import, division, print_function, with_statement\n\nimport os\nimport shlex\nimport tempfile\nimport time\nimport errno\nimport traceback\nimport datetime\nimport six\nimport tornado.gen\n\nfrom management.conf.configuration import ProgramConfig\nfrom management.controller import ControllerStates\nfrom management.dispatchers import POutputStream\nfrom management.enum import Enumeration\nfrom management import log\nfrom management import events\nfrom management.exception import ProcessException, BadCommand, PrivilegesException, NotRunning\nfrom management.system import System\nfrom management.util import RestartUnconditionally, get_path, check_execv_args, drop_privileges, compact_traceback, \\\n    signame, \\\n    decode_wait_status, close_fd, command_execv_args, expand, Automatic, existing_directory, total_seconds, PrioritySort\n\n\ndef mktempfile(suffix, prefix, dir):\n    os._urandomfd = None\n    fd, filename = tempfile.mkstemp(suffix, prefix, dir)\n    os.close(fd)\n    return filename\n\n\ndef create_autochildlogs(name, channel, childlogdir=None):\n    if not childlogdir:\n        childlogdir = tempfile.gettempdir()\n    prefix = '%s-%s---management-' % (name, channel)\n    logfile = mktempfile(\n        suffix='.log',\n        prefix=prefix,\n        dir=existing_directory(childlogdir))\n    return logfile\n\n\nclass ProcessStates(Enumeration):\n    STOPPED = 0\n    STARTING = 10\n    RUNNING = 20\n    BACKOFF = 30\n    STOPPING = 40\n    EXITED = 100\n    FATAL = 200\n    UNKNOWN = 1000\n\n\nRUNNING_STATES = (ProcessStates.RUNNING,\n                  ProcessStates.BACKOFF,\n                  ProcessStates.STARTING)\nCAN_RUN = (ProcessStates.EXITED, ProcessStates.FATAL, ProcessStates.BACKOFF, ProcessStates.STOPPED)\nCAN_STOP = (ProcessStates.RUNNING, ProcessStates.STARTING, ProcessStates.STOPPING)\n\n\nclass Program(PrioritySort):\n    def __init__(self, config, controller):\n        self.config = config\n        PrioritySort.__init__(self, self.config.name, self.config.priority)\n        self.controller = controller\n        self.processes = {}\n        self.single = self.config.get(\"numprocs\") <= 1\n        self.pid_history = {}\n        self.make_processes()\n\n    def stop_all(self):\n        for process in self.sort_processes(True):\n            state = process.get_state()\n            try:\n                if state == ProcessStates.RUNNING:\n                    # RUNNING -> STOPPING\n                    process.stop()\n                elif state == ProcessStates.STARTING:\n                    # STARTING -> STOPPING\n                    process.stop()\n                elif state == ProcessStates.BACKOFF:\n                    # BACKOFF -> FATAL\n                    process.give_up()\n            except:\n                pass\n\n    @property\n    def status(self):\n        s = self.__class__.__name__ + \":\" + self.config.name \\\n            + \"\\n\"\n        for process in self.sort_processes():\n            s += str(process) + \"\\n\"\n        return s\n\n    def __repr__(self):\n        return '<%s instance at %s named %s>' % (self.__class__, id(self),\n                                                 self.config.name)\n\n    @property\n    def mood(self):\n        return self.controller.mood\n\n    def make_processes(self):\n        processes = {}\n        assert self.config.get(\"numprocs\") >= 1\n        for process_num in range(self.config.get(\"numprocs_start\"),\n                                 self.config.get(\"numprocs\") + self.config.get(\"numprocs_start\")):\n            config = ProgramConfig(self.config.name,\n                                   priority=self.config.priority,\n                                   process_num=process_num,\n                                   expansions={\"process_num\": process_num}, parent=self.config)\n\n            process_name = \"%(program_name)s\"\n            if not self.single:\n                process_name += \":%(process_num)s\"\n            config.name = process_name = config.expand(process_name)\n            process = Process(process_name, process_num,\n                              config,\n                              self\n                              )\n            processes[process_name] = process\n        self.processes = processes\n        return self.processes\n\n    def sort_processes(self, reverse=False):\n        processes = self.processes.values()\n        processes.sort()\n        if reverse:\n            processes.reverse()\n        for process in processes:\n            yield process\n\n    def start(self):\n        _pid_history = {}\n        for process in self.sort_processes():\n            process.start()\n            if process.pid:\n                _pid_history[process.pid] = process\n        self.pid_history = _pid_history\n\n\nclass Process(object):\n    LOG = log.getLogger(\"Process\")\n    killing = False\n    pid = 0\n    backoff = 0\n    last_stop = None\n    last_start = None\n    exit_status = None\n    spawn_err = None\n    system_stop = False\n    stop_future = None\n    start_future = None\n\n\n    def __init__(self, name, process_num, config, program):\n        self.name = name\n        self.config = config\n        self.program = program\n        self.process_num = process_num\n        self.streams = {}\n        self.start_timeout = None\n        self.kill_timeout = None\n        self.change_state_time = time.time()\n        self.state = ProcessStates.STOPPED\n        self.system_util = System()\n\n    def __cmp__(self, other):\n        return (self.config.get(\"priority\") > other.config.get(\"priority\")) - (\n            self.config.get(\"priority\") < other.config.get(\"priority\"))\n\n    def get_state(self):\n        return self.state\n\n    def stop(self):\n        return self.kill(self.config.get(\"stopsignal\"))\n\n    def kill(self, sig):\n        if self.state == ProcessStates.BACKOFF:\n            msg = \"Attempted to kill which is in BACKOFF state.\"\n            self.LOG.debug(msg, process=self)\n            self.change_state(ProcessStates.STOPPED)\n            if not self.stop_future or self.stop_future.done():\n                self.stop_future = tornado.gen.Future()\n            self.stop_future.set_result(None)\n            return self.stop_future\n\n        if not self.pid:\n            msg = \"attempted to kill %s but it wasn't running\" % signame(sig)\n            self.LOG.debug(msg, process=self)\n            raise NotRunning(msg)\n        if self.state == ProcessStates.STOPPING:\n            killasgroup = self.config.get(\"killasgroup\")\n        else:\n            killasgroup = self.config.get(\"stopasgroup\")\n\n        as_group = \"\"\n        if killasgroup:\n            as_group = \"process group \"\n\n        self.LOG.debug('killing (pid %s) %swith signal %s', self.pid,\n                       as_group,\n                       signame(sig), process=self\n                       )\n        self.killing = True\n        self.change_state(ProcessStates.STOPPING, assert_state=(ProcessStates.RUNNING,\n                                                                ProcessStates.STARTING,\n                                                                ProcessStates.STOPPING))\n        pid = self.pid\n        if killasgroup:\n            # send to the whole process group instead\n            pid = -self.pid\n        try:\n            if not self.stop_future or self.stop_future.done():\n                self.stop_future = tornado.gen.Future()\n            self.system_util.kill(pid, sig)\n            # todo 重复kill\n            self.kill_timeout = self.program.controller.add_timeout(\n                datetime.timedelta(seconds=3),\n                self.stop\n            )\n        except:\n            tb = traceback.format_exc()\n            msg = 'unknown problem killing (%s):%s' % (self.pid, tb)\n            self.change_state(ProcessStates.UNKNOWN)\n            self.pid = 0\n            self.killing = False\n            self.LOG.error(msg, process=self)\n            e = Exception(msg)\n            if not self.stop_future.done():\n                self.stop_future.set_exception(e)\n            self.stop_future = None\n            raise e\n        return self.stop_future\n\n    def start(self):\n        state = self.state\n        # todo controller 必须启动中\n        if self.program.mood > ControllerStates.RESTARTING:\n            if state == ProcessStates.EXITED:\n                # todo 进程退出, 自动重启\n                if self.config.get(\"autorestart\"):\n                    if self.config.get(\"autorestart\") is RestartUnconditionally:\n                        # todo EXITED -> STARTING\n                        self.spawn()\n                else:\n                    # todo 异常退出, 自动重启\n                    if self.exit_status not in self.config.get(\"exitcodes\"):\n                        # todo EXITED -> STARTING\n                        self.spawn()\n            # todo 进程停止状态\n            elif state == ProcessStates.STOPPED and not self.last_start:\n                # todo 自动启动\n                if self.config.get(\"autostart\"):\n                    # todo STOPPED -> STARTING\n                    self.spawn()\n            # todo 进程启动失败，比如执行的文件不存在,\n            elif state == ProcessStates.BACKOFF:\n                # todo 重试次数\n                if self.backoff <= self.config.get(\"startretries\"):\n                    # todo BACKOFF -> STARTING\n                    self.spawn()\n\n                    # elif state == ProcessStates.STOPPING:\n                    #     time_left = self.delay - now\n                    #     if time_left <= 0:\n                    #         self.config.options.logger.warn(\n                    #             'killing %r (%s) with SIGKILL' % (self.name,\n                    #                                               self.pid))\n                    #         self.kill(signal.SIGKILL)\n\n    def _assert_in_state(self, *states):\n        if self.state not in states:\n            current_state = ProcessStates(self.state)\n            allowable_states = ' '.join(map(lambda state: str(state.key), states))\n            raise AssertionError('Assertion failed for %s: %s not in %s' % (\n                self.name, current_state.key, allowable_states))\n\n    event_map = {\n        ProcessStates.BACKOFF: events.ProcessStateBackoffEvent,\n        ProcessStates.FATAL: events.ProcessStateFatalEvent,\n        # ProcessStates.UNKNOWN: events.ProcessStateUnknownEvent,\n        ProcessStates.STOPPED: events.ProcessStateStoppedEvent,\n        ProcessStates.EXITED: events.ProcessStateExitedEvent,\n        ProcessStates.STARTING: events.ProcessStateStartingEvent,\n        ProcessStates.RUNNING: events.ProcessStateRunningEvent,\n        # ProcessStates.STARTING: events.ProcessStateStartingEvent,\n        # ProcessStates.STOPPING: events.ProcessStateStoppingEvent,\n    }\n\n    def change_running(self):\n        self.start_timeout = None\n        self.change_state(ProcessStates.RUNNING, assert_state=ProcessStates.STARTING)\n        if self.start_future and not self.start_future.done():\n            self.start_future.set_result(None)\n        self.start_future = None\n        msg = (\n            'entered RUNNING state, process has stayed up for '\n            '> than %s seconds (startsecs)' % self.config.get(\"startsecs\"))\n        self.LOG.info('success:%s', msg, process=self)\n\n    def change_state(self, new_state, assert_state=None, expected=True):\n        if assert_state:\n            if not isinstance(assert_state, (list, set, tuple)):\n                assert_state = [assert_state]\n            self._assert_in_state(*assert_state)\n        msg = \"%s -> %s\" % (self.state.key, new_state.key)\n        old_state = self.state\n        if new_state is old_state:\n            return False\n        start_seconds = -1\n        if new_state == ProcessStates.BACKOFF:\n            msg += \"  backoff:%s\" % self.backoff\n            self.backoff += 1\n            if self.backoff <= self.config.get(\"startretries\"):\n                # todo 重启\n                # todo BACKOFF -> STARTING\n                start_seconds = self.backoff\n        elif new_state == ProcessStates.EXITED:\n            # todo EXITED -> STARTING\n            if self.config.get(\"autorestart\"):\n                start_seconds = 1\n        if start_seconds > -1:\n            msg += \"  restart %ss\" % start_seconds\n            self.program.controller.add_timeout(\n                datetime.timedelta(seconds=start_seconds),\n                self.start\n            )\n        self.change_state_time = time.time()\n        self.LOG.info(msg, process=self)\n        self.state = new_state\n\n        event_class = self.event_map.get(new_state)\n        if event_class is not None:\n            event = event_class(self, old_state)\n            events.notify(event)\n\n    def give_up(self):\n        self.backoff = 0\n        self.system_stop = True\n        self.change_state(ProcessStates.FATAL, assert_state=ProcessStates.BACKOFF)\n\n    def record_spawnerr(self, msg):\n        self.spawn_err = msg\n        self.LOG.info(\"spawnerr: %s\", msg, process=self)\n\n    def make_stream(self):\n        # todo 生成子进程的输出输入管道\n        use_stderr = not self.config.get(\"redirect_stderr\")\n        stream = {\n            'stdin': None,\n            'stdout': None,\n            'stderr': None}\n\n        stdout_logfile = self.config.get(\"stdout_logfile\")\n        stderr_logfile = self.config.get(\"stderr_logfile\")\n\n        if stdout_logfile:\n            if stdout_logfile == Automatic:\n                stdout_logfile = create_autochildlogs(self.name, \"stdout\")\n            if isinstance(stdout_logfile, six.string_types):\n                stdout_logfile = self.config.expand(stdout_logfile)\n            self.config.set(\"stdout_logfile\", stdout_logfile)\n        if use_stderr:\n            if stderr_logfile:\n                if stderr_logfile == Automatic:\n                    stderr_logfile = create_autochildlogs(self.name, \"stderr\")\n                if isinstance(stderr_logfile, six.string_types):\n                    stderr_logfile = self.config.expand(stderr_logfile)\n                self.config.set(\"stderr_logfile\", stderr_logfile)\n        else:\n            if stderr_logfile:\n                self.LOG.warning(\n                    'redirect_stderr=true but stderr_logfile has also been set to a filename, the filename '\n                    'has been ignored', process=self)\n            if not stdout_logfile:\n                self.LOG.warning(\n                    'redirect_stderr=true, but stdout_logfile is None, the stderr_logfile has been ignored',\n                    process=self)\n\n        try:\n            if self.config.get(\"stdout_logfile\"):\n                stdout, child_stdout = self.system_util.pipe()\n                stream['stdout'] = POutputStream(self, \"stdout\", stdout, child_stdout)\n            if use_stderr:\n                if self.config.get(\"stderr_logfile\"):\n                    stderr, child_stderr = self.system_util.pipe()\n                    stream['stderr'] = POutputStream(self, \"stderr\", stderr, child_stderr)\n        except OSError:\n            for fd in stream.values():\n                if fd is not None:\n                    fd.close()\n            raise\n        return stream\n\n    def get_execv_args(self):\n        command = self.config.expand(self.config.get(\"command\"), process_name=self.name)\n        self.LOG.debug(\"command:%s\", command, process=self)\n        return command_execv_args(command)\n\n    def spawn(self):\n        if not self.start_future or self.start_future.done():\n            self.start_future = tornado.gen.Future()\n        if self.pid:\n            msg = 'process %r already running' % self.name\n            self.LOG.warn(msg, process=self)\n            self.start_future.set_exception(Exception(msg))\n            return self.start_future\n        self.exit_status = None\n        self.last_start = time.time()\n        self.change_state(ProcessStates.STARTING,\n                          assert_state=(ProcessStates.EXITED, ProcessStates.FATAL,\n                                        ProcessStates.BACKOFF, ProcessStates.STOPPED))\n        try:\n            filename, argv = self.get_execv_args()\n        except ProcessException as e:\n            self.record_spawnerr(e.args[0])\n            self.change_state(ProcessStates.BACKOFF, assert_state=ProcessStates.STARTING)\n            self.start_future.set_exception(e)\n            return self.start_future\n        try:\n            self.streams = self.make_stream()\n        except (OSError, IOError) as e:\n            code = e.args[0]\n            if code == errno.EMFILE:\n                # too many file descriptors open\n                msg = 'too many open files to spawn %r' % self.name\n            else:\n                msg = 'unknown error making streams for %r: %s' % (\n                    self.name, errno.errorcode.get(code, code))\n            self.record_spawnerr(msg)\n            self.change_state(ProcessStates.BACKOFF, assert_state=ProcessStates.STARTING)\n            self.start_future.set_exception(e)\n            return self.start_future\n        try:\n            pid = self.system_util.fork()\n        except OSError as e:\n            code = e.args[0]\n            if code == errno.EAGAIN:\n                # process table full\n                msg = ('Too many processes in process table to spawn %r' %\n                       self.name)\n            else:\n                msg = 'unknown error during fork for %r: %s' % (\n                    self.name, errno.errorcode.get(code, code))\n            self.record_spawnerr(msg)\n            self.change_state(ProcessStates.BACKOFF, assert_state=ProcessStates.STARTING)\n            self.start_future.set_exception(e)\n            return self.start_future\n        if pid != 0:\n            # todo 父进程\n            self._spawn_as_parent(pid)\n            return self.start_future\n        else:\n            # todo fork子进程\n            self._spawn_as_child(filename, argv)\n\n    def _prepare_child_fds(self):\n        # todo 复制管道\n        try:\n            # os.dup2(self.pipes['child_stdin'], 0)\n            if self.streams['stdout']:\n                self.streams['stdout'].child_dup2(1)\n                if self.config.get(\"redirect_stderr\"):\n                    self.streams['stdout'].child_dup2(2)\n            if not self.config.get(\"redirect_stderr\"):\n                if self.streams['stderr']:\n                    self.streams['stderr'].child_dup2(2)\n        except Exception as e:\n            traceback.print_exc()\n            raise e\n\n    def finish(self, pid, sts):\n        if self.start_timeout:\n            self.program.controller.remove_timeout(self.start_timeout)\n            self.start_timeout = None\n        if self.kill_timeout:\n            self.program.controller.remove_timeout(self.kill_timeout)\n            self.kill_timeout = None\n\n        es, msg = decode_wait_status(sts)\n        now = time.time()\n        self.last_stop = now\n        process_name = self.name\n        if self.killing:\n            # todo implies STOPPING -> STOPPED\n            self.killing = False\n            self.exit_status = es\n            msg = \"stopped: (%s)\" % msg\n            self.change_state(ProcessStates.STOPPED, assert_state=ProcessStates.STOPPING)\n        else:\n            if self.state == ProcessStates.STARTING:\n                # todo 启动中 STARTING -> BACKOFF\n                self.spawn_err = 'Exited too quickly (process log may have details)'\n                msg = \"exited: (%s)\" % (msg + \"; not expected\")\n                self.change_state(ProcessStates.BACKOFF, assert_state=ProcessStates.STARTING)\n            else:\n                # TODO RUNNING -> EXITED\n                exit_expected = es in self.config.get(\"exitcodes\")\n                self.backoff = 0\n                self.exit_status = es\n                self._assert_in_state(ProcessStates.RUNNING)\n                if exit_expected:\n                    # expected exit code\n                    msg = \"exited: (%s)\" % (msg + \"; expected\")\n                    self.change_state(ProcessStates.EXITED, expected=True)\n                else:\n                    # unexpected exit code\n                    self.spawn_err = 'Bad exit code %s' % es\n                    msg = \"exited: (%s)\" % (msg + \"; not expected\")\n                    self.change_state(ProcessStates.EXITED, expected=False)\n        if self.start_future and not self.start_future.done():\n            self.start_future.set_exception(Exception(msg))\n        if self.stop_future and not self.stop_future.done():\n            self.stop_future.set_result(None)\n            self.stop_future = None\n        self.LOG.info(msg, process=self)\n        self.pid = 0\n        self.close_streams()\n        self.streams = {}\n\n    def close_child_streams(self):\n        for stream in self.streams.values():\n            if stream is not None:\n                stream.close_child()\n\n    def close_streams(self):\n        # todo 关闭管道\n        for stream in self.streams.values():\n            if stream is not None:\n                stream.close()\n\n    def set_uid(self):\n        \"\"\"\n        设置进程的用户uid\n        :return:\n        \"\"\"\n        if self.config.get(\"uid\", None) is None:\n            return\n        drop_privileges(self.config.get(\"uid\"))\n\n    def _spawn_as_child(self, filename, argv):\n        try:\n            self.system_util.setpgrp()\n            self.set_uid()\n            self._prepare_child_fds()\n            # TODO set environment\n            env = os.environ.copy()\n            env['SUPERVISOR_ENABLED'] = '1'\n            env['SUPERVISOR_PROCESS_NAME'] = self.name\n            cwd = self.config.get(\"directory\", None)\n            try:\n                if cwd is not None:\n                    os.chdir(cwd)\n            except OSError as e:\n                code = errno.errorcode.get(e.args[0], e.args[0])\n                msg = \"couldn't chdir to %s: %s\\n\" % (cwd, code)\n                self.system_util.write(2, msg)\n                return\n            try:\n                if self.config.get(\"umask\", None) is not None:\n                    self.system_util.umask(self.config.get(\"umask\", None))\n                self.system_util.execve(filename, argv, env)\n            except OSError as e:\n                code = errno.errorcode.get(e.args[0], e.args[0])\n                msg = \"couldn't exec %s: %s\\n\" % (argv[0], code)\n                self.system_util.write(2, msg)\n            except:\n                (f, fun, line), t, v, tbinfo = compact_traceback()\n                error = '%s, %s: file: %s line: %s' % (t, v, f, line)\n                msg = \"couldn't exec %s: %s\\n\" % (filename, error)\n                self.system_util.write(2, msg)\n\n        except PrivilegesException as e:\n            self.system_util.write(2, e.message)\n            return\n        finally:\n            self.system_util.write(2, \"child process was not spawned\\n\")\n            os._exit(12)\n\n    def _spawn_as_parent(self, pid):\n        self.pid = pid\n        self.LOG.info('spawned: with pid %s', pid, process=self)\n        self.spawn_err = None\n        self.close_child_streams()\n        # todo 注册进程退出的事件\n        self.program.controller.register_process_pid(pid, self.finish)\n        if self.config.get(\"startsecs\") > 0:\n            # todo 注册进程启动时间检查事件\n            self.start_timeout = self.program.controller.add_timeout(\n                datetime.timedelta(seconds=self.config.get(\"startsecs\")),\n                self.change_running\n            )\n        else:\n            self.change_running()\n        return pid\n\n    def process_info(self):\n        now = int(time.time())\n        state = self.state\n        if state == ProcessStates.RUNNING:\n            start_dt = datetime.datetime(*time.gmtime(int(self.last_start))[:6])\n            now_dt = datetime.datetime(*time.gmtime(now)[:6])\n            uptime = now_dt - start_dt\n            if total_seconds(uptime) < 0:  # system time set back\n                uptime = datetime.timedelta(0)\n            desc = 'pid %s, uptime %s' % (self.pid, uptime)\n        elif state in (ProcessStates.FATAL, ProcessStates.BACKOFF):\n            desc = self.spawn_err\n            if not desc:\n                desc = 'unknown error (try \"tail %s\")' % self.name\n        elif state in (ProcessStates.STOPPED, ProcessStates.EXITED):\n            if self.last_start:\n                stop = int(self.last_stop)\n                stop_dt = datetime.datetime(*time.localtime(stop)[:7])\n                desc = stop_dt.strftime('%b %d %I:%M %p')\n            else:\n                desc = 'Not started'\n        else:\n            desc = ''\n        return desc\n\n    def process_state(self):\n        return {\n            \"name\": self.name,\n            \"change_state_time\": int(self.change_state_time) if self.change_state_time else None,\n            \"state\": self.state.value,\n            \"last_stop\": int(self.last_stop) if self.last_stop else None,\n            \"last_start\": int(self.last_start) if self.last_start else None,\n            \"pid\": self.pid,\n            \"backoff\": self.backoff,\n            \"exit_status\": self.exit_status,\n            \"spawn_err\": self.spawn_err,\n            \"description\": self.process_info(),\n        }\n\n    def __repr__(self):\n        return \"%s:pid=%s,state=%s,priority=%s\" % (self.name, self.pid, self.state, self.config.get(\"priority\"))\n","sub_path":"management/process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":25179,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"366768010","text":"import pygame\n#윈도우 크기를 결정합니다\nsize = width, height = 800,600\nwin = pygame.display.set_mode(size)\n\n#png 리소스를 불러옵니다 : script를 이용하는 경우 working directory 문제로 작동하지 않을 수 있음\nbg = pygame.transform.rotozoom(pygame.image.load('freetileset/png/BG/BG.png'),0,0.85)\nchar1 = pygame.transform.rotozoom(pygame.image.load('freeknight/png/Walk (1).png'),0,0.35)\nchar2 = pygame.transform.rotozoom(pygame.transform.flip(pygame.image.load('freeknight/png/Walk (1).png'),True, False),0,0.35)\n\n#배경을 표시합니다\nwin.blit(bg,(0,0))\npygame.display.update()\npygame.init()\nclock = pygame.time.Clock()\n\ndef loadSprite(action, direction):\n    spriteList = []\n    for i in range(1, 11):\n        if (direction == \"R\"):\n            spriteList.append(pygame.transform.rotozoom(pygame.image.load('freeknight/png/%s (%d).png' %(action, i)), -5, 0.35))\n        elif (direction ==\"L\"):\n            spriteList.append(pygame.transform.rotozoom(pygame.transform.flip(pygame.image.load('freeknight/png/%s (%d).png' %(action ,i)),True, False),1,0.35))\n\n    return spriteList\n\n# 오른쪽을 공격하는 스프라이트\nattackRight = loadSprite(\"Attack\", \"R\")\n\n# 오른쪽으로 걷는 스프라이트\nwalkRight = loadSprite(\"Walk\", \"R\")\n\n#왼쪽으로 걷는 스프라이트\nwalkLeft = loadSprite(\"Walk\",\"L\")\n\nvel = 10\njump = 20\nyvel = 0\nx = 50\ny = 350\nleft = False\nright = False\nattack = False\natkcount = 0\nwalkcount = 0\nstopd = True\n\n# 화면을 갱신(redraw)합니다\ndef redrawG():\n    global walkcount\n    win.blit(bg, (0, 0))\n\n    # walkCount를 초기화합니다\n    if walkcount >= 32:\n        walkcount = 0\n\n    # 오른쪽으로 걸을 때\n    if right:\n        win.blit(walkRight[walkcount//4],(x,y))\n        print(walkcount//4)\n        walkcount += 1\n\n    # 왼쪽으로 걸을 때\n    elif left:\n        win.blit(walkLeft[walkcount//4],(x,y))\n        walkcount += 1\n    # 오른쪽을 보고 멈춰 있을 때\n    elif stopd :\n        win.blit(char1, (x, y))\n        print(\"Right Direction\")\n    # 왼쪽을 보고 멈춰 있을 때\n    else:\n        win.blit(char2, (x,y))\n        print(\"Left Direction\")\n        pass\n    pygame.display.update()\n\nrun = True\n\nwhile run:\n    clock.tick(48)\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            run = False\n        if event.type == pygame.KEYUP:\n            if right:\n                right = False\n                stopd = True\n            if left:\n                left = False\n                stopd = False\n            pass\n\n    keys = pygame.key.get_pressed()\n\n    if keys[pygame.K_RIGHT]:\n        if x > width - 100:\n            x = width - 100\n        x += vel\n        right = True\n        left = False\n    if keys[pygame.K_LEFT]:\n        if x < -150:\n            x = -150\n        x -= vel\n        right = False\n        left = True\n    if keys[pygame.K_UP]:\n        if y < 500 and yvel == 0:\n            yvel -= jump\n    if keys[pygame.K_SPACE]:\n        attack = True\n\n    if yvel != 0:\n        y += yvel\n        yvel += 10\n        if y > 500:\n            y = 500\n            yvel = 0\n    print(y, yvel)\n\n    redrawG()\n\npygame.quit()\n","sub_path":"my_mario.py","file_name":"my_mario.py","file_ext":"py","file_size_in_byte":3179,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"512656972","text":"import prepare\nimport acquire\n\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport statsmodels.api as sm\n\n# ----------------- # \n#      Explore      #\n# ----------------- #\n\ndef find_range(df):\n    print('Date Range:', df.index.min(), 'to', df.index.max())\n\ndef numeric_hists(df, bins=20):\n    \"\"\"\n    Function to take in a DataFrame, bins default 20,\n    select only numeric dtypes, and\n    display histograms for each numeric column\n    \"\"\"\n    num_df = df.select_dtypes(include=np.number)\n    num_df.hist(bins=bins, color='thistle')\n    plt.suptitle('Numeric Column Distributions')\n    plt.show()\n\ndef seasonal_decomposition(df, col):\n    y = df[col].resample(\"W\").mean()\n\n    result = sm.tsa.seasonal_decompose(y)\n    decomposition = pd.DataFrame({\n            \"y\": result.observed,\n        \"trend\": result.trend,\n        \"seasonal\": result.seasonal,\n        \"resid\": result.resid,\n    })\n\n    decomposition.iloc[:,1:].plot()","sub_path":"time_series/explore.py","file_name":"explore.py","file_ext":"py","file_size_in_byte":977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"99457089","text":"import torch.utils.data\nimport torchvision.transforms as transforms\nimport torchvision.datasets as datasets\n\nfrom dataloader import sDataLoader\n\nfrom PIL import ImageFile, Image\nImageFile.LOAD_TRUNCATED_IMAGES = True\n\n\ndef im_loader(path):\n    return Image.open(path).convert('RGB')\n\n\ndef get_loader(data_dir, inp_size, batch_size, training=True, shuffle=True, num_workers=3):\n    transform_train = transforms.Compose([\n        transforms.RandomSizedCrop(inp_size),\n        transforms.RandomHorizontalFlip(),\n        transforms.ToTensor(),\n        transforms.Normalize(mean=[0.485, 0.456, 0.406],\n                             std=[0.229, 0.224, 0.225]),\n    ])\n\n    transform_test = transforms.Compose([\n        transforms.CenterCrop(inp_size),\n        transforms.ToTensor(),\n        transforms.Normalize(mean=[0.485, 0.456, 0.406],\n                             std=[0.229, 0.224, 0.225]),\n    ])\n\n    transform = transform_train if training else transform_test\n    dataset = datasets.ImageFolder(data_dir, transform, loader=im_loader)\n\n    data_loader = sDataLoader(dataset, batch_size, shuffle, num_workers=num_workers)\n    return data_loader\n","sub_path":"tnn/datasets/imagenet.py","file_name":"imagenet.py","file_ext":"py","file_size_in_byte":1145,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"209800455","text":"from Bio import Phylo\nfrom collections import defaultdict\nimport networkx, pylab, dendropy, argparse, os, json\n\ntree_loc = \"\"\nbase_loc = \"\"\n\ndef distance_matrix():\n\t# Calculate dist matrix\n\ttree = dendropy.Tree.get_from_path(tree_loc, \"newick\")\n\tpdm = tree.phylogenetic_distance_matrix()\n\n\tdist_mat = defaultdict(dict)\n\tfor i, t1 in enumerate(tree.taxon_namespace):\n\t    for t2 in tree.taxon_namespace[i+1:]:\n\t        dist_mat[t1.label][t2.label] = dist_mat[t2.label][t1.label] = pdm(t1,t2)\n\t        \n\twith open(base_loc+\"/distance_matrix.json\", 'wb') as outfile:\n\t\tjson.dump(dist_mat, outfile)\n\ndef draw_tree():\n\t# Draw tree\n\ttree = Phylo.read(tree_loc, 'newick')\n\n\ttree.ladderize()\n\tPhylo.draw(tree, do_show=False)\n\t# pylab.axis('off')\n\tpylab.savefig(base_loc+\"/phylo_tree.svg\",format='svg', bbox_inches='tight', dpi=300)\n\nif __name__ == '__main__':\n\tparser = argparse.ArgumentParser()\n\tparser.add_argument(\"newick_file\", help=\"newick file location\")\n\targs = parser.parse_args()\n\n\ttree_loc = args.newick_file\n\tbase_loc = os.path.dirname(tree_loc)\n\n\tdistance_matrix()\n\tdraw_tree()\n","sub_path":"resources/phylo-tree/Phylo.py","file_name":"Phylo.py","file_ext":"py","file_size_in_byte":1082,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"215264927","text":"\"\"\"\r\nComputer Vision plots\r\n\"\"\"\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\n\r\ndef plot_hist_df(df, x_col, x_labels):\r\n    \"\"\" To plot histogram from pandas.Series \"\"\"\r\n    \r\n    print(\"Plotting histogram for %s...\" %x_col)\r\n    print(\"Total counts:\", len(df))\r\n    uniq_labels = sorted(df.unique())\r\n    x_labels = x_labels\r\n    plt.figure(figsize=(20, 20))\r\n    plt.style.use('seaborn-whitegrid')\r\n    plt.rcParams.update({'font.size': 15})\r\n    #x_pos = np.arange(len(df))  #the label locations\r\n    ax = sns.countplot(df)\r\n    ax.set_xticklabels(x_labels, rotation=40, ha='right')\r\n    plt.tight_layout()\r\n    i = 0\r\n    for l in uniq_labels:\r\n        labels_ttl = (df == l).sum()\r\n        #if labels_ttl < 5:\r\n        #    print(l, labels_ttl)\r\n        plt.text(i, labels_ttl, str(labels_ttl), ha='center')\r\n        i += 1\r\n    plt.show()\r\n    \r\ndef plot_bar_prob(list_x, list_y, g_title, x_label, ax):\r\n    \"\"\" To plot bar chart from a list \"\"\"\r\n    \r\n    print(\"Plotting bar chart for prediction probabilities...\")\r\n    \r\n    s_ax = ax\r\n    x_labels = list_x\r\n    #x_pos = np.arange(len(list_x))  #the label locations\r\n    sns.barplot(x=list_x, y=list_y, ax=s_ax)\r\n    s_ax.set_title(g_title)\r\n    s_ax.set_xlabel(x_label)\r\n    s_ax.set_ylabel(\"probabilities\")\r\n    s_ax.set_xticklabels(x_labels, rotation=40, ha='right')\r\n    plt.tight_layout()\r\n    i = 0\r\n    for i in range(len(list_y)):\r\n        s_ax.text(i, list_y[i], str(list_y[i]), ha='center')\r\n        i += 1\r\n\r\ndef plot_donut(the_num, the_title, sh, lo):\r\n    #create data\r\n    the_data=[the_num, (1-the_num)]\r\n    \r\n    #add the subplot and create pie\r\n    plt.subplot2grid(sh, lo)\r\n    plt.title(the_title)\r\n    wedges, texts = plt.pie(the_data, wedgeprops=dict(width=0.5), colors=['b', 'w'], startangle=-270, counterclock=False)\r\n    the_text = '{0:.2f}%'.format(the_data[0] * 100)\r\n    plt.text(-0.2, 0, the_text, fontsize=15)\r\n\r\ndef plot_prec_rec(dict_cr, tag_id, list_class):\r\n    \"\"\" To plot Precision-Recall curve \"\"\"\r\n    \r\n    plt.figure(figsize=(20, 8))\r\n    plt.rcParams.update({'font.size': 13})\r\n    list_prec = []\r\n    list_rec = []\r\n\r\n    for tid in tag_id:\r\n        scores = dict_cr.get(str(tid))\r\n        if scores == None:\r\n            prec = 0.0\r\n            rec = 0.0\r\n        else:\r\n            prec = round(scores['precision'], 2)\r\n            rec = round(scores['recall'], 2)     \r\n        #print(\"tid:\", tid, prec, rec)\r\n        list_prec.append(prec)\r\n        list_rec.append(rec)\r\n\r\n    if len(list_class) != 0:\r\n        x_axis = [list_class[i] for i in tag_id]\r\n    else:\r\n        x_axis = tag_id\r\n    plt.plot(x_axis, list_prec, '-gD', markersize=12, label='Precision')\r\n    plt.plot(x_axis, list_rec, '--ro', label='Recall')\r\n    plt.xticks(rotation=90)\r\n    #plt.xlim([0.0, 1.0])\r\n    #plt.ylim([0.0, 1.05])\r\n    plt.xlabel('Bird Class')\r\n    plt.ylabel('Scores')\r\n    plt.title('Precision Recall scores per class')\r\n    plt.legend()\r\n    plt.show()\r\n","sub_path":"cv_plot.py","file_name":"cv_plot.py","file_ext":"py","file_size_in_byte":2962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"603045553","text":"__author__ = \"corka149\"\n\nfrom pykie.runner import MultiTaskVariables, ProcessRunner\nfrom pykie.core import models\nfrom unittest import TestCase\nimport unittest\n\n\nclass TestMultiTaskVariables(TestCase):\n\n    def test_matching(self):\n        json = {\"task-id\": 1, \"task-name\": \"CreateShoppingList\", \"task-created-on\": {\"java.util.Date\": 1539631552.8501}}\n        task = models.Task(json)\n        mtv = MultiTaskVariables()\n        mtv.add_conditional_variables({\"name\": \"Bob\"}, id=1)\n        mtv.add_conditional_variables({\"description\": \"Lorem ipsum\"}, name=\"ShoppingList\")\n        mtv.add_conditional_variables({\"items\": [\"Bread\", \"Apples\"]}, name=\"CreateShoppingList\")\n        vars = ProcessRunner.__match_variables__(mtv, task)\n        self.assertEqual(2, len(vars))\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"test/test_multitaskvariables.py","file_name":"test_multitaskvariables.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"198312606","text":"from django.shortcuts import render,redirect\nfrom .models import *\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth import login,logout,authenticate\nfrom django.contrib import messages\nfrom datetime import date\n# Create your views here.\ndef Home(request):\n    cat = Category.objects.all()\n    d = {'cat': cat}\n    return render(request, 'index.html', d)\n\ndef About(request):\n    return render(request, 'about.html')\n\ndef Contact(request):\n    return render(request, 'contact.html')\ndef Login(request):\n    error = False\n    if request.method == 'POST':\n        u = request.POST['uname']\n        p = request.POST['psw']\n        user = authenticate(username=u, password=p)\n        if user:\n            login(request, user)\n            return redirect('home')\n        else:\n            error = True\n    d = {'error': error}\n\n    return render(request, 'login.html', d)\n\ndef Logout(request):\n    if not request.user.is_authenticated:\n        return redirect('login')\n    logout(request)\n    return redirect('home')\n\ndef Login(request):\n    error = False\n    if request.method == 'POST':\n        u = request.POST['uname']\n        p = request.POST['psw']\n        user = authenticate(username=u, password=p)\n        if user:\n            login(request, user)\n            return redirect('home')\n        else:\n            error = True\n    d = {'error': error}\n\n    return render(request, 'login.html', d)\ndef Add_Profile(request):\n    error = False\n    if request.method == 'POST':\n        n = request.POST['name']\n        u = request.POST['uname']\n        p = request.POST['pwd']\n        d = request.POST['date']\n        c = request.POST['city']\n        ad = request.POST['add']\n        e = request.POST['email']\n        con = request.POST['contact']\n        user1 = User.objects.filter(username = u)\n        if user1:\n            error = True\n        else:\n            user = User.objects.create_user(username=u, email=e, password=p, first_name=n)\n            Profile.objects.create(user=user, dob=d, city=c, address=ad, contact=con)\n            return redirect('login')\n    d = {'error':error}\n    return render(request, 'profile.html',d)\n\ndef Login_Admin(request):\n    error = False\n    if request.method == 'POST':\n        u = request.POST['uname']\n        p = request.POST['psw']\n        user = authenticate(username=u, password=p)\n        if user.is_staff:\n            login(request, user)\n            return redirect('admin_base')\n        else:\n            error = True\n    d = {'error': error}\n\n    return render(request, 'login_admin.html', d)\n\ndef Admin_Base(request):\n    if not request.user.is_staff:\n        return redirect('login_admin')\n    book=Booking.objects.all()\n    pro=Product.objects.all()\n    profile=Profile.objects.all()\n    b=0\n    b1=0\n    b2=0\n    for i in book:\n        b+=1\n    for j in pro:\n        b1+=1\n    for k in profile:\n        b2+=1\n    d={'b':b,'b1':b1,'b2':b2}\n    return render(request, 'admin_home.html',d)\ndef Admin_Profile(request, pid):\n    if not request.user.is_staff:\n        return redirect('login_admin')\n    user1 = User.objects.get(id=pid)\n    data = Profile.objects.filter(user=user1).first()\n    d = {'data': data}\n    return render(request, 'admin_profile.html', d)\n\n\ndef View_Product(request):\n\n    pro = Product.objects.all()\n    d = {'pro': pro}\n    return render(request, 'view_product.html', d)\n\ndef Admin_View_Product(request):\n\n    pro = Product.objects.all()\n    d = {'pro': pro}\n    return render(request, 'admin_view_product.html', d)\n\ndef Product_booking(request,pid):\n    pro1 = Category.objects.get(id=pid)\n    pro=Product.objects.filter(category=pro1)\n    d = {'pro': pro}\n    return render(request, 'basekin.html', d)\ndef Add_Product(request):\n    if not request.user.is_staff:\n        return redirect('login_admin')\n    cat = Category.objects.all()\n    error=False\n    if request.method==\"POST\":\n        c = request.POST['cat']\n        p = request.POST['pname']\n        pr = request.POST['price']\n        i = request.FILES['img']\n        d = request.POST['desc']\n        ct = Category.objects.filter(name=c).first()\n        Product.objects.create(category=ct, name=p, price=pr, image=i, desc=d)\n        error=True\n    d = {'cat': cat,'error':error}\n    return render(request, 'add_product.html', d)\ndef Admin_View_Booking(request):\n    if not request.user.is_staff:\n        return redirect('login_admin')\n    book = Booking.objects.all()\n    d = {'book': book}\n    return render(request, 'admin_viewBokking.html', d)\n\ndef View_feedback(request):\n    if not request.user.is_staff:\n        return redirect('login_admin')\n    feed = Send_Feedback.objects.all()\n    d = {'feed': feed}\n    return render(request, 'view_feedback.html', d)\ndef Logout_Admin(request):\n    if not request.user.is_staff:\n        return redirect('login_admin')\n    logout(request)\n    return redirect('login_admin')\n\ndef View_Customer(request):\n    if not request.user.is_staff:\n        return redirect('login_admin')\n    cust = Profile.objects.all()\n    d = {'cus': cust}\n    return render(request, 'view_customer.html', d)\ndef Edit_Product(request, pid):\n    if not request.user.is_staff:\n        return redirect('login_admin')\n    pro = Product.objects.get(id=pid)\n    cat = Category.objects.all()\n    error=False\n    if request.method==\"POST\":\n        p = request.POST['pname']\n        pr = request.POST['price']\n        d = request.POST['desc']\n        pro.name = p\n        pro.price = pr\n        pro.desc = d\n        pro.save()\n        try:\n            c = request.POST['cat']\n            ct = Category.objects.filter(name=c).first()\n            pro.category = ct\n            pro.save()\n        except:\n            pass\n        try:\n            i = request.FILES['img']\n            pro.image = i\n            pro.save()\n        except:\n            pass\n        error=True\n    d = {'cat': cat, 'pro': pro,'error':error}\n    return render(request, 'edit_product.html', d)\ndef delete_product(request,pid):\n    if not request.user.is_staff:\n        return redirect('login_admin')\n    if Product.objects.filter(id=pid).exists():\n        pro = Product.objects.get(id=pid)\n        pro.delete()\n        message1 = messages.info(request, 'Product Cancelled')\n        return redirect('view_product')\ndef Delete_Customer(request, pid):\n    if not request.user.is_staff:\n        return redirect('login_admin')\n    if Profile.objects.filter(id=pid).exists():\n        pro = Profile.objects.get(id=pid)\n        pro.delete()\n        message1 = messages.info(request, 'Customer Deleted')\n        return redirect('view_customer')\ndef Edit_Profile(request, pid):\n    data = Profile.objects.get(id=pid)\n    if request.method == 'POST':\n        n = request.POST['name']\n        u = request.POST['uname']\n        c = request.POST['city']\n        ad = request.POST['add']\n        e = request.POST['email']\n        con = request.POST['contact']\n        data.user.first_name = n\n        data.user.username = u\n        data.user.email = e\n        data.contact = con\n        data.address = ad\n        data.city = c\n        data.save()\n        try:\n            d = request.POST['date']\n            data.dob = d\n            data.save()\n        except:\n            pass\n\n        return redirect('view_prifile')\n    d = {'data': data}\n    return render(request, 'edit_profile.html', d)\n\n\n\ndef View_profile(request):\n    if not request.user.is_authenticated:\n        return redirect('login')\n    data1 = User.objects.get(id=request.user.id)\n    data = Profile.objects.filter(user=data1).first()\n    d = {'data': data}\n    return render(request, 'view_profile.html', d)\n\ndef Booking_order(request, pid):\n    if not request.user.is_authenticated:\n        return redirect('login')\n    pro = Product.objects.get(id=pid)\n    pro1=Profile.objects.get(user=request.user.id)\n\n    data1 = User.objects.get(id=request.user.id)\n    data = Profile.objects.filter(user=data1).first()\n    date1 = date.today()\n    if request.method == \"POST\":\n        d = request.POST['date']\n        c = request.POST['name']\n        c1 = request.POST['city']\n        ad = request.POST['add']\n        e = request.POST['email']\n        con = request.POST['contact']\n        p = request.POST['pname']\n        pr = request.POST['price']\n        q = request.POST['quantity']\n        t = request.POST['total']\n        stat=Status.objects.get(name=\"pending\")\n\n        Book = Booking.objects.create(user=request.user,status=stat, book_date=d, c_name=c, email=e, contact=con, city=c1, address=ad, pro_name=p, price=pr, quantity=q, total=t)\n\n\n        return redirect('payment1',Book.id)\n\n\n    d = {'data': data, 'data1': data1, 'pro': pro, 'date1': date1, 'pro1': pro1}\n\n    return render(request, 'booking.html', d)\n\ndef View_Booking(request):\n    book = Booking.objects.filter(user=request.user)\n    if book:\n        pass\n    else:\n        message1 = messages.info(request, 'Not Found Any Data Related To Order')\n\n    d = {'book': book}\n    return render(request, 'view_booking.html', d)\n\ndef delete_booking(request, pid):\n    if not request.user.is_authenticated:\n        return redirect('login')\n    book = Booking.objects.get(id=pid)\n    book.delete()\n    message2 = messages.info(request, 'Booking Cancelled')\n    return redirect('view_booking')\ndef Payment1(request,pid):\n    if not request.user.is_authenticated:\n        return redirect('login')\n    book = Booking.objects.get(id=pid)\n\n    message1 = messages.info(request, 'Booking Your Order Successfully')\n    d = {'book': book}\n    return render(request, 'payment2.html', d)\n\ndef Payment(request):\n    if not request.user.is_authenticated:\n        return redirect('login')\n    return render(request, 'payment2.html')\ndef View_feedback(request):\n    if not request.user.is_staff:\n        return redirect('login_admin')\n    feed = Send_Feedback.objects.all()\n    d = {'feed': feed}\n    return render(request, 'view_feedback.html', d)\ndef Admin_View_Booking(request):\n    if not request.user.is_staff:\n        return redirect('login_admin')\n    book = Booking.objects.all()\n    d = {'book': book}\n    return render(request, 'admin_viewBokking.html', d)\ndef Feedback(request, pid):\n    if not request.user.is_authenticated:\n        return redirect('login')\n    date1 = date.today()\n    user = User.objects.get(id=pid)\n    pro = Profile.objects.filter(user=user).first()\n    if request.method == \"POST\":\n        d = request.POST['date']\n        u = request.POST['name']\n        e = request.POST['email']\n        con = request.POST['contact']\n        m = request.POST['message']\n        user = User.objects.filter(username=u, email=e).first()\n        pro = Profile.objects.filter(user=user, contact=con).first()\n        Send_Feedback.objects.create(profile=pro, date=d, message1=m)\n        message1 = messages.info(request, 'send_feedback successfully')\n        return redirect('send_feedback',pid)\n\n    d = {'pro': pro, 'date1': date1}\n\n    return render(request, 'feedback.html', d)\n\ndef Delete_feedback(request, pid):\n    if not request.user.is_staff:\n        return redirect('login_admin')\n    if Send_Feedback.objects.filter(id=pid).exists():\n        pro = Send_Feedback.objects.get(id=pid)\n        pro.delete()\n        message1 = messages.info(request, 'Selected Feedback Deleted')\n        return redirect('view_feedback')\n\ndef Edit_status(request,pid):\n    if not request.user.is_staff:\n        return redirect('login_admin')\n    book = Booking.objects.get(id=pid)\n    stat = Status.objects.all()\n    if request.method == \"POST\":\n        n = request.POST['book']\n        s = request.POST['st']\n        book.id = n\n        sta = Status.objects.filter(name=s).first()\n        book.status = sta\n        book.save()\n        return redirect('admin_viewBooking')\n\n    d = {'book': book, 'stat': stat}\n    return render(request, 'status.html', d)\ndef change_password(request):\n     error=\"\"\n     data = User.objects.get(username=request.user.username)\n     if request.method==\"POST\":\n        o=request.POST['password1']\n        n=request.POST['password2']\n        if o==n:\n            data.set_password(o)\n            data.save()\n            login(request,data)\n            error=\"yes\"\n        else:\n            error=\"not\"\n     d={'error':error}\n     return render(request,'change_password.html',d)\n\n","sub_path":"onlinebookshop/bookshop/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":12247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"151869180","text":"# -*- coding: utf-8 -*-\n\"\"\"\nHandle and interchange between different random number generators (numpy,\npython, torch, ...). Also defines useful random iterator functions and\n:func:`ensure_rng`.\n\n\nRandom Number Generator Patterns\n--------------------------------\n\nIf you need a seeded random number generator kwarray.ensure_rng is helpful with\nthat: :func:`kwarray.util_random.ensure_rng`\n\nIf the input is a number it returns a seeded random number generator. If it is\nNone is returns whatever the system level RNG is. If the input is an existing\nRNG it returns it without changing it. It also has the ability to switch\nbetween Python's random module RNG and numpys np.random RNG (it can translate\nthe internal state between the two).\n\n\nWhen I write randomized functions / class, a coding pattern I like is to\nhave a default keyword argument ``rng=None``. Then kwarray.ensure_rng coerces\nwhatever the input is into a :func:`random.Random` or\n:func:`numpy.random.RandomState` object.\n\n.. code:: python\n\n    def some_random_function(*args, rng=None):\n        rng = kwarray.ensure_rng(rng)\n\nThen if this random function calls any other random function, it passes the\ncoerced rng to all other subfunctions. This ensures that seeing the RNG at\nthe top level produces a completely determenistic process.\n\nFor a more involved example\n\n.. code:: python\n\n    import pandas as pd\n    import numpy\n    import kwarray\n\n    def random_subfunc1(rng=None):\n        rng = kwarray.ensure_rng(rng, api='python')\n        value: float = rng.betavariate(3, 2.3)\n        return value\n\n\n    def random_subfunc2(rng=None):\n        rng = kwarray.ensure_rng(rng, api='numpy')\n        arr: np.ndarray = rng.choice([1, 2, 3, 4], size=3, replace=0)\n        return arr\n\n\n    def random_method(rng=None):\n        value = random_subfunc1(rng=rng)\n        arr = random_subfunc2(rng=rng)\n        final = (arr * value).sum()\n        return final\n\n    def demo():\n        results = []\n        num = 10\n        for _ in range(num):\n            rng = np.random.RandomState(3)\n            row = {}\n            row['system'] = random_method(None)\n            row['seeded'] = random_method(0)\n            row['exiting'] = random_method(rng)\n            results.append(row)\n\n        df = pd.DataFrame(results)\n        print(df)\n\n\nThis results in:\n\n.. code::\n\n         system    seeded   exiting\n    0  3.642700  6.902354  4.869275\n    1  3.127890  6.902354  4.869275\n    2  4.317397  6.902354  4.869275\n    3  3.382259  6.902354  4.869275\n    4  1.999498  6.902354  4.869275\n    5  5.293688  6.902354  4.869275\n    6  2.984741  6.902354  4.869275\n    7  6.455160  6.902354  4.869275\n    8  5.161900  6.902354  4.869275\n    9  2.810358  6.902354  4.869275\n\"\"\"\nimport numpy as np\nimport random\nimport itertools as it\n\n_SEED_MAX = int(2 ** 32 - 1)\n\n\n__todo = \"\"\"\nMake a Coercable[RandomState] type that is\n\nCoercable[numpy.random.RandomState] = Union[int, float, None, numpy.random.RandomState, random.Random]\nCoercable[random.Random] = Union[int, float, None, numpy.random.RandomState, random.Random]\n\n\n\n\"\"\"\n\n\ndef seed_global(seed, offset=0):\n    \"\"\"\n    Seeds the python, numpy, and torch global random states\n\n    Args:\n        seed (int): seed to use\n        offset (int): if specified, uses a different seed for each\n            global random state separated by this offset. Defaults to 0.\n    \"\"\"\n    random.seed((seed) % _SEED_MAX)\n    np.random.seed((seed + offset) % _SEED_MAX)\n    try:\n        import torch\n    except ImportError:\n        pass\n    else:\n        torch.random.manual_seed((seed + 2 * offset) % _SEED_MAX)\n        torch.cuda.manual_seed_all((seed + 3 * offset) % _SEED_MAX)\n\n\ndef shuffle(items, rng=None):\n    \"\"\"\n    Shuffles a list inplace and then returns it for convinience\n\n    Args:\n        items (list | ndarray): data to shuffle\n        rng (int | float | None | numpy.random.RandomState | random.Random):\n            seed or random number gen\n\n    Returns:\n        list: this is the input, but returned for convinience\n\n    Example:\n        >>> list1 = [1, 2, 3, 4, 5, 6]\n        >>> list2 = shuffle(list(list1), rng=1)\n        >>> assert list1 != list2\n        >>> result = str(list2)\n        >>> print(result)\n        [3, 2, 5, 1, 4, 6]\n    \"\"\"\n    rng = ensure_rng(rng)\n    rng.shuffle(items)\n    return items\n\n\ndef random_combinations(items, size, num=None, rng=None):\n    \"\"\"\n    Yields ``num`` combinations of length ``size`` from items in random order\n\n    Args:\n        items (List):\n            pool of items to choose from\n\n        size (int):\n            Number of items in each combination\n\n        num (int | None):\n            Number of combinations to generate. If None, generate them all.\n\n        rng (int | float | None | numpy.random.RandomState | random.Random):\n            seed or random number generator. Defaults to the global state\n            of the python random module.\n\n    Yields:\n        Tuple: a random combination of ``items`` of length ``size``.\n\n    Example:\n        >>> # xdoctest: +REQUIRES(module:scipy)\n        >>> import ubelt as ub\n        >>> items = list(range(10))\n        >>> size = 3\n        >>> num = 5\n        >>> rng = 0\n        >>> # xdoctest: +IGNORE_WANT\n        >>> combos = list(random_combinations(items, size, num, rng))\n        >>> print('combos = {}'.format(ub.urepr(combos, nl=1)))\n        combos = [\n            (0, 6, 9),\n            (4, 7, 8),\n            (4, 6, 7),\n            (2, 3, 5),\n            (1, 2, 4),\n        ]\n\n    Example:\n        >>> # xdoctest: +REQUIRES(module:scipy)\n        >>> import ubelt as ub\n        >>> items = list(zip(range(10), range(10)))\n        >>> # xdoctest: +IGNORE_WANT\n        >>> combos = list(random_combinations(items, 3, num=5, rng=0))\n        >>> print('combos = {}'.format(ub.urepr(combos, nl=1)))\n        combos = [\n            ((0, 0), (6, 6), (9, 9)),\n            ((4, 4), (7, 7), (8, 8)),\n            ((4, 4), (6, 6), (7, 7)),\n            ((2, 2), (3, 3), (5, 5)),\n            ((1, 1), (2, 2), (4, 4)),\n        ]\n\n    \"\"\"\n    import sys\n    rng = ensure_rng(rng, api='python')\n    num_ = np.inf if num is None else num\n    # Ensure we dont request more than is possible\n    if sys.version_info[0:2] >= (3, 8):\n        import math\n        n_max = math.comb(len(items), size)\n    else:\n        try:\n            import scipy.special\n            n_max = int(scipy.special.comb(len(items), size))\n        except ImportError:\n            # https://stackoverflow.com/questions/26560726/python-binomial-coefficient\n            from math import factorial as fac\n            a = len(items)\n            b = size\n            n_max = int(fac(a) // fac(b) // fac(a - b))\n\n    num_ = min(n_max, num_)\n    if num is not None and num_ > n_max // 2:\n        # If num is too big just generate all combinations and shuffle them\n        combos = list(it.combinations(items, size))\n        rng.shuffle(combos)\n        for combo in combos[:num]:\n            yield combo\n    else:\n        # Otherwise yield randomly until we get something we havent seen\n        items = list(items)\n        combos = set()\n        while len(combos) < num_:\n            # combo = tuple(sorted(rng.choice(items, size, replace=False)))\n            combo = tuple(sorted(rng.sample(items, size)))\n            if combo not in combos:\n                # TODO: store indices instead of combo values\n                combos.add(combo)\n                yield combo\n\n\ndef random_product(items, num=None, rng=None):\n    \"\"\"\n    Yields ``num`` items from the cartesian product of items in a random order.\n\n    Args:\n        items (List[Sequence]):\n            items to get caresian product of packed in a list or tuple.\n            (note this deviates from api of :func:`itertools.product`)\n\n        num (int | None):\n            maximum number of items to generate. If None generat them all\n\n        rng (int | float | None | numpy.random.RandomState | random.Random):\n            Seed or random number generator. Defaults to the global state\n            of the python random module.\n\n    Yields:\n        Tuple: a random item in the cartesian product\n\n    Example:\n        >>> import ubelt as ub\n        >>> items = [(1, 2, 3), (4, 5, 6, 7)]\n        >>> rng = 0\n        >>> # xdoctest: +IGNORE_WANT\n        >>> products = list(random_product(items, rng=0))\n        >>> print(ub.urepr(products, nl=0))\n        [(3, 4), (1, 7), (3, 6), (2, 7),... (1, 6), (2, 5), (2, 4)]\n        >>> products = list(random_product(items, num=3, rng=0))\n        >>> print(ub.urepr(products, nl=0))\n        [(3, 4), (1, 7), (3, 6)]\n\n    Example:\n        >>> # xdoctest: +REQUIRES(--profile)\n        >>> rng = ensure_rng(0)\n        >>> items = [np.array([15, 14]), np.array([27, 26]),\n        >>>          np.array([21, 22]), np.array([32, 31])]\n        >>> num = 2\n        >>> for _ in range(100):\n        >>>     list(random_product(items, num=num, rng=rng))\n    \"\"\"\n    # NUMPY_RNG = True  # toggle new speedup on\n    try:\n        if not isinstance(items, (list, tuple)):\n            raise TypeError\n        idx_cards = np.array([len(g) for g in items], dtype=np.uint32)\n    except (TypeError, AttributeError):\n        items = [list(g) for g in items]\n        idx_cards = np.array([len(g) for g in items], dtype=np.uint32)\n\n    ndims = len(items)\n    # max_num = np.prod(idx_cards.astype(np.float))\n    max_num = np.multiply.reduce(idx_cards.astype(np.float32))\n\n    if num is None:\n        num = max_num\n    else:\n        num = min(num, max_num)\n        # if num > max_num:\n        #     raise ValueError('num exceedes maximum number of products')\n\n    # TODO: make this more efficient when num is large\n    if max_num > 100 and num > max_num // 2:\n\n        rng = ensure_rng(rng, 'python')\n\n        for prod in shuffle(list(it.product(*items)), rng=rng):\n            yield prod\n    else:\n        if True:  # NUMPY_RNG\n            rng = ensure_rng(rng, 'numpy')\n            # Need to use least-common-multiple so the mod of all idxs\n            # are equally likely\n            card_lcm = np.lcm.reduce(idx_cards)\n        else:\n            rng = ensure_rng(rng, 'python')\n\n        seen = set()\n        while len(seen) < num:\n\n            if True:  # NUMPY_RNG\n                idxs = rng.randint(0, card_lcm, size=ndims, dtype=idx_cards.dtype)\n                idxs %= idx_cards\n                idxs = tuple(idxs.tolist())\n            else:\n                idxs = tuple(rng.randint(0, n - 1) for n in idx_cards)\n\n            if idxs not in seen:\n                seen.add(idxs)\n                prod = tuple(g[x] for g, x in zip(items, idxs))\n                yield prod\n\n\ndef _npstate_to_pystate(npstate):\n    \"\"\"\n    Convert state of a NumPy RandomState object to a state\n    that can be used by Python's Random. Derived from [1]_.\n\n    References:\n        .. [1] https://stackoverflow.com/questions/44313620/convert-randomstate\n\n    Example:\n        >>> py_rng = random.Random(0)\n        >>> np_rng = np.random.RandomState(seed=0)\n        >>> npstate = np_rng.get_state()\n        >>> pystate = _npstate_to_pystate(npstate)\n        >>> py_rng.setstate(pystate)\n        >>> assert np_rng.rand() == py_rng.random()\n    \"\"\"\n    PY_VERSION = 3\n    version, keys, pos, has_gauss, cached_gaussian_ = npstate\n    keys_pos = tuple(map(int, keys)) + (int(pos),)\n    cached_gaussian_ = cached_gaussian_ if has_gauss else None\n    pystate = (PY_VERSION, keys_pos, cached_gaussian_)\n    return pystate\n\n\ndef _pystate_to_npstate(pystate):\n    \"\"\"\n    Convert state of a Python Random object to state usable\n    by NumPy RandomState. Derived from [2]_.\n\n    References:\n        .. [2] https://stackoverflow.com/questions/44313620/convert-randomstate\n\n    Example:\n        >>> py_rng = random.Random(0)\n        >>> np_rng = np.random.RandomState(seed=0)\n        >>> pystate = py_rng.getstate()\n        >>> npstate = _pystate_to_npstate(pystate)\n        >>> np_rng.set_state(npstate)\n        >>> assert np_rng.rand() == py_rng.random()\n    \"\"\"\n    NP_VERSION = 'MT19937'\n    version, keys_pos_, cached_gaussian_ = pystate\n    keys, pos = keys_pos_[:-1], keys_pos_[-1]\n    keys = np.array(keys, dtype=np.uint32)\n    has_gauss = cached_gaussian_ is not None\n    cached_gaussian = cached_gaussian_ if has_gauss else 0.0\n    npstate = (NP_VERSION, keys, pos, has_gauss, cached_gaussian)\n    return npstate\n\n\ndef _coerce_rng_type(rng):\n    \"\"\"\n    Internal method that transforms input seeds into an integer form.\n    \"\"\"\n    if rng is None or isinstance(rng, (random.Random, np.random.RandomState)):\n        pass\n    elif rng is random:\n        rng = rng._inst\n    elif rng is np.random:\n        rng = np.random.mtrand._rand\n    # elif isinstance(rng, str):\n    #     # todo convert string to rng\n    #     pass\n    elif isinstance(rng, (float, np.floating)):\n        rng = float(rng)\n        # Coerce the float into an integer\n        a, b = rng.as_integer_ratio()\n        if b == 1:\n            rng = a\n        else:\n            s = max(a.bit_length(), b.bit_length())\n            rng = (b << s) | a\n    elif isinstance(rng, (int, np.integer)):\n        rng = int(rng)\n    else:\n        raise TypeError(\n            'Cannot coerce {!r} to a random object'.format(type(rng)))\n    return rng\n\n\ndef ensure_rng(rng=None, api='numpy'):\n    \"\"\"\n    Coerces input into a random number generator.\n\n    This function is useful for ensuring that your code uses a controlled\n    internal random state that is independent of other modules.\n\n    If the input is None, then a global random state is returned.\n\n    If the input is a numeric value, then that is used as a seed to construct a\n    random state.\n\n    If the input is a random number generator, then another random number\n    generator with the same state is returned. Depending on the api, this\n    random state is either return as-is, or used to construct an equivalent\n    random state with the requested api.\n\n    Args:\n        rng (int | float | None | numpy.random.RandomState | random.Random):\n            if None, then defaults to the global rng. Otherwise this can\n            be an integer or a RandomState class. Defaults to the global\n            random.\n\n        api (str): specify the type of random number\n            generator to use. This can either be 'numpy' for a\n            :class:`numpy.random.RandomState` object or 'python' for a\n            :class:`random.Random` object. Defaults to numpy.\n\n    Returns:\n        (numpy.random.RandomState | random.Random) :\n            rng - either a numpy or python random number generator, depending\n            on the setting of ``api``.\n\n    Example:\n        >>> rng = ensure_rng(None)\n        >>> ensure_rng(0).randint(0, 1000)\n        684\n        >>> ensure_rng(np.random.RandomState(1)).randint(0, 1000)\n        37\n\n    Example:\n        >>> num = 4\n        >>> print('--- Python as PYTHON ---')\n        >>> py_rng = random.Random(0)\n        >>> pp_nums = [py_rng.random() for _ in range(num)]\n        >>> print(pp_nums)\n        >>> print('--- Numpy as PYTHON ---')\n        >>> np_rng = ensure_rng(random.Random(0), api='numpy')\n        >>> np_nums = [np_rng.rand() for _ in range(num)]\n        >>> print(np_nums)\n        >>> print('--- Numpy as NUMPY---')\n        >>> np_rng = np.random.RandomState(seed=0)\n        >>> nn_nums = [np_rng.rand() for _ in range(num)]\n        >>> print(nn_nums)\n        >>> print('--- Python as NUMPY---')\n        >>> py_rng = ensure_rng(np.random.RandomState(seed=0), api='python')\n        >>> pn_nums = [py_rng.random() for _ in range(num)]\n        >>> print(pn_nums)\n        >>> assert np_nums == pp_nums\n        >>> assert pn_nums == nn_nums\n\n    Example:\n        >>> # Test that random modules can be coerced\n        >>> import random\n        >>> import numpy as np\n        >>> ensure_rng(random, api='python')\n        >>> ensure_rng(random, api='numpy')\n        >>> ensure_rng(np.random, api='python')\n        >>> ensure_rng(np.random, api='numpy')\n\n    Ignore:\n        >>> np.random.seed(0)\n        >>> np.random.randint(0, 10000)\n        2732\n        >>> np.random.seed(0)\n        >>> np.random.mtrand._rand.randint(0, 10000)\n        2732\n        >>> np.random.seed(0)\n        >>> ensure_rng(None).randint(0, 10000)\n        2732\n        >>> np.random.randint(0, 10000)\n        9845\n        >>> ensure_rng(None).randint(0, 10000)\n        3264\n    \"\"\"\n    rng = _coerce_rng_type(rng)\n\n    if api == 'numpy':\n        if rng is None:\n            # This is the underlying random state of the np.random module\n            rng = np.random.mtrand._rand\n            # Dont do this because it seeds using dev/urandom\n            # rng = np.random.RandomState(seed=None)\n        elif isinstance(rng, int):\n            rng = np.random.RandomState(seed=rng % _SEED_MAX)\n        elif isinstance(rng, random.Random):\n            # Convert python to numpy random state\n            py_rng = rng\n            pystate = py_rng.getstate()\n            npstate = _pystate_to_npstate(pystate)\n            rng = np_rng = np.random.RandomState(seed=0)\n            np_rng.set_state(npstate)\n    elif api == 'python':\n        if rng is None:\n            # This is the underlying random state of the random module\n            rng = random._inst\n        elif isinstance(rng, int):\n            rng = random.Random(rng % _SEED_MAX)\n        elif isinstance(rng, np.random.RandomState):\n            # Convert numpy to python random state\n            np_rng = rng\n            npstate = np_rng.get_state()\n            pystate = _npstate_to_pystate(npstate)\n            rng = py_rng = random.Random(0)\n            py_rng.setstate(pystate)\n    else:\n        raise KeyError('unknown rng api={}'.format(api))\n    return rng\n\n\nif __name__ == '__main__':\n    \"\"\"\n    CommandLine:\n        xdoctest -m kwarray.util_random\n    \"\"\"\n    import xdoctest\n    xdoctest.doctest_module(__file__)\n","sub_path":"kwarray/util_random.py","file_name":"util_random.py","file_ext":"py","file_size_in_byte":17792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"227930221","text":"from pandas import Series as ser, DataFrame as DF\r\nimport numpy as np\r\n\r\nwather=['맑음','구름','비']\r\n\r\nLists={}\r\n\r\nfor month in range(1,4):\r\n    List={}\r\n    for day in range(1,4):\r\n        today=np.random.choice(wather,1,p=[0.6,0.3,0.1]) \r\n        # print(today)\r\n        List.update({day:today}) \r\n        # print(List)\r\n    Lists.update({month:List})\r\n    # print(Lists)\r\n# print(Lists)\r\n\r\n# year={month:{day:np.random.choice(wather,1,p=[0.6,0.3,0.1])for day in range(1,32)}for month in range(1,13)}\r\n# data=DF(year)\r\n\r\ndata2=DF(Lists)\r\n# print(data)\r\nprint(data2)","sub_path":"만든 작품/20180704.py","file_name":"20180704.py","file_ext":"py","file_size_in_byte":573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"250333587","text":"# coding: utf-8\r\nimport time\r\nimport random\r\n\r\n''' computerGuesses är funktionen som utför gissningarna och sökningen till talet. Där den gör detta genom att gissa\r\n    på ett tal mellan intervallet och sedan veta om talet är midnre eller störe och sedan sätta intervallet mellan\r\n    talet den gissade + 1 och det andra talet som beror på om svaret var mindre eller störe.''' \r\ndef computerGuesses(lowest, higest, answer = None):\r\n    correct = 1 \r\n    guessCount = 0 # guessCount räknar hur många gånger den har gissat på ett tal för att visa det senare.\r\n    while correct != 0: # Denna whole loop loopar tills programet har gissat rätt på talet där programet får värdet 0.\r\n        guess = random.randint(lowest, higest)\r\n        guessCount += 1\r\n        print('Jag gissar på att numret är',guess)\r\n        if answer: # Program för att göra så att datorn kan köra koden genom att söka sig närmare och närmare nummret.\r\n            if answer == guess:\r\n                correct = 0\r\n            elif answer < guess:\r\n                correct = -1\r\n            elif answer > guess:\r\n                correct = 1\r\n        else: # Programmet för att användaren kan manuelt låta datorn gissa talet.\r\n            correct = helpToGuess()\r\n        if correct == 0:\r\n            print('Yey! Jag gissade rätt och det tog mig bara', guessCount,'försök!')\r\n        elif correct > 0: # Kollar om nästa tal är störe.\r\n            print('-'*50)\r\n            print('Störe du säger? Okej låt mig gissa igen..')\r\n            if lowest < guess: # Ifall den nya gissningen är störe en den förra så ändras värdet.\r\n                lowest = guess + 1 \r\n        elif correct < 0: # kollar om nästa tal är mindre\r\n            print('-'*50)\r\n            print('Mindre du säger? Okej låt mig gissa igen..')\r\n            if higest > guess: # Ifall den nya gissningen är mindre en den förra så ändras värdet.\r\n                higest = guess - 1\r\n   \r\n   \r\n   \r\n''' helpToGuess är en funktion som gör att användaren kan hjälpa datorn att gissa rätt på vilket tal du har tänkt på.\r\n    Där användaren kan säga om det är rätt, eller om talet är större eller mindre. '''     \r\ndef helpToGuess():\r\n    while True:\r\n        try:\r\n            print('Skriv 0 om det är korekt, 1 eller mer om det är större, och -1 eller mindre ifall det är mindre')\r\n            correct = int(input('Svar (0, 1, -1): '))\r\n            return correct\r\n        except ValueError: # Ifall användaren inte ger ett korekt värde ältså ett heltal så skickas denna felkod.\r\n            print('Oops! du skrev inte in ett tal testa igen!')    \r\n            \r\n            \r\n            \r\n''' Denna funktion tar hand om det mästa med interactivitet och när den körs kan användaren skriva in hur den\r\n    vill att programmet ska köras.'''\r\ndef interactive():\r\n    print('-'*50)\r\n    while True:\r\n        try:\r\n            x = input(\"Ange sökintervall (ex '1-100'): \")\r\n            x = x.replace(' ', '') # Tar bort ALLA mellan rum så även om användaren skriver 1 00 så blir det 100.\r\n            x = x.split(\"-\") # Tar bort - från ex 1-100 och delar up det i en lista av två värden. \r\n            x.sort() # Soterar listan så att om användaren skriver 100-1 tar programmet det som 1-100.\r\n            lowest = int(x[0])\r\n            higest = int(x[1])\r\n            break\r\n        except (ValueError, IndexError): # Skickar denna felkod ifall användaren inte ger ut två heltal som värde.\r\n            print(\"Oops.. Du skrev inte in ett heltal testa igen\")\r\n    computerGuesses(lowest, higest)\r\n    playerInput = 'y'\r\n    while playerInput == 'y': # While loop som frågar användaren om dom vill köra igen eller inte.\r\n        playerInput = input(\"Får jag köra en gång till? ('y' för ja och 'n' för nej): \")\r\n        playerInput = playerInput.strip()\r\n        if playerInput == 'y' or playerInput == 'n': # Detta kollar om användaren skrev 'n' eller 'y'.\r\n            if playerInput == 'y':\r\n                print(\"Yey! Då kör vi igen!\")\r\n                find(lowest, higest)\r\n            if playerInput == 'n': \r\n                print(\"Det var synd..\")\r\n        else: # Ifall användaren inte angav 'n' eller 'y' så skickas denna felkod ut.\r\n            print(\"Du angav inte 'y' eller 'n' försök igen..\")\r\n\r\n\r\n''' Test kod som kollar om programmet fungerar genom automatiskt köra genom programmet med 3 värden \r\n    två för vilket intervall den ska gissa på och 1 för vad det korrekta gissningen är.'''\r\ndef runTestCode(lowest, higest, answer):\r\n    print('-'*50)\r\n    print('Talet datorn ska gissa är',answer)\r\n    print('Samt är intervallet',str(lowest)+'-'+str(higest))\r\n    print(\"Låt mig tänka\", end='') \r\n    for i in range(5): # Denna loop skapar punkter som gör att programmet syns ut som att den tänker.\r\n        time.sleep(0.5) # Även uppfyler detta att användaren kan läsa vilket intervall och vad svaret var.\r\n        print('.', end='')\r\n    print()\r\n    computerGuesses(lowest, higest, answer)\r\n    \r\n\r\ninteractive() # Kallar till interavtive så att användaren kan lägga in sina egna värden.\r\n\r\n''' För att ändra värden i runTestCode så är lowest det minsta värdet datorn kan gissa och highest är största\r\n    samt är answer vilket tal den ska försöka gissa på'''\r\nrunTestCode(20, 300, 42) # Används för att kalla till runTestCode för att testa programet där ","sub_path":"DatornGissar.py","file_name":"DatornGissar.py","file_ext":"py","file_size_in_byte":5453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"245604681","text":"from torch import save, random\nfrom train import train\nfrom create_sequence import create_sequence\nfrom sample import sample\nfrom rnn import RNN\nfrom argparse import ArgumentParser\nimport matplotlib.pyplot as plt\n\nrandom.manual_seed(0)\n\n\n# ***** DO NOT EDIT ABOVE ****\n\n\ndef text_generation(rnn_type='basic_rnn', input_file=\"shakespeare.txt\", sample_len=2000, seed=\"KING\"):\n    if rnn_type == \"basic_rnn\":\n        # specify the sequence length, hidden size and number of layers\n        seq_len = 100\n        hidden_size = 128\n        num_layers = 2\n        \n        # specify train opts\n        train_opts = {\n            \"num_epochs\": 120,\n            \"lr\": 0.001,\n            \"batch_size\": 128,\n            \"weight_decay\": 0.0001\n        }\n\n    elif rnn_type == \"lstm_rnn\":\n        # specify the sequence length, hidden size and number of layers\n        seq_len = 100\n        hidden_size = 256\n        num_layers = 2\n        \n        # specify train_opts\n        train_opts = {\n            \"num_epochs\": 120,\n            \"lr\": 0.001,\n            \"batch_size\": 128,\n            \"weight_decay\": 0.0001\n        }\n        \n    else:\n        raise ValueError(f\"Unknown RNN type {rnn_type}\")\n\n    train_ds, val_ds, char_to_index, index_to_char = create_sequence(input_file, seq_len=seq_len)\n    vocab_size = len(char_to_index)\n    model = RNN(vocab_size=vocab_size, hidden_size=hidden_size, num_layers=num_layers, rnn_type=rnn_type)\n\n    # specify an optional directory to save the model checkpoints\n    exp_dir = None\n\n    train(model, train_ds, val_ds, train_opts=train_opts, exp_dir=exp_dir)\n    state = {\n        'state': model.state_dict(), \"hidden_size\": hidden_size,\n        \"num_layers\": num_layers, \"vocab_size\": vocab_size\n    }\n\n    save(state, f\"{rnn_type}.pt\")\n\n    # time to compose essays\n    sampled_text = sample(\n        model=model,\n        seed=seed,\n        sample_len=sample_len,\n        char_to_index=char_to_index,\n        index_to_char=index_to_char\n    )\n\n    with open(f\"{rnn_type}_output.txt\", 'w') as f:\n        f.write(sampled_text)\n\n    print(\"\\n\", sampled_text)\n    plt.show()\n\n\nif __name__ == '__main__':\n    parser = ArgumentParser()\n    # change to \"lstm_rnn for an lstm cell\n    parser.add_argument(\"--rnn_type\", default=\"basic_rnn\", type=str, help=\"Specify rnn type\")\n    parser.add_argument(\"--input_file\", default=\"shakespeare.txt\", type=str, help=\"The source corpus\")\n    parser.add_argument(\"--sample_len\", default=2000, type=int, help=\"The length of text to generate\")\n    parser.add_argument(\"--seed\", default=\"KING LEAR\", type=str, help=\"The seed text\")\n    args, _ = parser.parse_known_args()\n\n    text_generation(**args.__dict__)\n","sub_path":"text_generation.py","file_name":"text_generation.py","file_ext":"py","file_size_in_byte":2672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"326815717","text":"#!python2\n\nfrom __future__ import division, print_function\nfrom visual import *\nfrom visual.graph import *\nfrom visual.controls import *\nimport wx\nfrom copy import deepcopy\n# Custom made imports\nfrom umi_parameters import UMI_parameters\nfrom umi_chessboard import UMI_chessboard\nfrom umi_student_functions import *\nimport numpy as np\nimport os.path\n\n#**********************************************\n# ROBOT PARAMETERS\n\n# Specifications of UMI ARE IMPORTED THROUGH umi_student_functions.\n\n#**********************************************\n# Functions that are called on various events\n\ndef setRiserHeight(evt): # called on slider events (output in mm)\n    value = s0.GetValue() / 1000.0\n    moveRiser(value)\n\ndef moveRiser(value):\n    '''\n    Sets the height of the riser, values are entered in meters\n    :param value: Value in meters, while the display is in mm.\n    '''\n    s0_label.SetLabel('Set Riser Height: %d mm' % (value * 1000.0))\n    s0.SetValue(value * 1000.0)\n    riser.pos.y = UMI.correct_height(value)\n    UMI_angles[0] = value\n\n\ndef setShoulderAngle(evt): # called on slider events (output in degrees)\n    value = s1.GetValue() / 1000.0\n    moveShoulder(value)\n\ndef moveShoulder(value):\n    '''\n    Sets the angle of the shoulder joint, values are entered in radians\n    :param value: Angle in radians.\n    '''\n    s1_label.SetLabel('Set Shoulder rotation: %.2f degrees' % degrees(value))\n    shoulder_joint.axis = (cos(value),0,sin(value))\n    UMI_angles[1] = value\n    s1.SetValue(value*1000.0)\n\ndef setElbowAngle(evt): # called on slider events (output in degrees)\n    value = s2.GetValue() / 1000.0\n    moveElbow(value)\n\ndef moveElbow(value):\n    '''\n    Sets the angle of the elbow joint, values are entered in radians\n    :param value: Angle in radians.\n    '''\n    s2_label.SetLabel('Set Elbow rotation: %.2f degrees' % degrees(value))\n    elbow_joint.axis = (cos(value),0,sin(value))\n    UMI_angles[2] = value\n    s2.SetValue(value*1000.0)\n\ndef setWristAngle(evt): # called on slider events] (output in degrees)\n    value = s3.GetValue() / 1000.0\n    moveWrist(value)\n\ndef moveWrist(value):\n    '''\n    Sets the angle of the wrist joint, values are entered in radians\n    :param value: Angle in radians.\n    '''\n    s3_label.SetLabel('Set Wrist rotation: %.2f degrees' % degrees(value))\n    wrist_joint.axis = (cos(value),0,sin(value))\n    UMI_angles[3] = value\n    s3.SetValue(value*1000.0)\n\ndef setGripperWidth(evt): # called on slider events] (output in degrees)\n    value = s4.GetValue() / 1000.0\n    moveGripper(value)\n\ndef moveGripper(value):\n    '''\n    Sets the distance between the grippers, values are entered in meters\n    :param value: Distance between grippers in meters.\n    '''\n    s4_label.SetLabel('Set Gripper opening: %d mm' % (value * 1000))\n    gripper_pos.pos = (0, gripper_pos.pos.y, 0.5*gripper_pos.width+value/2)\n    gripper_neg.pos = (0, gripper_pos.pos.y, -0.5*gripper_pos.width-value/2)\n    UMI_angles[4] = value\n    s4.SetValue(value*1000.0)\n\ndef read_input_file(evt):\n    \"\"\"\n        Upon clicking the button, reads the input from the file, and executes the instructions.\n    \"\"\"\n    joints_file = \"joints_simulator.txt\"\n    if os.path.isfile(joints_file):\n        (headers, sequence_list) = read_parameters_from_file(joints_file)\n        execute_sequence(sequence_list)\n\ndef store_input_text(evt):\n    \"\"\"\n        Upon clicking the button, reads the instruction in the textbox, and translates this to instruction for the\n        robot arm to execute this action.\n    \"\"\"\n    joints_file = \"joints_simulator.txt\"\n    input_text = input_field.GetValue()\n    if len(input_text) == 4:\n        parameter_lines = move(CHESSBOARD, input_text[0:2], input_text[2:4])\n        write_parameters_to_file(parameter_lines, joints_file)\n\nL = 600\n# Create a window. Note that w.win is the wxPython \"Frame\" (the window).\n# window.dwidth and window.dheight are the extra width and height of the window\n# compared to the display region inside the window. If there is a menu bar,\n# there is an additional height taken up, of amount window.menuheight.\n# The default style is wx.DEFAULT_FRAME_STYLE; the style specified here\n# does not enable resizing, minimizing, or full-sreening of the window.\nw = window(width=2*(L+window.dwidth), height=L+window.dheight,\n           menus=False, title='UMI RTX',\n           style= wx.CAPTION | wx.CLOSE_BOX)\n\n# Place a 3D display widget in the left half of the window.\nd = 20\ndisp = display(window=w, x=d, y=d, width=L-2*d, height=L-2*d, forward=-vector(1,0.25,1), center=vector(0,0.5,0))\ndisp.background=(0.859, 0.949, 0.957)\n# Place buttons, radio buttons, a scrolling text object, and a slider\n# in the right half of the window. Positions and sizes are given in\n# terms of pixels, and pos(0,0) is the upper left corner of the window.\np = w.panel # Refers to the full region of the window in which to place widgets\n\nwx.StaticText(p, pos=(d,4), size=(L-2*d,d), label='3D representation.',\n              style=wx.ALIGN_CENTRE | wx.ST_NO_AUTORESIZE)\n\ns0 = wx.Slider(p, pos=(1.0*L,0.1*L), size=(0.9*L,20), minValue=UMI.joint_ranges[\"Riser\"][0]*1000.0, maxValue=UMI.joint_ranges[\"Riser\"][1]*1000.0)\ns0.Bind(wx.EVT_SCROLL, setRiserHeight)\ns0_label = wx.StaticText(p, pos=(1.0*L,0.05*L), label='Set Riser height: %d mm' % (UMI.joint_ranges[\"Riser\"][1]*1000.0))\n\ns1 = wx.Slider(p, pos=(1.0*L,0.2*L), size=(0.9*L,20), minValue=radians(UMI.joint_ranges[\"Shoulder\"][0])*1000.0, maxValue=radians(UMI.joint_ranges[\"Shoulder\"][1])*1000.0)\ns1.Bind(wx.EVT_SCROLL, setShoulderAngle)\ns1_label = wx.StaticText(p, pos=(1.0*L,0.15*L), label='Set Shoulder rotation: 0 degrees')\n\ns2 = wx.Slider(p, pos=(1.0*L,0.3*L), size=(0.9*L,20), minValue=radians(UMI.joint_ranges[\"Elbow\"][0])*1000.0, maxValue=radians(UMI.joint_ranges[\"Elbow\"][1])*1000.0)\ns2.Bind(wx.EVT_SCROLL, setElbowAngle)\ns2_label = wx.StaticText(p, pos=(1.0*L,0.25*L), label='Set Elbow rotation: 0 degrees')\n\ns3 = wx.Slider(p, pos=(1.0*L,0.4*L), size=(0.9*L,20), minValue=radians(UMI.joint_ranges[\"Wrist\"][0])*1000.0, maxValue=radians(UMI.joint_ranges[\"Wrist\"][1])*1000.0, style=wx.SL_HORIZONTAL)\ns3.Bind(wx.EVT_SCROLL, setWristAngle)\ns3_label = wx.StaticText(p, pos=(1.0*L,0.35*L), label='Set Wrist rotation: 0 degrees')\n\ns4 = wx.Slider(p, pos=(1.0*L,0.5*L), size=(0.9*L,20), minValue=UMI.joint_ranges[\"Gripper\"][0]*1000.0, maxValue=UMI.joint_ranges[\"Gripper\"][1]*1000.0, style=wx.SL_HORIZONTAL)\ns4.Bind(wx.EVT_SCROLL, setGripperWidth)\ns4_label = wx.StaticText(p, pos=(1.0*L,0.45*L), label='Set Gripper opening: 50 mm')\n\nread_input = wx.Button(p, label='Execute joints.txt', pos=(1.0*L,0.75*L))\nread_input.Bind(wx.EVT_BUTTON, read_input_file)\n\nstore_input = wx.Button(p, label='Compute High Path', pos=(1.11*L,0.65*L))\nstore_input.Bind(wx.EVT_BUTTON, store_input_text)\n\ninput_field = wx.TextCtrl(p, pos=(1.0*L,0.65*L), value='a1a3',\n            size=(0.1*L,25))\ninput_field.SetInsertionPoint(len(input_field.GetValue())+1) # position cursor at end of text\ninput_field.SetFocus() # so that keypresses go to the TextCtrl without clicking it\n\n#***********************************************\n# ROBOT JOINTS\nframeworld = frame()\n\nframe0 = frame(frame=frameworld)\nframe0.pos = (-UMI.wpedestal/2.0, 0.5*UMI.hpedestal,0)\n\n# The shoulder joint location is now on world position (x,z) = (0,0)\nriser = frame(frame=frame0)\nriser.pos = (UMI.wpedestal/2.0,frame0.pos.y, 0)\n\nshoulder_joint = frame(frame=riser)\nshoulder_joint.pos = (0,-UMI.pedestal_offset, 0)\n#shoulder_joint.rotate(axis = (0, 1, 0), angle = pi/4)\n\nelbow_joint = frame(frame=shoulder_joint)\nelbow_joint.pos = (UMI.upper_length,-UMI.upper_height, 0)\n#elbow_joint.rotate(axis = (0, 1, 0), angle = pi/4)\n\nwrist_joint = frame(frame=elbow_joint)\nwrist_joint.pos = (UMI.lower_length,-UMI.lower_height, 0)\n#wrist_joint.rotate(axis = (0, 1, 0), angle = pi/4)\n#************************************************\n# ROBOT ARM\npedestal = box(frame = frame0,\n               pos = (0,0,0),\n               height = UMI.hpedestal,\n               length = UMI.wpedestal,\n               width = UMI.wpedestal,\n               color = (0.4, 0.4, 0.4))\nriser_part = cylinder(frame = riser,\n               pos = (0, -UMI.pedestal_offset, 0),\n               axis = (0, UMI.pedestal_offset, 0),\n               radius = UMI.wpedestal/2.0,\n               color = color.red)\n\nupper_arm = box(frame = shoulder_joint,\n               pos = (UMI.upper_length/2.0,-UMI.upper_height/2,0),\n               height = UMI.upper_height,\n               length = UMI.upper_length*1.25,\n               width = 0.08,\n               color = color.green)\nlower_arm = box(frame = elbow_joint,\n               pos = (UMI.lower_length/2.0,-UMI.lower_height/2,0),\n               height = UMI.lower_height,\n               length = UMI.lower_length*1.25,\n               width = 0.08,\n               color = color.green)\n\nwrist = box(frame = wrist_joint,\n               pos = (0,-UMI.wrist_height/8,0),\n               height = UMI.wrist_height/4,\n               length = 0.08,\n               width = 0.08,\n               color = color.green)\n\ngripper_pos = box(frame = wrist_joint,\n               pos = (0,-UMI.wrist_height/2,0.025),\n               height = UMI.wrist_height,\n               length = 0.03,\n               width = 0.005,\n               color = color.blue)\n\ngripper_neg = box(frame = wrist_joint,\n               pos = (0,-UMI.wrist_height/2,-0.025),\n               height = UMI.wrist_height,\n               length = 0.03,\n               width = 0.005,\n               color = color.blue)\ngripper_open = 1\n\nfloor = box(frame=frameworld,\n               pos = (0,0,0),\n               height = 0.001,\n               length = UMI.wpedestal + 0.6,\n               width = 0.6*2,\n               color = (0.5, 0.5, 0.5))\nfloor.pos = (floor.length/2 - UMI.wpedestal, 0, 0)\n#**************************************************************************\n# CHESSBOARD\n# frame, board_size=0.3, position_x_z = (0.15, -0.15), angle_degrees=0)\n# <<<<<<<<<<-------------------------------------------------------------------- CHANGE BOARD POSITION/ANGLE HERE\nCHESSBOARD = UMI_chessboard(frameworld, 0.3, (0.15, -0.15), 0)\n\n#***************************************************************************\n\n# CONTROLLER Functions\ndef get_gripper_bottom_position():\n    '''\n    Gives the position of the tip of the gripper in the real world coordinate system.\n    :return: Tuple in the format (x,y,z)\n    '''\n    return frame0.frame_to_world(\n        riser.frame_to_world(\n            shoulder_joint.frame_to_world(\n                elbow_joint.frame_to_world(\n                    wrist_joint.pos + vector(0,-UMI.wrist_height, 0)\n                )\n            )\n        )\n    )\n\ndef execute_sequence(sequence_list):\n    '''\n    Runs the commands as provided in a list\n    :param sequence_list: List where each row contains either a GUI command or a joints-setting for the arm.\n    '''\n    # First move up so you do not knock over anything.\n    safe_angles = deepcopy(UMI_angles)\n    safe_angles[0] = CHESSBOARD.get_board_height() + 0.2 + UMI.total_arm_height\n    # Set to a safe location before execution\n    loop_angles = deepcopy(UMI_angles)\n    # Then continue with the original plans.\n    total_list = [safe_angles] + sequence_list\n    chess_piece = None\n    for new_angles in total_list:\n        if len(new_angles) == 3 and new_angles[0] == \"GUI\":\n            [_, command, piece_position] = new_angles\n            if command == \"TAKE\" and chess_piece == None:\n                chess_piece = CHESSBOARD.remove_piece(piece_position)\n                if chess_piece != None:\n                    chess_piece[0].frame=wrist_joint\n                    if chess_piece[1] == \"Rook\":\n                        piece_offset = 0\n                    else:\n                        piece_offset = CHESSBOARD.pieces_height[chess_piece[1]]/2\n                    chess_piece[0].pos=(0,-UMI.wrist_height-piece_offset, 0)\n            if command == \"DROP\" and chess_piece != None:\n                (temp_x, temp_z) = to_coordinate(piece_position)\n                if temp_x > 7 or temp_x < 0 or temp_z > 7 or temp_z < 0:\n                    # Garbage field\n                    chess_piece[0].visible = False\n                    del chess_piece[0]\n                    chess_piece = None\n                else:\n                    f_size = CHESSBOARD.field_size\n                    chess_piece[0].frame=CHESSBOARD.framemp\n                    #chess_piece[0].pos=(f_size*(temp_x+1) - f_size/2.0, 0, (f_size*temp_z) + f_size/2),\n                    if chess_piece[1] == \"Rook\":\n                        piece_offset = CHESSBOARD.pieces_height[chess_piece[1]]/2\n                    else:\n                        piece_offset = 0\n                    chess_piece[0].pos=(f_size*(7-temp_x) + f_size/2.0, piece_offset, f_size*(7-temp_z) + f_size/2.0)\n                    CHESSBOARD.pieces[piece_position] = chess_piece\n                    chess_piece = None\n        else:\n            # Degrees to Radians.\n            new_angles = [new_angles[0]] + [radians(x) for x in new_angles[1:-1]] + [new_angles[-1]]\n            # Correct the height\n            animate_arm(loop_angles, new_angles)\n            loop_angles = deepcopy(UMI_angles)\n            sleep(0.5)\n\ndef animate_arm(from_angles, to_angles):\n    '''\n    Given two different joint combinations, animate the movement for the arm between those two.\n    :param from_angles: Original joint positions.\n    :param to_angles: New joint positions.\n    '''\n    # Compute the differences for all joints\n    old_a = np.array(from_angles)\n    new_a = np.array(to_angles)\n    delta_a = ( new_a - old_a )\n    # Move through these differences in 100 steps\n    for i in np.arange(0.0, 1.01, 0.01):\n        rate(100)\n        moveRiser(old_a[0] + delta_a[0]*i)\n        moveShoulder(old_a[1] + delta_a[1]*i)\n        moveElbow(old_a[2] + delta_a[2]*i)\n        moveWrist(old_a[3] + delta_a[3]*i)\n        moveGripper(old_a[4] + delta_a[4]*i)\n        disp.center=get_gripper_bottom_position()\n\ndef move(chessboard, from_pos, to_pos):\n    '''\n    Given two positions on the board [a1-h8] compute the required actions\n    :param chessboard: The chessboard object\n    :param from_pos: [a1-h8]\n    :param to_pos: [a1-h8]\n    :return: List of actions for the simulator to run.\n    '''\n    sequence_list = []\n    # Check if you are removing a piece from play by performing the action.\n    if to_pos in chessboard.pieces:\n        sequence_list += move_to_garbage(chessboard, to_pos)\n    sequence_list += high_path(chessboard, from_pos, to_pos)\n    # Write the output files.\n    write_parameters_to_file(sequence_list, \"joints_simulator.txt\")\n    write_parameters_to_umi_robot(sequence_list)\n    return sequence_list\n#**************************************************************************\n\n# INIT CONTROLS\ns0.SetValue(s0.GetMax())\ns1.SetValue(0) # update the slider\ns2.SetValue(0) # update the slider\ns3.SetValue(0) # update the slider\ns4.SetValue(50) # update the slider\n\n# Storage only used to make the movements of the arm appear smoothed.\nUMI_angles = [UMI.joint_ranges[\"Riser\"][1], 0, 0, 0, 0.05]\n#**************************************************************************\n\n\nwhile(True):\n    rate(100)\n    # TIP: If you want to know at all time, what the x,y,z of your robot arm is,\n    # print(get_gripper_bottom_position())\n    disp.center=get_gripper_bottom_position()\n#End Program\n0\n","sub_path":"umi_simulation.py","file_name":"umi_simulation.py","file_ext":"py","file_size_in_byte":15379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"486971398","text":"from student_data.__node import _Node\n\n\nclass _LinkedList:\n    def __init__(self):\n        self.head: _Node = None\n        self.tail: _Node = None\n\n    def append(self, node: _Node):\n        if not self.head:\n            self.head = node\n            self.tail = self.head\n        else:\n            self.tail.after = node\n            self.tail = self.tail.after\n\n    def insert(self, index: int, node: _Node):\n        if index == 0 and len(self) == 0:\n            self.append(node)\n            return\n        try:\n            if index == 0:\n                node.after = self.head\n                self.head = node\n            else:\n                node.after = self[index]\n        except IndexError:\n            if index == 0 and len(self) == 0 or index == len(self) + 1:\n                self.append(node)\n            else:\n                raise IndexError('Index out of range.')\n        if index != 0:\n            self[index - 1].after = node\n\n    def pop(self, index: int):\n        if index == 0:\n            to_return = self.head\n            self.head = self[1]\n            return to_return\n        else:\n            to_return = self[index]\n            self[index - 1].after = self[index].after\n            return to_return\n\n    def index(self, item):\n        for i in range(len(self)):\n            if self[i] is item:\n                return i\n\n    def __getitem__(self, index):\n        if isinstance(index, int):\n            current = self.head\n            if current is None:\n                raise IndexError(f'{self.__class__.__name__} out of range')\n            for _ in range(index):\n                try:\n                    current = current.after\n                except AttributeError:\n                    raise IndexError(f'{self.__class__.__name__} out of range')\n            return current\n        elif isinstance(index, slice):\n            start, stop, step = index.indices(len(self))\n            iter_lista = []\n            for i in range(start, stop, step):\n                iter_lista.append(self[i])\n            return list(iter_lista)\n        else:\n            raise TypeError(f'{self.__class__.__name__} indices must be int or slices, not {index.__class__.__name__}')\n\n    def __iter__(self):\n        return iter(self[:])\n\n    def __len__(self):\n        counter = 0\n        current = self.head\n        if current:\n            while current:\n                counter += 1\n                current = current.after\n            return counter\n        else:\n            return 0\n\n    def __str__(self):\n        return str(list(self))\n\n\nif __name__ == '__main__':\n    lista1 = _LinkedList()\n    node1 = _Node(3)\n    lista1.append(node1)\n","sub_path":"student_data/__linkedlist.py","file_name":"__linkedlist.py","file_ext":"py","file_size_in_byte":2646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"218681113","text":"# Copyright (c) 2012-2015 Netforce Co. Ltd.\n# \n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n# \n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n# \n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\n# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\n# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\n# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,\n# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR\n# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE\n# OR OTHER DEALINGS IN THE SOFTWARE.\n\nfrom netforce.model import Model, fields, get_model\nfrom datetime import *\nfrom dateutil.relativedelta import *\nimport time\nfrom netforce.access import get_active_company,set_active_user,set_active_company,get_active_user\n\nclass FixedAssetSell(Model):\n    _name = \"account.fixed.asset.sell\"\n    _string = \"Sell Asset\"\n    _fields = {\n        \"asset_id\": fields.Many2One(\"account.fixed.asset\", \"Asset\", readonly=True),\n        \"date\": fields.Date(\"Date\", required=True),\n        \"sale_acc_id\": fields.Many2One(\"account.account\", \"Account to Debit for Sale\", required=True),\n        \"gain_loss_acc_id\": fields.Many2One(\"account.account\", \"Gain / Loss Account\", required=True),\n        \"journal_id\" : fields.Many2One(\"account.journal\", \"Account Journal\", required=True),\n        \"price\": fields.Decimal(\"Sale price (Excluding Tax)\", required=True),\n        \"accum_depr_amount\": fields.Decimal(\"Accum. Depr. Amount\"),\n    }\n    _defaults = {\n        \"date\": lambda *a: time.strftime(\"%Y-%m-%d\"),\n    }\n\n    def onchange_date(self,context):\n        data=context[\"data\"]\n        asset_id = data.get(\"asset_id\")\n        date=data.get(\"date\")\n        if not asset_id or not date:\n           return data\n        days = None\n        accum_depr_amount = 0\n        obj_asset = get_model(\"account.fixed.asset\").browse(asset_id)\n        if not obj_asset.last_dep:\n           raise Exception(\"No Last depreciation\")\n        if obj_asset.last_dep:\n            d_from=datetime.strptime(obj_asset.last_dep,\"%Y-%m-%d\")+timedelta(days=1)\n        else:\n            d_from=datetime.strptime(obj_asset.date_purchase,\"%Y-%m-%d\")+timedelta(days=1)\n        d_to=datetime.strptime(date,\"%Y-%m-%d\")\n        days = (d_to - d_from).days+1\n        rate=obj_asset.get_daily_rate(d_from.strftime(\"%Y-%m-%d\"))\n        accum_depr_amount=get_model(\"currency\").round(None,days*rate)\n        data['accum_depr_amount'] = accum_depr_amount\n        return data\n\n    def sell_depreciation(self,ids,context):\n        obj=self.browse(ids)[0]\n        if obj.asset_id.last_dep:\n            d_from=datetime.strptime(obj.asset_id.last_dep,\"%Y-%m-%d\")+timedelta(days=1)\n        else:\n            d_from=datetime.strptime(obj.asset_id.date_purchase,\"%Y-%m-%d\")\n        d_to=datetime.strptime(obj.date,\"%Y-%m-%d\")\n        days = (d_to - d_from).days+1\n        if days > 30:\n           raise Exception(\" (Last depreciation : %s ) - (Date : %s ) Over 30 Days, Please Check Date\"%(obj.asset_id.last_dep,obj.date))\n        elif days <= 0:\n           raise Exception(\" (Last depreciation : %s ) - (Date : %s ) less 0 Days, Please Check Date\"%(obj.asset_id.last_dep,obj.date))\n        narration = \"Fixed asset depreciation [%s] %s\"%((obj.asset_id.number or \"\"),(obj.asset_id.name or \"\"))\n        get_model(\"account.fixed.asset\").depreciate([obj.asset_id.id],obj.date,context={\"narration\":narration})\n\n        if obj.asset_id.periods:\n            obj.write({\n                'accum_depr_amount': obj.asset_id.periods[0].amount,\n            })\n        return {\n            \"flash\":\"registered assets (%s) are depreciated to %s\"%(obj.asset_id.number,obj.date),\n        }\n\n    def sell(self, ids, context={}):\n        settings=get_model(\"settings\").browse(1)\n        obj = self.browse(ids)[0]\n        if settings.lock_date:\n            assert obj.date >= settings.lock_date, \"Accounting transaction is before lock date\"\n        journal_id=obj.journal_id.id\n        if not journal_id:\n            raise Exception(\"Account Journal not found\")\n        asset = obj.asset_id\n        # FIX ME cannot use asset.book_val\n        if asset.book_val is not None and asset.book_val == 0:\n            pass\n        elif not asset.last_dep:\n            raise Exception(\"No last depreciation\")\n        elif asset.last_dep and asset.last_dep > obj.date:\n            raise Exception(\"Cannot sell before last depreciation date %s\"%asset.last_dep)\n        elif asset.book_val is None or asset.salvage_value is None or asset.book_val > asset.salvage_value:\n            if asset.last_dep < obj.date and asset.dep_rate:\n                raise Exception(\"Last depreciation : %s  <  Date : %s, Please Depreciation First\"%(asset.last_dep,obj.date))\n        desc = \"Sell fixed asset [%s] %s\" % (asset.number, asset.name)\n        ## get number\n        number_jv = get_model(\"account.move\")._get_number(context={\"journal_id\":journal_id})\n        move_vals = {\n            \"journal_id\": journal_id,\n            \"date\": obj.date,\n            \"narration\": desc,\n            \"related_id\": \"account.fixed.asset,%s\"%asset.id\n        }\n        if number_jv:\n            move_vals[\"number\"] = number_jv\n        lines = []\n        amt = -asset.price_purchase\n        line_vals = {\n            \"description\": desc,\n            \"account_id\": asset.fixed_asset_account_id.id,\n            \"debit\": amt > 0 and amt or 0,\n            \"credit\": amt < 0 and -amt or 0,\n        }\n        if asset.fixed_asset_account_id.require_track and asset.track_id.id:\n            line_vals['track_id']=asset.track_id.id\n        lines.append(line_vals)\n\n        amt = asset.price_purchase - asset.book_val\n        line_vals = {\n            \"description\": desc,\n            \"account_id\": asset.accum_dep_account_id.id,\n            \"debit\": amt > 0 and amt or 0,\n            \"credit\": amt < 0 and -amt or 0,\n        }\n        if asset.accum_dep_account_id.require_track and asset.track_id.id:\n            line_vals['track_id']=asset.track_id.id\n        lines.append(line_vals)\n\n        amt = obj.price\n        line_vals = {\n            \"description\": desc,\n            \"account_id\": obj.sale_acc_id.id,\n            \"debit\": amt > 0 and amt or 0,\n            \"credit\": amt < 0 and -amt or 0,\n        }\n        if obj.sale_acc_id.require_track and asset.track_id.id:\n            line_vals['track_id']=asset.track_id.id\n        lines.append(line_vals)\n\n        amt = asset.book_val - obj.price\n        line_vals = {\n            \"description\": desc,\n            \"account_id\": obj.gain_loss_acc_id.id,\n            \"debit\": amt > 0 and amt or 0,\n            \"credit\": amt < 0 and -amt or 0,\n        }\n        if obj.gain_loss_acc_id.require_track and asset.track_id.id:\n            line_vals['track_id']=asset.track_id.id\n        lines.append(line_vals)\n\n        move_vals[\"lines\"] = [(\"create\", v) for v in lines]\n        move_id = get_model(\"account.move\").create(move_vals)\n        get_model(\"account.move\").post([move_id])\n        asset.write({\"state\": \"sold\", \"date_dispose\": obj.date})\n        return {\n            \"next\": {\n                \"name\": \"fixed_asset\",\n                \"mode\": \"form\",\n                \"active_id\": asset.id,\n            }\n        }\n\nFixedAssetSell.register()\n","sub_path":"day10/models/netforce_account/account_fixed_asset_sell.py","file_name":"account_fixed_asset_sell.py","file_ext":"py","file_size_in_byte":7768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"583292543","text":"#!/usr/bin/env python3\n\nimport json\nimport os\nimport subprocess\nimport sys\nimport urllib.request\n\nfrom utils import get_build, get_image_name, print_red, print_yellow, get_cbl_name\nfrom install_deps import install_deps\n\n\ndef fetch_logs(build):\n    url = build[\"download_url\"] + \"build.log\"\n    print_yellow(\"fetching logs from %s\" % build[\"download_url\"])\n    # TODO: use something more robust like python wget library.\n    response = urllib.request.urlopen(url).read().decode(\"UTF-8\")\n    print(response)\n\n\ndef check_log(build):\n    warnings_count = build[\"warnings_count\"]\n    errors_count = build[\"errors_count\"]\n    if warnings_count + errors_count > 0:\n        print_yellow(\"%d warnings, %d errors\" % (warnings_count, errors_count))\n        fetch_logs(build)\n\n\ndef fetch_dtb(build):\n    config = os.environ[\"CONFIG\"]\n    if config != \"multi_v5_defconfig\" and config != \"aspeed_g5_defconfig\":\n        return\n    dtb = {\n        \"multi_v5_defconfig\": \"aspeed-bmc-opp-palmetto.dtb\",\n        \"aspeed_g5_defconfig\": \"aspeed-bmc-opp-romulus.dtb\",\n    }[config]\n    dtb_path = \"dtbs/\" + dtb\n    url = build[\"download_url\"] + dtb_path\n    # mkdir -p\n    os.makedirs(dtb_path.split(\"/\")[0], exist_ok=True)\n    print_yellow(\"fetching DTB from: %s\" % url)\n    urllib.request.urlretrieve(url, dtb_path)\n    if os.path.exists:\n        print_yellow(\"Filesize: %d\" % os.path.getsize(dtb_path))\n    else:\n        print_red(\"Unable to download dtb\")\n        sys.exit(1)\n\n\ndef fetch_kernel_image(build):\n    image_name = get_image_name()\n    url = build[\"download_url\"] + image_name\n    print_yellow(\"fetching kernel image from: %s\" % url)\n    # TODO: use something more robust like python wget library.\n    urllib.request.urlretrieve(url, image_name)\n    # Suspect download is failing.\n    if os.path.exists:\n        print_yellow(\"Filesize: %d\" % os.path.getsize(image_name))\n    else:\n        print_red(\"Unable to download kernel image\")\n        sys.exit(1)\n\n\ndef cwd():\n    os.chdir(os.path.dirname(__file__))\n    return os.getcwd()\n\n\ndef run_boot(build):\n    cbl_arch = get_cbl_name()\n    kernel_image = cwd() + \"/\" + get_image_name()\n    boot_qemu = [\n        \"./boot-utils/boot-qemu.sh\", \"-a\", cbl_arch, \"-k\", kernel_image\n    ]\n    if cbl_arch == \"s390\":\n        boot_qemu += [\"--use-cbl-qemu\"]\n    # If we are running a sanitizer build, we should increase the number of\n    # cores and timeout because booting is much slower\n    if \"CONFIG_KASAN=y\" in build[\"kconfig\"] or \\\n       \"CONFIG_KCSAN=y\" in build[\"kconfig\"] or \\\n       \"CONFIG_UBSAN=y\" in build[\"kconfig\"]:\n        boot_qemu += [\"-s\", \"4\", \"-t\", \"10m\"]\n    try:\n        subprocess.run(boot_qemu, check=True)\n    except subprocess.CalledProcessError as e:\n        if e.returncode == 124:\n            print_red(\"Image failed to boot\")\n        raise e\n\n\ndef boot_test(build):\n    if build[\"result\"] == \"fail\":\n        print_red(\"fatal build errors encountered during build, skipping boot\")\n        sys.exit(1)\n    if \"BOOT\" in os.environ and os.environ[\"BOOT\"] == \"0\":\n        print_yellow(\"boot test disabled via config, skipping boot\")\n        return\n    fetch_kernel_image(build)\n    fetch_dtb(build)\n    install_deps()\n    run_boot(build)\n\n\nif __name__ == \"__main__\":\n    for var in [\"ARCH\", \"CONFIG\", \"LLVM_VERSION\"]:\n        if not var in os.environ:\n            print_red(\"$%s must be specified\" % var)\n            sys.exit(1)\n    build = get_build()\n    print(json.dumps(build, indent=4))\n    check_log(build)\n    boot_test(build)\n","sub_path":"check_logs.py","file_name":"check_logs.py","file_ext":"py","file_size_in_byte":3492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"286986230","text":"from selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nimport pytest\n\n\ndef pytest_addoption(parser):\n    parser.addoption('--browser_name', action='store', default='chrome', help=\"Choose browser: chrome, firefox\")\n    parser.addoption('--language', action='store', default='en-gb',\n                     help='Choose language: es, fr, en-gb, ru, de, it')\n\n@pytest.fixture(scope=\"session\")\ndef browser(request):\n    lang = request.config.getoption(\"browser_name\")\n    options = Options()\n    options.add_experimental_option('prefs', {'intl.accept_languages': lang})\n    print('\\nstart Chrome browser...')\n    browser = webdriver.Chrome(options=options)\n    print('\\nstart chrome browser for test..', 'language =', lang)\n    yield browser\n    print(\"\\nquit browser..\")\n    browser.quit()\n","sub_path":"final/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"254868117","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Mar 26 20:17:06 2014\n\n@author: stuart\n\"\"\"\nimport os\nimport tempfile\nimport datetime\nimport astropy.table\nimport astropy.time\nimport astropy.units as u\nimport pytest\n\nfrom sunpy.time import parse_time\nfrom sunpy.net.jsoc import JSOCClient, JSOCResponse\nfrom sunpy.net.download import Results\nimport sunpy.net.jsoc.attrs as attrs\nimport sunpy.net.vso.attrs as vso_attrs\n\nclient = JSOCClient()\n\n\ndef test_jsocresponse_double():\n    j1 = JSOCResponse(table=astropy.table.Table(data=[[1, 2, 3, 4]]))\n    j1.append(astropy.table.Table(data=[[1, 2, 3, 4]]))\n    assert isinstance(j1, JSOCResponse)\n    assert all(j1.table == astropy.table.vstack([astropy.table.Table(\n        data=[[1, 2, 3, 4]]), astropy.table.Table(data=[[1, 2, 3, 4]])]))\n\n\ndef test_jsocresponse_single():\n    j1 = JSOCResponse(table=None)\n    assert len(j1) == 0\n    j1.append(astropy.table.Table(data=[[1, 2, 3, 4]]))\n    assert all(j1.table == astropy.table.Table(data=[[1, 2, 3, 4]]))\n    assert len(j1) == 4\n\n\ndef test_payload():\n    start = parse_time('2012/1/1T00:00:00')\n    end = parse_time('2012/1/1T00:00:45')\n\n    payload = client._make_query_payload(start, end, 'hmi.M_42s', notify='@')\n\n    payload_expected = {\n        'ds': '{0}[{1}-{2}]'.format('hmi.M_42s',\n                                    start.strftime(\"%Y.%m.%d_%H:%M:%S_TAI\"),\n                                    end.strftime(\"%Y.%m.%d_%H:%M:%S_TAI\")),\n        'format': 'json',\n        'method': 'url',\n        'notify': '@',\n        'op': 'exp_request',\n        'process': 'n=0|no_op',\n        'protocol': 'FITS,compress Rice',\n        'requestor': 'none',\n        'filenamefmt':\n        '{0}.{{T_REC:A}}.{{CAMERA}}.{{segment}}'.format('hmi.M_42s')\n    }\n\n    assert payload == payload_expected\n\n\ndef test_payload_nocompression():\n    start = parse_time('2012/1/1T00:00:00')\n    end = parse_time('2012/1/1T00:00:45')\n\n    payload = client._make_query_payload(\n        start, end, 'hmi.M_42s', compression=None, notify='jsoc@cadair.com')\n\n    payload_expected = {\n        'ds': '{0}[{1}-{2}]'.format('hmi.M_42s',\n                                    start.strftime(\"%Y.%m.%d_%H:%M:%S_TAI\"),\n                                    end.strftime(\"%Y.%m.%d_%H:%M:%S_TAI\")),\n        'format': 'json',\n        'method': 'url',\n        'notify': 'jsoc@cadair.com',\n        'op': 'exp_request',\n        'process': 'n=0|no_op',\n        'protocol': 'FITS, **NONE**',\n        'requestor': 'none',\n        'filenamefmt':\n        '{0}.{{T_REC:A}}.{{CAMERA}}.{{segment}}'.format('hmi.M_42s')\n    }\n\n    assert payload == payload_expected\n\n\ndef test_payload_protocol():\n    start = parse_time('2012/1/1T00:00:00')\n    end = parse_time('2012/1/1T00:00:45')\n\n    payload = client._make_query_payload(\n        start, end, 'hmi.M_42s', protocol='as-is', notify='jsoc@cadair.com')\n\n    payload_expected = {\n        'ds': '{0}[{1}-{2}]'.format('hmi.M_42s',\n                                    start.strftime(\"%Y.%m.%d_%H:%M:%S_TAI\"),\n                                    end.strftime(\"%Y.%m.%d_%H:%M:%S_TAI\")),\n        'format': 'json',\n        'method': 'url',\n        'notify': 'jsoc@cadair.com',\n        'op': 'exp_request',\n        'process': 'n=0|no_op',\n        'protocol': 'as-is',\n        'requestor': 'none',\n        'filenamefmt':\n        '{0}.{{T_REC:A}}.{{CAMERA}}.{{segment}}'.format('hmi.M_42s')\n    }\n\n    assert payload == payload_expected\n\n\ndef test_process_time_string():\n    start = client._process_time('2012/1/1T00:00:00')\n    assert start == datetime.datetime(year=2012, month=1, day=1, second=34)\n\n\ndef test_process_time_datetime():\n    start = client._process_time(datetime.datetime(year=2012, month=1, day=1))\n    assert start == datetime.datetime(year=2012, month=1, day=1, second=34)\n\n\ndef test_process_time_astropy():\n    start = client._process_time(\n        astropy.time.Time(\n            '2012-01-01T00:00:00', format='isot', scale='utc'))\n    assert start == datetime.datetime(year=2012, month=1, day=1, second=34)\n\n\ndef test_process_time_astropy_tai():\n    start = client._process_time(\n        astropy.time.Time(\n            '2012-01-01T00:00:00', format='isot', scale='tai'))\n    assert start == datetime.datetime(year=2012, month=1, day=1, second=0)\n\n\n@pytest.mark.online\ndef test_status_request():\n    r = client._request_status('none')\n    assert r.json() == {\n        u'error':\n        u'requestid none is not an acceptable ID for the external export system (acceptable format is JSOC_YYYYMMDD_NNN_X_IN or JSOC_YYYYMMDD_NNN).',\n        u'status': 4\n    }\n\n\ndef test_empty_jsoc_response():\n    Jresp = JSOCResponse()\n    assert Jresp.table is None\n    assert Jresp.query_args is None\n    assert Jresp.requestIDs is None\n    assert str(Jresp) == 'None'\n    assert repr(Jresp) == 'None'\n    assert len(Jresp) == 0\n\n\n@pytest.mark.online\ndef test_query():\n    Jresp = client.search(\n        attrs.Time('2012/1/1T00:00:00', '2012/1/1T00:01:30'),\n        attrs.Series('hmi.M_45s'), vso_attrs.Sample(90 * u.second))\n    assert isinstance(Jresp, JSOCResponse)\n    assert len(Jresp) == 2\n\n\n@pytest.mark.flaky(reruns=5)\n@pytest.mark.online\ndef test_post_pass():\n    responses = client.search(\n        attrs.Time('2012/1/1T00:00:00', '2012/1/1T00:00:45'),\n        attrs.Series('hmi.M_45s'), attrs.Notify('jsoc@cadair.com'))\n    aa = client.request_data(responses, return_resp=True)\n    tmpresp = aa[0][0].json()\n    assert tmpresp['status'] == 2\n    assert tmpresp['protocol'] == 'FITS,compress Rice'\n    assert tmpresp['method'] == 'url'\n\n\n@pytest.mark.online\ndef test_post_wavelength():\n    responses = client.search(\n        attrs.Time('2010/07/30T13:30:00', '2010/07/30T14:00:00'),\n        attrs.Series('aia.lev1_euv_12s'), attrs.Wavelength(193 * u.AA) |\n        attrs.Wavelength(335 * u.AA), attrs.Notify('jsoc@cadair.com'))\n    aa = client.request_data(responses, return_resp=True)\n    tmpresp = aa[0][0].json()\n    assert tmpresp['status'] == 2\n    assert tmpresp['protocol'] == 'FITS,compress Rice'\n    assert tmpresp['method'] == 'url'\n    assert tmpresp['rcount'] == 151\n    tmpresp = aa[1][0].json()\n    assert tmpresp['status'] == 2\n    assert tmpresp['protocol'] == 'FITS,compress Rice'\n    assert tmpresp['method'] == 'url'\n    assert tmpresp['rcount'] == 151\n\n\n@pytest.mark.online\ndef test_post_notify_fail():\n    responses = client.search(\n        attrs.Time('2012/1/1T00:00:00', '2012/1/1T00:00:45'),\n        attrs.Series('hmi.M_45s'))\n    with pytest.raises(ValueError):\n        client.request_data(responses, return_resp=True)\n\n\n@pytest.mark.online()\ndef test_post_wave_series():\n    with pytest.raises(TypeError):\n        client.search(\n            attrs.Time('2012/1/1T00:00:00', '2012/1/1T00:00:45'),\n            attrs.Series('hmi.M_45s') | attrs.Series('aia.lev1_euv_12s'),\n            attrs.Wavelength(193 * u.AA) | attrs.Wavelength(335 * u.AA))\n\n\n@pytest.mark.online\ndef test_post_fail(recwarn):\n    res = client.search(\n        attrs.Time('2012/1/1T00:00:00', '2012/1/1T00:00:45'),\n        attrs.Series('none'), attrs.Notify('jsoc@cadair.com'))\n    client.request_data(res, return_resp=True)\n    w = recwarn.pop(Warning)\n    assert issubclass(w.category, Warning)\n    assert \"Query 0 retuned status 4 with error Series none is not a valid series accessible from hmidb2.\" == str(\n        w.message)\n    assert w.filename\n    assert w.lineno\n\n\n@pytest.mark.online\ndef test_request_status_fail():\n    resp = client._request_status('none')\n    assert resp.json() == {\n        u'status': 4,\n        u'error':\n        u\"requestid none is not an acceptable ID for the external export system (acceptable format is JSOC_YYYYMMDD_NNN_X_IN or JSOC_YYYYMMDD_NNN).\"\n    }\n    resp = client._request_status(['none'])\n    assert resp.json() == {\n        u'status': 4,\n        u'error':\n        u\"requestid none is not an acceptable ID for the external export system (acceptable format is JSOC_YYYYMMDD_NNN_X_IN or JSOC_YYYYMMDD_NNN).\"\n    }\n\n\n@pytest.mark.online\ndef test_wait_get():\n    responses = client.search(\n        attrs.Time('2012/1/1T1:00:36', '2012/1/1T01:00:38'),\n        attrs.Series('hmi.M_45s'), attrs.Notify('jsoc@cadair.com'))\n    path = tempfile.mkdtemp()\n    res = client.fetch(responses, path=path)\n    assert isinstance(res, Results)\n    assert res.total == 1\n\n\n@pytest.mark.online\ndef test_get_request():\n    responses = client.search(\n        attrs.Time('2012/1/1T1:00:36', '2012/1/1T01:00:38'),\n        attrs.Series('hmi.M_45s'), attrs.Notify('jsoc@cadair.com'))\n\n    bb = client.request_data(responses)\n    path = tempfile.mkdtemp()\n    aa = client.get_request(bb, path=path)\n    assert isinstance(aa, Results)\n\n\n@pytest.mark.online\ndef test_results_filenames():\n    responses = client.search(\n        attrs.Time('2014/1/1T1:00:36', '2014/1/1T01:01:38'),\n        attrs.Series('hmi.M_45s'), attrs.Notify('jsoc@cadair.com'))\n    path = tempfile.mkdtemp()\n    aa = client.fetch(responses, path=path)\n    assert isinstance(aa, Results)\n    files = aa.wait(progress=False)\n    assert len(files) == len(responses)\n    for hmiurl in aa.map_:\n        assert os.path.isfile(hmiurl)\n\n\n@pytest.mark.online\ndef test_invalid_query():\n    with pytest.raises(ValueError):\n        client.search(attrs.Time('2012/1/1T01:00:00', '2012/1/1T01:00:45'))\n","sub_path":"sunpy/net/jsoc/tests/test_jsoc.py","file_name":"test_jsoc.py","file_ext":"py","file_size_in_byte":9252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"461695262","text":"# This script is used to calculate some statistics for the Green View Results\n# Copyright(C) Xiaojiang Li, Ian Seiferling, Marwa Abdulhai, Senseable City Lab, MIT\n\n\ndef Read_GSVinfo_Text(GVI_Res_txt):\n    '''\n    This function is used to read the information in text files or folders\n    the fundtion will remove the duplicate sites and only select those sites\n    have GSV info in green month.\n\n    Return:\n        panoIDLst,panoDateLst,panoLonLst,panoLatLst,greenViewLst\n\n    Pamameters:\n        GVI_Res_txt: the file name of the GSV information txt file\n    '''\n\n    import os,os.path\n\n    # empty list to save the GVI result and GSV metadata\n    panoIDLst = []\n    panoDateLst = []\n    panoLonLst = []\n    panoLatLst = []\n    greenViewLst = []\n\n    # read the green view index result txt files\n    lines = open(GVI_Res_txt,\"r\")\n    for line in lines:\n        # check the completeness of each line, each line include attribute of, panoDate, lon, lat,greenView\n        if \"panoDate\" not in line or \"greenview\" not in line:\n            continue\n\n        panoID = line.split(\" panoDate\")[0][-22:]\n        panoDate = line.split(\" longitude\")[0][-7:]\n        coordinate = line.split(\"longitude: \")[1]\n        lon = coordinate.split(\" latitude: \")[0]\n        latView = coordinate.split(\" latitude: \")[1]\n        lat = latView.split(', greenview:')[0]\n        greenView = line.split(\"greenview:\")[1]\n\n        # check if the greeView data is valid\n        if len(greenView)<2:\n            continue\n\n        elif float(greenView) <= 0:\n##            print(greenView)\n            continue\n\n        # remove the duplicated panorama id\n        if panoID not in panoIDLst:\n            panoIDLst.append(panoID)\n            panoDateLst.append(panoDate)\n            panoLonLst.append(lon)\n            panoLatLst.append(lat)\n            greenViewLst.append(greenView)\n\n    return panoIDLst,panoDateLst,panoLonLst,panoLatLst,greenViewLst\n\n\n\n# read the green view index files into list, the input can be file or folder\ndef Read_GVI_res(GVI_Res):\n    '''\n        This function is used to read the information in text files or folders\n        the fundtion will remove the duplicate sites and only select those sites\n        have GSV info in green month.\n\n        Return:\n            panoIDLst,panoDateLst,panoLonLst,panoLatLst,greenViewLst\n\n        Pamameters:\n            GVI_Res: the file name of the GSV information text, could be folder or txt file\n\n        last modified by Xiaojiang Li, March 27, 2018\n        '''\n\n    import os,os.path\n\n    # empty list to save the GVI result and GSV metadata\n    panoIDLst = []\n    panoDateLst = []\n    panoLonLst = []\n    panoLatLst = []\n    greenViewLst = []\n\n\n    # if the input gvi result is a folder\n    if os.path.isdir(GVI_Res):\n        allTxtFiles = os.listdir(GVI_Res)\n\n        for txtfile in allTxtFiles:\n            print('k')\n            # only read the text file\n            if not txtfile.endswith('.txt'):\n                continue\n\n            txtfilename = os.path.join(GVI_Res,txtfile)\n\n            # call the function to read txt file to a list\n            [panoIDLst_tem,panoDateLst_tem,panoLonLst_tem,panoLatLst_tem,greenViewLst_tem] = Read_GSVinfo_Text(txtfilename)\n\n            panoIDLst = panoIDLst + panoIDLst_tem\n            panoDateLst = panoDateLst + panoDateLst_tem\n            panoLonLst = panoLonLst + panoLonLst_tem\n            panoLatLst = panoLatLst + panoLatLst_tem\n            greenViewLst = greenViewLst + greenViewLst_tem\n\n    else: #for single txt file\n        [panoIDLst_tem,panoDateLst_tem,panoLonLst_tem,panoLatLst_tem,greenViewLst_tem] = Read_GSVinfo_Text(txtfilename)\n\n\n    return panoIDLst,panoDateLst,panoLonLst,panoLatLst,greenViewLst\n\n\n\n## ----------------- Main function ------------------------\nif __name__ == \"__main__\":\n    import os\n    import sys\n    import numpy as np\n    import matplotlib.pyplot as plt\n    import seaborn as sns\n\n\n    inputGVIres = r'C:\\\\Users\\\\rangu_uhpmatw\\\\Documents\\\\GitHub\\\\customs\\\\Treepedia_Public\\\\SA\\\\SA_GVR'\n    outputShapefile = 'C:\\\\Users\\\\rangu_uhpmatw\\\\Documents\\\\GitHub\\\\customs\\\\Treepedia_Public\\\\SA\\\\SA_GVR.shp'\n    lyrname = 'greenView'\n    [_,_,_,_,SA] = Read_GVI_res(inputGVIres)\n    print ('The length of the SA is:', len(SA))\n    SA = np.array(SA).astype(np.float)\n    print(np.average(SA))\n    print(np.percentile(SA, 0))\n    print(np.percentile(SA, 25))\n    print(np.percentile(SA, 50))\n    print(np.percentile(SA, 75))\n    print(np.percentile(SA, 100))\n\n    inputGVIres = r'C:\\\\Users\\\\rangu_uhpmatw\\\\Documents\\\\GitHub\\\\customs\\\\Treepedia_Public\\\\LB\\\\LB_GVR'\n    outputShapefile = 'C:\\\\Users\\\\rangu_uhpmatw\\\\Documents\\\\GitHub\\\\customs\\\\Treepedia_Public\\\\LB\\\\LB_GVR.shp'\n    [_,_,_,_,LB] = Read_GVI_res(inputGVIres)\n    print ('The length of the LB is:', len(LB))\n    LB = np.array(LB).astype(np.float)\n    print(np.average(LB))\n    print(np.percentile(LB, 0))\n    print(np.percentile(LB, 25))\n    print(np.percentile(LB, 50))\n    print(np.percentile(LB, 75))\n    print(np.percentile(LB, 100))\n\n    sns.set_style('whitegrid')\n    sns.distplot(SA)\n    plt.show()\n    sns.distplot(LB)\n    plt.show()\n    \n\n    plt.boxplot([SA,LB], vert=False, whis=3.1)\n    plt.yticks([1, 2], ['Santa Ana', 'Long Beach'])\n    plt.show()\n\n##    plt.boxplot(LB, vert=False)\n##    plt.show()\n\n    print('Done!!!')\n","sub_path":"Treepedia/GVStats.py","file_name":"GVStats.py","file_ext":"py","file_size_in_byte":5312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"203097667","text":"import json\nimport parser\nimport os\nimport parser_urls\n\nbase_image_url = 'https://st.mafiaonline.ru/images/gifts/'\njson_load_url = 'https://www.mafiaonline.ru/api/api.php?action=gifts&param=gifts'\njson_categories_load_url = 'https://www.mafiaonline.ru/api/api.php?action=gifts&param=sections'\n\n\ndef save_image(base_url, path, filename, file_extention):\n    url = base_url + filename + file_extention\n    full_file_path = path + filename + file_extention\n    if not os.path.exists(full_file_path):\n        r = parser.get_url(url)\n        if r.status_code == 200:\n            print('Картинка загружена, сохраняем... ')\n            if not os.path.exists(path):\n                print('path ' + path + ' doesnt exist. Creating new one...')\n                os.makedirs(path)\n            with open(full_file_path, \"wb\") as f:\n                f.write(r.content)\n                print('Сохранение прошло успешно!')\n        else:\n            print('Не удалось загрузить картинку по ссылке ' + url)\n    else:\n        print(\"Файл уже существует\")\n\ndef read_all_gifts(path):\n    with open(path, 'r', encoding='utf8') as f:\n        return json.load(f)\n\ndef write_to_file(json_file, path):\n    with open(path, 'w', encoding='utf8') as f:\n        json.dump(json_file, f, indent=4, separators=(',', ': '), ensure_ascii=False)\n\ndef load_json_from_ulr(url):\n    r = parser.get_url(url)\n    return r.json()\n\ndef check_update():\n    print('---------- Проверяем наличие новых подарков. ---------------')\n    json_cached = read_all_gifts(parser_urls.GIFTS_ALL_JSON)\n    json_loaded = load_json_from_ulr(json_load_url)\n    num_cached = len(json_cached['gifts'])\n    num_loaded = len(json_loaded['gifts'])\n    print(' К-во картинок в кешированном json : ' + str(num_cached))\n    print(' К-во картинок в мафском json : ' + str(num_loaded))\n    if num_loaded > num_cached:\n        new_images = num_loaded - num_cached\n        print('Есть новые картинки. К-во : ' + str(new_images) + ' шт.')\n        start_index = num_loaded - new_images\n        filetype = '.' + json_loaded['filetype']\n        for id in range(start_index, num_loaded):\n            print(' Загружаю картинку № ' + str(id))\n            img_section = json_loaded['gifts'][id]['section']\n            img_name = str(json_loaded['gifts'][id]['filename'])\n            save_path = parser_urls.GIFTS_IMAGE_LOCATION + str(img_section) + '/'\n            print('full save path = ' + save_path + img_name + filetype)\n            save_image(base_image_url, save_path, img_name, filetype)\n        write_to_file(json_loaded, parser_urls.GIFTS_ALL_JSON)\n        print('Загруженный json сохранен!')\n    else:\n        print('Новых картинок нет!')\n    print('---------- Проверка наличия новых подарков окончена. ---------------')\n\ndef load_categories():\n    print('---------- Обновляем категории. ---------------')\n    json_loaded = load_json_from_ulr(json_categories_load_url)\n    write_to_file(json_loaded, parser_urls.GIFTS_CATEGORIES_JSON)\n    print('---------- Обновление категорий завершено. ---------------')\n\ndef load_all_images():\n    json_cached = read_all_gifts(parser_urls.GIFTS_ALL_JSON)\n    num_loaded = len(json_cached['gifts'])\n    filetype = '.' + json_cached['filetype']\n    for id in range(0, num_loaded):\n        print(' Загружаю картинку № ' + str(id))\n        img_section = json_cached['gifts'][id]['section']\n        img_name = str(json_cached['gifts'][id]['filename'])\n        save_path = parser_urls.GIFTS_IMAGE_LOCATION + str(img_section) + '/'\n        print('full save path = ' + save_path + img_name + filetype)\n        save_image(base_image_url, save_path, img_name, filetype)\n    print('Сохранение прошло успешно.')\n\ncheck_update()\nload_categories()","sub_path":"Parser/Check_gifts.py","file_name":"Check_gifts.py","file_ext":"py","file_size_in_byte":4069,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"607488197","text":"# coding: utf-8\n\nimport logging\nimport functools\nimport random\n\nimport yaml\nimport pika\nimport os\nimport time\nimport json\nimport base64\nimport threading\nimport ctypes\n\nfrom requests.exceptions import RequestException, Timeout\nfrom pycti import OpenCTIApiClient\n\nPROCESSING_COUNT = 5\nMAX_PROCESSING_COUNT = 30\n\n\nclass Consumer(threading.Thread):\n    def __init__(self, connector, api):\n        threading.Thread.__init__(self)\n        self.api = api\n        self.queue_name = connector[\"config\"][\"push\"]\n        self.pika_credentials = pika.PlainCredentials(\n            connector[\"config\"][\"connection\"][\"user\"],\n            connector[\"config\"][\"connection\"][\"pass\"],\n        )\n        self.pika_parameters = pika.ConnectionParameters(\n            connector[\"config\"][\"connection\"][\"host\"],\n            connector[\"config\"][\"connection\"][\"port\"],\n            \"/\",\n            self.pika_credentials,\n        )\n        self.pika_connection = pika.BlockingConnection(self.pika_parameters)\n        self.channel = self.pika_connection.channel()\n        self.channel.basic_qos(prefetch_count=1)\n        self.processing_count = 0\n\n    def get_id(self):\n        if hasattr(self, \"_thread_id\"):\n            return self._thread_id\n        for id, thread in threading._active.items():\n            if thread is self:\n                return id\n\n    def terminate(self):\n        thread_id = self.get_id()\n        res = ctypes.pythonapi.PyThreadState_SetAsyncExc(\n            thread_id, ctypes.py_object(SystemExit)\n        )\n        if res > 1:\n            ctypes.pythonapi.PyThreadState_SetAsyncExc(thread_id, 0)\n            logging.info(\"Unable to kill the thread\")\n\n    def nack_message(self, channel, delivery_tag):\n        if channel.is_open:\n            logging.info(\"Message (delivery_tag=\" + str(delivery_tag) + \") rejected\")\n            channel.basic_nack(delivery_tag)\n        else:\n            logging.info(\n                \"Message (delivery_tag=\"\n                + str(delivery_tag)\n                + \") NOT rejected (channel closed)\"\n            )\n            pass\n\n    def ack_message(self, channel, delivery_tag):\n        if channel.is_open:\n            logging.info(\n                \"Message (delivery_tag=\" + str(delivery_tag) + \") acknowledged\"\n            )\n            channel.basic_ack(delivery_tag)\n        else:\n            logging.info(\n                \"Message (delivery_tag=\"\n                + str(delivery_tag)\n                + \") NOT acknowledged (channel closed)\"\n            )\n            pass\n\n    def stop_consume(self, channel):\n        if channel.is_open:\n            channel.stop_consuming()\n\n    # Callable for consuming a message\n    def _process_message(self, channel, method, properties, body):\n        data = json.loads(body)\n        logging.info(\n            \"Processing a new message (delivery_tag=\"\n            + str(method.delivery_tag)\n            + \"), launching a thread...\"\n        )\n        thread = threading.Thread(\n            target=self.data_handler,\n            args=[self.pika_connection, channel, method.delivery_tag, data],\n        )\n        thread.start()\n\n        while thread.is_alive():  # Loop while the thread is processing\n            self.pika_connection.sleep(0.05)\n        logging.info(\"Message processed, thread terminated\")\n\n    # Data handling\n    def data_handler(self, connection, channel, delivery_tag, data):\n        # Set the API headers\n        applicant_id = data[\"applicant_id\"]\n        self.api.set_applicant_id_header(applicant_id)\n        work_id = data[\"work_id\"] if \"work_id\" in data else None\n        # Execute the import\n        self.processing_count += 1\n        content = \"Unparseable\"\n        try:\n            content = base64.b64decode(data[\"content\"]).decode(\"utf-8\")\n            types = (\n                data[\"entities_types\"]\n                if \"entities_types\" in data and len(data[\"entities_types\"]) > 0\n                else None\n            )\n            update = data[\"update\"] if \"update\" in data else False\n            processing_count = self.processing_count\n            if self.processing_count == PROCESSING_COUNT:\n                processing_count = None\n            self.api.stix2.import_bundle_from_json(\n                content, update, types, processing_count\n            )\n            # Ack the message\n            cb = functools.partial(self.ack_message, channel, delivery_tag)\n            connection.add_callback_threadsafe(cb)\n            if work_id is not None:\n                self.api.work.report_expectation(work_id, None)\n            self.processing_count = 0\n            return True\n        except Timeout as te:\n            logging.warn(\"A connection timeout occurred: { \" + str(te) + \" }\")\n            # Platform is under heavy load, wait for unlock & retry almost indefinitely\n            sleep_jitter = round(random.uniform(10, 30), 2)\n            time.sleep(sleep_jitter)\n            self.data_handler(connection, channel, delivery_tag, data)\n            return True\n        except RequestException as re:\n            logging.error(\"A connection error occurred: { \" + str(re) + \" }\")\n            time.sleep(60)\n            logging.info(\n                \"Message (delivery_tag=\" + str(delivery_tag) + \") NOT acknowledged\"\n            )\n            cb = functools.partial(self.nack_message, channel, delivery_tag)\n            connection.add_callback_threadsafe(cb)\n            self.processing_count = 0\n            return False\n        except Exception as ex:\n            error = str(ex)\n            if \"LockError\" in error and self.processing_count < MAX_PROCESSING_COUNT:\n                # Platform is under heavy load, wait for unlock & retry almost indefinitely\n                sleep_jitter = round(random.uniform(10, 30), 2)\n                time.sleep(sleep_jitter)\n                self.data_handler(connection, channel, delivery_tag, data)\n            elif (\n                \"MissingReferenceError\" in error\n                and self.processing_count < PROCESSING_COUNT\n            ):\n                # In case of missing reference, wait & retry\n                sleep_jitter = round(random.uniform(1, 3), 2)\n                time.sleep(sleep_jitter)\n                logging.info(\n                    \"Message (delivery_tag=\"\n                    + str(delivery_tag)\n                    + \") reprocess (retry nb: \"\n                    + str(self.processing_count)\n                    + \")\"\n                )\n                self.data_handler(connection, channel, delivery_tag, data)\n            else:\n                # Platform does not know what to do and raises an error, fail and acknowledge the message\n                logging.error(str(ex))\n                self.processing_count = 0\n                cb = functools.partial(self.ack_message, channel, delivery_tag)\n                connection.add_callback_threadsafe(cb)\n                if work_id is not None:\n                    self.api.work.report_expectation(\n                        work_id, {\"error\": str(ex), \"source\": content}\n                    )\n                return False\n\n    def run(self):\n        try:\n            # Consume the queue\n            logging.info(\"Thread for queue \" + self.queue_name + \" started\")\n            self.channel.basic_consume(\n                queue=self.queue_name, on_message_callback=self._process_message\n            )\n            self.channel.start_consuming()\n        finally:\n            self.channel.stop_consuming()\n            logging.info(\"Thread for queue \" + self.queue_name + \" terminated\")\n\n\nclass Worker:\n    def __init__(self):\n        self.logs_all_queue = \"logs_all\"\n        self.consumer_threads = {}\n        self.logger_threads = {}\n\n        # Get configuration\n        config_file_path = os.path.dirname(os.path.abspath(__file__)) + \"/config.yml\"\n        config = (\n            yaml.load(open(config_file_path), Loader=yaml.FullLoader)\n            if os.path.isfile(config_file_path)\n            else {}\n        )\n        self.log_level = os.getenv(\"WORKER_LOG_LEVEL\") or config[\"worker\"][\"log_level\"]\n        self.opencti_url = os.getenv(\"OPENCTI_URL\") or config[\"opencti\"][\"url\"]\n        self.opencti_token = os.getenv(\"OPENCTI_TOKEN\") or config[\"opencti\"][\"token\"]\n\n        # Check if openCTI is available\n        self.api = OpenCTIApiClient(\n            self.opencti_url, self.opencti_token, self.log_level\n        )\n\n        # Configure logger\n        numeric_level = getattr(logging, self.log_level.upper(), None)\n        if not isinstance(numeric_level, int):\n            raise ValueError(\"Invalid log level: \" + self.log_level)\n        logging.basicConfig(level=numeric_level)\n\n        # Initialize variables\n        self.connectors = []\n        self.queues = []\n\n    # Start the main loop\n    def start(self):\n        while True:\n            try:\n                # Fetch queue configuration from API\n                self.connectors = self.api.connector.list()\n                self.queues = list(map(lambda x: x[\"config\"][\"push\"], self.connectors))\n\n                # Check if all queues are consumed\n                for connector in self.connectors:\n                    queue = connector[\"config\"][\"push\"]\n                    if queue in self.consumer_threads:\n                        if not self.consumer_threads[queue].is_alive():\n                            logging.info(\n                                \"Thread for queue \"\n                                + queue\n                                + \" not alive, creating a new one...\"\n                            )\n                            self.consumer_threads[queue] = Consumer(\n                                connector,\n                                self.api,\n                            )\n                            self.consumer_threads[queue].start()\n                    else:\n                        self.consumer_threads[queue] = Consumer(\n                            connector,\n                            self.api,\n                        )\n                        self.consumer_threads[queue].start()\n\n                # Check if some threads must be stopped\n                for thread in list(self.consumer_threads):\n                    if thread not in self.queues:\n                        logging.info(\n                            \"Queue \" + thread + \" no longer exists, killing thread...\"\n                        )\n                        try:\n                            self.consumer_threads[thread].terminate()\n                            self.consumer_threads.pop(thread, None)\n                        except:\n                            logging.info(\n                                \"Unable to kill the thread for queue \"\n                                + thread\n                                + \", an operation is running, keep trying...\"\n                            )\n                time.sleep(60)\n            except KeyboardInterrupt:\n                # Graceful stop\n                for thread in self.consumer_threads.keys():\n                    if thread not in self.queues:\n                        self.consumer_threads[thread].terminate()\n                exit(0)\n            except Exception as e:\n                logging.error(e)\n                time.sleep(60)\n\n\nif __name__ == \"__main__\":\n    worker = Worker()\n    try:\n        worker.start()\n    except Exception as e:\n        logging.error(e)\n        exit(1)\n","sub_path":"opencti-worker/src/worker.py","file_name":"worker.py","file_ext":"py","file_size_in_byte":11332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"277005627","text":"from Crypto.Cipher import AES\nimport os\nfrom Crypto import Random\nimport base64\n\n\"\"\"\naes加密算法\npadding : PKCS7\nCBC\n\"\"\"\nclass AES256Util:\n\n    __BLOCK_SIZE_16 = BLOCK_SIZE_16 = AES.block_size\n\n    @staticmethod\n    def encryt(string, key, iv):\n        \"\"\"\n        加密文本\n        :param string: 文本\n        :param key: 密钥\n        :param iv: 偏移量/初始向量\n        :return: 密文\n        \"\"\"\n        cipher = AES.new(key, AES.MODE_CBC, iv)\n        x = AES256Util.__BLOCK_SIZE_16 - (len(string) % AES256Util.__BLOCK_SIZE_16)\n        # 如果最后一块不够16位需要用字符进行补全\n        if x != 0:\n            string = string + chr(x)*x\n        msg = cipher.encrypt(string.encode('utf-8'))\n        \n        # msg = base64.urlsafe_b64encode(msg).replace('=', '')\n        msg = base64.b64encode(msg)\n        return msg\n\n    @staticmethod\n    def decrypt(en_str, key, iv):\n        cipher = AES.new(key, AES.MODE_CBC, iv)\n        # en_str += (len(en_str) % 4)*\"=\"\n        # decryptByts = base64.urlsafe_b64decode(en_str)\n        en_str = base64.b64decode(en_str)\n\n        msg = cipher.decrypt(en_str)\n        padding_len = msg[len(msg)-1]\n        return msg[0:-padding_len]\n\n\n\nif __name__ == \"__main__\":\n    # import hashlib\n    # key = hashlib.md5().hexdigest()   # 随机生产一个md5值，32位\n    string = \"{'xss':'cssvv'}\"\n\n    import json\n    string = json.dumps( {\"username\":\"test\", \"sysname\":\"TestSys\", \n    \"redirect\": \"http://datastudio-sit.cpic.com.cn/webroot/decision/v5/design/report/dd83502efef34949acd8377a77152e17/view\",\n    \"ext\": {}} )\n\n    key = b\"uBdUx82vPHkDKb284d7NkjFoNcKWBuka\"   # 32位\n    iv = b\"c558Gq0YQK2QUlMc\"    # 16位\n    res = AES256Util.encryt(string, key, iv)\n    print(res)  # \\xbf\\xd7\\xf7\\xfc\\x1c\\x15\\x93\\xc1*Z\\xc3\\x0e\\xda\\x85\\xdb\\x9d\\x9a\\xb6z\\xbb\\xa7\\xb3&\\x8b\\xa1\\xd3\\xc6\\xf13;\\xf3\\n\n    print(AES256Util.decrypt(res, key, iv))    # {\"key\": \"123456\"}\n","sub_path":"utils/AES/AES.py","file_name":"AES.py","file_ext":"py","file_size_in_byte":1926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"133908323","text":"import socket\nimport pickle\nimport queue\nimport threading\n\nclientSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n\nip='127.0.0.1'\nport = 8080\nadd = (ip,port)\n\nz = ('ajdiwjidj', 'yea')\ny = pickle.dumps(z)\n\nclientSocket.sendto(y,add)\n\ninput_queue = queue.Queue()\n\nclass ThreadingExample(object):\n    \"\"\" Threading example class\n    The run() method will be started and it will run in the background\n    until the application exits.\n    \"\"\"\n\n    def __init__(self, interval=1):\n        \"\"\" Constructor\n        :type interval: int\n        :param interval: Check interval, in seconds\n        \"\"\"\n\n        thread = threading.Thread(target=self.run, args=())\n        thread.daemon = True                            # Daemonize thread\n        thread.start()                                  # Start the execution\n\n    def run(self):\n        \"\"\" Method that runs forever \"\"\"\n        while True:\n            # Do something\n            data = clientSocket.recv(1024)\n            print(data)\n            #data_queue.put(data)\n\nclass ThreadingExample1(object):\n    \"\"\" Threading example class\n    The run() method will be started and it will run in the background\n    until the application exits.\n    \"\"\"\n\n    def __init__(self, interval=1):\n        \"\"\" Constructor\n        :type interval: int\n        :param interval: Check interval, in seconds\n        \"\"\"\n\n        thread = threading.Thread(target=self.run, args=())\n        thread.daemon = True                            # Daemonize thread\n        thread.start()                                  # Start the execution\n\n    def run(self):\n        \"\"\" Method that runs forever \"\"\"\n        while True:\n            # Do something\n            x = input()\n            input_queue.put(x)\n\nexample = ThreadingExample()\nexample1 = ThreadingExample1()\n\ncount = 0\n\nwhile True:\n    try:\n        x = input_queue.get_nowait()\n        if x:\n            print(x)\n            clientSocket.sendto(y,add)\n        #data = data_queue.get_nowait()\n        #print(data)\n    except queue.Empty:\n        pass","sub_path":"test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":2033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"54"}
+{"seq_id":"279994615","text":"# -*- coding: utf-8 -*-\n#\n# This file is part of Invenio.\n# Copyright (C) 2016-2021 CERN.\n#\n# Invenio is free software; you can redistribute it and/or modify it\n# under the terms of the MIT License; see LICENSE file for more details.\n\n\"\"\"Invenio Communities Resource API.\"\"\"\n\nfrom flask import g\nfrom flask_resources import resource_requestctx, response_handler, route\nfrom invenio_records_resources.resources.files.resource import request_stream\nfrom invenio_records_resources.resources.records.resource import \\\n    RecordResource, request_data, request_headers, request_search_args, \\\n    request_view_args\nfrom invenio_records_resources.resources.records.utils import es_preference\n\n\nclass CommunityResource(RecordResource):\n    \"\"\"Communities resource.\"\"\"\n\n    def create_url_rules(self):\n        \"\"\"Create the URL rules for the record resource.\"\"\"\n\n        def p(prefix, route):\n            \"\"\"Prefix a route with the URL prefix.\"\"\"\n            return f\"{prefix}{route}\"\n\n        routes = self.config.routes\n        return [\n            route(\n                \"GET\",\n                p(routes[\"communities-prefix\"], routes[\"list\"]),\n                self.search\n            ),\n            route(\n                \"POST\",\n                p(routes[\"communities-prefix\"], routes[\"list\"]),\n                self.create\n            ),\n            route(\n                \"GET\",\n                p(routes[\"communities-prefix\"], routes[\"item\"]),\n                self.read\n            ),\n            route(\n                \"PUT\",\n                p(routes[\"communities-prefix\"], routes[\"item\"]),\n                self.update\n            ),\n            route(\n                \"DELETE\",\n                p(routes[\"communities-prefix\"], routes[\"item\"]),\n                self.delete\n            ),\n            route(\n                \"GET\",\n                p(routes[\"user-prefix\"], routes[\"list\"]),\n                self.search_user_communities\n            ),\n            route(\n                \"POST\",\n                p(routes[\"communities-prefix\"], routes[\"item\"]) + '/rename',\n                self.rename\n            ),\n            route(\n                \"GET\",\n                p(routes[\"communities-prefix\"], routes[\"item\"]) + '/logo',\n                self.read_logo\n            ),\n            route(\n                \"PUT\",\n                p(routes[\"communities-prefix\"], routes[\"item\"]) + '/logo',\n                self.update_logo\n            ),\n            route(\n                \"DELETE\",\n                p(routes[\"communities-prefix\"], routes[\"item\"]) + '/logo',\n                self.delete_logo\n            ),\n        ]\n\n    @request_search_args\n    @response_handler(many=True)\n    def search_user_communities(self):\n        \"\"\"Perform a search over the user's communities.\n\n        GET /user/communities\n        \"\"\"\n        hits = self.service.search_user_communities(\n            identity=g.identity,\n            params=resource_requestctx.args,\n            es_preference=es_preference()\n        )\n        return hits.to_dict(), 200\n\n    @request_headers\n    @request_view_args\n    @request_data\n    @response_handler()\n    def rename(self):\n        \"\"\"Rename a community.\"\"\"\n        item = self.service.rename(\n            resource_requestctx.view_args[\"pid_value\"],\n            g.identity,\n            resource_requestctx.data,\n            revision_id=resource_requestctx.headers.get(\"if_match\"),\n        )\n        return item.to_dict(), 200\n\n    @request_view_args\n    def read_logo(self):\n        \"\"\"Read logo's content.\"\"\"\n        item = self.service.read_logo(\n            resource_requestctx.view_args[\"pid_value\"],\n            g.identity,\n        )\n        return item.send_file(), 200\n\n    @request_view_args\n    @request_stream\n    @response_handler()\n    def update_logo(self):\n        \"\"\"Upload logo content.\"\"\"\n        item = self.service.update_logo(\n            resource_requestctx.view_args[\"pid_value\"],\n            g.identity,\n            resource_requestctx.data[\"request_stream\"],\n            content_length=resource_requestctx.data[\"request_content_length\"],\n        )\n        return item.to_dict(), 200\n\n    @request_view_args\n    def delete_logo(self):\n        \"\"\"Delete logo.\"\"\"\n        self.service.delete_logo(\n            resource_requestctx.view_args[\"pid_value\"],\n            g.identity,\n        )\n        return \"\", 204\n","sub_path":"invenio_communities/communities/resources/resource.py","file_name":"resource.py","file_ext":"py","file_size_in_byte":4345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"142979315","text":"from itasca import PFC3D_Connection\nfrom itasca import FishBinaryReader\nimport numpy as np\n\nclass pfcBridge(object):\n    def __init__(self):\n        self._pfc = PFC3D_Connection()\n        self._pfc.start(\"pfc_bridge_server.p3dat\")\n        self._pfc.connect()\n\n    def __del__(self):\n        self.quit()\n\n    def eval(self, fish_string):\n        \"\"\"Evaluate a FISH expression and return the result\n\n        The expression can either be an assignment or an\n        expression. Assignments return zero expressions return the\n        value they evaluate to.\n        \"\"\"\n        assert type(fish_string) == str\n\n        if fish_string.find(\"=\") == -1:\n            code = 11\n        else:\n            code = 12\n\n        self._pfc.send(code)\n        self._pfc.send(fish_string)\n        res = self._pfc.receive()\n        return self.map_return_type(res)\n\n    def map_return_type(self, val):\n        if not type(val)==str: return val\n        if not val.startswith(':'): return val\n        if val==\":null:\": return None\n\n        flag, idn = val.split()\n        if flag == ':ball:':\n            return pfc_ball(int(idn), self)\n        elif flag == ':wall:':\n            return pfc_wall(int(idn), self)\n        elif flag == ':contact:':\n            return pfc_contact(self)\n        elif flag == ':clump:':\n            return pfc_clump(int(idn), self)\n        elif flag == ':meas:':\n            return pfc_meas(int(idn), self)\n        else:\n            raise \"unknown type in bridge communication\"\n\n\n    def cmd(self, command):\n        \"execute a PFC3D command\"\n        assert type(command) == str\n        self._pfc.send(10)\n        self._pfc.send(command)\n        res = self._pfc.receive()\n        assert res == 0\n\n    def ball_radii(self):\n        \"\"\" returns a NumPy array of the ball radii \"\"\"\n        self.cmd(\"write_ball_radii\")\n        return self._read_v1_fish()\n\n    def ball_positions(self):\n        \"\"\" returns a NumPy array of the ball locations \"\"\"\n        self.cmd(\"write_ball_positions\")\n        return self._read_v3_fish()\n\n    def ball_velocities(self):\n        \"\"\" returns a NumPy array of the ball velocities \"\"\"\n        self.cmd(\"write_ball_velocities\")\n        return self._read_v3_fish()\n\n    def ball_list(self):\n        return ball_list(self)\n\n    def close(self):\n        \"\"\" Return control to PFC \"\"\"\n        self._pfc.send(-1)\n\n    def quit(self):\n        \"\"\" Close PFC \"\"\"\n        self._pfc.send(-2)\n\n    def _read_v3_fish(self, filename='bin.fish'):\n        a = FishBinaryReader(filename).asarray()\n        assert a.shape[0] % 3 == 0, \"bad fish array shape\"\n        return a.reshape(a.shape[0]/3, 3)\n\n    def _read_v1_fish(self, filename='bin.fish'):\n        return FishBinaryReader(filename).asarray()\n\n    def __getattr__(self, item):\n        \"\"\"\n        intercept method calls and try them as fish intrinsics\n        \"\"\"\n        # shoud check for rw here?\n        def handle_fishcall(*args):\n            if len(args)==0:\n                fish_string = \"%s\"  % (item)\n                return self.eval(fish_string)\n            else:\n                fish_string = \"%s(%s)\" % (item, \",\".join(map(str, args)))\n                return self.eval(fish_string)\n\n        return handle_fishcall\n\n\nclass ball_list(object):\n    \"iterator object for list of balls\"\n    def __init__(self, bridge):\n        self._bridge = bridge\n        self.current = pfc_ball(self._bridge.eval(\"ball_id_head\"),\n                                self._bridge)\n    def __iter__(self):\n        return self\n    def next(self):\n        if self.current.id==-1:\n            raise StopIteration\n        else:\n            retval = self.current\n            self._bridge.eval(\"current_id = %i\" % self.current.id)\n            self.current = pfc_ball(self._bridge.eval(\"ball_id_next\"),\n                                    self._bridge)\n            return retval\n\nclass pfc_object(object):\n    \"\"\" Catch uncought method calls and try them as FISH intrinsics.  \"\"\"\n    def __getattr__(self, item):\n        fname = self.prefix + item\n        if not fname in self.methods:\n            raise AttributeError(\"no fish intrinsic \" + fname)\n\n        # shoud check for rw here?\n        def handle_fishcall(arg=None):\n            if arg==None:\n                fish_string = \"%s(%s)\" % (fname, self.find)\n                return self._bridge.eval(fish_string)\n            else:\n                fish_string = \"%s(%s)=%s\" % (fname,\n                                             self.find,\n                                             str(arg))\n                res = self._bridge.eval(fish_string)\n                return arg\n        return handle_fishcall\n\n\nclass pfc_contact(pfc_object):\n    methods = \"\"\"c_next c_ball1 c_ball2 c_b1clist c_b2clist c_gobj1 c_gobj2 c_go1clist\nc_go2clist c_wseg c_type c_bflag c_broken c_x c_y c_z c_vpos c_nforce\nc_xsforce c_ysforce c_zsforce c_sforce c_kn c_ks c_hn c_hs c_fric\nc_nstrength c_sstrength c_ex c_pb c_xun c_yun c_zun c_vun c_jset\nc_slipwork c_inhibit c_prop c_model c_extra c_vsforce c_dampn c_damps\nc_thactive c_thlen c_thpipe c_thpow c_thres c_active c_installpb\nc_ondisk c_ondisk c_nvforce c_xsvforce c_ysvforce c_zsvforce c_svforce\nc_vsvforce c_knset c_ksset c_fricset c_dampnt c_ontri c_mom c_tmom\nc_vbmom\"\"\".split()\n\n    def __init__(self, idn, bridge):\n        self.prefix = \"c_\"\n        self.id = 0\n        self._bridge = bridge\n        self.methods = pfc_contact.methods\n        self.find = 'find_contact(current_contact)'\n    def __repr__(self):\n        return \"<pfc contact>\"\n\n\nclass pfc_wall(pfc_object):\n    methods = \"\"\"w_next w_clist w_id w_x w_y w_z w_pos w_ux w_uy w_uz w_vu w_xvel\nw_yvel w_zvel w_vvel w_xfob w_yfob w_zfob w_vfob w_kn w_ks w_fric w_ex\ndel_wall w_color w_flist w_wlist w_extra w_fix w_delete w_rxvel\nw_ryvel w_rzvel w_rvel w_vrvel w_xmom w_ymom w_zmom w_mom w_vmom\nw_type w_radvel w_radfob w_radend1 w_radend2 w_posend1 w_posend2 w_rad\"\"\".split()\n    def __init__(self, idn, bridge):\n        self.prefix = \"w_\"\n        self.id = idn\n        self._bridge = bridge\n        self.methods = pfc_wall.methods\n        self.find = 'find_wall(%i)' % (self.id)\n    def __repr__(self):\n        return \"<pfc wall id=%i>\" % (self.id)\n\n\nclass pfc_clump(pfc_object):\n    methods = \"\"\"cl_add cl_extra cl_id cl_list cl_next cl_rel cl_scale cl_color\ncl_damp cl_mass cl_realmass cl_moi cl_vmoi cl_vol cl_volgiven cl_vfix\ncl_xfix cl_yfix cl_zfix cl_rfix cl_vrfix cl_rxfix cl_ryfix cl_rzfix\ncl_vfap cl_xfap cl_yfap cl_zfap cl_map cl_vmap cl_xmap cl_ymap cl_zmap\ncl_vpos cl_x cl_y cl_z cl_vvel cl_xvel cl_yvel cl_zvel cl_rvel\ncl_vrvel cl_rxvel cl_ryvel cl_rzvel cl_vfob cl_xfob cl_yfob cl_zfob\ncl_mom cl_vmom cl_xmom cl_ymom cl_zmom cl_stress cl_vdisp cl_xdisp\ncl_ydisp cl_zdisp\"\"\".split()\n\n    def __init__(self, idn, bridge):\n        self.prefix = \"cl_\"\n        self.id = id\n        self._bridge = bridge\n        self.methods = pfc_clump.methods\n        self.find = 'find_clump(%i)' % (self.id)\n    def __repr__(self):\n        return \"<pfc clump id=%i>\" % (self.id)\n\n\nclass pfc_meas(pfc_object):\n    methods = \"\"\"m_coord m_poros m_sfrac m_s11 m_s12 m_s21 m_s22 m_s13 m_s31\nm_s23 m_s32 m_s33 m_ed11 m_ed12 m_ed21 m_ed22 m_ed13 m_ed31 m_ed23\nm_ed32 m_ed33 m_x m_y m_z m_vpos m_rad m_id m_next m_tc11 m_tc12\nm_tc21 m_tc22 m_tc13 m_tc31 m_tc23 m_tc32 m_tc33\"\"\".split()\n\n    def __init__(self, idn, bridge):\n        self.prefix = \"m_\"\n        self.id = idn\n        self._bridge = bridge\n        self.methods = pfc_meas.methods\n        self.find = 'find_meas(%i)' % (self.id)\n    def __repr__(self):\n        return \"<pfc measurement sphere id=%i>\" % (self.id)\n\n\nclass pfc_ball(pfc_object):\n    methods = \"\"\"b_next b_clist b_ctype b_xfix b_yfix b_zfix b_vfix\nb_rxfix b_ryfix b_rzfix b_rfix b_x b_y b_z b_vpos b_ux b_uy b_uz b_vu\nb_xvel b_yvel b_zvel b_vvel b_rxvel b_ryvel b_rzvel b_rvel b_xfob\nb_yfob b_zfob b_vfob b_xfap b_yfap b_zfap b_vfap b_xmom b_ymom b_zmom\nb_mom b_rad b_mass b_realmass b_moi b_dens b_kn b_ks b_shearmod\nb_poiss b_fric b_ex del_ball b_color b_xmap b_ymap b_zmap b_map\nb_shared b_type b_rot b_damp b_realmoi b_clump b_cllist b_extra\nb_stress b_vrvel b_vmom b_vmap b_vrfix b_xdisp b_ydisp b_zdisp b_vdisp\nb_delete b_thexp b_thfix b_thpob b_thpsrc b_thsheat b_thtemp\nb_thdeltemp b_perflag b_perBall b_xffap b_yffap b_zffap b_vffap\nb_multi_type b_realmassset b_realmoiset\"\"\".split()\n\n    def __init__(self, idn, bridge):\n        self.prefix = \"b_\"\n        self.id = idn\n        self._bridge = bridge\n        self.methods = pfc_ball.methods\n        self.find = \"find_ball(%i)\" % (self.id)\n    def __repr__(self):\n        return \"<pfc ball id=%i>\" % (self.id)\n","sub_path":"itasca/bridge_client.py","file_name":"bridge_client.py","file_ext":"py","file_size_in_byte":8587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"642156703","text":"# l1 = [1, 2, 3, 4, 5, 6, 7]\n# s1 = 'hello'\n#\n# count = 0\n# while count<len(s1):\n#     print(s1[count])\n#     count += 1\n\nd1 = {'a':1,'b':2,\"c\":3}\n#\n# count = 0\n# while count < len(d1):\n#     print(count)\n#     count += 1\n\n# for i in d1:\n#     print(d1[i])\n\n# for:给我们提供了一种不依赖于索引的取值方式\n# for取值方式更加简洁\n# l1 = [1, 2, 3, 4, 5, 6, 7]\n# for i in l1:\n#     print(i)\n\n# range(10) 等价于-->[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n\n# l1 = [1,2,3,4,5,6,7,8,9,10]\n#\n# for i in l1:\n#     print(i)\n\n# print(range(10))\n\n# range() 顾头不顾尾\n# start  stop  sep\n# for i in range(0,10,9):\n#     print(i)\n\n\n# for i in range(0,10):\n#     if i == 7:\n#         continue\n#     if i == 9:\n#         break\n#     print(i)\n\n# for循环正常执行结束，就会执行else对应的代码块\n# 非正常结束，例如break打断，就不会执行\n\nfor i in range(10):\n    if i == 5:\n        break\n    print(i)\nelse:\n    print('执行成功')","sub_path":"2.python/0.python基础/day4/代码/for 循环.py","file_name":"for 循环.py","file_ext":"py","file_size_in_byte":965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"290527635","text":"\n\n#calss header\nclass _CERVICAL():\n\tdef __init__(self,): \n\t\tself.name = \"CERVICAL\"\n\t\tself.definitions = [u'relating to the cervix (= the narrow, lower part of the uterus): ', u'relating to the cervical vertebrae (= the bones in the neck): ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'adjectives'\n\n\n\tdef run(self, obj1, obj2):\n\t\tself.jsondata[obj2] = {}\n\t\tself.jsondata[obj2]['properties'] = self.name.lower()\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/adjectives/_cervical.py","file_name":"_cervical.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"572086003","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Jesse Rubin\n\"\"\"\nDigit power sum\nProblem 119\nThe number 512 is interesting because it is equal to the sum of its digits\nraised to some power: 5 + 1 + 2 = 8, and 8**3 = 512. Another example of a\nnumber with this property is 614656 = 284.\n\nWe shall define an to be the nth term of this sequence and insist that a\nnumber must contain at least two digits to have a sum.\n\nYou are given that a2 = 512 and a10 = 614656.\n\nFind a30.\n\"\"\"\n\n\ndef digits_sum(number):\n    tot = 0\n    while number > 0:\n        tot += number % 10\n        number //= 10\n    return tot\n\n\ndef p119():\n    a = []\n    for num in range(2, 100):\n        for exponent in range(1, 20):\n            aaa = num ** exponent\n            if digits_sum(aaa) == num and aaa > 10:\n                a.append(aaa)\n\n    a.sort()\n    return a[30-1]\n\n\nif __name__ == '__main__':\n    ANSWER = p119()\n    print(\"Answer: {}\".format(ANSWER))","sub_path":"done/py/euler_119.py","file_name":"euler_119.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"363205351","text":"import huffman\nimport sys\nimport util\n\n\ndef run_compressor (filename):\n    with open(filename, 'rb') as uncompressed:\n        freqs = huffman.make_freq_table(uncompressed)\n        tree = huffman.make_tree(freqs)\n        uncompressed.seek(0)\n        with open(filename+'.huf', 'wb') as compressed:\n                util.compress(tree, uncompressed, compressed)\n\n\nif __name__ == '__main__':\n    if len(sys.argv) < 2:\n        print(\"Usage: {} <file1> <file2> ...\".format(sys.argv[0]))\n    else:\n        for filename in sys.argv[1:]:\n            print (\"Compressing '{0}' to '{0}.huf'\".format(filename))\n            run_compressor(filename)\n","sub_path":"CMPUT274275/Assignments/Huffman/compress.py","file_name":"compress.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"72854140","text":"from openerp.osv import fields, osv\nimport openerp.addons.decimal_precision as dp\n\nclass hr_contract(osv.osv):\n    _inherit = 'hr.contract'\n\n    _columns = {\n    \t'wage': fields.float('Wage', digits_compute=dp.get_precision('Payroll'), required=True, help=\"Basic Salary of the employee\"),\n        'nationalite': fields.char('Nationalite', size=64),\n        'qualif': fields.char('Qualification', size=64),\n        'niveau': fields.char('Niveau', size=64),\n        'coef': fields.char('Coefficient', size=64),\n    }\nhr_contract()\n\n\n\nclass res_company(osv.osv):\n    _inherit = 'res.company'\n\n    _columns = {\n        'plafond_secu': fields.float('Plafond de la Securite Sociale', digits_compute=dp.get_precision('Payroll')),\n        'nombre_employes': fields.integer('Nombre d\\'employes'),\n        'cotisation_prevoyance': fields.float('Cotisation Patronale Prevoyance', digits_compute=dp.get_precision('Payroll')),\n        'org_ss': fields.char('Organisme de securite sociale', size=64),\n        'conv_coll': fields.char('Convention collective', size=64),\n    }\n\nres_company()\n\n\n\nclass hr_payslip(osv.osv):\n    _inherit = 'hr.payslip'\n\n    _columns = {\n        'payment_mode': fields.char('Mode de paiement', size=64),\n    }\nhr_payslip()\n\nclass hr_employee(osv.osv):\n    _inherit = 'hr.employee'\n  \n\t\n    _columns = {\n\t\t'matricule_cnss':fields.integer('Matricule CNSS', size=10),\n\t\t'num_chezemployeur':fields.integer('Numero chez l\\'employeur'),\n\t}\nhr_employee()\n\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\n","sub_path":"l10n_cm_paye.py","file_name":"l10n_cm_paye.py","file_ext":"py","file_size_in_byte":1495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"48941818","text":"import time\nimport rclpy\nfrom rclpy.node import Node\nfrom std_msgs.msg import String\nfrom geometry_msgs.msg import Twist\nimport math\nimport serial\nfrom math import sin, cos, pi\nimport threading\n\nclass ReadLine:\n    def __init__(self, s):\n        self.buf = bytearray()\n        self.s = s\n\n    def readline(self):\n        i = self.buf.find(b\"\\n\")\n        if i >= 0:\n            r = self.buf[:i+1]\n            self.buf = self.buf[i+1:]\n            return r\n        while True:\n            i = max(1, min(2048, self.s.in_waiting))\n            data = self.s.read(i)\n            i = data.find(b\"\\n\")\n            if i >= 0:\n                r = self.buf + data[:i+1]\n                self.buf[0:] = data[i+1:]\n                return r\n            else:\n                self.buf.extend(data)               \n\nclass RobotDigitalTwin(Node):\n    #the location of the robot, x, y and rotation\n    x=0.0\n    y=0.0\n    z=0.5\n    angle = 0.0 #in degrees\n    th=0.0 #angle in radians\n\n    def listener_callback(self, msg):\n        command = msg.data[0:5] #the actual command string\n        value = int(msg.data[6:11]) #the data\n        print(command)\n        print(value)\n\n        if(command == \"MOVEF\"):\n            if(value == 600):\n                print(\"robot moving forward\")\n                vx = 0.5 #velocity of x, how much we move\n                # compute odometry\n                delta_x = vx * cos(self.th)\n                delta_y = vx * sin(self.th)\n                delta_th = 0 #no change in angle\n                #add the changes to the values\n                self.x += delta_x\n                self.y += delta_y\n                self.th += delta_th\n                \n        elif(command == \"FASTO\"):\n            if(value == 600):\n                print(\"robot moving fast\")\n                vx = 1 #velocity of x, how much we move\n                # compute odometry\n                delta_x = vx * cos(self.th)\n                delta_y = vx * sin(self.th)\n                delta_th = 0 #no change in angle\n                #add the changes to the values\n                self.x += delta_x\n                self.y += delta_y\n                self.th += delta_th\n              \n        elif(command == \"FASTT\"):\n            if(value == 600):\n                print(\"robot moving fastest\")\n                vx = 1.5 #velocity of x, how much we move\n                # compute odometry\n                delta_x = vx * cos(self.th)\n                delta_y = vx * sin(self.th)\n                delta_th = 0 #no change in angle\n                #add the changes to the values\n                self.x += delta_x\n                self.y += delta_y\n                self.th += delta_th     \n               \n        elif(command == \"MOVEB\"):\n            if(value == 600):\n                print(\"robot moving backwards\")\n                vx = 1 #velocity of x, how much we move\n                # compute odometry\n                delta_x = vx * cos(self.th)\n                delta_y = vx * sin(self.th)\n                delta_th = 0 #no change in angle\n                #add the changes to the values\n                self.x -= delta_x\n                self.y -= delta_y\n                self.th -= delta_th\n\n        #Obey the right-hand law. Orientation is counter-clockwise.\n        elif(command == \"TURNR\"):\n            print(\"robot turning right\")\n            if(value == 1000):\n                self.angle = (self.angle - 10) #if the result is +ve\n                if (self.angle < 0): self.angle = 360 - abs(self.angle) #if the result is -ve\n                self.th = math.radians(self.angle) #convert to radians\n\n        elif(command == \"TURNL\"):\n            print(\"robot turning left\")\n            if(value == 1000):\n                self.angle = ((10 + self.angle) % 360 ) #add to the angle, keeping mod 360\n                self.th = math.radians(self.angle) #convert to radians\n\n        print(self.x)\n        print(self.y)\n        print(self.z)\n        print(self.angle)\n        print(self.th)\n\n    def __init__(self):\n        super().__init__('robot_digital_twin')\n        self.subscription = self.create_subscription(\n            String,\n            '/robot/control',\n            self.listener_callback,\n            10)\n        self.subscription  # prevent unused variable warning\n        \n        \n        \n        super().__init__('robot_wall')\n        self.subscription_wall = self.create_subscription(\n            String,\n            '/robot/control',\n            self.listener_callback,\n            10)\n        self.subscription  # prevent unused variable warning\n\n        self.publish_thread = threading.Thread(target=self._publish_thread)\n        self.publish_thread.daemon = True\n        self.publish_thread.start()\n        \n    def _publish_thread(self):\n        self.publisher_front = self.create_publisher(String, '/robot/front', 10)\n      \n        ser = serial.Serial(\n            port='/dev/ttyACM0',\\\n            baudrate=9600,\\\n            parity=serial.PARITY_NONE,\\\n            stopbits=serial.STOPBITS_ONE,\\\n            bytesize=serial.EIGHTBITS,\\\n            timeout=0)\n        \n        rl = ReadLine(ser)\n        \n        #publish the pose regularly\n        while(True):\n            \n            #This will publish the robot position and update regularly\n            self.publisher = self.create_publisher(Twist, '/robot/pose', 10)\n            msg = Twist()\n            msg.linear.x = self.x\n            msg.linear.y = self.y\n            msg.linear.z = self.z #for height\n            msg.angular.x = 0.0\n            msg.angular.y = 0.0\n            msg.angular.z = self.th\n            self.publisher.publish(msg)\n            self.get_logger().info(\"Publishing to /robot/pose\")\n            time.sleep(0.1) #2/5 of a second\n            \n            \n            line = rl.readline().decode()\n            clean = line[1:] #removes the [\n            clean2 = clean[:-2] #removes the ]\\n\n            readings = clean2.split(',')\n            readings = readings[0]\n            wall_distance = String()  \n            wall_distance.data = readings.replace('cm','')\n            self.publisher_front.publish(wall_distance)\n            self.get_logger().info('Publishing: \"%s\"' % wall_distance.data)\n            \n            #TODO\n            #PUBLISH THE WALL POSE REGULARLY COMPARED TO ROBOT\n            \n            self.publisher = self.create_publisher(Twist, '/robot/wall', 10)\n            msg2 = Twist()\n            msg2.linear.x = float(wall_distance.data)\n            msg2.linear.y = self.y\n            msg2.linear.z = self.z #for height\n            msg2.angular.x = 0.0\n            msg2.angular.y = 0.0\n            msg2.angular.z = self.th\n            self.publisher.publish(msg2)\n            self.get_logger().info(\"Publishing to /robot/wall\")\n            \n            print(msg2)\n            \n            \n            \ndef main(args=None):\n    rclpy.init(args=args)\n\n    robot = RobotDigitalTwin()\n    rclpy.spin(robot)\n\n    robot.destroy_node()\n    rclpy.shutdown()\n\nif __name__ == '__main__':\n    main()\n","sub_path":"Feature1/digital_twin/digital_twin/robot_digital_twin.py","file_name":"robot_digital_twin.py","file_ext":"py","file_size_in_byte":6992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"596064697","text":"import sys\nimport os\nimport socketio\nimport base64\nimport json\nimport numpy as np\nimport time\nclass MyEncoder(json.JSONEncoder):\n    def default(self, obj):\n        if isinstance(obj, np.ndarray):\n            return obj.tolist()\n        elif isinstance(obj, bytes):\n            return str(obj, encoding='utf-8');\n        return json.JSONEncoder.default(self, obj)\n\nsys.path.append(os.path.abspath(os.path.dirname(__file__) + '/' + '..'))\nsys.path.append(\"..\")\n\nname_space = '/face-recognize'\nsio = socketio.Client()\ndef img_to_base64(img_path):\n    with open(img_path, 'rb')as read:\n        b64 = base64.b64encode(read.read())\n    return b64\n\n@sio.on('connect')\ndef on_connect():\n    \"\"\"创建连接\"\"\"\n    print('I am connected!')\n\n@sio.on('single_recognition_response', namespace=name_space)\ndef on_message(msg):\n    print('I received a single message!')\n    print(json.loads(msg).get('code'))\n    img_b64 = img_to_base64('asserts/3.jpg')\n    sio.emit('multi_recognition', json.dumps({'stream': img_b64}, cls=MyEncoder, indent=4), namespace='/multi-face-recognize')\n\n@sio.on('multi_recognition_response', namespace='/multi-face-recognize')\ndef on_multi(msg):\n    print('I received a multi message!')\n    print(json.loads(msg).get('code'))\n    img_b64 = img_to_base64('asserts/4.jpg')\n    sio.emit('single_recognition', json.dumps({'stream': img_b64}, cls=MyEncoder, indent=4), namespace=name_space)\n\n@sio.on('disconnect')\ndef on_disconnect():\n    \"\"\"关闭连接\"\"\"\n    print('I m disconnected!')\n\n@sio.on('callback message', namespace=name_space)\ndef my_event_handler(data):\n    # 客��端接收服务端的回调消息\n    print(\"my receive new msg:\", data)\n    # 接收完消息关闭连接\n    sio.disconnect()\n    return \"ok!\"\n\n# 建立连接对象\nprint('1')\nsio.connect('http://dev.1msoft.cn:8888')\nprint(2)\nimg_b64 = img_to_base64('asserts/4.jpg')\nprint(3)\nsio.emit('single_recognition', json.dumps({'stream': img_b64},cls=MyEncoder,indent=4), namespace=name_space)\nprint('4')\n\nprint('5')\n#sio.sleep(0)\n","sub_path":"test3.py","file_name":"test3.py","file_ext":"py","file_size_in_byte":2014,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"123466603","text":"import unittest\r\nfrom fs.container import *\r\n\r\nclass ContainerTest(unittest.TestCase):\r\n    \r\n    def test_fqn(self):\r\n        \r\n        root = VirtualDirectory()\r\n        root._name = 'fs'\r\n         \r\n        sub = VirtualDirectory()\r\n        sub._name = 'dir'\r\n        sub._parent = root\r\n         \r\n        file = VirtualFile()\r\n        file._name = 'foo.txt'\r\n        file._parent = sub\r\n         \r\n        self.assertEqual(file.fqn(), 'fs/dir/foo.txt')\r\n        \r\n    def test_get_resource_name(self):\r\n        \r\n        self.assertEqual(Resource.get_resource_name('fs/dir/foo.txt'), 'foo.txt')\r\n\r\n    if __name__ == '__main__':\r\n        unittest.main()\r\n","sub_path":"tests/test_container.py","file_name":"test_container.py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"202569402","text":"import os, time\nfrom selenium import webdriver\ncwd = os.getcwd() + '/'\ndriver = webdriver.Chrome(cwd + 'chromedriver')\ndriver.get('http://www.google.com/xhtml'); \ntime.sleep(5) \nsearch_box = driver.find_element_by_name('q')\nsearch_box.send_keys('ChromeDriver') \nsearch_box.submit() \ntime.sleep(5)\ndriver.quit() \n","sub_path":"drivers/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"426331735","text":"from django.test import TestCase\nfrom django.test import Client\nfrom django.core.files.uploadedfile import SimpleUploadedFile\nfrom django.conf import settings\nfrom books.models import Book, Chapter, Page\n\n\nclass BookViewTests(TestCase):\n    def setUp(self):\n        self.book = Book.objects.create(title=\"Book\", slug=\"book\")\n        self.chapter1 = Chapter.objects.create(\n            book=self.book, volume=1, chapter=1)\n        self.chapter2 = Chapter.objects.create(\n            book=self.book, volume=1, chapter=2)\n        image = SimpleUploadedFile(\n            \"name\",\n            open(settings.MEDIA_ROOT + \"/testing/test.jpg\", \"rb\").read())\n        Page.objects.create(\n            chapter=self.chapter1, page=1, image=image)\n        Page.objects.create(\n            chapter=self.chapter1, page=2, image=image)\n        Page.objects.create(\n            chapter=self.chapter1, page=3, image=image)\n        Page.objects.create(\n            chapter=self.chapter2, page=1, image=image)\n        Page.objects.create(\n            chapter=self.chapter2, page=2, image=image)\n        Page.objects.create(\n            chapter=self.chapter2, page=3, image=image)\n        self.client = Client()\n\n    def test_page_view_ch1_p1(self):\n        response = self.client.get('/book/1/1/1/')\n        self.assertEqual(response.status_code, 200)\n        self.assertContains(response, '/book/1/1/2/')\n        self.assertContains(response, '/book/1/1/1/')\n\n    def test_page_view_ch1_p2(self):\n        response = self.client.get('/book/1/1/2/')\n        self.assertEqual(response.status_code, 200)\n        self.assertContains(response, '/book/1/1/1/')\n        self.assertContains(response, '/book/1/1/3/')\n\n    def test_page_view_ch1_p3(self):\n        response = self.client.get('/book/1/1/3/')\n        self.assertEqual(response.status_code, 200)\n        self.assertContains(response, '/book/1/1/2/')\n        self.assertContains(response, '/book/1/2/1/')\n\n    def test_page_view_ch2_p3(self):\n        response = self.client.get('/book/1/2/3/')\n        self.assertEqual(response.status_code, 200)\n        self.assertContains(response, '/book/1/2/2/')\n        self.assertContains(response, '/book/1/1/1/')\n\n    def test_book_view(self):\n        response = self.client.get('/book/')\n        self.assertRedirects(response, '/book/1/2/3/')\n\n    def test_home_view(self):\n        response = self.client.get('/')\n        self.assertEqual(response.status_code, 200)\n","sub_path":"books/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}
+{"seq_id":"147199521","text":"import gym\nfrom gym import wrappers\nimport matplotlib\nmatplotlib.use('TkAgg')\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport numpy as np\nimport random\nfrom collections import deque\nimport pickle\nimport copy\n\n# Hyper Parameters for DQN\nGAMMA = 0.99  # discount factor for target Q\nINITIAL_EPSILON = 1.0  # starting value of epsilon\nFINAL_EPSILON = 0.01  # final value of epsilon\nEPSILIN_DECAY = 0.999\nSTART_TRAINING = 1000  # experience replay buffer size\nBATCH_SIZE = 64  # size of minibatch\nUPDATE_TARGET_NETwORK = 1000  # update eval_network params every 200 steps\nLEARNING_RATE = 0.001\nDEMO_RATIO = 0.1\nLAMBDA_1 = 1.0\nLAMBDA_2 = 10e-5\nPRETRAIN_STEPS = 1000\nMODEL_PATH = 'C:\\\\Users\\\\go2sea\\\\Desktop\\\\savers\\\\saver_Double_DQN\\\\model.ckpt'\nFRAME_HISTORY = 4\n\n\nclass DQfD_DDQN():\n    # DQN Agent\n    def __init__(self, env, demo_mode):\n        self.sess = tf.InteractiveSession()\n        # init experience replay\n        self.replay_buffer = deque(maxlen=2000)  # store the item generated from select_network\n        self.demo_buffer = deque()  # store the demo data\n        # init some parameters\n        self.time_step = 0\n        self.epsilon = INITIAL_EPSILON\n        # self.state_dim = env.observation_space.shape[0]\n        if len(env.observation_space.shape) == 1:\n            # This means we are running on low-dimensional observations (e.g. RAM)\n            self.state_dim = env.observation_space.shape\n        else:\n            img_h, img_w, img_c = env.observation_space.shape\n            self.state_dim = (img_h, img_w, FRAME_HISTORY * img_c)\n        self.action_dim = env.action_space.n\n        self.demo_mode = demo_mode\n\n        self.create_network()\n        self.create_training_method()\n\n        self.sess.run(tf.global_variables_initializer()) #TODO: check version initializer\n\n    # use the expert-demo-data to pretrain\n    def pre_train(self):\n        print('pre-training ...')\n        for i in range(PRETRAIN_STEPS):\n            if i % 200 == 0:\n                print(i, 'th step of pre-trianing ...')\n            self.train_Q_network(pre_train=True)\n        self.time_step = 0\n        print('pre-train finish ...')\n\n    def create_network(self):\n        def atari_model(img_in, num_actions, scope, reuse=False):\n            # as described in https://storage.googleapis.com/deepmind-data/assets/papers/DeepMindNature14236Paper.pdf\n            with tf.variable_scope(scope, reuse=reuse):\n                out = img_in\n                with tf.variable_scope(\"convnet\"):\n                    # original architecture\n                    out = layers.convolution2d(out, num_outputs=32, kernel_size=8, stride=4, activation_fn=tf.nn.relu)\n                    out = layers.convolution2d(out, num_outputs=64, kernel_size=4, stride=2, activation_fn=tf.nn.relu)\n                    out = layers.convolution2d(out, num_outputs=64, kernel_size=3, stride=1, activation_fn=tf.nn.relu)\n                out = layers.flatten(out)\n                with tf.variable_scope(\"action_value\"):\n                    out = layers.fully_connected(out, num_outputs=512, activation_fn=tf.nn.relu)\n                    out = layers.fully_connected(out, num_outputs=num_actions, activation_fn=None)\n\n                return out\n\n        # --------------------- build select network ----------------------\n\n        self.obs_input = tf.placeholder(tf.uint8, [None] + list(input_shape))# tf.placeholder(\"float\", [None, self.state_dim])\n        self.select_input = tf.cast(obs_input, tf.float32) / 255.0\n        with tf.variable_scope('select_net'):\n            c_names = ['select_net_params', tf.GraphKeys.GLOBAL_VARIABLES, tf.GraphKeys.REGULARIZATION_LOSSES]  # 注意：只有select网络的参数添加到正则化collection中\n            w_initializer = tf.random_uniform_initializer(-0.1, 0.1)\n            b_initializer = tf.constant_initializer(0.1)\n            self.Q_select = build_layers(self.select_input, c_names, 24, 24, w_initializer, b_initializer)\n        # --------------------- build eval network ----------------------\n        self.eval_input = tf.placeholder(\"float\", [None, self.state_dim])\n        with tf.variable_scope('eval_net'):\n            c_names = ['eval_net_params', tf.GraphKeys.GLOBAL_VARIABLES]\n            w_initializer = tf.random_uniform_initializer(-0.1, 0.1)\n            b_initializer = tf.constant_initializer(0.1)\n            self.Q_eval = build_layers(self.eval_input, c_names, 24, 24, w_initializer, b_initializer)\n\n    def save_model(self):\n        saver = tf.train.Saver()\n        save_path = saver.save(self.sess, MODEL_PATH)\n        print(\"Model saved in file: \", MODEL_PATH)\n\n    def restore_model(self):\n        saver = tf.train.Saver()\n        saver.restore(self.sess, MODEL_PATH)\n        print(\"Model restored.\")\n\n    def loss_l(self, state, ae, a):\n        return 0.0 if ae == a else 0.8\n\n    def loss_jeq(self, Q_select):\n        jeq = 0.0\n        for i in range(BATCH_SIZE):\n            ae = self.action_batch[i]\n            max_value = float(\"-inf\")\n            for a in range(self.action_dim):\n                max_value = tf.maximum(Q_select[i][a] + self.loss_l(self.select_input[i], ae, a), max_value)\n            jeq += self.isdemo[i] * (max_value - Q_select[i][ae])\n        return jeq\n\n    def create_training_method(self):\n        self.action_batch = tf.placeholder(\"int32\", [None])\n        self.y_input = tf.placeholder(\"float\", [None, self.action_dim])\n        self.isdemo = tf.placeholder(\"float\", [None])\n        Q_select = self.Q_select\n        loss_dq = tf.reduce_mean(tf.squared_difference(Q_select, self.y_input))\n        loss_jeq = self.loss_jeq(Q_select)\n        loss_l2 = tf.reduce_sum([tf.reduce_mean(reg_loss) for reg_loss in tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)])\n\n        if self.demo_mode == 'get_demo':\n            self.loss = loss_dq + LAMBDA_2 * loss_l2\n        else:\n            self.loss = loss_dq + LAMBDA_1 * loss_jeq + LAMBDA_2 * loss_l2\n\n        self.optimizer = tf.train.AdamOptimizer(LEARNING_RATE).minimize(self.loss)  # optimizer只更新selese_network中的参数\n\n    def perceive(self, state, action, reward, next_state, done, demo):\n        # epsilon是不断变小的，也就是随机性不断变小:开始需要更多的探索，所以动作偏随机，之后需要动作能够有效，因此减少随机。\n        self.epsilon = max(FINAL_EPSILON, self.epsilon * EPSILIN_DECAY)\n        # 经验池添加\n        self.replay_buffer.append((state, action, reward, next_state, done, demo))\n\n    def train_Q_network(self, pre_train=False):\n        if not pre_train and len(self.replay_buffer) < START_TRAINING:\n            return\n        self.time_step += 1\n        # 经验池随机采样minibatch\n        if pre_train:\n            minibatch = random.sample(self.demo_buffer, BATCH_SIZE)\n        elif self.demo_mode == 'get_demo':\n            minibatch = random.sample(self.replay_buffer, BATCH_SIZE)\n        elif self.demo_mode == 'use_demo':\n            demo_num = int(BATCH_SIZE * DEMO_RATIO)\n            minibatch = random.sample(self.replay_buffer, BATCH_SIZE - demo_num)\n            demo_batch = random.sample(self.demo_buffer, demo_num)\n            minibatch.extend(demo_batch)\n        else:\n            assert(False)\n\n        np.random.shuffle(minibatch)\n        state_batch = [data[0] for data in minibatch]\n        action_batch = [data[1] for data in minibatch]\n        reward_batch = [data[2] for data in minibatch]\n        next_state_batch = [data[3] for data in minibatch]\n        done = [data[4] for data in minibatch]\n        isdemo = [data[5] for data in minibatch]\n\n        Q_select = self.Q_select.eval(feed_dict={self.select_input: next_state_batch})\n        Q_eval = self.Q_eval.eval(feed_dict={self.eval_input: next_state_batch})\n\n        # convert true to 1, false to 0\n        done = np.array(done) + 0\n\n        y_batch = np.zeros((BATCH_SIZE, self.action_dim))\n        for i in range(0, BATCH_SIZE):\n            temp = self.Q_select.eval(feed_dict={self.select_input: state_batch[i].reshape((-1, 4))})[0]\n            action = np.argmax(Q_select[i])\n            temp[action_batch[i]] = reward_batch[i] + (1 - done[i]) * GAMMA * Q_eval[i][action]\n            y_batch[i] = temp\n\n        # 新产生的样本输入\n        self.optimizer.run(feed_dict={\n            self.y_input: y_batch,\n            self.select_input: state_batch,\n            self.action_batch: action_batch,\n            self.isdemo: isdemo\n        })\n\n    def egreedy_action(self, state):\n        if random.random() <= self.epsilon:\n            return random.randint(0, self.action_dim - 1)\n        return np.argmax(self.Q_select.eval(feed_dict={self.select_input: [state]})[0])\n\n    def update_target_network(self):\n        select_params = tf.get_collection('select_net_params')\n        eval_params = tf.get_collection('eval_net_params')\n        self.sess.run([tf.assign(e, s) for e, s in zip(eval_params, select_params)])\n\n\n# ---------------------------------------------------------\n# Hyper Parameters\nbenchmark = gym.benchmark_spec('Atari40M')\n# Change the index to select a different game.\nENV_NAME = benchmark.tasks[2]\n#ENV_NAME = \"CartPole-v1\"\nEPISODE = 5000  # Episode limitation\n\n\ndef map(dqfd_scores=None, ddqn_scores=None, xlabel=None, ylabel=None):\n    if dqfd_scores is not None:\n        plt.plot(dqfd_scores, 'r')\n    if ddqn_scores is not None:\n        plt.plot(ddqn_scores, 'b')\n    if xlabel is not None:\n        plt.xlabel(xlabel)\n    if ylabel is not None:\n        plt.ylabel(ylabel)\n    plt.show()\n\n\ndef run_DQfD(index, episode_limit, env):\n    with tf.variable_scope('DQfD_' + str(index)):\n        agent = DQfD_DDQN(env, 'use_demo')\n    with open('/Users/mahailong/DQfD/demo.p', 'rb') as f:\n        agent.demo_buffer = pickle.load(f)\n    # use the demo data to pre-train network\n    agent.pre_train()\n    scores = []\n    for e in range(EPISODE):\n        done = False\n        score = 0  # sum of reward in one episode\n        state = env.reset()\n        while done is False:\n            action = agent.egreedy_action(state)  # e-greedy action for train\n            next_state, reward, done, _ = env.step(action)\n            # Define reward for agent\n            score += reward\n            reward = reward if not done or score == 499 else -100\n            agent.perceive(state, action, reward, next_state, done, 0.0)\n            agent.train_Q_network(pre_train=False)\n            state = next_state\n\n        if done:\n            scores.append(score)\n            agent.update_target_network()\n            print(\"episode:\", e, \"  score:\", score, \"  memory length:\", len(agent.replay_buffer), \"  epsilon:\",\n                  agent.epsilon)\n            # stop traning when the mean of scores of the last 10 episode is bigger than 495\n            # if np.mean(scores[-min(10, len(scores)):]) > 495:\n            #     break\n\n            if e == episode_limit:\n                break\n    return scores\n\n\ndef run_DDQN(index, episode_limit, env):\n    with tf.variable_scope('DDQN_' + str(index)):\n        agent = DQfD_DDQN(env, 'get_demo')\n    scores = []\n    for e in range(EPISODE):\n        done = False\n        score = 0  # sum of reward in one episode\n        state = env.reset()\n        demo = []\n        while done is False:\n            action = agent.egreedy_action(state)  # e-greedy action for train\n            next_state, reward, done, _ = env.step(action)\n            # Define reward for agent\n            score += reward\n            reward = reward if not done or score == 499 else -100\n            agent.perceive(state, action, reward, next_state, done, 0.0)  # 0. means it is not a demo data\n            demo.append((state, action, reward, next_state, done, 1.0))  # record the data that could be expert-data\n            agent.train_Q_network(pre_train=False)\n            state = next_state\n\n        if done:\n            scores.append(score)\n            agent.update_target_network()\n            print(\"episode:\", e, \"  score:\", score, \"  demo_buffer:\", len(agent.demo_buffer),\n                  \"  memory length:\", len(agent.replay_buffer), \"  epsilon:\", agent.epsilon)\n            # stop traning when the mean of scores of the last 10 episode is bigger than 490\n            # if np.mean(scores[-min(10, len(scores)):]) > 490:\n            #     break\n            if e == episode_limit:\n                break\n    return scores\n\n\n# get expert demo data\ndef get_demo_data(env):\n    # env = wrappers.Monitor(env, '/tmp/CartPole-v0', force=True)\n    # agent.restore_model()\n    with tf.variable_scope('get_demo_data'):\n        agent = DQfD_DDQN(env, 'get_demo')\n    scores = []\n    for e in range(EPISODE):\n        done = False\n        score = 0  # sum of reward in one episode\n        state = env.reset()\n        demo = []\n        while done is False:\n            action = agent.egreedy_action(state)  # e-greedy action for train\n            next_state, reward, done, _ = env.step(action)\n            # Define reward for agent\n            score += reward\n            reward = reward if not done or score == 499 else -100\n            agent.perceive(state, action, reward, next_state, done, 0.0)  # 0. means it is not a demo data\n            demo.append((state, action, reward, next_state, done, 1.0))  # record the data that could be expert-data\n            # print(\"len(demo):\", len(demo))\n            agent.train_Q_network(pre_train=False)\n            state = next_state\n\n        if done:\n            scores.append(score)\n            if score == 500:  # expert demo data\n                agent.demo_buffer.extend(demo)\n            agent.update_target_network()\n            print(\"episode:\", e, \"  score:\", score, \"  demo_buffer:\", len(agent.demo_buffer),\n                  \"  memory length:\", len(agent.replay_buffer), \"  epsilon:\", agent.epsilon)\n            # stop traning when the mean of scores of the last 10 episode is bigger than 490\n            # if np.mean(scores[-min(10, len(scores)):]) > 490:\n            #     break\n            if len(agent.demo_buffer) >= 500 * 50:\n                break\n\n    # write the demo data to a file\n    with open('/Users/mahailong/DQfD/test_demo.p', 'wb') as f:\n        pickle.dump(agent.demo_buffer, f, protocol=2)\n\n\ndef main():\n    env = gym.make(ENV_NAME)\n    # env = wrappers.Monitor(env, '/tmp/CartPole-v0', force=True)\n\n    iteration = 5\n    episode_limit = 300\n\n    # ------------------------ get demo scores by DDQN -----------------------------\n    # get_demo_data(env)\n    # --------------------------  get DDQN scores ----------------------------------\n    ddqn_sum_scores = np.zeros(episode_limit)\n    for i in range(iteration):\n        scores = run_DDQN(i, episode_limit, env)\n        for e in range(episode_limit):\n            ddqn_sum_scores[e] += scores[e]\n    ddqn_mean_scores = ddqn_sum_scores / iteration\n    # write the scores to a file\n    with open('/Users/mahailong/DQfD/ddqn_mean_scores.p', 'wb') as f:\n        pickle.dump(ddqn_mean_scores, f, protocol=2)\n    # ----------------------------- get DQfD scores --------------------------------\n    dqfd_sum_scores = np.zeros(episode_limit)\n    for i in range(iteration):\n        scores = run_DQfD(i, episode_limit, env)\n        for e in range(episode_limit):\n            dqfd_sum_scores[e] += scores[e]\n    dqfd_mean_scores = dqfd_sum_scores / iteration\n    # write the scores to a file\n    with open('/Users/mahailong/DQfD/dqfd_mean_scores.p', 'wb') as f:\n        pickle.dump(dqfd_mean_scores, f, protocol=2)\n\n    map(dqfd_scores=dqfd_mean_scores, ddqn_scores=ddqn_mean_scores, xlabel='Red: dqfd         Blue: ddqn', ylabel='Scores')\n\n    # env.close()\n    # gym.upload('/tmp/carpole_DDQN-1', api_key='sk_VcAt0Hh4RBiG2yRePmeaLA')\n\n\nif __name__ == '__main__':\n    main()","sub_path":"hw3/DQfD/DQfD_CarPole.py","file_name":"DQfD_CarPole.py","file_ext":"py","file_size_in_byte":15700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}