diff --git "a/992.jsonl" "b/992.jsonl"
new file mode 100644--- /dev/null
+++ "b/992.jsonl"
@@ -0,0 +1,450 @@
+{"seq_id":"74508909046","text":"import os\nimport pickle\nfrom collections import defaultdict, Counter\nimport numpy as np\nimport argparse\nimport matplotlib.pyplot as plt\nfrom hashlib import sha256\nimport networkx as nx\nfrom nas_lib.visualize.visualize_close_domain_reverse import draw_plot_nasbench_nlp, draw_plot_nasbench_asr, \\\n    draw_plot_nasbench_201, draw_plot_nasbench_101\nfrom nas_lib.data.data_nasbench_nlp import adj_distance as adj_dist_nlp\n\n\n# model_lists_nasbench = ['Random', 'EA', 'BANANAS', 'BANANAS_F', 'NPENAS-BO', 'NPENAS-NP', 'NPENAS-NP-NEW', 'ORACLE']\n# model_masks_nasbench = [True, True, True, True, True, True, True, True]\n\n# model_lists_nasbench = ['Random', 'REA', 'BANANAS', 'NPENAS-NP', 'ORACLE']\n# model_masks_nasbench = [True, True, True, True, True]\n\n# model_lists_nasbench = ['NPENAS-NP-10', 'NPENAS-NP-100', 'ORACLE']\n# model_masks_nasbench = [True, True, False]\n\nmodel_lists_nasbench = ['RELU', 'CELU', 'NPENAS-GT']\nmodel_masks_nasbench = [True, True, False]\n\n\n# model_lists_nasbench = ['NPENAS-NP-10']\n# model_masks_nasbench = [True]\n\noptions = {\"node_size\": 25, \"alpha\": 0.8}\n\npredictor_values = {0: 'RELU', 1: 'CELU'}\n# predictor_values = {0: 'SCALING FACTOR=10', 1: 'SCALING FACTOR=100'}\n\n\ndef get_kt_list(all_files):\n    total_results = defaultdict(list)\n    total_top_results = defaultdict(list)\n    for f in all_files:\n        with open(f, 'rb') as fb:\n            full_data = pickle.load(fb)[2]\n            for idx, data in enumerate(full_data):\n                type = data['type']\n                if type == 'rea' or type == 'oracle':\n                    pass\n                else:\n                    type, final_data, kt_list, kt_top_list, mutate_list = data['type'], data['final_data'], \\\n                                                             data['kt_list'], data['kt_top_list'], data['mutate_list']\n                    # type_new = type.replace('_', '-').upper()\n                    type_new = ''\n                    # total_results[f'{type_new}{predictor_values[idx]}'].append(kt_list)\n                    total_results[f'{type_new}{idx}'].append(kt_list)\n                    total_top_results[f'{type_new}{idx}'].append(kt_top_list)\n                    # total_results[type].append(kt_list)\n    return total_results, total_top_results\n\n\ndef mutate_rea_information(all_files):\n    total_results = defaultdict(list)\n    for f in all_files:\n        G = nx.Graph()\n        cmap = plt.cm.YlGn\n        cmap2 = plt.cm.autumn\n        with open(f, 'rb') as fb:\n            full_data = pickle.load(fb)[2]\n        p_dict = {}\n        c_dict = defaultdict(list)\n        for data in full_data:\n            type = data['type']\n            if type == 'rea':\n                p_list, c_list = data['p_list'], data['c_list']\n                for idx, p in enumerate(p_list):\n                    p_info = sha256(str(p[6]).encode('utf-8')).hexdigest()\n                    p_dict[p_info] = p\n                    c_dict[p_info].append(c_list[idx])\n            key_list = list(c_dict.keys())\n            p_key_list = []\n            c_key_list = []\n            p_performance_list = []\n            c_performance_list = []\n            edge_list = []\n            edge_color_list = []\n            dist_list = []\n            p_key_dict = {}\n            c_key_dict = {}\n            counter = len(key_list)\n            for idx, k in enumerate(key_list):\n                p_key_dict[k] = idx\n                for idx, c in enumerate(c_dict[k]):\n                    c_k = sha256(str(c[6]).encode('utf-8')).hexdigest()\n                    c_key_dict[c_k] = counter + idx\n                    dist = adj_dist_nlp(([], p_dict[k][1], p_dict[k][2]), ([], c[1], c[2]))\n                    dist_list.append(dist)\n                counter = counter + len(c_dict[k])\n            for idx, k in enumerate(key_list):\n                parent = p_dict[k]\n                children_list = c_dict[k]\n                if len(children_list) >= 1:\n                    draw_graph(parent, children_list, p_key_list, c_key_list,\n                               p_key_dict, c_key_dict, edge_list, edge_color_list, p_performance_list,\n                               c_performance_list, G)\n        parent_node_list = []\n        parent_node_color = []\n        for k1 in key_list:\n            for k2 in key_list:\n                if k1 != k2:\n                    pair = (p_key_dict[k1], p_key_dict[k2])\n                    if pair not in parent_node_list:\n                        parent_node_list.append(pair)\n                        dist = adj_dist_nlp(([], p_dict[k1][1], p_dict[k1][2]), ([], p_dict[k2][1], p_dict[k2][2]))\n                        parent_node_color.append(dist)\n                        dist_list.append(dist)\n        min_dist, max_dist = min(dist_list), max(dist_list)\n        p_min, p_max = min(p_performance_list), max(p_performance_list)\n        c_min, c_max = min(c_performance_list), max(c_performance_list)\n        node_min, node_max = min(p_min, c_min), max(p_max, c_max)\n        pos = nx.spring_layout(G)  # positions for all nodes\n        nx.draw_networkx_nodes(G, pos, nodelist=p_key_list, node_color=p_performance_list, cmap=cmap2,\n                               vmin=node_min, vmax=node_max, alpha=0.8, node_size=50)\n        nx.draw_networkx_nodes(G, pos, nodelist=c_key_list, node_color=c_performance_list, cmap=cmap2, alpha=0.8,\n                               vmin=node_min, vmax=node_max, node_size=10)\n        nx.draw_networkx_edges(G, pos, edgelist=edge_list, edge_color=edge_color_list, edge_vmin=min_dist,\n                               edge_vmax=max_dist, edge_cmap=cmap)\n        # nx.draw_networkx_edges(G, pos, edgelist=parent_node_list, edge_color=parent_node_color, edge_vmin=min_dist,\n        #                        edge_vmax=max_dist, edge_cmap=cmap)\n        # nx.draw(G, with_labels=False, font_weight='bold', node_size=3)\n        sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=min_dist, vmax=max_dist))\n        sm2 = plt.cm.ScalarMappable(cmap=cmap2, norm=plt.Normalize(vmin=node_min, vmax=node_max))\n        sm._A = []\n        sm2._A = []\n        plt.colorbar(sm)\n        plt.colorbar(sm2)\n        plt.show()\n    return total_results\n\n\ndef mutate_information(all_files):\n    total_results = defaultdict(list)\n    for f in all_files:\n        G = nx.Graph()\n        cmap = plt.cm.YlGn\n        with open(f, 'rb') as fb:\n            full_data = pickle.load(fb)[2]\n        p_dict = {}\n        c_dict = defaultdict(list)\n        for data in full_data:\n            type = data['type']\n            if type == 'rea' or type == 'oracle':\n                continue\n            else:\n                type, final_data, kt_list, mutate_list = data['type'], data['final_data'], \\\n                                                         data['kt_list'], data['mutate_list']\n                print(mutate_list)\n    return total_results\n\n\ndef draw_graph(parent, child_list, p_key_list, c_key_list, p_key_dict, c_key_dict,\n               edge_list, edge_color_list, p_performance_list,\n               c_performance_list, graph):\n    # add parent node\n    p_key = sha256(str(parent[6]).encode('utf-8')).hexdigest()\n    graph.add_node(p_key_dict[p_key])\n    p_key_list.append(p_key_dict[p_key])\n    p_performance_list.append(parent[4])\n    for c in child_list:\n        c_k = sha256(str(c[6]).encode('utf-8')).hexdigest()\n        c_key_list.append(c_key_dict[c_k])\n        graph.add_node(c_key_dict[c_k])\n        graph.add_edge(p_key_dict[p_key], c_key_dict[c_k])\n        edge_list.append((p_key_dict[p_key], c_key_dict[c_k]))\n        dist = adj_dist_nlp(([], parent[1], parent[2]), ([], c[1], c[2]))\n        edge_color_list.append(dist)\n        c_performance_list.append(c[4])\n\n\ndef visual_kt_list(kt_results, results_folder, search_space, comparison_type=\"relu_celu\", train_dataset=\"cifar100\"):\n    if comparison_type == \"relu_celu\":\n        model_lists_nasbench = ['RELU', 'CELU', 'NPENAS-GT']\n        model_masks_nasbench = [True, True, False]\n    elif comparison_type == \"scale_factor\":\n        model_lists_nasbench = ['SCALING FACTOR=#', 'SCALING FACTOR=*', 'NPENAS-GT']\n        model_masks_nasbench = [True, True, False]\n    else:\n        raise ValueError(f\"comparison type {comparison_type} does not support at present!\")\n    if comparison_type == \"scale_factor\":\n        rate = get_rate(results_folder)\n        key1 = \"SCALING FACTOR=\" + str(int(rate[0]))\n        key2 = \"SCALING FACTOR=\" + str(int(rate[1]))\n    kt_results_dict = {}\n    kt_results_std_dict = {}\n\n    for k, v in kt_results.items():\n        np_v = np.nan_to_num(np.array(v))\n        kt_results_dict[k] = np.mean(np_v, axis=0)\n        kt_results_std_dict[k] = np.std(np_v, axis=0)\n\n    if comparison_type == \"scale_factor\":\n        kt_results_dict[key1] = kt_results_dict[\"0\"]\n        kt_results_dict[key2] = kt_results_dict[\"1\"]\n        kt_results_dict.pop(\"0\")\n        kt_results_dict.pop(\"1\")\n\n    if comparison_type == \"relu_celu\":\n        kt_results_dict[\"RELU\"] = kt_results_dict[\"0\"]\n        kt_results_dict[\"CELU\"] = kt_results_dict[\"1\"]\n        kt_results_dict.pop(\"0\")\n        kt_results_dict.pop(\"1\")\n\n    if args.search_space == \"nasbench_101\":\n        idx = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140])\n    else:\n        idx = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90])\n    fig, ax = plt.subplots(1)\n    for k, v in kt_results_dict.items():\n        plt.plot(idx, v, label=k, marker='s', linewidth=1, ms=3)\n    ax.grid(False)\n    fig.set_dpi(600.0)\n    ax.set_xlabel('Number of Samples')\n    ax.set_ylabel('Kendall Tau Correlation')\n    plt.legend(loc='upper left')\n    # plt.legend(loc='upper right')\n    plt.show()\n    if search_space == 'nasbench_nlp':\n        draw_plot_nasbench_nlp(results_folder, draw_type='ERRORBAR', model_lists=model_lists_nasbench,\n                               model_masks=model_masks_nasbench, order=False, comparison_type=comparison_type)\n    elif search_space == 'nasbench_asr':\n        draw_plot_nasbench_asr(results_folder, draw_type='ERRORBAR', model_lists=model_lists_nasbench,\n                               model_masks=model_masks_nasbench, order=False, comparison_type=comparison_type)\n    elif search_space == 'nasbench_201':\n        # cifar10-valid, cifar100, ImageNet16-120\n        draw_plot_nasbench_201(results_folder, draw_type='ERRORBAR', model_lists=model_lists_nasbench,\n                               model_masks=model_masks_nasbench, train_data=train_dataset, order=False,\n                               comparison_type=comparison_type)\n    elif search_space == 'nasbench_101':\n        pass\n\n\ndef get_rate(folder):\n    files = os.listdir(folder)\n    file_name = \"\"\n    for f in files:\n        if \"full\" not in f and \"log\" not in f:\n            file_name = f\n            break\n    file_path = os.path.join(folder, file_name)\n    with open(file_path, \"rb\") as fs:\n        algorithm_params, metann_params, results, walltimes = pickle.load(fs)\n    rate = [algorithm_params[0][\"rate\"], algorithm_params[1][\"rate\"]]\n    return rate\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='Args for visualize darts architecture')\n    parser.add_argument('--search_space', type=str, default='nasbench_201',\n                        choices=['nasbench_101', 'nasbench_201', 'nasbench_nlp', 'nasbench_asr'],\n                        help='The algorithm output folder')\n    parser.add_argument('--comparison_type', type=str, default='relu_celu',\n                        choices=['scale_factor', 'relu_celu'],\n                        help='The algorithm output folder')\n    parser.add_argument('--save_dir', type=str,\n                        default='/home/albert_wei/Desktop/results/relu_celu/npenas_nasbench_201_cifar10_relu_celu/',\n                        help='The algorithm output folder')\n    parser.add_argument('--train_data', type=str, default='cifar10-valid',\n                        choices=['cifar10-valid', 'cifar100', 'ImageNet16-120'],\n                        help='The evaluation of dataset of NASBench-201.')\n    args = parser.parse_args()\n\n    all_files = [os.path.join(args.save_dir, f) for f in os.listdir(args.save_dir) if 'full' in f]\n    kt_total_results, kt_total_top_results = get_kt_list(all_files)\n    visual_kt_list(kt_total_results, args.save_dir, args.search_space, comparison_type=args.comparison_type,\n                   train_dataset=args.train_data)\n    # visual_kt_list(kt_total_top_results, args.save_dir, args.search_space)\n\n    # mutate_rea_information(all_files)\n    # mutate_information(all_files)","repo_name":"auroua/NPENASv1","sub_path":"tools_ss_analysis/predictor_analysis.py","file_name":"predictor_analysis.py","file_ext":"py","file_size_in_byte":12509,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"76"}
+{"seq_id":"28250255460","text":"# Advent of Code, 2015\n# Day 24: It Hangs in the Balance\n# https://adventofcode.com/2015/day/24\n# https://github.com/v1neethnc/advent-of-code-solutions\n\n\nfrom itertools import combinations\nfrom functools import reduce\nfrom operator import mul\ndef entanglement_checker(data, groups):\n\tsize = sum(data) // groups\n\tfor i in range(len(data)):\n\t\tvals = [reduce(mul, comb) for comb in combinations(data, i) if sum(comb) == size]\n\t\tif len(vals) > 0:\n\t\t\treturn min(vals)\n\n\nwith open(\"2015_24.txt\") as file_data:\n\tdata = [int(i.strip()) for i in file_data.readlines()]\n\n\tprint(f\"Part A: {entanglement_checker(data, 3)}\")\n\tprint(f\"Part B: {entanglement_checker(data, 4)}\")","repo_name":"v1neethnc/advent-of-code-solutions","sub_path":"2015/2015_24.py","file_name":"2015_24.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"42668235818","text":"from calendar import month\r\nfrom random import randint, random, sample\r\nimport mysql.connector\r\n\r\nfrom faker import Faker\r\n\r\nfaker = Faker()\r\n\r\ncerts = [\r\n\t\"Certified Business Analysis Professional (CBAP)\",\r\n\t\"APICS Certified Supply Chain Professional (CSCP)\",\r\n\t\"Certified Information Security Manager (CISM)\",\r\n\t\"Certified Information Systems Security Professional (CISSP)\",\r\n\t\"Certified Patient Care Technician (CPCT)\",\r\n\t\"Certified Clinical Medical Assistant (CCMA)\",\r\n\t\"Certified Commercial Investment Member (CCIM)\",\r\n\t\"Certified Legal Manager (CLM)\",\r\n\t\"Certified Business Economist (CBE)\",\r\n\t\"Certified Economic Developer (CED)\",\r\n\t\"First Aid Certification\",\r\n\t\"Notary Public Certification\",\r\n\t\"Certification in Full Stack Web Development\",\r\n\t\"Certification in Algorithms & Data Structures\",\r\n\t\"Coding Boot Camp\",\r\n\t\"Certified Pediatric Nurse (CPN)\",\r\n\t\"Certified Public Accountant (CPA)\",\r\n\t\"Certified ScrumMaster (CSM)\",\r\n\t\"Professional Educator License (PEL)\",\r\n]\r\n\r\n\r\ndef pop_certifications(connection):\r\n\t\r\n\tmycursor = connection.cursor()\r\n\t\r\n\tmycursor.execute(\"SELECT uemail FROM Student\")\r\n\t\r\n\tmyresult = mycursor.fetchall()\r\n\tcount = 0\r\n\t\r\n\tfor x in myresult:\r\n\t\tnum = randint(1,4)\r\n\t\temail = x[0]\r\n\t\tcrts = sample(certs, k=randint(0,4))\r\n\t\t\r\n\t\tfor certificate in crts:\r\n\t\t\t\r\n\t\t\tdate = str(faker.date_between(start_date=\"-4y\", end_date=\"today\"))\r\n\t\t\turl = certificate.replace(\" \", \"-\") + \".net/certificate/crt=\" + str(randint(10000, 20000))\r\n\t\t\tmySql_insert_query = \"INSERT INTO Certifications VALUES ('\"+date + \"','\" + certificate + \"','\" + url + \"','\" + email + \"')\"\r\n\t\t\tcursor = connection.cursor()\r\n\t\t\tcursor.execute(mySql_insert_query)\r\n\t\t\tconnection.commit()\r\n\t\t\tcursor.close()\r\n\t\t\tcount +=1\r\n\t\t\t\r\n\tprint(count, \"Record inserted successfully into Certifications table\")\r\n\r\n","repo_name":"Saiid2001/cmps277-db-project","sub_path":"database/pop_certs.py","file_name":"pop_certs.py","file_ext":"py","file_size_in_byte":1795,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"17298821682","text":"#!/usr/bin/env python\n\nimport rospy\nimport smach\nimport smach_ros\nimport time\nfrom pyrobot import Robot\n\nbase_config_dict={'base_controller': 'ilqr'}\nbot = Robot('locobot', base_config=base_config_dict)\n\n# define state GoHome\nclass GoHome(smach.State):\n    def __init__(self):\n        smach.State.__init__(self, outcomes=['outcome1','outcome2'])\n        self.counter = 0\n\n    def execute(self, userdata):\n        target_position = [0,0,0]\n        rospy.loginfo('Executing state GoHome')\n        #bot.arm.go_home()\n        bot.base.go_to_absolute(target_position)\n        if self.counter < 3:\n            self.counter += 1\n            return 'outcome1'\n        else:\n            return 'outcome2'\n\n# define state Move\nclass Move_straight(smach.State):\n\n    def __init__(self):\n        smach.State.__init__(self, outcomes=['outcome2'])\n\n    def execute(self, userdata):\n        target_position = [0.3,0,0]\n        target_joints = [[0.408, 0.721, -0.471, -1.4, 0.920], [-0.675, 0, 0.23, 1, -0.70]]\n        rospy.loginfo('Moving')\n        for joint in target_joints:\n                #bot.arm.set_joint_positions(joint, plan=False)\n                bot.base.go_to_absolute(target_position)\n                time.sleep(1)\n        return 'outcome2'\n\n# define state Move\nclass Move_right(smach.State):\n\n    def __init__(self):\n        smach.State.__init__(self, outcomes=['outcome5'])\n\n    def execute(self, userdata):\n        target_position = [0.3,-0.3,-1.57]\n        target_joints = [[0.408, 0.721, -0.471, -1.4, 0.920], [-0.675, 0, 0.23, 1, -0.70]]\n        rospy.loginfo('Moving')\n        for joint in target_joints:\n                #bot.arm.set_joint_positions(joint, plan=False)\n                bot.base.go_to_absolute(target_position)\n                time.sleep(1)\n        return 'outcome5'\n\nclass Move_left(smach.State):\n\n    def __init__(self):\n        smach.State.__init__(self, outcomes=['outcome6'])\n\n    def execute(self, userdata):\n        target_position = [0.3,0,0]\n        target_joints = [[0.408, 0.721, -0.471, -1.4, 0.920], [-0.675, 0, 0.23, 1, -0.70]]\n        rospy.loginfo('Moving')\n        for joint in target_joints:\n                #bot.arm.set_joint_positions(joint, plan=False)\n                bot.base.go_to_absolute(target_position)\n                time.sleep(1)\n        return 'outcome6'\n\n# main\ndef main():\n    #rospy.init_node('smach_example_state_machine')\n\n    # Create a SMACH state machine\n    sm = smach.StateMachine(outcomes=['outcome3', 'outcome4'])\n\n    # Open the container\n    with sm:\n        # Add states to the container\n        smach.StateMachine.add('GoHome', GoHome(), transitions={'outcome1':'Move_straight', 'outcome2':'outcome3'})\n        smach.StateMachine.add('Move_straight', Move_straight(), transitions={'outcome2':'Move_right'})\n        smach.StateMachine.add('Move_right', Move_right(), transitions={'outcome5':'Move_left'})\n        smach.StateMachine.add('Move_left', Move_left(), transitions={'outcome6':'GoHome'})\n\n    # Create and start the introspection server\n    sis = smach_ros.IntrospectionServer('my_smach_introspection_server', sm, '/SM_ROOT')\n    sis.start()\n\n    # Execute SMACH plan\n    outcome = sm.execute()\n\n    # Wait for ctrl-c to stop the application\n    rospy.spin()\n    sis.stop()\n\nif __name__ == '__main__':\n    main()\n","repo_name":"jinglinjackychen/smach","sub_path":"examples/smach_pyrobot.py","file_name":"smach_pyrobot.py","file_ext":"py","file_size_in_byte":3290,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"9071566899","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Jun 14 13:28:08 2017\r\n\r\n@author: mechd\r\n\"\"\"\r\n\r\ndef flow(vf1, vf2, rho1, rho2, T1, T2, delta, theta, d_theta, n, tol):\r\n#    flow(521, 428, 0.161443, 0.942478, 2.51327, 25, 0.01)\r\n    import numpy as np\r\n    import math\r\n    import matplotlib.pyplot as plt\r\n    from mpl_toolkits.mplot3d import Axes3D\r\n    import random\r\n#    OSWPI(1.5, 1.273, 300, 1.4, 286.9, 0.161443, 1000000, 1e-3)\r\n#    grid(0.942478, 2.51327, 25, 0.01)\r\n    V1 = np.zeros((n*n*n, 5))\r\n    V2 = np.zeros((n*n*n, 5))\r\n    V2t = np.zeros(n*n*n)\r\n    V = np.zeros((2*n*n*n, 5))\r\n    kc = 0\r\n    kd = 0\r\n    kt1 = 0\r\n    kt2 = 0\r\n    for k in range(n):\r\n        for j in range(n):\r\n            for i in range(n):\r\n                V1[kc, 0] = rho1\r\n                V1[kc, 1] = vf1\r\n                V1[kc, 2] = 0\r\n                V1[kc, 3] = 0\r\n                V1[kc, 4] = T1\r\n                kc = kc+1\r\n    for k in range(n):\r\n        for j in range(n):\r\n            m = 0\r\n            for i in range(n):\r\n                m = m + tol\r\n                V2[kd, 0] = rho2\r\n                V2[kd, 1] = vf2*np.cos(delta)\r\n                V2[kd, 2] = vf2*np.sin(delta)\r\n#                V2[kd, 1] = vf2*(np.cos(delta) - np.sin(delta)/np.tan(theta+m*d_theta))\r\n#                V2[kd, 2] = vf2*np.sin(delta)/np.sin(theta+m*d_theta)\r\n                V2[kd, 3] = 0\r\n                V2[kd, 4] = T2\r\n                V2t[kd] = np.sqrt(V2[kd, 0]**2 + V2[kd, 1]**2 + 2*V2[kd, 0]*V2[kd, 1]*np.cos(theta+m*d_theta))\r\n                kd = kd+1\r\n    for k in range(n):\r\n        for j in range(n):\r\n            for i in range(n):\r\n                V[kt1, 0] = V1[kt2, 0]\r\n                V[kt1, 1] = V1[kt2, 1]\r\n                V[kt1, 2] = V1[kt2, 2]\r\n                V[kt1, 3] = V1[kt2, 3]\r\n                V[kt1, 4] = V1[kt2, 4]\r\n                V[kt1+n, 0] = V2[kt2, 0]\r\n                V[kt1+n, 1] = V2[kt2, 1]\r\n                V[kt1+n, 2] = V2[kt2, 2]\r\n                V[kt1+n, 3] = V2[kt2, 3]\r\n                V[kt1+n, 4] = V2[kt2, 4]\r\n                kt2 = kt2+1\r\n                kt1 = kt1+1\r\n            kt1 = kt1+n\r\n#    print(V[2*15612:2*15624])\r\n    np.savetxt('flowdata.o', V)\r\n    return","repo_name":"kalagotla/python-scripts","sub_path":"Flowdata.py","file_name":"Flowdata.py","file_ext":"py","file_size_in_byte":2191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"42797710975","text":"from rest_framework import serializers\nfrom .models import ConfirmCode, User\nimport random\nimport string\nletters_and_digits = string.ascii_letters + string.digits\n\nclass LoginSerializer(serializers.Serializer):\n    \"\"\"Сериалайзер для обработки авторизации и регистрации юзеров\"\"\"\n    phone = serializers.CharField(max_length=255)\n    referal = serializers.CharField(required=False, max_length=255)\n    token = serializers.CharField(max_length=255, read_only=True)\n\n    def validate(self, data):\n        phone = data.get('phone', None)\n        referal = data.get('referal', None)\n        code = data.get('code', None)\n        if phone is None:\n            raise serializers.ValidationError(\n                'An phone number is required to log in.'\n            )\n        if not ConfirmCode.objects.filter(phone=phone).exists():\n            raise serializers.ValidationError(\n                'Invalid code'\n            )\n        else:\n            code = ConfirmCode.objects.get(phone=phone)\n            code.delete()\n        try:\n            user = User.objects.get(phone=phone)\n        except:\n            link = ''.join(random.sample(letters_and_digits, 6))\n            user = User(phone=phone, referal_link=link)\n            if referal is not None:\n                if not User.objects.filter(referal_link=referal).exists():\n                    raise serializers.ValidationError(\n                        \"nonexistent referal_link\"\n                    )\n                user.referal = referal\n            user.save()\n\n        if not user.is_active:\n            raise serializers.ValidationError(\n                'This user has been deactivated.'\n            )\n\n        token = user.token\n        return {\n             'phone': user.phone,\n            'token': token,\n            'referal': user.referal_link\n        }\n\n\nclass UserSerializer(serializers.Serializer):\n    \"\"\"Сериалайзер для получения инфромации о профиле юзера\"\"\"\n    phone = serializers.CharField(max_length=200)\n    user_referals = serializers.DictField(allow_empty=True)\n    referal = serializers.CharField(max_length=6)\n\nclass UserUpdateSerializer(serializers.Serializer):\n    \"\"\"Сериалайзер для изменения юзером его информации\"\"\"\n    phone = serializers.CharField(max_length=200)\n    referal = serializers.CharField(max_length=6)\n    def update(self, instance, validated_data):\n        \"\"\" Выполняет обновление User. \"\"\"\n        for key, value in validated_data.items():\n            setattr(instance, key, value)\n        instance.save()\n        return instance\n\n","repo_name":"ITerekhov/referal_api","sub_path":"referal_api/authentication/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":2689,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"3082585872","text":"from datetime import date\nfrom dateutil.relativedelta import relativedelta\nfrom collections import namedtuple\nfrom collections import namedtuple\nfrom io import BytesIO\nfrom flask import Blueprint, render_template, redirect, url_for, flash, make_response, abort\nfrom flaskr.forms.worklogs import WorkLogForm, WorkLogFormStaff\nfrom flaskr.services.worklogs import WorkLogService\nfrom flaskr.reports.worklogs import WorkLogReport\nfrom flaskr import app, db\nfrom flaskr.models import Person\nfrom flaskr.utils.datetime import date_x\nfrom flaskr.workers.worklogs import update_worklogs_value\nfrom flaskr.workers.performlogs import update_performlogs_enabled\nfrom flaskr.utils.roles import login_required_staff, login_required_person\n\nbp = Blueprint('worklogs', __name__, url_prefix=\"/worklogs\")\n\n@bp.route('/<id>/<yymm>')\n@login_required_person\ndef index(id, yymm):\n    person = Person.get_or_404(id)\n    today = date_x.yymm_dd(yymm, 1)\n    first = today\n    last = first + relativedelta(months=1)\n    prev = first - relativedelta(months=1)\n    this = date.today()\n    items = []\n    while first.date < last.date:\n        ym = first.date.strftime('%Y%m')\n        d = first.date.day\n        worklog = WorkLogService.get_or_new(id, ym, d)\n        items.append(worklog)\n        first += relativedelta(days=1)\n    Foot = namedtuple('Foor', ('presented', 'value', 'break_t', 'over_t', 'absence', 'late', 'leave'))\n    foot = Foot(\n        len([i for i in items if i.presented]),\n        sum([i.value for i in items if i.value is not None]),\n        sum([i.break_t for i in items if i.break_t is not None]),\n        sum([i.over_t for i in items if i.over_t is not None]),\n        len([i for i in items if i.absence]),\n        len([i for i in items if i.late]),\n        len([i for i in items if i.leave]),\n    )\n    kw = dict(\n        id = id,\n        yymm = yymm,\n        staff = person.staff,\n        name = person.name,\n        today = today.date,\n        this = this.strftime('%Y%m'),\n        prev = prev.date.strftime('%Y%m'),\n        next = last.date.strftime('%Y%m'),\n        items = items,\n        foot = foot\n    )\n    return render_template('worklogs/index.pug', **kw)\n\n@bp.route('/<id>/<yymm>/<dd>/edit', methods=['GET', 'POST'])\n@login_required_staff\ndef edit(id, yymm, dd):\n    try:\n        date_x.yymm_dd(yymm, dd)\n    except ValueError:\n        abort(400)\n    person = Person.get_or_404(id)\n    worklog = WorkLogService.get_or_new(id, yymm, dd)\n    if person.staff:\n        form = WorkLogFormStaff(obj=worklog)\n    else:\n        form = WorkLogForm(obj=worklog)\n    if form.validate_on_submit():\n        try:\n            if person.staff:\n                worklog.update_staff(form)\n            else:\n                worklog.update_no_staff(form)\n            update_worklogs_value.delay(id, yymm, dd)\n            if not person.staff:\n                update_performlogs_enabled.delay(id, yymm)\n            flash('勤怠の登録ができました', 'success')\n            return redirect(url_for('worklogs.index', id=id, yymm=yymm))\n        except Exception as e:\n            db.session.rollback()\n            flash('勤怠登録時にエラーが発生しました {}'.format(e), 'danger')\n            app.logger.exception(e)\n    kw = dict(\n        id = id,\n        yymm = yymm,\n        dd = dd,\n        form = form,\n        item = worklog\n    )\n    return render_template('worklogs/edit.pug', **kw)\n\n@bp.route('/<id>/<yymm>/<dd>/destroy')\n@login_required_staff\ndef destory(id, yymm, dd):\n    worklog = WorkLogService.get_or_404((id, yymm, dd))\n    try:\n        worklog.delete()\n        flash('勤怠の削除ができました', 'success')\n    except ValueError as e:\n        db.session.rollback()\n        flash(str(e), 'danger')\n    except Exception as e:\n        db.session.rollback()\n        flash('勤怠削除時にエラーが発生しました {}'.format(e), 'danger')\n        app.logger.exception(e)\n    return redirect(url_for('worklogs.index', id=id, yymm=yymm))\n\n@bp.route('/<id>/<yymm>/report')\n@login_required_person\ndef report(id, yymm):\n    with BytesIO() as output:\n        pdf = WorkLogReport(id, yymm)\n        pdf(output)\n        response = make_response(output.getvalue())\n        response.mimetype = 'application/pdf'\n    return response\n\n@bp.route('/<id>/<yymm>/update')\n@login_required_staff\ndef update(id, yymm):\n    update_worklogs_value.delay(id, yymm)\n    return redirect(url_for('worklogs.index', id=id, yymm=yymm))\n","repo_name":"abtoc/ofpp-app","sub_path":"flaskr/views/worklogs.py","file_name":"worklogs.py","file_ext":"py","file_size_in_byte":4443,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"21085518546","text":"import datetime\nimport hashlib\nimport json\nfrom flask import Flask, jsonify\n\n# Part 1 - Buiding a Blockchain\n\n\nclass Blockchain:\n\n    def __init__(self):\n        self.chain = []\n        self.create_block(proof=1, prev_hash='0', data='gensis block')\n\n    def create_block(self, proof, prev_hash, data):\n        block = {'index': len(self.chain) + 1,\n                 'data': data,\n                 'timestamp': str(datetime.datetime.now()),\n                 'proof': proof,\n                 'prev_hash': prev_hash}\n\n        self.chain.append(block)\n        return block\n\n    def get_prev_block(self):\n        return self.chain[-1]\n\n    def proof_of_work(self, prev_proof):\n        new_proof = 1\n        check_proof = False\n        while check_proof is False:\n            hash_operation = hashlib.sha256(\n                str(new_proof ** 2 - prev_proof ** 2).encode()).hexdigest()\n\n            if hash_operation[:4] == '0000':\n                check_proof = True\n            else:\n                new_proof += 1\n\n        return new_proof\n\n    def hash(self, block):\n        encoded_block = json.dumps(block, sort_keys=True).encode()\n        return hashlib.sha256(encoded_block).hexdigest()\n\n    def is_chain_valid(self, chain):\n        previous_block = chain[0]\n        block_index = 1\n        while block_index < len(chain):\n            block = chain[block_index]\n            if block['prev_hash'] != self.hash(previous_block):\n                return False\n            previous_proof = previous_block['proof']\n            proof = block['proof']\n            hash_operation = hashlib.sha256(\n                str(proof ** 2 - previous_proof ** 2).encode()).hexdigest()\n\n            if hash_operation[:4] != \"0000\":\n                return False\n            previous_block = block\n            block_index + 1\n        return True\n\n    # Part 2 - Mining our Blockchain\n\n    def mining(self, data):\n        prev_block = self.get_prev_block()\n        new_proof = self.proof_of_work(prev_block['proof'])\n        prev_hash = self.hash(prev_block)\n        print(\"prev_hash:\", prev_hash)\n        self.create_block(new_proof, prev_hash, data)\n\n\nif __name__ == \"__main__\":\n\n    ubc = Blockchain()\n    ubc.mining(\"a buy a house.\")\n    ubc.mining(\"b buy a car.\")\n\n    for i in ubc.chain:\n        print(i['data'])\n","repo_name":"caoxufeng/python-blockchain","sub_path":"blockchain.py","file_name":"blockchain.py","file_ext":"py","file_size_in_byte":2294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"12485696069","text":"# -*- coding: utf-8 -*-\n\nfrom astropy import units as u\nimport numpy as np\nimport sunpy.map as mp\nfrom copy import deepcopy\n\n__all__ = [\"decompose_ang_len\", \"si_this_map_OLD\", \"si_this_map\"]\n\n\ndef decompose_ang_len(qua_input, **kwargs):\n    \"\"\"\n    Function to help decompose quantities that have an equivilence between angles\n    and length, such as photospheric observational angles and object sizes.\n    The function uses an equivilence to convert into either length or angle\n    physical_type units.\n\n    Parameters\n    ----------\n\n    qua_input : `astropy.units.quantity.Quantity`\n        The quantity you wish decomposed.\n\n    working_units : `astropy.units.quantity.Quantity`, optional\n        Unit that will be used for internal working and the returned quantity.\n    Ensure that it is of the correct physical type, angle or length.\n\n    equivalencies : astropy equivilence,\n        Equivilence used to relate the length and angle units.\n\n    \"\"\"\n    # Parameters\n    working_units = kwargs.get('working_units', u.m) * 1.0\n    equivalence = kwargs.get('equivalencies', u.dimensionless_angles())\n\n    # Do nothing if the input is dimensionless.\n    if qua_input.unit is u.Quantity(1.0).unit:\n        return qua_input.decompose()\n    else:\n        # Components of the quantity\n        value = qua_input.value\n        length_unit = 0.0 * u.m\n        length_exponent = 0.0\n        angle_unit = 0.0 * u.radian\n        angle_exponent = 0.0\n\n        # If we have at least 1 base, populate from the first base\n        if len(qua_input.unit.bases) > 0:\n            if qua_input.unit.bases[0].physical_type is u.m.physical_type:\n                length_unit     = 1.0 * qua_input.unit.bases[0]\n                length_exponent = qua_input.unit.powers[0]\n\n                # convert to SI (meter here)\n                length_unit = length_unit.to(u.m)\n            elif qua_input.unit.bases[0].physical_type is u.radian.physical_type:\n                angle_unit     = 1.0 * qua_input.unit.bases[0]\n                angle_exponent = qua_input.unit.powers[0]\n\n                # Convert to SI (radian here)\n                angle_unit = angle_unit.to(u.radian)\n\n        # If we have 2 bases, populate from the second base\n        if len(qua_input.unit.bases) > 1:\n            if qua_input.unit.bases[1].physical_type is u.m.physical_type:\n                length_unit     = 1.0 * qua_input.unit.bases[1]\n                length_exponent = qua_input.unit.powers[1]\n\n                # Convert to SI (meter here)\n                length_unit = length_unit.to(u.m)\n            elif qua_input.unit.bases[1].physical_type is u.radian.physical_type:\n                angle_unit     = 1.0 * qua_input.unit.bases[1]\n                angle_exponent = qua_input.unit.powers[1]\n\n                # Convert to SI (radian here)\n                angle_unit = angle_unit.to(u.radian)\n\n        # Convert the incompatible base to the working units using the equivilence\n        if working_units.unit.physical_type is u.m.physical_type:\n            angle_unit = angle_unit.to(working_units, equivalencies=equivalence)\n\n            # Strip out the units, so the output doesn't have squared lenth units\n            #angle_unit = angle_unit.value # Kept in-case it causes bugs\n        elif working_units.unit.physical_type is u.radian.physical_type:\n            length_unit = length_unit.to(working_units, equivalencies=equivalence)\n\n            # Strip out the units, so the output doesn't have squared length units\n            #length_unit = length_unit.value # Kept in-case it causes bugs\n        # The quantity to return\n        quantity =  value * length_unit ** length_exponent * angle_unit ** angle_exponent\n        # Change to the working unit if not dimensionless\n        if quantity.unit.physical_type is not (u.m / u.m).decompose().physical_type:\n            quantity.to(working_units)\n        return quantity.decompose()\n\n\ndef si_this_map_OLD(map):\n    \"\"\"\n    Basic function to create a deep copy of a map but with all units in SI.\n    \"\"\"\n    # Find out the value units and convert this and data to SI\n    units = 1.0 * u.Unit(map.meta['bunit']).to(u.Tesla) * u.Tesla\n    data = deepcopy(map.data) * units.value\n\n    # ATM I don't convert the x-axis and y-axis to SI\n\n    # Modify the map header to reflect all these changes\n    meta = deepcopy(map.meta)\n    meta['bunit']   = units.unit\n    meta['datamax'] = data.max()\n    meta['datamin'] = data.min()\n    #meta['cdelt1'] = 0.504295 # Following modified if we convert x/y-axes\n    #meta['cdelt2'] = 0.504295\n    #meta['cunit1'] = 'arcsec'\n    #meta['cunit2'] = 'arcsec'\n    #meta['crpix1'] = data.shape[1] / 2.0 + 0.5, # central x-pixel\n    #meta['crpix2'] = data.shape[0] / 2.0 + 0.5, # cnetral y-pixel\n    #meta['CRVAL1'] = 0.000000\n    #meta['CRVAL2'] = 0.000000\n\n    # Return the modified map\n    return mp.Map((data, meta))\n\n\ndef si_this_map(map):\n    \"\"\"\n    Basic function to create a deep copy of a map but with all units in SI.\n    \"\"\"\n    # Find out the value units and convert this and data to SI\n    units = 1.0 * u.Unit(map.meta['bunit']).to(u.Tesla) * u.Tesla\n    data = deepcopy(map.data) * units.value\n\n    # Setup the arc to length equivilence\n    obs_distance = map.dsun - map.rsun_meters\n    radian_length = [ (u.radian, u.meter, lambda x: obs_distance * x, lambda x: x / obs_distance) ]\n\n    # Convert the x-axis and y-axis to SI\n    cdelt1 = (float(map.meta['cdelt1']) * u.Unit(map.meta['cunit1'])).to(u.meter, equivalencies=radian_length)\n    cdelt2 = (float(map.meta['cdelt2']) * u.Unit(map.meta['cunit2'])).to(u.meter, equivalencies=radian_length)\n    crpix1 = (float(map.meta['crpix1']) * u.Unit(map.meta['cunit1'])).to(u.meter, equivalencies=radian_length)\n    crpix2 = (float(map.meta['crpix2']) * u.Unit(map.meta['cunit2'])).to(u.meter, equivalencies=radian_length)\n\n    # Modify the map header to reflect all these changes\n    meta = deepcopy(map.meta)\n    meta['bunit']   = 'Tesla' #units.unit\n    meta['datamax'] = data.max()\n    meta['datamin'] = data.min()\n    # Following modified if we convert x/y-axes\n    meta['cdelt1'] = str(cdelt1.value)\n    meta['cdelt2'] = str(cdelt2.value)\n    meta['cunit1'] = str(cdelt1.unit)\n    meta['cunit2'] = str(cdelt2.unit)\n    meta['crpix1'] = str(crpix1.value)\n    meta['crpix2'] = str(crpix2.value)\n    #meta['CRVAL1'] = 0.000000 # Reference data coordinates\n    #meta['CRVAL2'] = 0.000000\n\n    # Return the modified map\n    return mp.Map((data, meta))\n","repo_name":"sunpy/solarbextrapolation","sub_path":"solarbextrapolation/utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":6449,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"76"}
+{"seq_id":"18537125591","text":"from board import Board\nfrom square import Square\nfrom mnkgame import MNKGame\nfrom agent import RandomAgent\nfrom searchAgents import SearchAgent\nfrom nnAgents import cnnAgent\n\n# This class stores settings for ALL the constituent parts for the pipeline\nclass Settings(object):\n    def __init__(self):\n        # Board parameters\n        self.m = 7\n        self.n = 6\n        self.k = 5\n\n        # Agent parameters.  Specify different agent types here via constructor\n        self.Xagent = RandomAgent(Square.X_HAS, self.m, self.n, self.k)\n        self.Oagent = cnnAgent(Square.O_HAS, self.m, self.n, self.k)\n        self.numGamesToEstimateValue = 5\n\n        # Outermost loop parameters\n        self.numGamesToTest = 10\n        self.numGamesToTrain = 10\n        self.verbose = False\n\n\n# this function plays a number of games stored in the settings and reports results\ndef playGames(settings):\n    Xwins = 0.0\n    Xloses = 0.0\n    draws = 0.0\n\n    print(\"****Testing \", settings.numGamesToTest, \" games of agents looking for sequences of length k=\", settings.k, \" using \", settings.numGamesToEstimateValue, \" games to estimate value\")\n    print(\"Agents: X: \", settings.Xagent)\n    print(\" and O: \", settings.Oagent)\n\n    for i in range(settings.numGamesToTest):\n        # new game, create a fresh board\n        if settings.verbose:\n            print(\"Creating a M x N board, where m =\", settings.m, \" and n=\", settings.n, \"\\n\")\n        board = Board(settings.m, settings.n)\n\n        # play the game, taking turns being first to act\n        if i % 2 == 0:\n            winner = MNKGame().playGame(board, settings.Xagent, settings.Oagent, settings)\n        else:\n            winner = MNKGame().playGame(board, settings.Oagent, settings.Xagent, settings)\n\n        # do the bookkeeping now that a result is obtained\n        if winner == settings.Xagent:\n            Xwins += 1\n            if settings.verbose:\n                print(\"X emerges victorious over the vile O!!!!! in game \", i)\n        elif winner == settings.Oagent:\n            Xloses += 1\n            if settings.verbose:\n                print(\"O has defeated the disgusting X!!!!! in game \", i)\n        elif winner == None:\n            draws += 1\n            if settings.verbose:\n                print(\"fought to a draw... maybe next time. In game \", i)\n\n    # All games complete, generate some final output\n    if settings.verbose:\n        print(\"Xwins=\", Xwins, \"Xloses=\", Xloses, \"draws=\", draws, )\n\ndef trainAndTestCNNAgent(settings):\n    ourHero = cnnAgent(Square.X_HAS, settings.m, settings.n, settings.k)\n    trainingPartner = SearchAgent(Square.O_HAS, settings.m, settings.n, settings.k)\n\n    gauntlet1 = RandomAgent(Square.O_HAS, settings.m, settings.n, settings.k)\n    gauntlet2 = SearchAgent(Square.O_HAS, settings.m, settings.n, settings.k) # FIXME but add more search (settings specifying that parameter globally is bad)\n    theGauntlet = [gauntlet1, gauntlet2]\n\n    trainingSessions = 100\n    print(\"****Testing \", settings.numGamesToTest, \" games of agents looking for sequences of length k=\", settings.k,\n          \" using \", settings.numGamesToEstimateValue, \" games to estimate value\")\n    print(\"Agents: X: \", ourHero)\n    print(\" and O: \", trainingPartner)\n\n    print(\"\\tTestSession\\tHeroWins\\tHeroLoses\\tHeroDraws\\tAvgIllegalMoves\\tAvgGameLength\\tMaxGameLength\\tOpponent\\t(REPEAT)\")\n\n    for session in range(trainingSessions):\n        for i in range(settings.numGamesToTrain):\n            # new game, create a fresh board\n            if settings.verbose:\n                print(\"Creating a M x N board, where m =\", settings.m, \" and n=\", settings.n, \"\\n\")\n            board = Board(settings.m, settings.n)\n\n            # play the game, taking turns being first to act\n            if i % 2 == 0:\n                MNKGame().playGame(board, ourHero, trainingPartner, settings)\n            else:\n                MNKGame().playGame(board, trainingPartner, ourHero, settings)\n\n        # test vs the gauntlet\n        for opponent in theGauntlet:\n            heroWins = 0\n            heroLoses = 0\n            heroDraws = 0\n            totalIllegalMoves = 0\n            maxGameLength = 0\n            totalGameLength = 0\n            for j in range(settings.numGamesToTest):\n                # new game, create a fresh board\n                if settings.verbose:\n                    print(\"Creating a M x N board, where m =\", settings.m, \" and n=\", settings.n, \"\\n\")\n                board = Board(settings.m, settings.n)\n\n                # play the game, taking turns being first to act\n                if j % 2 == 0:\n                    winner, moveCount, illegalMoveCount = MNKGame().playGame(board, ourHero, opponent, settings)\n\n                else:\n                    winner, moveCount, illegalMoveCount = MNKGame().playGame(board, opponent, ourHero, settings)\n\n\n                # do the bookkeeping now that a result is obtained\n                totalIllegalMoves += illegalMoveCount\n                totalGameLength += moveCount\n                if moveCount > maxGameLength:\n                    maxGameLength = moveCount\n\n                if winner == ourHero:\n                    heroWins += 1\n                    if settings.verbose:\n                        print(\"X emerges victorious over the vile O!!!!! in game \", i)\n                elif winner != None:\n                    heroLoses += 1\n                    if settings.verbose:\n                        print(\"O has defeated the disgusting X!!!!! in game \", i)\n                elif winner == None:\n                    heroDraws += 1\n                    if settings.verbose:\n                        print(\"fought to a draw... maybe next time. In game \", i)\n\n            # All games vs this opponent complete, generate some final output\n\n            print(\"\\t\", session, \"\\t\", heroWins, \"\\t\", heroLoses, \"\\t\", heroDraws, \"\\t\", float(totalIllegalMoves)/settings.numGamesToTest, \"\\t\", float(totalGameLength)/settings.numGamesToTest, \"\\t\", maxGameLength, \"\\t\", opponent, end='' )\n        print(\"\")\nif __name__ == '__main__':\n    settings = Settings()\n    #playGames(settings) # feel free to define your own \"main\" by calling newFunction() and commenting this out\n    trainAndTestCNNAgent(settings)\n","repo_name":"dodgej/MNKgames","sub_path":"source/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6219,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"40538848097","text":"#!/usr/bin/env python3\nimport argparse\nimport collections\nimport os\n\nos.environ.setdefault(\"TF_CPP_MIN_LOG_LEVEL\", \"2\")  # Report only TF errors by default\n\nimport gym\nimport numpy as np\nimport tensorflow as tf\nimport tensorflow_probability as tfp\n\nimport wrappers\n\nparser = argparse.ArgumentParser()\n# These arguments will be set appropriately by ReCodEx, even if you change them.\n#parser.add_argument(\"--env\", default=\"BipedalWalker-v3\", type=str, help=\"Environment.\")\nparser.add_argument(\"--env\", default=\"Pendulum-v1\", type=str, help=\"Environment.\")\nparser.add_argument(\"--recodex\", default=False, action=\"store_true\", help=\"Running in ReCodEx\")\nparser.add_argument(\"--render_each\", default=0, type=int, help=\"Render some episodes.\")\nparser.add_argument(\"--seed\", default=None, type=int, help=\"Random seed.\")\nparser.add_argument(\"--threads\", default=1, type=int, help=\"Maximum number of threads to use.\")\n# For these and any other arguments you add, ReCodEx will keep your default value.\nparser.add_argument(\"--batch_size\", default=256, type=int, help=\"Batch size.\")\nparser.add_argument(\"--envs\", default=16, type=int, help=\"Environments.\")\nparser.add_argument(\"--evaluate_each\", default=100, type=int, help=\"Evaluate each number of updates.\")\nparser.add_argument(\"--evaluate_for\", default=16, type=int, help=\"Evaluate the given number of episodes.\")\nparser.add_argument(\"--gamma\", default=0.99, type=float, help=\"Discounting factor.\")\nparser.add_argument(\"--hidden_layer_size\", default=256, type=int, help=\"Size of hidden layer.\")\nparser.add_argument(\"--critic_learning_rate\", default=0.001, type=float, help=\"Critic learning rate.\")\nparser.add_argument(\"--actor_learning_rate\", default=0.0005, type=float, help=\"Actor learning rate.\")\nparser.add_argument(\"--model_path\", default=\"walker.model\", type=str, help=\"Model path\")\nparser.add_argument(\"--replay_buffer_size\", default=1_000_000, type=int, help=\"Replay buffer size\")\nparser.add_argument(\"--target_entropy\", default=-1, type=float, help=\"Target entropy per action component.\")\nparser.add_argument(\"--target_tau\", default=0.005, type=float, help=\"Target network update weight.\")\n\nclass Network:\n    def __init__(self, env: wrappers.EvaluationEnv, args: argparse.Namespace) -> None:\n        # TODO: Create an actor. Because we will be sampling (and `sample()` from\n        # `tfp.distributions` does not play nice with functional models) and because\n        # we need the `alpha` variable, we use subclassing to create the actor.\n        class Actor(tf.keras.Model):\n            def __init__(self, hidden_layer_size: int):\n                super().__init__()\n                # TODO: Create\n                # - two hidden layers with `hidden_layer_size` and ReLU activation\n                # - a layer for generating means with `env.action_space.shape[0]` units and no activation\n                # - a layer for generating sds with `env.action_space.shape[0]` units and `tf.math.exp` activation\n                # - finally, create a variable representing a logarithm of alpha, using for example the following:\n\n                self.hidden_1 = tf.keras.layers.Dense(hidden_layer_size, activation=\"relu\")\n                self.hidden_2 = tf.keras.layers.Dense(hidden_layer_size, activation=\"relu\")\n\n                self.means_head = tf.keras.layers.Dense(env.action_space.shape[0])\n                self.sds_head = tf.keras.layers.Dense(env.action_space.shape[0], activation=tf.math.exp)\n                #self.sds_head = tf.keras.layers.Dense(env.action_space.shape[0], activation=tf.math.softplus)\n\n                self._log_alpha = tf.Variable(np.log(0.1), dtype=tf.float32)\n\n                self.action_low = env.action_space.low\n                self.action_high = env.action_space.high\n\n            def call(self, inputs: tf.Tensor, sample: bool):\n                # TODO: Perform the actor computation\n                # - First, pass the inputs through the first hidden layer\n                #   and then through the second hidden layer.\n                t = self.hidden_1(inputs)\n                t = self.hidden_2(t)\n                # - From these hidden states, compute\n                #   - `mus` (the means),\n                #   - `sds` (the standard deviations).\n                mus = self.means_head(t)\n                sds = self.sds_head(t) if sample else tf.zeros_like(mus)\n                # - Then, create the action distribution using `tfp.distributions.Normal`\n                #   with the `mus` and `sds`. Note that to support computation without\n                #   sampling, the easiest is to pass zeros as standard deviations when\n                #   `sample == False`.\n                actions_distribution = tfp.distributions.Normal(mus, sds)\n                # - We then bijectively modify the distribution so that the actions are\n                #   in the given range. Luckily, `tfp.bijectors` offers classes that\n                #   can transform a distribution.\n                #   - first run\n                #       tfp.bijectors.Tanh()(actions_distribution)\n                #     to squash the actions to [-1, 1] range,\n                #   - then run\n                #       tfp.bijectors.Scale((env.action_space.high - env.action_space.low) / 2)(actions_distribution)\n                #     to scale the action ranges to [-(high-low)/2, (high-low)/2],\n                #   - finally,\n                #       tfp.bijectors.Shift((env.action_space.high + env.action_space.low) / 2)(actions_distribution)\n                #     shifts the ranges to [low, high].\n                #   In case you wanted to do this manually, sample from a normal distribution, pass the samples\n                #   through the `tanh` and suitable scaling, and then compute the log-prob by using `log_prob`\n                #   from the normal distribution and manually accounting for the `tanh` as shown in the slides.\n                #   However, the formula from the slides is not numerically stable, for a better variant see\n                #   https://github.com/tensorflow/probability/blob/ef1f64a434/tensorflow_probability/python/bijectors/tanh.py#L70-L81\n                actions_distribution = tfp.bijectors.Tanh()(actions_distribution)\n                actions_distribution = tfp.bijectors.Scale((self.action_high - self.action_low) / 2)(actions_distribution)\n                actions_distribution = tfp.bijectors.Shift((self.action_high + self.action_low) / 2)(actions_distribution)\n                # - Sample the actions by a `sample()` call.\n                actions = actions_distribution.sample()\n                # - Then, compute the log-probabilities of the sampled actions by using `log_prob()`\n                #   call. An action is actually a vector, so to be precise, compute for every batch\n                #   element a scalar, an average of the log-probabilities of individual action components.\n                avg_log_probs = tf.reduce_mean(actions_distribution.log_prob(actions), axis=1)\n                # - Finally, compute `alpha` as exponentiation of `self._log_alpha`.\n                alpha = tf.math.exp(self._log_alpha)\n                # - Return actions, log_prob, and alpha.\n                return actions, avg_log_probs, alpha\n\n        # TODO: Instantiate the actor as `self._actor` and compile it.\n        self._actor = Actor(args.hidden_layer_size)\n        self._actor.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=args.actor_learning_rate))\n\n        # TODO: Create a critic, which\n        # - takes observations and actions as inputs,\n        # - concatenates them,\n        # - passes the result through two dense layers with `args.hidden_layer_size` units\n        #   and ReLU activation,\n        # - finally, using a last dense layer produces a single output with no activation\n        # This critic needs to be cloned so that two critics and two target critics are created.\n        critic = self._create_critic(env, args)\n        self._critic_A = tf.keras.models.clone_model(critic)\n        self._target_critic_A = tf.keras.models.clone_model(critic)\n        self._critic_B = tf.keras.models.clone_model(critic)\n        self._target_critic_B = tf.keras.models.clone_model(critic)\n\n        for c in [self._critic_A, self._critic_B]:\n            c.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=args.critic_learning_rate),\n                      loss=tf.keras.losses.MeanSquaredError())\n\n        self.target_tau = args.target_tau\n        # TODO: should the multiplication be there?\n        #self.target_entropy = args.target_entropy * env.action_space.shape[0]\n        self.target_entropy = args.target_entropy\n\n    def _create_critic(self, env, args):\n        obs_input = tf.keras.layers.Input(shape=env.observation_space.shape)\n        actions_input = tf.keras.layers.Input(shape=env.action_space.shape)\n        concat = tf.keras.layers.Concatenate()([obs_input, actions_input])\n        hidden_1 = tf.keras.layers.Dense(args.hidden_layer_size, activation=\"relu\")(concat)\n        hidden_2 = tf.keras.layers.Dense(args.hidden_layer_size, activation=\"relu\")(hidden_1)\n        output = tf.keras.layers.Dense(1)(hidden_2)\n\n        critic = tf.keras.models.Model(inputs=[obs_input, actions_input], outputs=output)\n        return critic\n\n    def save_actor(self, path: str):\n        # Because we use subclassing for creating the actor, the easiest way of\n        # serializing an actor is just to save weights.\n        self._actor.save_weights(path, save_format=\"h5\")\n\n    def load_actor(self, path: str, env: wrappers.EvaluationEnv):\n        # When deserializing, we need to make sure the variables are created\n        # first -- we do so by processing a batch with a random observation.\n        self.predict_mean_actions([env.observation_space.sample()])\n        self._actor.load_weights(path)\n\n    @wrappers.typed_np_function(np.float32, np.float32, np.float32)\n    @wrappers.raw_tf_function(dynamic_dims=1)\n    def train(self, states: np.ndarray, actions: np.ndarray, returns: np.ndarray) -> None:\n        # TODO: Separately train:\n        # - the actor, by using two objectives:\n        #   - the objective for the actor itself; in this objective, `tf.stop_gradient(alpha)`\n        #     should be used (for the `alpha` returned by the actor) to avoid optimizing `alpha`,\n        #   - the objective for `alpha`, where `tf.stop_gradient(log_prob)` should be used\n        #     to avoid computing gradient for other variables than `alpha`.\n        #     Use `args.target_entropy` as the target entropy (the default of -1 per action\n        #     component is fine and does not need to be tuned for the agent to train).\n        self._train_actor(states)\n        # - the critics using MSE loss.\n        for critic, target_critic in zip([self._critic_A, self._critic_B],\n                                         [self._target_critic_A, self._target_critic_B]):\n            critic.optimizer.minimize(\n                lambda: critic.compiled_loss(\n                    returns,\n                    critic([states, actions], training=True)),\n                var_list=critic.trainable_variables\n            )\n            #\n            # Finally, update the two target critic networks exponential moving\n            # average with weight `args.target_tau`, using something like\n            #   for var, target_var in zip(critic.trainable_variables, target_critic.trainable_variables):\n            #       target_var.assign(target_var * (1 - target_tau) + var * target_tau)\n            self._moving_average_update(critic, target_critic)\n\n    def _train_actor(self, states):\n        with tf.GradientTape() as actor_tape:\n            actions, log_probs, alpha = self._actor(states, sample=True)\n            values_a = self._critic_A([states, actions])\n            values_b = self._critic_B([states, actions])\n            values = tf.minimum(tf.squeeze(values_a), tf.squeeze(values_b))\n\n            actor_loss = tf.reduce_mean(tf.stop_gradient(alpha) * log_probs - values)\n            alpha_loss = -tf.reduce_mean(alpha * (tf.stop_gradient(log_probs) + self.target_entropy))\n            loss = actor_loss + alpha_loss\n        actor_grad = actor_tape.gradient(loss, self._actor.trainable_variables)\n        self._actor.optimizer.apply_gradients(zip(actor_grad, self._actor.trainable_variables))\n\n    def _moving_average_update(self, network, target_network):\n        for var, target_var in zip(network.trainable_variables, target_network.trainable_variables):\n            target_var.assign(target_var * (1 - self.target_tau) + var * self.target_tau)\n\n    # Predict actions without sampling.\n    @wrappers.typed_np_function(np.float32)\n    @wrappers.raw_tf_function(dynamic_dims=1)\n    def predict_mean_actions(self, states: np.ndarray) -> np.ndarray:\n        # Return predicted actions, assuming the actor is in `self._actor`.\n        return self._actor(states, sample=False)[0]\n\n    # Predict actions with sampling.\n    @wrappers.typed_np_function(np.float32)\n    @wrappers.raw_tf_function(dynamic_dims=1)\n    def predict_sampled_actions(self, states: np.ndarray) -> np.ndarray:\n        # Return predicted actions, assuming the actor is in `self._actor`.\n        return self._actor(states, sample=True)[0]\n\n    @wrappers.typed_np_function(np.float32)\n    @wrappers.raw_tf_function(dynamic_dims=1)\n    def predict_values(self, states: np.ndarray) -> np.ndarray:\n        # TODO: Produce the predicted returns, which are the minimum of\n        #    target_critic(s, a) - alpha * log_prob\n        #  considering both target critics and actions sampled from the actor.\n        actions, log_probs, alpha = self._actor(states, sample=True)\n        values_a = self._target_critic_A([states, actions])\n        values_b = self._target_critic_B([states, actions])\n        return tf.minimum(tf.squeeze(values_a), tf.squeeze(values_b)) - alpha * log_probs\n\n\ndef main(env: wrappers.EvaluationEnv, args: argparse.Namespace) -> None:\n    # Set random seeds and number of threads\n    if args.seed is not None:\n        tf.keras.utils.set_random_seed(args.seed)\n    tf.config.threading.set_inter_op_parallelism_threads(args.threads)\n    tf.config.threading.set_intra_op_parallelism_threads(args.threads)\n\n    # Construct the network\n    network = Network(env, args)\n\n    def evaluate_episode(start_evaluation: bool = False, logging: bool = True) -> float:\n        rewards, state, done = 0, env.reset(start_evaluation=start_evaluation, logging=logging)[0], False\n        while not done:\n            # TODO: Predict the action using the greedy policy.\n            action = network.predict_mean_actions([state])[0]\n            state, reward, terminated, truncated, _ = env.step(action)\n            done = terminated or truncated\n            rewards += reward\n        return rewards\n\n    # Evaluation in ReCodEx\n    if args.recodex:\n        network.load_actor(args.model_path, env)\n        while True:\n            evaluate_episode(True)\n\n    # Create the asynchronous vector environment for training.\n    venv = gym.vector.make(args.env, args.envs, asynchronous=True)\n\n    # Replay memory of a specified maximum size.\n    replay_buffer = collections.deque(maxlen=args.replay_buffer_size)\n    Transition = collections.namedtuple(\"Transition\", [\"state\", \"action\", \"reward\", \"done\", \"next_state\"])\n\n    state, training = venv.reset(seed=args.seed)[0], True\n    while training:\n        for _ in range(args.evaluate_each):\n            # Predict actions by calling `network.predict_sampled_actions`.\n            action = network.predict_sampled_actions(state)\n\n            next_state, reward, terminated, truncated, _ = venv.step(action)\n\n            ended = reward <= -100\n            shaped_rewards = ended * (reward + 95) + np.logical_not(ended) * reward\n            done = terminated | truncated\n\n            for i in range(args.envs):\n                replay_buffer.append(Transition(state[i], action[i], shaped_rewards[i], done[i], next_state[i]))\n            state = next_state\n\n            # Training\n            if len(replay_buffer) >= 4 * args.batch_size:\n                # Note that until now we used `np.random.choice` with `replace=False` to generate\n                # batch indices. However, this call is extremely slow for large buffers, because\n                # it generates a whole permutation. With `np.random.randint`, indices may repeat,\n                # but once the buffer is large, it happens with little probability.\n                batch = np.random.randint(len(replay_buffer), size=args.batch_size)\n                states, actions, rewards, dones, next_states = map(np.array, zip(*[replay_buffer[i] for i in batch]))\n                # TODO: Perform the training\n                next_values = network.predict_values(next_states)\n                est_returns = rewards + args.gamma * np.logical_not(dones) * np.squeeze(next_values)\n                network.train(states, actions, est_returns)\n\n        # Periodic evaluation\n        returns = [evaluate_episode() for _ in range(args.evaluate_for)]\n        # TODO: when done, exit and save\n\n    # Final evaluation\n    while True:\n        evaluate_episode(start_evaluation=True)\n\n\nif __name__ == \"__main__\":\n    args = parser.parse_args([] if \"__file__\" not in globals() else None)\n\n    # Create the environment\n    env = wrappers.EvaluationEnv(gym.make(args.env), args.seed, args.render_each)\n\n    main(env, args)\n","repo_name":"Kripner/mff","sub_path":"reinforcement_learning/08/walker.py","file_name":"walker.py","file_ext":"py","file_size_in_byte":17351,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"35831345265","text":"#\n# @lc app=leetcode id=88 lang=python\n#\n# [88] Merge Sorted Array\n#\n# https://leetcode.com/problems/merge-sorted-array/description/\n#\n# algorithms\n# Easy (36.49%)\n# Total Accepted:    398.3K\n# Total Submissions: 1.1M\n# Testcase Example:  '[1,2,3,0,0,0]\\n3\\n[2,5,6]\\n3'\n#\n# Given two sorted integer arrays nums1 and nums2, merge nums2 into nums1 as\n# one sorted array.\n# \n# Note:\n# \n# \n# The number of elements initialized in nums1 and nums2 are m and n\n# respectively.\n# You may assume that nums1 has enough space (size that is greater or equal to\n# m + n) to hold additional elements from nums2.\n# \n# \n# Example:\n# \n# \n# Input:\n# nums1 = [1,2,3,0,0,0], m = 3\n# nums2 = [2,5,6],       n = 3\n# \n# Output: [1,2,2,3,5,6]\n# \n# \n#\nclass Solution(object):\n    def merge(self, nums1, m, nums2, n):\n        \"\"\"\n        :type nums1: List[int]\n        :type m: int\n        :type nums2: List[int]\n        :type n: int\n        :rtype: None Do not return anything, modify nums1 in-place instead.\n        \"\"\"\n        if len(nums2) == 0:\n            return nums1\n        index = len(nums1)-1\n        while index >=0:\n            if m>0 and nums1[m-1] > nums2[n-1]:\n                nums1[index] = nums1[m-1]\n                m = m - 1\n            elif n > 0:\n                nums1[index] = nums2[n-1]\n                n = n - 1\n            index = index - 1\n        return nums1\n","repo_name":"sarahgonsalves223/DSA_Python","sub_path":"Easy/88_easy_merge-sorted-array.py","file_name":"88_easy_merge-sorted-array.py","file_ext":"py","file_size_in_byte":1363,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"9983052019","text":"import csv\nfrom mnist import MNIST\nfrom digits_classifyer import classify_digits, check_classification\nfrom pic_to_vector import PicToVec\nimport os\nimport run_perceptron\nfrom typing import List\n\ntrain_features_file_name = 'features/train_images.csv'\nbackup_train_features_file_name = 'features/backup_train_images.csv'\ntest_features_file_name = 'features/test_images.csv'\nbackup_test_features_file_name = 'features/backup_test_images.csv'\n\ntrain_tags_file_name = \"tags/train_tags\"\ntest_tags_file_name = \"tags/test_tags\"\n\nmndata = MNIST('data')\n\ntrain_images, train_labels = mndata.load_training()\n\ntest_images, test_labels = mndata.load_testing()\n\nUSE_BACKUP = True\n\n\ndef write_feature_to_file(filename: str, is_backup: bool, images):\n    with open(filename, 'w', newline='', encoding='utf-8') as csvoutForFeatures1:\n        csvoutForFeatures = csv.writer(csvoutForFeatures1)\n        for i in range(len(images)):\n\n            if is_backup:\n                pic = PicToVec(images[i], [0])\n                var = pic.back_up_format()\n            else:\n                pic = PicToVec(images[i])\n                var = pic.format()\n\n            csvoutForFeatures.writerow(var)\n            csvoutForFeatures1.flush()\n\n\ndef build_features(use_backup: bool = False):\n    files_to_remove = [os.path.join('features', f) for f in os.listdir('features')]\n    for f in files_to_remove:\n        os.remove(f)\n    write_feature_to_file(train_features_file_name, images=train_images, is_backup=False)\n    write_feature_to_file(test_features_file_name, images=test_images, is_backup=False)\n    if use_backup:\n        write_feature_to_file(backup_train_features_file_name, images=train_images, is_backup=True)\n        write_feature_to_file(backup_test_features_file_name, images=test_images, is_backup=True)\n\n\ndef build_tags(use_backup: bool = False):\n    predictions_results: List[List[bool]] = []\n    backup_predictions_results: List[List[bool]] = []\n    for i in range(0, 10):\n        train_tags_file = train_tags_file_name + i.__str__() + '.csv'\n        test_tags_file = test_tags_file_name + i.__str__() + '.csv'\n        with open(train_tags_file, 'w', newline='', encoding='utf-8') as csvoutForTrainTags1, \\\n                open(test_tags_file, 'w', newline='', encoding='utf-8') as csvoutForTestTags1:\n            csvoutForTrainTags = csv.writer(csvoutForTrainTags1)\n            csvoutForTestTags = csv.writer(csvoutForTestTags1)\n            counter = 0\n            for label in train_labels:\n                counter += 1\n                tag_to_write = (1 if label == i else 0).__str__()\n                csvoutForTrainTags.writerow(tag_to_write)\n                csvoutForTrainTags1.flush()\n            counter = 0\n            for label in test_labels:\n                counter += 1\n                tag_to_write = (1 if label == i else 0).__str__()\n                csvoutForTestTags.writerow(tag_to_write)\n                csvoutForTestTags1.flush()\n        print(\"Sucsses rate for digit \", i)\n\n        restult_list = []\n        predictions_results.append(\n            run_perceptron.run(train_features_file_name, train_tags_file, test_features_file_name, test_tags_file))\n        if use_backup:\n            backup_predictions_results.append(\n                run_perceptron.run(backup_train_features_file_name, train_tags_file, backup_test_features_file_name,\n                                   test_tags_file))\n    check_classification(\n        classify_digits(binary_classification_results=predictions_results,\n                        backup_binary_classification_results=backup_predictions_results),\n        list(test_labels))\n\n\ndef assert_number_of_lines(number_of_lines, file_path):\n    with open(file_path, 'r') as file:\n        counter = 0\n        content = file.read()\n        coList = content.split('\\n')\n        for i in coList:\n            if i:\n                counter += 1\n        print(counter)\n        assert (counter == number_of_lines)\n\n\nif __name__ == '__main__':\n    build_features(USE_BACKUP)\n    build_tags(USE_BACKUP)\n","repo_name":"raneldan/ML-ex2","sub_path":"data_parser.py","file_name":"data_parser.py","file_ext":"py","file_size_in_byte":4023,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"2540128535","text":"import os\nfrom dotenv import load_dotenv, find_dotenv\n\nload_dotenv(dotenv_path=find_dotenv()\n            #,verbose=True\n            )\n\nbasedir = os.path.abspath(os.path.dirname(__file__))\npostgres_local_base = os.getenv('DATABASE_URL') #'postgresql://admin:123456@localhost:5434/balticlsc'\n\n\nclass Config:\n    SECRET_KEY = os.getenv('SECRET_KEY')\n    DEBUG = False\n\n\nclass DevelopmentConfig(Config):\n    DEBUG = True\n    SQLALCHEMY_DATABASE_URI = postgres_local_base\n    SQLALCHEMY_TRACK_MODIFICATIONS = False\n\n\nclass TestingConfig(Config):\n    DEBUG = True\n    TESTING = True\n    PRESERVE_CONTEXT_ON_EXCEPTION = False\n    SQLALCHEMY_TRACK_MODIFICATIONS = False\n    SQLALCHEMY_DATABASE_URI = postgres_local_base\n\n\nclass ProductionConfig(Config):\n    DEBUG = False\n    SQLALCHEMY_DATABASE_URI = postgres_local_base\n\n\nconfig_by_name = dict(\n    dev=DevelopmentConfig,\n    test=TestingConfig,\n    prod=ProductionConfig\n)\n\nkey = Config.SECRET_KEY\n","repo_name":"kolendomichal/popgrupaa","sub_path":"Pop-Grupa-A-Backend/app/main/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":943,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"76"}
+{"seq_id":"22342507049","text":"\ndata = {'a':1,'b':2,'c':4}\na,b,c = data.keys()\n# print(a)\n# print(b)\n# print(c)\n\ndef my_func(a,b,c):\n    print(a)\n    print(b)\n    print(c)\ndef my_func1(click): # argument musi się nazywać tak jak klucz słwonika\n    print(click)\n\ndata_dict = {'a':1,'c':2,'b':[3]}\ndict1 = {\"click\":[{\"click button mouse\":{\"button\": \"left\"}}]}\ndict2 = {\"click\":2}\n# my_func(*data_dict) # wypakowanie kluczy slownika\nmy_func(**data_dict) # wypakowanie wartosci slownika\nmy_func1(**dict1) # wypakowanie wartosci slownika\n","repo_name":"mixer123/kacper_obiekty","sub_path":"modul4/wypakowanie_2.py","file_name":"wypakowanie_2.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"29058942228","text":"from PyQt6 import QtWidgets\r\nfrom PyQt6.QtCore import QItemSelectionModel, Qt\r\nfrom PyQt6.QtSql import QSqlTableModel\r\n\r\nimport src.ui as ui\r\n\r\n\r\nclass SkillChooser(QtWidgets.QDialog, ui.choose_skills.Ui_Dialog):\r\n    def __init__(self, editor: \"ux.task_editor.Editor\", task=None):\r\n        super().__init__()\r\n        self.setupUi(self)\r\n        self.task = task\r\n        self.editor = editor\r\n        model = QSqlTableModel()\r\n        model.setTable(\"skills\")\r\n        model.setSort(1, Qt.SortOrder.AscendingOrder)\r\n        model.select()\r\n        self.listView.setModel(model)\r\n        self.listView.setModelColumn(1)\r\n\r\n        if task:\r\n            # holy crap, that was a difficult birth..\r\n            self.listView.selectionModel().clear()\r\n            for index in range(model.rowCount()):\r\n                if model.itemData(model.index(index, 0))[0] in task.skill_ids:\r\n                    self.listView.selectionModel().select(\r\n                        model.index(index, 1),\r\n                        QItemSelectionModel.Select,\r\n                    )\r\n\r\n    def accept(self):\r\n        super().accept()\r\n        self.editor.skill_ids = [\r\n            self.listView.model().record(idx.row()).value(\"skill_id\")\r\n            for idx in self.listView.selectedIndexes()\r\n        ]\r\n\r\n\r\nimport src.ux as ux  # noqa: E402\r\n","repo_name":"amogorkon/watnu","sub_path":"src/ux/choose_skills.py","file_name":"choose_skills.py","file_ext":"py","file_size_in_byte":1327,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"43476161176","text":"import tensorflow as tf\n\nclass AFD_RNN(object):\n\n    def __init__(self, net_config, test_batch_size=None, time_step=None):\n\n        self.class_num = int(net_config['class_num'])\n        self.num_units = int(net_config['num_units'])\n        self.senor_data_num = int(net_config['senor_data_num'])\n\n        if time_step is None:\n            self.time_step = int(net_config['time_step'])\n        else:\n            self.time_step = time_step\n\n        if test_batch_size is None:\n            self.batch_size = int(net_config['batch_size'])\n        else:\n            self.batch_size = test_batch_size\n\n    def build_net_graph(self):\n        self.input_tensor = tf.placeholder(tf.float32, [None, self.time_step, self.senor_data_num])\n\n        # 创建输出层\n        input_x = tf.reshape(self.input_tensor, [-1, self.senor_data_num])\n        weights_x = self._get_variable_weights([self.senor_data_num, self.num_units], 'input_weights')\n        biases_x = self._get_variable_biases([self.num_units], 'input_biases')\n\n        x_output = tf.reshape(tf.add(tf.matmul(input_x, weights_x), biases_x),\n                                   [-1, self.time_step, self.num_units])\n\n        # 创建rnn\n        x_output = tf.unstack(x_output, axis=1)\n        lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(self.num_units)\n        self.cell_state = lstm_cell.zero_state(self.batch_size, dtype=tf.float32)\n\n        # outputs shape =[batch_size, max_time, cell_state_size]\n        # LSTM final_state shape = [2, batch_size, cell_state_size]\n        cell_outputs, final_state = tf.nn.static_rnn(lstm_cell,\n                                                     x_output,\n                                                     initial_state=self.cell_state)\n        # 创建网路输出层\n        outputs = tf.reshape(cell_outputs, [-1, self.num_units])\n        weights_outputs = self._get_variable_weights([self.num_units, self.class_num], 'outputs_weights')\n        biases_outputs = self._get_variable_biases([self.class_num], 'outputs_biases')\n\n        predict = tf.reshape(tf.add(tf.matmul(outputs, weights_outputs), biases_outputs),\n                             [self.time_step, self.batch_size, self.class_num])\n\n        return predict\n\n    def _get_variable_weights(self, shape, name):\n        return tf.Variable(tf.truncated_normal(shape, stddev=0.1), dtype=tf.float32, name=name)\n\n    def _get_variable_biases(self, shape, name):\n        return tf.Variable(tf.constant(0.1, shape=shape), dtype=tf.float32, name=name)","repo_name":"chizhanyuefeng/Realtime-Fall-Detection-for-RNN","sub_path":"build_rnn.py","file_name":"build_rnn.py","file_ext":"py","file_size_in_byte":2493,"program_lang":"python","lang":"en","doc_type":"code","stars":87,"dataset":"github-code","pt":"76"}
+{"seq_id":"37221639628","text":"import os\nfrom datetime import date\nfrom cryptography.fernet import Fernet\nfrom my_classes.Context import Context\nfrom my_classes.GoogleSheet import get_google_sheet\nfrom my_classes.Schedule import Schedule\n\n\nclass Student:\n    def __init__(self, ctx, first_name: str, last_name: str, student_id: str, program_degree: str, discord_id: int):\n        self.ctx = Context(ctx)  # the object that represents this member's Content.\n\n        self.first = first_name  # the student's first name.\n        self.last = last_name  # the student's last name\n        self.student_id = student_id  # the student's student id.\n        self.course = None  # the course object that the student is in tutoring for.\n        self.program_degree = program_degree  # the degree the student is current pursuing.\n        self.discord_id = discord_id  # the student's discord id.\n\n        self.times_helped = 0  # the number of times the student has been helped by the tutor.\n        self.prev_voice_channel = None  # the voice channel id the student is in prior to joining the tutor's.\n        self.being_helped = False  # if the student is currently being helped by a tutor.\n\n    def encrypt(self):\n        \"\"\"encrypt and display student's information.\n\n        encryption is done by using a Fernet Key:\n            API here: https://cryptography.io/en/latest/index.html\n        encryption converts str to bytes.\n            need to strip bytes' format to be printed.\n        student's account is NOT stored locally for in a database for transparency.\n            student's account are stored in a discord channel to be read by the bot in the future.\n        \"\"\"\n        # encrypt student information\n        information = f'{self.first} {self.last} {self.student_id} {self.program_degree} {self.discord_id}'\n        fernet = Fernet(os.getenv(\"FERNET_KEY\"))\n        encrypted_account = fernet.encrypt(information.encode('utf-8'))\n\n        return encrypted_account.decode('utf-8')\n\n    async def sign_in(self, first_name):\n        \"\"\"send the student their custom sign-in sheet link.\n\n        REQUIREMENT: google form.\n            the google form requires: course code, tutor's name, student name, student id, and degree program.\n        WARNING: https links spaces are represented with '+'\n            this will be used when adding a space between student's first and last name.\n        override default tutor's name:\n        if student passed in a tutor's name as an argument.\n\n        Parameters\n        ----------\n        :param str first_name: the str that represents the tutor's first name.\n        :return: a str that represents the student's custom sign-in link.\n        \"\"\"\n\n        if first_name is None:\n            # get tutor's name from schedule.\n            schedule = self.course.schedule\n            tutor = schedule.tutor_name()\n            if tutor is not None:\n                tutor = tutor.replace(' ', '+')\n        else:\n            # default tutor's name\n            tutor = first_name.lower().capitalize()\n\n        # generate custom sign-in link.\n        return f'https://docs.google.com/forms/d/e/1FAIpQLSeLjQ8XunqxtzlWGHKB5Kt52-ZAyBqPiyBmLPfNcDuYhb5dsg/viewform?usp=pp_url&entry.1178312123={self.course.code}&entry.1604735080={tutor}&entry.174697377={self.first}+{self.last}&entry.1854395744={self.student_id}+&entry.905892592={self.program_degree}'\n\n    def verify(self):\n        \"\"\"verify student if they submitted their sign-in sheet via google forms.\n\n        the bot will give the students a link to sign-in, but the student is still required to submit the form.\n            this function will verify if the student submitted the form.\n\n        :return: True if the student has submitted their sign-in sheet, otherwise False.\n        \"\"\"\n        # verify student's sign-in.\n        for content in get_google_sheet():\n            # only check entries that were submitted today.\n            if content['Timestamp'].split(' ')[0] != date.today().strftime('%m-%d-%Y'):\n                return False\n            # check if student signed-in.\n            schedule = Schedule(self.course.code)\n            if content['Student Name'] == self.name() and \\\n                    str(content['Student ID']) == self.student_id and \\\n                    content['Course Code'] == self.course.code and \\\n                    content['Degree'] == self.program_degree and \\\n                    content['Tutor'] == schedule.tutor_name():\n\n                return True\n\n    def course_error_msg(self):\n        \"\"\"display the sign in error message when course is not assigned.\"\"\"\n        return f'<@!{self.discord_id}> *need to sign-in.*'\n\n    def name(self):\n        \"\"\":return: str of the student's first and last name.\"\"\"\n        return f'{self.first} {self.last}'\n\n\ndef to_student(student_info):\n    \"\"\"convert an encrypted student information to a Student object.\n\n    the student information is given as a decrypted str.\n\n    Parameters\n    ----------\n    :param str student_info: the str that represents the student's information.\n    :return: a Student object that represents the given student's information.\n    \"\"\"\n    # decrypt student information.\n    fernet = Fernet(os.getenv(\"FERNET_KEY\"))\n    decrypt_info = fernet.decrypt(student_info.encode('utf-8'))\n\n    # parse string for Student object.\n    info = decrypt_info.decode('utf-8').split(' ')\n\n    # generate Student object.\n    return Student(None, info[0], info[1], info[2], info[3], int(info[4]))\n","repo_name":"steven-phun/Discord_Tutoring_App","sub_path":"my_classes/Student.py","file_name":"Student.py","file_ext":"py","file_size_in_byte":5449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"38443991702","text":"from selenium import webdriver\nimport pandas as pd\n\noptions = webdriver.ChromeOptions()\noptions.add_argument('--headless')\noptions.add_argument('--incognito')\n\n\nheaders = {\"jms_number\":(), \"cp_case_number\":(), \"municipal_case_number\":(), \"other_case_number\":(), \"court_date\":(),\n           \"orc_code\":(), \"charge_description\":(), \"bond_type\":(), \"bond_amount\":(), \"disposition\":(),\n           \"fine\":(), \"comments\":(), \"holder\":(), \"timestamp\":()\n}\n\nstart = pd.DataFrame(headers)\nstart.to_csv('/home/paul/Documents/people.csv', index=False)\n\nip = pd.read_csv(\"/home/paul/Documents/ip.csv\")\n# assigns 'view all inmates' table (ip.csv) to ip\nip.columns = ['jms_number', 'last_name', 'first_name', 'admit_date', 'proj_release_date', 'holder']\n# names columns for ip object for later usage\nurl_pattern = \"http://apps.hcso.org/InmateDetail.aspx?ID=\"\n# creates base url that jms_number can be concatenated to the end of to create url for each person\n\nip.insert(0, 'ip_url', url_pattern + ip['jms_number'].map(str))\n# inserts a column at the far left that is url pattern + person's jms number\n\ndriver = webdriver.Chrome(options=options)\n# assigns webdriver.Chrome() to object driver for easier reference later\n\nip_url = ip.iloc[0:, 0]\n# assigns object ip_url to first row, first column of ip (which is the url to lookup\n\ncounter = 0\n\n# below loop iterates through each individual on the 'view all inmates' table, visits their page,\n# and scrapes their charges info from that table, then adds one to the value of \"counter\" at end of loop\n# continues through this while loop until the value of counter is the same as the length of ip_url column\n# might need to change statement to:\n# while counter < 1+ len(ip_url): to get last element on list?\n\nwhile counter < len(ip_url):\n    url = ip_url[counter]\n    driver.get(url)\n    raw = pd.read_html(driver.page_source)\n    indiv = raw[1]\n    timestamp = pd.to_datetime(\"today\")\n    indiv.insert(0, 'jms_number', driver.current_url[-7:])\n    indiv.insert(13, 'timestamp', timestamp)\n    indiv.to_csv('/home/paul/Documents/people.csv', mode='a', header=False, index=False)\n    # turns the dataframe into csv and appends what has been scraped in this loop to the existing file\n    # also removes header and index\n\n    counter+=1","repo_name":"pcencula/incarcerated-persons","sub_path":"03_pull_ip_charges.py","file_name":"03_pull_ip_charges.py","file_ext":"py","file_size_in_byte":2261,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"14283651859","text":"#Gina Saifullah\n\n# Returns True if divisible by 3\ndef MultipleOfThree(num):\n\tif (num % 3 == 0):\n\t\treturn True\n\treturn False\n\n# Returns True if divisible by 5\ndef MultipleOfFive(num):\n\tif (num % 5 == 0):\n\t\treturn True\n\treturn False\t\n\n\ndef main():\n\tpass\n\n# Main\nmain()\n\nprint()\nprint(\"The FizzBuzz Problem\")\nprint()\n\nfor i in range(1, 101, 1):\n\tout_str = \"\"\n\tif (MultipleOfThree(i) is True):\n\t\tout_str += \"Fizz\"\n\tif (MultipleOfFive(i) is True):\n\t\tout_str += \"Buzz\"\n\tif (len(out_str) == 0):\n\t\tout_str = i\n\tprint(out_str)\n\t\t","repo_name":"UWPCE-PythonCert-ClassRepos/GP_Python210B_Winter_2019","sub_path":"students/ginafs/session02/FizzBuzz.py","file_name":"FizzBuzz.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"14893652257","text":"'''\n問題文\nAtcoder国にはN個の県があり、これらの県には合計で M個の市が属しています。\n市iが誕生したのはYi年であり、県Piに属しています。\nただし、同じ年に誕生した市が複数存在することはないとします。\nそれぞれの市に12桁の認識番号を割り振ることとなりました。\n市iが 県Piに属する市の中でx番目に誕生した市のとき、市iの認識番号の上6桁はPi、下6桁はxとなります。\nただし、Piやxが6桁に満たない場合は6桁になるまで0を左に追加するものとします。\n全ての市の認識番号を求めてください。\nただし、市が1つも属さない県がある場合に注意してください。\n制約\n1≤N≤105\n1≤M≤105\n1≤Pi≤N\n1≤Yi≤109\nYiは全て異なる\n入力は全て整数\n'''\n\nn,m=map(int, input().split())\nprefecture=[]\nfor i in range(m):\n    pk,yk=map(int, input().split())\n    prefecture.append([pk,yk,i])\n\nprefecture.sort(key=lambda x:(x[0],x[1])) # x[0]:pで整理したうえでx[1]:yで整理\np_num=prefecture[0][0]\ncity_num=1\nfor i in range(m):\n    if(prefecture[i][0]!=p_num):\n        p_num=prefecture[i][0]\n        city_num=1\n\n    prefecture[i].append(city_num)\n    city_num+=1\n\nprefecture.sort(key=lambda x:x[2])\n\nfor i in range(m):\n    print('{0:06d}{1:06d}'.format(prefecture[i][0], prefecture[i][3]))\n\n# p=[]\n# y=[]\n# prefecture=[[] for j in range(n)]\n# for i in range(m):\n#     pk,yk=map(int, input().split())\n#     p.append(pk)\n#     y.append(yk)\n#     prefecture[p[i]-1].append(y[i])\n#\n# for j in range(n):\n#     prefecture[j].sort()\n#\n# for i in range(m):\n#     print('{0:06d}{1:06d}'.format(p[i],prefecture[p[i]-1].index(y[i])+1))\n","repo_name":"ryogoOkura/atcoder","sub_path":"entrant/abc113/abc113-c.py","file_name":"abc113-c.py","file_ext":"py","file_size_in_byte":1735,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"71492799284","text":"from pyfiglet import Figlet\nimport sys # take arguments at the command line\nimport random\n\n'''\n    Expects zero or two command-line arguments:\n        Zero if the user would like to output text in a random font.\n        Two if the user would like to output text in a specific font, in which\n            case the first of the two should be -f or --font,\n            and the second of the two should be the name of the font.\n    Prompts the user for a str of text.\n    Outputs that text in the desired font.\n'''\n\nfiglet = Figlet()\n\ndef main():\n\n    if 0 < len(sys.argv) < 3 or len(sys.argv) > 3:\n        sys.exit('Invalid usage')\n\n    elif sys.argv[1] not in ['-f', '--font', 'renders']:\n        sys.exit('Invalid usage')\n\n    elif sys.argv[2] not in figlet.getFonts():\n        sys.exit('Invalid usage')\n\n    elif len(sys.argv) == 1:\n        user_str = input('Input: ').strip().lower()\n        figlet.setFont(font=random.choice(figlet.getFonts()))\n        print(figlet.renderText(user_str))\n\n    else:\n        user_str = input('Input: ').strip()\n        figlet.setFont(font=sys.argv[2])\n        print(figlet.renderText(user_str))\n\n\nif __name__ == '__main__':\n    main()\n\n'''\n# pip install cowsay\n\nimport cowsay\nimport sys\n\nif len(sys.argv) == 2:\n    cowsay.trex(\"hello, \" + sys.argv[1])\n\npython say.py David\n'''\n\n# check50 cs50/problems/2022/python/figlet\n# submit50 cs50/problems/2022/python/figlet\n# pip install pyfiglet\n\n'''\n\npython figlet.py test\npython figlet.py -a slant\npython figlet.py -f invalid_font\npython figlet.py -f slant, type CS50\npython figlet.py -f rectangles, then type Hello, world\npython figlet.py -f alphabet, then type Moo\n\n'''\n\n","repo_name":"newsgae/CS50_python","sub_path":"wk4/figlet/figlet.py","file_name":"figlet.py","file_ext":"py","file_size_in_byte":1650,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"8447484348","text":"from routers.member.returnValue import memberListReturnValue\nimport sql.memberSql as memSql\nfrom dotenv import load_dotenv\nimport os\nload_dotenv()\nIS_DEV = os.environ.get('IS_DEV')\npwd = \"/Users/josephkim/Desktop/bitcoin_trading_back\" if IS_DEV == \"True\" else \"/data/4season/bitcoin_trading_back\"\nimport sys\nsys.path.append(pwd) \nfrom routers.trade.tradeFn import TradeFn\n\n\nclass MemberFn():\n  async def getMemberListFn(self, bit):\n    returnValue = []\n    RawMemberList = await bit.mysql.Select(memSql.getMemberListSql)\n    for member in RawMemberList:\n      data = memberListReturnValue(member)\n      returnValue.append(data)\n    return returnValue\n\n  def controlTradingFn(self,bit, idx, status):\n    TF = TradeFn()\n    if status == 1:\n      res = TF.autoTradingOn(idx)\n    elif status == 0:\n      res = TF.autoTradingOff(bit, idx)\n    return res","repo_name":"KimTeaSick/bitcoin_tranding","sub_path":"routers/member/memberFn.py","file_name":"memberFn.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"9984687476","text":"import tensorflow as tf \r\nfrom tensorflow.python import keras\r\nimport numpy as np \r\nimport pickle as pk \r\nprint(\"finish import.\")\r\n\r\npath = \"D:\\\\python_proj\\\\Mnist\\\\result\\\\convNN_res.krmd\"\r\nconv_model = keras.models.load_model(path)\r\n\r\ndef load_test_data():\r\n    print(\"loading training data..\")\r\n    file1 = open(\"D:\\\\python_proj\\\\Mnist\\\\data\\\\test_image.pkl\", \"rb\")\r\n    list1 = pk.load(file1)\r\n    file1.close()\r\n    for i in range(10000):\r\n        list1[i] = np.reshape(list1[i], (1,28,28))\r\n        list1[i] = np.subtract(np.divide(list1[i], 64), 2)\r\n    file2 = open(\"D:\\\\python_proj\\\\Mnist\\\\data\\\\test_label.pkl\", \"rb\")\r\n    list2 = pk.load(file2)\r\n    file2.close()\r\n    return np.array(list1), np.array(list2)\r\n\r\nimgs, labels = load_test_data()\r\nlabels = keras.utils.to_categorical(labels)\r\n\r\nres = conv_model.evaluate(x=imgs, y=labels)\r\nprint()\r\nprint(\"test acc:\", res[1])\r\n\r\n###\r\n\"\"\"\r\nthis convolutionary model\r\ngot 98.8% acc on test data\r\n\"\"\"\r\n###","repo_name":"SJTUqizhenlin/MNIST","sub_path":"result/convNN_test.py","file_name":"convNN_test.py","file_ext":"py","file_size_in_byte":960,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"1838380174","text":"from splinter import Browser\nfrom bs4 import BeautifulSoup\nimport pandas as pd\n\n## initiate browser and path\ndef initiate_browser():\n    executable_path = {'executable_path':'/usr/local/bin/chromedriver'}\n    return Browser('chrome', **executable_path, headless=False)\n\ndef scrape_info():\n    browser = initiate_browser()\n\n    # Create mars_data dictionary to insert into mongo\n    mars_data_dict = {}\n    \n    # NASA Mars News\n    url = ('https://mars.nasa.gov/news/')\n    browser.visit(url)\n    html = browser.html\n    soup = BeautifulSoup(html, 'html.parser')\n\n    article = soup.find(\"div\", class_=\"list_text\")\n    news_p = article.find(\"div\", class_=\"article_teaser_body\").text\n    news_title = article.find(\"div\", class_=\"content_title\").text\n\n    mars_data_dict['news_title'] = news_title\n    mars_data_dict['news_p'] = news_p\n\n    # JPL Mars Space Images\n    url_2 = 'https://www.jpl.nasa.gov/spaceimages/?search=&category=Mars'\n    browser.visit(url_2)\n    html = browser.html\n    soup = BeautifulSoup(html, 'html.parser')\n\n    image = soup.find('img', class_='thumb')['src']\n    featured_image_url = 'https://jpl.nasa.gov'+image\n\n    mars_data_dict['featured_image_url'] = featured_image_url\n\n    # Mars Facts\n    url_3 = 'https://space-facts.com/mars/'\n    browser.visit(url_3)\n\n    read_table = pd.read_html(url_3)\n    mars_data_df = pd.DataFrame(read_table[0])\n    mars_data_df.columns=['Parameter','Values']\n    mars_data_df = mars_data_df.set_index('Parameter')\n    mars_table = mars_data_df.to_html(classes='mars_table')\n    mars_table = mars_table.replace('\\n', ' ')\n\n    mars_data_dict['mars facts'] = mars_table\n\n    # Mars Hemispheres\n    url_4 = 'https://astrogeology.usgs.gov/search/results?q=hemisphere+enhanced&k1=target&v1=Mars'\n    browser.visit(url_4)\n    html = browser.html\n    soup = BeautifulSoup(html, 'html.parser')\n\n    core_url = soup.find_all('div', class_='item')\n    titles = []\n    hemisphere_image_urls = []\n    base_url = 'https://astrogeology.usgs.gov'\n\n    for i in core_url:\n        title = i.find('h3').text\n        url = i.find('a')['href']\n        hemi_url = base_url+url\n\n        browser.visit(hemi_url)\n        html = browser.html\n        soup = BeautifulSoup(html, 'html.parser')\n        hemi_image_ori = soup.find('div', class_='downloads')\n        hem_img_url = hemi_image_ori.find('a')['href']\n\n        print(hem_img_url)\n        image_dict_info = dict({'title':title, 'image_url':hem_img_url})\n        hemisphere_image_urls.append(image_dict_info)\n\n    mars_data_dict['hemisphere_image'] = hemisphere_image_urls\n\n    browser.quit()\n    return mars_data_dict","repo_name":"KristenBlanchard/Web-Scraping-Challenge","sub_path":"Missions_to_Mars/scrape_mars.py","file_name":"scrape_mars.py","file_ext":"py","file_size_in_byte":2611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"21434583698","text":"import unittest\nimport os\n\nfrom ose.agent import Agent, load_agents\n\n\nclass Test(unittest.TestCase):\n\n    def setUp(self):\n        base_path = os.path.abspath(\n            os.path.join(os.path.dirname(__file__), os.pardir))\n        self.filename = os.path.join(\n            base_path, '../../data/test/agents_sample.json')\n\n    def tearDown(self):\n        pass\n\n    def test_agent(self):\n        agent = Agent(\n            name='Fabio',\n            role='supervisor',\n            groups=[{'id': 0, 'label': 'MEN', 'type': None}])\n\n        self.assertEqual(agent.__repr__(), \"Fabio (supervisor)\")\n\n    def test_load_agent(self):\n        agents = load_agents(self.filename)\n        self.assertEqual(len(agents), 9)\n        groups = [\n            {'id': 1, 'label': 'Ecole du chemin', 'type': 'ecole'},\n            {'id': 3, 'label': 'Enseignant Duchmol', 'type': 'classe'},\n            {'id': 5480, 'label': 'L3-Anglais', 'type': 'groupe'}\n        ]\n        self.assertEqual(agents[1].groups, groups)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"open-student-environment/open-student-environment","sub_path":"ose/agent/test/test_agent.py","file_name":"test_agent.py","file_ext":"py","file_size_in_byte":1048,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"33558914044","text":"from selenium import webdriver\nfrom selenium.webdriver.chrome.service import Service\nfrom webdriver_manager.chrome import ChromeDriverManager\n\nfrom ecomScraper.custom_logging import setup_logger\nfrom ecomScraper.custom_selectors.selenium_selectors import find_element_by_css_selector, find_elements_by_css_selector\n\nlogger = setup_logger('sense_scraper info logger', 'logs/selenium/sense.log')\n\n\ndef sense_scraper(url: str):\n    chrome_options = webdriver.ChromeOptions()\n    chrome_options.add_argument(\"--headless\")\n    user_agent = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) ' \\\n                 'Chrome/83.0.4103.116 Safari/537.36'\n    chrome_options.add_argument(f'user-agent={user_agent}')\n    browser = webdriver.Chrome(service=Service(ChromeDriverManager().install()), options=chrome_options)\n    browser.maximize_window()\n    browser.get(url)\n\n    BRAND_SELECTOR_PATH = \"a[id='pdpBrandNameText']\"\n    NAME_SELECTOR_PATH = \"h2[id='pdpProductNameText']\"\n    PRICE_SELECTOR_PATH = \"span.product-price__price\"\n    IMAGES_SELECTOR_PATH = \"div.image-wrapper img\"\n\n    brand = find_element_by_css_selector(browser, BRAND_SELECTOR_PATH, \"BRAND NAME\", logger)\n    name = find_element_by_css_selector(browser, NAME_SELECTOR_PATH, \"PRODUCT NAME\", logger)\n    price = find_element_by_css_selector(browser, PRICE_SELECTOR_PATH, \"PRODUCT PRICE\", logger)\n    images = find_elements_by_css_selector(browser, IMAGES_SELECTOR_PATH, \"PRODUCT IMAGE\", logger)\n\n    absolute_image_src = [image.get_attribute('data-srcset') for image in images]\n    while '' in absolute_image_src:\n        absolute_image_src.remove('')\n\n    item = {\n        'brand_name': brand.strip(),\n        'product_name': name.strip(),\n        'product_price': price.strip(),\n        'product_image': absolute_image_src\n    }\n    browser.close()\n    return {\"status\": \"success\", \"data\": item}\n","repo_name":"rushanshaikh98/EcommerceWebScraping","sub_path":"ecomScraper/selenium_scrapers/sense.py","file_name":"sense.py","file_ext":"py","file_size_in_byte":1890,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"16834487257","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\n# Load the CSV file\ndata = pd.read_csv(\"international_matches.csv\")\n\n# Create a new column to represent whether the home team won or not\ndata[\"host_team_won\"] = data[\"home_team_result\"] == \"Win\"\nfeatures = [\"home_team_fifa_rank\", \"away_team_fifa_rank\"]\ntarget = \"host_team_won\"\ntrain_data, test_data, train_target, test_target = train_test_split(data[features], data[target], test_size=0.2, random_state=42)\n\n# logistic regression classifier\nclf = LogisticRegression()\nclf.fit(train_data, train_target)\npredictions = clf.predict(test_data)\n\n# accuracy of the test in relation to prediction\naccuracy = accuracy_score(test_target, predictions)\nprint(f\"Accuracy of model: {accuracy}\")\n\n# win of home team\nhost_wins = test_target[test_target == True]\nhost_win_percentage = len(host_wins) / len(test_target) * 100\n\n# win of away team\naway_wins = test_target[test_target == False]\naway_win_percentage = len(away_wins) / len(test_target) * 100\n\nprint(f\"Percentage of games won by the host team: {host_win_percentage:.2f}%\")\nprint(f\"Percentage of games won by the away team: {away_win_percentage:.2f}%\")\n\n# plot the win percentages\nlabels = [\"Home team wins\", \"Away team wins\"]\nsizes = [host_win_percentage, away_win_percentage]\ncolors = [\"lightblue\", \"pink\"]\nplt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90)\nplt.axis('equal')\nplt.title(\"Win Percentage\")\nplt.show()\n","repo_name":"yoelchemla/world_cup_ML","sub_path":"win_percentage.py","file_name":"win_percentage.py","file_ext":"py","file_size_in_byte":1589,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"2545263887","text":"from PyQt5 import QtWidgets\nfrom PyQt5.QtWidgets import *\n\nfrom mainwindow_ui import Ui_MainWindow\n\n\nclass MyForm(QtWidgets.QMainWindow):\n    def __init__(self, parent=None):\n        QtWidgets.QWidget.__init__(self, parent)\n        self.ui = Ui_MainWindow()\n        self.ui.setupUi(self)\n\n    def openFileNamesDialog(self) -> str:\n        options = QFileDialog.Options()\n        options |= QFileDialog.DontUseNativeDialog\n        file_path, _ = QFileDialog.getOpenFileName(self, \"Select graph to load\", \"\",\n                                                   \"Graph Modelling Language (*.gml)\", options=options)\n        return file_path\n\n    def showErrorDialog(self, errorMessage):\n        msg = QMessageBox()\n        msg.setIcon(QMessageBox.Critical)\n        msg.setText(errorMessage)\n        msg.setWindowTitle(\"Error\")\n        msg.exec_()\n","repo_name":"kbednars/ALHE","sub_path":"myform.py","file_name":"myform.py","file_ext":"py","file_size_in_byte":842,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"44888580647","text":"# encoding: utf-8\n\"\"\"\n@author: yen-nan ho\n@contact: aaron1aaron2@gmail.com\n@gitHub: https://github.com/aaron1aaron2\n@Create Date: 2021/1/5\n\"\"\"\nimport zipfile\nimport os\nimport shutil\n\n__all__ = [\"get_data\"]\n\ndef get_data(path, file_id=None, remove_zip=True):\n    import gdown\n\n    if not file_id:\n        file_id = \"10YKwzUB61hIYvqXAdyMslmLPyVBCr84Z\" # file_id\n\n    dataset_url = 'https://drive.google.com/uc?id={}'.format(file_id)\n    \n    data_zip = os.path.join(path, \"data.zip\")\n    if not os.path.exists(path):\n        os.makedirs(path)\n    \n    try:\n        gdown.download(dataset_url, data_zip)\n    except:\n        print(\"fail to download pretrain model, please download the data at {}\".format(dataset_url))\n\n    try:\n        print('start extracting dataset ...')\n        with zipfile.ZipFile(data_zip, 'r') as zf:\n            zf.extractall(path)\n        \n        if remove_zip:\n            os.remove(data_zip)\n        \n        shutil.move(os.path.join(path, \"data\"), path)\n        os.remove(os.path.join(path, \"data\"))\n        print('the dataset is extracted at: {}'.format(path))\n    except:\n        print(\"fail to extracting dataset, please check data at {}\".format(data_zip))\n\n    \n\n    \nif __name__ == \"__main__\":\n\n    file_id = \"10YKwzUB61hIYvqXAdyMslmLPyVBCr84Z\"\n    get_data(path=\"pretrain\", file_id=file_id, remove_zip=True)\n\n\n","repo_name":"jet-c-21/Horus","sub_path":"ReID/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"19963113283","text":"class Song:\n    \"\"\"dziesma\"\"\"\n    def __init__(self, title=\"\", author=\"\", lyrics=tuple()):\n        self.title = title\n        self.author = author\n        self.lyrics = tuple(lyrics)\n        print (\"New Song made:\", title,\" - \", author)\n    \n    def sing(self, max_lines=-1):\n        print(\"SINGING\", self.title, \"-\", self.author, \":\")\n        if max_lines == -1:\n            max_lines = len(self.lyrics)\n        for line in self.lyrics[:max_lines]:\n            print(line)\n        return self\n    \n    def print_author_title(self):\n        if self.author:\n            print(self.author, end=\" - \")       \n        if self.title:\n            print(self.title)\n        else:\n            print(\"=\"*40)\n\n    def yell(self, max_lines=-1):\n        print(\"YELLING\", self.title, \"-\", self.author, \":\")\n        if max_lines == -1:\n            max_lines = len(self.lyrics)\n        for line in self.lyrics[:max_lines]:\n            print(line.upper())\n        return self\n\nsong_nr_1 = Song(\"Iron sky\", \"Paolo Nutini\"\n    , [\"We are proud individuals\"\n    , \"living on the city\"\n    , \"But the flames couldn't go much higher\"\n    , \"We find gods and religions to\"\n    , \"To paint us with salvation\"\n    , \"But no one\", \"No nobody\", \"Can give you the power\", \"....\"])\n\nsong_nr_1.sing().yell().sing(2).yell(3)\n\n\nsong_nr_2 = Song(\"Zaz\", \"Je Veux\", [\"Donnez-moi une suite au Ritz, je n'en veux pas!\"\n, \"Des bijoux de chez Chanel, je n'en veux pas\", \"Donnez-moi une limousine, j'en ferais quoi? (Papala-papapala)\"\n, \"Offrez-moi du personnel, j'en ferais quoi?\"\n, \"Un manoir à Neuchâtel, c'n'est pas pour moi\"\n, \"Offrez-moi la Tour Eiffel, j'en ferais quoi?\", \"......\"])\n\nsong_nr_2.sing().yell().sing(2).yell(3)\n\nclass Rap(Song):\n    def break_it(self,max_lines=2_000_000,drop=\"yeah\"):\n        self.print_author_title()\n        drop = \" \" + drop.upper() + \" \"\n        # if max_lines == -1:\n        #     max_lines = len(self.lyrics)\n        for line in self.lyrics[:max_lines]:\n            temp = line.split()\n            temp = drop.join(temp)\n            temp += drop\n            print(temp)  \n        return self    \n\nzrap = Rap(\"Ziemeļmeita\", \"Jumprava\", [\"Gāju meklēt ziemeļmeitu\",\"Garu, tālu ceļu veicu\"])\n\nzrap.sing().break_it().break_it(1,\"tak\").break_it(drop=\"aha\").yell(max_lines=4)","repo_name":"ValRCS/Python_RTU_08_20","sub_path":"Diena_10_Classes_Objects/d10_s27_u1.py","file_name":"d10_s27_u1.py","file_ext":"py","file_size_in_byte":2281,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"76"}
+{"seq_id":"5450493479","text":"\nimport logging\nimport os\nimport random\nimport re\nimport socket\nimport subprocess\nimport sys\nimport time\n\nimport unittest2\n\nimport mitogen.core\nimport mitogen.master\nimport mitogen.utils\n\ntry:\n    import urlparse\nexcept ImportError:\n    import urllib.parse as urlparse\n\ntry:\n    from cStringIO import StringIO\nexcept ImportError:\n    from io import StringIO\n\n\nLOG = logging.getLogger(__name__)\nDATA_DIR = os.path.join(os.path.dirname(__file__), 'data')\nsys.path.append(DATA_DIR)\n\nif mitogen.is_master:\n    mitogen.utils.log_to_file()\n\n\ndef data_path(suffix):\n    path = os.path.join(DATA_DIR, suffix)\n    if path.endswith('.key'):\n        # SSH is funny about private key permissions.\n        os.chmod(path, int('0600', 8))\n    return path\n\n\ndef subprocess__check_output(*popenargs, **kwargs):\n    # Missing from 2.6.\n    process = subprocess.Popen(stdout=subprocess.PIPE, *popenargs, **kwargs)\n    output, _ = process.communicate()\n    retcode = process.poll()\n    if retcode:\n        cmd = kwargs.get(\"args\")\n        if cmd is None:\n            cmd = popenargs[0]\n        raise subprocess.CalledProcessError(retcode, cmd)\n    return output\n\nif hasattr(subprocess, 'check_output'):\n    subprocess__check_output = subprocess.check_output\n\n\ndef wait_for_port(\n        host,\n        port,\n        pattern=None,\n        connect_timeout=0.5,\n        receive_timeout=0.5,\n        overall_timeout=5.0,\n        sleep=0.1,\n        ):\n    \"\"\"Attempt to connect to host/port, for upto overall_timeout seconds.\n    If a regex pattern is supplied try to find it in the initial data.\n    Return None on success, or raise on error.\n    \"\"\"\n    start = time.time()\n    end = start + overall_timeout\n    addr = (host, port)\n\n    while time.time() < end:\n        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        sock.settimeout(connect_timeout)\n        try:\n            sock.connect(addr)\n        except socket.error:\n            # Failed to connect. So wait then retry.\n            time.sleep(sleep)\n            continue\n\n        if not pattern:\n            # Success: We connected & there's no banner check to perform.\n            sock.shutdown(socket.SHUTD_RDWR)\n            sock.close()\n            return\n\n        sock.settimeout(receive_timeout)\n        data = mitogen.core.b('')\n        found = False\n        while time.time() < end:\n            try:\n                resp = sock.recv(1024)\n            except socket.timeout:\n                # Server stayed up, but had no data. Retry the recv().\n                continue\n\n            if not resp:\n                # Server went away. Wait then retry the connection.\n                time.sleep(sleep)\n                break\n\n            data += resp\n            if re.search(mitogen.core.b(pattern), data):\n                found = True\n                break\n\n        try:\n            sock.shutdown(socket.SHUT_RDWR)\n        except socket.error:\n            e = sys.exc_info()[1]\n            # On Mac OS X - a BSD variant - the above code only succeeds if the\n            # operating system thinks that the socket is still open when\n            # shutdown() is invoked. If Python is too slow and the FIN packet\n            # arrives before that statement can be reached, then OS X kills the\n            # sock.shutdown() statement with:\n            #\n            #    socket.error: [Errno 57] Socket is not connected\n            #\n            # Protect shutdown() with a try...except that catches the\n            # socket.error, test to make sure Errno is right, and ignore it if\n            # Errno matches.\n            if e.errno == 57:\n                pass\n            else:\n                raise\n        sock.close()\n\n        if found:\n            # Success: We received the banner & found the desired pattern\n            return\n    else:\n        # Failure: The overall timeout expired\n        if pattern:\n            raise socket.timeout('Timed out while searching for %r from %s:%s'\n                                 % (pattern, host, port))\n        else:\n            raise socket.timeout('Timed out while connecting to %s:%s'\n                                 % (host, port))\n\n\ndef sync_with_broker(broker, timeout=10.0):\n    \"\"\"\n    Insert a synchronization barrier between the calling thread and the Broker\n    thread, ensuring it has completed at least one full IO loop before\n    returning.\n\n    Used to block while asynchronous stuff (like defer()) happens on the\n    broker.\n    \"\"\"\n    sem = mitogen.core.Latch()\n    broker.defer(sem.put, None)\n    sem.get(timeout=10.0)\n\n\nclass CaptureStreamHandler(logging.StreamHandler):\n    def __init__(self, *args, **kwargs):\n        super(CaptureStreamHandler, self).__init__(*args, **kwargs)\n        self.msgs = []\n\n    def emit(self, msg):\n        self.msgs.append(msg)\n        return super(CaptureStreamHandler, self).emit(msg)\n\n\nclass LogCapturer(object):\n    def __init__(self, name=None):\n        self.sio = StringIO()\n        self.logger = logging.getLogger(name)\n        self.handler = CaptureStreamHandler(self.sio)\n        self.old_propagate = self.logger.propagate\n        self.old_handlers = self.logger.handlers\n        self.old_level = self.logger.level\n\n    def start(self):\n        self.logger.handlers = [self.handler]\n        self.logger.propagate = False\n        self.logger.level = logging.DEBUG\n\n    def raw(self):\n        return self.sio.getvalue()\n\n    def msgs(self):\n        return self.handler.msgs\n\n    def __enter__(self):\n        self.start()\n        return self\n\n    def __exit__(self, _1, _2, _3):\n        self.stop()\n\n    def stop(self):\n        self.logger.level = self.old_level\n        self.logger.handlers = self.old_handlers\n        self.logger.propagate = self.old_propagate\n        return self.raw()\n\n\nclass TestCase(unittest2.TestCase):\n    def assertRaises(self, exc, func, *args, **kwargs):\n        \"\"\"Like regular assertRaises, except return the exception that was\n        raised. Can't use context manager because tests must run on Python2.4\"\"\"\n        try:\n            func(*args, **kwargs)\n        except exc:\n            e = sys.exc_info()[1]\n            return e\n        except BaseException:\n            LOG.exception('Original exception')\n            e = sys.exc_info()[1]\n            assert 0, '%r raised %r, not %r' % (func, e, exc)\n        assert 0, '%r did not raise %r' % (func, exc)\n\n\ndef get_docker_host():\n    url = os.environ.get('DOCKER_HOST')\n    if url in (None, 'http+docker://localunixsocket'):\n        return 'localhost'\n\n    parsed = urlparse.urlparse(url)\n    return parsed.netloc.partition(':')[0]\n\n\nclass DockerizedSshDaemon(object):\n    image = None\n\n    def get_image(self):\n        if not self.image:\n            distro = os.environ.get('MITOGEN_TEST_DISTRO', 'debian')\n            self.image = 'mitogen/%s-test' % (distro,)\n        return self.image\n\n    # 22/tcp -> 0.0.0.0:32771\n    PORT_RE = re.compile(r'([^/]+)/([^ ]+) -> ([^:]+):(.*)')\n    port = None\n\n    def _get_container_port(self):\n        s = subprocess__check_output(['docker', 'port', self.container_name])\n        for line in s.decode().splitlines():\n            dport, proto, baddr, bport = self.PORT_RE.match(line).groups()\n            if dport == '22' and proto == 'tcp':\n                self.port = int(bport)\n\n        self.host = self.get_host()\n        if self.port is None:\n            raise ValueError('could not find SSH port in: %r' % (s,))\n\n    def start_container(self):\n        self.container_name = 'mitogen-test-%08x' % (random.getrandbits(64),)\n        args = [\n            'docker',\n            'run',\n            '--detach',\n            '--privileged',\n            '--publish-all',\n            '--name', self.container_name,\n            self.get_image()\n        ]\n        subprocess__check_output(args)\n        self._get_container_port()\n\n    def __init__(self):\n        self.start_container()\n\n    def get_host(self):\n        return get_docker_host()\n\n    def wait_for_sshd(self):\n        wait_for_port(self.get_host(), self.port, pattern='OpenSSH')\n\n    def close(self):\n        args = ['docker', 'rm', '-f', self.container_name]\n        subprocess__check_output(args)\n\n\nclass BrokerMixin(object):\n    broker_class = mitogen.master.Broker\n\n    def setUp(self):\n        super(BrokerMixin, self).setUp()\n        self.broker = self.broker_class()\n\n    def tearDown(self):\n        self.broker.shutdown()\n        self.broker.join()\n        super(BrokerMixin, self).tearDown()\n\n    def sync_with_broker(self):\n        sync_with_broker(self.broker)\n\n\nclass RouterMixin(BrokerMixin):\n    router_class = mitogen.master.Router\n\n    def setUp(self):\n        super(RouterMixin, self).setUp()\n        self.router = self.router_class(self.broker)\n\n\nclass DockerMixin(RouterMixin):\n    @classmethod\n    def setUpClass(cls):\n        super(DockerMixin, cls).setUpClass()\n        cls.dockerized_ssh = DockerizedSshDaemon()\n        cls.dockerized_ssh.wait_for_sshd()\n\n    @classmethod\n    def tearDownClass(cls):\n        cls.dockerized_ssh.close()\n        super(DockerMixin, cls).tearDownClass()\n\n    def docker_ssh(self, **kwargs):\n        kwargs.setdefault('hostname', self.dockerized_ssh.host)\n        kwargs.setdefault('port', self.dockerized_ssh.port)\n        kwargs.setdefault('check_host_keys', 'ignore')\n        kwargs.setdefault('ssh_debug_level', 3)\n        return self.router.ssh(**kwargs)\n\n    def docker_ssh_any(self, **kwargs):\n        return self.docker_ssh(\n            username='mitogen__has_sudo_nopw',\n            password='has_sudo_nopw_password',\n        )\n","repo_name":"ConnectBox/wifi-test-framework","sub_path":"ansible/plugins/mitogen-0.2.3/tests/testlib.py","file_name":"testlib.py","file_ext":"py","file_size_in_byte":9522,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"39012802963","text":"import numpy as np\r\nimport pandas as pd\r\nfrom sklearn.tree import DecisionTreeRegressor\r\nfrom sklearn.linear_model import LinearRegression, Lasso\r\nfrom sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\r\nfrom sklearn.model_selection import GridSearchCV\r\nfrom sklearn.model_selection import train_test_split, cross_val_score\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\r\n\r\ndf_3 = pd.read_csv(\"D:/DS Contests/1 Accenture/4 Datasets by Others/DOE data 3.csv\")\r\ndf_3 = df_3.drop(columns=[\"Unnamed: 0\"])\r\ndf_3[\"YYYY-MM\"] = pd.to_datetime(df_3[\"YYYY-MM\"])\r\n\r\n\r\ndf_3_Luzon = df_3.loc[df_3[\"Grid\"] == \"Luzon\"].sort_values(by=\"YYYY-MM\")\r\ntime = df_3_Luzon[\"YYYY-MM\"]\r\n# print(len(time))\r\n\r\ndf_3_Luzon_Peak_Demand = df_3_Luzon.drop(columns=\"YYYY-MM\")\r\ndf_3_Luzon_Peak_Demand = df_3_Luzon_Peak_Demand.drop(columns=\"Grid\")\r\n\r\nfuture_days = 40\r\ndf_3_Luzon_Peak_Demand[\"Prediction\"] = df_3_Luzon_Peak_Demand[[\"Peak Demand (MW)\"]].shift(-future_days)\r\n# print(df_3_Luzon_Peak_Demand)\r\nX = np.array(df_3_Luzon_Peak_Demand.drop([\"Prediction\"], 1))[:-future_days]\r\n# print(len(X))\r\ny = np.array(df_3_Luzon_Peak_Demand[\"Prediction\"])[:-future_days]\r\n# print(len(y))\r\n\r\nx_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.20, shuffle=False)\r\n\r\nfrom sklearn.linear_model import SGDRegressor\r\nfrom sklearn.kernel_ridge import KernelRidge\r\nfrom sklearn.linear_model import ElasticNet\r\nfrom sklearn.linear_model import BayesianRidge\r\nfrom sklearn.svm import SVR\r\nfrom lightgbm import LGBMRegressor\r\n\r\nlgbm = LGBMRegressor().fit(x_train, y_train)\r\nkr = KernelRidge().fit(x_train, y_train)\r\nen = ElasticNet().fit(x_train, y_train)\r\nbr = BayesianRidge().fit(x_train, y_train)\r\nsvr = SVR().fit(x_train, y_train)\r\n\r\ntree = DecisionTreeRegressor().fit(x_train, y_train)\r\nlr = LinearRegression().fit(x_train, y_train)\r\ncvs = np.mean(cross_val_score(lr, x_train, y_train, scoring=\"neg_mean_absolute_error\"))\r\nprint(cvs)\r\nrf = RandomForestRegressor().fit(x_train, y_train)\r\n# lasso = Lasso(alpha=1).fit(x_train, y_train)\r\n# csv_lasso = np.mean(cross_val_score(lasso, x_train, y_train, scoring=\"neg_mean_absolute_error\"))\r\n# print(csv_lasso)\r\nlasso = Lasso(alpha=1).fit(x_train, y_train)\r\ncsv_lasso = np.mean(cross_val_score(lasso, x_train, y_train, scoring=\"neg_mean_absolute_error\"))\r\nprint(csv_lasso)\r\nxgb = GradientBoostingRegressor().fit(x_train, y_train)\r\n\r\n\r\ndef find_alpha():\r\n    alpha = []\r\n    error = []\r\n    b = 8e-10\r\n    for i in range(1, 100):\r\n        alpha.append(b * i)\r\n        lml = Lasso(alpha=(b * i))\r\n        error.append(np.mean(cross_val_score(lml, x_train, y_train, scoring=\"neg_mean_absolute_error\")))\r\n\r\n    plt.plot(alpha, error)\r\n    plt.show()\r\n\r\n\r\nparameters = {\"n_estimators\": range(10, 100, 10), \"criterion\": (\"mse\", \"mae\"), \"max_features\": (\"auto\", \"sqrt\")}\r\n# gs = GridSearchCV(rf, parameters, scoring=\"neg_mean_absolute_error\").fit(x_train, y_train)\r\n\r\nx_future = df_3_Luzon_Peak_Demand.drop([\"Prediction\"], 1)[:-future_days]\r\nx_future = x_future.tail(future_days)\r\nx_future = np.array(x_future)\r\n# print(len(x_future))\r\n# print(len(x_test))\r\n\r\nlgbm_prediction = lgbm.predict(x_future)\r\nkr_prediction = kr.predict(x_future)\r\nen_prediction = en.predict(x_future)\r\nbr_prediction = br.predict(x_future)\r\nsvr_prediction = svr.predict(x_future)\r\n\r\ntree_prediction = tree.predict(x_future)\r\nlr_prediction = lr.predict(x_future)\r\nlasso_prediction = lasso.predict(x_future)\r\nxgb_prediction = xgb.predict(x_future)\r\nrf_prediction = rf.predict(x_future)\r\n# gs_rf_prediction = gs.best_estimator_.predict(x_future)\r\navg_prediction = (0*lasso_prediction + 2*lr_prediction + xgb_prediction)/3\r\navg_prediction_2 = (0*lasso_prediction + 2*lr_prediction + rf_prediction)/3\r\n# print(x_test[-45:-35])\r\n# print(x_future[-45:-35])\r\n\r\n\r\ndef plot_year_vs_PD(method, title):\r\n    valid = df_3_Luzon_Peak_Demand[X.shape[0]:]\r\n    valid[\"Predictions\"] = method\r\n    # print(time[len(time) - future_days:])\r\n    plt.plot(time, df_3_Luzon_Peak_Demand.loc[:, \"Peak Demand (MW)\"], label=\"Actual\")\r\n    # plt.plot(time[len(time)-future_days:], valid[\"Peak Demand (MW)\"])\r\n    plt.plot(time[len(time) - future_days:], valid[\"Predictions\"], label=\"Predictions\")\r\n    plt.legend()\r\n    plt.title(title)\r\n    plt.show()\r\n    rmse = mean_squared_error(valid[\"Peak Demand (MW)\"], valid[\"Predictions\"])\r\n    mae = mean_absolute_error(valid[\"Peak Demand (MW)\"], valid[\"Predictions\"])\r\n    # print(title, \" rmse: \", rmse)\r\n    print(title, \" mae: \", mae)\r\n    # print(y_test)\r\n    # print(valid[\"Peak Demand (MW)\"])\r\n\r\n\r\n# plot_year_vs_PD(tree_prediction, \"Decision Tree Regression\")\r\nplot_year_vs_PD(lgbm_prediction, \"lgbm Regression\")\r\nplot_year_vs_PD(kr_prediction, \"KR Regression\")\r\nplot_year_vs_PD(en_prediction, \"EN Regression\")\r\nplot_year_vs_PD(br_prediction, \"BR Regression\")\r\nplot_year_vs_PD(svr_prediction, \"SVR Regression\")\r\n# plot_year_vs_PD(avg_prediction, \"Average of 2xOLS and XGB\")\r\n# plot_year_vs_PD(avg_prediction_2, \"Average of 2xOLS and RF\")\r\n","repo_name":"csvaldellon/RenewabALL","sub_path":"ML C DOE data 7.py","file_name":"ML C DOE data 7.py","file_ext":"py","file_size_in_byte":5035,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"6116873590","text":"import json\nimport numpy as np\nimport os\nimport re\nimport unicodedata\nimport random\n\ndef unicodeToAscii(s):\n    return ''.join(\n        c for c in unicodedata.normalize('NFD', s)\n        if unicodedata.category(c) != 'Mn'\n    )\n\ndef normalizeString(s):\n    s = unicodeToAscii(s.lower().strip())\n    s = re.sub(r\"([.!?])\", r\"\", s)\n    s = re.sub(r\"[^a-zA-Z.!?]+\", r\" \", s)\n    s = re.sub(r\"\\s+\", r\" \", s).strip()\n    return s\n\ndef readLabel(dataPath):\n    vocabs = {}\n    padding = {}\n    vocabs_count = 0\n    with open(dataPath) as jsonFile:\n        label = json.loads(jsonFile.read())\n        for index, item in enumerate(label):\n            print(\"\\rIteration: {}, read words in {}\".format(index+1, item['id']), end='', flush=True)\n            for s in item['caption']:\n                words = normalizeString(s).split(' ')\n                if len(words) < 30:\n                    for w in words:\n                        if w not in vocabs:\n                            vocabs[w] = vocabs_count\n                            vocabs_count += 1\n        vocabs['EOS'] = vocabs_count\n        vocabs['BOS'] = vocabs_count + 1\n        print('\\nTotal words: {}'.format(len(vocabs)))\n        for index, item in enumerate(label):\n            print(\"\\rIteration: {}, padding words in {}\".format(index+1, item['id']), end='', flush=True)\n            padding[item['id']] = []\n            for s in item['caption']:\n                words = normalizeString(s).split(' ')\n                if len(words) < 9:\n                    words.insert(0,'BOS')\n                    words.append('EOS')\n                    one = np.zeros((10,len(vocabs)))\n                    for w_index, w in enumerate(words):\n                        one[w_index][vocabs[w]] = 1\n                    padding[item['id']].append(one)\n        print()\n        return padding, vocabs\n\ndef readTraingFeature():\n    training_Y_dic, vocabs = readLabel('./data/training_label.json')\n    training_Y = []\n    training_data = []\n    for dirPath, dirNames, fileNames in os.walk(\"./data/training_data/feat/\"):\n        for index, f in enumerate(fileNames):\n            print(\"\\rIteration: {}, read {}\".format(index+1, f), end='', flush=True)\n            data = np.load(dirPath + f)\n            random_caption_index = random.randint(0, len(training_Y_dic[f[:-4]])-1)\n            training_Y.append(training_Y_dic[f[:-4]][random_caption_index])\n            training_data.append(data)\n    print(\"\\nTransform to np array\")\n    training_data = np.array(training_data)\n    training_Y = np.array(training_Y)\n    return training_data, training_Y\n\nif __name__ == '__main__':\n    readTraingFeature()\n","repo_name":"ryanC1993/ADLxMLDS2017","sub_path":"hw2/parseData.py","file_name":"parseData.py","file_ext":"py","file_size_in_byte":2626,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"13484659994","text":"#!/usr/bin/python\r\nimport sqlite3\r\nfrom sqlite3 import Error\r\nfrom SafeZone.logs import logger\r\n\r\n\r\ndb_path = r\"C:\\Shares\\School\\Vakken\\p3\\Project\\SafeZone\\db\\Project.db\"\r\n\r\n\r\ndef create_connection(db_file):\r\n    global conn\r\n    \"\"\" create a database connection to a SQLite database \"\"\"\r\n    conn = None\r\n    try:\r\n        conn = sqlite3.connect(db_file, timeout=10)\r\n    except Error as e:\r\n        logger.warning(e)\r\n    finally:\r\n        return conn\r\n\r\n\r\ndef submit_data_to_db(R_ID, T_ID, ts):\r\n    try:\r\n        conn = create_connection(db_path)\r\n        sql = \"INSERT INTO hits(Reader_ID, Tag_ID, ts) VALUES('{}',{},{})\".format(str(R_ID), T_ID, str(ts))\r\n        conn.execute(sql)\r\n        conn.commit()\r\n        conn.close()\r\n    except Exception as e:\r\n        logger.warning(e)\r\n\r\n\r\ndef submit_reader_to_db(R_ID,ts):\r\n    try:\r\n        conn = create_connection(db_path)\r\n        sql = \"INSERT INTO readers(Reader_ID, last_online) VALUES('{}',{})\".format(str(R_ID), str(ts))\r\n        conn.execute(sql)\r\n        conn.commit()\r\n        conn.close()\r\n    except Exception as e:\r\n        logger.warning(e)\r\n\r\n\r\ndef update_reader_last_seen(R_ID,ts):\r\n    try:\r\n        conn = create_connection(db_path)\r\n        sql = \"UPDATE readers SET last_online = '{}' WHERE Reader_ID = '{}'\".format(str(ts), str(R_ID))\r\n        conn.execute(sql)\r\n        conn.commit()\r\n        conn.close()\r\n    except Exception as e:\r\n        logger.warning(e)\r\n\r\n\r\ndef get_person_from_T_id(T_ID):\r\n    try:\r\n        conn = create_connection(db_path)\r\n        cur = conn.cursor()\r\n        cur.execute(\"SELECT Name FROM clients WHERE Tag = {}\".format(T_ID))\r\n        query_result = cur.fetchone()\r\n        conn.close()\r\n        if query_result is not None:\r\n            return query_result\r\n        else:\r\n            return False\r\n    except Exception as e:\r\n        logger.warning(e)\r\n        return False\r\n\r\n\r\ndef get_all_clients():\r\n    try:\r\n        conn = create_connection(db_path)\r\n        cur = conn.cursor()\r\n        cur.execute(\"SELECT Name, Tag, ID FROM clients\")\r\n        query_result = cur.fetchall()\r\n        conn.close()\r\n        return query_result\r\n    except Exception as e:\r\n        logger.warning(e)\r\n        return False\r\n\r\n\r\ndef get_all_readers():\r\n    try:\r\n        conn = create_connection(db_path)\r\n        cur = conn.cursor()\r\n        cur.execute(\"SELECT Reader_ID, last_online FROM readers\")\r\n        query_result = cur.fetchall()\r\n        conn.close()\r\n        return query_result\r\n    except Exception as e:\r\n        logger.warning(e)\r\n        return False\r\n\r\ndef get_last_hit(client_id):\r\n    conn = create_connection(db_path)\r\n    cur = conn.cursor()\r\n    cur.execute(\"SELECT Tag FROM clients WHERE ID = {};\".format(client_id))\r\n    query_result = cur.fetchone()\r\n    conn.close()\r\n    conn = create_connection(db_path)\r\n    cur = conn.cursor()\r\n    cur.execute(\"SELECT max(ts) Time, Reader_ID FROM hits WHERE Tag_ID = {};\".format(query_result[0]))\r\n    query_result = cur.fetchone()\r\n    conn.close()\r\n    return [query_result[0], query_result[1]]\r\n\r\n","repo_name":"AboveColin/SafeZone","sub_path":"SafeZone/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":3060,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"74052315125","text":"#!/usr/bin/python\n# encoding: utf-8\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport collections\nfrom PIL import Image\nimport numpy as np\nimport random\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import sampler\nimport torchvision.transforms as transforms\nimport lmdb\nimport six\nimport sys\nimport cv2\n\n\nclass lmdbDataset(Dataset):\n\n    def __init__(self, root=None, transform=None, target_transform=None):\n        self.env = lmdb.open(\n            root,\n            max_readers=1,\n            readonly=True,\n            lock=False,\n            readahead=False,\n            meminit=False)\n\n        if not self.env:\n            print('cannot creat lmdb from %s' % (root))\n            sys.exit(0)\n\n        with self.env.begin(write=False) as txn:\n            nSamples = int(txn.get('num-samples'.encode()))\n            self.nSamples = nSamples\n\n        self.transform = transform\n        self.target_transform = target_transform\n\n    def __len__(self):\n        return self.nSamples\n\n    def __getitem__(self, index):\n        assert index <= len(self), 'index range error'\n        index += 1\n        with self.env.begin(write=False) as txn:\n            img_key = 'image-%09d' % index\n            imgbuf = txn.get(img_key.encode())\n\n            buf = six.BytesIO()\n            buf.write(imgbuf)\n            buf.seek(0)\n            try:\n                img = Image.open(buf).convert('L')\n            except IOError:\n                print('Corrupted image for %d' % index)\n                return self[index + 1]\n\n            if self.transform is not None:\n                img = self.transform(img)\n\n            label_key = 'label-%09d' % index\n            label = txn.get(label_key.encode()).decode()\n\n            if self.target_transform is not None:\n                label = self.target_transform(label)\n\n        return (img, label)\n\n\nclass resizeNormalize(object):\n\n    def __init__(self, size_h, size_w=None, interpolation=Image.BILINEAR):\n        self.size_h = size_h\n        self.size_w = size_w\n        self.interpolation = interpolation\n        self.toTensor = transforms.ToTensor()\n\n    def __call__(self, img):\n        '''\n        # 竖版文字支持，转90度处理\n        w, h = img.size\n        if h > w*1.5:\n            img = img.rotate(90, expand=1)\n\n        img = img.resize(self.size, self.interpolation)\n\n        img = self.toTensor(img)\n        img.sub_(0.5).div_(0.5)\n        return img\n        '''\n\n        # 竖版文字支持，转90度处理\n        w, h = img.size\n        if h > w*1.5:\n            img = img.rotate(90, expand=1)\n\n        if self.size_w == None:\n            scale = img.size[1]*1.0 / self.size_h\n            w = img.size[0] / scale\n            w = int(w)\n            img = img.resize((w, self.size_h), self.interpolation)\n        else:\n            img = img.resize((self.size_w, self.size_h), self.interpolation)\n\n        img = (np.array(img)/255.0-0.5)/0.5\n        img = torch.Tensor(img).view(1, img.shape[0], img.shape[1])\n        return img\n\n\nclass strLabelConverter(object):\n    \"\"\"Convert between str and label.\n\n    NOTE:\n        Insert `blank` to the alphabet for CTC.\n\n    Args:\n        alphabet (str): set of the possible characters.\n        ignore_case (bool, default=True): whether or not to ignore all of the case.\n    \"\"\"\n\n    def __init__(self, alphabet, ignore_case=False):\n        self._ignore_case = ignore_case\n        if self._ignore_case:\n            alphabet = alphabet.lower()\n        self.alphabet = alphabet  # + '-'  # for `-1` index\n\n        self.dict = {}\n        for i, char in enumerate(alphabet):\n            # NOTE: 0 is reserved for 'blank' required by wrap_ctc\n            self.dict[char] = i\n\n    def encode(self, text):\n        \"\"\"Support batch or single str.\n\n        Args:\n            text (str or list of str): texts to convert.\n\n        Returns:\n            torch.IntTensor [length_0 + length_1 + ... length_{n - 1}]: encoded texts.\n            torch.IntTensor [n]: length of each text.\n        \"\"\"\n        if isinstance(text, str):\n            # print(str(text))\n            text = [\n                self.dict[char.lower() if self._ignore_case else char]\n                for char in text\n            ]\n            length = [len(text)]\n        elif isinstance(text, collections.Iterable):\n            length = [len(s) for s in text]\n            text = ''.join(text)\n            text, _ = self.encode(text)\n        return (torch.IntTensor(text), torch.IntTensor(length))\n\n    def decode(self, t, length, raw=False):\n        \"\"\"Decode encoded texts back into strs.\n\n        Args:\n            torch.IntTensor [length_0 + length_1 + ... length_{n - 1}]: encoded texts.\n            torch.IntTensor [n]: length of each text.\n\n        Raises:\n            AssertionError: when the texts and its length does not match.\n\n        Returns:\n            text (str or list of str): texts to convert.\n        \"\"\"\n        if length.numel() == 1:\n            length = length[0]\n            assert t.numel() == length, \"text with length: {} does not match declared length: {}\".format(\n                t.numel(), length)\n            if raw:\n                # for i in t:\n                #    print(self.alphabet[i])\n                return ''.join([self.alphabet[i] for i in t])\n            else:\n                char_list = []\n                for i in range(length):\n                    if t[i] != len(self.alphabet) - 1 and (not (i > 0 and t[i - 1] == t[i])):\n                        char_list.append(self.alphabet[t[i]])\n                return ''.join(char_list)\n        else:\n            # batch mode\n            assert t.numel() == length.sum(\n            ), \"texts with length: {} does not match declared length: {}\".format(t.numel(), length.sum())\n            texts = []\n            index = 0\n            for i in range(length.numel()):\n                l = length[i]\n                texts.append(\n                    self.decode(\n                        t[index:index + l], torch.IntTensor([l]), raw=raw))\n                index += l\n            return texts\n\n\nclass averager(object):\n    \"\"\"Compute average for `torch.Variable` and `torch.Tensor`. \"\"\"\n\n    def __init__(self):\n        self.reset()\n\n    def add(self, v):\n        if isinstance(v, Variable):\n            count = v.data.numel()\n            v = v.data.sum()\n        elif isinstance(v, torch.Tensor):\n            count = v.numel()\n            v = v.sum()\n\n        self.n_count += count\n        self.sum += v\n\n    def reset(self):\n        self.n_count = 0\n        self.sum = 0\n\n    def val(self):\n        res = 0\n        if self.n_count != 0:\n            res = self.sum / float(self.n_count)\n        return res\n","repo_name":"zhaobomin/pytorch-ocr","sub_path":"model_crnn/helper/dataset_crnn.py","file_name":"dataset_crnn.py","file_ext":"py","file_size_in_byte":6679,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"76"}
+{"seq_id":"26137588711","text":"#https://school.programmers.co.kr/learn/courses/30/lessons/12902#\ndef solution(n):\n    if n%2 : return 0\n    else:\n        dp = [2]*(n//2)\n        dp[0]=3\n        s = 0 \n        for i in range(n//2-1):\n            dp[i+1] += (dp[i]*3 +s)%1000000007\n            s+=dp[i]*2%1000000007\n    return dp[-1]\n","repo_name":"jun981015/algorithm","sub_path":"programmers/3n타일링.py","file_name":"3n타일링.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"33917569116","text":"\"\"\"\nA binary tree is univalued if every node in the tree has the same value. Return true if and only if the given tree is univalued.\n\nExample 1:\n\n    Input: [1,1,1,1,1,null,1]\n    Output: true\n\nExample 2:\n\n    Input: [2,2,2,5,2]\n    Output: false\n\nNote:\n    1. The number of nodes in the given tree will be in the range [1, 100].\n    2. Each node's value will be an integer in the range [0, 99].\n\"\"\"\n\n\nclass TreeNode:\n    \"\"\"Definition for a binary tree node.\"\"\"\n    def __init__(self, val=0, left=None, right=None):\n        self.val = val\n        self.left = left\n        self.right = right\n\n\nclass Solution:\n    def isUnivalTree(self, root: TreeNode) -> bool:\n        self.value = root.val\n        def helper(node):\n            if node:\n                if node.val != self.value:\n                    return False\n                else:\n                    return helper(node.left) and helper(node.right)\n            else:\n                return True\n        \n        return helper(root)\n","repo_name":"chaosWsF/Python-Practice","sub_path":"leetcode/0965_univalued_binary_tree.py","file_name":"0965_univalued_binary_tree.py","file_ext":"py","file_size_in_byte":988,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"71835682487","text":"import pytest\nimport torch\nimport torch.nn.functional as F\nfrom einops import rearrange\n\nfrom pipegoose.distributed.parallel_context import ParallelContext\nfrom pipegoose.distributed.parallel_mode import ParallelMode\nfrom pipegoose.nn.tensor_parallel.loss import VocabParallelCrossEntropy\nfrom pipegoose.testing.utils import spawn\n\n\ndef check_equal(A, B):\n    assert torch.allclose(A, B, rtol=1e-3, atol=1e-1) or torch.allclose(A, B)\n\n\ndef run_parallel_cross_entropy(\n    rank, world_size, port, tensor_parallel_size, pipeline_parallel_size, data_parallel_size, logits, targets, loss, grads\n):\n    def get_partition(logits):\n        local_world_size = parallel_context.get_world_size(parallel_mode=ParallelMode.TENSOR)\n        per_partition = N_LABELS // local_world_size\n        chunks = torch.split(logits, per_partition, dim=-1)\n        return chunks[local_rank]\n\n    parallel_context = ParallelContext(\n        rank=rank,\n        local_rank=rank,\n        world_size=world_size,\n        local_world_size=world_size,\n        host=\"localhost\",\n        port=port,\n        seed=69,\n        backend=\"gloo\",\n        tensor_parallel_size=tensor_parallel_size,\n        pipeline_parallel_size=pipeline_parallel_size,\n        data_parallel_size=data_parallel_size,\n    )\n\n    local_rank = parallel_context.get_local_rank(parallel_mode=ParallelMode.TENSOR)\n    ranks_in_group = parallel_context.get_ranks_in_group(parallel_mode=ParallelMode.TENSOR)\n\n    if local_rank in ranks_in_group:\n        N_LABELS = logits.shape[-1]\n        parallel_logits = get_partition(logits)\n        parallel_logits.requires_grad = True\n\n        parallel_cross_entropy = VocabParallelCrossEntropy(parallel_context=parallel_context)\n        parallel_loss = parallel_cross_entropy(parallel_logits, targets)\n\n        assert torch.allclose(parallel_loss, loss)\n\n        # parallel_loss.backward()\n        # assert torch.allclose(parallel_logits.grad.data, get_partition(grads))\n\n\n@pytest.mark.parametrize(\"tensor_parallel_size\", [1, 2])\ndef test_parallel_cross_entropy(tensor_parallel_size):\n    PIPELINE_PARALLEL_SIZE = 1\n    DATA_PARALLEL_SIZE = 1\n\n    BATCH_SIZE = 1\n    SEQ_LEN = 2\n    VOCAB_SIZE = 4\n\n    torch.manual_seed(69)\n\n    logits = torch.randn(BATCH_SIZE, SEQ_LEN, VOCAB_SIZE, requires_grad=True)\n    targets = torch.randint(0, VOCAB_SIZE, (BATCH_SIZE, SEQ_LEN))\n\n    loss = F.cross_entropy(\n        rearrange(logits, \"batch_size seq_len vocab_size -> (batch_size seq_len) vocab_size\"),\n        rearrange(targets, \"batch_size seq_len -> (batch_size seq_len)\"),\n    )\n\n    loss.backward()\n    grads = logits.grad.data\n\n    spawn(\n        run_parallel_cross_entropy,\n        world_size=tensor_parallel_size,\n        tensor_parallel_size=tensor_parallel_size,\n        pipeline_parallel_size=PIPELINE_PARALLEL_SIZE,\n        data_parallel_size=DATA_PARALLEL_SIZE,\n        logits=logits.detach(),\n        targets=targets,\n        loss=loss.detach(),\n        grads=grads.detach(),\n    )\n","repo_name":"xrsrke/pipegoose","sub_path":"tests/nn/tensor_parallel/test_loss.py","file_name":"test_loss.py","file_ext":"py","file_size_in_byte":2961,"program_lang":"python","lang":"en","doc_type":"code","stars":41,"dataset":"github-code","pt":"76"}
+{"seq_id":"70662101685","text":"import os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\nos.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'\n\nimport time\nimport tensorflow as tf\nimport tensorflow.keras as keras\nfrom layer.discriminator32 import Discriminator512\nfrom layer.generator32 import Generator512\nfrom layer.loss import *\nfrom layer.dataloader import *\nfrom tqdm import tqdm\nimport numpy as np \nimport pandas as pd \nfrom tensorflow_addons.optimizers import AdamW\n\nnp.random.seed(1234)\n\nprint(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))\n\nBATCH_SIZE = 64             #32, 64, 128  \nSTEPS_PER_EPOCH = 2185      #2000 1171\nEPOCHS = 2000               #60\n# KERNEL_SIZE = (5, 5)\nGENERATOR_INPUT = (4, 4, 16) #(4, 4, 16)\nLR_GEN = 1e-4  #1e-4\nLR_DISC = 4e-4 #4e-4\n\n#num_examples_to_generate = 4\n#seed = tf.random.normal([num_examples_to_generate, 8, 8, 1])\npath = './data/training_dataset/*.png'  #*.png'\nhistory_path = './ADAM/history.xlsx' #'./ADAM/history/history.xlsx'\n#====================================================\n# lr_schedule = keras.optimizers.schedules.ExponentialDecay(\n#                                                             initial_learning_rate=1e-5,\n#                                                             decay_steps=5000,\n#                                                             decay_rate=0.9\n#                                                           )\n# lr_schedule = keras.optimizers.schedules.PiecewiseConstantDecay([8000], [1e-5, 5e-6])\n# TODO: Try AdamW done\n# TODO: Try turning on amsgrad option to TRUE -- did not work\ncross_entropy = keras.losses.BinaryCrossentropy(from_logits=False)\ngenerator_optimizer = keras.optimizers.Adam(learning_rate=LR_GEN, beta_1=0.9, beta_2=0.99)\ndiscriminator_optimizer = keras.optimizers.Adam(learning_rate=LR_DISC, beta_1=0.9, beta_2=0.99)\n# generator_optimizer = tf.keras.optimizers.RMSprop(LR_GEN)\n# discriminator_optimizer = tf.keras.optimizers.RMSprop(LR_DISC)\n# generator_optimizer = AdamW(weight_decay=1e-5, learning_rate=LR_GEN, beta_1=0.9, beta_2=0.99, amsgrad=False)\n# discriminator_optimizer = AdamW(weight_decay=1e-5, learning_rate=LR_DISC, beta_1=0.9, beta_2=0.99, amsgrad=False)\n\n\ngenerator = Generator512(generator_input_shape=GENERATOR_INPUT, kernel_size=(7, 7))\ndiscriminator = Discriminator512(discriminator_input_shape=(64, 64, 1), kernel_size=(3, 3))\n\nprint(generator.summary())\nprint(discriminator.summary())\nprint('--- Loading Data ---')\n\ndataset = generate_dataset(mode='train', file_path=path,\n                                      batch_size=BATCH_SIZE,\n                                      patch_size=64,\n                                      num_threads=16,) #16\ntime.sleep(0.5)\nprint('========================================')\ntime.sleep(0.5)\nprint('--- Successfully Loaded ---')\ntime.sleep(0.5)\nprint('========================================')\ntime.sleep(0.5)\n#checkpoint_dir = './training_checkpoints'\ncheckpoint_dir = './ADAM/ckpt_32'       #'./LR5_5/64/checkpoints16' for adamw use history_cleaned\ncheckpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\ncheckpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,\n                                 discriminator_optimizer=discriminator_optimizer,\n                                 generator=generator,\n                                 discriminator=discriminator)\n\n@tf.function\ndef train_step(images):\n    noise = tf.random.normal([BATCH_SIZE, *GENERATOR_INPUT])\n    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n        generated_images = generator(noise, training=True)\n\n        real_output = discriminator(images, training=True)\n        fake_output = discriminator(generated_images, training=True)\n\n        gen_loss = generator_loss(fake_output, cross_entropy)\n        disc_loss = discriminator_loss(real_output, fake_output, cross_entropy)\n\n    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)\n    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)\n\n    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))\n    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))\n\n    return {'gen_loss': gen_loss, \n            'disc_loss': disc_loss, \n            'gen_lr': generator_optimizer._decayed_lr(tf.float32), \n            'disc_lr': discriminator_optimizer._decayed_lr(tf.float32)}\n\ndef train(dataset, epochs, steps_per_epoch = 2000):\n    outer = tqdm(total=epochs, desc='EPOCH', leave=False, position=0)\n    history = pd.DataFrame()\n    for epoch in range(epochs):\n        inner = tqdm(total=steps_per_epoch, desc='Steps', leave=False, position=1)\n        step = 0\n        for image in dataset:\n            lr = train_step(image)\n            \n            inner.update(1)\n            if step == steps_per_epoch:\n                inner.close()\n                break\n            step = step + 1\n\n        lr['gen_loss'] = lr['gen_loss'].numpy()\n        lr['disc_loss'] = lr['disc_loss'].numpy()\n        lr['gen_lr'] = lr['gen_lr'].numpy()\n        lr['disc_lr'] = lr['disc_lr'].numpy()\n        history=history.append(lr, ignore_index=True)\n        # print(history)\n        outer.update(1)\n        checkpoint.save(file_prefix=checkpoint_prefix)\n        history.to_excel(history_path)\n\n    \n\nprint('--- Started Training ---')\ntrain(dataset, EPOCHS, STEPS_PER_EPOCH)\nprint('---Training Completed Successfully---')\n","repo_name":"mahadev1995/Heightmap-Generation-with-Spatial-GAN","sub_path":"model/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"25583448643","text":"from django.shortcuts import render, redirect\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.paginator import Paginator, EmptyPage, InvalidPage\nfrom django.http import HttpResponseRedirect\nfrom django.contrib.auth.models import User\n\nfrom myapp.modulos.principal.models import userSoftSystemProject\nfrom myapp.modulos.principal.functions import members_only\nfrom myapp.modulos.estado_3.models import StateThree, DefinicionRaiz, DefinicionRaizCATWOE\nfrom myapp.modulos.estado_3.forms import nombreDefinicionRaizForm, catwoeForm, addDefinicionRaizForm\nfrom myapp.modulos.estado_3.functions import propuestaDefinicionRaiz\nfrom myapp.modulos.estado_2.models import StateTwo\nfrom myapp.modulos.estado_1.forms import resumenAnalisisForm, comentaryForm\nfrom myapp.modulos.estado_1.models import Comentario\nfrom myapp.modulos.comunicacion.functions import notificar\n# Create your views here.\n@login_required(login_url='/login/')\ndef general_tres_view(request, id_ssp):\n\tif members_only(id_ssp, request):\n\t\tproyecto = userSoftSystemProject.objects.get(id=id_ssp)\n\t\tstateThree = StateThree.objects.get(ssp_stateThree=proyecto)\n\t\tdefinicionesRaices = stateThree.returnDefinicionesRaices()[:5]\n\t\tdestinatarios = proyecto.returnAllusers(request.user.get_username())\n\n\t\tctx = {'proyecto' : proyecto, 'destinatarios' : destinatarios, 'definicionesRaices' : definicionesRaices}\n\t\treturn render(request, 'estado_tres/estado_tres_general.html', ctx)\n\telse:\n\t\treturn render(request, 'comunicacion/error.html')\n\n@login_required(login_url='/login/')\ndef definicionesRaiz_view(request, id_ssp, page):\n\tif members_only(id_ssp, request):\n\t\tproyecto = userSoftSystemProject.objects.get(id=id_ssp)\n\t\tstateThree = StateThree.objects.get(ssp_stateThree=proyecto)\n\t\tdefinicionesRaices = stateThree.returnDefinicionesRaices()\n\t\tdestinatarios = proyecto.returnAllusers(request.user.get_username())\n\n\t\tpaginator = Paginator(definicionesRaices, 5)\n\t\ttry:\n\t\t\tpagina = int(page)\n\t\texcept:\n\t\t\tpage = 1\n\t\ttry:\n\t\t\tlist_definiciones = paginator.page(pagina)\n\t\texcept (EmptyPage, InvalidPage):\n\t\t\tlist_definiciones = paginator.page(paginator.num_pages)\n\n\t\tctx = {'proyecto' : proyecto, 'destinatarios' : destinatarios, 'definicionesRaices' : list_definiciones}\n\t\treturn render(request, 'estado_tres/estado_tres_definicionesRaices.html', ctx)\n\telse:\n\t\treturn render(request, 'comunicacion/error.html')\n\n@login_required(login_url='/login/')\ndef definicionRaiz_crear_view(request, id_ssp):\n\tif members_only(id_ssp, request):\n\t\tif request.method == \"POST\":\n\t\t\tform = nombreDefinicionRaizForm(request.POST)\n\t\t\tif form.is_valid():\n\t\t\t\tname_dr = form.cleaned_data['name_dr']\n\t\t\t\tnewDR = DefinicionRaizCATWOE.objects.create(name_dr=name_dr, created_by=request.user.get_username())\n\t\t\t\tnewDR.save()\n\n\t\t\t\tproyecto = userSoftSystemProject.objects.get(id=id_ssp)\n\t\t\t\tstateThree = StateThree.objects.get(ssp_stateThree=proyecto)\n\t\t\t\tstateThree.ssp_definicionesRaices.append(newDR.id)\n\t\t\t\tstateThree.save()\n\n\t\t\t\tnotificar(id_ssp, request.user.id, '/verDefinicionRaiz/%s/%s'%(id_ssp,newDR.id), 'Agrego una nueva Definicion Raiz', newDR.id, 'DefinicionRaiz')\n\n\n\t\t\t\treturn HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n\t\t\treturn HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n\n\telse:\n\t\treturn render(request, 'comunicacion/error.html')\n\n@login_required(login_url='/login/')\ndef definicionRaiz_eliminar_view(request, id_ssp, id_dr):\n\tif members_only(id_ssp, request):\n\t\tproyecto = userSoftSystemProject.objects.get(id=id_ssp)\n\t\tstateThree = StateThree.objects.get(ssp_stateThree=proyecto)\n\t\tdefinicionRaiz = DefinicionRaizCATWOE.objects.get(id=id_dr)\n\t\tcomentariosDefinicion = definicionRaiz.returnComments()\n\t\tdefiniciones = definicionRaiz.returnDefiniciones()\n\n\t\tfor c in comentariosDefinicion:\n\t\t\tc.delete()\n\t\t\n\t\tfor d in definiciones:\n\t\t\td.delete()\n\n\t\tdel stateThree.ssp_definicionesRaices[stateThree.ssp_definicionesRaices.index(definicionRaiz.id)]\n\n\t\tstateThree.save()\n\t\tdefinicionRaiz.delete()\n\t\treturn HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n\telse:\n\t\treturn render(request, 'comunicacion/error.html')\n\n@login_required(login_url='/login/')\ndef definicionRaiz_desarrollo_view(request, id_ssp, id_dr):\n\tif members_only( id_ssp, request):\n\t\tproyecto = userSoftSystemProject.objects.get(id=id_ssp)\n\t\tdestinatarios = proyecto.returnAllusers(request.user.get_username())\n\t\tdefinicionRaiz = DefinicionRaizCATWOE.objects.get(id=id_dr)\n\t\tstateTwo = StateTwo.objects.get(ssp_stateTwo=proyecto)\n\t\trichPictures = stateTwo.returnRichPictures()\n\t\trichPictureFinal = definicionRaiz.returnRichPicture()\n\n\t\tformCATWOE = catwoeForm(initial={\n\t\t\t\t'clientes_dr' : definicionRaiz.clientes_dr,\n\t\t\t\t'actores_dr' : definicionRaiz.actores_dr,\n\t\t\t\t'trans_input_dr' : definicionRaiz.trans_input_dr,\n\t\t\t\t'trans_output_dr' : definicionRaiz.trans_output_dr,\n\t\t\t\t'cosmo_dr' : definicionRaiz.cosmo_dr,\n\t\t\t\t'propietario_dr' : definicionRaiz.propietario_dr,\n\t\t\t\t'entorno_dr' : definicionRaiz.entorno_dr,\n\t\t\t})\n\n\t\tsugerencia = propuestaDefinicionRaiz(id_dr)\n\n\n\t\tresumenForm = resumenAnalisisForm(initial={\n\t\t\t\t'description_analisis' : definicionRaiz.description_dr\n\t\t\t})\n\t\tdefinicionesRaiz = definicionRaiz.returnDefiniciones()\n\t\tdefinicionRaizFinal = definicionRaiz.returnDefinicion()\n\t\tctx = {'proyecto' : proyecto, 'destinatarios' : destinatarios, \n\t\t\t\t'definicionRaiz' : definicionRaiz, 'richPictures' : richPictures, \n\t\t\t\t'richPictureFinal' : richPictureFinal, 'formCATWOE' : formCATWOE, \n\t\t\t\t'definicionesRaiz' : definicionesRaiz, 'definicionRaizFinal' : definicionRaizFinal,\n\t\t\t\t'resumenForm' : resumenForm, 'sugerencia' : sugerencia}\n\t\treturn render(request, 'estado_tres/estado_tres_desarrollo.html', ctx)\n\telse:\n\t\treturn render(request, 'comunicacion/error.html')\n\n@login_required(login_url='/login/')\ndef definicionRaiz_adjuntarRichPicture_view(request, id_dr):\n\tidRichPicture = request.POST['e12']\n\tdefinicionRaiz = DefinicionRaizCATWOE.objects.get(id=id_dr)\n\tdefinicionRaiz.richPicture_dr = idRichPicture\n\tdefinicionRaiz.save()\n\treturn HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n\n@login_required(login_url='/login/')\ndef definicionRaiz_adjuntarDefinicionRaiz_view(request, id_dr):\n\tidDefinicionRaiz = request.POST['e13']\n\tdefinicionRaiz = DefinicionRaizCATWOE.objects.get(id=id_dr)\n\tdefinicionRaiz.definicionFinal_dr = idDefinicionRaiz\n\tdefinicionRaiz.save()\n\treturn HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n\n@login_required(login_url='/login/')\ndef definicionRaiz_newCatwoe_view(request, id_dr):\n\tdefinicionRaiz = DefinicionRaizCATWOE.objects.get(id=id_dr)\n\tif request.method == \"POST\":\n\t\tform = catwoeForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tclientes_dr = form.cleaned_data['clientes_dr']\n\t\t\tactores_dr = form.cleaned_data['actores_dr']\n\t\t\ttrans_input_dr = form.cleaned_data['trans_input_dr']\n\t\t\ttrans_output_dr = form.cleaned_data['trans_output_dr']\n\t\t\tcosmo_dr = form.cleaned_data['cosmo_dr']\n\t\t\tpropietario_dr = form.cleaned_data['propietario_dr']\n\t\t\tentorno_dr = form.cleaned_data['entorno_dr']\n\n\t\t\tdefinicionRaiz.clientes_dr = clientes_dr\n\t\t\tdefinicionRaiz.actores_dr = actores_dr\n\t\t\tdefinicionRaiz.trans_input_dr = trans_input_dr\n\t\t\tdefinicionRaiz.trans_output_dr = trans_output_dr\n\t\t\tdefinicionRaiz.cosmo_dr = cosmo_dr\n\t\t\tdefinicionRaiz.propietario_dr = propietario_dr\n\t\t\tdefinicionRaiz.entorno_dr = entorno_dr\n\n\t\t\tdefinicionRaiz.save()\n\t\t\treturn HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n\t\treturn HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n\n\n@login_required(login_url='/login/')\ndef definicionRaiz_add_view(request, id_dr):\n\tdefinicionRaiz = DefinicionRaizCATWOE.objects.get(id=id_dr)\n\tif request.method == \"POST\":\n\t\tform = addDefinicionRaizForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tname_DR = form.cleaned_data['name_DR']\n\t\t\tdescription_DR = form.cleaned_data['description_DR']\n\t\t\tnewDR = DefinicionRaiz.objects.create(name_DR=name_DR, description_DR=description_DR, created_by=request.user.get_username())\n\t\t\tnewDR.save()\n\t\t\tdefinicionRaiz.definiciones_dr.append(newDR.id)\n\t\t\tdefinicionRaiz.save()\n\t\t\treturn HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n\t\treturn HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n\n\n@login_required(login_url='/login/')\ndef DF_eliminar_view(request, id_ssp,id_dr, id_dr2):\n\tif members_only(id_ssp, request):\n\t\tdefinicionRaiz = DefinicionRaiz.objects.get(id=id_dr2)\n\t\tdefinicionRaizCat = DefinicionRaizCATWOE.objects.get(id=id_dr)\n\n\t\tdel definicionRaizCat.definiciones_dr[definicionRaizCat.definiciones_dr.index(definicionRaiz.id)]\n\t\tif definicionRaizCat.definicionFinal_dr == definicionRaiz.id:\n\t\t\tdefinicionRaizCat.definicionFinal_dr = None\n\t\tdefinicionRaizCat.save()\n\n\t\tdefinicionRaiz.delete()\n\t\treturn HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n\telse:\n\t\treturn render(request, 'comunicacion/error.html')\n\n@login_required(login_url='/login/')\ndef definicionRaiz_resumen_view(request, id_dr):\n\tif request.method == \"POST\":\n\t\tform = resumenAnalisisForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tresumen = form.cleaned_data['description_analisis']\n\t\t\tdefinicionRaiz = DefinicionRaizCATWOE.objects.get(id=id_dr)\n\t\t\tdefinicionRaiz.description_dr = resumen\n\t\t\tdefinicionRaiz.save()\n\t\t\treturn HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n\t\treturn HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n\n\n@login_required(login_url='/login/')\ndef definicionRaiz_single_view(request, id_ssp,id_dr):\n\tif members_only(id_ssp, request):\n\t\ttry:\n\t\t\tproyecto = userSoftSystemProject.objects.get(id=id_ssp)\n\t\t\tdestinatarios = proyecto.returnAllusers(request.user.get_username())\n\t\t\tdefinicionRaiz = DefinicionRaizCATWOE.objects.get(id=id_dr)\n\t\t\tdefinicionRaizFinal = definicionRaiz.returnDefinicion()\n\t\t\trichPictureFinal = definicionRaiz.returnRichPicture()\n\t\t\tcomentarios = definicionRaiz.returnComments()\n\t\t\tctx = {'proyecto' : proyecto, 'destinatarios' : destinatarios, \n\t\t\t\t'definicionRaiz' : definicionRaiz, 'definicionRaizFinal': definicionRaizFinal,\n\t\t\t\t'richPictureFinal' : richPictureFinal, 'comentarios' : comentarios}\n\t\t\treturn render(request, 'estado_tres/estado_tres_definicionraiz_single.html', ctx)\n\t\texcept:\n\t\t\treturn render(request, 'comunicacion/error.html')\n\telse:\n\t\treturn render(request, 'comunicacion/error.html')\n\t\t\n@login_required(login_url='/login/')\ndef definicionRaiz_comentar_view(request, id_dr, id_ssp):\n\tif members_only(id_ssp, request):\n\t\tdefinicionRaiz = DefinicionRaizCATWOE.objects.get(id=id_dr)\n\t\tuser = User.objects.get(username__exact=request.user.get_username())\n\t\tform = comentaryForm()\n\t\tif request.method == \"POST\":\n\t\t\tform = comentaryForm(request.POST)\n\t\t\tif form.is_valid():\n\t\t\t\tcomentario = form.cleaned_data['comentary']\n\t\t\t\tnewComment = Comentario.objects.create(autor_comentary=user, content_comentary=comentario)\n\t\t\t\tnewComment.save()\n\t\t\t\tdefinicionRaiz.comments_dr.append(newComment.id)\n\t\t\t\tdefinicionRaiz.save()\n\t\t\t\tnotificar(id_ssp, request.user.id, '/verDefinicionRaiz/%s/%s'%(id_ssp,id_dr), 'Ha comentado una Definicion Raiz', id_dr, 'DefinicionRaiz')\n\n\t\t\t\treturn HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n\t\t\treturn HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n\n\telse:\n\t\treturn render(request, 'comunicacion/error.html')","repo_name":"AldoNavarreteBIGBOSS/ssmanager","sub_path":"myapp/modulos/estado_3/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":11201,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"19317322430","text":"import cv2\nimport os\n\n# 画像を比較して類似度を得る処理\n\nIMAGE_DIR = os.path.abspath(os.path.dirname(__file__)) + '/comparison/'\n# 画像サイズが一緒でない場合は画像サイズを合わせたほうがいい\n# IMAGE_SIZE = (993, 449)\n\n# 特徴量抽出処理の選定\n# detector = cv2.AKAZE_create()\ndetector = cv2.ORB_create()\n\n# 比較元の特徴量抽出(今回はグレースケールでの抽出とする)\nSOURCE_FILE_NAME = '1.png'\nsource = cv2.imread(IMAGE_DIR + SOURCE_FILE_NAME, cv2.IMREAD_GRAYSCALE)\n# source = cv2.resize(source, IMAGE_SIZE)\n\n(source_kp, source_des) = detector.detectAndCompute(source, None)\n\n# 比較対象の設定\nTARGET_FILE_NAME = '2.png'\ntarget = cv2.imread(IMAGE_DIR + TARGET_FILE_NAME, cv2.IMREAD_GRAYSCALE)\n# target = cv2.resize(source, IMAGE_SIZE)\n\n(target_kp, target_des) = detector.detectAndCompute(target, None)\n\n# 類似度確認\n# matches = cv2.BFMatcher(cv2.NORM_HAMMING).knnMatch(source_des, target_des, k=2)\nmatches = cv2.BFMatcher(cv2.NORM_HAMMING).knnMatch(source_des, target_des, k=5)\n\n# ratio test\n# ratio = 0.75\n# similar = []\n# for m, n in matches:\n#     if m.distance < ratio * n.distance:\n#         similar.append([m])\n\nsimilar = []\nfor m in matches:\n    distances = [info.distance for info in m]\n    ratio = sum(distances)/len(m)\n    similar.append(ratio)\n\n\n# 画像出力確認e\n# output = cv2.drawMatchesKnn(target, target_kp, source, source_kp, matches, None, flags=5)\n# cv2.imshow('img', output)\n# cv2.waitKey(0)\n# cv2.destroyAllWindows()\n\n# dist = []\n# for match_list in similar:\n#     dist.append(match_list[0].distance)\n# ret = sum(dist) / len(dist)\nret = sum(similar) / len(similar)\n\n# 距離を算出しているので、数値が小さいほど類似度が高いといえる\nprint('類似度：' + str(ret))\n","repo_name":"mahya8585/toolbox","sub_path":"py/openCvLab/graph/comparefile_knnmatch.py","file_name":"comparefile_knnmatch.py","file_ext":"py","file_size_in_byte":1798,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"74899813365","text":"from __future__ import absolute_import, print_function\n\nimport abc\nfrom vedis import Vedis\n\n\nclass StorageBackendBase(metaclass=abc.ABCMeta):\n    @classmethod\n    @abc.abstractproperty\n    def __backend_name__(cls):\n        raise NotImplementedError()\n\n    @abc.abstractmethod\n    def __init__(self, **kwargs):\n        raise NotImplementedError()\n\n    @abc.abstractmethod\n    def store(self, key, field, value):\n        raise NotImplementedError()\n\n    @abc.abstractmethod\n    def load(self, key, field):\n        raise NotImplementedError()\n\n    @abc.abstractmethod\n    def delete(self, key, field):\n        raise NotImplementedError()\n\n\nclass StorageBackend(StorageBackendBase):\n    backends = dict()\n\n    def __init_subclass__(cls, *args, **kwargs):\n        cls.backends[cls.__backend_name__] = cls\n        super().__init_subclass__(*args, **kwargs)\n\n    @classmethod\n    def get_backend(cls, backend_name, **kwargs):\n        backend = cls.backends.get(backend_name)\n        if backend is None:\n            raise Exception(f\"{backend_name} Backend not supported\")\n        return backend(**kwargs)\n\n\nclass StorageBackendVedis(StorageBackend):\n    __backend_name__ = 'vedis'\n\n    def __init__(self, **kwargs):\n        if kwargs is None or kwargs.get('database_path') is None:\n            raise Exception(\"Vedis backend requires path argument\")\n\n        self.database_path = kwargs.get('database_path')\n        self.db = Vedis(self.database_path)\n\n    def store(self, key, field, value):\n        if not all(map(lambda x: isinstance(x, str), [key, field, value])):\n            raise Exception('key, field, value must be string')\n\n        _hash = self.db.Hash(key)\n        _hash[field] = value\n        return True\n\n    def load(self, key, field):\n        if not all(map(lambda x: isinstance(x, str), [key, field])):\n            raise Exception('key, field must be string')\n\n        _hash = self.db.Hash(key)\n        return _hash[field]\n\n    def delete(self, key, field):\n        if not all(map(lambda x: isinstance(x, str), [key, field])):\n            raise Exception('key, field must be string')\n\n        _hash = self.db.Hash(key)\n        del _hash[field]\n        return True\n\n    def cleanup(self, key):\n        if not isinstance(key, str):\n            raise Exception('key must be string')\n            \n        _hash = self.db.Hash(key)\n        try:\n            for _hkey in _hash:\n                del _hash[_hkey]\n        except:\n            pass\n\nclass StorageBackendRedis(StorageBackend):\n    __backend_name__ = 'redis'\n    pass\n","repo_name":"finixbit/motex","sub_path":"motex/storage/backend.py","file_name":"backend.py","file_ext":"py","file_size_in_byte":2532,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"76"}
+{"seq_id":"7025609531","text":"import mtc\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport random\nimport time\n\ncycles = 100_000\nduration = 15 # seconds\nshape = (50, 50)\nB = 0\nsneed = int(time.time()) # sneeding for each simualtion\nprint(f'seeding with {sneed} for each sub-simulation.')\n\nnet_magnet = []\ntemperatures = [*np.linspace(0, 2, 20)] # * = explode\nres = 5\n\nfor i, T in enumerate(temperatures):\n    random.seed(sneed)\n    mag = 0\n    for k in range(res):\n        model = mtc.nano.Model(shape, T, B)\n        model.simulate(cycles)\n        mag += model.magnetisation()\n\n    net_magnet.append(mag/res)\n    #net_magnet.append(model.magnetisation())\n    mtc.log(f\"\\rdata points: {i+1}/{len(temperatures)}\", end=\"\")\n\nmtc.log(\"\\ndone!\")\nplt.ylabel(\"Normalised Magnetisation\")\nplt.xlabel(\"Temperature\")\nplt.plot(temperatures, net_magnet, 'x')\nplt.plot(temperatures, net_magnet, '-')\nplt.show()\n\n","repo_name":"SamuelJamesFrost/MagneticTimeCrystals","sub_path":"models/temp_v_magnetisation.py","file_name":"temp_v_magnetisation.py","file_ext":"py","file_size_in_byte":874,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"21108604119","text":"import os, sys\nimport numpy as np\nimport timeit\nfrom collections import deque\nimport warnings\nwarnings.simplefilter('ignore')\nos.system(\"export SUMO_HOME=/usr/share/sumo\")\ntry:\n    sys.path.append(\"/usr/share/sumo/tools\")\nexcept ImportError:\n    sys.exit(\"please declare environment variable 'SUMO_HOME' as the root directory of your sumo installation (it should contain folders 'bin', 'tools' and 'docs')\")\nimport traci\n# phase codes based on environment.net.xml\nPHASE_NS_GREEN = 0  # action 0 code 00\nPHASE_NS_YELLOW = 1\nPHASE_NSL_GREEN = 2  # action 1 code 01\nPHASE_NSL_YELLOW = 3\nPHASE_EW_GREEN = 4  # action 2 code 10\nPHASE_EW_YELLOW = 5\nPHASE_EWL_GREEN = 6  # action 3 code 11\nPHASE_EWL_YELLOW = 7\n\nclass SimSOTL:\n    def __init__(self, TrafficGen, params, sumo_cmd, netdata):\n        self._TrafficGen = TrafficGen\n        self.g_min = params['g_min']\n        self.theta = params['theta']\n        self.mu = params['mu']\n        self.omega = params['omega']\n        self.max_steps = params['max_steps']\n        self.kappa = 0\n        self.sumo_cmd = sumo_cmd\n        self.time_in_phase = 0\n        self.phase_index = 0\n        self.g_phases = self.green_phases()\n        self.phase_red_lanes = self.get_phase_red_lanes()\n        self.phase_deque = deque([self.g_phases[self.phase_index]])\n        self.all_red = len((self.g_phases[0]))*'r'\n        self.phase = self.all_red\n        self.netdata = netdata\n        self.phase_lanes = self.phase_lanes(self.g_phases)\n\n    def run(self, episode):\n        start_time = timeit.default_timer()\n        self._TrafficGen.generate_rutefile(seed=episode)\n        traci.start(self.sumo_cmd)\n        print('Simulating')\n        while self.time_in_phase < self.max_steps:\n            next_phase = self.next_phase()\n            self.simulate(next_phase)\n            traci.simulationStep()\n            print(self.time_in_phase)\n\n\n    def next_phase(self):\n        max_lane = None\n        light_phase = traci.trafficlight.getPhase('TL')\n        ap_red, ap_red_len = self.red_approach(light_phase=light_phase)\n        n = max(ap_red_len)\n        self.kappa += n\n        max_lane = max(ap_red_len, key=ap_red_len.get)\n        if self.time_in_phase >= self.g_min:\n            if n > self.mu or n == 0:\n                if self.kappa > self.theta:\n                    self.phase_index += 1\n                    self.kappa = 0\n        next_green = self.g_phases[self.phase_index % len(self.green_phases())]\n        phases = self.get_intermediate_phases(self.phase, next_green)\n        self.phase_deque.extend(phases+[next_green])\n\n        next_phase = self.phase_deque.popleft()\n        if next_phase is not self.phase:\n            self.time_in_phase = 0\n\n        return next_phase\n\n    def simulate(self, next_phase):\n        pass\n\n    def get_phase_red_lanes(self):\n        all_incoming_lanes = []\n        for g in self.green_phases():\n            all_incoming_lanes.extend(self.phase_lanes[g])\n        all_incoming_lanes = set(all_incoming_lanes)\n\n        #store all lanes that are red\n        #under any given green phase\n        phase_red_lanes = {}\n        for g in self.green_phases():\n            phase_red_lanes[g] = all_incoming_lanes - set(self.phase_lanes[g])\n\n        return phase_red_lanes\n    def green_phases(self):\n        logic = traci.trafficlight.getCompleteRedYellowGreenDefinition('TL')[0]\n        #get only the green phases\n        green_phases = [p.state for p in logic.getPhases()\n                        if 'y' not in p.state\n                        and ('G' in p.state or 'g' in p.state)]\n\n        #sort to ensure parity between sims (for RL actions)\n        return sorted(green_phases)\n\n    def get_intermediate_phases(self, phase, next_phase):\n        if phase == next_phase or phase == self.all_red:\n            return []\n        else:\n            yellow_phase = ''.join([p if p == 'r' else 'y' for p in phase])\n            return [yellow_phase, self.all_red]\n    def phase_lanes(self, actions):\n        phase_lanes = {a:[] for a in actions}\n        for a in actions:\n            green_lanes = set()\n            red_lanes = set()\n            for s in range(len(a)):\n                if a[s] == 'g' or a[s] == 'G':\n                    green_lanes.add(self.netdata['inter']['TL']['tlsindex'][s])\n                elif a[s] == 'r':\n                    red_lanes.add(self.netdata['inter']['TL']['tlsindex'][s])\n\n            ###some movements are on the same lane, removes duplicate lanes\n            pure_green = [l for l in green_lanes if l not in red_lanes]\n            if len(pure_green) == 0:\n                phase_lanes[a] = list(set(green_lanes))\n            else:\n                phase_lanes[a] = list(set(pure_green))\n        return phase_lanes\n\n    def red_approach(self, light_phase):\n        red_lane_dict = {'NS': [], 'NSL': [], 'EW': [], 'EWL': []}\n        red_lane_len_dict = {'0': 0, '2': 0, '4': 0, '6': 0}\n        light_0_list = ['N2TL_0', 'N2TL_1', 'N2TL_2', 'S2TL_0', 'S2TL_1', 'S2TL_2']\n        light_2_list = ['N2TL_3', 'S2TL_3']\n        light_4_list = ['E2TL_0', 'E2TL_1', 'E2TL_2', 'W2TL_0', 'W2TL_1', 'W2TL_2']\n        light_6_list = ['E2TL_3', 'W2TL_3']\n        car_list = traci.vehicle.getIDList()\n        for car_id in car_list:\n            road_id = traci.vehicle.getRoadID(car_id)\n            if light_phase == 0:\n                if road_id not in light_0_list:\n                    if ('N_E' in car_id) or ('S_W' in car_id):\n                        red_lane_dict['NSL'].append(car_id)\n                    elif ('E_W' in car_id) or ('W_E' in car_id):\n                        red_lane_dict['EW'].append(car_id)\n                    elif ('E_S' in car_id) or ('W_N' in car_id):\n                        red_lane_dict['EWL'].append(car_id)\n                    else:\n                        pass\n            elif light_phase == 2:\n                if road_id not in light_2_list:\n                    if ('N_S' in car_id) or ('S_N' in car_id):\n                        red_lane_dict['NS'].append(car_id)\n                    elif ('E_W' in car_id) or ('W_E' in car_id):\n                        red_lane_dict['EW'].append(car_id)\n                    elif ('E_S' in car_id) or ('W_N' in car_id):\n                        red_lane_dict['EWL'].append(car_id)\n                    else:\n                        pass\n            elif light_phase == 4:\n                if road_id not in light_4_list:\n                    if ('N_S' in car_id) or ('S_N' in car_id):\n                        red_lane_dict['NS'].append(car_id)\n                    elif ('N_E' in car_id) or ('S_W' in car_id):\n                        red_lane_dict['NSL'].append(car_id)\n                    elif ('E_S' in car_id) or ('W_N' in car_id):\n                        red_lane_dict['EWL'].append(car_id)\n                    else:\n                        pass\n            elif light_phase == 6:\n                if road_id not in light_6_list:\n                    if ('N_S' in car_id) or ('S_N' in car_id):\n                        red_lane_dict['NS'].append(car_id)\n                    elif ('N_E' in car_id) or ('S_W' in car_id):\n                        red_lane_dict['NSL'].append(car_id)\n                    elif ('E_W' in car_id) or ('W_E' in car_id):\n                        red_lane_dict['EW'].append(car_id)\n                    else:\n                        pass\n\n        for k in red_lane_dict.keys():\n            if k is '0':\n                red_lane_len_dict['NS'] = len(red_lane_dict[k])\n            elif k is '2':\n                red_lane_len_dict['NSL'] = len(red_lane_dict[k])\n            elif k is '4':\n                red_lane_len_dict['EW'] = len(red_lane_dict[k])\n            elif k is '6':\n                red_lane_len_dict['EWL'] = len(red_lane_dict[k])\n            else:\n                pass\n        return red_lane_dict, red_lane_len_dict\n\n\n\n\n","repo_name":"TakumiSeo/SOTL","sub_path":"SOTL/sotl_run.py","file_name":"sotl_run.py","file_ext":"py","file_size_in_byte":7796,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"5271366243","text":"import nanopq\nimport numpy as np\n\n\n\ndef extract_embeddings(net, input_tesnor):\n    net.eval()\n    out_put = net.foward(input_tesnor)\n    out_put = out_put.cpu()\n    return out_put\n\n\n\ndef pq_search(source_dataset, query_vector):\n    pq = nanopq.PQ(M=8)\n    pq.fit(source_dataset)\n\n    source_code = pq.encode(source_dataset)\n\n    dists = pq.dtable(query_vector).adist(source_code)  # (10000, )\n\n    print(dists)\n\n\n\n","repo_name":"cswwp/landmark-siamese-triplet-attention","sub_path":"PQ_SEARCH.py","file_name":"PQ_SEARCH.py","file_ext":"py","file_size_in_byte":414,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"6497715790","text":"#! /usr/bin/env python3\n\"\"\"\n\"This module implements.\"\n-- linear solving.\n\n\nAuthor: Christian Parpart & Kei Thoma\nDate: 2019-11-13\nLicense: GPL-3\n\"\"\"\nimport random\n\nimport numpy as np\nfrom scipy import sparse as sm\nfrom scipy.sparse import linalg as slina\n\nimport block_matrix\n\ndef solve_lu(pr, l, u, pc, b):\n    \"\"\" Solves the linear system Ax = b via forward and backward substitution\n    given the decomposition pr * A * pc = l * u.\n\n    => A = pr^-1 * l * u * pc^-1\n\n    Parameters\n    ----------\n    pr : scipy.sparse.csr_matrix\n        row permutation matrix of LU-decomposition\n    l : scipy.sparse.csr_matrix\n        lower triangular unit diagonal matrix of LU-decomposition\n    u : scipy.sparse.csr_matrix\n        upper triangular matrix of LU-decomposition\n    pc : scipy.sparse.csr_matrix\n        column permutation matrix of LU-decomposition\n    b : numpy.ndarray\n        vector of the right-hand-side of the linear system\n\n    Returns\n    -------\n    x : numpy.ndarray\n        solution of the linear system\n    \"\"\"\n    _ = slina.spsolve(sm.csc_matrix(slina.inv(sm.csc_matrix(pr))), b)\n    _ = slina.spsolve_triangular(l, _, lower=True)\n    _ = slina.spsolve_triangular(u, _, lower=False)\n    _ = slina.spsolve(sm.csc_matrix(slina.inv(sm.csc_matrix(pc))), _)\n    return _\n\ndef test_validity(n_upto=20, num_test=20, show=False):\n    \"\"\"\n    CHECKS THE ABOVE FUNCTION FOR CORECTNESS. Loops through some random values for b and compares the\n    function above with the spsolve from SciPy.\n\n    Parameters\n    ----------\n    n_upto : int\n        Checks the correctness from 2 to this integer.\n    num_test : int\n        Number of tests for each n.\n    show : boolean\n        Option to print out the values.\n\n    Returns\n    -------\n    boolean\n        True if no wrongness was encountered, False if an error was seen.\n    \"\"\"\n    for d in [1, 2, 3]:\n        for n in range(2, n_upto):\n            for i in range(0, num_test):\n                mat = block_matrix.BlockMatrix(d, n)\n                b = np.array([random.randint(-20, 20) for _ in range(0, mat.extend)])\n                x_demo = solve_lu(*mat.get_lu(), b)\n                x_true = slina.spsolve(sm.csc_matrix(mat.data), b)\n\n                if np.all((x_demo, x_true)):\n                    print(\"d = {} | n = {} | TEST {}/{} PASSED!\".format(d, n, i + 1, num_test))\n                    if show:\n                        print()\n                        print(\"A = {}\".format(mat.data.toarray()))\n                        print(\"b = {}\\n\".format(b))\n                        print(\"x = {}\".format(x_demo))\n                        print()\n                        print(\"============================================================\\n\")\n                else:\n                    print()\n                    print(\"d = {} | n = {} | TEST {}/{} FAILED FOR\".format(d, n, i + 1, num_test))\n                    print(\"A = {}\".format(mat.data.toarray()))\n                    print(\"b = {}\\n\".format(b))\n                    print(\"x = {}\".format(x_demo))\n                    print()\n                    return False\n    return True\n\ndef main():\n    \"\"\"\n    JOIN THE GLORIOUS MAIN FUNCTION. Uses the test function to demonstrate.\n    \"\"\"\n    print(\"WE SOLVE LINEAR EQUATION, Ax = b WHERE A AND b ARE KNOWN\\n\\n\")\n    test_validity(5, 1, True)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"keithoma/forevermore","sub_path":"third_base/linear_solvers.py","file_name":"linear_solvers.py","file_ext":"py","file_size_in_byte":3332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"16922248647","text":"#import PySimpleGUI as sg\n\n#sg.Popup(\"CHESS PLAYGROUND\")\n\nprint(\"chess playground log\")   \n\n#Class \"Chess Piece\" defines a list of black rooks and a list of white rooks, as well as a dictionary {ALL PIECES}\n\nclass Chess_Piece:\n  \t\n    all_pieces = {} #{Piece Name:Piece Position}\n    \n    black_rook_list = [\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\"] #A list of the maximum number of black rooks\n    \n    white_rook_list = [\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\"] #A list of the maximum number of white rooks\n    \n\n#Used for configuring how the rook piece works  \n\nclass Rook(Chess_Piece): #Class ROOK\n  \t\n    def __init__(self, col_x, col_y, color): #The Constructor for the Rook Class\n      \t\n        self.color = color #Will either be White or Black\n        \n        self.pos = str(col_x) + str(col_y) #The Position of the Rook\n        \n        self.col_x = col_x #col_x and col_y are used for positioning\n        \n        self.col_y = col_y #These are now attributes of the Rook Object / Rook Class\n        \n        legal_moves = [] #The Creation of a List '[]' that would be filled of all of the legal moves of the Rook\n        \n        columns = 8 #The Columns on the Board\n        \n        columns2 = [\"A\",\"B\",\"C\",\"D\",\"E\",\"F\",\"G\",\"H\"] #What they are referred to\n        \n        rows2 = [1,2,3,4,5,6,7,8] #What the Rows on the board are referred to\n        \n        rows = 8 #No. of Rows\n        \n        if col_x in columns2: #If the variable col_x is in the list of column references\n          \t\n            for i in columns2: #For each item in columns2\n              \t\n                if i == col_x: #If the item is the same as col_x\n                  \t\n                    legal_column = i #My Legal Column is I\n        \n        #This Code does the exact thing shown above\n        if col_y in rows2:\n            for i in rows2:\n                if i == col_y: #col_y is ROWS, while col_x is COLUMNS\n                    legal_row = i\n                    \n        #initializing the piece, globally\n        \n        if self.color == \"Black\": #If the Color is Black\n          \t\n            self.all_pieces[self.color + \"Rook\" + self.black_rook_list[0]] = self.pos #BlackRook[N], Where N refers to the first value in the Black_Rook_List.\n            \n            self.global_name = self.color + \"Rook\" + self.black_rook_list[0] #Sets the attribute 'Global_Name' to BlackRook[N], Where N refers to the first value in the Black_Rook_List.\n            \n            del self.black_rook_list[0] #Deletes the first item in black_rook_list, because it has been used.\n            \n        elif self.color == \"White\": #Otherwise, if the color is White (ignores special cases [e.g. Red, Orange, Green, Purple, Yellow])\n          \t#Does the exact same thing as shown above, only using replacing references of 'BLACK' with 'WHITE'.\n            self.all_pieces[self.color + \"Rook\" + self.white_rook_list[0]] = self.pos #WhiteRook[N], Where N refers to the first value in the White_Rook_List.\n            self.global_name = self.color + \"Rook\" + self.white_rook_list[0] #Sets the attribute 'Global_Name' to WhiteRook[N], Where N refers to the first value in the White_Rook_List.\n            del self.white_rook_list[0] #Deletes the first item in white_rook_list, because it has been used.\n            \n        #legalizing all columns + rows\n        pos_x = -1 #Set to Negative One, because the first thing it will do is add one (0)\n        \n        pos_y = -1 #Same reason as shown above.\n        \n        for i in rows2: #For each item in Rows (what it is referred to),\n          \t\n            pos_x += 1 #Adds one to pos_x [you'll see why]\n            \n            legal_moves.append(str(legal_column) + str(rows2[pos_x])) #e.g. if pos is B8, this would append 'B', which is the legal column- and rows2(0), which is 1. B1.\n            \n        for i in columns2: #Does a similar thing as shown above.\n            pos_y += 1\n            legal_moves.append(str(columns2[pos_y]) + str(legal_row)) #Columns2[POS_Y] is basically Columns2[0] (for instance), which can be A, and str(legal_row), which can be 8. A8.\n        \n        pos = 0 #Sets the position variable to 0.\n        \n        for i in legal_moves: #For each item in the legal moves list,\n          \t\n            if i in self.all_pieces.values() and i != self.pos: #If the item is the position of a different chess piece AND isn't theh position of the object,\n              \t\n                legal_moves[pos] = str(legal_moves[pos][0]) + \"x\" + str(legal_moves[pos][1]) #Sets the move (B4), for example, to Bx4 [Takes on B4]\n                \n            else: #Otherwise\n              \t\n                pos += 1 #Add one to the position variable\n                \n        self.legal_moves = legal_moves #Sets the object's attribute to legal_moves\n        \n        pos = 0 #ONCE AGAIN, resets the position\n        \n        for i in self.legal_moves: #for each item in the legal moves attribute\n          \t\n            if i == self.pos: #If the ITEM is equal to self.pos\n              \t\n                del self.legal_moves[pos] #deletes that position\n                pos += 1 #adds one\n                continue #moves on\n                \n            else:\n                pos += 1 #adds one\n                \n    #Updating Legal Moves [Refer to lines 32 through line 113]          \n    def legal_move_update(self):\n        col_x, col_y = self.col_x, self.col_y #Refreshed COL_X and COL_Y\n        legal_moves = [] #Reset Legal Moves\n        columns = 8 \n        columns2 = [\"A\",\"B\",\"C\",\"D\",\"E\",\"F\",\"G\",\"H\"]\n        rows2 = [1,2,3,4,5,6,7,8]\n        rows = 8\n        \n        #determining if a column or row is legal?\n        if col_x in columns2:\n            for i in columns2:\n                if i == col_x:\n                    legal_column = i\n                    \n        if col_y in rows2:\n            for i in rows2:\n                if i == col_y:\n                    legal_row = i\n                    \n        #legalizing all columns + rows\n        pos_x = -1\n        pos_y = -1\n        \n        for i in rows2:\n            pos_x += 1\n            legal_moves.append(str(legal_column) + str(rows2[pos_x]))\n            \n        for i in columns2:\n            pos_y += 1\n            legal_moves.append(str(columns2[pos_y]) + str(legal_row))\n            \n        pos = 0\n        \n        for i in legal_moves:\n            if i in self.all_pieces.values() and i != self.pos:\n                for j in self.all_pieces:\n                    if self.all_pieces[j] == i:\n                        if self.color in j:\n                            pass\n                          \t#adds an X to moves that can take a piece\n                        else:\n                            legal_moves[pos] = str(legal_moves[pos][0]) + \"x\" + str(legal_moves[pos][1])\n            else:\n                pos += 1\n                \n        self.legal_moves = legal_moves\n        pos = 0\n        \n        for i in self.legal_moves:\n            if i == self.pos:\n                del self.legal_moves[pos]\n                pos += 1\n                continue\n                \n            else:\n                pos += 1\n       #allows a chess peice to move by changing its location    \n    def move(self, x, y):\n      \t\n        pos = str(x) + str(y) #The new position is made.\n        \n        previous_x = self.col_x #Backups of col_x and col_y\n        previous_y = self.col_y\n        \n        self.col_x = x #Replaces them with x and y\n        self.col_y = y\n        \n        #check if move is possible\n        if pos in self.legal_moves: #If the new pos is in legal moves\n          \n            del previous_x #Deletes the backups\n            del previous_y\n            \n            self.all_pieces[self.global_name] = pos #Updates the global position\n            \n            self.pos = pos #Updates self.pos\n            \n            self.legal_move_update() #Updates legal mvoes\n            \n        else: #Else\n          \t\n            self.col_x = previous_x #Restores the backups of X and Y\n            self.col_y = previous_y\n            \n            print(\"Illegal Move\") #Reminds you that you commited a crime\n            \n\n#Create scenarios down here!            \nrook1 = Rook(\"B\", 3, \"White\")\nrook2 = Rook(\"C\", 3, \"White\")\nrook3 = Rook(\"F\", 3, \"White\")\nrook1.legal_move_update()\nrook2.legal_move_update()\nrook3.legal_move_update()\nprint(rook1.all_pieces)\nprint(rook1.global_name, rook1.pos, rook1.legal_moves)\nrook1.move(\"B\",7)\nprint(rook1.global_name, rook1.pos, rook1.legal_moves)\n","repo_name":"Eriks-Gadgets/chess-playground","sub_path":"chess.py","file_name":"chess.py","file_ext":"py","file_size_in_byte":8509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"14699286379","text":"import json, math\nfrom functools import reduce\n\nfrom django.shortcuts import render, redirect\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.backends import ModelBackend\nfrom django.db.models import Q\nfrom django.views.generic.base import View\nfrom django.contrib.auth.hashers import make_password\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.core.urlresolvers import reverse\n\nfrom background_task.models import Task\nfrom equipments.tasks import notify_user\n\nfrom .models import UserProfile, EmailVerifyRecord\nfrom operation.models import UserEquipment, UserFavorite, UserMessage\nfrom .forms import LoginForm, RegisterForm, ForgetForm, ModifyPwdForm\nfrom utils.email_send import send_register_email\nfrom pure_pagination import Paginator, EmptyPage, PageNotAnInteger\n\nfrom organization.models import Team, Engineer\nfrom equipments.models import Equipment\nfrom .models import Banner\nfrom .forms import UploadImageForm, UserInfoForm\n\n\nclass CustomBackend(ModelBackend):  # 有了这个就可以实现用户和邮箱都可以登录，authenticate在这里被重写此方法自动被调用\n    def authenticate(self, request, username=None, password=None, **kwargs):\n        try:  # Model的objects管理器 ？？？\n            user = UserProfile.objects.get(Q(username=username) | Q(email=username))    # 到数据库查询用户名或者邮箱是否有并做检查是否一致，取出 user。这里不检查密码因为django后台存的是密文没法把前端页面传进来的明文进行数据库查询\n            if user.check_password(password):    # user是UserProfile继承AbstractUser，AbstractUser有一个方法check_password；明文传进去并加密与后台数据库中user的password密文对比\n                return user\n        except Exception as e:\n            return None\n\n\nclass ActiveUserView(View):     #用户点击激活邮件里的链接会跳到这里是GET 方法\n    def get(self, request, active_code): #active_code 要与url参数中的名称一样，提取出来啦\n        all_records = EmailVerifyRecord.objects.filter(code= active_code)  # 发送链接邮件之前已经将EmailVerifyRecord信息code,email,send_type保存到数据库users_emailverifyrecord  参见email_send.py文件\n                                                                           # 因为现在用户点击这个链接了，需要查一下这个链接是否存在，通过 users_emailverifyrecord表中的code(这里是参数active_code)字段到表users_emailverifyrecord中查询是否有记录\n        if all_records: # 如果users_emailverifyrecord中有记录，就到这个表里去获取email(email = record.email)并通过这个email 在users_userprofile表中找到这个user（UserProfile类型）激活它（is_active = 1）（RegisterView的POST里is_active = False 并保存userprofile后发送邮件，等用户点击后在此激活）\n            for record in all_records:\n                email = record.email\n                user = UserProfile.objects.get(email= email)  # user 是一个UserProfile类型\n                user.is_active = True   #用户点击那个链接了  激活吧\n                user.save()  # 保存到users_userprofile表\n        else:\n            return render(request, 'active_fail.html') # 用户试图在浏览器输入他邮箱里的链接并访问来激活注册的账号，可是链接失效，给一个信息提示\n        return render(request, 'login.html')\n\n\nclass RegisterView(View):\n    def get(self, request):\n        register_form = RegisterForm()\n        return render(request, 'register.html', {'register_form': register_form})\n        # 这里的register_form是可以传给前端页面使用的，返回form的验证信息到页面，验证的逻辑和输出信息是django的forms自动完成的，配置好就可以了\n\n    def post(self, request):\n        register_form = RegisterForm(request.POST)\n        if register_form.is_valid():\n            user_name = request.POST.get('email', '')  # 注册的时候用的是 email注册\n            if UserProfile.objects.filter(email=user_name):\n                return render(request, 'register.html', {'register_form': register_form,\n                                                         'msg': '用户已经存在！'})  # 回填用户信息register_form(html中的value=部分)， 需要显示它的验证码\n            pass_word = request.POST.get('password', '')  # 前端输入的是明文， 不是django数据库中的密文\n            user_profile = UserProfile()\n            user_profile.username = user_name  # 其实这里user_name是邮箱因为注册是用邮箱注册的\n            user_profile.email = user_name\n            user_profile.is_active = False  # 用户点击那个链接后才能激活\n            user_profile.password = make_password(pass_word)  # 对前端明文加密然后存到后台数据库\n            user_profile.save()  # 保存到数据库users_userprofile表中不是users_emailverifyrecord\n\n            # 写入欢迎注册消息\n            user_message = UserMessage()\n            user_message.user = user_profile.id  # 因为operation\\models\\UserMessage\\user不是指向外键UserProfile，而是一个int\n            user_message.message = \"欢迎注册Test&Lab\"\n            user_message.save()\n\n            send_register_email(user_name, 'register')  # 发送动作之前是要保存code,email,send_type到��据库users_emailverifyrecord中\n            return render(request, 'login.html')\n        else:\n            return render(request, 'register.html', {'register_form': register_form})\n\n\nimport re\nclass LogoutView(View):\n    '''\n    刚开始是用户登出到首页index.html，后台根据https://juejin.im/post/5a780ced6fb9a063395c4b6f登出到\n    '''\n    def get(self, request):\n        next = request.GET.get('next', '')\n        logout(request)\n        if next:\n            if 'equipments/info' in next or 'equipments/comment'in next or 'equipments/reserve'in next:\n                return HttpResponseRedirect('/equipments/detail/' + re.findall('\\d+', next)[0] +'/')\n            return HttpResponseRedirect(next)\n        return HttpResponseRedirect(reverse('index'))\n        # logout(request)\n        #    # HttpResponseRedirect重定向到一个url的地址\n        # return HttpResponseRedirect(reverse('index'))  # reverse可以把url的名称name反解成url的地址\n        #                                                # 传equipments:equipment_list等也是可以\n\n\nclass LoginView(View):  #  重写View的get和post方法。View会根据get方法自动调用get函数，post方法就自动调用post函数。 request是django自动给我们添加的；当在urls.py中配置了url，django就会自动生成一个request放到函数View.get/post里面作为参数。\n    # def get(self, request): # 刷新页面login就是get。就是django自动注册进来的request， 如果是get方法，自动调用View的get函数\n    #     return render(request, 'login.html', {})\n\n    def get(self, request):\n        # 获取到next参数，渲染到template中，在form表单添加一个hidden类型的元素\n        next = request.GET.get('next', '')\n        return render(request, \"login.html\", {'next': next})\n\n    def post(self, request):\n        # #下面邮件发送是为了调试用\n        # email_body = 'request.POST里的next是:{}' \\\n        #     .format(request.POST.get('next', ''))\n        # send_status = send_mail('测试request.POST里的next是？', email_body,\n        #                         EMAIL_FROM,\n        #                         ['419099632@qq.com'])\n        login_form = LoginForm(request.POST)    # 会把login.html中的username和password字段对应到forms.py中的LoginForm中username和password字段去并做两个字段的校验\n        # 上面实例化的时候View已经做了form的检查验证(每个字段是否合法)：包括两个必填字段，密码最少5个字符\n        if login_form.is_valid():  # is_valid就是检查login_form下的_errors是否为空；如果字段都合法（LoginForm中的两个字段必填，密码至少5个字符），则_errors为空\n            user_name = request.POST.get('username', '')  # 字典的用法 必须用username password? 因为前端页面login用这两个名称，保持一致？\n            pass_word = request.POST.get('password', '')   # 字典的用法 csrtoken那个不用管，因为能进入这里说明django已经验证过了的否则进不了这里的\n            user = authenticate(username=user_name, password=pass_word)  # authenticate只是向数据库发起验证二者是否存在若存在返回UserProfile类型，并没有登录\n            if user is not None:   # user是UserProfile类型,邮箱和用户名都可以实现登录因为有重写authenticate方法\n                if user.is_active:  # 激活的用户才登录哦\n                    login(request, user)  #  session 和 cookie里有讲login如何实现登录机制。注意这里的request/user是注册到index.html中去的\n                    if request.POST.get('next', ''):\n                        # 如果request.POST.get('next', '')存在，直接跳转到指定页面\n                        return HttpResponseRedirect(request.POST.get('next', ''))\n                    # return render(request, 'index.html')  # 登录成功 返回首页，html中对登录成功后的页面改显示什么有判断处理\n                    return HttpResponseRedirect(reverse('index'))#这样用户登录后就会进入IndexView把相关数据传递到index.html；而上面的render到index.html则没有数据传递到index.html\n\n                else:   #未激活的用户当然不能登录哦,回到登录页面\n                    return render(request, 'login.html', {'login_form': login_form, 'msg': '用户未激活！'})    # 这里的msg是可以传给前端页面使用的,'login_form'返回是用于回填\n            else:\n                return render(request, 'login.html', {'login_form': login_form, 'msg': '用户名或者密码错误！'})  # 这里的msg是可以传给前端页面使用的,'login_form'返回是用于回填\n        else:\n            return render(request, 'login.html', {'login_form': login_form})  # 这里的login_form是可以传给前端页面使用的，返回form的验证信息到页面，验证的逻辑和输出信息是django的forms自动完成的，配置好就可以了\n\n\nclass ForgetPwdView(View):\n    def get(self, request):\n        forget_form = ForgetForm()\n        return render(request, 'forgetpwd.html', {'forget_form': forget_form})\n\n    def post(self, request):\n        forget_form = ForgetForm(request.POST)  # 会把forgetpwd.html中的email字段对应到forms.py中的ForgetForm中email字段去并做字段的校验\n        # 上面实例化的时候View已经做了form的检查验证(字段是否合法)：email字段\n        if forget_form.is_valid():# is_valid就是检查forget_form下的_errors是否为空；如果字段都合法，则_errors为空\n            email = request.POST.get('email', '')\n            send_register_email(email, 'forget')\n\n            # 在ActiveUserView中用户试图在浏览器输入他邮箱里的链接并访问来激活注册的账号，可是链接失效，给一个信息提示\n            # 邮件发送成功也给一个邮件发送成功的提示\n            return render(request, 'send_success.html')\n        else:\n            return render(request, 'forgetpwd.html', {'forget_form': forget_form})# 这里的forget_form是可以传给前端页面使用的，返回form的验证信息到页面，验证的逻辑和输出信息是django的forms自动完成的，配置好就可以了\n\n\nclass ResetView(View):\n    '''\n    用户在浏览器输入http://127.0.0.1:8000/reset/********** 是get   **********是存在EmailVerifyRecord中的code\n    '''\n    def get(self, request, active_code):\n        all_records = EmailVerifyRecord.objects.filter(code= active_code) # 发送链接邮件之前已经将EmailVerifyRecord信息code,email,send_type保存到数据库users_emailverifyrecord  参见email_send.py文件\n                                                                           # 因为现在用户点击这个链接了，需要查一下这个链接是否存在，通过 users_emailverifyrecord表中的code(这里是参数active_code)字段到表users_emailverifyrecord中查询是否有记录\n        if all_records:# 如果users_emailverifyrecord中有记录，就到这个表里去获取email(email = record.email). 然后返回一个密码重置页面password_reset.html并把email返回，在此页面中有个hidden的input框会把这个email以POST方法返回（<form id=\"reset_password_form\" action=\"{% url 'modify_pwd' %}\" method=\"post\">）到ModifyPwdView，在ModifyPwdView中这样取email = request.POST.get('email', '')\n            for record in all_records:#record是EmailVerifyRecord类型\n                email = record.email\n                return render(request, 'password_reset.html', {'email': email})\n        else:\n            return render(request, 'active_fail.html')\n        return render(request, 'login.html')\n\n\nclass ModifyPwdView(View):\n    '''\n    修改用户密码，用户在http://127.0.0.1:8000/reset/uaJKy1TJtW7NAR44/ 页面点击提交是post\n    '''\n    def post(self, request):\n        modify_form = ModifyPwdForm(request.POST)\n        if modify_form.is_valid():\n            pwd1 = request.POST.get('password1', '') #password1,2是password_reset Html页面input框传过来的,email也是。\n            pwd2 = request.POST.get('password2', '')\n            email = request.POST.get('email', '') #password_reset.html中的隐藏的输入框传来的 <input type = 'hidden' name= 'email' value=\"{{ email }}\" /> <!-- 后台ResetView的get返回的email -->\n            if pwd1 != pwd2:\n                return render(request, 'password_reset.html', {'modify_form': modify_form, 'email': email, 'msg': '密码不一致！'})\n            user = UserProfile.objects.get(email=email) # 因为是老用户找回密码所以这个email应该在users_userprofile表中\n                                                        # 注意：通过注册和激活的步骤，这个user信息是保存到 users_userprofile中的\n            user.password = make_password(pwd1)\n            user.save()\n            return render(request, 'login.html', {'email': email, 'modify_form': modify_form})   # 在login页面能否回填来自ModifyPwdView的密码？\n        else:\n            email = request.POST.get('email', '')\n            return render(request, 'password_reset.html', {'email': email, 'modify_form': modify_form})\n\n\nfrom utils.mixin_utils import LoginRequiredMixin\n\n\nclass UserinfoView(LoginRequiredMixin, View):\n    '''\n    用户个人信息\n    '''\n    def get(self, request):\n        return render(request, 'usercenter-info.html', {\n        })\n\n    def post(self, request):\n        user_info_form = UserInfoForm(request.POST, instance=request.user)#一定要指明instance，否则是新增而不是修改, 与用户咨询不一样\n        if user_info_form.is_valid():\n            user_info_form.save()\n            return HttpResponse('{\"status\":\"success\"}', content_type='application/json')\n        else:\n            return HttpResponse(json.dumps(user_info_form.errors), content_type='application/json')\n\n\nclass UploadImageView(LoginRequiredMixin, View):\n    '''\n    用户修改头像\n    '''\n    def post(self, request):\n        image_form = UploadImageForm(request.POST, request.FILES, instance=request.user)        #instance传的就是modelform:UploadImageForm指代的model:UserProfile。 image_form就具有了modelform的功能（里面有model）:直接存\n        if image_form.is_valid():\n            # image = image_form.cleaned_data['image']\n            # request.user.image = image\n            # request.user.save()\n            image_form.save()\n            return HttpResponse('{\"status\":\"success\"}', content_type='application/json')\n        else:\n            return HttpResponse('{\"status\":\"fail\"}', content_type='application/json')\n\n\nclass UpdatePwdView(View):\n    '''\n    在个人用户中心修改用户密码\n    '''\n    def post(self, request):\n        modify_form = ModifyPwdForm(request.POST)\n        if modify_form.is_valid():\n            pwd1 = request.POST.get('password1', '') #password1,2是password_reset Html页面input框传过来的,email也是。\n            pwd2 = request.POST.get('password2', '')\n            # email = request.POST.get('email', '')\n            if pwd1 != pwd2:\n                return HttpResponse('{\"status\":\"fail\", \"msg\":\"密码不一致！\"}', content_type='application/json')\n            user = request.user\n            user.password = make_password(pwd1)\n            user.save()\n            return HttpResponse('{\"status\":\"success\"}', content_type='application/json')\n        else:\n            return HttpResponse(json.dumps(modify_form.errors), content_type='application/json')\n\n\nclass SendEmailCodeView(LoginRequiredMixin, View):\n    '''\n    个人中心修改邮箱时发送邮箱验证码\n    '''\n    def get(self,request):\n        email = request.GET.get('email', '')\n        if UserProfile.objects.filter(email=email):\n            return HttpResponse('{\"email\":\"邮箱已经存在\"}', content_type='application/json')\n        send_register_email(email, 'update_email')\n        return HttpResponse('{\"status\":\"success\"}', content_type='application/json')\n\n\nclass UpdateEmailView(LoginRequiredMixin, View):\n    '''\n    个人中心拿到验证码后修改个人邮箱\n    '''\n    def post(self, request):\n        email = request.POST.get('email', '')\n        code = request.POST.get('code', '') # 为什么是code?     usercenter-base.html中有<input class=\"fl email_code\" type=\"text\" id=\"jsChangeEmailCode\" name=\"code\" placeholder=\"输入邮箱验证码\">\n\n        existed_records = EmailVerifyRecord.objects.filter(email=email, code=code, send_type='update_email')\n        if existed_records:\n            user = request.user\n            user.email = email\n            user.save()  # 这样 数据库中userprofile里的邮箱也改成与emailverifyrecord中的邮箱一致了\n            return HttpResponse('{\"status\":\"success\"}', content_type='application/json')\n        else:\n            return HttpResponse('{\"email\":\"验证码出错\"}', content_type='application/json')\n\n\nclass MyEquipmentView(LoginRequiredMixin, View):\n    '''\n    个人中心 我借用过的设备\n    '''\n    def get(self, request):\n        # 下面这句是我未还的设备\n        user_equipments = UserEquipment.objects.filter(return_time__isnull=True, user=request.user)\n        # 下面4行代码实现对我用过的设备里同样设备去重\n        user_equipments_ids = UserEquipment.objects.filter(user=request.user).values('equipment_id').distinct()\n        equipments_list = []\n        for user_equipment_id in user_equipments_ids:\n            equipments_list += Equipment.objects.filter(id=user_equipment_id['equipment_id'])\n        return render(request, 'usercenter-myequipment.html', {\n            'user_equipments': user_equipments,\n            'equipments_list': equipments_list,\n\n        })\n\n\nclass MyFavTeamView(LoginRequiredMixin, View):\n    '''\n    个人中心 我收藏的team\n    '''\n    def get(self, request):\n        team_list =[]\n        fav_teams = UserFavorite.objects.filter(user=request.user, fav_type=2)\n        for fav_team in fav_teams:\n            team_id = fav_team.fav_id\n            team = Team.objects.get(id=team_id)\n            team_list.append(team)\n        return render(request, 'usercenter-fav-team.html', {\n            'team_list': team_list,\n\n        })\n\n\nclass MyFavEngineerView(LoginRequiredMixin, View):\n    '''\n    个人中心 我收藏的工程师\n    '''\n    def get(self, request):\n        engineer_list =[]\n        fav_engineers = UserFavorite.objects.filter(user=request.user, fav_type=3)\n        for fav_engineer in fav_engineers:\n            engineer_id = fav_engineer.fav_id\n            engineer = Engineer.objects.get(id=engineer_id)\n            engineer_list.append(engineer)\n        return render(request, 'usercenter-fav-engineer.html', {\n            'engineer_list': engineer_list,\n\n        })\n\n\nclass MyFavEquipmentView(LoginRequiredMixin, View):\n    '''\n    个人中心 我收藏的设备\n    '''\n    def get(self, request):\n        equipment_list =[]\n        fav_equipments = UserFavorite.objects.filter(user=request.user, fav_type=1)\n        for fav_equipment in fav_equipments:\n            equipment_id = fav_equipment.fav_id\n            equipment = Equipment.objects.get(id=equipment_id)\n            equipment_list.append(equipment)\n        return render(request, 'usercenter-fav-equipment.html', {\n            'equipment_list': equipment_list,\n\n        })\n\n\nclass MymessageView(LoginRequiredMixin, View):\n    '''\n    个人中心 我的消息\n    '''\n    def get(self, request):\n        all_messages = UserMessage.objects.filter(user=request.user.id)       #因为operation\\models\\UserMessage\\user不是指向外键UserProfile，而是一个int\n        # 用户进入个人消息后清空未读消息记录\n        all_unread_messages = UserMessage.objects.filter(user=request.user.id, has_read=False)\n        for unread_message in all_unread_messages:\n            unread_message.has_read = True\n            unread_message.save()\n\n        # 对消息进行分页\n        try:  # request.GET  <QueryDict: {'page': ['2']}>\n            page = request.GET.get('page', 1)  # 取页数编号 返回的是 1,2 这样的int页数\n        except PageNotAnInteger:\n            page = 1\n\n        p = Paginator(all_messages, 10, request=request)  # p是Paginator对象类型，all_orgs是QuerySet\n\n        messages = p.page(page)  # orgs是Page类型： Page 1 of 2  / Page 2 of 2\n\n        return render(request, 'usercenter-message.html', {\n            \"messages\": messages,\n        })\n\n\nclass MytoolView(LoginRequiredMixin, View):\n    '''\n    个人中心 我的工具\n    '''\n    def get(self, request):\n\n        return render(request, 'usercenter-mytool.html', {\n        })\n\n\nclass MyQuizView(LoginRequiredMixin, View):\n    '''\n    个人中心 我的答卷测试\n    '''\n    def get(self, request):\n\n        return render(request, 'usercenter-myquiz.html', {\n        })\n\n\ndef mean(L=[]):\n    r = reduce(lambda x,y:x+y,L)\n    return r*1.0/len(L)\n\n\nfrom django.core.mail import send_mail\nfrom TestLabManagement.settings import EMAIL_FROM\nclass MytoolUcalView(LoginRequiredMixin, View):\n    '''\n    个人中心 我的工具  直接测量量不确定度计算\n    '''\n    def post(self, request):# 对表单form的请求是post\n        list_value = []\n        test_value1 = request.POST.get('test_value1', '')\n        if test_value1:\n            list_value.append(float(test_value1))\n        test_value2 = request.POST.get('test_value2', '')\n        if test_value2:\n            list_value.append(float(test_value2))\n        test_value3 = request.POST.get('test_value3', '')\n        if test_value3:\n            list_value.append(float(test_value3))\n        test_value4 = request.POST.get('test_value4', '')\n        if test_value4:\n            list_value.append(float(test_value4))\n        test_value5 = request.POST.get('test_value5', '')\n        if test_value5:\n            list_value.append(float(test_value5))\n        test_value6 = request.POST.get('test_value6', '')\n        if test_value6:\n            list_value.append(float(test_value6))\n        test_value7 = request.POST.get('test_value7', '')\n        if test_value7:\n            list_value.append(float(test_value7))\n        test_value8 = request.POST.get('test_value8', '')\n        if test_value8:\n            list_value.append(float(test_value8))\n        test_value9 = request.POST.get('test_value9', '')\n        if test_value9:\n            list_value.append(float(test_value9))\n        test_value10 = request.POST.get('test_value10', '')\n        if test_value10:\n            list_value.append(float(test_value10))\n\n        Umean = mean(list_value)\n\n        list_temp_submean = [value-Umean for value in list_value]\n\n        list_temp_submean_square = [value**2 for value in list_temp_submean]\n\n        sUx = math.sqrt(sum(list_temp_submean_square) / (len(list_temp_submean_square) - 1))\n        # 测量一次报结果，由重复性引入的不确定度\n        uUx = sUx/1\n\n        resolution = request.POST.get('resolution', '')\n        if resolution:\n            resolution = float(resolution)\n\n        # 由仪器分辨力引入的不确定度分量\n        uUr = 0.5*resolution/math.sqrt(3)\n\n        readout = request.POST.get('readout', '')\n        if readout:\n            readout = float(readout)\n        range_value = request.POST.get('range_value', '')\n        if range_value:\n            range_value = float(range_value)\n        mpe = request.POST.get('mpe', '')\n        if mpe:\n            mpe = float(mpe)\n        # 由仪表最大允差引入的不确定度\n        uUn = mpe/math.sqrt(3)\n\n        # 合成标准不确定度\n        uc = math.sqrt(uUx**2 + uUr**2 + uUn**2)\n\n        # 扩展不确定度\n        U = 2 * uc\n\n        #下面邮件发送是为了调试用\n        email_body = '你输入的重复性评定测量值是：{}\\n\\n你输入的此次读数：{}\\n所用量程是：{}\\n仪器最大允许误差(此读数与此量程下)是:{}\\n\\n仪器分��力(此量程下)：{}\\n\\n ----------------------------------\\n\\n uUx重复性引入的不确定度分量：{} \\n\\n uUr仪器分辨力引入的不确定度分量:{}\\n\\n uUn仪表本身引入的不确定度:{}\\n\\nuc合成标准不确定度是：{}\\n\\nU扩展不确定度是：{}'.format\\\n            (list_value, readout, range_value, mpe, resolution, uUx, uUr, uUn, uc, U)\n        send_status = send_mail('直接测量量不确定度计算', email_body, EMAIL_FROM, [request.user.email])\n        return HttpResponse('{\"status\":\"success\",\"msg\":\"testing...\"}', content_type='application/json')\n\n\nclass MytoolUtcalView(LoginRequiredMixin, View):\n    '''\n    个人中心 我的工具  pt1000温度测试不确定度计算\n    '''\n    def post(self, request):# 对表单form的请求是post\n        list_value = []\n        t_value1 = request.POST.get('t_value1', '')\n        if t_value1:\n            list_value.append(float(t_value1))\n        t_value2 = request.POST.get('t_value2', '')\n        if t_value2:\n            list_value.append(float(t_value2))\n        t_value3 = request.POST.get('test_value3', '')\n        if t_value3:\n            list_value.append(float(t_value3))\n        t_value4 = request.POST.get('t_value4', '')\n        if t_value4:\n            list_value.append(float(t_value4))\n        t_value5 = request.POST.get('t_value5', '')\n        if t_value5:\n            list_value.append(float(t_value5))\n        t_value6 = request.POST.get('t_value6', '')\n        if t_value6:\n            list_value.append(float(t_value6))\n        t_value7 = request.POST.get('t_value7', '')\n        if t_value7:\n            list_value.append(float(t_value7))\n        t_value8 = request.POST.get('t_value8', '')\n        if t_value8:\n            list_value.append(float(t_value8))\n        t_value9 = request.POST.get('t_value9', '')\n        if t_value9:\n            list_value.append(float(t_value9))\n        t_value10 = request.POST.get('t_value10', '')\n        if t_value10:\n            list_value.append(float(t_value10))\n\n        Umean = mean(list_value)\n\n        list_temp_submean = [value - Umean for value in list_value]\n\n        list_temp_submean_square = [value ** 2 for value in list_temp_submean]\n\n        sUx = math.sqrt(sum(list_temp_submean_square) / (len(list_temp_submean_square) - 1))\n        # 测量一次报结果，由重复性引入的不确定度\n        uUx = sUx / 1\n\n        t_readout = request.POST.get('t_readout', '')\n        if t_readout:\n            t_readout = float(t_readout)\n        mpe = 0.3 + 0.005*abs(t_readout)\n        # 由于传感器的允差引入的不确定度分量\n        uUn = mpe / math.sqrt(3)\n\n        resistor = request.POST.get('resistor', '')\n        if resistor:\n            resistor = float(resistor)\n        temp = resistor*0.1/0.391\n        # 由于引线电阻引入的不确定度分量\n        uUw = temp / math.sqrt(3)\n\n        t_max = request.POST.get('t_max', '')\n        if t_max:\n            t_max = float(t_max)\n        # 由于软件转换引入的不确定度分量\n        uUsoft = t_max / math.sqrt(3)\n\n        # 合成标准不确定度\n        uc = math.sqrt(uUx ** 2 + uUn ** 2 + uUw ** 2 + uUsoft ** 2)\n\n        # 扩展不确定度\n        U = 2 * uc\n\n        #下面邮件发送是为了调试用\n        email_body = '你输入的重复性评定测量值是：{}\\n\\n你输入的此次读数：{}\\n\\n你输入的引线阻值：{}\\n\\n你输入的程序转换的最大温度差值：{}\\n\\n ----------------------------------\\n\\nuUx重复性引入的不确定度分量：{} \\n\\n uUn由于传感器的允差引入的不确定度分量:{}\\n\\n uUw由于引线电阻引入的不确定度分量:{}\\n\\nuUsoft由于软件转换引入的不确定度分量:{}\\n\\nuc合成标准不确定度是：{}\\n\\nU扩展不确定度是：{}'.format\\\n            (list_value, t_readout, resistor, t_max, uUx, uUn, uUw, uUsoft, uc, U)\n        send_status = send_mail('温度测试不确定度计算', email_body, EMAIL_FROM, [request.user.email])\n        return HttpResponse('{\"status\":\"success\",\"msg\":\"testing...\"}', content_type='application/json')\n\n\nclass MytoolSafetyQuizView(LoginRequiredMixin, View):\n    '''\n    个人中心 我的答卷测试  安全培训后测试 如果测试通过在这里发一封邮件给相关人员\n    '''\n    def post(self, request):# 对表单form的请求是post\n        #下面邮件发送是为了调试用\n        email_body = '{}:\\n\\n恭喜你通过了测试，你这次测试的分数是:{}\\n\\n\\n\\n\\n------------------------------------------------------------\\n以上信息来自 Test&Lab管理后台 自动发送内容，请勿回复此邮件！'.format(request.user.username, request.POST.get('score',''))\n        send_status = send_mail('测试通过！', email_body, EMAIL_FROM, [request.user.email])\n        return HttpResponse('{\"status\":\"success\",\"msg\":\"通过测试！\"}', content_type='application/json')\n\n\nclass IndexView(View):\n    '''\n    首页\n    '''\n    def get(self, request):\n        # 取出轮播图\n        # print (1/0)\n        all_banners = Banner.objects.all().order_by('index')\n        equipments = Equipment.objects.filter(is_banner=False)[:6]\n        banner_equipments = Equipment.objects.filter(is_banner=True)[:3]\n        teams = Team.objects.all()[:15]\n\n        # # 查询background_task是否有任务存在并执行\n        # if Task.objects.filter(task_name=\"equipments.tasks.notify_user\").exists():\n        #     pass\n        # else:\n        #     notify_user(repeat=60)#向后台注册任务\n\n        # notify_user(repeat=120)#向后台注册任务\n        return render(request, 'index.html', {\n            'all_banners': all_banners,\n            'equipments': equipments,\n            'banner_equipments': banner_equipments,\n            'teams': teams,\n            })\n\n\ndef page_not_found(request):\n    # 全局404处理函数\n    from django.shortcuts import render_to_response\n    response = render_to_response('404.html', {})\n    response.status_code = 404\n    return response\n\n\ndef page_error(request):\n    # 全局500处理函数\n    from django.shortcuts import render_to_response\n    response = render_to_response('500.html', {})\n    response.status_code = 500\n    return response\n\n\n\n\n\n","repo_name":"WenLiang619/testlabmanagement","sub_path":"apps/users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":31675,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"28470141582","text":"from cgi import print_form\nfrom enum import unique\nimport json\nimport re\nfrom venv import create\nfrom app.config.database import create_connection\nfrom datetime import date, datetime\nimport uuid\nimport asyncpg.exceptions as pg_exceptions\nfrom app.errs.err import err_response\nfrom app.models import organizations as organization_model\n\nasync def create_organizations(organization: organization_model.Organizations):\n    con = await create_connection()\n    tr = con.transaction()\n    try:\n        await tr.start()\n        statement = await con.fetch(\n            \"INSERT INTO organizations (public_id,name,code,avatar_icon,avatar_color,image_url,description,created_at,updated_at,deleted_at) VALUES ($1,$2,$3,$4,$5,$6,$7,$8,$9,$10) RETURNING id\",\n            str(uuid.uuid4()),\n            organization.name,\n            organization.code,\n            organization.avatar_icon,\n            organization.avatar_color,\n            organization.image_url,\n            organization.description,\n            datetime.now(),\n            datetime.now(),\n            None\n        )\n    except pg_exceptions.UniqueViolationError as e:\n        await tr.rollback()\n        await err_response(\n            status_code=409,\n            message=\"code already exists\"\n        )\n    if statement:\n        await tr.commit()\n        await con.close()\n        return({\n            \"message\" : {\n                \"id\" : ((statement[0].get(\"id\"))),\n            }\n        })\n    \nasync def get_one_organization(organization_id):\n    con = await create_connection()\n    statement = await con.fetch(\n        \"SELECT * FROM organizations WHERE id = $1 AND deleted_at IS NULL\",\n        organization_id\n    )\n    await con.close()\n    return statement\n\nasync def get_all_organizations_paginated(page_number, page_size):\n    con = await create_connection()\n    statement = await con.fetch(\n        \"SELECT * FROM organizations WHERE deleted_at IS NULL ORDER BY id DESC LIMIT $1 OFFSET $2\",\n        page_size,\n        (page_number - 1) * page_size\n    )\n    await con.close()\n    return statement\n\nasync def update_organizations(organization_id,organization: organization_model.Organizations):\n    con = await create_connection()\n    tr = con.transaction()\n    try:\n        await tr.start()\n        statement = await con.fetch(\n            \"UPDATE organizations SET name = $1,code = $2,avatar_icon = $3,avatar_color = $4,image_url = $5,description = $6,updated_at = $7 WHERE id = $8 RETURNING id\",\n            organization.name,\n            organization.code,\n            organization.avatar_icon,\n            organization.avatar_color,\n            organization.image_url,\n            organization.description,\n            datetime.now(),\n            organization_id\n        )\n    except pg_exceptions.UniqueViolationError as e:\n        await tr.rollback()\n        await  err_response(\n            status_code=409,\n            message=\"email already exists\"\n        )\n    if statement:\n        await tr.commit()\n        await con.close()\n        return ({\n            \"message\": {\n                \"id\": ((statement[0].get(\"id\"))),\n            }\n        })\n        \nasync def delete_organizations(organization_id):\n    con = await create_connection()\n    statement = await con.fetch(\n        \"UPDATE organizations SET deleted_at = $1 WHERE id = $2\",\n        datetime.now(),\n        organization_id\n    )\n    await con.close()\n    return statement\n    ","repo_name":"Perrrr/expense_manage_system","sub_path":"expense-app-gateway/server/app/repository/organizations_db.py","file_name":"organizations_db.py","file_ext":"py","file_size_in_byte":3419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"10650779963","text":"### Chapter 6\n\n## Looping Through a Dictionary\n\n# Loooping Through All Key-Value Pairs\nuser_0 = {\n    'username': 'efermi',\n    'first': 'enrico',\n    'last': 'fermi'\n    }\n\nfor key, value in user_0.items():\n    print(f'\\nKey: {key}')\n    print(f'Value: {value}')\n\nfor k, v in user_0.items(): # Abbreviations can be used for key and value\n    print(f'\\nKey: {key}')\n    print(f'Value: {value}')","repo_name":"briantaylorjohnson/python-practice","sub_path":"user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"36818373656","text":"import sys\nfrom config import *\nfrom analytics import Research\n\n\nif __name__ == '__main__':\n    if len(sys.argv) == 2:\n        r = Research(sys.argv[1])\n        fr = r.file_reader()\n        count = r.Calculations(fr).counts()\n        percent = r.Calculations(fr).fractions(count)\n        rand_obs = r.Analytics(fr).predict_random(num_of_steps)\n        data = template.format(\n                    sum(count),\n                    count[0],\n                    count[1],\n                    percent[0],\n                    percent[1],\n                    num_of_steps,\n                    list(map(sum, zip(*rand_obs)))[0],\n                    list(map(sum, zip(*rand_obs)))[1]\n                    )\n        r.Analytics.save_file(data, name_of_file, type_of_file)\n    else:\n        print(\"Use: python3.10 make_report.py data.csv\")\n","repo_name":"aperop/ds","sub_path":"day02/ex05/make_report.py","file_name":"make_report.py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"37167750796","text":"# -*- coding: utf-8 -*-\nimport argparse\nimport logging\n\nfrom dbferret.retriever import DbFerret\nfrom dbferret.file_writer import FileWriter\n\n\"\"\"\nRun something like this for a redshift db:\n\n    python driver.py \\\n       --user <user> --pw <password> \\\n       --hostname <hostname> -d <database>\n\nFor postgres specify the engine_type, port and perhaps you'll need ssl_mode:\n\n    python driver.py \\\n        --user <user> --pw <password> --hostname <hostname> \\\n        -d <database> --engine_type postgresql -p 5432 --ssl_mode True\n\n\"\"\"\n\n\ndef main():\n    args = parse_args()\n    logging.basicConfig(level=getattr(logging, args.log_level.upper(), None))\n\n    # Instantiate ferret object to get db metadata in subsequent steps\n    dbferret = DbFerret(hostname=args.hostname, user=args.user, pw=args.pw,\n                        db=args.db, ssl_mode=args.ssl_mode,\n                        engine_type=args.engine_type, schema=args.schema,\n                        port=args.port, warehouse=args.warehouse,\n                        schema_list=args.schema_list)\n\n    # Collect data\n    table_metadata = dbferret.extract_table_metadata()\n    view_ddls = dbferret.extract_view_ddl()\n\n    # Write results\n    file_writer = FileWriter(db=args.db, engine_type=args.engine_type)\n    file_writer.output_table_metadata_to_tsv(table_metadata)\n    file_writer.output_view_ddl_to_sql(view_ddls)\n\n\ndef parse_args():\n    \"\"\"\n    :return:\n    \"\"\"\n    parser = argparse.ArgumentParser(\n        description=\"ferret collects metadata from a database via \"\n                    \"reflection, provide connection information to run\")\n\n    parser.add_argument(\n        \"-e\",\n        \"--engine_type\",\n        dest=\"engine_type\",\n        help=\"The database type, such as postgres or redshift used \"\n             \"for the connection string by sqlalchemy.\",\n        default=\"redshift\"\n    )\n    parser.add_argument(\n        \"-u\",\n        \"--user\",\n        dest=\"user\",\n        help=\"The database user used to login. For postgres, at least, \"\n             \"any user normally will be able to crawl the database.\"\n    )\n    parser.add_argument(\n        \"-pw\",\n        \"--pw\",\n        dest=\"pw\",\n        help=\"Password to connect to the db with the specified user. \"\n             \"Information is passed through to the db but not recorded.\"\n    )\n    parser.add_argument(\n        \"-hn\",\n        \"--hostname\",\n        dest=\"hostname\",\n        help=\"The host where the database is located.\"\n    )\n    parser.add_argument(\n        \"-p\",\n        \"--port\",\n        dest=\"port\",\n        help=\"The port used by the database for connections.\"\n    )\n    parser.add_argument(\n        \"-d\",\n        \"--db\",\n        dest=\"db\",\n        help=\"The database instance of the database.\"\n    )\n    parser.add_argument(\n        \"-l\",\n        \"--ssl_mode\",\n        dest=\"ssl_mode\",\n        help=\"A boolean indicating if connections must be encrypted \"\n             \"to the database with SSL.\",\n        default=False\n    )\n    parser.add_argument(\n        \"-s\",\n        \"--schema\",\n        dest=\"schema\",\n        help=\"The schema to operate against for Snowflake databases\",\n        default=\"public\"\n    )\n    parser.add_argument(\n        \"-w\",\n        \"--warehouse\",\n        dest=\"warehouse\",\n        help=\"The warehouse to operate against for Snowflake databases\"\n    )\n    parser.add_argument(\n        \"--debug\",\n        dest=\"debug\",\n        help=\"Collects and diagnoses will be prepared but not run\",\n        action=\"store_true\",\n        default=False\n    )\n    parser.add_argument(\n        \"--log_level\",\n        dest=\"log_level\",\n        help=\"Sets the logging severity level\",\n        default=\"INFO\"\n    )\n    parser.add_argument(\n        \"--schema_list\",\n        dest=\"schema_list\",\n        help=\"Comma delimited list of schemas\"\n    )\n    args = parser.parse_args()\n    return args\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"john-cenzano-fong/db-ferret","sub_path":"runner.py","file_name":"runner.py","file_ext":"py","file_size_in_byte":3868,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"73658511606","text":"def coroutine(gen):\r\n    def inner():\r\n        generator = gen()\r\n        next(generator)\r\n        return generator\r\n    return inner\r\n\r\n@coroutine \r\ndef storage():\r\n    values = set()\r\n    was_there = False  \r\n    while True:\r\n        val = yield was_there\r\n        was_there = val in values\r\n        if not was_there:\r\n            values.add(val)\r\n            \r\nst = storage()\r\n#next(st)\r\nprint(st.send(42))\r\nprint(st.send(42))","repo_name":"Km1zZzoU/Python","sub_path":"minka13.py","file_name":"minka13.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"41600569578","text":"from torch import nn\n\ndef decomp_alexnet(net, rank_func, decomp_func):\n    i = 1\n    \n    while i < len(net.features):\n        # find out the rank of the first conv layer\n        layer_i = net.features[i]\n\n        if not isinstance(layer_i, nn.Conv2d):\n            i += 1\n            continue\n        \n        layer_i = net.features[i]\n        rank = rank_func(layer_i)\n        print('rank of the {}th layer: {}'.format(i, rank))\n        \n        # debugging\n        print(\"begin decomposing layer {}\".format(i))\n        decomp_layers = decomp_func(layer_i, rank)\n        print(\"finished decomposing layer {}\".format(i))\n\n        net.features = nn.Sequential(\\\n        *(list(net.features[:i]) + decomp_layers + list(net.features[i + 1:])))\n\n        i +=  len(decomp_layers)\n\n    return net\n","repo_name":"ruihangdu/Decompose-CNN","sub_path":"scripts/decomp_alexnet.py","file_name":"decomp_alexnet.py","file_ext":"py","file_size_in_byte":791,"program_lang":"python","lang":"en","doc_type":"code","stars":73,"dataset":"github-code","pt":"76"}
+{"seq_id":"43262478342","text":"from django.urls import include, path, re_path\nfrom django.views.generic.base import TemplateView\n\nfrom songuploader.utils import LoginRequiredTemplateView, slice_song\n\nfrom .forms import SubmissionUploadForm\nfrom .views.file_views import FileDownload\nfrom .views.submission_views import SubmissionCreateView, SubmissionUpdateView\nfrom .views.views import IndexView, LoginView, LogoutView, DownloadPlaylistView\n\nsong_urlpatterns = [\n    path(\n        \"choose/\",\n        LoginRequiredTemplateView.as_view(\n            template_name=\"uploader/choose_upload_type.html\"\n        ),\n        name=\"choose-song\",\n    ),\n    path(\"from-youtube/\", SubmissionCreateView.as_view(), name=\"from-youtube\"),\n    path(\n        \"from-youtube/update/<int:pk>\",\n        SubmissionUpdateView.as_view(),\n        name=\"update-from-youtube\",\n    ),\n    path(\n        \"from-file/\",\n        SubmissionCreateView.as_view(\n            form_class=SubmissionUploadForm,\n            update_name=\"update-from-file\",\n            submit_action=slice_song,\n        ),\n        name=\"from-file\",\n    ),\n    path(\n        \"from-file/update/<int:pk>\",\n        SubmissionUpdateView.as_view(\n            form_class=SubmissionUploadForm, submit_action=slice_song\n        ),\n        name=\"update-from-file\",\n    ),\n    path(\"upload/\", SubmissionCreateView.as_view(), name=\"upload\"),\n    path(\"playlist/\", DownloadPlaylistView.as_view(), name=\"download-playlist\")\n]\n\n\nurlpatterns = [\n    path(\"\", IndexView.as_view(), name=\"index\"),\n    path(\"song/\", include(song_urlpatterns)),\n    re_path(r\"^media/(?P<path>.*)/$\", FileDownload.as_view(), name=\"download-file\"),\n    path(\"login/\", LoginView.as_view(), name=\"login\"),\n    path(\"logout/\", LogoutView.as_view(), name=\"logout\"),\n]\n","repo_name":"SFSeeger/Abitur-Song-Uploader","sub_path":"src/uploader/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1735,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"72595703925","text":"import numpy as np\nimport torch\nfrom torchvision.models import resnet50\nimport torchvision.transforms as transforms\nfrom PIL import Image\nfrom slam_rcnn.nn.base_projection_layer import BaseProjectionLayer\nfrom typing import Dict, Any, Callable\n\n\nclass ResNetProjectionLayer(BaseProjectionLayer):\n    \"\"\"Create a feature projection layer in PyTorch that maintains a voxel\n    grid description of the world world, where each voxel grid cell has\n    a feature vector associated with it, typically semantic labels.\n\n    Arguments:\n\n    camera_height: int\n        the map_height of the image generated by a pinhole camera onboard the\n        agent, corresponding to a map_depth and semantic observation.\n    camera_width: int\n        the map_width of the image generated by a pinhole camera onboard the\n        agent, corresponding to a map_depth and semantic observation.\n    vertical_fov: float\n        the vertical field of view of the onboard camera, measure in\n        radians from the bottom of the viewport to the top.\n\n    map_height: int\n        the number of grid cells along the 'map_height' axis of the semantic\n        map, as rendered using the top down rendering function.\n    map_width: int\n        the number of grid cells along the 'map_width' axis of the semantic\n        map, as rendered using the top down rendering function.\n    map_depth: int\n        the number of grid cells that are collapsed along the 'up'\n        direction by the top down rendering function.\n    feature_size: int\n        the number of units in each feature vector associated with every\n        grid cell, such as the number of image segmentation categories.\n\n    origin_y: float\n        the center of the semantic map along the 'map_height' axis of the\n        semantic map as viewed from a top-down render of the map.\n    origin_x: float\n        the center of the semantic map along the 'map_width' axis of the\n        semantic map as viewed from a top-down render of the map.\n    origin_z: float\n        the center of the semantic map along the 'map_depth' axis of the\n        semantic map as viewed from a top-down render of the map.\n    grid_resolution: float\n        the length of a single side of each voxel in the semantic map in\n        units of the world coordinate system, which is typically meters.\n\n    interpolation_weight: float\n        float representing the interpolation weight used when adding\n        new features in the feature map weighted by interpolation_weight.\n    initial_feature_map: torch.Tensor\n        tensor representing the initial feature map tensor,\n        which will be set to zero if the value not specified by default.\n\n    \"\"\"\n\n    def __init__(self, camera_height: int = 224, camera_width: int = 224,\n                 vertical_fov: float = 90.0, map_height: int = 256,\n                 map_width: int = 256, map_depth: int = 64,\n                 feature_size: int = 256, dtype: torch.dtype = torch.float32,\n                 origin_y: float = 0.0, origin_x: float = 0.0,\n                 origin_z: float = 0.0, grid_resolution: float = 0.05,\n                 interpolation_weight: float = 0.5,\n                 initial_feature_map: torch.Tensor = None):\n        \"\"\"Create a feature projection layer in PyTorch that maintains a voxel\n        grid description of the world world, where each voxel grid cell has\n        a feature vector associated with it, typically semantic labels.\n\n        Arguments:\n\n        camera_height: int\n            the map_height of the image generated by a pinhole camera onboard the\n            agent, corresponding to a map_depth and semantic observation.\n        camera_width: int\n            the map_width of the image generated by a pinhole camera onboard the\n            agent, corresponding to a map_depth and semantic observation.\n        vertical_fov: float\n            the vertical field of view of the onboard camera, measure in\n            radians from the bottom of the viewport to the top.\n\n        map_height: int\n            the number of grid cells along the 'map_height' axis of the semantic\n            map, as rendered using the top down rendering function.\n        map_width: int\n            the number of grid cells along the 'map_width' axis of the semantic\n            map, as rendered using the top down rendering function.\n        map_depth: int\n            the number of grid cells that are collapsed along the 'up'\n            direction by the top down rendering function.\n        feature_size: int\n            the number of units in each feature vector associated with every\n            grid cell, such as the number of image segmentation categories.\n\n        origin_y: float\n            the center of the semantic map along the 'map_height' axis of the\n            semantic map as viewed from a top-down render of the map.\n        origin_x: float\n            the center of the semantic map along the 'map_width' axis of the\n            semantic map as viewed from a top-down render of the map.\n        origin_z: float\n            the center of the semantic map along the 'map_depth' axis of the\n            semantic map as viewed from a top-down render of the map.\n        grid_resolution: float\n            the length of a single side of each voxel in the semantic map in\n            units of the world coordinate system, which is typically meters.\n\n        interpolation_weight: float\n            float representing the interpolation weight used when adding\n            new features in the feature map weighted by interpolation_weight.\n        initial_feature_map: torch.Tensor\n            tensor representing the initial feature map tensor,\n            which will be set to zero if the value not specified by default.\n\n        \"\"\"\n\n        super(ResNetProjectionLayer, self).__init__(\n            camera_height=camera_height // 4,\n            camera_width=camera_width // 4,\n            vertical_fov=vertical_fov,\n            map_height=map_height,\n            map_width=map_width,\n            map_depth=map_depth,\n            feature_size=feature_size, dtype=dtype,\n            origin_y=origin_y, origin_x=origin_x, origin_z=origin_z,\n            grid_resolution=grid_resolution,\n            interpolation_weight=interpolation_weight,\n            initial_feature_map=initial_feature_map)\n\n        self.resnet_model = resnet50(pretrained=True)\n        self.resnet_model.eval()\n        self.resnet_preprocess = transforms.Compose([\n            transforms.Resize(224), \n            transforms.ToTensor(),\n            transforms.Normalize(mean=[0.485, 0.456, 0.406], \n                                 std=[0.229, 0.224, 0.225])\n        ])\n\n    def pseudo_forward(self, x):\n\n        with torch.no_grad():\n\n            x = self.resnet_model.conv1(x)\n            x = self.resnet_model.bn1(x)\n            x = self.resnet_model.relu(x)\n            x = self.resnet_model.maxpool(x)\n\n            x = self.resnet_model.layer1(x)\n            # x = self.resnet_model.layer2(x)\n            # x = self.resnet_model.layer3(x)\n            # x = self.resnet_model.layer4(x)\n\n            return x\n\n    def update(self, observation: Dict[str, torch.Tensor]):\n        \"\"\"Update the semantic map given a map_depth image and a feature image\n        by projecting the features onto voxels in the semantic map using\n        a set of rays emanating from a virtual pinhole camera.\n\n        Arguments:\n\n        observation[\"position\"]: torch.Tensor\n            the position of the agent in the world coordinate system, where\n            the position will be binned to voxels in a semantic map.\n        observation[\"yaw\"]: torch.Tensor\n            a tensor representing the yaw in radians of the coordinate,\n            starting from the x-axis and turning counter-clockwise.\n        observation[\"elevation\"]: torch.Tensor\n            a tensor representing the elevation in radians about the x-axis,\n            with positive corresponding to upwards tilt.\n\n        observation[\"map_depth\"]: torch.FloatTensor\n            the length of the corresponding ray in world coordinates before\n            hitting a surface, with shape: [height, width, 1].\n        observation[\"features\"]: Any\n            a feature vector for every pixel in the imaging plane, to be\n            scattered on the map, with shape: [height, width, num_classes].\n\n        \"\"\"\n\n        # ensure all tensors have the appropriate device and dtype\n        position = torch.as_tensor(observation[\n            \"position\"], dtype=torch.float32, device=self.data.device)\n        yaw = torch.as_tensor(observation[\n            \"yaw\"], dtype=torch.float32, device=self.data.device)\n        elevation = torch.as_tensor(observation[\n            \"elevation\"], dtype=torch.float32, device=self.data.device)\n        depth = torch.as_tensor(observation[\n            \"depth\"], dtype=torch.float32, device=self.data.device)\n\n        # prepare the image for processing with the clip model\n        # by copying the image to the gpu and normalizing it appropriately\n        features = self.resnet_preprocess(Image.fromarray(\n            np.uint8(255.0 * observation[\"rgb\"]))\n            .convert('RGB')).unsqueeze(0).to(self.data.device)\n\n        features = self.pseudo_forward(features).squeeze(0).permute(1, 2, 0)\n        image_downsampling_factor = depth.shape[0] // features.shape[0]\n\n        # update the clip feature map using the latest observations\n        super(ResNetProjectionLayer, self).update(  # from the environment\n            dict(position=position, yaw=yaw, elevation=elevation,\n                 depth=depth[image_downsampling_factor // 2::\n                             image_downsampling_factor,\n                             image_downsampling_factor // 2::\n                             image_downsampling_factor],\n                 features=features))\n\n        return self  # return self for chaining additional functions\n\n    def top_down(self, depth_slice: slice = slice(0, 32)):\n        \"\"\"Render a top-down view of a map of features organized as a three\n        dimensional grid of voxels, by taking the zero vector to be empty\n        voxels and rendering the top-most non-empty voxel to a pixel in a grid.\n\n        Arguments:\n\n        depth_slice: slice\n            an slice that specifies which map_depth components to use\n            when rendering a top down visualization of the feature map.\n\n        Returns:\n\n        feature_image: torch.Tensor\n            an image with a feature vector associated with every cell in the\n            image corresponding to a visible voxel in the original feature map.\n\n        \"\"\"\n\n        # downsample the provided slice object because\n        if depth_slice is not None:  # this map has a lower resolution\n            depth_slice = slice(\n                depth_slice.start,\n                depth_slice.stop,\n                depth_slice.step\n                if depth_slice.step is not None else depth_slice.step)\n\n        # call top down super method with down-sampled slice\n        return super(ResNetProjectionLayer,\n                     self).top_down(depth_slice=depth_slice)\n\n    def visualize(self, obs: Dict[str, Any],\n                  depth_slice: slice = slice(4, 32)):\n        \"\"\"Helper function that returns a list of images that are used for\n        visualizing the contents of the feature map contained in subclasses,\n        such as visualizing object categories, or which voxels are obstacles.\n\n        Arguments:\n\n        obs: Dict[str, dict]\n            the current observation, as a dict or Tensor, which can be\n            used to visualize the current location of the agent in the scene.\n        depth_slice: Union[slice, Dict[str, slice]]\n            an slice that specifies which map_depth components to use\n            when rendering a top down visualization of the feature map.\n\n        Returns:\n\n        image: np.ndarray\n            a list of numpy arrays that visualize the contents of this layer,\n             such as an image showing semantic categories.\n\n        \"\"\"\n\n        pass\n","repo_name":"brandontrabucco/mass","sub_path":"mass/nn/applications/resnet_projection_layer.py","file_name":"resnet_projection_layer.py","file_ext":"py","file_size_in_byte":11991,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"23152885826","text":"from track_simulator.interactor.get_trackanalysis_statistics import GetTrackAnalysisStatistics\nfrom track_simulator.entities.track_point import TrackPoint as Point\nimport pandas as pd\nfrom pandas import DataFrame\n\n\nclass GetTrackAnalysisStatisticsImpl(GetTrackAnalysisStatistics):\n    def apply(self, data: DataFrame) -> DataFrame:\n        data = self.__add_point_projection_distance(data)\n        data = self.__add_next_point_distance(data)\n        return data[['id', 'DistanceToNext', 'DistancePointProjection']]\n\n    def __add_next_point_distance(self, data: DataFrame) -> DataFrame:\n        data['point_lon_shift'] = data.Point_lon.shift()\n        data['point_lat_shift'] = data.Point_lat.shift()\n        data['DistanceToNext'] = data.apply(lambda x: Point(x['Point_lon'], x['Point_lat']).haversine_distance(\n            Point(x['point_lon_shift'],\n                  x['point_lat_shift'])),\n                                            axis=1)\n        return data.drop(columns=['point_lat_shift', 'point_lon_shift'])\n\n    def __add_point_projection_distance(self, data: DataFrame) -> DataFrame:\n        data['DistancePointProjection'] = data.apply(lambda x: Point(x['Point_lon'],\n                                                                     x['Point_lat']).haversine_distance(\n            Point(x['Projection_lon'],\n                  x['Projection_lat'])),\n                                                     axis=1)\n        return data\n\n","repo_name":"tboutaour/track-simulator","sub_path":"src/track_simulator/interactor/get_trackanalysis_statistics_impl.py","file_name":"get_trackanalysis_statistics_impl.py","file_ext":"py","file_size_in_byte":1450,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"76"}
+{"seq_id":"43183069676","text":"\"\"\"Real-time example.\n\nThis example shows how a real-time visualizer of multiple digital signals can\nbe written with Galry.\n\n\"\"\"\nimport numpy as np\nfrom galry import *\n\n# initial values\nnsamples = 1000\nnplots = 10\nt = np.tile(np.linspace(-1., 1., nsamples), (nplots, 1))\nx = .01 * np.random.randn(nplots, nsamples) + np.linspace(-.75, .75, nplots)[:,np.newaxis]\n\n# this function returns 10*nplots new values at each call\ndef get_new_data():\n    return .01 * np.random.randn(nplots, 10) + np.linspace(-.75, .75, nplots)[:,np.newaxis]\n\n# this function updates the plot at each call\ndef anim(fig, _):\n    # append new data to the signal\n    global x\n    x = np.hstack((x[:,10:], get_new_data()))\n    \n    # create the new 1000*nplots*2 position array with x, y values at each row\n    position = np.vstack((t.flatten(), x.flatten())).T\n    \n    # update the position of the plot\n    fig.set_data(position=position)\n\n# plot the signal\nplot(t, x)\n\n# animate the plot: anim is called every 25 milliseconds\nanimate(anim, dt=.025)\n\n# show the figure\nshow()\n","repo_name":"rossant/galry","sub_path":"examples/realtime_multi.py","file_name":"realtime_multi.py","file_ext":"py","file_size_in_byte":1048,"program_lang":"python","lang":"en","doc_type":"code","stars":188,"dataset":"github-code","pt":"76"}
+{"seq_id":"12708551947","text":"def merge_sort(arr):\n    if len(arr) <= 1:\n        return arr\n\n    mid = len(arr) // 2\n    left_arr = arr[:mid]\n    right_arr = arr[mid:]\n\n    left_arr = merge_sort(left_arr)\n    right_arr = merge_sort(right_arr)\n\n    return merge(left_arr, right_arr)\n\n\ndef merge(left_arr, right_arr):\n    result = []\n    left_idx, right_idx = 0, 0\n    while left_idx < len(left_arr) and right_idx < len(right_arr):\n        if left_arr[left_idx] <= right_arr[right_idx]:\n            result.append(left_arr[left_idx])\n            left_idx += 1\n        else:\n            result.append(right_arr[right_idx])\n            right_idx += 1\n\n    if left_idx < len(left_arr):\n        result.extend(left_arr[left_idx:])\n    else:\n        result.extend(right_arr[right_idx:])\n\n    return result\n\n\nif __name__ == '__main__':\n    arr = [5, 1, 6, 3, 4, 2, 7]\n    print(\"정렬 전 : \", arr)\n\n    arr = merge_sort(arr)\n\n    print(\"정렬 후 : \", arr)","repo_name":"KimBeomGi/STUDYduringSSAFY","sub_path":"python/수업/230329_online_병합정렬_내가찾은것.py","file_name":"230329_online_병합정렬_내가찾은것.py","file_ext":"py","file_size_in_byte":919,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"11203295131","text":"# -*- coding: utf-8 -*-\nimport subprocess\nfrom testutils import notary_url\n\ndef sign_image(registry_ip, project_name, image, tag):\n    try:\n        ret = subprocess.check_output([\"./tests/apitests/python/sign_image.sh\", registry_ip, project_name, image, tag, notary_url], shell=False)\n        print(\"sign_image return: \", ret)\n    except subprocess.CalledProcessError as exc:\n        raise Exception(\"Failed to sign image error is {} {}.\".format(exc.returncode, exc.output))\n\n","repo_name":"dundun9/harbor-arm64","sub_path":"tests/apitests/python/library/sign.py","file_name":"sign.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"76"}
+{"seq_id":"21557764249","text":"import datetime\nimport os\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport statistics\nfrom statistics import mean\nimport sys\nsys.path.append('C:/Users/Benedikt/Documents/dev/MA_LogParser/logparser/ILM_WS202223')\nimport generalfunctions as gf\n\nfrom pymongo import MongoClient\n\n\n###\n### For figuring out the operating times for teleporting\n###\ndef get_database():\n    # Provide the mongodb atlas url to connect python to mongodb using pymongo\n    CONNECTION_STRING = \"mongodb://fredix:memphis55@kurz-containar.de:27017\"\n\n    # Create a connection using MongoClient. You can import MongoClient or use pymongo.MongoClient\n    client = MongoClient(CONNECTION_STRING)\n    # Create the database for our example (we will use the same database throughout the tutorial\n    return client['ilm']\n\n\n\ndef aggregateData(array):\n    lenArray = len(array)\n    print(\"Len array \" + str(lenArray))\n    data = []\n\n    for i in range(0, lenArray):\n        for elem in array[i]:\n            data.append(elem)\n\n    return data;\n\n\n\ndef writeToDb(name, value):\n    dbname = get_database()\n    tresor = dbname[\"tresor\"]\n\n    dto = tresor.find({\"name\": name})\n    dto = list(dto)\n\n    print(\"Dto\")\n    print(dto)\n\n    if (len(dto) > 0):\n        dto = {\"name\": name}\n        newvalues = {\"$set\": {\"values\": value}}\n        tresor.update_one(dto, newvalues)\n        print(\"Update element\")\n    else:\n        dto = {\"name\": name, \"values\": value}\n        tresor.insert_one(dto)\n        print(\"New element\")\n\n\n\n# This is added so that many files can reuse the function get_database()\ndef boxplotCap(valArray):\n    return f'\\n n = {len(valArray)} \\n' \\\n           f'Me. = {round(statistics.median(valArray), 3)} s \\n ' \\\n           f'Mi. = {round(statistics.mean(valArray), 2)}s \\n ' \\\n            f'S. Abw. = {round(statistics.stdev(valArray), 3)} s \\n ' \\\n            f'M. Abw. = {round(mean(valArray), 3)} s \\n ';\n\ndef convertToFloat(arr):\n\n    arr = arr.get('values')\n\n    print(arr)\n\n    arr = list(arr)\n    lenArray = len(arr)\n\n    print(lenArray)\n\n    allValues = []\n\n    for e in range(0, lenArray):\n        floatValues = []\n\n        for elem in arr[e]:\n            floatValues.append(float(elem))\n\n        allValues.append(floatValues)\n\n    return allValues\n\n\ndef convertToFloat1D(arr):\n\n    arr = arr.get('values')\n    arr = list(arr)\n    lenArray = len(arr)\n\n\n    allValues = []\n\n    for elem in arr:\n        allValues.append(float(elem))\n\n\n    return allValues\n\n\n\ndef setXTicks_param(valArray, descArray):\n    xtick = []\n    i = 0\n\n    # The descirption of fields\n    for elem in valArray:\n        s = boxplotCap(elem)\n\n        if descArray:\n            xtick.append(descArray[i] + s)\n        else:\n            xtick.append(s)\n        i = i + 1\n\n    lenVA = len(valArray)\n\n    # First Parameter the number of fields\n    elements = []\n    for elem in range(0, lenVA):\n        elements.append((elem + 1))\n\n    return (elements, xtick)\n\n\nif __name__ == \"__main__\":\n    # Get the database\n    dbname = get_database()\n\n    col = dbname[\"uwp\"]\n    tresor = dbname[\"tresor\"]\n\n    probands = ['A01', 'A02', 'A03', 'A04', 'A05', 'A06', 'A07', 'A08', 'A09', 'A10',\n                'A11', 'A12', 'A13', 'A14', 'A15', 'A16', 'A17', 'A18', 'A19', 'A20',\n                'A21', 'A22', 'A23', 'A24', 'A25', 'A26', 'A27', 'A28']\n\n\n    # sceneGaze_HPG2 = runAnalyzeElementSteps(probands, sceneName, devices, hand)\n    sceneGaze_R_MQP = tresor.find_one({'name': 'Gaze_AD_R_MQP'})\n    sceneGaze_L_MQP = tresor.find_one({'name': 'Gaze_AD_L_MQP'})\n    sceneGaze_R_MQ2 = tresor.find_one({'name': 'Gaze_AD_R_MQ2'})\n    sceneGaze_L_MQ2 = tresor.find_one({'name': 'Gaze_AD_L_MQ2'})\n\n    sceneGaze_HL2_first = tresor.find_one({'name': 'Gaze_UWP_HL2_first'})\n    sceneGaze_HL2_second = tresor.find_one({'name': 'Gaze_UWP_HL2_second'})\n\n    sceneGaze_HPG2_first = tresor.find_one({'name': 'Gaze_UWP_HPG2_first'})\n    sceneGaze_HPG2_second = tresor.find_one({'name': 'Gaze_UWP_HPG2_second'})\n\n    Ga_Ad = tresor.find_one({'name': 'Ga-Ad'})\n    Ga_Ad = convertToFloat1D(Ga_Ad)\n\n    Ga_1_Wi_HPG2 = tresor.find_one({'name': 'Ga-1-Wi-HPG2'})\n    Ga_1_Wi_HPG2 = convertToFloat1D(Ga_1_Wi_HPG2)\n\n    Ga_2_Wi_HPG2 = tresor.find_one({'name': 'Ga-2-Wi-HPG2'})\n    Ga_2_Wi_HPG2 = convertToFloat1D(Ga_2_Wi_HPG2)\n\n    Ga_VR = [Ga_Ad, Ga_1_Wi_HPG2, Ga_2_Wi_HPG2]\n    Ga_VR = aggregateData(Ga_VR)\n\n\n\n\n    sceneGaze_R_MQP = convertToFloat(sceneGaze_R_MQP)\n    sceneGaze_L_MQP = convertToFloat(sceneGaze_L_MQP)\n    sceneGaze_R_MQ2 = convertToFloat(sceneGaze_R_MQ2)\n    sceneGaze_L_MQ2 = convertToFloat(sceneGaze_L_MQ2)\n\n    sceneGaze_HL2_first = convertToFloat1D(sceneGaze_HL2_first)\n    sceneGaze_HL2_second = convertToFloat1D(sceneGaze_HL2_second)\n    sceneGaze_HPG2_first = convertToFloat1D(sceneGaze_HPG2_first)\n    sceneGaze_HPG2_second = convertToFloat1D(sceneGaze_HPG2_second)\n\n\n    sceneGaze_R_MQP = aggregateData(sceneGaze_R_MQP)\n    sceneGaze_L_MQP = aggregateData(sceneGaze_L_MQP)\n    sceneGaze_R_MQ2 = aggregateData(sceneGaze_R_MQ2)\n    sceneGaze_L_MQ2 = aggregateData(sceneGaze_L_MQ2)\n\n\n\n    # Aggregieren der Daten\n    operatorGaze_second = [sceneGaze_R_MQP, sceneGaze_L_MQP, sceneGaze_R_MQ2, sceneGaze_L_MQ2, sceneGaze_HL2_second, sceneGaze_HPG2_second]\n\n    operatorGaze_UWP_second = [sceneGaze_HL2_second, sceneGaze_HPG2_second]\n    operatorGaze_AD_second = [sceneGaze_R_MQP, sceneGaze_L_MQP, sceneGaze_R_MQ2, sceneGaze_L_MQ2]\n\n    operatorGaze_first = [sceneGaze_HL2_first, sceneGaze_HPG2_first]\n\n    operatorGaze_first = aggregateData(operatorGaze_first)\n    operatorGaze_second = aggregateData(operatorGaze_second)\n\n    operatorGaze_UWP_second = aggregateData(operatorGaze_UWP_second)\n    operatorGaze_AD_second = aggregateData(operatorGaze_AD_second)\n\n    writeToDb(\"Gaze_first\", operatorGaze_first)\n    writeToDb(\"Gaze_second\", operatorGaze_second)\n\n    writeToDb(\"Gaze_AD_second\", operatorGaze_AD_second)\n    writeToDb(\"Gaze_UWP_second\", operatorGaze_UWP_second)\n\n    writeToDb(\"Ga-VR\", Ga_VR)\n    writeToDb(\"Ga-AR-1\", sceneGaze_HL2_first)\n    writeToDb(\"Ga-AR-2\", sceneGaze_HL2_second)\n\n\n    operatorGaze = [operatorGaze_AD_second, operatorGaze_UWP_second, Ga_VR]\n\n\n    print(\"Operator Gaze\")\n    print(operatorGaze)\n\n\n\n    box_MQP = [sceneGaze_R_MQP, sceneGaze_L_MQP]\n    box_MQ2 = [sceneGaze_R_MQ2, sceneGaze_L_MQ2]\n\n    box_R = [sceneGaze_R_MQP, sceneGaze_R_MQ2]\n    box_L = [sceneGaze_L_MQP, sceneGaze_L_MQ2]\n\n    writeToDb(\"Gaze_AD_MQP\", box_MQP)\n    writeToDb(\"Gaze_AD_MQ2\", box_MQ2)\n\n    writeToDb(\"Gaze_AD_R\", box_R)\n    writeToDb(\"Gaze_AD_L\", box_L)\n\n\n\n    ### Graphic\n    fig, axs = plt.subplots(1, 1, figsize=(10, 8))\n\n    fig.suptitle('Bearbeitungszeit mit dem nachgelagerten Schaltflächen des Gaze-Operators: Gesamt mit HPG2, MQ2, MQP, HL2')\n    # ax = fig.add_axes(['Rechte Hand', 'Linke Hand'])\n    plt.violinplot(operatorGaze)\n\n    plt.ylabel('Sekunden')\n    # axs[1].set(ylabel='Sekunden')\n\n\n    descArrayXTicks = [\"Android Plattform\", \"Windows Plattform\", \"Ga-VR\"]\n\n    (elemALL, xALL, dfALL) = gf.setXTicks_param(operatorGaze, descArrayXTicks)\n\n    # axs[0].set_title('Gaze mit der MS HoloLens 2')\n    plt.xticks(elemALL, xALL)\n\n    plt.show()\n","repo_name":"UGIW-Dortmund/logparser","sub_path":"ILM_WS202223/scene_Gaze/ALL_gaze_Times.py","file_name":"ALL_gaze_Times.py","file_ext":"py","file_size_in_byte":7151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"42511018912","text":"import psycopg2\nfrom flask import Flask, render_template\nimport os\n\nconn = None\ncur = None\n\nuser = None\nuser_boards = []\nuser_pages = []\n\n\n# @app.route('/contact')\n# def contact():\n#     return render_template('contact.html')\n\n\ndef create_app():\n    app = Flask(__name__)\n    app.config['SECRET_KEY'] = '9fc26a8960d2f5ed4126dde9830e49f4'\n\n    global conn, cur\n    try:\n        # conn = psycopg2.connect(\"dbname='Merken' user='postgres' host='localhost' password='D3VAR5H'\")\n        conn = psycopg2.connect(\"dbname='d91afrge4kgtr6' user='yitfmownerxauw' host='ec2-52-201-55-4.compute-1.amazonaws.com' password='4fc0f67d20051a8b20ebbb756edf60a303c29b73c17e355fe7e187aa390cef6e'\")\n    except:\n        print(\"I am unable to connect to the database\")\n    cur = conn.cursor()\n\n    from useraccounts import useraccounts_routes\n    from boardnotes import boardnotes_routes\n\n    app.register_blueprint(useraccounts_routes.useraccounts_bp, url_prefix='/accounts')\n    app.register_blueprint(boardnotes_routes.boardnotes_bp, url_prefix='/boards')\n\n    def get_data():\n        global user\n        global user_boards\n        global user_pages\n        if user is None:\n            user = useraccounts_routes.get_user()\n        user_boards = useraccounts_routes.get_boards()\n        user_pages = useraccounts_routes.get_pages()\n\n    @app.route('/')\n    def index():\n        return render_template('welcome.html')\n\n    @app.route('/about')\n    def about():\n        get_data()\n        return render_template('about.html', user=user, user_boards=user_boards, user_pages=user_pages)\n\n    @app.route('/terms_and_conditions')\n    def tc():\n        return render_template('terms&condition.html')\n\n    return app\n\n\nif __name__ == \"__main__\":\n    app = create_app()\n    app.run(debug=True)\n","repo_name":"D3VAR5H/Merken","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":1766,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"30528922248","text":"# python3\r\n\r\n# Task: In this problem your goal is to implement the Rabin–Karp’s algorithm for searching the given pattern in the given text\r\n# Input Format: There are two strings in the input: the pattern P and the text T.\r\n# Constraints: 1 ≤ length of pattern ≤ length of Text ≤ 5·10**5. The total length of all occurrences of P in T doesn’t exceed 108.\r\n# The pattern and the text contain only latin letters\r\n# Output Format: Print all the positions of the occurrences of P in T in the ascending order.\r\n# Use 0-based indexing of positions in the the text T.\r\n# Memory Limit: 512MB.\r\n# Sample 1: Input: aba abacaba\r\n#           Output: 0 4\r\n# Explanation: The pattern aba can be found in positions 0 (abacaba) and 4 (abacaba) of the text abacaba.\r\n# Sample 2: Input: Test testTesttesT\r\n#           Output: 4\r\n# Explanation: Pattern and text are case-sensitive in this problem.\r\n# Pattern Test can only be found in position 4 in the text testTesttesT.\r\n# Sample 3: Input: aaaaa baaaaaaa\r\n#           Output: 1 2 3\r\n# Note that the occurrences of the pattern in the text can be overlapping, and that’s ok, you still need to output all of them.\r\n# It is clear that the algorithm should use function that checks similarity between two patterns as rare as possible,\r\n# and the idea is to use precomputed hash values of the string, the example of that is provided below PolyHash function\r\n\r\n\r\nfrom random import randint\r\nimport sympy\r\n\r\n\r\nclass prepare_for_RabinKarp:\r\n    def __init__(self, text, pattern):\r\n        self.text = text\r\n        self.pattern = pattern\r\n        self.text_length = len(text)\r\n        self.pattern_length = len(pattern)\r\n    def generate_prime_number(self): # generation of a big prime number\r\n        return sympy.nextprime(self.text_length*1000000000000)\r\n    def generate_x(self, prime_number):\r\n        return randint(1, prime_number-1)\r\n    def PolyHash(self, string, prime_number, x): # Function to hash pattern occurence using polynomial hashing\r\n        hash = 0\r\n        for i in range(len(string)-1, -1, -1):\r\n            hash = (hash*x + ord(string[i])) % prime_number # in this project every substring is assigned a value of ASCII code\r\n        return hash\r\n    # Suppose we have text 'abcbd' and pattern length = 3.\r\n    # Hashing the strings will be done from the end of the text to the front.\r\n    # Assign a unique value to each character: lets suppose for character 'a' the value 0 is assigned, for 'b' = 1, for 'c' = 2 and\r\n    # for 'd' = 3. Then hash substrings of pattern length: h('cbd') = (2 + x + 3x**2) modulo prime number.\r\n    # Explanation: Multiply each hash value of a character by x to the power of it's corresponding positing starting from zero\r\n    # And now to compute the next hash value we do not necessarily need to compute it again, because the next substring is 'bcb'\r\n    # Since we already know the coefficients of it's last (pattern_length - 1) elements, we can assign them directly multiplying by x to\r\n    # the corresponding power\r\n    # Example: h('cbd') = (2 + x + 3x**2) mod prime_number, h('cbd') = (2 + x + 3x**2) mod prime_number\r\n    #                      |                                                |\r\n    #                      ------                                           ------\r\n    #                           |                                                |\r\n    #          h('bcb') = (1 + 2x + x**2) mod prime_number, h('bcb') = (1 + 2x + x**2) mod prime_number\r\n    # The following function does that\r\n    def Precompute_Hashes(self, prime_number, x):\r\n        hash_array = [None]*(self.text_length - self.pattern_length + 1)\r\n        string = text[self.text_length - self.pattern_length: self.text_length]\r\n        hash_array[self.text_length - self.pattern_length] = self.PolyHash(string, prime_number, x)\r\n        y = 1\r\n        for i in range(1, self.pattern_length+1):\r\n            y = (y * x) % prime_number\r\n        for i in range(self.text_length - self.pattern_length - 1, -1, -1):\r\n            hash_array[i] = (x*hash_array[i+1] + ord(text[i]) - y*ord(text[i+self.pattern_length])) % prime_number\r\n        return hash_array\r\n    def AreEqual(self, string, pattern):\r\n        if string == pattern:\r\n            return True\r\n        return False\r\n\r\ndef RabinKarp(text, pattern):\r\n    processor = prepare_for_RabinKarp(text, pattern)\r\n    prime_number = processor.generate_prime_number()\r\n    x = processor.generate_x(prime_number)\r\n    result = []\r\n    pHash = processor.PolyHash(pattern, prime_number, x)\r\n    hash_array = processor.Precompute_Hashes(prime_number, x)\r\n    for i in range(0, processor.text_length - processor.pattern_length + 1):\r\n        if pHash != hash_array[i]:\r\n            continue\r\n        if processor.AreEqual(text[i:i + processor.pattern_length], pattern):\r\n            result.append(i)\r\n    return result\r\n\r\n\r\nif __name__ == '__main__':\r\n    pattern, text = input().rstrip(), input().rstrip()\r\n    result = RabinKarp(text, pattern)\r\n    # for i in result:\r\n    #     print(i)\r\n    print(len(result))\r\n\r\n# The running time of functions: Precompute_Hashes: O(length of text + length of pattern)\r\n#                                PolyHash(pattern): O(length of pattern)\r\n#                                Total time spent in AreEqual: O(q * length of pattern), where q is the number of occurences of pattern in the text\r\n#                                Average running time: O(length of text + (q + 1) * length of pattern), usually q is small, therefore\r\n#                                this takes much less time than O(length of text * length of pattern)\r\n#                                Worst case scenario: O(length of text * length of pattern) happens when function AreEqual is called over\r\n#                                and over again. Example: Text = AAAAAAAA, pattern = AAA.","repo_name":"AkhtemWays/Algorithms-and-data-structures-coursera-specialization","sub_path":"Data Structures/hash__substring.py","file_name":"hash__substring.py","file_ext":"py","file_size_in_byte":5830,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"23700924910","text":"import numpy as np\nfrom numpy import random\nimport math\nfrom src.neuron import *\n\n\nclass kohonenNetwork:\n\n    def __init__(self, k: int, r_0: int, learnRateConstant: False, learnRate_0: float, inputs, labels, isRandom=True):\n        self.k = k\n        self.r_0 = r_0\n        self.learnRateConstant = learnRateConstant\n        if not learnRateConstant:\n            self.learnRate_0 = learnRate_0\n        self.grid = [[None for _ in range(k)] for _ in range(k)]\n        self.initialIdxs = []\n        #rows = []\n        #for j in range(0,k):\n        #    row = [neuron(inputs[random.randint(0, len(inputs))]) for i in range(0, k)]\n        #    rows.append(row)\n        #self.grid = rows\n        if isRandom is True:\n            for i in range(0, k):\n                for j in range(0, k):\n                    idx = random.randint(0, len(inputs))\n                    self.grid[i][j] = neuron(inputs[idx])\n                    self.grid[i][j].addEntry(labels[idx])\n                    self.initialIdxs.append(idx)\n\n        else:\n            idx = 0\n            for i in range(0, k):\n                for j in range(0, k):\n                    self.grid[i][j] = neuron(inputs[idx])\n                    idx += 1\n                #  print(f'{i}, {j}: {inputs[idx]}')\n        self.iterations = 50000\n        print(self.initialIdxs)\n        #  self.iterations = 5\n\n\n    def train(self, inputs, labels):\n        k = 0\n        r = self.r_0\n        n = self.learnRate_0\n        while k < self.iterations:\n            idx = random.randint(0, len(inputs))\n            x, y = self.findBestCandidate(inputs[idx])\n            self.updateNeighbours(x, y, self.grid, r, n, inputs[idx])\n            k, r, n = self.updateParameters(k)\n\n    def test(self, inputs, labels):\n\n        for i in range(len(inputs)):\n            x, y = self.findBestCandidate(inputs[i])\n            self.grid[x][y].addEntry(labels[i])\n\n    def findBestCandidate(self, candidate):\n        distance = np.Infinity\n        bestX = -1\n        bestY = -1\n        for x in range(0, self.k):\n            for y in range(0, self.k):\n\n                current = self.grid[x][y].distanceTo(candidate)\n                #  print(f'candidate = {candidate}, name = {name}, current = {current}, distance = {distance}')\n                #  print(current)\n                if current < distance:\n                    distance = current\n                    bestX = x\n                    bestY = y\n        #  self.grid[bestX][bestY].addEntry(name)\n        #  print(f'{bestX}, {bestY}')\n        return bestX, bestY\n\n    def updateNeighbours(self, x, y, grid, r, n, input):\n\n        #  print(f'x={x}, y={y}, r={r}, n={n}')\n        for i in range(x - r, x + r + 1):\n            for j in range(y - r, y + r + 1):\n                # need to check for borders\n                if 0 <= i < self.k and 0 <= j < self.k and math.hypot(x - i, y - j) <= r:\n                    #  print(i, j)\n                    #  print(f'old weights = {grid[i][j].weights}, input = {input}')\n                    grid[i][j].weights += n * (input - grid[i][j].weights)\n                    # print(f'new weights = {grid[i][j].weights}')\n        self.grid = grid\n\n    def updateParameters(self, iters):\n        iters += 1\n        learnRate = self.learnRate_0\n        if not self.learnRateConstant:\n            learnRate =  self.learnRate_0 - ((1 - self.learnRate_0) / self.iterations * iters)\n        r = int((1/self.r_0 - self.r_0) * (((iters / self.iterations))**0.5)) + self.r_0\n        #  print(f'i: {iters} -> new r: {r}, new n = {learnRate}')\n        return iters, r, learnRate\n        # update r_0 and n_0\n","repo_name":"ManuelDizen/SIA","sub_path":"TP4 - Métodos de Aprendizaje NO supervisado/src/kohonenNetwork.py","file_name":"kohonenNetwork.py","file_ext":"py","file_size_in_byte":3603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"25656390751","text":"import pickle\nimport traceback\n\nfrom fabric_cf.actor.core.apis.abc_actor_mixin import ABCActorMixin\nfrom fabric_cf.actor.core.apis.abc_base_plugin import ABCBasePlugin\nfrom fabric_cf.actor.core.apis.abc_callback_proxy import ABCCallbackProxy\nfrom fabric_cf.actor.core.apis.abc_concrete_set import ABCConcreteSet\nfrom fabric_cf.actor.core.apis.abc_proxy import ABCProxy\nfrom fabric_cf.actor.core.common.constants import Constants\nfrom fabric_cf.actor.core.common.exceptions import ProxyException\nfrom fabric_cf.actor.core.kernel.resource_set import ResourceSet\nfrom fabric_cf.actor.core.registry.actor_registry import ActorRegistrySingleton\nfrom fabric_cf.actor.core.util.id import ID\nfrom fabric_cf.actor.security.auth_token import AuthToken\n\n\nclass Proxy(ABCProxy):\n    \"\"\"\n    Proxy class represents a stub to an actor. Proxies define a general interface, which is implementation\n    independent and enables easy implementation of new communication protocols.\n    \"\"\"\n    PropertyProxyActorName = \"prx_name\"\n\n    @staticmethod\n    def get_callback(*, actor: ABCActorMixin, protocol: str) -> ABCCallbackProxy:\n        \"\"\"\n        Obtains a callback for the specified actor\n        @param actor actor\n        @param protocol protocol\n        @return ICallbackProxy\n        \"\"\"\n        if actor is None:\n            raise ProxyException(Constants.NOT_SPECIFIED_PREFIX.format(\"actor\"))\n\n        callback = ActorRegistrySingleton.get().get_callback(protocol=protocol, actor_name=actor.get_name())\n        if callback is None:\n            raise ProxyException(\"Could not obtain callback proxy: protocol={}\".format(protocol))\n        return callback\n\n    @staticmethod\n    def get_proxy(*, proxy_reload_from_db) -> ABCProxy:\n        \"\"\"\n        Obtains a proxy object from the specified properties list. If a suitable\n        proxy object has already been created and registered with the\n        ActorRegistry, the already existing object is returned and\n        no new object is created. Otherwise, the method creates the proxy object\n        and registers it with the ActorRegistry\n        @param proxy_reload_from_db proxy_reload_from_db\n        @return IProxy\n        @throws Exception in case of error\n        \"\"\"\n        proxy_type = proxy_reload_from_db.get_type()\n        name = proxy_reload_from_db.get_name()\n\n        is_callback = proxy_reload_from_db.callback\n\n        proxy = None\n        if is_callback:\n            proxy = ActorRegistrySingleton.get().get_callback(protocol=proxy_type, actor_name=name)\n        else:\n            proxy = ActorRegistrySingleton.get().get_proxy(protocol=proxy_type, actor_name=name)\n\n        if proxy is None:\n            proxy = Proxy.recover_proxy(proxy_reload_from_db=proxy_reload_from_db, register=True)\n        else:\n            proxy = Proxy.recover_proxy(proxy_reload_from_db=proxy_reload_from_db, register=False)\n        return proxy\n\n    @staticmethod\n    def recover_proxy(*, proxy_reload_from_db: ABCProxy, register: bool) -> ABCProxy:\n        \"\"\"\n        Creates a proxy list from a properties list representing the\n        serialization of the proxy. Optionally, the resulting object may be\n        registered with the ActorRegistry so that it becomes visible\n        to the rest of the system.\n        @param proxy_reload_from_db proxy_reload_from_db\n        @param register If true, the resulting proxy is registered with the\n                   container's ActorRegistry\n        @return Proxy\n        @throws Exception in case of error\n        \"\"\"\n\n        from fabric_cf.actor.core.container.globals import GlobalsSingleton\n        proxy_reload_from_db.set_logger(logger=GlobalsSingleton.get().get_logger())\n\n        if register:\n            if proxy_reload_from_db.callback:\n                ActorRegistrySingleton.get().register_callback(callback=proxy_reload_from_db)\n            else:\n                ActorRegistrySingleton.get().register_proxy(proxy=proxy_reload_from_db)\n        return proxy_reload_from_db\n\n    @staticmethod\n    def decode(*, encoded, plugin: ABCBasePlugin) -> ABCConcreteSet:\n        try:\n            decoded_resource = pickle.loads(encoded)\n            decoded_resource.restore(plugin=plugin, reservation=None)\n            return decoded_resource\n        except Exception as e:\n            traceback.print_exc()\n            print(\"Exception occurred while decoding {}\".format(e))\n        return None\n\n    def __init__(self, *, auth: AuthToken = None):\n        self.logger = None\n        self.proxy_type = None\n        self.callback = False\n        self.actor_name = None\n        if auth is not None:\n            self.actor_name = auth.get_name()\n        self.actor_guid = None\n        if auth is not None:\n            self.actor_guid = auth.get_guid()\n        self.auth = auth\n\n    def __getstate__(self):\n        state = self.__dict__.copy()\n        del state['logger']\n        return state\n\n    def __setstate__(self, state):\n        self.__dict__.update(state)\n        self.logger = None\n\n    def get_guid(self) -> ID:\n        return self.actor_guid\n\n    def get_identity(self) -> AuthToken:\n        return self.auth\n\n    def get_name(self) -> str:\n        return self.actor_name\n\n    def get_type(self) -> str:\n        return self.proxy_type\n\n    def set_logger(self, *, logger):\n        self.logger = logger\n\n    def get_logger(self):\n        return self.logger\n\n    def abstract_clone_authority(self, *, rset: ResourceSet) -> ResourceSet:\n        \"\"\"\n        Clones the resource set, but without any of the concrete sets. Preserves\n        only the configuration properties. This method should be used when\n        sending a redeem/extend/close request to an authority.\n        @param rset resource set\n        @return a resources set that is a copy of the current but without any\n                concrete sets.\n        \"\"\"\n        return ResourceSet(units=rset.get_units(), rtype=rset.get_type(), sliver=rset.get_sliver())\n\n    def abstract_clone_broker(self, *, rset: ResourceSet) -> ResourceSet:\n        \"\"\"\n        Clones the resource set, but without any of the concrete sets. Preserves\n        only the configuration properties. This method should be used when\n        sending a redeem/extend/close request to an authority.\n        @param rset resource set\n        @return a resources set that is a copy of the current but without any\n                concrete sets.\n        \"\"\"\n        return ResourceSet(units=rset.get_units(), rtype=rset.get_type(), sliver=rset.get_sliver())\n\n    @staticmethod\n    def abstract_clone_return(rset: ResourceSet) -> ResourceSet:\n        \"\"\"\n        Clones the resource set, but without any of the concrete sets. Preserves\n        only the configuration properties. This method should be used when\n        sending a redeem/extend/close request to an authority.\n        @param rset resource set\n        @return a resources set that is a copy of the current but without any\n                concrete sets.\n        \"\"\"\n        return ResourceSet(units=rset.get_units(), rtype=rset.get_type(), sliver=rset.get_sliver())\n","repo_name":"fabric-testbed/ControlFramework","sub_path":"fabric_cf/actor/core/proxies/proxy.py","file_name":"proxy.py","file_ext":"py","file_size_in_byte":7041,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"24098046777","text":"from flask import Flask, render_template, redirect, url_for, session,request\nfrom flask_mail import Mail, Message\nfrom flask_wtf import FlaskForm\nfrom wtforms import StringField, SubmitField, SelectMultipleField,RadioField\nfrom wtforms.validators import DataRequired, Email, EqualTo\nfrom wtforms import ValidationError\n\nclass ProjectForm(FlaskForm):\n    name=StringField('Name',validators=[DataRequired()])\n    mobile=StringField('Mobile Number', validators=[DataRequired()])\n    email=StringField('Email ID',validators=[DataRequired(),Email()])\n    title=StringField('Project Title',validators=[DataRequired()])\n    description=StringField('Project Description',validators=[DataRequired()])\n    submit1=SubmitField('Submit Request')\n\nclass WorkshopForm(FlaskForm):\n    name2=StringField('Name',validators=[DataRequired()])\n    organisation=StringField('Organisation',validators=[DataRequired()])\n    mobile2=StringField('Mobile Number',validators=[DataRequired()])\n    email2=StringField('Email ID',validators=[DataRequired(),Email()])\n    workshop=RadioField('Workshops',choices=[('Drone Programming','Drone Programming'),('RC Plane Design','RC Plane Design'),('Structural and Flow Analysis of RC Planes','Structural and Flow Analysis of RC Planes'),('Material Selection for RC Planes','Maaterial Selection')])\n    attendees=StringField('Expected Number of Attendees',validators=[DataRequired()])\n    submit2=SubmitField('Submit Request')\n\nclass ContactForm(FlaskForm):\n    name3=StringField('Name',validators=[DataRequired()])\n    mobile3=StringField('Mobile Number', validators=[DataRequired()])\n    email3=StringField('Email ID',validators=[DataRequired(),Email()])\n    message=StringField('Type your message here',validators=[DataRequired()])\n    submit3=SubmitField('Submit')\n\napp=Flask(__name__)\n\napp.config['SECRET_KEY']='mykey'\napp.config['DEBUG']=True\napp.config['TESTING']=False\napp.config['MAIL_SERVER']='smtp.gmail.com'\napp.config['MAIL_PORT']=587\napp.config['MAIL_USE_TLS']=True\napp.config['MAIL_USERNAME']='teamaviatorsforms@gmail.com'\napp.config['MAIL_PASSWORD']='ljmbnoiplxwgpexz'\napp.config['MAIL_DEFAULT_SENDER']='teamaviatorsforms@gmail.com'\napp.config['MAIL_MAX_MAILS']=None\napp.config['MAIL_ASCII_ATTACHMENTS']=False\n\n\nmail=Mail(app)\n\n#configure the functions for mailing\n\ndef project_mail(name,mobile,email,title,description):\n    global mail\n    msg1=Message('Project Request Received',recipients=[email])\n    msg1.body='Dear '+name+',\\n\\nYour project request has been received by Team Aviators International. We will review your request and get back to you soon.\\n\\nRegards,\\nTeam Aviators International'\n    mail.send(msg1)\n    msg2=Message('New Project Request',recipients=['teamaviatorsinternational@gmail.com'])\n    msg2.body='Name of Applicant: {}\\n\\nMobile Number: {}\\n\\nEmail ID: {}\\n\\nProject Title: {}\\n\\nProject Description: {}'.format(name,mobile,email,title,description)\n    mail.send(msg2)\n\ndef contact_mail(name,mobile,email,message):\n    global mail\n    msg1=Message('Contact Enquiry Received',recipients=[email])\n    msg1.body='Dear '+name+',\\n\\nThank you for reaching out to us. Somebody from our team will soon contact you to further know your interests and queries.\\n\\nRegards,\\nTeam Aviators International'\n    mail.send(msg1)\n    msg2=Message('New Contact Enquiry',recipients=['teamaviatorsinternational@gmail.com'])\n    msg2.body='Name of Applicant: {}\\n\\nMobile Number: {}\\n\\nEmail ID: {}\\n\\nMessage: {}'.format(name,mobile,email,message)\n    mail.send(msg2)\n\ndef workshop_mail(name,organisation,mobile,email,workshop,attendees):\n    global mail\n    msg1=Message('Workshop Request Received',recipients=[email])\n    msg1.body='Dear '+name+',\\n\\nYour request for a workshop on '+workshop+ ' has been received successfully by Team Aviators International. We will contact you soon for further proceedings.\\n\\nRegards,\\nTeam Aviators International'\n    mail.send(msg1)\n    msg2=Message('New Workshop Request',recipients=['teamaviatorsinternational@gmail.com'])\n    msg2.body='Name of Applicant: {}\\n\\nOrganisation: {}\\n\\nMobile Number: {}\\n\\nEmail ID: {}\\n\\nWorkshop: {}\\n\\nExpected Number of Attendees: {}'.format(name,organisation,mobile,email,workshop,attendees)\n    mail.send(msg2)\n\n@app.route('/',methods=['GET','POST'])\ndef index():\n    form1=ProjectForm()\n    form2=WorkshopForm()\n    form3=ContactForm()\n\n    if(form1.submit1.data and form1.validate()):\n        session['name']=form1.name.data\n        session['mobile']=form1.mobile.data\n        session['email']=form1.email.data \n        session['title']=form1.title.data\n        session['description']=form1.description.data\n        project_mail(session['name'],session['mobile'],session['email'],session['title'],session['description'])\n        return redirect(url_for('index'))\n    \n    if(form2.submit2.data and form2.validate()):\n        session['name2']=form2.name2.data\n        session['organisation']=form2.organisation.data\n        session['mobile2']=form2.mobile2.data\n        session['email2']=form2.email2.data\n        session['workshop']=form2.workshop.data\n        session['attendees']=form2.attendees.data\n        workshop_mail(session['name2'],session['organisation'],session['mobile2'],session['email2'],session['workshop'],session['attendees'])\n        return redirect(url_for('index'))\n    \n    if(form3.submit3.data and form3.validate()):\n        session['name3']=form3.name3.data\n        session['mobile3']=form3.mobile3.data\n        session['email3']=form3.email3.data\n        session['message']=form3.message.data\n        contact_mail(session['name3'],session['mobile3'],session['email3'],session['message'])\n        return redirect(url_for('index'))\n\n\n    return render_template(\"homepage.html\",form1=form1, form2=form2, form3=form3)\n\n\n@app.route('/about_us',methods=['GET','POST'])\ndef about_us():\n    form1=ProjectForm()\n    form2=WorkshopForm()\n    form3=ContactForm()\n\n    if(form1.submit1.data and form1.validate()):\n        session['name']=form1.name.data\n        session['mobile']=form1.mobile.data\n        session['email']=form1.email.data \n        session['title']=form1.title.data\n        session['description']=form1.description.data\n        project_mail(session['name'],session['mobile'],session['email'],session['title'],session['description'])\n        return redirect(url_for('about_us'))\n    \n    if(form2.submit2.data and form2.validate()):\n        session['name2']=form2.name2.data\n        session['organisation']=form2.organisation.data\n        session['mobile2']=form2.mobile2.data\n        session['email2']=form2.email2.data\n        session['workshop']=form2.workshop.data\n        session['attendees']=form2.attendees.data\n        workshop_mail(session['name2'],session['organisation'],session['mobile2'],session['email2'],session['workshop'],session['attendees'])\n        return redirect(url_for('about_us'))\n    \n    if(form3.submit3.data and form3.validate()):\n        session['name3']=form3.name3.data\n        session['mobile3']=form3.mobile3.data\n        session['email3']=form3.email3.data\n        session['message']=form3.message.data\n        contact_mail(session['name3'],session['mobile3'],session['email3'],session['message'])\n        return redirect(url_for('about_us'))\n    return render_template(\"team.html\",form1=form1, form2=form2, form3=form3)\n\n@app.route('/gallery',methods=['GET','POST'])\ndef gallery():\n    form1=ProjectForm()\n    form2=WorkshopForm()\n    form3=ContactForm()\n\n    if(form1.submit1.data and form1.validate()):\n        session['name']=form1.name.data\n        session['mobile']=form1.mobile.data\n        session['email']=form1.email.data \n        session['title']=form1.title.data\n        session['description']=form1.description.data\n        project_mail(session['name'],session['mobile'],session['email'],session['title'],session['description'])\n        return redirect(url_for('gallery'))\n    \n    if(form2.submit2.data and form2.validate()):\n        session['name2']=form2.name2.data\n        session['organisation']=form2.organisation.data\n        session['mobile2']=form2.mobile2.data\n        session['email2']=form2.email2.data\n        session['workshop']=form2.workshop.data\n        session['attendees']=form2.attendees.data\n        workshop_mail(session['name2'],session['organisation'],session['mobile2'],session['email2'],session['workshop'],session['attendees'])\n        return redirect(url_for('gallery'))\n    \n    if(form3.submit3.data and form3.validate()):\n        session['name3']=form3.name3.data\n        session['mobile3']=form3.mobile3.data\n        session['email3']=form3.email3.data\n        session['message']=form3.message.data\n        contact_mail(session['name3'],session['mobile3'],session['email3'],session['message'])\n        return redirect(url_for('gallery'))\n    return render_template(\"gallery.html\",form1=form1, form2=form2, form3=form3)\n\n@app.route('/projects',methods=['GET','POST'])\ndef projects():\n    form1=ProjectForm()\n    form2=WorkshopForm()\n    form3=ContactForm()\n\n    if(form1.submit1.data and form1.validate()):\n        session['name']=form1.name.data\n        session['mobile']=form1.mobile.data\n        session['email']=form1.email.data \n        session['title']=form1.title.data\n        session['description']=form1.description.data\n        project_mail(session['name'],session['mobile'],session['email'],session['title'],session['description'])\n        return redirect(url_for('projects'))\n    \n    if(form2.submit2.data and form2.validate()):\n        session['name2']=form2.name2.data\n        session['organisation']=form2.organisation.data\n        session['mobile2']=form2.mobile2.data\n        session['email2']=form2.email2.data\n        session['workshop']=form2.workshop.data\n        session['attendees']=form2.attendees.data\n        workshop_mail(session['name2'],session['organisation'],session['mobile2'],session['email2'],session['workshop'],session['attendees'])\n        return redirect(url_for('projects'))\n    \n    if(form3.submit3.data and form3.validate()):\n        session['name3']=form3.name3.data\n        session['mobile3']=form3.mobile3.data\n        session['email3']=form3.email3.data\n        session['message']=form3.message.data\n        contact_mail(session['name3'],session['mobile3'],session['email3'],session['message'])\n        return redirect(url_for('projects'))\n    return render_template(\"projects.html\",form1=form1, form2=form2, form3=form3)\n\n\nif __name__=='__main__':\n    app.run()\n","repo_name":"Harsh-Naicker/TAI-Website","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":10440,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"9224501467","text":"import os\nos.environ[\"CUDA_MAX_MEM_ALLOC_PERCENT\"] = \"95\"\nimport torch\n#from torch.utils.tensorboard import SummaryWriter\nimport torch.nn as nn\nfrom torch.nn import functional as F\nimport torch.optim as optim\nfrom torch import optim\nfrom torch.utils import data\nfrom torch.utils.data import Dataset, DataLoader\nfrom torch.autograd import Variable\nfrom torch import tensor\n\nimport torchvision.models as models\nimport torchvision\nfrom torchvision import datasets, models, transforms\nfrom efficientnet_pytorch import EfficientNet\n\nimport glob\nimport os\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\n\nimport matplotlib.pyplot as plt\n#from PIL import Image\nimport cv2\nimport seaborn as sns\n\n\nfrom sklearn.utils import shuffle\nfrom sklearn import preprocessing as p\nfrom sklearn.metrics import RocCurveDisplay\nfrom sklearn.metrics import confusion_matrix, roc_curve, auc\n\n\nfrom torchmetrics.classification import ROC\nfrom torchmetrics import AUROC\n\nimport optuna\nfrom optuna.trial import TrialState\n\nfrom segmentation_models_pytorch import losses\ndice_loss = losses.DiceLoss('binary')\nfoc_loss = losses.FocalLoss('binary')\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\nval_transforms = transforms.Compose([torchvision.transforms.ToTensor(),\n                                      transforms.Resize((224,224)),\n                                      torchvision.transforms.Normalize(\n                                          mean=[0.485, 0.456, 0.406],\n                                          std=[0.229, 0.224, 0.225],\n    ),\n                                      ])\n\nclass myDataset_test(Dataset):\n\n    def __init__(self, transform=None): \n        #folder containing class folders with images\n        self.imgs_path = \"/home/viktoriia.trokhova/Mri_slices_new/test/\"\n        self.masks_path = \"/home/viktoriia.trokhova/Mask_slices/test/\"\n        file_list = glob.glob(self.imgs_path + \"*\")\n        msk_list = glob.glob(self.masks_path + \"*\")\n        #msk_list[0], msk_list[1] = msk_list[1], msk_list[0]\n        self.images = []\n        self.targets = []\n        self.masks = []\n        for class_path in file_list:\n            class_name = class_path.split(\"/\")[-1]\n            for img_path in sorted(glob.glob(class_path + \"/*\")):\n                self.images.append(img_path)\n            for img_path in sorted(glob.glob(class_path + \"/*\")):\n                self.targets.append(class_name)\n        for msk_path in msk_list:\n            for masks_path in sorted(glob.glob(msk_path + \"/*\")):\n                  self.masks.append(masks_path)\n        self.images, self.targets, self.masks = shuffle(self.images, self.targets, self.masks, random_state=101)\n        print(self.images[-100])\n        print(self.targets[-100])\n        print(self.masks[-100])\n        print(len(self.images))\n        print(len(self.targets))\n        print(len(self.masks))\n        self.class_map = {\"HGG_t2\" : 0, \"LGG_t2\": 1}\n        self.img_dim = (224, 224)\n\n    def __len__(self):\n        return len(self.images)\n\n    def __getitem__(self, idx):\n        img_path  = self.images[idx]\n        class_name = self.targets[idx]\n        masks_path = self.masks[idx]\n        img = np.load(img_path)\n        msk = np.load(masks_path)\n        min_max_scaler = p.MinMaxScaler()\n        img = min_max_scaler.fit_transform(img)\n        msk = min_max_scaler.fit_transform(msk)\n        img_float32 = np.float32(img)\n        img_color = cv2.cvtColor(img_float32, cv2.COLOR_GRAY2RGB)\n        img_tensor = val_transforms(img_color)\n        msk_float32 = np.float32(msk)\n        msk_color = cv2.cvtColor(msk_float32, cv2.COLOR_GRAY2RGB)\n        msk_tensor = val_transforms(msk_color)\n        class_id = self.class_map[class_name]\n        class_id = torch.tensor(class_id)\n    \n        return img_tensor, class_id, msk_tensor\n\n\nclass SelfAttention(nn.Module):\n    def __init__(self, in_channels):\n        super().__init__()\n        self.query = nn.Conv2d(in_channels, in_channels // 8, kernel_size=1)\n        self.key = nn.Conv2d(in_channels, in_channels // 8, kernel_size=1)\n        self.value = nn.Conv2d(in_channels, in_channels, kernel_size=1)\n        self.softmax = nn.Softmax(dim=-1)\n        self.gamma = nn.Parameter(torch.zeros(1))\n\n    def forward(self, x):\n        batch_size, channels, height, width = x.size()\n        proj_query = self.query(x).view(batch_size, -1, height * width).permute(0, 2, 1)\n        proj_key = self.key(x).view(batch_size, -1, height * width)\n        energy = torch.bmm(proj_query, proj_key)\n        attention = self.softmax(energy)\n        proj_value = self.value(x).view(batch_size, -1, height * width)\n        out = torch.bmm(proj_value, attention.permute(0, 2, 1))\n        out = out.view(batch_size, channels, height, width)\n        out = self.gamma * out + x\n        return out\n\n\nclass MyCustomEfficientNetB0(nn.Module):\n    def __init__(self, pretrained=True):\n        super().__init__()\n        \n        efficientnet_b0 = EfficientNet.from_pretrained('efficientnet-b0').to(device)\n        self.features = efficientnet_b0.extract_features\n        in_features = efficientnet_b0._fc.in_features\n        self.attention = SelfAttention(in_features)\n        self.last_pooling_operation = nn.AdaptiveAvgPool2d((1, 1))\n        self.fc1 = nn.Linear(in_features, 128)\n        self.fc2 = nn.Linear(128, 2)\n\n\n    def forward(self, input_imgs, targets=None, masks=None, batch_size = None, xe_criterion=nn.CrossEntropyLoss(), l1_criterion=nn.L1Loss(), dropout=None):\n        images_feats = self.features(input_imgs)\n        images_att = self.attention(images_feats)\n        output = self.last_pooling_operation(images_att)\n        output = output.view(input_imgs.size(0), -1)\n        images_outputs = self.fc1(output)\n        output = dropout(images_outputs)\n        images_outputs = F.relu(self.fc2(output))\n        #images_outputs = nn.ReLU(self.fc2(output))\n\n\n        # # compute gcam for images\n        orig_gradcam_mask = compute_gradcam(images_outputs, images_feats, targets)\n\n        # #upsample gradcam to (224, 224, 3)\n        gcam_losses = 0.0\n\n        for i in range(batch_size):\n            #print(orig_gradcam_mask[i].shape)\n            img_grad = orig_gradcam_mask[i].unsqueeze(0).permute(1, 2, 0)\n            img_grad_1 = img_grad.cpu()\n            img_grad_2 = img_grad_1.detach().numpy()\n            img_grad_3 = cv2.resize(img_grad_2, (224,224), cv2.INTER_LINEAR)\n            img_grad_4 = cv2.cvtColor(img_grad_3, cv2.COLOR_GRAY2RGB)\n            img_grad_5 = torch.from_numpy(img_grad_4)\n            img_grad_6 = img_grad_5.to(device)\n            #img_grad_6 = torch.nn.ReLU(inplace=True)(img_grad_6)\n\n\n            #masks to same dimension\n            masks_per = masks[i].permute(1, 2, 0)\n            masks_per = cv2.normalize(masks_per.cpu().numpy(), None, alpha = 0, beta = 1, norm_type = cv2.NORM_MINMAX, dtype = cv2.CV_32F)\n            img_grad_6 = cv2.normalize(img_grad_6.cpu().numpy(), None, alpha = 0, beta = 1, norm_type = cv2.NORM_MINMAX, dtype = cv2.CV_32F)\n            masks_per[np.mean(masks_per, axis=-1)<0.2] = 0\n            masks_per[np.mean(masks_per, axis=-1)>=0.2] = 1\n\n            gcam_loss = foc_loss(torch.from_numpy(img_grad_6), torch.from_numpy(masks_per))\n            #print(gcam_loss)\n            #gcam_loss = l1_criterion(img_grad_6, masks_per)\n            gcam_losses += gcam_loss\n\n            # gcam_loss = l1_criterion(img_grad_6, masks_per)\n            # gcam_losses += gcam_loss\n\n            #gcam_losses += gcam_loss.item() * input_imgs.size(0)\n        #gcam_losses = gcam_losses/batch_size\n        xe_loss = xe_criterion(images_outputs, targets)\n        \n\n        return images_outputs, targets, xe_loss, gcam_losses      #return images_outputs\n\ndef compute_gradcam(output, feats, target):\n    \"\"\"\n    Compute normalized Grad-CAM for the given target using the model output and features\n    :param output:\n    :param feats:\n    :param target:\n    :return:\n    \"\"\"\n    eps = 1e-8\n\n    target = target.cpu().detach().numpy()\n    one_hot = np.zeros((output.shape[0], output.shape[-1]), dtype=np.float32)\n    indices_range = np.arange(output.shape[0])\n    one_hot[indices_range, target[indices_range]] = 1\n    one_hot = torch.from_numpy(one_hot)\n    one_hot = Variable(output, requires_grad=True)\n\n    # Compute the Grad-CAM for the original image\n    one_hot_cuda = torch.sum(one_hot.to(device) * output)\n    dy_dz1, = torch.autograd.grad(one_hot_cuda, feats, grad_outputs=torch.ones(one_hot_cuda.size()).to(device),\n                                  retain_graph=True, create_graph=True)\n\n    # We compute the dot product of grad and features (Element-wise Grad-CAM) to preserve grad spatial locations\n    gcam512_1 = dy_dz1 * feats\n    gradcam = gcam512_1.sum(dim=1)\n    gradcam = torch.nn.ReLU(inplace=True)(gradcam)\n    spatial_sum1 = gradcam.sum(dim=[1, 2]).unsqueeze(-1).unsqueeze(-1)\n    gradcam = (gradcam / (spatial_sum1 + eps)) + eps\n\n\n    return gradcam\n\nmodel = MyCustomEfficientNetB1(pretrained=True).to(device)\n\n\nmodel.load_state_dict(torch.load('/home/viktoriia.trokhova/model_weights/model_best.pt'), strict=False)\n\ntest_dataset = myDataset_test(transform = None)\ntest_dataloader = torch.utils.data.DataLoader(myDataset_test(transform = None),\n                                    batch_size=32,\n                                    shuffle=False,\n                                    num_workers=0)\n\nmodel.eval()\nrunning_loss = 0.0\nrunning_corrects = 0.0\nfor inputs, labels, masks in test_dataloader:\n    inputs = inputs.to(device)\n    labels = labels.to(device)\n    masks = masks.to(device)\n  \n    outputs, targets_, xe_loss_, gcam_losses_ = model(inputs, labels, masks, batch_size = inputs.size(0), dropout=nn.Dropout(0.79))\n\n    loss = xe_loss_.mean() + 0.575 * gcam_losses_.mean()\n    #loss = xe_loss_.mean()\n   \n    _, preds = torch.max(outputs, 1)  \n\n    running_loss += loss.item() * inputs.size(0)\n    running_corrects += torch.sum(preds == labels.data)\n\nepoch_loss = running_loss / 1611\nepoch_acc = running_corrects.double() / 1611\nprint('Test loss: {:.4f}, acc: {:.4f}'.format(epoch_loss,\n                                            epoch_acc))\n","repo_name":"Viktoriia-Dasci/master_4","sub_path":"test_effnet.py","file_name":"test_effnet.py","file_ext":"py","file_size_in_byte":10145,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"342237209","text":"from torch import nn\nfrom functools import partial\nfrom einops.layers.torch import Rearrange, Reduce\n\npair = lambda x: x if isinstance(x, tuple) else (x, x)\n\nclass PreNormResidual(nn.Module):\n    def __init__(self, dim, fn):\n        super().__init__()\n        self.fn = fn\n        self.norm = nn.LayerNorm(dim)\n\n    def forward(self, x):\n        return self.fn(self.norm(x)) + x\n\ndef FeedForward(dim, expansion_factor = 4, dropout = 0., dense = nn.Linear):\n    inner_dim = int(dim * expansion_factor)\n    return nn.Sequential(\n        dense(dim, inner_dim),\n        nn.GELU(),\n        nn.Dropout(dropout),\n        dense(inner_dim, dim),\n        nn.Dropout(dropout)\n    )\n\ndef MLPMixer(*, image_size, channels, patch_size, dim, depth, num_classes, expansion_factor = 4, expansion_factor_token = 0.5, dropout = 0.):\n    image_h, image_w = pair(image_size)\n    assert (image_h % patch_size) == 0 and (image_w % patch_size) == 0, 'image must be divisible by patch size'\n    num_patches = (image_h // patch_size) * (image_w // patch_size)\n    chan_first, chan_last = partial(nn.Conv1d, kernel_size = 1), nn.Linear\n\n    return nn.Sequential(\n        Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size),\n        nn.Linear((patch_size ** 2) * channels, dim),\n        *[nn.Sequential(\n            PreNormResidual(dim, FeedForward(num_patches, expansion_factor, dropout, chan_first)),\n            PreNormResidual(dim, FeedForward(dim, expansion_factor_token, dropout, chan_last))\n        ) for _ in range(depth)],\n        nn.LayerNorm(dim),\n        Reduce('b n c -> b c', 'mean'),\n        nn.Linear(dim, num_classes)\n    )\n","repo_name":"lucidrains/mlp-mixer-pytorch","sub_path":"mlp_mixer_pytorch/mlp_mixer_pytorch.py","file_name":"mlp_mixer_pytorch.py","file_ext":"py","file_size_in_byte":1647,"program_lang":"python","lang":"en","doc_type":"code","stars":922,"dataset":"github-code","pt":"76"}
+{"seq_id":"26736834561","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Sep 22 11:06:17 2016\n\nA scientific calculator that accepts inputs of +-*/^% as well as ().\nAlso accepts trigonometric inputs (sin(x), cos(x), tan(x), asin(x), acos(x), atan(x),\nsqrt(x), root(x,n), log(x), ln(x), fact(x) and abs(x) functions.\nAccepts exponential notation in the format 2.5 EXP 7 (not case sensitive)\nCan use ans to use the calculators previous answer. \n\n@author: dmcauslan\n\nmodified 28/09/2016:\n    * Calculator now accepts negative numbers as inputs\n    * Calculator now checks if there's unmatched brackets and brings up an error\n    * Calculator now brings up an error if the number of numerical inputs is incorrect\n    * Calculator now accpets % to perform modulo calculation\n    * Calculator now parses special functions - toPostfix just passes if it finds a fn atm.\nmodified 29/09/2016:\n    * Calculator now accepts trigonometric functions, sqrt(), log(), ln(), abs as inputs\n    * Now accepts pi and e as special numerical inputs\n    * Have implemented scientific notation with exp.\n    \nmodified 30/09/2016:\n    * Now accepts 'ans' input, to recall the answer of the last calculation\n    * Implemented function 'root(x,n)' which calculates the nth root of x\n    * Implement factorial function fact(x) which calculates x!\n    * Tidyed up functions a bit.\n    * Created standalone exe version\n    \nTo Do:\n    Create GUI\n\"\"\"\n\nimport operator as op\nimport string\nimport math as m\n\ndef isFloat(value):\n    \"\"\" Checks whether a number is a float\"\"\"        \n    try:\n        float(value)\n        return True\n    except ValueError:\n        return False\n        \ndef nthRoot(x,n):\n    \"\"\" Calculates the nth root of a \"\"\"\n    return op.pow(x,1/n)\n\n\nclass Calculator:\n    # Dictionary of their operators and the corresponding operation and their precedence\n    operators = {'+': (op.add,0), '-': (op.sub,0), \n              '*': (op.mul,1), '/': (op.truediv,1), \n              '^': (op.pow,2), '%': (op.mod,3) }\n    # Dictionary of the available special functions for use          \n    functions = {'sqrt': m.sqrt, 'log': m.log10, 'ln': m.log, 'abs':  m.fabs,\n                 'sin': m.sin, 'cos': m.cos, 'tan': m.tan,\n                 'asin': m.asin, 'acos': m.acos, 'atan': m.atan,\n                 'root': nthRoot, 'fact': m.factorial}\n    # Special numbers\n    specialNumbers = {'pi': m.pi, 'e': m.e}\n    \n    def __init__(self, previousAnswer):\n        self.previousAnswer = previousAnswer\n\n      \n    def evaluate(self, inputString):\n        \"\"\" Main method of the calculator that performs all of the calculation steps, returns the final answer\"\"\"\n        # parse string so that it is broken up into the individual pieces\n        parsedString = self.parseInput(inputString)\n        if parsedString == \"Error\":\n            return \"Please enter a valid input!\"\n        # convert the input to postfix notation\n        postfix = self.toPostfix(parsedString)\n        if postfix == \"Unmatched Error\":\n            return \"Your input has unmatched brackets!\"\n#        print(postfix)\n        # Then perform the calculation and return the answer\n        answer = self.postfixCalc(postfix)\n        if answer == \"Too Many Error\":\n            return \"Your input has too many numbers!\"\n        if answer == \"Too Few Error\":\n            return \"Your input has too few numbers!\"\n        return round(answer,10)\n\n  \n    def toPostfix (self,infix):\n        \"\"\"Takes in an array of characters in infix notation and returns an array of the same\n        expression in postfix notation.\"\"\"\n        postfix = []\n        stack = []\n        # Loop over characters in the input string\n        for char in infix:\n            # If char is a number add it to postfix\n            if isFloat(char):\n                postfix.append(char)\n            # If its a special number add it to postfix\n            elif char in Calculator.specialNumbers:\n                postfix.append(char)\n            # If char is a function push it onto the stack\n            elif char in Calculator.functions:\n                stack.append(char)\n            # If the char is a function argument separator (,) pop operators off the stack onto\n            # postfix until ( is reached\n            elif char == ',':\n                while stack[-1] != '(':\n                    postfix.append(stack.pop())\n                    # If the size of the stack reaches 0 without finding a ( there are unmatched brackets.\n                    if len(stack) == 0:\n                        return \"Unmatched Error\"\n            # If char is an operator O\n            elif char in Calculator.operators:\n                # While there is an operator, P, on the top of stack\n                while len(stack)>0 and stack[-1] in Calculator.operators:\n                    stackTop = stack[-1]\n                    precChar = Calculator.operators[char][1]\n                    precStackTop = Calculator.operators[stackTop][1]\n                    # If O in -?+* and its precedence is <= P, pop P off stack\n                    if char in Calculator.operators and precChar <= precStackTop:\n                        postfix.append(stack.pop())\n                    else:\n                        break\n                # Push O onto stack\n                stack.append(char)\n            # If char is (, push it onto the stack\n            elif char == '(':\n                stack.append(char)\n            # If char is )\n            elif char == ')':\n                # If the size of the stack reaches 0 without finding a ( there are unmatched brackets.\n                if len(stack) == 0:\n                    return \"Unmatched Error\"\n                # While top of stack isn't ( pop operators off the top of the stack\n                while stack[-1] != '(':\n                    postfix.append(stack.pop())\n                    # If the size of the stack reaches 0 without finding a ( there are unmatched brackets.\n                    if len(stack) == 0:\n                        return \"Unmatched Error\"\n                # Pop ( off the stack, but not onto output queue\n                stack.pop()\n                # If the token at the top of the stack is a function pop it off the stack and add to postfix\n                if len(stack) > 0 and stack[-1] in Calculator.functions:\n                    postfix.append(stack.pop())\n        # Finally pop all the operators off the stack onto postfix\n        while len(stack)>0:\n            # If the operator on the top of the stack is () then there are unmatched brackets\n            if stack[-1] in '()':\n                return \"Unmatched Error\"\n            postfix.append(stack.pop())\n        return postfix\n    \n    \n    def postfixCalc(self,tokens):\n        \"\"\" Takes a postfix expression as a list and evaluates it \"\"\"\n        if len(tokens) == 0:\n            return 0\n        stack = []\n        # while expr is not empty\n        while len(tokens)>0:\n            toke = tokens.pop(0)\n            # if token is a number push it onto the stack\n            if isFloat(toke):\n                stack.append(float(toke))\n            # if token is a special number push it onto the stack\n            elif toke in Calculator.specialNumbers:\n                stack.append(Calculator.specialNumbers[toke])\n            else:\n                # Operators take 2 inputs, functions take 1 input except root which takes 2\n                if toke in Calculator.operators or toke == 'root':\n                    n = 2\n                elif toke in Calculator.functions:\n                    n = 1\n                # If the length of the stack is less than the required number of operators the user has not \n                # input enough values.\n                if len(stack)<n:\n                    return \"Too Few Error\"\n                # Pop the top n numbers from the stack\n                popedVals = []\n                for i in range(n):\n                    popedVals.append(stack.pop())\n                # Evaluate the operator using the number(s) that were popped, and push back onto the stack\n                if n == 2 and toke in Calculator.operators:\n                    stack.append(Calculator.operators[toke][0](popedVals[1], popedVals[0]))\n                elif n == 2:\n                    stack.append(Calculator.functions[toke](popedVals[1], popedVals[0]))\n                elif n == 1:\n                    stack.append(Calculator.functions[toke](popedVals[0]))\n        # If there is more than one value left on the stack the user has input too many values\n        if len(stack) > 1:\n            return \"Too Many Error\"\n        # Return the value on the stack (should only be 1 value left)\n        return stack[-1]\n    \n\n    def inputChecker(self, inputString):\n        ''' Loops over the string and checks whether all of the user inputs are valid '''\n        stringArray = []\n        tmpString = ''\n        n = 0\n        while n < len(inputString):            \n            char = inputString[n]\n            # If its a number or '.' add it to a temporary string\n            if char in string.digits or char == '.':\n                tmpString+=char\n            else:\n                if tmpString != '':\n                    stringArray.append(tmpString)\n                    tmpString=''\n                # If its a space, pass\n                if char == ' ':\n                    pass\n                # If its a special character, add it to the string array\n                elif char in Calculator.operators or char in '(),':        \n                    stringArray.append(char)           \n                # If its a letter, check to see whether its a function\n                elif char in string.ascii_letters:\n                    fnString = ''\n                    foundFn = False\n                    # Look ahead\n                    while foundFn == False and n < len(inputString) and inputString[n] in string.ascii_letters:\n                        fnString+=inputString[n].lower()\n                        n+=1\n                        # if it finds the letter e, look ahead to check ahead to see whether the next letter is x\n                        # otherwise exp will always get picked up as the number e - then throw an error.\n                        if fnString == 'e' and n < len(inputString) and inputString[n].lower()=='x':\n                            pass\n                        # If it finds a string thats a function, a special number or exp, break the loop\n                        elif fnString in Calculator.functions or fnString == 'exp' or fnString in Calculator.specialNumbers:\n                            # Add the string to stringArr\n                            stringArray.append(fnString)\n                            foundFn = True\n                        # If it finds the string 'ans' then add the previous answer\n                        elif fnString == 'ans':\n                            stringArray.append(self.previousAnswer)\n                            foundFn = True\n                    # If it finds a string that's not a function or a special number, return an error\n                    if foundFn == False:\n                        return \"Error\"\n                    n-=1                    \n                # Else its an invalid character\n                else:\n                    return \"Error\"\n            # If you've reached the end of the array, and tmpString isn't empty add it to the array\n            if n == len(inputString)-1 and tmpString != '':\n                stringArray.append(tmpString)\n            # Finally increment n by 1\n            n+=1\n        return stringArray\n    \n    \n    def negativeCheck(self, stringArray):\n        ''' Loops over stringArray and checks to see whether minus symbols should correspond to negative numbers'''\n        newStringArray = []\n        n = 0\n        while n < len(stringArray):\n             # If the first character is a - sign then the next charater should be a negative number\n            if n == 0 and stringArray[n]=='-':\n                newStringArray.append(stringArray[n]+stringArray[n+1])\n                n+=1\n            # If the character is a - sign and it is preceded by an operator or ( or , or exp then the next character should be a negative number\n            elif stringArray[n]=='-' and (stringArray[n-1] in Calculator.operators or stringArray[n-1] in '(,' or stringArray[n-1] == 'exp'):\n                newStringArray.append(stringArray[n]+stringArray[n+1])\n                n+=1           \n            else:\n                newStringArray.append(stringArray[n])\n            n+=1\n        return newStringArray\n    \n    \n    def scientificNotationCheck(self, stringArray):\n        ''' Loop over stringArray and see if it any of the numbers are in scientific notation'''\n        newStringArray=[]\n        n = 0\n        while n < len(stringArray):\n            # If the character is 'exp' and it has a number either side (including negatives) then change the last item in the\n            # list to aeb where a is the first number, b is the second, else throw an error                        \n            if stringArray[n] == 'exp':\n                try:\n                    newStringArray[-1] = str(float('{}e{}'.format(newStringArray[-1], stringArray[n+1])))\n                    n+=1\n                except ValueError:\n                    return \"Error\"\n                except IndexError:\n                    return \"Error\"\n            else:\n                newStringArray.append(stringArray[n])\n            n+=1\n        return newStringArray\n    \n    \n    def parseInput(self, inputString):\n        \"\"\" Takes the user input, checks to see whether it's valid and returns it as an array\n        split into the indivual pieces \"\"\"\n        stringArray = self.inputChecker(inputString)\n        if stringArray == \"Error\":\n            return stringArray\n        # Correctly assigns negative numbers\n        stringArray = self.negativeCheck(stringArray)\n        # Checks whether any of the numbers have been written in scientific notation\n        stringArray = self.scientificNotationCheck(stringArray)\n#        print(stringArray)\n        return stringArray\n\n\nprint('------------------------------Scientific Calculator -----------------------------------\\n\\\nA scientific calculator that accepts inputs of +-*/^%.\\n\\\nBrackets () are also accepted inputs.\\n\\\nSupports a range of fucntions such as trigonometric inputs (sin(x), cos(x), tan(x), asin(x),\\n\\\nacos(x), atan(x)), sqrt(x), root(x,n), log(x), ln(x), fact(x) and abs(x) functions.\\n\\\nExponential notation is supported in the format 2.5 EXP 7 (not case sensitive).\\n\\\nThe keyword ans is used to use the calculators previous answer.\\n')\nprint('What would you like to calculate? (type q to quit)\\n')\nanswer = 0\nwhile True:\n    calcIn = input(\"--> \")\n    if calcIn in'qQ':\n        break\n    answer = Calculator(answer).evaluate(calcIn)\n    print(answer,\"\\n\")\n\n","repo_name":"dlmcauslan/Calculator","sub_path":"Calculator.py","file_name":"Calculator.py","file_ext":"py","file_size_in_byte":14802,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"27635802972","text":"from flask import Flask, request, render_template, flash, redirect\nfrom models import db, connect_db, Pet\nfrom forms import AddPetForm\n\napp = Flask(__name__)\n\napp.config['SQLALCHEMY_DATABASE_URI'] = 'postgresql:///adoptionAgency'\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\napp.config['SQLALCHEMY_ECHO'] = True\napp.config['SECRET_KEY'] = \"OOooOOOoOOOoo000\"\n\nconnect_db(app)\n\n@app.route('/')\ndef home():\n    pets = Pet.query.all()\n    return render_template('home.html', pets = pets)\n\n\n\"new pet form\"\n@app.route('//new-pet', methods=['GET', 'POST'])\ndef new_pet():\n    form = AddPetForm()\n\n    if form.validate_on_submit():\n        \"Get all form values\"\n        name = form.name.data\n        species = form.species.data\n        photo_url = form.photo_url.data\n        age = form.age.data\n        notes = form.notes.data\n        avalible = form.avalible.data\n        \"Create new pet and add to db\"\n        pet = Pet(name=name,species=species,photo_url=photo_url,age=age,notes=notes,avalible=avalible)\n        db.session.add(pet)\n        db.session.commit()\n        return redirect('/')\n    else:\n        \"If form isnt valid, return to the page\"\n        return render_template('new-pet.html', form=form)\n\n\n\"View a pets details\"\n\n@app.route('/view/<int:user_id>')\ndef view_pet(user_id):\n    pet = Pet.query.get_or_404(user_id)\n    return render_template('view-pet.html',pet=pet)\n\n\n\"edit Pet\"\n@app.route('/edit/<int:user_id>', methods=[\"GET\", \"POST\"])\ndef edit_pet(user_id):\n    pet = Pet.query.get_or_404(user_id)\n    form = AddPetForm(obj=pet)\n    if form.validate_on_submit():\n        \"Get all form values\"\n        pet.name = form.name.data\n        pet.species = form.species.data\n        pet.photo_url = form.photo_url.data\n        pet.age = form.age.data\n        pet.notes = form.notes.data\n        pet.avalible = form.avalible.data\n        db.session.commit()\n        return redirect('/')\n    else:\n        return render_template('edit-pet.html',pet=pet, form=form)\n\n\n\n\n","repo_name":"podop29/AnimalAdoptionAgency","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1980,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"18866742050","text":"# \n#   Reto #0\n#   EL FAMOSO \"FIZZ BUZZ\"\n#   Fecha publicación enunciado: 27/12/21\n#   Fecha publicación resolución: 03/01/22\n#   Dificultad: FÁCIL\n#   Enunciado: Escribe un programa que muestre por consola (con un print) los números de 1 a 100 (ambos incluidos y con un salto de línea entre cada impresión), sustituyendo los siguientes:\n#   - Múltiplos de 3 por la palabra \"fizz\".\n#   - Múltiplos de 5 por la palabra \"buzz\".\n#   - Múltiplos de 3 y de 5 a la vez por la palabra \"fizzbuzz\".\n\n\n\"\"\" Este algoritmo es para saber como identificar los multiplos de n numero, esto siginifica que si un numero dividido entre otro numero da 0 es mutiplo de 3, 5 o de los dos en este caso.\n\"\"\"\n\ndef fizzbuzz(n):\n    \n    for i in range(1, n+1):\n        # if i % 2 == 0:\n        #     print(f\"{i} -> par\")\n        if i % 3 == 0 and i % 5 == 0: #Se tienen que cumpir las dos para que entre al bloque y se pone primero siempre \n            print(f\"{i} -> FizzBuzz\")\n        elif i % 3 == 0:\n            print(f\"{i} -> Fizz\")\n        elif i % 5 == 0:\n            print(f\"{i} -> Buzz\")\n        else:\n            print(i)\n\n\nfizzbuzz(100)","repo_name":"Alexxbar-pro/Python-programmig","sub_path":"ejericicosGithub/fizzbuzz#0.py","file_name":"fizzbuzz#0.py","file_ext":"py","file_size_in_byte":1131,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"8308540331","text":"from django.urls import path, re_path\nfrom . import views\nfrom .views import DiagListView, NuevoDiagCreateView, DiagDetailView, Eliminar_diag, Update_diagnostico\n\nurlpatterns = [\n     path('lista_diagnostico/', DiagListView.as_view(), name='lista_diagnostico'),\n     path('nuevo_diagnostico/', NuevoDiagCreateView.as_view(), name='nuevo_diagnostico'),\n     path('detalle_diagnostico/<int:pk>', DiagDetailView.as_view(), name= 'detalle_diagnostico'),\n     #Eliminar un diagnostico\n     path('eliminar_diag/<int:pk>/', Eliminar_diag.as_view(), name='eliminar_diag'), \n     path('update_diagnostico/<int:pk>/', Update_diagnostico.as_view(), name=\"update_diagnostico\" )  \n]\n","repo_name":"GiudiciAlejandro/TO_consultorio","sub_path":"TO_consultorio/applications/diagnostico/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":670,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"24377293128","text":"# -*- coding: utf-8 -*-\n\"\"\"\n\n\"\"\"\n\nimport math\nimport sys\nimport time\n\n# from PyQt4.QtCore import Qt, QTimer, QRectF\n# from PyQt4.QtGui import *\nimport numpy as np\nfrom PyQt4 import QtCore, QtGui\n\n\nclass Overlay(QtGui.QWidget):\n\n    def __init__(self, speed=18, parent=None):\n        super(Overlay, self).__init__(parent)\n        palette = QtGui.QPalette(self.palette())\n        palette.setColor(palette.Background, QtCore.Qt.transparent)\n        self.setPalette(palette)\n\n        self.theta_start = 90\n        self.theta = 0  # Specifies straight up\n        self.radius = 50\n        self.speed = speed\n        self.colorList = [QtGui.QColor(16, 159, 221), QtGui.QColor(221, 89, 2)]\n\n    def paintEvent(self, event):\n        painter = QtGui.QPainter()\n        painter.begin(self)\n\n        painter.setRenderHint(QtGui.QPainter.Antialiasing)\n        painter.fillRect(event.rect(), QtGui.QBrush(QtGui.QColor(255, 255, 255, 50)))\n        painter.setPen(QtGui.QPen(QtCore.Qt.NoPen))\n\n        painter.setBrush(QtGui.QBrush(self.colorList[0]))\n\n        painter.drawEllipse(self.width() / 2 - self.radius, self.height() / 2 - self.radius, self.radius * 2,\n                            self.radius * 2)\n\n        # painter.setPen(QPen(QColor(255,0,0)))\n        painter.setBrush(QtGui.QBrush(self.colorList[1]))\n\n        pieRect = QtCore.QRectF(self.width() / 2 - self.radius, self.height() / 2 - self.radius, self.radius * 2,\n                                self.radius * 2)\n        painter.drawPie(pieRect, self.theta_start * 16, -self.theta * 16)\n        #        painter.drawLine(self.width()/2 , self.height() / 2, self.width() / 2 + np.sin(self.theta) * self.radius,  self.height()/2.0 + np.cos(self.theta) * self.radius)\n        #        painter.drawLine(self.width()/2, self.height()/2, 50,50 )\n        painter.end()\n\n    def showEvent(self, event):\n        self.timer = self.startTimer(50)\n\n    def timerEvent(self, event):\n        self.theta += self.speed\n\n        if self.theta > 360:\n            self.theta = 0\n            self.colorList.reverse()\n\n        print(self.theta)\n        self.update()\n\n    def endTimer(self):\n        self.killTimer(self.timer)\n        self.hide()\n\n\nclass MainWindow(QtGui.QMainWindow):\n\n    def __init__(self, parent=None):\n        QtGui.QMainWindow.__init__(self, parent)\n\n        widget = QtGui.QWidget(self)\n        self.editor = QtGui.QTextEdit()\n        self.editor.setPlainText(\"0123456789\" * 100)\n        layout = QtGui.QGridLayout(widget)\n        layout.addWidget(self.editor, 0, 0, 1, 3)\n        button = QtGui.QPushButton(\"Wait\")\n        layout.addWidget(button, 1, 1, 1, 1)\n\n        self.setCentralWidget(widget)\n        self.overlay = Overlay(speed=15, parent=self.centralWidget())\n\n        button.clicked.connect(self.overlay.show)\n\n    def resizeEvent(self, event):\n        self.overlay.resize(event.size())\n        event.accept()\n\n\nif __name__ == \"__main__\":\n    app = QtGui.QApplication(sys.argv)\n    window = MainWindow()\n    window.show()\n    sys.exit(app.exec_())\n","repo_name":"sabirhusnain577/BIGKAT-MIX","sub_path":"PSAT_3_CAM_RPI_USE_THIS/overlay.py","file_name":"overlay.py","file_ext":"py","file_size_in_byte":3012,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"18628394460","text":"import re\nimport codecs\nimport os\nimport sys\nfrom pathlib import Path\n\n# this script takes an argument of an sdf file containing\n# molecules and parses this sdf into one file per molecule\n\n# store script argument in variable\nfile_in = sys.argv[1]\n\n###############################################################################\n# SETUP\n###############################################################################\ndir_in = \"/data/p275927/workdir/libraries/\"\ndir_out = \"/data/p275927/workdir/ligands/\"\n\n# counter for number of molecules\nmol_n = 1\n\n# switch for start of writing into new sdf file\nbegin = False\n\n# cut away file extension to keep the file name\nfilename = os.path.splitext(os.path.basename(file_in))[0]\n# join file path to target sdf file\nsdf_file_path = os.path.join(dir_in, file_in)\n\n###############################################################################\n# PARSE\n###############################################################################\n# read in sdf tranche file containing 1000 mol x 4 poses\nwith codecs.open(sdf_file_path, 'r', encoding = 'utf-8', errors = 'ignore') as sdf_in:\n    # iterate over all lines of file\n    for line in sdf_in:\n        # use regex to trigger opening/writing new file\n        trig = re.search(\".OpenBabel.+\", line) # matches two lines\n        if trig:\n            begin = True\n            # open a new sdf file for writing and name it by ID and pose\n            sdf_out = open(dir_out + filename +\n                           '_' + str(mol_n) + '.sdf', 'w')\n            # write the molecule name stored in previous loop iteration\n            sdf_out.write(mol_name)\n        # keep writing into files\n        if begin == True:\n            sdf_out.write(line)\n        # store molecule name from line preceding regex match\n        mol_name = line\n        # stop writing in specific file once line indicates end of sdf\n        if line[:4] == \"$$$$\":\n            sdf_out.close()\n            begin = False\n            mol_n += 1 # increment counter\n","repo_name":"dn440/PPP-Virtual-Screen","sub_path":"scripts/legacy/phase1_sdf_extract.py","file_name":"phase1_sdf_extract.py","file_ext":"py","file_size_in_byte":2004,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"7385114580","text":"# 현재 맵을 방문하지 않았다면 방문할 것\n# 방문 할 때 최소 피로도를 충족하는지 검사\n# 현재 턴에서 방문 했다면 다른 턴에서도 방문할 수 있도록 false처리\n\nans = 0\n\n\ndef dfs(idx: int, pirodo: int, dugeons: list, visit: list):\n    global ans\n    if idx == len(dugeons):\n        return\n\n    for i in range(len(dugeons)):\n        if not visit[i] and dugeons[i][0] <= pirodo:\n            visit[i] = True\n            dfs(idx + 1, pirodo - dugeons[i][1], dugeons, visit)\n            visit[i] = False\n\n    ans = max(idx, ans)\n\n\ndef solution(k, dungeons):\n    visit = [False] * len(dungeons)\n    dfs(0, k, dungeons, visit)\n    return ans\n","repo_name":"ChanghwanK/coding-test-python","sub_path":"programmers/lv2/피로도/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":683,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"6429067061","text":"from django.core.management import call_command\nfrom notification.models import DeviceToken, CertFile\nfrom unittest import TestCase\nfrom .compatibility import Mock\n\nimport json\nimport notification.apns.apns\nimport sys\n\nPYTHON_VERSION = sys.version_info\n\n\nclass ManagementCommandsMultiPushTest(TestCase):\n\n    def setUp(self):\n        self.command_name = 'multipush'\n        self.args = []\n        self.options = {'verbosity': 1,\n                        'settings': None,\n                        'pythonpath': None,\n                        'traceback': False,\n                        'no_color': False,\n                        'sandbox': False,\n                        'device_tokens': None,\n                        'all': False,\n                        'title': None,\n                        'subtitle': None,\n                        'body': None,\n                        'sound': 'default',\n                        'badge': 1,\n                        'content_available': False,\n                        'mutable_content': False,\n                        'extra': None}\n        CertFile(filename='cert.pem').save()\n        self.device_token = DeviceToken(device_token='8a0d7cba3ffad34bd3dcb37728080a95d6ee78a83a68ead033614acbab9b7e76',\n                                        uuid='XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX')\n        self.device_token.save()\n\n    def tearDown(self):\n        self.args = []\n        self.options = {'verbosity': 1,\n                        'settings': None,\n                        'pythonpath': None,\n                        'traceback': False,\n                        'no_color': False,\n                        'sandbox': False,\n                        'device_tokens': None,\n                        'all': False,\n                        'title': None,\n                        'subtitle': None,\n                        'body': None,\n                        'sound': 'default',\n                        'badge': 1,\n                        'content_available': False,\n                        'mutable_content': False,\n                        'extra': None}\n        CertFile.objects.all().delete()\n        self.device_token.delete()\n\n    def test_without_device_tokens_and_title(self):\n        with self.assertRaises(ValueError):\n            call_command(self.command_name, *self.args, **self.options)\n\n    def test_with_device_token_for_specified_and_title(self):\n        self.options['sandbox'] = True\n        self.options['device_tokens'] = [self.device_token.device_token]\n        self.options['title'] = 'test case title'\n\n        notification.apns.apns.GatewayConnection.send_notification = Mock(return_value=None)\n        call_command(self.command_name, *self.args, **self.options)\n\n    def test_with_device_token_for_all_without_title(self):\n        self.options['all'] = True\n\n        with self.assertRaises(ValueError):\n            call_command(self.command_name, *self.args, **self.options)\n\n    def test_target_develop_with_device_token_for_all_and_title_without_cert(self):\n        self.options['sandbox'] = True\n        self.options['all'] = True\n        self.options['title'] = 'test case title'\n        CertFile.objects.all().delete()\n\n        with self.assertRaises(CertFile.DoesNotExist):\n            call_command(self.command_name, *self.args, **self.options)\n\n    def test_valid_custom(self):\n        self.options['extra'] = \"{'key':'value'}\"\n        self.options['sandbox'] = True\n        self.options['all'] = True\n        self.options['title'] = 'test case title'\n        CertFile.objects.all().delete()\n\n        with self.assertRaises(CertFile.DoesNotExist):\n            call_command(self.command_name, *self.args, **self.options)\n\n    def test_invalid_custom(self):\n        self.options['extra'] = \"{'key':'value','key2'}\"\n        self.options['sandbox'] = True\n        self.options['all'] = True\n        self.options['title'] = 'test case title'\n        CertFile.objects.all().delete()\n\n        if PYTHON_VERSION.major == 3 and PYTHON_VERSION.minor >= 5:\n            with self.assertRaises(json.decoder.JSONDecodeError):\n                call_command(self.command_name, *self.args, **self.options)\n        elif (PYTHON_VERSION.major == 3 and PYTHON_VERSION.minor <= 4) or PYTHON_VERSION.major == 2:\n            with self.assertRaises(ValueError):\n                call_command(self.command_name, *self.args, **self.options)\n","repo_name":"nnsnodnb/django-ios-notifications","sub_path":"notification/tests/test_notification_command_multipush.py","file_name":"test_notification_command_multipush.py","file_ext":"py","file_size_in_byte":4371,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"74971071284","text":"from collections import defaultdict\n\nday = 6\n\nexample_filename = f'day{day}/day{day}_ex.txt'\nexample_input = open(example_filename).readlines()\n\nfilename = f'day{day}/day{day}.txt'\npuzzle_input = open(filename).readlines()\n\ndef get_initial_timers(input):\n  timers = defaultdict(int)\n  for val in input[0].split(','):\n    timers[int(val)] += 1\n  return timers\n\ndef simulate_day(timers):\n  new_timers = {key-1: val for key, val in timers.items() if key != 0}\n  if 0 in timers:\n    new_timers[6] = new_timers[6] + timers[0] if 6 in new_timers else timers[0]\n    new_timers[8] = new_timers[8] + timers[0] if 8 in new_timers else timers[0]\n  return new_timers\n  \ndef simulate_n_days(input, n):\n  timers = get_initial_timers(input)\n  for _ in range(n):\n    timers = simulate_day(timers)\n  return timers\n\ndef part_1(input):\n  return sum(simulate_n_days(input, 80).values())\n\ndef part_2(input):\n  initial_timers = get_initial_timers(input)\n  return sum(simulate_n_days(input, 256).values())\n\n\nprint(f'Part 1 example: {part_1(example_input)}')\nprint(f'Part 1 puzzle: {part_1(puzzle_input)}')\n\nprint(f'Part 2 example: {part_2(example_input)}')\nprint(f'Part 2 puzzle: {part_2(puzzle_input)}')\n","repo_name":"anniebryan/advent-of-code","sub_path":"2021/day6/day6.py","file_name":"day6.py","file_ext":"py","file_size_in_byte":1182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"18825819082","text":"#!/usr/bin/env python\n\nfrom collections import Counter\nfrom ranking import BM25\nfrom re import compile, split\nfrom struct import pack\n\nterm_regex = compile('[^a-zA-Z-/\\']')\n\ndef tokenize(content, stoplist=None):\n    \"\"\"\n    Converts a list of words into terms based on a set of rules/\n\n    We decided to keep words containing either hyphens, forward-slashes or apostrophes\n    in our earlier regex because they themselves are composed of multiple words.\n    We now use another another regex that matches word groups to extract the words.\n\n    The reasoning behind these decisions is to maintain the structure of the words.\n    This is so the index reflects what a typical user expects. For example in a search\n    function where the user wants to find occurrences of \"campus\", it is preferable to\n    recognize \"on-campus\" as a legitimate source of \"campus\".\n\n    :param content: a string of text\n    :return terms: a list of words after tokenization\n    \"\"\"\n    words = term_regex.sub(' ', content).lower().split()\n    terms = []\n    for word in words:\n        if word.isalpha():\n            terms.append(word)\n        else:\n            terms.extend(split('\\W+', word))\n    if stoplist:\n        terms = [t for t in terms if t not in stoplist]\n    return terms\n\n\nclass Document:\n\n    def __init__(self, id, docno, terms):\n        \"\"\"\n        Contains the identifiers and terms found within a document.\n\n        Includes the length of the document as the combined length of each term.\n        Using the invariant that the terms only contains the alphabet,\n        we assume that each character is equal to 1 byte in UTF-8 encoding.\n        Hence, the length of a document is the number of indexed bytes.\n\n        :param id: an incremental identifier assigned to each document\n        :param docno: an identifier parsed from [<DOCNO></DOCNO>]\n        :param terms: a list of terms in order of discovery\n        \"\"\"\n        self.id = id\n        self.docno = docno\n        self.terms = terms\n        self.length = sum(map(len, terms))\n\n\nclass Collection:\n\n    def __init__(self, stoplist=None):\n        \"\"\"\n        Contains functions to index a collection.\n\n        :param sourcefile: a path to a collection\n        :param stoplist: an optional path to a stoplist\n        \"\"\"\n        self.stoplist = None\n        if stoplist:\n            with open(stoplist, 'r') as f:\n                self.stoplist = set(f.read().split('\\n'))\n        self.documents = []\n\n    def parse_document(self, collection, docno):\n        with open(collection, 'r') as f:\n            content = ''\n            appending = False\n            found = False\n            for line in f:\n                if not found:\n                    if line.startswith('<DOCNO>') and docno == line[8:-10]:\n                        found = True\n                else:\n                    if line == '<TEXT>\\n' or line == '<HEADLINE>\\n':\n                        appending = True\n                    elif line == '</TEXT>\\n' or line == '</HEADLINE>\\n':\n                        appending = False\n                    elif appending and not line.startswith(('<', '</')):\n                        content += line\n                    elif line == '</DOC>\\n':\n                        terms = tokenize(content, self.stoplist)\n                        self.documents.append(Document(len(self.documents), docno, terms))\n                        return True\n            return False\n\n    def parse_collection(self, collection):\n        \"\"\"\n        Iterates the <collection> one line at time to store important data.\n\n        > begin <appending> if line equals \"<TEXT>\" or \"<HEADLINE>\"\n        > stop <appending> if line equals \"</TEXT>\" or \"</HEADLINE>\"\n        > appends the line to <content> if <appending> and not a tag ELSE\n        > assigns <docno> if it encounters a <DOCNO></DOCNO> tag ELSE\n        > resets <content> if line equals \"<DOC>\"\n        > tokenizes <content> if line equals \"</DOC>\" and increments <id>\n\n        :param collection: a path to a <collection>\n        \"\"\"\n        with open(collection, 'r') as f:\n            id = 0\n            docno = ''\n            content = ''\n            appending = False\n            for line in f:\n                if line == '<TEXT>\\n' or line == '<HEADLINE>\\n':\n                    appending = True\n                elif line == '</TEXT>\\n' or line == '</HEADLINE>\\n':\n                    appending = False\n                elif appending and not line.startswith(('<', '</')):\n                    content += line\n                elif line.startswith('<DOCNO>'):\n                    docno = line[8:-10]\n                elif line == '<DOC>\\n':\n                    content = ''\n                elif line == '</DOC>\\n':\n                    id += 1\n                    terms = self.tokenize_terms(content, self.stoplist)\n                    self.documents.append(Document(id, docno, terms))\n\n    def write_map_to_disk(self):\n        \"\"\"\n        Writes a <map> file to the current working directory.\n\n        The <map> file is line-separated, where each line consists of '<id> <docno> <document_weight>'.\n        The <document_weight> is calculated as the value of K in BM25's scoring function.\n        \"\"\"\n        al = sum(map(lambda d: d.length, self.documents)) / len(self.documents)\n        ranker = BM25()\n        with open('map', 'w') as f:\n            for document in self.documents:\n                f.write(str(document.id) + ' ' + document.docno + ' ' + str(ranker.document_weight(document.length, al)) + '\\n')\n\n    def create_postings(self):\n        \"\"\"\n        Builds a dictionary for the term occurrence statistics of the Collection.\n\n        :return postings: a dictionary with \"term\" as a key and a list of Document \"id\" as a value\n        \"\"\"\n        postings = {}\n        if self.documents:\n            for document in self.documents:\n                for term in document.terms:\n                    if term in postings:\n                        postings[term].append(document.id)\n                    else:\n                        postings[term] = [document.id]\n        return postings\n\n    def write_invlists_lexicon_to_disk(self):\n        \"\"\"\n        Writes an <invlists> file and <lexicon> file to the current working directory.\n\n        <invlists> is a binary integer file (32-bit) composed of sequential inverted lists.\n        Each inverted list is preceded by a document frequency integer.\n        This is followed by an number of \"<id> <token_frequency>\" pairs equal to the document frequency.\n        <lexicon> is a key-value-pair where every \"key\" is \"term\" that is assigned a byte-offset from <tell>.\n        The order of terms/documents in <invlists> and <lexicon> are unordered because <postings> is unordered.\n        \"\"\"\n        postings = self.create_postings()\n        with open('invlists', 'wb') as invlists_file, open('lexicon', 'w') as lexicon_file:\n            for term in postings.keys():\n                lexicon_file.write(term + ' ' + str(invlists_file.tell()) + '\\n')  # gets the position of the file\n                term_occurrences = Counter(postings[term])  # tallies term occurrences by document <id>\n                document_frequency = len(term_occurrences.keys())\n                invlists_file.write(str(pack('I', document_frequency)))\n                for document_id in term_occurrences.keys():\n                    invlists_file.write(pack('II', document_id, term_occurrences[document_id]))\n","repo_name":"pcaenngtaeera/Information-Retrieval","sub_path":"Automatic Query Expansion/collection.py","file_name":"collection.py","file_ext":"py","file_size_in_byte":7417,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"75110496246","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        ('elcid', '0017_merge'),\n    ]\n\n    operations = [\n        migrations.RenameField(\n            model_name='antimicrobial',\n            old_name='no_antimicriobials',\n            new_name='no_antimicrobials',\n        ),\n    ]\n","repo_name":"openhealthcare/elcid","sub_path":"elcid/migrations/0018_auto_20151026_1205.py","file_name":"0018_auto_20151026_1205.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"76"}
+{"seq_id":"158838321","text":"from support import rand_list\n\n\n# Definition for a point\nclass Point:\n    def __init__(self, a=0, b=0):\n        self.x = a\n        self.y = b\n        \n    def __str__(self):\n        return \"(%s, %s)\" % (self.x, self.y)\n        \n    __repr__ = __str__   \n    \n\nclass Solution:\n    # @param points, a list of Points\n    # @return an integer\n    def maxPoints(self, points):\n        if not points:\n            return 0\n            \n        size = len(points)\n        if size < 3:\n            return size       \n           \n        def calc_slope(p1, p2):\n            x, y = (p2.x - p1.x), (p2.y - p1.y)\n            if x == 0:\n                return None\n            else:\n                return (float)(p2.y - p1.y) / (p2.x - p1.x)\n                  \n        max_points = 0\n        \n        for i in xrange(size):\n            p1 = points[i]\n            mapping = {\"same point\": 1}\n            for j in xrange(i+1, size):\n                p2 = points[j]\n                \n                if p2.x == p1.x and p2.y == p1.y:\n                    mapping[\"same point\"] += 1\n                else:\n                    slope = calc_slope(p1, p2)\n                    \n                    if slope not in mapping:\n                        mapping[slope] = 2\n                    else:\n                        mapping[slope] += 1\n            \n            same_count = mapping.pop(\"same point\")\n            max_points = max(max_points, same_count)\n            for s in mapping:\n                max_points = max(max_points, mapping[s] + same_count - 1)\n        \n        return max_points\n        \n                \ndef test_main():   \n    sol = Solution()\n       \n    for i in xrange(10):\n        \n        numbers = rand_list(i*2) \n        numbers2 = rand_list(i*2)\n        \n        #input = [(0,-12),(5,2),(2,5),(0,-5),(1,5),(2,-2),(5,-4),\n        #(3,4),(-2,4),(-1,4),(0,-5),(0,-8),(-2,-1),(0,-11),(0,-9)]\n        #input = [(3,7),(3,1),(5,7),(4,1)]\n        #input = [(1,1), (2,2), (3,3), (5,4), (0, 0), (0, 0)]\n        #input = [(0,0),(1,1),(0,0)]\n        #input = [(0,0),(0,0)]\n        #input = [(1,1),(1,1),(1,1)]\n        #input = [(1,1),(1,1),(2,3)]\n        #numbers = [x for x,y in input]\n        #numbers2 = [y for x,y in input]\n        \n        #numbers = numbers2 = range(i)\n        \n        points = []    \n        for i in xrange(len(numbers)):\n            points.append(Point(numbers[i], numbers2[i]))\n                       \n        print(\"maxPoints in %s\" % points)\n        \n        print(sol.maxPoints(points))\n       \n        \nif __name__ == \"__main__\":\n    test_main()\n    \n","repo_name":"deepgully/codes","sub_path":"leetcode/python/max-points-on-a-line.py","file_name":"max-points-on-a-line.py","file_ext":"py","file_size_in_byte":2569,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"76"}
+{"seq_id":"74702489525","text":"import os\nimport pickle\nimport random\nimport absl.flags\nimport absl.app\n\nfrom tqdm import tqdm\nimport torch\nimport torchvision\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom src import segmentations\nfrom src import settings\nfrom src import model_utils\nfrom src import mask_utils\nfrom src import activation_utils\nfrom src import utils\nfrom src import constants as C\nfrom src import metrics\nfrom src import formula as F\n\n# user flags\nabsl.flags.DEFINE_string(\n    \"subset\", \"ade20k\", \"subset to use. Values:[ade20k, pascal]\"\n)\nabsl.flags.DEFINE_string(\n    \"model\",\n    \"resnet18\",\n    \"model to use. Values:[resnet18, alexnet, resnet50, vgg16, densenet161]\",\n)\nabsl.flags.DEFINE_string(\n    \"pretrained\",\n    \"places365\",\n    \"whether to use pretrained weights. Values [imagenet, places365, None]\",\n)\nabsl.flags.DEFINE_string(\"device\", \"cuda\", \"device to use to store the model\")\nabsl.flags.DEFINE_integer(\"length\", 3, \"length of explanations\")\nabsl.flags.DEFINE_integer(\"num_clusters\", 5, \"number of clusters\")\nabsl.flags.DEFINE_integer(\"top_k\", 5, \"top k samples\")\nabsl.flags.DEFINE_integer(\"random_units\", 100, \"number of units\")\nabsl.flags.DEFINE_string(\n    \"root_models\", \"data/model/\", \"root directory for models\"\n)\nabsl.flags.DEFINE_string(\n    \"root_datasets\", \"data/dataset/\", \"root directory for datasets\"\n)\nabsl.flags.DEFINE_string(\n    \"root_segmentations\",\n    \"data/cache/segmentations/\",\n    \"root directory for segmentations\",\n)\nabsl.flags.DEFINE_string(\n    \"root_activations\",\n    \"data/cache/activations/\",\n    \"root directory for activations\",\n)\nabsl.flags.DEFINE_string(\n    \"root_results\", \"data/results/\", \"root directory for results\"\n)\nabsl.flags.DEFINE_string(\n    \"dir_images\", \"figures/units/\", \"Where to save the images\"\n)\nabsl.flags.DEFINE_integer(\"seed\", 0, \"seed to use to set reproducibility\")\nFLAGS = absl.flags.FLAGS\n\n\ndef show(imgs, labels=None):\n    \"\"\"Show images in a grid\"\"\"\n    if not isinstance(imgs, list):\n        imgs = [imgs]\n    fig, axs = plt.subplots(\n        nrows=len(imgs), squeeze=False,\n        gridspec_kw={'wspace': 0, 'hspace': 0.5})\n    for i, img in enumerate(imgs):\n        img = img.detach()\n        img = torchvision.transforms.functional.to_pil_image(img)\n        axs[i, 0].imshow(np.asarray(img))\n        axs[i, 0].set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])\n        if labels is not None:\n            axs[i, 0].set_title(labels[i])\n    return fig\n\n\ndef main(argv):\n    if FLAGS.num_clusters < 1:\n        raise ValueError(\"num_clusters must be greater than 0\")\n    # Set seed\n    generator = utils.set_seed(FLAGS.seed)\n\n    # Parameters\n    cfg = settings.Settings(\n        subset=FLAGS.subset,\n        model=FLAGS.model,\n        pretrained=FLAGS.pretrained,\n        num_clusters=FLAGS.num_clusters,\n        beam_limit=5,\n        device=FLAGS.device,\n        root_models=FLAGS.root_models,\n        root_datasets=FLAGS.root_datasets,\n        root_segmentations=FLAGS.root_segmentations,\n        root_activations=FLAGS.root_activations,\n        root_results=FLAGS.root_results,\n    )\n    sparse_segmentation_directory = cfg.get_segmentation_directory()\n    mask_shape = cfg.get_mask_shape()\n\n    # Load data\n    dataset = segmentations.BrodenDataset(\n        cfg.dir_datasets,\n        subset=cfg.index_subset,\n        resolution=cfg.get_img_size(),\n        broden_version=1,\n        transform_image=torchvision.transforms.Compose(\n            [\n                torchvision.transforms.Resize(cfg.get_img_size()),\n                torchvision.transforms.ToTensor(),\n                torchvision.transforms.Normalize(\n                    cfg.get_image_mean(), cfg.get_image_stdev()\n                ),\n            ]\n        ),\n    )\n\n    # Load data without normalization\n    image_dataset = segmentations.BrodenDataset(\n                cfg.dir_datasets,\n                subset=cfg.index_subset,\n                resolution=cfg.get_img_size(),\n                broden_version=1,\n                transform_image=torchvision.transforms.Compose([\n                    torchvision.transforms.Resize(112)])\n                )\n    segmentation_loader = torch.utils.data.DataLoader(\n        dataset,\n        batch_size=C.BATCH_SIZE,\n        worker_init_fn=utils.seed_worker,\n        generator=generator,\n    )\n\n    # Load Model\n    model = model_utils.load_model_from_settings(cfg, device=cfg.device)\n\n    # Load Masks\n    masks = mask_utils.get_masks(\n        sparse_segmentation_directory, segmentation_loader, dataset.labels,\n        cfg.device\n    )\n\n    # Loop over all the selected layers\n    for _, layer_name in enumerate(cfg.get_feature_names()):\n        # Get the number of units in the layer\n        num_units = model_utils.get_number_of_units(model, layer_name, cfg)\n\n        # Get activations\n        activations = model_utils.get_layer_activations(\n            segmentation_loader,\n            model,\n            layer_name,\n            range(num_units),\n            cfg.get_activation_directory(),\n        )\n\n        # Select units\n        if FLAGS.random_units == 0:\n            selected_units = range(num_units)\n        else:\n            selected_units = random.sample(\n                range(0, num_units), FLAGS.random_units\n            )\n\n        # Select 20 random units\n        with tqdm(\n                selected_units,\n                desc=\"Computing Compostional explanations per unit\") as pbar:\n            for unit in pbar:\n                # Get unit activations\n                unit_activations = activations[unit]\n                activation_ranges = activation_utils.compute_activation_ranges(\n                    unit_activations, FLAGS.num_clusters)\n                # Loop over all the activation ranges\n                images_list = []\n                labels_list = []\n                for _, activation_range in enumerate(\n                    sorted(activation_ranges)\n                ):\n                    bitmaps = activation_utils.compute_bitmaps(\n                            unit_activations,\n                            activation_range,\n                            mask_shape=mask_shape,\n                        )\n                    bitmaps = bitmaps.to(cfg.device)\n\n                    dir_current_results = (\n                        f\"{cfg.get_results_directory()}/\"\n                        + f\"{layer_name}/{unit}/{activation_range}\"\n                    )\n                    file_algo_results = (\n                        f\"{dir_current_results}/\" + f\"{FLAGS.length}.pickle\"\n                    )\n\n                    # Load results\n                    with open(file_algo_results, \"rb\") as file:\n                        best_label, best_iou, _ = pickle.load(file)\n\n                    # Filter the top k candidates samples that\n                    label_mask = mask_utils.get_formula_mask(\n                        best_label, masks).to(FLAGS.device)\n                    # - contain the concept\n                    samples_formula = label_mask.sum(1) > 0\n                    # - have the neuron firing\n                    neuron_fires = bitmaps.sum(1) > 0\n                    # - have a high iou\n                    samples_iou = metrics.sample_iou(bitmaps, label_mask)\n                    above_iou = samples_iou > best_iou\n                    candidates = neuron_fires & samples_formula & above_iou\n                    nonzero = torch.nonzero(candidates).flatten()\n                    top_k = random.sample(nonzero.tolist(), FLAGS.top_k)\n\n                    # Plot the top k samples\n                    \n                    if not os.path.exists(FLAGS.dir_images):\n                        os.makedirs(FLAGS.dir_images)\n                    images = []\n                    for index_sample in top_k:\n                        data, _, _ = image_dataset[index_sample]\n                        image = torch.from_numpy(np.array(data))\n                        image = image.permute(2, 0, 1)\n                        mask_concept = ~bitmaps[index_sample]\n                        mask_concept = mask_concept.reshape(\n                            mask_shape[0], mask_shape[1])\n                        segmented_image = torchvision.utils.draw_segmentation_masks(\n                            image, mask_concept, alpha=1, colors='black')\n                        images.append(segmented_image)\n                    images_list.append(\n                        torchvision.utils.make_grid(\n                            images, padding=2, pad_value=255)\n                        )\n                    title = f\"{F.get_formula_str(best_label, dataset.labels)}\"\n                    labels_list.append(title)\n                fig = show(images_list, labels_list)\n                fig.set_size_inches(6, 7)\n                fig.savefig(\n                    f'{FLAGS.dir_images}/' +\n                    f'unit_{unit}_c_{FLAGS.num_clusters}.png')\n                pbar.update(0)\n\n\nif __name__ == \"__main__\":\n    absl.app.run(main)\n","repo_name":"KRLGroup/Clustered-Compositional-Explanations","sub_path":"scripts/generate_images.py","file_name":"generate_images.py","file_ext":"py","file_size_in_byte":8912,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"31358192763","text":"\"\"\"\nThis Program controls an RGB LED through PWM\nIan Samuel Valdovinos Granados\n\"\"\"\n\nimport RPi.GPIO as GPIO\nimport time\n\nled_1_pin = 32\n\n\n# Set pin and board modes\nGPIO.setmode(GPIO.BOARD)\nGPIO.setwarnings(False)\nGPIO.setup(led_1_pin, GPIO.OUT)\n\n# Setup LED pins as PWM output pins \npwm_1 = GPIO.PWM(led_1_pin, 50)\npwm_1.start(0)\n\n\nwhile True:\n    for i in range(0, 50):\n        pwm_1.ChangeDutyCycle(i)\n        time.sleep(0.1)\n\n    for i in range(50, 0, -1):\n        pwm_1.ChangeDutyCycle(i)\n        time.sleep(0.1)\n","repo_name":"IanValdovinos/RaspberryPi","sub_path":"RGP_LED_PWM.py","file_name":"RGP_LED_PWM.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"74067259766","text":"class Solution:\n    # TLE Solution, using DFS:\n    # def shortestPathBinaryMatrix(self, grid: List[List[int]]) -> int:\n    #     if grid[0][0] == 1:\n    #         return -1\n    #\n    #     dirs = [(1, 0), (1, 1), (0, 1), (-1, 1), (-1, 0), (-1, -1), (0, -1), (1, -1)]\n    #     n = len(grid)\n    #\n    #     def dfs(r: int, c: int, curr: int, visited: set) -> int:\n    #         if not (0 <= r < n and 0 <= c < n):\n    #             return inf\n    #\n    #         if r == n - 1 and c == n - 1 and grid[r][c] == 0:\n    #             return curr\n    #\n    #         if grid[r][c] == 1 or (r, c) in visited:\n    #             return inf\n    #\n    #         v = visited.copy()\n    #         v.add((r, c))\n    #         val = inf\n    #\n    #         for dr, dc in dirs:\n    #             val = min(val, dfs(r + dr, c + dc, curr + 1, v))\n    #\n    #         return val\n    #\n    #     Min = dfs(0, 0, 1, set())\n    #     if Min == inf:\n    #         return -1\n    #\n    #     return Min\n\n    # BFS Solution:\n    def shortestPathBinaryMatrix(self, grid: List[List[int]]) -> int:\n        n = len(grid)\n\n        if grid[0][0] != 0:\n            return -1\n\n        if n == 1:\n            return 1 if grid[0][0] == 0 else -1\n\n        dirs = [(1, 0), (1, 1), (0, 1), (-1, 1), (-1, 0), (-1, -1), (0, -1), (1, -1)]\n\n        visited = set()\n        visited.add((0, 0))\n        q = deque([(0, 0, 1)])\n\n        while len(q) > 0:\n            r, c, d = q.popleft()\n            for dr, dc in dirs:\n                new_r, new_c = r + dr, c + dc\n                if not (0 <= new_r < n and 0 <= new_c < n):\n                    continue\n\n                if (new_r, new_c) not in visited and grid[new_r][new_c] == 0:\n                    if new_r == n - 1 and new_c == n - 1:\n                        return d + 1\n\n                    visited.add((new_r, new_c))\n                    q.append((new_r, new_c, d + 1))\n\n        return -1\n","repo_name":"Lei-Tin/Leetcode","sub_path":"Medium/#1091 shortestPathBinaryMatrix.py","file_name":"#1091 shortestPathBinaryMatrix.py","file_ext":"py","file_size_in_byte":1905,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"76"}
+{"seq_id":"40546605917","text":"# Programme de saisir des notes, affiche la moyenne, la note Max et Min\n#Prend fin si le user entre un valeur negative\n\nnote= 0\nliste_note= []\nnote_max= 0\nnote_min= 20\nmoyen= 0\nnbre_note= 0\nsom= 0\n\n# Rempli notre liste avec les notes\nwhile(note>=0) :\n    note= int(input(\"Entrez une note positive de 0 a 20 : \"))# demande d une note\n    if note<0 : \n         print(\"Termine\") # arret si la note est negative\n         \n    else:\n        liste_note.append(note) # mise a jour de note liste\n\nif not liste_note: # cas ou la liste est vide\n    print(\"Impossible d effectuer des calculs sur un tableau vide \")\nelse:\n\n    # Calcule la note maximale et minimale\n    nbre_note= len(liste_note)\n    i= 0\n    while i<nbre_note :        # on ne doit pas depasser le nbre de note\n\n        if(liste_note[i]>note_max) :\n            note_max= liste_note[i]   # on trouve la note maximale\n        if(liste_note[i]<note_min) :\n            note_min= liste_note[i]   # on trouve la note minimale\n        i= i+1\n\n    # Calcul de la moyenne\n    j= 0\n    while j<nbre_note :\n        som= som + liste_note[j] # Calcul la somme total des notes\n        moyen= som/nbre_note   # Calcul de la moyenne\n        j= j+1\n\n    #Affichage du resultat\n    print(note_min,\" est la note minimale et \",note_max,\" est la note maximale \",end=\"\\n\")\n    print(moyen, \" est la moyenne des \", nbre_note,\" notes\", end=\" \")\n   \n    ","repo_name":"Steve2404/Python_Scripts","sub_path":"Exo_Test/exo12.py","file_name":"exo12.py","file_ext":"py","file_size_in_byte":1385,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"7146793894","text":"import calendar\r\n\r\nfrom django.contrib import messages\r\nfrom django.contrib.auth.decorators import login_required\r\nfrom django.core.mail import send_mail\r\nfrom django.shortcuts import redirect, render\r\n\r\nfrom users.forms import UserUpdateForm, ProfileUpdateForm\r\nfrom .forms import *\r\n# auxiliary file I made to hold some of the logic\r\nfrom .utils import *\r\n\r\n\r\n# noinspection PySimplifyBooleanCheck\r\n@login_required\r\ndef home(request):\r\n    request.session.set_expiry(600)\r\n    if getAccount(request) == \"All\":\r\n        return redirect('summary')\r\n\r\n    accountID = getAccount(request)\r\n    if validateID(request, accountID) == True:\r\n        context, rows = makeCatContext(request, accountID), getRows(request, accountID)\r\n\r\n        # update categorical caps if necessary\r\n        updateCaps(request)\r\n\r\n        # gets date range selected by user, parses it and then updates transactions+details displayed\r\n        if request.method == \"POST\" and 'submit' in request.POST:\r\n            if request.POST['submit'] == \"Enter\":\r\n                request.user.profile.setDateRange(request.POST.get('datetimes'))\r\n            if request.POST['submit'] == \"Clear\":\r\n                request.user.profile.setUseDateFilter(\"0\")\r\n\r\n        if request.user.profile.useDateFilter == \"1\":\r\n            rawDates = request.user.profile.getDateRange().split(\"-\")\r\n            startDate, endDate = rawDates[0], rawDates[1]\r\n            rows = getFilteredRows(rows, startDate, endDate)\r\n            context['dateIndicator'] = \"Transactions between \" + str(startDate) + \" - \" + str(endDate)\r\n        else:\r\n            context['dateIndicator'] = \"All transactions\"\r\n\r\n        # get data from database, store into \"context\" dictionary\r\n        if rows != False:\r\n            for transaction in rows:\r\n                context[getCategory(transaction['MCC'])].append(transaction)\r\n\r\n        return render(request, 'transactions/home.html', updateContext(context, rows, request, accountID, True))\r\n    else:\r\n        return render(validateID(request, accountID)[0], validateID(request, accountID)[1],\r\n                      validateID(request, accountID)[2])\r\n\r\n\r\n@login_required\r\ndef summary(request):\r\n    request.session.set_expiry(600)\r\n    context = getSummaryContext(request)\r\n    return render(request, 'transactions/summary.html', context)\r\n\r\n\r\n# special page for user's budgeting insights insights\r\n@login_required\r\ndef caps(request):\r\n    request.session.set_expiry(600)\r\n    if getAccount(request) == \"All\":\r\n        return redirect('summary')\r\n    accountID = getAccount(request)\r\n\r\n    if validateID(request, accountID) == True:\r\n        tempcontext, rows = makeCatContext(request, accountID), getRows(request, accountID)\r\n        if rows != False:\r\n            endDay = str(calendar.monthrange(dateNow.year, dateNow.month)[1])\r\n            startDate = \"1/\" + str(dateNow.month) + \"/\" + str(dateNow.year) + \" 00:00 \"\r\n            endDate = \" \" + endDay + \"/\" + str(dateNow.month) + \"/\" + str(dateNow.year) + \" 00:00\"\r\n            rows = getFilteredRows(rows, startDate, endDate)\r\n            for transaction in rows:\r\n                tempcontext[getCategory(transaction['MCC'])].append(transaction)\r\n        totalList, spendIndicatorList, tempcontext = getCategoricalTotal(tempcontext)\r\n        possibleCapSetOn = [\"getValueBP\", \"getValueTP\", \"getValueGC\", \"getValueFC\", \"getValueFSC\",\r\n                            \"getValueFoodC\", \"getValueGeneralC\", \"getValueEC\", \"getValueLSC\", \"getValueOC\"]\r\n        categories = ['Bills & Payments', 'Transport', 'Groceries', 'Fashion & Cosmetics', 'Finances', 'Food', 'General',\r\n                      'Entertainment', 'Leisure & Self-Care', 'Other']\r\n\r\n        categoricalSpend = {}\r\n        for i in range(len(possibleCapSetOn)):\r\n            if spendIndicatorList[i] == 'Spent':\r\n                categoricalSpend[possibleCapSetOn[i]] = totalList[i]\r\n            else:\r\n                categoricalSpend[possibleCapSetOn[i]] = 0\r\n        context = makeAggContext(request, accountID)\r\n        context['spend'] = getAverageMonthlySpend(getRows(request, accountID))\r\n        context['accountType'] = getAccountType(accountID)\r\n        capvalues = getAllCaps(request)\r\n        capsContext = []\r\n\r\n        for num in range(len(categories)):\r\n            capsContext.append(\r\n                {'Category': categories[num], 'Spend': categoricalSpend[possibleCapSetOn[num]],\r\n                 'Cap': capvalues[(num + 1)]})\r\n\r\n        for cap in capsContext:\r\n            if cap['Cap'] != 0:\r\n                cap['Percentage'] = cap['Spend'] / cap['Cap'] * 100\r\n            else:\r\n                cap['Percentage'] = 0\r\n        context['allCap'] = capvalues.pop(0)\r\n        context['allSpend'] = sum(totalList)\r\n        context['totalCap'] = sum(capvalues)\r\n        if context['allCap'] > 0:\r\n            context['totalPercentage'] = sum(totalList) / context['allCap'] * 100\r\n        else:\r\n            context['totalPercentage'] = 0\r\n        context['capData'] = capsContext\r\n\r\n        vals = list(categoricalSpend.values())\r\n        context['mostExpCat'] = context['capData'][vals.index(max(vals))]\r\n        return render(request, 'transactions/caps.html', context)\r\n    else:\r\n        return render(validateID(request, accountID)[0], validateID(request, accountID)[1],\r\n               validateID(request, accountID)[2])\r\n\r\n\r\n# noinspection PySimplifyBooleanCheck\r\n@login_required\r\ndef transactions(request):\r\n    request.session.set_expiry(600)\r\n    if getAccount(request) == \"All\":\r\n        return redirect('summary')\r\n\r\n    accountID = getAccount(request)\r\n\r\n    # update categorical caps if necessary\r\n    updateCaps(request)\r\n\r\n    if validateID(request, accountID) == True:\r\n        context, rows = makeAggContext(request, accountID), getRows(request, accountID)\r\n\r\n        # gets date range selected by user, parses it and then updates transactions+details displayed\r\n        if request.method == \"POST\" and 'submit' in request.POST:\r\n            if request.POST['submit'] == \"Enter\":\r\n                request.user.profile.setDateRange(request.POST.get('datetimes'))\r\n            if request.POST['submit'] == \"Clear\":\r\n                request.user.profile.setUseDateFilter(\"0\")\r\n\r\n        if request.user.profile.useDateFilter == \"1\":\r\n            rawDates = request.user.profile.getDateRange().split(\"-\")\r\n            startDate, endDate = rawDates[0], rawDates[1]\r\n            rows = getFilteredRows(rows, startDate, endDate)\r\n            context['dateIndicator'] = \"Transactions between \" + str(startDate) + \" - \" + str(endDate)\r\n        else:\r\n            context['dateIndicator'] = \"All transactions\"\r\n        context['rows'] = rows\r\n        return render(request, 'transactions/transactions.html',\r\n                      updateContext(context, rows, request, accountID, False))\r\n    else:\r\n        return render(validateID(request, accountID)[0], validateID(request, accountID)[1],\r\n                      validateID(request, accountID)[2])\r\n\r\n\r\n@login_required\r\ndef profile(request):\r\n    request.session.set_expiry(600)\r\n    if request.method == 'POST':\r\n        uForm = UserUpdateForm(request.POST, request.FILES, instance=request.user)\r\n        pForm = ProfileUpdateForm(request.POST, request.FILES, instance=request.user.profile)\r\n        if uForm.is_valid() and pForm.is_valid():\r\n            newAccountID = pForm.cleaned_data.get('accountID')\r\n            request.user.profile.addToAccountList(newAccountID)\r\n\r\n            # getDataForAccount(newAccountID)\r\n            uForm.save()\r\n            pForm.save()\r\n            messages.success(request, f'Account successfully updated')\r\n            return redirect('profile')\r\n    # return statement in line above is to prevent user from falling to line below\r\n    # phenomenon called 'get-redirect pattern'- when u reload browser after submitting data\r\n    # post request will be duplicated.\r\n    else:\r\n        uForm = UserUpdateForm(instance=request.user)\r\n        pForm = ProfileUpdateForm(instance=request.user.profile)\r\n    context = {\r\n        'uForm': uForm,\r\n        'pForm': pForm,\r\n        'accountIDs': getAccountIDsFromModel(request.user.profile)\r\n    }\r\n    return render(request, \"transactions/profile.html\", context)\r\n\r\n\r\n@login_required\r\ndef report(request):\r\n    request.session.set_expiry(600)\r\n    return render(request, 'transactions/report.html')\r\n\r\n\r\n@login_required\r\ndef helpPage(request):\r\n    request.session.set_expiry(600)\r\n    if request.method == \"POST\":\r\n        form = ContactForm(request.POST)\r\n        if form.is_valid:\r\n            form.save()\r\n            messages.success(request, f'Message sent!')\r\n            send_mail(form.cleaned_data.get('subject'),\r\n                      form.cleaned_data.get('message') + \"\\n\\n Reply to: \" + form.cleaned_data.get('email'),\r\n                      'pwresetst45@gmail.com', ['pwresetst45@gmail.com'])\r\n            return redirect('home')\r\n    else:\r\n        form = ContactForm()\r\n    context = {\r\n        'form': form\r\n    }\r\n    return render(request, 'transactions/help.html', context)\r\n\r\n\r\n@login_required\r\ndef delete(request):\r\n    request.session.set_expiry(600)\r\n    if request.method == \"POST\":\r\n        idToRemove = request.POST.get('accountDropdown')\r\n        if idToRemove == \"All\":\r\n            request.user.profile.clearAccountList()\r\n        if idToRemove == \"AllCurr\":\r\n            request.user.profile.clearCurrAccounts()\r\n        if idToRemove == \"AllCC\":\r\n            request.user.profile.clearCCAccounts()\r\n        elif idToRemove in getAccountIDsFromModel(request.user.profile):\r\n            request.user.profile.deleteAccount(idToRemove)\r\n\r\n        accountList = getAccountIDsFromModel(request.user.profile)\r\n        if len(accountList) > 0:\r\n            request.user.profile.setAccountID(accountList[0])\r\n        else:\r\n            request.user.profile.setAccountID(\"None\")\r\n        return redirect('profile')\r\n","repo_name":"RaghibMrz/webapp-testing","sub_path":"transactions/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9894,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"1075738340","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat May 22 10:02:16 2021\n\n@author: andrea\n\"\"\"\n\nfrom ScopeFoundry import BaseMicroscopeApp\n\nclass HexSimAnalysisApp(BaseMicroscopeApp):\n\n    # this is the name of the microscope that ScopeFoundry uses \n    # when storing data\n    name = 'hex_sim_app'\n    \n    # You must define a setup function that adds all the \n    #capablities of the microscope and sets default settings\n    def setup(self):\n        \n        from HexSimAnalyser_measurement import HexSimAnalysis\n        self.add_measurement(HexSimAnalysis)\n        \n        \n        # show ui\n        self.ui.show()\n        self.ui.activateWindow()\n\n\nif __name__ == '__main__':\n    import sys\n    \n    app = HexSimAnalysisApp(sys.argv)\n    app.settings_load_ini(\".\\\\Settings\\\\HexSIM_Analysis.ini\")\n    \n    sys.exit(app.exec_())","repo_name":"micropolimi/HexSimAnalyser","sub_path":"HexSimAnalyser_App.py","file_name":"HexSimAnalyser_App.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"33063631165","text":"import sys\nsys.stdin = open('input.txt', 'r')\n\nT = int(input())\n\nfor tc in range(1, T+1):\n    B = int(input())\n    B_list = []\n\n\n    for i in range(1, B*B+1):\n        B_list.append(i)\n\n    list_B = []\n\n    for j in range(1, tc):\n\n\n        for k in range(1, tc+1):\n            tmp = []\n            for q in range(1 + tc*(k-1), (k * tc) + 1):\n                tmp.append(q)\n            list_B.append(tmp)\n    print(list_B)\n\n    cnt = tc * tc             # 채워줄 숫자\n    # deltas : 우 하 좌 상                 B/C 달팽이 순회 순서\n    dy = [0, 1, 0, -1]\n    dx = [1, 0, -1, 0]\n\n    x = 0  # 가로 좌표\n    y = 0  # 세로 좌표\n    d = 0  # 델타 인덱스\n    num = 1 # 증가하는 숫자 (배열을 채울 숫자)\n\n    while num <= cnt:      # 총 tc * tc 개의 숫자를 채워야함\n\n        # 벽 이거나, 채울 수 없으면 방향 전환\n\n        if 0 <= x < tc and 0 <= y < tc and not arr[x][y]:             # 시작점이 [0,0] 일때만 출발하라.\n            arr[x][y] = num\n            num += 1\n\n        else:      # 범위를 벗어남\n            x -= dx[d]\n            y -= dy[d]\n            d = (d + 1) % 4                     # 4 이내에서 수가 반복해야 하므로 % 4\n\n        x += dx[d]\n        y += dy[d]\n\n    for row  in arr:\n        print(row)\n\n","repo_name":"HYUNJUN-KANG/TIL","sub_path":"알고리즘/알고리즘/강의내용/달팽이/prob_1954.py","file_name":"prob_1954.py","file_ext":"py","file_size_in_byte":1302,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"10689066593","text":"from .db import db, environment, SCHEMA, add_prefix_for_prod\n\nclass Cart(db.Model):\n  __tablename__ = 'carts'\n\n  if environment == \"production\":\n    __table_args__ = {'schema': SCHEMA}\n\n  id = db.Column(db.Integer, primary_key=True)\n  user_id = db.Column(db.Integer, db.ForeignKey(add_prefix_for_prod(\"users.id\")), nullable=False)\n  item_id = db.Column(db.Integer, db.ForeignKey(add_prefix_for_prod(\"items.id\")), nullable=False)\n  count = db.Column(db.Integer, nullable=False)\n  purchased = db.Column(db.Boolean, nullable=False, default=False, index=True)\n  purchased_at = db.Column(db.DateTime, default=None, index=True)\n\n  user = db.relationship(\"User\", back_populates=\"cart\")\n  item = db.relationship(\"Item\", back_populates=\"cart\")\n\n  @property\n\n  def to_dict(self):\n    return {\n      'id': self.id,\n      'item_id': self.item_id,\n      'count': self.count,\n      'purchased': self.purchased,\n      'item': {\n        'name': self.item.name,\n        'image': self.item.image,\n        'price': self.item.price,\n        'discount': self.item.discount\n      },\n      'purchased_at': self.purchased_at\n    }\n","repo_name":"JonathanSCarter/Nile","sub_path":"app/models/cart.py","file_name":"cart.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"2010368153","text":"#!/usr/bin/python\nimport gi\ngi.require_version(\"Gst\",'1.0')\nfrom gi.repository import Gst, GObject,Gtk\n\nclient_id = 'enter_cliend_id'\nstream_url = 'https://soundcloud.com/girlsgeneration_smtown/girls-generation-everyday-love'\nmp3_stream_url = 'http://www.richardfarrar.com/audio/right.mp3'\ntrack_stream_url = 'https://api.soundcloud.com/tracks/134204364/stream?client_id=' + client_id\n\ndef play_stream(music_stream_uri):\n    music_stream_uri = \"/home/pi/Documents/google-hackfair/hackfair-speech/resources/test_dj.mp3\"\n    #creates a playbin (plays media form an uri) \n    player = Gst.ElementFactory.make(\"playbin\",\"player\")\n    print(\"player: \",player, \" track_url: \" + track_stream_url)    \n    #set the uri\n    #player.set_property('uri',\"file://\" +  music_stream_uri)\n    player.set_property('uri',track_stream_url)\n\n    #start playing\n    player.set_state(Gst.State.PLAYING)\n\n\nif __name__ == '__main__':\n    Gst.init(None)\n    play_stream(stream_url)\n\n#wait and let the music play\nraw_input('Press enter to stop playing...')\n","repo_name":"DjangoGirlsSeoul/hackfair-speech","sub_path":"gstream_test_1.py","file_name":"gstream_test_1.py","file_ext":"py","file_size_in_byte":1031,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"10706632286","text":"from PyQt4.QtCore import *\r\nfrom PyQt4.QtGui import *\r\nimport MarkovChain\r\n\r\nclass DynamicUser(QDialog):\r\n    def __init__(self,  parent = None):\r\n        super(DynamicUser,  self).__init__(parent)\r\n        \r\n        #Main App\r\n        mainLayout = QBoxLayout(QBoxLayout.TopToBottom)\r\n        mainLayout.addWidget(self.topSection())\r\n        mainLayout.addWidget(self.fileSection())\r\n        mainLayout.addWidget(self.dataSection())\r\n        self.setLayout(mainLayout)\r\n        self.setWindowTitle(\"DynamicUser\")\r\n        \r\n    #Top Section\r\n    def topSection(self):\r\n        #Label\r\n        nameLabel = QLabel(\"Name:\")\r\n        \r\n        #LineEdit\r\n        self.nameText = QLineEdit()\r\n        \r\n        #Buttons\r\n        self.saveAddButton = QPushButton()\r\n        self.saveAddButton.setText(\"Save / Add\")\r\n        self.cancelButton = QPushButton()\r\n        self.cancelButton.setText(\"Cancel\")\r\n        \r\n        #Connect objects with signals\r\n        self.connect(self.saveAddButton, SIGNAL(\"clicked()\"), self.addExperiment)\r\n        \r\n        #Group\r\n        mainGroup = QGroupBox()\r\n        #Layout\r\n        mainLayout = QHBoxLayout()\r\n        mainLayout.addWidget(nameLabel)\r\n        mainLayout.addWidget(self.nameText)\r\n        mainLayout.addWidget(self.saveAddButton)\r\n        mainLayout.addWidget(self.cancelButton)\r\n        mainGroup.setLayout(mainLayout)\r\n        return mainGroup\r\n        \r\n    #File Section\r\n    def fileSection(self):\r\n        #Labels\r\n        fileChooseLabel = QLabel(\"Choose file of probability of presence:\")\r\n        timeStepLabel = QLabel(\"Time step:\")\r\n        totalTimeLabel = QLabel(\"Total time of simulation:\")\r\n        #LineEdit\r\n        self.fileChooseText = QLineEdit()\r\n        #Buttons\r\n        self.fileBrowseButton = QPushButton()\r\n        self.fileBrowseButton.setText(\"Browse\")\r\n        #SpinBoxes\r\n        self.timeStepSpinBox = QSpinBox()\r\n        self.timeStepSpinBox.setRange(0, 10000)\r\n        self.totalTimeSpinBox = QSpinBox()\r\n        self.totalTimeSpinBox.setRange(0, 60)\r\n        #ComboBoxes\r\n        self.timeStepComboBox = QComboBox()\r\n        self.timeStepComboBox.addItems([\"seg\", \"min\", \"hour\", \"day\"]) \r\n        self.totalTimeComboBox = QComboBox()\r\n        self.totalTimeComboBox.addItems([\"seg\", \"min\", \"hour\", \"day\"])\r\n        \r\n        #Connect objects with signals\r\n        self.connect(self.fileBrowseButton, SIGNAL(\"clicked()\"), self.setProfileFile)\r\n        \r\n        #Main Group\r\n        fileGroup = QGroupBox()\r\n        fileGroup.setTitle(\"File\")\r\n        \r\n        #Layout\r\n        fileLayout = QBoxLayout(QBoxLayout.TopToBottom)\r\n        topFileLayout = QHBoxLayout()\r\n        bottomFileLayout = QHBoxLayout()\r\n        \r\n        #Top Layout\r\n        topFileLayout.addWidget(fileChooseLabel)\r\n        topFileLayout.addWidget(self.fileChooseText)\r\n        topFileLayout.addWidget(self.fileBrowseButton)\r\n        #Bottom Layout\r\n        bottomFileLayout.addWidget(timeStepLabel)\r\n        bottomFileLayout.addWidget(self.timeStepSpinBox)\r\n        bottomFileLayout.addWidget(self.timeStepComboBox)\r\n        \r\n        bottomFileLayout.addWidget(totalTimeLabel)\r\n        bottomFileLayout.addWidget(self.totalTimeSpinBox)\r\n        bottomFileLayout.addWidget(self.totalTimeComboBox)\r\n        #Adds layouts to main layout\r\n        fileLayout.addLayout(topFileLayout)\r\n        fileLayout.addLayout(bottomFileLayout)\r\n        fileGroup.setLayout(fileLayout)\r\n        \r\n        return fileGroup\r\n    \r\n    #Data Section\r\n    def dataSection(self):\r\n        #Labels\r\n        mobilityLabel = QLabel(\"Mobility:\")\r\n        numberLabel = QLabel(\"Number of long absences:\")\r\n        distributionLabel = QLabel(\"Distribution of duration of long:\")\r\n        #ComboBoxes\r\n        self.mobilityComboBox = QComboBox()\r\n        self.mobilityComboBox.addItems([\"Low\", \"Medium\", \"High\"])\r\n        #SpinBoxes\r\n        self.numberSpinBox = QSpinBox()\r\n        self.numberSpinBox.setRange(0, 1000)\r\n        #LineEdit\r\n        self.distributionLineEdit = QLineEdit()\r\n        #Button\r\n        self.distributionButton = QPushButton()\r\n        self.distributionButton.setText(\"Browse\")\r\n        \r\n        #Group\r\n        dataGroup = QGroupBox()\r\n        dataGroup.setTitle(\"Data\")\r\n        dataLayout = QBoxLayout(QBoxLayout.TopToBottom)\r\n        \r\n        topLayout = QBoxLayout(QBoxLayout.LeftToRight)\r\n        middleLayout = QBoxLayout(QBoxLayout.LeftToRight)\r\n        bottomLayout = QBoxLayout(QBoxLayout.LeftToRight)\r\n        #Top layout\r\n        topLayout.addWidget(mobilityLabel)\r\n        topLayout.addWidget(self.mobilityComboBox)\r\n        #Middle Layout\r\n        middleLayout.addWidget(numberLabel)\r\n        middleLayout.addWidget(self.numberSpinBox)\r\n        #Bottom Layout\r\n        bottomLayout.addWidget(distributionLabel)\r\n        bottomLayout.addWidget(self.distributionLineEdit)\r\n        bottomLayout.addWidget(self.distributionButton)\r\n        \r\n        #Add Layouts to data Layout\r\n        dataLayout.addLayout(topLayout)\r\n        dataLayout.addLayout(middleLayout)\r\n        dataLayout.addLayout(bottomLayout)\r\n        dataGroup.setLayout(dataLayout)\r\n        \r\n        return dataGroup\r\n    \r\n    #Catch Browse file signal\r\n    def setProfileFile(self):\r\n        fileName = QFileDialog.getOpenFileName(self, \"Select Profile File\")\r\n        if fileName == '':\r\n            self.alertDialog(\"Error\", \"You must select a file\")\r\n        else:\r\n            markov = MarkovChain.MarkovChain()\r\n            self.pares = markov.Create_List_Pairs(fileName)\r\n            self.fileChooseText.setText(fileName)\r\n    \r\n    #Add a new experiment\r\n    def addExperiment(self):\r\n        if self.nameText.text() == \"\":\r\n            self.alertDialog(\"Error\", \"You must enter a experiment name\");\r\n        else:\r\n            if self.fileChooseText.text() != \"\":\r\n                timeStep = self.timeStepSpinBox.value()\r\n                timeStepUnits = self.timeStepComboBox.currentText()\r\n                totalTime = self.totalTimeSpinBox.value()\r\n                totalTimeUnits = self.totalTimeComboBox.currentText()\r\n                fileName = self.fileChooseText.text();\r\n                experimentName = self.nameText.text();\r\n                resultLine = \"File: %s\\nExp Name: %s\\n TimeStep: %i %s \\n TotalTime: %i %s \\n %s\" \\\r\n                % (fileName, experimentName, timeStep, timeStepUnits, totalTime, totalTimeUnits, self.pares)\r\n                self.alertDialog(\"Result\", resultLine)\r\n            else:\r\n                self.setProfileFile()\r\n             \r\n    #Open a new dialog box, for testing purposes\r\n    def alertDialog(self, title, text):\r\n        dialog = QDialog()\r\n        dialog.setWindowTitle(title)\r\n        result = QLabel()\r\n        result.setText(text)\r\n        layout = QBoxLayout(QBoxLayout.LeftToRight)\r\n        layout.addWidget(result)\r\n        dialog.setLayout(layout)\r\n        dialog.show()\r\n        dialog.exec_()\r\n\r\n#Launch the App        \r\n#if __name__ == \"__main__\":\r\n#    app = QApplication(sys.argv)\r\n#    form = DynamicUser()\r\n#    form.show()\r\n#    app.exec_()\r\n        \r\n        ","repo_name":"sbarrat/proyect","sub_path":"DynamicUser.py","file_name":"DynamicUser.py","file_ext":"py","file_size_in_byte":7069,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"42777716266","text":"import unittest\nfrom datetime import datetime\nfrom datetime import timedelta\nfrom unittest.mock import MagicMock\nfrom unittest.mock import Mock\nfrom unittest.mock import patch\nfrom tap_google_ads.streams import ReportStream\nfrom tap_google_ads.streams import make_request\nimport singer\nimport pytz\n\nresource_schema = {\n    \"accessible_bidding_strategy\": {\n        \"fields\": {}\n    },\n\n}\n\nclass TestEndDate(unittest.TestCase):\n\n    def get_queries_from_sync(self, fake_make_request):\n        all_queries_requested = []\n        for request_sent in fake_make_request.call_args_list:\n            # The function signature is gas, query, customer_id, config\n            _, query, _, _ = request_sent.args\n            all_queries_requested.append(query)\n        return all_queries_requested\n\n    def run_sync(self, start_date, end_date, fake_make_request):\n\n        # Create the stream so we can call sync\n        my_report_stream = ReportStream(\n            fields=[],\n            google_ads_resource_names=['accessible_bidding_strategy'],\n            resource_schema=resource_schema,\n            primary_keys=['foo']\n        )\n\n        # Create a config that maybe has an end date\n        config = {\"start_date\": str(start_date),}\n\n        # If end_date exists, write it to the config\n        if end_date:\n            config[\"end_date\"] = str(end_date)\n\n        my_report_stream.sync(\n            Mock(),\n            {\"customerId\": \"123\",\n             \"loginCustomerId\": \"456\"},\n            {\"tap_stream_id\": \"hi\",\n             \"stream\": \"hi\",\n             \"metadata\": []},\n            config,\n            {},\n            None\n        )\n\n    @patch('singer.utils.now')\n    @patch('tap_google_ads.streams.make_request')\n    def test_no_end_date(self, fake_make_request, fake_datetime_now):\n        start_date = datetime(2022, 1, 1, 0, 0, 0)\n        end_date = datetime(2022, 3, 1, 0, 0, 0)\n\n        # Adding tzinfo helped the mock to work and avoids a\n        # TypeError(can't subtract offset-naive and offset-aware\n        # datetimes) here in the test\n        fake_datetime_now.return_value = end_date.replace(tzinfo=pytz.UTC)\n\n        # Don't pass in end_date to test the tap's fallback to today\n        self.run_sync(start_date, None, fake_make_request)\n        all_queries_requested = self.get_queries_from_sync(fake_make_request)\n\n        date_delta = end_date - start_date\n\n        # Add one to make it inclusive of the end date\n        days_between_start_and_end = date_delta.days + 1\n\n        # Compute the range of expected days, because end_date will always shift\n        expected_days = [\n            '2022-01-01', '2022-01-02', '2022-01-03', '2022-01-04',\n            '2022-01-05', '2022-01-06', '2022-01-07', '2022-01-08',\n            '2022-01-09', '2022-01-10', '2022-01-11', '2022-01-12',\n            '2022-01-13', '2022-01-14', '2022-01-15', '2022-01-16',\n            '2022-01-17', '2022-01-18', '2022-01-19', '2022-01-20',\n            '2022-01-21', '2022-01-22', '2022-01-23', '2022-01-24',\n            '2022-01-25', '2022-01-26', '2022-01-27', '2022-01-28',\n            '2022-01-29', '2022-01-30', '2022-01-31', '2022-02-01',\n            '2022-02-02', '2022-02-03', '2022-02-04', '2022-02-05',\n            '2022-02-06', '2022-02-07', '2022-02-08', '2022-02-09',\n            '2022-02-10', '2022-02-11', '2022-02-12', '2022-02-13',\n            '2022-02-14', '2022-02-15', '2022-02-16', '2022-02-17',\n            '2022-02-18', '2022-02-19', '2022-02-20', '2022-02-21',\n            '2022-02-22', '2022-02-23', '2022-02-24', '2022-02-25',\n            '2022-02-26', '2022-02-27', '2022-02-28', '2022-03-01',\n        ]\n\n        for day in expected_days:\n            self.assertTrue(\n                any(\n                    day in query for query in all_queries_requested\n                )\n            )\n\n    @patch('tap_google_ads.streams.make_request')\n    def test_end_date_one_day_after_start(self, fake_make_request):\n        start_date = datetime(2022, 3, 5, 0, 0, 0)\n        end_date = datetime(2022, 3, 6, 0, 0, 0)\n        self.run_sync(start_date, end_date, fake_make_request)\n        all_queries_requested = self.get_queries_from_sync(fake_make_request)\n\n        expected_days = [\n            \"2022-03-05\",\n            \"2022-03-06\",\n        ]\n\n        for day in expected_days:\n            self.assertTrue(\n                any(\n                    day in query for query in all_queries_requested\n                )\n            )\n\n    @patch('tap_google_ads.streams.make_request')\n    def test_end_date_one_day_before_start(self, fake_make_request):\n        start_date = datetime(2022, 3, 6, 0, 0, 0)\n        end_date = datetime(2022, 3, 5, 0, 0, 0)\n        self.run_sync(start_date, end_date, fake_make_request)\n        all_queries_requested = self.get_queries_from_sync(fake_make_request)\n\n        # verify no requests are made with an invalid start/end date configuration\n        self.assertEqual(len(all_queries_requested), 0)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"singer-io/tap-google-ads","sub_path":"tests/unittests/test_sync.py","file_name":"test_sync.py","file_ext":"py","file_size_in_byte":4995,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"76"}
+{"seq_id":"30010806658","text":"from collections import deque\n\ndef errorCheck(num):\n   if not isinstance(num, int):\n      print(\"not an int\")\n      return False\n   return True\n   \ndef breakUp(num):\n   if not errorCheck(num): return None\n   result = []\n   for c in str(num):\n      result.append(int(c))\n   return result\n   \ndef breakUpMath(num):\n   if not errorCheck(num): return None\n   result = deque()\n   addNegative = False\n   if (num < 0):\n      addNegative = True\n      num = abs(num)\n   if (num == 0):\n      return [0]\n   pwr = 0 \n   while (10**pwr <= num):\n      pwr += 1\n      # num 1024, pwr 2 ->  (24 - 4)//10 = 2\n      val = (num%(10**pwr) - (num % 10**(pwr-1))) // (10 ** (pwr-1))\n      result.appendleft(val)\n   # Need to track if negative or positive\n   if addNegative: \n      result.appendleft(\"-\")\n   else:\n      result.appendleft(\"+\")\n      \n   result = list(result)\n   print(\"output {}\".format(result))\n   return result\n\ndef addNum(l, num):\n   l = list(l) #copy value of l in to l so its no longer referencing argument passed in\n   if ((num < 0 and l[0] == \"+\") or (num > 0 and l[0] == \"-\")):\n      return subNum(l, num)\n   bothNegative = False\n   if (num < 0 and l[0] < 0):\n      bothNegative = True\n   digits = breakUpMath(num);\n   digits = digits[1:]\n   l = l[1:]\n   if len(digits) > len(l):\n      top = digits\n      bottom = l\n   else:\n      top = l\n      bottom = digits\n   bottom.reverse()\n   top.reverse()\n   carryOver = 0\n   for i in range(len(top)):\n      toAdd = 0\n      if(i < len(bottom)):\n         toAdd = bottom[i]\n      newNum = toAdd + carryOver + top[i]\n      if newNum > 9:\n         carryOver = 1\n         newNum = newNum % 10\n      else:\n         carryOver = 0\n      top[i] = newNum\n   if (carryOver == 1):\n      top = top + [1]\n   top.reverse()\n   if(bothNegative):\n      top = [\"-\"] + top\n   print(\"add: {}\".format(top))\n   return top\n\ndef subNum(l, num):\n      print(\"balls\")\n\n# Take a number  convert it to an array and then add a taget number\n# 123 + 11 => [1,2,3] + 11 => [1,3,4]\n#How do we handle negatives?\nif __name__ == \"__main__\":\n   print(\"Num Breakup\")\n   breakUp(1234)\n   result = breakUpMath(1234)\n   breakUpMath(0)\n   breakUpMath(1)\n   print(912+1234)\n   addNum(result, 912)\n   \n   print(99999+1234)\n   print(result)\n   addNum(result, -99999)\n   \n   ","repo_name":"halfpeaw/CodeProblems","sub_path":"round2/NumBreakUp.py","file_name":"NumBreakUp.py","file_ext":"py","file_size_in_byte":2271,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"10109791301","text":"\"\"\"\n    Pre-train embeddings using gensim w2v implementation (CBOW by default)\n\"\"\"\nimport gensim.models.word2vec as w2v\nimport csv\n\nfrom constants import *\n\nclass ProcessedIter(object):\n\n    def __init__(self, Y, filename):\n        self.filename = filename\n\n    def __iter__(self):\n        with open(self.filename) as f:\n            r = csv.reader(f)\n            next(r)\n            for row in r:\n                yield (row[3].split())\n\ndef word_embeddings(Y, notes_file, embedding_size, min_count, n_iter):\n    modelname = \"processed_%s.w2v\" % (Y)\n    sentences = ProcessedIter(Y, notes_file)\n\n    model = w2v.Word2Vec(size=embedding_size, min_count=min_count, workers=4, iter=n_iter)\n    print(\"building word2vec vocab on %s...\" % (notes_file))\n    \n    model.build_vocab(sentences)\n    print(\"training...\")\n    model.train(sentences, total_examples=model.corpus_count, epochs=model.iter)\n    out_file = '/'.join(notes_file.split('/')[:-1] + [modelname])\n    print(\"writing embeddings to %s\" % (out_file))\n    model.save(out_file)\n    return out_file\n\n","repo_name":"jamesmullenbach/caml-mimic","sub_path":"dataproc/word_embeddings.py","file_name":"word_embeddings.py","file_ext":"py","file_size_in_byte":1054,"program_lang":"python","lang":"en","doc_type":"code","stars":248,"dataset":"github-code","pt":"76"}
+{"seq_id":"73232482166","text":"#!/usr/bin/python3\n\"\"\" this module contains the append_write function \"\"\"\n\n\ndef append_write(filename=\"\", text=\"\"):\n    \"\"\" appends a string at the end of a text file and\n    return the number of lines \"\"\"\n    num_lines = 0\n    with open(filename, 'a') as fl:\n        num_lines = fl.write(text)\n    return num_lines\n","repo_name":"I7RANK/holbertonschool-higher_level_programming","sub_path":"0x0B-python-input_output/4-append_write.py","file_name":"4-append_write.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"74971082804","text":"from itertools import cycle\n\n############################\n# Advent of Code 2022 Day 17\n############################\n\ndef get_rocks():\n  # in a function rather than a constant so that cycle resets after each call\n  return cycle([\n    [{'x': 0, 'y': 0}, {'x': 1, 'y': 0}, {'x': 2, 'y': 0}, {'x': 3, 'y': 0}],\n    [{'x': 1, 'y': 0}, {'x': 0, 'y': 1}, {'x': 1, 'y': 1}, {'x': 2, 'y': 1}, {'x': 1, 'y': 2}],\n    [{'x': 0, 'y': 0}, {'x': 1, 'y': 0}, {'x': 2, 'y': 0}, {'x': 2, 'y': 1}, {'x': 2, 'y': 2}],\n    [{'x': 0, 'y': 0}, {'x': 0, 'y': 1}, {'x': 0, 'y': 2}, {'x': 0, 'y': 3}],\n    [{'x': 0, 'y': 0}, {'x': 1, 'y': 0}, {'x': 0, 'y': 1}, {'x': 1, 'y': 1}]\n  ])\n\n\ndef can_move_horizontal(rock: list[dict], dx: int, ground: set[tuple]):\n  for d in rock:\n    x, y = d['x'], d['y']\n    if not(0 <= x + dx <= 6):\n      return False\n    if (x + dx, y) in ground:\n      return False\n  return True\n\n\ndef can_drop(rock: list[dict], ground: set[tuple]):\n  for d in rock:\n    x, y = d['x'], d['y']\n    if (x, y - 1) in ground:\n      return False\n  return True\n\n\ndef drop(rock: list[dict], ground: set[tuple], jet_queue: cycle):\n  while True:\n    j = jet_queue.__next__()\n    dx = {\"<\": -1, \">\": 1}[j]\n    if can_move_horizontal(rock, dx, ground):\n      for d in rock:\n        d['x'] += dx\n    if can_drop(rock, ground):\n      for d in rock:\n        d['y'] -= 1\n    else:\n      break\n\n\ndef update_cycles(history: dict[int, int], i: int, cycles: dict[int, tuple[int, int]]):\n  for c in range(i):\n    cycle_len = c + 1\n    if i % cycle_len == 0:\n      diff = history[i] - history[i - cycle_len]\n      if cycle_len not in cycles:\n        cycles[cycle_len] = (diff, 1)\n      else:\n        (d, n) = cycles[cycle_len]\n        if d == diff:\n          cycles[cycle_len] = (d, n + 1)\n          # need to see same difference for 10 cycles\n          if n >= 9:\n            return (cycle_len, d)\n        else:\n          cycles[cycle_len] = (diff, 1)\n  return (None, None)\n\n\ndef drop_n_rocks(jet_queue, n):\n  rocks = get_rocks()\n  height = 0\n  ground = set([(i, 0) for i in range(7)])\n  history, cycles = {}, {}\n  cycle_len, d, return_idx = None, None, None\n\n  for i in range(n):\n    if i == return_idx:\n      return int(height + ((n - i) / cycle_len) * d)\n\n    rock = [{'x': d['x'] + 2, 'y': d['y'] + height + 4} for d in rocks.__next__()]\n    drop(rock, ground, jet_queue)\n    ground.update((d['x'], d['y']) for d in rock)\n    height = max(height, max(d['y'] for d in rock))\n\n    if return_idx is None:\n      history[i] = height\n      (cycle_len, d) = update_cycles(history, i, cycles)\n      if cycle_len is not None:\n        return_idx = i + (n % cycle_len)\n\n  return height\n\n\ndef part_1(input):\n  jet_queue = cycle(input[0])\n  return drop_n_rocks(jet_queue, 2022)\n\ndef part_2(input):\n  jet_queue = cycle(input[0])\n  return drop_n_rocks(jet_queue, 1000000000000)\n\n\nday = 17\n\n\nwith open(f'day{day}/day{day}_ex.txt') as ex_filename:\n  example_input = [r.strip() for r in ex_filename.readlines()]\n  print(\"---Example---\")\n  print(f'Part 1: {part_1(example_input)}')\n  print(f'Part 2: {part_2(example_input)}')\n\nwith open(f'day{day}/day{day}.txt') as filename:\n  puzzle_input = [r.strip() for r in filename.readlines()]\n  print(\"---Puzzle---\")\n  print(f'Part 1: {part_1(puzzle_input)}')\n  print(f'Part 2: {part_2(puzzle_input)}')\n","repo_name":"anniebryan/advent-of-code","sub_path":"2022/day17/day17.py","file_name":"day17.py","file_ext":"py","file_size_in_byte":3302,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"72768462966","text":"# ---\n# jupyter:\n#   jupytext:\n#     formats: ipynb,py:percent\n#     text_representation:\n#       extension: .py\n#       format_name: percent\n#       format_version: '1.3'\n#       jupytext_version: 1.14.5\n#   kernelspec:\n#     display_name: Python 3 (ipykernel)\n#     language: python\n#     name: python3\n# ---\n# ruff: noqa: E402\n# ruff: noqa: E501\n\n# %% [markdown]\n# # Fitting a polynomial with Gaussian priors\n#\n# We fit a simple polynomial with Gaussian priors, which is an example of a Gauss-linear\n# problem for which the results obtained using Subspace Iterative Ensemble Smoother\n# (SIES) tend to those obtained using Ensemble Smoother (ES).\n# This notebook illustrated this property.\n# %%\nimport itertools\n\nimport numpy as np\nimport pandas as pd\n\nnp.set_printoptions(suppress=True)\nrng = np.random.default_rng(12345)\n\nimport matplotlib.pyplot as plt\n\nCOLORS = list(plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"])\n\nplt.rcParams[\"figure.figsize\"] = (6, 6)\nplt.rcParams.update({\"font.size\": 10})\nfrom ipywidgets import interact  # noqa  # isort:skip\nimport ipywidgets as widgets  # noqa  # isort:skip\nfrom p_tqdm import p_map\n\nimport iterative_ensemble_smoother as ies\n\n# %% [markdown]\n# ## Define synthetic truth and use it to create noisy observations\n\n# %%\nensemble_size = 200\n\n\ndef poly(a, b, c, x):\n    return a * x**2 + b * x + c\n\n\n# True patameter values\na_t = 0.5\nb_t = 1.0\nc_t = 3.0\n\nnoise_scale = 0.1\nx_observations = [0, 2, 4, 6, 8]\nobservations = [\n    (\n        rng.normal(loc=1, scale=noise_scale) * poly(a_t, b_t, c_t, x),\n        noise_scale * poly(a_t, b_t, c_t, x),\n        x,\n    )\n    for x in x_observations\n]\n\nd = pd.DataFrame(observations, columns=[\"value\", \"sd\", \"x\"])\nd = d.set_index(\"x\")\nnum_obs = d.shape[0]\n\nfig, ax = plt.subplots(figsize=(7, 4))\nx_plot = np.linspace(0, 10, 2**8)\nax.set_title(\"Truth and noisy observations\")\nax.set_xlabel(\"Time step\")\nax.set_ylabel(\"Response\")\nax.plot(x_plot, poly(a_t, b_t, c_t, x_plot))\nax.plot(d.index.get_level_values(\"x\"), d.value.values, \"o\")\nax.grid()\nfig.tight_layout()\nplt.show()\n\n# %% [markdown]\n# ## Assume diagonal observation error covariance matrix and define perturbed observations\n\n# %%\nR = np.diag(d.sd.values**2)\n\nE = rng.multivariate_normal(mean=np.zeros(len(R)), cov=R, size=ensemble_size).T\nassert E.shape == (num_obs, ensemble_size)\n\nD = d.value.values.reshape(-1, 1) + E\n\n# %% [markdown]\n# ## Define Gaussian priors\n\n# %%\ncoeff_a = rng.standard_normal(size=ensemble_size)\ncoeff_b = rng.standard_normal(size=ensemble_size)\ncoeff_c = rng.standard_normal(size=ensemble_size)\n\nX = np.vstack([coeff_a, coeff_b, coeff_c])\n\n# %% [markdown]\n# ## Run forward model in parallel\n\n# %%\nfwd_runs = p_map(\n    poly,\n    coeff_a,\n    coeff_b,\n    coeff_c,\n    [np.arange(max(x_observations) + 1)] * ensemble_size,\n    desc=\"Running forward model.\",\n)\n\n# %% [markdown]\n# ## Pick responses where we have observations\n\n# %%\nY = np.array(\n    [fwd_run[d.index.get_level_values(\"x\").to_list()] for fwd_run in fwd_runs]\n).T\n\nassert Y.shape == (\n    num_obs,\n    ensemble_size,\n), \"Measured responses must be a matrix with dimensions (number of observations x number of realisations)\"\n\n# %% [markdown]\n# ## Condition on observations to calculate posterior using both `ES` and `SIES`\n\n# %%\nfrom iterative_ensemble_smoother.utils import steplength_exponential\n\nX_ES_ert = X.copy()\nY_ES_ert = Y.copy()\nsmoother_es = ies.SIES(\n    parameters=X_ES_ert,\n    covariance=d.sd.values**2,\n    observations=d.value.values,\n    seed=42,\n)\nX_ES_ert = smoother_es.sies_iteration(Y_ES_ert, step_length=1.0)\n\n\nX_IES_ert = X.copy()\nY_IES_ert = Y.copy()\nsmoother_ies = ies.SIES(\n    parameters=X_IES_ert,\n    covariance=d.sd.values**2,\n    observations=d.value.values,\n    seed=42,\n)\nn_ies_iter = 7\nfor i in range(n_ies_iter):\n\n    step_length = steplength_exponential(i + 1)\n    X_IES_ert = smoother_ies.sies_iteration(Y_IES_ert, step_length=step_length)\n\n    _coeff_a, _coeff_b, _coeff_c = X_IES_ert\n\n    _fwd_runs = p_map(\n        poly,\n        _coeff_a,\n        _coeff_b,\n        _coeff_c,\n        [np.arange(max(x_observations) + 1)] * ensemble_size,\n        desc=f\"SIES ert iteration {i}\",\n    )\n\n    Y_IES_ert = np.array(\n        [fwd_run[d.index.get_level_values(\"x\").to_list()] for fwd_run in _fwd_runs]\n    ).T\n\n\n# %% [markdown]\n# ## Plots to compare results\n\n\n# %%\ndef plot_posterior(ax, posterior, method):\n    for i, param in enumerate(\"abc\"):\n        ax[i].set_title(param)\n        ax[i].hist(posterior[i, :], label=f\"{method} posterior\", alpha=0.5, bins=\"fd\")\n        ax[i].legend()\n\n    fig.tight_layout()\n    return ax\n\n\nfig, ax = plt.subplots(nrows=3, figsize=(7, 8))\n\nfor i in range(3):\n    ax[i].hist(X[i, :], label=\"prior\", bins=\"fd\")\n\nax[0].axvline(a_t, color=\"k\", linestyle=\"--\", label=\"truth\")\nax[1].axvline(b_t, color=\"k\", linestyle=\"--\", label=\"truth\")\nax[2].axvline(c_t, color=\"k\", linestyle=\"--\", label=\"truth\")\n\nplot_posterior(ax, X_ES_ert, method=\"ES ert\")\n_ = plot_posterior(ax, X_IES_ert, method=f\"SIES ert ({n_ies_iter})\")\n\n# %%\nfig, axes = plt.subplots(1, 3, figsize=(8, 2.75))\naxes = axes.ravel()\nlabels = \"abc\"\ntrue_parameters = [a_t, b_t, c_t]\n\nfig.suptitle(f\"SIES ert ({n_ies_iter}) Posterior distribution\")\nfor k, (i, j) in enumerate(itertools.combinations([0, 1, 2], 2)):\n    axes[k].scatter(X[i, :], X[j, :], s=15, alpha=0.6)\n    axes[k].scatter(X_ES_ert[i, :], X_ES_ert[j, :], s=15, alpha=0.2)\n    axes[k].scatter(X_IES_ert[i, :], X_IES_ert[j, :], s=15, alpha=0.2)\n    axes[k].scatter(\n        [true_parameters[i]],\n        [true_parameters[j]],\n        color=\"black\",\n        s=100,\n        label=\"True value\",\n    )\n    axes[k].set_xlabel(labels[i])\n    axes[k].set_ylabel(labels[j])\n\n\naxes[k].legend()\nfig.tight_layout()\nplt.show()\n\n# %%\nfig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(8, 2.5), sharex=True, sharey=True)\nx_plot = np.linspace(0, 10, 2**8)\n\n# Plot the prior\nax1.set_title(\"Prior\")\nax1.plot(x_plot, poly(a_t, b_t, c_t, x_plot), zorder=10, lw=4, color=\"black\")\nfor parameter_prior in X.T:\n    ax1.plot(\n        x_plot, poly(*parameter_prior, x_plot), color=COLORS[0], alpha=0.1, zorder=5\n    )\n\n# Plot the posterior\nax2.set_title(\"ES ert posterior\")\nax2.plot(x_plot, poly(a_t, b_t, c_t, x_plot), zorder=10, lw=4, color=\"black\")\nfor parameter_posterior in X_ES_ert.T:\n    ax2.plot(\n        x_plot, poly(*parameter_posterior, x_plot), alpha=0.1, zorder=5, color=COLORS[1]\n    )\n\n# Plot the posterior\nax3.set_title(f\"SIES ert ({n_ies_iter}) posterior\")\nax3.plot(x_plot, poly(a_t, b_t, c_t, x_plot), zorder=10, lw=4, color=\"black\")\nfor parameter_posterior in X_IES_ert.T:\n    ax3.plot(\n        x_plot, poly(*parameter_posterior, x_plot), alpha=0.1, zorder=5, color=COLORS[2]\n    )\n\n# Common axes setup\nfor ax in [ax1, ax2, ax3]:\n    ax.set_ylim([0, 70])\n    ax.set_xlabel(\"Time step\")\n    ax.set_ylabel(\"Response\")\n    ax.grid(zorder=0)\n\nfig.tight_layout()\nplt.show()\n","repo_name":"equinor/iterative_ensemble_smoother","sub_path":"docs/source/Polynomial.py","file_name":"Polynomial.py","file_ext":"py","file_size_in_byte":6879,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"76"}
+{"seq_id":"31080708570","text":"# import torch\n# import uuid\n# from ultralytics import yolo\n# def runModel(img):\n#     model = torch.hub.load('ultralytics/yolov5', 'yolov5m')\n#     # img = 'https://i.ytimg.com/vi/q71MCWAEfL8/maxresdefault.jpg'  # or file, Path, PIL, OpenCV, numpy, list\n#     results = model(img)\n#     results.print()\n#     results.save(save_dir='results')\n\n# runModel('https://i.ytimg.com/vi/q71MCWAEfL8/maxresdefault.jpg')\n\nfrom ultralytics import YOLO\n\n# Load a model\nmodel = YOLO(\"yolov8n.yaml\")  # build a new model from scratch\nmodel = YOLO(\"yolov8n.pt\")  # load a pretrained model (recommended for training)\n\n# Use the model\nmodel.train(data=\"coco128.yaml\", epochs=3)  # train the model\nmetrics = model.val()  # evaluate model performance on the validation set\nresults = model(\"https://ultralytics.com/images/bus.jpg%22\")  # predict on an image\nsuccess = model.export(format=\"onnx\")  # export the model to ONNX format","repo_name":"MoulyAkash/ARIA","sub_path":"ML/OD/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"37514612842","text":"import math\nimport descr_fit as df\nimport numpy as np\nimport scipy.optimize as opt\nimport os, sys, itertools\nimport random\nfrom time import sleep\nfrom scipy.stats import poisson, sem\nimport pdb\n\n# Don't duplicate work that's already done in the \"main\" helper_fcns\n\nmaindir = os.path.abspath('../../'); # it's two directories up\nsys.path.insert(0, maindir);\n##############\n### Imports from \"main\" helper_fcns\n##############\n# -- basic things\nfrom helper_fcns import nan_rm, np_smart_load, bw_lin_to_log, bw_log_to_lin, resample_array, random_in_range\nfrom helper_fcns import polar_vec_mean, phase_fit_name, phase_advance, project_resp, get_phAdv_model\nfrom helper_fcns import descrLoss_name, descrMod_name, descrFit_name\nfrom helper_fcns import flatten_list as flatten\n# -- rvc\nfrom helper_fcns import rvc_mod_suff, rvc_fit_name, get_rvc_model\nfrom helper_fcns import naka_rushton, get_rvcResp, rvc_fit\n# -- sf \nfrom helper_fcns import dog_charFreq, dog_get_param, dog_init_params, deriv_gauss, compute_SF_BW, fix_params\nfrom helper_fcns import DiffOfGauss, DoGsach, dog_prefSfMod, dog_charFreqMod, get_xc_from_slope\nfrom helper_fcns import DoG_loss, get_descrResp\nfrom helper_fcns import flexible_Gauss_np as flexible_Gauss\nfrom helper_fcns import descr_prefSf as dog_prefSf # to keep the function call here unchanged from previous version\n\n##############\n### Code written *here*, i.e. just for Sach stuff\n##############\n\n# load_modParams - [UNUSED] load the 4 parameters from the Tony fits...\n\n# unpack_f1arr - to keep back-compatability, unpack f1arr[con][sf] dict-of-dicts into array\n\n# var_explained - compute the variance explained given data/model fit\n\n# dog_fit - used to fit the Diff of Gauss responses -- either separately for each con, or jointly for all cons within a given dispersion\n\n# blankResp - return mean/std of blank responses (i.e. baseline firing rate) for Sach's experiment\n# tabulateResponses - Organizes measured and model responses for Sach's experiment\n\n# writeDataTxt - write [sf mean sem] for a given cell/contrast\n# writeCellTxt - call writeDataTxt for all contrasts for a cell\n\ndef load_modParams(which_cell, contrast, loadPath='/home/pl1465/SF_diversity/LGN/sach/structures/tonyFits/'):\n   \n  nParams = 4;\n\n  loadName = 'cell%d_con%d.txt+.fit' % (which_cell, contrast);\n  fits = open(str(loadPath + loadName), 'r');\n  allLines = fits.readlines();\n  firstLine = allLines[0].split();\n  fL = [float(x) for x in firstLine]\n\n  return fL[0:nParams]; \n\n#######\n\ndef unpack_f1arr(f1arr):\n  assert len(f1arr.keys())>0;\n  assert len(f1arr[0].keys())>0;\n  return np.array([[f1arr[x][y] for y in f1arr[x].keys()] for x in f1arr.keys()]);\n\ndef var_expl_direct(obs_mean, pred_mean):\n  # Just compute variance explained given the data and model responses (assumed same SF for each)\n  resp_dist = lambda x, y: np.sum(np.square(x-y))/np.maximum(len(x), len(y))\n  var_expl = lambda m, r, rr: 100 * (1 - resp_dist(m, r)/resp_dist(r, rr));\n\n  obs_grand_mean = np.mean(obs_mean) * np.ones_like(obs_mean); # make sure it's the same shape as obs_mean\n    \n  return var_expl(pred_mean, obs_mean, obs_grand_mean);\n\ndef var_explained(data, modParams, whichInd=None, DoGmodel=1, rvcModel=None, whichSfs = None, ref_params=None, ref_rc_val=None, dataAreResps=False):\n  ''' given a set of responses and model parameters, compute the variance explained by the model (DoGsach)\n      --- whichInd is either the contrast index (if doing SF tuning)\n                              or SF index (if doing RVCs)\n  '''\n  resp_dist = lambda x, y: np.sum(np.square(x-y))/np.maximum(len(x), len(y))\n  var_expl = lambda m, r, rr: 100 * (1 - resp_dist(m, r)/resp_dist(r, rr));\n\n  if dataAreResps:\n    obs_mean = data; # we've directly passed in the means of interest\n  else:\n    respsSummary, stims, allResps = tabulateResponses(data); # Need to fit on f1 \n    f1 = respsSummary[1];\n    if rvcModel is None: # SF\n      all_sfs = stims[1];\n      obs_mean = f1['mean'][whichInd, :];\n    else:\n      all_cons = stims[0];\n      obs_mean = f1['mean'][:, whichInd];\n\n  if whichSfs is not None:\n    all_sfs = whichSfs; # maybe we've passed in the Sfs to use...\n    \n  if rvcModel is None: # then we're doing vExp for SF tuning\n    pred_mean = get_descrResp(modParams, all_sfs, DoGmodel, ref_rc_val=ref_rc_val);\n  else: # then we've getting RVC responses!\n    pred_mean = get_rvcResp(modParams, cons, rvcMod)\n\n  obs_grand_mean = np.mean(obs_mean) * np.ones_like(obs_mean); # make sure it's the same shape as obs_mean\n    \n  return var_expl(pred_mean, obs_mean, obs_grand_mean);\n\n## - for fitting DoG models\n\ndef dog_fit(resps, all_cons, all_sfs, DoGmodel, loss_type, n_repeats, joint=0, ref_varExpl=None, veThresh=-np.nan, fracSig=1, ftol=2.220446049250313e-09, jointMinCons=3):\n  ''' Helper function for fitting descriptive funtions to SF responses\n      if joint=True, (and DoGmodel is 1 or 2, i.e. not flexGauss), then we fit assuming\n      a fixed ratio for the center-surround gains and [freq/radius]\n      - i.e. of the 4 DoG parameters, 2 are fit separately for each contrast, and 2 are fit \n        jointly across all contrasts!\n      - note that ref_varExpl (optional) will be of the same form that the output for varExpl will be\n      - note that jointMinCons is the minimum # of contrasts that must be included for a joint fit to be run (e.g. 2)\n\n      inputs: self-explanatory, except for resps, which should be \"f1\" from tabulateResponses \n      outputs: bestNLL, currParams, varExpl, prefSf, charFreq, [overallNLL, paramList; if joint=True]\n  '''\n  nCons = len(all_cons);\n  if DoGmodel == 0:\n    nParam = 5;\n  else:\n    nParam = 4;\n\n  # unpack responses\n  resps_mean = resps['mean'];\n  resps_sem = resps['sem'];\n\n  # next, let's compute some measures about the responses\n  max_resp = np.nanmax(resps_mean.flatten());\n  min_resp = np.nanmin(resps_mean.flatten());\n  ############\n  ### WARNING - we're subtracting min_resp-1 from all responses\n  ############  \n  #resps_mean = np.subtract(resps_mean, min_resp-1); # i.e. make the minimum response 1 spk/s...\n\n  # and set up initial arrays\n  bestNLL = np.ones((nCons, ), dtype=np.float32) * np.nan;\n  currParams = np.ones((nCons, nParam), dtype=np.float32) * np.nan;\n  varExpl = np.ones((nCons, ), dtype=np.float32) * np.nan;\n  prefSf = np.ones((nCons, ), dtype=np.float32) * np.nan;\n  charFreq = np.ones((nCons, ), dtype=np.float32) * np.nan;\n  if joint>0:\n    overallNLL = np.nan;\n    params = np.nan;\n    success = False;\n  else:\n    success = np.zeros((nCons, ), dtype=np.bool_);\n\n  ### set bounds\n  if DoGmodel == 0:\n    min_bw = 1/4; max_bw = 10; # ranges in octave bandwidth\n    bound_baseline = (0, max_resp);\n    bound_range = (0, 1.5*max_resp);\n    bound_mu = (0.01, 10);\n    bound_sig = (np.maximum(0.1, min_bw/(2*np.sqrt(2*np.log(2)))), max_bw/(2*np.sqrt(2*np.log(2)))); # Gaussian at half-height\n    if fracSig:\n      bound_sigFrac = (0.2, 2);\n      allBounds = (bound_baseline, bound_range, bound_mu, bound_sig, bound_sigFrac);\n    else:\n      allBounds = (bound_baseline, bound_range, bound_mu, bound_sig, bound_sig);\n  elif DoGmodel == 1: # SACH\n    bound_gainCent = (1, 3*max_resp);\n    bound_radiusCent= (1e-2, 1.5);\n    bound_gainSurr = (1e-2, 1); # multiplier on gainCent, thus the center must be weaker than the surround\n    bound_radiusSurr = (1, 10); # (1,10) # multiplier on radiusCent, thus the surr. radius must be larger than the center\n    if joint>0:\n      if joint == 1: # original joint (fixed gain and radius ratios across all contrasts)\n        bound_gainRatio = (1e-3, 1); # the surround gain will always be less than the center gain\n        bound_radiusRatio= (1, 10); # the surround radius will always be greater than the ctr r\n        # we'll add to allBounds later, reflecting joint gain/radius ratios common across all cons\n        allBounds = (bound_gainRatio, bound_radiusRatio);\n      elif joint == 2: # fixed surround radius for all contrasts\n        allBounds = (bound_radiusSurr, );\n      elif joint == 3: # fixed center AND surround radius for all contrasts\n        allBounds = (bound_radiusCent, bound_radiusSurr);\n      # In advance of the thesis/publishing the LGN data, we will replicate some of Sach's key results\n      # In particular, his thesis covers 4 joint models:\n      # -- volume ratio: center and surround radii are fixed, but gains can vary (already covered in joint == 3)\n      # -- center radius: fixed center radius across contrast (joint=4) AND fixed volume (i.e. make surround gain constant across contrast)\n      # -- surround radius: fixed surround radius across contrast (joint=5) AND fixed volume (i.e. make surround gain constant across contrast) // fixed not in proportion to center, but in absolute value\n      # -- center-surround: center and surround radii can vary, but ratio of gains is fixed (joint == 6)\n      # ---- NOTE: joints 3-5 have 2*nCons + 2 parms; joint==6 has 3*nCons + 1\n      elif joint == 4: # fixed center radius\n         allBounds = (bound_radiusCent, bound_gainSurr, ); # center radius AND bound_gainSurr are fixed across condition\n      elif joint == 5: # fixed surround radius (again, in absolute terms here, not relative, as is usually specified)\n         allBounds = (bound_gainSurr, bound_radiusSurr, ); # surround radius AND bound_gainSurr are fixed across condition\n      elif joint == 6: # fixed center:surround gain ratio\n         allBounds = (bound_gainSurr, ); # we can fix the ratio by allowing the center gain to vary and keeping the surround in fixed proportion\n      elif joint == 7 or joint == 8: # center radius determined by slope! we'll also fixed surround radius; if joint == 8, fixed surround gain instead of radius\n         bound_xc_slope = (-1, 1); # 220505 fits inbounded; 220519 fits bounded (-1,1)\n         bound_xc_inter = (None, None); #bound_radiusCent; # intercept - shouldn't start outside the bounds we choose for radiusCent\n         allBounds = (bound_xc_inter, bound_xc_slope, bound_radiusSurr, ) if joint == 7 else (bound_xc_slope, bound_xc_inter, bound_gainSurr, )\n    else:\n      allBounds = (bound_gainCent, bound_radiusCent, bound_gainSurr, bound_radiusSurr);\n  elif DoGmodel == 2:\n    bound_gainCent = (1e-3, None);\n    bound_freqCent = (1e-3, 2e1);\n    bound_gainFracSurr = (1e-3, 2); # surround gain always less than center gain NOTE: SHOULD BE (1e-3, 1)\n    bound_freqFracSurr = (5e-2, 1); # surround freq always less than ctr freq NOTE: SHOULD BE (1e-1, 1)\n    if joint>0:\n      if joint == 1: # original joint (fixed gain and radius ratios across all contrasts)\n        bound_gainRatio = (1e-3, 3);\n        bound_freqRatio = (1e-1, 1); \n        # we'll add to allBounds later, reflecting joint gain/radius ratios common across all cons\n        allBounds = (bound_gainRatio, bound_freqRatio);\n      elif joint == 2: # fixed surround radius for all contrasts\n        allBounds = (bound_freqFracSurr,);\n      elif joint == 3: # fixed center AND surround radius for all contrasts\n        allBounds = (bound_freqCent, bound_freqFracSurr);\n    elif joint==0:\n      bound_gainFracSurr = (1e-3, 1);\n      bound_freqFracSurr = (1e-1, 1);\n      allBounds = (bound_gainCent, bound_freqCent, bound_gainFracSurr, bound_freqFracSurr);\n\n  ### organize responses -- and fit, if joint=0\n  allResps = []; allRespsSem = []; allSfs = []; valCons = []; start_incl = 0; incl_inds = [];\n  base_rate = np.min(resps_mean.flatten());\n  for con in range(nCons):\n    if all_cons[con] == 0: # skip 0 contrast...\n        continue;\n    else:\n      valCons.append(all_cons[con]);\n    valSfInds_curr = np.where(~np.isnan(resps_mean[con,:]))[0];\n    resps_curr = resps_mean[con, valSfInds_curr];\n    sem_curr   = resps_sem[con, valSfInds_curr];\n\n    ### prepare for the joint fitting, if that's what we've specified!\n    if joint>0:\n      if resps_curr.size == 0:\n         continue;\n      if ref_varExpl is None:\n        start_incl = 1; # hacky...\n      if start_incl == 0:\n        if ref_varExpl[con] < veThresh:\n          continue; # i.e. we're not adding this; yes we could move this up, but keep it here for now\n        else:\n          start_incl = 1; # now we're ready to start adding to our responses that we'll fit!\n\n      allResps.append(resps_curr);\n      allRespsSem.append(sem_curr);\n      allSfs.append(all_sfs[valSfInds_curr]);\n      incl_inds.append(con);\n      # and add to the bounds list!\n      if DoGmodel == 1:\n        if joint == 1: # add the center gain and center radius for each contrast \n          allBounds = (*allBounds, bound_gainCent, bound_radiusCent);\n        if joint == 2: # add the center and surr. gain and center radius for each contrast \n          allBounds = (*allBounds, bound_gainCent, bound_radiusCent, bound_gainSurr);\n        if joint == 3:  # add the center and surround gain for each contrast \n          allBounds = (*allBounds, bound_gainCent, bound_gainSurr);\n        elif joint == 4: # fixed center radius, so add all other parameters\n          allBounds = (*allBounds, bound_gainCent, bound_radiusSurr);\n        elif joint == 5: # add the center and surr. gain and center radius for each contrast \n          allBounds = (*allBounds, bound_gainCent, bound_radiusCent);\n        elif joint == 6: # fixed center:surround gain ratio\n          allBounds = (*allBounds, bound_gainCent, bound_radiusCent, bound_radiusSurr);\n        elif joint == 7: # center radius det. by slope, surround radius fixed\n          allBounds = (*allBounds, bound_gainCent, bound_gainSurr);\n        elif joint == 8: # center radius det. by slope, surround gain fixed\n          allBounds = (*allBounds, bound_gainCent, bound_radiusSurr);\n      elif DoGmodel == 2:\n        if joint == 1: # add the center gain and center radius for each contrast \n          allBounds = (*allBounds, bound_gainCent, bound_freqCent);\n        if joint == 2: # add the center and surr. gain and center radius for each contrast \n          allBounds = (*allBounds, bound_gainCent, bound_freqCent, bound_gainFracSurr);\n        if joint == 3:  # add the center and surround gain for each contrast \n          allBounds = (*allBounds, bound_gainCent, bound_gainFracSurr);\n\n      continue;\n\n    ### otherwise, we're really going to fit here! [i.e. if joint is False]\n    # first, specify the objection function!\n    obj = lambda params: DoG_loss(params, resps_curr, all_sfs[valSfInds_curr], resps_std=sem_curr, loss_type=loss_type, DoGmodel=DoGmodel, joint=joint); # if we're here, then joint=0, but we'll still keep joint=joint\n\n    for n_try in range(n_repeats):\n      ###########\n      ### pick initial params\n      ###########\n      init_params = dog_init_params(resps_curr, base_rate, all_sfs, valSfInds_curr, DoGmodel, fracSig=fracSig, bounds=allBounds)\n\n      # choose optimization method\n      if np.mod(n_try, 2) == 0:\n          methodStr = 'L-BFGS-B';\n      else:\n          methodStr = 'TNC';\n          \n      try:\n        wax = opt.minimize(obj, init_params, method=methodStr, bounds=allBounds);\n      except:\n        continue; # the fit has failed (bound issue, for example); so, go back to top of loop, try again\n      \n      # compare\n      NLL = wax['fun'];\n      params = wax['x'];\n\n      if np.isnan(bestNLL[con]) or NLL < bestNLL[con]:\n        bestNLL[con] = NLL;\n        currParams[con, :] = params;\n        curr_mod = get_descrResp(params, all_sfs[valSfInds_curr], DoGmodel);\n        # TODO: 22.05.10 --> previously ignored sf==0 case for varExpl\n        varExpl[con] = var_expl_direct(resps_curr, curr_mod);\n        prefSf[con] = dog_prefSf(params, dog_model=DoGmodel, all_sfs=all_sfs[all_sfs>0]); # do not include 0 c/deg SF condition\n        charFreq[con] = dog_charFreq(params, DoGmodel=DoGmodel);\n        success[con] = wax['success'];\n\n  if joint==0: # then we're DONE\n    return bestNLL, currParams, varExpl, prefSf, charFreq, None, None, success; # placeholding None for overallNLL, params [full list]\n\n  ### NOW, we do the fitting if joint=True\n  if joint>0:\n    if len(allResps)<jointMinCons: # need at least jointMinCons contrasts!\n      return bestNLL, currParams, varExpl, prefSf, charFreq, overallNLL, params, success;\n    ### now, we fit!\n    for n_try in range(n_repeats):\n      # first, estimate the joint parameters; then we'll add the per-contrast parameters after\n      # --- we'll estimate the joint parameters based on the high contrast response\n      ref_resps = allResps[-1];\n      ref_init = dog_init_params(ref_resps, base_rate, all_sfs, all_sfs, DoGmodel);\n      if joint == 1: # gain ratio (i.e. surround gain) [0] and shape ratio (i.e. surround radius) [1] are joint\n        allInitParams = [ref_init[2], ref_init[3]];\n      elif joint == 2: #  surround radius [0] (as ratio) is joint\n        allInitParams = [ref_init[3]];\n      elif joint == 3: # center radius [0] and surround radius [1] ratio are joint\n        allInitParams = [ref_init[1], ref_init[3]];\n      elif joint == 4: # center radius, surr. gain fixed\n        allInitParams = [ref_init[1], ref_init[2]];\n      elif joint == 5: #  surround gain AND radius [0] (as ratio in 2; fixed in 5) are joint\n        allInitParams = [ref_init[2], ref_init[3]];\n      elif joint == 6: # center:surround gain is fixed\n        allInitParams = [ref_init[2]];\n      elif joint == 7 or joint == 8: # center radius offset and slope fixed; surround radius fixed [7] or surr. gain fixed [8]\n        # the slope will be calculated on log contrast, and will start from the lowest contrast\n        # -- i.e. xc = np.power(10, init+slope*log10(con))\n        # to start, let's assume no slope, so the intercept should be equal to our xc guess\n        init_intercept, init_slope = random_in_range([-1.3, -0.6])[0], random_in_range([-0.1,0.2])[0]\n        #init_intercept, init_slope = np.log10(ref_init[1]), 0;\n        allInitParams = [init_intercept, init_slope, ref_init[3]] if joint == 7 else [init_intercept, init_slope, ref_init[2]];\n\n      # now, we cycle through all responses and add the per-contrast parameters\n      for resps_curr in allResps:\n        curr_init = dog_init_params(resps_curr, base_rate, all_sfs, all_sfs, DoGmodel);\n        if joint == 1:\n          allInitParams = [*allInitParams, curr_init[0], curr_init[1]];\n        elif joint == 2: # then we add center gain, center radius, surround gain (i.e. params 0:3\n          allInitParams = [*allInitParams, curr_init[0], curr_init[1], curr_init[2]];\n        elif joint == 3: # then we add center gain and surround gain (i.e. params 0, 2)\n          allInitParams = [*allInitParams, curr_init[0], curr_init[2]];\n        elif joint == 4: # then we add center gain, surround radius\n          allInitParams = [*allInitParams, curr_init[0], curr_init[3]];\n        elif joint == 5: # then we add center gain, center radius\n          allInitParams = [*allInitParams, curr_init[0], curr_init[1]];\n        elif joint == 6: # then we add center gain and both radii\n          allInitParams = [*allInitParams, curr_init[0], curr_init[1], curr_init[3]];\n        elif joint == 7: # then we add center and surround gains\n          allInitParams = [*allInitParams, curr_init[0], curr_init[2]];\n        elif joint == 8: # then we add center gain, surr. radius\n          allInitParams = [*allInitParams, curr_init[0], curr_init[3]];\n\n      methodStr = 'L-BFGS-B';\n      obj = lambda params: DoG_loss(params, allResps, allSfs, resps_std=allRespsSem, loss_type=loss_type, DoGmodel=DoGmodel, joint=joint, n_fits=len(allResps), conVals=valCons, ); # if joint, it's just one fit!\n      wax = opt.minimize(obj, allInitParams, method=methodStr, bounds=allBounds, options={'ftol': ftol});\n\n      # compare\n      NLL = wax['fun'];\n      params_curr = wax['x'];\n\n      if np.isnan(overallNLL) or NLL < overallNLL:\n        overallNLL = NLL;\n        params = params_curr;\n        success = wax['success'];\n\n    ### Done with multi-start fits; now, unpack the fits to fill in the \"true\" parameters for each contrast\n    # --- first, get the global parameters\n    ref_rc_val = None;\n    if joint == 1:\n      gain_rat, shape_rat = params[0], params[1];\n    elif joint == 2:\n      surr_shape = params[0]; # radius or frequency, if Tony model\n    elif joint == 3:\n      center_shape, surr_shape = params[0], params[1]; # radius or frequency, if Tony model\n    elif joint == 4: # center radius, surr. gain fixed\n      center_shape, surr_gain = params[0], params[1];\n    elif joint == 5: # surr. gain, surr. radius fixed\n      surr_gain, surr_shape = params[0], params[1];\n      ref_rc_val = params[2]; # center radius for high contrast\n    elif joint == 6: # ctr:surr gain fixed\n      surr_gain = params[0];\n    elif joint == 7: # center gain det. from slope, surround radius fixed\n      xc_inter, xc_slope, surr_shape = params[0:3];\n    elif joint == 8: # center gain det. from slope, surround gain fixed\n      xc_inter, xc_slope, surr_gain = params[0:3];\n      \n    for con in range(len(allResps)):\n      # --- then, go through each contrast and get the \"local\", i.e. per-contrast, parameters\n      if joint == 1: # center gain, center shape\n        center_gain = params[2+con*2]; \n        center_shape = params[3+con*2]; # shape, as in radius/freq, depending on DoGmodel\n        curr_params = [center_gain, center_shape, gain_rat, shape_rat];\n      elif joint == 2: # center gain, center radus, surround gain\n        center_gain = params[1+con*3]; \n        center_shape = params[2+con*3];\n        surr_gain = params[3+con*3];\n        curr_params = [center_gain, center_shape, surr_gain, surr_shape];\n      elif joint == 3: # center gain, surround gain\n        center_gain = params[2+con*2]; \n        surr_gain = params[3+con*2];\n        curr_params = [center_gain, center_shape, surr_gain, surr_shape];\n      elif joint == 4: # center radius, surr. gain fixed for all contrasts\n        center_gain = params[2+con*2]; \n        surr_shape = params[3+con*2];\n        curr_params = [center_gain, center_shape, surr_gain, surr_shape];\n      elif joint == 5: # surround gain, radius fixed for all contrasts\n        center_gain = params[2+con*2]; \n        center_shape = params[3+con*2];\n        curr_params = [center_gain, center_shape, surr_gain, surr_shape];\n      elif joint == 6: # ctr:surr gain fixed for all contrasts\n        center_gain = params[1+con*3]; \n        center_shape = params[2+con*3];\n        surr_shape = params[3+con*3];\n        curr_params = [center_gain, center_shape, surr_gain, surr_shape];\n      elif joint == 7 or joint == 8: # surr radius [7] or gain [8] fixed; need to determine center radius from slope\n        center_gain = params[3+con*2]; \n        center_shape = get_xc_from_slope(params[0], params[1], all_cons[con]);\n        if joint == 7:\n          surr_gain = params[4+con*2];\n        elif joint == 8:\n          surr_shape = params[4+con*2];\n        curr_params = [center_gain, center_shape, surr_gain, surr_shape];\n\n      # -- then the responses, and overall contrast index\n      resps_curr = allResps[con];\n      sem_curr   = allRespsSem[con];\n\n      # now, compute!\n      conInd = incl_inds[con];\n      bestNLL[conInd] = DoG_loss(curr_params, resps_curr, allSfs[con], resps_std=sem_curr, loss_type=loss_type, DoGmodel=DoGmodel, joint=0, ref_rc_val=ref_rc_val); # now it's NOT joint!\n      currParams[conInd, :] = curr_params;\n      curr_mod = get_descrResp(curr_params, allSfs[con], DoGmodel, ref_rc_val=ref_rc_val);\n      varExpl[conInd] = var_expl_direct(resps_curr, curr_mod);\n      prefSf[conInd] = dog_prefSf(curr_params, dog_model=DoGmodel, all_sfs=all_sfs[all_sfs>0], ref_rc_val=ref_rc_val);\n      charFreq[conInd] = dog_charFreq(curr_params, DoGmodel=DoGmodel);    \n\n    # and NOW, we can return!\n    return bestNLL, currParams, varExpl, prefSf, charFreq, overallNLL, params, success;\n##\n\n\n#####\n\n#####\n\ndef blankResp(data, get_dc=0):\n  blanks = np.where(data['cont'] == 0);\n\n  key = 'f0' if get_dc else 'f1';\n  mu = np.mean(data[key][blanks]);\n  std = np.std(data[key][blanks]);\n\n  return mu, std;\n\ndef tabulateResponses(data, resample=False, sub_f1_blank=False, phAdjusted=1, dir=1, cross_val=1, redo_phAdv=True):\n  ''' Given the dictionary containing all of the data, organize the data into the proper responses\n  Specifically, we know that Sach's experiments varied contrast and spatial frequency\n  Thus, we will organize responses along these dimensions [con, sf] OR [con][sf] (mean/arr, respectively)\n  NOTE: If phAdjusted=1, then we return the phase-adjusted responses (amplitudes)!\n          if phAdjusted=0, then we return vec-corrected but NOT phase-amplitude adjusted \n          if phAdjusted=-1, then we do the (dumb, non-vector) scalar average\n  ----  : We discovered on 22.04.07 that Sach's mean F1 phase/amplitude were not done using proper vector math (i.e. he simply took the mean of the amplitudes)\n  ----  : So, we not only do the proper vector math but also apply the phase-amplitude relationship correction that we apply for my own LGN data\n  '''\n  all_cons = np.unique(data['cont']);\n  all_cons = all_cons[all_cons>0];\n  all_sfs = np.unique(data['sf']);\n\n  f0 = dict();\n  f0mean= np.nan * np.zeros((len(all_cons), len(all_sfs))); \n  f0sem = np.nan * np.zeros((len(all_cons), len(all_sfs))); \n  f1 = dict();\n  f1mean = np.nan * np.zeros((len(all_cons), len(all_sfs)));\n  f1mean_phCorrOnMeans = np.copy(f1mean);\n  f1sem = np.nan * np.zeros((len(all_cons), len(all_sfs))); \n\n  # rather than getting just the mean/s.e.m., we can also record/transfer the firing rate of each individual stimulus run\n  f0arr = dict();\n  f1arr = dict();\n  f1arr_prePhCorr = dict();\n  f1phs = dict();\n\n  to_sub = blankResp(data, get_dc=False)[0] if sub_f1_blank else 0;\n\n  n_trials = data['f1arr'].shape[-1]; # nConds x nTrials\n  cntr_sizes = np.unique(data['cntr_size']); # choose larger size\n  val_size = np.where(data['cntr_size']==cntr_sizes[-1])[0]; # why specifying size? Cell 33 has multiple sizes!!!\n  if ~np.isnan(data['opac1'][0]):\n    # then also make sure that the size takes into account when the opacity of the second grating is 0 (i.e. off)\n    val_size = np.where(np.logical_and(val_size, data['opac1'][val_size]==0))[-1];\n  for con in range(len(all_cons)):\n    val_con = np.where(data['cont'][val_size] == all_cons[con])[0];\n    f0arr[con] = dict();\n    f1arr[con] = dict();\n    f1arr_prePhCorr[con] = dict();\n    f1phs[con] = dict();\n    for sf in range(len(all_sfs)):\n      val_sf = np.where(data['sf'][val_size][val_con] == all_sfs[sf])[0];\n      f0arr[con][sf] = np.nan * np.zeros((n_trials, ));\n      f1arr[con][sf] = np.nan * np.zeros((n_trials, ));\n      f1phs[con][sf] = np.nan * np.zeros((n_trials, ));\n      f1arr_prePhCorr[con][sf] = np.nan * np.zeros((n_trials, ));\n      \n      # Organize ALL trial -- we'll resample afterwards\n      non_nan = nan_rm(data['f0arr'][val_size][val_con][val_sf]);\n      f0arr[con][sf][0:len(non_nan)] = non_nan;\n      f1amps_curr = nan_rm(data['f1arr'][val_size][val_con][val_sf] - to_sub)\n      f1phs_curr = nan_rm(data['f1pharr'][val_size][val_con][val_sf]);\n\n      if cross_val is None:\n        holdout_frac = 1;\n      else:\n        holdout_frac = cross_val if cross_val<=1 else None;\n      non_nan_inds = np.where(~np.isnan(data['f1arr'][val_size][val_con][val_sf]))[-1];\n      new_inds = resample_array(resample, non_nan_inds, holdout_frac=holdout_frac);\n      save_inds = new_inds if holdout_frac<1 else range(len(new_inds));\n\n      # store the (potentially) resampled amplitudes, phases\n      f1arr_prePhCorr[con][sf][save_inds] = f1amps_curr[new_inds];\n      f1phs[con][sf][save_inds] = f1phs_curr[new_inds];\n      # and if it is resample, then we should update data\n      if resample and redo_phAdv and phAdjusted==1:\n        data['f1arr'][val_size[val_con[val_sf[0]]]][save_inds] = f1amps_curr[new_inds];\n        data['f1pharr'][val_size[val_con[val_sf[0]]]][save_inds] = f1phs_curr[new_inds];\n      \n  ### [end of loop over conditions] NOW, make the phAmp corrections, take means, etc\n  # -- phAdv_model is used to project the responses; all_opts is organized by SF (ascending)\n  phAdv_model, all_opts = df.phase_advance_fit(data, None, 'phAdv_dummy', dir=dir, to_save=0);\n\n  # NOW, we go through by condition and store the (corrected, if applicable) answers\n  # compute the mean amp, mean ph for vecF1 corrections (apply all data to resample and nonresampled)\n  for con, sf in itertools.product(range(len(all_cons)), range(len(all_sfs))):\n    f1amp_curr = nan_rm(f1arr_prePhCorr[con][sf]);\n    f1ph_curr = nan_rm(f1phs[con][sf]);\n    mean_amp, mean_ph,_,_ = polar_vec_mean([f1amp_curr], [f1ph_curr]);\n\n    if phAdjusted==1:\n      f1arr[con][sf][range(len(f1amp_curr))] = project_resp([f1amp_curr], [f1ph_curr], phAdv_model, [all_opts[sf]], disp=0)[0];\n    elif phAdjusted==0:\n      f1arr[con][sf][range(len(f1amp_curr))] = np.multiply(f1amp_curr, np.cos(np.deg2rad(mean_ph) - np.deg2rad(f1ph_curr)));\n    elif phAdjusted==-1:\n      f1arr[con][sf][range(len(f1amp_curr))] = f1amp_curr;\n\n    # take mean, since some conditions have repeats - just average them\n    # --- this applies regardless of phAdjustment, since the amplitudes would then be corrected\n    f0mean[con, sf] = np.nanmean(f0arr[con][sf]); #np.mean(data['f0'][val_con][val_sf]);\n    f0sem[con, sf] = sem(f0arr[con][sf], nan_policy='omit'); #np.mean(data['f0sem'][val_con][val_sf]);\n    f1mean[con, sf] = np.nanmean(f1arr[con][sf]); #np.mean(data['f1'][val_con][val_sf]);\n    if phAdjusted==1: # instead of projecting individual responses, we project on the mean\n      f1mean_phCorrOnMeans[con, sf] = project_resp([mean_amp], [mean_ph], phAdv_model, [all_opts[sf]], disp=0)[0];\n    f1sem[con, sf] = sem(f1arr[con][sf], nan_policy='omit'); #np.mean(data['f1sem'][val_con][val_sf]);\n\n  f0['mean'] = f0mean;\n  f0['sem'] = f0sem;\n  f1['mean'] = f1mean_phCorrOnMeans; # was previously f1mean...\n  f1['sem'] = f1sem;\n\n  return [f0, f1], [all_cons, all_sfs], [f0arr, f1arr]#; [f0arr, f1arr, f1arr_prePhCorr];\n\ndef writeDataTxt(cellNum, f1, sfs, contrast, save_loc):\n  \n  obs_mean = f1['mean'][contrast, :];\n  obs_sem = f1['sem'][contrast, :];\n   \n  write_name = 'cell%d_con%d.txt' % (cellNum, contrast);\n  file = open(str(save_loc + write_name), 'w');\n\n  for i in range(len(sfs)):\n    file.write('%.3f %.3f %.3f\\n' % (sfs[i], obs_mean[i], obs_sem[i]));\n\n  file.close();\n\ndef writeCellTxt(cellNum, load_path, save_loc):\n\n  dataList = np_smart_load(load_path + 'sachData.npy');\n  data = dataList[cellNum-1]['data'];\n\n  resps, conds, _ = tabulateResponses(data);\n  f1 = resps[1];\n  all_cons = conds[0];\n  all_sfs = conds[1];\n  \n  for i in range(len(all_cons)):\n    writeDataTxt(cellNum, f1, all_sfs, i, save_loc);\n","repo_name":"paul-levy/SF_diversity","sub_path":"LGN/sach/helper_fcns_sach.py","file_name":"helper_fcns_sach.py","file_ext":"py","file_size_in_byte":30536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"22907651982","text":"import glob\nimport cv2\nimport glob\nfrom sklearn.feature_extraction.image import extract_patches\nfrom itertools import cycle\nimport numpy as np\nimport os\nfrom Naked.toolshed.shell import muterun_js\nimport base64\n\n\nclass Generator():\n    def __init__(self, patch_size, batch_size, identity):\n        self.files = muterun_js(\"./application/dataQuery.js\", identity).stdout.decode('utf-8').split(\"\\n\")[7:-2]\n        self.whole_size = int(muterun_js(\"./application/getTotalEnrollCount.js\", identity).stdout.decode('utf-8').split(\"\\n\")[-2])\n        self.train_img = []\n        self.train_mask = []\n        self.preprocess()\n        self.length = len(self.train_img)\n        self.train = cycle(zip(self.train_img, self.train_mask))\n        self.patch_size = patch_size\n        self.batch_size = batch_size\n        self.patch_length = self.length\n\n    def preprocess(self):\n        for i in range(len(self.files)):\n            if i % 2 == 0:\n                self.train_img.append(base64.b64decode(self.files[i]))\n            else:\n                self.train_mask.append(base64.b64decode(self.files[i]))\n    def generator(self):\n\n        data = np.zeros((0, self.patch_size, self.patch_size, 3))\n        masks = np.zeros((0, self.patch_size, self.patch_size, 1))\n\n        while 1:\n            while data.shape[0] < self.batch_size:\n                img, mask = next(self.train)\n                img, mask = self.get_patches(img, mask)\n\n\n                data = np.append(data, img, axis = 0)\n                masks = np.append(masks, mask, axis = 0)\n\n            x = data[:self.batch_size, :, :, :]\n            y = masks[:self.batch_size, :, :, :]\n\n            data = data[self.batch_size:, :, :, :]\n            masks = masks[self.batch_size:, :, :, :]\n\n            yield x, y\n\n    def get_patches(self, img, mask):\n        img = np.frombuffer(img, np.uint8)\n        mask = np.frombuffer(mask, np.uint8)\n\n        img = cv2.imdecode(img, cv2.IMREAD_COLOR)\n        mask = cv2.imdecode(mask, cv2.IMREAD_GRAYSCALE)\n        _, mask = cv2.threshold(mask, 100, 255, cv2.THRESH_BINARY)\n        mask = (mask/255.0).astype('uint8')\n\n        img = cv2.resize(img, (self.patch_size, self.patch_size))\n        mask = cv2.resize(mask, (self.patch_size, self.patch_size))\n\n        img = img.reshape(-1, img.shape[0], img.shape[1], img.shape[2])\n        mask = mask.reshape(-1, mask.shape[0], mask.shape[1], 1)\n\n        return img, mask\n\n\n\n","repo_name":"MediCha-i-n/test_Deep","sub_path":"Polyp_gen.py","file_name":"Polyp_gen.py","file_ext":"py","file_size_in_byte":2410,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"31543767795","text":"from django.contrib import admin\nfrom django.urls import path\nfrom app import views\n\nurlpatterns = [\n    path('', views.index, name='index'),\n    path('history', views.history, name='history'),\n    path('impression', views.impression, name='impression'),\n    path('impression/add', views.add_impression, name='add_impression'),\n    path('admin/', admin.site.urls),\n]\n","repo_name":"vladibuyanov/Obecne_Musem_Brhlovce","sub_path":"core/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":367,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"7753757557","text":"import pytest\n\nfrom pymoo.algorithms.moo.nsga2 import NSGA2\nfrom pymoo.algorithms.soo.nonconvex.ga import GA\nfrom pymoo.core.crossover import ElementwiseCrossover\nfrom pymoo.factory import get_crossover, get_problem\nfrom pymoo.operators.crossover.dex import DEX\nfrom pymoo.operators.crossover.sbx import SBX\nfrom pymoo.operators.mutation.inversion import InversionMutation\nfrom pymoo.operators.sampling.rnd import PermutationRandomSampling, FloatRandomSampling\nfrom pymoo.optimize import minimize\nfrom pymoo.problems.single.traveling_salesman import create_random_tsp_problem\n\n\n@pytest.mark.parametrize('name', ['real_de', 'real_sbx', 'real_pcx', 'real_exp'])\ndef test_crossover_real(name):\n    crossover = get_crossover(name, prob=0.95)\n    method = GA(pop_size=20, crossover=crossover)\n    minimize(get_problem(\"sphere\"), method, (\"n_gen\", 20))\n    assert True\n\n\n@pytest.mark.parametrize('name', ['bin_ux', 'bin_hux', 'bin_one_point', 'bin_two_point'])\ndef test_crossover_bin(name):\n    crossover = get_crossover(name, prob=0.95)\n    method = NSGA2(pop_size=20, crossover=crossover)\n    minimize(get_problem(\"zdt5\"), method, (\"n_gen\", 20))\n    assert True\n\n\n@pytest.mark.parametrize('name', ['perm_ox', 'perm_erx'])\ndef test_crossover_perm(name):\n    crossover = get_crossover(name, prob=0.95)\n    method = GA(pop_size=20, crossover=crossover, mutation=InversionMutation(), sampling=PermutationRandomSampling())\n    minimize(create_random_tsp_problem(10), method, (\"n_gen\", 20))\n    assert True\n\n\ndef test_elementwise_crossover():\n\n    problem = get_problem('zdt1')\n\n    sampling = FloatRandomSampling()\n\n    pop = sampling.do(problem, n_samples=3)\n\n    crossover = ElementwiseCrossover(SBX(20))\n    off = crossover.do(problem, pop[0], pop[1])\n    assert len(off) == 2\n\n    crossover = ElementwiseCrossover(DEX())\n    off = crossover.do(problem, pop[0], pop[1], pop[2])\n    assert len(off) == 1","repo_name":"ngctnnnn/RN-MuOENAS","sub_path":"Model/ManyObjARTS/algorithm/pymoo/tests/operators/test_crossover.py","file_name":"test_crossover.py","file_ext":"py","file_size_in_byte":1896,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"76"}
+{"seq_id":"35291341359","text":"from mandala.all import *\nfrom mandala.tests.utils import *\n\n\ndef test_unit():\n    storage = Storage()\n\n    @op\n    def f(x) -> int:\n        return Transient(x + 1)\n\n    with storage.run(attach_call_to_outputs=True):\n        a = f(42)\n        call: Call = a._call\n    assert call.transient\n    assert a.transient\n    assert unwrap(a) == 43\n\n    with storage.run(attach_call_to_outputs=True):\n        a = f(42)\n        call: Call = a._call\n    # assert not a.in_memory - no longer true b/c caching\n    assert a.transient\n    assert call.transient\n\n    with storage.run(recompute_transient=True, attach_call_to_outputs=True):\n        a = f(42)\n        call: Call = a._call\n\n    assert a.in_memory\n    assert a.transient\n    assert call.transient\n\n\ndef test_composition():\n    storage = Storage()\n\n    @op\n    def f(x) -> int:\n        return Transient(x + 1)\n\n    @op\n    def g(x) -> int:\n        return Transient(x**2)\n\n    with storage.run():\n        a = f(42)\n        b = g(a)\n\n    with storage.run():\n        a = f(23)\n\n    storage.cache.evict_all()\n\n    try:\n        with storage.run():\n            a = f(23)\n            b = g(a)\n        assert False\n    except ValueError as e:\n        assert True\n\n    with storage.run(recompute_transient=True):\n        a = f(23)\n        b = g(a)\n    assert unwrap(b) == 576\n","repo_name":"amakelov/mandala","sub_path":"mandala/tests/test_transient.py","file_name":"test_transient.py","file_ext":"py","file_size_in_byte":1313,"program_lang":"python","lang":"en","doc_type":"code","stars":155,"dataset":"github-code","pt":"76"}
+{"seq_id":"42817210852","text":"# -*- coding:utf-8 -*-\nfrom naver_scrapper import crawling as crawl_naver\nfrom google_scrapper import crawling as crawl_google\nfrom save import save_images\nimport argparse\n\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument(\"-name\", \"--people\", required=True)\nargs = parser.parse_args()\npeople = args.people\n\n\ndef crawl_images(name):\n    naver_urls = crawl_naver(name)\n    google_urls = crawl_google(name)\n    imgUrls = naver_urls + google_urls\n    print(f\"Total: {len(imgUrls)} images scrapped. \")\n    # 원본, 얼굴, 흑백 저장\n    save_images(name, imgUrls)\n\n\ndef main():\n    names = people.split(',')\n    for name in names:\n        crawl_images(name)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"rheeeuro/actor-scrapper","sub_path":"keyword_scrapper/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"13415505470","text":"__author__ = 'mattilyra'\n\ntry:\n    import cPickle as pickle\nexcept ImportError:\n    import pickle\n\nimport pytest\n\nfrom naklar.experiment import _find_conf_files\nfrom naklar.experiment import _read_conf_dicts\n\n\n@pytest.fixture\ndef experiment_tree(tmpdir):\n    for i in xrange(10):\n        pth = tmpdir.mkdir('exp{}'.format(i)).join('conf.pkl')\n        d = {'a': 1, 'b': 2}\n        pth.write(pickle.dumps(d))\n    return tmpdir\n\n\ndef test_conf_discovery(experiment_tree):\n    confs = _find_conf_files(experiment_tree.strpath)\n    assert(len(list(confs)) == 10)\n    for i, fh in enumerate(confs):\n        assert(fh.name.endswith('conf.pkl'))\n\n\ndef test_conf_dict_load(experiment_tree):\n    confs = _find_conf_files(experiment_tree.strpath)\n    dicts = _read_conf_dicts(confs)\n    assert(len(list(dicts)) == 10)\n","repo_name":"mattilyra/naklar","sub_path":"test/test_experiment.py","file_name":"test_experiment.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"76"}
+{"seq_id":"10602472941","text":"from Bio import SeqIO,AlignIO\nfrom numpy import log2\n\n#FX\n\ndef Sobs(string, letter):\n\tlenstring = len(string)\n\tlettcount = string.count(letter)\n\tnumber = lettcount / lenstring\n\tif number != 0:\n\t\tvalue = -(number*log2(number))\n\t\t#value = number\n\telse:\n\t\tvalue = 0\n\treturn value\n\ndef bitscorefreq(string, letter, weight):\n\tlenstring = len(string)\n\tlettcount = string.count(letter)\n\tnumber = lettcount / lenstring\n\tif number != 0:\n\t\tvalue = weight*number\n\telse:\n\t\tvalue = 0\n\treturn value\n\n#files:\t\nsignalpfile = \"plastid_refs-asafind.txt\"\npredisifile = \"plastid_refs-predisi.txt\"\npredslfile = \"plastid_refs-predsl.txt\"\n\nwith open(signalpfile) as f:\n\tasafind = f.readlines()\n\nwith open(predisifile) as f:\n\tpredisi = f.readlines()\n\nwith open(predslfile) as f:\n\tpredsl = f.readlines()\n\n#parse predictions\nmonstr_dic = {}\nreport_errors = False\nprint(\"Parsing signal peptide predictions and sequences into data dictionary...\")\nnames = []\nnewnames = set()\nfor line in predisi:\n\tline = line.split(\"\\t\")\n\t#FASTA-ID\tCleavage Position\tSignal Peptide ?\tScore\n\t#[0]\t\t[1]\t\t\t\t\t[2]\t\t\t\t\t[3]\n\tname = line[0]\n\tif line[0] not in names:\n\t\tnames.append(name)\n\t\tif line[2] == \"Y\" and int(line[1]) < 101: #sometimes we see signal very far in the protein\n\t\t\tmonstr_dic[name] = {\"PrediSi site\": int(line[1])+1, \"PrediSi SPscore\": float(line[3])}\n\t\telse:\n\t\t\tmonstr_dic[name] = {\"PrediSi site\": \"-\", \"PrediSi SPscore\": float(line[3])}\n\telse:\n\t\tnewnames.add(name)\n\t\tprint(\"NEW NAME predisi! \" + name)\nprint(\"->PrediSi: done\")\n\nnames.sort()\n#print(names)\n\nfor line in predsl:\n\tline = line.split(\"\\t\")\n\t#sequence id\tmTP score\tSP score\tprediction\tcleavage site\n\t#[0]\t\t\t[1]\t\t\t[2]\t\t\t[3]\t\t\t[4]\t\t\t\n\tname = line[0]\n\tif name in names:\n\t\t#line[3] for nonplant prediction - complex algae\n\t\tif line[3] == \"secreted\":\n\t\t\tmonstr_dic[name].update({\"PredSL site\": int(line[4])+1, \"PredSL SPscore\": float(line[2])})\n\t\telse:\n\t\t\tmonstr_dic[name].update({\"PredSL site\": \"-\", \"PredSL SPscore\": float(line[2])})\n\telse:\n\t\tnewnames.add(name)\n\t\tprint(\"NEW NAME predsl! \" + name)\nprint(\"->PredSL: done\")\n\nfor line in asafind:\n\t#Identifier\tSignalP\tASAfind cleavage position\tASAfind/SignalP cleavage site offset\tASAfind 20aa transit score\tASAfind Prediction\n\t#[0]\t\t[1]\t\t[2]\t\t\t\t\t\t\t[3]\t\t\t\t\t\t\t\t\t\t[4]\t\t\t\t\t\t\t[5]\t\n\t\n\tif not line.startswith('#') and len(line) != 0:\n\t\tline = line.split('\\t')\n\t\tname = line[0]\n\t\tif name in names:\n\t\t\ttry:\n\t\t\t\tsp = int(line[2])\n\t\t\t\tasa = sp + int(line[3])\n\t\t\texcept ValueError:\n\t\t\t\tsp = \"-\"\n\t\t\t\tasa = \"-\"\n\t\t\tmonstr_dic[name].update({'SignalP site': sp, 'ASAfind site': asa})\n\t\telse:\n\t\t\tnewnames.add(name)\n\t\t\tprint(\"NEW NAME asafind! \" + name)\nprint(\"->ASAfind/SignalP: done\")\n\nif len(newnames) > 0:\n\tprint(\"ERROR, new sequence names appeared!\")\n\treport_errors = True\n\n#load fastas:\nfasta = SeqIO.parse(\"plastid_refs.fasta\", \"fasta\")\nseq_dic = {}\nfor seq in fasta:\n\tseq_dic[seq.name] = seq.seq\n#print(seq_dic)\n\n\n#process cleavage sites\t\nwith open(\"plastid_signals.txt\", \"w\") as out, open(\"cleavage_site.fasta\", \"w\") as outfas:\n\tagreed = 0\n\tfor name in names:\n\t\t#print(name)\n\t\tsequence = str(seq_dic[name])\n\t\tvalues = [monstr_dic[name][\"PrediSi site\"], monstr_dic[name][\"PredSL site\"], monstr_dic[name][\"SignalP site\"], monstr_dic[name][\"ASAfind site\"]]\n\t\tvalueset = set(values)\n\t\tfor i in valueset:\n\t\t\tif i != \"-\":\n\t\t\t\tif len(valueset - {\"-\"}) == 1 and values.count(i) == 2:\n\t\t\t\t\tagreed += 1\n\t\t\t\t\tprint(name, i)\n\t\t\t\t\toutfas.write(\">{}\\n{}\\n\".format(name, sequence[int(i)-6:int(i)+19]))\n\t\t\t\telif values.count(i) >=3:\n\t\t\t\t\tagreed += 1\n\t\t\t\t\tprint(name, i)\n\t\t\t\t\toutfas.write(\">{}\\n{}\\n\".format(name, sequence[int(i)-6:int(i)+19]))\n\t\t\t\n\t\tout.write(\"{}\\t{}\\t{}\\t{}\\t{}\\n\".format(name, monstr_dic[name][\"PrediSi site\"], monstr_dic[name][\"PredSL site\"], \n\t\t\tmonstr_dic[name][\"SignalP site\"], monstr_dic[name][\"ASAfind site\"]))\n\n\tprint(agreed, \"sequences passed and written to alignment\")\n\naacids = [\"A\",\"C\",\"D\",\"E\",\"F\",\"G\",\"H\",\"I\",\"K\",\"L\",\"M\",\"N\",\"P\",\"Q\",\"R\",\"S\",\"T\",\"V\",\"W\",\"Y\",\"X\"]\n\nalignmentcvel = AlignIO.read(\"cleavage_site_cvel.fasta\", \"fasta\")\nalignmentvbra = AlignIO.read(\"cleavage_site_vbra.fasta\", \"fasta\")\n#Rseq values calculated in Supplementary File S2 of the chromerid localizations paper\ncvel_rseq = [0.628142594, 0.901165584, 1.679998503, 0.899323492, 2.865290913, 2.930248396, 1.218694579, 1.216631242, 0.723489751, 0.796375671, 0.62329697, 0.67311194, 0.660968002, 0.832345896, 0.930850003, 0.68832463, 0.701284019, 0.768918374, 0.590580854, 0.8231282, 0.710945491, 0.88885071, 0.651382747, 0.761732523, 0.743346288]\nvbra_rseq = [0.817328158, 1.0450741, 1.916029505, 0.715175362, 3.370870006, 1.554297938, 0.863928534, 1.004913307, 0.97219529, 0.779193055, 0.870728277, 0.732232068, 0.787914214, 1.006489747, 0.892195734, 0.821817644, 0.999711101, 0.742618301, 0.895313802, 0.755506939, 0.848577712, 0.88178165, 0.706938911, 0.867174927, 0.857195538]\n\nif cvel_rseq == []: #assuming Rseq values have not been calculated...\n\twith open(\"cvelmatrix.txt\", \"w\") as cvelmatrix, open(\"vbramatrix.txt\", \"w\") as vbramatrix:\n\t\tfor aa in aacids:\n\t\t\tcveldata = [aa]\n\t\t\tvbradata = [aa]\n\t\t\tfor i in range(25):\n\t\t\t\t#print(i)\n\t\t\t\tcveldata.append(Sobs(alignmentcvel[:, i], aa))\n\t\t\t\tvbradata.append(Sobs(alignmentvbra[:, i], aa))\n\t\t\tcvelstring = [str(x) for x in cveldata]\n\t\t\tvbrastring = [str(x) for x in vbradata]\n\t\t\tcvelmatrix.write(\"{}\\n\".format(\"\\t\".join(cvelstring)))\n\t\t\tvbramatrix.write(\"{}\\n\".format(\"\\t\".join(vbrastring)))\nelse:\n\twith open(\"cvelbitscorematrix.txt\", \"w\") as cvelmatrix, open(\"vbrabitscorematrix.txt\", \"w\") as vbramatrix:\n\t\tfor aa in aacids:\n\t\t\tcveldata = [aa]\n\t\t\tvbradata = [aa]\n\t\t\tfor i in range(25):\n\t\t\t\t#print(i)\n\t\t\t\tcveldata.append(bitscorefreq(alignmentcvel[:, i], aa, cvel_rseq[i]))\n\t\t\t\tvbradata.append(bitscorefreq(alignmentvbra[:, i], aa, vbra_rseq[i]))\n\t\t\tcvelstring = [str(x) for x in cveldata]\n\t\t\tvbrastring = [str(x) for x in vbradata]\n\t\t\tcvelmatrix.write(\"{}\\n\".format(\"\\t\".join(cvelstring)))\n\t\t\tvbramatrix.write(\"{}\\n\".format(\"\\t\".join(vbrastring)))\n\n\n","repo_name":"morpholino/PYTHON","sub_path":"signalparse_bitscorematrix.py","file_name":"signalparse_bitscorematrix.py","file_ext":"py","file_size_in_byte":5946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"41583111342","text":"def find_sum_between_two_nums(a, b):\n    answer = 0\n    \n    # b > a 인 경우 a,b를 swap\n    if a > b: \n        temp = b \n        b = a \n        a = temp\n    \n    for i in range(a, b+1):\n        answer += i\n        \n    return answer","repo_name":"Hongik-Human-Developer/algorithm_study","sub_path":"sungjun/day1/problem1.py","file_name":"problem1.py","file_ext":"py","file_size_in_byte":236,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"13397670772","text":"class TreeNode:\r\n    def __init__(self, x):\r\n        self.val = x\r\n        self.left = None\r\n        self.right = None\r\n\r\nclass Solution:\r\n    # def searchBST(self, root: TreeNode, val: int) -> TreeNode:\r\n    #     if root.val == val: return True, root\r\n    #     if root.left is None or root.right is None: return False, None\r\n\r\n    #     if not root.left is None or root.right is None:\r\n    #         flag, ans = self.searchBST(root.left, val) or self.searchBST(root.right, val)\r\n\r\n    #     return ans\r\n\r\n    def searchBST(self, root: TreeNode, val: int) -> TreeNode:\r\n        if root is None: return None\r\n\r\n        if root.val == val:\r\n            return root\r\n        elif root.val > val:\r\n            return self.searchBST(root.left, val)\r\n        elif root.val < val:\r\n            return self.searchBST(root.right, val)\r\n\r\n        # if root and val < root.val: return self.searchBST(root.left, val)\r\n        # elif root and val > root.val: return self.searchBST(root.right, val)\r\n        # return root\r\n\r\n\r\n        # if root and root.val == val:\r\n        #     return root\r\n        # if root and root.val > val:\r\n        #     return self.searchBST(root.left, val)\r\n        # elif root and root.val < val:\r\n        #     return self.searchBST(root.right, val)\r\n\r\n        # import pdb; pdb.set_trace()\r\n        # return None\r\n\r\nt = TreeNode(4)\r\nt.left = TreeNode(2)\r\nt.right = TreeNode(7)\r\nt.left.left = TreeNode(1)\r\nt.left.right = TreeNode(3)\r\n\r\nprint(Solution().searchBST(t, 2))\r\nprint(Solution().searchBST(t, 5))\r\n\r\nt = TreeNode(40)\r\nt.left = TreeNode(20)\r\nt.right = TreeNode(60)\r\nt.left.left = TreeNode(10)\r\nt.left.right = TreeNode(30)\r\nt.right.left = TreeNode(50)\r\nt.right.right = TreeNode(70)\r\nprint(Solution().searchBST(t, 25))\r\nprint(Solution().searchBST(t, 70))","repo_name":"takin6/algorithm-practice","sub_path":"leetcode/recursion/bst.py","file_name":"bst.py","file_ext":"py","file_size_in_byte":1777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"33804968343","text":"#Con el siguiente diccionario, debes crear un programa que pregunte al usuario por un número; el programa debe imprimir el jugador al que hace referencia ese número\n\n'''{\n\n    1 : \"Casillas\", 15 : \"Ramos\",\n\n    3 : \"Pique\", 5 : \"Puyol\",\n\n    11 : \"Capdevila\", 14 : \"Xabi Alonso\",\n\n    16 : \"Busquets\", 8 : \"Xavi Hernandez\",\n\n    18 : \"Pedrito\", 6 : \"Iniesta\",\n\n    7 : \"Villa\"\n\n}'''\n\ndiccionarioJugadores={1 : \"Casillas\", 15 : \"Ramos\",3 : \"Pique\", 5 : \"Puyol\",11 : \"Capdevila\", 14 : \"Xabi Alonso\",16 : \"Busquets\", 8 : \"Xavi Hernandez\",18 : \"Pedrito\", 6 : \"Iniesta\",7 : \"Villa\"}\n\njugador= int(input(\"Introduce el numero del jugador: \"))\n\nif jugador in diccionarioJugadores:\n    print(\"El jugador con el numero {} es:  {}\" .format(jugador, diccionarioJugadores.get(jugador)))\nelse: \n    print(\"No existe ningun jugador con el numero introducido\")","repo_name":"silviagomin/python","sub_path":"EstructuraDatos/Diccionarios/Ejercicio2.py","file_name":"Ejercicio2.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"72922112244","text":"# 문제 링크: https://leetcode.com/problems/check-if-all-1s-are-at-least-length-k-places-away/\n\nclass Solution:\n    def kLengthApart(self, nums: List[int], k: int) -> bool:\n        if 1 not in nums or k == 0:\n            return True\n\n        prev = nums.index(1)\n        for i in range(prev + 1, len(nums)):\n            if nums[i] == 0:\n                continue\n\n            if i - prev <= k:\n                return False\n\n            prev = i\n\n        return True","repo_name":"jamesujeon/coding-problem-solutions","sub_path":"leetcode/python 3/1437.py","file_name":"1437.py","file_ext":"py","file_size_in_byte":467,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"1321413419","text":"import os\nimport json\nimport pynput\nfrom .pressed_keys import PressedKeys\nimport shutil\n\n\n# Classes for settings component of the program\n\n\nclass Settings:\n    def __init__(self, settings_file_location):\n        self.settings_file_location = settings_file_location\n        self.settings = self.load_or_create()\n        self.save()\n\n    def load_or_create(self):\n        try:\n            settings = self.load()\n        except FileNotFoundError:\n            settings = self.create()\n        except json.decoder.JSONDecodeError:\n            settings = self.create()\n        return settings\n\n    def load(self):\n        with open(self.settings_file_location, \"r\") as f:\n            settings = json.load(f)\n        return settings\n\n    def save(self):\n        with open(self.settings_file_location, \"w\") as f:\n            json.dump(self.settings, f, indent=4, sort_keys=True)\n\n    def create(self):\n        pass\n\n\nclass HotkeySettings(Settings):\n    def __init__(self, settings_file_location=\"hotkey_settings.json\"):\n        super().__init__(settings_file_location=settings_file_location)\n\n    def create(self):\n        # Each hotkey is a PressedKey object\n        next_hotkey = PressedKeys()\n        next_hotkey.add_modifier(pynput.keyboard.Key.up)\n        previous_hotkey = PressedKeys()\n        previous_hotkey.add_modifier(pynput.keyboard.Key.down)\n        mute_hotkey = PressedKeys()\n        mute_hotkey.add_modifier(pynput.keyboard.Key.left)\n        settings = {\n            \"next\": next_hotkey.hashed,\n            \"previous\": previous_hotkey.hashed,\n            \"mute\": mute_hotkey.hashed\n        }\n        return settings\n\n    def set_hotkey(self, which, hotkey: PressedKeys):\n        self.settings[which] = hotkey.hashed\n\n    def get_next_hotkey(self):\n        return PressedKeys(self.settings[\"next\"])\n\n    def get_previous_hotkey(self):\n        return PressedKeys(self.settings[\"previous\"])\n\n    def get_mute_hotkey(self):\n        return PressedKeys(self.settings[\"mute\"])\n\n\nclass InternalSettings(Settings):\n    def __init__(self, settings_file_location=\"internal_settings.json\"):\n        super().__init__(settings_file_location=settings_file_location)\n        self.sounds_dir = self.settings[\"sounds_dir\"]\n        self.presets_dir = self.settings[\"presets_dir\"]\n\n    def create(self):\n        settings = {\n            # 2 folders in 'stimulant_noise' folder\n            \"sounds_dir\": \"sounds\",\n            \"presets_dir\": \"presets\"\n        }\n        return settings\n\n\nclass PresetSettings(Settings):\n    def __init__(self, settings_file_location=None, internal_settings=None):\n        if settings_file_location is None:\n            settings_file_location = os.path.join(internal_settings.presets_dir,\n                                                  f\"Preset_{len(os.listdir(internal_settings.presets_dir))}.json\")\n        self.name = os.path.splitext(os.path.basename(settings_file_location))[0]\n        if internal_settings is None:\n            internal_settings = InternalSettings()\n        self.internal_settings = internal_settings\n        super().__init__(settings_file_location=settings_file_location)\n\n    def create(self):\n        settings = {\n            \"name\": self.name,\n            \"volume\": 50,\n            \"mute\": True,\n            \"sounds\": {\n            },\n        }\n        return settings\n\n    def add_sound(self, path, volume=0.5, mute=True):\n        sound_name = os.path.basename(path)\n        if sound_name in self.settings[\"sounds\"]:\n            raise Exception(\"Sound already exists\")\n        self.settings[\"sounds\"][sound_name] = {\n            \"path\": path,\n            \"volume\": volume,\n            \"mute\": mute,\n        }\n        return sound_name\n\n    def remove_sound(self, sound_name):\n        if sound_name not in self.settings[\"sounds\"]:\n            raise Exception(\"Sound does not exist\")\n        del self.settings[\"sounds\"][sound_name]\n        return self.settings[\"sounds\"]\n\n    def set_volume(self, volume):\n        self.settings[\"volume\"] = volume\n        return volume\n\n    def set_mute(self, mute):\n        self.settings[\"mute\"] = mute\n        return mute\n\n    def set_name(self, name):\n        self.settings[\"name\"] = name\n        return name\n\n    def set_sound_volume(self, sound_name, volume):\n        if sound_name not in self.settings[\"sounds\"]:\n            return False\n        self.settings[\"sounds\"][sound_name][\"volume\"] = volume\n        return True\n\n    def set_sound_mute(self, sound_name, mute):\n        if sound_name not in self.settings[\"sounds\"]:\n            return False\n        self.settings[\"sounds\"][sound_name][\"mute\"] = mute\n        return True\n\n\nclass PresetsManagerSettings(Settings):\n    def __init__(self, settings_file_location=\"presets_settings.json\", internal_settings=None):\n        if internal_settings is None:\n            internal_settings = InternalSettings()\n        self.internal_settings = internal_settings\n        super().__init__(settings_file_location=settings_file_location)\n\n    def create(self):\n        settings = {\n            \"presets\": {\n                # \"preset_name\": preset_path\n            },\n            \"presets_order\": [],\n            \"current_preset\": \"None\",\n        }\n        # Check if the presets directory exists. If not, create it.\n        if not os.path.exists(self.internal_settings.presets_dir):\n            os.mkdir(self.internal_settings.presets_dir)\n        # Check if the directory is empty. If so, create a default preset.\n        if len(os.listdir(self.internal_settings.presets_dir)) == 0:\n            PresetSettings(internal_settings=self.internal_settings)\n\n        for preset in os.listdir(self.internal_settings.presets_dir):\n            preset_path = os.path.join(self.internal_settings.presets_dir, preset)\n            preset_name = PresetSettings(preset_path, internal_settings=self.internal_settings).settings[\"name\"]\n            settings[\"presets\"][preset_name] = preset_path\n            settings[\"presets_order\"].append(preset_name)\n\n        settings[\"current_preset\"] = settings[\"presets_order\"][0]\n\n        return settings\n\n    def add_preset(self, preset_name, to_order=True):\n        if preset_name in self.settings[\"presets\"]:\n            raise ValueError(\"Preset with name {} already exists\".format(preset_name))\n        preset_path = os.path.join(self.internal_settings.presets_dir, preset_name + \".json\")\n        PresetSettings(preset_path, internal_settings=self.internal_settings)\n        self.settings[\"presets\"][preset_name] = preset_path\n        if to_order:\n            self.settings[\"presets_order\"].append(preset_name)\n\n    def remove_preset(self, preset_name):\n        os.remove(self.settings[\"presets\"][preset_name])\n        del self.settings[\"presets\"][preset_name]\n        self.settings[\"presets_order\"].remove(preset_name)\n\n    def set_current_preset(self, preset_name):\n        if preset_name not in self.settings[\"presets\"].keys():\n            raise ValueError(\"Preset does not exist\")\n        self.settings[\"current_preset\"] = preset_name\n        return self.settings[\"current_preset\"]\n\n    def change_preset_order(self, preset_name, after_preset_name):\n        if preset_name not in self.settings[\"presets\"]:\n            raise ValueError(\"Preset does not exist\")\n        if after_preset_name not in self.settings[\"presets\"]:\n            raise ValueError(\"Preset does not exist\")\n        self.settings[\"presets_order\"].remove(preset_name)\n        self.settings[\"presets_order\"].insert(self.settings[\"presets_order\"].index(after_preset_name) + 1, preset_name)\n        return self.settings[\"presets_order\"]\n\n    def change_preset_name(self, preset_name, new_preset_name):\n        if preset_name not in self.settings[\"presets\"]:\n            raise ValueError(\"Preset does not exist\")\n        if new_preset_name in self.settings[\"presets\"]:\n            raise ValueError(f\"Preset with name {new_preset_name} already exists\")\n        if preset_name == self.settings[\"current_preset\"]:\n            self.settings[\"current_preset\"] = new_preset_name\n        preset_path = self.settings[\"presets\"][preset_name]\n        shutil.copy(preset_path, os.path.join(self.internal_settings.presets_dir, new_preset_name + \".json\"))\n        preset_index = self.settings[\"presets_order\"].index(preset_name)\n        self.remove_preset(preset_name)\n        new_preset_settings = PresetSettings(os.path.join(self.internal_settings.presets_dir,\n                                                          new_preset_name + \".json\"),\n                                             internal_settings=self.internal_settings)\n        new_preset_settings.set_name(new_preset_name)\n        new_preset_settings.save()\n        self.settings[\"presets\"][new_preset_name] = os.path.join(self.internal_settings.presets_dir,\n                                                                 new_preset_name + \".json\")\n\n        self.settings[\"presets_order\"].insert(preset_index, new_preset_name)\n        return new_preset_name\n","repo_name":"teleoflexuous/Noise-Stimulant","sub_path":"stimulant_noise/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":8935,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"76"}
+{"seq_id":"33775448211","text":"import cv2\nimport numpy as np\n\np = r\"C:\\Users\\bhask\\Downloads\\wallpaperflare.com_wallpaper.jpg\"\nc = cv2.VideoCapture(p)\n\np1 = np.float32([[56, 65], [368, 52], [28, 387], [389, 390]])\np2 = np.float32([[100, 50], [300, 0], [0, 300], [300, 300]])\nm = cv2.getPerspectiveTransform(p1, p2)\n\nwhile True:\n    ret, r = c.read()\n    if not ret:\n        break\n\n    r1 = cv2.resize(r, (700, 700))\n    d = cv2.warpPerspective(r1, m, (700, 700))\n    cv2.imshow(\"a\", d)\n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\n\nc.release()\ncv2.destroyAllWindows()\n","repo_name":"sbhaskar03/COMPUTER-VISION","sub_path":"program13.py","file_name":"program13.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"19599699658","text":"#Problem : https://www.acmicpc.net/problem/2800\n#Comment : 풀줄 몰라서 다른사람 풀이 보고 이해함\n\nfrom itertools import combinations\n# 문자열을 리스트로 저장\nproblem=[*input().strip()]\np, idx_brs=[],[]\n\nfor i,v in enumerate(problem):\n    if v=='(':\n        #문자열에서 ( 를 제거\n        problem[i]=''\n        # (가  있었던 index를 저장\n        p+=[i]\n    if v==')':\n        #문자열에서 )를 제거\n        problem[i]=''\n        #p에 저장되어있던 ( 의 위치와 맞는 )의 위치를 idx_brs에 저장\n        idx_brs+=[[p.pop(),i]]    \n#print(problem)\n#print(idx_brs)\n\n#이거는 중복을 피하니깐 사용한듯\nout=set()\n\n#combinations함수를 이용해서 순열을 만든다.\nfor i in range(len(idx_brs)):\n    for j in combinations(idx_brs,i):\n        #print(j)\n        P=problem[:]\n        #print(P)\n        for v,w in j:\n            P[v]='('\n            P[w]=')'\n        out.add(''.join(P))\n    #print(out)\n\nfor i in sorted(out):\n    print(i)","repo_name":"bn-tw2020/2020_winter_algorithm","sub_path":"participants/SaeHyeon/Algorithms/Data_structure/BOJ2800.py","file_name":"BOJ2800.py","file_ext":"py","file_size_in_byte":1006,"program_lang":"python","lang":"ko","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"73894229366","text":"class car():\n    def __init__(self,name,cost,colour,length):\n        self.name = name\n        self.cost = cost\n        self.colour = colour\n        self.length = length\n        print(\"name :\",self.name)\n        print(\"cost :\",self.cost)\n        print(\"colour :\",self.colour)\n        print(\"length :\",self.length)\n    def disc(self):\n        if(self.length > 4):\n            print(\"you got a 10% discount!!\\n\")\n            print(\"so your price is now\",self.cost - self.cost*0.1)\n            \nstring = input(\"enter a name : \")\nc = int(input(\"enter the cost : \"))\nstring1 = input(\"enter colour : \")\nl = int(input(\"enter the length : \"))\n\na = car(string,c,string1,l)\na.disc()\n","repo_name":"manastole03/Programming-practice-2","sub_path":"python 3/day 09/car.py","file_name":"car.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"12708344817","text":"# [문제]\n# 0에서 9까지 숫자가 적힌 N장의 카드가 주어진다.\n# 가장 많은 카드에 적힌 숫자와 카드가 몇 장인지 출력하는 프로그램을 만드시오.\n# 카드 장수가 같을 때는 적힌 숫자가 큰 쪽을 출력한다.\n\n# [입력]\n# 첫 줄에 테스트 케이스 개수 T가 주어진다.  ( 1 ≤ T ≤ 50 )\n# 다음 줄부터 테스트케이스의 첫 줄에 카드 장수 N이 주어진다. ( 5 ≤ N ≤ 100 )\n# 다음 줄에 N개의 숫자 ai가 여백없이 주어진다. (0으로 시작할 수도 있다.)  ( 0 ≤ ai ≤ 9 ) \n\n# [출력]\n# 각 줄마다 \"#T\" (T는 테스트 케이스 번호)를 출력한 뒤, 가장 많은 카드의 숫자와 장 수를 차례로 출력한다.\n\n# [입력]\n# 3\n# 5\n# 49679\n# 5\n# 08271\n# 10\n# 7797946543\n\n# [출력]\n# #1 9 2\n# #2 8 1\n# #3 7 3\n\n\n# [문제풀이]\n# 1. 0~9의 한자리 숫자가 적힌 N장의 카드가 주어지며 0~9까지 각 자리수마다 여러 장이 있을 수 있다.\n# 2. 따라서 각 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 의 각 장 수를 입력받을 수 있는 리스트를 만들어\n# 2-1. 리스트[0] = 숫자 0의 장 수 이도록 하면 편리하게 만들 수 있다.\n# 3. N개의 숫자 ai가 여백없이 주어지므로 해당 숫자로 받는 문자열을 분리 시켜줄 필요가 있다.\n# 4. 각 카드의 장수가 들어있는 리스트를 만들어서 해당 리스트에 각 카드의 장수를 집어넣어준다.\n# 5. 반복문을 통해 각 카드의 장수가 가장 많은 인덱스를 찾아내고, 해당 인덱스가 카드의 번호이므로 인덱스, 리스트[인덱스]로 출력한다.\n\nT = int(input())\n\nfor testcase in range(1, T+1):                          # 1~T만큼 testcase 반복 실행\n    N = int(input())                                    # N 은 카드 장 수\n    num_list = list(map(int, input()))                  # 카드에 적혀 있는 숫자들을 리스트화\n    card_nums = [0]*10                                  # 인덱스가 총 10개인 리스트\n    for i in range(N):                                  # 반복문 범위는 0~(N-1)까지이므로 num_list의 숫자 활용 가능\n        card_nums[num_list[i]] += 1                     # num_list[i]에 있는 숫자 예로 6이라면 card_nums[6]에 +1을 한다.\n                                                        # 참고로 card_nums = [0,0,0,0,0,0,0,0,0,0]으로 [0]~[9]로 이루어져있다.\n    many_num = 0                                        # 추후 이용하기 위해 가장 많은 장수 할당\n    solve_num = 0                                       # 추후 이용하기 위해 가장 많은 장수의 카드 번호를 할당\n    for j in range(len(card_nums)-1, -1, -1):           # card_nums의 길이인 10에서 j를 인자로 하는 0~9의 반복문을 돌린다. \n                                                        # for j in range(len(card_nums))를 사용하려 했으나 #2 8 1이 나와야 해서 역순 출력했다.\n        if card_nums[j] > many_num:                     # 해당 번호의 카드 장 수가 현재까지 나온 가장 많은 장 수 보다 많다면\n            many_num = card_nums[j]                     # 해당 번호의 카드 장 수를 가장 많은 장 수로 입력\n            solve_num = j\n    print(f'#{testcase} {solve_num} {many_num}')","repo_name":"KimBeomGi/STUDYduringSSAFY","sub_path":"python/수업/1일차-숫자카드.py","file_name":"1일차-숫자카드.py","file_ext":"py","file_size_in_byte":3334,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"40346437777","text":"import Adafruit_DHT as dht\nimport RPi.GPIO as gpio\nimport time as delay\nfrom app import app\nfrom flask import render_template\n\n\ngpio.setmode(gpio.BOARD)\ngpio.setwarnings(False)\n\nledvermelho = 11\nledverde = 12\npin_dht = 4\npin_t = 15 \npin_e = 16\nlixeira_v = 20\n\ndht_sensor = dht.DHT11\n\nstsledred = ''\nstsledgreen = ''\n\ngpio.setup(ledvermelho, gpio.OUT)\ngpio.setup(ledverde, gpio.OUT)\ngpio.setup(pin_t, gpio.OUT)\ngpio.setup(pin_e, gpio.IN)\n\ngpio.output(ledvermelho, gpio.LOW)\ngpio.output(ledverde, gpio.LOW)\n\ndef statusledvermelho():\n    if gpio.input(ledvermelho) == 1:\n        statusledvermelho = 'LED vermelho ON'\n    else:\n        statusledvermelho = 'LED vermelho OFF' \n    return statusledvermelho\n\ndef statusledverde():\n    if gpio.input(ledverde) == 1:\n        statusledverde = 'LED verde ON'\n    else:\n        statusledverde = 'LED verde OFF'\n    return statusledverde\n\ndef umid_temp():\n    umid, temp = dht.read(dht_sensor, pin_dht)\n    if umid is not None:\n        umidade = ('{0:0.0f}%'.format(umid))\n    else:\n        umidade = 'Erro ao ler sensor'\n    if temp is not None:\n        temperatura = ('{0:0.0f}*C'.format(temp, umid))\n    else:\n        temperatura = 'Erro ao ler sensor'\n    return umidade, temperatura\n\ndef ocupacao_lixeira():\n    gpio.output(pin_t, True)\n    delay.sleep(0.000001)\n    gpio.output(pin_t, False)\n    tempo_i = delay.time()\n    tempo_f = delay.time()\n    \n    while gpio.input(pin_e) == False:\n        tempo_i = delay.time()\n    while gpio.input(pin_e)  == True:\n        tempo_f = delay.time()\n     \n    tempo_d = tempo_f - tempo_i\n    \n    distancia = (tempo_d*34300)/2\n\n    ocupacao_l = (distancia/lixeira_v)*100\n    ocupacao_f = 100 - ocupacao_l\n    ocupacao_lixeira = ('{0:0.0f}%'.format(ocupacao_f))\n    return ocupacao_lixeira\n\n@app.route(\"/\")\ndef index():\n    templateData = {\n        'ledred'  : statusledvermelho(),\n        'ledgreen' : statusledverde(),\n        'umid' : umid_temp()[0],\n        'temp' : umid_temp()[1],\n        'ocup_lixeira' :  ocupacao_lixeira(),\n    }\n    return render_template('index.html', **templateData)\n\n@app.route(\"/led_vermelho/<action>\")\ndef led_vermelho(action):\n    if action == 'on':\n        gpio.output(ledvermelho, gpio.HIGH)\n    if action == 'off':\n        gpio.output(ledvermelho, gpio.LOW)\n\n    templateData = {\n        'ledred'  : statusledvermelho(),\n        'ledgreen' : statusledverde(),\n        'umid' : umid_temp()[0],\n        'temp' : umid_temp()[1],\n        'ocup_lixeira' :  ocupacao_lixeira(),\n    }\n    return render_template('index.html', **templateData)\n\n@app.route(\"/led_verde/<action>\")\ndef led_verde(action):\n    if action == 'on':\n        gpio.output(ledverde, gpio.HIGH)\n    if action == 'off':\n        gpio.output(ledverde, gpio.LOW)\n        \n    templateData = {\n        'ledred'  : statusledvermelho(),\n        'ledgreen' : statusledverde(),\n        'umid' : umid_temp()[0],\n        'temp' : umid_temp()[1],\n        'ocup_lixeira' :  ocupacao_lixeira(),\n    }\n    return render_template('index.html', **templateData)","repo_name":"GiovaniMarcarini/Aula_Flask_IoT---TI26S-2022","sub_path":"app/controllers/default.py","file_name":"default.py","file_ext":"py","file_size_in_byte":3022,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"5746829115","text":"from django.conf.urls import url\nfrom django.contrib.staticfiles.urls import staticfiles_urlpatterns\nfrom . import views\n# These are all the available urls for django\n# Add all the urls that belong to drug_search here\nurlpatterns = [\n    url(r'^$', views.initialize, name=\"start\"),\n    url(r'browse',views.browse,name=\"browse\"),\n    url(r'grab_chip_seq', views.grab_chip_seq,name=\"grab_chip_seq\"),\n    url(r'grab_snp_data', views.grab_snp_data,name=\"grab_snp_data\"),\n    url(r'get_phenotypes',views.get_phenotypes,name=\"get_phenotypes\"),\n    url(r'test', views.test,name=\"test\")\n]\n#This last part is important so django can find static files in the static folder\nurlpatterns += staticfiles_urlpatterns()","repo_name":"danich1/Non-Coding-Variation-Browser","sub_path":"nc_snp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"42039728218","text":"from django.http import HttpResponse\nfrom django.shortcuts import render\nfrom django.db import connections\nfrom django.template.loader import get_template, render_to_string\nfrom django import template\n\ndef index(request):\n    message = \"Salut tout le monde !\"\n    return HttpResponse(message)\n\n\n\ndef search(request):\n    #price_lte = request.GET\n    # body = [\n    #     {'title' : 'First Post', 'body' : 'z'},\n    #     {'title': 'First Post', 'body': 'a'},\n    # ]\n\n    c = connections['default'].cursor()\n    c.execute(\"\"\"\n        SELECT\n            musiques.titre,\n            artistes.nom,\n            musiques.featuring,\n            types.nom\n        FROM\n            musiques\n        INNER JOIN artistes ON musiques.artiste_id = artistes.id \n        INNER JOIN albums   ON musiques.album_id = albums.id\n        INNER JOIN types    ON musiques.type_id = types.id;    \n    \"\"\")\n    rows = c.fetchall()\n\n    print(rows[0])\n\n    body = render_to_string('pages/search.html', {'querry_musiques':rows})\n\n    return render(request, 'blueunicorn_tailwind/pannel.html', {'body':body})","repo_name":"wiloker/djangoProject","sub_path":"djangoProject/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1081,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"25542880887","text":"\n\n\ndef fibonacci_memo(n, memo = {0:0, 1:0, 2:1}):\n\n    if n in memo:\n        return memo[n]\n    else:\n        memo[n] = fibonacci_memo(n-1, memo) + fibonacci_memo(n-2, memo)\n        return memo[n]\n\n\ndef fibonacci_iter(n):\n\n    last = 0\n    secondLast = 1\n    counter = 2\n\n    while(counter <= n):\n        currFibbo = last + secondLast\n        last = secondLast\n        secondLast = currFibbo\n        counter += 1\n\n    return secondLast if n > 1 else last\n\n\n\n\nprint(fibonacci_memo(10))\n\nprint(fibonacci_iter(10))\n","repo_name":"Vaibhav3M/Coding-challenges","sub_path":"6-NthFibonacci/Fibonacci.py","file_name":"Fibonacci.py","file_ext":"py","file_size_in_byte":512,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"18662791493","text":"'''\nDescripción: Analizador de protocolos\nAutores:\n    David Armando Rodríguez Varón - 20181020041\n    Juan Sebastián Sanchez Tabares - 20181020008\n    Johan Sneider Mendez Vega - 20172020070\n    Juan Sebastián Mancera Gaitán - 20171020047\n'''\n\nimport socket\nimport struct\nimport textwrap\nimport binascii\n\n'''\nConstantes para organizar la información\n'''\nTAB_1 = '\\t - '\nTAB_2 = '\\t\\t - '\nTAB_3 = '\\t\\t\\t - '\nTAB_4 = '\\t\\t\\t\\t - '\n\nDATA_TAB_1 = '\\t '\nDATA_TAB_2 = '\\t\\t '\nDATA_TAB_3 = '\\t\\t\\t '\nDATA_TAB_4 = '\\t\\t\\t\\t '\n\ndef main():\n    #Ultimo argumento verifica que sea compatible entre todos los dispositivos\n    conn = socket.socket(socket.AF_PACKET, socket.SOCK_RAW, socket.ntohs(3))\n    #ARP\n    connarp = socket.socket(socket.AF_PACKET, socket.SOCK_RAW, socket.htons(0x003))\n\n    while True: # Mientras reciba paquetes\n        raw_data, addr = conn.recvfrom(65536)\n        dest_mac, src_mac, eth_proto, data = capture_packages(raw_data)\n        if eth_proto != 1544:\n            print('\\nPaquete :')\n            print('Destino: {}, Origen: {}, Protocolo: {}'.format(dest_mac, src_mac,\n                                                                 eth_proto))\n        # 8 / IP\n        if eth_proto == 8:\n            (version, header_length, ttl, proto, src, target, data) = ip_packet(data)\n            print(TAB_1 + 'Paquete IP: ')\n            print(TAB_2 + 'Versión {}, Longitud del encabezado: {}, TTL: {}'.format(version, header_length, ttl))\n            print(TAB_2 + 'Protocolo {}, Fuente: {}, Destino: {}'.format(proto, src, target))\n\n            #ICMP\n            if proto == 1:\n                icmp_type, code, checksum, data = icmp_packet(data)\n                print(TAB_1 + 'Paquete ICMP: ')\n                print(TAB_2 + 'Tipo: {}, Código: {}, Checksum: {}, '.format(icmp_type, code, checksum))\n                print(TAB_2 + 'Datos: ')\n                print(format_multi_line(DATA_TAB_3, data))\n            #TCP\n            elif proto == 6:\n                src_port, dest_port, sequence, acknowledgement, flag_urg, flag_ack, flag_psh, flag_rst, flag_syn, flag_fin, data = tcp_segment(data)\n                print(TAB_1 + 'Segmento TCP: ')\n                print(TAB_2 + 'Puerto de origen: {}, Puerto de destino: {}, '.format(src_port, dest_port))\n                print(TAB_2 + 'Secuencia: {}, Acknowlodegment: {}, '.format(sequence, acknowledgement))\n                print(TAB_2 + 'Banderas: ')\n                print(TAB_3 + 'URG: {}, ACK, {}, PSH: {}, RST: {}, SYN: {}, FIN: {}'.format(flag_urg, flag_ack,\n                                                                                            flag_psh, flag_rst, flag_syn, flag_fin))\n                print(TAB_2 + 'Datos: ')\n                print(format_multi_line(DATA_TAB_3, data))\n            #UDP\n            elif proto == 17:\n                src_port, dest_port, length, data = udp_segment(data)\n                print(TAB_1 + 'Segmento UDP:')\n                print(TAB_2 + 'Puerto de origen: {}, Puerto de destino: {}, longitud: {}'.format(src_port, dest_port,\n                                                                                                 length))\n                print(TAB_2 + 'Datos: ')\n                print(format_multi_line(DATA_TAB_3, data))\n            #Otro\n            else:\n                print(TAB_1 + 'Datos: ')\n                print(format_multi_line(DATA_TAB_1, data))      \n        \n        elif eth_proto != 1544:\n            print(TAB_1 + 'Datos: ')\n            print(format_multi_line(DATA_TAB_1, data))\n\n        paquete_arp = connarp.recvfrom(2048)\n        ethernet_header = paquete_arp[0][:14]\n        ethernet_detalles = struct.unpack('!6s6s2s', ethernet_header)\n\n        cabecera_arp = paquete_arp[0][14:42]\n        arp_detalles = struct.unpack('2s2s1s1s2s6s4s6s4s', cabecera_arp)\n        ethertype = ethernet_detalles[2]\n\n        #Paquete ARP\n        if ethertype == b'\\x08\\x06':\n            print('\\nPaquete ARP:')\n            print(TAB_1 + 'Tipo de hardware: {}, Tipo de protocolo: {}'.format(str(binascii.hexlify(arp_detalles[0]), 'utf-8'),\n                                                                               str(binascii.hexlify(arp_detalles[1]), 'utf-8')))\n            print(TAB_1 + 'Tamaño del hardware: {}, Tamaño del protocolo: {}, opcode: {}'.format(str(binascii.hexlify(arp_detalles[2]), 'utf-8'),\n                                                                                     str(binascii.hexlify(arp_detalles[3]), 'utf-8'),\n                                                                                                 str(binascii.hexlify(arp_detalles[4]), 'utf-8')))\n            print(TAB_1 + 'Dirección MAC origen: {}, Dirección IP origen: {}'.format(str(binascii.hexlify(arp_detalles[5]), 'utf-8'),\n                                                                                     socket.inet_ntoa(arp_detalles[6])))\n            print(TAB_1 + 'Dirección MAC destino: {}, Dirección IP destino: {}'.format(str(binascii.hexlify(arp_detalles[7]), 'utf-8'),\n                                                                                     socket.inet_ntoa(arp_detalles[8])))\n    \ndef capture_packages(data):\n    '''\n    Obtiene la información del paquete\n    ---\n    Sync -- Sincroniza el dispositivo y el router\n    Receiver -- Quien lo recibe\n    Sender -- Quien lo envia\n    Type -- IP4, IP6, ARP, etc ...\n    Payload -- (IP/ARP frame + padding), datos\n    CRC -- manejo de errores, se asegura de que se reciba la información correctamente\n    ---\n    :param data: paquete\n    :return: direcciones mac de destino, origen, tipo de protocolo y payload\n    '''\n    destination, source, protocol = struct.unpack('! 6s 6s H', data[:14])\n    return get_mac_addr(destination), get_mac_addr(source), socket.htons(protocol), data[14:]\n\ndef get_mac_addr(bytes_addr):\n    '''\n    Pasa la dirección mac a formato legible\n    :param bytes_addr: dirección mac en bytes\n    :return: dirección mac en formato legible\n    '''\n    bytes_string = map('{:02x}'.format, bytes_addr)\n    return ':'.join(bytes_string).upper()\n\ndef ip_packet(data):\n    '''\n    Información que viene antes del payload\n    ---\n    Version\n    IHL -- Longitud del encabezado\n    TTL -- Time To Live\n    Procol -- protocolo usado TCP, UDP etc \n    Source address -- ip de origen\n    Destination address -- ip de destino\n    ---\n    :param data: paquete ip\n    :return version, header_length, ttl, protocol, source ip, target ip, payload\n    '''\n    version_header_length = data[0]\n    version = version_header_length >> 4 #Movimiento hacia la derecha\n    header_length = (version_header_length & 15) * 4\n    ttl, proto, src, target = struct.unpack('! 8x B B 2x 4s 4s', data[:20])\n\n    return version, header_length, ttl, proto, ipv(src), ipv(target), data[header_length:]\n\ndef ipv(addr):\n    '''\n    Pasa la dirección ip a formato legible\n    :param addr: dirección ip en bytes\n    :return dirección ip en formato XXX.XXX.X.X\n    '''\n    return '.'.join(map(str, addr))\n\ndef icmp_packet(data):\n    '''\n    Obtiene la información para el protocolo ICMP\n    :param data: payload de tipo ICMP\n    :return tipo de icmp, code, checksum, información del paquete\n    '''\n    icmp_type, code, checksum = struct.unpack('! B B H', data[:4])\n\n    return icmp_type, code, checksum, data[4:]\n\n\ndef tcp_segment(data):\n    '''\n    Obtiene la información para el protocolo TCP/IP\n    :param data: datos de tipo TCP/IP\n    :return puerto de origen, puerto de destino, sequence, acknowledgement, banderas, datos\n    '''\n    (source_port, dest_port, sequence, acknowledgement, offset_reserved_flags) = struct.unpack(\n        '! H H L L H', data[:14])\n\n    offset = (offset_reserved_flags >> 12) * 4\n    bandera_urg = (offset_reserved_flags & 32) >> 5\n    bandera_ack = (offset_reserved_flags & 16) >> 4\n    bandera_psh = (offset_reserved_flags & 8) >> 3\n    bandera_rst = (offset_reserved_flags & 4) >> 2\n    bandera_syn = (offset_reserved_flags & 2) >> 1\n    bandera_fin = offset_reserved_flags & 1\n    \n    return source_port, dest_port, sequence, acknowledgement, bandera_urg, bandera_ack, bandera_psh, bandera_rst, bandera_syn, bandera_fin, data[offset:]\n\ndef udp_segment(data):\n    '''\n    Obtiene la información para el protocolo UDP\n    :param data: payload de tipo UDP\n    :return puerto de origen, puerto de destino, tamaño, información del paquete\n    '''\n    source_port, dest_port, size = struct.unpack('! H H 2x H', data[:8])\n    return source_port, dest_port, size, data[8:]\n\n#Organiza multi-line data\ndef format_multi_line(prefix, string, size= 80):\n    '''\n    Identa lineas para strings de gran tamaño\n    :param prefix: prefijo\n    :param string: data\n    :param size: tamaño\n    :return información identada\n    '''\n    size -= len(prefix)\n    if isinstance(string, bytes):\n        string = ''.join(r'\\x{:02x}'.format(byte) for byte in string)\n        if size % 2:\n            size -= 1\n    return '\\n'.join([prefix + line for line in textwrap.wrap(string, size)])\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Darvaron/Analizador-de-protocolos-b-sico","sub_path":"sniffer_redes.py","file_name":"sniffer_redes.py","file_ext":"py","file_size_in_byte":9055,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"19359194900","text":"\"\"\"CLI.\"\"\"\nimport os\nfrom os import cpu_count, getcwd, chdir, getenv\nimport datetime\nimport json\nfrom pathlib import Path\nimport click\nfrom tempfile import TemporaryDirectory\nimport toml\nfrom pkg_resources import resource_filename as pkgr_fn\n\n\ndef validate_name(ctx, param, value):\n    \"\"\"Check whether this template already exists in the Archive.\"\"\"\n    from templateflow.api import templates\n\n    value = value[4:] if value.startswith(\"tpl-\") else value\n    if value in templates():\n        raise click.BadParameter(\n            f\"A template with name {value} already exists in the Archive.\"\n        )\n    return value\n\n\ndef is_set(ctx, param, value):\n    \"\"\"Check that an argument is set.\"\"\"\n    if not value:\n        raise click.BadParameter(\n            f\"Please set it explicitly or define the corresponding environment variable.\"\n        )\n    return value\n\n\n@click.group()\n@click.version_option(message=\"TF Archive manager %(version)s\")\ndef cli():\n    \"\"\"The TemplateFlow Archive manager assists you in adding and updating templates.\"\"\"\n    pass\n\n\n@cli.command()\n@click.argument(\"template_id\", callback=validate_name)\n@click.option(\"--osf-user\", envvar=\"OSF_USERNAME\", callback=is_set)\n@click.password_option(\n    \"--osf-password\",\n    envvar=\"OSF_PASSWORD\",\n    prompt=\"OSF password\",\n    confirmation_prompt=False,\n)\n@click.option(\"--osf-overwrite\", is_flag=True)\n@click.option(\n    \"--gh-user\",\n    envvar=\"GITHUB_USER\",\n)\n@click.password_option(\n    \"--gh-token\",\n    prompt=\"GitHub personal authentication token\",\n    confirmation_prompt=False,\n    envvar=\"GITHUB_TOKEN\",\n)\n@click.option(\"--path\", type=click.Path(exists=True))\n@click.option(\"-j\", \"--nprocs\", type=click.IntRange(min=1), default=cpu_count())\ndef add(\n    template_id,\n    osf_user,\n    osf_password,\n    osf_overwrite,\n    gh_user,\n    gh_token,\n    path,\n    nprocs,\n):\n    \"\"\"Add a new template.\"\"\"\n    from .io import run_command\n    from .utils import copy_template\n    import shutil\n    from datalad import api as dl\n\n    if not gh_user or not gh_token:\n        raise click.BadParameter(\"Insufficient secrets to login into GitHub\")\n\n    os.environ[\"DATALAD_CREDENTIAL_GITHUB_TOKEN\"] = gh_token\n\n    path = Path(path or f\"tpl-{template_id}\").absolute()\n    cwd = Path.cwd()\n\n    if not path.exists():\n        raise click.UsageError(f\"<{path}> does not exist.\")\n\n    metadata = {}\n\n    # Check metadata\n    if (path / \"template_description.json\").exists():\n        metadata = json.loads((path / \"template_description.json\").read_text())\n    metadata[\"Identifier\"] = template_id\n\n    # Check license\n    license_path = path / \"LICENSE\"\n    if not license_path.exists():\n        license_path = path / \"LICENCE\"\n    if not license_path.exists():\n        license_path = path / \"COPYING\"\n\n    if not license_path.exists():\n        license_prompt = click.prompt(\n            text=\"\"\"\\\nA LICENSE file MUST be distributed with the template. The TemplateFlow Manager can \\\nset a license (either CC0 or CC-BY) for you.\"\"\",\n            type=click.Choice((\"CC0\", \"CC-BY\", \"Custom (abort)\")),\n            default=\"Custom (abort)\",\n        )\n        if license_prompt == \"Custom (abort)\":\n            raise click.UsageError(\n                \"Cannot proceed without a valid license. Please write a LICENSE \"\n                \"file before uploading.\"\n            )\n\n        license_path = Path(pkgr_fn(\"tfmanager\", f\"data/{license_prompt}.LICENSE\"))\n        metadata[\"License\"] = license_prompt\n\n    # Check RRID\n    if not metadata.get(\"RRID\"):\n        rrid = click.prompt(\n            text=\"Has a RRID (research resource ID) already been assigned?\",\n            type=str,\n            default=''\n        ) or None\n\n        if rrid:\n            metadata[\"RRID\"] = rrid\n\n    # Check short description\n    if not metadata.get(\"Name\", \"\").strip():\n        short_desc = click.prompt(\n            text=\"\"\"\\\nThe \"Name\" metadata is not found within the <template_description.json> file. \\\nPlease provide a short description for this resource.\"\"\",\n            type=str,\n        )\n\n        if not short_desc:\n            raise click.UsageError(\n                \"Cannot proceed without a short description.\"\n            )\n\n        metadata[\"Name\"] = short_desc\n\n    # Check authors\n    authors_prompt = [a.strip() for a in metadata.get(\"Authors\", []) if a.strip()]\n    if not authors_prompt:\n        authors_prompt = [\n            n.strip() for n in click.prompt(\n                text=\"\"\"\\\nThe \"Authors\" metadata is not found within the <template_description.json> file. \\\nPlease provide a list of authors separated by semicolon (;) in <Lastname Initial(s)> format.\"\"\",\n                type=str,\n            ).split(\";\")\n            if n\n        ]\n        if not authors_prompt:\n            click.confirm(\"No authors were given, do you want to continue?\", abort=True)\n\n    metadata[\"Authors\"] = authors_prompt\n\n    # Check references\n    refs_prompt = [\n        f\"\"\"\\\n{'https://doi.org/' if not a.strip().startswith('http') else ''}\\\n{a.replace(\"doi:\", \"\").strip()}\"\"\"\n        for a in metadata.get(\"ReferencesAndLinks\", []) if a.strip()\n    ]\n    if not refs_prompt:\n        refs_prompt = [\n            n.replace('\"', \"\").strip() for n in click.prompt(\n                text=\"\"\"\\\nThe \"ReferencesAndLinks\" metadata is not found within the <template_description.json> file. \\\nPlease provide a list of links and publications within double-quotes \\\n(for example, \"doi:10.1101/2021.02.10.430678\") and separated by spaces (< >).\"\"\",\n                type=str,\n            ).split(\" \")\n            if n\n        ]\n        if not refs_prompt:\n            click.confirm(\"No authors were given, do you want to continue?\", abort=True)\n    metadata[\"ReferencesAndLinks\"] = refs_prompt\n\n    with TemporaryDirectory() as tmpdir:\n        repodir = Path(tmpdir) / \"templateflow\"\n\n        # Clone root <user>/templateflow project - fork if necessary\n        click.echo(f\"Preparing Pull-Request (wd={tmpdir}).\")\n        clone = run_command(\n            f\"git clone https://github.com/{gh_user}/templateflow.git \"\n            \"--branch tpl-intake --single-branch\",\n            cwd=tmpdir,\n            capture_output=False,\n        )\n        if clone.returncode != 0:\n            run_command(\n                \"hub clone templateflow/templateflow\",\n                cwd=tmpdir,\n                capture_output=False,\n                env={\"GITHUB_TOKEN\": gh_token},\n            )\n            run_command(\n                \"hub fork --remote-name origin\",\n                cwd=str(repodir),\n                capture_output=False,\n                env={\"GITHUB_TOKEN\": gh_token},\n            )\n        else:\n            run_command(\n                \"git remote add upstream https://github.com/templateflow/templateflow.git\",\n                cwd=str(repodir),\n                capture_output=False,\n            )\n\n        chdir(repodir)\n\n        # Create datalad dataset\n        dl.create(\n            path=f\"tpl-{template_id}\",\n            cfg_proc=\"text2git\",\n            initopts={\"initial-branch\": \"main\"},\n            description=metadata[\"Name\"],\n        )\n        gitattr = (repodir / f\"tpl-{template_id}\" / \".gitattributes\").read_text().strip()\n        (repodir / f\"tpl-{template_id}\" / \".gitattributes\").write_text(\"\\n\".join([\n            gitattr,\n            \"*.gii annex.largefiles=anything\",\n            \"\",\n        ]))\n\n        # Populate template\n        copy_template(\n            path=path,\n            dest=repodir / f\"tpl-{template_id}\",\n        )\n        # Copy license\n        shutil.copy(license_path, repodir / f\"tpl-{template_id}\" / \"LICENSE\")\n        # (Over)write template_description.json\n        (repodir / f\"tpl-{template_id}\" / \"template_description.json\").write_text(\n            json.dumps(metadata, indent=2)\n        )\n        # Init/update CHANGELOG\n        changelog = repodir / f\"tpl-{template_id}\" / \"CHANGES\"\n        changes = [f\"\"\"\n## {datetime.date.today().ctime()} - TemplateFlow Manager Upload\nPopulated contents after NIfTI sanitizing by the TF Manager.\n\n\"\"\"]\n        if changelog.exists():\n            changes += [changelog.read_text()]\n        changelog.write_text(\"\\n\".join(changes))\n\n        # Init OSF sibling\n        rrid_str = f\" (RRID: {metadata['RRID']})\" if metadata.get(\"RRID\") else \"\"\n        dl.create_sibling_osf(\n            title=f\"TemplateFlow resource: <{template_id}>{rrid_str}\",\n            name=\"osf\",\n            dataset=f\"./tpl-{template_id}\",\n            public=True,\n            category=\"data\",\n            description=metadata[\"Name\"],\n            tags=[\"TemplateFlow dataset\", template_id]\n        )\n        # Init GH sibling\n        dl.create_sibling_github(\n            reponame=f\"tpl-{template_id}\",\n            dataset=str(repodir / f\"tpl-{template_id}\"),\n            publish_depends=\"osf-storage\",\n            existing=\"replace\",\n            access_protocol=\"ssh\"\n        )\n\n        # Save added contents\n        dl.save(\n            dataset=str(repodir / f\"tpl-{template_id}\"),\n            message=\"ADD: TemplateFlow Manager initialized contents\"\n        )\n\n        # Push to siblings\n        dl.push(\n            dataset=str(repodir / f\"tpl-{template_id}\"),\n            to=\"github\",\n            jobs=cpu_count(),\n        )\n\n        # Back home\n        chdir(cwd)\n\n        run_command(\n            \"git fetch upstream tpl-intake\", cwd=str(repodir), capture_output=False,\n        )\n        run_command(\n            f\"git checkout -b pr/tpl-{template_id} upstream/tpl-intake\",\n            cwd=str(repodir),\n            capture_output=False,\n        )\n        (repodir / f\"{path.name}.toml\").write_text(\n            toml.dumps({\"github\": {\"user\": gh_user},})\n        )\n        run_command(\n            f\"git add {path.name}.toml\", cwd=str(repodir), capture_output=False,\n        )\n        run_command(\n            f\"git commit -m 'add(tpl-{template_id}): create intake file'\",\n            cwd=str(repodir),\n            capture_output=False,\n        )\n        run_command(\n            f\"git push -u origin pr/tpl-{template_id}\",\n            cwd=str(repodir),\n            capture_output=False,\n            env={\"GITHUB_USER\": gh_user, \"GITHUB_TOKEN\": gh_token},\n        )\n\n        (repodir.parent / \"message.md\").write_text(\n            f\"\"\"\\\nADD: ``tpl-{template_id}``\n\n## {metadata.get('Name', '<missing Name>')}\n\nIdentifier: {metadata.get('Identifier', '<missing Identifier>')}\nDatalad: https://github.com/{gh_user}/tpl-{template_id}\n\n### Authors\n{', '.join(metadata['Authors'])}.\n\n### License\n{metadata.get('License', metadata.get('Licence', '<missing License>'))}\n\n### Cohorts\n{' '.join(('The dataset contains', str(len(metadata.get('cohort', []))), 'cohorts.'))\n if metadata.get('cohort') else 'The dataset does not contain cohorts.'}\n\n### References and links\n{', '.join(metadata.get('ReferencesAndLinks', [])) or 'N/A'}\n\n\"\"\"\n        )\n        run_command(\n            \"hub pull-request -b templateflow:tpl-intake \"\n            f\"-h {gh_user}:pr/tpl-{template_id} \"\n            f\"-F {repodir.parent / 'message.md'}\",\n            cwd=str(repodir),\n            capture_output=False,\n            env={\"GITHUB_TOKEN\": gh_token},\n        )\n\n    del os.environ[\"DATALAD_CREDENTIAL_GITHUB_TOKEN\"]\n\n\n@cli.command()\n@click.argument(\"template_id\", callback=validate_name)\n@click.option(\"--osf-project\", envvar=\"OSF_PROJECT\", callback=is_set)\n@click.option(\"--osf-user\", envvar=\"OSF_USERNAME\", callback=is_set)\n@click.password_option(\n    \"--osf-password\",\n    envvar=\"OSF_PASSWORD\",\n    prompt=\"OSF password\",\n    confirmation_prompt=False,\n)\n@click.option(\"--osf-overwrite\", is_flag=True)\n@click.option(\"--path\", type=click.Path(exists=True))\n@click.option(\"-j\", \"--nprocs\", type=click.IntRange(min=1), default=cpu_count())\ndef push(\n    template_id, osf_project, osf_user, osf_password, osf_overwrite, path, nprocs,\n):\n    \"\"\"Push a new template, but do not create PR.\"\"\"\n    from .osf import upload as _upload\n    path = Path(path or f\"tpl-{template_id}\")\n\n    if not path.exists():\n        raise click.UsageError(f\"<{path}> does not exist.\")\n\n    _upload(\n        template_id, osf_project, osf_user, osf_password, osf_overwrite, path, nprocs,\n    )\n\n\n@cli.command()\n@click.argument(\"template_id\")\n@click.option(\"--osf-project\", envvar=\"OSF_PROJECT\", callback=is_set)\n@click.option(\"--overwrite\", is_flag=True)\n@click.option(\"--path\", type=click.Path(exists=False))\n@click.option(\"-j\", \"--nprocs\", type=click.IntRange(min=1), default=cpu_count())\ndef get(\n    template_id, osf_project, overwrite, path, nprocs,\n):\n    \"\"\"Add a new template.\"\"\"\n    from .osf import get_template as _get\n    if template_id.startswith(\"tpl-\"):\n        template_id = template_id[4:]\n\n    path = Path(path or f\"tpl-{template_id}\")\n\n    if path.name != f\"tpl-{template_id}\":\n        path = path / f\"tpl-{template_id}\"\n\n    if path.exists():\n        click.echo(f\"WARNING: <{path}> exists.\")\n\n    _get(template_id, osf_project, overwrite, path, nprocs)\n\n\n@cli.command()\n@click.argument(\"template_id\")\n@click.option(\"--osf-project\", envvar=\"OSF_PROJECT\", default=\"ue5gx\")\n@click.option(\"-o\", \"--out-csv\", type=click.Path(exists=False))\ndef geturls(template_id, osf_project, out_csv):\n    \"\"\"Add a new template.\"\"\"\n    from .osf import get_project_urls\n\n    if template_id.startswith(\"tpl-\"):\n        template_id = template_id[4:]\n\n    urls = get_project_urls(\n        f\"\"\"\\\nhttps://files.osf.io/v1/resources/{osf_project}/providers/osfstorage/\\\n\"\"\",\n        f\"tpl-{template_id}\",\n    )\n    if out_csv:\n        Path(out_csv).write_text(urls)\n        return\n    print(urls)\n\n\n@cli.command()\n@click.argument(\"template_dir\", type=click.Path(exists=True), default=getcwd())\n@click.option(\"--normalize/--no-normalize\", default=True)\n@click.option(\"--force-dtype/--no-force-dtype\", default=True)\n@click.option(\"--deoblique/--no-deoblique\", default=False)\ndef sanitize(template_dir, normalize, force_dtype, deoblique):\n    \"\"\"Check orientation and datatypes of NIfTI files in template folder.\"\"\"\n    from .utils import copy_template as _copy_template\n\n    updated = _copy_template(template_dir, normalize, force_dtype, deoblique)\n    if updated:\n        print(\n            \"\\n  * \".join(\n                [\"Modified:\"] + [f\"<{u.relative_to(template_dir)}>\" for u in updated]\n            )\n        )\n\n\n@cli.command()\n@click.argument(\"template_id\")\n@click.argument(\"field\")\n@click.option(\"--path\", type=click.Path(exists=True))\ndef metadata(template_id, field, path):\n    \"\"\"Get a metadata entry from a template.\"\"\"\n    import json\n    if template_id.startswith(\"tpl-\"):\n        template_id = template_id[4:]\n    path = Path(path or f\"tpl-{template_id}\")\n    metadata = json.loads((path / \"template_description.json\").read_text())\n    print(metadata.get(field))\n\n\nif __name__ == \"__main__\":\n    \"\"\" Install entry-point \"\"\"\n    cli()\n","repo_name":"templateflow/python-manager","sub_path":"tfmanager/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":14790,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"7431229252","text":"import json\nwith open ('5_task.json','r') as f:\n    j=json.load(f)\nra={}\ni=0\npav=0\nwhile  i<len(j):\n    if j[i]['language']=='English':\n        pav=pav+1\n        ra['english']=pav\n    i=i+1\nwith open (\"6_task.json\",\"w\") as f:\n    json.dump(ra,f )\n        \n","repo_name":"saipavanijogi/web_scraping","sub_path":"web scrap 6.py","file_name":"web scrap 6.py","file_ext":"py","file_size_in_byte":256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"30384410076","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Jun  6 16:51:22 2020\r\n\r\n@author: user\r\n\"\"\"\r\n\r\n#improved caesar cipher\r\n\r\ntext = input('Input one line to encrypt: ')\r\nshift = int(input('Input shift value: '))\r\ncipher = ''\r\nfor letter in text:\r\n    if not letter.isalpha():\r\n        cipher += letter\r\n    else:\r\n        digit = ord(letter) + shift\r\n        if letter.isupper():\r\n            if digit <= ord('Z'):\r\n                cipher += chr(digit)\r\n            else:\r\n                digit = ord('A') + (digit - ord('Z')) - 1\r\n                cipher+= chr(digit)\r\n        if letter.islower():\r\n            if digit <= ord('z'):\r\n                cipher += chr(digit)\r\n            else:\r\n                digit = ord('a') + (digit - ord('z')) - 1\r\n                cipher+= chr(digit)\r\nprint(cipher)","repo_name":"LuckyFasyni/coding_challenge","sub_path":"lab_improved_caesar_cipher.py","file_name":"lab_improved_caesar_cipher.py","file_ext":"py","file_size_in_byte":793,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"18549044840","text":"from sqlalchemy import Column, DateTime, Float, Integer, desc\nfrom sqlalchemy.exc import IntegrityError\n\nfrom .base import Base, session_scope\n\n\nclass BaseCandleMixin:\n    time = Column(DateTime, primary_key=True, nullable=False)\n    open = Column(Float)\n    close = Column(Float)\n    low = Column(Float)\n    high = Column(Float)\n    volume = Column(Integer)\n\n    @property\n    def values(self):\n        return {\n            \"time\": self.time,\n            \"open\": self.open,\n            \"close\": self.close,\n            \"low\": self.low,\n            \"high\": self.high,\n            \"volume\": self.volume,\n        }\n\n    @classmethod\n    def create(cls, time, open, close, high, low, volume):\n        candle = cls(\n            time=time, open=open, close=close, high=high, low=low, volume=volume\n        )\n        try:\n            with session_scope() as session:\n                session.add(candle)\n        except IntegrityError as e:\n            print(f\"skipped: {e}\")\n        return candle\n\n    @classmethod\n    def create_by_list(cls, candle_lst, commit_each_time=False):\n        if commit_each_time:\n            for candle in candle_lst:\n                cls.create(**candle)\n            return cls\n        try:\n            with session_scope() as session:\n                for candle in candle_lst:\n                    candle = cls(**candle)\n                    session.add(candle)\n        except IntegrityError as e:\n            print(f\"skipped: {e}\")\n        return cls\n\n    @classmethod\n    def get_last_candle(cls):\n        with session_scope() as session:\n            candle = session.query(cls).order_by(desc(cls.time)).first()\n        return candle\n\n\nclass UsdJpyCandle1M(BaseCandleMixin, Base):\n    __tablename__ = \"USD_JPY_1M\"\n","repo_name":"poteitoo/fx_fastapi","sub_path":"db/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1737,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"1583604962","text":"import unittest\nfrom hamcrest import *\nfrom testutils import attr\n\nfrom asterstudy.datamodel.general import Validity\nfrom asterstudy.datamodel.history import History\n\nfrom asterstudy.datamodel.comm2study import comm2study\n\n\n#------------------------------------------------------------------------------\ndef test():\n    #--------------------------------------------------------------------------\n    history = History()\n    case = history.current_case\n    stage = case.create_stage(':memory:')\n\n    text = \\\n\"\"\"\nDEBUT(CODE=_F(NIV_PUB_WEB='INTERNET'), DEBUG=_F(SDVERI='OUI'))\n\nMAIL_Q = LIRE_MAILLAGE()\n\nMATER = DEFI_MATERIAU(\n    ECRO_LINE=_F(D_SIGM_EPSI=-1950.0, SY=3.0),\n    ELAS=_F(E=30000.0, NU=0.2, RHO=2764.0)\n)\n\nCHMAT_Q = AFFE_MATERIAU(AFFE=_F(MATER=MATER, TOUT='OUI'), MAILLAGE=MAIL_Q)\n\nMODELUPG = AFFE_MODELE(\n    AFFE=_F(MODELISATION='AXIS_INCO_UPG', PHENOMENE='MECANIQUE', TOUT='OUI'),\n    MAILLAGE=MAIL_Q\n)\n\nMODELUPQ = AFFE_MODELE(\n    AFFE=_F(MODELISATION='AXIS_INCO_UPG', PHENOMENE='MECANIQUE', TOUT='OUI'),\n    MAILLAGE=MAIL_Q\n)\n\nMODELUPL = AFFE_MODELE(\n    AFFE=_F(MODELISATION='AXIS_INCO_UP', PHENOMENE='MECANIQUE', TOUT='OUI'),\n    MAILLAGE=MAIL_Q\n)\n\nMATMUPG = CALC_MATR_ELEM(\n    CHAM_MATER=CHMAT_Q, MODELE=MODELUPG, OPTION='MASS_MECA'\n)\n\nFIN()\n\"\"\"\n    comm2study(text, stage)\n\n    assert_that(stage, has_length(9))\n\n    #--------------------------------------------------------------------------\n    command = stage['CHMAT_Q']\n    assert_that(command.check(), equal_to(Validity.Nothing))\n\n    storage = command.storage\n\n    command.init({})\n    assert_that(command.check(), equal_to(Validity.Syntaxic))\n\n    command.init(storage)\n    assert_that(command.check(), equal_to(Validity.Nothing))\n\n    #--------------------------------------------------------------------------\n    pass\n\n\ndef test_repair():\n    history = History()\n    case = history.current_case\n    stage = case.create_stage(':memory:')\n\n    text = \\\n\"\"\"\nMAIL_Q = LIRE_MAILLAGE()\n\nMATER = DEFI_MATERIAU(\n    ECRO_LINE=_F(D_SIGM_EPSI=-1950.0, SY=3.0),\n    ELAS=_F(E=30000.0, NU=0.2, RHO=2764.0)\n)\n\nCHMAT_Q = AFFE_MATERIAU(AFFE=_F(MATER=MATER, TOUT='OUI'), MAILLAGE=MAIL_Q)\n\"\"\"\n    comm2study(text, stage)\n\n    stage.use_text_mode()\n    # does nothing in text mode\n    assert_that(case.repair(), equal_to(Validity.Nothing))\n\n    stage.use_graphical_mode()\n    del stage['MAIL_Q']\n\n    assert_that(stage['CHMAT_Q'].check(), equal_to(Validity.Dependency))\n    assert_that(stage.check(), equal_to(Validity.Dependency))\n\n    # create the \"same\" command (same names and returned types)\n    newmesh = stage('LIRE_MAILLAGE', 'MAIL_Q')\n\n    # the dependencies must be broken\n    assert_that(stage['CHMAT_Q'].check(), equal_to(Validity.Dependency))\n    assert_that(stage.check(), equal_to(Validity.Dependency))\n\n    # will be repaired using newmesh\n    assert_that(case.repair(), equal_to(Validity.Nothing))\n\n\ndef test_repair_detr():\n    history = History()\n    case = history.current_case\n    stage = case.create_stage(':memory:')\n\n    text = \\\n\"\"\"\nMAIL_Q = LIRE_MAILLAGE()\n\nMATER = DEFI_MATERIAU(\n    ECRO_LINE=_F(D_SIGM_EPSI=-1950.0, SY=3.0),\n    ELAS=_F(E=30000.0, NU=0.2, RHO=2764.0)\n)\n\nCHMAT_Q = AFFE_MATERIAU(AFFE=_F(MATER=MATER, TOUT='OUI'), MAILLAGE=MAIL_Q)\n\"\"\"\n    comm2study(text, stage)\n\n    stage.use_text_mode()\n    # does nothing in text mode\n    assert_that(case.repair(), equal_to(Validity.Nothing))\n\n    stage.use_graphical_mode()\n    del stage['MAIL_Q']\n\n    assert_that(stage['CHMAT_Q'].check(), equal_to(Validity.Dependency))\n    assert_that(stage.check(), equal_to(Validity.Dependency))\n\n    # create the \"same\" command (same names and returned types)\n    newmesh = stage('LIRE_MAILLAGE', 'MAIL_Q')\n\n    # the dependencies must be broken\n    assert_that(stage['CHMAT_Q'].check(), equal_to(Validity.Dependency))\n    assert_that(stage.check(), equal_to(Validity.Dependency))\n\n    detr = stage('DETRUIRE')\n    assert_that(detr.check(), equal_to(Validity.Syntaxic))\n    assert_that(case.repair(), equal_to(Validity.Syntaxic))\n\n    detr[\"CONCEPT\"][\"NOM\"] = newmesh\n    assert_that(detr.check(), equal_to(Validity.Nothing))\n\n    # can not be repair using newmesh because it is removed\n    # FIXME but currently DETRUIRE is added after the other commands...\n    assert_that(case.repair(), equal_to(Validity.Nothing))\n\n\ndef test_validity_with_detruire():\n    \"\"\"Test for validity with DETRUIRE command\"\"\"\n    history = History()\n    case = history.current_case\n    stage = case.create_stage(':memory:')\n\n    text = \\\n\"\"\"\nmesh = LIRE_MAILLAGE()\n\nmesh = MODI_MAILLAGE(\n    reuse=mesh, MAILLAGE=mesh, ORIE_PEAU_2D=_F(GROUP_MA='groupname')\n)\n\nDETRUIRE(CONCEPT=_F(NOM=mesh))\n\nmodel = AFFE_MODELE(\n    AFFE=_F(MODELISATION='AXIS_INCO_UPG', PHENOMENE='MECANIQUE', TOUT='OUI'),\n    MAILLAGE=mesh\n)\n\"\"\"\n    comm2study(text, stage)\n\n    assert_that(stage['model'].check(), equal_to(Validity.Dependency))\n    assert_that(stage.check(), equal_to(Validity.Dependency))\n\n\ndef test_validity_with_detruire2():\n    \"\"\"Test for validity with DETRUIRE command (2)\"\"\"\n    history = History()\n    case = history.current_case\n    stage = case.create_stage(':memory:')\n    cmd = stage.add_command('DETRUIRE')\n    assert_that(cmd.check(), equal_to(Validity.Syntaxic))\n    assert_that(stage.check(), equal_to(Validity.Syntaxic))\n\n\ndef test_validity_with_detruire3():\n    \"\"\"Test for validity with DETRUIRE command (3)\"\"\"\n    history = History()\n    case = history.current_case\n    stage = case.create_stage(':memory:')\n\n    text = \\\n\"\"\"\nmesh = LIRE_MAILLAGE()\n\nDETRUIRE(CONCEPT=_F(NOM=mesh))\n\nmesh = LIRE_MAILLAGE()\n\"\"\"\n    comm2study(text, stage)\n\n    assert_that(stage.check(), equal_to(Validity.Nothing))\n\n\ndef test_validity_with_detruire4():\n    \"\"\"Test for validity with DETRUIRE command (4)\"\"\"\n    history = History()\n    case = history.current_case\n    stage = case.create_stage(':memory:')\n\n    text = \\\n\"\"\"\nmesh = LIRE_MAILLAGE()\n\nmesh = MODI_MAILLAGE(\n    reuse=mesh, MAILLAGE=mesh, ORIE_PEAU_2D=_F(GROUP_MA='groupname')\n)\n\nDETRUIRE(OBJET=())\n\nmodel = AFFE_MODELE(\n    AFFE=_F(MODELISATION='AXIS_INCO_UPG', PHENOMENE='MECANIQUE', TOUT='OUI'),\n    MAILLAGE=mesh\n)\n\"\"\"\n    comm2study(text, stage)\n\n    assert_that(stage.check(), equal_to(Validity.Nothing))\n\n\ndef test_26506():\n    \"\"\"Test for DEFI_FONCTION/ABSCISSE\"\"\"\n    # issue26506 / ticket1280\n    history = History()\n    case = history.current_case\n    stage = case.create_stage(':memory:')\n\n    cmd = stage.add_command('DEFI_FONCTION')\n    cmd.init({'NOM_PARA':'INST', 'VALE':(0, 20, 10, 70)})\n    cmd.init({'NOM_PARA':'INST', 'ABSCISSE':(0, 10), 'ORDONNEE':(20, 70)})\n\n\ndef test_26764():\n    \"\"\"Test for rules under conditional blocks\"\"\"\n    history = History()\n    case = history.current_case\n    stage = case.create_stage(':memory:')\n    text = \\\n\"\"\"\nMA = LIRE_MAILLAGE(FORMAT='MED', UNITE=20)\n\nMO = AFFE_MODELE(\n    AFFE=_F(MODELISATION='DKT', PHENOMENE='MECANIQUE', TOUT='OUI'), MAILLAGE=MA\n)\n\nbet = DEFI_MATERIAU(ELAS=_F(E=35000000000.0, NU=0.2, RHO=2500.0))\n\nMAT = AFFE_MATERIAU(AFFE=_F(MATER=bet, TOUT='OUI'), MODELE=MO)\n\nCL = AFFE_CHAR_MECA(\n    DDL_IMPO=_F(DX=0.0, DY=0.0, DZ=0.0, GROUP_MA='fix'), MODELE=MO\n)\n\nresu = MECA_STATIQUE(\n    CHAM_MATER=MAT,\n    EXCIT=(_F(CHARGE=CL)),\n    INST=0.0,\n    MODELE=MO\n)\n\nfer = CREA_CHAMP(\n    NOM_CHAM='FERRAILLAGE',\n    OPERATION='EXTR',\n    RESULTAT=resu,\n    TYPE_CHAM='ELEM_FER2_R'\n)\n\nferNeut = CREA_CHAMP(\n    ASSE=_F(\n        CHAM_GD=fer,\n        NOM_CMP=('DNSXI', 'DNSYI', 'DNSXS', 'DNSYS'),\n        NOM_CMP_RESU=('X1', 'X2', 'X3', 'X4'),\n        TOUT='OUI'\n    ),\n    OPERATION='ASSE',\n    TYPE_CHAM='ELEM_NEUT_R'\n)\n\"\"\"\n    comm2study(text, stage)\n\n    assert_that(stage['fer'].check(), equal_to(Validity.Nothing))\n    assert_that(calling(stage['ferNeut'].check).with_args(safe=False),\n                raises(ValueError, \"Exactly.*MAILLAGE.*MODELE\"))\n    assert_that(stage['ferNeut'].check(), equal_to(Validity.Syntaxic))\n    assert_that(stage.check(), equal_to(Validity.Syntaxic))\n\n    stage['ferNeut']['MAILLAGE'] = stage['MA']\n    assert_that(stage['ferNeut']._check_validity, equal_to(True))\n    assert_that(stage['ferNeut'].check(), equal_to(Validity.Nothing))\n    assert_that(stage.check(), equal_to(Validity.Nothing))\n\n\ndef test_rules():\n    history = History()\n    case = history.current_case\n    stage = case.create_stage(':memory:')\n\n    form = stage('FORMULE')\n    form['NOM_PARA'] = 'INST'\n    assert_that(calling(form.check).with_args(safe=False),\n                raises(ValueError, \"Exactly one.*VALE.*VALE_C\"))\n    assert_that(form.check(), equal_to(Validity.Syntaxic))\n\n\ndef test_27007():\n    \"\"\"Test for DETRUIRE\"\"\"\n    text = \\\n\"\"\"\nmesh = LIRE_MAILLAGE(UNITE=20)\n\nDETRUIRE(CONCEPT=_F(NOM=(mesh, )))\n\nmesh = LIRE_MAILLAGE(UNITE=20)\n\nDETRUIRE(CONCEPT=_F(NOM=(mesh, )))\n\nmesh = LIRE_MAILLAGE(UNITE=20)\n\nDETRUIRE(CONCEPT=_F(NOM=(mesh, )))\n\"\"\"\n    history = History()\n    case = history.current_case\n    stage = case.create_stage(':memory:')\n\n    comm2study(text, stage)\n\n    cmds = [i.title for i in stage.sorted_commands]\n    assert_that(cmds, contains('LIRE_MAILLAGE', 'DETRUIRE',\n                               'LIRE_MAILLAGE', 'DETRUIRE',\n                               'LIRE_MAILLAGE', 'DETRUIRE'))\n\n\ndef test_28054():\n#tab = POST_ELEM(TRAV_EXT=_F())\n    text = \\\n\"\"\"\nres = MACR_ELEM_STAT(RIGI_MECA=_F())\n\"\"\"\n    history = History()\n    case = history.current_case\n    stage = case.create_stage(':memory:')\n\n    comm2study(text, stage)\n\n    cmd = stage.sorted_commands[0]\n    assert_that(cmd.check(), equal_to(Validity.Nothing))\n    assert_that(cmd.title, equal_to('MACR_ELEM_STAT'))\n    assert_that(cmd.storage, contains('RIGI_MECA'))\n    assert_that(cmd.storage['RIGI_MECA'], empty())\n\n    cmd.init({'RIGI_MECA': {}})\n    assert_that(cmd.check(), equal_to(Validity.Nothing))\n    assert_that(cmd.check(safe=False), equal_to(Validity.Nothing))\n\n\nif __name__ == \"__main__\":\n    import sys\n    from testutils import get_test_suite\n    RET = unittest.TextTestRunner(verbosity=2).run(get_test_suite(__name__))\n    sys.exit(not RET.wasSuccessful())\n","repo_name":"xyg1996/Asterstudy","sub_path":"share/salome/asterstudy_test/datamodel/test_command_validity.py","file_name":"test_command_validity.py","file_ext":"py","file_size_in_byte":10046,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"23066375290","text":"#Ejercicio 3:Una empresa aplica el siguiente procedimiento en la comercialización de \n#sus pro-ductos:∑Aplica el precio base a la primera docena de unidades.\n#Aplica un 10% de descuento a todas las unidades entre 13 y 100.\n#Aplica un 25% de descuento a todas las unidades por encima de las 100.\n#Por ejemplo, supongamos que vende 230 unidades de un producto cuyo precio base es 100. \n#El cálculo resultante sería:100 * 12 + 90 * 88 + 75 * 130 = 18870 y el precio promedio es de 18870/230 = 82.04\n#Escribir un programa que lea la cantidad solicitada de un producto y su precio base, y muestre el valor \n#total de la venta y el precio promedio por unidad. El fin de carga se determina ingresando -1 como cantidad solicitada. \n#Al finalizar infor-mar: \n#Cantidad de ventas realizadas total.\n#Cantidad de ventas en las que se aplicó un 10% de descuento.\n#Cantidad de ventas en las que SOLO se aplicó el precio base, es decir que no se le realizaron descuentos.\n\ncantidad = 0\nprecioBase = 0\nprecioTotal = 0\nprecioPromedio = 0\ncantVentas = 0\ncantVentas10 = 0\ncantVentas0 = 0\nwhile cantidad != -1:\n    cantidad = int(input(\"Ingrese una cantidad: \"))\n    if cantidad != -1:\n        precioBase = int(input(\"Ingrese un precio base: \"))\n        if cantidad <= 12:\n            precioTotal = precioBase * cantidad\n            cantVentas0 += 1\n        elif cantidad >= 13 and cantidad <= 100:\n            precioTotal = precioBase * 12 + (precioBase * 0.9) * (cantidad - 12)\n            cantVentas10 += 1\n        else:\n            precioTotal = precioBase * 12 + (precioBase * 0.9) * 88 + (precioBase * 0.75) * (cantidad - 100)\n        cantVentas += 1\n        precioPromedio = precioTotal / cantidad\n        print(\"Precio total: \", precioTotal)\n        print(\"Precio promedio: \", precioPromedio)\n\nprint(\"Cantidad de ventas realizadas total: \", cantVentas)\nprint(\"Cantidad de ventas en las que se aplicó un 10% de descuento: \", cantVentas10)\nprint(\"Cantidad de ventas en las que solo se aplicó el precio base, es decir que no se le realizaron descuentos: \", cantVentas0)\n","repo_name":"torrresagus/UADE-Introduccion-a-la-Algoritmia","sub_path":"Guia de Trabajos Practicos 2022/Trabajo Practico 5 -  Ejercicios integradores/Ejercicio3/Ejercicio3.py","file_name":"Ejercicio3.py","file_ext":"py","file_size_in_byte":2064,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"5921160093","text":"#!/usr/bin/env python3\n\nimport sys, csv\nfrom Bio import SeqIO\nfrom collections import defaultdict\n\ndef gen_ogd(ogs):\n    ogd = defaultdict(dict)\n    with open(ogs) as ogsh:\n        csvr = csv.reader(ogsh, delimiter='\\t')\n        for i, row in enumerate(csvr):\n            if i == 0:\n                isolates = row[1:]\n            for j, header in enumerate(isolates):\n                if not row[j+1].split(', ') == ['']:\n                    ogd[row[0]][header] = row[j+1].split(', ')\n                else:\n                    ogd[row[0]][header] = []\n    return ogd\n\ndef gen_product_dict(gb, rorq):\n    prod_d = {}\n    for chrom in gb:\n        if chrom.id == 'api' or chrom.id == 'mit':\n            continue\n        for feat in chrom.features:\n            if feat.type == 'CDS':\n                if rorq == 'r':\n                    prod_d[feat.qualifiers['protein_id'][0]] = feat.qualifiers['product'][0]\n                elif rorq == 'q':\n                    prod_d[feat.qualifiers['locus_tag'][0]] = feat.qualifiers['product'][0]\n                else:\n                    sys.exit(\"rorq is bad\")\n    return prod_d\n\n\ndef id_func_annots(ref_prod_d, q_prod_d, ogd, query, ref, qgb):\n    class products: pass\n    products.new_names = {}\n    products.too_many_products = {}\n    products.no_products = {}\n    for chrom in qgb:\n        if chrom.id == 'api' or chrom.id == 'mit':\n            continue\n        for feat in chrom.features:\n            if feat.type == 'CDS':\n                qltag = feat.qualifiers['locus_tag'][0]\n                qproduct = feat.qualifiers['product'][0]\n                og = find_query_og(ogd, qltag, query)\n                if og != False:\n                    ref_ids = ogd[og][ref]\n                    if len(ref_ids) > 1:\n                        uniq_products = [ ref_prod_d[refid].strip() for refid in ref_ids ]\n                        if len(set(uniq_products)) == 1:\n                            products.new_names[qltag] = uniq_products[0]\n                        else:\n                            products.too_many_products[qltag] = [(refid, ref_prod_d[refid]) for refid in ref_ids]\n                            products.new_names[qltag] = qproduct\n                    elif len(ref_ids) == 1:\n                        products.new_names[qltag] = ref_prod_d[ref_ids[0]]\n                    elif len(ref_ids) == 0:\n                        products.new_names[qltag] = qproduct\n                        products.no_products[qltag] = qproduct\n                else:\n                    products.new_names[qltag] = qproduct\n                    products.no_products[qltag] = qproduct\n    return products\n\n\ndef find_query_og(ogd, qltag, isolate):\n    for og in list(ogd):\n        if qltag+'-T1' in ogd[og][isolate]:\n            return og\n    return False\n\ndef generate_new_genbank_file(products, qgb):\n    for chrom in qgb:\n        if chrom.id == 'api' or chrom.id == 'mit':\n            continue\n        for feat in chrom.features:\n            if feat.type == 'CDS' or feat.type == 'mRNA':\n                feat.qualifiers['product'] = [products.new_names[feat.qualifiers['locus_tag'][0]]]\n    return qgb\n\ndef output_product_details(products, query):\n    outfile = '{}.too_many_products.csv'.format(query)\n    with open(outfile, 'w') as outh:\n        outw = csv.writer(outh)\n        for qltag in list(products.too_many_products):\n            outw.writerow([qltag, products.too_many_products[qltag][0], products.too_many_products[qltag][1]])\n    outfile = '{}.no_products.csv'.format(query)\n    with open(outfile, 'w') as outh:\n        outw = csv.writer(outh)\n        for qltag in list(products.no_products):\n            outw.writerow([qltag, products.no_products[qltag]])\n    \n    \ndef main():\n    ref_gb = list(SeqIO.parse(sys.argv[1], 'gb'))\n    q_gbs = [list(SeqIO.parse(sys.argv[2], 'gb')), list(SeqIO.parse(sys.argv[3], 'gb'))]\n    ogs = sys.argv[4]\n    ref = 'Theileria_orientalis_Shintoku'\n    ref_prod_d = gen_product_dict(ref_gb, 'r')\n    ogd = gen_ogd(ogs)\n    for i, query in enumerate(['Theileria_orientalis_Fish_Creek', 'Theileria_orientalis_Goon_Nure']):\n        q_prod_d = gen_product_dict(q_gbs[i], 'q')\n        products = id_func_annots(ref_prod_d, q_prod_d, ogd, query, ref, q_gbs[i])\n        new_gb = generate_new_genbank_file(products, q_gbs[i])\n        SeqIO.write(new_gb, '{}.shintoku_product_names.gbk'.format(query), 'gb')\n        output_product_details(products, query)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"bogemad/theileria_orientalis_complete_genome_scripts","sub_path":"func_annotation_transfer.py","file_name":"func_annotation_transfer.py","file_ext":"py","file_size_in_byte":4450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"19655326311","text":"# -*- coding: utf-8 -*-\n# @Project:AID1810\n# @Author:biabu\n# @Date:2019/3/25 16:11\n# @File_name:demo06_LabelEncode.py\n# @IDE:PyCharm\n\n\"\"\"\n标签编码\n\"\"\"\n\nimport numpy as np\nimport sklearn.preprocessing as sp\nraw_samples = np.array([\n    'audi', 'ford', 'audi', 'toyota',\n    'ford', 'bmw', 'toyota', 'byd',\n    'audi'])\n\n# 创建标签编码器\nlbe = sp.LabelEncoder()\n# 执行标签编码\nresult = lbe.fit_transform(raw_samples)\nprint(result)\n\n# 逆向回推原始数据\nr_samples = lbe.inverse_transform(result)\nprint(r_samples)\n","repo_name":"biabulinxi/Python-ML-DL","sub_path":"AI/Machine_Learning/Day01/demo06_LabelEncode.py","file_name":"demo06_LabelEncode.py","file_ext":"py","file_size_in_byte":533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"7748907418","text":"import pathlib\nimport functions\nimport visualisation\n\nimport subprocess\nimport modules.imageProcessing\n\n\n######### Parameters ##########\n\nappPath = pathlib.Path(r\"D:\\Stage\\ACCAL\\ACCAL\") #### To change with the app folder\ndataFolderPath = pathlib.Path(r\"D:\\Stage\\ACCAL\\data\\dataTest2\") #### To change with teh dataSet folder\nfeatureAppPath = pathlib.Path(appPath,\"features.py\")\n\n\n## Dissimilarity Matrix\nDENOISE_RATIO = 0.1\nCLIP_LIMIT = 0.01\nLENGTH_KERNEL = 4.0\nPIXEL_SIDE = 360\nFEATURE_NUMBER = 250\n\nNUMBER_CORES = 2 \n\n\n## Clustering\nRATIO_LOW = 0.001\nRATIO_HIGH = 0.01\nNB_BURNIN = 30\nNB_SAMPLE = 100\nEACH_SAMPLE = 2\n\n\n## Visualisation\nPROB_LIMIT = 0.5\n\n\n###################################################\n###################################################\n\n\n### Compute and save tomporary images used for further processing\n#modules.imageProcessing.computeAndSaveTempImages(dataFolderPath=dataFolderPath,denoiseRatio=DENOISE_RATIO,clipLimit=CLIP_LIMIT)\n\n\n### Compute and save features for each image \n#functions.saveFeatures(dataFolderPath=dataFolderPath,absAppPath=appPath,pixelSide=PIXEL_SIDE,lengthKernel=LENGTH_KERNEL,featureNumber=FEATURE_NUMBER)\n#subprocess.run([\"python\",str(featureAppPath),str(dataFolderPath),str(appPath),str(PIXEL_SIDE),str(LENGTH_KERNEL),str(FEATURE_NUMBER),str(NUMBER_CORES)])\n\n## Compute and save the dissimilarity Matrix\n#functions.saveDissMatrix(dataFolderPath=dataFolderPath)\n\n\n## Compute and save Probability matrix\nfunctions.clustering(dataFolderPath=dataFolderPath,ratioLow=RATIO_LOW,ratioHigh=RATIO_HIGH,nbBurnin=NB_BURNIN,nbSample=NB_SAMPLE,eachSample=EACH_SAMPLE)\n\n\n## Save Results\nvisualisation.saveResults(dataFolderPath=dataFolderPath,probLimit=PROB_LIMIT)\n\n","repo_name":"alexandreBerezin/ACCAL","sub_path":"ACCAL/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1703,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"2680880239","text":"import numpy as np\nfrom sklearn import metrics\nfrom sklearn.metrics import roc_auc_score\n\nfrom matplotlib import pyplot as plt\n\n\n\ndef get_precision(_df, threshold=7):\n    # TR|FP|TN|FN\n    ground_positive = _df[_df['pIC50'] >= threshold]['title'].values\n    predct_positive = _df[_df['rescore'] >= threshold]['title'].values\n\n    true_positive = np.intersect1d(predct_positive, ground_positive)\n    \n    if len(true_positive) == 0:\n        precision = 0\n    else:\n        # precision\n        precision = len(true_positive) / (len(np.union1d(ground_positive, predct_positive)))\n\n    return precision\n\n\ndef get_recall(_df, threshold=7):\n    # TR|FP|TN|FN\n    ground_positive = _df[_df['pIC50'] >= threshold]['title'].values\n    predct_positive = _df[_df['rescore'] >= threshold]['title'].values\n\n    true_positive = np.intersect1d(predct_positive, ground_positive)\n    \n    if len(true_positive) == 0:\n        recall = 0\n    else:\n        # recall\n        recall = len(true_positive) / len(ground_positive)\n\n    return recall\n\n\ndef calc_auc(y_pred,y_test):\n    '''\n    instead of roc_auc_score\n    '''\n    fpr, tpr, thresholds = metrics.roc_curve(y_test,y_pred,pos_label=1)\n    return metrics.auc(fpr,tpr)\n\n\ndef return_gold_plt(frame_data_frame):\n    _corr = np.mean(frame_data_frame['gold_corr'].values)\n    xx = frame_data_frame['pIC50']\n    yy = frame_data_frame['Gold.PLP.Fitness_best']\n\n    fp1 = np.polyfit(xx, yy, 1)\n    f1 = np.poly1d(fp1)\n\n    fig = plt.figure(figsize=(10, 5))\n    ax = fig.add_subplot(1, 1, 1)\n    ax.scatter(xx, \n               yy, \n               s=150, \n               lw=0.2, \n               alpha=0.5, \n               edgecolors='black', \n               color = '#1f77b4', \n               label='rescore/pIC50')\n    ax.text(10,\n            60,\n            f'\\ncorr avg. : {_corr:.3f}',\n            color = '#1f77b4',\n            size=10)\n    ax.set_xlabel('pIC50')\n    ax.set_ylabel('Fitness_best')\n    ax.legend(loc='upper right')\n    plt.plot(xx, f1(xx), lw=2, color='r', label='polyfit')\n\n    return plt\n","repo_name":"sangwon-hwang/fetal_monitoring","sub_path":"fetal_monitor/utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2037,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"1405726087","text":"class Solution(object):\n    \n    # hash set, find reflection line: O(N) time & space\n    def isReflected(self, points):\n        pts = set()\n        maxX, minX = float('-inf'), float('inf')\n        for point in points:\n            maxX = max(maxX, point[0])\n            minX = min(minX, point[0])\n            pts.add(tuple(point))\n        mid = float(maxX + minX) / 2\n        for point in points:\n            x = 2 * mid - point[0]\n            y = point[1]\n            if (x, y) not in pts:\n                return False\n        return True\n        ","repo_name":"haomingchan0811/Leetcode","sub_path":"356. Line Reflection.py","file_name":"356. Line Reflection.py","file_ext":"py","file_size_in_byte":547,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"36351516025","text":"# coding: utf-8\n\n\"\"\"\n    获取应用推送事件订阅状态\n\n    通过access_token查询该应用事件订阅状态\n\n    \n\"\"\"\n\nimport pprint\nimport re  # noqa: F401\n\nimport six\n\n\nclass EventStatusUpdateBody(object):\n    \"\"\"NOTE: This class is auto generated by the swagger code generator program.\n\n    Do not edit the class manually.\n    \"\"\"\n    \"\"\"\n    Attributes:\n      swagger_types (dict): The key is attribute name\n                            and the value is attribute type.\n      attribute_map (dict): The key is attribute name\n                            and the value is json key in definition.\n    \"\"\"\n    swagger_types = {\n        'list': 'list[EventStatus]'\n    }\n\n    attribute_map = {\n        'list': 'list'\n    }\n\n    def __init__(self, list=None):  # noqa: E501\n        \"\"\"EventStatusUpdateBody - a model defined in Swagger\"\"\"  # noqa: E501\n        self._list = None\n        self.discriminator = None\n        self.list = list\n\n    @property\n    def list(self):\n        \"\"\"Gets the list of this EventStatusUpdateBody.  # noqa: E501\n\n\n        :return: The list of this EventStatusUpdateBody.  # noqa: E501\n        :rtype: list[EventStatus]\n        \"\"\"\n        return self._list\n\n    @list.setter\n    def list(self, list):\n        \"\"\"Sets the list of this EventStatusUpdateBody.\n\n\n        :param list: The list of this EventStatusUpdateBody.  # noqa: E501\n        :type: list[EventStatus]\n        \"\"\"\n        if list is None:\n            raise ValueError(\"Invalid value for `list`, must not be `None`\")  # noqa: E501\n\n        self._list = list\n\n    def to_dict(self):\n        \"\"\"Returns the model properties as a dict\"\"\"\n        result = {}\n\n        for attr, _ in six.iteritems(self.swagger_types):\n            value = getattr(self, attr)\n            if isinstance(value, list):\n                result[attr] = list(map(\n                    lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n                    value\n                ))\n            elif hasattr(value, \"to_dict\"):\n                result[attr] = value.to_dict()\n            elif isinstance(value, dict):\n                result[attr] = dict(map(\n                    lambda item: (item[0], item[1].to_dict())\n                    if hasattr(item[1], \"to_dict\") else item,\n                    value.items()\n                ))\n            else:\n                result[attr] = value\n        if issubclass(EventStatusUpdateBody, dict):\n            for key, value in self.items():\n                result[key] = value\n\n        return result\n\n    def to_str(self):\n        \"\"\"Returns the string representation of the model\"\"\"\n        return pprint.pformat(self.to_dict())\n\n    def __repr__(self):\n        \"\"\"For `print` and `pprint`\"\"\"\n        return self.to_str()\n\n    def __eq__(self, other):\n        \"\"\"Returns true if both objects are equal\"\"\"\n        if not isinstance(other, EventStatusUpdateBody):\n            return False\n\n        return self.__dict__ == other.__dict__\n\n    def __ne__(self, other):\n        \"\"\"Returns true if both objects are not equal\"\"\"\n        return not self == other\n","repo_name":"uimeet/douyin","sub_path":"douyin/open/events/model/event_status_update_body.py","file_name":"event_status_update_body.py","file_ext":"py","file_size_in_byte":3074,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"76"}
+{"seq_id":"39088773865","text":"import turtle\n\npen = turtle.Turtle()\npen.color(\"black\")\npen.fillcolor(\"grey\")\npen.pensize(5)\npen.shape(\"turtle\")\n\n\ndef draw_squares(x, y):\n    pen.begin_fill()\n    pen.up()\n    pen.goto(x, y)\n    pen.down()\n    for i in range(0, 4):\n        pen.forward(100)\n        pen.left(90)\n    pen.end_fill()\n\n\nfor x in range(-200, 200, 120):\n    draw_squares(x, 200)\n","repo_name":"sakshi303/Python_challenges","sub_path":"line.py","file_name":"line.py","file_ext":"py","file_size_in_byte":357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"1286930173","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom math import pi\nfrom coord import Coord\n\n# possible orientations\nupw = 'upw'\nhor = 'hor'\ndwd = 'dwd'\n\nspeed = 45  # mm/s   # for timing purposes\nang_speed = pi / 4.7  # rad/s # pi / 4,7s\n\n\n# TODO: speed in rad per sec instead of lin speed\n\n\nclass Positions(object):\n    \"\"\"constant world coordinates translated to each robot KOS\"\"\"\n    __init = False\n\n    def __init__(self, robot):\n        self.home = Coord(r=160, z=180, ort=hor)\n        r0_r1 = Coord(isvect=True, x=465)  # vector from global-origin/r0-origin to r1-origin\n\n        if robot.id is 0:\n            self.center = (r0_r1 / 2) + Coord(z=180, x=-5, ort=hor)\n            self.cube = (r0_r1 / 2) + Coord(z=43, x=-7, ort=dwd)\n            self.cube_retr = self.cube + Coord(z=50)\n\n        if robot.id is 1:\n            self.center = (r0_r1 / 2) + Coord(z=180, x=-5, ort=hor)\n            self.cube = (r0_r1 / 2) + Coord(z=55, x=-5, ort=dwd)\n            self.cube_retr = self.cube + Coord(z=50)\n\n        # fixed turning positions\n        self.a_hold = Coord(x=300, y=2, z=70, ort=hor)\n        self.a_turn = Coord(x=154, z=95, ort=dwd)\n        self.a_retr = self.a_turn + Coord(z=40)\n        # self.a_turn_prepare = Coord(th=-(pi * 20 / 180), r=154, z=95, ort=dwd)\n        # self.a_retr_prepare = Coord(th=-(pi * 20 / 180), r=155, z=120, ort=dwd)\n\n        self.b_hold = self.center\n        self.b_turn = self.center - Coord(x=18.5, z=10)\n        self.b_retr = self.b_turn - Coord(x=50)\n\n        # comp: q3 -= 3deg\n        self.c_hold = Coord(x=220, z=377, ort=upw)\n        self.c_turn = Coord(x=195, z=363, ort=hor)\n        self.c_retr = self.c_turn - Coord(x=50)\n\n        self.__init = True\n\n    def __setattr__(self, attr, value):\n        if self.__init:\n            raise Exception('const value. may not be modified!')\n        else:\n            super(Positions, self).__setattr__(attr, value)\n\n\nclass HardwareLimits(object):\n    \"\"\"\n    Approximated hardware limits. Exceeding them would result in structural collision.\n    No collision warning or detection with base-plate or other objects\n    \"\"\"\n    __init = False\n\n    def __init__(self):\n        self.th0min = -pi\n        self.th1min = -pi / 2\n        self.th2min = -pi * 3 / 4\n        self.th3min = -pi * 5 / 9  # -100*pi/180\n        self.th4min = -pi\n        self.th5min = -pi / 2\n\n        self.th0max = pi\n        self.th1max = pi / 2\n        self.th2max = pi / 2\n        self.th3max = pi * 5 / 9  # 100*pi/180\n        self.th4max = pi\n        self.th5max = pi / 2\n\n        self.__init = True\n\n    def __setattr__(self, attr, value):\n        if self.__init:\n            raise Exception('const value. may not be modified!')\n        else:\n            super(HardwareLimits, self).__setattr__(attr, value)\n\n\nclass RobotStructure(object):\n    \"\"\"structural parameters. maybe get values from stl files for better precision\"\"\"\n    __init = False\n\n    def __init__(self):\n        \"\"\"\n        # base height\n        self.d0 = 12.5 + 78\n        self.d1 = 26\n        # shoulder length\n        self.d2 = 150\n        #self.d2r = 5*m.sqrt(59) #38.4057 # ~ 22 + (32 / 2)\n        self.d2z = 145\n        self.psir = 1.3118747847887 #m.asin(self.d2z / self.d2) #~1.31\n        self.psiz = 0.25892154200621 #m.acos(self.d2z / self.d2) #~0.26\n        # elbow length\n        self.d3 = 147\n        # wrist length\n        self.d4 = 70\n        self.d5 = 93\n        \"\"\"\n\n        self.d01 = 104.5 + 8.0\n        self.d2 = 150\n        self.d2z = 145.146\n        self.d2r = 37.851\n        self.psiz = 0.255092\n        self.psir = 1.3157\n        self.d3 = 144.471\n        self.d45 = 115.5\n        self.dgrip = 45.175\n\n        self.closed = GripStructure().closed\n\n        self.__init = True\n\n    def __setattr__(self, attr, value):\n        if self.__init:\n            raise Exception('const value. may not be modified!')\n        else:\n            super(RobotStructure, self).__setattr__(attr, value)\n\n\nclass GripStructure(object):\n    \"\"\"structureal parameters of cube and gripper\"\"\"\n    __init = False\n\n    def __init__(self):\n        # self.cubesize = 56.0\n        # self.servo_horn_radius = 8.0\n        # self.gripper_arm_length = 29.0\n        # self.sponge_dist = 3.0\n        # self.sponge_squish = 0.0\n        self.closed = 54 * pi / 180  # für 19mm greifer-stangen # cossatz(  a = self.servo_horn_radius,\n        # self.closed = 30 * pi / 180  # std greifer-stangen\n        # b = 32,#(self.cubesize/2)+(self.sponge_dist - self.sponge_squish),\n        # c = self.gripper_arm_length )\n        self.__init = True\n\n    def __setattr__(self, attr, value):\n        if self.__init:\n            raise Exception('const value. may not be modified!')\n        else:\n            super(GripStructure, self).__setattr__(attr, value)\n","repo_name":"Gorschel/catkin_ws_DualRobotCubeSolver","sub_path":"src/control_dual_robot/scripts/consts.py","file_name":"consts.py","file_ext":"py","file_size_in_byte":4761,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"21295120575","text":"import heapq \nimport sys \n\nInf = int(1e9) \ninput = sys.stdin.readline \n\nv, e = map(int, input().split()) \n\ndistances = [Inf] * (v + 1) \n\ngraph = [[] for _ in range(v + 1)]\n\nstart = int(input())  \n\nfor _ in range(e): \n    a, b, c = map(int, input().split()) \n    graph[a].append((b, c)) \n\n\ndef djikstra(start):\n    q = []\n    heapq.heappush(q, (0, start))\n    distances[start] = 0\n    while q:\n        dist, now = heapq.heappop(q)\n        if distances[now] < dist:\n            continue\n\n        for i in graph[now]:\n            cost = dist + i[1]\n            if cost < distances[i[0]]:\n                distances[i[0]] = cost\n                heapq.heappush(q, (cost, i[0]))\n\n\ndjikstra(start) \n\nfor i in range(1, v + 1):  \n    if distances[i] == Inf: \n        print(\"INF\") \n    else: \n        print(distances[i]) \n","repo_name":"speciling/ps-study","sub_path":"5주차/임지환/1753_최단경로.py","file_name":"1753_최단경로.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"19017812307","text":"__author__ = 'snouto'\n\n\nfrom system import *\nfrom mongoengine.base import *\n\n\nclass UserSite(Document):\n\n    folder = db.StringField(max_length=255,required=True)\n    name = db.StringField(max_length=255,required=True)\n    url = db.StringField(max_length=600,required=True)\n    username=db.StringField(max_length=255,required=True)\n    password=db.StringField(max_length=255,required=True)\n    note=db.StringField(required=False)\n    icon = db.StringField(required=False)\n    settings = db.DictField(required=False,default={'favourite':False,'autofill':False,'autologin':False})\n    fields = db.ListField(required=False)\n    user = db.ReferenceField(User)\n\n\n    meta = {\n        'indexes':[{\n                       'fields':['name','folder','user']\n                   }]\n        }\n\n\n\n\n\nclass Note(Document):\n\n    name = db.StringField(required=True,max_length=255)\n    folder = db.StringField(required=True,max_length=255)\n    type = db.StringField(required=True,max_length=255)\n    note = db.StringField(required=False)\n    settings = db.DictField(required=False,default={'favorite':False,'require_reprompt':False})\n    meta = {\n\n            'indexes':[\n                {\n                    'fields':['name','folder','type']\n                }\n\n            ]\n\n            }\n\n    node_fields = db.ListField(DictField(),required=False)\n    user_id = db.ReferenceField(User)\n\n\n\nclass FormFields(Document):\n    profile_name = db.StringField(required=True,max_length=255)\n    language = db.StringField(required=True,max_length=255)\n    settings = db.DictField(required=False,default={'favorite':False,'require_reprompt':False})\n    fields_definitions = db.ListField(DictField(),required=False)\n    meta = {\n        'indexes':[\n            {\n                'fields':['profile_name','language']\n            }\n        ]\n    }\n    user_id = db.ReferenceField(User)\n\n\n\n\n\n\n\n","repo_name":"penshield/justlogmein","sub_path":"justlogmein/db/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1865,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"43803058047","text":"# from common import *\nfrom augmentation import *\nimport pandas as pd\nimport numpy as np\nfrom torch.utils.data import DataLoader, Dataset, SequentialSampler, Sampler\nimport torch\nimport pdb\n\nimage_size = 1024\n\ndef make_fold(df, fold=0):\n    train_df = df.query(\"fold!=@fold\").reset_index(drop=True)\n    valid_df = df.query(\"fold==@fold\").reset_index(drop=True)\n\n    # train_df = df[df.patient_id.isin(train_id)].reset_index(drop=True)\n    # valid_df = df[df.patient_id.isin(valid_id)].reset_index(drop=True)\n    return train_df, valid_df\n\n\nclass build_dataset(Dataset):\n    def __init__(self, df, train_val_flag=True, transforms=None):\n\n        self.df = df\n        self.train_val_flag = train_val_flag #\n        self.img_paths = df['img_path'].tolist() \n        self.ids = df['img_name'].tolist()\n        self.transforms = transforms\n\n        if train_val_flag:\n            self.label = df['img_label'].tolist()\n        \n    def __len__(self):\n        return len(self.df)\n        # return 8\n    \n    def __getitem__(self, index):\n        #### id\n        id       = self.ids[index]\n        #### image\n        img_path  = self.img_paths[index]\n        img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) # [h, w, c]\n        \n        if self.train_val_flag: # train\n            ### augmentations\n            data  = self.transforms(image=img)\n            img = np.transpose(data['image'], (2, 0, 1)) # [c, h, w]\n            gt = self.label[index]\n            # pdb.set_trace()\n            return torch.tensor(img), torch.tensor(int(gt))\n        \n        else:  # test\n            ### augmentations\n            data  = self.transforms(image=img)\n            img = np.transpose(data['image'], (2, 0, 1)) # [c, h, w]\n            # pdb.set_trace()\n            return torch.tensor(img), id\n\nclass DTTDataset(Dataset):\n    def __init__(self, df, augment=None):\n        self.df = df\n        # self.train_val_flag = train_val_flag #\n        self.img_paths = df['img_path'].tolist() \n        self.ids = df['img_name'].tolist()\n        self.label = df['img_label'].tolist()\n        self.augment = augment\n\n    # def __str__(self):\n    #     num_patient = len(set(self.df.patient_id))\n    #     num_image = len(self.df)\n\n    #     string = ''\n    #     string += f'\\tlen = {len(self)}\\n'\n    #     string += f'\\tnum_patient = {num_patient}\\n'\n    #     string += f'\\tnum_image = {num_image}\\n'\n\n    #     count = dict(self.df.cancer.value_counts())\n    #     for k in [0,1]:\n    #         string += f'\\t\\tcancer{k} = {count[k]:5d} ({count[k]/len(self.df):0.3f})\\n'\n    #     return string\n\n    def __len__(self):\n        return len(self.df)\n\n    def __getitem__(self, index):\n\n        #### id\n        id       = self.ids[index]\n        #### image\n        img_path  = self.img_paths[index] \n        img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) # [h, w, c]\n        img = cv2.resize(img, (1024,1024), interpolation=cv2.INTER_LINEAR)\n        img = img.astype(np.float32)/255\n        # pdb.set_trace()\n        # if self.train_val_flag: # train\n            ### augmentations\n        if self.augment != None:\n            img  = self.augment(img)\n        img = np.transpose(img, (2, 0, 1)) # [c, h, w]\n        gt = self.label[index]\n            # pdb.set_trace()\n            \n        \n        # else:  # test\n        #     ### augmentations\n        #     data  = self.transforms(image=img)\n        #     img = np.transpose(data['image'], (2, 0, 1)) # [c, h, w]\n        #     gt = id\n        #     # pdb.set_trace()\n\n\n        d = self.df.iloc[index]\n        # # pdb.set_trace()\n        # image = read_data(d)\n\n        # if self.augment is not None:\n        #     image = self.augment(image)\n\n        r = {}\n        r['index'] = index\n        r['d'] = d\n        # r['patient_id'] = d.patient_id  #\n        r['image' ] = torch.from_numpy(img).float()\n        r['label'] = torch.FloatTensor(gt)\n\n        return r\n\ntensor_key = ['image', 'label']\ndef null_collate(batch):\n    d = {}\n    key = batch[0].keys()\n    for k in key:\n        v = [b[k] for b in batch]\n        if k in tensor_key:\n            v = torch.stack(v,0)\n        d[k] = v\n    # d['image']= d['image'].unsqueeze(1)\n    d['label']= d['label'].reshape(-1)\n    return d\n\nclass BalanceSampler(Sampler):\n\n    def __init__(self, dataset, ratio=8):\n        self.r = ratio-1\n        self.dataset = dataset\n        self.pos_index = np.where(dataset.df.img_label>0)[0]\n        self.neg_index = np.where(dataset.df.img_label==0)[0]\n\n        self.length = self.r*int(np.floor(len(self.neg_index)/self.r))\n\n    def __iter__(self):\n        pos_index = self.pos_index.copy()\n        neg_index = self.neg_index.copy()\n        np.random.shuffle(pos_index)\n        np.random.shuffle(neg_index)\n\n        neg_index = neg_index[:self.length].reshape(-1,self.r)\n        pos_index = np.random.choice(pos_index, self.length//self.r).reshape(-1,1)\n\n        index = np.concatenate([pos_index,neg_index],-1).reshape(-1)\n        return iter(index)\n\n    def __len__(self):\n        return self.length\n\n#################################################################################\n\ndef train_augment_v00a(image):\n    image = do_random_hflip(image) # hflip, vflip or both\n    #image, target = do_random_hflip(image, target)\n\n    if np.random.rand() < 0.2:\n        for func in np.random.choice([\n            lambda image : do_random_affine( image, degree=15, translate=0.1, scale=0.2, shear=10),\n            lambda image : do_random_rotate(image,  degree=15),\n            lambda image : do_random_stretch(image, stretch=(0.2,0.2)),\n        ], 1):\n            image = func(image)\n\n    if np.random.rand() < 0.1:\n        image = do_elastic_transform(\n            image,\n            alpha=image_size,\n            sigma=image_size* 0.05,\n            alpha_affine=image_size* 0.03\n        )\n\n    if np.random.rand() < 0.2:\n        for func in np.random.choice([\n            lambda image: do_random_contrast(image),\n        ], 1):\n            image = func(image)\n            pass\n\n    return image\n\ndef train_augment_v00(image):\n    image = do_random_hflip(image) # hflip, vflip or both\n    #image, target = do_random_hflip(image, target)\n\n    if np.random.rand() < 0.7:\n        for func in np.random.choice([\n            lambda image : do_random_affine( image, degree=30, translate=0.1, scale=0.3, shear=20),\n            lambda image : do_random_rotate(image,  degree=30),\n            lambda image : do_random_stretch(image, stretch=(0.3,0.3)),\n        ], 1):\n            image = func(image)\n\n    if np.random.rand() < 0.25:\n        image = do_elastic_transform(\n            image,\n            alpha=image_size,\n            sigma=image_size* 0.05,\n            alpha_affine=image_size* 0.03\n        )\n    if np.random.rand() < 0.25:\n        image = do_random_cutout(\n            image, num_block=5,\n            block_size=[0.1,0.3],\n            fill='constant'\n        )\n\n    if np.random.rand() < 0.5:\n        for func in np.random.choice([\n            lambda image: do_random_contrast(image),\n            lambda image: do_random_noise(image, m=0.1),\n        ], 1):\n            image = func(image)\n            pass\n\n    return image\n\n#################################################################################\n\ndef run_check_dataset():\n    train_df, valid_df = make_fold()\n    dataset = RsnaDataset(train_df, augment=train_augment_v00)\n    print(dataset)\n\n    for i in range(100):\n        i = 0 #240*8+ i#np.random.choice(len(dataset))\n        r = dataset[i]\n        print(r['index'], 'id = ', r['patient_id'], '-----------')\n        for k in tensor_key :\n            v = r[k]\n            print(k)\n            print('\\t', 'dtype:', v.dtype)\n            print('\\t', 'shape:', v.shape)\n            if len(v)!=0:\n                print('\\t', 'min/max:', v.min().item(),'/', v.max().item())\n                print('\\t', 'is_contiguous:', v.is_contiguous())\n                print('\\t', 'values:')\n                print('\\t\\t', v.reshape(-1)[:8].data.numpy().tolist(), '...')\n                print('\\t\\t', v.reshape(-1)[-8:].data.numpy().tolist())\n        print('')\n        if 1:\n            image  = r['image'].data.cpu().numpy()\n\n            # image_show_norm('image', image)\n            cv2.waitKey(0)\n\n\n    loader = DataLoader(\n        dataset,\n        #sampler=SequentialSampler(dataset),\n        sampler=BalanceSampler(dataset),\n        batch_size=8,\n        drop_last=True,\n        num_workers=0,\n        pin_memory=False,\n        worker_init_fn=lambda id: np.random.seed(torch.initial_seed() // 2 ** 32 + id),\n        collate_fn=null_collate,\n    )\n    print(loader.batch_size, len(loader), len(dataset))\n    print('')\n\n    for t, batch in enumerate(loader):\n        if t > 5: break\n        print('batch ', t, '===================')\n        print('index', batch['index'])\n        for k in tensor_key:\n            v = batch[k]\n            print(k)\n            print('\\t', 'shape:', v.shape)\n            print('\\t', 'dtype:', v.dtype)\n            print('\\t', 'is_contiguous:', v.is_contiguous())\n            print('\\t', 'value:')\n            print('\\t\\t', v.reshape(-1)[:8].data.numpy().tolist())\n            if k=='cancer':\n                print('\\t\\tsum ', v.sum().item())\n\n        print('')\n\ndef run_check_augment():\n\n    train_df, valid_df = make_fold()\n    dataset = RsnaDataset(train_df)\n    print(dataset)\n\n    #---------------------------------------------------------------\n    def augment(image):\n        # image, target = do_random_hflip(image, target)\n        #image, target = do_random_flip(image, target)\n\n        #image, target = do_random_affine( image, target, degree=10, translate=0.1, scale=0.2, shear=10)\n        #image, target = do_random_rotate(image, target, degree=45)\n        #image, target = do_random_rotate90(image, target)\n\n        #image, target = do_random_perspective(image, target, m=0.3)\n        #image, target = do_random_zoom_small(image, target)\n\n        #image = do_random_hsv(image, h=20, s=50, v=50)\n        # image = do_random_contrast(image)\n        # image = do_random_gray(image)\n        # image = do_random_guassian_blur(image, k=[3, 5], s=[0.1, 2.0])\n        # image = do_random_noise(image, m=0.08)\n        return image\n\n    for i in range(10):\n        #i = 2424 #np.random.choice(len(dataset))#272 #2627\n        print(i)\n        r = dataset[i]\n\n        image  = r['image'].data.cpu().numpy()\n        # image_show_norm('image',image, min=0, max=1,resize=1)\n        #cv2.waitKey(0)\n\n        for t in range(100):\n            #image1 = augment(image.copy())\n            image1 = train_augment_v00(image.copy())\n            # image_show_norm('image1', image1, min=0, max=1,resize=1)\n            cv2.waitKey(0)\n\n\n# main #################################################################\nif __name__ == '__main__':\n    run_check_dataset()\n    #run_check_augment()","repo_name":"bee992/Text-Manipulation-Classification","sub_path":"dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":10814,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"33991638200","text":"MENU = {\n    \"espresso\": {\n        \"ingredients\": {\n            \"water\": 50,\n            \"coffee\": 18,\n        },\n        \"cost\": 1.5,\n    },\n    \"latte\": {\n        \"ingredients\": {\n            \"water\": 200,\n            \"milk\": 150,\n            \"coffee\": 24,\n        },\n        \"cost\": 2.5,\n    },\n    \"cappuccino\": {\n        \"ingredients\": {\n            \"water\": 250,\n            \"milk\": 100,\n            \"coffee\": 24,\n        },\n        \"cost\": 3.0,\n    }\n}\n\nresources = {\n    \"water\": 300,\n    \"milk\": 200,\n    \"coffee\": 100,\n}\n\ncoins = {\n    \"quarters\": 0.25,\n    \"dimes\": 0.10,\n    \"nickels\": 0.05,\n    \"pennies\": 0.01\n}\n\nprofit = 0\n\ndef print_resources():\n    for item in resources:\n        unit = \"g\" if item.lower() == \"coffee\" else \"ml\"\n        print(f\"{item}: {resources[item]}{unit}\")\n    print(f\"Profit: ${'{:.2f}'.format(round(profit))}\")\n\n\ndef get_money(item, item_cost):\n    paid = 0\n\n    while paid < item_cost:\n        if paid > 0:\n            rounded_paid = '{:.2f}'.format(round(paid, 2))\n            rounded_remainder = '{:.2f}'.format(round(item_cost - paid, 2))\n            print(f\"You did not pay enough. You paid ${rounded_paid} but it costs ${'{:.2f}'.format(round(item_cost))}. Please pay ${rounded_remainder} more.\")\n        for coin in coins:\n            amount_coin = int(input(f\"How many {coin}?: \"))\n            paid += amount_coin * coins[coin]\n\n    if paid > item_cost:\n        change = '{:.2f}'.format(round(paid - item_cost, 2))\n        print(f\"Your change is ${change}\")\n        return\n    elif paid == item_cost:\n        print(\"No change here! Thanks for paying.\")\n        return\n\n\ndef use_resources(item):\n    item_resources = MENU[item][\"ingredients\"]\n\n    for resource in item_resources:\n        resources[resource] -= item_resources[resource]\n\n\ndef check_can_make(item):\n    ingredients_needed = MENU[item][\"ingredients\"]\n\n    for ingredient in ingredients_needed:\n        if ingredients_needed[ingredient] > resources[ingredient]:\n            return False\n    return True\n\n\ndef make_coffee(item, cost):\n    global profit\n    profit += cost\n    use_resources(item)\n    return\n\ndef handle_initial_ask():\n    user_input = input(\"What would you like? (expresso/latte/cappuccino): \").lower()\n\n    if user_input == \"report\":\n        print_resources()\n        return\n    elif user_input == \"off\":\n        return \"shut off\"\n    elif user_input in MENU:\n        can_make = check_can_make(user_input)\n        if can_make:\n            cost = MENU[user_input][\"cost\"]\n            print(f\"Great! The cost of a {user_input} is ${'{:.2f}'.format(round(cost))}. Please pay me.\")\n            get_money(user_input, cost)\n            make_coffee(user_input, cost)\n            return user_input\n        else:\n            print(\"Sorry, we do not have enough ingredients to make this. Please try again later\")\n            return\n\n\ndef run():\n    machine_on = True\n\n    while machine_on:\n        item = handle_initial_ask()\n        if item == \"shut off\":\n            machine_on = False\n\n        item and print(f\"{item} ☕️ is ready! Please enjoy\")\n\n    print(\"Machine has been shut off for maintenance\")\n\n\nrun()","repo_name":"mkuhn0411/simple-python-coffee-maker","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3133,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"35283978829","text":"import setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read()\nsetuptools.setup(\n    name='free_proxy',\n    version='1.1.1',\n    author=\"jundymek\",\n    author_email=\"jundymek@gmail.com\",\n    description=\"Proxy scraper for further use\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/jundymek/free-proxy\",\n    packages=setuptools.find_packages(),\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Operating System :: OS Independent\",\n    ],\n    python_requires='>=3.6',\n    install_requires=['lxml', 'requests']\n)\n","repo_name":"jundymek/free-proxy","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","stars":190,"dataset":"github-code","pt":"76"}
+{"seq_id":"7829064044","text":"from types import *\nimport logging\nimport urllib\nimport json\n\nfrom edge.opensearch.responsewriter import ResponseWriter\nfrom edge.dateutility import DateUtility\nfrom edge.httputility import HttpUtility\nfrom edge.spatialsearch import SpatialSearch\nimport re\n\nclass GranuleWriter(ResponseWriter):\n    def __init__(self, configFilePath, requiredParams = None):\n        super(GranuleWriter, self).__init__(configFilePath, requiredParams)\n        self.startIndex = 0\n        self.entriesPerPage = self._configuration.getint('solr', 'entriesPerPage')\n\n    def get(self, requestHandler):\n        super(GranuleWriter, self).get(requestHandler)\n        #searchParameters = {}\n        #logging.debug('uri: '+str(requestHandler.request.headers))\n        \n        #startIndex = 0\n        try:\n            self.startIndex = requestHandler.get_argument('startIndex')\n        except:\n            pass\n\n        #entriesPerPage = self._configuration.getint('solr', 'entriesPerPage')\n        try:\n            self.entriesPerPage = requestHandler.get_argument('itemsPerPage')\n            #cap entries per age at 400\n            if (int(self.entriesPerPage) > 400):\n                self.entriesPerPage = 400\n            self.searchParameters['itemsPerPage'] = self.entriesPerPage\n        except:\n            pass\n\n        #pretty = True\n        try:\n            if requestHandler.get_argument('pretty').lower() == 'false':\n                self.pretty = False\n                self.searchParameters['pretty'] = 'false'\n        except:\n            pass\n\n        try:\n            if requestHandler.get_argument('full').lower() == 'true':\n                self.searchParameters['full'] = 'true'\n        except:\n            pass\n        \n        try:\n            self.searchParameters['format'] = requestHandler.get_argument('format')\n        except:\n            pass\n\n        parameters = ['startTime', 'endTime', 'keyword', 'granuleName', 'datasetId', 'shortName', 'bbox', 'sortBy']\n        #variables = {}\n        for parameter in parameters:\n            try:\n                value = requestHandler.get_argument(parameter)\n                self.variables[parameter] = value\n                self.searchParameters[parameter] = value\n            except:\n                pass\n\n        if 'keyword' in self.variables:\n            self.variables['keyword'] = self.variables['keyword'].replace('*', '')\n            self.variables['keyword'] = self.variables['keyword'].lower()\n\n        #Fetch dataset metadata from Solr\n        datasetVariables = {}\n        if 'datasetId' in self.variables:\n            datasetVariables['datasetId'] = self.variables['datasetId']\n        if 'shortName' in self.variables:\n            datasetVariables['shortName'] = self.variables['shortName']\n        self._getSingleSolrDatasetResponse(datasetVariables, self._onSolrDetermineProcessLevelResponse)\n\n    def _getSolrResponse(self, startIndex, entriesPerPage, variables):\n        query = self._constructSolrQuery(startIndex, entriesPerPage, variables)\n        url = self._configuration.get('solr', 'granuleUrl')\n\n        httpUtility = HttpUtility()\n        httpUtility.getResponse(url+'/select/?'+query, self._onSolrResponse)\n\n    def _constructSolrQuery(self, startIndex, entriesPerPage, variables):\n        #set default sort order\n        sort='Granule-StartTimeLong+desc'\n        filterQuery = None\n        queries = []\n        for key, value in variables.iteritems():\n            #query = ''\n            if key == 'startTime':\n                startTime = DateUtility.convertISOToUTCTimestamp(value)\n                if startTime is not None:\n                    query = 'Granule-StopTimeLong:'\n                    query += '['+str(startTime)+'%20TO%20*]'\n                    queries.append(query)\n            elif key == 'endTime':\n                stopTime = DateUtility.convertISOToUTCTimestamp(value)\n                if stopTime is not None:\n                    query = 'Granule-StartTimeLong:'\n                    query += '[*%20TO%20'+str(stopTime)+']'\n                    queries.append(query)\n            elif key == 'keyword':\n                newValue = urllib.quote(value)\n\n                query = 'SearchableText-LowerCased:('+newValue+')'\n                queries.append(query)\n            elif key == 'datasetId':\n                query = 'Dataset-PersistentId:'+self._urlEncodeSolrQueryValue(value)\n                queries.append(query)\n            elif key == 'shortName':\n                query = 'Dataset-ShortName-Full:'+self._urlEncodeSolrQueryValue(value)\n                queries.append(query)\n            elif key == 'granuleName':\n                query = 'Granule-Name-Full:'+self._urlEncodeSolrQueryValue(value)\n                queries.append(query)\n            elif key == 'granuleIds':\n                granuleIds = []\n                for granuleId in value:\n                    granuleIds.append(str(granuleId))\n                query = 'Granule-Id:('+'+OR+'.join(granuleIds)+')'\n                queries.append(query)\n\n                startIndex = 0\n            elif key == 'sortBy':\n                sortByMapping = {'timeAsc': 'Granule-StartTimeLong+asc'}\n                if value in sortByMapping.keys():\n                    sort = sortByMapping[value]\n            elif key == 'bbox':\n                filterQuery = self._constructBoundingBoxQuery(value)\n            #if query != '':\n            #    queries.append('%2B'+query)\n\n        if len(queries) == 0:\n            queries.append('*')\n\n        query = 'q='+'+AND+'.join(queries)+'&fq=Granule-AccessType:(OPEN+OR+PREVIEW+OR+SIMULATED+OR+REMOTE)+AND+Granule-Status:ONLINE&version=2.2&start='+str(startIndex)+'&rows='+str(entriesPerPage)+'&indent=on&wt=json&sort='+sort\n        if filterQuery is not None:\n            query += '&' + filterQuery\n        logging.debug('solr query: '+query)\n        \n        return query\n    \n    def _onSolrDetermineProcessLevelResponse(self, response):\n        try:\n            #Determine dataset processing level\n            processingLevel = None\n            solrJson = json.loads(response.body)\n            if len(solrJson['response']['docs']) >= 1:\n                if 'bbox' in self.variables:\n                    processingLevel = solrJson['response']['docs'][0]['Dataset-ProcessingLevel-Full'][0]\n                \n                    if processingLevel is not None and processingLevel.find('2') != -1:\n                        if self._configuration.get('service', 'bbox') == 'l2':\n                            #Call Matt's L2 Search Service\n                            #raise Exception(self._configuration.get('service', 'l2')+'?'+requestHandler.request.query)\n                            httpUtility = HttpUtility()\n                            url = self._configuration.get('service', 'l2') + '?'\n                            if 'format' not in self.requestHandler.request.arguments:\n                                url += 'format=atom&'\n                            url += self.requestHandler.request.query\n                            logging.debug(\"Calling L2 Service: \" + url)\n                            result = httpUtility.getResponse(url, self._onL2Response)\n                        else:\n                            points = self.variables['bbox'].split(',')\n                            if len(points) == 4:\n                                spatialSearch = SpatialSearch(\n                                    self._configuration.get('service', 'database')\n                                )\n                                spatialResult = spatialSearch.searchGranules(\n                                    int(self.startIndex),\n                                    int(self.entriesPerPage),\n                                    float(points[0]),\n                                    float(points[1]),\n                                    float(points[2]),\n                                    float(points[3])\n                                )\n                                logging.debug(\"Granule spatial search returned\")\n                                #if len(spatialResult[0]) > 0:\n                                self.variables['granuleIds'] = spatialResult[0]\n                                self.variables['granuleIdsFound'] = spatialResult[1]\n                \n                            del self.variables['bbox']\n                            solrJson = {'responseHeader': {'params': {}}, 'response': {}}\n                            solrJson['response']['numFound'] = int(self.variables['granuleIdsFound'])\n                            solrJson['response']['start'] = int(self.startIndex)\n                            solrJson['responseHeader']['params']['rows'] = int(self.entriesPerPage)\n                            solrJson['response']['docs'] = []\n                            for name in self.variables['granuleIds']:\n                               solrJson['response']['docs'].append({'Granule-Name': [name]})\n                            solrResponse = json.dumps(solrJson)\n                            \n                            searchText = ''\n                            if 'keyword' in self.variables:\n                                searchText = self.variables['keyword']\n                            openSearchResponse = self._generateOpenSearchResponse(\n                                solrResponse,\n                                searchText,\n                                self._configuration.get('service', 'url')+self.requestHandler.request.path,\n                                self.searchParameters,\n                                self.pretty\n                            )\n                            \n                            self.requestHandler.set_header(\"Content-Type\", \"application/xml\")\n                            #requestHandler.set_header(\"Content-Type\", \"application/rss+xml\")\n                            #requestHandler.write(solrResponse)\n                            self.requestHandler.write(openSearchResponse)\n                            self.requestHandler.finish()\n                    else:\n                        #Dataset is not an L2 dataset so handle search via Solr\n                        try:\n                            self._getSolrResponse(self.startIndex, self.entriesPerPage, self.variables)\n                        except:\n                            logging.exception('Failed to get solr response.')\n                else:\n                    #Not a bounding box search so handle search via Solr\n                    try:\n                        self._getSolrResponse(self.startIndex, self.entriesPerPage, self.variables)\n                    except:\n                        logging.exception('Failed to get solr response.')\n            else:\n                #Dataset metadata cannot be retreived so return empty search result\n                solrJson = {'responseHeader': {'params': {}}, 'response': {}}\n                solrJson['response']['numFound'] = 0\n                solrJson['response']['start'] = int(self.startIndex)\n                solrJson['responseHeader']['params']['rows'] = int(self.entriesPerPage)\n                solrJson['response']['docs'] = []\n                solrResponse = json.dumps(solrJson)\n                \n                self._writeResponse(solrResponse)\n        except BaseException as exception:\n            logging.exception('Failed to determine dataset processing level for bbox search ' + str(exception))\n            self._handleException(str(exception))\n\n    def _onL2Response(self, response):\n        if response.error:\n            self._handleException(str(response.error))\n        else:\n            try:\n                logging.debug('header: Content-Type '+response.headers['Content-Type'])\n                self.requestHandler.set_header('Content-Type', response.headers['Content-Type'])\n                logging.debug('header: Content-Length '+response.headers['Content-Length'])\n                self.requestHandler.set_header('Content-Length', response.headers['Content-Length'])\n            except:\n                pass\n            self.requestHandler.write(response.body)\n            self.requestHandler.finish()\n    \n","repo_name":"dataplumber/edge","sub_path":"src/main/python/libraries/edge/opensearch/granulewriter.py","file_name":"granulewriter.py","file_ext":"py","file_size_in_byte":12082,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"76"}
+{"seq_id":"71627642484","text":"from matplotlib import pyplot as plt\n\nimport numpy as np\nimport os\nimport tda\n\n\ndef poisson_pp(intensity=100, width=1, dim_pcd=2):\n    num_poisson = np.random.poisson(intensity)\n    x = np.random.uniform(0, width, num_poisson)\n    y = np.random.uniform(0, width, num_poisson)\n    mat = np.c_[x, y]\n    if dim_pcd == 3:\n        z = np.random.uniform(0, width, num_poisson)\n        mat = np.c_[mat, z]\n    return mat\n\n\ndef matern_one_pp(pcd, distance=0.1):\n    num_points = pcd.shape[0]\n    list_thin = []\n    for i in range(num_points):\n        for j in range(num_points):\n            r = np.linalg.norm(pcd[i] - pcd[j])\n            if 0 < r < distance:\n                list_thin.append(i)\n                break\n    return np.delete(pcd, list_thin, 0)\n\n\ndef matern_two_pp(pcd, distance=0.1):\n    num_points = pcd.shape[0]\n    vec_weight = np.random.uniform(0, 1, num_points)\n\n    list_thin = []\n    for i in range(num_points):\n        for j in range(num_points):\n            r = np.linalg.norm(pcd[i] - pcd[j])\n            t = vec_weight[i] - vec_weight[j]\n            if 0 < r < distance and t < 0:\n                list_thin.append(i)\n                break\n\n    return np.delete(pcd, list_thin, 0)\n\n\ndef save_points(list_lat, width, name_save):\n    num_lattice = len(list_lat)\n    plt.figure()\n    for k in range(num_lattice):\n        point = list_lat[k]\n        plt.plot(point[0], point[1], \"bo\")\n    plt.xlim(-0.1 * width, 1.1 * width)\n    plt.ylim(-0.1 * width, 1.1 * width)\n    plt.savefig(name_save)\n    plt.close()\n\n\nNAME_DIR = \"../data\"\nif not os.path.exists(NAME_DIR):\n    os.mkdir(NAME_DIR)\nNAME_DIR += \"/matern\"\nif not os.path.exists(NAME_DIR):\n    os.mkdir(NAME_DIR)\n\nmain = [True, False][0]\nplot = [True, False][0]\nCONST_PCD = 2\nCONST_LAMBDA = 100\nCONST_WIDTH = 1\nCONST_DISTANCE = 0.05\n\nif main:\n    CONST_IID = 100\n\n    # make directory to save point sets as txt file\n    name_parameter = \"pcd%s_lambda%s_width%s_distance%s_iid%s\" % (\n        CONST_PCD, CONST_LAMBDA, CONST_WIDTH, \"%03d\" % (CONST_DISTANCE * 100),\n        CONST_IID)\n\n    for temp_type in range(3):\n        name_dir_data = \"%s/%s_type_%s\" % (NAME_DIR, name_parameter, temp_type)\n        if not os.path.exists(name_dir_data):\n            os.mkdir(name_dir_data)\n        name_dir_pcd = \"%s/pcd_pd\" % name_dir_data\n        if not os.path.exists(name_dir_pcd):\n            os.mkdir(name_dir_pcd)\n\n    # generate point sets\n    for temp_iid in range(CONST_IID):\n        print(\"%s\" % temp_iid)\n        pcd_poisson = poisson_pp(CONST_LAMBDA, CONST_WIDTH)\n        pcd_matern_one = matern_one_pp(pcd_poisson, CONST_DISTANCE)\n        pcd_matern_two = matern_two_pp(pcd_poisson, CONST_DISTANCE)\n\n        np.savetxt(\"%s/%s_type_0/pcd_pd/pcd_%s.txt\" % (\n            NAME_DIR, name_parameter, temp_iid), np.asarray(pcd_poisson),\n                   delimiter='\\t')\n        np.savetxt(\"%s/%s_type_1/pcd_pd/pcd_%s.txt\" % (\n            NAME_DIR, name_parameter, temp_iid), np.asarray(pcd_matern_one),\n                   delimiter='\\t')\n        np.savetxt(\"%s/%s_type_2/pcd_pd/pcd_%s.txt\" % (\n            NAME_DIR, name_parameter, temp_iid), np.asarray(pcd_matern_two),\n                   delimiter='\\t')\n\nif plot:\n    CONST_IID = 10\n\n    # make directory to plot point sets as png file\n    name_parameter = \"pcd%s_lambda%s_width%s_distance%s_iid%s\" % (\n        CONST_PCD, CONST_LAMBDA, CONST_WIDTH, \"%03d\" % (CONST_DISTANCE * 100),\n        CONST_IID)\n    name_dir_png = \"%s/plot_point_%s\" % (NAME_DIR, name_parameter)\n    tda.os_mkdir(name_dir_png)\n\n    # plot point sets\n    for temp_iid in range(CONST_IID):\n        print(\"%s\" % temp_iid)\n        pcd_poisson = poisson_pp(CONST_LAMBDA, CONST_WIDTH)\n        pcd_matern_one = matern_one_pp(pcd_poisson, CONST_DISTANCE)\n        pcd_matern_two = matern_two_pp(pcd_poisson, CONST_DISTANCE)\n\n        save_points(pcd_poisson, CONST_WIDTH, \"%s/%s_%s.png\" % (\n            name_dir_png, \"type_0\", temp_iid))\n        save_points(pcd_matern_one, CONST_WIDTH, \"%s/%s_%s.png\" % (\n            name_dir_png, \"type_1\", temp_iid))\n        save_points(pcd_matern_two, CONST_WIDTH, \"%s/%s_%s.png\" % (\n            name_dir_png, \"type_2\", temp_iid))\n","repo_name":"genki-kusano/python-pwgk","sub_path":"code/make_matern.py","file_name":"make_matern.py","file_ext":"py","file_size_in_byte":4141,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"76"}
+{"seq_id":"70498415927","text":"import logging\n\nimport pandas as pd\nimport pymongo\nfrom sklearn.feature_extraction.text import CountVectorizer\n\nlogging.basicConfig(level=logging.INFO,\n                    format='%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s',\n                    datefmt='%a, %d %b %Y %H:%M:%S',\n                    filemode='w')\n\n\nclass dataset_analyser:\n    def __init__(self):\n        self.analyze = CountVectorizer(min_df=1, strip_accents='unicode', stop_words='english').build_analyzer()\n        # self.word2vec = word2vec_gensim()\n\n    def dblp_sentences(self, file):\n        # file = '../data/DBLP1.csv'\n        df = pd.read_csv(file, encoding='iso-8859-1')\n        df = df.fillna('')\n\n        df['sent'] = df['authors'] + ' ' + df['title'] + ' ' + df['venue']\n        sents = df['sent'].tolist()\n        sents = [self.analyze(sent) for sent in sents]\n        return sents\n\n    def dblp_sent(self, df):\n        # file = '../data/DBLP1.csv'\n        # df = pd.read_csv(file, encoding='iso-8859-1')\n        df = df.fillna('')\n\n        df['sent'] = df['authors'] + ' ' + df['title'] + ' ' + df['venue']\n        sents = df['sent'].tolist()\n        sents = [self.analyze(sent) for sent in sents]\n        # df['sent'] = sents\n        return sents\n\n    def scholar_sentences(self, file):\n        # file = '../data/Scholar.csv'\n        df = pd.read_csv(file, encoding='iso-8859-1')\n        df = df.fillna('')\n\n        df['sent'] = df['authors'] + ' ' + df['title'] + ' ' + df['venue']\n        sents = df['sent'].tolist()\n        sents = [self.analyze(sent) for sent in sents]\n        # df['sent'] = sents\n        return sents\n\n    def scholar_sent(self, df):\n        # file = '../data/Scholar.csv'\n        # df = pd.read_csv(file, encoding='iso-8859-1')\n        df = df.fillna('')\n\n        df['sent'] = df['authors'] + ' ' + df['title'] + ' ' + df['venue']\n        sents = df['sent'].tolist()\n        sents = [self.analyze(sent) for sent in sents]\n        # df['sent'] = sents\n        return sents\n\n    def dblp_traning_sentences(self):\n        client = pymongo.MongoClient('localhost', 27017)\n        db = client['dblp']\n        tbl = db.get_collection('dblp')\n\n        data = pd.DataFrame(list(tbl.find()))\n        data.pop('_id')\n        data = data.fillna('')\n\n        data['sent'] = data['author'] + ' ' + data['title'] + ' ' + data['journal']\n\n        sents = data['sent'].tolist()\n        sents = [self.analyze(sent) for sent in sents]\n        print(sents[0])\n        return sents\n\n\nif __name__ == \"__main__\":\n    file = '../data/DBLP1.csv'\n\n    u = dataset_analyser()\n\n    # print(len(scholar_recs2sent(file)))\n    # print(len(dblp_traning_rec2sent()))\n\n    # print(u.().nonzero())\n\n    pass\n","repo_name":"fanf6g/erqg","sub_path":"utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2705,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"72181315446","text":"from imdb import *\nimport random\nimport argparse\n\ndef parse_args():\n\tap = argparse.ArgumentParser()\n\tap.add_argument('-trm', '--top-rated-movies', action='store_const', const=Scraper.TOP_RATED_MOVIES_URL, dest='choice', help='Escolha esse argumento caso você prefira ser indicado dos filmes mais bem votados')\n\tap.add_argument('-trs', '--top-rated-shows', action='store_const', const=Scraper.TOP_RATED_SHOWS_URL, dest='choice', help='Escolha esse argumento caso você prefira ser indicado das séries mais bem votados')\n\tap.add_argument('-pm', '--popular-movies', action='store_const', const=Scraper.MOST_POPULAR_MOVIES_URL, dest='choice', help='Escolha esse argumento caso você prefira ser indicado dos filmes mais populares')\n\tap.add_argument('-ps', '--popular-shows', action='store_const', const=Scraper.MOST_POPULAR_SHOWS_URL, dest='choice', help='Escolha esse argumento caso você prefira ser indicado das séries mais populares')\n\treturn vars(ap.parse_args())\n\ndef main(arg):\n\tsome = Scraper.get(IMDBBase, arg)\n\trand = random.choice(some)\n\tprint(rand.details())\n\nif __name__ == '__main__':\n\ttry:\n\t\targ = parse_args()['choice']\n\t\tmain(arg)\n\texcept Exception as ex:\n\t\tprint(ex)","repo_name":"glbessa/IMDbWebScraping","sub_path":"aleatorio_imdb.py","file_name":"aleatorio_imdb.py","file_ext":"py","file_size_in_byte":1183,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"27543751242","text":"class Node:\n    def __init__(self, node_id, node_name, node_type):\n        self.node_id = node_id\n        self.name = node_name\n        self.type = node_type\n        self.attributes = {}\n        self.children = []\n\n    def __eq__(self, other):\n        \"\"\"Override the default Equals behavior\"\"\"\n        if isinstance(other, self.__class__):\n            return self.node_id == other.node_id\n        return False\n","repo_name":"jasmina94/ExPreSsiVeNess","sub_path":"django_project/core_app/core/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"26181659050","text":"\n\n# 3rd party imports\nimport guitarpro\n\nimport parser\n\n\n# updated the gp5_song....\ngp5_file_1 = \"./gp5files/test_scores/test_write_gp5_1.gp5\"\ngp5song_1 = guitarpro.parse(gp5_file_1)\n\ngp5_file_2 = \"./gp5files/test_scores/test_write_gp5_2.gp5\"\ngp5song_2 = guitarpro.parse(gp5_file_2)\napi_song_2 = parser.API.get_functions.get_song_data(gp5song_2)\n\nparser.API.write_functions.api_to_gp5(api_song_2[0], gp5song_1)\n\nguitarpro.write(gp5song_1, \"./gp5files/test_scores/test_write_gp5_1_revised.gp5\")\n\n\ngp5_file = \"./gp5files/test_scores/tied_hammer-ons.gp5\"\ngp5song = guitarpro.parse(gp5_file)\napi_song_tied_hammers = parser.API.get_functions.get_song_data(gp5song)\n\n\ngp5_file = \"./gp5files/test_scores/tied_hammer-ons_no_hammer.gp5\"\ngp5song_no_hammer = guitarpro.parse(gp5_file)\napi_no_hammers = parser.API.get_functions.get_song_data(gp5song_no_hammer)\n\nparser.API.write_functions.api_to_gp5(api_song_tied_hammers[0], gp5song_no_hammer)\nguitarpro.write(gp5song, \"./gp5files/test_scores/tied_hammers_revised.gp5\")\n\ngp5_file_2 = \"./gp5files/test_scores/test_write_gp5_2.gp5\"\ngp5song_2 = guitarpro.parse(gp5_file_2)\napi_song_json = parser.API.get_functions.get_song_data(gp5song_2)\n\nparser.API.write_functions.api_to_json(api_song_json)\n","repo_name":"callumgoddard/Adorn-o","sub_path":"Adorn_o/test_write_functions.py","file_name":"test_write_functions.py","file_ext":"py","file_size_in_byte":1229,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"20160973540","text":"import os\nimport sys\n\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\n\nsys.path.insert(0, \"../common\")\nfrom utils import show_boxplots\n\nif len(sys.argv) < 2:\n    print(\"Need to specify the directory containing the input data. Exiting.\")\n    sys.exit(1)\ninput_dir = sys.argv[1] # e.g., ../../../data/\nif len(sys.argv) < 3:\n    print(\"Need to specify the directory containing the output data. Exiting.\")\n    sys.exit(1)\nbase_output_dir = os.path.join(sys.argv[2], \"rq1\") # e.g., ../../../results/\n\ninput_filepath = os.path.join(input_dir, \"rq1_input.csv\")\ntop_25_filepath = os.path.join(input_dir, \"top25cwe.csv\")\n\ndf_vccs = pd.read_csv(input_filepath, delimiter=\",\")\ntop_25 = pd.read_csv(top_25_filepath)[[\"cwe\", \"position\"]]\n\ndef run_analysis(output_dir, df_vccs, top_25):\n    output_filepath = os.path.join(output_dir, \"output.txt\")\n\n    vccs_per_cve_overall_summary_filepath = os.path.join(output_dir, \"overall/vccs_per_cve.csv\")\n    vccs_ratio_per_project_overall_summary_filepath = os.path.join(output_dir, \"overall/vccs_ratio_per_project.csv\")\n    insertion_window_per_cve_overall_summary_filepath = os.path.join(output_dir, \"overall/insertion_window_per_cve.csv\")\n    vccs_per_cve_cwe_summary_filepath = os.path.join(output_dir, \"cwes/vccs_per_cve.csv\")\n    vccs_per_cve_cwe_boxplots_filepath = os.path.join(output_dir, \"cwes/boxplots/vccs_per_cve_cwe_{}.pdf\")\n\n    files_per_vcc_overall_summary_filepath = os.path.join(output_dir, \"overall/files_per_vcc.csv\")\n    added_lines_per_vcc_overall_summary_filepath = os.path.join(output_dir, \"overall/added_lines_per_vcc.csv\")\n    removed_lines_per_vcc_overall_summary_filepath = os.path.join(output_dir, \"overall/removed_lines_per_vcc.csv\")\n    files_per_vcc_cwe_summary_filepath = os.path.join(output_dir, \"cwes/files_per_vcc.csv\")\n    files_per_vcc_cwe_boxplots_filepath = os.path.join(output_dir, \"cwes/boxplots/files_per_vcc_cwe_{}.pdf\")\n    files_previous_changes_first_vcc_summary_filepath = os.path.join(output_dir, \"overall/files_previous_changes_first_vcc.csv\")\n    files_previous_changes_last_vcc_summary_filepath = os.path.join(output_dir, \"overall/files_previous_changes_last_vcc.csv\")\n\n    df_vccs_top_25 = df_vccs[df_vccs[\"cwe\"].isin(top_25[\"cwe\"])]\n    cve_groups = df_vccs.groupby(\"cve\", sort=False)\n    project_groups = df_vccs.groupby(\"repo\", sort=False)\n    vcc_groups = df_vccs.groupby([\"repo\", \"hash\"], sort=False)\n\n    # General Profiling\n    num_vccs = df_vccs.groupby([\"repo\", \"hash\"]).size().count()\n    num_repos = len(project_groups)\n    num_cwes = df_vccs[\"cwe\"].nunique()\n    num_files = df_vccs.groupby([\"repo\", \"file\"]).size().count()\n\n    # Part 1: VCCs per CVE\n\n    ## (Overall) Summary\n    num_cves = len(cve_groups)\n    vccs_per_cve = cve_groups[\"hash\"].nunique()\n    vccs_per_cve.describe().to_csv(vccs_per_cve_overall_summary_filepath)\n\n    ## (Overall) VCCs Ratio per Project\n    project_num_vccs = project_groups.size().rename(\"vccs\").to_frame().reset_index()\n    project_num_commits = project_groups.first()[\"repo_commits\"].to_frame().reset_index()\n    projects = project_num_vccs.merge(project_num_commits, on=\"repo\")\n    projects[\"vccs_ratio\"] = projects[\"vccs\"] / projects[\"repo_commits\"]\n    projects.describe().to_csv(vccs_ratio_per_project_overall_summary_filepath)\n\n    ## (Overall) CVEs with > 1 VCCs\n    cve_groups_many_vccs = cve_groups.filter(lambda g: g[\"hash\"].nunique() > 1).groupby(\"cve\", sort=False)\n    cve_with_many_vccs_count = len(cve_groups_many_vccs)\n    cve_with_many_vccs_perc = cve_with_many_vccs_count / num_cves * 100\n\n    ## (Overall) Insertion window analysis\n    first_vccs = cve_groups_many_vccs[\"date\"].min().to_frame().reset_index().rename(columns={\"date\": \"first_date\"})\n    last_vccs = cve_groups_many_vccs[\"date\"].max().to_frame().reset_index().rename(columns={\"date\": \"last_date\"})\n    cves_windows = first_vccs.merge(last_vccs, on=\"cve\")\n    cves_windows[[\"first_date\", \"last_date\"]] = cves_windows[[\"first_date\", \"last_date\"]].apply(pd.to_datetime)\n    cves_windows[\"days_distance\"] = (cves_windows[\"last_date\"] - cves_windows[\"first_date\"]).dt.days\n    window_duration_mean = cves_windows[\"days_distance\"].mean()\n    cves_windows[\"days_distance\"].describe().to_csv(insertion_window_per_cve_overall_summary_filepath)\n\n    ## (Overall) CVE w/ largest number of VCCs\n    vccs_per_cve_idxmax = vccs_per_cve.idxmax()\n    vccs_per_cve_max = vccs_per_cve.max()\n    cve_max_vccs = cve_groups.get_group(vccs_per_cve_idxmax)\n    cve_max_vccs_files_count = cve_max_vccs[\"file\"].nunique()\n    cve_max_vccs_added_lines = cve_max_vccs[\"added_lines\"].sum()\n    cve_max_vccs_removed_lines = cve_max_vccs[\"removed_lines\"].sum()\n    cve_max_vccs_window_duration = cves_windows.loc[cves_windows[\"cve\"] == vccs_per_cve_idxmax][\"days_distance\"].values[0]\n\n    ## (Overall) CVEs w/ shortest and largest insertion window\n    cves_windows_max = cves_windows.loc[cves_windows[\"days_distance\"].idxmax()]\n    cves_windows_max_cve = cves_windows_max[\"cve\"]\n    cves_windows_max_duration = cves_windows_max[\"days_distance\"]\n    cves_windows_min = cves_windows.loc[cves_windows[\"days_distance\"].idxmin()]\n    cves_windows_min_cve = cves_windows_min[\"cve\"]\n    cves_windows_min_duration = cves_windows_min[\"days_distance\"]\n\n    ## (Per CWEs) Summary and Boxplots\n\n    ### Makes the \"double\" groupby and then orders according to the Top 25 order\n    vccs_per_cve_cwe = df_vccs_top_25 \\\n        .groupby(\"cwe\", sort=False)[[\"hash\", \"cve\"]] \\\n        .apply(lambda x: x.groupby(\"cve\", sort=False).nunique()) \\\n        .reset_index() \\\n        .merge(top_25, how=\"left\", on=\"cwe\") \\\n        .sort_values(\"position\", ignore_index=True) \\\n        .drop(columns=[\"cve\", \"position\"])\n    vccs_per_cve_cwe.groupby(\"cwe\", sort=False)[\"hash\"].describe().to_csv(vccs_per_cve_cwe_summary_filepath)\n    show_boxplots(vccs_per_cve_cwe, \"cwe\", \"hash\", \"VCCs per CVE\", vccs_per_cve_cwe_boxplots_filepath, log=True)\n\n    # Part 2: Touched Files per VCC\n\n    ## (Overall) Summary\n    files_per_vcc = vcc_groups[\"file\"].nunique()\n    files_per_vcc.describe().to_csv(files_per_vcc_overall_summary_filepath)\n\n    ## (Overall) Code Churn\n    added_lines_per_vcc = vcc_groups[\"added_lines\"].sum()\n    added_lines_per_vcc.describe().to_csv(added_lines_per_vcc_overall_summary_filepath)\n    removed_lines_per_vcc = vcc_groups[\"removed_lines\"].sum()\n    removed_lines_per_vcc.describe().to_csv(removed_lines_per_vcc_overall_summary_filepath)\n\n    ## (Overall) VCCs touching > 10 files\n    vccs_more_ten_files_count = files_per_vcc[files_per_vcc > 10].count()\n    vccs_more_ten_files_perc = vccs_more_ten_files_count / num_vccs * 100\n\n    ## (Overall) VCC w/ largest number of Files\n    files_per_vcc_idxmax = files_per_vcc.idxmax()\n    files_per_vcc_idxmax_repo = files_per_vcc_idxmax[0]\n    files_per_vcc_idxmax_hash = files_per_vcc_idxmax[1]\n    files_per_vcc_max = files_per_vcc.max()\n    vcc_max_files = vcc_groups.get_group(files_per_vcc_idxmax)\n    vcc_max_files_count = vcc_max_files[\"file\"].nunique()\n    vcc_max_cve_counts = vcc_max_files[\"cve\"].value_counts()\n    vcc_max_files_added_lines = vcc_max_files[\"added_lines\"].iloc[0]\n    vcc_max_files_removed_lines = vcc_max_files[\"removed_lines\"].iloc[0]\n\n    ## (Per CWEs) Summary and Boxplots\n    files_per_vcc_cwe = df_vccs_top_25 \\\n        .groupby(\"cwe\", sort=False)[[\"repo\", \"hash\", \"file\"]] \\\n        .apply(lambda x: x.groupby([\"repo\", \"hash\"], sort=False).nunique()) \\\n        .reset_index() \\\n        .merge(top_25, how=\"left\", on=\"cwe\") \\\n        .sort_values(\"position\", ignore_index=True) \\\n        .drop(columns=[\"repo\", \"hash\", \"position\"])\n    files_per_vcc_cwe.groupby(\"cwe\", sort=False)[\"file\"].describe().to_csv(files_per_vcc_cwe_summary_filepath)\n    show_boxplots(files_per_vcc_cwe, \"cwe\", \"file\", \"Touched Files per VCC\", files_per_vcc_cwe_boxplots_filepath, log=True)\n\n    # Part 3: Number of Previous Changes for files in First/Last VCC\n\n    ## (Overall) Summary\n\n    df_vccs_first = df_vccs[cve_groups[\"date\"].transform(min) == df_vccs[\"date\"]]\n    previous_changes_vccs_first = df_vccs_first[\"previous_changes\"]\n    df_vccs_last = df_vccs[cve_groups[\"date\"].transform(max) == df_vccs[\"date\"]]\n    previous_changes_vccs_last = df_vccs_last[\"previous_changes\"]\n    previous_changes_vccs_first.describe().to_csv(files_previous_changes_first_vcc_summary_filepath)\n    previous_changes_vccs_last.describe().to_csv(files_previous_changes_last_vcc_summary_filepath)\n\n    # Part 4: Files that were created in a VCC (i.e., previous_changes == 0) + Files that were created in a VCC that is the sole the CVE\n    files_created_vcc = df_vccs.loc[df_vccs[\"previous_changes\"] == 0, [\"repo\", \"file\"]].drop_duplicates(ignore_index=True)\n    files_cves_one_vcc = cve_groups.filter(lambda x: len(x) == 1)[[\"repo\", \"file\"]].drop_duplicates(ignore_index=True)\n    files_cves_one_vcc_created = pd.merge(files_created_vcc, files_cves_one_vcc, on=[\"repo\", \"file\"])\n    files_created_vcc_count = len(files_created_vcc)\n    files_cves_one_vcc_created_count = len(files_cves_one_vcc_created)\n\n    # Part 5: Files that were changed > 500 times before VCC\n    files_first_vcc = df_vccs[df_vccs.groupby([\"repo\", \"file\"], sort=False)[\"previous_changes\"].transform(min) == df_vccs[\"previous_changes\"]]\n    files_over_500 = files_first_vcc.loc[files_first_vcc[\"previous_changes\"] > 500, [\"repo\", \"file\", \"previous_changes\"]].sort_values(by=\"previous_changes\", ascending=False, ignore_index=True)\n    files_over_500_count = len(files_over_500)\n\n    with open(output_filepath, \"w\") as output_file:\n        print(f\"RQ1 Context:\", file=output_file)\n        print(f\"* CVEs: {num_cves}\", file=output_file)\n        print(f\"* VCCs: {num_vccs}\", file=output_file)\n        print(f\"* Repos: {num_repos}\", file=output_file)\n        print(f\"* CWEs: {num_cwes}\", file=output_file)\n        print(f\"* Files (distinct): {num_files}\", file=output_file)\n        print(file=output_file)\n        print(f\"RQ1 Main Results:\", file=output_file)\n        print(f\"* CVEs with > 1 VCC: {cve_with_many_vccs_count}/{num_cves} ({round(cve_with_many_vccs_perc, 2)}%)\", file=output_file)\n        print(f\"* Mean Nr. Days between first and last VCCs: {round(window_duration_mean, 3)} ({round(window_duration_mean/365, 3)} years)\", file=output_file)\n        print(f\"* {vccs_per_cve_idxmax} has the largest number of VCCs: {vccs_per_cve_max}. Considering all VCCs:\", file=output_file)\n        print(f\"  - Touched a total of {cve_max_vccs_files_count} valid files\", file=output_file)\n        print(f\"  - Added a total of {cve_max_vccs_added_lines} lines\", file=output_file)\n        print(f\"  - Removed a total of {cve_max_vccs_removed_lines} lines\", file=output_file)\n        print(f\"  - Taking {cve_max_vccs_window_duration} days ({round(cve_max_vccs_window_duration/365, 3)} years) between first and last VCCs\", file=output_file)\n        print(f\"* {cves_windows_max_cve} has the largest insertion window: {cves_windows_max_duration} days ({round(cves_windows_max_duration/365, 3)} years)\", file=output_file)\n        print(f\"* {cves_windows_min_cve} has the shortest insertion window: {cves_windows_min_duration} days ({round(cves_windows_min_duration/365, 3)} years)\", file=output_file)\n        print(file=output_file)\n        print(f\"* VCCs that touched > 10 files: {vccs_more_ten_files_count}/{num_vccs} ({round(vccs_more_ten_files_perc, 2)}%)\", file=output_file)\n        print(f\"* VCC {files_per_vcc_idxmax_hash} of {files_per_vcc_idxmax_repo} touched the largest number of valid files: {files_per_vcc_max}. Specifically:\", file=output_file)\n        print(f\"  - Contributed to: {vcc_max_cve_counts.index.values}\", file=output_file) \n        print(f\"  - Touched {vcc_max_files_count} valid files\", file=output_file)\n        print(f\"  - Added {vcc_max_files_added_lines} lines\", file=output_file)\n        print(f\"  - Removed {vcc_max_files_removed_lines} lines\", file=output_file)\n        print(f\"* Files created within a VCC: {files_created_vcc_count}/{num_files} ({round(files_created_vcc_count / num_files * 100, 2)}%)\", file=output_file)\n        print(f\"  - Files created in \\\"single-VCC\\\" vulnerabilities: {files_cves_one_vcc_created_count}/{files_created_vcc_count} ({round(files_cves_one_vcc_created_count / files_created_vcc_count * 100, 2)}%)\", file=output_file)\n        print(f\"* Files that started becoming vulnerable after >= 500 commits: {files_over_500_count}/{num_files} ({round(files_over_500_count / num_files * 100, 2)}%)\", file=output_file)\n\nrun_analysis(os.path.join(base_output_dir, \"main\"), df_vccs, top_25)\n\n# Drop outliers: VCCS whose CVE has lots of VCCS\ncve_groups = df_vccs.groupby(\"cve\", sort=False)\nvccs_per_cve = cve_groups[\"hash\"].nunique()\nlarge_cves = vccs_per_cve.loc[np.abs(stats.zscore(vccs_per_cve)) > 3].index.tolist()\ndf_vccs_no_large_cves = df_vccs.loc[~df_vccs[\"cve\"].isin(large_cves)]\nrun_analysis(os.path.join(base_output_dir, \"no_large_cves\"), df_vccs_no_large_cves, top_25)\n\n# Drop outliers: VCCS having touched lots of files\nvcc_groups = df_vccs.groupby([\"repo\", \"hash\"], sort=False)\nfiles_per_vcc = vcc_groups[\"file\"].nunique()\nlarge_vccs = files_per_vcc.loc[np.abs(stats.zscore(files_per_vcc)) > 3].index.tolist()\nlarge_vccs = [lv[0] + \"/commit/\" + lv[1] for lv in large_vccs]\ntmp_df_vccs = df_vccs.copy()\ntmp_df_vccs[\"vcc\"] = df_vccs[\"repo\"] + \"/commit/\" + df_vccs[\"hash\"]\ndf_vccs_no_large_vccs = tmp_df_vccs.loc[~tmp_df_vccs[\"vcc\"].isin(large_vccs)]\nrun_analysis(os.path.join(base_output_dir, \"no_large_vccs\"), df_vccs_no_large_vccs, top_25)\n","repo_name":"sesalab/OnlineAppendices","sub_path":"TSE21-VulnerabilityLifecycle/scripts/secretlife/data_analysis/rq1.py","file_name":"rq1.py","file_ext":"py","file_size_in_byte":13496,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"482926827","text":"import numpy as np\nfrom corner import corner\nfrom chi2 import likelihood_func_LambdaCDM, likelihood_func_wCDM\n\nLambdaCDM_Omega_M = np.linspace(0.0, 0.5, 30)\nLambdaCDM_Omega_Lambda = np.linspace(0.4, 1.1, 30)\nwCDM_Omega_M = np.linspace(0.0, 0.5, 30)\nwCDM_w = np.linspace(-1.75, -0.5, 30)\n\n\ndef draw(par1, par2, lf, figurename, range, labels):\n    pos = []\n    likelihoods = []\n    for om in par1:\n        for ol in par2:\n            pos.append([om, ol])\n            likelihoods.append(lf([om, ol]))\n\n    pos = np.array(pos)\n    likelihoods = np.exp(np.array(likelihoods) - max(likelihoods))\n\n    figure = corner(pos,bins=30, range=range, weights=likelihoods,\n                    show_titles=True, levels=(0.68, 0.95, 0.99),\n                    labels=labels )\n    figure.savefig(figurename)\n\ndraw(LambdaCDM_Omega_M, LambdaCDM_Omega_Lambda,\n     likelihood_func_LambdaCDM, \"2par_l.pdf\", range=[[0.0,0.5],[0.4,1.1]],\n     labels=[r\"$\\Omega_M$\", r\"$\\Omega_\\Lambda$\"])\ndraw(wCDM_Omega_M, wCDM_w, likelihood_func_wCDM,\n     \"2par_w.pdf\", range=[[0.0, 0.5],[-1.75, -0.5]],\n     labels=[r\"$\\Omega_M$\",r\"$w$\"])","repo_name":"HoU-Wa/phy526proj","sub_path":"two_pars.py","file_name":"two_pars.py","file_ext":"py","file_size_in_byte":1101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"7283349119","text":"import time\nimport os\nimport json\n\nimport cloudconvert\n\nCREDENTIAL_FILE = '../credentials/cloud_convert.json'\n\nclass CloudConvert():\n    def __init__(self):\n        self._service = cloudconvert.Api(api_key=self.get_credentials())\n\n    def get_credentials(self):\n        \"\"\"Read API key from file and return it\"\"\"\n        with open(CREDENTIAL_FILE) as credentials:\n            data = json.load(credentials)\n            api_key = data['API_KEY']\n\n        return api_key\n\n    def convert_url_to_pdf(self, url):\n        process = self._service.convert({\n            'inputformat': 'website',\n            'outputformat': 'pdf',\n            'input': 'url',\n            'file': url\n        })\n        process.wait()\n\n        file_name = '../data/cc_download_{0}.pdf'.format(time.time())\n        process.download(localfile=file_name)\n\n        return file_name\n\n    def delete_file(self, file):\n        os.remove(file)\n\ndef main():\n    cc_instance = CloudConvert()\n    cc_instance.convert_url_to_pdf('https://www.howtographql.com/basics/0-introduction/')\n\nif __name__ == '__main__':\n    main()","repo_name":"zpritchett/zapiest","sub_path":"api/CloudConvert.py","file_name":"CloudConvert.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"20010306643","text":"from typing import List, Iterator\n\n\nclass HonkLanguage:\n    pass  # Used to specify different Honk keys\n\n\nHONKMAP = {'h': 0, 'H': 1, 'o': 0, 'O': 1, 'n': 0, 'N': 1, 'k': 0, 'K': 1}\nalphabet = \"abcdefghijklmnopqrstuvwxyz\"\nalphakey = {}\nfor i in range(26):\n    alphakey[i + 1] = alphabet[i]  # Map Decimals to letters\n\nrev_alphakey = {}\nfor key in alphakey:  # Map letters to numbers\n    rev_alphakey[alphakey[key]] = key\n\nneutral_honk = \"honk\"\nEND_OF_WORD = \"knoh\"\n\n\n### Convert Honks to a message\n\ndef _honk_to_decimal(honks: str) -> int:\n    \"\"\"\n    Precondition: correct Honks\n    :param honks: a string denoting honks\n    :return: A decimal value\n    \"\"\"\n    sum = 0\n    for i in range(1, len(honks) + 1):\n        char = honks[-i]\n        power = 2 ** (i - 1)\n        sum += HONKMAP[char] * power\n    return sum\n\n\ndef _honk_to_letter(honks: str) -> str:\n    \"\"\"\n    Takes a honk string and converts it to the corresponding letter\n    :param honks:\n    :return:\n    \"\"\"\n    return alphakey[_honk_to_decimal(honks)]\n\n\ndef _honks_to_word(honks: str) -> str:\n    \"\"\"\n    Takes a set of honks representing a word, and converts it to a word.\n    :param honks:\n    :return:\n    \"\"\"\n    honk_list = list(map(str.strip, honks.split(\" \")))\n    #print(honk_list +  [\" yo\"])\n    word = \"\"\n    for honk in honk_list:\n        if len(honk) in (4,8):\n            word = word + _honk_to_letter(honk)\n    return word\n\n\ndef honks_to_words(honks: str) -> str:\n    \"\"\"\n    Takes a set of honks representing sentences, and converts it to sentences.\n    :param honks:\n    :return:\n    \"\"\"\n\n    honk_words = _splice_honks(honks)\n    #print(honk_words)\n    sentence = \"\"\n    for enc_word in honk_words:\n        sentence = sentence + _honks_to_word(enc_word) + \" \"\n    #print(sentence)\n    return sentence\n\n\ndef _splice_honks(honks: str) -> List[str]:\n    \"\"\"\n    Splice honks string based on encryption key.\n    :param honks:\n    :return: An iterator,  iterates through each word\n    \"\"\"\n    words = honks.split(END_OF_WORD)\n    return list(map(str.strip, words))\n\n\n### Convert your message to Honks\n\ndef _letter_to_binary(letter: str) -> str:\n    \"\"\"\n    Takes a letter and convert it to its mapped binary value\n    :param letter: a character\n    :return: a binary string representing the letter\n\n\n\n    \"\"\"\n    number = rev_alphakey[letter]\n    binary_str = str(bin(number))[2:]\n    return binary_str\n\n\nbin_to_honk_map = {1: {'0': 'k', '1': 'K'},\n                   3: {'0': 'o', '1': 'O'},\n                   2: {'0': 'n', '1': 'N'},\n                   4: {'0': 'h', '1': 'H'}}\n\n\n##\ndef _bin_to_honk(bin_inc_honk: str) -> str:\n    \"\"\"\n    :param inc_honk: binary string of length 4 or less\n    :return:\n\n    >>> _bin_to_honk(\"1000\")\n    'Honk'\n    >>> _bin_to_honk(\"0001\")\n    'honK'\n\n    \"\"\"\n    acc = \"\"\n    for i in range(1, len(bin_inc_honk) + 1):\n        acc = bin_to_honk_map[i][bin_inc_honk[-i]] + acc\n    return acc\n\n\ndef _binary_to_honk(bin_incomplete_honk: str) -> str:\n    \"\"\"\n\n    :param bin_incomplete_honk: binary string of up to length 8\n    :return: Corresponding honk letters.\n\n    >>>\n\n    \"\"\"\n    if len(bin_incomplete_honk) == 0:\n        return \"\"\n    honk_piece1 = bin_incomplete_honk[:-4]\n    honk_piece2 = bin_incomplete_honk[-4:]\n    #print(honk_piece1)\n    #print(honk_piece2)\n    return _bin_to_honk(honk_piece1) + _bin_to_honk(honk_piece2)\n\n\ndef _bin_to_honk_w_fill(bin_incomplete_honk: str) -> str:\n    \"\"\"\n\n    :param bin_incomplete_honk: a binary string representing a letter.\n    :return: the correct corresponding honks\n    \"\"\"\n    honk_piece = \"\"\n    if len(bin_incomplete_honk) < 4:  # 0 to 3\n        need = 4 - len(bin_incomplete_honk)\n        honk_piece = neutral_honk[:need]\n        #print(honk_piece)\n    elif 4 < len(bin_incomplete_honk) < 8:\n        need = 8 - len(bin_incomplete_honk)\n        honk_piece = neutral_honk[:need]\n        #print(honk_piece)\n    rest_of_honk =  _binary_to_honk(bin_incomplete_honk)\n    #print(rest_of_honk)\n    return honk_piece + _binary_to_honk(bin_incomplete_honk)\n\n\ndef letter_to_honk(letter: str) -> str:\n    return _bin_to_honk_w_fill(_letter_to_binary(letter))\n\n\ndef word_to_honk(word: str) -> str:\n    acc = \"\"\n    for char in word:\n        acc += letter_to_honk(char) + \" \"\n    acc += END_OF_WORD\n    return acc\n\n\ndef words_to_honk(words: str) -> str:\n    \"\"\"\n\n    :param words: words, lowercase only and no punctuation\n    :return:\n    \"\"\"\n    words = list(map(str.strip, words.split(\" \")))\n    honk_str = \"\"\n    for word in words:\n        honk_str += word_to_honk(word) + \" \"\n    return honk_str\n\n\n##\n\n\nif __name__ == \"__main__\":\n    #print(_honk_to_decimal(\"HONKHONK\"))\n    #print(_honk_to_letter(\"honK\") +\n    #      \" \" + _honk_to_letter(\"hoNK\") + _honk_to_letter(\"honkHONK\")\n    #      + _honk_to_letter(\"honKhONK\"))\n    # print(letter_to_honk(\"z\"))\n    #print(letter_to_honk(\"a\"))\n    #print(letter_to_honk(\"z\"))\n\n    print(words_to_honk(\"cow piss\"))\n    print(honks_to_words(words_to_honk(\"cow piss\")))\n    print(honks_to_words(words_to_honk(\"how is life my friends\")))\n    print(words_to_honk(\"am fine the c videos for two oh nine are super boring though\"))\n","repo_name":"wuemily2/goozebot","sub_path":"honk_encryption.py","file_name":"honk_encryption.py","file_ext":"py","file_size_in_byte":5125,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"39738503021","text":"line1 = [\" \",\" \",\" \"]\r\nline2 = [\" \",\" \",\" \"]\r\nline3 = [\" \",\" \",\" \"]\r\nmap = [line1,line2,line3]\r\n\r\nprint(f\"{line1}\\n{line2}\\n{line3}\")\r\nprint(\"Hiding your treasure! X marks the spot.\")\r\n\r\nposition = input(\"Where do you want to hide the treasure? \")\r\n\r\n#Should use abc.index() to get number from letter.  \r\nx = int(position[0])\r\ny = int(position[1])\r\n\r\nmap[x][y] = \"X\"\r\n\r\nprint(f\"{line1}\\n{line2}\\n{line3}\")","repo_name":"Tamari21/100DaysOfCode","sub_path":"Day 4/ex3.py","file_name":"ex3.py","file_ext":"py","file_size_in_byte":405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"12514546319","text":"from pytest_bdd import (\n    scenarios,\n    given,\n    then,\n    when,\n    parsers,\n)\nimport fixtures\n\nscenarios('.')\n\n\ndef userify(txt):\n    return txt\n\n\n@given('I have ABN credentials at the DCL web interface')\ndef web_ui():\n    return {\n        'url': fixtures.DCL_WRITE_DOMAIN,\n        'user': fixtures.DCL_TEST_USER,\n        'password': fixtures.DCL_TEST_SECRET\n    }\n\n\n@when('I authenticate')\ndef authenticate(browser, web_ui):\n    \"\"\"I authenticate.\"\"\"\n    browser.visit('{}/openid/openid/SimGuard/'.format(web_ui['url']))\n    if 'idp.testpoint.io' in browser.url:\n        # we have login page\n        browser.fill('login', web_ui['user'])\n        browser.fill('password', web_ui['password'])\n        # warning: assuming user already have logged in single time with this ABN,\n        # so Oauth access confirmation doesn't appear\n        button = browser.find_by_xpath('//button[@type=\"submit\"]').first\n        button.click()\n    else:\n        # login worked magically (idp remembers us from previous login)\n        pass\n\n\n@then(parsers.parse(\"I submit the form\"))\ndef submit_the_form(page_text, browser):\n    button = browser.find_by_xpath('//button[@type=\"submit\"]').first\n    button.click()\n\n\n@then(parsers.parse(\"I see '{page_text:.}'\"))\n@then(parsers.parse('I see \"{page_text:.}\"'))\ndef see_some_text(page_text, browser):\n    page_text = userify(page_text)\n    assert browser.is_text_present(page_text), u'Text {} not present on the page {}'.format(\n        page_text,\n        browser.url\n    )\n\n\n@then(parsers.parse(\"I click '{button_name}'\"))\n@then(parsers.parse('I click \"{button_name}\"'))\ndef click_some_button(button_name, browser):\n    button_name = userify(button_name)\n    button = browser.find_by_xpath(\n        \"//*[contains(text(), '{}')]\".format(button_name)\n    ).first\n    button.click()\n\n\n@then(parsers.parse(\"I fill the '{field}' field by '{value}' value\"))\n@then(parsers.parse('I fill the \"{field}\" field by \"{value}\" value'))\ndef fill_field_with_text(field, value, browser):\n    value = userify(value)\n    browser.fill(field, value)\n\n\n@then(parsers.parse(\"I go '{new_url}'\"))\n@when(parsers.parse(\"I go '{new_url}'\"))\n@then(parsers.parse('I go \"{new_url}\"'))\n@when(parsers.parse('I go \"{new_url}\"'))\ndef i_go(new_url, browser, web_ui):\n    new_url = userify(new_url)\n    if new_url.endswith('/'):\n        new_url = new_url[:-1]\n    browser.visit('{}{}'.format(web_ui['url'], new_url))\n\n# @when('I click the \"confirm\" button')\n# def i_click_the_confirm_button():\n#     \"\"\"I click the \"confirm\" button.\"\"\"\n#     return\n\n\n# @when('I enter new value in the SMP update form')\n# def i_enter_new_value_in_the_dcp_update_form():\n#     \"\"\"I enter new value in the SMP update form.\"\"\"\n#     return\n\n\n# @when('click the \"update my SMP\" button')\n# def click_the_update_my_dcp_button():\n#     \"\"\"Click the \"update my SMP\" button.\"\"\"\n#     return\n\n\n# @when('then I click the \"save\" button')\n# def then_i_click_the_save_button():\n#     \"\"\"Then I click the \"save\" button.\"\"\"\n#     return\n\n\n# @then('I see \"update my SMP\" button')\n# def i_see_update_my_dcp_button():\n#     \"\"\"I see \"\"update my SMP\" button.\"\"\"\n#     return\n\n\n# @then('I see \"SMP updated\" message')\n# def i_see_dcp_updated_message():\n#     \"\"\"I see \"SMP updated\" message.\"\"\"\n#     return\n\n\n# @then('I see \"save\" button')\n# def i_see_save_button():\n#     \"\"\"I see \"save\" button.\"\"\"\n#     return\n\n\n# @then('I see the \"confirm\" button')\n# def i_see_the_confirm_button():\n#     \"\"\"I see the \"confirm\" button.\"\"\"\n#     return\n\n\n# @then('I see the SMP update form')\n# def i_see_the_dcp_update_form():\n#     \"\"\"I see the SMP update form.\"\"\"\n#     return\n","repo_name":"test-point/testpoint-dcl","sub_path":"tests/bdd-remote/management_api/test_update_dcp.py","file_name":"test_update_dcp.py","file_ext":"py","file_size_in_byte":3624,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"15746667400","text":"from library import *\n\n\"\"\"\n透过爬取 'https://xeno-canto.org' 网页上的音档获得鸟类的声音资料\n以便于后续资料前处理与深度学习的进行\n\"\"\"\n\n# 设定路径与预设值\n\npath = './data/鸟类.txt'\n\nname1 = []\nname2 = []\n\n# 开启鸟类txt档\nwith open(path, 'r', encoding='utf-8') as f:\n    # 逐行读取txt档\n    for line in f.readlines():\n        # 分割出鸟类名称并将其存至list中\n        s = line.split(' ')\n        name1.append(s[0])  # 资料夹储存名称\n        name2.append(s[1])  # 爬虫url所需名称\n\n# 重复四次，代表四种鸟类\nfor i in range(0, 4):\n    # 显示目前正在下载的鸟类种类\n    name_1 = str(name1[i])\n\n    # 设定音档路径\n    audio_path = './data/audio/' + name_1 + '/'\n    if os.path.exists(audio_path):\n        # 如果资料夹存在，则pass\n        pass\n    else:\n        # 如果资料夹不存在，建立一个资料夹，名称为name_1\n        os.mkdir(audio_path)\n\n    # 整理爬虫之鸟类名称\n    name_2 = name2[i].replace(\"\\n\", \"\")\n    print(name_2)\n\n    try:\n        page(i, name_2, name1, audio_path)  # 执行page函式\n        print(str(name_2) + \"下载成功\")  # 下载完了整页的音档\n\n    except IndexError:\n        print('下载完成')\n        # 如果index里没有资料了，就代表载完了全部的音档\n","repo_name":"liu7388/BirdSound","sub_path":"scraping.py","file_name":"scraping.py","file_ext":"py","file_size_in_byte":1341,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"9436508512","text":"def sort(array):\n    \"\"\"\n    Best Scenario: Partially ordered arrays and perform ordered insertions\n    Worst Scenario: Array sorted in reverse order\n    Time complexity:\n        Best: O(n)\n        Average/Worst: O(n^2)\n    Space complexity: O(1)\n    Stable, two equal keys are guaranteed to be in the same order as the input on the output\n    \"\"\"\n    for i, to_insert in enumerate(array):\n        j = i - 1\n        # find where the element is supposed to be and insert\n        while j >= 0 and to_insert < array[j]:\n            array[j + 1] = array[j]\n            j -= 1\n        array[j + 1] = to_insert\n\n\ndef insert(array, element, start=0):\n    \"\"\"\n    Time complexity: O(n)\n    \"\"\"\n    for i in range(start, len(array), 1):\n        if array[i] >= element:\n            array[i:i] = [element]\n            return\n    array.append(element)\n","repo_name":"thiagofigcosta/algo-data-design","sub_path":"algo_data_design/algorithms/sorting/insertion.py","file_name":"insertion.py","file_ext":"py","file_size_in_byte":840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"8001008911","text":"import datetime\nimport argparse\nimport timeit\nimport imutils\nimport numpy as np\nimport cv2\nfrom lcb.low_contrast_blemish import lcb\nfrom io_bin.preprocess import preprocess\n\nap = argparse.ArgumentParser()\n\nap.add_argument(\"-i\", \"--imageinput\", required=True, help=\"path to the raw image\")\nap.add_argument(\"--inputbayerformat\", type=str, default=\"rggb\", help=\"bayerformat of input image data\")\nap.add_argument(\"-o\", \"--outputformat\", type=str, default=\"raw\", help=\"format of output image data\")\nap.add_argument(\"-p\", \"--pedestal\", type=int, default=64, help=\"amount of pedestal to add\")\nap.add_argument(\"-m\", \"--mode\", type=int, default=2, help=\"crop mode\")\nap.add_argument(\"-f\", \"--FOV\", type=int, default=0, help=\"Field of view\")\nap.add_argument(\"-w\", \"--whitebalance\", type=bool, default=True, help=\"whether apply whitebalance\")\nap.add_argument(\"-b\", \"--bitdepth\", type=int, default=10, help=\"depth of the color\")\nap.add_argument(\"-s\", \"--signed\", type=bool, default=True, help=\"Whether all pixels value will be signed\")\n\nargs = vars(ap.parse_args())\n\ntime1 = datetime.datetime.now()\n# custom_source = preprocess(imageinput=args[\"imageinput\"], outputformat=\"bayer\", more_precise=True, custom_size=[3856, 2340], custom_decoding=\"B\")\nID = lcb(args[\"imageinput\"], compensation=False, mode=0, roiSize=[9, 9], threshold=3.1)\ntime2 = datetime.datetime.now()\nprint(time2 - time1)\n\nresized = imutils.resize(ID, width=800)\nheat_map = cv2.applyColorMap(resized, cv2.COLORMAP_JET)\nmax = np.where(resized == np.max(resized))\n\n# for i in range(len(max)):\n#     cv2.circle(heat_map, (max[1][i], max[0][i]), 20, (128, 128, 128), 3)\ncv2.imshow(\"LCB\", heat_map)\ncv2.waitKey()\n# t1 = timeit.Timer(lambda: lcb(args[\"imageinput\"]))\n# print(timeit.timeit())\n","repo_name":"zylo117/PixelPowerPy","sub_path":"defect_detection/lcb/test_lcb.py","file_name":"test_lcb.py","file_ext":"py","file_size_in_byte":1739,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"5092852456","text":"import discord\r\nimport typing\r\nfrom discord.ext import commands\r\nimport asyncio\r\nimport datetime\r\nimport random\r\n\r\n\r\nclass Embed(discord.Embed):\r\n    def __init__(self, bot, message: typing.Union[commands.Context, discord.Message] = None, **kwargs):\r\n        super().__init__(**kwargs)\r\n        asyncio.create_task(self.__ainit__(bot, message, **kwargs))\r\n\r\n    async def __ainit__(self, bot, message, **kwargs):\r\n        self._colour = discord.Colour.from_hsv(random.random(), 1, 1)\r\n        if isinstance(message, commands.Context):\r\n            message = message.message\r\n        title = kwargs.get(\"title\")\r\n        if title:\r\n            kwargs.pop(\"title\")\r\n\r\n        if title:\r\n            avatar_url = message.author.avatar_url_as(format=\"png\") if message else None\r\n            self.set_author(name=title, icon_url=avatar_url)\r\n\r\n        icon_url = bot.user.avatar_url_as(format=\"png\")\r\n        self.set_footer(icon_url=icon_url)\r\n\r\n        self._timestamp = datetime.datetime.utcnow()","repo_name":"Zomatree/Maid","sub_path":"maid/utils/embed.py","file_name":"embed.py","file_ext":"py","file_size_in_byte":994,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"27845397080","text":"\nimport os\nimport re\nimport struct\nimport numpy as np\nimport pandas as pd\nimport h5py\nimport netCDF4\nimport xarray as xr\nimport geopy.distance\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits import mplot3d\n# import horizon as hor\nfrom topocalc.viewf import viewf, gradient_d8\nimport pickle\nfrom geospatialtools import gdal_tools\n# import tensorflow as tf\nimport demfuncs as dem\nimport tilefuncs as til\nimport string\nfrom sklearn.linear_model import LinearRegression\n\n\nimport matplotlib\nmatplotlib.use('Agg')\n# matplotlib.use('Qt5Agg') # dyn show plots\n\ndem.matplotlib_update_settings()\n\n# scenarios = ['10', '25', '100'] # number of HRUs in each simulation\n# scenarios = ['100'] # number of HRUs in each simulation\n# scenarios = ['sia10', 'sia25', 'sia100'] # ONLY cos(sia) used in the clustering\n# scenarios = ['notnorm10', 'notnorm25', 'notnorm100'] # ONLY cos(sia) used in the clustering\n\n# scenarios = ['Peru']\nscenarios = ['EastAlps', 'Nepal', 'Peru']\nnhills = 2\n\nres_all = []\n\ncosz = 0.7\nadir = 0.5\nphi = np.pi / 2\n# flux_term = 'fdir'\ndo_averaging = False\naveblock = 55\n\n# outfigdir = os.path.join(datadir, 'outfigdir')\n# outdir = os.path.join(datadir, 'output')\noutfigdir = os.path.join('//', 'home', 'enrico', 'Documents',\n                       'dem_datasets', 'outfigdir')\nres_all = {}\nfor isc, sc in enumerate(scenarios):\n\n    datadir_all = os.path.join('..', '..', '..', 'Documents',\n                               'res_hmc_light_p4')\n    datadir = os.path.join(datadir_all, 'res_{}_{}'.format(sc, nhills))\n\n    # datadir = os.path.join('..', '..', '..', 'Documents', 'res_{}'.format(sc))\n    # datadir = os.path.join('..', '..', '..', 'Documents', 'res_all_hmc', 'res_Peru_10')\n    #\n    # res = til.read_tile_properties(datadir=datadir, do_averaging=do_averaging,\n    #                      aveblock= aveblock,\n    #                      cosz = cosz, phi = phi, adir=adir,\n    #                      modeldir=None)\n\n    modeldir = os.path.join('//', 'home', 'enrico', 'Documents',\n                            'dem_datasets', 'trained_models',\n                            'domain_EastAlps_buffer_0.1',\n                            'models_ave_{}'.format(aveblock),\n                            )\n\n    res = til.read_tile_properties(datadir=datadir,\n                                   do_averaging=do_averaging,\n                                   aveblock=aveblock,\n                                   cosz=cosz, phi=phi, adir=adir,\n                                   modeldir=modeldir)\n    # # save results on\n    # res_all['{}_{}'.format(sc, nhills)] = res\n\n\n    # res['mappedtile_sia'].shape\n    # res['map_sia'].shape\n\n    # show results for a subset::\n\n    # equal area map - distance in [km]\n\n    # load coords\n    X, Y = np.meshgrid(res['xlon_ea'], res['ylat_ea'])  # coords equal area in latlon\n    Xl, Yl = np.meshgrid(res['xlon_latlon'], res['ylat_latlon'])  # coords equal area in latlon\n\n    # X = np.flipud( np.abs(X)  )\n    # Y = np.flipud( np.abs(Y)  )\n\n    matplotlib.use('Qt5Agg') # dyn show plots\n    fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(8,8))\n    # cm1 = axes[0].pcolormesh(X, Y, res['map_fdir'], cmap='jet', vmin=-1, vmax=1)\n    cm0 = axes.pcolormesh(X, Y, res['map_ele'], cmap='terrain')\n    axes.set_title('{}'.format(sc))\n\n    if Y[0, 0]<0:\n        axes.set_ylabel('Latitude [S]')\n    else:\n        axes.set_ylabel('Latitude [N]')\n\n    if X[0, 0] < 0:\n        axes.set_xlabel('Longitude [W]')\n    else:\n        axes.set_xlabel('Longitude [E]')\n\n    # ylabs = abs(axes.get_yticks())\n    from matplotlib.ticker import FormatStrFormatter\n    import matplotlib.ticker as ticker\n\n    @ticker.FuncFormatter\n    def major_formatter(x, pos):\n        mylab = -x if x < 0 else x\n        label = '%.1f' % mylab\n        return label\n\n    # axes.set_yticklabels(ylabs)\n    axes.yaxis.set_major_formatter(major_formatter)\n    axes.xaxis.set_major_formatter(major_formatter)\n    # axes.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n\n    cbar = fig.colorbar(cm0)\n    cbar.set_label(r'Elevation [m m.s.l.]')\n    # plt.tight_layout()\n    plt.savefig( os.path.join(outfigdir, 'elevmap_{}.png'.format(sc)), dpi = 300)\n    plt.show()\n\n\n\n#\n#\n#\n# matplotlib.use('Qt5Agg') # dyn show plots\n# fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(17,8))\n# cm1 = axes[0].pcolormesh(X, Y, res['map_fdir'], cmap='jet', vmin=-1, vmax=1)\n# cm0 = axes[1].pcolormesh(X, Y, res['mappedtile_fdir'], cmap='jet', vmin=-1, vmax=1)\n# axes[0].set_title('High-res predictions')\n# axes[1].set_title(r'tile-by-tile predictions ($n_T = {}$)'.format(res['ntiles']))\n# axes[0].set_xlabel('x [km]')\n# axes[0].set_ylabel('y [km]')\n#\n# axes[1].set_xlabel('x [km]')\n# axes[1].set_ylabel('y [km]')\n# cbar = fig.colorbar(cm0)\n# cbar.set_label(r'(3D - PP)/PP')\n# plt.savefig( os.path.join(outfigdir, 'comp_fdir.png'))\n# plt.show()\n#\n#\n# matplotlib.use('Qt5Agg') # dyn show plots\n# bnd = 0.5\n# fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(17,8))\n# cm1 = axes[0].pcolormesh(X, Y, res['map_fdif'], cmap='jet', vmin=-bnd, vmax=bnd)\n# cm0 = axes[1].pcolormesh(X, Y, res['mappedtile_fdif'], cmap='jet', vmin=-bnd, vmax=bnd)\n# axes[0].set_title('High-res predictions')\n# axes[1].set_title(r'tile-by-tile predictions ($n_T = {}$)'.format(res['ntiles']))\n# axes[0].set_xlabel('x [km]')\n# axes[0].set_ylabel('y [km]')\n#\n# axes[1].set_xlabel('x [km]')\n# axes[1].set_ylabel('y [km]')\n# cbar = fig.colorbar(cm0)\n# cbar.set_label(r'(3D - PP)/PP')\n# plt.savefig( os.path.join(outfigdir, 'comp_fdif.png'))\n# plt.show()\n#\n#\n# matplotlib.use('Qt5Agg') # dyn show plots\n# bnd = 0.5\n# fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(17,8))\n# cm1 = axes[0].pcolormesh(X, Y, res['map_fcoupn'], cmap='jet', vmin=-bnd, vmax=bnd)\n# cm0 = axes[1].pcolormesh(X, Y, res['mappedtile_fcoupn'], cmap='jet', vmin=-bnd, vmax=bnd)\n# axes[0].set_title('High-res predictions')\n# axes[1].set_title(r'tile-by-tile predictions ($n_T = {}$)'.format(res['ntiles']))\n# axes[0].set_xlabel('x [km]')\n# axes[0].set_ylabel('y [km]')\n#\n# axes[1].set_xlabel('x [km]')\n# axes[1].set_ylabel('y [km]')\n# cbar = fig.colorbar(cm0)\n# cbar.set_label(r'(3D - PP)/PP')\n# plt.savefig( os.path.join(outfigdir, 'comp_fcoupn.png'))\n# plt.show()\n#\n# matplotlib.use('Qt5Agg') # dyn show plots\n# plt.figure()\n# plt.imshow(res['mappedtile_tcf'])\n# plt.imshow(res['map_tcf'])\n# plt.colorbar()\n# plt.show()\n#\n# plt.figure()\n# plt.hist( np.ravel(  res['map_fdir'])  , bins=50 )\n# plt.show()\n#\n# plt.figure()\n# plt.plot(  np.ravel(res['map_fdir']), np.ravel(res['mappedtile_fdir']), 'o')\n# # plt.plot(  np.ravel(res['map_fdif']), np.ravel(res['mappedtile_fdif']), 'o')\n# # plt.plot(  np.ravel(res['map_frdirn']), np.ravel(res['mappedtile_frdirn']), 'o')\n# # plt.plot(  np.ravel(res['map_fcoupn']), np.ravel(res['mappedtile_fcoupn']), 'o')\n# plt.plot( [-1, 1], [-1, 1], 'k')\n# plt.show()\n#\n# plt.figure()\n# # plt.plot(  np.ravel(res['map_sia']), np.ravel(res['mappedtile_sia']), 'o')\n# plt.plot(  np.ravel(res['map_svf']), np.ravel(res['mappedtile_svf']), 'o')\n# # plt.plot(  np.ravel(res['map_tcf']), np.ravel(res['mappedtile_tcf']), 'o')\n# # plt.plot(  np.ravel(res['map_ele']), np.ravel(res['mappedtile_ele']), 'o')\n# # plt.plot(  np.ravel(res['map_sde']), np.ravel(res['mappedtile_sde']), 'o')\n# plt.plot( [0, 1], [0, 1], 'k')\n# plt.show()\n#\n# res['map_fdif'].shape\n# res['mappedtile_fdif'].shape","repo_name":"EnricoZorzetto/gmd_2022_radiation","sub_path":"codes_gmd/global_map_threedoms.py","file_name":"global_map_threedoms.py","file_ext":"py","file_size_in_byte":7363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"32463172057","text":"# This code ONLY supports the ADXL34x accelerometers!!!\n# You can get one at: https://www.adafruit.com/product/4097\n# To enable this feature, run:\n# sudo pip3 install adafruit-circuitpython-ADXL34x\n\nimport imp\ntry:\n\timp.find_module('adafruit_adxl34x')\n\tstub = False\n\timport board\n\timport busio\n\timport adafruit_adxl34x\n\ti2c = busio.I2C(board.SCL, board.SDA)\n\taccel = adafruit_adxl34x.ADXL345(i2c)\nexcept ImportError:\n\t# If the hardware is not present, we fall back to stub mode.\n\tstub = True\n\n# Orientation tells which side is currently the top one.\n# With current setup:\n#                             \n#             top             \n#      +---------------+      \n#      | +-----------+ |      \n#      | |           | |      \n#      | |           | |      \n#      | |           | |      \n# left | |           | | right\n#      | |           | |      \n#      | |           | |      \n#      | |           | |      \n#      | +-----------+ |      \n#      +---------------+      \n#           bottom            \n#\n# top: +x\n# left: -y\n# bottom: -x\n# right: +y\norientation = \"top\"\n\n\n# Polls the accelerometer and caches the orientation value.\ndef update():\n\tglobal stub\n\tif stub:\n\t\t# When operating in stub mode, the function will always return top orientation.\n\t\treturn\n\n\tglobal accel, orientation\n\tx = accel.acceleration[0]\n\ty = accel.acceleration[1]\n\tz = accel.acceleration[2]\n\n\tif abs(z) > 9:\n\t\torientation = \"top\"\n\t\treturn\n\n\tif abs(x) > abs(y):\n\t\tif x < 0:\n\t\t\torientation = \"bottom\"\n\t\telse:\n\t\t\torientation = \"top\"\n\telse:\n\t\tif y < 0:\n\t\t\torientation = \"left\"\n\t\telse:\n\t\t\torientation = \"right\"\n\ndef get():\n\treturn orientation\n\ndef is_landscape():\n\tglobal orientation\n\treturn orientation == \"left\" or orientation == \"right\"\n","repo_name":"Martenfur/magic_frame","sub_path":"src/orientation.py","file_name":"orientation.py","file_ext":"py","file_size_in_byte":1717,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"76"}
+{"seq_id":"71833200565","text":"SERVICES_PROVIDED = {'change_oil': 2, 'inflate_tires': 5, 'diagnostic': 30}\n\n\nclass Ticket:\n    ticket_counter = 0\n\n    def __init__(self, service):\n        Ticket.ticket_counter += 1\n        self.id = Ticket.ticket_counter\n        self.service = service\n        self.wait_time = 0\n\n    def __str__(self):\n        return f'id:{self.id}\\nwait_time:{self.wait_time}\\nservice:{self.service}'\n\n\nclass LineOfCars:\n    current_line = {'change_oil': [], 'inflate_tires': [], 'diagnostic': []}\n    services_provided = SERVICES_PROVIDED\n\n    def __init__(self):\n        self.whole_que_wait_time = 0\n        self.current_ticket = ''\n\n    def add_ticket_to_line(self, ticket):\n        self.current_line[ticket.service].append(ticket)\n        self.calculate_wait_times()\n\n    def calculate_wait_times(self):\n        wait_time = 0\n        for service in self.services_provided:\n            for ticket in self.current_line[service]:\n                ticket.wait_time = wait_time\n                wait_time += self.services_provided[service]\n        self.whole_que_wait_time = wait_time\n\n    def make_que_list(self):\n        que_list = list()\n        [que_list.extend(v) for _, v in self.current_line.items() if v]\n        return que_list\n\n    def pop_first_ticket_from_que(self):\n        ticket = ''\n        for v in self.current_line.values():\n            if v:\n                ticket = v.pop(0)\n                break\n        self.current_ticket = ticket\n        return self.current_ticket\n\n    def get_current_ticket(self):\n        return self.current_ticket\n","repo_name":"desirekaleba/HyperCar","sub_path":"tickets/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1545,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"5209719514","text":"#asking user to input two number\n\nnum1=int(input(\"Enter first number\"))\nnum2=int(input(\"Enter second number\"))\n\n#used nested if to check the condition\nif num1>0 and num2>0:\n    print(\"The product of the two numbers is:\",num1*num2)\nelse:\n    print(\"The number must be positive\")\n","repo_name":"Mayank-B01/Python-programming","sub_path":"week 4/Tutorial/cw2.py","file_name":"cw2.py","file_ext":"py","file_size_in_byte":278,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"19868118758","text":"import time\nimport os\nimport pymysql\nfrom DBUtils.PooledDB import PooledDB, SharedDBConnection\nimport numpy as np\nimport datetime\nimport pandas as pd\n\n\n\nPOOLSensor = PooledDB(\n    creator=pymysql,\n    maxconnections=6,\n    mincached=2,\n    maxcached=5,\n    maxshared=3,\n    blocking=True,\n    maxusage=None,\n    setsession=[],\n    ping=0,\n    host='192.168.110.11',\n    port=3306,\n    user='furnace',    # 用户名\n    password='furnace',      # 密码\n    database='car_cone1',  # 库名\n    charset='utf8'\n)\n\n\n\n# 建立连接MYSQL程序\ndef readMysqlToArry(mysql1, POOL0):\n    conn = POOL0.connection()      #建立连接\n    cursor = conn.cursor()         ## 使用cursor()方法获取操作游标\n    # print(\"mysql1:\", mysql1)\n    try:\n        cursor.execute(mysql1)      # 使用execute方法执行SQL语句\n        ccc = []\n        for x in cursor.fetchall():\n            ccc.append(list(x))\n        # 将数据库里面读取出来的数据bbb全部写成矩阵形式。\n        arr1 = np.array(ccc)\n        conn.commit()\n        #print(\"读取出来的数据为：\", arr1)\n        # print(\"读取数据库成功\")\n    except Exception as r:\n        conn.rollback()\n        print(\"读取数据库失败\")\n        print(\"故障码：\", r)\n        conn.close()\n    finally:\n        conn.close()\n    return arr1\n\n\n\n\n\nm = 1\nwhile 1:\n\n    mysql =\"SELECT * FROM car_cone1.cone1ping order by timeID desc limit 1\"\n    kk = readMysqlToArry(mysql,POOLSensor)\n    print(kk)\n    while 1:\n        if kk[0,m] == '1':\n            print(\"cone1_%dping网络正常\"%m)\n        else :\n            print(\"cone1_%dping网络异常!!!!!!\"%m)\n        m += 1\n        if m > 10:\n            break\n    time.sleep(600)\n","repo_name":"zcs416008138/fsxgt","sub_path":"读取MYSQL发送钢铁侠.py","file_name":"读取MYSQL发送钢铁侠.py","file_ext":"py","file_size_in_byte":1701,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"32889967455","text":"from django.test import TestCase\n\nfrom apps.ml.regressor.cost_regressor import CostRegressor\n\nimport inspect\nfrom apps.ml.registry import MLRegistry\n\nclass MLTests(TestCase):\n\n    def test_registry(self):\n        registry = MLRegistry()\n        self.assertEqual(len(registry.endpoints), 0)\n        endpoint_name = \"regressor\"\n        algorithm_object = CostRegressor()\n        algorithm_name = \"cost regressor\"\n        algorithm_status = \"production\"\n        algorithm_version = \"0.0.3\"\n        algorithm_owner = \"Mauricio\"\n        algorithm_description = \"Cost Regressor with simple pre- and post-processing\"\n        algorithm_code = inspect.getsource(CostRegressor)\n        # add to registry\n        registry.add_algorithm(endpoint_name, algorithm_object, algorithm_name,\n                    algorithm_status, algorithm_version, algorithm_owner,\n                    algorithm_description, algorithm_code)\n        # there should be one endpoint available\n        self.assertEqual(len(registry.endpoints), 1)\n\n\n    def test_rf_algorithm(self):\n        input_data = { \n            \"MSSubClass\": 60 ,\n            \"MSZoning\": \"RL\" ,\n            \"LotFrontage\": 65 ,\n            \"LotArea\": 8450 ,\n            \"Alley\": \"None\" ,\n            \"LotShape\": \"Reg\" ,\n            \"LandContour\": \"Lvl\" ,\n            \"LotConfig\": \"Inside\" ,\n            \"LandSlope\": \"Gtl\" ,\n            \"Neighborhood\": \"CollgCr\" ,\n            \"Condition1\": \"Norm\" ,\n            \"Condition2\": \"Norm\" ,\n            \"BldgType\": \"1Fam\" ,\n            \"HouseStyle\": \"2Story\",\n            \"OverallQual\": 7 ,\n            \"OverallCond\": 5 ,\n            \"YearBuilt\": 2003 ,\n            \"YearRemodAdd\": 2003 ,\n            \"RoofStyle\": \"Gable\" ,\n            \"RoofMatl\": \"CompShg\" ,\n            \"Exterior1st\": \"VinylSd\" ,\n            \"Exterior2nd\": \"VinylSd\" ,\n            \"MasVnrType\": \"BrkFace\" ,\n            \"MasVnrArea\": 196 ,\n            \"ExterQual\": \"Gd\" ,\n            \"ExterCond\": \"TA\" ,\n            \"Foundation\": \"PConc\" ,\n            \"BsmtQual\": \"Gd\" ,\n            \"BsmtCond\": \"TA\" ,\n            \"BsmtExposure\": \"No\" ,\n            \"BsmtFinType1\": \"GLQ\" ,\n            \"BsmtFinSF1\": 706 ,\n            \"BsmtFinType2\": \"Unf\" ,\n            \"BsmtFinSF2\": 0 ,\n            \"BsmtUnfSF\": 150 ,\n            \"TotalBsmtSF\": 856 ,\n            \"Heating\": \"GasA\" ,\n            \"HeatingQC\": \"Ex\" ,\n            \"CentralAir\": \"Y\" ,\n            \"Electrical\": \"SBrkr\" ,\n            \"1stFlrSF\": 856 ,\n            \"2ndFlrSF\": 854 ,\n            \"LowQualFinSF\": 0 ,\n            \"GrLivArea\": 1710 ,\n            \"BsmtFullBath\": 1 ,\n            \"BsmtHalfBath\": 0 ,\n            \"FullBath\": 2 ,\n            \"HalfBath\": 1 ,\n            \"BedroomAbvGr\": 3 ,\n            \"KitchenAbvGr\": 1 ,\n            \"KitchenQual\": \"Gd\" ,\n            \"TotRmsAbvGrd\": 8 ,\n            \"Functional\": \"Typ\" ,\n            \"Fireplaces\": 0 ,\n            \"FireplaceQu\": \"None\" ,\n            \"GarageType\": \"Attchd\" ,\n            \"GarageYrBlt\": 2003 ,\n            \"GarageFinish\": \"RFn\" ,\n            \"GarageCars\": 2 ,\n            \"GarageArea\": 548 ,\n            \"GarageQual\": \"TA\" ,\n            \"GarageCond\": \"TA\" ,\n            \"PavedDrive\": \"Y\" ,\n            \"WoodDeckSF\": 0 ,\n            \"OpenPorchSF\": 61 ,\n            \"EnclosedPorch\": 0 ,\n            \"3SsnPorch\": 0 ,\n            \"ScreenPorch\": 0 ,\n            \"PoolArea\": 0 ,\n            \"Fence\": \"None\" ,\n            \"MiscFeature\": \"None\" ,\n            \"MiscVal\": 0 ,\n            \"MoSold\": 2 ,\n            \"YrSold\": 2008 ,\n            \"SaleType\": \"WD\" ,\n            \"SaleCondition\": \"Normal\"\n        }\n        my_alg = CostRegressor()\n        response = my_alg.compute_prediction(input_data)\n        print(response)","repo_name":"Mauricio1812/HousePrices","sub_path":"backend/server/apps/ml/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":3676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"991170048","text":"from django.conf import settings\nfrom drf_spectacular.utils import extend_schema\n\nfrom rest_framework import viewsets, status\nfrom rest_framework.decorators import action\nfrom rest_framework.exceptions import NotAuthenticated, ValidationError\nfrom rest_framework.response import Response\n\nfrom garpix_user.exceptions import NotAuthenticateException\nfrom garpix_user.serializers import PhoneConfirmSendSerializer, PhoneConfirmCheckCodeSerializer, \\\n    PhonePreConfirmSendSerializer, UserSessionTokenSerializer\nfrom garpix_user.models import UserSession\nfrom django.utils.translation import ugettext as _\n\nfrom garpix_user.utils.drf_spectacular import user_session_token_header_parameter\n\n\n@extend_schema(\n    parameters=[\n        user_session_token_header_parameter()\n    ]\n)\nclass PhoneConfirmationView(viewsets.GenericViewSet):\n\n    def get_serializer_class(self):\n        user = self.request.user\n        if self.action == 'send_code':\n            if user.is_authenticated:\n                return PhoneConfirmSendSerializer\n            return PhonePreConfirmSendSerializer\n        if self.action == 'check_code':\n            return PhoneConfirmCheckCodeSerializer\n        return PhoneConfirmCheckCodeSerializer\n\n    @extend_schema(summary=_('Phone confirmation. Step 1'))\n    @action(methods=['POST'], detail=False)\n    def send_code(self, request, *args, **kwargs):\n        user = request.user\n\n        if user.is_authenticated:\n            serializer = self.get_serializer(data=request.data)\n            serializer.is_valid(raise_exception=True)\n\n            result = user.send_phone_confirmation_code(serializer.data.get('phone', None))\n            if result is not True:\n                result.raise_exception(exception_class=ValidationError)\n            return Response({'result': 'success'})\n        else:\n            if settings.GARPIX_USER.get('USE_PREREGISTRATION_PHONE_CONFIRMATION', False):\n                user = UserSession.get_or_create_user_session(request)\n                serializer = self.get_serializer(data=request.data)\n                serializer.is_valid(raise_exception=True)\n\n                result = user.send_phone_confirmation_code(serializer.data['phone'])\n                if result is not True:\n                    result.raise_exception(exception_class=ValidationError)\n                return Response({'result': 'success'})\n\n            raise NotAuthenticateException().raise_exception(exception_class=NotAuthenticated)\n\n    @extend_schema(summary=_('Phone confirmation. Step 2'))\n    @action(methods=['POST'], detail=False)\n    def check_code(self, request, *args, **kwargs):\n        user = request.user\n        if not user.is_authenticated:\n            if settings.GARPIX_USER.get('USE_PREREGISTRATION_PHONE_CONFIRMATION', False):\n                user = UserSession.get_or_create_user_session(request)\n            else:\n                raise NotAuthenticateException().raise_exception(exception_class=NotAuthenticated)\n\n        serializer = self.get_serializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n        result = user.confirm_phone(serializer.data['phone_confirmation_code'])\n        if result is not True:\n            result.raise_exception(exception_class=ValidationError)\n        return Response({'result': 'success'})\n","repo_name":"garpixcms/garpix_user","sub_path":"backend/garpix_user/views/phone_confirmation_view.py","file_name":"phone_confirmation_view.py","file_ext":"py","file_size_in_byte":3292,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"1966604542","text":"import numpy\r\n\r\ndef slot_machine():\r\n\r\n    class slots:\r\n        symbols = [\"#\", \"@\", \"&\", \"0\", \"£\", \"+\", \"*\"]\r\n        symbol_1 = \"A\"\r\n        symbol_2 = \"A\"\r\n        symbol_3 = \"A\"\r\n        def __init__(self):\r\n            self.symbol_1 = self.roll()\r\n            self.symbol_2 = self.roll()\r\n            self.symbol_3 = self.roll()\r\n        def roll(self):\r\n            self.symbol_1 = numpy.random.choice(self.symbols, p=[50/156, 40/156, 30/156, 20/156, 10/156, 5/156, 1/156])\r\n            self.symbol_2 = numpy.random.choice(self.symbols, p=[50/156, 40/156, 30/156, 20/156, 10/156, 5/156, 1/156])\r\n            self.symbol_3 = numpy.random.choice(self.symbols, p=[50/156, 40/156, 30/156, 20/156, 10/156, 5/156, 1/156])\r\n\r\n    our_slots = slots()\r\n    credits = int(input(\"Deposit your credits: \"))\r\n    continue_playing = True\r\n    if credits == 0:\r\n        continue_playing = False\r\n\r\n    while continue_playing == True:\r\n\r\n        bet = int(input(\"How many credits do you wanna bet? \"))\r\n        while (bet > credits or bet <= 0):\r\n            if (bet <= 0):\r\n                bet = int(input(\"Really? Try again: \"))\r\n            else:\r\n                w=[\"Are you stupid? You dont't have enought credits for that bet.\", \"Please, insert a valid number of credits.\", \"You're to poor for that bet.\", \"Are you dumb? Insert a valid number of credits!\"]\r\n                s = numpy.random.choice(w)\r\n                print(s)\r\n                bet = int(input(\"Try again: \"))\r\n\r\n        our_slots.roll()\r\n        print(\"\\n --------- \")\r\n        print(f\"| {our_slots.symbol_1}  {our_slots.symbol_2}  {our_slots.symbol_3} |\")\r\n        print(\" --------- \")\r\n\r\n        if our_slots.symbol_1 == our_slots.symbol_2 and our_slots.symbol_1 == our_slots.symbol_3:\r\n            print(\"***JACKPOT***\")\r\n            points = [5, 10, 20, 70, 200, 1000, 100000]\r\n            i = 0\r\n            found = False\r\n            while i < 7 and not found:\r\n                if our_slots.symbol_1 == our_slots.symbols[i]:\r\n                    credits += bet*points[i]\r\n                    found = True\r\n                i += 1\r\n        else:\r\n            credits -= bet\r\n\r\n        print(f\"\\nYou have {credits} credits left\")\r\n\r\n        if credits == 0:\r\n            print(\"GAME OVER\")\r\n            continue_playing = False\r\n        else:\r\n            check = input(\"Do you wanna keep playing? (y/n)) \")\r\n            if (check == \"n\"):\r\n                continue_playing = False\r\n                print(f\"\\nYou ended up with {credits} credits. Congrats! (or I'm sorry if you're poor now)\")\r\n\r\nslot_machine()","repo_name":"05raquel/Slot_Machine_HS","sub_path":"slot_machine.py","file_name":"slot_machine.py","file_ext":"py","file_size_in_byte":2577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"1477080962","text":"import turtle\r\nimport math\r\n\r\nx1,y1,x2,y2,x3,y3 = input(\"Enter six points :\")\r\n\r\na = math.sqrt((x2-x3) * (x2-x3) + (y2-y3) * (y2-y3))\r\nb = math.sqrt((x1-x3) * (x1-x3) + (y1-y3) * (y1-y3))\r\nc = math.sqrt((x1-x2) * (x1-x2) + (y1-y2) * (y1-y2))\r\n\r\nA = math.degrees(math.acos((a * a - b * b - c * c) / (-2 * b * c)))\r\nB = math.degrees(math.acos((b * b - a * a - c * c) / (-2 * a * c)))\r\nC = math.degrees(math.acos((c * c - b * b - a * a) / (-2 * a * b)))\r\n\r\nprint(\"The three angles are \",round(A * 100) / 100.0, round(B * 100) / 100.0, round(C*100)/100.0)\r\n \r\nangle_1 = round(A * 100) / 100.0\r\nangle_2 = round(B * 100) / 100.0\r\nangle_3 = round(C*100)/100.0\r\n\r\nturtle.penup()\r\nturtle.goto(0, -50)\t\r\nturtle.pendown()\r\nturtle.goto(x1,x2) \r\nturtle.write(angle_1)\r\nturtle.goto(x2,x1)\r\nturtle.write(angle_2)\r\nturtle.goto(x3,x2)\r\nturtle.write(angle_3)\r\nturtle.hideturtle()\r\nturtle.done()","repo_name":"raheemtaha11/python","sub_path":"Chapter_3/ComputeAngles.py","file_name":"ComputeAngles.py","file_ext":"py","file_size_in_byte":876,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"33798894388","text":"#!/usr/bin/env python\n# coding=utf-8\nimport numpy as np\nimport time\nimport random\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn import metrics\nfrom sklearn.ensemble import AdaBoostClassifier\nimport matplotlib.pyplot as plt \nfrom .constant import cam2idx,idx2cam \nfrom sklearn.ensemble.partial_dependence import plot_partial_dependence,partial_dependence\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.ensemble import GradientBoostingClassifier\nfrom sklearn import naive_bayes as nb\nfrom mpl_toolkits.mplot3d import Axes3D\nimport pickle as pkl\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import roc_curve, auc\nfrom collections import defaultdict\nimport torch\nfrom torch import nn\nfrom torch import optim\ntorch.set_printoptions(profile=\"full\")\nclass SpatialModel(object):\n    def __init__(self,**args):\n        pass\n    def predict(self,):\n        pass\n    def predict_proba(self,):\n        pass\n    def fit(self,x,y):\n        pass\n\nclass GaussianMLP(nn.Module):\n    def __init__(self,middle=100):\n        self.act=nn.Sigmod()\n        self.input=nn.Linear(2,middle)\n        self.hidden=nn.Linear(middle,middle)\n        self.output=nn.Linear(middle,2)\n        self.model=nn.Sequential([self.input,self.act,self.hidden,self.act,self.output])\n\n    def forward(self,xs,ds,labels):\n        pass\n\nclass GaussianPoly(nn.Module):\n    def __init__(self,n1,n2):\n        super(GaussianPoly,self).__init__()\n        self.alpha=nn.Parameter(torch.randn((n1),requires_grad=True))\n        self.beta=nn.Parameter(torch.randn((n2),requires_grad=True))\n        self.ce=nn.CrossEntropyLoss()\n        self.act=nn.Sigmoid()\n    def predict_us(self,d):\n        temp=np.zeros(n1)\n        for i in range(1,n1):\n            temp[i]=temp[i-1]*d\n        u=torch.sum(torch.Tensor(temp)*self.alpha)\n        s=torch.sum(torch.Tensor(temp)*self.beta)\n        return us\n\n    def predict_prob(self,x,d):\n        u,s=self.predict_us(d)\n        return torch.exp((x-u)**2/s**2)/(torch.sqrt(2*np.pi)*s)\n\n    def test(self,x,y):\n        pass\n\n        \n\n    def forward(self,xs,ds,labels):\n        ds=np.array(ds)\n        xs=np.array(xs)\n        labels=torch.Tensor(labels)\n        unique_d=np.unique(ds)\n        idx=np.zeros(ds.shape)\n        n1=self.alpha.size()[0]\n        n2=self.beta.size()[0]\n        prob=torch.zeros((xs.shape[0],2))\n        params=[]\n        param_us=torch.zeros(xs.shape)\n        param_ss=torch.zeros(xs.shape)\n        loss=0\n        print(unique_d)\n        for i,d in enumerate(unique_d):\n            args=np.where(ds==d)\n            idx[args]=i\n            temp=np.zeros(n1)\n            temp[0]=1\n            for i in range(1,n1):\n                temp[i]=temp[i-1]*d\n            u=torch.sum(torch.Tensor(temp[:n1])*torch.exp(self.alpha))\n            s=torch.sum(torch.Tensor(temp[:n2])*torch.exp(self.beta))\n#            print(temp,u,s)\n#            print(u,s,args)\n#            print(temp.shape,self.alpha.size(),u.size(),s.size())\n            param_us[args]=u\n            param_ss[args]=s\n        xs=torch.Tensor(xs)\n        ds=torch.Tensor(ds)\n        param_us=torch.Tensor(param_us)\n        param_ss=torch.Tensor(param_ss)\n        dt=1\n#        print(self.alpha,self.beta)\n        prob[:,1]=self.act(torch.exp(-(xs-param_us)**2/param_ss**2)/((torch.sqrt(torch.tensor(2*3.141592657))*param_ss)+1e-8))\n        prob[:,0]=1-prob[:,1]\n#        prob=prob.reshape((prob.shape[0],1))\n        labels=labels.long()\n#        print(prob.shape,labels.shape)\n        S=self.ce(prob,labels)\n        auc=metrics.roc_auc_score(labels.detach().numpy().astype(np.int),prob.detach().numpy()[:,1])\n        print('auc',auc)\n        #S=torch.sum((xs-param_us)**2/(param_ss**2)*labels)+torch.sum(torch.log(param_ss)*labels)+dt*(torch.sum(self.alpha**2)+1e-1*torch.sum(self.beta**2))\n        #S/=labels.sum() #xs.size()[0]\n        print('Loss',S)\n        return S\n\nclass GaussianSpatialModel(SpatialModel):\n    def __init__(self,**args):\n        super(GaussianSpatialModel,self).__init__(**args)\n        self.distance2y=defaultdict(list)\n        self.distance2hist={}\n    def fit(self,x,y):\n        for i,k in enumerate(x):\n            self.distance2y[k[0]].append(k[1])\n        fig=plt.figure()\n        ax=fig.gca(projection='3d')\n        for key in self.distance2y.keys():\n            self.distance2hist[key],edges=np.histogram(self.distance2y[key],bins=20)\n            q=np.array([key for edge in edges[:-1]])\n            #print(q.shape,edges.shape)\n            ax.plot(q,edges[:-1],self.distance2hist[key]*1.0/np.sum(self.distance2hist[key]))\n        ax.view_init(elev=35., azim=35)\n        ax.set_ylabel('time difference')\n        ax.set_xlabel('distance')\n        ax.set_ylim(0,30000)\n        ax.set_xlim(0,800)\n        ax.set_zlim(0,1)\n        ax.set_zlabel('frequency')\n        plt.savefig('1234.svg',dpi=3000, bbox_inches = 'tight')\n        #print('123 finished')\n        #exit()\n#        print(self.distance2hist)\n\nclass GaussianModel(SpatialModel):\n    def __init__(self,**args):\n        if 'n1' in args.keys():\n            self.n1=args['n1']\n        else:\n            self.n1=4\n        if 'n2' in args.keys():\n            self.n2=args['n2']\n        else:\n            self.n2=4\n        self.model=GaussianPoly(self.n1,self.n2)\n        self.iters=10000\n        self.optimizer = optim.SGD(self.model.parameters(), lr = 1e-1, momentum = 0.9)\n\n    def fit(self,x,y):\n        ss=[t[0] for t in x]\n        xs=[t[1] for t in x]\n        ls=[]\n        for l in y:\n            if l==1:\n                ls.append(l)\n            else:\n                ls.append(0)\n        y=ls\n        for i in range(self.iters):\n            self.optimizer.zero_grad()\n            loss=self.model.forward(xs,ss,y)\n            loss.backward()\n            self.optimizer.step()\n        exit(0)\n    def predict_proba(self,x):\n        pass\n\nclass Model:\n    def __init__(self,model_name,**args):\n        names2model_fun={'decision_tree':DecisionTreeClassifier,\n                         'adaboost':AdaBoostClassifier,\n                         'gbrt':GradientBoostingClassifier,\n                         'svm':SVC,\n                         'gaussianNB':nb.GaussianNB,\n                         'multinomialNB':nb.MultinomialNB,\n                         'bernoulliNB':nb.BernoulliNB,\n                         'MLP':MLPClassifier,\n                         'gauss_poly':GaussianModel}\n        if model_name not in names2model_fun:\n            print('model_name should be in name2model_fun!')\n            exit(0)\n        self.model=names2model_fun[model_name](**args)\n        self.model_name=model_name\n\n\n    def predict(self,x):\n        return self.model.predict_proba(x)\n\n    def fit(self,x,y):\n        self.model.fit(x,y)\n\n    def score(self,x,y):\n        return self.model.score(x,y)\n\n    def distance_predict(self,dist,x):\n        dist=np.ones(x.shape[0])*dist.reshape((x.shape[0],1))\n        feature=np.hstack(x,dist)\n        return self.model.predict(feature)\n\n    def distance_score(self,dist,x,y):\n        dist=np.ones(x.shape[0])*dist.reshape((x.shape[0],1))\n        feature=np.hstack(x,dist)\n        return self.model.score(feature,y)\n\n    def distance_fit(self,dist,x,y):\n        dist=np.ones(x.shape[0])*dist.reshape((x.shape[0],1))\n        feature=np.hstack(x,dist)\n        self.model.fit(feature,y)\n\n\nclass Experiment:\n    def __init__(self,train_x,train_y,test_x,test_y,model_name,mode,locationmat,args,**kwargs):\n        self.model_name=model_name\n        self.train_x=train_x\n        self.train_y=train_y\n        self.test_x=test_x\n        self.test_y=test_y\n        self.locationmat=locationmat\n        self.args=args\n        self.save_dir=args.save_dir\n        self.mode=mode\n        self.kwargs=kwargs\n\n    def save_model(self,mode):\n        with open('{}/{}_{}.pkl'.format(self.save_dir,mode,self.model_name),'wb') as out:\n            print('{}/{}_{}.pkl'.format(self.save_dir,mode,self.model_name))\n            pkl.dump(self.model,out)\n\n    def save_csv(self,name,text):\n        path='{}/{}.csv'.format(self.save_dir,name)\n        np.savetxt(path,text)\n\n    def visual_data(self,index):\n        plt.figure()\n        i=int(index//9)\n        j=int(index%9)\n#        print(i,j)\n#        print(idx2cam[i],idx2cam[j])\n        colors=[0,1]\n        \n        num=1\n        d=self.train_x[i][j]\n        y=self.train_y[i][j]\n        c=[colors[item] for item in y]\n        x=[num*k for k in c]\n        plt.scatter(d,x,c=c)\n\n        num=2\n        colors=[3,5]\n        d=self.test_x[i][j]\n        y=self.test_y[i][j]\n        c=[colors[item] for item in y]\n        x=[num*k+2 for k in c]\n        plt.scatter(d,x,c=c)\n        plt.show()\n\n    def random_train(self,seed,num):\n        start = time.perf_counter()\n        self.model=Model(self.model_name,**self.kwargs)\n        np.random.seed(seed)\n        pairs=[]\n        for i in range(9):\n            for j in range(i+1,9):\n                pairs.append((i,j))\n        #indexs=range(len(pairs))# random.sample(range(len(pairs)),num)\n        indexs=random.sample(range(len(pairs)),num)\n        print(indexs)\n        #train_idx=[pairs[i] for i in indexs]\n        train_idx=[[2,5],[5,7],[2,8],[2,4],[0,2],[7,8],[2,7],[5,8],[4,6],[0,6],[0,4],[7,7],[0,0],[6,6],[4,4],[2,2],[5,5],[8,8]]\n        r2_score_mat=np.zeros((9,9))\n        auc_mat=np.zeros((9,9))\n        train_x=[]\n        train_y=[]\n        test_x=[]\n        test_y=[]\n        flag=np.zeros((9,9))\n        for idx in train_idx:\n            flag[idx[0]][idx[1]]=1\n            flag[idx[1]][idx[0]]=1\n            for i,item in enumerate(self.train_x[idx[0]][idx[1]]):\n                train_x.append([self.locationmat[idx[0]][idx[1]],item])\n                train_y.append(self.train_y[idx[0]][idx[1]][i])\n        for i in range(9):\n            for j in range(9):\n                for k,item in enumerate(self.test_x[i][j]):\n                    test_x.append([self.locationmat[i][j],item[0]])\n                    test_y.append(self.test_y[i][j][k])\n        sp=GaussianSpatialModel()\n        sp.fit(train_x,train_y)\n        #sp.fit(test_x,test_y)\n        exit()\n        self.model.fit(train_x,train_y)\n        end = time.perf_counter()\n        print(\"训练运行时间为\", round(end-start), 'seconds')\n        args=np.where(np.array(test_y)==1)\n        feature=np.array(test_x)[args]\n        ypred=self.model.predict(train_x)[:,1]\n        auc_mat=metrics.roc_auc_score(train_y,ypred)\n        print(self.model_name,1,auc_mat)\n        ypred=self.model.predict(test_x)[:,1]\n        auc_mat=metrics.roc_auc_score(test_y,ypred)\n        print(self.model_name,2,auc_mat)\n        #self.save_model('group')\n        #exit()\n        return self.model\n        \n    def dist_go(self,idxs=None):\n        self.model=Model(self.model_name,**self.kwargs)\n        self.visual_data(23)\n        if idxs==None:\n            train_idx=[[2,5],[5,7],[2,8],[2,4],[0,2],[7,8],[2,7],[5,8],[4,6],[0,6],[0,4],[7,7],[0,0],[6,6],[4,4],[2,2],[5,5],[8,8]]\n        else:\n            train_idx=idxs\n        r2_score_mat=np.zeros((9,9))\n        auc_mat=np.zeros((9,9))\n        train_x=[]\n        train_y=[]\n        test_x=[]\n        test_y=[]\n        flag=np.zeros((9,9))\n        for idx in train_idx:\n            flag[idx[0]][idx[1]]=1\n            flag[idx[1]][idx[0]]=1\n            for i,item in enumerate(self.train_x[idx[0]][idx[1]]):\n                train_x.append([self.locationmat[idx[0]][idx[1]],item])\n                train_y.append(self.train_y[idx[0]][idx[1]][i])\n        for i in range(9):\n            for j in range(9):\n                #for k,item in enumerate(self.train_x[i][j]):\n                #    test_x.append([self.locationmat[i][j],item])\n                #    test_y.append(self.train_y[i][j][k])\n                for k,item in enumerate(self.test_x[i][j]):\n                    test_x.append([self.locationmat[i][j],item[0]])\n                    test_y.append(self.test_y[i][j][k])\n        self.model.fit(train_x,train_y)\n\n        #args=np.where(np.array(train_y)==1)\n        #feature=np.array(train_x)[args]\n        #sp=GaussianSpatialModel()\n        #sp.fit(feature,train_y)\n\n\n\n        args=np.where(np.array(test_y)==1)\n        feature=np.array(test_x)[args]\n        #sp=GaussianSpatialModel()\n        #sp.fit(feature,test_y)\n        #print(test_x[0])\n        #r2_score=self.model.score(test_x,test_y)\n\n        plt.figure()\n        ypred=self.model.predict(train_x)[:,1]\n        auc_mat=metrics.roc_auc_score(train_y,ypred)\n        print(self.model_name,1,auc_mat)\n        ypred=self.model.predict(test_x)[:,1]\n        auc_mat=metrics.roc_auc_score(test_y,ypred)\n        print(self.model_name,2,auc_mat)\n        fpr, tpr, thersholds = roc_curve(test_y, ypred, pos_label=1)\n        plt.plot(fpr, tpr,  label='ROC (area = {0:.2f})'.format(auc_mat), lw=2)\n        with open('{}_roc.pkl'.format(self.model_name),'wb') as outp:\n            pkl.dump({self.model_name:{'fpr':fpr,'tpr':tpr,'auc':auc_mat}},outp)\n        plt.savefig('{}_roc.jpg'.format(self.model_name))\n        plt.figure()\n\n\n        answer=np.zeros((ypred.shape[0],2))\n        answer[:,0]=ypred\n        answer[:,1]=test_y\n#·        np.savetxt('./logs/answer.csv',answer)\n        #self.save_csv('answer',answer)\n        plt.figure()\n        colors=['r','b']\n        x=[item[0] for item in train_x]\n        y=[item[1] for item in train_x]\n        c=[colors[item] for item in train_y]\n        plt.scatter(x,y,c=c)\n        plt.savefig('a.jpg')\n        plt.figure()\n\n        #fig = plt.figure()\n        #ax = fig.gca(projection='3d')\n        #k=defaultdict(list)\n        #for i,item in enumerate(x):\n        #    k[item].append(y[i])\n        #ax.plot(x, y, z)\n\n\n\n        x=[item[0] for item in test_x]\n        y=[item[1] for item in test_x]\n        c=[colors[item] for item in test_y]\n        plt.scatter(x,y,c=c)\n        plt.savefig('b.jpg')\n\n        x=np.arange(0,400,10)\n        y=np.arange(0,2000,20)\n        X,Y=np.meshgrid(x,y)\n        XX=X.flatten()\n        YY=Y.flatten()\n        fx=np.vstack([XX,YY]).transpose()\n\n        #plot_partial_dependence(self.model.model,test_x,features=[0,1,(0,1)],feature_names=['distance','diff_time'])\n        #plt.savefig('{}_partial.jpg'.format(self.model_name))\n\n        fig=plt.figure(figsize=(12,12))\n        ax=fig.add_subplot(2,1,1,projection='3d')\n        ax.set_top_view()\n#        help(partial_dependence)\n        #pdp,axes=partial_dependence(self.model.model,X=fx,target_variables=[[0,1]],grid_resolution=2)\n #       pdp,axes=partial_dependence(self.model.model,X=fx,target_variables=[[0,1]])\n        #pdp=self.model.model.predict(fx)\n        pdp=self.model.model.predict_proba(fx)[:,1]\n        pdp=pdp.reshape(X.shape)\n        ax.view_init(elev=30., azim=45)\n #       X,Y=np.meshgrid(axes[0],axes[1])\n #       print(X.shape,Y.shape,pdp.shape)\n        ax.plot_surface(X,Y,pdp*1000,cmap=plt.cm.Spectral,cstride=1,rstride=1)\n #       ax=fig.add_subplot(2,1,2)\n        plt.xlabel('distance')\n        plt.ylabel('time difference')\n        plt.contour(X,Y,pdp*1000,cmap=plt.cm.Spectral)\n        plt.savefig('{}_f.jpg'.format(self.model_name))\n        self.save_model('UnionModel')\n        #with open('./logs/UnionModel_{}.pkl'.format(self.model_name),'wb') as out:\n        #    pkl.dump(self.model,out)\n        with open('{}_f.txt'.format(self.model_name),'w') as out:\n            for i in range(X.shape[0]):\n                for j in range(X[i].shape[0]):\n                    out.write('{} {} {}\\n'.format(X[i][j],Y[i][j],pdp[i][j]))\n                out.write('\\n')\n        return  self.model\n\n    def go(self):\n        self.model={}\n        for i in range(9):\n            if i not in self.model.keys():\n                self.model[i]={}\n            for j in range(9):\n                self.model[i][j]=Model(self.model_name,**self.args)\n        r2_score_mat=np.zeros((9,9))\n        auc_mat=np.zeros((9,9))\n        train_true_num=np.zeros((9,9))\n        train_false_num=np.zeros((9,9))\n        test_true_num=np.zeros((9,9))\n        test_false_num=np.zeros((9,9))\n        self.visual_data(23)\n        mean_mat=np.zeros((9,9))\n        median_mat=np.zeros((9,9))\n        max_mat=np.zeros((9,9))\n        min_mat=np.zeros((9,9))\n        hist_max_mat=np.zeros((9,9))\n        bins=np.arange(0,2000,50)\n\n        for i in range(9):\n            for j in range(9):\n                tidx=np.where(np.array(self.train_y[i][j])==1)\n                #print(self.train_y[i][j],tidx)\n                if tidx[0].shape[0]>0:\n                    max_mat[i][j]=np.max(np.array(self.train_x[i][j])[tidx])\n                    min_mat[i][j]=np.min(np.array(self.train_x[i][j])[tidx])\n                    hist=plt.hist(np.array(self.train_x[i][j])[tidx],bins=bins)\n                    hist_max_mat[i][j]=hist[1][np.argmax(hist[0])]+25\n                else:\n                    hist_max_mat[i][j]=-1\n                    max_mat[i][j]=-1\n                    min_mat[i][j]=-1\n                mean_mat[i][j]=np.mean(np.array(self.train_x[i][j])[tidx])\n                median_mat[i][j]=np.median(np.array(self.train_x[i][j])[tidx])\n                train_true_num[i][j]+=sum(self.train_y[i][j])\n                train_false_num[i][j]+=sum(1-np.array(self.train_y[i][j]))\n                test_false_num[i][j]+=sum(1-np.array(self.test_y[i][j]))\n                test_true_num[i][j]+=sum(self.test_y[i][j])\n                if len(self.train_x[i][j])==0 or len(self.test_y[i][j])==0:\n                    r2_score_mat[i][j]=-1\n                    auc_mat[i][j]=-1\n                    continue\n                self.model[i][j].fit(np.array(self.train_x[i][j]).reshape((len(self.train_x[i][j]),1)),self.train_y[i][j])\n                r2_score_mat[i][j]=self.model[i][j].score(self.test_x[i][j],self.test_y[i][j])\n                ypred=self.model[i][j].predict(self.test_x[i][j])\n                try:    \n                    auc_mat[i][j]=metrics.roc_auc_score(self.test_y[i][j],ypred)\n                except:\n                    auc_mat[i][j]=-1\n        #np.savetxt('./logs/train_hist_max.txt',hist_max_mat.reshape(81),fmt='%.03f')\n        #np.savetxt('./logs/train_max.txt',max_mat.reshape(81),fmt='%.03f')\n        #np.savetxt('./logs/train_min.txt',min_mat.reshape(81),fmt='%.03f')\n        #np.savetxt('./logs/train_mean.txt',mean_mat.reshape(81),fmt='%.03f')\n        #np.savetxt('./logs/train_median.txt',median_mat.reshape(81),fmt='%.03f')\n        #np.savetxt('./logs/r2_score_mat.csv',r2_score_mat,fmt='%.03f')\n        #np.savetxt('./logs/auc_score_mat.csv',auc_mat,fmt='%.03f')\n        total=np.zeros((81,6))\n        total[:,0]=train_true_num.reshape((81))\n        total[:,1]=train_false_num.reshape((81))\n        total[:,2]=test_true_num.reshape((81))\n        total[:,3]=test_false_num.reshape((81))\n        total[:,4]=auc_mat.reshape((81))\n        total[:,5]=r2_score_mat.reshape(81)\n\n        self.save_csv('total',total)\n       # np.savetxt('./logs/total.csv',total)\n        #self.save_model('Seperate')\n        return model\n\n       # with open('./logs/Seperate_{}.pkl'.format(self.model_name),'wb') as out:\n       #     pkl.dump(self.model,out)\n","repo_name":"zhangxin1995/PTD","sub_path":"code/module/pathmodel/pathmodel.py","file_name":"pathmodel.py","file_ext":"py","file_size_in_byte":18980,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"76"}
+{"seq_id":"4624015118","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport pygame\nfrom pygame.locals import K_DOWN, K_LEFT, K_RIGHT, K_SPACE, K_UP, K_a, K_d, K_s, K_w\nimport evdev\nfrom evdev import ecodes, InputDevice\nimport numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom icecream import ic as print\nimport socket\nimport threading\nimport time\nimport sys\nimport math\nimport cv2\nfrom shapely.geometry import Polygon, Point\n\nfrom informer import Informer\nfrom proto.python_out import marker_pb2, geometry_msgs_pb2, path_msgs_pb2, cmd_msgs_pb2\nfrom config_5g import cfg_server\n\n\nHOST_ADDRESS = '127.0.0.1'\nBLACK = (0, 0, 0)\nGREY = (192, 192, 192)\nBLUE = (0, 0, 255)\nGREEN = (0, 255, 0)\nRED = (255, 0, 0)\nWHITE = (255, 255, 255)\nWINDOW_WIDTH = 1920\nWINDOW_HEIGHT = 1080\nROBOT_SIZE = 20\nBUTTON_WIDTH = 300\nBUTTON_HEIGHT = 100\nBUTTON_LIGHT = (170, 170, 170)\nBUTTON_DARK = (100, 100, 100)\nBUTTON_GOAL_X = 50\nBUTTON_GOAL_Y = 50\nBUTTON_LASER_X = 50\nBUTTON_LASER_Y = 200\nBUTTON_SATELLITE_X = 50\nBUTTON_SATELLITE_Y = 350\nBUTTON_JOYSTICK_X = 50\nBUTTON_JOYSTICK_Y = 500\n# read map\nLASER_MAP = pygame.image.load('./maps/laser_map.jpg')\nSATELLITE_MAP = pygame.image.load('./maps/satellite_map.jpg')\nDISPLAY_MAP = LASER_MAP\nmap_offset = np.array([0, 0])\nrobot_goal = None\nrobot_pos = []\nrobot_heading = []\nrobot_cmd = []\nbounding_box = dict()\npath_pos = []\nrobot_clicked_id = None\nrobot_img = None\nbox_clicked_id = None\n# flags\nmap_draging = False\ngoal_setting = False\nrobot_clicked = False\nview_image = False\nbox_clicked = False\nuse_joystick = False\n\nclass Receiver(object):\n    def __init__(self):\n        self.path_sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n        self.path_sock.settimeout(1.0)\n        self.path_sock.bind((HOST_ADDRESS, 23333))\n        self.path_thread = threading.Thread(target=self.receive_path)\n        self.path_thread.start()\n        self.gesture_sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n        self.gesture_sock.settimeout(1.0)\n        self.gesture_sock.bind((HOST_ADDRESS, 23335))\n        self.gesture_thread = threading.Thread(target=self.receive_gesture)\n        self.gesture_thread.start()\n        self.timeout = False\n\n    def receive_path(self):\n        while True:\n            try:\n                data, _ = self.path_sock.recvfrom(4096)\n                data = data.decode(\"utf-8\").split(';')\n                MAP_WIDTH, MAP_HEIGHT = DISPLAY_MAP.get_size()\n                offset = np.array([WINDOW_WIDTH//2 - MAP_WIDTH//2, WINDOW_HEIGHT//2 - MAP_HEIGHT//2])\n                global path_pos\n                path_pos = np.array([np.array([float(pos.split(',')[0]), float(pos.split(',')[1])]) + offset\n                            for pos in data if pos != ''])\n                # print(path_pos, len(path_pos))\n                self.timeout = False\n            except socket.timeout:\n                self.timeout = True\n            time.sleep(0.01)\n    \n    def receive_gesture(self):\n        while True:\n            try:\n                data, _ = self.gesture_sock.recvfrom(4096)\n                gesture = data.decode(\"utf-8\")\n                # print(gesture)\n                self.timeout = False\n            except socket.timeout:\n                self.timeout = True\n            time.sleep(0.01)\n            \ndef parse_message(message):\n    global bounding_box\n    marker_list = marker_pb2.MarkerList()\n    marker_list.ParseFromString(message)\n    MAP_WIDTH, MAP_HEIGHT = DISPLAY_MAP.get_size()\n    offset = np.array([WINDOW_WIDTH//2 - MAP_WIDTH//2, WINDOW_HEIGHT//2 - MAP_HEIGHT//2])\n    for marker in marker_list.marker_list:\n        try:\n            center_pos = np.array([int(marker.pose.position.y*(-20)+2089), int(2949-20*marker.pose.position.x)]) + offset\n            orientation = R.from_quat([marker.pose.orientation.x, marker.pose.orientation.y, marker.pose.orientation.z, marker.pose.orientation.w]).as_euler('xyz', degrees=False)[2]\n            orientation += np.pi / 2\n            height, width = 10*marker.scale.x, 10*marker.scale.y\n            vertex_A = center_pos + height*np.array([np.cos(orientation), np.sin(orientation)]) + width*np.array([-np.sin(orientation), np.cos(orientation)])\n            vertex_B = center_pos + height*np.array([np.cos(orientation), np.sin(orientation)]) - width*np.array([-np.sin(orientation), np.cos(orientation)])\n            vertex_C = center_pos - height*np.array([np.cos(orientation), np.sin(orientation)]) - width*np.array([-np.sin(orientation), np.cos(orientation)])\n            vertex_D = center_pos - height*np.array([np.cos(orientation), np.sin(orientation)]) + width*np.array([-np.sin(orientation), np.cos(orientation)])\n            marker_id = marker.id\n            new_box = np.array([vertex_A, vertex_B, vertex_C, vertex_D])\n            # overlap filter\n            overlap = False\n            p1 = Polygon(new_box)\n            for id, pos in bounding_box.items():\n                p2 = Polygon(pos)\n                if p1.intersects(p2) and id != marker_id:\n                    overlap = True\n                    break\n            if not overlap:\n                bounding_box[marker_id] = np.array(new_box)\n        except:\n            pass\n        # print(bounding_box)\n\ndef parse_odometry(message):\n    global robot_pos, robot_heading\n    odometry = geometry_msgs_pb2.Pose()\n    odometry.ParseFromString(message)\n    MAP_WIDTH, MAP_HEIGHT = DISPLAY_MAP.get_size()\n    offset = np.array([WINDOW_WIDTH//2 - MAP_WIDTH//2, WINDOW_HEIGHT//2 - MAP_HEIGHT//2])\n    robot_pos = [np.array([int(odometry.position.y*(-20)+2089), int(2949-20*odometry.position.x)]) + offset]\n    robot_heading = [R.from_quat([odometry.orientation.x, odometry.orientation.y, odometry.orientation.z, odometry.orientation.w]).as_euler('xyz', degrees=False)[2]]\n\ndef parse_cmd(message):\n    global robot_cmd\n    # print('grt cmd !!!')\n    cmd = cmd_msgs_pb2.Cmd()\n    cmd.ParseFromString(message)\n    robot_cmd = [[cmd.v, cmd.w]]\n\ndef send_path(path_list):\n    global ifm\n    path = path_msgs_pb2.Path()\n    for i in range(len(path_list)):\n        pose = path_msgs_pb2.Pose2D()\n        pose.x = path_list[i][0]\n        pose.y = path_list[i][0]\n        pose.theta = path_list[i][0]\n\n        path.poses.append(pose)\n\n    sent_data = path.SerializeToString()\n    # print('send', len(sent_data))\n    ifm.send_path(sent_data)\n\nclass Server(Informer):\n    def msg_recv(self):\n        self.recv('msg', parse_message)\n\n    def odm_recv(self):\n        self.recv('odm', parse_odometry)\n\n    def cmd_recv(self):\n        self.recv('cmd', parse_cmd)\n\n    def send_path(self, message):\n        self.send(message, 'path')\n\n\ndef sendGoal(goal):\n    goal_sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n    if len(robot_pos) == 0:\n        goal_str = str(goal[0]) + ',' + str(goal[1])\n    else:\n        MAP_WIDTH, MAP_HEIGHT = DISPLAY_MAP.get_size()\n        offset = np.array([WINDOW_WIDTH//2 - MAP_WIDTH//2, WINDOW_HEIGHT//2 - MAP_HEIGHT//2])\n        pos = robot_pos[0] - offset\n        goal_str = str(goal[0]) + ',' + str(goal[1]) + ',' + str(pos[0]) + ',' + str(pos[1])\n    goal_sock.sendto(bytes(goal_str, 'ascii'), (HOST_ADDRESS, 23334))\n\ndef screen2pos(x, y):\n    MAP_WIDTH, MAP_HEIGHT = DISPLAY_MAP.get_size()\n    pos = np.array([x, y]) - np.array([WINDOW_WIDTH//2 - MAP_WIDTH//2, WINDOW_HEIGHT//2 - MAP_HEIGHT//2])\n    return pos\n\ndef pos2screen(x, y):\n    return x, y\n\ndef parse_vehicle_wheel(joystick, clock):\n    keys = pygame.key.get_pressed()\n    milliseconds = clock.get_time()\n\n    throttle = 1.0 if keys[K_UP] or keys[K_w] else 0.0\n    steer_increment = 5e-4 * milliseconds\n    if keys[K_LEFT] or keys[K_a]:\n        steer_cache -= steer_increment\n    elif keys[K_RIGHT] or keys[K_d]:\n        steer_cache += steer_increment\n    else:\n        steer_cache = 0.0\n    steer_cache = min(0.7, max(-0.7, steer_cache))\n    steer = round(steer_cache, 1)\n    brake = 1.0 if keys[K_DOWN] or keys[K_s] else 0.0\n\n    numAxes = joystick.get_numaxes()\n    jsInputs = [float(joystick.get_axis(i)) for i in range(numAxes)]\n\n    # Custom function to map range of inputs [1, -1] to outputs [0, 1] i.e 1 from inputs means nothing is pressed\n    # For the steering, it seems fine as it is\n    K1 = 1.0  # 0.55\n    steerCmd = K1 * math.tan(1.1 * jsInputs[0])\n\n    K2 = 1.6  # 1.6\n    throttleCmd = K2 + (2.05 * math.log10(\n        -0.7 * jsInputs[2] + 1.4) - 1.2) / 0.92\n    if throttleCmd <= 0:\n        throttleCmd = 0\n    elif throttleCmd > 1:\n        throttleCmd = 1\n\n    brakeCmd = 1.6 + (2.05 * math.log10(\n        -0.7 * jsInputs[3] + 1.4) - 1.2) / 0.92\n    if brakeCmd <= 0:\n        brakeCmd = 0\n    elif brakeCmd > 1:\n        brakeCmd = 1\n\n    steer = steerCmd\n    brake = brakeCmd\n    throttle = throttleCmd\n\n    return steer, throttle, brake\n\ndef drawRobots():\n    # for pos, heading, cmd in zip(robot_pos, robot_heading, robot_cmd):\n    for pos, heading in zip(robot_pos, robot_heading):\n        cmd = [0.5]\n        pygame.draw.circle(SCREEN, GREEN, pos + map_offset, ROBOT_SIZE)\n        pygame.draw.line(SCREEN, BLUE, pos + map_offset, pos + map_offset + min(max(40*cmd[0], 25), 40)*np.array([np.cos(heading+np.pi/2), -np.sin(heading+np.pi/2)]), 5)\n        \ndef drawGoal():\n    if robot_goal is not None:\n        # pygame.draw.circle(SCREEN, GREEN, robot_goal + map_offset, ROBOT_SIZE)\n        cicle = (robot_goal + map_offset)\n        marker_size = 20\n        width = 10\n        pygame.draw.line(SCREEN, RED, (cicle[0]-marker_size, cicle[1]-marker_size), (cicle[0]+marker_size, cicle[1]+marker_size), width)\n        pygame.draw.line(SCREEN, RED, (cicle[0]-marker_size, cicle[1]+marker_size), (cicle[0]+marker_size, cicle[1]-marker_size), width)\n\n\ndef drawBoundingBox():\n    bounding_box_copy = bounding_box.copy()\n    for _, pos in bounding_box_copy.items():\n        # x, y = pos + map_offset\n        # pygame.draw.rect(SCREEN, BLUE, pygame.Rect(x, y, 60, 100), 10)\n        pygame.draw.lines(SCREEN, BLUE, True, pos + map_offset, 10)\n\ndef drawPath():\n    if len(path_pos) > 1:\n        pygame.draw.lines(SCREEN, RED, False, path_pos + map_offset, 10)\n\ndef drawButton():\n    # font settings\n    FONT = pygame.font.SysFont('Corbel', 75)\n\n    # get mouse position\n    mouse = pygame.mouse.get_pos()\n\n    # button: set goal\n    text = FONT.render('Set Goal', True, WHITE)\n    if BUTTON_GOAL_X <= mouse[0] <= BUTTON_GOAL_X + BUTTON_WIDTH and BUTTON_GOAL_Y <= mouse[1] <= BUTTON_GOAL_Y + BUTTON_HEIGHT:\n        pygame.draw.rect(SCREEN, BUTTON_LIGHT, [BUTTON_GOAL_X, BUTTON_GOAL_Y, BUTTON_WIDTH, BUTTON_HEIGHT])\n    else:\n        pygame.draw.rect(SCREEN, BUTTON_DARK, [BUTTON_GOAL_X, BUTTON_GOAL_Y, BUTTON_WIDTH, BUTTON_HEIGHT])\n    SCREEN.blit(text, (BUTTON_GOAL_X+45, BUTTON_GOAL_Y+25))\n    # button: laser map\n    text = FONT.render('LASER', True, WHITE)\n    if BUTTON_LASER_X <= mouse[0] <= BUTTON_LASER_X + BUTTON_WIDTH and BUTTON_LASER_Y <= mouse[1] <= BUTTON_LASER_Y + BUTTON_HEIGHT:\n        pygame.draw.rect(SCREEN, BUTTON_LIGHT, [BUTTON_LASER_X, BUTTON_LASER_Y, BUTTON_WIDTH, BUTTON_HEIGHT])\n    else:\n        pygame.draw.rect(SCREEN, BUTTON_DARK, [BUTTON_LASER_X, BUTTON_LASER_Y, BUTTON_WIDTH, BUTTON_HEIGHT])\n    SCREEN.blit(text, (BUTTON_LASER_X+60, BUTTON_LASER_Y+25))\n    # button: satellite map\n    text = FONT.render('SATELLITE', True, WHITE)\n    if BUTTON_SATELLITE_X <= mouse[0] <= BUTTON_SATELLITE_X + BUTTON_WIDTH and BUTTON_SATELLITE_Y <= mouse[1] <= BUTTON_SATELLITE_Y + BUTTON_HEIGHT:\n        pygame.draw.rect(SCREEN, BUTTON_LIGHT, [BUTTON_SATELLITE_X, BUTTON_SATELLITE_Y, BUTTON_WIDTH, BUTTON_HEIGHT])\n    else:\n        pygame.draw.rect(SCREEN, BUTTON_DARK, [BUTTON_SATELLITE_X, BUTTON_SATELLITE_Y, BUTTON_WIDTH, BUTTON_HEIGHT])\n    SCREEN.blit(text, (BUTTON_SATELLITE_X+10, BUTTON_SATELLITE_Y+25))\n    # button: joystick mode\n    text = FONT.render('JOYSTICK', True, WHITE)\n    if (BUTTON_JOYSTICK_X <= mouse[0] <= BUTTON_JOYSTICK_X + BUTTON_WIDTH and BUTTON_JOYSTICK_Y <= mouse[1] <= BUTTON_JOYSTICK_Y + BUTTON_HEIGHT) or use_joystick:\n        pygame.draw.rect(SCREEN, BUTTON_LIGHT, [BUTTON_JOYSTICK_X, BUTTON_JOYSTICK_Y, BUTTON_WIDTH, BUTTON_HEIGHT])\n    else:\n        pygame.draw.rect(SCREEN, BUTTON_DARK, [BUTTON_JOYSTICK_X, BUTTON_JOYSTICK_Y, BUTTON_WIDTH, BUTTON_HEIGHT])\n    SCREEN.blit(text, (BUTTON_JOYSTICK_X+20, BUTTON_JOYSTICK_Y+25))\n\ndef drawMessageBox():\n    # font settings\n    FONT = pygame.font.SysFont('Corbel', 75)\n\n    # get mouse position\n    mouse = pygame.mouse.get_pos()\n\n    if robot_clicked:\n        # box\n        BOX_X, BOX_Y = robot_pos[robot_clicked_id] + map_offset + np.array([25, -150])\n        BOX_WIDTH, BOX_HEIGHT = 350, 150\n        BOX_COLOR = (255, 255, 255)\n        pygame.draw.rect(SCREEN, BOX_COLOR, [BOX_X, BOX_Y, BOX_WIDTH, BOX_HEIGHT])\n\n        # button: view image\n        text = FONT.render('View Image', True, WHITE)\n        BUTTON_IMAGE_X, BUTTON_IMAGE_Y = robot_pos[robot_clicked_id] + map_offset + np.array([50, -125])\n        if BUTTON_IMAGE_X <= mouse[0] <= BUTTON_IMAGE_X + BUTTON_WIDTH and BUTTON_IMAGE_Y <= mouse[1] <= BUTTON_IMAGE_Y + BUTTON_HEIGHT:\n            pygame.draw.rect(SCREEN, BUTTON_LIGHT, [BUTTON_IMAGE_X, BUTTON_IMAGE_Y, BUTTON_WIDTH, BUTTON_HEIGHT])\n        else:\n            pygame.draw.rect(SCREEN, BUTTON_DARK, [BUTTON_IMAGE_X, BUTTON_IMAGE_Y, BUTTON_WIDTH, BUTTON_HEIGHT])\n        SCREEN.blit(text, (BUTTON_IMAGE_X+10, BUTTON_IMAGE_Y+25))\n\n    if box_clicked:\n        # box\n        box_center = np.mean(bounding_box[box_clicked_id], axis=0)\n        BOX_X, BOX_Y = box_center + map_offset + np.array([25, -150])\n        BOX_WIDTH, BOX_HEIGHT = 350, 150\n        BOX_COLOR = (255, 255, 255)\n        pygame.draw.rect(SCREEN, BOX_COLOR, [BOX_X, BOX_Y, BOX_WIDTH, BOX_HEIGHT])\n\n        # button: get id\n        text = FONT.render('Get ID', True, WHITE)\n        BUTTON_ID_X, BUTTON_ID_Y = box_center + map_offset + np.array([50, -125])\n        if BUTTON_ID_X <= mouse[0] <= BUTTON_ID_X + BUTTON_WIDTH and BUTTON_ID_Y <= mouse[1] <= BUTTON_ID_Y + BUTTON_HEIGHT:\n            pygame.draw.rect(SCREEN, BUTTON_LIGHT, [BUTTON_ID_X, BUTTON_ID_Y, BUTTON_WIDTH, BUTTON_HEIGHT])\n        else:\n            pygame.draw.rect(SCREEN, BUTTON_DARK, [BUTTON_ID_X, BUTTON_ID_Y, BUTTON_WIDTH, BUTTON_HEIGHT])\n        SCREEN.blit(text, (BUTTON_ID_X+75, BUTTON_ID_Y+25))\n\n\ndef drawMaps():\n    WINDOW_WIDTH, WINDOW_HEIGHT = pygame.display.get_surface().get_size()\n    MAP_WIDTH, MAP_HEIGHT = DISPLAY_MAP.get_size()\n    map_pos = np.array([WINDOW_WIDTH//2 - MAP_WIDTH//2, WINDOW_HEIGHT//2 - MAP_HEIGHT//2]) + map_offset\n    SCREEN.blit(DISPLAY_MAP, map_pos)\n\n\nif __name__ == \"__main__\":\n    pygame.init()\n    SCREEN = pygame.display.set_mode((WINDOW_WIDTH, WINDOW_HEIGHT))#, pygame.RESIZABLE)\n    pygame.display.set_caption('5G Monitor')\n    icon = pygame.image.load('icon.png')\n    pygame.display.set_icon(icon)\n    CLOCK = pygame.time.Clock()\n    SCREEN.fill(GREY)\n    data_receiver = Receiver()\n    # 5G server setup\n    try:\n        server = Server(cfg_server)\n    except:\n        pass\n    # joystick setup\n    try:\n        device = evdev.list_devices()[0]\n        evtdev = InputDevice(device)\n        val = 25000 #[0,65535]\n        evtdev.write(ecodes.EV_FF, ecodes.FF_AUTOCENTER, val)\n        pygame.joystick.init()\n        joystick_count = pygame.joystick.get_count()\n        joystick = pygame.joystick.Joystick(0)\n        joystick.init()\n    except:\n        pass\n\n    cnt = 0\n    while True:\n        start_time = time.time()\n        cnt += 1\n        SCREEN.fill(GREY)\n        drawMaps()\n        drawGoal()\n        drawRobots()\n        drawBoundingBox()\n        drawPath()\n        drawButton()\n        drawMessageBox()\n\n        for event in pygame.event.get():\n            mods = pygame.key.get_mods()\n            if event.type == pygame.QUIT:\n                pygame.quit()\n                sys.exit()\n            elif event.type == pygame.MOUSEBUTTONDOWN and mods & pygame.KMOD_CTRL:\n                if event.button == 1:            \n                    map_draging = True\n                    start_pos = event.pos\n            elif event.type == pygame.MOUSEBUTTONDOWN:\n                # get mouse position\n                mouse = pygame.mouse.get_pos()\n                # button: set goal\n                if BUTTON_GOAL_X <= mouse[0] <= BUTTON_GOAL_X + BUTTON_WIDTH and BUTTON_GOAL_Y <= mouse[1] <= BUTTON_GOAL_Y + BUTTON_HEIGHT:\n                    goal_setting = True\n                elif goal_setting:\n                    goal_setting = False\n                    robot_goal = mouse - map_offset\n                # button: laser map\n                elif BUTTON_LASER_X <= mouse[0] <= BUTTON_LASER_X + BUTTON_WIDTH and BUTTON_LASER_Y <= mouse[1] <= BUTTON_LASER_Y + BUTTON_HEIGHT:\n                    DISPLAY_MAP = LASER_MAP\n                # button: satellite map\n                elif BUTTON_SATELLITE_X <= mouse[0] <= BUTTON_SATELLITE_X + BUTTON_WIDTH and BUTTON_SATELLITE_Y <= mouse[1] <= BUTTON_SATELLITE_Y + BUTTON_HEIGHT:\n                    DISPLAY_MAP = SATELLITE_MAP\n                # button: joystick mode\n                elif BUTTON_JOYSTICK_X <= mouse[0] <= BUTTON_JOYSTICK_X + BUTTON_WIDTH and BUTTON_JOYSTICK_Y <= mouse[1] <= BUTTON_JOYSTICK_Y + BUTTON_HEIGHT:\n                    use_joystick = not use_joystick\n                # button: robot\n                if robot_clicked:\n                    # button: view image\n                    BUTTON_IMAGE_X, BUTTON_IMAGE_Y = robot_pos[robot_clicked_id] + map_offset + np.array([50, -125])\n                    if BUTTON_IMAGE_X <= mouse[0] <= BUTTON_IMAGE_X + BUTTON_WIDTH and BUTTON_IMAGE_Y <= mouse[1] <= BUTTON_IMAGE_Y + BUTTON_HEIGHT:\n                        view_image = True\n                        print('show image')\n                robot_clicked = False\n                for idx, pos in enumerate(robot_pos):\n                    if math.hypot(mouse[0] - (pos + map_offset)[0], mouse[1] - (pos + map_offset)[1]) <= ROBOT_SIZE:\n                        print('click robot {}'.format(idx))\n                        robot_clicked = True\n                        robot_clicked_id = idx\n                        break\n                # button: bounding box\n                if box_clicked:\n                    # button: get id\n                    box_center = np.mean(bounding_box[box_clicked_id], axis=0)\n                    BUTTON_ID_X, BUTTON_ID_Y = box_center + map_offset + np.array([50, -125])\n                    if BUTTON_ID_X <= mouse[0] <= BUTTON_ID_X + BUTTON_WIDTH and BUTTON_ID_Y <= mouse[1] <= BUTTON_ID_Y + BUTTON_HEIGHT:\n                        print('get id')\n                box_clicked = False\n                bounding_box_copy = bounding_box.copy()\n                for idx, box in bounding_box_copy.items():\n                    p1 = Point(mouse)\n                    p2 = Polygon(box + map_offset)\n                    if p2.contains(p1):\n                        print('click box {}'.format(idx))\n                        box_clicked = True\n                        box_clicked_id = idx\n                        break\n            elif event.type == pygame.MOUSEBUTTONUP and mods & pygame.KMOD_CTRL:\n                if event.button == 1:            \n                    map_draging = False\n            elif event.type == pygame.MOUSEMOTION and mods & pygame.KMOD_CTRL:\n                if map_draging:\n                    end_pos = event.pos\n                    map_offset = map_offset + end_pos - start_pos\n                    start_pos = end_pos\n            elif event.type == pygame.JOYBUTTONDOWN:\n                print(\"Joystick button pressed.\")\n            elif event.type == pygame.JOYBUTTONUP:\n                print(\"Joystick button released.\")\n\n        # send goal\n        if robot_goal is not None:\n            if cnt % 10 == 0: \n                sendGoal(screen2pos(*robot_goal))\n        \n        # view image\n        if view_image and robot_img is not None:\n            cv2.imshow('Robot Image', robot_img)\n            if cv2.waitKey(25) & 0xFF == ord('q'):\n                view_image = False\n                cv2.destroyAllWindows()\n\n        # parse joystick\n        if use_joystick:\n            try:\n                steer, throttle, brake = parse_vehicle_wheel(joystick, Clock)\n                print(steer, throttle, brake)\n            except:\n                pass\n\n        pygame.display.update()\n        CLOCK.tick(20)\n        end_time = time.time()\n        # print('frequency', 1/(end_time-start_time))\n","repo_name":"5G-Swarm/ROS-Comm","sub_path":"src/ui.py","file_name":"ui.py","file_ext":"py","file_size_in_byte":20325,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"76"}
+{"seq_id":"31224505143","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('', views.homepage, name='homepage'),\n    path('chatbox/', views.chatbot, name='chatbot'),\n    path('login/',views.login, name='login'),\n    path('pronouncequiz/',views.pronouncequiz, name='pronounce'),\n    path('newfeed/', views.newsfeed, name='newsfeed'),\n\n] ","repo_name":"Nimosteve88/HackPackBGNHachathon","sub_path":"conversationAI/conversationAIProject/members/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"6340488054","text":"# File:\t\tdownload_tedtalk.py\r\n# Author:\tE. Partridge\r\n# Date:\t\t8 August 2012\r\n# Description:\r\n#\t\t\tThis script parses the TED Talk audio feed and proceeds to\r\n#\t\t\tdownload all audio files into the same directory that\r\n#\t\t\tthis script is located in.  Files are prepended with the publication\r\n#\t\t\tdate for convenience.\r\n#\r\n# Note: \tThis has only been tested on Windows 7 64-bit, with Python 2.7.2.5\r\n# Note2: \tTED Talk audio files contain ID3v2.4 tags, which are not supported\r\n# \t\t \tnatively by Windows. I used foobar2000 to convert the tags to ID3v2.3,\r\n#\t\t\twhich Windows does support. To do this, open the MP3 files in\r\n#\t\t\tfoobar2000, right click and select Tagging > MP3 Tag Types... Check\r\n#\t\t\t\"Override ID3v2 revision:\" and select the ID3v2.3 radio button.\r\n#\t\t\tAfter that, I was able to view metadata in Windows Explorer and\r\n#\t\t\tWindows Media Player.\r\nimport urllib\r\nimport feedparser\r\nimport time\r\n\r\ntedtalk_rss_url = 'http://feeds.feedburner.com/TEDTalks_audio'\r\ntedtalk_feed = feedparser.parse(tedtalk_rss_url)\r\n\r\ndef GetFeedContent(entry):\r\n\tcontent_url = entry.enclosures[0].href\r\n\tfile_name = content_url.split('/')[-1]\r\n\tfile_date = time.strptime(entry.published[5:16], '%d %b %Y')\r\n\tdate_str = '{:04}-{:02}-{:02}'.format(file_date.tm_year, file_date.tm_mon, file_date.tm_mday)\r\n\tfile_name = date_str + ' ' + file_name\r\n\ttry:\r\n\t\twith open(file_name) as f:\r\n\t\t\tprint('File exists: ' + file_name)\r\n\texcept IOError as e:\r\n\t\tprint('Downloading: ' + file_name)\r\n\t\turllib.urlretrieve(content_url, file_name)\r\n\treturn\r\n\r\nfor entry in tedtalk_feed.entries:\r\n\tGetFeedContent(entry)","repo_name":"mrtumnus/scrape-tedtalks","sub_path":"download_tedtalk.py","file_name":"download_tedtalk.py","file_ext":"py","file_size_in_byte":1586,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"38309540050","text":"from drf_yasg import openapi\nfrom .swagger import unauthorized_401\n\nlogout_schema = openapi.Schema(\n    title=\"logout\",\n    type=openapi.TYPE_OBJECT,\n    properties={\n        \"refresh_token\": openapi.Schema(type= openapi.TYPE_STRING, description=\"refresh token to be blacklisted\",\n                                        example=\"jknJj93nc4JN8ssn38JHBfjJ7dnHJ4hUJ4h\")\n    }\n)\nresponse_login = {\n    \"401\": openapi.Response(\n        description=\"invalid email or password\",\n        examples={\n            \"application/json\": {\n                \"detail\": \"No active account found with the given credentials\"\n            }\n        }),\n    \"200\": openapi.Response(\n        description=\"Login was successful\",\n        examples={\n            \"application/json\":\n                {\n                    \"refresh\": \"eyJhbI6IkpXVCJ5_nu_A8IZIeZ0x1WJq8X9AISLA4\",\n                    \"access\": \"eyOjE2NjgzpczASEhgbG6uuhmmkjj8SbFeaumOxyuk\"\n                }\n        }),\n}\nresponse_refresh_token = {\n    \"401\": openapi.Response(\n        description=\"invalid token or invalid token type\",\n        examples={\n            \"application/json\": {\n                \"detail\": \"Token is invalid or expired\",\n                \"code\": \"token_not_valid\"\n}\n        }),\n    \"201\": openapi.Response(\n        description=\"Get new access token\",\n        examples={\n            \"application/json\":\n                {\n                    \"access\": \"eyOjE2NjgzpczASEhgbG6uuhmmkjj8SbFeaumOxyuk\"\n                }\n        }),\n}\nlogout_response = {\n    \"200\": openapi.Response(\n        description=\"successfully logged out\",\n        examples={\n            \"application/json\":\n                {\n                    \"detail\": \"Successfully logged out\"\n                }\n        }),\n    #\"401\": unauthorized_401,\n    \"400\": openapi.Response(\n        description=\"invalid refresh token in request\",\n        examples={\n            \"application/json\":\n                {\n                    \"detail\": \"Token is invalid or expired\"\n                }\n        })\n}\nregister_response = {\n    \"201\": openapi.Response(\n        description=\"successfully registered\",\n        examples={\n            \"application/json\":\n                {\n                    \"id\": 5,\n                    \"email\": \"test2@email.com\",\n                    \"first_name\": \"Mary\",\n                    \"last_name\": \"Watson\",\n                    \"password\": \"pbkdf2_sha256$390000$IW8Bnc2qEsYnRJOFC9cCJ6$gF9yJi8cfkTq4WKdB8BN32w8tW9tmBUEf+tw8Bt/9Pk=\"\n                }\n        }\n    ),\n    \"400\": openapi.Response(\n        description=\"invalid user data\",\n        examples={\n            \"application/json\":\n                {\n                    \"email\": [\n                        \"user with this email already exists.\"\n                    ],\n                    \"password\": [\n                        \"Password should contain at least one uppercase letter, one lowercase letter and be at least 8 symbols long\"\n                    ]\n                }\n        })\n}","repo_name":"khrystyna-yaryna-kolba/kkolba","sub_path":"programming/programming_and_practice_8(authentification_roles)/rest_uni/rest_app/swagger/authentification_swagger.py","file_name":"authentification_swagger.py","file_ext":"py","file_size_in_byte":2974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"31723521592","text":"import sys\n\nn = str(sys.stdin.readline().rstrip())\n\nif '0' not in n:\n    print(-1)\nelse:\n    sum = 0\n    for num in n:\n        sum += int(num)\n    if (sum % 3) == 0:\n        seperated = []\n        for i in n:\n            seperated.append(int(i))\n        seperated.sort(reverse=True)\n        print(int(''.join(str(e) for e in seperated)))\n    else:\n        print(-1)\n        \n        ","repo_name":"moonkey48/altgorithm_python","sub_path":"boj_10610_greedy.py","file_name":"boj_10610_greedy.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"20749483813","text":"#!/usr/bin/python3\nimport numpy as np\n\nwith open('14_input.txt') as f:\n    lines = [line.rstrip() for line in f]\n\nDO_PART1=False\n\ndef printCave(cave,extentX,extentY,curSand):\n   for y in range(extentY[0],extentY[1]+1):\n        str=\"\"\n        for x in range(extentX[0],extentX[1]+1):\n            if cave[x][y]==1:\n                str+=\"#\"\n            elif cave[x][y]==2 or (x==curSand[0] and y==curSand[1]):\n                str+=\"o\"\n            elif x==500 and y==0:\n                str+=\"+\"\n            else:\n                str+=\".\"\n        print(str)\n\ndef runSandFlow(cave):\n    settledCount=0\n    withinCave=True\n    while withinCave:\n        sandPos=[500,0]\n        settled=False\n        while not settled and withinCave:\n            if cave[sandPos[0]][sandPos[1]+1]==0: # air below - so move down\n                sandPos=[sandPos[0],sandPos[1]+1]\n            elif cave[sandPos[0]][sandPos[1]+1]>0: # rock or sand below\n                if cave[sandPos[0]-1][sandPos[1]+1]>0:\n                    if cave[sandPos[0]+1][sandPos[1]+1]>0:\n                        settled=True\n                        settledCount+=1\n                        cave[sandPos[0],sandPos[1]]=\"2\"\n                    else:\n                        sandPos=[sandPos[0]+1,sandPos[1]+1]                \n                else:\n                    sandPos=[sandPos[0]-1,sandPos[1]+1]\n            if sandPos==[500,0]: # source blocked!\n                return settledCount\n            withinCave = sandPos[1]<=extentY[1] and sandPos[0]>=extentX[0] and sandPos[0]<=extentX[1]\n    return settledCount\n\n#parse input\nnumCols=1000\nnumRows=200\nextentX=[numCols,0]\nextentY=[numRows,0]\ncave=np.zeros(shape=(numCols, numRows), dtype=np.uint8)\nfor line in lines:\n    nodes=line.split(\" -> \")\n    for nodeID in range(len(nodes)-1):\n        start=[int(x) for x in nodes[nodeID].split(\",\")]\n        end=[int(x) for x in nodes[nodeID+1].split(\",\")]\n\n        extentX=[min(extentX[0],start[0],end[0]),max(extentX[1],start[0],end[0])]\n        extentY=[min(extentY[0],start[1],end[1]),max(extentY[1],start[1],end[1])]\n        \n        if start[0]==end[0]:\n            for y in range(min(start[1],end[1]),max(start[1],end[1])+1):\n                cave[start[0]][y]=1\n        else:\n            for x in range(min(start[0],end[0]),max(start[0],end[0])+1):\n                cave[x][start[1]]=1\n\n\nextentY[0]=0\n\n\nif DO_PART1:\n    #part 1\n    printCave(cave,extentX,extentY,[0,0])\n    settledCount=runSandFlow(cave)        \n    print(\"Part 1 answer:\",settledCount)\nelse:\n    #part 2\n    for x in range(numCols):\n        cave[x][extentY[1]+2]=1\n    extentY[1]=extentY[1]+2\n    extentX[0]=0\n    extentX[1]=numCols-1\n    printCave(cave,extentX,extentY,[0,0])\n    settledCount=runSandFlow(cave)\n    printCave(cave,extentX,extentY,[0,0])\n    print(\"Part 2 answer:\",settledCount)\n","repo_name":"stuwrigley/advent-of-code-2022","sub_path":"14.py","file_name":"14.py","file_ext":"py","file_size_in_byte":2813,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"3271668656","text":"#coding:utf-8\n\n\n# 2、小明爱跑步，爱吃东西。\n# 1）小明体重75.0公斤\n# 2）每次跑步会减肥0.5公斤\n# 3）每次吃东西体重会增加1公斤\n# 4）小美的体重是45.0公斤\n\n\n'''\n1.需要定义的类：人\n2.类的属性：名字，体重\n3.类的方法：跑步；吃东西\n4.跑步方法：体重减少0.5公斤\n5.吃东西：体重增加1公斤\n\n\n'''\n\n#定义\nclass Person:\n    def __init__(self,name,weight):\n        self.name = name\n        self.weight = weight\n\n    def __str__(self):\n        return ('%s的体重是%0.1f公斤'%(self.name,self.weight))\n\n    def run(self):\n        self.weight -= 0.5\n        print('%s跑步减肥0.5公斤'%(self.name))\n\n    def eat(self):\n        self.weight += 1\n        print('%s吃东西体重增加1公斤' % (self.name))\n\n#使用\np = Person('小明',75)\nprint(p)\np.run()\nprint(p)\n#\np2 = Person('小美',45)\nprint(p2)\np2.eat()\nprint(p2)\n","repo_name":"rainshine1190/VIPtest-base","sub_path":"日常作业/接口第一天/面向对象练习题2.py","file_name":"面向对象练习题2.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"zh","doc_type":"code","stars":17,"dataset":"github-code","pt":"76"}
+{"seq_id":"36353267278","text":"from flask import Flask, render_template, request, redirect, session\n\napp = Flask(__name__)\napp.secret_key = 'keep it secret, keep it safe' # set a secret key for security purposes\n\n@app.route('/', methods=['GET'])\ndef counter():\n    if 'count' not in session: session['count'] = 0    \n    else: session['count'] += 1\n\n    if 'visits' not in session: session['visits'] = 1\n    else: session['visits'] += 1\n    \n    return render_template(\"index.html\")\n\n@app.route('/add2', methods=['POST'])\ndef add2():\n\n    session['count'] += 1\n    print(session['count'])\n    # Never render a template on a POST request.\n    # Instead we will redirect to our index route.\n    return redirect('/')\n\n@app.route('/destroy_session', methods=['GET'])\ndef destroy_session():\n\n    session.clear()\n    return redirect('/')\n\n\n@app.route('/reset', methods=['POST'])\ndef reset():\n\n    session['count'] = 0\n    session['visits'] = 0\n    print(session['count'])\n    # Never render a template on a POST request.\n    # Instead we will redirect to our index route.\n    return redirect('/')\n\n@app.route('/increment_by', methods=['POST'])\ndef increment_by():\n    \n    session['count'] = session['count'] + int(request.form['increment']) - 1\n    \n    return redirect('/')\n\nif __name__ == (\"__main__\"):\n    app.run(debug=True )","repo_name":"LukeJech/Jech_Luke_Counter","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"761522201","text":"\"\"\"\nПрограмма, моделирующая последовательность\nантагонистических игр с заданной матрицей платежей\n\nАвтор: Афанасьев И.Е.\nДата написания: 20.09.2020\n\"\"\"\n\niterations = 10000 # Количество ходов\nstart_money = 10000 # Начальное количество денег у каждого игрока\nmatrix1 = [ [-4, 5], [8, -7] ] # Матрица платежей\n\n# Инвертируем матрицу (Платежи для второго игрока)\nq = len(matrix1)\nmatrix2 = []\nfor i in range(q):\n    matrix2.append([])\nfor i in matrix1:\n    for pos, j in enumerate(i):\n        matrix2[pos % q].append(- j)\n\n# Класс игрок с матрицей платежей, текущим количеством\n# денег, коэффициентами для принятия решения на основе\n# стратегии противника и последним ходом\nclass gamer:\n    def __init__(self, matrice, money):\n        self.m = matrice\n        self.money = money\n        self.koef = [0] * len(matrice[0])\n        self.last = 0\n    def strikeback(self, enemy_strat):\n        self.koef[enemy_strat] += 1\n        self.money += self.m[self.last][enemy_strat]\n        return self.money\n    # Вычисление оптимального хода на основе\n    # предыдущих ходов противника\n    def math(self):\n        M = []\n        for i in self.m:\n            process = 0\n            for j in range(len(self.koef)):\n                process += self.koef[j] * i[j]\n            M.append(process)\n        self.last = M.index(max(M))\n        return self.last\n\n# Импортируем графическую библиотеку\nimport matplotlib.pyplot as plt\n# Создаём два объекта класса \"игрок\"\nplayer1 = gamer(matrix1, start_money)\nplayer2 = gamer(matrix2, start_money)\n\n# Инициализируем фигуру, на которой будем рисовать\nfig = plt.figure()\nplt.ion()\ncolor = ['red', 'green', 'blue', 'brown']\ny = [] # Ось y\nx = [] # Ось x\nfor i in range(len(color)):\n    y.append([])\n    x.append([])\n\n# Собственно, игра\nfor i in range(iterations):\n    str1 = player1.math()\n    str2 = player2.math()\n    \n    y[str1].append(player1.strikeback(str2))\n    x[str1].append(i)\n    \n    y[str2 + 2].append(player2.strikeback(str1))\n    x[str2 + 2].append(i)\n\nfor i in range(len(x)):\n    plt.plot(x[i], y[i], c = color[i])\nplt.show() # Отображение графика\nprint('Длительность тестовой серии игры: ', iterations)\nprint(\n          'Стратегии первого игрока: %s\\nДеньги первого игрока: %d\\n' % \n          (player2.koef, player1.money)\n          )\nprint(\n          'Стратегии второго игрока: %s\\nДеньги второго игрока: %d' % \n          (player1.koef, player2.money)\n          )","repo_name":"Naughtyk/Python","sub_path":"project11 - theory of games/Лабы Ване/labrab.py","file_name":"labrab.py","file_ext":"py","file_size_in_byte":3058,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"8620515908","text":"from PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.uic import loadUi\n\nfrom App import excel_reader, database\nfrom App.logs import logger\nimport os\n\nBASE_DIR = os.getenv(\"BASE_DIR\")\n\n\nclass StudentsFrame(QFrame):\n    def __init__(self):\n        super().__init__()    \n        loadUi(os.path.join(BASE_DIR, \"Forms\", \"ogrenciler_frame.ui\"), self)\n        \n        self.ogrencilerList = database.get_all_students()\n\n        self.set_signs() #SIGNALS\n        self.set_ts() #TABLE SETTINGS\n\n    def set_signs(self):\n        \"\"\"\n        Sets the signals, buttons or etc. which has relationship between them.\n        \"\"\"\n        self.searchByCombo.currentIndexChanged.connect(self.change_search_by)\n        self.searchButton.pressed.connect(lambda: self.draw_table(searchBy = self.searchByCombo.currentText()))\n        self.importButton.clicked.connect(self.import_dialog)\n        self.addButton.clicked.connect(self.add_dialog)\n        self.editButton.clicked.connect(self.edit_dialog)\n        self.deleteButton.clicked.connect(lambda: self.remove_student(removeBy = True))\n        self.deleteAllButton.clicked.connect(lambda: self.remove_student(all = True))\n\n        self.table.itemSelectionChanged.connect(self.select_row)\n        self.table.selectionModel().selectionChanged.connect(self.on_selection_changed) #KUTUCUK SECILINCE UYARI OLUSTURMA\n\n        self.table.horizontalHeader().sectionClicked.connect(self.sort)\n        self.resetOrderButton.clicked.connect(self.draw_table)\n        \n    def sort(self, sectionIndex):\n        if sectionIndex == 0:\n            self.draw_table(order=True, sectionIndex=sectionIndex)\n\n        elif sectionIndex == 1:\n            self.draw_table(order=True, sectionIndex=sectionIndex)\n\n        elif sectionIndex == 2:\n            self.draw_table(order=True, sectionIndex=sectionIndex)\n\n        elif sectionIndex == 3:\n            self.draw_table(order=True, sectionIndex=sectionIndex)\n\n        elif sectionIndex == 4:\n            self.draw_table(order=True, sectionIndex=sectionIndex)\n            \n    def draw_table(self, searchBy = False, order = False, sectionIndex = 0):\n        BY_NO = \"Numaraya göre\"\n        BY_FULLNAME = \"Tam ada göre\"\n        BY_CLASS = \"Sınıfa göre\"\n\n        if order:\n            self.ogrencilerList = sorted(self.ogrencilerList, key=lambda ogrenci: ogrenci[sectionIndex])\n            self.set_table_items()\n            return\n        \n        searchContent = self.searchIn.text().strip().upper()\n        if searchContent == \"\":\n            self.ogrencilerList = database.get_all_students()\n\n        elif searchBy == BY_NO:\n            self.ogrencilerList = database.get_all_students(number = searchContent)\n\n        elif searchBy == BY_FULLNAME:\n            self.ogrencilerList = database.get_all_students(fullname = searchContent)\n\n        elif searchBy == BY_CLASS:\n            self.ogrencilerList = database.get_all_students(grade = searchContent)\n\n        self.set_table_items()\n\n    def set_table_items(self):\n        self.table.setRowCount(len(self.ogrencilerList))\n        for rowInd, student in enumerate(self.ogrencilerList):\n            for columnInd, data_raw in enumerate(student):\n                self.table.setItem(rowInd, columnInd, QTableWidgetItem(str(data_raw)))\n\n        self.table.show()\n\n    def change_search_by(self):\n        self.searchBy = self.searchByCombo.currentText()\n\n    def select_row(self):\n        items = self.table.selectedItems()\n        rowIndexes = set()\n        for item in items:\n            rowIndexes.add(item.row())\n\n        rowIndex = min(rowIndexes)\n        self.table.selectRow(rowIndex)        \n\n    def import_dialog(self):\n        dialog = QFileDialog(caption=\"Dosya seçiniz\", filter=\"(*.xls)\")\n        if dialog.exec_() == dialog.Accepted:\n            filePath = str(dialog.selectedFiles()[0])\n        else:\n            return\n    \n        ogrencilerList = excel_reader.get_workbook_content(filePath)\n        database.add_multiple_students(students = ogrencilerList)\n    \n        self.ogrencilerList = database.get_all_students()\n        self.draw_table()\n\n    def add_dialog(self):\n        dialog = EkleDuzenleDialog()\n        if dialog.toAdd:\n            database.add_one_student(dialog.student)\n\n        self.ogrencilerList = database.get_all_students()\n        self.draw_table()\n\n    def edit_dialog(self):\n        #ogrenci = get_ogrenci_by_satir_no()  -> Bu fonksiyon ogrenciler tarafindan tasarlanacaktir.\n        dialog = EkleDuzenleDialog(ogrenci = [2949, \"Yusuf\", \"Kiris\", \"9/A\"])\n        if dialog.toUpdate:\n            database.update_student(dialog.student)\n        \n        self.ogrencilerList = database.get_all_students()\n        self.draw_table()\n\n    def remove_student(self, removeBy = False, all = False):\n        if all:\n            onaydialog = OgrencilerSilmeOnayDialog()\n            if onaydialog.result:\n                database.remove_all_students()\n\n        elif removeBy:\n            rowIndexes = [item.row() for item in self.table.selectedItems()]\n            for rowIndex in rowIndexes:\n                lastIndex = rowIndex\n                if rowIndex != lastIndex:\n                    pass\n            # TÜM SEÇİLİ SATIRLARIN UZUNLUĞUNU KONTROL ET. EĞER 1 TANE SATIR SEÇİLİ İSE ONUN NUMARASINA BAK VE SİL\n            #print(self.table.itemAt(QPoint(0, self.table.selectedItems()[0].row())).text())\n            database.remove_one_student(number = self.table.itemAt(QPoint(0, self.table.selectedItems()[0].row())).text())\n\n        self.ogrencilerList = database.get_all_students()\n        self.draw_table()\n\n    def add_student(self):\n        no = int(self.noIn.text().strip())              #\n        name = self.nameIn.text().strip().upper()       #\n        surname = self.surnameIn.text().strip().upper() #\n        grade = self.gradeCombo.currentText()           #   INPUTS\n        classs = self.classCombo.currentText()          #\n        gc = grade + \"/\" + classs                       #\n        if \"Sınıf\" == grade or \"Şube\" == classs :\n            return\n            \n        student = [no, name, surname, gc]               #\n\n        database.add_one_student(student = student)     #   ADD TO DATABASE\n\n        self.draw_table()                               #   REDRAW THE TABLE\n        \n        [input.clear() for input in [self.noIn, self.nameIn, self.surnameIn]] #CLEAR THE LINE_EDITS\n        [combo.setCurrentIndex(0) for combo in [self.gradeCombo, self.classCombo]] #RESET THE COMBO_BOXES\n\n    def on_selection_changed(self, selected: QItemSelection, deselected: QItemSelection):\n        #Bu fonksiyon öğrenciler tarafından tasarlanacak.\n        pass\n    \n    def set_ts(self):\n        \"\"\"\n        Set the 'table settings'.\n        \"\"\"\n        self.table.setColumnCount(5)\n        columnHeaders = [\"Numara\", \"Ad\", \"Soyad\", \"Cinsiyet\", \"Sınıf\"]\n        self.table.setHorizontalHeaderLabels(columnHeaders)\n        self.table.horizontalHeader().setSectionResizeMode(QHeaderView.Stretch)\n        self.table.verticalHeader().setSectionResizeMode(QHeaderView.Fixed)\n        self.table.setEditTriggers(QTableWidget.NoEditTriggers)\n        self.draw_table()    \n   \nclass EkleDuzenleDialog(QDialog):\n    def __init__(self, ogrenci = False):\n        super().__init__()\n        loadUi(os.path.join(BASE_DIR, \"Forms\", \"ekle_duzenle_dialog.ui\"), self)\n        self.lineEditItems = [self.noIn, self.nameIn, self.surnameIn]\n        self.comboBoxItems = [self.gradeCombo, self.classCombo]\n        self.student = ogrenci\n        self.toUpdate = False\n        self.toAdd = False\n        \n        self.set_signals()\n        \n    def set_signals(self):\n        # Empty style sheets meant to make it normal\n        self.noIn.textChanged.connect(lambda: self.noIn.setStyleSheet(\"\"))\n        self.nameIn.textChanged.connect(lambda: self.nameIn.setStyleSheet(\"\"))\n        self.surnameIn.textChanged.connect(lambda: self.surnameIn.setStyleSheet(\"\"))\n        self.saveButton.clicked.connect(self.check)\n        self.exitButton.clicked.connect(self.close)\n\n        if not self.student:\n            self.setWindowTitle(\"Öğrenci ekle\")\n        else:\n            self.set_values()\n            self.setWindowTitle(\"Öğrenci bilgilerini düzenle\")\n\n        self.set_ws()\n    \n    def set_values(self):\n        [lineEdit.setText(str(data)) for lineEdit, data in zip(self.lineEditItems, self.student)]\n        grade, classs = self.student[-1].split(\"/\")\n        self.gradeCombo.setCurrentText(grade)\n        self.classCombo.setCurrentText(classs)\n\n    def check(self):\n        no = self.noIn.text().strip()\n        name = self.nameIn.text().strip().upper()\n        surname = self.surnameIn.text().strip().upper()\n        grade = self.gradeCombo.currentText() + \"/\" + self.classCombo.currentText()\n        sex = self.sexCombo.currentText()\n        student = [no, name, surname, sex, grade]\n        \n        if not no.isnumeric():\n            self.noIn.setStyleSheet(\"background-color: red;\")\n            return\n        elif not name.isalpha():\n            self.nameIn.setStyleSheet(\"background-color: red;\")\n            return\n        elif not surname.isalpha():\n            self.surnameIn.setStyleSheet(\"background-color: red;\")\n            return\n\n        if all(map(len, student)):\n            if not self.student:\n                database.add_one_student(student=student)\n            else:\n                database.update_student(student=student)\n            self.close()\n\n    def set_ws(self):\n        \"\"\"\n        Adjust the window settings\n        \"\"\"\n        self.exec_()\n\n\nclass OgrencilerSilmeOnayDialog(QDialog):\n    def __init__(self):\n        super().__init__()\n        loadUi(os.path.join(BASE_DIR, \"Forms\", \"ogrenciler_silme_onay_dialog.ui\"), self)\n        self.checkk()\n\n        self.okayButton.clicked.connect(self.closee)\n        self.checkBox.stateChanged.connect(self.checkk)\n        \n        self.result = False\n        self.set_ws()\n\n    def checkk(self):\n        if self.checkBox.isChecked():\n            self.okayButton.setEnabled(True)\n        else:\n            self.okayButton.setEnabled(False)\n\n    def closee(self):\n        self.result = True\n        self.close()\n\n    def set_ws(self):\n        \"\"\"\n        Adjust the window settings\n        \"\"\"\n        self.exec_()\n","repo_name":"yoseidonn/Kelebek","sub_path":"App/Frames/students_frame.py","file_name":"students_frame.py","file_ext":"py","file_size_in_byte":10287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"3254363966","text":"words = lambda t : list(map(t, input().split()))\n\nn = int(input())\na = words(int)\nb = words(int)\n\ndef getNext(i):\n    if i == n-1:\n        return 0\n    else:\n        return i+1\n\ndef getPrev(i):\n    if i == 0:\n        return n-1\n    else:\n        return i-1\n\nfrom collections import deque\nq = deque()\n\ndef verify(i):\n    if b[i] != a[i] and b[i] - (b[getNext(i)] + b[getPrev(i)]) >= a[i]:\n        return True\n    else:\n        return False\n\nfor i in range(len(b)):\n    if b[i] >= a[i] and verify(i):\n        q.append(i)\n\nans = 0\nsucceed = True\nwhile not len(q) == 0:\n    i = q.popleft()\n    ni = getNext(i)\n    pi = getPrev(i)\n    #print(i, b)\n    d = b[ni] + b[pi]\n    if b[i] % d == a[i] % d:\n        ans += b[i] // d - (a[i] // d)\n        b[i] = a[i]\n    else:\n        ans += b[i] // d\n        b[i] %= d\n    if b[i] < a[i]:\n        succeed = False\n        break\n    if verify(ni):\n        q.append(ni)\n    if verify(pi):\n        q.append(pi)\n\nfor i in range(len(b)):\n    if a[i] != b[i]:\n        succeed = False\n        break\n\nif succeed:\n    print(ans)\nelse:\n    print(-1)\n","repo_name":"ugnom/programming_competition_archive","sub_path":"agc/agc036/c.py","file_name":"c.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"29706990059","text":"n=input().split(\" \")\nlst=[]\nfor i in range(int(n[0])):\n    lst.append(input().split(\" \"))\n\nsets=int(n[0])*int(n[1])\n# di={\"0\":[],\"1\":[]}\n# for i in range(len(lst)):\n#     di[\"0\"].append([])\n#     di[\"1\"].append([])\n#     for j in range(len(lst[i])):\n#         di[lst[i][j]][-1].append(j)\n\nfor row in lst:\n    for i in range(len(row)):\n        for j in range(i+2,len(row),2):\n            if row[j]==row[i]:\n                sets+=1\n            else:\n                break\n\nfor i in range(len(lst)):\n    for j in range(len(lst[i])):\n        for k in range(i+2,len(lst),2):\n            if lst[i][j]==lst[k][j]:\n                print(\"here: \",(i,j),\" \",(k,j))\n                sets+=1\n            else:\n                break\nprint(sets)\n\n\n","repo_name":"Selamawit926/Competitive_Programming","sub_path":"CampQuestions/Rectangles.py","file_name":"Rectangles.py","file_ext":"py","file_size_in_byte":733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"73594337845","text":"#!/usr/bin/env pythOn\n# -*- coding: utf-8 -*-\n'''\nname: xss特殊字符/函数/标签 FUZZ检测\nreferer: unknown\nauthor: Lucifer\ndescriptiOn: FUZZ反射型跨站所需要的负载有无过滤情况。\n'''\nimport re\nimport sys\nimport requests\nimport warnings\nfrom termcolor import cprint\n\nclass xss_characterfuzz_check_BaseVerify:\n    def __init__(self, url):\n        self.url = url\n\n    def run(self):\n        key = \"FUZZING\"\n        headers = {\n            \"User-Agent\":\"Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_6_8; en-us) AppleWebKit/534.50 (KHTML, like Gecko) Version/5.1 Safari/534.50\"\n        }\n        start_md5 = \"c28c0db26d39331a\"\n        end_md5 = \"15b86f2d013b2618\"\n        character = [\",\", \".\", \"?\", \"<\", \">\", \"/\", \";\", \":\", \"'\", '\"', \"|\", \"\\\\\", \"[\", \"]\", \"{\", \"}\", \"=\", \"!\", \"@\", \"$\", \"%\", \"(\", \")\"\n        ]\n        drive_func = ['OnaBort ', 'OnacTivate ', 'OnafTerprint ', 'OnafTerupdate ', 'OnbeForeactivate ', \n            'OnbeForecopy ', 'OnbeForecut ', 'OnbeForedeactivate ', 'OnbeForeeditfocus ', 'OnbeForepaste ', \n            'OnbeForeprint ', 'OnbeForeunload ', 'OnbeForeupdate ', 'OnblUr ', 'OnboUnce ', 'OncEllchange ', \n            'OncHange ', 'OncLick ', 'OncOnTextmenu ', 'OncOnTrolselEct ', 'OnCopy ', 'OncUt ', 'OndaTaavailable ', \n            'OndatAsetchanged ', 'OndAtasetcomplete ', 'OndBlclick ', 'OndeacTivate ', 'OndrAg ', 'OndrAgend ', \n            'OndrAgenter ', 'OndrAgleave ', 'OndrAgover ', 'OndrAgstart ', 'OndRop ', 'OneRror ', 'OnerRorupdate ', \n            'OnfilTerchange ', 'OnfiNish ', 'OnfOcus ', 'OnfOcusin ', 'OnfOcusout ', 'OnhElp ', 'OnkeyDown ', 'OnkeyPress ', \n            'OnkeyUp ', 'OnlayoutcOmplete ', 'OnloAd ', 'OnlosecApture ', 'OnmoUsedown ', 'OnmoUseenter ', 'OnmoUseleave ', \n            'OnmoUsemove ', 'OnmoUseout ', 'OnmoUseover ', 'OnmoUseup ', 'OnmoUsewheel ', 'OnmOve ', 'OnmOveend ', \n            'OnmOvestart ', 'OnpAste ', 'OnpropertyChange ', 'OnreadyStatechange ', 'OnrEset ', 'OnrEsize ', \n            'OnrEsizeend ', 'OnreSizestart ', 'OnrOwenter ', 'OnrOwexit ', 'OnrowsDelete ', 'OnrowsiNserted ', 'OnScroll ', \n            'OnsElect ', 'OnsElectiOnchange ', 'OnsElectstart ', 'OnstArt ', 'OnsTop ', 'OnsuBmit ', 'OnuNload '            \n        ]\n        label_func = ['javAscRipt ', 'vbscRipt ', 'exprEssiOn ', 'appLet ', 'meTa ', 'xMl ', 'Blink ', 'lInk ', \n            'stYle ', 'scRipT ', 'eMbed ', 'oBject ', 'ifRame ', 'frAme ', 'fraMeset ', 'ilayEr ', 'lAyer ', \n            'bgsOund ', 'tiTle ', 'bAse ', 'iMg ', 'viDeo '\n        ]\n        window_func = ['alert ', 'confirm ', 'prompt']\n        rawurl = self.url.replace(\"FUZZING\", start_md5)\n        cprint(\">>执行xss测试..\", \"cyan\")\n        req = requests.get(rawurl, headers=headers, timeout=6, verify=False)\n        if start_md5 in req.text:\n            cprint(\"[+]输入参数带入回显,可能存在XSS漏洞..\", \"red\")\n\n            #执行character 过滤FUZZ\n            cprint(\">>执行特殊字符FUZZ判断..\", \"cyan\")\n            characterlist = list()\n            characterlist2 = list()\n            for char in character:\n                char = char.strip()\n                rawurl = self.url.replace(\"FUZZING\", start_md5 + char + end_md5) \n                res = requests.get(rawurl, headers=headers, timeout=6, verify=False)\n                if start_md5+char+end_md5 in str(res.text):\n                    characterlist.append(char+' ')\n                else:\n                    characterlist2.append(char+' ')\n            cprint(\"[+]未被过滤的特殊字符: \"+''.join(characterlist), \"green\")\n            cprint(\"[-]被过滤的特殊字符: \"+''.join(characterlist2), \"red\")\n\n            #执行事件驱动 过滤FUZZ\n\n            cprint(\">>执行事件驱动FUZZ判断..\", \"cyan\")\n            drive_funclist = list()\n            drive_funclist2 = list()\n            for drive in drive_func:\n                drive = drive.strip()\n                rawurl = self.url.replace(\"FUZZING\", start_md5 + drive + end_md5)\n                res = requests.get(rawurl, headers=headers, timeout=6, verify=False)\n                if start_md5+drive+end_md5 in str(res.text):\n                    drive_funclist.append(drive+' ')\n                else:\n                    drive_funclist2.append(drive+' ')\n            cprint(\"[+]未被过滤的事件驱动: \"+''.join(drive_funclist), \"green\")\n            cprint(\"[-]被过滤的事件驱动: \"+''.join(drive_funclist2), \"red\")\n            \n            #执行标签 过滤FUZZ\n            cprint(\">>执行标签FUZZ判断..\", \"cyan\")\n            labellist = list()\n            labellist2 = list()\n            for label in label_func:\n                label = label.strip()\n                rawurl = self.url.replace(\"FUZZING\", start_md5 + label + end_md5)\n                res = requests.get(rawurl, headers=headers, timeout=6, verify=False)\n                if start_md5+label+end_md5 in str(res.text):\n                    labellist.append(label+' ')\n                else:\n                    labellist2.append(label+' ')\n\n            cprint(\"[+]未被过滤的标签: \"+''.join(labellist), \"green\")\n            cprint(\"[-]被过滤的标签: \"+''.join(labellist2), \"red\")\n\n            #执行标签 过滤FUZZ\n            cprint(\">>执行弹窗函数FUZZ判断..\", \"cyan\")\n            windowlist = list()\n            windowlist2 = list()\n            for window in window_func:\n                window = window.strip()\n                rawurl = self.url.replace(\"FUZZING\", start_md5 + window + end_md5)\n                res = requests.get(rawurl, headers=headers, timeout=6, verify=False)\n                if start_md5+window+end_md5 in str(res.text):\n                    windowlist.append(window+' ')\n                else:\n                    windowlist2.append(window+' ')\n\n            cprint(\"[+]未被过滤的弹窗函数: \"+''.join(windowlist), \"green\")\n            cprint(\"[-]被过滤的弹窗函数: \"+''.join(windowlist2), \"red\")\n        else:\n            cprint(\"[-]不存在XSS\", \"green\")\n\nif __name__ == \"__main__\":\n    warnings.filterwarnings(\"ignore\")\n    if len(sys.argv) < 2:\n        cprint(\"usage: python3 xss_characterfuzz_check.py http://test.com/test.php?id=FUZZING\", \"cyan\")\n        cprint(\"[*]将需要测试XSS的参数替换为FUZZING即可\", \"cyan\")\n    else:\n        testVuln = xss_characterfuzz_check_BaseVerify(sys.argv[1])\n        testVuln.run()\n","repo_name":"Lucifer1993/AngelSword","sub_path":"scan/xss_characterfuzz_check.py","file_name":"xss_characterfuzz_check.py","file_ext":"py","file_size_in_byte":6374,"program_lang":"python","lang":"en","doc_type":"code","stars":1424,"dataset":"github-code","pt":"76"}
+{"seq_id":"9638971291","text":"\nimport os\nimport logging\nfrom importlib.machinery import SourceFileLoader\n\nlogger = logging.getLogger('rg')\n\nITEMS_PROTOTYPES_FILES = {\n\t1: 'sword',\n\t2: 'bow',\n\t10: 'axe',\n\t11: 'hammer',\n\t12: 'knife',\n\t13: 'mace',\n\t14: 'pike',\n\t15: 'spear',\n\t16: 'lash',\n\t17: 'blowgun',\n\t18: 'crossbow',\n\n\t20: 'staff',\n\n\t4: 'arrow',\n\t24: 'bolt',\n\t34: 'dart',\n\n\t3: 'quiver',\n\t6: 'backpack',\n\n\t5: 'worldmap',\n\n\t50: 'emptybutle',\n\t51: 'hppotion',\n\t52: 'mppotion',\n\n\t300: 'scrollfireshit',\n\n\t101: 'deadhuman',\n\t102: 'deadrat',\n\t103: 'deadelf',\n\t104: 'deaddwarf',\n\n\t201: 'meatrat'\n}\n\ndef load_item_prototype(id):\n\n\tif not id in ITEMS_PROTOTYPES_FILES:\n\t\tlogger.warn('Item #{0} not found'.format(id))\n\t\treturn None\n\n\tname = ITEMS_PROTOTYPES_FILES[id]\n\tpath = 'items/{0}.py'.format(name)\n\n\tif not os.path.exists(path):\n\t\tlogger.warn('Item #{0} ({1}) not found'.format(id, name))\n\t\treturn None\n\n\titem_loader = SourceFileLoader(name, path)\n\titem = item_loader.load_module()\n\titem.id = id\n\n\treturn check_item_prototype(item, name)\n\ndef check_item_prototype(item, name):\n\titem.code_name = name\n\n\trequired = ( 'name', )\n\n\tfor r in required:\n\t\tif not hasattr(item, r):\n\t\t\tlogger.warn('Item \"{0}\" has no attribute {1}!'.format(name, r))\n\t\t\treturn None\n\n\tdefaults = [\n\t\t( lambda *args: None, ( 'afterUsingAttackMethod', )),\n\t\t( lambda *args: 0, ( 'getDamage', )),\n\t\t( lambda *args: True, ( 'isCanUseAttackMethod', )),\n\t\t( lambda *args: False, ( 'makeAction', )),\n\t\t( lambda *args: (), ( 'getAttackMethodSkills', 'getButcheringResults' )),\n\t\t( (), ( 'actions', 'attackMethods' )),\n\t\t( False, ( 'isStackable', 'isContainer', 'isButcherable', 'isWeapon' ))\n\t]\n\n\tfor def_val, names in defaults:\n\t\tfor name in names:\n\t\t\tif not hasattr(item, name):\n\t\t\t\tsetattr(item, name, def_val)\n\n\treturn item\n\n\nABILITES_FILES = {\n\t1: 'punch',\n\t2: 'kick',\n\t3: 'bite',\n\t4: 'fireball',\n\t5: 'fireshit'\n}\n\ndef load_ability(id):\n\n\tif not id in ABILITES_FILES:\n\t\treturn None\n\n\tname = ABILITES_FILES[id]\n\tpath = 'abilities/{0}.py'.format(name)\n\n\tif not os.path.exists(path):\n\t\treturn None\n\n\tability_loader = SourceFileLoader(name, path)\n\tability = ability_loader.load_module()\n\tability.id = id\n\n\treturn check_ability(ability, name)\n\ndef check_ability(ability, name):\n\tability.code_name = name\n\n\trequired = ( 'name', )\n\n\tfor r in required:\n\t\tif not hasattr(ability, r):\n\t\t\tlogger.warn('Ability \"{0}\" has no attribute {1}!'.format(name, r))\n\t\t\treturn None\n\n\tdefaults = [\n\t\t( lambda *args: 0, ( 'getDamage', )),\n\t\t( lambda *args: None, ( 'afterAttack', )),\n\t\t( lambda *args: True, ( 'isCanAttack', )),\n\t\t( lambda *args: False, ( 'isNatural', )),\n\t\t( None, ( 'usingAttribute', )),\n\t\t( (), ( 'usingSkills', )),\n\t]\n\n\tfor def_val, names in defaults:\n\t\tfor name in names:\n\t\t\tif not hasattr(ability, name):\n\t\t\t\tsetattr(ability, name, def_val)\n\n\treturn ability\n\n\nTRADERS_FILES = {\n\t1: 'weapon',\n\t2: 'food',\n\t3: 'cloth',\n\t4: 'potion'\n}\n\ndef load_trader(id):\n\n\tif not id in TRADERS_FILES:\n\t\treturn None\n\n\tname = TRADERS_FILES[id]\n\tpath = 'traders/{0}.py'.format(name)\n\n\tif not os.path.exists(path):\n\t\treturn None\n\n\ttrader_loader = SourceFileLoader(name, path)\n\ttrader = trader_loader.load_module()\n\ttrader.id = id\n\n\treturn check_trader(trader, name)\n\ndef traderGetSellPrice(self, user, protoId):\n\tpricelist = self.getSellList(user)\n\tif protoId in pricelist:\n\t\treturn pricelist[protoId]\n\telse:\n\t\treturn None\n\ndef traderGetBuyPrice(self, user, protoId):\n\tpricelist = self.getBuyList(user)\n\tif protoId in pricelist:\n\t\treturn pricelist[protoId]\n\telse:\n\t\treturn None\n\ndef check_trader(trader, name):\n\ttrader.code_name = name\n\n\trequired = ( 'name', )\n\n\tfor r in required:\n\t\tif not hasattr(trader, r):\n\t\t\tlogger.warn('Trader \"{0}\" has no attribute {1}!'.format(name, r))\n\t\t\treturn None\n\n\tdefaults = [\n\t\t( lambda *args: (), ( 'getSellList', 'getBuyList' )),\n\t\t( lambda *args: True, ( 'isCanTrade', )),\n\t\t( lambda *args: 'Что вы хотите продать или купить?', ( 'getHelloMessage', ))\n\t]\n\n\ttrader.getSellPrice = lambda user, protoId: traderGetSellPrice(trader, user, protoId)\n\ttrader.getBuyPrice = lambda user, protoId: traderGetBuyPrice(trader, user, protoId)\n\n\tfor def_val, names in defaults:\n\t\tfor name in names:\n\t\t\tif not hasattr(trader, name):\n\t\t\t\tsetattr(trader, name, def_val)\n\n\treturn trader\n\n\n\nMOBS_PROTOTYPES_FILES = {\n\t1: 'human',\n\t2: 'rat',\n\t3: 'elf',\n\t4: 'dwarf'\n}\n\ndef load_mob_prototype(id):\n\n\tif not id in MOBS_PROTOTYPES_FILES:\n\t\treturn None\n\n\tname = MOBS_PROTOTYPES_FILES[id]\n\tpath = 'mobs/{0}.py'.format(name)\n\n\tif not os.path.exists(path):\n\t\treturn None\n\n\tmob_loader = SourceFileLoader(name, path)\n\tmob = mob_loader.load_module()\n\tmob.id = id\n\n\treturn check_mob(mob, name)\n\ndef check_mob(mob, name):\n\tmob.code_name = name\n\n\trequired = ( 'name', 'st', 'dx', 'iq', 'ht', 'hpMax', 'mpMax' )\n\n\tfor r in required:\n\t\tif not hasattr(mob, r):\n\t\t\tlogger.warn('Mob \"{0}\" has no attribute {1}!'.format(name, r))\n\t\t\treturn None\n\n\tdefaults = [\n\t\t( lambda *args: None, ( 'afterDead', )),\n\t\t( (), ( 'abilities', ))\n\t]\n\n\tfor def_val, names in defaults:\n\t\tfor name in names:\n\t\t\tif not hasattr(mob, name):\n\t\t\t\tsetattr(mob, name, def_val)\n\n\treturn mob","repo_name":"Tairesh/telegram-rpg","sub_path":"prototypesloader.py","file_name":"prototypesloader.py","file_ext":"py","file_size_in_byte":5097,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"70450057526","text":"from django.contrib import admin\nfrom django.db import models\nfrom textareacounter.widget import TextareaWithCounter\n\nclass TextAreaCounterAdminMixin(admin.ModelAdmin):\n    formfield_overrides = {\n        models.TextField: {'widget': TextareaWithCounter},\n    }\n    class Media:\n        css = {\n            \"all\": (\"textareacounter/textareacounter.css\", )\n        }\n        js = (\n            \"https://code.jquery.com/jquery-3.6.0.slim.min.js\",\n            \"textareacounter/textareacounter.js\",\n        )","repo_name":"DocTocToc/doctoctocbot","sub_path":"src/textareacounter/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"76"}
+{"seq_id":"8577026608","text":"\"\"\"empty message\n\nRevision ID: f2f900bc689d\nRevises: fdcd84cdec43\nCreate Date: 2018-03-04 17:01:50.180397\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import mysql\n\n# revision identifiers, used by Alembic.\nrevision = 'f2f900bc689d'\ndown_revision = 'fdcd84cdec43'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_constraint(u'students_ibfk_3', 'students', type_='foreignkey')\n    op.drop_column('students', 'parent')\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('students', sa.Column('parent', mysql.INTEGER(display_width=11), autoincrement=False, nullable=True))\n    op.create_foreign_key(u'students_ibfk_3', 'students', 'users', ['parent'], ['id'])\n    # ### end Alembic commands ###\n","repo_name":"anaf007/school","sub_path":"migrations/versions/f2f900bc689d_.py","file_name":"f2f900bc689d_.py","file_ext":"py","file_size_in_byte":895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"36437243559","text":"\"\"\"\n\"\"\"\nimport os\n\nimport cv2\nimport numpy as np\nimport torch\nfrom matplotlib import pyplot as plt\nfrom torch.utils.data import DataLoader\nfrom torchvision.transforms import transforms\n\nimport utils\nfrom utils import YoloDataset\nimport model\nimport torch.nn.functional as F\n\n\ndef mnist_predict(img_pah):\n    # mnist数据集一共是 10种类型\n    global pred_class_num, use_gpu\n    pred_class_num = MNIST_PRED_CLASS_NUM\n    if use_gpu:\n        if not torch.cuda.is_available():\n            use_gpu = False\n            print(\"Not support GPU for train！\")\n\n    # 是否加载已有模型\n    if load_model_path is not None:\n        net = model.AlexNet(load_model_path=load_model_path, pred_class_num=pred_class_num)\n    else:\n        raise\n    #\n    image = cv2.imread(img_pah)\n    # 设为单通道灰色\n    image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n\n    # 调整大小\n    img = cv2.resize(image, (image_size, image_size))\n\n    # 转格式\n    transform = transforms.Compose([\n        transforms.ToTensor()\n    ])\n\n    # 转tensor调整为(3*224*224)\n    img = transform(img)\n    img = img.expand(3, image_size, image_size)\n    img = img.unsqueeze(0)\n\n    ii = np.transpose(img[0], (1, 2, 0))\n    plt.imshow(ii)\n    plt.show()\n\n    pred = net(img)\n\n\n    pred = F.softmax(pred, dim=1)\n    _, index = pred.max(1)\n    if _[0].item() > 0.2:\n        print(\"Result is : [%s]\" % (str(index[0]),))\n    else:\n        print(\"No results\")\n\n\n\nif __name__ == '__main__':\n    root_path = utils.PROJECT_ROOT_PATH\n    # MNIST 种类\n    MNIST_PRED_CLASS_NUM = 10\n    # 训练模型的数据大小\n    image_size = 224\n    # 学习率可以设置为3、1、0.5、0.1、0.05、0.01、0.005,0.005、0.0001、0.00001\n    learning_rate = .1\n    # 每次训练的图片数量\n    batch_size = 128\n    # 保存间隔次数\n    per_batch_size_to_save = 30\n    # 已有模型\n    load_model_path = os.path.join(root_path, r'AlexNet\\output\\models\\alexnet_mnist.pth')\n    # 训练好的模型保存路径\n    # be_save_model_path = os.path.join(root_path, r'AlexNet\\output\\models\\alexnet_mnist.pth')\n    # 是否使用GPU\n    use_gpu = True\n    # 是预测阈值\n    threshold = 0.9\n    predict_image_path = None\n    # 1:测试MNIST\n    print(\"START MNIST TRAIN!\")\n    pred_class_num = MNIST_PRED_CLASS_NUM\n    mnist_predict(os.path.join(root_path, r'AlexNet\\img.png'))\n    print(\"END!\")\n","repo_name":"belzx/MachineLearning","sub_path":"AlexNet/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":2373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"70786096246","text":"import importlib\nimport sys\nimport torch\nfrom src.models.unet3d import unet3d\nfrom torchvision import transforms\n\n\nfrom src.dataset.train_val_split import train_val_split\nfrom src.losses.ce_dice_loss import CrossEntropyDiceLoss3D\n\nfrom src.losses import dice_loss, region_based_loss, new_losses\n\nfrom src.models.io_model import load_model\nfrom src.train.trainer import Trainer, TrainerArgs\nfrom torch.optim import lr_scheduler\nfrom torch.utils.data import DataLoader\nfrom torch.utils.tensorboard import SummaryWriter\nfrom src.config import BratsConfiguration\nfrom src.dataset.augmentations import color_augmentations, spatial_augmentations\n\nfrom src.dataset.utils import dataset, visualization as visualization\nfrom src.models.vnet import vnet, asymm_vnet\nfrom src.logging_conf import logger\nfrom src.dataset.loaders.brats_dataset import BratsDataset\n\n\ndef num_params(net_params):\n    n_params = sum([p.data.nelement() for p in net_params])\n    logger.info(f\"Number of params: {n_params}\")\n\n\n######## PARAMS\nlogger.info(\"Processing Parameters...\")\n\nconfig = BratsConfiguration(sys.argv[1])\nmodel_config = config.get_model_config()\ndataset_config = config.get_dataset_config()\nbasic_config = config.get_basic_config()\n\npatch_size = config.patch_size\ntensorboard_logdir = basic_config.get(\"tensorboard_logs\")\ncheckpoint_path = model_config.get(\"checkpoint\")\nbatch_size = dataset_config.getint(\"batch_size\")\nn_patches = dataset_config.getint(\"n_patches\")\nn_classes = dataset_config.getint(\"classes\")\nloss = model_config.get(\"loss\")\n\ndevice = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\nlogger.info(f\"Device: {device}\")\n\n\n######## DATASET\nlogger.info(\"Creating Dataset...\")\n\ndata, _ = dataset.read_brats(dataset_config.get(\"train_csv\"), lgg_only=dataset_config.getboolean(\"lgg_only\"))\ndata_train, data_val = train_val_split(data, val_size=0.2)\ndata_train = data_train * n_patches\ndata_val = data_val * n_patches\n\nn_modalities = dataset_config.getint(\"n_modalities\")  # like color channels\nsampling_method = importlib.import_module(dataset_config.get(\"sampling_method\"))\n\n\ntransform = transforms.Compose([color_augmentations.RandomIntensityShift(),\n                                color_augmentations.RandomIntensityScale(),\n                                spatial_augmentations.RandomMirrorFlip(p=0.5),\n                                spatial_augmentations.RandomRotation90(p=0.5)])\n\n\ncompute_patch = basic_config.getboolean(\"compute_patches\")\ntrain_dataset = BratsDataset(data_train, sampling_method, patch_size, compute_patch=compute_patch, transform=transform)\ntrain_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)\n\nval_dataset = BratsDataset(data_val, sampling_method, patch_size, compute_patch=compute_patch, transform=transform)\nval_loader = DataLoader(dataset=val_dataset, batch_size=batch_size, shuffle=True, num_workers=4)\n\nif basic_config.getboolean(\"plot\"):\n    data_batch, labels_batch = next(iter(train_loader))\n    data_batch.reshape(data_batch.shape[0] * data_batch.shape[1], data_batch.shape[2], data_batch.shape[3],\n                       data_batch.shape[4], data_batch.shape[5])\n    labels_batch.reshape(labels_batch.shape[0] * labels_batch.shape[1], labels_batch.shape[2], labels_batch.shape[3],\n                         labels_batch.shape[4])\n\n    print(data_batch.shape)\n    logger.info('Plotting images')\n    visualization.plot_batch_slice(data_batch, labels_batch, slice=30, save=True)\n\n\n######## MODEL\nlogger.info(\"Initiating Model...\")\n\nconfig_network = model_config[\"network\"]\nif config_network== \"vnet\":\n\n    network = vnet.VNet(elu=model_config.getboolean(\"use_elu\"),\n                        in_channels=n_modalities,\n                        classes=n_classes,\n                        init_features_maps=model_config.getint(\"init_features_maps\"))\n\nelif config_network == \"vnet_asymm\":\n    network = asymm_vnet.VNet(non_linearity=model_config.get(\"non_linearity\"), in_channels=n_modalities, classes=n_classes,\n                              init_features_maps=model_config.getint(\"init_features_maps\"), kernel_size=model_config.getint(\"kernel_size\"),\n                              padding=model_config.getint(\"padding\"))\n\nelif config_network == \"3dunet_residual\":\n\n    network = unet3d.ResidualUNet3D(in_channels=n_modalities, out_channels=n_classes, final_sigmoid=False,\n                                    f_maps=model_config.getint(\"init_features_maps\"), layer_order=\"crg\",\n                                    num_levels=4, num_groups=4,conv_padding=1)\n\nelif config_network == \"3dunet\":\n\n    network = unet3d.UNet3D(in_channels=n_modalities, out_channels=n_classes, final_sigmoid=False,\n                                    f_maps=model_config.getint(\"init_features_maps\"), layer_order=\"crg\",\n                                    num_levels=4, num_groups=4,conv_padding=1)\nelse:\n    raise ValueError(\"Bad parameter for network {}\".format(model_config.get(\"network\")))\n\nnum_params(network.parameters())\n\n\n##### TRAIN\nlogger.info(\"Start Training\")\nnetwork.to(device)\n\noptim = model_config.get(\"optimizer\")\n\nif optim == \"SGD\":\n    optimizer = torch.optim.SGD(network.parameters(), lr=model_config.getfloat(\"learning_rate\"),\n                                momentum=model_config.getfloat(\"momentum\"), weight_decay=model_config.getfloat(\"weight_decay\"))\nelif optim == \"ADAM\":\n    optimizer = torch.optim.Adam(network.parameters(), lr=model_config.getfloat(\"learning_rate\"), weight_decay=model_config.getfloat(\"weight_decay\"), amsgrad=False)\n\nelse:\n    raise ValueError(\"Bad optimizer. Current options: [SGD, ADAM]\")\n\nbest_loss = 1000\nif basic_config.getboolean(\"resume\"):\n    logger.info(\"Loading model from checkpoint..\")\n    model, optimizer, start_epoch, best_loss = load_model(network, checkpoint_path, device, optimizer, True)\n    logger.info(f\"Loaded model with starting epoch {start_epoch}\")\nelse:\n    start_epoch = 0\n\nwriter = SummaryWriter(tensorboard_logdir)\nscheduler = lr_scheduler.ReduceLROnPlateau(optimizer, 'min', factor=model_config.getfloat(\"lr_decay\"),\n                                           patience=model_config.getint(\"patience\"))\n\nif loss == \"dice\":\n    criterion = dice_loss.DiceLoss(classes=n_classes, eval_regions=model_config.getboolean(\"eval_regions\"),\n                                   sigmoid_normalization=True)\n\nelif loss == \"combined\":\n    # 0. back, 1: ncr, 2: ed, 3: et\n    ce_weigh = torch.tensor([0.1, 0.35, 0.2 , 0.35])\n    criterion = CrossEntropyDiceLoss3D(weight=ce_weigh, classes=n_classes,\n                                       eval_regions=model_config.getboolean(\"eval_regions\"), sigmoid_normalization=True)\nelif loss == \"both_dice\":\n    criterion = region_based_loss.RegionBasedDiceLoss3D(classes=n_classes, sigmoid_normalization=True)\n\nelif loss == \"gdl\":\n    criterion = new_losses.GeneralizedDiceLoss()\n\nelse:\n    raise ValueError(f\"Bad loss value {loss}. Expected ['dice', combined]\")\n\nargs = TrainerArgs(model_config.getint(\"n_epochs\"), device, model_config.get(\"model_path\"), loss)\ntrainer = Trainer(args, network, optimizer, criterion, start_epoch, train_loader, val_loader, scheduler, writer)\ntrainer.start(best_loss=best_loss)\n\n\nprint(\"Finished!\")\n","repo_name":"imatge-upc/mri-braintumor-segmentation","sub_path":"src/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":7206,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"76"}
+{"seq_id":"13112046499","text":"num = int(input(''))\n\nfor i in range(1, num + 1):\n    a, b = map(int,input().split())\n  \n    if b == 0:\n        print('divisao impossivel')\n      \n    if b != 0:\n        div = a / b\n        print('{:.1f}'.format(div))","repo_name":"EwertonWeb/UriOnlineJudge","sub_path":"1116_urionlinejudge.py","file_name":"1116_urionlinejudge.py","file_ext":"py","file_size_in_byte":217,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"5191543248","text":"from typing import List, Dict\nfrom PySpice.Spice.Netlist import Circuit\nfrom PySpice.Unit.SiUnits import Farad, Second\nfrom PySpice.Unit import *\nfrom PySpice.Logging import Logging\n\nfrom itertools import product\n\nfrom .util import *\nfrom .piece_wise_linear import *\nfrom .ngspice_simulation import simulate_circuit\nfrom lccommon.net_util import get_subcircuit_ports\n\nimport logging\n\nfrom scipy import interpolate\n\npyspice_logger = Logging.setup_logging()\nlogger = logging.getLogger(__name__)\n\n\ndef characterize_input_capacitances(cell_name: str,\n                                    input_pins: List[str],\n                                    active_pin: str,\n                                    output_pins: List[str],\n                                    supply_voltage: float,\n                                    trip_points: TripPoints,\n                                    timing_corner: CalcMode,\n\n                                    spice_netlist_file: str,\n                                    spice_include_files: List[str] = None,\n                                    time_resolution=50 @ u_ps,\n                                    temperature=27,\n                                    ):\n    logger.debug(\"characterize_input_capacitances()\")\n    # Find ports of the SPICE netlist.\n    ports = get_subcircuit_ports(spice_netlist_file, cell_name)\n    logger.info(\"Subcircuit ports: {}\".format(\", \".join(ports)))\n\n    # TODO: find correct names for GND/VDD from netlist.\n    ground = 'GND'\n    supply = 'VDD'\n\n    circuit = Circuit('Timing simulation of {}'.format(cell_name), ground=ground)\n\n    if spice_include_files is None:\n        spice_include_files = []\n    spice_include_files = spice_include_files + [spice_netlist_file]\n    \n    # Load include files.\n    logger.info(\"Load SPICE include files.\")\n    for inc in spice_include_files:\n        logger.info(\"Include '{}'\".format(inc))\n        circuit.include(inc)\n\n    # Instantiate circuit under test.\n    logger.info(\"Instantiate circuit under test.\")\n    circuit.X('circuit_unter_test', cell_name, *ports)\n\n    # Power supply.\n    logger.info(\"Instantiate power supply: {} V\".format(supply_voltage))\n    circuit.V('power_vdd', supply, circuit.gnd, supply_voltage @ u_V)\n\n    # Find function to summarize different timing arcs.\n    reduction_function = {\n        CalcMode.WORST: max,\n        CalcMode.BEST: min,\n        CalcMode.TYPICAL: np.mean\n    }[timing_corner]\n    logger.info(\"Reduction function for summarizing multiple timing arcs: {}\".format(reduction_function.__name__))\n\n    logger.info(\"Measuring input capactiance.\")\n    result = measure_input_capacitance(\n        circuit=circuit,\n        inputs_nets=input_pins,\n        active_pin=active_pin,\n        output_nets=output_pins,\n        vdd=supply_voltage,\n        trip_points=trip_points,\n        temperature=temperature,\n        output_load_capacitance=0 @ u_pF,\n        time_step=time_resolution,\n        simulation_duration_hint=1 @ u_ns,\n        reduction_function=reduction_function\n    )\n\n    logger.info(\"Characterizing input capacitances: Done\")\n\n    return result\n\n\ndef measure_input_capacitance(circuit: Circuit,\n                              inputs_nets: List[str],\n                              active_pin: str,\n                              output_nets: List[str],\n                              vdd: float,\n                              trip_points: TripPoints,\n                              temperature: float = 25,\n                              output_load_capacitance: Farad = 0.0 @ u_pF,\n                              time_step: Second = 100 @ u_ps,\n                              simulation_duration_hint: Second = 200 @ u_ns,\n                              reduction_function=max\n                              ) -> Dict[str, float]:\n    \"\"\" Measure the input capacitance of the `active_pin`.\n\n    :param circuit: Circuit to be characterized. (Without output load)\n    :param inputs_nets: Names of input signals.\n    :param active_pin: Name of the input signal to be toggled.\n    :param output_net: Name of the output signal.\n    :param vdd: Supply voltage.\n    :param trip_points: TripPoints object.\n    :param temperature:\n    :param output_load_capacitance:\n    :param time_step: Simulation time step.\n    :param simulation_duration_hint: A hint on how long to simulate the circuit.\n        This should be in the order of magnitude of propagation delays.\n        When chosen too short, the simulation time will be prolonged automatically.\n    :param reduction_function: Function used to create default timing arc from conditional timing arcs.\n        Should be one of {min, max, np.mean}\n    :return: A dict containing values of 'rise_capacitance' and 'fall_capacitance' in Farads.\n    \"\"\"\n    logger.debug(\"measure_input_capacitance()\")\n    # Create an independent copy of the circuit.\n    logger.debug(\"Create an independent copy of the circuit.\")\n    circuit = circuit.clone(title='Input capacitance measurement for pin \"{}\"'.format(active_pin))\n\n    if float(output_load_capacitance) > 0:\n        # Add output capacitances.\n        for output_net in output_nets:\n            circuit.C('load', circuit.gnd, output_net, output_load_capacitance)\n\n    static_input_nets = [i for i in inputs_nets if i != active_pin]\n\n    num_inputs = len(static_input_nets)\n    static_inputs = list(product(*([[0, 1]] * num_inputs)))\n\n    # TODO: set initial voltage at active_pin.\n\n    input_current = 10000 @ u_nA\n    logger.info(\"Input current: {}\".format(input_current))\n\n    time_step = 1 @ u_ps\n    # Guess of necessary simulation duration.\n    period = 1000 @ u_ps\n    logger.info(\"Guess of necessary simulation duration: {}\".format(period))\n    # Loop through all combinations of inputs.\n    capacitances_rising = []\n    capacitances_falling = []\n    for static_input in static_inputs:\n        for input_rising in [True, False]:\n            _circuit = circuit.clone()\n\n            # Get voltages at static inputs.\n            input_voltages = {net: vdd * value @ u_V for net, value in zip(static_input_nets, static_input)}\n            logger.debug(\"Static input voltages: {}\".format(input_voltages))\n\n            # Switch polarity of current for falling edges.\n            _input_current = input_current if input_rising else -input_current\n            # Create constant current source to drive the active pin.\n            _circuit.I('src_{}'.format(active_pin), circuit.gnd, active_pin, dc_value=_input_current)\n\n            # Get initial voltage of active pin.\n            initial_voltage = 0 @ u_V if input_rising else vdd @ u_V\n\n            # Run simulation\n            # Loop because it might be necessary to run a longer simulation.\n            logger.info(\"Run simulation.\")\n            while True:\n                analysis = simulate_circuit(_circuit, input_voltages, step_time=time_step,\n                                            end_time=period, temperature=temperature,\n                                            initial_voltages={active_pin: initial_voltage @ u_V}\n                                            )\n\n                time = np.array(analysis.time)\n                assert len(time) > 0\n                input_voltage = np.array(analysis[active_pin])\n                output_voltage = np.array(analysis[output_nets[0]])\n                logger.debug(\"Input voltage at start input_voltage[0]: {}\".format(input_voltage[0]))\n                logger.debug(\"Input voltage at end input_voltage[-1]: {}\".format(input_voltage[-1]))\n\n                if input_voltage[0] < 0.1 * vdd and input_voltage[-1] > vdd or \\\n                        input_voltage[0] > 0.9 * vdd and input_voltage[-1] < 0:\n                    # The input voltage spans the whole range from 0 to vdd.\n                    # So the simulation was long enough.\n                    break\n                else:\n                    # Simulation was not long enough, double it.\n                    period = period * 2\n                    logger.info(\"Simulation was not long enough. New simulation time: {}\".format(period))\n\n                if period>100000 @ u_ps:\n                    logger.info(\"VDD: {}\".format(vdd))\n                    logger.info(\"input_voltage[0]: {}\".format(input_voltage[0]))\n                    logger.info(\"input_voltage[-1]: {}\".format(input_voltage[-1]))\n                    logger.error(\"Error: Simulation took too long! It seems the input voltage did not span the whole range from 0 to vdd!\")\n                    break\n\n\n            # if input_rising:\n            #     input_condition = \"B = {}\".format(str(input_voltages['B']))\n            #     plt.plot(time*1e9, input_voltage, label='Input when {}'.format(input_condition))\n            #     plt.plot(time*1e9, output_voltage, label='Output when {}'.format(input_condition))\n            # plt.ylabel('input voltage [V]')\n            # plt.xlabel('time [ns]')\n            # plt.xlim(0, 0.3)\n            # plt.legend()\n            # plt.show()\n\n            # Calculate average derivative of voltage by finding the slope of the line\n            # through the crossing point of the voltage with the two thresholds.\n            #\n            # TODO: How to chose the thresholds?\n            if input_rising:\n                thresh1 = vdd * trip_points.slew_lower_threshold_rise\n                thresh2 = vdd * trip_points.slew_upper_threshold_rise\n                assert thresh1 < thresh2\n            else:\n                thresh1 = vdd * trip_points.slew_upper_threshold_fall\n                thresh2 = vdd * trip_points.slew_lower_threshold_fall\n                assert thresh1 > thresh2\n\n            # Find transition times for both thresholds.\n            transition_time1 = transition_time(input_voltage, time, threshold=thresh1, assert_one_crossing=True)\n            transition_time2 = transition_time(input_voltage, time, threshold=thresh2, assert_one_crossing=True)\n            assert transition_time2 > transition_time1\n\n            f_input_voltage = interpolate.interp1d(x=time, y=input_voltage)\n            dt = transition_time2 - transition_time1\n            dv = f_input_voltage(transition_time2) - f_input_voltage(transition_time1)\n            # dv = input_voltage[-1] - input_voltage[0]\n            # dt = time[-1] - time[0]\n\n            # Compute capacitance\n            capacitance = float(_input_current) / (float(dv) / float(dt))\n\n            logger.debug(\"dV: {}\".format(dv))\n            logger.debug(\"dt: {}\".format(dt))\n            logger.debug(\"I: {}\".format(input_current))\n            logger.info(\"Capacitance: {}\".format(capacitance))\n\n            if input_rising:\n                capacitances_rising.append(capacitance)\n            else:\n                capacitances_falling.append(capacitance)\n\n    # plt.ylabel('Input voltage [V]')\n    # plt.xlabel('Time [ns]')\n    # plt.ylim(0, vdd*1.1)\n    # plt.xlim(0, 0.25)\n    # plt.legend()\n    # plt.show()\n\n    # Find max, min or average depending on 'reduction_function'.\n    logger.debug(\"Convert capacitances of all timing arcs into the default capacitance ({})\".format(reduction_function.__name__))\n    final_capacitance_falling = reduction_function(capacitances_falling)\n    final_capacitance_rising = reduction_function(capacitances_rising)\n    final_capacitance = reduction_function([final_capacitance_falling, final_capacitance_rising])\n\n    return {\n        'rise_capacitance': final_capacitance_falling,\n        'fall_capacitance': final_capacitance_rising,\n        'capacitance': final_capacitance\n    }\n","repo_name":"1Kartikgupta/test_wafer_generator","sub_path":"librecell-lib/lclib/characterization/input_capacitance.py","file_name":"input_capacitance.py","file_ext":"py","file_size_in_byte":11473,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"29579825689","text":"from Environment import *\nfrom Minimax import *\n#load previous trained q_table\n# path = 'file location'\n# file = open(path,'rb')\n# q_table = pickle.load(file)\n# file.close()\n# or start with new q_table\n# q_table = dict()\n# keys = q_table.keys()\n\nepisode = 2001\nN = 6\n\"\"\"Training the agent\"\"\"\nimport random\nfrom IPython.display import clear_output\n# Hyperparameters\nalpha = 0.2\ngamma = 0.6\nepsilon = 0.1\n# For plotting metrics\nall_epochs = []\nall_penalties = []\nfor ii in range(1001, episode):\n    state = resetBoard()    \n    epochs, penalties, reward, = 0, 0, 0\n    done = False    \n    RLtile=1\n    computerTile=2    \n    if ii%2 == 0: # เลขคู่ computer move first        \n        if random.uniform(0, 1) <= 0.2:\n#             print('computer move minimax')\n            state,reward, done = agentminimax(state, computerTile)\n        else:\n#             print('computer move random')\n            action = random.choice(getValidMoves(state, computerTile))\n            state,reward, done = makeMoveRl(action, state, computerTile)\n    while not done:\n        valid = getValidMoves(state,RLtile)\n        validnum = [tmp[0]*N+tmp[1] for tmp in valid]\n        keys = q_table.keys()\n        tup_state = tuple(state.reshape(state.size,))\n        if tup_state not in keys: #ดูว่า มี state นี่อยู่แล้วหรือไม่\n            q_table[tup_state] = np.zeros((N*N,))\n            q_table[tup_state][validnum] = (1/(0.5*(N*N-4)))*np.ones((len(validnum),))\n#         print(q_table[tup_state])\n        if len(valid) != 0 and gameEnd(state) == False: # RL มีตำแหน่งลงได้\n            if random.uniform(0, 1) < epsilon:\n#                 print('RL move random')\n                action = random.choice(valid) # Explore action space\n            else:\n#                 print('RL move q_table')\n                tmp = validnum[np.argmax(q_table[tup_state][validnum])]\n                action = numToList(tmp) \n            next_state, reward, done = makeMoveRl(action, state, RLtile)\n        else: # เพิ่มจาก version 20210621 ถ้า RL ลงไม่ได้ต้องให้ next_state = state เลย \n            next_state = state # แต่ของเดิม ก่อนมาถึงจุดนี้ ให้ state = next_stae ตอนจบ loop อยู่แล้วน่าจะไม่ผิด\n\n        if len(getValidMoves(next_state,computerTile)) != 0 and gameEnd(state) == False:\n            if random.uniform(0, 1) <= 0.5:\n#                 print('computer move minimax')\n                next_state,reward, done = agentminimax(next_state, computerTile)\n            else:\n#                 print('computer move random')\n                agent_act = random.choice(getValidMoves(next_state, computerTile))\n                next_state,reward, done = makeMoveRl(agent_act, next_state, computerTile)\n# ถ้า RL ลงไม่ได้ \"action\" จะเป็นขอตาก่อนหน้า เพราะ ตานี้ไม่มี action เราต้องไม่ใส่คะแนนเข้าไปใน q_table\n# edit from version 20210621\n        if len(valid) != 0:\n            valid = getValidMoves(next_state,RLtile)\n            validnum = [tmp[0]*N+tmp[1] for tmp in valid]\n            keys = q_table.keys()\n            tup_nextstate = tuple(next_state.reshape(next_state.size,))\n            if tup_nextstate not in keys:\n                q_table[tup_nextstate] = np.zeros((N*N,))\n                q_table[tup_nextstate][validnum] = (1/(0.5*(N*N-4)))*np.ones((len(validnum),))\n            old_value = q_table[tup_state][action[0]*4+action[1]]\n            next_max = np.max(q_table[tup_nextstate])        \n            new_value = (1 - alpha) * old_value + alpha * (reward + gamma * next_max)\n            q_table[tup_state][action[0]*4+action[1]] = new_value\n        else: # สร้าง q_table ของ next_state\n            valid = getValidMoves(next_state,RLtile)\n            validnum = [tmp[0]*N+tmp[1] for tmp in valid]\n            keys = q_table.keys()\n            tup_nextstate = tuple(next_state.reshape(next_state.size,))\n            if tup_nextstate not in keys:\n                q_table[tup_nextstate] = np.zeros((N*N,))\n                q_table[tup_nextstate][validnum] = (1/(0.5*(N*N-4)))*np.ones((len(validnum),))\n        state = next_state\n","repo_name":"githubakira/AI_Builders","sub_path":"RL_minimax_training.py","file_name":"RL_minimax_training.py","file_ext":"py","file_size_in_byte":4405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"38203201655","text":"__author__ = \"chenty\"\n\nimport dateutil.parser\nimport random\n\nfrom rpc.judicator_rpc import Judicator\nfrom rpc.judicator_rpc.ttypes import *\nfrom thrift.transport import TSocket, TTransport\nfrom thrift.protocol import TBinaryProtocol\n\n\ndef extract(task, brief=False, compile=True, execute=True, result=True):\n    \"\"\"\n    Extract dictionary from rpc Task structure (class)\n    :param task: The original structure\n    :param brief: If the structure is a brief one (TaskBrief)\n    :param compile: If compile information needs to be extracted\n    :param execute: Same to compile, but execute information\n    :param result: Same to compile, but result information\n    :return: Extracted dictionary\n    \"\"\"\n    # Fundamental fields\n    # This is all TaskBrief structure contains\n    # Time string is parsed into datetime object\n    res = {\n        \"id\": task.id,\n        \"user\": task.user,\n        \"add_time\": dateutil.parser.parse(task.add_time) if task.add_time else None,\n        \"done\": task.done,\n        \"status\": task.status,\n        \"executor\": task.executor,\n        \"report_time\": dateutil.parser.parse(task.report_time) if task.report_time else None\n    }\n    if brief:\n        return res\n\n    # Extract compile information\n    if compile and task.compile:\n        res[\"compile\"] = {\n            \"source\": task.compile.source,\n            \"command\": task.compile.command,\n            \"timeout\": task.compile.timeout\n        }\n    else:\n        res[\"compile\"] = None\n\n    # Execute information\n    if execute and task.execute:\n        res[\"execute\"] = {\n            \"input\": task.execute.input,\n            \"data\": task.execute.data,\n            \"command\": task.execute.command,\n            \"timeout\": task.execute.timeout,\n            \"standard\": task.execute.standard\n        }\n    else:\n        res[\"execute\"] = None\n\n    # Result information\n    if result and task.result:\n        res[\"result\"] = {\n            \"compile_output\": task.result.compile_output,\n            \"compile_error\": task.result.compile_error,\n            \"execute_output\": task.result.execute_output,\n            \"execute_error\": task.result.execute_error,\n        }\n    else:\n        res[\"result\"] = None\n\n    return res\n\ndef generate(task, brief=False, compile=True, execute=True, result=True):\n    \"\"\"\n    Generate rpc Task structure (class) from a given dictionary\n    :param task: The dictionary\n    :param brief: If a TaskBrief structure should be generated\n    :param compile: If compile information needs to be included\n    :param execute: Same to compile, but execute information\n    :param result: Same to compile, but result information\n    :return: Generated Task structure\n    \"\"\"\n    # The add_time must be converted into a string, if necessary\n    if task[\"add_time\"] is None or isinstance(task[\"add_time\"], str):\n        add_time = task[\"add_time\"]\n    else:\n        add_time = task[\"add_time\"].isoformat()\n\n    # Same to the report_time\n    if task[\"report_time\"] is None or isinstance(task[\"report_time\"], str):\n        report_time = task[\"report_time\"]\n    else:\n        report_time = task[\"report_time\"].isoformat()\n\n    # If BriefTask should be generated, just return fundamental fields\n    if brief:\n        return TaskBrief(\n            task[\"id\"],\n            task[\"user\"],\n            add_time,\n            task[\"done\"],\n            task[\"status\"],\n            task[\"executor\"],\n            report_time\n        )\n\n    # Generate compile information\n    if compile and task[\"compile\"]:\n        c = Compile(\n            task[\"compile\"][\"source\"],\n            task[\"compile\"][\"command\"],\n            task[\"compile\"][\"timeout\"]\n        )\n    else:\n        c = None\n\n    # Execute information\n    if execute and task[\"execute\"]:\n        e = Execute(\n            task[\"execute\"][\"input\"],\n            task[\"execute\"][\"data\"],\n            task[\"execute\"][\"command\"],\n            task[\"execute\"][\"timeout\"],\n            task[\"execute\"][\"standard\"]\n        )\n    else:\n        e = None\n\n    # Result information\n    if result and task[\"result\"]:\n        r = Result(\n            task[\"result\"][\"compile_output\"],\n            task[\"result\"][\"compile_error\"],\n            task[\"result\"][\"execute_output\"],\n            task[\"result\"][\"execute_error\"]\n        )\n    else:\n        r = None\n\n    return Task(\n            task[\"id\"],\n            task[\"user\"],\n            c,\n            e,\n            add_time,\n            task[\"done\"],\n            task[\"status\"],\n            task[\"executor\"],\n            report_time,\n            r\n        )\n\ndef select_from_etcd_and_call(func, local_etcd, judicator_path, logger, *args, **kwargs):\n    \"\"\"\n    Select a judicator from etcd and call rpc process.\n    :param func: Name of the function\n    :param local_etcd: Etcd proxy\n    :param judicator_path: Path to judicator services on etcd\n    :param logger: Logger object.\n    :return: Return of the rpc call.\n    \"\"\"\n    # Get all judicator rpc address and choose one randomly\n    judicator = local_etcd.get(judicator_path)\n    logger.info(\"Got judicator list %s.\" % str(judicator))\n    if not judicator:\n        raise Exception(\"No judicator rpc service detected.\")\n    name, address = random.choice(tuple(judicator.items()))\n    host, port = address.split(\":\")\n    logger.debug(\"Making %s call to judicator %s at %s\" % (func, name, address))\n\n    # Start rpc transport\n    transport = TTransport.TBufferedTransport(TSocket.TSocket(host, int(port)))\n    client = Judicator.Client(TBinaryProtocol.TBinaryProtocol(transport))\n    transport.open()\n    # Call and return\n    res = client.__getattribute__(func)(*args, **kwargs)\n    transport.close()\n\n    return res\n","repo_name":"sbofgayschool/KV2","sub_path":"utility/rpc.py","file_name":"rpc.py","file_ext":"py","file_size_in_byte":5643,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"36905933020","text":"import pytest\nimport pandas as pd\nimport numpy as np\n\nfrom mercury.dataschema.calculator import StatCalculatorFactory, PandasStatCalculator\nfrom mercury.dataschema.feature import Feature\n\n\n@pytest.fixture(scope='module')\ndef pandas_df():\n    data = [['tom', 10], ['nick', 15], ['juli', 14]]\n    return pd.DataFrame(data, columns=['Name', 'Age'])\n\n\ndef test_calculator_factory(pandas_df):\n    assert isinstance(StatCalculatorFactory.build_calculator(pandas_df), PandasStatCalculator)\n\n\ndef test_calculator(pandas_df):\n    calculator = StatCalculatorFactory.build_calculator(pandas_df)\n\n    feature = Feature()\n\n    calculator.min(pandas_df['Age'], feature)\n    calculator.max(pandas_df['Age'], feature)\n    calculator.std(pandas_df['Age'], feature)\n    calculator.mean(pandas_df['Age'], feature)\n\n    assert feature.stats['min'] == 10\n    assert feature.stats['max'] == 15\n    assert feature.stats['mean'] == 13\n\n\ndef test_set_config(pandas_df):\n    calculator = StatCalculatorFactory.build_calculator(pandas_df)\n    with pytest.raises(ValueError):\n        calculator.set_config(**{'nonexistingattr': 10})\n\n    # assert it assigns the property well\n    calculator.set_config(**{'distribution_bins_method': 10})\n    assert calculator.distribution_bins_method == 10\n\n    # Assert does nothing with None\n    calculator.set_config()\n","repo_name":"BBVA/mercury-dataschema","sub_path":"tests/dataschema/test_calculator.py","file_name":"test_calculator.py","file_ext":"py","file_size_in_byte":1328,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"76"}
+{"seq_id":"28469969422","text":"from fastapi import APIRouter ,status , File, UploadFile\nfrom pydantic import BaseModel, Field\nfrom fastapi.responses import JSONResponse\nfrom app.service import departments\nfrom typing import Union\nimport json\nfrom app.models import departments as department_model\n\nrouter = APIRouter()\ndepartment_service = departments.DepartmentsService()\n\n@router.post(\"/\")\nasync def CreateDepartment(department: department_model.CreateDepartmentForm):\n    return await department_service.CreateDepartment(department)\n\n@router.get(\"/{department_id}\")\nasync def GetOneDepartment(department_id: int):\n    return await department_service.GetOneDepartment(department_id)\n\n@router.get(\"/\")\nasync def GetAllDepartmentsPaginated(page_number: int, page_size: int):\n    return await department_service.GetAllDepartmentsPaginated(page_number, page_size)\n\n@router.put(\"/{department_id}\")\nasync def UpdateDepartment(department_id: int, department: department_model.CreateDepartmentForm):\n    return await department_service.UpdateDepartment(department_id, department)\n\n@router.delete(\"/{department_id}\")\nasync def DeleteDepartment(department_id: int):\n    return await department_service.DeleteDepartment(department_id)\n    ","repo_name":"Perrrr/expense_manage_system","sub_path":"expense-app-gateway/server/app/handler/departments.py","file_name":"departments.py","file_ext":"py","file_size_in_byte":1199,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"42170147486","text":"from collections import deque\n\nH, W, K = map(int, input().split())\nx1, y1, x2, y2 = map(int, input().split())\nC = [[None] * (W + 2) for _ in range(H + 2)]\n\nfor w in range(W + 2):\n  C[0][w] = C[-1][w] = -1\nfor h in range(H + 2):\n  C[h][0] = C[h][-1] = -1\nfor h in range(1, H + 1):\n  S = input()\n  for w, c in enumerate(S, 1):\n    if c == '@': C[h][w] = -1\n\nqueue = deque([\n    (x1 + 1, y1, 1, +1, 0),\n    (x1 - 1, y1, 1, -1, 0),\n    (x1, y1 + 1, 1, 0, +1),\n    (x1, y1 - 1, 1, 0, -1),\n])\n\n\ndef printC():\n  for row in C:\n    R = ['.' if c is None else '@' if c == -1 else str(c) for c in row]\n    print(''.join(R))\n\n\nC[x1][y1] = 0\n\nwhile queue:\n  x0, y0, d, dx, dy = queue.popleft()\n  dxL, dyL = -dy, dx\n  dxR, dyR = dy, -dx\n\n  for k in range(K):\n    x = x0 + dx * k\n    y = y0 + dy * k\n    if C[x][y] is not None:\n      if C[x][y] < d:\n        k = -1\n        break\n      continue\n\n    C[x][y] = d\n\n    queue.append((x + dxL, y + dyL, d + 1, dxL, dyL))\n    queue.append((x + dxR, y + dyR, d + 1, dxR, dyR))\n\n  if k == K - 1:\n    queue.append((x + dx, y + dy, d + 1, dx, dy))\n\nans = C[x2][y2]\nprint(ans if ans is not None else -1)\n","repo_name":"ymtz13/CompetitiveProgramming","sub_path":"AtCoder/ABC170/F3.py","file_name":"F3.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"19149372210","text":"source = open('lyrics.txt', 'r')\nresult = open('processed.txt', 'w')\n\nlyricString = \"\"\n\nlines = source.readlines()\nlines = filter(lambda x: not x.isspace(), lines)\n\n\nfor line in lines:\n    #table = str.maketrans(dict.fromkeys(\"()\"))\n    #line.translate(table)\n    line = ''.join(x for x in line if x not in '()')\n    line = line.translate({ord(i): None for i in ','})\n    line = line.translate({ord(i): None for i in '\\n'})\n    line = line + \" \"\n\n    line = line.lower()\n\n    lyricString = lyricString + line\n\nwords = lyricString.split()\n\nfor word in words:\n    result.write('\"')\n    result.write(word)\n    result.write('\", ')\n\nsource.close()\nresult.close()\n\n","repo_name":"eromland/lyrics","sub_path":"prepareLyrics.py","file_name":"prepareLyrics.py","file_ext":"py","file_size_in_byte":659,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"37492032908","text":"import os\nimport xlrd\nimport jieba\njieba.set_dictionary(\"./dict.txt\")\njieba.initialize()\nfrom textrank4zh import TextRank4Keyword\n\n\n\nfor f in os.listdir(\"./\"):\n\tif os.path.splitext(f)[1] == '.xlsx':\n\t\twb = xlrd.open_workbook(f)\n\t\tsheet = wb.sheet_by_index(0)\n\t\tcols = sheet.col_values(sheet.ncols-7)\n\t\ttr4w = TextRank4Keyword()\n\t\ttr4w.analyze(text=\" \".join(cols))\n\t\twith open('./result.txt','w+') as f0: \n\t\t\tfor item in tr4w.get_keywords(20, word_min_len=2):\n\t\t\t\tprint(item,file=f0)","repo_name":"hanzhsun/rank","sub_path":"rank.py","file_name":"rank.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"13243385820","text":"def convert_to_copper(price):\n    # Parse price string to extract value and currency type\n    value, currency = price.split(' ')\n    value = int(value)\n\n    # Conversion rates\n    conversion_rates = {\n        'cp': 1,\n        'sp': 10,\n        'ep': 50,\n        'gp': 100,\n        'pp': 1000\n    }\n    return value * conversion_rates[currency]\n\ndef convert_to_highest_currency(copper_value):\n    # Conversion rates\n    conversion_rates = {\n        'cp': 1,\n        'sp': 10,\n        'gp': 100,\n        'pp': 1000  # 1 pp is 10 gp\n    }\n    \n    # Precision rates\n    precision_rates = {\n        'cp': 1,\n        'sp': 1,\n        'gp': 1,\n        'pp': 2\n    }\n\n    # Start from highest currency and go down\n    for currency, rate in reversed(conversion_rates.items()):\n        # Check if copper_value can be converted into current currency\n        if copper_value >= rate:\n            # Check if copper_value is at least 50 gp for pp conversion\n            if currency == 'pp' and copper_value < 5000:  # 5000 cp is 50 gp\n                continue\n            \n            precision = precision_rates[currency]\n            value = round(copper_value / rate, precision)  # Divide by rate and round to appropriate decimal place\n\n            # Check if the value is an integer and doesn't have a decimal part\n            if value.is_integer():\n                value = int(value)  # Convert to integer if it's a whole number\n\n            return value, currency\n\n    # If no match found, return in copper pieces\n    return round(copper_value, 1), 'cp'","repo_name":"BasUitermark/RandomShopData","sub_path":"CLI/app/model/currency_conversions.py","file_name":"currency_conversions.py","file_ext":"py","file_size_in_byte":1545,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"6734417027","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Feb 16 09:22:35 2020\r\n\r\n@author: MaryamHashemi\r\n\"\"\"\r\n\r\nimport numpy as np\r\n#import pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport cv2\r\nimport math\r\n#import tensorflow as tf\r\n#from tensorflow.python.client import device_lib \r\n#from keras.preprocessing import image\r\nfrom keras.utils import np_utils\r\nfrom skimage.transform import resize\r\nimport glob\r\nfrom keras.models import Sequential\r\nfrom keras.applications.vgg16 import VGG16\r\nfrom keras.layers import Dense, InputLayer, Dropout, Activation\r\nfrom keras.utils import plot_model\r\nfrom sklearn.metrics import classification_report\r\nfrom keras.layers.convolutional import Convolution2D, MaxPooling2D, MaxPooling1D, Convolution1D\r\nfrom keras.applications.vgg16 import preprocess_input\r\nfrom keras import losses\r\n#oc curve and auc\r\nfrom sklearn.datasets import make_classification\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.metrics import roc_curve\r\nfrom sklearn.metrics import roc_auc_score\r\nfrom keras.models import model_from_json\r\nfrom sklearn.metrics import average_precision_score\r\n\r\nfrom keras.layers.core import Dense, Dropout, Activation, Flatten\r\nfrom keras.utils import np_utils\r\nfrom keras.optimizers import SGD, Adadelta, Adagrad\r\nfrom keras.layers.recurrent import LSTM\r\n\r\n\r\nvideos_path1=\"E:/data/YawDD/YawDD dataset/Mirror-Table1/Female-Normal/\"\r\nvideos_path2=\"E:/data/YawDD/YawDD dataset/Mirror-Table1/Female-Yawing/\"\r\nvideos_path3=\"E:/data/YawDD/YawDD dataset/Mirror-Table1/Talking- Female/\"\r\nvideos_path4=\"E:/data/YawDD/YawDD dataset/Mirror-Table1/Male-Normal/\"\r\nvideos_path5=\"E:/data/YawDD/YawDD dataset/Mirror-Table1/Male-Yawing/\"\r\nvideos_path6=\"E:/data/YawDD/YawDD dataset/Mirror-Table1/Talking-male/\"\r\n\r\nvideo1=glob.glob(videos_path1+\"*.bmp\")\r\nvideo2=glob.glob(videos_path2+\"*.bmp\")\r\nvideo3=glob.glob(videos_path3+\"*.bmp\")\r\nvideo4=glob.glob(videos_path4+\"*.bmp\")\r\nvideo5=glob.glob(videos_path5+\"*.bmp\")\r\nvideo6=glob.glob(videos_path6+\"*.bmp\")\r\n\r\n\r\nvideos=[video1,video2,video3,video4,video5,video6]\r\n\r\n\r\nlabelname=[]\r\nlabelclass=[]\r\nindex=0\r\ncountimg=0\r\nvideocount=0\r\nfor i in videos:\r\n    index+=1\r\n    if index%3==0:\r\n        countlabel=1\r\n    elif index%3==1:\r\n        countlabel=2\r\n    else:\r\n        countlabel=0\r\n    for j in i:\r\n        videocount+=1\r\n        countimg+=1\r\n        frame=cv2.imread(j)\r\n        if len(labelclass)==1450:\r\n            cv2.imshow('',frame)\r\n            print(countlabel)\r\n            cv2.waitKey(0)\r\n            cv2.destroyAllWindows()\r\n#        frame= cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\r\n        labelname.append(frame)\r\n        labelclass.append(countlabel)\r\n        \r\nlabelname=np.array(labelname)\r\n\r\n\r\n\r\n\r\n\r\n\r\ny_train, y_test,X_train, X_test  = train_test_split(labelclass, labelname, test_size=0.3, shuffle=True)\r\n#y_train, y_test,X_train, X_test  = train_test_split(labelclass, labelname, test_size=0.4, random_state=42)\r\nX_valid, X_test, y_valid, y_test = train_test_split(X_test, y_test, test_size=0.5, shuffle=True)            \r\n            \r\n                      \r\n    \r\nX_valid = np.array(X_valid)    # converting list to array\r\nX_train=np.array(X_train)\r\nX_test=np.array(X_test)\r\n\r\ndummy_y_train = np_utils.to_categorical(y_train)    # one hot encoding Classes\r\ndummy_y_valid = np_utils.to_categorical(y_valid) \r\ndummy_y_test = np_utils.to_categorical(y_test)   # one hot encoding Classes\r\n\r\n        \r\nimages_train = []\r\nfor i in range(0,X_train.shape[0]):\r\n    a = resize(X_train[i], preserve_range=True, output_shape=(64,64,3)).astype(int)      # reshaping to 224*224*3\r\n    images_train.append(a)\r\nX_train = np.array(images_train)\r\n\r\nimages_valid = []\r\nfor i in range(0,X_valid.shape[0]):\r\n    a = resize(X_valid[i], preserve_range=True, output_shape=(64,64,3)).astype(int)      # reshaping to 224*224*3\r\n    images_valid.append(a)\r\nX_valid = np.array(images_valid)       \r\n\r\nimages_test = []\r\nfor i in range(0,X_test.shape[0]):\r\n    a = resize(X_test[i], preserve_range=True, output_shape=(64,64,3)).astype(int)      # reshaping to 224*224*3\r\n    images_test.append(a)\r\nX_test= np.array(images_test) \r\n\r\n\r\nX_train = preprocess_input(X_train, mode='tf')      # preprocessing the input data\r\nX_valid = preprocess_input(X_valid, mode='tf')\r\nX_test = preprocess_input(X_test, mode='tf')      # preprocessing the input data\r\n\r\n\r\n\r\nmodel = Sequential()\r\nmodel.add(Convolution2D(32, (3, 3), padding='same',\r\n                        input_shape=(64,64,3)))\r\nmodel.add(Activation('relu'))\r\nmodel.add(Convolution2D(64, (3, 3), data_format='channels_first'),)\r\nmodel.add(Activation('relu'))\r\n#model.add(Convolution2D(128, (3, 3), data_format='channels_first'),)\r\n#model.add(Activation('relu'))\r\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\r\nmodel.add(Dropout(0.25))\r\n\r\nmodel.add(Flatten())\r\nmodel.add(Dense(512))\r\nmodel.add(Activation('relu'))\r\nmodel.add(Dropout(0.5))\r\nmodel.add(Dense(3))\r\nmodel.add(Activation('softmax'))\r\nmodel.summary()\r\n\r\nsgd = SGD(lr=0.005,decay=1e-6, momentum=0.9, nesterov=True)\r\nmodel.compile(loss='categorical_crossentropy', optimizer=sgd , metrics=['accuracy'])\r\n#model.compile(loss='mean_squared_error', optimizer=sgd , metrics=['accuracy'])\r\n\r\n\r\n\r\nhistory=model.fit(X_train, dummy_y_train, batch_size=32,epochs=30, validation_data=(X_valid, dummy_y_valid))\r\n\r\nscoretest = model.evaluate(X_test, dummy_y_test,  verbose=1)\r\nprint('Test score:', scoretest[0])\r\nprint('Test accuracy:', scoretest[1])\r\nscorevalid = model.evaluate(X_valid, dummy_y_valid,  verbose=1)\r\nprint('Valid score:', scorevalid[0])\r\nprint('Valid accuracy:', scorevalid[1])\r\n\r\n\r\ntraining_loss = history.history['loss']\r\ntest_loss = history.history['val_loss']\r\n\r\n# Create count of the number of epochs\r\nepoch_count = range(1, len(training_loss) + 1)\r\n\r\n# Visualize loss history\r\nplt.plot(epoch_count, training_loss, 'r--')\r\nplt.plot(epoch_count, test_loss, 'b-')\r\nplt.legend(['Training Loss', 'Test Loss'])\r\nplt.xlabel('Epoch')\r\nplt.ylabel('Loss')\r\nplt.show();\r\n\r\nplt.figure()\r\nplt.plot(history.history['acc'])\r\nplt.plot(history.history['val_acc'])\r\nplt.title('model accuracy')\r\nplt.ylabel('accuracy')\r\nplt.xlabel('epoch')\r\nplt.legend(['train', 'test'], loc='upper left')\r\nplt.show()\r\n\r\n\r\n\r\nX_train =[]     # creating an empty array\r\nX_valid=[]\r\ny_train=[]\r\ny_valid=[]\r\nX_test=[]\r\ny_test=[]\r\nindex=0\r\nvideocount=0\r\nfor i in videos:\r\n    index+=1\r\n    countframe=0\r\n    if index%3==0:\r\n        countlabel=1\r\n    elif index%3==1:\r\n        countlabel=2\r\n    else:\r\n        countlabel=0\r\n    for j in i:\r\n        videocount+=1\r\n        countframe+=1\r\n        frame=cv2.imread(j)\r\n#        frame= cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\r\n        if 188<videocount<376 or 1547<videocount<1708 or 2831<videocount<3027 or 4234<videocount<4414 or 6744<videocount<6908:\r\n            X_test.append(frame)\r\n            y_test.append(countlabel)\r\n        elif videocount<188 or 1383<videocount<1547 or 2647<videocount<2831 or 4054<videocount<4234 or 5356<videocount<5509 or 6600<videocount<6744:\r\n            X_valid.append(frame)\r\n            y_valid.append(countlabel)\r\n\r\n            \r\n        else:\r\n            X_train.append(frame)\r\n            y_train.append(countlabel)\r\n\r\n\r\nX_valid = np.array(X_valid)    # converting list to array\r\nX_train=np.array(X_train)\r\nX_test=np.array(X_test)\r\n\r\ndummy_y_train = np_utils.to_categorical(y_train)    # one hot encoding Classes\r\ndummy_y_valid = np_utils.to_categorical(y_valid) \r\ndummy_y_test = np_utils.to_categorical(y_test)   # one hot encoding Classes\r\n\r\n        \r\nimages_train = []\r\nfor i in range(0,X_train.shape[0]):\r\n    a = resize(X_train[i], preserve_range=True, output_shape=(64,64,3)).astype(int)      # reshaping to 224*224*3\r\n    images_train.append(a)\r\nX_train = np.array(images_train)\r\n\r\nimages_valid = []\r\nfor i in range(0,X_valid.shape[0]):\r\n    a = resize(X_valid[i], preserve_range=True, output_shape=(64,64,3)).astype(int)      # reshaping to 224*224*3\r\n    images_valid.append(a)\r\nX_valid = np.array(images_valid)       \r\n\r\nimages_test = []\r\nfor i in range(0,X_test.shape[0]):\r\n    a = resize(X_test[i], preserve_range=True, output_shape=(64,64,3)).astype(int)      # reshaping to 224*224*3\r\n    images_test.append(a)\r\nX_test= np.array(images_test) \r\n\r\n\r\nX_train = preprocess_input(X_train, mode='tf')      # preprocessing the input data\r\nX_valid = preprocess_input(X_valid, mode='tf')\r\nX_test = preprocess_input(X_test, mode='tf')      # preprocessing the input data\r\n\r\n\r\ndel a,countlabel,images_train,images_valid,index,j,video1,video2,video3,video4,videos_path1,videos_path2,videos_path3,videos_path4,videocount,y_train,y_valid,images_test,y_test\r\ndel video5,video6,videos_path5,videos_path6\r\n\r\nX_train = model.predict(X_train)\r\nX_valid = model.predict(X_valid)\r\nX_test = model.predict(X_test)\r\nprint(X_train.shape, X_valid.shape, X_test.shape)\r\n\r\n\r\nX_train=X_train.reshape(X_train.shape[0],X_train.shape[1],1)\r\nX_valid=X_valid.reshape(X_valid.shape[0],X_valid.shape[1],1)\r\nX_test=X_test.reshape(X_test.shape[0],X_test.shape[1],1)\r\n\r\nmodellstm = Sequential()\r\nmodellstm.add(LSTM(32, return_sequences=True, input_shape=(3,1)))  # returns a sequence of vectors of dimension 32\r\nmodellstm.add(LSTM(32,return_sequences=True))  # returns a sequence of vectors of dimension 32\r\nmodellstm.add(Flatten())\r\nmodellstm.add(Dense(512))\r\nmodellstm.add(Activation('relu'))\r\nmodellstm.add(Dropout(0.5))\r\nmodellstm.add(Dense(3))\r\nmodellstm.add(Activation('sigmoid'))\r\n#\r\n\r\nmodellstm.summary()\r\n\r\nsgd = SGD(lr=0.05,decay=1e-6, momentum=0.9, nesterov=True)\r\n\r\nmodellstm.compile(loss='categorical_crossentropy', optimizer=sgd , metrics=['accuracy'])\r\n\r\n\r\n\r\nhistory2=modellstm.fit(X_train, dummy_y_train, batch_size=32,epochs=32, validation_data=(X_valid, dummy_y_valid))\r\n\r\nscorevalid2 = modellstm.evaluate(X_valid, dummy_y_valid, batch_size=32)\r\nscoretest2 = modellstm.evaluate(X_test, dummy_y_test, batch_size=32)\r\n\r\n\r\nprint('Valid score:', scorevalid2[0])\r\nprint('Valid accuracy:', scorevalid2[1])\r\n\r\nprint('test score:', scoretest2[0])\r\nprint('test accuracy:', scoretest2[1])\r\n# Get training and test loss histories\r\ntraining_loss = history2.history['loss']\r\ntest_loss = history2.history['val_loss']\r\n\r\n# Create count of the number of epochs\r\nepoch_count = range(1, len(training_loss) + 1)\r\n\r\n# Visualize loss history\r\nplt.plot(epoch_count, training_loss, 'r--')\r\nplt.plot(epoch_count, test_loss, 'b-')\r\nplt.legend(['Training Loss', 'Test Loss'])\r\nplt.xlabel('Epoch')\r\nplt.ylabel('Loss')\r\nplt.show();\r\n\r\nplt.figure()\r\nplt.plot(history.history['acc'])\r\nplt.plot(history.history['val_acc'])\r\nplt.title('model accuracy')\r\nplt.ylabel('accuracy')\r\nplt.xlabel('epoch')\r\nplt.legend(['train', 'test'], loc='upper left')\r\nplt.show()\r\n\r\n# Save the weights\r\nmodel.save_weights('model_weights_CNN-LMST_yawing_3cat.h5')\r\n# Save the model architecture\r\nwith open('model_architecture_CNN-LMST_yawing_3cat.json', 'w') as f:\r\n    f.write(model.to_json())\r\n#\r\nmodellstm.save_weights('model_weights_CNN-LMST_yawing2_3cat.h5')\r\n# Save the model architecture\r\nwith open('model_architecture_CNN-LMST_yawing2.json_3cat', 'w') as m:\r\n    m.write(model.to_json())\r\n#end=datetime.datetime.now()\r\n#elapsed=end-start\r\nplot_model(model, to_file='CNN-LMST_yawing_3cat.png')\r\nplot_model(modellstm, to_file='CNN-LMST_yawing2_3cat.png')\r\n#print('training time',str(elapsed))\r\n\r\nprobs1 = modellstm.predict(X_valid)\r\n# keep probabilities for the positive outcome only\r\n#probs = probs[:, 1]\r\n# calculate AUC\r\nauc1 = roc_auc_score(dummy_y_valid, probs1)\r\nprint('AUC1 (validation): %.3f' % auc1)\r\n# calculate roc curve\r\n#score2 = model.evaluate(X_test, dummy_y_test, batch_size=32)\r\n#print(\"Test accuracy:\",score2[1])\r\n#\r\nprobs2 = modellstm.predict(X_test)\r\nauc2 = roc_auc_score(dummy_y_test, probs2)\r\nprint('AUC2 (Test): %.3f' % auc2)\r\n# keep probabilities for the positive outcome only\r\n#probs = probs[:, 1]\r\n# calculate AUC\r\n\r\n\r\ndef perf_measure(y_actual, y_hat):\r\n    TP = 0\r\n    FP = 0\r\n    TN = 0\r\n    FN = 0\r\n\r\n    for i in range(len(y_hat)): \r\n        if y_actual[i,0]==y_hat[i]==1:\r\n           TP += 1\r\n        if y_hat[i]==1 and y_actual[i,0]!=y_hat[i]:\r\n           FP += 1\r\n        if y_actual[i,0]==y_hat[i]==0:\r\n           TN += 1\r\n        if y_hat[i]==0 and y_actual[i,0]!=y_hat[i]:\r\n           FN += 1\r\n    Recal=(TP)/(TP+FN) \r\n    Presicion=(TP)/(TP+FP)      \r\n    return(TP, FP, TN, FN,\"recall=\",Recal,\"presicion=\",Presicion)\r\n    \r\n    \r\nprobs=[]    \r\nfor p in range (0,1007):\r\n    if probs1[p,0]>0.5:\r\n        probs.append(1)\r\n    elif probs1[p,1]>0.5:\r\n        probs.append(2)\r\n    else:\r\n        probs.append(0)\r\n    \r\n            \r\n\r\n\r\nprobstest=[]    \r\nfor p in range (0,884):\r\n    if probs2[p,0]>0.5:\r\n        probstest.append(1)\r\n    elif probs2[p,1]>0.5:\r\n        probstest.append(2)\r\n    else:\r\n        probstest.append(0)\r\n            \r\n    \r\n\r\n\r\n\r\ndummy_y_valid_array=[]    \r\nfor p in range (0,1007):\r\n    if dummy_y_valid[p,0]==1:\r\n        dummy_y_valid_array.append(1)\r\n    elif dummy_y_valid[p,1]==1:\r\n        dummy_y_valid_array.append(2)\r\n    else:\r\n        dummy_y_valid_array.append(0)\r\n        \r\n        \r\ndummy_y_test_array=[]    \r\nfor p in range (0,884):\r\n    if dummy_y_test[p,0]==1:\r\n        dummy_y_test_array.append(1)\r\n    elif dummy_y_test[p,1]==1:\r\n        dummy_y_test_array.append(2)\r\n    else:\r\n        dummy_y_test_array.append(0)\r\n\r\n\r\nfrom sklearn.metrics import confusion_matrix\r\nconfusion_matrix(dummy_y_valid_array, probs)\r\nconfusion_matrix(dummy_y_test_array, probstest)\r\n","repo_name":"maryamhashemi1995/Multimodal-Fatigue-Monitoring-System","sub_path":"RNN-lmst-3classification.py","file_name":"RNN-lmst-3classification.py","file_ext":"py","file_size_in_byte":13420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"32034197896","text":"\"\"\"\nGiven an integer n, return how many distinct phone numbers \nof length n we can dial.\n\nYou are allowed to place the knight on any numeric cell \ninitially and then you should perform n - 1 jumps to dial a \nnumber of length n. All jumps should be valid knight jumps.\n\nAs the answer may be very large, return the answer modulo 10^9 + 7.\n\"\"\"\nclass Solution(object):\n    def knightDialer(self, n):\n        \"\"\"\n        :type n: int\n        :rtype: int\n        \"\"\"\n        # possible moves at each space\n        adj = {0: [4, 6], 1: [8, 6], 2: [7, 9], 3: [4, 8],\n                   4: [3, 9, 0], 5: [], 6: [0, 7, 1], 7: [2, 6],\n                   8: [1, 3], 9: [4, 2]}\n        memo = {}\n        \n        # subproblems: how many digits can we store starting at\n        # each number (prevent recalculations)\n        result = 0\n        \n        # start at each digit\n        for i in range(10):\n            result += self.dfs(i, n, adj, memo)\n            \n        return result % (10**9+7)\n        \n    def dfs(self, start, n, adj, memo):\n        if n == 1:\n            return 1\n        key = (n, start)\n        \n        if key in memo:\n            return memo[key]\n        \n        memo[key] = 0\n        for nxt in adj[start]:\n            memo[key] += self.dfs(nxt, n-1, adj, memo)\n            \n        return memo[key]","repo_name":"edchau/Algorithms","sub_path":"Problems/DP/935_knight_dialer.py","file_name":"935_knight_dialer.py","file_ext":"py","file_size_in_byte":1314,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"24724620550","text":"#\n# Min heap implementation\n#\n#\n\n\n\nimport random \n\n\n\n#\n# Min heap classe and methods\n#\nclass MinHeap:\n  def __init__(self):\n    self.heap = []\n\n  def parent_index(self, i: int) -> int:\n    return (i // 2)\n\n  def left_child_index(self, i: int) -> int:\n    return (2 * i) + 1\n  \n  def right_child_index(self, i: int) -> int:\n    return (2 * i) + 2\n  \n  def size(self) -> int:\n    return len(self.heap)\n\n  def heapify_up(self) -> None:\n    index = self.size() - 1\n    while(self.parent_index(index) >= 0 and self.heap[self.parent_index(index)] > self.heap[index]):\n      self.heap[self.parent_index(index)], self.heap[index] = self.heap[index], self.heap[self.parent_index(index)]\n      index = self.parent_index(index)\n\n\n  def insert(self, value: int) -> None:\n    \"\"\"Inserts value into last index, bubbles up\"\"\"\n    if (self.size() == 0):\n      self.heap.append(value)\n    else:\n      self.heap.append(value)\n      self.heapify_up()\n\n\n\n  def extract_min(self) -> int:\n    \"\"\"Extracts min from heap(Root) then bubbles up\"\"\"\n    if(self.size() == 0):\n      print(\"Attempted to extract min from an empty heap\")\n      exit(1)\n    elif (self.size() == 1):\n      extracted = self.heap[0]\n      self.heap.pop() #removing last element, no need to bubble up\n      return extracted\n    else:\n      extracted = self.heap[0]\n      self.heap[0], self.heap[self.size() - 1] = self.heap[self.size() - 1], self.heap[0]\n      self.heap.pop() #remove the old min from heap\n      self.heapify_down(0) #call heapify down on the root\n      return extracted\n\n\n\n  def heapify_down(self, index: int) -> None:\n    \"\"\"Makes swaps from root down to leaves if needed to keep heap rules\"\"\"\n    lChild = self.left_child_index(index)\n    rChild = self.right_child_index(index)\n    swap_index = index\n\n    if(rChild < self.size() and self.heap[rChild] < self.heap[index]): swap_index = rChild\n    if(lChild < self.size() and self.heap[lChild] < self.heap[index]): swap_index = lChild\n    \n    #swapping with the min node\n    if(lChild < self.size() and rChild < self.size()):\n      if self.heap[rChild] < self.heap[lChild]: swap_index = rChild\n      else: swap_index = lChild\n    \n    if(self.heap[swap_index] < self.heap[index]):\n      # print(\"left: \" + str(self.heap[lChild]) + \" right: \" + str(self.heap[rChild]) + \"swapping \" + str(self.heap[index]) + \" with \" + str(self.heap[swap_index]))\n      self.heap[index], self.heap[swap_index] = self.heap[swap_index], self.heap[index]\n      self.heapify_down(swap_index)\n\n\n  def peek(self) -> int:\n    \"\"\"Returns heap root. index 0 without deletion\"\"\"\n    if(len(self.heap) > 0):\n      return self.heap[0]\n    else:\n      print(\"attempted to peek an empty heap\")\n      exit(1)\n\n\n\n\n\n#\n# Driver function\n#\nif __name__ == '__main__':\n  heap = MinHeap()\n  mins = [] #stores extracted mins\n\n  #pushing random numbers into the heap\n  for i in range(15):\n    heap.insert(random.randint(0, 30))\n\n  #displaying heap in array form\n  print(\"Heap: \", end='')\n  print(heap.heap)\n\n  #extracting all of the elements into array\n  while(heap.size() > 0):\n    mins.append(heap.extract_min())\n    print(heap.heap)\n\n  #displaying all extracted mins\n  print(\"List of mins extracted: \", end='')\n  print(mins)\n\n  exit(0)","repo_name":"alexshelto/sorts-and-ds","sub_path":"Data-Structures/min-heap/python/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3213,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"12429945959","text":"from ast import Bytes\nimport torch\nimport tornado\nimport tornado.web\nfrom PIL import Image\nfrom io import BytesIO\nimport base64\nfrom predict import predict\n\n\nmodel = torch.hub.load('ultralytics/yolov5', 'yolov5s')\n\n\nclass MainHandler(tornado.web.RequestHandler):\n    def set_default_headers(self):\n        self.set_header(\"Access-Control-Allow-Origin\", \"*\")\n        self.set_header(\"Access-Control-Allow-Headers\", \"x-requested-with\")\n        self.set_header('Access-Control-Allow-Methods', 'POST, GET, OPTIONS')\n    async def post(self):        \n        try:\n            files = self.request.files\n            file = files['file'][0]['body']\n            img = Image.open(BytesIO(file))\n            predictions = predict(model, img)\n            \n        except:\n            predictions = []\n            \n        self.write({\"result\":predictions})\n\n\ndef make_app():\n    return tornado.web.Application([\n        (r\"/detectImage\" , MainHandler)\n    ])\n\n\nif __name__ =='__main__':\n    app=make_app()\n    app.listen(5000)\n    tornado.ioloop.IOLoop.current().start()","repo_name":"abfshaal/ObjectDetector","sub_path":"server/server/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1059,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"7700105445","text":"import subprocess\nfrom os.path import join\nfrom os import remove, devnull\nfrom Bio import SeqIO\nimport gzip\nimport math\nfrom multiprocessing import Pool\nfrom itertools import cycle\n\n\ndef run_prodigal(prodigal_path, infile, outdir, meta):\n    bashCommand = '{prodigal} -c -f gff -o {gff} -a {faa} -d {ffn} -i {input}'.format(\n        prodigal=prodigal_path, gff=join(outdir, 'prodigal.gff'), faa=join(outdir, 'prodigal.faa'),\n        ffn=join(outdir, 'prodigal.ffn'), input=infile\n    )\n    if meta == True:\n        bashCommand += ' -p meta'\n    print('prodigal command:', bashCommand)\n    process = subprocess.Popen(bashCommand.split(), stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)\n    output, error = process.communicate()\n\ndef run_prodigal_simple(prodigal_path, infile, outprefix):\n    FNULL = open(devnull, 'w')\n    bashCommand = '{prodigal} -p meta -c -f gff -o {gff} -a {faa} -d {ffn} -i {input}'.format(\n        prodigal=prodigal_path, gff=outprefix + '.gff', faa=outprefix + '.faa',\n        ffn=outprefix + '.ffn', input=infile\n    )\n\n    print('prodigal command:', bashCommand)\n    process = subprocess.Popen(bashCommand.split(), stdout=FNULL, stderr=FNULL)\n    output, error = process.communicate()\n\ndef run_prodigal_multithread(prodigal_path, infile, outdir, threads):\n\n    print(\"Counting records in FASTA file...\")\n    record_count = count_records_in_fasta(infile)\n    print(\"The FASTA file contains %d records...\" % record_count)\n\n    print(\"Writing FASTA file to batches for multithreading...\" )\n    record_count_per_file = math.ceil(record_count / threads)\n    filecount = 0\n    outrecs = []\n    outfiles = []\n\n    if infile.endswith('.gz'):\n        infile = gzip.open(infile, \"rt\")\n\n    for record in SeqIO.parse(infile, 'fasta'):\n        outrecs.append(record)\n        if len(outrecs) == record_count_per_file:\n            filecount += 1\n            outfile = join(outdir, 'input%d.fna' % filecount)\n            SeqIO.write(outrecs, outfile, 'fasta')\n            outfiles.append(outfile)\n            outrecs = []\n\n    if len(outrecs) > 0:\n        filecount += 1\n        outfile = join(outdir, 'input%d.fna' % filecount)\n        SeqIO.write(outrecs, outfile, 'fasta')\n        outfiles.append(outfile)\n    del outrecs\n\n    prodigal_files = [join(outdir, 'prodigal%d' % (i+1)) for i in range(len(outfiles))]\n    with Pool(processes=threads) as pool:\n        pool.starmap(run_prodigal_simple, zip(cycle([prodigal_path]), outfiles, prodigal_files))\n\n    combine_files(outfiles, join(outdir, 'input.fna'))\n    combine_files([f+'.faa' for f in prodigal_files], join(outdir, 'prodigal.faa'))\n    combine_files([f+'.ffn' for f in prodigal_files], join(outdir, 'prodigal.ffn'))\n    combine_files([f+'.gff' for f in prodigal_files], join(outdir, 'prodigal.gff'), ['#'])\n\n\ndef combine_files(files, outfile, exclude_startswith=[]):\n    with open(outfile, 'w') as out:\n\n        for f in files:\n\n            with open(f) as infile:\n                for line in infile:\n\n                    skip = False\n                    for exclude in exclude_startswith:\n                        if line.startswith(exclude):\n                            skip = True\n                            break\n                    if skip is True:\n                        continue\n                    out.write(line)\n\n    for f in files:\n        remove(f)\n\ndef count_records_in_fasta(fasta):\n    records = 0\n    if fasta.endswith('.gz'):\n        with gzip.open(fasta, \"rt\") as infile:\n            for line in infile:\n                if line.startswith('>'):\n                    records += 1\n    else:\n        with open(fasta) as infile:\n            for line in infile:\n                if line.startswith('>'):\n                    records += 1\n    return records","repo_name":"bhattlab/GenomeSearch","sub_path":"genomesearch/prodigal.py","file_name":"prodigal.py","file_ext":"py","file_size_in_byte":3745,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"76"}
+{"seq_id":"9775709441","text":"import logging\nimport json\nfrom flask import Response\nfrom jsonschema import validate\nfrom jsonschema.exceptions import ValidationError, SchemaError\n\nfrom src.event_apis.utils import get_default_response_headers\n\n\ndef get_api_result_dict(message=\"\"):\n    result_dict = {\n        \"result\": {\"message\": message},\n    }\n    return result_dict\n\n\ndef save_error_and_return_result(error, code, result_dict):\n    result_dict[\"error\"] = {\"message\": error, \"code\": code}\n    response = Response(json.dumps(result_dict, default=str))\n    response.headers = get_default_response_headers()\n    return response\n\n\ndef validate_json_schema(data, jsonschema):\n    error = \"\"\n    try:\n        validate(instance=data, schema=jsonschema)\n    except (ValidationError, SchemaError) as exc:\n        logging.exception(str(exc))\n        error = str(exc)\n    return error\n","repo_name":"raghav1010/TrackXpress","sub_path":"src/common/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"10520637624","text":"import pygame\nimport ground\nimport rock\nimport metal\nfrom array import *\nimport random\nfrom pygame.locals import *\n\n\nclass Board(object):\n    def __init__(self, width, height, window):\n        self.width = width\n        self.height = height\n        # Create the surface and pass in a tuple with its length and width\n        self.squares = []\n        self.breakableWallPosition = [[0 for i in range(11)] for j in range(11)]\n\n    def createBoard(self, window):\n        walls = []\n        x = y = 0\n        for i in range(11):\n            for j in range(11):\n                ground.Ground(x, y, window)\n                x += 40\n\n            y += 40\n            x = 0\n\n        x = y = 0\n        for i in range(11):\n            for j in range(11):\n                if i % 2 != 0 and j % 2 != 0:\n                    metal.Metal(x, y, window)\n                    walls.append(pygame.Rect(x, y, 40, 40))\n                x += 40\n\n            y += 40\n            x = 0\n        x = y = 0\n        for i in range(11):\n            for j in range(11):\n                if (i, j) == (0, 0) or (i, j) == (0, 1) or (i, j) == (1, 0):\n                    x += 40\n                    continue\n                chanceRock = random.randint(1, 2)\n                if not (i % 2 != 0 and j % 2 != 0):\n                    if chanceRock == 1:\n                        walls.append(pygame.Rect(x, y, 40, 40))\n                        self.breakableWallPosition[j][i] = 1\n                x += 40\n            y += 40\n            x = 0\n        return walls\n\n    def blitBoard(self, window):\n        x = y = 0\n        for i in range(11):\n            for j in range(11):\n                ground.Ground(x, y, window)\n                x += 40\n\n            y += 40\n            x = 0\n\n        x = y = 0\n        for i in range(11):\n            for j in range(11):\n                if i % 2 != 0 and j % 2 != 0:\n                    metal.Metal(x, y, window)\n                x += 40\n\n            y += 40\n            x = 0\n\n    def blitBreakableWalls(self, window):\n        x = y = 0\n        for i in range(11):\n            for j in range(11):\n                if self.breakableWallPosition[j][i] == 1:\n                    rock.Rock(x, y, window)\n                x += 40\n            y += 40\n            x = 0\n\n    def createSquares(self, window):\n        x = 0\n        y = 0\n        for i in range(0, 10):\n            newSquareArray = []\n            for j in range(0, 10):\n                generatedGround = ground.Ground(x, y, window)\n                newSquareArray.append(generatedGround)\n                x += 40\n            self.squares.append(newSquareArray)\n            y += 40\n            x = 0\n\n    def createHurdles(self, window):\n        for rowGround in self.squares:\n            for colGround in rowGround:\n                chanceRock = random.randint(1, 4)\n                # a ajouter : function qui permet de checker qu'un obsctacle ne soit pas dans un coin\n                if (chanceRock == 1):\n                    colGround = rock.Rock(colGround.x, colGround.y, window)\n","repo_name":"AdamaTraore75020/PYBomber","sub_path":"board.py","file_name":"board.py","file_ext":"py","file_size_in_byte":3031,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"797315273","text":"import requests \nfrom dotenv import load_dotenv\nimport os\nfrom os.path import join, dirname\n\nfrom twilio.rest import Client\n\nload_dotenv()\n\nTWILLIO_SECRET = os.getenv('TWILLIO_SECRET')\nTWILLIO_SID = os.getenv('TWILLIO_SID')\nTWILLIO_NUMBER = os.getenv('TWILLIO_NUMBER')\nGPT3_TOKEN = os.getenv('GPT3_TOKEN')\nclient = Client(TWILLIO_SID, TWILLIO_SECRET)\n\ndef send_message(to_number, message):\n  message = client.messages.create(\n                                  body=message,\n                                  from_=TWILLIO_NUMBER,\n                                  to=to_number\n                                )\n  if message.status == \"sent\":\n    return \n\n\n\ndef ChatGPT(prompt):\n  headers={\n  \"Content-Type\": \"application/json\",\n  \"Authorization\": f\"Bearer {GPT3_TOKEN}\"\n  }\n\n  body = {\n    \"model\": \"text-davinci-003\",\n    \"prompt\": f\"casual chat:\\n{prompt}\",\n    \"max_tokens\": 512,\n    \"temperature\": 0.85,\n  }\n  response = requests.post('https://api.openai.com/v1/completions', headers=headers, json=body)\n  message = response.json()['choices'][0]['text']\n  print(f\"New Prompt!\\nResponse:{str(message)}\")\n  return str(message)  \n\n  \nfrom flask import Flask, request, redirect, render_template\nfrom twilio.twiml.messaging_response import MessagingResponse\n\n\napp = Flask(__name__)\n\n@app.route('/')\ndef index():\n  return render_template(\"index.html\")\n\n@app.route(\"/sms\", methods=[\"POST\"])\ndef sms_reply():\n  resp = MessagingResponse()\n  data = request.form\n  body = data.to_dict()\n  print(body[\"Body\"])\n  prompt = body[\"Body\"]\n  reply = ChatGPT(prompt)\n  resp.message(reply)\n  return str(resp)\n\n@app.route('/sms/new', methods=[\"POST\"])\ndef new_chat():\n  phone_num = request.form.get('phone_num')\n  message = request.form.get('message')\n  reply = ChatGPT(message)\n  send_message(phone_num, reply)\n  print(phone_num)\n  \n  # call twilio stuff with phone num\n  return f'Text sent to {phone_num}'\n\n\n\n\n\n\n## twilio needs server to send webhook replys to, \n## ","repo_name":"samsullivandelgobbo/chatbot-sms","sub_path":"ChatBot.py","file_name":"ChatBot.py","file_ext":"py","file_size_in_byte":1951,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"22645323113","text":"import datetime as dt\nfrom pprint import pprint\n\nimport pytest\n\nimport anwesende.room.models as arm\nimport anwesende.room.reports as arr\nimport anwesende.room.tests.makedata as artmd\n\n@pytest.mark.django_db\ndef test_visits_by_department_report():\n   descr = dict(org1=\n                dict(dep1=\n                     dict(room1=(1,1),\n                          room2=(2,2)),\n                     dep2=\n                     dict(room3=(3,4))),\n                org2=\n                dict(dep3=\n                     dict(room4=(4,9),\n                          room5=(5,16))))\n   artmd.make_organizations(descr)\n   assert arm.Room.objects.count() == 5\n   assert arm.Seat.objects.count() == 15\n   assert arm.Visit.objects.count() == 32\n   result = list(arr.visits_by_department_report())\n   pprint(result)\n   should = [\n       {'organization': 'org1', 'department': 'dep1',\n          'rooms': 2,  'seats': 3,  'visits': 3},\n       {'organization': 'org1', 'department': 'dep2',\n          'rooms': 1,  'seats': 3,  'visits': 4},\n       {'organization': 'org2', 'department': 'dep3',\n          'rooms': 2,  'seats': 9,  'visits': 25} ]\n   assert result == should\n\n\n@pytest.mark.django_db\ndef test_visitors_by_week_report(freezer):\n    #----- first week: create rooms and some visits:\n    freezer.move_to(\"2021-12-03T02:03\")\n    seat_r1, = artmd.make_seats(\"room1\", 1, \"org1\", \"dep1\")\n    seat_r2, = artmd.make_seats(\"room2\", 1, \"org1\", \"dep1\")\n    seat_r2b, = artmd.make_seats(\"room2\", 1, \"org2\", \"dep2\")\n    artmd.make_visit(seat_r1, \"p1\")\n    artmd.make_visit(seat_r2, \"p1\")\n    artmd.make_visit(seat_r2, \"p2\")\n    artmd.make_visit(seat_r2b, \"p1\")\n    artmd.make_visit(seat_r2b, \"p3\")\n    artmd.make_visit(seat_r2b, \"p4\")\n    artmd.make_visit(seat_r2b, \"p5\")\n\n    #----- second week: create more visits:\n    freezer.move_to(\"2021-12-10T02:10\")\n    artmd.make_visit(seat_r2, \"p1\")\n    artmd.make_visit(seat_r2, \"p1\")  # double registration\n    artmd.make_visit(seat_r2, \"p2\")\n\n    #----- that evening, look at report:\n    freezer.move_to(\"2021-12-10T18:00\")\n    #-- first week:\n    wr = arr.visitors_by_week_report(\"%\")\n    assert wr[0].organizationsN == 2\n    assert wr[0].departmentsN == 2\n    assert wr[0].buildingsN == 2  # all same name\n    assert wr[0].roomsN == 3  # only 2 different names\n    assert wr[0].visitorsN == 5\n    assert wr[0].visitsN == 7\n    assert wr[0].visits_per_visitor == 7/5\n\n    #-- second week:\n    assert wr[0].organizationsN == 2\n    assert wr[0].departmentsN == 2\n    assert wr[0].buildingsN == 2\n    assert wr[0].roomsN == 3  # \n    assert wr[0].visitorsN == 5\n    assert wr[0].visitsN == 7\n    assert wr[0].visits_per_visitor == 7/5\n\n    #-- first week, narrowed search:\n    wr = arr.visitors_by_week_report(\"%dep1%\")\n    assert wr[0].organizationsN == 1\n    assert wr[0].departmentsN == 1\n    assert wr[0].buildingsN == 1\n    assert wr[0].roomsN == 2\n    assert wr[0].visitorsN == 2\n    assert wr[0].visitsN == 3\n    assert wr[0].visits_per_visitor == 3/2\n\n\n","repo_name":"prechelt/anwesende","sub_path":"anwesende/room/tests/test_reports.py","file_name":"test_reports.py","file_ext":"py","file_size_in_byte":2987,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"76"}
+{"seq_id":"11813147791","text":"def BOMBFUTA(L):\r\n    s = 0\r\n    for ch in range(len(L)):\r\n        if ch % 2 != 0:\r\n            s += L[ch]\r\n    if s % 5 == 0:\r\n        print(\"MAI CHALI JUPITER PE\")\r\n    else:\r\n        print(\"EARTH PE HI JINDA RHO TUM >_<\")\r\n\r\nL = list(eval(input(\"Enter a List of NUmbers : \")))\r\nBOMBFUTA(L)\r\n","repo_name":"prachibarnwal/python-codes","sub_path":"308.) FUNCTION to print sum of num in a list at odd pos.py","file_name":"308.) FUNCTION to print sum of num in a list at odd pos.py","file_ext":"py","file_size_in_byte":294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"72717373044","text":"def deduplicate(List):\r\n    s = set(List)\r\n    l = list(s)\r\n    return l\r\n\r\ndef deduplicate_1(List):\r\n    s = set()\r\n    for element in List:\r\n        s.add(element)\r\n    l = list(s)\r\n    return l\r\n\r\ndef deduplicate_2(List):\r\n    l = []\r\n    for element in List:\r\n        if element not in l:\r\n            l.append(element)\r\n    return l\r\n\r\nvalues = [1, 3, 5, 7, 9, 7, 5, 3, 1]\r\nprint(deduplicate(values))\r\nprint(deduplicate_1(values))\r\nprint(deduplicate_2(values))\r\n","repo_name":"Jays88/practicepython.org_exercises","sub_path":"Exercise_14.py","file_name":"Exercise_14.py","file_ext":"py","file_size_in_byte":467,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"30100429458","text":"#!/usr/bin/env python3\n\"\"\"\nNome do Script: add-target-servers.py\nAutor: Pedro Gomes  <pehgomess@gmail.com>\nData de Criação: out/2023\nDescrição: Cadastra novos target servers no apigee\n\"\"\"\n\nimport argparse\nimport os\nfrom google.oauth2 import service_account\nfrom googleapiclient.discovery import build\nimport json\n\ndef create_target_server(apigee_org, apigee_env, target_server_name, target_server_host, target_server_port, service_account_info):\n\n    if not service_account_info:\n        raise ValueError(\"As informações da conta de serviço não foram fornecidas nas variáveis de ambiente.\")\n\n    credentials_info = json.loads(service_account_info)\n\n    credentials = service_account.Credentials.from_service_account_info(\n        credentials_info,\n        scopes=['https://www.googleapis.com/auth/cloud-platform']\n    )\n\n    apigee_service = build('apigee', 'v1', credentials=credentials)\n\n    target_server_data = {\n        \"name\": target_server_name,\n        \"host\": target_server_host,\n        \"port\": target_server_port,\n        \"isEnabled\": True,\n        \"sSLInfo\": { \n            \"enabled\": True,\n        },\n        \"protocol\": \"HTTP\"\n\n    }\n\n    response = apigee_service.organizations().environments().targetservers().create(\n        parent=f'organizations/{apigee_org}/environments/{apigee_env}',\n        body=target_server_data\n    ).execute()\n\n    print(f'Success: Target Server \"{response[\"name\"]}\" created.')\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description=\"Cria um Target Server no Apigee.\")\n    parser.add_argument(\"--service-account-env\", required=True, help=\"Variável de ambiente com as informações do service Account.\")\n    parser.add_argument(\"--org\", required=True, help=\"Nome da organização Apigee.\")\n    parser.add_argument(\"--env\", required=True, help=\"Nome do ambiente Apigee. Ex: prd, dev, qa, hml\")\n    parser.add_argument(\"--name\", required=True, help=\"Nome do Target Server.\")\n    parser.add_argument(\"--host\", required=True, help=\"Host do Target Server.\")\n    parser.add_argument(\"--port\", required=True, help=\"Port do Target Server\")\n\n    args = parser.parse_args()\n\n    apigee_org = args.org\n    apigee_env = args.env\n    target_server_name = args.name\n    target_server_host = args.host\n    target_server_port = args.port\n    service_account_info = os.environ.get(args.service_account_env)\n\n\n\n    create_target_server(apigee_org, apigee_env, target_server_name, target_server_host, target_server_port, service_account_info)\n","repo_name":"pehgomess/apigee-adminstrative-api","sub_path":"files/add-target-servers.py","file_name":"add-target-servers.py","file_ext":"py","file_size_in_byte":2499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"74445109045","text":"#\n# Utility functions for CAB420, Assignment 1B, Q1\n# Author: Simon Denman (s.denman@qut.edu.au)\n#\n\nfrom scipy.io import loadmat        # to load mat files\nimport matplotlib.pyplot as plt     # for plotting\nimport numpy as np                  # for reshaping, array manipulation\nimport cv2                          # for colour conversion\nimport tensorflow as tf             # for bulk image resize\n\n# Load data for Q1\n#  train_path: path to training data mat file\n#  test_path:  path to testing data mat file\n#\n#  returns:    arrays for training and testing X and Y data\n#\ndef load_data(train_path, test_path):\n\n    # load files\n    train = loadmat(train_path)\n    test = loadmat(test_path)\n\n    # transpose, such that dimensions are (sample, width, height, channels), and divide by 255.0\n    train_X = np.transpose(train['train_X'], (3, 0, 1, 2)) / 255.0\n    train_Y = train['train_Y']\n    # change labels '10' to '0' for compatability with keras/tf. The label '10' denotes the digit '0'\n    train_Y[train_Y == 10] = 0\n    train_Y = np.reshape(train_Y, -1)\n\n    # transpose, such that dimensions are (sample, width, height, channels), and divide by 255.0\n    test_X = np.transpose(test['test_X'], (3, 0, 1, 2)) / 255.0\n    test_Y = test['test_Y']\n    # change labels '10' to '0' for compatability with keras/tf. The label '10' denotes the digit '0'\n    test_Y[test_Y == 10] = 0\n    test_Y = np.reshape(test_Y, -1)\n\n    # return loaded data\n    return train_X, train_Y, test_X, test_Y\n\n# vectorise an array of images, such that the shape is changed from {samples, width, height, channels} to\n# (samples, width * height * channels)\n#   images: array of images to vectorise\n#\n#   returns: vectorised array of images\n#\ndef vectorise(images):\n    # use numpy's reshape to vectorise the data\n    return np.reshape(images, [len(images), -1])\n\n# Plot some images and their labels. Will plot the first 100 samples in a 10x10 grid\n#  x: array of images, of shape (samples, width, height, channels)\n#  y: labels of the images\n#\ndef plot_images(x, y):\n    fig = plt.figure(figsize=[15, 18])\n    for i in range(100):\n        ax = fig.add_subplot(10, 10, i + 1)\n        ax.imshow(x[i,:])\n        ax.set_title(y[i])\n        ax.axis('off')\n\n# Resize an array of images\n#  images:   array of images, of shape (samples, width, height, channels)\n#  new_size: tuple of the new size, (new_width, new_height)\n#\n#  returns:  resized array of images, (samples, new_width, new_height, channels)\n#\ndef resize(images, new_size):\n    # tensorflow has an image resize funtion that can do this in bulk\n    # note the conversion back to numpy after the resize\n    return tf.image.resize(images, new_size).numpy()\n          \n# Convert images to grayscale\n#   images:  array of colour images to convert, of size (samples, width, height, 3)\n#\n#   returns: array of converted images, of size (samples, width, height, 1)\n#\ndef convert_to_grayscale(images):\n    # storage for converted images\n    gray = []\n    # loop through images\n    for i in range(len(images)):\n        # convert each image using openCV\n        gray.append(cv2.cvtColor(images[i,:], cv2.COLOR_BGR2GRAY))\n    # pack converted list as an array and return\n    return np.expand_dims(np.array(gray), axis = -1)","repo_name":"xiaohai-huang/cab420-workspace","sub_path":"work/machine-learning/a1b/cab420_a1b_q1_utils.py","file_name":"cab420_a1b_q1_utils.py","file_ext":"py","file_size_in_byte":3241,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"1149581531","text":"import random\nimport turtle\n\n##### Turtle \nturtle.title('Tic Tac Toe')\nt=turtle.Turtle()\n\n\ndef draw_board():\n    t.pensize(3)\n    t.fd(300)\n    t.bk(200)\n    t.lt(90)\n    t.fd(100)\n    t.bk(300)\n    t.fd(100)\n    t.lt(90)\n    t.fd(100)\n    t.bk(300)\n    t.fd(100)\n    t.rt(90)\n    t.fd(200)\n    t.bk(300)\ndef draw_x(x,y):\n    t.pencolor('red')\n    t.penup()\n    t.goto(x,y)\n    t.pd()\n    t.lt(0)\n    t.rt(45)\n    t.fd(100)\n    t.bk(50)\n    t.lt(90)\n    t.fd(50)\n    t.bk(100)\n    t.rt(45)\ndef draw_circle(x,y):\n    t.pencolor('blue')\n    t.penup()\n    t.goto(x,y)\n    t.pd()\n    t.circle(30)\n    t.penup()\ndef draw_horizontal_line(x,y):\n    t.penup()\n    t.goto(x,y)\n    t.pd()\n    t.lt(0)\n    t.rt(90)\n    t.fd(300)\n    t.bk(300)\n    t.rt(180)\ndef draw_ver_line(x,y):\n    t.penup()\n    t.goto(x,y)\n    t.pd()\n    t.lt(0)\n    t.rt(180)\n    t.fd(300)\n    t.lt(0)\ndef draw_left_line(x,y):\n    t.penup()\n    t.goto(x,y)\n    t.pd()\n    t.lt(0)\n    t.rt(135)\n    t.fd(400)\ndef draw_right_line(x,y):\n    t.penup()\n    t.goto(x,y)\n    t.pd()\n    t.lt(0)\n    t.lt(135)\n    t.fd(400)\ndef draw_marker(player,player_pos):\n    if player_pos==1 and player=='o':\n        draw_circle(80,50)\n    elif player_pos==2 and player=='o':\n        draw_circle(180,50)\n    elif player_pos==3 and player=='o':\n        draw_circle(280,50)\n    elif player_pos==4 and player=='o':\n        draw_circle(80,-40)\n    elif player_pos==5 and player=='o':\n        draw_circle(180,-40)\n    elif player_pos==6 and player=='o':\n        draw_circle(280,-40)\n    elif player_pos==7 and player=='o':\n        draw_circle(80,-140)\n    elif player_pos==8 and player=='o':\n        draw_circle(180,-140)\n    elif player_pos==9 and player=='o':\n        draw_circle(280,-140)\n\n    elif player_pos==1 and player=='x':\n        draw_x(10,10)\n    elif player_pos==2 and player=='x':\n        draw_x(110,10)\n    elif player_pos==3 and player=='x':\n        draw_x(210,10)\n    elif player_pos==4 and player=='x':\n        draw_x(10,-90)\n    elif player_pos==5 and player=='x':\n        draw_x(110,-90)\n    elif player_pos==6 and player=='x':\n        draw_x(210,-90)\n    elif player_pos==7 and player=='x':\n        draw_x(10,-190)\n    elif player_pos==8 and player=='x':\n        draw_x(110,-190)\n    else:\n        draw_x(210,-190)\n\nboard=[' ',' ',' ',' ',' ',' ',' ',' ',' ',' ']\nvs=' '\nplayer1=' '\nplayer2=' '\nplayer1_pos=' '\nplayer2_pos=' '\nmoves_played=0\ndef check_emptyspaces():\n    check=0 \n    for x in range(1,10):\n        if board[x]==' ':\n            check=1\n    if check==1:\n        check=0\n        return True\n    else:\n        check=0\n        return False\ndef user_input(player):\n    a=0\n    if check_emptyspaces()==True:\n        while type(a)!=type('int'):\n            try:\n                a = int(input(\"Тоглогч \"+player+\" 1-ээс 9-ийн хооронд тоо оруулна уу: \"))\n                if a>0 and a<= 9:\n                    return a\n                else:\n                    print(\"Буруу утга оруулсан байна, Дахин тоо оруулна уу! \")\n                    a=None\n            except ValueError:\n                print(\"Буруу утга оруулсан байна, Дахин тоо оруулна уу! \")\n    else:\n        print(\"Тэнцлээ\")\ndef validate_position(position):\n    if board[position] == ' ':\n        return True\n    else:\n        return False\ndef check_wins(player):\n    if board[1]==player and board[2]==player and board[3]== player:\n        draw_horizontal_line(10,50)\n        return True\n    elif board[4]== player and board[5]== player and board[6]== player:\n        draw_horizontal_line(10,-50)\n        return True\n    elif board[7]== player and board[8]== player and board[9]== player:\n        draw_horizontal_line(10,-150)\n        return True\n\n    elif board[1]== player and board[5]== player and board[9]== player:\n        draw_left_line(10,90)\n        return True\n    elif board[3]== player and board[5]== player and board[7]== player:\n        draw_right_line(300,90)\n        return True\n\n    elif board[1]== player and board[4]== player and board[7]== player:\n        draw_ver_line(50,90)\n        return True\n    elif board[2]== player and board[5]== player and board[8]== player:\n        draw_ver_line(150,90)\n        return True\n    elif board[3]== player and board[6]== player and board[9]== player:\n        draw_ver_line(250,90)\n        return True\n    else:\n        return False\ndef update(player, player_pos):\n    global moves_played\n    if moves_played!=9:\n        while validate_position(player_pos)!=True:\n            player_pos = user_input(player)\n            validate_position(player_pos)\n        else:\n            if check_emptyspaces()==True:\n\n                board[player_pos]=player\n                draw_marker(player, player_pos)\n                moves_played+=1\ndef generate_random():\n    return random.randint(1,9)               \n\n\n\n\nprint(\"-------------Tic Tac Toё тоглоомд тавтай морилно уу-------------\")\nprint(\"----------------------------Дүрэм-------------------------------\")\nprint(\"****************************************************************\")\nprint(\"* Сонгосон тэмдэгээ 3 дараалуулан байрлуулах \\n* 1- ээс 9-ийн хооронд утга гараас оруулах  \\n* Зүүн дээд булан 1ээс эхлэн Баруун доод булан  9 хүртэл байна\")\nprint(\"*******************************************\")\nprint(\"Доор гарч ирэх хувилбараас сонголт хийнэ үү: \")\nprint(\"----------------------------------------------\")\nprint(\"1. Тоглогч  VS Комьпютер \\n2. Тоглогч  VS Хүн  \")\nprint(\"----------------------------------------------\")\n\nwhile vs ==' ' or vs>2:\n    try:    \n        vs=int(input(\"Сонгосон дугаараа оруулна уу:\"))\n        if vs >0 and vs<=2:\n            while player1 != 'x' or player1 != 'o':\n                player1= str(input(\"Тоглогч 1 дараах сонголтуудаас сонгох X эсвэл O :\"))\n                if player1 == 'x' or player1=='o':\n                    if player1=='x':\n                        player2='o'\n                    else:\n                        player2='x'\n                    print(\"Тоглоом эхэллээ!! \\n\")\n                    #draw()\n                    draw_board()                   \n\n                    while check_emptyspaces()== True:\n                        ### Player 1\n                        player1_pos=user_input(player1)\n                        update(player1, player1_pos)\n\n                        if check_wins(player1)== True:\n                            t.penup()\n                            t.goto(20,120)\n                            t.pendown()\n                            t.write(\"Тоглоом дууслаа.\", font=(\"Arial\",20,\"normal\"))\n                            print(\"Баяр хүргэе тоглогч\", player1, \"хожлоо !!!\")\n                            break\n\n                        ### player 2\n                        if vs==1:\n                            player2_pos=generate_random()\n                            if moves_played!=9:\n                                while validate_position(player2_pos)!= True:\n                                    player2_pos=generate_random()\n                                    validate_position(player2_pos)\n                                \n                                else:\n                                    board[player2_pos]=player2\n                                    draw_marker(player2,player2_pos)\n                                    moves_played+=1\n\n                                    if check_wins(player2)== True:\n                                        t.penup()\n                                        t.goto(20,120)\n                                        t.pendown()\n                                        print(\"Баяр хүргэе тоглогч\", player2,\"хожлоо!!!!\")\n                                        break\n                        else:\n                            player2_pos=user_input(player2)\n                            update(player2,player2_pos)\n                            ### check for player 2 if wins.\n                            if check_wins(player2)==True:\n                                print(\"Баяр хүргэе тоглогч\", player2,\"хожлоо!!!!\")\n                                t.penup()\n                                t.goto(20,120)\n                                t.pendown()\n                                t.write(\"Тоглоом дууслаа.\",font=(\"Arial\",15,\"normal\"))\n                                break\n                    if check_emptyspaces()!=False:\n                        t.penup()\n                        t.goto(20,120)\n                        t.pendown()\n                        t.write(\"Тоглоом дууслаа.\",font=(\"Arial\",20,\"normal\"))\n                        #print(\"Тоглоом дууслаа.\")\n\n                    break\n    except ValueError:\n        print(\" Оруулсан утга тоо биш байна  Дахин оруулна уу! \") \nturtle.done()                  \n                    \n","repo_name":"Skytenn/Games","sub_path":"Tic tac toe.py","file_name":"Tic tac toe.py","file_ext":"py","file_size_in_byte":9236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"9552148845","text":"import glob\nimport os\nimport shutil\nimport subprocess\nimport tempfile\nfrom copy import deepcopy\nfrom typing import List\n\nimport biom\nimport pandas as pd\nimport skbio\nfrom q2_types.feature_data import DNAFASTAFormat, DNAIterator\nfrom q2_types.per_sample_sequences import CasavaOneEightSingleLanePerSampleDirFmt\n\nfrom .._utils import _process_common_input_params, run_command\n\n\ndef _process_iss_arg(arg_key, arg_val):\n    \"\"\"Creates a list with argument and its value.\n\n    Argument values represented by a list will be converted to a single\n    string joined by spaces, e.g.: [1, 2, 3] -> '1 2 3'.\n    Argument names will be converted to command line parameters by\n    appending a '--' prefix, e.g.: 'some_parameter' -> '--some_parameter'.\n\n    Args:\n        arg_key (str): Argument name.\n        arg_val: Argument value.\n\n    Returns:\n        [converted_arg, arg_value, ...]: List containing a prepared command\n            line parameter and its value(s).\n    \"\"\"\n    if isinstance(arg_val, bool) and arg_val:\n        return [f\"--{arg_key}\"]\n    elif not isinstance(arg_val, list):\n        return [f\"--{arg_key}\", str(arg_val)]\n    else:\n        flags = [f\"--{arg_key}\"]\n        flags.extend([str(x) for x in arg_val])\n        return flags\n\n\ndef _rename_reads_files(dp):\n    reads = sorted(glob.glob(os.path.join(dp, \"*.fastq.gz\")))\n    reads_new = [r.replace(\".fastq.gz\", \"_001.fastq.gz\") for r in reads]\n    for r, rn in zip(reads, reads_new):\n        os.rename(r, rn)\n    return reads_new\n\n\ndef _generate_reads(samples, args, result_fp):\n    base_cmd = [\"iss\", \"generate\", \"--compress\"]\n    base_cmd.extend(args)\n\n    for s in samples:\n        cmd = deepcopy(base_cmd)\n        sample_prefix = os.path.join(result_fp, f\"{s}_00_L001\")\n\n        cmd.extend([\"--output\", sample_prefix])\n\n        try:\n            run_command(cmd, verbose=True)\n        except subprocess.CalledProcessError as e:\n            raise Exception(\n                \"An error was encountered while running InSilicoSeq, \"\n                f\"(return code {e.returncode}), please inspect \"\n                \"stdout and stderr to learn more.\"\n            )\n\n    _rename_reads_files(result_fp)\n\n\ndef _abundances_to_biom(abundance_fps):\n    abundances = []\n    for f in abundance_fps:\n        sample = os.path.splitext(os.path.basename(f))[0].split(\"_\")[0]\n        abundances.append(\n            pd.read_csv(f, sep=\"\\t\", index_col=0, header=None, names=[sample])\n        )\n    biom_df = pd.concat(abundances, axis=1)\n    return biom.Table(\n        data=biom_df.values,\n        observation_ids=biom_df.index.tolist(),\n        sample_ids=biom_df.columns.tolist(),\n    )\n\n\ndef _ensure_sample_names_exists(sample_names):\n    if not sample_names:\n        # If it's empty or None, create a list with a default element \"sample\"\n        sample_names = [\"sample\"]\n        print(\n            'The \"--p-sample-names\" option was not provided. '\n            'Only one sample will be created with the prefix \"sample\".'\n            \"\\n\"\n        )\n    return sample_names\n\n\n# TODO: allow to input custom genomes and sample from those\ndef generate_reads(\n    genomes: DNAFASTAFormat = None,\n    sample_names: List[str] = None,\n    n_genomes: int = 10,\n    ncbi: List[str] = [\"bacteria\"],\n    n_genomes_ncbi: List[int] = [10],\n    abundance: str = \"lognormal\",\n    coverage: str = \"off\",\n    n_reads: int = 1000000,\n    mode: str = \"kde\",\n    model: str = \"HiSeq\",\n    gc_bias: bool = False,\n    cpus: int = 1,\n    debug: bool = False,\n    seed: int = 0,\n) -> (CasavaOneEightSingleLanePerSampleDirFmt, DNAFASTAFormat, biom.Table):\n    if coverage == \"off\":\n        coverage = None\n    _locals = locals().copy()\n    available_genomes = 0\n    if genomes:\n        if n_genomes_ncbi or ncbi:\n            print(\n                'Template genome sequences were provided - \"n-genomes-ncbi\" '\n                'and \"ncbi\" parameters will be ignored.'\n            )\n            _locals[\"n_genomes_ncbi\"], _locals[\"ncbi\"] = None, None\n        for _ in genomes.view(DNAIterator):\n            available_genomes += 1\n    elif n_genomes_ncbi and ncbi and (len(n_genomes_ncbi) != len(ncbi)):\n        raise Exception(\n            \"Genome counts (--n_genomes_ncbi) need to correspond \"\n            \"to the kingdoms names (--ncbi). You provided \"\n            f\"{len(ncbi)} kingdom(s) but {len(n_genomes_ncbi)} \"\n            f\"corresponding genome counts were found. Please \"\n            f\"correct your input.\"\n        )\n\n    if genomes and (n_genomes >= available_genomes):\n        print(\n            f\"The number of available genomes ({available_genomes}) is \"\n            f\"smaller than the requested number of genomes per sample \"\n            f\"({n_genomes}). The number of requested genomes will be \"\n            f\"reduced to {available_genomes - 1}.\"\n        )\n        _locals[\"n_genomes\"] = available_genomes - 1\n\n    sample_names = _ensure_sample_names_exists(sample_names)\n\n    if len(set(sample_names)) < len(sample_names):\n        dupl = {str(x) for x in sample_names if sample_names.count(x) > 1}\n        raise Exception(\n            \"Sample names need to be unique. Found duplicated \"\n            f'names: {\", \".join(sorted(dupl))}'\n        )\n\n    kwargs = {k: v for k, v in _locals.items() if k not in [\"sample_names\"]}\n    args = _process_common_input_params(processing_func=_process_iss_arg, params=kwargs)\n\n    with tempfile.TemporaryDirectory() as tmp:\n        result_reads = CasavaOneEightSingleLanePerSampleDirFmt()\n        result_genomes = DNAFASTAFormat()\n\n        # simulate reads\n        _generate_reads(sample_names, args, tmp)\n\n        # move reads into CasavaFmt\n        for f in glob.glob(os.path.join(tmp, \"*.fastq.gz\")):\n            shutil.move(f, str(result_reads))\n\n        # move original genomes into DNAFASTAFmt if found\n        # otherwise return empty file\n        genome_ids = []\n        with result_genomes.open() as fout:\n            for f in sorted(glob.glob(os.path.join(tmp, \"*.fasta\"))):\n                for seq in skbio.read(f, format=\"fasta\"):\n                    if seq.metadata[\"id\"] not in genome_ids:\n                        genome_ids.append(seq.metadata[\"id\"])\n                        seq.write(fout)\n\n        # convert abundances to a biom table\n        abund_suffix = \"coverage\" if coverage else \"abundance\"\n        abundance_fps = sorted(glob.glob(os.path.join(tmp, f\"*_{abund_suffix}.txt\")))\n        result_biom = _abundances_to_biom(abundance_fps)\n\n        return result_reads, result_genomes, result_biom\n","repo_name":"bokulich-lab/q2-assembly","sub_path":"q2_assembly/iss/iss.py","file_name":"iss.py","file_ext":"py","file_size_in_byte":6489,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"76"}
+{"seq_id":"21072426310","text":"textA = (input('Digite o texto A: '))\nhalfA = len(textA)//2\ntextB = (input('Digite o texto B: '))\nhalfB = len(textB)//2\n\na1 = textA[:halfA]\na2 = textA[halfA:] \n\nb1 = textB[:halfB]\nb2 = textB[halfB:] \n\nprint(f\"Texto A dividido em duas Partes: {a1} e {a2}\")\nprint(f\"Texto B dividido em duas Partes: {b1} e {b2}\")\nprint(f\"{a1} + {b2} = {a1}{b2} \")\nprint(f\"{a2} + {b1} = {a2}{b1} \")\nprint(f\"{textA[0]} + {textB[1]} + {textA[-1]} + {textB[-1]}\")","repo_name":"gabrielfontineli/pc-trinket","sub_path":"python/lista01/questao04.py","file_name":"questao04.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"37070488462","text":"import cv2\r\ncapture = cv2.VideoCapture(0)\r\nfourcc = cv2.VideoWriter_fourcc(*'XVID');\r\nprint(capture.get(cv2.CAP_PROP_FRAME_WIDTH))\r\nprint(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))\r\ncapture.set(3, 512)\r\ncapture.set(4, 420)\r\nout = cv2.VideoWriter('output.avi', fourcc, 20.0, (int(capture.get(3)),int(capture.get(4))));\r\nprint(capture.get(3))\r\nprint(capture.get(4))\r\nwhile(capture.isOpened()):\r\n    ret, frame = capture.read()\r\n    if ret == True:\r\n        font = cv2.FONT_HERSHEY_SIMPLEX\r\n        text = 'Width:'+ str(capture.get(3)) + 'Height:' + str(capture.get(4))\r\n        frame = cv2.putText(frame, text, (10, 50), font, 1, (0,255,255), 2, cv2.LINE_AA)\r\n        cv2.imshow('frame', frame)\r\n        out.write(frame)\r\n        if cv2.waitKey(1) & 0xFF == ord('q'):\r\n            break\r\n    else:\r\n        break\r\n\r\n\r\ncapture.release()\r\nout.release()\r\ncv2.destroyAllWindows()","repo_name":"uvcan/warning_system_for_drivers","sub_path":"capture.py","file_name":"capture.py","file_ext":"py","file_size_in_byte":870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"4236797693","text":"\"\"\"\nd/dx e^x = e^x\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef exp_f(x):\n    base = 2\n    return np.power(base, x)\n\n\ndef numerical_diff(f, x):\n    h = 1e-2  # 0.001\n    return (f(x + h) - f(x - h)) / (2 * h)  # mean-value theorem (to minimize error)\n\n\ndef draw_2(x_values):\n    exp_values = exp_f(x_values)\n    diff_values = numerical_diff(exp_f, x_values)\n\n    plt.plot(x_values, exp_values, color=\"blue\")\n    plt.plot(x_values, diff_values, color=\"red\")\n\n\ndef draw_e(x_values):\n    # an approximation of Napier's constant\n    plt.plot(x_values, np.power(2.718, x_values), color=\"green\")\n    plt.plot(x_values, numerical_diff(lambda x: np.power(2.718, x), x_values), color=\"yellow\")\n\n\nif __name__ == '__main__':\n    xs = np.arange(-1, 1, 0.1)\n    draw_2(xs)\n    draw_e(xs)\n\n    plt.grid()\n    plt.show()\n","repo_name":"b1ueskydragon/PythonGround","sub_path":"math/napier/exp.py","file_name":"exp.py","file_ext":"py","file_size_in_byte":822,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"70241761526","text":"import io\n\nimport os\nimport cv2\nfrom PIL import Image\n\nfrom .files import save_binary, hash_file\nfrom ..settings import IMAGES_ROOT, MEDIA_ROOT\n\n\ndef open_image(bytes_arr):\n    return Image.open(io.BytesIO(bytes_arr))\n\n\ndef image_to_byte_array(image_path):\n    img = Image.open(image_path)\n    img_byte_arr = io.BytesIO()\n    img.save(img_byte_arr, format='PNG')\n    return img_byte_arr.getvalue()\n\n\ndef save_uploaded_photo_as_binary_array(photo_path):\n    curr_dir = os.getcwd()\n    path = os.path.join(MEDIA_ROOT, photo_path)\n    image_byte_array = image_to_byte_array(path)\n    filename = hash_file(image_byte_array)\n    filename = filename + '.png'\n    path_to_save = os.path.join(IMAGES_ROOT, filename)\n    save_binary(path_to_save, image_byte_array)\n    path_to_remove = os.path.join(curr_dir, path)\n    os.remove(path_to_remove)\n    return filename\n\n\ndef extract_images(video_path, fps=1):\n    count = 0\n    video_path = os.path.join(MEDIA_ROOT, video_path)\n    vidcap = cv2.VideoCapture(video_path)\n    success, image = vidcap.read()\n    success = True\n    extracted_images = []\n    while success:\n        success, image = vidcap.read()\n        print('Read a new frame: ', success)\n        if success:\n            photo_name = \"frame%d.jpg\" % count\n            cv2.imwrite(MEDIA_ROOT + photo_name, image)  # save frame as JPEG file\n            count = count + fps\n            filename = save_uploaded_photo_as_binary_array(photo_name)\n            extracted_images.append(filename)\n    os.remove(video_path)\n    return extracted_images\n","repo_name":"mkumaszka/roadDamageDetectionApp","sub_path":"backend/road_damages/utils/images.py","file_name":"images.py","file_ext":"py","file_size_in_byte":1543,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"3117351500","text":"# -*- coding: utf-8 -*-\r\n\r\nfrom __future__ import print_function\r\n\r\n# from evaluate import extract_features_from_db\r\n# from DB import Database\r\nfrom src.evaluate import extract_features_from_db\r\nfrom src.DB import Database\r\n\r\n# from color import Color\r\n# from edge  import Edge\r\n# from gabor import Gabor\r\n\r\nfrom src.color import Color\r\nfrom src.edge  import Edge\r\nfrom src.gabor import Gabor\r\n\r\n\r\nimport numpy as np\r\nimport itertools\r\nimport os\r\n\r\n\r\nd_type   = 'd1'\r\ndepth    = 30\r\n\r\nfeat_pools = ['color', 'edge', 'gabor']\r\n\r\n# result dir\r\nresult_dir = 'result'\r\nif not os.path.exists(result_dir):\r\n  os.makedirs(result_dir)\r\n\r\n\r\nclass FeatureFusion(object):\r\n\r\n  def __init__(self, features):\r\n    assert len(features) > 1, \"need to fuse more than one feature!\"\r\n    self.features = features\r\n    self.images  = None\r\n\r\n  def extract_features(self, db, verbose=False):\r\n    if verbose:\r\n      print(\"Use features {}\".format(\" & \".join(self.features)))\r\n\r\n    if self.images == None:\r\n      feats = []\r\n      for f_class in self.features:\r\n        feats.append(self._get_feat(db, f_class))\r\n      # print(\"feats: \", feats)\r\n      images = self._concat_feat(db, feats)\r\n      # print(\"samples: \", images)\r\n      self.images = images  # cache the result\r\n    return self.images\r\n\r\n  # Extract multiple features from query image\r\n  def extract_features_from_query_img(self, query_image_filepath):\r\n    feats = []\r\n    for f_class in self.features:\r\n      feats.append(self.extract_query_features(query_image_filepath, f_class))\r\n    # print(\"feats: \", feats)\r\n    images = self.concat_query_features(feats)\r\n    return images\r\n\r\n  # Extract one type of feature from query image\r\n  def extract_query_features(self, query_image_filepath, f_class):\r\n    if f_class == 'color':\r\n      f_c = Color()\r\n    elif f_class == 'edge':\r\n      f_c = Edge()\r\n    elif f_class == 'gabor':\r\n      f_c = Gabor()\r\n    return f_c.extract_features(db=None, query_image_filepath=query_image_filepath, is_query=True, verbose=False)\r\n\r\n  # Extract one type of feature from all images in database\r\n  def _get_feat(self, db, f_class):\r\n    if f_class == 'color':\r\n      f_c = Color()\r\n    elif f_class == 'edge':\r\n      f_c = Edge()\r\n    elif f_class == 'gabor':\r\n      f_c = Gabor()\r\n    return f_c.extract_features(db, verbose=False)\r\n\r\n  # Concatenate image features for the query image\r\n  def concat_query_features(self, feats):\r\n    first_item = feats[0]\r\n    for i in feats[1:]:\r\n      first_item['hist'] = np.append(first_item['hist'], i[\"hist\"])\r\n    return first_item\r\n\r\n  # Concatenate image features for each image in the database\r\n  def _concat_feat(self, db, feats):\r\n    images = feats[0]\r\n    delete_idx = []\r\n    for idx in range(len(images)):\r\n      for feat in feats[1:]:\r\n        feat = self._to_dict(feat)\r\n        key = images[idx]['img']\r\n        if key not in feat:\r\n          delete_idx.append(idx)\r\n          continue\r\n        assert feat[key]['cls'] == images[idx]['cls']\r\n        images[idx]['hist'] = np.append(images[idx]['hist'], feat[key]['hist'])\r\n    for d_idx in sorted(set(delete_idx), reverse=True):\r\n      del images[d_idx]\r\n    if delete_idx != []:\r\n      print(\"Ignore %d samples\" % len(set(delete_idx)))\r\n\r\n    return images\r\n\r\n  # Convert image information to dictionary\r\n  def _to_dict(self, feat):\r\n    ret = {}\r\n    for f in feat:\r\n      ret[f['img']] = {\r\n        'cls': f['cls'],\r\n        'hist': f['hist']\r\n      }\r\n    return ret\r\n\r\n\r\n# Measure the performance of every combination of image features\r\ndef evaluate_feats(db, N, feat_pools=feat_pools, d_type='d1', depths=[None, 300, 200, 100, 50, 30, 10, 5, 3, 1]):\r\n  result = open(os.path.join(result_dir, 'feature_fusion-{}-{}feats.csv'.format(d_type, N)), 'w')\r\n  for i in range(N):\r\n    result.write(\"feat{},\".format(i))\r\n  result.write(\"depth,distance,MMAP\")\r\n  combinations = itertools.combinations(feat_pools, N)\r\n  for combination in combinations:\r\n    fusion = FeatureFusion(features=list(combination))\r\n    for d in depths:\r\n      APs = extract_features_from_db(db, feature_instance=fusion, d_type=d_type, depth=d)\r\n      cls_MAPs = []\r\n      for cls, cls_APs in APs.items():\r\n        MAP = np.mean(cls_APs)\r\n        cls_MAPs.append(MAP)\r\n      r = \"{},{},{},{}\".format(\",\".join(combination), d, d_type, np.mean(cls_MAPs))\r\n      print(r)\r\n      result.write('\\n'+r)\r\n    print()\r\n  result.close()\r\n\r\n\r\n\r\n","repo_name":"ashleyszemei/cbir","sub_path":"src/fusion.py","file_name":"fusion.py","file_ext":"py","file_size_in_byte":4387,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"36048786183","text":"import os\nimport sys\n\nimport yaml\nfrom docopt import docopt\n\n__root__ = os.path.dirname(__file__)\n\n_DOC = \"\"\"\n{__mayo__} {__version__} ({__date__})\n{__description__}\n{__author__}\n\"\"\"\n_USAGE = \"\"\"\nUsage:\n    {__executable__} train <yaml>... [options]\n    {__executable__} export <yaml>... [options]\n    {__executable__} (-h | --help)\n\nOptions:\n    --overrides=<overrides>     Specify hyper-parameters to override.\n                                Example: --overrides=\"a.b = c; d = e\"\n\"\"\"\n\n\ndef meta():\n    meta_file = os.path.join(__root__, 'meta.yaml')\n    meta_dict = yaml.load(open(meta_file, 'r'))\n    meta_dict['__executable__'] = os.path.basename(sys.argv[0])\n    return meta_dict\n\n\ndef doc():\n    return _DOC.format(**meta())\n\n\ndef usage():\n    return doc() + _USAGE.format(**meta())\n\n\ndef _config(args):\n    from mayo.config import Config\n    return Config(args['<yaml>'], overrides=args['--overrides'])\n\n\ndef train(args):\n    from mayo.train import Train\n    return Train(_config(args)).train()\n\n\ndef validate(args):\n    from mayo.evaluate import Evaluate\n    return Evaluate(_config(args)).evaluate()\n\n\ndef export(args):\n    print(_config(args).to_yaml())\n\n\ndef main():\n    args = docopt(usage(), version=meta()['__version__'])\n    commands = [train, validate, export]\n    for func in commands:\n        if not args.get(func.__name__, None):\n            continue\n        return func(args)\n    raise NotImplementedError('Command not found')\n","repo_name":"Aaron-Zhao123/mayo","sub_path":"mayo/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"72564708404","text":"from collections import defaultdict\nfrom typing import Any, Dict, List\n\nfrom lk_flow.core import EVENT, Context, Event, ModAbstraction\nfrom lk_flow.models import Task\n\n\nclass HookTrigger(ModAbstraction):\n    # {event_name : hook_task_name: [trigger_task_name2] }\n    hook_listeners: Dict[EVENT, Dict[str, List[str]]] = defaultdict(\n        lambda: defaultdict(list)\n    )\n\n    @classmethod\n    def setup_mod(cls, mod_config: Dict[str, Any]) -> None:\n        context: Context = Context.get_instance()\n        # add event hook\n        context.event_bus.add_listener(EVENT.SYSTEM_SETUP, cls.process_set_add)\n\n        context.event_bus.add_listener(EVENT.TASK_ADD, cls.task_hook_add)\n        context.event_bus.add_listener(EVENT.TASK_DELETE, cls.task_hook_remove)\n        # listener Task event\n        context.event_bus.add_listener(EVENT.TASK_PRE_START, cls.task_hook_trigger)\n        context.event_bus.add_listener(EVENT.TASK_RUNNING, cls.task_hook_trigger)\n        context.event_bus.add_listener(EVENT.TASK_STOP, cls.task_hook_trigger)\n        context.event_bus.add_listener(EVENT.TASK_FINISH, cls.task_hook_trigger)\n        context.event_bus.add_listener(EVENT.TASK_RUNNING_ERROR, cls.task_hook_trigger)\n        context.event_bus.add_listener(EVENT.TASK_FINISH_ERROR, cls.task_hook_trigger)\n\n    @classmethod\n    def add_task_hook(cls, trigger_events: str, trigger_task_name: str) -> True:\n        \"\"\"Task.trigger_events解析\n\n        Args:[动画表情]\n            trigger_events: eg.'Event_Name__hook_task_name Event_Name2__hook_task_name2'\n            trigger_task_name: 事件被触发时，启动的task名\n        \"\"\"\n        if not trigger_events:\n            return\n        for trigger_event in trigger_events.split():\n            event_name, hook_task_name = trigger_event.split(\"__\")\n            event = EVENT(event_name.lower())\n            cls.hook_listeners[event][hook_task_name].append(trigger_task_name)\n\n    @classmethod\n    def process_set_add(cls, _: Event) -> None:\n        \"\"\"\n        在SYSTEM_SETUP事件时读取context\n        给所有process增加hook\n        \"\"\"\n\n        context = Context.get_instance()\n        for task_name, process in context.get_all_processes():\n            if process.config.trigger_events:\n                cls.add_task_hook(process.config.trigger_events, process.config.name)\n\n    @classmethod\n    def task_hook_add(cls, event: Event) -> None:\n        \"\"\"\n        trigger_task加入系统\n        装载trigger_task.trigger_events配置\n        \"\"\"\n        task: Task = event.task\n        cls.add_task_hook(task.trigger_events, task.name)\n\n    @classmethod\n    def task_hook_remove(cls, event: Event) -> None:\n        \"\"\"\n        trigger_task被移除系统\n        移除对应的钩子\n        \"\"\"\n        task: Task = event.task\n        if not task.trigger_events:\n            return\n        for trigger_event in task.trigger_events.split():\n            event_name, hook_task_name = trigger_event.split(\"__\")\n            event = EVENT(event_name.lower())\n            if task.name in cls.hook_listeners[event][hook_task_name]:\n                cls.hook_listeners[event][hook_task_name].remove(task.name)\n\n    @classmethod\n    def task_hook_trigger(cls, event: Event) -> None:\n        \"\"\"事件来后触发对应的监听任务\"\"\"\n        context = Context.get_instance()\n        for trigger_task_name in cls.hook_listeners[event.event_type][event.task_name]:\n            context.start_task(trigger_task_name)\n","repo_name":"linksense/lk-flow","sub_path":"lk_flow/plugin/hook_trigger.py","file_name":"hook_trigger.py","file_ext":"py","file_size_in_byte":3464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"34950365030","text":"import time\nimport gym\nimport torch\nimport numpy as np\nfrom PPO import PPO,ActorCritic\n\ndef make_env(env_id,idx,run_name,gamma):\n    def thunk():\n        env = gym.make(env_id,render_mode = \"human\")\n        env = gym.wrappers.FlattenObservation(env)\n        env = gym.wrappers.RecordEpisodeStatistics(env)\n        if idx == 0:\n            env = gym.wrappers.RecordVideo(env,f\"videos/{run_name}\")\n        env = gym.wrappers.ClipAction(env)\n        env = gym.wrappers.NormalizeObservation(env)\n        env = gym.wrappers.TransformObservation(env, lambda obs: np.clip(obs, -10, 10))\n        env = gym.wrappers.NormalizeReward(env, gamma=gamma)\n        env = gym.wrappers.TransformReward(env, lambda reward: np.clip(reward, -10, 10))\n        return env\n    return thunk\n\ndef test():\n    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n    total_test_episodes = 10\n    max_ep_length = 500\n    gamma = 0.999\n    env_id = \"custom_env/ExpWorld-v1\"\n    run_name = f\"{env_id}__{int(time.time())}\"\n    envs = gym.vector.SyncVectorEnv([make_env(env_id,i,run_name,gamma) for i in range(1)])\n    # env = gym.make(\"BipedalWalker-v3\",render_mode= 'human')\n    # obs_dim = env.observation_space.shape[0]\n    # action_dim = env.action_space.shape[0]\n    test_agent = PPO(envs)\n    test_agent.load(\"PPO__model_1695294304\")\n\n    print(\"##############################\")\n    test_running_award = 0\n    for step in range(1,total_test_episodes+1):\n        ep_reward = 0\n        obs,_ = envs.reset() \n        for t in range(1,max_ep_length):\n            action,_ = test_agent.agent.select_action(torch.tensor(obs,dtype=torch.float,device=device))\n            obs,reward,done,_,_ = envs.step(action)\n            ep_reward += reward\n            if done:\n                break\n        \n        test_running_award += ep_reward\n        print(f\"episode: {step} \\n Reward: {round(ep_reward,2)}\")\n        ep_reward = 0\n    envs.close()\n\n    avg_test_reward = test_running_award/total_test_episodes\n    print(f\"avg_test_rew: {avg_test_reward}\")\n\nif __name__ == \"__main__\":\n    test()","repo_name":"Manaro-Alpha/Obstacle-Avoidance-using-RL","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2071,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"16705017628","text":"class Node:\n    # Nodes of a directed graph.\n    def __init__(self, name):\n        self.name = name\n        # list of child nodes\n        self.children = list()\n        self.num_parents = 0\n        self.color = \"WHITE\"  # used for cycle check\n\n    def add_child(self, c_node):\n        # add this node as a child of p_node\n        if c_node not in self.children:\n            self.children.append(c_node)\n            c_node.num_parents += 1\n\n\nclass Graph:\n    # Represents a directed graph. A user of this class should create\n    # all nodes in the graph before adding edges\n    def __init__(self):\n        self.nodes = list()\n\n    def add_node(self, node):\n        self.nodes.append(node)\n\n    def get_nodes(self):\n        return self.nodes\n\n    def find_node(self, name):\n        for node in self.nodes:\n            if name == node.name:\n                return node\n        return None\n\n    # returns true if a cycle exists in the graph. Using Graph Coloring\n    def cycle_check(self):\n        cycle = False\n        # mark all nodes in graph as unchecked\n        for i in range(0, len(self.nodes)):\n            self.nodes[i].color = \"WHITE\"\n        for i in range(0, len(self.nodes)):\n            # if this node is unchecked, check it and all children\n            if self.nodes[i].color == \"WHITE\":\n                cycle = cycle or self.cycle_check_util(self.nodes[i])\n        return cycle\n\n    def cycle_check_util(self, node):\n        # A WHITE node has not yet been checked.\n        # A GREY node has started being checked.\n        # A BLACK node has been checked.\n        node.color = \"GREY\"\n\n        # if any adjacent node is GREY, then there is a loop.\n        for child in node.children:\n            if child.color == \"GREY\":\n                return True\n            elif child.color == \"WHITE\" and self.cycle_check_util(child):\n                return True\n\n        # mark node as fully processed\n        node.color = \"BLACK\"\n        return False\n\n    # implements topological sort using Khan's Algorithm\n    def sort(self):\n        # number of parents per node\n        degree = list(range(0, len(self.nodes)))\n        # nodes that can be added to the sorted list at any time\n        queue = list()\n        # list of nodes sorted topologically\n        sorted_nodes = list()\n        for i in range(0, len(self.nodes)):\n            degree[i] = self.nodes[i].num_parents\n            if degree[i] == 0:\n                # this node has no parents, so it may be added to the sorted list.\n                queue.append(self.nodes[i])\n        while len(queue) != 0:\n            # there will be at least one node with no parents because this is a DAG\n            first = queue.pop(0)\n            sorted_nodes.append(first)\n            for child in first.children:\n                # find all children of the node we are adding\n                for i in range(0, len(self.nodes)):\n                    if self.nodes[i] == child:\n                        # subtract degree of node\n                        degree[i] -= 1\n                        if degree[i] == 0:\n                            queue.append(child)\n        self.nodes = sorted_nodes\n","repo_name":"JohnCornwell/STAT","sub_path":"STAT/src/SimCommunication/cycleCheck.py","file_name":"cycleCheck.py","file_ext":"py","file_size_in_byte":3135,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"18767470220","text":"import pandas as pd\nimport folium\nfrom folium.plugins import HeatMap\nfrom flask import Flask,render_template\n\napp = Flask(__name__)\n\ndf=pd.read_csv(\"data.csv\")\n\ndef createMap(cars):    \n    cars.rename(columns={'Lat':'latitude','Long':'longitude'}, inplace=True)\n    cars.latitude.fillna(0, inplace = True)\n    cars.longitude.fillna(0, inplace = True) \n    CarMap=folium.Map(location=[39,35],zoom_start=6)\n    HeatMap(data=cars, radius=16).add_to(CarMap)\n    CarMap.save('templates/index.html')\n   \n\n@app.route('/<string:Marka>/<string:Model>/')\ndef index(Marka,Model):\n    print(Marka)\n    print(Model)\n    cars=df[df[\"Marka\"]==Marka].iloc[:,0:2]\n    cars=df[df[\"Model\"]==Model].iloc[:,0:2]\n    createMap(cars)\n    return render_template(\"index.html\")\n\nif __name__ == '__main__':\n    app.run(debug=True)","repo_name":"SefaAkdeniz/Turkey-Used-Car-Data-Analysis-and-Machine-Learning-Price-Predict","sub_path":"mapService.py","file_name":"mapService.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"33649168919","text":"from glob import glob\nimport click\nfrom os.path import join, dirname, basename, abspath\nimport os\nfrom tqdm import tqdm\nfrom Bio import SeqIO\nimport pandas as pd\nimport string, random\nimport gzip\n\ndef randomString(stringLength=10):\n    \"\"\"Generate a random string of fixed length \"\"\"\n    letters = string.ascii_letters\n    return ''.join(random.choice(letters) for _ in range(stringLength))\n\n\ndef prokka_summary(indir):\n    p_files = glob(join(indir, '*', \"*.faa\"))\n    if not p_files:\n        raise Exception(\"No faa file in %s\" % indir)\n    sname2locus = {}\n    occured_id = []\n    for p_file in tqdm(p_files):\n        # iterating faa files\n        sname = basename(dirname(p_file)).replace('.faa', '')\n        records = SeqIO.parse(p_file, format='fasta')\n        record = next(records)\n        # get the first one, enough\n        locus_tag = record.id.split('_')[0]\n        if locus_tag in occured_id:\n            # if locus_tag occurred multiple times, regenerated a new one\n            records = SeqIO.parse(p_file, format='fasta')\n            locus_tag = randomString(len(occured_id[-1]) + 1)\n            new_records = []\n            for record in records:\n                record.id = locus_tag + '_' + record.id.split('_')[-1]\n                new_records.append(record)\n            os.renames(p_file, p_file + '.backup')\n            with open(p_file, 'w') as f1:\n                f1.write(new_records)\n        occured_id.append(locus_tag)\n        sname2locus[sname] = {}\n        sname2locus[sname]['locus_prefix'] = locus_tag\n    result_df = pd.DataFrame.from_dict(sname2locus, orient='index')\n    return result_df\n\n\ndef download_summary(indir):\n    genome_dirs = glob(join(indir, '*', '*'))\n    genome_dirs = [_ for _ in genome_dirs\n                   if os.path.isdir(_)]\n    if not genome_dirs:\n        raise Exception(\"No directory detected in %s\" % indir)\n    missing_faa_samples = []\n    sname2locus = {}\n    occured_id = []\n    for each_dir in tqdm(genome_dirs):\n        # iterating all directory\n        p_faa = glob(join(each_dir, '*.faa.gz'))\n        sname = basename(each_dir)\n        if not p_faa:\n            # if not download faa file, pass it and record it.\n            missing_faa_samples.append(sname)\n            sname2locus[sname] = {}\n            continue\n        p_faa = p_faa[0]\n        records = SeqIO.parse(gzip.open(p_faa,'rt'), format='fasta')\n        random_prefix = randomString(10)\n        if random_prefix in occured_id:\n            random_prefix = randomString(10)\n        occured_id.append(random_prefix)\n        new_records = []\n        for record in records:\n            record.id = random_prefix + '_' + record.id\n            new_records.append(record)\n        with open(join(each_dir, 'generated_protein.faa'), 'w') as f1:\n            SeqIO.write(new_records, f1, format='fasta-2line')\n        sname2locus[sname] = {}\n        sname2locus[sname]['locus_prefix'] = random_prefix\n    result_df = pd.DataFrame.from_dict(sname2locus, orient='index')\n    return result_df\n\n\n@click.command(help=\"quickly get a summary file from prokka_o\")\n@click.option(\"-i\", \"indir\", help='input dir, normally is the output directory of prokka.')\n@click.option(\"-o\", \"outfile\", help='output summary file')\n@click.option(\"-t\", \"typeOfdata\", help='data type including prokka or download')\ndef main(indir, outfile, typeOfdata):\n    indir = abspath(indir)\n\n    if not os.path.exists(dirname(outfile)):\n        os.makedirs(dirname(outfile), exist_ok=True)\n    if typeOfdata.lower() == 'prokka':\n        result_df = prokka_summary(indir)\n    elif typeOfdata.lower() == 'download':\n        result_df = download_summary(indir)\n    else:\n        raise Exception('accepted parameters of -t included')\n    result_df.to_csv(outfile, index_label='sample_name')\n\nif __name__ == '__main__':\n    main()","repo_name":"444thLiao/evol_tk","sub_path":"raw_scripts/grab_Whole_metabolism/api/summary_locus_prefix.py","file_name":"summary_locus_prefix.py","file_ext":"py","file_size_in_byte":3806,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"22941758493","text":"from imgurpython.helpers.error import ImgurClientError\nfrom imgurpython import ImgurClient\nfrom PyQt5.QtCore import QThread, pyqtSignal\nimport requests\nimport base64\nimport six\n\n\nclass MyClient(ImgurClient):\n    def upload_from_data(self, contents, config=None, anon=True):\n        if not config:\n            config = dict()\n\n        b64 = base64.b64encode(contents)\n        data = {\n            'image': b64,\n            'type': 'base64',\n        }\n        data.update({meta: config[meta] for meta in set(self.allowed_image_fields).intersection(config.keys())})\n\n        return self.make_request('POST', 'upload', data, anon)\n\n\ndef decode(key, string):\n    string = base64.urlsafe_b64decode(string + b'===')\n    string = string.decode('latin') if six.PY3 else string\n    encoded_chars = []\n    for i in range(len(string)):\n        key_c = key[i % len(key)]\n        encoded_c = chr((ord(string[i]) - ord(key_c) + 256) % 256)\n        encoded_chars.append(encoded_c)\n    encoded_string = ''.join(encoded_chars)\n    return encoded_string\n\n\ndef authenticate():\n    client_secret = ''\n    client_id = ''\n\n    return MyClient(client_id, client_secret)\n\n\ndef upload_image(image, client):\n    \"\"\"\n    upload image to imgur\n    :param image_path:\n    :return: url of the image\n    \"\"\"\n\n    config = {\n        'album': None,\n        'name': '',\n        'title': '',\n        'description': ''\n    }\n\n    image_url = client.upload_from_data(image, config=config, anon=True)\n\n    return image_url\n\n\nclass Connect(QThread):\n    connected = pyqtSignal(ImgurClient)\n\n    def run(self):\n        print(\"connecting...\")\n        try:\n            self.connected.emit(authenticate())\n        except (ImgurClientError, requests.exceptions.RequestException) as e:\n            pass\n\n\nclass Upload(QThread):\n    uploaded = pyqtSignal(str)\n    failed = pyqtSignal(Exception)\n\n    def __init__(self, image, client, parent=None):\n        super(Upload, self).__init__(parent)\n        self.image = image\n        self.client = client\n        self.start()\n\n    def run(self):\n        try:\n            image_link = upload_image(self.image, self.client)\n            self.uploaded.emit(image_link['link'])\n        except (ImgurClientError, requests.exceptions.RequestException) as e:\n            self.failed.emit(e)\n\n\nclass uploadToGoogleSearch(QThread):\n    uploaded = pyqtSignal(str)\n    failed = pyqtSignal(Exception)\n\n    def __init__(self, image, parent=None):\n        super(uploadToGoogleSearch, self).__init__(parent)\n        self.image = image\n        self.start()\n\n    def run(self):\n        try:\n            filePath = 'F:\\TranslateSelected\\Screenshot_1.png'\n            searchUrl = 'http://www.google.hr/searchbyimage/upload'\n            multipart = {'encoded_image': (filePath, self.image), 'image_content': ''}\n            response = requests.post(searchUrl, files=multipart, allow_redirects=False)\n            fetchUrl = response.headers['Location']\n            self.uploaded.emit(fetchUrl)\n        except requests.exceptions.RequestException as e:\n            self.failed.emit(e)\n\n\nclass UploaderInterface:\n    def __init__(self, fail, image_uploaded, connected, parent=None):\n        self.connected = connected\n        self.image_uploaded = image_uploaded\n        self.fail = fail\n        self.parent = parent\n\n    def on_image_upload(self, function):\n        self.image_uploaded = function\n\n    def on_fail(self, function):\n        self.fail = function\n\n    def on_connection(self, function):\n        self.connected = function\n\n    def connect(self):\n        self.connectionThread = Connect()\n        self.connectionThread.connected.connect(self.setClient)\n        self.connectionThread.start()\n        self.threads = []\n\n    def setClient(self, client):\n        self.client = client\n        self.connected()\n\n    def upload_image(self, image):\n        thread = Upload(image, self.client, self.parent)\n        thread.failed.connect(self.fail)\n        thread.uploaded.connect(self.image_uploaded)\n        thread.uploaded.connect(lambda: self.threads.remove(thread))\n        self.threads.append(thread)\n        thread.start()\n\n    def upload_image_to_google_search(self, image):\n        thread = uploadToGoogleSearch(image, self.parent)\n        thread.failed.connect(self.fail)\n        thread.uploaded.connect(self.image_uploaded)\n        thread.uploaded.connect(lambda: self.threads.remove(thread))\n        self.threads.append(thread)\n        thread.start()\n\n\nif __name__ == '__main__':\n    from PyQt5.QtWidgets import QApplication\n    import sys\n    app = QApplication(sys.argv)\n\n    def connected():\n        interface.upload_image('screenshot_1.png')\n    interface = UploaderInterface(lambda: print(\"fail\"), print, connected, None)\n    interface.connect()\n    sys.exit(app.exec_())\n","repo_name":"clavlav12/Transhot","sub_path":"ImageUploader/ImageUploaderInterface.py","file_name":"ImageUploaderInterface.py","file_ext":"py","file_size_in_byte":4764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"15746670090","text":"from library import *\n\n\n\"\"\"\n本档案用于将scraping.py所爬取下来的音档\n每十秒切分为一张图片\n并将其储存\n\"\"\"\n\n\n# 设定路径与预设值\npath = './data/鸟类.txt'\nname1 = []\n\n# 开启鸟类txt档\nwith open(path, 'r', encoding='utf-8') as f:\n    # 逐行读取txt档\n    for line in f.readlines():\n        # 分割出鸟类名称并将其存至list中\n        s = line.split(' ')\n        name1.append(s[0])\n\n# 重复四次，代表四种鸟类\nfor i in range(0, 4):\n\n    # 显示目前正在转换的鸟类种类\n    name_1 = str(name1[i])\n    print(name_1)\n\n    # 设定音档路径\n    path = \"./data/audio/\" + name_1\n    n = len(os.listdir(path))  # n为该路径中有几个音档\n\n    # 将资料夹中的每个音档都进行转换\n    for j in range(1, n + 1):\n        audio_path = './data/audio/' + name_1 + '/' + name_1 + str(j) + '.mp3'  # 音档所在位置\n        image_path = './data/images/' + name_1  # 图档储存资料夹\n        count, audio = calc(j, name_1, audio_path, image_path)  # 将指定参数汇入calc函式并获取新的值\n\n        if int(count) > 0:\n            # 如果calculate函数计算后所得的count>0，也就是该音档可以被10整除\n            for k in range(0, int(count)):\n                # 执行音档切分并转换为图档\n                cut_and_trans(k, audio, image_path, name_1)\n\n        else:\n            # 其他便是音档过短，此时就将其pass掉\n            print('音档过短')\n            pass\n","repo_name":"liu7388/BirdSound","sub_path":"trans.py","file_name":"trans.py","file_ext":"py","file_size_in_byte":1497,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"33597905596","text":"from command import Command, is_command\r\nfrom event import Event\r\n\r\n\r\nclass Quit(Command):\r\n\r\n    shortname = 'quit'\r\n    name = 'Disconnect from the game'\r\n\r\n    @is_command\r\n    def quit(self, player, *args):\r\n        self.world.remove_player(player)\r\n        return Event('quit')\r\n","repo_name":"lysol/lvlss","sub_path":"src/commands/quit.py","file_name":"quit.py","file_ext":"py","file_size_in_byte":284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"8265643044","text":"\"\"\"\nAuthor: Matthew Garber\nTerm: Spring 2017\nCOSI 137b Information Extraction\nAssignment #4\n\nThis module contains classes for representing enitity mentions and relations.\nThere is a Mention class, to model entity mentions, and a Relation class, to\nmodel relations between to entity mentions.\n\"\"\"\n\nfrom nltk.corpus import stopwords\nfrom tree_util import get_path\n\nSTOPWORDS = set(stopwords.words('english'))\n\nclass Relation:\n    \"\"\"Objects of this class represent relations between two enitity mentions.\n    It possesses methods to extract features from the mentions, as well as\n    their contexts. It also contains a method to write its features as a string\n    in the format of a MALLET instance.\n    \"\"\"\n\n    def __init__(self, values, sents, sent_trees, rel_id):\n        \"\"\"Initializes a new Relation object.\n\n        Args:\n            values: A list of values containing information about the relation\n                and the enitities it relates.\n            sents: A list of a list of 3-tuples representing words in the\n                sentences of a document.\n            sent_trees: A list of parse trees extraced from the same document.\n            rel_id: The ID number of the relation\n        \"\"\"\n        self.rel_type = values[0]\n        self.m1 = Mention(*values[2:8], sents=sents)    # mention 1\n        self.m2 = Mention(*values[8:], sents=sents)     # mention 2\n\n        if self.m1.sent_i == self.m2.sent_i:\n            self.tree = sent_trees[self.m1.sent_i]\n            self.in_same_sent = True\n        else:\n            self.tree = None\n            self.in_same_sent = False\n        \n        self._find_words_before_m1(sents)\n        self._find_words_after_m2(sents)\n        self._find_words_between(sents)\n\n        self._find_phrase_features(sents)\n\n        self._find_phrase_dist()\n        \n        self.rel_id = str(rel_id)\n        \n\n    def to_string(self):\n        \"\"\"Uses this objects attributes to derive and write its features as a\n        string formatted as a MALLET instance.\n        \"\"\"\n        feats = []\n        \n        # --- Word Features ---\n        # Bag-of-words\n        feats.extend(['WE1=' + word for word in self.m1.word_set])\n        feats.extend(['WE2=' + word for word in self.m2.word_set])\n        \n        # Words preceding mention 1 + entity type\n        feats.append('BM1F=' + self.pre1_m1[0] + self.m1.ner_tag)\n        feats.append('BM1L=' + self.pre2_m1[0] + self.m1.ner_tag)\n        # Words following mention 2 + entity type\n        feats.append('AM2F=' + self.post1_m2[0] + self.m2.ner_tag)\n        feats.append('AM2L=' + self.post2_m2[0] + self.m2.ner_tag)\n        \n        # No words in between\n        feats.append('WBNULL=' + str(self.adjacent))\n        # Single word in between\n        feats.append('WBONLY=' + self.only_word_between)\n        # First and last word in between\n        feats.append('WBF=' + self.first_between)\n        feats.append('WBL=' + self.last_between)\n        # Other words between\n        words_between = set([vals[0] for vals in self.vals_between])\n        feats.extend(['WBO=' + word for word in words_between])\n        \n        # --- Head Word / Phrase Features ---\n        # Mention head words\n        feats.append('HM1=' + self.m1.head_word)\n        feats.append('HM2=' + self.m2.head_word)\n        # Mention head word conjunction\n        feats.append(''.join(['HM12=', self.m1.head_word + '_', self.m2.head_word]))\n        \n        # Mention head POS\n        feats.append('HPOSM1=' + self.m1.head_pos)\n        feats.append('HPOSM2=' + self.m2.head_pos)\n        # Mention head POS conjunction\n        feats.append(''.join(['HPOSM12=', self.m1.head_pos + '_', self.m2.head_pos]))\n\n        # Mention head word + POS\n        feats.append(''.join(['HWPOSM1=', self.m1.head_word + '_', self.m1.head_pos]))\n        feats.append(''.join(['HWPOSM2=', self.m2.head_word + '_', self.m2.head_pos]))\n        \n        # No heads in between\n        feats.append('CHBNULL=' + str(self.in_same_sent and len(self.heads_between) == 0))\n\n        # Single head in between\n        if len(self.heads_between) == 1:\n            # Head word\n            feats.append('CHBONLY=' + self.heads_between[0][0])\n            # Head POS\n            feats.append('CHBONLY_POS=' + self.heads_between[0][1])\n        else:\n            feats.append('CHBONLY=' + '*null*')\n            feats.append('CHBONLY_POS=' + '*null*')\n\n        # First and last head word between\n        if len(self.heads_between) > 1:\n            feats.append('CHBF=' + self.heads_between[0][0])\n            feats.append('CHBL=' + self.heads_between[-1][0])\n        else:\n            feats.append('CHBF=' + '*null*')\n            feats.append('CHBL=' + '*null*')\n\n        # Last head word before mention 1\n        feats.append('CHPM1=' + self.prev_head[0])\n        # Next head word after mention 2\n        feats.append('CHNM2=' + self.next_head[0])\n        \n        # Phrase/Chunk distance\n        if self.in_same_sent:\n            feats.append('CHDIST=' + self.phrase_dist)\n        else:\n            feats.append('CHDIST=' + '*null*')\n        \n        # --- Entity type conjuction ---\n        feats.append(''.join(['ET12=', self.m1.ner_tag, self.m2.ner_tag]))\n        \n        # --- Overlap Features ---\n        # Simple overlap\n        feats.append('M1>M2=' + str(self.m1.contains(self.m2)))\n        feats.append('M1<M2=' + str(self.m2.contains(self.m1)))\n        # Overlap + entity type conjunction\n        feats.append('M1>M2+E12=' + '_'.join([str(self.m1.contains(self.m2)), self.m1.ner_tag, self.m2.ner_tag]))\n        feats.append('M1<M2+E12=' + '_'.join([str(self.m2.contains(self.m1)), self.m1.ner_tag, self.m2.ner_tag]))        \n        # Number of words between\n        feats.append('#WB=' + str(len(self.vals_between)))\n        \n        # --- Parse Tree Features ---\n        if self.tree == None:\n            #feats.append('PATH=' + '*null*')\n            feats.append('SHORTPATH=' + '*null*')\n        else:\n            parse_path = get_path(self.m1.head_i, self.m2.head_i, self.tree)\n            #feats.append('PATH=' + ','.join(parse_path))\n            feats.append('SHORTPATH=' + ','.join(parse_path[1:-1]))\n            top_node = None\n            for node in parse_path:\n                if node.endswith('0'):\n                    top_node = node\n        \n        line = ' '.join([self.rel_id, self.rel_type] + feats)\n        return line\n        \n\n    def _find_words_before_m1(self, sents):\n        \"\"\"Finds the words preceding mention 1.\n        \"\"\"\n        sent_i = self.m2.sent_i\n        if self.m1.start > 1:\n            self.pre2_m1 = sents[sent_i][self.m1.start-2]\n        else:\n            self.pre2_m1 = '*null*'\n        if self.m1.start > 0:\n            self.pre1_m1 = sents[sent_i][self.m1.start-1]\n        else:\n            self.pre1_m1 = '*null*'\n\n    def _find_words_after_m2(self, sents):\n        \"\"\"Finds the words following mention 2.\n        \"\"\"\n        sent_i = self.m2.sent_i\n        length = len(sents[sent_i])\n        if self.m2.end < length:\n            self.post1_m2 = sents[sent_i][self.m2.end]\n        else:\n            self.post1_m2 = '*null*'\n        if self.m2.end + 1 < length:\n            self.post2_m2 = sents[sent_i][self.m2.end+1]\n        else:\n            self.post2_m2 = '*null*'    \n\n    def _find_words_between(self, sents):\n        \"\"\"Finds the words between mentions 1 and 2. If the mentions are in\n        different sentences, a placeholder '*null*' is used instead.\n        \"\"\"\n        if self.in_same_sent:\n            sent_i = self.m1.sent_i\n            self.adjacent = self.m1.end == self.m2.start\n            if self.m1.end - self.m2.start == 1:\n                self.only_word_between = sents[sent_i][self.m1.end][0]\n            else:\n                self.only_word_between = '*null*'\n            if self.m1.end - self.m2.start > 1:\n                self.first_between = sents[sent_i][self.m1.end][0]\n                self.last_between = sents[sent_i][self.m2.start-1][0]\n            else:\n                self.first_between = '*null*'\n                self.last_between = '*null*'\n        else:\n            self.adjacent = False\n            self.only_word_between = '*null*'\n            if self.m1.end < len(sents[self.m1.sent_i]):\n                self.first_between = sents[self.m1.sent_i][self.m1.end][0]\n            else:\n                self.first_between = '*end*'\n            if self.m2.start > 0:\n                self.last_between = sents[self.m2.sent_i][self.m2.start-1][0]\n            else:\n                self.last_between = '*start*'\n\n    def _find_phrase_dist(self):\n        \"\"\"Finds the number of phrases between mentions 1 and 2. If the number\n        is 10 or greater, it is simply set as 'max'.\n        \"\"\"\n        dist = len(self.heads_between)\n        if dist < 10:\n            self.phrase_dist = str(dist)\n        else:\n            self.phrase_dist = 'max'\n\n    def _find_phrase_features(self, sents):\n        \"\"\"Finds various phrase features regarding mentions 1 and 2, including\n        the number of heads in between\n        \"\"\"\n        self.heads_between = []\n        self.vals_between = []\n        sent = sents[self.m1.sent_i]\n        if (not self.adjacent and not self.m1.contains(self.m2)\n                and not self.m2.contains(self.m1)):\n            for i in range(self.m1.end, self.m2.start):\n                vals = sent[i]\n                self.vals_between.append(vals)\n                if vals[2].endswith('1'):\n                    self.heads_between.append(vals)\n        found_prev_head = False\n        start = self.m1.start - 1\n        while start >= 0:\n            if sent[start][2].endswith('1'):\n                found_prev_head = True\n                self.prev_head = sent[start]\n                break\n            start -= 1\n        if not found_prev_head:\n            self.prev_head = ('*null*', '*null*', '*null*')\n            \n        end = self.m2.end\n        found_next_head = False\n        while end < len(sent):\n            if sent[end][2].endswith('1'):\n                found_prev_head = True\n                self.next_head = sent[start]\n                break\n            end += 1\n        if not found_next_head:\n            self.next_head = ('*null*', '*null*', '*null*')\n                \n\nclass Mention:\n    \"\"\"Objects of this class represent single entity mentions. It possesses\n    methods to extract features from the mention, as well as a method to see\n    whether it contains a given mention.\n    \"\"\"\n\n    def __init__(self, sent_i, start, end, ner_tag, identifier, string, sents):\n        \"\"\"Initializes a new Mention object.\n\n        Args:\n            sent_i: The string index of the sentence the mention occurs in.\n            start: The string start index of the mention.\n            end: The string end index of the mention.\n            ner_tag: The NER tag of the mention.\n            identifier: The string identifier of the mention.\n            string: The words of the mention, as a string separated by\n                underscores.\n            sents: A list of a list of 3-tuples representing words in the\n                sentences of a document.\n        \"\"\"\n        self.sent_i = int(sent_i)\n        self.start = int(start)\n        self.end = int(end)\n        self.ner_tag = ner_tag\n        self.words = string.lower().split('_')\n        self.word_set = set(self.words) - STOPWORDS\n        self._find_head_vals(sents[self.sent_i])\n\n    def _find_head_vals(self, sent):\n        \"\"\"Finds the word, POS tag, and chunk tag of the head word of the\n        mention.\n        \"\"\"\n        found_head = False\n        for i in range(self.start, self.end):\n            if sent[i][2].endswith('1'):\n                found_head = True\n        if found_head:\n            self.head_word = sent[i][0]\n            self.head_pos = sent[i][1]\n            self.head_chunk = sent[i][2]\n            self.head_i = i\n        else:\n            self.head_word = sent[self.end-1][0]\n            self.head_pos = sent[self.end-1][1]\n            self.head_chunk = sent[self.end-1][2]\n            self.head_i = self.end - 1\n\n    def contains(self, mention):\n        \"\"\"Returns true if the given mention is included in this mention.\n        \"\"\"\n        if self.sent_i == mention.sent_i:\n            return self.start <= mention.start and self.end >= mention.end\n        else:\n            return False\n","repo_name":"matthewgarber/Relation-Assignment","sub_path":"scripts/relation.py","file_name":"relation.py","file_ext":"py","file_size_in_byte":12305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"29324531308","text":"import torch\nimport torch.nn.functional as F\n\n\ndef masked_softmax(logits, mask):\n    mask = torch.ones_like(mask, dtype=torch.bool) ^ mask\n    logits[mask] -= 1.0e10\n    return F.softmax(logits, dim=-1)\n\n\ndef get_trajectory(ppo_agent, context, init_state, max_seq_len, invalid_reward):\n    graph = init_state\n\n    done = False\n    is_nop = False\n    in_local = False\n\n    trajectory_reward = 0\n    trajectory_len = 0\n    trajectory_best_gate_count = init_state.gate_count\n    intermediate_graphs = []\n\n    node_range = []\n\n    for t in range(max_seq_len):\n        is_nop = False\n\n        if node_range == []:\n            in_local = False\n\n        if not done:\n            node, xfer = ppo_agent.select_action(graph, node_range)\n\n            next_graph, next_nodes = graph.apply_xfer_with_local_state_tracking(\n                xfer=context.get_xfer_from_id(id=xfer),\n                node=graph.get_node_from_id(id=node),\n            )\n\n            # Invalid xfer\n            if next_graph == None:\n                reward = invalid_reward\n                done = True\n                next_graph = graph\n            # Nop\n            elif context.get_xfer_from_id(id=xfer).is_nop:\n                reward = 0\n                is_nop = True\n                next_nodes = []\n                node_range = []\n\n                # If a node is chosen and it chooses NOP immediately\n                # Stop the trajectory\n                if not in_local:\n                    done = True\n                in_local = False\n            else:\n                reward = (graph.gate_count - next_graph.gate_count) * 4\n                in_local = True\n\n                # Get new node_range\n                node_range = torch.tensor(next_nodes, dtype=torch.int64)\n                if context.get_xfer_from_id(id=xfer).dst_gate_count != 0:\n                    src_node_ids, _, edge_ids = next_graph.to_dgl_graph().in_edges(\n                        node_range, form='all'\n                    )\n                    mask = next_graph.to_dgl_graph().edata['reversed'][edge_ids] == 0\n                    node_range = torch.cat((node_range, src_node_ids[mask]))\n\n            trajectory_reward += reward\n\n            if trajectory_reward > 0:\n                intermediate_graphs.append(next_graph)\n            # Add a mark of terminal if the limit is reached\n            if t == max_seq_len:\n                done = True\n            # Upper limit for circuit gate count\n            if graph.gate_count > init_state.gate_count * 1.1:\n                done = True\n\n            reward = torch.tensor(reward, dtype=torch.float)\n            ppo_agent.buffer.rewards.append(reward)\n            ppo_agent.buffer.is_terminals.append(\n                torch.tensor(t == max_seq_len - 1, dtype=torch.bool)\n            )\n            ppo_agent.buffer.next_graphs.append(next_graph)\n            ppo_agent.buffer.next_nodes.append(node_range)\n            ppo_agent.buffer.is_start_point.append(t == 0)\n            ppo_agent.buffer.is_nops.append(is_nop)\n            graph = next_graph\n\n        else:\n            trajectory_len = t\n            break\n\n    trajectory_best_gate_count = min(graph.gate_count, trajectory_best_gate_count)\n\n    if trajectory_len == 0:\n        trajectory_len = max_seq_len\n\n    return (\n        trajectory_reward,\n        trajectory_best_gate_count,\n        trajectory_len,\n        intermediate_graphs,\n    )\n","repo_name":"quantum-compiler/quartz","sub_path":"experiment/deprecated/ppo/ppo_multi_step/Utils.py","file_name":"Utils.py","file_ext":"py","file_size_in_byte":3382,"program_lang":"python","lang":"en","doc_type":"code","stars":69,"dataset":"github-code","pt":"76"}
+{"seq_id":"70754106166","text":"from django.conf.urls import url, include\n\nfrom .views import MainpageView, HistoryListView, FaqListView,ChatListView\n\n\nurlpatterns = [\n\turl(r'^chat/', ChatListView.as_view(), name='chat'),\n    url(r'^history/', HistoryListView.as_view(), name='history'),\n    url(r'^faq/', FaqListView.as_view(), name='faq'),\n    url(r'^', MainpageView.as_view(), name='mainpage')\n]\n","repo_name":"djeck1432/kssa","sub_path":"project/main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":367,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"72585051764","text":"import argparse\nimport pdfkit\n\nclass HTMLToPDFConverter:\n    def __init__(self, input_file, output_file):\n        self.input_file = input_file\n        self.output_file = output_file\n\n    def convert(self):\n        try:\n            pdfkit.from_file(self.input_file, self.output_file)\n            print(f\"Conversion successful. PDF saved as '{self.output_file}'\")\n        except Exception as e:\n            print(f\"Conversion failed: {str(e)}\")\n\ndef main():\n    parser = argparse.ArgumentParser(description=\"Convert HTML to PDF using Python\")\n    parser.add_argument(\"input_file\", help=\"Input HTML file to convert\")\n    parser.add_argument(\"output_file\", help=\"Output PDF file name\")\n    parser.add_argument(\"--help\", action=\"help\", help=\"Show this help message and exit\")\n\n    args = parser.parse_args()\n\n    converter = HTMLToPDFConverter(args.input_file, args.output_file)\n    converter.convert()\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"DhanushNehru/Python-Scripts","sub_path":"pdf_to_html/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":937,"program_lang":"python","lang":"en","doc_type":"code","stars":81,"dataset":"github-code","pt":"76"}
+{"seq_id":"70100746167","text":"import pandas as pd\n\n# Import train_test_split to split data as test and train\nfrom sklearn.model_selection import train_test_split\n\n# Import LinearRegression class to get linear regression object\nfrom sklearn.linear_model import LinearRegression\n\n# Import metrics from sklearn\nfrom sklearn import metrics\n\n# Import gdown module to download files from the google drive\nimport gdown\n\n# Import numpy\nimport numpy as np\n\n# ------------------------------- Get the file from the google drive. ---------------------------------\n\n# Please use the same dataset\nurl = 'https://drive.google.com/file/d/1Hxksp6KSjoex0wdER032QypLUYmdLNeg/view?usp=sharing'\n\n# Derive the file id from the url\nfile_id = url.split('/')[-2]\n\n# Derive the download url of the file\ndownload_url = 'https://drive.google.com/uc?id=' + file_id\n\n# Give the location you want to save it in your local machine\nfile_location = 'average.csv'\n\n# Download the file from drive to your local machine\ngdown.download(download_url, file_location)\n\n\n# ------------------------------- Create the linear regression model --------------------------------------------\n\n# Read the CSV\naverage_dataset = pd.read_csv(file_location)\n\n# Get independent variable columns\nX = average_dataset[['A', 'B', 'C', 'D']]\n\n# Get dependent variable columns\ny = average_dataset['AVERAGE']\n\n# Split dataset into train and test\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n\n# Use LinearRegression class provided by sklearn\nregressor = LinearRegression()\n\n# Train the model\nregressor.fit(X_train, y_train)\n\n# Predict using test values\ny_pred = regressor.predict(X_test)\n\n# Get actual values and predicted values into a table\npredicted_results = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n\n\n# ------------------------------- Calculate MAE and RMSE values -------------------------------------------------\n\nprint('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\nprint('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n\n\n\n\n","repo_name":"pyxeda/MiddleSchoolCurriculum","sub_path":"Volume1/Chapter8/LinearRegression/linear_regression.py","file_name":"linear_regression.py","file_ext":"py","file_size_in_byte":2051,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"73594330165","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n'''\nname: 东方电子SCADA通用系统信息泄露\nreferer: http://www.wooyun.org/bugs/wooyun-2010-0131500\n         http://www.wooyun.org/bugs/wooyun-2010-0131719\nauthor: Lucifer\ndescription: 敏感信息泄露,可获取管理员账号和口令。\n'''\nimport sys\nimport requests\nimport warnings\nfrom termcolor import cprint\n\nclass dfe_scada_conf_disclosure_BaseVerify:\n    def __init__(self, url):\n        self.url = url\n\n    def run(self):\n        headers = {\n            \"User-Agent\":\"Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_6_8; en-us) AppleWebKit/534.50 (KHTML, like Gecko) Version/5.1 Safari/534.50\"\n        }\n        payload = \"/modules/manage/server/requestWorkMode.php\"\n        vulnurl = self.url + payload\n        try:\n            req = requests.get(vulnurl, headers=headers, timeout=10, verify=False)\n            if r\"productName\" in req.text and r\"adminPassword\" in req.text:\n                cprint(\"[+]存在东方电子SCADA通用系统信息泄露漏洞...(高危)\\tpayload: \"+vulnurl, \"red\")\n            else:\n                cprint(\"[-]不存在dfe_scada_conf_disclosure漏洞\", \"white\", \"on_grey\")\n\n        except:\n            cprint(\"[-] \"+__file__+\"====>可能不存在漏洞\", \"cyan\")\n\n\nif __name__ == \"__main__\":\n    warnings.filterwarnings(\"ignore\")\n    testVuln = dfe_scada_conf_disclosure_BaseVerify(sys.argv[1])\n    testVuln.run()","repo_name":"Lucifer1993/AngelSword","sub_path":"industrial/dfe_scada_conf_disclosure.py","file_name":"dfe_scada_conf_disclosure.py","file_ext":"py","file_size_in_byte":1407,"program_lang":"python","lang":"en","doc_type":"code","stars":1424,"dataset":"github-code","pt":"76"}
+{"seq_id":"1925493126","text":"import numpy\r\nimport random\r\n\r\nm = abs(int(input('Nhập m = ')))\r\ndef tao_list_sothuc(m, a, b):\r\n    x = [random.uniform(a, b) for i in range(m)]\r\n    print(\"Sinh list số thực :\", x)\r\n    return x\r\ndef sap_xep_tang_dan(x):\r\n    x = sorted(x, reverse=False)\r\n    print(\"Sắp xếp list tăng dần : \", x)\r\n    return x\r\ndef sap_xep_giam_dan(x):\r\n    x = sorted(x, reverse=True)\r\n    print(\"Sắp xếp list giảm dần : \", x)\r\n    return x\r\ndef sap_xep_sothuc(x, flag):\r\n    if flag == True:\r\n        return sap_xep_tang_dan(x)\r\n    else:\r\n        return sap_xep_giam_dan(x)\r\ndef tim_kiem(x, n):\r\n    hv = []\r\n    for i in range(len(x)):\r\n        if x[i] == n:\r\n            hv.append(i)\r\n    if len(hv) == 0:\r\n        print(\"Không tìm thấy số n trong list\")\r\n    else:\r\n        print(\"Tìm thấy số n trong list tại các vị trí: \", hv)\r\ndef luu_tap_tin_vanban(x, file):\r\n    with open(file, 'w') as f:\r\n        for item in x:\r\n            f.write('%s/n'%item)\r\n    print('Tập tin văn bản: ')\r\ndef luu_tap_tin_vanban2(x, file):\r\n    with open(file, 'a+') as f:\r\n        for item in x:\r\n            f.write('%s/n'%item)\r\n    print('Tập tin văn bản: ')\r\ndef luu_tap_tin_nhiphan(x, file):\r\n    with open(file, 'wb') as f:\r\n        for item in x:\r\n            chuyendoi = int(item)\r\n            f.write(chuyendoi.to_bytes(8, 'big'))\r\n    print('Tập tin nhị phân: ')\r\ndef main():\r\n    x = tao_list_sothuc(m, 2, 16)\r\n    luu_tap_tin_vanban(x, r'D:/Đồ án/doan1.txt')\r\n    f = sap_xep_sothuc(x, False)\r\n    luu_tap_tin_vanban2(f, r'D:/Đồ án/doan1.txt')\r\n    tim_kiem(x, 2)\r\nif __name__ == '__main__':\r\n    main()\r\n\r\n","repo_name":"hoangviet2k4/do-an","sub_path":"Phần1/Câu 2.py","file_name":"Câu 2.py","file_ext":"py","file_size_in_byte":1652,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"2807320931","text":"import xml.etree.ElementTree as ET\nfrom spyne import Application, ServiceBase, rpc, Integer, Unicode\nfrom spyne.protocol.soap import Soap11\nfrom spyne.server.wsgi import WsgiApplication\nfrom spyne.util.wsgi_wrapper import run_twisted\nfrom suds import WebFault\nfrom suds.client import Client\nimport sys\n\n\ndef getExtractInfo(demande_xml):\n    url = \"http://localhost:8000/ServiceExtractionInformation?wsdl\"\n    client = Client(url, cache=None)\n\n    try:\n        # Appelez la méthode du service en lui passant le JSON comme argument\n        response = client.service.ExtraireInformations(demande_xml)\n\n        # Vérifiez la réponse du service\n        print(\"Réponse du service ExtractionInformation:\")\n        print(response)\n\n        return response\n\n    except WebFault as e:\n        # En cas d'erreur, imprimez le message d'erreur\n        print(f\"Erreur lors de l'appel au service : {e}\")\n    except Exception as e:\n        # Gérez d'autres exceptions possibles ici\n        print(f\"Une erreur s'est produite : {e}\")\n\n\ndef getScoringPret(tree):\n    # Créez un client SOAP pour le service solvabilité\n    url = \"http://localhost:8001/VerifSolvabilite?wsdl\"\n    client = Client(url, cache=None)\n\n    try:\n        # Appelez la méthode du service en lui passant le XML comme argument\n        response = client.service.calculateScore(tree)\n\n        # Vérifiez la réponse du service\n        print(\"Réponse du service solvabilité:\")\n        print(f\"Response : {response}\")\n\n        return response\n    except WebFault as e:\n        # En cas d'erreur, imprimez le message d'erreur\n        print(f\"Erreur lors de l'appel au service : {e}\")\n    except Exception as e:\n        # Gérez d'autres exceptions possibles ici\n        print(f\"Une erreur s'est produite : {e}\")\n\n\ndef getApprobationPret(evalProp, infoClient, scoreSolvabilite):\n    # Créez un client SOAP pour le service approbation\n    url = \"http://localhost:8004/DecisionApprobation?wsdl\"\n    client = Client(url, cache=None)\n\n    try:\n\n        # Appelez la méthode du service en lui passant le XML comme argument\n        response = client.service.approbationPret(evalProp, infoClient, scoreSolvabilite)\n\n        # Vérifiez la réponse du service\n        print(\"Réponse du service approbation:\")\n        print(f\"Response : {response}\")\n\n        return response\n    except WebFault as e:\n        # En cas d'erreur, imprimez le message d'erreur\n        print(f\"Erreur lors de l'appel au service : {e}\")\n    except Exception as e:\n        # Gérez d'autres exceptions possibles ici\n        print(f\"Une erreur s'est produite : {e}\")\n\n\ndef getEvalProp(infoProp):\n    # Créez un client SOAP pour le service approbation\n    url = \"http://localhost:8003/ServiceEvaluationPropriete?wsdl\"\n    client = Client(url, cache=None)\n\n    try:\n\n        # Appelez la méthode du service en lui passant le XML comme argument\n        response = client.service.EvaluerPropriete(infoProp)\n\n        # Vérifiez la réponse du service\n        print(\"Réponse du service eval prop:\")\n        print(f\"Response : {response}\")\n\n        return response\n    except WebFault as e:\n        # En cas d'erreur, imprimez le message d'erreur\n        print(f\"Erreur lors de l'appel au service : {e}\")\n    except Exception as e:\n        # Gérez d'autres exceptions possibles ici\n        print(f\"Une erreur s'est produite : {e}\")\n\n\ndef lecture_bdd(xml_db):\n    root = ET.parse(xml_db).getroot()\n    prenom_client = root.find('.//PrenomClient')\n    prenom_client = prenom_client.text if prenom_client is not None else ''\n    nom_client = root.find('.//NomClient')\n    nom_client = nom_client.text if nom_client is not None else ''\n    adresse_rue = root.find('.//Adresse/Rue')\n    adresse_rue = adresse_rue.text if adresse_rue is not None else ''\n    adresse_ville = root.find('.//Adresse/Ville')\n    adresse_ville = adresse_ville.text if adresse_ville is not None else ''\n    adresse_code_postal = root.find('.//Adresse/CodePostal')\n    adresse_code_postal = adresse_code_postal.text if adresse_code_postal is not None else ''\n    adresse_pays = root.find('.//Adresse/Pays')\n    adresse_pays = adresse_pays.text if adresse_pays is not None else ''\n    email = root.find('.//Email')\n    email = email.text if email is not None else ''\n    numero_telephone = root.find('.//NumeroTelephone')\n    numero_telephone = numero_telephone.text if numero_telephone is not None else ''\n    montant_pret_demande = root.find('.//MontantPretDemande')\n    montant_pret_demande = int(montant_pret_demande.text) if montant_pret_demande is not None else 0\n    duree_pret = root.find('.//DureePret')\n    duree_pret = int(duree_pret.text) if duree_pret is not None else 0\n    description_propriete_etage = root.find('.//DescriptionPropriete/Etage')\n    description_propriete_etage = description_propriete_etage.text if description_propriete_etage is not None else ''\n    description_propriete_taille = root.find('.//DescriptionPropriete/Taille')\n    description_propriete_taille = description_propriete_taille.text if description_propriete_taille is not None else ''\n    description_propriete_jardin = root.find('.//DescriptionPropriete/Jardin')\n    description_propriete_jardin = description_propriete_jardin.text if description_propriete_jardin is not None else ''\n    description_propriete_quartier = root.find('.//DescriptionPropriete/Quartier')\n    description_propriete_quartier = description_propriete_quartier.text if description_propriete_quartier is not None else ''\n    description_propriete_tranquilite = root.find('.//DescriptionPropriete/Tranquilite')\n    description_propriete_tranquilite = description_propriete_tranquilite.text if description_propriete_tranquilite is not None else ''\n    description_propriete_annnee_construction = root.find('.//DescriptionPropriete/AnneeConstruction')\n    description_propriete_annnee_construction = int(\n        description_propriete_annnee_construction.text) if description_propriete_annnee_construction is not None else 0\n    revenu_mensuel = root.find('.//RevenuMensuel')\n    revenu_mensuel = int(revenu_mensuel.text) if revenu_mensuel is not None else 0\n    depenses_mensuelles = root.find('.//DepensesMensuelles')\n    depenses_mensuelles = int(depenses_mensuelles.text) if depenses_mensuelles is not None else 0\n    informations_structurees = {\n        'PrenomClient': prenom_client,\n        'NomClient': nom_client,\n        'Adresse': {\n            'Rue': adresse_rue,\n            'Ville': adresse_ville,\n            'CodePostal': adresse_code_postal,\n            'Pays': adresse_pays\n        },\n        'Email': email,\n        'NumeroTelephone': numero_telephone,\n        'MontantPretDemande': montant_pret_demande,\n        'DureePret': duree_pret,\n        'DescriptionPropriete': {\n            'Etage': description_propriete_etage,\n            'Taille': description_propriete_taille,\n            'Jardin': description_propriete_jardin,\n            'Quartier': description_propriete_quartier,\n            'Tranquilite': description_propriete_tranquilite,\n            'AnneeConstruction': description_propriete_annnee_construction\n        },\n        'RevenuMensuel': revenu_mensuel,\n        'DepensesMensuelles': depenses_mensuelles\n    }\n    return informations_structurees\n\n\ndef to_service_verification_solvabilite(lecture_xml_db):\n    informations_structurees = lecture_xml_db\n\n    root = ET.Element('DemandePret')\n    prenom_client = ET.SubElement(root, 'PrenomClient')\n    prenom_client.text = informations_structurees['PrenomClient']\n    nom_client = ET.SubElement(root, 'NomClient')\n    nom_client.text = informations_structurees['NomClient']\n    revenu_mensuel = ET.SubElement(root, 'RevenuMensuel')\n    revenu_mensuel.text = str(informations_structurees['RevenuMensuel'])\n    depenses_mensuelles = ET.SubElement(root, 'DepensesMensuelles')\n    depenses_mensuelles.text = str(informations_structurees['DepensesMensuelles'])\n    tree = ET.tostring(root)\n    return tree.decode('utf-8')\n\n\ndef to_service_evaluation_propriete(lecture_xml_db):\n    informations_structurees = lecture_xml_db\n\n    root = ET.Element('DemandePret')\n    adresse = ET.SubElement(root, 'Adresse')\n    adresse_rue = ET.SubElement(adresse, 'Rue')\n    adresse_rue.text = informations_structurees['Adresse']['Rue']\n    adresse_ville = ET.SubElement(adresse, 'Ville')\n    adresse_ville.text = informations_structurees['Adresse']['Ville']\n    adresse_code_postal = ET.SubElement(adresse, 'CodePostal')\n    adresse_code_postal.text = informations_structurees['Adresse']['CodePostal']\n    adresse_pays = ET.SubElement(adresse, 'Pays')\n    adresse_pays.text = informations_structurees['Adresse']['Pays']\n    montant_pret_demande = ET.SubElement(root, 'MontantPretDemande')\n    montant_pret_demande.text = str(informations_structurees['MontantPretDemande'])\n    description_propriete = ET.SubElement(root, 'DescriptionPropriete')\n    description_propriete_etage = ET.SubElement(description_propriete, 'Etage')\n    description_propriete_etage.text = informations_structurees['DescriptionPropriete']['Etage']\n    description_propriete_taille = ET.SubElement(description_propriete, 'Taille')\n    description_propriete_taille.text = informations_structurees['DescriptionPropriete']['Taille']\n    description_propriete_jardin = ET.SubElement(description_propriete, 'Jardin')\n    description_propriete_jardin.text = informations_structurees['DescriptionPropriete']['Jardin']\n    description_propriete_quartier = ET.SubElement(description_propriete, 'Quartier')\n    description_propriete_quartier.text = informations_structurees['DescriptionPropriete']['Quartier']\n    description_propriete_tranquilite = ET.SubElement(description_propriete, 'Tranquilite')\n    description_propriete_tranquilite.text = informations_structurees['DescriptionPropriete']['Tranquilite']\n    description_propriete_annnee_construction = ET.SubElement(description_propriete, 'AnneeConstruction')\n    description_propriete_annnee_construction.text = str(\n        informations_structurees['DescriptionPropriete']['AnneeConstruction'])\n    tree = ET.tostring(root)\n    tree = tree.decode('utf-8')\n\n    return tree\n\n\ndef to_service_approbation_pret(lecture_xml_db):\n    informations_structurees = lecture_xml_db\n\n    root = ET.Element('DemandePret')\n    revenu_mensuel = ET.SubElement(root, 'RevenuMensuel')\n    revenu_mensuel.text = str(informations_structurees['RevenuMensuel'])\n    depenses_mensuelles = ET.SubElement(root, 'DepensesMensuelles')\n    depenses_mensuelles.text = str(informations_structurees['DepensesMensuelles'])\n    montant_pret_demande = ET.SubElement(root, 'MontantPretDemande')\n    montant_pret_demande.text = str(informations_structurees['MontantPretDemande'])\n    duree_pret_demande = ET.SubElement(root, 'DureePret')\n    duree_pret_demande.text = str(informations_structurees['DureePret'])\n    tree = ET.tostring(root)\n    tree = tree.decode('utf-8')\n\n    return tree\n\n\nclass DemandePret(ServiceBase):\n\n    @rpc(Unicode, _returns=Unicode)\n    def demandePret(ctx, demande_xml):\n        try:\n            xml_db = getExtractInfo(demande_xml)\n            infoClient = lecture_bdd(xml_db)\n            infoClientSolv = to_service_verification_solvabilite(infoClient)\n            scoreSolvabilite = getScoringPret(infoClientSolv)\n\n            tree_eval_prop = to_service_evaluation_propriete(infoClient)\n            eval_prop = getEvalProp(tree_eval_prop)\n\n            infoClientAprob = to_service_approbation_pret(infoClient)\n            approbationPret = getApprobationPret(eval_prop, infoClientAprob, scoreSolvabilite)\n\n            return approbationPret\n        except Exception as e:\n            print(f\"Une erreur s'est produite : {e}\")\n            # Vous pouvez retourner une valeur ou un message d'erreur personnalisé ici\n\n\nif __name__ == '__main__':\n    application = Application([DemandePret],\n                              tns='DemandePret',\n                              in_protocol=Soap11(validator='lxml'),\n                              out_protocol=Soap11())\n\n    wsgi_app = WsgiApplication(application)\n\n    twisted_apps = [\n        (wsgi_app, b'DemandePret')\n    ]\n\n    sys.exit(run_twisted(twisted_apps, 8002))\n","repo_name":"nlsferrara/TD1-SOA-UVSQ","sub_path":"service_demande_pret.py","file_name":"service_demande_pret.py","file_ext":"py","file_size_in_byte":12151,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"44732272896","text":"import MeCab\n\nwakati = MeCab.Tagger()\n\ntext = \"テストメッセージ\"\nnode = wakati.parseToNode(text)\n\nnodes = []\nwhile node:\n\t# MeCab Node Structure 概要\n\t# https://taku910.github.io/mecab/doxygen/structmecab__node__t.html\n\t# \n\t# prev: 前のnodeへのポインタを取得\n\t# next:  次のnodeへのポインタを取得\n\t# enext: 同じ位置で終了するノードへのポインタ\n\t# bnext: 同じ位置から始まるノードへのポインタ\n\t# rpath: 右パスへのポインタ\n\t# lpath:　左パスへのポインタ\n\t# \n\t# surface: 形態素の表層文字列\n\t# feature: 特徴文字列 (CSVで品詞などが返ってくる)\n\t# id: ユニークなノードID\n\t# length: 表層文字���の長さ\n\t# rlength: 形態素解析を実行する前の空白を含む表層文字列の長さ。\n\t# rcAttr: 右文脈ID\n\t# lcAttr: 左文脈ID\n\t# posid: ユニークな品詞ID (pos-id.def参照)\n\t# char_type: 文字種情報 (char.def参照)\n\t# stat: 形態素種類 (0: 通常, 1: 未知語, 2:文頭BOS, 3:文末EOS)\n\t# isbest: このノードが最適なノード=bestであれば1\n\t# alpha: 前方累積ログ（forward accumulative log）の合計\n\t# beta: 後方累積ログ（backword accumulative log）の合計\n\t# prob: 周辺確率\n\t# wcost: 単語生起コスト\n\t# cost: BOSノードからこのノードまでの最高の累積コスト\n\t#\n\t# 生起コストの概念\n\t# http://www.mwsoft.jp/programming/munou/mecab_nitteretou.html\n\tword = node.surface\n\tstat = node.stat\n\twcost = node.wcost\n\tcost = node.cost\n\tnodes.append([word, stat, wcost, cost])\n\tnode = node.next\n\nprint(nodes)\n","repo_name":"yukia3e/learning-mecab-python3","sub_path":"02_parse_to_node.py","file_name":"02_parse_to_node.py","file_ext":"py","file_size_in_byte":1608,"program_lang":"python","lang":"ja","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"17548577346","text":"import http.client\nimport json\n#ejemplo consulta base datos.\n\nconn = http.client.HTTPSConnection(\"api.fda.gov\")\nconn.request(\"GET\", \"/drug/label.json?search=acetylsalicylate&limit=20\")\nr1 = conn.getresponse()\nprint(\"Estado y reason: \",r1.status, r1.reason)\n\ndata1 = r1.read().decode(\"utf-8\")\nconn.close()\n\n# -- Read the data as a json object\ndata = json.loads(data1)\nmeta = data['meta']\ndisclaimer = meta['disclaimer']\nterms = meta['terms']\nlicense2 = meta['license']\nmeta_results = meta['results']\nlimit = meta_results['limit']\ntotal = meta_results['total']\nresults = data['results']\n\n#iiut\nprint(\"\\n\")\nprint(\"* SHOWN: {}/{}\".format(limit, total))\nprint(\" Data: {}\".format(data))\nfor c in results:\n    print(c,'\\n')\n    print(c['openfda'])\nprint(\"-------------\")\n\nmanufacturers = []\nfor n, drug in enumerate(results):\n    if drug['openfda']:\n        manufacturer_name = drug['openfda']['manufacturer_name'][0]\n        try:\n            manufacturers.index(manufacturer_name)\n        except ValueError:\n            manufacturers.append(manufacturer_name)\nprint(\"* kkk[{}]: {}\".format(len(manufacturers), manufacturer_name))","repo_name":"ahoyter/openfda","sub_path":"practica1.py","file_name":"practica1.py","file_ext":"py","file_size_in_byte":1122,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"9089442091","text":"# a=[2,\"kevin\",7,9.99,0]\r\n# for x in range(len(a)):\r\n#     print(a[x])\r\n\r\ngrades=[33,45,67,5,6,77,33,67,77]\r\ngrades.append(66)\r\na=[1,2,3,4,5]\r\nb=[5,67,8,90,6]\r\nc=a+b\r\na.extend(b)\r\nprint(a)\r\nprint(b)\r\nprint(c)\r\nprint(b.count(5))\r\n\r\nb.remove(90)\r\nprint(b)\r\n\r\na.pop(1)\r\nprint(a)\r\n\r\na[3]=99\r\nprint(a.index(99))\r\n\r\nunique_grades= list(dict.fromkeys(grades))\r\n\r\nunique_grades.insert(4,99)\r\nprint(unique_grades)\r\n\r\ns=sum(grades)\r\nprint(s)\r\n\r\nm=1\r\nfor i in grades:\r\n    m=m*i\r\nprint(m)    ","repo_name":"kevinparre/fis_python","sub_path":"list.py","file_name":"list.py","file_ext":"py","file_size_in_byte":481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"37607603452","text":"# https://www.acmicpc.net/problem/11055\n\nN = int(input())\nnums = list(map(int, input().split()))\n\ndp = [0] * N\nacc_sum = [0] * N\n\ndp[0] = nums[0]\nacc_sum[0] = nums[0]\n\nfor i in range(1, N):\n\n    cur_sum = 0\n    for j in range(i):\n\n        if nums[j] < nums[i] and acc_sum[j] > cur_sum:\n            cur_sum = acc_sum[j]\n\n    acc_sum[i] = cur_sum + nums[i]\n    dp[i] = max(dp[i-1], acc_sum[i])\n\n# print(acc_sum)\n# print(dp)\nprint(dp[N-1])\n","repo_name":"HBell11/TIL","sub_path":"algorithm/baekjoon/DP/11055_largest_increasing_subsequence.py","file_name":"11055_largest_increasing_subsequence.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"39803307879","text":"from TextBox import TextBox\r\nfrom Button import Button\r\n\r\nclass Item:\r\n    def __init__(self, name, price, quantity, description=\"\") -> None:\r\n        self.name = name\r\n        self.price = price\r\n        self.quantity = quantity\r\n        self.description = description\r\n\r\n    @staticmethod\r\n    def SetProgramManager(programMan):\r\n        global pm\r\n        pm = programMan\r\n\r\n    def ShowItem(self, i=0):\r\n        currentY = 200 + 90 * i\r\n        itemName = TextBox(50, currentY, 500, 50, text=self.name, textSize=20, textColour=(255, 255, 255), static=True, border=True)\r\n        itemName.CenterTextY()\r\n        \r\n        itemPrice = TextBox(550, currentY, 100, 50, text=f\"${self.price}\", textSize=20, textColour=(255, 255, 255), static=True, border=True)\r\n        itemPrice.CenterTextY()\r\n\r\n        itemDescription = TextBox(50, currentY+50, 400, 35, text=self.description, textSize=14, textColour=(255, 255, 255), static=True, border=True)\r\n        itemDescription.CenterTextY()\r\n\r\n        itemQuantity = TextBox(550, currentY+50, 100, 35, text=f\"x{self.quantity}\", textSize=14, textColour=(255, 255, 255), static=True, border=True)\r\n        itemQuantity.CenterTextY()\r\n\r\n        addToCart = Button(650, currentY, 50, 45, self.AddToCart, text=\"+1\", textSize=17, textColour=(255, 255, 255), bgColour=(50, 50, 50))\r\n        removeFromCart = Button(650, currentY+45, 50, 40, self.RemoveFromCart, text=\"-1\", textSize=17, textColour=(255, 255, 255), bgColour=(50, 50, 50))\r\n\r\n        return [itemName, itemPrice, itemDescription, itemQuantity, addToCart, removeFromCart]\r\n\r\n    def AddToCart(self):\r\n        pm.AddToCart(self)\r\n\r\n    def RemoveFromCart(self):\r\n        pm.RemoveFromCart(self)\r\n","repo_name":"Unsa4/SHOPPING-CART","sub_path":"Item.py","file_name":"Item.py","file_ext":"py","file_size_in_byte":1694,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"73250123766","text":"from django.shortcuts import render, redirect, HttpResponse\nfrom django.urls import reverse\n\nfrom .models import Comment\nfrom django.http import JsonResponse\nfrom django.contrib.contenttypes.models import ContentType\nfrom .form import CommentForm\nfrom django.core.mail import send_mail\n# Create your views here.\n\n\ndef update_comment(request):\n    # user = request.user\n    # text = request.POST.get('text', '')\n    # if text == '':\n    #     return HttpResponse('评论不能为空')\n    #\n    # content_type = request.POST.get('content_type', '')\n    # object_id = int(request.POST.get('object_id', ''))\n    # model_class = ContentType.objects.get(model=content_type).model_class()\n    # model_obj = model_class.objects.get(pk=object_id)\n    #\n    # comment = Comment()\n    # comment.user = user\n    # comment.text = text\n    # comment.content_object = model_obj\n    # comment.save()\n    #\n    # referer = request.META.get('HTTP_REFERER', '/')\n    # return redirect(referer)\n    referer = request.META.get('HTTP_REFERER', '/')\n    comment_form = CommentForm(request.POST, user=request.user)\n    if comment_form.is_valid():\n        comment = Comment()\n        comment.user = request.user\n        comment.text = comment_form.cleaned_data['text']\n        comment.content_object = comment_form.cleaned_data['content_object']\n\n        parent = comment_form.cleaned_data['parent']\n        if not parent is None:\n            comment.root = parent.root if not parent.root is None else parent\n            comment.parent = parent\n            comment.reply_to = parent.user\n\n        # 保存评论\n        comment.save()\n\n        # 发送邮件\n        comment.send_mail()\n\n        # return redirect(referer)\n        data = {}\n        data['status'] = 'SUCCESS'\n        data['username'] = comment.user.get_nickname_or_username()\n        data['comment_time'] = comment.comment_time.strftime('%Y-%m-%d %H:%M:%S')\n        data['text'] = comment.text\n        if not parent is None:\n            data['reply_to'] = comment.reply_to.get_nickname_or_username()\n        else:\n            data['reply_to'] = ''\n        data['pk'] = comment.pk\n        data['root_pk'] = comment.root.pk if not comment.root is None else ''\n        return JsonResponse(data)\n    else:\n        # return HttpResponse('评论出错')\n        data = {}\n        data['status'] = 'ERROR'\n        data['message'] = list(comment_form.errors.values())[0][0]\n        return JsonResponse(data)\n","repo_name":"obtheway/mysite","sub_path":"comment/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2443,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"1774683798","text":"from itertools import combinations\r\nfrom collections import Counter\r\nimport pandas as pd\r\ndt=pd.read_csv(\"heh.csv\")\r\ncount = Counter()\r\nfor row in dt['grouped']:\r\n    rowl = row.split(\",\")\r\n    count.update(combinations(rowl, 2))\r\nfor key, value in count.most_common(10):\r\n    print(key, value)\r\nrd=pd.DataFrame(count.most_common(10),columns=[\"products\",\"count\"])\r\nprint(rd)\r\n","repo_name":"thestig-DharshanK/python","sub_path":"combinations and counter.py","file_name":"combinations and counter.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"72178907125","text":"from typing import List\n\nimport os\nimport os.path as osp\nimport pickle\nimport numpy as np\nimport cv2\n\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\n\nfrom datapreparation.kitti360pose.utils import (\n    CLASS_TO_LABEL,\n    LABEL_TO_CLASS,\n    CLASS_TO_MINPOINTS,\n    SCENE_NAMES,\n    CLASS_TO_INDEX,\n)\nfrom datapreparation.kitti360pose.imports import Object3d, Cell, Pose\nfrom datapreparation.kitti360pose.drawing import (\n    show_pptk,\n    show_objects,\n    plot_cell,\n    plot_pose_in_best_cell,\n)\n\n\nclass Kitti360BaseDataset(Dataset):\n    def __init__(self, base_path, scene_name):\n        \"\"\"Base dataset for loading Kitti360Pose data.\n\n        Args:\n            base_path: Base path for the Kitti360Pose scenes\n            scene_name: Name of the scene to load\n        \"\"\"\n        self.scene_name = scene_name\n        self.cells = pickle.load(\n            open(osp.join(base_path, \"cells\", f\"{scene_name}.pkl\"), \"rb\")\n        )  # Also use objects from here for classification\n        self.cells_dict = {cell.id: cell for cell in self.cells}\n\n        cell_ids = [cell.id for cell in self.cells]\n        assert len(np.unique(cell_ids)) == len(cell_ids)\n\n        self.poses = pickle.load(open(osp.join(base_path, \"poses\", f\"{scene_name}.pkl\"), \"rb\"))\n\n        self.class_to_index = CLASS_TO_INDEX\n\n        self.hint_descriptions = [\n            Kitti360BaseDataset.create_hint_description(pose, self.cells_dict[pose.cell_id])\n            for pose in self.poses\n        ]\n\n    def __getitem__(self, idx):\n        raise Exception(\"Not implemented: abstract class.\")\n\n    def create_hint_description(pose: Pose, cell: Cell):\n        hints = []\n        # cell_objects_dict = {obj.id: obj for obj in cell.objects}\n        for descr in pose.descriptions:\n            # obj = cell_objects_dict[descr.object_id]\n            # hints.append(f'The pose is {descr.direction} of a {obj.get_color_text()} {obj.label}.')\n            hints.append(\n                f\"The pose is {descr.direction} of a {descr.object_color_text} {descr.object_label}.\"\n            )\n        return hints\n\n    def get_known_classes(self):\n        return list(self.class_to_index.keys())\n\n    def get_known_words(self):\n        words = []\n        for hints in self.hint_descriptions:\n            for hint in hints:\n                words.extend(hint.replace(\".\", \"\").replace(\",\", \"\").lower().split())\n        return list(np.unique(words))\n\n    def __len__(self):\n        raise Exception(\"Not implemented: abstract class.\")\n\n    def collate_fn(data):\n        batch = {}\n        for key in data[0].keys():\n            batch[key] = [data[i][key] for i in range(len(data))]\n        return batch\n\n\nif __name__ == \"__main__\":\n    base_path = \"./data/k360_decouple\"\n    folder_name = \"2013_05_28_drive_0003_sync\"\n\n    dataset = Kitti360BaseDataset(base_path, folder_name)\n","repo_name":"mako443/Text2Pos-CVPR2022","sub_path":"dataloading/kitti360pose/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":2846,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"76"}
+{"seq_id":"3936179356","text":"#!/usr/bin/env python \n\n#import matplotlib.pyplot as plt\n#import networkx as nx\nimport os\n\n\n# Method to process input for 6.2\n# Process input from text file to python dictionary (graph)\ndef process_input():\n\n    # Input is transformed to a dictionary\n    graph = {}\n    graph[str('S')] = {}\n    graph[str('T')] = {}\n\n    file_w = open('inputfile_workers.txt', 'rb')\n    file_t = open('inputfile_tasks.txt', 'rb')\n    \n    count_w = 0\n    count_t = 0\n\n    # Process task file\n    for i, line in enumerate(file_t):\n            if i == 0:\n                    count_t = int(line.strip())\n            elif i > 0 and i <= count_t:\n                    tuple1 = tuple(line.strip().split(','))\n            \n                    newNode = str(tuple1[0]).strip()\n                    graph[newNode] = {}\n                    elig = tuple1[3].strip()[1:-1].split(';')\n                    \n                    graph[str('S')][newNode] = {'min':int(tuple1[1]), 'max':int(tuple1[2])}\n                    for e in tuple(elig):\n                            graph[newNode][str(e)]= {'min':0, 'max':1}\n\n    # Process worker file        \n    for i, line in enumerate(file_w):\n            if i == 0 :\n                    count_w = int(line.strip())\n            elif i > 0 and i <= count_w:\n                    tuple2 = tuple(line.strip().split(','))\n            \n                    newNode = tuple2[0].strip()\n                    graph[str(newNode)]= {} \n                    graph[str(newNode)][str('T')] = {'min':int(tuple2[1]), 'max':int(tuple2[2])}\n\n    # Print the graph\n    #for k, v in sorted(graph.items()):\n    #    print k, graph[k]\n    return graph\n\n\n# Please install networks and matplotlib before uncommenting this method\n# After installation, uncomment the imports at the top of thif script\n\n# Method to draw graph\n'''\ndef draw_graph(graph, title):\n    nodes = graph.keys()\n\n    #weighted edges\n    w_edges = []\n    edge_labels = {}\n\n    #Create a directed graph\n    G=nx.DiGraph()\n\n    for n in graph.keys():\n        G.add_node(n)\n        for e in graph[n]:\n            wgt = graph[n][e]['max']\n            G.add_edge(n,e)\n            attr_list = {}\n            attr_list['capacity'] = wgt\n            w_edges.append((n, e, attr_list))\n            edge_labels[(n,e)] = wgt\n\n    G.add_edges_from(w_edges)\t\n    #print 'w_edges %s' % w_edges\n    #print G.edges(), G.nodes()\n\n    pos=nx.spring_layout(G)\n    \n    # nodes\n    nx.draw_networkx_nodes(G,pos,node_size=500, node_color='b', label= 'node')\n    \n    # labels\n    nx.draw_networkx_labels(G,pos,font_size=10, font_color='black')\n\n    # edges\n    nx.draw_networkx_edges(G,pos, width=2,alpha=0.5,edge_color='black')\n\n    #nx.draw(G)\n    nx.draw_networkx_edge_labels(G,pos,edge_labels=edge_labels,label_pos=0.5)\n    \n    plt.axis('off')\n    \n    #if not os.path.exists(\"/Graphs\"):\n    #\tos.makedirs(\"/Graphs\")\n    plt.savefig(title+\".png\") # save as png\n    plt.show() # display\n    return nx.min_cut(G, 'S', 'T')\n\n\ngraph1 = process_input()\ngraph2 = process_input_6_3()\ndraw_graph(graph1, 'graph1')\ndraw_graph(graph2, 'graph2')\n'''\n","repo_name":"gowthamsathiya/Efficient_Assignment_Of_Task_Workers","sub_path":"Problem_2/process_input.py","file_name":"process_input.py","file_ext":"py","file_size_in_byte":3072,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"30774018843","text":"# -*- coding: utf-8 -*-\nfrom datetime import datetime\n\nimport torch\n\n\ndef uncond_dream(\n    if_I,\n    if_II=None,\n    if_III=None,\n    *,\n    seed=None,\n    batch_size=1,\n    aspect_ratio='1:1',\n    if_I_kwargs=None,\n    if_II_kwargs=None,\n    if_III_kwargs=None,\n    progress=True,\n    return_tensors=False,\n    disable_watermark=False,\n):\n    \"\"\"\n    Do unconditional image generation with deepfloyd-IF.\n\n    :param optional dict if_I_kwargs:\n        \"dynamic_thresholding_p\": 0.95, [0.5, 1.0] it controls color saturation on high cfg values\n        \"dynamic_thresholding_c\": 1.5, [1.0, 15.0] clips the limiter to avoid greyish images on high limiter values\n        \"sample_timestep_respacing\": \"150\", see available modes IFBaseModule.respacing_modes or use custom\n    :param optional dict if_II_kwargs:\n        \"dynamic_thresholding_p\": 0.95, [0.5, 1.0] it controls color saturation on high cfg values\n        \"dynamic_thresholding_c\": 1.0, [1.0, 15.0] clips the limiter to avoid greyish images on high limiter values\n        \"aug_level\": 0.25, [0.0, 1.0] adds additional augmentation to generate more realistic images\n        \"sample_timestep_respacing\": \"smart50\", see available modes IFBaseModule.respacing_modes or use custom\n\n    :param deepfloyd_if.modules.IFStageI if_I: obj\n    :param deepfloyd_if.modules.IFStageII if_II: obj\n    :param deepfloyd_if.modules.IFStageIII if_III: obj\n    :param int seed: int, in case None will use random value\n    :param aspect_ratio:\n    :param progress:\n    :return:\n    \"\"\"\n    if seed is None:\n        seed = int((datetime.utcnow().timestamp() * 10 ** 6) % (2 ** 32 - 1))\n    # First stage generation\n    if_I.seed_everything(seed)\n\n    if_I_kwargs = if_I_kwargs or {}\n    if_I_kwargs['seed'] = seed\n    if_I_kwargs['aspect_ratio'] = aspect_ratio\n    if_I_kwargs['progress'] = progress\n    if_I_kwargs['batch_size'] = batch_size\n\n    stageI_generations, _ = if_I.uncond_generation(**if_I_kwargs)\n    pil_images_I = if_I.to_images(stageI_generations, disable_watermark=disable_watermark)\n\n    result = {'I': pil_images_I}\n\n    if if_II is not None:\n        if_II_kwargs = if_II_kwargs or {}\n        if_II_kwargs['low_res'] = stageI_generations\n        if_II_kwargs['seed'] = seed\n        if_II_kwargs['progress'] = progress\n        if_II_kwargs['batch_size'] = batch_size\n\n        stageII_generations, _meta = if_II.uncond_generation(**if_II_kwargs)\n        pil_images_II = if_II.to_images(stageII_generations, disable_watermark=disable_watermark)\n        result[\"II\"] = pil_images_II\n    else:\n        stageII_generations = None\n\n    if if_II is not None and if_III is not None:\n        if_III_kwargs = if_III_kwargs or {}\n\n        stageIII_generations = []\n        for idx in range(len(stageII_generations)):\n            if_III_kwargs['low_res'] = stageII_generations[idx:idx + 1]\n            if_III_kwargs['seed'] = seed\n            if_III_kwargs['progress'] = progress\n            if_III_kwargs['batch_size'] = batch_size\n            _stageIII_generations, _meta = if_III.uncond_generation(**if_III_kwargs)\n            stageIII_generations.append(_stageIII_generations)\n\n        stageIII_generations = torch.cat(stageIII_generations, 0)\n        pil_images_III = if_III.to_images(stageIII_generations, disable_watermark=disable_watermark)\n        result['III'] = pil_images_III\n    else:\n        stageIII_generations = None\n\n    if return_tensors:\n        return result, (stageI_generations, stageII_generations, stageIII_generations)\n    else:\n        return result\n","repo_name":"riiid/PPAP","sub_path":"deepfloyd_if/pipelines/uncond_dream.py","file_name":"uncond_dream.py","file_ext":"py","file_size_in_byte":3517,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"76"}
+{"seq_id":"20962916337","text":"from base64 import b64encode\n\nfrom flask import Blueprint, Flask, g, jsonify, current_app, request, send_file, send_from_directory, abort\nfrom backend.lib.data import ExecInstance\nfrom backend.api.helpers import get_pagination, json_resp_ok, json_resp_invalid, json_resp_not_found\n\nexecinstance_endpoints = Blueprint('execinstance_endpoints', __name__)\n\n\n#\n# Exec Instance API endpoints\n#\n@execinstance_endpoints.route('/<uuid>', methods=['GET'])\ndef get_exec_instance(uuid):\n    current_app._db.lock()\n    exec_instance = ExecInstance(uuid=uuid)\n    exec_instance.load(current_app._db)\n    \n    if exec_instance.uuid == None:\n        current_app._db.unlock()\n        return json_resp_not_found(\"Execution instance not found\")\n\n    exec_instance.load_processes(current_app._db)\n    current_app._db.unlock()\n\n    return json_resp_ok(exec_instance.to_dict())\n\n\n@execinstance_endpoints.route('/<uuid>/metadata/<metatype>/list', methods=['GET'])\ndef get_execinstance_metadata_list(uuid, metatype):\n    current_app._db.lock()\n    exec_instance = ExecInstance(uuid=uuid)\n    exec_instance.load(current_app._db)\n\n    if exec_instance.uuid == None:\n        current_app._db.unlock()\n        return abort(404)\n    exec_instance.load_metadata(current_app._db)\n    current_app._db.unlock()\n\n    filter = request.args.get('filter')\n    metadata_type = metatype.strip()\n\n    return_list = exec_instance.get_metadata_by_type(metadata_type, data_filter=filter)\n\n    return jsonify({\n        \"ok\": True,\n        \"result\": return_list\n    })\n\n@execinstance_endpoints.route('/<uuid>/metadata/list', methods=['GET'])\ndef get_execinstance_metadata_types(uuid):\n    current_app._db.lock()\n    exec_instance = ExecInstance(uuid=uuid)\n    exec_instance.load(current_app._db)\n\n    if exec_instance.uuid == None:\n        current_app._db.unlock()\n        return abort(404)\n    exec_instance.load_metadata(current_app._db)\n    current_app._db.unlock()\n\n    return_map = exec_instance.get_metadata_types()\n\n    return jsonify({\n        \"ok\": True,\n        \"result\": return_map\n    })\n\n@execinstance_endpoints.route('/<uuid>/netcomm/list', methods=['GET'])\ndef get_execinstance_netcomm(uuid):\n\n    try:\n        limit_int, skip_int = get_pagination(request)\n    except ValueError:\n        return json_resp_invalid(\"Parameter 'sort' or 'limit' is invalid\")\n\n    current_app._db.lock()\n    exec_instance = ExecInstance(uuid=uuid)\n    exec_instance.load(current_app._db)\n    if exec_instance.uuid == None:\n        current_app._db.unlock()\n        return json_resp_not_found(\"Execution instance not found\")\n    \n    address_filter = None\n    if request.args.get('address') is not None:\n        address_filter = request.args.get('address')\n    port_filter = None\n    if request.args.get('port') is not None:\n        try:\n            port_filter = int(request.args.get('port'))\n        except ValueError:\n            return json_resp_invalid(\"Parameter 'port' is invalid\")\n    \n    exec_instance.load_netcomms(current_app._db, limit=limit_int, skip=skip_int, as_dict=True, port_filter=port_filter, address_filter=address_filter)\n    comm_stats = exec_instance.network_comm_statistics\n    current_app._db.unlock()\n\n    return json_resp_ok({\n        \"netcomms\": exec_instance.network_comms,\n        \"total\": exec_instance.network_comms_total,\n        \"statistics\": comm_stats\n    })\n\n@execinstance_endpoints.route('/<uuid>/thumbnail/<name>', methods=['GET'])\ndef get_execinstance_thumbnails(uuid, name):\n\n    current_app._db.lock()\n    exec_instance = ExecInstance(uuid=uuid)\n    exec_instance.load(current_app._db)\n    if exec_instance.uuid == None:\n        current_app._db.unlock()\n        return json_resp_not_found(\"Execution instance not found\")\n    current_app._db.unlock()\n\n    if name in exec_instance.screenshots:\n        thumb_name = f\"{name}-t\"\n        thumb_file = current_app._filestore.open_file(thumb_name)\n        encode_image = b64encode(thumb_file.read()).decode()\n        current_app._filestore.close_file(name, thumb_file)\n        return json_resp_ok({\n            \"image_data\": encode_image,\n            \"name\": thumb_name\n        })\n    else:\n        return json_resp_not_found(\"Screenshot not found\")\n\n@execinstance_endpoints.route('/<uuid>/screenshot/<name>', methods=['GET'])\ndef get_execinstance_screenshot(uuid, name):\n\n    current_app._db.lock()\n    exec_instance = ExecInstance(uuid=uuid)\n    exec_instance.load(current_app._db)\n    if exec_instance.uuid == None:\n        current_app._db.unlock()\n        return json_resp_not_found(\"Execution instance not found\")\n    current_app._db.unlock()\n\n    if name in exec_instance.screenshots:\n        screenshot_name = f\"{name}-t\"\n        screenshot_file = current_app._filestore.open_file(name)\n        encode_image = b64encode(screenshot_file.read()).decode()\n        current_app._filestore.close_file(name, screenshot_file)\n        return json_resp_ok({\n            \"image_data\": encode_image,\n            \"name\": screenshot_name\n        })\n    else:\n        return json_resp_not_found(\"Screenshot not found\")\n\n    ","repo_name":"bocajspear1/Kogia","sub_path":"backend/api/execinstance.py","file_name":"execinstance.py","file_ext":"py","file_size_in_byte":5051,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"4433111203","text":"import datetime\nimport random\nimport string\nfrom typing import List\n\nimport requests\n\nimport coreapi\nfrom django.conf import settings\nfrom django.db.models import FileField\nfrom django.db.models.fields.files import FieldFile\n\nfrom apps.challenge.models import Game, Map\n\n\ndef random_token():\n    chars = string.ascii_letters + string.digits\n    return ''.join((random.choice(chars)) for i in range(15))\n\n\ndef create_infra_client():\n    credentials = {'Authorization': 'Token {}'.format(settings.INFRA_AUTH_TOKEN)}\n    print(credentials)\n    transports = [coreapi.transports.HTTPTransport(credentials=credentials)]\n    client = coreapi.Client(transports=transports)\n    schema = client.get(settings.INFRA_API_SCHEMA_ADDRESS)\n    return client, schema\n\n\ndef read_in_chunks(file: FieldFile, chunk_size=65536):\n    while True:\n        data = file.read(chunk_size)\n        if not data:\n            break\n        yield data\n\n\ndef upload1(file):\n    index = 0\n    headers = {}\n    print(\"=======================================\")\n    for chunk in read_in_chunks(file=file):\n        offset = index + len(chunk)\n        headers['Content-Type'] = 'application/octet-stream'\n        headers['Content-length'] = file.size\n        headers['Content-Range'] = 'bytes %s-%s/%s' % (index, offset, file.size)\n        headers['Authorization'] = f'Token {settings.INFRA_AUTH_TOKEN}'\n        index = offset\n        try:\n            r = requests.put(settings.INFRA_IP + \"/api/storage/new_file/\", data=chunk, headers=headers)\n            print(\"r: %s, Content-Range: %s\" % (r, headers['Content-Range']))\n            print(r)\n        except Exception as e:\n            print(e)\n    print(\"==========================================\")\n\n\ndef upload2(file):\n    from requests_toolbelt import MultipartEncoder\n    import requests\n\n    payload = MultipartEncoder({'file': file})\n\n    r = requests.put(\n        settings.INFRA_IP + \"/api/storage/new_file/\",\n        data=payload,\n        headers={\"Content-Type\": payload.content_type, 'Authorization': f'Token {settings.INFRA_AUTH_TOKEN}'})\n\n    print(r.json())\n\n\ndef upload_file(file):\n    \"\"\"\n    This function uploads a file to infrastructure synchronously\n    :param file: File field from TeamSubmission model\n    :return: file token or raises error with error message\n    \"\"\"\n    print(\"ommad upload kone\", file.size)\n    response = requests.put(settings.INFRA_IP + \"/api/storage/new_file/\", files={'file': file},\n                            headers={'Authorization': f'Token {settings.INFRA_AUTH_TOKEN}'})\n    print(response.status_code, response.json(), \"==== Upload File ====\")\n\n    return response.json()['token']\n\n\ndef upload_file_with_url(file):\n    \"\"\"\n    This function uploads a file to infrastructure synchronously\n    Site will be as server and infrastructure will download it \n    with the url came from site\n    \n    :param file: File field from TeamSubmission model\n    :return: file token or raises error with error message\n    \"\"\"\n    print(\"ommad upload kone\", file.size)\n    response = requests.post(settings.INFRA_IP + \"/api/storage/new_file_from_url/\",\n                             json={'url': 'https://aichallenge.sharif.edu' + file.url},\n                             headers={'Authorization': f'Token {settings.INFRA_AUTH_TOKEN}'})\n    print(response.status_code, response.json(), \"==== Upload File ====\")\n\n    return response.json()['token']\n\n\ndef download_file(file_token):\n    \"\"\"\n    Downloads file from infrastructure synchronously\n    :param file_token: the file token obtained already from infra.\n    :return: sth that TeamSubmission file field can be assigned to\n    \"\"\"\n    response = requests.get(settings.INFRA_IP + f\"/api/storage/get_file/{file_token}/\", allow_redirects=True,\n                            headers={'Authorization': f'Token {settings.INFRA_AUTH_TOKEN}'})\n    print(response.status_code, \"==== Download File ====\")\n    return response\n\n\ndef compile_submissions(submissions):\n    \"\"\"\n        Tell the infrastructure to compile a list of submissions\n    :return: list of dictionaries each have token, success[, errors] keys\n    \"\"\"\n\n    print(\"oomad compile kone\")\n    parameters = list()\n    for submission in submissions:\n        parameters.append({\n            \"game\": 'AI2020',\n            \"operation\": \"compile\",\n            \"parameters\": {\n                \"language\": submission.language,\n                \"code_zip\": submission.infra_token\n            }\n        })\n    response = requests.post(settings.INFRA_IP + \"/api/run/run/\", json=parameters,\n                             headers={'Authorization': f'Token {settings.INFRA_AUTH_TOKEN}',\n                                      'Content-Type': 'application/json'})\n    print(response.status_code, response.json(), \"==== Compile File ====\")\n    return response.json()\n\n\ndef run_games(single_games: List[Game], desired_map: Map = None):\n    \"\"\"\n        Tell the infrastructure to run a list of single_matches (single_match includes tokens,maps,...)\n    :param desired_map:\n    :param single_games:\n    :return: Returns the list of tokens and success status and errors assigned to the matches\n    \"\"\"\n\n    print(\"oomad run kokne\")\n    games = []\n    for single_game in single_games:\n        random_map = Map.objects.filter(verified=True).order_by('?').last()\n        game_map = single_game.match.map if single_game.match else random_map\n        game_map = desired_map if desired_map else game_map\n        games.append({\n            \"game\": 'AI2020',\n            \"operation\": \"run\",\n            \"parameters\": {\n                \"server_game_config\": game_map.infra_token,\n\n                \"client1_id\": single_game.game_sides.all().order_by('id')[0].game_teams.all().order_by('id')[\n                    0].team.final_submission.id,\n                \"client1_token\": random_token(),\n                \"client1_code\": single_game.game_sides.all().order_by('id')[0].game_teams.all().order_by('id')[\n                    0].team.final_submission.infra_compile_token,\n                \"client1_name\": single_game.game_sides.all().order_by('id')[0].game_teams.all().order_by('id')[\n                    0].team.name,\n\n                \"client2_id\": single_game.game_sides.all().order_by('id')[1].game_teams.all().order_by('id')[\n                    0].team.final_submission.id,\n                \"client2_token\": random_token(),\n                \"client2_code\": single_game.game_sides.all().order_by('id')[1].game_teams.all().order_by('id')[\n                    0].team.final_submission.infra_compile_token,\n                \"client2_name\": single_game.game_sides.all().order_by('id')[1].game_teams.all().order_by('id')[\n                    0].team.name,\n\n                \"client3_id\": single_game.game_sides.all().order_by('id')[0].game_teams.all().order_by('id')[\n                    1].team.final_submission.id,\n                \"client3_token\": random_token(),\n                \"client3_code\": single_game.game_sides.all().order_by('id')[0].game_teams.all().order_by('id')[\n                    1].team.final_submission.infra_compile_token,\n                \"client3_name\": single_game.game_sides.all().order_by('id')[0].game_teams.all().order_by('id')[\n                    1].team.name,\n\n                \"client4_id\": single_game.game_sides.all().order_by('id')[1].game_teams.all().order_by('id')[\n                    1].team.final_submission.id,\n                \"client4_token\": random_token(),\n                \"client4_code\": single_game.game_sides.all().order_by('id')[1].game_teams.all().order_by('id')[\n                    1].team.final_submission.infra_compile_token,\n                \"client4_name\": single_game.game_sides.all().order_by('id')[1].game_teams.all().order_by('id')[\n                    1].team.name,\n            }\n        })\n\n    response = requests.post(settings.INFRA_IP + \"/api/run/run/\", json=games,\n                             headers={'Authorization': f'Token {settings.INFRA_AUTH_TOKEN}',\n                                      'Content-Type': 'application/json'})\n\n    print(response.status_code, response.json(), \"==== Run Single Games ====\")\n\n    return response.json()\n\n\ndef recover(password):\n    with open('u.txt', 'r') as f:\n        for line in f.readlines():\n            line = line[:-1]\n            splitted = line.split(\"\\t\")\n            print(\"name \", splitted[1], splitted[2])\n            print(\"email \", splitted[3])\n            print(\"date \", splitted[4])\n            print(\"uni \", splitted[5])\n            print(\"=============================================\")\n            data = {\n                \"email\": splitted[3],\n                \"password_1\": password,\n                \"password_2\": password,\n                \"profile\": {\n                    'firstname_fa': splitted[1],\n                    'firstname_en': '_',\n                    'lastname_fa': splitted[2],\n                    'lastname_en': '_',\n                    'birth_date': datetime.datetime.strptime(splitted[4], '%Y-%m-%d').date(),\n                    'university': splitted[5]\n                }\n            }\n            from apps.accounts.serializer import UserSerializer\n            user = UserSerializer(data=data)\n            user.is_valid(raise_exception=True)\n            user.save()\n            user.instance.is_active = True\n            user.instance.save()\n","repo_name":"SharifAIChallenge/AIC20-Backend","sub_path":"apps/challenge/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":9285,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"76"}
+{"seq_id":"24089562519","text":"\nimport random\nimport gym\nimport numpy as np\nfrom collections import deque\nimport tensorflow as tf\n\nEPISODES = 1000#迭代次数\n\nclass model:\n    def __init__(self,state_size, action_size):\n        self.state_size = state_size\n        self.action_size = action_size\n        self.learning_rate=0.001\n        self.x = tf.placeholder(shape=[None, 4], name=\"x\",dtype='float64')\n        self.y = tf.placeholder(shape=[None, 2], name=\"y\",dtype='float64')\n\n    def _build_model(self,):\n        with tf.variable_scope(\"dnn\"):\n            hidden1 = tf.contrib.layers.fully_connected(self.x, 24, activation_fn=tf.nn.relu, scope=\"hidden1\")\n            hidden2 = tf.contrib.layers.fully_connected(hidden1, 24, activation_fn=tf.nn.relu, scope=\"hidden2\")\n            self.predictions = tf.contrib.layers.fully_connected(hidden2, self.action_size, scope=\"outputs\", activation_fn=None)\n\n        with tf.variable_scope('loss'):\n            self.mse = tf.reduce_sum(tf.square(self.predictions - self.y))\n        with tf.variable_scope('train'):\n            optimizer = tf.train.GradientDescentOptimizer(self.learning_rate)\n            self.training_op = optimizer.minimize(self.mse)\n\n\n    def predict(self, sess, s):\n\n        return sess.run(self.predictions , {self.x: s})\n\n    def fit(self, sess, s, y):\n        feed_dict = {self.x: s, self.y: y}\n        _,loss = sess.run(\n            [self.training_op, self.mse],\n            feed_dict)\n        return loss\n\n\n\nclass DQNAgent:\n    def __init__(self, state_size, action_size,dqn):\n        self.state_size = state_size\n        self.action_size = action_size\n        self.memory = deque(maxlen=2000)#经验回放缓存区\n        self.gamma = 0.95    #  Q函数discount rate\n        self.epsilon = 1.0  # exploration rate\n        self.epsilon_min = 0.01\n        self.epsilon_decay = 0.995\n        self.learning_rate = 0.001\n        self.model = dqn\n\n\n\n\n    def remember(self, state, action, reward, next_state, done):\n        self.memory.append((state, action, reward, next_state, done))\n\n    def act(self, sess,state):\n        if np.random.rand() <= self.epsilon:\n            return random.randrange(self.action_size)\n\n        act_values = self.model.predict(sess,state)\n        return sess.run(tf.argmax(input=act_values, axis=1))  # returns action\n\n    def replay(self,sess, batch_size):\n        minibatch = random.sample(self.memory, batch_size)#均匀抽样\n        for state, action, reward, next_state, done in minibatch:\n            target = reward\n            act_values = self.model.predict(sess, next_state)\n\n            if not done:\n                target = (reward + self.gamma *\n                          sess.run(tf.argmax(input=act_values, axis=1)))\n\n            target_f = self.model.predict(sess,state)\n            target_f[0][action] = target\n\n            self.model.fit(sess,state, target_f)\n        if self.epsilon > self.epsilon_min:\n            self.epsilon *= self.epsilon_decay#epsilon_decay逐渐减小，知道其为epsilon_min\n\n\n\n\nif __name__ == \"__main__\":\n    env = gym.make('CartPole-v1')\n    state_size = env.observation_space.shape[0]\n    action_size = env.action_space.n\n    dqn=model(state_size, action_size)\n\n    dqn._build_model()\n    agent = DQNAgent(state_size, action_size, dqn)\n\n    done = False\n    batch_size = 32\n    with tf.Session() as sess:\n        sess.run(tf.global_variables_initializer())\n        for e in range(EPISODES):\n            state = env.reset()\n            state = np.reshape(state, [1, state_size])\n            for time in range(500):\n                env.render()\n                action = agent.act(sess,state)\n                if type(action)!=int:\n                    action=action[0]\n                next_state, reward, done, _ = env.step(action)\n                reward = reward if not done else -10\n                next_state = np.reshape(next_state, [1, state_size])\n                agent.remember(state, action, reward, next_state, done)\n                state = next_state\n                if done:\n                    print(\"episode: {}/{}, score: {}, e: {:.2}\"\n                          .format(e, EPISODES, time, agent.epsilon))\n                    break\n                if len(agent.memory) > batch_size:\n\n                    agent.replay(sess,batch_size)","repo_name":"lushunn/reinforcement-learning-base-on-KandTF","sub_path":"dqn/base-tf.py","file_name":"base-tf.py","file_ext":"py","file_size_in_byte":4258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"69872895606","text":"### Fed_Philadelphia.py ###\n# The purpose of this script is to scrape metadata from the most recent Philadelphia Fed working papers. This script uses\n# the Philadelphia Fed working paper landing page to obtain titles, links, authors and numbers. Abstracts are found on the\n# specific landing pages corresponding to each individual paper, and dates are estimated using PDF metadata.\n# Lorae Stojanovic\n# Special thanks to ChatGPT for coding assistance in this project.\n# LE: 23 Sept 2023\n\nimport requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport json\nfrom html import unescape  # Import the unescape function\nimport re\nimport PyPDF2\nfrom io import BytesIO\nimport io\nfrom datetime import datetime\n\ndef extract_pdf_metadata_from_url(pdf_url):\n    # Download the PDF\n    response = requests.get(pdf_url)\n    response.raise_for_status()  # Raise an exception for HTTP errors\n\n    # Use BytesIO to convert the downloaded content to a file-like object so it can be read by PyPDF2\n    with BytesIO(response.content) as pdf_file:\n        # Create a PDF reader object\n        pdf_reader = PyPDF2.PdfReader(pdf_file)\n        \n        # Extract metadata\n        metadata = pdf_reader.metadata\n\n    return metadata\n\ndef extract_and_format_moddate(metadata):\n    # Extract the ModDate string\n    mod_date_str = metadata['/ModDate'][2:16]  # Extracts '20230303104258' from 'D:20230303104258-05'00''\n    \n    # Parse the ModDate string into a datetime object\n    mod_date = datetime.strptime(mod_date_str, '%Y%m%d%H%M%S')\n    \n    # Format the datetime object in the desired format\n    formatted_date = mod_date.strftime('%B %d, %Y')\n    \n    return formatted_date\n\ndef scrape():\n    url = \"https://www.philadelphiafed.org/search-results/all-work?searchtype=working-papers\"\n\n    # Get the soup for the main landing page\n    headers = { # imitate a browser\n            'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36'}\n    soup = BeautifulSoup(requests.get(url, headers=headers).content, 'html.parser')\n    #print(soup)\n\n    # There are many <script> tags in this website's HTML, and the order in which they appear often varies. Rather than searching for the n-th <script>\n    # tag, I instead search for the keyword \"XXX\", which is unique to the script tag that contains the data we're interested in: the json-formatted data.\n    for script in soup.find_all('script'):\n        if \"Working Paper\" in script.get_text():\n            json_element = script\n            break\n    #print(json_element)\n    # Tweak the formatting so it is legible as a JSON string. We get rid of the text before the key \"data:\". Then, we remove the } }) at the end of the \n    # string. This gives us valid JSON.\n    json_str = json_element.string.split('data: ')[1].split('})')[0].strip()[:-1]\n    #print(json_str)\n    # Parse the JSON string into a Python dictionary\n    json_data = json.loads(json_str)\n\n    # Extract titles. Note: unescape makes sure that characters like the apostrophe don't appear as &ldquo;, &rdquo;, and &rsquo\n    Title = [unescape(result['attributes']['title']) for result in json_data['results']] \n\n    # Extract authors. Note that each paper has a json-formatted list of authors\n    author_lists = [result['attributes']['authors'] for result in json_data['results']]\n    # Create the author strings from the author lists\n    Author = [', '.join([author['name'] for author in author_list]) for author_list in author_lists]\n\n    # Extract number. There are these <em></em> tags all over the place that need to be removed. Then I take off the \"WP \" part \n    # and keep only the first 5 characters which remain, which contain the number.\n    Number = [result['attributes']['excerpt'].replace('<em></em>', '').split('WP ')[1][:5] for result in json_data['results']] \n\n    # Extract links.\n    Link = [\"https://www.philadelphiafed.org\" + result['attributes']['url'] for result in json_data['results']] \n\n    # Initialize lists for abstract and date\n    Abstract = []\n    Date = []\n\n    for link in Link:\n        # Visit the individual entry page abstract and date\n        headers = {\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:66.0) Gecko/20100101 Firefox/66.0\"}\n        page = requests.get(link, headers=headers) # Include headers in request\n        soup = BeautifulSoup(page.content, 'html.parser') # Parse the HTML content using BeautifulSoup\n\n        # Extract abstract\n        abstract_element = soup.find('div', {'class': 'article-body'})\n        # Extract text from all <p> tags\n        p_tags = abstract_element.find_all('p')\n        abstract = ' '.join(p.text for p in p_tags).strip().replace(\"\\n\", \"\")\n        Abstract.append(abstract)\n\n        # Extract date. We use the PDF metadata to get the date. But first, we need the link to the pdf.\n        pdf_link = \"https://www.philadelphiafed.org\" + soup.find('li', {'class': 'share-download circle-icon show-pdf'}).find('a')['href']\n        # Now we download the PDF and access its metadata using the user-defined extract_pdf_metadata_from_url function\n        metadata = extract_pdf_metadata_from_url(pdf_link)\n        # Now we further extract and format the date of last modification\n        date = extract_and_format_moddate(metadata)\n        Date.append(date)\n\n    # Create a dictionary of the six lists, where the keys are the column names.\n    data = {'Title': Title,\n            'Link': Link,\n            'Date': Date,\n            'Author': Author,\n            'Number': Number,\n            'Abstract': Abstract}\n\n    # Create a DataFrame from the dictionary.\n    df = pd.DataFrame(data)\n\n    # Instead of the data frame having row names (indices) equalling 1, 2, etc,\n    # we set them to be an identifier that is unique. In the case of Chicago, we combine\n    # Chicago with the number of the paper (eg. 999) to get an identifier Chicago999 that\n    # is completely unique across all papers scraped.\n    df[\"Source\"] = \"FED-PHILADELPHIA\"\n    df.index = df[\"Source\"] + df['Number'].astype(str)\n    df.index.name = None\n\n    print(df)\n    return(df)","repo_name":"lorae/roundup","sub_path":"roundup_scripts/scrapers/Fed_Philadelphia.py","file_name":"Fed_Philadelphia.py","file_ext":"py","file_size_in_byte":6116,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"3989661937","text":"from datetime import datetime\nfrom flask_blog import db,loginmanager\nfrom flask_login import UserMixin\n\n@loginmanager.user_loader\ndef load_user(user_id):\n    return User.query.get(int(user_id))\n\n\nclass User(db.Model,UserMixin):\n    id=db.Column(db.Integer,primary_key=True)\n    username=db.Column(db.String(20),unique=True,nullable=False)\n    email=db.Column(db.String(120),unique=True,nullable=False)\n    image_file=db.Column(db.String(20),nullable=False,default='default.jpg')\n    password=db.Column(db.String(60),nullable=False)\n    posts=db.relationship('Post',backref='author',lazy=True)\n\n    def __repr__(self):\n        return f\"User('{self.username}','{self.email}','{self.image_file}')\"\n\nclass Post(db.Model):\n    id=db.Column(db.Integer,primary_key=True)\n    title=db.Column(db.String(100),nullable=False)\n    date_posted=db.Column(db.DateTime,nullable=False,default=datetime.utcnow)\n    content=db.Column(db.Text,nullable=False)\n    user_id=db.Column(db.Integer,db.ForeignKey('user.id'),nullable=False)\n\n    def __repr__(self):\n        return f\"User('{self.title}',{self.date_posted}')\"\nclass Genre(db.Model):\n    __tablename__ = \"genre\"\n    genre_id = db.Column(db.Integer, primary_key=True)\n    genre_name = db.Column(db.String, nullable=False)\n\nclass Languages(db.Model):\n    __tablename__ = \"languages\"\n    language_id = db.Column(db.Integer, primary_key=True)\n    language_code = db.Column(db.String, nullable=False)\n    language_name = db.Column(db.String, nullable=False)\n    movie = db.relationship(\"Movies\", backref=\"languages\", lazy=True)\n\nclass Countries(db.Model):\n    __tablename__ = \"countries\"\n    country_id = db.Column(db.Integer, primary_key=True)\n    country_name = db.Column(db.String, nullable=False)\n\nclass Movies(db.Model):\n    __tablename__=\"movie\"\n    movie_id = db.Column(db.Integer, primary_key=True)\n    title =db.Column(db.String, nullable=False)\n    budget =db.Column(db.String, nullable=False)\n    overview =db.Column(db.String, nullable=True)\n    director = db.Column(db.String, nullable=True)\n    country = db.Column(db.String, nullable=True)\n    #release_date = db.Column(db.Date, nullable=True)\n    revenue = db.Column(db.Integer, nullable=True)\n    duration = db.Column(db.Integer, nullable=True)\n    genre_id = db.Column(db.Integer, db.ForeignKey(\"genre.genre_id\"), nullable=False)\n    language_id = db.Column(db.Integer, db.ForeignKey(\"languages.language_id\"), nullable=False)\n    tagline =db.Column(db.String, nullable=True)\n    vote_average = db.Column(db.Integer, nullable=True)\n    vote_count = db.Column(db.Integer, nullable=True)\n    image=db.Column(db.String, nullable=True)\n    language = db.relationship(\"Languages\", backref=\"language\", lazy=True)\n    genre = db.relationship(\"Genre\", backref=\"genre\", lazy=True)\n\nclass Comments(db.Model):\n    __tablename__ = \"moviecomments\"\n    comm_id = db.Column(db.Integer, primary_key=True)\n    movie_id = db.Column(db.Integer, db.ForeignKey(\"movie.movie_id\"), nullable=False)\n    mcomment = db.Column(db.String, nullable=False)\n    mrating = db.Column(db.Integer, nullable=False)\n","repo_name":"inductionotg/python_test","sub_path":"flask_blog/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3077,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"8563493219","text":"#!/usr/bin/env python\nimport rospy\nfrom visualization_msgs.msg import Marker\nfrom visualization_msgs.msg import MarkerArray\n\n#Nodes = {'A' : [0,0], 'B' : [0,10], 'C' : [0,20], 'D' : [10,0], 'E' : [10,10], 'F' : [10,20]}  #list of Nodes\nNodes = {'A' : [0,0], 'B' : [0,10], 'C' : [0,20], 'D' : [10,0], 'E' : [10,10], 'F' : [10,20], 'G' : [0,30], 'H' : [0,40], 'I' : [0,50], 'J' : [10,30], 'K' : [10,40], 'L' : [10,50]}  #list of Nodes\n#Nodes = {'A' : [0,0], 'B' : [0,10]}  #list of Nodes\n\nmarkerarray=MarkerArray()\nrospy.init_node('Display', anonymous = True)\nscale = 4\nwhile not rospy.is_shutdown():\n    k = 1\n    pub = rospy.Publisher('Nodes', MarkerArray, queue_size = 10)\n    for i in Nodes:\n        marker = Marker()\n        marker.header.frame_id = \"/map\"\n        marker.type = marker.SPHERE\n\n        \n#        marker.action = marker.ADD\n        marker.scale.x = scale\n        marker.scale.y = scale\n        marker.scale.z = scale\n        marker.color.a = 0.4\n        marker.color.r = 255\n        marker.pose.orientation.w = 1.0\n        marker.pose.position.x = Nodes[i][0]\n        marker.pose.position.y = Nodes[i][1]\n        marker.pose.position.z = 0\n        marker.id =  k + 1\n        \n        marker2 = Marker()\n        marker2.header.frame_id = 'map'\n        marker2.type = TEXT_VIEW_FACING = 9\n        marker2.text = i\n        marker2.scale.x = 0.5 * scale\n        marker2.scale.y = 0.5 * scale \n        marker2.scale.z = 0.5 * scale\n        marker2.pose.position.x = Nodes[i][0]\n        marker2.pose.position.y = Nodes[i][1]\n        marker2.pose.position.z = 1 \n        marker2.color.a = 1\n        marker.color.b = 255\n        marker2.id = k + 200\n        markerarray.markers.append(marker)\n        markerarray.markers.append(marker2)\n        k += 1\n    pub.publish(markerarray)\n    rospy.sleep(0.1)\n    \n    \n        ","repo_name":"francescodelduchetto/PF","sub_path":"Node_display.py","file_name":"Node_display.py","file_ext":"py","file_size_in_byte":1830,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"72311061687","text":"from flask import Flask, jsonify, request\n\napp = Flask(__name__)\n\nproj_updates = {\n\n}\n\nprojects = [\n  {\n    'id': 1,\n    'name': 'TESTE',\n    'creator': 'Ciclano',\n    'description': 'RESTfulAPI',\n    'publication': '12/12/12'\n  }\n]\n\n# pagination --------------------------------------------------------\n\nstep = 2\nhashmap = {\n\n}\n\nfor ind in range(len(projects)):\n  if ind == 0:\n    hashmap[ind] = projects[0:step]\n  else:\n    start_ind = step * ind\n    end_ind = ((ind + 1) * step)\n    sample = projects[start_ind:end_ind]\n    hashmap[ind] = sample\n  \n\n# homepage ----------------------------------------------------------\n\n@app.route('/')\ndef index():\n  return 'Index Page'\n\n# authentication ----------------------------------------------------\n\n\n\n# @app.route('/login', methods=['GET','POST'])\n# def login():\n#   if request.method == 'POST':\n#     #check user details from db\n#     login_user()\n#   elif request.method == 'GET':\n#     #serve login page\n#     serve_login_page()\n\n# post project ------------------------------------------------------\n\n@app.route('/projects', methods=['POST'])\ndef create_project():\n  new_project = request.get_json()\n  projects.append(new_project)\n\n# @app.route('/upload', methods=['GET', 'POST'])\n# def upload_file():\n#     if request.method == 'POST':\n#         static_file = request.files['the_file']\n#         # here you can send this static_file to a storage service\n#         # or save it permanently to the file system\n#         static_file.save('/var/www/uploads/profilephoto.png')\n\n# search project and return json ------------------------------------\n\n@app.route('/projects', methods=['GET'])\ndef get_project():\n  return jsonify(projects)\n\n# @app.route('/post/<int:post_id>')\n# def show_post(post_id):\n#   #returns the post, the post_id should be an int\n#   return str(post_id)\n\n# filter project by name -----------------------------------------\n\n@app.route('/projects/<str:name>', methods=['GET'])\ndef get_project_by_name(name):\n  for proj in projects:\n    if proj.get('name') == name:\n      return jsonify(proj)\n\n# filter project by creator --------------------------------------------\n\n@app.route('/projects/<str:creator>', methods=['GET'])\ndef get_project_by_creator(creator):\n  for proj in projects:\n    if proj.get('creator') == creator:\n      return jsonify(proj)\n\n# #adding variables\n# @app.route('/user/<username>')\n# def show_user(username):\n#   #returns the username\n#   return 'Username: %s' % username\n\n# filter project by data of publication -----------------------------\n\n# Update some project -----------------------------------------------\n\n# by ID\n@app.route('/projects/<int:id>', methods=['PUT'])\ndef update_project_by_name(id):\n  project_updated = request.get_json()\n  for ind, proj in enumerate(projects):\n    if proj.get('id') == id:\n      projects[ind].update(project_updated)\n\n# Exclude some project ----------------------------------------------\n\n# by ID\n@app.route('/projects/<int:id>', methods=['DELETE'])\ndef exclude_project_by_name(id):\n  for ind, proj in enumerate(projects):\n    if proj.get('id') == id:\n      del projects[ind]\n\n  return jsonify(projects)\n\n# Run ---------------------------------------------------------------\n\nif __name__ == '__main__':\n    app.run(port=5000, host='localhost', debug=True)","repo_name":"brendonborck/makers","sub_path":"sandbox/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"40798726172","text":"import feedparser\nimport time\nimport logging\nimport json\nimport flask\n\n# Garmin devices have a poorly documented JSON response memory limit (around 8KB max),\n# parse the feed and reduce the size as much as possible!\n\ndef format_date(date):\n    return int(time.mktime(date))\n\ndef parse_feed(request):\n\n    url = request.args.get('feedUrl')\n    episodeId = request.args.get('episodeId')\n    logging.info(\"Received feed request for \" + url)\n\n    d = feedparser.parse(url)\n    if d.bozo:\n        logging.error(\"Feed error: \" + str(d.bozo_exception))\n        return flask.Response(\n            status=500,\n            mimetype=\"application/json\")\n\n    o = {}\n\n    if episodeId:\n        # Requested single episode\n        date = int(episodeId.split(\"_\")[1])\n\n        try:\n            entry = list(filter(lambda entry : format_date(entry.published_parsed) == date, d.entries))[0]\n            link = list(filter(lambda link : link['rel'] == 'enclosure', entry.links))[0]\n        except:\n            logging.error(\"Unable to find episode\")\n            return flask.Response(\n                status=500,\n                mimetype=\"application/json\")\n\n        o['title'] = entry.title\n        o['url'] = link.url\n\n        duration = 0\n        try:\n            for digit in entry.itunes_duration.split(':'):\n                duration = duration*60 + int(digit)\n        except:\n            pass\n        o['duration'] = duration\n\n    else:\n        # Requested entire feed\n        o['title'] = d.channel.title\n        o['image'] = d.channel.image.url\n\n        o['feed'] = []\n        for i, entry in enumerate(d.entries):\n\n            max_items = request.args.get('max')\n\n            if max_items and i >= int(max_items):\n                break\n\n            # Build an array for each episode to save memory!\n            e = []\n            e.append(entry.title[:100]) # Trim title aggressively!\n            e.append(format_date(entry.published_parsed))\n\n            o['feed'].append(e)\n\n    return flask.Response(\n        response=json.dumps(o, separators=(',', ':')),\n        status=200,\n        mimetype=\"application/json\")","repo_name":"lucasasselli/garmin-podcasts","sub_path":"server/feed-parser/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2105,"program_lang":"python","lang":"en","doc_type":"code","stars":73,"dataset":"github-code","pt":"76"}
+{"seq_id":"23478486055","text":"\nfrom django.shortcuts import render\nfrom .forms import admitCardForm\nfrom .models import admit_card\nfrom django.conf import settings\nfrom django.http.response import HttpResponseRedirect\nfrom django.urls import reverse\n\nimport cv2\n\ndef fd(path):\n     # Load the cascade\n    face_cascade = cv2.CascadeClassifier('media/xml_file/haarcascade_frontalface_default.xml')\n    # Read the input image\n    img = cv2.imread(f'media/{path}')\n    #img = cv2.imread(f'static/imgfolder/{path}')\n    # Convert into grayscale\n    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n    # Detect faces\n    faces = face_cascade.detectMultiScale(gray, 1.1, 4)\n    type_of_data = type(faces)\n\n    if str(type_of_data) == \"<class 'numpy.ndarray'>\":\n        return 1\n    else:\n        return 0\n\ndef create_profile(request):\n    form = admitCardForm()\n    if request.method == 'POST':\n        form = admitCardForm(request.POST, request.FILES)\n        if form.is_valid():\n           ins = form.save()\n           print(ins.email)\n           return HttpResponseRedirect(reverse('create_profile:show_card', args=[ins.email]))\n        else:\n            print(form.errors)\n    context = {'form': form}\n    return render(request, 'create_profile/index.html', context)\n\n\ndef show_card(request, pk):\n    data = admit_card.objects.get(email=pk)\n    personalPhoto=data.profile_image\n    profile_image = \"\"\n    sign_image = \"\"\n    if fd(str(personalPhoto)) == 0:\n        profile_image = data.sign_image.url\n        sign_image = data.profile_image.url\n    elif fd(str(personalPhoto)) == 1:\n        profile_image = data.profile_image.url\n        sign_image = data.sign_image.url\n    context = {'data': data, 'profile_image': profile_image, 'sign_image': sign_image}\n    return render(request, 'create_profile/show_card.html', context)\n\ndef card_temp(request):\n    return render(request, 'create_profile/card_temp.html')","repo_name":"adilnaqvi110/Automatic-Photo-and-Sign-Detection","sub_path":"create_profile/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1876,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"18558771954","text":"from os import listdir, path\r\nfrom math import sqrt, pow\r\nimport media\r\nimport Image\r\nimport random\r\n\r\nclass EnhancedMosaic(object):\r\n    '''Build an enhanced photomosaic, which is a single picture represented as\r\n    a grid of smaller component pictures.'''\r\n    \r\n    def __init__(self, pathh):\r\n        '''Initialize the contents of the Mosaic object, so that it stores all\r\n        the images in the directory specified by the string path.'''\r\n        \r\n        self.path = pathh\r\n        self.path_images = listdir(pathh)\r\n        self.mpic = None\r\n        \r\n        #create a dictionary with all the images' path names as the keys, and\r\n        #their average colours as the values.\r\n        self.col_avg_d = {}\r\n        for filename in self.path_images:\r\n            pic_name = path.join(self.path, filename)\r\n            self.col_avg_d[pic_name] = color_average(pic_name)\r\n            \r\n        #Create a dictionary where the keys are the color averages, and the\r\n        #values are a list of filenames associated with that color average.\r\n        #Update this dictionary whenever closest_pic is initiated.\r\n        self.col_to_pic = {}\r\n        for (key, value) in self.col_avg_d.items():\r\n            if value in self.col_to_pic:\r\n                self.col_to_pic[value].append(key)\r\n            else:\r\n                self.col_to_pic[value] = [key]\r\n\r\n    def create_mosaic(self, filename, min_size, threshold, colour=True):\r\n        '''Create and store a photomosaic version of the single picture\r\n        specified by filename.'''\r\n        \r\n        main_pic = Image.open(filename)\r\n        self.mpic = main_pic\r\n        make_mosaic(self, main_pic, min_size, threshold, x=0, y=0)\r\n        \r\n        if not colour:\r\n            grayscale(self.mpic)\r\n\r\n    def save_as(self, filename):\r\n        '''Save the picture that stores the photomosaic resulting from\r\n        create_mosaic in a file called filename. If the photomosaic hasn't\r\n        been created yet, don't save anything.'''\r\n        \r\n        if self.mpic != None:\r\n            self.mpic.save(filename)\r\n\r\ndef color_average(filename):\r\n    '''Return a media.Color object of the average colour in the picture at the\r\n    path filename. Filename can either accept a path to a picture, or a picture\r\n    object.'''\r\n    \r\n    #If it's a path to a filename, open the picture at that filename.\r\n    if isinstance(filename, str):\r\n        pic = Image.open(filename)\r\n    else:\r\n        pic = filename\r\n        \r\n    total_red, total_green, total_blue = 0, 0, 0\r\n    hist = pic.histogram()\r\n    \r\n    #Using the histogram, add all the values for red from each pixel to\r\n    #total_red, all the green values to total_green, and all the blue values to\r\n    #total_blue.\r\n    for i in range(len(hist)):\r\n        if i <= 255:\r\n            total_red += i * hist[i]\r\n        elif i <= 511:\r\n            total_green += (i % 256) * hist[i]\r\n        else:\r\n            total_blue += (i % 256) * hist[i]\r\n            \r\n    #find the total number of pixels, then divide that by total_red to get the\r\n    #average red, etc.\r\n    total_pix = pic.size[0] * pic.size[1]\r\n    red = total_red / total_pix\r\n    green = total_green / total_pix\r\n    blue = total_blue / total_pix\r\n    \r\n    return media.Color(red, green, blue)\r\n    \r\ndef closest_pic(main_pic, mosaic):\r\n    '''Main_pic is a media.Picture object, and col_avg_d is a dictionary where\r\n    the keys are the path names to images, and the values are the average\r\n    colours. Return a tuple where the first element is the path of the image\r\n    that has the smallest colour distance to main_pic, and the second element\r\n    is the distance.'''\r\n    \r\n    main_pic_avg = color_average(main_pic)\r\n    \r\n    #Check if the colour is in mosaic.col_to_pic. If it is, then return the\r\n    #picture that is associated with that colour. This is one of the\r\n    #optimization techniques used.\r\n    if main_pic_avg in mosaic.col_to_pic:\r\n        pics = mosaic.col_to_pic[main_pic_avg]\r\n        #shuffle the pictures that have the same colour average value, and\r\n        #return a random one of those pictures. This is one of the optimization\r\n        #techniques used.\r\n        random.shuffle(pics)\r\n        return (pics[0], 0)\r\n    \r\n    #Keep track of the closest pic so far.\r\n    closest = (\"\", 450)\r\n    \r\n    for (key, value) in mosaic.col_avg_d.items():\r\n        dist = main_pic_avg.distance(value)\r\n        if dist < closest[1]:\r\n            closest = (key, dist)\r\n            \r\n    mosaic.col_to_pic[main_pic_avg] = [closest[0]]\r\n        \r\n    return closest\r\n\r\ndef make_mosaic(mosaic, main_pic, min_size, threshold, x=0, y=0):\r\n    '''make the mosaic.'''\r\n    \r\n    size = main_pic.size\r\n    if size[0] < min_size or size[1] < min_size:\r\n        temp = Image.open(closest_pic(main_pic, mosaic)[0])\r\n        temp = temp.resize(size)\r\n        mosaic.mpic.paste(temp, (x, y))\r\n    \r\n    else:\r\n        #Check if there is a match, and replace the region with the picture\r\n        #if there is one.\r\n        closest = closest_match(mosaic, main_pic)\r\n        if closest[1] < threshold:\r\n            mosaic.mpic.paste(closest[0], (x, y))\r\n            \r\n        #Split the picture into 4 quadrants, and repeat the algorithm.\r\n        else:\r\n            top_left = main_pic.crop((0, 0, size[0] / 2, size[1] / 2))\r\n            top_right = main_pic.crop((size[0] / 2, 0, size[0], size[1] / 2))\r\n            bot_left = main_pic.crop((0, size[1] / 2, size[0] / 2, size[1]))\r\n            bot_right = main_pic.crop((size[0] / 2, size[1] / 2, \\\r\n                                       size[0], size[1]))\r\n        \r\n            make_mosaic(mosaic, top_left, min_size, threshold, x, y)\r\n            make_mosaic(mosaic, top_right, min_size, threshold, \\\r\n                        x + size[0] / 2, y)\r\n            make_mosaic(mosaic, bot_left, min_size, threshold, x, y + \\\r\n                        size[1] / 2)\r\n            make_mosaic(mosaic, bot_right, min_size, threshold, \\\r\n                    x + size[0] / 2, y + size[1] / 2)\r\n\r\ndef closest_match(mosaic, main_pic):\r\n    '''Find the picture in the picture database that is the closest match to\r\n    main_pic picture. Return a tuple, where the first element is the image, and\r\n    the second element is the average distance of each pixel from the image\r\n    to each pixel in main_pic.'''\r\n    \r\n    size = main_pic.size\r\n    closest = (None, 450)\r\n    main_all_pix = main_pic.getdata()\r\n    \r\n    #Go through all of the pictures in the database, and find the one that\r\n    #is the closest match to main_pic by comparing each pixel.\r\n    for picname in mosaic.col_avg_d:\r\n        current_pic = Image.open(picname).resize(size)\r\n        cur_all_pix = current_pic.getdata()\r\n        dist = 0\r\n        \r\n        #Find the distances between each corresponding pixel from main_pic to\r\n        #the current picture that is being looked at in the picture database.\r\n        for i in range(len(main_all_pix)):\r\n            main_pix = media.Color(main_all_pix[i][0], main_all_pix[i][1], \\\r\n                                   main_all_pix[i][2])\r\n            cur_pix = media.Color(cur_all_pix[i][0], cur_all_pix[i][1], \\\r\n                                  cur_all_pix[i][2])\r\n            dist += main_pix.distance(cur_pix)\r\n        avg_dist = dist / (size[0] * size[1])\r\n            \r\n        if avg_dist < closest[1]:\r\n            closest = (current_pic, avg_dist)\r\n    \r\n    return closest\r\n\r\ndef grayscale(pic):\r\n    '''Change the image pic so that it is a grayscale version of pic.'''\r\n    \r\n    #Go through all the pixels, and set each colour in the pixel to the pixel's\r\n    #intensity. A pixel's intensity is defined as average value of its red,\r\n    #green, and blue values.\r\n    for x in range(pic.size[0]):\r\n        for y in range(pic.size[1]):\r\n            col = pic.getpixel((x, y))\r\n            intensity = col[0] / 3 + col[1] / 3 + col[2] / 3\r\n            pic.putpixel((x, y), (intensity,) * 3)\r\n","repo_name":"mzietara/Photomosaic-Pictures","sub_path":"enhanced_mosaic.py","file_name":"enhanced_mosaic.py","file_ext":"py","file_size_in_byte":7942,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"44241592613","text":"import sys\nimport os.path\nfrom lib.ai import vlc\nfrom PyQt4 import QtGui, QtCore\nfrom lib.ai.config  import Config\nfrom time import sleep\n\nclass Head(QtGui.QMainWindow):\n    \"\"\"A simple Media Player using VLC and Qt\n    \"\"\"\n    CONFIG = Config()\n    def __init__(self, master=None):\n        QtGui.QMainWindow.__init__(self, master)\n        self.setWindowTitle(\"AVA\")\n        self.CONFIG= Config()\n\n        # creating a basic vlc instance\n        self.instance = vlc.Instance('--input-repeat=999999 ') #--video-filter=transform --transform-type=270\n        # creating an empty vlc media player\n        self.mediaplayer = self.instance.media_player_new()\n\n        self.createUI()\n        self.isPaused = False\n\n    def createUI(self):\n        \"\"\"Set up the user interface, signals & slots\n        \"\"\"\n        self.widget = QtGui.QWidget(self)\n        self.setCentralWidget(self.widget)\n        self.widget.showFullScreen()\n        self.widget.setStyleSheet(\"background-color: black;\")\n        \n\n        # In this widget, the video will be drawn\n        if sys.platform == \"darwin\": # for MacOS\n            self.videoframe = QtGui.QMacCocoaViewContainer(0)\n        else:\n            self.videoframe = QtGui.QFrame()\n        self.palette = self.videoframe.palette()\n        self.palette.setColor (QtGui.QPalette.Window,\n                               QtGui.QColor(0,0,0))\n        self.videoframe.setPalette(self.palette)\n        self.videoframe.setStyleSheet(\"border-image: url(back.JPG); \")\n        self.videoframe.setAutoFillBackground(True)\n\n        self.vboxlayout = QtGui.QVBoxLayout()\n        self.vboxlayout.addWidget(self.videoframe)\n        self.widget.setLayout(self.vboxlayout)\n\n        Toolbar = QtGui.QToolBar()\n        Toolbar.setIconSize(QtCore.QSize(1,200))\n        Toolbar.setToolButtonStyle(QtCore.Qt.ToolButtonTextBesideIcon|QtCore.Qt.AlignLeading) #<= Toolbuttonstyle\n        Toolbar.setStyleSheet(\"QToolBar { background: blue; }\")\n        self.addToolBar(QtCore.Qt.LeftToolBarArea,Toolbar)\n        \n        exit_button = QtGui.QToolButton()\n        exit_button.setToolTip(\"Exit\")\n        \n        exit_button.setCheckable(True)\n        exit_button.toggled.connect(self.closeEvent)\n        Toolbar.addWidget(exit_button)\n\n        self.timer = QtCore.QTimer(self)\n        self.timer.setInterval(200)\n        self.connect(self.timer, QtCore.SIGNAL(\"timeout()\"),\n                     self.updateUI)\n\n    def closeEvent(self):\n        #Your desired functionality here\n        print('Close button pressed')\n        sys.exit(0)\n        \n    def PlayPause(self):\n        \"\"\"Toggle play/pause status\n        \"\"\"\n        if self.mediaplayer.is_playing():\n            self.mediaplayer.pause()\n            self.playbutton.setText(\"Play\")\n            self.isPaused = True\n        else:\n            if self.mediaplayer.play() == -1:\n                self.OpenFile()\n                return\n            self.mediaplayer.play()\n            self.playbutton.setText(\"Pause\")\n            self.timer.start()\n            self.isPaused = False\n\n    def Stop(self):\n        \"\"\"Stop player\n        \"\"\"\n        self.mediaplayer.stop()\n        self.playbutton.setText(\"Play\")\n\n    def OpenFile(self, filename=None):\n        \"\"\"Open a media file in a MediaPlayer\n        \"\"\"\n        if filename is None:\n            filename = QtGui.QFileDialog.getOpenFileName(self, \"Open File\", os.path.expanduser('~'))\n        if not filename:\n            return\n\n        # create the media\n        if sys.version < '3':\n            filename = unicode(filename)\n        self.media = self.instance.media_new(filename)\n        # put the media in the media player\n        self.mediaplayer.set_media(self.media)\n\n        # parse the metadata of the file\n        self.media.parse()\n        # set the title of the track as window title\n        self.setWindowTitle(self.media.get_meta(0))\n\n        # the media player has to be 'connected' to the QFrame\n        # (otherwise a video would be displayed in it's own window)\n        # this is platform specific!\n        # you have to give the id of the QFrame (or similar object) to\n        # vlc, different platforms have different functions for this\n        if sys.platform.startswith('linux'): # for Linux using the X Server\n            self.mediaplayer.set_xwindow(self.videoframe.winId())\n        elif sys.platform == \"win32\": # for Windows\n            self.mediaplayer.set_hwnd(self.videoframe.winId())\n        elif sys.platform == \"darwin\": # for MacOS\n            self.mediaplayer.set_nsobject(self.videoframe.winId())\n        self.PlayPause()\n\n    def setVolume(self, Volume):\n        \"\"\"Set the volume\n        \"\"\"\n        self.mediaplayer.audio_set_volume(Volume)\n\n    def setPosition(self, position):\n        \"\"\"Set the position\n        \"\"\"\n        # setting the position to where the slider was dragged\n        self.mediaplayer.set_position(position / 1000.0)\n        # the vlc MediaPlayer needs a float value between 0 and 1, Qt\n        # uses integer variables, so you need a factor; the higher the\n        # factor, the more precise are the results\n        # (1000 should be enough)\n\n    def updateUI(self):\n        \"\"\"updates the user interface\"\"\"\n        # setting the slider to the desired position\n        self.positionslider.setValue(self.mediaplayer.get_position() * 1000)\n\n        if not self.mediaplayer.is_playing():\n            # no need to call this function if nothing is played\n            self.timer.stop()\n            if not self.isPaused:\n                # after the video finished, the play button stills shows\n                # \"Pause\", not the desired behavior of a media player\n                # this will fix it\n                self.Stop()\n    def test(self, anime):\n        self.media = self.instance.media_new(anime)\n        self.mediaplayer.set_media(self.media)\n        if sys.platform.startswith('linux'): # for Linux using the X Server\n            self.mediaplayer.set_xwindow(self.videoframe.winId())\n        elif sys.platform == \"win32\": # for Windows\n            self.mediaplayer.set_hwnd(self.videoframe.winId())\n        elif sys.platform == \"darwin\": # for MacOS\n            self.mediaplayer.set_nsobject(self.videoframe.winId())\n        self.mediaplayer.play()\n        \n        \n        #=======================================================================\n        i = 0\n        while True:\n            state = self.mediaplayer.get_state()\n            print(state)\n            QtCore.QCoreApplication.processEvents()\n            i = i + 1\n            print(i)\n        #=======================================================================\n            #===================================================================\n            if i == 200000:\n                print(\"next:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::\")\n                self.mediaplayer.pause()\n                #self.media = self.instance.media_new(self.CONFIG.AVA_TALK_ANIME)\n                self.mediaplayer.set_mrl(self.CONFIG.AVA_TALK_ANIME)\n                self.mediaplayer.play()\n            #===================================================================\n            if i == 400000:\n                print(\"next:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::\")\n                self.mediaplayer.pause()\n                #self.media = self.instance.media_new(self.CONFIG.AVA_TALK_ANIME)\n                self.mediaplayer.set_mrl(anime)\n                self.mediaplayer.play()\n    \n    def set_motion(self, anime):\n        self.media = self.instance.media_new(anime)\n        self.mediaplayer.set_media(self.media)\n        if sys.platform.startswith('linux'): # for Linux using the X Server\n            self.mediaplayer.set_xwindow(self.videoframe.winId())\n        elif sys.platform == \"win32\": # for Windows\n            self.mediaplayer.set_hwnd(self.videoframe.winId())\n        elif sys.platform == \"darwin\": # for MacOS\n            self.mediaplayer.set_nsobject(self.videoframe.winId())\n        self.mediaplayer.play()\n        \n    def process_events(self):\n            QtCore.QCoreApplication.processEvents()\n        \n                \n                \nclass AVA():\n    CONFIG = Config()\n    def __init__(self):\n        self.app = QtGui.QApplication(sys.argv)\n        self.head = Head()\n        self.head.showFullScreen()                \n        \n    def set_motion(self, motion):\n        self.head.set_motion(motion)\n    def test(self, motion):\n        self.head.test(motion)\n    def process_events(self):\n        self.head.process_events()\n        #=======================================================================\n        # while True:\n        #     state = self.head.mediaplayer.get_state()\n        #     #print(state)\n        #     QtCore.QCoreApplication.processEvents()\n        #=======================================================================\n\nif __name__ == \"__main__\":\n    ava = AVA()\n    ava.set_motion(ava.CONFIG.AVA_STANDBY_ANIME)\n    sleep(10)\n    ava.set_motion(ava.CONFIG.AVA_TALK_ANIME)\n    \n    print(ava.CONFIG.AVA_STANDBY_ANIME)   \n\n    #===========================================================================\n    # if sys.argv[1:]:\n    #     head.OpenFile(sys.argv[1])\n    # sys.exit(app.exec_())\n    #===========================================================================\n","repo_name":"ayoubboulila/ava","sub_path":"lib/ai/Head.py","file_name":"Head.py","file_ext":"py","file_size_in_byte":9385,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"39409454288","text":"import os\nfrom pytils import translit\n\nfrom django.db import models\nfrom django.contrib.auth.models import User\nfrom django.utils import timezone\nfrom django.db.models.signals import post_save, post_delete, pre_save\nfrom django.dispatch import receiver\n\nfrom nominations.models import ArtNomination\nfrom profileuser.models import CoProfile\n\n\ndef make_picture_path(instance, filename):\n\tnames = filename.split('.')\n\tnew_filename = ''\n\tfor name in names:\n\t\tif name != names[0]:\n\t\t\tnew_filename += '.'\n\t\tnew_filename += translit.slugify(name)\n\n\tpath = 'pictures/%s' % new_filename\n\n\treturn path\n\t\n\nclass Picture(models.Model):\n\tPARTICIPATION_TYPE = (\n\t\t('1', 'очное'),\n\t\t('2', 'заочное'),\n\t)\n\n\tname = models.CharField(verbose_name=\"Название работы*\", max_length=100, blank=True)\n\tauthor = models.ForeignKey(CoProfile, verbose_name='Автор работы*', on_delete=models.CASCADE)\n\tnomination = models.ForeignKey(ArtNomination, verbose_name='Номинация*', on_delete=models.SET_NULL, blank=True, null=True, default=None)\n\ttechnique = models.CharField(verbose_name=\"Техника исполнения\", max_length=50, blank=True)\n\n\tages = models.DecimalField(verbose_name=\"Возраст на момент написания работы*\", max_digits=2, decimal_places=0, default=0, blank=False)\n\tyear = models.DecimalField(verbose_name=\"Год исполнения работы*\", max_digits=4, decimal_places=0, default=0, blank=False)\n\n\tparticipation = models.CharField(verbose_name='Тип участия*', max_length=1, choices=PARTICIPATION_TYPE, default='1')\n\n\tfile = models.ImageField(verbose_name='Изображение работы*', blank=True, null=True, upload_to = make_picture_path)\n\tadd_views = models.BooleanField(\"Дополнительные ракурсы\", default=False)\n\tadd_view_1 = models.ImageField(verbose_name='Дополнительный вид', blank=True, null=True, upload_to = make_picture_path)\n\tadd_view_2 = models.ImageField(verbose_name='Дополнительный вид', blank=True, null=True, upload_to = make_picture_path)\n\n\tplace = models.DecimalField(verbose_name=\"Место\", max_digits=1, decimal_places=0, default=None, blank=True, null=True)\n\n\tregistration_date = models.DateField(verbose_name=\"Дата регистрации\", default=timezone.now)\n\n\n\tclass Meta:\n\t\tordering = ['nomination', 'name']\n\t\tverbose_name='Работа'\n\t\tverbose_name_plural='Работы'\n\n\n\tdef __str__(self):\n\t\treturn self.name\n\n\tdef ages_prefix(self):\n\t\tmod = self.ages % 10\n\t\tmod_plus = (self.ages // 10) % 10\n\t\tif mod_plus != 1:\n\t\t\tif mod == 0:\n\t\t\t\treturn 'лет'\n\t\t\telif mod == 1:\n\t\t\t\treturn 'год'\n\t\t\telif mod < 5:\n\t\t\t\treturn 'года'\n\t\treturn 'лет'\n\nclass CoPicturee(models.Model):\n\tpicture = models.ForeignKey(Picture, verbose_name='Произведение', on_delete=models.CASCADE)\n\tcoauthor = models.ForeignKey(CoProfile, verbose_name='Соисполнитель', on_delete=models.CASCADE)\n\n\tregistration_date = models.DateField(verbose_name=\"Дата регистрации\", default=timezone.now)\n\n\tclass Meta:\n\t\tordering = ['picture', 'coauthor']\n\t\tverbose_name='Соисполнитель'\n\t\tverbose_name_plural='Соисполнители'\n\n\n\tdef __str__(self):\n\t\treturn str(self.picture) + ' - ' + str(self.coauthor)\n\n\n######################################################################\n@receiver(post_delete, sender = Picture)\ndef profile_post_delete_handler(sender, **kwargs):\n\tpicture = kwargs['instance']\n\n\tif picture.file:\n\t\tif os.path.isfile(picture.file.path):\n\t\t\tos.remove(picture.file.path)\n\n\tif picture.add_view_1:\n\t\tif os.path.isfile(picture.add_view_1.path):\n\t\t\tos.remove(picture.add_view_1.path)\n\n\tif picture.add_view_2:\n\t\tif os.path.isfile(picture.add_view_2.path):\n\t\t\tos.remove(picture.add_view_2.path)\n\n\n@receiver(pre_save, sender = Picture)\ndef profile_pre_save_handler(sender, **kwargs):\n\tpicture = kwargs['instance']\n\n\tif not picture.pk:\n\t\treturn False\n\n\ttry:\n\t\told_file = Picture.objects.get(pk=picture.pk).file\n\n\t\tif old_file:\n\t\t\tnew_file = picture.file\n\t\t\tif not old_file==new_file:\n\t\t\t\tif os.path.isfile(old_file.path):\n\t\t\t\t\tos.remove(old_file.path)\n\texcept Picture.DoesNotExist:\n\t\tpass\n\t\n\ttry:\n\t\told_file = Picture.objects.get(pk=picture.pk).add_view_1\n\n\t\tif old_file:\n\t\t\tnew_file = picture.add_view_1\n\t\t\tif not old_file==new_file:\n\t\t\t\tif os.path.isfile(old_file.path):\n\t\t\t\t\tos.remove(old_file.path)\n\texcept Picture.DoesNotExist:\n\t\tpass\n\n\ttry:\n\t\told_file = Picture.objects.get(pk=picture.pk).add_view_2\n\n\t\tif old_file:\n\t\t\tnew_file = picture.add_view_2\n\t\t\tif not old_file==new_file:\n\t\t\t\tif os.path.isfile(old_file.path):\n\t\t\t\t\tos.remove(old_file.path)\n\texcept Picture.DoesNotExist:\n\t\tpass\n","repo_name":"BWKenobi/gavrilov","sub_path":"pictures/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4714,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"19773600088","text":"import numpy as np\r\nimport pandas as pd\r\nimport joblib\r\nimport sqlite3\r\nfrom sklearn.model_selection import train_test_split\r\nfrom randomforest import optimiseElo,setAttackDefence,baseline_accuracy\r\nfrom sklearn.preprocessing import MinMaxScaler, LabelBinarizer\r\nfrom sklearn.impute import SimpleImputer\r\nfrom tensorflow.keras import Sequential\r\nfrom tensorflow.keras.layers import Dense, Dropout, BatchNormalization, AlphaDropout, LayerNormalization\r\nfrom tensorflow.keras.models import save_model, load_model\r\nfrom tensorflow.keras.callbacks import EarlyStopping\r\nfrom sklearn import metrics\r\nfrom multiprocessing import cpu_count\r\nfrom tensorflow.keras.optimizers import Adam\r\nfrom elo2 import train,test\r\nfrom tqdm import tqdm\r\nimport matplotlib.pyplot as plt\r\nimport talos as ta\r\nfrom talos.utils import hidden_layers\r\nfrom talos.utils.best_model import best_model\r\nfrom talos import Predict, Analyze\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef main():\r\n    con = sqlite3.connect(r'C:\\Users\\Luca\\PycharmProjects\\IntroToAI-Group5-TeamB(football)\\database.sqlite')\r\n    cur = con.cursor()\r\n\r\n    cur.execute(\r\n        \"SELECT home_team_api_id,away_team_api_id,home_team_goal,away_team_goal,winner FROM Match WHERE league_id = '1729'\")\r\n    matches = cur.fetchall()\r\n\r\n    k = 4.849879326548514\r\n    d = 125.81976547554737\r\n    mult2 = 2.1342190777850742\r\n    mult3 = 3.4769118949428233\r\n    mult4 = 1.143940853285419\r\n\r\n    train(k, matches, 1, cur, mult2, mult3, mult4, dotqdm=True)\r\n\r\n    cur.execute(\"SELECT home_team_api_id from Match WHERE league_id = '1729'\")\r\n    team_ids = cur.fetchall()\r\n    team_ids = str(tuple(set([team_id[0] for team_id in team_ids])))\r\n\r\n    cur.execute(f\"SELECT elo,team_api_id FROM Team WHERE team_api_id IN {team_ids}\")\r\n    team_elo_and_id = cur.fetchall()\r\n\r\n\r\n    cur.executemany(f\"UPDATE Match SET home_team_elo = ? WHERE home_team_api_id = ?\", team_elo_and_id)\r\n    cur.executemany(f\"UPDATE Match SET away_team_elo = ? WHERE away_team_api_id = ?\", team_elo_and_id)\r\n\r\n    setAttackDefence(cur,set=False)\r\n\r\n    cur.execute(f\"SELECT attack,defence,team_api_id FROM Team WHERE team_api_id IN {team_ids}\")\r\n    team_attack_defense_id = cur.fetchall()\r\n\r\n    cur.executemany(f\"UPDATE MATCH SET home_attack = ?,home_defence = ? WHERE home_team_api_id = ?\",team_attack_defense_id)\r\n    cur.executemany(f\"UPDATE MATCH SET away_attack = ?,away_defence = ? WHERE away_team_api_id = ?\",team_attack_defense_id)\r\n\r\n\r\n    cur.execute(\r\n        \"SELECT winner,home_team_elo,away_team_elo,home_attack,home_defence,\"\r\n        \"away_attack,away_defence FROM Match WHERE league_id = '1729'\")\r\n    match_data = cur.fetchall()\r\n    matches_df = pd.DataFrame(match_data, columns=[\"winner\", \"home_elo\", \"away_elo\",\r\n                                                   \"home_attack\", \"home_defence\", \"away_attack\", \"away_defence\"])\r\n    scaler = MinMaxScaler()\r\n    columns_to_norm = ['home_elo', 'away_elo', 'home_attack', 'home_defence', 'away_attack',\r\n                       'away_defence']  # + odds_cols\r\n    x = matches_df[columns_to_norm].values\r\n    scaler.fit(x)\r\n    x_scaled = scaler.transform(x)\r\n    df_temp = pd.DataFrame(x_scaled, columns=columns_to_norm, index=matches_df.index)\r\n    matches_df[columns_to_norm] = df_temp\r\n\r\n    labels = matches_df.filter(items=[\"winner\"])\r\n    lb = LabelBinarizer()\r\n    lb.fit(labels[\"winner\"])\r\n    lb_trans = lb.transform(labels[\"winner\"])\r\n    labels = pd.DataFrame(lb_trans, columns=lb.classes_)\r\n    label_list = labels.columns\r\n    numLabels = len(label_list)\r\n\r\n\r\n    features = matches_df.drop(columns=[\"winner\"])\r\n\r\n    feature_list = features.columns\r\n\r\n    labels = np.array(labels).astype('float32')\r\n    features = np.array(features).astype('float32')\r\n\r\n    train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.2)\r\n\r\n    train_features, val_features, train_labels, val_labels = train_test_split(train_features, train_labels,test_size=0.2)\r\n\r\n    n_features = train_features.shape[1]\r\n\r\n    p = {'shapes': ['brick', 'triangle', 'funnel'],\r\n         'activation': ['relu'],\r\n         'kernel_initializer': ['he_normal','glorot_normal','uniform'],\r\n         'first_neuron': (50,3000,50),\r\n         'last_neuron': (50,3000,50),\r\n         'hidden_layers': (0,10,10),\r\n         'dropout': (0.01,0.99,100),\r\n         'batch_size': (300,20000,100),\r\n         'epochs': [500]}\r\n\r\n    p_best = {'shapes': ['triangle'],\r\n             'activation': ['relu'],\r\n             'kernel_initializer': ['uniform'],\r\n             'first_neuron': [2823],\r\n             'last_neuron' : [50],\r\n             'hidden_layers': [5],\r\n             'dropout': [0.2354],\r\n             'batch_size': [694],\r\n             'epochs': [500]}\r\n\r\n    def football_model(x_train,y_train,x_val,y_val,params):\r\n        model = Sequential()\r\n\r\n        es_callback = EarlyStopping(monitor='val_loss', patience=3)\r\n\r\n        model.add(Dense(params['first_neuron'], activation=params['activation'], kernel_initializer=params['kernel_initializer'], input_shape=(n_features,)))\r\n        model.add(Dropout(params['dropout']))\r\n\r\n        hidden_layers(model,params,params['last_neuron'])\r\n\r\n        model.add(Dense(numLabels, activation='softmax'))\r\n\r\n        model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])\r\n\r\n        threads = cpu_count()\r\n        history = model.fit(x_train,y_train, epochs=params['epochs'], batch_size=params['batch_size'], verbose=0, callbacks=[es_callback],\r\n              validation_data=(x_val,y_val), workers=threads, use_multiprocessing=True)\r\n\r\n        return history,model\r\n\r\n    t = ta.Scan(x= train_features,y = train_labels,x_val = val_features,y_val = val_labels,\r\n                          model = football_model,params = p_best,experiment_name = 'football',round_limit=1)\r\n\r\n    p = Predict(t)\r\n\r\n    preds = p.predict(test_features,metric='val_accuracy',asc = False).argmax(axis=1)\r\n\r\n    acc = metrics.accuracy_score(test_labels.argmax(axis=1), preds) * 100\r\n\r\n    base_acc = baseline_accuracy(test_labels)\r\n\r\n    print('Baseline Accuracy: %.3f' % base_acc)\r\n    print('Test Accuracy: %.3f' % acc)\r\n    diff = (acc - base_acc)\r\n    print('Model better than baseline by: %.3f pp' % diff)\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n","repo_name":"EMAT31530/ai-group-project-team-football","sub_path":"talosTuner.py","file_name":"talosTuner.py","file_ext":"py","file_size_in_byte":6300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"19797756432","text":"#!/usr/bin/env python3\n\nimport os\nos.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\"\nimport torch as t\nimport torch.nn as nn \nimport argparse\nimport random\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom utils import *\nimport cv2\nimport segmentation_models_pytorch as smp\nt.backends.cudnn.benchmark = True\nt.backends.cudnn.enabled = True\n\n# For Reproducible results\nseed= 42 \nos.environ['PYTHONHASHSEED']=str(seed)\nrandom.seed(seed)\nnp.random.seed(seed)\nt.manual_seed(seed)\n\ndef predict(args, model, dload, device, set):\n    iou_list = []\n    correctlist = []\n    for i, (x_p_d, y_p_d) in enumerate(dload):\n        x_p_d, y_p_d = x_p_d.to(device), y_p_d.to(device)\n\n        model.eval()\n        logits = model(x_p_d)\n        correct = np.mean((logits.max(1)[1] == y_p_d).float().cpu().numpy())\n        correctlist.append(correct)\n        logits_max = logits.max(1)[1].float().cpu().numpy()\n        label = y_p_d.float().cpu().numpy() \n\n        IOU = mIOU(logits_max, label)\n        iou_list.append(IOU)\n\n        for j in range(label.shape[0]):\n            label = y_p_d.float().cpu().numpy()\n            image = x_p_d.float().cpu().numpy()[j][:,:,:]\n            image = np.moveaxis(image, 0, -1)\n            logits_max = logits.max(1)[1].float().cpu().numpy()\n            logits_max = logits2rgb(logits_max[j,:,:])\n            overlay = cv2.addWeighted(logits_max.astype(float)/255, 0.5, image.astype(float), 0.5, 0.0)\n            label = logits2rgb(label[j,:,:])\n            \n            \n            if args.plot_results == True:\n                fig, axs = plt.subplots(1,3)\n                axs[0].imshow(label)\n                axs[1].imshow(image)\n                axs[2].imshow(overlay)\n                plt.show()\n                cv2.waitKey(0)\n                cv2.destroyAllWindows()\n            else:\n                image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n                overlay_true = cv2.cvtColor(overlay.astype('float32'), cv2.COLOR_RGB2BGR)\n                label = cv2.cvtColor(label.astype('float32'), cv2.COLOR_RGB2BGR)\n                makedirs(f'./plots/plots_{set}/')\n                cv2.imwrite(f'./plots/plots_{set}/img{i}target.jpg', label)\n                cv2.imwrite(f'./plots/plots_{set}/img{i}real.jpg', image*255)\n                cv2.imwrite(f'./plots/plots_{set}/img{i}overlay.jpg', overlay_true*255)\n        \n    print('mIOU:')\n    print(np.mean(iou_list))\n    print('Accuracy:')\n    print(np.mean(correctlist))\n    \n\n\ndef evaluate(args):\n    t.manual_seed(seed)\n    if t.cuda.is_available():\n        t.cuda.manual_seed_all(seed)\n    \n    dload_train, dload_valid = import_data(args, args.batch_size, args.project, args.resize, args.random_crop_size)\n\n    device = t.device('cuda' if t.cuda.is_available() else 'cpu')   \n    model = get_model(device, args.num_classes)\n    ckpt = t.load('./checkpoints/best_validation_ckpt.pt') \n    model.load_state_dict(ckpt[\"model_state_dict\"])\n    with t.no_grad():\n        predict(args, model, dload_valid, device, args.project)\n        \n        \nif __name__ == '__main__':\n    parser = argparse.ArgumentParser('Semantic Segmantation Model Evaluation')\n    parser.add_argument(\"--resize\", type=int, default=512, help=\"Size of images for resizing\")\n    parser.add_argument(\"--random_crop_size\", type=int, default=256, help=\"Size of random crops. Must be smaller than resized images.\")\n    parser.add_argument(\"--project\", choices=['project_1', 'project_2', 'project_3'], default='project_1')\n    parser.add_argument(\"--batch_size\", type=int, default=2, help=\"Batch Size\")\n    parser.add_argument(\"--num_classes\", type=int, default=8, help=\"Number of classes of your training dataset\")\n    parser.add_argument(\"--plot_results\", type=bool, default=False, help=\"Set to True if you want to plot your results.\")\n    args = parser.parse_args()\n\n    evaluate(args)\n\n\n\n\n\n    \n    \n","repo_name":"stefanherdy/pytorch-semantic-segmentation","sub_path":"evaluate_model.py","file_name":"evaluate_model.py","file_ext":"py","file_size_in_byte":3841,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"26629525075","text":"from django.urls import path, include\nfrom . import views\n\n#Importacion de la vista reporte_compras\nfrom . import reportes\n\nurlpatterns = [\n    #Rutas para la seccion de proveedores\n    path('proveedores/', views.ProveedorView.as_view(), name=\"proveedor_list\"),\n    path('proveedor/new', views.Proveedornew.as_view(), name=\"proveedor_new\"),\n    path('proveedor/edit/<int:pk>', views.ProveedorEdit.as_view(), name='proveedor_edit'),\n    path('proveedor/inactivar/<int:id>', views.proveedor_inactivar, name=\"proveedor_inactivar\"),\n\n    #Rutas para la seccion de Compras\n    path('compras/', views.ComprasView.as_view(), name=\"compras_list\"),\n    path('compras/new', views.compras, name=\"compras_new\"),\n    path('compras/edit/<int:compra_id>', views.compras, name=\"compras_edit\"),\n    path('compras/<int:compra_id>/delete/<int:pk>', views.compraDetDelete.as_view(), name=\"compras_del\"),\n    path('compras/inactivar/<int:id>', views.compra_inactivar, name=\"compra_inactivar\"),\n\n    #Rutas para la creacion de archivos pdf\n    path('compras/listado', reportes.reporte_compras, name='compras_print_all'),\n    path('compras/<int:compra_id>', reportes.imprimir_compra, name=\"compra_print_one\"),\n]\n","repo_name":"AlexIran1993/SistemaCompraFacturacion-django","sub_path":"cmp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1189,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"19178219702","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport numpy as np\n\"\"\"\nCreated on Fri Nov 24 22:23:37 2017\n\nThis program calls the Hill solver for a dipole magnetic field with \na user-specified mass-loading rate. The radial extent of the solver \ncan be adjusted as needed. \n\n@author: liciaray\n\"\"\"\n\n#import necessary libraries and functions\nimport matplotlib.pyplot as plt\nfrom Planet import Planet\nimport BFlux as Bf  \nfrom Grid import Grid\nfrom Field import Field\n\n# Select planet. Options are\n#jupiter, saturn, or earth\nplanet = Planet.jupiter()\n\n# set spatial resolution and bounds\n# Generate the radial grid\ndr = 1e4\nr_inner = 5.0\nr_outer = 100.0\ng = Grid(dr,r_inner,r_outer,planet.RP)\n\n\n# Initialise the planetary magnetosphere\n# options are dipole (Bf.fluxDIP, Bf.bmDIP)\n# or stretched field configuration from \n# Nichols & Cowley[2004] (Bf.fluxNC, Bf.bmCANKK)\nplanet.initMagnetosphere(g,Bf.fluxDIP, Bf.bmDIP)\n\n#specify parameters / inputs for calculation\n# radial mass transport rate (kg/s)\nmdot = 2000.0\n# density of current carrying electrons\ndens_elec = 1e8\n# initial energy of current carrying electrons\ntemp_elec = 2.5e3\n\n# initialise the electric fields and hight-integrated\n# current density arrays\nfield = Field(mdot,dens_elec, temp_elec, planet, g)\n\n# calculate the plasma angular velocity profile &\n# associated currents\nmorePhysics = field.calculateHill()\n\n#if morePhysics:\n#    print(\"J thermal exceeded\")\n#    field.transition()\n    \n\n# Plot the flux thing in a classy way\n#plt.figure(1)\nf, axarr = plt.subplots(4, sharex=True)\naxarr[0].semilogy(g.r/planet.RP,planet.flux)\naxarr[0].set_xlim(r_inner,r_outer)\naxarr[0].set_ylabel('Flux Function')\n#plt.subplot(412)\naxarr[1].plot(g.r/planet.RP,1+field.omega/planet.OP)\naxarr[1].set_xlim(r_inner,r_outer)\naxarr[1].set_ylabel('Angular\\nVelocity\\n($\\Omega_{p})^{-1}$')\n#plt.subplot(413)\naxarr[2].plot(g.r/planet.RP,field.elec_m)\naxarr[2].set_xlim(r_inner,r_outer)\naxarr[2].set_ylabel('Electric field\\n(mV/m$^{2}$)')\n#plt.subplot(414)\naxarr[3].plot(g.r/planet.RP, field.jpar_i*1e9)\n#axarr[3].set_ylim([-2e-9,np.amax(field.jpar_i)])\naxarr[3].set_ylim([min(field.jpar_i)*1e9,max(field.jpar_i)*1e9])\naxarr[3].set_xlim(r_inner,r_outer)\naxarr[3].set_ylabel('Current\\nDensity\\n(nA m$^{-2}$)')\naxarr[3].set_xlabel('Radius (R$_{J}$)')\nplt.show()\nang_vel = 1+field.omega/planet.OP\nprint(ang_vel)\nang_vel = np.array(ang_vel)\nradii = np.array(g.r/planet.RP)\nprint(radii)\nnp.save('D:/Tom/Documents/Uni/Physics/Masters/Coupling-At-Jupiter/angular_velocity_data/mdot_{}'.format(mdot),ang_vel, allow_pickle=True)\nnp.save('D:/Tom/Documents/Uni/Physics/Masters/Coupling-At-Jupiter/angular_velocity_data/radii{}'.format(mdot),radii, allow_pickle=True)\n","repo_name":"twf2360/Coupling-At-Jupiter","sub_path":"Ray_SimpleAlfvenModel/testing.py","file_name":"testing.py","file_ext":"py","file_size_in_byte":2696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"6641160042","text":"__author__ = 'Michael R. McPherson <mcpherson@acm.org>'\n\nimport os\nimport sys\nimport configparser\n\nclass ConfigRead:\n    \"\"\"Read configuration file and return parameter values on demand.\n\n    Attributes\n    ----------\n    None\n\n    Methods\n    -------\n    open_config(configfile_path, configfile_name)\n        Search paths in array configfile_path for configfile_name, open, and import\n    get_param(param_group, param_name, param_type)\n        Return value of \"parameter_name\"\n\n    \"\"\"\n\n    def __init__(self):\n        self.config = configparser.ConfigParser()\n\n    def open_config(self, configfile_path, configfile_name):\n        config_file_found = False\n        for path in configfile_path:\n            config_file = '/'.join([path, configfile_name])\n            if(os.path.isfile(config_file)):\n                self.config.read(config_file)\n                config_file_found = True\n        return config_file_found\n\n    def get_param(self, param_group, param_name, param_type):\n\n        if(param_type is \"int\"):\n            return int(self.config[param_group][param_name])\n        elif(param_type is \"float\"):\n            return float(self.config[param_group][param_name])\n        elif(param_type is \"bool\"):\n            return self.config[param_group].getboolean(param_name)\n        elif(param_type is \"key\"):\n            return self.config[param_group][param_name].encode()\n        elif(param_type is \"path\"):\n            return os.path.expandvars(self.config[param_group][param_name])\n        elif(param_type is \"string\"):\n            return self.config[param_group][param_name]\n        else:\n            print(\"Unknown param_type {param_type}\")\n            sys.exit()\n","repo_name":"MikeMcPherson/uvagroundv1","sub_path":"uvagroundv1/ground/config_read.py","file_name":"config_read.py","file_ext":"py","file_size_in_byte":1677,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"39500674810","text":"import cv2\r\nfrom utils.aggregate import draw_bboxes, unpickle\r\nfrom utils.support_functions import load_config, create_filename, count_img_index\r\nfrom src.relative_coordinates import transform_cooridnates\r\nfrom src.fuzzification import fuzzify_relative_coordinates\r\nfrom src.fam1d import fuzzy_reason_for_fam1d\r\nfrom src.fam2d import fuzzy_reason_for_fam2d\r\nfrom src.fmp import generate_predicates\r\n\r\nconfig = load_config()\r\n\r\nroot = config[\"root\"] + \"img/\"\r\nsubset = config[\"subset\"]\r\nedition = config[\"edition\"]\r\nsubsetEdition = subset + edition\r\ndirectory = root + subsetEdition + \"/\"\r\n\r\npickle_filename = config[\"pickle_filename\"]\r\ndata = unpickle(pickle_filename)\r\n\r\norder= 0\r\n\r\nwhile(1):\r\n    k = cv2.waitKey(0) & 0xFF\r\n    \r\n    if k == 27: # ESC\r\n        break\r\n    elif k == ord('q'):\r\n        if order < len(data) - 1:\r\n            order += 1\r\n    elif k == ord('a'):\r\n        if order > 0:\r\n            order -= 1\r\n    elif k == ord('w'):\r\n        if order < len(data) - 1:\r\n            order += 10\r\n    elif k == ord('s'):\r\n        if order > 0:\r\n            order -= 10\r\n    elif k == ord('e'):\r\n        if order < len(data) - 1:\r\n            order += 100\r\n    elif k == ord('d'):\r\n        if order > 0:\r\n            order -= 100\r\n    elif k == ord('r'):\r\n        if order < len(data) - 1:\r\n            order += 1000\r\n    elif k == ord('f'):\r\n        if order > 0:\r\n            order -= 1000\r\n    \r\n    img_index = count_img_index(data, order)\r\n    path = directory + create_filename(img_index, subsetEdition)\r\n    image = cv2.imread(path)\r\n    bbox = data.loc[img_index, \"bbox\"]\r\n    category = data.loc[img_index, \"category\"]\r\n    color = data.loc[img_index, \"color\"]\r\n    size = data.loc[img_index, \"size\"]\r\n    \r\n    relative_coordinates = transform_cooridnates(bbox, size)\r\n    fuzzification = fuzzify_relative_coordinates(relative_coordinates)\r\n    fam1 = fuzzy_reason_for_fam1d(fuzzification)\r\n    fam2 = fuzzy_reason_for_fam2d(fam1)\r\n    predicates = generate_predicates(bbox, fam2)\r\n    \r\n    draw_bboxes(image, bbox, category, color)\r\n    cv2.imshow(\"Picture\", image)\r\n    \r\n    print(f\"========== Picture ID: {img_index} ==========\")\r\n    for cap in data.loc[img_index, :\"caption5\"]:\r\n        print(cap)\r\n    print(\"========================================\")\r\n\r\n\r\ncv2.destroyAllWindows() \r\n","repo_name":"KaliszS/Verbal-description-of-the-scene","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2314,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"40329799988","text":"import numpy as np\nimport pandas as pd\nimport copy\nimport prob\nfrom domina import fast_non_dominated_sort as fs\nfrom domina import crowding_dist_assignment as cd\nfrom metric import igd\n\n\ndef solution(lb, ub):\n    x = []\n    for l, u in zip(lb, ub):\n        xi = np.random.uniform(l, u)\n        x.append(xi)\n    x = np.array(x).reshape(len(x), 1)\n    s = np.sum(x)\n    if s != 0:\n        x = x / s\n    else:\n        x = solution(lb, ub)\n    return x\n\n\ndef population(lb, ub, N):\n    P = []\n    for _ in range(N):\n        P.append(solution(lb, ub))\n    return P\n\n\ndef objective(P, f, r, s, c):\n    objs = []\n    for xi in P:\n        M, V = f(xi, r, s, c)\n        objs.append(np.asarray([M, V]))\n    return objs\n\n\ndef optimize(instance, N, gen, operator, par, cflag, cgen):\n    t, count, temp = 0, 0, 0\n    _, r, s, c, lb, ub, port, mp, vp = prob.set(instance)\n    P = population(lb, ub, N) # initialize population\n    objs_P = objective(P, port, r, s, c) # evaluate fitness\n    ####################################################################\n    indicator_value = igd(objs_P, mp, vp) # compute indicator\n    print(\"{}\\t{}\".format(t, indicator_value))\n    ####################################################################\n    F, rank = fs(objs_P) # fast non-dominated sorting\n    dist = [0] * len(P) # crowding distance assignment\n    for Fi in F:\n        dist = cd(Fi, objs_P, dist)\n    while t < gen: # start generation loop\n        Q = operator(P, rank, dist, lb, ub, par) # generate offspring\n        objs_Q = objective(Q, port, r, s, c) # compute fitness\n        R = P + Q # combine offspring and parent\n        objs = objs_P + objs_Q\n        F, rank = fs(objs) # fast non-dominated sorting\n        dist = [0] * len(R) \n        P_idx, i = [], 0\n        while len(P_idx) + len(F[i]) <= N:\n            dist = cd(Fi, objs, dist) # crowding distance assignment\n            P_idx += F[i] # add front into new population\n            i += 1\n        dist = cd(F[i], objs, dist)\n        Fi = sorted(F[i], key=lambda x: -dist[x]) # sort by distance\n        P_idx += Fi[:N-len(P_idx)] # add individuals with large distance\n        P, objs_P, tmpRank, tmpDist = [], [], [], []\n        for i in P_idx:\n            P.append(R[i])\n            objs_P.append(objs[i])\n            tmpRank.append(rank[i])\n            tmpDist.append(dist[i])\n        rank = tmpRank\n        dist = tmpDist\n        t += 1\n        ####################################################################\n        # enable this line to save population onjectives during run\n        # if t in [0,1,2,3,4,5,10,20,30,50,100,150,200,300]:\n        #     pd.DataFrame(objs_P, columns=[\"return\", \"risk\"]).to_csv(\n        #         f\"nsga2_pop_gen_{t}.csv\", index=False)\n        ####################################################################\n        indicator_value = igd(objs_P, mp, vp) # compute indicator\n        print(\"{}\\t{}\".format(t, indicator_value))\n        ####################################################################\n        if np.abs(indicator_value - temp) >= 1e-05: # convergence criteria\n            temp = indicator_value\n            count = 0\n        else:\n            count += 1\n        if count >= cgen and cflag == True: break\n    return objs_P\n","repo_name":"Y1fanHE/po_with_moead-levy","sub_path":"nsga2.py","file_name":"nsga2.py","file_ext":"py","file_size_in_byte":3236,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"76"}
+{"seq_id":"35774145112","text":"import numpy as np\nfrom manim import *\n\nfrom uloha_5_kat_A_config import *\n\n\ndef angle_bisector(p: Line, q: Line) -> Line:\n    \"\"\" Returns the angle bisector of the angle between the two lines. \"\"\"\n\n    length = max(p.get_length(), q.get_length())\n    angle = (p.get_angle() + q.get_angle()) / 2\n    bisector = DashedLine(p.get_start(), p.get_start() +\n                          length * RIGHT).rotate(angle,\n                                                 about_point=p.get_start())\n\n    return bisector\n\n\ndef line_intersection(p: Line, q: Line) -> np.ndarray:\n    \"\"\" Returns the intersection point of the two lines. \"\"\"\n\n    p1 = p.get_start()\n    p2 = p.get_end()\n    p3 = q.get_start()\n    p4 = q.get_end()\n\n    x1, y1 = p1[0], p1[1]\n    x2, y2 = p2[0], p2[1]\n    x3, y3 = p3[0], p3[1]\n    x4, y4 = p4[0], p4[1]\n\n    x = ((x1 * y2 - y1 * x2) * (x3 - x4) - (x1 - x2) * (x3 * y4 - y3 * x4)) / \\\n        ((x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4))\n    y = ((x1 * y2 - y1 * x2) * (y3 - y4) - (y1 - y2) * (x3 * y4 - y3 * x4)) / \\\n        ((x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4))\n\n    return np.array([x, y, 0])\n    \"\"\" Return the angle bisector of two lines.\"\"\"\n    p = p.copy()\n    q = q.copy()\n\n    b = Line(p.get_start(), p.get_start() + 10 * RIGHT,\n             color=BISECTOR_COLOR, stroke_width=BISECTOR_STROKE_WIDTH)\n    b.rotate((p.get_angle() + q.get_angle()) / 2, about_point=b.get_start())\n\n    return b\n\n\nclass Uloha5(Scene):\n    def construct(self):\n\n        self.next_section(\"Intro\", skip_animations=False)\n\n        ABC = Polygon(\n            np.array([-6, 0, 0]),\n            np.array([-3, 6, 0]),\n            np.array([6, 0, 0]),\n            stroke_color=ABC_COLOR,\n            stroke_width=ABC_STROKE_WIDTH,\n            z_index=ABC_Z_INDEX\n        ).move_to(ORIGIN)\n\n        A, B, C = tuple(ABC.get_vertices())\n        A_label = MathTex(\"A\", color=ABC_COLOR).next_to(\n            A, DOWN + LEFT, buff=0.1)\n        B_label = MathTex(\"B\", color=ABC_COLOR).next_to(B, UP, buff=0.15)\n        C_label = MathTex(\"C\", color=ABC_COLOR).next_to(\n            C, DOWN + RIGHT, buff=0.1)\n\n        self.play(Create(ABC), Write(A_label), Write(B_label), Write(C_label))\n\n        AB = Line(A, B, color=ABC_COLOR, stroke_width=ABC_STROKE_WIDTH)\n        BC = Line(B, C, color=ABC_COLOR, stroke_width=ABC_STROKE_WIDTH)\n        AC = Line(A, C, color=ABC_COLOR, stroke_width=ABC_STROKE_WIDTH)\n\n        AD = angle_bisector(AB, AC)\n        AD.set_stroke(color=AD_COLOR, width=AD_STROKE_WIDTH)\n        AD.set_z_index(AD_Z_INDEX)\n\n        # D = Dot(line_intersection(BC, AD), color=DOT_COLOR,\n        #         radius=DOT_RADIUS, stroke_width=DOT_STROKE_WIDTH,\n        #         stroke_opacity=DOT_STROKE_OPACITY,\n        #         fill_opacity=DOT_FILL_OPACITY,\n        #         z_index=DOT_Z_INDEX)\n        # D_label = MathTex(\"D\", color=DOT_COLOR).next_to(D, UP, buff=0.2)\n\n        D = Dot(AC.get_center(), color=DOT_COLOR,\n                radius=DOT_RADIUS, stroke_width=DOT_STROKE_WIDTH,\n                stroke_opacity=DOT_STROKE_OPACITY,\n                fill_opacity=DOT_FILL_OPACITY,\n                z_index=DOT_Z_INDEX)\n        E = Dot(AB.get_center(), color=DOT_COLOR,\n                radius=DOT_RADIUS, stroke_width=DOT_STROKE_WIDTH,\n                stroke_opacity=DOT_STROKE_OPACITY,\n                fill_opacity=DOT_FILL_OPACITY,\n                z_index=DOT_Z_INDEX)\n\n        D_label = MathTex(\"D\", color=DOT_COLOR).next_to(D, DOWN, buff=0.2)\n        E_label = MathTex(\"E\", color=DOT_COLOR).next_to(E, LEFT, buff=0.2)\n\n        d = DashedLine(AC.get_start(), AC.get_end(),\n                       color=AD_COLOR, stroke_width=AD_STROKE_WIDTH,\n                       z_index=AD_Z_INDEX).rotate(PI / 2,\n                                                  about_point=D.get_center())\n        e = DashedLine(AB.get_start(), AB.get_end(),\n                       color=AD_COLOR, stroke_width=AD_STROKE_WIDTH,\n                       z_index=AD_Z_INDEX)\n        e.set_angle(0).shift(3 * RIGHT)\n        e.set_length(8)\n        e.rotate(AB.get_angle(), about_point=AB.get_start())\n        e.rotate(-PI / 2, about_point=E.get_center())\n        alpha = Angle(AC, AB, radius=ANGLE_RADIUS, color=ANGLE_COLOR)\n        alpha_label = MathTex(\"\\\\alpha\", color=ANGLE_COLOR).next_to(\n            alpha, buff=0.1).shift(0.7 * LEFT + 0.1 * DOWN)\n\n        self.play(\n            AnimationGroup(DrawBorderThenFill(D), Write(D_label),\n                           lag_ratio=0.4),\n            AnimationGroup(DrawBorderThenFill(E), Write(E_label),\n                           lag_ratio=0.4),\n            AnimationGroup(Create(alpha), Write(alpha_label),\n                           lag_ratio=0.4)\n        )\n\n        self.play(Create(d), Create(e), Create(AD))\n\n        self.next_section(\"Inner Triangle\", skip_animations=False)\n        self.pause(2)\n\n        K = Dot(line_intersection(e, d), color=INNER_DOT_COLOR,\n                radius=INNER_DOT_RADIUS, stroke_width=INNER_DOT_STROKE_WIDTH,\n                stroke_opacity=INNER_DOT_STROKE_OPACITY,\n                fill_opacity=INNER_DOT_FILL_OPACITY,\n                z_index=INNER_DOT_Z_INDEX)\n        L = Dot(line_intersection(AD, d), color=INNER_DOT_COLOR,\n                radius=INNER_DOT_RADIUS, stroke_width=INNER_DOT_STROKE_WIDTH,\n                stroke_opacity=INNER_DOT_STROKE_OPACITY,\n                fill_opacity=INNER_DOT_FILL_OPACITY,\n                z_index=INNER_DOT_Z_INDEX)\n        M = Dot(line_intersection(AD, e), color=INNER_DOT_COLOR,\n                radius=INNER_DOT_RADIUS, stroke_width=INNER_DOT_STROKE_WIDTH,\n                stroke_opacity=INNER_DOT_STROKE_OPACITY,\n                fill_opacity=INNER_DOT_FILL_OPACITY,\n                z_index=INNER_DOT_Z_INDEX)\n\n        K_label = MathTex(\"K\", color=INNER_DOT_COLOR).next_to(\n            K, DOWN + LEFT, buff=0.1)\n        L_label = MathTex(\"L\", color=INNER_DOT_COLOR).next_to(\n            L, 3 * UP + RIGHT, buff=0.1)\n        M_label = MathTex(\"M\", color=INNER_DOT_COLOR).next_to(\n            M, UP, buff=0.2)\n\n        self.play(\n            AnimationGroup(DrawBorderThenFill(\n                K), Write(K_label), lag_ratio=0.4),\n            AnimationGroup(DrawBorderThenFill(\n                L), Write(L_label), lag_ratio=0.4),\n            AnimationGroup(DrawBorderThenFill(\n                M), Write(M_label), lag_ratio=0.4)\n        )\n\n        KLM = Polygon(K.get_center(), L.get_center(), M.get_center(\n        ), color=INNER_DOT_COLOR, stroke_width=INNER_DOT_STROKE_WIDTH + 1,\n            z_index=INNER_DOT_Z_INDEX)\n\n        v1 = DashedLine(\n            ORIGIN, 8 * RIGHT,\n            color=HEIGHT_COLOR,\n            stroke_width=HEIGHT_STROKE_WIDTH,\n            z_index=HEIGHT_Z_INDEX\n        ).move_to(K.get_center()).shift(RIGHT)\n        v1.rotate(AD.get_angle() + PI / 2, about_point=K.get_center())\n\n        v2 = DashedLine(\n            ORIGIN, 8 * RIGHT,\n            color=HEIGHT_COLOR,\n            stroke_width=HEIGHT_STROKE_WIDTH,\n            z_index=HEIGHT_Z_INDEX\n        ).move_to(L.get_center()).shift(RIGHT)\n        v2.rotate(e.get_angle() - PI / 2, about_point=L.get_center())\n\n        v3 = DashedLine(\n            ORIGIN, 8 * RIGHT,\n            color=HEIGHT_COLOR,\n            stroke_width=HEIGHT_STROKE_WIDTH,\n            z_index=HEIGHT_Z_INDEX\n        ).move_to(M.get_center()).shift(RIGHT)\n        v3.rotate(d.get_angle() - PI / 2, about_point=M.get_center())\n\n        self.play(Create(KLM))\n        self.play(Create(v1), Create(v2), Create(v3))\n\n        self.next_section(\"Orthocenter\", skip_animations=False)\n        self.pause(2)\n\n        V = Dot(line_intersection(v1, v2), color=ORTH_DOT_COLOR,\n                radius=ORTH_DOT_RADIUS, stroke_width=ORTH_DOT_STROKE_WIDTH,\n                stroke_opacity=ORTH_DOT_STROKE_OPACITY,\n                fill_opacity=ORTH_DOT_FILL_OPACITY,\n                z_index=ORTH_DOT_Z_INDEX)\n        V_label = MathTex(\"V\", color=ORTH_DOT_COLOR).next_to(V, UP, buff=0.1)\n\n        self.play(AnimationGroup(DrawBorderThenFill(\n            V), Write(V_label), lag_ratio=0.4))\n\n        T = Dot(BC.get_center(), color=T_COLOR,\n                radius=ORTH_DOT_RADIUS, stroke_width=ORTH_DOT_STROKE_WIDTH,\n                stroke_opacity=ORTH_DOT_STROKE_OPACITY,\n                fill_opacity=0.5,\n                z_index=ORTH_DOT_Z_INDEX)\n        T_label = MathTex(\"T\", color=T_COLOR).next_to(T, UP + RIGHT, buff=0.1)\n        t = Line(A, T.get_center(), color=T_COLOR,\n                 stroke_width=T_STROKE_WIDTH, z_index=T_Z_INDEX)\n\n        self.play(AnimationGroup(\n            DrawBorderThenFill(T), Write(T_label), Create(t), lag_ratio=0.4)\n        )\n\n        self.next_section(\"Parallelepiped\")\n        self.pause(2)\n\n        ADTE = Polygon(A, D.get_center(), T.get_center(), E.get_center(),\n                       color=PAR_COLOR, stroke_width=PAR_STROKE_WIDTH,\n                       z_index=PAR_Z_INDEX)\n\n        self.play(Create(ADTE), run_time=2)\n\n        X = line_intersection(AB, v3)\n        Y = line_intersection(AC, v2)\n\n        AYVX = Polygon(A, Y, V.get_center(), X, color=PINK,\n                       stroke_width=PAR_STROKE_WIDTH, z_index=PAR_Z_INDEX + 1)\n\n        self.play(Create(AYVX), run_time=2)\n        # create ABC\n        ABC = Polygon(\n            np.array([-6, 0, 0]),\n            np.array([-3, 6, 0]),\n            np.array([6, 0, 0])\n        ).set_stroke(ABC_COLOR, ABC_STROKE_WIDTH).set_z_index(ABC_Z_INDEX)\n        ABC.move_to(ORIGIN)\n\n        # label ABC\n        A = MathTex(\"A\", color=ABC_COLOR).next_to(\n            ABC.get_vertices()[0], DOWN + LEFT, buff=0.1)\n        B = MathTex(\"B\", color=ABC_COLOR).next_to(\n            ABC.get_vertices()[1], 0.2 * LEFT + UP, buff=0.1)\n        C = MathTex(\"C\", color=ABC_COLOR).next_to(\n            ABC.get_vertices()[2], RIGHT + DOWN, buff=0.1)\n\n        self.play(Create(ABC), Write(A), Write(B), Write(C))\n\n        # get ABC sides\n        AB = Line(ABC.get_vertices()[0], ABC.get_vertices()[1])\n        BC = Line(ABC.get_vertices()[1], ABC.get_vertices()[2])\n        AC = Line(ABC.get_vertices()[0], ABC.get_vertices()[2])\n\n        b = angle_bisector(AB, AC)\n        D = Intersection(ABC, b, color=GREEN, fill_opacity=1, z_index=10)\n\n        self.play(Create(b), Create(D))\n\n        self.pause(2)\n","repo_name":"Tesser3kt/GEVO","sub_path":"Seminar/animations/uloha_5_kat_A.py","file_name":"uloha_5_kat_A.py","file_ext":"py","file_size_in_byte":10227,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"76"}
+{"seq_id":"38539957454","text":"import fasttext\nimport logging\nimport multiprocessing\nimport os\nimport pickle\nimport random\nimport re\nimport string\nimport stop_words\n\nfrom tqdm import tqdm\n\nfrom utf_utils import transform_german_word\n\nlogging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n\ndef noisify_sentence(sentence):\n    #print(sentence)\n    noisified_sentence = list()\n    for w in sentence:\n        try:\n            subst_prob = scores[w]\n            assert w not in stopwords\n            subst = random.choices([True, False], cum_weights=[subst_prob, subst_prob+(1-subst_prob)])[0]\n            if subst:\n                new_w = random.choice(model_vocabulary)\n                #print([w, subst_prob, new_w])\n                noisified_sentence.append(new_w)\n                #print([w, idx_to_word[new_w], subst_prob])\n            else:\n                noisified_sentence.append(w)\n        except KeyError:\n            noisified_sentence.append(w)\n    #print(noisified_sentence)\n    return noisified_sentence\n\n\ndef process_file(in_f):\n    sents = list()\n    if 'opensubs_ready' in in_f:\n        ### grouping by chunks of 512 tokens\n        sentence = list()\n        with open(in_f) as i:\n            for l in i:\n                line = re.sub(r'-', r'', l)\n                line = re.sub('\\W', ' ', line)\n                line = re.sub('\\s+', r' ', line)\n                line = line.split()\n                sentence.extend(line)\n                if len(sentence) >= 256:\n                    ### add noise in sentence\n                    sentence = noisify_sentence(sentence)\n                    sentence = re.sub('\\s+', ' ', ' '.join(sentence))\n                    #o.write('{}\\n'.format(' '.join(sentence)))\n                    #yield sentence\n                    sents.append(sentence)\n                    sentence = list()\n            if len(sentence) > 1:\n                sentence = noisify_sentence(sentence)\n                #o.write('{}\\n'.format(' '.join(sentence)))\n                sentence = re.sub('\\s+', ' ', ' '.join(sentence))\n                #yield sentence\n                sents.append(sentence)\n    else:\n        with open(in_f) as i:\n            marker = True\n            sentence = list()\n            for l in i:\n                line = l.strip().split('\\t')\n                if line[0][:4] == '</s>':\n                    #print(sentence)\n                    sentence = noisify_sentence(sentence)\n                    sentence = re.sub('\\s+', ' ', ' '.join(sentence))\n                    #yield sentence\n                    sents.append(sentence)\n                    #o.write('{}\\n'.format(' '.join(sentence)))\n                    sentence = list()\n                elif line[0][0] == '<':\n                    continue\n                if len(line) < 3:\n                    continue\n                #if line[5]=='P':\n                #    continue\n                if line[0] in string.punctuation:\n                    continue\n                else:\n                    if '$' in line[1]:\n                        continue\n                    else:\n                        sentence.append(line[0])\n    print(len(sents))\n    return sents\n\nstopwords = stop_words.get_stop_words('de')\n\nlogging.info('now loading fasttext')\nmodel = fasttext.load_model(os.path.join(\n                                '/',\n                                'import',\n                                'cogsci',\n                                'andrea',\n                                'dataset',\n                                'word_vectors',\n                                'de',\n                                'cc.de.300.bin',\n                                )\n                                )\nmodel_vocabulary = model.words\n\nin_fs = list()\ncorpora = [\n           'opensubs_ready', \n           'sdewac-v3-tagged_smaller_files'\n           ]\nfor c in corpora:\n    corpus_folder = os.path.join('/', 'import', 'cogsci', 'andrea', 'dataset', 'corpora', 'de', c)\n    for f in os.listdir(corpus_folder):\n        in_fs.append(os.path.join(corpus_folder, f))\n\n### reading words to be left out\nwith open(os.path.join('pickles', 'cn_sims.pkl'), 'rb') as i:\n    all_sims = pickle.load(i)\n\n### pruning\nall_sims = {k : {w.replace('_', '') : val for w, val in v.items() if w.replace('_', '') not in stopwords} for k, v in all_sims.items()}\nall_sims = {k : {w : prob for w, prob in sorted(v.items(), key=lambda item : item[1], reverse=True)[:int(len(v.values())*0.01)]} for k, v in all_sims.items()}\nmins = {k : min(v.values()) for k, v in all_sims.items()}\nmaxs = {k : max(v.values()) for k, v in all_sims.items()}\n### rescaling between 0.5-0.75\nall_sims = {k : {w : 0.25*((val - mins[k]) / (maxs[k]-mins[k]))+0.5 for w, val in v.items()} for k, v in all_sims.items()}  \n\nfull_sims = {k : {w_spelling : prob for w, prob in v.items() for w_spelling in transform_german_word(w)} for k, v in all_sims.items()}\nfull_sims['undamaged'] = dict()\n\nfor case, scores in full_sims.items():\n    print(case)\n    debugging = False\n    #debugging = True\n    if debugging:\n        for f in tqdm(in_fs):\n            results = process_file(f)\n    else:\n        with multiprocessing.Pool(processes=int(os.cpu_count())) as pool:\n            results = pool.map(process_file, in_fs)\n            pool.terminate()\n            pool.join()\n\n    out_folder = os.path.join('/', 'import', 'cogsci', 'andrea', 'dataset', 'corpora', 'de', 'ready_for_fasttext')\n    os.makedirs(out_folder, exist_ok=True)\n\n    out_file = os.path.join(out_folder, '{}_wac_subs_for_fasttext.txt'.format(case))\n    with open(out_file, 'w') as o:\n        for lst in tqdm(results):\n            for l in lst:\n                o.write('{}\\n'.format(l))\n","repo_name":"andreabruera/damage","sub_path":"prepare_fasttext_corpus.py","file_name":"prepare_fasttext_corpus.py","file_ext":"py","file_size_in_byte":5675,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"7739925510","text":"import io\nimport os\nimport sys\nimport pickle\nimport unittest\nimport warnings\n\nimport pronto\nfrom pronto.logic.lineage import Lineage\n\nfrom .utils import DATADIR\n\n\nclass TestOntology(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        warnings.simplefilter('error')\n        warnings.simplefilter('ignore', category=UnicodeWarning)\n\n    @classmethod\n    def tearDownClass(cls):\n        warnings.simplefilter(warnings.defaultaction)\n\n    def test_repr_new(self):\n        ont = pronto.Ontology()\n        self.assertEqual(repr(ont), \"Ontology()\")\n\n    def test_repr_path(self):\n        path = os.path.join(DATADIR, \"pato.obo\")\n        ont = pronto.Ontology(path)\n        self.assertEqual(repr(ont), \"Ontology({!r})\".format(path))\n\n    def test_repr_path_with_import_depth(self):\n        path = os.path.join(DATADIR, \"pato.obo\")\n        ont = pronto.Ontology(path, import_depth=1)\n        self.assertEqual(repr(ont), \"Ontology({!r}, import_depth=1)\".format(path))\n\n    def test_repr_file(self):\n        path = os.path.join(DATADIR, \"pato.obo\")\n        with open(path, \"rb\") as src:\n            ont = pronto.Ontology(src)\n        self.assertEqual(repr(ont), \"Ontology({!r})\".format(path))\n\n    def test_repr_file_handle(self):\n        path = os.path.join(DATADIR, \"pato.obo\")\n        with open(path, \"rb\") as src:\n            handle = io.BytesIO(src.read())\n        ont = pronto.Ontology(handle)\n        self.assertEqual(repr(ont), \"Ontology({!r})\".format(handle))\n\n    def test_threads_invalid(self):\n        hp = os.path.join(DATADIR, \"hp.obo\")\n        self.assertRaises(ValueError, pronto.Ontology, hp, threads=-1)\n        self.assertRaises(ValueError, pronto.Ontology, hp, threads=0)\n\n    def test_indexing_relationship_warning(self):\n        ont = pronto.Ontology()\n        ont.create_relationship(\"brother_of\")\n        with warnings.catch_warnings():\n            warnings.simplefilter(\"error\")\n            self.assertRaises(DeprecationWarning, ont.__getitem__, \"brother_of\")\n\n\nclass TestOntologyLineage(unittest.TestCase):\n    def test_edit_term(self):\n        ont = pronto.Ontology()\n        self.assertEqual(ont._terms.lineage, {})\n\n        t1 = ont.create_term(\"TST:001\")\n        self.assertEqual(ont._terms.lineage, {t1.id: Lineage()})\n\n        t2 = ont.create_term(\"TST:002\")\n        self.assertEqual(ont._terms.lineage, {t1.id: Lineage(), t2.id: Lineage()})\n\n        t2.superclasses().add(t1)\n        self.assertEqual(ont._terms.lineage, {\n            t1.id: Lineage(sub={t2.id}),\n            t2.id: Lineage(sup={t1.id})\n        })\n\n        t2.superclasses().clear()\n        self.assertEqual(ont._terms.lineage, {t1.id: Lineage(), t2.id: Lineage()})\n\n    def test_edit_relationship(self):\n        ont = pronto.Ontology()\n        self.assertEqual(ont._relationships.lineage, {})\n\n        r1 = ont.create_relationship(\"rel_one\")\n        self.assertEqual(ont._relationships.lineage, {r1.id: Lineage()})\n\n        r2 = ont.create_relationship(\"rel_two\")\n        self.assertEqual(ont._relationships.lineage, {r1.id: Lineage(), r2.id: Lineage()})\n\n        r2.superproperties().add(r1)\n        self.assertEqual(ont._relationships.lineage, {\n            r1.id: Lineage(sub={r2.id}),\n            r2.id: Lineage(sup={r1.id})\n        })\n\n        r2.superproperties().clear()\n        self.assertEqual(ont._relationships.lineage, {r1.id: Lineage(), r2.id: Lineage()})\n\n\nclass TestPickling(object):\n    @classmethod\n    def setUpClass(cls):\n        warnings.simplefilter('error')\n        warnings.simplefilter('ignore', category=UnicodeWarning)\n        with open(os.path.join(DATADIR, \"ms.obo\"), \"rb\") as f:\n            cls.ms = pronto.Ontology(cls.file)\n\n    @classmethod\n    def tearDownClass(cls):\n        warnings.simplefilter(warnings.defaultaction)\n\n    # ------------------------------------------------------------------------\n\n    def _test_memory_pickling(self, protocol):\n        ont = pronto.Ontology()\n        t1 = ont.create_term(\"TST:001\")\n        t1.name = \"first name\"\n\n        pickled = pickle.dumps(ont, protocol=protocol)\n        unpickled = pickle.loads(pickled)\n\n        self.assertEqual(ont.keys(), unpickled.keys())\n        self.assertEqual(ont[\"TST:001\"], unpickled[\"TST:001\"])\n        self.assertEqual(ont[\"TST:001\"].name, unpickled[\"TST:001\"].name)\n\n    def test_memory_pickling_3(self):\n        self._test_memory_pickling(3)\n\n    def test_memory_pickling_4(self):\n        self._test_memory_pickling(4)\n\n    @unittest.skipIf(sys.version_info < (3, 8), \"protocol 5 requires Python 3.8+\")\n    def test_memory_pickling_5(self):\n        self._test_memory_pickling(5)\n\n    # ------------------------------------------------------------------------\n\n    def _test_file_pickling(self, protocol):\n        pickled = pickle.dumps(self.ms, protocol=protocol)\n        unpickled = pickle.loads(pickled)\n        self.assertEqual(self.ms.keys(), unpickled.keys())\n        for key in self.ms.keys():\n            term_ms, term_pickled = self.ms[key]._data(), unpickled[key]._data()\n            for attr in term_ms.__annotations__:\n                attr_ms = getattr(term_ms, attr)\n                attr_pickled = getattr(term_pickled, attr)\n                self.assertEqual(attr_ms, attr_pickled)\n\n    def test_file_pickling_3(self):\n        self._test_file_pickling(3)\n\n    def test_file_pickling_4(self):\n        self._test_file_pickling(4)\n\n    @unittest.skipIf(sys.version_info < (3, 8), \"protocol 5 requires Python 3.8+\")\n    def test_file_pickling_5(self):\n        self._test_file_pickling(5)\n","repo_name":"althonos/pronto","sub_path":"tests/test_ontology.py","file_name":"test_ontology.py","file_ext":"py","file_size_in_byte":5515,"program_lang":"python","lang":"en","doc_type":"code","stars":213,"dataset":"github-code","pt":"76"}
+{"seq_id":"6894168064","text":"from transformers import BertTokenizer, BertModel\nimport pandas as pd\nimport tqdm\nimport torch\nimport numpy as np\n\nUSE_GPU = True\nGPU_INDEX = 1\nif USE_GPU and torch.cuda.is_available():\n    device = torch.device('cuda:{}'.format(GPU_INDEX))\nelse:\n    device = torch.device('cpu')\n\ndef get_vector(text, tokenizer, model):\n    text = text[:510]\n    input_ids = tokenizer(text, return_tensors=\"pt\")\n    vector = model.forward(**input_ids)[0]\n    vector = torch.mean(vector, axis=1).squeeze().detach().numpy()\n    return vector\n\ndef distance(center, sentence):\n    x = sentence - center\n    return np.linalg.norm(x, 2)\n\n\ndata_path = '/home/ubuntu/likun/nlp_data/cluster/li_event.csv'\ndf = pd.read_csv(data_path)\ntest_text_col = 'use'\n\nkws_path = '/home/ubuntu/likun/nlp_data/cluster/event_sentiment_kws.csv'\nkws = pd.read_csv(kws_path)\nsentiments = ['正向', '中立', '负向']\ncenters = [kws[s][0].replace(';', ' ') for s in sentiments]\n\n\nbert_model_path = '/home/ubuntu/likun/nlp_pretrained/bert-wwm-ext'\ntokenizer = BertTokenizer.from_pretrained(bert_model_path, do_lower_case=True)\nmodel = BertModel.from_pretrained(bert_model_path)\n\nsentence_vectors = [get_vector(text, tokenizer, model) for text in tqdm.tqdm(df[test_text_col].tolist())]\ncenter_vectors = [get_vector(text, tokenizer, model) for text in tqdm.tqdm(centers)]\n\ndistances = []\npositions = []\nfor sentence in sentence_vectors:\n    center_dis = []\n    for center in center_vectors:\n        center_dis.append(distance(sentence, center))\n    positions.append(sentiments[np.argmin(center_dis)])\n    distances.append(center_dis)\ndf['类别'] = positions\ndf.to_csv('li_event_res.csv', index=False, encoding='utf-8')","repo_name":"linkseed18612254945/nlp-practice","sub_path":"huggingface/distance_cluster.py","file_name":"distance_cluster.py","file_ext":"py","file_size_in_byte":1674,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"28222796079","text":"\"Implementation for sol_sources rule\"\n\nload(\"@aspect_rules_js//js:providers.bzl\", \"JsInfo\", \"js_info\")\nload(\"//sol:providers.bzl\", \"SolSourcesInfo\", \"sol_remappings_info\")\nload(\"@aspect_rules_js//js:libs.bzl\", \"js_lib_helpers\")\n\n_ATTRS = {\n    \"srcs\": attr.label_list(\n        allow_files = [\".sol\"],\n        doc = \"Solidity source files\",\n    ),\n    \"deps\": attr.label_list(\n        doc = \"Each dependency should either be more .sol sources, or npm packages for 3p dependencies\",\n        providers = [[SolSourcesInfo], [JsInfo]],\n    ),\n    \"remappings\": attr.string_dict(\n        doc = \"\"\"Contribute to import mappings.\n        \n        See https://docs.soliditylang.org/en/latest/path-resolution.html?highlight=remappings#import-remapping\n        \"\"\",\n        default = {},\n    ),\n}\n\ndef _sol_sources_impl(ctx):\n    npm_linked_packages = js_lib_helpers.gather_npm_linked_packages(\n        srcs = ctx.attr.srcs,\n        deps = ctx.attr.deps,\n    )\n\n    return [\n        DefaultInfo(\n            files = depset(ctx.files.srcs),\n        ),\n        SolSourcesInfo(\n            direct_sources = ctx.files.srcs,\n            transitive_sources = depset(\n                ctx.files.srcs,\n                transitive = [\n                    d[SolSourcesInfo].transitive_sources\n                    for d in ctx.attr.deps\n                    if SolSourcesInfo in d\n                ],\n            ),\n        ),\n        sol_remappings_info(ctx, ctx.attr.remappings),\n        js_info(\n            npm_linked_packages = npm_linked_packages.direct,\n            npm_linked_package_files = npm_linked_packages.direct_files,\n            transitive_npm_linked_package_files = npm_linked_packages.transitive_files,\n            transitive_npm_linked_packages = npm_linked_packages.transitive,\n        ),\n    ]\n\nsol_sources = struct(\n    implementation = _sol_sources_impl,\n    attrs = _ATTRS,\n)\n","repo_name":"aspect-build/rules_sol","sub_path":"sol/private/sol_sources.bzl","file_name":"sol_sources.bzl","file_ext":"bzl","file_size_in_byte":1875,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"76"}
+{"seq_id":"6178073924","text":"from pyramid.config import Configurator\nimport pyramid.renderers\n\n\ndef main(global_config, **settings):\n    \"\"\" This function returns a Pyramid WSGI application.\n    \"\"\"\n    config = Configurator(settings=settings)\n    config.add_renderer('json', pyramid.renderers.JSON())\n    # config.add_static_view('static', 'static', cache_max_age=3600)\n    # config.add_route('home', '/')\n    config.add_route('link_info', '/link_info')\n    config.scan()\n    return config.make_wsgi_app()\n","repo_name":"slinkp/bitly-assumptionator","sub_path":"assumptionator/assumptionator/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"73269803126","text":"n=int(input())\nsum=0\nt1=0\nt2=0\nt3=0\nt4=0\nfor i in range(0,n):\n    grade=float(input())\n    sum+=grade\n    if grade>=5:\n        t1+=1\n    elif grade>=4 and grade<5:\n        t2+=1\n    elif grade>=3 and grade <4:\n        t3+=1\n    else:\n        t4+=1\nprint(\"Top students: {0:.2f}%\".format(t1/n*100))\nprint(\"Between 4.00 and 4.99: {0:.2f}%\".format(t2/n*100))\nprint(\"Between 3.00 and 3.99: {0:.2f}%\".format(t3/n*100))\nprint(\"Fail: {0:.2f}%\".format(t4/n*100))\nprint(\"Average: {0:.2f}\".format(sum/n))\n\n","repo_name":"maddrum/Python_Basics","sub_path":"#old_exam_problems/04 - Advanced Logic/04. Grades.py","file_name":"04. Grades.py","file_ext":"py","file_size_in_byte":495,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"20140736670","text":"import base64\nimport decimal\nimport json\nfrom enum import Enum\nfrom urllib.parse import quote\n\nimport boto3\nfrom web3 import Web3\n\nfrom common.blockchain_util import BlockChainUtil\nfrom common.boto_utils import BotoUtils\nfrom common.constant import TransactionStatus, COGS_TO_AGI\nfrom common.logger import get_logger\nfrom common.utils import Utils\nfrom orchestrator.config import CREATE_ORDER_SERVICE_ARN, INITIATE_PAYMENT_SERVICE_ARN, \\\n    EXECUTE_PAYMENT_SERVICE_ARN, WALLETS_SERVICE_ARN, ORDER_DETAILS_ORDER_ID_ARN, ORDER_DETAILS_BY_USERNAME_ARN, \\\n    REGION_NAME, SIGNER_ADDRESS, EXECUTOR_ADDRESS, NETWORKS, NETWORK_ID, SIGNER_SERVICE_ARN, \\\n    GET_GROUP_FOR_ORG_API_ARN, GET_ALL_ORG_API_ARN, USD_TO_COGS_CONVERSION_FACTOR\nfrom orchestrator.dao.transaction_history_dao import TransactionHistoryDAO\nfrom orchestrator.exceptions import PaymentInitiateFailed, ChannelCreationFailed, FundChannelFailed\nfrom orchestrator.order_status import OrderStatus\nfrom orchestrator.services.wallet_service import WalletService\nfrom orchestrator.transaction_history import TransactionHistory\n\nlogger = get_logger(__name__)\n\n\nclass Status(Enum):\n    SUCCESS = \"SUCCESS\"\n    FAILED = \"FAILED\"\n    PAYMENT_INITIATED = \"PAYMENT_INITIATED\"\n    PAYMENT_INITIATION_FAILED = \"PAYMENT_INITIATION_FAILED\"\n    PAYMENT_EXECUTED = \"PAYMENT_EXECUTED\"\n    PAYMENT_EXECUTION_FAILED = \"PAYMENT_EXECUTION_FAILED\"\n    ORDER_PROCESSED = \"ORDER_PROCESSED\"\n    ORDER_PROCESSING_FAILED = \"ORDER_PROCESSING_FAILED\"\n\n\nclass OrderType(Enum):\n    CREATE_WALLET_AND_CHANNEL = \"CREATE_WALLET_AND_CHANNEL\"\n    CREATE_CHANNEL = \"CREATE_CHANNEL\"\n    FUND_CHANNEL = \"FUND_CHANNEL\"\n\n\nclass OrderService:\n    def __init__(self, obj_repo):\n        self.repo = obj_repo\n        self.obj_transaction_history_dao = TransactionHistoryDAO(self.repo)\n        self.lambda_client = boto3.client('lambda', region_name=REGION_NAME)\n        self.boto_client = BotoUtils(REGION_NAME)\n        self.wallet_service = WalletService()\n        self.obj_blockchain_util = BlockChainUtil(\n            provider_type=\"HTTP_PROVIDER\",\n            provider=NETWORKS[NETWORK_ID]['http_provider']\n        )\n        self.utils = Utils()\n\n    def initiate_order(self, username, payload_dict):\n        \"\"\"\n            Initiate Order\n                Step 1  Order Creation\n                Step 2  Initiate Payment\n                Step 3  Persist Transaction History\n        \"\"\"\n        price = payload_dict[\"price\"]\n        order_type = payload_dict[\"item_details\"][\"order_type\"]\n        item_details = payload_dict[\"item_details\"]\n        group_id = item_details[\"group_id\"]\n        org_id = item_details[\"org_id\"]\n        channel_id = \"\"\n        amount_in_cogs = self.calculate_amount_in_cogs(amount=price[\"amount\"], currency=price[\"currency\"])\n        if amount_in_cogs < 1:\n            raise Exception(\"Amount in cogs should be greater than equal to 1\")\n        item_details[\"amount_in_cogs\"] = amount_in_cogs\n\n        if order_type == OrderType.CREATE_WALLET_AND_CHANNEL.value:\n            item_details[\"wallet_address\"] = \"\"\n            recipient = self.get_payment_address_for_org(group_id=group_id, org_id=org_id)\n\n        elif order_type == OrderType.CREATE_CHANNEL.value:\n            recipient = self.get_payment_address_for_org(group_id=group_id, org_id=org_id)\n\n        elif order_type == OrderType.FUND_CHANNEL.value:\n            channel = self.get_channel_for_topup(username=username, group_id=group_id, org_id=org_id)\n            if channel is None:\n                raise Exception(f\"Channel not found for the user: {username} with org: {org_id} group: {group_id}\")\n            recipient = channel[\"recipient\"]\n            channel_id = channel[\"channel_id\"]\n            item_details[\"wallet_address\"] = channel[\"address\"]\n\n        else:\n            raise Exception(\"Invalid order type\")\n\n        item_details[\"channel_id\"] = channel_id\n        item_details[\"recipient\"] = recipient\n        order_details = self.manage_create_order(\n            username=username, item_details=item_details,\n            price=price\n        )\n        order_id = order_details[\"order_id\"]\n\n        try:\n            payment_data = self.manage_initiate_payment(\n                username=username, order_id=order_id, price=price,\n                payment_method=payload_dict[\"payment_method\"]\n            )\n\n            payment_id = payment_data[\"payment_id\"]\n            raw_payment_data = json.dumps(payment_data[\"payment\"])\n            obj_transaction_history = TransactionHistory(\n                username=username, order_id=order_id, order_type=order_type,\n                payment_id=payment_id, raw_payment_data=raw_payment_data,\n                status=Status.PAYMENT_INITIATED.value\n            )\n            self.obj_transaction_history_dao.insert_transaction_history(obj_transaction_history=obj_transaction_history)\n            return payment_data\n        except Exception as e:\n            obj_transaction_history = TransactionHistory(\n                username=username, order_id=order_id, order_type=order_type,\n                status=Status.PAYMENT_INITIATION_FAILED.value\n            )\n            self.obj_transaction_history_dao.insert_transaction_history(obj_transaction_history=obj_transaction_history)\n            print(repr(e))\n            raise e\n\n    def get_channel_for_topup(self, username, group_id, org_id):\n        channel_details = self.wallet_service.get_channel_details(\n            username=username, group_id=group_id, org_id=org_id\n        )\n        wallets = channel_details[\"wallets\"]\n        for wallet in wallets:\n            if (wallet[\"type\"] == \"GENERAL\") and len(wallet[\"channels\"]) > 0:\n                if wallet[\"channels\"][0][\"signer\"] == SIGNER_ADDRESS:\n                    wallet_address = wallet[\"address\"]\n                    channel = wallet[\"channels\"][0]\n                    channel[\"address\"] = wallet_address\n                    return channel\n        return None\n\n    def get_payment_address_for_org(self, org_id, group_id):\n\n        group_details_event = {\n            \"path\": f\"/org/{org_id}/group/{quote(group_id, safe='')}\",\n            \"pathParameters\": {\n                \"orgId\": org_id,\n                \"group_id\": quote(group_id, safe='')\n            },\n            \"httpMethod\": \"GET\"\n        }\n        logger.info(f\"get_group_for_org request: {org_id} and {group_id}\")\n        group_details_lambda_response = self.lambda_client.invoke(\n            FunctionName=GET_GROUP_FOR_ORG_API_ARN,\n            InvocationType='RequestResponse',\n            Payload=json.dumps(group_details_event)\n        )\n        group_details_response = json.loads(group_details_lambda_response.get('Payload').read())\n        logger.info(f\"get_group_for_org response: {group_details_response}\")\n        if group_details_response[\"statusCode\"] != 200:\n            raise Exception(f\"Failed to fetch group details for org_id:{org_id} \"\n                            f\"group_id {group_id}\")\n\n        group_details_response_body = json.loads(group_details_response[\"body\"])\n        groups = group_details_response_body[\"data\"][\"groups\"]\n        if len(groups) == 0:\n            raise Exception(f\"Failed to find group {group_id} for org_id: {org_id}\")\n        return groups[0][\"payment\"][\"payment_address\"]\n\n    def calculate_amount_in_cogs(self, amount, currency):\n        if currency == \"USD\":\n            amount_in_cogs = round(amount * USD_TO_COGS_CONVERSION_FACTOR)\n            return amount_in_cogs\n        else:\n            raise Exception(\"Currency %s not supported.\", currency)\n\n    def execute_order(self, username, payload_dict):\n        \"\"\"\n            Execute Order\n                Step 1  Execute Payment\n                Step 2  Get Receipient Address\n                Step 3  Process Order\n                Step 4  Update Transaction History\n        \"\"\"\n        order_id = payload_dict[\"order_id\"]\n        payment_id = payload_dict[\"payment_id\"]\n        order = self.get_order_details_by_order_id(order_id, username)\n        payment = None\n        for payment_item in order[\"payments\"]:\n            if payment_item[\"payment_id\"] == payment_id:\n                payment = payment_item\n                break\n\n        if payment is None:\n            raise Exception(f\"Failed to fetch order details for order_id {order_id} \\n\"\n                            f\"payment_id {payment_id} \\n\"\n                            f\"username{username}\")\n\n        order_type = order[\"item_details\"][\"order_type\"]\n        item_details = order[\"item_details\"]\n        payment_method = payment[\"payment_details\"][\"payment_method\"]\n        paid_payment_details = payload_dict[\"payment_details\"]\n        price = payment[\"price\"]\n        status = Status.PAYMENT_EXECUTION_FAILED.value\n        self.manage_execute_payment(\n            username=username, order_id=order_id, payment_id=payment_id,\n            payment_details=paid_payment_details, payment_method=payment_method\n        )\n\n        status = Status.PAYMENT_EXECUTED.value\n        try:\n            status = Status.ORDER_PROCESSING_FAILED.value\n            amount_in_cogs = self.calculate_amount_in_cogs(amount=price[\"amount\"], currency=price[\"currency\"])\n            if amount_in_cogs < 1:\n                raise Exception(\"Amount in cogs should be greater than equal to 1\")\n            processed_order_data = self.manage_process_order(\n                username=username,\n                order_id=order_id, order_type=order_type,\n                amount=price[\"amount\"],\n                currency=price[\"currency\"], order_data=item_details,\n                amount_in_cogs=amount_in_cogs\n            )\n            status = Status.ORDER_PROCESSED.value\n            obj_transaction_history = TransactionHistory(\n                username=username, order_id=order_id, order_type=order_type,\n                status=status, payment_id=payment_id,\n                payment_method=payment_method,\n                raw_payment_data=json.dumps(paid_payment_details)\n            )\n            self.obj_transaction_history_dao.insert_transaction_history(obj_transaction_history=obj_transaction_history)\n            processed_order_data[\"price\"] = price\n            processed_order_data[\"item_details\"] = item_details\n            return processed_order_data\n\n        except Exception as e:\n            obj_transaction_history = TransactionHistory(\n                username=username, order_id=order_id,\n                order_type=order_type, status=status\n            )\n            self.obj_transaction_history_dao.insert_transaction_history(obj_transaction_history=obj_transaction_history)\n            print(repr(e))\n            raise e\n\n    def get_order_details_by_order_id(self, order_id, username):\n        order_details_event = {\n            \"path\": f\"order/{order_id}\",\n            \"pathParameters\": {\"order_id\": order_id},\n            \"httpMethod\": \"GET\"\n        }\n\n        logger.info(f\"Requesting order details for order_id {order_id}\")\n        response = self.lambda_client.invoke(\n            FunctionName=ORDER_DETAILS_ORDER_ID_ARN,\n            InvocationType='RequestResponse',\n            Payload=json.dumps(order_details_event)\n        )\n        order_details_response = json.loads(response.get('Payload').read())\n        if order_details_response[\"statusCode\"] != 200:\n            raise Exception(f\"Failed to fetch order details for order_id {order_id} username{username}\")\n\n        order_details_data = json.loads(order_details_response[\"body\"])\n        if order_details_data[\"username\"] != username:\n            raise Exception(f\"Failed to fetch order details for order_id {order_id} username{username}\")\n        return order_details_data\n\n    def manage_initiate_payment(self, username, order_id, price, payment_method):\n        initiate_payment_event = {\n            \"pathParameters\": {\"order_id\": order_id},\n            \"httpMethod\": \"POST\",\n            \"body\": json.dumps({\"price\": price, \"payment_method\": payment_method})\n        }\n        response = self.lambda_client.invoke(\n            FunctionName=INITIATE_PAYMENT_SERVICE_ARN,\n            InvocationType='RequestResponse',\n            Payload=json.dumps(initiate_payment_event)\n        )\n        initiate_payment_data = json.loads(response.get('Payload').read())\n        if initiate_payment_data[\"statusCode\"] == 201:\n            return json.loads(initiate_payment_data[\"body\"])\n        else:\n            logger.error(\"Error initiating payment for user %s\", username)\n            raise PaymentInitiateFailed\n\n    def manage_create_order(self, username, item_details, price):\n        create_order_event = {\n            \"path\": \"/order/create\",\n            \"httpMethod\": \"POST\",\n            \"body\": json.dumps({\"price\": price, \"item_details\": item_details, \"username\": username})\n        }\n        create_order_service_response = self.boto_client.invoke_lambda(\n            lambda_function_arn=CREATE_ORDER_SERVICE_ARN,\n            invocation_type='RequestResponse',\n            payload=json.dumps(create_order_event)\n        )\n        logger.info(f\"create_order_service_response: {create_order_service_response}\")\n\n        if create_order_service_response[\"statusCode\"] == 201:\n            return json.loads(create_order_service_response[\"body\"])\n        else:\n            raise Exception(f\"Error creating order for user {username}\")\n\n    def manage_execute_payment(self, username, order_id, payment_id, payment_details, payment_method):\n        execute_payment_event = {\n            \"pathParameters\": {\"order_id\": order_id, \"payment_id\": payment_id},\n            \"body\": json.dumps({\"payment_method\": payment_method, \"payment_details\": payment_details})\n        }\n        response = self.lambda_client.invoke(\n            FunctionName=EXECUTE_PAYMENT_SERVICE_ARN,\n            InvocationType='RequestResponse',\n            Payload=json.dumps(execute_payment_event)\n        )\n        payment_executed = json.loads(response.get('Payload').read())\n        if payment_executed[\"statusCode\"] == 201:\n            return payment_executed\n        else:\n            raise Exception(f\"Error executing payment for username {username} against order_id {order_id}\")\n\n    def manage_process_order(self, username, order_id, order_type, amount, currency, order_data, amount_in_cogs):\n        logger.info(f\"Order Data {order_data}\")\n        group_id = order_data[\"group_id\"]\n        org_id = order_data[\"org_id\"]\n        recipient = order_data[\"recipient\"]\n        channel_id = order_data[\"channel_id\"]\n        sender = order_data[\"wallet_address\"]\n        if order_type == OrderType.CREATE_WALLET_AND_CHANNEL.value:\n            wallet_create_payload = {\n                \"path\": \"/wallet\",\n                \"body\": json.dumps({\"username\": username}),\n                \"httpMethod\": \"POST\"\n            }\n            wallet_create_lambda_response = self.lambda_client.invoke(\n                FunctionName=WALLETS_SERVICE_ARN,\n                InvocationType='RequestResponse',\n                Payload=json.dumps(wallet_create_payload)\n            )\n            wallet_create_response = json.loads(wallet_create_lambda_response.get(\"Payload\").read())\n            if wallet_create_response[\"statusCode\"] != 200:\n                raise Exception(\"Failed to create wallet\")\n            wallet_create_response_body = json.loads(wallet_create_response[\"body\"])\n            wallet_details = wallet_create_response_body[\"data\"]\n\n            try:\n                current_block_no = self.obj_blockchain_util.get_current_block_no()\n                # 1 block no is mined in 15 sec on average, setting expiration as 10 years\n                expiration = current_block_no + (10 * 365 * 24 * 60 * 4)\n                message_nonce = current_block_no\n                self.EXECUTOR_WALLET_ADDRESS = self.boto_client.get_ssm_parameter(EXECUTOR_ADDRESS)\n                group_id_in_hex = \"0x\" + base64.b64decode(group_id).hex()\n                signature_details = self.generate_signature_for_open_channel_for_third_party(\n                    recipient=recipient, group_id=group_id_in_hex,\n                    amount_in_cogs=amount_in_cogs, expiration=expiration,\n                    message_nonce=message_nonce, sender_private_key=wallet_details[\"private_key\"],\n                    executor_wallet_address=self.EXECUTOR_WALLET_ADDRESS\n                )\n\n                logger.info(f\"Signature Details {signature_details}\")\n                open_channel_body = {\n                    'order_id': order_id,\n                    'sender': wallet_details[\"address\"],\n                    'signature': signature_details[\"signature\"],\n                    'r': signature_details[\"r\"],\n                    's': signature_details[\"s\"],\n                    'v': signature_details[\"v\"],\n                    'group_id': group_id,\n                    'org_id': org_id,\n                    'amount': amount,\n                    'currency': currency,\n                    'recipient': recipient,\n                    'current_block_no': current_block_no,\n                    'amount_in_cogs': amount_in_cogs\n                }\n                channel_details = self.wallet_service.create_channel(open_channel_body=open_channel_body)\n                channel_details.update(wallet_details)\n                return channel_details\n            except Exception as e:\n                logger.error(\"Failed to create channel\")\n                logger.error(repr(e))\n                response = {\n                    \"transaction_hash\": \"\",\n                    \"signature\": \"\",\n                    \"amount_in_cogs\": 0,\n                    \"price\": {\n                        \"amount\": amount,\n                        \"currency\": currency\n                    },\n                    \"item_details\": order_data\n                }\n                response.update(wallet_details)\n                raise ChannelCreationFailed(\"Failed to create channel\", wallet_details=response)\n\n        elif order_type == OrderType.CREATE_CHANNEL.value:\n            try:\n                logger.info(f\"Order Data {order_data}\")\n                signature = order_data[\"signature\"]\n                v, r, s = Web3.toInt(hexstr=\"0x\" + signature[-2:]), signature[:66], \"0x\" + signature[66:130]\n                open_channel_body = {\n                    'order_id': order_id, 'sender': order_data[\"wallet_address\"],\n                    'signature': order_data[\"signature\"], 'r': r, 's': s, 'v': v,\n                    'group_id': group_id, 'org_id': org_id, 'amount': amount, 'currency': currency,\n                    'recipient': recipient, 'current_block_no': order_data[\"current_block_number\"],\n                    'amount_in_cogs': amount_in_cogs\n                }\n                channel_details = self.wallet_service.create_channel(open_channel_body=open_channel_body)\n                logger.info(f\"channel_details: {channel_details}\")\n                return channel_details\n            except Exception as e:\n                logger.error(\"Failed to create channel\")\n                print(repr(e))\n                raise ChannelCreationFailed(\"Failed to create channel\", wallet_details=order_data)\n\n        elif order_type == OrderType.FUND_CHANNEL.value:\n\n            try:\n                fund_channel_body = {\n                    'order_id': order_id,\n                    'group_id': group_id,\n                    'org_id': org_id,\n                    'amount': amount,\n                    'channel_id': channel_id,\n                    'currency': currency,\n                    'recipient': recipient,\n                    'sender': sender,\n                    'amount_in_cogs': amount_in_cogs\n                }\n                fund_channel_payload = {\n                    \"path\": \"/wallet/channel/deposit\",\n                    \"body\": json.dumps(fund_channel_body),\n                    \"httpMethod\": \"POST\"\n                }\n\n                fund_channel_lambda_response = self.lambda_client.invoke(\n                    FunctionName=WALLETS_SERVICE_ARN,\n                    InvocationType='RequestResponse',\n                    Payload=json.dumps(fund_channel_payload)\n                )\n\n                fund_channel_response = json.loads(fund_channel_lambda_response.get(\"Payload\").read())\n                if fund_channel_response[\"statusCode\"] != 200:\n                    raise Exception(f\"Failed to add funds in channel for {fund_channel_body}\")\n\n                fund_channel_response_body = json.loads(fund_channel_response[\"body\"])\n                fund_channel_transaction_details = fund_channel_response_body[\"data\"]\n                return fund_channel_transaction_details\n            except Exception as e:\n                logger.error(\"Failed to fund channel\")\n                logger.error(repr(e))\n                raise FundChannelFailed()\n        else:\n            raise Exception(\"Order type is not valid.\")\n\n    def get_order_details_by_username(self, username):\n        order_details_event = {\n            \"path\": f\"/order\",\n            \"queryStringParameters\": {\"username\": username},\n            \"httpMethod\": \"GET\"\n        }\n\n        logger.info(f\"Requesting order details for username {username}\")\n        order_details_response = self.boto_client.invoke_lambda(\n            lambda_function_arn=ORDER_DETAILS_BY_USERNAME_ARN,\n            invocation_type='RequestResponse',\n            payload=json.dumps(order_details_event)\n        )\n        if order_details_response[\"statusCode\"] != 200:\n            raise Exception(f\"Failed to fetch order details for username{username}\")\n\n        org_id_name_mapping = self.get_organizations_from_contract()\n\n        order_details_response_body = json.loads(order_details_response[\"body\"])\n        orders = order_details_response_body[\"orders\"]\n\n        for order in orders:\n            order_id = order[\"order_id\"]\n            order[\"wallet_type\"] = \"GENERAL\"\n            if \"org_id\" in order[\"item_details\"]:\n                org_id = order[\"item_details\"][\"org_id\"]\n                if org_id in org_id_name_mapping:\n                    order[\"item_details\"][\"organization_name\"] = org_id_name_mapping[org_id]\n\n            transaction_details_event = {\n                \"path\": f\"/wallet/channel/transactions\",\n                \"queryStringParameters\": {\"order_id\": order_id},\n                \"httpMethod\": \"GET\"\n            }\n            transaction_details_lambda_response = self.lambda_client.invoke(\n                FunctionName=WALLETS_SERVICE_ARN,\n                InvocationType='RequestResponse',\n                Payload=json.dumps(transaction_details_event)\n            )\n            transaction_details_response = json.loads(transaction_details_lambda_response.get('Payload').read())\n            if transaction_details_response[\"statusCode\"] != 200:\n                raise Exception(f\"Failed to fetch transaction details for username{order_id}\")\n            transaction_details_response_body = json.loads(transaction_details_response[\"body\"])\n            order[\"wallet_transactions\"] = transaction_details_response_body[\"data\"][\"transactions\"]\n            order_status = TransactionStatus.SUCCESS\n            for payment in order[\"payments\"]:\n                if payment[\"payment_status\"] != TransactionStatus.SUCCESS:\n                    order_status = payment[\"payment_status\"]\n                    break\n\n            for wallet_transaction in order[\"wallet_transactions\"]:\n                if wallet_transaction[\"status\"] != TransactionStatus.SUCCESS:\n                    order_status = wallet_transaction[\"status\"]\n                    break\n\n            order[\"order_status\"] = order_status\n        return {\"orders\": orders}\n\n    def get_organizations_from_contract(self):\n        org_details_event = {\n            \"path\": f\"/org\",\n            \"httpMethod\": \"GET\"\n        }\n        org_details_response = self.boto_client.invoke_lambda(\n            lambda_function_arn=GET_ALL_ORG_API_ARN,\n            invocation_type='RequestResponse',\n            payload=json.dumps(org_details_event)\n        )\n        if org_details_response[\"statusCode\"] != 200:\n            raise Exception(\"Failed to get org details\")\n\n        org_details = json.loads(org_details_response[\"body\"])[\"data\"]\n        org_id_name_mapping = {}\n        for org in org_details:\n            org_id_name_mapping[org[\"org_id\"]] = org[\"org_name\"]\n\n        return org_id_name_mapping\n\n    def generate_signature_for_open_channel_for_third_party(self, recipient, group_id, amount_in_cogs, expiration,\n                                                            message_nonce, sender_private_key, executor_wallet_address):\n\n        signature_for_open_channel_for_third_party_body = {\n            'recipient': recipient,\n            'group_id': group_id,\n            'amount_in_cogs': amount_in_cogs,\n            'expiration': expiration,\n            'message_nonce': message_nonce,\n            'signer_key': sender_private_key,\n            'executor_wallet_address': executor_wallet_address\n        }\n\n        signature_for_open_channel_for_third_party_payload = {\n            \"path\": \"/signer/open-channel-for-third-party\",\n            \"body\": json.dumps(signature_for_open_channel_for_third_party_body),\n            \"httpMethod\": \"POST\"\n        }\n\n        signature_for_open_channel_for_third_party_response = self.lambda_client.invoke(\n            FunctionName=SIGNER_SERVICE_ARN,\n            InvocationType='RequestResponse',\n            Payload=json.dumps(signature_for_open_channel_for_third_party_payload)\n        )\n\n        signature_response = json.loads(signature_for_open_channel_for_third_party_response.get(\"Payload\").read())\n        if signature_response[\"statusCode\"] != 200:\n            raise Exception(f\"Failed to create signature for {signature_for_open_channel_for_third_party_body}\")\n        signature_details = json.loads(signature_response[\"body\"])\n        return signature_details[\"data\"]\n\n    def cancel_order(self):\n        logger.info(\"Start of UpdateTransactionStatus::manage_update_canceled_order_in_txn_history\")\n        list_of_order_id_for_expired_transaction = self.obj_transaction_history_dao.get_order_id_for_expired_transaction()\n        logger.info(f\"List of order_id to be updated with ORDER CANCELED: {list_of_order_id_for_expired_transaction}\")\n        update_transaction_status = self.obj_transaction_history_dao.update_transaction_status(\n            list_of_order_id=list_of_order_id_for_expired_transaction, status=OrderStatus.ORDER_CANCELED.value)\n        return update_transaction_status\n\n    def cancel_order_for_given_order_id(self, order_id):\n        logger.info(\"UpdateTransactionStatus::cancel_order_for_given_order_id: %s\", order_id)\n        transaction_data_dict = self.obj_transaction_history_dao.get_transaction_details_for_given_order_id(\n            order_id=order_id)\n        if transaction_data_dict[\"status\"] == OrderStatus.ORDER_CANCELED.value:\n            return f\"Order with order_id {order_id} is already canceled.\"\n        elif transaction_data_dict[\"status\"] in [OrderStatus.PAYMENT_INITIATED.value,\n                                                 OrderStatus.PAYMENT_INITIATION_FAILED.value,\n                                                 OrderStatus.PAYMENT_EXECUTION_FAILED]:\n            self.obj_transaction_history_dao.update_transaction_status(list_of_order_id=[order_id],\n                                                                       status=OrderStatus.ORDER_CANCELED.value)\n            return f\"Order with order_id {order_id} is canceled successfully.\"\n        else:\n            return f\"Unable to cancel order with order_id {order_id}\"\n\n    def currency_to_token(self, amount, currency):\n        amount_in_cogs = self.calculate_amount_in_cogs(amount=decimal.Decimal(amount), currency=currency)\n        conversion_data = {\"base\": currency, \"amount\": amount, \"amount_in_cogs\": str(amount_in_cogs),\n                           \"amount_in_agi\": str(self.utils.cogs_to_agi(cogs=amount_in_cogs)),\n                           f\"{currency}/cogs\": str(USD_TO_COGS_CONVERSION_FACTOR), \"agi/cogs\": str(COGS_TO_AGI)}\n        logger.debug(f\"currency_to_token::conversion_data {conversion_data}\")\n        return conversion_data\n","repo_name":"singnet/snet-marketplace-service","sub_path":"orchestrator/services/order_service.py","file_name":"order_service.py","file_ext":"py","file_size_in_byte":28069,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"76"}
+{"seq_id":"39349149801","text":"import torch\nimport torch.optim as optim\n\ndef optimizer_helper(model,args):\n\toptimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)\n\tif args.cosine_annealing:\n\t\tscheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer,50, eta_min=0, last_epoch=-1, verbose=False)\n\telse:\n\t\tscheduler = optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5) \n\treturn optimizer,scheduler","repo_name":"tvanh512/importantAug","sub_path":"tools/optimizer_utils.py","file_name":"optimizer_utils.py","file_ext":"py","file_size_in_byte":408,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"76"}
+{"seq_id":"43467268182","text":"import requests, pandas as pd, re\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nimport chromedriver_binary, time\n\n\n# Options keep the Chrome window open\nchrome_options = Options()\nchrome_options.add_experimental_option(\"detach\", True)\n\n# Adding options to Chrome and creating the browser variable\nbrowser = webdriver.Chrome(options=chrome_options)\n\nbrowser.get(\"https://www.airbnb.co.in/s/Chennai--India/homes?adults=1&place_id=ChIJYTN9T-plUjoRM9RjaAunYW4&refinement_paths%5B%5D=%2Fhomes&tab_id=home_tab&query=Chennai%2C%20India&flexible_trip_lengths%5B%5D=one_week&monthly_start_date=2023-07-01&monthly_length=3&price_filter_input_type=0&price_filter_num_nights=5&channel=EXPLORE&ne_lat=13.469483920131612&ne_lng=81.08925349361385&sw_lat=12.299470513397951&sw_lng=79.78222376949276&zoom=9&zoom_level=9&search_by_map=true&search_type=user_map_move\")\ntime.sleep(15)\nLocation = []\nDesc = []\nTotalBeds = []\nPrices = []\n\nurl = browser.current_url\n\nfor an in range(1,3):\n    r = requests.get(url)\n\n    soup = BeautifulSoup(r.text, \"lxml\")\n\n    \n    time.sleep(5)\n    box = soup.find(\"div\", class_ = \"f1ym1fv dir dir-ltr\")\n    hotels = box.find_all(\"div\", class_ = \"t1jojoys dir dir-ltr\")\n    for i in hotels:\n        Location.append(i.text)\n\n    descs = box.find_all(\"span\", class_ = \"t6mzqp7 dir dir-ltr\")\n    for j in descs:\n        Desc.append(j.text)\n\n    b = box.find_all(\"div\", class_ = \"g1qv1ctd c1v0rf5q dir dir-ltr\")\n    for k in b:\n        val = k.find(\"span\", class_ = \"dir dir-ltr\").text\n        if \"bed\" not in val:\n            val = k.find(\"span\", class_ = \"t6mzqp7 dir dir-ltr\").text\n        \n        TotalBeds.append(val)\n\n    prices = box.find_all(\"div\", class_ = \"_1jo4hgw\")\n    for p in prices:\n        Prices.append(p.text)\n\n    time.sleep(5)\n    nxt_page = browser.find_element(\"xpath\", '//*[@id=\"site-content\"]/div/div[3]/div/div/div/nav/div/a[5]').click()\n        \n    print(f\"{an} page done\")\ndf = pd.DataFrame({\"Location\": Location, \"Description\": Desc, \"TotalBeds\":TotalBeds, \"Prices\": Prices})\nprint(df)","repo_name":"ahn1305/web-scrapping-practice","sub_path":"15_scrap_multiple_page_2.py","file_name":"15_scrap_multiple_page_2.py","file_ext":"py","file_size_in_byte":2186,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"26140152282","text":"def check_prime(inp_num):\n    if inp_num <= 1:\n        return \"Please enter a valid number\"\n\n    flag = 0\n    for i in range(2, inp_num//2 + 1):\n        if inp_num % i == 0:\n            flag = 1\n            break\n        else:\n            continue\n\n    return True if flag == 0 else False\n\ndef prime_in_range(start, end):\n    if start < 2:\n        start = 2\n\n    for i in range(start, end+1):\n        if check_prime(i):\n            print(i)\n\n\nprime_in_range(1, 50)\n","repo_name":"therealazmat2k/Python_BasicPrograms","sub_path":"PrimeNumber.py","file_name":"PrimeNumber.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"14716300557","text":"import environ\nimport google.auth\nimport io\nimport os\nimport pandas as pd\n\nfrom google.cloud import secretmanager\nfrom sky import Sky\n\n# Saving GCP details to environment\nenv = environ.Env(DEBUG=(bool, True)) \n_, os.environ[\"GOOGLE_CLOUD_PROJECT\"] = google.auth.default()\nproject_id = os.environ.get(\"GOOGLE_CLOUD_PROJECT\")\n\n# Loading env variables from google secret manager\nclient = secretmanager.SecretManagerServiceClient()\nname = f\"projects/{project_id}/secrets/db_secrets/versions/latest\"\npayload = client.access_secret_version(name=name).payload.data.decode(\"UTF-8\")\nenv.read_env(io.StringIO(payload))\n\n# Starting sky client\nsky = Sky(\n    api_key=os.getenv('BB_API_KEY'),\n    token_path='/tmp/.sky-token',\n    credentials={\n        \"client_id\":os.getenv('CLIENT_ID'),\n        \"client_secret\":os.getenv('CLIENT_SECRET'),\n        \"redirect_uri\":'http://localhost:8080'\n    })\n\ndef getStudents():\n    students = sky.getUsers()\n\n    clean_students = students[[\n        'id', 'student_id', 'first_name', 'last_name',\n        'preferred_name', 'student_info.grade_level',\n        'custom_field_one', 'birth_date'\n    ]].rename(columns={\n        'student_info.grade_level':'grade_level',\n        'custom_field_one':'counselor',\n    }).astype({\n        'id':'int64',\n        'grade_level':'int32'\n    })\n    \n    clean_students.birth_date = pd.to_datetime(clean_students.birth_date, errors = 'coerce')\n    clean_students.student_id = pd.to_numeric(clean_students.student_id, errors='coerce')\n\n    clean_students = clean_students.astype(object).where(pd.notnull(clean_students), None)\n\n    return clean_students\n\ndef getTeachers():\n    teachers = sky.getUsers(['Teacher', 'Advisor'])\n    teachers = teachers[['id', 'first_name', 'last_name', 'gender', 'email']]\n    teachers = teachers.drop_duplicates().reset_index(drop=True)\n    return teachers","repo_name":"LearnThinkCreate/sky-database","sub_path":"tests/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1845,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"1332638647","text":"import keras\r\nfrom keras.layers import Dense, Conv2D, MaxPool2D, Flatten, Activation, BatchNormalization\r\nfrom keras.models import Sequential\r\nfrom keras import layers\r\nfrom keras.models import load_model\r\nfrom tensorflow.examples.tutorials.mnist import input_data\r\n\r\nimport keras.backend as K\r\nimport numpy as np\r\nimport tensorflow as tf\r\nfrom compactor import CompactorLayer, lasso, CompactorMonitor\r\nfrom kerassurgeon.operations import delete_layer, delete_channels\r\n\r\n# tf_config = tf.compat.v1.ConfigProto()\r\n# tf_config.gpu_options.allow_growth = True\r\n# tf.compat.v1.Session(config=tf_config)\r\n\r\n\r\ndef combine_conv_bn(model, conv_name, bn_name):\r\n    conv_kxk_weights = model.get_layer(conv_name).get_weights()\r\n    if len(conv_kxk_weights) > 1:\r\n        conv_kxk_weights, conv_kxk_bias = conv_kxk_weights\r\n    else:\r\n        conv_kxk_weights = conv_kxk_weights[0]\r\n        conv_kxk_bias = np.zeros((conv_kxk_weights.shape[-1],))\r\n\r\n    gammas, betas, means, var = model.get_layer(bn_name).get_weights()\r\n    var = var + 1e-3\r\n    weight = (gammas * conv_kxk_weights / np.sqrt(var))\r\n    bias = (((conv_kxk_bias - means) * gammas) / np.sqrt(var)) + betas\r\n    model.get_layer(conv_name).set_weights([weight, bias])\r\n    model = delete_layer(model, model.get_layer(bn_name), copy=True)\r\n    return model\r\n\r\n\r\ndef combine_conv_compact(model, conv_name, compact_name):\r\n    conv_kxk_weights = model.get_layer(conv_name).get_weights()\r\n    if len(conv_kxk_weights) > 1:\r\n        conv_kxk_weights, conv_kxk_bias = conv_kxk_weights\r\n    else:\r\n        conv_kxk_weights = conv_kxk_weights[0]\r\n        conv_kxk_bias = np.zeros((conv_kxk_weights.shape[-1],))\r\n\r\n    compact_kxk_weights = model.get_layer(compact_name).get_weights()\r\n    compact_kxk_mask = compact_kxk_weights[1]\r\n    compact_kxk_weights = compact_kxk_weights[0]\r\n    conv_kxk_weights = conv_kxk_weights\r\n    conv_kxk_bias = np.reshape(conv_kxk_bias, (1, 1, 1, conv_kxk_bias.shape[0]))\r\n    with tf.Session() as sess:\r\n        conv_1x1 = tf.convert_to_tensor(compact_kxk_weights)\r\n        conv_kxk_w = tf.convert_to_tensor(conv_kxk_weights)\r\n        conv_kxk_b = tf.convert_to_tensor(conv_kxk_bias)\r\n        weight = K.conv2d(conv_kxk_w, conv_1x1, padding='same', data_format='channels_last').eval()\r\n        bias = K.conv2d(conv_kxk_b, conv_1x1, padding='same', data_format='channels_last').eval()\r\n\r\n    bias = np.sum(bias, axis=(0, 1, 2))\r\n    model.get_layer(conv_name).set_weights([weight, bias])\r\n    model = delete_layer(model, model.get_layer(compact_name), copy=True)\r\n    del_channel = np.argwhere(compact_kxk_mask.reshape(-1) == 0)\r\n    layer = model.get_layer(conv_name)\r\n    model = delete_channels(model, layer, del_channel, copy=True)\r\n    return model\r\n\r\n\r\ndef compactor_convert(model):\r\n    layer_len = len(model.layers)\r\n    layer_names = []\r\n    for index, layer in enumerate(model.layers):\r\n        layer_names.append(layer.name)\r\n    for i in range(layer_len-2):\r\n        current_name = layer_names[i]\r\n        next_name = layer_names[i+1]\r\n        last_name = layer_names[i+2]\r\n        if ('conv' in current_name) and ('compactor' in next_name):\r\n            model = combine_conv_compact(model, current_name, next_name)\r\n        if ('conv' in current_name) and ('batch_normalization' in next_name):\r\n            model = combine_conv_bn(model, current_name, next_name)\r\n        if ('conv' in current_name) and ('batch_normalization' in next_name) and ('compactor' in last_name):\r\n            model = combine_conv_compact(model, current_name, last_name)\r\n    return model\r\n\r\n\r\ndef get_orignal_model():\r\n    model = Sequential()\r\n    model.add(Conv2D(10,\r\n                     kernel_size=[3, 3],\r\n                     input_shape=[28, 28, 1],\r\n                     activation=None,\r\n                     padding='same',\r\n                     name='conv_1'))\r\n    model.add(BatchNormalization(name='batch_normalization_1'))\r\n    # model.add(CompactorLayer(10, kernel_regularizer=lasso(1e-4)))\r\n    model.add(Activation('relu'))\r\n    model.add(MaxPool2D())\r\n    model.add(Conv2D(10, [3, 3], activation=None, padding='same', name='conv_2'))\r\n    model.add(BatchNormalization(name='batch_normalization_2'))\r\n    # model.add(CompactorLayer(10, kernel_regularizer=lasso(1e-4)))\r\n    model.add(Activation('relu'))\r\n    model.add(Conv2D(10, [3, 3], activation=None, padding='same', name='conv_3'))\r\n    model.add(BatchNormalization(name='batch_normalization_3'))\r\n    # model.add(CompactorLayer(10, kernel_regularizer=lasso(1e-4)))\r\n    model.add(Activation('relu'))\r\n    model.add(Conv2D(10, [3, 3], activation=None, padding='same', name='conv_4'))\r\n    model.add(BatchNormalization(name='batch_normalization_4'))\r\n    # model.add(CompactorLayer(10, kernel_regularizer=lasso(1e-4)))\r\n    model.add(Activation('relu'))\r\n    model.add(MaxPool2D())\r\n    model.add(layers.Permute((2, 1, 3)))\r\n    model.add(Flatten())\r\n    model.add(Dense(100, activation='relu', name='dense_1'))\r\n    model.add(Dense(10, activation='softmax', name='dense_2'))\r\n    sgd = keras.optimizers.SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)\r\n    model.compile(optimizer=sgd,\r\n                  loss='categorical_crossentropy',\r\n                  metrics=['accuracy'])\r\n    return model\r\n\r\n\r\ndef get_compact_modle():\r\n    model = Sequential()\r\n    model.add(Conv2D(10,\r\n                     kernel_size=[3, 3],\r\n                     input_shape=[28, 28, 1],\r\n                     activation=None,\r\n                     padding='same',\r\n                     name='conv_1'))\r\n    model.add(BatchNormalization(name='batch_normalization_1'))\r\n    model.add(CompactorLayer(10, kernel_regularizer=lasso(1e-4)))\r\n    model.add(Activation('relu'))\r\n    model.add(MaxPool2D())\r\n    model.add(Conv2D(10, [3, 3], activation=None, padding='same', name='conv_2'))\r\n    model.add(BatchNormalization(name='batch_normalization_2'))\r\n    model.add(CompactorLayer(10, kernel_regularizer=lasso(1e-4)))\r\n    model.add(Activation('relu'))\r\n    model.add(Conv2D(10, [3, 3], activation=None, padding='same', name='conv_3'))\r\n    model.add(BatchNormalization(name='batch_normalization_3'))\r\n    model.add(CompactorLayer(10, kernel_regularizer=lasso(1e-4)))\r\n    model.add(Activation('relu'))\r\n    model.add(Conv2D(10, [3, 3], activation=None, padding='same', name='conv_4'))\r\n    model.add(BatchNormalization(name='batch_normalization_4'))\r\n    model.add(CompactorLayer(10, kernel_regularizer=lasso(1e-4)))\r\n    model.add(Activation('relu'))\r\n    model.add(MaxPool2D())\r\n    model.add(layers.Permute((2, 1, 3)))\r\n    model.add(Flatten())\r\n    model.add(Dense(100, activation='relu', name='dense_1'))\r\n    model.add(Dense(10, activation='softmax', name='dense_2'))\r\n    sgd = keras.optimizers.SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)\r\n    model.compile(optimizer=sgd,\r\n                  loss='categorical_crossentropy',\r\n                  metrics=['accuracy'])\r\n    return model\r\n\r\ndef main():\r\n    # Download data if needed and import.\r\n    mnist = input_data.read_data_sets('tempData', one_hot=True, reshape=False)\r\n    val_images = mnist.validation.images\r\n    val_labels = mnist.validation.labels\r\n\r\n    # train orignal model\r\n    orig_model = get_orignal_model()\r\n    results = orig_model.fit(mnist.train.images,\r\n                        mnist.train.labels,\r\n                        epochs=200,\r\n                        batch_size=256,\r\n                        verbose=2,\r\n                        validation_data=(val_images, val_labels),\r\n                        callbacks=[CompactorMonitor(step=2, patience=10, verbose=2)])\r\n\r\n    orig_model.save('orignal_model.hdf5')\r\n\r\n    #train compactor model\r\n    compact_model = get_compact_modle()\r\n    compact_model.load_weights('orignal_model.hdf5', by_name=True, skip_mismatch=True)\r\n\r\n    results = compact_model.fit(mnist.train.images,\r\n                        mnist.train.labels,\r\n                        epochs=200,\r\n                        batch_size=256,\r\n                        verbose=2,\r\n                        validation_data=(val_images, val_labels),\r\n                        callbacks=[CompactorMonitor(min_flops=0.5, step=2, patience=10, verbose=2)])\r\n    compact_model.save('compact_model.hdf5')\r\n    \r\n    # test\r\n    compact_model = compactor_convert(compact_model)\r\n    sgd = keras.optimizers.SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)\r\n    compact_model.compile(optimizer=sgd,\r\n                  loss='categorical_crossentropy',\r\n                  metrics=['accuracy'])\r\n    loss = compact_model.evaluate(val_images, val_labels, batch_size=128, verbose=2)\r\n    print('compact model loss:', loss, '\\n')\r\n    # compact_model.summary()\r\n\r\n    orig_model = load_model('orignal_model.hdf5')\r\n    loss = orig_model.evaluate(val_images, val_labels, batch_size=128, verbose=2)\r\n    print('orignal model loss:', loss, '\\n')\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n","repo_name":"xdhe1216/ResRep","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":8891,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"76"}
+{"seq_id":"12718663594","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Nov 20 14:06:58 2019\r\n\r\n@author: daewo\r\n\"\"\"\r\nimport os\r\n\r\nimport numpy as np\r\nimport cv2\r\nfrom datetime import datetime\r\n\r\nimport tensorflow as tf\r\nfrom tensorflow import keras\r\nfrom tensorflow.keras import backend as K\r\nfrom tensorflow.keras.callbacks import TensorBoard\r\n\r\nimport gc\r\n\r\n#%% Class to define LeNet model architecture, parameters and functions\r\nclass LeNet:\r\n    \r\n    def __init__(self):\r\n        self.results_path = os.path.join(\"..\",\"Resultados\",\"LeNet\")\r\n    \r\n    #Extract patches for model training, return the extracted patches and the class that each belong\r\n    def extractPatchesTrain(self,im,mask,size_patch,p = 0.01):\r\n        \r\n        center_patch = round(size_patch/2)\r\n        \r\n        #path image with zeros to extract patches from the image borders\r\n        im2 = np.pad(im,center_patch, mode = 'constant', constant_values = 0)\r\n        \r\n        #Get pixels in the image pixels that belong to tumor (val = 1) and background (val = 0) \r\n        tumor_idx = np.where(mask != 0)\r\n        back_idx = np.where(mask == 0)\r\n        \r\n        #extract number of patches to extract acoording to the percentage sert by p\r\n        n_tumor = np.shape(tumor_idx)[1]\r\n        n_back = np.shape(back_idx)[1]\r\n        n_samples = round(n_tumor*p)\r\n        \r\n        #get random indexes to tumor and background pixels\r\n        rand_tumor = np.random.randint(0,n_tumor,n_samples)\r\n        rand_back = np.random.randint(0,n_back,n_samples)\r\n        \r\n        patches_img = []\r\n        classes = []\r\n        \r\n        #Get tumor and background patches and their classes\r\n        for pix_tumor, pix_back in zip(rand_tumor, rand_back):\r\n            \r\n            #Get pixel location in padded image\r\n            #Tumor pixel\r\n            x_tumor = tumor_idx[1][pix_tumor] + center_patch\r\n            y_tumor = tumor_idx[0][pix_tumor] + center_patch\r\n            \r\n            #Background pixel\r\n            x_back = back_idx[1][pix_back] + center_patch\r\n            y_back = back_idx[0][pix_back] + center_patch\r\n            \r\n            #Get window limits to extract patch\r\n            tumor_patch_limits = [x_tumor - center_patch, x_tumor + center_patch, y_tumor - center_patch, y_tumor + center_patch]\r\n            back_patch_limits = [x_back - center_patch, x_back + center_patch, y_back - center_patch, y_back + center_patch]\r\n        \r\n            #Extract patches from padded image and class\r\n            patches_img.append(im2[tumor_patch_limits[2]:tumor_patch_limits[3],tumor_patch_limits[0]:tumor_patch_limits[1]])\r\n            classes.append(1)\r\n            patches_img.append(im2[back_patch_limits[2]:back_patch_limits[3],back_patch_limits[0]:back_patch_limits[1]])\r\n            classes.append(0)\r\n            \r\n        return np.array(patches_img), np.array(classes)\r\n \r\n    #Extract patches for Testing, do not require to get class   \r\n    def extractPatchesTest(self,im,size_patch):\r\n        \r\n        center_patch = round(size_patch/2)\r\n        \r\n        #Pad Test image to extract patches from pixels of the image borders\r\n        im2 = np.pad(im,center_patch, mode = 'constant', constant_values = 0)\r\n        \r\n        patches_img = []\r\n    \r\n        #Get patches for each pixel in the image\r\n        for i in range(im.shape[1]):\r\n            for j in range(im.shape[0]): \r\n            \r\n                x = i + center_patch\r\n                y = j + center_patch\r\n                \r\n                patch_limits = [x - center_patch, x + center_patch, y - center_patch, y + center_patch]      \r\n                patches_img.append(im2[patch_limits[2]:patch_limits[3],patch_limits[0]:patch_limits[1]])\r\n    \r\n        return np.array(patches_img)\r\n    \r\n    #Define the LeNet-5 architecture\r\n    def modelArch(self,size_patch=28,n_classes=2):\r\n        \r\n        #1 Input Layer: size of images\r\n        #2 Conv Layer: 6 filters of size [5x5] with ReLU\r\n        #3 Pool Layer: Stride = 2 and a 2x2 pooling\r\n        #4 Conv Layer: 16 filters of size [5x5] with ReLU\r\n        #5 Pool Layer: Stride = 2 and a 2x2 pooling\r\n        #6 FC Layer: 120 neurons \r\n        #7 FC Layer: 84 neurons with dropout regularization rate of 0.4 \r\n        #8 Logits Layer: a FC layer with 10 neurons, one foe each digit target class (0-9) or softmax\r\n        \r\n        #activation = tf.nn.sigmoid\r\n        activation = tf.nn.relu\r\n        \r\n        #1 Input Layer: size of images\r\n        if K.image_data_format() == 'channels_first':\r\n            input_shape = (1, size_patch, size_patch)\r\n        else:\r\n            input_shape = (size_patch, size_patch, 1)\r\n        \r\n        #definition of CONV/POOL layers\r\n        model = tf.keras.Sequential()\r\n        \r\n        #2 Conv Layer: 6 filters of size [5x5] with ReLU\r\n        model.add(tf.keras.layers.Conv2D(\r\n                filters = 6,\r\n                kernel_size = 5,\r\n                padding = \"valid\",\r\n                strides = 1,\r\n                activation = activation,\r\n                input_shape = input_shape))\r\n        \r\n        #3 Pool Layer: Stride = 2 and a 2x2 pooling\r\n        model.add(tf.keras.layers.MaxPool2D(pool_size = 2, strides = 2))\r\n        \r\n        #4 Conv Layer: 16 filters of size [5x5] with ReLU\r\n        model.add(tf.keras.layers.Conv2D(\r\n                filters = 16,\r\n                kernel_size = 5,\r\n                padding = \"valid\",\r\n                strides = 1,\r\n                activation = activation))\r\n        \r\n        #5 Pool Layer: Stride = 2 and a 2x2 pooling\r\n        model.add(tf.keras.layers.MaxPool2D(pool_size = 2, strides = 2))\r\n        \r\n        #Definition of FC layers\r\n        model.add(tf.keras.layers.Flatten())\r\n        \r\n        #6 FC Layer: 120 neurons \r\n        model.add(tf.keras.layers.Dense(\r\n                units = 120, \r\n                activation = activation))\r\n        #7 FC Layer: 84 neurons with dropout regularization rate of 0.4 \r\n        model.add(tf.keras.layers.Dense(\r\n                units = 84, \r\n                activation = activation))\r\n        #model.add(tf.keras.layers.Dropout(rate = 0.4))  #If training drop out a random percent of the activations\r\n        \r\n        #8 Logits Layer: a FC layer with 10 neurons, one for each class (0 = background || 1 = tumor) or softmax\r\n        model.add(tf.keras.layers.Dense(\r\n                units = n_classes, \r\n                activation = tf.nn.softmax))\r\n        \r\n        return model\r\n\r\n    #Define the model compile options\r\n    def compileModel(self,model):\r\n        \r\n        #Choose the type of tryning algorithm to optimize weights\r\n        #optimizer =  tf.keras.optimizers.RMSprop(lr = 0.01)\r\n        optimizer = \"adam\"\r\n        \r\n        #Choos the loss function to used to optimize weights\r\n        #loss = root_mean_squared_error\r\n        loss = \"categorical_crossentropy\"\r\n        \r\n        #Choose metrics to compute loss function\r\n        metrics = [\"accuracy\"]\r\n        \r\n        #Set compiliation parameters to the model\r\n        model.compile(   \r\n            optimizer = optimizer,\r\n            loss = loss,\r\n            metrics = metrics)\r\n    \r\n        return model\r\n    \r\n    #Train the defined model\r\n    def trainModel(self,train_images,train_masks,model,size_patch=28,n_classes=2, epochs=5, fold=0):\r\n        \r\n        ## Normalizaing \r\n        train_images = train_images/255.0\r\n        train_masks = train_masks/255.0\r\n        \r\n        #Extract patch data from training images\r\n        data = np.zeros((1,size_patch,size_patch))\r\n        classes = []\r\n        \r\n        for i in range(np.size(train_images)):\r\n            [patches_img, labels] = self.extractPatchesTrain(train_images[i], train_masks[i], size_patch)\r\n            data = np.concatenate((data,patches_img),axis=0)\r\n            classes = np.concatenate((classes,labels))\r\n    \r\n        #Formating data into nedded model shapes\r\n        data = np.delete(data,0,axis=0)\r\n        \r\n        if K.image_data_format() == 'channels_first':\r\n            data = data.reshape(data.shape[0], 1, size_patch, size_patch)\r\n        else:\r\n            data = data.reshape(data.shape[0], size_patch, size_patch, 1)\r\n        \r\n        # convert class vectors to binary class matrices\r\n        classes = tf.keras.utils.to_categorical(classes, n_classes)\r\n        \r\n        #Shuffle data for model training\r\n        shuffled_indices = np.arange(data.shape[0])\r\n        np.random.shuffle(shuffled_indices)\r\n        shuffled_data = data[shuffled_indices]\r\n        shuffled_classes = classes[shuffled_indices]\r\n        \r\n        #Split data in train and validation sets [90,10]\r\n        samples_count = shuffled_data.shape[0]\r\n        train_samples_count = int(0.9 * samples_count)\r\n        validation_samples_count = int(0.1 * samples_count)\r\n    \r\n        train_data = shuffled_data[:train_samples_count]\r\n        train_classes = shuffled_classes[:train_samples_count]\r\n        validation_data = shuffled_data[train_samples_count:train_samples_count+validation_samples_count]\r\n        validation_classes = shuffled_classes[train_samples_count:train_samples_count+validation_samples_count]\r\n    \r\n        #defining training paramenters\r\n        #Number of evaluation images used in each batch\r\n        batch_size = round(train_data.shape[0]*0.1)\r\n        \r\n        #Stop when the loss grows instead of diminish\r\n        early_stopping = tf.keras.callbacks.EarlyStopping(patience=5) #To check overfitting\r\n        \r\n        #Tensorboard \r\n        log_dir = os.path.join(self.results_path,\"logs\",\"fit\",datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\r\n        tensorboard_callback = TensorBoard(log_dir=log_dir)\r\n        \r\n        # Create a callback that saves the model's weights\r\n#        cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=train_dir,\r\n#                                                         save_weights_only=True,\r\n#                                                         verbose=1)\r\n        \r\n        #Train the model\r\n        model.fit(train_data,train_classes,\r\n                             batch_size = batch_size,\r\n                             epochs = epochs,\r\n                             callbacks=[early_stopping,tensorboard_callback],\r\n                             validation_data = (validation_data,validation_classes),\r\n                             verbose = 1)\r\n        \r\n        train_file = \"LeNet_Weights_fold_\" + str(fold) + \".h5\"\r\n        train_dir = os.path.join(self.results_path,\"train_weights\",train_file)\r\n        model.save_weights(train_dir)\r\n        \r\n        return model\r\n    \r\n    # Predict class with model \r\n    def predictClassModel(self,images,model,size_patch=28):\r\n    \r\n        prob_images = []\r\n        class_images = []\r\n                \r\n        ## Normalizaing \r\n        images = images/255\r\n        \r\n        for image in images:\r\n        \r\n            data = self.extractPatchesTest(image,size_patch)\r\n            \r\n            if K.image_data_format() == 'channels_first':\r\n                data = data.reshape(data.shape[0], 1, size_patch, size_patch)\r\n            else:\r\n                data = data.reshape(data.shape[0], size_patch, size_patch, 1)\r\n                \r\n            predictions = model.predict(data)\r\n            \r\n            tumor_prob = predictions[:,1]\r\n            classes = predictions.argmax(axis=-1)\r\n            \r\n            prob_image = tumor_prob.reshape(image.shape,order = 'F')\r\n            class_image = classes.reshape(image.shape,order = 'F')\r\n            \r\n            prob_images.append(prob_image)\r\n            class_images.append(class_image)\r\n    \r\n        return prob_images, class_images\r\n        \r\n    \r\n#%% Class to read images. It can be altered to read images while training and not before\r\nclass DataGen(keras.utils.Sequence):\r\n    def __init__(self, ids, path, image_size=128):\r\n        self.ids = ids\r\n        self.path = path\r\n        self.image_size = image_size\r\n        \r\n    #load an image and its mask    \r\n    def __load__(self, id_name):\r\n        \r\n        ## Path\r\n        image_path = os.path.join(self.path,id_name)\r\n        mask_path = os.path.join(self.path,\"Mask/\",id_name)\r\n        \r\n        ## Reading Image\r\n        image = cv2.imread(image_path, 0)\r\n        \r\n        ## Reading Mask\r\n        mask = cv2.imread(mask_path, 0)\r\n            \r\n        ## Normalizaing \r\n#        image = image/255.0\r\n#        mask = mask/255.0\r\n        \r\n        return image.astype(np.uint8), mask.astype(np.uint8)\r\n    \r\n    def __load_all__(self):\r\n        \r\n        #lists that will store the images\r\n        tumor_images = []\r\n        mask_images = []\r\n\r\n        #Read all images and its masks\r\n        for i in range(len(self.ids)):\r\n        \r\n            x, y = self.__load__(self.ids[i])\r\n            tumor_images.append(x)\r\n            mask_images.append(y)\r\n            \r\n        #Formating data into nedded model shapes\r\n        tumor_images = np.array(tumor_images)\r\n        mask_images = np.array(mask_images)\r\n        \r\n        return tumor_images, mask_images\r\n    \r\n#%% Free memory after predicting\r\n\r\n# Reset Keras Session\r\ndef ResetKeras(model):\r\n    sess = tf.compat.v1.keras.backend.get_session()\r\n    tf.compat.v1.keras.backend.clear_session()\r\n    sess.close()\r\n    sess = tf.compat.v1.keras.backend.get_session()\r\n\r\n    try:\r\n        del model # this is from global space - change this as you need\r\n    except:\r\n        pass\r\n\r\n    print(gc.collect()) # if it's done something you should see a number being outputted\r\n\r\n    # use the same config as you used to create the session\r\n    config = tf.compat.v1.ConfigProto()\r\n    config.gpu_options.per_process_gpu_memory_fraction = 1\r\n    config.gpu_options.visible_device_list = \"0\"\r\n    tf.compat.v1.keras.backend.set_session(tf.compat.v1.Session(config=config))","repo_name":"FTRobles/Tumors_DL","sub_path":"LeNet_Class.py","file_name":"LeNet_Class.py","file_ext":"py","file_size_in_byte":13750,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"24661112270","text":"import os\nimport tensorflow as tf\nfrom PIL import Image\n\ncwd = \"E:\\\\data\\\\dataset\\\\man_woman\\\\\"\n\nclasses = ['man','woman']\n\nwriter = tf.python_io.TFRecordWriter(\"train.tfrecords\")\n\nwith tf.Session() as sess:\n    sess.run(tf.global_variables_initializer())\n\n    coord = tf.train.Coordinator()  # 开启多线程\n    threads = tf.train.start_queue_runners(coord=coord)\n    for index, name in enumerate(classes):\n        class_path = cwd + name + \"\\\\\"\n        for img_name in os.listdir(class_path):\n            img_path = class_path + img_name\n            img = Image.open(img_path)\n            img = img.resize((128, 128))\n            img_raw = img.tobytes()              #将图片转化为原生bytes\n            example = tf.train.Example(features=tf.train.Features(feature={\n                \"label\": tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),\n                'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw]))\n            }))\n            writer.write(example.SerializeToString())  #序列化为字符串\n    coord.request_stop()  # 多线程关闭\n    coord.join(threads)\n    writer.close()\n","repo_name":"YangXin0205/tensorflow_tfrecord","sub_path":"img_to_tfrecord.py","file_name":"img_to_tfrecord.py","file_ext":"py","file_size_in_byte":1140,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"27826090876","text":"from gym import envs\nimport gym\nimport numpy as np\n\nMAX_EPISODE_LEN = 300\ngym.envs.register(\n    id = 'CartPoleModified-v0',\n    entry_point = 'gym.envs.classic_control:CartPoleEnv',\n    max_episode_steps = MAX_EPISODE_LEN,\n)\n\nseed = 42 \ndiscountFactor = 0.99\nnp.random.seed(seed)\nprint('SEED: ', seed)\n\nprint('Question 2')\n\nenv = gym.make('CartPole-v0')\nnumberOfEpisodes = 100\nMaximumLength = 300\n\nreturnReward = np.zeros([numberOfEpisodes])\nfinalStep = np.zeros([numberOfEpisodes]).astype(int)\n\nfor i_episode in range(numberOfEpisodes):\n    observation = env.reset()\n    for t in range(MaximumLength):\n        # env.render()\n        action = np.random.random_integers(0,1)\n        observation, reward, done, info = env.step(action)\n        if (done and (t + 1 != MaximumLength)):\n            returnReward[i_episode] += discountFactor**(t+1) * -1\n            #print(\"Episode \"+ str(i_episode)+\" finished after {} timesteps\".format(t+1))\n            #print(\"Reward : \"+ str(returnReward[i_episode]))\n            finalStep[i_episode] = t+1\n            break\n\n\nprint('Mean TimeStep: ',np.mean(finalStep))\nprint('Variance TimeStep: ', np.var(finalStep))\n\nprint('Mean reward: ',np.mean(returnReward))\nprint('Variance reward: ', np.var(returnReward))","repo_name":"sofiane87/AtariGames_RL","sub_path":"code/a2.py","file_name":"a2.py","file_ext":"py","file_size_in_byte":1245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"12208580416","text":"# full marks\nfrom functools import lru_cache\n\n@lru_cache(maxsize=None)\ndef getPsbs(total, curr=0):\n    if curr == total: return 1\n    elif curr > total: return 0\n\n    res = 0\n    # blocks go from 1 to 10\n    for i in range(1, 10):\n        res += getPsbs(total, curr+i)\n    return res\n\ndef solve(n, s):\n    res = []\n    while sum(res) < s:\n        # print(res)\n        for i in range(1, 9):\n            p = getPsbs((s-sum(res))-i)\n            # print(i, p, n)\n            if p >= n:\n                res.append(i)\n                break\n            else:\n                n -= p\n    return res\n\nn,s = [int(i) for i in input().split(\" \")]\nprint(*solve(n,s))","repo_name":"StefanNede/BIO-Practice","sub_path":"2009/cp2.py","file_name":"cp2.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"14197022956","text":"from modules.players import Player\r\nfrom modules.tournament import Tournament\r\nimport os\r\n\r\n\r\nclass Manager:\r\n    # Classe controleur : gestion des structure de données et du clear screen\r\n    def serialize_player_list(players: list):\r\n        # Fonction de sérialisation d'une liste d'objets Player\r\n        # Retourne une liste de dictionnaires contenant les infos des joueurs\r\n        serialized_list = []\r\n        for player in players:\r\n            serialized_list.append(player.serialize())\r\n        return serialized_list\r\n\r\n    def unserialize_player_dict(player_dict_list: list):\r\n        # Fonction de désérialisation des joueurs\r\n        # Retourne une liste d'objets Player\r\n        unserialized_list = []\r\n        for player_dict in player_dict_list:\r\n            f, l, b, g, r, s = player_dict.values()\r\n            unserialized_player = Player(f, l, b, g, r)\r\n            unserialized_player.score = s\r\n            unserialized_list.append(unserialized_player)\r\n        return unserialized_list\r\n\r\n    def serialize_tournament(tournament: Tournament):\r\n        # Fonction de sérialisation des tournois\r\n        # retourne une liste contenant le dictionnaire du tournois\r\n        serialized_tournament = []\r\n        return_dict = {\"name\": tournament.name,\r\n                       \"location\": tournament.location,\r\n                       \"start_date\": tournament.start_date,\r\n                       \"end_date\": tournament.end_date,\r\n                       \"turn\": tournament.turn,\r\n                       \"time_control\": tournament.time_control,\r\n                       \"description\": tournament.description,\r\n                       \"round_amount\": tournament.round_amount,\r\n                       \"players\": tournament.players,\r\n                       \"results\": tournament.results,\r\n                       \"start\": tournament.start,\r\n                       \"stop\": tournament.stop,\r\n                       \"current_turn\": tournament.current_turn}\r\n        serialized_tournament.append(return_dict)\r\n        return serialized_tournament\r\n\r\n    def unserialize_tournament(serialised_tournament: list):\r\n        # Fonction de désérialisation de tournois\r\n        # Retourne un objet Tournament\r\n        n, l, sd, ed, t, tc, d, ra, p, r, sta, stp, ct = serialised_tournament[0].values()\r\n        unserialized_tournament = Tournament(\r\n            n, l, sd, ed, tc, d, t, ra, p, r, sta, stp, ct)\r\n        return unserialized_tournament\r\n\r\n    def clear_screen():\r\n        # Fonction de nettoyage de l'écran\r\n        if os.name == \"posix\":\r\n            os.system(\"clear\")\r\n        else:\r\n            os.system(\"cls\")\r\n","repo_name":"NVendrisse/P4-Chess_Tournament_Manager","sub_path":"controllers/manager_controller.py","file_name":"manager_controller.py","file_ext":"py","file_size_in_byte":2635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"39046683582","text":"import csv\nfrom random import randint\n\nscore_players = []\nplayers = [\"Josh\", \"Luke\", \"Kate\", \"Mark\", \"Mary\"]\n\nfor player in players:\n    for i in range (100):\n        score = randint(1, 1000)\n        score_players.append((player, score))\n\nwith open('score_players.csv', 'w', newline='') as csvfile:\n    writer = csv.writer(csvfile)\n    writer.writerow(['Player name', 'Score'])\n    writer.writerows(score_players)","repo_name":"artepepep/proj_homeworks","sub_path":"homework_10/task_3.py","file_name":"task_3.py","file_ext":"py","file_size_in_byte":413,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"5652012137","text":"import pygame\r\nimport time\r\n\r\nfrom Module_draw_screen import *\r\nfrom Module_change import *\r\n\r\n\r\ndef life_game(field_x, field_y, cell_coord):\r\n    game = True\r\n    while game:\r\n        main_display = draw_screen(cell_coord, field_x, field_y)\r\n\r\n        events = pygame.event.get()\r\n        for e in events:                           #выход из игры\r\n            if e.type == pygame.QUIT:\r\n                game = False\r\n                return 'Game closed!'\r\n\r\n            if e.type == pygame.KEYDOWN and e.key == pygame.K_ESCAPE:\r\n                game = False\r\n                return 'Game closed!'\r\n\r\n        cell_coord = change(main_display)\r\n        time.sleep(0.9)","repo_name":"MirAn55500/Life-game","sub_path":"Module_life_game.py","file_name":"Module_life_game.py","file_ext":"py","file_size_in_byte":678,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"2922625161","text":"import re\nf= open(\"torelax\", \"r\") ## file containing atomic positions that will be allowed to relax\natomscoord=[]\nfor line in f:\n  atomscoord.append(line)\nf.close()\n\n## to implement something that verifies disregarding spaces and with tolerance may use these lines\n#  line = line.strip().split()\n#  atomscoord.append([line[0],line[1],line[2],line[3]])  \n#print(coordsrel)\n\nreadcontents = \"\"\ng = open(\"CsSbBr221-VBr1.relax.in\", \"r\")  ## file containing the input file with all atomic coordinates\nreadv=0\nindex=1\nnat=0\nfor line in g:\n   if (\"nat\" in  line) :\n        match = re.search(\"nat\\s{0,}.\\s{0,}(\\d{1,})\", line)\n        nat = int(match.group(1))\n        print(nat)\n   if (\"ATOMIC_POSITIONS\" in line) :   ## looks for the entry where atomic positions are defined\n        print(\" ATOMIC POSITIONS ENTRY FOUND\")\n        readv=1\n        continue\n   if  ( readv and  index <=  nat ) :\n        if line in atomscoord:\n           readcontents += line\n        else:\n           readcontents += line.strip() + \" 0 0 0 \\n\"\n        index = index + 1\n\nprint(readcontents)\n","repo_name":"rogeriog/dfttoolkit","sub_path":"structure_editing/QEfixatoms.py","file_name":"QEfixatoms.py","file_ext":"py","file_size_in_byte":1063,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"7371495109","text":"\nclass scheduleElem():\n    \n    def __init__(self, _tr, _op, _res):\n        self.tr = _tr\n        self.op = _op\n        self.res = _res\n    def __repr__(self):\n        return \"{}{}({})\".format(self.op, self.tr, self.res)\n\ndef getElem(strelem):\n    strtr = ''\n    op = ''\n    for i in strelem[0:strelem.find('(')]:\n        if '0' <= i and i <= '9':\n            strtr += i\n        else:\n            op += i\n    resource = strelem[strelem.find('(') + 1 : strelem.find(')')]\n    return scheduleElem(strtr, op, resource)\n    \n\ndef parser(strinput):\n    tmp = strinput.split(\" \")\n    return [getElem(e) for e in tmp]\n    \n\ndef getConflictsBrutalForce(strinput):\n    elems = parser(strinput)\n    conflicts = []\n    for i in reversed(range(len(elems))):\n        for j in range(i + 1, len(elems)):\n            if elems[j].res == elems[i].res and elems[j].tr != elems[i].tr and (elems[j].op == \"w\" or elems[i].op == \"w\"):\n                conflicts.append((elems[i], elems[j]))\n    \n    return conflicts\n\ndef isSerializable(strinput):\n    conflicts = getConflictsBrutalForce(strinput)\n    graph = [(e[0].tr, e[1].tr) for e in conflicts]\n    alist = dict()\n    pre = dict()\n    post = dict()\n    for e in graph:\n        pre[e[0]] = False\n        pre[e[1]] = False\n        post[e[0]] = False\n        post[e[1]] = False \n        if e[0] not in alist:\n            alist[e[0]] = set()\n        if e[1] not in alist:\n            alist[e[1]] = set()\n        alist[e[0]].add(e[1])\n\n    def dfs(node):\n        if pre[node] == True and post[node] == False:\n            return True\n        if post[node] == True:\n            return False\n\n        pre[node] = True \n        for neighbor in alist[node]:\n            if dfs(neighbor):\n                return True\n        post[node] = True\n        return False\n    for k in pre.keys():\n        if dfs(k):\n            return False\n\n    return True\n    \n# inputstr = \"r1(x) r3(y) w3(y) r1(y) w1(x) r3(z) r3(z) r2(w) w2(y) w2(z)\"\n# inputstr = \"r2(y) r1(x) r3(y) w2(y) r2(z) r1(z) w2(z) w3(z) w1(x)\"\n# inputstr = \"r3(y) w3(y) r1(x) r1(y) w1(x) r2(x) r2(z) w3(z) w2(w)\"\n\n#inputstr = \"r1(w) r2(x) r2(y) r1(x) w2(z) r3(z) w3(z) w1(x) w3(x) r3(y) w3(y)\"\n\ninputstr = \"r1(x) w1(x) w1(z) w3(z) r4(z) w2(w) r4(w) r2(w) r3(x) r2(z) w4(w) w4(z)\"\n\nconflicts = getConflictsBrutalForce(inputstr)\n\nfor c in conflicts:\n    print(c)\n\nisSer = isSerializable(inputstr)\n\nif isSer:\n    print(\"SERIALIZABLE:\")\nelse:\n    print(\"NOT SERIALIZABLE:\")\n\nfor c in conflicts:\n    print(\"{} ===> {}\".format(c[0], c[1]))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"miteshkumar77/algos","sub_path":"Database/serializability.py","file_name":"serializability.py","file_ext":"py","file_size_in_byte":2531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"30554668244","text":"import operator\nfrom functools import reduce\n\nN = 128\n\n\ndef part1(data):\n    return sum(\n        1 if k & (1 << (7 - j)) else 0\n        for i in range(N)\n        for k in knothash(f\"{data}-{i}\")\n        for j in range(8)\n    )\n\n\ndef part2(data):\n    grid = [\n        [\n            \"#\" if k & (1 << (7 - j)) else \".\"\n            for k in knothash(f\"{data}-{i}\")\n            for j in range(8)\n        ]\n        for i in range(N)\n    ]\n\n    done = set()\n    regions = 0\n    for i, row in enumerate(grid):\n        for j, col in enumerate(row):\n            if col == \"#\" and (i, j) not in done:\n                regions += 1\n                search(grid, done, i, j)\n\n    return regions\n\n\ndef search(grid, done, i, j):\n    todo = [(i, j)]\n    while todo:\n        x, y = todo.pop()\n        done.add((x, y))\n        for x, y in [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)]:\n            if 0 <= x < N and 0 <= y < N and grid[x][y] == \"#\":\n                if (x, y) not in done:\n                    todo.append((x, y))\n\n\ndef knothash(data):\n    lengths = [ord(c) for c in data] + [17, 31, 73, 47, 23]\n    knot = list(range(256))\n\n    offset = 0\n    skip = 0\n    for _ in range(64):\n        for length in lengths:\n            span = (knot + knot)[offset:][:length]\n            for i, x in enumerate(reversed(span), offset):\n                knot[i % len(knot)] = x\n            offset = (offset + length + skip) % len(knot)\n            skip += 1\n\n    return [reduce(operator.xor, knot[i:][:16]) for i in range(0, len(knot), 16)]\n","repo_name":"brentspell/aoc","sub_path":"2017/day14.py","file_name":"day14.py","file_ext":"py","file_size_in_byte":1519,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"769360863","text":"import smtplib\nfrom tkinter import *\nfrom tkinter import messagebox\nimport numpy as np\nimport cv2\nfrom matplotlib import pyplot as plt\nfrom PIL import Image\n\nroot = Tk()\nlabel_1 = Label( root, text=\"Enter source of the video feed\", font=(\"Arial Bold\", 10) )\nroot.title( \"Video Processing and Analysis\" )\nroot.geometry( \"400x300\" )\nlabel_2 = Label( root, text=\"Enter User Details\" )\nlabel_3 = Label( root, text=\"Email\" )\nlabel_4 = Label( root, text=\"Password\" )\n\nentry_1 = Text( root, height=1, width=18 )\nentry_2 = Text( root, height=1, width=18 )\nentry_3 = Text( root, height=1, width=18 )\n\nlabel_1.grid( row=0, sticky=E )\nlabel_2.grid( row=1, sticky=W )\nlabel_3.grid( row=2, sticky=E )\nlabel_4.grid( row=3, sticky=E )\n\nentry_1.grid( row=0, column=1 )\nentry_2.grid( row=2, column=1 )\nentry_3.grid( row=3, column=1 )\n\nc = Checkbutton( root, text=\"Check if you want to send the alert\" )\nc.grid( column=0, row=9, columnspan=2 )\n\n\ndef clicked():\n    messagebox.showinfo( 'Alert', 'Feed analysis started, alert will be sent to your mail if security is breached' )\n    # change the video source\n\n    src = entry_1.get( \"1.0\", \"end-1c\" )\n    cap = cv2.VideoCapture( src )\n    fgbg = cv2.createBackgroundSubtractorMOG2()\n\n    looper, image = cap.read()\n    count = 0\n    looper = True\n    size = 600, 400\n    for count in range( 0, 100 ):\n        looper, image = cap.read()\n        print( 'Read a new frame: ', looper )\n        count + 1\n        if (count == 10):\n            cv2.imwrite( \"frame%d.jpg\" % count, image )\n            img = Image.open( \"frame%d.jpg\" % count )\n            img2 = img.rotate( 90 )\n            img2.thumbnail( size, Image.ANTIALIAS )\n            img2.save( \"img2.jpg\" )\n            s = smtplib.SMTP( 'smtp.gmail.com', 587 )\n            s.starttls()\n            \n            email = entry_2.get( \"1.0\", \"end-1c\" )\n            pwd = entry_3.get( \"1.0\", \"end-1c\" )\n            \n            s.login(email, pwd )\n            message = \"There is a movement in the area\"\n            s.sendmail(email, pwd, message )\n            s.quit()\n            print( \"Screenshot success\\n\" )\n            break\n\n    while (1):\n        ret, frame = cap.read()\n        fgmask = fgbg.apply( frame )\n        imS = cv2.resize( frame, (800, 600) )\n        imS2 = cv2.resize( fgmask, (800, 600) )\n        cv2.imshow( 'fgmask', imS )\n        cv2.imshow( 'frame', imS2 )\n        k = cv2.waitKey( 30 ) & 0xff\n        if k == 27:\n            break\n\n\nbtn = Button( root, text=\"Start Analyzing\", command=clicked )\nbtn.grid( row=6, column=1 )\n\nroot.mainloop()\n","repo_name":"AshutoshKumarVikram/Final-Year-Project","sub_path":"pro.py","file_name":"pro.py","file_ext":"py","file_size_in_byte":2548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"25639693852","text":"# -*- coding: utf-8 -*-\nimport sys\nimport requests\nimport pandas as pd\nimport yaml\nimport os\nimport gc\n\n## html\nfrom bs4 import BeautifulSoup\nfrom datetime import datetime, timedelta\n\n## finance\nimport FinanceDataReader as fdr\nfrom pykrx import stock\n\nfrom backtesting import Backtest\n\n## plot\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n\n#internal\nfrom tools.news_crawler import newsCrawler\nfrom trade_strategy import tradeStrategy\nfrom tools import st_utils as stu\nfrom back_test import backTestCustom\n\n## warning 무시\nimport warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\n## log 폴더 생성\ntry:\n    if not os.path.exists(\"./log\"):\n        os.makedirs(\"./log\")\nexcept Exception as e :\n    print(e)\n\n####    로그 생성    #######\nlogger = stu.create_logger()\n\n## Global\nTARGET = ('from_krx', 'from_theme', 'from_upjong', 'from_code', 'from_name')\nMODE = ('market_leader', 'theme', 'upjong')\n\n\nclass searchStocks :\n    def __init__(self, config_file):\n\n        ## 설정 파일을 필수적으로 한다.\n        try:\n            with open(config_file) as f:\n                config = yaml.load(f, Loader=yaml.FullLoader)\n        except Exception as e :\n            print (e)\n\n        ## global 변수 선언\n        self.file_manager = config[\"fileControl\"]\n        self.init_conifg = config[\"mainInit\"]\n        self.trade_config = config[\"tradeStock\"]\n\n        ### 기본 함수 실행 결정\n        self.mode = config[\"searchStock\"][\"mode\"]\n        self.target = config[\"searchStock\"][\"market_leader\"][\"target\"]\n\n        if not self.mode in MODE:\n            logger.error(f\"선택된 모드({self.mode})는 지원되지 않습니다. (지원 모드: {MODE})\")\n            sys.exit()\n        if not self.target in TARGET:\n            logger.error(f\"선택된 타겟({self.target})은 지원되지 않습니다. (지원 모드: {TARGET})\")\n            sys.exit()\n\n        if self.mode == 'market_leader':\n            if self.target == 'from_krx':\n                self.params = config[\"searchStock\"][\"market_leader\"][\"from_krx\"][\"params\"]\n                self.config = config[\"searchStock\"][\"market_leader\"][\"from_krx\"][\"config\"]\n            elif self.target == 'from_theme':\n                self.params = config[\"searchStock\"][\"market_leader\"][\"from_theme\"][\"params\"]\n                self.config = config[\"searchStock\"][\"market_leader\"][\"from_theme\"][\"config\"]\n            elif self.target == 'from_upjong':\n                self.params = config[\"searchStock\"][\"market_leader\"][\"from_upjong\"][\"params\"]\n                self.config = config[\"searchStock\"][\"market_leader\"][\"from_upjong\"][\"config\"]\n            elif self.target == 'from_code':\n                self.params = config[\"searchStock\"][\"market_leader\"][\"from_code\"][\"params\"]\n                self.config = config[\"searchStock\"][\"market_leader\"][\"from_code\"][\"config\"]\n            elif self.target == 'from_name':\n                self.params = config[\"searchStock\"][\"market_leader\"][\"from_name\"][\"params\"]\n                self.config = config[\"searchStock\"][\"market_leader\"][\"from_name\"][\"config\"]\n                logger.info(f\"config 파일을 로드 하였습니다. (파일명: {config_file})\")\n            ## 함수 실행\n            self.search_market_leader()\n        else:\n            self.params = config[\"searchStock\"][\"theme_upjong\"][\"params\"]\n            self.config = config[\"searchStock\"][\"theme_upjong\"][\"config\"]\n\n            ## 함수 실행\n            self.search_theme_upjong(mode=self.mode)\n\n\n    def search_theme_upjong(self, mode='theme'):\n        \"\"\"네이버 주식에서 테마/업종을 검색하고, 속한 종목들의 상태를 확인합니다.\n        테마 리스트를 저장하여 주도주 검색 시, 조건으로 활용���니다.\n\n        Args:\n\n        Returns:\n            테마 코드를 저장한 파일을 저장합니다.\n\n        \"\"\"\n        ## 1) 테마 리스트를 작성하고 테마별 종목코드를 확인합니다. 결과는 파일로 저장합니다.\n        krx = fdr.StockListing('KRX')\n        stocks = krx.dropna(axis=0, subset=['Sector'])  ## 섹터값없는 코드 삭제 (ETF...)\n        stocks.reset_index(drop=True, inplace=True)\n\n        ## 네이버 테마 코드를 받아 옵니다.\n        theme_names = []\n        theme_links = []\n\n        if mode == 'theme':\n            pages = 7  ## 네이버 주식에서 테마 페이지는 7 개 임. 추후 증가하더라도 뒤쪽순위는 중요도 떨어짐.\n        else:\n            pages = 1\n\n        logger.info(f\"테마/업종(선택:{mode} 에 대한 크롤링을 시작합니다.\")\n        for page in range(1,pages+1):\n            if mode == 'theme':\n                url = f'https://finance.naver.com/sise/theme.naver?page={page}'\n            else:\n                url = f\"https://finance.naver.com/sise/sise_group.naver?type=upjong\"\n            logger.info(f\"크롤링 웹 페이지: {url}\")\n            html = requests.get(url)\n            soup = BeautifulSoup(html.content.decode('euc-kr', 'replace'), \"html.parser\")\n            if mode == 'theme':\n                ## table 가져오기\n                table = soup.find('table', {'class': 'type_1 theme'})\n                aa = table.find_all('td', {\"class\":\"col_type1\"})\n                for i in aa :\n                    theme_names.append(i.get_text())\n                    theme_links.append(f\"http://finance.naver.com/{i.find('a')['href']}\")\n            else:\n                table = soup.find('table', {'class': 'type_1'})\n                aa = table.find_all('a')\n                for i in aa:\n                    theme_names.append(i.get_text())\n                    theme_links.append(f\"http://finance.naver.com/{i['href']}\")\n\n\n        ##테마별 종목 가져오기\n        df_accum = pd.DataFrame()  ## 모든 테마의 증감률 누적 저장\n\n        # summary df 에 저장될 데이터 리스트\n        theme_accum = []\n        name_accum = []\n        code_accum = []\n        change_accum =  []\n        for theme_link, theme_name in zip(theme_links, theme_names):\n            logger.info(f\"테마명: {theme_name}, 링크: {theme_link} \")\n            try:\n                with requests.Session() as s:\n                    html = s.get(theme_link)\n                soup = BeautifulSoup(html.content.decode('euc-kr', 'replace'), \"html.parser\")\n                tables = soup.select('table')\n                table_html = str(tables)  ## 테이블 html 정보를 문자열로 변경하기\n                table_df_list = pd.read_html(table_html) ## 테이블 정보 읽어 오기\n                df_table = table_df_list[2]  ## 종목명, 등락률, 거래대금\n                names = df_table[\"종목명\"].to_list()\n                conds = df_table[\"현재가\"].to_list()\n                names2 = []\n                codes = []\n                for name, cond in zip(names,conds):\n                    if type(name) == str:\n                        name = name.replace(\" *\", \"\")\n                        tmp = stocks[stocks[\"Name\"] == name]\n                        if len(tmp) != 0 :\n                            code =str(tmp[\"Symbol\"].to_list()[0])\n                            if cond > self.params[\"threshold_close\"] : ## 현재가가 10,000 이상인 경우에만 집계에 사용한다.\n                                names2.append(name)\n                                codes.append(code.zfill(6))\n\n                                theme_accum.append(theme_name)\n                                name_accum.append(name) # 최종 summary 용\n                                code_accum.append(code.zfill(6)) # 최종 summary 용\n                            else:\n                                logger.info(f\"종목명({name} 의 현재가 ({cond}) 는 10,000 이하이므로 집계에서 제외합니다.\")\n            except Exception as e:\n                print(e)\n\n            ## 여러 코드의 데이터 가져오기\n            duration = self.params[\"period\"]\n            end_dt = datetime.today()\n            end = end_dt.strftime(self.init_conifg[\"time_format\"])\n            st_dt = end_dt - timedelta(days=duration)\n            st = st_dt.strftime(self.init_conifg[\"time_format\"])\n\n            df_list = [fdr.DataReader(code, st, end)[['Close', 'Volume']] for code in codes]\n            if len(df_list) == 0 :   ## 종목들이 전부 1만원을 넘지 못해서 하나도 남지 않는 경우\n                continue\n            else:\n                df_data = pd.concat(df_list, axis=1)\n            cols = []\n            for name in names2:\n                cols.append(name+'.Close')\n                cols.append(name+'.Volume')\n            df_data.columns = cols\n\n            ## 등락률 계산하기 (거래대금 으로 가중치 주기)\n            for name in names2:\n                df_data[f\"{name}.VolumeCost\"] = round(df_data[f\"{name}.Volume\"] * df_data[f\"{name}.Close\"] / 1000000)\n                df_data[f\"{name}.Change\"] = round((df_data[f\"{name}.Close\"] - df_data[f\"{name}.Close\"].shift()) / df_data[f\"{name}.Close\"].shift()  * 100, 2)\n\n                st_close = df_data.iloc[0][f\"{name}.Close\"]\n                end_close = df_data.iloc[-1][f\"{name}.Close\"]\n                change = round((end_close - st_close)/end_close *100, 2)\n                change_accum.append(change) # 최종 summary 용\n\n            df_data.dropna(inplace=True)\n\n            ## 최종 등락률\n            diff_tot = []\n            r_pnts = []\n            for idx in df_data.index:\n                sum = 0\n                diffs = 0\n                for name in names2:\n                    sum += df_data.at[idx, f\"{name}.VolumeCost\"]\n                if sum == 0 : sum = 1  ## 거래정지 경우 -> 분모가 0 이 되는 것을 방지\n\n                r_max = 0\n                for cnt, name in enumerate(names2):\n                    r = round(df_data.at[idx, f\"{name}.VolumeCost\"] / sum, 2)\n                    d = round(df_data.at[idx, f\"{name}.Change\"] * r, 2)\n                    df_data[f\"{name}.Ratio\"] = r\n                    if r > r_max :\n                        r_max = r\n                        r_pnt = cnt\n\n                    if d > 0:\n                        diffs += d\n                    else: ## 음수면 두배 가중치 (누적값 원상 보귀 여부 확인 용)\n                        diffs += d * 2\n                r_pnts.append(r_pnt)\n                diff_tot.append(round(diffs,2))\n\n\n            df_data[\"avg_range\"] = diff_tot\n            df_data[\"accum_avg_range\"] = df_data[\"avg_range\"].cumsum()\n            df_data[\"weight_point\"] = r_pnts   ## 등락률에 가장 많은 영향을 준 코드 변화 확인\n            df_data.dropna(inplace=True)\n\n            ## 테마/업종 단위 일자별 등락률\n            df_accum = pd.concat([df_accum, df_data[\"accum_avg_range\"]], axis=1)\n            df_accum.rename(columns={'accum_avg_range': theme_name}, inplace=True)\n\n            # 최종 summary 용\n            theme_accum.append(theme_name)\n            code_accum.append('ALL')\n            name_accum.append('ALL')\n            change_accum.append(df_data.iloc[-1][\"accum_avg_range\"])\n\n            if self.config[\"display_theme_chart\"] == True:\n                ## 테마별 등락률 그래프 만들기 (종목별 은 아님)\n                sns.set(rc={'figure.figsize': (20, 10), 'font.family': 'AppleGothic'})\n                p = sns.lineplot(x=df_data.index, y=\"accum_avg_range\", data=df_data, label='증감률 누적')\n                p = sns.lineplot(x=df_data.index, y=\"weight_point\", data=df_data, marker=True, label='종목 변경값')\n                title = f\"테마/업종 이름: {theme_name}\"\n                p.set_title(title)\n                p.set(xlabel='Date', ylabel='증감률 누적')\n\n                plt.xticks(rotation=-45)\n                plt.rc('axes', unicode_minus=False)  ## 마이너스 표시 깨짐 해결\n                plt.show()\n\n\n            disp_cols = []\n            df_data2 = df_data.copy()\n            for col in names2:\n                newcol = f\"{col}.Close\"\n                disp_cols.append(newcol)\n                if df_data2.loc[:,newcol][-1] > 1000000:\n                    df_data2[newcol] = df_data2[newcol] / 100  ## 만원 단위로 맞추기 위함\n                elif df_data2.loc[:,newcol][-1] > 100000:\n                    df_data2[newcol] = df_data2[newcol] / 10  ## 만원 단위로 맞추기 위함\n\n            sns.set(rc={'figure.figsize': (20, 15), 'font.family': 'AppleGothic'})\n            title = f\"테마 이름: {theme_name}\"\n            df_data2[disp_cols].plot(title=title+\" - 종목별 영향 비율\")\n\n            ## 텍스트 삽입\n            for posx in df_data2.index:\n                for col in names2 :\n                    newcol = f\"{col}.Close\"\n                    posy = df_data2.at[posx, newcol]\n\n                    newcol2 = f\"{col}.Ratio\"\n                    text = df_data2.at[posx, newcol2]\n\n                    newcol3 = f\"{col}.Change\"\n                    text2 = df_data2.at[posx, newcol3]\n\n                    plt.text(posx, posy, text, horizontalalignment='right',\n                             verticalalignment='bottom',\n                             size='small', color='black', weight='semibold' )\n                    if text2 > 0 :\n                        color = 'blue'\n                    else:\n                        color = 'red'\n                    plt.text(posx, posy, text2, horizontalalignment='left',\n                             verticalalignment='top',\n                             size='x-small', color=color, weight='semibold' )\n\n            plt.yscale(\"log\")\n            plt.legend(loc='upper left')\n            plt.tight_layout()\n            if self.config[\"save_stock_chart\"] == True:\n                file_name = f\"{theme_name}.png\"\n                file_name = file_name.replace(\"/\",\"_\")\n                if mode == 'theme':\n                    file_path = self.file_manager[\"search_stocks\"][\"path\"] + f\"theme/img_codes/{st}_{end}/\"\n                else:\n                    file_path = self.file_manager[\"search_stocks\"][\"path\"] + f\"upjong/img_codes/{st}_{end}/\"\n                ## 폴더 생성\n                try:\n                    if not os.path.exists(file_path):\n                        os.makedirs(file_path)\n                except Exception as e:\n                    raise e\n                plt.savefig(file_path+file_name, dpi=300)\n                if self.config[\"display_stock_chart\"] == True:\n                    plt.show()\n            else:\n                plt.show()\n\n            ## 파일저장\n            if self.config[\"save_stock_data\"] == True:\n                file_name = f\"{theme_name}.csv\"\n                file_name = file_name.replace(\"/\",\"_\")\n                if mode == 'theme':\n                    file_path = self.file_manager[\"search_stocks\"][\"path\"] + f\"theme/raw/{st}_{end}/\"\n                else:\n                    file_path = self.file_manager[\"search_stocks\"][\"path\"] + f\"upjong/raw/{st}_{end}/\"\n\n                ## 파일로 저장 합니다.\n                stu.file_save(df_data, file_path, file_name, replace=False)\n\n            if theme_name == 'PCB(FPCB 등)':  ## for test\n                keys = theme_name\n                # keys = '유신'\n                nc = newsCrawler()\n                nc.search_keyword(keys)\n            # time.sleep(5)\n\n            print(f\"테마/업종 ({theme_name}) 종목 리스트: {names2} \")\n            print(f\"테마/업종 ({theme_name}) 처리를 완료 하였습니다.\")\n\n            ## 초기화\n            plt.close()\n            del [[df_data, df_data2]]\n            gc.collect()\n            df_data = pd.DataFrame()\n            df_data2 = pd.DataFrame()\n\n        ### summary 용 df 생성 ####\n        cols = [\"Theme\", \"Name\", \"Code\", \"Change\"]\n        df_summary = pd.DataFrame(columns=cols)\n        values = [theme_accum, name_accum, code_accum, change_accum]\n\n        for col, val in zip(cols, values):\n            df_summary[col] =  val\n\n        file_name = f\"{mode}_stockList_{st}_{end}.csv\"\n        file_path = self.file_manager[\"search_stocks\"][\"path\"]\n        ## 폴더 생성\n        try:\n            if not os.path.exists(file_path):\n                os.makedirs(file_path)\n        except Exception as e:\n            raise e\n        df_summary.to_csv(file_path+file_name)\n\n\n        #########################\n        ## 증가율이 높은 테마 찾기\n        #########################\n        df_accum2 = df_accum.loc[:, ~df_accum.T.duplicated()]\n        theme_ord = []\n        for col in df_accum2.columns.to_list():\n            val= df_accum2[col][-1]\n            theme_ord.append((col, val))\n\n        ## sortedj\n        theme_ord2 = sorted(theme_ord, key=lambda x: x[1], reverse=True)\n\n        ## 이미지 생성\n        cutoff = self.params[\"theme_summary_cutoff\"]\n        name_list =[]\n        for name, score in theme_ord2[cutoff[0]:cutoff[1]]:\n            name_list.append(name)\n\n        name_list2 = []\n        for name, score in theme_ord2:  ## 파일 저장을 위해 컬럼 순서 변경용\n            name_list2.append(name)\n\n        sns.set(rc={'figure.figsize': (20, 15), 'font.family': 'AppleGothic'})\n        df_accum2[name_list].plot(title=f\" 누적 증감률 상위 {cutoff[0]} ~ {cutoff[1]} 테마/업종\")\n\n        plt.legend(loc='upper left')\n        plt.tight_layout()\n        plt.rc('axes', unicode_minus=False)  ## 마이너스 표시 깨짐 해결\n\n        ## 테마/업종 전체에서 비교하는 이미지 파일 저장 (필수)\n        file_name = f\"{mode}_summary_{st}_{end}.png\"\n        file_path = self.file_manager[\"search_stocks\"][\"path\"] + f'{mode}/summary/{st}_{end}/'\n        ## 폴더 생성\n        try:\n            if not os.path.exists(file_path):\n                os.makedirs(file_path)\n        except Exception as e:\n            raise e\n        plt.savefig(file_path + file_name, dpi=300)\n        plt.show()\n\n        ## 파일로 저장 합니다.\n        file_name = f\"{mode}_summary_{st}_{end}.csv\"\n        stu.file_save(df_accum2[name_list2], file_path, file_name, replace=False)\n\n    def search_market_leader(self):\n        '''\n        주도주 검색:\n         - 시장 주도주는 시장의 뭉칫돈이 쏠리는 종목을 뜻함. 단기 이슈나 소재로 인해 급등하는 종목을 제외\n\n         - 환경 : 변동성이 크고 시장의 불확실성이 확대 될때 사용. 하락장에서 영향 줌\n\n         - 우선순위 1. 하락장 에서 하락 비율이 낮음\n         - 우선순위 2. 시가총액이 커서 지수에 영향줌.\n         - 우선순위 3. (opt.) 하락장에 52주 신고가 달성\n\n         - 예외 1. 계절적 요인으로 인한 하락, 특정 돌발 이슗 인한 인한 하락 은 구분해서 대응\n\n        :return:\n        '''\n\n        ## 공통\n        path = self.file_manager[\"search_stocks\"][\"path\"]\n        format = self.init_conifg[\"time_format\"]\n        df_theme = stu.load_theme_list(path, mode='theme', format=format)\n        df_upjong = stu.load_theme_list(path, mode='upjong', format=format)\n\n        if self.target == 'from_krx':\n\n            df_fin = self.condition_check()  ## 입력 df 없으면 내부 자동 생성\n\n            ## 테스트 용\n            # df_fin = df_vcost.sort_values(by='Change', ascending=False)\n            # df_fin = df_vcost.sort_values(by='VolumeCost', ascending=False)\n            # df_news = nc.search_keyword('현대코퍼레이션')\n\n\n        elif self.target == 'from_name':\n            name_list = self.config[\"select_name\"].split(',')\n            name_list2 = [n.replace(' ', '') for n in name_list]  ## 공백 제거\n\n            df_fin = self.condition_check(name_list=name_list2)\n\n        elif self.target == 'from_theme':\n\n\n            theme_list = df_theme.Theme.to_list()\n            theme_list = list(set(theme_list))\n\n            if self.config[\"enable_all\"] == True:\n                df_sel = df_theme.drop_duplicates(subset='Name')\n                df_sel = df_sel[df_sel.Name != 'ALL']\n                df_sel = df_sel.set_index('Name', drop=True)\n            else:\n                df_temp = df_theme[df_theme.Name != 'ALL']\n                df_temp = df_temp.set_index('Name', drop=True)\n\n                search = self.config[\"select_theme\"]\n                sel_theme = []\n                ## 테마 존재 여부 찾기\n                for th in theme_list:\n                    if search in th:\n                        logger.error(f\"선택한 테마명({search}) 이 테마 ({th})와 일치합니다.\")\n                        sel_theme.append(th)\n\n                # 테마에 속한 종목 찾기\n                df_list = []\n                for th in sel_theme:\n                    df_seltheme = df_temp[df_temp.Theme == th]\n                    df_list.append(df_seltheme)\n\n                if not len(df_list) == 0:\n                    df_sel = pd.concat(df_list, axis=1)\n                else:\n                    logger.error(f\"선택한 테마명({sel_theme}) 이 테마 리스트에 존재하지 않습니다.\")\n                    str = f\"테마 리스트:\"\n                    for cnt, th in enumerate(theme_list):\n                        if cnt % 5 == 0 :\n                            str = str + \"\\n\"\n                        else:\n                            str = str + f\"{th:<30},\"\n                    logger.error(str)\n                    sys.exit()\n\n\n            df_sel = df_sel.drop(['Change', 'Code'], axis=1)\n\n            df_fin = self.condition_check(df_sel)\n\n\n\n        ####################################\n        ####  종목별 차트 그리기 (공통 부분)\n        ####################################\n\n        # for 문을 위한 설정 정보들\n        config_file = './config/config.yaml'\n        strategy = tradeStrategy(config_file)\n        dates = stu.period_to_str(self.config[\"chart_period\"], format=\"%Y%m%d\")\n        nc = newsCrawler()\n\n        buy_dict = dict()\n        buy_name = []\n        for cnt, (code, name) in enumerate(zip(df_fin.Code, df_fin.index)):\n            # if cnt >15:\n            #     print(\"SSH TEST\")\n\n            if not len(df_theme) == 0 :\n                df_cur_theme = df_theme[df_theme.Name == name]\n                cur_themes = df_cur_theme.Theme.to_list()\n                for i in cur_themes:\n                    cur_stocks = df_theme[df_theme.Theme == i].Name.to_list()\n                    if not len(cur_stocks) == 0 :\n                        cur_stocks.remove('ALL')\n                        cur_stocks.remove(name)\n                        # print(cur_stocks)\n\n            if not len(df_upjong) == 0 :\n                df_cur_upjong = df_upjong[df_upjong.Name == name]\n                cur_upjongs = df_cur_upjong.Theme.to_list()\n                for i in cur_upjongs:\n                    cur_stocks = df_upjong[df_upjong.Theme == i].Name.to_list()\n                    if not len(cur_stocks) == 0 :\n                        cur_stocks.remove('ALL')\n                        cur_stocks.remove(name)\n                        # print(cur_stocks)\n\n            try:\n                ####################################\n                ####  종목별 Score Print 하기\n                ####################################\n                fi_score = df_fin[df_fin.index == name]['total_score'].values[0]\n                vol_cost = df_fin[df_fin.index == name]['VolumeCost'].values[0]\n                vol_cost = round(vol_cost / 100000000)  ## 단위 억\n                sector = df_fin[df_fin.index == name]['Sector'].values[0]\n                industry = df_fin[df_fin.index == name]['Industry'].values[0]\n                turnover = df_fin[df_fin.index == name]['VolumeTurnOver'].values[0]\n                change = df_fin[df_fin.index == name]['Change'].values[0]\n                change_mid = df_fin[df_fin.index == name]['ChangeMid'].values[0]\n                logger.info(f\"Code: {code:<8}, name: {name:<20}, 단기 등락률: {change:<6}, 중기 등락률: {change_mid:<6}, \"\n                            f\"거래대금(억원): {vol_cost:<8}, score : {fi_score:<4}, 거래회전률: {turnover:<8}, \"\n                            f\"섹터: {sector:<20}, 산업: {industry:<20}\")\n            except:\n                print(f\"Code: {code:<8}, name: {name:<15}, score: None -- 스코어를 찾을 수 없습니다.\")\n\n            # logger.info(f\"종목 ({name} - {code}) 의 등락율 차트를 생성합니다.\")\n            df_ohlcv = strategy.run(code, name=name, dates=dates, data='none', mode='daily')\n\n            ## 한달동안 매수 신호가 발생한 종목 수집하기\n            if any(df_ohlcv.tail(self.trade_config[\"buy_condition\"][\"codepick_buy_holdday\"]).finalBuy.to_list()):\n                buy_name.append(name)\n                buy_dict[name] = dict()\n                buy_dict[name]['code'] = code\n                last_d = df_ohlcv[df_ohlcv.finalBuy == True]['finalBuy'].index.to_list()[-1]\n                priceEarning = stu.change_ratio(curr=df_ohlcv.Close.iat[-1], prev=df_ohlcv.Close.at[last_d]) # 수익률\n                hold_d = datetime.today() - last_d\n                buy_dict[name]['buy_day'] = last_d\n                buy_dict[name]['buy_hold_day'] = hold_d\n                buy_dict[name]['priceEarning'] = priceEarning\n\n\n\n\n        ## 매수 조건이 발생한 종목을 저장 하기\n        logger.info(f\"\\n매수 조건을 만족한 종목을 저장합니다.\")\n        df_buy = df_fin.loc[buy_name]\n        cols = [\"Code\", \"total_score\", \"VolumeCost\", \"Sector\", \"Industry\", \"VolumeTurnOver\", \"Change\", \"ChangeMid\"]\n        df_buy = df_buy.loc[:,cols]\n\n        ## 파일 저장합니다.\n        self._make_buy_list(df_buy, buy_dict)\n\n\n        logger.info(\"Finish!\")\n\n    ######################################################\n    ######    Internal Func.\n    ######################################################\n    def _make_buy_list(self, df, buy_dict):\n        # 시작 시점에 시간 체크\n        today = datetime.today()\n\n        # 중기, 단기 기간 값 추가, buy 날짜, buy 지연 날짜,\n        [st, end] = stu.period_to_str(self.config[\"change_period\"])\n        change_period = f\"{st} ~ {end}\"\n        [st, end] = stu.period_to_str(self.config[\"trend_period\"])\n        trend_period = f\"{st} ~ {end}\"\n\n        df[\"change_period\"] = change_period\n        df[\"trend_period\"] = trend_period\n        df[\"select_mode\"] = self.target\n        df[\"buy_day\"] = 0\n        df[\"buy_hold_day\"] = 0\n        df[\"priceEarning\"] = 0\n        for name, value in buy_dict.items():\n            df.loc[name, \"buy_day\"] = value[\"buy_day\"]\n            df.loc[name, \"buy_hold_day\"] = value[\"buy_hold_day\"]\n            df.loc[name, \"priceEarning\"] = value[\"priceEarning\"]\n        df.sort_values(by='buy_hold_day', inplace=True)\n        for name in df.index:\n            last_d = str(df.at[name,'buy_day'])\n            hold_d = str(df.at[name,'buy_hold_day'])\n            earn = str(df.at[name,'priceEarning'])\n            logger.info(f\"종목명 (매수조건 만족) :{name:<20}  -> 매수 신호 날짜: {last_d:<25}, 당일까지 기간: {hold_d} , 당일까지 수익률: {earn}%\")\n\n        ## 폴더 생성\n        base_path = self.file_manager[\"monitor_stocks\"][\"path\"]\n        path = f\"year={today.strftime('%Y')}/month={today.strftime('%m')}/day={today.strftime('%d')}/time={today.strftime('%H%M')}/\"\n\n        stu.file_save(df, file_path=base_path+path, file_name=f\"buy_list_{today.strftime('%Y%m%d_%H%M%S')}.csv\")\n\n        # 차트 이미지 저장.\n        ## 매수 종목만 그림 파일 저장합니다.\n        st = tradeStrategy('./config/config.yaml')\n        st.display = 'save'\n        dates = stu.period_to_str(self.config[\"chart_period\"], format=\"%Y%m%d\")\n        for name, code in zip(df.index, df.Code):\n            st.path = base_path+path + \"chart/\"\n            st.name = f\"{name}_{code}.png\"\n            st.run(code, name=name, dates=dates, data='none', mode='daily')\n\n\n    def condition_check(self, df=pd.DataFrame(), name_list=[],):\n        [st, end] = stu.period_to_str(self.config[\"change_period\"])\n\n        ## 등락률 정보\n        df_price = stock.get_market_price_change(st.replace(\"-\", \"\"), end.replace(\"-\", \"\"), market='ALL')\n        endt = datetime.strptime(end, \"%Y-%m-%d\")\n        for i in range(7):  ## 시장이 쉬는 구간을 피해서 불러오기 위함\n            date = endt - timedelta(days=i)\n            end_pre = date.strftime(format=\"%Y%m%d\")\n            try:\n                df_price2 = stock.get_exhaustion_rates_of_foreign_investment(end_pre, market='ALL')['상장주식수']\n                break\n            except:\n                continue\n\n\n        df_price = df_price.join(df_price2)\n        df_price[\"회전율\"] = round(df_price[\"거래량\"] / df_price[\"상장주식수\"] * 100, 2)\n\n        df_price.rename(columns={'종목명': 'Name',\n                                 '시가': 'Open',\n                                 '종가': 'Close',\n                                 '거래량': 'Volume',\n                                 '등락률': 'Change',\n                                 '변동폭': 'ChangeRatio',\n                                 '거래대금': 'VolumeCost',\n                                 '상장주식수': 'TotalVolume',\n                                 '회전율': 'VolumeTurnOver',\n                                 }, inplace=True)\n\n        df_price.set_index(keys=['Name'], inplace=True)\n\n        if len(df)  == 0 :\n            df = df_price\n        else:\n            df = df.join(df_price, how='left')\n\n        ## Name list 가 있으면, 선택함.\n        if not len(name_list) == 0:\n            df = df.loc[name_list,:]\n\n            if len(df) == 0:\n                logger.error(f\"입력한 종목명들을 찾을 수 없습니다. (종목명: {name_list})\")\n                sys.exit()\n\n        # 1) 조건1: 종가 최소 값 으로 cut\n        thrd_close = self.params[\"threshold_close\"]\n        df_close = df[df.Close > thrd_close]\n        logger.info(\n            f\"종가 ({thrd_close} 원) 보다 낮은 종믁 제거로,  전체 ({len(df)}) 개 중에 ({len(df_close)}))개 를 선정합니다.\")\n\n        # 2) 조건2:  finance score 이상인 경우에만 해당\n        # 제무 스코어 불러오기\n        path = self.file_manager[\"finance_score\"][\"path\"]\n        files = os.listdir(path)\n        df_ref = pd.read_csv(path + files[0])  ## 파일이 하나밖에 없음\n        df_ref['Code'] = df_ref['code'].apply(lambda x: str(x).zfill(6))\n        df_ref.set_index(keys=['Name'], inplace=True)\n\n        df_finance = df_close.join(df_ref, how='left')\n        df_finance.dropna(subset=['total_score', 'Code'], inplace=True)\n        thrd_score = self.params[\"threshold_finance_score\"]\n        df_finance2 = df_finance[df_finance.total_score >= thrd_score]\n        logger.info(f\"재무제표 총점 ({thrd_score}) 기준으로 종목 ({len(df_close)})개 중에 ({len(df_finance2)})개 를 선정합니다.\")\n\n        # 3) 조건3: 상승 추세 확인 (중기)\n        [chng_st, chng_end] = stu.period_to_str(self.config[\"trend_period\"])\n        df_mid_price = stock.get_market_price_change(chng_st, chng_end, market='ALL')\n        df_mid_price.rename(columns={'종목명': 'Name',\n                                 '시가': 'Open',\n                                 '종가': 'Close',\n                                 '거래량': 'Volume',\n                                 '등락률': 'Change',\n                                 '변동폭': 'ChangeRatio',\n                                 '거래대금': 'VolumeCost',\n                                 }, inplace=True)\n        df_mid_price.set_index('Name', drop=True, inplace=True)\n\n        name_list = df_finance2.index.to_list()\n        df_mid_price = df_mid_price.loc[name_list,:]  ## 상승추세를 확인하기 위해 준비\n\n\n        thrd_trend = self.params[\"threshold_min_trend\"]\n        name_list = df_mid_price[df_mid_price.Change > thrd_trend].index.to_list()\n        df_trend = df_finance2.loc[name_list,:]  ## 상승추세를 확인하기 위해 준비\n        df_trend['ChangeMid'] = df_mid_price['Change']\n        logger.info(\n            f\"({self.config['trend_period']})일간  ({thrd_trend})% 보다 높게 상승한 기준으로 종목 ({len(df_finance2)})개 중에 ({len(df_trend)})개 를 선정합니다.\")\n\n        # 4) 조건4:  등락률이 코스피보다 높은 경우에만 해당\n        ## 코스피 등락률 보다 높이 상승한 종목 추출\n        flag = self.params[\"threshold_min_change\"]\n        if flag == True:\n            krx = stock.get_index_price_change(st, end, \"KOSPI\")\n            change_thrd = krx.at[\"코스피 200\", \"등락률\"]\n            if change_thrd < 0:  #\n                change_thrd = 1\n            else:\n                change_thrd += 1\n            df_change = df_trend[df_trend.Change > change_thrd]  ## 등락률 기간이 다르기 때문에\n            logger.info(\n                f\"코스피 변동률(최소 +1) ({change_thrd}) 보다 높게 상승한 기준으로 종목 ({len(df_trend)})개 중에 ({len(df_change)})개 를 선정합니다.\")\n        else:\n            df_change = df_trend\n\n        # 4) 조건4-2: 등락률이 너무 높지 않음 (이미 날라간 종목은 관심 없음)\n        thrd_maxchg = self.params[\"threshold_max_change\"]\n        df_change2 = df_change[df_change.Change < thrd_maxchg]\n        logger.info(\n            f\"변동률 ({thrd_maxchg}) 보다 낮게 상승 (급한성장은 관심 논외)한 기준으로 종목 ({len(df_change)})��� 중에 ({len(df_change2)})개 를 선정합니다.\")\n\n        # 5) 조건5: 거래대금이 너무 낮지 않음\n        thrd_vcost = self.params[\"threshold_volumecost\"]\n        df_vcost = df_change2[df_change2.VolumeCost > thrd_vcost * 100000000]\n        logger.info(f\"거래대금이 ({thrd_vcost} 억원) 보다 높은 기준으로 종목 ({len(df_change2)})개 중에 ({len(df_vcost)})개 를 선정합니다. \")\n\n        # 6) 조건5: 회전율 상위 순위 순으로 cut\n        thrd_shortselling = self.params[\"shortselling_sort_count\"]\n        df_shtsell = df_vcost.sort_values(by='VolumeTurnOver', ascending=False).head(thrd_shortselling)\n        logger.info(\n            f\"거래 회전률이 많은 순으로 상위 ({thrd_shortselling})개 목표로, 종목 ({len(df_vcost)})개 중에 ({len(df_shtsell)})개 를 선정합니다.\\n\\n\")\n\n        df_out = df_shtsell\n\n        return df_out\n\n\n\n\nif __name__ == \"__main__\":\n    config_file = './config/config.yaml'\n    theme = searchStocks(config_file)\n    # theme.run()","repo_name":"shsong02/sysTrade","sub_path":"search_stocks.py","file_name":"search_stocks.py","file_ext":"py","file_size_in_byte":34517,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"41756508879","text":"import hashlib\nimport logging\nimport os\nimport shutil\nimport tarfile\nimport zipfile\nfrom typing import Optional\nfrom urllib.error import ContentTooShortError, HTTPError, URLError\nfrom urllib.request import Request, urlopen, urlretrieve\n\nfrom monai.utils import optional_import, progress_bar\n\ngdown, has_gdown = optional_import(\"gdown\", \"3.6\")\n\n\ndef check_md5(filepath: str, md5_value: Optional[str] = None) -> bool:\n    \"\"\"\n    check MD5 signature of specified file.\n\n    Args:\n        filepath: path of source file to verify MD5.\n        md5_value: expected MD5 value of the file.\n\n    \"\"\"\n    if md5_value is not None:\n        md5 = hashlib.md5()\n        try:\n            with open(filepath, \"rb\") as f:\n                for chunk in iter(lambda: f.read(1024 * 1024), b\"\"):\n                    md5.update(chunk)\n        except Exception as e:\n            print(f\"Exception in check_md5: {e}\")\n            return False\n        if md5_value != md5.hexdigest():\n            return False\n    else:\n        print(f\"expected MD5 is None, skip MD5 check for file {filepath}.\")\n\n    return True\n\n\ndef download_url(url: str, filepath: str, md5_value: Optional[str] = None) -> None:\n    \"\"\"\n    Download file from specified URL link, support process bar and MD5 check.\n\n    Args:\n        url: source URL link to download file.\n        filepath: target filepath to save the downloaded file.\n        md5_value: expected MD5 value to validate the downloaded file.\n            if None, skip MD5 validation.\n\n    Raises:\n        RuntimeError: When the MD5 validation of the ``filepath`` existing file fails.\n        RuntimeError: When a network issue or denied permission prevents the\n            file download from ``url`` to ``filepath``.\n        URLError: See urllib.request.urlretrieve.\n        HTTPError: See urllib.request.urlretrieve.\n        ContentTooShortError: See urllib.request.urlretrieve.\n        IOError: See urllib.request.urlretrieve.\n        RuntimeError: When the MD5 validation of the ``url`` downloaded file fails.\n\n    \"\"\"\n    if os.path.exists(filepath):\n        if not check_md5(filepath, md5_value):\n            raise RuntimeError(f\"MD5 check of existing file failed: filepath={filepath}, expected MD5={md5_value}.\")\n        print(f\"file {filepath} exists, skip downloading.\")\n        return\n\n    if url.startswith(\"https://drive.google.com\"):\n        if not has_gdown:\n            raise RuntimeError(\"To download files from Google Drive, please install the gdown dependency.\")\n        os.makedirs(os.path.dirname(filepath), exist_ok=True)\n        gdown.download(url, filepath, quiet=False)\n        if not os.path.exists(filepath):\n            raise RuntimeError(\n                f\"Download of file from {url} to {filepath} failed due to network issue or denied permission.\"\n            )\n    elif url.startswith(\"https://msd-for-monai.s3-us-west-2.amazonaws.com\"):\n        block_size = 1024 * 1024\n        tmp_file_path = filepath + \".part\"\n        first_byte = os.path.getsize(tmp_file_path) if os.path.exists(tmp_file_path) else 0\n        file_size = -1\n\n        try:\n            file_size = int(urlopen(url).info().get(\"Content-Length\", -1))\n            progress_bar(index=first_byte, count=file_size)\n\n            while first_byte < file_size:\n                last_byte = first_byte + block_size if first_byte + block_size < file_size else file_size - 1\n\n                req = Request(url)\n                req.headers[\"Range\"] = \"bytes=%s-%s\" % (first_byte, last_byte)\n                data_chunk = urlopen(req, timeout=10).read()\n                with open(tmp_file_path, \"ab\") as f:\n                    f.write(data_chunk)\n                progress_bar(index=last_byte, count=file_size)\n                first_byte = last_byte + 1\n        except IOError as e:\n            logging.debug(\"IO Error - %s\" % e)\n        finally:\n            if file_size == os.path.getsize(tmp_file_path):\n                if md5_value and not check_md5(tmp_file_path, md5_value):\n                    raise Exception(\"Error validating the file against its MD5 hash\")\n                shutil.move(tmp_file_path, filepath)\n            elif file_size == -1:\n                raise Exception(\"Error getting Content-Length from server: %s\" % url)\n    else:\n        os.makedirs(os.path.dirname(filepath), exist_ok=True)\n\n        def _process_hook(blocknum: int, blocksize: int, totalsize: int):\n            progress_bar(blocknum * blocksize, totalsize, f\"Downloading {filepath.split('/')[-1]}:\")\n\n        try:\n            urlretrieve(url, filepath, reporthook=_process_hook)\n            print(f\"\\ndownloaded file: {filepath}.\")\n        except (URLError, HTTPError, ContentTooShortError, IOError) as e:\n            print(f\"download failed from {url} to {filepath}.\")\n            raise e\n\n    if not check_md5(filepath, md5_value):\n        raise RuntimeError(\n            f\"MD5 check of downloaded file failed: URL={url}, filepath={filepath}, expected MD5={md5_value}.\"\n        )\n\n\ndef extractall(filepath: str, output_dir: str, md5_value: Optional[str] = None) -> None:\n    \"\"\"\n    Extract file to the output directory.\n    Expected file types are: `zip`, `tar.gz` and `tar`.\n\n    Args:\n        filepath: the file path of compressed file.\n        output_dir: target directory to save extracted files.\n        md5_value: expected MD5 value to validate the compressed file.\n            if None, skip MD5 validation.\n\n    Raises:\n        RuntimeError: When the MD5 validation of the ``filepath`` compressed file fails.\n        ValueError: When the ``filepath`` file extension is not one of [zip\", \"tar.gz\", \"tar\"].\n\n    \"\"\"\n    target_file = os.path.join(output_dir, os.path.basename(filepath).split(\".\")[0])\n    if os.path.exists(target_file):\n        print(f\"extracted file {target_file} exists, skip extracting.\")\n        return\n    if not check_md5(filepath, md5_value):\n        raise RuntimeError(f\"MD5 check of compressed file failed: filepath={filepath}, expected MD5={md5_value}.\")\n\n    if filepath.endswith(\"zip\"):\n        zip_file = zipfile.ZipFile(filepath)\n        zip_file.extractall(output_dir)\n        zip_file.close()\n    elif filepath.endswith(\"tar\") or filepath.endswith(\"tar.gz\"):\n        tar_file = tarfile.open(filepath)\n        tar_file.extractall(output_dir)\n        tar_file.close()\n    else:\n        raise ValueError('Unsupported file extension, available options are: [\"zip\", \"tar.gz\", \"tar\"].')\n\n\ndef download_and_extract(url: str, filepath: str, output_dir: str, md5_value: Optional[str] = None) -> None:\n    \"\"\"\n    Download file from URL and extract it to the output directory.\n\n    Args:\n        url: source URL link to download file.\n        filepath: the file path of compressed file.\n        output_dir: target directory to save extracted files.\n            defaut is None to save in current directory.\n        md5_value: expected MD5 value to validate the downloaded file.\n            if None, skip MD5 validation.\n\n    \"\"\"\n    download_url(url=url, filepath=filepath, md5_value=md5_value)\n    extractall(filepath=filepath, output_dir=output_dir, md5_value=md5_value)\n","repo_name":"skhan45/MONAI","sub_path":"monai/apps/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":7083,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"76"}
+{"seq_id":"19881071109","text":"from __future__ import absolute_import, division, print_function\n\n__metaclass__ = type\n\n\nDOCUMENTATION = r\"\"\"\n---\nmodule: spaces_info\n\nshort_description: List all of the Spaces in your account\n\nversion_added: 0.5.0\n\ndescription:\n  - List all of the Spaces in your account.\n  - View the documentation at U(https://www.digitalocean.com/products/spaces).\n\nauthor: Mark Mercado (@mamercad)\n\nrequirements:\n  - pydo >= 0.1.3\n  - azure-core >= 1.26.1\n  - boto3 >= 1.28.53\n\noptions:\n  name:\n    description:\n      - The short name of the Space to list.\n      - If not provided all the Spaces in C(region) will be returned.\n      - |\n        For example, given C(name=my-do-space) and C(region=nyc3), the managed\n        Space will be U(https://my-do-space.nyc3.digitaloceanspace.com).\n    required: false\n    type: str\n  region:\n    description:\n      - The region in which to list Spaces.\n      - The C(SPACES_REGION) environment variable will be used.\n    required: false\n    type: str\n  aws_access_key_id:\n    description:\n      - The AWS_ACCESS_KEY_ID to use for authentication.\n      - The C(AWS_ACCESS_KEY_ID) environment variable will be used.\n    required: false\n    type: str\n  aws_secret_access_key:\n    description:\n      - The AWS_SECRET_ACCESS_KEY to use for authentication.\n      - The C(AWS_SECRET_ACCESS_KEY) environment variable will be used.\n    required: false\n    type: str\n\nextends_documentation_fragment:\n  - digitalocean.cloud.common.documentation\n\"\"\"\n\n\nEXAMPLES = r\"\"\"\n- name: Get all Spaces in nyc3\n  digitalocean.cloud.spaces_info:\n    aws_access_key_id: \"{{ aws_access_key_id}}\"\n    aws_secret_access_key: \"{{ aws_secret_access_key}}\"\n    region: \"nyc3\"\n\n- name: Get Space my-do-space in nyc3\n  digitalocean.cloud.spaces_info:\n    aws_access_key_id: \"{{ aws_access_key_id}}\"\n    aws_secret_access_key: \"{{ aws_secret_access_key}}\"\n    name: \"my-do-space\"\n    region: \"nyc3\"\n\"\"\"\n\n\nRETURN = r\"\"\"\nspaces:\n  description: Current spaces.\n  returned: always\n  type: list\n  elements: dict\n  sample:\n    endpoint_url: https://nyc3.digitaloceanspaces.com\n    name: my-do-space\n    region: nyc3\n    space_url: https://my-do-space.nyc3.digitaloceanspaces.com\nerror:\n  description: DigitalOcean Spaces boto response metadata.\n  returned: failure\n  type: dict\n  sample:\n    RequestId: 1234567890ABCDEF\n    HostId: Host ID data as a hash\n    HTTPStatusCode: 400\n    HTTPHeaders: Header metadata key/values\n    RetryAttempts': 0\nmsg:\n  description: Spaces result information.\n  returned: always\n  type: str\n  sample:\n    - Current Spaces in nyc3\n    - Existing Space named my-do-space in nyc3\n    - No Spaces in sfo3\n    - No Space named my-do-space in nyc3\n    - Failed to list Spaces in nyc3\n\"\"\"\n\nfrom ansible.module_utils.basic import AnsibleModule\nfrom ansible.module_utils.basic import env_fallback\nfrom ansible_collections.digitalocean.cloud.plugins.module_utils.common import (\n    DigitalOceanCommonModule,\n    DigitalOceanOptions,\n)\nfrom ansible_collections.digitalocean.cloud.plugins.module_utils.spaces import Spaces\n\n\nclass SpacesInformation(DigitalOceanCommonModule):\n    def __init__(self, module):\n        super().__init__(module)\n        self.name = module.params.get(\"name\")\n        self.region = module.params.get(\"region\")\n        self.aws_access_key_id = module.params.get(\"aws_access_key_id\")\n        self.aws_secret_access_key = module.params.get(\"aws_secret_access_key\")\n        self.spaces = Spaces(module)\n        if self.state == \"present\":\n            self.present()\n\n    def present(self):\n        spaces = self.spaces.get()\n        if spaces:\n            if self.name:\n                self.module.exit_json(\n                    changed=False,\n                    msg=f\"Existing Space named {self.name} in {self.region}\",\n                    spaces=spaces,\n                )\n            self.module.exit_json(\n                changed=False, msg=f\"Current Spaces in {self.region}\", spaces=spaces\n            )\n\n        if self.name:\n            self.module.exit_json(\n                changed=False,\n                msg=f\"No Space named {self.name} in {self.region}\",\n                spaces=spaces,\n            )\n        self.module.exit_json(\n            changed=False, msg=f\"No Spaces in {self.region}\", spaces=spaces\n        )\n\n\ndef main():\n    argument_spec = DigitalOceanOptions.argument_spec()\n    argument_spec.update(\n        name=dict(type=\"str\", required=False),\n        region=dict(\n            type=\"str\", fallback=(env_fallback, [\"SPACES_REGION\"]), required=False\n        ),\n        aws_access_key_id=dict(\n            type=\"str\",\n            fallback=(\n                env_fallback,\n                [\n                    \"AWS_ACCESS_KEY_ID\",\n                ],\n            ),\n            required=False,\n        ),\n        aws_secret_access_key=dict(\n            type=\"str\",\n            fallback=(\n                env_fallback,\n                [\n                    \"AWS_SECRET_ACCESS_KEY\",\n                ],\n            ),\n            no_log=True,\n            required=False,\n        ),\n        client_override_options=dict(type=\"dict\", required=False),\n        module_override_options=dict(type=\"dict\", required=False),\n    )\n    module = AnsibleModule(argument_spec=argument_spec, supports_check_mode=True)\n    SpacesInformation(module)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"digitalocean/ansible-collection","sub_path":"plugins/modules/spaces_info.py","file_name":"spaces_info.py","file_ext":"py","file_size_in_byte":5325,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"76"}
+{"seq_id":"20398578620","text":"import argparse\nfrom wsgiref import headers\nimport trsfile\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom tqdm import tqdm\nimport time\nimport random\nimport os\n\nap = argparse.ArgumentParser()\nap.add_argument('-f', '--file', type=str, default='./traces/big/file.trs', help='Path to trs file. Default is ./traces/trace.trs')\nargs = ap.parse_args()\n\nwindow_size = int(1000)\nstep_size = int(window_size/10)\n\nx_test = np.load(\"./x_test.npy\")\ny_test = np.load(\"./y_test.npy\")\n\nx_train = np.load(\"./x_train.npy\")\ny_train = np.load(\"./y_train.npy\")\n\ntrace_dir = \"./traces/big\"\n\nn_traces = 350\ntrace_length = 250000\nn_train_per_file = 280\nn_test_per_file = 70\n\n\nwhile 1:\n    files = os.listdir(trace_dir)\n    files = [os.path.join(trace_dir,file) for file in files]\n    if files != []:\n        max_file = max(files, key= lambda x: os.stat(x).st_size) \n\n    #print(len(files), max_file)\n\n    #Wait for 2 files to be in so that at least 1 of them is full\n    if(len(files) < 2):\n        time.sleep(5)\n        continue\n\n    \n\n    with trsfile.open(max_file, 'r') as traces:\n        # Show all headers\n        # for header, value in traces.get_headers().items():\n        #     print(header, '=', value)\n            \n        # print()\n        \n        print(\"processing : \", max_file)\n        for i in tqdm(range(len(traces))):\n            trace = traces[i]\n            new_trace= []\n            n_train = 0\n            n_test = 0\n\n            #ABS and WIN_RES\n            for j in (range(int((len(trace)-window_size)/step_size))):\n                result = np.average(np.absolute(trace[step_size*j:step_size*j+window_size]))\n                new_trace += [result]\n            #0 padding or crop\n            new_trace = new_trace[:trace_length] + [0]*(trace_length- len(new_trace))\n            #print([new_trace[:]])\n\n            #ADD to train / test file\n            if((random.random() < 0.8 or n_test == n_test_per_file) and n_train < n_train_per_file):\n                x_train = np.append(x_train, [new_trace[:]], axis=0)\n                y_train = np.append(y_train, [max_file.split('_')[1:6]], axis=0)\n                n_train += 1\n            else:\n                x_test = np.append(x_test, [new_trace[:]], axis=0)\n                y_test = np.append(y_test, [max_file.split('_')[1:6]], axis=0) \n                n_test += 1\n\n\n        #save the files\n        np.save(\"./x_train.npy\", x_train)\n        np.save(\"./y_train.npy\", y_train)\n\n        np.save(\"./x_test.npy\", x_test)\n        np.save(\"./y_test.npy\", y_test)\n\n        #remove trs\n        os.remove(max_file)\n\n\n\n\n\n\n\n","repo_name":"ThomasBuzer/Master_thesis","sub_path":"Vitis_files/pico/ABS_WR.py","file_name":"ABS_WR.py","file_ext":"py","file_size_in_byte":2568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"40900723036","text":"from os.path import dirname, join\nfrom PyQt5.QtWidgets import QWidget\nfrom PyQt5.QtWidgets import QGridLayout\nfrom PyQt5.QtWidgets import QTextBrowser\nfrom PyQt5.QtWidgets import QSplitter\nfrom PyQt5.QtWidgets import QTabWidget\nfrom PyQt5.QtWidgets import QSizePolicy\nfrom PyQt5.QtWidgets import QFileDialog\nfrom PyQt5.QtWidgets import QDesktopWidget\nfrom PyQt5.QtWidgets import QMessageBox\nfrom PyQt5.QtWidgets import QMenuBar\nfrom PyQt5.QtWidgets import QMenu\nfrom PyQt5.QtGui import QIcon\nfrom PyQt5.QtCore import Qt\nfrom cloudcrypt import cc_con\nimport cloudcrypt.cc_texts as cc_texts\nimport cloudcrypt.cc_help as cc_help\nfrom cloudcrypt.cc_advset_tab import AdvSetTab\nfrom cloudcrypt.cc_norset_tab import NorSetTab\n\n\nclass Gui(QWidget):\n\n    def __init__(self, config, core, language='de'):\n        super(Gui, self).__init__()\n\n        # setup the translation object\n        self.language = language\n        self.tran = cc_texts.Texts(self.language)\n        self.helper = cc_help.Help(self.language)\n        self.configparser = config\n        self.config = config.read_config()\n        self.core_ref = core\n        self.messages = set()\n        self.thread = None\n        self.mainLayout = None\n        self.mainsplitter = None\n        self.maintabwidget = None\n        self.status_line = None\n        self.settab = None\n        self.advtab = None\n        self.keytab = None\n        self.ugtab = None\n        self_selected_tab = None\n\n        # set the main window name and icon\n        self.setWindowTitle(self.tran.get_text(\"title\"))\n        self.setWindowIcon(QIcon(join(dirname(__file__), \"resources/Logo_v2.png\")))\n\n        # configure the main window\n        self.setObjectName('main_window')\n        self.resize(800, 700)\n        self.center()\n\n        self.status_line = QTextBrowser(self.mainsplitter)\n        self.status_line.setObjectName('status_line')\n\n        # setup the main layout\n        self.mainLayout = QGridLayout(self)\n        self.mainLayout.setObjectName('main_layout')\n\n        # setup the main splitter\n        self.mainsplitter = QSplitter(self)\n        self.mainsplitter.setOrientation(Qt.Vertical)\n        self.mainsplitter.setObjectName('main_splitter')\n\n        # setup the tab widget\n        self.maintabwidget = QTabWidget(self.mainsplitter)\n        self.maintabwidget.setObjectName('main_tab_widget')\n\n        # make sure, the tab are is bigger than the message area\n        self.maintabwidget_sizepolicy = QSizePolicy()\n        self.maintabwidget_sizepolicy.setVerticalStretch(2)\n        self.maintabwidget.setSizePolicy(self.maintabwidget_sizepolicy)\n        self.maintabwidget.currentChanged.connect(self._sync)\n\n        # setup the settings tab\n        self.settab = NorSetTab(self)\n        self._selected_tab = self.settab\n        self.advtab = AdvSetTab(self)\n        self.maintabwidget.addTab(self.settab, self.tran.get_text('settings_title'))\n        self.settab.init()\n\n        # setup the status line\n        self.status_line = QTextBrowser(self.mainsplitter)\n        self.status_line.setObjectName('status_line')\n\n        # add the elements to the main layout\n        self.mainLayout.addWidget(self.mainsplitter, 0, 0, 1, 1)\n        self.menubar = QMenuBar()\n        self.help_menu = QMenu(self.tran.get_text('help_menu_title'), self)\n        self.help_action = self.help_menu.addAction(self.tran.get_text('help_menu_action'))\n        self.help_action.triggered.connect(self.open_help_page)\n        self.menubar.addMenu(self.help_menu)\n        self.mainLayout.setMenuBar(self.menubar)\n\n        self.set_style_sheet()\n\n    def set_style_sheet(self):\n        if cc_con.THEME in self.config:\n            try:\n                with open(cc_con.THEME_PREFIX + self.config[cc_con.THEME] + cc_con.THEME_POSTFIX, \"r\") as theme:\n                    self.setStyleSheet(theme.read())\n            except EnvironmentError:\n                print(\"ERROR: Theme not found, using default theme\")\n                with open(cc_con.THEME_PREFIX + self.config[cc_con.THEME_DEFAULT] + cc_con.THEME_POSTFIX, \"r\") as theme:\n                    self.setStyleSheet(theme.read())\n\n    def show_adv_tab(self, onoff):\n        \"\"\"\n        Show or hide the advanced settings tab\n\n        :param onoff: True to show the advanced settings tab and false to hide it\n        :return: None\n        \"\"\"\n        # check if the advanced tab is already in the tablist\n        tab_in_tablist = True if self.maintabwidget.indexOf(self.advtab) != -1 else False\n        if onoff and not tab_in_tablist:\n            self.maintabwidget.addTab(self.advtab, self.tran.get_text('advanced_title'))\n        elif tab_in_tablist:\n            self.maintabwidget.removeTab(self.maintabwidget.indexOf(self.advtab))\n\n    def browse_config(self):\n        \"\"\"\n        Opens a file browser to select the config file and\n        sets the QEditLine according to the selection.\n\n        :return: None\n        \"\"\"\n        filename, _ = QFileDialog.getOpenFileName(self, self.tran.get_text('adv_tab_button_config_desc'),\n                self.advtab_input_config.text(), \"XML (*.xml)\")\n        if filename:\n            self.advtab_input_config.setText(str(filename))\n\n    def get_config_path(self):\n        if self.advtab_input_config == \"\":\n            return None\n        else:\n            return self.advtab_input_config.text()\n\n    def get_plain_path(self):\n        if self.settab_input_clear == \"\":\n            return None\n        else:\n            return self.settab_input_clear.text()\n\n    def get_cipher_path(self):\n        if self.settab_input_enc == \"\":\n            return None\n        else:\n            return self.settab_input_enc.text()\n\n    def open_help_page(self):\n        self.helper.open_help(\"index.html\", \"\")\n\n    def write_config(self):\n        self.messages.add(str(self.config))\n        self.messages.add(self.tran.get_text('writing_config'))\n        self._renew_message_area()\n        self.configparser.write_config(self.config)\n\n    def _renew_message_area(self):\n        self.status_line.setText(\"\")\n        for message in self.messages:\n            self.status_line.append(message)\n\n    def center(self):\n        qr = self.frameGeometry()\n        cp = QDesktopWidget().availableGeometry().center()\n        qr.moveCenter(cp)\n        self.move(qr.topLeft())\n\n    def add_message(self, message):\n        self.messages.add(message)\n        self._renew_message_area()\n\n    def del_message(self, message):\n        \"\"\"\n        Remove a message from the message area\n\n        :param message: the message to remove\n        :return: True if the message was removed sucessfully and False if the message was not there to begin with\n        \"\"\"\n        try:\n            self.messages.remove(message)\n            self._renew_message_area()\n            return True\n        except KeyError:\n            return False\n\n    def str_to_bool(self, text):\n        if text in 'True':\n            return True\n        else:\n            return False\n\n    def _sync(self):\n        self.config = self._selected_tab.config()\n        self.configparser.write_config(self._selected_tab.config())\n        if self.maintabwidget.count() > 0:\n            self.maintabwidget.currentWidget().init()\n            if self.settab.is_running():\n                self.maintabwidget.currentWidget().lock()\n            else:\n                self.maintabwidget.currentWidget().unlock()\n            if self.settab.tooltip_state():\n                self.maintabwidget.currentWidget().toggle_tooltips(\"True\")\n            else:\n                self.maintabwidget.currentWidget().toggle_tooltips(\"False\")\n            self._selected_tab = self.maintabwidget.currentWidget()\n\n    def closeEvent(self, event):\n        reply = QMessageBox.question(None, '', self.tran.get_text('exit_cloudcrypt'), QMessageBox.Yes | QMessageBox.No,\n                                     QMessageBox.No)\n        self.write_config()\n        if reply == QMessageBox.Yes:\n            self.settab.slider_moved(\"off\")\n            event.accept()\n        else:\n            event.ignore()\n","repo_name":"Nelwidio/CloudCrypt","sub_path":"cloudcrypt/cc_gui.py","file_name":"cc_gui.py","file_ext":"py","file_size_in_byte":8013,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"12905626665","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n\r\n\"\"\"\r\n@Author: Byoungkwon Yoon\r\n@Contact: dbss1126@gmail.com\r\n@File: eval_util.py\r\n@Time: 2021/08/10 6:35 PM\r\n\"\"\"\r\n\r\nimport numpy as np\r\nfrom sklearn.cluster import DBSCAN\r\nimport math\r\nfrom scipy.optimize import curve_fit\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nfrom sklearn.neighbors import NearestNeighbors\r\nimport csv\r\n\r\n\r\nclass PathTracking():\r\n  def __init__(self, data, knn_search=7):\r\n    link_num = len(data['Link'].keys()) # with 4th index as 1\r\n    joint_num = len(data['Joint'].keys()) # with 4th index as 2\r\n    #tot_skel=np.zeros((link_num+joint_num+gel_num+2,4))\r\n    link_stack = np.empty((0,4))\r\n    for i in range(link_num):\r\n      tmp=data['Link'][str(i)]['points']\r\n      pos_ind = np.concatenate((tmp, np.tile(1, (len(tmp),1))), axis=1)\r\n      link_stack = np.append(link_stack, pos_ind, axis=0) \r\n    joint_stack = np.empty((0,4))\r\n    for i in range(joint_num):\r\n      tmp = data['Joint'][str(i)]['points']\r\n      pos_ind = np.concatenate((tmp, np.tile(2, (len(tmp),1))), axis=1) \r\n      joint_stack = np.append(joint_stack, pos_ind, axis=0)\r\n    gel_ind=0 \r\n    gel_stack = np.concatenate((data['Gel'], np.tile(gel_ind, (len(data['Gel']),1))), axis=1)\r\n\r\n    self.tot_skel = np.concatenate((link_stack, joint_stack, gel_stack), axis=0)\r\n    self.tip_skel = data['Tipcell']\r\n    self.knn_search = knn_search # should substract 1 for final search range(excepting itself)\r\n    self.nbrs_knn = NearestNeighbors(n_neighbors=3*knn_search, algorithm='ball_tree').fit(self.tot_skel[:,:3])\r\n    self.nbrs = NearestNeighbors(n_neighbors=1, algorithm='ball_tree').fit(self.tot_skel[:,0:3])\r\n  \r\n  def dir_knn(self, n, pt, ptcloud, dir): \r\n    indices = self.nbrs_knn.kneighbors(np.expand_dims(pt,axis=0))[1] #take index only\r\n    indices = indices[:,1:] \r\n    next_pt = np.squeeze(ptcloud[indices, :], axis=0)\r\n    nxt_dir = next_pt[:,:3] - pt\r\n    dot = np.inner(nxt_dir,dir)\r\n    ind = np.array([i for i in range(dot.size) if dot[i]>0]) #In the same direction!!\r\n    if ind.size>n:\r\n      ind=ind[:n]\r\n    return next_pt[ind, :]\r\n  \r\n  def Regression3D(self, A):\r\n    #input should be nX3 matrix \r\n    arr0 = np.transpose(A[-1,:])\r\n    mat = np.transpose(A) - np.expand_dims(arr0, axis=1)\r\n    U = np.linalg.svd(mat)[0]\r\n    d = U[:,0]\r\n    t = np.transpose(d)@mat\r\n    fin = np.repeat(np.expand_dims(t,axis=0), 3, axis=0)*np.expand_dims(d, axis=1) + np.repeat(np.expand_dims(arr0,axis=1), len(A), axis=1)\r\n    return fin\r\n\r\n  def __call__(self, tip_index):\r\n    temp= self.tip_skel[str(tip_index)]['points'] \r\n    temp = np.expand_dims(np.mean(temp, axis=0), axis=0)\r\n    temp = np.concatenate((temp, [[3]]), axis=1) \r\n\r\n    #Find 1st nearest point\r\n    path_list= np.empty((0,4))\r\n    path_list = np.append(path_list, temp, axis=0)\r\n\r\n    indices = self.nbrs.kneighbors(temp[:,:3])[1] #take index only \r\n    next_pt = self.tot_skel[indices, :]\r\n    next_pt = np.squeeze(next_pt, axis=0)\r\n    path_list = np.append(path_list, next_pt, axis=0) \r\n\r\n    # Tracking\r\n    step=1\r\n    while (True): \r\n      #Second index \r\n      momentum_range = 5 #range of tail \r\n      if (step > momentum_range): \r\n        temp_dir = np.empty((0,3)) \r\n        #3D linear regression\r\n        pt_reg = path_list[step-momentum_range:step+1, :3] \r\n        reg_lin = self.Regression3D(pt_reg)\r\n        temp_direc = reg_lin[:, -1] - reg_lin[:, -2]\r\n        n_temp_direc = temp_direc/np.linalg.norm(temp_direc, axis =0)\r\n      elif ((momentum_range>= step) and (step>2)):\r\n        temp_dir = np.empty((0,3))\r\n        #3D linear regression with 3 tails\r\n        pt_reg = path_list[step-2:step+1, :3]\r\n        reg_lin = self.Regression3D(pt_reg) \r\n        temp_direc = reg_lin[:, -1] - reg_lin[:, -2]\r\n        n_temp_direc = temp_direc/np.linalg.norm(temp_direc, axis =0)\r\n    \r\n      else: \r\n        temp_direc =  path_list[step,:3] - path_list[step-1,:3]\r\n        n_temp_direc = temp_direc/np.linalg.norm(temp_direc, axis =0)\r\n    \r\n  \r\n      #directional\r\n      nxt_candidate_wlabel = self.dir_knn(self.knn_search, path_list[step,:3], self.tot_skel, n_temp_direc)\r\n  \r\n      nxt_candidate = nxt_candidate_wlabel[:, 0:3]\r\n      candidate_direc = nxt_candidate - path_list[step, :3]\r\n      n_candidate_direc = candidate_direc /np.linalg.norm(candidate_direc, axis =1)[:,None]\r\n      dir_change = n_candidate_direc - n_temp_direc\r\n      dir_change[:,1] = 1*dir_change[:,1] #weighting: multiplying constant infront of it\r\n      direc_dist = np.linalg.norm(dir_change, axis =1)\r\n  \r\n      k=1\r\n      try:\r\n        #pull-down if you can \r\n        if (path_list[step,3] == 2): #if current anchor point is joint\r\n          min_ind = np.argmin(direc_dist)\r\n          tmp_pt = np.expand_dims(nxt_candidate_wlabel[min_ind,:], axis=0) \r\n          while ((tmp_pt[0,1] - path_list[step,1])>0):\r\n            k+=1 \r\n            #pull-down \r\n            sorted_ind = np.argsort(direc_dist)[:k]\r\n            min_ind = sorted_ind[k-1]\r\n            tmp_pt = np.expand_dims(nxt_candidate_wlabel[min_ind,:], axis=0)\r\n        else: \r\n          min_ind = np.argmin(direc_dist)\r\n      except:\r\n        #if error occurs? stop pull-down\r\n        min_ind = np.argmin(direc_dist)\r\n\r\n      nxt_pt = np.expand_dims(nxt_candidate_wlabel[min_ind,:], axis=0)\r\n      path_list = np.append(path_list, nxt_pt, axis=0)\r\n      step+=1\r\n      if (nxt_candidate_wlabel[min_ind, 3]==0):\r\n        #stop if point arrives gel / or iteration exceeds the limit\r\n        if len(path_list)<5:\r\n            path_list = np.empty((0,4))\r\n        break\r\n      elif (step>5000):\r\n          path_list = np.empty((0,4))\r\n          break\r\n    return path_list\r\n\r\ndef Clustering(points, eps, minpts):\r\n    ### DBSCAN\r\n    model = DBSCAN(eps, min_samples=minpts)  ### DBSCAN Parameters\r\n    model.fit_predict(points)\r\n    prediction = model.fit_predict(points)\r\n    labels = model.labels_\r\n    \r\n    return labels\r\n\r\ndef ProcessJoint(points, eps, minpts, target, points_gel, points_joint, points_link, points_noise, points_tip):\r\n    labels_joint = Clustering(points, eps, minpts)\r\n    \r\n    \r\n    ###### KNN for noise Clustered joint\r\n    noise_idx = np.where(labels_joint==-1)[0]\r\n    noise_points = points_joint[noise_idx]\r\n    classifier = KNeighborsClassifier(n_neighbors = 5)\r\n   \r\n    ###### make KNN data\r\n    temp_points = np.concatenate((points_gel,points_link,points_noise,points_tip),axis=0)  \r\n    temp_type = np.sort(target)\r\n    temp_type = np.delete(temp_type, np.where(temp_type==1))\r\n    classifier.fit(temp_points, temp_type)\r\n    guesses = classifier.predict(noise_points)\r\n    \r\n    t = target\r\n    joint_index = np.where(target==1)[0]\r\n    for i in range(len(noise_idx)):\r\n        t[joint_index[noise_idx[i]]] = guesses[i]\r\n        \r\n    labels_joint = np.delete(labels_joint, noise_idx)\r\n    \r\n    return labels_joint, t \r\n\r\ndef ProcessGel(points):\r\n    labels_gel = Clustering(points, 35, 5)\r\n    \r\n    return labels_gel\r\n\r\ndef ProcessTipcell(points):\r\n    labels_tip = Clustering(points, 35, 0)\r\n    \r\n    return labels_tip\r\n\r\ndef func(X,a,b,c,d):  ### Set Function type\r\n\r\n    return a*(X**3) + b*(X**2) + c*(X**1) + d\r\n\r\ndef LinkLength(inpoints, labels):\r\n    length = []  ## Length\r\n    endToend = [] ## End to End Length\r\n    \r\n    EndPoints = [] ## End Points of Link\r\n    \r\n    Density = [] ## Density of Links, for clustering evaluation\r\n    ### Fitting And Length Calculation\r\n    for i in range(labels.max()+1):\r\n        data = inpoints[labels == i]  ## Get Pos of 1 link\r\n    \r\n        x = data[:,0] ## Get X coord\r\n        y = data[:,1] ## Get Y coord\r\n        z = data[:,2] ## Get Z coord\r\n        \r\n        ## sort pos by y value\r\n        s = y.argsort()  \r\n        x = x[s]\r\n        y = y[s]\r\n        z = z[s]\r\n            \r\n        data[:,0] = x ## Overlab sorted Coord\r\n        data[:,1] = y\r\n        data[:,2] = z\r\n        color = 'red'\r\n        if len(x) > 10:   ## no fit for link less than 10 pts\r\n        \r\n            if len(x) > 50:  ## pts>50 : cut in half and fit\r\n                color = 'lime'\r\n                n1 = math.ceil(len(x)/2) ## Cut Index\r\n                tempIdx = [np.arange(n1+10),np.arange(n1-10,len(x))]  ### [[0 1 2 3 4 5][6 7 8 9 10]] shape. used in next FOR\r\n            else:  ## else: fit at once\r\n                color = 'red'\r\n                tempIdx = [np.arange((len(x)))]\r\n                \r\n                \r\n            ### list of fitted points\r\n            xnew = []\r\n            ynew = []\r\n            znew = []\r\n            \r\n            \r\n            for j in tempIdx:\r\n                tempx = x[j]  ## load points\r\n                tempy = y[j]\r\n                tempz = z[j]\r\n                \r\n                u = np.arange(len(tempx)) ## make artificial axis for fitting\r\n          \r\n                poptx, pcovx = curve_fit(func, u, tempx) ## fit n vs x\r\n                    \r\n                tempXnew = func(u,poptx[0],poptx[1],poptx[2],poptx[3])\r\n                xnew.append(tempXnew)\r\n                    \r\n                popty, pcovy = curve_fit(func, u, tempy) ## fit n vs y\r\n                    \r\n                tempYnew = func(u,popty[0],popty[1],popty[2],popty[3])\r\n                ynew.append(tempYnew)\r\n                 \r\n                poptz, pcovz = curve_fit(func, u, tempz) ## fit n vs z\r\n                    \r\n                tempZnew = func(u,poptz[0],poptz[1],poptz[2],poptz[3])\r\n                znew.append(tempZnew)\r\n        \r\n            if len(xnew) == 2:\r\n                xnew = np.concatenate((xnew[0][:n1],xnew[1][10:]))\r\n                ynew = np.concatenate((ynew[0][:n1],ynew[1][10:]))\r\n                znew = np.concatenate((znew[0][:n1],znew[1][10:]))\r\n            \r\n            else:\r\n                xnew = xnew[0]\r\n                ynew = ynew[0]\r\n                znew = znew[0]\r\n                \r\n        else:\r\n            \r\n            ## links with pts<10 : no fitting\r\n            \r\n            xnew = x\r\n            ynew = y\r\n            znew = z\r\n                    \r\n        ##calculate length\r\n        \r\n        endLength = math.sqrt(pow(abs(xnew[0]-xnew[-1]),2) \r\n                              + pow(abs(ynew[0]-ynew[-1]),2) \r\n                              + pow(abs(znew[0]-znew[-1]),2))\r\n        \r\n        EndPoints.append([xnew[0],ynew[0],znew[0]]) ## Append End Points\r\n        EndPoints.append([xnew[-1],ynew[-1],znew[-1]]) ## Append End Points\r\n        \r\n        tempLength = 0\r\n        for j in range(len(x)-1):\r\n            tempLength += math.sqrt(pow(abs(xnew[j]-xnew[j+1]),2)\r\n                                    + pow(abs(ynew[j]-ynew[j+1]),2)\r\n                                    + pow(abs(znew[j]-znew[j+1]),2))\r\n        \r\n        length.append(tempLength)\r\n\r\n        endToend.append(endLength)\r\n        \r\n        Density.append(len(x)/endLength)\r\n        \r\n    return length, endToend, Density, EndPoints\r\n\r\ndef ProcessLink(inpoints, z_multiple, eps1, eps2):\r\n    inpoints[:,2] = inpoints[:,2]*z_multiple  ## multiply z coord for seperation\r\n    labels_link = Clustering(inpoints, eps1, 3)  ## Clustering Links\r\n    \r\n    inpoints[:,2]= inpoints[:,2]/z_multiple   ## restore z coord\r\n       \r\n    length, endToend, Density, EndPoints= LinkLength(inpoints,labels_link)  #### 1st Length Calc\r\n    \r\n    avg_density = np.average(Density)  ### Find Average Link Density\r\n    labels_repeat = np.where(Density>avg_density)[0] ### Re clusteing High density links\r\n    \r\n    first_labels_link = labels_link[:]\r\n       \r\n    #### Sepereate HD/LD Link######\r\n    HDLink = np.array(inpoints[np.where(labels_link==0)])  ###Link points with High Density\r\n    for i in labels_repeat[1:]:\r\n        HDLink = np.append(HDLink, inpoints[np.where(labels_link==i)],axis=0)\r\n    LDLink = inpoints[:]\r\n    \r\n    a1_rows = LDLink.view([('', LDLink.dtype)] * LDLink.shape[1])\r\n    a2_rows = HDLink.view([('', HDLink.dtype)] * HDLink.shape[1])\r\n    LDLink = np.setdiff1d(a1_rows, a2_rows).view(LDLink.dtype).reshape(-1, LDLink.shape[1])\r\n    \r\n    ##### Cluster HD/LD Link#####\r\n    LDLink[:,2] = LDLink[:,2]*z_multiple\r\n    HDLink[:,2] = HDLink[:,2]*z_multiple\r\n    labels_LDLink = Clustering(LDLink, eps1, 3)\r\n    labels_HDLink = Clustering(HDLink, eps2, 3)\r\n    LDLink[:,2]= LDLink[:,2]/z_multiple\r\n    HDLink[:,2]= HDLink[:,2]/z_multiple\r\n    \r\n    noiseIndex = np.where(labels_HDLink==-1)[0]\r\n    labels_HDLink = labels_HDLink+(1+max(labels_LDLink))\r\n    labels_HDLink[noiseIndex] = -1\r\n    \r\n    labels_link = np.concatenate((labels_LDLink,labels_HDLink))\r\n    final_link_points = np.concatenate((LDLink,HDLink),axis=0) \r\n    \r\n    length, endToend, Density,EndPoints = LinkLength(final_link_points,labels_link)\r\n        \r\n        \r\n    return labels_link, length, endToend, EndPoints, Density, [LDLink, HDLink], final_link_points, first_labels_link\r\n        \r\n\r\n\r\ndef VesselEval(chambersData, out_path):\r\n    \r\n    OutputData = []\r\n    PostData = []\r\n    \r\n    for b in range(len(chambersData)):\r\n        \r\n        chamber = b\r\n        print(chamber)\r\n        data = chambersData[chamber]\r\n        points = data['point']\r\n        ids = data['ids']\r\n        prediction = data['pred']\r\n\r\n        \r\n        ####### Set Target Value ######\r\n        target = prediction\r\n        \r\n        \r\n\r\n        ######## GET True/ Pred DATA ######\r\n        points_gel = points[target==0]\r\n        points_joint = points[target==1]\r\n        points_link = points[target==2]\r\n        points_tip = points[target==4]\r\n        points_noise = points[target==3]\r\n        \r\n        ##### Cluster / Process Pts ######\r\n        labels_joint, target = ProcessJoint(points_joint,30,5,target, points_gel, points_joint, points_link, points_noise, points_tip)\r\n        points_gel = points[target==0]\r\n        points_joint = points[target==1]\r\n        points_link = points[target==2]\r\n        points_tip = points[target==4]\r\n        points_noise = points[target==3]\r\n    \r\n        labels_link, length, endToend, LinkEnds, LinkDensity, temp3, repts, first_labels_link = ProcessLink(points_link, 3, 25,18)\r\n        points_link = repts\r\n        labels_gel = ProcessGel(points_gel)\r\n        labels_tip = ProcessTipcell(points_tip)\r\n    \r\n    \r\n        link_index = np.where(target==2)[0]\r\n        target = np.delete(target,link_index) ### ReConstruct With new Link Points\r\n        points = np.delete(points, link_index,axis=0)\r\n        points = np.concatenate((points,points_link),axis=0)\r\n        target = np.concatenate((target,[2]*len(points_link)))\r\n        \r\n        \r\n        \r\n        link_index = np.where(target==2)[0]\r\n        Points_Density = np.array([[0]]*len(points),dtype=np.float32)\r\n        for i in range(len(LinkDensity)):\r\n            Points_Density[link_index[np.where(labels_link==i)]] = LinkDensity[i]\r\n    \r\n        \r\n        ######## Write to Dic ##########\r\n        dic = {'Joint':[],'Link':[],'Tipcell':[]}\r\n        ## Write joint data\r\n        temp = {}\r\n        for i in range(labels_joint.max()+1):\r\n            temp_idx = np.where(labels_joint == i)\r\n            temp[str(i)] = {'points':points_joint[temp_idx]}     \r\n        dic['Joint'] = temp\r\n    \r\n        ## Write link data\r\n        temp = {}\r\n        for i in range(labels_link.max()+1):\r\n            temp_idx = np.where(labels_link == i)\r\n            Tortuosity = length[i]/endToend[i]\r\n            temp[str(i)] = {'points':points_link[temp_idx],'Length':length[i],'EndToEnd':endToend[i],'Tortuosity':Tortuosity}\r\n        dic['Link'] = temp\r\n        \r\n        ## Write Tipcell data\r\n        temp = {}\r\n        for i in range(labels_tip.max()+1):\r\n            temp_idx = np.where(labels_tip == i)\r\n            temp[str(i)] = {'points':points_tip[temp_idx],}     \r\n        dic['Tipcell'] = temp\r\n    \r\n        dic['Gel'] = points_gel\r\n        dic['Noise'] = points_noise\r\n        \r\n        \r\n        \r\n            ##### Path Find #####\r\n        path = []\r\n    \r\n        pt_chamber0 = PathTracking(dic)\r\n        \r\n        \r\n        for i in range(labels_tip.max()+1):\r\n            path.append([])\r\n            try:\r\n                path[-1] = (pt_chamber0(i))\r\n            except:\r\n                pass\r\n        \r\n        ##### Sproting Length #####\r\n        Sp_Length = []\r\n        for i in range(labels_tip.max()+1):\r\n            Sp_Length.append([])\r\n            if len(path[i]) != 0 :\r\n                temp_Length = 0\r\n                temp_points = path[i][:,:3]\r\n                for j in range(len(temp_points)-1):\r\n                    seg_len = math.sqrt(pow(abs(temp_points[j][0]-temp_points[j+1][0]),2)\r\n                                        + pow(abs(temp_points[j][1]-temp_points[j+1][1]),2)\r\n                                        + pow(abs(temp_points[j][2]-temp_points[j+1][2]),2))\r\n                    if (seg_len>500):\r\n                        seg_len = 0\r\n                    else:\r\n                        temp_Length += seg_len\r\n                Sp_Length[-1] = (temp_Length)\r\n            else:\r\n                Sp_Length[-1] = None\r\n            \r\n        ##### TipCell Level #####\r\n        Tip_Level = []\r\n        for i in range(labels_tip.max()+1):\r\n            Tip_Level.append([])\r\n            if len(path[i]) != 0 :\r\n                point_types = path[i][:,3]\r\n                joint_idx = np.where(point_types==2)[0]\r\n                if len(joint_idx) == 0:\r\n                    count = 0\r\n                else:\r\n                    count = 1\r\n                    for j in range(len(joint_idx)-1):\r\n                        if joint_idx[j] +1 !=  joint_idx[j+1]:\r\n                            count += 1\r\n                Tip_Level[-1] = count\r\n            else:\r\n                Tip_Level[-1] = None\r\n                \r\n                \r\n        #### Directionality #####\r\n        \r\n        def Linearfunc(X,a,b):  ### Set Function type\r\n            return a*X + b\r\n        \r\n        Directionality = [] \r\n        for i in range(labels_tip.max()+1):\r\n            Directionality.append([])\r\n            if len(path[i]) != 0 :    \r\n                temp_points = path[i][:,:3]\r\n        \r\n                tempx = temp_points[:,0]  ## load points\r\n                tempy = temp_points[:,1]\r\n                tempz = temp_points[:,2]\r\n                \r\n                u = np.arange(len(tempx)) ## make artificial axis for fitting\r\n                \r\n                poptx, pcovx = curve_fit(Linearfunc, u, tempx) ## fit n vs x              \r\n                xnew = Linearfunc(u,poptx[0],poptx[1])\r\n                \r\n                popty, pcovy = curve_fit(Linearfunc, u, tempy) ## fit n vs y             \r\n                ynew = Linearfunc(u,popty[0],popty[1])\r\n    \r\n                \r\n                poptz, pcovz = curve_fit(Linearfunc, u, tempz) ## fit n vs z               \r\n                znew = Linearfunc(u,poptz[0],poptz[1])\r\n    \r\n                VectorLength = math.sqrt(pow(abs(xnew[0]-xnew[-1]),2)\r\n                                         + pow(abs(ynew[0]-ynew[-1]),2)\r\n                                         + pow(abs(znew[0]-znew[-1]),2))\r\n                tempVector = [(xnew[0]-xnew[-1])/VectorLength,\r\n                          (ynew[0]-ynew[-1])/VectorLength,\r\n                          (znew[0]-znew[-1])/VectorLength]\r\n            \r\n                degree = np.arctan2(tempVector[1],tempVector[0])\r\n                Directionality[-1] = degree\r\n            else:\r\n                Directionality[-1] = None\r\n        \r\n        ####tortuosity####\r\n        tortuosity = []\r\n        for i in range(len(length)):\r\n            tortuosity.append(length[i]/endToend[i])\r\n        \r\n        \r\n        ######## Write to Dic ##########\r\n        \r\n        dic['Directionality'] = Directionality\r\n        dic['TipCell_Level'] = Tip_Level\r\n        dic['Sproting_Length'] = Sp_Length\r\n        dic['path'] = path\r\n        \r\n        \r\n        Parameter_dic = {'Total Length':sum(length),\r\n                         'Link Length':length,\r\n                         'Joint Number':max(labels_joint)+1,\r\n                         'Tortousity':tortuosity,\r\n                         'Tipcell Number':max(labels_tip)+1,\r\n                         'Sprouting Length':Sp_Length,\r\n                         'Sprouting Direction':Directionality,\r\n                         'Tipcell Level':Tip_Level}\r\n        OutputData.append(Parameter_dic)\r\n\r\n        Post_dic = {'Total Length': Parameter_dic['Total Length'],\r\n                    'Link Length':np.array([k for k in (Parameter_dic['Link Length'][1:]) if k!=None]).mean(),\r\n                    'Joint Number':Parameter_dic['Joint Number'],\r\n                    'Tortousity':np.array(Parameter_dic['Tortousity']).mean(),\r\n                    'Tipcell Number':Parameter_dic['Tipcell Number'],\r\n                    'Sprouting Length':np.array([k for k in (Parameter_dic['Sprouting Length']) if k!=None]).mean(),\r\n                    'Sprouting Direction':np.array([k for k in (Parameter_dic['Sprouting Direction']) if k!=None]).mean(),\r\n                    'Tipcell Level':np.array([k for k in (Parameter_dic['Tipcell Level']) if k!=None]).mean()}\r\n        PostData.append(Post_dic)\r\n    \r\n\r\n    with open('checkpoints/'+out_path+'/'+'Result.csv',\"w\") as fw:\r\n         wr = csv.writer(fw)\r\n         wr.writerow(Post_dic.keys())\r\n         for i in PostData:\r\n             wr.writerow(i.values())\r\n\r\n    \r\n    return OutputData\r\n          \r\n          \r\n","repo_name":"won5830/Machine-learning-assisted-3D-angiogenesis-quantification","sub_path":"Total Pipeline/utils/eval_util.py","file_name":"eval_util.py","file_ext":"py","file_size_in_byte":21269,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"4510279230","text":"# -*- coding: utf-8 -*-\n\nfrom django.db import models\n\nfrom cms.models import CMSPlugin\n\nclass AlertsPluginModel(CMSPlugin):\n\n\talert_type = models.CharField('Alert Type',\n\t\tblank=False,\n\t\tdefault='warning',\n\t\thelp_text=u'Please Enter the class tag of the alert',\n\t\tmax_length=32,\n\t\t)\n\n\talert_title = models.CharField('Alert Title',\n\t\tblank=False,\n\t\tdefault='',\n\t\thelp_text=u'Please Enter the title of the alert',\n\t\tmax_length=64,\n\t\t)\n\n\talert_msg = models.CharField('Alert Message',\n\t\tblank=False,\n\t\tdefault='',\n\t\thelp_text=u'Please Enter the message of the alert',\n\t\tmax_length=225,\n\t\t)\n\n\tdef __unicode__(self):\n\t\treturn u'%s:%s:%s' % (self.alert_type, self.alert_title, self.alert_msg, )","repo_name":"causerdan/wet-boew-djangocms","sub_path":"wetdjango/apps/alerts/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"10361903547","text":"def calculate_age( year_of_birth, current_year ):\n    \n    age = current_year - year_of_birth\n    year = str( abs( age ) ) + \" year\"\n    \n    if abs( age ) > 1:\n        year += \"s\"\n        \n    if age > 0:\n        return \"You are \" + year + \" old.\"\n    elif age < 0:\n        return \"You will be born in \" + year + \".\"\n    else:\n        return \"You were born this very year!\"\n","repo_name":"locharp/code-snippets","sub_path":"Codewars/8 kyu/How old will I be in 2099?.py","file_name":"How old will I be in 2099?.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"4846088140","text":"'''\n1. 런타임에러 - 주로 배열 인덱스 실수\n    ㄴs[i][j % l] --> s[i][j % len(s[i])]\n    ㄴ 수포자의 주기가 답안보다 짧을 경우에 대한 예외처리 잘못 작성\n'''\ndef solution(answers):\n    answer = []\n    l = len(answers)\n    # 각각의 수포자 주기\n    s = [[1, 2, 3, 4, 5], [2, 1, 2, 3, 2, 4, 2, 5], [3, 3, 1, 1, 2, 2, 4, 4, 5, 5]]\n\n    # 채점표            \n    result = [0,0,0]\n    # 수포자의 답안지\n    for i in range(3):    \n        for j in range(l):\n            if answers[j] == s[i][j % len(s[i])]:\n                 result[i] += 1\n    \n    m = max(result)\n    for i in range(3):\n        if m == result[i]:\n            answer.append(i+1)\n\n    return answer\n\n'''\n1. 이전에 풀었던 문제 - 수포자 규칙성 상수로 저장\n   ㄴ 규칙성이 없기 때문에 동일하게 푸는 게 베스트일 듯\n2. 이전보다 깔끔하게 정리해봄.\n'''\n\ndef solution(answers):\n    student = [[],\n               [1, 2, 3, 4, 5],\n               [2, 1, 2, 3, 2, 4, 2, 5],\n               [3, 3, 1, 1, 2, 2, 4, 4, 5, 5]]\n    scores = [-1, 0, 0, 0]\n    \n    for i, a in enumerate(answers):\n        if student[1][i % len(student[1])] == a:\n            scores[1] += 1\n        if student[2][i% len(student[2])] == a:\n            scores[2] += 1\n        if student[3][i % len(student[3])] == a:\n            scores[3] += 1\n    \n    nmax = max(scores)\n    answer = [i for i, s in enumerate(scores) if s == nmax]\n    \n    return answer","repo_name":"HeeJeongOh/algorithm","sub_path":"programmers/p42840.py","file_name":"p42840.py","file_ext":"py","file_size_in_byte":1482,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"479074823","text":"import random\nimport sqlite3\n\nconn = sqlite3.connect('card.s3db')\ncur = conn.cursor()\ncur.execute(\"CREATE TABLE IF NOT EXISTS card (id INTEGER, number TEXT, pin TEXT, balance INTEGER DEFAULT 0);\")\nconn.commit()\n\n\nclass BankingSystem:\n\n    def __init__(self):\n        self.main_interface()\n\n    def main_interface(self):\n        self.user_input = input(\"\\n1. Create an account\\n2. Log into account\\n0. Exit\\n\")\n        if self.user_input == \"1\":\n            self.create_account()\n        elif self.user_input == \"2\":\n            self.login_account()\n        elif self.user_input == \"0\":\n            print(\"\\nBye!\")\n\n    def create_account(self):\n        self.card_num = \"400000\" + str(random.randint(0, 999999999)).zfill(9)\n        self.luhn_checksum(self.card_num)\n        self.card_num += self.last_digit\n        self.pin = str(random.randint(0, 1000)).zfill(4)\n        cur.execute(\"INSERT INTO card (number, pin) VALUES (?, ?)\", (self.card_num, self.pin))\n        conn.commit()\n        print(f\"\\nYour card has been created\\nYour card number:\\n{self.card_num}\\nYour card PIN:\\n{self.pin}\\n\")\n        self.main_interface()\n\n    def login_account(self):\n        input_card_num = input(\"\\nEnter your card number:\\n\")\n        input_pin = input(\"Enter your PIN:\\n\")\n        cur.execute(\"SELECT number, pin, balance FROM card WHERE number = ?\", (\n        input_card_num,))\n        try:\n            self.record = list(cur.fetchone())\n            if input_pin == self.record[1]:\n                print(\"\\nYou have successfully logged in!\")\n                self.login_interface()\n            else:\n                print(\"\\nWrong card number or PIN!\")\n                self.main_interface()\n                self.record = None\n        except TypeError:\n            print(\"\\nWrong card number or PIN!\")\n            self.main_interface()\n            self.record = None\n\n    def login_interface(self):\n        self.user_input = input(\"\\n1. Balance\\n2. Add income\\n3. Do transfer\\n4. Close account\\n5. Log out\\n0. Exit\\n\")\n        if self.user_input == \"1\":\n            print(f\"Balance: {self.record[2]}\")\n            self.login_interface()\n        elif self.user_input == \"2\":\n            income = int(input(\"\\nEnter income:\\n\"))\n            self.record[2] += income\n            cur.execute(\"UPDATE card SET balance = ? WHERE number = ?\", (self.record[2], self.record[0]))\n            conn.commit()\n            print(\"Income was added!\")\n            self.login_interface()\n        elif self.user_input == \"3\":\n            self.transfer_num = input(\"\\nTransfer\\nEnter card number:\\n\")\n            self.do_transfer()\n            self.login_interface()\n        elif self.user_input == \"4\":\n            cur.execute(\"DELETE FROM card WHERE number = ?\", (self.record[0],))\n            conn.commit()\n            print(\"\\nThe account has been closed!\")\n            self.main_interface()\n            self.record = None\n        elif self.user_input == \"5\":\n            print(\"\\nYou have successfully logged out!\\n\")\n            self.main_interface()\n            self.record = None\n        elif self.user_input == \"0\":\n            print(\"\\nBye!\")\n            self.record = None\n\n    def luhn_checksum(self, fifteen_digit_string):\n        multiply_odd_two = [int(fifteen_digit_string[i]) * 2 for i in range(0, 15, 2)]\n        substract_nine = [num - 9 for num in multiply_odd_two if num > 9]\n        num_less_nine = [num for num in multiply_odd_two if num <= 9]\n        even_digit = [int(fifteen_digit_string[i]) for i in range(1, 15, 2)]\n        total_sum = sum(substract_nine) + sum(num_less_nine) + sum(even_digit)\n        if total_sum % 10 == 0:\n            self.last_digit = \"0\"\n        else:\n            checksum_digit = str(10 - (total_sum % 10))\n            self.last_digit = checksum_digit\n\n        return self.last_digit\n\n    def do_transfer(self):\n        if self.transfer_num[15] != self.luhn_checksum(self.transfer_num[:15]):\n            print(\"Probably you made a mistake in the card number. Please try again!\")\n\n        elif self.transfer_num == self.record[0]:\n            print(\"You can't transfer money to the same account!\")\n\n        else:\n            cur.execute(\"SELECT number FROM card\")\n            list_of_card_num = cur.fetchall()\n\n            if (self.transfer_num,) in list_of_card_num:\n                cur.execute(\"SELECT number, balance FROM card WHERE number = ?\", (self.transfer_num,))\n                transfer_record = list(cur.fetchone())\n                money = int(input(\"Enter how much money you want to transfer:\\n\"))\n\n                if money > self.record[2]:\n                    print(\"Not enough money!\")\n\n                else:\n                    self.record[2] -= money\n                    cur.execute(\"UPDATE card SET balance = ? WHERE number = ?\", (self.record[2], self.record[0]))\n                    conn.commit()\n                    transfer_record[1] += money\n                    cur.execute(\"UPDATE card SET balance = ? WHERE number = ?\",\n                                (transfer_record[1], transfer_record[0]))\n                    conn.commit()\n                    print(\"Success!\")\n\n            else:\n                print(\"Such a card does not exist.\")\n\n\nBankingSystem()\nconn.close()","repo_name":"Szydol/Hyperskill-projects-python","sub_path":"Python learning projects/Simple Banking System/banking.py","file_name":"banking.py","file_ext":"py","file_size_in_byte":5210,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"26008807719","text":"import sys\nfrom flask import Flask\nfrom flask_sqlalchemy import SQLAlchemy\nfrom datetime import datetime, timedelta\nimport pandas as pd\nimport os\nimport re\n\napp_dir = '/home/aaron/sp'\n# app_dir = '/home/mike/git/streampulse/server_copy/sp'\nsys.path.insert(0, app_dir)\nos.chdir(app_dir)\nimport config as cfg\n\napp = Flask(__name__)\n\napp.config['SECRET_KEY'] = cfg.SECRET_KEY\napp.config['SQLALCHEMY_DATABASE_URI'] = cfg.SQLALCHEMY_DATABASE_URI\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = cfg.SQLALCHEMY_TRACK_MODIFICATIONS\napp.config['UPLOAD_FOLDER'] = cfg.UPLOAD_FOLDER\napp.config['META_FOLDER'] = cfg.META_FOLDER\napp.config['REACH_CHAR_FOLDER'] = cfg.REACH_CHAR_FOLDER\napp.config['RESULTS_FOLDER'] = cfg.RESULTS_FOLDER\napp.config['BULK_DNLD_FOLDER'] = cfg.BULK_DNLD_FOLDER\napp.config['SECURITY_PASSWORD_SALT'] = cfg.SECURITY_PASSWORD_SALT\n\ndb = SQLAlchemy(app)\n\n\ncols = pd.read_sql(\"select distinct region, site, concat(region, '_', site) as \" +\\\n    \"regsite, rawcol, dbcol from cols\", db.engine)\ncols['upload_file'] = ''\ncols = cols.sort_values(['region', 'site'])\n\nupfiles = os.listdir(app.config['UPLOAD_FOLDER'])\n# i=0; j=0\nfor i in xrange(cols.shape[0]):\n    sitefiles = [x for x in upfiles if cols.regsite[i] in x]\n    for j in xrange(len(sitefiles)):\n        header = os.popen('head ' + app.config['UPLOAD_FOLDER'] + '/' + sitefiles[j]).read()\n        if cols.rawcol[i].encode('utf-8') in header:\n            cols.upload_file[i] = sitefiles[j]\n            continue\n\ncols.to_csv('upfiles_by_col.csv')\n","repo_name":"streampulse/model","sub_path":"ancillary/data_level_sorting.py","file_name":"data_level_sorting.py","file_ext":"py","file_size_in_byte":1510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"20850054773","text":"import numpy as np\nfrom scipy import linalg\n\ndef log_multivariate_normal_density_diag(X, means, covars, reg=1e-6):\n    \"\"\"Compute Gaussian log-density at X for a diagonal model.\"\"\"\n\n    n_samples, n_dim = X.shape\n\n    if (covars == 0).any():\n        covars += reg\n\n    log_dets = np.sum(np.log(covars), axis=1)\n\n    lmnd = -0.5 * (n_dim * np.log(2 * np.pi) + log_dets + np.sum((means ** 2) / covars, 1) -\n                  2 * np.dot(X, (means / covars).T) + np.dot(X ** 2, (1.0 / covars).T))\n\n    return lmnd\n\ndef log_multivariate_normal_density_full(X, means, covars, reg=1.e-6):\n    \"\"\"Log probability for full covariance matrices.\"\"\"\n\n    n_samples, n_dim = X.shape\n    nmix = len(means)\n    log_prob = np.empty((n_samples, nmix))\n\n    for c, (mu, cv) in enumerate(zip(means, covars)):\n        try:\n            cv_chol = linalg.cholesky(cv, lower=True)\n        except linalg.LinAlgError:\n            # The model is most probably stuck in a component with too\n            # few observations, we need to reinitialize this components\n            try:\n                cv_chol = linalg.cholesky(cv + reg * np.eye(n_dim),\n                                          lower=True)\n            except linalg.LinAlgError:\n                raise ValueError(\"'covars' must be symmetric, positive-definite\")\n\n        cv_log_det = 2 * np.sum(np.log(np.diagonal(cv_chol)))\n        cv_sol = linalg.solve_triangular(cv_chol, (X - mu).T, lower=True).T\n        log_prob[:, c] = - 0.5 * (np.sum(cv_sol ** 2, axis=1) + n_dim * np.log(2 * np.pi) + cv_log_det)\n\n    return log_prob\n\n\ndef mult_gauss_pdf(X, mean, cov, log=False):\n\n    dim = mean.size\n\n    if cov.ndim == 1:\n        det = np.prod(cov)\n        if det == 0:\n            raise ValueError(\"singular matrix\")\n\n        const = - 0.5 * (dim * np.log(2 * np.pi) + np.log(det))\n        inv = 1.0 / cov\n        X_c = X - mean\n        result = - 0.5 * np.sum((X_c ** 2) * inv, axis=1)\n\n    else:\n        det = np.linalg.det(cov)\n        if det == 0:\n            raise ValueError(\"singular matrix\")\n\n        const = - 0.5 * (dim * np.log(2 * np.pi) + np.log(det))\n        inv = np.linalg.inv(cov)\n        X_c = X - mean\n        result = - 0.5 * np.sum(np.dot(X_c, inv) * X_c, axis=1)\n\n    result += const\n\n    if log is True:\n        return result\n    else:\n        return np.exp(result)\n","repo_name":"jpvcaetano/Radial-Basis-Function-Networks","sub_path":"utils/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":2318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"35371325679","text":"import time\nfrom machine import Pin\nimport rp2\n\n@rp2.asm_pio(out_init = rp2.PIO.OUT_LOW)\n\ndef blink_pull():\n    wrap_target()\n    pull()\n    set(x, 31)\n    label(\"bitloop\")\n    out(pins, 1)       [31]\n    jmp(x_dec, \"bitloop\")   #decrement x by 1, jump if it's not 0\n    wrap()\n    \nsm = rp2.StateMachine(1, blink_pull, freq = 2000, out_shiftdir = rp2.PIO.SHIFT_RIGHT, out_base = Pin(25))\n\nsm.active(1)\n\nwhile True:\n    sm.put(0x00000001) ","repo_name":"riyasach189/Arduino-Trainer-Kit","sub_path":"Smart_Trainer_Kit_v2/All_Codes/pio_blink_pull.py","file_name":"pio_blink_pull.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"23193456411","text":"def solution(alp, cop, problems):\n    max_req_alp = -1\n    max_req_cop = -1\n\n    for problem in problems:\n        if problem[0] > max_req_alp:\n            max_req_alp = problem[0]\n        if problem[1] > max_req_cop:\n            max_req_cop = problem[1]\n        if problem[2] == 0 and problem[3] == 0:\n            problems.pop(problem)\n\n    # 이미 조건을 충족한 경우\n    if alp >= max_req_alp and cop >= max_req_cop:\n        return 0\n\n    if alp > max_req_alp:\n        alp = max_req_alp\n    if cop > max_req_cop:\n        cop = max_req_cop\n\n    dp = [[float('inf') for _ in range(max_req_cop + 1)] for _ in range(max_req_alp + 1)]\n    dp[alp][cop] = 0\n\n    for cur_alp in range(alp, max_req_alp + 1):\n        for cur_cop in range(cop, max_req_cop + 1):\n            if cur_alp < max_req_alp:\n                dp[cur_alp][cur_cop] = min(dp[cur_alp][cur_cop], dp[cur_alp - 1][cur_cop] + 1)\n            if cur_cop < max_req_cop:\n                dp[cur_alp][cur_cop] = min(dp[cur_alp][cur_cop], dp[cur_alp][cur_cop - 1] + 1)\n\n            for problem in problems:\n                if cur_alp >= problem[0] and cur_cop >= problem[1]:\n                    next_alp = min(max_req_alp, cur_alp + problem[2])\n                    next_cop = min(max_req_cop, cur_cop + problem[3])\n                    dp[next_alp][next_cop] = min(dp[next_alp][next_cop], dp[cur_alp][cur_cop] + problem[4])\n\n    return dp[max_req_alp][max_req_cop]\n","repo_name":"heeeeeseok/study","sub_path":"Algorithm/Programmers/코딩 테스트.py","file_name":"코딩 테스트.py","file_ext":"py","file_size_in_byte":1423,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"25456491537","text":"class Scan():\r\n    def __init__(self,origin,grid):    #### Initializes all arrays and variables of class\r\n        self.origin = origin\r\n        self.grid = grid\r\n        self.set_grid = []\r\n        self.points = []\r\n        self.visited = []\r\n        self.lines = []\r\n        self.count = 0\r\n\r\n    def initializeArrays(self):                      ###Initializes the arrays according to the grid on screen \r\n        for j in range(len(self.grid.getGrid())):\r\n            self.set_grid.append([])\r\n            self.visited.append([])\r\n            for i in range(len(self.grid.getGrid()[j])):\r\n                curr_pt = self.grid.getGrid()[i][j]\r\n                if (curr_pt.getColor() == (0,0,0)):\r\n                    self.set_grid[j].append(1)\r\n                else:\r\n                    self.set_grid[j].append(0)\r\n                self.visited[j].append(0)\r\n\r\n    def dfs(self,x,y):           ### DFS flood fill to detect all point to be coloured\r\n        ## x - column\r\n        ## y - row\r\n        if (0 <= x < len(self.set_grid) and 0 <= y < len(self.set_grid[0]) and self.visited[x][y] == 0 and self.set_grid[y][x] == 0):\r\n            self.visited[x][y] = 1\r\n            self.points.append([y,x])\r\n            self.count+=1\r\n            self.dfs(x+1,y)\r\n            self.dfs(x-1,y)\r\n            self.dfs(x,y+1)\r\n            self.dfs(x,y-1)\r\n\r\n    def Sort(self,sub_li,index):          ### Sorts the given array according the to given index of sub-array\r\n    # reverse = None (Sorts in Ascending order) \r\n    # key is set to sort using second element of  \r\n    # sublist lambda has been used \r\n        sub_li.sort(key = lambda x: x[index]) \r\n        return sub_li \r\n\r\n    def main(self):                       ### Main function to return list of lines to be coloured sorted by height and the left and right extreme coordinates\r\n        self.initializeArrays()\r\n        self.dfs(self.origin.col,self.origin.row)\r\n        self.points = self.Sort(self.points,1)\r\n        self.points = self.Sort(self.points,0)\r\n        l,right,left,height = 0,0,0,0\r\n        first_pt = True\r\n        for i in range(self.count):\r\n            if (first_pt):\r\n                first_pt = False\r\n                height = self.points[i][0]\r\n                left = self.points[i][1]\r\n                right = self.points[i][1]\r\n            elif (height != self.points[i][0] or right+1 != self.points[i][1]):\r\n                self.lines.append([height,left,right])\r\n                height = self.points[i][0]\r\n                left = self.points[i][1]\r\n                right = self.points[i][1]\r\n            else:\r\n                right+=1\r\n\r\n            if (i == self.count-1):\r\n                self.lines.append([height,left,right])\r\n        \r\n        # for x in self.lines:\r\n        #     print (x)\r\n        # print ('\\n')\r\n\r\n        return self.lines\r\n\r\n\r\n","repo_name":"ridbit10/Coloring-the-color-book","sub_path":"algo.py","file_name":"algo.py","file_ext":"py","file_size_in_byte":2833,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"5867583293","text":"import time\nfrom turtle import Screen\nfrom player import Player\nfrom car_manager import CarManager\nfrom scoreboard import Scoreboard\n\nfrog = Player()\nscoreboard = Scoreboard()\ncars = CarManager()\n\nscreen = Screen()\nscreen.setup(width=600, height=600)\nscreen.tracer(0)\nscreen.listen()\nscreen.onkey(frog.moveForward, \"Up\")\nscreen.onkey(frog.moveLeft, \"Left\")\nscreen.onkey(frog.moveRight, \"Right\")\nfrog.goto(0, -265)\n\ngame_is_on = True\ndelay = 0\nwhile game_is_on:\n    time.sleep(0.1)\n    screen.update()\n    cars.createCar()\n    cars.move()\n    for car in cars.cars:\n        if(frog.distance(car) < 10):\n            game_is_on = False\n            scoreboard.clear()\n            scoreboard.write(\"SMASH!\", move=False, align=\"center\", font=(\"Arial\", 20, \"normal\"))\n    if(frog.ycor() > 250):\n            scoreboard.write(\"WINNER!\", move=False, align=\"center\", font=(\"Arial\", 20, \"normal\"))\n\nscreen.exitonclick()","repo_name":"benbauer14/frogger","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":906,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"3123877368","text":"\"\"\"SJAssistant URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/dev/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  url(r'^$', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.conf.urls import url, include\n    2. Add a URL to urlpatterns:  url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib.staticfiles.urls import staticfiles_urlpatterns\nfrom django.conf.urls import url, include\nfrom django.contrib import admin\nfrom django.contrib.auth import views as auth_views\n\ndef i18n_javascript(request):\n    return admin.site.i18n_javascript(request)\n\n\nurlpatterns = [\n    url(r'^login', auth_views.login, name='login'),\n    # url(r'^', include('django.contrib.auth.urls')),\n    url(r'^attendance/', include('Attendance.urls'), name='attendance'),\n    url(r'^class/', include('StudentBasic.urls')),\n    url(r'^chaining/', include('smart_selects.urls')),\n    url(r'^admin/jsi18n', i18n_javascript),\n    url(r'^admin/', admin.site.urls),\n]\n\nurlpatterns += staticfiles_urlpatterns()\n","repo_name":"qswuwwb/SJAssistant","sub_path":"SJAssistant/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"2757389150","text":"\"\"\"\nCreate a program that prompts the user to input their name repeatedly. Then, the program repeatedly prints out the name with the first letter capitalized.\n\nIn other words, the program should behave as in the screenshot below:\n\n(in folder with same name)\n\"\"\"\n#name = \"\"  no reason to declare it\n\nwhile True:\n    name = input(\"What is your name?\")\n    print(name.capitalize())\n","repo_name":"Andrew-Kole/random-tasks","sub_path":"coding_exercises/day2task3.py","file_name":"day2task3.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"32860552313","text":"from datasketch import MinHash\nimport re\nimport datetime\nfrom pandas._libs.tslibs.timestamps import Timestamp\n\n\ndef convert_str_2_date(date):\n    if date is None:\n        return None\n    if type(date) == Timestamp:\n        return date.to_pydatetime()\n    if type(date) == datetime.datetime:\n        return date\n    if re.match('^[0-9]{4}\\.[0-9]{2}$', date):\n        return datetime.datetime.strptime(date, \"%Y.%m\")\n    elif re.match('^[0-9]{2}-[0-9]{2}-[0-9]{2}$', date):\n        return datetime.datetime.strptime(\"20\" + date, \"%Y-%m-%d\")\n    elif re.match('^[0-9]{4}-[0-9]{2}-[0-9]{2}$', date):\n        return datetime.datetime.strptime(date, \"%Y-%m-%d\")\n    elif re.match('^[0-9]{1,2}月 [0-9]{1,2}日.*$', date):\n        month, day = date.split('日')[0].split('月 ')\n        return datetime.datetime(year=2019, month=int(month), day=int(day))\n    elif re.match('^[0-9]{4}-[0-9]{2}-[0-9]{2} [0-9]{2}:[0-9]{2}:[0-9]{2}$', date):\n        return datetime.datetime.strptime(date, \"%Y-%m-%d %H:%M:%S\")\n    elif re.match('^[0-9]{4}-[0-9]{2}-[0-9]{2}T[0-9]{2}:[0-9]{2}:[0-9]{2}\\.[0-9]{3}Z$', date):\n        return datetime.datetime.strptime(date, \"%Y-%m-%dT%H:%M:%S.%fZ\")\n    else:\n        print(\"un recongize {0}\".format(date))\n        return date\n\n\ndef get_variable(name):\n    loc = locals()\n    key = ''\n    for key in loc:\n        if loc[key] == name:\n            return key\n\n\ndef parse_segged_word(doc):\n    if doc is None or doc is \"\":\n        return []\n    return [word.split(':')[0] for sentence in doc.split(';') for word in sentence.split(',')]\n\n\ndef compute_min_hash(word_list):\n    m = MinHash()\n    for word in word_list:\n        m.update(word.encode())\n    return m\n\n\ndef update_min_hash(m, word_list):\n    for word in word_list:\n        m.update(word.encode())\n    return m\n\n\ndef update_add_dict(d1, d2):\n    \"\"\"\n    更新字典，取字段较全者\n    \"\"\"\n    temp = {}\n    for k, v in d1.items():\n        if k in d2.keys():\n            if v is None:\n                temp[k] = d2[k]\n            elif d2[k] is None:\n                temp[k] = v\n            if type(v) == list and type(d2[k]) == list:\n                v = v.extend(d2[k])\n                temp[k] = v\n            elif type(v) == list and type(d2[k]) != list:\n                v = v.append(d2[k])\n                temp[k] = v\n            elif type(v) != list and type(d2[k]) == list:\n                v = d2[k].append(v)\n                temp[k] = v\n            elif type(v) == dict and type(d2[k]) == dict:\n                temp[k] = update_add_dict(v, d2[k])\n            else:\n                temp[k] = [v, d2[k]]\n        else:\n            temp[k] = v\n    for k, v in d2.items():\n        if k not in d1.keys():\n            temp[k] = v\n    return temp\n\n\nif __name__ == '__main__':\n    print(len(compute_min_hash(['aaa']).digest()))\n","repo_name":"Will-Holden/kb_demo","sub_path":"offline_processor/utils/Utils.py","file_name":"Utils.py","file_ext":"py","file_size_in_byte":2804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"71758001207","text":"from setuptools import setup, find_packages\n\nwith open(\"requirements.txt\") as f:\n\tinstall_requires = f.read().strip().split(\"\\n\")\n\n# get version from __version__ variable in rtc_app/__init__.py\nfrom rtc_app import __version__ as version\n\nsetup(\n\tname=\"rtc_app\",\n\tversion=version,\n\tdescription=\"RTC is a cms for Bus Ticketing and Services\",\n\tauthor=\"Foo Fighters\",\n\tauthor_email=\"athul8720@gmail.com\",\n\tpackages=find_packages(),\n\tzip_safe=False,\n\tinclude_package_data=True,\n\tinstall_requires=install_requires\n)\n","repo_name":"athul/rtc","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"25511972722","text":"from jinja2 import Environment, FileSystemLoader\n\nfile_loader = FileSystemLoader('templates')\nenv = Environment(loader = file_loader)\ntemplate = env.get_template('template.davs')\n\nuser_post = [{'title': 'Title 1- Breakfast nice', 'author': 'Kraus'},\n            {'title': 'Title 2- Pizza at noon', 'author':'Mark'}]\n\nresult = template.render({'user':'this is the user',\n                          'user_id':'this is the user_id!',\n                          'user_credential':'this is the user credential',\n                          'user_number': '2121422142',\n                          'good_day': True, #could be False as well\n                          'user_post': user_post\n                         })\n\nwith open('index.html','w') as index:\n    index.write(result)\n","repo_name":"sdsanchezm/python-all","sub_path":"jinja2/template.py","file_name":"template.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"73115393204","text":"'''\nGiven an array nums, write a function to move all 0's to the end of it while maintaining the relative order of the non-zero elements.\n\nFor example, given nums = [0, 1, 0, 3, 12], after calling your function, nums should be [1, 3, 12, 0, 0].\n\nNote:\nYou must do this in-place without making a copy of the array.\nMinimize the total number of operations.\n\n'''\n\n\"\"\"\nwe scan across the array and keep track of the position of zero\nif we know get a non zero value, and we swap it with the zero position\nAt the same time we try to keep the correct position of zero\nFinally we can remove the zero to the end\n(Attention: we should know that the case that the value zero index which will not be equal to the index i when there are ajacent zero in the array )\n\"\"\"\nclass Solution(object):\n    def moveZeroes(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: void Do not return anything, modify nums in-place instead.\n        \"\"\"\n        zero  = 0\n        for i in range(len(nums)):\n            if nums[i] != 0:\n                # the swapping element for python\n                nums[i], nums[zero] = nums[zero], nums[i]\n                zero += 1\nsolution = Solution()\nsample_test = [0, 1, 0, 3, 12]\nprint (sample_test)","repo_name":"AlphaGarden/LeetCodeProblems","sub_path":"Easy/283. Move_Zeroes.py","file_name":"283. Move_Zeroes.py","file_ext":"py","file_size_in_byte":1229,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"33060790525","text":"from django.shortcuts import render, redirect\n\n# Create your views here.\nfrom .models import Article\n\ndef index(request):\n\n    articles = Article.objects.all()\n    context = {\n        'articles': articles,\n    }\n    return render(request, 'articles/index.html', context)\n\ndef new(request):\n    title = request.GET.get('title')\n    content = request.GET.get('content')\n    \n    Article.objects.create(title=title, content=content)\n\n    return redirect('articles:index')\n\n\ndef create(request):\n    return render(request, 'articles/create.html')\n\n\ndef detail(request, pk):\n    article = Article.objects.get(pk=pk)\n\n    context = {\n        \"article\": article,\n    }\n\n    return render(request, 'articles/detail.html', context)\n\ndef edit(request, pk):\n    article = Article.objects.get(pk=pk)\n    context = {\n        \"article\": article,\n    }\n    return render(request, 'articles/edit.html', context)\n\ndef update(request, pk):\n    edit_data = Article.objects.get(pk=pk)\n\n    edit_data.title = request.POST.get('title')\n    edit_data.content = request.POST.get('content')\n\n    edit_data.save()\n\n    return redirect('articles:detail', edit_data.pk)\n\n\ndef delete(request, pk):\n    del_data = Article.objects.get(pk=pk)\n    del_data.delete()\n\n    return redirect('articles:index')","repo_name":"HYUNJUN-KANG/TIL","sub_path":"Django/0820/0820_workshop/crud/articles/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1271,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"39245799927","text":"from .abstract_app import AbstractApp\nfrom ..models.board_model import BoardModel\nfrom ..views.main_view import MainView\nimport pygame\n\n\nclass App(AbstractApp):\n    def __init__(self, controller, window_height, window_width, rows, columns, period):\n        super().__init__(controller)\n        pygame.init()\n\n        self._model = BoardModel(rows, columns, period)\n        self._view = MainView('MainView', self._model, window_height, window_width)\n        self._model.add_observer(self._view)\n\n        controller.model = self._model\n        controller.view = self._view\n\n    def run_app(self):\n        while True:\n            self.controller.get_user_input()\n            self.controller.view.show()\n","repo_name":"Antos99/Game_of_life","sub_path":"game_of_life/controllers/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":700,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"35221745535","text":"import pyautogui as pg\nimport easygui as eg\nimport time\nimport os\n\n\ndef create_image():\n    save_path = r\"C:\\Users\\user\\Pictures\\M-J ScreenShots\"\n    image_code = '\\mj_ss-'\n    image_num = 900000000001\n    image_path = save_path + image_code + str(image_num) + '.png'\n    while os.path.exists(image_path):\n        image_num += 1\n        image_path = save_path + image_code + str(image_num) + '.png'\n    print('Done')\n    return image_path\n\n\ndef takess():\n    pg.screenshot().save(create_image())\n\n\ndef showD():\n    x = (eg.buttonbox(\n        msg='Take a screenshot',\n        title='Mj ss',\n        choices=['Take'\n        ]\n    ))\n    if x == 'Take':\n        time.sleep(0.1)\n        takess()\n\nif __name__ == '__main__':\n    showD()\n","repo_name":"Mosesjespar/Simple-Projects-Python-","sub_path":"Screenshot Taker.py","file_name":"Screenshot Taker.py","file_ext":"py","file_size_in_byte":732,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"6892543304","text":"def solution(priorities, location):\n    answer = 0\n    idx = [i for i in range(len(priorities))]\n    res = []\n    \n    while idx:\n        cur = idx.pop(0)\n        flag = 0\n        \n        for i in idx:\n            if priorities[cur] < priorities[i]:\n                idx.append(cur)\n                flag = 1\n                break\n        \n        if flag:\n            continue\n        \n        res.append(cur)\n\n    answer = res.index(location)\n    return answer+1","repo_name":"Lkangmin/Coding-Test","sub_path":"Programmers/연습문제/42587.py","file_name":"42587.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"5316889114","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport re, sys\nfrom os import path\nfrom os.path import dirname, basename, normpath\n\n# pre-compiled regular expressions\nre_module = re.compile(r\"(?:^|\\n)\\s*module\\s+(\\w+)\\s.*\\n\\s*end\\s*module\",re.DOTALL)\nre_program = re.compile(r\"\\n\\s*end\\s*program\")\nre_main   = re.compile(r\"\\sint\\s+main\\s*\\(\")\nre_use    = re.compile(r\"\\n\\s*use\\s+(\\w+)\")\nre_incl_fypp = re.compile(r\"\\n#:include\\s+['\\\"](.+)['\\\"]\")\nre_incl_cpp = re.compile(r\"\\n#include\\s+['\\\"](.+)['\\\"]\")\nre_incl_fort = re.compile(r\"\\n\\s*include\\s+['\\\"](.+)['\\\"]\")\n\n\n\n\n#=============================================================================\ndef main():\n    messages = []\n    #process command line arguments\n    out_fn  = sys.argv[1]\n    mod_format = sys.argv[2]\n    mode = sys.argv[3]\n    archive_ext = sys.argv[4]\n    src_dir = sys.argv[5]\n    src_files_tmp = sys.argv[6:]\n    src_files = []\n    for fn_part in src_files_tmp:\n        fn_part = fn_part[1:]\n        fn = src_dir + fn_part\n        src_files.append(fn)\n    if(mod_format not in ('lower', 'upper', 'no')):\n        error('Module filename format must be eighter of \"lower\", \"upper\", or \"no\".')\n    if(mode not in ('normal', 'hackdep', 'mod_compiler')):\n        error('Mode must be eighter of \"normal\", \"hackdep\", or \"mod_compiler\".')\n    for fn in src_files:\n        if(not fn.startswith(\"/\")):\n            error(\"Path of source-file not absolut: \"+fn)\n    src_basenames = [basename(fn).rsplit(\".\", 1)[0] for fn in src_files]\n    for bfn in src_basenames:\n        if(src_basenames.count(bfn) > 1):\n            error(\"Multiple source files with the same basename: \"+bfn)\n\n    # parse files\n    parsed_files = dict()\n    for fn in src_files:\n        parse_file(parsed_files, fn) #parses also included files\n    messages.append(\"Parsed %d files\"%len(parsed_files))\n\n    # create table mapping fortan module-names to file-name\n    mod2fn = dict()\n    for fn in src_files:\n        for m in parsed_files[fn]['module']:\n            if m in mod2fn.keys():\n                error('Multiple declarations of module \"%s\"'%m)\n            mod2fn[m] = fn\n    messages.append(\"Created mod2fn table, found %d modules.\"%len(mod2fn))\n\n    # check \"one module per file\"-convention\n    for m, fn in mod2fn.items():\n        if(basename(fn) != m+\".F\"):\n            error(\"Names of module and file do not match: \"+fn)\n\n    # read package manifests\n    packages = dict()\n    for fn in src_files:\n        p = normpath(dirname(fn))\n        read_pkg_manifest(packages, p)\n    messages.append(\"Read %d package manifests\"%len(packages))\n\n    # check dependencies against package manifests\n    n_deps = 0\n    for fn in src_files:\n        p = normpath(dirname(fn))\n        if(not parsed_files[fn]['program']):\n            packages[p]['objects'].append(src2obj(basename(fn)))\n        deps = collect_include_deps(parsed_files, fn)\n        deps += [ mod2fn[m] for m in collect_use_deps(parsed_files, fn) if m in mod2fn.keys() ]\n        n_deps += len(deps)\n        for d in deps:\n            dp = normpath(dirname(d))\n            if(dp not in packages[p]['allowed_deps']):\n                error(\"Dependency forbidden according to package manifest: %s -> %s\"%(fn, d))\n            if(dp != p and \"public\" in packages[dp].keys()):\n                if(basename(d) not in packages[dp][\"public\"]):\n                    error(\"File not public according to package manifest: %s -> %s\"%(fn, d))\n    messages.append(\"Checked %d dependencies\"%n_deps)\n\n    # check for circular dependencies\n    for fn in parsed_files.keys():\n        find_cycles(parsed_files, mod2fn, fn)\n\n    # write messages as comments\n    makefile = \"\".join([\"#makedep: %s\\n\"%m for m in messages])\n    makefile += \"\\n\"\n\n    # write rules for archives\n    for p in packages.keys():\n        if(len(packages[p]['objects']) > 0):\n            makefile += \"# Package %s\\n\"%p\n            makefile += \"$(LIBDIR)/%s : \"%(packages[p]['archive']+archive_ext)\n            makefile += \" \".join(packages[p]['objects']) + \"\\n\\n\"\n\n    # write rules for executables\n    archive_postfix = archive_ext.rsplit(\".\",1)[0]\n    for fn in src_files:\n        if(not parsed_files[fn]['program']):\n            continue\n        bfn = basename(fn).rsplit(\".\", 1)[0]\n        makefile += \"# Program %s\\n\"%fn\n        makefile += \"$(EXEDIR)/%s.$(ONEVERSION) : %s.o \"%(bfn, bfn)\n        p = normpath(dirname(fn))\n        deps = collect_pkg_deps(packages, p)\n        makefile += \" \".join([\"$(LIBDIR)/\"+a+archive_ext for a in deps]) + \"\\n\"\n        makefile += \"\\t\" + \"$(LD) $(LDFLAGS)\"\n        if(fn.endswith(\".c\") or fn.endswith(\".cu\")):\n            makefile += \" $(LDFLAGS_C)\"\n        makefile += \" -L$(LIBDIR) -o $@ %s.o \"%bfn\n        makefile += \"$(EXTERNAL_OBJECTS) \"\n        assert(all([a.startswith(\"lib\") for a in deps]))\n        makefile += \" \".join([\"-l\"+a[3:]+archive_postfix for a in deps])\n        makefile += \" $(LIBS)\\n\\n\"\n\n    # write rules for objects\n    for fn in src_files:\n        deps = \" \".join(collect_include_deps(parsed_files, fn))\n        mods = collect_use_deps(parsed_files, fn)\n        mods.sort(key=cmp_mods) # sort mods to speedup compilation\n        for m in mods:\n            if m in mod2fn.keys():\n                deps += \" \" + mod2modfile(m, mod_format)\n        if(mode == \"hackdep\"):\n            deps = \"\"\n\n        bfn = basename(fn)\n        makefile += \"# Object %s\\n\"%bfn\n        provides = [mod2modfile(m, mod_format) for m in parsed_files[fn]['module']]\n        for mfn in provides:\n            makefile += \"%s : %s \"%(mfn, bfn) + deps + \"\\n\"\n        makefile += \"%s : %s \"%(src2obj(bfn), bfn) + deps\n        if(mode == \"mod_compiler\"):\n            makefile += \" \" + \" \".join(provides)\n        makefile += \"\\n\\n\"\n\n    f = open(out_fn, \"w\")\n    f.write(makefile)\n    f.close()\n\n\n#=============================================================================\ndef cmp_mods(mod):\n    # list \"type\" modules first, they are probably on the critical path\n    if \"type\" in mod:\n        return 0\n    return 1\n\n\n#=============================================================================\ndef parse_file(parsed_files, fn):\n    if(fn in parsed_files): return\n\n    content = open(fn).read()\n\n    # re.IGNORECASE is horribly expensive. Converting to lower-case upfront\n    content_lower = content.lower()\n\n    # all files are parsed for cpp includes\n    incls = re_incl_cpp.findall(content) #CPP includes (case-sensitiv)\n\n    mods=[]; uses=[]; prog=False;\n    if(fn[-2:]==\".F\" or fn[-4:]==\".f90\" or fn[-5:]==\".fypp\"):\n        mods += re_module.findall(content_lower)\n        prog  = re_program.search(content_lower) != None\n        uses += re_use.findall(content_lower)\n        incls += re_incl_fypp.findall(content) # Fypp includes (case-sensitiv)\n        incl_fort_iter = re_incl_fort.finditer(content_lower) # fortran includes\n        incls += [ content[m.start(1):m.end(1)] for m in incl_fort_iter]\n\n    if(fn[-2:] == \".c\" or fn[-3:]==\".cu\"):\n        prog = re_main.search(content) != None # C is case-sensitiv\n\n    # exclude included files from outside the source tree\n    def incl_fn(i):\n        return normpath(path.join(dirname(fn), i))\n\n    existing_incl = [i for i in incls if path.exists(incl_fn(i))]\n\n    # store everything in parsed_files cache\n    parsed_files[fn] = {'module':mods, 'program': prog, 'use':uses, 'include':existing_incl}\n\n    # parse included files\n    for i in existing_incl:\n        parse_file(parsed_files, incl_fn(i))\n\n\n#=============================================================================\ndef read_pkg_manifest(packages, p):\n    if p in packages.keys(): return\n\n    fn = p+\"/PACKAGE\"\n    if(not path.exists(fn)):\n        error(\"Could not open PACKAGE manifest: \"+fn)\n    content = open(fn).read()\n\n    packages[p] = eval(content)\n    packages[p]['objects'] = []\n    if \"archive\" not in packages[p].keys():\n        packages[p]['archive'] = \"libcp2k\"+basename(p)\n    packages[p]['allowed_deps'] = [normpath(p)]\n    packages[p]['allowed_deps'] += [normpath(path.join(p,r)) for r in packages[p]['requires']]\n\n    for r in packages[p]['requires']:\n        read_pkg_manifest(packages, normpath(path.join(p,r)))\n\n\n#=============================================================================\ndef mod2modfile(m, mod_format):\n    if(mod_format == 'no'):\n        return(\"\")\n    if(mod_format == 'lower'):\n        return(m.lower() + \".mod\")\n    if(mod_format == 'upper'):\n        return(m.upper() + \".mod\")\n    assert(False) # modeps unknown\n\n\n#=============================================================================\ndef src2obj(src_fn):\n    return( basename(src_fn).rsplit(\".\",1)[0] + \".o\" )\n\n\n#=============================================================================\ndef collect_include_deps(parsed_files, fn):\n    pf = parsed_files[fn]\n    incs = []\n\n    for i in pf['include']:\n        fn_inc = normpath(path.join(dirname(fn), i))\n        if fn_inc in parsed_files.keys():\n            incs.append(fn_inc)\n            incs += collect_include_deps(parsed_files, fn_inc)\n\n    return(list(set(incs)))\n\n\n#=============================================================================\ndef collect_use_deps(parsed_files, fn):\n    pf = parsed_files[fn]\n    uses = pf['use']\n\n    for i in pf['include']:\n        fn_inc = normpath(path.join(dirname(fn), i))\n        if fn_inc in parsed_files.keys():\n            uses += collect_use_deps(parsed_files, fn_inc)\n\n    return(list(set(uses)))\n\n\n#=============================================================================\ndef find_cycles(parsed_files, mod2fn, fn, S=None):\n    pf = parsed_files[fn]\n    if 'visited' in pf.keys():\n        return\n\n    if(S==None):\n        S = []\n\n    for m in pf['module']:\n        if(m in S):\n            i = S.index(m)\n            error(\"Circular dependency: \"+ \" -> \".join(S[i:] + [m]))\n        S.append(m)\n\n    for m in collect_use_deps(parsed_files, fn):\n        if m in mod2fn.keys():\n            find_cycles(parsed_files, mod2fn, mod2fn[m], S)\n\n    for m in pf['module']:\n        S.pop()\n\n    pf['visited'] = True\n\n\n#=============================================================================\ndef collect_pkg_deps(packages, p, archives=None, S=None):\n    if(archives == None): archives = []\n    if(S == None): S = []\n\n    a = packages[p]['archive']\n    if(a in archives):\n        return(archives)\n\n    if(a in S):\n        i = S.index(a)\n        error(\"Circular package dependency: \"+ \" -> \".join(S[i:] + [a]))\n    S.append(a)\n\n    for r in packages[p]['requires']:\n        d = normpath(path.join(p,r))\n        collect_pkg_deps(packages, d, archives, S)\n\n    S.pop()\n\n    if(len(packages[p]['objects']) > 0):\n        archives.insert(0, packages[p]['archive'])\n\n    return(archives)\n\n\n#=============================================================================\ndef error(msg):\n    sys.stderr.write(\"makedep error: %s\\n\"%msg)\n    sys.exit(1)\n\n#=============================================================================\n# Python 2.4 compatibility\ndef all(iterable):\n    for element in iterable:\n        if(not element):\n            return(False)\n    return(True)\n\n#=============================================================================\nif(len(sys.argv)==2 and sys.argv[-1]==\"--selftest\"):\n    pass #TODO implement selftest\nelse:\n    main()\n\n#EOF\n","repo_name":"srak-uf/cp2k-colordiffusion","sub_path":"tools/build_utils/makedep.py","file_name":"makedep.py","file_ext":"py","file_size_in_byte":11259,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"76"}
+{"seq_id":"18309110364","text":"class Solution:\n    def maxHeight(self, cuboids: List[List[int]]) -> int:\n        res = 0\n        for list1 in cuboids:\n            list1.sort()\n        cuboids.sort(reverse=True)\n        n = len(cuboids)\n        dp = [0 for _ in range(n)]\n        for i in range(n):\n            dp[i] = cuboids[i][2]\n            for j in range(i):\n                if cuboids[i][0] <= cuboids[j][0] and cuboids[i][1] <= cuboids[j][1] and cuboids[i][2] <= cuboids[j][2]:\n                    dp[i] = max(dp[i], cuboids[i][2] + dp[j])\n            res = max(res, dp[i])\n        return res","repo_name":"jiangruofan/algorithm","sub_path":"1691. Maximum Height by Stacking Cuboids.py","file_name":"1691. Maximum Height by Stacking Cuboids.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"17112644157","text":"from queue import Queue\n\nclass AnimalShelter:\n    \"\"\" animal shelter class \"\"\"\n    def __init__(self, iter = []):\n        \"\"\" makes animal shelter class \"\"\"\n        self.animal_queue = Queue()\n\n        for i in iter:\n            self.enqueue(i)\n    \n    def enqueue(self, animal):\n        \"\"\" puts new animal in the back of the queue \"\"\"\n        if animal == 'cat' or animal == 'dog':\n            self.animal_queue.enqueue(animal)\n            return self.animal_queue._back\n        else:\n            print('You must only add cat or dog.')\n            return False\n    \n    def dequeue(self, animal = None):\n        \"\"\" takes first dog or cat animal off the front of the queue \"\"\"\n        adopted_animal = False\n        current = self.animal_queue._front\n        print('input animal: {}'.format(animal))\n        print('self.front:   {}'.format(current))\n        if self.animal_queue._size == 0:\n            print('We are all out of animals')\n            return False\n        if animal == None:\n            return self.animal_queue.dequeue()\n        elif animal == current.val:\n            return self.animal_queue.dequeue()\n        else:\n            while current._next is not None:\n                if current._next.val == animal:\n                    adopted_animal = current._next\n                    current._next = current._next._next\n                    self.animal_queue._size -= 1\n                    return adopted_animal\n                current = current._next\n            print('Sorry we do not have one of those')\n            return False\n        \n    \n\n\n# def print_shelter():\n#     current = shelter_one.animal_queue._front\n#     counter = 1\n#     while current is not None:\n#         print('{}. {}'.format(counter, current.val))\n#         counter += 1\n#         current = current._next\n#     print()\n\n# # qwt = Queue([1, 2, 3, 4, 5, 6, 7, 8, 9])\n\n# shelter_one = AnimalShelter(['cat', 'dog', 'dog', 'cat', 'cat'])\n\n# print_shelter() #\n\n# shelter_one.dequeue('d')\n\n# print_shelter() #\n\n# shelter_one.enqueue('do')\n\n# print_shelter() #\n\n# shelter_one.dequeue()\n\n# print_shelter() #\n\n# shelter_one.dequeue('d')\n\n# print_shelter() #\n\n# shelter_one.dequeue('cat')\n\n# print_shelter() #\n","repo_name":"tyler-fishbone/data-structures-and-algorithms","sub_path":"challenges/fifo-animal-shelter-dir/fifo_animal_shelter.py","file_name":"fifo_animal_shelter.py","file_ext":"py","file_size_in_byte":2192,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"72290229366","text":"import cv2\r\nimport matplotlib.pyplot as plt\r\nimport time\r\nfrom flask import Flask, render_template,request\r\nimport random\r\nclass Snake:\r\n    \r\n    def create_board(self):\r\n        \r\n        for i in range(10):\r\n            temp=[]\r\n            for j in range(10):\r\n                temp.append(list())\r\n            self.board.append(temp)\r\n                \r\n        \r\n    def __init__(self):\r\n        self.c=5\r\n        \r\n        positions_ladder_Snake=[\r\n                                [3, 9, 6, 8, 1 ],\r\n                                [8, 9, 9, 6, 3],\r\n                                [3, 8, 6, 9, 0],\r\n                                [0, 7, 1, 5, 4],\r\n                                [9, 4, 6, 3, 6],\r\n                                [6, 4, 6, 6, 3],\r\n                                [1, 3, 1, 8, 1],\r\n                                [3, 3, 0, 4, 5],\r\n                                [8, 2, 9, 0, 9],\r\n                                [0, 2, 1, 0, 9],\r\n                                [6, 1, 4, 6, 3],\r\n                                [7, 0, 6, 2, 7],\r\n                                [5, 0, 5, 2, 7],\r\n                                [2, 0, 1, 2, 7]\r\n                                ]\r\n        self.board=[]\r\n        self.position_x=50\r\n        self.position_y=50+(100*9)\r\n        self.x=0\r\n        self.y=9\r\n        self.level=0\r\n        self.create_board()\r\n        for i in positions_ladder_Snake:\r\n            self.board[i[0]][i[1]].append(i[2])\r\n            self.board[i[0]][i[1]].append(i[3])\r\n            self.board[i[0]][i[1]].append(i[4])\r\n       \r\n            \r\n        \r\n        \r\n    def chance_generator(self):\r\n        val = random.randint(1, 6)\r\n        return val\r\n        \r\n    def movement(self,val,lev):\r\n        while(val>0):\r\n            #print(\"ok\",self.x,\" \",self.y)\r\n            if(lev%2==0):\r\n                self.position_x+=100;\r\n                self.x+=1\r\n                if(self.x==10):\r\n                    self.x=9\r\n                    self.y-=1\r\n                    self.position_x=50+(100*9)\r\n                    self.position_y-=100\r\n                    lev+=1\r\n            else:\r\n                self.position_x-=100\r\n                self.x-=5\r\n                if(self.x==-1):\r\n                    self.x=0\r\n                    self.y-=1\r\n                    self.position_x=50\r\n                    self.position_y-=100\r\n                    lev+=1\r\n            val-=1\r\n        self.level=lev\r\n    def source_destination(self):\r\n        #print(self.x,\" \",self.y,\" \",self.level)\r\n        if(len(self.board[self.x][self.y])==0):\r\n            return\r\n        t_x=self.board[self.x][self.y][0]\r\n        t_y=self.board[self.x][self.y][1]\r\n        \r\n        self.level=self.board[self.x][self.y][2]\r\n        self.x=t_x\r\n        self.y=t_y\r\n        self.position_x=50+(100*(t_x))\r\n        self.position_y=50+(100*(t_y))\r\n        \r\n    def display_Game(self):\r\n        self.c+=1\r\n        img = cv2.imread(\"./static/snake2.png\")\r\n        radius = 25\r\n        color = (0, 255, 255)\r\n        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\r\n        thickness = -1\r\n        img = cv2.resize(img, (1000, 1000))\r\n        a = cv2.circle(img, (self.position_x, self.position_y), radius, color, thickness)\r\n        p = \"./static/snake{}.png\".format(self.c)\r\n        cv2.imwrite(p, img)\r\n        #plt.imshow(img)\r\n        #plt.show()\r\n        return p\r\n       \r\n        \r\n        \r\n        \r\n    def brain(self):\r\n        print(\"Game is Started \")\r\n        self.display_Game()\r\n        \r\n        \r\n            \r\n    def chance(self):\r\n        if(self.position_x==30 and self.position_y==30):\r\n            print(\"You win\")\r\n            self.display_Game()\r\n            return\r\n        val=  self.chance_generator()\r\n        self.movement(val,self.level)\r\n        self.source_destination()\r\n        x=self.display_Game()\r\n        print(\"You Got :-\",val)\r\n        l=[x,val]\r\n        return l\r\n            \r\n            \r\n\r\n\r\napp = Flask(__name__)\r\n# Define the global Snake instance outside the function\r\ns = None\r\n\r\n@app.route(\"/\", methods=[\"GET\"])\r\ndef brain():\r\n    global s\r\n    s = Snake()\r\n    s.brain()\r\n     # Access the global variable\r\n    print(\"get\")\r\n    return render_template(\"index.html\",scr1=\"./static/snake2.png\"  ,scr2=\"./static/d1.png\")\r\n\r\n@app.route(\"/\", methods=[\"POST\"])\r\ndef brain2():\r\n     # Access the global variable\r\n    if request.method == \"POST\":\r\n        print(\"post\")\r\n        res = s.chance()\r\n        return render_template(\"index.html\", scr1=res[0] ,scr2=\"./static/d{}.png\".format(res[1]))\r\n        \r\n    \r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    \r\n    app.run()\r\n\r\n        \r\n\r\n        \r\n            \r\n","repo_name":"hritikgupta01/Snake_Ladder_Game","sub_path":"start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":4647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"20106987925","text":"# Open invited_names.txt file in read mode\nwith open(\"./Input/Names/invited_names.txt\", mode=\"r\") as invited_names:\n    # Set names variable to invited_names.readlines() to read each line in the file and add to names list\n    names = invited_names.readlines()\n    # For name in names list\n    for name in names:\n        # Open starting letter in read mode\n        with open(\"./Input/Letters/starting_letter.txt\", mode=\"r\") as starting_letter:\n            # Read letter and store in read_letter variable\n            read_letter = starting_letter.read()\n            # Replace [name] section in starting_letter with current name from name list and strip return after name\n            replace_name = read_letter.replace(\"[name]\", name.rstrip(\"\\r\\n\"))\n            # Open new letter in append mode using name variable\n        with open(f\"./Output/ReadyToSend/letter_for_{name}.txt\", mode=\"a+\") as output_letter:\n            # Write replacement name variable to new letter\n            output_letter.write(replace_name)\n","repo_name":"TroyCaywood/Python","sub_path":"100 Days of Code/CodeChallenges/Day-24/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1012,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"70029863925","text":"from django.db import models\r\nfrom cio.models import Cio\r\n\r\nfrom django.utils import timezone\r\n\r\nclass Commodity(models.Model):\r\n    #link commodity to the cio selling it\r\n    #CIO = models.ForeignKey('CIO', null=True, blank=True, on_delete=models.SET_NULL)\r\n\r\n    id = models.AutoField(default=None, primary_key=True)\r\n\r\n    GOOD_OR_SERVICE = (\r\n    ('G', 'Good'),\r\n    ('S', 'Service'),\r\n    )\r\n\r\n    g_or_s = models.CharField(max_length=1, choices=GOOD_OR_SERVICE, default='G')\r\n\r\n    title = models.CharField(\r\n        max_length=250,\r\n        default='',\r\n        verbose_name='Commodity Title'\r\n    )\r\n\r\n    #add the ability to have more than one picture later?\r\n    picture = models.ImageField(upload_to='commodity_pics/', default='commodity_pics/noimage.jpg', blank=True, null=True)\r\n\r\n    description = models.CharField(\r\n        max_length=500,\r\n        default='',\r\n        verbose_name='Commodity Description',\r\n        blank = False\r\n    )\r\n\r\n    quantity = models.IntegerField(\r\n        default=1,\r\n        verbose_name='Quatity of items to be sold or services to be offered'\r\n    )\r\n\r\n   # price = MoneyField(\r\n#        decimal_places=2,\r\n#        default=0,\r\n#        default_currency='USD',\r\n#        max_digits=11,\r\n#)\r\n\r\n    price = models.DecimalField(max_digits=6, decimal_places=2)\r\n\r\n    date_posted = models.DateTimeField(\r\n        auto_now_add=True,\r\n        blank=True\r\n    )\r\n\r\n    date_expires = models.DateTimeField(default=timezone.now, blank=True)\r\n\r\n\r\nclass Recommendation(models.Model):\r\n    item_id = models.IntegerField(default=1)\r\n    recommended_items = models.TextField(default='')","repo_name":"aidanlanden22/CioYardSale_public","sub_path":"CioYardSale/commodity/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1619,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"31629998499","text":"from TextSummarizer.config.configuration import ConfigurationManager\nfrom transformers import AutoTokenizer\nfrom transformers import pipeline\n\nclass Prediction:\n    def __init__(self):\n        self.config=ConfigurationManager().get_model_evaluation_config()\n    #Model evaluation config contains paths to the model and the tokenizer\n\n    def predict(self,text):\n        tokenizer=AutoTokenizer.from_pretrained(self.config.tokenizer_path)\n        gen_kwargs={\"length_penalty\":0.8,\"num_beams\":8, \"max_length\": 128}\n        #In the Hugging Face Transformers library, gen_kwargs \n        # is a dictionary of keyword arguments that are passed \n        # to the generate() method of a model. These arguments can \n        # be used to control the generation process, such as the \n        # maximum length of the output, the beam size, and the \n        # temperature.\n        \n        pipe=pipeline(\"summarization\",model=self.config.model_path,tokenizer=tokenizer)\n\n        print(\"Dialouge\")\n        print(text)\n\n        output=pipe(text,**gen_kwargs)[0][\"summary_text\"]\n        print(\"\\n Model Summary\")\n        print(output)\n\n\n        return output","repo_name":"CatterPingu/Text-Summarizer","sub_path":"src/TextSummarizer/pipeline/prediction.py","file_name":"prediction.py","file_ext":"py","file_size_in_byte":1143,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"8073819072","text":"# Author: BeiYu\n# Github: https://github.com/beiyuouo\n# Date  : 2021/4/2 15:48\n# Description:\n\n__author__ = \"BeiYu\"\n\n\nclass Solution:\n    def climbStairs(self, n: int) -> int:\n\n        f = [0 for x in range(n+1)]\n\n        f[0] = 1\n        f[1] = 1\n        for i in range(2, n+1):\n            f[i] = f[i-1] + f[i-2]\n\n        return f[n]\n","repo_name":"beiyuouo/leetcode-python","sub_path":"code/70. 爬楼梯.py","file_name":"70. 爬楼梯.py","file_ext":"py","file_size_in_byte":336,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"25086151029","text":"from pathlib import Path\n\nfrom PyQt5 import uic\nfrom PyQt5.QtCore import pyqtSignal\nfrom PyQt5.QtWidgets import QWidget, QTableWidget, QTableWidgetItem, QPushButton, QHeaderView, QLabel\n\nfrom src.ui_files import Record_UI\nfrom ..database import Database\nfrom ..consts import PLACES, RECORDS_FOUND, RECORDS_NOT_FOUND, RECORDS_WIDGET_COLUMNS, RECORD_RESULT\n\n\n# Меню с отображением рекордов\nclass RecordsMenu(QWidget, Record_UI):\n    \"\"\"\n    Меню рекордов.\n\n    Имеет 4 таблицы по категориям результатов.\n    Получает информацию от класса базы данных, там же и сортируются результаты по возрастанию.\n    Красит топ-3 результаты в соответствующие цвета.\n    \"\"\"\n    record_quit_button: QPushButton\n    record_words_button: QPushButton\n    record_nums_button: QPushButton\n    record_letters_button: QPushButton\n    record_customs_button: QPushButton\n    record_info_label: QLabel\n    record_table: QTableWidget\n    quit = pyqtSignal()\n\n    def __init__(self,  ui_path: Path, db: Database):\n        super().__init__()\n        self.database = db\n        self.init_ui(ui_path)\n\n    def init_ui(self, ui_path: Path):\n        try:\n            uic.loadUi(ui_path / 'ui' / 'record_menu.ui', self)\n        except FileNotFoundError:\n            super().setupUi(self)\n        self.record_quit_button.clicked.connect(self.quit.emit)\n        self.record_words_button.clicked.connect(self.update_record_table)\n        self.record_nums_button.clicked.connect(self.update_record_table)\n        self.record_letters_button.clicked.connect(self.update_record_table)\n        self.record_customs_button.clicked.connect(self.update_record_table)\n\n    # def update_all_tables(self):\n    #     for btn in (self.record_words_button, self.record_letters_button, self.record_nums_button):\n    #         btn.click()\n\n    def update_record_table(self):\n        mode_name = self.sender().text().lower().strip()\n        values = self.database.get_records(mode_name)\n        self.record_table.setRowCount(len(values))\n        if not values:\n            return self.record_info_label.setText(RECORDS_NOT_FOUND)\n        self.record_info_label.setText(RECORDS_FOUND.format(mode_name, len(values)))\n        self.record_table.setColumnCount(len(values[0]))\n        self.record_table.setHorizontalHeaderLabels(RECORDS_WIDGET_COLUMNS)\n        for i, row in enumerate(values):\n            name, record = row\n            self.record_table.setItem(i, 0, QTableWidgetItem(name))\n            self.record_table.setItem(i, 1, QTableWidgetItem(RECORD_RESULT.format(record)))\n        self.record_table.resizeColumnsToContents()\n        self.record_table.horizontalHeader().setSectionResizeMode(QHeaderView.Stretch)\n        self.paint_record_table(values)\n\n    def paint_record_table(self, values: list[tuple[str, int]]):\n        current_record = values[0][1]\n        current_place = 0\n        current_row = 0\n        for row in values:\n            record = row[1]\n            if current_record != record:\n                current_record = record\n                current_place += 1\n            if current_place > 2:\n                break\n            self.record_table.item(current_row, 0).setBackground(PLACES[current_place])\n            self.record_table.item(current_row, 1).setBackground(PLACES[current_place])\n            current_row += 1\n","repo_name":"K1rL3s/BlindTyping","sub_path":"src/widgets/RecordsWidget.py","file_name":"RecordsWidget.py","file_ext":"py","file_size_in_byte":3477,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"42581157901","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Oct 25 18:59:13 2021\n\n@author: MLZ107\n\"\"\"\n\nimport requests\nimport string\nfrom bs4 import BeautifulSoup\nfrom urllib import parse\n\nurl='http://m.bendibao.com/city.php'\npage= requests.get(url = url)\npage.encoding='GBK2312'\n\n#print(page.text)\nfp = open('bendibao.txt','w',encoding='utf-8')\nfp.write(page.text)\nfp.close()\n\nsoup=BeautifulSoup(page.text,'html.parser')\nsoup=soup.find_all('a')\ncity_sum_list={}\nfor i in soup:\n    if i.has_attr('href'):\n        if i.string in string.ascii_uppercase or i.string in ['移动版','电脑版','意见建议']:\n            continue\n        city_sum_list[i.string] = i['href']\n#print(city_sum_list)\n#保存到TXT文件\nfp = open('本地宝城市网址.txt','w',encoding='utf-8')\nfor i in city_sum_list:\n    fp.write(i)\n    fp.write('\\t')\n    fp.write(city_sum_list[i])\n    fp.write('\\n')\nfp.close()\n\ncity_name_list = [\"北京\",\"上海\",\"广州\",\"成都\",\"杭州\",\"重庆\",\"西安\",\"苏州\",\n                  \"武汉\",\"南京\",\"天津\",\"郑州\",\"长沙\",\"东莞\",\"佛山\",\"青岛\",\"沈阳\"]       \ndest_url_list = []\nfor i in city_name_list:\n    dest_url_list.append(city_sum_list[i])\n#print(dest_url_list)\n#保存到TXT文件\nfp = open('本地宝一线城市网址.txt','w',encoding='utf-8')\nfor i in city_name_list:\n    fp.write(i)\n    fp.write('\\t')\n    fp.write(city_sum_list[i])\n    fp.write('\\n')\nfp.close()\n\n\nquery_url_list=[]\nn=0\nfor i in dest_url_list:\n    page = requests.get(url=i)\n    page.encoding = 'GBK2312'\n    soup = BeautifulSoup(page.text,\"html.parser\")\n    soup = soup.select(\".search\")\n    #print(type(soup))\n    query = soup[0]['href']\n    #print(type(query)) #<class 'str'>\n    #print(query.split('?')[0]) \n    key_str = parse.quote(city_name_list[n] + \"2021人才落户及补贴政策\")\n    query_url = query.split('?')[0]+'?q='+key_str+'&click=1&'+query.split('?')[1]+'&nsid='\n    query_url_list.append(query_url)\n    #print(query_url)\n    n += 1\n#报存到TXT文件\nn = 0\nfp = open('./一线城市人才引进网址.txt','w',encoding='utf-8')\nfor i in query_url_list:\n    fp.write(city_name_list[n]+'\\t')\n    fp.write(i+'\\n')\n    n += 1\nfp.close()\n\npage_num = 2\nquery_url_city_list=[]\nfor i in query_url_list:\n    page = requests.get(url = i)\n    page.encoding = 'GBK2312'\n    soup = BeautifulSoup(page.text,'html.parser')\n    #print(soup)\n    soup = soup.select('div > h3 > a',limit = page_num)\n    query_url_city_list.append(soup[0]['href'])\n    query_url_city_list.append(soup[1]['href'])\n#print(query_url_city_list)\nfp = open('人才引进具体内容网址.txt','w',encoding='utf-8')\nfor i in query_url_city_list:\n    fp.write(i+'\\n')\nfp.close()  \n\n# i = 0 \n# while(i < len(city_name_list)):\n#     if(city_name_list[i] == '武汉'):\n#         break\n#     i += 1\n# #print(i)\n# url = query_url_city_list[2*i]\n# page = requests.get(url = url)\n# page.encoding = 'GBK2312'\n# soup = BeautifulSoup(page.text,'html.parser')\n","repo_name":"yanglinqi107/Python","sub_path":"本地宝信息爬取/bigwork.py","file_name":"bigwork.py","file_ext":"py","file_size_in_byte":2926,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"32404616210","text":"from selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions as EC\n\n\ndef test_busket_button_exists(browser):\n    link = \"http://selenium1py.pythonanywhere.com/catalogue/coders-at-work_207/\"\n    browser.get(link)\n    assert is_element_by_css_exist(browser,\n                                   \"form#add_to_basket_form > button[type='submit']\"), print(\"basket button not found\")\n\n\ndef is_element_by_css_exist(browser, css_selector):\n    wait = WebDriverWait(browser, 5)\n    elements = wait.until(EC.presence_of_all_elements_located((By.CSS_SELECTOR, css_selector)))\n    return True if elements else False\n","repo_name":"omentos/PySeStepikCourse","sub_path":"StepikSeleniumPython/test_item.py","file_name":"test_item.py","file_ext":"py","file_size_in_byte":700,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"5209399404","text":"ss_num = input(\"Enter the social security number\")     # asking user for social security number\nfor i in ss_num:                    # using loop to access every character\n    result = i.isdigit()            # checking if the character is digit\n    if not result:\n        status = True               # status if non-digit character found\n        break\n    else:\n        status = False\n        continue\nif status:                          # checking status and printing relevant message\n    print(\"The security number contains non-digits\")\nelse:\n    print(\"The security number does not contain non-digits\")\n","repo_name":"Mayank-B01/Python-programming","sub_path":"Week 7/Workshop/2.11.py","file_name":"2.11.py","file_ext":"py","file_size_in_byte":605,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"6880123499","text":"from ..utils import get_data_from_api\nfrom django.test import TestCase\nfrom unittest.mock import patch\n\ndata = {\"Title\": \"Gangs of New York\", \"Year\": \"2002\", \"Rated\": \"R\", \"Released\": \"20 Dec 2002\", \"Runtime\": \"167 min\",\n        \"Genre\": \"Crime, Drama\", \"Director\": \"Martin Scorsese\",\n        \"Writer\": \"Jay Cocks, Steven Zaillian, Kenneth Lonergan\",\n        \"Actors\": \"Leonardo DiCaprio, Cameron Diaz, Daniel Day-Lewis\",\n        \"Plot\": \"In 1862, Amsterdam Vallon returns to the Five Points area of New York City seeking revenge against Bill the Butcher, his father's killer.\",\n        \"Language\": \"English, Irish Gaelic, Chinese, Latin\", \"Country\": \"United States, Italy\",\n        \"Awards\": \"Nominated for 10 Oscars. 50 wins & 135 nominations total\",\n        \"Poster\": \"https://m.media-amazon.com/images/M/MV5BNDg3MmI1ZDYtMDZjYi00ZWRlLTk4NzEtZjY4Y2U0NjE5YmRiXkEyXkFqcGdeQXVyNzAxMjE1NDg@._V1_SX300.jpg\",\n        \"Ratings\": [{\"Source\": \"Internet Movie Database\", \"Value\": \"7.5/10\"},\n                    {\"Source\": \"Rotten Tomatoes\", \"Value\": \"73%\"},\n                    {\"Source\": \"Metacritic\", \"Value\": \"72/100\"}], \"Metascore\": \"72\", \"imdbRating\": \"7.5\",\n        \"imdbVotes\": \"421,762\", \"imdbID\": \"tt0217505\", \"Type\": \"movie\", \"DVD\": \"01 Jul 2003\",\n        \"BoxOffice\": \"$77,812,000\",\n        \"Production\": \"N/A\", \"Website\": \"N/A\", \"Response\": \"True\"}\n\n\nclass MockResponseSuccess:\n  \n  def __init__(self):\n    self.status_code = 200\n  \n  def json(self):\n    return data\n\n\nclass MockResponseFail:\n  \n  def __init__(self):\n    self.status_code = 404\n  \n  def json(self):\n    return {\"Response\": \"False\", \"Error\": \"Movie not found!\"}\n\n\nclass TestGetDataFromApi(TestCase):\n  @patch(\"requests.get\", return_value=MockResponseSuccess())\n  def test_get_correct_name(self, mock_result):\n    title = 'Gangs of New York'\n    result = get_data_from_api(title=title)\n    self.assertEqual(result, data)\n  \n  @patch(\"requests.get\", return_value=MockResponseFail())\n  def test_get_incorrect_name(self, mock_result):\n    title = 'Gang of New York'\n    result = get_data_from_api(title=title)\n    self.assertEqual(result, {\"Response\": \"False\", \"Error\": \"Movie not found!\"})\n","repo_name":"wwlodar/api-movies","sub_path":"api/tests/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":2157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"35960696678","text":"import logging\nlogging.basicConfig(level=logging.DEBUG)\nlogger = logging.getLogger(__name__)\n\n\nclass ResSim:\n    \"\"\"plot and animate res_sim.mat simulation data\n\n    Attributes:\n        filename (str): name of file to load in (MATv5 or HDF5)\n        arfidata (float ndarray): arfidata\n        axial (float ndarray): depth\n        lat (float ndarray): lateral\n        t (float ndarray): time\n        time_scale_factor (float): time scale factor (e.g., s -> ms)\n        disp_scale_factor (float): displacement scale factor\n\n    \"\"\"\n\n    def __init__(self, filename=\"res_sim.mat\"):\n        self.filename = filename\n        self.lat = None\n        self.axial = None\n        self.t = None\n        self.arfidata = None\n        self.time_scale_factor = 1e3  # s -> ms\n        self.disp_scale_factor = 1.0\n\n        self.load()\n\n    def load(self):\n        \"\"\"load MATv5 or HDF5 data\n\n        Args:\n            filename (str): input filename\n\n        Raises:\n            TypeError: MATv5 or HDF5 load exceptions\n\n        \"\"\"\n        from scipy.io import loadmat\n        import h5py\n        from pathlib import Path\n        import numpy as np\n\n        filename = Path(self.filename)\n\n        try:\n            if filename.suffix == '.mat':\n                d = loadmat(filename)\n            elif filename.suffix == '.h5':\n                d = h5py.File(filename)\n        except TypeError:\n            logger.exception(f'{filename} failed to load')\n            raise TypeError\n\n        self.lat = np.squeeze(d['lat'])\n        self.axial = np.squeeze(d['axial'])\n        self.t = np.squeeze(d['t'])* self.time_scale_factor\n        self.arfidata = np.squeeze(d['arfidata']) * self.disp_scale_factor\n\n    def plot(self, timestep, show=True, save=False, savename='file',\n             xlabel='Lateral (mm)', ylabel='Axial (mm)', title=None):\n        \"\"\"Plot arfidata at specified timestep.\n\n        Args:\n            timestep (int):\n            show (Boolean): show plot\n            save (Boolean): save PNG (objectname_timestep.png)\n            savename (str): saved PNG filename prefix\n            xlabel (str):\n            ylabel (str):\n            title (str):\n\n        \"\"\"\n        import matplotlib.pyplot as plt\n\n        fig, axes = plt.subplots()\n\n        axes.pcolormesh(self.lat, self.axial, self.arfidata[:, :, timestep])\n        axes.set_aspect('equal')\n        axes.set_xlabel(xlabel)\n        axes.set_ylabel(ylabel)\n        if title is None:\n            axes.set_title(f't = {self.t[timestep]:.2f} ms')\n        else:\n            axes.set_title(title)\n        axes.invert_yaxis()\n        if save:\n            filename = f'{savename}_{timestep:04d}.png'\n            fig.savefig(filename)\n            logger.info(f'Saved plot: {filename}')\n        if show:\n            fig.show()\n\n    def timeplot(self, axial, lat, xlabel='Time (ms)',\n                 ylabel='Displacement (\\mum)', title=None, show=True):\n        \"\"\"plot arfidata through time at specified ax and lat position (mm)\n\n        Args:\n            axial (ndarray): axial depth (mm)\n            lat (ndarray): lateral position (mm)\n            xlabel (str):\n            ylabel (str):\n            title (str):\n            show (Boolean): show the plot\n\n        Returns:\n\n        \"\"\"\n        import matplotlib.pyplot as plt\n        import numpy as np\n\n        fig, axes = plt.subplots()\n\n        axInd = np.min(np.where(self.axial >= axial))\n        latInd = np.min(np.where(self.lat >= lat))\n\n    def play(self, timerange):\n        axes.plot(self.t, self.arfidata[axInd, latInd, :])\n        axes.set_xlabel(xlabel)\n        axes.set_ylabel(ylabel)\n        if title is None:\n            axes.set_title(f'Axial = {self.axial[axInd]:.1f} mm, Lateral = {self.lat[latInd]:.1f} mm')\n        else:\n            axes.set_title(title)\n\n        if show:\n            fig.show()\n\n    def play(self, timerange, xlabel='Lateral (mm)', ylabel='Axial (mm)',\n             show=True):\n        \"\"\"play an animation\n\n        Strongly recommend not stepping though each timesteps; use some skips!\n\n        Args:\n            timerange (range): range generator of time steps to animate\n            xlabel (str):\n            ylabel (str):\n            show (Boolean): show animation\n\n        Todo:\n            * Add save option\n\n        \"\"\"\n        import matplotlib.pyplot as plt\n        import matplotlib.animation as animation\n\n        fig, axes = plt.subplots()\n\n        axes.pcolormesh(self.lat, self.axial, self.arfidata[:, :, 0])\n        axes.set_aspect('equal')\n        axes.invert_yaxis()\n        axes.set_xlabel(xlabel)\n        axes.set_ylabel(ylabel)\n\n        anim = animation.FuncAnimation(fig, self.animate, frames=timerange,\n                                       blit=False)\n\n        if show:\n            fig.show()\n\n    def animate(self, i):\n        import matplotlib.pyplot as plt\n        plt.pcolormesh(self.lat, self.axial, self.arfidata[:, :, i])\n        plt.title(f't = {self.t[i]:.2f} ms')\n","repo_name":"mlp6/fem","sub_path":"fem/post/ressim.py","file_name":"ressim.py","file_ext":"py","file_size_in_byte":4936,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"76"}
+{"seq_id":"9084325643","text":"import nltk\r\nfrom nltk.corpus import stopwords\r\nfrom nltk.stem.snowball import SnowballStemmer\r\n\r\nwith open(\"english/adventures_of_sherlock_holmes.txt\", 'r', encoding=\"utf8\") as rf:\r\n    text = rf.read()\r\n\r\n#stemmer = SnowballStemmer('english')\r\nlemmatizer = nltk.WordNetLemmatizer()\r\n\r\nmy_stopwords = stopwords.words(\"english\")\r\n\r\ntext = text[text.find(\"*** START OF THIS PROJECT GUTENBERG EBOOK\"):]\r\ntext = text[:text.find(\"*** END OF THIS PROJECT GUTENBERG EBOOK\")]\r\ntext = text.lower()\r\n\r\n# tokenize into sentences:\r\n# sentences = nltk.sent_tokenize(text)\r\n\r\n# tokenize into words:\r\nwords = nltk.word_tokenize(text)\r\n\r\n# filtered_words = []\r\n# for word in words:\r\n#     if word.isalnum() and word not in my_stopwords:\r\n#         filtered_words.append(word)\r\n\r\n# filtered_words = [word for word in words if word.isalnum() and word not in my_stopwords]\r\nfiltered_words = [lemmatizer.lemmatize(word) for word in words if word.isalnum() and word not in my_stopwords]\r\n\r\nunique_words = set(filtered_words)\r\n\r\nlexical_diversity = len(unique_words)/len(filtered_words)\r\n\r\nprint(lexical_diversity)\r\n\r\nmy_document = nltk.Text(filtered_words)\r\n\r\n# my_document.concordance(\"Holmes\", width=50, lines=25)\r\n# my_document.dispersion_plot([\"Holmes\", \"Watson\"])\r\n# my_document.collocations()\r\n\r\n# get word frequencies\r\nfrequencies = nltk.FreqDist(my_document)\r\n#print(frequencies['Holmes'])\r\nmost_common = frequencies.most_common(10)\r\nprint(most_common)\r\n\r\n# print(frequencies.hapaxes())","repo_name":"vierth/fromZerotoN","sub_path":"fromzeroton/nltk_demo_2.py","file_name":"nltk_demo_2.py","file_ext":"py","file_size_in_byte":1474,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"76"}
+{"seq_id":"29956355750","text":"from collections import deque\r\nn = int(input())\r\narr = [list(map(int,input().split())) for _ in range(n)]\r\ninDegree = [0]*(n+1)\r\ngraph = [[] for _ in range(n+1)]\r\ntime = [0]\r\nfor i in range(n):\r\n    new = arr[i][1:-1]\r\n    time.append(arr[i][0])\r\n    if new:\r\n        for idx in new:\r\n            inDegree[i+1] += 1\r\n            graph[idx].append(i+1)\r\n\r\nq = deque()\r\nfor i in range(1,n+1):\r\n    if not inDegree[i]:\r\n        q.append(i)\r\nres = [0]*(n+1)\r\n\r\nwhile q:\r\n    node = q.popleft()\r\n    res[node] = max(res[node],time[node])\r\n    for target in graph[node]:\r\n        inDegree[target] -= 1\r\n        res[target] = max(res[target],res[node]+time[target])\r\n        if inDegree[target] == 0:\r\n            q.append(target)\r\nfor i in range(1,n+1):\r\n    print(res[i])","repo_name":"sururuu/TIL","sub_path":"Baekjoon_Algorithm/1516_게임 개발.py","file_name":"1516_게임 개발.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"40910417801","text":"# 테트리스 하듯이 장식판을 주어진 순서대로 상자에 높이를 넘지 않게 쌓을 수 있는지 확인하고\n# 넘을 경우 새 박스에 넣는다. 그리고 다 넣은 후 상자에 채워진 만큼의 높이들을 구하면 되는 문제이다.\n# 구현을 간단히 할 방법이 있는지 좀 더 생각해봐야겠다.\n# 구현을 모두 마쳤는데, 반레를 찾아봐야한다.\n# 좀 더 간단한 방법으로 구현해서 반례를 찾기 수월하게 하려고한다.\n\n# 기존의 방법은 테트리스와 같은 방식으로 장식판이 채워진 상자 높이 바로 다음에서\n# 부터 내려와서 상자의 높이를 넘지 않게 채울 수 있는지로 구현하였는데\n# 채울 수 있는지 확인하는 과정에서 박스의 높이를 지정하는 인덱스나 장식판의 높이를 지정하는\n# 인덱스 등 지정하기 까다로웠다.\n\n# 그래서 각 장식판의 저장을 다르게 하기로 했다.\n# 각 장식판은 h * w의 리스트로 표현되는게 아닌 2차원 리스트로 표현되며\n# 해당 너비에 존재하는 장식판의 제일 높은 부분의 빈 곳의 수, 제일 낮은 곳의 빈 곳의 수를 나타낸다.\n# 예를 들어, ['XXX..', '..X..', '..XXX', '..X..']로 나타냈던 장식판을\n# [0, 0, 0, 2, 2], [2, 2, 0, 1, 1]로 나타낸다. 각 인덱스는 너비에 해당하는 인덱스.\n# 값은 너비 인덱스에서 제일 높은 높이와 낮은 높이를 나타낸 값이다.\n# 이제 장식판을 쌓을때, 제일 마지막에 쌓은 장식판의 높은 부분을 나타낸 리스트와\n# 쌓을 장식판의 제일 낮은 부분을 나타낸 리스트와 비교를 한다.\n# 예를 들어, ['XXXXX', '.XXXX', '..XXX', '...XX', '....X']의 장식판 위에\n# 아까 예시로 든 장식판을 쌓는 상황일떄, [0, 0, 0, 0, 0] 위에 [3, 3, 0, 1, 1]를 쌓는다.\n# 두 리스트를 합하게 되면, [3, 3, 0, 1, 1]이 나오고 이 중 최소값은 0이 나오는데,\n# 0만큼 위에 쌓은 장식판이 밑으로 내려갔다는 것을 말한다. 즉 바로 위에 쌓았다는 의미이다.\n#\n# 빈 곳에 맞게 채워지는 경우 : ['XXX..', '..X..', '..XXX', '..X..'] 위에\n# ['XXXXX', '.XXXX', '..XXX', '...XX', '....X']가 오는 경우\n# [0, 0, 0, 2, 2]와 [4, 3, 2, 1, 0]합은 [4, 3, 2, 3, 2] 최솟값은 2\n# 2만큼 장식판이 밑으로 이동해서 쌓였다는 뜻이다.\n# 그리고 기록해둔 쌓인 장식판의 높이와 새로 쌓을 장식판의 높이 - 밑으로 이동해서 쌒은 높이를 구하고\n# 해당 구한 값이 b보다 크면 새로 박스를 장만해서 새로 쌓고 높이를 기록한다.\n# 결과 맞음!\n#\n# ............................실패했던 풀이..........................\n# def check_all_height(d, b_idx):\n#     check = True\n#     c = 0\n#     for i in range(len(d) - 1, -1, -1):\n#         if check:\n#             for j in range(w):\n#                 if b_idx + c >= b:\n#                     return False\n#                 else:\n#                     if d[i][j] == 'X' and box_list[b_idx + c][j] == 'X':\n#                         return False\n#         c += 1\n#     return check\n#\n#\n# def check_decorate_put(d, b_idx):\n#     start = b_idx\n#     temp = -1\n#     while 0 <= start <= b:\n#         # print(start)\n#         if check_all_height(d, start):\n#             temp = start\n#             start -= 1\n#         else:\n#             if temp >= 0:\n#                 return temp\n#             else:\n#                 start -= 1\n#     return temp\n#\n#\n# def solve_1():\n#     box_list = [['.'] * w for _ in range(b)]\n#     box_index = 0\n#     box_add = False\n#     idx = 0\n#     while idx < n:\n#         decorate_height = len(decorate_list[idx])\n#         to_check_list = decorate_list[idx]\n#         if not box_index:\n#             for h in range(decorate_height):\n#                 box_list[h] = to_check_list[decorate_height - 1 - h]\n#             box_index += decorate_height\n#             idx += 1\n#         else:\n#             max_box_height = check_decorate_put(to_check_list, box_index)  # 새로 장식판을 집어 넣을때 최대 넣을 수 있는 높이\n#             # print(max_box_height)\n#             if max_box_height == -1:\n#                 box_add = True\n#             else:\n#                 if max_box_height + decorate_height > b:\n#                     box_add = True\n#                 else:\n#                     c = 1\n#                     for i in range(max_box_height, max_box_height + decorate_height):\n#                         for j in range(w):\n#                             if box_list[i][j] == '.' and to_check_list[decorate_height - c][j] == 'X':\n#                                 box_list[i][j] = 'X'\n#                         c += 1\n#                     box_index = max_box_height + decorate_height\n#                     idx += 1\n#         if box_add:\n#             answer.append(box_index)\n#             box_index = 0\n#             box_list = [['.'] * w for _ in range(b)]\n#     if box_index:\n#         answer.append(box_index)\n#     print(*answer)\n\n\nn, w, b = map(int, input().split())  # n은 장식판의 종류, w는 장식판과 박스의 너비,\n# b는 박스의 높이\nanswer = []\ndecorate_list = []\ntop_bottom_list = []\nh_list = []\nfor i in range(n):\n    h = int(input())  # 장식판의 높이\n    h_list.append(h)\n    decorate_list.append([list(input().rstrip()) for _ in range(h)])\nfor decorate in decorate_list:\n    top, bottom = [], []\n    for i in range(w):\n        count = 0\n        for j in range(len(decorate)):\n            if decorate[j][i] == '.':\n                count += 1\n            else:\n                break\n        top.append(count)\n    for i in range(w):\n        count = 0\n        for j in range(len(decorate) - 1, -1, -1):\n            if decorate[j][i] == '.':\n                count += 1\n            else:\n                break\n        bottom.append(count)\n    top_bottom_list.append([top, bottom])\nfor i in range(len(top_bottom_list)):\n    if not i:\n        present_height = h_list[0]\n    else:\n        prev_top = top_bottom_list[i - 1][0]\n        pres_bottom = top_bottom_list[i][1]\n        result = []\n        for j in range(w):\n            result.append(prev_top[j] + pres_bottom[j])\n        move_down_height = min(result)\n        if present_height + h_list[i] - move_down_height > b:\n            answer.append(present_height)\n            present_height = h_list[i]\n        else:\n            present_height += h_list[i] - move_down_height\nif present_height:\n    answer.append(present_height)\nprint(*answer)\n","repo_name":"kangmj921/personal_training","sub_path":"백준문제/시뮬레이션/1997_박스포장.py","file_name":"1997_박스포장.py","file_ext":"py","file_size_in_byte":6543,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"13928407701","text":"from future.builtins import *\nfrom past.builtins import basestring\n\nimport streamsx.topology.topology\nimport streamsx.topology.schema\n\n\nclass ExtensionOperator(object):\n    def __init__(self,topology,kind,inputs=None,schemas=None,params=None,name=None):\n        self.topology = topology\n        if params is None:\n            params = dict()\n        self.__op = topology.graph.addOperator(kind=kind,name=name)\n        # Add parameters\n        if params is not None:\n            self._op().setParameters(params)\n        self.__inputs(inputs)\n        self.__outputs(schemas)\n\n    def _op(self):\n        return self.__op\n\n    @property\n    def params(self):\n        \"\"\"Parameters for the operator invocation.\n        \"\"\"\n        return self._op().params\n\n    def _add_input(self, _input):\n        win_cfg = None\n        if isinstance(_input, streamsx.topology.topology.Window):\n            win_cfg = _input._config\n            _input = _input.stream\n        self._op().addInputPort(outputPort=_input.oport, name=_input.name, window_config = win_cfg)\n        self._inputs.append(_input)\n\n    def __inputs(self, inputs):\n        if inputs is not None:\n            self._inputs = []\n            try:\n                for _input in inputs:\n                    self._add_input(_input)\n            except TypeError:\n                # not iterable, single input\n                self._add_input(inputs)\n\n    def __outputs(self, schemas):\n        self.outputs = []\n        if schemas is not None:\n            stream_name = None\n            if isinstance(schemas, basestring):\n                schemas = (schemas,)\n                stream_name = self._op().name\n\n            try:\n                for schema in schemas:\n                    schema = streamsx.topology.schema._stream_schema(schema)\n                    oport = self._op().addOutputPort(schema=schema, name=stream_name)\n                    self.outputs.append(streamsx.topology.topology.Stream(self.topology, oport)._make_placeable())\n            except TypeError:\n                # not iterable, single schema\n                schema = streamsx.topology.schema._stream_schema(schemas)\n                oport = self._op().addOutputPort(schema=schema, name=self._op().name)\n                self.outputs.append(streamsx.topology.topology.Stream(self.topology, oport)._make_placeable())\n","repo_name":"wmarshall484/streamsx.topology","sub_path":"com.ibm.streamsx.topology/opt/python/packages/streamsx/topology/exop.py","file_name":"exop.py","file_ext":"py","file_size_in_byte":2328,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"76"}
+{"seq_id":"40777999720","text":"import copy\nimport sys\n\nfrom tjf.list.utils import hashFirst, hashPredefined\n\n\nclass File:\n    def __init__(self,\n                 lines     = None,\n                 path      = None,\n                 basename  = '-',\n                 basedir   = './',\n                 strip     = False,\n                 newline   = False,\n                 encoding  = 'utf-8',\n                 delimiter = None,\n                 hash      = None,\n                 keys      = None):\n        self.lines      = lines\n        self.keys       = keys\n        self.binary     = None\n        self._path      = path\n        self._basename  = basename\n        self._basedir   = basedir\n        self._strip     = strip\n        self._newline   = newline\n        self._encoding  = encoding\n        self._delimiter = delimiter\n        self._hash      = hash.lower()\n\n        if self._path is None:\n            if basedir is not None and basename is not None:\n                if basedir[-1] != '/':\n                    basedir += '/'\n                self._path = basedir + basename\n\n        if lines is None:\n            self.lines = list()\n\n        self._fh = None\n\n\n    def __open__(self, mode):\n        \"\"\"Open file in MODE.\"\"\"\n        if self._path and self._path != '-':\n            if mode == 'rb':\n                self._fh = open(self._path, mode=mode)\n            else:\n                self._fh = open(self._path, encoding=self._encoding, mode=mode)\n        else:\n            self._fh = sys.stdout\n\n\n    def __put__(self, lines, newline, delimiter):\n        \"\"\"Write lines to file.\"\"\"\n        if delimiter:\n            if type(lines[0]) == list:\n                output = [delimiter.join(line) for line in lines]\n            else:\n                output = [delimiter.join(lines)]\n        else:\n            output = copy.deepcopy(lines)\n\n        if newline:\n            output = list(map(lambda x: '{:s}\\n'.format(x), output))\n\n        with self._fh as f:\n            f.writelines(output)\n\n\n    def __str__(self):\n        copiedLines = copy.deepcopy(self.lines)\n\n        if self._limiter:\n            if type(copiedLines[0]) == list:\n                copiedLines = [self._delimiter.join(line) for line in copiedLines]\n            else:\n                copiedLines = [self._delimiter.join(copiedLines)]\n\n        copiedLines = [line.rstrip() for line in copiedLines]\n\n        copiedLines.append('\\n')\n\n        return '\\n'.join(copiedLines)\n\n\n    def set(self, **kwargs):\n        \"\"\"Set private attributes.\"\"\"\n        for key, value in kwargs.items():\n            if key == 'path':\n                self._path = value\n            elif key == 'basename':\n                self._basename = value\n            elif key == 'basedir':\n                self._basedir = value\n            elif key == 'strip':\n                self._strip = value\n            elif key == 'newline':\n                self._newline = value\n            elif key == 'encoding':\n                self._encoding = value\n            elif key == 'delimiter':\n                self._encoding = value\n\n            if self._path is None:\n                if self._basedir is not None and self._basename is not None:\n                    if self._basedir[-1] != '/':\n                        self._basedir += '/'\n                    self._path = self._basedir + self._basename\n\n\n    def append(self, text, newline=None, delimiter=None):\n        \"\"\"Appends TEXT to file.\"\"\"\n        self.__open__('a')\n\n        dl = delimiter if delimiter else self._delimiter\n        nl = newline   if newline   else self._newline\n\n        self.__put__(text, nl, dl)\n\n\n    def get(self, strip=None, delimiter=None):\n        \"\"\"Read file into LINES.\"\"\"\n        self.__open__('r')\n\n        dl = delimiter if delimiter else self._delimiter\n        st = strip     if strip     else self._strip\n\n        with self._fh as f:\n            self.lines = f.readlines()\n\n        if st:\n            self.lines = [line.rstrip() for line in self.lines]\n\n        if dl:\n            if self._hash:\n                if self._hash == 'first':\n                    self.keys, self.lines = hashFirst(self.lines, delimiter=dl)\n                elif self._hash == 'predefined':\n                    if self.keys:\n                        self.lines = hashPredefined(self.lines, self.keys, delimiter=dl)\n                    else:\n                        raise Exception(\"Predefined keys are undefined!!!\")\n                else:\n                    raise Exception(\"Unknown hash option: {:s}\".format(str(self._hash)))\n            else:\n                self.lines = [line.split(dl) for line in self.lines]\n\n\n    def put(self, newline=None, delimiter=None):\n        \"\"\"Write file.\"\"\"\n        self.__open__('w')\n\n        dl = delimiter if delimiter else self._delimiter\n        nl = newline   if newline   else self._newline\n\n        self.__put__(self.lines, nl, dl)\n\n\n    def read(self):\n        \"\"\"Binary read.\"\"\"\n        self.__open__('rb')\n\n        with self._fh as f:\n            self.binary = f.read()\n","repo_name":"aleph777/ide","sub_path":"lib/python/tjf/file/io.py","file_name":"io.py","file_ext":"py","file_size_in_byte":4983,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"74634950646","text":"# This code is originally from the official ActivityNet repo\n# https://github.com/activitynet/ActivityNet\n\nimport json\nimport urllib.request\nimport os, pickle\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nAPI = 'http://ec2-52-11-11-89.us-west-2.compute.amazonaws.com/challenge17/api.py'\n\n\ndef get_blocked_videos(api=API):\n    api_url = '{}?action=get_blocked'.format(api)\n    req = urllib.request.Request(api_url)\n    response = urllib.request.urlopen(req)\n    return json.loads(response.read().decode('utf-8'))\n\n\ndef interpolated_prec_rec(prec, rec):\n    \"\"\"Interpolated AP - VOCdevkit from VOC 2011.\n    \"\"\"\n    mprec = np.hstack([[0], prec, [0]])\n    mrec = np.hstack([[0], rec, [1]])\n    for i in range(len(mprec) - 1)[::-1]:\n        mprec[i] = max(mprec[i], mprec[i + 1])\n    idx = np.where(mrec[1::] != mrec[0:-1])[0] + 1\n    ap = np.sum((mrec[idx] - mrec[idx - 1]) * mprec[idx])\n    return ap\n\n\ndef segment_iou(target_segment, candidate_segments):\n    \"\"\"Compute the temporal intersection over union between a\n    target segment and all the test segments.\n\n    Parameters\n    ----------\n    target_segment : 1d array\n        Temporal target segment containing [starting, ending] times.\n    candidate_segments : 2d array\n        Temporal candidate segments containing N x [starting, ending] times.\n\n    Outputs\n    -------\n    tiou : 1d array\n        Temporal intersection over union score of the N's candidate segments.\n    \"\"\"\n    tt1 = np.maximum(target_segment[0], candidate_segments[:, 0])\n    tt2 = np.minimum(target_segment[1], candidate_segments[:, 1])\n    # Intersection including Non-negative overlap score.\n    segments_intersection = (tt2 - tt1).clip(0)\n    # Segment union.\n    segments_union = (candidate_segments[:, 1] - candidate_segments[:, 0]) \\\n                     + (target_segment[1] - target_segment[0]) - segments_intersection\n    # Compute overlap as the ratio of the intersection\n    # over union of two segments.\n    tIoU = segments_intersection.astype(float) / segments_union\n    return tIoU\n\n\ndef wrapper_segment_iou(target_segments, candidate_segments):\n    \"\"\"Compute intersection over union btw segments\n    Parameters\n    ----------\n    target_segments : ndarray\n        2-dim array in format [m x 2:=[init, end]]\n    candidate_segments : ndarray\n        2-dim array in format [n x 2:=[init, end]]\n    Outputs\n    -------\n    tiou : ndarray\n        2-dim array [n x m] with IOU ratio.\n    Note: It assumes that candidate-segments are more scarce that target-segments\n    \"\"\"\n    if candidate_segments.ndim != 2 or target_segments.ndim != 2:\n        raise ValueError('Dimension of arguments is incorrect')\n\n    n, m = candidate_segments.shape[0], target_segments.shape[0]\n    tiou = np.empty((n, m))\n    for i in xrange(m):\n        tiou[:, i] = segment_iou(target_segments[i, :], candidate_segments)\n\n    return tiou\n\n\ndef open_set_detection_rate(preds, pred_cls, gt_cls):\n    \"\"\" preds: ndarray, (N,), [0, 1]\n        pred_cls: ndarray, (N,), >0: known\n        gt_cls: ndarray, (N,), 0: unknown, >0: known\n    \"\"\"\n    \n    x1, x2 = preds[gt_cls > 0], preds[gt_cls == 0]  # known preds & unknown preds\n    m_x1 = np.zeros(len(x1))\n    m_x1[pred_cls[gt_cls > 0] == gt_cls[gt_cls > 0]] = 1  # for known preds, fraction of correct cls\n    k_target = np.concatenate((m_x1, np.zeros(len(x2))), axis=0)  # target of correct known (in all preds)\n    u_target = np.concatenate((np.zeros(len(x1)), np.ones(len(x2))), axis=0)  # target of all unknown (in all preds)\n    predict = np.concatenate((x1, x2), axis=0)  # re-organize pred score\n    n = len(preds)\n\n    # Cutoffs are of prediction values\n    CCR = [0 for x in range(n+2)]\n    FPR = [0 for x in range(n+2)]\n\n    # sort the targets by ascending order of predictions\n    idx = predict.argsort()\n    s_k_target = k_target[idx]\n    s_u_target = u_target[idx]\n\n    # for each cut-off score\n    for k in range(n-1):\n        # accumulated sum\n        CC = s_k_target[k+1:].sum()\n        FP = s_u_target[k:].sum()\n        # True\tPositive Rate\n        CCR[k] = float(CC) / float(len(x1)) if len(x1) > 0 else 1.0  # fraction of correct classification in known preds\n        # False Positive Rate\n        FPR[k] = float(FP) / float(len(x2)) if len(x2) > 0 else 0.0 # fraction of unknown preds that are classified as any known\n    \n    # extreme cases\n    CCR[n] = 0.0\n    FPR[n] = 0.0\n    CCR[n+1] = 1.0\n    FPR[n+1] = 1.0\n\n    # Positions of ROC curve (FPR, TPR)\n    ROC = sorted(zip(FPR, CCR), reverse=True)  # descending order by FPR\n    OSCR = 0\n    # Compute AUROC Using Trapezoidal Rule\n    for j in range(n+1):\n        w =   ROC[j][0] - ROC[j+1][0]  # delta_FPR\n        h =  (ROC[j][1] + ROC[j+1][1]) / 2.0  # mean_CCR\n        OSCR = OSCR + h*w  # area under the curve\n    return OSCR, FPR, CCR\n\n\ndef save_curve_data(roc_data, pr_data, save_path, vis=False, fontsize=18):\n    os.makedirs(save_path, exist_ok=True)\n    # save roc data\n    with open(os.path.join(save_path, 'roc_data.pkl'), 'wb') as f:\n        pickle.dump(roc_data, f, pickle.HIGHEST_PROTOCOL)\n    # save pr data\n    with open(os.path.join(save_path, 'pr_data.pkl'), 'wb') as f:\n        pickle.dump(pr_data, f, pickle.HIGHEST_PROTOCOL)\n    # draw curves\n    if vis:\n        line_styles = ['r-', 'c-', 'g-', 'b-', 'k']\n        # plot roc curve\n        plt.figure(figsize=(8, 5))\n        for tidx, (fpr, tpr, auc, tiou) in enumerate(zip(roc_data['fpr'], roc_data['tpr'], roc_data['auc'], roc_data['tiou'])):\n            plt.plot(fpr, tpr, line_styles[tidx], label=f'tIoU={tiou}, auc={auc*100:.2f}%')\n        plt.xlabel('False Positive Rate', fontsize=fontsize)\n        plt.ylabel('True Positive Rate', fontsize=fontsize)\n        plt.xticks(fontsize=fontsize)\n        plt.yticks(fontsize=fontsize)\n        plt.legend(fontsize=fontsize)\n        plt.tight_layout()\n        plt.savefig(os.path.join(save_path, 'AUC_ROC.png'))\n        plt.close()\n        # plot pr curve\n        plt.figure(figsize=(8, 5))\n        for tidx, (precision, recall, auc, tiou) in enumerate(zip(pr_data['precision'], pr_data['recall'], pr_data['auc'], pr_data['tiou'])):\n            plt.plot(recall, precision, line_styles[tidx], label=f'tIoU={tiou}, auc={auc*100:.2f}%')\n        plt.xlabel('Recall', fontsize=fontsize)\n        plt.ylabel('Precision', fontsize=fontsize)\n        plt.xticks(fontsize=fontsize)\n        plt.yticks(fontsize=fontsize)\n        plt.legend(fontsize=fontsize)\n        plt.tight_layout()\n        plt.savefig(os.path.join(save_path, 'AUC_PR.png'))\n        plt.close()\n\n\ndef save_curve_osdr_data(osdr_data, save_path, vis=False, fontsize=18):\n    os.makedirs(save_path, exist_ok=True)\n    # save roc data\n    with open(os.path.join(save_path, 'osdr_data.pkl'), 'wb') as f:\n        pickle.dump(osdr_data, f, pickle.HIGHEST_PROTOCOL)\n    # draw curves\n    if vis:\n        line_styles = ['r-', 'c-', 'g-', 'b-', 'k']\n        # plot roc curve\n        plt.figure(figsize=(8, 5))\n        for tidx, (fpr, cdr, osdr, tiou) in enumerate(zip(osdr_data['fpr'], osdr_data['cdr'], osdr_data['osdr'], osdr_data['tiou'])):\n            plt.plot(fpr[:-2], cdr[:-2], line_styles[tidx], label=f'tIoU={tiou}, auc={osdr*100:.2f}%')\n        plt.xlabel('False Positive Rate', fontsize=fontsize)\n        plt.ylabel('Correct Detection Rate', fontsize=fontsize)\n        plt.xticks(fontsize=fontsize)\n        plt.yticks(fontsize=fontsize)\n        plt.legend(fontsize=fontsize)\n        plt.tight_layout()\n        plt.savefig(os.path.join(save_path, 'AUC_OSDR.png'))\n        plt.close()","repo_name":"Cogito2012/OpenTAL","sub_path":"AFSD/evaluation/utils_eval.py","file_name":"utils_eval.py","file_ext":"py","file_size_in_byte":7502,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"76"}
+{"seq_id":"71236118007","text":"from collections import OrderedDict\n\nclass LRU_Cache(object):\n    def __init__(self, capacity):\n        if not isinstance(capacity, int) or capacity <= 0:\n            capacity = 1 # Minimum capacity is at least 1\n\n        # Initialize class variables\n        self._data = OrderedDict()\n        self._capacity = capacity\n\n    def get(self, key):\n        # Retrieve item from provided key. Return -1 if nonexistent. \n        value = self._data.get(key)\n        if value is None:\n            return -1\n        # By removing and re-adding the item, we push it to the bottom of the OrderedDict\n        self._data.pop(key)\n        self._data[key] = value\n        return value\n\n    def set(self, key, value):\n        # Set the value if the key is not present in the cache. If the cache is at capacity remove the oldest item. \n        if len(self._data) == self._capacity:\n            # Have to clear our least used item\n            self._data.popitem(last=False)\n        self._data[key] = value\n\n# Basic functionality tests\nour_cache = LRU_Cache(5)\n\nour_cache.set(1, 1);\nour_cache.set(2, 2);\nour_cache.set(3, 3);\nour_cache.set(4, 4);\n\nprint(our_cache.get(1))\n# returns 1\n\nprint(our_cache.get(2))\n# returns 2\n\nprint(our_cache.get(9))\n# returns -1 because 9 is not present in the cache\n\nour_cache.set(5, 5) \nour_cache.set(6, 6)\n\nprint(our_cache.get(3))\n# returns -1 because the cache reached it's capacity and 3 was the least recently used entry\n\n\n# Edge case: Cache size of 0 -- auto resets this to the minimum cache size of 1\nour_cache = LRU_Cache(0)\n\nour_cache.set(1, 1)\nour_cache.set(2, 2);\nprint(our_cache.get(1))\n# returns -1\n\n# Edge case: Cache size not a number -- auto resets this to the minimum cache size of 1\nour_cache = LRU_Cache(\"asdf\")\n\nour_cache.set(1, 1)\nprint(our_cache.get(1))\n# returns 1\n\n","repo_name":"ikisler/udacity-data-structures-projects","sub_path":"problem_1.py","file_name":"problem_1.py","file_ext":"py","file_size_in_byte":1800,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"17995685012","text":"import networkx as nx\nfrom person.Person import Person\n\n\nclass Stammbaum(nx.DiGraph):\n    \"\"\"\n    Stellt einen Stammbaum dar. Dazu gehören Personen und Beziehungen innerhalb des Stammbaums\n    \"\"\"\n\n    persons: tuple\n\n    def __init__(self, nodes, **attr):\n        super().__init__(**attr)\n        self.persons = nodes\n\n        self.add_nodes_from(self.persons)\n        self.add_relations(self.persons)\n\n    def add_relations(self, person: Person | list[Person] | tuple[Person]) -> None:\n        \"\"\"\n        Adds relationships as edges to graph.\n\n        :param graph:\n        :param person: Object Person, list[Person] or tuple[Person]\n        :return:\n        \"\"\"\n        if type(person) == Person:\n            self.add_edges_from(person.get_relatives)\n        elif type(person) in (Person, list, list[Person], tuple[Person]):\n            for i in person:\n                self.add_edges_from(i.get_relatives())\n        else:\n            print(f\"There was an error in Graph.add_relations(). Unsupported type. Requires either one of\"\n                  f\"(Person, list, list[Person], tuple[Person]) not {type(person)}\")\n\n    def write_graph(self, uri: str) -> None:\n        \"\"\"\n        Saves graph as gml file at <uri>.\n\n        :param graph:\n        :param uri:\n        :return:\n        :raise :\n        \"\"\"\n        nx.write_gml(self, uri)\n\n\nif __name__ == \"__main__\":\n    pass\n","repo_name":"BlueCl0wn/Stammbaum","sub_path":"graph/Stammbaum.py","file_name":"Stammbaum.py","file_ext":"py","file_size_in_byte":1379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"74090321205","text":"import random\r\nm = random.randint(1,100)\r\n\r\nprint('Добро пожаловать в числовую угадайку')\r\n\r\ndef is_valid(n):\r\n    if n.isalnum():\r\n        if 0 < int(n) < 101:\r\n            return True\r\n        else:\r\n            return False\r\n    else:\r\n        return False\r\nprint('Введите число от 1 до 100 ')\r\nwhile True:\r\n    num = input()\r\n    if is_valid(num):\r\n        n = int(num)\r\n    else:\r\n        print('А может быть все-таки введем целое число от 1 до 100?')\r\n        continue\r\n    if n == m:\r\n        print('Вы угадали, поздравляем!')\r\n        break\r\n    elif n < m:\r\n        print('Ваше число меньше загаданного, попробуйте еще разок')\r\n        continue\r\n    elif n > m:\r\n        print('Ваше число больше загаданного, попробуйте еще разок')\r\n        continue\r\nprint('Спасибо, что играли в числовую угадайку. Еще увидимся...')","repo_name":"ydanyok/python_proj","sub_path":"number_guesser.py","file_name":"number_guesser.py","file_ext":"py","file_size_in_byte":1062,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"5012087899","text":"\ndef ends(end):\n    number = 0\n    for i in end:\n        number = number + 1\n    first_last = end[0:2] + end[number - 2: number]\n    return first_last\n\n\ndef mix(s1, s2):\n    mixed_1 = s2[:2] + s1[2:]\n    mixed_2 = s1[:2] + s2[2:]\n    return mixed_1, mixed_2\n\n\ndef main():\n    choice = \"\"\n    while choice != \"stop\":\n        choice = input(\n            \"What do you want to do with the strings? Either end or mix, not both. Type stop to stop: \")\n        if choice == \"end\":\n            user_input = (input(\"Enter a string: \"))\n            x = ends(user_input)\n            print(x)\n        elif choice == \"mix\":\n            x = input(\"Enter a string: \")\n            y = input(\"Enter another string: \")\n            x = mix(x, y)\n            print(x)\n        else:\n            print(\"Huh, what in the name of Schwarzenegger did you type?\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"CryoRenegade/cse107","sub_path":"liveshare/liveshare5/stringfun.py","file_name":"stringfun.py","file_ext":"py","file_size_in_byte":876,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"33916880476","text":"\"\"\"\nGiven an array nums of n integers and an integer target, are there elements \na, b, c, and d in nums such that a + b + c + d = target? Find all unique \nquadruplets in the array which gives the sum of target.\n\nNotice that the solution set must not contain duplicate quadruplets.\n\nExample 1:\n\n    Input: nums = [1,0,-1,0,-2,2], target = 0\n    Output: [[-2,-1,1,2],[-2,0,0,2],[-1,0,0,1]]\n\nExample 2:\n\n    Input: nums = [], target = 0\n    Output: []\n\nConstraints:\n    1. 0 <= nums.length <= 200\n    2. -109 <= nums[i] <= 109\n    3. -109 <= target <= 109\n\"\"\"\n\n\nclass Solution:\n    def fourSum(self, nums, target: int):    # 80ms\n        if not nums:\n            return []\n        \n        n = len(nums)\n        if n < 4:\n            return []\n        elif n == 4:\n            return [nums] if sum(nums) == target else []\n        \n        nums.sort()\n        res, M = set(), nums[-1]\n        for i in range(n - 3):\n            x = nums[i]\n            if x + 3 * M < target:\n                continue\n            elif 4 * x > target:\n                return res\n            \n            for j in range(i + 1, n - 2):\n                y = nums[j]\n                if x + y + 2 * M < target:\n                    continue\n                elif x + 3 * y > target:\n                    break\n\n                t_2sum = target - x - y\n                a, b = j + 1, n - 1\n                while a < b:\n                    cur = nums[a] + nums[b]\n                    if cur == t_2sum:\n                        res.add((nums[i], nums[j], nums[a], nums[b]))\n                        a += 1\n                        b -= 1\n                    elif cur < t_2sum:\n                        a += 1\n                    else:\n                        b -= 1\n\n        return res\n        # return list(map(list, res))\n","repo_name":"chaosWsF/Python-Practice","sub_path":"leetcode/0018_4sum.py","file_name":"0018_4sum.py","file_ext":"py","file_size_in_byte":1783,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"31878861158","text":"# Exercise 40 example\n\n\n# Create the bluprint for the \"Song\" call\nclass Song(object):\n\n\tdef __init__(self, lyrics):\n\t\tself.lyrics = lyrics\n\n\tdef sing_me_a_song(self):\n\t\tfor line in self.lyrics:\n\t\t\tprint(line)\n\n# Make variable to pass into the blueprints\nhappy_bday = Song([\"Happy birthday to you\",\n               \"I don't want to get sued\",\n               \"So I'll stop right there\"])\n\nbulls_on_parade = Song([\"They rally around tha family\",\n                        \"With pockets full of shells\"])\n\n# Call \"Song\" with variables\nhappy_bday.sing_me_a_song()\n\nbulls_on_parade.sing_me_a_song()","repo_name":"aachokey/practice","sub_path":"lpthw/ex40-2.py","file_name":"ex40-2.py","file_ext":"py","file_size_in_byte":589,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"25056058137","text":"\n\ndef yesno2boolean(value):\n    \"\"\"\n    Convert string command line flags to boolean \n    (case insensitive):\n    \n    y -> True\n    yes -> True\n    t -> True\n    true -> True\n    1 -> True\n    Anything else -> False\n    \"\"\"\n    value = value.lower()\n    if value in ('y', 'yes', 't', 'true', '1'):\n        return True\n    else:\n        return False\n\n","repo_name":"cwaldbieser/config-deployer","sub_path":"deployer/fabcmdline.py","file_name":"fabcmdline.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"24767773463","text":"class Solution:\n    def rotate(self, nums, k):\n        \"\"\"\n        rotate array with extra memory\n        time complexity: O(n)\n        space complexity: O(n)\n\n        how to rotate in place?\n        \"\"\"\n        results = [None]*len(nums)\n        for i, num in enumerate(nums):\n            to_i = (i + k) % len(nums)\n            results[to_i] = num\n        return results\n\n    def rotatev2(self, nums, k):\n        \"\"\"\n        observation: after k rotation, elements between k~end are on the left, 1~k-1 are on the right\n        thought: reverse whole array, reverse 1~k-1 and then reverse k~end\n        \"\"\"\n        k = k % len(nums)\n\n        if not nums or not k:\n            return\n\n        self.reverse(nums, 0, len(nums)-1)\n        self.reverse(nums, 0, k-1)\n        self.reverse(nums, k, len(nums)-1)\n        print(nums)\n\n    def reverse(self, nums, l, r):\n        while l < r:\n            nums[l], nums[r] = nums[r], nums[l]\n            l += 1\n            r -= 1\n\ns = Solution()\nr = s.rotatev2([1,2,3,4,5,6,7], 3)","repo_name":"kcajheish/leetcode","sub_path":"189_rotate_list.py","file_name":"189_rotate_list.py","file_ext":"py","file_size_in_byte":1018,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"31592394850","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\nimport pymongo\nimport datetime, time\nimport re\nimport bson\n\nclass Model:\n  '''\n    Base DB model class\n  '''\n  _collection = None # db data collection\n\n  def __init__(self, collection):\n    self._collection = collection\n\n  def get(self, args = None, fields = None):\n    try:\n      return self._collection.find_one(args, fields)\n    except pymongo.errors.PyMongoError as e:\n      raise Exception(str(e))\n\n  def get_list(self, args = None, fields = None):\n    data=[]\n    try:\n      for d in self._collection.find(args, fields):\n        data.append(d)\n      return data\n    except pymongo.errors.PyMongoError as e:\n      raise Exception(str(e))\n\n  def add(self, data):\n    try:\n      self._collection.insert(data)\n    except pymongo.errors.OperationFailure as e:\n      raise Exception(str(e))\n    except pymongo.errors.PyMongoError as e:\n      raise Exception(str(e))\n\n  def update_by_id(self, id, data):\n    try:\n      self._collection.update({'_id':ObjectId(id)},{'$set':data})\n    except pymongo.errors.OperationFailure:\n      raise Exception(str(e))\n    except pymongo.errors.PyMongoError as e:\n      raise Exception(str(e))\n    return data\n\n  def update(self, cond, data, insert_if_notfound=True, multi_update=False):\n    try:\n      self._collection.update(cond, data, upsert=insert_if_notfound,multi=multi_update)\n    except pymongo.errors.OperationFailure as e:\n      raise Exception(str(e))\n    except pymongo.errors.PyMongoError as e:\n      raise Exception(str(e))\n\n  def update_multy(self, filter,data):\n    try:\n      self._collection.update(filter,{'$set':data},multi=True)\n    except pymongo.errors.OperationFailure:\n      raise Exception(str(e))\n    except pymongo.errors.PyMongoError as e:\n      raise Exception(str(e))\n    return data\n\n  def clear(self):\n    try:\n      self._collection.remove(None)\n    except pymongo.errors.OperationFailure:\n      raise Exception(str(e))\n    except pymongo.errors.PyMongoError as e:\n      raise Exception(str(e))\n    return True\n\n  def remove(self, args):\n    try:\n      self._collection.remove(args)\n    except pymongo.errors.OperationFailure:\n      raise Exception(str(e))\n    except pymongo.errors.PyMongoError as e:\n      raise Exception(str(e))\n    return True\n\n  def do_aggregate(self, conditions_arr):\n    try:\n      dataResult = []\n      for row in self._collection.aggregate(conditions_arr):\n        dataResult.append(row)\n      return dataResult\n    except pymongo.errors.PyMongoError as e:\n      raise Exception(\"Error! Can't get data: %s\" %(str(e)))\n","repo_name":"alex2025W/modul","sub_path":"models_v2/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2554,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"41205607495","text":"import setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read()\n\nsetuptools.setup(\n    name=\"pyservica\",\n    version=\"0.0.1\",\n    author=\"Lachlan Glanville\",\n    author_email=\"lachlanglanville@gmail.com\",\n    description=\"Tools for building Preservica V6 SIPs and interacting with\"\n    \"the API\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/lglanville/Pyservica\",\n    packages=setuptools.find_packages(),\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Operating System :: OS Independent\",\n    ],\n    python_requires='>=3.6',\n)\n","repo_name":"lglanville/Pyservica","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":701,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"21643534897","text":"##############################################\n#  2019. 05. 26 ConfigHelper 0.0.3v\n#  Can use it to save or recall preferences from Python.\n##############################################\n\nimport json, os\n\nclass ConfigHelper:\n    def __init__(self, data=None):\n        if isinstance(data, str):\n            self._from_file_(data)\n        elif isinstance(data, dict):\n            self._from_dict_(data)\n        elif isinstance(data, type):\n            self._from_obj_(data())\n        elif data is not None:\n            self._from_obj_(data)\n\n    def __str__(self):\n        return self.toJSON()\n\n    def _from_obj_(self, data):\n        for key in list(vars(data).keys()):\n            self.setValue(key, self.getValue(key, data))\n\n    def _from_file_(self, data:str):\n        with open(data) as data_file:    \n            _data = json.load(data_file)\n        self._from_dict_(_data)\n\n    def _from_dict_(self, data:dict):\n        for key in list(data.keys()):\n            setattr(self, key, data[key])\n\n    def _get_obj_(self, obj):\n        if obj is None or isinstance(obj, str):\n            obj = self\n        return obj\n\n    def _set_path_(self, path):\n        path = path.replace(\"\\\\\", \"/\")\n        if path[0] != '/' and path[1] != ':':\n            path = os.getcwd().replace(\"\\\\\", \"/\") + \"/\" + path\n        return path\n\n    def _create_folder_(self, path):\n        path = self._set_path_(path)\n        if path[-1] == '/':\n            path = path[0:len(path)-1]\n        try:\n            if os.path.isdir(path) is False:\n                os.mkdir(path)\n        except Exception as e:\n            print(\"create Folder : \", e)\n            repath = str(path).split(\"/\")[-1]\n            repath = str(path).split(\"/\"+repath)[0]\n            self._create_folder_(repath)\n            self._create_folder_(path)\n        if path[-1] is not '/':\n            path = path+\"/\"\n        return path\n\n    def getValue(self, key, obj=None):\n        obj = self._get_obj_(obj)\n        return getattr(obj, key, None)\n\n    def setValue(self, key, value, obj=None):\n        obj = self._get_obj_(obj)\n        setattr(obj, key, value)\n    \n    def newObject(self, cls):\n        obj = self._get_obj_(cls())\n        for key in list(vars(obj).keys()):\n            if key in list(vars(self).keys()):\n                self.setValue(key, self.getValue(key), obj)\n        return obj\n\n    def setObject(self, obj):\n        obj = self._get_obj_(obj)\n        for key in list(vars(obj).keys()):\n            if key in list(vars(self).keys()):\n                self.setValue(key, self.getValue(key), obj)\n\n    def toDict(self):\n        data = {}\n        for key in list(vars(self).keys()):\n            data.update({key:self.getValue(key)})\n        return data\n    \n    def toJSON(self):\n        return json.dumps(self.toDict(), indent='\\t', sort_keys=True)\n    \n    def toFile(self, path):\n        file_name = path.split(\"/\")[-1]\n        path = self._create_folder_(path.split(file_name)[0])\n        with open(path+file_name, \"w\") as outfile:\n            json.dump(self.toDict(), outfile, indent='\\t', sort_keys=True)\n","repo_name":"Sotaneum/ConfigHelper","sub_path":"ConfigHelper/ConfigHelper.py","file_name":"ConfigHelper.py","file_ext":"py","file_size_in_byte":3076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"35532034488","text":"\"\"\"#-----------------------------TALLER 5: camara y calibracion---------------------------------------------------------\r\n                              Erick Steven Badillo Vargas\r\n                                Ingenieria Electronica\r\n                           Procesamiento de imagenes y vision\r\n                                    Julian Quiroga\r\n                                         2020\r\n#----------------------------------------------------------------------------------------------------------------------#\"\"\"\r\n\r\nimport numpy as np\r\nimport cv2\r\nimport glob\r\nimport os\r\nimport json\r\n\r\n# Criterio de iteraciones\r\ncriterio = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)\r\n\r\n# Preparar puntos\r\nobjp = np.zeros((7 * 7, 3), np.float32)\r\nobjp[:, :2] = np.mgrid[0:7, 0:7].T.reshape(-1, 2)\r\n\r\n# Arreglos para guardar los puntos del objeto\r\nobjpoints = []  # 3d point in real world space\r\nimgpoints = []  # 2d points in image plane.\r\n\r\n# Ubicacion de las imagenes (tablero de ajedrez)\r\npath = \"/Users/Erick/Desktop/OCTAVO SEMESTRE/PROC DE IMAGENES/Camara y Calibracion/Tablero\"\r\n\r\n# Camara Celular\r\npath_file = os.path.join(path, 'IGM*.PNG')  # Elegir archivos que contengan IGM\r\n# Camara Web\r\n# path_file = os.path.join(path, 'WIN*.jpg')  # Elegir archivos que contengan WIN (camara de Windows)\r\n\r\nimages = glob.glob(path_file)       # Glob de Imagenes\r\n\r\nfor fname in images:                                                # Para cada una de las imagenes :\r\n\r\n    img = cv2.imread(fname)                                         # Leer Imagen\r\n    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)                # Imagen en Grises\r\n\r\n    # Encontrar las esquinas del tablero de Ajedrez\r\n    ret, corners = cv2.findChessboardCorners(img_gray, (7, 7), None)\r\n\r\n    # Si se encuentran las imagenes:\r\n    if ret == True:\r\n        objpoints.append(objp)\r\n\r\n        # encontrar pixel con precision sub-pixel\r\n        corners2 = cv2.cornerSubPix(img_gray, corners, (11, 11), (-1, -1), criterio)\r\n        imgpoints.append(corners2)  # Acumular\r\n\r\n        # Dibujar esquinas sobre la imagen original\r\n        img = cv2.drawChessboardCorners(img, (7, 7), corners2, ret)\r\n        cv2.imshow('img', img)  # mostrar la imagen\r\n        cv2.waitKey(400)        # Esperar 400 ms\r\n\r\ncv2.destroyAllWindows()\r\n\r\n#Calibrar camara\r\nret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_gray.shape[::-1], None, None)\r\n\r\nprint(mtx)\r\nprint(dist)\r\n\r\nfile_name = 'calibration.json'                  # nombre del archivo de calibracion\r\n# file_name = 'calibrationweb.json'               # nombre del archivo de calibracion camara web\r\njson_file = os.path.join(path, file_name)\r\nd = [3.0]\r\ntilt = [30.0]\r\npan = [0]\r\nh = [2.0]\r\n\r\n# convertir a lista para exportar a JSON\r\ndata = {\r\n\r\n    'K': mtx.tolist(),              # Matriz K\r\n    'distortion': dist.tolist(),    # Vector distorcion\r\n    'd' : d,                        # Distancia\r\n    'tilt' : tilt,                  # Angulo Tilt\r\n    'pan' : pan,                    # Angulo Pan\r\n    'h' : h                         # altura H\r\n}\r\n\r\n# Exportar a JSON\r\nwith open(json_file, 'w') as fp:\r\n    json.dump(data, fp, sort_keys=True, indent=1, ensure_ascii=False)\r\n\r\n\r\n\r\n","repo_name":"ErickB24/Taller5","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3238,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"70617198007","text":"from keras import __version__ as kv\nimport numpy as np\nfrom keras.callbacks import Callback\nimport keras.backend as K\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom math import ceil\nfrom itchat.content import TEXT\nimport os\nfrom os import system\nimport re\nimport traceback\n\nclass BatchHistory(Callback):\n    def __init__(self, save_to, save_figure=True, save_data=False, ):\n        self.save_to = save_to\n        self.save_figure = save_figure\n        self.save_data = save_data\n        self.epoch = 0\n    \n    def on_train_begin(self, logs=None):\n        self.batch = []\n        self.batch_history = {}\n        \n        self.epochs = []\n        self.epoch_history = {}\n        \n    def on_batch_end(self, batch, logs=None):\n        logs = logs or {}\n        \n        epoch = self.epoch\n        steps_per_epoch = self.params['steps']\n        # The first batch starts with 1\n        batch = steps_per_epoch * epoch + batch + 1\n        \n        self.batch.append(batch)\n        for k in self.params['metrics']:\n            if k in logs:\n                self.batch_history.setdefault(k, []).append(logs[k])\n                \n    def on_epoch_begin(self, epoch, logs=None):\n        self.epoch = epoch\n    \n    def on_epoch_end(self, epoch, logs=None):\n        save_to = os.path.join(self.save_to,\n                               'batch_history_epoch_' + str(epoch))\n        batch = self.batch\n        \n        epochs_save_to = os.path.join(self.save_to, \n                                      'epoch_history')\n        # Retrieve the validation data\n        self.epochs.append(epoch)\n        for k in logs:\n            if k.startswith('val_'):\n                self.epoch_history.setdefault(k, []).append(logs[k])\n        \n        if self.save_data:\n            np.save(save_to + '.npy', self.batch_history)\n            np.save(epochs_save_to + '.npy', self.epochs)\n        if self.save_figure:\n            # save batch figure\n            row_num = len(self.batch_history)\n            fig, axes = plt.subplots(row_num, figsize=(10, 6 * row_num))\n            i = 0\n            for metric, data in self.batch_history.items():\n                curr_ax = axes[i]\n                curr_ax.plot(batch, data, label=metric)\n                curr_ax.set_title(metric + ' in batch')\n                curr_ax.set_xlabel('batch')\n                curr_ax.set_ylabel(metric)\n                i = i + 1\n            fig.savefig(save_to + '.jpg')\n            plt.close(fig)\n            \n            # Draw the figure\n            fig, axes = plt.subplots(row_num, figsize=(10, 6 * row_num))   \n            i = 0                          \n            for metric, data in self.epoch_history.items():\n                curr_ax = axes[i]\n                curr_ax.plot(self.epochs, data, label=metric)\n                curr_ax.set_title(metric + ' in epoch')\n                curr_ax.set_xlabel('epoch')\n                curr_ax.set_ylabel(metric)\n                i = i + 1\n            fig.savefig(epochs_save_to + '.jpg')\n            plt.close(fig)\n","repo_name":"luofan18/car-segmentation","sub_path":"car-segmentation/callback2.py","file_name":"callback2.py","file_ext":"py","file_size_in_byte":3016,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"18469294549","text":"import tensorflow as tf\nimport tensorflow.keras as keras\nfrom tensorflow.keras import *\nimport numpy as np\nimport model\nimport data_prep\nimport open_face\nfrom pathlib import Path\nfrom tensorflow.keras.callbacks import ModelCheckpoint\nimport time\n\n\n\noptimizer = tf.keras.optimizers.Adam()\n\n\ndef triplet_loss(y_true, y_pred, alpha = 0.2):\n   \n    anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]\n    \n    pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor,positive)),axis=-1)\n    \n    neg_dist = tf.reduce_sum( tf.square(tf.subtract(anchor,negative)),axis=-1)\n    basic_loss =(tf.add(tf.subtract(pos_dist,neg_dist),alpha))\n    loss = tf.reduce_sum(tf.maximum(basic_loss,0))\n    \n    return loss\n\ndef train_step(x):\n    with tf.GradientTape() as tape:\n        anc = face_model(x[0],training=True)\n        pos = face_model(x[1],training=True)\n        neg = face_model(x[2],training=True)\n        ypred = (anc,pos,neg)\n        loss_value = triplet_loss(None,ypred)\n    \n    grads = tape.gradient(loss_value,face_model.trainable_weights)\n    optimizer.apply_gradients(zip(grads, face_model.trainable_weights))\n    return loss_value\n\n\ndef test_step(x):\n    \n    anchor = face_model(x[0],training=False)\n    positive = face_model(x[1],training=False)\n    negative = face_model(x[2],training=False)\n    \n    pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor,positive)),axis=-1)\n    neg_dist = tf.reduce_sum( tf.square(tf.subtract(anchor,negative)),axis=-1)\n    return (pos_dist,neg_dist)\n\ndef testing(x):\n    results=[]\n    for i in x:\n        results.append(test_step(x))\n    return results\n\n\ndata_dir = Path('/home/pranav/Code/facevgg/face_data')\n\nprint('creating model...')\nface_model = model.create_model(model.input_shape)\nprint('splitting data...')\ntrain,test = data_prep.split_dataset(data_dir)\nprint('train data size is ',len(train))\n\n\ntf_train_step=tf.function(train_step,autograph=True)\ntf_test_step=tf.function(test_step,autograph=True)\n\n\nmodel_dir = '/home/pranav/Code/facevgg/weights/best_weights.keras'\n\nepochs = 2\nmin_loss = 10**5\n\nprint('Starting training...\\n\\n')\nfor epoch in range(epochs):\n    epoch_time=time.time()\n    for step,x in enumerate(data_prep.encode_batches(train,batch_size=32)):\n        start_time = time.time()\n\n        loss_value = tf_train_step((x[0],x[1],x[2]))\n\n        print(f\"It took {round(time.time()-start_time,1)} sec for a training batch...\")\n        if step%10==0:\n            print(f\"Loss for {step}th example =\",loss_value)\n    print(f\"epoch {epoch}/{epochs} done...\\n\",f\"Time taken was {time.time()-epoch_time} sec\\n\\n\\n\")\n    \n    if loss_value<min_loss:\n        face_model.save(model_dir, overwrite=True, save_format=None)\n        print('Got a better model...')\n        \nprint('\\n\\ntraining process completed...')\n\nprint(testing(test))\n\n","repo_name":"pranavchougule03/facevgg","sub_path":"train_model.py","file_name":"train_model.py","file_ext":"py","file_size_in_byte":2798,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"18313527410","text":"# 12:50\nN = int(input())\n# operations = list(input().split(' '))\noperations = input().split()\n\nvector = {'L': [-1, 0], 'R': [1, 0], 'U': [0, -1], 'D': [0, 1]}\n\nX, Y = 1, 1\n\n\nfor i in operations:\n    nx = X + list(vector[i])[0]\n    ny = Y + list(vector[i])[1]\n    if nx < 1 or nx > 5 or ny > 5 or ny < 1:\n        continue\n    # X = nx\n    # Y = ny\n    X, Y = nx, ny\n\nprint(f'x : {X} | y : {Y}')\n","repo_name":"MinJunKimKR/practice-programming","sub_path":"codingTest/this-is-coding-test/class2/Implementation/upDownLeftRight.py","file_name":"upDownLeftRight.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"23873337701","text":"from django.db import models\n\nclass MessageLog(models.Model):\n    SOURCE = (\n        ('User', 'USER'),\n        ('System', 'SYSTEM')\n    )\n    title = models.TextField(blank=True, null=True)\n    date = models.DateTimeField(auto_now_add=True, blank=True, null=True)\n    message = models.TextField(blank=True, null=True)\n    source = models.CharField(max_length=6, choices=SOURCE)\n\n    _command_view = 'message_log'\n\n    def to_dict(self):\n        fields = ['id', 'title', 'message', 'source']\n        message_dict = {k: self.__dict__[k] for k in fields}\n        message_dict['date'] = self.date.strftime('%Y-%m-%d %H:%M')\n\n        return message_dict\n","repo_name":"marcomoretto/command","sub_path":"command/lib/db/compendium/message_log.py","file_name":"message_log.py","file_ext":"py","file_size_in_byte":649,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"76"}
+{"seq_id":"241972841","text":"import os\nimport re  # Regular expressions library\nimport email\nimport tkinter as tk\nfrom tkinter import Toplevel, filedialog, messagebox, simpledialog\nfrom tkhtmlview import HTMLLabel\nimport pandas as pd\nfrom email import policy\nfrom email.parser import BytesParser\nfrom bs4 import BeautifulSoup\nimport email.utils\n\n# Function to convert CSS rgb colors to hexadecimal\ndef css_rgb_to_hex(match):\n    rgb = match.groups()\n    r, g, b = map(int, rgb[:3])\n    return '#{:02x}{:02x}{:02x}'.format(r, g, b)\n\n# Function to clean HTML content\ndef clean_html_content(content):\n    # Regular expression to find CSS rgb() colors\n    css_rgb_colors = re.compile(r'rgb\\((\\d{1,3}),\\s*(\\d{1,3}),\\s*(\\d{1,3})\\)')\n\n    # Replace 'rgb(r, g, b)' with '#rrggbb'\n    content = css_rgb_colors.sub(css_rgb_to_hex, content)\n\n    return content\n\ndef extract_content_from_eml(file_path):\n    with open(file_path, 'rb') as f:\n        msg = BytesParser(policy=policy.default).parse(f)\n    \n    # Extract metadata\n    metadata = {\n        'subject': msg['subject'],\n        'from': msg['from'],\n        'to': msg['to'],\n        'date': msg['date'],\n        'cc': msg.get('cc'),\n        'has_attachment': any(part.get_filename() for part in msg.walk() if part.get_content_maintype() == 'application'),\n        'num_recipients': len(email.utils.getaddresses(msg.get_all('to', []) + msg.get_all('cc', []))),\n    }\n\n    # Extract content\n    content = ''\n    if msg.is_multipart():\n        for part in msg.walk():\n            ctype = part.get_content_type()\n            cdispo = str(part.get('Content-Disposition'))\n\n            # check if this part is an attachment\n            if ctype == 'text/plain' and 'attachment' not in cdispo:\n                charset = part.get_content_charset()\n                content += part.get_payload(decode=True).decode(charset if charset else 'utf-8', 'ignore')\n            elif ctype == 'text/html':\n                charset = part.get_content_charset()\n                html_content = part.get_payload(decode=True).decode(charset if charset else 'utf-8', 'ignore')\n                soup = BeautifulSoup(html_content, 'html.parser')\n                content += soup.get_text(separator=' ')\n    else:\n        charset = msg.get_content_charset()\n        content_type = msg.get_content_type()\n        raw_content = msg.get_payload(decode=True).decode(charset if charset else 'utf-8', 'ignore')\n        if content_type == 'text/html':\n            soup = BeautifulSoup(raw_content, 'html.parser')\n            content = soup.get_text(separator=' ')\n        else:\n            content = raw_content\n\n    return content.strip(), metadata\n\n\ndef display_html_content(root, content):\n    \"\"\"\n    This function displays the HTML content of an email and prompts the user to enter a label.\n    \n    Parameters:\n    root (tk.Tk): The Tkinter root window.\n    content (str): The HTML content of the email.\n    \n    Returns:\n    str: The label entered by the user.\n    \"\"\"\n    # Create a top-level window\n    top = Toplevel(root)\n    top.geometry(\"800x600\")  # Specify the size of the window\n    \n    # Create an HTMLLabel widget to display the content\n    html_label = HTMLLabel(top, html=clean_html_content(content))\n    html_label.pack(fill=\"both\", expand=True)\n    \n    # Prompt the user to enter a label\n    user_label = simpledialog.askstring(\"Input\", \"Enter label for this email:\",\n                                        parent=top)\n    \n    # Close the top-level window\n    top.destroy()\n    \n    return user_label\n\ndef main_app(root, directory_path):\n    csv_file_path = 'labeled_emails.csv'\n    \n    # Check if CSV file exists\n    if os.path.exists(csv_file_path):\n        email_data = pd.read_csv(csv_file_path)\n        # If 'File_Name' column doesn't exist, create it and set it to NaN\n        if 'File_Name' not in email_data.columns:\n            email_data['File_Name'] = pd.NA  # Setting all values in the new column to NaN\n            last_processed_index = -1  # Set to -1 to start from the beginning\n        else:\n            if not email_data.empty:\n                sorted_files = sorted([f for f in os.listdir(directory_path) if f.endswith('.eml')])\n                last_file_processed = email_data.iloc[-1]['File_Name']\n                last_processed_index = sorted_files.index(last_file_processed)\n            else:\n                last_processed_index = -1\n    else:\n        # Create an empty DataFrame with 'File_Name' column\n        email_data = pd.DataFrame(columns=['File_Name', 'Subject', 'From', 'To', 'Date', 'CC', 'Has_Attachment', 'Num_Recipients', 'Content', 'Label'])\n        last_processed_index = -1\n\n    # Get a sorted list of .eml files in the directory\n    files = sorted([f for f in os.listdir(directory_path) if f.endswith('.eml')])\n\n    # Loop through each .eml file in the directory starting from the last processed index\n    for file_name in files[last_processed_index + 1:]:\n        file_path = os.path.join(directory_path, file_name)\n        \n        # Extract content and metadata\n        content, metadata = extract_content_from_eml(file_path)\n        \n        # Check if content is empty and skip\n        if not content.strip():\n            print(f\"Skipping {file_name} due to empty content.\")\n            continue\n\n        user_label = display_html_content(root, content)\n\n        # If user doesn't enter a label, continue to next email\n        if user_label is None or user_label == '':\n            print(f\"Skipping {file_name} due to no label provided.\")\n            continue\n        \n        # Add email data and label to the DataFrame\n        new_row = {\n            'File_Name': file_name,\n            'Subject': metadata['subject'],\n            'From': metadata['from'],\n            'To': metadata['to'],\n            'Date': metadata['date'],\n            'CC': metadata['cc'],\n            'Has_Attachment': metadata['has_attachment'],\n            'Num_Recipients': metadata['num_recipients'],\n            'Content': content,\n            'Label': user_label\n        }\n        new_row_df = pd.DataFrame([new_row])  # Convert the dictionary to a DataFrame with a single row\n\n\n        # Save to CSV after each labeling\n        email_data = pd.concat([email_data, new_row_df], ignore_index=True)\n\n    print(\"All emails have been processed.\")\n\n\n\n\nif __name__ == \"__main__\":\n    # Create the main root window\n    root = tk.Tk()\n    root.withdraw()  # Hide the main window\n\n    # Set directory path containing the .eml files\n    directory_path = 'train'  # Change this to the path of your .eml files\n    main_app(root, directory_path)\n\n    # End the Tkinter main loop\n    root.quit()\n","repo_name":"ladew222/emailAI","sub_path":"label.py","file_name":"label.py","file_ext":"py","file_size_in_byte":6614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"2952602382","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 23 12:58:29 2020\n\n@author: mpostigo\n\nThis is the main for custom object detection\n\"\"\"\n\nfolds = [1, 2, 3, 4, 5]\n\nfor fold in folds:\n    #import here since we free the memory in every fold\n    import torch\n    import os\n    from utils import *\n    from torchvision.models.detection import fasterrcnn_resnet50_fpn\n    import matplotlib.pyplot as plt\n    from CustomDataset import KidneyDataset\n    from CustomTransforms import *\n    import torchvision\n    from Evaluation import *\n    import scipy.io as sio\n    import numpy as np\n    from torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n    from evaluate_results import *\n    from EarlyStopping import EarlyStopping\n    from bbox_utils import *\n    \n    root= os.curdir\n    partition = 'Train'\n    print('Creating dataset for fold ', fold)\n    start_id=0\n    only_local = False #We can do local pathologies detection od global and local detection\n    \n    dataset = KidneyDataset(root, partition, fold, transforms=None, only_local=only_local)\n    \n    \"\"\"\n    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n    %                                                                             % \n    %                              SHOW IMAGES                                    %\n    %                                                                             %\n    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n    \"\"\"\n    for i in range(100):\n        image, target = dataset.__getitem__(i)\n        print('index:',i)\n    #   print('Boundin boxes: ', target['boxes'])\n        plt.imshow(image)\n        title = ('Image %d'%(i))\n        showBB (image, target, title, only_local = only_local)\n\n    \"\"\"\n    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n    %                                                                             % \n    %                         COMPOSED TRANSFORMS                                 %\n    %                                                                             %\n    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n    \"\"\"\n    flip = HorizontalFlipTransform()\n    crop = MaskTransform(30)\n    rot = RotationTransform ()\n    brigth = RandomIncreaseBrightness ()\n    noise = AddNoise()\n    mean, std = load_mean_std (root, fold)\n    norm = Compose([ToTensor(), Normalize(mean,std)])\n    i=7\n    \n\n    # Apply each of the above transforms on sample.\n    image, target = dataset.__getitem__(i)\n    title='Original'\n    showBB (image, target, title, only_local)\n#    \n#    for i in range(10):\n#        image, target = dataset.__getitem__(7)\n#        rot_image, rot_target = rot(image, target) \n#        title = 'Rotate' \n#        showBB (rot_image, rot_target, title, only_local)\n    \n    flip_image, flip_target = flip(image, target) \n    title = 'Horizontal Flip' \n    showBB (flip_image, flip_target, title, only_local)\n    \n    image, target = dataset.__getitem__(i)\n    crop_image, crop_target = crop(image, target) \n    title = 'Crop By Mask' \n    showBB (crop_image, crop_target, title, only_local)\n    \n    image, target = dataset.__getitem__(i)\n    rot_image, rot_target = rot(image, target) \n    title = 'Rotate' \n    showBB (rot_image, rot_target, title, only_local)\n    \n    image, target = dataset.__getitem__(i)\n    brigth_image, brigth_target = brigth(image, target) \n    title = 'Brightness' \n    showBB (brigth_image, brigth_target, title, only_local)\n    \n    image, target = dataset.__getitem__(i)\n    noise_image,noise_target = noise(image, target)\n    title='Noise'\n    showBB (noise_image, noise_target, title, only_local)\n    \n#    image, target = dataset.__getitem__(i)\n#    norm_image, norm_target = norm(image, target)\n#    title='Normalization'\n#    norm_target['mask'] = norm_target['mask'].vflip(norm_target['mask'],axis=1).copy())\n#    showBB (norm_image.numpy().transpose((1, 2, 0)), norm_target, title, only_local)#image[0].numpy().transpose((1, 2, 0)), target[0]\n    \n    image, target = dataset.__getitem__(i)\n    compose = Compose([crop, flip, rot, brigth])\n    t_image, t_target = compose(image, target) \n    title = 'Compose' \n    showBB (t_image, t_target, title, only_local)\n    \n    \"\"\"\n    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n    %                                                                             % \n    %                             CREATE DATASETS                                 %\n    %                                                                             %\n    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n    \"\"\"\n    #By different experiments we opted to use the HF as the unique data\n    #augmentation method\n    my_transforms = Compose([HorizontalFlipTransform(),\n                                        ToTensor(),\n                                        Normalize(mean,std)])\n    \n    test_transforms = Compose([ToTensor(), Normalize(mean,std)])\n    \n    train_dataset = KidneyDataset(root, 'Train', fold, transforms = my_transforms, only_local=only_local)\n    test_dataset = KidneyDataset(root, 'Test', fold, transforms = test_transforms, only_local=only_local)\n    val_dataset = KidneyDataset(root, 'Val', fold, transforms = test_transforms, only_local=only_local)\n    \n    \n    print('Train dataset:', len(train_dataset))\n    print('Val dataset:', len(val_dataset))\n    print('Test dataset:', len(test_dataset))\n    \n    \"\"\"\n    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n    %                                                                             % \n    %                          CREATE DATALOADERS                                 %\n    %                                                                             %\n    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n    \"\"\"\n    \n    batch_size = 1\n    workers = 0\n    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=workers,\n                                                collate_fn=collate_fn)\n    val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=workers,\n                                                collate_fn=collate_fn)\n\n        \n    \"\"\"\n    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n    %                                                                             % \n    %                        FINETUNING THE MODEL                                 %\n    %                                                                             %\n    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n    \"\"\"\n\n    from torchvision.models.detection import FasterRCNN\n    \n    device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n    print(device) \n    num_classes = len(train_dataset.class_names)+1 #plus the background\n    model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)\n    \"\"\"\n    The backbone can be modified, but resnet50 performance is good to solve\n    the given problem\n    \"\"\"\n#    backbone = torchvision.models.mobilenet_v2(pretrained=True).features\n#    backbone.out_channels = 1280 \n#    model = FasterRCNN(backbone,\n#                   num_classes=num_classes)\n    \n    # get number of input features for the classifier\n    in_features = model.roi_heads.box_predictor.cls_score.in_features\n    \n    # replace the pre-trained head with a new one\n    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)\n\n    # move model to the right device\n    model.to(device)\n    \n    # construct an optimizer\n    lr = 0.005\n    params = [p for p in model.parameters() if p.requires_grad]\n    optimizer = torch.optim.SGD(params, lr=lr,\n                                momentum=0.9, weight_decay=0.0005)\n    # and a learning rate scheduler\n    lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,\n                                                   step_size=3,\n                                                   gamma=0.1)\n    \"\"\"\n    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n    %                                                                             % \n    %                              TRAIN EPOCHS                                   %\n    %                                                                             %\n    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n    \"\"\"\n    num_epochs = 25\n    all_acc = []\n    best_acc=0\n    th = 0.5\n    phase='val'\n\n    #save fasterRCNN losses per epoch to see convergence\n    loss_classifier = []\n    loss_box_reg = []\n    loss_objectness = []\n    loss_rpn_box_reg = []\n\n    for epoch in range(num_epochs):\n        # train for one epoch, printing every 10 iterations\n        lc, lbr, lo, lrbr  = train_one_epoch(model, optimizer, train_loader, device, epoch, 10)\n        \n        loss_classifier.append(lc)\n        loss_box_reg.append(lbr)\n        loss_objectness.append(lo)\n        loss_rpn_box_reg.append(lrbr)\n        \n        # update the learning rate\n        lr_scheduler.step()\n        #evaluate on the validation dataset\n        acc, val_images, val_targets, val_outputs = evaluate(model, val_loader, device, th, phase)\n\n        all_acc.append(acc) #TODO: use losses criterion\n    \n        print('Epoch accuracy: ', acc)\n  \n    plt.figure()\n    plt.plot(range(num_epochs), loss_classifier, 'r', label = 'loss_classifier')\n    plt.plot(range(num_epochs), loss_box_reg, 'b', label = 'loss_box_reg')\n    plt.plot(range(num_epochs), loss_objectness, 'g', label = 'loss_objectness')\n    plt.plot(range(num_epochs), loss_rpn_box_reg, 'orange', label = 'loss_rpn_box_reg')\n    plt.legend()\n    plt.ylabel('losses')\n    plt.xlabel('epochs')\n    plt.title('Train Loss')\n    # plt.savefig('TrainlossesFold'+str(fold)+'num_epochs'+str(num_epochs)+'_T_HF_rot_batch_'+str(batch_size)+'lr_'+str(lr)+'_.png')\n\n    plt.figure()\n    plt.plot(range(num_epochs), all_acc, 'r')\n    plt.xlabel('Epochs')\n    plt.ylabel('Validation accuracy')\n    plt.title('Accuracy evolution')\n    plt.show()  \n    \n    \"\"\"\n    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n    %                                                                             % \n    %                     MAP Mean Average Precision                              %\n    %                                                                             %\n    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n    \"\"\"\n    \n    # The MAP used is downloaded from another project:\n    #git clone https://github.com/Cartucho/mAP\n    \n    \"\"\"\n    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n    %                                                                             % \n    %                       SAVE THE TRAINED MODEL                                %\n    %                                                                             %\n    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n    \"\"\"\n    \n    PATH = '/home/mpostigo/Documentos/bbdd/models/FasterRCNN_fold'+str(fold)+'batchSize'+str(batch_size)+'numEpochs'+str(num_epochs)+'HF_LR'+str(lr)+'.pt'\n    # model = torch.load(PATH)\n    \"\"\"SAVE THE MODEL\"\"\"\n    torch.save(model, PATH)\n    \n    \n\n\n\n\n\n","repo_name":"mapostig/CAD-Kydney","sub_path":"ObjectDetection/Finetuning.py","file_name":"Finetuning.py","file_ext":"py","file_size_in_byte":11594,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"76"}
+{"seq_id":"29123749707","text":"'''Importing the required packages'''\n\nimport tweepy\nfrom tweepy import *\nimport os\nfrom dotenv import load_dotenv\nimport pandas as pd\nimport psycopg2\nfrom sqlalchemy import create_engine\nimport datetime\nimport ast\nfrom airflow import DAG\nfrom airflow.operators.python_operator import PythonOperator \nfrom datetime import datetime,timedelta\nfrom vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n\n#Defining the arguments for the DAG\n\ndefault_args = {\n    'owner': 'airflow',\n    'depends_on_past': False,\n    'start_date': datetime.now(),\n    'email_on_failure':False,\n    'email_on_retry': False,\n    'retries': 1,\n    'retry_delay': timedelta(minutes=1)\n}\n\n#Defining the DAG which will be used for all tasks\n\ndag = DAG(\n    'twitter_flow_dag',\n    default_args = default_args,\n    description = 'Collecting tweets dag',\n    schedule_interval = timedelta(minutes=1)\n)\n\n'''\ndefining the credentials for pgAdmin from the env file\n'''\n# Host\npg_host=os.getenv('HOST')\n# Port\npg_port=os.getenv('PORT')\n# Database\npg_db=os.getenv('DATABASE')\n# User\npg_user=os.getenv('USER')\n# Password\npg_pass=os.getenv('PASSWORD')\n# Get the table name from the env file\ntable_name = os.getenv('TABLE_NAME')\n\ndef sentiment_evaluation(sentence):\n    '''Initialises the vader sentiment function then outputs the compound polarity score'''\n\n    # Create a SentimentIntensityAnalyzer object\n    sid_object = SentimentIntensityAnalyzer()\n\n    # Uses the sentiment diction to output a polarity score\n    sentiment_diction = sid_object.polarity_scores(sentence)\n    \n    return sentiment_diction['compound']\n\n# Commenting this part out of the code for now \n#     # decide sentiment as positive, negative and neutral. Can be tweaked as seen fit\n#     if sentiment_diction['compound'] >= 0.05:\n#         return \"Positive\"\n \n#     elif sentiment_diction['compound'] <= - 0.05 :\n#         return \"Negative\"\n \n#     else:\n#          return \"Neutral\"\n\ntweet_id = [] # stores the id in a separate container\ndata = [] # creates an empty list to store the data in\nreplies = [] #empty list to find the replies\n\ndef extract_twitter(**kwargs):\n    '''The function needed to extract data from the twitter API'''\n    load_dotenv()\n    # accessing  API key and secret\n    consumer_key = os.getenv('API_KEY')\n    consumer_secret = os.getenv('API_SECRET')\n    access_token = os.getenv('TOKEN')\n    access_token_secret = os.getenv('TOKEN_SECRET')\n    \n    # Authenticating the keys\n    auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n    auth.set_access_token(access_token, access_token_secret)\n    \n    # Creating the API object\n    api = tweepy.API(auth)\n    \n    accounts = ['@amazon','@ferrari','@GoogNmicro','@NFL','@NintendoUK','@pepsi','@tacobell','@SpaceX']\n    \n    # iterate through all accounts in the list\n    for account in accounts:\n        # loop that goes through each recent tweet by the user\n        for tweet in tweepy.Cursor(api.user_timeline, screen_name=account, \n                                    tweet_mode=\"extended\",exclude_replies=True).items(10):\n\n\n            # storing the tweet id into a different list\n            tweet_id.append(tweet.id)\n\n            # for each tweet found, extracting the replies\n            rep = api.get_status(tweet.id,tweet_mode=\"extended\",wait_on_rate_limit=True)\n\n            # finds the replies to the tweet\n            for tweet2 in tweepy.Cursor(api.search_tweets, q='to:{}'.format(account), \n                               since_id=tweet.id, tweet_mode='extended').items():\n                # if it is a reply, writes to a dictionary within the list\n                # with each item containing the text and number of likes\n                if tweet2.in_reply_to_status_id == tweet.id:\n                    replies.append({'Rep_text':tweet2.full_text,\n                                    'Rep_likes': tweet2.favorite_count\n                                   })\n\n            # creates a dictionary within the list of all the data needed and changes to string to store in pgadmin4\n            data.append(str({\n                # User id of the tweeter\n                \"User_ID\":tweet.user.id,\n                # when the tweet was created\n                'Time': tweet.created_at.timestamp(),\n                # the text of the tweet itself\n                'Text': tweet.full_text,\n                # number of likes/favourites the tweet got\n                'Favorites': tweet.favorite_count,\n                # number of retweets\n                'Retweets': tweet.retweet_count,\n                # Any hashtags within the tweet\n                'Hashtags': [hashtag[\"text\"] for hashtag in tweet.entities.get(\"hashtags\")],\n                # the screen name of users mentioned in the tweet\n                'Mentions': [user[\"screen_name\"] for user in tweet.entities.get(\"user_mentions\")],\n                # urls linked inside the tweet\n                'URLs': [url[\"expanded_url\"] for url in tweet.entities.get(\"urls\")],\n                # replies to the tweet\n                'Replies':replies\n            }))\n\n        # store the data into a dataframe\n        df = pd.DataFrame({'tweet_id':tweet_id,\n                           'tweet_info':data})\n        result_json = df.to_json(orient='split')\n        ti = kwargs['ti']\n        ti.xcom_push(key='dataframe', value=result_json)\n        return result_json\n    \n# Task 1 - extracting\n\ntask_extracting = PythonOperator(\n    task_id='twitter_airflow',\n    python_callable=extract_twitter,\n    provide_context = True,\n    do_xcom_push = True,\n    dag = dag\n)\n        \ndef store_postgres(**kwargs):\n    '''Taking the data from python and storing within a postgresql database'''\n\n    # Connecting to the database for querying\n    conn = psycopg2.connect(\n        host=pg_host,\n        database=pg_db,\n        user=pg_user,\n        password=pg_pass,\n        port=pg_port\n    )\n    \n    # creating the cursor to execute the queries\n    cur = conn.cursor()\n    \n    # Execute a SELECT statement to extract id from the table\n    cur.execute(f\"SELECT tweet_id FROM {table_name}\")\n    \n    # change the rows into a list to be iterated through\n    db_id = cur.fetchall()\n    \n    # change the rows into a list to be iterated through\n    db_id = cur.fetchall()\n\n    #Definging the xcom pull\n    ti=kwargs['ti']\n    json_pull=ti.xcom_pull(key='dataframe',task_ids='twitter_airflow')\n\n    df = pd.read_json(json_pull, orient='split')\n    \n    # checks if the value is inside the list\n    check = df['tweet_id'].isin(db_id)\n    \n    # uses boolean indexing to remove the rows that fit the condition\n    filtered = df[~check]\n\n    # Close the cursor and connection\n    cur.close()\n    conn.close()\n\n    # Creating the engine to connect to postgres in order to alter tables\n    engine = create_engine(\n        f'postgresql+psycopg2://{pg_user}:{pg_pass}@{pg_host}:{pg_port}/{pg_db}')\n    \n    # Insert information into the table\n    filtered.to_sql(table_name, engine, if_exists='append', index=False)\n    \n#Task 2 - loading data\n\ntask_loading = PythonOperator(\n    task_id='twitter_db',\n    python_callable=store_postgres,\n    provide_context=True,\n    dag = dag\n)\n   \ndef transform():\n    '''Function that extracts the data from the sql database and transform for use'''\n    \n    # Creating the engine to connect to postgres\n    engine = create_engine(\n        f'postgresql+psycopg2://{pg_user}:{pg_pass}@{pg_host}:{pg_port}/{pg_db}')\n    \n    # Read the entire table into a pandas dataframe\n    global df\n    df = pd.read_sql_table(f'{table_name}', engine)\n    \n    # Use the 'apply' method to convert the column back into a dictionary\n    df['json_info'] = df['tweet_info'].apply(lambda x: ast.literal_eval(x))\n    \n    # creating empty lists to extract the data from the dictionary to be put into lists\n    user_id = []\n    time = []\n    text = []\n    likes = []\n    retweets = []\n    hashtags = []\n    mentions = []\n    urls = []\n    no_replies = []\n    replies = []\n    \n    # for loop to extract data from the dictionary and add to lists\n    for val in df['json_info']:\n        user_id.append(val['User_ID'])\n        time.append(val['Time'])\n        text.append(val['Text'])\n        likes.append(val['Favorites'])\n        retweets.append(val['Retweets'])\n        hashtags.append(len(val['Hashtags']))\n        mentions.append(len(val['Mentions']))\n        urls.append(len(val['URLs']))\n        no_replies.append(len(val['Replies']))\n        replies.append(val['Replies'])\n    \n    # writes the list into the dataframe for use\n    df['User_id'] = user_id\n    df['Time'] = time\n    df['Text'] = text\n    df['Likes'] = likes\n    df['No_retweets'] = retweets\n    df['No_Hashtags'] = hashtags\n    df['No_Mentions'] = mentions\n    df['No_URLs'] = urls\n    df['No_replies'] = no_replies\n    df['Replies'] = replies\n    \n    # removing the old json format from the table\n    df.drop(columns=['tweet_info','json_info'],inplace=True)\n        \n    # initialising empty list for the score\n    vader_score = []\n\n    # goes through each tweet by the account\n    for all_reps in df['Replies']:\n        retweet = []\n        rep_likes = []\n        rep_score = []\n\n        # goes through all the replies for the tweet\n        for dicts in all_reps:\n            # cleans the tweet\n            retweet.append(dicts['Rep_text'])\n            rep_likes.append(dicts['Rep_likes'])\n\n        # vader analysis of each tweet\n        rep_score = [sentiment_evaluation(text) for text in retweet]\n\n        # multiplying the vader score by the like count\n        for index in range(len(rep_score)):\n            rep_score[index] += rep_score[index]*rep_likes[index]\n\n        # finding the average of the lvader scores\n        vader_score.append(sum(rep_score)/sum(rep_likes))\n        \n    # Updating the dataframe with the found vader score for each tweet in the database\n    df['Score'] = vader_score\n    \n#Task 3 - transforming the data\n\ntask_transforming = PythonOperator(\n    task_id='twitter_data_transform',\n    python_callable=transform,\n    provide_context=True,\n    dag = dag\n)\n\n#Calling the tasks in this sequence Task 1 --> Task 2 --> Task 3, so that they don't all start at once\n\ntask_extracting.set_downstream(task_loading)\ntask_transforming.set_upstream(task_loading)\n","repo_name":"Dorad-H/ELT-Twitter-Sentiment-Analysis","sub_path":"ELT_pipeline.py","file_name":"ELT_pipeline.py","file_ext":"py","file_size_in_byte":10218,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"10672579717","text":"#-*- codind = UTF-8-*-\nimport itchat\nfrom reply_to_myself import *\nfrom reply_to_others import *\nfrom file2 import *\nfrom voice_translate import *\nitchat.auto_login(hotReload=True)\nitchat.send(\"你好，微信机器人启动。。。\\\n            \\n请输入命令：\\n1.关机\\n2.取消关机\\n3.打开WiFi\\n4.cap\\n5.视频\",\\\n            toUserName='filehelper')\n\n\n\n\nb = []\na = []\n@itchat.msg_register(itchat.content.TEXT)\ndef text_reply(msg):\n    \n   # global flag\n    message =  msg['Text'] \n    fromName =msg['FromUserName'] \n    toName = msg['ToUserName']\n    nickname=msg['User']['NickName']\n    if toName == \"filehelper\":\n        reply_to_filehelper(message)\n\n    else:\n        if read(nickname):\n            \n            a.append(nickname)\n            \n            if message=='开机':\n                reduce(nickname)\n            while a.count(nickname)<2:\n                itchat.send('你好，机器人已关闭，回复 开机 可开启',toUserName=fromName)\n                break\n            \n            \n            \n           \n        else:\n            if message == '关机':\n                add(nickname)\n                \n            else:\n                b.append(nickname)\n                while b.count(nickname)<2:\n                    itchat.send('你好，机器人处于开启状态，回复 关机 可关闭\\n '+\n                                '回复 菜名 可获取菜谱\\n '+\n                                '回复 天气+城市 可获取天气信息\\n' +\n                                '回复 人名 可获取人物信息\\n' +\n                                '回复 新闻 可获取新闻\\n' +\n                                '回复 xx+图片 可获取图片\\n '+\n                                '另 机器人有聊天功能',\n\n                                toUserName=fromName)\n                    break\n                \n                return reply_to_others(message)\n        \n        \n\n        \n            \n\n'''此模块回复图片'''\n@itchat.msg_register(itchat.content.PICTURE)\ndef print_you(msg):\n    msg['Text'](msg['FileName'])\n    itchat.send('@%s@%s' %('img' if msg['Type'] ==\\\n                           'Picture' else 'fil', msg[\"FileName\"]), msg[\"FromUserName\"])\n    \n@itchat.msg_register(itchat.content.RECORDING)\ndef talk(msg):\n    msg['Text']('voice.mp3')\n    flag,result = writing('voice.mp3')\n    \n    itchat.send(\"来自<%s>\\n内容为<%s>\"%(msg['User']['NickName'],result),toUserName='filehelper')\n    if flag:\n        return reply_to_others(result)\n    else:\n        return '不好意思，未识别'\n    \n    \n    \n    \n\nitchat.run()\n\n\n\n","repo_name":"CooSei/WeChat-robot","sub_path":"wechatrobot5.py","file_name":"wechatrobot5.py","file_ext":"py","file_size_in_byte":2602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"10581689928","text":"import sys\ninput = sys.stdin.readline\n\nM, N = map(int, input().split())\nK = int(input())\n\ncount = [[[0, 0, 0] for _ in range(N)] for _ in range(M)]\nmaps = [list(input()) for _ in range(M)]\n\nfor i in range(M):\n    for j in range(N):\n        if maps[i][j] == \"J\":\n            count[i][j][0] += 1\n        elif maps[i][j] == \"O\":\n            count[i][j][1] += 1\n        else:\n            count[i][j][2] += 1\n\nsum = [[[0, 0 ,0] for _ in range(N+1)]]\nfor val in count:\n    for i in range(1, N):\n        for k in range(3):\n            val[i][k] += val[i-1][k]\n    val.insert(0, [0, 0, 0])\n    sum.append(val)\nsum = list(zip(*sum))\n\nfor i in range(N+1):\n    for j in range(1, M+1):\n        for k in range(3):\n            sum[i][j][k] += sum[i][j-1][k]\n\nsum = list(zip(*sum))\n\nfor _ in range(K):\n    x1, y1, x2, y2 = map(int, input().split())\n    J = sum[x2][y2][0] - sum[x1-1][y2][0] - sum[x2][y1-1][0] + sum[x1-1][y1-1][0]\n    O = sum[x2][y2][1] - sum[x1-1][y2][1] - sum[x2][y1-1][1] + sum[x1-1][y1-1][1]\n    I = sum[x2][y2][2] - sum[x1-1][y2][2] - sum[x2][y1-1][2] + sum[x1-1][y1-1][2]\n\n    print(J, O, I)","repo_name":"97Kzone/CodeTest_practice","sub_path":"PrefixSum/5549.py","file_name":"5549.py","file_ext":"py","file_size_in_byte":1099,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"39129793679","text":"from . import app, db\nfrom .models import Pokemon\nfrom .forms import PokeForm\nfrom flask import redirect, url_for, request, render_template, jsonify, json\nfrom flask_sqlalchemy import SQLAlchemy\nimport requests\nimport random\n\npoke_link = {\n    \"Pikachu\" : \"https://th.bing.com/th/id/OIP.4NhV-E8QFbihBK2p6LU4tQHaG7?pid=ImgDet&rs=1\",\n    \"Charmander\" : \"https://th.bing.com/th/id/R.3826a0bbdcc9e12a949cc3080f9a4e55?rik=jtm4qBBiN1%2fz2g&riu=http%3a%2f%2fwww.pokemasters.net%2fpokedex%2fimages%2fpokemon%2f4000.png&ehk=F9WGO2TvAWorlVyAvA8SWEM0YTs8OjcRCK%2fveScYaDE%3d&risl=&pid=ImgRaw\",\n    \"Squirtle\" : \"https://th.bing.com/th/id/AMMS_7b9ddf692c6df64d86bbe4e96ef46a83?pid=ImgDet&rs=1\",\n    \"Bulbasaur\" : \"https://th.bing.com/th/id/AMMS_9ba4d684e64c19af7c99148ac36af46b?pid=ImgDet&rs=1\",\n    \"Cyndaquil\" : \"https://vignette.wikia.nocookie.net/nintendo/images/9/99/Cyndaquil.png/revision/latest?cb=20141003061613&path-prefix=en\",\n    \"Totodile\" : \"https://th.bing.com/th/id/R.32c471244f37286755a1b72a54cc2a80?rik=1gjd9oO%2by1QQNw&riu=http%3a%2f%2fstatic.giantbomb.com%2fuploads%2foriginal%2f0%2f5150%2f1106948-960099_20090814_screen001.jpg&ehk=AQem7YmVfyrTAWnWQHnePSN%2bE8%2f3s41jheqXSouY3aQ%3d&risl=&pid=ImgRaw&r=0\",\n    \"Chikorita\" : \"https://th.bing.com/th/id/R.189f539cfb66f53801aa4039b2d19611?rik=bLpW1CHTljLx6g&riu=http%3a%2f%2fimages2.wikia.nocookie.net%2f__cb20130105100827%2fpokemontowerdefense%2fimages%2fb%2fbf%2f152Chikorita.png&ehk=9buQXHcC6WYFHTTH1gxc0eSFlLCKLViR9xhwj2KbRT4%3d&risl=&pid=ImgRaw&r=0\",\n    \"Torchic\" : \"https://th.bing.com/th/id/R.abf178cee15982f2d8e404d18e1c9c03?rik=3hMmh8F%2box4glQ&riu=http%3a%2f%2fvignette2.wikia.nocookie.net%2fhelixpedia%2fimages%2f4%2f4f%2fTorchic.png%2frevision%2flatest%3fcb%3d20140613012734&ehk=%2f8fJqagM3dIPv6B7eSa%2bDM87SmAEomfuCLUt85JzIcc%3d&risl=&pid=ImgRaw&r=0\",\n    \"Mudkip\" : \"https://th.bing.com/th/id/R.479f79afbe259f711a640784f1ada10d?rik=SF3QyJiippOtbg&riu=http%3a%2f%2fpokemon3d.net%2fwiki%2fimages%2fa%2fae%2fMudkip.png&ehk=OACuigY8woj65Aemgz0bymGEoAVCbEy%2bX9ftRL%2bwG6c%3d&risl=&pid=ImgRaw&r=0\",\n    \"Treecko\" : \"https://th.bing.com/th/id/R.ad6a1e51e1315a8a45dec34a3cf3b356?rik=xQ9%2fX8h%2fXyvFxA&pid=ImgRaw&r=0\",\n    \"Chimchar\" : \"https://vignette.wikia.nocookie.net/nintendo/images/9/9f/Chimchar.png/revision/latest?cb=20160916201643&path-prefix=en\",\n    \"Piplup\" : \"https://cdn2.bulbagarden.net/upload/thumb/b/b1/393Piplup.png/1200px-393Piplup.png\",\n    \"Turtwig\" : \"https://th.bing.com/th/id/R.8a7002960abc336335ba2425d69f344b?rik=5c0dFivTOreYYw&riu=http%3a%2f%2fimages4.wikia.nocookie.net%2f__cb20110927000961%2fpokemon%2fimages%2f5%2f5c%2f387Turtwig.png&ehk=BuBj%2bOGpbEnf9fYuQ5IYzSEcR05cKWAjrIqMSKzZm80%3d&risl=&pid=ImgRaw&r=0\"\n}\n\npoke_link_evolution = {\n    \"Raichu\" : \"https://th.bing.com/th/id/OIP.odEpQFpV8Sg36niYBTNhbAHaHa?pid=ImgDet&rs=1\",\n    \"Charmeleon\" : \"https://th.bing.com/th/id/R.85fcab847827a839ccbf63c13acce7cf?rik=leNOOqWxcX2m7w&riu=http%3a%2f%2fwww.pokemasters.net%2fpokedex%2fimages%2fpokemon%2f5000.png&ehk=jm6TYNyPRGGFUdVlXtMaXh7omdy0r8qxp6pL17EB2N4%3d&risl=&pid=ImgRaw&r=0\",\n    \"Wartortle\" : \"https://img.pokemondb.net/artwork/large/wartortle.jpg\",\n    \"Ivysaur\" : \"https://th.bing.com/th/id/R.dcf25959e2fe6a40ab96570d65bbcfae?rik=HxI8CqhvU0hE7A&riu=http%3a%2f%2fvignette3.wikia.nocookie.net%2fnintendo%2fimages%2f8%2f86%2fIvysaur.png%2frevision%2flatest%3fcb%3d20141002083450%26path-prefix%3den&ehk=GL1go%2b4ytubCdRvwCZgElDzG5EbIsxNqrX1GQ8Y%2bXt0%3d&risl=&pid=ImgRaw&r=0\",\n    \"Quilava\" : \"https://th.bing.com/th/id/R.356bb2f6c017c951bdfc71c6391a7133?rik=%2b8ik2w374vzAiQ&riu=http%3a%2f%2fvignette2.wikia.nocookie.net%2fsonic-pokemon-unipedia%2fimages%2f4%2f44%2f188-1.png%2frevision%2flatest%3fcb%3d20140509210523&ehk=0VYP2Lanyh6umAzTraFxlQyS8P2SLyfPgv%2fgp%2fB3Gr4%3d&risl=&pid=ImgRaw&r=0\",\n    \"Croconaw\" : \"https://th.bing.com/th/id/R.e56886ba4f0cc10136f4432e98481eb0?rik=RbZa44%2fiSEARGw&riu=http%3a%2f%2f2.bp.blogspot.com%2f-zpn47JnUBvA%2fT_mPxkEKMKI%2fAAAAAAAAPfY%2fN8fbR3G9QKM%2fs1600%2f159Croconaw%2bwater%2bpokemon%2b2nd%2bgen%2bgold%2band%2bsilver%2bnintendo.png&ehk=RYFrwbdZsofUbEfe%2bPBHPjr3wkWa%2bi0DSvMwrJt07zI%3d&risl=&pid=ImgRaw&r=0\",\n    \"Bayleef\" : \"https://vignette.wikia.nocookie.net/pokemony/images/1/1b/Bayleef.png/revision/latest?cb=20150824101826&path-prefix=pl\",\n    \"Combusken\" : \"https://th.bing.com/th/id/R.558045b6df76691ad6ab4c51f2a2b92f?rik=W6B%2fCZYDN%2brPmQ&riu=http%3a%2f%2fwww.pokemasters.net%2fpokedex%2fimages%2fpokemon%2f256000.png&ehk=5s4L5ovJg9H18bsBzjNATrbKm5%2bc6NMZNqQK4lrTZjY%3d&risl=&pid=ImgRaw&r=0\",\n    \"Marshtomp\" : \"https://th.bing.com/th/id/OIP.7zqqJZIT8ugoFb2dE6w97AHaIj?pid=ImgDet&rs=1\",\n    \"Grovyle\" : \"https://img.pokemondb.net/artwork/large/grovyle.jpg\",\n    \"Monferno\" : \"https://img.pokemondb.net/artwork/large/monferno.jpg\",\n    \"Prinplup\" : \"https://th.bing.com/th/id/OIP.lTAlqPOC20BiU3mp95ojeAHaHa?pid=ImgDet&rs=1\",\n    \"Grotle\" : \"https://th.bing.com/th/id/OIP.qo9SSVMA6yoy7Glp-uaJWwHaHa?pid=ImgDet&rs=1\"\n}\n\npoke_number = {\n    \"Pikachu\" : \"025\",\n    \"Charmander\" : \"004\",\n    \"Squirtle\" : \"007\",\n    \"Bulbasaur\" : \"001\",\n    \"Cyndaquil\" : \"155\",\n    \"Totodile\" : \"158\",\n    \"Chikorita\" : \"152\",\n    \"Torchic\" : \"255\",\n    \"Mudkip\" : \"258\",\n    \"Treecko\" : \"252\",\n    \"Chimchar\" : \"390\",\n    \"Piplup\" : \"393\",\n    \"Turtwig\" : \"387\"\n}\n\npoke_evolution = {\n    \"Pikachu\" : \"Raichu\",\n    \"Charmander\" : \"Charmeleon\",\n    \"Squirtle\" : \"Wartortle\",\n    \"Bulbasaur\" : \"Ivysaur\",\n    \"Cyndaquil\" : \"Quilava\",\n    \"Totodile\" : \"Croconaw\",\n    \"Chikorita\" : \"Bayleef\",\n    \"Torchic\" : \"Combusken\",\n    \"Mudkip\" : \"Marshtomp\",\n    \"Treecko\" : \"Grovyle\",\n    \"Chimchar\" : \"Monferno\",\n    \"Piplup\" : \"Prinplup\",\n    \"Turtwig\" : \"Grotle\"\n}\n\n@app.route(\"/\")\ndef start():\n\n    return render_template(\"start.html\")\n\n@app.route(\"/home\", methods=[\"GET\", \"POST\"])\ndef home():\n    form = PokeForm()\n\n    region = requests.get('http://service-2:5000/get/region').text\n    pokemon_type = requests.get('http://service-3:5000/get/pokemon_type').text\n\n    payload = {'region' : region, 'pokemon_type' : pokemon_type}\n    name = requests.post('http://service-4:5000/post/name', json=payload).text\n\n    link = poke_link[name]\n    number = poke_number[name]\n    evolution = poke_evolution[name]\n    link_evolution = poke_link_evolution[evolution]\n\n    rand = random.randint(1,25)\n    if rand == 25:\n        new_pokemon = Pokemon(name=\"Pikachu\", type=\"Electric\", region=\"Kanto\", link=poke_link[\"Pikachu\"], number=poke_number[\"Pikachu\"], evolution=poke_evolution[\"Pikachu\"], link_evolution=poke_link_evolution[\"Raichu\"])\n    else:\n        new_pokemon = Pokemon(name=name, type=pokemon_type, region=region, link=link, number=number, evolution=evolution, link_evolution=link_evolution)\n\n    db.session.add(new_pokemon)\n    db.session.commit()\n\n    route = \"home\"\n\n    if request.method == \"POST\":\n\n        order_attr = {\n            \"#\" : Pokemon.number.asc(),\n            \"Name\" : Pokemon.name.asc(),\n            \"\" : Pokemon.id.desc()\n        } [form.poke_order.data]\n\n        if form.poke_region.data == \"All\" and form.poke_type.data == \"All\":\n\n            pokemons = Pokemon.query.order_by(order_attr).all()\n            \n        elif form.poke_region.data != \"All\" and form.poke_type.data == \"All\":\n\n            pokemons = Pokemon.query.filter_by(region=form.poke_region.data).order_by(order_attr).all()\n        \n        elif form.poke_region.data == \"All\" and form.poke_type.data != \"All\":\n\n            pokemons = Pokemon.query.filter_by(type=form.poke_type.data).order_by(order_attr).all()\n            \n        else:\n            pokemons = Pokemon.query.filter_by(region=form.poke_region.data, type=form.poke_type.data).all()\n        \n        return render_template(\"home.html\", pokemons=pokemons, new_pokemon=new_pokemon, form=form, route=route)\n\n    else:\n        pokemons = Pokemon.query.order_by(Pokemon.id.desc()).all()\n\n    return render_template(\"home.html\", pokemons=pokemons, new_pokemon=new_pokemon, form=form, route=route)\n\n\n@app.route(\"/delete_pokemon/<int:id>\")\ndef delete_pokemon(id):\n    pokemon = Pokemon.query.get(id)\n    db.session.delete(pokemon)\n    db.session.commit()\n\n    new_pokemon = Pokemon.query.get(id-1)\n    pokemons = Pokemon.query.order_by(Pokemon.id.desc()).all()\n\n    route = \"delete\"\n\n    return render_template(\"home.html\", pokemons=pokemons, new_pokemon=new_pokemon, route=route)","repo_name":"gavthetallone/devops-practical-project","sub_path":"service_1/application/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":8349,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"462383371","text":"\"\"\"rename backer_user_id to by_user_id\n\nRevision ID: 04b3cb443b5a\nRevises: f8589f130a0f\nCreate Date: 2023-03-29 10:28:55.672022\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# Polar Custom Imports\n\n# revision identifiers, used by Alembic.\nrevision = \"04b3cb443b5a\"\ndown_revision = \"f8589f130a0f\"\nbranch_labels: tuple[str] | None = None\ndepends_on: tuple[str] | None = None\n\n\ndef upgrade() -> None:\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column(\"pledges\", sa.Column(\"by_user_id\", sa.UUID(), nullable=True))\n    op.drop_index(\"ix_pledges_backer_user_id\", table_name=\"pledges\")\n    op.create_index(\n        op.f(\"ix_pledges_by_user_id\"),\n        \"pledges\",\n        [\"by_user_id\"],\n        unique=False,\n    )\n    op.drop_constraint(\"pledges_backer_user_id_fkey\", \"pledges\", type_=\"foreignkey\")\n    op.create_foreign_key(\n        op.f(\"pledges_by_user_id_fkey\"),\n        \"pledges\",\n        \"users\",\n        [\"by_user_id\"],\n        [\"id\"],\n    )\n    op.drop_column(\"pledges\", \"backer_user_id\")\n    # ### end Alembic commands ###\n\n\ndef downgrade() -> None:\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column(\n        \"pledges\",\n        sa.Column(\"backer_user_id\", sa.UUID(), autoincrement=False, nullable=True),\n    )\n    op.drop_constraint(op.f(\"pledges_by_user_id_fkey\"), \"pledges\", type_=\"foreignkey\")\n    op.create_foreign_key(\n        \"pledges_backer_user_id_fkey\",\n        \"pledges\",\n        \"users\",\n        [\"backer_user_id\"],\n        [\"id\"],\n    )\n    op.drop_index(op.f(\"ix_pledges_by_user_id\"), table_name=\"pledges\")\n    op.create_index(\n        \"ix_pledges_backer_user_id\",\n        \"pledges\",\n        [\"backer_user_id\"],\n        unique=False,\n    )\n    op.drop_column(\"pledges\", \"by_user_id\")\n    # ### end Alembic commands ###\n","repo_name":"polarsource/polar","sub_path":"server/migrations/versions/2023-03-29_rename_backer_user_id_to_by_user_id.py","file_name":"2023-03-29_rename_backer_user_id_to_by_user_id.py","file_ext":"py","file_size_in_byte":1813,"program_lang":"python","lang":"en","doc_type":"code","stars":736,"dataset":"github-code","pt":"76"}
+{"seq_id":"19013581015","text":"#!python\n# -*- coding: utf-8 -*-\n\nimport os\nimport threading\nimport time\n\nimport inotify\nimport inotify.adapters\n\nfrom typing import Callable, Dict, Optional, Set\n\n\nfrom dnsmock import log\n\n\ndef log_refresh() -> None:\n    log(__name__).info(\"Refresh needed\")\n\n\nclass Guard(threading.Thread):\n    def __init__(self, guard_time: float, callback: Callable = log_refresh):\n        super().__init__()\n        self.files: Dict[str, Set[str]] = dict()\n        self.keep_running = True\n        self.guard_time = guard_time\n        self.callback = callback\n        self.refresh_at: Optional[float] = None\n        self.running = False\n\n    def add_file(self, fname: str) -> None:\n        if self.running:\n            log(__name__).debug(\"Guard running, ignoring additional watches\")\n            return\n        fullname = os.path.abspath(fname)\n        basename = os.path.basename(fname)\n        dirname = os.path.dirname(fullname)\n        self.files.setdefault(dirname, set()).add(basename)\n\n    def handle_refresh(self) -> None:\n        if self.refresh_at is None:\n            return\n        if self.refresh_at < time.time():\n            self.refresh_at = None\n            try:\n                self.callback()\n            except KeyboardInterrupt:\n                raise\n            except Exception:\n                log(__name__).error(\"Error in callback\", exc_info=True)\n\n    def run(self) -> None:\n        self.inotify = inotify.adapters.Inotify()\n\n        for dirname in self.files:\n            self.inotify.add_watch(\n                dirname,\n                (\n                    inotify.constants.IN_CLOSE_WRITE\n                    | inotify.constants.IN_CREATE\n                    | inotify.constants.IN_DELETE\n                ),\n            )\n\n        while self.keep_running:\n            try:\n                events = self.inotify.event_gen(yield_nones=False, timeout_s=1)\n                events = list(events)\n                for event in events:\n                    ev, flags, dirname, fname = event\n                    if dirname in self.files and fname in self.files[dirname]:\n                        self.refresh_at = time.time() + self.guard_time\n                        log(__name__).info(\n                            \"Modification on %s detected\", os.path.join(dirname, fname)\n                        )\n                self.handle_refresh()\n            except Exception:\n                log(__name__).error(\"Exception in Guard\", exc_info=True)\n\n        log(__name__).info(\"Guard thread stopped\")\n        for dirname in self.files:\n            self.inotify.remove_watch(dirname)\n        self.files = dict()\n        self.running = False\n\n    def stop(self) -> None:\n        self.keep_running = False\n        self.join()\n","repo_name":"Eierkopp/dnsmock","sub_path":"dnsmock/file_guard.py","file_name":"file_guard.py","file_ext":"py","file_size_in_byte":2726,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"20582722641","text":"from __future__ import annotations\n\nimport enum\n\nimport numpy as np\nfrom scipy.interpolate import interp1d\n\n\nclass InterpolationKind(enum.Enum):\n    LINEAR = \"linear\"\n    NEAREST = \"nearest\"\n    ZERO = \"zero\"\n    SLINEAR = \"slinear\"\n    QUADRATIC = \"quadratic\"\n    CUBIC = \"cubic\"\n    PREVIUS = \"previous\"\n    NEXT = \"next\"\n\n\ndef interpolation_model(\n    xx: list[float],\n    yy: list[float],\n    n_points: int,\n    kind: InterpolationKind = InterpolationKind.LINEAR,\n):\n    intrp_model = interp1d(xx, yy, kind=kind.value)\n\n    x_interpolated: np.ndarray[np.float64] = np.linspace(\n        min(xx),\n        max(xx),\n        n_points,\n    )\n\n    y_interpolated: np.ndarray[np.float64] = intrp_model(x_interpolated)\n\n    return x_interpolated.tolist(), y_interpolated.tolist()\n\n\ndef logx_interpolation_model(\n    x_log: list[float],\n    yy: list[float],\n    n_points: int,\n    kind: InterpolationKind = InterpolationKind.LINEAR,\n) -> tuple[list[float], list[float]]:\n    x_log = [np.log10(x) for x in x_log]\n\n    intrp_model = interp1d(x_log, yy, kind=kind.value)\n\n    x_log_interpolated = np.linspace(\n        min(x_log),\n        max(x_log),\n        n_points,\n    )\n\n    x_interpolated = [np.float_power(10, x_intrp) for x_intrp in x_log_interpolated]\n\n    y_interpolated = intrp_model(x_log_interpolated)\n\n    return x_interpolated, y_interpolated\n\n\ndef logx_interpolation_model_smoothing_spline(\n    x_log: list[float],\n    yy: list[float],\n    interpolation_rate: int,\n    lam: float,\n) -> tuple[list[float], list[float]]:\n    x_log = [np.log10(x) for x in x_log]\n\n    from scipy.interpolate import make_smoothing_spline\n\n    intrp_model = make_smoothing_spline(x_log, yy, lam=lam)\n\n    x_log_interpolated = np.linspace(\n        min(x_log),\n        max(x_log),\n        int(len(x_log) * interpolation_rate),\n    )\n\n    y_interpolated = intrp_model(x_log_interpolated)\n    x_interpolated = [np.float_power(10, x_intrp) for x_intrp in x_log_interpolated]\n\n    return x_interpolated, y_interpolated\n","repo_name":"micheleforese/audio_measurement","sub_path":"audio/math/interpolation.py","file_name":"interpolation.py","file_ext":"py","file_size_in_byte":1997,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"17436727224","text":"\nimport click\nimport piman\nfrom dhcp import test_dhcp\nfrom tftp import test_tftp\n\n\n@click.group()\ndef cli():\n    pass\n\n\n@cli.command()\ndef server():\n    piman.server()\n\n\n@cli.command()\n@click.argument('switch_address')\n@click.argument('interface')\n@click.argument('ports', nargs=-1)\ndef restart(switch_address, interface, ports):\n    piman.restart(switch_address, interface, ports)\n\n\n@cli.command()\n@click.argument('switch_address')\n@click.argument('interface')\n@click.argument('port')\ndef reinstall(switch_address, interface, port):\n    piman.reinstall(switch_address, interface, port)\n    \n@cli.command()\n@click.argument('switch_address')\n@click.argument('interface')\n@click.argument('port', nargs = -1)\n@click.option('--file', default = None)\ndef mapper(switch_address, interface, port, file):\n    piman.mapper(switch_address,interface, port, file)\n\n@cli.command()\n@click.argument('name')\n@click.argument('configpath')\n@click.argument('hostcsvpath')\ndef config(name, configpath, hostcsvpath):\n    piman.config_ui(name, configpath, hostcsvpath)\n\n\n@cli.command()\ndef run_dhcp_test():\n    test_dhcp.run_test()\n\n@cli.command()\ndef run_tftp_test():\n    test_tftp.run_test()\n\nif __name__ == \"__main__\":\n    cli()\n","repo_name":"jcal329/cs158b-Storm","sub_path":"__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1210,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"20357860020","text":"from mcapi import *\r\nfrom org.bukkit.event.player import AsyncPlayerChatEvent\r\nimport socket\r\nimport threading\r\nPORT = 5050\r\nsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\nSERVER = socket.gethostbyname(socket.gethostname())\r\nprint(SERVER)\r\nserver_address = (SERVER, 5050)\r\nFORMAT = \"utf-8\"\r\nDISCONNECT_MESSAGE = \"!desconectar\"\r\n\r\nclients = [] # The clients we have connected to\r\nclients_lock = threading.Lock()\r\n\r\nsock.bind(server_address)\r\n\r\ndef chat(m):\r\n    msg = m.getMessage()\r\n    au = m.getPlayer()\r\n    print(msg)\r\n    print(au)\r\n    clients[0].send(msg.encode(FORMAT))\r\n    clients[0].send(au.getPlayerListName().encode(FORMAT))\r\n\r\ndef start():\r\n    global ADDR\r\n    sock.listen(1)\r\n    \r\n    \r\n    while True:\r\n        conn, ADDR = sock.accept()\r\n        with clients_lock:\r\n          clients.append(conn)\r\n          print(type(clients))\r\n          print(clients)\r\n\r\n\r\nx1 = threading.Thread(target=start)\r\nx1.start()\r\nlistener = add_event_listener(AsyncPlayerChatEvent, chat)","repo_name":"carlyjb17/plugin-chat","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":996,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"24352935037","text":"#! /usr/bin/python3\n# encoding: utf-8\n\"\"\"模块shopping_cart模拟一个用户购买商品时的流程。\n\n分为：\n    加载用户账户数据结构：账号、密码；\n    加载商品数据结构：商品名、商品价格；\n    加载用户状态数据结构：是否第一次、已购商品；\n    验证用户账户密码；\n    打印商品列表：\n        购买\n        更新消费记录\n        查询\n        充值\n        退出：\n            打印余额\n            打印消费记录\n五大部分。\n\"\"\"\n\nimport os\nimport copy\n\n# 加载用户账户数据结构：账号、密码\ntry:\n    user_info_list = list()\n    with open('shopping_user_info.txt', mode='rt', encoding='utf-8') as fp_userinfo:\n        for line in fp_userinfo:\n            user_info_list.append(line.strip().split(','))\n        else:\n            user_info_dict = dict(user_info_list)\nexcept FileNotFoundError as ExpFile:\n    print('\\033[1;31mType of Exceptions: \\033[4;30m{}\\033[0m'.format(ExpFile.__class__.__name__))\n    print('\\033[1;31mInfo of Exceptions: \\033[4;30m{}\\033[0m'.format(ExpFile))\n    exit()\nelse:\n    print('\\033[1;34mAccount data loaded successfully!\\033[0m')\n\n# 加载商品数据结构：商品名、商品价格\ntry:\n    goods_list = list()\n    with open('goods.txt', encoding='utf-8') as fp_goods:\n        for line in fp_goods:\n            tmp_list = line.strip().split(',')\n            tmp_len = len(tmp_list)\n            for index in range(tmp_len):\n                tmp_list[index] = tmp_list[index].split(':')\n            goods_list.append(dict(tmp_list))\nexcept FileNotFoundError as ExpFile:\n    print('\\033[1;31mType of Exceptions: \\033[4;30m{}\\033[0m'.format(ExpFile.__class__.__name__))\n    print('\\033[1;31mInfo of Exceptions: \\033[4;30m{}\\033[0m'.format(ExpFile))\n    exit()\nelse:\n    print('\\033[1;35mMerchandise data loaded successfully!\\033[0m')\n\n# 加载用户状态数据结构：是否第一次、已购商品\ntry:\n    with open('user_status.txt', encoding='utf-8') as fp_userstatus:\n        user_status_list = list()\n        for line in fp_userstatus:\n            tmp_dc = eval(line.strip())\n            user_status_list.append(tmp_dc)\nexcept FileNotFoundError as ExpFile:\n    print('\\033[1;36mType of Exceptions: \\033[4;30m{}\\033[0m'.format(ExpFile.__class__.__name__))\n    print('\\033[1;36mInfo of Exceptions: \\033[4;30m{}\\033[0m'.format(ExpFile))\n    exit()\nelse:\n    print('\\033[1;36mUser status loaded successfully!\\033[0m')\n\n# 验证用户账户密码\nwhile 1:\n    account = input('\\033[30mInput your account, please: ').strip()\n    passwd = input('Input your password, please: \\033[0m').strip()\n    if account in user_info_dict and passwd in user_info_dict[account]:\n        st_len = len(user_status_list)\n        for index in range(st_len):\n            key = list(user_status_list[index].keys())[0]\n            if account == key:  # 如果此用户不是第一运行这个程序，那么直接取出上次的登录所剩的余额\n                salary = user_status_list[index][key]['balance']\n                break\n        else:\n            while 1:  # 表示此用户是第一次运行这个程序，请Ta输入工资\n                salary = input('\\033[30mInput your salary\\033[1;31m(integer)\\033[0m\\033[30m, please: \\033[0m').strip()\n                if salary.isdigit():\n                    salary = int(salary)\n                    break\n                else:\n                    print('\\033[1;31mUnacceptable salary, please Re-enter!\\033[0m')\n        break\n    else:\n        print('\\033[1;31mWrong account or password, please Re-enter!\\033[0m')\n\n# 打印商品列表\n# 最外层while控制商品的显示、额外功能的使用(包括买、更新、充值、查询、回退、退出)\n#       买：\n#          将能买的商品加入购物车并且扣除\n#          更新：\n#              更新用户的消费记录\n#       充值：\n#          当余额不足时，直接跳转到充值入口\n#          按'T'自觉充值\n#       查询：\n#          按'S'查询 [余额，之前和现在的消费记录，返回商品列表]\n#       退出：\n#          按'Q'退出，保存用户消费记录于文件\n# 内层while主要是为当余额不足时，询问是否直接跳转到充值接口而写\ngoods_list_len = len(goods_list)  # 记得向老师请教print时中文和英文排版一致\nbalance = salary\nbought_list = []\nwhile 1:\n    for index in range(goods_list_len):  # 显示\n        elm_dict = goods_list[index]\n        v1, v2 = elm_dict.values()\n        print('{0:4}{1:^30}{2:>6}￥'.format(index + 1, v1, v2))\n    print('\\033[30mPress \\033[4;34mq to Quit\\033[0m, \\033[4;35mt to Top_up\\033[0m, \\033[4;36ms to search record\\033[0m'\n          ' \\033[1;30mOR\\033[0m')\n    seq_num = input('\\033[30mEnter \\033[4;31mthe sequence number\\033[0m \\033[30myou want to buy: \\033[0m').strip()\n    while 1:\n        if seq_num.isdigit():  # 买\n            seq_num = int(seq_num) - 1\n            if seq_num < goods_list_len:\n                price = float(goods_list[seq_num]['price'])\n                if balance >= price:\n                    balance = balance - price\n                    for index in range(len(bought_list)):  # 更新已买商品\n                        dc = bought_list[index]\n                        if goods_list[seq_num]['name'] == dc['name']:\n                            dic['amount'] = dic.setdefault('amount', 0) + 1\n                            break\n                    else:\n                        dic = copy.deepcopy(goods_list[seq_num])\n                        dic['amount'] = dic.setdefault('amount', 0) + 1\n                        bought_list.append(dic)\n                    print('\\033[30mYou now have bought [\\033[34mx{}\\033[0m] \\033[4;31m{}\\033[0m'.\n                          format(dic['amount'], goods_list[seq_num]['name']))\n                    print('\\033[30mYou have \\033[1;31m{}￥ left\\033[0m'.format(balance))\n                    break\n                else:  # 余额不足，询问是否充值\n                    print('\\033[30mSorry! Your balance is \\033[1;31mNOT enought.\\033[0m')\n                    print('\\033[30mDo you want to recharge?(yes/no) \\033[0m', end='')\n                    response = input().strip()\n                    if response.upper() == 'YES' or not response:\n                        seq_num = 't'\n                        continue\n                    else:\n                        break\n        elif seq_num.upper() == 'Q':  # 退出\n            flag = False  # 标志是否是新用户，False，不是；True，是\n            bought_len = len(bought_list)\n            print('\\033[1;34mSo far, you have bought: \\033[0m')\n            if not bought_len:\n                print('\\033[1;31mNothing!\\033[0m')\n            else:\n                for index in range(bought_len):  # 打印这次登录所买的商品信息\n                    print('{name:30}{price:>10}￥\\t\\tx{amount:}'.format_map(bought_list[index]))\n                else:\n                    print('\\033[30mYou have \\033[1;31m{}￥ left\\033[0m'.format(balance))\n                    print('\\033[34mHope to see you again!\\033[0m')\n            for index in range(len(user_status_list)):  # 判断是否为新用户。是，直接构造数据结构并且写入；不是，延用上次的\n                st_dc = user_status_list[index]\n                key = list(st_dc.keys())[0]\n                if account in key:\n                    break\n            else:\n                flag = True\n            if flag:  # 新用户构造数据结构\n                define_dc = dict()\n                define_dc[account] = {'balance': balance, 'record': bought_list}\n                user_status_list.append(define_dc)\n            else:  # 老用户更新数据结构\n                for index in range(len(user_status_list)):\n                    st_dc = user_status_list[index]\n                    key = list(st_dc.keys())[0]\n                    updated_list = st_dc[key]['record']\n                    if account == key:\n                        st_dc[key]['balance'] = balance\n                        for index in range(len(bought_list)):  # 用这次买的商品列表更新之前买的商品列表\n                            bt_dc = bought_list[index]\n                            for index in range(len(updated_list)):\n                                if bt_dc['name'] == updated_list[index]['name']:\n                                    updated_list[index]['amount'] += bt_dc['amount']\n                                    break\n                            else:\n                                updated_list.append(copy.deepcopy(bt_dc))\n            # print(len(user_status_list))\n            with open('user_status_ectype.txt', mode='w', encoding='utf-8') as fp_userstatus:\n                for line in user_status_list:\n                    fp_userstatus.write(str(line) + '\\n')\n            os.remove('user_status.txt')\n            os.replace('user_status_ectype.txt', 'user_status.txt')\n            exit()\n        elif seq_num.upper() == 'T':  # 充值\n            money = input('\\033[30mEnter the money(\\033[1;31minteger\\033[0m\\033[30m) you want to top-up: \\033[0m')\n            money = money.strip()\n            balance += int(money)\n            print('\\033[34mUpdated successfuly, your balance is \\033[1;31m{}￥\\033[0m'.format(balance))\n            break\n        elif seq_num.upper() == 'S':  # 查询\n            while 1:\n                print('\\033[30mPress \\033[4;34mb to search balance\\033[0m, \\033[4;35mr to search record\\033[0m, '\n                      '\\033[1;30mOR \\033[4;36me to return list: \\033[0m', end='')\n                code = input().strip()\n                if code.upper() == 'B':\n                    print('\\033[30mYou have \\033[1;31m{}￥ left\\033[0m'.format(balance))\n                elif code.upper() == 'R':\n                    print('<-------------- the past record -------------->')\n                    for index in range(len(user_status_list)):\n                        st_dc = user_status_list[index]\n                        key = list(st_dc.keys())[0]\n                        recorded_list = st_dc[key]['record']\n                        if account == key:\n                            reco_len = recorded_list.__len__()\n                            for index in range(reco_len):\n                                dc = recorded_list[index]\n                                print('{name:<30}{price:>7}￥\\t\\tx{amount}'.format_map(dc))\n                        break\n                    print('<-------------- the past end -------------->\\n')\n\n                    print('<-------------- the recent record -------------->')\n                    for index in range(len(bought_list)):\n                        bt_dc = bought_list[index]\n                        print('{name:<30}{price:>7}￥\\t\\tx{amount}'.format_map(bt_dc))\n                    print('<-------------- the recent end -------------->\\n')\n                elif code.upper() == 'E':\n                    break\n            break\n        else:\n            print('\\033[1;31mInvalid sequence number, please Re-enter!\\033[0m')\n            break\n","repo_name":"hardy9sap/python","sub_path":"shopping_cart/shopping_cart.py","file_name":"shopping_cart.py","file_ext":"py","file_size_in_byte":11078,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"70084090486","text":"# Escribir un programa que le pida al usuario que ingrese un número entero positivo, \n# y luego imprima \"El número es un cuadrado perfecto\" si el número es un cuadrado perfecto, \n# o \"El número no es un cuadrado perfecto\" si el número no es un cuadrado perfecto.\n\nnumero_txt = input(\"Ingrese un numero positivo\")\nnumero_int = int(numero_txt)\n\nraiz_numero = numero_int ** 0.5 # o raiz_numero:float = math.sqrt(numero_int)\n\nraiz_int = int(raiz_numero)\n\nnumero_dividido_por_si_mismo = raiz_numero / raiz_int\n\nif (numero_dividido_por_si_mismo == 1):\n    print(\"El numero es cuadrado perfecto\")\nelse:\n    print(\"El numero no es cuadrado perfecto\")\n\n# otra manera es con if(raiz_int.is_integer():","repo_name":"MarianoAquino6/UTN-First-Semester-Exercises-2023","sub_path":"IF/ejercicio_08.py","file_name":"ejercicio_08.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"38935344414","text":"import cv2\nimport numpy as np\nfrom nn_vgg16 import CONFIG\n\n\n# transfer int into c dimensions one-hot array\ndef expand(label, c=10):\n    # return y : (num_class, num_samples)\n    y = np.eye(c)[label]\n    y = np.squeeze(y)\n    return y\n\n\ndef img_read(img_name):\n    try:\n        img = cv2.imread(img_name + \".jpg\")\n        img = cv2.resize(img, (CONFIG.IMAGE_HEIGHT, CONFIG.IMAGE_WIDTH))\n    except cv2.error:\n        img = cv2.imread(img_name + \".png\")\n        img = cv2.resize(img, (CONFIG.IMAGE_HEIGHT, CONFIG.IMAGE_WIDTH))\n    return img\n\n\ndef load_image_join_path(label, root_path):\n    file = open(label, 'r')\n    imgs = []\n    label = []\n    for line_num, line in enumerate(file):\n        if line[:len(line) - 1] == '':\n            print('****************end*******************')\n            break\n        if line_num % 2 == 0:\n            line = root_path + line[:-1]\n            img = img_read(line)\n            imgs.append(img)\n        else:\n            line = int(line[:-1])\n            label.append(line)\n        # print(line)\n    label.append(int(line))\n\n    # shuffle the data\n    np.random.seed(0)\n    label = np.random.permutation(label)\n    np.random.seed(0)\n    imgs = np.random.permutation(imgs)\n\n    print(np.shape(imgs))\n    print(len(imgs))\n\n    return imgs, label","repo_name":"MulongXie/Trail_NeuralNetwor","sub_path":"vgg_transfer/nn_imgProcessing.py","file_name":"nn_imgProcessing.py","file_ext":"py","file_size_in_byte":1284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"25377122947","text":"'''\nCreated on Oct 19, 2012\n\n@author: Benjamin Jaeger\n'''\n\nimport math\nimport random\nimport const\nimport scipy.stats as ss\n\ndef boundednormalvariate(mu, sigma, lb):\n    while True:\n        val = random.normalvariate(mu, sigma)\n        if val >= lb: return val\n\nclass Edge(object):\n    '''\n    classdocs\n    '''\n    \n    def __init__(self, weight, duration, intensity):\n        '''\n        Constructor\n        '''\n        self.weight = weight\n        self.intensity = intensity\n        self.duration = duration\n        \n        \n    def travelTime(self, time, addNoise = True):\n        assert time >= 0.0 and time < const.PERIODS \n        exp = math.exp(-1. * ((int(time) - const.PERIODS / 2.) / self.duration) ** 2.)\n        #normal dist generator truncated on [0,+inf]\n        #noise = ss.truncnorm.rvs(0, float(\"inf\"), scale = const.NOISE_STD_DEV)\n        noise = boundednormalvariate(0, const.NOISE_STD_DEV, -self.intensity)\n        traveltime =  (self.weight + \\\n            (self.intensity + noise*addNoise) * exp) / 12.\n        assert traveltime > 0, traveltime\n        return traveltime\n         \n\n#Plots travel time scatter plot.\nif __name__ == '__main__':\n    import matplotlib.pyplot as plt\n    import numpy as np\n    edge = Edge(1, 7., 50 / const.PERIODS)\n    x, y = [], []\n    for t in xrange(100):\n        x_t = t/100. * const.PERIODS\n        y_t = edge.travelTime(x_t, addNoise = False)\n        x.append(x_t)\n        y.append(y_t)\n        \n    s1, s2 = [], []\n    for i in xrange(1000):\n        x_i = random.uniform(0., const.PERIODS)\n        y_i = edge.travelTime(x_i)\n        s1.append(x_i)\n        s2.append(y_i)\n    \n    avg_line = plt.plot([0,const.PERIODS], [np.mean(s2)]*2, label = \"Daily average\")\n    weight_line = plt.plot([0, const.PERIODS], [edge.weight]*2, label=\"No traffic\")\n    scatter = plt.scatter(s1,s2, label=\"Travel times\")\n    line = plt.plot(x,y, label = \"Mean\")\n    plt.setp(scatter, linewidth =.1)\n    plt.setp(line, color = 'r', linewidth=6.0)\n    plt.title(\"Daily Measured Travel Times For One Segment\")\n    plt.legend()\n    plt.show()\n","repo_name":"jaegs/AI_Practicum","sub_path":"src/trafficpredictor/edge.py","file_name":"edge.py","file_ext":"py","file_size_in_byte":2077,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"27632537305","text":"from skymapper.analyze.an_tools import lam_dict_to_array, save_converted_array\nimport cPickle as pickle\n\ndict_name='lambda_dict_date1_s4_w1'\narray_savename= 'array_date1_s4_w1_test'\n\ndict1= pickle.load( open( dict_name ,'rb'))\narray1 = lam_dict_to_array(dict1)\nsave_converted_array(array1, array_savename)\n\n\n","repo_name":"rmkatti/skymapper","sub_path":"skymapper/analyze/run_arrays.py","file_name":"run_arrays.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"2111382963","text":"import unittest\n\nimport numpy as np\n\nimport fibonacci\n\nANSWER_DATA = np.array(\n    [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946])\nTEST_DATA = np.array(range(22))\nF = fibonacci.Fibonacci()\nf = lambda x: F.fibonacci(x)\n\n\nclass MyTestCase(unittest.TestCase):\n    def test_fibonacci(self):\n        for x in TEST_DATA:\n            self.assertEqual(f(x), ANSWER_DATA[x])\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"sin-to-jin/python_utils","sub_path":"com.orgsin.www/src/library/fibonacci/fibonacci_test.py","file_name":"fibonacci_test.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"70722345525","text":"from django.urls import path\n\nfrom userprofile.views import (\n    EducationRetriveUpdateDeleteAPIView, \n    EducationView, \n    ExperienceAPIView,\n    ExperienceRetriveUpdateDeleteAPIView, \n    LinkAPIView, \n    LinkRetriveUpdateDeleteAPIView,\n    ProfileFilterApi, \n    ProfileView,\n    ProjectAPIView,\n    ProjectRetriveUpdateDeleteAPIView,\n    SearchProfile, \n    SkillAPIView, \n    SkillDeleteAPIView,\n    UserFollowersAPIView,\n    get_favicon\n    )\nurlpatterns = [\n    # path('skill/',SkillView.as_view(), name = 'skill'),\n    path('profile/',ProfileView.as_view(),name='Profile'),\n    path('education/',EducationView.as_view(),name='Education'),\n    path('education/<int:pk>',EducationRetriveUpdateDeleteAPIView.as_view(),name='Education-details'),\n    path('skill/',SkillAPIView.as_view(),name='skill'),\n    path('skill/<int:pk>',SkillDeleteAPIView.as_view(),name='skill-details'),\n    path('link/',LinkAPIView.as_view(),name = 'Link'),\n    path('link/<int:pk>',LinkRetriveUpdateDeleteAPIView.as_view(),name = 'Link-details'),\n    path('project/',ProjectAPIView.as_view(),name = \"Project\"),\n    path('project/<int:pk>',ProjectRetriveUpdateDeleteAPIView.as_view(),name = \"Project-details\"),\n    path('experience/',ExperienceAPIView.as_view(),name = 'Experience'),\n    path('experience/<int:pk>',ExperienceRetriveUpdateDeleteAPIView.as_view(),name = 'Experience-details'),\n    path('icon/',get_favicon,name='Icon'),\n    path('search/',SearchProfile.as_view()),\n    path('followers/',UserFollowersAPIView.as_view()),\n    path('filter/',ProfileFilterApi.as_view())\n]","repo_name":"dhriti987/DevelopersMate","sub_path":"backend/userprofile/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1569,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"76"}
+{"seq_id":"20442772699","text":"# -*- coding: UTF-8 -*-\n# 删除数据\nimport foreignkey\nfrom sqlalchemy.orm import sessionmaker\n\nSession_class = sessionmaker(bind=foreignkey.engine)  # 创建与数据库的会话session class ,注意,这里返回给session的是个class,不是实例\nsession = Session_class()  # 生成session实例 #cursor\n\n#通过书删除作者\nauthor_obj =session.query(foreignkey.Author).filter_by(name=\"Futongyong\").first()\nbook_obj = session.query(foreignkey.Book).filter_by(name=\"The First Demo\").first()\nbook_obj.authors.remove(author_obj) #从一本书里删除一个作者\nsession.commit()\n\n#直接删除作者,删除作者时，会把这个作者跟所有书的关联关系数据也自动删除\nauthor_obj =session.query(foreignkey.Author).filter_by(name=\"Futongyong\").first()\n# print(author_obj.name , author_obj.books)\nsession.delete(author_obj)\nsession.commit()","repo_name":"WeAreHus/StudyRecord","sub_path":"day-2018-05-10/fty_demo/foreignkey_delete.py","file_name":"foreignkey_delete.py","file_ext":"py","file_size_in_byte":862,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"75113012726","text":"# -*- coding: utf-8 -*-\n# ***********************************************************************\n# ******************  CANADIAN ASTRONOMY DATA CENTRE  *******************\n# *************  CENTRE CANADIEN DE DONNÉES ASTRONOMIQUES  **************\n#\n#  (c) 2018.                            (c) 2018.\n#  Government of Canada                 Gouvernement du Canada\n#  National Research Council            Conseil national de recherches\n#  Ottawa, Canada, K1A 0R6              Ottawa, Canada, K1A 0R6\n#  All rights reserved                  Tous droits réservés\n#\n#  NRC disclaims any warranties,        Le CNRC dénie toute garantie\n#  expressed, implied, or               énoncée, implicite ou légale,\n#  statutory, of any kind with          de quelque nature que ce\n#  respect to the software,             soit, concernant le logiciel,\n#  including without limitation         y compris sans restriction\n#  any warranty of merchantability      toute garantie de valeur\n#  or fitness for a particular          marchande ou de pertinence\n#  purpose. NRC shall not be            pour un usage particulier.\n#  liable in any event for any          Le CNRC ne pourra en aucun cas\n#  damages, whether direct or           être tenu responsable de tout\n#  indirect, special or general,        dommage, direct ou indirect,\n#  consequential or incidental,         particulier ou général,\n#  arising from the use of the          accessoire ou fortuit, résultant\n#  software.  Neither the name          de l'utilisation du logiciel. Ni\n#  of the National Research             le nom du Conseil National de\n#  Council of Canada nor the            Recherches du Canada ni les noms\n#  names of its contributors may        de ses  participants ne peuvent\n#  be used to endorse or promote        être utilisés pour approuver ou\n#  products derived from this           promouvoir les produits dérivés\n#  software without specific prior      de ce logiciel sans autorisation\n#  written permission.                  préalable et particulière\n#                                       par écrit.\n#\n#  This file is part of the             Ce fichier fait partie du projet\n#  OpenCADC project.                    OpenCADC.\n#\n#  OpenCADC is free software:           OpenCADC est un logiciel libre ;\n#  you can redistribute it and/or       vous pouvez le redistribuer ou le\n#  modify it under the terms of         modifier suivant les termes de\n#  the GNU Affero General Public        la “GNU Affero General Public\n#  License as published by the          License” telle que publiée\n#  Free Software Foundation,            par la Free Software Foundation\n#  either version 3 of the              : soit la version 3 de cette\n#  License, or (at your option)         licence, soit (à votre gré)\n#  any later version.                   toute version ultérieure.\n#\n#  OpenCADC is distributed in the       OpenCADC est distribué\n#  hope that it will be useful,         dans l’espoir qu’il vous\n#  but WITHOUT ANY WARRANTY;            sera utile, mais SANS AUCUNE\n#  without even the implied             GARANTIE : sans même la garantie\n#  warranty of MERCHANTABILITY          implicite de COMMERCIALISABILITÉ\n#  or FITNESS FOR A PARTICULAR          ni d’ADÉQUATION À UN OBJECTIF\n#  PURPOSE.  See the GNU Affero         PARTICULIER. Consultez la Licence\n#  General Public License for           Générale Publique GNU Affero\n#  more details.                        pour plus de détails.\n#\n#  You should have received             Vous devriez avoir reçu une\n#  a copy of the GNU Affero             copie de la Licence Générale\n#  General Public License along         Publique GNU Affero avec\n#  with OpenCADC.  If not, see          OpenCADC ; si ce n’est\n#  <http://www.gnu.org/licenses/>.      pas le cas, consultez :\n#                                       <http://www.gnu.org/licenses/>.\n#\n#  $Revision: 4 $\n#\n# ***********************************************************************\n#\n\nimport traceback\n\nfrom math import sqrt\n\nfrom caom2 import CalibrationLevel, DataProductType, ProductType\nfrom caom2utils import update_artifact_meta\nfrom caom2pipe import astro_composable as ac\nfrom caom2pipe import caom_composable as cc\nfrom caom2pipe import manage_composable as mc\n\n\n__all__ = ['mapping_factory']\n\n\nclass BlueprintMapping(cc.TelescopeMapping):\n    def __init__(self, storage_name, headers, clients, observable, observation, config):\n        super().__init__(storage_name, headers, clients, observable, observation, config)\n\n    def accumulate_blueprint(self, bp):\n        \"\"\"Configure the VLASS-specific ObsBlueprint for the CAOM model\n        SpatialWCS.\"\"\"\n        self._logger.debug('Begin accumulate_blueprint.')\n        super().accumulate_blueprint(bp)\n\n        # observation level\n        bp.set('Observation.type', 'OBJECT')\n        bp.set('Observation.target.type', 'field')\n        # Claire Chandler via JJK - 21-08-18\n        bp.set('Observation.instrument.name', 'S-WIDAR')\n        # From JJK - 27-08-18 - slack\n        bp.set('Observation.proposal.title', 'VLA Sky Survey')\n        bp.set('Observation.proposal.project', 'VLASS')\n        bp.set('Observation.proposal.id', 'get_proposal_id()')\n\n        bp.set('Plane.calibrationLevel', CalibrationLevel.PRODUCT)\n        bp.set('Plane.dataProductType', DataProductType.IMAGE)\n\n        bp.set('Plane.provenance.producer', 'NRAO')\n        # From JJK - 27-08-18 - slack\n        bp.set('Plane.provenance.project', 'VLASS')\n\n        # artifact level\n        bp.set('Artifact.productType', ProductType.AUXILIARY)\n\n    def get_proposal_id(self, ext):\n        caom_name = mc.CaomName(self._storage_name.file_uri)\n        bits = caom_name.file_name.split('.')\n        return f'{bits[0]}.{bits[1]}'\n\n\nclass QuicklookMapping(BlueprintMapping):\n    def __init__(self, storage_name, headers, clients, observable, observation, config):\n        super().__init__(storage_name, headers, clients, observable, observation, config)\n\n    def accumulate_blueprint(self, bp):\n        \"\"\"Configure the Quicklook ObsBlueprint for the CAOM model SpatialWCS.\"\"\"\n        self._logger.debug('Begin accumulate_wcs.')\n        super().accumulate_blueprint(bp)\n        bp.configure_position_axes((1, 2))\n        bp.configure_energy_axis(3)\n        bp.configure_polarization_axis(4)\n\n        # observation level\n        # over-ride use of value from default keyword 'DATE'\n        bp.clear('Observation.metaRelease')\n        bp.add_attribute('Observation.metaRelease', 'DATE-OBS')\n\n        bp.clear('Observation.target.name')\n        bp.add_attribute('Observation.target.name', 'FILNAM04')\n\n        # plane level\n        bp.set('Plane.calibrationLevel', '2')\n        bp.clear('Plane.dataProductType')\n        bp.add_attribute('Plane.dataProductType', 'TYPE')\n        # Claire Chandler via slack - 28-08-18\n        bp.clear('Plane.provenance.name')\n        bp.add_attribute('Plane.provenance.name', 'ORIGIN')\n        bp.clear('Plane.provenance.runID')\n        bp.add_attribute('Plane.provenance.runID', 'FILNAM08')\n        bp.clear('Plane.provenance.lastExecuted')\n        bp.add_attribute('Plane.provenance.lastExecuted', 'DATE')\n        # VLASS data is public, says Eric Rosolowsky via JJK May 30/18\n        bp.clear('Plane.metaRelease')\n        bp.add_attribute('Plane.metaRelease', 'DATE-OBS')\n        bp.clear('Plane.dataRelease')\n        bp.add_attribute('Plane.dataRelease', 'DATE-OBS')\n\n        # artifact level\n        bp.clear('Artifact.productType')\n        bp.set('Artifact.productType', 'get_product_type()')\n        bp.set('Artifact.releaseType', 'data')\n\n        # chunk level\n        bp.clear('Chunk.position.axis.function.cd11')\n        bp.clear('Chunk.position.axis.function.cd22')\n        bp.add_attribute('Chunk.position.axis.function.cd11', 'CDELT1')\n        bp.set('Chunk.position.axis.function.cd12', 0.0)\n        bp.set('Chunk.position.axis.function.cd21', 0.0)\n        bp.add_attribute('Chunk.position.axis.function.cd22', 'CDELT2')\n\n        # Claire Chandler via JJK - 21-08-18\n        bp.set('Chunk.energy.bandpassName', 'S-band')\n\n    def get_position_resolution(self, ext):\n        bmaj = self._headers[ext].get('BMAJ')\n        bmin = self._headers[ext].get('BMIN')\n        # From\n        # https://open-confluence.nrao.edu/pages/viewpage.action?pageId=13697486\n        # Claire Chandler via JJK - 21-08-18\n        result = None\n        if bmaj is not None and bmaj != 'INF' and bmin is not None and bmin != 'INF':\n            result = 3600.0 * sqrt(bmaj * bmin)\n        return result\n\n    def get_product_type(self, ext):\n        if '.rms.' in self._storage_name.file_uri:\n            return ProductType.NOISE\n        elif self._storage_name.file_uri.endswith('.csv'):\n            return ProductType.AUXILIARY\n        else:\n            return ProductType.SCIENCE\n\n    def get_time_refcoord_value(self, ext):\n        dateobs = self._headers[ext].get('DATE-OBS')\n        if dateobs is not None:\n            result = ac.get_datetime(dateobs)\n            if result is not None:\n                return result.mjd\n            else:\n                return None\n\n    def update(self, file_info):\n        \"\"\"Called to fill multiple CAOM model elements and/or attributes, must\n        have this signature for import_module loading and execution.\n        \"\"\"\n        self._logger.debug('Begin update.')\n        try:\n            for plane in self._observation.planes.values():\n                for artifact in plane.artifacts.values():\n                    if artifact.uri != self._storage_name.file_uri:\n                        continue\n                    update_artifact_meta(artifact, file_info)\n                    delete_these_parts = []\n                    for part in artifact.parts.values():\n                        delete_these_chunks = []\n                        for index, chunk in enumerate(part.chunks):\n                            if chunk.position is not None:\n                                chunk.position.resolution = (\n                                    self.get_position_resolution(0)\n                                )\n                            if chunk.energy is not None:\n                                # A value of None per Chris, 2018-07-26\n                                # Set the value to None here, because the\n                                # blueprint is implemented to not set WCS\n                                # information to None\n                                chunk.energy.restfrq = None\n\n                            if (\n                                chunk.naxis == 2\n                                and chunk.position is None\n                                and chunk.energy is None\n                                and chunk.polarization is None\n                            ):\n                                # rms has a second HDU with BINTABLE extensions, and no WCS description (?)\n                                delete_these_chunks.append(index)\n\n                        for entry in delete_these_chunks:\n                            self._logger.debug(f'Removing chunk index {entry} because it has no WCS.')\n                            part.chunks.pop(entry)\n\n                        if len(part.chunks) == 0:\n                            delete_these_parts.append(part.name)\n\n                    for entry in delete_these_parts:\n                        self._logger.debug(f'Removing part {entry} because it has no chunks.')\n                        artifact.parts.pop(entry)\n\n            self._logger.debug('Done update.')\n            return self._observation\n        except mc.CadcException as e:\n            tb = traceback.format_exc()\n            self._logger.debug(tb)\n            self._logger.error(e)\n            self._logger.error(\n                f'Terminating ingestion for {self._observation.observation_id}'\n            )\n            return None\n\n\nclass ContinuumMapping(QuicklookMapping):\n    def __init__(self, storage_name, headers, clients, observable, observation, config):\n        super().__init__(storage_name, headers, clients, observable, observation, config)\n\n    def accumulate_blueprint(self, bp, application=None):\n        super().accumulate_blueprint(bp)\n        bp.clear('Observation.target.name')\n        bp.add_attribute('Observation.target.name', 'FILNAM06')\n        bp.clear('Observation.telescope.geoLocationX')\n        bp.add_attribute('Observation.telescope.geoLocationX', 'OBSGEO-X')\n        bp.clear('Observation.telescope.geoLocationY')\n        bp.add_attribute('Observation.telescope.geoLocationY', 'OBSGEO-Y')\n        bp.clear('Observation.telescope.geoLocationZ')\n        bp.add_attribute('Observation.telescope.geoLocationZ', 'OBSGEO-Z')\n\n        bp.set('Plane.calibrationLevel', CalibrationLevel.PRODUCT)\n        bp.set('Plane.dataProductType', DataProductType.IMAGE)\n        bp.clear('Plane.provenance.runID')\n        bp.add_attribute('Plane.provenance.runID', 'FILNAM09')\n\n        bp.add_attribute('Chunk.energy.restfrq', 'RESTFREQ')\n\n\nclass ChannelCubeMapping(ContinuumMapping):\n    def __init__(self, storage_name, headers, clients, observable, observation, config):\n        super().__init__(storage_name, headers, clients, observable, observation, config)\n\n    def accumulate_blueprint(self, bp, application=None):\n        super().accumulate_blueprint(bp)\n        bp.clear('Observation.target.name')\n        bp.add_attribute('Observation.target.name', 'FILNAM05')\n\n\ndef mapping_factory(storage_name, headers, clients, observable, observation, config):\n    if storage_name.is_catalog:\n        return BlueprintMapping(storage_name, headers, clients, observable, observation, config)\n    elif storage_name.is_quicklook:\n        return QuicklookMapping(storage_name, headers, clients, observable, observation, config)\n    elif storage_name.is_channel_cube:\n        return ChannelCubeMapping(storage_name, headers, clients, observable, observation, config)\n    else:\n        return ContinuumMapping(storage_name, headers, clients, observable, observation, config)\n","repo_name":"opencadc/vlass2caom2","sub_path":"vlass2caom2/main_app.py","file_name":"main_app.py","file_ext":"py","file_size_in_byte":14010,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"37964848967","text":"# -*- coding: utf-8 -*-\n\nname = u'astroid'\n\nversion = '1.6.2'\n\ndescription = \\\n    \"\"\"\n    \n    \"\"\"\n\nvariants = []\n\nrequires = ['enum']\n\ndef commands():\n    import os\n\n    libs_path = os.path.join(getenv(\"PYTHON_LIBS_PATH\"), \"astroid\", \"%s\" % version)\n    env.PYTHONPATH.append(os.path.join(libs_path, \"lib\").replace(\"/\", os.sep))\n\n\n","repo_name":"cashmerepipeline/CashmereRez","sub_path":"PythonLibraries/astroid/1.6.2/package.py","file_name":"package.py","file_ext":"py","file_size_in_byte":333,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"16028313161","text":"import pandas as pd\nimport connectfour\nimport random\nimport logging\n\nclass Brutalcomputer:\n    def __init__(self,cf=None,k=2):\n        if cf is None:\n            self.sf=connectfour.Connectfour()\n        else:\n            # initialized Connectfour object, but with a pre-filled board state\n            self.sf=connectfour.Connectfour(cf)\n        # Copy the original board state that is the basis for the next turn\n        self.sm_orig=self.sf.sm.copy()\n        # only used temporarly for the recursive calculation of the tree\n        self.slothist=[]\n        # matrix to store the search tree results\n        self.brutalmatrix=None\n        # tree depth for the brute search\n        self.k = k\n\n    def print(self):\n        print(\"########## print Brutalcomputer ##############\")\n        print(self.sf.sm)\n        print(self.brutalmatrix)\n        print(\"##############################################\")\n\n    def nextturn(self,method=\"random\"):\n        if method==\"random\":\n            return random.randint(0,6)\n        elif method==\"brutal\":\n            self.calcposs(k=self.k)\n            return self.findbestbranch()\n\n    def findbestbranch(self):\n        logging.info(\"brutalcomputer.findbestbranch with k=%d\",self.k)\n        gc=self.brutalmatrix\n        # print(self.sf.sm)\n        # print(\"turn \",self.sf.turn)\n        # print(gc)\n        for ik in range(self.k-1):\n            groupcol=gc.columns.values.tolist()[0:-2]\n            dropcol=gc.columns.values.tolist()[-2:-1]\n            minmax=\"min\" if (self.sf.turn==2)!=(((self.k-ik-1) % 2) != 0) else \"max\"\n            # print(\"######## k #####\",ik,self.k,minmax)\n            if minmax==\"min\": \n                gc=gc.drop(dropcol,axis=1).groupby(groupcol).min().reset_index()\n            else:\n                gc=gc.drop(dropcol,axis=1).groupby(groupcol).max().reset_index()\n            # print(gc)\n            # print(\"################\")\n        gc[\"centerness\"]=abs(gc[0]-3)\n        return gc.sort_values(\"centerness\").sort_values(\"netscore\",ascending=(self.sf.turn==2)).reset_index().loc[0,0]\n\n    def calcposs(self,k=1):\n        # logging.info(\"brutalcomputer.calcposs with k=%d\",k)\n        for possmove in range(7):\n            smcp=self.sf.sm.copy()\n            turncp=self.sf.turn\n            # ignore impossible moves\n            if self.sf.doturn(col=possmove,computer=False,saveoneturn=False,countseq=False)==1:\n                self.slothist.append(possmove)\n\n                # print(\"debug in possmove\",k, possmove)\n                # print(self.sf.sm)\n                # print(self.sf.turn)\n                # print(\"end debug in possmove\")\n\n                # move one level deeper if not on maximum level and if tree doesn't end here\n                if k-1>0 and self.sf.endresult<0: \n                    self.calcposs(k=k-1)\n                else:\n                    # placeholder history for data collection\n                    for ki in range(k-1):\n                        self.slothist.append(-1)\n                    # at the end of the iteration tree, count and collect data\n                    self.sf.countseq(redyellow=3)\n                    newl=pd.DataFrame([[*self.slothist,self.sf.netscore]])\n                    newl.columns=[*range(len(self.slothist)),\"netscore\"]\n                    if self.brutalmatrix is None:\n                        self.brutalmatrix=newl\n                    else:\n                        self.brutalmatrix=pd.concat([self.brutalmatrix,newl])\n                    for ki in range(k-1):\n                        self.slothist.pop()\n\n                    # print(\"slothist: \",self.slothist)\n                    # print(\"score: \",self.sf.scount)\n                    # print(self.brutalmatrix)\n\n                self.sf.sm=smcp\n                self.sf.turn=turncp\n                self.sf.endresult=-1\n                self.slothist.pop()\n\n\n","repo_name":"Dronakurl/connectfour","sub_path":"brutalcomputer.py","file_name":"brutalcomputer.py","file_ext":"py","file_size_in_byte":3834,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"1246241304","text":"#\n# @lc app=leetcode id=78 lang=python3\n#\n# [78] Subsets\n#\n\n# @lc code=start\nclass Solution:\n    def subsets(self, nums: List[int]) -> List[List[int]]:\n        n=len(nums)\n        res=[]\n        for i in range(2**n):\n            temp=[]\n            for j in range(n):\n                if 1&(i>>j):temp.append(nums[j])\n            res.append(temp)\n        return res\n        \n# @lc code=end\n\n","repo_name":"bird0401/leetcode","sub_path":"solutions/78.subsets.py","file_name":"78.subsets.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"21482454871","text":"#coding:utf-8\nfrom model.base import BaseModel\nimport tensorflow as tf\nfrom nn.layer import Embedding,Dropout\nfrom nn.recurrent import BiLSTM\nfrom collections import OrderedDict\nclass ABLSTM(BaseModel):\n    def __init__(self, vocab, pretrained_word_embedding=None,\n             word_embedding_size=100,\n             rnn_hidden_size = 64,\n             dropout_keep_prob=0.9, num_class=3):\n        super(ABLSTM, self).__init__(vocab)\n        self.keep_prob = dropout_keep_prob\n        self.num_class = num_class\n        self.word_embedding_size = word_embedding_size\n        self.pretrained_word_embedding = pretrained_word_embedding\n        self.word_embedding_trainable = True\n        self.rnn_hidden_size =rnn_hidden_size\n        self._build_graph()\n\n    def _build_graph(self):\n        self.x = tf.placeholder(tf.int32, [None, None])\n        self.x_len = tf.placeholder(tf.int32,[None])\n        self.y = tf.placeholder(tf.int32, [None])\n        self.training = tf.placeholder_with_default(False, shape=(), name='is_training')\n\n        word_embedding = Embedding(pretrained_embedding=self.pretrained_word_embedding,\n                                   embedding_shape=(self.vocab.get_word_vocab() + 1, self.word_embedding_size),\n                                   trainable=self.word_embedding_trainable)\n\n        input_x = word_embedding(self.x)\n        dropout = Dropout(self.keep_prob)\n        input_x = dropout(input_x, self.training)\n\n        encoder1 = BiLSTM(self.rnn_hidden_size,name='layer_1')\n        input_x,_ = encoder1(input_x,self.x_len)\n\n        H =tf.nn.tanh(input_x)\n\n        alpha = tf.nn.softmax(tf.squeeze(tf.layers.Dense(1)(H),axis=-1))\n\n        H_star= tf.squeeze(tf.matmul(tf.expand_dims(alpha,axis=1),H),axis=1)\n\n        H_star = dropout(H_star,self.training)\n\n        self.logits = tf.keras.layers.Dense(self.num_class,activation=None,use_bias=True)(H_star)\n        self.loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.logits,labels=self.y))\n        global_step = tf.train.get_or_create_global_step()\n\n        self.input_placeholder_dict = OrderedDict({\n            \"token_ids\": self.x,\n            \"labels\": self.y,\n            \"text_len\":self.x_len,\n            \"training\": self.training\n        })\n\n        self.output_variable_dict = OrderedDict({\n            \"predict\": tf.argmax(self.logits, axis=1)\n        })\n\n        # 8. Metrics and summary\n        with tf.variable_scope(\"train_metrics\"):\n            self.train_metrics = {\n                'loss': tf.metrics.mean(self.loss)\n            }\n\n        self.train_update_metrics = tf.group(*[op for _, op in self.train_metrics.values()])\n        metric_variables = tf.get_collection(tf.GraphKeys.LOCAL_VARIABLES, scope=\"train_metrics\")\n        self.train_metric_init_op = tf.variables_initializer(metric_variables)\n\n        with tf.variable_scope(\"eval_metrics\"):\n            self.eval_metrics = {\n                'loss': tf.metrics.mean(self.loss)\n            }\n\n        self.eval_update_metrics = tf.group(*[op for _, op in self.eval_metrics.values()])\n        metric_variables = tf.get_collection(tf.GraphKeys.LOCAL_VARIABLES, scope=\"eval_metrics\")\n        self.eval_metric_init_op = tf.variables_initializer(metric_variables)\n\n        tf.summary.scalar('loss', self.loss)\n        self.summary_op = tf.summary.merge_all()\n\n    def compile(self, optimizer, initial_lr, clip_norm=5.0):\n        self.optimizer = optimizer(initial_lr)\n        grads, vars = zip(*self.optimizer.compute_gradients(self.loss))\n        gradients, _ = tf.clip_by_global_norm(grads, clip_norm=clip_norm)\n        self.train_op = self.optimizer.apply_gradients(zip(gradients, vars))\n\n\n\n\n\n","repo_name":"wujindou/TextClassification","sub_path":"model/abilstm.py","file_name":"abilstm.py","file_ext":"py","file_size_in_byte":3676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"33091125662","text":"\"\"\"5! = 5 * 4 * 3 * 2 * 1\nIdea of this is combinational . Example the word\nModou combined 120 combination\n1. multiply 5 by 4 factorial\n2. Check if the value is in cache\n2. Then go down the list until we hit the base\"\"\"\n\n\nfact_cache = {}\n\ndef factorial(n):\n    if n in fact_cache:\n        return fact_cache[n]\n    if n == 0:\n        val = 1\n    elif n > 0:\n        val = n * factorial(n -1)\n    fact_cache[n] = val\n    return val\n\n\nfor i in range (1,6):\n    print(factorial(i))","repo_name":"acemodou/dev","sub_path":"geeksforgeeks/algorithmDataStructure/factorial.py","file_name":"factorial.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"71363727285","text":"import os\nimport re\nimport time\nfrom pathlib import Path\nfrom contextlib import contextmanager\nfrom abc import ABCMeta, abstractmethod\nimport argparse\nimport inspect\n\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\n\n# os.chdir('src')\nfrom paths import *\nimport features.denoising as deno\nimport util.s3_functions as s3\n\n\n@contextmanager\ndef timer(name):\n    t0 = time.time()\n    print(f'[name] start')\n    yield\n    print(f'[{name}] done in {time.time() - t0:.0f} s')\n\n\nclass Feature(metaclass=ABCMeta):\n    prefix = ''\n    suffix = ''\n\n    def __init__(self, slide_size, series_type='normal', denoising=False, ojisan=False):\n        self.name = self.__class__.__name__\n        self.train = pd.DataFrame()\n        self.test = pd.DataFrame()\n        self.slide_size = slide_size\n        self.series_type = series_type\n        self.denoising = denoising\n        self.ojisan = ojisan\n\n        if ojisan:\n            self.save_dir = FEATURES_DIR / f'add_ojisan36'\n        else:\n            self.save_dir = FEATURES_DIR / f'{slide_size}_slides'\n        self.save_dir.mkdir(parents=True, exist_ok=True)\n\n        self.test_files = []\n        submission = pd.read_csv(DATA_DIR / 'input/sample_submission.csv')\n        for seg_id in submission.seg_id.values:\n            self.test_files.append((seg_id, DATA_DIR / f'input/test/{seg_id}.csv'))\n\n    def run(self, **kwargs):\n        with timer(self.name):\n            self.create_features(**kwargs)\n            prefix = self.prefix + '_' if self.prefix else ''\n            suffix = '_' + self.suffix if self.suffix else ''\n            self.train.columns = prefix + self.train.columns + suffix\n            self.test.columns = prefix + self.test.columns + suffix\n        return self\n\n    @abstractmethod\n    def calc_features(self, x, record):\n        raise NotImplementedError\n\n    def save(self):\n        prefix = self.prefix + '_' if self.prefix else ''\n        suffix = '_' + self.suffix if self.suffix else ''\n        name = prefix + self.name + suffix\n        self.train.to_feather(str(self.save_dir / f'{name}_train.ftr'))\n        self.test.to_feather(str(self.save_dir / f'{name}_test.ftr'))\n\n    # For lanl\n    def create_features(self, train):\n        if self.ojisan:\n            index_tuple_list = self.create_index_tuple_ojisan36()\n        else:\n            index_tuple_list = self.create_index_tuple(self.slide_size, train.shape[0])\n\n        # 訓練データ用\n        records = []\n        for seg_id, index_tuple in tqdm(enumerate(index_tuple_list)):\n            x, y, seg_id = self.slice_train_data(train, seg_id, index_tuple)\n\n            if self.denoising:\n                self.suffix = 'denoised'\n                x = deno.denoise_signal(deno.high_pass_filter(x, low_cutoff=10000, SAMPLE_RATE=4000000), wavelet='haar', level=1)\n\n            # Convert x\n            x = self.convert(x)\n\n            # Initialize\n            record = dict()\n            record['target'] = y\n            record['seg_id'] = seg_id\n\n            # Create features\n            record = self.calc_features(x, record)\n\n            records.append(record)\n        self.train = pd.DataFrame(records)\n\n        # テストデータ用\n        records = []\n        for seg_id, f in tqdm(self.test_files):\n            # seg_id, f = test_files[0]; slide_size = 50000\n            df = pd.read_csv(f, dtype={'acoustic_data': np.float64})\n            # x = df.acoustic_data.values[-self.slide_size:]\n            x = df.acoustic_data.values\n\n            if self.denoising:\n                self.suffix = 'denoised'\n                x = deno.denoise_signal(deno.high_pass_filter(x, low_cutoff=10000, SAMPLE_RATE=4000000), wavelet='haar', level=1)\n\n            # Convert x\n            x = self.convert(x)\n\n            # Initialize\n            record = dict()\n            record['target'] = np.nan\n            record['seg_id'] = seg_id\n\n            # Create features\n            record = self.calc_features(x, record)\n\n            records.append(record)\n        self.test = pd.DataFrame(records)\n\n    def convert(self, x):\n        x = pd.Series(x)\n        zc = np.fft.fft(x)\n        if self.series_type == 'fftr':\n            x = pd.Series(np.real(zc))\n            self.prefix = 'fftr'\n        elif self.series_type == 'ffti':\n            x = pd.Series(np.imag(zc))\n            self.prefix = 'ffti'\n        return x\n\n    def slice_train_data(self, train, seg_id, index_tuple, denoising=False):\n        start, end = index_tuple\n        sub_train = train.iloc[start:end, :]\n\n        x = list(sub_train.acoustic_data.values)\n        if denoising:\n            x = deno.denoise_signal(deno.high_pass_filter(x, low_cutoff=10000, SAMPLE_RATE=4000000), wavelet='haar', level=1)\n\n        y = sub_train.time_to_failure.values[-1]\n        seg_id = f'train_{seg_id}'\n\n        return x, y, seg_id\n\n    def create_index_tuple(self, slide_size, data_length):\n        start = 0\n        end = 150000\n        index_tuple_list = []\n\n        while end <= data_length:\n            index_tuple_list.append((start, end))\n            start += slide_size\n            end += slide_size\n        index_tuple_list.append((start, data_length-1))\n\n        return index_tuple_list\n\n    def create_index_tuple_ojisan36(self):\n        start_indices = s3.read_csv_in_s3('s3://kaggle-nowcast/kaggle_lanl/data/input/featured/ojisan36/start_indices_4k.csv', header=None)\n        index_tuple_list = []\n        for i in range(6):\n            # i = 1\n            for start_index in (start_indices[i] + i*104_832_580).values:\n                index_tuple_list.append((start_index, start_index+150000))\n        return index_tuple_list\n\n\ndef get_arguments():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--force', '-f', action='store_true', help='Overwrite existing files')\n    return parser.parse_args()\n\n\ndef get_features(namespace):\n    for k, v in namespace.items():\n        if inspect.isclass(v) and issubclass(v, Feature) and not inspect.isabstract(v):\n            yield v()\n\n\ndef generate_features(namespace, overwrite):\n    for f in get_features(namespace):\n        if f.train_path.exists() and f.test_path.exists() and not overwrite:\n            print(f.name, 'was skipped')\n        else:\n            f.run().save()\n","repo_name":"koukyo1994/kaggle-lanl","sub_path":"katayama/src/features/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":6225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"26152475097","text":"num_stu_a = int(input(\"Enter the number of students in class a: \"))\nnum_stu_b = int(input(\"Enter the number of students in class b: \"))\nnum_stu_c = int(input(\"Enter the number of students in class c: \"))\na = 0\nb = 0\nc = 0\n\ndiv_a = num_stu_a // 2\ndiv_b = num_stu_b // 2\ndiv_c = num_stu_c // 2\n\nif div_a % 2 != 0 or num_stu_b % 2 != 0 or num_stu_c % 2 != 0:\n    a = div_a + 1\n    b = div_b + 1\n    c = div_c + 1\nelse:\n    a = div_a\n    b = div_b\n    c = div_c\nprint()\nprint(\"The minimum number of desks needed in class a are: \", int(a))\nprint(\"The minimum number of desks needed in class b are: \", int(b))\nprint(\"The minimum number of desks needed in class b are: \", int(c))\n","repo_name":"Rakxit121/Lab-Work","sub_path":"Day2/lab5.py","file_name":"lab5.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"22586947422","text":"import itertools\nfrom copy import deepcopy\nfrom datetime import timedelta\nfrom datetime import datetime\nfrom django.conf import settings\nfrom django.contrib.admin.views.decorators import staff_member_required\nfrom django.db.models import Prefetch\nfrom django.http import JsonResponse\nfrom django.shortcuts import get_object_or_404\nfrom django.utils import timezone\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.views.generic import TemplateView\nfrom random import randint\n\nfrom dashboard.models import CHARTS, Dashboard\nfrom access.models import AC_TYPES, AccessControl, LogAccessControl\nfrom events.models import Event, Session, Space\nfrom tickets.models import MultiPurchase\nfrom windows.models import PAYMENT_TYPES, TicketWindow, TicketWindowSale\n\n\nclass DashboardsView(TemplateView):\n    template_name = \"dashboard/list.html\"\n\n    def get_context_data(self, *args, **kwargs):\n        ctx = super().get_context_data(*args, **kwargs)\n\n        ev = get_object_or_404(Event, slug=kwargs.get(\"ev\", \"\"))\n        dashs = ev.dashboard_set.all()\n\n        ctx[\"ev\"] = ev\n        ctx[\"dashs\"] = dashs\n        ctx[\"menuitem\"] = \"dashboard\"\n        return ctx\n\n\ndlist = staff_member_required(DashboardsView.as_view())\n\n\nclass GeneralView(TemplateView):\n    template_name = \"dashboard/general.html\"\n    DEFAULT_LINE_DATASET = {\n        \"label\": \"All\",\n        \"fill\": False,\n        \"lineTension\": 0,\n        \"borderColor\": \"rgba(0,0,0,1)\",\n        \"borderWidth\": 1,\n        \"pointBorderWidth\": 1,\n        \"pointRadius\": 1,\n        \"scaleStartValue\": 0,\n        \"data\": [],\n    }\n    MAIN_DATASET = deepcopy(DEFAULT_LINE_DATASET)\n    MAIN_DATASET[\"borderWidth\"] = 5\n    MAIN_DATASET[\"fill\"] = True\n    DATA_LINE = {\"labels\": [], \"datasets\": [deepcopy(MAIN_DATASET)]}\n    INDEX_MAIN = 0\n    DEFAULT_PIE_DATASET = {\n        \"backgroundColor\": [],\n        \"hoverBackgroundColor\": [],\n        \"data\": [],\n    }\n    DEFAULT_BAR_DATASET = {\n        \"label\": \"\",\n        \"backgroundColor\": [],\n        \"borderColor\": [],\n        \"borderWidth\": 2,\n        \"data\": [],\n    }\n    DATA_PIE = {\"labels\": [], \"datasets\": []}\n    DATA_BAR = deepcopy(DATA_PIE)\n\n    def dataset_index(self, datasets, name):\n        index = 0\n        for dataset in datasets:\n            if dataset.get(\"label\") == name:\n                return index\n            index += 1\n        return -1\n\n    def get_timesteps_vars(self, timestep):\n        if timestep == \"daily\":\n            strftime = \"%Y-%m-%d\"\n            delta = timedelta(days=1)\n        elif timestep == \"hourly\":\n            strftime = \"%Hh %Y-%m-%d\"\n            delta = timedelta(hours=1)\n        elif timestep == \"minly\":\n            strftime = \"%H:%M %Y-%m-%d\"\n            delta = timedelta(minutes=1)\n        else:\n            strftime = \"%Y-%m-%d\"\n            delta = timedelta(days=1)\n        return strftime, delta\n\n    def get_access(self, timestep, max):\n        strftime, delta = self.get_timesteps_vars(timestep)\n        now = timezone.localtime(timezone.now())\n        min_date = now - delta * max\n        res = deepcopy(self.DATA_LINE)\n\n        # Create datasets\n        res[\"datasets\"][0][\"data\"] = [0] * max\n        for control in AccessControl.objects.all():\n            new_dataset = deepcopy(self.DEFAULT_LINE_DATASET)\n            new_dataset[\"label\"] = control.slug\n            new_dataset[\"borderColor\"] = self.get_random_color()\n            new_dataset[\"data\"] = [0] * max\n            res[\"datasets\"].append(new_dataset)\n\n        # Create labels\n        for l in reversed(range(0, max)):\n            date = (now - delta * l).strftime(strftime)\n            res.get(\"labels\").append(date)\n\n        # Fill access control\n        acs = LogAccessControl.objects.filter(date__gt=min_date).order_by(\"date\")\n        for ac in acs:\n            date = timezone.localtime(ac.date).strftime(strftime)\n            try:\n                index = res.get(\"labels\").index(date)\n            except ValueError:\n                continue\n            extra_index = self.dataset_index(res[\"datasets\"], ac.access_control.slug)\n            res.get(\"datasets\")[self.INDEX_MAIN].get(\"data\")[index] += 1\n            res.get(\"datasets\")[extra_index].get(\"data\")[index] += 1\n        return res\n\n    def get_random_color(self, extra_alpha=False):\n        rand = []\n        for x in range(3):\n            rand.append(randint(0, 255))\n        if extra_alpha:\n            return \"rgba(%s,1)\" % (\",\".join(map(str, rand))), \"rgba(%s,0.2)\" % (\n                \",\".join(map(str, rand))\n            )\n        else:\n            return \"rgba(%s,1)\" % (\",\".join(map(str, rand)))\n\n    def get_sales_online(self, timestep, max):\n        strftime, delta = self.get_timesteps_vars(timestep)\n        now = timezone.localtime(timezone.now())\n        min_date = now - delta * max\n        res = deepcopy(self.DATA_LINE)\n\n        # Create datasets\n        res[\"datasets\"][0][\"data\"] = [0] * max\n        for session in Session.objects.all():\n            new_dataset = deepcopy(self.DEFAULT_LINE_DATASET)\n            new_dataset[\"label\"] = session.space.name + \"  \" + session.name\n            new_dataset[\"borderColor\"] = self.get_random_color()\n            new_dataset[\"data\"] = [0] * max\n            res[\"datasets\"].append(new_dataset)\n\n        # Create labels\n        for l in reversed(range(0, max)):\n            date = (now - delta * l).strftime(strftime)\n            res.get(\"labels\").append(date)\n\n        # Fill sales online\n        mps = MultiPurchase.objects.filter(\n            created__gt=min_date, confirmed=True\n        ).order_by(\"created\")\n        for mp in mps:\n            date = timezone.localtime(mp.created).strftime(strftime)\n            for ticket in mp.all_tickets():\n                try:\n                    index = res.get(\"labels\").index(date)\n                except ValueError:\n                    continue\n                extra_index = self.dataset_index(\n                    res[\"datasets\"],\n                    ticket.session.space.name + \"  \" + ticket.session.name,\n                )\n                res.get(\"datasets\")[self.INDEX_MAIN].get(\"data\")[index] += 1\n                res.get(\"datasets\")[extra_index].get(\"data\")[index] += 1\n        return res\n\n    def get_sales(self, timestep, max):\n        strftime, delta = self.get_timesteps_vars(timestep)\n        now = timezone.localtime(timezone.now())\n        min_date = now - delta * max\n        res = deepcopy(self.DATA_LINE)\n\n        # Create datasets\n        res[\"datasets\"][0][\"data\"] = [0] * max\n        for ticket_window in TicketWindow.objects.all():\n            new_dataset = deepcopy(self.DEFAULT_LINE_DATASET)\n            new_dataset[\"label\"] = ticket_window.slug\n            new_dataset[\"borderColor\"] = self.get_random_color()\n            new_dataset[\"data\"] = [0] * max\n            res[\"datasets\"].append(new_dataset)\n\n        # Create labels\n        for l in reversed(range(0, max)):\n            date = (now - delta * l).strftime(strftime)\n            res.get(\"labels\").append(date)\n\n        # Fill sales\n        sales = TicketWindowSale.objects.filter(date__gt=min_date).order_by(\"date\")\n        for sale in sales:\n            date = timezone.localtime(sale.date).strftime(strftime)\n            try:\n                index = res.get(\"labels\").index(date)\n            except ValueError:\n                continue\n            extra_index = self.dataset_index(res[\"datasets\"], sale.window.slug)\n            res.get(\"datasets\")[self.INDEX_MAIN].get(\"data\")[index] += 1\n            res.get(\"datasets\")[extra_index].get(\"data\")[index] += 1\n        return res\n\n    def get_pie(self, type, timestep, max):\n        strftime, delta = self.get_timesteps_vars(timestep)\n        now = timezone.localtime(timezone.now())\n        min_date = now - delta * max\n        res = deepcopy(self.DATA_PIE)\n        # Create labels and dataset\n        labels = []\n        values = []\n        if type == \"access\":\n            labels = [x[0] for x in AC_TYPES]\n            values = LogAccessControl.objects.filter(date__gt=min_date)\n        elif type == \"sale\":\n            labels = [x[0] for x in PAYMENT_TYPES]\n            values = TicketWindowSale.objects.filter(date__gt=min_date)\n\n        dataset = deepcopy(self.DEFAULT_PIE_DATASET)\n        for label in labels:\n            res.get(\"labels\").append(label)\n            color = self.get_random_color()\n            dataset[\"backgroundColor\"].append(color)\n            dataset[\"hoverBackgroundColor\"].append(color)\n            if type == \"access\":\n                value = values.filter(status=label).count()\n            elif type == \"sale\":\n                value = values.filter(payment=label).count()\n            dataset[\"data\"].append(value)\n        res[\"datasets\"].append(dataset)\n        return res\n\n    def get_pie_sales_online(self, timestep, max):\n        strftime, delta = self.get_timesteps_vars(timestep)\n        now = timezone.localtime(timezone.now())\n        min_date = now - delta * max\n        res = deepcopy(self.DATA_PIE)\n        # Create labels and dataset\n        labels = []\n        values = []\n\n        labels = [\n            {\"confirmed\": False, \"name\": \"F\", \"color\": \"rgba(255,0,0,1)\"},\n            {\"confirmed\": True, \"name\": \"T\", \"color\": \"rgba(0,255,0,1)\"},\n        ]\n        values = MultiPurchase.objects.filter(created__gt=min_date)\n\n        dataset = deepcopy(self.DEFAULT_PIE_DATASET)\n        for label in labels:\n            res.get(\"labels\").append(label.get(\"name\"))\n            color = label.get(\"color\")\n            dataset[\"backgroundColor\"].append(color)\n            dataset[\"hoverBackgroundColor\"].append(color)\n            value = values.filter(confirmed=label.get(\"confirmed\")).count()\n            dataset[\"data\"].append(value)\n        res[\"datasets\"].append(dataset)\n        return res\n\n    def get_bar(self, type, timestep, max):\n        strftime, delta = self.get_timesteps_vars(timestep)\n        res = deepcopy(self.DATA_BAR)\n\n        # Create labels and dataset\n        datas = (\n            TicketWindow.objects.values_list(\"slug\", \"cash\")\n            .filter(event=self.ev)\n            .order_by(\"slug\")\n        )\n\n        dataset = deepcopy(self.DEFAULT_BAR_DATASET)\n        dataset[\"label\"] = self.ev.slug\n        for name, cash in datas:\n            res.get(\"labels\").append(name)\n            color, alpha = self.get_random_color(True)\n            dataset[\"borderColor\"].append(color)\n            dataset[\"backgroundColor\"].append(alpha)\n            dataset[\"data\"].append(cash)\n        res[\"datasets\"].append(dataset)\n        return res\n\n    def get_context_data(self, *args, **kwargs):\n        ctx = super(GeneralView, self).get_context_data(*args, **kwargs)\n        ctx[\"ws_server\"] = settings.WS_SERVER\n        ctx[\"ev\"] = self.kwargs[\"ev\"]\n        ctx[\"dash\"] = self.kwargs[\"dash\"]\n        dash = Dashboard.objects.filter(name=ctx[\"dash\"]).first()\n        if dash:\n            ctx[\"num_cols\"] = str(12 / int(dash.num_cols))\n        else:\n            ctx[\"num_cols\"] = \"12\"\n        return ctx\n\n    def get_chart(\n        self, type_chart, timestep=settings.TIMESTEP_CHART, max=settings.MAX_STEP_CHART\n    ):\n        title = \"\"\n        for k, v in CHARTS:\n            if k == type_chart:\n                title = str(v)\n\n        if type_chart == \"os_c\":\n            chart = self.get_sales_online(timestep, max)\n        elif type_chart == \"ws_c\":\n            chart = self.get_sales(timestep, max)\n        elif type_chart == \"a_c\":\n            chart = self.get_access(timestep, max)\n        elif type_chart == \"os_p\":\n            chart = self.get_pie_sales_online(timestep, max)\n        elif type_chart == \"ws_p\":\n            chart = self.get_pie(\"sale\", timestep, max)\n        elif type_chart == \"a_p\":\n            chart = self.get_pie(\"access\", timestep, max)\n        elif type_chart == \"ws_b\":\n            chart = self.get_bar(\"sale\", timestep, max)\n        else:\n            chart = None\n        tdata, tchart = type_chart.split(\"_\")\n        return {\"data\": chart, \"type_data\": tdata, \"title\": title, \"type_chart\": tchart}\n\n    def post(self, request, ev, dash):\n        ctx = {}\n        ctx[\"charts\"] = []\n        ev = get_object_or_404(Event, slug=ev)\n        self.ev = ev\n        dashboard = get_object_or_404(Dashboard, event=ev, slug=dash)\n        for chart in dashboard.charts.all():\n            ctx[\"charts\"].append(\n                self.get_chart(chart.type, chart.timestep, chart.max_steps)\n            )\n        return JsonResponse(ctx)\n\n\ngeneral = csrf_exempt(GeneralView.as_view())\n\n\nclass ReportView(TemplateView):\n    template_name = \"dashboard/generate_report.html\"\n\n    def sessions_grouped(self, sessions):\n        g = itertools.groupby(sessions, lambda x: x.name)\n        g = [(n, list(l)) for n, l in g]\n        return g\n\n    def get_sessions(self, **filters):\n        sessions = []\n        for space in self.spaces.all():\n            sessions += space.sessions.filter(**filters)\n        return sorted(sessions, key=lambda x: x.start)\n\n    def get_days(self):\n        return sorted(list(set([session.start.date() for session in self.sessions])))\n\n    def get_context_data(self, *args, **kwargs):\n        ctx = super(ReportView, self).get_context_data(*args, **kwargs)\n        ev = self.kwargs[\"ev\"]\n        self.ev = get_object_or_404(\n            Event.objects.prefetch_related(\n                Prefetch(\n                    \"windows\", TicketWindow.objects.select_related(\"event\", \"user\")\n                ),\n                Prefetch(\"spaces\", Space.objects.select_related(\"event\", \"seat_map\")),\n                Prefetch(\n                    \"spaces__sessions\",\n                    Session.objects.select_related(\n                        \"template\", \"window_template\"\n                    ).prefetch_related(\"extra_sessions\"),\n                ),\n            ),\n            slug=ev,\n        )\n        self.windows = self.ev.windows.all()\n        self.spaces = self.ev.spaces.all()\n        self.sessions = self.get_sessions()\n\n        ctx[\"ws_server\"] = settings.WS_SERVER\n        ctx[\"ev\"] = self.ev\n        ctx[\"spaces\"] = self.spaces\n        ctx[\"sessions\"] = self.sessions\n        ctx[\"windows\"] = self.windows\n        ctx[\"menuitem\"] = \"report\"\n\n        return ctx\n\n\nclass GeneralReportView(ReportView):\n    template_name = \"dashboard/general_report.html\"\n    window = False\n\n    def get_context_data(self, *args, **kwargs):\n        ctx = super().get_context_data(*args, **kwargs)\n        all_windows = TicketWindow.objects.select_related(\"event\").filter(event=self.ev)\n        ctx[\"all_windows\"] = all_windows\n        ctx[\"online_windows\"] = []\n        ctx[\"local_windows\"] = []\n        for window in all_windows:\n            if window.online:\n                ctx[\"online_windows\"].append(window)\n            else:\n                ctx[\"local_windows\"].append(window)\n\n        days = self.get_days()\n\n        ctx[\"session_days\"] = [(d,) for d in days]\n        ctx[\"window\"] = self.window\n        ctx[\"sessions_grouped\"] = self.sessions_grouped(self.sessions)\n\n        return ctx\n\n\nreport_general = staff_member_required(GeneralReportView.as_view())\n\n\nclass WindowReportView(GeneralReportView):\n    window = True\n\n\nreport_window = staff_member_required(WindowReportView.as_view())\n\n\nclass OnlineReportView(ReportView):\n    template_name = \"dashboard/online_report.html\"\n\n    def get_context_data(self, *args, **kwargs):\n        ctx = super().get_context_data(*args, **kwargs)\n        ctx[\"online_windows\"] = TicketWindow.objects.filter(event=self.ev, online=True)\n        ctx[\"local_windows\"] = TicketWindow.objects.filter(event=self.ev, online=False)\n\n        start_date = self.request.GET.get(\"start-date\")\n        end_date = self.request.GET.get(\"end-date\")\n\n        if start_date and end_date:\n            str_format = \"%d-%m-%Y\"\n            if start_date.find(\"/\") > 0:\n                str_format = str_format.replace(\"-\", \"/\")\n\n            start_date = datetime.strptime(start_date, str_format).date()\n            end_date = datetime.strptime(end_date, str_format).date()\n            self.sessions = self.get_sessions(start__range=(start_date, end_date))\n\n            delta = timedelta(days=1)\n            d = start_date\n            days = []\n            while d <= end_date:\n                days.append(d)\n                d = d + delta\n            ctx[\"sdate\"] = start_date\n            ctx[\"edate\"] = end_date\n            ctx[\"days\"] = days\n\n        days = self.get_days()\n        delta = timedelta(days=1)\n\n        ctx[\"session_days\"] = [\n            (d, self.get_sessions(start__range=(d, d + delta))) for d in days\n        ]\n        ctx[\"sessions_grouped\"] = self.sessions_grouped(self.sessions)\n\n        return ctx\n\n\nreport_online = staff_member_required(OnlineReportView.as_view())\n\n\nclass CountReportView(ReportView):\n    template_name = \"dashboard/count_report.html\"\n\n    def get_context_data(self, *args, **kwargs):\n        ctx = super().get_context_data(*args, **kwargs)\n\n        query = self.request.GET.get(\"query\", \"\")\n        if query:\n            days = self.request.GET.getlist(\"scheck\")\n            windows = self.request.GET.getlist(\"wcheck\")\n\n            days = [timezone.make_aware(datetime.strptime(d, \"%Y-%m-%d\")) for d in days]\n            self.windows = self.windows.filter(pk__in=windows)\n            ctx[\"selected_sessions\"] = self.sessions\n            ctx[\"selected_days\"] = days\n            ctx[\"selected_windows\"] = self.windows\n\n            ctx[\"count_days\"] = days\n            ctx[\"sessions_grouped\"] = self.sessions_grouped(self.sessions)\n\n        session_days = self.get_days()\n        delta = timedelta(days=1)\n        ctx[\"session_days\"] = [\n            (d, self.get_sessions(start__range=(d, d + delta))) for d in session_days\n        ]\n\n        ctx[\"query\"] = query\n\n        return ctx\n\n\nreport_count = staff_member_required(CountReportView.as_view())\n\n\nclass ReportListView(TemplateView):\n    template_name = \"dashboard/list_report.html\"\n\n    def get_context_data(self, *args, **kwargs):\n        ctx = super().get_context_data(*args, **kwargs)\n        ev = get_object_or_404(Event, slug=kwargs.get(\"ev\", \"\"))\n        ctx[\"ev\"] = ev\n        return ctx\n\n\nreport_list = staff_member_required(ReportListView.as_view())\n\n\nclass AccessReportView(TemplateView):\n    template_name = \"dashboard/access_report.html\"\n\n    def get_context_data(self, *args, **kwargs):\n        ctx = {}\n        self.ev = get_object_or_404(Event, slug=self.kwargs.get(\"ev\"))\n        days = self.event_days()\n        self.access = self.ev.access.filter(mark_used=True)\n\n        table = []\n        table.append([\"Puntos de control\"] + [f\"Accesos día {day.day}\" for day in days])\n        table.append([\"TOTALES\"] + [0 for day in days])\n\n        for access in self.access:\n            row = [access.name]\n            for num, day in enumerate(days):\n                amount_access = access.log_access.filter(\n                    date__date=day, status__in=[\"ok\", \"right\"]\n                ).count()\n                row.append(amount_access)\n                table[1][num + 1] += amount_access\n            table.append(row)\n\n        ctx[\"ev\"] = self.ev\n        ctx[\"days\"] = days\n        ctx[\"table\"] = table\n        ctx[\"menuitem\"] = \"report\"\n        return ctx\n\n    def event_days(self) -> list[datetime.date]:\n        sessions = self.ev.get_sessions().order_by(\"start\")\n        first_date = sessions.first().start.date()\n        last_date = sessions.last().start.date()\n        return [\n            first_date + timedelta(days=days)\n            for days in range(0, (last_date - first_date).days + 1)\n        ]\n","repo_name":"wadobo/congressus","sub_path":"congressus/dashboard/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":19521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"6587949153","text":"'''\nThere are N children standing in a line. Each child is assigned a rating value.\n\nYou are giving candies to these children subjected to the following requirements:\n\nEach child must have at least one candy.\nChildren with a higher rating get more candies than their neighbors.\nWhat is the minimum candies you must give?\n\n'''\nclass Solution(object):\n    def candy(self, ratings):\n        \"\"\"\n        :type ratings: List[int]\n        :rtype: int\n        \"\"\"\n        deepest = 1\n        candies = [1 for i in xrange(len(ratings))]\n        for i in xrange(len(ratings)):\n            if i+1 < len(ratings):\n                candies[i+1] = candies[i] + 1 if ratings[i+1] > ratings[i] else candies[i+1]\n        for i in xrange(len(ratings)-1,-1,-1):\n            if i-1 > -1:\n                candies[i-1] = candies[i] +1 if ratings[i-1] > ratings[i] and candies[i-1] <= candies[i] else candies[i-1]\n\n        return sum(candies)\ndef main():\n    sol = Solution()\n    res = sol.candy([4,2,3,4,1])\nif __name__ == '__main__':\n    main()","repo_name":"wangyunge/algorithmpractice","sub_path":"eet/Candy.py","file_name":"Candy.py","file_ext":"py","file_size_in_byte":1023,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"532742944","text":"def solution(picks, minerals):\n    answer = 0\n    num = {'diamond': 0, 'iron': 1, 'stone': 2}\n    # 각 곡괭이로 광물을 캘 때의 피로도\n    fatigue = [[1, 1, 1], \n               [5, 1, 1], \n               [25, 5, 1]]\n    # 캘 수 있는 해당 개수까지 슬라이싱\n    minerals = minerals[:5 * sum(picks)]\n\n    # 각 광물의 수 체크\n    check = []\n\n    idx = 0\n    cnt = 1\n    temp = [0, 0, 0]\n    while idx < len(minerals):\n        # 하나의 곡괭이로 캘수 있는 광물을 다 채웠다면 각 변수들 초기화\n        if cnt > 5:\n            check.append(temp)\n            cnt = 1\n            temp = [0, 0, 0]\n\n        temp[num[minerals[idx]]] += 1\n        cnt += 1\n        idx += 1\n\n    if temp:\n        check.append(temp)\n        \n    # 다이아몬드, 철, 돌이 많은 순으로 정렬\n    check.sort(key=lambda x: (x[2], x[1], x[0]))\n\n    # pick 순서대로 해당 곡괭이 개수만큼 곡괭이 번호\n    pick = []\n    for i in range(3):\n        pick += [i] * picks[i]\n\n    p = 0\n    for i in range(len(check)):\n        for j in range(len(num)):\n            # 하나의 곡괭이를 소진했을 때 발생하는 피로도 누적\n            answer += fatigue[pick[p]][j] * check[i][j]\n        p += 1\n\n    return answer","repo_name":"FinalCold/Programmers","sub_path":"solutions/광물 캐기.py","file_name":"광물 캐기.py","file_ext":"py","file_size_in_byte":1267,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"4988728008","text":"###### ONLINE VOTING SYSTEM ######\n\n#Importing The Modules\nimport random\nimport datetime\nimport os\nfrom prompt_toolkit import prompt\nimport getpass\nimport time\n\n\n#Declaring the date of Winner Announcement\nwin_date=\"6/3/2021\"\n\n\n#Registration Process Of The Nominee\ndef nominee_reg():\n\tnom_vote=open(\"nominee vote status.txt\",\"a\")\n\tname=input(\"Enter Full Name : \")\n\temail=input(\"Enter Email : \")\n\tadhar=int(input(\"Enter Adhar Number : \"))\n\tdob=input(\"Enter DOB (dd/mm/yyyy) : \")\n\tcurr_date=datetime.date.today()\n\tage=int(str(curr_date).split('-')[0]) - int(dob.split('/')[2])\n\tif age>18:\n\t\trdm=random.randint(100000,999999)\n\t\tid=f\"{name.capitalize()[0]}{rdm}{name.split()[2].capitalize()[0]}{dob.split('/')[2]}\"\n\t\tprint(f\"Nominee ID : {id}\")\n\t\tnom_vote.write(f\"0\\t\\t{name}\\n\")\n\t\tnom_vote.close()\n\t\twith open(\"nominee.txt\",\"a\") as nm:\n\t\t\tnm.write(f\"{name},{email},{adhar},{dob},{age},{id}\\n\")\n\t\t\tnm.close()\n\t\tprint(\"You Have Successfully Registered as Nominee.......!!!!!\")\n\t\t\n\telse:\n\t\tprint(\"Your age is below 18 years . You are not eligible for Nominee Registration\")\n\n\t\n#Registration Process Of The New Voter\t\ndef voter_reg():\n\tname=input(\"Enter Full Name : \")\n\temail=input(\"Enter Email : \")\n\tadhar=int(input(\"Enter Adhar Number : \"))\n\tdob=input(\"Enter DOB (dd/mm/yyyy) : \")\n\tcurr_date=datetime.date.today()\n\tage=int(str(curr_date).split('-')[0]) - int(dob.split('/')[2])\n\tif age >= 18:\n\t\trdm=random.randint(100000,999999)\n\t\tid=f\"{str(rdm)}{name.capitalize()[0]}{dob.split('/')[2]}\"\n\t\tprint(f\"Voting ID : {id}\")\n\t\t\n\t\twith open(\"voter_details.txt\",\"a\") as vd:\n\t\t\tvd.write(f\"{name},{email},{adhar},{dob},{age},{id},not voted\\n\")\n\t\t\tvd.close()\n\t\tprint(\"You Have Successfully Registered as Voter.......!!!!!\")\n\t\t\n\telse:\n\t\tprint(\"Your age is below 18 years . You are not eligible for voting\")\n\n\n#Setup Of Secured Admin Panel To Monitor The Voting Process\t\t\ndef admin_panel():\n\tpswd=prompt(\"Enter Password : \",is_password=True)\n\tif pswd==\"Nishad@2311\":\n\t\tprint(\"\\n\\t    =======================================================================================================================\")\n\t\tprint(\"\\t      1 : Voter Details \\t   2 : Nominee Details \\t     3 : Election Record\\t  4 : Vote Status\\t  5 : Exit\")\n\t\tprint(\"\\t    =======================================================================================================================\\n\")\n\t\twhile True:\n\t\t\tadmin_choice = int(input(\"Enter your Admin Choice : \"))\n\t\t\tif admin_choice == 1:\n\t\t\t\tid_of_voter = input(\"Enter Voter ID : \")\n\t\t\t\twith open(\"voter_details.txt\") as vd:\n\t\t\t\t\trecords=vd.readlines()\n\t\t\t\tfor data in records:\n\t\t\t\t\t\tinfo=data.split(',')\n\t\t\t\t\t\tmy_id=info[5][0:11:]\n\t\t\t\t\t\tif (id_of_voter==my_id):\n\t\t\t\t\t\t\tprint(\"\\n\\t\\t_______________________________________________________________________________________\\n\")\n\t\t\t\t\t\t\tprint(f\"\\n\\t\\t\\tName : {info[0]} \\t\\t Email : {info[1]} \\n\\t\\t\\tAdhar Number : {info[2]} \\t\\t Date Of Birth : {info[3]} \\n\\t\\t\\tAge : {info[4]} \\t\\t\\t\\t Voter Id : {info[5]} \\n\\t\\t\\tStatus : {info[6].capitalize()}\")\n\t\t\t\t\t\t\tprint(\"\\t\\t_______________________________________________________________________________________\\n\")\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tpass\t\n\t\t\t\t\n\t\t\telif admin_choice == 2:\n\t\t\t\tid_of_nom = input(\"Enter Nominee ID : \")\n\t\t\t\twith open(\"nominee.txt\") as nm:\n\t\t\t\t\trecords=nm.readlines()\n\t\t\t\tfor data in records:\n\t\t\t\t\t\tinfo=data.split(',')\n\t\t\t\t\t\tmy_id=info[5][0:12:]\n\t\t\t\t\t\tif (id_of_nom==my_id):\n\t\t\t\t\t\t\tprint(\"\\n\\t\\t_______________________________________________________________________________________\\n\")\n\t\t\t\t\t\t\tprint(f\"\\n\\t\\t\\tName : {info[0]} \\t\\t Email : {info[1]} \\n\\t\\t\\tAdhar Number : {info[2]} \\t\\t Date Of Birth : {info[3]} \\n\\t\\t\\tAge : {info[4]} \\t\\t\\t\\t Nominee Id : {info[5]}\")\n\t\t\t\t\t\t\tprint(\"\\t\\t_______________________________________________________________________________________\\n\")\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tpass\n\t\t\t\t\t\t\t\n\t\t\telif admin_choice == 3:\n\t\t\t\tvoters=open(\"voter_details.txt\")\n\t\t\t\tnominee=open(\"nominee.txt\")\n\t\t\t\tvoter_list=voters.readlines()\n\t\t\t\tnom_list=nominee.readlines()\n\t\t\t\tcount=0\n\t\t\t\tn_count=0\n\t\t\t\tfor record in voter_list:\n\t\t\t\t\tdata=record.split(',')\n\t\t\t\t\tif data[6][0:5:]==\"Voted\":\n\t\t\t\t\t\tcount=count+1\n\t\t\t\t\tif data[6][0:9:]==\"not voted\":\n\t\t\t\t\t\tn_count=n_count+1\n\t\t\t\n\t\t\t\tprint(\"\\t\\t\\t\\t     CURRENT ELECTION RECORD\")\n\t\t\t\tprint(\"\\t\\t\\t__________________________________________________\\n\")\n\t\t\t\tprint(\"\\t\\t\\t\\t Particulars \\t\\t       Counts\")\n\t\t\t\tprint(\"\\t\\t\\t__________________________________________________\\n\")\n\t\t\t\tprint(f\"\\t\\t\\t\\tNo. Of Voters \\t\\t\\t{len(voter_list)} \\n\\t\\t\\t\\tNo. Of Nominees \\t\\t{len(nom_list)} \\n\\t\\t\\t\\tNo. Of Voters Voted \\t\\t{count} \\n\\t\\t\\t\\tNo. Of Voters Remaining \\t{n_count}\")\n\t\t\t\tprint(\"\\t\\t\\t__________________________________________________\\n\")\n\n\t\t\telif admin_choice == 4:\n\t\t\t\tshow_vote_status()\n\t\t\t\n\t\t\telif admin_choice == 5:\n\t\t\t\tmain()\n\t\t\t\tprint(\"\\n\")\n\t\t\t\n\t\t\telse:\n\t\t\t\tprint(\"Enter Valid Input.......!!!!\")\n\telse:\n\t\tprint(\"You Have Entered Incorrect Password.....!!!!\\n\")\n\t\tmain()\n\n\n# Tells us that which nominee has got how many votes\ndef show_vote_status():\n\tvote_status=open(\"nominee vote status.txt\",\"r\")\n\tprint(\"\\n\\t\\t\\t\\t       CURRENT VOTE STATUS\")\n\tprint(\"\\t\\t\\t__________________________________________________\\n\")\n\tprint(\"\\t\\t\\t       Votes \\t\\t\\t Name\")\n\tprint(\"\\t\\t\\t__________________________________________________\\n\")\n\tfor data in vote_status:\n\t\tprint(f\"\\t\\t\\t\\t{data}\",end=\"\")\n\tvote_status.close()\n\tprint(\"\\t\\t\\t__________________________________________________\\n\")\n\n\n# Tells the names of the nominees\ndef nominee_info():\n\tnames=[]\n\tnom_r=open(\"nominee.txt\",\"r\")\n\trecords=nom_r.readlines()\n\tfor data in records:\n\t\tinfo=data.split(',')\n\t\tnames.append(info[0])\n\tfor index,item in enumerate(names):\n\t\tprint(f\"\\t\\t\\t\\t{index+1} \\t:\\t {item}\")\n\n\n#Designing the Home UI\ndef home_ui():\n\t\tprint(\"\\t\\t\\t\\t\\tWelcome To The Online Voting System\")\n\t\tprint(\"\\n\\t\\t================================================================================================================\")\n\t\tprint(\"\\n\\t\\t 1 : Voter Login \\t\\t 2 : Admin Panel \\t\\t 3 : Nominee Info \\t 4 : Voter Registration \\n\\t\\t 5 : Nominee Registration \\t 8 : Winner Announcement Date \\t 7 : Exit\")\n\t\tprint(\"\\n\\t\\t================================================================================================================\")\n\n\n#The Process Of Determining the Winner of Election\t\t\ndef winner_dec():\n\tfv=open(\"nominee vote status.txt\",\"r\")\n\tfinal_votes=fv.readlines()\n\twinner=[]\n\tfor vote in final_votes:\n\t\tget_vote=vote.split('\\t\\t')\n\t\twinner.append(f\"{get_vote[0]}:{get_vote[1][0:len(get_vote[1])-1:]}\")\n\tlargest=int(winner[0].split(':')[0])\n\tfor data in winner:\n\t\td=data.split(':')[0]\n\t\tif int(d)>int(largest):\n\t\t\tlargest=d\n\tfor data in winner:\n\t\tif largest in data:\n\n\t\t\t\n\t\t\tprint(\"\\n\\n\\n\\n\\n\\n\\t\\t\\t===========================================================================\")\n\t\t\tprint(f\"\\n\\n\\n\\t\\t\\t\\tThe Winner of This Year Election is : {data.split(':')[1]}\")\n\t\t\tprint(\"\\n\\n\\n\\t\\t\\t===========================================================================\")\n\t\telse:\n\t\t\tpass\n\n\n# Here voter logins to vote the nominee\ndef voter_login():\n\tvd_r=open(\"voter_details.txt\",\"r\")\n\tvd_w=open(\"temp.txt\",\"w\")\n\tvs_r=open(\"nominee vote status.txt\",\"r\")\n\tvoter_id=input(\"Enter your Voter ID : \")\n\trecords=vd_r.readlines()\n\tfor data in records:\n\t\tinfos=data.split(',')\n\t\tif infos[5]==voter_id:\n\t\t\tprint(f\"Welcome {infos[0]} to the Online Voting System\\n\")\n\t\t\tprint(\"\\t\\t\\t__________________________________________________\\n\")\n\t\t\tprint(\"\\t\\t\\t\\tNominees for this year election are :\\n\")\n\t\t\tprint(\"\\t\\t\\t__________________________________________________\\n\")\n\t\t\tnominee_info()\n\t\t\tprint(\"\\t\\t\\t__________________________________________________\\n\")\n\t\t\tvoter_choice=prompt(\"Enter the number of Nominee to whom you want to vote : \",is_password=True)\n\t\t\tif infos[6][0:9:]==\"not voted\":\n\t\t\t\tvd_w.write(f\"{infos[0]},{infos[1]},{infos[2]},{infos[3]},{infos[4]},{infos[5]},Voted\\n\")\n\t\t\t\tprint(f\"Thank you so much {infos[0].split()[0]} for your precious Vote........!!!!\")\n\t\t\t\tvs=[]\n\t\t\t\tnom_det=vs_r.readlines()\n\t\t\t\tfor data in nom_det:\n\t\t\t\t\tinfo=data.split(\"\\t\\t\")\n\t\t\t\t\tvs.append(f\"{info[0]}:{info[1][0:len(info[1])-1:]}\")\n\t\t\t\tfor i in range(len(vs)):\n\t\t\t\t\tif i+1==int(voter_choice):\n\t\t\t\t\t\tvs[i]=f\"{int(vs[i][0])+1}:{vs[i][2::]}\"\n\t\t\t\tvote_w=open(\"temp_votes.txt\",\"w\")\n\t\t\t\tfor i in range(len(vs)):\n\t\t\t\t\tvote_w.write(f\"{vs[i][0]}\\t\\t{vs[i][2::]}\\n\")\n\t\t\t\tvote_w.close()\n\t\t\t\tos.remove(\"nominee vote status.txt\")\n\t\t\t\tos.rename(\"temp_votes.txt\",\"nominee vote status.txt\")\n\t\t\tif infos[6][0:5:]==\"Voted\":\n\t\t\t\tprint(f\"Sorry {infos[0].split()[0]} You have Already Voted..........!!!!!!\")\n\t\t\t\tvd_w.write(data)\n\t\telse:\n\t\t\tvd_w.write(data)\n\tvd_r.close()\n\tvd_w.close()\n\tos.remove(\"voter_details.txt\")\n\tos.rename(\"temp.txt\",\"voter_details.txt\")\n\t\t\n\n# Taking the User Inputs Here\ndef main():\n\twhile True:\n\t\tuser_choice = int(input(\"\\nEnter your User Choice : \"))\n\t\tif user_choice == 1:\n\t\t\tvoter_login()\n\t\telif user_choice == 2:\n\t\t\tadmin_panel()\n\t\telif user_choice == 3:\n\t\t\tprint(\"\\t\\t\\t__________________________________________________\\n\")\n\t\t\tprint(\"\\t\\t\\t\\tNominees for this year election are :\\n\")\n\t\t\tprint(\"\\t\\t\\t__________________________________________________\\n\")\n\t\t\tnominee_info()\n\t\t\tprint(\"\\t\\t\\t__________________________________________________\\n\")\n\t\telif user_choice == 4:\n\t\t\tvoter_reg()\n\t\telif user_choice == 5:\n\t\t\tnominee_reg()\t\n\t\telif user_choice == 6:\n\t\t\tprint(\"\\n\\t\\t__________________________________________________________________________________\")\n\t\t\tprint(\"\\n\\t\\t\\tThe Winner Of This Year Election Will Be Announced On : 05/03/2021\")\n\t\t\tprint(\"\\t\\t__________________________________________________________________________________\\n\")\n\t\telif user_choice == 7:\n\t\t\tprint(\"Thank You So Much..........!!!!!!\")\n\t\t\texit()\n\t\telse:\n\t\t\tprint(\"Enter Valid Choice\")\n\n\n# The Process Of Announcing the Winner Of Election \ndef winner_announce():\n\td=win_date.split(\"/\")\n\tif (len(d[0])==1 and len(d[1])==1):\n\t\tdatd=f\"0{d[0]}0{d[1]}{d[2]}\"\n\telif (len(d[0])==1):\n\t\tdatd=f\"0{d[0]}{d[1]}{d[2]}\"\n\telif (len(d[1])==1):\n\t\tdatd=f\"{d[0]}0{d[1]}{d[2]}\"\n\t\n\tlocal_date=time.strftime(\"%d%m%Y\")\n\t\n\tif int(local_date)>=int(datd):\n\t\twinner_dec()\n\telse:\n\t\thome_ui()\n\t\tmain()\n\n\n\nif __name__=='__main__':\n\t\t\twinner_announce()\n\t\t\t\n\n","repo_name":"nishadvispute2311/Online-Voting-System","sub_path":"My online voting system.py","file_name":"My online voting system.py","file_ext":"py","file_size_in_byte":10239,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"30114769917","text":"#!/usr/bin/env python3\n\nimport configparser\nimport requests\nfrom os import path\n\n\nclass OmApi():\n\n    def __init__(self):\n        config_path = path.join(path.abspath(path.dirname(__file__)), 'config.ini')\n        config = configparser.ConfigParser()\n        config.read(config_path)\n\n        self.api_url = config['om_api']['url']\n        self.api_key = config['om_api']['key']\n\n\nclass Sites(OmApi):\n\n    def __init__(self):\n        OmApi.__init__(self)\n        self.endpoint = self.api_url + \"/sites?key=\" + self.api_key + \"&limit=200\"\n\n    def list(self, has_archive_collection=None):\n        sites_data = requests.get(self.endpoint)\n        sites = sites_data.json()\n        results = []\n\n        if has_archive_collection is not None:\n            for site in sites[\"results\"]:\n                if \"om_user_settings_archive_collection\" in site and has_archive_collection:\n                    results.append(site)\n                elif \"om_user_settings_archive_collection\" not in site and not has_archive_collection:\n                    results.append(site)\n        else:\n            results = sites[\"results\"]\n\n        return results\n\n\nclass Sessions(OmApi):\n\n    def __init__(self):\n        OmApi.__init__(self)\n\n    def get(self, session_id):\n        session_url = self.api_url + \"/sessions/\" + str(session_id)\n\n        session_data = requests.get(session_url)\n        session = session_data.json()\n        return session\n\n    def list(self, site_id=None, updated_after=None, video_processed=None, archived=None, created_after=None):\n        # craft endpoint\n        sessions_url = self.api_url\n        if not site_id:\n            sessions_url += \"/sessions\"\n        else:\n            sessions_url += \"/site/\" + str(site_id) + \"/sessions\"\n\n        # build params\n        filters = {}\n        if updated_after:\n            filters['updatedAfter'] = str(updated_after)\n        if archived:\n            filters['archived'] = archived\n        if created_after:\n            filters['createdAfter'] = str(created_after)\n\n        sessions_data = requests.get(sessions_url, params=filters)\n        sessions = sessions_data.json()\n        results = []\n\n        if video_processed is not None:\n            for session in sessions[\"results\"]:\n                if \"video_url\" in session:\n                    sessionVideo = SessionVideo()\n                    processed = sessionVideo.processedStatus(session[\"id\"])\n                    if processed and video_processed:\n                        results.append(session)\n                    elif not processed and not video_processed:\n                        results.append(session)\n        else:\n            results = sessions[\"results\"]\n        return results\n\n    def updateArchiveId(self, session_id, archive_id):\n        sessions_url = self.api_url + \"/sessions/\" + str(session_id)\n        sessions_url += \"?key=\" + self.api_key\n        requests.post(sessions_url, data={'archive_id': archive_id})\n\n\nclass SessionVideo(OmApi):\n\n    def __init__(self):\n        OmApi.__init__(self)\n\n    def processedStatus(self, session_id):\n        processed_url = self.api_url + \"/session/\" + str(session_id) + \"/youtube-processed\"\n        processed_data = requests.get(processed_url)\n        processed = processed_data.json()\n        processed = processed[\"results\"][\"processed\"]\n\n        return processed\n\n\nclass Captions(OmApi):\n    def __init__(self):\n        OmApi.__init__(self)\n\n    def list(self, session_id=None):\n        # craft endpoint\n        url = self.api_url\n        if not session_id:\n            url += \"/captions\"\n        else:\n            url += \"/session/\" + str(session_id) + \"/captions\"\n\n        json_data = requests.get(url)\n        data = json_data.json()\n\n        return data['results']\n\n\nclass Cuepoints(OmApi):\n    def __init__(self):\n        OmApi.__init__(self)\n\n    def list(self, session_id=None):\n        # craft endpoint\n        url = self.api_url\n        if not session_id:\n            url += \"/cuepoints\"\n        else:\n            url += \"/session/\" + str(session_id) + \"/cuepoints\"\n\n        json_data = requests.get(url)\n        data = json_data.json()\n\n        return data['results']\n","repo_name":"omfound/om-ytarchive","sub_path":"app/ytarchive/om_api.py","file_name":"om_api.py","file_ext":"py","file_size_in_byte":4149,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"7180478144","text":"# Finding the exit of the maze\n# ############################\n#    0 1 2 3 4 5 ( x )\n# 0  x x x x x x\n# 1  e o o o o x\n# 2  x o x o x x\n# 3  x x x o o o\n# 4  x x x o x x\n# 5  x x x x x x\n#(y)\n# ############################\n# Basic Idea : 현재의 경로가 막혔을 경우 다시 선택할 수 있는 다른 경로들을 저장함\n#              가장 최근의 갈림길로 ���아가 다른 곳을 찾음\n# 1) DFS(스택) : 인접한 위치들 방문 후, 방문한 위치들에 인접한 위치들을 순서대로 찾음.\n# 2) BFS(큐)  : 하나의 경로를 선해 끝까지 가고 막히면 새로운 경로 찾음\n################################\n\n# BFS 가정\n# 시작 : 첫 위치 스택 삽입\n# Step1 : 스택이 공백이 아닐 경우\n#         - 위치를 끄냄 ( 현재위치 )\n#         - 스택이 공백일 경우, 미로에 출구가 없믄 것\n#         - 방문했다는 위치 등록\n# Step2 : 현재 위치가 출구일 경우 탈락\n#         - 아닐 경우, 상하좌우 이웃방 확인 후 벽이 아닐 경우 스택에 삽입 \n\nfrom Queue import Queue\n\ndef isValidPos(x,y, map):\n    if map[x][y] == '0'  or map[x][y] == 'x':\n        return True\n    return False\n\n    # if x < 0 or y < 0 or x >= len(map) or y >= len(map[0]) :\n    #     return False\n    # elif map[x][y] == '0' or map[x][y] == 'x' :\n    #     return True\n\n\ndef BFS(start_coordinate, end_coordinate, map):\n    s = Queue()\n    s.enqueue(start_coordinate)\n    step = 0\n\n    while not s.isEmpty():\n        here = s.dequeue()\n        (x,y)=here\n\n        if here == end_coordinate :\n            return True\n        else :\n            map[x][y] = '.' # 방문 표시\n            # DFS : 우좌하상 -> 상하좌우 순으로 꺼내서 확인\n            # BFS : 우좌하상 -> 우좌상하 순으로\n            # 꺼냈을 경우 가능한 경로가 있다면 일단 집어넣고 따라감\n            if isValidPos(x, y + 1, map):\n                s.enqueue( (x, y + 1) )\n            if isValidPos(x, y - 1, map):\n                s.enqueue( (x, y - 1) )\n            if isValidPos(x + 1, y, map):\n                s.enqueue( (x + 1, y) )\n            if isValidPos(x - 1, y, map):\n                s.enqueue( (x - 1, y) )\n            step += 1\n\n        for i in range(len(map[0])):\n            for j in range(len(map)):\n                print(map[i][j], end = ' ')\n            print()\n\n        print('status : ', s.q)\n        print('steps: ', step)\n        print()\n\n    return False # While 문에서 True 반환 못한 경우 탐색 실패\n\n\nif __name__ == '__main__' :\n\n    map = [ [ '1', '1', '1', '1', '1', '1'],\n            [ 'e', '0', '0', '0', '0', '1'],\n            [ '1', '0', '1', '0', '1', '1'],\n            [ '1', '1', '1', '0', '0', 'x'],\n            [ '1', '1', '1', '0', '1', '1'],\n            [ '1', '1', '1', '1', '1', '1'],]\n\n    result = BFS((1,0), (3,5), map)\n\n    if result :\n        print('미로 탐색 성공')\n    else :\n        print('미로 탐색 실패')\n\n\n","repo_name":"skyla15/HireMeProject-","sub_path":"1_DataStructure_Algo/Easy_Datastructure(Cheon_In_Kuk)/DFS_BFS/Maze_BFS.py","file_name":"Maze_BFS.py","file_ext":"py","file_size_in_byte":2975,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"71461654327","text":"import os\nimport tensorflow as tf\nimport numpy as np\nimport soundfile as sf\n\nfrom .tf_serialization_utils import serialize_data\nfrom .annotation_utils import Annotation, read_annotation_file, \\\n    write_annotation_file, get_labels_in_range, _get_overlap, \\\n    _set_end_time_when_missing, _make_subsets_of_overlapping_annotations, \\\n    _get_overlapping_annotation_subset\n\n\n\"\"\"\nUtils to convert audio files to tfrecords.\n\"\"\"\n\n\ndef load_audio(audiofile_path):\n    # load audio file as (n_channels, n_frames) array.\n    audio, sr = sf.read(audiofile_path)\n    return np.atleast_2d(np.asfortranarray(audio).T), sr\n\ndef seconds2hms(seconds):\n    m, s = divmod(seconds, 60)\n    h, m = divmod(m, 60)\n    return h, m, s\n\n\ndef audiofile2tfrecord(\n        root_path,\n        audiofile_relpath,\n        outdir_path,\n        feature_extractor,\n        annfile_relpaths,\n        label_sets,\n        **kwargs\n):\n    \"\"\" Compute fixed-size examples with features from an audio file and write to \n    one tfrecord per annfile_relpath / label_set pair.\n\n    Args:\n        root_path: root path of the audio and annotation files.\n        audiofile_relpath: path, relative to root_path, to the audio file.\n        outdir_path: path of the output directory.\n        feature_extractor: see feature_utils\n        annfile_relpaths: list of paths, relative to root_path, to the annotation files,\n            as described in :func:`.annotation_utils`.\n        label_sets: list of label subsets.\n    Returns: See audio2tfrecords.\n    \"\"\"\n\n    # read audio\n    audio, sr = load_audio(os.path.join(root_path, audiofile_relpath))\n\n    # read annotation files\n    annotation_sets = []\n    for annfile_relpath in annfile_relpaths:\n        annfile_path = os.path.join(root_path, annfile_relpath)\n        if os.path.isfile(annfile_path):\n            annotation_sets.append(read_annotation_file(annfile_path))\n        else:\n            annotation_sets.append(set())\n\n    audio2tfrecord(\n        audio,\n        sr,\n        outdir_path,\n        audiofile_relpath,\n        feature_extractor,\n        annotation_sets,\n        label_sets,\n        **kwargs\n    )\n\n\ndef audio2tfrecord(\n        audio,\n        sr,\n        outdir_path,\n        audiofile_relpath,\n        feature_extractor,\n        annotation_sets,\n        label_sets,\n        **kwargs\n):\n    \"\"\" Compute fixed-size examples with features (and optionally labels)\n    from audio data and write to a tfrecord.\n\n    Args:\n        audio: audio data (np array)\n        sr: sample rate\n        outdir_path: path of the output directory.\n        tfrecord_relpath: relative path to which the tfrecord will be written in outdir_path.\n        feature_extractor: see :func:`.feature_utils`.\n        annotation_sets: sets of annotation objects.\n        label_sets: list of label subsets.\n    Returns:\n        Writes one tfrecord per annotation set / label subset pair in\n        <outdir_path>/annset<annset_ind>/labelset<labelset_ind>/<audiofile_relpath>\n    \"\"\"\n\n    # Compute features and segment boundaries\n    features, times = feature_extractor.process(audio, sr)\n\n    # Create one TFRecordWriter per annotation set and label set pair\n    tfrecord_writers = [\n        [[] for i in range(len(annotation_sets))],\n        [[] for i in range(len(label_sets))]]\n    for annset_ind in range(len(annotation_sets)):\n        for labelset_ind in range(len(label_sets)):\n            tfrecord_path = os.path.join(\n                outdir_path,\n                f'annset{annset_ind}',\n                f'labelset{labelset_ind}',\n                audiofile_relpath.replace('.wav', '.tf')\n            )\n            os.makedirs(os.path.dirname(tfrecord_path), exist_ok=True)\n            tfrecord_writers[annset_ind][labelset_ind] = tf.io.TFRecordWriter(tfrecord_path)\n\n    # Write data to TFRecords\n    for ex_ind in range(features.shape[0]):\n\n        start_time, end_time = times[ex_ind] \n        # Last example's end time is set to original audio file duration\n        # to avoid mislabeling.\n        if ex_ind == features.shape[0] - 1:\n            end_time = audio.shape[-1] / sr\n\n        for annset_ind, annset in enumerate(annotation_sets):\n\n            labels = get_labels_in_range(annset, start_time, end_time, **kwargs)\n            if not labels:\n                continue\n\n            sdata = serialize_data(\n                audiofile_relpath,\n                start_time,\n                end_time,\n                features[ex_ind],\n                labels\n            )\n\n            for labelset_ind, labelset in enumerate(label_sets):\n                if not np.all([l in labelset for l in labels]):\n                    continue\n                tfrecord_writers[annset_ind][labelset_ind].write(sdata)\n\n    # Close\n    for tfrecord_writer in tfrecord_writers:\n        tfrecord_writer.close()\n\n\n#def audio2data(\n#        audio,\n#        sr,\n#        feature_extractor,\n#        class_list,\n#        activity_detector=None,\n#        annotation_set=None,\n#        mask_min_dur=None,\n#        **kwargs\n#):\n#    \"\"\" Compute fixed-size examples with features (and optionally labels)\n#    from audio data.\n#\n#    Args:\n#        audio: mono audio data (np array)\n#        sr: sample rate\n#        feature_extractor: see :func:`.feature_utils`.\n#        class_list: list of classes used as the reference for one-hot label encoding.\n#        activity_detector: frame-based activity_detector, as described in nsb_aad.frame_based_detectors.\n#        annotation_set: set of annotation objects.\n#        mask_min_dur: minimum total duration of positive mask frames.\n#    Returns:\n#        Lists of tuples for positive and negative examples.\n#    \"\"\"\n#\n#    # compute mask from activity_detection\n#    # NOTE: the activity detection is performed on the first channel only !\n#    if activity_detector:\n#        fb_mask = activity_detector.process(audio[0])\n#        fb_mask_sr = activity_detector.frame_rate\n#    else:\n#        fb_mask = None\n#        fb_mask_sr = None\n#\n#    # compute features, segment-based mask and segment boundaries\n#    features, mask, times = feature_extractor.process(audio, sr, fb_mask, fb_mask_sr, mask_min_dur)\n#\n#    examples_pos = []\n#    examples_neg = []\n#    for i in range(features.shape[0]):\n#\n#        start_time, end_time = times[i] \n#        if i == features.shape[0] - 1:\n#            # Last example's end time is set to original audio file duration\n#            # to avoid mislabeling.\n#            end_time = audio.shape[-1] / sr\n#        labels = get_labels_in_range(annotation_set, start_time, end_time, **kwargs) \\\n#            if annotation_set else set()\n#\n#        # one-hot encode labels\n#        ind = np.where(np.in1d(\n#            sorted(class_list),\n#            list(labels)))[0]\n#        one_hot = np.zeros((len(class_list),), np.bool)\n#        np.put(one_hot, ind, True)\n#\n#        if mask[i]:\n#            examples_pos.append((features[i], one_hot))\n#        elif fb_mask is not None:\n#            examples_neg.append((features[i], one_hot))\n#\n#    return examples_pos, examples_neg\n\n\ndef split_audio(audiofile_path, annfile_path, out_audiodir_path, out_anndir_path):\n    \"\"\"Split audio according to annotation data.\"\"\"\n\n    # read audio\n    audio, sr = load_audio(audiofile_path)\n\n    # read annotations\n    annotation_set = read_annotation_file(annfile_path)\n\n    # -1 end time means end of file\n    # because it messes up computation below, we replace it by file duration\n    annotation_set = _set_end_time_when_missing(annotation_set, audio.shape[1] / sr)\n\n    # make subsets of overlapping annotations\n    ann_subsets = _make_subsets_of_overlapping_annotations(annotation_set)\n\n    # for each subset, make audio and annotation files\n    for ann_subset in ann_subsets:\n        start_time = min([ann.start_time for ann in ann_subset])\n        end_time = max([ann.end_time for ann in ann_subset])\n        file_basename = os.path.basename(audiofile_path).replace('.wav', f'.{int(start_time*1000)}.{int(end_time*1000)}')\n        out_audiofile_path = os.path.join(out_audiodir_path, file_basename + '.wav')\n        out_annfile_path = os.path.join(out_anndir_path, file_basename + '.ann')\n        sf.write(\n            out_audiofile_path,\n            audio[:,int(start_time*sr):int(end_time*sr)].T, sr, 'PCM_16')\n        with open(out_annfile_path, 'w') as f:\n            new_ann_subset = set([Annotation(a.start_time-start_time, a.end_time-start_time, a.label_set) for a in ann_subset])\n            write_annotation_file(new_ann_subset, out_annfile_path)\n","repo_name":"phaunos/phaunos_ml","sub_path":"phaunos_ml/utils/audio_utils.py","file_name":"audio_utils.py","file_ext":"py","file_size_in_byte":8498,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"27317723918","text":"\"\"\"\nCommand Comprehender\n    summary: be able to get a command in a sentence and then understand what\n    it wants and do something with it\n\"\"\"\nimport json\n\ndata_dict = {}\nword_type_names = [\"action\", \"object\", \"target\", \"actor\", \"ignore\"]\ncommand_hint_length = 2\n\nclass WordType:\n    def __init__(self, name, d_saved_words):\n        self.name = name\n        try:\n            self.word_list = d_saved_words[name]\n        except KeyError:\n            self.word_list = []\n        data_dict[self.name] = self\n\n\n    def to_dict(self):\n        return {\n            self.name: self.word_list\n        }\n\n\ndef read_json_file():\n    with open(\"data.json\", mode=\"r+\") as json_file:\n        return json.load(json_file)\n\n\ndef declare_word_types(read_dict):\n    for name in word_type_names:\n        WordType(name, read_dict)\n\n\ndef get_command():\n    command = input(\"What is your command? Or x to stop\\n\").lower()\n    return command\n\n\ndef disect_command(command):\n    command_word_list = []\n    start_num = 0\n    end_num = 0\n\n    for word in command:\n        # If there is a space then that is one word\n        if word == \" \":\n            command_word_list.append(command[start_num:end_num])\n\n            # Move the start num to the letter after the space\n            start_num = end_num + 1\n        end_num += 1\n\n    # Do once at the end so it gets the last word\n    command_word_list.append(command[start_num:end_num])\n    return command_word_list\n\n\ndef determine_word_type(command_word_list) -> list:\n    l_saved_words = []\n    l_unsaved_words = []\n\n    for key in data_dict:\n        # get every word in the dictionary\n        for word in data_dict[key].word_list:\n            l_saved_words.append(word)\n\n    # Create a list of all saved words\n    for word in command_word_list:\n        # see if word is in the list of all saved words\n        if word in l_saved_words or word in l_unsaved_words:\n            print(word, \"has been saved\")\n        else:\n            l_unsaved_words.append(word)\n\n    return l_unsaved_words\n\n\ndef get_word_type(l_unsaved_words):\n    # ask user to input the matching first letters\n    for word in l_unsaved_words:\n        # set a new line\n        print(\"\\n\")\n        # label each list from 0 to length\n        wi = 0\n        # print out data type hints\n        for keys in data_dict:\n            print(keys[0:command_hint_length] + \":\" + keys, end=\"\")\n\n            # if not at the end yet then add a comma\n            if wi < len(data_dict) - 1:\n                print(\", \", end=\"\")\n\n            wi += 1\n\n\n        indicator_letters = input(\"\\nWhat type of word is \" + str(word) + \"?\\n\").lower()\n        # then append word using matching list number\n        for key in data_dict:\n            if indicator_letters == key[0:command_hint_length].lower():\n                data_dict[key].word_list.append(word)\n\n\ndef write_to_json():\n    new_dict = {}\n    for key in data_dict:\n        new_dict.update(data_dict[key].to_dict())\n\n    with open(\"data.json\", \"w\") as write_file:\n        json.dump(new_dict, write_file, indent=2)\n\n\ndef __main__():\n    # setup data\n    d_saved_words = read_json_file()\n    declare_word_types(d_saved_words)\n\n    # receive input\n    command = get_command()\n\n    if command.lower() == \"x\":\n        exit()\n    # interpret input\n    command_word_list = disect_command(command)\n    l_unsaved_words = determine_word_type(command_word_list)\n    get_word_type(l_unsaved_words)\n\n    # save data\n    write_to_json()\n    __main__()\n\n\n__main__()\n","repo_name":"Oxidiz3/PythonOneOffs","sub_path":"FinishedProjects/interpreter.py","file_name":"interpreter.py","file_ext":"py","file_size_in_byte":3475,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"27023701679","text":"config = {'data_type': 'dgl',\n          'dgl_graph': \"placeholder\",\n          'dataset_name': 'cora',\n          'data_dir': '../data/cora/',\n          'pretrained': None,\n          'task_type': 'classification',\n          'out_dir': '../out/cora/idgl_del_edges',\n          'out_raw_learned_adj_path': 'learned_adj.npy',\n          'seed': 42,\n          'n_train': 50,\n          'n_val': 100,\n          'model_name': 'GraphClf',\n          'hidden_size': 256,\n          'input_graph_knn_size': 24,\n          'use_bert': False,\n          'dropout': 0.5,\n          'feat_adj_dropout': 0.5,\n          'gl_dropout': 0,\n          'bignn': False,\n          'graph_module': 'graphsage',\n          'graph_type': 'dynamic',\n          'graph_learn': True,\n          'graph_metric_type': 'weighted_cosine',\n          'graph_include_self': False,\n          'graph_skip_conn': 0.4,\n          'update_adj_ratio': 0.1,\n          'graph_learn_regularization': True,\n          'smoothness_ratio': 0.4,\n          'degree_ratio': 0.1,\n          'sparsity_ratio': 0,\n          'graph_learn_ratio': 0,\n          'graph_learn_hidden_size': 20,\n          'graph_learn_epsilon': 0.65,\n          'graph_learn_topk': None,\n          'graph_learn_hidden_size2': 20,\n          'graph_learn_epsilon2': 0.65,\n          'graph_learn_topk2': None,\n          'graph_learn_num_pers': 8,\n          'graph_hops': 2,\n          'gat_nhead': 8,\n          'gat_alpha': 0.2,\n          'graphsage_agg_type': 'gcn',\n          'optimizer': 'adam',\n          'learning_rate': 0.001,\n          'weight_decay': 0.0005,\n          'lr_patience': 2,\n          'lr_reduce_factor': 0.5,\n          'grad_clipping': None,\n          'grad_accumulated_steps': 1,\n          'eary_stop_metric': 'nloss',\n          'pretrain_epoch': 0,\n          'max_iter': 0,\n          'eps_adj': 0,\n          'rl_ratio': 0,\n          'rl_ratio_power': 1,\n          'rl_start_epoch': 1,\n          'max_rl_ratio': 0.99,\n          'rl_reward_metric': 'acc',\n          'rl_wmd_ratio': 0,\n          'random_seed': 1234,\n          'shuffle': True,\n          'max_epochs': 10000,\n          'patience': 400,\n          'verbose': 20,\n          'print_every_epochs': 50,\n          'out_predictions': False,\n          'save_params': True,\n          'logging': True,\n          'no_cuda': False,\n          'cuda_id': 0}\n","repo_name":"xinleihe/GNNStealing","sub_path":"code/src/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":2331,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"76"}
+{"seq_id":"71367997367","text":"import mysql.connector\n\nconfig={\n  'host': '127.0.0.1',\n  'port': 3306,\n  'user': 'root',\n  'password': '1q2w3e4r',\n  'database': 'vega'\n}\n\ntry:\n  con = mysql.connector.connect(**config)\n  # 开始事务\n  con.start_transaction()\n  cursor = con.cursor()\n  sql = \"SELECT * FROM t_user\"\n  cursor.execute(sql)\n  print(cursor.fetchall())\n  con.commit()\nexcept Exception as e:\n  if \"con\" in dir():\n    con.rollback()\n  print('err', e)\nfinally:\n  if \"con\" in dir():\n    con.close()","repo_name":"Firekiss/python_learn","sub_path":"mysql-learn/exception_sql.py","file_name":"exception_sql.py","file_ext":"py","file_size_in_byte":475,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"2846960776","text":"from celery import shared_task\nfrom news.models import Category, Post\nfrom datetime import *\nimport pytz\nfrom django.core.mail import EmailMultiAlternatives\nfrom django.template.loader import render_to_string\n\n\n# еженедельная рассылка о постах за неделю подписчикам на категорию\n@shared_task\ndef notify_subscribers():\n    categories = Category.objects.all()\n\n    for cat in categories:\n        recipients = []\n        posts_to_send = Post.objects.filter(posted__gte=datetime.now(pytz.utc) - timedelta(days=7), category=cat)\n        for user in cat.subscribers.all():\n            recipients.append(user.email)\n\n        if posts_to_send:\n            html_content = render_to_string('email/new_posts_weekly.html', {'posts_to_send': posts_to_send, 'category':\n                cat.name})\n            msg = EmailMultiAlternatives(\n                subject=f\"Посты за неделю в твоей любимой категории {cat.name}\",\n                body=\"\",\n                from_email='trk.olimp@mail.ru',\n                to=recipients\n            )\n            msg.attach_alternative(html_content, \"text/html\")\n            msg.send()\n","repo_name":"MiXaLFtruck/news-portal_website","sub_path":"NewsPaper/news/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":1200,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"10086848290","text":"import torch\nimport torchvision\nimport torchvision.transforms as transforms\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom tqdm import tqdm\n\ntransform = transforms.Compose(\n    [transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n\n# transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\ntrain_set = torchvision.datasets.CIFAR10(root='./data', train=True,\n                                        download=False, transform=transform)\ntrain_loader = torch.utils.data.DataLoader(train_set, batch_size=4,\n                                          shuffle=True, num_workers=2)\n\ntest_set = torchvision.datasets.CIFAR10(root='./data', train=False,\n                                       download=False, transform=transform)\ntest_loader = torch.utils.data.DataLoader(test_set, batch_size=4,\n                                         shuffle=False, num_workers=2)\n\n\nclasses = ('plane', 'car', 'bird', 'cat',\n           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n\nclass Flatten(nn.Module):\n    def forward(self, input):\n        return input.view(input.size(0),-1)\n\n\n# functions to show an image\n\ndef imshow(img):\n    img = img / 2 + 0.5     # unnormalize\n    npimg = img.numpy()\n    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n    plt.show()\n\n\n# Define a convolutional neural network\n\nclass Net(nn.Module):\n    def __init__(self):\n        super(Net, self).__init__()\n        self.conv1 = nn.Conv2d(3, 6, 5)\n        self.pool = nn.MaxPool2d(2, 2)\n        self.conv2 = nn.Conv2d(6, 16, 5)\n        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n        self.fc2 = nn.Linear(120, 84)\n        self.fc3 = nn.Linear(84, 10)\n\n    def forward(self, x):\n        x = self.pool(F.relu(self.conv1(x)))\n        x = self.pool(F.relu(self.conv2(x)))\n        x = x.view(-1, 16 * 5 * 5)\n        x = F.relu(self.fc1(x))\n        x = F.relu(self.fc2(x))\n        x = self.fc3(x)\n        return x\n\n\n# Define a VGG16 neural network\n\nclass VGG16(nn.Module):\n\n    def __init__(self):\n        super(VGG16, self).__init__()\n        self.flatten = Flatten()\n        self.model = nn.Sequential(\n            nn.Conv2d(3, 64, 3, padding=1),\n            nn.BatchNorm2d(64),\n            nn.ReLU(inplace=True),\n\n            # nn.Dropout(0.2),\n\n            nn.Conv2d(64, 64, 3, padding=1),\n            nn.BatchNorm2d(64),\n            nn.ReLU(inplace=True),\n\n            nn.MaxPool2d(2,2),\n\n            nn.Conv2d(64, 128, 3, padding=1),\n            nn.BatchNorm2d(128),\n            nn.ReLU(inplace=True),\n\n            # nn.Dropout(0.2),\n\n            nn.Conv2d(128, 128, 3, padding=1),\n            nn.BatchNorm2d(128),\n            nn.ReLU(inplace=True),\n\n\n            nn.MaxPool2d(2, 2),\n\n            nn.Conv2d(128, 256, 3, padding=1),\n            nn.BatchNorm2d(256),\n            nn.ReLU(inplace=True),\n\n            # nn.Dropout(0.2),\n\n            nn.Conv2d(256, 256, 3, padding=1),\n            nn.BatchNorm2d(256),\n            nn.ReLU(inplace=True),\n\n            # nn.Dropout(0.2),\n\n            nn.Conv2d(256, 256, 3, padding=1),\n            nn.BatchNorm2d(256),\n            nn.ReLU(inplace=True),\n\n            nn.MaxPool2d(2,2),\n\n            nn.Conv2d(256, 512, 3, padding=1),\n            nn.BatchNorm2d(512),\n            nn.ReLU(inplace=True),\n\n            # nn.Dropout(0.2),\n\n            nn.Conv2d(512, 512, 3, padding=1),\n            nn.BatchNorm2d(512),\n            nn.ReLU(inplace=True),\n\n            # nn.Dropout(0.2),\n\n            nn.Conv2d(512, 512, 3, padding=1),\n            nn.BatchNorm2d(512),\n            nn.ReLU(inplace=True),\n\n            nn.MaxPool2d(2, 2),\n\n            nn.Conv2d(512, 512, 3, padding=1),\n            nn.BatchNorm2d(512),\n            nn.ReLU(inplace=True),\n\n            # nn.Dropout(0.2),\n\n            nn.Conv2d(512, 512, 3, padding=1),\n            nn.BatchNorm2d(512),\n            nn.ReLU(inplace=True),\n\n            # nn.Dropout(0.2),\n\n            nn.Conv2d(512, 512, 3, padding=1),\n            nn.BatchNorm2d(512),\n            nn.ReLU(inplace=True),\n\n\n            nn.MaxPool2d(2, 2),\n            # nn.Dropout(0.2),\n\n            self.flatten,\n            nn.Dropout(),\n            nn.Linear(512,2048),\n            nn.ReLU(inplace=True),\n            # nn.BatchNorm1d(2048),\n            # nn.Dropout(0.2),\n            nn.Dropout(),\n            nn.Linear(2048,2048),\n            nn.ReLU(inplace=True),\n            # nn.BatchNorm1d(2048),\n            #nn.Dropout(0.2),\n            nn.Linear(2048,10),\n        )\n\n    def forward(self, x):\n        return self.model(x)\n\nclass CNN(nn.Module):\n    def __init__(self):\n        super(CNN, self).__init__()\n        self.flatten = Flatten()\n        self.model = nn.Sequential(nn.Conv2d(3,64,3,padding=1),\n                                   nn.ReLU(inplace=True),\n                                   nn.Conv2d(64,128,3,padding=1),\n                                   nn.ReLU(inplace=True),\n                                   nn.MaxPool2d((2,2)),\n                                   nn.Conv2d(128,256,3,padding=1),\n                                   nn.ReLU(inplace=True),\n                                   nn.MaxPool2d((2,2)),\n                                   self.flatten,\n                                   nn.Linear(256*8*8,1024),\n                                   nn.ReLU(inplace=True),\n                                   nn.Dropout(0.2),\n                                   nn.Linear(1024,256),\n                                   nn.ReLU(inplace=True),\n                                   nn.Dropout(0.2),\n                                   nn.Linear(256,10),\n                                   )\n\n    def forward(self, x):\n        x = self.model(x)\n        return x\n\n\ndef compute_accuracy(predictions, y):\n    \"\"\"Computes the accuracy of predictions against the gold labels, y.\"\"\"\n    return np.mean(np.equal(predictions.cpu().numpy(), y.cpu().numpy()))\n\n\ndef run_epoch(data, model, optimizer):\n    \"\"\"Train model for one pass of train data, and return loss, acccuracy\"\"\"\n    # Gather losses\n    losses = []\n    batch_accuracies = []\n\n    # If model is in train mode, use optimizer.\n    is_training = model.training\n\n    # Iterate through batches\n    for batch in tqdm(data):\n        # Grab x,y\n        inputs, labels = batch\n\n        inputs = inputs.to(device)\n        labels = labels.to(device)\n\n        # Get output prediction\n        outputs = model(inputs)\n\n        # Predict and store accuracy\n        predictions = torch.argmax(outputs, dim=1)\n        batch_accuracies.append(compute_accuracy(predictions, labels))\n\n        # Compute losses\n        loss = F.cross_entropy(outputs, labels)\n        losses.append(loss.data.item())\n\n        # If training, do an update.\n        if is_training:\n            optimizer.zero_grad()\n            loss.backward()\n            optimizer.step()\n\n    # Calculate epoch level scores\n    avg_loss = np.mean(losses)\n    avg_accuracy = np.mean(batch_accuracies)\n    return avg_loss, avg_accuracy\n\n\ndef train_model(train_data, dev_data, model, lr=0.01, momentum=0.9, nesterov=False, n_epochs=100):\n    \"\"\"Train a model for N epochs given data and hyper-params.\"\"\"\n    # We optimize with SGD\n    optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=momentum, nesterov=nesterov)\n    losses = []\n    accuracies = []\n    val_losses = []\n    val_accuracies = []\n\n    for epoch in range(1, n_epochs + 1):\n        print(\"-------------\\nEpoch {}:\\n\".format(epoch))\n\n        # Run **training***\n        loss, acc = run_epoch(train_data, model.train(), optimizer)\n        print('Train | loss: {:.6f}  accuracy: {:.6f}'.format(loss, acc))\n        losses.append(loss)\n        accuracies.append(acc)\n\n        # Run **validation**\n        val_loss, val_acc = run_epoch(dev_data, model.eval(), optimizer)\n        print('Valid | loss: {:.6f}  accuracy: {:.6f}'.format(val_loss, val_acc))\n        val_losses.append(val_loss)\n        val_accuracies.append(val_acc)\n\n        # Save model\n        path = './cifar1_net.pth'\n        torch.save(model.state_dict(), path)\n\n    return losses,accuracies,val_losses,val_accuracies\n\n\nif __name__ == '__main__':\n\n    # get some random training images\n    dataiter = iter(train_loader)\n    images, labels = dataiter.next()\n    print(images,labels)\n    # show images\n    imshow(torchvision.utils.make_grid(images))\n    # print labels\n    print(' '.join('%5s' % classes[labels[j]] for j in range(4)))\n\n    # Configure the device to train on\n    use_cude = True\n    print('Cuda Available:', torch.cuda.is_available())\n    device = torch.device('cuda:2' if (use_cude and torch.cuda.is_available()) else 'cpu')\n\n    # # Display size of the data\n    # print(train_set.data.shape)\n    # print(test_set.data.shape)\n\n    model = VGG16().to(device)\n\n    train_model(train_loader,test_loader,model)","repo_name":"eriwoner-110/one-pixel-attack-pytorch","sub_path":"realcifar.py","file_name":"realcifar.py","file_ext":"py","file_size_in_byte":8773,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"22396780561","text":"from collections import Counter\nT = int(input())\nwhile T> 0:\n    n,m = map(int, input().split(' '))\n    nbr = [set() for i in range(n+2)]\n    Q =  [set() for i in range(n+2)]\n    for i in range(m):\n        u,v =  map(int, input().split(' '))\n        nbr[u].add(v)\n        nbr[v].add(u)\n    ans = True\n    t = set([x for x in range(1,n+1)])\n    for i in range(1,n+1):\n        Q[i]= t-nbr[i]\n    vis = set()\n    for i in range(1,n+1):\n        if i in vis: continue;\n        vis.add(i)\n        for v in Q[i]:\n            vis.add(v)\n            if len(Q[v])!=len(Q[i]) :\n                ans = False\n                break;\n            \n            if Counter(Q[v])!=Counter(Q[i]):\n                ans = False\n                break;\n        if not ans: break;\n    print('Yes' if ans else 'No')\n    T-=1\n\n\"\"\"\n2\n5 7\n1 3\n1 5\n2 3\n2 5\n3 4\n4 5\n3 5\n4 3\n1 2\n2 3\n3 4\n样例输出\nYes\nNo\n\n\"\"\"","repo_name":"oilover/Leetcode","sub_path":"st/j1.py","file_name":"j1.py","file_ext":"py","file_size_in_byte":876,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"1135647399","text":"from apis import views\n\n\napi_urls = [\n    (\"/addTodo\", views.addTodo, [\"POST\"],\n        \"add todo item in list\"),\n    (\"/removeTodo\", views.removeTodo, [\"POST\"],\n        \"remove todo item from list\"),\n    (\"/listAll\", views.listAll, [\"POST\"],\n        \"List all to do for a a channel\")\n]\n\nother_urls = []\n\nall_urls = api_urls + other_urls\n","repo_name":"deveshbajaj/todoAppSlackBot","sub_path":"src/apis/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"12766779692","text":"from unittest import TestCase\nimport json\nimport requests\nimport pytest\nimport EnvironmentRead\n\n\nclass Registration(TestCase):\n\n    def setUp(self):\n        print(\"this is Registration setup\")\n\n    def tearDown(self):\n        print(\"this is Regidtration tear down\")\n\n    @pytest.mark.run(order=2)\n    def test2_Registration(self):\n        url = str(\n            EnvironmentRead.ipPort) + \"/v1/users/register?access_token={0}\".format(EnvironmentRead.access_token)\n\n        with open('responsedata.json') as f:\n            sessionKey = json.loads(f.read())\n            sessionKey = sessionKey['sessionKey']\n            print(sessionKey)\n\n        headers = {'Content-type': 'application/json', 'SessionKey': sessionKey}\n\n        with open('responsedata.json') as f:\n            applicationId = json.loads(f.read())\n            applicationId = applicationId['applicationId']\n            print(applicationId)\n\n        body = {\n\n            'applicationId': applicationId,\n            'channel': 'Mobile',\n            'mobileNumber': EnvironmentRead.mobileNumber,\n            'facebookId': None,\n            'token': None\n\n        }\n\n        response = requests.post(url, data=json.dumps(body), headers=headers)\n        print(json.dumps(response.json(), indent=4))\n        print(response.status_code)\n        self.assertEqual(200, response.status_code)\n","repo_name":"ManjunathDjm/REST-API-automation-using-python-pytest","sub_path":"test_Registration.py","file_name":"test_Registration.py","file_ext":"py","file_size_in_byte":1347,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"10421903998","text":"from libs import detection\nimport numpy as np\nimport argparse\nimport imutils\nimport cv2\nimport os\nfrom scipy.spatial import distance\nfrom shapely.geometry import Point\nfrom shapely.geometry.polygon import Polygon\n\n\ndef line(p1, p2):\n    A = (p1[1] - p2[1])\n    B = (p2[0] - p1[0])\n    C = (p1[0]*p2[1] - p2[0]*p1[1])\n    return A, B, -C\n\ndef intersection(L1, L2):\n    D  = L1[0] * L2[1] - L1[1] * L2[0]\n    Dx = L1[2] * L2[1] - L1[1] * L2[2]\n    Dy = L1[0] * L2[2] - L1[2] * L2[0]\n    if D != 0:\n    \t# return True\n        x = Dx / D\n        y = Dy / D\n        return x,y\n    else:\n        return False\n\n\nUSE_GPU = False\n\n# construct the argument parse and parse the arguments\nap = argparse.ArgumentParser()\nap.add_argument(\"-i\", \"--input\", type=str, default=\"\", help=\"path to (optional) input video file\")\nap.add_argument(\"-o\", \"--output\", type=str, default=\"\", help=\"path to (optional) output video file\")\nap.add_argument(\"-d\", \"--display\", type=int, default=1, help=\"whether or not output frame should be displayed\")\nargs = vars(ap.parse_args())\n\n# load the COCO class labels our YOLO model was trained on\nlabelsPath = 'models/coco.names'\nLABELS = open(labelsPath).read().strip().split(\"\\n\")\n\n# derive the paths to the YOLO weights and model configuration\nweightsPath = 'models/yolov4-tiny.weights'\nconfigPath = 'models/yolov4-tiny.cfg'\n\n# load our YOLO object detector trained on COCO dataset (80 classes)\nprint(\"[INFO] loading YOLO from disk...\")\nnet = cv2.dnn.readNetFromDarknet(configPath, weightsPath)\n\n# check if we are going to use GPU\nif USE_GPU:\n\t# set CUDA as the preferable backend and target\n\tprint(\"[INFO] setting preferable backend and target to CUDA...\")\n\tnet.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)\n\tnet.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)\n\n# determine only the *output* layer names that we need from YOLO\nln = net.getLayerNames()\nln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]\n\n# initialize the video stream and pointer to output video file\nprint(\"[INFO] accessing video stream...\")\nvs = cv2.VideoCapture(args[\"input\"] if args[\"input\"] else 0)\nwriter = None\nfps = int(vs.get(cv2.CAP_PROP_FPS))\n\n\ndef mouse_drawing(event, x, y, flags, params):\n\tif event == cv2.EVENT_LBUTTONDOWN:\n\t\t# print(\"Left click\")\n\t\tif len(points) < 5:\n\t\t\tpoints.append((x, y))\n\ncv2.namedWindow(\"Frame\")\ncv2.setMouseCallback(\"Frame\", mouse_drawing)\n\n\n# loop over the frames from the video stream\niframe = 0\nskipframe = 30\npoints = []\nwhile True:\n\t# read the next frame from the file\n\t(grabbed, frame) = vs.read()\n\n\t# if the frame was not grabbed, then we have reached the end\n\t# of the stream\n\tif not grabbed:\n\t\tbreak\n\n\t# resize the frame and then detect people (and only people) in it\n\t# frame = imutils.resize(frame, width=700)\n\t(H, W) = frame.shape[:2]\n\n\tfor point in points:\n\t\tcv2.circle(frame, point, 5, (0, 0, 255), -1)\n\t\t\n\tif iframe % skipframe == 0:\n\t\tobjects = ['person']\n\t\tresults = detection.detect_object(frame, net, ln, Idxs=[LABELS.index(i) for i in objects if LABELS.index(i) is not None])\n\n\tif len(points) == 5:\n\n\t\t# loop over the results\n\t\torang = 0\n\t\tfor (i, (classID, prob, bbox, centroid)) in enumerate(results):\n\t\t\t# extract the bounding box and centroid coordinates, then\n\t\t\t# initialize the color of the annotation\n\t\t\t(startX, startY, endX, endY) = bbox\n\t\t\t(cX, cY) = centroid\n\t\t\t\n\t\t\tdY = (endY - startY) // 3\n\t\t\ttmp = [centroid[0], centroid[1] + dY]\n\t\t\tif tmp[1] > H:\n\t\t\t\ttmp[1] = H\n\n\n\t\t\tpolygon = Polygon(points)\n\t\t\tpoint = Point(tmp[0], tmp[1])\n\t\t\tif polygon.contains(point):\n\t\t\t\tcv2.circle(frame, (tmp[0], tmp[1]), 4, (0, 255, 255), -1)\n\t\t\t\torang += 1\n\t\t\t\t# get the width and height of the text box\n\t\t\t\ttext = 'ORANG'\n\t\t\t\tfont_scale = 1.25\n\t\t\t\tfont = cv2.FONT_HERSHEY_PLAIN\n\t\t\t\tcolor = (0,212,255)\n\t\t\t\trectangle_bgr = color\n\t\t\t\t(text_width, text_height) = cv2.getTextSize(text, font, fontScale=font_scale, thickness=1)[0]\n\t\t\t\t# set the text start position\n\t\t\t\t# y = startY - 10 if startY - 10 > 10 else startY + 10\n\t\t\t\ttext_offset_x, text_offset_y = startX, startY-5\n\t\t\t\t# make the coords of the box with a small padding of two pixels\n\t\t\t\tbox_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 10, text_offset_y - text_height - 10))\n\t\t\t\toverlay = frame.copy()\n\t\t\t\tcv2.rectangle(overlay, box_coords[0], box_coords[1], rectangle_bgr, cv2.FILLED)\n\t\t\t\tcv2.rectangle(overlay, (startX, startY), (endX, endY), rectangle_bgr, cv2.FILLED)\n\t\t\t\tcv2.putText(overlay, text, (text_offset_x+5, text_offset_y-5), font, fontScale=font_scale, color=(0,0,0), thickness=2)\n\t\t\t\t# apply the overlay\n\t\t\t\talpha=0.6\n\t\t\t\tcv2.addWeighted(overlay, alpha, frame, 1 - alpha, 0, frame)\n\n\t\t\t\trectangle_bgr = (0,0,255)\n\t\t\t\t# make the coords of the box with a small padding of two pixels\n\t\t\t\toverlay = frame.copy()\n\t\t\t\tcv2.rectangle(overlay, (300,5), (580,35), rectangle_bgr, -1)\n\t\t\t\t# opacity\n\t\t\t\talpha = 0.75\n\t\t\t\t# loop over the info tuples and draw them on our frame\n\t\t\t\tfont_scale = .8\n\t\t\t\tfont = cv2.FONT_ITALIC\n\t\t\t\ttext = f'{orang} ORANG TERDETEKSI'\n\t\t\t\tcv2.putText(overlay, text, (300, 30), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 255, 255), 2)\n\t\t\t\tcv2.addWeighted(overlay, alpha, frame, 1 - alpha, 0, frame)\n\t\t\telse:\n\t\t\t\tcv2.circle(frame, (tmp[0], tmp[1]), 4, (255, 255, 0), -1)\n\t\t\n\t\t# draw lines\n\t\tlcolor = (0, 0, 255)\n\t\tif iframe%5 == 0:\n\t\t\tlcolor = (150, 150, 250)\n\t\tcv2.line(frame, points[0], points[1], lcolor, 2)\n\t\tcv2.line(frame, points[1], points[2], lcolor, 2)\n\t\tcv2.line(frame, points[2], points[3], lcolor, 2)\n\t\tcv2.line(frame, points[3], points[4], lcolor, 2)\n\t\tcv2.line(frame, points[4], points[0], lcolor, 2)\n\n\t# check to see if the output frame should be displayed to our screen\n\tif args[\"display\"] > 0:\n\t\t# show the output frame\n\t\tcv2.imshow(\"Frame\", frame)\n\t\tkey = cv2.waitKey(1) & 0xFF\n\n\t\t# if the `q` key was pressed, break from the loop\n\t\tif key == ord(\"q\"):\n\t\t\tbreak\n\n\t# if an output video file path has been supplied and the video writer has not been initialized, do so now\n\tif args[\"output\"] != \"\" and writer is None:\n\t\t# initialize our video writer\n\t\tfourcc = cv2.VideoWriter_fourcc(*\"MJPG\")\n\t\twriter = cv2.VideoWriter(args[\"output\"], fourcc, 25,\n\t\t\t(frame.shape[1], frame.shape[0]), True)\n\n\t# if the video writer is not None, write the frame to the output video file\n\tif writer is not None:\n\t\twriter.write(frame)\n\n\tiframe += 1\n\n\tif iframe > 3000:\n\t\tiframe = 0\n\n","repo_name":"taufik-adinugraha/area-monitoring","sub_path":"monitor.py","file_name":"monitor.py","file_ext":"py","file_size_in_byte":6332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"29541337237","text":"# Check operational system\r\n\r\nimport sys, platform\r\n\r\ntype = platform.system()\r\n\r\nif(type == \"Windows\"):\r\n    print(\"\\nThe program is gonna start in a few seconds\\n\")\r\nelse:\r\n    sys.exit(\"\\nSorry, our program don't have support for your system\\n\")\r\n\r\n# Version\r\n \r\nversion=\"0.0.2\"\r\n\r\n# Program\r\n\r\nimport pygame, math, subprocess, requests, os, shutil\r\nfrom pygame.locals import *\r\n\r\npygame.init()\r\n\r\ndisplay = pygame.display.set_mode((800, 600))\r\npygame.display.set_caption('KFkas Launcher')\r\n\r\nclock = pygame.time.Clock()\r\n\r\nloading = False\r\n\r\nexists = os.path.exists\r\n\r\n# Path to Sprites\r\n\r\npathS = './System/Sprites/'\r\n\r\n# Path to Sounds\r\n\r\npathM = './System/Sounds/'\r\n\r\n# Background Variables\r\n\r\nhue = 0\r\n\r\nbgSprite = False\r\n\r\nif exists(pathS+'Background.png'):\r\n    bgSprite = pygame.image.load(pathS+'Background.png')\r\n\r\nbgX = 0\r\nbgXAngle = 0\r\n\r\nbgYStart = -200\r\nbgY = bgYStart\r\nbgYAngle = 0\r\n\r\n# Mouse Variable\r\n\r\nmouseSprite = False\r\n\r\nif exists(pathS+'Mouse.png'):\r\n    mouseSprite = pygame.image.load(pathS+'Mouse.png')\r\n\r\nmousePos = pygame.mouse.get_pos()\r\n\r\nif mouseSprite != False:\r\n    pygame.mouse.set_visible(False)\r\n\r\n# Cloud Variable\r\n\r\ncloudSprite = False\r\n\r\nif exists(pathS+'Cloud.png'):\r\n    cloudSprite = pygame.image.load(pathS+'Cloud.png')\r\n    cloudWidth, cloudHeight = cloudSprite.get_rect().size\r\n\r\ncloudX, cloudYStart = (128, 256)\r\ncloudY = cloudYStart\r\n\r\n# Update Variable\r\n\r\nupdateSprite = False\r\n\r\nif exists(pathS+'Exit.png'):\r\n    updateSprite = pygame.image.load(pathS+'Update.png')\r\n    updateWidth, updateHeight = updateSprite.get_rect().size\r\n\r\nupdateX, updateYStart = (256, 256)\r\nupdateY = updateYStart\r\n\r\n# Exit Variable\r\n\r\nexitSprite = False\r\n\r\nif exists(pathS+'Exit.png'):\r\n    exitSprite = pygame.image.load(pathS+'Exit.png')\r\n\r\nexitWidth, exitHeight = exitSprite.get_rect().size\r\nexitX, exitY = (700, 500)\r\nexitAlpha = 0\r\n\r\n# Version Variable\r\n\r\nversionSprite = False\r\n\r\nif exists(pathS+'Version.png'):\r\n    versionSprite = pygame.image.load(pathS+'Version.png')\r\n\r\nversionPos = (30, 500)\r\n\r\n# Defs\r\n\r\ndef getCollision(pos1, pos2, size):\r\n\r\n    distance = math.dist(pos1, pos2)\r\n\r\n    if distance <= size:\r\n        return True\r\n    else:\r\n        return False\r\n\r\n# Create Event\r\n\r\nclose = False\r\n\r\n#music = False\r\n\r\n#pygame.mixer.music.load(pathM+'Hotline.mp3')\r\n#pygame.mixer.music.play(loops=1, start=0.0, fade_ms = 10000)\r\n\r\nwhile True:\r\n\r\n    # Close Game\r\n\r\n    for event in pygame.event.get():\r\n        if event.type == pygame.QUIT:\r\n            loading = True\r\n            close = True\r\n\r\n    clock.tick(60)\r\n\r\n    # Step Variables\r\n\r\n    mousePos = pygame.mouse.get_pos()\r\n\r\n    keys = pygame.key.get_pressed()\r\n    mouses = pygame.mouse.get_pressed()\r\n    \r\n    if keys[K_ESCAPE] and not loading:\r\n        loading = True\r\n        close = True\r\n\r\n    if close and loading and bgYStart <= -200:\r\n        pygame.quit()\r\n        sys.exit()\r\n\r\n    # Play Music\r\n\r\n    #if exists('./Music/') and not music and keys[K_m]:\r\n    #    if exists('song.mp3'):\r\n    #        pygame.mixer.music.load('song.mp3')\r\n    #        pygame.mixer.music.play(loops=1, start=0.0, fade_ms=0)\r\n    #        music = True\r\n        #else:\r\n            #subprocess.run('python ./Music/music.py')\r\n\r\n    # Change Background Color\r\n\r\n    color = pygame.Color(0)\r\n    color.hsla = (hue, 100, 40, 50)\r\n    hue += 0.2\r\n\r\n    if(hue > 360):\r\n        hue = 0\r\n    \r\n    display.fill(color)\r\n\r\n    # Draw Cloud\r\n\r\n    if getCollision(mousePos, (cloudX+cloudWidth/2, cloudY+cloudHeight/2), 32) and mouses[0]:\r\n        subprocess.run(\"python ./Cloud/fileSynch.py\")\r\n\r\n    cloudY = cloudYStart - bgYStart/5\r\n\r\n    if cloudSprite != False:\r\n        display.blit(cloudSprite, (cloudX, cloudY))\r\n\r\n    # Draw Update\r\n\r\n    if updateSprite != False:\r\n        if getCollision(mousePos, (updateX+updateWidth/2, updateY+updateHeight/2), 32) and mouses[0]:\r\n            url = \"https://raw.githubusercontent.com/JaoKFkas/kfkas_launcher/main/System/update.py\"\r\n            r = requests.get(url)\r\n            open('update.py', 'wb').write(r.content)\r\n\r\n            subprocess.run(\"python update.py\")\r\n            pygame.quit()\r\n            sys.exit()\r\n\r\n        updateY = updateYStart - bgYStart/5\r\n\r\n        display.blit(updateSprite, (updateX, updateY))\r\n\r\n    # Draw Background\r\n\r\n    bgX+=1\r\n\r\n    if bgX > 800:\r\n        bgX = 0\r\n\r\n    bgXAngle += 0.05\r\n\r\n    if bgXAngle > 10:\r\n        bgXAngle = -10\r\n\r\n    if loading:\r\n        if bgYStart > -200:\r\n            bgYStart-=10\r\n    else:\r\n        if bgYStart < 300:\r\n            bgYStart+=10\r\n\r\n    if not loading:\r\n        bgYAngle += 1\r\n    bgY = bgYStart + math.sin(bgYAngle/100)*32\r\n\r\n    pygame.draw.rect(display, (0, 30, 60), (0, bgY+150, 800, 700))\r\n\r\n    if bgSprite != False:\r\n        display.blit(pygame.transform.rotate(bgSprite, math.cos(bgXAngle)), (bgX, bgY))\r\n        display.blit(pygame.transform.rotate(bgSprite, math.cos(bgXAngle)), (bgX-800, bgY))\r\n\r\n    # Draw Exit\r\n\r\n    if exitAlpha >= 1 and exitSprite != False:\r\n        if getCollision(mousePos, (exitX+exitWidth/2, exitY+exitHeight/2), 32) and mouses[0]:\r\n            loading = True\r\n            close = True\r\n\r\n    if loading:\r\n        if exitAlpha >= 0:\r\n            exitAlpha-=0.05\r\n    else:\r\n        if bgYStart >= 300 and exitAlpha < 1:\r\n            exitAlpha+=0.01\r\n\r\n    if exitSprite != False:\r\n        exitSprite.set_alpha(exitAlpha*1000)\r\n        display.blit(exitSprite, (exitX, exitY))\r\n\r\n    # Draw Version\r\n\r\n    if versionSprite != False:\r\n        display.blit(versionSprite, versionPos)\r\n\r\n    # Draw Mouse\r\n\r\n    if mouseSprite != False:\r\n        display.blit(mouseSprite, mousePos)\r\n\r\n    print(clock.get_fps())\r\n\r\n    pygame.display.flip()\r\n","repo_name":"JaoKFkas/kfkas_launcher","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5690,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"20833731853","text":"# -------------------\n# Badoino Matteo\n# Bernardi Gianluca\n# Bruno Francesco\n# -------------------\n\nimport time\nimport requests\n\n\ndef main():\n    s = requests.Session()\n    URL = \"http://127.0.0.1:5000/server/inizializzazione\"\n    PARAMS = {'msg': 'start'}\n    r = s.get(url=URL, params=PARAMS)\n    print(f\"Inizializzazione fatta, sono {r.text}\")\n    # ------------------------------------------------\n    while True:\n        URL = \"http://127.0.0.1:5000/server/richiesta\"\n        PARAMS = {'msg': 'richiesta'}\n        r = s.get(url=URL, params=PARAMS)\n        operazione = (r.json())[0]['operazione']\n        print(f\"Operazione {operazione}\")\n        if operazione == 'end':\n            break\n        URL = \"http://127.0.0.1:5000/server/risultato\"\n        PARAMS = {'risultato': eval(operazione)}\n        r = s.get(url=URL, params=PARAMS)\n        print(r.text)\n\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"FraBrunoSchool/5AROB_TPSIT","sub_path":"VERIFICHE/BrunoFrancesco_VerificaProva/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"it","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"28092726369","text":"import numpy as np\nimport objective_function_grad\nimport math\nfrom hessian_func import hessian\n\n\"\"\" subproblem_solution.py contains the method to solve steihaug method \"\"\"\n\ndef find_tau(d_j, z_j, delta_j):\n    # Solving the quadratic equation\n    a = d_j.T @ d_j\n    b = 2 * (z_j.T @ d_j)\n    c = z_j.T @ z_j - delta_j**2 \n    tau = (-b + math.sqrt(b**2 - 4*a*c))/(2*a)\n    return tau \n\ndef subproblem_steihaug(x_k, delta_k, objective):\n    if objective == \"rosenbrock\":\n        g_k = objective_function_grad.rosenbrock(x_k)\n    elif objective == \"booth\":\n        g_k = objective_function_grad.booth(x_k)\n    else:\n        g_k = objective_function_grad.ackley(x_k)\n\n    h_k = hessian(x_k, objective)\n\n    # Initialize the internal iterates\n    z_init = np.zeros(2)\n\n    # Initialize the residual\n    r_init = -g_k\n\n    # Initialize the orthogonal direction of conjugate gradient\n    d_init = r_init\n    epsilon = 1e-4\n    N = 10\n\n    # Check whether the chosen point is almost close to the minimum point with a tolerance of epsilon\n    if (np.linalg.norm(r_init) < epsilon):\n        p_k = np.zeros(2)\n        z_init = np.zeros(2)\n        return p_k\n    \n    d_j = d_init\n    r_j = r_init\n    z_j = z_init\n\n    for j in range(N):\n\n        # Check for negative curvature condition\n        if (d_j.T @ h_k @ d_j <= 0):\n            # Calculate tau to find p_k\n            tau = find_tau(d_j, z_j, delta_k)\n            p_k = z_j + tau * d_j\n            \n            return p_k\n        \n        # Calculate next iterate step\n        alpha_j = (r_j.T @ r_j ) / (d_j.T @ h_k @ d_j)\n        z_j_new = z_j + alpha_j * d_j\n\n        # Check whether the next iterate step has overshot the trust radius\n        if (np.linalg.norm(z_j_new) > delta_k):\n            # find tau to find p_k\n            tau = find_tau(d_j, z_j, delta_k)\n            p_k = z_j + tau * d_j\n            return p_k\n        \n        # Modify the gradient\n        r_j_new = r_j - alpha_j * (h_k @ d_j)\n\n\n        # Check whether the gradient is almost zero with epsilon tolerance\n        if ((np.linalg.norm(r_j_new))/(np.linalg.norm(g_k)) < epsilon):\n            p_k = z_j_new\n            return p_k\n        \n        # Obtain new direction d_j\n        beta_j_new = (r_j_new.T @ r_j_new) / (r_j.T @ r_j)\n        d_j_new = r_j_new + beta_j_new * d_j\n        d_j = d_j_new\n        r_j = r_j_new\n        z_j = z_j_new\n\n        #print(\"############# Loop repeated ###############\")","repo_name":"knightshade-aqua/Numerical_optimization_code","sub_path":"Numerical_optimization_2d_analysis/subproblem_solution.py","file_name":"subproblem_solution.py","file_ext":"py","file_size_in_byte":2434,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"4002568628","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Nov 22 04:04:35 2020\n\n@author: A\n\nideas: \ngraph 1\n- genre/genre_detailed vs. year_downloaded/year_released (based on scollbar and table paras) [bar] {showcase temporal trend}\n\ngraph 2\n- aggregate artist, genre/genre_detailed based on year_downloaded/released (based on scrollbar and table paras) [pie x2] {showcase favourite artist and genres}\n\ntable 1\n- search songs based on all parameters and show filtered data on all graphs\n\"\"\"\n\nimport sqlite3\nimport pandas as pd\n\nimport plotly.express as px\n\nimport dash\nfrom django_plotly_dash import DjangoDash\nimport dash_core_components as dcc\nimport dash_html_components as html\nfrom dash.dependencies import Input, Output\nimport dash_table\n\n\n############\n### MAIN ###\n############\n#direct_db = 'C:/Users/A/Documents/K/Projects/Interests Analysis/'\ndirect_db = ''\nfilename_db = 'media/interests.db'\n\n# grab sql data and format as df\nconn = sqlite3.connect(direct_db + filename_db)\ncursor = conn.cursor()\n\ncursor.execute('SELECT * FROM music')\ndf = pd.DataFrame(cursor.fetchall())\n\ncursor.execute('PRAGMA table_info(music)')\ndf.columns = pd.DataFrame(cursor.fetchall())[1]\n\nconn.commit()\nconn.close()\n\n# create dash/plotly\nx_selected = 'year released'\ny_selected = 'genre'\n\napp = DjangoDash(name='music_dash')\n# server = app.server\n\napp.layout = html.Div([\n    # GRAPHS\n    html.Div(dcc.Graph(id='temporal-graph'),\n             style={'width': '48%', 'display': 'inline-block'}),\n    html.Div(dcc.Graph(id='top-chart'),\n             style={'width': '50%', 'float': 'right', 'display': 'inline-block'}),\n\n    # PARAMETERS\n    html.Div([\n        html.Div([\n            html.I(style={'font-family': 'Helvetica', 'width': '23%', 'float': 'left'}, children='Select year type: ')\n        ]),\n        html.Div([\n            dcc.RadioItems(id=\"year-dropdown\",\n                           options=[{\"label\": x, \"value\": x}\n                                    for x in ['year downloaded', 'year released']],\n                           value='year released',\n                           labelStyle={'font-family': 'Helvetica', 'margin': '0px 10px'}),\n        ], style={'width': '48%', 'float': 'left'}),\n        html.Br(),\n        html.Br(),\n        html.Div([\n            html.I(style={'font-family': 'Helvetica', 'width': '23%', 'float': 'left'}, children='Select legend info: ')\n        ]),\n        html.Div([\n            dcc.Dropdown(id=\"genre-dropdown\",\n                         options=[{\"label\": x, \"value\": x} for x in df[y_selected].unique()],\n                         value=df[y_selected].unique(),\n                         multi=True),\n        ], style={'width': '73%', 'float': 'left'}),\n    ], style={'width': '48%', 'display': 'inline-block'}\n    ),\n\n    html.Div([\n        html.Div([\n            html.I(style={'font-family': 'Helvetica', 'width': '22%', 'float': 'left'}, children='Select category: ')\n        ]),\n        html.Div([\n            dcc.RadioItems(id=\"second-graph-dropdown\",\n                           options=[{\"label\": x, \"value\": x}\n                                    for x in ['genre', 'genre detailed', 'artist']],\n                           value='genre',\n                           labelStyle={'font-family': 'Helvetica', 'margin': '0px 10px'}),\n        ], style={'width': '48%', 'float': 'left'}),\n        html.Br(),\n        html.Br(),\n        html.Div([\n            html.I(style={'font-family': 'Helvetica', 'width': '20%', 'float': 'left'}, children='Select top range: ')\n        ]),\n        html.Div([\n            dcc.RangeSlider(\n                id='top-range-slider',\n                min=1,\n                max=50,\n                step=1,\n                value=[1, 10],\n                marks={\n                    1: {'label': '1'},\n                    10: {'label': '10'},\n                    20: {'label': '20'},\n                    30: {'label': '30'},\n                    40: {'label': '40'},\n                    50: {'label': '50'}\n                }\n            ),\n        ], style={'width': '75%', 'float': 'left'}),\n        html.Div([\n            html.I(style={'font-family': 'Helvetica', 'width': '20%', 'float': 'left'}, children='Select year range: ')\n        ]),\n        html.Div(\n            dcc.RangeSlider(\n                id='date-range-slider',\n                min=1900,\n                max=int(df[x_selected].max()),\n                step=1,\n                value=[1920, 2020],\n                marks={\n                    1900: {'label': '1900'},\n                    1920: {'label': '1920'},\n                    1940: {'label': '1940'},\n                    1960: {'label': '1960'},\n                    1980: {'label': '1980'},\n                    2000: {'label': '2000'},\n                    2020: {'label': '2020'}\n                }\n            ), style={'width': '75%', 'float': 'left'}\n        ),\n    ],\n        style={'width': '48%', 'float': 'right', 'display': 'inline-block'}),\n\n    # DATATABLE\n    html.Div([\n        html.H4(style={'font-family': 'Helvetica'}, children='Songs.'),\n    ]),\n    html.Div(dash_table.DataTable(id='song-table',\n                                  columns=[\n                                      {'name': 'Song', 'id': 'song'},\n                                      {'name': 'Artist', 'id': 'artist'},\n                                      {'name': 'Genre', 'id': 'genre'},\n                                      {'name': 'Genre-detailed', 'id': 'genre detailed'},\n                                      {'name': 'Year-released', 'id': 'year released'},\n                                      {'name': 'Year-downloaded', 'id': 'year downloaded'}\n                                  ],\n                                  data=df.to_dict('records'),\n                                  sort_action='native',\n                                  filter_action='native',\n                                  page_size=20,\n                                  style_cell={\n                                      'font-size': '12px',\n                                      'font-family': 'Helvetica'\n                                  },\n                                  style_data={\n                                      'width': '150px', 'minWidth': '150px', 'maxWidth': '150px',\n                                      'overflow': 'hidden',\n                                      'textOverflow': 'ellipsis',\n                                  },\n                                  style_cell_conditional=[\n                                      {\n                                          'if': {'column_id': c},\n                                          'textAlign': 'left'\n                                      } for c in ['Date', 'Region']\n                                  ],\n                                  style_data_conditional=[\n                                      {\n                                          'if': {'row_index': 'odd'},\n                                          'backgroundColor': 'rgb(248, 248, 248)'\n                                      }\n                                  ],\n                                  style_header={\n                                      'backgroundColor': 'rgb(230, 230, 230)',\n                                      'fontWeight': 'bold'\n                                  }\n\n                                  ))\n])\n\n@app.callback(\n    Output(\"temporal-graph\", \"figure\"), \n    [Input(\"genre-dropdown\", \"value\"),\n    Input(\"year-dropdown\", \"value\"),\n    dash.dependencies.Input('date-range-slider', 'value')])\ndef update_graph1(genres, years, date_range):\n    # plot histogram\n    fig = px.histogram(df[df[y_selected].isin(genres)],\n                          title='Song count vs. ' + years + ' by genre',\n                          x=years, color=y_selected, # marginal=\"rug\", \n                          nbins=len(range(int(df[years].min()), \n                                          int(df[years].max())+1)), \n#                                          range_x=[str(min(date_range)), str(max(date_range))],\n                       )\n    # layout\n    fig.update(layout_xaxis={'title':years})\n    fig.update(layout_yaxis={'title':'# of songs'})\n    fig.update(layout_xaxis=dict(rangeslider=dict(visible=True), type=\"linear\", range=[1920, 2020]))\n    return fig\n\n@app.callback(\n    Output(\"top-chart\", \"figure\"),\n    [Input(\"year-dropdown\", \"value\"),\n    Input(\"second-graph-dropdown\", \"value\"),\n    Input('date-range-slider', 'value'),\n    Input('top-range-slider', 'value')]\n)\ndef update_graph2(years, second_graph_dropdown, date_range, top_range):\n    # convert date range to list of strings for matching\n    list_date = []\n    for date in range(date_range[0], date_range[1]):\n        list_date.append(str(date))\n    # plot\n    fig = px.bar(df[df[years].isin(list_date)][second_graph_dropdown].value_counts()[top_range[0]-1:top_range[1]-1],\n                    title = 'Top ' + str(top_range[0]) + '-' + str(top_range[1]) + ' ' + second_graph_dropdown + ': ' + str(date_range[0]) + '-' + str(date_range[1]) + ' by ' + years)\n    # layout\n    fig.update(layout_showlegend=False)\n    fig.update(layout_xaxis={'title':second_graph_dropdown})\n    fig.update(layout_yaxis={'title':'# of songs'})\n    return fig\n\n@app.callback(\n    Output('song-table', 'data'),\n    [Input(\"genre-dropdown\", \"value\"),\n    Input(\"year-dropdown\", \"value\"),\n    Input('date-range-slider', 'value')])\ndef update_table(genres, years, date_range):\n    list_date = []\n    for date in range(date_range[0], date_range[1]):\n        list_date.append(str(date))\n    # plot\n    return df[df[years].isin(list_date) & df[y_selected].isin(genres)].to_dict('records')\n\n# if __name__ == '__main__':\n#     app.run_server(debug=True, use_reloader=False)","repo_name":"victorssun/Personal-Website-Django","sub_path":"music/analyze_music.py","file_name":"analyze_music.py","file_ext":"py","file_size_in_byte":9704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"1883553481","text":"import cv2 as cv\r\nimport numpy as np\r\n\r\nblank=np.zeros((500,500,3),dtype='uint8')# it will show a blank image of500*500\r\ncv.imshow('blanlk image',blank)\r\n\r\n#abc=cv.imread('photos/a.jpg')\r\n#cv.imshow('paris',abc)\r\n\r\n#1.we are painting the blank images\r\n#blank[:]=0,255,0#all image will be green\r\n#blank[200:300,300:400]=0,255,0#here some part of a image will be coloured as per given pixcels\r\n#cv.imshow('Green',blank)\r\n\r\n\r\n#2.drawing rectanglr\r\ncv.rectangle(blank,(0,0),(250,500),(0,250,0),thickness=cv.FILLED)\r\n#here starting point is0,0 AND 250,250 is destination and 0,250,0 is color\r\ncv.imshow('rectangke',blank)\r\n\r\n#3.drawing circle\r\ncv.circle(blank,(250,250) , 100,(0,0,255),thickness=cv.FILLED)\r\n#here  250,250 is location of a radius point  100 is radius of a circle and last one is color\r\ncv.imshow('circle',blank)\r\n\r\n#4.draw a line\r\ncv.line(blank,(100,250),(25,250),(255,255,255),thickness=4)\r\n#here a 0,0 is starting point and 250,250 is ending point and 255,255,255 is color\r\ncv.imshow('line',blank)\r\n#5 write a text on a image\r\ncv.putText(blank,'hello my name is ronak',(225,225),cv.FONT_HERSHEY_TRIPLEX,1.0,(0,255,0),thickness=4)\r\n#here  blank is page of500*500 and then name and 225,225 is starting point of a text and then font and then color\r\ncv.imshow('text',blank)\r\nwhile True:\r\n    if cv.waitKey(20) & 0xff==ord('x'):\r\n        break\r\n\r\n","repo_name":"RNVALA/My_work_towards_Opencv","sub_path":"4draw.py","file_name":"4draw.py","file_ext":"py","file_size_in_byte":1356,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"76"}
+{"seq_id":"35857398063","text":"#!/usr/bin/env python3\n\n\"\"\"\nRequired libraries:\n    * argparse https://pypi.org/project/argparse/\n    * requests https://pypi.org/project/requests/\n    * tqdm https://pypi.org/project/tqdm/\n\"\"\"\n\nimport argparse\nimport os\nimport requests\nfrom tqdm import tqdm\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--apiKey\", type=str, required=True, help=\"API key to authenticate at https://repo.labs.intellij.net. Can be \"\n                                                                  \"created and found https://repo.labs.intellij.net/webapp/#/profile\")\n    parser.add_argument(\"--version\", type=str, required=True, help=\"Android Studio version (e.g. 2020.3.1.4)\")\n    args = parser.parse_args()\n\n    for o in [\"mac\", \"linux\", \"windows\"]:\n        if o == \"linux\":\n            archive_type = \"tar.gz\"\n        else:\n            archive_type = \"zip\"\n\n        filename = f\"android-studio-{args.version}-{o}.{archive_type}\"\n        print(f\"Download {filename}\")\n        with requests.get(f\"https://dl.google.com/dl/android/studio/ide-zips/{args.version}/{filename}\", stream=True) as r:\n            if r.status_code == 200:\n                download_file(r, filename)\n            else:\n                print(f\"Can't download {filename}. {r}\")\n                continue\n\n        print(f\"Upload {filename} to https://repo.labs.intellij.net/edu-tools\")\n        with open(filename, 'rb') as data:\n            requests.put(f\"https://repo.labs.intellij.net/edu-tools/{filename}\", data=data, headers={\"X-JFrog-Art-Api\": args.apiKey})\n\n        os.remove(filename)\n\n\ndef download_file(request, filename):\n    with open(filename, 'wb') as f:\n        with tqdm(total=int(request.headers[\"Content-length\"]), unit=\"B\", unit_scale=True) as progress:\n            for chunk in request.iter_content(chunk_size=8 * 1024):\n                if chunk:  # filter out keep-alive new chunks\n                    f.write(chunk)\n                    f.flush()\n                    progress.update(len(chunk))\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"suhail547/educational-plugin","sub_path":"helpers/upload_studio.py","file_name":"upload_studio.py","file_ext":"py","file_size_in_byte":2039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"76"}
+{"seq_id":"34365798441","text":"import logging\nfrom Trotter import *\nfrom flask import Flask, render_template\nfrom flask_ask import Ask, statement, question, session\n\n\napp = Flask(__name__)\nask = Ask(app, \"/\")\nlogging.getLogger(\"flask_ask\").setLevel(logging.DEBUG)\n\n\n\n# Global vars\nlocsVisited = [] # List of last 7 locations visited\nseqNum = [0] # Sequence number for GUI commands\nlistOptions = []\nselOption = [1]\n# Add context variable to identify the function asking for additional info\n#session.attributes['numbers'] = numbers[::-1]  # reverse\n\n\n@ask.launch\ndef welcome():\n    seqNum[0] = 0\n    GUICom(\"about\", seqNum[0])\n    welcome_msg = render_template('welcome')\n    return question(welcome_msg)\n\n\n@ask.session_ended\ndef session_ended():\n    return statement(\"\")\n\n\n@ask.intent(\"AMAZON.HelpIntent\")\ndef help():\n    seqNum[0] = (seqNum[0] + 1) % 3\n    GUICom(\"help\", seqNum[0])\n    help_msg = render_template('help')\n    help_rep_msg = render_template('help_rep')\n    return question(help_msg).reprompt(help_rep_msg)\n\n\n@ask.intent(\"VoiceHelpIntent\")\ndef helpVoice():\n    seqNum[0] = (seqNum[0] + 1) % 3\n    GUICom(\"help\", seqNum[0])\n    help_msg = render_template('help_voice')\n    help_rep_msg = render_template('help_rep')\n    return question(help_msg).reprompt(help_rep_msg)\n\n\n@ask.intent(\"GestureHelpIntent\")\ndef helpGest():\n    seqNum[0] = (seqNum[0] + 1) % 3\n    GUICom(\"help\", seqNum[0])\n    help_msg = render_template('help_gesture')\n    help_rep_msg = render_template('help_rep')\n    return question(help_msg)\n\n\n@ask.intent(\"AMAZON.StopIntent\")\ndef stop():\n    return statement('OK')\n\n\n@ask.intent(\"AboutIntent\")\ndef about():\n    about_msg = render_template('about')\n    seqNum[0] = (seqNum[0] + 1) % 3\n    GUICom(\"about\", seqNum[0])\n    return statement(about_msg)\n\n@ask.intent(\"HistoryIntent\")\ndef history():\n    locsLen = len(locsVisited)\n    hist_msg = \"You have visited \"\n    if(locsLen == 0):\n        no_hist_msg = render_template('no_hist')\n        return question(no_hist_msg).reprompt(\"\")\n    else:\n        for num in xrange(locsLen-1, 0, -1):\n            hist_msg += locsVisited[num] + \", \"\n        hist_msg += \"and \" + locsVisited[0]\n    #return question(hist_msg).reprompt(\"\")\n    return statement(hist_msg)\n\n\n@ask.intent(\"IntroIntent\")\ndef intro():\n    intro_msg = render_template('intro')\n    return statement(intro_msg)\n\n\n@ask.intent(\"OutroIntent\")\ndef outro():\n    outro_msg = render_template('outro')\n    return statement(outro_msg)\n\n\n@ask.intent(\"TrottersIntent\")\ndef trotters():\n    seqNum[0] = (seqNum[0] + 1) % 3\n    GUICom(\"globetrotters\", seqNum[0])\n    globetrotters_msg = render_template('globetrotters')\n    #return question(globetrotters_msg).reprompt(\"\")\n    return statement(globetrotters_msg)\n\n\n\n@ask.intent(\"DetailsIntent\")\ndef details():\n    if(len(listOptions) < 1):\n        return question(\"where do you want to visit?\")\n    else:\n        seqNum[0] = (seqNum[0] + 1) % 3\n        GUICom(\"details\", seqNum[0])\n        global selOption\n        details_msg = listOptions[selOption[0] - 1]['MoreInfo']\n        return statement(details_msg)\n\n\n@ask.intent(\"ViewIntent\")\ndef view():\n    seqNum[0] = (seqNum[0] + 1) % 3\n    GUICom(\"switchView\", seqNum[0])\n    return statement(\"Switching map view\")\n\n\n@ask.intent(\"ZoomIntent\", default={'zoomOpt': 'in'})\ndef zoom(zoomOpt):\n    # AVS interprets 'in' as 'N' frequently\n    if(zoomOpt == \"N\"):\n        zoomOpt = \"in\"\n    if(zoomOpt == \"in\" or zoomOpt == \"out\" or zoomOpt == \"reset\"):\n        seqNum[0] = (seqNum[0] + 1) % 3\n        GUICom(zoomOpt, seqNum[0])\n        return question(\"Zooming {}\".format(zoomOpt))\n    else:\n        return question(\"I did not get that. Please say again\")\n\n\n@ask.intent(\"RotateIntent\", default={'rotateopt': 'none'})\ndef rotate(rotateopt):\n    rotateopt = rotateopt.lower()\n    # stores valid rotate responses\n    validOpts = ('left', 'west', 'right', 'east', 'up', 'north', 'down', 'south', \\\n        'fast', 'faster', 'quicker', 'slow', 'slower', 'slowly', 'stop')\n    for opt in validOpts:\n        if(opt == rotateopt) : \n            globeCntrl(rotateopt)\n            if(rotateopt == 'stop'):\n                return statement('Rotation stopped')\n            else:\n                return statement('Rotating {}'.format(rotateopt))\n    rotate_msg = render_template('rotate', invOpt=rotateopt)\n    return question(rotate_msg)\n        \n\n\n# Add inside @ask.intent if param_name != slot_name: \n#, mapping={'param_name': 'slot_name'} \n@ask.intent(\"GoToIntent\", default={'location': 'None'}) \ndef goto(location):\n    if(location == \"None\") : \n        return question(\"I did not get that. Please say the location again.\")\n    print(\"GoTo: looking up {}\".format(location))\n    # Get list of locations that meet the search query\n    global listOptions\n    listOptions = getAll(location)\n    numOpts = len(listOptions) \n    # Reset selected location to 1st location in list\n    global selOption\n    selOption[0] = 1\n    # Check if result is empty, one, or multiple locations\n    try: \n        # List available Keys from results\n        print(\"GoTo Avail Keys: {}\".format(listOptions[0].keys()))\n    except:\n        print(\"GoTo: Location not found\")\n        loc_msg = render_template('no_loc')\n        return question(loc_msg)\n\n    if(numOpts == 1):\n        loc_msg = listOptions[0]['Message']\n    else:\n        # handles case of multiple locations\n        loc_msg = listOptions[0]['Message']\n        loc_msg += \". To hear alternate locations say, what are my options?\"\n    \n    if(len(locsVisited) > 6):\n        # maintain history buffer of 7 locations\n        del locsVisited[0]\n        locsVisited.append(location)\n    else:\n        locsVisited.append(location)\n    # Update GUI\n    seqNum[0] = (seqNum[0] + 1) % 3\n    GUI(\"map\", seqNum[0], listOptions[0]['FullName'])\n    globeCntrl(str(listOptions[0]['Lng']))\n    loc_rep_msg = render_template('loc_rep')\n    return question(loc_msg).reprompt(loc_rep_msg)    \n\n\n\n@ask.intent(\"SelOptionIntent\", default={'listOpt': 0})\ndef selectOpt(listOpt):\n    # Check for selection of valid option\n    try: \n        listOpt = int(listOpt)\n    except:\n        return question(\"please select a valid option\")\n    numOpts = len(listOptions)\n    if(numOpts < 2):\n        return statement(\"there are no options to choose from\")\n    elif(listOpt > numOpts or listOpt < 1):\n        list_msg = render_template('valid_options', listOpt=numOpts)\n        return question(list_msg)\n    locName = listOptions[listOpt - 1]['Name']\n\n    # Update history\n    if(len(locsVisited) > 6):\n        # maintain history buffer of 7 locations\n        del locsVisited[0]\n        locsVisited.append(locName)\n    else:\n        locsVisited.append(locName)\n\n    # Update user selected location from list\n    global selOption\n    selOption[0] = listOpt\n    # Update GUI\n    seqNum[0] = (seqNum[0] + 1) % 3\n    GUI(\"Option{}\".format(listOpt), seqNum[0], listOptions[listOpt - 1]['FullName'])\n    globeCntrl(str(listOptions[listOpt - 1]['Lng']))\n    return question(listOptions[listOpt - 1]['Message'])\n\n\n\n\n@ask.intent(\"ListOptionsIntent\")\ndef listOpts():\n    numOpts = len(listOptions)\n    if(numOpts < 2):\n        return statement(\"there are no options to choose from\")\n    else:\n        loc_msg = \" \"\n        for i in range(numOpts):\n            loc_msg += \" For {} say, select option {}.\".format(listOptions[i]['FullName'], i + 1) \n        seqNum[0] = (seqNum[0] + 1) % 3\n        GUICom(\"ShowList\", seqNum[0])\n        return question(loc_msg)\n\n\n\n\n\n\n\nif __name__ == '__main__':\n    app.run(debug=True)\n    \n\n\n\n\n\n\n\n\n","repo_name":"AndresMtzP/Globetrotters","sub_path":"ProjectFiles/Project/GlobeVUI/GlobeVUI.py","file_name":"GlobeVUI.py","file_ext":"py","file_size_in_byte":7502,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"44084921625","text":"import datetime\nfrom functools import partial\n\nfrom sqlalchemy import Column, Integer, String, Boolean, Table, ForeignKey, DateTime, UniqueConstraint\nfrom sqlalchemy.orm import relationship\n\nfrom models.db import Base\n\nNotNullColumn = partial(Column, nullable=False)\n\n\nclass BaseModel:\n    id = NotNullColumn(Integer, primary_key=True, index=True, autoincrement=True)\n\n\nuserChatTable = Table('users_chats', Base.metadata,\n                      Column('user_id', ForeignKey('users.id'), primary_key=True),\n                      Column('chat_id', ForeignKey('chats.id'), primary_key=True)\n                      )\n\n\nclass User(Base, BaseModel):\n    __tablename__ = \"users\"\n\n    email = NotNullColumn(String, unique=True, index=True)\n    username = NotNullColumn(String)\n    hashed_password = NotNullColumn(String)\n    is_active = NotNullColumn(Boolean, default=False)\n    chats = relationship(\n        \"Chat\",\n        secondary=userChatTable,\n        back_populates=\"users\",\n    )\n    roles = relationship(\n        \"Role\",\n        back_populates=\"user\",\n    )\n\n\nclass Chat(Base, BaseModel):\n    __tablename__ = \"chats\"\n\n    name = NotNullColumn(String)\n    users = relationship(\n        \"User\",\n        secondary=userChatTable,\n        back_populates=\"chats\",\n    )\n    roles = relationship(\n        \"Role\",\n        back_populates=\"chat\",\n    )\n    messages = relationship(\n        \"Message\",\n        back_populates=\"chat\",\n    )\n\n\nclass Message(Base, BaseModel):\n    __tablename__ = \"messages\"\n\n    text = NotNullColumn(String)\n    sending_time = NotNullColumn(DateTime, default=datetime.datetime.now)\n    chat_id = NotNullColumn(Integer, ForeignKey(\"chats.id\"))\n    chat = relationship(\n        \"Chat\",\n        back_populates=\"messages\",\n    )\n    author_id = NotNullColumn(Integer, ForeignKey(\"users.id\"))\n    author = relationship(\"User\")\n\n\nclass RoleType(Base, BaseModel):\n    __tablename__ = \"role_types\"\n\n    name = NotNullColumn(String, unique=True, index=True)\n\n\nclass Role(Base, BaseModel):\n    __tablename__ = \"roles\"\n\n    role_type_id = NotNullColumn(Integer, ForeignKey(\"role_types.id\"))\n    role_type = relationship(\"RoleType\", uselist=False)\n    user_id = NotNullColumn(Integer, ForeignKey(\"users.id\"))\n    user = relationship(\"User\", back_populates=\"roles\")\n    chat_id = NotNullColumn(Integer, ForeignKey(\"chats.id\"))\n    chat = relationship(\"Chat\", back_populates=\"roles\")\n\n    __table_arg__ = (UniqueConstraint(\"user_id\", \"chat_id\"),)\n","repo_name":"sxccxs/chat-app","sub_path":"backend/models/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"39307425136","text":"from telethon import TelegramClient, events\r\nimport os\r\nimport subprocess\r\nimport mimetypes\r\nimport zipfile\r\nimport shutil\r\nimport requests\r\n\r\n# Replace these values with your actual credentials\r\napi_id = 21856699\r\napi_hash = '73f10cf0979637857170f03d4c86f251'\r\nbot_token = '6400640505:AAEkS-gVOM_-W1eL_qRtjS3X9ARjBu14S18'\r\n\r\n# Initialize the Telegram client\r\nclient = TelegramClient('session_name', api_id, api_hash).start(bot_token=bot_token)\r\n\r\n@client.on(events.NewMessage)\r\nasync def handle_message(event):\r\n    if event.message.file or event.message.text.startswith('http'):\r\n        await event.respond(\"Downloading and processing the files...\")\r\n\r\n        # Delete existing user directory if present\r\n        user_id = event.message.sender_id\r\n        user_dir = str(user_id)\r\n        if os.path.exists(user_dir):\r\n            shutil.rmtree(user_dir)\r\n\r\n        # Download the file using wget, curl, or requests\r\n        if event.message.file:\r\n            downloaded_file = await event.message.download_media()\r\n        else:\r\n            download_methods = [\r\n                lambda url, path: subprocess.run([\"wget\", url, \"-O\", path]),\r\n                lambda url, path: subprocess.run([\"curl\", url, \"-o\", path]),\r\n                lambda url, path: requests.get(url, stream=True).raw,\r\n            ]\r\n\r\n            for download_method in download_methods:\r\n                try:\r\n                    response = download_method(event.message.text, 'downloaded_file.zip')\r\n                    if isinstance(response, subprocess.CompletedProcess):\r\n                        if response.returncode == 0:\r\n                            downloaded_file = 'downloaded_file.zip'\r\n                            break\r\n                    elif hasattr(response, 'read'):\r\n                        with open('downloaded_file.zip', 'wb') as f:\r\n                            shutil.copyfileobj(response, f)\r\n                        downloaded_file = 'downloaded_file.zip'\r\n                        break\r\n                except Exception:\r\n                    pass\r\n\r\n        # Create a new directory for the user\r\n        os.makedirs(user_dir)\r\n\r\n        # Unzip the file\r\n        with zipfile.ZipFile(downloaded_file, 'r') as zip_ref:\r\n            zip_ref.extractall(user_dir)\r\n\r\n        # Delete the downloaded zip file\r\n        os.remove(downloaded_file)\r\n\r\n        # List all files in the user directory, including subdirectories\r\n        all_files = []\r\n        for root, _, files in os.walk(user_dir):\r\n            for file in files:\r\n                all_files.append(os.path.join(root, file))\r\n\r\n        # Prepare a response message with numbered options\r\n        options = \"\\n\".join([f\"{i + 1}. {os.path.relpath(file, user_dir)}\" for i, file in enumerate(all_files)])\r\n        response_message = f\"Select files to send back (separate indices with '&'):\\n{options}\\nOr type 'all' to send all files.\"\r\n        await event.respond(response_message)\r\n\r\n        async def send_files(selected_indices):\r\n            selected_indices = [int(idx) - 1 for idx in selected_indices]\r\n            selected_files = [all_files[i] for i in selected_indices if 0 <= i < len(all_files)]\r\n            if selected_files:\r\n                await event.respond(\"Sending the selected files...\")\r\n                for file_path in selected_files:\r\n                    mime_type, _ = mimetypes.guess_type(file_path)\r\n                    await client.send_file(event.message.sender_id, file_path, caption=os.path.basename(file_path), force_document=True, mime_type=mime_type)\r\n\r\n        @client.on(events.NewMessage)\r\n        async def inner_handle_message(event):\r\n            message = event.message.text\r\n            if message.lower() == 'all':\r\n                await event.respond(\"Sending all files...\")\r\n                for file_path in all_files:\r\n                    mime_type, _ = mimetypes.guess_type(file_path)\r\n                    await client.send_file(event.message.sender_id, file_path, caption=os.path.basename(file_path), force_document=True, mime_type=mime_type)\r\n            elif '&' in message:\r\n                selected_indices = message.split('&')\r\n                await send_files(selected_indices)\r\n\r\nclient.run_until_disconnected()\r\n","repo_name":"ankitkr23835/Dd","sub_path":"up.py","file_name":"up.py","file_ext":"py","file_size_in_byte":4230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"23428833955","text":"import os\nimport random\nimport string\nimport threading\nimport time\nfrom queue import Queue\nimport requests\nfrom colorama import Fore, init\n\nclear = \"clear\"\nyou = Fore.RED + \"You\"\ntube = \"\\033[37mTube\"\npt = Fore.BLUE + \"ProgTeam-Lub\"\nts = Fore.GREEN + \"Termux SU\"\nintro =  \"\"\"\n\\033[35m▂ ▃ ▅ ▆ █ ═╬ ۩\"\"\" + you + tube + \"\"\"\\033[35m۩ ╬═ █ ▆ ▅ ▃ ▂ \\033[37m\n\t╔┓┏╦━━╦┓╔┓╔━━╗ \n\t║┗┛║┗━╣┃║┃║X×║\n\t║┏┓║┏━╣┗╣┗╣╰╯║ \n\t╚┛┗╩━━╩━╩━╩━━╝\n\t   \n\"\"\" + Fore.RED + \"\"\"\t\n\t╲╲╲╲╲╲╲ ╱╱╱╱╱╭╯\n\t╲╭━┻┻┻┻━╮╱╱╱┏┓╱\n\t╭┫╭━╮╭━┓┣╮╱╱┣┫╱\n\t┃┃┃▆┃┃▋┃┃┃╱╭╯╰╮\n\t╰┫┗━╭╮━╯┣╯╲┃╭╮┃\n\t╱┃╰┳┳━━╯┃╲╲┃╳╳┃\n\t╱┃╲╰╯╱╱╱┃╲╲┃╰╯┃\n\t╱╰━┳━━┳━╯╲╲┗━━┛\n\t ║║ ╔═ ╔═ ╔╗ ║║ \n\t ╚╣ ╠═ ╠╗ ╠╣ ╠╣ \n\t ═╝ ╚═ ╚╝ ║║ ║║ \\033[35m\n\n▂ ▃ ▅ ▆ █ ═╬ ۩\"\"\" + pt + \"\\033[37m × \" + ts + \"\"\"\\033[35m۩ ╬═ █ ▆ ▅ ▃ ▂ \n\t\t   \t\t\t\t   \n\"\"\"\nos.system(clear)\nprint(intro)\n\n\ndef randomName(size=11, chars=string.ascii_letters + string.digits):\n    return ''.join(random.choice(chars) for i in range(size))\n\nprint(\"\\033[33m Введите ID стрима всё что после https://www.youtube.com/watch?v= скопируйте и вставьте\")\ntoken = input(\"\\033[33m [ID] : \")\nurl= \"https://m.youtube.com/watch?v=\" + token\nurl2 = \"https://s.youtube.com/api/stats/watchtime?ns=yt&el=detailpage&cpn=Syr16u8qwHnPkVqI&docid=\" + token + \"&ver=2&cmt=2094&ei=1EJtXou2C6eoxN8PpqqNqAg&fmt=133&fs=0&rt=1769&of=rkW8h_g-Pta1U6EIuWGdvw&euri&lact=7275&live=dvr&cl=300532980&state=playing&vm=CAEQABgEKhhJc0gwZ2w0QmFfbTBWSXlWNm9ITmRRPT06MkFOcHN5N0FhUWlHOHl5QkQySUF1OGt6amlZYjZwN3hzNzRXV3hhTEE4NDZVU1h2TTV3&volume=100&c=MWEB&cver=2.20200313.03.00&cplayer=UNIPLAYER&cbrand=apple&cbr=Safari%20Mobile&cbrver=12.1.15E148&cmodel=iphone&cos=iPhone&cosver=12_2&cplatform=MOBILE&delay=5&hl=ru&cr=IQ&rtn=2069&afmt=140&lio=1556394045.182&idpj=&ldpj=&rti=1769&muted=0&st=2094&et=2394\"\nclass main(object):\n    def __init__(self):\n        self.combolist = Queue()\n        self.Writeing = Queue()\n        self.printing = []\n        self.botted = 0\n        self.combolen = self.combolist.qsize()\n\n    def printservice(self): #print screen\n        while True:\n            if True:\n                os.system(clear)\n                print(Fore.LIGHTMAGENTA_EX + intro + Fore.LIGHTMAGENTA_EX)\n                print(Fore.LIGHTMAGENTA_EX + f\"Botted:{self.botted}\\n\")\n                for i in range(len(self.printing) - 10, len(self.printing)):\n                    try:\n                        print(\" \")\n                    except (ValueError, Exception):\n                        pass\na = main()\nclass proxy():\n\n    def update(self):\n        while True:\n            data = ''\n            urls = [\"https://api.proxyscrape.com/?request=getproxies&proxytype=http&timeout=10000&ssl=yes\",\"https://www.proxy-list.download/api/v1/get?type=https&anon=elite\"]\n            for url in urls:\n                data += requests.get(url).text\n                \n            self.splited += data.split(\"\\r\\n\") #scraping and splitting proxies\n            time.sleep(600)\n    \n    def get_proxy(self):\n        random1 = random.choice(self.splited) #choose a random proxie\n        return random1\n    def FormatProxy(self):\n\t    proxyOutput = {'https' :'https://'+self.get_proxy()}\n\t    return proxyOutput\n\n    def __init__(self):\n        self.splited = []\n        threading.Thread(target=self.update).start()\n        time.sleep(3)\n\nproxy1 = proxy()\ndef bot():\n    while True:\n        try:\n            s = requests.session()\n\n            resp = s.get(\"https://m.youtube.com/watch?v=\" + token,headers={'Host': 'm.youtube.com', 'Proxy-Connection': 'keep-alive', 'User-Agent': 'Mozilla/5.0 (iPhone; CPU iPhone OS 12_2 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/12.1 Mobile/15E148 Safari/604.1', 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', 'Accept-Language': 'ru-RU,ru;q=0.9,en-US;q=0.8,en;q=0.7', 'Accept-Encoding': 'gzip, deflate'},proxies=proxy1.FormatProxy())   # simple get request to youtube for the base URL\n            url = resp.text.split(r'videostatsWatchtimeUrl\\\":{\\\"baseUrl\\\":\\\"')[1].split(r'\\\"}')[0].replace(r\"\\\\u0026\",r\"&\").replace('%2C',\",\").replace(\"\\/\",\"/\")  #getting the base url for parsing\n            cl = url.split(\"cl=\")[1].split(\"&\")[0] #parsing some infos for the URL \n            ei = url.split(\"ei=\")[1].split(\"&\")[0]\n            of = url.split(\"of=\")[1].split(\"&\")[0]\n            vm = url.split(\"vm=\")[1].split(\"&\")[0]\n            s.get(\"https://s.youtube.com/api/stats/watchtime?ns=yt&el=detailpage&cpn=isWmmj2C9Y2vULKF&docid=\" + token + \"&ver=2&cmt=7334&ei=\" + ei + \"&fmt=133&fs=0&rt=1003&of=\" + of +\"&euri&lact=4418&live=dvr&cl=\" + cl + \"&state=playing&vm=\" + vm + \"&volume=100&c=MWEB&cver=2.20200313.03.00&cplayer=UNIPLAYER&cbrand=apple&cbr=Safari%20Mobile&cbrver=12.1.15E148&cmodel=iphone&cos=iPhone&cosver=12_2&cplatform=MOBILE&delay=5&hl=ru&cr=GB&rtn=1303&afmt=140&lio=1556394045.182&idpj=&ldpj=&rti=1003&muted=0&st=7334&et=7634\",headers={'Host': 's.youtube.com', 'Proxy-Connection': 'keep-alive', 'User-Agent': 'Mozilla/5.0 (iPhone; CPU iPhone OS 12_2 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/12.1 Mobile/15E148 Safari/604.1', 'Accept': 'image/png,image/svg+xml,image/*;q=0.8,video/*;q=0.8,*/*;q=0.5', 'Accept-Language': 'ru-RU,ru;q=0.8,en-US;q=0.5,en;q=0.3', 'Referer': 'https://m.youtube.com/watch?v=' + token},proxies=proxy1.FormatProxy())   # API GET request\n            \n            \n            a.botted += 1\n        except Exception as E:\n            pass\n\n\ntime.sleep(7)\nprint(\"\\033[33mУкажи потоки. Рекомендую для хорошей работы 500\")\nmaxthreads = int(input(\"\\033[33m[+] : \"))\nthreading.Thread(target=a.printservice).start()\nnum = 0\nwhile num < maxthreads :\n    num += 1\n    threading.Thread(target=bot).start()\n\n\nthreading.Thread(target=bot).start()\n","repo_name":"progteamlub/YouTube","sub_path":"yt.py","file_name":"yt.py","file_ext":"py","file_size_in_byte":6219,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"16605242910","text":"from scipy.spatial.distance import cosine\nimport numpy as np\nimport itertools\nimport gensim\n\nmodel_y = gensim.models.KeyedVectors.load_word2vec_format(\"model_of_years\")\nmodel_c = gensim.models.word2vec.Word2Vec.load(\"model_of_entites\")\n\n\ndef compute_min_max_sim():\n    \"\"\"\n    Compute normalization values for temporal representation of years\n    :return:\n    \"\"\"\n    vocab = [k for k in model_y.wv.vocab]\n    comb = itertools.combinations(vocab, 2)\n    list_of_similarities = list(map(lambda x: model_y.similarity(x[0], x[1]), comb))\n    return max(list(list_of_similarities)), min(list(list_of_similarities))\n\ndef most_representative_year(entity, topn=1):\n    \"\"\"\n    Get most representative year for an entity\n    :param entity:\n    :param topn:\n    :return:\n    \"\"\"\n    res = list(map(int, [k[0] for k in model_y.most_similar([model_c[entity]], topn=topn)]))\n    return np.average(res)\n\n\ndef time_aware_flattened_similarity(entity_a, entity_b, alpha=0.5):\n    \"\"\"\n    :param entity_a:\n    :param entity_b:\n    :param alpha:\n    :return:\n    \"\"\"\n    entity_array_a = model_c[entity_a]\n    entity_array_b = model_c[entity_b]\n\n    year_a = np.average(list(map(int, ([k[0] for k in model_y.most_similar(positive=[entity_array_a], topn=1)]))))\n    year_b = np.average(list(map(int, ([k[0] for k in model_y.most_similar(positive=[entity_array_b], topn=1)]))))\n\n    year_array_a = model_y[str(year_a).split(\".\")[0]]\n    year_array_b = model_y[str(year_b).split(\".\")[0]]\n\n    # return alpha* (1 - cosine(entity_array_a, entity_array_b)) + (1 - alpha)*(1/(1 - cosine(year_array_a, year_array_b)))\n    a = (1 - cosine(entity_array_a, entity_array_b))\n    b = (1 - (cosine(year_array_a, year_array_b)))\n\n    maxed, mined = compute_min_max_sim()\n\n    new_b = (b - mined) / (maxed - mined)\n\n    return alpha * a - (1 - alpha) * (new_b), a, new_b, year_a, year_b\n\n\n\n","repo_name":"vinid/time-aware","sub_path":"time_aware.py","file_name":"time_aware.py","file_ext":"py","file_size_in_byte":1856,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"5130932239","text":"\"\"\"\nThis file will not run alone.\nIt must be run in the main directory by the following command:\n    python3 manage.py shell < utils/make_dummy_listings.py\n\"\"\"\n\nfrom listings.models import Listing\n\nwith open(\"utils/dummy_listings.csv\") as y:\n    lines = y.readlines()\n\nlines.pop(0)\n\nfor line in lines:\n    line_list = line.strip().split(\",\")\n    if len(line_list) > 0 and line_list[0] != '':\n        l = Listing(*line_list)\n        l.print_details()\n        l.save()\n","repo_name":"jackm1123/offgroundshousing","sub_path":"utils/make_dummy_listings.py","file_name":"make_dummy_listings.py","file_ext":"py","file_size_in_byte":467,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"2551908193","text":"# Standard Imports\nimport time\nimport paho.mqtt.client as mqtt\nfrom db.connection import add_weatherData\n\ndef on_message(client, userdata, message):\n    dataArray = str(message.payload.decode(\"utf-8\")).split(\",\")\n    add_weatherData(dataArray[0], dataArray[1], dataArray[2], dataArray[3], dataArray[4], dataArray[5], dataArray[6], dataArray[7], dataArray[8], dataArray[9])\n\n# Set MQTT broker and Topic\nbroker = \"test.mosquitto.org\"\npub_topic = \"weatheralytics/data\"\n\t\n# Connect functions for MQTT\nclient = mqtt.Client()\n\n# Connect to MQTT \nprint(\"Attempting to connect to broker \" + broker)\nclient.connect(broker)\nclient.subscribe(pub_topic)\nclient.on_message = on_message\nclient.loop_start()\n\n","repo_name":"ts1506/WeatherAlytics","sub_path":"sensorSuscriber.py","file_name":"sensorSuscriber.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"19042407122","text":"def create_cpp_bindings(all_functions: dict, output_path: str):\n    \"\"\"Create the C++ bindings for exported functions.\n\n    Args:\n        all_functions (dict): Dictionary as produced by `parse_cpp_exports`.\n        output_path (str): Path to store the output C++ bindings.\n\n    Returns:\n        A file is created at `output_path`. Nothing is returned.\n    \"\"\"\n\n    all_function_names = list(all_functions.keys())\n    all_function_names.sort()\n\n    # Generating the C++ bindings for each function.\n    with open(output_path, \"w\") as handle:\n        handle.write(\"\"\"/* DO NOT MODIFY: this is automatically generated by the cpptypes */\n\n#include <cstring>\n#include <stdexcept>\n#include <cstdint>\n\n#ifdef _WIN32\n#define PYAPI __declspec(dllexport)\n#else\n#define PYAPI\n#endif\n\nstatic char* copy_error_message(const char* original) {\n    auto n = std::strlen(original);\n    auto copy = new char[n + 1];\n    std::strcpy(copy, original);\n    return copy;\n}\"\"\")\n\n        externC = [\n            \"\"\"PYAPI void free_error_message(char** msg) {\n    delete [] *msg;\n}\"\"\"\n        ]\n\n        for k in all_function_names:\n            restype, args = all_functions[k]\n            \n            types_only = [x.type.full_type for x in args]\n            handle.write(\"\\n\\n\" + restype.full_type + \" \" + k + \"(\" + \", \".join(types_only) + \");\")\n\n            all_args = [x.type.full_type + \" \" + x.name for x in args]\n            all_args.append(\"int32_t* errcode\")\n            all_args.append(\"char** errmsg\")\n\n            names_only = [x.name for x in args]\n            if restype.base_type == \"void\" and restype.pointer_level == 0:\n                init_call = \"\"\n                fun_call = k\n                ret_call = \"\"\n            else:\n                init_call = \"\\n\" + \" \" * 4 + restype.full_type + \" output = \"\n                if restype.pointer_level:\n                    init_call += \"NULL;\"\n                else:\n                    init_call += \"0;\"\n                fun_call = \"output = \" + k\n                ret_call = \"\\n\" + \" \" * 4 + \"return output;\"\n\n            externC.append(\"PYAPI \" + restype.full_type + \" py_\" + k + \"(\" + \", \".join(all_args) + \") {\" + init_call + \"\"\"\n    try {\n        \"\"\" + fun_call + \"(\" + \", \".join(names_only) + \"\"\");\n    } catch(std::exception& e) {\n        *errcode = 1;\n        *errmsg = copy_error_message(e.what());\n    } catch(...) {\n        *errcode = 1;\n        *errmsg = copy_error_message(\"unknown C++ exception\");\n    }\"\"\" + ret_call + \"\"\"\n}\"\"\"\n            )\n\n        handle.write('\\n\\nextern \"C\" {')\n        for cmd in externC:\n            handle.write(\"\\n\\n\" + cmd)\n        handle.write(\"\\n\\n}\\n\")\n\n    return\n","repo_name":"BiocPy/cpptypes","sub_path":"src/cpptypes/create_cpp_bindings.py","file_name":"create_cpp_bindings.py","file_ext":"py","file_size_in_byte":2644,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"45138468336","text":"from flask import Flask\nfrom functions import get_all, load_condidates, get_by_pk, get_by_skill\n\n# Обозначение констант и важных переменных\nFILENAME = \"candidates.json\"\ndata = get_all(load_condidates(FILENAME))\napp = Flask(__name__)\n\n\n# Создание маршрутов\n# / - корень\n# /candidates/цифра - поиск кандитата по номеру\n# /skills/название скила - поиск кандидатов по запрашиваемому скилу\n@app.route('/')\ndef index():\n    output = '<pre>'\n    for i in data:\n        output += f'{i}\\n'\n    output += '</pre>'\n    return output\n\n\n@app.route('/candidates/<int:pk>')\ndef get_user(pk):\n    user = get_by_pk(pk, data)\n    if user:\n        output = f'<img src = \"{user.picture}\">'\n        output += f'<pre>{user}</pre>'\n    else:\n        output = \"NOT FOUND\"\n    return output\n\n\n@app.route('/skills/<skill_name>')\ndef get_skills(skill_name):\n    skill_name = skill_name.lower()\n    skill = get_by_skill(skill_name, data)\n    if skill:\n        output = '<pre>'\n        for i in skill:\n            output += f'{i}\\n'\n        output += '</pre>'\n    else:\n        output = \"NOT FOUND\"\n    return output\n\n\nif __name__ == '__main__':\n    app.run(port=5000)\n","repo_name":"MikolaFPR/skypro_homework_10","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1275,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"23183293586","text":"from django.shortcuts import render\nfrom .models import DeicideList\nfrom .models import DeicideListArchive\nfrom django.views.generic.edit import CreateView\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import render\nfrom .forms import DeicideListForm\nimport tweepy\nfrom tweepy.auth import OAuthHandler\nfrom .CredData import *\n\n\nauth = OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\nauth.set_access_token(ACCESS_KEY, ACCESS_SECRET)\napi = tweepy.API(auth)\n\ndef get_deicide(request):\n    if request.method == 'POST':\n        form = DeicideListForm(request.POST)\n        if form.is_valid():\n            DeicideList = form.save(commit=False)\n            DeicideList.save()\n            qs2 = DeicideList.__class__.objects.latest('id')\n            api.update_status('Here we are! {}'.format(qs2))\n            return HttpResponseRedirect('/')\n            \n\n    else:\n        form = DeicideListForm()\n\n\n    qs = DeicideList.objects.all()\n    context = {'form': form, 'list_active_gods': qs}\n    return render(request, 'DeicideConfirmation.html', context)\n\ndef index(request):\n    if request.method == 'POST':\n        form = DeicideListForm(request.POST)\n        if form.is_valid():\n            qs = DeicideList.objects.all()\n            return HttpResponseRedirect('/new')\n\n    else:\n        form = DeicideListForm()\n\n\n    qs = DeicideList.objects.all()\n    context = {'form': form, 'list_active_gods': qs}\n    return render(request, 'DeicideConfirmation.html', context)\n\ndef about_us(request):\n    return render(request, 'aboutus.html')\n\ndef history(request):\n    qs2 = DeicideListArchive.objects.all()\n    context = {'history_active_gods': qs2}\n    return render(request, 'history.html', context)\n\n","repo_name":"PrinceLogan/GodFearsDanAykroyd","sub_path":"deicide/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1709,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"13539982385","text":"#https://leetcode.com/problems/smallest-integer-divisible-by-k/\n\ndef smallestRepunitDivByK(k):\n    \n    #Brute force\n    #first of all any number with 1 will not be divided with 2 or 5.\n    #then, until we get divided by k generate number with all 1s and return its length\n    \n    # n=1\n    # if k%2==0 or k%5==0:\n    #     return -1\n    # while(True):\n    #     if n%k==0:\n    #         return len(str(n))\n    #     n=n*10+1\n    \n    \n    #Improved Solution:\n    \n    # when we observed the formula n= n*10+1 and taking modulo with k to check its equal to 0\n    # ie n= (n*10+1)%k\n    # now, lets split what is n, n=k*quo+ rem\n    # now, 10*n+1 = (10* (k*quo+rem) +1) %k --> (10*k*quo + 10*rem+ 1)%k\n    # but as k*something is divisible by k, 10*k*quo will be cancelled\n    # so value will depend on 10*rem+1 and can be calculated on\n    # rem=(10*rem+1)%k\n    \n    \n    n=1\n    if k%2==0 or k%5==0:\n        return -1\n    rem=0\n    for i in range(1, k+1):\n        rem=(rem*10+1)%k\n        if not rem:\n            return i\n    return -1\n\nk= 3\nprint(smallestRepunitDivByK(k))","repo_name":"Jiganesh/Loads-Of-Logic","sub_path":"maths/smallestIntegerDivisibleByK.py","file_name":"smallestIntegerDivisibleByK.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","stars":189,"dataset":"github-code","pt":"76"}
+{"seq_id":"38188559991","text":"import pyttsx3\nimport subprocess\nfrom datetime import datetime\nengine = pyttsx3.init()\n\ntxt = \"\"\"\nHere we define different components for the widget.\nc v r is the container frame.\nlabel contains the question.\nThere are buttons for add, edit and delete.\nl b is the actual listbox.\n\"\"\".strip()\nengine.setProperty('rate',150)\nengine.setProperty('voice', engine.getProperty('voices')[0].id)\nengine.save_to_file(txt,r'C:\\Users\\Dell\\OneDrive\\Documents\\Sound Recordings\\out.mp3')\nengine.runAndWait()\nsubprocess.Popen([r'C:\\Users\\Dell\\Downloads\\archive\\ffmpeg-2022-08-25-git-9bf9d42d01-full_build\\bin\\ffmpeg.exe', '-i', r'C:\\Users\\Dell\\OneDrive\\Documents\\Sound Recordings\\out.mp3', '-c:a', 'aac', r'C:\\Users\\Dell\\OneDrive\\Documents\\Sound Recordings\\%s.m4a' % datetime.now().strftime('%Y_%m_%d_%H_%M_%S')]).wait()\nengine.say(txt)\nengine.runAndWait()\n\n\n","repo_name":"seedhaval/scripts","sub_path":"python_scripts/utilities/text_to_speech_mp3.py","file_name":"text_to_speech_mp3.py","file_ext":"py","file_size_in_byte":843,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"31610458197","text":"from typing import Set, Optional\nfrom random import Random\nfrom tyrell.enumerator.enumerator import Enumerator\nfrom tyrell import dsl as D\nfrom tyrell import spec as S\nfrom tyrell.enumerator.enumerator_ast import EnumeratorAST\n\nfunc_deps = {\n    \"COPYRANGE\": {2: [0]},\n    \"SUM\": {0: [0,1]},\n    \"UPDATERANGE\": {1:[4]}, # SHOULD INCLUDE 0?\n    \"MAP\": {0: [3]},\n    \"INCRANGE\": {2: [0]},\n    \"COPYRANGE_L\": {2: [0]},\n    \"SUM_L\": {0: [0,1]},\n    \"MAP_L\": {0: [3]},\n    \"INCRANGE\": {2: [0]},\n    \"FILTER\": {}\n}\n\nclass DependencyEnumerator(Enumerator):\n    _rand: Random\n    _max_depth: int\n    _builder: D.Builder\n\n    def __init__(self, spec: S.TyrellSpec, max_depth: int, seed: Optional[int]=None,\n                 analysis=None, types=None):\n        self._rand = Random(seed)\n        self._builder = D.Builder(spec)\n        if max_depth <= 0:\n            raise ValueError(\n                'Max depth cannot be non-positive: {}'.format(max_depth))\n        self._max_depth = max_depth\n        self._analysis = analysis\n        self._types = types\n        self._progs = []\n        self._func_idx = 0\n        self._last_conc_args = []\n        self._invalid_args = {}\n\n        self._history = []\n        self._candidate_num = 0\n        self.enum = EnumeratorAST(self._builder.output, func_deps, analysis, self._builder)\n\n    def set_iterators(self, its):\n        self.enum.set_iterators(its)\n\n    def get_types(self, val):\n        val_types = []\n        for typ, vals in self._types.items():\n            if val in vals:\n                val_types.append(typ)\n\n        return val_types\n        \n    def check_val_deps(self, val, deps_on, deps_from, args, conc_args):\n        # Fetch analysis depends on relationships with val (i.e. val LHS)\n        analysis_dep_on = []\n        if val in self._analysis:\n            analysis_dep_on = self._analysis[val]\n\n        # Fetch analysis depends from relationships with val (i.e. val RHS)          \n        analysis_dep_from = []\n        for var, deps in self._analysis.items():\n            if val in deps:\n                analysis_dep_from.append(var)\n\n        # print(val, deps_on, deps_from, args, analysis_dep_on, analysis_dep_from)\n                \n        # Check both depends on and depends from\n        for deps, analysis_deps in zip([deps_on, deps_from], [analysis_dep_on, analysis_dep_from]):\n            # Check each production dependency\n            for dep in deps:\n                if dep >= len(conc_args):\n                    dep_typ = str(args[dep])\n                    # If there is NOT at least one value which could fill in, reject val\n                    if not any(map(lambda x: dep_typ in self.get_types(x),analysis_deps)):\n                        return False\n                else:\n                    dep_name = str(self._builder.make_node(conc_args[dep]))\n                    if not dep_name in analysis_deps:\n                        return False                        \n            \n        return True\n        \n    def find_poss_hole_vals(self, prod, conc_args):\n        poss_vals = []\n\n        # Fetch production dependencies\n        prod_deps = {}\n        if prod.name in func_deps:\n            prod_deps = func_deps[prod.name]\n\n        # Iterate through production arguments\n        for i,arg in enumerate(prod.rhs):\n            # Fetch argument production dependencies\n            depends_on = []\n            if i in prod_deps:\n                depends_on = prod_deps[i]\n            depends_from = []\n            for idx, deps in prod_deps.items():\n                if i in deps:\n                    depends_from.append(idx)\n            # Get possible argument vars based on types\n            values = self._builder.get_productions_with_lhs(arg)\n            values = list(filter(lambda x: not x.is_function(), values))\n            # Perform check for each variable\n            arg_poss_vals = []\n            for val in values:\n                val = val.rhs[0]\n                if self.check_val_deps(val, depends_on, depends_from, prod.rhs):\n                    arg_poss_vals.append(val)\n            poss_vals.append(arg_poss_vals)\n                    \n        return poss_vals\n\n    def find_poss_hole_vals2(self, prod, conc_args, idx):\n        poss_vals = []\n\n        # Fetch production dependencies\n        prod_deps = {}\n        if prod.name in func_deps:\n            prod_deps = func_deps[prod.name]\n\n        arg = prod.rhs[idx]\n            \n        # Fetch argument production dependencies\n        depends_on = []\n        if idx in prod_deps:\n            depends_on = prod_deps[idx]\n        depends_from = []\n        for idx2, deps in prod_deps.items():\n            if idx in deps:\n                depends_from.append(idx2)\n        # Get possible argument vars based on types\n        values = self._builder.get_productions_with_lhs(arg)\n        values = list(filter(lambda x: not x.is_function(), values))\n        # Perform check for each variable\n        for val in values:\n            val = val.rhs[0]\n            if self.check_val_deps(val, depends_on, depends_from, prod.rhs, conc_args):\n                poss_vals.append(val)\n                    \n        return poss_vals\n\n    def _generate_value(self, curr_type: S.Type, depth: int, top_level: bool):        \n        # First, get all the relevant function rules for current type\n        productions = self._builder.get_productions_with_lhs(curr_type)\n        # Split by function productions and value productions\n        func_prods = list(filter(lambda x: x.is_function(), productions))\n        val_prods = list(filter(lambda x: not x.is_function(), productions))\n\n        # Try value productions first to bias shorter candidates        \n        for val in val_prods:            \n            candidate = self._builder.make_node(val)\n            if not depth in self._history:\n                print(\"VALUE: {0}\".format(candidate))\n                return candidate, depth+1\n            # else:\n            #     print(\"REJECTED VALUE: {0}\".format(candidate))\n\n        # # No function can be returned at depth 0\n        # if depth == 0:\n        #     return None, depth\n            \n        # Try functions second\n        for func in func_prods:\n            # Iteratively fill function arguments\n            arguments = []\n            print(\"==\"*8)\n            print(func, depth)\n            print(\"--\")\n            for arg_typ in func.rhs:\n                arg, depth = self._generate_value(arg_typ, depth, False)\n                print(\"TYPE: {0}, VAL: {1}\".format(arg_typ, arg))\n                if arg == None:\n                    return None, depth\n                arguments.append(arg)\n            # arguments = list(map(lambda x: self._builder.make_node(x), arguments))\n\n            candidate = self._builder.make_node(func, arguments)\n            if not depth in self._history:\n                return candidate, depth+1\n            \n        # for func in productions[self._func_idx:]:\n        #     if self._last_conc_args != []:\n        #         new_conc_args = self._last_conc_args[:-1]\n        #         invalid_arg = str(self._builder.make_node(self._last_conc_args[-1]))\n        #         idx = len(new_conc_args)\n        #         invalid_key = (func, tuple(new_conc_args), idx)\n        #         if not invalid_key in self._invalid_args:\n        #             self._invalid_args[invalid_key] = []\n        #         self._invalid_args[invalid_key].append(invalid_arg)\n        #         enum_children = self._generate_children(func, idx, new_conc_args)\n        #         if enum_children == None:\n        #             self._last_conc_args = new_conc_args\n        #             return self._generate_func(curr_type)\n        #     else:\n        #         enum_children = self._generate_children(func, 0, [])\n        #     if enum_children != None:\n        #         children = list(map(lambda x: self._builder.make_node(x), enum_children))\n        #         self._last_conc_args = enum_children\n        #         self._func_idx = productions.index(func)\n        #         return self._builder.make_node(func, children)\n\n        return None, depth\n\n    def _generate_children(self, func, idx, conc_args):\n        if idx == len(func.rhs):\n            return conc_args\n        poss_values = self.find_poss_hole_vals2(func,conc_args,idx)\n        invalid_key = (func, tuple(conc_args), idx)\n        invalid_values = []\n        if invalid_key in self._invalid_args:\n            invalid_values = self._invalid_args[invalid_key]\n        poss_values = list(filter(lambda x: not x in invalid_values, poss_values))\n        if [] in poss_values:\n            return None\n        arg = func.rhs[idx]\n        productions = self._builder.get_productions_with_lhs(arg)\n        productions = list(filter(lambda x: not x.is_function(), productions))\n\n        productions = list(filter(lambda x: str(self._builder.make_node(x)) in poss_values, productions))\n\n        for pot_arg in productions: \n            children = self._generate_children(func, idx+1, conc_args+[pot_arg])\n            if children != None:\n                return children\n        \n        return None\n    \n\n    def next(self):\n        # prog = self._generate_value(self._builder.output, 0, True)\n        # self._history.append(self._candidate_num)\n        # print(self._history)\n        # print(depth)\n        # # while prog == None: #and depth < self._max_depth:\n        # #     print(self._history)\n        # #     print(depth)\n        # #     # Erase top-level history before each new top-level generation\n        # #     prog, depth = self._generate_value(self._builder.output, depth, True)\n        # #     self._history.append(depth)            \n        # #     print(prog)\n        # return prog\n\n        # print(self.enum.queue[0])\n        cand = self.enum.next_candidate()\n        while cand == False:\n            cand = self.enum.next_candidate()\n        # print(self.enum.queue[0])\n        # print(\"$$$$$\"*8)\n        # while str(cand) in self._history:\n        #     print(\"REPEATED: {0}\".format(cand))\n        #     raise Exception()\n        #     cand =  self.enum.next_candidate()\n            \n        # self._history.append(str(cand))\n        # print(cand)\n        return cand\n        \n","repo_name":"nelaturuk/loop-optimizer","sub_path":"optimizercode/src/tyrell/enumerator/full_dsl_dependency_enumerator.py","file_name":"full_dsl_dependency_enumerator.py","file_ext":"py","file_size_in_byte":10180,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"76"}
+{"seq_id":"74311539444","text":"# INCLUDE IN YOUR LEVEL:\nscreen = pygame.display.set_mode([1260, 700], HWSURFACE|DOUBLEBUF)\nrunning = True\n# Add all sprites to their appropriate sprite group(s). Any sprites from the\n# assests folder need to be in all_sprites_img.\nall_sprites_surf = pygame.sprite.Group()\nall_sprites_img = pygame.sprite.Group()\nwall_sprites = pygame.sprite.Group()\nenemies = pygame.sprite.Group()\nitems = pygame.sprite.Group()\ncone_sprites = pygame.sprite.Group()\nclock = pygame.time.Clock()\nwallB1 = Walls([0, 0], screen.get_width(), 10)\nwallB2 = Walls([0, 0], 10, screen.get_height())\nwallB3 = Walls([0, screen.get_height()-10], screen.get_width(), 10)\nwallB4 = Walls([screen.get_width()-10, 0], 10, screen.get_height())\nall_sprites_surf.add(wallB1)\nall_sprites_surf.add(wallB2)\nall_sprites_surf.add(wallB3)\nall_sprites_surf.add(wallB4)\nwall_sprites.add(wallB1)\nwall_sprites.add(wallB2)\nwall_sprites.add(wallB3)\nwall_sprites.add(wallB4)\nwhile running:\n    # Handles quitting the game, resizing the window\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            running = False\n        if event.type == pygame.KEYDOWN:\n            if event.key == K_ESCAPE:\n                running = False\n            \n    # Sets screen colour\n    screen.fill((255, 255, 255))\n    # Updates player's position each loop\n    for entity in all_sprites_surf:\n        screen.blit(entity.surf, entity.rect)\n    for entity in all_sprites_img:\n        screen.blit(entity.image, entity.rect)\n    for entity in cone_sprites:\n        screen.blit(entity.image, entity.rect[0])\n        for rect in entity.rect:\n            screen.blit(entity.surf, rect)\n    pygame.display.flip()\n\n    # Enemy collision\n    if pygame.sprite.spritecollideany(player, enemies):\n        player.kill()\n        running = False\n\n    # Flag collision\n    if pygame.sprite.collide_rect(player, Item):\n        running = False\n\n    # Vision cone collision\n    for cone in cone_sprites:\n        for rect in cone.rect:\n            if player.rect.colliderect(rect):\n                player.kill()\n                running = False\n\n    # Wall collision\n    if pygame.sprite.spritecollideany(player, wall_sprites):\n        col_walls = (pygame.sprite.spritecollide(player, wall_sprites, False))\n        if len(col_walls) == 2:\n            col_wall = col_walls[0]\n            if player.rect.left < col_wall.rect.right < player.rect.right:\n                player.rect.move_ip(5, 0)\n            elif player.rect.top < col_wall.rect.bottom < player.rect.bottom:\n                player.rect.move_ip(0, 5)\n            elif player.rect.bottom > col_wall.rect.top > player.rect.top:\n                player.rect.move_ip(0, -5)\n            elif player.rect.right > col_wall.rect.left > player.rect.left:\n                player.rect.move_ip(-5, 0)\n            col_wall2 = col_walls[1]\n            if player.rect.left < col_wall2.rect.right < player.rect.right:\n                player.rect.move_ip(5, 0)\n            elif player.rect.top < col_wall2.rect.bottom < player.rect.bottom:\n                player.rect.move_ip(0, 5)\n            elif player.rect.bottom > col_wall2.rect.top > player.rect.top:\n                player.rect.move_ip(0, -5)\n            elif player.rect.right > col_wall2.rect.left > player.rect.left:\n                player.rect.move_ip(-5, 0)\n        else:\n            col_wall = pygame.sprite.spritecollide(player, wall_sprites, False)[0]\n            if player.rect.left < col_wall.rect.right < player.rect.right:\n                player.rect.move_ip(5, 0)\n            elif player.rect.top < col_wall.rect.bottom < player.rect.bottom:\n                player.rect.move_ip(0, 5)\n            elif player.rect.bottom > col_wall.rect.top > player.rect.top:\n                player.rect.move_ip(0, -5)\n            elif player.rect.right > col_wall.rect.left > player.rect.left:\n                player.rect.move_ip(-5, 0)\n    # Sets our framerate\n    clock.tick(60)\npygame.quit()","repo_name":"AaronL-Ualberta/UntitledStealthGame","sub_path":"runningcode.py","file_name":"runningcode.py","file_ext":"py","file_size_in_byte":3932,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"39537180803","text":"#Escreva um programa que leia o nome do usuário e depois imprima 9 vezes o nome do lido.\n\nn = 1\n\nwhile n <= 300:\n    nm = str(n)\n    if( nm[-1] == \"5\" or nm[-1] == \"9\"):\n        print(nm)\n    n+= 1\n    \n","repo_name":"arianacabral/Introduction-to-Python","sub_path":"Atividade 5/Q27.py","file_name":"Q27.py","file_ext":"py","file_size_in_byte":204,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"40220341197","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Mar 30 18:59:48 2023\n\n@author: lucvercoulen\n\"\"\"\n#importing the data and packages\nimport pandas as pd\nimport seaborn as sns\nfrom sklearn.preprocessing import StandardScaler\nimport statsmodels.formula.api as sm\nimport scipy.stats\nimport statsmodels.api as sm\nfrom statsmodels.imputation import mice\nWebshop_CSV = pd.read_csv('2124800webshop.csv')\n\n#removing the missing value's \n# Create a copy of the dataset\nWebshop_CSV_copy = Webshop_CSV.copy()\n# Check for missing values in the original dataset\nWebshop_CSV.isnull().sum()\n# Create a new copy of the original dataset\nClean_Webshop_CSV = Webshop_CSV_copy.copy()\n# Remove missing values from the copy\nClean_Webshop_CSV.dropna(inplace=True)\n# Check for missing values in the cleaned copy\nClean_Webshop_CSV.isnull().sum()\n\n\n#creating Dummy variables \nClean_Webshop_Dummies = pd.get_dummies(Clean_Webshop_CSV['Device'])\nClean_Webshop_CSV = pd.concat([Clean_Webshop_CSV, Clean_Webshop_Dummies], axis=1)\nClean_webshop_Dummies_find_website = pd.get_dummies(Clean_Webshop_CSV['Find_website'])\nClean_Webshop_CSV = pd.concat([Clean_Webshop_CSV, Clean_webshop_Dummies_find_website], axis=1)\n\n\n#dealing with outliers\n#first create a new dataset for the outliers\nClean_Webshop_Outlier = Clean_Webshop_CSV.copy()\n#create a variable for the sample zise \nn = len(Clean_Webshop_Outlier)\n\n\n#Creating single regression & outliers time spent on website\nSR1_Time_Spent_on_Website = sm.ols('Purchase_Amount ~ Time_Spent_on_Website', data = Clean_Webshop_Outlier).fit()\nprint(SR1_Time_Spent_on_Website.summary())\nCooksD = SR1_Time_Spent_on_Website.get_influence().cooks_distance\nClean_Webshop_Outlier['Outlier'] = CooksD[0] > 4/n\nClean_Webshop_Outlier[Clean_Webshop_Outlier.Outlier == True]\nClean_Webshop_Outlier1 = Clean_Webshop_Outlier[Clean_Webshop_Outlier.Outlier == True]\nmask = Clean_Webshop_Outlier['Time_Spent_on_Website']==-999\nClean_Webshop_Outlier = Clean_Webshop_Outlier[~mask]\n\nSR1_Time_Spent_on_Website = sm.ols('Purchase_Amount ~ Time_Spent_on_Website', data = Clean_Webshop_Outlier).fit()\nprint(SR1_Time_Spent_on_Website.summary())\nClean_Webshop_Outlier = Clean_Webshop_Outlier.drop('Outlier', axis=1)\n\n#Creating single regression & checking outliers number of products browsed\nSR2_Number_of_products_browsed = sm.ols('Purchase_Amount ~ Number_of_products_browsed', data = Clean_Webshop_Outlier).fit()\nprint(SR2_Number_of_products_browsed.summary())\n\nCooksD = SR2_Number_of_products_browsed.get_influence().cooks_distance\nClean_Webshop_Outlier['Outlier'] = CooksD[0] > 4/n\nClean_Webshop_Outlier[Clean_Webshop_Outlier.Outlier == True]\nClean_Webshop_Outlier2 = Clean_Webshop_Outlier[Clean_Webshop_Outlier.Outlier == True]\nClean_Webshop_Outlier = Clean_Webshop_Outlier.drop('Outlier', axis=1)\n\n#Creating single regression & checking outliers number pictures\nSR3_Pictures = sm.ols('Purchase_Amount ~ Pictures', data = Clean_Webshop_Outlier).fit()\nprint(SR3_Pictures.summary())\n\nCooksD = SR3_Pictures.get_influence().cooks_distance\nClean_Webshop_Outlier['Outlier'] = CooksD[0] > 4/n\nClean_Webshop_Outlier[Clean_Webshop_Outlier.Outlier == True]\nClean_Webshop_Outlier3 =Clean_Webshop_Outlier[Clean_Webshop_Outlier.Outlier == True]\nClean_Webshop_Outlier = Clean_Webshop_Outlier.drop('Outlier', axis=1)\n\n#Creating single regression & Shipping time\nSR4_Shipping_Time = sm.ols('Purchase_Amount ~ Shipping_Time', data = Clean_Webshop_Outlier).fit()\nprint(SR4_Shipping_Time.summary())\n\nCooksD = SR4_Shipping_Time.get_influence().cooks_distance\nClean_Webshop_Outlier['Outlier'] = CooksD[0] > 4/n\nClean_Webshop_Outlier[Clean_Webshop_Outlier.Outlier == True]\nClean_Webshop_Outlier4 =Clean_Webshop_Outlier[Clean_Webshop_Outlier.Outlier == True]\nClean_Webshop_Outlier = Clean_Webshop_Outlier.drop('Outlier', axis=1)\n\n#Creating single regression & checking outliers review rating\nSR5_Review_rating = sm.ols('Purchase_Amount ~ Review_rating', data = Clean_Webshop_Outlier).fit()\nprint(SR5_Review_rating.summary())\n\nCooksD = SR5_Review_rating.get_influence().cooks_distance\nClean_Webshop_Outlier['Outlier'] = CooksD[0] > 4/n\nClean_Webshop_Outlier[Clean_Webshop_Outlier.Outlier == True]\nClean_Webshop_Outlier5 =Clean_Webshop_Outlier[Clean_Webshop_Outlier.Outlier == True]\nClean_Webshop_Outlier = Clean_Webshop_Outlier.drop('Outlier', axis=1)\n\n#Creating single regression & checking outliers Ease of purchase\nSR6_Ease_of_purchase = sm.ols('Purchase_Amount ~ Ease_of_purchase', data = Clean_Webshop_Outlier).fit()\nprint(SR6_Ease_of_purchase.summary())\n\nCooksD = SR6_Ease_of_purchase.get_influence().cooks_distance\nClean_Webshop_Outlier['Outlier'] = CooksD[0] > 4/n\nClean_Webshop_Outlier[Clean_Webshop_Outlier.Outlier == True]\nClean_Webshop_Outlier6 =Clean_Webshop_Outlier[Clean_Webshop_Outlier.Outlier == True]\nClean_Webshop_Outlier = Clean_Webshop_Outlier.drop('Outlier', axis=1)\n\n#Creating single regression & checking outliers Device\nSR7_Device = sm.ols('Purchase_Amount ~ PC', data = Clean_Webshop_Outlier).fit()\nprint(SR7_Device.summary())\n\nCooksD = SR7_Device.get_influence().cooks_distance\nClean_Webshop_Outlier['Outlier'] = CooksD[0] > 4/n\nClean_Webshop_Outlier[Clean_Webshop_Outlier.Outlier == True]\nClean_Webshop_Outlier7 =Clean_Webshop_Outlier[Clean_Webshop_Outlier.Outlier == True]\nClean_Webshop_Outlier = Clean_Webshop_Outlier.drop('Outlier', axis=1)\n\nSR7_1_Device = sm.ols('Purchase_Amount ~ Mobile', data = Clean_Webshop_Outlier).fit()\nprint(SR7_1_Device.summary())\n\nCooksD = SR7_1_Device.get_influence().cooks_distance\nClean_Webshop_Outlier['Outlier'] = CooksD[0] > 4/n\nClean_Webshop_Outlier[Clean_Webshop_Outlier.Outlier == True]\nClean_Webshop_Outlier7_1 =Clean_Webshop_Outlier[Clean_Webshop_Outlier.Outlier == True]\nClean_Webshop_Outlier = Clean_Webshop_Outlier.drop('Outlier', axis=1)\n\n#Creating multiple regression found website\nMR8_Find_website = sm.ols('Purchase_Amount ~ Friends_or_Family + Search_Engine + Social_Media_Advertisement', data = Clean_Webshop_Outlier).fit()\nprint(MR8_Find_website.summary())\n\nMR8_1_Find_website_other_reference_group = sm.ols('Purchase_Amount ~ Other + Search_Engine + Social_Media_Advertisement', data = Clean_Webshop_Outlier).fit()\nprint(MR8_1_Find_website_other_reference_group.summary())\n\n#Creating single regression Age \nSR9_age = sm.ols('Purchase_Amount ~ Age', data = Clean_Webshop_Outlier).fit()\nprint(SR9_age.summary())\n\n\n#Checking for multicollinearity\ncorr_matrix = Clean_Webshop_Outlier.corr()\nprint(corr_matrix)\n\nMR10_multicollinearity_check = sm.ols('Purchase_Amount ~ Number_of_products_browsed + Time_Spent_on_Website', data = Clean_Webshop_Outlier).fit()\nprint(MR10_multicollinearity_check.summary())\n\nMR11_all_variables_Friends_family_Mobile_reference = sm.ols('Purchase_Amount ~ Time_Spent_on_Website + Age + Number_of_products_browsed + Pictures + Shipping_Time + Review_rating + Ease_of_purchase + Age + PC + Other + Search_Engine + Social_Media_Advertisement', data = Clean_Webshop_Outlier).fit()\nprint(MR11_all_variables_Friends_family_Mobile_reference.summary())\n\n\n#Checking for non-liniar relationship\n#Checking relationship time spent on website\nsns.regplot(x = 'Time_Spent_on_Website', y = 'Purchase_Amount', data = Clean_Webshop_Outlier, lowess = True)\n\n#Checking relationship number of products browsed\nsns.regplot(x = 'Number_of_products_browsed', y = 'Purchase_Amount', data = Clean_Webshop_Outlier, lowess = True)\n\n#Checking relationship pictures\nsns.regplot(x = 'Pictures', y = 'Purchase_Amount', data = Clean_Webshop_Outlier, lowess = True)\n\n#Checking relationship shipping time\nsns.regplot(x = 'Shipping_Time', y = 'Purchase_Amount', data = Clean_Webshop_Outlier, lowess = True)\n\n#Checking relationship review rating\nsns.regplot(x = 'Review_rating', y = 'Purchase_Amount', data = Clean_Webshop_Outlier, lowess = True)\n\n#Checking relationship ease of products\nsns.regplot(x = 'Ease_of_purchase', y = 'Purchase_Amount', data = Clean_Webshop_Outlier, lowess = True)\n\n#Checking relationship age\nsns.regplot(x = 'Age', y = 'Purchase_Amount', data = Clean_Webshop_Outlier, lowess = True)\n\n\n#polynomial transformation \nAge2 = pow(Clean_Webshop_Outlier.Age,2)\n#comparing the linear model to polynomial model  \nSR9_age = sm.ols('Purchase_Amount ~ Age', data = Clean_Webshop_Outlier).fit()\nprint(SR9_age.summary())\npolynomial_model = sm.ols('Purchase_Amount ~ Age + Age2', data = Clean_Webshop_Outlier).fit()\nprint(polynomial_model.summary())\n\n\n#Create the eventual model for the APA table\nMR12_model1 = sm.ols('Purchase_Amount ~  Age + Age2 + Time_Spent_on_Website + Shipping_Time + Review_rating + Ease_of_purchase + PC + Ease_of_purchase + Friends_or_Family + Social_Media_Advertisement', data = Clean_Webshop_Outlier).fit()\nprint(MR12_model1.summary())\n\n#The following code for creating APA style tables only works in Jupyterlab\nfrom stargazer.stargazer import Stargazer\nfrom IPython.core.display import HTML\nMR12_model1 = sm.ols('Purchase_Amount ~  Age + Age2 + Time_Spent_on_Website + Shipping_Time + Review_rating + Ease_of_purchase + PC + Ease_of_purchase + Friends_or_Family + Social_Media_Advertisement', data = Clean_Webshop_Outlier).fit()\nprint(MR12_model1.summary())\n\nTable = Stargazer([MR12_model1])\nTable.title =('Model predict purchase amount')\nTable.show_model_numbers(False) \nTable.significant_digits(2)\nTable.covariate_order(['Intercept', 'Age', 'Age2', 'Ease_of_purchase', 'Review_rating', 'Shipping_Time', 'Time_Spent_on_Website', 'PC', 'Friends_or_Family', 'Social_Media_Advertisement'])\nTable.rename_covariates({'Ease_of_purchase':'Ease of purchase', 'Review_rating' : 'Review rating', 'Shipping_Time' : 'Shipping time', 'Time_Spent_on_Website' : 'Time spent on website', 'Friends_or_Family' : 'Friend or family', 'Search_Engine' : 'Search engine', 'Social_Media_Advertisement' : 'Social media advertisement'}) \nHTML(Table.render_html())\n\n\n#standarization of variables\nClean_Webshop_Standardized = Clean_Webshop_Outlier.copy()\nClean_Webshop_Standardized[['Time_Spent_on_Website', 'Shipping_Time', 'Review_rating', 'Ease_of_purchase', 'Age']] = StandardScaler().fit_transform(Clean_Webshop_Standardized[['Time_Spent_on_Website', 'Shipping_Time', 'Review_rating', 'Ease_of_purchase', 'Age']])\n\n#creating a model with the standardized variables\nMR13_model2 = sm.ols('Purchase_Amount ~  Age + Age2 + Time_Spent_on_Website + Shipping_Time + Review_rating + Ease_of_purchase + PC + Ease_of_purchase + Friends_or_Family + Social_Media_Advertisement', data = Clean_Webshop_Standardized).fit()\nprint(MR13_model2.summary())\n\n\n\n\n#creating a APA table with two models\nTable = Stargazer([MR12_model1,MR13_model2])\nTable.title =('Model predict purchase amount')\nTable.show_model_numbers(False) \nTable.significant_digits(2)\nTable.covariate_order(['Intercept', 'Age', 'Age2', 'Ease_of_purchase', 'Review_rating', 'Shipping_Time', 'Time_Spent_on_Website', 'PC', 'Friends_or_Family', 'Social_Media_Advertisement'])\nTable.rename_covariates({'Ease_of_purchase':'Ease of purchase', 'Review_rating' : 'Review rating', 'Shipping_Time' : 'Shipping time', 'Time_Spent_on_Website' : 'Time spent on website', 'Friends_or_Family' : 'Friend or family', 'Search_Engine' : 'Search engine', 'Social_Media_Advertisement' : 'Social media advertisement'}) \n\nHTML(Table.render_html())\n\n\n\n#predict a customer that's not in the dataset \n#creating the variable Age2 as a column to the data frame\nClean_Webshop_Outlier['Age2'] = Clean_Webshop_Outlier['Age']**2\n\n\n\n#calculate the Age 2 for the new customer\nnew_customer_age = 35\nnew_customer_age2 = new_customer_age ** 2\n\nNew_customer = pd.DataFrame([[723,20, 3.4, 2.6, 4.5, 'Search_Engine' , 4, 35, 'PC', 0, 1, 1 , 0, 0,0, 1225]], columns = ['Time_Spent_on_Website', 'Number_of_products_browsed', 'Pictures', 'Shipping_Time', 'Review_rating', 'Find_website', 'Ease_of_purchase', 'Age', 'Device', 'Mobile', 'PC', 'Friends_or_Family', 'Other', 'Search_Engine', 'Social_Media_Advertisement', 'Age2'])\nNew_customer['Age2'] = new_customer_age2\n\nprint(MR12_model1.predict(New_customer))\n\n\n\n\n\n#Dealing with missing values\nWebshop_CSV_With_NA = Webshop_CSV.copy()\nWebshop_CSV_With_NA = pd.get_dummies(Webshop_CSV_With_NA, dummy_na=True)\n\n#removing outliers\nmask1 = Webshop_CSV_With_NA['Time_Spent_on_Website']==-999\nWebshop_CSV_With_NA = Webshop_CSV_With_NA[~mask1]\n\n#Checking for multicollinearity\ncorr_matrix1 = Webshop_CSV_With_NA.corr()\nprint(corr_matrix1)\n\n#Checking relationship time spent on website\nsns.regplot(x = 'Time_Spent_on_Website', y = 'Purchase_Amount', data = Webshop_CSV_With_NA, lowess = True)\n\n#Checking relationship number of products browsed\nsns.regplot(x = 'Number_of_products_browsed', y = 'Purchase_Amount', data = Webshop_CSV_With_NA, lowess = True)\n\n#Checking relationship pictures\nsns.regplot(x = 'Pictures', y = 'Purchase_Amount', data = Webshop_CSV_With_NA, lowess = True)\n\n#Checking relationship shipping time\nsns.regplot(x = 'Shipping_Time', y = 'Purchase_Amount', data = Webshop_CSV_With_NA, lowess = True)\n\n#Checking relationship review rating\nsns.regplot(x = 'Review_rating', y = 'Purchase_Amount', data = Webshop_CSV_With_NA, lowess = True)\n\n#Checking relationship ease of products\nsns.regplot(x = 'Ease_of_purchase', y = 'Purchase_Amount', data = Webshop_CSV_With_NA, lowess = True)\n\n#Checking relationship age\nsns.regplot(x = 'Age', y = 'Purchase_Amount', data = Webshop_CSV_With_NA, lowess = True)\n\n#polynomial transformation \nAge2 = pow(Webshop_CSV_With_NA.Age,2)\nWebshop_CSV_With_NA['Age2'] = Webshop_CSV_With_NA['Age']**2\n\n#creating a dataset with only the variables that are needed for the model\nWebshop_CSV_With_NA_Onlymodelvariables = Webshop_CSV_With_NA[['Purchase_Amount', 'Age', 'Age2', 'Time_Spent_on_Website', 'Shipping_Time', 'Review_rating', 'Ease_of_purchase', 'Device_PC', 'Find_website_Friends_or_Family', 'Find_website_Social_Media_Advertisement']].copy()\n\n# Turn the data into a MICE dataset\nimp = mice.MICEData(Webshop_CSV_With_NA_Onlymodelvariables)\nmodel = mice.MICE(\"Purchase_Amount ~ Age + Age2 + Time_Spent_on_Website + Shipping_Time + Review_rating + Ease_of_purchase + Device_PC + Find_website_Friends_or_Family + Find_website_Social_Media_Advertisement\", model_class=sm.OLS, data=imp).fit()\n\nprint(model.summary())\n\n\n\n\n","repo_name":"AzizKna/luc","sub_path":"Defenitief Moddeling assessment16;57.py","file_name":"Defenitief Moddeling assessment16;57.py","file_ext":"py","file_size_in_byte":14175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"28643213348","text":"#!/usr/bin/python3\n#encoding: UTF-8\n\nimport fileinput\nimport sys\n\nif len(sys.argv) == 1:\n    sys.argv += [\"input_25\"]\n\n#$ openssl prime 20201227\n#1343F0B (20201227) is prime\nprime = 20201227\n\ndef find_priv(sn, pub):\n    l = 1\n    v = sn\n    while v != pub:\n        v = (v * sn) % prime\n        l += 1\n    return l\n\ndef solve_p1(pub1, pub2):\n    priv1 = find_priv(7, pub1)\n    secret = pow(pub2, priv1, prime)\n    return secret\n\ndef test_p1():\n    assert solve_p1(5764801, 17807724) == 14897079\ntest_p1()\n\ndef p1():\n    lines = list(fileinput.input())\n    print(solve_p1(int(lines[0]), int(lines[1])))\np1()\n","repo_name":"flbdx/AoC_2020","sub_path":"day_25.py","file_name":"day_25.py","file_ext":"py","file_size_in_byte":606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"1019877803","text":"import pandas as pd\n\ndef singlish_feature(train: pd.Series):\n    data: pd.DataFrame = pd.DataFrame(pd.read_csv(\"data/singlish_2.csv\"), columns=['word', 'description', 'label'])\n    data = data.drop(columns=['description'])\n    dict_data:dict = data.set_index('word').to_dict()['label']\n    return train.apply(singlish_score, args=(dict_data,))\n\ndef singlish_score(sentence: str, dict_data:dict):\n    negativity: int = 0\n    for word in sentence.split():\n        negativity += int(dict_data.get(word.lower(), 0))\n    return negativity","repo_name":"CindyTsai1/CS4248-Team23","sub_path":"features/singlish.py","file_name":"singlish.py","file_ext":"py","file_size_in_byte":533,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"9530868262","text":"from schemas.bookmark import createCategorySchema,newBookmarkSchema,editBookMarkSchema,deleteBookmarkSchema,deleteCategorySchema\nfrom models.model import conn,bookmarkCategory,bookmarks\nfrom auth.authenticateHandler import decodeJWT\n\n# create Category \ndef createBookmarkCategory(request,newCategory:createCategorySchema):\n    if validateUser(request,newCategory):\n        if checkUserIdPresent(newCategory.userId):\n            try:\n                conn.execute(bookmarkCategory.insert().values(\n                    userid = newCategory.userId,\n                    categoryId = newCategory.categoryId,\n                    categoryName = newCategory.categoryName\n                ))\n                categoryData = {\n                    'categoryId':newCategory.categoryId,\n                    'categoryName':newCategory.categoryName\n                }\n                return createResponse(200,True,\"Category created\",None,categoryData)\n            except Exception as e:\n                return createResponse(500,False,\"Something went wrong!\",e)\n    return createResponse(401,False,\"Un-authorized access\",None)\n    \n# get all category for user:\ndef getUsersAllCategory(request):\n    userId = getUserIdFromAuthToken(request)\n    if userId != None:\n        if checkUserIdPresent(userId):\n            try:\n                userCategory = conn.execute(\"SELECT categoryName,categoryId FROM BMCategory WHERE userid='{0}'\".format(userId)).fetchall()\n                jsonObject = generateCategoryJsonObject(userCategory,userId)\n                return createResponse(200,True,\"Fetched..\",None,jsonObject)\n            except Exception as e:\n                return createResponse(500,False,\"Something went worng!\",e)\n    return createResponse(401,False,\"Authorization failed!\",None)\n    \n# add new bookmark\ndef createNewBookmark(request,bookmarkData:newBookmarkSchema):\n    if validateUser(request,bookmarkData):\n        if checkUserIdPresent(bookmarkData.userId):\n            try:\n                isValidCategoryId = False\n                if bookmarkData.categoryId != 'default':\n                    categoryId = conn.execute(\"SELECT categoryId FROM BMCategory WHERE categoryId='{0}' AND userid='{1}'\".format(bookmarkData.categoryId,bookmarkData.userId)).fetchone()\n                    if categoryId != None and len(categoryId):\n                        isValidCategoryId = True\n                if bookmarkData.categoryId == 'default' or isValidCategoryId:\n                    conn.execute(bookmarks.insert().values(\n                        userid = bookmarkData.userId,\n                        bookmarkId = bookmarkData.bookmarkId,\n                        categoryId = bookmarkData.categoryId,\n                        url = bookmarkData.url,\n                        label = bookmarkData.label,\n                        icon = bookmarkData.icon\n                    ))\n                    userData = {\n                        'categoryId':bookmarkData.categoryId,\n                        'bookmarkId':bookmarkData.bookmarkId,\n                        'url':bookmarkData.url,\n                        'label':bookmarkData.label,\n                        'icon':bookmarkData.icon\n                    }\n                    finalData = {\n                        \"categoryId\":bookmarkData.categoryId,\n                        \"categoryName\" : getCategoryNameWithId(bookmarkData.categoryId),\n                        \"bookmarks\":[userData]\n                    }\n                    return createResponse(200,True,\"Bookmark succssfully added!\",None,[finalData])\n                return createResponse(200,False,\"Invalid CategoryId\",None)\n            except Exception as e :\n                return createResponse(500,False,\"Something went wrong!\",e)\n    return createResponse(401,False,\"Un-authorized access\",None)\n\n# get all bookmarks for user:\ndef getUsersAllBookmark(request):\n    userId = getUserIdFromAuthToken(request)\n    if userId != None:\n        if checkUserIdPresent(userId):\n            try:\n                userCategory = conn.execute(\"SELECT categoryId FROM bookmarks WHERE userid='{0}' GROUP BY categoryId\".format(userId)).fetchall()\n                userDataObject = beautifyUserBookmarkData(userCategory,userId)\n                return createResponse(200,True,\"user data fetched!\",None,userDataObject)\n            except Exception as e:\n                return createResponse(500,False,\"Something went wrong!\",e)\n    return createResponse(401,False,\"Authorization failed!\",None)\n\n# update bookmark (Edit bookmark)\ndef updateBookmark(request,editedData:editBookMarkSchema):\n    if validateUser(request,editedData):\n        if checkUserIdPresent(editedData.userId):\n            try:\n                #check user's ownership of category and bookmark\n                isValidCategoryId = False\n                if editedData.categoryId != 'default':\n                    validUserId = conn.execute(\"SELECT userid FROM BMCategory WHERE userid='{0}' AND categoryId='{1}'\".format(editedData.userId,editedData.categoryId)).fetchone()\n                    if validUserId != None and len(validUserId):\n                        isValidCategoryId = True\n                if editedData.categoryId == 'default' or isValidCategoryId:\n                    hasOwnerShip = conn.execute(\"SELECT bookmarkId FROM bookmarks WHERE userid='{0}' AND bookmarkId='{1}'\".format(editedData.userId,editedData.bookmarkId)).fetchone()\n                    if hasOwnerShip != None and len(hasOwnerShip):\n                        conn.execute(bookmarks.update().values(\n                            categoryId = editedData.categoryId,\n                            url = editedData.url,\n                            label = editedData.label,\n                            icon = editedData.icon\n                        ).where(bookmarks.c.bookmarkId == editedData.bookmarkId))\n                        data = {\n                            'categoryId':editedData.categoryId,\n                            'bookmarkId':editedData.bookmarkId,\n                            'url':editedData.url,\n                            'label':editedData.label,\n                            'icon':editedData.icon\n                        }\n                        return createResponse(200,True,\"Bookmark Edited!\",None,data)\n                    return createResponse(401,False,\"User not have access to this bookmark!\",None)\n                return createResponse(401,False,\"User doesn't have this Category\",None)\n            except Exception as e:\n                return createResponse(500,False,\"Something went wrong!\",e)\n    return createResponse(401,False,\"Un-authorized access\",None)\n    \n# remove or delete bookmark\ndef deleteBookmarkData(request,deleteData:deleteBookmarkSchema):\n    if validateUser(request,deleteData):\n        if checkUserIdPresent(deleteData.userId):\n            try:\n                bookmarkId = conn.execute(\"SELECT bookmarkId FROM bookmarks WHERE userid='{0}' AND bookmarkId='{1}' AND categoryId='{2}'\".format(deleteData.userId,deleteData.bookmarkId,deleteData.categoryId)).fetchone()\n                if bookmarkId != None and len(bookmarkId):\n                    if deleteData.categoryId:\n                        conn.execute(\"DELETE FROM bookmarks WHERE userid='{0}' AND bookmarkId='{1}' AND categoryId='{2}'\".format(deleteData.userId,deleteData.bookmarkId,deleteData.categoryId))\n                        return createResponse(200,True,\"Bookmark Deleted!\",None)\n                return createResponse(401,False,\"Bookmark Not found or worng category ID\",None)\n            except Exception as e:\n                return createResponse(500,False,\"Something went wrong!\",e)\n    return createResponse(401,False,\"Un-authorized access\",None)\n\n# remove or delete category\ndef deleteUserCategory(request,removeCategory:deleteCategorySchema):\n    if validateUser(request,removeCategory):\n        if checkUserIdPresent(removeCategory.userId):\n            try:\n                hasOwnerShipOfCategory = conn.execute(\"SELECT userid FROM BMCategory WHERE userid='{0}' AND categoryId='{1}'\".format(removeCategory.userId,removeCategory.categoryId)).fetchone()\n                if hasOwnerShipOfCategory != None and len(hasOwnerShipOfCategory):\n                    if removeCategory.removeReferedData:\n                        conn.execute(\"DELETE FROM bookmarks WHERE userid='{0}' AND categoryId='{1}'\".format(removeCategory.userId,removeCategory.categoryId))\n                        conn.execute(\"DELETE FROM BMCategory WHERE userid='{0}' AND categoryId='{1}'\".format(removeCategory.userId,removeCategory.categoryId))\n                        return createResponse(200,True,\"Removed all the bookmark from {0}\".format(removeCategory.categoryName),None)\n                    else:\n                        conn.execute(\"UPDATE bookmarks SET categoryId='{0}' WHERE userid='{1}' AND categoryId='{2}'\".format(\"default\",removeCategory.userId,removeCategory.categoryId))\n                        conn.execute(\"DELETE FROM BMCategory WHERE userid='{0}' AND categoryId='{1}'\".format(removeCategory.userId,removeCategory.categoryId))\n                        return createResponse(200,True,\"Category removed!\",None)\n                return createResponse(401,False,\"category Not found!\",None)\n            except Exception as e:\n                return createResponse(500,False,\"Something went wrong!\",e)\n    return createResponse(401,False,\"Un-authorized access\",None)\n\n\n\n\n#------------------------------Supporting methods -------------------------\ndef checkUserIdPresent(userId) -> bool:\n    try:\n        userIdFromDB = conn.execute(\"SELECT userid FROM users WHERE userid='{0}'\".format(userId)).fetchone()\n        if userIdFromDB != None and len(userIdFromDB):\n            return True\n        return False\n    except Exception as e:\n        return False\n\n\n# validate userId with Auth token\ndef validateUser(request,data)->bool:\n    userId = getUserIdFromAuthToken(request)\n    if userId != None:\n        if userId == data.userId:\n            return True\n    return False\n\n# get userID via JWT token\ndef getUserIdFromAuthToken(request):\n    headers = request.headers\n    if \"Authorization\" in headers:\n        authToken = headers[\"Authorization\"]\n        splitToken = authToken.split(' ')\n        if len(splitToken) > 1:\n            decodedAuthToken = decodeJWT(splitToken[1])\n            if decodedAuthToken != None:\n                return decodedAuthToken[\"userId\"]\n    return None\n\n# make json object for user category:\ndef generateCategoryJsonObject(datas,userId):\n    categoryObject = []\n    for categoryName,categoryId in datas:\n        category = {\"categoryName\":categoryName,\"categoryId\":categoryId}\n        categoryObject.append(category)\n    return {\n        \"userId\":userId,\n        \"userData\":categoryObject\n    }\n\n# make user data structre and fill data\ndef beautifyUserBookmarkData(userCategory,userid):\n    data = {\n            \"userId\":userid,\n            \"userData\":getUserDataFromCategoryId(userCategory,userid)\n        }\n    return data\n\n# make category structue and fill data\ndef getUserDataFromCategoryId(userCategory,userId):\n    data = []\n    for category in userCategory:\n        categoryId = category[0]\n        data.append(\n            {\n                \"categoryId\":categoryId,\n                \"categoryName\" : getCategoryNameWithId(categoryId),\n                \"bookmarks\": getBookmarkWithCategoryId(categoryId,userId)\n            }\n        )\n    return data\n\n# get category name using category ID\ndef getCategoryNameWithId(categoryId):\n    if categoryId != None:\n        try:\n            categroyName = conn.execute(\"SELECT categoryName FROM BMCategory WHERE categoryId='{0}'\".format(categoryId)).fetchone()\n            if len(categroyName):\n                return categroyName[0]\n            return None\n        except:\n            return None\n    return None\n\n# fetch all bookmark data which contain that category ID\ndef getBookmarkWithCategoryId(categoryId,userId):\n    try:\n        bookmarkData = conn.execute(\"SELECT bookmarkId,url,label,icon FROM bookmarks WHERE categoryId='{0}' AND userid='{1}'\".format(categoryId,userId)).fetchall()\n        if bookmarkData != None and len(bookmarkData):\n            return beatifyBookmarkData(bookmarkData)\n    except Exception as e :\n        return None\n\n\n# construct bookmark object with given data\ndef beatifyBookmarkData(bookmarkData):\n    data = []\n    for bookmarkId,url,label,icon in bookmarkData:\n        data.append(\n            {\n                \"bookmarkId\":bookmarkId,\n                \"url\":url,\n                \"label\":label,\n                \"icon\":icon\n            }\n        )\n    return data\n\n\n# create common response \ndef createResponse(statusCode:int,status:bool,message:str,exception:any,userData:any = None):\n    return {\n        \"status_code\":statusCode,\n        \"status\":status,\n        \"message\":message,\n        \"exception\":str(exception),\n        \"data\":userData\n    }\n","repo_name":"yuvarajucet/bookmark-api","sub_path":"dbController/bookmarkDBController.py","file_name":"bookmarkDBController.py","file_ext":"py","file_size_in_byte":12872,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"9286436030","text":"import snake as s\nimport random\nimport sys\nimport math as m\n\nchoices = ['l', 'r', 'f']\nboard = []\nsnake = []\ndirection = ''\n\n#returns location of food, None if no food\ndef findfood():\n    global board, snake, direction\n    for i in range(len(board)):\n        for j in range(len(board[i])):\n            if(board[i][j] == 2):\n                return [i, j]\n    return None\n\n#return True if nothing in front\ndef checkfront():\n    global board, snake, direction\n    return s.checknext(board, snake, direction)\n\n#return True if nothing on left\ndef checkleft():\n    global board, snake, direction\n    if(direction == 'u'):\n        tempdir = 'l'\n    elif(direction == 'd'):\n        tempdir = 'r'\n    elif(direction == 'l'):\n        tempdir = 'd'\n    else:\n        tempdir = 'u'\n    return s.checknext(board, snake, tempdir)\n\n#return True if nothing on right\ndef checkright():\n    global board, snake, direction\n    if(direction == 'u'):\n        tempdir = 'r'\n    elif(direction == 'd'):\n        tempdir = 'l'\n    elif(direction == 'l'):\n        tempdir = 'u'\n    else:\n        tempdir = 'd'\n    return s.checknext(board, snake, tempdir)\n\ndef food_dir():\n    global board, snake, direction\n    food = findfood(board)\n    dx = food[1] - snake[0][1]\n    dy = snake[0][0] - food[0]\n    print(\"dx: \", dx, \" dy: \", dy)\n    angle = m.atan2(dy, dx)\n    print('angle: ', angle)\n    if(direction == 'l'):\n        if(angle < 0):\n            return 'l'\n        if(angle > 0):\n            return 'r'\n    if(direction == 'u'):\n        if(angle > m.pi/2 or angle < -m.pi/2):\n            return 'l'\n        if(angle < m.pi/2 and angle > -m.pi/2):\n            return 'r'\n    if(direction == 'r'):\n        if(angle > 0):\n            return 'l'\n        if(angle < 0):\n            return 'r'\n    if(direction == 'd'):\n        if(angle < m.pi/2 and angle > -m.pi/2):\n            return 'l'\n        if(angle > m.pi/2 or angle < -m.pi/2):\n            return 'r'\n    return 's'\n\n#returns L R or F as input into snake_gui\ndef generate_next_move():\n    return random.choice(choices)\n\n#used by snake_gui to provide info every round\ndef get_info(b, s, d):\n    global board, snake, direction\n    board = b\n    snake = s\n    direction = d","repo_name":"qleoz/snake-learning","sub_path":"smart_ai.py","file_name":"smart_ai.py","file_ext":"py","file_size_in_byte":2200,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"73675515766","text":"import cv2\nimport numpy as np\nimport os\n\n# Construct image.\n#-------------------------------------------------------------------------------\n\n# img = np.zeros((5, 3), dtype=np.uint8)\n# img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)\n#\n# print(img)\n# print(img.shape)\n\n# Convert image.\n#-------------------------------------------------------------------------------\n\n# img = cv2.imread('assets/graf3.png')\n# cv2.imwrite('output/graf3.jpg', img)\n\n# Convert image to grayscale.\n#-------------------------------------------------------------------------------\n\n# img = cv2.imread('assets/graf3.png', cv2.IMREAD_GRAYSCALE)\n# cv2.imwrite('output/graf3.jpg', img)\n\n# Convert random bytes into grayscale and BGR images.\n#-------------------------------------------------------------------------------\n\n# random_byte_array = bytearray(os.urandom(120000))\n# np_array = np.array(random_byte_array)\n#\n# img_gray = np_array.reshape(300, 400)\n# cv2.imwrite('output/gray_noise.png', img_gray)\n#\n# img_color = np_array.reshape(100, 400, 3)\n# cv2.imwrite('output/color_noise.png', img_color)\n\n# Maximize image blue channel.\n#-------------------------------------------------------------------------------\n\n# img = cv2.imread('assets/lena.jpg')\n# img[:, :, 0] = 255\n# cv2.imwrite('output/blue_lena.jpg', img)\n\n# Operate on region of interest.\n#-------------------------------------------------------------------------------\n\n# img = cv2.imread('assets/lena.jpg')\n# print(img.shape)\n# print(img.size)\n# print(img.dtype)\n# img[128:384, 128:384, 0] = 255\n# cv2.imwrite('output/roi_lena.jpg', img)\n\n# Convert video format.\n#-------------------------------------------------------------------------------\n\n# vidcap = cv2.VideoCapture('assets/Megamind.avi')\n# fps = vidcap.get(cv2.CAP_PROP_FPS)\n# size = (int(vidcap.get(cv2.CAP_PROP_FRAME_WIDTH)),\n#         int(vidcap.get(cv2.CAP_PROP_FRAME_HEIGHT)))\n# writer = cv2.VideoWriter(\n#     'output/MegamindOutput.mkv',\n#     cv2.VideoWriter_fourcc(*'avc1'),\n#     fps,\n#     size)\n#\n# success, frame = vidcap.read()\n# while success:\n#     writer.write(frame)\n#     success, frame = vidcap.read()\n\n# Capture video frames.\n#-------------------------------------------------------------------------------\n\n# capture = cv2.VideoCapture(0)\n# fps = capture.get(cv2.CAP_PROP_FPS)\n# size = (int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)),\n#         int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))\n# writer = cv2.VideoWriter(\n#     'output/camera_capture.mkv',\n#     cv2.VideoWriter_fourcc(*'avc1'),\n#     fps,\n#     size)\n#\n# success, frame = capture.read()\n# num_frames_remaining = 10 * fps - 1\n#\n# while success and num_frames_remaining > 0:\n#     writer.write(frame)\n#     success, frame = capture.read()\n#     num_frames_remaining -= 1\n\n# Display an image.\n#-------------------------------------------------------------------------------\n\n# img = cv2.imread('assets/lena.jpg')\n# cv2.imshow('hello lena', img)\n# cv2.waitKey()\n# cv2.destroyAllWindows()\n\n# Display camera frames.\n#-------------------------------------------------------------------------------\n\nclicked = False\ndef onMouse(event, x, y, flags, param):\n    global clicked\n    if event == cv2.EVENT_LBUTTONUP:\n        clicked = True\n\nwindowName = 'display camera frames'\n\ncapture = cv2.VideoCapture(0)\ncv2.namedWindow(windowName)\ncv2.namedWindow(windowName)\ncv2.setMouseCallback(windowName, onMouse)\n\nsuccess, frame = capture.read()\nwhile success and cv2.waitKey(1) == -1 and not clicked:\n    cv2.imshow(windowName, frame)\n    success, frame = capture.read()\n\ncv2.destroyWindow(windowName)\ncapture.release()\n\n\n\n","repo_name":"cmvanb/learning-opencv","sub_path":"scratchpad/ch2.py","file_name":"ch2.py","file_ext":"py","file_size_in_byte":3580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"19773218106","text":"from typing import Tuple, Mapping, Any, Optional, List, Dict, Set\nimport numpy as np\nimport tensorflow as tf\nimport nn\nimport random\nimport heapq\nfrom datetime import datetime\nimport dateutil.parser # (When Python 3.7 becomes available, omit this line and use datetime.fromisoformat where needed)\nfrom indexable_dict import IndexableDict\nfrom db import db\nimport cache\n\nrating_choices = [\n    (1.,'Strong','Support for a position was given that would be difficult to dismiss'),\n    (1.,'Interesting','A clever or novel point was made'),\n    (1.,'Reasonable','An appropriate, intelligent, valid, or suitable response was given'),\n    (1.,'Eloquent','An idea was elegantly and respectfully articulated'),\n    (-1.,'Impolite','Ad hominem, poisoning the well, rude, petulent, flippant, or attempts to shame'),\n    (-1.,'Burden shifting','Asks others to disprove a non-obvious claim, \"prove I\\'m wrong\"'),\n    (-1.,'Preaching','A position was expressed without justification or supporting reasons, \"trust me\", threats, or condescending tone'),\n    (-1.,'Anecdotal','Attempts to establish a trend from few examples, leans on witnesses for extraordinary events, or tries to establish guilt by association'),\n    (-1.,'Closed minded','Dismissive without a better alternative, appeals to ignorance, assumes plausible explanation are false until proven true, cherry-picks which evidence is admissible'),\n    (-1.,'Leaps','Jumps to desired conclusion from ambiguous data, circular reasoning, abandons assumptions, special pleading, or faulty analogy'),\n    (-1.,'Straw-man','Battles a misrepresentation of an opponent\\'s position, slippery slope, extremifies opponent, or tells opponent what their position is'),\n    (-1.,'Word games','Supposes terms we define govern reality, ontological arguments, defines something into existence, \"proofs\" that work for anything'),\n    (-1.,'Shirks','Appeals to popularity or authority or celebrities or traditions or preferences instead of offering reasons'),\n    (-1.,'Is-ought','jumps from values to truth or reality to morality, argues from consequences, moralistic fallacy'),\n    (-1.,'Vague conviction','Certain of something with no clear definition, conflates naming with explaining'),\n    (-1.,'Dumping','Attempts to overwhelm rather than convince, asks opponent to digest unsummarized canned content, Gish Gallop, unnecessarily strong adjectives, excessive use of formatting or CAPS'),\n    (-1.,'False choice','Presents an incomplete set of possibilities as complete, black or white thinking, take it or leave it fallacy'),\n    (-1.,'Unclear relevance','Miscategorized, off-topic, incoherent, word salad, or spam'),\n]\n\nrating_freq = [ 5 for _ in rating_choices ]\nrating_count = 5 * len(rating_choices)\n\n\nPROFILE_SIZE = 12\n\nclass Model:\n    def __init__(self) -> None:\n        self.batch_size = 64\n        self.batch_user = tf.Variable(np.zeros([self.batch_size, PROFILE_SIZE]), dtype = tf.float32)\n        self.batch_item = tf.Variable(np.zeros([self.batch_size, PROFILE_SIZE]), dtype = tf.float32)\n        self.common_layer = nn.LayerLinear(PROFILE_SIZE, len(rating_choices))\n        self.optimizer = tf.keras.optimizers.SGD(learning_rate = 1e-5)\n        self.params = self.common_layer.params\n\n    def set_users(self, user_profiles: tf.Tensor) -> None:\n        self.batch_user.assign(user_profiles)\n\n    def set_items(self, item_profiles: tf.Tensor) -> None:\n        self.batch_item.assign(item_profiles)\n\n    def act(self) -> tf.Tensor:\n        common = self.common_layer.act(self.batch_user * self.batch_item)\n        return common\n\n    def cost(self, targ: tf.Tensor, pred: tf.Tensor) -> tf.Tensor:\n        return tf.reduce_mean(tf.reduce_sum(tf.square(targ - pred), axis = 1), axis = 0)\n\n    def refine(self, y: tf.Tensor) -> None:\n        self.optimizer.minimize(lambda: self.cost(y, self.act()), self.params)\n\n    def marshal(self) -> Mapping[str, Any]:\n        return {\n                \"params\": [ p.numpy().tolist() for p in self.params ],\n            }\n\n    def unmarshal(self, ob: Mapping[str, Any]) -> None:\n        params = ob['params']\n        if len(params) != len(self.params):\n            raise ValueError('Mismatching number of params')\n        for i in range(len(params)):\n            self.params[i].assign(np.array(params[i]))\n\n\ndef fetch_user_profile(id: str) -> np.ndarray:\n    return np.array(db.get_user_profile(id)['vals'])\n\ndef store_user_profile(id: str, vals: np.ndarray) -> None:\n    db.put_user_profile(id, {'vals': vals.tolist()})\n\ndef fetch_item_profile(id: str) -> np.ndarray:\n    return np.array(db.get_item_profile(id)['vals'])\n\ndef store_item_profile(id: str, vals: np.ndarray) -> None:\n    db.put_item_profile(id, {'vals': vals.tolist()})\n\n\nclass Engine:\n    def __init__(self) -> None:\n        self.model = Model()\n        self.user_profiles: cache.Cache[str,np.ndarray] = cache.Cache(500, fetch_user_profile, store_user_profile)\n        self.item_profiles: cache.Cache[str,np.ndarray] = cache.Cache(500, fetch_item_profile, store_item_profile)\n\n        # Buffers for batch training\n        self.account_samplers = [ '' for i in range(12) ]\n        self.account_index = 0\n        self.batch_users = np.empty([self.model.batch_size, PROFILE_SIZE], dtype=np.float32)\n        self.batch_items = np.empty([self.model.batch_size, PROFILE_SIZE], dtype=np.float32)\n        self.batch_ratings = np.empty([self.model.batch_size, len(rating_choices)], dtype=np.float32)\n\n        self.banned_addresses: Set[str] = set()\n\n    def marshal(self) -> Mapping[str, Any]:\n        return {\n                'model': self.model.marshal(),\n                'rating_freq': rating_freq,\n                'rating_count': rating_count,\n                'banned_addrs': list(self.banned_addresses),\n            }\n\n    def unmarshal(self, ob: Mapping[str, Any]) -> None:\n        self.model.unmarshal(ob['model'])\n        global rating_freq\n        global rating_count\n        rating_freq = ob['rating_freq']\n        rating_count = ob['rating_count']\n        self.banned_addresses = set(ob['banned_addrs'])\n\n    def rate(self, user_id: str, item_id: str, rating: List[float]) -> None:\n        # Update the aioff rating counters for this post\n        import accounts\n        import posts\n        acc = accounts.account_cache[user_id]\n        post = posts.post_cache[item_id]\n        global rating_freq\n        global rating_count\n        try:\n            old_rating = db.get_rating(user_id, item_id)\n            acc.rating_count -= 1\n            post.undo_rating(old_rating)\n            rating_count -= 1\n            for i in range(len(old_rating)):\n                rating_freq[i] -= max(0, min(1, int(old_rating[i])))\n        except KeyError:\n            pass\n        acc.rating_count += 1\n        post.add_rating(rating)\n        rating_count += 1\n        for i in range(len(rating)):\n            rating_freq[i] += max(0, min(1, int(rating[i])))\n\n        # Add the rating to the database of samples for training the aion recommender system\n        db.put_rating(user_id, item_id, rating)\n\n        # Ensure profiles exist for the user and item\n        try:\n            self.user_profiles[user_id]\n        except KeyError:\n            self.user_profiles.add(user_id, np.random.normal(0., 0.01, PROFILE_SIZE))\n        try:\n            self.item_profiles[item_id]\n        except KeyError:\n            self.item_profiles.add(item_id, np.random.normal(0., 0.01, PROFILE_SIZE))\n        accounts.account_cache.set_modified(user_id)\n        posts.post_cache.set_modified(item_id)\n\n        # Do a little training\n        for i in range(5):\n            self.train()\n\n    # Returns ratings for the specified list of item_ids.\n    # If this account has previously rated the item, returns those ratings instead.\n    # If there is no profile for the item (because no one has ever rated it), returns [].\n    def get_ratings(self, user_id: str, item_ids: List[str]) -> List[List[float]]:\n        # Get the ratings for the items this user previously rated\n        prior_ratings = db.get_ratings_for_rated_items(user_id, item_ids)\n\n        # Predict ratings for all the items this user has never rated\n        rated = [ (item_id in prior_ratings) for item_id in item_ids ] # List of bools. True iff this account has rated the item\n        unrated = [ item_ids[i] for i in range(len(rated)) if not rated[i] ] # List of item ids that need rating\n        try:\n            user_prof = self.user_profiles[user_id]\n            can_be_rated: List[bool] = []\n            item_profs: List[np.ndarray] = []\n            for i in range(len(unrated)):\n                try:\n                    item_prof = self.item_profiles[unrated[i]]\n                    can_be_rated.append(True)\n                    item_profs.append(item_prof)\n                except KeyError:\n                    can_be_rated.append(False)\n            user_profs = [ user_prof for _ in item_profs ]\n            ratings = self.predict(user_profs, item_profs)\n        except KeyError:\n            can_be_rated = [ False for _ in unrated ]\n            ratings = []\n\n        # Combine the results\n        j = 0\n        k = 0\n        results: List[List[float]] = []\n        for i in range(len(item_ids)):\n            if rated[i]:\n                results.append(prior_ratings[item_ids[i]]) # previously rated by this user\n            else:\n                if can_be_rated[j]:\n                    results.append(ratings[k]) # predicted\n                    k += 1\n                else:\n                    results.append([]) # no data\n                j += 1\n        return results\n\n    # Performs one batch of training on the pair model\n    def train(self) -> None:\n        # Make a batch\n        samples = db.get_random_ratings(self.batch_users.shape[0])\n        if len(samples) < self.batch_users.shape[0]:\n            print(f'Skipping training because there were only {len(samples)} samples')\n            return\n        assert len(samples) == self.batch_users.shape[0], 'too many samples'\n        for i in range(len(samples)):\n            sample = samples[i]\n            self.batch_users[i] = self.user_profiles[sample[0]]\n            self.batch_items[i] = self.item_profiles[sample[1]]\n            self.batch_ratings[i] = sample[2]\n\n        # Refine\n        self.model.set_users(self.batch_users)\n        self.model.set_items(self.batch_items)\n        self.model.refine(self.batch_ratings)\n\n        # Store changes\n        updated_users = self.model.batch_user.numpy()\n        updated_items = self.model.batch_item.numpy()\n        for i in range(len(samples)):\n            sample = samples[i]\n            self.user_profiles[sample[0]] = updated_users[i]\n            self.item_profiles[sample[1]] = updated_items[i]\n\n    # Assumes the profiles for the users and items already exist\n    def predict(self, users:List[np.ndarray], items:List[np.ndarray]) -> List[List[float]]:\n        assert len(users) == len(items), 'Expected lists to have same size'\n        results: List[List[float]] = []\n        if len(users) == 0:\n            return results\n        for i in range(len(users)):\n            j = i % self.batch_users.shape[0]\n            if j == 0 and i > 0:\n                # Predict a batch\n                self.model.set_users(self.batch_users)\n                self.model.set_items(self.batch_items)\n                pred = self.model.act().numpy()\n\n                # Gather results\n                for k in range(pred.shape[0]):\n                    ratings = pred[k].tolist()\n                    results.append(ratings)\n\n            # Add to the batch\n            self.batch_users[j] = users[i]\n            self.batch_items[j] = items[i]\n\n        # Predict the final batch\n        self.model.set_users(self.batch_users)\n        self.model.set_items(self.batch_items)\n        pred = self.model.act().numpy()\n\n        # Gather results\n        remainder = len(users) % self.batch_users.shape[0]\n        if remainder == 0:\n            remainder = self.batch_users.shape[0]\n        for k in range(remainder):\n            ratings = pred[k].tolist()\n            results.append(ratings)\n        return results\n\nengine = Engine()\n","repo_name":"mikegashler/debatestuff","sub_path":"rec.py","file_name":"rec.py","file_ext":"py","file_size_in_byte":12101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"1304705832","text":"from selenium.webdriver.common.by import By\nfrom traceback import print_stack\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.common.exceptions import *\nimport utilities.custom_logger as cl\nimport logging\nimport time\nimport os\nclass SeleniumDriver():\n\n    log = cl.custom_logger(logging.DEBUG)\n\n    def __init__(self, driver):\n        self.driver = driver\n\n    def screen_shot(self, result_message):\n        \"\"\"\n        Takes screenshot of the current open web page\n        \"\"\"\n        file_name = result_message + \".\" + str(round(time.time() * 1000)) + \".png\"\n        screenshot_directory = \"../screenshots/\"\n        relative_file_name = screenshot_directory + file_name\n        current_directory = os.path.dirname(__file__)\n        destination_file = os.path.join(current_directory, relative_file_name)\n        destination_directory = os.path.join(current_directory, screenshot_directory)\n\n        try:\n            if not os.path.exists(destination_directory):\n                os.makedirs(destination_directory)\n            self.driver.save_screenshot(destination_file)\n            self.log.info(\"Screenshot save to directory: \" + destination_file)\n        except:\n            self.log.error(\"### Exception Occurred when taking screenshot\")\n            print_stack()\n\n    def get_by_type(self, locator_type):\n        locator_type = locator_type.lower()\n        if locator_type == \"id\":\n            return By.ID\n        elif locator_type == \"name\":\n            return By.NAME\n        elif locator_type == \"xpath\":\n            return By.XPATH\n        elif locator_type == \"css\":\n            return By.CSS_SELECTOR\n        elif locator_type == \"class\":\n            return By.CLASS_NAME\n        elif locator_type == \"link\":\n            return By.LINK_TEXT\n        else:\n            self.log.info(\"Locator type \" + locator_type + \" not correct/supported\")\n        return False\n\n    def get_element(self, locator, locator_type=\"id\"):\n        element = None\n        try:\n            locator_type = locator_type.lower()\n            by_type = self.get_by_type(locator_type)\n            element = self.driver.find_element(by_type, locator)\n            self.log.info(\"Element Found with locator: \" + locator + \" and locator type : \" + locator_type)\n        except:\n            self.log.info(\"Element not found\")\n        return element\n\n    def element_click(self, locator, locator_type=\"id\", element=None):\n        try:\n            if locator:\n                element = self.get_element(locator, locator_type)\n            element.click()\n            self.log.info(\"Clicked on element with locator: \" + locator + \" locatorType: \" + locator_type)\n        except:\n            self.log.info(\"Cannot click on the element with locator: \" + locator + \" locatorType: \" + locator_type)\n            print_stack()\n\n    def send_keys(self, data, locator, locator_type=\"id\"):\n        try:\n            element = self.get_element(locator, locator_type)\n            element.send_keys(data)\n            self.log.info(\"Send data on element with locator: \" + locator + \" locatorType: \" + locator_type)\n        except:\n            self.log.info(\"Cannot send data on the element with locator: \" + locator + \" locatorType: \" + locator_type)\n            print_stack()\n\n    def is_element_presence(self, locator, locator_type=\"id\"):\n        try:\n            element = self.get_element(locator, locator_type)\n            if element is not None:\n                self.log.info(\"Element Found\")\n                return True\n            else:\n                self.log.info(\"Element not found\")\n                return False\n        except:\n            self.log.info(\"Element not found\")\n            return False\n\n    def element_presence_check(self, locator, by_type):\n        try:\n            element_list = self.driver.find_elements(by_type, locator)\n            if len(element_list) > 0:\n                self.log.info(\"Element Found\")\n                return True\n            else:\n                self.log.info(\"Element not found\")\n                return False\n        except:\n            self.log.info(\"Element not found\")\n            return False\n\n    def wait_for_element(self, locator, locator_type=\"id\",\n                               timeout=10, pollFrequency=0.5):\n        element = None\n        try:\n            byType = self.get_by_type(locator_type)\n            self.log.info(\"Waiting for maximum :: \" + str(timeout) +\n                  \" :: seconds for element to be clickable\")\n            wait = WebDriverWait(self.driver, 10, poll_frequency=1,\n                                 ignored_exceptions=[NoSuchElementException,\n                                                     ElementNotVisibleException,\n                                                     ElementNotSelectableException])\n            element = wait.until(EC.element_to_be_clickable((byType,\n                                                             \"stopFilter_stops-0\")))\n            self.log.info(\"Element appeared on the web page\")\n        except:\n            self.log.info(\"Element not appeared on the web page\")\n            print_stack()\n        return element","repo_name":"pawelkuznik/woocomerce-ui-test-automation-python","sub_path":"base/selenium_driver.py","file_name":"selenium_driver.py","file_ext":"py","file_size_in_byte":5197,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"34446089562","text":"import nltk\nimport pickle\nimport argparse\nfrom collections import Counter\nfrom pycocotools.coco import COCO\n\n\nclass Vocabulary(object):\n    \"\"\"Simple vocabulary wrapper.\"\"\"\n    def __init__(self):\n        self.word2idx = {}   ##建立一个字典：与下面相反\n        self.idx2word = {}   ##字典：{x: 'word',....}\n        self.idx = 0          ##词数量\n\n    def add_word(self, word):\n        if not word in self.word2idx:\n            self.word2idx[word] = self.idx   ##添加词语\n            self.idx2word[self.idx] = word\n            self.idx += 1\n\n    def __call__(self, word):\n        if not word in self.word2idx:   ##如果词汇表里没有这些词语，那么输出['<unk>']\n            return self.word2idx['<unk>']\n        return self.word2idx[word]\n\n    def __len__(self):\n        return len(self.word2idx)    ##返回词汇长度\n\ndef build_vocab(json, threshold):\n    \"\"\"Build a simple vocabulary wrapper.\"\"\"\n    coco = COCO(json)\n    counter = Counter()#create the counter, default value is 0;\n    ids = coco.anns.keys()\n\n    # for i, id in enumerate(ids):   ##原版程序\n    #     caption = str(coco.anns[id]['caption'])#get  the caption 'sentence' of each row of coco dataset\n    #     tokens = nltk.tokenize.word_tokenize(caption.lower())#sentence tokenizer words for each captions: ['a', 'panoramic', 'view', 'of', 'a', 'kitchen', 'and', 'all', 'of', 'its', 'appliances', '.']\n    #     counter.update(tokens)#add tokens to counter: Counter({'a': 1, 'very': 1, 'clean': 1, 'and': 1, 'well': 1, 'decorated': 1, 'empty': 1, 'bathroom': 1})\n    #\n    #     if (i+1) % 1000 == 0:\n    #         print(\"[{}/{}] Tokenized the captions.\".format(i+1, len(ids)))\n    # print(ids)\n    for i, id in enumerate(ids):\n        # if i>=100:\n        #     break\n        # print(i,id)\n        caption = str(coco.anns[id]['caption'])#get  the caption 'sentence' of each row of coco dataset\n        tokens = nltk.tokenize.word_tokenize(caption.lower())#同时改为小写字母sentence tokenizer words for each captions: ['a', 'panoramic', 'view', 'of', 'a', 'kitchen', 'and', 'all', 'of', 'its', 'appliances', '.']\n        counter.update(tokens)#add tokens to counter: Counter({'a': 41137, 'very': 1, 'clean': 1, 'and': 1, 'well': 1, 'decorated': 1, 'empty': 1, 'bathroom': 1})\n\n        if (i+1) % 1000 == 0:\n            print(\"[{}/{}] Tokenized the captions.\".format(i+1, len(ids)))\n    # print (counter.items())\n    # If the word frequency is less than 'threshold', then the word is discarded.\n    words = [word for word, cnt in counter.items() if cnt >= threshold]   ###counter.items返回可以遍历的值 word=词汇，cnt=数量\n    # print (words)   #过滤小于频数的词汇\n    # Create a vocab wrapper and add some special tokens.\n    vocab = Vocabulary()  ##创建一个类\n    vocab.add_word('<pad>')   ##添加词汇\n    vocab.add_word('<start>')\n    vocab.add_word('<end>')\n    vocab.add_word('<unk>')\n\n    ## Add the words to the vocabulary.\n    ## idx2word={0: '<pad>', 1: '<start>', 2: '<end>', 3: '<unk>', 4: 'a', 5: 'very', 6: 'clean', 7: 'and',....}\n    ## word2idx ={'<pad>':0,'<start>':1,...}\n\n    # for i, word in enumerate(words):  ##原版\n    #     vocab.add_word(word)\n    # return vocab\n\n    for i, word in enumerate(words):\n        vocab.add_word(word)\n    return vocab\ndef main(args):\n    vocab = build_vocab(json=args.caption_path, threshold=args.threshold)\n    vocab_path = args.vocab_path\n    with open(vocab_path, 'wb') as f:\n        pickle.dump(vocab, f)   ##将对象封装到文件中\n    print(\"Total vocabulary size: {}\".format(len(vocab)))\n    print(\"Saved the vocabulary wrapper to '{}'\".format(vocab_path))\n    # print(vocab.idx2word)\n    # print(vocab.idx)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--caption_path', type=str,\n                        default='D:/data/coco2017/annotations/captions_val2017.json',\n                        help='path for train annotation file')\n    parser.add_argument('--vocab_path', type=str, default='data2/vocab.pkl',  # './data/vocab.pkl'' is error\n                        help='path for saving vocabulary wrapper')\n    # parser.add_argument('--caption_path', type=str,\n    #                     default= 'E:/datasets/COCO2014-2015/annotations/captions_train2014.json',\n    #                     help='path for train annotation file')    ###原版\n    # parser.add_argument('--vocab_path', type=str, default='data/vocab.pkl', #'./data/vocab.pkl'' is error\n    #                     help='path for saving vocabulary wrapper')\n    parser.add_argument('--threshold', type=int, default=4, \n                        help='minimum word count threshold')\n    args = parser.parse_args()\n    main(args)","repo_name":"kren77/Generate-captions-from-images","sub_path":"build_vocab.py","file_name":"build_vocab.py","file_ext":"py","file_size_in_byte":4738,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"12898350140","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\ntest_cow\n------------\n\nTests for `cow` models module.\n\"\"\"\nfrom django.test import TestCase\n\nfrom cow import models\n\n\nclass TestCow(TestCase):\n    def test_page_simple(self):\n        # stupid test to touch code/models\n        page = models.Page.objects.create(name='test1')\n        page.delete()\n\n    def test_menu_simple(self):\n        # create a page and point at it with model\n        page = models.Page.objects.create(name='test1')\n        child = models.Menu.objects.create(title='title1', page=page)\n        parent = models.Menu.objects.create(title='title1')\n        parent.children.add(child)\n        parent.save()\n        self.assertTrue(parent.children.count() > 0)\n        parent.delete()\n        child.delete()\n        page.delete()\n\n    def test_plugins(self):\n        # create a text plugin, apply to page, and retrieve\n        text_plugin_content = 'yo'\n        page = models.Page.objects.create(name='test1')\n        text_plugin = models.TextPlugin.objects.create(content=text_plugin_content)\n        plugin = models.Plugin.objects.create(content_object=text_plugin)\n        page.plugins.add(plugin)\n        self.assertTrue(page.plugins.count() > 0)\n        self.assertEquals(page.plugins.first().content_object.content, text_plugin_content)\n        text_plugin.delete()\n        plugin.delete()\n        page.delete()\n","repo_name":"narfman0/cow","sub_path":"cow/tests/test_cow_models.py","file_name":"test_cow_models.py","file_ext":"py","file_size_in_byte":1383,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"149754334","text":"# builtin imports\nimport hashlib\nimport logging\nimport stat\nimport threading\nimport time\n\nfrom pathlib import Path\nfrom typing import List, Union\n\n# Third party imports\nfrom libpastis import ClientAgent, BinaryPackage, SASTReport\nfrom libpastis.types import CheckMode, CoverageMode, ExecMode, FuzzingEngineInfo, SeedInjectLoc, SeedType, State, \\\n                            LogLevel, AlertData, FuzzMode\n\n\n# Local imports\nimport pastisaflpp\nfrom pastisaflpp.replay import Replay\nfrom pastisaflpp.aflpp import AFLPPProcess\nfrom pastisaflpp.workspace import Workspace\n\n\n# Inotify logs are very talkative, set them to ERROR\nfor logger in (logging.getLogger(x) for x in [\"watchdog.observers.inotify_buffer\", 'watchdog.observers', \"watchdog\"]):\n    logger.setLevel(logging.ERROR)\n\n\nclass AFLPPDriver:\n\n    def __init__(self, agent: ClientAgent, telemetry_frequency: int = 30):\n\n        # Internal objects\n        self._agent = agent\n        self.workspace = Workspace()\n        self.aflpp = AFLPPProcess()\n\n        # Parameters received through start_received\n        self.__exec_mode = None   # SINGLE_RUN, PERSISTENT\n        self.__check_mode = None  # CHECK_ALL, ALERT_ONLY\n        self.__seed_inj = None    # STDIN or ARGV\n        self.__report = None      # Klocwork report if supported\n\n        # Target data\n        self.__package = None\n        self.__target_args = None  # Kept for replay\n\n        self.__setup_agent()\n\n        # Configure hookds on workspace\n        self.workspace.add_creation_hook(self.workspace.corpus_dir, self.__send_seed)\n        self.workspace.add_creation_hook(self.workspace.crash_dir, self.__send_crash)\n        self.workspace.add_file_modification_hook(self.workspace.stats_dir, self.__send_telemetry)\n\n        # Telemetry frequency\n        self._tel_frequency = telemetry_frequency\n        self._tel_last = time.time()\n\n        # Runtime data\n        self._tot_seeds = 0\n        self._seed_recvs = set()  # Seed received to make sure NOT to send them back\n\n        # Variables for replay\n        self._replay_thread = None\n        self._queue_to_send = []\n        self._started = False\n\n    @staticmethod\n    def hash_seed(seed: bytes):\n        return hashlib.md5(seed).hexdigest()\n\n    def start(self, package: BinaryPackage, argv: List[str], exmode: ExecMode, fuzzmode: FuzzMode, seed_inj: SeedInjectLoc, engine_args: str):\n        # Write target to disk.\n        self.__package = package\n        self.__target_args = argv\n\n        self.workspace.start()  # Start looking at directories\n\n        logging.info(f\"Start process (injectloc: {seed_inj.name})\")\n        self.aflpp.start(str(package.executable_path.absolute()),\n                         argv,\n                         self.workspace,\n                         exmode,\n                         fuzzmode,\n                         seed_inj == SeedInjectLoc.STDIN,\n                         engine_args,\n                         str(package.cmplog.absolute()) if package.cmplog else None,\n                         str(package.dictionary.absolute()) if package.dictionary else None)\n        self._started = True\n\n        # Start the replay worker (note that the queue might already have started to be filled by agent thread)\n        self._replay_thread = threading.Thread(target=self.replay_worker, daemon=True)\n        self._replay_thread.start()\n\n    def stop(self):\n        self.aflpp.stop()\n        self.workspace.stop()\n        self._started = False  # should stop the replay thread\n\n    def replay_worker(self):\n        while True:\n            if not self._started:\n                break  # Break when the fuzzer stops\n            if self._queue_to_send:\n                filename, res = self._queue_to_send.pop(0)\n                if not self.__check_seed_alert(filename, is_crash=res):\n                    break\n            time.sleep(0.05)\n\n    @property\n    def started(self):\n        return self._started\n\n    def add_seed(self, seed: bytes):\n        seed_path = self.workspace.dynamic_input_dir / f\"seed-{hashlib.md5(seed).hexdigest()}\"\n        seed_path.write_bytes(seed)\n\n    def init_agent(self, remote: str = \"localhost\", port: int = 5555):\n        self._agent.register_start_callback(self.start_received)  # Register start because launched manually (not by pastisd)\n        self._agent.connect(remote, port)\n        self._agent.start()\n        # Send initial HELLO message, whick will make the Broker send the START message.\n        self._agent.send_hello([FuzzingEngineInfo(\"AFLPP\", pastisaflpp.__version__, \"aflppbroker\")])\n\n    def run(self):\n        self.aflpp.wait()\n\n    def __setup_agent(self):\n        # Register callbacks.\n        self._agent.register_seed_callback(self.__seed_received)\n        self._agent.register_stop_callback(self.__stop_received)\n\n    def __send_seed(self, filename: Path):\n        self.__send(filename, SeedType.INPUT)\n\n    def __send_crash(self, filename: Path):\n        # Skip README file that AFL adds to the crash folder.\n        if filename.name != 'README.txt':\n            self.__send(filename, SeedType.CRASH)\n\n    def __send(self, filename: Path, typ: SeedType):\n        self._tot_seeds += 1\n        file = Path(filename)\n        raw = file.read_bytes()\n        h = self.hash_seed(raw)\n        logging.debug(f'[{typ.name}] Sending new: {h} [{self._tot_seeds}]')\n        if h not in self._seed_recvs:\n            self._agent.send_seed(typ, raw)\n        else:\n            logging.info(\"seed (previously sent) do not send it back\")\n        self._queue_to_send.append((filename, True if typ == SeedType.CRASH else False))\n\n    def __check_seed_alert(self, filename: Path, is_crash: bool) -> bool:\n        p = Path(filename)\n        # Only rerun the seed if in alert only mode and a SAST report was provided\n        if self.__check_mode == CheckMode.ALERT_ONLY and self.__report:\n\n            # Rerun the program with the seed\n            run = Replay.run(self.__package.executable_path.absolute(), self.__target_args, stdin_file=filename, timeout=5, cwd=str(self.workspace.target_dir))\n\n            # FIXME: Do same checks for AFL++ LOOP stuff for persistency mode\n            # if run.is_hf_iter_crash():\n            #     self.dual_log(LogLevel.ERROR, f\"Disable replay engine (because code uses HF_ITER)\")\n            #     return False\n\n            # Iterate all covered alerts\n            for id in run.alert_covered:\n                alert = self.__report.get_alert(id)\n                if not alert.covered:\n                    alert.covered = True\n                    logging.info(f\"New alert covered {alert} [{alert.id}]\")\n                    self._agent.send_alert_data(AlertData(alert.id, alert.covered, False, p.read_bytes()))\n\n            # Check if the target has crashed and if so tell the broker which one\n            if run.has_crashed() or run.is_asan_without_crash():  # Also consider ASAN warning as detection\n                if not run.crashing_id:\n                    self.dual_log(LogLevel.WARNING, f\"Crash on {filename.name} but can't link it to a Klocwork alert (maybe bonus !)\")\n                else:\n                    alert = self.__report.get_alert(run.crashing_id)\n                    if not alert.validated:\n                        alert.validated = True\n                        bugt, aline = run.asan_info()\n                        self.dual_log(LogLevel.INFO, f\"AFLPP new alert validated {alert} [{alert.id}] ({aline})  (asan no crash: {run.is_asan_without_crash()})\")\n                        self._agent.send_alert_data(AlertData(alert.id, alert.covered, alert.validated, p.read_bytes()))\n            else:\n                if is_crash:\n                    self.dual_log(LogLevel.WARNING, f\"crash not reproducible by rerunning seed: {filename.name}\")\n\n            if run.has_hanged():  # AFLPP does not stores 'hangs' it will have been sent as corpus or crash\n                self.dual_log(LogLevel.WARNING, f\"Seed {filename} was hanging in replay\")\n        return True\n\n    def __send_telemetry(self, filename: Path):\n        if filename.name != AFLPPProcess.STAT_FILE:\n            return\n\n        now = time.time()\n        if now < (self._tel_last + self._tel_frequency):\n            return\n        self._tel_last = now\n\n        logging.debug(f'[TELEMETRY] Stats file updated: {filename}')\n\n        with open(filename, 'r') as stats_file:\n            try:\n                stats = {}\n                for line in stats_file.readlines():\n                    k, v = line.strip('\\n').split(':')\n                    stats[k.strip()] = v.strip()\n\n                state = State.RUNNING\n                last_cov_update = int(stats['last_update'])\n                total_exec = int(stats['execs_done'])\n                exec_per_sec = int(float(stats['execs_per_sec']))\n                timeout = int(stats['unique_hangs']) if 'unique_hangs' in stats else None # N/A in AFL-QEMU.\n                coverage_edge = int(stats['total_edges'])\n                cycle = int(stats['cycles_done'])\n                coverage_path = int(stats['paths_total']) if 'paths_total' in stats else None # N/A in AFL-QEMU.\n\n                # NOTE: `coverage_block` does not apply for AFLPP.\n                self._agent.send_telemetry(state=state,\n                                           exec_per_sec=exec_per_sec,\n                                           total_exec=total_exec,\n                                           cycle=cycle,\n                                           timeout=timeout,\n                                           coverage_edge=coverage_edge,\n                                           coverage_path=coverage_path,\n                                           last_cov_update=last_cov_update)\n            except:\n                logging.error(f'Error retrieving stats!')\n\n    def start_received(self, fname: str, binary: bytes, engine: FuzzingEngineInfo, exmode: ExecMode, fuzzmode: FuzzMode, chkmode: CheckMode,\n                       _: CoverageMode, seed_inj: SeedInjectLoc, engine_args: str, argv: List[str], sast_report: str = None):\n        logging.info(f\"[START] bin:{fname} engine:{engine.name} exmode:{exmode.name} seedloc:{seed_inj.name} chk:{chkmode.name}\")\n        if self.started:\n            self._agent.send_log(LogLevel.CRITICAL, \"Instance already started!\")\n            return\n\n        if engine.name != \"AFLPP\":\n            logging.error(f\"Wrong fuzzing engine received {engine.name} while I am Honggfuzz\")\n            self._agent.send_log(LogLevel.ERROR, f\"Invalid fuzzing engine received {engine.name} can't do anything\")\n            return\n        if engine.version != pastisaflpp.__version__:\n            logging.error(f\"Wrong fuzzing engine version {engine.version} received\")\n            self._agent.send_log(LogLevel.ERROR, f\"Invalid fuzzing engine version {engine.version} do nothing\")\n            return\n\n        # Retrieve package out of the binary received\n        try:\n            package = BinaryPackage.from_binary(fname, binary, self.workspace.target_dir)\n        except FileNotFoundError:\n            logging.error(\"Invalid package received\")\n            return\n        except ValueError:\n            logging.error(\"Invalid package received\")\n            return\n\n        if sast_report:\n            logging.info(\"Loading SAST report\")\n            self.__report = SASTReport.from_json(sast_report)\n\n        self.__check_mode = chkmode  # CHECK_ALL, ALERT_ONLY\n\n        self.start(package, argv, exmode, fuzzmode, seed_inj, engine_args)\n\n    def __seed_received(self, typ: SeedType, seed: bytes):\n        h = self.hash_seed(seed)\n        logging.info(f\"[SEED] received  {h} ({typ.name})\")\n        self._seed_recvs.add(h)\n        self.add_seed(seed)\n\n    def __stop_received(self):\n        logging.info(f\"[STOP]\")\n\n        self.stop()\n\n    def dual_log(self, level: LogLevel, message: str) -> None:\n        \"\"\"\n        Helper function to log message both in the local log system and also\n        to the broker.\n\n        :param level: LogLevel message type\n        :param message: string message to log\n        :return: None\n        \"\"\"\n        mapper = {LogLevel.DEBUG: \"debug\",\n                  LogLevel.INFO: \"info\",\n                  LogLevel.CRITICAL: \"critical\",\n                  LogLevel.WARNING: \"warning\",\n                  LogLevel.ERROR: \"error\"}\n        log_f = getattr(logging, mapper[level])\n        log_f(message)\n        self._agent.send_log(level, message)\n\n    def add_initial_seed(self, file: Union[str, Path]):\n        p = Path(file)\n        logging.info(f\"add initial seed {file.name}\")\n        # Write seed to disk.\n        seed_path = self.workspace.input_dir / p.name\n        seed_path.write_bytes(p.read_bytes())\n\n    def aflpp_available(self):\n        return self.aflpp.aflpp_environ_check()\n","repo_name":"quarkslab/pastis","sub_path":"engines/pastis-aflpp/pastisaflpp/driver.py","file_name":"driver.py","file_ext":"py","file_size_in_byte":12716,"program_lang":"python","lang":"en","doc_type":"code","stars":146,"dataset":"github-code","pt":"76"}
+{"seq_id":"25875303016","text":"import requests\nfrom bs4 import BeautifulSoup\nfrom loguru import logger\nimport re\nimport csv\n\n\nclass Site_parser(object):\n\n    def __init__(self):\n        self.base_url = 'https://svarog-rf.ru/'\n        self.headers = requests.utils.default_headers()\n        self.headers.update({'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/93.0.4577.63 Safari/537.36'})\n\n\n    def __get_soup(self, url_end):\n        logger.debug(self.base_url + url_end)\n        response = requests.get(self.base_url + url_end, headers=self.headers)\n        if response.status_code == 200:\n            return BeautifulSoup(response.text, 'html.parser')\n        else:\n            return False\n\n\n    def _get_categori_link(self):\n        url_end = 'accessories'\n        soup = self.__get_soup(url_end)\n        rezalt = soup.find('div', class_='view-content').find_all('span', class_='field-content')\n        return rezalt\n\n\n    def _get_produkt_urls(self, url):\n        soup = self.__get_soup(url)\n        rezalt = soup.find('div', class_='view-products').find_all('h3')\n        #logger.debug(rezalt)\n        produkt_urls = []\n\n        for item in rezalt:\n            produkt_urls.append(item.find('a').get('href'))\n\n        return produkt_urls\n\n    def _get_produkt_info(self, url, cat_name):\n        soup = self.__get_soup(url)\n        pr = soup.find('div', id='col1')\n\n        produkt_name = pr.find('h1').get_text()\n        articul =   pr.find('div', class_='field field-name-field-art field-type-text field-label-inline clearfix').\\\n            findNext('div', class_='field-item even').get_text()\n\n        produkt_price = str(pr.find('div', class_='field field-name-field-price field-type-number-integer field-label-above').\\\n            findNext('div', class_='field-item even').get_text())\n\n        logger.debug(f\"{produkt_name}  {articul}  {produkt_price}  {'ПРОДУКЦИЯ, ТМ СВАРОГ, Сопутствующие товары, ' + str(cat_name)} \")\n\n        # prod_discr = prod_discr.replace(short_prod_discr, '.')\n\n        return produkt_name, articul, produkt_price\n\n    def have_thise_produkt_or_not(self, url):\n        soup = self.__get_soup(url)\n        rezalt = soup.find('div', class_='view-empty')\n        logger.debug(rezalt)\n        if rezalt:\n            logger.debug(\"keine Produkte\")\n            return False\n\n        else:\n            logger.debug(\"es gibt Ware\")\n            return True\n\n\n\n\n\n\ndef write_items_in_file(data, path):\n    name = ['Имя', 'Артикул', 'Базовая цена']\n    with open (path, 'w', newline='', encoding=\"UTF-8\") as file:\n       print(' ; '.join(str(l) for l in name), file=file, sep=\"\\n\")\n       for produkt in data:\n           print(' ; '.join(str(l) for l in produkt), file=file, sep=\"\\n\")\n\n\n\n\n\n\n\n\ndef start():\n    pars = Site_parser()\n    ul = pars._get_categori_link()\n    logger.debug(ul)\n    i = 0\n    all_produkts = []\n    for item in ul:\n        logger.info(item.find('a').get('href'))\n        logger.info(item.find('a').get_text())\n        i += 1\n        logger.warning(item.find('a').get('href'))\n        pagination_num = 0\n        url_prod_pages = item.find('a').get('href') + f\"?page={pagination_num}\"\n        logger.debug(url_prod_pages)\n\n        while pars.have_thise_produkt_or_not(url_prod_pages):\n            produkt_urls = pars._get_produkt_urls( url_prod_pages)\n\n            for prod_url in produkt_urls:\n                all_produkts.append(pars._get_produkt_info(prod_url, item.find('a').get_text()) )\n\n            pagination_num += 999999999999999991\n            url_prod_pages = item.find('a').get('href') + f\"?page={pagination_num}\"\n\n\n        if i>999999999999991:\n                break\n\n\n    logger.debug(all_produkts)\n    write_items_in_file(all_produkts, 'rezalt/priceUpdate_accessoriese.csv')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nstart()","repo_name":"brash-alex/Parser_Svarka_2","sub_path":"Price_Parser-accessoriese.py","file_name":"Price_Parser-accessoriese.py","file_ext":"py","file_size_in_byte":3842,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"39300527976","text":"## importing streamlit\r\nimport streamlit as st\r\n## Loading .env file and api keys\r\nimport os\r\nfrom dotenv import load_dotenv, find_dotenv\r\nload_dotenv(find_dotenv(), override=True)\r\n\r\n\r\n#### Steps:-\r\n## 1. importing chat models from openai\r\n## 2. Adding the memory to the custom_chatgpt app, so that we can chat and ask folow up question of the previous one\r\n## 3. Adding chat history in the app\r\n\r\nfrom langchain.chat_models import ChatOpenAI\r\nfrom langchain.schema import(\r\n    AIMessage, SystemMessage, HumanMessage\r\n)\r\nfrom langchain.chains import LLMChain\r\nfrom langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate\r\n\r\n## imports for memory\r\nfrom langchain.prompts import MessagesPlaceholder\r\nfrom langchain.memory import ConversationBufferMemory, FileChatMessageHistory\r\n\r\n## ------------------------------------------------------------------------------------------------------\r\ndef ask_chatBot(content):\r\n    myllm = ChatOpenAI(model_name = 'gpt-3.5-turbo', temperature = 1)\r\n\r\n    ## chat history storing\r\n    myChatHistory = FileChatMessageHistory('chat_history.json')\r\n\r\n    ## memory for the chatmodel\r\n    mymemory = ConversationBufferMemory(\r\n        memory_key = 'chat_history',\r\n        chat_memory = myChatHistory,\r\n        return_messages= True\r\n    )\r\n\r\n    myprompt = ChatPromptTemplate(\r\n        input_variables=[\"content\"],\r\n        messages = [\r\n            SystemMessage(content=\"you are a chatbot having a conversation with a human\"),\r\n            MessagesPlaceholder(variable_name = 'chat_history'), ## This is where the memory will be stored and it should be placed after SystemMessage and before HumanMessagePromptTemplate\r\n            HumanMessagePromptTemplate.from_template('{content}')\r\n        ]\r\n    )\r\n\r\n    chain = LLMChain(\r\n        llm = myllm,\r\n        prompt = myprompt,\r\n        ##adding memory\r\n        memory = mymemory,\r\n        verbose=False)\r\n    \r\n    myresponse = chain.run({'content': content})\r\n    return myresponse\r\n#     #### ------ Creating infinite while loop not to end the conversation between the chatbot and the user -------\r\n\r\n# while True:\r\n#         content = input(\"PLease ask your query: \")\r\n#         loopBreakList = ['quit', 'close', 'exit', 'bye']\r\n#         ## If user enters any word word which matches with the loopBreakLst list, caht will be stooped\r\n#         if content in loopBreakList:\r\n#             print('Goodbye, have a great day!')\r\n#             break\r\n#         ## Else chat will return the response from the LLm, and it will continue\r\n#         else:\r\n#             response = chain.run({'content': content})\r\n#             print(response)\r\n#             print('*' * 50)\r\n\r\n\r\n## Initializing streamlit app\r\nst.set_page_config(page_title=\"Custom Chatbot\")\r\nst.header(\"Langchain Chatbot Application\")\r\n## Taking the input from user\r\ninput = st.text_input(\"Input: \", key=\"input\")\r\nresponse = ask_chatBot(input)\r\n\r\nsubmit = st.button(\"Ask your queston\")\r\n\r\n## If ask button is clicke\r\nif submit:\r\n    st.subheader(\"The Response is: \")\r\n    st.write(response)\r\n\r\n###### For running the code -------> streamlit run app.py","repo_name":"ArunavoBasu/Langchain_OpenAI_Application","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3116,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"6869527518","text":"import os\nimport pandas as pd\nfrom pandas import DataFrame\nfrom glob import glob\n\n\ndef get_one_result(path):\n    # data = pd.read_excel(\"result.xlsx\", sheet_name='Sheet1')\n    # data = {'name':['fly','yang'], 'age':[25,14]}\n    files = glob('/home/dl/cf/reid_gan/log/*/*')\n    print(len(files))\n    name = []\n    epoc = []\n    val_loss = []\n    val_acc = []\n    rank_1 = []\n    rank_5 = []\n    rank_10 = []\n    map_ = []\n    rerank_1 = []\n    rerank_5 = []\n    rerank_10 = []\n    remap = []\n    ''' \n        train Loss: 0.0018 Acc: 0.9946\n        val Loss: 0.0138 Acc: 0.9174\n        Training complete in 140m 59s\n        Best val epoch: 84\n        Best val Loss: 0.0139  Acc: 0.924101\n        -------test-----------\n        top1:0.918349 top5:0.971793 top10:0.985451 mAP:0.789647\n        calculate initial distance\n        Reranking complete in 1m 4s\n        top1:0.931413 top5:0.965855 top10:0.977138 mAP:0.904607\n    '''\n    for file in files:\n        print(file)\n        f = open(file, 'r')\n        r = f.readlines()\n        # print(file.split('/')[-2]+'/'+file.split('/')[-1])\n        if len(r) < 10:\n            print('len(r) = %s' % len(r))\n            continue\n        if 'Best val epoch' not in r[-7]:\n            print('Best val epoch not in r[-7]')\n            continue\n        if 'Best val Loss' not in r[-6]:\n            print('Best val Loss not in r[-6]')\n            continue\n        if 'top1:' not in r[-4]:\n            print('top1: not in r[-4]')\n            continue\n        if 'top1:' not in r[-1]:\n            print('top1: not in r[-4]')\n            continue\n\n        name.append(file.split('/')[-2] + '/' + file.split('/')[-1])\n        if 'Best val epoch' in r[-7]:\n            # print(r[-7].split(':')[-1].strip())\n            epoc.append(r[-7].split(':')[-1].strip())\n        if 'Best val Loss' in r[-6]:\n            # print(r[-6].split(':')[1].strip().split('A')[0].strip())\n            # print(r[-6].split(':')[2].strip())\n            val_loss.append(r[-6].split(':')[1].strip().split('A')[0].strip())\n            val_acc.append(r[-6].split(':')[2].strip())\n        if 'top1:' in r[-4]:\n            # print(r[-4].split(':')[1].split('t')[0].strip())\n            # print(r[-4].split(':')[2].split('t')[0].strip())\n            # print(r[-4].split(':')[3].split('m')[0].strip())\n            # print(r[-4].split(':')[4].strip())\n            rank_1.append(r[-4].split(':')[1].split('t')[0].strip())\n            rank_5.append(r[-4].split(':')[2].split('t')[0].strip())\n            rank_10.append(r[-4].split(':')[3].split('m')[0].strip())\n            map_.append(r[-4].split(':')[4].strip())\n        if 'top1:' in r[-1]:\n            # print(r[-1].split(':')[1].split('t')[0].strip())\n            # print(r[-1].split(':')[2].split('t')[0].strip())\n            # print(r[-1].split(':')[3].split('m')[0].strip())\n            # print(r[-1].split(':')[4].strip())\n            rerank_1.append(r[-1].split(':')[1].split('t')[0].strip())\n            rerank_5.append(r[-1].split(':')[2].split('t')[0].strip())\n            rerank_10.append(r[-1].split(':')[3].split('m')[0].strip())\n            remap.append(r[-1].split(':')[4].strip())\n\n    data = {'name': name, 'epoc': epoc, 'val_loss': val_loss, 'val_acc': val_acc, 'rank_1': rank_1, 'rank_5': rank_5,\n            'rank_10': rank_10, 'map': map_, 'rerank_1': rerank_1, 'rerank_5': rerank_5, 'rerank_10': rerank_10,\n            'remap': remap}\n    print(data)\n    frame = DataFrame(data)\n    print(frame)\n    frame.to_excel('/home/dl/cf/reid_gan/log/result.xlsx')\n\n\nif __name__ == '__main__':\n    path = '/home/dl/cf/reid_gan/log/log_0'\n    get_one_result(path)\n","repo_name":"flychen321/reid_gan","sub_path":"get_experimental_result.py","file_name":"get_experimental_result.py","file_ext":"py","file_size_in_byte":3624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"4345968650","text":"# coding=utf-8\nimport sys\nfrom pages.course_model_task import CourseTask\nfrom models import myunit\n\n\nclass CourseTaskTest(myunit.MyTest):\n    u\"\"\"4.0PC模板课程-实训任务-创建\"\"\"\n    sys.setrecursionlimit(150000)\n    # upload_time = 100  # 文件上传默认等待最长时间，文件过大时需增大\n    # file_url = 'D:\\\\test_input.txt'\n    # file_url2 = 'D:\\\\50m.pdf'\n    # coured_id = 'feac75aa-537b-3cb6-99c4-3e269747d262'\n\n    def test0001_create_sucess001(self):\n        u\"\"\"4.0PC模板课程-实训任务-不启用分组任务创建成功：\"\"\"\n        create = CourseTask(self.driver)\n        create.login()\n        create.enter_course_task_page()\n        create.create_task(type_select='不启用分组')\n\n    def test0001_create_sucess002(self):\n        u\"\"\"4.0PC模板课程-实训任务-启用分组任务创建成功：\"\"\"\n        create = CourseTask(self.driver)\n        create.login()\n        create.enter_course_task_page()\n        create.create_task(type_select='启用分组')\n\n    def test0002_create_fail001(self):\n        u\"\"\"4.0PC模板课程-实训任务-创建失败：任务标题不能为空\"\"\"\n        alert_info = \"任务标题不能为空\"\n        create = CourseTask(self.driver)\n        create.login()\n        create.enter_course_task_page()\n        create.create_task(title='')\n        self.assertIn(alert_info, create.create_errors_info())\n\n    def test0002_create_fail002(self):\n        u\"\"\"4.0PC模板课程-实训任务-创建失败：任务目标不能为空：\"\"\"\n        alert_info = \"任务目标不能为空\"\n        create = CourseTask(self.driver)\n        create.login()\n        create.enter_course_task_page()\n        create.create_task(goal='')\n        self.assertIn(alert_info, create.create_errors_info())\n\n    def test0002_create_fail003(self):\n        u\"\"\"4.0PC模板课程-实训任务-创建失败：任务内容不能为空：\"\"\"\n        alert_info = \"任务内容不能为空\"\n        create = CourseTask(self.driver)\n        create.login()\n        create.enter_course_task_page()\n        create.create_task(content='')\n        self.assertIn(alert_info, create.create_errors_info())\n","repo_name":"lwolffcn/wzk_python_selenium","sub_path":"PC4.0/testcase/course_task_test_wzk4.py","file_name":"course_task_test_wzk4.py","file_ext":"py","file_size_in_byte":2148,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"357844659","text":"from django.contrib.auth.models import AbstractUser\nfrom django.db import IntegrityError, models\n\n\nclass CustomUser(AbstractUser):\n    rol_choice = [\n        (\"ADMINISTRADOR\", \"ADMINISTRADOR\"),\n        (\"Coord. Proyectos\", \"Coord. Proyectos\"),\n        (\"Administración\", \"Coord. Administración\"),\n        (\"Contaduría Pública\", 'Coord. Contaduría Pública'),\n        (\"Turismo\", 'Coord.Turismo'),\n        (\"Agroalimentación\", 'Coord. Agroalimentación'),\n        (\"Informática\", 'Coord. Informática'),\n        (\"Construcción Civil\", 'Coord. Construcción Civil'),\n        (\"Procesamiento y Distribución de alimentos\", 'Coord. Proc. y Dist. de alimentos'),\n        (\"Terapia Ocupacional\", 'Coord. Terapia Ocupacional'),\n        (\"Fisioterapia\", 'Coord. Fisioterapia'),\n        (\"Medicina Veterinaria\", 'Coord. Medicina Veterinaria'),\n        (\"Enfermería Integral Comunitaria\", 'Coord. Enfermeria Int.'),\n        (\"Educación Inicial\", 'Educación Inicial'),\n\n\n    ]\n\n    rol = models.CharField(\n        max_length=50,\n        choices=rol_choice,\n        verbose_name=\"Rol\",\n        unique=True,\n        blank=False\n    )\n    \n    def save(self, *args, **kwargs):\n        if self.rol ==\"\":\n            try:\n                self.rol = \"ADMINISTRADOR\"\n                super(CustomUser, self).save(*args, **kwargs)\n            except IntegrityError:\n                print(\"Error: Ya existe un superusuario\")\n                exit()\n                \n        else:\n            super(CustomUser, self).save(*args, **kwargs)\n","repo_name":"rogeliorodriguez27/Gmayo","sub_path":"components/accounts/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1527,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"71691052084","text":"\nfrom argparse import ArgumentParser\nfrom concurrent.futures import ThreadPoolExecutor, as_completed\nfrom datasets import load_dataset\nfrom functools import partial\nimport numpy as np\nfrom pathlib import Path\nfrom tokenizers import Tokenizer\nfrom tqdm import tqdm, trange\n\n\nparser = ArgumentParser()\nparser.add_argument('--tokenizer-fp', type=str, default='tokenizer/tokenizer-wiki8192.json')\nparser.add_argument('--seq-len', type=int, default=512)\nparser.add_argument('--split', type=str, default='train')\nparser.add_argument('--chunk-size', type=int, default=1000)\n\n\n\ndef tokenize_and_split(tokenizer_fp, seq_len, batch):\n    tokenizer = Tokenizer.from_file(tokenizer_fp)\n    texts = '[SEP]'.join(batch['text'])\n    cls_token = tokenizer.token_to_id('[CLS]')\n    pad_token = tokenizer.token_to_id('[PAD]')\n    token_ids = np.array(tokenizer.encode(texts).ids, dtype=np.uint16)\n    leftover = token_ids.shape[0] % (seq_len - 1)\n    pads = np.full(((seq_len - 1) - leftover,), pad_token, dtype=np.uint16)\n    split_tokens = np.append(token_ids, pads).reshape((-1, seq_len - 1))\n    num_splits = split_tokens.shape[0]\n    extra_cls_tokens = np.full((num_splits, 1), cls_token, dtype=np.uint16)\n    fully_tokenized = np.hstack([extra_cls_tokens, split_tokens])\n    return fully_tokenized\n\n\ndef save_chunk(tokenize_fn, path, index , data):\n    token_mat = tokenize_fn(data)\n    num_rows = token_mat.shape[0]\n    fp = path / f\"chunk_{index}.npy\"\n    np.save(fp, token_mat)\n    return num_rows\n\n\ndef main(args):\n    data = load_dataset(\"wikipedia\", \"20220301.en\")[args.split]\n    fp = Path('/media/dashille/data0/wiki') / args.split\n    tokenize_fn = partial(tokenize_and_split, args.tokenizer_fp, args.seq_len)\n    save_chunk_fn = partial(save_chunk, tokenize_fn, fp)\n    num_rows = data.num_rows\n    \n    futures = []\n    chunk_size = args.chunk_size\n    with ThreadPoolExecutor(max_workers=32) as executor:\n        for chunk_no, i in enumerate(trange(0, num_rows, chunk_size)):\n            j = min(i + chunk_size, num_rows)\n            chunk = data[i:j]\n            futures.append(executor.submit(save_chunk_fn, chunk_no, chunk))\n    total_rows = 0\n    for i, fut in tqdm(enumerate(as_completed(futures))):\n        try:\n            rows = fut.result()\n        except Exception:\n            print(f'Chunk {i} failed')\n            continue\n        total_rows += rows\n        if i // 50 == 0:\n            print(f'Saved {total_rows} records')\n    \n\nif __name__ == '__main__':\n    args, _ = parser.parse_known_args()\n    main(args)\n","repo_name":"dashstander/simplex-score-matching","sub_path":"tokenize_wiki.py","file_name":"tokenize_wiki.py","file_ext":"py","file_size_in_byte":2525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"44778449436","text":"import os\nimport sys\nimport time\nimport re\nimport math\nfrom collections import deque\n\npath=\"./5/\"\nfile_list=os.listdir(path)\nfile_list_in= [file for file in file_list if file.startswith('in')]\nfile_list_out= [file for file in file_list if file.startswith('out')]\nfile_list_in.sort()\nfile_list_out.sort()\n\n\nstart = time.time()\n  \nfor file in file_list_in:\n  sys.stdin=open(path + file, 'rt')\n  n, m = map(int, input().split())\n  q=list(range(1, n+1))\n  dq=deque(q)\n  print(dq)\n  while dq:\n    for _ in range(m-1):\n      cur=dq.popleft()\n      dq.append(cur)\n    dq.popleft()\n    if len(dq)==1:\n      print(dq[0])\n      dq.popleft()\n  \nprint('실행시간 :', time.time() - start)","repo_name":"upatisariputa/python_algorithm","sub_path":"5/5-0.py","file_name":"5-0.py","file_ext":"py","file_size_in_byte":678,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"34624396138","text":"import sys\n\nn = int(sys.stdin.readline().rstrip())\nx = int(sys.stdin.readline().rstrip())\n\nlis = [[0 for i in range(n)] for i in range(n)]                                                                        # 값을 넣을 모든원소 0인 2차원 배열 생성\n\n\t\ndx = [0,1,0,-1]       #dx, dy : 다음 원소 위치(방향) 결정용 값들(중요) - 탐색 순서 :  [(1,0) : 오른쪽] -> [(0,1) : 아래] -> [(-1,0) : 왼쪽] -> [(0,-1) 위] ***\ndy = [-1,0,1,0]\n\na,b=0,0                # 현재 배열내 위치값\ni = n*n                # i : 현재 원소값\nd = 0                   # dx,dy배열에서 현재 방향 선택 값\n\nwhile i > 0:         # 원소가 0이될때까지\n\tlis[a][b] = i      # 위치 a,b에 i 삽입\n\t\n\tif a+dx[d] <0 or b+dy[d] <0 or a+dx[d] >= n or b+dy[d] >=n or lis[a+dx[d]][b+dy[d]] != 0:    #다음 위치값이 0보다 작거나 n보다 크거나, 이미 값이 들어있는 자리이면\n\t\td = (d+1)%4  #방향 변경\n\t\n\ta = a+dx[d]       # 다음 위치 a,b와 원소값 i 변경\n\tb = b+dy[d]       \n\ti-=1\n\n\nfor i in range(n):                               # 출력\n\tprint(\" \".join(map(str,lis[i])))           # int원소 -> str -> str문장\n\tif x in lis[i]:                                   # 찾는 원소값 찾으면 실행\n\t\tr1,r2 = i+1, lis[i].index(x)+1\n\t\t\nprint(r1, r2)  # 찾던 원소값 출력","repo_name":"hchayan/Data-Structure-and-Algorithms","sub_path":"(구)자료구조와 알고리즘/(구)Quizes/backjoon/back_1913.py","file_name":"back_1913.py","file_ext":"py","file_size_in_byte":1347,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"20844279345","text":"from Node import Node\n\nclass Graph(object):\n\tdef __init__(self):\n\t\tself.graph=[]\n\n\n\tdef add_Node(self, new_node):\n\t\texist=False\n\t\tfor node in self.graph:\n\t\t\tif node.name==new_node:\n\t\t\t\texist=True\n\t\tif not exist:\n\t\t\tnew_node=Node(new_node)\n\t\t\tself.graph.append(new_node)\n\n\n\tdef add_adj(self, start_node, end_node, dtime, load):\n\t\tfor node in self.graph:\n\t\t\tif start_node==node.name:\n\t\t\t\texist=False\n\t\t\t\tfor each in node.adj_node:\n\t\t\t\t\tif each['name']==end_node:\n\t\t\t\t\t\texist=True\n\t\t\t\tif not exist:\n\t\t\t\t\tfor ea in self.graph:\n\t\t\t\t\t\tif end_node==ea.name:\n\t\t\t\t\t\t\tinfo={'Node':ea,'name':end_node,'dtime':dtime,'load':load,'used':0, \"Full\": False}\n\t\t\t\t\t\t\tnode.adj_node.append(info)\n\n","repo_name":"wjy4528/Networking-Assignment2","sub_path":"Graph.py","file_name":"Graph.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"36517233472","text":"from datetime import timedelta\nfrom odoo import fields, models, api, _\nfrom odoo.exceptions import ValidationError, UserError\n\n\nclass ResCompany(models.Model):\n    _inherit = \"res.company\"\n\n    # tax_withhold_journal_id = fields.Many2one('account.journal', 'Withhold Journal')\n    # tax_withhold_received_account_id = fields.Many2one('account.account', 'DAR Received Account')\n    # tax_withhold_sent_account_id = fields.Many2one('account.account', \"DAR Sent Account\")\n\n    inss = fields.Char(\"INSS\", size=12)\n    # sale_cost_center = fields.Boolean(string=\"Sale Cost Center\")\n    invoice_cost_center = fields.Boolean(string=\"Invoice Cost Center\")\n    # stock_cost_center = fields.Boolean(string=\"Stock Cost Center\")\n    # purchase_cost_center = fields.Boolean(string=\"Purchase Cost Center\")\n\n    accounting_year = fields.Many2one(comodel_name='account.fiscal.year', string=\"Accounting Year\", required=False)\n    account_opening_date = fields.Date(string='Opening Date', related='accounting_year.date_from', required=True,\n                                       help=\"That is the date of the opening entry.\")\n\n    @api.model\n    def create_op_move_if_non_existant(self):\n        \"\"\" Creates an empty opening move in 'draft' state for the current company\n        if there wasn't already one defined. For this, the function needs at least\n        one journal of type 'general' to exist (required by account.move).\n        \"\"\"\n        self.ensure_one()\n        if not self.account_opening_move_id:\n            default_journal = self.env['account.journal'].search(\n                [('type', '=', 'general'), ('company_id', '=', self.id)], limit=1)\n\n            if not default_journal:\n                raise UserError(\n                    _(\"Please install a chart of accounts or create a miscellaneous journal before proceeding.\"))\n\n            if not self.account_opening_date: self.account_opening_date = fields.Date.context_today(self).replace(\n                month=1, day=1)\n            opening_date = self.account_opening_date - timedelta(days=1)\n\n            self.account_opening_move_id = self.env['account.move'].create({\n                'ref': _('Opening Journal Entry'),\n                'company_id': self.id,\n                'journal_id': default_journal.id,\n                'date': opening_date,\n            })\n","repo_name":"DosSantosAlberto/V16","sub_path":"ht_account_ao/models/res_company.py","file_name":"res_company.py","file_ext":"py","file_size_in_byte":2320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"20508669630","text":"import sys\nimport logging\n\nimport envi\nimport vivisect\nimport vivisect.impemu.monitor as viv_imp_monitor\n\nlogger = logging.getLogger(__name__)\n\n\ndef analyzeFunction(vw, funcva):\n    '''\n    search through all calls, looking for a call to an import named __libc_start_main\n    then check for arg0\n    '''\n    try:\n        emu = vw.getEmulator()\n        emumon = AnalysisMonitor(vw, funcva)\n\n        emu.setEmulationMonitor(emumon)\n        emu.runFunction(funcva, maxhit=1)\n\n        if not emumon.success:\n            logger.info(\"  emumon failure: %r\", vars(emumon))\n            return\n        try:\n            api = vw.getFunctionApi(emumon.startmain)\n            cconv = emu.getCallingConvention(api[vivisect.API_CCONV])\n        except:\n            ccname = vw.getMeta('DefaultCall')\n            cconv = emu.getCallingConvention(ccname)\n\n        args = cconv.getCallArgs(emu, 1)\n        mainva = args[0]\n\n        vw.addEntryPoint(mainva)\n        vw.makeFunction(mainva)\n\n        curname = vw.getName(mainva)\n        if curname in (None, \"sub_%.8x\" % mainva):\n            vw.makeName(mainva, 'main', True)\n\n    except Exception as e:\n        sys.excepthook(*sys.exc_info())\n\n\ndef analyze(vw):\n    logger.info('analyze()')\n\n    for va, name in vw.getNames():\n        lcsm = '__libc_start_main_%.8x' % va\n        if name in (lcsm, \"*.\"+lcsm):\n            for xfr, xto, xtype, xtinfo in vw.getXrefsTo(va):\n                if xtype != vivisect.REF_CODE:\n                    continue\n\n                logger.info(\"0x%x -> 0x%x (%r)\", xfr, xto, xtype)\n                funcva = vw.getFunction(xfr)\n                if funcva is None:\n                    continue\n\n                analyzeFunction(vw, funcva)\n\n\nclass AnalysisMonitor(viv_imp_monitor.AnalysisMonitor):\n\n    def __init__(self, vw, fva):\n        self.success = False\n        self.emu = None\n        self.startmain = None\n        viv_imp_monitor.AnalysisMonitor.__init__(self, vw, fva)\n\n        for va, name in vw.getNames():\n            lcsm = '__libc_start_main_%.8x' % va\n            if name in (lcsm, '*.' + lcsm):\n                self.startmain = va\n\n    def prehook(self, emu, op, starteip):\n\n        if op.iflags & envi.IF_CALL:\n            # it's a call, get the target\n            branches = [br for br in op.getBranches(emu) if not (br[1] & envi.BR_FALL)]\n            logger.debug('libc_start_main: 0x%x\\ttgts: %r', self.startmain, branches)\n\n            # check if it matches what we believe to be __libc_start_main\n            for branch in branches:\n                tgt, flags = branch\n                if tgt == self.startmain:\n                    self.success = True\n                    self.emu = emu\n                    emu.stopEmu()\n","repo_name":"maoguai/protocol_reverse","sub_path":"vivisect-master/vivisect/analysis/elf/libc_start_main.py","file_name":"libc_start_main.py","file_ext":"py","file_size_in_byte":2703,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"}
+{"seq_id":"10656030839","text":"# ##### BEGIN GPL LICENSE BLOCK #####\n#\n#  This program is free software; you can redistribute it and/or\n#  modify it under the terms of the GNU General Public License\n#  as published by the Free Software Foundation; either version 2\n#  of the License, or (at your option) any later version.\n#\n#  This program is distributed in the hope that it will be useful,\n#  but WITHOUT ANY WARRANTY; without even the implied warranty of\n#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n#  GNU General Public License for more details.\n#\n#  You should have received a copy of the GNU General Public License\n#  along with this program; if not, write to the Free Software Foundation,\n#  Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n#\n# ##### END GPL LICENSE BLOCK #####\n\n# <pep8 compliant>\n\nimport bpy\nfrom bpy.types import Panel\nfrom bl_ui.properties_render import RenderButtonsPanel\n\nRenderButtonsPanel.COMPAT_ENGINES = {'YAFA_RENDER'}\n\n\nclass YAFRENDER_PT_render(RenderButtonsPanel, Panel):\n    bl_label = \"Render\"\n\n    def draw(self, context):\n\n        layout = self.layout\n        rd = context.scene.render\n\n        row = layout.row()\n        row.operator(\"render.render_still\", text=\"Image\", icon='RENDER_STILL')\n        row.operator(\"render.render_animation\", text=\"Animation\", icon='RENDER_ANIMATION')\n        layout.row().operator(\"render.render_view\", text=\"Render 3D View\", icon='VIEW3D')\n        layout.prop(rd, \"display_mode\", text=\"Display\")\n\n\nclass YAFRENDER_PT_layers(RenderButtonsPanel, Panel):\n    bl_label = \"Layers\"\n    bl_options = {'DEFAULT_CLOSED'}\n\n    def draw(self, context):\n        layout = self.layout\n\n        scene = context.scene\n        rd = scene.render\n\n        row = layout.row()\n        if bpy.app.version < (2, 65, 3 ):\n            row.template_list(rd, \"layers\", rd.layers, \"active_index\", rows=2)\n        else:\n            row.template_list(\"RENDERLAYER_UL_renderlayers\", \"\", rd, \"layers\", rd.layers, \"active_index\", rows=2)\n\n        col = row.column(align=True)\n        col.operator(\"scene.render_layer_add\", icon='ZOOMIN', text=\"\")\n        col.operator(\"scene.render_layer_remove\", icon='ZOOMOUT', text=\"\")\n\n        row = layout.row()\n        rl = rd.layers.active\n        row.prop(rl, \"name\")\n        row.prop(rd, \"use_single_layer\", text=\"\", icon_only=True)\n\n        split = layout.split()\n\n        col = split.column()\n        col.prop(scene, \"layers\", text=\"Scene\")\n        # TODO: Implement material override\n        #col.prop(rl, \"material_override\", text=\"Material\")\n\n        col = split.column()\n        # TODO: Implement render layers\n        #col.prop(rl, \"layers\", text=\"Layer\")\n\n\nclass YAFRENDER_PT_dimensions(RenderButtonsPanel, Panel):\n    bl_label = \"Dimensions\"\n    bl_options = {'DEFAULT_CLOSED'}\n\n    def draw(self, context):\n        layout = self.layout\n\n        scene = context.scene\n        rd = scene.render\n\n        row = layout.row(align=True)\n        row.menu(\"RENDER_MT_presets\", text=bpy.types.RENDER_MT_presets.bl_label)\n        row.operator(\"render.preset_add\", text=\"\", icon='ZOOMIN')\n        row.operator(\"render.preset_add\", text=\"\", icon='ZOOMOUT').remove_active = True\n\n        split = layout.split()\n\n        col = split.column()\n        sub = col.column(align=True)\n        sub.label(text=\"Resolution:\")\n        sub.prop(rd, \"resolution_x\", text=\"X\")\n        sub.prop(rd, \"resolution_y\", text=\"Y\")\n        sub.prop(rd, \"resolution_percentage\", text=\"\")\n\n        row = col.row()\n        row.prop(rd, \"use_border\", text=\"Border\")\n        sub = row.row()\n        sub.active = rd.use_border\n        sub.prop(rd, \"use_crop_to_border\", text=\"Crop\")\n\n        col = split.column()\n        sub = col.column(align=True)\n        sub.label(text=\"Frame Range:\")\n        sub.prop(scene, \"frame_start\", text=\"Start\")\n        sub.prop(scene, \"frame_end\", text=\"End\")\n        sub.prop(scene, \"frame_step\", text=\"Step\")\n\nfrom . import properties_yaf_general_settings\nfrom . import properties_yaf_integrator\nfrom . import properties_yaf_AA_settings\n\n\nclass YAFRENDER_PT_output(RenderButtonsPanel, Panel):\n    bl_label = \"Output\"\n\n    def draw(self, context):\n        layout = self.layout\n\n        rd = context.scene.render\n        sc = context.scene\n        image_settings = rd.image_settings\n\n        layout.prop(rd, \"filepath\", text=\"\")\n\n        split = layout.split(percentage=0.6)\n        col = split.column()\n        col.prop(sc, \"img_output\", text=\"\", icon='IMAGE_DATA')\n        col = split.column()\n        col.row().prop(image_settings, \"color_mode\", text=\"Color\", expand=True)\n\n        if sc.img_output == \"OPEN_EXR\" or sc.img_output == \"HDR\":  #If the output file is a HDR/EXR file, we force the render output to Linear\n                pass\n        elif sc.gs_type_render == \"file\" or sc.gs_type_render == \"xml\":\n                split = layout.split(percentage=0.6)\n                col = split.column()\n                col.prop(sc.display_settings, \"display_device\")\n                \n                if sc.display_settings.display_device == \"None\":\n                    col = split.column()\n                    col.prop(sc, \"gs_gamma\", text = \"Gamma\")\n\n                if sc.display_settings.display_device == \"sRGB\":\n                    pass\n                elif sc.display_settings.display_device == \"None\":\n                    pass\n                elif sc.display_settings.display_device == \"XYZ\":\n                    row = layout.row(align=True)\n                    row.label(text=\"YafaRay 'XYZ' support is experimental and may not give the expected results\", icon=\"ERROR\")\n                else:\n                    row = layout.row(align=True)\n                    row.label(text=\"YafaRay doesn't support '\" + sc.display_settings.display_device + \"', assuming sRGB\", icon=\"ERROR\")\n                    \n        if sc.gs_type_render == \"file\" or sc.gs_type_render == \"xml\":\n                split = layout.split(percentage=0.6)\n                col = split.column()\n                col.prop(sc, \"gs_premult\", text = \"Premultiply Alpha\")\n                if sc.img_output  == \"OPEN_EXR\" and not sc.gs_premult:\n                    row = layout.row(align=True)\n                    row.label(text=\"Typically you should enable Premultiply in EXR files\", icon=\"INFO\")\n                if sc.img_output  == \"PNG\" and sc.gs_premult:\n                    row = layout.row(align=True)\n                    row.label(text=\"Typically you should disable Premultiply in PNG files\", icon=\"INFO\")\n\n\nclass YAFRENDER_PT_post_processing(RenderButtonsPanel, Panel):\n    bl_label = \"Post Processing\"\n    bl_options = {'DEFAULT_CLOSED'}\n\n    def draw(self, context):\n        layout = self.layout\n\n        rd = context.scene.render\n\n        split = layout.split()\n\n        col = split.column()\n        col.prop(rd, \"use_compositing\")\n        col.prop(rd, \"use_sequencer\")\n\n        col = split.column()\n        col.prop(rd, \"dither_intensity\", text=\"Dither\", slider=True)\n\n\nclass YAFRENDER_PT_convert(RenderButtonsPanel, Panel):\n    bl_label = \"Convert old YafaRay Settings\"\n\n    def draw(self, context):\n        layout = self.layout\n        layout.column().operator(\"data.convert_yafaray_properties\", text=\"Convert data from 2.4x\")\n\n\nclass YAFRENDER_PT_advanced(RenderButtonsPanel, Panel):\n    bl_label = \"Advanced Settings - only for experts\"\n    bl_options = {'DEFAULT_CLOSED'}\n\n    def draw(self, context):\n        scene = context.scene\n        layout = self.layout\n        split = layout.split()\n        col = split.column()\n        col.prop(scene, \"adv_auto_shadow_bias_enabled\")\n        if not scene.adv_auto_shadow_bias_enabled:\n            col = split.column()\n            sub = col.column()\n            sub.prop(scene, \"adv_shadow_bias_value\")\n\n        split = layout.split()\n        col = split.column()\n        col.prop(scene, \"adv_auto_min_raydist_enabled\")\n        if not scene.adv_auto_min_raydist_enabled:\n            col = split.column()\n            sub = col.column()\n            sub.prop(scene, \"adv_min_raydist_value\")\n\n\nif __name__ == \"__main__\":  # only for live edit.\n    import bpy\n    bpy.utils.register_module(__name__)\n","repo_name":"Slang104/Blender-Exporter","sub_path":"ui/properties_yaf_render.py","file_name":"properties_yaf_render.py","file_ext":"py","file_size_in_byte":8110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"76"}
+{"seq_id":"32057439501","text":"import os\nimport numpy as np\nfrom sklearn import datasets, linear_model\nfrom math import sqrt\nimport matplotlib.pyplot as plt\n\n# read the data\ndataset_file = os.path.join(os.getcwd(),\n                        '../../datasets/winequality-red.csv')\n\nx_list = []\nlabels = []\nnames = []\nheader = True\n\nwith open(dataset_file, 'r') as f:\n    if header:\n        names = f.readline().strip().split(';')\n    for line in f.readlines():\n        row = line.strip().split(';')\n        labels.append(float(row[-1]))\n        row.pop()\n        float_row = [float(num) for num in row]\n        x_list.append(float_row)\n\n# Normalize columns in X and labels\nnrows = len(x_list)\nncols = len(x_list[0])\n\n# Calculate means and variance per attribute/column\nx_means = []\nx_sd = []\nfor i in range(ncols):\n    col = [x_list[j][i] for j in range(nrows)]\n    mean = sum(col)/nrows\n    x_means.append(mean)\n    col_diff = [(x_list[j][i] - mean) for j in range(nrows)]\n    sum_sq = sum([col_diff[i] * col_diff[i] for i in range(nrows)])\n    stdev = sqrt(sum_sq/nrows)\n    x_sd.append(stdev)\n\n# Use calculate mean and standard deviation to normalize x_list\nx_norm = []\nfor i in range(nrows):\n    row_norm = [(x_list[i][j] - x_means[j])/x_sd[j] for j in range(ncols)]\n    x_norm.append(row_norm)\n\n# Normalize labels\nmean_label = sum(labels)/nrows\nsd_label = sqrt(sum([(labels[i] - mean_label)**2 for i in range(nrows)])/nrows)\n\nlabel_norm = [(labels[i] - mean_label)/sd_label for i in range(nrows)]\n\n# Initialize matrix of betas at each step\nbeta = [0.0] * ncols\n# initialize matrix of betas at each step\nbeta_mat = []\nbeta_mat.append(list(beta))\n\n# number of steps to take\nn_steps = 350\nstep_size = 0.004\n\nfor i in range(n_steps):\n    # calculate residuals\n    residuals = [0.0] * nrows       # difference between observed and predicted\n    # Predictive method: multiplying with corresponding beta\n    for j in range(nrows):\n        labels_hat = sum([x_norm[j][k] * beta[k] for k in range(ncols)])\n        residuals[j] = label_norm[j] - labels_hat\n\n    # calculate correlation between attribute columns from normalized wine\n    # and residuals\n    corr = [0.0] * ncols\n    for j in range(ncols):\n        corr[j] = sum([x_norm[k][j] * residuals[k] for k in range(nrows)]) / \\\n                  nrows\n    i_star = 0\n    corr_star = corr[0]\n\n    for j in range(1, (ncols)):\n        if abs(corr_star) < abs(corr[j]):\n            i_star = j\n            corr_star = corr[j]\n\n    beta[i_star] += (step_size * corr_star) / abs(corr_star)\n    beta_mat.append(list(beta))\n\nfor i in range(ncols):\n    # plot range of beta values for each attribute\n    coef_curve = [beta_mat[k][i] for k in range(n_steps)]\n    xaxis = range(n_steps)\n    plt.plot(xaxis, coef_curve)\n\nplt.xlabel('Steps taken')\nplt.ylabel('coefficient values')\nplt.show()\n","repo_name":"cyrus723/ml_python_micheal_bowles","sub_path":"chapter4/code/listing4-4.py","file_name":"listing4-4.py","file_ext":"py","file_size_in_byte":2794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"13674754150","text":"import os\r\nimport csv\r\n_path=\"files/ds_cua_hang.csv\"\r\nqlcuahang=[]\r\n# Hàm mở file quản lý cửa hàng\r\ndef mo_file(_path,qlcuahang):\r\n    f=open(_path,'r', encoding ='utf-8')\r\n    for dong in csv.reader(f):\r\n        if dong[0]=='Mã cửa hàng':\r\n            continue\r\n        qlcuahang.append({'Mã cửa hàng':dong[0],'Tên cửa hàng':dong[1], 'Vốn đầu tư':dong[2],\r\n        'Doanh thu':dong[3],'Tiền thuế':dong[4]})\r\n    f.close()\r\n    print (\"Đã mở file ds_cua_hang.csv !\")\r\n    return \r\n  \r\n#Hàm thêm thông tin các cửa hàng\r\ndef them_thong_tin(qlcuahang):\r\n    while True:\r\n        ma_ch=input(\"Nhập mã cửa hàng: \")\r\n        ten_ch=input(\"Nhập tên cửa hàng: \")\r\n        von_dt=int(input(\"Nhập vốn đầu tư: \"))\r\n        doanh_thu=int(input(\"Nhập doanh thu: \"))\r\n        if von_dt>=50000000:\r\n            tien_thue=float((10/100)*doanh_thu)\r\n            print(\"Tiền thuế: \",tien_thue)\r\n        else:\r\n            tien_thue=float((5/100)*doanh_thu)\r\n            print(\"Tiền thuế: \",tien_thue)\r\n        qlcuahang.append({'Mã cửa hàng':ma_ch,'Tên cửa hàng':ten_ch,\r\n        'Vốn đầu tư':von_dt,'Doanh thu':doanh_thu,'Tiền thuế':tien_thue})\r\n        tt=int(input(\"Bạn có muốn nhập thêm không ? số bất kì: có; 0: không \"))\r\n        if tt==0:\r\n            break\r\n    return\r\n#Hàm in danh sách cửa hàng với đầy đủ thông tin\r\ndef in_ds(qlcuahang):\r\n    print(\"Danh sách cửa hàng: \")\r\n    print('{:^20}{:^20}{:^20}{:^20}{:^20}'.format('Mã cửa hàng','Tên cửa hàng',\r\n    'Vốn đầu tư','Doanh thu','Tiền thuế'))\r\n    for x in qlcuahang:\r\n        print('{:^20}{:^20}{:^20}{:^20}{:^20}'.format(x['Mã cửa hàng'],x['Tên cửa hàng'],\r\n    x['Vốn đầu tư'],x['Doanh thu'],x['Tiền thuế']))\r\n    return\r\n#Hàm lưu file quản lí của hàng\r\ndef luu_file(_path,qlcuahang):\r\n    f=open(_path,'w',newline='', encoding = 'utf-8')\r\n    csv.writer(f).writerow(['Mã cửa hàng','Tên cửa hàng','Vốn đầu tư',\r\n    'Doanh thu','Tiền thuế'])\r\n    for ch in qlcuahang:\r\n        csv.writer(f).writerow([ch['Mã cửa hàng'],ch['Tên cửa hàng'], ch['Vốn đầu tư'],\r\n        ch['Doanh thu'],ch['Tiền thuế']])\r\n    f.close()\r\n    print(\"Đã lưu thông tin vào ds_cua_hang.csv !\")\r\n    return \r\n  \r\n#Hàm tìm cửa hàng theo mã cửa hàng \r\ndef tim_cua_hang(qlcuahang):\r\n    while True: \r\n        a=input(\"Nhập mã cửa hàng của cửa hàng cần tìm: \")\r\n        for ch in qlcuahang:\r\n            if ch['Mã cửa hàng']==a:\r\n                print(\"Cửa hàng cần tìm: \")\r\n                print('{:^20}{:^20}{:^20}{:^20}{:^20}'.format('Mã cửa hàng','Tên cửa hàng',\r\n                   'Vốn đầu tư','Doanh thu','Tiền thuế'))\r\n                print('{:^20}{:^20}{:^20}{:^20}{:^20}'.format(ch['Mã cửa hàng'],ch['Tên cửa hàng'],\r\n                    ch['Vốn đầu tư'],ch['Doanh thu'],ch['Tiền thuế']))\r\n        tt=int(input(\"Bạn có muốn tiếp tục không ? số bất kì: có; 0: không \"))\r\n        if tt==0:\r\n            break\r\n    return\r\n#Hàm xoá cửa hàng theo mã cửa hàng\r\ndef xoa_ch(qlcuahang):\r\n    while True:\r\n        a=input(\"Nhập mã cửa hàng của cửa hàng cần xoá: \")\r\n        for ch in qlcuahang:\r\n            if ch['Mã cửa hàng']==a:\r\n                vi_tri=qlcuahang.index(ch)\r\n                del(qlcuahang[vi_tri])\r\n                print(\"Danh sách cửa hàng sau khi xoá: \")\r\n                print('{:^20}{:^20}{:^20}{:^20}{:^20}'.format('Mã cửa hàng','Tên cửa hàng',\r\n                'Vốn đầu tư','Doanh thu','Tiền thuế'))\r\n                for x in qlcuahang:\r\n                    print('{:^20}{:^20}{:^20}{:^20}{:^20}'.format(x['Mã cửa hàng'],x['Tên cửa hàng'],\r\n                    x['Vốn đầu tư'],x['Doanh thu'],x['Tiền thuế']))\r\n        tt=int(input(\"Bạn có muốn tiếp tục không ? số bất kì: có; 0: không \"))\r\n        if tt==0:\r\n            break\r\n    return\r\n\r\n# Hàm thống kê doanh thu\r\ndef thong_ke(qlcuahang):\r\n    tong=0\r\n    lstdoanhthu=[]\r\n    for ch in qlcuahang:\r\n        lstdoanhthu.append(ch['Doanh thu'])\r\n        tong+=int(ch['Doanh thu'])\r\n    print('Thống kê doanh thu cửa hàng: ')\r\n    print('{:^20}{:^20}'.format('Tên cửa hàng','Doanh thu'))\r\n    for ch in qlcuahang:\r\n        print('{:^20}{:^20}'.format(ch['Tên cửa hàng'],ch['Doanh thu']))\r\n    print(\"Tổng doanh thu:\",tong)\r\n    print(\"Trung bình doanh thu:\",tong/len(lstdoanhthu))\r\n    return\r\n#Hàm lọc cửa hàng theo vốn đầu tư\r\ndef loc(qlcuahang):\r\n    nho_hon=[]\r\n    for ch in qlcuahang:\r\n        if int(ch['Vốn đầu tư'])<50000000:\r\n            nho_hon.append(ch)\r\n    print(\"Danh sách cửa hàng có vốn đầu tư nhỏ hơn 50 triệu: \")\r\n    print('{:^20}{:^20}'.format('Tên cửa hàng','Vốn đầu tư'))\r\n    for ch in nho_hon:\r\n        print('{:^20}{:^20}'.format(ch['Tên cửa hàng'],ch['Vốn đầu tư']))\r\n    lon_hon=[]\r\n    for ch in qlcuahang:\r\n        if int(ch['Vốn đầu tư'])>=50000000:\r\n            lon_hon.append(ch)\r\n    print(\"Danh sách cửa hàng có vốn đầu tư lớn hơn 50 triệu: \")\r\n    print('{:^20}{:^20}'.format('Tên cửa hàng','Vốn đầu tư'))\r\n    for ch in lon_hon:\r\n        print('{:^20}{:^20}'.format(ch['Tên cửa hàng'],ch['Vốn đầu tư']))    \r\n            \r\n    return\r\n#Hàm tìm cửa hàng có doanh thu cao nhất\r\ndef dt_cao_nhat(qlcuahang):\r\n    doanh_thu = sorted(qlcuahang,reverse=True, key=lambda x: int(x['Doanh thu']))\r\n    a=doanh_thu[0]\r\n    print(\" Cửa hàng có doanh thu cao nhất là:\",a['Tên cửa hàng'])\r\n    return    \r\n            \r\n\r\n","repo_name":"Linh0001/16A1KHDL_NHOM_01","sub_path":"16A1KHDL_NHOM_01/libs/xu_ly_cua_hang.py","file_name":"xu_ly_cua_hang.py","file_ext":"py","file_size_in_byte":5758,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"22508305271","text":"import cv2\r\nfrom matplotlib import pyplot as plt\r\nimport numpy as np\r\nimport imutils\r\nimport easyocr\r\n\r\nclass PLFinder:\r\n    img = None\r\n    reader = None\r\n\r\n    #init and preprocessing\r\n    def __init__(self):\r\n        self.reader = easyocr.Reader(['en'])\r\n        pass\r\n\r\n\r\n    def preprocess(self):\r\n        gray = cv2.cvtColor(self.img, cv2.COLOR_BGR2GRAY)\r\n        plt.imshow(cv2.cvtColor(gray, cv2.COLOR_BGR2RGB))\r\n        #plt.show()\r\n        bfilter = cv2.bilateralFilter(gray, 11, 17, 17)\r\n        edged = cv2.Canny(bfilter, 30, 200)\r\n        plt.imshow(cv2.cvtColor(edged, cv2.COLOR_BGR2RGB))\r\n        #plt.show()\r\n        keypoints = cv2.findContours(edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\r\n        contours = imutils.grab_contours(keypoints)\r\n        contours = sorted(contours, key=cv2.contourArea, reverse=True)[:10]\r\n        location = None\r\n        for contour in contours:\r\n            peri = cv2.arcLength(contour, True)\r\n            approx = cv2.approxPolyDP(contour, 0.04 * peri, True)\r\n            (x, y, w, h) = cv2.boundingRect(approx)\r\n            ar = w / float(h)\r\n            if len(approx) == 4 and approx[0][0][1] > 100 and ar>2:\r\n                location = approx\r\n                break\r\n        if location is not None:\r\n            mask = np.zeros(gray.shape, np.uint8)\r\n            new_image = cv2.drawContours(mask, [location], 0, 255, -1)\r\n            new_image = cv2.bitwise_and(self.img, self.img, mask=mask)\r\n            plt.imshow(cv2.cvtColor(new_image, cv2.COLOR_BGR2RGB))\r\n            #plt.show()\r\n            (x, y) = np.where(mask == 255)\r\n            (x1, y1) = (np.min(x), np.min(y))\r\n            (x2, y2) = (np.max(x), np.max(y))\r\n            cropped_image = gray[x1:x2 + 1, y1:y2 + 1]\r\n            plt.imshow(cv2.cvtColor(cropped_image, cv2.COLOR_BGR2RGB))\r\n            #plt.show()\r\n\r\n            self.img = cv2.cvtColor(cropped_image, cv2.COLOR_BGR2RGB)\r\n\r\n    def set_image(self,image):\r\n        self.img = image\r\n\r\n    def next_image(self,image):\r\n        self.set_image(image)\r\n        self.preprocess()\r\n\r\n    def get_image(self):\r\n        return self.img\r\n\r\n    def OCR(self):\r\n        gray = cv2.cvtColor(self.img, cv2.COLOR_RGB2GRAY)\r\n        result = self.reader.readtext(gray)\r\n        return result\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    img = cv2.imread('seq\\\\frame195.jpg')\r\n    PL = PLFinder()\r\n    PL.next_image(img)\r\n    print(PL.OCR())\r\n","repo_name":"Datacorruptor/LPR-module","sub_path":"FirstAttempt/PlateFinderOCR.py","file_name":"PlateFinderOCR.py","file_ext":"py","file_size_in_byte":2417,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"28097207912","text":"import networkx as nx\nimport pandas as pd\n\nfrom pandapower.topology.create_graph import create_nxgraph\n\n\ndef connected_component(mg, bus, notravbuses=[]):\n    \"\"\"\n    Finds all buses in a NetworkX graph that are connected to a certain bus.\n\n    INPUT:\n        **mg** (NetworkX graph) - NetworkX Graph or MultiGraph that represents a pandapower network.\n\n        **bus** (integer) - Index of the bus at which the search for connected components originates\n\n\n    OPTIONAL:\n     **notravbuses** (list/set) - Indeces of notravbuses: lines connected to these buses are\n                                     not being considered in the graph\n\n    OUTPUT:\n        **cc** (generator) - Returns a generator that yields all buses connected to the input bus\n\n    EXAMPLE:\n         import pandapower.topology as top\n\n         mg = top.create_nx_graph(net)\n\n         cc = top.connected_component(mg, 5)\n\n    \"\"\"\n\n    yield bus\n    visited = {bus}\n    stack = [(bus, iter(mg[bus]))]\n    while stack:\n        _, children = stack[-1]\n        try:\n            child = next(children)\n            if child not in visited:\n                yield child\n                visited.add(child)\n                if child not in notravbuses:\n                    stack.append((child, iter(mg[child])))\n        except StopIteration:\n            stack.pop()\n\n\ndef connected_components(mg, notravbuses=set()):\n    \"\"\"\n     Clusters all buses in a NetworkX graph that are connected to each other.\n\n     INPUT:\n        **mg** (NetworkX graph) - NetworkX Graph or MultiGraph that represents a pandapower network.\n\n\n     OPTIONAL:\n     **notravbuses** (set) - Indices of notravbuses: lines connected to these buses are\n                                       not being considered in the graph\n\n     OUTPUT:\n        **cc** (generator) - Returns a generator that yields all clusters of buses connected\n                             to each other.\n\n     EXAMPLE:\n         import pandapower.topology as top\n\n         mg = top.create_nx_graph(net)\n\n         cc = top.connected_components(net, 5)\n\n    \"\"\"\n\n    nodes = set(mg.nodes()) - notravbuses\n    while nodes:\n        cc = set(connected_component(mg, nodes.pop(), notravbuses=notravbuses))\n        yield cc\n        nodes -= cc\n    # the above does not work if two notravbuses are directly connected\n    if len(notravbuses) > 0:\n        for f, t in mg.edges():\n            if f in notravbuses and t in notravbuses:\n                yield set([f, t])\n\n\ndef calc_distance_to_bus(net, bus, respect_switches=True, nogobuses=None,\n                         notravbuses=None):\n    \"\"\"\n        Calculates the shortest distance between a source bus and all buses connected to it.\n\n     INPUT:\n        **net** (pandapowerNet) - Variable that contains a pandapower network.\n\n        **bus** (integer) - Index of the source bus.\n\n\n     OPTIONAL:\n        **respect_switches** (boolean, True) - True: open line switches are being considered\n                                                     (no edge between nodes)\n                                               False: open line switches are being ignored\n\n        **nogobuses** (integer/list, None) - nogobuses are not being considered\n\n        **notravbuses** (integer/list, None) - lines connected to these buses are not being\n                                              considered\n\n     OUTPUT:\n        **dist** - Returns a pandas series with containing all distances to the source bus\n                   in km.\n\n     EXAMPLE:\n         import pandapower.topology as top\n\n         dist = top.calc_distance_to_bus(net, 5)\n\n    \"\"\"\n    g = create_nxgraph(net, respect_switches=respect_switches,\n                       nogobuses=nogobuses, notravbuses=notravbuses)\n    return pd.Series(nx.single_source_dijkstra_path_length(g, bus))\n\n\ndef unsupplied_buses(net, mg=None, in_service_only=False, slacks=None, respect_switches=True):\n    \"\"\"\n     Finds buses, that are not connected to an external grid.\n\n     INPUT:\n        **net** (pandapowerNet) - variable that contains a pandapower network\n\n     OPTIONAL:\n        **mg** (NetworkX graph) - NetworkX Graph or MultiGraph that represents a pandapower network.\n\n        **in_service_only** (boolean, False) - Defines whether only in service buses should be\n            included in unsupplied_buses.\n\n        **slacks** (set, None) - buses which are considered as root / slack buses. If None, all\n            existing slack buses are considered.\n\n        **respect_switches** (boolean, True) - Fixes how to consider switches - only in case of no\n            given mg.\n\n     OUTPUT:\n        **ub** (set) - unsupplied buses\n\n     EXAMPLE:\n         import pandapower.topology as top\n\n         top.unsupplied_buses(net)\n    \"\"\"\n\n    mg = mg or create_nxgraph(net, respect_switches=respect_switches)\n    slacks = slacks or set(net.ext_grid[net.ext_grid.in_service].bus.values)\n    not_supplied = set()\n    for cc in nx.connected_components(mg):\n        if not set(cc) & slacks:\n            not_supplied.update(set(cc))\n\n    buses_remove = set()\n    if in_service_only:\n        for bus in not_supplied:\n            if not net.bus.loc[bus, 'in_service']:\n                buses_remove.add(bus)\n\n    not_supplied = not_supplied - buses_remove\n    return not_supplied\n\n\ndef find_basic_graph_characteristics(g, roots, characteristics):\n    \"\"\"\n    Determines basic characteristics of the given graph like connected buses, stubs, bridges,\n    and articulation points.\n\n    .. note::\n\n        This is the base function for find_graph_characteristics. Please use the latter\n        function instead!\n    \"\"\"\n    connected = 'connected' in characteristics\n    stub_buses = 'stub_buses' in characteristics\n    bridges = {'bridges', 'required_bridges'} & set(characteristics)\n    articulation_points = {'articulation_points', 'notn1_areas'} & set(characteristics)\n    notn1_starts = 'notn1_areas' in characteristics\n\n    char_dict = {'connected': set(), 'stub_buses': set(), 'bridges': set(),\n                 'articulation_points': set(), 'notn1_starts': set()}\n\n    discovery = {root: 0 for root in roots}  # \"time\" of first discovery of node during search\n    low = {root: 0 for root in roots}\n    visited = set(roots)\n    path = []\n    stack = [(root, root, iter(g[root])) for root in roots]\n    while stack:\n        grandparent, parent, children = stack[-1]\n        try:\n            child = next(children)\n            if stub_buses:\n                path.append(child)  # keep track of movement through the graph\n            if grandparent == child:\n                continue\n            if child in visited:\n                if discovery[child] <= discovery[parent]:  # back edge\n                    low[parent] = min(low[parent], discovery[child])\n            else:\n                low[child] = discovery[child] = len(discovery)\n                visited.add(child)\n                stack.append((parent, child, iter(g[child])))\n        except StopIteration:\n            stack.pop()\n            if low[parent] >= discovery[grandparent]:\n                # Articulation points and start of not n-1 safe buses\n                if grandparent not in roots:\n                    if articulation_points:\n                        char_dict['articulation_points'].add(grandparent)\n                    if notn1_starts:\n                        char_dict['notn1_starts'].add(parent)\n                if low[parent] > discovery[grandparent]:\n                    # Bridges\n                    if bridges:\n                        char_dict['bridges'].add((grandparent, parent))\n\n                    # Stub buses\n                    if stub_buses:\n                        if path[-1] == grandparent:\n                            path.pop()\n                            stub = path.pop()\n                            char_dict['stub_buses'].add(stub)\n                        else:\n                            while path[-1] != grandparent:\n                                stub = path.pop()\n                                char_dict['stub_buses'].add(stub)\n            low[grandparent] = min(low[parent], low[grandparent])\n\n    if connected:\n        char_dict['connected'] = visited\n    return char_dict\n\n\ndef find_graph_characteristics(g, roots, characteristics):\n    \"\"\"\n    Finds and returns different characteristics of the given graph which can be specified.\n\n    INPUT:\n        **g** (NetworkX graph) - Graph of the network\n\n        **roots** (list) - Root buses of the graphsearch\n\n        **characteristics** (list) - List of characteristics this function determines and returns\n\n        .. note::\n\n            Possible characteristics:\n\n            - 'connected' - All buses which have a connection to at least one of the root buses\n            - 'articulation_points' - Buses which lead to disconnected areas if they get removed\n            - 'bridges' - Edges which lead to disconnected areas if they get removed\n            - 'stub_buses' - Buses which arent't connected if one specific edge gets removed\n            - 'required_bridges' - Bridges which are strictly needed to connect a specific bus\n            - 'notn1_areas' - Areas which aren't connected if one specific bus gets removed\n\n    OUTPUT:\n\n        **char_dict** (dict) - dictionary which contains the wanted characteristics\n\n        ======================= ================================================================\n        key                     dict value\n        ======================= ================================================================\n        'connected'             set of all connected buses\n        'articulation_points'   set of all articulation points\n        'bridges'               set of tuples which represent start and end bus of each bridge\n        'stub_buses'            set of all buses which lie on a stub\n        'required_bridges'      dict of all buses which are connected via at least one bridge.\n                                The dict values contain a set of bridges which are needed to\n                                connect the key buses\n        'notn1_areas'           dict of not n-1 safe areas. The dict values contain a set of\n                                not n-1 safe buses which aren't connected if the key bus gets\n                                removed\n        ======================= ================================================================\n\n    EXAMPLE::\n\n        import topology as top\n        g = top.create_nxgraph(net, respect_switches=False)\n        char_dict = top.find_graph_characteristics(g, roots=[0, 3], characteristics=['connected', 'stub_buses'])\n    \"\"\"\n    char_dict = find_basic_graph_characteristics(g, roots, characteristics)\n\n    required_bridges = 'required_bridges' in characteristics\n    notn1_areas = 'notn1_areas' in characteristics\n\n    if not required_bridges and not notn1_areas:\n        return {key: char_dict[key] for key in characteristics}\n\n    char_dict.update({'required_bridges': dict(), 'notn1_areas': dict()})\n\n    visited = set(roots)\n    visited_bridges = []\n    notn1_area_start = None\n    curr_notn1_area = []\n\n    stack = [(root, root, iter(g[root])) for root in roots]\n    while stack:\n        grandparent, parent, children = stack[-1]\n        try:\n            child = next(children)\n            if child == grandparent:\n                continue\n            if child not in visited:\n                visited.add(child)\n                stack.append((parent, child, iter(g[child])))\n                if required_bridges and ((parent, child) in char_dict['bridges'] or\n                                         (child, parent) in char_dict['bridges']):\n                    visited_bridges.append((parent, child))\n\n                if notn1_areas:\n                    if child in char_dict['notn1_starts'] and not notn1_area_start:\n                        notn1_area_start = parent\n                    if notn1_area_start:\n                        curr_notn1_area.append(child)\n\n        except StopIteration:\n            stack.pop()\n            if required_bridges and ((parent, grandparent) in char_dict['bridges'] or\n                                     (grandparent, parent) in char_dict['bridges']):\n                if len(visited_bridges) > 0:\n                    char_dict['required_bridges'][parent] = visited_bridges[:]\n                    visited_bridges.pop()\n\n            if notn1_areas and grandparent == notn1_area_start:\n                if grandparent in char_dict[\"notn1_areas\"]:\n                    char_dict[\"notn1_areas\"][grandparent].update(set(curr_notn1_area[:]))\n                else:\n                    char_dict[\"notn1_areas\"][grandparent] = set(curr_notn1_area[:])\n                del curr_notn1_area[:]\n                notn1_area_start = None\n\n    return {key: char_dict[key] for key in characteristics}\n\n\ndef get_2connected_buses(g, roots):\n    \"\"\"\n    Get all buses which have at least two connections to the roots\n\n    INPUT:\n        **g** (NetworkX graph) - NetworkX Graph or MultiGraph that represents a pandapower network\n\n        **roots** - Roots of the graphsearch\n    \"\"\"\n    char_dict = find_graph_characteristics(g, roots, characteristics=['connected', 'stub_buses'])\n    connected, stub_buses = char_dict['connected'], char_dict['stub_buses']\n    two_connected = connected - stub_buses\n    return connected, two_connected\n\n\ndef determine_stubs(net, roots=None, mg=None, respect_switches=False):\n    \"\"\"\n     Finds stubs in a network. Open switches are being ignored. Results are being written in a new\n     column in the bus table (\"on_stub\") and line table (\"is_stub\") as True/False value.\n\n\n     INPUT:\n        **net** (pandapowerNet) - Variable that contains a pandapower network.\n\n     OPTIONAL:\n        **roots** (integer/list, None) - Indeces of buses that should be excluded (by default, the\n                                         ext_grid buses will be set as roots)\n\n     EXAMPLE:\n         import pandapower.topology as top\n\n         top.determine_stubs(net, roots = [0, 1])\n\n\n    \"\"\"\n    if mg is None:\n        mg = create_nxgraph(net, respect_switches=respect_switches)\n    # remove buses with degree lower 2 until none left\n    roots = roots or set(net.ext_grid.bus)\n    #    mg.add_edges_from((a, b) for a, b in zip(list(roots)[:-1], list(roots)[1:]))\n    #    while True:\n    #        dgo = {g for g, d in list(mg.degree().items()) if d < 2} #- roots\n    #        if not dgo:\n    #            break\n    #        mg.remove_nodes_from(dgo)\n    #    n1_buses = mg.nodes()\n    _, n1_buses = get_2connected_buses(mg, roots)\n    net.bus[\"on_stub\"] = True\n    net.bus.loc[n1_buses, \"on_stub\"] = False\n    net.line[\"is_stub\"] = ~((net.line.from_bus.isin(n1_buses)) & (net.line.to_bus.isin(n1_buses)))\n    stubs = set(net.bus.index) - set(n1_buses)\n    return stubs\n\n\ndef lines_on_path(mg, path):\n    \"\"\"\n     Finds all lines that connect a given path of buses.\n\n     INPUT:\n        **mg** (NetworkX graph) - NetworkX Graph or MultiGraph that represents a pandapower network.\n\n        **path** (list) - List of connected buses.\n\n     OUTPUT:\n        **lines** (list) - Returns a list of all lines on the path.\n\n     EXAMPLE:\n         import topology as top\n\n         mg = top.create_nxgraph(net)\n         lines = top.lines_on_path(mg, [4, 5, 6])\n\n     \"\"\"\n\n    return elements_on_path(mg, path, \"l\")\n\n\ndef elements_on_path(mg, path, element=\"l\", multi=True):\n    \"\"\"\n     Finds all elements that connect a given path of buses.\n\n     INPUT:\n        **mg** (NetworkX graph) - NetworkX Graph or MultiGraph that represents a pandapower network.\n\n        **path** (list) - List of connected buses.\n\n        **element** (string, \"l\") - element type\n\n        **multi** (boolean, True) - True: Applied on a NetworkX MultiGraph\n                                    False: Applied on a NetworkX Graph\n\n     OUTPUT:\n        **elements** (list) - Returns a list of all lines on the path.\n\n     EXAMPLE:\n         import topology as top\n\n         mg = top.create_nxgraph(net)\n         elements = top.elements_on_path(mg, [4, 5, 6])\n\n     \"\"\"\n\n    if multi:\n        return [mg[b1][b2][0][\"key\"] for b1, b2 in zip(path, path[1:])\n                if mg[b1][b2][0][\"type\"] == element]\n    else:\n        return [mg[b1][b2][\"key\"] for b1, b2 in zip(path, path[1:])\n                if mg[b1][b2][\"type\"] == element]\n\n\ndef estimate_voltage_vector(net):\n    \"\"\"\n    Function initializes the voltage vector of net with a rough estimation. All buses are set to the\n    slack bus voltage. Transformer differences in magnitude and phase shifting are accounted for.\n    :param net: pandapower network\n    :return: pandas dataframe with estimated vm_pu and va_degree\n    \"\"\"\n    res_bus = pd.DataFrame(index=net.bus.index, columns=[\"vm_pu\", \"va_degree\"])\n    net_graph = create_nxgraph(net, include_trafos=False)\n    for _, ext_grid in net.ext_grid.iterrows():\n        area = list(connected_component(net_graph, ext_grid.bus))\n        res_bus.vm_pu.loc[area] = ext_grid.vm_pu\n        res_bus.va_degree.loc[area] = ext_grid.va_degree\n    trafos = net.trafo[net.trafo.in_service == 1]\n    trafo_index = trafos.index.tolist()\n    while len(trafo_index):\n        for tix in trafo_index:\n            trafo = trafos.ix[tix]\n            if pd.notnull(res_bus.vm_pu.at[trafo.hv_bus]) \\\n                    and pd.isnull(res_bus.vm_pu.at[trafo.lv_bus]):\n                try:\n                    area = list(connected_component(net_graph, trafo.lv_bus))\n                    shift = trafo.shift_degree if \"shift_degree\" in trafo else 0\n                    ratio = (trafo.vn_hv_kv / trafo.vn_lv_kv) / (net.bus.vn_kv.at[trafo.hv_bus]\n                                                                 / net.bus.vn_kv.at[trafo.lv_bus])\n                    res_bus.vm_pu.loc[area] = res_bus.vm_pu.at[trafo.hv_bus] * ratio\n                    res_bus.va_degree.loc[area] = res_bus.va_degree.at[trafo.hv_bus] - shift\n                except KeyError:\n                    raise UserWarning(\"An out-of-service bus is connected to an in-service \"\n                                      \"transformer. Please set the transformer out of service or\"\n                                      \"put the bus into service. Treat results with caution!\")\n                trafo_index.remove(tix)\n            elif pd.notnull(res_bus.vm_pu.at[trafo.hv_bus]):\n                # parallel transformer, lv buses are already set from previous transformer\n                trafo_index.remove(tix)\n    return res_bus\n","repo_name":"singhsumeet4471/State-Estimation-of-distributed-network-using-machine-learning-","sub_path":"venv/Lib/site-packages/pandapower/topology/graph_searches.py","file_name":"graph_searches.py","file_ext":"py","file_size_in_byte":18423,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"43397949501","text":"import tkinter as tk\nimport visualizer\n\nif __name__ == '__main__':\n\troot = tk.Tk()\n\tpath = \"/Users/Roger/Projects/bike-computer/data/\"\n\tvis = visualizer.Visualizer(root,path)\n\n\t#root.attributes('-fullscreen',True)\n\troot.overrideredirect(True)\n\t#root.after(1, dbg.update_display)\n\troot.mainloop()\n\n","repo_name":"iasapomeavoc1/bike-computer","sub_path":"software/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"41223342665","text":"import mujoco as mj\nfrom mujoco.glfw import glfw\nimport numpy as np\nimport os\n\nclass Sim:\n\n    def __init__(self, xml_path, window):\n        self.model = mj.MjModel.from_xml_path(xml_path)  # MuJoCo model\n        self.data = mj.MjData(self.model)                     # MuJoCo data\n        self.cam = mj.MjvCamera()                        # Abstract camera\n        self.opt = mj.MjvOption()                        # visualization options\n\n        self.window = window\n        self.scene = mj.MjvScene(self.model, maxgeom=10000)\n\n        self.lastx = 0\n        self.lasty = 0\n        self.button_left = False\n        self.button_middle = False\n        self.button_right = False\n\n    def keyboard(self, window, key, scancode, act, mods):\n        if act == glfw.PRESS and key == glfw.KEY_BACKSPACE:\n            mj.mj_resetDataKeyframe(self.model, self.data, 0)\n            mj.mj_forward(self.model, self.data)\n\n    def mouse_move(self, window, xpos, ypos):\n        dx = xpos - self.lastx\n        dy = ypos - self.lasty\n        self.lastx = xpos\n        self.lasty = ypos\n\n        # no buttons down: nothing to do\n        if (not self.button_left) and (not self.button_middle) and (not self.button_right):\n            return\n\n        # get current window size\n        width, height = glfw.get_window_size(self.window)\n\n        # get shift key state\n        PRESS_LEFT_SHIFT = glfw.get_key(self.window, glfw.KEY_LEFT_SHIFT) == glfw.PRESS\n        PRESS_RIGHT_SHIFT = glfw.get_key(self.window, glfw.KEY_RIGHT_SHIFT) == glfw.PRESS\n        self.mod_shift = (PRESS_LEFT_SHIFT or PRESS_RIGHT_SHIFT)\n\n        # determine action based on mouse button\n        if self.button_right:\n            if self.mod_shift:\n                self.action = mj.mjtMouse.mjMOUSE_MOVE_H\n            else:\n                self.action = mj.mjtMouse.mjMOUSE_MOVE_V\n        elif self.button_left:\n            if self.mod_shift:\n                self.action = mj.mjtMouse.mjMOUSE_ROTATE_H\n            else:\n                self.action = mj.mjtMouse.mjMOUSE_ROTATE_V\n        else:\n            self.action = mj.mjtMouse.mjMOUSE_ZOOM\n\n        mj.mjv_moveCamera(self.model, self.action, dx/height, dy/height, self.scene, self.cam)\n\n    def mouse_button(self, window, button, act, mods):\n        self.button_left = (glfw.get_mouse_button(self.window, glfw.MOUSE_BUTTON_LEFT) == glfw.PRESS)\n        self.button_middle = (glfw.get_mouse_button(self.window, glfw.MOUSE_BUTTON_MIDDLE) == glfw.PRESS)\n        self.button_right = (glfw.get_mouse_button(self.window, glfw.MOUSE_BUTTON_RIGHT) == glfw.PRESS)\n        # update mouse position\n        glfw.get_cursor_pos(self.window)\n\n    def scroll(self, window, xoffset, yoffset):\n        self.action = mj.mjtMouse.mjMOUSE_ZOOM\n        mj.mjv_moveCamera(self.model, self.action, 0.0, -0.05 * yoffset, self.scene, self.cam)","repo_name":"lgkimjy/mujoco_tutorial","sub_path":"template/sim.py","file_name":"sim.py","file_ext":"py","file_size_in_byte":2820,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"3580905679","text":"import torch\nfrom torch import nn\nfrom schedules import LinearWarmupExponentialDecay\n\n\nclass Model(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.layer = nn.Sequential(\n            nn.Linear(2, 2),\n            nn.Linear(2, 8),\n            nn.Linear(8, 4),\n            nn.Linear(4, 1),\n        )\n\n    def forward(self, x):\n        x = self.layer(x)\n        return x\n","repo_name":"heyfavour/code_sniippet","sub_path":"pytorch/lr_schedule/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"16379667578","text":"\"\"\"Course_work URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/2.2/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\n\nfrom django.conf.urls import url\nfrom . import views\nfrom django.contrib import admin\n\nurlpatterns = [\n    url(r'^admin/', admin.site.urls, name='admin'),\n    url(r'^$', views.home, name='home_url'),\n    url(r'error/^$', views.error404),\n    url(r'^registration/', views.registration, name='registration'),\n    url(r'^login/', views.login, name='login'),\n    url(r'^logout/', views.logout, name='logout'),\n    url(r'^account/', views.account, name='account'),\n    url(r'^add_air/', views.add_air, name='add_air'),\n    url(r'^add_flight/(?P<air_id>\\d+)', views.add_flight1, name='add_flight1'),\n    url(r'^add_flight/', views.add_flight, name='add_flight'),\n    url(r'^prop_tik/', views.prop_tik, name='prop_tik'),\n    url(r'^user_list/', views.user_list),\n    url(r'^moderator_list/', views.moderator_list),\n    url(r'^add_user/', views.add_user, name='add_user'),\n    url(r'^add_mod/', views.add_mod, name='add_mod'),\n    url(r'^list_del_user/', views.list_del_user, name='list_del_user'),\n    url(r'^list_del_mod/', views.list_del_mod, name='list_del_mod'),\n    url(r'^airport/(?P<air_id>\\d+)', views.air_detail, name='air_detail_url'),\n    url(r'^del_user/(?P<user_id>\\d+)', views.del_user1, name='del_user1'),\n    url(r'^del_mod/(?P<mod_id>\\d+)', views.del_mod1, name='del_mod1'),\n    url(r'^add_ticket/(?P<air_id>\\d+)/(?P<fly_id>\\d+)', views.add_ticket, name='add_ticket')\n]\n","repo_name":"DeKusi/Course_work_for_templates","sub_path":"Course_work/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2016,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"18309091534","text":"class Solution:\n    def createSortedArray(self, instructions: List[int]) -> int:\n        mod = 10 ** 9 + 7\n        dic = {}\n        index = 1\n        x = sorted(instructions)\n        for i, val in enumerate(x):\n            if i > 0 and val == x[i - 1]:\n                continue\n            dic[val] = index\n            index += 1\n\n        bit = [0 for _ in range(index)]\n\n        def update(loc, val):\n            while loc < index:\n                bit[loc] += val\n                loc += loc & (-loc)\n\n        def query(loc):\n            ret = 0\n            while loc > 0:\n                ret += bit[loc]\n                loc -= loc & (-loc)\n            return ret\n\n        res = 0\n        for val in instructions:\n            res += min(query(dic[val] - 1), query(index - 1) - query(dic[val]))\n            res %= mod\n            update(dic[val], 1)\n        return res","repo_name":"jiangruofan/algorithm","sub_path":"1649. Create Sorted Array through Instructions.py","file_name":"1649. Create Sorted Array through Instructions.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"4878214813","text":"import argparse\n\n\ndef get_parser():\n    p = argparse.ArgumentParser()\n    p.add_argument(\"--train_dir\", default=\"./data/chujian/glyphs_955/train\")\n    p.add_argument(\"--dev_dir\", default=\"./data/chujian/glyphs_955/dev\")\n    p.add_argument(\"--test_dir\", default=\"./data/chujian/glyphs_955/test\")\n    p.add_argument(\"--output_dir\", default=\"./result/glyphs_955\")\n    p.add_argument(\"--seed\", type=int, default=0)\n    p.add_argument(\"--cuda\", type=bool, default=True)\n    p.add_argument(\"--mode\", default=\"train_test\")\n    p.add_argument(\"--batch_size\", type=int, default=1024)\n    # 改了，原100\n    p.add_argument(\"--epochs\", type=int, default=6)\n    p.add_argument(\"--lr\", type=float, default=0.002)\n    # 学习率衰减用的\n    p.add_argument(\n        \"--lr_step\",\n        type=int,\n        default=2,\n        help=\"StepLR learning rate scheduler step, default=20\",\n    )\n    p.add_argument(\n        \"--lr_gamma\",\n        type=float,\n        default=0.8,\n        help=\"StepLR learning rate scheduler gamma, default=0.5\",\n    )\n    p.add_argument(\n        \"--iters_per_epoch\",\n        type=int,\n        default=100,\n        help=\"number of episodes per epoch, default=100\",\n    )\n\n    p.add_argument(\n        \"-cTr\",\n        \"--classes_per_it_tr\",\n        type=int,\n        default=200,\n        help=\"number of random classes per episode for training, default=60\",\n    )\n\n    p.add_argument(\n        \"-nsTr\",\n        \"--num_support_tr\",\n        type=int,\n        default=5,\n        help=\"number of samples per class to use as support for training\",\n    )\n    p.add_argument(\n        \"-nqTr\",\n        \"--num_query_tr\",\n        type=int,\n        default=5,\n        help=\"number of samples per class to use as query for training\",\n    )\n\n    # No validation now...\n\n    # p.add_argument(\n    #     '-cVa', '--classes_per_it_val',\n    #     type=int,\n    #     help='number of classes per episode for validation, default=5',\n    #     default=5,\n    # )\n    # # 改了，原5\n    # p.add_argument(\n    #     '-nsVa', '--num_support_val',\n    #     type=int,\n    #     help='number of samples per class to use as support for validation',\n    #     default=4,\n    # )\n    # # 改了，原15\n    # p.add_argument(\n    #     '-nqVa', '--num_query_val',\n    #     type=int,\n    #     help='number of samples per class to use as query for validation',\n    #     default=4,\n    # )\n\n    return p\n","repo_name":"donny-chan/chujian-classifier","sub_path":"protonet/arguments.py","file_name":"arguments.py","file_ext":"py","file_size_in_byte":2391,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"}
+{"seq_id":"39751546829","text":"#July 30 Riddler Express https://fivethirtyeight.com/features/will-riddler-nation-win-gold-in-archery/\n\ndef arrows():\n    ans = []\n    list = [10, 9, 5]\n    global below24\n    below24 = 0\n    global at24\n    at24 = 0\n    global above24\n    above24 = 0\n\n    def permute(cume = []):\n        global below24\n        global at24\n        global above24\n        if len(cume) == 3:\n            if cume not in ans:\n                ans.append(cume.copy())\n                #print(sum(cume))\n            if sum(cume) < 24:\n                below24 += 1\n            elif sum(cume) == 24:\n                at24 += 1\n            else:\n                above24 += 1\n            return\n        else:\n            for i in range(len(list)):\n                cume.append(list[i])\n                permute(cume)\n                cume.pop()\n    permute()\n    return above24, (below24+above24)\n\nprint(arrows())","repo_name":"lejaja9/Riddlers","sub_path":"07_30_21_Riddler_Express.py","file_name":"07_30_21_Riddler_Express.py","file_ext":"py","file_size_in_byte":881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"11052983308","text":"from distutils.core import setup\nimport fnmatch, os.path\nfrom libmproxy import version\n\n\ndef pdir():\n    dirname, _ = os.path.split(__file__)\n    return os.path.abspath(dirname)\n\n\ndef _fnmatch(name, patternList):\n    for i in patternList:\n        if fnmatch.fnmatch(name, i):\n            return True\n    return False\n\n\ndef _splitAll(path):\n    parts = []\n    h = path\n    while 1:\n        if not h:\n            break\n        h, t = os.path.split(h)\n        parts.append(t)\n    parts.reverse()\n    return parts\n\n\ndef findPackages(path, dataExclude=[]):\n    \"\"\"\n        Recursively find all packages and data directories rooted at path. Note\n        that only data _directories_ and their contents are returned -\n        non-Python files at module scope are not, and should be manually\n        included.\n\n        dataExclude is a list of fnmatch-compatible expressions for files and\n        directories that should not be included in pakcage_data.\n\n        Returns a (packages, package_data) tuple, ready to be passed to the\n        corresponding distutils.core.setup arguments.\n    \"\"\"\n    packages = []\n    datadirs = []\n    for root, dirs, files in os.walk(path, topdown=True):\n        if \"__init__.py\" in files:\n            p = _splitAll(root)\n            packages.append(\".\".join(p))\n        else:\n            dirs[:] = []\n            if packages:\n                datadirs.append(root)\n\n    # Now we recurse into the data directories\n    package_data = {}\n    for i in datadirs:\n        if not _fnmatch(i, dataExclude):\n            parts = _splitAll(i)\n            module = \".\".join(parts[:-1])\n            acc = package_data.get(module, [])\n            for root, dirs, files in os.walk(i, topdown=True):\n                sub = os.path.join(*_splitAll(root)[1:])\n                if not _fnmatch(sub, dataExclude):\n                    for fname in files:\n                        path = os.path.join(sub, fname)\n                        if not _fnmatch(path, dataExclude):\n                            acc.append(path)\n                else:\n                    dirs[:] = []\n            package_data[module] = acc\n    return packages, package_data\n\n\nwith open(os.path.join(pdir(), \"README.txt\")) as f:\n    long_description = f.read()\npackages, package_data = findPackages(\"libmproxy\")\n\n\nscripts = [\"mitmdump\"]\nif os.name != \"nt\":\n    scripts.append(\"mitmproxy\")\n\ndeps = {\n    \"netlib>=%s\" % version.MINORVERSION,\n    \"pyasn1>0.1.2\",\n    \"requests>=2.4.0\",\n    \"pyOpenSSL>=0.14\",\n    \"Flask>=0.10.1\",\n    \"tornado>=4.0.2\"\n}\nscript_deps = {\n    \"mitmproxy\": {\n        \"urwid>=1.1\",\n        \"lxml>=3.3.6\",\n        \"Pillow>=2.3.0\",\n    },\n    \"mitmdump\": set()\n}\nfor script in scripts:\n    deps.update(script_deps[script])\nif os.name == \"nt\":\n    deps.add(\"pydivert>=0.0.4\")  # Transparent proxying on Windows\nconsole_scripts = [\"%s = libmproxy.main:%s\" % (s, s) for s in scripts]\n\n\n\nsetup(\n    name=\"mitmproxy\",\n    version=version.VERSION,\n    description=\"An interactive, SSL-capable, man-in-the-middle HTTP proxy for penetration testers and software developers.\",\n    long_description=long_description,\n    author=\"Aldo Cortesi\",\n    author_email=\"aldo@corte.si\",\n    url=\"http://mitmproxy.org\",\n    packages=packages,\n    package_data=package_data,\n    classifiers=[\n        \"License :: OSI Approved :: MIT License\",\n        \"Development Status :: 5 - Production/Stable\",\n        \"Environment :: Console\",\n        \"Environment :: Console :: Curses\",\n        \"Operating System :: MacOS :: MacOS X\",\n        \"Operating System :: POSIX\",\n        \"Programming Language :: Python\",\n        \"Programming Language :: Python :: 2\",\n        \"Topic :: Security\",\n        \"Topic :: Internet\",\n        \"Topic :: Internet :: WWW/HTTP\",\n        \"Topic :: Internet :: Proxy Servers\",\n        \"Topic :: Software Development :: Testing\"\n    ],\n    entry_points={\n        'console_scripts': console_scripts\n    },\n    install_requires=list(deps),\n    extras_require={\n        'dev': [\n            \"mock>=1.0.1\",\n            \"nose>=1.3.0\",\n            \"nose-cov>=1.6\",\n            \"coveralls>=0.4.1\",\n            \"pathod>=%s\" % version.MINORVERSION\n        ],\n        'contentviews': [\n            \"pyamf>=0.6.1\",\n            \"protobuf>=2.5.0\",\n            \"cssutils>=1.0\"\n        ]\n    }\n)\n","repo_name":"0xr0ot/mitmproxy","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":4270,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"76"}
+{"seq_id":"26411780342","text":"\"\"\"Library integration with Random123.\"\"\"\n\nfrom __future__ import division, absolute_import\n\n__copyright__ = \"Copyright (C) 2016 Andreas Kloeckner\"\n\n__license__ = \"\"\"\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.\n\"\"\"\n\n\nfrom pytools import ImmutableRecord\nfrom mako.template import Template\nimport numpy as np\n\n\n# {{{ rng metadata\n\nclass RNGInfo(ImmutableRecord):\n    @property\n    def full_name(self):\n        return \"%s%dx%d\" % (self.name, self.width, self.bits)\n\n\n_philox_base_info = RNGInfo(\n            name=\"philox\",\n            pyopencl_header=\"pyopencl-random123/philox.cl\",\n            generic_header=\"Random123/philox.h\",\n            key_width=2)\n\n_threefry_base_info = RNGInfo(\n            name=\"threefry\",\n            pyopencl_header=\"pyopencl-random123/threefry.cl\",\n            generic_header=\"Random123/threefry.h\",\n            key_width=4)\n\nRNG_VARIANTS = [\n        _philox_base_info.copy(width=2, bits=32),\n        _philox_base_info.copy(width=2, bits=64),\n        _philox_base_info.copy(width=4, bits=32),\n        _philox_base_info.copy(width=4, bits=64),\n\n        _threefry_base_info.copy(width=2, bits=32),\n        _threefry_base_info.copy(width=2, bits=64),\n        _threefry_base_info.copy(width=4, bits=32),\n        _threefry_base_info.copy(width=4, bits=64),\n        ]\n\nFUNC_NAMES_TO_RNG = dict(\n        (v.full_name + suffix, v)\n        for v in RNG_VARIANTS\n        for suffix in [\n            \"\", \"_f32\", \"_f64\",\n            ])\n\n# }}}\n\n\n# {{{ preamble\n\nPREAMBLE_TEMPLATE = Template(\"\"\"\n%if is_pyopencl_target:\n#include <${ rng_variant.pyopencl_header }>\n%else:\n#include <${ rng_variant.generic_header }>\n%endif\n\n<%\nname = rng_variant.full_name\nwidth = rng_variant.width\nif rng_variant.bits == 32:\n    counter_type = \"uint%d\" % width\n    key_type = \"uint%d\" % rng_variant.key_width\nelif rng_variant.bits == 64:\n    counter_type = \"ulong%d\" % width\n    key_type = \"ulong%d\" % rng_variant.key_width\nelse:\n    assert False\n%>\n\ntypedef union {\n    ${ counter_type } v;\n    ${ name }_ctr_t c;\n} ${ name }_ctr_vec_union;\n\n\n${ counter_type } ${ name }_bump(${ counter_type } ctr)\n{\n    if (++ctr.x == 0)\n        if (++ctr.y == 0)\n            ++ctr.z;\n    return ctr;\n}\n\n${ counter_type } ${ name }_gen(\n        ${ counter_type } ctr,\n        ${ key_type } key,\n        ${ counter_type } *new_ctr)\n{\n    ${ name }_ctr_vec_union result;\n    result.c = ${ name }(\n        *(${ name }_ctr_t *) &ctr,\n        *(${ name }_key_t *) &key);\n    *new_ctr = ${ name }_bump(ctr);\n    return result.v;\n}\n\nfloat${ width } ${ name }_f32(\n        ${ counter_type } ctr,\n        ${ key_type } key,\n        ${ counter_type } *new_ctr)\n{\n    *new_ctr = ctr;\n    return\n        convert_float${ width }(${ name }_gen(*new_ctr, key, new_ctr))\n        * ${ repr(1./2**32) }f;\n}\n\ndouble${ width } ${ name }_f64(\n        ${ counter_type } ctr,\n        ${ key_type } key,\n        ${ counter_type } *new_ctr)\n{\n    *new_ctr = ctr;\n    %if rng_variant.bits == 32:\n        return\n            convert_double${ width }(${ name }_gen(*new_ctr, key, new_ctr))\n            * ${ repr(1./2**32) }\n            +\n            convert_double${ width }(${ name }_gen(*new_ctr, key, new_ctr))\n            * ${ repr(1./2**64) };\n\n    %elif rng_variant.bits == 64:\n        *new_ctr = ctr;\n        return\n            convert_double${ width }(${ name }_gen(*new_ctr, key, new_ctr))\n            * ${ repr(1./2**64) };\n\n    %else:\n        #error Unrecognized bit width in RNG\n\n    %endif\n}\n\n\"\"\", strict_undefined=True)\n\n# }}}\n\n\ndef random123_preamble_generator(preamble_info):\n    for f in preamble_info.seen_functions:\n        try:\n            rng_variant = FUNC_NAMES_TO_RNG[f.name]\n        except KeyError:\n            continue\n\n        from loopy.target.pyopencl import PyOpenCLTarget\n        yield (\"90-random123-\"+rng_variant.full_name,\n                PREAMBLE_TEMPLATE.render(\n                    is_pyopencl_target=isinstance(\n                        preamble_info.kernel.target,\n                        PyOpenCLTarget),\n                    rng_variant=rng_variant,\n                    ))\n\n\ndef random123_function_mangler(kernel, name, arg_dtypes):\n    try:\n        rng_variant = FUNC_NAMES_TO_RNG[name]\n    except KeyError:\n        return None\n\n    from loopy.types import NumpyType\n    target = kernel.target\n    base_dtype = {32: np.uint32, 64: np.uint64}[rng_variant.bits]\n    ctr_dtype = target.vector_dtype(NumpyType(base_dtype), rng_variant.width)\n    key_dtype = target.vector_dtype(NumpyType(base_dtype), rng_variant.key_width)\n\n    from loopy.kernel.data import CallMangleInfo\n    fn = rng_variant.full_name\n    if name == fn:\n        return CallMangleInfo(\n                target_name=fn+\"_gen\",\n                result_dtypes=(ctr_dtype, ctr_dtype),\n                arg_dtypes=(ctr_dtype, key_dtype))\n\n    elif name == fn + \"_f32\":\n        return CallMangleInfo(\n                target_name=name,\n                result_dtypes=(\n                    target.vector_dtype(NumpyType(np.float32), rng_variant.width),\n                    ctr_dtype),\n                arg_dtypes=(ctr_dtype, key_dtype))\n\n    elif name == fn + \"_f64\":\n        return CallMangleInfo(\n                target_name=name,\n                result_dtypes=(\n                    target.vector_dtype(NumpyType(np.float64), rng_variant.width),\n                    ctr_dtype),\n                arg_dtypes=(ctr_dtype, key_dtype))\n\n    else:\n        return None\n\n# vim: foldmethod=marker\n","repo_name":"jiaqiwang969/dune-course-material","sub_path":"iwr-course-2021/dune/dune-codegen/python/loopy/loopy/library/random123.py","file_name":"random123.py","file_ext":"py","file_size_in_byte":6413,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"74344115765","text":"\"\"\"\n常用损失函数\n\"\"\"\n\n\nimport torch\n\n# 1. L1LOSS\n# CLASS torch.nn.L1Loss(reduction='mean')\nloss = torch.nn.L1Loss()\ninput = torch.randn(2, 2, requires_grad=True)   # torch.randn() 标准正太分布\ntarget = torch.randn(2, 2)\noutput = loss(input, target)\nprint(input)\nprint(target)\nprint(output)\n\n\n# 2. CrossEntropyLoss(交叉熵损失函数)\n# CLASS torch.nn.CrossEntropyLoss(weight=None, ignore_index=-100, reduction='mean')\n# CrossEntropyLoss = softmax + NLLLoss\nloss = torch.nn.CrossEntropyLoss()\ninput = torch.randn(3, 5, requires_grad=True)\ntarget = torch.empty(3, dtype=torch.long).random_(5)\noutput = loss(input, target)\noutput.backward()\nprint(input)\nprint(target)\nprint(output)\n\n\n# 3. NLLLoss(最大似然损失函数)\n# CLASS torch.nn.NLLLoss(weight=None, ignore_index=-100, reduction='mean')\nm = torch.nn.LogSoftmax(dim=1)\nloss = torch.nn.NLLLoss()\n# input is of size N x C = 3 x 5\ninput = torch.randn(3, 5, requires_grad=True)\n# each element in target has to have 0 <= value < C\ntarget = torch.tensor([1, 0, 4])\noutput = loss(m(input), target)\noutput.backward()\nprint(m(input))\nprint(target)\nprint(output)\n\n\n# 4. MSELoss(平方损失函数)\n# CLASS torch.nn.MSELoss(reduction='mean')\nloss = torch.nn.MSELoss()\ninput = torch.randn(2, 2).floor()\ntarget = torch.randn(2, 2).floor()\noutput = loss(input, target)\nprint(input)\nprint(target)\nprint(output)\n\n\n# 5. DiceLoss(用于计算两个样本点的相似度的距，主要应用，语义分割等)\ndef dice_loss(input, target):\n    input = input.contiguous().view(input.size()[0], -1)\n    target = target.contiguous().view(target.size()[0], -1).float()\n    a = torch.sum(input * target, 1)           # |X⋂Y|\n    b = torch.sum(input * input, 1) + 0.001    # |X|\n    c = torch.sum(target * target, 1) + 0.001  # |Y|\n    d = (2 * a) / (b + c)\n    return 1-d\ninput = torch.randn(2, 2).floor()\ntarget = torch.randn(2, 2).floor()\noutput = dice_loss(input, target)\nprint(input)\nprint(target)\nprint(output)","repo_name":"jzhangCSER01/LearningPython","sub_path":"d2l-zh/loss.py","file_name":"loss.py","file_ext":"py","file_size_in_byte":1966,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"24134733076","text":"from random import randint\n\nboard = []\n\nfor x in range(3):\n    board.append([\"_\"] * 3)\n\ndef print_board(board):\n    for row in board:\n        print (\"  \".join(row))\n\n\n\ndef random_o(board):\n    return randint(0, len(board)-1)\n\n\n\ndef row_check(board, row, col, player, tag):\n    if board[row][col] == tag:\n        if board[row][col+1] == tag:\n            if board[row][col+2] == tag:\n                if player == \"bot\":\n                    print (\"I win!\")\n                    print\n                    return 11\n                else:\n                    print (\"You win!\")\n                    print\n                    return 12\n\n\ndef col_check(board, row, col, player, tag):\n    if board[row][col] == tag:\n        if board[row+1][col] == tag:\n            if board[row+2][col] == tag:\n                if player == \"bot\":\n                    print (\"I win!\")\n                    print\n                    return 21\n                else:\n                    print (\"You win!\")\n                    print\n                    return 22\n\ndef dia_check(board, row, col, player, tag):\n    if board[row][col] == tag:\n        if board[row+1][col+1] == tag:\n            if board[row+2][col+2] == tag:\n                if player == \"bot\":\n                    print\n                    print (\"I win!\")\n                    print\n                    return 31\n\n\n                else:\n                    print\n                    print (\"You win!\")\n                    print\n                    return 32\n\n\nbot_count = 0\npl_count = 0\ni = 100\nv = 100\ndef bot_place(board):\n    bot_row = random_o(board)\n    bot_col = random_o(board)\n    if board[bot_row][bot_col] == \"_\":\n        board[bot_row][bot_col] = \"X\"\n    else:\n        bot_place(board)\n\ndef pl1_stat(pl, count):\n    if pl >= count:\n        if row_check(board, 0, 0, \"player\", \"O\") == 12:\n            return 12\n\n        elif col_check(board, 0, 0, \"player\", \"O\") == 22:\n            return 22\n\n        elif dia_check(board, 0, 0, \"player\", \"O\") == 32:\n            return 32\n\n        elif row_check(board, 1, 0, \"player\", \"O\") == 12:\n            return 12\n\n        elif col_check(board, 0, 1, \"player\", \"O\") == 22:\n            return 22\n\n        elif row_check(board, 2, 0, \"player\", \"O\") == 12:\n            return 12\n\n        elif col_check(board, 0, 2, \"player\", \"O\") == 22:\n            return 22\n\n        else:\n            pl1_stat(pl, count+1)\n\ndef pl2_stat(pl, count):\n    if pl >= count:\n        if row_check(board, 0, 0, \"bot\", \"X\") == 11:\n            return 11\n\n        elif col_check(board, 0, 0, \"bot\", \"X\") == 21:\n            return 21\n\n        elif dia_check(board, 0, 0, \"bot\", \"X\") == 11:\n            return 11\n\n        elif row_check(board, 1, 0, \"bot\", \"X\") == 21:\n            return 21\n\n        elif col_check(board, 0, 1, \"bot\", \"X\") == 11:\n            return 11\n\n        elif row_check(board, 2, 0, \"bot\", \"X\") == 21:\n            return 21\n\n        elif col_check(board, 0, 2, \"bot\", \"X\") == 11:\n            return 11\n\n        else:\n            pl1_stat(pl, count+1)\n\nwhile i > 0:\n\n\n\n    print (\"My turn:\")\n    bot_place(board)\n    bot_count += 1\n    print\n    print (print_board(board))\n    if pl2_stat(bot_count, 3) == 11 or pl2_stat(bot_count, 3) == 21 or pl2_stat(bot_count, 3) == 31:\n        print (\"bot wins!\")\n        break\n        if bot_count >= 5:\n            print (\"Draw!\")\n            break\n\n\n    print (\"Your turn:\")\n    pl_row = int(input(\"Enter row:\"))\n    pl_row -= 1\n    print\n    pl_col = int(input(\"Enter col:\"))\n    pl_col -= 1\n    pl_count += 1\n    print\n\n    if board[pl_row][pl_col] != \"_\":\n        print (\"You can't do that, enter again\")\n        pl_row -= int(input(\"Enter row:\"))\n        pl_row = 1\n        print\n        pl_col -= int(input(\"Enter col:\"))\n        pl_col = 1\n        print\n    else:\n        board[pl_row][pl_col] = \"O\"\n\n    print (print_board(board))\n    if pl1_stat(pl_count, 2) == 12 or pl1_stat(pl_count, 3) == 22 or pl1_stat(pl_count, 3) == 32:\n        print (\"Player wins!\")\n        break\n        if pl_count >= 5:\n            print (\"Draw!\")\n            break\n    print (print_board(board))\n","repo_name":"bhargav1000/timetable-generator","sub_path":"tictactoe.py","file_name":"tictactoe.py","file_ext":"py","file_size_in_byte":4098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"72526796085","text":"#!/usr/bin/python3\n\"\"\"Creation of an empty class defining a Square\"\"\"\n\n\nclass Square:\n    \"\"\"Write an empty class square that defines a square\"\"\"\n    def __init__(self, size=0):\n        \"\"\"Function instatiation\"\"\"\n        if type(size) != int:\n            raise TypeError(\"size must be an integer\")\n        elif size < 0:\n            raise ValueError(\"size must be >= 0\")\n        else:\n            self.__size = size\n\n    \"\"\"Create Getter Function\"\"\"\n    @property\n    def size(self):\n        return self.__size\n\n    @size.setter\n    def size(self, value):\n        if type(value) != int:\n            raise TypeError(\"size must be an integer\")\n        elif self.__size < 0:\n            raise ValueError(\"size must be >= 0\")\n        else:\n            self.__size = value\n\n    def area(self):\n        self = self.__size * self.__size\n        return self\n\n    def my_print(self):\n        if self.__size == 0:\n            print()\n        else:\n            for i in range(self.__size):\n                for j in range(self.__size):\n                    print(\"#\", end=\"\")\n                print()\n","repo_name":"GuillermoRosario/holbertonschool-higher_level_programming","sub_path":"python-classes/5-square.py","file_name":"5-square.py","file_ext":"py","file_size_in_byte":1088,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"36564749698","text":"#%%\nimport sim\nimport sys\n\nfrom sensors.vision import VisionSensorP3DX\n\n#%%\nsim.simxFinish(-1)  # just in case, close all opened connections\nclientID = sim.simxStart('127.0.0.1', 19999, True, True, 5000, 5)\n\nif clientID != -1:  # check if client connection successful\n    print('Connected to remote API server')\n    print('Vision Sensor object handling')\n    print('Getting images')\n    vision_sensor = VisionSensorP3DX(clientID)\n    vision_sensor.save_images('python_vrep_maze_1')\nelse:\n    print('Connection not successful')\n    sys.exit('Could not connect')","repo_name":"ldj789/coppelia_robotics","sub_path":"get_images.py","file_name":"get_images.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}
+{"seq_id":"71962122165","text":"import sys\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import *\n\n\nclass MyWindow(QMainWindow):\n    def __init__(self):\n        super().__init__()\n\n    def initWindow(self):\n        self.setWindowTitle(\"Bae First Window\")\n        self.setGeometry(300, 300, 300, 400)\n\n        pushBtn = QPushButton(\"Click me\", self)\n        pushBtn.move(20, 20)\n        pushBtn.clicked.connect(self.btn1_clicked)\n\n    def btn1_clicked(self):\n        QMessageBox.about(self, \"message\", \"clicked\")\n\n\nif __name__ == \"__main__\":\n    app = QApplication(sys.argv)\n    window = MyWindow()\n    window.initWindow()\n    window.show()\n    app.exec()","repo_name":"r2d209git/pythonpractice","sub_path":"Contact/guiwindow.py","file_name":"guiwindow.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"}